ASSESSMENT OF THE IMPACT OF BURNING ON BIODIVERSITY … · My work would have been very difficult without the support of my course mates; Xanat Antonio, Emmanuel Tabi-Gyansah, Ibrahim

ASSESSMENT OF THE IMPACT OF BURNING ON BIODIVERSITY USING GEOSTATISTICS, GEOGRAPHICAL INFORMATION

SYSTEMS (GIS) AND FIELD SURVEYS. A case study on Budongo forest in Uganda

Grace Nangendo

February, 2000

ASSESSMENT OF THE IMPACT OF BURNING ON BIODIVERSITY USING GEOSTATISTICS, GEOGRAPHICAL

INFORMATION SYSTEMS (GIS) AND FIELD SURVEYS. A case study on Budongo forest in Uganda

by

Grace Nangendo

February, 2000

Submitted to the Forest Science Division in partial fulfilment of the requirements for the

degree of Master of Science in Geo-information for Forest and Tree Resource Management

Degree Assessment Board:

Prof. Dr. Ir. A. de Gier

Chairman and Head of Forest Science Division

Prof. Dr. Ir. F. Mohren

External Examiner

Dr. Y. A. Hussin

Member and Director of Studies

Prof. A Stein

Member

Ir. M. Gelens

First Supervisor

R. Albricht M.sc

Second Supervisor

Forest Science Division International Institute for Aerospace Survey and Earth Sciences (ITC)

Enschede, The Netherlands

ABSTRACT

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Abstract The impact of burning on biodiversity in Budongo Forest Reserve in Western Uganda was analysed with a focus on woody species as the indicators. Two areas; an undisturbed forest area and a grassland located within the forest, were selected for study. A grid-like set of points with varying distance intervals between them was set for the data collection over each area. The tree species were aggregated onto 3 groups using diameter classes. In the past, forest management in Uganda was focused on timber production. Grasslands were regarded as areas that were mismanaged by local people. With attention turning to biodiversity conservation, core forest sites have been the focus habitat protection. But grassland/woodland mosaics also support high levels of tree species diversity with some taxa that may occur no where else. Unfortunately, Forestry policies, especially concerning grasslands, have remained unchanged. One of the main reasons for the neglect of the grassland/woodland interface is lack of information, convincing enough, to foster greater appreciation and valuation. Little interest has been taken in studying woodland/grassland tree species and, the species flow between these areas and core forests. Consequently, the management systems these species require have not been taken into consideration in planning for biodiversity conservation. In the process of keeping out the local people and allowing the forest colonise these areas, woodland/grassland, species are being threatened with probable extinction. And local people are also being denied the forest resources which are very important for their lives such as the animal protein . The analysis involved using both statistical and geostatistical methods for analysing species diversity. Both actual species counts and calculated index values at plot level were mapped to observe the tree species distribution over the area. The results show that the forest has higher diversity distribution per plot while the grassland has a higher variation in distribution over the area. Overall species diversity calculations however reveal that the grassland has higher tree species diversity than the forest. Considering the current need for biodiversity conservation, protecting these woodland/grassland edges poses a challenge. If they hold high levels and significant biodiversity, they may function as key seed sources that not only enrich respective ecosystems but also adjacent forest. Therefore, along with the conservation of the interior forest, woodland/grassland ecotones should be conserved as well. Certain kinds of traditional burning still carried out within these forest-savanna mosaics should be maintained and some discontinued practices re-established.

ACKNOWLEDGEMENTS

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Acknowledgements I wish to thank the Netherlands Government for having granted me this fellowship and the Uganda government for opening the way for me to undertake this study. I would like to thank my primary Supervisor, Mr. Martien Gelens, for the guidance he has given me throughout this work and for the supervision during fieldwork where he had to endure heavy rains. I thank my second supervisor, Mr. Robert Albricht, for the ever constructive comments he gave. They really helped shape my thinking. I also thank him for being there at the times when I needed technical help. I would like to express my sincere thanks to Professor Alfred Stein, from Wageningen Agricultural University, for the time he dedicated to guiding me in this work. What you have invested in me, I promise to use and also pass on to others that may need it. I would like to sincerely thank my lecturers in the Forest Science division for the encouragement and guidance they constantly gave me throughout my study. I also thank all the Lectures in ITC who have participated in shaping my career. I wish to thank Dr. John Aluma of National Agriculture Research Organisation (NARO), Uganda and Dr. John Kabogoza of Makerere University, Uganda who encouraged me to take up this study and for their constant interest in my work. I also wish to thank the staff of Nyabyeya Forestry College and the forest officer at the station, Mr. Steven Khawuka, who provided a conducive atmosphere for my fieldwork work. Special thanks go to Arwai, the taxonomist, and Dele and Rufino, the line cutters, who worked tirelessly enduring all the rough conditions. My work would have been very difficult without the support of my course mates; Xanat Antonio, Emmanuel Tabi-Gyansah, Ibrahim Khan, Armand Natta and Heri Sunuprapto. I will always remember the joys and struggles we shared and your willingness to help in times of need. I thank my family for constantly encouraging me through out this study. I also thank them for being patient with me at the times when I have not lived up to their expectations, especially when the book pressure was on. For all that has been achieved in this work, I give the glory to the Lord Almighty; for without Him I can do nothing.

TABLE OF CONTENTS

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TABLE OF CONTENTS

Abstract i Acknowledgements ii LIST OF FIGURES v LIST OF TABLES vi

1. INTRODUCTION 1 1.1 BACKGROUND OF THE STUDY 1 1.2 COUNTRY PROFILE 2 1.3 THE FOREST POLICY OF UGANDA 3 1.4 PROBLEM FORMULATION 5 1.5 STUDY OBJECTIVES 6 1.6 HYPOTHESIS 7 1.7 RESEARCH QUESTIONS 7 1.8 RESEARCH APPROACH 8

2. CONCEPTS 9 2.1 BIODIVERSITY 9

2.1.1 A general overview 9 2.1.2 Diversity indices 10

2.2 GEOSTATISTICS 12 2.2.1 A brief overview 12 2.2.2 Kriging 14 2.2.3 Map making 15

3. METHODS AND MATERIALS 16 3.1 STUDY AREA 16

3.1.1 Location 16 3.1.2 Climate 16 3.1.3 Vegetation condition 16 3.1.4 The community around the forest 17 3.1.5 History of Budongo Forest Reserve 17 3.1.6 Soils 18

3.2 SAMPLING 18 3.2.1 Sampling design and sample plot selection 18 3.2.2 Plot shape and size 20 3.2.3 Sample size 21 3.2.4 Site selection 21 3.2.5 Organisation of crew 22

3.3 SECONDARY DATA 22 3.3.1 Aerial Photographs 22 3.3.2 Participatory Rapid Rural Appraisal (PRRA) 23

3.4 METHODOLOGY 24 3.5 SECONDARY INFORMATION ANALYSIS 25

3.5.2 Change Detection 26 4. DATA ANALYSIS AND RESULTS 28

4.1. DESCRIPTIVE STATISTICS 28 4.1.1. Total tree count (TC) 28 4.1.2. Number of species (NS) 29

4.2 CORRELATION ANALYSIS 29 4.2.1 Total tree count 29 4.2.2 Number of species 30

4.3 CHECKING OF REPRESENTATIVENESS 30 4.3.1 Total tree count 30 4.3.2 Number of species 31

TABLE OF CONTENTS

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4.4 SPECIES COMPOSITION 31 4.4.1 Comparison between forest and grassland 31 4.4.2 Comparison within each area 324.4.3 Comparison of grassland and forest with the edge group 33 4.4.4 Analysis of areas where species occur in the three groups 33

4.5 TC AND NS GEOSTATISTICAL ANALYSIS 35 4.5.1 Total tree count (TC) 35 4.5.2 Number of species (NS) 38

4.6 INDICES RESULTS 41 4.6.1 Descriptive statistics 42

4.7 GEOSTATISTICAL ANALYSIS FOR THE SHANNON INDEX 42 4.7.1 Trees' group 42 4.7.2 Saplings’ group 44 4.7.3 Seedlings’ group 46

4.8 SIMPSON AND SHANNON-WIENER DESCRIPTIVE STATISTICS 48 4.9 CORRELATION 49 4.10 GEOSTATISTICAL ANALYSIS FOR SIMPSON AND SHANNON-WIENER INDICES 50

4.10.1 Trees’ group 50 4.10.2 Saplings’ group 52 4.10.3 Seedlings’ group 54

4.11 STATISTICAL ANALYSIS 56 4.11.1 Overall species diversity using Shannon index 56 4.11.2 T-test using Shannon index values per group 57 4.11.3 Overall species diversity for the Simpson and Shannon-Wiener indices 58

4.12 ANALYSIS OF RELATIONSHIP BETWEEN BIODIVERSITY AND ENVIRONMENTAL FACTORS 58

5. DISCUSSION 61 5.1 LOCAL PEOPLE AND THE FOREST 61 5.2 TREE SPECIES DISTRIBUTION IN GRASSLAND AREA 62 5.3 TREE SPECIES DISTRIBUTION IN FOREST AREA 64 5.4 SPECIES COMPOSITION AND ITS DIFFERENCE IN THE TWO AREAS 65 5.5 DIFFERENCE IN SPECIES DISTRIBUTION BETWEEN THE TWO AREAS 66 5.6 RELATION OF SPECIES DIVERSITY TO ENVIRONMENTAL FACTORS 67

6. CONCLUSION AND RECOMMENDATIONS 68 6.1 CONCLUSION 68 6.2. RECOMMENDATIONS 70

References 71 Appendix 74

LIST OF FIGURES

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LIST OF FIGURES Figure 1-1: Map showing the location of Uganda 3 Figure 1-2: Flow chart for conceptual framework 8 Figure 2-1: Graph showing important model parameters 13 Figure 2-2: Graphs of models used 13 Figure 3-1: NDVI of the grassland selected for intense data collection 19 Figure 3-2: Plot lay out demonstration 14 Figure 3-3: Display of plot arrangement at a single plot 21 Figure 3-4: Map of Budongo Forest Reserve showing the data collection sites 22 Figure 3-5: Flow chart for methodology 24 Figure 3-6: The hunters’ grassland map 26 Figure 3-7: Change detection maps 27 Figure 4-1: Graphs showing the total tree count model fit 36 Figure 4-2: Total tree count spatial maps 37 Figure 4-3: Graphs showing the number of species model fit 38 Figure 4-4: Tree group number of species spatial maps 39 Figure 4-4: Sapling group number of species spatial maps 40 Figure 4-5: Seedling group number of species spatial map 41 Figure 4-6: Graphs showing the Shannon index tree group model fit 42 Figure 4-7: Shannon index tree group maps 43 Figure 4-8: Shannon index tree group error maps 44 Figure 4-9: Graphs showing the Shannon index sapling group model fit 44 Figure 4-10: Shannon index sapling group maps 45 Figure 4-11: Shannon index sapling group error maps 46 Figure 4-12: Graphs showing the Shannon index seedling group model fit 46 Figure 4-13: Shannon index seedling group maps 47 Figure 4-14: Shannon index seedling group error maps 47 Figure 4-15: Graphs showing Simpson and Shannon-Wiener tree group model fit 50 Figure 4-16: Simpson and Shannon-Wiener tree group maps 51 Figure 4-17: Simpson and Shannon-Wiener tree group error maps 52 Figure 4-18: Graphs showing Simpson and Shannon-Wiener sapling group model fit 52 Figure 4-19: Simpson and Shannon-Wiener sapling group maps 53 Figure 4-20: Simpson and Shannon-Wiener sapling group error maps 54 Figure 4-21: Graphs showing Simpson and Shannon-Wiener seedling group model fit 54 Figure 4-22: Simpson and Shannon-Wiener seedling group maps 55 Figure 4-23: Simpson and Shannon-Wiener seedling group error maps 56 Figure 4-24: Scatter plots for slope and Simpson index at area level 59 Figure 4-25: Scatter plots for slope and Shannon index at group level 60

LIST OF TABLES

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LIST OF TABLES Table 3-1: Variation in plot size in relation to vegetation type 21 Table 3-2: The number of plots collected in each area 21 Table 4-1: Total tree count descriptive statistics for intensively sampled areas 28 Table 4-2: Number of species descriptive statistics for intensively sampled areas 29 Table 4-3: Correlation for total tree count 29 Table 4-4: Correlation for number of species 30 Table 4.5 Total tree count descriptive statistics for less intensively sampled areas 30 Table 4-6: Number of species descriptive statistics for less intensively sampled area 31 Table 4-7: Species distribution between forest and grassland 32 Table 4-8: Checking for dominant species 32 Table 4-9: Species distribution in groups within each area 32 Table 4-10: Species exchange between forest and grassland through the edge 33 Table 4-11: Location of species which occur in all the 3 groups 33 Table 4-12: Total tree count model parameters 36 Table 4-13: Number of species model parameters 39 Table 4-14: Shannon index descriptive statistics 42 Table 4-15: Shannon index trees’ group model parameters 43 Table 4-16: Shannon index saplings’ group model parameters 45 Table 4-17: Shannon index seedlings’ group model parameters 46 Table 4-18: Simpson and Shannon-Wiener indices’ descriptive statistics 48 Table 4-19: Correlation between the indices 49 Table 4-20: Simpson and Shannon-Wiener indices trees’ group model parameters 50 Table 4-21: Simpson and Shannon-Wiener indices saplings’ group model parameters 53 Table 4-22: Simpson and Shannon-Wiener indices seedlings group model parameters 55 Table 4-23: Overall species diversity for the Shannon index 57 Table 4-24: Shannon index t-test results 57 Table 4-25: Simpson and Shannon-Wiener indices’ overall diversity 58 Table 4-26: Environmental factors’ influence test at area level 59 Table 4-27: Environmental factors’ influence test at area level 59

CHAPTER 1 INTRODUCTION

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1. INTRODUCTION

1.1 BACKGROUND OF THE STUDY The term “biodiversity” or “biological diversity” has been defined as “the variability among living organisms and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems” (Parviainen and Päivine 1998). An ecosystem is a community of organisms and their environment, which functions as an integrated unit. Conserving biodiversity is receiving international attention. World-wide, numerous species are going extinct, and even more that have not yet been identified are likely to be similarly threatened. The “red lists” and the “red data books” published by the “world conservation monitoring centre” indicate that in 1994, just for species about which enough is known to asses their status, nearly 5,400 animal species and more than 26,100 plant species were threatened (Dallmeier, 1998). The convention on biological diversity, signed in 1992 in Rio de Janerio, also advocates for sustainable management of biodiversity. Forest ecosystems hold the highest amount of biodiversity and so play an important role as biodiversity banks. The rapidly increasing destruction of forest ecosystems is therefore a major threat to biodiversity conservation. Biodiversity in the tropical rainforests is known to be larger than in other types of forests. Therefore, the tropical rainforests are of primary importance for conservation of biodiversity. In the effort to conserve tropical rainforest biodiversity, forest ecosystems have been treated as independent entities which may then need a buffer zone around them just to keep off human disturbance. The biodiversity within these buffer zones is usually not considered to be of great importance. Forests, however, are a part of a landscape that can be recognised by the spatial repetitive cluster of interacting ecosystems, morphology and disturbance regimes. So the spatial relationship and the interactions among the ecosystems need to be considered as major factors in the survival of the landscape and more so the forest within it. A heterogeneous landscape favours an abundance in plant species and animals requiring two or more landscape elements and enhances the potential total species coexistence (Forman and Godron, 1986). In such a landscape species and species clusters differ greatly and so a wide range of patterns and measurements need to be used in order to describe it. Among these are species composition, species richness and species dominance. The outer part of any patch has a significantly different environment from the interior and different species composition and abundance is found there. This is called the edge effect and it is often wider where the matrix, the continuous piece of terrain or binding, and the patch differ more in their vertical structure. In many of the tropical landscapes, grasslands are the matrix. Traditionally, foresters have for good reasons considered wildfire as an enemy to be excluded at all costs but there is growing evidence that fire plays an important and either a beneficial or advantageous role in some ecosystems. Organisms native to fire dependent areas may grow better with a natural fire frequency than with no fire. So there is need to know more about the effects of fire on different ecosystems so as to prevent



the destructive fire and to maintain it where it is a desirable environmental factor (Forman and Godron, 1986). Most specialists believe that natural savannas are exceptions and that most savannas have been created and maintained by human influence, especially using fire (Forman and Godron, 1986, Chandler et al, 1983, Paterson, 1991). The periodic occurrence of fire ensures the continued existence of the fire dependent ecosystems and if it occurs with sufficient regularity, the ecosystem may be stable for millennia. Each species in a fire dependent ecosystem develops survival strategies depending on how often the fire is expected to occur in the lifetime of the individual. Where fire is frequent, the individual must develop characteristics that will enable it to at least grow to sexual maturity. Where a fire is expected once in a plant’s lifetime, the basic requirement of the plant is the ability to ensure immediate and prolific reproduction following a fire. Such plants require fire so that their post fire reproductive advantage can be realised (Chandler et al, 1983). The ecosystems identified in Uganda include forests, savannah/grasslands, wetlands and other aquatic ecosystems. Each of these supports a wide range of animals and plants including endemic species. Steps towards biodiversity conservation are underway; several forests have been analysed for their biodiversity content and a number of them have been selected for conservation. Preferred sites within the forests for nature reserves are the undisturbed core areas of each forest covering the widest possible range of altitude and a variety of vegetation types (Howard et al, 1998). This in many ways eliminates the grasslands since they are an outcome of regular human disturbance. In this study, the major focus is on bringing to light the uniqueness of two major ecosystem types; the forest and the grassland. At the same time, the way the diversity in one area is enriched by the other through species dispersal will be highlighted. This process is commonly referred to as plant dispersal and species flow across mosaics dominated by trees and by grassland.

1.2 COUNTRY PROFILE

Uganda is located in the eastern region of Africa, situated between latitudes 1°30′ South and 4° North and longitudes 29°30′ and 35° East. The Republic of Kenya borders the country in the east, Tanzania and Rwanda in the south, the Democratic Republic of Congo (former Zaire) in the west and Sudan in the North (figure 1-1). Uganda covers an area of about 241,500 sq. km of which about 15.3% is open water, 3.0% permanent wetlands and 9.4% seasonal wetlands. Africa has a number of distinct bio-geographic regions; Uganda is located in an area where several of them meet. There are seven major bio-geographic regions in Uganda, each with its distinct flora and possibly a similar distribution of fauna. Each bio-geographic region has more than 50% of its species confined to it; this is the basis of Uganda’s endemism. The country is also in a privileged position because of its proximity to the Pleistocene forest refugium of eastern Congo. Most of Uganda’s biodiversity is in the natural forests. Uganda is estimated to have a quarter of a million species, with flowering plants numbering over 4500. Due to Uganda’s location in a zone between the ecological communities characteristic of the drier East African



savannahs and the more moist West African rain forests, combined with high altitude range, the country exhibits great biological diversity (NEMA 1996).

Figure 1-1: The map showing the location of Uganda, in Africa, and of Budongo

Forest Reserve, in Uganda. The central, western and eastern parts of the country are characterised by flat topped hills. The rise of the plateau in eastern and western parts of the country is represented by the mountainous topology found along the boarders forming the Rwenzori mountains and the Mufumbira volcanoes in the west and Mt. Elgon and Mt. Kadam in the east. The forests are classified into two broad categories, tropical high forests and plantations. Forests in Uganda occur as gazetted forest reserves, protected national parks and private and ungazetted public land. Only about 2.2% of the gazetted forests is covered by plantations (NEMA, 1996).

1.3 THE FOREST POLICY OF UGANDA Because of the central position of forests in Uganda’s ecosystems, a forestry policy has been in place since 1929 and has been revised several times thereafter. The focus has shifted from the management of forests for timber production and afforestation of more land to an emphasis on the role of forestry in the protection of the environment and community participation in forest management (Howard, 1991, Obua, 1998). The revised policy of 1988 (The Uganda Gazette 81 (2). 15th January 1988), outlined below, emphasises protective forestry.



1. To maintain and safeguard enough forest land so as to ensure that:

i. Sufficient supply of timber, fuel, pulp, paper and poles and other forest products are available in the long term for the needs of the country, and where feasible for export;

ii. Water supplies and soils are protected, plants and animals (including endangered

ones) are conserved in natural ecosystems, and forests are also available for amenity and recreation.

2. To manage the forest estate so as to optimise economic and environmental benefits in the country by ensuring that:

i. The conversion of the forest resource into timber, charcoal, fuelwood, poles and other products is carried out efficiently;

ii. The forest estate is protected against encroachment, illegal tree cutting, pests,

diseases and fires; iii. The harvesting of timber, charcoal, fuelwood, poles and other products

applies appropriate silvicultural methods which ensures sustainable yields and preserves environmental services and biotic diversity;

iv. Research is undertaken:

♦To improve seed sources for planting stock and the silvicultural and protection methods needed to regenerate the forest and increase its growth and yield; ♦Into new and existing forest products including tourism and education with the objective of maximising their utilisation potential; ♦To monitor and promote the preservation of environmental services and conservation of biotic diversity.

3. To promote an understanding of the forests and trees by:

i. Establishing extension and research services aimed at helping farmers, organisations and individuals to grow and protect their own trees for timber, fuel and poles and to encourage agro-forestry processes;

ii. Publicising the availability and suitability of various types of timber and wood

products for domestic and industrial use, and publicising the importance of environmental services provided by forests;

iii. Holding open days at regular intervals in all districts to demonstrate working

techniques and bring attention to the positive benefits of forestry. The forest department is the government agency responsible for the implementation of the national forest policy. It is responsible for the selection and management of forest reserves, the protection of reserved trees outside forest reserves, research and extension work.



The process of revising the forest policy and act is on-going and the drafts are being discussed. Currently the department is in the process of finalising an operational plan called the “nature conservation master plan”. This is a guideline for sustainable forest management. In it, the forests have been categorised into production forest (50%), strict nature reserve (20%) and buffer (30%). These divisions are a result of the biodiversity inventory carried out in most of the forests in the country. This information is also currently being used to re-mark management zones.

