Designing an integrated protected area network for Maputaland, South .

Robert J. Smith

Thesis submitted for the degree of Doctor of Philosophy Durrell Institute of Conservation & Ecology University of Kent at Canterbury March 2001

KwaZulu-Natal Nature Conservation Service Project Number: NA/111/06 Table of contents

TABLE OF CONTENTS ...... i LIST OF FIGURES...... v LIST OF TABLES………...... vii ABSTRACT……… ...... viii ACKNOWLEDGEMENTS ...... ix LIST OF ACRONYMS...... x

CHAPTER 1: GENERAL INTRODUCTION...... 1 1.1 Introduction ...... 1 1.2 Protected areas and biodiversity conservation...... 2 1.2.1 Social costs of PAs...... 3 1.2.2 Ecological costs of PAs ...... 3 1.2.3 Modern approaches to PA management...... 4 1.3 Methods for selecting PA location ...... 4 1.3.1 Combinatorial scoring and hotspots...... 5 1.3.2 Complementarity...... 6 1.3.3 Planning and implementing stages...... 7 1.4 PA selection and biodiversity surrogates...... 8 1.4.1 Species as surrogates...... 9 1.4.2 Higher taxa as surrogates...... 10 1.4.3 Environmental surrogates...... 11 1.5 Incorporating viability into PA selection...... 12 1.5.1 PA selection and the theory of island biogeography ...... 12 1.5.2 Maintaining ecological processes...... 13 1.5.3 Selecting for persistence ...... 14 1.6 PA selection in the real world...... 14 1.6.1 Finances and fund raising ...... 15 1.6.2 Scale ...... 16 1.7 Aims ...... 16 1.8 Thesis structure...... 17

CHAPTER 2: A DESCRIPTION OF MAPUTALAND ...... 18 2.1 Introduction ...... 18 2.2 The physical characteristics of Maputaland...... 20 2.2.1 The climate of Maputaland...... 20 2.2.2 Geology and soils ...... 21 2.2.3 Rivers, lakes and wetlands...... 21 2.3 The flora and fauna of Maputaland ...... 24 2.3.1 Flora ...... 24 2.3.2 Fauna...... 24 2.4 Human involvement in Maputaland ...... 26 2.4.1 A history of Maputaland ...... 26 2.4.2 Culture...... 27 2.4.3 Demographics...... 27 2.5 Conservation in Maputaland...... 29 2.5.1 A history of biodiversity conservation in Maputaland...... 30 2.5.2 Protected areas of Maputaland ...... 31 2.6 The future of biodiversity conservation in Maputaland...... 33 2.6.1 Threats to biodiversity in Maputaland...... 35 2.6.2 Future conservation developments ...... 36 2.7 Chapter summary...... 36

CHAPTER 3: CREATING A GIS FOR MAPUTALAND...... 38 3.1 Introduction ...... 38 3.2 Representing spatial phenomena in a GIS...... 39 3.3 GIS and error propagation...... 40 i 3.4 Landsat Thematic Mapper imagery...... 41 3.5 Image restoration and enhancement ...... 42 3.5.1 Methods ...... 43 3.5.2 Results...... 43 3.5.3 Discussion...... 44 3.6 Contrast stretching the images...... 44 3.6.1 Methods ...... 45 3.6.2 Results...... 46 3.6.3 Discussion...... 48 3.7 Geo-registering the Landsat TM images ...... 49 3.7.1 Methods ...... 49 3.7.2 Results...... 49 3.7.3 Discussion...... 51 3.8 Deriving the Maputaland GIS coverages...... 51 3.8.1 Methods ...... 51 3.8.2 Results and discussion ...... 53 3.9 Chapter summary...... 53

CHAPTER 4: CREATING THE LAND-COVER COVERAGE ...... 55 4.1 Introduction ...... 55 4.2 Designing a land-cover classification scheme for Maputaland ...... 55 4.2.1 Past descriptions of the vegetation of Maputaland...... 56 4.2.2 Methods ...... 59 4.2.3 Results...... 60 4.3 Collecting the ground-truth data...... 65 4.3.1 Methods ...... 65 4.3.2 Results...... 68 4.3.3 Discussion...... 68 4.4 Creating the land-cover coverage ...... 70 4.4.1 Methods ...... 70 4.4.2 Results...... 72 4.4.3 Discussion...... 75 4.5 The effects of reducing the resolution of the land-cover coverage...... 76 4.5.1 Methods ...... 77 4.5.2 Results...... 78 4.5.3 Discussion...... 80 4.6 Chapter summary...... 81

CHAPTER 5: MODELLING LAND-COVER TRANSFORMATION ...... 82 5.1 Introduction ...... 82 5.2 Mapping habitat transformation ...... 83 5.2.1 Methods ...... 83 5.2.2 Results...... 85 5.2.3 Discussion...... 85 5.3 Modelling risk of future habitat transformation ...... 87 5.3.1 Methods ...... 87 5.3.2 Results...... 88 5.3.3 Discussion...... 92 5.4 Human population density and transformation risk...... 93 5.4.1 Methods ...... 93 5.4.2 Results...... 94 5.4.3 Discussion...... 95 5.5 Identifying land-cover types most at risk of transformation...... 95 5.5.1 Methods ...... 95 5.5.2 Results...... 95 5.5.3 Discussion...... 97 5.6 Predicting future patterns of agricultural transformation...... 97 5.6.1 Methods ...... 97 5.6.2 Results...... 99 5.6.3 Discussion...... 101 5.7 Chapter summary...... 101

ii CHAPTER 6: MAPPING THE DISTRIBUTION OF MAPUTALAND'S BIRD SPECIES ..... 102 6.1 Introduction ...... 102 6.2 A description of the SABAP data for Maputaland ...... 103 6.2.1 Methods ...... 103 6.2.2 Results...... 104 6.2.3 Discussion...... 105 6.3 Factors affecting species richness in the SABAP grid squares...... 105 6.3.1 Methods ...... 105 6.3.2 Results...... 106 6.3.3 Discussion...... 108 6.4 Modelling the distribution of Maputaland’s bird species ...... 108 6.4.1 Methods ...... 110 6.4.2 Results...... 112 6.4.3 Discussion...... 116 6.5 Finding the factors that determined recorded bird distributions...... 117 6.5.1 Methods ...... 118 6.5.2 Results...... 119 6.5.3 Discussion...... 124 6.6 A method for reducing the effects of sampling bias...... 125 6.6.1 Methods ...... 126 6.6.2 Results...... 127 6.6.3 Discussion...... 129 6.7 Chapter summary ...... 129

CHAPTER 7: A COMPLEMENTARITY ANALYSIS OF MAPUTALAND...... 131 7.1 Introduction ...... 131 7.2 The PA status of Maputaland’s bird species and vegetation types...... 132 7.2.1 Methods ...... 132 7.2.2 Results...... 132 7.2.3 Discussion...... 135 7.3 Identifying biodiversity hotspots in Maputaland...... 136 7.3.1 Methods ...... 136 7.3.2 Results...... 138 7.3.3 Discussion...... 143 7.4 A gap analysis of Maputaland ...... 144 7.4.1 Methods ...... 146 7.4.2 Results...... 147 7.4.3 Discussion...... 147 7.5 A gap analysis of Maputaland allowing for risk...... 149 7.5.1 Methods ...... 150 7.5.2 Results...... 150 7.5.3 Discussion...... 152 7.6 Identifying near-minimum sets for PA targets ...... 152 7.6.1 Methods ...... 153 7.6.2 Results...... 153 7.6.3 Discussion...... 158 7.7 Assessing the biodiversity value of land-claim areas ...... 159 7.7.1 Methods ...... 159 7.7.2 Results...... 160 7.7.3 Discussion...... 163 7.8 Chapter summary...... 163

CHAPTER 8: IDENTIFYING AREAS OF HIGH CONSERVATION VALUE ...... 165 8.1 Introduction ...... 165 8.2 Deciding the structure of the conservation scoring system...... 167 8.2.1 Methods ...... 167 8.2.2 Results...... 167 8.2.3 Discussion...... 173 8.3 Producing the factor data...... 174 8.3.1 Methods ...... 174 8.3.2 Results...... 177 8.3.3 Discussion...... 182 iii 8.4 Producing the factor weights ...... 182 8.4.1 Methods ...... 182 8.4.2 Results...... 184 8.4.3 Discussion...... 190 8.5 Producing the sub-component weights...... 190 8.5.1 Methods ...... 190 8.5.2 Results...... 191 8.5.3 Discussion...... 196 8.6 Comparing the results from the AHP with the gap analysis...... 196 8.6.1 Methods and results...... 196 8.6.2 Discussion...... 198 8.7 Chapter summary...... 199

CHAPTER 9: CONCLUSIONS...... 200 9.1 Introduction ...... 200 9.2 Scale and conservation planning ...... 200 9.3 Applicability of methods for general use...... 202 9.4 Choosing biodiversity surrogates ...... 203 9.5 Increasing the applicability of conservation planning methods...... 204 9.6 PA planning and land-ownership ...... 205

REFERENCES…….…………………...…………………………………………………………………..207 APPENDIXES……..………………………………...……………………………………………………...226

iv List of figures

Figure 2-1: A map of showing the position of Greater Maputaland...... 18 Figure 2-2: The towns, major roads, rivers and lakes of Maputaland...... 19 Figure 2-3: A digital elevation model of Maputaland ...... 22 Figure 2-4: A geological map of Maputaland...... 23 Figure 2-5: The ecological zones of Maputaland ...... 25 Figure 2-6: The human population in Ubombo and from 1936 to 1990 ...... 28 Figure 2-7: The PAs of KwaZulu-Natal ...... 29 Figure 2-8: The PAs of Maputaland ...... 32 Figure 2-9: Land-claim areas in Maputaland...... 34

Figure 3-1: The three components used in the vector data model ...... 39 Figure 3-2: A comparison of the way that vector and raster data models represent a polygon...... 40 Figure 3-3: A DN value histogram before contrast stretching...... 44 Figure 3-4: A comparison of contrast stretching on real and integer pixel values...... 45 Figure 3-5: A detail from band 7 before and after a linear stretch with 2 % saturation...... 46 Figure 3-6: Pixel values for bands 1-7 before and after contrast stretching ...... 46 Figure 3-7: Position of geo-registering points ...... 50

Figure 4-1: The CSIR land-cover coverage of Maputaland...... 57 Figure 4-2: The location of the ground-truth points ...... 69 Figure 4-3: The land-cover coverage of Maputaland ...... 74 Figure 4-4: A comparison between Maputaland GIS data and global land-cover data ...... 77 Figure 4-5: 1 km resolution land-cover coverage of Maputaland...... 78 Figure 4-6: The relationship between patch area and reducing the land-cover resolution...... 80

Figure 5-1: A schematic representation of the methods described in sub-section 5.2.1 ...... 84 Figure 5-2: Mean distance to existing agriculture for transformed and untransformed points...... 89 Figure 5-3: The transformation status of the sample points grouped according to ecological zone ...... 89 Figure 5-4: Mean log10 of slope for transformed and untransformed points...... 90 Figure 5-5: Mean log10 of elevation for transformed and untransformed points ...... 90 Figure 5-6: Risk of the remaining natural vegetation being cleared for subsistence agriculture ...... 91 Figure 5-7: Frequency distribution of risk status derived from the modelled coverage ...... 92 Figure 5-8: The relationship between population density and proportion of subsistence agriculture...... 94 Figure 5-9: The relationship between area and risk of transformation ...... 96 Figure 5-10: Equal probability categories ...... 98 Figure 5-11: Equal area categories ...... 98 Figure 5-12: Modelled changes in area of natural vegetation...... 100 Figure 5-13: Modelled changes in number of vegetation patches ...... 100 Figure 5-14: Modelled changes in patch area...... 100

Figure 6-1: The number of species recorded in each SABAP grid square ...... 104 Figure 6-2: Species density in the SABAP grid squares...... 106 Figure 6-3: Record density in the SABAP grid squares ...... 106 Figure 6-4: The relationship between log10 of species density and log10 land-cover type density...... 107 Figure 6-5: The relationship between log10 of bird species density and log10 record density...... 107 Figure 6-6: The modelled habitat areas of Maputaland’s bird species ...... 114 Figure 6-7: Number of land-cover types associated with bird species ...... 114 Figure 6-8: Bird species richness in Maputaland...... 115 Figure 6-9: The relationship between record number and proportion of species recorded in grid squares ... 120 Figure 6-10: The proportion of recording success for birds with distinctive and indistinctive appearances . 121 Figure 6-11: The proportion of recording success for birds with distinctive and indistinctive songs ...... 122 Figure 6-12: The proportion of recording success for carnivorous and herbivorous birds...... 122 Figure 6-13: The proportion of recording success for birds associated with different habitat types ...... 123 Figure 6-14: The proportion of recording success for the different combinations of colour, song and diet.. 123 Figure 6-15: The relationship between record numbers and proportion of distinctive species...... 127 Figure 6-16: A comparison of the two recording success models ...... 128

v Figure 7-1: The PA status of Maputaland’s natural land-cover types ...... 134 Figure 7-2: The relationship between land-cover type area and PA status...... 134 Figure 7-3: The PA status of the habitats of Maputaland’s bird species ...... 135 Figure 7-4: The relationship between bird habitat area and PA status ...... 135 Figure 7-5: Bird species richness...... 139 Figure 7-6: Species proportional richness ...... 139 Figure 7-7: Endemic bird species richness ...... 139 Figure 7-8: Endemic species proportional richness...... 139 Figure 7-9: Threatened bird species richness...... 140 Figure 7-10: Threatened species proportional richness ...... 140 Figure 7-11: Bird species hotspots in Maputaland ...... 141 Figure 7-12: Bird species proportional hotspots...... 142 Figure 7-13: Gap analysis priority sites using three different surrogates for biodiversity...... 148 Figure 7-14: Priority sites allowing for habitat transformation risk ...... 151 Figure 7-15: Protection given to land-cover types based on 10 % conservation target ...... 155 Figure 7-16: Protection given to all bird species based on 10 % target...... 155 Figure 7-17: Protection given to all bird species based on 10 % target for distinctive birds...... 155 Figure 7-18: Protection given to bird species based on 10 % target for land-cover types...... 156 Figure 7-19: Set for land-cover types ...... 156 Figure 7-20: Set for distinctive bird spp ...... 156 Figure 7-21: Near-minimum set for three conservation targets using bird species distributions...... 157 Figure 7-22: Minimum set for target of 10 % using bird species and grid squares with PA status...... 162

Figure 8-1: A landscape coverage of Maputaland ...... 170 Figure 8-2: The complementarity factors ...... 179 Figure 8-3: The risk factors ...... 179 Figure 8-4: The viability factors...... 181 Figure 8-5: Group A complementarity score...... 187 Figure 8-6: Group B complementarity score ...... 187 Figure 8-7: Final complementarity sub-component score ...... 187 Figure 8-8: Group A risk score...... 188 Figure 8-9: Group B risk score ...... 188 Figure 8-10: Final risk sub-component score ...... 188 Figure 8-11: Group A viability score...... 189 Figure 8-12: Group B viability score...... 189 Figure 8-13: Final viability sub-component score...... 189 Figure 8-14: Grid squares chosen in sensitivity analysis...... 192 Figure 8-15: Group A final score...... 193 Figure 8-16: Group B final score...... 193 Figure 8-17: Group C final score...... 193 Figure 8-18: Final conservation scores for Maputaland ...... 194 Figure 8-19: A sensitivity analysis of the final AHP conservation scoring system...... 195 Figure 8-20: Priority sites chosen by gap analysis and AHP methods ...... 197

vi List of tables

Table 2-1: Statistics describing the human population of three districts in Maputaland ...... 28 Table 2-2: The human population in Ubombo and Ingwavuma from 1936 to 1990 ...... 28 Table 2-3: A description of the PAs of Maputaland ...... 33

Table 3-1: A description of the bands that make up a Landsat TM satellite image...... 42 Table 3-2: The % variance of the Landsat TM bands explained by each PCA image...... 43

Table 4-1: Summary of Tinley’s vegetation classification (Tinley & Van Riet 1981)...... 58 Table 4-2: Summary of the methods used to collect of the ground-truth points...... 68 Table 4-3: Accuracy assessment of land-cover coverage ...... 72 Table 4-4: Area of land-cover types found in Maputaland...... 73 Table 4-5: Differences between 30 m and 1 km resolution land-cover coverages ...... 79

Table 5-1: The area of natural vegetation transformed between 1986 and 1998...... 85 Table 5-2: Percentage of subsistence agric. found outside PAs in the ecological zones in 1986 ...... 85 Table 5-3: Details of the factors that determined risk of agricultural clearance ...... 88 Table 5-4: Details of the amount of variance explained by each factor in the model...... 89 Table 5-5: The relationship between population density and proportion of subsistence agriculture...... 94 Table 5-6: The transformation risk and vulnerability of Maputaland’s vegetation types...... 96 Table 5-7: Predicted habitat loss and fragmentation in Maputaland...... 99

Table 6-1: Details of the Southern African Bird Atlas data for Maputaland ...... 104 Table 6-2: Details of the factors that significantly determined log10 bird species density...... 107 Table 6-3: A list of species that were identified as possibly being range restricted ...... 111 Table 6-4: Details of the bird species/land-cover type association matrix ...... 113 Table 6-5: A comparison of recorded and modelled number of species in SABAP grid squares ...... 119 Table 6-6: Results from the analysis of factors that determined recording success...... 120 Table 6-7: Modelled contribution of factors to recording success...... 121 Table 6-8: Details of the relationship between record no. and proportion of distinctive species ...... 127 Table 6-9: Descriptions of the two recording success models...... 128 Table 6-10: A comparison of predicted recording success for the two models ...... 128 Table 6-11: A comparison of the habitat associations for species used in the two models ...... 129

Table 7-1: PA status of the natural land-cover types of Maputaland...... 133 Table 7-2: Endemic bird species found in Maputaland (Barnes, 1998)...... 137 Table 7-3: Endangered and vulnerable bird species found in Maputaland (Barnes, 2000) ...... 138 Table 7-4: Details of hotspots in Maputaland...... 140 Table 7-5: Coincidence within richness and proportional richness hotspots...... 143 Table 7-6: Coincidence between richness and proportional richness hotspots...... 143 Table 7-7: Results from the gap analysis...... 147 Table 7-8: Results from the gap analysis allowing for habitat transformation risk ...... 150 Table 7-9: Coincidence sites chosen using gap analysis and gap analysis allowing for risk...... 150 Table 7-10: Protection given to biodiversity elements based on 10 % conservation target...... 154 Table 7-11: Results from near-minimum set analysis using different conservation targets ...... 154 Table 7-12: Coincidence of priority sites chosen using different biodiversity surrogates...... 154 Table 7-13: Conservation importance of land-claim areas ...... 161

Table 8-1: An example of the table used to determine factor weightings...... 183 Table 8-2: Weightings given to complementarity factors...... 184 Table 8-3: Coincidence between the different complementarity sub-component results ...... 184 Table 8-4: Weightings given to risk factors...... 185 Table 8-5: Coincidence between the different risk sub-component results ...... 185 Table 8-6: Weightings given to viability factors ...... 185 Table 8-7: Coincidence between the different viability sub-component results...... 186 Table 8-8: Weightings given to sub-components ...... 191 Table 8-9: Coincidence between the different conservation score results...... 192

vii Abstract

Recent developments in conservation planning theory have the potential to increase the efficiency and viability of protected area (PA) networks. However, much of this work was done at an inappropriate scale, used unsuitable biodiversity surrogates or biased data and did not involve the relevant conservation practitioners. This thesis describes how a GIS-based approach was used to provide the fine-scale data and analysis needed by the KwaZulu-Natal Nature Conservation Service (NCS) to develop a new PA strategy for Maputaland, .

Maputaland is an area of 9500 km2 that contains 17 protected areas (PAs), covering 25 % of the . Most of these PAs are the subject of land-claims from former occupants and the region’s biodiversity outside the PAs is threatened by subsistence agriculture. The NCS plans to adapt the PA system to increase revenue-earning and co-management with local communities and the private sector, whilst ensuring full protection for a representative proportion of the region’s biodiversity in state-owned PAs. The information needed to develop this conservation strategy was produced, allowing a comparison of the PA systems derived using different methods. A methodology for creating the fine-scale data required for regional conservation planning was also developed and the applicability of pre-existing selection methods was assessed.

The analysis was based on dividing Maputaland into a series of 1 km2 grid squares and using satellite imagery to map the region’s land-cover, to identify areas at risk of agricultural transformation and to map the distribution of Maputaland’s bird species. This information was used to identify under-represented biodiversity elements and locate unprotected squares containing them. Most of the under-represented elements were found in the but these were also the least threatened by transformation. Another analysis incorporated data on biodiversity, transformation risk and ecological viability and there was little coincidence between the sets of priority squares identified using the two methods.

Only 694 squares were needed to achieve a target of 10 % protection for each bird species, whereas 696 were needed if the choice was restricted to squares with PA status. This showed that selection methods based entirely on biodiversity data were inadequate, as many combinations of squares would have achieved the target with near-equal efficiency. Instead, my research has shown the need to develop suitable software to allow conservation practitioners to identify a smaller number of PA options by incorporating financial, political and ecological constraints in the planning process.

viii Acknowledgements

During this research I relied on the help of dozens of people and I would like to thank them all for making my time spent in KwaZulu-Natal and Kent so enjoyable. I would particularly like to thank the staff of the KwaZulu-Natal Nature Conservation Service, as I feel very fortunate to have had the opportunity to work with them. I would like to thank DICE and the Durrell Trust for generously providing funding and British Airways for providing flights as part of their British Airways Assisting Conservation scheme.

Thanks to my supervisors, Pete Goodman, who originally suggested this project, and Nigel Leader- Williams for all their help and advice. I would also like to thank Andy Blackmore, Dave Johnson, Athol Marchant, Wayne Matthews and Nigel Robson for their help with producing the land-cover map and the bird habitat association matrix. I am also grateful to Adrian Armstrong, Dave Balfour, Grant Benn, Tony Bowland, Mike Coke, Ant Maddock, Craig Mulqueeny and Rob Scott-Shaw for their advice and input at the conservation scoring system workshops.

Thanks also to Ian Felton, Cathy Greaver, Catherine Hanekom, Simon Nxumalo and Sipho Sibaya who helped me collect my field data and the staff at Mkhuze Game Reserve, and the Eastern Shores Reserve for making my stay there so enjoyable.

In Pietermaritzburg, I would like to thank Carmen McKenzie and Bee Kasseepursad for all their help, as well as Martin Brooks, John Craigie, Rose Hamilton, Lynn Harrison, Neil Langley, Pete Le Roux, Ian Rushworth, Shannon Rushworth and Heidi Snyman.

I would also like to thank the various people who provided satellite imagery or help with GIS data and software and these include Greg Botha in Pietermaritzburg, Paul Desanker at the University of Virginia, Paul Eastwood at Christchurch College in Canterbury, Dave Woods at Kruger National Park and Paul Williams at the Natural History Museum in London.

In Canterbury, I would like to thank Mike Fischer for writing the reserve selection software and Richard Griffiths for statistical advice. I would also like to thank Christine Eagle, Joan England, Shelly Hills and Nicola Kerry-Yoxall. Many thanks also to Annette Huggins and Thomasina Oldfield for all their help, as well as to Fiona Carlston, Matt Linkie and Matt Walpole.

Finally, I would like to thank my parents for all their support and encouragement.

ix List of acronyms

AVHRR Advanced Very High Resolution Radiometer AHP Analytical Hierarchy Process CSIR Council for Scientific and Industrial Research DEM Digital Elevation Model DN Digital Number EBA Endemic Bird Area EROS Earth Resources Observation System FR Forest Reserve GAP Gap Analysis Program GIS Geographical Information System GPS Global Positioning System GR Game Reserve IGBP International Geosphere Biosphere Programme IUCN World Conservation Union KDNC KwaZulu Department of Nature Conservation KZN KwaZulu-Natal NCS Nature Conservation Service NDVI Normalised Difference Vegetation Index NGO Non-Governmental Organisation NPB Natal Parks Board NR Nature Reserve PA Protected Area PCA Principle Component Analysis RLCC Restitution of Land Claims Commission SABAP Southern African Bird Atlas Project TEP Tembe Elephant Park TFCA Transfrontier Conservation Area TM Thematic Mapper UK United Kingdom US United States

x Chapter 1: General Introduction

1.1 Introduction

The recent actions of humans have been responsible for a huge loss in biodiversity and this trend is continuing (Meffe & Carroll, 1997). Rates of species loss are still uncertain but it is estimated that they range between 100 and 1000 times greater than pre-human levels (Pimm et al., 1995), leading many to argue that this represents the opening phase of a mass extinction event (Soulé, 1991). There any many reasons for this biodiversity loss but four factors, known as the “evil quartet”, have traditionally been identified as the most important (Diamond, 1984). These factors are unsustainable harvesting, habitat destruction and fragmentation, the impact of introduced species and chains of extinction.

Unsustainable harvesting was probably the first of the evil quartet to be widely recognised as a conservation threat, when several large mammal species were affected by over-hunting in the early 20th century. In turn, this led to the creation of several now prominent conservation non- governmental organisations (NGOs) and to treaties such as the Convention of International Trade in Endangered Species (Hutton & Dickson, 2000). However, many more species are now threatened because human population growth and technological developments have increased the impact of previously sustainable harvesting methods (Attwell & Cotterill, 2000). Questions of ownership also play a large role in unsustainable harvesting because competition for common- property resources tends to be high and difficult to regulate (Ludwig et al., 1993).

High human population growth has had a large impact on rates of habitat destruction, which is famously illustrated by the loss of tropical rainforest (Laurance et al., 2001). Many other biomes are similarly threatened because of unsuitable farming techniques, land-ownership issues and poor land-use planning (Groombridge, 1992). This destruction also produces habitat fragmentation, which has a more long term affect on biodiversity. Smaller habitat patches are more affected by edge affects (Lovejoy et al., 1986; Kapos, 1989; Avery et al., 1989) and cannot support viable populations of species associated with core habitat (Saunders et al., 1991). This can then produce an example of chains of extinctions, where the loss of one species leads to the extinction of others.

Introduced species have played a major role in increasing extinction rates, although this influence is expected to decline in the future (Balmford et al., 1998). Island species have been especially affected by introduced predators but introduced species have had a great influence on both islands and . Introduced pathogens have also had an important affect but disease in general could be added to the list of important causes of extinctions (Tompkins & Wilson, 1998). This is because many threatened populations are small and have low levels of genetic variability,

Chapter 1: General Introduction 1 making them particularly susceptible. Finally, another future cause of extinctions is likely to be climate change. This change will probably proceed too rapidly for an evolved response, so species will have to change their range instead. This may not be possible for many species that are poor dispersers and habitat loss and fragmentation will exacerbate this problem (Hill et al., 1999).

Biodiversity is recognised as having high levels of economic and cultural value (Wilson, 1992), so many governments, organisations and individuals have developed strategies that try to mitigate the severity of this extinction event. A whole range of methods and policies are in place to try to reduce pollution levels, increase sustainability, restore degraded habitats and directly prevent species loss. One of the most widespread strategies to stem the loss of biodiversity is the establishment of protected areas (PAs) and this chapter will investigate their role in biodiversity conservation. The advantages and disadvantages of PAs will be discussed in section 1.2 and the methods used to choose PA selection will be reviewed in section 1.3. Section 1.4 will discuss the problems of finding a suitable surrogate for biodiversity in conservation planning and sections 1.5 and 1.6 will discuss how ecological, financial and political factors can be incorporated in PA planning. Section 1.7 will discuss the aims of this study and section 1.8 will describe the structure of the rest of thesis.

1.2 Protected areas and biodiversity conservation

Protected areas (PAs) have been defined as “areas of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means” (IUCN, 1994). Several internationally recognised terms are used to describe PAs, such as “National Park”, “Game Reserve” or “Forest Reserve” but the management objectives of these can differ between countries. To overcome this, the World Conservation Union (IUCN) has devised a set of international categories into which all of these national categories can be fitted. This IUCN classification consists of 6 main categories, from Category I, which describes strict nature reserves/wilderness areas to Category VI, which describes PAs that are mainly managed for the sustainable use of natural ecosystems (IUCN, 1994).

There are more than 8600 formally recognised PAs and terrestrial PAs had an area of twelve million km2 in 1998, covering 7.9 % of the Earth’s land surface (IUCN, 1998). PAs are the main component of most countries’ strategy for biodiversity conservation because they aim to prevent over-exploitation, habitat loss and habitat fragmentation. In addition, they may raise revenue by attracting tourists (Maille & Mendelsohn, 1993; Walpole et al., 2001), help protect water catchments, allow important ecological research (Arcese & Sinclair, 1997) and support populations of species, such as grey wolves (Canis lupus) and lions (Panthera leo), that threaten humans and their livestock and are rarely tolerated outside PAs (du Toit, 1995). Another advantage of PAs is the simplicity of their underlying philosophy and the apparent ease with which they can be

Chapter 1: General Introduction 2 maintained. In the past, all that was required to create a PA was to remove any people living in the proposed PA and prevent any further resource use. Unfortunately, this approach usually had severe social and ecological repercussions and this section discusses these problems and their potential resolution.

1.2.1 Social costs of PAs The history of PA creation has often involved forced removals and denied access to areas where many people previously grazed domestic animals, collected fuel-wood and other plant resources and practiced subsistence hunting (Ghimire & Pimbert, 1997). In addition, PAs often act as refuges for species, such as elephants, primates and large carnivores, which crop-raid or kill livestock in neighbouring areas (Naughton-Treves, 1998). This often leads to conflict between local communities and PA staff and widespread flouting of the laws, in the form of poaching, poisoning problem animals and agricultural encroachment.

It was recognised that a different approach was needed, especially as the spread of democracy meant that politicians were more likely to respond to the demands of their rural electorates (Hackel, 1999). It was felt that local communities should benefit from neighbouring PAs as compensation for loss of resources and a large range of schemes have been established by governments and NGOs. These include adding a community levy on PA entrance fees and trophy hunting, allowing limited resource use and building capacity to encourage local people to exploit tourism-based employment opportunities (MacKinnon et al., 1986). More recent developments have included involving local communities in management decisions or returning ownership rights of a PA in return for a commitment to maintaining its status (Getz et al., 1999).

