Biodiversity Plan v1.0 Free State ProvinceFree State Biodiversity Plan v1.0: Technical Report 2016 The targets of only 83 (75.5%) features of the 110 features that were included in

Biodiversity Plan v1.0

Free State Province Technical Report

(FSDETEA/BPFS/2016_1.0)

DRAFT 1

JUNE 2016

Report Title: Free State Province Biodiversity Plan: Technical Report v1.0

Date: $20 June 2016

Version: 1.0

Authors & contact details:

Nacelle Collins Free State Department of Economic Development, Tourism and Environmental Affairs [email protected] 051 4004775 082 4499012 Physical address: 34 Bojonala Buidling Markgraaf street Bloemfontein 9300 Postal address: Private Bag X20801 Bloemfontein 9300

Citation:

Report: Collins, N.B. 2016. Free State Province Biodiversity Plan: Technical Report v1.0. Free State Department of Economic, Small Business Development, Tourism and Environmental Affairs. Internal Report.

Map: Collins, N.B. 2015. Free State Province Biodiversity Plan: CBA map. Free State Department of Economic, Small Business Development, Tourism and Environmental Affairs. Internal Report.

______________________________

Free State Biodiversity Plan v1.0: Technical Report 2016

1. Summary

$what is a biodiversity plan

This report contains the technical information that details the rationale and methods followed to produce the first terrestrial biodiversity plan for the Free State Province. Because of low confidence in the aquatic data that were available at the time of developing the plan, the aquatic component is not included herein and will be released as a separate report.

The biodiversity plan was developed with cognisance of the requirements for the determination of bioregions and the preparation and publication of bioregional plans (DEAT, 2009). To this extent the two main products of this process are:

• A map indicating the different terrestrial categories (Protected, Critical Biodiversity Areas, Ecological Support Areas, Other and Degraded)

• Land-use guidelines for the above mentioned categories

This plan represents the first attempt at collating all terrestrial biodiversity and ecological data into a single system from which it can be interrogated and assessed. Biodiversity and ecological data included are:

• Land cover data

• Inselbergs

• Species distribution data (from records and expert mapping)

• Modelled species distribution

• A range of national data sets (Vegetation types, NFEPA sub-catchments, $list others)

• The existing Ekangala spatial biodiversity plan

• Biodiversity plans of neighbouring provinces

• Existing provincial plans that guide development within the Free State Province, most notably the Provincial Spatial Development Framework (PSDF)

Interrogation and assessment of the data was done according to national accepted biodiversity planning principles, i.e. classification of the landscape was done according to a systematic and a quantitative approach.

Included in the assessment was the incorporation of edge matching principles to ensure that planning units across provincial boundaries have similar classifications (CBA, ESA, etc.) where appropriate.

Large portions of the Free State have been degraded and are not available for conservation. According to the 2009 land cover map of the Free State (GeoterraImage, 2011) $% of the province is degraded while 33.67% is transformed ($% urban development, $% agriculture). Only $% of the Free State is covered by Formal Protected areas (Provincial Nature Reserves and SANParks)


The targets of only 83 (75.5%) features of the 110 features that were included in the Marxan analysis were achieved while the targets of 27 features could not be achieved (24.5). Most of the targets not achieved were of species (the targets for 22 of 36 species features were not achieved, i.e. the targets of only 62.1% of species features were achieved). The targets of all threatened vegetation types were achived within the remaining areas.

Table 1: Summarized Area and percentage contribution of CBA map categories

Category Area (km2) %

Protected 1267 1 CBAs 15400 12 ESAs 68525 53 Other 20914 16 Degraded 22785 18

Total 128891

Of the CBA map categories, protected area areas make up only 1% of province while degraded areas account for 18%. CBAs and ESAs account for 12% and 53% respectively. Remaining natural areas (natural areas not classified as CBA or ESA) therefore constitute 16% of the province.

Land use guidelines1 facilitate the incorporation of the CBA map into land use planning. CBA map categories were also aligned with the Spatial Planning Categories of the Provincial Spatial Development Framework to allow for their inclusion in SPISYS. In addition to facilitate spatial planning within urban area, it will also facilitate land use planning within the broader municipal areas as required by SPLUMA.

The 2013-2014 land cover map of South Africa (Geoterraimage, 2015) was released soon after the publication of this document. It follows that its content, as well as the data that informed the systematic biodiversity planning process and the resulting maps, are not the most current. Future releases of the biodiversity plan will incorporate such data.

1 Adapted from land use management guidelines for the Limpopo Province (Desmet, Holness, Skowno, & Egan, 2013)which in turn are based on the land use management guidelines of Mpumalanga, KNZ, and Gauteng.


2. Table of Contents

1. SUMMARY ............................................................................................................................................................................................................................................................... 2

2. TABLE OF CONTENTS .......................................................................................................................................................................................................................................... 4

3. EXPLANATION OF TERMS AND CONCEPTS................................................................................................................................................................................................ 15

4. LIST OF ACRONYMS ........................................................................................................................................................................................................................................... 19

5. ACKNOWLEDGEMENTS .................................................................................................................................................................................................................................... 20

6. INTRODUCTION .................................................................................................................................................................................................................................................. 21 6.1. GENERAL .................................................................................................................................................................................................................................................................................... 21 6.1. PURPOSE OF THE CONSERVATION PLAN ............................................................................................................................................................................................................................... 21 6.2. DEVELOPING THE BIODIVERSITY PLAN ................................................................................................................................................................................................................................ 22 6.3. LIMITATIONS OF THE CONSERVATION PLAN ....................................................................................................................................................................................................................... 22 6.4. APPLICATION OF THE CONSERVATION PLAN ....................................................................................................................................................................................................................... 23 6.5. PROJECT PRODUCTS ................................................................................................................................................................................................................................................................. 23

7. CONSERVATION PLANNING APPROACH..................................................................................................................................................................................................... 24 7.1. BIOREGIONAL PLAN REQUIREMENTS OF A SYSTEMATIC CONSERVATION PLAN.............................................................................................................................................................. 24

8. MATERIALS AND METHODS ........................................................................................................................................................................................................................... 26 8.1. INTRODUCTION ......................................................................................................................................................................................................................................................................... 26 8.2. INPUT DATA ............................................................................................................................................................................................................................................................................... 27

8.2.1. Species data.................................................................................................................................................................................................................................................................................. 27 8.2.2. Ecosystem data ........................................................................................................................................................................................................................................................................... 32 8.2.3. Aquatic ........................................................................................................................................................................................................................................................................................... 40 8.2.4. Ecological corridors ................................................................................................................................................................................................................................................................. 42 8.2.5. Ecological Processes................................................................................................................................................................................................................................................................. 58 8.2.6. Unique features .......................................................................................................................................................................................................................................................................... 59 8.2.7. Climate change ........................................................................................................................................................................................................................................................................... 60 8.2.8. Land Cover .................................................................................................................................................................................................................................................................................... 64 8.2.9. Protected Areas .......................................................................................................................................................................................................................................................................... 66 8.2.10. Existing Spatial Planning Products................................................................................................................................................................................................................................... 67 8.2.11. Ecological support areas (ESAs) ........................................................................................................................................................................................................................................ 68

8.3. PROJECT DESIGN ....................................................................................................................................................................................................................................................................... 70 8.3.1. Technical specifications ......................................................................................................................................................................................................................................................... 70 8.3.2. Cost ................................................................................................................................................................................................................................................................................................... 74


8.3.3. Planning units ............................................................................................................................................................................................................................................................................. 86 8.3.4. Planning unit cost ..................................................................................................................................................................................................................................................................... 91 8.3.5. Planning unit boundary cost ................................................................................................................................................................................................................................................ 92 8.3.6. Planning unit status ................................................................................................................................................................................................................................................................. 93 8.3.7. Ecological Support Areas (ESAs) ....................................................................................................................................................................................................................................... 93 8.3.8. Targets ........................................................................................................................................................................................................................................................................................... 94 8.3.9. Edge Matching .......................................................................................................................................................................................................................................................................... 102 8.3.10. CBAs ............................................................................................................................................................................................................................................................................................... 104 8.3.11. The CBA map ............................................................................................................................................................................................................................................................................. 107

8.4. ANALYSIS .................................................................................................................................................................................................................................................................................. 111 8.4.1. Calibration .................................................................................................................................................................................................................................................................................. 111

9. RESULTS ............................................................................................................................................................................................................................................................. 126 9.1. THE FREQUENCY OF SELECTION MAP .................................................................................................................................................................................................................................. 126 9.2. COMPOSITION OF CBAS ......................................................................................................................................................................................................................................................... 128 9.3. TARGET ACHIEVEMENT IN CRITICAL BIODIVERSITY AREAS............................................................................................................................................................................................ 133

10. LAND-USE GUIDELINES ............................................................................................................................................................................................................................. 134 10.1. RELATION WITH THE PROVINCIAL SDF ......................................................................................................................................................................................................................... 136 10.2. SPISYS ................................................................................................................................................................................................................................................................................ 141

11. FUTURE IMPROVEMENTS: ....................................................................................................................................................................................................................... 142

12. REFERENCES ................................................................................................................................................................................................................................................. 143

13. APPENDIX 1: MODELLING ........................................................................................................................................................................................................................ 145

14. APPENDIX 2: TARGETS ............................................................................................................................................................................................................................. 147

1. AVIFAUNA ......................................................................................................................................................................................................................................................... 147 14.1.1. Rationale for inclusion .......................................................................................................................................................................................................................................................... 147 14.1.2. Distribution mapping/modeling general ..................................................................................................................................................................................................................... 147 14.1.3. Raw distribution data sources .......................................................................................................................................................................................................................................... 147 14.1.4. Distribution mapping/modeling technical.................................................................................................................................................................................................................. 147 14.1.5. Target rationale ....................................................................................................................................................................................................................................................................... 148 14.1.6. Targets ......................................................................................................................................................................................................................................................................................... 148 14.1.7. Rationale for inclusion .......................................................................................................................................................................................................................................................... 150 14.1.8. Distribution mapping/modeling general ..................................................................................................................................................................................................................... 150 14.1.9. Raw distribution data sources .......................................................................................................................................................................................................................................... 150 14.1.10. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 150


14.1.11. Target rationale.................................................................................................................................................................................................................................................................. 151 14.1.12. Targets .................................................................................................................................................................................................................................................................................... 151 14.1.13. Rationale for inclusion ..................................................................................................................................................................................................................................................... 153 14.1.14. Distribution mapping/modeling general ................................................................................................................................................................................................................ 153 14.1.15. Raw distribution data sources ..................................................................................................................................................................................................................................... 153 14.1.16. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 154 14.1.17. Target rationale.................................................................................................................................................................................................................................................................. 154 14.1.18. Targets .................................................................................................................................................................................................................................................................................... 155 14.1.19. Rationale for inclusion ..................................................................................................................................................................................................................................................... 156 14.1.20. Distribution mapping/modeling general ................................................................................................................................................................................................................ 156 14.1.21. Raw distribution data sources ..................................................................................................................................................................................................................................... 157 14.1.22. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 157 14.1.23. Ecological niche modelling ............................................................................................................................................................................................................................................ 158 14.1.24. Target rationale.................................................................................................................................................................................................................................................................. 158 14.1.25. Targets .................................................................................................................................................................................................................................................................................... 158 14.1.26. Rationale for inclusion ..................................................................................................................................................................................................................................................... 160 14.1.27. Distribution mapping/modeling general ................................................................................................................................................................................................................ 160 14.1.28. Raw distribution data sources ..................................................................................................................................................................................................................................... 160 14.1.29. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 160 14.1.30. Ecological Niche Modelling ........................................................................................................................................................................................................................................... 161 14.1.31. Target rationale.................................................................................................................................................................................................................................................................. 161 14.1.32. Targets .................................................................................................................................................................................................................................................................................... 161 14.1.33. Rationale for inclusion ..................................................................................................................................................................................................................................................... 163 14.1.34. Distribution mapping/modeling general ................................................................................................................................................................................................................ 163 14.1.35. Raw distribution data sources ..................................................................................................................................................................................................................................... 164 14.1.36. Distribution mapping/modelling technical ........................................................................................................................................................................................................... 164 14.1.37. Target rationale.................................................................................................................................................................................................................................................................. 165 14.1.38. Targets .................................................................................................................................................................................................................................................................................... 165 14.1.39. Rationale for inclusion ..................................................................................................................................................................................................................................................... 167 14.1.40. Distribution mapping/modeling general ................................................................................................................................................................................................................ 167 14.1.41. Raw distribution data sources ..................................................................................................................................................................................................................................... 167 14.1.42. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 168 14.1.43. Ecological niche modelling ............................................................................................................................................................................................................................................ 168 14.1.44. Target rationale.................................................................................................................................................................................................................................................................. 168 14.1.45. Targets .................................................................................................................................................................................................................................................................................... 168 14.1.46. Rationale for inclusion ..................................................................................................................................................................................................................................................... 170


14.1.47. Distribution mapping/modelling general .............................................................................................................................................................................................................. 170 14.1.48. Raw distribution data sources ..................................................................................................................................................................................................................................... 170 14.1.49. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 170 14.1.50. Target rationale.................................................................................................................................................................................................................................................................. 171 14.1.51. Targets .................................................................................................................................................................................................................................................................................... 171 14.1.52. Rationale for inclusion ..................................................................................................................................................................................................................................................... 174 14.1.53. Distribution mapping/modeling general ................................................................................................................................................................................................................ 174 14.1.54. Raw distribution data sources ..................................................................................................................................................................................................................................... 174 14.1.55. Distribution mapping/modelling technical ........................................................................................................................................................................................................... 174 14.1.56. Target rationale.................................................................................................................................................................................................................................................................. 175 14.1.57. Targets .................................................................................................................................................................................................................................................................................... 175 14.1.58. Rationale for inclusion ..................................................................................................................................................................................................................................................... 177 14.1.59. Distribution mapping/modeling general ................................................................................................................................................................................................................ 177 14.1.60. Raw distribution data sources ..................................................................................................................................................................................................................................... 177 14.1.61. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 178 14.1.62. Ecological niche modelling ............................................................................................................................................................................................................................................ 178 14.1.63. Target rationale.................................................................................................................................................................................................................................................................. 178 14.1.64. Targets .................................................................................................................................................................................................................................................................................... 178 14.1.65. Rationale for inclusion ..................................................................................................................................................................................................................................................... 181 14.1.66. Distribution mapping/modeling general ................................................................................................................................................................................................................ 181 14.1.67. Raw distribution data sources ..................................................................................................................................................................................................................................... 181 14.1.68. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 181 14.1.69. Ecological niche modelling ............................................................................................................................................................................................................................................ 182 14.1.70. Target rationale.................................................................................................................................................................................................................................................................. 182 14.1.71. Targets .................................................................................................................................................................................................................................................................................... 182 14.1.72. Rationale for inclusion ..................................................................................................................................................................................................................................................... 184 14.1.73. Distribution mapping/modeling general ................................................................................................................................................................................................................ 185 14.1.74. Raw distribution data sources ..................................................................................................................................................................................................................................... 185 14.1.75. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 185 14.1.76. Ecological niche modelling ............................................................................................................................................................................................................................................ 186 14.1.77. Target rationale.................................................................................................................................................................................................................................................................. 186 14.1.78. Targets .................................................................................................................................................................................................................................................................................... 186

2. FLORA ................................................................................................................................................................................................................................................................ 188 1.13. ALEPIDEA AMATYMBICA ................................................................................................................................................................................................................................................... 188

14.1.79. Rationale for inclusion ..................................................................................................................................................................................................................................................... 188 14.1.80. Distribution mapping/modeling general ................................................................................................................................................................................................................ 189


14.1.81. Raw distribution data sources ..................................................................................................................................................................................................................................... 189 14.1.82. Distribution mapping/modelling technical ........................................................................................................................................................................................................... 189 14.1.83. Ecological niche modelling ............................................................................................................................................................................................................................................ 190 14.1.84. Target rationale.................................................................................................................................................................................................................................................................. 190 14.1.85. Targets .................................................................................................................................................................................................................................................................................... 190

1.14. KNIPHOFIA TYPHOIDES ..................................................................................................................................................................................................................................................... 192 14.1.86. Rationale for inclusion ..................................................................................................................................................................................................................................................... 192 14.1.87. Distribution mapping/modeling general ................................................................................................................................................................................................................ 192 14.1.88. Raw distribution data sources ..................................................................................................................................................................................................................................... 192 14.1.89. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 192 14.1.90. Ecological niche modelling ............................................................................................................................................................................................................................................ 193 14.1.91. Target rationale.................................................................................................................................................................................................................................................................. 193 14.1.92. Targets .................................................................................................................................................................................................................................................................................... 193

1.15. STRUMARIA TENELLA SUBSP. ORIENTALIS..................................................................................................................................................................................................................... 195 14.1.93. Rationale for inclusion ..................................................................................................................................................................................................................................................... 195 14.1.94. Distribution mapping/modeling general ................................................................................................................................................................................................................ 195 14.1.95. Raw distribution data sources ..................................................................................................................................................................................................................................... 195 14.1.96. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 195 14.1.97. Target rationale.................................................................................................................................................................................................................................................................. 196 14.1.98. Targets .................................................................................................................................................................................................................................................................................... 196

1.16. ISOETES AEQUINOCTIALIS ................................................................................................................................................................................................................................................. 197 14.1.99. Rationale for inclusion ..................................................................................................................................................................................................................................................... 197 14.1.100. Distribution mapping/modeling general ................................................................................................................................................................................................................ 197 14.1.101. Raw distribution data sources ..................................................................................................................................................................................................................................... 197 14.1.102. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 197 14.1.103. Target rationale.................................................................................................................................................................................................................................................................. 198 14.1.104. Targets .................................................................................................................................................................................................................................................................................... 198

1.17. STENOSTELMA UMBELLULIFERUM .................................................................................................................................................................................................................................. 199 14.1.105. Rationale for inclusion ..................................................................................................................................................................................................................................................... 199 14.1.106. Distribution mapping/modeling general ................................................................................................................................................................................................................ 199 14.1.107. Raw distribution data sources ..................................................................................................................................................................................................................................... 199 14.1.108. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 199 14.1.109. Target rationale.................................................................................................................................................................................................................................................................. 200 14.1.110. Targets .................................................................................................................................................................................................................................................................................... 200

1.18. PENTZIA OPPOSITIFOLIA ................................................................................................................................................................................................................................................... 201 14.1.111. Rationale for inclusion ..................................................................................................................................................................................................................................................... 201


14.1.112. Distribution mapping/modeling general ................................................................................................................................................................................................................ 201 14.1.113. Raw distribution data sources ..................................................................................................................................................................................................................................... 201 14.1.114. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 202 14.1.115. Target rationale.................................................................................................................................................................................................................................................................. 202 14.1.116. Targets .................................................................................................................................................................................................................................................................................... 202

1.19. PROTEA SUBVESTITA (LIP-FLOWER SUGARBUSH) ....................................................................................................................................................................................................... 203 14.1.117. Rationale for inclusion ..................................................................................................................................................................................................................................................... 203 14.1.118. Distribution mapping/modeling general ................................................................................................................................................................................................................ 203 14.1.119. Raw distribution data sources ..................................................................................................................................................................................................................................... 203 14.1.120. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 203 14.1.121. Target rationale.................................................................................................................................................................................................................................................................. 204 14.1.122. Targets .................................................................................................................................................................................................................................................................................... 204

1.20. PROTEA DRACOMONTANA ................................................................................................................................................................................................................................................ 205 14.1.123. Rationale for inclusion ..................................................................................................................................................................................................................................................... 205 14.1.124. Distribution mapping/modeling general ................................................................................................................................................................................................................ 206 14.1.125. Raw distribution data sources ..................................................................................................................................................................................................................................... 206 14.1.126. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 206 14.1.127. Target rationale.................................................................................................................................................................................................................................................................. 206 14.1.128. Targets .................................................................................................................................................................................................................................................................................... 206

1.21. HOODIA OFFICINALIS SUBSP. OFFICINALIS ..................................................................................................................................................................................................................... 208 14.1.129. Rationale for inclusion ..................................................................................................................................................................................................................................................... 208 14.1.130. Distribution mapping/modeling general ................................................................................................................................................................................................................ 208 14.1.131. Raw distribution data sources ..................................................................................................................................................................................................................................... 208 14.1.132. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 208 14.1.133. Target rationale.................................................................................................................................................................................................................................................................. 209 14.1.134. Targets .................................................................................................................................................................................................................................................................................... 209

1.22. SCHIZOGLOSSUM MONTANUM .......................................................................................................................................................................................................................................... 210 14.1.135. Rationale for inclusion ..................................................................................................................................................................................................................................................... 211 14.1.136. Distribution mapping/modeling general ................................................................................................................................................................................................................ 211 14.1.137. Raw distribution data sources ..................................................................................................................................................................................................................................... 211 14.1.138. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 211 14.1.139. Target rationale.................................................................................................................................................................................................................................................................. 211 14.1.140. Targets .................................................................................................................................................................................................................................................................................... 211

1.23. HELICHRYSUM HAYGARTHII ............................................................................................................................................................................................................................................. 213 14.1.141. Rationale for inclusion ..................................................................................................................................................................................................................................................... 213 14.1.142. Distribution mapping/modeling general ................................................................................................................................................................................................................ 213


14.1.143. Raw distribution data sources ..................................................................................................................................................................................................................................... 213 14.1.144. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 213 14.1.145. Target rationale.................................................................................................................................................................................................................................................................. 214 14.1.146. Targets .................................................................................................................................................................................................................................................................................... 214

1.24. DRACOSCIADIUM SANICULIFOLIUM ................................................................................................................................................................................................................................. 214 14.1.147. Rationale for inclusion ..................................................................................................................................................................................................................................................... 215 14.1.148. Distribution mapping/modeling general ................................................................................................................................................................................................................ 215 14.1.149. Raw distribution data sources ..................................................................................................................................................................................................................................... 215 14.1.150. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 215 14.1.151. Target rationale.................................................................................................................................................................................................................................................................. 215 14.1.152. Targets .................................................................................................................................................................................................................................................................................... 215

1.25. NERINE BOWDENII ............................................................................................................................................................................................................................................................. 216 14.1.153. Rationale for inclusion ..................................................................................................................................................................................................................................................... 216 14.1.154. Distribution mapping/modeling general ................................................................................................................................................................................................................ 216 14.1.155. Raw distribution data sources ..................................................................................................................................................................................................................................... 217 14.1.156. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 217 14.1.157. Target rationale.................................................................................................................................................................................................................................................................. 217 14.1.158. Targets .................................................................................................................................................................................................................................................................................... 217

1.26. BRACHYSTELMA DIMORPHUM SUBSP. GRATUM ............................................................................................................................................................................................................ 218 14.1.159. Rationale for inclusion ..................................................................................................................................................................................................................................................... 218 14.1.160. Distribution mapping/modeling general ................................................................................................................................................................................................................ 218 14.1.161. Raw distribution data sources ..................................................................................................................................................................................................................................... 218 14.1.162. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 218 14.1.163. Target rationale.................................................................................................................................................................................................................................................................. 219 14.1.164. Targets .................................................................................................................................................................................................................................................................................... 219

1.27. CHORLOLIRION LATIFOLIUM ............................................................................................................................................................................................................................................ 220 14.1.165. Rationale for inclusion ..................................................................................................................................................................................................................................................... 220 14.1.166. Distribution mapping/modeling general ................................................................................................................................................................................................................ 220 14.1.167. Raw distribution data sources ..................................................................................................................................................................................................................................... 220 14.1.168. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 220 14.1.169. Target rationale.................................................................................................................................................................................................................................................................. 220 14.1.170. Targets .................................................................................................................................................................................................................................................................................... 220

1.28. LITHOPS SALICOLA ............................................................................................................................................................................................................................................................. 222 14.1.171. Rationale for inclusion ..................................................................................................................................................................................................................................................... 222 14.1.172. Distribution mapping/modeling general ................................................................................................................................................................................................................ 222 14.1.173. Raw distribution data sources ..................................................................................................................................................................................................................................... 222


14.1.174. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 222 14.1.175. Ecological niche modelling: ........................................................................................................................................................................................................................................... 222 14.1.176. Target rationale.................................................................................................................................................................................................................................................................. 222 14.1.177. Targets .................................................................................................................................................................................................................................................................................... 223

3. INVERTEBRATES .......................................................................................................................................................................................................................................... 224 14.1.178. Rationale for inclusion ..................................................................................................................................................................................................................................................... 224 14.1.179. Distribution mapping/modeling general ................................................................................................................................................................................................................ 224 14.1.180. Raw distribution data sources ..................................................................................................................................................................................................................................... 224 14.1.181. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 224 14.1.182. Target rationale.................................................................................................................................................................................................................................................................. 225 14.1.183. Targets .................................................................................................................................................................................................................................................................................... 225 14.1.184. Rationale for inclusion ..................................................................................................................................................................................................................................................... 226 14.1.185. Distribution mapping/modeling general ................................................................................................................................................................................................................ 227 14.1.186. Raw distribution data sources ..................................................................................................................................................................................................................................... 227 14.1.187. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 227 Target rationale ........................................................................................................................................................................................................................................................................................... 228 Targets .............................................................................................................................................................................................................................................................................................................. 228 14.1.188. Rationale for inclusion ..................................................................................................................................................................................................................................................... 229 14.1.189. Distribution mapping/modeling general ................................................................................................................................................................................................................ 229 14.1.190. Raw distribution data sources ..................................................................................................................................................................................................................................... 229 14.1.191. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 229 Target rationale ........................................................................................................................................................................................................................................................................................... 230 Targets .............................................................................................................................................................................................................................................................................................................. 230 14.1.192. Rationale for inclusion ..................................................................................................................................................................................................................................................... 232 14.1.193. Distribution mapping/modeling general ................................................................................................................................................................................................................ 232 14.1.194. Raw distribution data sources ..................................................................................................................................................................................................................................... 232 14.1.195. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 232 Target rationale ........................................................................................................................................................................................................................................................................................... 233 Targets .............................................................................................................................................................................................................................................................................................................. 233 14.1.196. Rationale for inclusion ..................................................................................................................................................................................................................................................... 234 14.1.197. Distribution mapping/modeling general ................................................................................................................................................................................................................ 234 14.1.198. Raw distribution data sources ..................................................................................................................................................................................................................................... 234 14.1.199. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 234 14.1.200. Ecological niche modelling ............................................................................................................................................................................................................................................ 235 Target rationale ........................................................................................................................................................................................................................................................................................... 235 Targets .............................................................................................................................................................................................................................................................................................................. 235


14.1.201. Rationale for inclusion ..................................................................................................................................................................................................................................................... 237 14.1.202. Distribution mapping/modeling general ................................................................................................................................................................................................................ 237 14.1.203. Raw distribution data sources ..................................................................................................................................................................................................................................... 237 14.1.204. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 237 Target rationale ........................................................................................................................................................................................................................................................................................... 237 Targets .............................................................................................................................................................................................................................................................................................................. 238 14.1.205. Rationale for inclusion ..................................................................................................................................................................................................................................................... 239 14.1.206. Distribution mapping/modeling general ................................................................................................................................................................................................................ 239 14.1.207. Raw distribution data sources ..................................................................................................................................................................................................................................... 239 14.1.208. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 239 Target rationale ........................................................................................................................................................................................................................................................................................... 240 Targets .............................................................................................................................................................................................................................................................................................................. 240

4. REPTILES .......................................................................................................................................................................................................................................................... 241 14.1.209. Rationale for inclusion ..................................................................................................................................................................................................................................................... 241 14.1.210. Distribution mapping/modeling general ................................................................................................................................................................................................................ 242 14.1.211. Raw distribution data sources ..................................................................................................................................................................................................................................... 242 14.1.212. Distribution mapping/modelling technical ........................................................................................................................................................................................................... 242 Target rationale ........................................................................................................................................................................................................................................................................................... 243 Targets .............................................................................................................................................................................................................................................................................................................. 243 14.1.213. Rationale for inclusion ..................................................................................................................................................................................................................................................... 245 14.1.214. Distribution mapping/modeling general ................................................................................................................................................................................................................ 245 14.1.215. Raw distribution data sources ..................................................................................................................................................................................................................................... 245 14.1.216. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 245 14.1.217. Target rationale.................................................................................................................................................................................................................................................................. 245 14.1.218. Targets .................................................................................................................................................................................................................................................................................... 246 14.1.219. Rationale for inclusion ..................................................................................................................................................................................................................................................... 247 14.1.220. Distribution mapping/modeling general ................................................................................................................................................................................................................ 247 14.1.221. Raw distribution data sources ..................................................................................................................................................................................................................................... 247 14.1.222. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 247 Target rationale ........................................................................................................................................................................................................................................................................................... 248 Targets .............................................................................................................................................................................................................................................................................................................. 248 14.1.223. Rationale for inclusion ..................................................................................................................................................................................................................................................... 250 14.1.224. Distribution mapping/modeling general ................................................................................................................................................................................................................ 250 14.1.225. Raw distribution data sources ..................................................................................................................................................................................................................................... 250 14.1.226. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 250 Target rationale ........................................................................................................................................................................................................................................................................................... 251


Targets .............................................................................................................................................................................................................................................................................................................. 251 14.1.227. Rationale for inclusion ..................................................................................................................................................................................................................................................... 253 14.1.228. Distribution mapping/modeling general ................................................................................................................................................................................................................ 253 14.1.229. Raw distribution data sources ..................................................................................................................................................................................................................................... 253 14.1.230. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 253 Target rationale ........................................................................................................................................................................................................................................................................................... 254 Targets .............................................................................................................................................................................................................................................................................................................. 254

5. MAMMALS - SMALL ...................................................................................................................................................................................................................................... 255 1.41. MYSTROMYS ALBICAUDATUS (WHITE-TAILED RAT) ................................................................................................................................................................................................... 256

14.1.231. Rationale for inclusion ..................................................................................................................................................................................................................................................... 256 14.1.232. Distribution mapping/modeling general ................................................................................................................................................................................................................ 256 14.1.233. Raw distribution data sources ..................................................................................................................................................................................................................................... 256 14.1.234. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 256 14.1.235. Target rationale.................................................................................................................................................................................................................................................................. 256 14.1.236. Targets .................................................................................................................................................................................................................................................................................... 257

1.42. LAEPHOTIS WINTONI (DE WINTON’S LONG-EARED BAT) .......................................................................................................................................................................................... 258 14.1.237. Rationale for inclusion ..................................................................................................................................................................................................................................................... 258 14.1.238. Distribution mapping/modeling general ................................................................................................................................................................................................................ 258 14.1.239. Raw distribution data sources ..................................................................................................................................................................................................................................... 258 14.1.240. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 259 14.1.241. Target rationale.................................................................................................................................................................................................................................................................. 259 14.1.242. Targets .................................................................................................................................................................................................................................................................................... 259

1.43. CISTUGO LESUEURI (LESUEUR’S WING-GLAND BAT) ................................................................................................................................................................................................... 260 14.1.243. Rationale for inclusion ..................................................................................................................................................................................................................................................... 261 14.1.244. Distribution mapping/modeling general ................................................................................................................................................................................................................ 261 14.1.245. Raw distribution data sources ..................................................................................................................................................................................................................................... 261 14.1.246. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 261 14.1.247. Target rationale.................................................................................................................................................................................................................................................................. 261 14.1.248. Targets .................................................................................................................................................................................................................................................................................... 262

1.44. POECILOGALE ALBINUCHA (AFRICAN WEASEL) ........................................................................................................................................................................................................... 263 14.1.249. Rationale for inclusion ..................................................................................................................................................................................................................................................... 263 14.1.250. Distribution mapping/modeling general ................................................................................................................................................................................................................ 263 14.1.251. Raw distribution data sources ..................................................................................................................................................................................................................................... 263 14.1.252. Distribution mapping/modeling technical ............................................................................................................................................................................................................ 263 14.1.253. Target rationale.................................................................................................................................................................................................................................................................. 264 14.1.254. Targets .................................................................................................................................................................................................................................................................................... 264


15. APPENDIX 3: COST ...................................................................................................................................................................................................................................... 266

16. APPENDIX 4: CONNECTIVITY .................................................................................................................................................................................................................. 266

17. APPENDIX 5: ECOSYSTEM THREAT STATUS ..................................................................................................................................................................................... 266

18. APPENDIX 6: EDGE MATCHING .............................................................................................................................................................................................................. 266

19. CALIBRATION ............................................................................................................................................................................................................................................... 266


3. Explanation of terms and concepts

Areas of climate change resilience

These are areas that represent the remaining natural or near-natural areas that are important for climate change resilience at the landscape scale under a range of climate scenarios. These were identified as described as part of the National Biodiversity Assessment

C-Plan

C-Plan is a conservation-planning tool developed in Australia specifically for protected areas planning. It identifies area that are required to satisfy pre-determined biodiversity targets and ranks such areas according to the necessity of such an area to be included in a conservation portfolio to achieve the targets. Al also allows for an assessment of the extent to which the options to achieve the targets will be lost in the event of sites becoming unavailable for conservation.

Connectivity

Connectivity is the result of employing a network of corridors. High connectivity is achieved when all or selected features in the landscape are connected through corridors that will allow for the free movement of species. Low connectivity implies that the movement of species is restricted by human activities that have resulted in fragmentation of the landscape.

Corridor

A corridor is an area of habitat that connects wildlife populations that are otherwise separated by human activities (such as roads, development, or logging). This allows an exchange of individuals and genes between populations, which may help prevent the negative effects of inbreeding. Corridors may also help facilitate the re-establishment of populations. Corridors are also used to mitigate some of the effects of habitat fragmentation.

Cost layer

A layer which represent the cost that is incurred when a planning unit is selected as part of a conservation portfolio. Cost can represent the surface area of the planning unit, the actual cost to acquire the land, the level of resistance expected from competing land uses, etc. The cost layer is typically used by C-Plan and Marxan. Because these programs try to minimize the cost when selecting planning units to achieve the conservation targets, they first try and satisfy the targets by selecting planning units from areas with low cost.


Critical Biodiversity Area (CBA)

The Critical Biodiversity Areas constitute the planning units which if not included in the final portfolio (selection of planning units) will result in the pre-defined targets not being achieved. They are therefore identified based in the irreplaceability output of C-Plan or the frequency of selection analysis of Marxan. For Marxan it will constitute all planning units that were selected during each o the individual runs (i.e. a frequency selection of 1). Typically CBA1 and CBA2 areas are identified where the former discussion relates to CBA1 areas. CBA2 areas represent areas of high biodiversity significance but will not necessarily result in the target not being achieved if they were excluded from the final portfolio, i.e. they represent areas for which options exist.

Ecological Support Area (ESA)

Areas that are required to support the persistence of species.

Focal point

Focal points are used with Linkage Mapper and represents points to and from which corridors need to be created. Although referred to as points, they can also be represented by an area.

$Freshwater Ecosystem Priority Areas (FEPA)

A FEPA is an output of the NFEPA process, i.e. it is the spatial priority area and can constitute rivers, wetlands, sub-quaternary catchments, etc.

Frequency selection

Unlike C-Plan which does a single run through a dataset, Marxan performs a number of runs (defined by the user). The planning units that are selected to satisfy the targets during each run are not necessarily always the same. However, some planning units will, because of their key contribution towards achieving targets, always be selected, while other will not. Frequency selection is therefore an expression of the number of times that a specific planning unit was selected during each of the runs. A frequency selection value of 1 indicates that the planning unit was selecting during each of the individual runs.

Habitat Modification Layer


The habitat modification is a representation of land surface which is classified into a number of categories according on the land cover presebt at that site (previously referred to as land cover).

Irreplaceability

The term irreplaceability is most closely related to C-Plan analysis. It refers to the planning units which if not selected, will result in the pre-determined conservation targets not being met. They are therefore considered to be irreplaceable. In Marxan analysis planning units which were selected during each of the many runs can also be considered to be irreplaceable.

Marxan

Although using different algorithms, MARXAN is very similar to C-Plan in that it also aims to aid systematic reserve design based on pre-determined targets. In addition it also aims to identify the optimal spatial configuration for protected areas design by incorporating the principle of connectivity.

$National Freshwater Ecosystem Priority Areas (NFEPA)

NFEPA is the project through which FEPAs were identified.

Planning unit

Biodiversity features occur all over the landscape. To identify which areas are more important relative to others the landscape needs to be subdivided into smaller sections. These smaller sections (areas) are the planning units and usually consists of a grid regular or irregular shapes such as squares, hexagons, administrative boundaries, etc.

Significance score

Each biodiversity feature was assigned a significance score according to its perceived conservation importance. Scarce biodiversity features that are at high risk of extinction were assigned high significance scores while those will low conservation importance were assigned lower significance scores. Assigned scores ranges from 3 to 10.

Standard deviation


The standard deviation is a measure of the spread of values as compared to the average of the range. A low standard deviation means that all values in the range are approximately similar (if all are exactly the same then the standard deviation is 0) while a high standard deviation implies that the values have a wide range.

Target (conservation target)

Targets are the quantitative amount of each biodiversity feature that needs to be included in the final selection of planning units. It follows that once all planning units are selected by C-Plan or Marxan, that all targets must be represented within the final selection. Targets typically represent the absolute minimum of that feature that must remain for it to be viable, i.e. for it to persist over time.


4. List of acronyms

CAR Coordinated Avifaunal Roadcounts

CBA Critical Biodiversity Area

Degraded Portions of land that are not in climax condition due to factors other than physical disturbance

ESA Ecological Support Area

FEPA Freshwater Ecosystem Priority Areas

FS DESTEA Free State Department of Economic, Small Business Development, Tourism and Environmental Affairs

FSBP Free State Biodiversity Plan

GIS Geographical Information System

MPM

$NFEPA National Freshwater Ecosystem Priority Areas

SPF Species Penalty Factor

SPLUMA

Transformed Portions land that have been physically altered


5. Acknowledgements


6. Introduction

6.1. General

All living organisms, including humans, rely on the natural environment for their continued survival. They all require space and the resources that are found within this space. Of all, it is humans who require most of these resources and are, as a result, responsible for the majority of land degradation. The competition for resources is not limited to that which exists between organisms of different taxa or species, but also occurs within such groups of which humans is a prime example. The same piece of land may be required for conservation, mining and agricultural purposes.

Various tools to manage such conflicting land use requirements exist. Most of these are located within the domain of land use planning of which the Municipal Spatial Development Frameworks (SDFs) and Integrated Development Plans (IDPs) are the most prominent. These in turn are guided by higher order frameworks such as the Spatial Planning and Land Use Management Act (Act no 16 of 2013) SPLUMA and provincial Spatial Development Frameworks. However, all of these operate at a strategic level which in turn requires operational plans and tools for their effective implementation. A systematic biodiversity plans in one of such operational tools.

A systematic biodiversity plan is founded on two important principles, these being the principle of (i) representation and (ii) the principle of persistence. The former involves knowing where biodiversity (species, ecosystems, habitats and ecological processes) is and the latter involves accounting for their ecological requirements so that they can persist over time. A key product of the provincial biodiversity plan is a map indicating areas that are critical to account for the principles or representation and persistence and which if not protected implies that the affected biodiversity features will be lost. Such areas are referred to as Critical Biodiversity Areas (CBAs). The fact that an area is indicated to be critical does not necessarily mean that all development within such an area is forbidden. The provincial biodiversity plan provides guidelines as to what types of development are permissible so as to ensure the persistence of the biodiversity features responsible for their classicfation as CBAs.

To support economic development the biodiversity plan was designed so that CBAs are not selected in areas that have already been identified for alternative uses or that has the potential to contribute to other economic developement initiatves. Where options are available, such areas are avoided and only identified as CBAs where their exclusion will result in the principles of representation or persistecnce not being achieved.

6.1. Purpose of the Conservation Plan

The draft report of the Free State Provincial Spatial Development Plan (PSDP) was published in May 2013 (National Department of Rural Development and Land Reform, 2013). It has the sustainable use of resources as a primary objective to unlock meaningful and lasting benefit for both the people of the province (i.e. enhancing human well-being) and the environment (i.e. enhancing the integrity of the environment). This means that any resource use must, on balance, ‘improve the state of’ the conditions or circumstances prevalent in the area to be affect by the resource use.

A key objectives of the PSDF as it relates to spatial planning is to integrate and standardize planning at all spheres of government in the province with specific reference to amongst others facilitating land-use classification of the entire land surface of the province. To this extent a set of dedicated Spatial Planning Categories (SPCs) were developed which provide a spatial framework to guide decision-making regarding land-use at all levels of


planning. The SPCs represent a classification system that indicates the most suitable, or a range of, land use options for a certain piece of land. Associated with each SPC category is land use guidelines which when implemented ensures a balance between development and conservation. Mainstreaming of the biodiversity plan into spatial planning process will be achieved by alligning the biodiviersity plan categories with thos of the SPCs so that planning acording to SPC will then automatically also adopt the biodiversity plan categories and their associated land use guidelines.

The PSDF aims amongst others to support the district and local municipalities in the preparation of their Spatial Development Frameworks (SDFs) in terms of the Local Government: Municipal Systems Act (Act 32 of 2000). Such support and guidance include recording the land-use plans (SPC) and associated strategies and guidelines in an innovative Spatial Planning Information System (SPISYS).

SPISYS is a GIS based spatial planning tool to serve as a standard Spatial Planning Information System (SPISYS) in the Free State Province. Its purpose is to facilitate land-use management, integrated planning and governance throughout the province in terms of standard formats and procedures adopted by the Free State sectoral departments, while also accounting for a range of other national and international programs and obligations that need to be considered during land use planning exercises. SPISYS is structured in that all land is categorized as belonging to any one of six SPCs (Core conservation areas, natural buffer areas, agricultural areas, urban related areas, industrial areas, and surface infrastructure and buildings). It follows that once the biodiversity plan categories have been related to the SPCs, that the former will become an integral part of SPISYS and therefore of spatial planning within the Free State Province.

It follows that the purpose of the biodiversity plan is to provide the spatial component that will inform and support existing (and possible new) systems that support spatial planing within the Free State province. In addition to the latter, the biodiversity plan will also support other decision making and planning processes such as EIAs and SEAs.

6.2. Developing the Biodiversity Plan

An objective during development of the biodiversity plan was to create systems that would allow for all actions and tasks perfomed to be repeated accurately in future updates of the plan. This is to ensure that where the manner in which the plan was compiled is not altered, that changes in future updates reflect changes to data and not changes to the manner in which the plan was compiled, specifically where such changes are unintentional oversights. In addition to ensuring repeatability, such systems will obviously also require that much less user involvement is required than in their absence making future updates much less cumbersome and time consuming. This objective was achievd by making etensive use of the modelbuilder function of ArcGIS (Section 8.3.1).

6.3. Limitations of the Conservation Plan

The Free State Biodiversity Plan is a static map of a dynamic environment. It follows that it is representative of the environment as it was at the time of development. Furthermore, although the most accurate and most recent data were used during its development, these are in very few instances complete. It follows that the biodiversity plan inevitably suffers from spatial and temporal inaccuracies which need to be recognised and accounted for during its application.


6.4. Application of the Conservation Plan

The conservation plan should ideally not be applied at a scale finer than 50 000. Application at a finer scale will exacerbate the above mentioned limitations.

Following from the above, although the CBA map must be consulted during EIAs, it can not be used as a substitute for on-site specialist studies. Depending on the feature/s responsible for the classification of a planning unit as a CBA, it merely indicates the possibility of such features being present on sites. Similarly, the absence of features does not necessarily mean that features of conservation concern are not present on site.

6.5. Project Products


7. Conservation planning approach

This section provides the rationale and technical background on which the Free State biodiversity plan was developed.

7.1. Bioregional plan requirements of a systematic conservation plan

The Guideline regarding the Determination of Bioregions and the Preparation and Publication of Bioregional Plans (DEAT, 2009) states that Bioregional Plans must be a spatial plan that indicates areas that are important for the representation and persistence of terrestrial and aquatic species and ecosystems. These areas are referred to as Critical Biodiversity Areas (CBAs) and are the minimum area required to ensure the persistence and representation of biodiversity. A bioregional plan must also include land use management guidelines which if implemented, must avoid further loss and degradation within the CBAs.

The guideline also state that a bioregional plan must be based on a systematic biodiversity plan. The key characteristics of a systematic biodiversity plan are:

• The principle of representation

The spatial output must account for all biodiversity pattern, including species and their habitats. While the vegetation types serve as surrogate for all species, species that are being threatened by extinction, as well as species which according to experts are of conservation concern, were mapped individually. The vegetation types also account for the range of habitats that occur in the Free State. Details on the mapping procedure are provided in Error! Reference source not found..

• The principle of persistence

A main objective of the biodiversity plan is to identify areas, which if conserved, will allow for their associated process and species to persist. Areas selected for species conservation must therefore support and provide all processes and resources for the species to survive, these consisting of areas that are suitable for breeding, feeding and resting. However, the ability of areas to support species may change over time. Such changes may be brought about by amongst others long term climate change. In the face of such long term climate changes the persistence of species is facilitated through the establishment of corridors that connect areas across ecological gradients (Appendix 4).

• Feature targets

$Revise the section about setting separate targets for different Pseudo features

The systematic biodiversity planning process requires that a target needs to be set for all biodiversity features. For species the quantitative targets represent the minimum area required for the species to persist, while for habitats and ecosystems the target represent the minimum area that is required to serve as representative sample of that system. Species for which targets were set (i.e. species that were included in the FS biodiversity plan) are those that are classified as being threatened (i.e. are at risk of extinction), are endemic to the Free State Province or those that experts consider to be conservation worthy. The number or each species that needs to be conserved was set following the guidelines of Pfab, Victor, &


Armstrong (2011), according to which the recommended number of individuals are the minimum amount required to prevent the species from being classified to a higher threat status. To account for the principle of persistence the number of individuals was converted to an area requirement.