1.4 PROBLEM FORMULATION It is generally accepted that the characteristic vegetation of most of East Africa is a sub-climax resulting from burning and that most grasslands and much of the savannah are maintained by fire. The most commonly burnt vegetation is the grassland but to a lesser extent all scrub and light woodland areas are subject to burning. Only closed forest in areas of high rainfall can be considered as unaffected by burning. Uganda experiences a more rapid regeneration of vegetation following burning than occurs elsewhere and there may be a marked growth of dense bush following the cessation of burning or a change from late to early burning (Paterson, 1991). In the study area, burning can be cited as far back as the human occupation of the area. It was mainly used by the cattle keepers for preparing ground for fresh grass and by hunters for acquiring game. The regular burning practised in the human occupied west, east and south of the Budongo forest in the earlier years maintained pure stands of napier grass which prevented the forest from expanding. With the coming in of the colonial rule that lead to the gazetting of forests, the burning was heavily controlled. This allowed more trees and taller grass to come up and several other ecological effects accompanied the change. One of these was the colonisation of a large part of the grassland area (Paterson, 1991). This process is explained in further detail in chapter 3. Burning has facilitated the existence of unique ecosystems within the forest although it is one of those activities that have for a long time been considered to have negative effects on the forest. Originally, the basic interest in the forests all over the country was timber production. Currently the attention is being given to biodiversity conservation and several forests, including Budongo Forest Reserve, have come to the limelight as biodiversity banks because of their biodiversity richness. Unfortunately many of the policies originally geared towards improvement of the forests for timber production, especially those concerning grassland areas within and adjacent to the forests, have remained unchanged. At UNCED in Rio de Janerio in 1992, it was pointed out that the environment is no longer the specialist concern for the few. The UNCED biodiversity convention, signed by many countries including Uganda, will require all signatory countries at the very least to take biological stock, in addition to other reasons, to ascertain which species are there to conserve and which ones are threatened (Cowie, 1992). McNeely (1994) states that the available evidence indicates that human activities are eroding biological resources and greatly reducing the planet’s biodiversity. Little data is available to assess which genes or species are particularly important in the functioning of ecosystems, so it is difficult to estimate to which extent people are suffering from the



loss of biodiversity. The desired future is where the entire landscape is being sustainably managed to conserve tree species diversity and where biological resources are used for the benefit of current and future citizens. Holdgate et al. (1994) say conserving the richness, integrity and productivity of life can mainly be achieved through a triple mechanism of saving it, studying it and using it. It is increasingly recognised as neither politically feasible nor ethically justifiable to attempt to deny the poor from the use of natural resources without providing them with alternative means of livelihood. Enlisting the co-operation and support of local people has thus emerged as a major priority of in situ biodiversity conservation. (McNeely et al., 1990, Brandon and Wells, 1992). The hunters that make use of the grasslands within Budongo forest are no exception to this. It is therefore necessary to seek ways and means by which the local people can obtain the much needed resources and yet have the grasslands remain sustainably managed for the future generations. Most of Uganda’s biodiversity is in the tropical rainforests and this is the area mainly focused on for biodiversity conservation. These forests do not stand as islands but are part of a landscape stretching from the forest through the grassland to the farmland. So far, in the selection of areas for conservation, it is the tropical rainforests and national parks that have been considered. The grasslands within and adjacent to the rainforests, at the best, have been considered as buffer zones, yet these grasslands are a unique type of ecosystem which has existed for years because of the activities carried out in there. The problem lies in the fact that, considering the history of forest management in Uganda, grasslands have been regarded as areas that have been mismanaged by the local people. Therefore, little interest has been taken in studying the species they support, the species flow between them and the core forest, and what management systems would be required to maintain these species. In the process of keeping out the local people and letting the forest colonise these areas, species are being lost and some may be lost forever. So it is imperative that grasslands adjacent to the tropical rain forest be independently assessed, the species flow between them and the forest analysed and specific sustainable management measures be put in place for their biodiversity conservation.

1.5 STUDY OBJECTIVES The main objective is to establish the extent to which burning carried out by hunters is affecting biodiversity in Budongo forest (Biiso and Budongo Subcounties in particular). The following are the more specific objectives: 1. To investigate the views of the current users of the grassland (local people/hunters)

on the importance of the grassland to them, the present status of the grasslands and the processes occurring in them.

2. To assess the state of biodiversity within the grassland area from the physical as well as the spatial point of view.

3. To assess the state of biodiversity within the undisturbed forest area from the physical as well as the spatial point of view.

4. To compare biodiversity composition and distribution of the grassland area with that of the undisturbed forest area.

5. To bring out the uniqueness of the two ecosystems within the landscape and their importance in biodiversity conservation.



1.6 HYPOTHESIS The act of repeated burning does not reduce biodiversity, but rather changes species composition through colonisation of the affected area by particular species with specific characteristics and then maintains the ecosystem thus created.

1.7 RESEARCH QUESTIONS 1 How do local people (hunters) currently benefit from the grassland areas within the

forest?

2 How do the local people (hunters) asses the current status of the grassland areas and

the processes taking place within them?

3 What is the tree species composition in the burnt-over (grassland) area?

Hypothesis: There are very many species but they occur in small quantities

4 What is the tree species distribution in the burnt-over (grassland) area?

Hypothesis: The tree species are not evenly distributed over the area.

5 Is there a recognisable relationship between the species distribution pattern and the

burning?

Hypothesis: The tree species increase as one moves from the centre of the grassland,

where more burning is taking place, to the edge.

6 What is the tree species composition in an area of forest?

Hypothesis: The forest has many species and they occur in large over the forest area.

7 What is the tree species distribution in an area of forest?

Hypothesis: The tree species are generally evenly distributed over the whole area.

8 Is there a recognisable and statistically significant difference in species

distribution and composition between the burnt-over (grassland) and the forest areas?

Hypothesis: There is a big difference in species composition and distribution between

the two areas. There is a more recognisable pattern in the grassland than in the forest.

9 Do aspect, slope or elevations have any statistically significant influence on the

biodiversity in the grassland or the forest area?



1.8 RESEARCH APPROACH

Figure 1.2 Flow chart showing the conceptual framework.

Sustainable forest management

Forest fires

Problem formulation

Forest area

Biodiversity measurement

Grassland area

Experimental design

Biodiversity measurement

Species abundance Species distribution

Management and Policy

recommendations

Geostatistics

Krigging Mapping

Shannon index Shannon-wiener index Simpson index

Biodiversity indices

Biodiversity status

Analysis Literature review

Literature review

Indigenous knowledge

Biodiversity conservation

Change detection

CHAPTER 2 CONCEPTS


2. CONCEPTS

2.1 BIODIVERSITY

2.1.1 A general overview The term biodiversity has been defined by several scientists in many different ways. With each definition a new aspect may be brought to light or re-emphasised in a different way to suite the work at hand. This section will highlight but a few that are relevant to the study. OTA (1987) defines biodiversity as the variety and variability among living organisms and the ecological complexes in which they occur. The OTA document describes diversity at three different fundamental levels: genetic, species and ecosystem diversity. The genes, species and ecosystem aspects are also brought out by McNeely (1994) and, Holdgate and Giovannini, (1994). Franklin et al. (1981) builds it deeper by recognising three primary attributes of ecosystems: composition, structure and function. Composition has to do with the identity and variety of elements in a collection, and includes species lists and measures of species and genetic diversity. Structure is the physical organisation or pattern of a system. Function involves ecological and evolutionary processes. These three are interconnected and we cannot define one without considering the other. This study will focus on the composition and the pattern/distribution of species within an ecosystem and between ecosystems. The community and spatial aspects are brought in by Magurran (1988), who defines diversity as a measure of species richness and/or relative abundance within a sample or community, Krebs (1978) who defines a community as ‘a group of populations of plants and animals in a given place’, Begon et al. (1990) who describes it as an assemblage of species populations which occur together in space and Southwood (1988), who sees a community as an organised body of individuals in a specified location. In all these definitions a community is viewed as a group of interacting organisms which exist within defined spatial boundaries. According to Whittaker (1978), two types of diversity exist – alpha and beta diversity. Alpha diversity is the number of species within a chosen area community such as one type of woodland or grassland. Beta diversity is the difference in species diversity between areas or communities. Beta diversity is thus the difference between species composition of different areas or environments and the rapidity of change of those habitats. Diversity is therefore measured by recording number of species and their abundance in a specific spatial location. Also Magurran (1988) defines Beta (B) diversity as the degree of change in (species) diversity along a transect or between habitats. In this study both a change in species within an ecosystem as well as a change between ecosystems will be considered. According to Reid et al. (1993), the fundamental goal of biodiversity conservation is to support sustainable development by protecting and using biological resources in ways that do not diminish the world’s variety of genes and species or destroy important habitats or ecosystems. At higher levels of organisation i.e. communities, ecosystems and landscapes, many different ‘units’ of diversity are involved. These include the pattern of habitats in a community, the relative abundance of species, the age structure of populations, the pattern of communities on the landscape, trophic structure, and patch

CHAPTER 2 CONCEPTS


dynamics. At these levels the statement of concern for biodiversity conservation is in terms of ‘management’ of biodiversity to ensure the maintenance of the species comprising the community. There has been acceleration of human impact on ecosystems (podolsky, et al., 1992) and this has caused preserving biodiversity to become internationally stated as a common target. This study aims at bringing to light the uniqueness of the grassland ecosystem within the forest with the hope that their conservation will be considered necessary.

2.1.2 Diversity indices

Several diversity indices have been developed and each seeks to express the diversity of a sample or quadrant as a single number. For this study, it is necessary to check both the abundance and the evenness of spread of the species. So indices have been chosen that can be used to provide these parameters. A commonly used one according to Kent & Coker (1992), is the Shannon index. It is calculated from the equation:

∑=

−=s

i

ii ppH1

ln'

Where s = the number of species

Pi = the proportion of individuals or the abundance of the ith Species expressed as the total cover. ln = log basen

(Kent and Coker, 1996) The formula of the Shannon index starts with a negative sign to cancel out the minus sign created when taking algorithms of the population. Values of the index usually lie between 1.5 and 3.5, but in exceptional cases the value can exceed 4.5 (Kent and Coker, 1996). In this study the Shannon index will be considered for analysing the combination of species evenness and abundance. This index is also correctly referred to as the Shannon-Wiener since Shannon and Wiener independently derived this function that has become known as the Shannon index. In this study, however, a measure of equitability or evenness (E) of species for the sampled area will also be analysed and it has been decided that it be referred to as the Shannon-Wiener. So for checking on the evenness as an independent parameter, the Shannon-Wiener index will be used. It is calculated from the equation:

s

pp

HHE

s

i

ii

ln

ln' 1

max

∑===

Where s = the total number of species pi = the proportion of the ith species or the abundance of the ith species expressed as a proportion of total cover ln = log basen (Kent and Coker, 1996)

CHAPTER 2 CONCEPTS


The higher the value of E the more even the species are in their distribution within the plot or ecosystem.

In addition to the Shannon index, Magurran (1988) also gives a method for calculating the variance of H′ for the Shannon index and a method of calculating t and the degrees of freedom to test the significant differences between quadrats or samples. The equation for variance is:

( )

2

22

21ln)(ln

'N

sN

ppppVarH

iiii −+−

= ∑ ∑

The equation for t is:

( )21

21

'var'var''

HHHHt

+−=

Where H′1 = is the diversity of sample 1 and Var H′1 = is its variance The equation for the degrees of freedom is:

( )( ) ( ) 2

221

21

221

varvarvarvar

NHNHHHdf

++=

N1 and N2 being the total number of individuals in samples 1 and 2 respectively. (Magurran, 1988) For abundance, the Simpson index will be used. It is calculated from the equation:

∑ ⎟⎟⎠

⎞⎜⎜⎝

⎛−−=

)1()1(

NNnnD ii

Where ni = the number of individuals in the ith species and N = the total number of individuals (Magurran, 1988) As biodiversity increases, the index reduces. To get a clear picture of species dominance, a ratio is used: either1/D or 1-D. For this study, 1-D will be used (Magurran, 1988). Knowledge about species abundance, evenness and composition is all of great importance in this study. So all the indices indicated above will in one way or another be used in this study but the Shannon index has been chosen to be used as the main body and the others as complementary indices in the analysis. Reasons for this choice are: 1. The index takes into account both species abundance and species richness unlike the

others mentioned above. 2. It can also be used for further statistical analysis for the comparison within and

between the areas. This is by use of the t test formula provided.

CHAPTER 2 CONCEPTS


3. It has been reported to be sensitive to changes in the importance of the rarest classes (Heusèrr, 1998).

4. It is also better understood than all the rest. The Simpson index is also of value in this analysis because it helps capture the dominant species within a community. The Shannon-Wiener index also will help us capture the species wealth within a community. So it is evident that though these diversity measures are often used independently, each has a unique characteristic that it brings out in the study of a community.

2.2 GEOSTATISTICS

2.2.1 A brief overview

Environmental data are likely to vary throughout a region and such variation takes place in space and time. This is referred to as spatial variation. Relatively large variation within small distances indicates that the variable is subject to very local influences. On the other hand, very gradual changes in a variable indicate an influence at a more global scale. Geostatistics allows to quantitatively deal with such spatial variation in large sets of data. This is carried out in four major stages; - an analysis of the spatial dependence i.e. how large is the variation as a function of

the distance between observations - creating of computerised maps - determination of the probabilities of exceeding a threshold value - determination of sampling schemes which are optimal in some predefined sense The main distinction with statistics is that in Geostatistics the variables used are linked to locations. Observations in space are linked to their co-ordinates and each observation has its specific place in space. These are also sometimes referred to as “regionalized variables” or “geovariables”. One of the ways of summarising data is the use of graphs of the cumulative relative variance for increasing distance. These show the distance at which important increases in variance occur (Saldaña et al,1998). Geostatistical methods can be used to analyse spatial variability of a variable at different observation points since each variable that is measured is associated with its observation location x. A variogram γ(h) for variable zi(x) can then be estimated from the equation: γi(h) = 1/2E[zi(x) – zi(x + h)]2, where x and x + h are two locations, separated by distance h, at which the regionalized variable is measured, and E donates the mathematical expectation. Commonly, pairs of observations are grouped into a limited number of distance classes and each class contains pairs with approximately the same distance. The sample variograms were estimated using the programme SPATANAL (Stein, 1993). The variogram describes the spatial correlation of a regionalized variable. Three important parameters characterise the variogram: the nugget variance, the sill variance and the range.

CHAPTER 2 CONCEPTS


Range

Nugget

Sill

Figure 2-1: The graph shows the three important parameters that characterise a

model; nugget, sill and range. The nugget is the positive intercept of the variogram with the ordinate and represents unexplained spatially dependant variation or purely random variance. The sill is the value at which transitive variograms level out and the distance at which the levelling occurs is known as the range of spatial dependence. The variogram models with a clear range and sill are known as transitive models (Burrough, 1988). Common transitive models are the Spherical, Exponential, Gaussian, Hole effect (wave) and Pure nugget. A commonly used nontransitive model is the linear model. The Gaussian describes continuous, gradually varying attributes while the Spherical model describes attributes with abrupt boundaries at discrete and regular spacings (range) and the distance between the abrupt changes is not clearly defined. Attributes characterised by abrupt changes at all distances are described by the exponential model and the pure nugget model indicates that there is no spatial dependence at the scale of investigation. The Linear model describes attributes varying at all scales. Model fitting is required for the interpolation procedures and it is the preceding step to the creation of a map. Model selection is based on a combination of R2 of a weighted linear regression and interactive interpretation of the sample variogram values (Saldaña et.al, 1998). The models were fitted using the programme WLSFIT (Heuvelink, 1992 )

Figure 2-2: An example of the Gaussian (a), Spherical (b) and the Exponential (c)

models. These are the models that have been used in this study.

(a) (b) (c )

CHAPTER 2 CONCEPTS


2.2.2 Kriging In studies on spatial variability, one of the central factors is to move from point observations towards statements concerning the area. This requires estimation of the prediction of the variable at unvisited locations. This is achieved through interpolation/extrapolation techniques.

Two types of kriging that have been used in the study are simple kriging and ordinary kriging. Simple kriging assumes that the local mean is constant and equal to the population mean which is assumed to be known. The population mean is used in each local estimate. Ordinary kriging assumes that local means are not necessarily closely related to the population mean and uses only samples in the local neighbourhood for the estimate. Ordinary kriging is often associated with the acronym B.L.U.E. for “best linear unbiased estimator”. It is Linear because its estimates are weighted linear combinations of the available data; it is Unbiased since it tries to have the mean residual or error, mR, equal to 0; it is best because it aims at minimising the variance of the errors, σ2

R. Of all these characteristics, the minimising of error variance is unique to ordinary kriging. These characteristics are hard to attain since the mR and σ2

R are always unknown. So a probability model in which the bias and the error variance can both be calculated is used and then weighted for the nearby samples which ensures that the average error for our model, mR, is exactly zero and that our modelled error variance, σ2

R is minimised (Isaaks and Srivastava, 1989). A measure of the quality of the prediction such as the variance or the standard deviation is also required to assess the reliability of an interpreted map. When predicting in the presence of spatial dependence, observations close to each other are more likely to be similar than those far apart. Therefore, the mean, μ, of the estimations will not be the same. The difference between the true value and the estimated value is known as the error of estimation and since the true value is often not known, an average error is introduced based on the probablistic models. Ordinary Kriging system can be written in a matrix notation as: Γ11 … Γ1n 1 w1 Γ10

ΓnI … Γnn 1 wn = Γn0 I … 1 0 λ 1

(n+1)×(n+1) (n+1)×1 (n+1) ×1 where Γij are the variogram values between samples i and j and Wi is the weight factor assigned to the ith sample, λ is the Lagrange multiplier and the Γi0 are the variogram values between samples and the prediction location (Isaaks and Srivastava, 1989).

. . . ... 1. . . . . .

123 .

...

123 123

...

CHAPTER 2 CONCEPTS


2.2.3 Map making

In order to come up with a map, the following procedures have to be used: 1. Determine the variogram 2. Fit a model to the variogram 3. Predict the values at the nodes of a fine meshed grid 4. Present the results in a two- or a three-dimensional perspective by linking individual

predictions with line elements The map of the prediction of the error variance is also obtained at the same nodes of the fine meshed grid as the predictions themselves and it displays the reliability of the map. The uncertainty expressed on this map usually reflects the variogram and hence the spatial variability. Low standard variations correspond with low sill value, whereas a high standard deviation corresponds with a high sill value. Also variation visible on such a map is related to the values of the range. Close to observation points low standard deviations occur, whereas reliability decreases with increasing distance from observations. Extrapolation allows for prediction outside the observation area but it is always risky and the prediction error variance rapidly increases as the distance from the observation area increases.

CHAPTER 3 METHODS AND MATERIALS


3. METHODS AND MATERIALS

3.1 STUDY AREA

3.1.1 Location

Budongo Forest Reserve is located in the north-western part of Uganda and it consists of Budongo, Siba, Busaju and Kaniyo-Pabidi forests. It is situated in the districts of Masindi and Hoima with the largest part falling in the former. It is located between 1° 35′-1° 55′ N and 31° 18′ – 31° 42′ E on the edge of the western rift valley. Budongo forest is classified, based on the concepts of Langdale-Brown and Osmaston (1967), as a medium altitude, moist semi-deciduous forest (NEMA 1996). Budongo Forest Reserve was gazetted as a central forest reserve in 1932. The reserve, which is a mixture of tropical high forest with a large population of mahogans, woodlands and savannah grasslands thought to be capable of supporting forest, covers 82,530 ha, making it Uganda’s largest forest reserve (Hamilton 1984). It consists of 53.7% forest and 46.3% grassland. Budongo forest is of exceptional biodiversity importance, ranking third in overall importance in the country. There are about 465 species of trees and shrubs, 366 bird species, 289 butterfly species and 130 species of large moths.

3.1.2 Climate

Budongo forest is located in a zone described as transitional between the Congo forest and the Uganda savannah climates and is characterised by high temperatures. The minimum temperature is 23 – 290C during June – July, while the maximum temperature is 29 – 320C during December to February. The rainfall received varies between 1,397 and 1,524 mm annually on 100 to 150 rainy days. It is predominantly of the thunderstorm type and it occurs mainly in the afternoon. The peaks of the rainy season are during the months of April – May and October –November. The east and south parts of the forest receive more rain compared to the north and north west (Forest Department, 1997).

3.1.3 Vegetation condition

The phytology of Budongo forest resembles that of the Congo basin but supports lower species diversity and a rare and significant climax community. It contains two types of climax and three distinct seres. The climaxes are Cynometra forest and an edaphic climax of the swamp forest. The seres are the colonizers; Maesopsis, woodland forests and the mixed forests. The most common genera in the mixed forests are Chrysophyllum, Cynometra, Khaya and Trichilia. Economically this is the most important component of the forest. The swamp forest covers the smallest part of the forest and it is found on soils that are flooded for part of the year and water logged for the remainder. The Maesopsis forest is found on slightly better soils than the woodland and is dominated by Maesopsis eminii. The woodland forest is often found on the sides of ridges. The colonizing forest types expand into the savannah areas located in the forest and on its fringes. The colonizing process usually starts with Acanthus arboreus which is replaced by Maesopsis eminii and then a mixed forest. With the absence of Acanthus the colonization is much slower. A natural boundary between a colonizing forest and the tree savanna is usually dominated by Albizzia and Coloncoba. Such areas are widest where conditions of soil



fertility and water supply favour forest expansion and where fire has not been intense enough to kill off the young trees (Paterson, 1991).

3.1.4 The community around the forest

The forest is surrounded by several agro-pastoral ethnic groups of Sudanic and Congo origin. Crop production is the major economic activity. According to Langoya et al. (1997), the local population has changed in composition during this century. People from other parts of Uganda, Sudan and Congo settled and joined the traditional inhabitants, the Banyoro in the villages surrounding the forest. As a result, the local community today is very heterogeneous in terms of culture, language and nationality. With the population growth of the community living around Budongo forest, human pressure on the forest has also increased. Some of the emigrant tribes practice game hunting as a means of providing supplementary protein for their family. In a research carried out by Obua et al. (1998) in the communities around Budongo forest, it was recorded that 55.5% of the respondents secured bush meat from the forest. Howard, (1991) records the hunting methods commonly used in 9 of the 12 main forests reserves. Budongo Forest Reserve is one of those that lack this information.

3.1.5 History of Budongo Forest Reserve

The major modifiers of the forest patterns in Africa, fire (most frequently of Anthropogenic origin) and elephants, have been active in Uganda, and Budongo forest in particular, for thousands of years. The coming of the Europeans brought in new controls. They suppressed and controlled fires, periodically removed large animals and managed the forest for timber production. These conditions favoured the expansion of the forest. The value of Budongo forest was noted as early as 1905. It was noted that the forest had larger and better trees than those around lake Victoria and that they were some of the most valuable timber stands in Uganda. This sparked off the management of the forest for timber production. By 1926 timber extraction from the forest had started and in 1936 a resident research officer started drawing plans for timber extraction and regeneration control. These extraction plans were for selective logging but after 1957, selective cutting plans were abandoned in favour of plans for clear cutting. During the same period, the use of chemicals to remove “weed” trees and growth impeders was started in some parts of the forest (Paterson, 1991). Among the British colonials, any form of wooding was preferred to grass or shrub cover and any factor inhibiting the growth of wooded species was condemned. The “forestry ordinance” was established in 1903 and the “careless use of fire ordinance” in 1920. This resulted in controlled burning and concentrating burns in the early dry season when the fire would have less effect. The precolonial burning eliminated the savanna bush and the colonial policies to reduce the burning later encouraged the spread of the tse tse fly and the exclusion of both humans and cattle from the area. The other notable result was the new disease regime to both cattle and the humans coming from the tremendous increase of the tse tse fly in the area. The attack was so heavy on the northern and western part of the forest that by 1910 an evacuation of the entire population had to be carried out. The other side effect was the increase in the large animal population, especially elephant, since their main competitors, the human and cattle population, were almost extinct. Their



“control”, a word euphemistically used for legalised extermination, was started in 1926 and by the late 1960s they had been totally eliminated from the forest. The control was carried out to protect the seedling plantations both inside the forest and at the edges of Budongo forest (Paterson, 1991).

3.1.6 Soils

(Fo 43 - 2b) The underlying rock for most of Budongo Forest Reserve is of pre-cambrian origin consisting of gneiss, schists and granulites. Part of the southern Siba is underlain by Bunyoro-Kyoga series type of rock of pluvio-glacial origin. The main soils of the area are Orthic Ferralsols. These are deep and highly weathered, very infertile soils which allow rapid water movement. They have very good physical characters and they are least susceptible to erosion. The soils have low nutrient reserve. They are of a medium texture (not too sandy and not too clayey) and they have a low pH . The associated soils are Feric Acrisols, which consist of an upper horizon which is sandy and a subsurface horizon which is clayey, and Xanthic Ferralsols which are yellow soils. All these soils make up about 90% of the soils in the area. The other 10% consists of Lithosols which are typically shallow soils. They are found on hill tops. They are underlain by Quartzite rocks and vary from red loam containing small quantities of ironstone concentrations which supports forest vegetation, to ridge top pavements of solid cellular iron sheets. Eutric Fluvisols are found in river valleys. These are much more fertile than the other soils and they sometimes get flooded. Other soils in this category are the Histosols which are organic soils made of organic materials. These are found in low-lying areas where water stays permanently (FAO - Unesco, 1977).