1.2.2 Ecological costs of PAs Many local communities practice sustainable use of natural resources and their exclusion from PAs prevented a series of actions that directly or indirectly maintained biodiversity (Sinclair, 1998). Humans may help to disperse wild (Harris & Hillman, 1989), reduce localised elephant feeding pressure on particular habitats (Ville, 1995), reduce numbers of dominant prey species (Novaro et al., 2000) and maintain grasslands by burning (Huntley & Walker, 1982). Another problem arises from the social costs of establishing PAs because this can lead to high poverty levels and so the land around PAs becomes particularly degraded and affected by over-hunting. These “islands” of untransformed vegetation then tend to have much higher densities of large mammal species that use them as refuges. Both these factors affect ecological processes and they are particularly acute in small PAs. This means that many PAs need to be intensively managed to maintain their biodiversity.

Chapter 1: General Introduction 3 1.2.3 Modern approaches to PA management The problems associated with PAs have led some people to argue that they are unnecessary and that they should be returned to the former occupants. This view is probably overly naive because the sustainable resource-use practiced by local communities was partly due to their low population densities and inefficient harvesting techniques and so could not be guaranteed in the future (Attwell & Cotterill, 2000). However, if PAs are to be successful in the future then there needs to be a radical change in their management. There must be a further inclusion of local communities in benefit sharing and decision making, in addition to effective law enforcement and management of the PAs to maintain biodiversity and ecological processes.

The importance of these factors is generally well understood but all three rely on adequate funding. A recent questionnaire survey of PA managers would seem to contradict this statement, as most respondents felt that land-clearing was significantly lower even within poorly-funded PAs when compared with neighbouring areas (Bruner et al., 2001). These PA managers also felt that logging, hunting, burning and grazing were lower inside the PAs. However, this only illustrates that PAs are more effective than areas with no PA status that often contain large numbers of poor people. Other studies have shown that the cost of effectively retaining key elements by preventing poaching of large mammals is much higher (Leader-Williams & Albon, 1988). Indeed, the cost of managing ecological processes is such that it is not implemented by most state conservation organisations (James et al., 2000). Establishing and maintaining community conservation projects can also be very expensive and time-consuming, as is illustrated by a scheme in Uganda that cost more than two million US dollars over seven years (Infield & Namara, 2001).

This shows that PAs will only be a long term success if new strategies are developed to increase their funding. Community conservation projects will undoubtedly play a role but a great deal of progress needs to be made before they are self-sustaining. Another related approach is to diversify the methods used to raise income from PAs and so encourage involvement from local communities and private companies (Inamdar et al., 1999). However, there is also a need for publicly financed PAs that act to maintain biodiversity elements that are unlikely to protected by the private sector (James et al., 2000). Therefore, it is extremely important when establishing a new PA or rationalising the management of an existing PA system to use methods that act to maximise biodiversity protection (Pressey et al., 1993). Such methods have been developed in the previous twenty years and are discussed in the following section.

1.3 Methods for selecting PA location

The first PAs were generally established to protect wilderness and large mammal populations but these areas normally remained in pristine condition because they had little financial value (Shands & Healey, 1977). They were thus highly favoured for PA status because they could be proclaimed

Chapter 1: General Introduction 4 with relatively little opposition from those who felt that PAs might otherwise impede economic progress (Runte, 1979). This trend of giving PA status to “the land that nobody wanted” is a global phenomenon. PAs in New Zealand were often located on high, infertile ground (Mark, 1985), protected forests in Canada were generally too inaccessible for commercial utilisation (Henderson, 1992) and those in Zambia tended to be in areas that were infested with tsetse fly (Leader-Williams et al., 1990).

This commercial pressure also led to the establishment of reserves in areas where it was expected that tourists would pay to visit (Pressey, 1994). Again, this included many places that had spectacular scenery or contained charismatic mega-fauna (Williams et al., 2000). It is partly for this reason that by 1990, approximately 7 % of Africa's savanna had PA status, as compared to only 3 % of the natural forest (Sayer et al., 1992). However, the influence of financial issues should not be over-emphasised because most people felt that the most important role of PAs was to protect wilderness. However, this view gradually changed with the acceptance that PAs should aim to reduce biodiversity loss, leading to a re-evaluation of the effectiveness of the existing PAs in achieving this goal. It was apparent that the global PA network did not adequately represent either terrestrial or marine biodiversity and that new PAs would have to be established.

The methods used to choose where new PAs should be located have increased in complexity and sophistication but they are still criticised for their lack of practicality. Some people have argued that the money spent on this type of planning would be better used for biological surveys and land acquisition (Pressey, 1999a). However, such criticisms are generally ill-founded because alternative approaches tend to follow the path of least political resistance and to protect biodiversity elements that are already well represented in the PA system (Pressey & Tully, 1994). Any method for PA selection needs to be data-driven, goal-directed, transparent, repeatable, efficient and flexible (Pressey, 1999a). Hence, this section will review the methods that have been used for PA selection and illustrate how they have developed to meet these criteria. The application of these methods is also related to the adopted biodiversity surrogate but this topic will be discussed in section 1.4.

1.3.1 Combinatorial scoring and hotspots Combinatorial scoring systems were the first set of methods to be widely used to measure conservation value (Usher, 1986). The technique involved identifying a series of categories that were felt to be relevant, scoring each area and combining these results to produce a final score (Bedward et al., 1991). However, this method was criticised for not being transparent and for theoretical reasons (see later in chapter eight).

An alternative approach was developed as part of growing concerns about global biodiversity loss. The first step was the identification of global “hotspots” that had high levels of plant species

Chapter 1: General Introduction 5 richness and endemism and were also threatened by habitat loss (Myers, 1988; 1990). This methodology was seen as promising because it was hoped that, by protecting hotspots for one species group, other elements of biodiversity would also be protected (Prendergast et al., 1993). However, a relationship between different components of biodiversity only tended to exist at coarse scale (Pearson & Cassola, 1992; Curnutt et al., 1994). Hence, hotspot analysis has generally been superseded for reasons that are explained in chapter seven.

Another problem with the hotspot approach was that it did not allow for the existing levels of protection given to different biodiversity elements by existing PAs. This led to the development of “gap analysis” techniques that have been widely used throughout the United States (US) (Scott et al., 1993; Strittholt & Boerner, 1995), as well as Brazil, India and Costa Rica (Fearnside & Ferraz, 1995; Ramesh et al., 1997; Powell et al., 2000). A gap analysis involves mapping the distribution of biodiversity elements, such as vegetation communities or vertebrate species, and calculating the area of each element that has PA status. Originally, hotspots of under-represented elements were then identified (Scott et al., 1987) but this system was shown to be inefficient. Instead, gap analysis adopted methods that used complementarity to identify priority sites (Kiester et al; 1996) and these are described below.

1.3.2 Complementarity The biodiversity crisis described in section 1.1 suggests that many biodiversity elements are at risk of extinction and so approaches that focus only on the most threatened elements are insufficient (Groves, 1992). A better strategy is to protect a given amount of each biodiversity element and this goal of representativeness is seen as an important part of most PA planning (Dasmann, 1972; Austin & Margules, 1986). The best way to achieve this goal is to use methods based on the concept of complementarity, which is defined as the degree to which an area contributes biodiversity elements that are otherwise unrepresented in another sets of areas (Vane-Wright et al., 1991). A complementarity analysis can be used to identify the smallest number of areas that represent each biodiversity element by a set amount or to identify sites that would maximise representativeness given a set area that can be added to the existing PA system (Williams, 1998a).

This type of analysis was first used in conservation planning in the late 1980s (Kirkpatrick, 1983; Margules & Nicholls, 1987) and a large range of methods has been used since (Kershaw et al., 1995). The most efficient results are produced by using linear programming techniques but these tend to require a large amount of computing time and cannot be used for complex selection goals (Underhill, 1994; Pressey et al., 1996). Instead, most conservation planners have used heuristic algorithms to identify priority sites, which are much faster, more transparent and can produce near- optimal results. The first to be developed chose areas sequentially based on the extent that they complimented previously chosen sites (Dobson et al., 1997). These are known as “greedy richness”

Chapter 1: General Introduction 6 algorithms because they are generally less efficient than algorithms that first select preferentially for areas with the more range-restricted elements (Csuti et al., 1997).

Most of these algorithms were used to analyse presence/absence data that divided the study area into a series of selection units, with each unit having a list of associated biodiversity elements (Williams, 1996). There are two ways in which a unit can be identified as having conservation importance, the most obvious of which is that its inclusion in the selected-area set is vital to achieve the conservation goal. For example, the goal may be to represent a group of plant species in at least one unit and so any unit containing a species found nowhere else would be irreplaceable in the final selected-area set. However, some chosen units do not contain an element that is found nowhere else. Instead they contain more widespread elements that are needed to achieve the conservation goal. These are known as flexible units because they could be substituted for other units that contain the same element. They are important because they allow conservation planners to choose between alternative units without affecting the conservation goal (Williams, 1998b).

However, there is a problem with this approach because it does not provide any information on the potential contribution of units that are not part of the selected-area set (Pressey, 1999b). This has led to the concept of irreplaceability that is described below, together with other aspects of conservation planning.

1.3.3 Planning and implementing stages The development of complementarity analysis has been extremely important because it has introduced a more rigorous approach to conservation planning. It allows the identification of broad scale priority sites (Lombard et al., 1995; Castro Parga et al., 1996; Williams et al., 1996a; Hacker et al., 1998) and can be used to identify sets of existing reserves that should have strict PA status (Howard et al., 1998). However, conservation planning often takes place at a finer scale and involves making choices between large numbers of different selection units. Instead, a measure of value is needed based on the importance of each selection unit in achieving a conservation goal and this is why the concept of irreplaceability has been developed (Pressey, 1999b).

The irreplaceability of a land unit can be defined either as the likelihood that the unit will be required as part of an expanded conservation system that achieves the set of targets or as the extent to which the options for achieving the set of targets are reduced if the area is unavailable for conservation (Pressey et al., 1994). Hence, if a unit is totally irreplaceable, then it will be part of any selected-area set, whereas units with low irreplaceability scores are less likely to be part of a selected-area set and their destruction will have less of impact on the achievement of the conservation target (Pressey, 1999b). This approach has been criticised because the scoring process is less transparent than methods based on complementarity (Williams 1999a) but this difference in transparency tends to be reduced when dealing with area-based conservation targets.

Chapter 1: General Introduction 7 Another problem with the original definitions of irreplaceability occurs in some areas where the majority of units can be totally irreplaceable but will vary greatly in the numbers of unique species that would remain at risk if they were not protected (Lombard et al., 1999). This has been addressed by using an index called “summed irreplaceability” which is based on separate estimated irreplaceability values for each biodiversity element. Another important step in this process has been to allow conservation planners to test different scenarios themselves by linking the process to a geographical information system (GIS) and producing faster methods of calculating irreplaceability values (Pressey et al., 1995; Ferrier at al., 2000).

Conservation planners have also developed a series of methods that increase the applicability of their results. One strategy is based on dividing the process into the following stages: prescription, preselection, selection, prioritisation and postselection (Bedward et al., 1992; Williams, 1998a). Prescription (or goal setting), selection and postselection (or investigating flexibility) have been discussed above but the other stages are an important part of the planning process. Preselection involves identifying land units that should either be excluded from the analysis or automatically selected. Excluded units may be highly transformed (Wessels et al., 2000) or owned by groups that would block any attempt to give the land conservation status. Automatically selected units may be part of an existing PA or included for other reasons, such as watershed conservation.

Prioritisation is another very important stage because rates of habitat conversion are usually much higher than the rate of PA creation. Therefore, chosen units can be ranked according to their vulnerability when this threat is known or can be estimated (Margules & Pressey, 2000). Alternatively, when threat data are unavailable it is better to rank sites by their conservation value so high-scoring sites are protected first (Williams et al., 1996b).

The development of all these methods, whether using complementarity or irreplaceability, has imposed an important structure on conservation planning and helped increase the efficiency of final selected-area sets. However, the quality of the results is also dependent on the biodiversity data used in the analysis. It would be impossible to measure the distributions of a fraction of the biodiversity elements found in a region, so all studies have to use a surrogate for biodiversity. The following section describes the different surrogates used and compares their efficacy.

1.4 PA selection and biodiversity surrogates

The term “biodiversity” includes a whole hierarchy of biological form and function, from DNA and genes to landscapes and ecosystems (Noss, 1990). Unfortunately, almost nothing is known about the identity and distribution of the vast majority of these components. Information at a local or regional level is usually restricted to the distribution of well-known taxa such as birds, mammals or

Chapter 1: General Introduction 8 flowering plants (Williams et al., 1997) and data on genes or demes is lacking for even these groups. However, there is a pressing need to make conservation decisions based on biodiversity data and so suitable surrogates need to be identified and used. Therefore, an important feature of a surrogate group is that the distributions of its elements are well known or can be easily surveyed and are easily distinguished (Pearson, 1994).

However, the most important feature of a surrogate group is that when used in conservation planning it should produce a selected-area set that is representative of biodiversity as a whole (Williams, 1998a). Unfortunately, it has proved impossible to measure how well surrogates represent genes or demes. However, a range of other surrogates have been used and tested and these are discussed in this section.

1.4.1 Species as surrogates Species are the most widely used biodiversity surrogate group because of the wealth of data that has been collected on the distributions of certain taxa. This might seem surprising because various studies have shown that there is little correlation between the distributions of different taxonomic groups. For example, none of the nine taxonomic groups surveyed in a Cameroonian rainforest showed similar patterns of species richness (Lawton et al., 1998) and species richness hotspots for British butterflies, dragonflies, liverworts, aquatic plants and birds showed poor levels of coincidence (Prendergast et al., 1993). However, results from the relevant test of whether the selected-area set for the surrogate group adequately represents other groups are more positive (Margules & Pressey, 2000).

A study from Uganda investigated woody plants, large moths, butterflies, birds and small mammals distributions in 50 forest reserves (Howard et al., 1998). The data were analysed using pairwise comparisons, where each pair consisted of one forest and the next smallest forest to avoid differences driven by species-area relationships. The complementarity score was then calculated for each taxon, as the sum of the number of species found at just one of a pair of sites, divided by the combined total found at either or both sites. The study found that these complementarity scores showed significant cross-taxon correlations and explained why priority sites based on one taxon often captured species richness in other groups with nearly the same efficiency as when using information from all the taxa at once (Balmford, 1998). In addition, a study from found that a selected-area set designed to protect 90 % of species also protected 65 % of the bryophytes and 87 % of the lichen species (Pharo et al., 2000).

These results are in contrast to one study that used distribution data from northern South Africa to identify selected-area sets for eight different taxa (van Jaarsveld et al., 1998). The coincidence between sites selected for these different taxa were then calculated in a series of pairwise comparisons and the mean coincidence was less than 10 %. However, this study did not use the key

Chapter 1: General Introduction 9 test of effectiveness, ie, the number of species belonging to one group that were contained in the selected-area set of another. When this type of analysis was carried out on the same data set it was found that the selected-area sets for one taxon were effective at capturing others, although this approach did exclude some rare and endemic species (Reyers et al., 2000). A similar study from the United Kingdom (UK) found that methods that measured coincidence levels between the selected-area sets for two surrogate groups, tended to be unduly pessimistic. This was because the identified set for each surrogate usually contained many flexible sites and so was only one of a large number of possible sets. Coincidence levels between all the possible selected area sets for two groups were shown to be much higher than expected from random (Hopkinson et al., 2001).

Species data are widely used as a biodiversity surrogate in reserve selection exercises and recent results support the efficacy of this approach. However, there is still a need for further research to confirm this important assumption and to determine the extent that sampling bias can disrupt inter- taxon complementarity relationships. In the meantime, conservation planners will continue to use species data in the hope that they act as a biodiversity surrogate and the knowledge that the results will at least protect a representative sample of the focal taxa. However, accurate species data is not available for many areas and collecting this information can be expensive (Margules & Austin, 1991). Any alternative approach is to use higher taxa as surrogates and this is described below.

1.4.2 Higher taxa as surrogates Using higher taxa as a biodiversity surrogate instead of species data obviously reduces the precision of the exercise. This is particularly the case for species rich areas that may be the focus of greatest conservation concern (Balmford et al., 1996a). Therefore, higher taxa need to fulfil two criteria to be considered a viable alternative. Firstly, they need to pass the standard test for all the surrogates described in this section, ie does a selected-area set for one surrogate using complementarity or irreplaceability capture a representative amount of other biodiversity elements. Secondly, it needs to be shown that costs of collecting information on higher taxa distributions are low enough to justify their use instead of species data (Balmford et al., 1996b).

A large number of studies have looked at patterns of genera or family richness (Gaston & Williams, 1993; Gaston & Blackburn, 1995; Roy et al., 1996; Williams et al., 1997) but there has been relatively little work on their effectiveness in conservation planning (Balmford et al., 2000). However it has been shown that selected-area sets based vascular plant genera were also effective at representing plant species at medium and fine scales (Balmford et al., 1996a; Balmford et al., 1996b). Similar results were found for macrofungi (Balmford et al., 2000) and all three studies found that using families instead of genera reduced the effectiveness of the surrogate in representing species. Results from the fine scale analysis of vascular plants also found that costs for genus level surveys achieved a minimum saving of 60 % (Balmford et al., 1996b).

Chapter 1: General Introduction 10 Despite these advantages, the higher taxon approach is not widely used. This is probably because most species-based approaches use data that have already been collected and so have minimal associated costs. Another reason is that many studies use modelled distributions (Scott et al., 1993) and there has been little work on modelling the ranges of genera or families. Instead, another approach that attempts to map higher levels of biodiversity is widely used and is described below.

1.4.3 Environmental surrogates Very little is known about the distribution of most species and so conservation planners frequently need a broader measure of biodiversity that can be more easily mapped (Franklin, 1993). These surrogates may also be important when selected-area sets based on available species data are shown to be unrepresentative (Fairbanks et al., 2001). The first attempts to achieve this often used vegetation types but the only suitable maps were usually too broad for practical use (Dasmann, 1972). More recent approaches have used a wide range of surrogate measures, including climatic and edaphic variables (Belbin, 1993), landscape ecosystems (Lapin & Barnes, 1995) and landform- vegetation classes (Awimbo et al., 1996). These surrogates are generally derived by combining a whole series of biotic and abiotic factors that are available in digital format from remote sensing or other sources (Roy & Tomar, 2000).

It has been shown that the distributions of most biodiversity elements are partly dependent on these factors, suggesting that this approach is valid (Pressey et al., 2000). However, there are problems with the methods used because they make two important assumptions. Firstly, it assumes that the underlying data is accurate and this may not always be the case. Many climatic data sets are created by interpolating from a few sample points or by deriving one set of information from another. Some data sets may be out of date or the information may have been collected at different scales (Estes & Mooneyhan, 1994). Any errors are then compounded further when the data are combined (Lunetta et al., 1991). Secondly, it assumes that the distribution of each biodiversity element is determined by the same combination of variables and this problem is compounded when a continuous factor is used to produce categorical variables.

There is a great need, therefore, for testing the efficacy of many environmental surrogates but this would be very difficult because these surrogates were developed in response to the lack of suitable taxon data. One study found that the selected-area set of land facets, defined as areas with uniform slope, soils and hydrological conditions, was effective at representing bird and dung beetle species but the study only covered an area of 350 km2 and used fine resolution abiotic data (Wessels et al., 1999). A larger scale analysis also found a large degree of coincidence between sites chosen using species and environmental data although both selected-area sets failed to capture some important elements (Kirkpatrick & Brown, 1994). In contrast a study that looked for similarities between the distributions of vegetation types and ecoregion types, as defined by two classification systems, found little overlap (Wright et al., 1998).

Chapter 1: General Introduction 11 It appears that these derived environmental surrogates, therefore, should only be used when no other data source is available to identify conservation priorities. The best strategy would probably be to use these surrogates at a very broad scale, where data error is more likely to be masked by biogeographic patterns, and then carry out a species or genera based analysis in areas that were identified as conservation priorities. However, there is another advantage of using these other factors in conservation planning because they can be used to produce a more ecologically viable PA network and this subject is described in the next section.

1.5 Incorporating viability into PA selection

The methods for selecting PAs described above have been designed to be as efficient as possible by identifying the smallest amount of land needed to achieve a conservation goal. However, the aim of these PAs is to maintain the associated biodiversity and so these efficiency-led methods may not be adequate (Cowling et al., 1999). For example, a study from the UK found that the most efficient PA network to represent plants found on limestone pavements in 1974/5 would have lost 36 % of these species by 1985 (Margules et al., 1994). Similar results were found from a study in Finland, where 12 % of plant species would have been lost over 63 years from the original selected-area set (Virolainen et al., 1999). Therefore, there is a need to incorporate methods in a PA planning exercise that will promote the long term viability of biodiversity. Three main methods have been used to achieve this and are described below.

1.5.1 PA selection and the theory of island biogeography One of the first set of ideas to influence PA design were based on the theory of island biogeography (MacArthur & Wilson, 1963) and these stated that ideally PAs should be large, circular, grouped in clusters and joined by corridors (Diamond, 1975). One reason for promoting these factors was that the theory of island biogeography predicted that these PAs would contain more species and this provoked the SLOSS (Single Large Or Several Small) debate. Others argued that a series of smaller PAs containing different habitats would contain more species than a large, homogenous PA (Higgs & Usher, 1980) and this has been supported by the literature described in section 1.3.

There is general agreement, however, on one reason for expecting more species in a larger PA. It was argued that larger areas would contain larger populations that were less likely to become extinct (Baz & Garcia-Boyero, 1996) and the importance of a large population size is now widely recognised (Soulé, 1987). Despite the general acceptance of this concept, there are no set rules for calculating the necessary population size for a particular species (Caughley, 1994) and so the general rule of aiming to maximise PA size is still the most useful. The importance of PA shape has also been recognised because of the edge effects described in section 1.1. Therefore, some reserve selection algorithms have been developed that give preference to the selection of neighbouring

Chapter 1: General Introduction 12 units (Nicholls & Margules, 1993; Lombard et al., 1997), thereby both increasing the mean area of individual PAs and reducing edge. The importance of connectivity has also been widely recognised because of the role it plays in many ecological processes and this will be discussed below.

1.5.2 Maintaining ecological processes It has long been recognised that ecological processes are important for maintaining biodiversity. This has led to the suggestion that conservation planning should be based on ecologically valid units such as watersheds or landscapes (Noss, 1983; Akçakaya et al., 1995; Margules & Pressey, 2000). Another important factor is ensuring connectivity between PAs and this has been focus of much conservation effort (Newmark, 1995; Mann & Plummer, 1995; Westing, 1998). Connectivity brings benefits because it increases the effective size of PAs, producing more viable populations and providing more space for successional changes (Taylor et al., 1993). It also allows the recolonisation of habitat patches after local extinctions, as has been shown by studies on metapopulations (Hanski, 1989). In addition, connectivity maintains nutrient cycles, migrations and a range of other ecosystem processes (Forman & Godron, 1986) and this is particularly important when considering the effects of predicted climate change (Hill et al., 1999).

There are two approaches that have been identified as maintaining connectivity and the most obvious of these is constructing corridors between PAs (Hobbs, 1992). These corridors were often designed for large mammals (Johnsingh, 1990; Beier, 1993) and were criticised for lack of data proving their effectiveness (Simberloff et al., 1992). However, supporting evidence now exists (Beier & Noss, 1998) and corridors for a range of other groups have been created or protected (Bennett et al., 1994; Hill, 1995; Bentley & Catterall, 1997). Another approach is to improve the conservation value of the human-dominated land found around PAs by changing agricultural practices and other land-use policies (Balmford et al., 1998). This increases not only connectivity between PAs but also the area of land containing important levels of biodiversity.

More recently, there have been suggestions that PA planners should span substantial environmental gradients and maintain evolutionary processes (Margules & Pressey; Fairbanks et al., 2001). The first suggestion is important because protecting areas with important environmental gradients will allow the biodiversity elements to change their distributions in response to future climate change. However, in theory this should be achieved automatically by using standard reserve selection methods, as long as the appropriate biodiversity surrogates are used (Faith & Walker, 1996) and the selection units are large enough to include transitional areas. Selecting PAs to maintain evolutionary processes is more problematic because it is difficult to predict the future course of evolution. In addition, evolutionary hotspots tend to contain many closely related species that are not representative of biodiversity as a whole (Crozier, 1997; Williams, 1998a).

Chapter 1: General Introduction 13 Most of these conservation strategies aim to increase the viability of PAs once they have been identified as priority sites. An alternative approach is to develop methods that identify the most viable populations of the biodiversity surrogate and this is discussed below.

1.5.3 Selecting for persistence It has been shown that selected-area sets used to represent a series of biodiversity elements are unlikely to contain all of the elements over time (Margules et al., 1994). However, the factors causing the extirpations that produce this phenomenon are not uniform and so some populations are more resilient (Rodrigues et al., 2000). One strategy is to select more sites and so increase the chances of including those with viable populations. However, this method, although probably effective, would be inefficient.

Another strategy is to base selection on the area of each biodiversity element found in a selection unit instead of relying on presence/absence data. This method would prevent the selection of units near human habitation that tend to be relatively over-sampled and so appear to have high species richness despite containing little suitable habitat (Wessels et al., 2000). It would also prevent the selection of units that have high species richness because they fall on an ecotone (Branch et al., 1995). An obvious limitation with this method is that the data are more difficult to produce but modelling species distributions is now common and would provide the continuous data, either as presence probabilities or area, which are required. However, this method is still not ideal because it may identify units that contain habitats that act as population sinks (Pulliam, 1988; Nicholls, 1998).

One solution to this problem is to base selection on abundance data and this was shown to be effective when identifying priority conservation sites in the UK (Rodrigues et al., 2000). Another study from the US showed the importance of only using habitat patches in a gap analysis that were large enough to sustain viable populations of 30 focal mammal species (Allen et al., 2001). An alternative strategy has been developed for use with presence/absence data that aims to use distribution data to fit models and then predict persistence using available information on threats and species vulnerability (Araújo & Williams, 2000). These methods would probably increase the ecological viability of any resulting PA network but lack of funding means that it is unlikely that they can be repeated in most parts of the world. In fact, there are many occasions where non- biological factors play a role in PA selection and a number of these are described in the next section.

1.6 PA selection in the real world

There is a large range of factors that determine the success of a PA network, including the expertise of the staff and the effectiveness of the management system (McRae, 1998). However, one of the most important is the availability of funding and the influence it has on conservation planning. This

Chapter 1: General Introduction 14 section will describe two of the most important results of these constraints and methods to overcome them.

1.6.1 Finances and fund raising Most PAs are partly funded by national governments, although spending in most developing countries is low (James et al., 2001). Despite this shortage of funds some countries have developed national and provincial PA systems and this may lead to inefficient representation (Erasmus et al., 1999). However, an increasingly important source of funding comes from international funding agencies and NGOs and these groups can play a large role in affecting conservation policy (Wells, 1998). In the past there was a particular emphasis on developing PAs for the protection of large charismatic species, such as giant pandas and tigers (MacKinnon & Wulf, 1994; Wikramanayake et al., 1998). These “flagship” species are often used to promote conservation awareness and raise funding or support (Leader-Williams & Dublin, 2000) and it was argued that protecting these species could also protect habitat for a range of other biodiversity elements, at least in or .

However, these species tend to be poor biodiversity surrogates in Africa (Williams et al., 2000) and even species associated with biodiversity hotspots may be habitat generalists found in the poor quality agricultural land that is least threatened. These shortcomings mean that the larger conservation NGOs tend not to use flagship species as a biodiversity surrogates. However, “flagship ecosystems”, such as tropical rainforests, are still used and may bias conservation planning (Pressey et al., 2000). The size of the NGO may also play a role in determining conservation strategy, perhaps explaining why the Worldwide Fund for Nature has identified more than 200 important conservation sites compared to the 25 of Conservation International (Olson & Dinerstein, 1998; Myers et al., 2000).

However, the negative aspects of the influence of conservation NGOs and funding bodies should not be over-emphasised because they have played an important role in biodiversity conservation. The resources they provide are an essential part of conservation budgets in many countries and several have led the way in applying reserve selection planning techniques. However, this funding could be used even more effectively if they worked with governments to design a strategy for representative PA systems. Different funding bodies often have different areas of interest and it is only by producing a comprehensive PA strategy that the needs and wishes of the two groups can be integrated (Pressey et al., 2000). Another important effect of funding difficulties is that most conservation organisations cannot afford to collect the necessary distributional data and the results from this problem are described below.

Chapter 1: General Introduction 15 1.6.2 Scale Conservation planning needs to take place using a range of spatial scales but most work has been done at a coarse resolution (ie > than 100 km2). These studies are extremely useful for identifying broad conservation priorities but they have little relevance when dealing with national conservation strategies. This is because the selection units tend to include major human population centres. In addition, they are often larger than most PAs (Siegfried et al., 1998) and in some cases may be only slightly smaller than the whole country. Despite these obvious disadvantages, there are two main reasons why coarse scale studies are still predominant in the literature. The first is that most work aims to develop the theoretical aspects of conservation planning and so there is less emphasis on producing concrete plans to be used by the relevant state organisations. This has produced great developments in the methods available for conservation planners, although few of these have been tested at a finer scale.

The second reason is that academic departments and conservation bodies tend to be poorly funded and so often cannot afford to collect data at the necessary spatial scale. Instead, they tend to collate existing data from museums and volunteer groups, which may be out of date or affected by sampling bias (Freitag et al., 1998). Consequently, there is a great need for researchers to develop affordable methods for collecting and analysing fine scale data that can be used to design realistic PA networks. This thesis describes one such study that focussed on designing a PA network for Maputaland, South Africa. This was an ideal study area because existing information and expertise allowed the development of a suitably fine-scale data set. In addition, this research was seen as a priority by the KwaZulu-Natal Nature Conservation Service (NCS), an organisation that is widely recognised for its efficiency and skilled personnel. This meant that NCS staff were willing to be involved in the planning process and are likely to act on any findings.

1.7 Aims

The main purpose of this study was to use a GIS, satellite imagery and the expertise of NCS staff to produce a data set that could be used to design an integrated PA network for Maputaland. This data set needed to describe the biodiversity of the region, as well as to incorporate issues of land- ownership, predicted risk of habitat transformation and value judgements. Such a process involves several important stages and so this research had the following aims:

1) To create a GIS that describes the topography, geology, PA network and roads of Maputaland at a fine scale.