For ecosystems the targets used were those set by SANBI.. Ecosystems were subdivided where different regions of the same ecosystem (vegetation type) are known to be floristically dissimilar (section $0; i.e. floristic dissimilar regions of the same vegetation type were entered as separate features, each with its own target). In such instances all subdivisions (i.e. each feature) were assigned the national target (Table 11). This approach ensures that Marxan attempts to achieve the national target for that ecosystem, but that the selection of planning units is more representative of the diversity contained within such ecosystems.

For a detailed discussion on target setting please refer to Appendix 2.

• Conflict avoidence

While being responsible for the long term sustainable use of the natural resources of the Free State Province, the FS DESTEA also has as its mandate to ensure economic development and social upliftment of its people. It is inevitable that land which is suitable for conservation purposes will also be key areas for economic development. Areas for conservation should therefore be located in areas that have potentially little or no economic value. To steer the selection of conservation worthy areas away from areas required to support economic development, a cost layer is incorporated into the biodiversity planning process where area with high economic value or opportunity are assigned a high cost and areas of low economic value or opportunity are assigned a low cost. When selecting a planning unit the cost of that unit is added to the portfolio cost total. Becasue Marxan tries to achieve the target at the lowest cost. Planning units of low cost will be selected where a choice exists, i.e. areas of low economoc opportunity will preferentially be selected where optins are available.

For a detailed discussion on development of the cost layer please refer to


Appendix 3.

• Connectivity

Within the broader context of the Republic of South Africa, the Free State is considered to be a transitional zone between the dry and hot climate of the western regions and the moist cooler eastern regions of the country. The ecosystems and habitats of the Free State are therefore in most part not considered to be rare or unique. However, given its central location, it is considered to be strategically important as a link between habitats and ecosystems of the neighboring provinces and Lesotho.

The Free State shares it boundary with six other provinces and the Kingdom of Lesotho. Many of the neighbouring provinces have already completed their provincial biodiversity plans, which include the mapping of ecological corridors. Corridor analysis was done using Linkage Mapper (McRae & Kavanagh, 2011) which is an ArcGIS tool to support regional wildlife habitat connectivity analyses. Input data requires a resistance (cost) layer and focal points.

Focal points along the Free State border were informed by the most recent published provincial biodiversity plans while inland focal points consisted mostly of protected areas. The resistance layer was compiled from two separated sources, these being the national coverage of areas resilient to climate change (Holness & Bradshaw, 2012) and the Free State land cover map (Appendix 4: Connectivity).

Creating the final map of ecological corridors was an iterative process. After the first run of Linkage Mapper the results were inspected after which additional focal points were subjectively added to re-route or to create additional corridors.

For a detailed discussion on identifying ecological corridors please refer to Appendix 4.

• Edge matching

Edge matching refers to the approach followed to preferentially have CBAs selected adjacent to CBAs of neighbouring provinces.

With the exception of the Northern Cape Province, all provinces that border the Free State have already developed provincial biodiversity plans. All of these plans were compiled following the systematic biodiversity planning approach, i.e. they all have maps that indicate Critical Biodiversity Ares (CBAs) as well as ecological corridors. To preferentially select CBAs adjacent to the CBAs of neighbouring provinces the cost of planning units that are adjacent to neighbouring CBAs as well as the planning unit boundary cost were lowered. It follows that because of their lowered cost, that Marxan will preferentially select such planning units when e.g. it is attempting to satisfy the ecosystem targets.

For a detailed discussion on edge matching please refer to Appendix 6.


8. Materials and methods

8.1. Introduction

A range of data types were used to inform the biodiversity planning process. Depending on the data contained in the data sets they either encourage or deter the selection of corresponding planning units. Data sets that encourage the selection of planning units are those that contain features that need to be conserved and protected, whereas those that deter the selection of planning units are those that contain features that should be avoided.. The latter is commonly those features that exert some form of pressure on biodiversity or ecological process. These are commonly acquiered from a range of sources and then captured as a collective in the cost layer. Other data that in addition to species and ecosystem services encourage the selection of planning units are existing protected areas. Data sets included in the biodiversity planning process are indicated in Table x ($still need to create this table and

Biodiversity features included in the Free State Biodiversity Plan account mainly for terrestrial ecosystems and habitats. Inclusion of the aquatic component was limited to FEPA catchments (included in the cost layer and for the identification of ESAs) and wetland clusters (included as ecological support areas only). Also included are features that represent scarce or unique habitats and that accounts for ecological processes, including connectivity.

8.2. Input Data

8.2.1. Species data

The large number of species that occur within the Free State makes it impossible to include all species as biodiversity features (i.e. to have them mapped and their potential distribution modelled). All species for which suitable spatial data were available and that satisfy any one of the following criteria were included:

o Nationally Threatened species as per the IUCN 2001 classification (species listed as Critically Endangered, Endangered or Vulnerable). o Species that are Endemic to South Africa. Avifauna species classified as near-endemic or suspected to be near endemic, as well as species

suspected to be winter endemic or breeding endemic were also included. o Near Threatened species (as per the IUCN 2001 classification) that are also endemic to South Africa. o Species that are endemic to the Free State, irrespective of their red data classification (Species that occur only within the Free State and

Lesotho were considered to be endemic to the Free State). o Any other species that according to expert opinion are of conservation concern and need to be included in the provincial biodiversity plan.

The number of species included as features with targets per taxa are as per Table 2.

Table 2: The number of species per taxa included in the Free State biodiversity plan.

Taxa Number of species


Taxa Number of species

Avifauna 12 species

Amphibians 0 species

Flora 16 species

Invertebrates 7 species

Mammals - Small 4 species

Mammals - Large 1 species

Reptiles 5 species

Species data were obtained from a wide range of sources, including national data sets, private collections, expert knowledge and other published data (Table 3).

Table 3: Species data features included in the Free State biodiversity plan.

Feature Source Date Comments

Avifauna Avifauna distribution data were supplied by:

• SANBI

• National Museum, Bloemfontein (Nat. Mus, Bftn)

• Expert mapping (B. Colahan, FS DETEA, M. Pretorius, FS DETEA)

• Ekangala dataset

• Sonja Kruger/Ian Rushworth (KZN Wildlife)

• N.B. Collins (FS DETEA)

• EWT

• J. du Preez (UFS)

• Dawie Kleynhans (submitted by B. Colahan)

• Mark Kirk (submitted by B. Colahan)

Varied (Friedman & Daly, 2004)

Flora Avifauna distribution data were supplied by:

• SANBI

• Expert mapping (Prof. J du Preez, UFS; Dr. Z. Zietsman, Nat. Mus., Bftn)

• Craig $Whitthington Jones (GDACE)

• Dawie de Swardt (Nas. Mus. Bftn)

• Robert Lotze (FS DETEA)

• Michael Cunningham (submitted by B. Colahan)

• Faansie Peacock (submitted by B. Colahan)

• David Weaver (submitted by B. Colahan)

Varied (Raimondo, et al., 2009)



Invertebrates (Butterflies)

Invertebrate distribution data were supplied by:

• SANBI

• SABCA/ADU

Varied (Mecenero, et al., 2013)

Mammals - small Small mammal distribution data were supplied by:

• (Kuyler, 2000)

• Expert mapping (M. Pretorius, FS DETEA)

• Expert mapping (J. Watson, FS DETEA)


Mammals - large Large mammal distribution data were supplied by:

• Morne Pretorius (FS DETEA)

• N.B. Collins (FS DETEA)

• Ekangala dataset


Reptiles Reptile distribution data were supplied by:

• ADU

• N.B. Collins

• Dr. M. Cunningham

• (Bates, 2007)

• (McIntyre & Whiting, 2012)

Varied (Bates, et al., 2014)

The objective while developing the species spatial layers (GIS maps) was to distinguish between areas of different probability of occurrence. This was achieved by considering the nature of the data that were included, where the nature of the data refers to:

• Whether it is an actual observation of the species (the term 'actual' implies that a specimen was observed at that point or within the indicated area; point and polygon locations are recognized and mapped separately),

• Suitable habitat adjacent to an actual observation, or

• Modelled habitat in which the species may potentially occur (Appendix 1).

The above order represents a ranking of probability of occurrence, i.e. the probability of finding a species in an area where it has been recorded before (an actual observation) is higher than finding it within the modelled area.

The different observation types (probability of occurrence) were mapped separately for each species. To indicate the different probability of occurrences a system of 'Pseudo species' was used. Of all the Pseudo layers it is only the Pseudo_1 observations that were included as featrures with targets to inform the selection of CBAs. The remainder of the Pseudo layers informed the selection of ESA (section 8.2.11) while all Pseudo layers were included as areas of lowered cost within the cost layer (section 8.3.2). The mapping protocol for different taxa are as per


Table 4: Mapping protocol for avifauna

Pseudo layer per taxa2

Observation type Avifauna Flora, Reptiles & Invertebrates

Small mammals Large mammals

Actual point observation (nesting & roosting) Pseudo_1 (point)

Actual polygon observation (nesting & roosting) Pseudo_1 (polygon)

Actual point observation (other than nesting & roosting) Pseudo_2 (point) Pseudo_1 (point) Pseudo_1 (point) Pseudo_1 (point)

Actual polygon observation (other than nesting & roosting) Pseudo_2 (polygon) Pseudo_1 (polygon) Pseudo_1 (polygon) Pseudo_1 (polygon)

Suitable habitat adjacent to Pseudo_1 and 2 observations Pseudo_3 Pseudo_2

Large scale actual observations Pseudo_4 Pseudo_4

Modelled distribution Pseudo_5 Pseudo_5 Pseudo_4 Pseudo_3

• For avifauna:

Pseudo_3 layers were developed specifically, although not exclusively, for those species that are habitat specialists and that therefore requires a specific patch of land (e.g. a wetland, a rocky outcrop, etc.) to ensure their persistence. A Pseudo_3 layer was therefore not mapped for species that utilize large patches of land that extend over considerable distances. In such instances a buffer (discussed below), was applied to the actual Pseudo_1 observations to select the portions of suitable habitat that are in the immediate vicinity of the actual observations.

The Pseudo_4 layer was developed to account for observation data that were collected as part of large scale assessments, specifically the SABAP1 QDSs and the SABAP2 pentads. These are considered to be temporal observations, i.e. the species that were recorded at the time of observation do not necessarily permanently reside at that site. Although based on actual observations, this is considered to be a modelled layer based on the large scale and the temporal nature of such observations.

The Pseudo_5 layer represents areas outside of large scale assessments (Pseudo_4) but which are located within the modelled geographic extent (Appendix 1).

To account for representation all actual observations (Pseudo_1 and Pseudo_2) were buffered by 500 m. Where the home range of the species is known to be greater or less than r = 500 m, the home range buffer was applied. Semlitsch & Bodie (2003) in Compaan (2013) recommend retaining 350 m of terrestrial habitat around wetlands and rivers as a life zone for reptiles and amphibians. Therefore, where actual observations were of wetland dependant species, the associated wetland was buffered by 350 m (except where the known home range is larger, in which case the sighting was buffered to account for the home range).

2 'Point' refers to a point observation, e.g. a single nesting site while 'Polygon' refers to an area observation, e.g. a rock face on which Bald Ibis breed.


• For flora, reptiles and invertebrates:

In some instances the flora and reptile actual point and polygon observations do not overlap with modelled habitat. In other instances actual point and polygon observations are located within large continuous sections of suitable habitat that cover hundreds of square kilometers. In such instances a Pseudo_2 layer was not created. To account for the latter the actual observations (Pseudo_1) were buffered by 100 m to include a portion of such areas. This buffer represents an area that should not be disturbed to enable the species recorded at the actual observation to persist and also to account for other specimens of that species that may occur in close vicinity of actual observations. In the case of certain invertebrate species a buffer was also in applied to the Pseudo_2 layer to include habitat that fringe rivers and streams.

• For small mammals:

A standard buffer of 100 m was applied to all actual observations of terrestrial small mammals while a standard 500 m buffer was applied to all actual observations of bats (Pseudo_1). This buffer represents an area that should not be disturbed to enable the species recorded at the actual observation to persist and to also to account for other species that may occur in close vicinity that were not recorded.

• For large mammals:

A buffer of 500 m was applied to all actual observations of oribi (the only large mammal included in the biodiversity plan). This buffer represents an area that should not be disturbed to enable the species recorded at the actual observation to persist and to also to account for other species that may occur in close vicinity that were not recorded.

The different observation types (Pseudo layers) were therefore mapped separately for each species and a target was set for each (section 8.3.8). However, as mentioned previously, only Pseudo_1 data were included as features with targets.while the remainderof the observation types (probability of occurrence).

To account for persistence buffers were applied to Pseudo_1 point and polygon data. Buffers were applied as follows: o For species of which no data on their home range could be found but for which a recommende buffer width is set by the edge-matching

technical guidelines, the latter was applied. o Where the home range of species was found to differ from the buffer width as recomemnded by the edge-matching technical guidelines,

the mapped areas were buffered by the former (the smaller or larger home range). o It is only in instances were no information of the species home range could be found and where the species is not included in the edge-

matching technical guidleine that the standard taxa specific buffer (Table 5) was applied.

Table 5: Taxa dependant buffers that were applied where species home ranges are unknown.

Taxa Standard buffer width

Flora 100 m

Avifauna 500 m


Invertebrates 100 m

Reptiles 100 m

Mammals - small (terrestrial) 100 m

Mammals - small (bats) 500 m

Mammals - large 500 m

For avifauna species with exceptional large home ranges and daily travelling distances (e.g. vulture) a 2 km buffer was applied. Species specfic buffer distances are provided in Appendix 2. In addition to the individually mapped species, forests ($DWAF reference) also accounted for species associated with forests, specifically Plectranthus grallatus, Streptocarpus gardenia, Bradypodion dracomontanum, Lioptilus nigricapillus, Anhydrophryne hewitti, Heleophryne natalensis, Osyridicarpus schimperianus, Cryptocarya woodii, Curtisia dentata, and Podocarpus falcatus.

8.2.2. Ecosystem data

There are 40 vegetation (ecosystem) types in the Free State (excluding forests) of which 1 is classified as Endangered (Vaal-vet Sandy Grassland) and 6 are classified as Vulnerable (Bloemfontein Dry Grassland, Eastern Free State Clay Grassland, Eastern Temperate Freshwater Wetlands, Rand Highveld Grassland, Soweto Highveld Grassland, and Vredefort Dome Granite Grassland). Three vegetation types are endemic to the Free State, these being the Bloemfontein Dry Grassland (VU, 4914 km2), the Western Free State Clay Grassland (6667 km2) and the Winburg Grassy Shrubland (1570 km2); together comprising 10% of the Free State surface area. Representation of thereatned vegetation types within protetced areas are shown in Table 6.


Table 6: Representation of threatened vegetation types within proteted areas.

Area (ha) of threatened vegetation types of the Free State (Mucina and Rutherford, 2009)

Protected Areas Gh 5 (VU) Gm 3 (VU) Azf 3 (VU) Gm 11 (VU) Gm 8 (VU) Gh 10 (EN) Gh 11 (VU) Remainder of PA Grand Total

Bathurst NR 151.7 151.7

Caledon NR 3770.4 3770.4

Erfenis Dam NR 877.3 877.3

Gariep NR 27895.0 27895.0

Golden Gate Highlands NP 31274.2 31274.2

Kalkfontein Dam NR 5718.6 5718.6

Koppies Dam NR 5042.7 5042.7

Maria Moroka NR 4863.7 4863.7

Rustfontein Dam NR 4536.7 4536.7

Sandveld NR 151.1 30702.9 30854.1

Seekoeivlei NR 1369.3 2328.4 3697.7

Soetdoring NR 1139.1 83.3 5760.4 6982.8

Sterkfontein Dam NR 17984.4 17984.4

Tussen die Riviere NR 18981.7 18981.7

WP NR 1034.6 10180.8 11215.4

Remainder of VegType 490152.1 1400047.0 6450.0 45785.5 46210.6 1464002.0 86644.5 3539291.8

Grand Total 491442.9 1401081.6 7819.3 45785.5 46210.6 1464236.5 86644.5 169917.3 3713138.2

Code Vegetation type

Gh 5 Bloemfontein Dry Grassland

Gm 3 Eastern Free State Clay Gassland

Azf 3 Eastern Temperate Freshwater Wetlands

Gm 11 Rand Highveld Grassland

Gm 8 Soweto Highveld Grassland

Gh 10 Vaal-vet Sandy Grassland

Gh 11 Vredefort Dome Granite Grassland


Ecosystem data were ued to account for species that do not satisfy the criteria for species inclusion (section 8.2.1), i.e. ecosystems were used as surrogate for overall biodiversity. This approach is based on the assumption that there exists some correlation between habitats and the species that they support, and that the inclusion of the vegetation types as a biodiversity features will sufficiently account for the species that are not included as features.

The vegetation types of Mucina and Rutherford (2009) was used as the base map. The following amendments were performed to the base map:

• Forests

The Vegetation type data was supplemented by the forest types as mapped and classified by the Department of Water Affairs and Forestry ($date and reference). The forest data mapped by DWAF was considered to be of higher accuracy than those mapped by Mucina and Rutherford (2009). Where the DWAF spatial data included forests that intersect those mapped by Mucina and Rutherford (2009), the latter were removed and replaced with those of DWAF. In addition to the above mentioned forests, the Nelsonskop Forests were also included as a forest features (with 100% target; section 8.3.8). At an altitude of over 2000 m.a.s. the Nelsonkop forests are the highest lying Afromontane Forest in South Africa (Cooper, 1982).

• Regionalization of the Highveld Salt Pans,the Highveld Alluvial Vegetation and the Eastern Temperate Freshwater Wetlands

In adition to having included the DWAF forests, the Eastern Temperate Freshwater Wetlands, Highveld Alluvial vegetation and Highveld Salt Pans vegetation types were regionalized according to Collins (2011). Regionalization implies the subdivion of vegetation types (Mucina & Rutherford, 2006) into regions where the vegetation of a region is similar, but is dissimilar to that of another region. Regionalisation was done on account of the imilarity and dissimilary of the plant communities of depression and valley-bottom wetlands. Seperate regions were identified for valley-bottom (Figure 1) and depression (Figure 2) wetlands.


Figure 1: Distribution of valley-bottom wetland regions (Collins, 2011). Region 1 is charaterised by the Cynodon dactylon-Panicum coloratum and the Pennisetum sphacelatum-Andropogon appendiculatus communities as well as assemblages containing Eragrostis planiculmis, Setaria incrassata, and Setaria sphacelata. Region 3 is characterised by the Eragrostis bicolor community while Region 3 contains assemblages of other species composition.


Figure 2: Distribution depression of wetland regions (Collins, 2011). Region 1 is charactyerized by the Eleocharis dregeana sub-community, Region 2 by the Eleocharis dregeana and Leptochloa fusca sub-communities, Region 3 by Schoenoplectus decipiens sub-communities and Region 4 by bare pans and Leptochloa fusca (creeping form) community.

Portions of the Eastern Temperate Freshwater Wetlands, Highveld Alluvial vegetation and Highveld Salt Pans vegetation types as they occur within the different wetland regions were mapped as separate features. The Eastern Temperate Freshwater Wetlands was subsequently mapped as 2 separate features (according to Figure 1) while the Highveld Alluvial vegetation and Highveld Salt Pans vegetation types were mappes as 3 separate features as per Figure 1and Figure 2 respectively.

$see if the content of 0_Ecosystems_Technical report for compiling the Ecosystem layer_v1.0.docx has been included herein.


Table 7. Ecosystem features included in the Free State biodiversity plan.


Ecosystems (Vegetation types)

(Mucina & Rutherford, 2006)

2006 Highveld Salt Pans, Highveld Alluvial Vegetation and Eastern Temperate Freshwater Wetlands were regionalised as described in this section..

Forests $ DWAF $ Where forsets of DWAF intersect with forests of Mucina and Rutherford (2009), the latter were removed and replaced by those of DWAF.

• Ecosystem status Provincial adjustments for planning purposes

The vegetation types of Mucina and Rutherford were accepted to represent ecosystems for which eight criteria3 were developed to classify them as CR, EN, or VU (SANBI, 2011). Because the criteria used for the national list were quantitative, they are explicit and repeatable, and therefore suitable to be used for a provincial list. For the national assessment Criterion A1 (Irreversible loss of natural habitat) was applied using an outdated national land cover dataset. The availability of an updated provincial land cover layer (GeoterraImage, 2011) allowed for a re-assessment of the ecosystem threat status using criterion A1. The remainder of the criteria were not applied because the data required for their application were not available at the time of analysis. For criterion A2; although the new provincial land cover data does include a degraded category, the accuracy of this classification is considered insufficient for the purpose of identifying threatened ecosystems. For criterion C; although the extent of the vegetation types are known, the level of threat is not. Also unavailable at the time were the number of threatened plant species that are associated with the individual vegetation types (criterion D1), as well as the areas required to meet explicit biodiversity targets as determined from a systematic conservation plan. It should be noted that duer to the fact that a natiobnal land cover map was not available at the time of analysis, that that the re-assessment was only applied to vegetation types as they occur within the Free State, i.e. portions of Free State vegetation types that occur outside of

the province were not included in the assessment. The results are presented graphically in Figure 3 and in table format in Appendix 5 (Table 8). The newly assigned ecosystem status influenced the feature penalty factors as discussed in section 8.4.1.

3 One of the criteria was applied to forests only while three of the criteria were not applied to any of the ecosystems.


Figure 3: Visual presentation of the national and provincial list of threatened ecosystems (the National list of threatened ecosystems are as per SANBI, 2011).

To summarise:

• The Highveld Salt Pans vegetation type was not originally classified as threatened and that was upgraded to CRs.

• The Upper Gariep Alluvial vegetation type was not originally classified as threatened and that was upgraded to EN

• Vegetation types that were not originally classified as threatened and that were upgraded to VU are the Amersfoort Highveld Clay Grassland, Andesite Mountain Bushveld, Carletonville Dolomite Grassland, Central Free State Grassland, Eastern Free State Sandy Grassland, Frankfort Highveld Grassland, Gold Reef Mountain Bushveld, Highveld Alluvial Vegetation, Schmidtsdrif Thornveld, Senqu Montane Shrubland, Vaal Reefs Dolomite Sinkhole Woodland and the Zastron Moist Grassland vegetation types

• Vegetation types that were upgraded from VU to EN are the Eastern Free State Clay Grassland and the Soweto Highveld Grassland vegetation types.


• The Eastern Temperate Freshwater Wetlands Vegetation type was upgraded from VU to CR.

• The Vaal-Vet Sandy Grassland Vegetation type was upgraded from EN to CR.

Table 8: Adjusted vegetation type status on account of criterion A1 for identifying threatened ecosystems.

Vegetation type Natural

(ha) Transformed

(ha) Total (ha)

% Natural

% Transformed Target

National Status

Adjusted Status

Aliwal North Dry Grassland 469765.7 208731.6 678497.2 69.23619 30.76381 24

Amersfoort Highveld Clay Grassland 164579.3 225494.5 390073.8 42.19183 57.80817 244 VU

Andesite Mountain Bushveld 22647.98 31754.09 54402.07 41.63073 58.36927 24 VU

Basotho Montane Shrubland 138394 51429.81 189823.9 72.90656 27.09344 28

Besemkaree Koppies Shrubland 645267.9 74479.54 719747.4 89.65199 10.34801 28

Bloemfontein Dry Grassland 214900.8 276542.1 491442.9 43.72853 56.27147 24 VU VU

Bloemfontein Karroid Shrubland 6649.02 1392.766 8041.786 82.68089 17.31911 28

Carletonville Dolomite Grassland 4668.96 10051.7 14720.66 31.71706 68.28294 24 VU

Central Free State Grassland 883248.8 713807 1597056 55.30482 44.69518 24 VU

Drakensberg Afroalpine Heathland 120352 1473.467 121825.5 98.79051 1.20949 27

Drakensberg-Amathole Afromontane Fynbos 333.7129 6.706371 340.4193 98.02997 1.970033 27

Eastern Free State Clay Grassland 383801.9 1119566 1503368 25.52948 74.47052 24 VU EN

Eastern Free State Sandy Grassland 525722.9 762230.6 1287954 40.81847 59.18153 24 VU

Eastern Temperate Freshwater Wetlands 802.3689 7445.77 8248.139 9.727878 90.27212 24 VU CR

Eastern Upper Karoo 4071653 555979.8 4627633 87.98566 12.01434 21

Frankfort Highveld Grassland 445198.4 542323.9 987522.2 45.08236 54.91764 24 VU

Gold Reef Mountain Bushveld 13945.56 32190.26 46135.82 30.22719 69.77281 24 VU

Highveld Alluvial Vegetation 150514.3 172795.6 323309.9 46.55418 53.44582 31 VU

Highveld Salt Pans 4215.645 74712.22 78927.87 5.341137 94.65886 24 CR

Kimberley Thornveld 1345758 567721.8 1913480 70.3304 29.6696 16

Lesotho Highland Basalt Grassland 1482291 423516.8 1905808 77.77757 22.22243 27

4 Target is stated as "refer to text"; the target of 24% was subjectively chosen


Vegetation type Natural

(ha) Transformed

(ha) Total (ha)

% Natural

% Transformed Target

National Status

Adjusted Status

Low Escarpment Moist Grassland 141377.7 21399.37 162777.1 86.85357 13.14643 23

Northern Afrotemperate Forest 907.066 259.5339 1166.6 77.75296 22.24704 31

Northern Drakensberg Highland Grassland 74056.79 16773.97 90830.76 81.53272 18.46728 27

Northern Free State Shrubland 1967.613 390.9214 2358.534 83.42524 16.57476 28

Northern Upper Karoo 660157.5 306329.8 966487.3 68.30483 31.69517 21

Rand Highveld Grassland 76777.13 161833.2 238610.3 32.17678 67.82322 24 VU VU

Schmidtsdrif Thornveld 67791.98 65594.52 133386.5 50.82372 49.17628 16 VU

Senqu Montane Shrubland 199719.7 173759.5 373479.2 53.47546 46.52454 28 VU

Soweto Highveld Grassland 308776.1 961664.3 1270440 24.30465 75.69535 24 VU EN

uKhahlamba Basalt Grassland 145313.3 5076.816 150390.1 96.62423 3.375765 27

Upper Gariep Alluvial Vegetation 20677.67 42103.73 62781.4 32.93598 67.06402 31 EN

Vaal Reefs Dolomite Sinkhole Woodland 20653.71 14013.46 34667.18 59.57714 40.42286 24 VU

Vaalbos Rocky Shrubland 26306.39 1919.794 28226.18 93.19853 6.801465 16

Vaal-Vet Sandy Grassland 475504.1 1797196 2272700 20.92243 79.07757 24 EN CR

Vredefort Dome Granite Grassland 32336.07 59751.57 92087.64 35.11445 64.88555 24 VU VU

Western Free State Clay Grassland 402431.9 264294.8 666726.7 60.35936 39.64064 24

Winburg Grassy Shrubland 119190.1 37897.31 157087.4 75.87502 24.12498 28

Xhariep Karroid Grassland 1025947 312258.8 1338206 76.66586 23.33414 24

Zastron Moist Grassland 163742.6 251395.5 415138.1 39.44292 60.55708 24 VU

8.2.3. Aquatic

With the exception of FEPA catchments (included in the cost layer and for the identification of ESAs) and wetland clusters (included as ecological support areas only), aquatic features were not included in this assessment. The most recent and complete coverages of the wetlands of the Free State available is the NFEPA wetlands (Nel, et al., 2011) and the wetlands that were mapped as part of the updated Free State land over map (GeoterraImage, 2011). However, both these provide poor coverage of especially the long linear valley bottom wetlands. Manual mapping of wetlands was not considered to be a practical solution. The mapping of wetlands was therefore automated using an ArcGIS model. The main purpose of the model was:

• To improve the coverage of existing wetland spatial data

• To remove the isolated non-wetland 'headlands' that according to the land cover data are mapped and classified as wetlands


• To string together the isolated and fragmented wetlands units of the land cover map that are actually part of a single linear wetland.

The result is a watercourse probability map that provides improved coverage and better connectivity amongst the isolated wetland fragments of the Free State wetlands land cover class while also omitting non-wetland headlands (Figure 4).

Figure 4: Comparison of the wetland coverage of existing data sets (NFEPA and the Free State land cover) with the modelled wetland coverage. Table 9: Comparison of the number of features and area of different wetland coverages, namely NFEPA (national wetland inventory), the Free State land cover map and the modelled coverage.

Coverage Number of features Total area (ha) % of modelled area

National Inventory 44 604 257 475 17 %

Land Cover 397 359 317 948 21 %


Modelled 37 589 1 508 763 -

The modelled watercourse layer covers 1 508 763 ha compared to the 257 475 ha and 317 948 ha of the NFEPA and land cover data respectively. Although the spatial information was improved by the modelled coverage, it contained no attribute data to render it suitable for inclusion in the Free State Biodiversity Plan. The aquatic component was therefore omitted from analysis. However, the biodiversity plan outputs should be informed by the aquatic products that are available (NFEPA and the land cover spatial data). Although the latter are considered to be of too low accuracy to be included in this analysis, they are still considered to be of sufficient significance to be considered during decision making.

8.2.4. Ecological corridors

Ecologcal corridors were mapped to account for climate change and the possible negative effect of habitat (and population) fragmentation. Changes in climate may force certain species to move along environmental gradients as existing habitats become unsuitable. Isolated populations, which includes those in protected areas that are not managed for genetic fitness, are more susceptible to the effects of climate change than those that are witrhin open systems and that are exposed to periodic gene flow. Ecologcal corridors may potentially negate some the negative effects by:

• Allowing species to migrate as climate changes to move to more suitable habitats

• Promoting gene flow between populations

• Providing habitat that offers range of microclimate refugia. The central location of the Free State makes it strategically important for maintaining landscape connectivity. Ecological corridors were identified by using Linkage Mapper (McRae & Kavanagh, 2011) which requires a resistance (cost) layer and focal points. $Say it is a least cost path analysis.

• Resistance layer (cost) The resistance layer was compiled from two separated sources, these being the national coverage of areas resilient to climate change (Holness & Bradshaw, 2012) and the Free State land cover map (GeoterraImage, 2011). The approach followed was to use the climate change resilient areas as the primary layer to inform corridor analysis with the land cover map informing the analysis only when the corridors need to extend beyond the areas of climate change resilience. This was achieved by assigning lower resistence values rto features of the climeta change resilience map (1 - 900) and higher resistence values to features of the land cover map (2000 - 10 000). In addition to the above mentioned two layers, the NFEPA rivers and streams (Nel, et al., 2011) and wetland clusters (Nel, et al., 2011) were also considered to be preferential areas for ecological corridors. The rivers and stream were subsequently buffered by 50 m and the wetland clusters by 500 m and assigned resistence values of 600 and 900 respectively. All of the above mentioned were merged into a single layer. This approach effectively prioritises the layers, with the climate change resilience map being the first priority for identifying ecological corridors, followed by the rivers, wetland clusters and then the different land cover classes. The outcome of this approach is that corridors will firstly be aligned with areas of climate change resilience (except in areas with resitsence cost >600 and


in which rivers are embedded). It is only when the corridor analysis has to find corridors outside of the climate change resilience preferential areas that the land cover classes data will be considered (it is for this reason that land cover classes that represent natural vegetation were assigned a cost of 2000, i.e. the algorithym will first try and direct the corridor through the climate change resilience map, ond only after this is not possible will it revert to the land cover data, of which the natural areas have the lowest cost (2000)]. Land cover classes that are not suitable for inclusion in ecological corridors (e.g. built up areas) were assigned "NoData". The latter implies that Linkage Mapper will not attempt to create corridors through such areas. All headlands were removed from the final resistance layer to prevent corridors being directed along these thin stretches of natural land that do not satisfy the basic requirements for ecological corridors (Thomas, 1991). The following procedures were followed to prepare the resistance layer: o The climate change resilience layer was inverted (reclassified) according to Table 10 so that the original low values indicate low resilience to

climate change and high values indicate high resilience to climate change.

Table 10: Table indicating the reclassification (inverting) of the climate change resilience layer (Holness & Bradshaw, 2012). Assigned resistence values are provided in the column with geading ' New Value (Resistence)'.

Original value New Value (Resistence)

0 NoData

1 900

2 800

3 700

4 600

5 500

7 300

9 100

10 1

NoData NoData

o Land cover data were reclassified according to Table 11. Assigned resistence value are provided in the column with geading ' New Value

(Resistence)'. Land cover classes assigned 'NoData' were considered to be unsuitable for the establishment of corridorts

Table 11: Resistance values assigned to individual land cover classes (fields 'Resistance' and 'State' respectively). 'NoData' indicates land cover classes that act as complete barriers (e.g. urban areas). ($table still needs to be finalised in terms of which field should be removed and which ones not). $Adjust collumn widths so that Free State legend class is not truncated.

Class no.

Primary land-cover Free State legend class Resistance State CBA availability

1 Forest & woodland Forest 2000 Natural Available 2 Forest & woodland Woodland 2000 Natural Available


Class no.


3 Forest & woodland Open woodland 2000 Natural Available 4 Thicket, bushland, tall fynbos Thicket 2000 Natural Available 5 Thicket, bushland, tall fynbos Scrub forest 2000 Natural Available 6 Thicket, bushland, tall fynbos Open bushland 2000 Natural Available 7 Thicket, bushland, tall fynbos Bush clumps 2000 Natural Available 8 Shrubland, low fynbos Low shrubland 2000 Natural Available 9 Shrubland, low fynbos Sparse / open low shrubland 2000 Natural Available 10 Shrubland, low fynbos Sparse / open low shrubland (gravel/rocky substrate) 2000 Natural Available 11 Grassland Grassland 2000 Natural Available 12 Grassland Sparse / open grassland 2000 Natural Available 13 Grassland Sparse grassland: (gravel/rocky substrate) 2000 Natural Available 14 Grassland Planted grass, golf & sports grounds 8 000 Transformed Not available 15 Forest plantations Plantations (pine) 5 000 Transformed Not available 16 Forest plantations Plantations (eucalypt) 5 000 Transformed Not available 17 Forest plantations Plantations (wattle) 5 000 Transformed Not available 18 Forest plantations Plantations (other) 5 000 Transformed Not available 19 Forest plantations Plantations (clear-felled) 5 000 Transformed Not available 20 Forest plantations Woodlots 8 000 Transformed Not available 21 Water bodies Water (man-made) NoData Transformed Not available 22 Water bodies Water (sewage) NoData Transformed Not available 23 Water bodies Water (natural) 2000 Natural Available 24 Water bodies Water (natural pan) 2000 Natural Available 25 Wetlands Wetlands (non-pan) 2000 Natural Available 26 Wetlands Wetlands (vegetated pans) 2000 Natural Available 27 Wetlands Wetlands (dry pans) 2000 Natural Available 28 Barren land Natural bare / non-vegetated (non-rock) 2000 Natural Available 29 Barren land Natural non-vegetated (bare rock) 2000 Natural Available 30 Barren land Salt mines in pans 2000 Transformed Not available 31 Barren land Erosion (dongas) 3 000 Transformed Not available 32 Barren land Erosion (sheet) 3 000 Transformed Not available 33 Barren land Degraded 3 000 Degraded Available5 34 Barren land Degraded (less severe) 3 000 Degraded Available6 35 Barren land Land-fills NoData Transformed Not available

5 In field inspection of land cover class 33 revealed that many large areas of natural vegetation were, due to the natural sparse nature of this vegetation type, incorrectly classified as degraded and barren land. It was therefore considered to be suitable for inclusion in CBAs. 6 In field inspection of land cover class 34 revealed that many large areas of natural vegetation were, due to the natural sparse nature of this vegetation type, incorrectly classified as degraded and barren land. It was therefore considered to be suitable for inclusion in CBAs.


Class no.


36 Barren land Feedlots NoData Transformed Not available 37 Cultivated lands Cultivated (orchards) NoData Transformed Not available 38 Cultivated lands Cultivated (pricklv pear) NoData Transformed Not available 39 Cultivated lands Cultivated - dryland commercial 10 000 Transformed Not available 40 Cultivated lands Cultivated - dryland subsistence 10 000 Transformed Not available 41 Cultivated lands Cultivated - irrigated (non-pivot) 10 000 Transformed Not available 42 Cultivated lands Cultivated - irrigated annuals (pivot) 10 000 Transformed Not available 43 Cultivated lands Smallholding cultivated 10 000 Transformed Not available 44 Cultivated lands Smallholdings 10 000 Transformed Not available 45 Cultivated - old fields Cultivated - old fields (open woodland) 5000 Degraded Not available 46 Cultivated - old fields Cultivated - old fields (thicket) 5000 Degraded Not available 47 Cultivated - old fields Cultivated - old fields (open bush) 5000 Degraded Not available 48 Cultivated - old fields Cultivated - old fields (bush clumps) 5000 Degraded Not available 49 Cultivated - old fields Cultivated - old fields (low shrub) 5000 Degraded Not available 50 Cultivated - old fields Cultivated - old fields (open / sparse low shrub) 5000 Degraded Not available 51 Cultivated - old fields Cultivated - old fields (rocky open / sparse low shrub) 5000 Degraded Not available 52 Cultivated - old fields Cultivated - old fields (grassland) 5000 Degraded Not available 53 Cultivated - old fields Cultivated - old fields (sparse grass) 5000 Degraded Not available 54 Cultivated - old fields Cultivated - old fields (degraded) 5000 Degraded Not available 55 Cultivated - old fields Cultivated - old fields (erosion) 5000 Degraded Not available 56 Urban / built-up Urban residential / high density NoData Transformed Not available 57 Urban / built-up Urban commercial NoData Transformed Not available 58 Urban / built-up Urban industrial i transport NoData Transformed Not available 59 Urban / built-up Animal batteries NoData Transformed Not available 60 Urban / built-up Greenhouses / tunnels NoData Transformed Not available 61 Urban / built-up Urban village / low density NoData Transformed Not available 62 Urban / built-up Golf & trout residential estates NoData Transformed Not available 64 Urban / built-up Urban vegetation NoData Transformed Not available 65 Urban / built-up Transport networks 2000 Transformed Not available 66 Mines & quarries Mine (extraction & tailings ) NoData Transformed Not available 67 Mines & quarries Mine (infrastructure) NoData Transformed Not available 68 Riparian Riparian (A.karoo) woodland 2000 Natural Available 69 Riparian Riparian (A.karoo) open woodland 2000 Natural Available 70 Riparian Riparian (A.karoo) thicket 2000 Natural Available 71 Riparian Riparian (A.karoo) scrub forest 2000 Natural Available 72 Riparian Riparian (A.karoo) open bushland 2000 Natural Available 73 Riparian Riparian (A.karoo) bush clumps 2000 Natural Available 74 Riparian Riparian (other) woodland 2000 Natural Available 75 Riparian Riparian (other) open woodland 2000 Natural Available


Class no.


76 Riparian Riparian (other) thicket 2000 Natural Available 77 Riparian Riparian (other) scrub forest 2000 Natural Available 78 Riparian Riparian (other) open bushland 2000 Natural Available 79 Riparian Riparian (other) bush clumps 2000 Natural Available 80* Mesic highveld grassland low

shrub communities Low shrub in mesic highveld grass, slopes > 25 2000 Natural Available

81* Mesic highveld grassland low shrub communities

Low shrub in mesic highveld grass, slopes < 25 2000 Natural Available 82 Mesic highveld grassland low

shrub communities Open woodland in non mesic highveld grass 2000 Natural Available

83 Rocky outcrops Rocky hills open / sparse low shrub 2000 Natural Available 84 Rocky outcrops Rocky hills open woodland 2000 Natural Available 85 Bush canopy density modelling Sparse bushland (10- 40% cc) 2000 Natural Available

Although land cover classes 23 (natural open water) and 65 (transport networks) are considered to be unsuitable for the establishment of ecological ecological corridors, they were classified as being suitable and were assigned a resitence value of 2000 to prevent them acting as in-corridor blockages. o Rivers and streams were assigned a resitence value of 600

o Wetland clusters were assigned a resitence value of 900.

• Focal points Focal points represent the areas that need to be connected. Two types of focal points are recognized; inter-provincial (focal points that ensure connectivity and alignment with corridors of neighboring provinces) and inner-provincial focal points (focal points to ensure connectivity across the Free State province) (Table 12). The inter-provincial focal points were identified by consulting the corridors and focal points from neighboring provinces to inform the position of the Free State focal points. The Free State focal points that are positioned along the provincial boundary indicate the areas to which connectivity must be created to ensure that the network of Free State corridors are aligned with the corridors and focal areas of the neighbouring provinces. Where neighboring provinces have not yet been determined focal points these were mapped in areas subjectively considered to be suitable. Preference was given to areas that are in or are in close proximity to protected areas, which are aligned with the climate change resilience map, are in close proximity to river/stream confluences and of which there is little transformation in the immediate vicinity.


Figure 5: Inter- and inner provincial focal points used for corridor analysis. Focal points located on the provincial boundary represent the inter-provincial focal points while those located within the province are the inner-provincial focal points. Descriptive comments associated with each o fthe focal points are provided in Table 12 (the number in field 'ID' corresponds with the number displayed in this figure.


Table 12: Descriptive information for inter- and inner provincial focal points. The values in the column "Centrality ranking' is a ranking of the individuall focal points pertaining to their importance for keeping the overall coriidor netword connected (the lower the value the more important is the corrsponding focal point).

ID Descriptive comments Type Centrality

ranking

1 Borders Mpumalanga focal area. Positioned at stream/river confluence for increased connectivity. Alligned with CCR corridor. Inter-provincial 28

2 Arbitraty point for connectivity between NE highlands and SE lowlands. Alligned with CCR corridor, Inter-provincial 14

3 Borders Ingula and borders KZN CBA mandatory sites. Alligned with CCR corridor. Inter-provincial 30

4 Positioned opposite araes IDd as irreplaceable and important in natural area to allow linkage. Alligned with CCR corridor. Inter-provincial 37

5 Positioned to allow for improved linkage; avoiding old field, urban areas and other forms of transformation. Alligned with CCR corridor. Inter-provincial 36

6 Borders Mpumalanga focal area. Positioned at stream/river confluence for increased connectivity Inter-provincial 20

7 Borders Mpumalanga focal area. Positioned along a stream/wetland for increased connectivity. Alligned with CCR corridor. Inter-provincial 34

8 At confluence of corridors. Alligned with CCR corridors. Inner-provincial 21

9 Borders KZN CBA mandatory area in close proximity to FS area of high species occurence and Seekoeivlei NR. Alligned with CCR corridor. Inter-provincial 27

10 Positioned at river confluence to maximise connectivity. Alligned with CCR corridor. Inter-provincial 18

11 In close proximity to two preotected areas (Doringkloof & Gariep) is area of low land transformation. Alligned with CCR corridor. Inter-provincial 32

12 Stepping stone for corridor allignment; Willem Pretorius Game Reserve Inner-provincial 1

13 Stepping stone for corridor allignment; Soetdoring Nature Reserve Inner-provincial 15

14 Stepping stone for corridor allignment; Golden Gate Highlands National Park Inner-provincial 5

15 Stepping stone for corridor allignment; Caledon Nature Reserve Inner-provincial 13

16 Positioned to maximise linkage opportunity. Was therefore plotted on the border of the Sandveld Nature Reserve. Alligned with CCR corridor; Sandveld Nature Reserve Inter-provincial 7

17 Stepping stone for corridor allignment; Koppies Dam Nature Reserve Inner-provincial 9


ID Descriptive comments Type Centrality

ranking

18 Stepping stone for corridor allignment; Kalkfontein Dam Nature Reserve Inner-provincial 10

19 Stepping stone for corridor allignment; Sterkfontein Dam Nature Reserve Inter-provincial 4

20 Stepping stone for corridor allignment; Maria Maroka National Park Inner-provincial 8

21 Stepping stone for corridor allignment; Seekoeivlei Nature Reserve Inner-provincial 23

22 Positioned in close proximity to Mokala National Park and along river for connectivity. Area is heavily transformed. Alligned with CCR corridor. Inter-provincial 17

23 No transformed or disturbed areas on the FS side to account for. Alligned with CCR corridor; Tussen-die-rivier Nature Reserve Inter-provincial 26

24 Stepping stone for corridor allignment; Erfenis Dam Nature Reserve Inner-provincial 2

25 Subjective modification; Core_Area_1 Inner-provincial 40

26 Subjective modification; Core_Area_2 Inter-provincial 3

27 Subjective modification; Sentinel Inter-provincial 6

28 Borders North West corridor. Positioned at stream/river confluence for increased connectivity. Alligned with CCR corridor. Inter-provincial 12

29 Borders North West corridor. Positioned at stream/river confluence for increased connectivity. Alligned with CCR corridor. Inter-provincial 33



32 Positioned along river for connectivity. Alligned with CCR corridor. Inter-provincial 39

33 Positioned along river for connectivity. Alligned with inland located CCR corridor. Inter-provincial 38








Inter-provincial focal points o Eastern Cape Province


Focal points for the Eastern Cape Province were obtained from the Eastern Cape Province biodiversity plan. Ecological corridors stretched along an extended area of the southern Free State border. Focal points were identified by overlaying the ecological corridors with areas of high irreplaceability as determined through Marxan analysis in the Eastern Cape Province biodiversity plan. One focal area which accounts for Eastern Cape corridors and which is also aligned with a number of Eastern Cape planning units classified as CBAs was identified. It was positioned to be aligned with the Tussen-die-Riviere Nature Reserve and areas of high climate change resilience while simultaneously avoiding areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer. Large water bodies, e.g. the Xhariep and Van Der Kloof Dams, were also avoided as these were considered to represent discontinuities across which certain organisms will not be able to move, e.g. reptiles. An additional two focal points were added to steer the corridors along the southern border (Orange River) and for improved connectivity with the Eastern Cape corridors.

o Gauteng

Climate change corridors and species migration corridors were obtained from the Department of Agriculture and Rural Development, Gauteng. These were identified as part of the Gauteng Conservation Plan (version 3.3, 2011). Two focal points where the climate change corridors and species migration corridors intersect the Free State boundary were included. One of these focal points is also located within a grouping of planning units that have been classified as CBAs (irreplaceable and important areas) and was considered to be important to maintain linkage with. No ESAs were considered as focal points. The final two focal points were subjectively positioned to maximize linkage opportunity, i.e. they were subjectively placed to avoid areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer. Large water bodies, e.g. the Vaal Dam, was also avoided as these were considered to represent a discontinuities across which certain organisms will not be able to move, e.g. reptiles.

o North West

Areas considered for focal points were those that contain both CBA corridors and CBA links and where CBA nodes overlap with CBA corridors and CBA links. These sites were considered most suitable for where which linkages should be maintained. One focal area which accounts for Eastern Cape corridors and which is also aligned with a number of Eastern Cape planning units classified as CBAs was identified. It was positioned to be aligned with the Tussen-die-Riviere Nature Reserve and areas of high climate change resilience while simultaneously avoiding areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer. Large water bodies, e.g. the Xhariep and Van Der Kloof Dams, were also avoided as these were considered to represent discontinuities across which certain organisms will not be able to move, e.g. reptiles. An additional two


focal points were added to steer the corridors along the southern border (Orange River) and for improved connectivity with the Eastern Cape corridors. Three focal points were subjectively positioned to maximize linkage opportunity with biodiversity corridors, biodiversity corridor linkages and biodiversity nodes (CBA2) as identified in the North West Province biodiversity plan. One of these is aligned with the Sandveld Nature Reserve while all are aligned with areas of high climate change resilience while simultaneously avoiding areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer. Although the Sandveld Nature Reserve includes a large water body (the Bloemhof Dam) it was included as it is strategically located to maintain connectivity with an important climate change resilience corridor (along the Vet River). One additional focal point was added.

o KwaZulu-Natal According to the KwaZulu-Natal conservation plan the entire escarp along the Free State/KwaZulu-Natal boundary is an ecological corridor (Alpine corridor, Berg corridor or Chelmsford corridor). The entire Free State/KwaZulu-Natal boundary has been identified as an important climate change resilience area. Three focal points were identified. One of these is aligned with the Sterkfontein Dam Nature Reserve while the other is aligned with the Ingula pumped storage scheme. The latter is Eskom owned and although not protected, is managed according to conservation principles. An application to have it proclaimed as a protected area has been submitted to the National Department of Environmental Affairs. The third focal point borders a KwaZulu-Natal CBA mandatory area which in close proximity to a Free State area of high species occurrence and the Seekoeivlei Nature Reserve (a Ramsar site). Areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer were avoided.

o Mpumalanga

Focal areas identified foe the Mpumalanga corridor analysis were obtained from the Mpumalanga Tourism and Parks Agency. The corresponding Free State focal points were positioned so that are aligned with areas of climate change resilience and where possible, with stream/river confluences while also avoiding areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer. The most southern of the Mpumalanga focal points is strategically located in close proximity to the Seekoeivlei Nature Reserve (a Ramsar site).

o Northern Cape Province

At the time of compiling the Free State biodiversity plan the Northern Cape Province was still in the process of finalizing its plan, i.e. ecological corridors and focal areas along the Free State/Northern Cape border was not yet available. Focal points were therefore subjectively identified to maximize alignment with existing protected areas (Doringkloof Nature Reserve, Gariep Nature Reserve and the Mokala National


Park) and areas of climate change resilience while avoiding areas of transformation or other forms of disturbance on the Free State side of the provincial border as identified from the Free State land cover data layer.