3.2 SAMPLING

3.2.1 Sampling design and sample plot selection

The sampling design selected involved a combination of stratified and systematic sampling methods. Considering the type of study at hand, stratification was a major prerequisite for site selection. According to Kent and Cooker, (1998), stratification is carried out on the basis of major and usually very obvious variations within the area under study. The area was stratified into burnt over and unburnt areas. The burnt over areas were found in the grasslands and the unburnt areas in the forest. Then a sampling method that would enable the researcher to collect as much data as possible from the selected site was chosen. The most appropriate method of sampling was systematic sampling which, according to Kent and Coker (1992), involves the location of sampling points at regular or systematic intervals. Here the size of the sampling interval is of great importance and is usually fixed. Sampling was then carried out on each of the strata. Two types of sampling were carried out; 1. Intensive sampling for analysing spatial variation and; 2. Less intensive sampling at selected points in one grassland and one undisturbed forest

area for checking on how representative the intensively sampled area is for the rest of the forest.



The intensive sampling was in the direction of the stretch of the grassland area with a spacing of 300m between the plots along the transect. Another sample plot was also taken at 75m after every 300m distance sample plot. This was to help produce a more realistic variance during the analysis. At each of the 300m points, data was also collected, at intervals of 75m, along a perpendicular line to the initial transect. This was done throughout the burnt-over area to produce a grid-like coverage of the area. This sampling at varying distances provides several spatial scales. This allows for the establishment of short- and medium-range variations occurring within the sampled area. In one other grassland area , data was collected in nine (9) plots, which were regularly spaced at a distance of 300m in one direction, and 150m in another from one another. These samples were used to establish the representativeness of the fully covered patch. The initial plan was to do atleast 2 areas in each stratum but time constraints could not allow for this. Tentative burnt over areas for intense data collection were identified on the image before going to the field. This was done by making an NDVI of a SPOT.XS satellite image and identifying areas with reflectance values characteristic of grasslands. These were identified on a topographic map of the area which was then used to identify the places in the field. In the field, fire indicators were used to identify the areas. These includes: 1. Charcoal in the soil 2. Scars on surviving trees 3. Remains of burnt trees 4. Remains of burnt grass The direction of the long transect was then determined and line cutting for data collection started. There was no hard rule about the actual starting point of the transect. All we had to be sure of is that we were within the desired stratum. The finally selected burnt-over area is indicated below;

Figure 3-1: An NDVI of the grassland area selected for intense data collection. Similar work was also done in parts of undisturbed forest areas. This way of laying plots ensures that no part of the desired area is left sampled. Below is a diagrammatic display of the plot lay out: -



300m

75m

75m

300m

maintransect

Figure 3-2: A demonstration of the plot lay out for intense data collection. To eliminate other kinds of human disturbance as causes of biodiversity difference, a grassland area, which is not close to human settlements, was selected. Also any disturbances noticed in the area of study were recorded. In the data analysis stratification of the data into grassland and undisturbed forest areas has been carried out. In this study all the woody plants have been considered. So any reference to trees means all woody species. This ensures a better accuracy since there is nothing to ignore apart from grass. Every woody species in each plot had to be enumerated. In order to get a full picture about the diversity in the area of study, it was necessary to cover all diameter classes. The tree species were divided into three DBH groups:- 1. Trees’ group > 10 cm 2. Saplings’ group < 10 cm and >2cm) 3. Seedlings’ group < 2cm DBH measurements were carried out on the trees’ and saplings’ group. For the seedlings’ group, species were identified and counted. To avoid mistakes in identifying species in the Seedlings’ group, a minimum height of 20cm was decided. The plot was split into four sections to avoid missing out on identifying any of the trees, saplings or seedlings in the plot.

3.2.2 Plot shape and size

Circular plots were used for this study so that the same plot centre could be used for the three groups (trees, saplings and seedlings). Though the plot size has no hard rule, the size chosen should be large enough to cover the variation in species within a locality and it should relate to the size of the vegetation being studied i.e. larger quadrants for trees and small ones for small plants. Kent and Coker (1994) have suggested a range of plot sizes for the different classes that may be studied: -



Table 3-1: Table showing the variation of plot size in relation to vegetation type.

Vegetation type Quadrant size Bryophyte and lichen communities Grasslands, dwarf heaths Shrubby heaths, tall herbs and grassland communities Scrub, woodland shrubs Woodland canopies

0.5m × 0.5m 1m × 1m-2m × 2m 2m × 2m-4m × 4m 10m × 10m 20m × 20m-50m × 50m (use plotless sampling)

Source; Kent and Coker, 1994 In this study a plot size of 400m2 was used for the tree group and 200m2 for the sapling group and 50m2 for the seedling group.

400m2 (radius – 11.29m)200m2 (radius – 7.98m)50m2 (radius – 3.99m)

Figure 3- 3: A display of how the plots of the different sizes were laid out at a point in the field during data collection

3.2.3 Sample size

The sample size in a study area, according to Trangmar (1985), is based on the objective of the study and the cost of sampling and measurement and the accuracy desired. In this case also time available was a major factor. A sample size of 40-50 is statistically acceptable. For this study 162 plots were measured. 101 of them were in the grassland area, 76 plots within the grassland and 17 on the edge of the grassland area in the intensively sampled area and 9 in the less intensively sampled grassland area. 61 plots were taken in the undisturbed forest area with 52 of them in the intensively sampled area and 9 in the less intensively sampled area. Table 3-2: The table shows the number of plots that were collected in each area

Number of plots Area Intensive sampling Less intensive sampling

Forest 52 9 Grassland 76 9 Grassland edge 17 -

3.2.4 Site selection

The researcher together with the forest officer analysed on the management map the originally identified sites. Three grasslands were then picked out as most suitable and reconnaissance trips were conducted to make the final selection. A grassland in Siba block (S7 & S8) was selected for the intensive sampling and one adjacent to Nyakafunjo block (N15) was selected to check for representativeness. Similar activities were carried out for the selection of the forest areas. Care was taken to avoid areas that have been



disturbed through application of aboricides to eliminate “weed” trees or logging. N15 in Nyakafunjo block was selected for the intensive sampling and a portion crossing N14 and W17 in Nyakafunjo and Waibira blocks respectively was selected for checking for representativeness.

Less Intense datacollection sites

Intense datacollectionsites

Figure 3-4: Budongo Forest Reserve map showing the sites selected for data

collection

3.2.5 Organisation of crew

A crew of 4 people, the researcher, a taxonomist and two line cutters was required. A taxonomist, well versed with tree species was identified and he was responsible for the species identification and measuring of the DBH which he would then call out to the researcher who did all the recording. Two line cutters who were well versed with the area and could carry out good ranging with minimum supervision were also identified. The researcher carried out the slope, aspect and Global Positioning System (GPS) readings. The taxonomist and the researcher handled the chain for the laying out of the plots and from time to time checked on the line cutters to be sure they were cutting the lines at the desired bearing.

3.3 SECONDARY DATA

3.3.1 Aerial Photographs

Apart from analysing what exists today in the grassland, it is important that we observe what changes have taken place in this area in the past years. This will be done through carrying out a change detection on aerial photographs. Photos for 1950, 1962 and 1988 were obtained from the National Biomass Study Project which is part of the forest department in Uganda and they have been used for this purpose. Since the areas are small, even a small displacement in the polygons causes misinterpretation. So it has



proved necessary to carry out photo interpretation on stereo pairs and then using stereo plotters to plot the change which is then digitized for further analysis.

3.3.2 Participatory Rapid Rural Appraisal (PRRA)

The main tools used were the semi-structured interviews and group discussions. The hunters know the burnt over areas in the forest better than any other people because of their regular interaction with these grasslands. They are a valuable source of information that can not be ignored. The grassland is what it is today because of their activities in it and if change is to be achieved, as far as burning is concerned, the hunters ought to be involved. So it is imperative that information concerning their attitude to the management practices and possible solutions to burning is solicited. An elderly man who has been carrying out hunting for over 50 years was used as a key informant to help in the location of the grassland data collection site. He was later incorporated in the data collection crew as a line cutter and he proved useful in identifying recently burnt areas and pointing out the different types of traps that we came across as we worked in the grassland. At a later stage a total of 31 hunters were involved in the discussions that were held at different times during the data collection period. A transect walk through the grassland was carried out with two of the hunters towards the end of the data collection period. This was for the purpose of verification of the information gathered during the discussions.



3.4 METHODOLOGY

Groupdiscussions

Field verifications

Analysis

Indigenousknowledge(Hunters)

Perceived changeHunters’ Activities

Digitize

Spatialchange

Change Map

Secondarydata

PhotoInterpretation

Stereo Plotting

Aerial photos1950 & 1988

Geostatistics

MAPITSURFER

Kriging

SpatanalWLSFIT

Variograms

Maps

Analysis

Descriptivestatistics

-Total count-Species count-DBH

Shannon indexShannon wiener indexSimpson index

Indices

BiodiversityStatus

Significancecorrelation

Statisticalanalysis

Data collection

Sampling design &Sample plot selection

Sample size

Plot size & shape

Site selection

ForestGrassland

Figure 3-5: Flow chart of the methodology starting from the field preparations.



3.5 SECONDARY INFORMATION ANALYSIS Before getting into the statistical analysis, its important to be familiar with the activities that take place in area and the changes that have taken place within the area. This will be achieved through the analysis of the secondary information. 3.5.1 Socio-economic This information was obtained through focus group discussions and a transect walk through the grassland. The respondents live in the trading centres located on the main road that passes through the forest area and all of them hunted in the grassland where the intensive sampling was carried out. The oldest of the respondents had been living in the area and hunting in the grassland since 1918. Others interviewed had hunted in the area for 20 to 30 years. Their main source of income is farming and meat provides extra income in the times when they get a good catch. Most of their hunting is carried out in the grasslands because that is where the greatest number of the animals of interest are found. a) Hunted animals The animals hunted include bush pigs, bush bucks, duikers, baboons, pythons, porcupines, squirrels and edible rats. Their preference in animals was based on two criteria; the ease of catching the animal and the quantity of meat acquired. The edible rats were ranked number one because they were not dangerous and bush pig number two because it yields a large quantity of meat. b) Hunters’ View of the changes in the grassland The forest is colonising part of the grassland and forest species are coming in. The whole area used to be covered by grass but now there are patches of large trees. According to the hunters this is because of the reduction in the rate of burning. Before, the burning cycle was one year i.e. burning used to be carried out every 6 months but the area burnt in the first part of the year would not be burnt again. Now the burning cycle is two years. c) Hunters’ view about the animals They are no longer as available as before. Some of the animals have retreated to the thicker parts of the forest because of disturbance. The buffaloes and elephants have been made extinct by a combination of hunters and forest management operations. d) Burning mechanism The grassland area is divided into several sections and each section is burnt in a different week. The areas next to each other are not burnt in consecutive weeks. Instead, if an area on the lower part of the grassland is burnt this week, the next week’s burning would be on the upper part of the grassland. On the day of burning, a line is cut close to the forest on the lower side of the slope and nets are set up along this line. Hunters are also stationed behind the nets so that they can shoot the animals that run through the nets. A fire line is cut to separate the area to be burnt from the rest of the grassland and hunters are stationed along the line to chase back the animals which run that way. Hunters are also stationed on any other side where there may be no net. Then the fire is started on the upper side of the slope.



The area is usually divided into 9 portions and a portion is burnt per week. Not all portions are burnt in one burning season. Each area is burnt once in two years. This is done to allow enough debris to accumulate in the area. e) Timing Burning is carried out in the hottest season of the year and this is between January and March. This is when the debris will burn hottest and clear the desired area. f) Benefits of burning -A larger, one time catch is obtained in such times and this ensures a good supply of meat for the family and also some extra income. Meat selling is, however, done through house to house contacts to avoid getting into conflict with the forest department officials. -The after effects of the burning are provision of an open area for easy chasing and visibility of the animals in future hunting, and the coming of new grass which attracts more animals. - Also medicinal plants that are used by the local people come up after the burning. g) Other hunting methods used In the rest of the year, other methods other than burning are used to acquire the game. These include the use of deadfall traps, triggered snares, bows and arrows and poison. The poison is mainly used for the edible rats. h) The map of the hunters in comparison to the grassland map During one of the group discussion, the hunters were asked to draw the map for the area where they carry out their activities. They also gave an explanation of how they divide the area for burning and some of the actual boundaries were visited during the transect walk. The resultant map in relation to the actual grassland map is shown below:

Hunters’ mapDemarcations for burningThe rest of the grassland

Figure 3-6: The hunters’ map in relation to the actual grassland map. The area that the hunters included in their map is actually the area that had evidence of recent burning. This included remains of burnt grass, especially elephant grass and spear grass, killed young trees and mature resistant trees with thick layers of charred like bark.

3.5.2 Change Detection

Vegetation change as observed from aerial photographs. In the 1950 photographs the area was mainly covered by low grasses and there are no visible trees. The grassland has a very clear boundary all around. In the 1988 photographs several clusters of young trees can be observed on the eastern part of the grassland and the edge is fuzzy.



Change 1950 to 1962 Change 1962 to 1988

Overall - Change 1950 t0 1988

Grassland to Forest Forest to Grassland Grassland Forest


c)

a)


(b)

Changes have been more in the decrease in the grassland area than increase. The little increase in the area of study is in the area where burning is still actively practised. Between 1950 and 1962, colonisation occurred mainly on the western side of the grassland and between 1962 and 1988 the colonisation was mainly on the eastern side of the grassland under study. The grassland under study used to be connected to another grassland but with time the area between them has been colonised by the forest as can be seen from map c.

Figure 3-7: The maps showing the change in the area occupied by the grassland under study between 1950 and 1988. (a) shows change between 1950 and 1962, (b) the change between 1962 and 1988 and (c) the overall change between 1950 and 1988.

CHAPTER 4 DATA ANALYSIS AND RESULTS


4. DATA ANALYSIS AND RESULTS First the actual figures of the variables, total tree count and number of species, collected from the field will be used in the analysis and then the outcome of the indices calculations at plot level will follow. While the actual species count is the least ambiguous measure of species diversity, it is not able to differentiate between areas that have the same number of species but with varying number of individuals per species. This can only be brought out using diversity indices. So the numbers will provide a good overview while the indices show the uniqueness within each area. The analysis will involve both statistical and geostatistical methods. Before getting into the geostatistical analysis, the species composition of the two areas will be analysed.

4.1. DESCRIPTIVE STATISTICS Summary statistics provide an overview of the variation within the data set and also an idea of trends, this in turn will give a basis for further analysis.

4.1.1. Total tree count (TC)

Table 4-1: Total tree count (TC) descriptive statistics of the intensively sampled areas.

Total tree count (TC) Area groups No of

plots Total Count

Range Median Mean Std Deviation

Grassland Trees 76 912 0-38 11 12 9.63 Saplings 76 1311 0-68 15 17 12.33 Seedlings 76 1911 1-81 22 25 17.39 Forest Trees 52 1107 5-39 22 21 5.79 Saplings 52 1383 11-62 28 27 9.42 Seedlings 52 2019 10-123 35 39 22.34

For the total count in each group, both areas follow the same trend. The seedlings have the highest value and the trees the lowest. The forest generally has more trees in all the three groups than the grassland. The grassland however shows higher variation, except for the seedlings group. Both areas have a funnel like arrangement of quantities with the lowest TC value in the trees’ group and the highest in the seedlings’ group. This is a sign of continuity of an ecosystem. The highest median in the grassland area equals the lowest median in the forest area. The seedlings in the forest have the highest value in all the parameters that have been calculated.



4.1.2. Number of species (NS)

Table 4-2: Number of Species (NS) descriptive statistics for the intensively sampled areas.

Number of species (NS) Area groups No of

plots No. of

Species (NS)Range Median Mean Std

Deviation Grassland Trees 76 61 0-13 4 5 3.34 Saplings 76 74 0-16 6 7 4.13 Seedlings 76 65 1-15 5 6 3.57 Forest Trees 52 45 3-12 7 8 2.13 Saplings 52 44 3-13 7 7 2.41 Seedlings 52 38 2-13 7 7 2.20

For the actual count of species per area, in the grassland, the saplings have the highest numbers and the trees the lowest while in the forest, the trees have the highest number and the seedlings the lowest. The lowest NS value in the grassland equals 0 while in the forest it equals 2. A reverse occurs in the highest figures where grassland has a maximum of 16, and the forest a maximum of 13. The forest has the same median in all groups while the grassland median varies between 4 and 6. Forest generally has a higher number of species per plot in all groups, trees, saplings and seedlings, but grassland has a higher variance in the three groups. The higher standard deviation of the grassland is an indicator of higher variability in the grassland as compared to the forest.

4.2 CORRELATION ANALYSIS A correlation analysis gives insight into the strength of the relationships that exist between the three groups in each sampled area. The resultant statistics of a correlation lies between –1 through 0.0 to +1 and this indicates the degree of the strength of the relationship between the two variables. A positive correlation means that as one group increases the other also increases or vice versa. A negative one means that as one group increases the other decreases. SPSS (statistical package for social scientists) was used for this analysis and in addition to giving the correlation it gives the level of significance of the correlation as well. This has also been indicated in the results.

4.2.1 Total tree count

Table 4-3: Total tree count (TC) correlation between the trees, saplings and seedlings within each area.

Area Group Saplings Seedlings Grassland Trees 0.583 0.329 Saplings 0.543 Forest Trees 0.185 -0.163 Saplings 0.213

Significant (P = 0.05) Highly significant (P = 0.001)



The trees, saplings and seedlings in the grassland have a significant correlations unlike in the forest where none is observed. In the grassland one group is directly related to the other, while in the forest each group exists as an almost independent entity.

4.2.2 Number of species

Table 4-4: Number of species (NS) correlation between the trees, saplings and seedlings in each area.

Area Group Saplings Seedlings Grassland Trees 0.707 0.606 Saplings 0.686 Forest Trees 0.058 0.214 Saplings 0.346 The grassland has very high correlations between all groups while in the forest correlation is only observed between saplings and seedlings. An explanation of this difference is that there is a continuous flow of species from the seedlings through to the trees in the grassland because the species that survive the seedling stage need specific fire adaptive characteristics. In the forest, the correlation between seedlings and saplings is because the two groups have many species that are shade lovers and they occur in both groups.

4.3 CHECKING OF REPRESENTATIVENESS The data analysed in this section are those that were collected in the less intensively sampled areas. These have been used to check for any similarities between the areas where intensive sampling was done and the other parts of the forest which are under the same disturbance regime.

4.3.1 Total tree count

Table 4-5: Total tree count (TC) descriptive statistics for the less intensively sampled areas.

Total tree count (TC)

Area Group No. of plots

Range Mean Std. Deviation

Grassland Trees 10 1-10 6 3.30 Saplings 10 7-60 31 17.82 Seedlings 10 7-72 33 24.15

Forest Trees 9 20-28 24 2.96 Saplings 9 17-34 23 5.50 Seedlings 9 8-85 31 22.57

The grassland has wider ranges for saplings and seedlings than the forest area. The mean for the trees in the forest area equals 4 times that of the grassland though their standard deviations are not that different. The standard deviation in both areas follows the same trend as in the TC table of the intensively sampled area. It is lowest in trees and highest in seedlings.

Significant (P = 0.05) Highly significant (P = 0.001)




Table 4-6: Number of species (NS) descriptive statistics for the less intensively sampled areas.

Number of species (NS)

Area Groups No. of plots

Range Mean Std. Deviation

Grassland Trees 10 1-4 2 0.82 Saplings 10 1-7 4 1.99 Seedlings 10 1-6 3 1.62

Forest Trees 9 6-14 11 2.45 Saplings 9 5-10 8 1.67 Seedlings 9 2-10 6 2.52

The NS value is low in the grassland and much higher in the forest. The standard deviation in grassland follows the same trend as in the intensively sampled area. In the forest a divergence of order occurs but the figures do not differ very much from those of the main data set. From this analysis we can say that the data collected is fairly representative of the other areas of the Budongo Forest Reserve which are under the same disturbance regime. After here all analysis is going to be based on the data from the intensively sampled areas.

4.4 SPECIES COMPOSITION Before going into the detailed statistical analysis, it is necessary to know which species occur where and in what quantities. While NS gives an overview of the species distribution, it does not deal with the issue of where the species are able to grow and seed. The analysis in this section is to further refine the issue of species composition and give insight into the species distribution with a direct focus on the physical occurrence of specific species in each area. This is for research questions 3 and 6.

4.4.1 Comparison between forest and grassland

Analysis has been carried out to find out the relationship that exist within the three groups in each area and the relationship between the two areas. The table below gives the summary of the species distribution within the two areas. The full lists of the individual species, the total count per species and the relationships found are given in appendix 1a



Table 4 –7 Comparison of quantities of species that occur in the forest with those found in grassland

Area Number of species

Total Shared Unique to respective area

Grassland 89 40 49 Forest 54 40 14

The grassland has more species than the forest and there are more species that are found only in the grassland than only in the forest. Using the total count per species in each area enables us to select the species which are dominant in each area. This analysis was carried out on species that occur in both areas. The result is shown below and the detailed list is in appendix 1b Table 4-8 Species that are dominant in each area

No. of species

Percentage

Dominant in grassland 16 40 Dominant in forest 10 25 Almost equal in both 14 35

The species that were selected as dominant in a specific area were at least three times more in occurrence in that area than the other. The rest were grouped as almost equal.

4.4.2 Comparison within each area

For each area, the species in each group were listed with the total count per group and an analysis was made to find out whether a species occurred in more than one group and which ones occurred in only one group. Summary of the results is indicated below and the detailed list is in appendix 1c and 1d. Table 4-9: Species distribution within each area

Area Group Number of species Found

in all In Trees & Saplings

In Saplings & Seedlings

In Tree & Seedlings

Unique to respective

group Grassland Trees 42 10 2 7 Saplings 42 10 14 8 Seedlings 42 14 2 6 Forest Trees 27 9 3 6 Saplings 27 9 6 2 Seedlings 27 6 3 2



4.4.3 Comparison of grassland and forest with the edge group

During data collection in the grassland, after reaching the boundary of the grassland, an extra plot was taken in the forest for each of the lines. Two of the plots could not however be accessed due to the presence of a succession of colonies of various biting insects. So finally 17 plots were collected instead of 19. A collection of these plots is what has been called the edge group. A list of the species with their total count for this group has been compared with the lists of those found in the forest and in the grassland. A summary of the results is shown below and the details are shown in appendix 1e. Table 4-10 Distribution of species between the forest, grassland and the forest edge

Area Number of species Found in

all areas In Grassland

& Forest In Grassland

& Edge In Forest & Edge

Unique to respective

group Edge 37 19 4 5

Grassland 37 3 19 30 Forest 37 3 4 10

Though the edge group had few plots, it has a high number of species that it shares with both the forest and the grassland. The number, 37 species, is very close to the value, 40, obtained for the species that are shared between the forest and the grassland. The edge also has a high number of species it shares with the grassland. The grassland area has the highest number of species that do not occur in any of the other areas.