2) To develop a land-cover classification system and map the land-cover of Maputaland using Landsat Thematic Mapper (TM) satellite imagery.

Chapter 1: General Introduction 16 3) To map agricultural transformation in Maputaland between 1986 and 1998, use logistic regression to identify any determining factors and model future risk with the GIS.

4) To model the distribution of Maputaland’s bird species by using a land-cover type/bird species association matrix based on expert review and the land-cover coverage.

5) To use the modelled data to investigate whether the Southern African Bird Atlas Project data for Maputaland is affected by sampling bias and to identify any factors determining such bias.

6) To use a complementarity-based gap analysis to identify priority sites for the conservation of bird species and land-cover types that are under-represented in the present PA network.

7) To carry out a gap analysis that includes information on agricultural transformation risk.

8) To identify the near-minimum set of priority sites that would be needed to protect set percentages of each biodiversity element in Maputaland.

9) To use information from the near-minimum set analyses to rank areas that are the subject of land-claims from local communities in terms of their conservation importance.

10) To use analytical hierarchy process methodology to incorporate ecological viability and preferred weightings for different biodiversity elements in a decision making process to identify priority sites for future conservation development.

1.8 Thesis structure

This thesis consists of eight further chapters. Chapter two describes the study region and its role in biodiversity conservation. Chapter three gives an introduction to GIS and describes the GIS coverages that were produced for this study. Chapter four reviews past descriptions of the land- cover of Maputaland and describes the methods used to produce a land-cover coverage of the region based on Landsat TM imagery. Chapter five uses this land-cover information to investigate the factors that determine agricultural transformation and chapter six uses the same information to model the distributions of the region’s bird species. Chapter seven uses a newly developed reserve selection algorithm to identify priority sites for conservation and chapter eight describes a similar analysis based on a different method that incorporated issues of ecological viability. Chapter nine concludes by discussing previous findings and suggesting future developments that would increase the quality and relevance of PA planning.

Chapter 1: General Introduction 17 Chapter 2: A description of Maputaland

2.1 Introduction

Greater Maputaland is an area of approximately 20 000 km2 that covers part of South Africa, Moçambique and Swaziland (Figure 2-1). It consists of the Lebombo Mountains and the coastal plain that lies between the mountains and the . The northern boundary of Greater Maputaland is set by Delagoa Bay in Moçambique and the southerly boundary by Lake St Lucia in South Africa (Cowling and Hilton-Taylor, 1994). These boundaries are based on ecological factors, as the flora and fauna of this region share many affinities (Van Wyk, 1994). However, the name “Maputaland” has been used to describe sub-sections of this larger area, so caution is needed when using the term. This study focuses only on the South African section, which will be referred to as Maputaland, whilst the larger area will be referred to as Greater Maputaland.

ZIMBABWE

MOÇAM- BOTSWANA BIQUE Northern Province

N nga g ala en m t u u p North-West Ga SWAZI M

NAMIBIA LAND n

a e atal c N O Free State u- ul n Z ia a d n w I LESO- K

Northern Cape

A THO

t l a n t i c 300 km O c Eastern Cape e a Key n

Western Cape Greater Maputaland

Maputaland, South Africa

Figure 2-1: A map of Southern Africa showing the position of Greater Maputaland.

Within South Africa, Maputaland has an area of 9790 km2 and is found between latitude 26.78° and 28.5° South and longitude 31.95° and 32.9° East. In this study the boundaries of the region are set by the Moçambique border in the north, the Indian Ocean in the east and the road that connects Mtubatuba with St Lucia in the south (Figure 2-2). The western boundary is set by the Lebombo Mountains and by a line that connects the southern part of the mountains with the St Lucia estuary (Figure 2-3). This area was known as Tongaland and this name is still occasionally used (Bruton 1980a).

Chapter 2: A description of Maputaland 18 Usutu

Kosi Lake System INGWAVUMA KwaNgwanase Ingwavuma

Ingwavuma

Pongola Lake Sibaya Pongolapoort Dam

Jozini Mbazwane UBOMBO Ubombo

Mkhuze Mkhuze

Msunduzi

Mzinene

Hluhluwe

Lake St Lucia Nyalazi

20 km N2 highway Hluhluwe Road Mpate River Lake HLABISA Town

St Lucia Mtubatuba

Figure 2-2: The towns, major roads, rivers and lakes of Maputaland

Chapter 2: A description of Maputaland 19 This chapter describes the main factors that make Maputaland an area of international conservation importance. Section 2.2 describes the topography, climate, geology and hydrology of the region and section 2.3 describes the rich biodiversity that these physical factors have partly fostered. Section 2.4 describes the people of the region and the ways in which their culture and demographics have shaped the natural resources. Section 2.5 describes the history of biodiversity conservation in Maputaland, together with the PA system that has developed. Section 2.6 describes how this biodiversity is threatened and discusses future conservation policy that aims to reduce the impact of these threats and this chapter is summarised in section 2.7.

2.2 The physical characteristics of Maputaland

Maputaland consists of two major topographical features: the Lebombo Mountains and the coastal plain (Watkeys et al., 1993). Both features extend northwards into Moçambique, where they are even more extensively developed. The Lebombo Mountains are at their widest (over 13 km) and highest (over 700 m asl.) in the north (Figure 2-3). They consist of undulating plateaux at an elevation of between 300 and 600 m asl. and are bounded by a steep western scarp face with a gentle dip-slope to the east. There are also several steep valleys, which were formed by the region’s rivers cutting through the mountains (Figure 2-2, Figure 2-3).

This mountain range, together with those found to the north in Kenya and Tanzania, mark the position of the coastline that was formed in the Jurassic period by the break-up of the of Gondwana (Groenewald et al., 1991). The sea level has risen and fallen a number of times since then, depositing, eroding and reworking a variety of sands, silts and clays (Watkeys et al., 1993). The resultant coastal plain is made up of three main bands, all of which follow the general north- south direction of the mountain range. The first of these bands consists of gently undulating terrain at the base of the Lebombo Mountains, which generally has a lower elevation than the sandy ridges that make up the central band of the coastal plain. The third band is characterised by the narrow, high coastal dunes that are formed by the prevailing coast-parallel winds (Tinley, 1985). The coastal plain is geologically quite recent, especially in the east where it continues to expand. The formation of this plain, together with the topography of the region, has had a major influence on the abiotic characteristics of Maputaland, which are described below.

2.2.1 The climate of Maputaland

The most striking feature of the climate of Maputaland is the variation in rainfall across the region. In the east, by the coast, the annual rainfall averages between 1000 and 1100 mm. This decreases progressively inland, so that the mean rainfall is approximately 600 mm at the foot of the Lebombo Mountains. This then increases with altitude so that the crest of the mountains receives approximately 800 mm annually (Maud, 1980). However, these annual rainfall figures can vary dramatically between years as is illustrated by data from Mkhuze Game Reserve (GR). The Chapter 2: A description of Maputaland 20 maximum annual rainfall recorded there is 1048 mm, which is over three times greater than the minimum recorded value of 345 mm (Watkeys et al., 1993). Maputaland has hot, wet summers that last from November to March and mild, dry winters that last from May to August. The mean annual temperature for the region varies between 21 and 23 °C (Schulze, 1982).

2.2.2 Geology and soils

The geology of the region is very diverse, consisting of Mesozoic, Tertiary and Quaternary sequences (Figure 2-4). In general, these formations decrease in age the closer they are to the coast. The oldest exposed rocks are 180 million years old Jozini Formation rhyolites that make up the Lebombo Mountains (Watkeys et al., 1993). At the foot of the mountains are a series of Cretaceous sediments, which have both riverine and marine origins, whilst Pleistocene sediments cover the two thirds of Maputaland that is closest to the sea. These form a thin veneer on the Tertiary and Cretaceous rocks which is rarely thicker than 50 m (Maud, 1980). This layer has been reworked by wind-action in the later Pleistocene and recent times, giving rise to extensive dunes of yellowish sand that characterises most of Maputaland. Some of these sand dune ridges have been subject to more prolonged weathering and are red in colour.

2.2.3 Rivers, lakes and wetlands

Six major rivers flow through Maputaland. The Pongolo, Ingwavuma and Usuthu rivers are found in the north of the region and cut through the Lebombo Mountains (Figure 2-2). The Mkhuze and Msunduze rivers meander through the Lebombo Mountains and join before flowing into the north of Lake St Lucia. The Hluhluwe River flows to the south of the mountains and also enters into Lake St Lucia (Figure 2-2). In addition, a number of smaller rivers originate either in the Lebombo Mountains or on the coastal plain. All of the rivers that flow through the coastal plain are seasonal, flowing only during the wet summer and usually drying out by mid-winter (Watkeys et al., 1993).

There are three major natural lake systems in Maputaland, all of which are found in the east of the region (Figure 2-2). Lake Sibaya is the largest natural freshwater lake in South Africa and covers an area of 70 km2 (Pitman, 1980). The Kosi Lake system consists of four interconnected lakes, which range in salinity from close to seawater in the tidal basin to freshwater in Lake Amanzimnyama (Wright et al., 2000). Lake St Lucia is 40 km long, up to 10 km wide and it has an average depth of 0.9 metres. It is connected to the sea by The Narrows, a tidal channel of about 20 km that together with the lake forms the largest estuary in Africa (CSIR Environmental Services, 1993). There are a number of other, smaller natural water bodies in Maputaland, many of which have associated wetlands. The other significant water body in the region is artificial and was formed in 1974 by damming the Pongolo River near the town of Jozini (Bembridge, 1991). The Pongolapoort Dam is the fifth largest lake in South Africa and it provides water for people living in the Jozini area and electricity from a hydroelectric power station. Chapter 2: A description of Maputaland 21 20 km Elevation profile (in m) 694 between points A and B

0 A B

Figure 2-3: A digital elevation model of Maputaland

Chapter 2: A description of Maputaland 22 Rhyodacite Conglomerate Red sandy clay Siltstone Young alluvium Red sand Argillaceous sand Red dune cordon sand Blown sand 20 km Marsh Yellowish redistributed sand Dune and beach sand

Water

Figure 2-4: A geological map of Maputaland

Chapter 2: A description of Maputaland 23 2.3 The flora and fauna of Maputaland

The position and geological history of Maputaland has had a profound effect on its flora and fauna. The region is found at the most southerly point of the East African coastal plain, which stretches as far north as Somalia and many species reach the southernmost limit of their range in Maputaland. The region also contains many endemic species that evolved to occupy the ecological niches created by the geologically recent formation of the coastal plain. In addition, the well-defined climatic and geological conditions have produced a series of ecological zones (Figure 2-5), which in turn contain many distinct habitat types (Tinley & van Riet, 1981). This biodiversity is generally well known and is described in more detail below.

2.3.1 Flora

The flora of Maputaland belongs to the Indian Ocean Coastal Belt phytogeographical zone (Moll & White, 1978; White, 1983). The flora is a mixture of several floristic elements and communities, including , Cape, Afroalpine and paleoendemic elements (Moll, 1980). However, the strongest affinities are with the more tropical parts of Africa, as Maputaland is found at the edge of the tropics. This range of affinities means that Maputaland is an area of high plant species richness and it contains an estimated 1800 species of vascular plants. Maputaland has also been defined as a centre of plant endemism as it contains at least 168 species/infraspecific taxa and four genera which are endemic or near endemic to the region (Van Wyk, 1994). The vegetation types of the region are equally diverse and rich but these are discussed in more detail in chapter four.

2.3.2 Fauna

The fauna of Maputaland shows similar patterns to that of the flora by being exceptionally rich and containing the southernmost range of many components of the East African fauna (Poynton, 1961; Van Wyk, 1994). For example, Maputaland contains 472 bird species (57% of South Africa's total) of which 47 sub-species are endemic or near endemic. It also makes up the southernmost part of the South East African coast Endemic Bird Area (EBA), which contains three endemic, range- restricted bird species (Stattersfield et al., 1998). The mammals, reptiles, frogs and freshwater fishes show similar patterns (Van Wyk, 1994; Rowe-Rowe & Taylor, 1996). The invertebrates of the region are less well known but a study of dung beetles (Scarabaeidae) found that species assemblages in this group differed with habitat type, suggesting similar levels of species richness (Van Rensburg et al., 1999).

Chapter 2: A description of Maputaland 24 Lebombo zone Cretaceous zone 20 km Alluvial zone Coastal plain zone Coastal dune zone

Figure 2-5: The ecological zones of Maputaland

Chapter 2: A description of Maputaland 25 2.4 Human involvement in Maputaland

The oldest signs of human presence in Maputaland were found in the Border Cave, on the western face of the Lebombo Mountains. Over 69 000 implements were found in the cave and these are between 110 000 and 30 000 years old (Avery, 1980; Klein & Cruz-Uribe, 1996). Iron Age people lived in Maputaland by 290 AD and charcoal from an iron-smelting furnace found in Ndumo GR by Dutton (1970) has been dated at 630 AD. People with knowledge of iron working and crop growing reached the area of Mkhuze GR by about 1440 and human presence in Maputaland had a profound influence on the local ecology. These people used fire to clear areas for agriculture and improve grazing for cattle and cut down hardwood trees for fuel and building materials. This reduced the amount of forest, which was replaced by thickets and secondary, fire-maintained grasslands (Feely, 1980). Most of the present inhabitants of Maputaland are a mixture of Nguni and Thonga speaking people and their history, demographics and culture are described in the following sections.

2.4.1 A history of Maputaland

The Nguni and Thonga speakers migrated down the low-lying East Coast of Africa. By 1550 they had reached southern Moçambique and both groups had arrived in Greater Maputaland by the 1700s. The end of the 18th century saw the rise of the Mabhudu-Tembe ruling lineage, which was based in southern Moçambique and collected tributes from the people of Maputaland (Bruton et al., 1980). The beginning of the 19th century saw the introduction of an expansionist policy by the Zulu king Shaka and his successor Dingaan. This had a massive knock-on effect on the rest of the people in the region. Most of the expansion was into fertile, crop-growing areas and so the Zulus largely ignored Maputaland. However, the expansionist policy led to many people who fled the Zulu armies settling in Maputaland, which became home to various isiZulu or siSwati-speaking groups.

The 19th century also saw the increased influence of Europeans in Maputaland and the region became part of the colonial aspirations of several countries. The Portuguese, Dutch and British all vied for control of the harbour at Delagoa Bay to the north of Maputaland. This dispute was settled by arbitration in 1875 and the resultant boundary still marks the border between South Africa and Moçambique, but it split the Mabhudu-Tembe tribe into two. In 1897 the British formally took control of Maputaland by annexing the region to Zululand and incorporating both into Natal.

In 1910, Natal became a part of the Union of South Africa and successive governments introduced the apartheid system to separate the different ethnic groups. The black people of Maputaland were relatively unaffected by this system as their land had little agricultural potential, so they were not forcibly moved to new areas. The region was more affected by the Zululand Commission (1902- 1904), which declared parts of Maputaland to be Crown Land. This took away the legal status of the people living there and this continued after the South African government took over from the

Chapter 2: A description of Maputaland 26 British. In these areas all the community facilities, such as schools and roads, had to be financed, constructed and maintained by the community (CORD, 1991). These communities were very poor and the lack of employment opportunities forced many men to work elsewhere. It is argued that the absence of these migrant workers deprived the region of able-bodied people and reduced the agricultural success of those that remained (Webster, 1986).

In 1971, the South African government decided to create a series of semi-autonomous homelands. One of these was KwaZulu, which consisted of 10 areas that were surrounded by Natal. The majority of Maputaland was classified as belonging to KwaZulu but those areas in the south, around Lake St Lucia and Mkhuze town, remained in Natal. This system remained until 1994 when the new democratically elected government merged the two and formed KwaZulu-Natal.

2.4.2 Culture

The human culture of Maputaland has been greatly influenced by the poor soils and high levels of biodiversity in the region. The people of Maputaland have diverse origins but those belonging to the Tembe clan of the Thonga people dominate both in terms of numbers and social and political power (Mountain, 1990). The Tembe-Thonga are known for their use of the natural resources of the region and they exploit a large number of animal and plant species for food, medicine, fuel, building materials and household utensils (Pooley, 1980). The low agricultural potential of the region has forced people from other tribes to adopt these practices and even the Zulu people who migrated into the area have been assimilated into their culture. This is very unusual and explains why Maputaland was relatively unaffected by the expansion of the Zulu kingdom in the 19th century (Felgate, 1982). For example, the land tenure system and the customs and laws that govern marriage, funeral rites, rituals and taboos are all more closely related to Thonga customs than to Zulu customs (Torres, 1980; Mountain, 1990).

2.4.3 Demographics

Maputaland contains sections of three magisterial districts, Ingwavuma, Ubombo and Hlabisa, which contained 376 800 people in 1990 (CSIR Environmental Services, 1993). However, approximately 10% of Ubombo and Hlabisa fall outside of Maputaland so the population size of the region is likely to be smaller (Table 2-1). Long-term population census data are not available for the whole of the region but good records exist for the Ubombo magisterial district (Bruton, 1980b). These show that the populations increased by 413%, from 21 705 to 89 700, between 1936 and 1990 (Table 2-2, Figure 2-6). This is a change in population density from 5.2 to 21.4 people km-2 and similar trends have been shown in Ingwavuma and Hlabisa. Maputaland is also home to some of the poorest people in South Africa and in 1986 it was estimated that the majority of the population had a mean household income of approximately US $250 (CORD, 1991). Levels of

Chapter 2: A description of Maputaland 27 unemployment and illiteracy are high and figures from 1990 showed that each wage earner had to support at least five others (Table 2-1).

Table 2-1: Statistics describing the human population of three districts in Maputaland

Magisterial Popn Popn Popn Unemploy Depen Illiteracy district density growth -ment -dency rate rate rate ratio † (km-2) % % %

Ingwavuma 112 900 27.8 0.6 17.9 5.6 62.3 Ubombo 89 700 21.4 0.8 15.7 5.1 57.8 Hlabisa 174 200 40.8 2.5 49.8 5.0 32.1 † Dependency ratio = Dependants / Wage-earner

Table 2-2: The human population in Ubombo and Ingwavuma from 1936 to 1990

Year Ubombo Ingwavuma Total 1936 21 705 - - 1946 22 376 - - 1951 20 028 - - 1960 26 390 - - 1970 46 638 62 326 108 964 1980 62 146 86 307 148 453 1990 89 700 112 900 202 600

Figure 2-6: The human population in Ubombo and Ingwavuma from 1936 to 1990 Chapter 2: A description of Maputaland 28 2.5 Conservation in Maputaland

South Africa is an extremely biodiverse country and PAs play a central part in its conservation policy. These PAs cover 72 000 km2, or 5.9% of the country’s land area and have played a major role in the success of a number of large mammal protection programmes, such as the one that saved the white rhinoceros (Ceratotherium simum) from near extinction (Huntley, 1996). They also protect other taxa, with 90% of the amphibian, reptilian, avian and mammalian species having breeding populations in at least 1 of the 576 formally PAs (Siegfried, 1989). They are managed both by national and provincial organisations. The South African National Parks has responsibility for 17 National Parks, which are found throughout the country apart from KwaZulu-Natal. Most of the other PAs are managed by state conservation bodies in each of South Africa’s nine provinces.

Maputaland is part of KwaZulu-Natal, the most easterly of South Africa’s provinces (Figure 2-1). The KwaZulu-Natal Nature Conservation Service (NCS) is responsible for conserving its biodiversity. The NCS manage 108 terrestrial PAs that have a combined area of 7545 km2 (Figure 2-7) and contain populations of more than 95 % of the province's vertebrate species, 61 % of the Cetoniid beetle species and 94 % of the tree species (Bourquin et al., 1996). KwaZulu-Natal is also home to 8.55 million people and over 50 % of its land has been transformed by agriculture, silviculture and urbanisation (Bourquin et al., 1996). It was estimated that by 1986, 65 % of the vegetation complexes in the province had been totally or partially transformed and this process is ongoing (Scott-Shaw et al., 1996). Forests and wetlands have been particularly affected by transformation, with most of the wetlands having been reduced by over 50 % (Kotze et al., 1995).

Protected Area

50 km

Figure 2-7: The PAs of KwaZulu-Natal

Chapter 2: A description of Maputaland 29 The conservation importance of Maputaland has long been recognised by those living in Southern Africa because it has high levels of species richness and endemism, a large number of different habitat types and some of South Africa's few remaining large mammal assemblages. Furthermore, in recent years various international organisations have confirmed the value of the region. In particular, Maputaland has been identified as being globally important for the conservation of birds (Stattersfield et al., 1998) and vascular plants (Van Wyk, 1994). In addition, the Worldwide Fund for Nature has identified Maputaland as making up part of one of the 200 most important eco- (Olson & Dinerstein, 1998). This conservation importance is also reflected by the fact that five of the wetland sites are registered under the RAMSAR convention and that the Greater St Lucia Wetland Park is recognised as a World Heritage Site.

The history of formal biodiversity conservation in Maputaland goes back more than a century and is discussed below, together with a description of the PA network that has been produced.

2.5.1 A history of biodiversity conservation in Maputaland

The first Europeans to visit Maputaland arrived in the 16th century and noted the abundance of large mammals that lived there. The region had become a popular destination for Europeans by the middle of the 19th century who came to shoot large numbers of the available game. For example, George Shadwell shot 150 elephant (Loxodonta africana) and 91 hippopotamus (Hippopotamus amphibius) in one season (Baldwin, 1863), while John Dunn shot 23 dugong (Dugong dugong) in one morning (Dunn, 1886). This type of hunting, together with habitat loss and increasing human population pressures, drastically reduced the numbers of large mammals in the region. Several early hunters were appalled by the amount of hunting that had taken place in Maputaland and there was a call for the establishment of controlled-hunting areas. In 1897, this pressure led to the proclamation of the Lake St Lucia game reserve and other reserves, such as Mkhuze and Ndumo, were established at the beginning of the 20th century.

In 1947 the Natal Parks, Game and Fish Preservation Board was established and given responsibility for the management of Maputaland's PAs. This organisation was eventually renamed the Natal Parks Board (NPB) and its staff played an increasingly important role in conserving the region’s biodiversity. Change came in the late 1970's when the South African government established a series of semi-autonomous homeland states within South Africa as part of its apartheid policies. This resulted in the northern half of Maputaland being designated as part of KwaZulu and the KwaZulu Department of Nature Conservation (KDNC) was established to manage its biodiversity.

Chapter 2: A description of Maputaland 30 It had long been recognised that the existing PAs in Maputaland were mainly established to protect large mammal populations and recreational fishing sites. This meant that many aspects of Maputaland's biodiversity were not represented in the PA system. The KDNC acted to rectify this situation by establishing several new PAs, including Tembe Elephant Park (TEP) and the Lebombo Mountain Nature Reserve (NR) (Tinley & van Riet, 1981). This involved the forced relocation of many people, with one estimate suggesting that the creation of Ndumo GR, the Coastal Forest Reserve (FR) and TEP involved the forced relocation of 30 % of the population of Ingwavuma and Ubombo districts (AFRA, 1990). This led to widespread resentment amongst the affected communities towards PAs and the organisations that managed them.

In recent years, Maputaland has been the location of two important test cases with regards to conservation in South Africa. The first of these was the proposal to mine an area on the eastern shores of Lake St Lucia for titanium. The government commissioned an Environmental Impact Assessment that was reviewed by a panel of eminent lay-people and they decided that the mining should not go ahead because of the perceived value of the area and because of the inability to make exact predictions of the magnitude of expected impacts (Kruger et al., 1997). There was widespread public opposition to the mining and support for eco-tourism and sustainable use of the natural resources as an alternative land-use option (Douglas, 1996). The second example was the illegal occupation of parts of the Dukuduku forest by local people and Moçambiquan refugees. These people destroyed the natural vegetation by clearing land for agriculture and it took pressure from conservation groups before a resettlement programme was organised (Yeld et al., 1992).

In 1994, the newly elected democratic government decided to abolish the former homelands and KwaZulu and Natal were merged to form KwaZulu-Natal. This was followed by an amalgamation of the NPB and KDNC to form the NCS. Both the NPB and KDNC had developed conservation projects that involved local communities (Kyle et al., 1997a & b) and the NCS aimed to strengthen these links and encourage community-based natural resource management (Douglas, 1998).

2.5.2 Protected areas of Maputaland

The PAs of Maputaland have a combined area of 2482 km2, covering 25.4 % of the region (Table 2-3, Figure 2-8). However, these PAs include Lake Sibaya and Lake St Lucia and other smaller water bodies, so the area of land with PA status is 2010 km2. The largest PA is the Greater St Lucia Wetland Park, an area of 1672 km2 that consists of 8 different sections that gained PA status at different times. The other PAs range in size from less than 3 km2 (Manguzi FR) to 300 km2 (TEP). Most of these PAs are completely fenced to reduce poaching, livestock encroachment and human- wildlife conflict. There are other conservation areas in Maputaland, including the Makasa Biosphere Reserve and the privately owned Phinda GR but these are not directly managed by the NCS and so will not be considered as strict PAs.

Chapter 2: A description of Maputaland 31 Ndumo Tembe Elephant Game Manguzi Reserve Park Forest Coastal Reserve Forest Reserve

Sileza Nature Reserve

Lake Hlatikhulu Sibaya Forest Reserve

Pongola Game Reserve

Sodwana Bay Lebombo Mountain Sodwana Nature Mkhuze State Reserve Game Forest Reserve

False Bay Park Lake St Lucia

Western Eastern Shores Shores

20 km

St Lucia Game Reserve

Figure 2-8: The PAs of Maputaland

Chapter 2: A description of Maputaland 32 Table 2-3: A description of the PAs of Maputaland

Area Area Protected area name Protected area name (km2) (km2) Coastal Forest Reserve 265.31 Greater St Lucia Wetland Park: Hlatikhulu Forest Reserve 12.10 - Eastern Shores 250.67 Lake Sibaya 60.60 - False Bay Park 29.63 Lebombo Mountain Nature Reserve* 14.59 - Lake St Lucia 370.05 Manguzi Forest Reserve 2.38 - Mkhuze Game Reserve 376.25 118.67 - Sodwana Bay 13.84 Sileza Nature Reserve 21.25 - Sodwana State Forest 546.72 Tembe Elephant Park 300.11 - St Lucia Game Reserve 30.07 Pongola Biosphere Reserve* 15.76 - Western Shores 54.49 * Area within Maputaland

2.6 The future of biodiversity conservation in Maputaland

The legacy of South Africa’s political policies could have a major impact on the biodiversity of Maputaland. The region is home to some of the country’s poorest people, many of whom were affected by the forced relocations that took place to establish the PA system. Both these factors suggest that biodiversity conservation will be seen as increasingly unimportant by the people who live in Maputaland and the politicians that represent them. Any negative attitudes towards conservation could be particularly important because most of the region’s PAs are the subject of land-claims by dispossessed communities (Figure 2-9). These claims are being investigated by the Restitution of Land Claims Commission (RLCC) and if the land is given community ownership then it could be converted to agriculture.

There are, however, two reasons for thinking that this view is unnecessarily bleak. Firstly, the sandy soils have little agricultural value and so biodiversity conservation, with associated eco- tourism and sustainable use projects, is often the most profitable form of land-use. This is recognised by local communities, several of whom are either involved in conservation projects or are in the process of establishing them. This has also influenced the RLCC, as the first two land- claims to be decided gave ownership to the relevant communities but insisted that the land was co- managed by the NCS and should continue to be used for biodiversity conservation.

Secondly, Maputaland is the focus of much international conservation interest and the NCS is a widely respected organisation. This suggests that these conservation projects will attract more funding and have a greater chance of success than in most developing countries. This section discusses the major threats to biodiversity in Maputaland and describes the new conservation policies that aim to redress them.

Chapter 2: A description of Maputaland 33 Mbangweni Corridor Ndumo Tembe Elephant Park Manguzi Forest Coastal Forest Reserve

Sileza

Manzengwenya Plantation

Lake Sibaya Hlatikhulu

Sodwana Triangle North Sodwana State Forest Triangle Ubombo Sodwana Mountain State Mkhuze Forest Lower Link Properties

Makhasa

False Bay

Cape Vidal

20 km Eastern Shores Western State Forest Shores

St Lucia Park

Figure 2-9: Land-claim areas in Maputaland

Chapter 2: A description of Maputaland 34 2.6.1 Threats to biodiversity in Maputaland

Despite the conservation importance of Maputaland and the large amount of the region that has PA status, much of the biodiversity that remains is threatened by anthropogenic factors. The region is affected by two pressures that are common in developing countries, namely an increasing human population that needs land for subsistence agriculture and a demand from government and industry to commercially develop any available resource. This has produced the following problems:

• Habitat loss. Much of the vegetation that was found on nutritionally rich soils outside PAs has been cleared for agriculture. This has been done both by commercial and subsistence farmers and has particularly affected riverine vegetation communities. More recently, increasing human population pressure has forced people to clear areas that have much lower agricultural potential. This is particularly serious as it affects vegetation types, such as sand forest, which are very slow growing.

• Habitat fragmentation. This is a natural consequence of habitat loss and it is likely to play an increasing role in biodiversity loss in Maputaland. An illustration of its effects comes from the status of the black-backed jackal (Canis mesomelas), which was once abundant in Mkhuze GR but is now locally extinct (Dixon, 1964). This species, together with many other mammals in the region, was generally restricted to PAs and so vulnerable to extirpation.

• Disruption of ecological processes. Habitat loss and fragmentation naturally lead to the disruption of ecological processes, such as nutrient cycles and migration patterns. In addition, the damming of the has disrupted the natural flooding patterns of the river systems and Eucalyptus and pine plantations have dramatically affected the region’s water table. Most of the PAs are intensively managed to maintain the disrupted ecological processes, with culling used to simulate the affects of missing large carnivores.

• Over-exploitation. The Tembe-Thonga people have traditionally utilised a large number of species. This exploitation was sustainable but increasing population pressure and the development of commercial markets has led to the near extirpation of many species outside of the PAs. In particular, most large mammal species (> 5 kg) are only found in the PAs and some species of medicinal plants are similarly restricted.