Inner-provincial focal points

In addition to the border focal points, focal points were also mapped within the interior of the province. These serve as 'stepping stones' through which corridors should preferably go. Preference was given to protected areas that are also aligned with the areas of climate change resilience as well as Important Bird Areas (IBAs).

Analysis:

Linkage mapper Ecological corridors were identified using the ' Build Network and Map Linkages' tool of the ' Linkage Mapper Arc10.tbx' toolbox (Error!

Reference source not found.).


Figure 6: Output of Linkage Mapper. The coloured Line features indicate the optimum corridor paths and are color coded according to the cost per Euclidean distance ratio (cooler colors indicate better corridors; lower cost per distance unit). The background indicates corridor routes (different shades of grey). Darker shades indicate optimum corridor routes. The thicker black sections in the southern and north eastern Free State indicate more options as to where the corridors can be established, compared to the northern and eastern portions where options are limited to the indicated narrow paths.


Pinchpoint mapper

After having mapped the ecological corridors using Linkage mapper, Pinchpoint mapper is used to run Circuitscape (McRae & Shah, Circuitscape.org, 2009) within the resulting corridors to identify bottlenecks (pinchpoints) in the corridors (Figure 7). The pinchpoints represent areas where corridors are at highest risk of failing due to the narrow nature of the corridors at such points.

Figure 7: Current flow analysis projected onto corridor routes (different shades of grey). High current flow (higher values, i.e. warmer colours) indicate pinchpoints areas within individual corridors.

Centrality mapper After having mapped the ecological corridors using Linkage mapper the centrality mapper tool of the ' Linkage Mapper Arc10.tbx' toolbox was run to determine the importance of the indivual corridors and core areas for keeping the overall coriidor netword connected (Figure 8).


Figure 8: Result of centrality mapper indicating the importance of the indivual focal points and corridors for keeping the overall corridor network connected. Focal points and corridors are classified into 5 categories according to the centrality scores. The higher the centrality score the more important that node/corridor is for mainatining overall corridor connectivity. Legend for the focal point labels indicate the Focal point number, the Centrality score and the Rank, e.g. 1) 34.658 (4) Focal point 1 with centrality score 34.658 and is ranked 4th important..


Discussion:

It is acknowledged that the width of ecological buffers needs to be informed by the ecological requirement of the species that will be using them. Corridors identified for the biodiversity plan do not target any specific species and will therefore need to act as a general pathway for all species. Reasons for species movement may vary and can range from dispersal movement to 'forced' movement. Dispersal movement is where species move away from their existing habitat in spite of it still being suitable, whereas 'forced' movement is considered to be instances where the current habitat is no longer suitable to support the species. The former can be the result of social pressures whereas the latter can be the result of e.g. climate change.

Wider corridors have always been assumed to be more effective and desirable than narrow corridors (Fleury & Brown, 1997). However, other competing land uses will necessitate that ecological corridors are no longer or wider than what they need to be to serve their purpose. Optimal corridor width is species specific, time specific, habitat specific and landscape specific (Fleury & Brown, 1997). The corridors included in the Free State biodiversity plan will need to serve a number of species over an extended period of time and across a range and habitats and landscapes. The optimal width is therefore the width that is required by the species with the highest demand. Desired corridor width according to (Fleury & Brown, 1997) is 90 m. However, according to (Jain, 2013) a rule of thumb is 1000 ft (304.8 meters).

Of all the species the large mammals are expected to have the highest corridor width requirement. However, the movement of large mammals is restricted by fences and as such they will not be able to utilize corridors unless when having escaped from their confinements. Corridors will therefore mainly serve birds, reptiles, amphibians and invertebrates, while plants will also utilize corridors should they present suitable habitat.

Ecological corridors were classified as being either Regional (Landscape) or sub-regional (Local) corridors. Landscape corridors are those that are responsible for connectivity across the broader landscape. In general this implies that they connect inter-provincial focal points that occur within different biogeographically regions and therefore having their starting and end points on the provincial boundary. Local corridors are those that connect landscape corridors. It follows that either the starting or end point of local corridors, or both, is at an inner-provincial focal point. However, regional corridors were selected based on their importance to maintain connectivity between focal points to and from which regional corridors extend.


Figure 9: Regional (red) and sub-regional (blue) ecological corridors. Labels indicate the centrality score rank of the focal points. To demarcate the different corridor paths regional corridors were buffered by 250 m (i.e. a 500 m corridor width) while sub-regional corridors were buffered by 150 m (i.e. a 300 m corridor width).


8.2.5. Ecological Processes

Table 13. Features related to ecological process and climate change adaptation that were included in the Free State biodiversity plan.

Feature Source Date Selection criteria Comments

Important bird areas • Centres of endemism

• Dolomite regions with unique biodiversity

• Forest pattern and process areas

DAFF

• High water yield areas

CSIR 2013

• Ecological process layers linked to climate change resilience and adaptation

Feature Dataset Data Type

Date Source Comment Target Description of usage

Other systematic spatial plans

Ekangala 100% target for CBAs



Date Source Comment Target Description of usage

8.2.6. Unique features

The inselbergs of the Free State as well as the geological formations that deine the Vredefort Dome were considered to be unique and were included as seperate features (Table 14). Table 14: Unique features included in the Free State biodiversity plan


Inselbergs; Afro Montane

Modelled (FS DETEA) 2013 Modelled Afro Montane inselbergs were included as separate features

Inselbergs; Karoo Modelled (FS DETEA) 2013 Modelled Karoo inselbergs were included as separate features

Unique Geological features

$ 2014 Unique geological features. The hills of the Vredefort dome were included as a separate features

Inselbergs Inselbergs act as repositories for a number of plants and animals. In biogeographical terms the biodiversity associated with inselbergs represent remnants of previous periods and therefore provide a view of evolution over a certain period of time. They are gene stores from previous cycles of climate change and should be protected into the future to allow for this potential to continue and not be lost. They also represent island within the broader landscape where they are often underlain by geology that is dissimilar to that of their immediate surroundings, while also, because of their degree of isolation, are not subjected to the same type and frequencies of disturbances as whet their immediate surroundings are.


Plant an animal communities and their associated species are therefore often dissimilar to those that have established in their immediate surroundings. Inselbergs were modelled according to criteria provided by Dr. Robert Brandt7.

• Altitude and elevation The elevation of 1950 m.a.s is of significance as it represents the altitude at which a distinct and significant change starts to occur in terms of species composition and most probable community structure. This threshold is higher than White's criteria of 1800 m which he used to define Afro alpine or Afro Montane sub-alpine plant communities. Inselbergs were subsequently regionalized on account of altitude, with Afro Montane inselbergs occurring at altitudes in excess of 1960 m.a.s. and Karroo inselbergs occurring at altitudes below 1960 m.a.s.

• Degree of isolation Inselbergs are characterised by being isolated from their immediate surroundings. They are typically difficult to access due to vertical or near-vertical surrounding slopes. Inselbergs were subsequrnly identified as portions of land that are surrounding by vertical or near-vertical slopes so that that they will be difficult or impossible to access on foot. Modelled inselbergs were seprted into the Afro Montane and Karoo inselbergs on account of altitide, wherhe the former occurs at altitudes in excess of 1960 m.a.s with the Karoo inselbergs occuring at lower elevations. Vredefort Dome The Vredefort Dome is an astroid impact crater that was formed about two billion years ago. It is the largest visible asteroid impact crater and is also the second-oldest impact structure with visible evidence at Earth's surface. The impact resulted in the creation of a ring of hills that surround the western to northern regions of the impact site. It is these hills that were included as features. A submission to have it declared a world haritage site has been submittd to UNESCO.

8.2.7. Climate change

To account for climate change the features as per Table 15were included in the Free State biodiversity plan. Table 15: The following features were included in the Free State biodiversity plan to account for climate change.


7 Dr. Robert Brandt. $adress



Areas of climate change resilience

(Holness & Bradshaw, 2012)

Areas of climate change resilience were not included as a separate feature. They were incorporated by having them included in the resistance layer which informs the mapping of ecological corridors (Seection 8.2.4), which in turn informs the Marxan cost layer (low cost assigned to areas of climate change resilience)

Areas of biome stability

(SANBI, 2011a) Areas of biome stability indicate those portions of a biome that are unlike to change in the face of cliamte change. $See section 8.2.7 for justification of target). Only the portions with value 0 of Figure 10 (areas of stability) that occur within the grassland biome were included as features. The most western portion of this category occurs mostly within the Savannah biome which according to Figure 11 will increase in extent at the cost of the grasslands. Although the Nama-Karoo biome (Figure 11) is also indicated to be an area with a high likelihood of change, there are no areas of stability locted within this biome.

Ecological corridors

See Section 8.3.11 Ecological corridors were not included as a separate feature and are accounted for by their inclusion in the cost layer (Section 8.3.2), while also informing the identifiaction of ESAs (Section 8.3.11).


Figure 10: Areas of biome stability. Cooler colors (greens) indicate areas that are least likely to change floristically while areas in the warmers color (reds) are most likely to change (SANBI, 2011a).


Figure 11: Comparison of biomes: Current distribution as per (Mucina & Rutherford, 2006) and future distribution under a high risk (worst case) scenario (SANBI, 2011b).


8.2.8. Land Cover

Key to any biodiversity planning process is to distinguish between areas that are still available for conservation and those that have been lost. Land cover for the Free State is based on the 2009 SPOT images (GeoterraImage, 2011). Eighty-five land cover classes, including some modelled, were identified. Depending on the nature of the land cover class these were collapsed into 2 classes; natural and modified. The natural class is further subdivided into areas that are degraded and those that are not (Figure 12). Areas classified as natural are considered to represent functioning ecosystems with intact or near-intact ecological and evolutionary processes. The surface areas of the primary land cover classes are prsented in Table 16.

Figure 12: Land Cover (GeoterraImage, 2011) with land cover classes reclassified according to field "State" of Table 11 (Natural, Degraded and Transformed).

$ percent of the Free State is natural (of which $ percent is degraded) while $ percent is transformed (Table 16 ).


Table 16: Surface area of primary land cover classes (GeoterraImage, 2011) Primary land cover class Area (km2)

Natural8

Forest & woodland 1075.22

Thicket, bushland, tall fynbos 2594.31

Shrubland, low fynbos 4737.56

Grassland 67645.36

Waterbodies 1522.51

Wetlands 3057.78

Riparian 333.14

Mesic highveld grassland low shrub communities 411.11

Rocky outcrops9 497.84

Bush canopy density modelling10 4149.28

Not natural

Forest plantations 928.21

Barren land 4107.49

Cultivated lands 36669.16

Cultivated - old fields 3037.23

Urban / bullt-up 1191.51

Mines & quarries 159.47

TOTAL 132117.211

8 Although most of the secondary land cover classes that make up the primary land cover classes are natural, some are coonsideredd to be not natural, e.g. the primary class 'waterbodies' also includes man-made waterbodies. Similalry, although consisting of natural vegetation, some of the secondary land cover classes may represent an unnatural state, e.g. areas of low shrubland may represent degraded grasslands that have been invaded by by e.g. Seriphium plumosum.

9 A modelled primary landcover class indicating low shrub areas located on rocky outcrops. 10 A modelled primary landcover class indicating sanavva areas based on grasslands (0 – 9% tree cover) and sparse (10 – 40% tree cover) and open (41 – 70%) bushland

areas. 11 Includes additional modelld layers


8.2.9. Protected Areas

Sixteen protected areas with a combined surface area of 185 686 ha have been proclaimed. However, of the sixteen only 13 are currently being managed as protected areas. The Karee Nature Reserve serves as a nursery while the Wuras Dam and Fiscksburg Nature Reserves are not managed as nature reserves due to their small size (262 and 134 ha respectively. The above excludes the Golden Gate Highlands National Park (31 274.1 ha) which is managed by SANParks.

Problems with proclaiming protected areas has resulted in some portions being included into and managed as protected areas that have not been proclaimed, while proclaimed areas outside of protected areas that are not being managed also exist. As such there is some uncertainty as to the legal protection that is afforded to all areas of the FS protected area network. The spatial layers of the FS protected areas are therefore a representation of areas that are being managed as protected area, but which includes proclaimed and not-proclaimed portions.

Although other formal protected areas such as the Maloti Drakensberg Transboundary World Heritage Site (MDTP) and a number of municipal nature reserves and private nature reserves occur, these were not included as features targets due to their weak state of legal protection. Informal protected areas (game farms and stewardship areas) are also excluded for similar reasons.

Table 17. Summary of formal protected areas in the Free State Province

Protected Area Type Agency Number ha

National Parks SANParks 1 31 274

Provincial Nature Reserves FS DESTEA 1612 185 686

Local Authority Local Municipalities 12 10 977

Total 227 937

Prtected area features included in the Free State biodiversity plan are discussed in Table 18.

Table 18: Protected area features included in the Free State biodiversity plan. Feature Source Date Selection criteria Comments

Private nature reserves

FS DETEA 2014 Due to low confidence in the accuracy of this dataset the private nature reserves were not included as a feature but was incorporated into the Marxan cost layer (low cost assigned to areas declared as private nature reserves) (Section 8.3.2)

World Although an application to have the Vredefort crater declared as a World

12 Three of the 16 protected areas areas are not managed as protected areas, these being the Karee Nature Reserve (Nursery; 10 ha), the Wuras Dam Nature Reserve (262 ha) and the Ficksburg Nature Reserve (134 ha).



Heritage Sites

Heritage Site, this has not yet been concluded. The Vredefort area was therefore not included as a feature. It was, however, because of its unique geological, historical, scientific and heritage value, incorporated into the Marxan cost layer (as part of the NPAES category, Section 8.3.2). However, the unique geological features of the site were included as a separate feature (see Section 8.2.6).

Buffers around protected areas

Buffers around protected areas were not included as features but was incorporated into the Marxan cost layer (low cost assigned to areas declared as private nature reserves; Section 8.3.2). A 5 km buffer was applied to provincial nature reserves and a 10 km to the Golden Gate Highlands National Park.

NPAES priority areas

SANBI FS DETEA

Priorities areas for protected area expansion were not included as features but were incorporated into the Marxan cost layer (low cost assigned to areas declared as private nature reserves). Included in this category are the NPAES focus areas, Vredefort Dome area, the Ingula area and the MDTP area (see Section Section 8.3.2).

8.2.10. Existing Spatial Planning Products

The only systematic and target driven plan that extends into the Free State is that of the Ekangala region. This plan included areas of the Free State, Mpumalanga, and KwaZulu-Natal provinces. The Ekangala plan differs from the Free State biodfversity plan in that it used the C_Plan software instead of Marxan to identify areas that are critical for biodiversity (CBAs).

The Ekangala plan was incorporated into the Free State bidiversity plan as follows:

• All CBAs identified by the Ekangala plan were included as features with a 100% target.

• Species data that informed the C-Plan were included as features in the Free State Bidoiversity plan with targets as per Table $(refer to the table that lists the features and their targets).


Other spatial plans:

To avoid conflict between different spatial plans, the FS biodiversity plan was aligned with other spatial plans that already exist for the study area. Spatial plans considered were:

• The Free State Spatial Development Framework (FS SDF)

• Ekangala biodiversity assessment

The FS SDF is the provincial spatial and strategic planning policy that responds to and complies with, in particular, the National Development Plan (NDP) Vision 2030. In this regard the FS SDF provides the spatial and strategic context for land-use throughout the Free State. It therefore gives effect by illustrating the desired future spatial patterns that provide for integrated, efficient and sustainable land-use throughout the province based upon the development priorities set the FSGDS. In practical land-use terms, the PSDF provides guidance pertaining to amongst others:

• What type of land-use should be undertaken at any particular location. This is to be achieved in conjunction with the FS GDS.

• How such land-use should be undertaken.

The FS SDF was integrated into the FS biodiversity plan by assigning high cost values to areas that the FS SDF has identified as important development nodes (emerging farmers agricultural projects, high priority development nodes and high mining potential; Section 8.3.2). A lower cost was not assigned to tourism related features, e.g. tourism corridors. Tourism corridors are routes that link development nodes and that provide access to well known attractions such as Clarens, Ficksburg, QwaQwqa, and the Golden Gate Highlands National Park. These are therefore areas in which future development may focus and they afford no additional protection (other than provided by the NEMA) to biodiversity features that occur in them or that are in their close vicinity. Large geographic regions that the FS SDF indicates to be important for economic growth, e.g. the Goldfield region and the Diamond region, were not included as these indicate large geographic regions in which future development may or may not take place and/or in which many of the mining operations will happen under ground. The exclusion of these regions are accounted for by the inclusion of areas known to have mineral deposits and high mining potential

The Ekangala spatial plan was, as the FS Biodiversity Plan, developed using a quantitative and systematic approach with the frequency of selection analysis done using C-Plan. The CBAs were therefore determined using a very similar approach, but for a different study area which included portions of the Free State, KZN and Mpumalanga. The Ekangala plan was incorporated into the Free State bidiversity plan as follows:

• All CBAs identified by the Ekangala plan were included as features with a 100% target.

• Species data that informed the C-Plan were included as features in the Free State Bidoiversity plan with targets as per Table $(refer to the table that lists the features and their targets).

8.2.11. Ecological support areas (ESAs)

The following features were included as ESAs:

• All Pseudo_2 and Pseudo_3 species coverages


• Ecological corridors (The optimum corridor routes as per Figure 6)

• Buffers around protected areas (protected areas: 5 km buffer; national parks: 10 km buffer)

• Strategic Water Source areas

• NFEPA Rivers: FEPACODE = 1

• NFEPA Rivers: FEPACODE = 1, 2 or 3

• NFEPA Rivers: FEPACODE = 1, 2 or 3

• NFEPA Rivers: ID_NFEPA = 1

• NFEPA Rivers: ORDER = 1, 2 or 3

• NFEPA Wetland clusters: FEPA = 1

All of the above features were merged and dissolved to create a single coverage of ESAs (Figure 13). CBAs as included in the CBA map were identified according to the procedure described in Table 31.


Figure 13: Areas from which CBAs were identified.

8.3. Project design

8.3.1. Technical specifications

Analysis was performed using Marxan systematic consrrvation planning software (Ball, Possingham, & Watts, 2009). The most important input features for a Marxan analysis are:

• The planning unit grid (Section 8.3.3)

• The biodiversity features with associated cost (referred to as the feature penalty factor; FPF) (Section $)


• The cost of each planning unit (Section 8.3.2)

• The planning unit boundary cost (Section 8.3.5)

Creating the neccesary inputs and setting the various Marxan parameters involved various tasks for which a number of GIS related and other tools were used. These are summarised in Table 19.

Table 19: Summary of the technical tasks performed and tools used to perform such tasks.

Task Tool

Data preparation Data preparation and all GIS related tasks were performed using ArcGIS 10.1 Extensive use was made of ArcGIS modelbuilder to create the neccesry outputs and product (Figure 14).

Ecological niche modelling (Section $) Ecological niche moddeling was perfomed for species feature target setting (Section $). Ecological nuch modelling was performed using Maxent (Phillips, Dudik, & Schapire). (Jenness, Export to Circuitscape, 2010)

Modelling: Watercourse probability map (Section $)

A watercourse probability map was created which used Toogrtaphical Position Index (TPI) as input feature. The TPI was created using the 6-Category Topographic Position Index tool which is part of the Land Facet Corridor Designer toolset (Jenness, Land Facet Corridor Designer, 2013)

Creation of planning units (Section 8.3.3) The 100 ha hexagon grid base layer was created using the Repeating shapes tool (Jenness, Repeating Shapes, 2012).

Marxan input files (Section $) Marxan input files were created using QMarxan (Apropos Information Systems Inc.) with Quantum GIS 1.8.0 (QGIS). However, the 'Calculate Planning Grid Conservation Factor Values' was found ineffective and this function was subsequently performed using a custom developed ArcGIS model.

With the exception of the input.dat file, all Marxan input files (spec.dat, pu.dat, puvspr.dat & bound.dat), were created using QMarxan (Apropos Information Systems Inc.). The planning unit boundary cost was assigned a value equal to the length of the boundary and the boundary cost for planning units at the edge of the study area were assigned their full value. The input.dat file was created using the Inedit.exe utility that is downloaded along with Marxan.

Marxan parameters were as follows:

• Version: Marxan v2.4.3 (Ball, Possingham, & Watts, 2009)

• Repeat runs: 100 (2 step)

• Boundary length modifier: 0.06

• Simulated annealing: Enabled

• Number of iterations 1 000 000 000

• Cost threshold: Not applied


Task Tool

• Starting prop: 0

Updating the bound.dat file After having created the bound.dat file using QMarxan it was updated by adjusting the cost of the planning unit boundary that is next to CBAs of neighbouring provinces to 0. Similarly, the planning unit boundary costs of planning units adjacent to protected areas were also adjusted to 0. Converting such boundary costs to 0 was performed using two custom designed program created using VB.NET.

Assigning Feature Penalty Factor values

Calibration (Section 8.4.1) Feature Penalty Factor (FPF) The selection of planning units is also influenced amongst others the Feature Penalty Factor (FPF). The feature penalty factor represents an additional cost that is incurred should Marxan not be able to satisfy a specific features target with the selected planning units. The FPF was initially assigned to species, ecosystems and other features based on their threatened status classification as per Table 34 (Section 8.4.1), after which the FPFs were adjusted dusring calibration using a custom developed tool created with VB.NET. Boundary Length Modifier (BLM) Calibration of the Boundary Length Modifier parameter was done using a custom developed VB.NET tool.

Number of runs (Section 8.4.1) The process for determining the most appropriate number of runs is described in Section 8.4.1.


Figure 14: A schematic presentation of how individual tollboxes (each containing a number of models as illustrated by the inserts indicted by the blue arrows) models were strung together to create the various outputs and products required for developing the Free State biodiversity plan.


8.3.2. Cost

The selection of a planning unit by Marxan is determined not only by the presence and/or absence of features, but also by the cost that is associated with selecting the planning unit. The cost incurred when selecting a planning unit is determined by:

• the planning unit cost and

• the planning unit boundary cost Each planning unit is assigned a planning unit cost. The planning unit cost assigned to each planning unit is the mean of the costs valujues as informed by the cost layer. The latter is a composite layer where the cost at any given location is informed by a number of features, each with its own characteristics. The cost assigned to each planning unit is a measure of its desirability to be selected by Marxan to satisfy the biodiversity feature targets. With all else being equal, planning units with low cost will selected prior to those with higher cost.

The composite cost layer consists of features with the following characteristics:

• Proximity cost (the cost is related to the Euclidean distance from the feature)

• Inherent cost (the cost is related to the inherent properties of the features itself) Negative and positive proximity cost is recognised:

• Positive proximity cost (ArcGIS model names starting with High) does not represent a disturbance factor but rather a likelihood or desirability factor. These are areas with lowered cost because of the presence of some feature or condition that make them desirable for selection. For example, within its modelled range a species is more likely to occur within the immediate surroundings of known observations as opposed to some distance from such observations. When planning units that cover the modelled habitat are selected to achieve the target, then it is desirable that such planning units are closer to actual observations to increase the likelihood of the species actually occurring within the selected area. The cost in the immediate surroundings of the actual observations should therefore be less than those some distance away.

• Negative proximity cost (ArcGIS model names starting with Low) is related to features that have a negative influence or exerts some form of pressure on its immediate surroundings. These are areas with increased cost due to some existing and/or potential future pressure or disturbance that make them undesirable for selection. Habitat in the immediate vicinity of e.g. urban areas, mining operations, roads, etc, are known to be less suitable than those further away because of the disturbances associated with the areas and activities. When planning units that cover the modelled habitat are selected to achieve the target, then it is desirable that such planning units are some distance away from such disturbances as opposed to those that are in close proximity. The cost in the immediate surroundings of the actual observations should therefore be higher than those some distance away.


Cost were assigned as follows: Features of which their impact is not limited to the location of the feature itself were buffered so that planning units in their immediate vacinity are less favourable for selcetion by Mearxan than those that are some distance away. Buffers applied were determined from literature or where no informatin could be found, were subjectively determined (field 'Reference'; Table 22). Features were group into primary classes as per Table 21. Primary classes therefore represent features that are considered to be relatively similar in their perceived impact. For pair-wise comparison the project objective was stated to be the establishment of CBAs and ESAs. The suitability of each of the primary classes relative to all other primary classes for the establishemnt of CBAs and ESAs was judged using a two-way table after which a weight was assigned to each of the primary classes. The cost of each feature was subsequently adjusted (multiplied) using the assigned primary class weight after which the results were adjusted and expressed as a proportion of 100 (i.e. so that the minimum of the newly calculated cost is 1 and the maximum is 100). The results are presented in Table 22. The 'Species' cost layer listed in Table 21 is a mosaiced cost layer of which the asosiated cost is an indication of the likelihood of a threatened and/or other conservation worthy species occuring at that site. The Pseudo species mapping used represents a ranking of likelihood of occurance (Section 8.2.1). Although only actual observations of species were included as features with targtes, it was considered desirable to have Marxan select planning units for exosystems (vegetation types) in locations that also have a high probability of threatened and/or other conservation worthy species being present within the selected planning units. To bias the selection of planning units that satisfy ecosystem targets toward lpanning units with high probability of species occurences. the cost of planning units that have a high likelihood of such species being present were lowered compared to those planning unit with a lower likelihood of containing such species. To this extent a cost layer was created for each species whereby the cost is lowest at the observation site itself, and increases with increasing distance from that site. Only site specific observations (Pseudo 1) were considered for creating the species specific cost layers. The increased cost with increased distance from the point of actual observation, multiple buffers with allocated costs as per Table 20 were mapped around such points. Table 20: Buffer distances with assigned cost (in parenthsis) for different taxa.

Taxa Standard buffer width; distance m (cost).

Flora 100 m (1); 200 m (10)

Avifauna 1000 m (1); 2000 m (10); 5000 m (20); 10000 m (40)

Invertebrates 100 m (1); 200 m (10); 500 m (20); 1000 m (40)

Reptiles 50 m (1); 100 m (10)

Mammals - small (terrestrial) 50 m (1); 100 m (10)

Mammals - small (bats) 100 m (1); 200 m (10); 500 m (20); 1000 m (40)

Mammals - large 100 m (1); 200 m (10); 500 m (20); 1000 m (40)


The cost layer was developed by using a base cost map of which all cells have a value of 50. The base cost layer value of 50 is considered to be 'neutral', i.e. areas or features for which there is no pre-disposition in terms of their suitability for establishing CBAs were assigned a cost of 50. A cost value was subsequently assigned to each feature/activity and its buffers which reflects its perceived impact or suitability for the establishment of CBAs. Features considered not suitable for the establishment of CBAs were assigned a high in escess of the base cost (51 - 100) while features that are favourable for the establishment of CBAs were assigned cost that is lower than the base cost (1 - 49) (field 'Cost'; Table 22). The cost allocated to each feature and its buffers was subsequently adjusted based on the importance of the feature relative to all other features. These adjustments weights were determined by means of pair-wise comparison analysis using a Priority Estimation Tool (PriEsT) (Siraj, Mikhailov, & Keane, 2014). An advantage of PriEsT is its ability to detect and report congruence (weight) and dissonance (direction) errors in judgements. Table 21: Relative importance (weight) of the individual Primary classes using pair-wise comparison.

Primary class Feature Weight

Negative proximity features

Agric opportunity Areas identified for the establishment of emerging farmers and other agricultural projects 0.077

Urban opportunity Areas identified to be priority future development nodes 0.077

Mining opportunity Areas with high mining potential 0.089

Agriculture lc15-lc20, lc37-lc44 0.101

Degraded Lc31-lc34, lc45-lc55 0.045

Urban lc14-lc22, lc30, lc35-lc44, lc54-lc67 & Settlements: Includes formal and informal 0.106

Mining lc30, lc66-lc67 0.106

Main & High Main roads and Highways 0.065

Secondary Secondary roads 0.054

Railway Railway lines 0.065

Powerlines Powerlines (high and medium voltage) 0.054

Positive proximity features

Edge matching CBAs of neighbouring provinces 0.016

NPAES NPAES 0.033

Formal Provincial Nature Reserves and National Parks 0.011

Informal Private Nature Reserves 0.016

Climate change Area resilient to climate change 0.022


Ecological services Catchments of river FEPAs 0.022

Ecological corridors Ecological corridors 0.022

Species Species (Actual locations) 0.017

Total 0.998

The abbreviation 'lc' referes to land cover classes and the following number correspond with the land cover classes listed in Table 11, e.g. lc45 refers to land cover class 45 (Cultivated - old fields (open woodland).

Table 22: Primary classes, features and their associated costs and adjusted costs. The values in column with heading "Weight (Primary class)" is the assigned Primary class weight which was determined using pair-wise comparison. The adjusted cost is the final cost value assigned to the individual features (values of column "Cost" multiplied with values of column "Cost * weight and adjusted to the range 1 - 100).

Primary class Features Applied buffer width Reference Cost Weight

(Primary class)

Cost * weight

Adjusted Cost

Agric opportunity Areas identified for establishing emerging farmers (National Department of Rural Development and Land Reform (DRDLR), 2015)

No buffers applied. All areas assigned a cost of 100

Subjectively determined 100 0.077 7.7 73

Agric opportunity

Areas identified for AVMP projects (National Department of Rural Development and Land Reform (DRDLR), 2015) Within 100 m of AVMP project area Subjectively determined 100 0.077 7.7 73

Within 500 m of AVMP project area Subjectively determined 60 0.077 4.62 44

Agric opportunity

Commonages (National Department of Rural Development and Land Reform (DRDLR), 2015) Within 100 m of AVMP project area Subjectively determined 100 0.077 7.7 73


Agric opportunity

PLAS projects (National Department of Rural Development and Land Reform (DRDLR), 2015) Within 100 m of AVMP project area Subjectively determined 100 0.077 7.7 73


Agric opportunity

Land acquisition and allocation farms (National Department of Rural Development and Land Reform (DRDLR), 2015) Within 100 m of AVMP project area Subjectively determined 100 0.077 7.7 73


Urban opportunity Areas identified in the FS SDF as high priority development nodes

No buffers applied. All areas assigned a cost of 100




(Primary class)

Cost * weight

Adjusted Cost

(Department of Cooperative Governance and Traditional Affairs (DCGTA), 2014)

Mining opportunity Areas identified in the FS SDF with mining potential (Department of Cooperative Governance and Traditional Affairs (DCGTA), 2014)

Areas with high mining potential


Mining opportunity Areas with minerals (point data) ($Source unknown)

Within 500 m of mineral point Subjectively determined 100 0.089 8.9 84

Mining opportunity Within 1000 m of mineral point 80 0.089 7.12 67

Mining opportunity Within 1500 m of mineral point 70 0.089 6.23 59

Agriculture Cultivated land: Small holdings (lc44) (GeoterraImage, 2011)

Within 100 m of smallholding cultivated land

Subjectively determined. Accepted half distances as for Settlements

100 0.101 10.1 95

Agriculture Within 2500 m of smallholding cultivated land

60 0.101 6.06 57

Degraded lc32-lc34; lc45-lc55 (GeoterraImage, 2011)

No buffers applied. Degraded areas (lc32-lc34; lc45-lc55) were assigned a cost value of 80. The remainder were assigned NoData.


Urban Transformed and undesirable land cover classes (lc14-lc22, lc30-lc31; lc35-lc44, lc56-lc67) (GeoterraImage, 2011)

No buffers applied. Transformed areas (lc14-lc22, lc30-lc31; lc35-lc44, lc56-lc67) were assigned a cost value of 100. The remainder were assigned NoData.


Urban Landfill sites lc35 (GeoterraImage, 2011)

Within 20 m of a landfill site (GDACE, 2002) 100 0.106 10.6 100

Urban Within 400 m of a landfill site 80 0.106 8.48 80

Urban Feedlots lc36 (GeoterraImage, 2011) Within 10 m of a feedlot (Beacon Environmental Ltd., 2012) (Learmonth, Whitehead, Boyd, & Fletcher)

100 0.106 10.6 100

Urban Within 200 m of a feedlot 80 0.106 8.48 80

Urban Urban / built-up: Urban commercial (lc57) (GeoterraImage, 2011)

Within 50 m of a commercial urban development

(Beacon Environmental Ltd., 2012)

100 0.106 10.6 100

Urban Within 100 m of a commercial urban development

80 0.106 8.48 80

Urban Urban / built-up: Animal batteries (lc59) (GeoterraImage, 2011)

Within 100 m of an animal battery (Learmonth, Whitehead, Boyd, & Fletcher)

100 0.106 10.6 100



(Primary class)

Cost * weight

Adjusted Cost

Urban Within 250 m of an animal battery 80 0.106 8.48 80

Urban Urban / built-up: Golf & trout residential estates (lc62) (GeoterraImage, 2011)

Within 100 m of a golf course Subjectively determined 100 0.106 10.6 100

Urban Within 200 m of a golf course 80 0.106 8.48 80

Urban Formal and informal settlements (see below)

Within 200 m of a formal or informal settlement

Subjectively determined (to allow for urban expansion)

100 0.106 10.6 100

Urban Within 1500 m of a formal or informal settlement

80 0.106 8.48 80

Mining Salt mines in pans (lc30) (GeoterraImage, 2011)

Within 200 m of a salt mine Subjectively determined 100 0.106 10.6 100

Mining Within 400 m of a salt mine 80 0.106 8.48 80

Mining Mining areas (quarries, extraction pits and tailings) (lc66) (GeoterraImage, 2011)

Within 50 m of a quarries, extraction pit or tailings

(Rademeyer, 2007) 100 0.106 10.6 100

Mining Within 100 m of a quarries, extraction pit or tailings

80 0.106 8.48 80

Mining Within 1500 m of a quarries, extraction pit or tailings

70 0.106 7.42 70

Main & High Highways and main roads (1:50 000 feature files)

Within 50 m of a highway or main road For main roads: 0. 100, 1000 is concluded from (Beacon Environmental Ltd., 2012), However the subjectively determined thresholds were preferred

100 0.065 6.5 61

Main & High Within 100 m of a highway or main road 90 0.065 5.85 55



Secondary Secondary roads (1:50 000 feature files)

Within 50 m of a secondary road (Beacon Environmental Ltd., 2012)

80 0.054 4.32 41

Secondary Within 500 m of a secondary road 60 0.054 3.24 31

Secondary Urban / built-up: Urban industrial / transport (lc58) (GeoterraImage, 2011)

Within 100 m of a industrial/transport urban development

(Beacon Environmental Ltd., 2012)

100 0.054 5.4 51

Secondary Within 2000 m of a industrial/transport urban development

80 0.054 4.32 41

Railway Railway lines (1:50 000 feature files) Within 50 m of a railway line Subjectively determined 80 0.065 5.2 49



(Primary class)

Cost * weight

Adjusted Cost

Railway Within 500 m of a railway line (adopted from 'Secondary roads')

60 0.065 3.9 37

Powerlines HV (High Voltage) lines (Source unknown)

Within 50 m of a high voltage power line Subjectively determined (adopted from 'Secondary roads')

100 0.054 5.4 51

Powerlines Within 100 m of a high voltage power line 80 0.054 4.32 41

Powerlines Within 500 m of a high voltage power line 60 0.054 3.24 31

Powerlines MV (Medium Voltage) lines (Source unknown)

Within 50 m of a medium voltage power line

Subjectively determined (adopted from 'Secondary roads')

100 0.054 5.4 51

Powerlines Within 100 m of a medium voltage power line

80 0.054 4.32 41

Edge matching CBAs of neighbouring provinces (Provincial CBA maps)

Within 5000 m of a CBA of a neighbouring province


NPAES Areas identified for protected area expansion or with potential for such expansion (Eskom)

Ingula Non-public areas with a management authority in place were assigned a cost of 1

1 0.033 0.03 1

NPAES ($reference, BGIS?) No buffer applied:; MDTP, Vredefort: Core and buffer areas

Subjectively determined: Public areas with a management authority in place were assigned a cost of 20

20 0.033 0.66 6

NPAES (SANBI, 2010) No buffer applied: NPAES focus areas Subjectively determined: Public areas without a management authority in place were assigned a cost of 30

30 0.033 0.99 9

Formal Provincial Protected Areas (Hayter & Schulze, 2015)

Within 5 km of a provincial nature reserve

NEMA; Listing Notice 3 10 0.011 0.11 1

Formal National Parks (Hayter & Schulze, 2015)

Within 5 km of a national park NEMA; Listing Notice 3 10 0.011 0.11 1

Within 10 km of a national park 20 0.011 0.22 2

Informal Private Nature Reserves (Hayter, Spatial map (GIS): Private Nature Reserves, 2015)

No buffer applied: Private Naure Reserves

20 0.016 0.32 3



(Primary class)

Cost * weight

Adjusted Cost

Climate change Areas resilient to limate change (Holness & Bradshaw, 2012)

No buffer applied: Climate change resilience categories 5 - 10


No buffer applied: Climate change resilience categories 1 - 4

20 0.022 0.44 4

Ecological services Catchments of river FEPAs No buffer applied: River FEPAs Subjectively determined

1 0.022 0.02 1

Ecological corridors Ecological corridors (Section 8.2.4) No buffer applied: Ecological corridors 1 0.022 0.02 1

Species Actual species locations (Table 3, Section 8.2.1)

Avifauna: within 1 km of observation 1 0.017 0.02 1

Species Avifauna: within 2 km of observation 10 0.017 0.17 2



Species Reptiles: within 50 m of observation 1 0.017 0.02 1

Species Reptiles: within 100 m of observation 10 0.017 0.17 2

Species Invertebrate: within 100 m of observation 1 0.017 0.02 1



Species Invertebrate: within 1000 m of observation

40 0.017 0.68 6

Species Flora: within 100 m of observation 1 0.017 0.02 1

Species Flora: within 200 m of observation 10 0.017 0.17 2

Species Mammal-small (non-bat): within 50 m of observation

1 0.017 0.02 1

Species Mammal-small (non-bat): within 100 m of observation

10 0.017 0.17 2

Species Mammal-small (bat): within 100 m of observation

1 0.017 0.02 1


10 0.017 0.17 2


20 0.017 0.34 3

Species Mammal-small (bat): within 1000 m of 40 0.017 0.68 6



(Primary class)

Cost * weight

Adjusted Cost

observation

Species Mammal-large: within 100 m of observation

1 0.017 0.02 1


10 0.017 0.17 2


20 0.017 0.34 3


40 0.017 0.68 6

Note:

• Many of the buffer widths are from references that provide guidelines or a summary of various findings as to suitable buffer widths for various sources of disturbances. The listed buffers are therefore not necessarily as stated within these references, but rather an approximation of a suitable buffer width as taken from such references.

The settlement layer was as follows:

• Areas classified as high density or large settlements are those where (Stats SA, 2001): o towns, townships and cities contain 20 000 people or more o the main places contain a population 1000 people or more and with a density of 1000 or more people per km². o All other places are classified as low density.

• The criteria of a density of 1000 or more people per km² equals 0.1 person/ha. A circular shape with an area of 10 000 m2 (1 ha) requires a radius of 56.41 m Buffer.

• The Eskom residential supply point dataset was used to indicate the location of residential areas. Two datasets were available of which one was downloaded from SPISYS13. The datasets were compared. The second dataset contained numerous points that were not included from the dataset downloaded from SPISYS. The additional points were exported and copied into the SPISYS dataset (C:\GIS\SCP\Cost\Settlements\Eskom_residences.shp).

• All features of the Eskom residential supply point dataset (C:\GIS\SCP\Cost\Settlements\Eskom_residences.shp) were buffered by 56 m of which the output was dissolved (C:\GIS\SCP\Cost\Settlements\Eskom_residences_56mBuffer_Dissolved.shp) (with the 'Create multipart features' option unchecked to create single features) and the surface area of each features was calculated (field "Area_ha" was added).

13 Downloaded from SPISYS on 21 March 2014


• A spatial Join was performed to count the number of points in each of the features contained within C:\GIS\SCP\Cost\Settlements\Eskom_residences_56mBuffer_Dissolved.shp during which the total number if residences in each of the features of C:\GIS\SCP\Cost\Settlements\Eskom_residences_56mBuffer_Dissolved.shp was determined to create C:\GIS\SCP\Cost\Settlements\Eskom_residences_56mBuffer_Dissolved_Join.shp.

• A field "Ratio" was added to C:\GIS\SCP\Cost\Settlements\Eskom_residences_56mBuffer_Dissolved_Join.shp and was populated with the result of dividing the area (field "Area_ha") with the number of residences (field "Count") to obtain a 'density ratio', i.e. the surface area per residence.

• Features considered with potential to exert pressure on their surrounding environment were those with 50 residential units or more, or those with more than 20 residential units within a third or less of 10 000 m2 (i.e. 1 residential unit on 3 000 m2; 1/3 of a hectare). These were selected using the following SQL statement; "Count_" > 50 OR ("Count_" >=20 AND "Ratio" <= 0.29). The selected features were exported to C:\GIS\SCP\Cost\Settlements\Eskom_Settlements.shp

• C:\GIS\SCP\Cost\Settlements\Eskom_Settlements.shp was merged with the urban edges as downloaded from the SPISYS system (C:\GIS\SCP\Cost\Settlements\Eskom_Settlements_Urban_edge_Merge.shp) after which all features were dissolved (with the 'Create multipart features' option unchecked to create single features) to create C:\GIS\SCP\Cost\Settlements\Settlements.shp which represent the final settlement layer.

Features that were considered but which were not included in the cost layer are presented in Table 23.

Table 23: Feature that were considered but not included in the final cost layer.

Primary class Features Description Reasons for exclsuion

Agric opportunity

Recapitalization programme

Consist of already black owned farms that are supported by government. These farms are synonymous with other farming areas that make up the greater portion of the Free State. Where they consist of natural vegettaion they will have been assigned the base cost value of 50. Where they have been impacted on they would have been assigned cost values as per Table 22, depending on features present.

Built-up areas Urban / built-up: Urban village / low density (lc61)

Source file: C:\GIS\SCP\Z_Supplement\Landcover\lc61.shp

The content of this feature consists mostly of low density farm homesteads (farm houses) that are already accounted for in the Formal and informal settlements feature layer.