4.4.4 Analysis of areas where species occur in the three groups

Due to time limitations, in-depth analysis has been limited to a smaller part of the species. To find out which species actually need conservation and in what area, a relationship was sought between the species that are found in all the three groups (trees, saplings and seedlings) in the grassland with those found in all the three groups in the forest. The summary of the results is shown below and the details are shown in appendix 1f. Table 4-11 Species that occur in the three groups (trees, saplings and seedlings) and

their location

Description Forest Grassland Species in the 3 groups of both areas 14 14 Species occurring in both areas but with the 3 groups in one area 11 (a) 7 (c) Species unique to respective area and occur in the 3 groups there. 2 (b) 19 (d) Of the species that occur in all the 3 groups, the grassland has more than the forest. Of those found in both areas, the forest has more which occur in all the three groups. The species found in all the three groups in both areas can survive almost anywhere under the prevailing conditions. So they do not need specific attention for conservation. On the



other hand, species which occur in all the three groups in only one area need that kind of environment for their continuity. This second category is the one that will be focussed on. The species have been grouped into groups with letters, a, b, c and d, for description purposes. A short description will be given for the species that occur in large quantities in each group with the hope that a few of their ecological needs can be identified. This will highlight the essential biodiversity conservation needs. a. Celtis waghtii – An evergreen understorey tree with a crooked trunk and fairly

spreading crown. The bark is smooth and thin usually with prominent lenticels and flakes on older trees.

- Rinorea ilicifolia – An understorey tree with a rather narrow crown. Has a thin flaking bark.

- Lasiodiscus mildbreadii – An understorey tree with a crooked trunk and an untidy, deep crown. The bark is of medium thickness and flaking in places. It is very abundant under Cynometra in Budongo.

- Trichilia rubensens – A spreading understorey species with a crooked, cylindrical or fluted trunk. The bark is thin, smooth and sometimes flaking.

- Entandrophragma utile – A very large deciduous tree with a straight, cylindrical, wide trunk and a large spreading, open crown. The bark is thick and rough.

- Chrysophyllum albidum – A tall tree with a straight trunk and a dense rounded crown. The bark is quite thin with a general smooth effect.

b. Celtis mildbraedii – Tree with a long straight, rather thin trunk and small, rounded

deciduous or evergreen crown. Bark is thin and flaking in large pieces. It is a co-dominant species. The fruit is dehiscent. In the forest under study, it mainly occurred under the shade of larger trees.

- - Chryophyllum perpulchrum – The trunk is thin and straight, with a rounded crown. The trunk is fluted and the bark is smooth. Its mainly found in Budongo forest

c. Antaris toxicaria – The tree is large and spreading and its bark is thin and smooth. It

is a light lover. - Cola gigantia – Has a fairly spreading crown of large, dark coloured leaves. The bark

is quite thick and rough. The fruit contains arrilate seeds. In the area of study, it was mainly found where fire had not taken place for some time.

- Sapium elipticum – Has a crooked trunk, usually branched low down with spreading crown with drooping branches. The bark is thick and rough, sometimes flaking. It is a light requiring species often found on forest edges, in large gaps and in open forest types.

- Albizzia zygia – Tree has a cylindrical trunk and layered foliage. Bark has dual characteristics; in forest it has a smooth bark and in Savanna a rough bark.

d. Securinega virosa – A much branched shrub or small tree. It has small leaves. It is a

light requiring species found on forest edges and large clearings. Also common in savanna.

- Bridelia micrantha – Small tree with trunk branched near base and a dense crown. The bark is fairly thick and vertically fissured. A common tree on forest edges.

- Vernonia amygdalina – A shrub or small tree growing on forest edges. This species was mainly found in the areas that had recently been burnt. It is a light lover.

- **Combretun polinum – The tree has a thick bark and it usually has little debris under it.



- **Terminalia glaucescens – The tree has a very thick back and it showed high ability to regenerate after a fire.

This information was obtained from Hamilton, 1991. ** Description is from field observations by the researcher. The species found in a and b are either shade lovers or large domineering trees with large crowns and sometimes thin barks. So the shade lovers and the domineering species co-exist in the forest. The species found in c and d mainly have a spreading crown and a number of them have a thick bark. The spreading crown is a characteristic of trees that desire a lot of light while the thick bark is usually for protection from disturbances especially fire. A number of those found in (d) occur on forest edges. In the study area, they were abundant in the grassland area.

4.5 TC AND NS GEOSTATISTICAL ANALYSIS All geostatistical analysis focus on the uniqueness in the species distribution, both in the forest and in the grassland; research questions 4, 5,7 and part of 8. Total tree count is a preliminary variable of this analysis.

4.5.1 Total tree count (TC)

Spatial continuity of an h-scatter plot can be summarised using different indices and these are; the correlation function, ρ(h), the covariance function, C(h) and the moment of inertia traditionally called the variogram, γ(h) (Isaaks and Srivastava, 1989). Throughout the geostatistical analysis the variogram has been used and for each variable the values of its main features; the nugget, sill and range, have been indicated. For a measure of how well the model fit on the data the ratio of the Sum of Square Deviations to the Total Sum of the Square (SSD/SST) has been indicated. The closer this ratio is to 0 the better the fit. The variogram calculations have been done using two software packages; SPATANAL (Stein, 1993) and WLSFIT (Weighted Least Squares Fitting of variograms). SPATANAL was used to calculate the variance function for the different variables and to determine the weighted linear regression coefficients for the variogram. The output file from here was then used in WLSFIT to fit the models and select the best fit model basing on the SSD/SST value each model gives for a particular set of data. This combination of software was chosen in preference to ILWIS, a more familiar package, because it provides a finer set of results due to its in-built algorithms for determining the nugget, sill and range. This is especially important when the values being handled are between 0 and 1. For ILWIS, these parameters are visually determined from the scatter plot by the analyst. This results in a low level of accuracy. The graphs, for visual purposes, have been made using excel because the packages used for their calculations are DOS programmes and so the presentation was not very clear on paper and also ILWIS does not have the ability to combine scatter plots using the average distance obtained at model fitting. It scatters all data collection points and it does not give the true picture of the model fit. In ILWIS, when the nugget and sill were between 0 and 1, the model was almost flat on the h-axis



while the scatter plots were way up above it. The formulae for the models used, Exponential (E), Spherical (S) and Gaussian (G), are indicated in appendix 4 Model fitting (a) (b) (c)

Figure 4-1: The graphs showing the model fitting for the total tree count for trees (a),

saplings (b) and seedlings (c) in the grassland area. Table 4-12: Total tree count model parameters

Area Variable Model Nugget Sill Range SSD/SST Grassland Trees G 80.6 234.6 3350 0.413 Sapling G 114.0 933.0 4380 0.442 Seedling S 190.0 313.0 570 0.255 Forest Trees Nugget - - - - Sapling Nugget - - - - Seedling Nugget - - - -

The total tree count (TC) in the grassland displays patterns of distribution which can be fitted with either a Gaussian or Spherical model while the forest had a nugget pattern for all the groups. This means that in the grassland where fire occurs, it influences the distribution while in the forest where there is none the trees are rather randomly distributed. So the rest of the description will be for the grassland area. The seedlings show the highest nugget, which is an indicator of a higher variance, as compared to the other groups in the grassland. This is confirmed by the standard deviation in the descriptive statistics where the seedlings group has the highest value. For the trees and saplings, at very short distances no pattern is observed and as distance increases a pattern emerges. The distance within which no further change will be encountered, the range, for both groups cannot be accurately estimated because the range obtained at model fitting is beyond the maximum distance covered in the data collection. This range is rather an estimate since it was obtained through extrapolation. It could be a bit larger or smaller. This range indicates that these two groups have a high spatial dependence i.e. they require large space in order for their pattern to be fully observed. The seedlings have abrupt boundaries and the distance between the boundaries is not clearly defined but the change in the pattern of distribution can be observed even within short distances up to a maximum distance of 570m. This fairly short range obtained at model fitting means that the seedlings’ group TC has a much lower spatial dependence.



TC maps The Surfer software has been used for the generation of all the geostatistical maps. Here kriging is carried out using its in-built algorithms to produce a grid file which is then used to produce a contour map for visualisation of the variable’s distribution over the area. The maps obtained are very fine and it was decided that the actual contours be eliminated because they obscure the visualisation of the fine details in the variation of the variable over the area. ILWIS, the available alternative software, on the other hand does a lot of smoothing that the details of variation are overlooked. So Surfer was preferred. All the three maps have been put on the same colour scale so that the difference in TC can be directly observed. (a) (b)

( c )

Figure 4-2: The total tree count distribution for the trees’, saplings’ and seedlings’

group in the grassland area. The trees’ group and the saplings’ group display the same pattern of spread. Where there are more saplings, there are also more trees, but the seedlings have a different pattern.



There is higher number of trees and saplings on the eastern side of the grassland and it decreases westwards. The map getting darker westwards and southwards displays this. The two maps are fuzzy because of the high spatial dependence (large range) obtained at model fitting. The seedlings’ group shows a clearer pattern because of the lower spatial dependence obtained at model fitting. It also covers a wider part of the colour scale with several areas having higher values than the trees’ and saplings’ group. The distribution pattern differs quite a bit from the other two in the areas where high values are located. Here the edge of the eastern side has more seedlings because of the incoming colonising trees. A bigger part of the arm on the western end also has a high number of seedlings because of the regeneration after the fire. Being on the same scale, the existence of higher numbers of seedlings and very low numbers of trees per plot is displayed by the seedling map being much lighter than the others.


a) Model fitting

(a) Grassland area Trees Saplings Seedlings

(b) Forest area Trees Saplings

Figure 4-3: Models for the number of species for the grassland (a) and the forest (b)

areas.



Table 4-13: Number of species model parameters

Area Groups Model Nugget Sill Range SSD/SST Grassland Tree E 4.11 11.29 114 0.167 Sapling E 9.18 17.92 194 0.139 Seedling G 8.66 15.52 465 0.230 Forest Trees S 1.44 4.47 231 0.182 Sapling G 4.77 32.47 3770 0.408 Seedling Nugget - - - -

For all the groups that have a model fitted, the forest groups have lower nuggets than the grassland groups. This is an indicator of lower variance in the forest. There are generally better model fits for the grassland than the forest area for the respective groups. This is because of the existence of an influencing factor in the grassland unlike the forest where none exists. In the grassland, the trees’ and saplings’ groups display the same pattern and it can be observed within the sampled area since they both have a range, 114m and 194m respectively, which is less than the maximum distance covered in the data collection. Both groups change gradually starting from short distances and the change continues up to the distance of the range where it becomes stable. The seedlings’ group exhibits a nugget behaviour over short distances but emerges into a pattern after some distance and this pattern develops up to a maximum distance of 465m, the range, where the changes stop being significant. In the forest, the trees’ group’s distribution varies abruptly throughout the forest area. The variation is evident even within short distances and these changes can be observed over a distance of 231m, the range, after which no significant change may be evident. The forest saplings’ group, however, shows a different pattern; at short distances it displays a nugget effect and a pattern emerges as distance increases. The maximum distance over which no further change may be evident cannot be accurately estimated with the data collected since the model levels off at a distance, 3770m, which is beyond the maximum distance covered during data collection. The forest Seedlings group displays a pure nugget which means that the seedlings’ species occur randomly throughout the sampled area.

Figure 4-4: Distribution of the number of species for the trees’ group over the sampled areas. The maps also show the plot lay out in both areas.



There is generally a higher NS in the forest area than in the grassland area. This is evident on the maps where the forest map has lighter colours (lies higher on the colour scale) than the grassland map. The grassland map, however, shows more variation than the forest map. This was also shown by the standard deviation in the grassland for all the groups (trees, saplings and seedlings) being higher than that of the respective groups in the forest. The NS in the grassland generally decrease westwards and southwards along the arm. The map also shows an increase in the number of species starting from along the main transect outwards towards the forest.

Figure 4-5: Distribution of the number of species for the saplings’ group over the

sampled areas. The forest shows less variation than the grassland area and though there is a pattern, it is not very clear from the map. This is because the range obtained on the variogram was much larger than the maximum distance covered during data collection. The forest is generally in the middle of the colour scale while the grassland covers a wider part. For the seedlings’ group, a good model was only obtained for the grassland. The forest showed a nugget effect. So here the map is only for the grassland.



Figure 4-6: Distribution of the number of species for the seedlings’ group in the grassland area.

The eastern part showed a higher number of species than the western part of the grassland. This is mainly because of the incoming colonising species on the eastern side.

4.6 INDICES RESULTS Three indices; the Shannon index (a measure of species abundance and richness), Shannon-Wiener index (a measure of species evenness) and Simpson index (a measure of species dominance) have been used. For each index, calculations have been made at plot level and the outcome of the calculation is the one that has been used in the analysis. The tables for the indices’ results per plot can be found in appendix 2 and 3. First, preliminary analysis has been carried out using descriptive statistics and correlation. Then the in-depth analysis is done using geostatistics followed by further statistical analysis. In geostatistics, the best fit models for each group in each area have been worked out and kriged maps made for each one of them. Throughout this section, independent analysis of each group in each area will be made followed by a comparison between the forest and the grassland. The Shannon index will be used for the main body and then the uniqueness of each group will further be highlighted using the Simpson and the Shannon-Wiener indices. For each index, an error map has also been worked out and it will be displayed after the map. During the kriging, estimations of diversity are made, through the process of interpolation and extrapolation, for the areas that had no data collected. In this process some errors occur. A grid of these errors is what is mapped over the whole sampled area to come up with the error map.



4.6.1 Descriptive statistics

Table 4.14: Shannon index descriptive statistics

Area Group No. of plots

Range Median Mean Std. Deviation

Grassland Trees 68 0-2.369 1.187 1.221 0.630 Saplings 75 0-2.553 1.437 1.419 0.640 Seedlings 76 0-2.476 1.156 1.190 0.644 Forest Trees 52 0.92-2.380 1.694 1.679 0.307 Saplings 52 0.64-2.260 1.512 1.447 0.375 Seedlings 52 0.33-2.391 1.406 1.419 0.365

In the grassland the total number of plots varies because the plots which had no trees had to be eliminated since an undefined value would be obtained in the calculations. These were 8 plots for the trees’ group and 1 plot for the saplings’ group. For the trees group they were all located on the western side and the sapling plot was located on the south-eastern side of the grassland. The minimum for all groups in the grassland equals 0 while in the forest there is a higher value. The standard deviation is higher in the grassland for the respective groups than in the forest. In the grassland, especially where burning is currently active, there were several plots that had one species yet the forest had more than one species in all the sampled plots.

4.7 GEOSTATISTICAL ANALYSIS FOR THE SHANNON INDEX

4.7.1 Trees’ group

Model fitting

(a) (b)

Figure 4-6: The models of the Shannon index for the tree group in the grassland (a)

and the forest (b).



Table 4-15: The Shannon index model parameters for the trees’ group

Area Model Nugget Sill Range SSD/SST Grassland G 0.3180 1.3380 1340 0.162 Forest S 0.0441 0.0931 274 0.579

For the grassland, the Gaussian model, indicates that no distribution pattern may be observed at very short distances but the distribution becomes clear as the distance increases up to the observed range distance. The range is fairly large but it still lies within the maximum distance covered within the sampled area. So a pattern will be evident. For the forest, the spherical model indicates that even at very short distances a distribution pattern can be observed but it ends abruptly after a distance. The range obtained here is much shorter than in the grassland which is an indicator of a much lower spatial dependence in the forest than in the grassland. For the trees’ group, putting the grassland and the forest maps on the same scale made the pattern in the forest not very evident. So to analyse the pattern in the forest, a separate map has been made and placed next to the combined map.

Figure 4-7: Shannon index trees’ group maps for both the forest and grassland. The tree species richness and abundance in the grassland decreases westwards and southwards and gets to the extreme of 0 diversity at the end of the long arm. In the forest, small islands of low species abundance and richness occur. Generally, the species abundance and richness is high throughout the forest area. Comparing the two areas, the forest is on the higher part of the scale. The forest has a very narrow range and so it is almost one shade while the grassland shows a much wider range and so it covers a wider part of the scale which means it has a higher variation over the sample area. This was also reflected in the descriptive statistics where the grassland has a higher standard deviation. Apart from observing the variation of the species richness and abundance over the sampled area, the error of estimation of this variable during the process of interpolation



and extrapolation over the unvisited parts of the study area also needs to be considered. It shows how accurately the estimations were done. This has been done using the error maps. The contours have been included in most of the error maps because the variation in the error is so small that the successive colour change is almost not visible to the naked eye. To come up with the error map, a best fit model has to be selected which fits both areas. This is done using SPATANAL and WLSFIT software packages. For the kriging, MAPIT, a software developed by Stein (1986), was used. It concurrently carries out the calculation for the main map and the error for each point of estimation. The error grid obtained is then used in Surfer to make the contour error map for visualisation.

Figure 4-8: Shannon index error maps for the trees’ group. In the grassland the uncertainty of estimation increases outwards towards the forest while in the forest the least uncertainty was recorded around the actual data collection points and it increases in the areas between the lines. Further increase is observed as distance increases from the data collection area which indicates that uncertainty increases with extrapolation. The forest has a lower uncertainty than the grassland because of the low nugget obtained at model fitting and low spatial variability within the plots.

4.7.2 Saplings’ group

(a) (b)

Figure 4-9: Shannon index model fit for the sapling group in the grassland (a) and the

forest (b).



Table 4-16: Shannon index model parameters for the saplings’ group

AREA MODEL NUGGET SILL RANGE SSD/SST Grassland S 0.218 0.416 432 0.246 Forest G 0.118 0.592 3060 0.036

For the saplings, the Shannon index in the grassland shows changes even at short distances and a full pattern emerges within a distance of 432m while the forest shows no change at short distances and a pattern develops as distance increases. The changes in the forest go on over a much wider range (3060m) and since this is beyond the maximum distance covered during data collection, a fully developed spatial pattern will not observe in the analysed data. The grassland has a higher nugget and thus higher variance than the forest but the forest has a higher spatial dependence.

Figure 4-10: Shannon index saplings’ group for both the forest and grassland. In the grassland, the sapling species abundance and richness shows a great variation in distribution. The areas of low species abundance and richness are close to the edge and in the lower part of the grassland. The eastern end has higher species abundance and richness than the western side. The narrow centre part has the highest species abundance and richness. The forest generally has a more regular species abundance and richness distribution throughout the area. The southern part has higher species abundance and richness and it gradually decreases towards the north-west. Comparing the two areas, the forest lies at the upper end of the scale while the grassland covers the whole scale. So generally the forest has higher diversity than the grassland but the grassland has higher variation in species abundance and richness.



Figure 4-11: Shannon index error map for the saplings’ group for both the grassland and the forest.

The forest shows much lower uncertainty of estimation than the grassland. The variation in the forest area starts just after the sampled area, which means that uncertainty increases as distance from the sampled area increases. In the grassland, the uncertainty increases with distance from the data collection points and it shows several levels of increase outwards. It is lowest at the crossing points of the main transect with the crossing lines. So the sampling intensity plays a part here; the higher it is the lower the uncertainty.

4.7.3 Seedlings’ group

Model fitting (a) (b)

Figure 4-12: Shannon index model fit for the seedlings group in the grassland (a) and the forest (b).

Table 4-17: Shannon index model parameters for the seedling group.

AREA MODEL NUGGET SILL RANGE SSD/SST Grassland S 0.197 0.402 321 0.204 Forest S 0.114 0.1352 412 0.190

The Shannon index has the same model for both areas and it has a short range in both areas. This is an indicator of low spatial dependence in both areas. The nugget is higher in the grassland than the forest which means that there is higher variance in the grassland than the forest.



Figure 4-13: Shannon index maps for the seedlings’ group for both the forest and grassland. In the grassland, the seedlings’ species abundance and richness is higher on the south eastern side and the central part. The other parts are quite low in species abundance and richness but the very low areas occur as islands over the area. The forest shows a rather uniform species abundance and richness although there are some areas that show slightly higher species diversity than others. Comparing the two areas, the forest lies on the upper end of the colour scale but it has no area that lies at the very top of the scale. The grassland on the other hand covers the whole scale with several parts having extremely low species abundance and richness and a few parts which are very high.

Figure 4-14: Shannon index error maps for the seedlings’ group. The grassland shows higher uncertainty of estimation as compared to the forest and the forest variance lies within a narrow range. This is also evident in the descriptive statistics



where the grassland standard deviation is almost double that of the forest. The grassland, apart from showing a higher uncertainty, shows a high variation in the estimation. There is less uncertainty along the data collection lines and it slowly increases as distance from the plots increases but afterwards, it suddenly gives a big increase. This indicates that with high intensity of sampling the uncertainty can be reduced. Overall Looking at the colour scales, in the forest, the trees’ group has the highest species abundance and richness followed by the saplings and then the seedlings. In the grassland it is the opposite; the seedlings show the highest species abundance and richness followed by saplings and the trees show the lowest. The grassland saplings have fewer areas with low diversity. The error maps go the opposite way for both areas; where there is higher diversity, there is low uncertainty. Overall there is higher abundance and richness in the forest than in the grassland but the grassland shows a higher variation over the sampled area. The uncertainty is lower in the forest than in the grassland. The trees group shows both the lowest and the highest uncertainty. This is because in the forest area, the trees have the highest species abundance and richness while in the grassland, the trees have the lowest.

4.8 SIMPSON AND SHANNON-WIENER DESCRIPTIVE STATISTICS For sections 4.8 to 4.10, mainly the unique characteristics of each area that are brought out by the Simpson and the Shannon-Wiener indices will be high lighted. Table 4-18: Simpson and Shannon-Wiener indices descriptive statistics Area Group Index No. of

plots Range Median Mean Std.

Deviation Grassland Trees Simpson 68 0-1.000 0.714 0.698 0.254 Shannon-Wiener 68 0-1.000 0.795 0.745 0.287 Saplings Simpson 75 0-1.000 0.785 0.720 0.241 Shannon-Wiener 75 0-1.000 0.874 0.805 0.219 Seedlings Simpson 76 0-1.000 0.696 0.616 0.267 Shannon-Wiener 76 0-1.000 0.831 0.733 0.271 Forest Trees Simpson 52 0.54-0.940 0.812 0.800 0.077 Shannon-Wiener 52 0.66-0.990 0.855 0.846 0.067 Saplings Simpson 52 0.27-0.910 0.734 0.705 0.134 Shannon-Wiener 52 0.35-0.960 0.790 0.769 0.104 Seedlings Simpson 52 0.19-0.964 0.698 0.692 0.135 Shannon-Wiener 52 0.47-0.991 0.750 0.745 0.114 The lowest value for both indices in grassland equals 0 and the highest equals 1 while in the forest there is a higher value for the minimum and the highest value is less than 1. The standard deviation is generally higher in the grassland for both indices than for the respective indices in the forest. This is the same trend as with the Shannon index.



Overall, the Shannon index has the highest standard deviation in both areas in the respective groups.

4.9 CORRELATION The correlation analysis is to help us establish if a relationship exists between the indices’ values within each group. Since it is a comparison, all results for the three indices will be displayed in one table: Table 4-20: Correlation between indices in each group.

Area Group Index Shannon Shannon- Wiener

Grassland Trees Simpson 664 592 Shannon 754 Saplings Simpson 822 891 Shannon 613 Seedlings Simpson 901 885 Shannon 671 Forest Trees Simpson 889 759 Shannon 441 Saplings Simpson 926 858 Shannon 649 Seedlings Simpson 930 839 Shannon 681

Highly significant (p = 0.001)

All indices in all groups in both areas have highly significant correlation. The lowest correlation in the grassland occurs between the Simpson and the Shannon-Wiener in the trees’ group and the highest is between the Simpson and the Shannon index in the seedlings’ group. In the forest the lowest occurs between the Shannon and Shannon-Wiener in the trees' group and the highest is between the Simpson and the Shannon indices in the seedlings’ group. The latter two are also the lowest and highest overall.