• Introduced species. Maputaland contains a large number of introduced species, some of which have had a dramatic effect on the region's biodiversity. The most important of these are probably alien plants, such as Chromolaena odorata, which have formed large patches throughout the region, especially in disturbed areas

Chapter 2: A description of Maputaland 35 2.6.2 Future conservation developments

The NCS has already introduced a series of projects throughout KwaZulu-Natal to include local communities in decision making and to encourage sustainable resource use. In addition, they have established a community levy on all entrance fees to provide funds for capacity-building projects. In Maputaland, the NCS has worked with local communities to form the Makasa Biosphere Reserve and to set sustainable harvest limits for reeds and shellfish found within the PAs. More recently they established two “Local Boards” that will be involved with making management plans for the Ndumo/Tembe and Tembe/Coastal FR area. These boards will include representatives from local communities, commercial agriculture, tourism and special interest groups. The region is also part of the proposed Maputaland Transfrontier Conservation Area (TFCA), which aims to establish a conservation area that would link PAs in South Africa with those in Swaziland and Moçambique. Such a large-scale project will take many years but in 1999 the necessary legislation was agreed by all three of the governments concerned.

The involvement of the NCS with these projects is part of a general commitment to involve local communities in biodiversity conservation. The organisation has identified a range of strategies to achieve this aim, which include promoting sustainable natural resource management and developing and enabling access to conservation-based entrepreneurial opportunities (NCS, 2000). These are most likely to succeed in Maputaland, as the region is still rich in biodiversity and could be a prime eco-tourism destination. However, developing these strategies is costly and so the NCS needs to identify where their involvement would most benefit biodiversity conservation.

2.7 Chapter summary

• Maputaland is a region in the north-east of KwaZulu-Natal, South Africa with an area of 9790 km2. It has high levels of species richness and endemism and its conservation importance for plants and birds has been internationally recognised.

• The human population of the region was 376 800 in 1990 and poverty levels are some of the highest in South Africa. Many people rely on subsistence agriculture and are forced to clear the natural vegetation because the sandy soils have low agricultural potential.

• Maputaland contains 17 PAs and these cover 25 % of the region. The first PAs were established to protect over-hunted large mammal species but later ones were created to conserve threatened and endemic habitat types.

• The majority of the PAs are the subject of land-claims by local communities but it is likely that most of the areas will continue to be used for biodiversity conservation, even if the ownership status changes. These PAs may also be included in the proposed Maputaland Transfrontier Conservation Area. Chapter 2: A description of Maputaland 36 • The NCS has identified a need to determine whether the present PA system is representative of the region’s biodiversity and to produce a land-use strategy that integrates the requirements of biodiversity conservation, local communities and private enterprise.

One of the best ways to integrate all the necessary spatial information needed for this type of land- use planning exercise is to use a GIS. However, acquiring and developing these data sets can be a time-consuming process and this is discussed in the next chapter.

Chapter 2: A description of Maputaland 37 Chapter 3: Creating a GIS for Maputaland

3.1 Introduction

Geographical information systems (GIS) have been defined as “a set of computer programs, together with associated hardware, that are designed to store, manipulate and display data that are recorded according to geographic location” (Marble, 1990). The spatial data that a GIS contains can be thought of as a series of digital maps (known as coverages) that describe different information about the same area of interest. This technology has the potential to be widely used by conservation biologists, as most ecological relationships involve a spatial element (Haslett, 1990). In addition, most biodiversity managers deal with issues of land-use monitoring and planning, both of which benefit from using a GIS-based approach.

Despite these advantages, most GIS-based research has focused on single species conservation. This is because creating a GIS can be time consuming and expensive, so projects that study large charismatic species, such as grey wolves (Mladenoff et al., 1995) and elephants (Lindeque & Lindeque, 1991), are more able to attract the necessary funding. However, these costs have reduced over time, allowing an increase in the number of species studied (Sperduto & Congalton, 1996; Weiss & Weiss, 1998). There has also been an increase in the ways in which these single species projects have been applied. Initially, they were only used to find the characteristics of suitable habitat for the species of interest, so that the determining factors could be identified. However, it was soon realised that the resultant models could be used to predict the presence of suitable habitat elsewhere (Austin et al., 1996), calculate population estimates (Smith, 1996) and predict the effects of management actions (Liu et al., 1995; Pearlstine et al., 1995). More recently, these techniques have also been used to model other phenomena, such as human-elephant conflict (Smith & Kasiki, 2000) and deforestation (Linkie, 1999). They have also been used to map elements of biodiversity from the genetic (Jones et al., 1997) to the landscape level (Homer et al., 1997).

The increasing availability of these distribution data, together with the recent affordability of the required technology, has also led to an increase in the use of GIS for conservation planning and monitoring (Lewis, 1995: Fox et al., 1996). For example, information on species and habitat distributions, current and predicted human settlement and the economic potential of natural forest was used to determine the boundaries of the Masoala National Park in (Kremen et al., 1999) and a similar approach was used to determine the buffer zone of the Yancheng Biosphere Reserve in China (Li et al., 1999a). At a larger spatial scale, information on wolf and road distributions in Italy was used to identify a new area, which if protected, would allow wolves to avoid road-kill black spots (Corsi et al., 1999).

Chapter 3: Creating a GIS for Maputaland 38 These examples illustrate the range of ways in which GIS can be used by conservation biologists. One of the key factors in their widespread use is the relatively low cost of the necessary equipment and software. However, a GIS is useless without spatial data, and collecting this information can be time-consuming and costly. This chapter will describe how the GIS coverages that were needed for this research were produced. Section 3.2 reviews the two commonly used spatial data models in GIS and section 3.3 discusses GIS and error propagation. Section 3.4 describes Landsat TM satellite imagery and sections 3.5, 3.6 and 3.7 describe how these images were manipulated to allow later analysis. Section 3.8 describes the methods used to produce the various GIS layers and section 3.9 summarises the chapter.

3.2 Representing spatial phenomena in a GIS

Two important geographical data models are commonly used in GIS. The vector data model represents space as a series of discrete entity-defined point, line or polygon units that are geographically represented by Cartesian co-ordinates (Figure 3-1). The choice of how to represent a spatial feature depends on the resolution of the data stored in the GIS. For example, a town may be represented as a point entity at a continental level but as a polygon entity at a regional level (Burrough & McDonnell, 1998). Some GIS also represent spatial entities using arcs, which can be thought of as lines with specified beginnings and ends (Figure 3-1). Arcs are particularly useful when defining lines that have a definite direction, such as rivers and traffic lanes. Polygons can also be defined by identifying the arcs that form their perimeter.

Points Arcs Polygon

Figure 3-1: The three components used in the vector data model

An alternative to this vector system is the raster data model that represents space as a continuous surface of tessellating shapes. Squares are the most commonly used tessellating shape, so a raster feature has the same format as a computer graphics bitmap file (Figure 3-2). Each square, or pixel, in the grid has a numeric value that may either indicate its membership to a particular class or describe the value of the measured phenomena at that point. For instance, a pixel with a value of 2 may indicate that it contains the predefined vegetation type 2 or that the pixel is 2 km away from a

Chapter 3: Creating a GIS for Maputaland 39 feature of interest. Decreasing the size of a pixel increases the resolution of the feature that is being represented but also increases the size of the resultant computer file. Therefore, pixel size in a raster system tends to be a trade-off between these two conflicting factors.

Vector Lower-resolution raster grid Higher-resolution raster grid

Figure 3-2: A comparison of the way that vector and raster data models represent a polygon.

In the past, different GIS software has tended to specialise in either vector or raster data models. Newer versions of these software packages are generally able to recognise both types but they are typically much better at manipulating one of them. Both data models have their advantages and disadvantages, and this means that each is better suited for a particular purpose. For example, vector-based systems are often more appropriate when dealing with anthropogenic features, such as buildings and roads. These have discrete boundaries and are well represented by lines or polygons. Raster systems are more suited to representing natural phenomena and to modelling interactions over continuous surfaces. Therefore, it was decided to adopt a raster-based approach for this research, as the majority of the data were derived from satellite imagery (which has a raster format) or they described continuous surfaces.

3.3 GIS and error propagation

It is impossible to produce error free spatial data, but it is important to understand the source of these errors so their effects can be minimised. One unavoidable source comes from the vector and raster data models themselves, which cannot perfectly represent the phenomena of interest. It is assumed that each raster grid cell and vector polygon is a homogenous unit but this is rarely the case (Fisher, 1997). These errors are compounded when coverages that use one data model are converted to the other. This is particularly obvious when converting raster data into vectors, as the resultant polygons will retain the pixellated appearance of the former (Congalton, 1997).

A range of other errors can occur at various stages in the process from observation to presentation (Burrough & McDonnell, 1998). Some of the most obvious are as a result of errors in measuring geographical location, which depends on the accuracy of the instruments used and the surveying Chapter 3: Creating a GIS for Maputaland 40 skills of the people involved. Some aspects of collecting data, such as vegetation mapping, may involve mis-classifying objects, which is particularly likely when relying on remotely sensed data such as aerial photographs or satellite imagery.

Converting paper maps into digital data is also a large source of error. The two main methods that are used to create digital data are digitising and scanning and both need extensive checking and correcting to produce a useable product. The quality of the data also depends on the age and the resolution of the paper maps that were used (Thapa & Bossler, 1992). In addition, it is often impossible to accurately measure and geographically locate a spatial feature, so it is common to sample at selected points and extrapolate between them. The accuracy of this process obviously depends on the number of sampling points, the distance between them and the model used.

Despite their ubiquity, errors in spatial data are often considered as something that should not be advertised. Unfortunately, GIS products are often judged by their visual appearance and the beauty of a map is rarely dependent on its quality. Therefore, a different approach is needed if GIS is to serve any useful purpose. The approach adopted for the Maputaland GIS was to reduce errors by using the best data sources that were compiled by myself or by other qualified personnel. However, it was also recognised that good quality data was not always available and so compromises had to be made (Estes & Mooneyhan, 1994). This made it important to document the source of all of the spatial data and the methods used to process them.

3.4 Landsat Thematic Mapper imagery

The easiest way to produce GIS coverages is to digitise the available paper maps, but these are not always available in developing countries. The only alternative is to obtain the necessary data from other sources and the most cost-effective method for large areas is to use satellite imagery. Remote- sensing satellites have been designed by several countries and companies to produce different types of imagery. One of these image types is produced by the Landsat TM satellites, which orbit the Earth at a height of 705 km above the ground (Lillesand & Kiefer, 1994). These measure the reflectance value of the ground beneath them in seven different bands of light and record this information with a resolution of 30 m (Table 3-1). These images have been designed to allow the successful mapping of vegetation and land-cover, and their relatively low cost (US $600 per image in 2001) has ensured their wide use (Lauer et al., 1997).

The reflectance values in each band are converted to a binary format to reduce the size of the resultant computer files. This conversion involves applying a linear transformation, so that all the numbers are converted to integers that fall between 0 and 255, and the resultant reflectance values are known as Digital Numbers (DNs). These data are grouped together into a series of scenes, where each scene has a pre-defined position that measures approximately 180 km by 180 km.

Chapter 3: Creating a GIS for Maputaland 41 These data contain a large amount of interesting information but they need to be greatly manipulated before they can be converted into a useful product. The following sections describe the steps that were taken to produce the corrected bands, which could then be used to produce the required GIS coverages.

Table 3-1: A description of the bands that make up a Landsat TM satellite image

Band number Wavelength Range Description of band (µm)

Band 1 0.45 - 0.52 Blue Band 2 0.52 - 0.60 Green Band 3 0.63 - 0.69 Red Band 4 0.76 - 0.90 Near infrared Band 5 1.55 - 1.75 Middle infrared Band 6 10.4 - 12.5 Thermal infrared Band 7 2.08 - 2.35 Middle infrared

The satellite data used in this study were derived from three different Landsat TM scenes. The first of these scenes (Path 167, Row 079) was recorded on 5th April 1998 and was purchased from the Satellite Applications Centre, South Africa. The second scene covered the same area but was taken in 14th November 1986 and this was purchased from the Eros Data Centre in the United States. Neither of these scenes covered the whole of the study region and so the extra data were obtained from a TM image that was taken in April 1995 and purchased by the NCS. The 1995 and 1998 images were purchased mainly to produce land-cover coverages of Maputaland and so it was important that they were taken in April, at the end of the rainy season, when vegetation types were at their most distinct. All three of these images had to be corrected using the same methodology. To avoid repetition the following will describe only the specifics for the 1998 scene.

3.5 Image restoration and enhancement

The information in a Landsat TM image is recorded in the satellite by reflecting the light from a scanning mirror on to 16 light detectors. The final image is built up of a series of blocks, which are 16 pixels high, and the width of the whole scene. These light detectors are carefully calibrated before the launch of the satellite, but it is common for individual detectors to vary in their response to the different wavelengths, which produces obvious striping on the resultant image. Problems are also caused by atmospheric dust and smoke which both reduce the light that illuminates the ground and reflects extra light that will be recorded by the detectors (Lillesand & Kiefer, 1994). This produces a “haze” effect on the TM bands that reduces the accuracy of their representation of the ground below. Chapter 3: Creating a GIS for Maputaland 42 The effect of this haze and striping can be reduced by carrying out a Principal Component Analysis (PCA) on the data contained in the seven TM bands. Much of the information contained within these bands is correlated, as it is common for each land-cover type to be equally reflective in several bands. A PCA converts these bands into a series of images, where each contains information that is completely uncorrelated with the others. The high levels of correlations between the bands typically mean that the first three PCA images often explain more than 95% of the information of the seven bands (Eastman, 1999). In contrast, the atmospheric conditions that produce haze do not reflect and absorb different light wavelengths in the same way and are seldom correlated. This means that the information in the reflection values caused by haze (and often striping) tend to be contained in separate PCA images. The PCA images can be used to reconstruct the original TM bands, based on the results of the PCA analysis, and so the haze and striping can be removed by excluding their associated PCA images when reconstructing the new bands. The methods used to achieve this are described below.

3.5.1 Methods

The Landsat TM bands were imported into Idrisi using the BILIDRIS module, and the WINDOW module was used to remove those areas that did not cover the study area. The DESTRIPE module was used to remove some of the striping on the seven bands and the resultant images were then analysed using the PCA module. The seven bands were reconstructed from the PCA images using the Image calculator in Idrisi.

3.5.2 Results

This module produced 7 PCA images, with the first five PCA images explaining 99.67% of the information contained in the seven bands. The sixth and seventh bands were judged visually to contain information produced only striping and haze (Table 3-2). Therefore, it was decided to reconstruct the seven TM bands from the first five PCA images.

Table 3-2: The % variance of the Landsat TM bands explained by each PCA image

PCA image 1st 2nd 3rd 4th 5th 6th 7th

Variance explained (%) 84.28 11.56 2.95 0.48 0.40 0.27 0.06

Chapter 3: Creating a GIS for Maputaland 43 3.5.3 Discussion

The process of restoring and enhancing the images was relatively quick (< 8 hours) when using a Pentium 2 350 MHz personal computer. Therefore, it should be considered a vital part of preparing Landsat TM images for further analysis. As well as removing the effects of haze, these methods produced images that were in real number format that had not been rounded to the nearest integer. This was particularly beneficial for reasons that will be described in the next section.

3.6 Contrast stretching the images

As was explained in section 3.4, the values recorded by the light detectors on the Landsat satellites are converted using a linear transformation to Digital Numbers (DNs) between 0 and 255. These DNs are also converted into integers to minimise the digital space needed to store them. Unfortunately, this means that if one pixel in a band has an exceptionally high or low value then the remaining pixels will have very similar DN values after the linear transformation. These pixels with very different values tend to be produced by errors in the detection or recording process, and they are not uncommon in a TM band, given the large number of pixels they contain.

The results of this process are illustrated in Figure 3-3, which shows a DN value histogram for a band before manipulation. The histogram has a long “tail” because the band contains a small number of pixels with very high and low DNs. Therefore, the linear transformation gives the majority of pixels very similar DN values, despite possibly large differences in their wavelength reflectance properties. This obviously makes any land-cover classification based on these images much more prone to errors, and there are great benefits in carrying out some type of “contrast stretching” to correct this problem.

Figure 3-3: A DN value histogram before contrast stretching

Chapter 3: Creating a GIS for Maputaland 44 One commonly used method re-classifies a set percentage of the lowest values as 0, reclasses the same percentage of the highest values as 255 and applies a linear transformation to the remaining DNs so that they fall between 1 and 254. This method is called “linear stretching with saturation” and the number of pixels that are initially reclassed (the percentage saturation) is commonly between 1 and 5 %. This method can greatly reduce the size of the 'tails' in the DN value histograms, and so increase the contrast in values given to the majority of pixels found in an image. However, this process is only useful if dealing with pixels with a large spread of values. If the pixels have integer values, then contrast stretching will only increase the size of the gaps between the values, rather than increasing the overall spread (Figure 3-4).

Stretching of real numbers Stretching of integer numbers

Figure 3-4: A comparison of contrast stretching on real and integer pixel values

Therefore, it is preferable to use bands that have been reconstructed from PCA images, as these contain real numbers that have not been rounded to the nearest integer. This method was used to achieve the contrast stretching of the seven bands for the Maputaland GIS and is described below.

3.6.1 Methods

Idrisi is unable to carry out linear stretching with saturation on images containing real numbers. However, it is possible to use a linear stretch on these images and to specify the lower and upper parameters of the numbers to be stretched. Therefore, the same procedure as the linear stretch with saturation can be achieved by using a linear stretch and specifying cut-off values that exclude the required percentage of lowest and highest valued pixels. The information needed to calculate these cut-off values was found by first using the CONVERT module to convert each band to an integer format. The AREA module was then used to calculate the number of pixels with each DN value, and the cut-off values were identified as those that excluded 945406 (2%) of the pixels with the highest and lowest values. The STRETCH module was then used to carry out a linear stretch on each band, using the lower and upper parameters that were calculated from the integer images.

Chapter 3: Creating a GIS for Maputaland 45 3.6.2 Results

The methods described above dramatically improved the visual contrast in each band (Figure 3-5) and increased the amount of information contained in each band (Figure 3-6). This is illustrated by the increase in standard deviation of the DNs in the bands, which varied between a 131% increase for band 5 and a 800% increase for band 2.

Figure 3-5: A detail from band 7 before and after a linear stretch with 2 % saturation.

Band 1 before stretching Band 1 after stretching

Mean = 67.92xxxStandard deviation = 6.93 Mean = 86.77xxxStandard deviation = 49.23 Band 2 before stretching Band 2 after stretching

Mean = 28.44xxxStandard deviation = 6.02 Mean = 108.75xxxStandard deviation = 54.16

Figure 3-6: Pixel values for bands 1-7 before and after contrast stretching Chapter 3: Creating a GIS for Maputaland 46 Band 3 before stretching Band 3 after stretching

Mean = 24.91xxxStandard deviation = 8.51 Mean = 103.01xxxStandard deviation = 59.25 Band 4 before stretching Band 4 after stretching

Mean = 50.07xxxStandard deviation = 24.49 Mean = 142.42xxxStandard deviation = 76.36 Band 5 before stretching Band 5 after stretching

Mean = 54.28xxxStandard deviation = 31.23 Mean = 133.28xxxStandard deviation = 72.27

Figure 3.6: Pixel values for bands 1-7 before and after contrast stretching (continued)

Chapter 3: Creating a GIS for Maputaland 47 Band 6 before stretching Band 6 after stretching

Mean = 145.24xxxStandard deviation = 7.56 Mean = 105.00xxxStandard deviation = 56.45 Band 7 before stretching Band 7 after stretching

Mean = 22.06xxxStandard deviation = 14.22 Mean = 97.20xxxStandard deviation = 55.84

Figure 3.6: Pixel values for bands 1-7 before and after contrast stretching (continued)

3.6.3 Discussion

The results showed that contrast stretching dramatically increased the amount of information contained in the TM bands. This made it possible to distinguish between pixels that had very similar DN values in the raw images. However, these results were dependent on using pixels with real number values in the contrast stretching that were produced using a PCA. This suggests that caution is needed when judging the success of this process because some of the differences between pixels may have been an artefact produced by the modelling process. The ideal solution would be to use satellite imagery that used a wider range of DN values in the original raw data but this is much more expensive. Therefore, contrast stretching of Landsat TM imagery will continue to be the most suitable option for conservation projects but users should be aware that it may be more difficult to distinguish between pixels of the same land-cover type than might be expected given the appearance of the final TM bands.

Chapter 3: Creating a GIS for Maputaland 48 3.7 Geo-registering the Landsat TM images

When taking an aerial photograph the amount of distortion in the image increases with distance from the camera. This means that the edges of the image are less accurate then the centre and so the whole photograph needs to be corrected to represent accurately the size and shape of the land-cover they describe (Lillesand & Kiefer, 1994). The same process needs to be followed to ensure the accuracy of satellite images and the methods used for this study are described in the next section.

3.7.1 Methods

The restored, enhanced bands were used to produce colour composite images using the COMPOSITE module in Idrisi. This combines the information from three bands to produce a 24- bit colour image, which allows the easy identification of any features that show reflectance variation in the chosen bands. It was decided to use bands 1 (blue light), 2 (green light) and 3 (red light) in this composite, as this produced an image that resembled a colour photograph of the study region. In addition, a Normalised Difference Vegetation Index (NDVI) image was produced as many of the features of interest, such as roads and water, have low NDVI values.

The next stage was to choose a series of points that could be identified both on the images and on 1:10 000 orthophotos. The points used were all road junctions, fence corners, dam walls or rocky features along the coastline, as this reduced the chances of their location changing over time. These points were also chosen to attempt to evenly cover the whole of the study region. The positions of these points on the orthophotos were found by placing each orthophoto on a digitising board and recording their co-ordinates using the digitising puck. The corresponding positions of these points on the satellite image were found by identifying them on the computer screen using Idrisi and recording their co-ordinates. The image was then geo-corrected and geo-referenced using the RESAMPLE module in Idrisi.

3.7.2 Results

The data were resampled using a quadratic equation, which was based on 85 data points and had a root mean square (RMS) error of 54.18 m. This error was within acceptable limits, given the resolution of the TM bands (30 m) and the dispersed nature of the geo-registration points.

Chapter 3: Creating a GIS for Maputaland 49 20 km Geo-registration point N2 highway Road PAs

Figure 3-7: Position of geo-registering points

Chapter 3: Creating a GIS for Maputaland 50 3.7.3 Discussion

The accuracy of the geo-registration process is obviously extremely important for any further analysis because the coverages derived from the Landsat TM data were combined with others and any mis-match between them would further compound errors. Therefore, I aimed to use location data from as many points as possible but two factors limited this to 85 points. The first was the cost of the orthophotos but more important was the difficulty in identifying widely dispersed points that were visible on both the orthophotos and the images. The orthophotos had a much higher resolution and so it was generally easier to identify points on them, although this was not always the case because they were in black and white. In addition, most of the orthophotos were taken in 1979 or 1986 and some of the road junctions had changed position in the intervening period.

Despite these problems, the accuracy of this process was improved greatly by using orthophotos. Unfortunately, these are not always available in other developing countries. Nevertheless, the decision by the United States Government in 2000 to remove the selective availability feature on GPS satellites means that the same accuracy of information can now be collected using a GPS unit.

3.8 Deriving the Maputaland GIS coverages

Producing GIS coverages is a very costly process but the NCS have recognised their value and devoted a lot of resources on producing the necessary data for KwaZulu-Natal. The NCS did this by directly capturing some spatial information, but also by working with other organisations to share the available data. This section describes the methods that were used to modify the available coverages so that they were suitable for this research.

3.8.1 Methods

A variety of methods were used to produce the Maputaland GIS coverages and these are described below. In each case the coverages were modified to have the same geographical reference system (lo33) that has the following characteristics:

Projection: Transverse Mercator Datum: Cape Delta WGS84: -136 -108 -292 Ellipsoid: Modified Clarke 1880 Major s-ax: 6378249.1453260 Minor s-ax: 6356514.9667204 Central meridian: 33 Reference latitude: 0 False easting: 0 False northing: 0 Scale factor: 1.0 Units: metres

In addition, the raster coverages were converted using the RESAMPLE module in Idrisi to have the same 30 m resolution as the Landsat TM images.

Chapter 3: Creating a GIS for Maputaland 51 A. Road coverage Road coverages were available from the National Roads Department but these did not prove to be sufficiently accurate. Instead the position of the roads was digitised directly from the Landsat TM images. Bands 1, 2 and 3 of the TM images were used to produce a colour composite image in Idrisi and this was exported into ArcView for on-screen digitising.

B. Maputaland boundary coverage The Maputaland boundary was derived from two sources. The coastline and borders with Moçambique and Swaziland were digitised by NCS staff from 1:10 000 orthophotos. These orthophotos are geo-referenced aerial photographs available from the South African Surveyor General. It was decided to define the eastern and southern boundaries of Maputaland as being delineated by the N2 and by the Mtubatuba to St Lucia roads, respectively. These boundaries were digitised directly from the TM-derived colour composite image of Maputaland.

C. Geology coverage The geology coverage was derived from the 1:250 000 land types map of Mkhuze (Land Type Survey Staff, 1986). This divided Maputaland into 410 polygons, which ranged in size from 0.02 to 1057 km2 (mean = 23.74 km2, standard deviation = 75.66). Recent work has updated this geological information and the coverage was changed accordingly (G. Botha, pers. comm.). This involved reclassifying those polygons that contained waterlogged sands as "marsh", reclassifying the polygons of argillaceous sand in Mkhuze Game Reserve as red sand and reclassifying the riparian intrusions into the rhyolites of the Lebombo Mountains as young alluvium (Figure 2-4).

D. Protected area coverage The PA coverage was digitised from 1:10 000 orthophotos by NCS staff.

E. Digital elevation model The digital elevation model (DEM) was derived from a 200 m resolution coverage produced by the Surveyor General of South Africa.

F. Land claim coverage This was derived from the PA coverage and information provided by the Restitution of Land Claims Commission. In some cases a point location was used to identify the land claim site and it was assumed that the land under claim formed a 3 km diameter circle around this point.

Chapter 3: Creating a GIS for Maputaland 52 G. 1km grid coverage This coverage divided the study area into a series of 1 km by 1 km grid squares and labelled each with a unique identifier. It was decided at the outset of this study to use this coverage as the basis of several different analyses. Hence, it was important to ensure that the positioning of these squares was independent of any of the features that it was used to measure. Therefore, it was decided to ensure that the corner coordinates of each square should be exactly divisible by 1000. This number was chosen to simplify the calculations needed to find the corners of the resultant coverage and its arbitrary nature reduced the chances of any resultant non-random sampling.

The coverage was created by using the INITIAL module in Idrisi to produce a 1km resolution coverage that contained 97 columns and 200 rows. This image was then converted to ASCII format and a text editor was used to replace its contents with the numbers 1 to 19400. This was converted back to binary format to produce a coverage that gave each 1 km pixel a unique identifier. The RESAMPLE module was then used to create a coverage that had a 30 m resolution and the OVERLAY module was used to remove those grid squares that were found outside the region.

3.8.2 Results and discussion

The final coverages were used to produce the figures that were illustrated in chapter two and they would have been difficult to produce without using data that had already been collected by the NCS. This may suggest that producing such a GIS would be too time consuming for a similar study that did not have access to these resources. However, the NCS have used these coverages for a range of other planning and research projects that justifies the effort that went into creating them. In addition, many of these coverages describe features, such as geology and elevation, which will not need updating.

3.9 Chapter summary

• The Maputaland GIS contained data that described the geology, topography, human infrastructure and PA network of the region. These data were obtained from different sources, which ranged from 1:10 000 orthophotos to 1:250 000 paper maps.

• Another important source of information came from Landsat TM satellite imagery that had a resolution of 30 m and recorded reflective values in different parts of the light spectrum.

• The TM images used in the Maputaland GIS had to be corrected to reduce the effects of atmospheric haze and recording error, so increasing their information content.

• The images were also geo-referenced by identifying 85 points that were visible both on orthophotos and the Landsat TM images and recording their location on the orthophotos.

Chapter 3: Creating a GIS for Maputaland 53 The images were then manipulated using a quadratic transformation so that the geographic location of the points on the images matched those on the orthophotos.

The GIS coverages described above are an important part of the Maputaland GIS but they do not describe the biodiversity of the region. This is addressed in the next chapter, which describes how the Landsat TM imagery was used to produce a detailed land-cover coverage.

Chapter 3: Creating a GIS for Maputaland 54 Chapter 4: Creating the land-cover coverage

4.1 Introduction

Mapping land-cover provides information that is important for land-use planning, monitoring and modelling (Eastman, 1999). Traditionally these maps have been based on aerial photographs but the advent of remote sensing satellites have greatly reduced the costs of mapping large areas. The resolution of these remotely sensed images ranges from 1 km (used to map land-cover at a continental scale) to sub-metre levels. This chapter describes the methods that were used to produce a land-cover coverage of Maputaland. Section 4.2 describes the methods used to develop the land-cover classification scheme and section 4.3 describes the methods used to collect data to test the accuracy of the resultant coverage. Section 4.4 describes the coverage, section 4.5 discusses the effectiveness of producing land-cover coverages from lower resolution satellite imagery and the chapter is summarised in section 4.6.

4.2 Designing a land-cover classification scheme for Maputaland

Classification has been defined as “the ordering or arrangement of objects into groups or sets on the basis of their relationships” (Sokal, 1974). A classification scheme describes the names of the classes and the criteria used to distinguish them and should be independent of scale and the means used to collect the information (Di Gregorio & Jansen, 1996). The classification can be done in two ways, either a priori or a posteriori. A posteriori classification is based on sampling the land-cover at various points and grouping the sites according to their similarity or dissimilarity. For example, the Braun-Blanquet method (1964) involves recording a list of the plant species present in a sample plot, together with a cover-abundance value for each species. The data from these plots can then be grouped into classes using techniques such as Two-way Indicator Species Analysis (TWINSPAN) (Hill, 1979).

This type of classification is extremely important and has been used by several authors in Maputaland to classify the vegetation (Lubbe, 1996; Matthews, 1999). However, this research used an a priori classification scheme, which defines the categories before any data collection or mapping takes place. The main advantage of this approach is that the categories are standardised and so independent of the study area. It is also useful for mapping land-cover from satellite images, as an a posteriori approach may not identity categories of interest. Defining these categories obviously relies on previous knowledge and so this section will start by reviewing the existing literature on the vegetation of Maputaland. This is followed by a description of the methods used to derive the classification scheme used here and the classification scheme itself.