Competing land uses:

High agric potential

Model: C:\GIS\SCP\Tools\SCP_Cost.tbx\High_Agric_potential (Done) Source file: C:\GIS\SCP\Z_Supplement\Agriculture\Land_capability_Dissolve.shp

The data is not of high enough precision which results in the entire FS being assigned high proximity cost values. Because the final cost layer is created by merging the low and high proximity cost layers of which the output contains the maximum of these values all low costs are lost.

Electricity: SIPS Grid corridors

C:\GIS\SCP\Z_Supplement\Infrastructure\EGI_corridors_FS.shp

The SIPS corridors represent a large area in which power lines will be constructed (100 km wide of which only a small portion will eventually be occupied by the grid). It follows that not all of this area will be developed. This development will be included as a negative proximity cost once the lines have been constructed (i.e. the extent of the power lines will be mapped and


subjected to the C:\GIS\SCP\Tools\SCP_Cost.tbx\High_Cost_Infr_Powerlines Model for inclusion in the cost layer).

Renewable: Solar priority areas

Model: None National CSIR study areas are available, but independent developers site potential plants according to their own data

Although priority areas for the development of solar farms have been determined by the CSIR, independent developers are identifying their own priorities, i.e. development of such facilities are not limited to the CSIR priority areas and can potentially be developed anywhere in the province.

Renewable: Wind priority areas

Model: None National CSIR study areas are available, but independent developers site potential plants according to their own data

Although priority areas for the development of wind farms have been determined by the CSIR, independent developers are identifying their own priorities, i.e. development of such facilities are not limited to the CSIR priority areas and can potentially be developed anywhere in the province.

Infrastructure Development corridors

Model: None Source file: (C:\GIS\SCP\Z_Supplement\SDF_2014\Negative\Collated_Negative_FS.shp) is for annotation purposes and does not represent the area that will targeted for development.

Development corridors along roads as indicated in the FS SDF were not included as these are already accounted for in the roads layer (Highways and main roads) which includes a 1 km buffer.

Low cost features

Priority areas for conservation

Priority areas for conservation as per (Driver A. M., 2005)

Moel: None Source file: D:\GIS\AGIS\South_Africa\Biodiversity_assessments\NSBA_2004\GIS_Layers\Terrestrial\Shapefiles\Priority_areas.shp Geographic priority areas identified as part of the NSBA 2004

Not included as these represent very large geographic areas within which the urgency for protection differs between individual portions of land.

Protected areas :Conservancies

Model: None Conservancies represent a voluntary association of environmentally conscious landowners and land-users who choose to cooperatively manage their natural resources in an environmentally sustainable manner.

Being voluntarily, conservencies may be desolved at any time. Land use within conservancies is not regulated and can easily resort to unsustainable land use practices when land owners or land usres change, or if the conservancy members decide to do so.

SANDF Model: None Areas that are classified as State Land and which is reserved for Department of Defence purposes. The land is legally owned by the South

Being owned by the South African National Defence Force, thay are subjected to the land use requirements of the SANDF. These areas are frequently subjected to Dpartment of Defeence related operations and activities, many of which are not considered to be compatible with the objectives of CBAs. Although many of the areas are managed by the


African Government with the Department of Defence having a reservation of use.

SANDF as natural environements, they have not been declared proteted in terms of any act. As such they are afforded no protection other than what is provided for in the NEMA.

Demographics (FS SDF)

Tourism corridors Model: C:\GIS\SCP\Tools\SCP_Cost.tbx\Low_Tourism_corridors (Done) Source file: C:\GIS\SCP\Z_Supplement\SDF_2014\Positive\Collated_Positive_FS.shp (as taken from the FS SDF spatial data)

Tourism corridors are routes hat link development nodes and that provide access to well known attractions such as Clarens, Ficksburg, QwaQqa, and the Golden Gate Highlands National Park. These are therefore areas in which future development may focus and they afford no additional protection (other than provided by the NEMA) to biodiversity features that occur in them or that are in their close vicinity.

Protected areas

Municipal reserves

Municipal reserves were not included as history has shown that these are not safeguarded against development and other pressures. The Bethlehem Municipal Reserve of Wolhuterskop serves as an example where a portion of the rotectd area that contained the theatened sungazr (Smaug giganteus) was developed to accomodate a church group.

Biosphere Reserves

Areas within which land owners and land users have voluntarily agreed to manage their natural resources in an environmentally sustainable manner and which as been declared as a biosphere reserve in terms of Man and the Biosphere Programme (MAB, or MaB) of UNESCO.

There are no biosphere reserves in the Free State Province. Biosphere reserves were not included as they provide no additional protection (other than provided by the NEMA and other pieces of legislation) to biodiversity features that occur in them or that are in their close vicinity. Also, Biosphere Reserves are not recognized in the National Environmental Management: Protected Areas Act (Act No. 57 of 2003).

World Heritage sites

An area or place of cultural or natural significance that is deemed to have outstanding universal value and that has been listed ed by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as being an area of special cultural or physical significance.

Although World Heritage sites are recognized in the National Environmental Management: Protected Areas Act (Act No. 57 of 2003), the Vredefort area has not been registered as a World Heritage Site at the time of developing the FS Biodiversity Plan.

Positive and negative pre-dispositions are represented by the positive and negative proximity maps respectively. The final cost layer was created as follows:

1. A base cost layer was created consisting of the entire Free State with all cells value = 50. (output 1). The base value of 50 represents a neutral cost, i.e. it represents no pre-disposition in terms of its suitability for establishing CBAs.

2. Cost layers for the individual features listed in field 'Features' of Table 22 were created. 3. All positive proximity species features (cost < 50) were merged using the 'Cell statistics' tool (Ignore NoData in calculation; Checked) to create

a single layer that contains the minimum value of the positive species proximity layers (output 2) 4. All other positive proximity rasters were merged using 'Cell statistics' tool (Ignore NoData in calculation; Checked) to create a single layer

that contains the mean value of the positive species proximity layers (output 3) 5. All negative proximity features (cost > 50) were merged using 'Cell statistics' tool (Ignore NoData in calculation; Checked) to create a single

layer that contains the maximum value of the negative species proximity layers (output 4)

https://en.wikipedia.org/wiki/UNESCO

https://en.wikipedia.org/wiki/United_Nations_Educational,_Scientific_and_Cultural_Organization

https://en.wikipedia.org/wiki/United_Nations_Educational,_Scientific_and_Cultural_Organization


6. The positive proximity rasters (output 2 and output 3) were merged to create a single layer that contains the mean value of the positive species proximity layers (using the Cell Statistics tool) (output 5)

7. The result (output 5) was Mosaiced with the negative proximity layer (output 4) using the "Mosaic to New Raster" tool so that where the values of the negative proximity layer (output 4) are greater than those of the positive proximity layer (output 5), then the value of the negative proximity (output 4) was accepted (output 6)

8. The base layer was adjusted to a value halfway between the maximum of the positive proximity layer (output 3) and the minimum of the negative proximity layer (output 4). This was done to account for the fact that many of the costs originally assigned to negative proximity features were adjusted to values below 50 (Table 22).

9. To create the final cost layer the result (output 6 with adjusted values) was Mosaiced with the base cost layer (output 1) using the "Mosaic to New Raster" tool so that where the values of (output 6) are different from those of the base layer, then the value of (output 6) is accepted.

8.3.3. Planning units

Creating the planning units consisted of the following tasks:

• Creating a spatial planning unit layer.

• Assigning an appropriate planning unit status to each planning unit.

The spatial planning units is what Marxan uses as its unit of selection while the planning unit status indicates whether the planning unit is available for selection or not, and if so, whether it constitutes a protected area or not.

A grid of 100 ha hexagons (Jenness, Repeating Shapes, 2012) was used as the base planning unit layer the Free State Biodiversity Plan. CBAs are in most cases expected to be in good ecological condition. To isolate degraded portions of the 100 ha hexagons and to identify planning units that entirely contain degraded land (and should therefore be excluded as CBAs), the transformed and degraded areas as identified from the Free State land cover map (GeoterraImage, 2011) were Unioned with the hexagon grid. All subdivisions smaller than 10 ha were dissolved into neighbouring planning units using the ArcGIS 'Eliminate' tool. All planning units of which more than 10% of its suraface area constitutes transformed or degraded land were assigned a planning unit status value of 3 so that they were not available for CBA selection. Such planning units were, however, available to be included as ESA2's. The criteria according to which planning status values were assigned to planning units are summarized in Table 25.


Table 24 . Steps for developing the planning unit layer and assigment of planning unit status values

Feature Dataset Data Type Date Source Comment

A grid of 100 ha hexagons

(Jenness, Repeating Shapes, 2012)

A grid of 100 ha hexagons was created using the repeating shapes tool of (Jenness, Repeating Shapes, 2012). This serves as the base layer.

Land cover data (GeoterraImage, 2011)

Land cover data were reclassified according to Table 11 after which it was Unioned with the 100 ha base layer. Planning units were categorised according to whether they contain transformed of degreaded land, or neither.

Sub-quarternary catchments

(Nel, et al., 2011) The above was Unioned with the sub-quarternary catchments as per the NFEPA assessment (Nel, et al., 2011).

Protected areas (Hayter & Schulze, 2015)

The above was Unioed with the protected area layer. Planning units containing land that is managed by the FS DETEA or SANPARKS as per the Protected Areas Act (Act 57 of 2003), were categorised as such, i.e. municipal and private nature reserves were not included in this category.

Planning units that were categorised as protected were not amalgamated to form a single large planning unit constituting the entire protected area.

Urban edge ####$Spisys Urban areas were included (Unioed to the above) to augment the 'urban/built up' land cover classes as per Table 11. These were categorised as transformed.

Large water bodies (Dam area)

Large water bodies were clipped from the planning unit layer.


Feature Dataset Data Type Date Source Comment

Put in how Pus were elimanted into neighbouring, e.g. <10 ha

Following creation of the planning unit layer each planning unit was assigned a planning unit status according to the rationale as per Table 25:

Table 25: Summary of criteria according to which planning status values were assigned to planning units.

Selection criteria PU Status Potential CBA map category

All planning units located within a protected area 2 Protected

PUS with degraded >= 50 % or PUS with transformed >= 50% or PUS with degraded + transformed >= 50 %, and not assigned Protected

3 Degraded

All other planning units 0 CBA Irreplaceable, CBA Optimal, ESA1, ESA2, Other

Potential CBA map category

Selection criteria PU Status

Protected All planning units located within a protected area 2

CBA Irreplaceable Frequency of selection >=80 AND (degraded <= 10% AND transformed <= 10%)

0

CBA Optimal Frequency of selection (>=50 AND <80) AND (degraded <= 10% AND transformed <= 10%)

0

ESA1 All planning units identified as ESAs (value 1 in field 'ESA') that have not been categorised as a CBA and that are <=10% degraded and <=10% transformed. ["ESA" =1 AND "Parea_ESA1" >=10 AND "Parea_ESA2" <=10

0


AND "Perc_trsf" <=10]

ESA2 All planning units identified as ESAs (value 1 in field 'ESA') and that have not been categorised as an ESA1. ["ESA" =1 AND "ESA1" =0 AND "Perc_trsf" <=50]

0

Other All planning units not categorised as a CBA or ESA and with planning unit status = 0. PUS with degraded < 50 %

0

Degraded All planning units not categorised as CBA, ESA, or other, i.e. all planning units with planning unit status = 3. PUS with degraded >= 50 %

3

All large water bodies were clipped from the planning unit layer to prevent ecosystem (vegetation type) targets from being satisfied within such areas. The Formal Protected Areas of the province were also Unioned with the planning unit layer after which all portions smaller than 30 ha were dissolved with adjacent planning units that also constitute protected areas. All such planning units were assigned a planning unit status value of 2, i.e. they are automatically selected by Marxan.

The availability of land cover classes for selection as CBAs are as per Table 11 (column ' CBA availability')

The use of farm portions as planning units were considered but was abandoned due to the poor quality of the data, specifically the multitude of gaps between farms and the multitude of 'undershoots' of farm boundaries as they extent to the Free State border. Planning units are therefore based on a 100 ha hexagon grid that has been intersected (Unioned) by:

• Protected Areas

• Sub-quaternary catchments

• Transformed land (comprising transformed land cover classes as per Table 11 which was supplemented with urban edges ($reference). The planning unit status of planning units that occur within protected areas were assigned a value of 2 and dissolved so that protected areas are not subdivided. Planning units occurring within transformed land were assigned a value of 3. Planning units with an area of 10 ha or less were dissolved into adjacent planning units using the ArcGIS 'Eliminate' tool ($bounday length). Data geometry was inspected using the Quantum GIS '$Check geometry' tool was identified errors that were not detected by the ArcGIS 'Repair Geometry' tool. Errors were repaired manually.


Planning Unit Grid Conservation Factor Values Biodiversity feature data were $ using a custom ArcGis 10.1 script. Input files for Marxan were created using QMarxan (Apropos Information Systems Inc.). To bias the selection of planning units towards those that are adjacent to the CBAs of neighbouring provinces, the joining boundaries were assigned a boundary cost of 0 using a custom developed program ($reference).

$To transpose the final 'frequency of selection' map to farm portions all farms portions that intersect planning units categorised as CBAs ($frequency of selection >= 0.8) were selected and classified as CBA.

Planning unit statistics are:

Note: Two planning u nits <10 ha remained. The were not disolved into neighbouring planning units for reasons unknown.


8.3.4. Planning unit cost

The planning unit cost is derived from a cost layer which, for the Free State biodiversity plan, is a composite layer indicating the suitability of the planning unit to be categorised as a CBA. Because Marxan attempts to satisfy the features targets within the area of lowest cost, it will, where there are options to satisfy features targets, select the planning units that will add the least cost to the solution.

The suitability of a planning unit to be categorised as a CBA is determined by the proximity of that planning unit of a number of features which may either enhance or detract from its suitability. Features that may enhance the suitability of a planning unit are termed 'positive proximity' features (e.g.


planning units within informal protected areas) while those that detract from the suitability are termed 'negative proximity' features (e.g. planning units within or surrounding mines). The technical detail on how the composite cost layer was created is explained in


Appendix 3: Cost.

8.3.5. Planning unit boundary cost

The cost that is assigned to each individual line14 of the planning unit boundary is termed the planning unit boundary cost. The setting of a cost is optional and the value is determined by calibration (See section 8.4.1). The planning unit boundary cost incurred when selecting a planning unit is the sum of the cost of all its boundaries, unless such a boundary is shared by a neighbouring planning unit that has also been selected. Therefore, because Marxan attempts to minimise the cost of the final solution, it will preferentially select neighbouring planning units to satisfy feature targets instead of planning units that are detached.

Table 26 . The cost surface is made up from the layers below


Date Source Comment Inclusion Description of usage

Species

14 The euclidean distance between two vertices


8.3.6. Planning unit status

Marxan also allows for the classification of planning units depending on their suitability and desirability for inclusion as CBAs. To this extent planning units were classified as:

• available for selection but not part of an existing protected area(planning unit status = 0)

• available for selection and that fall within an existing protected area (planning unit status = 2)

• areas that are transformed and not available for selection (planning unit status = 3)

Planning units that are not available for selection are those that cover degraded or transformed land. These are identified and assigned a planning unit status value of 3 during the process of creating the planning unit layer (Section Error! Reference source not found.). With te exception of planning units that contain degraded or transformed land, planning units located within protected areas were assigned a planning unit status of 2. This ensures their selection by Marxan. The remainder of the planning units are assigned a planning unit status value of 0, i.e. they are available for selection should they contain features. To ensure that planning units that do not contain features are not selected by Marxan, the minimum planning unit cost was set as 1 and not 0 (a 0 planning unit cost implies that there is no penalty for its selection and such planning units may be selected by Marxan even if they do not contain any features).

Planning units within formal protected areas but that cover degraded or transformed areas are assigned a planning unit status value of 3.

8.3.7. Ecological Support Areas (ESAs)

Ecological support areas:

Ecological support areas were included by hard-wiring the following feature into the Marxan options map.

• Pseudo_2 and Pseudo_3 coverages

• Wetland clusters

• Buffers surrounding protected areas (5 km around provincial protected areas; 10 km around the Golden Gate Highlands National Park (the Vredefor Dome was included as a features as it had not yet been declared a world Heritage Site at the time of compiling the FS Biodiversity Plan)

• Ecological corridors

• Buffers along major rivers ($how are these identified)

• Areas of NFEPA catchments that were not selected as CBAs ("ID_NFEPA" = 1)

• Areas of Strategic Water Resource Areas catchments that were not selected as CBAs

• FEPA Rivers ("ORDER" = 1, 2 or 3) (100 m buffer applied)


• FEPA Rivers ("FEPACODE" = 1) not selected as CBAs (100 m buffer applied)

• FEPA Rivers ("FEPACODE" = 2, 3 or 4; 2 = Fish Support Area or Fish Corridor, 3 = Phase 2 FEPA and 4 = Upstream Management Area) (100 m buffer applied)

8.3.8. Targets

The quantitative species targets were set according to the guidelines of Pfab, Victor & Armstrong (2011). To summarize the recommendations of Pfab, Victor, & Armstrong (2011) are:

• All known populations for any rare taxa with fewer than 11 known localities (localities being geographically separate sites at which the taxon has been recorded) should be included, independent of how that rarity is defined or assessed, in order to avoid an immediate threatened listing in the event of a decline or extreme fluctuations in specified population parameters (assuming that one locality would be equivalent to one location)

• All known populations of Critically Endangered, Endangered and Vulnerable taxa listed under the IUCN Red List criteria of B, C or D, should be included in a conservation plan.

• Eleven locations (or in the absence of any potential threat, 11 populations or 11 localities) and at least 10 000 mature individuals for all threatened (Critically Endangered, Endangered, Vulnerable) species solely listed under the IUCN Red List criteria of A or E as well as any other conservation worthy species (e.g. Near Threatened species).

The Free State (as other provinces) is responsible only for its proportional contribution of the required target. For example, should the Free Sate account for only 10% of a species distribution, then it is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). The proportional target was based on the proportional extent of the species distribution amongst provinces as determined through modelling (Appendix 1: Modelling). For example, if according to Pfab, Victor & Armstrong (2011) the target is 10 000 mature individuals and the Free State accounts for only 10% of the species distribution, then the target was set at 1 000 individuals.

Where data were available, the targets were set by converting the required number of populations/individuals to an area based on the home range of species. For example, where the proportional target for the Free State is 1 000 mature individuals for a species with a home range of 3 km2, then the target was set at 3 000 km2 (1 000 individuals x 3 km2).

For many species the required 10 000 mature individuals exceeds the known or estimated RSA population size. In such instances the FS proportional contribution and subsequent proportional targets were based on the estimated RSA population size and not the required 10 000 mature individuals.


Where the estimated RSA population is unknown, the target was based on the required 10 000 mature individuals. Where the proportional target is more than the number of individuals that occur within the Free State the target was set to the latter.

NOTE: The number of populations, the number of mature individuals and subsequent required surface area as stated by Pfab, Victor, & Armstrong (2011) were not followed unconditionally but rather used as guidelines. In many instances large areas of potential habitat were modelled for species that are rare and of which the likelihood of them occuring within these areas are low. In such instances best professional judgement was used for setting targets (implying that smaller targets were set than what is requried to satisfy the target as per Pfab, Victor & Armstrong (2011). $NOTE THAT THIS APPLIES TO THE PSEUDO_4 AND 5 AREAS WHICH IN FINAL ANALYSIS MAY NOT BE INLCUDED AS TARGETS FOR BIRDS, BUT WILL RATHER INFORM THE ECA AND BEST DESIGN SITES, IN WHICH CASE THIS PARAGRAPH NEEDS TO BE REMOVED

Modelled distribution: Areas where species could potentially occur was modelled (see Appendix 1)

Of the forest types map by DWAF it is only the Drakensberg montane forest type that occur within the Free State Province where it covers an area of 51.74 ha (0.73 percent of this forest type with the remaining occuring in other provinces). According to DWAF the target of this forest type is 997.7 ha which implies that the proportional FS contribution is 0.07 ha. Considering this small area, targets for forests were not set based on the FS proportional contribution but rather on account of the species that occur within the forest patches. To this extent a 100% target was set for all forest patches that contain species of which all known populations are to be conserved (Plectranthus grallatus, Streptocarpus gardenia, and Bradypodion dracomontanum). No forest patches in which Asplenium simii ocurs (of which all populations need to be conserved) occur in the Free State. Cumaltively the forest patches account for 33.24 ha (64.24% of the FS coverage of 51.74 ha and 0.47% of the total coverage of the national distribution of Drakensberg montane forest of 7024.73 ha). Other species that also occur within these forest patches are Lioptilus nigricapillus, Anhydrophryne hewitti, Heleophryne natalensis, Osyridicarpus schimperianus, Cryptocarya woodii, Curtisia dentata, and Podocarpus falcatus. The greater portion of these forests occurs within the Sterkfontein Dam Nature Reserve.

The regionalised portions of the Eastern Temperate Freshwater Wetlands, Highveld Alluvial and Highveld Salt Pans vegetation types were all assigned the same target as is assigned by National, i.e. all three portions of the Highveld Salt Pans are assigned a target of 24%.


Table 27: Biodiversity features and their associated parameters. Note that with the exception for the Eastern Temperate Freshwater Wetlands, that the conservation status (field 'Status") for ecosystems was adjusted as per Table 8 (Appendix 5).

Co

mm

on

nam

e

Sta

tus

Cri

teri

a

Tar

get

Est

imat

ed R

SA

po

pu

lati

on

Est

imat

ed R

SA

ran

ge

(km

2)

FS

nu

mb

er

FS

po

pu

lati

on

s

Ap

pro

xim

ate

RS

A in

div

idu

als

per

are

a

(in

d/h

a)

Ap

pro

xim

ate

RS

A a

rea

per

ind

ivid

ual

(km

2/in

d)

Ho

me

ran

ge/

Occ

up

ancy

(km

2)

Pro

po

rtio

nal

FS

dis

trib

uti

on

(%

)

FS

pro

po

rtio

nal

po

pu

lati

on

s (o

f 11

)

FS

pro

po

rtio

nal

ind

ivid

ual

s (o

f 10

000

)

FS

pro

po

rtio

nal

ind

ivid

ual

s (o

f E

stim

ated

RS

A p

op

ula

tio

n)

FS

pro

po

rtio

nal

are

a o

f es

tim

ated

RS

A

ran

ge

(km

2)

Are

a re

qu

ired

fo

r F

S n

um

ber

Are

a re

qu

ired

fo

r p

rop

ort

ion

al 1

0 00

0 in

div

idu

als

(km

2)

Are

a re

qu

ired

fo

r p

rop

ort

ion

al R

SA

p

op

ula

tio

n (

km2)

Blue Crane VU A1a,c,d,e; A2b,c 11/10000 21000 500000 0.04 23.8 3.8 18.4 3 1838.8 3861.4 91937.8 0.0 3493.6 7336.6

Yellowbreasted Pipit VU A2c; B1+2c; C1 100 6500 3000 7 2.17 0.5 0.0078 15.8 2 1579.3 1026.5 473.8 0.0 6.2 4.0

Grey Crowned Crane VU A1a,c; A2b,c; C1 100 3000 20000 5 0.15 6.7 6.3 10.9 2 1090.2 327.0 2180.3 0.0 3434.0 1030.2

Greater Bittern CR A1c; A2b,c 11/10000 350 50 1 7.00 0.1 12.7 2 1266.1 44.3 6.3 0.0 180.9 6.3

Southern Ground Hornbill VU C1 100 2000 160000 1 0.01 80.0 0.3 1 32.4 6.5 518.1 0.0 2590.4 518.1

Wattled Crane CR A2c; C1; C2a 100 230 1000 5 0.23 4.3 16.64 5.3 1 531.6 12.2 53.2 0.0 4422.9 101.7

Black Harrier NT D1 100 1000 416500 2 0.00 416.5 13.4 2 1336.6 133.7 55669.8 0.0 556698.2 55669.8

African Marsh Harrier VU A1c; A2b,c; C1 100 5000 33500 6 0.15 6.7 14.5 2 1452.4 726.2 4865.5 0.0 9731.0 4865.5

Corncrake VU A1a; C1 100 2500 35000 4 0.07 14.0 0.09 2.3 1 225.0 56.3 787.5 0.0 20.3 5.1

Saddlebilled Stork EN C1 100 150 700000 2 0.00 4666.7 13.1 2 1312.2 19.7 91850.9 0.0 6123393.5 91850.9

Blue Korhaan NT A2c 11/10000 5000 260000 0.02 52.0 0.468 41.6 5 4163.7 2081.8 108255.3 0.0 1948.6 974.3

Whitebellied Korhaan VU A1c; A2c; C1 100 5000 138000 5 0.04 27.6 24.2 3 2415.4 1207.7 33332.6 0.0 66665.3 33332.6

Bald Ibis VU A2c; C1; C2b 100 10000 180000 8 0.06 18.0 23.5 3 2351.8 2351.8 42332.7 0.0 42332.7 42332.7


Bearded Vulture EN C2b 100 200 100000 6 0.00 500.0 0 0.0 0.0 0.0 0.0 0.0 0.0

African Whitebacked Vulture VU C1 100 3500 200000 200

3 0.02 57.1 50 9.1 1 908.1 317.8 18161.7 5000.0 22702.1 7945.7

Rudd's Lark CR A2c 11/10000 5000 500 5 10.00 0.1 0.016 11.5 2 1151.0 575.5 57.6 0.0 9.2 4.6

Bush Blackcap NT C1; C2a 100 5000 1500 1 3.33 0.3 2.2 1 220.4 110.2 33.1 0.0 66.1 33.1

Melodious Lark NT A1c; A2c 11/10000 110400 2 0.00 #DIV/0! 0.25 47.5 6 4746.0 0.0 52395.8 0.0 593.2 0.0

Stanley's Bustard VU A1a,c; A2b,c; C1 100 5000 215000 9 0.02 43.0 0.4 1 37.3 18.7 802.3 0.0 1604.7 802.3

Martial Eagle VU A1a; C1 100 600 750000 3 0.00 1250.0 130 8.6 1 862.6 51.8 64697.0 0.0 56070.8 3364.2

Whitewinged Flufftail CR

A2c; B1+2a,b,c,d+3a,b,c; C1; C2a 100

250 40 5 6.25 0.2 38.1 5 3811.0 95.3 15.2 0.0 609.8 15.2

Botha's Lark EN A1c; A2c; B1+2c,d,e 100

5000 500 3 10.00 0.1 38.1 5 3811.0 1905.5 190.6 0.0 381.1 190.6

Grass Owl VU A2c; C1 100 5000 13900 9 0.36 2.8 3.13 24.0 3 2396.5 1198.3 3331.2 0.0 3750.6 1875.3

Secretary Bird NT A1c, A2c 11/10000 87094439 17 0.00 #DIV/0! 50 14.7 2 1474.2 0.0 12839119.6 0.0 73708.0 0.0

Lesser Kestrel VU A1a,c,e 11/10000 60000 580000 43 0.10 9.7 1000 14.4 2 143.6 8615.4 83282.0 0.0 115.6 6936.7

Targets were there assigned to layers in descending order of probability of occrence. Table 28 provides an example of such sequential target setting for a species of which 100 ha needs to be conserved: Table 28:

Species target: 1000 ha

Observation type Observation Area Target Cumulative target

Nesting site 20 ha 100 % (20 ha) 20 ha

Presence observation 320 ha 100 % (320 ha) 340 ha

Survey observation 2400 ha 30 % (720 ha) 1060 ha

Modelled distribution 50 875 ha 0 % (0 ha) 1060 ha

The rationale of this approach is to prioritise areas with higher probability of occurrence for selection.


Table 29:

Feature PU field Targets Status spf

C:\GIS\SCP\FINAL\AV_Anthropoides_paradiseus_Blue_Crane_Actual_point_Pseudo_1.shp Blue_Crane 100 VU 80

C:\GIS\SCP\FINAL\AV_Anthus_chloris_Yellowbreasted_Pipit_Actual_point_Pseudo_1.shp Yellowbr_1 100 VU 80

C:\GIS\SCP\FINAL\AV_Anthus_chloris_Yellowbreasted_Pipit_Actual_polygon_Pseudo_1.shp Yellowbr_2 100 VU 80

C:\GIS\SCP\FINAL\AV_Balearica_regulorum_Crowned_Crane_Actual_point_Pseudo_1.shp Crowned_Cr 100 VU 80

C:\GIS\SCP\FINAL\AV_Bugeranus_carunculatus_Wattled_Crane_Actual_point_Pseudo_1.shp Wattled_Cr 100 CR 100

C:\GIS\SCP\FINAL\AV_Falco_naumanni_Lesser_Kestrel_Actual_polygon_Pseudo_1.shp Lesser_Kes 100 VU 80

C:\GIS\SCP\FINAL\AV_Geronticus_calvus_Bald_Ibis_Actual_polygon_Pseudo_1.shp Bald_Ibis 100 VU 80

C:\GIS\SCP\FINAL\AV_Gypaetus_barbatus_Bearded_Vulture_Actual_point_Pseudo_1.shp Bearded_Vu 100 EN 90

C:\GIS\SCP\FINAL\AV_Gyps_africanus_Whitebacked_Vulture_Actual_point_Pseudo_1.shp Whiteb_Vu 100 VU 80

C:\GIS\SCP\FINAL\AV_Heteromirafra_ruddi_Rudds_Lark_ Actual point_Pseudo_1.shp Rudds_Lark 100 CR 100

C:\GIS\SCP\FINAL\AV_Polemaetus_bellicosus_Martial_Eagle_Actual_point_Pseudo_1.shp Mart_Eag_1 100 VU 80

C:\GIS\SCP\FINAL\AV_Polemaetus_bellicosus_Martial_Eagle_Actual_polygon_Pseudo_1.shp Mart_Eag_2 100 VU 80

C:\GIS\SCP\FINAL\AV_Sagittarius_serpentarius_Secretarybird_Actual_point_Pseudo_1.shp Secretaryb 100 NT 70

C:\GIS\SCP\FINAL\AV_Tyto_capensis_Grass_Owl_Actual_point_Pseudo_1.shp Grass_Owl 100 VU 80

C:\GIS\SCP\FINAL\CC_Biomes_resilient_to_change_grasslands.shp Bio_Cl_res 100 Bio_Cl_res 80

C:\GIS\SCP\FINAL\EC_Ecological_corridors.shp Ecol_Corr 100 Ecol_Corr 80

C:\GIS\SCP\FINAL\FL_Alepidea_amatymbica_Actual_point_Pseudo_1.shp Alep_ama_1 100 VU 80

C:\GIS\SCP\FINAL\FL_Alepidea_amatymbica_Actual_polygon_Pseudo_1.shp Alep_ama_2 100 VU 80

C:\GIS\SCP\FINAL\FL_Brachystelma_dimorphum_Actual_polygon_Pseudo_1.shp Brach_dim 100 Rare 100

C:\GIS\SCP\FINAL\FL_Chortolirion_latifolium_Actual_polygon_Pseudo_1.shp Chort_lat 100 Not assesed 30

C:\GIS\SCP\FINAL\FL_Dracosciadium_saniculifolium_Actual_point_Pseudo_1.shp Dracosci_1 100 Rare 100

C:\GIS\SCP\FINAL\FL_Dracosciadium_saniculifolium_Actual_polygon_Pseudo_1.shp Draco_sa_2 100 Rare 100

C:\GIS\SCP\FINAL\FL_Helichrysum_haygarthii_Actual_point_Pseudo_1.shp Hel_hayg_1 100 Rare 100

C:\GIS\SCP\FINAL\FL_Helichrysum_haygarthii_Actual_polygon_Pseudo_1.shp Hel_hayg_2 100 Rare 100

C:\GIS\SCP\FINAL\FL_Hoodia_officinalis_Actual_polygon_Pseudo_1.shp Hoodia_off 100 NT 70

C:\GIS\SCP\FINAL\FL_Isoetes_aequinoctialis_Actual_point_Pseudo_1.shp Isoet_aeq 100 VU 80



C:\GIS\SCP\FINAL\FL_Kniphofia_typhoides_Actual_polygon_Pseudo_1.shp Kniph_typh 100 NT 70

C:\GIS\SCP\FINAL\FL_Lithops_salicola_Actual_polygon_Pseudo_1.shp Lithops_sa 100 LC 30

C:\GIS\SCP\FINAL\FL_Nerine_bowdeni_Actual_point_Pseudo_1.shp Nerine_bow 100 Rare 100

C:\GIS\SCP\FINAL\FL_Pentzia_oppositifolia_Actual_point_Pseudo_1.shp Pentzia_op 100 Not classified 30

C:\GIS\SCP\FINAL\FL_Protea_dracomontana_Actual_point_Pseudo_1.shp Protea_dra 100 LC 30

C:\GIS\SCP\FINAL\FL_Protea_subvestita_Actual_point_Pseudo_1.shp Prot_sub_1 100 VU 80

C:\GIS\SCP\FINAL\FL_Protea_subvestita_Actual_polygon_Pseudo_1.shp Prot_sub_2 100 VU 80

C:\GIS\SCP\FINAL\FL_Schizoglossum_montanum_Actual_polygon_Pseudo_1.shp Schiz_mont 100 Rare 100

C:\GIS\SCP\FINAL\FL_Stenostelma_umbelluliferum_Actual_point_Pseudo_1.shp Stenost_um 100 NT 70

C:\GIS\SCP\FINAL\FL_Strumaria_tenella_Actual_point_Pseudo_1.shp Strum_te_1 100 LC 30

C:\GIS\SCP\FINAL\FL_Strumaria_tenella_Actual_polygon_Pseudo_1.shp Strum_te_2 100 LC 30

C:\GIS\SCP\FINAL\GE_Unique_geological_feature_Vredefort_Dome.shp Unique_geo 100 Geology 30

C:\GIS\SCP\FINAL\IB_Afro_Montane_Inselbergs.shp Insel_MfrM 100 Inselberg 100

C:\GIS\SCP\FINAL\IB_Karoo_Inselbergs.shp Insel_Kar 100 Inselberg 100

C:\GIS\SCP\FINAL\IN_Metisella_meninx_Marsh_Sylph_Actual_point_Pseudo_1.shp Marsh_Sylp 100 Rare (HS) 100

C:\GIS\SCP\FINAL\IN_Orachrysops_mijburghi_Mijburghs_Blue_Actual_point_Pseudo_1.shp Mijb_Blue 100 EN 90

C:\GIS\SCP\FINAL\IN_Orachrysops_montanus_Golden_Gate_Blue_Actual_point_Pseudo_1.shp GG_blue_1 100 LC (Extremely Rare) 100

C:\GIS\SCP\FINAL\IN_Orachrysops_montanus_Golden_Gate_Blue_Actual_polygon_Pseudo_1.shp GG_blue_2 100 LC (Extremely Rare) 100

C:\GIS\SCP\FINAL\IN_Pseudonympha_paragaika_Golden_Gate_Brown_Actual_point_Pseudo_1.shp GG_brown_1 100 VU 80

C:\GIS\SCP\FINAL\IN_Pseudonympha_paragaika_Golden_Gate_Brown_Actual_polygon_Pseudo_1.shp GG_brown_2 100 VU 80

C:\GIS\SCP\FINAL\IN_Thestor_p_terblanchei_Terblanches_Skolly_Actual_point_Pseudo_1.shp Terbl_Skol 100 VU 80

C:\GIS\SCP\FINAL\IN_Torynesis_orangica_Orange_Widow_Actual_point_Pseudo_1.shp Orange_Wid 100 Rare (RR, HS) 100

C:\GIS\SCP\FINAL\IN_Tuxentius_melaena_gr_Griqua_Black_Pie_Actual_point_Pseudo_1.shp Griqua_BlP 100 DD 100

C:\GIS\SCP\FINAL\ML_Ourebia_oribi_Oribi_Actual_point_Pseudo_1.shp Oure_ori_1 100 EN 90

C:\GIS\SCP\FINAL\ML_Ourebia_oribi_Oribi_Actual_polygon_Pseudo_1.shp Oure_ori_2 100 EN 90

C:\GIS\SCP\FINAL\MS_Cistugo_lesueuri_Lesueurs_Bat_Actual_point_Pseudo_1.shp Lesueurs_B 100 NT 70

C:\GIS\SCP\FINAL\MS_Laephotis_wintoni_De_Wintons_Bat_Actual_point_Pseudo_1.shp De_Wintons 100 VU 80

C:\GIS\SCP\FINAL\MS_Mystromy_albicaudatus_Whitetailed_Rat_Actual_point_Pseudo_1.shp Whitetai_1 100 EN 90

C:\GIS\SCP\FINAL\MS_Mystromy_albicaudatus_Whitetailed_Rat_Actual_polygon_Pseudo_1.shp Whitetai_2 100 EN 90

C:\GIS\SCP\FINAL\MS_Poecilogale_albinucha_African_weasel_Actual_polygon_Pseudo_1.shp African_we 100 DD 100



C:\GIS\SCP\FINAL\RE_Pseudocordylus_langi_Langs_Gridled_Lizard_Actual_point_Pseudo_1.shp Langs_Gr_1 100 NT 70

C:\GIS\SCP\FINAL\RE_Pseudocordylus_langi_Langs_Gridled_Lizard_Actual_polygon_Pseudo_1.shp Langs_Gr_2 30 NT 70

C:\GIS\SCP\FINAL\RE_Pseudocordylus_spinosus_Spiny_Crag_Lizard_Actual_point_Pseudo_1.shp Spiny_Crag 100 NT 70

C:\GIS\SCP\FINAL\RE_Smaug_giganteus_Sungazer_Actual_point_Pseudo_1.shp Sungazer_1 100 VU 80

C:\GIS\SCP\FINAL\RE_Smaug_giganteus_Sungazer_Actual_polygon_Pseudo_1.shp Sungazer_2 100 VU 80

C:\GIS\SCP\FINAL\RE_Tetradactylus_breyeri_Longtailed_Seps_Actual_point_Pseudo_1.shp Longt_Seps 100 VU 80

C:\GIS\SCP\FINAL\RE_Tropidosaura_cottrelli_Cottrells_Mnt_Lizard_Actual_point_Pseudo_1.shp Cott_Mnt_L 100 NT 70

C:\GIS\SCP\FINAL\SP_Ekangala_CBAs.shp Ekang_CBAs 100 LC 30

C:\GIS\SCP\FINAL\VE_Aliwal_North_Dry_Grassland.shp Aliwal_Nor 24 LC 30

C:\GIS\SCP\FINAL\VE_Amersfoort_Highveld_Clay_Grassland.shp Amersfoort 24 LC 30

C:\GIS\SCP\FINAL\VE_Andesite_Mountain_Bushveld.shp Andesite_M 24 VU 80

C:\GIS\SCP\FINAL\VE_Basotho_Montane_Shrubland.shp Basotho_Mo 28 LC 30

C:\GIS\SCP\FINAL\VE_Besemkaree_Koppies_Shrubland.shp Besemkaree 28 LC 30

C:\GIS\SCP\FINAL\VE_Bloemfontein_Dry_Grassland.shp Bl_Dry_Gra 24 VU 80

C:\GIS\SCP\FINAL\VE_Bloemfontein_Karroid_Shrubland.shp Bl_Kar_Shr 28 LC 30

C:\GIS\SCP\FINAL\VE_Carletonville_Dolomite_Grassland.shp Carletonvi 24 VU 80

C:\GIS\SCP\FINAL\VE_Central_Free_State_Grassland.shp Central_Fr 24 CR 100

C:\GIS\SCP\FINAL\VE_Drakensberg_Afroalpine_Heathland.shp Drak_Afr_H 27 LC 30

C:\GIS\SCP\FINAL\VE_Drakensberg_Amathole_Afromontane_Fynbos.shp Drak_Ama_A 27 LC 30

C:\GIS\SCP\FINAL\VE_Drakensberg_montane_with_prior_features.shp Drak_Monta 100 LC 30

C:\GIS\SCP\FINAL\VE_Eastern_Free_State_Clay_Grassland.shp East_FS_Cl 24 CR 100

C:\GIS\SCP\FINAL\VE_Eastern_Free_State_Sandy_Grassland.shp East_FS_Sg 24 LC 30

C:\GIS\SCP\FINAL\VE_Eastern_Temperate_Freshwater_Wetlands_Region1.shp Eas_Wet_R1 24 VU 80

C:\GIS\SCP\FINAL\VE_Eastern_Temperate_Freshwater_Wetlands_Region2.shp Eas_Wet_R2 24 VU 80

C:\GIS\SCP\FINAL\VE_Eastern_Upper_Karoo.shp Eastern_Up 21 CR 100

C:\GIS\SCP\FINAL\VE_Frankfort_Highveld_Grassland.shp Frankfort_ 24 LC 30

C:\GIS\SCP\FINAL\VE_Gold_Reef_Mountain_Bushveld.shp Gold_Reef 24 LC 30

C:\GIS\SCP\FINAL\VE_Highveld_Alluvial_Vegetation_Region1.shp High_Al_R1 31 LC 30





C:\GIS\SCP\FINAL\VE_Highveld_Salt_Pans_Region2.shp High_Pa_R2 24 LC 30



C:\GIS\SCP\FINAL\VE_Kimberley_Thornveld.shp Kimb_Thv 16 LC 30

C:\GIS\SCP\FINAL\VE_Lesotho_Highland_Basalt_Grassland.shp Lesotho_Hi 27 LC 30

C:\GIS\SCP\FINAL\VE_Low_Escarpment_Moist_Grassland.shp Low_Escarp 23 LC 30

C:\GIS\SCP\FINAL\VE_Northern_Afrotemperate_Forest.shp N_AfrT_For 31 LC 30

C:\GIS\SCP\FINAL\VE_Northern_Drakensberg_Highland_Grassland.shp N_Dr_H_Gr 27 LC 30

C:\GIS\SCP\FINAL\VE_Northern_Free_State_Shrubland.shp N_FS_Shrub 28 LC 30

C:\GIS\SCP\FINAL\VE_Northern_Upper_Karoo.shp N_Up_Kar 21 LC 30

C:\GIS\SCP\FINAL\VE_Rand_Highveld_Grassland.shp Rand_Hv_Gr 24 VU 80

C:\GIS\SCP\FINAL\VE_Schmidtsdrif_Thornveld.shp Schmid_Thv 16 VU 80

C:\GIS\SCP\FINAL\VE_Senqu_Montane_Shrubland.shp Senq_M_Shr 28 LC 30

C:\GIS\SCP\FINAL\VE_Soweto_Highveld_Grassland.shp Sow_Hig_Gr 24 VU 80

C:\GIS\SCP\FINAL\VE_uKhahlamba_Basalt_Grassland.shp uK_Bas_Gr 27 LC 30

C:\GIS\SCP\FINAL\VE_Upper_Gariep_Alluvial_Vegetation.shp UG_All_veg 31 LC 30

C:\GIS\SCP\FINAL\VE_Vaal_Reefs_Dolomite_Sinkhole_Woodland.shp Vl_R_Dol_S 24 LC 30

C:\GIS\SCP\FINAL\VE_Vaal_Vet_Sandy_Grassland.shp Vaal_Vet_S 24 EN 90

C:\GIS\SCP\FINAL\VE_Vaalbos_Rocky_Shrubland.shp Vlb_Ro_Shr 16 LC 30

C:\GIS\SCP\FINAL\VE_Vredefort_Dome_Granite_Grassland.shp Vred_Gr_Gr 24 VU 80

C:\GIS\SCP\FINAL\VE_Western_Free_State_Clay_Grassland.shp W_FS_Cl_Gr 24 LC 30

C:\GIS\SCP\FINAL\VE_Winburg_Grassy_Shrubland.shp Winb_Gr_Sh 28 LC 30

C:\GIS\SCP\FINAL\VE_Xhariep_Karroid_Grassland.shp Xh_Kar_shr 24 LC 30

C:\GIS\SCP\FINAL\VE_Zastron_Moist_Grassland.shp Zast_Mo_gr 24 LC 30

C:\GIS\SCP\FINAL\WET_RAMSAR_sites.shp RAMSAR_sit 100 Ramsar 100

C:\GIS\SCP\FINAL\WET_Rivers_FEPACODE_1.shp Wet_cluste 100 Clusters 100

C:\GIS\SCP\FINAL\WET_Rivers_FEPACODE_1.shp Riv_FEPA_1 100 FEPA 80

C:\GIS\SCP\FINAL\WET_Wetlands_WETFEPA_1.shp WETFEPA_1 80 FEPA 80


8.3.9. Edge Matching

Edge matching relates to the process of aligning different aspects of the biodicersty plan with similar aspects of the biodiversity plans of neighbouring provinces. Such aspects are:

• CBAs

Ideally the CBAs of the Free State Biodiversity Plan should align with the CBAs of neighbouring provinces. For the Free State Biodiversity Plan CBAs considered for edge matching were those that were selected to account for ecosystems and species.

The CBAs of neighbouring provinces are selected to account for ecosystem targets. The planning units that are selected is a function of the location of the planning units relative to other features; planning units that include more than one feature are morelikely to be selected than those that include a single feature while those that are in close proximity to selected planning units are also more likely to be selected (a function of the boundary length modifier). It follows that to bias the selection of Free State planning units as CBAs, that the data which determines the selction of CBAs should be aligned. The latter implies that features should be mapped along ecological boundaries so that their distributions extend across provincial boundaries. This is, however, not the case. Becasue provinces are not using the same data sets the actual and modelled species distributions are not aligned. The Marsh Sylph (Metisella meninx) is presented as an example (Figure 15). This species is known to occur on the border of the Free State and Mpumalanga. Planning units on the Mpumalanga side of the provincial boundary were selected as CBAs to satisfy the target for this species (and the vegetation type). To edge match the Free State biodiversity plan with that of Mpumalanga, the distribution records of the Marsh Sylph available for the Free State biodiversity plan were used to model its potential distribution. However, the distribution records were such that the modelled distribution did not extent to the habitat mapped in Mpumalanga. This example serves to illustrate that it was not possible to achieve edge matching by aligning features.