4.10 GEOSTATISTICAL ANALYSIS FOR SIMPSON AND SHANNON-WIENER INDICES

4.10.1 Trees’ group

Model fitting

(a1) (a2)

(b1) (b2)

Figure 4-15: The model fits for the Simpson index (a) and the Shannon-Wiener index (b)

in the grassland (1) and the forest (2). Table 4-20: Simpson and Shannon-Wiener indices model parameters for the trees’

group. Area Index Model Nugget Sill Range SSD/SST Grassland Simpson (1-D) G 0.0485 0.0684 492 0.595 Shannon-Wiener (E) G 0.0802 0.0867 3320 0.200 Forest Simpson (1-D) S 0.0039 0.0064 343 0.320 Shannon-Wiener (E) G 0.0042 0.0099 3040 0.302

In the grassland, both indices have the same model as the Shannon index which indicates that they all follow the same pattern. In the forest however, the Shannon-Wiener differs from the other two. The species evenness pattern is not visible at very shot distances but the pattern develops as distance increases. This index also has a much higher spatial dependence in both areas than the Shannon index. The Simpson and the Shannon-Wiener have lower nugget values than the Shannon index.



(a)

(b)

Figure 4-16: Simpson (a) and Shannon-Wiener (b) indices’ maps for the trees’ group.

The Simpson index maps also show the plot lay out. As much as the Shannon index shows the difference in the level of diversity between the two areas, the Shannon-Wiener shows a higher contrast. The forest has one shade all over while the grassland covers the whole scale and it shows sharp boundaries in levels of species evenness. In the separate forest map, the Shannon-Wiener shows an increase in species evenness northwards. The Simpson index shows much higher dominance in the centre of the eastern side of the grassland.



Figure 4-17: Simpson and Shannon-Wiener indices error maps for the trees’ group. Higher variation in uncertainty is shown by the Simpson index in the grassland area than with the Shannon index. Much lower uncertainty is observed in the centre parts of the sampled area and it rapidly increases outwards. These two indices generally show lower uncertainty than the Shannon index

4.10.2 Saplings’ group

Model Fitting

(a1) (a2)

(b1) (b2)

Figure 4-18: Simpson index (a) and Shannon-Wiener index (b) model fit for the sapling

group in the grassland (1) and the forest (2).



Table 4-21: Simpson and Shannon-Wiener indices’ model parameters for the sapling group

Area Index Model Nugget Sill Range SSD/SST Grassland Simpson ratio E 0.044 0.0632 430 0.267 Shannon-Wiener G 0.0413 0.426 875 0.342 Forest Simpson ratio G 0.0146 0.0238 920 0.028 Shannon-Wiener G 0.00896 0.0125 613 0.104 For the forest area, the same model is maintained by all the three indices which indicates that the distribution pattern follows the same trend but the Shannon index has a much higher range than the Simpson and the Shannon-Wiener indices. In the grassland however, each index has a different model. The Simpson index has an exponential model, which indicates that the change in the sapling dominance is gentle over the sampled area, while the Shannon-Wiener index has a Gaussian model. This is an indicator of the variation in the distribution pattern.

Figure 4-19: Simpson (a) and Shannon-Wiener (b) indices’ maps for the sapling group. In the forest, both indices show the same trend of tree species diversity distribution as the Shannon index but they show more variation over the sampled area than the Shannon index. In the grassland there is however a difference in the areas that have been indicated as having high diversity. While the Shannon index smoothes out the species diversity with a few areas showing low diversity, the Shannon-Wiener index shows a clear difference in species evenness between the eastern and the western part of the grassland. The eastern side of the grassland has lower species evenness than the western side. On the eastern end, the evenness also reduces slightly from the centre, along the main transect, outwards and on the western side the evenness reduces southwards. The Simpson index on the other hand, shows high species dominance for most parts of the grassland. It is also higher in the western than the eastern part of the grassland.

(a) (b)



(a) (b)

Figure 4-20: Simpson (a) and Shannon-Wiener (b) indices’ error for the sapling group. These two indices show lower uncertainty than the Shannon index. Their highest values on the scales are even much lower than the lowest value of the Shannon index. This can be traced back even to the descriptive statistics where the forest which had a lower standard deviation for the Shannon index, has a higher value than the Simpson index which had the highest value within the saplings in both areas for both indices. There is also lower variation within the grassland area. With the Shannon-Wiener, the increase in uncertainty in the forest area starts earlier than with the Shannon index.

4.10.3 Seedlings’ group

Model fitting (a1) (a2)

(b1) (b2)

Figure 4-21: Simpson index (a) and Shannon-Wiener index (b) model fit for the seedling

group in the grassland (1) and the forest (2).



Table 4-22: Simpson and Shannon-Wiener indices model parameters for the seedling group.

Area Index Model Nugget Sill Range SSD/SST Grassland Simpson ratio S 0.0622 0.0749 1310 0.499 Shannon-Wiener G 0.0697 0.1493 3000 0.693 Forest Simpson ratio S 0.0139 0.0192 476 0.499 Shannon-Wiener S 0.00255 0.0132 139 0.602 For the forest area a similar model was obtained for all the indices which is an indicator of the similarity in the pattern of species dominance, evenness and the combination of the two aspects in the Shannon index. The grassland however shows a difference with the Shannon-Wiener index. The pattern for species evenness is not evident at very short distances but it develops as distance increases. The index also has a range higher than the maximum sampled distance which means it has a higher spatial dependence. In the forest, the Shannon-Wiener has a very low range. The Shannon-Wiener index also shows the highest range in the grassland area for the seedlings’ group.

Figure 4-22: Simpson index (a) and Shannon-Wiener index (b) maps for the seedlings’

group. The islands of low diversity in the grassland area shown by the Shannon index do not occur with the Simpson and the Shannon-Wiener indices. The Shannon-Wiener index, however, shows high species evenness over the whole grassland. In the forest, the Shannon-Wiener index brings out another aspect of the existence of islands of very low and very high species evenness all over the forest. The very low spatial dependence observed during model fitting was an indicator of this phenomenon.

(a) (b)



Figure 4-23: Simpson index (a) and Shannon-Wiener index (b) error maps for the

seedling group. In the forest, the Simpson and the Shannon-Wiener indices are affected by the sampling intensity. Lower uncertainty is observed at the centre part of the forest area. The Shannon-Wiener index also shows low uncertainty along the lines of data collection where it slowly increases towards the gaps between the lines. In the grassland, the Simpson and the Shannon-Wiener indices do not show any variation in uncertainty in relation to sampling intensity as is shown by the Shannon index. So there is less variation of uncertainty within the grassland area with the Simpson and the Shannon-Wiener indices than with the Shannon index. The Shannon index has highest uncertainty as compared to the Simpson and the Shannon-Wiener indices.

4.11 STATISTICAL ANALYSIS This part of the analysis is to help answer research question 8. To get the full detail of the differences, the analysis will be carried out to compare species diversity in the groups (trees, saplings and seedlings) within each area and then between the two areas.

4.11.1 Overall species diversity using Shannon index

All species within each group have been combined and a single index value has been calculated for each group. The Excel package has been used to calculate these values. The Shannon variance has also been calculated for each group. The results are indicated below:

(a) (b)



Table 4-23: Overall species diversity for the Shannon index per group.

Area Total Count No. of Species Shannon VarH' (TC) (NS) (H')

Grassland Trees 912 61 2.9065 0.00255 Saplings 1311 74 3.3778 0.00110 Seedlings 1911 65 2.9627 0.00115 Grassland joined 4134 89 3.3188 0.00045Forest Trees 1107 45 2.3158 0.00180 Saplings 1383 44 2.0765 0.00150 Seedlings 2019 38 1.8686 0.00090 Forest joined 4509 54 2.2075 0.00044

In the grassland, the Shannon index is highest in the saplings’ and lowest in the trees’ group while in the forest it is highest in the trees and lowest in the seedlings group. Comparing the values for the two areas, the grassland has values higher than the forest. This indicates that the grassland has higher species abundance and richness than the forest.

4.11.2 T-test using Shannon index values per group

Magurran (1988) further provides a method for calculating the t-test value from the Shannon index to test the significant differences in species abundance and richness between sampled areas. The formulae are indicated in the chapter on concepts. This was used to carry out species abundance and richness comparisons between the groups within each area and between the areas. For these calculations, the Shannon index values indicated in table 4-23 have been used. The results obtained are indicated below: Table 4-24: Shannon index t-test results obtained on comparing the species abundance

and evenness of the groups in the grassland (a), the forest (b) and between the two areas (c).

a) Grassland Groups t df Sapling/trees 10.036 601.938 saplings/seedlings 8.742 3134.786 seedlings/trees 0.067 1751.046 b) Forest Groups t df trees/saplings 4.168 2390.285 saplings/seedlings 4.244 2842.519 trees/seedlings 8.604 2190.397

The comparisons are significant at p = 0.05 except between seedlings and trees.

All comparisons are significant at p = 0.05.



c) Comparison between grassland and forest Groups t df Trees 8.952 1881.282 Saplings 25.523 2652.406 Seedlings 24.144 3843.088

Total species 37.123 8620.063 There is a highly significant difference between trees, saplings and seedlings species abundance and richness in the forest as compared to the grassland.

4.11.3 Overall species diversity for the Simpson and Shannon-Wiener indices

Table 4- 25: Overall species diversity for the Simpson and the Shannon-Wiener indices.

Area Group Simpson Shannon-Wiener (1-D) (E)

Grassland Trees 0.8699 0.7070 Saplings 0.9445 0.7848 Seedlings 0.8842 0.7097 Grassland joined 0.9291 0.7394 Forest trees 0.8320 0.6084 saplings 0.7919 0.5487 seedlings 0.7661 0.5137 Forest joined 0.8189 0.5534

The Simpson and the Shannon-Wiener indices follow the same trend as the Shannon index.

4.12 ANALYSIS OF RELATIONSHIP BETWEEN BIODIVERSITY AND ENVIRONMENTAL FACTORS So far, the underlying assumption of the analysis is that fire is the factor affecting the biodiversity. Here we want to find out if some environmental factors may also be affecting the level of biodiversity found in the study area. The selection of the factors depended mainly on the ease of acquiring their data, considering the time and the financial limitations at the time. The factors selected are, slope, elevation and aspect. They were first analysed using a General Linear model to see if they had any correlation with tree species diversity. The analysis was carried out on the Shannon index in relation to the three environmental factors. This was done using SPSS. The summary of the results is shown in the table below:



Table 4- 26 Environmental factors’ influence significance tests for each area

Covariate Shannon Slope 0.0632 Elevation 0.2364 Aspect 0.7638

Covariate (%) = 7.8281

Here no significance was directly evident but slope showed a much lower value. A scatter plot for all the index values against slope was made and a line fitted on it. A linear and a second degree polynomial were fitted to see which one gives the better fit. The output is shown below: (a) (b)

Figure 4-24: Graphs showing the scatter plot for the Shannon index with slope for both

forest and grassland combined (a-linear and b-polynomial trendline). The second degree polynomial gives a better R2 than the linear trend line fit. So for the next scatter plots we will use the polynomial. Further analysis was carried out to check for the area that is responsible for the probable relationship. The test was carried out for all the three environmental factors in both areas. The results are shown below: Table 4-27 Environmental factors’ influence at group level in the grassland

Grasslands Forest

Covariate Shannon Covariate Shannon Slope 0.000196 Slope 0.950688 Elevation 0.648686 Elevation 0.882224 Aspect 0.013192 Aspect 0.526842

Covariate (%) = 5.6053

A significant correlation was still found with only slope.



Scatter plots for each group in the grassland, where significance occurs, versus slope have been made and a line fitted on them to see where the relationship is highest. (a) (b) (c)

Figure 4-25 Graphs showing the scatter plot of grassland trees (a), saplings (b) and seedlings (c) for the Shannon index with slope. An average index value was calculated for each slope level in the scatter plot. The best R2 was got with the saplings followed by seedlings and trees had the lowest. Although there exists a relationship, there is very little that can be explained by this relationship. The best R2 equals 0.0888, which is very low.

CHAPTER 6 CONCLUSION AND RECOMENDATIONS


5. DISCUSSION

5.1 LOCAL PEOPLE AND THE FOREST The presence of indigenous people close to any forest has, in the eyes of a forester, been a threat to the survival of the forests. Fire is one of the tools that have been used through history for several domestic activities such as clearing and fertilising the land, recharging pastures, purging fields of pests and disease, vaccinating lands against later wildfire and for hunting (Pyne 1996). In this study, several participatory methods were used to discover why the local people are attached to the forest, how they use it and to view the state of the forest through their eyes. Results show that the grassland areas found within and adjacent to the forest were of great importance to them as a main source of animal protein in addition to other supplies like medicinal plants. In the grassland under study, most of their activities are limited to one side. It is in this area that we found animal traps and evidence of fresh burns. The grassland is located right inside the gazetted forest and although they could face prosecution for illegal hunting, this does not deter them from going there. This is in line with the findings of Obua et al (1998) and Howard (1991) concerning the local people’s use of forests. Hunting is often accompanied by burning and this is the main issue that has been condemned by the foresters. The concern is that forest area is being converted to grassland due to fires. In my study area this, however, was not true. Much more area had turned into forest, as shown by figure 3-7c, than from forest to grassland. Where burning was actively being carried out, no change in grassland cover was recorded after 26 years, (between 1962 and 1988), instead tree regeneration, which was not there before, could be observed on the aerial photos. The change detection carried out gave insight into a management system that has been in place. As far as burning is concerned, it has been reactive type of management. The eastern side of the grassland where there was no cover change between 1950 and 1962, reduced between 1962 and 1988. This could suggest that more burning was taking place on the eastern side than the western during the period between 1950 and 1962; figure 3-7a. Consequently, in the following years, more attention was given to this problematic area by keeping a closer watch over it. The hunters in turn must have shifted their activities to the western side. This being the case, between 1962 and 1988, the grassland area that was colonised is on the eastern side where there was now more guarding and thus less burning; figure 3-7b. Now the hunters seem to have stuck to the western side of the grassland because even during the field data collection, recently burnt areas were mainly observed on the western side and more trees were observed on the eastern side. The map that the hunters drew also only covered the western part of the grassland (figure 3-6).



5.2 TREE SPECIES DISTRIBUTION IN GRASSLAND AREA Grassland-woodland occupies more surface area world-wide than any other cover type. In areas with a short fire season, the type of cover is directly correlated with the frequency of fire. Where fire is excluded or suppressed, woodlands predominate. Fire frequency plays a major role in determining vegetation structure and it affects the floristic composition of ecosystems. Fire frequency has shaped the evolution of vegetation and any change in the fire cycle of a community will automatically entail a change in its floristic composition and structure. In a fire dependent ecosystem, the individual species develop survival strategies depending on how many times fire may occur in the lifetime of an individual plant Chandler et al, (1983). This is a typical description of the study area. Trees in the grassland such as Combretum polinun were observed to have protective mechanisms such as a thick bark which enable them survive the fires; see appendix 7. To get a clear picture of the biodiversity in the grassland area, it was necessary to divide the woody species into groups (trees, saplings and seedlings). In the tree group, the western side of the grassland, which experiences a higher fire frequency, had lower diversity as can be seen in figures 4-4 and 4-7. These areas are dominated by fire resistant tree species such as Combretum polinun, Acacia hockii, Securinega virosa and Terminalia glaucescens. Still fewer of the individual plants are able to grow to maturity. The eastern side which has not had fire for some time showed higher diversity because of the many species that have regenerated. Here, species that require less frequent fires can be found especially those that may need fire only once in their life time e.g. for germination. This difference in species diversity, for the trees’ group, between the eastern and the western side of the grassland was strongly brought out through spatial mapping. The saplings’ group in the grassland area covers both the young trees and small woody plants like Vitex doniana and Dombeya mukole. Many of these small woody plants have a protective method of locating their buds on the underground roots where they are protected from the fire by the soil and sprouting is chemically induced after a fire or other disturbance. At the same time, an adaptive trait must be considered in the context of the plant itself and the fire cycle to which it is exposed. In this way, species that do not possess the “classic” adaptive traits may also survive and reproduce in fire environments. The western and the southern isolated part of the grassland which experiences more burning showed lower species abundance and richness but species like Vernonia amygdalina, which are easily destroyed by fire but have ability to quickly regenerate after a fire, were also found in high numbers. Chandler et al, (1983) says that the period which a grassland can survive without being converted to forest when burning has stopped depends on its neighbours since some communities nearby may have the ability to invade the territory. This is true of the eastern and the central part of the grassland. Forest and forest edge species such as Fantumia elastica had regenerated in high numbers. So, although there is lower abundance and richness on the western part, the overall species abundance and richness is generally high in the saplings’ group because of this variation in disturbance regimes. Of the measures used, the NS and the Shannon index indicate the eastern side having higher sapling diversity than the western side. The Simpson and the Shannon-Wiener



indices, which are independent parameter measures, show the western side to have higher diversity. This is because on the western side, only those species that are ecologically adapted could be found while the eastern has a variety of species but in small quantities. On the eastern side, the lower level of disturbance has allowed many species to regenerate. This shows that where fires are less frequent, there is high overall species diversity (abundance and richness) but high fire frequency allows specific species to be dominant over the area. So a combination of these measures brings out a better picture of species distribution in this group. The future forest is affected by the seed tree availability in the neighbouring locations and seedlings are often found growing in the vicinity of their parents (Acharya, 1999). This was true of the grassland under study. For the seedlings’ group, the western part of the grassland which is burnt more often showed lower species abundance and richness because the ecologically adapted species which are mature enough to produce seed were also few. Much of the eastern and the central part of the grassland, which shows higher species abundance and richness, has the forest and the forest edge as its seed source in addition to the species within the grassland. Other species that were found in this area were those whose seed is dispersed by wind e.g. Fantumia elastica. The Shannon index shows the eastern side to have higher seedling species diversity. This agrees with the assumptions made that more species are found where there is less fire. The Shannon-Wiener index, however, shows almost uniform species evenness over the area which indicates that, irrespective of what species exist in an area, they are well distributed within each plot. Overall, the grassland has higher diversity on the eastern than the western side which has had a higher fire frequency. There is, however, a difference in the location of areas of higher dominance and evenness in the saplings’ and seedlings’ group. The combination of statistical and geostatistical methods in the analysis greatly helped bring out these unique features. While the statistical analysis enabled us to know which group has high overall diversity, the spatial analysis showed the exact location of the areas with high diversity. Considering the number of species distribution, the trees group shows the lowest diversity and the seedlings the highest (figure 4-4 – 4-6). This is logical because for a species to survive to the tree size, it must be ecologically adapted to the fire environment. In this case the fire frequency also plays a major role in controlling species distribution. There are fewer trees with these characteristics. In the seedling stage, however, many species can be found because after a fire, the place is open and any seed that lands there can germinate. The sorting only comes with the next fire. At the time of data collection, several months had passed after the last fire and so the regeneration was all still present. Using the indices, however, the saplings’ group showed the higher diversity, 3.3778. This is because indices take into account the proportional abundance of each species. This indicated that while there is high regeneration after a fire, only the ecologically adapted species survive to the saplings’ group. Many of the incoming species in the area which has not been burnt for sometime are still in the sapling’ group. In the area where fire has been applied recently, a number of the survivors belonging to the fire resistant species also occur in this group. These two categories combined with the fast growing light lovers makes the saplings soar above the other groups. The trees group is mainly composed of the fire resistant species and a few colonisers that have grown thus far. So this makes it the lowest in species abundance and richness, 2.9065.



5.4 TREE SPECIES DISTRIBUTION IN FOREST AREA Ecologists and foresters have for long known that it is extremely difficult to find distinct plant associations in the moist tropical forests. Most species are rare and few species are frequent. The tropical forests are, however, not as homogeneous as aerial photographs would make one to believe (Forman and Godron, 1986). At a quick glance Budongo forest appears to be one homogeneous mass with no order. This was also reflected by the existence of high overall species diversity. The reason is that the area has not had disturbance for a very long time and so trees have been able to grow in very high quantities. The use of biodiversity measures and their mapping, however, distinctly brought out the variation in species distribution over the forest area. Grouping the trees further emphasised the uniqueness of each group. The seed dispersal process, especially by birds, mammals and insects, often affects the spatial landscape homogeneity. Animals living in bands often choose areas with fruit concentrations as their “dining room” and these areas are a potential source of future forest heterogeneity. The heterogeneity-producing process is, however, compensated by the process of seed transport over considerable distances to the dining room, since few barriers may exist to inhibit movement in the low–contrast rainforest landscape. (Forman and Godron, 1986). These species distribution processes appear to have played a part in the species distribution in the forest under study. Considering the trees’ group, the areas that show a much higher diversity than the rest of the forest may have been dining rooms for certain animals in the past years. The increase in the tree species evenness northwards could be due to other factors like variation in the physical environment which were not explored during this study. In the saplings’ group, the species distribution homogeneity is more evident since most of the species in this group are the shade lovers. Their only distinction here would be the nutrient availability and the tree species that exist in the top canopy since some understorey species have preferences in the uperstorey species. So the sapling species diversity was found to be generally high over the whole forest but it slightly increased northwards. The seedlings’ group even showed higher homogeneity as far as species diversity is concerned. These have almost the same environment as the saplings but also change in the local conditions may affect their evenness. It was observed that in places where a tree had fallen, thus opening up the canopy, there were more seedlings. These are the areas with higher species evenness on the spatial map (Figure 4-22b). So the spatial mapping was of great help in highlighting this phenomenon. Ecosystem and community stability is ultimately dependent on environmental stability (Kent and Coker, 1998). The overall picture, for the tropical rainforest, is a relatively homogeneous, low contrast landscape with some heterogeneity present (Forman and Godron, 1986). This applies to the forest area that was studied. The environment being relatively stable, there exists a fully developed forest with a closed canopy. The seedlings only serve to replace the trees or saplings that may die. With the closed canopy, seeds do not get enough energy for germination. This makes the trees group have the highest diversity, 2.3158, and the seedlings the lowest, 1.8686 (table 4-23). The heterogeneity in species distribution was mainly observed in the trees and the seedlings group. The spatial mapping was of great importance in bringing out these contrasts.