Chapter 4: Creating the land-cover coverage 55 4.2.1 Past descriptions of the vegetation of Maputaland

South Africa has a long tradition of producing highly skilled botanists and so there are many descriptions of the vegetation in Maputaland. These can be divided into three main classes, based on their resolution, which are described below:

4.2.1.1 National scale There have been three major descriptions of the vegetation of South Africa, which by necessity have been very broad. The first description was by Acocks (1953), who divided the country into broad vegetation types, based largely on their agriculture potential. The vegetation of Maputaland was described as consisting of “Zululand thornveld” in the Lebombo Mountains, “lowveld” in the centre of the region and “coastal forest and thornveld” in the East. This system was followed by Low & Rebelo (1996), who adapted the Acocks system based on the structure and floristics of the vegetation. This system divided Maputaland into five vegetation categories, which moving from west to east are: “sweet lowveld ”, “Lebombo arid mountain bushveld”, “Natal lowveld bushveld”, “subhumid lowveld bushveld” and “coastal bushveld/grassland”.

These two vegetation classification schemes, and the maps that describe the distributions of each vegetation type, are important. However, they are based on potential vegetation and do not always describe the vegetation types found in a particular location, which may have been transformed by agriculture or urbanisation. More accurate information was provided by the South African national land-cover database, which is based on Landsat TM satellite imagery and has a minimum mapping unit of 25 hectares. It was completed by the Council for Scientific and Industrial Research (CSIR) in 1997 and classified the land-cover of South Africa into 12 broad categories, some of which were further divided to produce 23 categories in total (Thompson, 1996; Fairbanks & Thompson, 1996). The land-cover coverage for Maputaland described the area as mostly consisting of “thicket” and “forest and woodland”, with a broad band of “unimproved grassland” in the east (Figure 4-1). This coverage also identified the timber plantations in the south and east of the region and the agricultural land that tended to border the region's rivers.

4.2.1.2 Regional scale The high levels of biodiversity found in Maputaland are partly due to the great variety of vegetation types it supports (van Wyk, 1994). This variety is masked when using the broad land-cover categories developed for national level mapping, so detailed land-cover systems are more valuable when considering the region's natural resources. The first of these systems was developed by Moll (1977; 1980), who divided the vegetation of Maputaland into the following 15 categories: Lebombo forest; Lebombo range; mixed bushveld; thicket; red-sand bushveld; floodplain vegetation; sand forest; pallid-sand bushveld; Muzi swamp; papyrus swamp; palmveld; coast grassland; swamp forest; mangroves and dune forest.

Chapter 4: Creating the land-cover coverage 56 Forest Thicket Forest and woodland Unimproved grassland Wetland Water body Rock and bare soil Degraded forest Degraded shrubland Degraded thicket Timber plantation 20 km Permanent agriculture Temporary agriculture Urban

Figure 4-1: The CSIR land-cover coverage of Maputaland (adapted from Thompson, 1996)

Chapter 4: Creating the land-cover coverage 57 This was followed by a more elaborate system developed by Tinley (Tinley & Van Riet, 1981) as part of a planning document for the KDNC. This focussed on the part of Maputaland that fell within KwaZulu (approximately the part of Maputaland found to the north of Mkhuze GR) and divided its vegetation into 40 categories, with several sub-types (Table 4-1). Tinley also grouped these types into eight classes based on their distributions, which were mostly determined by the underlying geology. There were many similarities between the two classification schemes, as all of Moll’s categories were used by Tinley (Table 4-1).

Table 4-1: Summary of Tinley’s vegetation classification (Tinley & Van Riet 1981).

Lebombo Pongolo Clayveld Sandveld

Aquatic Floodplain Vlei grassland Woodl. on red sands Grassland Riverine forest Thicket Sand forest Rock-faces Marginal woodland Acacia tortilis woodl. Woodland Tall reed beds Thicket (6 types) Medium height grass Forest Lawn grass Aquatic communities

Sand forest Muzi/Palmveld Coastal Lakes Coastal

Sand forest Palmveld Salt herb communities Hummock dunes Sand thicket Dambo grassland Mangrove Seaward thicket Terminalia woodl. Swamp forest Aquatic herbs Dune forest Termitaria thicket Evergreen thicket Sedge & grass swamp Acacia karroo thicket Open grassland Termitaria pan system Swamp forest Beach Acacia scrub Grassland Woodland Thicket Forest Categories in bold were also described in Moll's (1980) vegetation classification of Maputaland

4.2.1.3 Local scale The vegetation of many of Maputaland’s PAs has been mapped by NCS staff or by affiliated botanists. This is both because these coverages provide important information for the PA managers and because the vegetation of the region is so diverse that it is seen as an important area for further research. The vegetation of Ndumo GR was the first to be mapped (Pooley, 1978) and this was followed by work that involved mapping the vegetation of Mkhuze GR (Goodman, 1990), the Eastern Shores of Lake St Lucia (CSIR, 1993), the Coastal FR (Lubbe, 1996), Sileza NR (Matthews, 1999) and TEP (Matthews et al., in press). The fine resolution of these coverages

Chapter 4: Creating the land-cover coverage 58 meant that many of them included detailed vegetation types. These are too detailed for classifying larger areas but they can often be grouped into broader categories.

4.2.2 Methods

The review above illustrates that a great deal of work had already been done on classifying and mapping the vegetation and land-cover of Maputaland. However, these were developed by people working at different scales and so their classification schemes were not necessarily suitable for this study. The first stage in producing the land-cover classification scheme for this study was to determine the approximate number of land-cover types that there should be in the final classification system. A small number of categories would not represent the land-cover adequately and would make later modelling of the bird species distributions difficult. However, a large number of categories would:

• Increase the computing time needed by the GIS to produce land-cover coverages. • Increase the chances of producing categories that were indistinguishable on the satellite image. • Produce a coverage that may be difficult to interpret. • Make distinctions between categories that may be ecologically indistinct.

In addition, it was important to ensure that all of the natural vegetation categories had the same level of ecological distinctiveness. This was because a gap analysis is partly based on the percentage of each vegetation type that is protected. If one ecologically coherent vegetation type was sub-divided into several types, then each of these “pseudo-types” would be given the same weight in a final analysis as the other less-subdivided vegetation types.

Based on the above, the best compromise appeared to be a classification system with approximately 35 types. Hence, it was decided to base this on Tinley’s scheme, as he divided the natural vegetation of northern Maputaland into 34 types (Tinley & Van Riet, 1981). Tinley's classification was given to NCS ecologists A. Blackmore, P. Goodman, D. Johnson and W. Matthews for comment and each was asked to identify types that should be added or removed. Johnson, the NCS bird specialist, was asked to focus his comments on whether these types would be adequate for mapping the distributions of the bird species. These four sets of comments were used to produce a first draft of the proposed land-cover classification, which was then distributed to each person for further comment. The comments from each person were discussed with the other three contributors and the resultant changes led to the final land-cover classification scheme.

Chapter 4: Creating the land-cover coverage 59 4.2.3 Results

The group thought that Tinley’s decision to group vegetation types according to their geology was an important one but they felt that his eight classes (Table 4-1) should be reduced to five. This is because the sandveld, sand forest, Muzi/palmveld and coastal lakes were all found on similar underlying geology and their associated vegetation types often shared ecological affinities. Hence, these were merged into one class to produce five classes that were the same as the ecological zones mentioned in section 2.3. It was then assumed that a particular vegetation type could only be found within its associated ecological zone (Figure 2-5). The final Maputaland land-cover classification scheme included 34 different types, 29 of which were natural vegetation types. A description of each ecological zone, its associated vegetation types and the other land-cover types is given below:

4.2.3.1 Lebombo zone The Lebombo zone is defined as the area containing the Lebombo Mountains and it is 16 km at its widest and contains poor rocky soils (Figure 2-5). It contains six main land-cover types that are: 1. Lebombo aquatic. This is the vegetation found around the pans, streams, marshes, springs and gorges of the Lebombo Mountains. 2. Rock-faces. Some rock-faces are covered by the root mats of Selaginella dregei, which also contain dwarf grasses, such as Orpetium capense. In addition, a range of shrubs, trees and succulents are found in clumps on the rock-faces or in crevices and around outcrops. These include fig species, such as Ficus soldanella, F. ingens, F. glumosa, the white impala-lily (Pachypodium saundersiae), aloes (Aloe marlothii and A. sessiliflora) and the tree euphorbias Euphorbia cooperi, E. evansii, E. tirucalli and E. triangularis. 3. Lebombo grassland. This grassland is mostly found on rhyolitic soils and consists of acid, wiry, species that have poor pasture value. The most common grass species are: Elionurus argenteus, Andropogon gayanus, Schizachyrium semiberbe, Tristachya hispida, Brachiaria serrata and Hyperthelia dissoluta. On the heavier dolerite soils, other species such as Heteropogon contortus, Themeda triandra, Cymbopogon excavatus and Hyparrhenia filipendula are found and these produce good quality pasture. 4. Lebombo woodland. The trees in this woodland are generally 4 to 7 m in height, with the species dominance being influenced by the aspect of the slopes on which they are found. The xeroclines are mostly dominated by Combretum apiculatum, whereas the mesoclines are mostly dominated by Acacia species. Typical trees also include: Combretum molle, C. zeyheri, Terminalia phanerophlebia, Acacia burkei, A. caffra, A. davyi, A. swazica, Lannea discolor and Pterocarpus rotundifolius. 5. Lebombo thicket. Thicket in the Lebombo Mountains is found in a range of conditions and has a varying species composition. Valley thicket species are noted for Olea europea, Ptaeroxylon obliquum, whereas Ficus abutilifolia, F. ingens, F. glumosa, Pavetta edentula, Olax dissitiflora and Cussonia natalensis are associated with rock outcrops. Typical termitaria

Chapter 4: Creating the land-cover coverage 60 thicket components on the summit plateaux are Pappea capensis, Cadaba natalensis, Sideroxylon inerme, Euclea racemosa and Schotia brachypetala. 6. Lebombo forest. This has a canopy that attains 20 m in height and is formed by trees such as Chrysophyllum viridifolium, Heywoodia lucens, Homalium dentatum, Combretum kraussii and three Celtis species, C. africana, C. durandii and C. mildbraedii. This is a mixed or transition forest composed of elements from both tropical and Afrotemperate forests.

4.2.3.2 Cretaceous zone The cretaceous zone is found between the Lebombo Mountains and the large coastal plain and is 18 km at its widest (Figure 2-5). Its soils have high agricultural value and it contains four natural land- cover types. 1. Acacia tortilis woodland. This is dominated by Acacia tortilis but is also associated with Spirostachys africana and Schotia brachypetala. The main grasses are Dactyloctenium australe, Chloris virgata and Eragrostis rigidior, but the herbaceous layer is undeveloped. 2. Acacia nigrescens woodland. The tree layer is dominated by Acacia nigrescens with Dichrostachys cinerea common in the sub-story. The herbaceous layer is diverse with Themeda triandra, Panicum coloratum, Panicum maximum, Urochloa mosambicensis and Bothriochloa insculpta as the dominant grasses. 3. Acacia grandicornuta bushland. This is found on the better-drained textured soils with a red surface and impervious subsoil. It typically contains A. grandicornuta (although other Acacia species are generally present), together with a rich mixture of other trees and shrubs. 4. Acacia luederitzii thicket. These are generally in flat, poorly drained areas on dark calcareous vertic clays. The thicket is dense and low (3 to 5 m high) and is dominated by Acacia luederitzii and Euclea divinorum.

4.2.3.3 Alluvial zone The alluvial zone is found mostly along the Pongola and Mkhuze rivers that cross both the cretaceous and coastal plain zone. This is the only zone that does not have a distinct north-south orientation (Figure 2-5) and it contains four main natural land-cover types. 1. Floodplain grassland. This can consist of the medium height Echinochloa pyramidalis- Hemarthria altissima grass community, the shorter Cynodon dactylon lawn grass community or small patches of reeds. These three grass species form the most important dry season pasture in the entire region and their distributions are determined by grazing and burning patterns. 2. Reed bed. This consists of tall beds made up of Phragmites mauritianus and P. australis. 3. Riverine thicket. This is mostly composed of Acacia schweinfurthii, Azima tetracantha and the alien Eupatorium odoratum. 4. Riverine forest and woodland. The most striking tree species found in this vegetation type are Ficus sycomorus and Acacia xanthophloea as these have a canopy height of up to 25 metres. Other trees include: Cordyla africana, Syzygium guineense, Rauvolfia caffra and Trichilia

Chapter 4: Creating the land-cover coverage 61 dregeana (Rogers, 1980). Between the large trees and the water there is often a fringe of dense vegetation overhanging the river bank formed by Phoenix reclinata, Ficus capreifolia, Grewia caffra and Pisonia aculeata.

4.2.3.4 Coastal plain zone The coastal plain zone is the largest of the ecological zones and is 55 km at its widest (Figure 2-5). It lies on several different types of sand (Figure 2-3) and has little agricultural value. There are ten natural land-cover types in this zone and the distribution of each are affected by the position of the water table. 1. Sedge and grass swamp. This vegetation type is found in freshwater lakes and marshes and is dominated by two species of grass Leersia hexandra and Panicum meyerianum that grow out from the margins to form dense floating patches. The taller marsh and swamp grasses and sedges include: Phragmites spp., Scirpus littoralis, Caldium spp., Cyperus papyrus, C. immensus and Typha latifolia. Other grasses include Andropogon eucomis, Ischaemum arcuatum, Sporobolus subtilis and Eragrostis lappula. 2. Hygrophilous grasslands. These grasslands tend to be found on flat ground or on inter-dune depressions and are waterlogged for most of the year. The most commonly found species are the grasses Themeda triandra, Ischaemum arcuatum, Andropogon gayanus, Sporobolus subtilis, Imperata cylindrica and Brachiaria arrecta. The forb Centella asiatica, the creeper Desmodium dregeanum and the sedges Cyperus obtusiflorus, C. natalensis, C. tenax and Bulbostylis contexta are also generally present. 3. Woody grassland. Woody grasslands are found on dune crests, slopes and relatively high lying level plains. Diagnostic species include the geoxylic rhizomatous suffrutex Parinari capensis, which together with other plants, such as Eugenia albanensis, Ancylobothrys petersiana, Salacia kraussii and a dwarf form of Dichrostachys cinerea, add a woody component to the grassland. Common grass species include Themeda triandra, Diheteropogon amplectens, Urelytrum agropyroides and Trichoneura grandiglumis. 4. Terminalia woodland. A wide range of tree species is found in this woodland, although Terminalia sericea is the main diagnostic species. Other typical examples of sand woodland trees, which are between 5 and 12 m in height, include: Sclerocarya caffra, Combretum zeyheri, C. molle, C. collinum, S. madagascariensis, S. spinosa, Acacia burkei and Ozoroa engleri. The grass stratum of these sand woodlands is formed by medium to tall sourveld species such as Hyperthelia dissoluta, Pogonarthria squarrosa, Perotis patens, Triraphis andropgonoides, Digitaria macroglossa and Panicum species. 5. Woodland on red sands. The sand woodland trees on red sands are chiefly Combretum molle, C. zeyheri, Sclerocarya caffra (marula), Strychnos spinosa, S. madagascariensis, Acacia burkei, Lannea stuhlmannii and Sterculia rogersii. Grasses are chiefly acid, wiry or thatch- grass species such as Aristida spp, and Hyperthelia dissoluta, but better grazing grasses such as

Chapter 4: Creating the land-cover coverage 62 Panicum maximum and Digitaria occur, the former especially in the crown shade zone of trees such as the marula. 6. Sand thicket. The sand thicket is mostly composed of woodland species that form bush clumps beneath large woodland trees. Species include: Sclerocarya birrea, Strychnos spinosa, S. madagascariensis, Acacia burkei, A. robusta, Terminalia sericea, Peltophorum africanum, Spirostachys africana, Dichrostachys cinerea, Tabernaemontana elegans, Commiphora neglecta, Albizia versicolor, A. petersiana and Ziziphus mucronata. 7. Sand forest. The tree component of sand forest forms a canopy of 5 to 12 m that includes: Newtonia hildebrandtii, schlechteri, Pteleopsis myrtifolia, Hymenocardia ulmoides, Cassipourea mossambicensis, Craibia zimmermannii, Dialium schlechteri, Haplocoelum gallense, Balanites maughamii and Erythrophleum lasianthum (Kirkwood & Midgley, 1999). This land-cover type is seen as a conservation priority as it is only found in Greater Maputaland and contains many endemic plants species (Tinley & van Riet, 1981). 8. Inland evergreen forest. This type of forest is found in the west of the region and contains a wide range of species, having similarities with both sand and dune forest. The drier forests, such as Nyaneni Forest near Lake Sibaya, have more sand forest species such as Hymenocardia ulmoides, Dialium schlechteri and Euphorbia grandidens and the wetter forests tend to be taller and are composed of both tropical and Afrotemperate trees (Van Wyk et al., 1996). 9. Swamp forest. Swamp forests occur on many of the perennial bog drainage lines that enter the coastal lakes. The canopy is generally between 8 and 18 m high. The trees grow on mounds of bog soil that are surrounded by standing or slow-moving water. The predominant trees are Ficus trichopoda, although other species such as Syzygium cordatum, Voacanga thouarsii and Barringtonia racemosa may assume local dominance. This is an equatorial rainforest type but it also contains moist forest trees typical of the temperate uplands e.g. Podocarpus falcatus, Rapanea melanophloeos, Ilex mitis, Erythrina caffra and Halleria lucida. 10. Mangroves. Five species of mangrove occur in the salt-water estuaries of Maputaland and the height of these trees varies between 2 and 10 m. These species are Avicennia marina, Bruguiera gymnorrhiza, Rhizophora mucronata, Ceriops tagal and Lumnitzera racemosa.

4.2.3.5 Coastal dune zone This zone is the narrowest, as it only includes the wind-formed sand dunes that border the Indian Ocean (Figure 2-5). It contains three main natural vegetation types. 1. Beach. This includes the dune pioneer community that is found above the high water mark and dominated by the short robust shrub Scaevola plumieri and the forb Gazania uniflora. 2. Dune thicket. This community is found on the seaward slopes of coastal dunes and has a height of between 1 and 5 m (Weisser, 1980). The vegetation has a “clipped hedge” appearance caused by the effect of sea spray and mechanical blasting by windblown sand. Typical trees and shrubs include: Eugenia capensis, Diospyros rotundifolia, Mimusops caffra, Brachylaena discolor and Sideroxylon inerme. The vegetation is extremely dense and bound together by

Chapter 4: Creating the land-cover coverage 63 tangles of creepers and climbers, of which the most common are Rhoicissus digitata and Smilax kraussiana. 3. Dune forest. This community is found on the landward dunes that run parallel to the coast from north to south. Dune forest tends to have an uneven upper canopy between 6 and 15 m in height. Typical forest trees include: Diospyros inhacaensis, Celtis africana, Inhambanella henriquesii, Diospyros natalensis, Euclea racemosa and Croton gratissimus. The fern Microsorium scolopendrium and the shrubby forb Isoglossa woodii are found as ground cover.

4.2.3.6 Transformed land-cover types 1. Towns. This consists of high-density housing, in addition to commercial and industrial areas. 2. Roads. There are two main types of road in Maputaland. Tarred roads include the N2 motorway, which forms the westerly boundary of the study region and primary roads that link the major towns with the motorway. The others are dirt roads, which are found throughout the region and in some cases are classed by the National Roads Department as secondary roads. 3. Subsistence agriculture. Subsistence agriculture is typified by small fields growing maize and vegetables, as well as by grazing areas for cattle and goats, and it is found throughout the region. Many of these fields are smaller than that of the resolution of the satellite imagery and so cannot be individually identified. However, areas of subsistence agriculture tend to be distinctive on these images as they typically have lower vegetation biomass levels because of livestock grazing and fuel-wood collection. 4. Large-scale commercial agriculture. A variety of crops are grown commercially in Maputaland, including maize, cotton, sugar cane and pineapples. Most of this agriculture is found in the Cretaceous and alluvial zone and less commonly in the coastal plain zone. 5. Plantations of non-native species. The most widespread type of plantation contains Eucalyptus spp. and Pinus elliotii. Most of these are very large and found in the coastal plain zone but the companies that own them have also encouraged local landowners to set up smaller plantations that are found through the region. Some native species also tend to be present, especially in young plantations, together with the alien Chromolaena odorata (CSIR, 1993; Lubbe, 1996). The second type of plantation consists of stands of Casuarina equisetifolia, which were planted to stabilise the sand dunes along the coastline. The NCS are in the process of removing them because dune movement is now seen as an important ecological process.

4.2.3.7 Other land-cover types 1) Open water. 2) Mud flats.

Chapter 4: Creating the land-cover coverage 64 4.3 Collecting the ground-truth data

The accuracy of land-cover coverages produced using Landsat TM imagery can vary widely. This is partly due to the resolution of TM images, as most classification methods assume that each 30 m pixel contains only one land-cover type (Fisher, 1997). This is rarely the case, especially in fragmented landscapes, and so the combined reflectance of the several land-cover types measured in one pixel may be categorised as a different type. Another major problem occurs when the reflectance values are changed by the topography of the study area. Hills and mountains can change the reflectance values in two ways. Firstly, they produce shadows that can dramatically reduce the reflectance values of affected areas. Secondly, vegetation on hillsides may differ structurally from similar habitats on flat ground when viewed from above.

It is almost impossible to remove errors from the mapping process but it is important to quantify them (Janssen & van der Wel, 1994). This allows people to judge whether to use a particular coverage and to illustrate possible accuracy levels of any resultant analysis. The best way to measure this accuracy is to compare what the land-cover coverage predicts will be present at a series of points with what is actually present (Eastman, 1999). This process is called ground- truthing and can be a time-consuming and costly process. It is obviously preferable to collect a large number of points over a wide area but there is inevitably a trade-off with the costs involved. Therefore, when planning a sampling strategy the following factors should be considered:

• Each of the land-cover type should be sampled effectively, with a minimum of 10 ground-truth points collected for each class.

• The ground-truth points should be spaced as widely as possible to ensure that different patches of the same vegetation type (possibly with different reflectance values) were sampled.

• Whenever possible, the ground-truth points should be chosen at random to avoid sampling bias.

• Data should be collected as quickly and cheaply as possible to allow the maximum number of points to be visited.

4.3.1 Methods

Due to the conflicting nature of the factors described above, it was decided to use four different sampling methods. The first method involved collecting data from randomly chosen points that were close to roads. This tends to under-sample certain land-cover types and so the second method involved collecting data in known patches of these types. The third method used data that were collected as part of previous studies to map the vegetation of PAs in northern Maputaland. These ground-truth points were not chosen randomly but they comprised a very valuable source of data Chapter 4: Creating the land-cover coverage 65 that were used as part of this research. The fourth method was used for the few types that were inaccessible and the points were chosen using the GIS, based on existing land-cover coverages. Details of the methods used for each strategy are given below.

4.3.1.1 Collecting data close to roads Caution is needed when choosing points close to roads as they are more likely to be affected by rain run-off (which tends to increase the vegetation density) and to be cleared for fuel-wood. Therefore, it was decided to choose points in a “sampling area” that was more than 50 m but less than 250 m from roads. One thousand sample points were initially chosen within this area, as a compromise between avoiding pseudo-replication and reducing the distance between them. The sampling area was made using the “Create Buffers” option in ArcView based on the roads coverage. The “Random point” option in the Animal Movement extension was then used to create and label the 1000 points and their co-ordinates were downloaded into a Garmin 12XL GPS unit.

For logistical reasons, it was decided to use this sampling method for two months and to try to visit as many of the thousand points as possible. It was also decided to start in those areas that were closest to Mkhuze GR, TEP and St Lucia town, where accommodation was available. These points were printed out on a map, together with their identifier codes and the GPS unit was used to locate the position of each selected point. The following information was recorded at each ground-truth point visited: i) The co-ordinates of the ground-truth point. Without correction, a GPS unit can give inaccurate readings and so it is quite possible for a unit to show that a point of interest had been reached when its actual location was more than 100 m away. Therefore, the initial location that the unit identified was still used, but its co-ordinates were recalculated more accurately using the GPS unit's averaging facility. ii) The physiognomic characteristics of the vegetation. This was classed as grassland, woodland, thicket, forest or agricultural land. iii) The presence of any characteristic plant species. The species name of the most abundant tree and shrub species were recorded whenever possible, using Pooley (1997). In addition, the identity of any species that were indicative of the presence of a certain vegetation type was recorded. Some plant samples were collected to allow later identification. iv) The presence of any human related factors. The presence of any buildings near the ground-truth points was recorded as well as the presence of any nearby fields or domestic animals.

Chapter 4: Creating the land-cover coverage 66 It was not always possible to reach a particular ground-truth point, either because it was fenced or part of a wetland. However, the position of these points could often be estimated because of their proximity to the road. Sometimes it was possible to estimate where the point was and to identify the land-cover type from a distance and in these cases the land-cover type was recorded without visiting the actual point. No recording was made if it was not possible to see the estimated position of the point, or if the estimated position was close to several different land-cover types. This information was then used to assign each of the points into one of the 34 land-cover categories.

4.3.1.2 Collecting data in under-sampled land-cover types After two months of collecting ground-truth data using the first method, it was possible to identify those land-cover types that had been under-sampled. Known patches of these land- cover types were then visited and 10 points were collected for each of these types. The GPS unit's “nearest waypoint” feature was used to ensure that each point was at least 200 m from any other. It was not possible to enter patches of some of the wetland vegetation types, such as swamp forest and grass and sedge swamp. In this case, a point was recorded at the edge of a vegetation patch and the distance to a sampling point was estimated. It was then possible to calculate the position of the sampling point.

4.3.1.3 Using data that were collected as part of other studies The data used in this method were collected as part of the work done by Matthews et al (in press) in their description of the vegetation of TEP and the work done by Lubbe (1996) on the vegetation of the Coastal FR. Both these studies used a much more detailed vegetation classification than that used in this research but the recorded information allowed most of these finer categories to be re-assigned.

4.3.1.4 Deriving data from GIS coverages It was not possible to collect any ground-truthing data in open water, mud flats or mangroves. However, the position of these features had been recorded by Lubbe (1996) in his land-cover coverage of the Coastal FR and so the “Random point” option in ArcView was used to choose a total of ten points in the polygons belonging to each of these land-cover types.

The coordinates of each ground-truth point, together with the recorded land-cover type, were then imported into ArcView and used to produce a vector coverage. The projection of this coverage was then changed to lo33 using the “Projection Wizard” option.

Chapter 4: Creating the land-cover coverage 67 4.3.2 Results

The land-cover types were recorded at 723 points throughout Maputaland (Table 4-2; Figure 4-2). Many of these points were chosen randomly but a large number of points were collected as part of previous studies and as a supplement to ensure that each land-cover category was sampled adequately.

Table 4-2: Summary of the methods used to collect of the ground-truth points

Method Number of points Percentage Random sampling 272 37.6 Previously collected 217 30.0 Supplemental sampling 204 28.2 From GIS coverage 30 4.1

4.3.3 Discussion

The sampling methods described above were a compromise between avoiding sampling bias and reducing travel costs. The result was that more than 700 points were visited, which should be sufficient to measure the accuracy of the land-cover coverage. However, there were limitations, the most obvious of which was that more than 60 % of these points were not chosen randomly. This was particularly the case for land-cover types that had a limited distribution or were difficult to reach by road. The ideal method would have been to choose the ground-truth points after the land- cover coverage had been created, as this would have allowed the same number of points to be chosen for each type. It would also have been possible to choose points that were close to roads to minimise the cost of data collection. Unfortunately, the Landsat TM images only became available for purchase after the period that had been allocated for fieldwork, so this was not possible. However, it is strongly suggested that such an approach should be used for future work.

Chapter 4: Creating the land-cover coverage 68 Random sampling Previously collected 20 km Supplement sampling From GIS coverage N2 highway Road PAs

Figure 4-2: The location of the ground-truth points

Chapter 4: Creating the land-cover coverage 69 4.4 Creating the land-cover coverage

Two techniques are commonly used to produce land-cover coverages from satellite imagery. The first is based on the idea that natural land-cover categories can be identified by plotting frequency histograms of the reflectance values in each satellite band. Any “troughs” in these histograms are then assumed to mark the reflectance boundaries between land-cover types. The advantage of these “unsupervised classification” techniques is that they do not require any previous knowledge of the study area and produce results quickly. However, the land-cover categories that they produce may not have ecological significance and may not correspond with any a priori classification scheme that has been developed for the study area. This was shown in a study that found that an unsupervised classification was unable to distinguish between different ecological communities in a 5 x 5.5 km landscape in the Western Ghats of India (Nagendra & Gadgil, 1999).

Therefore, it was decided to use “supervised classification” techniques to produce the Maputaland land-cover coverages. This method allows the satellite image to be classified into a series of pre- determined types by using “training sites” to calculate each of their reflectance value characteristics. These training sites can either be identified in the field or on colour composite coverages by those with relevant expertise. Each land-cover type must be represented by at least one training site, although it is preferable to use several, widely dispersed sites. In addition, the training sites for each category must be large enough to contain at least ten times the number of pixels as the number of satellite bands that are analysed (Eastman, 1999). For example, calculating the reflectance characteristics of each training site from seven bands requires the training sites to be at least 70 pixels (or 0.063 km2) in area.

4.4.1 Methods

The first step in identifying the training sites was to use Idrisi to produce two colour composite images. The first of these images combined information from bands 1, 2 and 3 of the TM image and the second combined bands 2, 3 and 4. These images were then printed on A3 paper, laminated and given to NCS ecologists A. Blackmore, P. Goodman & W. Matthews. These three people used their knowledge of the area to mark the position of known patches of each land-cover type. The positions of these patches were then digitised in Idrisi. The reflectance values of each were calculated from the seven bands of the 1998 Landsat TM images using the MAKESIG function and the TM bands were then classified using the MAXLIKE function in Idrisi. This used information on the mean and variance of reflectance values of each land-cover type to identify the type that each pixel most closely resembles. The 1998 image did not include areas to the south of Mkhuze GR and so the same methods were applied using the 1996 TM image to map the remaining part of Maputaland.

Chapter 4: Creating the land-cover coverage 70 The MAXLIKE function also allows “prior probability images” to be used, so that the distribution of each type can be influenced by prior knowledge. These images predict the probability (between 0 and 1) of each pixel belonging to a particular class. Five of these prior probability images were created to increase the likelihood of each pixel being correctly classified, where each image marked the location of the five different ecological zones used in the land-cover classification. The boundaries of each zone were based on the 1: 250 000 geology coverage described in chapter two (Figure 2-4; Figure 2-5).