Figure 15: The Free State records of the Marsh Sylph (red triangles) are clearly well separated from the Mpumalanga records of the Marsh Sylph (green triangles). The modelled distribution of the Marsh Sylph within the Free State is therefore not aligned with the records of Mpumalanga around which CBAs were selected.

Edge matching was subsequently achieved by biasing the selection of CBAs towards planning units that are adjacent to the CBAs of neighbouring provinces. All planning units within 5 km of neighbouring province CBAs were assigned a cost of 1 (the lowest cost value). To further bias selection towards planning units that border CBAs of neighbouring provinces the boundary cost of the boundary that borders such CBAs were set to 0 (using a custom developed VB.Net application).

• Corridors

Edge-matching of corridors were accomplished through the mapping of inter-provincial focal points as discussed in Appendix 4: Connectivity.


• $Buffers (home ranges)

A workshop was held amongst the biodiversity planners of the Free State, Mpumalanga and KwaZulu-Natal province to facilitate edge matching across the Ekangala region, which includes all three mentioned provinces. The workshop focussed specifically on 'standardising' species feature targets and buffers. Although the targets were not adopted in the Free State Biodiversity Plan, the buffers were adopted to some extent (section 8.2.1).

• $Targets ($how was targets included as part of edge matching, if at all?)

$say somewhere that the plnanning unit boundary cost of planning unist bordering protected areas were also set to 0. Verify if the planning unit cost was also reduced to 1 (or some other value).

8.3.10. CBAs

Spatial alignment

In addition to having an international boundary with the Kingdom of Lesotho, the Free State also shares it boundary with six other provinces of which all but the Northern Cape Province have already developed biodiversity plans. The Free State biodiversity planning process attempted to achieve spatial alignment with the CBAs of neighboring provinces. An overview of previously completed biodiversity plans reveal that CBAs of different provinces are not aligned at provincial boundaries (Figure 16).

Figure 16: Example of non-alignment of CBAs across provincial boundaries

Non-alignment of CBAs that were selected to satisfy species targets could be because while the species was sampled or sighted on the one side of the boundary, that it was not sampled or searched for on the other. The distribution of the Marsh Sylph (Metisella meninx) serves as example (Figure 17).

Figure 17: Non-alignment of the Marsh Sylph (Metisella meninx) across the Free State, Gauteng and Mpumalanga border


All ecological corridors categorised as CBAs were excluded (i.e. Free State PUs that are adjacent to neighbouring PUs that were categorised as CBAs on account of them being ecological corridors alone were excluded from the edge matching process). Free State PUs that are adjacent to ecological corridors of neighbouring provinces that have not been categorised as CBAs were not included in the edge matching process.

With the exception of corridors, all CBAs of neighbouring provinces were considered irrespective of the reasons for them being selected as CBAs, e.g. to satisfy targets, areas identified by experts as critical to biodiversity conservation, important catchment areas, etc. Edge matching did not account for ecological support areas. CBAs of neighbouring provinces were obtained from the most recent published CBA maps. A summary of the CBA types per province are provided in Table 30.

Table 30: Table indicated the CBA types per province. The column with heading 'Field' indicates the field name that was queried to identify CBAs, while the values indicated in the corresponding provincial columns indicate the value in the field that indicate the presence of a CBA.

Provinces (field value)

Species/Biodiversity Field MP KZN EC NW GT

Terrestrial

Hyperdiversity Sites ID’d in previous studies (CBA2) CBA_sites1 2

Biodiversity Features ID’d in previous studies (CBA2) CBA_fea1 2

Biodiversity Nodes (CBA2) CBA_node1 2

Biodiversity areas id’d by experts (CBA1) CBA_expT1 1

Ecosystem status of vegtype (SA vegtype) (CR&EN=CBA1 VU=CBA2)

CBA_saveg1 1, 2

Biodiversity feature description CATEGORY2 CBA

Ecosystem status from SA veg map CBA_saveg3 T1,T2,T3

Ecosystem status from MDTP veg map CBA_mdtpvg3 T1,T2,T3

High irreplaceability sites from Marxan analysis for province CBA_marx3 T1

MDTP selected areas from Marxan analysis CBA_mdtpmx3 T1, T2

Ecosystem status from step veg map CBA_step3 T1,T2,T3

Forest patches CBA_forpat3 T1, T2

Expert mapped important areas CBA_expert3 T2

Forest clusters CBA_forest3 T1, T2

CBA1 Mandatory Legend4 R0

CBA2 Mandatory Legend4 R1

Critical Biodiversity Area 3 Optimal Legend4 R2

Irreplaceable ASSESSMENT5 Irreplaceable


Provinces (field value)

Species/Biodiversity Field MP KZN EC NW GT

Highly Significant ASSESSMENT5 Highly Significant

Important & Necessary ASSESSMENT5 Important & Necessary

Aquatic

Areas id’d by experts as critical to biodiversity conservation (CBA1) CBA_ExpA6 1

Wetland areas (Classification as CBA 1 or CBA 2 based on the percentage transformation of the buffer and whether wetland situate din priority subcatchment (<40% trans of buffer plus in a priority sub catch – CBA all other CBA2)

CBA_wet6 1,2

Priority sub quaternary catchments (CBA1 and 2) CBA_SQ46 1,2

Highest CBA value for each polygon (Note: the above CBA can be selecting by selecting all features of 6 with value 1 or 2 in field CBA_Aall)

CBA_Aall6 1,2

Migratory catchments (no impoundments) CBA_migr7 A2b

Important primary catchments (no further flow reduction) CBA_est7 E3a,E3b

Ec freshwater assessment (sub- quaternary catchments) CBA_aqua7 A1,A2a

Mdtp selected sub-catchments from analysis CBD_mdtpbn7 A1,A2

Mdtp important aquatic habitats (subcatchment level) CBA_mdtpaq7 A1,A2

Irreplaceable CATEGORY8 Irreplaceable

Highly Significant CATEGORY8 Highly Significant

Important & Necessary CATEGORY8 Important & Necessary

The selection of FS planning units that neighbor the CBAs of neighboring provinces units were encourages by:

• Lowering the cost of these planning units

• Assigning a zero cost to the outer boundary (i.e. the planning unit boundary that represents the provincial boundary) of planning unit that neighbour the CBAs of neighbouring provinces


The will encourage the selection of FS planning units even if not for the same reason as what the neighboring planning unit was selected for (e.g. the ecosystem target can be achieved in these planning units).

Spatial framework

To achieve some form of planning unit standardization all provinces are required to incorporate the NFEPA sub-catchments into their planning design (Nel, et al., 2011). The NFEPA sub-catchments were therefore used as spatial framework to determine the extent to which the edge-matching principles should apply. In addition to providing some form of planning unit standardization, it also provides a spatially and ecological sensible unit for this purpose.

Targets and buffers

In addition to spatial alignment, the process of edge matching also attempted to align the targets and buffers of inter-provincial species. The first of such an attempt was between the provinces of KwaZulu Natal and Mpumalanga of which the principles for alignment across the Ekangala region are described in a document entitled 'Target Setting Resolutions across Ekangala region' (November 2012) ($reference). Although aimed at the Ekangala region specifically, the concept of standardizing species buffers was applied as guidelines to the FS biodiversity planning process while targets were set according to the guidelines of Pfab, Victor, & Armstrong (2011). The resolution for species buffers was applied to species of which their home ranges require similar buffers and species of which their home ranges are unknown. However, where the home ranges are known, these were applied irrespective if the home range requires a larger or smaller buffer area.

8.3.11. The CBA map

Because the number of possible solutions is almost endless, Marxan performs a number of runs on the data. During each run it attempts to satisfy the targets (i.e. select planning units so that they account for the biodiversity feature targets) while simultaneously minimising the cost .e, it will first attempt to satisfy the targets in planning units with lowest cost, while also attempting to select neighbouring as opposed to remote planning units so as to minimise the planning unit boundary cost portion. Although each run produces a dissimilar result, there are certain planning units which if not selected will result in the biodiversity feature targets not being met. These planning units will be selected during most if not all of the runs and have a high frequency of selection. These are the planning units that are considered to be the Critical Biodiversity Areas (CBAs). Other planning units that are not essential for achieving the biodiversity feature targets are selected less often. Although these also contribute towards achieving the biodiversity feature targets, they are not essential, i.e. the targets can be achieved through the selection of other planning units. The graphical


presentation of planning units and the number of times that they were selected during the various runs is one of the key outputs of Marxan and is commonly referred to as the 'frequency of selection map'.

After analysis the run containing the best solution was identified and the results of that run were imported into the planning unit file using the QMarxan 'Import Marxan Results' function.

After importing the Marxan results two addition field were added to the planning unit layer, '$Ptranf' and '$Pdegr'. Using the 'Tabulate area' tool the area of each planning that is transformed and degraded was calculated and converted to percentage area of the planning unit. The latter were captured in the '$Ptranf' and '$Pdegr' respectively.

A field 'ESA' was added to the planning unit layer after Marxan analysis. All planning units that intersect the ESA map discussed in Section 8.3.7 were selected and field 'ESA' was populated with value 1 for such planning units.

A key output of the biodiversity planning process is the CBA map. Categories included in the CBA map are (descriptions are from $Lexicon):

• CBA Irreplaceable A site that is irreplaceable or near-irreplaceable for meeting biodiversity targets. There are no or very few other options for meeting biodiversity targets for the features associated with the site. Such sites are therefore critical and they need to be maintained to ensure that features targets are achieved and that such features persist.

• CBA Optimal A site that has been selected based on its complementarity for meeting biodiversity targets.CBA Optimal sites are therefore important but their maintenance is not critical to ensure that features targets are achieved and that such features persist.

• ESAs An area that plays an important role in supporting the ecological functioning of a protected area or Critical Biodiversity Area, or in delivering ecosystem services. In most cases ESAs are currently in at least fair ecological condition, and should remain in at least fair functioning condition.

o ESA1 ESA1 sites are those with minimal degradation.

o ESA2 ESA2 sites are those with degradation, i.e. they can be totally degraded, but not totally transformed.

• Other


An area of natural habitat not required to meet biodiversity targets for ecosystem types, species or ecological processes, i.e. natural areas not selected as CBA or ESA.

• Degraded An area of degraded or transformed habitat that has not been selected as an ESA, i.e. all remaining areas.

The selection and categorisation of planning units into CBA Irreplaceable, CBA Optimal, ESA1, and ESA2 were done using a Modelbuilder tool which employs the following logic:

• All planning units located within protected areas were designated 'Protected'.

• Planning units with planning unit status 0 and that were not designated Protected and with a frequency of selection >=80 were designated 'CBA1'.

• Planning units with planning unit status 0 and that were not designated Protected and with a frequency of selection >=50 and <80 were designated 'CBA2'.

• Planning units with planning unit status <>3 and wich have not been designated Protected or CBA and which contain ecological support area features (section 8.3.7) and of which both transformed and degraded areas constitute <=10%, were designated 'ESA1'.

• Planning units with planning unit status <>3 and wich have not been designated Protected, CBA or ESA1 and which contain ecological support area features (section 8.3.7) and of which transformed areas constitute <=50%, were designated 'ESA2'.

• All planning units with planning unit status 0 that were not designated Protected, CBA or ESA were designated 'Other'.

• All planning units with planning unit status 3 were designated 'Degraded' (all remaining areas).

Table 31: Procedure followed according to which planning units were assigned to the CBA map categories of CBA1, CBA2, ESA1, ESA2, Other and Degraded.

Potential CBA map category

Selection criteria PU Status

Protected All planning units located within a protected area 2

CBA Irreplaceable Frequency of selection >=80 AND (degraded <= 10% AND transformed <= 10%)

0

CBA Optimal Frequency of selection (>=50 AND <80) AND (degraded <= 10% AND transformed <= 10%)

0


ESA1 All planning units identified as ESAs (value 1 in field 'ESA') that have not been categorised as a CBA and that are <=10% degraded and <=10% transformed. ["ESA" =1 AND "Parea_ESA1" >=10 AND "Parea_ESA2" <=10 AND "Perc_trsf" <=10]

0

ESA2 All planning units identified as ESAs (value 1 in field 'ESA') and that have not been categorised as an ESA1. ["ESA" =1 AND "ESA1" =0 AND "Perc_trsf" <=50]

0

Other All planning units not categorised as a CBA or ESA and with planning unit status = 0. PUS with degraded < 50 %

0

Degraded All planning units not categorised as CBA, ESA, or other, i.e. all planning units with planning unit status = 3. PUS with degraded >= 50 %

3

Table 32: Indicators and criteria according to which planning units were categorised

CBA map category Selection criteria

CBA Irreplaceable Frequency of selection >=80

CBA Optimal Frequency of selection >=50 AND <80

ESA1 All planning units identified as ESAs (value 1 in field 'ESA') that have not been categorised as a CBA and that are <=10% degraded and <=10% transformed.

ESA2 All planning units identified as ESAs (value 1 in field 'ESA') and that have not been categorised as an ESA1.

Other All planning units not categorised as a CBA or ESA and with planning unit status = 0.

Degraded All planning units not categorised as CBA, ESA, or other, i.e. all planning units with planning unit status = 3.


In addition to having idenditified CBAs based on the logic as per Table 32, planning units that are $natural and that intersect corridor pinch points as

well as planning units that intersect biodiversity features with a 100% target that were not selected by Marxan at a high enough frequency for them

to become CBAs were hardwired in as CBAs.

8.4. Analysis

8.4.1. Calibration

When running Marxan it is important that the planning unit boundary cost and feature penalty factors are in 'balance', thereby implying that the values of the one are not such that it effectively nullifies the influence of the other. To ensure that both have opportunity to influence the selection of planning units these variables need to be calibrated. In addition to calibrating the

• FPFs

• BLM

• Iterations

• Repetitions (numreps)

FPF and the number of iteration

The principle used for calibrating the FPF is very similar to approach used in the Zonae Cogito FPF calibration function. However, it differs in its approach in that unlike Zonae Cogito which tries to meet a desired percentage of target achievement, the approach followed for the Free State biodiversity plan is based on an assessment of how many of the features targets have been achieved for different FPF values. The latter has the advantage that unlike the approach of Zonae Cogito, it does not require a dataset of which all targets must be achievable.


To calibrate the Feature Penalty Factor (FPF) two Marxan runs were initially performed with the FPF range was set between the values of 0.00001 and 100000000 for all features. These values represent theoretical minimum and maximum FPF values below and above which no more or no less of the biodiversity feature targets will be achieved. These runs resulted in 7 and 83 targets being met and total MPM15 values of 16.66 and 101.48 respectively (as determined from the 'output_mvbest.txt' file). This was done for 100 and 500 runs of which each produced a similar result. It was therefore concluded that there is no advantage in doing more than 100 runs.

The FPF was subsequently adjusted to 1 which resulted in 82 targets being achieved (1 less than for SPF 100000000) while the total MPM value was 100.87 (similar figure were obtained from 500 runs). The SPF was then adjusted to 5 which resulted in 83 targets being achieved (similar for SPF 100000000) while the total MPM value was 101.46. It was therefore concluded that the FPF value needed to be in the range between 1 and 5. Incremental Marxan runs with varying FPF values resulted in identifying 2.7 as the most efficient FPF value (Table 33) interms of the number of features targets being achievd and the MPM value.

Table 33: Summary of incremental Marxan runs with varying FPF values. The most efficient FPF value was found to be 2.7.

FPF value Total (MPM)

Not achieved Achieved

0.00001 16.66 103 7

100000000 101.48 27 83

1 100.87 28 82

2 101.29 28 82

3 101.39 27 83

2.5 101.35 28 82

2.6 101.36 28 82

2.7 101.37 27 83

5 101.46 27 83

Final analysis 82.60249 48 61

The FPF was subsequently scaled to a range of 2.7 to 10 and assigned to features based on the criteria as per Table 34.

Table 34: The species penalty factor for biodiversity features

Feature criteria FPF

15 "Minimum Proportion Met", defined as the minimum target achievement ratio across all features in a Marxan solution. The higher the value the more of the targets are achieved.


Critically Endangered, Rare, Extremely Rare, Inserbergs, Ramsar site 10

Endangered 9

Vulnerable, 16Biomes resilient to climate change, Ecological corridors, FEPAs, SWSA

8

Near Threatened 7

Not assessed, Not classified, Least Concern 2.7

FPF values were assigned to features using the following Microsoft Excel formula: [Where the feature criteria is located in cell A1] = IF(OR(A1="CR",A1="Rare",A1="DD",A1="LC (Extremely Rare)",A1="Rare (HS)",A1="Rare (RR, HS)",A1="Inselberg",A1="Ramsar",A1="Clusters"),100,IF(A1="EN",90,IF(OR(A1="VU",A1="Bio_Cl_res",A1="Ecol_Corr",A1="FEPA",A1="SWSA"),80, IF(A1="NT",70,30))))

Table 35: FPF values for individual features and their resulting MPM scores.

Results: Calibrated FPF; 2.7 Results: Feature specific FPF

Feature Name Status prop Feature target

Feature FPF MPM17

Target Met

Feature FPF

MPM Target Met

Anthropoides paradiseus Blue Crane_point VU 0 3759 2.7 0.920706 No 8 0.920706 no

Anthus chloris Yellowbreasted Pipit_point VU 0 78 2.7 1 Yes 8 1 yes

Anthus chloris Yellowbreasted Pipit_polygon VU 0 1947 2.7 0.956248 Yes 8 0.956248 yes

Balearica regulorum Crowned Crane_point VU 0 16435 2.7 0.81187 No 8 0.812019 no

Bugeranus carunculatus Wattled Crane_point CR 0 11242 2.7 0.926272 No 10 0.926272 no

Falco naumanni Lesser Kestrel_polygon VU 0 0 2.7 1 Yes 8 1 yes

Geronticus calvus Bald Ibis_polygon VU 0 4511 2.7 0.715373 No 8 0.716006 no

Gypaetus barbatus Bearded Vulture_point EN 0 2914 2.7 1 Yes 9 1 yes

Gyps africanus Whitebacked Vulture_point VU 0 75869 2.7 0.803603 No 8 0.805177 no

Heteromirafra ruddi Rudds Lark Actual_point CR 0 12 2.7 1 Yes 10 1 yes

Polemaetus bellicosus Martial Eagle_point VU 0 9459 2.7 0.792912 No 8 0.792978 no

16 A relative high species penalty factor is afforded to biomes that are resilient to climate change. This constitutes mainly grassland areas of which little is expected to remain (Figure 11). 17 Indicates how close the target is to being achieved (1=100% achieved, 0.55 = 55% of the target is achieved, etc.)




Feature FPF MPM17

Target Met

Feature FPF

MPM Target Met

Polemaetus bellicosus Martial Eagle_polygon VU 0 155 2.7 1 Yes 8 1 yes

Sagittarius serpentarius Secretarybird_point NT 0 75869 2.7 0.803603 No 7 0.805177 no

Tyto capensis Grass Owl_point VU 0 314 2.7 0.938446 No 8 0.938446 no

Biomes resilient to change grasslands Bio_Cl_res 0 1553004 2.7 0.612731 No 8 0.64697 no

Alepidea amatymbica_point VU 0 0 2.7 1 Yes 8 1 yes

Alepidea amatymbica_polygon VU 0 3 2.7 0.958118 Yes 8 0.958118 yes

Brachystelma dimorphum_polygon Rare 0 2023 2.7 0 No 10 0 no

Chortolirion latifolium_polygon Not assesed 0 21 2.7 0.253768 No 2.7 0.253768 no

Dracosciadium saniculifolium_point Rare 0 12808 2.7 1 Yes 10 1 yes

Dracosciadium saniculifolium_polygon Rare 0 6 2.7 1 Yes 10 1 yes

Helichrysum haygarthii_point Rare 0 693 2.7 1 Yes 10 1 yes

Helichrysum haygarthii_polygon Rare 0 3 2.7 1 Yes 10 1 yes

Hoodia officinalis_polygon NT 0 23 2.7 0.98855 Yes 7 0.98855 yes

Isoetes aequinoctialis_point VU 0 1810 2.7 1 Yes 8 1 yes

Kniphofia typhoides_polygon NT 0 5 2.7 0.740104 No 7 0.740104 no

Lithops salicola_polygon LC 0 141 2.7 0.987166 Yes 2.7 0.988334 yes

Nerine bowdeni_point Rare 0 20856 2.7 1 Yes 10 1 yes

Pentzia oppositifolia_point Not classified 0 3 2.7 1 Yes 2.7 1 yes

Protea dracomontana_point LC 0 6 2.7 1 Yes 2.7 1 yes

Protea subvestita_point VU 0 3 2.7 1 Yes 8 1 yes

Protea subvestita_polygon VU 0 18 2.7 1 Yes 8 1 yes

Schizoglossum montanum_polygon Rare 0 55 2.7 1 Yes 10 1 yes

Stenostelma umbelluliferum_point NT 0 3 2.7 0.293217 No 7 0.293217 no

Strumaria tenella_point LC 0 3 2.7 1 Yes 2.7 1 yes

Strumaria tenella_polygon LC 0 3 2.7 0.355386 No 2.7 0.355386 no

Unique geological feature Vredefort Dome Geology 0 2266 2.7 0.87368 No 2.7 0.873126 no

Afro Montane Inselbergs Inselberg 0 23505 2.7 0.972022 Yes 10 0.974048 yes

Karoo Inselbergs Inselberg 0 35824 2.7 0.90159 No 10 0.907225 no




Feature FPF MPM17

Target Met

Feature FPF

MPM Target Met

Metisella meninx Marsh Sylph_point Rare (HS) 0 36997 2.7 0.540328 No 10 0.540328 no

Orachrysops mijburghi Mijburghs Blue_point EN 0 9 2.7 0.940719 No 9 0.940719 no

Orachrysops montanus Golden Gate Blue_point LC (Extremely Rare) 0 21 2.7 1 Yes 10 1 yes

Orachrysops montanus Golden Gate Blue_polygon LC (Extremely Rare) 0 9 2.7 1 Yes 10 1 yes

Pseudonympha paragaika Golden Gate Brown_point VU 0 421 2.7 1 Yes 8 1 yes

Pseudonympha paragaika Golden Gate Brown_polygon VU 0 3 2.7 1 Yes 8 1 yes

Thestor p terblanchei Terblanches Skolly_point VU 0 421 2.7 1 Yes 8 1 yes

Torynesis orangica Orange Widow_point Rare (RR, HS) 0 6 2.7 0.915458 No 10 0.915458 no

Tuxentius melaena gr Griqua Black Pie_point DD 0 19 2.7 0.522915 No 10 0.522915 no

Ourebia oribi Oribi_point EN 0 6 2.7 1 Yes 9 1 yes

Ourebia oribi Oribi_polygon EN 0 157 2.7 0.840783 No 9 0.841155 no

Cistugo lesueuri Lesueurs Bat_point NT 0 24645 2.7 0.998748 Yes 9 0.998748 yes

Laephotis wintoni De Wintons Bat_point VU 0 235 2.7 1 Yes 8 1 yes

Mystromy albicaudatus Whitetailed Rat_point EN 0 78 2.7 1 Yes 9 1 yes

Mystromy albicaudatus Whitetailed Rat_polygon EN 0 3 2.7 0.901881 No 9 0.902314 no

Poecilogale albinucha African weasel_polygon DD 0 3804 2.7 0.949794 No 10 0.949794 no

Pseudocordylus langi Langs Gridled Lizard_point NT 0 2270 2.7 1 Yes 7 1 yes

Pseudocordylus langi Langs Gridled Lizard_polygon NT 0 8 2.7 1 Yes 7 1 yes

Pseudocordylus spinosus Spiny Crag Lizard_point NT 0 20 2.7 1 Yes 7 1 yes

Smaug giganteus Sungazer_point VU 0 9 2.7 0.542509 No 8 0.542509 no

Smaug giganteus Sungazer_polygon VU 0 12 2.7 0.77516 No 8 0.775598 no

Tetradactylus breyeri Longtailed Seps_point VU 0 2200 2.7 1 Yes 8 1 yes

Tropidosaura cottrelli Cottrells Mnt Lizard_point NT 0 12 2.7 1 Yes 7 1 yes

Ekangala CBAs LC 0 6 2.7 0.878752 No 2.7 0.883132 no

Aliwal North Dry Grassland LC 0 61309 2.7 1 Yes 2.7 1 yes

Amersfoort Highveld Clay Grassland VU 0 123280 2.7 1 Yes 8 1 yes

Andesite Mountain Bushveld VU 0 22327 2.7 0.999907 Yes 8 1 yes

Basotho Montane Shrubland LC 0 6809 2.7 1 Yes 2.7 1 yes




Feature FPF MPM17

Target Met

Feature FPF

MPM Target Met

Besemkaree Koppies Shrubland LC 0 48276 2.7 1 Yes 2.7 1 yes

Bloemfontein Dry Grassland VU 0 121325 2.7 1 Yes 8 1 yes

Bloemfontein Karroid Shrubland LC 0 117946 2.7 1 Yes 2.7 1 yes

Carletonville Dolomite Grassland VU 0 2251 2.7 1 Yes 8 1 yes

Central Free State Grassland VU 0 1403 2.7 1 Yes 8 0.999999 yes

Drakensberg Afroalpine Heathland LC 0 381306 2.7 1 Yes 2.7 1 yes

Drakensberg Amathole Afromontane Fynbos LC 0 427 2.7 1 Yes 2.7 1 yes

Drakensberg montane with prior features LC 0 90 2.7 1 Yes 2.7 1 yes

Eastern Free State Clay Grassland EN 0 33 2.7 1 Yes 9 1 yes

Eastern Free State Sandy Grassland VU 0 336017 2.7 1 Yes 8 1 yes

Eastern Temperate Freshwater Wetlands Region1 CR 0 269455 2.7 1 Yes 10 1 yes

Eastern Temperate Freshwater Wetlands Region2 CR 0 360 2.7 1 Yes 10 1 yes

Eastern Upper Karoo LC 0 1520 2.7 0.00568 No 2.7 0.00568 no

Frankfort Highveld Grassland VU 0 164 2.7 1 Yes 8 1 yes

Gold Reef Mountain Bushveld VU 0 224118 2.7 1 Yes 8 1 yes

Highveld Alluvial Vegetation Region1 VU 0 4774 2.7 1 Yes 8 1 yes



Highveld Salt Pans Region2 CR 0 39262 2.7 1 Yes 10 1 yes



Kimberley Thornveld LC 0 15816 2.7 1 Yes 2.7 1 yes

Lesotho Highland Basalt Grassland LC 0 111874 2.7 1 Yes 2.7 1 yes

Low Escarpment Moist Grassland LC 0 12505 2.7 1 Yes 2.7 1 yes

Northern Afrotemperate Forest LC 0 4959 2.7 1 Yes 2.7 1 yes

Northern Drakensberg Highland Grassland LC 0 185 2.7 1 Yes 2.7 1 yes

Northern Free State Shrubland LC 0 13800 2.7 1 Yes 2.7 1 yes

Northern Upper Karoo LC 0 660 2.7 1 Yes 2.7 1 yes




Feature FPF MPM17

Target Met

Feature FPF

MPM Target Met

Rand Highveld Grassland VU 0 161578 2.7 1 Yes 8 0.99995 yes

Schmidtsdrif Thornveld VU 0 10917 2.7 1 Yes 8 1 yes

Senqu Montane Shrubland VU 0 17393 2.7 1 Yes 8 1 yes

Soweto Highveld Grassland EN 0 108 2.7 1 Yes 9 1 yes

uKhahlamba Basalt Grassland LC 0 10990 2.7 1 Yes 2.7 1 yes

Upper Gariep Alluvial Vegetation EN 0 3061 2.7 1 Yes 9 1 yes

Vaal Reefs Dolomite Sinkhole Woodland VU 0 12278 2.7 1 Yes 8 1 yes

Vaal Vet Sandy Grassland CR 0 1154 2.7 0.952252 Yes 10 0.999996 yes

Vaalbos Rocky Shrubland LC 0 351393 2.7 1 Yes 2.7 1 yes

Vredefort Dome Granite Grassland VU 0 4485 2.7 0.999867 Yes 8 0.999966 yes

Western Free State Clay Grassland LC 0 20816 2.7 1 Yes 2.7 1 yes

Winburg Grassy Shrubland LC 0 160014 2.7 1 Yes 2.7 1 yes

Xhariep Karroid Grassland LC 0 43984 2.7 0.999993 Yes 2.7 1 yes

Zastron Moist Grassland VU 0 320572 2.7 1 Yes 8 1 yes

RAMSAR sites Ramsar 0 49452 2.7 1 Yes 10 1 yes

BLM

After having set the FPF value the Boundary Length Modifier (BLM) value was calibrated. The boundary cost was set to be equal to the length of the boundary using the 'Export data to Marxan input files' function on QMarxan. The BLM was calibrated using a custom designed tool which was designed to work with the Marxan input files as created by the QMarxan 'Export data to Marxan Input files' module. The operation and rationale of the tool is very similar to the BLM calibration function of Zonae Cogito. The summarized operations of the tool are as follows:

• Upper and lower BLM values are set as well an incremental value with which the lower value will be increased incrementally until it exceeds the upper value

• A Marxan run is launched for each of the lower, upper and incremental BLM values during which the BLM value of the input.dat file is adjusted to the incremental value of that run.

• The best run for each run as reported in the '_log.dat' file is determined.

• The total boundary length of each run is plotted on the Y-axis while the total cost of that run is potted on the X-axis.


The initial upper and lower BLM values were set to 0 and 10 respectively, with the increment set to 2. The results of the incremental runs are presented in Table 36 and Figure 18.

Table 36: Total solution cost and boundary length for Marxan runs with BLM ranging from 0 to 10 and with an incremental value of 3.

BLM Cost Boundary length

0 1454561 90646384

2 1737098 61920935

4 1759359 62010898

6 1746986 61103139

8 1765870 61905650

10 1751930 61702936

Figure 18: Graphical presentation of the total solution cost and boundary length for Marxan runs with BLM ranging from 0 to 10 and with an incremental value of 2.

It can be seen from Figure 18 that there is little decrease in cost for BLM value greater than 2 while the number of planning units selected was also greater for all BLM values larger than 3. The most efficient BML value was therefore considered to be between 0 and 2. The most efficient BLM was subsequently determined by performing a number of Marxan runs with BLM values ranging from 0 to 2 with an increment value of 0.2. The results are presented in Table 37and Figure 19.

Table 37: Total solution cost and boundary length for Marxan runs with BLM ranging from 0 to 2 and with an incremental value of 0.2.



0 1427609 89029424

0.2 1671517 65940756

0.4 1701605 63872580

0.6 1722172 63244424

0.8 1721704 63894202

1 1729772 62626420

1.2 1733661 63485559

1.4 1742208 63357845

1.6 1771908 62886674

1.8 1751901 62743774

2 1743122 61684371

Figure 19: Graphical presentation of the total solution cost and boundary length for Marxan runs BLM ranging from 0 to 2 and with an incremental value of 0.2.

It can be seen from Figure 19 that there is little decrease in cost for BLM value greater than 0.2. The most efficient BML value was therefore considered to be between 0 and 0.2. The most efficient BLM was subsequently determined by performing a number of Marxan runs with BLM values ranging from 0 to 0.2 with an increment value of 0.02. The results are presented in Table 38 and Figure 20.

Table 38: Total solution cost and boundary length for Marxan runs with BLM ranging from 0 to 0.2 and with an incremental value of 0.02.


0 1439894 89675595


0.02 1546657 76842113

0.04 1576182 71793749

0.06 1601544 69227701

0.08 1648171 70137277

0.1 1647275 67734686

0.12 1653423 67740753

0.14 1655252 66342117

0.16 1672776 66187094

0.18 1660834 65794598

0.2 1680909 66117382

Figure 20: Total solution cost and boundary length for Marxan runs with BLM ranging from 0 to 0.2 and with an incremental value of 0.02.

It can be seen from Figure 20 that there is little decrease in cost for BLM value greater than 0.08. The most efficient BML value was therefore considered to be between 0.04 and 0.08. The most efficient BLM was subsequently determined by performing a number of Marxan runs with BLM values ranging from 0.04 to 0.08 with an increment value of 0.02. The results are presented in Table 39 and Figure 21.

Table 39: Total solution cost and boundary length for Marxan runs with BLM ranging from 0.04 to 0.08 and with an incremental value of 0.02.


0.04 1578238 71954983

0.06 1602915 69348022

0.08 1639907 69167087


Figure 21: Total solution cost and boundary length for Marxan runs with BLM ranging from 0.04 to 0.08 and with an incremental value of 0.02.

It can be seen from Figure 21 that there is no marked decrease in the total boundary length with BLM value greater than 0.06. Considering Figure 21 the most efficient BLM value was determined to be 0.06 ("C:\GIS\SCP\0_PUs\Calibrate\BLM\Cost\Output\BLM_0_02_002_0.06").

Conservation Feature

Feature Name Target

Amount Held

Occurrence Target

Occurrences Held

Separation Target

Separation Achieved

Target Met MPM

5 Blue_Crane 3759 3460.934 0 108 0 0 no 0.920706

6 Yellowbr_1 78 78.51912 0 5 0 0 yes 1

7 Yellowbr_2 1947 1861.816 0 45 0 0 yes 0.956248

8 Crowned_Cr 16435 13345.53 0 252 0 0 no 0.812019

9 Wattled_Cr 11242 10414.32 0 178 0 0 no 0.926377

10 Lesser_Kes 0 216569.6 0 3720 0 0

1

11 Bald_Ibis 4511 3229.905 0 91 0 0 no 0.716006

12 Bearded_Vu 2914 2914.395 0 51 0 0 yes 1

13 Whiteb_Vu 75869 61101.6 0 1110 0 0 no 0.805357

14 Rudds_Lark 12 12.5581 0 2 0 0 yes 1

15 Mart_Eag_1 9459 7501.936 0 175 0 0 no 0.7931

16 Mart_Eag_2 155 155.3663 0 13 0 0 yes 1

17 Secretaryb 75869 61101.6 0 1110 0 0 no 0.805357

18 Grass_Owl 314 294.6722 0 11 0 0 no 0.938446


19 Bio_Cl_res 1553004 1009305 0 18107 0 0 no 0.649905

20 Alep_ama_1 3 3.1375 0 2 0 0 yes 1

21 Alep_ama_2 2023 1938.272 0 45 0 0 yes 0.958118

22 Brach_dim 21 0 0 0 0 0 no 0

23 Chort_lat 12808 3250.262 0 89 0 0 no 0.253768

24 Draco_sa_1 6 6.27499 0 1 0 0 yes 1

25 Draco_sa_2 693 693.063 0 24 0 0 yes 1

26 Hel_hayg_1 3 3.13749 0 1 0 0 yes 1

27 Hel_hayg_2 23 23.42843 0 5 0 0 yes 1

28 Hoodia_off 1810 1789.276 0 32 0 0 yes 0.98855

29 Isoet_aeq 5 5.55777 0 4 0 0 yes 1

30 Kniph_typh 141 104.3547 0 7 0 0 no 0.740104

31 Lithops_sa 20856 20617.88 0 326 0 0 yes 0.988582

32 Nerine_bow 3 3.13749 0 1 0 0 yes 1

33 Pentzia_op 6 6.27499 0 3 0 0 yes 1

34 Protea_dra 3 3.13749 0 2 0 0 yes 1

35 Prot_sub_1 18 18.5031 0 10 0 0 yes 1

36 Prot_sub_2 55 55.63171 0 3 0 0 yes 1

37 Schiz_mont 3 3.13749 0 2 0 0 yes 1

38 Stenost_um 3 0.87965 0 1 0 0 no 0.293217

39 Strum_te_1 3 3.13749 0 1 0 0 yes 1

40 Strum_te_2 2266 805.304 0 29 0 0 no 0.355386

41 Unique_geo 23505 20546.36 0 357 0 0 no 0.874127

42 Insel_AfrM 35824 34979.62 0 1291 0 0 yes 0.97643

43 Insel_Kar 36997 33574.59 0 1536 0 0 no 0.907495

44 Marsh_Sylp 9 4.86295 0 2 0 0 no 0.540328

45 Mijb_Blue 21 19.75509 0 7 0 0 no 0.940719

46 GG_blue_1 9 9.41247 0 4 0 0 yes 1

47 GG_blue_2 421 421.1643 0 11 0 0 yes 1

48 GG_brown_1 3 3.13749 0 1 0 0 yes 1

49 GG_brown_2 421 421.2039 0 11 0 0 yes 1


50 Terbl_Skol 6 6.27499 0 5 0 0 yes 1

51 Orange_Wid 19 17.39371 0 9 0 0 no 0.915458

52 Griqua_BlP 6 3.13749 0 1 0 0 no 0.522915

53 Oure_ori_1 157 157.0382 0 7 0 0 yes 1

54 Oure_ori_2 24645 20731.13 0 331 0 0 no 0.84119

55 Lesueurs_B 235 234.7057 0 11 0 0 yes 0.998748

56 De_Wintons 78 78.51911 0 3 0 0 yes 1

57 Whitetai_1 3 3.13749 0 1 0 0 yes 1

58 Whitetai_2 3804 3433.363 0 98 0 0 no 0.902566

59 African_we 2270 2156.106 0 62 0 0 no 0.949826

60 Langs_Gr_1 8 8.42691 0 4 0 0 yes 1

61 Langs_Gr_2 20 68.90187 0 6 0 0 yes 1

62 Spiny_Crag 9 9.94178 0 4 0 0 yes 1

63 Sungazer_1 12 6.51011 0 6 0 0 no 0.542509

64 Sungazer_2 2200 1706.525 0 57 0 0 no 0.775693

65 Longt_Seps 12 12.54997 0 7 0 0 yes 1

66 Cott_Mnt_L 6 6.27498 0 2 0 0 yes 1

67 Ekang_CBAs 61309 54207.07 0 1216 0 0 no 0.884162

68 Aliwal_Nor 123280 123351.1 0 1840 0 0 yes 1

69 Amersfoort 22327 41440.41 0 747 0 0 yes 1

70 Andesite_M 6809 6909.032 0 111 0 0 yes 1

71 Basotho_Mo 48276 130799.2 0 3866 0 0 yes 1

72 Besemkaree 121325 121364 0 2749 0 0 yes 1

73 Bl_Dry_Gra 117946 117950.8 0 1647 0 0 yes 1

74 Bl_Kar_Shr 2251 2257.594 0 105 0 0 yes 1

75 Carletonvi 1403 1404.593 0 27 0 0 yes 1

76 Central_Fr 381306 381306.7 0 5780 0 0 yes 1

77 Drak_Afr_H 427 1583.738 0 53 0 0 yes 1

78 Drak_Ama_A 90 333.8516 0 13 0 0 yes 1

79 Drak_Monta 33 33.16668 0 18 0 0 yes 1

80 East_FS_Cl 336017 372704 0 7989 0 0 yes 1


81 East_FS_Sg 269455 418608.6 0 8572 0 0 yes 1

82 Eas_Wet_R1 360 374.466 0 25 0 0 yes 1

83 Eas_Wet_R2 1520 3548.804 0 151 0 0 yes 1

84 Eastern_Up 164 0.9316 0 1 0 0 no 0.00568

85 Frank_HGr 224118 224123.3 0 3069 0 0 yes 1

86 Gold_Reef 4774 17472.76 0 278 0 0 yes 1

87 High_Al_R1 44832 44837.82 0 841 0 0 yes 1

88 High_Al_R2 4425 4461.642 0 91 0 0 yes 1

89 High_Al_R3 39262 39272.41 0 954 0 0 yes 1

90 High_Pa_R2 2938 9346.791 0 468 0 0 yes 1

91 High_Pa_R3 41 53.26741 0 6 0 0 yes 1

92 High_Pa_R4 15816 15827.91 0 617 0 0 yes 1

93 Kimb_Thv 111874 111878.4 0 1559 0 0 yes 1

94 Lesotho_Hi 12505 42693.91 0 970 0 0 yes 1

95 Low_Escarp 4959 17607.73 0 500 0 0 yes 1

96 N_AfrT_For 185 461.2243 0 79 0 0 yes 1

97 N_Dr_H_Gr 13800 34253.75 0 763 0 0 yes 1

98 N_FS_Shrub 660 1214.35 0 102 0 0 yes 1

99 N_Up_Kar 161578 161585 0 2058 0 0 yes 1

100 Rand_Hv_Gr 10917 10921.46 0 202 0 0 yes 1

101 Schmid_Thv 17393 17398.58 0 255 0 0 yes 1

102 Senq_M_Shr 108 325.0154 0 7 0 0 yes 1

103 Sow_Hig_Gr 10990 10992.9 0 251 0 0 yes 1

104 uK_Bas_Gr 3061 10918.91 0 181 0 0 yes 1

105 UG_All_veg 12278 12279.92 0 305 0 0 yes 1

106 Vl_R_Dol_S 1154 1158.505 0 27 0 0 yes 1

107 Vaal_Vet_S 351393 351392.2 0 9204 0 0 yes 0.999998

108 Vlb_Ro_Shr 4485 5490.747 0 179 0 0 yes 1

109 Vred_Gr_Gr 20816 20815.85 0 358 0 0 yes 0.999993

110 W_FS_Cl_Gr 160014 160016.6 0 2358 0 0 yes 1

111 Winb_Gr_Sh 43984 43991.31 0 1021 0 0 yes 1


112 Xh_Kar_shr 320572 320573.9 0 4627 0 0 yes 1

113 Zast_Mo_gr 49452 49448.89 0 959 0 0 yes 0.999937

114 RAMSAR_sit 3697 3697.737 0 61 0 0 yes 1


9. Results

The results are presented in Figure $ (Marxan map).

9.1. The frequency of selection map

Figure 22

It should be noted from Figure 22 planning units contained within protected areas may, in addition to being categorised as 'Protected', also have been classified as CBAs. It follows that irrespective of planning units within protected areas satisfying the criteria to be classified as CBAs, all such planning units


located with protected areas are categorised as "Protected". It follows that planning units within protected areas originally classified as 'Other' or 'Degraded' are also indicated as 'Protected'.

Achievement of targets

A total of 110 features were included in the Marxan analysis of which the targets for 83 features were achieved. It follws the targets of 27 feartures (24.5%) could not be achieved of which most (20%) ar of species features. A summary of the number of targets achieved is provided in Table 40.

Table 40: Summary of the number of targets achieved

Feature category Achieved Not achieved

Total features

% of feature category achieved

% of total features achieved

Species 36 22 58 62.1 32.7

Vegetatioin types 45 1 46 97.8 40.9

Other18 2 4 6 33.3 1.8

Total 83 27 110

75.5 However, closer inspection of the results reveal that many of the ecosystem (vegetation type) targets have been achieved within planning units of which a large portion of their surface areas cosist of wetland habitat. The course scale of the ecosystems map (Mucina & Rutherford, 2006) has resulted in many wetland ecosystems not being included in this coverage, resulting in such areas being mapped as terrestrial instead of aquatic (azonal) habitat. The high degree of association between CBAs and wetands are indicated in Figure 23. Although is advantageous for the conservatin of wetlands, it is undesirable as large areas that were selected so satisfy vegettaion type targets are in actual fact notr representative of those vegetation types. It is postulated that that these areas were selected to satisfy vegetation type tarets becasue no other natural areas avilable for selection of such vegetation types remain and that, should such planning unist consisting of wetland habitat haven been made not available for selection, that the targtes of such vegetation types will not be achieved. The reported extent to which feature targets, specifically those of thevegetation types, have been achieved is thereore considerd to be false representation of the actual amount of that vegetation type that is actually included in the selection. This is not a error in analysis but rather a consequence of the coarse scale at whcih the vegetaion types have been mapped. This isue will be addressed in future releases of the Free State Biodiversity Plan.

18 Other features include $biomes resilient to climate change (Verify what this is), unique geological structures, inselbers, CBAs of the Ekangala region and Ramsar sites.


Figure 23: Figure inidcating the high degree of association beween CBAs (green areas) and wetlands (black outlines).

9.2. Composition of CBAs

Due to the high levels of transformation the targets of the following features could not be achieved:

Table 41: Area and percentage contribution of CBA map categories


Protected 1275 1.0 CBA Irreplaceable 13089 10.2

CBA Optional 2095 1.6

ESA1 37126 28.8

ESA2 31524 24.5

Other 20998 16.3

Degraded 22784 17.7


Total 128891

Table 42: Summarized Area and percentage contribution of CBA map categories


Protected 1275 1.0 CBAs 15183 11.8

ESAs 68650 53.3

Other 20998 16.3

Degraded 22784 17.7

Total 128891

It should be noted that planning units that according to Table 32 are classified as belonging to either the CBA or ESA categories, but which are located within a protected area, are accounted for in the 'Protected' category. An analysis of the distribution of the different categories amongst those that occur within protected areas is presented in Table 43. It follows that 87.6 of planning units categorised as 'Protected' were, after applying the indicators and criteria as per Table 32, categorised as belonging to the CBA category.

Table 43: Area and percentage area of CBA categories that are located within protected.


CBAs 1117 87.6 ESAs 142 11.1

Other 16 1.2

Degraded 1 0.1

Total (Protected) 1275

A closer inspection of

An initial run of Marxan revealed that due to high levels of transformation the ecosystem features (vegetation types) that their targets were satisfied by selecting the only other available planning units, i.e. planning units that contain watercourses and wetlands (Figure 24 and Figure 25). Such watercourses and wetlands are azonal and are not representative of the vegetation types in which they are nested and their selection to satisfy the vegetation type target is considered to be undesirable. Such planning units were subsequently also assigned a planning unit status value of 3.