5.4 SPECIES COMPOSITION AND ITS DIFFERENCE IN THE TWO AREAS An ecological community may be described as the assemblage of species populations at a particular place and time. Because both species and species clusters differ greatly within an ecosystem, a wide range of patterns and measurements are used. Among these are species composition, species richness and species dominance. Species composition as defined by Forman and Godron (1986), are the particular species present in a particular area. Species dominance looks at what species are most abundant and by how much, while species richness focuses on how many species are present. A focus on the rare species also bring out another aspect of a community. Each of these measures would bring out some uniqueness in the ecosystem. Analysing the species present in the study area showed that there is a higher variation of species in grassland than in the forest. The forest species occur in high abundance, TC equals 4509 while the grassland species show a lower abundance with a TC of 4134. The existence of a high number of species that occur in both areas and yet few of them occurring in all the three groups (trees, saplings and seedlings), was an indicator of the species exchange between the two areas. Most of the species have a home either in the grassland, edge or interior forest. The home is where they find their most suitable environment and they can grow and produce seed. These seeds are often dispersed to the other areas. The areas where they do not seed is where they were not occurring in all the three groups. The existence of variation in disturbance regimes, thus creating varying environments for species survival, has enabled a higher number of species to find their home in the grassland than in the forest. The grassland area has species that require a lot of light and yet they are not very tall trees. So they either survive on forest edges where conditions are not very rough or develop characteristics, like a thick bark or reduction of debris under them, that enable them survive the fire. The species that can not persist in presence of disturbance or those that need the shelter of the larger trees occurred in the three groups in the interior forest. Landscape stability has been defined as the resistance of a landscape to disturbance and its recovery from the disturbance. In a landscape, three types of stability may be identified. These are; physical system stability, recovery stability and resistance stability. Physical stability refers to ecosystems that can not be changed easily because of their possession of little amount of energy stores in biomass. Recovery stability is the ability of the ecosystem to return after being disturbed, while resistance is the ability of the system, when subjected to an environmental change or potential disturbance, to withstand or resist the variation (Forman and Godron, 1986). In both areas the species counts for the three groups (trees, saplings and seedlings) resulted in a bell shaped occurrence; table 4-2. In the grassland, the saplings having the highest number of species and the trees the lowest is an indicator of an ecosystem that exhibits recovery stability. After a fire, many more seeds germinate and so the trees that are destroyed can be eventually replaced. This influence of one group on the other was also showed by the NS correlations, table 4-4. The forest, however, had more species in the trees’ group and the least number in the seedlings’ group. The changes within the ecosystem are minimal since trees have a long life span and the seedlings only serve to replace those that may fall due to old age or wind-throw. The forest edge is a major seed corridor between the grassland and the forest and so it was found to have many species in common with both areas. Each area contributes to the richness of the other through seed dispersal. So while considering areas for biodiversity conservation, both areas plus the edge, which is the seed corridor, need to be considered.



Overall, the grassland has a higher number of species, 89, and also it has a higher number that occur in the 3 groups, 42, than the forest which has 54 and 27 respectively. This indicates that the variation in the disturbance regimes fosters an increase in species diversity over the area. This is a process that should be explored further. Due to the time constraints, it was not possible to carry out in-depth analysis on the difference in species composition of the two areas. This is an area that would require further exploration. Also the relationship of species in one group to another needs to be studied further.

5.5 DIFFERENCE IN SPECIES DISTRIBUTION BETWEEN THE TWO AREAS

Assessment and management of biodiversity requires that its different components are defined as clearly as possible, and that the factors underlying the loss can be defined (Angelstam, 1998). The level of disturbance in each disturbance regime also needs to be considered. In defining the biodiversity of any ecosystem, the statistical analysis alone does not take care of the spatial variation i.e., variation tied to location in place (Acharya, 1999). The statistics showed that there is a significant difference in tree species diversity between the two areas. The grassland area has higher tree species diversity, 3.3188, than the forest area, 2.2075 (table 4-23). This outcome is truly a contradiction to the commonly believed theory that the tropical rainforests hold the highest diversity. A few issues need to be taken into account here; the grassland is located right within the forest area and so there is continual seed outpour into it from both the edge and the interior forest. There is also variation in the disturbance regimes within the grassland area which allows a variety of species to exist. Chandler et al. (1983) agree with this, saying that fire does not cause all that much damage to an ecosystem but only slows down, halts, or rejuvenates the development of an ecosystem. The forest, however, has had no disturbance over a long period and so the main controlling factor is competition for resources. Here, many trees have a good chance of survival. So all the tree species that can compete favourably for the resources and also require the prevailing environmental conditions were found in good quantities. Many species were found in each plot but there was little variation between the plots. This low variation was shown by the standard deviation of both the NS and the indices’ descriptive statistics; tables 4-2, 4-14 and 4-18. The spatial mapping brings out the other unique features in both areas. The high tree species diversity shown by the forest maps is due to the high species diversity found at plot level although the variation of species between plots is small. In the grassland, however, the different disturbance regimes favour the existence of a variety of species and also in varying quantities. As the species diversity varies over the area, also the actual species vary. In the grassland, the model fitting generally showing higher spatial dependence than in forest highlighted the impact of fire disturbance over the area. This means that for purposes of observing species diversity, larger distances must be covered in the grassland than in the forest. These distances, however, vary between the groups in each area and also with each diversity measure used. The high variation within the ecosystem was also shown by the grassland models having higher nuggets than the forest models and the standard deviation also being higher for the grassland area in all the groups. The grassland error maps also occurring on the higher part of the colour scale than the forest



error maps re-emphasised this. The forest area which showed higher diversity at plot level, also showed much lower uncertainty of estimation over the area which means that the higher the biodiversity, the lower the uncertainty. The sampling intensity also has effect on the amount of uncertainty over the sampled area. The higher it is the lower the uncertainty. The use of three different indices also brings out the issue highlighted by Acharya (1999) that no single measure is sufficient for describing diversity. The Shannon index consistently showed a higher error of estimation than the Simpson and the Shannon-Wiener indices. In the saplings’ group, the Simpson and the Shannon-Wiener indices showed a divergence from the Shannon index in the location of the areas with higher diversity (figure 4-10 and 4-19). In the seedlings’ group, the Shannon-Wiener brought out unique features of both areas, which were not evident with the Shannon index (figure 4-22b). Here species evenness is more regular over the grassland as compared to the forest. So, although the forest shows high and more regular tree species distribution, the grassland has higher overall tree species diversity. There is also a higher species variation over the grassland area than over the forest.

5.6 RELATION OF SPECIES DIVERSITY TO ENVIRONMENTAL FACTORS Several species that are associated in a community are often assumed to display similar abundance behaviour but studies of species’ response to environmental gradients suggest that in practice each species’ behaviour greatly varies. Further complication is that each species found growing at a point on the earth’s surface will usually be responding to more than one environmental factor (Kent and Coker, 1996). Forman and Godron (1986) highlight more complication in the whole system in that usually environmental factors are also unevenly or patchily distributed over space. The result is a sharper ecotone, overlap zone, where the amount of factor changes abruptly in a short space. With all these factors in play, its hard to characterise the diversity of an ecosystem, which involves several species, to an environmental factor and find a good relationship. This was the case with the study area. Although the variation in environmental factors tested over the area was not very high, relationship was only found with slope in the grassland. The relationship was also low with the highest r2 being 0.0888. As can be observed on the scatter plots, (figure 4-25), only the areas that have high slope have low diversity and these are very few plots to create any meaningful discussion. The only deductions from this is that only ecologically adapted species are able to survive on the slopes in the presence of disturbance. Slope was also mentioned by the hunters as one of the criteria for selection of the starting point of a fire.



6. CONCLUSION AND RECOMMENDATIONS

6.1 CONCLUSION

1. The local people’s use of the forest Burning at face value could be judged as destructive to forest. But this analysis revealed that forest tracts in areas where burning has been maintained have remained unchanged in size. At the same time, local people have been supplied with the much needed protein. While the opposition to and fear of fire by most foresters can be justified due to the enormous destruction to forests every year, there is need to know more about benefits of burning on some forest ecosystems. Such insights could guide determination of fires that warrant suppression along with those that are beneficial and could be sanctioned. It is of great importance that for each forest mosaic, the management objectives for each landscape be fully considered as the fire laws are applied. Due to time constraints in this study, the fire used by the local people was not fully explored and time was not available to have in-depth study of the extent to which the fire management plans that exist, have been implemented. The one fact that came out is that in Ugandan forestry fire is considered an evil and thus uniformly condemned. So this lack of site specificity around fire and suppression of burning is a topic that warrants further exploration.

2. Species composition and distribution in the burnt-over area

The species composition in the grassland illustrates some issues that should be of interest to conservationists concerned with tree species diversity. While grassland/woodland mosaics are often believed to have low diversity compared to forest cores, the study revealed otherwise. The grassland area has a very high number of species and some occur in high quantities. There is, however, a high variation of species at plot level and a variation in the tree species in the often burnt area as compared to the less often burnt area. This was mainly attributed to the variation in the disturbance regimes within the grassland area. Many of the species that were found in the trees’ group showed adaptive characteristics that enable them to survive the disturbance. So the variation in the disturbance regimes allows tree species with various ecological requirements to persist in grassland.

3. Species composition and distribution in the forest area

The forest area showed a high number of species over the area but with generally a low variation between the plots. The species that are rare also occur in very small quantities while for each group there were species that were found in high quantities. These characteristic species possessed ecological adaptations that may enable relative co-existence within this ecosystem. Overall, many of the tree species that require low levels of disturbance do not occur outside of interior forest.

4. Comparison of the two areas

In the comparison of the two areas, mapping of the tree species diversity, at plot level, revealed that the forest often had higher species diversity. The grassland, on the other hand, had higher variation of tree species diversity over the area. This is probably related to higher levels of disturbance. Calculation of a single index value and the species composition analysis revealed the grassland to have higher diversity than the undisturbed forest area. As the levels of diversity vary, the niche architecture between forest and woodland/grassland may well diverge. The grassland, apart from



the tree species within it that can seed, is in the vicinity of many seeding species of the forest edge and the interior forest and the variation in the disturbance regimes within it provides an ecologically suitable environment for many species. The forest, however, has fewer edges and so less seed can become established. Forest also has a closed canopy which may not give chance to many species to come up. At the same time, there are species that can only survive in low disturbance environments that interior forests provide. So while burning provides habitat for many species, interior forest is also a complementary refuge for tree diversity.

5. Influence of environmental factors on tree species diversity

A statistically significant environmental influence on tree species diversity was only observed with slope and only in the grassland area. But the percentage of influence detected is low. What can be said at the moment is that only ecologically adapted species survive high slope areas. This question warrants further study for the grassland area.

6. Conclusion concerning methodology The use of various indices, when considered on the statistical side only, showed a similar trend for all indices i.e. the group which one index showed as having high diversity, was the same one another index showed as high. This phenomenon was indicated by the very high correlation observed between the indices in both areas. Using geostatistical methods to map the tree species diversity over the areas, however, brought out the uniqueness of the groups within each area. There was variation in the location of the high tree species diversity over the area. This factor that is essential for species conservation since in most rainforests little is yet known about whether the rare species that compose much of the forest are distributed randomly, regularly, or in aggregated pattern.



6.2. RECOMMENDATIONS

1. To have long-term preservation of tree species diversity in this part of Uganda, it is

necessary to shift land management to better imitate the disturbances that are carried out by the local people. Fire suppression type of management should to be avoided. The system that the hunters use in the burning warrants further analysis. Advantages identified could be incorporated into a comprehensive burning plan which would ensure biodiversity conservation along with access to woodland/grassland.

2. For the grassland to persist, sufficient edges as a landscape matrix must be

maintained. But too much grassland would destroy larger stands of forest. 3. In the forests where core forest sites have been selected as nature reserves, the species

flow, both of fauna and of flora, should be mapped precisely so that other ecosystems which may require varying management systems may also be preserved.

4. Since the local people can not be excluded from the forest, ways of acknowledging

and valuing their priorities for forest protection can be explored. For example, for the hunters, there must be ways they can obtain their much needed protein while at the same time conserve the forest. A balanced mix of incentives and disincentives is needed in protecting the forest and the grassland. Institutional and legal frameworks should be put in place to ensure that conservation and sustainable use of natural resources are integrated into the wide range of social, cultural and economic context in which actions must be taken.

5. In order to set effective protected area boundaries including cores and buffers,

indicator species for the forest interior, the edge and grassland area can be selected and their flow across various ecological gradients mapped. This would provide, for each conserved tree species, a habitant for seed establishment and an adjacent area for colonising other ecosystems. Protected area designs can better reflect these gradients, including environmental factors such as soil types, fire regimes (in which season burning takes place and how), microclimate and topography. Such designs would provide more comprehensive sets of habitat conditions and selection factors as a sound basis for biodiversity conservation.

6. Considering the time available, the level of uncertainty acceptable and the ecosystem

to be assessed, care must be taken in selecting the best combination of indices for a study. A preliminary survey is highly recommended so that the important measures can be established for the particular area under study. In future studies, mapping of rare species in each ecosystem would enable conservationists to establish density areas of overlap and clustering of these species. This would contribute to a more precise and credible delineation of areas for conservation.

Appendices


References

Acharya B., 1999. Forest biodiversity assessment: A spatial analysis of tree species diversity in Nepal. International Institute for Aerospace Survey and Earth Sciences ITC, Enschede, The Netherlands, pp 32, 37, 157. PhD Thesis Rijksherbarium/Hortus Botanicus of Leiden University (ITC publication; 72) Angelstam A., 1998. Towards a logic for assessing Biodiversity in Boreal forest. In: Proceedings of the conference on Assessment of Biodiversity for Improved Forest Planning, 7-11 October 1996, held in Monte Verità, Switzerland, pp 302 Begon M, Harper J. L. and C. R. Townsend, 1990. Ecology: Individuals, populations and communities. Blackwel Scientific. Bradon, K E and Wells, M, P (1992), Planning for people & parks; design dilemmas’, World development 20(4): pp 557 – 570 Burrough P.A., 1988. Principles of geographical information systems for land resources assessment. Clarendon press Oxford. pp 157. ISBN 0-19-854592-4 (pbk) Chandler C., Cheney P., Thomas P., Trabaud L., Williams D., 1983. Fire in Forestry. Volume 1. Forest fire behavior and effects. John Wiley & sons. New York Chichester Brisbane Toronto Singapore, pp 2-12, 146 – 148,159-165, 266, 283, 357 Cowie J., 1992. Conserving biodiversity. Biologist (1992) Vol. 39 nr. 4, pp 142 Dallmeir, F. 1998. Measuring and Monitoring Forest Biodiversity: the SI/MAB Model. In: Proceedings of the conference on Assessment of Biodiversity for Improved Forest Planning, 7-11 October 1996, held in Monte Verità, Switzerland, pp15 - 17 FAO - Unesco, 1977. Soil map of the world 1:5 000 000 Volume VI Africa and associated map: 84 Forest Depertment, 1997. Forest management plan for Budongo Forest Reserve. Republic of Uganda, Ministry of natural resources, Kampala, Uganda. Forman R.T.T. and Godron M., 1986. Landscape ecology. John Wiley & sons, Inc. New York, Chichester, Brisbane, Toronto, Singapore, pp 4-527 Frannklin J. F., Cromack K., Denison W, et al. 1981. Ecological characteristics of old-growth Douglas-fir forests. In: USDA Forest Science General Technology Report PNW – 118. Pacific North West Forest and range Experiment Station, Portland, Oregon in Conservation Biology. Hamilton A. C., 1984. Deforestation in Uganda. Oxford University Press, Nairobi. Hamilton A., 1991. A guide to Ugandan forest trees. Makerere University, Kampala, Uganda, pp 73 - 276 Heusèrr M.J.J., 1998. Putting diversity indices into practice. In: Proceedings of the conference on Assessment of Biodiversity for Improved Forest Planning, 7-11 October 1996, held in Monte Verità, Switzerland, pp. 173

Appendices


Holdgate M. & Giovannini B., 1994. Biodiversity conservation: Foundations of the 21st Century. In: Widening Perspectives on Biodiversity. IUCN – The World Conservation Union, Grand, Switzerland and Cambridge, UK, and International Academy of the Environment, Geneva, Switzerland, pp 3-6. Howard p. 1991. Nature conservation in Uganda’s Tropical Forest reserves. IUCN, Gland, Switzerland and Cambridge, UK: pp 46-48, 313 Howard P.C et al, 1998. Biodiversity assessment for conservation planning in Uganda’s forests. In: Proceedings of the conference on Assessment of Biodiversity for Improved Forest Planning, 7-11 October 1996, held in Monte Verità, Switzerland, pp 263-270 Isaaks E.H. and Srivastava R.M., 1989. An introduction to applied geostatistics. Oxford University Press, 198 Madiason Avenue, New York, 10016-4314, pp 55-60, 278-287, 374-375. Kent M. and Coker P. 1996. Vegetation description and analysis. John Wiley & sons. New York Chichester Brisbane Toronto Singapore, pp 14-15, 96-105 Krebs C. J. 1978. Ecology: the experimental analysis of distribution and abundance. Harper and Row. Langoya, C. D. and Long, C. 1977. Local communities and ecotourism development in Budongo Forest Reserve, Uganda. Rural Development Forestry Network Paper 22e. ODI, London. Magurran A. E., 1988. Ecological diversity and its measurement. Princeton University Press, Princeton, New Jersey, Pp 1 – 11, 37 – 39, 57, 72 – 79, 104 McNeely J.A., Miller K, R., Reid W. V., Mittermeier R. A. and Werner T. B., 1990. Conserving world’s biological diversity. IUCN, WRI, CI, WWF and World Bank. McNeely J.A., 1994. Critical Issues in the Implementation of the conservation on Biological Diversity. In: Widening Perspectives on Biodiversity. IUCN – The World Conservation Union, Grand, Switzerland and Cambridge, UK, and International Academy of the Environment, Geneva, Switzerland, pp 7-9. National Environment Management Authority (NEMA), 1996. State of the environment report for Uganda. NEMA, Kampala, Uganda, pp 1-15, 73, 145 National Environment Management Authority (NEMA), 1998. State of the environment report for Uganda. Obua J., Banana A.Y. and Turyahabwe N. 1998. Attitudes of local communities towards forest management practices in Uganda: the case of Budongo Forest Reserve. Commonwealth Forestry Review, Vol. 77 nr. 2, pp 113-117 OTA (Office of Technology Assessment of the U.S. Congress) 1987. The technologies to maintain biological diversity. Report No. OTA-F-330. Washington, DC: U.S. Government Printing Office. Paterson J. D., 1991. The Ecology and History of Uganda’s Budongo Forest. In: Forest and Conservation History, Vol. 35: p179 - 186

Appendices


Pyne S.J., 1996. Wild Heath; A proleomenon to the cultural fire history of Nothern Eurasia. In: Fire in Ecosystems of Borial Eurasia. Kluwer Academic publishers, Dordorecht, Boston, London, pp 23-28. Parviainen J., and Päivinen R., 1998. Information needs for biodiversity assessment derived from international forestry discussions. In: Proceedings of the conference on Assessment of Biodiversity for Improved Forest Planning, 7-11 October 1996, held in Monte Verità, Switzerland, pp. 63 – 70. Podolsky R, Freilich J. and R. Knehr, 1992. Predicting Plant Species Richness from Remotely Sensed Data in a High Desert Ecosystem, Joshua Tree National Monument, Twentynine Palms, CA 92277, Bureau of Land management North Palm Springs, CA 92258. Reid W.V, McNeely A.J, Tunstall B.D, Bryant A.D and Winograd M, 1993. Biodiversity indicators for policy makers. Washington, D.C: World resources institute, pp1-33. Saldaña A., Stein A, and Zink J.A., 1998. Spatial variability of soil properties at different scales within three terraces of the Henares River (Spain). Catena Vol. 33, nr 139 – 153, pp 140 –152. Senanayake R., 1994. The need for the measurement and evaluation of biodiversity. In: Widening Perspectives on Biodiversity. IUCN – The World Conservation Union, Grand, Switzerland and Cambridge, UK, and International Academy of the Environment, Geneva, Switzerland, Southwood T. R. E, 1977. Habitat, the template for ecological strategies?: Presidential address to the British ecological society, Journal of animal ecology, 46(1977). pp 337-365. Stein A., 1993. SPATANAL PC software. Department of Soil Science and Geology. Agriculture University of Wageningen, Wageningen. Trangmar B. B., Yost R. S. and G. Uehara, 1985. Application of Geostatistics to spatial studies of soil properties. In: Advances in Agronomy, Vol 38. Academic Press, Inc. pp 45-91. Whittaker R.H., 1978. Ordination of plant communities. Dr. Junk W. The Hague 1978, pp 40 -41 Wright J.S. 1996. Plant species diversity and ecosystem functioning in tropical forests. In: Biodiversity and ecosystem Processes in Tropical Forests, Springer, Berlin, Heidelberg, New York, Barcelona, Budapest, Hong Kong, London, Milan, Paris, Santa Clare, Singapore, Tokyo. pp 12.

Appendices


Appendix 1: Species composition

a) Analysis of species found in the grassland and the forest. Shared species TC TC Species unique to Species unique to forest TC

Forest Grassland Grassland TC

Albizzia zygia 2 771 Acacia hockii 35 Albizia gummifera 3

Alstonia boonei 16 1 Acacia sieberiana 39 Bosqueia phoberos 17

Aningeria altissima 15 27 Acassia seyal 39 Cassia siemea 3

Antiaris toxicaria 10 19 Albizzia coriaria 7 Celtis durandii 15

Blighia unijugata 11 24 Albizzia grandibracteata 1 Celtis milbraedii 581

Caloncoba schweinfurthii 2 23 Alchornea spp 1 Chrysophyllum perpulchrum 27

Canarium schweinfurthii 1 1 Annona senegalensis 159 Entandrophragma cylindricum 3

Celtis waghtii 31 2 Apodytes dimidiata 11 Erythrina excelsa 2

Celtis zenkeri 64 91 Balanites wilsoniana 1 Macaranga schweinfurthii 8

Chrysophyllum albidum 38 17 Bombax buonopozense 1 Mildbraediodendron excelsum 4

Cleistopholis patens 4 12 Bridelia micrantha 160 Mitragyna stipulosa 1

Coffea spp 82 71 Chaetacme aristata 36 Morinda lucida 10

Cola gigantea 1 56 Chlorophora excelsa 4 Trichilia emetica 3

Croton megalocarpus 9 107 Combretum mole 21 Trichilia prieuriana 5

Cynometra alexandrii 541 12 Combretum polinum 472

Desplatsia dewevrei 7 10 Cordia millenii 4

Entandrophragma angolense 9 1 Cussonia arborea 3

Entandrophragma utile 4 2 Dichristachys cinerea 54

Fagaropsis angolensis 7 14 Dichrostachys cinera 1

Funtumia elastica 96 416 Dombeya mukole 44

Guarea cedrata 20 1 Elepsia senegalensis 2

Holoptelea grandis 2 2 Erythrina abyssinnica 3

Khaya anthotheca 88 23 Fagara leprieurii 1

Klainedoxa gabunensis 6 29 Ficus brachypoda 11

Lasiodiscus mildbraedii 1128 1 Ficus exasperata 3

Maerua duchensii 6 1 Ficus magalocarphus 7

Maesopsis eminii 4 30 Ficus natalensis 5

Mammea africana 16 52 Ficus sur 22

Myrianthus holstii 15 63 Grewia tricocapa 42

Phyllanthus discoideus 8 37 Guava spp 1

Pseudospondias microcarpa 5 13 Haronga madagascariensis 42

Rinorea ilicifolia 1312 13 Hymenocardia acida 4

Sapium ellipticum 4 6 Lannea kerstingii 3

Spanthodia campanulata 1 5 Lovoa brownii 1 Species unique to

Strombosia scheffleri 3 7 Lovoa trichiliodes 25 Grassland TCTabernaemontana holstii 102 84 Markhamia platycalyx 1 continued Tapura fisheri 12 42 Monodora angolensis 1

Teclea nobilis 16 79 Morus lactea 13 Stereospumum kunthianum 46

Tetrapleura tetraptera 2 15 Olea welwitschii 8 Terminalia glaucescens 90

Trichilia rubensens 127 49 Piliostgma thonningii 37 Trema orientalis 6

Prunus africana 3 Trichilia dregeana 1

Raphia spp 90 Vernonia amygdalina 273

Rauvolfia oxyphylla 2 Vitex doniana 4

Securinega virosa 65

Appendices


b) Dominant species in each area Dominant in Forest Dominant in grassland Almost equal