The resultant land-cover coverage then had to be greatly modified for several reasons. Firstly, it was decided to remove any group of five pixels or less to reduce the complexity of the final coverage and consequently the effects of errors in the original Landsat TM data. In addition, the MAXLIKE function was unable to distinguish between forest types, so each patch of mixed forest was re-classified as containing the dominant type. Finally, visual observation was used to rectify any classification errors, such as finding pixels of mangroves in non-coastal areas or swamp forest in high-lying areas. All of these corrections were made by displaying the land-cover coverage and a false colour composite image in ArcView for comparison, while editing the land-cover coverage in the PhotoPaint graphics software.

In addition, several land-cover types were mapped using different methods. Patches of rock-face were too small to distinguish from subsistence agriculture. Hence, they were digitised from false colour composite images by assuming that they bordered deep shadows in the Lebombo Mountains. It was also impossible to distinguish towns from subsistence agriculture and so the known position of the two towns in the region was also digitised separately. On-screen digitising of colour composite images was also used to identify patches of commercial agriculture and plantations and to digitise the position of roads. These vector coverages were converted to rasters using the POLYRAS module and then combined with the land-cover coverage using the OVERLAY module.

Different methods were also used for hygrophilous grassland, which proved difficult to distinguish from woody grassland. Its distribution was modelled by using the COST module in Idrisi based on the DEM and a coverage that showed the distribution of reed beds, sedge and grass swamp, swamp forest, open water and mudflats. The resultant coverage showed the likelihood of hygrophilous grassland occurring, assuming that it was found near water or aquatic vegetation and was less likely to occur uphill from these features. The cut-off cost value was estimated from the CFR land-cover coverage produced by Lubbe (1996) and the OVERLAY module was used to reclassify all those areas of woody grassland as hygrophilous grassland if they fell within the suitable area identified in the cost surface coverage.

Chapter 4: Creating the land-cover coverage 71 The predicted land-cover at each of the ground-truth points was then found using the EXTRACT module in Idrisi and the results were compared with the actual data collected in the field. It was decided to increase the resolution of the land-cover coverage to 25 m so that the same number of pixels fell within each of the 1 km x 1 km grid squares used in the later analysis. This was done using the RESAMPLE module and the area of each land-cover type was then calculated.

4.4.2 Results

There were 723 ground-truth points and the land-cover type at 629 of these (86.9%) was correctly predicted (Table 4-3). The accuracy for each type varied between 80 % and 100 % and the mean value was 88. 8%. The number of points recorded in each class varied between ten for fourteen types and 151 for woody grassland.

Table 4-3: Accuracy assessment of land-cover coverage

Land cover type Land cover type Correct Correct Incorrect Incorrect % correct % correct Lebombo aquatic 8 2 80.0 Terminalia woodland 59 9 86.8 Rock-faces* - - Woodland on red soils 9 1 90.0 Lebombo grassland 8 2 80.0 Sand thicket 8 2 80.0 Lebombo woodland 15 3 83.3 Sand forest 9 1 90.0 Lebombo thicket 9 1 90.0 Evergreen forest 17 1 94.4 Lebombo forest 11 0 100.0 Swamp forest 10 1 90.9 Mangroves 10 0 100.0 A. tortilis woodland 16 3 84.2 A. nigrescens woodland 25 6 80.6 Beach 15 0 100.0 Bushland (A. grandi.) 10 2 83.3 Dune thicket 9 1 90.0 Thicket (A. luederitzii) 9 1 90.0 Dune forest 9 1 90.0

Lawn grass 8 2 80.0 Roads 10 0 100.0 Reed beds 8 2 80.0 Buildings/settlement † -- Riverine thicket 8 2 80.0 Subsistence agriculture 80 14 85.1 Riverine forest 17 1 94.4 Commercial agriculture 14 0 100.0 Plantations 29 0 100.0 Freshwater swamp 9 2 81.8 Hygrophilous grasslands 37 7 84.1 Open Water 10 0 100.0 Woody grassland 124 27 82.1 Mud Flats 9 1 90.0 * It was not possible to collect ground-truth data for this land-cover type † This land-cover type was classified using prior information

Chapter 4: Creating the land-cover coverage 72 The area of each land-cover type varied between 1.4 km2 for mangroves and towns and 2199.6 km2 for subsistence agriculture (Table 4-4). The mean area for the different types was 279.1 km2 and the final coverage is shown in Figure 4-3.

Table 4-4: Area of land-cover types found in Maputaland

Land cover type Area Land cover type Area (km2) (km2) Lebombo zone Coastal plain zone Lebombo aquatic 39.8 Sedge & grass swamp 169.1 Rock-faces 5.7 Hygrophilous grasslands 520.5 Lebombo grassland 25.7 Woody grassland 760.4 Lebombo woodland 649.8 Terminalia woodland 1660.8 Lebombo thicket 328.3 Woodland on red sands 36.9 Lebombo forest 21.4 Sand thicket 76.6 Sand forest 144.0 Cretaceous zone Inland evergreen forest 154.93 Acacia tortilis woodland 116.3 Swamp forest 31.3 Acacia nigrescens woodland 170.9 Mangroves 1.4 Acacia grandicornuta bushland 119.8 Acacia luederitzii thicket 210.8 Coastal dune zone Beach 39.3 Alluvial zone Dune thicket 22.8 Floodplain grassland 114.7 Dune forest 112.4 Reed beds 139.5 Riverine thicket 76.1 Transformed land-cover types Riverine forest 30.7 Roads 35.7 Towns 1.4 Subsistence agriculture 2199.6 Commercial agriculture 167.5 Plantations 684.6

Other land-cover types Open Water 531.0 Mud Flats 88.5

Chapter 4: Creating the land-cover coverage 73 20 km

Figure 4-3: The land-cover coverage of Maputaland

Chapter 4: Creating the land-cover coverage 74 4.4.3 Discussion

The Maputaland land-cover coverage created for this study is an extremely important source of information because it used a standard methodology to map the whole of the study region. It is also highly accurate, meeting the accuracy requirements used by the GAP programme in the United States, which specifies that each land-cover type must be classified with more than 80 % accuracy and the mean accuracy of all the types should be greater than 85 % (Crist & Deitner, 2000). Previous land-cover coverages either used a classification system that did not distinguish between important habitat types (Thompson, 1996) or covered small parts of the region (Goodman, 1990; Lubbe, 1996). Therefore, it would not be possible to carry out any useful conservation planning exercise for Maputaland without using this land-cover coverage.

Nevertheless, this coverage does have limitations that must be recognised, especially when considering natural vegetation types that were not included. Many of these had limited distributions and formed patches that were too small to be mapped accurately. In addition, some land-cover types were excluded because they could not be distinguished on the satellite images. This was the case for Acacia karroo thickets, which form large patches on disturbed land in the coastal dune zone. It could also be argued that land-cover types such as Terminalia woodland should have been divided into several categories based on their physiognomy but the resultant classes would have been indistinct and would have complicated the final coverage. With regards to the land-cover types that were included, most caution is needed when considering the distribution of rock-faces. This is because it was difficult to identify this type from the TM images and for safety reasons it proved difficult to collect ground-truth data. This land-cover type is also likely to be under- represented in any coverage because it forms near-vertical blocks that seem much smaller when viewed from above (Stone et al., 1997).

Given the importance of the land-cover coverage for conservation planning it could be argued that this methodology should be more widely adopted. This will depend on the cost of the process, how easy it was to achieve and whether equally good alternatives exist. The financial cost was relatively low and would be even lower in the future, as the new Landsat 7 TM images only cost US $600. However, it took more than eight months to organise the necessary workshops to decide the classification system, collect the ground-truth data, produce the draft coverage using the GIS and to rectify errors using the graphics software. It is also possible that it would be more difficult to produce a similar coverage for different areas, as the geology of Maputaland produces vegetation patches with distinct boundaries.

Such classification difficulties can be reduced by using a priori information in a supervised classification (Franklin, 1995). This was used to produce the Maputaland land-cover coverage to ensure that the distribution of each land-cover type was restricted to its associated ecological zone. Incorporating extra information, such as water table height and elevation, would have increased the Chapter 4: Creating the land-cover coverage 75 accuracy of the coverage produced by the MAXLIKE module. The need for this type of analysis is in contrast with Nagendra & Gadgil (1999) who found that a simple supervised classification was sufficient to identify different land-cover types. However, their study site was only 5 km x 5.5 km and it is unlikely that the same methods would be accurate over a larger area. This was illustrated by findings in Maputaland, where forest could be distinguished from woodland and grassland but it was much more difficult to identify the forest type. This may have been because the TM bands used in this analysis contained relatively little information (section 3.6) but most TM images are equally affected by errors and so the results described here are probably typical of using such imagery to produce land-cover coverages.

Therefore, it seems that this approach to land-cover mapping is an important one but its associated costs and reliance on the availability of people with relevant expertise suggests that in some cases other methods may be more appropriate. One such method used 1 km resolution satellite imagery to produce land-cover coverages and this is described in the next section.

4.5 The effects of reducing the resolution of the land-cover coverage

It is widely recognised that conservation planning should be based on the type of fine scale data that is exemplified by the Maputaland land-cover coverage. Unfortunately, these types of data are not available for most parts of the developing world and so alternative approaches have been identified (Da Fonseca et al., 2000). One potential source of data is the 1 km resolution global land- cover coverages available without charge from the Earth Resources Observation System (EROS) Data Center. These data are based on AVHRR satellite imagery collected between April 1992 and March 1993. Each coverage shows the results of using different classification algorithms to identify land-cover types and each uses different land-cover categories.

Unfortunately, there are several problems with this data set. Firstly, the different land-cover classification schemes contain a wide range of category types, ranging from the 10 of the Simple Biosphere 2 system (Sellers et al., 1996) to 197 for the Africa Seasonal Land Cover system (USGS, 2000). These options would allow conservation planners to choose a scheme that was most suited to their requirements but it also means that the people involved would have to have a good knowledge of the study area to make that decision. It is also unlikely that the categories developed for the whole continent would be ideal for a particular region, so planners would need to make some modifications.

There are however more important limitations that relate to the quality of the data used. Firstly, the data are not accurately geo-referenced and would have to be corrected before being used. A comparison of data from the Simple Biosphere 2 land-cover coverage with coastline and lake boundaries derived from Landsat TM imagery shows differences of two to three km between the

Chapter 4: Creating the land-cover coverage 76 two types of data (Figure 4-4). This may seem negligible when using the dataset to map vegetation at a continental scale but this is a very important margin of error when planning the position of new PAs. Secondly, several different sources of data were used to produce these global datasets, the results seem to contain many errors (pers. obs.) and the resolution and origin of the original AVHRR data were not always documented (pers. comm. S. Howard at EROS).

Key Lake boundary

Coastline

Water

Sand or mud flats

10 km Other land-cover types

Figure 4-4: A comparison between Maputaland GIS data and global land-cover data

Therefore, it seems that conservation planners should not use these global land-cover datasets, unless they are working at a very broad scales. Despite these problems, the strategy of using 1 km resolution data could be important if the source of the information were more reliable. However, the process of reducing this resolution could have important results by under-representing the area of vegetation types with relatively small patch sizes (Mack et al., 1997). This section tests this hypothesis by reducing the resolution of the Maputaland land-cover coverage to 1 km to determine whether the areas of each land-cover type differ from those in the 30 m resolution coverage.

4.5.1 Methods

The 25 m land-cover coverage, which had been resampled from the 30 m coverage, and the 1 km grid coverage were imported into ArcView. The “Tabulate areas” was then used to find the area of each land-cover type in each 1 km2 grid square. The results were imported into Excel, which was used to identify which land-cover type had the largest area in each grid square and these results were joined to the 1km grid vector file in ArcView. This was used to produce a raster coverage and the area of each land-cover type in the new coverage was calculated in Idrisi using the AREA module.

Chapter 4: Creating the land-cover coverage 77 A Χ2 test was used to determine whether the area of pixels belonging to the different land-cover types differed between the 25 m and 1 km resolution coverages. The observed areas of each type were taken from the 1 km coverage and the expected areas were found for each type by calculating their proportional area in the 25 m resolution coverage and multiplying this by the total area. The town and mangrove land-cover types had expected values that were less than five, so the town types was merged with the road types and the mangrove types was merged with the swamp forest types. These data were then analysed in Excel using the CHITEST function.

The relationship between patch area and percentage difference for the different land-cover types was found using the “Curve estimation” option in SPSS. Ten different mathematical functions were fitted to the data and the one that explained the most of the variance (ie had the highest R2 value) was used to model the relationship between the two variables. This model was then used to predict the patch area value that would be unaffected by changing the resolution from 25 m to 1 km.

4.5.2 Results

The area of the land-cover types in the 1 km coverage ranged between 2 km2 for mangroves and towns and 2375 km2 for subsistence agriculture (Figure 4-5).

20 km

Figure 4-5: 1 km resolution land-cover coverage of Maputaland (see Figure 4-3 for key) Chapter 4: Creating the land-cover coverage 78 There was a significant difference between the observed and expected areas of the land-cover types in the 1 km resolution coverage (Χ2 = 89823.75, df = 32, p < 0.001). Three land-cover types (Lebombo aquatic, rock-faces and roads) were completely absent from the 1 km resolution coverage and three (towns, mangroves and dune forest) increased their area by more than 30 % (Table 4-5). There was a significant logarithmic relationship between patch area and percentage difference in area between the 30 m and 1 km resolution coverages (F = 33.17, df = 32, p < 0.001). This model had an R2 value of 0.509 and predicted that any land-cover type with a mean patch area of less than 0.13 km2 would be under-represented at the 1 km resolution (Figure 4-6).

Table 4-5: Differences between 30 m and 1 km resolution land-cover coverages

Patch Patch Difference Difference Land cover type area Land cover type area % % (km2) (km2) Lebombo aquatic 0.111 -100.0 Terminalia woodl. 0.458 7.2 Rock-faces 0.002 -100.0 Woodl. on red sands 0.326 7.7 Lebombo grassland 0.005 -80.6 Sand thicket 0.005 -58.5 Lebombo woodland 0.304 20.0 Sand forest 0.151 -41.3 Lebombo thicket 0.167 -19.2 Evergreen forest 0.239 -8.3 Lebombo forest 0.351 -11.9 Swamp forest 0.127 -39.6 Mangroves 0.130 37.9 A. tortilis woodland 0.144 -11.1 A. nigrescens woodl. 0.208 4.7 Beach 0.292 -11.5 A. grandi. bushland 0.242 -12.1 Dune thicket 0.496 -4.2 A. luederitzii thicket 0.260 -13.7 Dune forest 1.628 34.4

Floodplain grassland 0.110 -0.4 Roads <0.001 -100.0 Reed beds 0.105 -14.5 Towns 0.153 43.7 Riverine thicket 0.007 -73.9 Subsistence agr. 0.268 7.3 Riverine forest 0.008 -19.1 Commercial agr. 1.9015 7.4 Plantations 1.9226 1.9 Sedge swamp 0.009 -49.5 Hygro. grasslands 0.228 8.0 Open Water 0.411 -4.6 Woody grassland 0.299 3.7 Mud Flats 0.282 -7.9

Chapter 4: Creating the land-cover coverage 79 60

40

20

0

-20

-40

-60 Percentage difference

-80

-100

-120 0 0.5 1 1.5 2 Patch size (km2)

Figure 4-6: The effects of reducing land-cover resolution on patch area of land-cover types

4.5.3 Discussion

The disadvantages of reducing the land-cover coverage resolution to 1 km are obvious. The results described above show that it would be less accurate and would tend to under-represent land-cover types that have a small mean patch area. These types would then be under-represented in any resulting conservation policy based on protecting set percentages of features of interest. The advantages are less obvious because the only 1 km land-cover coverages that are currently available are not suitable and so the same process of deciding a classification system, producing the coverage and ground-truthing it would have to be followed. There would be savings in sampling time as the coverage would contain fewer types because types with small patch sizes would be excluded from the classification system. However, given the cost of producing an efficient conservation strategy based on poor information (Balmford & Gaston, 1999), it would probably be worth spending more to produce the fine resolution land-cover coverage described in section 4.3.

The advantage of using the Maputaland land-cover coverage can be seen when considering the results from the modelled relationship between patch area and change in area. This showed that any land-cover type with a mean patch area of less than 0.13 km2 would tend to be under-represented in a 1 km resolution coverage. Twelve of the Maputaland types had mean patch areas below this value, including types such as riverine forest, sedge and grass swamp and swamp forest, which all have high conservation value because they are threatened by agricultural transformation and contain many rare and restricted species (Tinley and van Riet, 1981).

Chapter 4: Creating the land-cover coverage 80 4.6 Chapter summary

• The land-cover of Maputaland has been described and mapped by several different authors but the available data either used very broad land-cover types or focussed on a small part of the region. Therefore, it was necessary to develop a new land-cover classification system for Maputaland based on the existing literature and the expertise of NCS ecologists.

• The classification system described 29 untransformed land-cover types and 5 transformed land-cover types found in the region. The system also recognised that each of the 26 untransformed vegetation types were associated with one of five ecological zones and grouped them accordingly.

• Supervised classification techniques were used to map the distributions of these land-cover types based on Landsat TM satellite imagery. The area of each land-cover type varied between 1.4 km2 for mangroves and towns and 2199.6 km2 for subsistence agriculture, with a mean area of 279.1 km2.

• The accuracy of this land-cover coverage was measured by recording the actual land-cover type at 723 points located throughout the region. The coverage met standard accuracy requirements, as the land-cover type was correctly predicted at more than 86 % of these points, with the accuracy levels for each type varying between 80 % and 100 %.

• Reducing the resolution of the land-cover coverage from 30 m to 1 km had important effects. Three land-cover types were completely absent from the 1 km resolution coverage and it was predicted that changing the resolution in this way would under-represent any land-cover type with a mean patch size of less than 0.13 km2. Twelve of the Maputaland types had a mean patch area below this value, including types with high conservation value such as riverine and swamp forest.

The Maputaland land-cover coverage is an important source of data for conservation planning but threats to biodiversity in the region are not uniformly distributed. The next chapter describes how Landsat TM imagery was used to map the spread of subsistence agriculture and model which areas are most at risk in the future.

Chapter 4: Creating the land-cover coverage 81 Chapter 5: Modelling land-cover transformation

5.1 Introduction

The need to select priority areas for biodiversity conservation comes from a limit on the availability of land and resources. This is why it is important to map biodiversity to determine which elements need further protection. However, this protection is a response to the threats imposed by habitat loss, fragmentation and other factors and these threats are not uniformly distributed (Faith & Walker, 1996). Threatened areas tend either to have high economic value and are transformed into commercial agriculture and urban landscapes (Pearlstine et al., 1995; White et al., 1997) or they have a high human population density, with high population growth and poverty levels that leads to unsustainable agriculture and resource-use (Cincotta et al., 2000).

This led to the suggestion that the concept of ‘triage’ should be adopted when deciding conservation policy (Myers, 1979). This concept requires that untransformed areas be divided into three categories. The first includes all areas that will be lost, irrespective of the amount of time and effort spent trying to protect them. The second includes areas that are threatened but can be saved by expending resources and the third includes those areas that will be unaffected by future habitat loss. Applying the triage process has definite benefits and it has influenced some conservation planning (Williams et al., 1996; Margules & Pressey, 2000). However, it should probably be considered as an important influence on conservation theory rather than as a process that should be followed in practice. This is because most areas could be saved if sufficient resources are committed to their protection. This is illustrated by examples from species conservation programmes, where army units protect groups of black rhinoceros (Diceros bicornis) in Zimbabwe and U.S. zoos and conservation bodies spend thousands of dollars on maintaining whooping crane (Grus americana) numbers (Emslie & Brooks, 1999; Cannon, 1996).

Therefore, a new set of criteria is needed to make planning decisions based on assessments of risk. Such assessments should include predictions on how long it will take for an area of interest to be transformed and how much it will cost to establish, maintain and protect this area. A range of factors would have to be considered when calculating these costs, including the higher costs of maintaining biodiversity in small areas and of solving resource-use conflict in areas with high population density or poverty levels (Newmark et al., 1994). Such a cost-calculating process has to involve people with a wide range of expertise and should be done on a case-by-case basis. Therefore, the first step in this process is to identify which areas are most at risk of being transformed. This chapter describes how this risk was assessed for Maputaland.

Chapter 5: Modelling land-cover transformation 82 Section 5.2 describes how recent habitat loss was mapped and sections 5.3 and 5.4 explain how the factors that caused the spatial distribution of this loss were identified and used to produce a risk of transformation coverage. Sections 5.5 and 5.6 describe how this coverage was used to identify those vegetation types most at risk and to predict future habitat loss and fragmentation trends and section 5.7 summarises the chapter.

5.2 Mapping habitat transformation

Protected area managers and conservation biologists have always seen mapping habitat transformation and loss as important because it provides information on vegetation dynamics and threats to biodiversity (Dublin, 1991; Ite & Adams, 1998). Aerial photography was widely used to provide these data but most people now use satellite imagery because of the lower costs. Initially, most of this mapping used 1 km resolution Advanced Very High Resolution Radiometer (AVHRR) data to quantify deforestation rates in tropical rainforests (Skole & Tucker, 1993). However, the increasing affordability of Landsat satellite data allowed more people to study a wider range of habitats and at a less coarse scale (Mendoza & Dirzo; Hudak & Wessman, 2000).

In southern Africa, such data have been used to measure woody biomass loss due to fuel-wood collection (Abbot & Homewood, 1999) and habitat transformation caused by subsistence farming (Ringrose et al., 1996). Both studies used two or more satellite images and created colour composite images using infrared and near infrared bands (Fiorella & Wimple, 1993). These methods were adopted for this study because habitat transformation for subsistence agriculture is the most important threat to biodiversity in Maputaland. Large parts of the region contain commercial agriculture and forestry (Figure 4-3) but this is generally restricted to privately owned land and unlikely to grow much further. However, any further expansion is likely to occur on the few remaining areas of higher quality soils, which are also preferred for subsistence agriculture. Hence, the following sub-section describes how the spread of subsistence agriculture was mapped.

5.2.1 Methods Two Landsat TM satellite images from 1986 and 1998 were used to map changes in subsistence agriculture. It was decided not to use the 1986 image to produce a land-cover coverage as experience gained from classifying the 1998 image showed that this was a very time consuming process. Instead, an alternative method was developed that used the PhotoPaint v7.0 graphics software.

The first stage in this method was to produce a false colour composite image using the 1986 image that only showed areas that were classified as subsistence agriculture in the 1998 image (Figure 5-1). This involved using the RECLASS module in Idrisi to produce a coverage from the 1998 land-cover coverage that only showed the position of subsistence agriculture. This mask coverage

Chapter 5: Modelling land-cover transformation 83 was then multiplied in turn by bands 2, 3 and 4 of the 1986 image using the OVERLAY module. These bands were then combined using the COMPOSITE module to produce the required false colour composite image of Maputaland.

Bands 2, 3 and 4 were chosen to produce this image because they accentuate differences in vegetation biomass, allowing those areas that had been cleared for agriculture to be identified. The reflectance values of subsistence agriculture tended to vary between the different ecological zones because of the underlying geology. Therefore, the original colour composite image was used to produce four new images, where each image only showed reflectance values in one zone. There were no reflectance values for the costal dune zone because it fell entirely within PAs.

The second stage was to import these four images into PhotoPaint to use the “Selection wand” tool to identify pixels that obviously contained subsistence agriculture (Table 5-1). These pixels were white or grey in appearance and they were coloured bright yellow to distinguish them from the others. The bitmap images were then displayed in ArcView, together with false colour composite images of the whole of Maputaland using the 1986 and 1998 Landsat TM bands.

The third stage was to compare all the remaining pixels in the subsistence agriculture images with the Maputaland images. These pixels were coloured green if they showed patches of natural vegetation that were present in 1986, or coloured yellow if they were subsistence agriculture in both years (Figure 5-1). Some pixels appeared to change from transformed to untransformed vegetation types (especially thicket) during this period. However, these were ignored as they were very few in number and probably contained weedy species with little conservation value.

Stage 1 Stage 2 Stage 3 Manipulate false colour Use PhotoPaint to identify areas Manually check the remaining composite image taken in 1986 that were subsistence agriculture pixels and colour green those to show those areas that were in 1986 and shade them yellow. containing natural vegetation in subsistence agriculture in 1998. 1986

Figure 5-1: A schematic representation of the methods described in sub-section 5.2.1

Chapter 5: Modelling land-cover transformation 84 This process was completed for each of the four ecological zones (Figure 2-5) and the four images were combined in Idrisi. Idrisi was also used to calculate the total area that had been cleared, in addition to the total area in each ecological zone.

5.2.2 Results A total of 127.68 km2 (Table 5-1) of natural vegetation was cleared for subsistence agriculture between November 1986 and April 1998, at a rate of 11.1 km2 yr-1. The amount cleared varied between zones, with 1.38 km2 cleared in the Lebombo zone and 84.96 km2 cleared in the coastal plain zone (Table 5-1). This meant that 3.19 % of the natural vegetation that was found outside PAs in 1986 had been converted to subsistence agriculture by 1998. The percentages cleared varied between 0.16 % in the Lebombo zone and 8.48 % in the Cretaceous zone. The Cretaceous zone also had the largest percentage of subsistence agriculture in 1986 (59 %), whereas the Lebombo zone had the lowest (26.33 %) (Table 5-2).

Table 5-1: The area of natural vegetation transformed between 1986 and 1998

Total area transformed Percentage (km2) transformed Lebombo zone 1.38 0.16 Cretaceous zone 35.87 8.48 Alluvial zone 5.47 3.15 Coastal plain zone 84.96 3.38

Total 127.68 3.19

Table 5-2: Percentage of subsistence agric. found outside PAs in the ecological zones in 1986

Zone Lebombo Cretaceous Alluvial Coastal pl. Total

% subsistence agric. 26.33 59.00 44.22 27.98 33.79

5.2.3 Discussion These results show that more than 3 % of Maputaland’s natural vegetation was cleared for subsistence agriculture between 1986 and 1998. If this continued at the same rate of 11.1 km2 yr-1 then there would be no natural vegetation outside the PA system within 305 years. However, such estimations are crude, as they do not allow for changes in human population, farming methods and a whole range of other factors. In addition, it is likely that the most productive and accessible land has already been transformed so that the remaining natural vegetation is less threatened. In addition, caution is needed when interpreting results from remote sensing data because of problems with distinguishing examples of biodiversity loss that do not affect vegetation physiognomy. In Chapter 5: Modelling land-cover transformation 85 particular, it is difficult to identify the spread of subsistence agriculture under thick tree canopies, as appears to be happening in some sand forest patches, and it is impossible to measure the over- exploitation of species, such a large mammals and medicinal plants.

The methods used to produce these results were based on visually judging which pixels in the colour composite images contained subsistence agriculture. This could be criticised for its subjective nature, but it had several advantages over the supervised classification techniques described in chapter four. Firstly, this process was much quicker, taking two weeks instead of the several months it took to produce the land-cover coverage. It also allowed the status of a pixel to be judged based on whether its vegetation density differed from that of its neighbours. For example, a pixel of transformed thicket might have had the same reflectance values as one of grassland but it could be identified because of the high vegetation biomass values of the neighbouring pixels. However, there were problems with this method, as it was easier to distinguish agricultural transformation when it involved large changes in vegetation biomass. This meant that the position of transformed forest and thicket was probably more accurately mapped than that of transformed grassland.

This rate of clearance of 0.28 % yr-1 was low compared to rates described in the literature for rainforest and woodland, which ranged between 0.16 % yr-1 (Sánchez-Azofeifa et al., 1999) and 4 % yr-1 (Sader & Joyce, 1988). There are several possible reasons why the rate was not as high in Maputaland. Firstly, many of these studies focus on forests, which are often easier to distinguish from agricultural land by remote sensing. Small patches of subsistence agriculture in a matrix of woodland or grassland are much harder to identify and so may result in an underestimate of loss in these habitats. Despite these potential problems, the results described above have important implications. The most obvious of these is that the measured rate of habitat loss in Maputaland appears to be quite low. Losing 0.28 % yr-1 suggests that biodiversity loss in the region will not be as rapid as might be feared. This was probably because few people decided to clear new areas to farm, despite a high population growth rate, because of the poor agricultural potential in the region.

Another reason for this low rate may be because this study looked at the transformation of the whole range of vegetation types. Most of the studies cited above focussed on particular habitat types and these were chosen because they are particularly at risk of clearance. This is illustrated by comparing results from the four affected ecological zones in Maputaland. The Cretaceous and alluvial zones contained soil of much higher agricultural value (Land Type Survey Staff, 1986) and these zones had much higher rates of transformation (Table 5-1). This is particularly important given that they were heavily transformed before 1986, both by subsistence and commercial agriculture. This suggests that the Cretaceous zone would lose its natural vegetation outside the PAs almost three times faster than the estimate for the whole region. This illustrates that estimates of agricultural transformation rates can differ greatly, even within relatively small study areas such

Chapter 5: Modelling land-cover transformation 86 as Maputaland. The results also show that risk of agricultural transformation was not uniformly spread and highlights the importance of identifying which areas are most at risk. The following section describes the methods that were used to provide this information.

5.3 Modelling risk of future habitat transformation

Mapping plays an important role in quantifying habitat loss but it can also be seen as a first step in trying to understand and mitigate future loss. A great deal of work has been done to determine factors causing habitat loss and the results have shown that these tend to be site specific and scale- dependent. At the broadest level, factors causing tropical deforestation differed between continents, with Africa being most affected by population density, Latin America by cattle density and Asia by cropland area (Bawa & Dayanandan, 1997). Nevertheless, there were still many similarities, with all three continents being to some extent affected by each of these factors, as well as by levels of external debt.

Studies at a finer resolution have shown that topographical features often play a role in determining where agricultural transformation takes place. In general, slope seems to be particularly important, with areas on steep slope less susceptible to deforestation (Green & Sussman, 1990; Trejo & Dirzo, 2000). In addition, elevation (Dirzo & Garcia, 1992), soil type (Ochoa-Gaona & González- Espinosa, 2000) and distance to roads or navigable rivers (Sader & Joyce, 1988; Linkie, 1999) have been shown to be important. In woodland it appears that distance from boreholes is significant, with most transformation occurring within two kilometres of these features in southern Botswana (Ringrose et al., 1996). Finally, where the data were available, most studies found that habitat loss was partly determined by human density (Vina & Cavelier, 1999).