Figure 24: Results of first analysis where watercourses and wetland were assigned a planning unit status value of 0 (i.e. available for selection) and subsequently selected by Marxan to satisfy the ecosystem (vegetation type) targets.


Figure 25: Close up of the black frame of Figure 24 with comparison of Marxan selection with planning units that contain watercourses and wetlands available for selection on the left and such planning units not available for selection on the right. CBAs (red) are those planning units with a frequency of selection of 80 or more.

Table 44: List comparing the features of which their targets were not achieved before and after excluding planning units containing watercourses and wetlands from analysis. MPM indicates how close the target is to being achieved (0.55 = 55% of the target is achieved, etc.). Feature targets were considered to have been not met if the missing proportion was less than 0.95. MPM_1 represents the values after excluding planning units containing watercourses and wetlands from analysis, while MPM_2 represents the values before excluding planning units containing watercourses and wetlands from analysis. "---" Indicates features of which the targets were achieved.

Feature Name MPM_1 MPM_2

Anthropoides paradiseus Blue Crane 0.592283 0.920694

Anthus chloris Yellowbreasted Pipit 0.698532 ---

Balearica regulorum Crowned Crane 0.4811 0.812019

Bugeranus carunculatus Wattled Crane 0.694909 0.926187

Geronticus calvus Bald Ibis 0.29015 0.716006

Gyps africanus Whitebacked Vulture 0.642795 0.805179

Heteromirafra ruddi Rudds Lark 0.525081 ---

Polemaetus bellicosus Martial Eagle 0.608471 0.792979

Sagittarius serpentarius Secretarybird 0.642795 0.805179

Tyto capensis Grass Owl 0.938446 0.938446

Biomes resilient to change grasslands 0.23657 0.647052

Alepidea amatymbica 0.710374 ---

Brachystelma dimorphum 0 0

Chortolirion latifolium 0.0712 0.253768

Helichrysum haygarthii 0 ---

Isoetes aequinoctialis 0.660662 ---

Kniphofia typhoides 0 0.740104

Lithops salicola 0.900946 ---

Stenostelma umbelluliferum 0 0.293217

Strumaria tenella_1 0 ---

Strumaria tenella_2 0.085412 0.35459

Unique geological feature: Vredefort Dome 0.431664 0.840358

Afro Montane Inselbergs 0.799327 ---

Karoo Inselbergs 0.552044 0.907076


Metisella meninx Marsh Sylph 0 0.540328

Orachrysops mijburghi Mijburghs Blue 0.00136 0.940719

Thestor protumnus terblanchei Terblanches Skolly 0.826595 ---

Torynesis orangica Orange Widow 0.645507 0.915458

Tuxentius melaena griqua Griqua Black Pie 0 0.522915

Ourebia oribi Oribi 0.364461 0.841155

Cistugo lesueuri Lesueurs Bat 0.668791 ---

Mystromys albicaudatus Whitetailed Rat 0.566955 0.902566

Poecilogale albinucha African weasel 0.875649 0.949794

Smaug giganteus Sungazer_1 0.256786 0.542509

Smaug giganteus Sungazer_2 0.351483 0.775555

Ekangala CBAs 0.509718 0.856992

Carletonville Dolomite Grassland 0.480183 ---

Eastern Free State Clay Grassland 0.238526 ---

Eastern Free State Sandy Grassland 0.886152 ---

Eastern Temperate Freshwater Wetlands Region1 0.40446 ---

Eastern Upper Karoo 0 0.00568

Frankfort Highveld Grassland 0.579042 ---

Highveld Alluvial Vegetation Region2 0.824857 ---

Highveld Alluvial Vegetation Region3 0.834216 ---

Highveld Salt Pans Region2 0.777406 ---

Rand Highveld Grassland 0.196706 ---

Soweto Highveld Grassland 0.240882 ---

Vaal Vet Sandy Grassland 0.159925 ---

Vredefort Dome Granite Grassland 0.392771 ---

It can be seen from Error! Reference source not found. and Table 45 that the targets of 49 of the 110 features were not achieved (44.5%). It follows that only 55.5 percent of feature targets were achieved. This low percentage of targets being achieved is attributed mainly to the high levels of transformation. Also contributing to the low level of target achievement is the fact that many of the planning units that contain watercourses and wetlands were excluded from analysis to prevent vegetation type targets (terrestrial ecosystems) from being achieved within planning units containing such azonal aquatic ecosystems. It can be concluded from Table 45 that although features types other than ecosystems were also affected by this $setup, that it mostly affected the ecosystems. The exclusion of such planning units resulted in 13 vegetation type planning units not being achieved compared to only 1 prior to their exclusion.

Table 45: Summary of target achievement after and prior to excluding planning units that contain watercourses and wetlands from analysis. Feature type N targets not N targets not Percentage


achieved after exclusion

achieved after exclusion

decrease

Avifauna 10 8 20% Flora 10 5 50% Invertebrates 5 4 20% Large mammals 1 1 0% Small Mammals 3 2 33% Reptile 2 2 0% Ecosystems 13 1 92% Biomes resilient to change grasslands

1 1 0%

Unique geological feature: Vredefort Dome

1 1 0%

Afro Montane Inselbergs 1 0 50% Karoo Inselbergs 1 1 0% Ekangala CBAs 1 1 0%

Total 49 27 44.8%

Although the low target achievement after the exclusion of planning units containing azonal ecosystems seems extreme$

9.3. Target achievement in Critical Biodiversity Areas


10. Land-Use Guidelines

Broad land use guidelines are associated with the Spatial Planning Categories as contained in the FS SDF, these being

SPC Type of development Condition

A No development allowed.

B a) Resort-related development. b) Infrastructure for non-consumptive land-use.

a) SPC to be amended to SPC D, depending on the proposed type of development. b) To be undertaken in accordance with site-specific design and planning guidelines (refer to Chapter C6).

C a) Agricultural development and

a) SPC to be amended to SPC D, depending on the proposed type of


infrastructure required for extensive and intensive agricultural land-uses. b) Resort development. c) Agricultural industry.

development. b) To be undertaken in accordance with site-specific design and planning guidelines.

D All urban-related developments.

a) To be undertaken in accordance with site-specific design and planning guidelines.

E Full spectrum of industrial developments required by the economic sectors.

a) Must be undertaken in accordance with site specific design and planning guidelines. b) All industrial activities must be regulated and managed in accordance with sustainability standards (e.g. ISO 14001).

F All surface infrastructure and buildings that are required for sustainable socio-economic development and resource use.

a) To be undertaken in accordance with site-specific design and planning guidelines. b) All industrial activities must be regulated and managed in accordance with sustainability standards (e.g. ISO 14001).


10.1. Relation with the provincial SDF

Until such time as the Provincial Spatial Planning and Land Use Management Bill has been promulgated the overarching legislative premise for the preparation and implementation of the PSDF is provided by the Land Use Management Bill (2012). Implementation of the PSDF is the responsibility of The Free State COGTA, on behalf of the Premier, and other sectoral departments. It is, amongst other, in response to this responsibility that the FS DETEA has developed and published this provincial biodiversity plan. The PSDF, in conjunction with the FSGDS, is to facilitate the application of the National Development Plan Vision 2030 in the Free State by defining a place-specific spatial vision and direction around which to align the Provincial Strategic Growth and Development Pillars of the FSGDS. This is given effect by illustrating the desired future spatial patterns that provide for integrated, efficient and sustainable land-use throughout the province based upon the development priorities set the FSGDS. In practical land-use terms, the PSDF provides guidance amongst others pertaining to what type of land-use should be undertaken at any particular location. The PSDF is to serve as a framework and manual for integrated spatial planning and land-use management in accordance with the principles of sustainability and sustainable development. To this end, the PSDF focuses on amongst others the following:

• Supporting the district and local municipalities in the preparation of their SDFs in terms of the Local Government: Municipal Systems Act 32 of 2000. Such support and guidance include the following: o Providing a standard spatial format for giving effect to, among others, the FSGDS and the associated development programmes and projects

throughout the province. o Facilitating the land-use classification of the province in a standard format in accordance with defined Spatial Planning Categories (SPCs). o Recording the land-use (SPC) plans and associated strategies and guidelines in an innovative Spatial Planning Information System (SPISYS).

• Providing a basis for co-ordinated decision-making and policy-formulation regarding future land-use with specific reference to the following: o Serving as a basis for decision-makers in respect of development applications throughout the province. o Facilitating cross-boundary co-operation and co-ordination between metropolitan, district and local municipalities, adjoining provinces, and Lesotho in

respect of issues that are of mutual interest for their respective areas of jurisdiction (refer to, among others, issues pertaining to land-use, biodiversity conservation, and resource utilisation).

The PSDF therefore serves as a framework and manual for integrated spatial planning and land-use management in accordance with the principles of sustainability and sustainable development. This implies amongst others (p 13 of PSDF) a) Enabling intergovernmental alignment and guiding the activities of the relevant role-players and agencies (including national and provincial

sectoral departments and municipalities). b) Ensuring uniformity of application of planning processes and methodologies.


(i) Supporting the district and local municipalities in the preparation of their SDFs in terms of the Local Government: Municipal Systems Act 32 of 2000. Such support and guidance include the following:

• Providing a standard spatial format for giving effect to, among others, the FSGDS and the associated development programmes and projects throughout the province.

• Facilitating the land-use classification of the province in a standard format in accordance with defined Spatial Planning Categories (SPCs).

• Recording the land-use (SPC) plans and associated strategies and guidelines in an innovative Spatial Planning Information System (SPISYS).

• Illustrating the desired future spatial patterns that provide for integrated, efficient and sustainable settlements based upon development priorities.

(ii) Providing a basis for co-ordinated decision-making and policy-formulation regarding future land-use with specific reference to the following:

• Serving as a basis for decision-makers in respect of development applications.

• Facilitating the replacement of inappropriate policy frameworks with a more integrated approach to planning.

• Facilitating cross-boundary co-operation and co-ordination between metropolitan, district and local municipalities, adjoining provinces, and Lesotho in respect of issues that are of mutual interest for their respective areas of jurisdiction.


Figure 26:

A4.2 CURRENT CHALLENGES FACING THE SPATIAL PLANNING SYSTEM p16 According to the NDP, the current spatial planning system faces a number of difficulties and challenges and a renewed effort is needed to ensure that national, provincial and local government work together in reshaping the built environment to achieve smarter and fairer development. The following challenges and difficulties have been listed in the NDP: a) Spatial planning is dispersed across ministries, and is subject to parallel and sometimes conflicting legislation. b) South Africa’s intergovernmental system of spatial planning has been slow to develop and has been poor. The complex division of powers

and functions between local, provincial and national government has contributes to the problem and, in addition, ambiguities in the Constitution about who is responsible for spatial planning has created uncertainty.

c) The planning system has cemented municipal and provincial boundaries, making it almost impossible to plan across borders or to collaborate between one province or municipality and another.

d) Municipal IDPs vary in quality, and many municipalities are still struggling to produce credible IDPs.


e) Inefficiencies in processing planning applications have sometimes deterred job-creating investment. The costs associated with long approval processes are carried by the private sector with negative consequences for growth and job-creation.

f) Planners (and other development and built environment professionals) who lack an understanding of economic principles, market forces and commercial realities to negotiate better development outcomes, etc. Many municipalities struggle to appoint qualified planners and urban designers, who are in short supply and are often not considered a priority. As a result quality standards are sometimes poor, and because opportunities are limited, too few people study planning and urban design.

g) Community and public participation in spatial planning is generally not adequate. A4.3 REFORM OF CURRENT PLANNING SYSTEM p17 The PSDF responds to the following recommendations of the NDP as it relates to reforming the current planning system: a) Actively support the development of plans that cross municipal, and even provincial boundaries, and which would promote collaborative

action in fields such as biodiversity protection, climate change adaptation, tourism and transportation. b) Develop a capability framework for spatial governance together with professional bodies, educational institutions and relevant government

agencies. This framework should deal with strengthening the education and training of planners and other spatial professionals, improving quality of professional work, etc.

c) Eliminate inefficiencies in administrative procedures for land development without compromising the need for careful evaluation of proposals.

d) Ensure that every municipality has an explicit spatial restructuring strategy that is linked to instruments for implementation. This includes identifying priority precincts for spatial restructuring.

e) If necessary, tools must be developed that empower municipalities to make critical interventions to redress past social segregation. f) Require all municipal and provincial plans, including IDPs and their SDF components, to be translated into spatial contracts that are binding

across national, provincial and local government. g) Retool the instruments of land-use management to achieve spatial objectives by, for example, municipalities introducing land use zoning,

incorporating the social value of land imperatives and the fiscal instruments to achieve spatial objectives. h) Strengthen planning capabilities with local government through extension of existing initiatives, but also through institutional innovations

that may include the regionalisation of planning and service delivery, or at least arrangements that allow for cross-border sharing of planning capacity.

i) Strengthen the enforcement of local planning and building control. P20 Driver 12: Integrate environmental limitations and change into growth and development planning.


a) Improve water quantity and quality management. b) Mitigate the causes and effects of climate change. c) Conserve and consolidate functional natural areas. d) Facilitate alternative habitat usage (in marginal areas). e) Broaden environmental capacity and skills in the environment sector (specifically) and in the cross-sectoral situation (generally). f) Internalise a cross-sectoral practice of environmental education and the raising of environmental awareness in the Free State. P22 Driver 2: Enhancing the integrity of the environment as an imperative for long-term sustainability. a) Maintain essential ecological processes, preservation of genetic diversity and the insurance of the sustainable utilisation of natural

resources. b) Plan and design the cultural (human) environment in a manner that enhances the intrinsic value (including heritage and traditional

legacy) of the subject places and the Free State as a whole, and creates places where people can live with dignity and pride (a key element of social equity).

Driver 3: Incorporating biodiversity into the management of all biological resources. a) Biodiversity conservation is a prerequisite for sustainable development, and for biodiversity conservation to succeed, the maintenance of

environmental integrity (as defined by ecological, economic and social criteria) must be one of the primary determinants of land-use planning. b) The mix of species in an ecosystem enables that system both to provide a flow of ecosystem services under given environmental

conditions, and to maintain that flow if environmental conditions change. The loss of biodiversity therefore limits the resilience of the affected ecosystem, which in turn, may have direct negative economic implications.

Driver 4: Supporting conservation initiatives in the private sector. a) Conservation on private land should become an integral part of the provincial conservation strategy. This, in turn, requires that forward

planning must be done on a holistic bioregional basis. Environmental health is the key to sustainable development. The primary threat to environmental health is fragmentation of community-supporting ecosystems. Fragmentation generally leads to a cycle of environmental degradation, which subsequently influences the well-being of the dependent communities. It is, therefore, of paramount importance that issues, such as biodiversity conservation, economic growth, human resources development, and social development, should be addressed in all SDFs.

P28 See Figure A9 P31 Therefore, sustainability is more than maintaining the regenerative capacities of natural ecosystems. It is about simultaneously meeting our material needs, striving for social and economic equitability and justice, and preserving all aspects of biodiversity and the natural environment. While this is a


tall order, the interconnected and complex nature of these three facets of society makes it easier, more logical, and, hopefully, more effective to address them simultaneously and holistically than to artificially divide them. P159 - Prioritising of towns ito A key objective of the PSDF is to serve as a reliable first-cut premise for prioritising and directing state funding and private investment in the province. P165 a) Prioritise government spending and private sector investment to the best benefit of province as a whole. P159 The investment curve in large-scale development in industrial amenities and bulk infrastructure is determined by the comparative economic advantages of the various regions of the province vested in the availability of critical resources (e.g. minerals, energy, agricultural produce, etc.). This trend has largely determined the growth potential and the economic potential of settlements and the development needs of the inhabitant communities. The Human needs assessmnt is therefore the result of its economic and urban growth potential. The PSDF provides an appropriate spatial and strategic context for land-use throughout the Free State. It provides generic land use guidelines with more detailed land use guidelines provided at the district and local municipal level. The PSDF therefore provides the overarching framework with which finer scale land use plans need to be aligned. To this extent any land-use amendment has to conform to the PSDF. This means that the relevant organs of state must take account of, and apply relevant provisions of the PSDF when making decisions that affect the use of land and other resources. The relationship between the PSDF and municipal SDFs is determined by the principle that if a municipal SDF is not consistent with the PSDF, the municipal SDF must be amended in order to align it with the latter. However, in cases where detail planning at the municipal sphere provides new information applicable to the PSDF, the latter must be amended accordingly

10.2. SPISYS


11. Future improvements:


12. References



13. Appendix 1: Modelling

None of the species included in this assessment were systematically sampled across the entire extent of the Free State Province. It follows that actual observations only represent sites at which the different species have been observed, while sites without actual observations merely implies that they have not been sampled or that they have been sampled, but that their absence was not recorded. To account for this deficiency the regional extent (the geographic area in which the species is likely to occur) of each species was determined using ecological niche modelling (using known occurrences as training points to model its potential distribution according to certain environmental variables). Where ecological niche modelling was not possible or suitable, the regional extent was determined by:

• Georeferencing and mapping published distribution maps, or

• By applying some environmental variable that is known to limit the species distribution range, e.g. altitude.

Ecological niche modelling was done using Maxent. As mentioned ecological niche modelling was not to identify specific sites where species are likely to occur, but rather to demarcate broad geographic regions to which the distribution of the species will be limited. Areas of suitable habitat within these regions were identified using cartographic modelling.

According to landscape ecology theory different landscape hierarchical levels are recognised which are nested so that higher levels of the hierarchy contain elements (patches) of the lower levels. The hierarchical levels are characterised by the different scale at which they operate (Holling, 1992). As such three different hierarchical levels are recognised, these being (Holling, 1992):

• Processes that determine plant growth, plant structure and soil structure at the local scale. These processes occupy centimeters to tens of meters in space and days to decades in time. These represent the first-order landscape elements.

• Contagious disturbance processes such as fire, insect outbreak or plant disease. The scale of operation is hundreds of meters to thousands of kilometers in space and years to decades in time. These represent the second-order landscape elements.

• Geomorphological processes that dominate the formation of the topography and soil structure at a larger scale. The scale of operation is hundreds to thousands of kilometres in space and centuries to millennia in time. These represent the third-order landscape elements.

The regional distribution of organisms is therefore related to the environmental variables that operate at a broader environment scale, i.e. the third order landscape elements. To this extent only environmental variables that operate at the broader scale were used during ecological niche modelling, these being:

o Altitude (continuous data) o Mean annual evaporation (continuous data) o Mean annual precipitation (continuous data) o Rainfall seasonality (categorical data) o Mean annual temperature (continuous data) o Vegetation type (categorical data)

Ecological niche modelling was used to determine the potential national distribution of the species in question. In addition to informing the potential broader distribution of the species throughout the Free State, the national distribution was used to determine the proportional target for which the Free State is responsible. It follows that the proportional target was based on the percentage area of the species distribution that occurs within the Free State relative to


other provinces (Error! Not a valid bookmark self-reference.). However, application of cartographic modelling to determine suitable habitat within the broader geographic range of the species, was limited to the Free State only.

Training points for ecological niche modelling were obtained from the actual (Pseudo_1) point and polygon observations. In the case of polygon data the training point was accepted to be the centroid of the polygon. Large scale data were also used as training points where available. The latter included, for example, systematic assessments of species occurrences where the accuracy of recording is based on some form of spatial framework, e.g. quarter degree squares or pentads. The inclusion of large scale observations as training points for ecological niche modelling (centre point of QDSs and pentads) was considered to be acceptable in view of the fact that environmental variables used for ecological niche modelling was limited to those that operate at the third hierarchical landscape level, while not including those that operate at the lower hierarchical levels.

The output of ecological niche modelling was supported by cartographic modelling which identified the specific habitat within the broader region in which the particular species can potentially occur. Unlike for ecological niche modeling where the same environmental variables were used for all species, cartographic modelling was species specific (i.e. different environmental variables were used depending on the ecological requirements of the species in question). The latter was constrained by the availability of spatial data, i.e. if a species selected for a particular soil type but no data that accurately describes the spatial occurrence of such soils were available, then soil types were not included as part of the cartographic modelling process. The cartographically mapped areas represent the modelled species distribution, i.e. the Pseudo_3 (large mammals), Pseudo_4 (flora, reptiles, invertebrates and small mammals) and Pseudo_5 (flora, reptiles, invertebrates, and flora) distribution.


14. Appendix 2: Targets

1. Avifauna 1.1. Anthropoides paradiseus (Blue crane)

14.1.1. Rationale for inclusion

VU (A1a,c,d,e; A2b,c)

14.1.2. Distribution mapping/modeling general

CAR data indicates that the blue crane occurs throughout the entire Free State Province.

According to the Gauteng C-Plan technical document the average home range of Blue Cranes in KZN is 3.8 km2 (380 ha; r = 1.09 km) (SACWG) (Compaan, 2013). In the absence of equivalent data the home range of Blue Crane in the Free State was accepted to be similar.

14.1.3. Raw distribution data sources

A single actual roosting location was provided by Mr. Morné Pretorius during and expert mapping exercise. Eleven actual point breeding localities were obtained from the EWT (Me. Michelle Wheeler).

General distribution data were obtained from CAR data.

14.1.4. Distribution mapping/modeling technical

Pseudo_1 (point)

• All known breeding and roosting sites were buffered by 1 km. The 1 km buffer is as per the edge-matching technical guidelines (Escott & Lotter, 2012)) and also satisfies the average home range of 3.8 km (r = 1.09 km). Although records of breeding blue crane are contained in the CAR dataset, these were considered to be of insufficient accuracy for inclusion.

Pseudo_2 (point)

• Actual point locations were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.


Pseudo_2 (polygon)

• Actual point locations were buffered by 500 m. The presence of the speceis indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.

Pseudo_4

• Blue Crane can potentially occur almost anywhere within the Free State. To bias selection towards areas of highest probability of occurrence, the regional extent was accepted to be a selection of 1000 ha hexagons in which more than 3 CAR observations were made.

• Because Blue Crane utilize transformed habitats these were not removed from either of the previously discussed Pseudo layers.

• Features of all sizes were included features were

Because the modelled distribution is based on the CAR data, no additional habitat modelling was done for the Blue Crane.

14.1.5. Target rationale

Eleven locations (or in the absence of any potential threat, 11 populations or 11 localities) and at least 10,000 mature individuals for all threatened (Critically Endangered, Endangered, Vulnerable) species solely listed under the IUCN Red List criteria of A or E as well as any other conservation worthy species (e.g. Near Threatened species) (Pfab, Victor, & Armstrong, 2011).

14.1.6. Targets

The target is based on the FS proportion of 10 000 individuals for a home range of 3.8 km2 per pair (2 birds).

Taxa Avifauna

Species Anthropoides paradiseus

Common name Blue Crane

Status VU

Criteria A1a,c,d,e; A2b,c

Endemic Trend Target 11/10000

National target: Populations 11

National target: Mature individuals 10000

Estimated RSA population 21000

Estimated RSA range (km2) 500000

FS populations


Type Pair

Approximate RSA individuals per area (ind/ha) 0.042

Approximate RSA area per individual (km2/ind) 23.80952

Home range/Occupancy (km2) 3.8

Proportional FS distribution (%) 18.38755 (as per ecological niche modelling)

FS proportional populations (of 11) 3

FS proportional individuals (of 10 000) 1838.755

FS proportional individuals (of Estimated RSA population) 3861.386

FS proportional area of estimated RSA range (km2) 91937.75

Area required for proportional 10 000 individuals (km2) 3493.63519

Area required for proportional RSA population (km2) 7336.633

Blue Crane VU (A1a,c,d,e; A2b,c)

Modelled area 14810.8331 km2 Proportional target (km2): Included as: 3493.63453 km2 % Target Target expression

Pseudo_1 area (point) Features & Cost 37.694124 km2 100 % 37.694 km2

Pseudo_2 area (point) ESA & Cost 0.785191 km2 0 0 km2

Pseudo_2 area (polygon) ESA & Cost 12.662014 km2 0 0 km2

Pseudo_4 area Cost 14759.6918 km2 0 0 km2

37.694 km2

Discussion: In the case of Anthropoides paradiseus the Free Satte accounts 18.38% of the estimated range of the species. The proportional target for the FS is therefore: 3 populations or 1838.75 mature individuals (3493.63 km2), whichever is the most.

• 100% of all known actual Pseudo_1 (point) localities (nesting sites) which accounts for 37.69 km2. The achievable target (37.694 km2) is therefore -98.92% of the proportional target (3493.63453 km2).

19 The area required to account for 1838.755 indivduals is 3493.635 km2 (1838.755/2*3.8); the FS proportion of 10 000 inidividuals is divided by 2 because the stated home range accounts for 1 pair, i.e. 2 birds.


Although Blue Crane occurs throughout the entire Free State, the modelled habitat was limited to areas of high occurrence as determined from CAR data. This is to optimize the likelihood of Blue Crane actually occurring within a selected planning unit (as compared to selecting planning units in areas of low probability of occurrence).

1.2. Anthus chloris (Yellowbreasted pipit)


VU (A2c; B1+2c; C1)


The Yellowbreasted Pipit generally favours high altitude areas (2000 – 2300 m.a.s.l.) in submontane flat or gently sloping grasslands (Clanvy 1985b in Barnes 2000). Recently grazed and burnt areas are avoided (Keith et al. 1992 in Barnes 2000). After breeding they will move down to lower altitudes (Clancy 1990 in Barnes 2000) where they are often found in pasture and fallow land.

Nests are at least 20 meters apart, but usually more than 100 meters ($Roberts). When accepting 100 m as the usual distance between nests and this spatial configuration is accepted to represent home range size, then it follows that the home range for a single nest (1 pair) = 31415 m2 (r = 100 m).

SABAP1 and SABAP2 data are available for the Yellowbreasted pipit.


Eight actual point observations (representing 4 populations, 1 breeding and 7 sightings) were provided by Mr. Brian Colahan (FS DESTEA) while four actual polygon locations (representing 3 populations) were obtained from Mr. Morné Pretorius during an expert mapping exercise. A total of 7 populations are recognised.


Pseudo_1 (point)

• All known point breeding and roosting sites were buffered by 500 m. The 500 m buffer is applied to include a reasonable sample of this habitat to account for persistence. Comments accompanying the observations suggest that there are more breeding pairs than the reported observations. A 500 m buffer was therefore applied to account for this possibility.

Pseudo_1 (polygon)

• Actual polygon breeding sites were not buffered (the polygon locality is on top of Platberg near Harrismith, i.e. a buffer will extend beyond the area of suitable habitat).

Pseudo_2 (point)


• Actual point sightings were buffered by 500 m (which exceeds the home range of 31415 m2; r = 99.99 m). The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat and to account for persistence.

Pseudo_2 (polygon)

• Actual polygon sightings were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat and to account for persistence.

Pseudo_4

• The regional extent was determined using ecological niche modelling. Training points included all actual point and polygon observations (using the centroid of the polygon in the latter case) as well as all SABAP1 QDS centers. To remove scattered fractions of the ecological niche modelled extent, only features larger than 500 ha were retained.

• Pseudo_4 distribution included areas of grasslands that occur on slopes 0 - 8 percent and that are at altitudes of 2000 m.a.s. or higher that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively (of which the most westerly lying features were manually removed).

• Only areas larger than 100 ha were retained

Pseudo_5

• Pseudo_5 consists of the remainder of the modelled range that occurs outside of the SABAP1 and SABAP 2 QDSs and Pentads records respectively.

• Only areas larger than 100 ha were retained.

Pseudo_3 areas surrounding breeding sites were not mapped as the area of suitable habitat surrounding the actual breeding point observation is already accounted for in the actual polygon breeding observation (Pseudo_1).


All known populations of Critically Endangered, Endangered and Vulnerable taxa listed under the IUCN Red List criteria of B, C or D, should be included in a conservation plan (Pfab, Victor, & Armstrong, 2011).

14.1.12. Targets

Taxa Avifauna

Species Anthus chloris

Common name Yellowbreasted Pipit

Status VU

Criteria A2c; B1+2c; C1

Endemic RSA

Trend Target 100






FS populations 7

Type Pair











Yellowbreasted Pipit VU (A2c; B1+2c; C1)

Modelled area 2575.07906 km2 Proportional target (km2): Included as: 4.00353336 km2 % Target Target expression

Pseudo_1 area (point) 0.785191 km2 100 % 0.7852 km2

Pseudo_1 area (polygon) 19.474446 km2 100 19.474 km2

Pseudo_2 area (point) 0 km2 0 0 km2

Pseudo_2 area (polygon) 0 km2 0 0 km2

Pseudo_4 area 0 km2 0 0 km2

20 The area required to account for 1026.54 indivduals ) is 4.00 km2 (1026.547/2*0.0078; the FS proportion of known individuals (6500) is divided by 2 because the stated home range accounts for 1 pair, i.e. 2 birds.



20.2592 km2

Discussion:

The estimated RSA population (6500) is less than the otherwise required 10 000 mature individuals. The targets are therefore based on the estimated RSA population and are subsequently adjusted to 2 populations or 1026.54 mature individuals (4.0 km2), whichever is the most.

• 100% of all known actual Pseudo_1 (point) localities (breeding) which amounts to 0.78 km2.

• 100% of all known actual Pseudo_1 (polygon) localities (breeding) which amounts to 19.47 km2. The Pseudo_1 (point) and Pseudo_1 (polygon) targets exceeds the FS proportional target by 506%.

The achievable target (20.2592 km2) is therefore 406.03% of the proportional target (4.0 km2). Although the achievable targets exceed the required FS proportional target, a 100% target for both the Pseudoe_1 point and Pseudo_1 polygon features were set on account of $provide reasoning for this

1.3. Balearica regulorum (Grey Crowned Crane)


VU (A1a,c; A2b,c; C1)


Grey Crowned Crane require both wetland and grassland areas. The edges of permanent wet wetlands or temporary wet wetlands are used for breeding. Well vegetated farm dams are also often used. Short to medium high open grasslands are preferred for foraging, but will also forage extensively in agricultural lands, including pastures, irrigated land, fallow land and crop fields that have recently been planted or harvested (Barnes, 2000)

According to Gishuki (in Meine & Archibald) the Grey Crowned Crane has an average breeding territory of 630 ha and an average home range of 2880 ha in Kenya (r = 3028 m). However, the home ranges of individual birds may vary with age, breeding conditions, and season of the year (Meine & Archibald, Google Books, 1996). In the absence of other data this was accepted. The area required to support a breeding pair was accepted to be 630 ha (6.3 km2)


Known breeding sites were obtained from expert mapping (Mr. Morné Pretorius and Mr. Nacelle Collins) and from EWT records. Sites indicating occurrences were obtained from the Ekangala Grasslands project (received from Dr. Boyd Escott of KZN Wildlife). The latter were, however, not used. The five actual point locations were considered to represent 5 populations.



Pseudo_1 (point)

• All actual known breeding and/or roosting point locations were buffered by 1.4 km [6.3 km2; r = 1.4 km) and was accepted to represent the home range for this species. This buffer exceeds the 1 km buffer required for edge matching as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_2 (point)

• All actual Pseudo_2 points were buffered by buffered by 500 m which is less than the 1 km buffer required for nesting sites as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_2 (polygon)

• All actual Pseudo_2 points were buffered by buffered by 500 m which is less than the 1 km buffer required for nesting sites as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_3 (polygon)

• Wetland habitat within 100 m of the actual Pseudo_1 point observations (not buffered). Not all actual Pseudo_1 point observations are located within wetlands. The 100 m selection buffer was subsequently applied to all wetland in close proximity of the actual point observations to be selected and included in this layer.

Pseudo_4

• To remove scattered fractions of the ecological niche modelled extent, only features larger than 500 ha were retained

• Cartographic modelling consisted of identifying and mapping wetlands as identified from the land cover dataset [landcover classes 24 (WATER

(natural pan)), 25 (WETLANDS (non-pan)), 26 (WETLANDS (vegetated pans)) and 27 (WETLANDS (dry pans)] that occur within the SABAP 1 and SABAP2

QDSs and Pentads respectively

• Because agricultural land is often used for forging, these were not removed from buffered areas


Pseudo_5

• Pseudo_5 consists of the remainder of the modelled range that occurs outside of the SABAP1 and SABAP 2 QDSs and Pentads respectively.





14.1.18. Targets

Taxa Avifauna

Species Balearica regulorum

Common name Grey Crowned Crane

Status VU

Criteria A1a,c; A2b,c; C1

Endemic No

Trend Target 100





FS number

FS populations 5

Size 2




Proportional FS distribution (%) 10.90160641







Grey Crowned Crane VU (A1a,c; A2b,c; C1)

Modelled area 728.936885 km2


Proportional target (km2): Included as: 4709.49397 km2 % Target Target expression







172.42 km2

Discission:

The estimated RSA population (3000) is less than the otherwise required 10 000 mature individuals. The targets are therefore based on the estimated RSA population and are subsequently adjusted to 2 populations or 327.04 mature individuals (4709.49 km2), whichever is the most.

• 100% of all known actual Pseudo_1 (point) localities which exceeds the FS proportion of 2 populations, but does not account for the required area to accommodate the FS proportional number of individuals (327.04 individuals; 1030.2 ha)

The achievable target (172.42 km2) is therefore -96.3% of the proportional target (4709.49397 km2).

1.4. Bugeranus carunculatus (Wattled Crane)


CR (A2c; C1; C2a)


Active breeding pairs are year round residents and reside in highland wetlands of variable size. Seasonal wetlands are sometimes used opportunistically. Pairs will defend territories several kilometers in size. They breed specifically in permanently inundated wetlands with predominantly sedge like vegetation. Utilized wetlands are characteristically of high altitude (>1500 m.a.s.l.) in the upper catchments of high rainfall grassland areas. Clutch failure is often the result of prolonged disturbance.

Wattled Cranes have an average home range size of 16.64 km2 (r = 2.3 km), comprising primarily open natural grassland, but often including temporary irrigated and dry land cultivated agriculture. The wetland constituted only 2.3% of the home range (McCann & Benn, 2006).

SABAP1 and SABAP2 data are available for the Wattled Crane.



Two actual breeding locations were obtained from expert mapping (Mr. Morné Pretorius and Dr. Nacelle Collins; FS DETEA) while an additional 5 actual breeding/roosting sites were obtained from the EWT (Me. Mariette Wheeler). Five populations are recognized (two of the actual point observations are considered to represent a single population).


Pseudo_1 (point)

• Actual nesting/roosting point records of nesting sites were buffered by 2.5 km. The 2.5 km buffer exceeds the recommended 2 km buffer is as per the edge-matching technical guidelines as well as the required 2.3 km buffer to include an area of 16.64 km2 .

Pseudo_2 (point)

• Actual point locations were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.

Pseudo_2 (polygon)

• Actual polygon locations were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.

Pseudo_3 (polygon)

• Consists of Wetland habitat (as from the Free State land cover data) within 100 m of the actual Pseudo_1 point observations (not buffered). Not all actual Pseudo_1 point observations are located within wetlands. The 100 m selection buffer was subsequently applied to allow wetland in close proximity of the actual point observations to be selected and included in this layer.

Pseudo_4

o Pseudo_4 consists of the carthographic modelled selected wetlands that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively

o The regional extent was accepted to be the ecological niche modelled range of which fragments were removed manually. o Consists of Valley bottom wetlands [landcover class lc25; WETLANDS (non-pan)] that are greater than 50 ha (to remove small fragments and to select

only relatively large wetlands and to give priority to larger wetland systems).

• Wetlands were limited to the those east of the most western occurrence of this species as per the CAR data.

• Wetlands were assessed individually to determine by subjective opinion whether the vegetation represents suitable habitat for Wattled Crane. Wetlands of which the vegetation was considered to be unsuitable were removed.


Pseudo_5


• Pseudo_5 consists of the remainder of the carthographic modelled selected wetlands that occur outside of the SABAP1 and SABAP 2 QDSs and Pentads respectively.


14.1.23. Ecological niche modelling

Potential distribution was modelled using Maxent ($reference). All actual Pseudo_1 and Pseudo_2 point localities as well as all point data from CAR observations were used as training points. Altitude, mean annual rainfall and mean annual temperature were used as environmental gradients. Small fragments were manually removed.



14.1.25. Targets

Taxa Avifauna

Species Bugeranus carunculatus

Common name Wattled Crane

Status CR

Criteria A2c; C1; C2a

Endemic No

Trend Target 100





FS populations 5

Type Pair












Wattled Crane CR (A2c; C1; C2a)


Proportional RSA population target (km2): Included as: 101.727246 km2 % Target Target expression







116.42 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. To account for habitat in addition to those where the species are known to occur, targets were also set for the modelled distrubion. The estimated RSA population (230) is less than the otherwise required 10 000 mature individuals. The targets for modelled areas are therefore based on the estimated RSA population and are subsequently adjusted to 1 population or 12.22 mature individuals (101.72 km2), whichever is the most.

• 100% of all known actual Pseudo_1 (point) localities which accounts for 116.42 km2 and exceeds the required FS proportional number of populations as well as the area required to account for the FS proportion of 12.22 individuals (101.72 km2).

The achievable target (116.42 km2) is therefore 14.44% of the proportional target (101.727246 km2). Considering its classification as being Critically

Endangered as the small amount with which the achievable target exceeds the proportional target, the target was set at 100%.

1.5. Geronticus calvus (Bald Ibis)



VU (A2c; C1; C2b)


The preferred habitat is high-rainfall (>700 mm p.a.) sour and alpine grasslands that are characterized by an absence of trees and a dense, short grass sward (Manry 2985 in Barnes, 2000). They will utilize recently burnt veld for foraging as wel as older burnt areas with emerging regrowth (Manry, 1985 in Barnes, 2000; Tarbotton et al. 1987 in Barnes, 2000) while also utilizing unburnt natural grasslands, cultivated pastures, reaped maize fields and ploughed lands. Although initial thoughts were that it required cliffs for breeding sites, recent sightings revealed that they will also breed in trees (Tarbotton in litt. in Barnes, 2000).

Both SABAP1 and SABAP2 data are for the Bald Ibis


Actual point localities (30 points) were obtained from the Ekangala database. Breeding and roosting locations (eight polygons locations) were obtained from the Free State Sensitive Area data. All eight locations were considered to represent separate populations.


Pseudo_1 (polygon)

• Actual polygon locations of nesting and/or roosting sites were buffered by 1 km. The 1 km buffer is as per the edge-matching technical guidelines (Escott & Lotter, 2012).

Pseudo_2 (point)

• Actual point locations were buffered by 500 m $Confirm if the model also applies a 500m or a 1km buffer. If a 1km buffer applies to the Pseudo_1 polygon then it must also apply to the Pseudo_1 point. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.

Pseudo_4


• Pseudo_4 distribution included land cover class 29 [NATURAL NON-VEGETATED (bare rock)] and classes 11 and 12 [GRASSLAND and SPARSE / OPEN GRASSLAND] that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively, but still within the ecological niche modelled extent.

• Only areas larger than 100 ha were retained (to remove small fragments and to bias selection towards larger open rock faces where there should be a higher chance of finding suitable breeding and roosting habitat). $Check first bullet and confirm which was retained >500 or >100 ha


Pseudo_5

• Pseudo_5 consists of the remainder of the cartographic modelled habitat that occurs outside of the SABAP1 and SABAP2 QDSs and Pentads, but still within the ecological niche modelled extent.

• Only areas larger than 100 ha were retained (to remove small fragments and to bias selection towards larger open rock faces where there should be a higher chance of finding suitable breeding and roosting habitat).

A Pseudo_3 layer (suitable habitat adjacent to actual breeding and roosting locations) was not created as this will imply the selection of features that are already included in the Pseudo_1 locations.

14.1.30. Ecological Niche Modelling

For Bald Ibis the actual data points were not added to the CSV file used for modelling as the SABAP1 data and resulting modelled output already include all areas in which actual observations were made.



14.1.32. Targets

Taxa Avifauna

Species Geronticus calvus

Common name Bald Ibis

Status VU

Criteria A2c; C1; C2b

Endemic RSA (near endemic)

Trend Target 100





FS populations 8

Type Approximate RSA individuals per area (ind/ha) 0.055556


Approximate RSA area per individual (km2/ind) 18

Home range/Occupancy (km2) Proportional FS distribution (%) 23.51814







Bald Ibis VU (A2c; C1; C2b)



Pseudo_1 area (polygon) 46.565578 km2 100 % 46.566 km2




46.566 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. To account for habitat in addition to those where the species are known to occur, targets were also set for the modelled distrubion. The targets for modelled areas of Geronticus calvus are therefore 3 populations or 2351.81.54 mature individuals (42332.66 km2), whichever is the most.

• 100% of the actual Pseudo_1 (polygon) area which accounts for 46.56 km2. The 100% target exceeds the FS prportional target of 3 populations but does not account for the required 42332.66 km2 to accommodate the required FS proportional targets of 2351.81 individuals.


Table 46: Land cover classes that represent habitat types not suitable to the Bald Ibis to create C:\GIS\SCP\Targets\Avifauna\Bald_Ibis\bi_habitat.


Class no. Suitability

1 - 10 Unavailable

11 - 12 Available

13 - 28 Unavailable

29 Available

30 - 85 Unavailable

$Instead of the above table, maybe consider putting in a table that summarized which land cover types were considered suitable and unsuitable for the different species. E.g.

Land cover class

Species1 Species2 Species3

Lc1 Available Unavailable Available

Lc2 Unavailable Unavailable Available

Lc2 Available Available Unavailable

1.6. Gypaetus barbatus (Bearded Vulture)


EN (C2b)


The bearded vulture is associated with mountaneous areas that occur within the region of alpine, subalpine and montane grasslands. Breeding sites are in areas that are in excess of 1800 m a.s.l. (Barnes, 2000).

Both SABAP1 and SABAP2 data are available for Bearded Vulture



Actual (currently active, old potential sites as well as recently active sites but not utilized as present) were obtained from Ms. Sonja Kruger (KZNWildlife; Drakensberg area & KZN Vulture Count Day Coordinator). Two polygon features indicating breeding/roosting sites were obtained from the Free State Sensitive Area Atlas. Only features that indicate currently active breeding/roosting locations were considered to be included in the Pseudo_1 layer and to represent exiting populations. The two polygon features were accepted to indicate old nesting sites not currently utilized and were included in the Peudo_2 (polygon) layer, i.e. 6 populations are recognized.

Pseudo_2 (polygon) features therefore include 3 features from the Free State Sensitive Area Atlas and 3 obtained from Mr. Morné Pretorius during an expert mapping exercise.

Pseudo_2 (point) features (received from Ms. Sonja Kruger) that overlap with Pseudo_2 (polygon) features (Free State Sensitive Area Atlas) were not removed as the point data indicate specific locations of concern within the broader region of the polygon area in which the species is known to occur and has been observed.

14.1.36. Distribution mapping/modelling technical

Pseudo_1 (point)

• The 2 km buffer for nesting and roosting sites as suggested for the Cape Vulture in the edge matching guidelines (Escott & Lotter, 2012) was adopted for the Bearded Vulture.

Pseudo_2 (point)

• Actual point locations were not buffered as these usually indicate the presence of opportunistic feedings due to the ad hoc presence of carcasses rather than sites that were selected for some other specific reason.

Pseudo_2 (polygon)

• Actual polygon locations were not buffered as these usually indicate the presence of opportunistic feedings due to the ad hoc presence of carcasses rather than sites that were selected for some other specific reason.

Pseudo_3

• Pseudo_3 locations were not buffered.

Pseudo_4


• Pseudo_3 distribution included land cover class 29 [NATURAL NON-VEGETATED (bare rock)] that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively, but still within the ecological niche modelled extent.


• Only areas larger than 0.5 ha were retained (NOTE: The selection of features > 0.5 ha was arbitrarily chosen to bias selection towards larger open rock faces where there should be a higher chance of finding suitable breeding and roosting habitat). $Note that first buffer says >500 ha obtained and here it says >0.5ha retained - sort this out.

Pseudo_5


• Only areas larger than 0.5 ha were retained (NOTE: The selection of features > 0.5 ha is was arbitrarily chosen to bias selection towards larger open rock faces where there should be a higher chance of finding suitable breeding and roosting habitat).



14.1.38. Targets

Taxa Avifauna

Species Gypaetus barbatus

Common name Bearded Vulture

Status EN

Criteria C2b

Endemic No

Trend Target 100





FS populations 6

Type Pair



Home range/Occupancy (km2) Proportional FS distribution (%)



FS proportional individuals (of 10 000) 0

FS proportional individuals (of Estimated RSA population) 0

FS proportional area of estimated RSA range (km2) 0

Area required for proportional 10 000 individuals (km2) 0

Area required for proportional RSA population (km2) 0

Bearded Vulture EN (C2b)

Modelled area 383.192183 km2 Proportional target (km2): Included as: Unknown % Target Target expression


Pseudo_2 area (point) 0.000022 km2 0 0 km2

Pseudo_2 area (polygon) 231.033456 km2 0 0 km2

Pseudo_3 area21 0 km2 0 0 km2

Pseudo_4 area 68.630646 km2 0 0 km2


43.372 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. The proportional contribution of the Free State for the Bearded Vulture is unknown.

• 100% of the actual Pseudo_1 (point) area which accounts for 43.37 km2.

Table 47: Land cover classes that represent habitat types not suitable to the Bearded vulture $

21 Extent of the Pseudo_3 layer is 0 km2 as it is already accounted for in the higher Pseudo levels



1 - 28 Unavailable

29 Available

30 - 85 Unavailable

1.7. Heteromirafra ruddi (Rudd's Lark)


CR (A2c)


Rudd's Lark is highly selective for its favoured habitat; open, moderate to heavily grazed , level grasslands without forb invasion in high rainfall areas (>600 mm pa.; sourveld). The occupancy of a single sight may vary, having a number of individuals the one year and none the next.