Alstonia boonei Albizzia zygia Aningeria altissima Celtis waghtii Caloncoba schweinfurthii Antiaris toxicaria Cynometra alexandrii Cleistopholis patens Blighia unijugata Entandrophragma angolense Cola gigantea Canarium schweinfurthii Guarea cedrata Croton megalocarpus Celtis zenkeri Khaya anthotheca Funtumia elastica Chrysophyllum albidum Lasiodiscus mildbraedii Klainedoxa gabunensis Coffea spp Maerua duchensii Maesopsis eminii Desplatsia dewevrei Rinorea ilicifolia Mammea africana Entandrophragma utile Trichilia rubensens Myrianthus holstii Fagaropsis angolensis Phyllanthus discoideus Holoptelea grandis Pseudospondias microcarpa Sapium ellipticum Spanthodia campanulata Strombosia scheffleri Tapura fisheri Tabernaemontana holstii Teclea nobilis Tetrapleura tetraptera

Appendices


c) Analysis of species found in the forest area Species found in all groups Species found in two groups Species unique to group

F-Trees/Saplings

Species name TC Species name Trees Sap. F - Trees Trees Sap. Seed. TC TC Species name TC

Aningeria altissima 4 6 5 Albizzia gummifera 2 1 Caloncoba schweinfurthii 2 Celtis milbraedii 272 282 27 Alstonia boonei 15 1 Canarium schweinfurthii 1 Celtis waghtii 6 14 11 Bosqueia phoberos 7 10 Celtis durandii 15 Celtis zenkeri 45 10 9 Cassia siamea 2 1 Holoptelea grandis 2 Chrysophyllum albidum 8 7 23 Cleistopholis patens 2 2 Mitragyna stipulosa 1 Chrysophyllum perpulchrum 13 6 8 Entandrophragma angolense 2 6 Trichilia prieuriana 5 Coffea spp 19 38 25 Entandrophragma cylindricum 2 1 Croton megalocarpus 2 4 3 Macaranga schweinfurthii 7 1 Cynometra alexandrii 56 19 466 Morinda lucida 2 8

Desplatsia dewevrei 2 2 3 Entandrophragma utile 2 1 1 F-Trees/Seedlings F - Saplings

Fagaropsis angolensis 3 2 2 Species name Trees Seed. Species name TC

Funtumia elastica 52 28 16 TC TC Entandrophragma gabunensis 1

Guarea cedrata 2 10 8 Maesopsis eminii 1 3 Spanthodia campanulata 1

Khaya anthotheca 9 32 47 Sapium ellipticum 2 2

Klainedoxa gabunensis 2 2 2 Strombosia scheffleri 2 1

Lasiodiscus mildbraedii 308 320 500

Maerua duchensii 3 2 1 Mammea africana 7 3 6 Myrianthus holstii 1 7 7 F-Saplings/Seedlings F - Seedlings

Phyllanthus discoideus 4 2 2 Species name Sap. Seed. Species name TC

Pseudospondias microcarpa 2 2 1 TC TC Albizzia zygia 2

Rinorea ilicifolia 164 458 690 Antiaris toxicaria 2 8 Cola gigantea 1

Tabernaemontana holstii 3 27 72 Blighia unijugata 5 6

Tapura fisheri 4 2 6 Erythrina excelsa 1 1

Teclea nobilis 1 6 9 Mildbraediodendron excelsum 2 2 Trichilia rubensens 41 46 40 Tetrapleura tetraptera 1 1

Trichilia emetica 1 2

Appendices


d) Analysis of species found in the grassland area Species found in all groups Species found in two groups Species unique to group

G-Trees/Saplings

Species name TC Species name Trees Sap. G - Trees Trees Sap. Seed. TC TC Species name TC

Acacia hockii 14 12 9 Chaetacme aistrata 21 15 Elepsia senegalensis 2 Acacia sieberiana 8 1 30 Chlorophora excelsa 2 2 Erythrina abyssinnica 3 Acassia seyal 15 3 21 Fagaropsis angolensis 5 9 Fagara leprieurii 1 Albizzia coriaria 2 2 3 Ficus magalocarphus 2 5 Ficus exasperata 3 Albizzia zygia 78 130 563 Ficus natalensis 2 3 Guava spp 1 Annona senegalensis 33 69 57 Hymenocardia acida 2 2 Lovoa brownii 1 Antiaris toxicaria 2 4 13 Lannea kerstingii 2 1 Maerua duchensii 1 Apodytes dimidiata 8 1 2 Maesopsis eminii 29 1 Bridelia micrantha 23 70 67 Prunus africana 1 2 Caloncoba schweinfurthii 8 14 1 Trema orientalis 3 3 Celtis zenkeri 4 35 52 G - Saplings Cleistopholis patens 2 7 3 Species name TC Coffea spp 1 26 44 Alchornea spp 1 Cola gigantea 13 25 18 Alstonia boonei 1 Combretum mole 2 4 15 G-Trees/Seedlings Balanites wilsoniana 1 Combretum polinum 301 113 58 Species name Trees Seed. Canarium Schweinfurthii 1 Croton megalocarpus 25 27 55 TC TC Guarea cedrata 1

Cynometra alexandrii 1 2 9 Cordia millenii 3 1 Markhamia platycalyx 1

Dichrostachys cinera 27 24 4 Raphia spp 49 41 Monodora angolensis 1

Ficus brachypoda 1 7 3 Rauvolfia oxyphylla 2

Ficus sur 11 5 6

Funtumia elastica 28 180 208 G-Saplings/Seedlings Grewia tricocapa 18 14 10 Species name Sap. Seed. Haronga madagascariensis 21 13 8 TC TC Khaya anthotheca 3 16 4 Aningeria altissima 6 21 G - Seedlings

Klainedoxa gabunensis 5 5 19 Blighia unijugata 16 8 Species name TC

Lovoa trichiliodes 1 7 17 Celtis waghtii 1 1 Albizzia grandibracteata 1

Mammea africana 1 4 47 Chrysophyllum albidum 3 14 Bombax buonopozense 1

Morus lactea 1 5 7 Desplatsia dewevrei 3 7 Cussonia arborea 3 Myrianthus holstii 4 27 32 Dombeya mukole 31 13 Entandroplagma angolense 1 Phyllanthus discoideus 12 16 9 Entandrophragma utile 1 1 Lasiodiscus mildbraedii 1 Piliostgma thonningii 21 8 8 Holoptelea grandis 1 1 Trichilia dregeana 1 Pseudospondias microcarpa 5 7 1 Olea welwitschii 5 3 Sapium ellipticum 2 3 1 Rinorea ilicifolia 5 8 Securinega virosa 1 60 4 Strombosia scheffleri 2 5 Spanthodea campanurata 1 3 1 Tetrapleura tetraptera 1 14 Stereospumum kunthianum 18 20 8 Trichilia rubensens 23 26

Tabernaemontana holstii 6 37 41 Vitex doniana 2 2

Tapura fisheri 2 22 18

Teclea nobilis 1 7 71

Terminalia glaucescens 45 24 21

Vernonia amygdalina 4 100 169

Appendices


e) Species found in all the three areas Species found in all the 3 areas Species found in two areas Species unique to respective area Forest/Grassland

Species name TC Species name For. Grass. Edge Forest Grass. edge TC TC Species name TC

Albizzia zygia 2 771 13 Canarium schweinfurthii 1 1 Acassia seibriana 3 Alstonia boonei 16 1 1 Maesopsis eminii 4 30 Acassia senia 1 Aningeria altissima 15 27 10 Spanthodia campanulata 1 5 Fantumia africana 15 Antiaris toxicaria 10 19 16 Musanga cecropioides 1 Blighia unijugata 11 24 21 Newtonia buchananii 1 Caloncoba schweinfurthii 2 23 7 Forest/Edge Celtis waghtii 31 2 4 Species name For. Edge Celtis zenkeri 64 91 40 TC TC Chrysophyllum albidum 38 17 40 Bosqueia phoberos 17 2 Cleistopholis patens 4 12 2 Celtis milbraedii 581 9 Grassland Coffea spp 82 71 91 Chrysophyllum perpulchrum 27 1 Species name TCCola gigantea 1 56 20 Trichilia emetica 3 7 Acacia hockii 35 Croton megalocarpus 9 107 22 Acacia sieberiana 39 Cynometra alexandrii 541 12 97 Albizzia coriaria 7 Desplatsia dewevrei 7 10 6 Grassland\Edge Albizzia grandibracteata 1 Entandrophragma angolense 9 1 2 Species name Grass

. Edge Annona senegalensis 159

Entandrophragma utile 4 2 3 TC TC Bombax buonopozense 1 Fagaropsis angolensis 7 14 3 Acassia seyal 39 1 Chlorophora excelsa 4 Funtumia elastica 96 416 33 Alchornea cordifolia 1 1 Combretum mole 21 Guarea cedrata 20 1 3 Apodytes dimidiata 11 2 Cussonia arborea 3 Holoptelea grandis 2 2 4 Balanites wilsoniana 1 2 Dombeya mukole 44 Khaya anthotheca 88 23 20 Bridelia micrantha 160 9 Elepsia senegalensis 2 Klainedoxa gabunensis 6 29 22 Combretum polinum 472 5 Erythrina abyssinnica 3 Lasiodiscus mildbraedii 1128 1 22 Cordia millenii 4 7 Fagara leprieurii 1 Maerua duchensii 6 1 17 Dichristachys cinerea 55 2 Ficus exasperata 3 Mammea africana 16 52 38 Ficus brachypoda 11 1 Ficus magalocarphus 7 Myrianthus holstii 15 63 20 Ficus natalensis 5 4 Grewia tricocapa 42 Phyllanthus discoideus 8 37 1 Ficus sur 22 1 Guava spp 1 Pseudospondias microcarpa 5 13 17 Markhamia platycalyx 1 3 Haronga madagascariensis 42 Rinorea ilicifolia 1312 13 16 Morus lactea 13 3 Hymenocardia acida 4 Sapium ellipticum 4 6 12 Olea welwitschii 8 1 Lannea kerstingii 3 Strombosia scheffleri 3 7 2 Prunus africana 3 1 Lovoa brownii 1 Tabernaemontana holstii 102 84 70 Raphia spp 90 2 Lovoa trichiliodes 25 Tapura fisheri 12 42 16 Chaetacme aristata 36 13 Monodora angolensis 1 Teclea nobilis 16 79 59 Trema orientalis 6 19 Piliostgma thonningii 37 Tetrapleura tetraptera 2 15 11 Trichilia dregeana 1 2 Rauvolfia oxyphylla 2 Trichilia rubensens 127 49 26 Securinega virosa 65

Stereospumum kunthianum 46 Terminalia glaucescens 90 Vernonia amygdalina 273 Vitex doniana 4

Appendices


f). Indication of where each species occurs in the 3 groups. Found in forest & grasssland v Found in both areas & in all forest Found in all 3gp of forest

Celtis zenkeri Aningeria altissima and occurs only in forest

Coffea spp Celtis waghtii Celtis milbraedii

Croton megalocarpus Chrysophyllum albidum Chrysophyllum perpulchrum

Cynometra alexandrii Entandrophragma utile 2

Funtumia elastica Fagaropsis angolensis Khaya anthotheca Guarea cedrata

Klainedoxa gabunensis Lasiodiscus mildbraedii Found in all 3gp of grassland

Mammea africana Maerua duchensii and occur only in grassland

Myrianthus holstii Rinorea ilicifolia Acacia hockii

Phyllanthus discoideus Trichilia rubensens Acacia sieberiana

Pseudospondias microcarpa Desplatsia dewevrei Acassia seyal(hockii)

Tabernaemontana holstii 11 Albizzia coriaria

Tapura fisheri Apodytes dimidiata

Teclea nobilis Bridelia micrantha

14 Found in both areas & in all Grassland Combretum mole

Albizzia zygia Combretum polinum

Antiaris toxicaria Dichrostachys cinera

Caloncoba schweinfurthii Ficus brachypoda

Cola gigantea Ficus sur

Sapium ellipticum Grewia tricocapa

Spanthodia campanulata Haronga madagascariensis

6 Lovoa trichiliodes

Mammea africana

Morus lactea

Securinega virosa

Stereospumum kunthianum

Terminalia glaucescens

Vernonia amygdalina

20

Appendices


Appendix 2: Data tables used in statistics and Geostatistics analysis: (a) Forest trees

Plot id Easting Northing Total treecount (N)

No. of species(S)

Simpson (1-D)

Shannon (H') Shannon-Wiener (E)

0-0 335350 189475 25 7 0.806667 1.654959 0.850481 0-75 335350 189545 23 8 0.790514 1.687748 0.811635 1//1 335050 189775 23 10 0.814229 1.865559 0.810202 1//2 335125 189775 5 3 0.800000 1.054920 0.960230 1//3 335200 189775 18 5 0.725490 1.377608 0.855956 1//4 335275 189775 20 7 0.689474 1.442783 0.741444 1//5 335350 189775 25 9 0.840000 1.880695 0.855941 1//6 335425 189775 20 6 0.784211 1.540123 0.859559 1//7 335500 189775 16 5 0.683333 1.229919 0.764191

1//75 335350 189850 18 8 0.830065 1.769036 0.850727 1//8 335575 189775 18 6 0.718954 1.406678 0.785082 1//9 335650 189775 22 7 0.735931 1.492738 0.767115 2//1 335050 190075 24 7 0.833333 1.715825 0.881760 2//2 335125 190075 23 8 0.833992 1.789251 0.860448 2//3 335200 190075 32 9 0.737903 1.624353 0.739275 2//4 335275 190075 18 7 0.738562 1.531641 0.787108 2//5 335350 190075 23 9 0.869565 1.978048 0.900248 2//6 335425 190075 22 9 0.835498 1.857201 0.845249 2//7 335500 190075 18 7 0.810458 1.671531 0.858997

2//75 335350 190150 12 6 0.848485 1.632631 0.911189 2//8 335575 190075 21 10 0.895238 2.111590 0.917052 2//9 335650 190075 22 7 0.714286 1.463300 0.751987 3//1 335050 190375 24 5 0.724638 1.352209 0.840175 3//2 335125 190375 30 7 0.696552 1.408287 0.723717 3//3 335200 190375 30 8 0.793103 1.725414 0.829749 3//4 335275 190375 14 6 0.813187 1.569153 0.875761 3//5 335350 190375 16 7 0.858333 1.771016 0.910122 3//6 335425 190375 22 8 0.792208 1.700370 0.817705 3//7 335500 190375 27 10 0.840456 1.918490 0.833189

3//75 335350 190450 19 7 0.748538 1.538834 0.790804 3//8 335575 190375 17 9 0.882353 1.972104 0.897543 3//9 335650 190375 23 13 0.928854 2.378475 0.927299 4//1 335050 190675 24 6 0.728261 1.408419 0.786054 4//2 335125 190675 21 8 0.790476 1.674409 0.805220 4//3 335200 190675 25 9 0.853333 1.911288 0.869865 4//4 335275 190675 20 8 0.857895 1.844440 0.886988 4//5 335350 190675 11 3 0.618182 0.916465 0.834202 4//6 335425 190675 18 6 0.777778 1.503461 0.839097 4//7 335500 190675 24 4 0.543478 0.917965 0.662172

4//75 335350 190750 23 5 0.802372 1.534643 0.953527 4//8 335575 190675 16 7 0.841667 1.717076 0.882402 4//9 335650 190675 25 7 0.830000 1.733666 0.890928 5//1 335050 190975 28 10 0.854497 1.991981 0.865106 5//2 335125 190975 16 8 0.875000 1.873452 0.900940 5//3 335200 190975 26 12 0.895385 2.195067 0.883360 5//4 335275 190975 38 12 0.816501 1.939863 0.780658 5//5 335350 190975 30 12 0.873563 2.144143 0.862867 5//6 335425 190975 19 10 0.918129 2.159732 0.937960 5//7 335500 190975 16 4 0.791667 1.370502 0.988609

5//75 335350 191050 23 8 0.830040 1.774477 0.853343 5//8 335575 190975 22 7 0.722944 1.490231 0.765827 5//9 335650 190975 12 9 0.939394 2.094729 0.953352

Appendices


(b) Forest saplings

Plot id Easting Northing Total tree count (N)

No. of species(S)

Simpson (1-D)

Shannon (H')

Shannon-Wiener (E)

0-0 335350 189475 34 11 0.82531 1.90854 0.79592 0-75 335350 189525 32 11 0.82258 1.94863 0.81264 1//1 335050 189775 17 11 0.91176 2.19676 0.91612 1//2 335125 189775 15 7 0.85714 1.74897 0.89879 1//3 335200 189775 9 4 0.58333 1.00272 0.72331 1//4 335275 189775 47 6 0.46346 0.97081 0.54182 1//5 335350 189775 29 11 0.81773 1.92559 0.80303 1//6 335425 189775 22 5 0.58009 1.11202 0.69094 1//7 335500 189775 14 4 0.78022 1.33374 0.96209

1//75 335350 189850 39 13 0.89069 2.25593 0.87952 1//8 335575 189775 14 7 0.85714 1.74787 0.89823 1//9 335650 189775 21 5 0.70952 1.30080 0.80823 2//1 335050 190075 62 6 0.26864 0.63530 0.35457 2//2 335125 190075 24 8 0.82246 1.75655 0.84472 2//3 335200 190075 27 9 0.71795 1.61937 0.73701 2//4 335275 190075 19 6 0.70175 1.33783 0.74666 2//5 335350 190075 28 5 0.70899 1.28042 0.79557 2//6 335425 190075 29 9 0.81281 1.78408 0.81197 2//7 335500 190075 19 9 0.84795 1.88212 0.85659

2//75 335350 190150 21 9 0.86667 1.93847 0.88223 2//8 335575 190075 18 6 0.71895 1.40668 0.78508 2//9 335650 190075 19 7 0.75439 1.51061 0.77630 3//1 335050 190375 28 6 0.60847 1.21596 0.67864 3//2 335125 190375 24 8 0.78261 1.64921 0.79310 3//3 335200 190375 30 8 0.73333 1.54305 0.74205 3//4 335275 190375 24 8 0.74275 1.57332 0.75661 3//5 335350 190375 32 7 0.70363 1.38889 0.71375 3//6 335425 190375 24 4 0.68841 1.17616 0.84842 3//7 335500 190375 36 10 0.81429 1.83558 0.79718

3//75 335350 190450 28 8 0.76455 1.59801 0.76848 3//8 335575 190375 30 7 0.81379 1.68205 0.86440 3//9 335650 190375 29 5 0.70197 1.31870 0.81936 4//1 335050 190675 45 4 0.61515 1.06727 0.76987 4//2 335125 190675 19 9 0.77778 1.75772 0.79997 4//3 335200 190675 32 4 0.53629 0.96482 0.69597 4//4 335275 190675 29 3 0.48768 0.81692 0.74359 4//5 335350 190675 17 5 0.50735 0.99761 0.61985 4//6 335425 190675 33 9 0.74242 1.58690 0.72223 4//7 335500 190675 22 4 0.63636 1.09717 0.79144

4//75 335350 190750 10 3 0.64444 0.94335 0.85867 4//8 335575 190675 31 8 0.77419 1.66255 0.79952 4//9 335650 190675 31 8 0.53978 1.22053 0.58695 5//1 335050 190975 22 3 0.39394 0.68892 0.62708 5//2 335125 190975 32 7 0.73387 1.51381 0.77794 5//3 335200 190975 23 6 0.78261 1.54144 0.86029 5//4 335275 190975 22 6 0.75325 1.45146 0.81008 5//5 335350 190975 29 5 0.61330 1.10789 0.68837 5//6 335425 190975 29 4 0.57389 1.01829 0.73454 5//7 335500 190975 21 9 0.68095 1.55856 0.70933

5//75 335350 191050 40 7 0.58333 1.20714 0.62035 5//8 335575 190975 12 8 0.84848 1.81431 0.87250 5//9 335650 190975 31 8 0.75699 1.63801 0.78772

Appendices


( c ) Forest seedlings


No. of species(S)

Simpson (1-D)

Shannon (H')

Shannon-Wiener (E)

0-0 335350 189475 23 8 0.85375 1.83601 0.88293 0-75 335350 189525 22 9 0.86147 1.94400 0.88475 1//1 335050 189775 11 9 0.96364 2.14584 0.97661 1//2 335125 189775 19 5 0.73099 1.32960 0.82613 1//3 335200 189775 23 5 0.62846 1.11574 0.69325 1//4 335275 189775 35 9 0.69076 1.57056 0.71479 1//5 335350 189775 39 8 0.75574 1.59518 0.76712 1//6 335425 189775 41 6 0.72317 1.40224 0.78260 1//7 335500 189775 33 9 0.71402 1.54824 0.70463

1//75 335350 189850 33 13 0.92235 2.39106 0.93221 1//8 335575 189775 12 3 0.53030 0.82396 0.75000 1//9 335650 189775 23 6 0.51383 1.06344 0.59352 2//1 335050 190075 68 9 0.50922 1.17103 0.53296 2//2 335125 190075 59 7 0.66219 1.33057 0.68378 2//3 335200 190075 25 5 0.67667 1.22887 0.76354 2//4 335275 190075 39 7 0.62078 1.17993 0.60637 2//5 335350 190075 40 7 0.59231 1.26796 0.65160 2//6 335425 190075 20 10 0.89474 2.08201 0.90420 2//7 335500 190075 74 6 0.60533 1.14115 0.63689

2//75 335350 190150 55 10 0.70101 1.49292 0.64837 2//8 335575 190075 59 4 0.64816 1.10772 0.79905 2//9 335650 190075 20 2 0.18947 0.32508 0.46900 3//1 335050 190375 24 8 0.75725 1.59332 0.76623 3//2 335125 190375 25 8 0.75333 1.65155 0.79423 3//3 335200 190375 27 6 0.78063 1.52719 0.85234 3//4 335275 190375 123 3 0.34706 0.64429 0.58646 3//5 335350 190375 10 6 0.84444 1.60944 0.89824 3//6 335425 190375 27 10 0.80342 1.81646 0.78888 3//7 335500 190375 48 6 0.64007 1.20101 0.67030

3//75 335350 190450 16 7 0.81667 1.66746 0.85691 3//8 335575 190375 34 7 0.67201 1.33559 0.68636 3//9 335650 190375 39 10 0.75439 1.72750 0.75024 4//1 335050 190675 50 4 0.67673 1.14972 0.82935 4//2 335125 190675 39 10 0.77193 1.71898 0.74654 4//3 335200 190675 95 6 0.52049 0.96789 0.54019 4//4 335275 190675 39 9 0.79622 1.75325 0.79794 4//5 335350 190675 23 5 0.74704 1.40010 0.86993 4//6 335425 190675 78 8 0.68898 1.41053 0.67832 4//7 335500 190675 69 6 0.69309 1.29825 0.72457

4//75 335350 190750 20 3 0.69474 1.08890 0.99116 4//8 335575 190675 70 7 0.74617 1.48012 0.76063 4//9 335650 190675 38 7 0.69559 1.41197 0.72561 5//1 335050 190975 21 5 0.71429 1.29727 0.80604 5//2 335125 190975 41 7 0.57683 1.21619 0.62500 5//3 335200 190975 63 6 0.63697 1.26312 0.70496 5//4 335275 190975 19 9 0.81287 1.82246 0.82944 5//5 335350 190975 20 7 0.63684 1.34000 0.68862 5//6 335425 190975 36 8 0.69365 1.43591 0.69052 5//7 335500 190975 42 10 0.80604 1.82140 0.79102

