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Application of GLOBIO3
Biodiversity Modelling to KENYA
2ND JANUARY 2007
MOSES MALOBA
GLOBIO 3- Developed by Netherlands environmental assessment agency (MNP), UNEP WCMC & UNEP GRID ARENDAL
Globio3 –Describes biodiversity by calculating remaining mean species abundance of original species relative to their abundance in primary vegetation (pristine condition)
Model considers various pressure factors (driving forces) that are either direct or indirect
MODEL DESIGNThe core of the model is the description of the major relationships between the pressures/ drivers and their impacts on biodiversity
Biodiversity of an ecosystem is considered as a stock entity i.e. the complete set of characteristic species & their abundance.
Drivers are divided into two
• Dependent
• Independent
GLOBIO3 Design
MODEL INPUTS
Land use (agriculture, forestry, settlement) Climate change Infrastructure Fragmentation Nitrogen Deposition
Design of model framework for GLOBIO 3
GLC 2000IMAGE GLOBIO 2
Land use
Nitrogen ClimateInfrastructure
Land-useeffect
Nitrogeneffect
Climateeffect
Patch sizeeffect
Infrastructureeffect
MSAGLOBIO3
Results from individual pressures are then combined and overall change in biodiversity calculated as Mean species abundance (MSA) Globio3 model depend on other models for some of the input data-IMAGE &Globio2
THE PROCESS OF BIODIVERSITY LOSS
Biodiversitydecrease
100%
50%Map color
0%
GLOBIO3 OUTPUT
Maps figures tables
PRELIMINARY RESULTS
NATIONAL MSA MAP OF KENYA
MEAN SPECIES ABANDANCE
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8
Total
MSA GRAPH FOR 8 PROVINCES
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8
corrected reduction by agriculture corrected reduction by grazing
corrected reduction by forestry corrected reduction by built up
corrected reduction by nitrogen corrected reduction by climate
corrected reduction by infra corrected reduction by fragmenation
corrected remaining biodiversity
PRESSURE FRACTION CONTRIBUTION TO MSA
Total contribution for each pressure
5.67%
1.34%
6.15%6.17%
0.01%
0.17%
0.09%
0.43%
79.97%
corrected reduction by agriculture corrected reduction by grazingcorrected reduction by forestry corrected reduction by built upcorrected reduction by nitrogen corrected reduction by climatecorrected reduction by infra corrected reduction by fragmenationcorrected remaining biodiversity
The Wildlife Conservation ProblemThe Wildlife Conservation Problem
Decline in Wildlife population
Habitat lossHuman -wildlife
conflict
Drought and diseases
High populationgrowth
PoachingIncreased Poverty
Cultivation in Wildlife areas
Loss of genetic biodiversity
PAC
Key policy questions relevant to KWS
What are impacts of pressures on species, ecosystems & ecosystem goods and services?
Where are the changes occurring?
Notable land use changes at: 1 –Transmara; 2-Narok-Nakuru; 3-Laikipia-Samburu; 4-Chyulu-Ngai Ndeithya; 5-Taita; 6- Coastal strip; 7-Tana PNR
1 2
3 3
1 2
44
5 5 66
77
Expansion of Agriculture : 1981-2000
Which are the environmental hotspots?
What is the state of biodiversity in the protected areas?
5.67%
1.34%
6.15%6.17%
0.01%
0.17%
0.09%
0.43%
79.97%
corrected reduction by agriculture corrected reduction by grazingcorrected reduction by forestry corrected reduction by built upcorrected reduction by nitrogen corrected reduction by climatecorrected reduction by infra corrected reduction by fragmenationcorrected remaining biodiversity
What are the key pressure factors contributing to biodiversity loss?
NATIONAL BIODIVERSITY MODELLING
SUPPORT TO POLICY MAKERS
African group
Robby, Carla and Moses Enschede, ITCJune 29, 2007
Scenario 1: OECD baseline (IMAGE results)
Lu demands• Increase of agricultural area demands (39%)• Reduction of forest and woodlands (10%)• Reduction of shrublands (39%)• Reduction of Grasslands (27%)
Policy option for conservation• Complete Protection of all the reserves
CLUE (Conversion of Land Use and its Effects
0 – permanent crops1 – intensive agriculture2 – extensive agriculture3 – forest4 – woodland5 – scrubland6 – grassland7 – others
2000 2030
0 – permanent crops1 – intensive agriculture2 – extensive agriculture3 – forest4 – woodland5 – scrubland6 – grassland7 – others
2000
2030
TRENDS FOR KENYA 2000 – 2030 SCENARIO 2
Increasing in agriculture by 30% (extensive and intensive combined). Keeping proportion constant between extensive and intensive from the beginning
Increasing in perennials by 10%
Increasing in built up areas by 15%
Decreasing in savannas and natural areas
Conversions into agriculture and built up areas are not permitted inside protected areas
POLICY: Increase intensive agriculture by 5% & reduction in extensive agric by same.
2000 2015 2030
RESULTS FOR SCENARIO 2
Results communication_ Policy makers
Target Organisations : Environment
Government Departments – e.g. and Natural Resources Management, etc.
Policy Mandate
Environmental conservation – Biodiversity (Fauna and Flora), water.
Geo-Information and biodiversity Modeling
can benefit this Policy • Spatial and temporal visualization of biodiversity status• Data Integration from different sources (socio-economic,
biophysical, administrative, etc)• Results are aggregated and presented in a series of clear,
communicative and policy relevant indices and indicators.• Use of scenarios to project future trends• Test different policy option outcomes• Supports decision making at both national and local levels• Scenarios for the future are relevant for policy formulation
over a range of spatial scales from local to National and global.
Policy Target - Environmental Conservation Policy
Biodiversity conservation strategy
IMPLEMENT
POLICYFORMULATION
EVALUATE
CONTROL
PROBLEMRECOGNITION
• Which areas are most vulnerable to Biodiversity loss (hot spots)?
• What is the relative importance of the different pressures (and interactions)?
• What trends in land use patterns can be expected (under various scenarios)?
• What are the likely effects of various response options, i.e. policies and strategies
• What is the rate of biodiversity loss (in terms of targets) in the future?
Key questions – Addressed by Modelling
2000
60%
31%
0%2%7% 0%
MSA remaining
Land_use
Ndep
Climate
infrastructure
fragmentation
Per regionPer pressure factor
Current biodiversity status
Information that can be provided
2000
2030
Future Biodiversity Trend
MSA 2000-2030
0%10%20%30%40%50%60%70%80%90%
100%
2000 2030
fragmentation
infrastructure
Climate
Ndep
Land_use
MSA remainingLegend
MSA
Value
High : 0.9807
Low : 0
Land use contribution
Land_use contribution
0
0.10.2
0.30.4
0.5
0.60.7
0.8
2000 2030
Year
MS
A
Others
Grasslands
Shrublands
Woodlands
Forest
Extensive
Crop area
Land_use
Poverty map overlay
Combining all Layers: Poverty and Competing Demands for Ecosystem Services in the Upper Tana River Basin
Sources: Kenya Central Bureau of Statistics, International Water Management Institute, Africover – Food and Agriculture Organization of the United Nations, Kenya National Environment Management Authority, and World Conservation Monitoring Centre.
Mt. Kenya
Meru NationalPark
Aberdare R
ange
Tana R.
Tana
R.
Role of our organizations and supporting Partners
• Provision of information to support policy
• Create awareness on importance of biodiversity conservation
• Conduct research and communicate the results
• Ensure sustainability of the GI and Biodiversity Modelling