This section describes how the available GIS data were used to determine which factors caused agricultural transformation in Maputaland. The tested factors were elevation, slope, soil type, distance to roads and distance to existing subsistence agriculture. The available human density coverage did not include the whole of the study region, so this factor was excluded from the analysis. It was decided instead to test its possible importance on a more localised basis and this is described in section 5.4.

5.3.1 Methods Idrisi was used to produce two coverages. The first (the cleared vegetation coverage) showed the areas that had been cleared for cultivation between 1986 and 1998. The second (the threatened vegetation coverage) showed those areas of natural vegetation outside the PA system that remained untransformed. Both coverages were vectorised and imported into ArcView and 200 random points were chosen, both in the cleared and threatened areas. This was done using the “Random point theme” module that makes up part of the Animal Movement extension for ArcView (Hooge &

Chapter 5: Modelling land-cover transformation 87 Eichenlaub, 1997). It was decided to ensure that the points were separated by at least 300 m (10 pixels) to reduce spatial autocorrelation. These points were then imported into Idrisi and rasterised.

Idrisi was also used to produce a distance from roads coverage and a distance from transformed land-cover types in 1986 coverage using the DISTANCE module. The EXTRACT module was then used to find the characteristics of each of the 200 randomly chosen points, based on the distance from roads, distance from subsistence agriculture, soil, slope and elevation coverages. This information was then exported into the SPSS statistical software for analysis. These data were analysed using stepwise logistic regression to determine whether any of the independent variables significantly affected the probability of a pixel in the 1986 land-cover coverage being transformed for subsistence agriculture by 1998. The resultant model was then converted into a 30 m resolution risk coverage using the “Image calculator” in Idrisi. The HISTO module was used to produce a histogram showing the number of pixels belonging to ten equally divided risk classes.

5.3.2 Results The probability of an area being cleared for agriculture between 1986 and 1998 was significantly related to its log10 distance from existing subsistence agriculture (Table 5-3, Figure 5-2), its log10 slope (Table 5-3, Figure 5-4), its log10 elevation (Table 5-3, Figure 5-5) and the ecological zone in which it was found (Figure 5-3; Table 5-3).

Distance to subsistence agriculture explained more than 50 % of the observed variation in the data, whereas the other three factors when combined explained less than 16 % of the variance (Table 5-4). Agricultural clearance was more likely to occur close to existing agriculture, in the Cretaceous or alluvial zones, at lower elevations and on flatter ground (Figure 5-6). However, most of the region was at little risk, with the majority of pixels having a predicted probability of being cleared for subsistence agricultural of less than 0.1 (Figure 5-7).

Table 5-3: Details of the factors that determined risk of agricultural clearance

Regression Factor df Wald Sig. coefficient

Log10 distance to existing subsistence agriculture -4.016 1 78.951 0.000 Lebombo zone -3.448 1 9.974 0.002 Cretaceous & Alluvial zone 2.325 1 20.268 0.000 Coastal plain zone* - - - -

Log10 of slope 2.377 1 7.970 0.004

Log10 of elevation -1.505 1 3.456 0.046 Constant 10.082 0 32.142 0.000 * This factor has no details because its effects were incorporated into the constant by SPSS

Chapter 5: Modelling land-cover transformation 88 Table 5-4: Details of the amount of variance explained by each factor in the model

% observations % variance Factor explained explained

Log10 distance to existing subsistence agriculture 81.0 51.4 Ecological zone 3.5 12.8

Log10 of slope 1 1.6

Log10 of elevation 0.5 0.7

Figure 5-2: Mean log10 of distance to existing subsistence agriculture for transformed and untransformed points

Figure 5-3: The mean transformation status of the sample points grouped according to the ecological zone in which they were found

Chapter 5: Modelling land-cover transformation 89 Figure 5-4: Mean log10 of slope for transformed and untransformed points

Figure 5-5: Mean log10 of elevation for transformed and untransformed points

Chapter 5: Modelling land-cover transformation 90 1

Transformation probability

0 20 km Protected Area

Agricultural land or water

Figure 5-6: Risk of the remaining natural vegetation being cleared for subsistence agriculture

Chapter 5: Modelling land-cover transformation 91 Figure 5-7: Frequency distribution of risk status derived from the modelled coverage

5.3.3 Discussion All of the factors tested, other than distance to roads, significantly determined the probability of an area being cleared for subsistence agriculture between 1986 and 1998. Distance to roads was probably not significant because most of Maputaland is made accessible by a network of unplanned roads, many of which were not visible on the satellite images. Ecological zone was significant, with areas in the Cretaceous and alluvial zones having a much higher risk of clearance. This was expected, as the agricultural value of these zones is much higher because of the underlying geology (Watkeys et al., 1993). Slope and elevation were also significant but when combined they explained only 1.5 % of the variation in the model (Table 5-4). This was probably because most of Maputaland consists of a coastal plain that is generally very flat. It appears that the steep slopes in the Lebombo Mountains were less susceptible to transformation but these make up a small part of the region as a whole and so had little influence in the final model.

The most important factor was distance to existing agriculture, which explained more than half of the variance in the model (Table 5-4) and this was probably for several reasons. The first is that agricultural transformation tended to occur at the interface between transformed and pristine vegetation. Second, distance to subsistence agriculture could be interpreted as distance to areas that had already been seen as suitable for agricultural clearance. Finally, distance to subsistence agriculture probably acted as a surrogate for human population density. Hence, the relationship between these two factors is investigated in the following section.

Chapter 5: Modelling land-cover transformation 92 5.4 Human population density and transformation risk

Human population density is widely recognised as being highly related to risk of agricultural transformation. Myers (1988) originally defined his biodiversity hotspots partly based on their population density and these areas have since been shown to contain nearly 20 % of world population in an area covering about 12% of the Earth’s terrestrial surface (Cincotta et al., 2000). Much of the literature described in the previous section identifies the causal role of this factor and other research has shown a link between population density and local plant extinction in the UK (Thompson & Jones, 1999).

Despite this link, it is probably not human density per se that leads directly to habitat loss, as the financial status and livelihoods of the people involved will also play a large role. For example, in Enseleni District, an area that lies close to the southern boundary of Maputaland, it was found that environmental degradation was related to the local poverty levels, rather than population density (Schwabe et al., 1996). The effects of higher population density can be mediated by changes in land-tenure arrangement and technology and so increased habitat loss is not inevitable (Bilsborrow & Okoth Ogendo, 1992). However, it is usually much more difficult to quantify appropriate socio- economic measures. Hence, human population density, which is generally related to poverty levels at a local scale, can act as a useful surrogate.

This section investigates whether population density had an effect on risk of agricultural transformation in Maputaland. The South African Medical Research Council created the population density coverage by using GPS units to record the position of each household. These data were collected for a malaria-control project and so did not include the malaria-free Lebombo Mountains. Therefore, it was decided to exclude population density from the analysis described in section 5.3 but to test whether it was correlated with the position of subsistence agriculture in the areas where data were available.

5.4.1 Methods The human population density coverage had a resolution of 500 m, so it was resampled using the nearest-neighbour option of the RESAMPLE module in Idrisi to produce a coverage that had a resolution of 25 m. The land-cover coverage was resampled to the same resolution and manipulated using the RECLASS module in Idrisi to show only subsistence agriculture. The OVERLAY module was then used to manipulate the 1 km grid coverage to exclude those squares that were outside the original human population density coverage.

The EXTRACT module was then used in Idrisi to calculate the mean population density for each of the remaining 1 km grid squares. The subsistence agriculture coverage and the grid coverage were then imported into ArcView and the “Summarize Zones” option was used to calculate the proportion of each grid square that contained subsistence agriculture. There were 5379 grid squares Chapter 5: Modelling land-cover transformation 93 with suitable data, so 200 of these were chosen at random to avoid pseudo-replication. The data were imported into SPSS, where the proportional data were transformed using an arcsine transformation and both sets of data were transformed logarithmically to achieve normality. A linear regression model was then used to test whether the log10 of population density in a grid square significantly determined the log10 of the transformed proportion of that square that contained subsistence agriculture.

5.4.2 Results 2 There was a significant, positive relationship between the log10 of population density of a 1 km grid square and the log10 of the transformed proportion of that square that contained subsistence agriculture (Table 5-5, Figure 5-8). The model had an adjusted R2 value of 0.320.

Table 5-5: The relationship between population density and proportion of subsistence agriculture.

Regression Factor tSig. coefficient

-2 Log10 of population density (km ) 0.473 9.736 0.000 Constant 0.007 6.669 0.000

Figure 5-8: The relationship between population density and proportion of subsistence agriculture.

Chapter 5: Modelling land-cover transformation 94 5.4.3 Discussion

There was a significant relationship between log10 population density and the transformed log10 proportion of subsistence agriculture in the 1 km resolution grid squares. This was expected, as people in Maputaland tend to farm around their homesteads and allow their livestock to graze nearby. The model only explained 32 % of the variance (Figure 5-8), suggesting that other factors played a part in determining the proportion of subsistence agriculture. It is likely that the influence of soil type and slope may have been important but it was decided to exclude these from the analysis as they had already been shown to determine risk of transformation in section 5.3 and including them would have introduced issues of auto-correlation. These results suggest that the agricultural transformation rate will increase with increasing population growth but the relationship is unlikely to be linear. This is because some people are likely to leave the region rather than trying to farm on the remaining poor soils.

5.5 Identifying land-cover types most at risk of transformation

The risk of agricultural transformation coverage can also be used to identify which land-cover types are most at risk, which is particularly important for determining conservation priorities. A land-cover type could be considered a particular priority if it was highly threatened and had a limited distribution. This is because widely distributed types are more likely to contain ecologically viable patches that are less at risk. In contrast, threatened small patches of vegetation would be much more affected by edge effects and fragmentation. Therefore, this section calculates the modelled transformation risk for each natural land-cover type and gives each a vulnerability score based on this risk and its area in Maputaland.

5.5.1 Methods The risk coverage was reclassed in Idrisi to produce a mask coverage showing only those areas that were at risk of transformation. The OVERLAY module was then used to multiply this mask coverage by the land-cover coverage to produce a new coverage that only showed those pixels of natural vegetation that were at risk of transformation. The mean transformation risk of each natural land-cover type was then calculated using the EXTRACT module and the AREA module was used to calculate the area of each of these types. Results from Lebombo aquatic and rock faces were excluded because they were not seen as being at risk of transformation. A vulnerability score was calculated for each natural land-cover type by dividing its mean transformation risk by its area for all those patches found outside the PAs. This number was then multiplied by 1000 to produce scores with numbers > 0.01 that were easier to interpret.

5.5.2 Results The mean risk of the natural land-cover types being transformed ranged between 0.017 for Lebombo forest and 0.226 for A. luederitzii thicket (Table 5-6, Figure 5-9). The vulnerability score

Chapter 5: Modelling land-cover transformation 95 ranged between 0.07 for Lebombo woodland and reed beds and 27.72 for swamp forest (Table 5-6), with a mean of 4.71.

Table 5-6: The transformation risk and vulnerability of Maputaland’s vegetation types

Mean Vuln. Mean Vuln. Land cover type Land cover type risk score risk score Lebombo grassland 0.034 1.48 Floodplain grassland 0.113 6.75 Lebombo woodland 0.040 0.07 Riverine thicket 0.060 3.08 Lebombo thicket 0.035 0.12 Lebombo forest 0.017 0.97 Sand forest 0.060 0.87 Sand thicket 0.142 3.67 Acacia luederitzii thicket 0.226 4.69 Terminalia woodland 0.106 0.10 A. grandicornuta bushland 0.085 3.81 Woodland on red sands 0.081 11.23 Acacia tortilis woodland 0.045 1.34 Woody grassland 0.143 0.40 A. nigrescens woodland 0.034 0.48 Hygrophilous grasslands 0.175 1.14 Sedge & grass swamp 0.179 3.65 Riverine forest 0.07 2.43 Swamp forest 0.168 27.72 Reed beds 0.11 24.28 Inland evergreen forest 0.061 0.73

Figure 5-9: The relationship between area and risk of transformation

Chapter 5: Modelling land-cover transformation 96 5.5.3 Discussion The results described show that caution is needed when predicting habitat loss for a large area, as there can be great differences between different natural land-cover types. There was also no relationship between land-cover type area and modelled risk, as can be seen from Figure 5-9. The validity of the vulnerability score is supported by the weighting it gave to swamp forest and riverine forest, both of which have been identified as being particularly threatened by habitat loss (Castley & Kerley, 1996; Lubbe, 1996).

5.6 Predicting future patterns of agricultural transformation

The model of agricultural transformation risk described in section 5.3 can be used to predict future patterns of agricultural encroachment and its effects on the biodiversity of Maputaland. Certain phenomena, such as habitat loss and fragmentation are known to increase biodiversity loss (Fahrig & Merriam, 1994; Turner, 1996) and these can be described using a series of landscape indexes (Akçakaya et al., 1995). This section will model two different scenarios to predict changes in land- cover.

• The “equal probability” model assumes transformation to subsistence agriculture will occur at a rate that is proportional to the modelled risk, ie the time taken for all the pixels with a risk probability of 0.2 to be cleared will be twice that of those with a probability of 0.1 (Figure 5-10). This model could be seen as relatively optimistic, as most of the pixels have a low risk of transformation (Figure 5-7) and so habitat loss would initially be low.

• The “equal area” model is more pessimistic and assumes that the need for new agricultural land will be constant and so will habitat transformation levels. This model still assumes that the pixels with the highest risk probabilities will be cleared first but it also assumes that this rate will be continuous, so that the number of pixels cleared after two years would be double that cleared after one (Figure 5-11).

The presented results predict the spread of subsistence agriculture according to the two models. Both of these model the distribution of untransformed vegetation in ten stages, from the present to the stage where no vegetation remains outside the PAs. The results also measure the level of habitat fragmentation at each stage for both models, by finding the number of patches and the mean patch area. A whole range of other landscape metrics can also be calculated but their relation to biodiversity loss is less well understood and so they were excluded from these models.

5.6.1 Methods The risk coverage was multiplied by 10 000 and then converted to integer format in Idrisi. The equal probability coverage was created by the using the STRETCH module to reclassify the pixels

Chapter 5: Modelling land-cover transformation 97 with values between 1 and 10 000 into 10 categories. The equal area coverage was created by first using the AREA module to calculate the area of each of the 10 000 risk classes. There were 4 339 691 pixels with a risk value greater than zero, so each of the 10 classes in the equal area coverage had to have an area of 433 969 pixels. The area data were imported into Excel and used to calculate the required boundary values. The RECLASS module was then used to classify the integer risk coverage to produce the final equal area coverage.

The equal probability coverage was then used to produce a series of ten new coverages. The first was changed so that the class with the highest risk of transformation was reclassed as zero. The second coverage had the first and second class reclassed to zero and the process was repeated so that the tenth coverage had all of the classes reclassed to zero. The OVERLAY module was then used to add the position of the vegetation found in the PAs to each of the ten coverages. This produced a series of 10 coverages that showed the predicted sequential loss of natural vegetation in Maputaland, based on the assumptions of the equal probability model (Figure 5-10). The same process was used to produce another ten coverages, based on the equal area model (Figure 5-11).

Transformation Transformation probability probability

0.91 - 1.0 0.997 - 1 0.81 - 0.9 0.992 - 0.996 0.71 - 0.8 0.980 - 0.991 0.61 - 0.7 0.953 - 0.979 0.51 - 0.6 0.900 - 0.952 0.41 - 0.5 0.800 - 0.899 0.31 - 0.4 0.629 - 0.799 0.21 - 0.3 0.347 - 0.628 0.11 - 0.2 0.131 - 0.346 0 - 0.1 0 - 0.130 20 km Protected Area 20 km Protected Area Agricultural land Agricultural land or water or water

Figure 5-10: Equal probability categories Figure 5-11: Equal area categories

The 20 coverages were then imported into ArcView and the “Patch Grid” extension was used to calculate the number of patches and mean patch area of each. The resolution of the coverages was first reduced from 30 m to 50 m because the software was not able to calculate the required landscape statistics of the original larger and more complicated files. Chapter 5: Modelling land-cover transformation 98 5.6.2 Results The two models show markedly different results, mainly because most of the pixels in the risk coverage had low values (Figure 5-7). The area of natural vegetation illustrates the difference between the models, as the equal area model by definition shows a linear decline over time (Table 5-7, Figure 5-12). The equal probability model shows a much less steep, near-linear decline until the ninth stage when it drops rapidly. The number of vegetation patches follows a similar pattern to those shown by the natural vegetation changes (Figure 5-13) but the change in mean patch area is more complicated (Figure 5-14). Both models show a gradual increase in patch area, as the smaller patches are completely transformed, and a large increase by stage 10 when the only remaining patches are those in the PAs. However, the equal probability model shows a more pronounced drop in patch area before this large increase, presumably as the last remaining large unprotected blocks begin to fragment.

Table 5-7: Predicted habitat loss and fragmentation in Maputaland

Equal probability scenario Equal area scenario

Trans. Area No of Patch area Area No of Patch area stage (km2) patches (km2) (km2) patches (km2) 1 5580 4969 1.12 5497 5265 1.04 2 5372 4950 1.09 5107 4359 1.17 3 5180 4496 1.15 4714 3402 1.39 4 5042 4140 1.22 4324 3107 1.39 5 4899 3734 1.31 3933 2809 1.4 6 4758 3416 1.39 3543 2100 1.69 7 4569 3192 1.43 3154 1417 2.23 8 4323 3103 1.39 2766 1125 2.46 9 3930 2794 1.41 2381 803 2.97 10 1981 104 19.05 1981 104 19.05

Chapter 5: Modelling land-cover transformation 99 Figure 5-12: Modelled changes in area of natural vegetation

Figure 5-13: Modelled changes in number of vegetation patches

Figure 5-14: Modelled changes in patch area

Chapter 5: Modelling land-cover transformation 100 5.6.3 Discussion It is difficult to predict which of the two models described here will most accurately describe future patterns of habitat loss in Maputaland. It was shown in section 5.4 that levels of subsistence agriculture were highest in areas with high human population density, so it could be expected that this will increase with the growing population. However, past rates of clearance have not shown a linear relationship with population growth and this is probably because people have moved out of the region to areas with better agricultural potential. These results suggest that the spread of subsistence agriculture can be reduced if alternative sources of income are found for people living in the area. If such schemes are successful, then rates of agricultural transformation will probably remain low and could be reduced to negligible levels.

5.7 Chapter summary

• Between 1986 and 1998 approximately 128 km2 of natural vegetation in Maputaland was converted to subsistence agriculture. This was a rate of 0.28 % per year, which was lower than most rates of loss from studies of rainforest and woodland described in the literature.

• The probability of a pixel of natural land-cover being cleared for subsistence agriculture was highest in those areas that were close to existing agriculture, on nutrient-rich soils and on low-lying, flat ground. This model explained 66 % of the variance and predicted that most of the remaining natural land-cover in Maputaland had a low risk of transformation.

• Human density also probably played a part in determining transformation risk, although data were not available for the whole of the region. Where human density was known, it was shown to be positively correlated with the proportion of subsistence agriculture found in a series of 1 km2 grid squares.

• The mean risk of transformation varied between natural land-cover types, with values ranging between 0.017 for Lebombo forest and 0.226 for A. luederitzii thicket. It was assumed that land-cover types with a limited distribution were most vulnerable, suggesting that swamp forest was particularly susceptible.

• The effects of two different predicted patterns of agricultural transformation were modelled. The more optimistic model assumed that transformation to subsistence agriculture would occur at a rate that is proportional to the modelled risk and predicted that initial rates of loss would be low.

The risk coverage produced in this chapter is extremely important for conservation planning. However, it is only one element and the next chapter describes how another important source of data was produced by modelling the distribution of Maputaland’s bird species.

Chapter 5: Modelling land-cover transformation 101 Chapter 6: Mapping the distribution of Maputaland's bird species

6.1 Introduction

There are approximately 9700 bird species (Sibley & Monroe, 1990) and 462 of these have been recorded in Maputaland (Cooper, 1980). Maputaland is part of an Endemic Bird Area (Stattersfield et al., 1998) and is internationally recognised as important for bird conservation (Johnson et al., 1998). For example, Ndumo GR has an area of only 119 km2 but contains 395 species. The distribution of these bird species are generally well known in Maputaland, so birds are a useful surrogate for biodiversity in any conservation planning exercise (Pearson, 1994). In fact, so little is known about most other species in the region that birds are probably the only surrogate group that could be used without resorting to using taxa with far fewer species.

Despite this advantage, birds are only one element of biodiversity and they may not be ideal surrogates because many are habitat generalists and highly mobile. This may make them less vulnerable to extinctions, as is illustrated by data on threatened species, where only 12 % of all bird species have some type of threatened status, compared to 24 % for all mammals (Hilton-Taylor, 2000). The previous two chapters have shown that land-cover types can be accurately mapped and have their risk of transformation assessed. Hence, it could be argued that these data should be used in preference as a biodiversity surrogate, as some plant and animal species are only associated with one or two land-cover types.

The great disadvantage with using land-cover data, however, is that the habitat classification system may not reflect how different species actually perceive these habitats. For example, Acacia tortilis and A. nigrescens woodland were mapped as two different land-cover types in this study (chapter four) but they share many plant species, have similar physiognomic characteristics and many species are found in both. Therefore, any conservation planning exercise benefits from using a biodiversity surrogate with elements that are not defined or classified by researchers or managers. Obviously, there are still arguments about which species concept should be used and this can affect results (Peterson & Navarro-Sigüenza, 1999). However, there are still many advantages in using distributional data from species, or higher taxa, rather than habitat classifications in conservation planning exercises. Unfortunately, obtaining these distributional data for species can be difficult and this chapter describes how it was achieved for the birds of Maputaland.

There are two broad methods used for mapping the distribution of species. The first is based on observational data collected in the field and such information is available for Maputaland from the Southern African Bird Atlas Project (SABAP). Sections 6.2 and 6.3 describe these data and Chapter 6: Mapping the distribution of Maputaland's bird species 102 investigate the extent to which they are affected by sampling bias. The second method involves modelling the distribution of the species of interest and section 6.4 describes how these data were derived from the land-cover coverage and section 6.5 explains how this information was used to identify which bird species are most likely to be under-recorded in bird atlas projects. Section 6.6 uses these results to suggest a method to reduce the affect of sampling bias, so increasing the accuracy of conservation planning and section 6.7 summarises the chapter.

6.2 A description of the SABAP data for Maputaland

The SABAP was launched in 1987 with the aim of documenting the distribution and seasonal movements of the 900 or more bird species found in the region (Harrison, 1992). The project divided the region into a series of quarter degree grid squares (approximately 25 x 25 km) and relied on qualified volunteers to visit each square and record the bird species that were present. The recorders then submitted bird lists to their regional atlas committee for vetting and the processed field cards were sent to the project co-ordinator (Harrison, 1992). The volunteers did not record the number of individuals seen, as this would have been heavily dependent on sampling effort. However, each grid square was generally visited several or many times, so a crude index of species abundance can be estimated from the number of bird lists on which each species was listed.

The final SABAP database is available in both electronic and paper format (Harrison et al., 1997) and is an extremely important resource for ornithologists and conservation biologists. It has been used to assess the impact of land-use on bird species (Allan et al., 1997; Herremans, 1998), to identify priority areas for the protection of endemic species (Barnard et al., 1998; Jarvis & Robertson, 1999) and to predict the population size and biogeography of focal species (Robertson et al., 1995; Robinson et al., 1998). This data set does have limitations though, which are inevitable given the scope of the SABAP project. Firstly, the quarter degree resolution means that it is unsuitable for fine resolution conservation planning exercises. However, the data could be used as a basis for modelling species distributions at a more relevant resolution. The second limitation is that the amount of sampling effort can vary widely for each SABAP grid due to differences in accessibility. This section investigates whether the SABAP data for Maputaland is affected by sampling bias and discusses whether they should be used in the modelling process.

6.2.1 Methods This section and section 6.3 used SABAP data from the 22 quarter degree grid squares that fell partly or completely within Maputaland (Figure 6-1). It would have been preferable to only use the 17 squares that fell completely within the study region but this would have reduced the sample size used in the later analysis. The data were extracted in MS Access format from the KwaZulu-Natal SABAP database that is held by the NCS. MS Access was used to count the number of bird species

Chapter 6: Mapping the distribution of Maputaland's bird species 103 found in each SABAP grid square and the number of records collected. ArcView was used to calculate the land area of each SABAP square.

6.2.2 Results There were 104 789 records for these 22 grid squares and these described the distributions of 452 species. Species number in each square ranged between 120 and 393, with a mean of 272.6, and record number varied between 190 and 19382, with a mean of 4763.1 (Table 6-1; Figure 6-1).

Table 6-1: Details of the Southern African Bird Atlas data for Maputaland

Grid Number of Number Area Grid Number of Number Area square records of species (km2) square records of species (km2) 2632CC 5409 331 457.0 2732CA 19382 393 683.7 2632CD 13319 373 384.8 2732CB 14234 374 683.7 2632DC 1420 230 368.8 2732DA 1367 246 419.0 2632DD 4642 283 191.4 2732CC 1260 235 682.2 2732AA 2441 283 686.7 2732CD 3935 328 682.1 2732AB 2978 248 686.7 2732DC 461 120 262.8 2732BA 1311 189 686.6 2832AA 5141 301 680.6 2732BB 2002 211 215.9 2832AB 6987 346 680.5 2732AC 822 241 685.2 2832BA 2021 259 137.0 2732AD 190 133 685.2 2832AC 2654 242 679.1 2732BC 5372 290 621.4 2832AD 7441 342 524.7

Figure 6-1: The number of species recorded in each SABAP grid square Chapter 6: Mapping the distribution of Maputaland's bird species 104 6.2.3 Discussion There was a large difference between the number of species recorded in each SABAP grid square, with square 2732CA containing more than three times the number of species found in square 2732DC (Figure 6-1). As there were also large differences in the number of records, it was important to determine whether this had an effect on the recorded number of species and this analysis is described in the next section.

6.3 Factors affecting species richness in the SABAP grid squares

Two types of factor are likely to affect the number of species recorded in a SABAP grid square. The first set affects the actual number of species present and these would include habitat diversity and resource availability. The second set affects the probability of the species being identified and includes factors such as the expertise of the recorders, the visibility conditions and sampling effort. Sampling effort generally plays the largest part in recording success and the relationship between species recorded and effort expended has been investigated by a number of authors (Colwell & Coddington, 1994). In general, the number of new species recorded increases rapidly with effort at the beginning of the sampling period but then gradually decreases until an asymptote is reached when all the present species have been identified. The relationship between these two factors is called a “species accumulation curve” and can be described mathematically, although there is no universally agreed model (Soberón & Llorente, 1993; Veech, 2000).

This suggests that if sampling effort varies between SABAP grid squares then there will be a corresponding variation in species density, unless sampling effort in each square was sufficient for the asymptote to have been reached. It is also likely that factors that determine actual species density, such as habitat variability, will be significant irrespective of sampling effort. This is because species-rich squares will tend to have steeper species accumulation curves for any given sampling effort. Therefore, it was decided to test whether record density, land-cover type density, proportion of pristine vegetation and proportion with PA status determined the recorded species density in each SABAP grid square. The CSIR land-cover vector coverage (Figure 4.1) was used to determine land-cover type density and proportion of pristine vegetation because the land-cover coverage described in chapter four did not cover all of the 22 SABAP squares.

6.3.1 Methods The number of records and species for each grid square were extracted from the Maputaland SABAP database. The intersect function of ArcView’s “Geoprocessing wizard” was used to determine the area of pristine vegetation (defined as vegetation that was not classified as agricultural or degraded), the area with PA status and the number of CSIR land-cover types within each SABAP square. These data were then used to calculate the species (Figure 6-2), record

Chapter 6: Mapping the distribution of Maputaland's bird species 105 (Figure 6-3) and land-cover density of each grid square, as well as the proportion of each square that had PA status and the proportion that consisted of pristine vegetation. The proportional factors were subjected to an arcsine transformation and all the factors were transformed logarithmically to meet the assumptions of multiple regression modelling.

Figure 6-2: Species density in the SABAP Figure 6-3: Record density in the SABAP grid squares grid squares

These data were then imported into SPSS and a stepwise multiple regression model was used to test whether log10 species density in each square was determined by log10 record density, log10 CSIR land-cover type density, log10 PA proportion and log10 pristine vegetation proportion.

6.3.2 Results

The log10 of species density was significantly and positively determined by log10 of CSIR land- cover type density (Table 6-2; Figure 6-4) and log10 of record density (Table 6-2; Figure 6-5), in a model that had an adjusted R2 value of 0.936.

Chapter 6: Mapping the distribution of Maputaland's bird species 106 Table 6-2: Details of the factors that significantly determined log10 bird species density

% Variance Factor Regr. coefficient t value Sig. explained

Log10 land-cover type density 12.715 12.587 0.000 82.4

Log10 record density 0.00848 5.975 0.000 11.2 Constant 0.000413 0.313 0.758 -

Figure 6-4: The relationship between log10 of species density and log10 land-cover type density

Figure 6-5: The relationship between log10 of bird species density and log10 record density Chapter 6: Mapping the distribution of Maputaland's bird species 107 6.3.3 Discussion The SABAP data set is widely recognised as an extremely important source of information that can be used to identify priority sites at a broad scale and to predict and monitor species’ distributions. However, these results show that bird density in the 22 SABAP grid squares was partly determined by record density. The influence of sampling bias on this data set must be recognised, although it is probably less severe in other parts of Southern Africa when compared to Maputaland. This is because some SABAP grid squares in Maputaland are difficult to access, whereas those that contain Mkhuze and Ndumo GRs are internationally known for their avifauna and widely visited by ornithologists (Johnson et al., 1998).

Bird density was also determined by the density of CSIR land-cover types, which was expected, given that an increase in habitat types generally results in an increase in the number of associated species. The model explained most of the recorded variance, with log10 land-cover type density being the most important factor (Table 6-2). The log10 proportion of the grid square with PA status was not significant, which is similar to the pattern found with small mammals in northern South

Africa (Freitag et al., 1998). The log10 proportion containing pristine vegetation was also not significant, suggesting species density outside the PAs was not affected by exploitation of habitats. However, it is probably more likely that the influence of record density masked these apparently non-significant relationships.