5//75 335350 191050 27 6 0.72934 1.46734 0.81894 5//8 335575 190975 33 8 0.71212 1.48380 0.71356 5//9 335650 190975 50 7 0.49878 1.08552 0.55784

Appendices


Appendix 2 (a) Grassland trees

Plot id Easting Northing Total tree

count (N) No. of

species(S) Simpson

(1-D) Shannon

(H') Shannon-Wiener

(E) 2 328937.50 188200.00 0 0 -1.00000 -1.00000 -1.00000 3 328975.00 188135.00 3 1 0.00000 0.00000 0.00000 4 329016.25 188068.75 12 10 0.96970 2.25386 0.97884 6 328900.00 188263.00 0 0 -1.00000 -1.00000 -1.00000 8 328871.25 188162.50 5 2 0.40000 0.50040 0.72193 9 328680.00 188050.00 3 2 0.66667 0.63651 0.91830

10 328718.75 187986.25 15 3 0.25714 0.48509 0.44155 11 328756.25 187922.50 30 7 0.46207 1.02457 0.52653 13 328615.00 188013.50 4 2 0.50000 0.56234 0.81128 15 328643.75 188117.50 6 3 0.60000 0.86756 0.78969 16 328605.00 188181.25 6 6 1.00000 1.79176 1.00000 17 328567.50 188247.50 26 1 0.00000 0.00000 0.00000 18 328528.75 188311.25 34 7 0.57041 1.22547 0.62977 19 328492.50 188376.00 13 6 0.82051 1.58577 0.88504

A10 329277.50 188861.25 21 11 0.92381 2.25235 0.93930 A11 329311.25 188928.75 1 1 1.00000 0.00000 0.00000 A12 329342.00 188997.50 1 1 1.00000 0.00000 0.00000 A13 329375.00 189066.50 19 3 0.36842 0.63304 0.57622 A14 329405.00 189132.50 25 9 0.87000 1.97067 0.89689 A2 329025.00 188318.75 8 4 0.64286 1.07354 0.77440 A3 329056.00 188397.50 12 7 0.87879 1.79176 0.92078 A4 329087.50 188453.00 23 8 0.69960 1.48914 0.71613 A5 329118.75 188523.75 25 8 0.71667 1.56598 0.75308 A6 329150.00 188587.50 20 9 0.89474 2.02968 0.92375 A7 329183.75 188660.00 24 5 0.74275 1.39824 0.86877 A75 329120.00 188697.50 5 3 0.70000 0.95027 0.86497 A8 329213.00 188726.00 6 5 0.93333 1.56071 0.96972 A9 329247.50 188794.00 28 6 0.48677 1.02518 0.57216 AA 329725.00 188400.00 18 8 0.75163 1.63773 0.78758

AA-75 329656.00 188446.00 19 5 0.63743 1.19137 0.74024 B1b 329328.75 188256.50 9 6 0.88889 1.67699 0.93594 B2 329358.35 188358.35 13 6 0.82051 1.58577 0.88504 B3 329392.00 188400.00 18 6 0.81699 1.61500 0.90135 B4 329422.00 188468.75 12 4 0.68182 1.12693 0.81291 B5 329454.00 188537.50 31 9 0.75699 1.67177 0.76085 B6 329487.50 188604.00 27 13 0.76923 1.93879 0.75588 B7 329517.50 188668.75 10 5 0.82222 1.47081 0.91386

B-75 329387.50 188566.50 9 3 0.63889 0.93689 0.85279 B8 329550.00 188737.50 4 2 0.66667 0.69315 1.00000 B9 329581.25 188806.25 33 11 0.66288 1.59118 0.66357 C2 328883.75 188712.50 21 4 0.27143 0.56706 0.40905 C3 328916.50 188781.25 11 4 0.78182 1.29455 0.93382 C4 328949.00 188850.00 11 5 0.76364 1.36671 0.84919

C75 328846.25 188816.25 10 7 0.91111 1.83437 0.94268 D2 328550.00 188703.75 11 10 0.98182 2.27187 0.98666 D3 328580.00 188773.00 26 12 0.87692 2.14063 0.86145 D4 328612.50 188840.00 11 6 0.72727 1.42057 0.79284 D5 328641.25 188906.00 12 11 0.98485 2.36938 0.98811 D6 328674.00 188975.00 24 9 0.73551 1.64679 0.74949 D75 328575.00 188937.50 10 6 0.88889 1.69574 0.94641 E10 328186.50 188623.00 4 4 1.00000 1.38629 1.00000 E11 328215.00 188690.00 4 2 0.50000 0.56234 0.81128 E12 328250.00 188758.75 22 7 0.54113 1.18163 0.60724 E13 328277.50 188826.25 1 1 1.00000 0.00000 0.00000 E14 328312.50 188892.50 0 0 -1.00000 -1.00000 -1.00000

Appendices


E15 328342.50 188962.50 10 4 0.71111 1.16828 0.84274 E16 328375.00 189031.25 21 6 0.70476 1.40858 0.78614 E17 328406.25 189100.00 9 1 0.00000 0.00000 0.00000 E18 328437.70 189180.00 10 4 0.64444 1.08890 0.78548 E19 328468.75 189235.00 5 2 0.60000 0.67301 0.97095 E2 327932.50 188076.00 0 0 -1.00000 -1.00000 -1.00000

E20 328502.50 189303.00 0 0 -1.00000 -1.00000 -1.00000 E21 328532.50 189373.75 5 4 0.90000 1.33218 0.96096 E3 327966.25 188143.75 0 0 -1.00000 -1.00000 -1.00000 E4 327997.50 188212.50 6 2 0.33333 0.45056 0.65002 E5 328028.75 188280.00 4 3 0.83333 1.03972 0.94639 E6 328062.50 188350.00 4 4 1.00000 1.38629 1.00000 E7 328092.50 188416.25 4 1 0.00000 0.00000 0.00000

E75 328306.25 189062.50 14 8 0.90110 1.93281 0.92948 E8 328125.00 188487.50 11 4 0.60000 1.03356 0.74556 E9 328155.00 188554.00 0 0 -1.00000 -1.00000 -1.00000 F1 328000.00 188952.50 13 8 0.91026 1.95126 0.93836 F2 328031.25 189018.75 25 6 0.72333 1.42697 0.79641 F3 328063.00 189088.75 0 0 -1.00000 -1.00000 -1.00000 F4 328093.65 189160.71 6 3 0.80000 1.09861 1.00000 F5 328127.50 189225.00 20 5 0.80000 1.48869 0.92498

Appendices


(b) Grassland saplings


No. of species(S)

Simpson (1-D)


2 328937.50 188200.00 2 1 0.00000 0.00000 0.00000 3 328975.00 188135.00 7 3 0.52381 0.79631 0.72483 4 329016.25 188068.75 12 8 0.89394 1.90728 0.91721 6 328900.00 188263.00 1 1 0.00000 0.00000 0.00000 8 328871.25 188162.50 6 3 0.73333 1.01140 0.92062 9 328680.00 188050.00 13 3 0.50000 0.79027 0.71933

10 328718.75 187986.25 9 2 0.22222 0.34883 0.50326 11 328756.25 187922.50 12 10 0.96970 2.25386 0.97884 13 328615.00 188013.50 8 4 0.75000 1.21301 0.87500 15 328643.75 188117.50 16 4 0.70000 1.21301 0.87500 16 328605.00 188181.25 4 3 0.83333 1.03972 0.94639 17 328567.50 188247.50 18 7 0.86275 1.79811 0.92404 18 328528.75 188311.25 45 14 0.68889 1.79112 0.67870 19 328492.50 188376.00 9 5 0.86111 1.52296 0.94627

A10 329277.50 188861.25 26 10 0.86462 2.01163 0.87364 A11 329311.25 188928.75 10 1 0.00000 0.00000 0.00000 A12 329342.00 188997.50 5 2 0.40000 0.50040 0.72193 A13 329375.00 189066.50 29 10 0.65517 1.54499 0.67098 A14 329405.00 189132.50 67 14 0.71235 1.82321 0.69085 A2 329025.00 188318.75 0 0 -1.00000 -1.00000 -1.00000 A3 329056.00 188397.50 13 8 0.92308 1.99151 0.95771 A4 329087.50 188453.00 17 9 0.91176 2.05772 0.93651 A5 329118.75 188523.75 17 10 0.89706 2.08443 0.90526 A6 329150.00 188587.50 7 3 0.52381 0.79631 0.72483 A7 329183.75 188660.00 11 4 0.74545 1.24068 0.89496 A75 329120.00 188697.50 6 4 0.80000 1.24245 0.89624 A8 329213.00 188726.00 8 8 1.00000 2.07944 1.00000 A9 329247.50 188794.00 39 11 0.85155 2.00356 0.83555 AA 329725.00 188400.00 37 14 0.89039 2.29321 0.86895

AA-75 329656.00 188446.00 10 6 0.84444 1.60944 0.89824 B1b 329328.75 188256.50 28 12 0.80159 1.95149 0.78534 B2 329358.35 188358.35 24 9 0.86232 1.94278 0.88420 B3 329392.00 188400.00 19 8 0.88304 1.93720 0.93160 B4 329422.00 188468.75 31 8 0.66882 1.42718 0.68633 B5 329454.00 188537.50 29 6 0.51970 1.09202 0.60947 B6 329487.50 188604.00 41 14 0.80366 2.02717 0.76814 B7 329517.50 188668.75 9 4 0.75000 1.21489 0.87636

B-75 329387.50 188566.50 8 3 0.46429 0.73562 0.66959 B8 329550.00 188737.50 20 5 0.74737 1.40014 0.86996 B9 329581.25 188806.25 29 10 0.69212 1.62834 0.70718 C2 328883.75 188712.50 36 9 0.69683 1.56232 0.71104 C3 328916.50 188781.25 8 5 0.85714 1.49418 0.92838 C4 328949.00 188850.00 15 8 0.89524 1.93381 0.92997

C75 328846.25 188816.25 25 10 0.89333 2.08963 0.90751 D2 328550.00 188703.75 30 12 0.90115 2.24558 0.90369 D3 328580.00 188773.00 30 16 0.93563 2.55258 0.92065 D4 328612.50 188840.00 15 8 0.79048 1.70754 0.82115 D5 328641.25 188906.00 27 10 0.86325 1.99244 0.86531 D6 328674.00 188975.00 21 13 0.95238 2.46658 0.96165 D75 328575.00 188937.50 19 9 0.88304 1.98262 0.90233 E10 328186.50 188623.00 17 6 0.74265 1.44692 0.80754 E11 328215.00 188690.00 11 5 0.76364 1.36671 0.84919 E12 328250.00 188758.75 19 3 0.54971 0.87853 0.79967 E13 328277.50 188826.25 22 4 0.61905 1.11051 0.80107 E14 328312.50 188892.50 17 3 0.48529 0.80374 0.73160 E15 328342.50 188962.50 13 6 0.78205 1.52471 0.85096

Appendices


E16 328375.00 189031.25 38 17 0.91750 2.53349 0.89421 E17 328406.25 189100.00 18 4 0.67974 1.16159 0.83791 E18 328437.70 189180.00 12 5 0.78788 1.42413 0.88486 E19 328468.75 189235.00 6 3 0.73333 1.01140 0.92062 E2 327932.50 188076.00 5 2 0.60000 0.67301 0.97095

E20 328502.50 189303.00 33 2 0.37879 0.55386 0.79905 E21 328532.50 189373.75 3 3 1.00000 1.09861 1.00000 E3 327966.25 188143.75 1 1 0.00000 0.00000 0.00000 E4 327997.50 188212.50 20 6 0.85263 1.72280 0.96151 E5 328028.75 188280.00 4 4 1.00000 1.38629 1.00000 E6 328062.50 188350.00 3 3 1.00000 1.09861 1.00000 E7 328092.50 188416.25 21 6 0.65714 1.31069 0.73151

E75 328306.25 189062.50 20 12 0.90000 2.22410 0.89504 E8 328125.00 188487.50 9 5 0.83333 1.46482 0.91014 E9 328155.00 188554.00 12 2 0.30303 0.45056 0.65002 F1 328000.00 188952.50 13 6 0.71795 1.41051 0.78722 F2 328031.25 189018.75 17 6 0.79412 1.53359 0.85591 F3 328063.00 189088.75 7 2 0.57143 0.68291 0.98523 F4 328093.65 189160.71 2 2 1.00000 0.69315 1.00000 F5 328127.50 189225.00 39 16 0.92308 2.51961 0.90876

Appendices


(c) Grassland seedlings

Plot id Easting (m)

Northing (m) Total tree count (N)

No. of species(S)

Simpson (1-D)


2 328937.50 188200.00 8 2 0.53571 0.66156 0.95443 3 328975.00 188135.00 14 4 0.71429 1.19734 0.86370 4 329016.25 188068.75 6 5 0.93333 1.56071 0.96972 6 328900.00 188263.00 3 1 0.00000 0.00000 0.00000 8 328871.25 188162.50 3 3 1.00000 1.09861 1.00000 9 328680.00 188050.00 2 1 0.00000 0.00000 0.00000

10 328718.75 187986.25 7 2 0.47619 0.59827 0.86312 11 328756.25 187922.50 42 7 0.74332 1.54894 0.79600 13 328615.00 188013.50 6 3 0.73333 1.01140 0.92062 15 328643.75 188117.50 10 3 0.60000 0.89795 0.81735 16 328605.00 188181.25 15 6 0.76190 1.48782 0.83037 17 328567.50 188247.50 36 8 0.51429 1.16509 0.56029 18 328528.75 188311.25 61 10 0.68525 1.46277 0.63527 19 328492.50 188376.00 39 7 0.57085 1.21789 0.62587

A10 329277.50 188861.25 13 5 0.69231 1.26363 0.78513 A11 329311.25 188928.75 37 3 0.10661 0.24775 0.22551 A12 329342.00 188997.50 10 1 0.00000 0.00000 0.00000 A13 329375.00 189066.50 44 6 0.56554 1.14770 0.64054 A14 329405.00 189132.50 81 9 0.35401 0.87768 0.39945 A2 329025.00 188318.75 22 9 0.83550 1.87000 0.85108 A3 329056.00 188397.50 24 6 0.71377 1.37413 0.76692 A4 329087.50 188453.00 20 8 0.87368 1.90023 0.91382 A5 329118.75 188523.75 41 12 0.81829 2.00573 0.80717 A6 329150.00 188587.50 5 3 0.70000 0.95027 0.86497 A7 329183.75 188660.00 29 4 0.58867 1.01916 0.73517 A75 329120.00 188697.50 11 5 0.81818 1.46814 0.91221 A8 329213.00 188726.00 1 1 0.00000 0.00000 0.00000 A9 329247.50 188794.00 21 7 0.80000 1.64620 0.84598 AA 329725.00 188400.00 40 15 0.92051 2.47632 0.91443

AA-75 329656.00 188446.00 30 11 0.86207 2.09048 0.87180 B1b 329328.75 188256.50 42 11 0.87456 2.10734 0.87883 B2 329358.35 188358.35 32 8 0.70161 1.55434 0.74748 B3 329392.00 188400.00 16 8 0.91667 2.01404 0.96855 B4 329422.00 188468.75 31 8 0.83011 1.84258 0.88610 B5 329454.00 188537.50 22 8 0.82684 1.77040 0.85138 B6 329487.50 188604.00 31 15 0.90538 2.38857 0.88202 B7 329517.50 188668.75 4 2 0.50000 0.56234 0.81128

B-75 329387.50 188566.50 7 4 0.80952 1.27703 0.92119 B8 329550.00 188737.50 20 6 0.80000 1.56573 0.87385 B9 329581.25 188806.25 48 13 0.85993 2.12987 0.83037 C2 328883.75 188712.50 55 14 0.84646 2.16896 0.82187 C3 328916.50 188781.25 6 4 0.80000 1.24245 0.89624 C4 328949.00 188850.00 33 5 0.58333 1.09279 0.67899

C75 328846.25 188816.25 18 9 0.90850 2.06207 0.93849 D2 328550.00 188703.75 29 11 0.91133 2.24345 0.93559 D3 328580.00 188773.00 42 9 0.78630 1.74522 0.79428 D4 328612.50 188840.00 3 2 0.66667 0.63651 0.91830 D5 328641.25 188906.00 9 3 0.63889 0.93689 0.85279 D6 328674.00 188975.00 22 12 0.93939 2.36577 0.95206 D75 328575.00 188937.50 26 9 0.82769 1.82777 0.83186 E10 328186.50 188623.00 48 6 0.74291 1.44558 0.80679 E11 328215.00 188690.00 32 3 0.64315 1.03580 0.94283 E12 328250.00 188758.75 38 3 0.24324 0.47793 0.43503 E13 328277.50 188826.25 80 4 0.36772 0.73758 0.53205 E14 328312.50 188892.50 36 1 0.00000 0.00000 0.00000 E15 328342.50 188962.50 13 3 0.51282 0.83052 0.75597

Appendices


E16 328375.00 189031.25 17 6 0.82353 1.61107 0.89915 E17 328406.25 189100.00 40 1 0.00000 0.00000 0.00000 E18 328437.70 189180.00 20 2 0.18947 0.32508 0.46900 E19 328468.75 189235.00 9 3 0.72222 1.06086 0.96563 E2 327932.50 188076.00 17 2 0.52941 0.69142 0.99750

E20 328502.50 189303.00 31 3 0.60860 0.98280 0.89458 E21 328532.50 189373.75 13 5 0.53846 1.04379 0.64855 E3 327966.25 188143.75 19 2 0.19883 0.33650 0.48546 E4 327997.50 188212.50 15 2 0.47619 0.63651 0.91830 E5 328028.75 188280.00 48 3 0.12145 0.27357 0.24902 E6 328062.50 188350.00 31 3 0.59785 0.95864 0.87259 E7 328092.50 188416.25 39 6 0.75439 1.48499 0.82879

E75 328306.25 189062.50 23 7 0.71542 1.51490 0.77851 E8 328125.00 188487.50 57 5 0.46867 0.88258 0.54838 E9 328155.00 188554.00 16 4 0.74167 1.28205 0.92480 F1 328000.00 188952.50 12 5 0.57576 1.09861 0.68261 F2 328031.25 189018.75 24 4 0.58696 0.96475 0.69592 F3 328063.00 189088.75 17 2 0.44118 0.60580 0.87398 F4 328093.65 189160.71 8 2 0.57143 0.69315 1.00000 F5 328127.50 189225.00 31 7 0.82796 1.71965 0.88373

Appendices


Appendix 3 An example of the index calculations BUDONGO FOREST FOREST PLOTS Saplings group

n Plot id

n-1 n(n-1) N N(N-1) n(n-1)/N(N-) pi In pi pi In pi (In pi)^2 pi(In pi)^2 S

Bosquia poberos 1 5//9 0 0 930 0 0.032258 -3.43399 -0.11077 11.79227 0.3803958 Celtis milbraedii 14 5//9 13 182 930 0.195698925 0.451613 -0.79493 -0.35900 0.631914 0.2853803 Celtis Zenkeri 1 5//9 0 0 930 0 0.032258 -3.43399 -0.11077 11.79227 0.3803958 coffea spp 6 5//9 5 30 930 0.032258065 0.193548 -1.64223 -0.31785 2.696912 0.5219830 Khaya anthoseca 3 5//9 2 6 930 0.006451613 0.096774 -2.33537 -0.22600 5.453976 0.5278041 Lasiodiscus mildbraedii 1 5//9 0 0 930 0 0.032258 -3.43399 -0.11077 11.79227 0.3803958 Rinolia ilicifolia 2 5//9 1 2 930 0.002150538 0.064516 -2.74084 -0.17683 7.512204 0.4846583 Trichilia rubensens 3 5//9 2 6 930 0.006451613 0.096774 -2.33537 -0.22600 5.453976 0.5278041

totals 5//9 31 -1.63801 3.488817 8

Simpson (D) 0.243 Simpson (1-D) 0.757 Shannon (H') 1.638 Shannon-Wiener (E) 0.788 VarH' = 0.030 Appendix 4 Formulae used for calculating the models in Excel Spherical model

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛−⎟

⎠⎞

⎜⎝⎛+=

3

5.05.1ah

ahBAhγ

Gaussian model

( )⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛−−+=

23exp1ahBAhγ

Exponential model

( ) ⎟⎠

⎞⎜⎝

⎛⎟⎠⎞

⎜⎝⎛−−+=

ahBAh exp1γ

where A is the nugget, B is the sill, h is the distance and a is the range. (Source: Isaaks and Srivastava, and Stein, 1996 figure 4-10,)

Appendices


Appendix 5 Semi-structured interview questions When did you start living in this area? What are your main income generating activities? For how long have you been hunting? Which are the "hot spots" for your hunting? Why? Which animals do you look for? Which ones do you value most (rank them)? Why? How do you rate the ease of getting your desired animals, now as compared to when you started hunting? If harder now, what do you think makes it so? What changes have you noticed in the forest cover over the period you have been hunting? Are they of any advantage to you as a hunter? I noticed some burnt areas; how does the burning facilitate your hunting? When do you decide that a place is due for burning? Are there ways you could still get the without more burning? What problems do you encounter in your hunting? Are there ways these problems can be solved; by forest management or (you) the hunters? ISSUES TO NOTE Sketch maps of forest -Can be used in change detection. Transect -Note change from their perspective. Mark where change is perceived and go back to measure distances between change. Walk with the locals over the area, along the same lines of data collection and let them indicate areas of change. Historical profile of burnt over areas Appendix 6 Areas for further study 1. Mapping of species location in relation to the edge. 2. A closer look at species’ conservation needs especially those found in the

grassland and the edge areas. 3. Generation of a historical vegetation map of Uganda. 4. Analysis of the management needs of buffer zones. 5. Mapping the distribution of the rare species in the rainforests

Appendices


Appendix 7 (a) (b)

The photos above were taken on part of the grassland where burning is currently taking place. Photo (a) shows a live tree, with a thick bark, which has experienced burning for several years, while photo (b) shows other trees found along the transect which were recovering from the previous fire.

The photo shows a mature forest tree that had fallen and thus opening up the canopy.

A photo showing the hunters and the researcher involved in a group discussion.

Appendices


APPENDIX 8 An example of the form used for data collection

INVENTORY FORM FOR BUDONGO FOREST RESERVE

Name of recorder .............................. Date............................................. Plot radius size..................................... Sampling Plot

No. Map scale Map No. Grid ref. Land Mark

X Y Dist. to centre

of plot. (m) Bearing to centre

of plot. Elevation Slope

(%) Aspect

Forest characteristics:

Land cover Slope position Stand composition

Fire indicators Other disturbances Undergrowth

F D A AF S SH BS FS TS B P N P N G N Tree Species DBH Tree class Tree Species DBH Tree class No. (cm) 1 2 3 4 d No. (cm) 2 3 4 d1 16 2 17 3 18 4 19 5 20 6 21 7 22 8 23 9 24 10 25 11 26 12 27 13 28 14 29 15 30

Appendices


Legend / Guide for Field Work Inventory form.

Land cover: Slope position Fire indicators Sample plot characteristics: Tree class : F = Forest. S = Summit P = Present DBH = diameter at breast height 1- dominant. D = Deforestation. SH = Shoulder N = Not present 2- co-dominant. A= Agriculture BS = Back slope 3- dominated. AF=Agroforestry FS = Foot slope 4- suppressed. TS = Toe slope d- dead/

diseased/dying. B = Bottom

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ASSESSMENT OF THE IMPACT OF BURNING ON BIODIVERSITY … · My work would have been very difficult without the support of my course mates; Xanat Antonio, Emmanuel Tabi-Gyansah, Ibrahim