Allan (1999) observed 2 nests that were simultaneously active and which were 150 m apart, as well as group of 5 nests that were all found in a c. 8 ha area fringing a small grassy pan in open grassland. About 11 displaying males were estimated to be present in this area (Allan, 1999).

SABAP data were not included. The SABAP1 data database did not contain any records of Rudd's Lark, while seven SABAP2 pentads were recorded with Rudd's Lark. NOTE – when joining the SABP2 data spreadsheet with the Pentads layer and selecting Sp_num, then 473 pentads are selected which when the exported selection is interrogated does not contain Rudds lark in the Sp_name filed, but also does not contain the value 473 in the Sp_num field.


Five actual Pseuod_2 (polygon) locations were obtained from Mr. Morné Pretorius (FS DETEA) during an expert mapping exercise. Five populations are recognized.



Pseudo_1 (point)

• The single actual point location (breeding) buffered by 200 m as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_2 (polygon)

• Actual polygon locations were buffered by 200 m as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_4


• Pseudo_3 distribution included land cover classes 11 and 12 (grasslands as identified from the Free State land cover map [landcover classes 11 (GRASSLAND)

and 12 (SPARSE / OPEN GRASSLAND)] that occur on slopes of 0 - 8 percent within the QDS and Pentads of SABAP1 and SABAP2 records respectively, but still within the ecological niche modelled extent.

• Only areas larger than 1 ha were retained. $Resolve discrepancy of 500 ha retained of the first bullet compared to the 1 ha retained stated here

Pseudo_5




• For Rudd's Lark an additional actual observation point was added to the CSV file used for ecological niche modelling (Note: this point was not included as an actual Pseudo_2 (point) observation as it represents the centroid of the farm on which the species was observed).



14.1.45. Targets

Taxa Avifauna

Species Heteromirafra ruddi

Common name Rudd's Lark


Status CR

Criteria A2c

Endemic RSA

Trend Target 11/10000





FS populations 5

Type Pair

Approximate RSA individuals per area (ind/ha) 10


Home range/Occupancy (km2) 0.016 (5 nests in 8 ha = 8/5 = 1.6 ha/nest = 0.016 km2)








Rudds Lark CR (A2c)

Modelled area 1635.86402 km2 Proportional RSA population target (km2): Included as: 4.604 km2 % Target Target expression




Pseudo_5 area 1413.17514 km2 0 0 km2

0.1256 km2


Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. The proportional contribution of the FS toward the Bearded Vulture is unknown.

• 100% of the actual Pseudo_1 (point) areas which accounts for 0.12 km2. This accounts for 1 of the required 2 populations and does not account for the required 4.6 km2 (to account for the proportional RSA population)


Table 48: Land cover classes that represent habitat types not suitable to Rudds Lark$.


1 - 10 Unavailable

11 - 12 Available

13 - 85 Unavailable

1.8. Polemaetus bellicosus (Martial Eagle)


VU (A1a; C1)

14.1.47. Distribution mapping/modelling general

According to Barnes (2000) the Martial Eagle can occupy a range of habitats including open grassland, woodlands and Karoo.


Eight actual breeding point locations were provided by Mr. Brian Colahan (FS DESTEA) and a single actual polygon breeding location was sourced from the Free State Sensitive Area Atlas. The latter coincides with one of the point breeding locations. Two actual polygon observations were obtained from Mr. Morné Pretorius during an Expert mapping exercise. The eight breeding sites are recognized as separate populations.


Pseudo_1 (point)


• The 2 km buffer for nesting and roosting sites as suggested for the Cape Vulture in the edge matching guidelines (Escott & Lotter, 2012) was adopted for the Martial Eagle.

Pseudo_1 (polygon)

• Actual polygon locations of nesting and/or roosting sites were buffered by 2 km.

Pseudo_2 (polygon)

• Actual polygon locations were not buffered as these usually indicate the presence of opportunistic feedings due to the ad hoc presence of carcasses rather than sites that were selected for some other specific reason.

Pseudo_4


• Pseudo_3 distribution included land cover classes 1 - 13, 15 - 20, 33, 34, and 68 - 84 [Woodlands, thickets, bushland, plantations, grasslands (including degraded areas) riparian areas and low shrubland] that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively, but still within the ecological niche modelled extent.

• Only areas larger than 100 ha were retained. $Resolve dicrepency wit hfirst bullet

Pseudo_5



Because the actual Pseudo_1 (polygon) localities are located within the larger modelled habitat of Pseudo_ 4 and Pseudo_5, a Pseudo_3 layer was not created (suitable habitat in the immediate surroundings of the actual point observations will be accounted for by the 500 m buffer around the actual point observations).



14.1.51. Targets

Taxa Avifauna

Species Polemaetus bellicosus

Common name Martial Eagle


Status VU

Criteria A1a; C1

Endemic No

Trend Target 100





FS populations 8

Type Pair



Home range/Occupancy (km2) 130








Martial Eagle VU (A1a; C1)





102.08 km2

Discussion:


According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. To account for habitat in addition to those where the species are known to occur, targets were also set for the modelled distrubion. The estimated RSA population (600) is less than the otherwise required 10 000 mature individuals. The targets for modelled areas are therefore based on the estimated RSA population and are subsequently adjusted to 1 population or 51.75 mature individuals (3364.24 km2), whichever is the most.

• 100% of the actual Pseudo_1 (point) area which accounts for 100.52 km2. The 100% target exceeds the FS proportional target of 1 population but does not account for the required 3364.24 km2 to accommodate the FS proportional targets of 51.75 indviduals of the estimated total RSA population of 600 birds.

• 100% of the actual Pseudo_1 (polygon) area which accounts for 1.55 km2. The 100% target exceeds the FS proportional target of 1 population but does not account for the required 3364.24 km2 to accommodate the FS proportional targets of 51.75 indviduals of the estimated total RSA population of 600 birds.


Table 49: Land cover classes that represent habitat types not suitable to the Martial Eagle.


0 Excluded

1-13 Available

14 Excluded

15-20 Available

21-32 Excluded

33-34 Available

35-67 Excluded

68-84 Available

It should be noted that land cover classes that were considered suitable includes stands of alien woodlots an plantation (potential nesting sites) as well as vegetated wetlands and degraded areas (vegetated wetlands and degraded areas could still support suitable prey species for the Martial Eagle).


1.9. Tyto capensis (Grass Owl)


VU (A2c; C1)


The Grass Owl almost exclusively occurs in typical high altitude rank grass, although they occur in lower altitudes of similar vegetation cover as well (Tarboton, et al., 1987 in Barnes, 2000). Although it typically breeds in permanent and seasonal wetlands, it will also breed in areas with very long grass, i.e. it is not necessarily associated with wetlands (Tarboton, in litt. in Barnes, 2000).

High densities occur in KwaZulu Natal where Mendelson (1989) considers it to be common with up to 22 birds being recorded in 69km2; an average home range of one bird per 314ha (or 69 km2/22 birds = 3.13 km2 per bird; r = 0.99 km = 3.07 km2) (Maclean). $find a date for the reference

Both SABAP1 and SABAP2 data are available for the Grass Owl.


Only one breeding locality (Pseudo_1; point) was confirmed through expert mapping (Mr. Morné Pretorius, FS DESTEA). In addition, six actual point observations were obtained from expert mapping [two from Mr. Brian Colahan, two from Dr. Craig Whittington Jones ($GDARD) and two from Mr. Rob Teifel of which his notes with these observations were provided by Mr. Brian Colahan (FS DESTEA)]. Two Actual polygon obtained were provided by expert mapping (Mr. Morné Pretorius; FS DETEA). Nine populations are therefore recognized.


Pseudo_1 (point)

• All known breeding and roosting sites were buffered by 1 km which accounts for the home range of this species.

Pseudo_2 (point)

• Actual point locations were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat. This exceeds the 200 m buffer required by the technical guidelines for edge matching.

Pseudo_2 (polygon)

• Actual polygon locations were buffered by 500 m. The presence of the species indicate suitable habitat at that site. The 500 m buffer is applied to include a reasonable sample of this habitat.


Pseudo_4


• Pseudo_3 distribution includes land cover classes 11, 25 and 26 (Natural grasslands and vegetated valley bottom and depression wetlands) that occur within the QDS and Pentads of SABAP1 and SABAP2 records respectively, but still within the ecological niche modelled extent.

• Only areas larger than 100 ha were retained. $Resolve area discrepancy with first bullet

Pseudo_5





14.1.57. Targets

Taxa Avifauna

Species Tyto capensis

Common name Grass Owl

Status VU

Criteria A2c; C1

Endemic No

Trend Target 100





FS populations 9

Type Pair












Grass Owl VU (A2c; C1)



Pseudo_2 area (point) 4.711147 km2 0 0 km2


Pseudo_4 area 14008.3819 km2 0 0 km2

Pseudo_5 area 116373.761 km2 0 0 km2

3.1412 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. The estimated RSA population (5000) is less than the otherwise required 10 000 mature individuals. The target is therefore based on the estimated RSA population and is subsequently adjusted to 3 populations or 1198.26 mature individuals (1875.28 km2), whichever is the most.

• 100% of the actual Pseudo_1 (polygon) area which accounts for 3.14 km2. The 100% target (9 populations) accounts for the FS proportional target of 3 populations but does not account for the required 1875.23 km2 to accommodate the required FS proportional targets of 1198.26 individuals of the estimated total RSA population of 5000 birds.


Table 50: Land cover classes that represent habitat types not suitable to Grass Owl to $Put into a single habitat table.



1 - 10 Unsuitable

11 Suitable

12 - 24 Unsuitable

25 - 26 Suitable

27 - 85 Unsuitable

1.10. Falco naumanni (Lesser Kestrel)


VU (A1a, c, e)


The lesser kestrel distribution is centered on the highveld of the Free State, north Eastern Cape, the North West Province, and more marginally the Western Cape, Gauteng, and KwaZulu-Natal, with scattered populations that occur throughout the remainder of the country (Barnes, 2000).

It prefers semi-arid grasslands, and in particular grassy Karoo, as well as sweet and mixed grasslands and the vegetation types of the central Kalahari. Wooded areas are avoided. They will forage in agricultural lands and small scale pasture. The regional population is estimated to not exceed 50 000 - 60 000 birds (Barnes, 2000). Its occurrence in the Free State is limited to roosting colonies which are suspected to average

The range of a roosting colony can exceed 1 000 km2 (McCann, 1994 in Barnes 2000) and roosts can continuously be occupied for up to 30 years (R. Martin per. Comm. in Barnes 2000). Known roost sites should be protected as well as suitable foraging habitat within 9 km around them (r = 9 km = 254.469 km2) (De Frutos et al., 2009 in (SEO Birdlife & Birdlife International) ($correct reference, change to static test but ensure it is included in the reference list). Although the home range for a roosting colony is accepted to be 1 000 km2, roosts were buffered by 9 km. According to (Van Zyl & Benn) the average roost size in the

Western Cape Province is 1242 individuals. In the absence of province specific data this is also accepted for the Free State.

Both SABAP1 and SABAP2 data are available for the Lesser Kestrel.


Point localities of roosting sites were obtained from expert mapping (Mr. Brian Colahan; FS DESTEA). Fourty-three (43) roosting sites (populations) are recognized.



The Lesser Kestrel utilizes a wide range of habitats. As such it was not possible to map specific habitat types using cartographic modelling. It was therefore assumed that the species can potentially occur anywhere within the Free State Province.

Pseudo_1 (point)

• All known roosting sites were buffered by 9 km which accounts for the reported range of a roosting colony (r = 9 km = 254.46 km2).

Pseudo_4

• The entire Free State was accepted as regional distribution of the Lesser Kestrel.

• Pseudo_4 distribution excluded the wooded land cover classes 1 - 7, as well as other land cover classes considered to be unsuitable (land cover classes 14 - 31, 35, 36, and 56 - 79). It therefore includes the remainder of the land cover classes that occur within the QDS and Pentads of SABAP1 and

SABAP2 records.


Pseudo_5

• Pseudo_5 consists of the remainder of the Free State province that occurs outside of the SABAP1 and SABAP2 QDSs and Pentads but still within suitable land cover classes as indicated for Pseudo_4 observations


A Pseudo_3 layer (suitable habitat directly adjacent to an actual point observation) was not created as habitat directly adjacent to the actual point observations are already included in the actual Pseudo_1 (point) layer due to the 17 km buffer around such points.


The centroids of all SABAP1 QDSs and SABNAP2 Pentads in which Lesser Kestrel were recorded were used as training points. Actual Pseudo_1 observations points were also included and used as training points.



14.1.64. Targets

Taxa Avifauna

Species Falco naumanni


Common name Lesser Kestrel

Status VU

Criteria A1a,c,e

Endemic No






FS number

FS populations 43

Size 1242









Area required for FS number 0.000



Lesser Kestrell CR (A2c)



Pseudo_1 area (polygon) 10924.416 km2 100 % 10924 km2

Pseudo_4 area 558.046173 km2 0 0 km2



10924 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. The estimated RSA population (60 000) is more than the required 10 000 mature individuals.

• 100% of the actual Pseudo_1 (polygon) area which accounts for 10924.41 km2. The 100% target accounts for the FS proportional target of 3 populations as well as the required 115.61 km2.

The achievable target (10924km2) is therefore 9348.9% of the proportional target (115.611605 km2).

Note: The Pseudo_1 observations are all located within urban areas. The Lesser Kestrel was therefore not included in Marxan analysis.

Table 51: Land cover classes that represent habitat types not suitable to the Lesser Kestrel $Capture in a single table for all species


1 - 7 Unavailable

8 - 13 Available

14 - 31 Unavailable

32 - 34 Available

35 - 36 Unavailable

37 - 55 Available

56 - 79 Unavailable

80 - 85 Available

86 - 255 Unavailable


1.11. Sagittarius serpentarius (Secretarybird)


NT (A1c, A2c)


The species inhabits grasslands, ranging from open plains to lightly wooded savanna, but is also found in agricultural areas (BirdLife International). They seem to disappear from areas that have undergone bush encroachment.

Territory size seems to vary according to the ecological integrity of the habitat. In the Kruger National Park a pair will occupy an area of 20 km2, while in the former Transkei pairs occupy areas of 100 km2 and 230 km2 (Steyn, 1982 in Barnes 2000). In the south-western and north-eastern Free State densities are reported to be 1.8 birds/100 km and 1.6 birds/100 km respectively ($reference). If an observation distance of 1 km is accepted then density is 1.8 birds/200 km2 for the south-western Free State and 1.6 birds/200 km2 for the north-eastern Free State. For the Free State the home range was accepted to be 50 km2 (r =

4 km = 50.26 km2) and the density 0.000085 birds/ha [(1.8 + 1.6)/2 = 1.7 birds/200 km2 = 0.000085 birds/ha].

No attempt at modelling the distribution of the Secretary Bird was done as this species can potentially occur throughout the entire Free State.

Both SABAP1 and SABAP2 data are available for the Secretary Bird.


Point localities of nesting sites were obtained from expert mapping (Mr. Brian Colahan (FS DESTEA). Seventeen (17) nesting sites (populations) are recognized. A single Pseudo_2 observation (personal observation of Mr. Robert Lotze of the FS DESTEA) is also included (an abandoned breeding site).


The Secretary Bird utilizes a wide range of habitats. As such it was not possible to map specific habitat types using cartographic modelling. It was therefore assumed that the species can potentially occur anywhere within the Free State Province.

Pseudo_1 (point)

• All known nesting sites were buffered by 4 km which accounts for the accepted home range of 50 km2 (r = 4 km = 50.26 km2). This buffer exceeds the 500 m buffer required as per the edge matching technical guidelines (Escott & Lotter, 2012).

Pseudo_2 (point)

• The single actual observation (old nesting site) was buffered by 500 m as per the edge matching technical guidelines(Escott & Lotter, 2012) .


Pseudo_4

• NOTE: ALTHOUGH A PSEUDO_5 COVERAGE IS RECOGNIZED, IT WAS NOT INCLUDED IN ANALSYS AS THE PSEUDO_4 COVERAGE ACCOUNTS FOR THE GREATEST PROTION OF THE FREE STATE PROVINCE $Considering this statement see if it was included in the cost layer

• The entire Free State was accepted as regional distribution of the Secretary Bird.

• Pseudo_4 distribution excluded areas with densely wooded vegetation (land cover classes 1, 2 and 4 - 6). Other land cover classes that were considered to represent unsuitable habitat were also excluded (land cover classes 14, and 56 - 79). It therefore includes the remainder of the land cover classes that occur within the QDS and Pentads of SABAP1 and SABAP2 records.


Pseudo_5

• NOTE: ALTHOUGH A PSEUDO_5 COVERAGE IS RECOGNIZED, IT WAS NOT INCLUDED IN ANALSYS AS THE PSEUDO_5 COVERAGE, ALAONG WITH THE PSEUDO_4 COVERAGE, ALREADY ACCOUNTS FOR ALNMOST THE ENTIRE FREE STATE PROVINCE $Considering this statement see if it was included in the cost layer

• Pseudo_5 consists of the remainder of the Free State province that occurs outside of the SABAP1 and SABAP2 QDSs and Pentads.




The centroids of all SABAP1 QDSs and SABNAP2 Pentads were used as training points. Actual Pseudo_1 observations points were also included and used as training points.



14.1.71. Targets

Taxa Avifauna

Species Sagittarius serpentarius

Common name Secretary Bird

Status NT

Criteria A1c, A2c


Endemic Trend Target 11/10000



Estimated RSA population Estimated RSA range (km2) 87094439

FS populations 17

Size 1


Approximate RSA area per individual (km2/ind) #DIV/0!









Secretarybird CR (A2c)



Pseudo_2 area (point) 0.785191 km2 0 0.7852 km2

809.85 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the Free State account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals).


In the case of Sagittarius serpentarius the Free State accounts 14.74% of the estimated range of the species. The estimated RSA population is unknown. The target for the FS is therefore set at 2 populations or 1474.16 mature individuals (73708.03 km2), whichever is the most.

• 100% of the actual Pseudo_1 (point) area which accounts for 809.85 67 km2. The 100% target exceeds the FS proportional target of 2 populations but does not account for the required 73708.03 km2 to accommodate the required FS proportional targets of 1474.16 individuals.


Table 52: Land cover classes that represent habitat types not suitable to the Secretarybird. %


0 - 2 Excluded

3 Available

4 - 6 Excluded

7 - 13 Available

14 Excluded

15-55 Available

56-79 Excluded

80 -85 Available

1.12. Gyps africanus (African Whitebacked Vulture)


VU (C1)



The African Whitebacked Vulture occurs mostly in the northern and eastern regions of South Africa. Its distribution correlates with the drier woodlands and tall trees, amongst others those of the Acacia spp. $get new genus for acacia, which are used for roosting and breeding. This being the most probable reason for its absence from the Karoo and grassland regions (Barnes, 2000).

The total RSA population is estimated to be 3500 pairs (9000 - 9500 individuals) of which 100 pairs occur in the Free State (±2.86%) (Barnes, 2000).

Home ranges vary and depends on the availability of food. Immature birds (less than 4 years old) have been recorded to travel approximately 22 km to 48 km per day, while some birds travelled more than 220 km in a single day. Foraging ranges of six immature birds varied from 2 300 km2 to 11 400 km2, while most were in the 3 780 km2 range (Phipps, Willis, Wolter, & Naidoo). Although the average actual home range is accepted to be 3 848 km2 [(22+48)/2 = 35; r = 35 km = 3 848 km2], for the purpose of this study the home range is accepted to be the distance that the average bird will cover in a 1 day period, which was

taken to be 50 km.

Both SABAP1 and SABAP2 data are available for the African Whitebacked Vulture.


Point localities of nesting sites were obtained from expert mapping (Mr. Brian Colahan; FS DESTEA). Many of the recorded points are in close proximity to each other and were considered to represent a single population. A total of 124 nesting sites were recorded which represent 3 populations.


Pseudo_1 (polygon)

• The 2 km buffer for nesting and roosting sites as suggested for the Cape Vulture in the edge matching guidelines (Escott & Lotter, 2012) was adopted for the Martial Eagle.

Pseudo_4


• Pseudo_4 distribution excluded the densely wooded areas (land cover classes 1 and 4) as well as those considered to represent unsuitable habitat [grasslands and other areas where trees are absent (land cover classes 8 to 85)]. It therefore includes the remainder of the land cover classes that occur

within the QDS and Pentads of SABAP1 and SABAP2 records.

• Only areas larger than 100 ha were retained. $resolve difference with first bullet

Pseudo_5






The centroids of all SABAP1 QDSs and SABNAP2 Pentads were used as training points. Actual Pseudo_1 observations points were also included and used as training points.



14.1.78. Targets

Taxa Avifauna

Species Gyps africanus

Common name African Whitebacked Vulture

Status VU

Criteria C1

Endemic Trend Target 100





FS number 200

FS populations 3

Size 2










Area required for FS number 5000.000



African Whitebacked Vulture VU (C1)

Modelled area 15644.6556 km2 Proportional RSA population target (km2): Included as: 5000 km2 % Target Target expression


Pseudo_4 area 7252.59642 km2 0 4714.2 km2

Pseudo_5 area 8149.95651 km2 0 0 km2

242.1 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. The estimated RSA population (3 500) is less than the otherwise required 10 000 mature individuals. The FS proportion of 3 500 individuals is 317.83 individuals which exceeds the estimated FS population of 200, i.e. the target is set for the area required to accommodate the 200 individuals (5 000 km2).

• 100% of the actual Pseudo_1 (point) area which accounts for 242.10 km2. The 100% target exceeds the FS proportional target of 1 population but does not account for the 5000 km2 required to accommodate the required FS proportional targets of 317.83 individuals.

The achievable target (5000km2) is therefore -95.1% of the proportional target (242.1 km2).

Table 53: Land cover classes that represent habitat types not suitable to the African Whitebacked Vulture $Compile in a single table




1 Unavailable

2 - 3 Available

4 Unavailable

5 - 9 Available

10 Unavailable

11 Available

12 - 84 Unavailable

85 Available

Note: Land cover class 11 reoresents grasslands, This land cover class was included as large protions of the class falls within the Kimberley Thornveld vegetation type of Mucina and Rutherford (2006).

2. FLORA

1.13. Alepidea amatymbica


VU (A2d)



According to Raimondo, et al. (2009) this species occurs in boulder strewn slopes and in grassland near streams, moist areas or drainage lines up to 2500 m

a.s.l.. Its habitat is also limited to the rocky slopes of southerly aspects (O'Connor, 2004). According to Prof. J. du Preez (pers. comm., $date) all mountainous areas of the eastern Free State region can be considered to be potential habitat for this species.

Of the two localities available, one is on top of Platberg (Harrismith) while the other is on a north west facing slope. These locations are therefore in agreement with the view of Prof. du Preez and the species seems to be able to establish and persist on the slopes of mountainous areas, as well on the mountainous features themselves. Potential habitat was therefore considered to be the inselbergs and the slopes of grasslands, irrespective of their aspect, as well as

grassland areas adjacent to rivers and streams. No distinction was made between bouldered and non-bouldered slopes as a suitable dataset on this variable was not available.


Actual point location data were received from the Ekangala conservation plan database as provided by Dr. Boyd Escott (KZN Wildlife). One of the point locations is located within the polygon area (Platberg) and was therefore excluded from analysis.


Pseudo_1 (point)

• The single actual point was buffered by 100 m.

Pseudo_1 (polygon)

• Actual polygons were not buffered (the polygon locality is on top of Platberg, i.e. a buffer will extend beyond the area of suitable habitat while the adjacent slopes are included in the Pseudo_2 layer).

Pseudo_2

• Includes grassland slopes, irrespective of aspect and whether they are bouldered or not, in the immediate surroundings of the actual point and polygon data, i.e. the slopes of Platberg and Manyenyeza Hill.

• Pseudo_2 was not buffered.

• All features were included (no size limitation was applied).

Pseudo_4

• $The geographic extent was limited to the ecological niche modelled area. Training points were limited to the two known actual Pseudo_1 locations. No size limitation to remove small isolated fragments was applied.

• $Alepediea amatymbica is known to occur within the Eastern Cape, Free State, KwaZulu-Natal, Limpopo, Mpumalanga Provinces. To limit the ecological niche modeled extent to these provinces all features in the mentioned provinces not associated with the Drakensberg range were removed (i.e. only areas associated with the Drakensberg were retained).


• All grassland areas with slope greater than 15 percent and at altitude less than 2500 m a.s.l. that occur within the ecological niche modelled extent were mapped.

• Pseudo_4 was not buffered

• All features larger than 10 ha were retained (to remove small isolated fragments).


The ecological niche modelled range is considered to be more than the actual distribution of this species, especially those areas in the south eastern Free State. The range needs to be reduced by expert opinion.



14.1.85. Targets

Taxa Flora

Species Alepidea amatymbica

Common name Status VU

Criteria A2d

Endemic No

Trend Decreasing

Target 11 populations /10 000 mature individuals



Estimated RSA population Unknown


FS populations 2





Alepidea amatymbica VU (A2d)


Proportional target (km2): Included as: % Target Target area (km2)



Pseudo_2 area 34.840498 km2 0 34.840498 km2

Pseudo_4 area 535.309564 km2 0 160.5928692 km2

Total target area: 20.290962km2

Discussion: The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Alepidea amatymbica the FS accounts 12.7% of the estimated range of the species. The proportional target for the FS is therefore: 2 populations or 1270 mature individuals, whichever is the most.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.03 km2. For the FS to achieve its proportional target of 2 populations, all of the Pseudo_1 records (1 point location) need to be included.

• 100% of all known actual polygon localities which amounts to 20.25 km2 (Pseudo_2). This accounts for 1 population which along with the single location accounted for under Pseudo_1 (point) amounts to two populations which equals the FS proportional target.


Table 54: Land cover classes that represent habitat types suitable to Protea subvestita


1 - 10 Unsuitable

11 Suitable

12 - 85 Unsuitable

1.14. Kniphofia typhoides


NT (A2ac)

Although not threatened, it was suggested that Kniphofia typhoides be included during an expert mapping exercise. Reasoning being that the status of this species needs to be reviewed at a national level. If it is found to be threatened, then is should be included, if not, then it can be omitted. Until further revision Kniphofia typhoides is included.


Kniphofia typhoides is reported to occur in low lying wetlands and seasonally wet areas in climax Themeda triandra grasslands where these occur on heavy black clay soils. The species tends to disappear from degraded grasslands (Raimondo, et al., 2009).


The only actual Pseudo_1 location of this species was provided by Prof. J. du Preez (University of the Free State) during an expert mapping exercise.


Pseudo_1 (polygon)

• The single actual polygon location was buffered by 100 m.


Pseudo_4

• The regional extent was determined by means of ecological niche modeling which was done using the centre of the SANBI QDSs as training points. No size limitation to remove small isolated fragments was applied.

• Cartographic modelling was done by mapping areas considered to be suitable for Kniphoifia typhoides from the Free State land cover map, these being; land cover classes 24 (naturally flooded pans), 25 (vegetated wetlands; non-pan) and 26 (vegetated pans).

• Only features greater than 1 ha were retained.


The ecological niche modelled range is considered to be more than the actual distribution of this species, especially those areas in the south eastern Free State. The range needs to be reduced by expert opinion.



14.1.92. Targets

Taxa Flora

Species Kniphofia typhoides

Common name Status NT

Criteria A2ac

Endemic RSA

Trend Decreasing





Estimated RSA range (km2) FS populations 1

Proportional FS distribution (%) Unknown

FS proportional populations (of 11) Unknown


FS proportional individuals (of 10 000) Unknown

Kniphofia typhoides NT (A2ac)




Pseudo_4 area 220.744742 km2 0 66.2234226 km2

1.411282 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Kniphofia typhoides the FS proportional contribution is unknown. Considering the fact that this species is known from a single location only, a precautionary approach is followed and the proportional target is set at 100% to account for the required 11 populations and 10 000 mature individuals.

• 100% of the single Pseudo_1 actual location which accounts for 1.41 km2.

Table 55: Land cover classes that represent habitat types suitable to Kniphofia typhoides.


1 - 23 Unavailable

24 - 26 Available

27 - 85 Unavailable


1.15. Strumaria tenella subsp. orientalis


LC

Although Strumaria tenella subsp. orientalis is not classified as threatened, it was identified for inclusion during an expert mapping workshop. Reasoning provided is that little information is known about this species. This includes amongst others poor information on its distribution. It is therefore considered to be data deficient. Although the species occurs in the Free State and Lesotho, its populations in the latter are under threat and their future existence is questioned. Strumaria tenella subsp. orientalis is associated with the Bloemfontein Karoid Shrubland which is typically fragmented with the largest portion being located directly north of Bloemfontein. This area is already fragmented and is being threatened by residential development.

According to Prof. J. du Preez (Pers. Comm..; $date) Strumaria tenella subsp. orientalis can be expected to occur within the Bloemfontein Karoid Shrubland (Mucina & Rutherford, 2006) that are located north of Bloemfontein and west of Verkeerdevlei.


The species is known to occur in the Bethulie area and in the Free State National Botanical Garden near Bloemfontein. Here it grows in the topsoil of dolerite rocks that are surrounded by Nama Karoo vegetation (Rambuwani).


Point localities were obtained from the PRECIS database. The latter included 12 records of the same location and one which is located within the actual polygon location (Pseudo_1 polygon). Only one of the twelve similar records were retained. Three actual area (polygon) distributions were obtained from Prof. J. du Preez (University of the Free State) during an expert mapping session. It follows that four populations are recognized.


Pseudo_1 (point)

• Actual point locations were buffered by 100 m.

Pseudo1 (polygon)

• Actual polygon locations were buffered by 100 m.

Pseudo_4

• No ecological niche modelling was attempted as the regional extent of its distribution was accepted to be limited to the Bloemfontein Karoid Shrubland (Mucina & Rutherford, 2006) that are located north of Bloemfontein and west of Verkeerdevlei.

• Portions already accounted for in Pseudo_2 as well as the portion between Bultfontein and Theunissen were excluded from the Pseudo_4 layer.

• Features were not buffered as such buffers will extend beyond the suitable habitat into areas in which the species is unlikely to occur.


• All features were included (i.e. no size limitation was applied).



14.1.98. Targets

Taxa Flora

Species Strumaria tenella subsp. orientalis

Common name Status LC

Criteria Endemic FS

Trend Target 11 populations /10 000 mature individuals





Proportional FS distribution (%) 100



Strumaria tenella subsp. orientalis LC





Pseudo_4 area 36.022543 km2 0 10.8067629 km2


22.701116km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Strumaria tenella subsp. orientalis the FS accounts for 100% of the estimated range of the species (it is considered to be an FS endemic). The proportional target for the FS is therefore set at 100% (i.e. all known populations) to account for the required 11 populations and 10 000 mature individuals.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.03 km2. This accounts for 1 of the required 11 populations.

• 100% of all known actual polygon localities which amounts to 22.66 km2 (Pseudo_1). This accounts for an additional 3 of the required 11 populations.

1.16. Isoetes aequinoctialis


VU (D2)


Isoetes aequinoctialis is known from only a single locality in the Free State, this being two rock pools on top of the Thaba Patswa mountain. It is also known to occur in temporarily wet places such as shallow depressions and stream banks at altitudes ranging from 1225 - 1350 m a.s.l. (Raimondo, et al., 2009). However, the altitude of the two rock pools on top of Thaba Patswa is located at 2000 m a.s.l.

Although the species is indicated to be limited to seasonally moist depressions and pans, it does not seem to be geographically limited. This is concluded from the fact that this species has been recorded in the Northern Cape, Namibia, Mpumalanga and the high altitude montane grasslands of the Free State (Burrows & von Staden, Isoetes aequinoctialis Welw. ex A.Braun, 2013).

According to the above all wetland areas, with the exception of the salt pans, are considered to be potential habitat for this species.


Actual distribution data (2 locations) were obtained from Prof. J du Preez (University of the Free State) during an expert mapping exercise. Because of their close proximity to each other these were considered to be a single population.


Pseudo_1 (point)



Pseudo_4

• No ecolgical niche modelling was attempted. $why not?

• All wetland areas (landcover classes 24 to 27) within 10 km of the actual point locations were selected and included (i.e. considered to be potential habitat).

• Only features larger than 1 ha were included (the threshold of 1 ha was arbitrarily chosen to remove small fragments of which many do not seem to be wetlands).



14.1.104. Targets

Taxa Flora

Species Isoetes aequinoctialis

Common name Status VU

Criteria D2

Endemic No

Trend Target All populations

FS populations 1

Isoetes aequinoctialis VU (D2)



Pseudo_1 area 0.055579 km2 100 % 0.055579 km2

Pseudo_4 area 1.6931 km2 0 0.50793 km2

0.055579 km2

Discussion:


According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.05 km2.

1.17. Stenostelma umbelluliferum


NT [B1ab(ii,iii,iv,v)]


Stenostelma umbelluliferum is known to occur on deep black turf soils in open woodland, mainly in the vicinity of drainage lines. Its extent of occurrence is estimated to be 9700 km2 and it is known from only 13 locations in South Africa. This species is declining as a result of urban expansion (Raimondo, et al., 2009).

The single point locality for the Free State is located in an unchannelled valley bottom wetland area (as determined from SPOT5 2009 imagery) which although not associated with a drainage line, is in close proximity thereof. It is therefore concluded that although Stenostelma umbelluliferum is reported to be closely associated with drainage lines, that it can also occur in wetland areas adjacent to such drainage lines.

The single actual point location and habitat data were considered to be insufficient to do any ecological niche modelling. Modelling was therefore limited to a cartographic approach.


Although two locality points for Stenostelma umbelluliferum were obtained, both of these were of the same location. Only one, that received from PRECIS (Dr. Domatilla Raimondo; SANBI), was retained.


Pseudo_1 (point)

• The single actual point location was buffered by 100 m.

Pseudo_2

• The stream along which the single actual point location is located was buffered by 10 m. This to include areas adjacent to the stream that may support other specimens of this species.

Pseudo_4


• No ecological niche modelling was attempted. $Why not

• The regional extent was accepted to be the vegetation type (Soweto Highveld Grassland and the Frankfort Highveld Grassland, Mucina & Rutherford, 2006) in which the single actual point is located (the single point location lies within a small pocket of the Soweto Highveld Grassland; it was assumed that Stenostelma umbelluiferum will also occur in the adjacent Frankfort Highveld Grassland). In the case of the Soweto Highveld Grassland vegetation type only the most eastern portion was included. Also included were the isolated areas of the Eastern Temperate Freshwater Wetlands vegetation type.

• The 1:50 000 rivers and streams were buffered by 10 m.

• Only those that occur within the accepted regional extent were retained.

• The extent of the Vaal Dam was excluded.

• To remove small isolated fragments only features larger than 2 ha were retained.



14.1.110. Targets

Taxa Flora

Species Stenostelma umbelluliferum


Criteria B1ab(ii,iii,iv,v)

Endemic RSA

Trend Target 11 populations /10 000 mature individuals





FS populations 1





Stenostelma umbelluliferum NT [B1ab(ii,iii,iv,v)]




Pseudo_2 area 0.197779 km2 0 0.197779 km2

Pseudo_4 area 211.055971 km2 0 63.3167913 km2

0.031375 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Stenostelma umbelluliferum the FS proportional contribution is unknown. Considering the fact that this species is known from a single location only, a precautionary approach is followed and the proportional target is set at 100% to account for the required 11 populations and 10 000 mature individuals.


1.18. Pentzia oppositifolia


Not threatened by extinction (Raimondo, et al., 2009).

Although not classified as threatened, Pentzia oppositifolia was identified for inclusion during an expert mapping exercise. $Reasnons?


Pentzia oppositifolia is restricted to the alluvium in and around the dolomitic limestone pans around Danielskuil in the Northern Cape Province and Boshof in the Free State Province (Magee & Tilney, 2012).


All point data (2 records) were obtained from the PRECIS database (received from Dr. Domatilla Raimondo; SANBI). Two populations are recognized.



Pseudo_1 (point)

• Actual point locations were buffered by 100 m. Pseudo_2

• Because Pentzia oppositifolia is known to occur on the fringes depression wetlands, the depression wetlands along which the actual point locations are located were buffered by 10 m.

Pseudo_4

• The geographic extent was limited to the ecological niche modelled area. Training points included the two known actual Pseudo_1 locations only. No size limitation to remove small isolated fragments was applied.

• NFEPA wetlands and depression wetlands from the FS land cover data [lc24 (WATER (natural pan), lc26 (WETLANDS (vegetated pans) and lc27 (WETLANDS (dry pans)] were considered suitable habitat.

• Because Pentzia oppositifolia occurs on the edge of pans, the depression wetlands were buffered by 10 m

• All features were retained (i.e. no size limitation was applied).



14.1.116. Targets

Taxa Flora

Species Pentzia oppositifolia

Common name Status Not threatened by extinction

Criteria Endemic RSA

Trend Stable

Target All populations

FS populations 2 $where is the rest of the tab;e?

Pentzia oppositifolia Rare





Pseudo_2 area 0.571044 km2 0 0.571044 km2

Pseudo_4 area 510.678483 km2 0 153.2035449 km2

0.062751 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Pentzia oppositifolia the FS proportional contribution is unknown. Considering the fact that this species is known from only two location, a precautionary approach is followed and the proportional target is set at 100% to account for the required 11 populations and 10 000 mature individuals.


1.19. Protea subvestita (Lip-flower Sugarbush)


VU [B2ab(iii,v)]


Protea subvestita is confined to infrequently burnt habitats and is often associated with gullies, scarps and forest margins. Occasional fires are required for successful recruitment.(Raimondo, et al., 2009).


All point data (10 points which represent 3 populations) were obtained from the PRECIS database. Three of these points overlay the single polygon location obtained from the FS Sensitive Area Atlas (Prof. J. du Preez, University of the Free State, during an expert mapping exercise). These three points were removed. The polygon data is considered to represent the same population as one of the remaining point locations, i.e. the point and polygon data together represent 3 populations.


Pseudo_1 (point)

• All actual point localities were buffered by 100 m.


Pseudo_1 (polygon)

• All actual polygon localities were buffered by 100 m.

Pseudo_4

• The geographic extent was limited to the ecological niche modelled area. All known actual Pseudo_1 point data were used as training points.

• To remove small isolated fragments only portions larger than 1 ha were retained.

• Features of land cover classes associated with actual observations of Protea subvestita are land cover classes 2 (woodland), 6 (open bushland), 29 [natural non-vegetated (bare rock)] and 85 [sparse bushland (10- 40% cc)] and were included where they occur within the ecological niche modelled extent.

• All features of the mapped land cover classes were included (i.e. no size limitation was applied).



14.1.122. Targets

Taxa Flora

Species Protea subvestita

Common name Lip-flower Sugarbush

Status VU

Criteria B2ab(iii,v)

Endemic No

Trend Decreasing


FS populations 3

Protea subvestita VU [B2ab(iii,v)]





Pseudo_4 area 88.650401 km2 30 26.5951203 km2


27.3396493 km2

Discussion:

According to (Pfab, Victor, & Armstrong, 2011) all known populations needs to be included in a systematic conservation plan. To account for habitat in addition to those where the species are known to occur, targets were also set for the modelled distribution. In the absence of the known FS proportional contribution, a default target of 30% was accepted for modelled areas.




1 Unsuitable

2 Suitable

3 - 5 Unsuitable

6 Suitable

7 - 28 Unsuitable

29 Suitable

30 - 84 Unsuitable

85 Suitable

1.20. Protea dracomontana


LC


Although not threatened the inclusion of Protea dracomontana was recommended for included during an expert mapping exercise. Reasoning was that this species has a very restricted distribution range, some of which is located in Lesotho, i.e. the long term survival of these populations is uncertain.


Protea dracomontana is reported to occur on Subalpine grassland on basalt, 1600-2600 m a.s.l. (http://www.proteaatlas.org.za/sugar11.htm).


The single point location was obtained from expert mapping (Mr. Peter Nelson; Eskom).


Pseudo_1 (point)

• All actual point localities were buffered by 100 m.

Pseudo_4

• The geographic extent was limited to the ecological niche modelled area. All known actual Pseudo_1 point data were used as training points, as well as point data received from KZN (the centre of the planning unit was taken as the approximate location). No size limitation to remove small isolated fragments was applied.

• $Verify all of the below

• All features of the ecological modelled extent were accepted.

• Only features larger than 1 ha of the modelled area were included. $Contradiction of statements here

• Landcover classes 2, 6, 29 and 85 that occur within the modelled area were included.

• All features of landcover class 11 (Grassland) that occur within the modelled extent were included. $Contradiction of statements here

• All features were accepted (i.e. no size limitation was applied).



Considering the fact that Protea dracomontana is not classified as threatened, the more conservative target of 11 populations/or at least 10 000 mature individuals is adopted as opposed to setting the target to all known populations.

14.1.128. Targets


Taxa Flora

Species Protea dracomontana

Common name Drakensberg Dwarf Sugarbush

Status LC


Trend Stable






Proportional FS distribution (%) FS proportional populations (of 11) 0


Protea dracomontana NT [B1ab(v)]



Pseudo_1 (polygon) 0.031375 km2 100 % 0.031375 km2

Pseudo_4 100.518561 km2 0 30.1555683 km2

0.031375 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case


of Protea dracomontana the FS proportional contribution is unknown. Although the conservative approach of 11 populations/or at least 10 000 mature individuals is adopted, the single population requires a target of 100% (i.e. all known populations).




1 - 10 Unsuitable

11 Suitable

12 - 85 Unsuitable

1.21. Hoodia officinalis subsp. officinalis


NT [B1ab(v)]

$REasonong for inclusion?


Hoodia officinalis subsp. officinalis occurs inside bushes in flat or gently sloping areas (http://redlist.sanbi.org/species.php?species=2705-21).


A single actual polygon distribution was obtained by expert mapping (Prof. J. du Preez; University of the Free State).


Pseudo_1 (polygon)


• The single actual polygon locality was buffered by 100 m.

Pseudo_4

• The regional extent of this species was taken to be the vegetation type in which the species was recorded (the Northern Upper Karoo vegetation type).

• Features of land cover classes associated with actual observations of Hoodia officinalis subsp. officinalis are landcover class 12 [sparse / open grassland (which may include a very scattered tree, bush and / or shrub component (< 10% tree, bush or shrub cover)] and land cover class 13 [sparse grassland; (gravel/rocky substrate) (may also include a very scattered tree, bush and/or shrub component) and were included where they occur within the ecological niche modelled extent. Although extensive areas of land cover class 32 (EROSION SHEET) are also present within the indicated polygon area, this land cover class was not included as potential habitat for this species.




Becasue Hoodia officinalis subsp. officinalis is not threatened the more conservative target of 11 populations/or at least 10 000 mature individuals is adopted as opposed to setting the target to all known populations

14.1.134. Targets

Taxa Flora

Species Hoodia officinalis subsp. officinalis


Criteria B1ab(v)

Endemic No

Trend Decreasing






FS populations 1





Hoodia officinalis subsp. officinalis NT [B1ab(v)]




Pseudo_4 311.234902 km2 0 93.3704706 km2

18.105222 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Hoodia officinalis subsp. officinalis the FS proportional contribution is unknown. Although the conservative approach of 11 populations/or at least 10 000 mature individuals is adopted, the single population requires a target of 100% (i.e. all known populations).


Table 58: Land cover classes that represent habitat types suitable to Hoodia officinalis


1 - 11 Unsuitable

12 - 13 Suitable

14 - 85 Unsuitable

1.22. Schizoglossum montanum



Rare


Schizoglossum montanum is reported to occur on Subalpine grassland and Drakensberg Afroalpine Heathland, 2100 - 2900 m a.s.l.

(http://redlist.sanbi.org/species.php?species=2687-89).


A single actual point distribution point was obtained from the PRECIS database as provided by Dr. Domatilla Raimondo.


Pseudo_1 (point)

• The single actual point locality was buffered by 100 m.

Pseudo_4

• The regional extent of this species was taken to be the vegetation type in which the species was recorded (the uKhahlamba Basalt Grassland vegetation type).

• Features of land cover classes associated with this species are land cover class 11 (grassland), land cover class 12 [sparse / open grassland (which may include a very scattered tree, bush and/or shrub component (< 10% tree, bush or shrub cover)], land cover class 80 (Low Shrub In Mesic

Highveld Grass, slopes > 25) and land cover class 81 (Low Shrub In Mesic Highveld Grass, slopes < 25). These were included where they occur within the associated vegetation type.



All known populations for any rare taxa with fewer than 11 known localities (localities being geographically separate sites at which the taxon has been recorded) should be included, independent of how that rarity is defined or assessed, in order to avoid an immediate threatened listing in the event of a decline or extreme fluctuations in specified population parameters (assuming that one locality would be equivalent to one location) (Pfab, Victor, & Armstrong, 2011).

14.1.140. Targets

Taxa Flora


Species Schizoglossum montanum

Common name Status Rare

Criteria Endemic No

Trend Stable


FS populations 1

Schizoglossum montanum Rare



Pseudo_1 (point) 0.031375 km2 100 % 0.031375 km2

Pseudo_4 105.266464 km2 0 31.5799392 km2

0.031375 km2

Discussion:



Table 59: Land cover classes that represent habitat types suitable to Schizoglossum montanum


1 - 10 Unsuitable

11 - 12 Suitable

13 - 79 Unsuitable



80 - 81 Suitable

82 - 85 Unsuitable

1.23. Helichrysum haygarthii


Rare


Helichrysum haygarthii occurs on high altitude cliffs that are inaccessible to livestock (http://redlist.sanbi.org/species.php?species=3240-183).


A single actual point location was obtained from the PRECIS database while actual polygon locations were obtained during expert mapping (Prof J. Du Preez; University of the Free State).


Pseudo_1 (point)

• The single actual point locality was buffered by 100 m.

Pseudo_1 (polygon)

• The actual polygon locations were not buffered (these occur on inselbergs and buffering implies that the area will extend beyond the edge of the inselberg and include adjacent slopes that are not suitable habitat for this species).

Pseudo_4

• All inselbergs at altitude greater than 1900 m.a.s. (the regional extent of this species was taken to be an altitude of 1900 m a.s.l.; 1900 m.a.s. is based on the elevation of the lowest lying actual observation).





14.1.146. Targets

Taxa Flora

Species Helichrysum haygarthii



Trend Stable


FS populations 2

Helichrysum haygarthii Rare





Pseudo_4 area 360.69697 km2 0 108.209091 km2

0.031375 km2

Discussion:



1.24. Dracosciadium saniculifolium



Rare


Dracosciadium saniculifolium is reported to occur on steep, east-facing, basalt slopes in montane grassland, 2000-2400 m a.s.l. (http://redlist.sanbi.org/species.php?species=2164-2).


Two actual point locations were obtain from the PRECIS database while six actual polygon locations were obtained during expert mapping (Prof J. Du Preez; University of the Free State).


Pseudo_1 (point)

• The actual point localities were buffered by 100 m.

Pseudo_1 (polygon)

• The actual polygon locations were buffered by 100 m

Pseudo_4

• $Is there a regional extent

• All grasslands on east to south west facing aspect that are located on the basalts of the Drakensberg formation and that are at an altitude greater than 2000 m.a.s. (the regional extent was limited by the altitude threshold of greater than 2000 m.a.s.; the range of east to south west aspect is as determined from the actual observation data).




14.1.152. Targets

Taxa Flora


Species Dracosciadium saniculifolium



Trend Stable


FS populations 6

Dracosciadium saniculifolium Rare





Pseudo_4 area 85.587109 km2 0 25.6761327 km2

0.062751 km2

Discussion:



1.25. Nerine bowdenii


Rare


Nerine bowdenii is reported to occur in Subalpine grassland and Drakensberg-Amathole Afromontane Fynbos in cool moist pockets of cliffs and steep slopes where deep humic soils accumulate (http://redlist.sanbi.org/species.php?species=2078-4).



A single point distribution location was obtained from the PRECIS database as provided by Dr. Domatilla Raimondo..


Pseudo_1 (point)

• The actual point localities were buffered by 100 m.

Pseudo_4

• The regional extent was accepted to be the vegetation types in which the actual point observation is located

• All slopes greater than 100 percent that occur within the Drakensberg-Amathole Afromontane Fynbos (as per http://redlist.sanbi.org/species.php?species=2078-4) and the uKhahlamba Basalt Grassland vegetation types (the vegetation type in which the actual point observation is located). The slope threshold is based on the actual point location being located on a slope of 109 percent.




14.1.158. Targets

Taxa Flora

Species Nerine bowdenii




FS populations 1


Nerine bowdenii Rare



Pseudo_1 (point) 0.031375 km2 100 % 0.031375 km2

Pseudo_4 1.603885 km2 0 1.603885 km2

0.031375 km2

Discussion:



1.26. Brachystelma dimorphum subsp. gratum


Rare


Brachystelma dimorphum subsp. gratum is reported to occur in seasonally wet areas (Raimondo, et al., 2009).


A single actual polygon location was obtained by expert mapping (Prof. J. du Preez; University of the Free State).


Pseudo_1 (polygon)

• The single known actual polygon locality was buffered by 100 m.

Pseudo_4


• Wetlands as identified from the FS land cover data within 1 km of the known actual polygon location (accepted as the regional extent) were considered to be areas of high probability of occurrence.




14.1.164. Targets

Taxa Flora

Species Brachystelma dimorphum subsp. gratum




FS populations 1

Brachystelma dimorphum subsp. gratum

Rare




Pseudo_4 0.071884 km2 0 0.0215652 km2

0.211947 km2

Discussion:



• 100% of all known actual polygon localities (Pseudo_1) which accounts for 0.21 km2.

1.27. Chorlolirion latifolium


Not assessed (considered to be very rare) (Fritz, 2012).


Chortolirion latifolium is known to occur in the Free State and Gauteng where it shows a preference for short grasslands in sandy soils that are not prone to

annual fires (Fritz, 2012).


Five actual polygon locations were mapped during an expert mapping exercise as were provided by Prof. J. du Preez (UFS). Four populations are recognized due to the close proximity of some of the mapped populations to each other.


Pseudo_1 (polygon)

• Actual polygon data were not buffered as the buffered areas will extend beyond what is considered to be suitable habitat.

Pseudo_4

• Grasslands (landcover classes 11 and 12) that occur on sandy soils in the western Free State were identified by means of cartographic modelling. These were considered to represent areas in which Chortolirion latifolium has a high probability of occurence.

• Only areas that are equal to or greater than 10 ha were included.



14.1.170. Targets

Taxa Flora


Species Chortolirion latifolium

Common name Status Not assessed


Trend 4


FS populations 4

Chortoliroin latifolium Very rare (not assessed)

Actual & Modelled area 5674.68043 km2



Pseudo_4 5546.59474 km2 0 55.46594739 km2

128.085688 km2

Discussion:



Table 60: Land cover classes that represent habitat types suitable to Chorlolirion latifolium


1 - 10 Unsuitable

11 - 12 Suitable



13 - 85 Unsuitable

1.28. Lithops salicola


LC.

Lithops salicola is included on account of expert opinion based on its very limited distribution (is considered to be a habitat specialist) and the fact that it is endemic to the Free State Province.


Lithops salicola is limited to a few isolated calcareous locations in the south western Free State (west of Luckhoff).


Seven actual polygon locations were mapped during an expert mapping exercise as provided by Prof. J. du Preez (UFS). Three populations are recognized due to the close proximity of some of the mapped populations to each other.


Pseudo_1 (polygon)

• Actual polygon data were not buffered as the buffered areas will extend beyond what is considered to be potential suitable habitat.

14.1.175. Ecological niche modelling:

It was not possible to model the potential extent of Lithops salicola because the land cover data does not contain a calcarous land cover class or any other class with which potential habitat can be identified.


All known populations for any rare taxa with fewer than 11 known localities (localities being geographically separate sites at which the taxon has been recorded) should be included, independent of how that rarity is defined or assessed, in order to avoid an immediate threatened listing in the event of a


decline or extreme fluctuations in specified population parameters (assuming that one locality would be equivalent to one location) (Pfab, Victor, & Armstrong, 2011).

14.1.177. Targets

Taxa Flora

Species Lithops salicola

Common name Status LC

Criteria Endemic FS

Trend Stable

Target 100

FS populations 3

Lithops salicola LC




208.562979 km2

Discussion:




3. INVERTEBRATES

1.29. Pseudonympha paragaika (Golden Gate Brown)


VU (D2)

Pseudonympha paragaika is endemic to the Free State and is only known from a single population.


This insect is found in south-facing montane grassland with rocks (Mecenero, et al., 2013; Mucina & Rutherford, 2006) in the in the Eastern Free State Sandy

Grassland of the Mesic Highveld Grassland Bioregion. The altitudinal band where adults of this species are found is between 2 000 and 2 400m (Mecenero, et al., 2013). The predominant grass in its habitat is a tall Merxmuellera sp. (Poaceae) (Henning, Terblanche, & BAll, 2009).


Actual point distribution data were obtained from Southern African Butterfly Conservation Assessment (SABCA), SABCA Field Surveys and the ADU. The points that overlap with the actual polygon location were removed so that only a single actual point observation was retained.

Pseudonympha paragaika is known from one location only, the Golden Gate Highlands National Park (Mecenero, et al., 2013). The actual polygon location was mapped as the south facing slopes in the Golden Gate Highlands National Park as per Mecenero, et al. (2013).


Pseudo_1 (point)

• Actual point locations were buffered by 500 m. The 500 m buffer is as per the edge-matching technical guidelines which require a 500 m buffer for CR, EN & VU taxa (Escott & Lotter, 2012).

Pseudo_1 (polygon)

• Actual polygon locations were buffered by 500 m. The 500 m buffer is as per the edge-matching technical guidelines which require a 500 m buffer for CR, EN & VU taxa (Escott & Lotter, 2012).

Pseudo_4

• No ecological niche modelling was attempted. The regional extent was accepted to be the vegetation type within which actual Pseudo_1 observations were made (point and polygon).


• Grasslands of the Eastern Free State Sandy Grasslands vegetation type (Mucina & Rutherford, 2006) as well as the vegetation types in which actual occurrences were recorded that occur on south facing aspects, are at an altitude of 2000 m.a.s and higher, are on slopes greater than 8 percent and that occur within the vegetation types in which known actual observations were made.


Pseudo_5

• Grasslands of the Eastern Free State Sandy Grasslands vegetation type as well as the vegetation types in which actual occurrences were recorded that occur on south facing aspects, are at an altitude of 2000 m.a.s and higher, are on slopes greater than 8 percent and that occur outside the

vegetation types in which known actual observations were made.



All known populations of Critically Endangered, Endangered and Vulnerable taxa listed under the IUCN Red List criteria of B, C or D, should be included in a conservation plan.

14.1.183. Targets

Taxa Invertebrate

Species Pseudonympha paragaika

Common name Golden Gate Brown

Status VU

Criteria D2

Endemic FS

Trend Target 100



Estimated RSA population


FS populations 1












Golden Gate Brown VU (D2)


Proportional target (km2): Included as: % Target Target expression



Pseudo_4 area 13.506597 km2 0 4.052 km2

Pseudo_5 area 26.904445 km2 0 8.0713 km2

8.9737km2

Discussion:


• 100% of all known actual point localities (Pseudo_1) which accounts for 0.78 km2).

• 100% of all known actual polygon localities which amounts to 8.18 km2 (Pseudo_1; polygon).

1.30. Orachrysops mijburghi (Mijburgh's Blue)


EN [B1ab(ii,iii)]



Orachrysops mijburghi is known from a few localities in the Free State, specifically around the Heilbron region.

This species occurs in moist habitats that fringe ephemeral steams in undulating grasslands (Mecenero, et al., 2013). It is also closely associated with the

plant species Indigodera dimidiata (Mecenero, et al., 2013) and Indigofera evansiana which are its larval food (Williams, 2008.; Mecenero, et al., 2013). Little additional information on its habitat preferences is available. Modelling was done by first identifying the region of occurrence after which suitable micro-habitats within this region were identified. The former was identified using ecological niche modelling (Maxent) while the latter was identified by applying cartographic mapping technique to the identified ecological niche.


Only two populations are known to occur in the Free State (Mecenero, et al., 2013) while all the actual records included in this study are from the same population $Verify this, it does not make sence. Nine actual point locations that are considered to represent a single population were obtained from the ADU (LepSoc, Dr. René Navarro). A total of 2 populations are therefore recognized.


Pseudo_1 (point)


Pseudo_2 (polygon)

• The wetland areas (streams) adjacent to the actual point observations were buffered by 100 m to include habitat that fringe the streams.

Pseudo_4

• The geographic extent was limited to the ecological niche modelled area. Training points were limited to the nine known actual Pseudo_1 locations.

• Rivers and streams that occur within the ecological niche modelled extent buffered with 100 m. In many instances minimum training presence area of 0.495 extend well beyond what is considered to be the natural range of the species. Buffered rivers that occur in these areas were manually removed. Buffered rivers that occur within the mountainous areas just north of Vredefort were also considered to be not suitable habitat and were also removed.

The buffer of 100 m around river and streams as used during the modelling process was arbitrarily chosen to account for surrounding moist grasslands as no data on such habitats were available. It is therefore possible that this species may venture further than 100 m from streams should the area contain suitable habitat.



Target rationale

All known populations of Critically Endangered, Endangered and Vulnerable taxa listed under the IUCN Red List criteria of B, C or D, should be included in a conservation plan.

Targets

Taxa Invertebrate

Species Orachrysops mijburghi

Common name Mijburgh’s Blue

Status EN

Criteria B1ab(ii,iii)

Endemic RSA

Trend Target 100





FS populations 2



Home range/Occupancy (km2) Proportional FS distribution (%) FS proportional populations (of 11) 0




Area required for proportional 10 000 individuals (km2) #DIV/0!

Area required for proportional RSA population (km2) #DIV/0!

Mijburgh's Blue EN [B1ab(ii,iii)]






Pseudo_4 area 1053.36087 km2 0 105.34 km2

3.4116 km2

Discussion:



1.31. Orachrysops montanus (Golden Gate Blue)


LC (Extremely rare)

Although classified as least concern this species has a very limited range and is known from only one site (the Golden Gate Highlands National Park), i.e. it is a Free State endemic.


This insect is found in moist areas on the foot of south-facing slopes of mountains in shallow gullies or on the banks of streams. The larval host plant

Indigofera dimidiate grows in bare areas very close to the water's edge (Mecenero, et al., 2013).


Seven actual point observations, representing 3 populations, were received from the Southern African Butterfly Conservation Assessment (SABCA), SABCA Field Surveys and the ADU. Four of these points occur within the actual polygon location and were removed. The remaining points, along with the actual polygon observation, are considered to represent the three different populations.


Pseudo_1 (point)



Pseudo_1 (polygon)

• Actual polygon locations were buffered by 500 m. The 500 m buffer is as per the edge-matching technical guidelines which require a 500 m buffer for CR, EN & VU taxa (Escott & Lotter, 2012).

Pseudo_2 (polygon)

• The wetland areas (streams) adjacent to the actual point observations were buffered by 20 m. The 20 m buffer was applied to include habitat that fringe streams.

Pseudo_4

• Ecological niche modelling produced poor results and was therefore not considered. Modelling was therefore done using cartographic modelling techniques only.

• Cartographic modelling comprised of selecting all rivers and streams (buffered with 20 meter) that occur on SE, S, and SW aspects and at altitudes in excess of 1800 m.a.s.

• In the absence of ecological niche modelling, cartographic modelled areas that were subjectively considered not to be representative of the species potential distribution were removed. These included all areas south and west of the GGHNP that extend into Phuthaditjhaba. Areas north of the Rooiberge and to the west of Clarens were also removed.

• All features were included (i.e. no size limitation was applied)

Target rationale


Targets

Taxa Invertebrate

Species Orachrysops montanus

Common name Golden Gate Blue

Status LC (Extremely Rare)

Criteria Endemic FS

Trend Target 100






FS populations 3










Golden Gate Blue EN [B1ab(ii,iii)]





Pseudo_2 area 0.004178 km2 0 0.0042 km2

Pseudo_4 area 29.894229 km2 0 8.9683 km2

9.7843km2

Discussion:



• 100% of all modelled Pseudo_2 localities which accounts for 7.42 km2.


1.32. Thestor protumnus terblanchei (Terblanche’s Skolly)


VU (D12)


Thestor protumnus terblanchei is known from only one location, the Korannaberg near Excelsior (Mecenero, et al., 2013). Although it is stated to only occur on south-western facing slopes, one of the point localities is located on the berg plateau. Cartographic modelling therefore included both the south-western facing slopes and the plateau.


Two actual point locations were obtained from the Southern African Butterfly Conservation Assessment (SABCA). These are from the same location and are considered to represent a single population.


Pseudo_1 (point)


Pseudo_2 (polygon)

• Ecological niche modelling was not applied to determine the regional extent of this species. Following the habitat description of this species as per Mecenero, et al. (2013), cartographic modelling was done by manually demarcating the southern and eastern slopes of the Korannaberg, as well as it

plateau.

• It was considered to extend the modelled area to the nearby Vierfonteinberg which at its closet point (plateau to plateau) is appeoximately 5km from the Korannaberg. The Vierfonteinberg represents similar habitat in term of its vegetation and topography. It is, as Korannaberg, also an inselberg. It does, however, differ on account of the extent of the elevation where the slopes of the Korannaberg are characterized by greater elevation (ranging from 1480m to 1860m = 380m difference) than those of the Vierfonteinberg (ranging from 1560m to 1850m = 290m difference). Vierfonteinberg was subsequently not included in the modelled habitat on account of the latter as well as the fact that the slopes of the Vierfonteinberg are in general also of steeper incline.

• All features were included (i.e. no size limitation was applied)


Target rationale


Targets

Taxa Invertebrate

Species Thestor protumnus terblanchei

Common name Terblanche’s Skolly

Status VU

Criteria D12

Endemic FS

Trend Target 100





FS populations 1










Terblanche's Skolly Rare





Pseudo_2 area 82.438781 km2 0 82.439 km2

1.5704 km2

Discussion:



$NOTE: Although the existing records suggest that the species is limited to the Korannaberg only, it is not inconceivable that the species may actually extend to the close by Vierfonteinberg. This warrants further investigation. Should the nearby Vierfonteinberg be included as potential habitat for this species?

1.33. Metisella meninx (Marsh Sylph)


Rare (Habitat specialist)


Metisella meninx occurs along marshes and stream banks in open grassland at altitudes of 1400 to 1700 m.a.s. Such marshes are often in the headwaters of streams (Henning & Roos in Mecenero, et al., 2013).


Three actual point locations were obtained from the Southern African Butterfly Conservation Assessment (SABCA). These are considered to represent three seperate populations.


Pseudo_1 (point)

• Actual point locations were buffered by 500 m. According to the edge-matching technical guidelines a 500 m buffer needs to be applied to species classified as CR, EN or VU (Escott & Lotter, 2012). Although Metisella meninx is not classified as CR, EN or VU, it is considered to be rare. A 500 m buffer was therefore applied.


Pseudo_2 (polygon)

• The area of suitable habitat directly adjacent to the actual point observation was buffered by 500 m. According to the edge-matching technical guidelines a 500 m buffer needs to be applied to species classified as CR, EN or VU (Escott & Lotter, 2012). Although Metisella meninx is not classified as CR, EN or VU, it is considered to be rare. A 500 m buffer was therefore applied.

Pseudo_4

• No ecological niche modelling was attempted. The regional extent was accepted to be suitable habitat within a distance of 1.5 km from the edge of the Vaal River. The distance of 1.5 km is based on the fact that all known localities within the Free State are within a distance of approximately 1.5 km from the edge of the Vaal River. This seems to be the most southern distribution of this species.

• Cartographic modelling was subsequently done by selecting all valley bottom wetlands (land cover class 25) that occur within the vegetation types of actual observations and that are within 10 km of the Vaal River $Resolve discrepancy of 1.5 km stated in first bullet and that are greater than 1 ha

in extent (wetlands in most eastern portion of the Soweto Highveld Grassland were exclude, as were the wetlands in the small portion of selected vegetation types that occur to the east of the main body of the vegetation types along the Vaal River, these were manually selected and removed).

• No size limitation was applied other than the already reported 1 ha as applied during cartographic modeling.


All available data from the FS actual point data were used, as well as the two localities from the Mpumalanga biodiversity plan (Still need to get hold of Gauteng data)

Target rationale


Targets

Taxa Invertebrate

Species Metisella meninx

Common name Marsh Sylph

Status Rare (HS)

Criteria Endemic


Trend Target 100



Estimated RSA population Estimated RSA range (km2) FS populations 3

Type Approximate RSA individuals per area (ind/ha) #DIV/0!








Marsh Sylph EN [B1ab(ii,iii)]





Pseudo_4 area 8.1609 km2 0 2.4483 km2

2.3556 km2

Discussion:




1.34. Torynesis orangica (Orange Widow)


Rare (Restricted range and Habitat specialist). Also endemic to the Free State.


Torynesis orangica occurs on rocky sandstone outcrops at high altitude (Mecenero, et al., 2013)


Actual point distribution data (8 locations) were obtained from the SANBI database as provided by Dr. Domatilla Raimondo. These represent four populations.


Pseudo_1 (point)

• Actual point locations were buffered by 500 m. According to the edge-matching technical guidelines a 500 m buffer needs to be applied to species classified as CR, EN or VU (Escott & Lotter, 2012). Although Torynesis orangica is not classified as CR, EN or VU, it is considered to be rare. A 500 m buffer was therefore applied.

Pseudo_2 (polygon)

• The area of suitable habitat directly adjacent to the actual point observation was not buffered.

Pseudo_4

• Cartographic modelling was applied to identify rocky outcrops [landcover class 29 (NATURAL NON-VEGETATED, bare rock)] that occur at altitudes of more than 1800 m.a.s. (the latter was acepted as the regional extent).


Target rationale



Targets

Taxa Invertebrate

Species Torynesis orangica

Common name Orange Widow

Status Rare (RR, HS)

Criteria Endemic FS

Trend Target 100



Estimated RSA population Estimated RSA range (km2) FS populations Type Approximate RSA individuals per area (ind/ha) #DIV/0!








Orange Widow EN [B1ab(ii,iii)]






Pseudo_4 area 52.363456 km2 0 15.709 km2

3.752 km2

Discussion:



1.35. Tuxentius melaena griqua (Griqua Black Pie)


DD (Mecenero, et al., 2013).


Tuxentius melaena griqua is known from only 2 locations with each considered to be separate populations (Woodland Hills Estate in Bloemfontein and Soutpan). This species is reported to occur in arid savanna and riverine forest that fringe the Vaal River. According to the distribution map as per (Mecenero, et al., 2013), Tuxentius melaena griqua is limited to the far western region of the Free State Province (in the Jacobsdal district). The point localities included in this study are therefore considered to be an extension of it known range.


Actual point distribution data (2 populations) were obtained from the SANBI database as provided by Dr. Domatilla Raimondo .


Pseudo_1 (point)

• Actual point locations were buffered by 500 m. According to the edge-matching technical guidelines a 500 m buffer needs to be applied to species classified as CR, EN or VU (Escott & Lotter, 2012). Although Tuxentius melaena griqua is not classified as CR, EN or VU, it is considered to be rare, which includes species classified as data defficient. A 500 m buffer was therefore applied.

Pseudo_2 (polygon)

• The area of suitable habitat directly adjacent to the actual point observation was not buffered. $why not

Pseudo_4

• The regional extent was accepted to be:


o Vegetation types in which actual observations were made o Vegetation types with which this species is associated (savannah) [Kimberley Thornveld (SVk 4) and the Schmidsfrif Thornveld (SCK 6)]

according to (Mecenero, et al., 2013). Unsuitable landcover types (transformed areas) were removed.

• Wooded land cover classes (land cover classes 1 - 7, 84 and 85) were identified and those that are not wooded were removed from the merged layer of vegetation types in which actual observations were made and the vegetation types with which this species is associated (i.e. transformed areas were removed)

• Only features greater than 1 ha were retained.

Target rationale

Species classified as data deficient were considered to be synonymous with Rare. All known populations for any rare taxa with fewer than 11 known localities (localities being geographically separate sites at which the taxon has been recorded) should be included, independent of how that rarity is defined or assessed, in order to avoid an immediate threatened listing in the event of a decline or extreme fluctuations in specified population parameters (assuming that one locality would be equivalent to one location) (Pfab, Victor, & Armstrong, 2011).

Targets

Taxa Invertebrate

Species Tuxentius melaena griqua

Common name Griqua Black Pie

Status DD


Trend Target 100



Estimated RSA population Estimated RSA range (km2) FS populations Type Approximate RSA individuals per area (ind/ha) #DIV/0!


Home range/Occupancy (km2) Proportional FS distribution (%)








Griqua Black Pie EN [B1ab(ii,iii)]





Pseudo_4 area 2086.44029 km2 0 625.93 km2

1.5704 km2

Discussion:



4. REPTILES

1.36. Smaug giganteus (Giant Spiny-tailed Lizard)


VU (A2c)



As per the recommendation of Dr. Mike Bates of the National Museum, Bloemfontein, the geographical distribution was taken to be the distribution map as per the (Animal Demography Unit, 2013) $Make reference static text but ensure it is included in the reference list instead of applying ecological niche modelling.

The burrows are usually 17 m apart (Unknown). Each burrow is usually occupied by a single individual, although adults will often share their burrow with juveniles (Unknown).

Soil types in which burrows are formed include sand, loams, black clays and solonetzic types. No accurate representative estimates of burrow distribution and densities are available within the current distribution range of Sungazers. Reported burrow densities range from 4 to 19 burrows per hectare. However, large open grassland areas were found without any burrows present. Usually a single adult will occupy a burrow although up to seven individuals have been recorded in a burrow and juveniles may share burrows with adults (Alexander & Marais, 2007).


Point data were received from the ADU (Dr. René Navarro) as well as the private database of Dr. Michael Cunningham. Point data provided by Dr. M. Cunningham that were of a single population were mapped as a polygon feature and the points were removed from the point dataset. Polygon data were also provided by expert opinion (Dr. N. Collins; FS DETEA). Point data represent 4 populations.

Point data of four populations were also received from Dr. Trevor McIntyre of the University of Pretoria. Point data provided by Dr. Trevor McIntyre that were of a single population were mapped as a polygon feature and the points were removed from the point dataset. Polygon data represent 11 populations.


Pseudo_1 (point)


Pseudo_1 (polygon)


Pseudo_4

• The regional extent was accepted to be the georeferenced ADU range.

• Pseudo_4 distribution included areas of grassland (land cover classes 11 and 12) that occur within the accepted ADU regional extent.

• Only portions larger than 1 ha were included


Target rationale


Targets

Taxa Reptile

Species Smaug giganteus

Common name Giant Spiny-tailed Lizard

Status VU


Trend Target 100



Estimated RSA population Gr size 1


FS population 11











Giant Spiny-tailed Lizard (Sungazer) VU (A2c)


Modelled area 15968.8065 km2 Proportional target (km2): 6.34379055 km2 % Target Target expression



Pseudo_4 area 15940.8891 km2 0 0 km2

27.917 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Smaug giganteus the FS accounts 79.29% of the estimated range of the species. The estimated RSA population is unknown. The target for the FS is therefore set at 9 populations or 7929.73 mature individuals (6.34 km2), whichever is the most.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.5 km2 and which also accounts for the FS proportional target of 9 populations, but does not account for the required FS proportional area of 6.34 km2.

• 100% of all known actual polygon localities (Pseudo_1) which accounts for 27.41 km2. Smaug giganteus is currently under severe preasure from threats including habitat desctruction and trade. For this reason the traget was set at 100%, even though it exceeds the required 6.34 km2.

Table 61: Land cover classes that represent habitat types suitable to Smaug giganteus [lc11 (Grassland) and lc12 (Sparse/open grassland)].


1 - 10 Unsuitable

11 - 12 Suitable

13 - 85 Unsuitable

1.37. Tetradactylus breyeri (Breyer's Longtailed Seps)



VU (A2c)


T. breyeri occurs in what appears to be three isolated populations in South Africa: a) montane and highveld grasslands of the Transvaal Drakensberg escarpment, h) montane grasslands of the Natal Drakensberg, and c) Patchy Highveld to Cymbopogogon-Themeda Veld Transition (grassland), veld type 53 (Acocks 1988) in the north-eastern Free State. The latter locality (Zwartkoppies farm) is intermediate between Mpumalanga and KwaZulu-Natal populations, but may be the north-westerly limit of the Natal Drakensberg population (Bates, Taxonomic status and distribution of the South African lizard Tetradactylus breyeri Roux (Gerrhosauridae), 1996).


Two actual point observations received from the ADU as well one observation from the private database of Dr. Michael Cunningham. One point locality was also obtained from expert mapping with Dr. N. Collins of the FS DETEA. Three populations are recognised.


Pseudo_1 (point)


Pseudo_2 (polygon)

• Suitable habitat adjacent to actual point observations were not buffered.

Pseudo_4

• The regional extent was accepted to be all vegetation types in which centroids of QDSs with observations as well as actual observations are located.

• Pseudo_3 distribution included areas of grasslands (land cover class 11 only) that occur on slopes of 0 - 8 percent (arbitrarily chosen).

• Although it is accepted that the species may occur in other grassland areas, i.e. of steeper slope or other vegetation types, the flatter areas are accepted to be areas of highest probability of occurrence.

• Only portions larger than 1 ha were included




14.1.218. Targets

Taxa Reptile

Species Tetradactylus breyeri

Common name Breyer's Long-tailed Seps

Status VU


Trend Target 100




Gr size 1


FS population 3

Approximate RSA individuals per area (ind/ha) #VALUE!

Approximate RSA area per individual (km2/ind) #VALUE!

Home range/Occupancy (km2) Unknown




FS proportional individuals (of Estimated RSA population) #VALUE!


Area required for proportional 10 000 individuals (km2) #VALUE!

Area required for proportional RSA population (km2) #VALUE!

Breyer's Long-tailed Seps VU (A2c)


Proportional target (km2): % Target Target expression


Pseudo_2 9.139742 km2 0 9.1397 km2


Pseudo_4 7751.58698 km2 0 0 km2

0.4838 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Tetradactylus breyeri the FS accounts 49.27% of the estimated range of the species. The estimated RSA population is unknown. The target for the FS is therefore set at 6 populations.

• 100% of all known actual point localities (Pseudo_1) which accounts for 3 populations and 0.48 km2.

1.38. Pseudocordylus langi (Lang's Gridled Lizard)


NT


Pseudocordylus langi is known from only a small, high elevation area (2805-3048 m) of the Drakensberg in the Mont-aux-Sources-Organ Pipes Pass area (KwaZulu-Natal, Free State, Lesotho) (Broadley 1964; M. Cunningham, pers. comm. 2005 in Bates, 2007). It may, however, occur in a more-or-less continuous band along the rim and summit of the escarpment from the Mont-aux-Sources area to at least the top of Sani Pass in Lesotho. There may be isolated populations of this species on unsampled mountain peaks such as Sentinel and Inner Tower (Bates, 2007).


All point and polygon records were obtained from the ADU (Dr. René Navarro) and the personal dataset of Dr. Michael Cunningham. Point data from Dr. Michael Cunnionham that were considered to represent a single population were mapped as a polygon features and the corresponding points were removed from the actual point dataset. These represent 3 populations.


Pseudo_1 (point)


Pseudo_1 (polygon)

• The single polygon location was buffered by 200 m.

Pseudo_4


• The regional extent was acepted to be the area determind by ecological niche modelling. All available actual Pseudo_1 locations were used as training points as well as all other actual observations that were excluded from the Pseudo_1 layer because of too coarse precision. To remove small isolated fragments only features greater than 1 ha were retained.

• Pseudo_4 distribution included areas of bare rock [(land cover class 29, NATURAL NON-VEGETATED (bare rock)] as limited to the ecological niche modelled extent.

• All features were included (no size limitation was applied)

Target rationale


Because Pseudocordylus langi is not threatened the more conservative target of 11 populations/or at least 10 000 mature individuals is adopted as opposed to setting the target to all known populations.

Targets

Taxa Reptile

Species Pseudocordylus langi

Common name Lang's Girdled Lizard

Status NT






Estimated RSA range (km2) 235.1275

FS population 3












Lang's Gridled Lizard NT





Pseudo_4 area 0.998695 km2 0 0.2996 km2

1.5085km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Pseudocordylus langi the FS accounts 12.66% of the estimated range of the species. The proportional target for the FS is therefore: 3 populations or 2640.64 mature individuals (unknown range), whichever is the most.


• 100% of all known actual polygon localities (Pseudo_1) which accounts for 1.12 km2. Pseudo_1 (point) and Pseudo_1 (polygon) account for all 3 of the FS proportional target of 3 populations.

Table 62: Land cover classes that represent habitat types not suitable to Langs Gridled Lizard


1 - 28 Unsuitable



29 Suitable

30 - 85 Unsuitable

1.39. Pseudocordylus spinosus (Spiny Crag Lizard)


NT


Pseudocordylus spinosus occurs on the lower (900 m) to middle (2517 m) slopes of the Drakensberg in KwaZulu-Natal and the Free State (Bates, 2007)


Three actual point records were obtained from a publication22 by Dr. Mike Bates ($put in the reference here and not as a afootnote), two were obtained from the ADU (Dr. René Navarro) and another two from the personal database of Dr. Michael Cunningham. These represent 3 populations.


Pseudo_1 (polygon)


Pseudo_4

• The regional extent was acepted to be the area determind by ecological niche modelling. All available actual Pseudo_1 locations were used as training points as well as all other actual observations that were excluded from the Pseudo_1 layer because of too coarse precision.

• Pseudo_4 distribution included areas of natural grassland (land cover class 11; GRASSLAND) as identified from the Free State land cover data and as limited to ecological niche modelled extent.


22 Bates, M.F. 2007. An analysis of the Pseudocordylus melanotus complex (Sauria: Cordylidae). PhD thesis. University of Stellenbosch.


Target rationale


Because Pseudocordylus spinosus is not threatened the more conservative target of 11 populations/or at least 10 000 mature individuals is adopted as opposed to setting the target to all known populations.

Targets

Taxa Reptile

Species Pseudocordylus spinosus

Common name Spiny Crag Lizard

Status NT







FS population 3












Spiny Crags Lizard NT




Pseudo_4 163.47568 km2 0 49.043 km2

0.3873 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). Information on the area required per individual or group of individuals could not be obtained. It was therefore not possible to express the FS proportional target into an area unit. The FS accounts for 5.83% of the estimated range of Pseudocordylus spinosus. The proportional target for the FS is therefore set at 1 population and 30% (arbitrarily chosen) of the mapped modelled habitat.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.38 km2 while also exceeding the proportional target of 1 population.

Table 63: Land cover classes that represent habitat types not suitable to Langs Gridled Lizard


1 - 10 Unsuitable

11 Suitable

12 - 85 Unsuitable


1.40. Tropidosaura cottrelli (Cottrell's mountain lizard)


NT


Its range is restricted to a narrow band at very high elevations along the Drakensberg escarpment, with an estimated extent of occurrence of approximately 3,180 km2 (http://www.iucnredlist.org/details/178302/0

Tropidosaura cottrelli is associated with altitudes of 2700-3000 m. and Montane rocky grasslands. (http://www.durban.gov.za/City_Services/ParksRecreation/museums/nsm/Documents/Novitates%2029/KZN%20Reptiles.pdf?Mobile=1&Source=%2FCity_Services%2FParksRecreation%2Fmuseums%2Fnsm%2F_layouts%2Fmobile%2Fview.aspx%3FList%3D27bbe6c1-0dee-4bb5-b934-b28cba7c1a96%26View%3Dafef5f14-be97-4b63-a953-1eec64996ba8%26RootFolder%3D%252FCity_Services%252FParksRecreation%252Fmuseums%252Fnsm%252FDocuments%252FNovitates%252029%26CurrentPage%3D1)


Two actual point localities were obtained from the personal database of Dr. Michael Cunningham. These represent two populatoins of which the one is a range extention.


Pseudo_1 (polygon)


Pseudo_4

• The regional extent was accepted to be the area determined by ecological niche modelling. Training points used were the two actual Pseudo_1 point locations as well as an observation from KZN (the planning unit was converted to a point). To remove small isolated fragments only features greater

than 1 ha were retained.

• Pseudo_4 distribution include areas of natural grassland (land cover class 11; GRASSLAND) as identified from the Free State land cover data and as limited to the ecological niche modelled extent.

• Only areas larger than 1 ha were retained. $Duplication with first bullet


Target rationale


Because Tropidosaura cottrelli is not threatened the more conservative target of 11 populations/or at least 10 000 mature individuals is adopted as opposed to setting the target to all known populations.

Targets

Taxa Reptile

Species Tropidosaura cottrelli

Common name Cottrell's Mountain Lizard

Status NT







FS population 2











Cottrells Mountain Lizard NT




Pseudo_4 69.44179 km2 0 20.833 km2

0.2512 km2

Discussion:

The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). Information on the area required per individual or group of individuals could not be obtained. It was therefore not possible to express the FS proportional target into an area unit. The FS accounts for 17.82% of the estimated range of Pseudocordylus spinosus. The proportional target for the FS is therefore set at 2 populations and 30% (arbitrarily chosen) of the mapped modelled habitat.

• 100% of all known actual point localities (Pseudo_1) which accounts for 0.25 km2 while also exceeding the proportional target of 2 populations.

Table 64: Land cover classes that represent habitat types suitable to Cottrells Mountain Lizard


1 - 10 Unsuitable

11 Suitable

12 - 85 Suitable

5. MAMMALS - SMALL


1.41. Mystromys albicaudatus (White-tailed rat)


EN (A3c)


Very little information on the ecology of this species is known.


Four point data locations were obtained from Mr. Johan Watson (FS DETEA) and an additional 4 points were obtained from the M.Sc. thesis of Mr. P. Kuyler. Eleven polygon records were obtained from Mr. Johan Watson (FS DETEA) during an expert mapping session; each considered to represent a separate population. $? Confirm this as C:\GIS\SCP\Biodiversity\Expert\Expert_All_fin.shp does not indicate where these records were obtained from. The four points from Mr. J. Watson were considered to represent a single population and were converted to a single polygon after which the points were removed from the actual point database.


Actual point data were considered to be temporal data, i.e. it does not necessarily indicate permanent residence of the species at that specific location, but rather serves as an indication that the species is more like to occur in the vicinity of such an observation compared to other areas where no observations were made.

Pseudo_1 (point)

• Actual point locations were buffered by 1 km to indicate an area of highest likelihood of occurrence.


Pseudo_1 (polygon)

• Actual polygon locations were buffered by 1 km to indicate an area of highest likelihood of occurrence.


Because of the wide ecological amplitude of Mystromys albicaudatus there was no attempt at modelling its potential distribution as it can potentially occur anywhere within the FS.




14.1.236. Targets

The proportional contribution of the Free State was calculated using the georeferenced distribution map of Friedman and Daly (2004).

Taxa Mammal small

Species Mystromys albicaudatus

Common name White-tailed rat

Status EN

Criteria A3c

Endemic RSA






FS populations 13

Approximate RSA individuals per area (ind/ha) --

Approximate RSA area per individual (km2/ind) --

Home range/Occupancy (km2) 2.001 (As per Friedman and Daly, 2004)




FS proportional individuals (of Estimated RSA population) --



Area required for proportional RSA population (km2) --

White-tailed Rat EN (A3c)

Actual & Modelled area 145.166643 km2 Proportional target (km2): Included as: 4663.134 km2 % Target Target area (km2)




145.17 km2

Discussion: The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Mystromys albicaudatus the FS accounts for 23.30% of the estimated range of the species (as determined from the georeferenced distribution map of Friedman and Daly, 2004). The proportional target for the FS is therefore: 3 populations or 2330 mature individuals (4663.13 km2), whichever is the most. Because only actual observations is included of which their mapped distribution is less than the required FS proportional target, a 100% target was set for all Pseudo layers.

• 100% of the single Pseudo_1 actual location which accounts for 3.14 km2 which exceeds accounts for the required proportional FS target of 1 population.

• 100% of all actual Pseudo_1 polygon localities which amounts to 42.6 km2 of the 142.02 km2. A 100% target is set to account for the required FS proportional target of 4663.13 km2.

1.42. Laephotis wintoni (De Winton’s long-eared bat)


VU (D2)


This species was collected from the Clarens area by Mr. Johan Watson (Watson, J., 2013) where it is associated with the sandstone hills ranging from Clarens

through the Golden Gate Highlands National Park, QWA-QWA to the Sterkfontein Dam Nature Reserve. It is therefore concluded by Watson (2013) that it is likely for this species to occur within these regions. Data on the general biology and habitat of this bat species are very limited and the animals are only known from four localities in South Africa (Skinner & Chimimba 2005; Watson 1990a). Taxonomically there is no consensus if this bat is a recognised species or if it is a subspecies of L. botswanae (Seamark et. al 2012). It is not clear if these animals are crawlers utilising crevices or a cave dwelling species.


A single locality point was obtained during expert mapping from Mr. Johan Watson (FS DETEA).



The proportional contribution of the Free State was calculated using the georeferenced distribution map of Friedman and Daly (2004).

Pseudo_1 (point)


Pseudo_4

• No ecological niche modelling of the potential distribution of the species was attempted. The regional extent was limited to exposed rock of the Clarens sandstone formation.

• Pseudo_4 distribution included areas of exposed rock (land cover class 29) of the Clarens sandstone formation.

• To exclude the areas of the south eastern Free State where the species is not known to occur, the features of the north eastern Free State were manually selected and retained. $Resolve contradiction of south eastern FS and north eastern FS as stated in this bullet

• Only features greater than 0.1 ha were retained (the 0.1 ha threshold was arbitrarily chosen to remove small isolated pockets and to improve the probability of occurrence by retaining only relative large portions of potential habitat).



$Include the following this for all species

The estimated RSA population is 50 individuals (Friedman & Daly, 2004). If the maximum reported home range of 500 km2 is accepted, then a total area of 25 000 km2 (50 x 500 km2) needs to be included in systematic conservation plans. The FS proportional target amounts to 4360.547 km2 (17.44% of 25 000km2).

14.1.242. Targets

Species Laephotis wintoni

Common name De Winton’s long-eared bat

Status VU

Criteria D2

Endemic

Trend

Target 100






FS populations 1

Gr size 1











De Winton’s long-eared bat VU (D2) Actual & Modelled area 121.6011 km2 Proportional target (km2): Included as: 218.027373 km2 % Target Target area (km2)


Pseudo_4 area 120.815909 km2 0 36.245 km2

0.7852 km2

Discussion:


• 100% of the single Pseudo_1 actual location which accounts for 0.78 km2.

1.43. Cistugo lesueuri (Lesueur’s wing-gland bat)



NT


According to Watson (2013) very little is known about this species. According to literature this species was collected in the vicinity of broken country and open water (Lynch 1994; Watson 1998; Skinner & Chimimba 2005; Seamark et. al 2012). The species is therefore expected to occur in similar habitats. Outside the drainage line was gentle sloping open grassland that could yield sufficient food items for this species. In the Sterkfontein Dam Nature Reserve it was collected in a wooded drainage line with rock face.

According to (Watson, 1998) a group of 40 animals was sampled in the Free State. An average group size of 20 animals is assumed.


Three actual point localities were obtained by expert mapping from Mr. Johan Watson (FS DESTEA).


Because only three locality points were available no modeling of the potential distribution of the species was attempted. The proportional contribution of the Free State was calculated using the georeferenced distribution map of Friedman and Daly (2004).

Pseudo_1 (point)


Pseudo_4

• No ecological niche modelling of the potential distribution of the species was attempted. The georeferenced IUCN distribution map was accepted as the regional extent of distribution.

• Pseudo_4 distribution included areas of all open bushland (land cover class 6), bushland (land cover class 78) and riparian vegetation (other) that intersect rivers and streams.

• All features were buffered by 300 m to account for foraging areas along such wooded habitats.





According to Friedman and Daily (2004) the estimated RSA population is unknown. If the maximum reported home range of 500 km2 is accepted (Friedman & Daly, 2004) for a targeted national population of 10 000 mature individuals, then a total area of 5 million km2 (10 000 x 500 km2) needs to be included in systematic conservation plans. The FS proportional target amounts to 312 553.4 km2 $Verify – this figure is not shown in the table below (6.25% of 5 million km2).

14.1.248. Targets

Species Cistugo lesueuri

Common name Lesueur’s wing-gland bat

Status NT

Criteria Not provided

Endemic RSA






FS populations 3

Gr size 20

Approximate RSA individuals per area (ind/ha) #VALUE!

Approximate RSA area per individual (km2/ind) #VALUE!





FS proportional individuals (of Estimated RSA population) #VALUE!




Lesueur’s wing-gland bat NT



Proportional target (km2): Included as: 15627.6718 km2 % Target Target area (km2)


Pseudo_4 1976.73829 km2 0 593.02 km2

0.7852 km2

Discussion: The FS is only responsible for its proportional contribution of the target. For example, should the FS account for only 10% of a species distribution, then the FS is responsible for only 10% of the required 11 population (i.e. 1 population) or 10% of the required 10 00 mature individuals (i.e. 1 000 individuals). In the case of Cistugo lesueuri the FS accounts for 6.25% of the estimated range of the species (as determined from the georeferenced distribution map of Friedman and Daly, 2004 - $is this true or was it ecological niche modelling - verify for each species). The estimated RSA population is unknown. The target for the FS is therefore set at 1 population or 625.10 mature individuals (15627.674 km2), whichever is the most.

• 100% of the single Pseudo_1 actual location which accounts for 2.35 km2. This accounts for the FS proportional target of 1 population.

1.44. Poecilogale albinucha (African weasel)


DD


Although Poecilogale albinucha is classified as data deficient, there are numerous records for this species throughout the entire Free State (Friedman & Daly, 2004). Because the species in known to occur throughout the Free State no attempt at modelling its potential distribution was done. Only the two areas of known occurrence as provided by Mr. Morné Pretorius were included.


The two areas of occurrence (recognized as 2 separate populations) were captured during the expert mapping process, Mr. Morné Pretorius (FS DETEA).


Pseudo_1 (polygon)

• Actual polygon locations were buffered by 1 km.


According to Friedman & Daly (2004) Poecilogale albinucha occurs throughout the entire Free State province. No modelling of its potential distribution was subsequently done.


Species classified as data defficient were considered to be synonymous with Rare. All known populations for any rare taxa with fewer than 11 known localities (localities being geographically separate sites at which the taxon has been recorded) should be included, independent of how that rarity is defined or assessed, in order to avoid an immediate threatened listing in the event of a decline or extreme fluctuations in specified population parameters (assuming that one locality would be equivalent to one location) (Pfab, Victor, & Armstrong, 2011).

The estimated range of Poecilogale albinucha is reported to be 20 000 km2 (Friedman & Daly, 2004). The FS proportional target is therefore 2870.57 km2.

14.1.254. Targets

Taxa Mammal small

Species Poecilogale albinucha

Common name African weasel

Status DD

Criteria Not provided

Endemic Trend


FS populations 2

African weasel DD




22.7 km2


Discussion:


• 100% of the two known Pseudo_1 (polygon) locations which amounts to 3.65 km2.


15. Appendix 3: Cost

16. Appendix 4: Connectivity

17. Appendix 5: Ecosystem threat status

18. Appendix 6: Edge matching

19. Calibration