These results have important implications for the use of the SABAP data as a basis for modelling the distribution of Maputaland’s bird species. The regression model showed that record density had a large and significant influence on species density. The record density in the 22 SABAP grid squares varied between 0.277 and 34.62 records km-2, suggesting that any bird species distribution coverage derived from the SABAP data would be heavily influenced by sampling effort and so would not resemble the actual distribution. Freitag & van Jaarsveld (1998) found that removing more than 80 % of records from a mammalian fauna database dramatically reduced the accuracy of their attempt to identify priority conservation sites. The results here suggest that under-recording in some SABAP grid squares far exceeds 80 %, so these data should not be used and alternative methods are needed to model the region’s bird species and these are described in the next section.

6.4 Modelling the distribution of Maputaland’s bird species

A variety of techniques have been used to model species distributions, depending on the type of data that are available and the number of species involved. When dealing with one or several species, it is common to use standard statistical techniques, which rely on sampling data that measure the presence or abundance of the focal species at a series of spatially independent points (Smith & Kasiki, 2000). The GIS can then be used to determine the spatial characteristics of these

Chapter 6: Mapping the distribution of Maputaland's bird species 108 points by extracting data from the available coverages (Sperduto & Congalton, 1996). The influence of these factors on the presence data can then be determined and the resultant statistical model turned into a suitability map (Austin et al., 1996).

The actual statistical test used depends on the type of data that were collected. For example, presence data, where no information is collected on where the species is not present, can be analysed using the Mahalanobis statistic. This defines the “optimum” habitat as a multivariate mean of the presence habitat variables and the GIS can be used to identify areas that approach this mean (Clark et al., 1993). Presence/absence data is generally analysed using logistic regression (Mace et al., 1996; Lindenmayer et al., 1999). However, other methods, such as discriminant analysis, principal component analysis and neural networks have also been used (Dettmers & Bart, 1999; Manel et al., 1999). Finally, density data, which are generally much more difficult to collect, can be analysed using multiple regression (Mladenoff et al., 1995; Smith, 1996).

These statistical methods have several advantages, as they allow a greater understanding of the factors that determine spatial distributions and do not rely on any a priori knowledge about the focal species. However, this approach is less commonly used when producing a data set for identifying priority conservation areas. This is because such data usually describe the distributions of a large number of species and so obtaining and analysing the necessary sampling data would be a very time consuming process (Pearce & Ferrier, 2000). One way to bypass this problem is to use existing data but this often is affected by sampling bias (see section 6.3). For example, over one quarter of the grid cells used to identify priority conservation areas in Amazonia had no museum records for any of their 421 focal species (Kress et al., 1998).

An alternative approach is to use expert knowledge or information from the scientific literature. This is usually done for coarse scale data sets (ie 1º x 1º), by collating information from different reference sources and using expert opinion to remove the influence of any perceived sampling bias in the original data (Lovett et al., 2000; Williams et al., 2000). Another method has been developed to model distributions at a finer resolution and this assumes that the distribution of species is determined by another factor that is more easily mapped. This method has been most extensively used as part of the gap analysis project (GAP) in the United States, which has mapped the distribution of the country’s vertebrate species based on their land-cover associations (Scott et al., 1993). This involves an eight step process that includes collating available data, producing wildlife/habitat relationship models, producing draft distribution maps and integrating range limit information (Csuti & Crist, 2000). This whole process relies heavily on expert opinion in the development of range limits, habitat associations and to review the predicted distributions.

The main problem with these methods is that it is often difficult to measure their accuracy. This is inevitable for coarse scale data sets because they were only developed to overcome the paucity of

Chapter 6: Mapping the distribution of Maputaland's bird species 109 distributional data at continental or sub-continental level. Distribution maps based on wildlife/habitat relationship models are more easily tested but this is still very expensive and has not even been implemented by the relatively wealthy GAP project. In addition, GAP argues that their distribution maps show potential habitat and that failure to find a species in an area where it was predicted to occur does not mean that it will never be found there (Crist, 2000).

This has led to some criticism of the GAP approach. The most obvious of these is that species distributions are heavily dependent on the accuracy of the distribution maps (Conroy & Noon, 1996; Schmidt, 1996). A range of errors can affect these maps, including the use of low resolution vegetation maps and random errors. Dean et al (1997) found that predictions of species richness in the GAP database were very sensitive to introduced errors of omission and commission. Edwards et al (1994) found that actual and predicted amphibian species lists for eight PAs in Utah had omission and commission errors of 16 and 15 %, respectively. There are also more fundamental criticisms from Beard et al (1999) who found that vegetation type might not be the best predictor of species distributions and that other factors, such as climate, may produce better results. In fact, climate data are sometimes used in the production of GAP distribution maps but only as a supplement to the vegetation data (Crist, 2000)

Despite these criticisms, the GAP modelling methods were adopted in a modified form to predict bird distributions in Maputaland. The main change was that the Maputaland land-cover coverage had a much finer resolution than those studied in the GAP project. The coverages produced by the GAP program are in vector format and have a minimum polygon area of 1 km2 for most types and 0.4 km2 for riparian areas (Stoms, 2000). There were also reasons to believe that the Maputaland distributional data would be more accurate than their GAP equivalents. This is because the region is a part of the biogeographically coherent Greater Maputaland and any changes in climate or elevation are reflected by vegetation changes. Therefore, it was felt unnecessary to include climatic data when modelling distribution data. In addition, birds tend to be less affected by these other factors, as was shown in Utah where omission and commission rates for this group were 1.8 and 7.5 % respectively (Edwards et al., 1994). At a more practical level, there was also no other way to accurately map the distribution of Maputaland’s bird species based on the available data, so this section describes how it was achieved.

6.4.1 Methods The avifauna of Maputaland is well known and has been described by a number of authors (Clancey, 1964; Cooper, 1980; Cyrus et al., 1980; Maclean, 1996). All of these sources provide valuable information on the habitat associations of these bird species but the habitat categories that they used were broader than those of the land-cover coverage described in chapter four. Therefore, it was necessary to ask relevant experts to produce the required land-cover type/bird species matrix based on their own knowledge and the available literature.

Chapter 6: Mapping the distribution of Maputaland's bird species 110 The first stage in this process involved Dr David Johnson, the bird specialist in the Biodiversity Division of the NCS, producing the first draft of the land-cover type/bird species matrix. He also identified which of these species were vagrants (ie occasional visitors that might appear on bird lists) and which were range restricted to the north or south of Maputaland (Table 6-3). This information was then sent to Athol Marchant, Research Technician in the Ecological Services Division of the NCS, and Nigel Robson, author of several descriptions of KwaZulu-Natal’s bird species (Cyrus et al., 1980; Cyrus & Robson, 1980). They made comments on this draft and these were used to produce the final matrix (Appendix 3). The matrix recorded species as being absent, present or found in the edge of each land-cover type and these data were entered into an Excel spreadsheet. It was decided to divide the subsistence agriculture category into sub-categories according to their associated ecological zone, with the coastal plain zone divided into woodland and grassland areas.

Table 6-3: A list of species that were identified as possibly being range restricted

Restricted to north Restricted to south Roberts Roberts Common name Common name No* No 147 Palmnut vulture 578 Spotted thrush 231 Stanley’s bustard 796 Cape white-eye 797 Yellow white-eye 840 Blue-billed firefinch 841 Jameson’s firefinch 871 Lemonbreasted canary * The Roberts Number is a widely adopted coding system for bird species in southern Africa

The matrix was used to produce a database that listed the modelled area of suitable habitat for each bird species in each of the 1 km2 grid squares. The first stage in this process was to identify edge habitat for each of the land-cover types. This was done using the 25 m resolution land-cover map described in chapter four, which was reclassed to produce a series of coverages that showed the distribution of each land-cover type. The FILTER module in Idrisi was then used to carry out a 3 x 3 pixel mean filter on each of these new coverages, so that all of the edge pixels in a coverage had a lower value than those pixels that were completely surrounded by the same land-cover type. The RECLASS module was then used to reclassify these edge pixels to have the same code value as their parent land-cover type plus one hundred (ie Lebombo forest was coded as 6, whereas Lebombo forest edge was coded as 106). The subsistence agriculture was divided into five new categories based on their associated ecological zone using the OVERLAY module. The subsistence agriculture in the coastal plain zone was divided further into woodland and grassland ecological

Chapter 6: Mapping the distribution of Maputaland's bird species 111 sub-zones by on-screen digitising the land-cover coverage. The data from these different coverages were then combined with the original land-cover coverage using the OVERLAY module.

The new land-cover coverage was then imported into ArcView and the “Tabulate areas” option was used to calculate the area of each land-cover type (including edge habitat) in each of the 1 km grid squares. These data were imported into Excel and a spreadsheet was created that automatically calculated the area of suitable habitat in each grid square for a species based on its land-cover associations. This was calculated for each species in turn and the data were added to the final bird distribution database, which was used to calculate the total suitable habitat for each species, in addition to the number of grid squares in which they were found. It was assumed that any patch of suitable habitat could support an associated bird species, irrespective of its size, because none of Maputaland’s species are restricted to core habitat (Johnson pers. comm.). The predicted distributions for the range restricted species were modified to exclude data from 1 km2 squares that fell within SABAP grid squares where the species had not been recorded in the SABAP database.

6.4.2 Results Twenty species that were recorded in the SABAP database for Maputaland were not predicted to occur in the bird/land-cover type matrix. The distribution maps in Maclean (1996) showed that eight of these species had ranges outside of the region and four others had been purposely excluded from the matrix because they were vagrants. Each of the remaining eight species had fewer than five records in the SABAP database (mean = 2.13). In contrast, nine species that were predicted to occur in Maputaland according to the bird/land-cover type matrix were not recorded in the SABAP database. The distribution maps in Maclean (1996) showed that eight of these nine species had their ranges within Maputaland.

The bird/land-cover matrix predicted that 441 species should be found in Maputaland. The feral pigeon (Columba livia), Indian myna (Acridotheres tristis) and house sparrow (Passer domesticus) are introduced species and so were excluded from any further analysis. The range of the remaining species varied between 12.4 km2 for the mangrove kingfisher (Halcyon senegaloides) and 8072.1 km2 for five species of swift (Apus spp. and Cypsiurus parvus), with a mean of 1910.3 km2 (Figure 6-6).

The matrix predicted that riverine forest was the most species rich land-cover type, with 181 associated species, 50 of which were edge species. Lebombo woodland was predicted as having no edge species but still had the second highest predicted number of associated species (Table 6-4). The mean number of predicted land-cover types for the bird species was 7.86, with a range between 1 and 37 (Figure 6-7). The highest number of bird species found in a grid square was 377 and the lowest was 21 (Figure 6-8).

Chapter 6: Mapping the distribution of Maputaland's bird species 112 Table 6-4: Details of the bird species/land-cover type association matrix

No of No of No of No of Land cover type spp in Land cover type spp in spp spp edge edge Lebombo aquatic 73 - Sand thicket 69 30 Rock-faces 35 - Sand forest 72 35 Lebombo grassland 85 - Evergreen forest 61 41 Lebombo woodland 179 - Swamp forest 55 26 Lebombo thicket 94 27 Mangroves 29 - Lebombo forest 58 29 Beach 31 - A. tortilis woodland 159 - Dune thicket 48 25 A. nigrescens woodland 165 - Dune forest 46 12 A. grandi. bushland 104 7 A. luederitzii thicket 93 8 Roads 27 - Buildings/settlement 46 - Lawn grass 86 4 Subsist. – Lebombo zone 34 - Reed beds 99 22 Subsist. – Cretaceous zone 37 - Riverine thicket 89 33 Subsist. – Alluvial zone 41 - Riverine forest 131 50 Subsist. – Coastal woodl. 33 - Subsist. – Coastal grassl. 34 - Sedge & grass swamp 125 17 Commercial agriculture 21 - Hygrophilous grassland 113 7 Plantations 25 25 Woody grassland 156 - Terminalia woodland 155 - Open Water 61 - Woodland on red soils 148 - Mud Flats 40 -

Chapter 6: Mapping the distribution of Maputaland's bird species 113 Figure 6-6: The modelled habitat areas of Maputaland’s bird species

Number of associated land-cover types

Figure 6-7: Number of land-cover types associated with bird species

Chapter 6: Mapping the distribution of Maputaland's bird species 114 Species richness

377

20 km

21

Figure 6-8: Bird species richness in Maputaland (species km-2)

Chapter 6: Mapping the distribution of Maputaland's bird species 115 6.4.3 Discussion The data set described above has a resolution of 1 km2 and has been corrected for sampling bias. This overcomes many of the problems associated with the SABAP data, and many other examples that rely on recorded information or data modelled at large spatial scales (Freitag et al., 1998; Williams et al., 2000). Hence, the techniques described above could be widely adopted but its disadvantages must be recognised. The most obvious of these is that this exercise relied on the expertise of six ecologists to determine the relevant vegetation communities and to predict the bird species that are associated with them. Nevertheless, these people collectively had over sixty years of accumulated experience and had access to a large amount of literature by others who have studied the ecology of Maputaland. Therefore, it is unlikely that many other sites in developing countries will have been studied so intensively.

A further problem with this method is that testing the predicted results would be very time consuming and were outside the scope of this study. The land-cover types described in chapter four were partly chosen because they could be distinguished on Landsat satellite imagery and so did not exactly correspond with previous classification systems. This meant that previous bird/habitat association data could not be used directly in this study because these only used some of the land- cover types. Other sources of data, such as PA bird lists, could provide data to check the veracity of the association models but these tended to group birds from different land-cover types and also included vagrant species. The necessary data could be collected in the future, especially if the SABAP records included the latitude/longitude coordinates of any sightings. This should be encouraged and is quite practical, given the increasing affordability of GIS and GPS units.

In the meantime, the SABAP data were relied upon to provide the only test of the accuracy of the association models. Twenty species that were recorded in the SABAP database were not predicted to occur in the bird/land-cover matrix. However, only 12 of these were shown as occurring in Maputaland by Maclean (1996) and four of these had been purposely excluded from the matrix as vagrants. The remaining species were recorded between one and four times in the SABAP database, suggesting that their omission was probably justified. Of course, the same logic could be applied to the eight species that were not recorded in the SABAP database but were actually included in the matrix. However, these species were included because seven of them were described as occurring by Maclean (1996) and they had been seen regularly by the experts who compiled the matrix.

Testing the accuracy of the matrix by comparing it with the SABAP database was a very crude approach but was used only because no other methods were available. It appears to support the matrix approach but it should be remembered that it does not test the accuracy of the species/land- cover types associations. Any errors would be compounded by the omission of several known

Chapter 6: Mapping the distribution of Maputaland's bird species 116 vegetation types from the land-cover coverage that were not distinguishable on the Landsat TM images. In addition, some vegetation patches were too small to appear on these images and the necessity of defining the edge of each land-cover type as the outermost pixel (ie 25 m) probably exaggerated the amount of available habitat for the associated species.

Despite these reservations, the results in Table 6-4 show that species richness varied dramatically between the different land-cover types and these data have a resolution that is fine enough to account for the observed diversity. The accuracy of the modelled data set can also be viewed with more optimism because many bird species are habitat generalists and so their distributions are unlikely to be affected by any unmeasured factors. This suggests that the modelled data set is extremely valuable because it overcomes many of the limitations of the other available information but this presumption can only be tested with the acquisition of spatially referenced sighting data. However, this data set does also allow an investigation into which factors affect the reliability of the SABAP data and this is described in the next section.

6.5 Finding the factors that determined recorded bird distributions

Sampling bias is known to influence the number of different species recorded at a particular site and so affect patterns of species richness (Gentry, 1992; Freitag et al., 1998). The data sets described in sections 6.2 and 6.4 are important because they allow data affected by sampling bias to be compared with modelled data corrected for such bias. Therefore, it is possible to test whether the bird species richness in the SABAP grid squares was dependent on sampling effort and this is described in the first part of the following section. However, these data can also be used for a more important analysis that is based on the likelihood that some species may be more prone to under- estimation of their recorded distributions. This may be because the species is less abundant, less distinctive or is associated with a habitat type that is difficult to survey. Any affected species would generally only be found in well-sampled grid squares.

Therefore, the second part of this section will describe an analysis to find the factors that determine the recording success of different bird species, where recording success is defined as the number of SABAP grid squares where a species was recorded divided by the number in which it was predicted to occur from the process described in section 6.4. The factors for investigation were chosen based on the requirement that data were available for each species and from the same data source (Maclean, 1996). These factors comprised of body mass, visual distinctiveness, song distinctiveness, diet, habitat and group size. Body mass and diet were chosen because it was assumed that smaller, herbivorous species would be more abundant and so might be easier to encounter. Visual distinctiveness, song distinctiveness and group size were chosen because it was assumed that species that formed large flocks and had a distinctive song or appearance might be easier to identify. Finally, it was assumed that species associated with habitat types that made

Chapter 6: Mapping the distribution of Maputaland's bird species 117 viewing birds difficult, such as forest or reed beds, might be more difficult to record. The methods that were used to test these assumptions are described below.

6.5.1 Methods The land-cover coverage was exported into ArcView and the area of each land-cover type in each SABAP grid square was determined using the “Tabulate areas” option. A bird list for each square was then compiled based on the land-cover type/bird species association matrix described in section 6.4. A list for each square was also derived from the SABAP database and both sets of data were imported into Excel. The proportion of recording success was then calculated for each species by dividing the number of squares that a species was recorded in by the number of species in which it was modelled to occur. This analysis was restricted to the 17 SABAP grid squares that fell entirely within the Maputaland land-cover coverage.

The characteristics of each of these species were obtained from Maclean (1996) and were grouped into the following categories:

• Body mass. The information on the mass of each species described in Maclean (1996) varied and so different approaches were used when appropriate. For some species, the mass of both sexes was listed and so the mean of these values was used to calculate the species' mass. In other cases, only the mass of the species was listed and so this was used instead. The listed data often came from several sources and so results based on the largest sample size and located closest to Maputaland were used in preference.

• Visual distinctiveness. The visual distinctiveness of the bird species was classed as either visually distinctive (coded 2) or visually indistinctive (coded 1). Visually distinctive species were defined as those where one or both of the sexes either had plumage, bills or legs that contained red, yellow, pink or purple coloration or had legs, bills or tails that were more than 50% of their body length.

• Song distinctiveness. If a species had a song that was described by Maclean (1996) as “loud”, “characteristic”, “penetrating”, “far-carrying”, “raucous”, “strident”, “booming” or “piercing” then it was classified as having a noticeable song (coded 2). All other species were classified as not having a noticeable song (coded 1).

• Diet. Omnivorous and carnivorous species were classed as carnivorous (coded 1), while herbivores were classed as herbivorous (coded 2).

Chapter 6: Mapping the distribution of Maputaland's bird species 118 • Habitat. The habitats associated with each bird species were classified into seven groups, based on the descriptions that were most commonly used in Maclean (1996). These were bushveld, forest, grassland, marsh, reed beds, water and woodland.

• Group size. If a species was recorded as generally being seen alone or in pairs it was classified as having a small group size (coded 1), while all other species were classified as having a large group size (coded 2). Some species are seen in much larger groups when breeding but these were still classified according to their non-breeding behaviour.

These data were imported in to SPSS for analysis with the original intention of carrying out a general linear model to find which factors determined recording success. However, despite using several transformations, the data did not meet the assumptions of this test and so non-parametric tests were used instead. A Wilcoxon signed rank test was used to find whether there was a significant relationship between recording success and body mass. The significance of the categorical factors was tested using a Kruskal-Wallis H test. Each factor was tested in turn and the significant factors were then combined, where possible, to produce a new factor that included all the possible combinations of its constituent factors. The significance of this new factor was then also tested with a Kruskal-Wallis H test and the explanatory power of each of the constituent factors was modelled using a multiple linear regression model.

6.5.2 Results The proportion of modelled species that were recorded in the SABAP grid squares varied between

0.34 and 0.97 (Table 6-5). There was a significant positive relationship between the log10 of this proportion and the log10 of the record number (n = 17, Z = -3.621, p < 0.001) (Figure 6-9).

Table 6-5: A comparison of recorded and modelled number of species in SABAP grid squares

Grid Recorded Modelled Proportion Grid Recorded Modelled Proportion square no of spp no of spp recorded square no of spp no of spp recorded 2632CC 329 407 0.81 2732AD 132 384 0.34 2632CD 371 381 0.97 2732BC 287 393 0.73 2632DC 228 390 0.58 2732CB 372 429 0.87 2632DD 277 395 0.70 2732DA 242 397 0.61 2732AA 281 407 0.69 2732CD 326 428 0.76 2732AB 247 405 0.61 2732DC 119 354 0.34 2732BA 188 392 0.48 2832AB 343 409 0.84 2732BB 207 390 0.53 2832BA 255 388 0.66 2732AC 239 408 0.59

Chapter 6: Mapping the distribution of Maputaland's bird species 119 Figure 6-9: The relationship between log10 record number and log10 transformed proportion of species recorded in SABAP grid squares

Birds with a distinctive appearance had a significantly higher mean recording success than birds with an indistinctive appearance (Table 6-6; Figure 6-10). In addition, birds with a distinctive song had a significantly higher mean recording success than birds with an indistinctive song (Table 6-6; Figure 6-11) and herbivores had a significantly higher mean recording success than carnivores (Table 6-6; Figure 6-12).

There were significant differences between mean recording success of birds associated with different habitat types (Table 6-6). Recording success was highest for birds associated with bushveld and open water but lowest for birds associated with reed beds and marshes (Figure 6-13).

Table 6-6: Results from the analysis of factors that determined recording success

Degrees of Factors Χ2 Sig. freedom Visual distinctiveness 1 10.6 0.001 Song distinctiveness 1 5.05 0.025 Diet 1 7.23 0.007 Habitat 6 19.92 0.003

The visual distinctiveness, song distinctiveness and diet factors were combined to produce a new factor that included the eight possible combinations of its three binary constituents. For example,

Chapter 6: Mapping the distribution of Maputaland's bird species 120 one category in this new factor could be “distinctive appearance/distinctive song/carnivore”. The habitat factor was excluded from this process, as combining this with the other three factors would have produced a new factor with 56 categories, some of which would have contained too few sample points for analysis.

There was also a significant difference between the mean recording successes of birds belonging to these new categories (df = 7, Χ2 = 20.73, p = 0.004). The effect of these constituent factors appeared to be cumulative, so herbivorous birds with a distinctive appearance and song had the highest mean recording success (Figure 6-14). The modelled contribution of these three factors to the mean recorded success of the eight new categories found that a distinctive appearance was more important than being a herbivore, which was in turn more important than having a distinctive song (Table 6-7).

Table 6-7: Modelled contribution of factors to recording success

Factor Modelled contribution

Distinctive appearance 0.095 Distinctive song 0.062 Herbivore 0.069 Constant 0.545

Figure 6-10: The mean proportion of recording success for birds with distinctive and indistinctive appearances

Chapter 6: Mapping the distribution of Maputaland's bird species 121 Figure 6-11: The mean proportion of recording success for birds with distinctive and indistinctive songs

Figure 6-12: The mean proportion of recording success for carnivorous and herbivorous birds

Chapter 6: Mapping the distribution of Maputaland's bird species 122 Figure 6-13: The mean proportion of recording success for birds associated with different habitat types

Figure 6-14: The mean proportion of recording success for the different combinations of colour, song and diet

Chapter 6: Mapping the distribution of Maputaland's bird species 123 6.5.3 Discussion These results have shown that that the species lists from nine of the seventeen SABAP squares were less than 70 % complete (assuming the complete accuracy of the modelled data) and that some species were under-recorded because of their physical characteristics or behaviour. There were large differences in recording success between the habitat types but the relationships were more complicated than was initially presumed. Recording success was high in habitat types with good visibility conditions, such as open water, grassland and woodland but it was also high in bushveld and forest. This was probably because birds can also be recognised by their song and species may be more likely to sing in thick vegetation where predation risks are lower. It is much easier to explain the low recording success of species in marsh and reed beds, as these are difficult to reach and have thick vegetation.

The significance of diet was interesting as this factor probably acted as a surrogate for abundance, with herbivores having higher densities because they feed at a lower trophic level. The importance of distinctive appearance and song was more expected because both these features increase the chances of seeing or hearing a species and it being correctly identified. However, despite the significance of all of these factors, it is important to note that the models did not explain much of the observed variation in recording success. For example, herbivorous birds with distinctive appearance and song had a mean recording success of 0.77, compared to 0.54 for those carnivorous birds with neither. This could be for several reasons, including the accuracy of the modelled distributions and the crude manner in which the factors and their categories were assigned. In addition, the analysis did not allow for the variation in the number of times a species was recorded in a grid square, as comparable information was not available from the modelled data.

Despite having low explanatory powers, these results have two important implications. Firstly, species associated with some habitat types, such as reed beds, have lower recording success and so will tend to be only recorded in well-sampled grid squares. This means that these habitat types will be generally under-represented in a reserve selection exercise and will increase the apparent importance of the well-sampled squares. This shows the advantages of basing such decisions on remotely sensed data. On a more practical note, these results show that recorded data on some bird species is likely to have higher than average accuracy levels. Obviously, excluding carnivorous or reed bed dwelling species will under-represent important elements of a region’s avifauna. However, restricting data to those species with a distinctive appearance and song may be less problematic and this is discussed in the following section.

Chapter 6: Mapping the distribution of Maputaland's bird species 124 6.6 A method for reducing the effects of sampling bias

The effect of sampling effort can have large effects on species richness data and caution is needed when using these data for conservation planning. This is illustrated by an example from the Brazilian Amazon, where six out of seven proposed plant endemism hotspots were shown to be located in plant collection centres (Nelson et al., 1990). This problem is not overcome by using a complementarity approach, as many of the commonly used algorithms give a high priority to areas that contain species with limited distributions (Csuti et al., 1997). This suggests that any conservation planning exercise based on biased data will generally select those areas that have been sampled the most. The obvious answer is to ensure that future sampling strategies avoid this bias, either by dividing areas into equal blocks and sampling equally or by sampling in proportion to the size of the areas of interest (Howard et al., 2000).

Nevertheless, there are two types of data set that cannot follow these strategies and these are likely to be increasingly used for conservation planning for reasons of cost and availability. The first type is collected by volunteers and the second type is from museum records and old record books (McCarthy, 1998). Both types can be augmented by targeted sampling to fill any gaps but this is usually limited because of funding constraints. Therefore, it is important to develop methods to overcome sampling bias and increase the value of these data.

The main methods that are used are based on extrapolating richness from species accumulation curves (Colwell & Coddington, 1994) or from neighbouring areas (Prendergast et al., 1993). However, the results from such extrapolation only revise species richness estimates for a particular area and cannot provide information on the distributions of individual species. This makes them unsuitable for any of the reserve selection algorithms that are seen as more suitable for conservation planning. However, the results from section 6.5 suggest a new method that could overcome these problems. These results showed that recording success was higher for birds with a distinctive appearance and/or song, which in turn suggests that these species would be less affected by sampling bias.

This section tests three hypotheses that would determine the efficacy of this approach. The first tests the assumption that distinctive species will be recorded first in a grid square, such that the proportion of distinctive species recorded in a SABAP square will decrease with increased sampling. The second determines whether the proportion of recorded species (compared to the number of modelled species) in the SABAP squares increases with sampling effort and whether this relationship changes when only using distinctive species. The third investigates whether the distinctive species show similar habitat associations to the patterns for all the bird species, as any differences would affect the suitability of using distinctive species as a surrogate.

Chapter 6: Mapping the distribution of Maputaland's bird species 125 6.6.1 Methods The methods consisted of three different parts that are described in the following sub-sections.

6.6.1.1 Finding whether the proportion of distinctive species recorded in a grid square changed with sampling effort The analysis described in section 6.5 produced a recorded species list for each of the 17 SABAP grid squares and had identified which of these species had a distinctive appearance and/or song. This information was used to calculate the proportion of recorded species that were distinctive for each SABAP square. The number of records for each grid square was then imported into SPSS, where the proportional data were manipulated using an arcsine transformation. Both factors were then transformed logarithmically to meet the assumptions of linear regression modelling. A regression model was then used to find whether the log10 number of records for a grid square determined the proportion of recorded species that had a distinctive appearance and/or song.

6.6.1.2 Finding whether the relationship between recording success and sampling effort was reduced when restricting the analysis to data on distinctive species The methods described in section 6.5 produced a recorded and modelled species list for each of the SABAP grid squares and also identified which of these species were distinctive (defined as either having a distinctive appearance and/or a distinctive song). Therefore, it was possible to calculate for each square the recording success (number of recorded species divided by the number of modelled species) for all the species and for only those distinctive species. These data were imported into SPSS and the ‘Curve estimation’ option was used to identify which of the ten mathematical models available best fitted the two sets of data. Other mathematical models are also used to produce these species accumulation curves but as there are no generally agreed methods, this analysis was limited to those available in SPSS.

6.6.1.3 Finding whether distinctive species had unrepresentative habitat associations The broad habitat associations of the bird species were derived from Maclean (1996), as described in section 6.5. Therefore, it was possible to count the number of bird species associated with each habitat type, both for all species and only for the distinctive species. The expected number of distinctive species associated with each habitat type was calculated by multiplying the proportion of species found in the habitat for all the bird species by the total number of distinctive species. These data were then analysed using a Χ2 test to find whether there was a significant difference between the observed and expected number of species associated with each habitat type.

Chapter 6: Mapping the distribution of Maputaland's bird species 126 6.6.2 Results 6.1.1.1 The effect of sampling effort on the proportion of distinctive species recorded

There was a significant negative relationship between the log10 record number and the log10 transformed proportion of distinctive species recorded in a SABAP grid square and the model had an adjusted R2 value of 0.506 (Table 6-8, Figure 6-15).

Table 6-8: Details of the relationship between record no. and proportion of distinctive species

% Regression Factors n t value Sig. Variance coefficient explained

Log10 prop. of distinctive species 17 -0.00105 25.422 0.000 50.6 Constant - 0.220 -4.169 0.001 -

Figure 6-15: The relationship between log10 record numbers and log10 proportion of recorded species with distinctive appearance and/or song

6.6.2.2 The relationship between recording success and sampling effort The best model fitted both for the distinctive species and for the complete data set was the logarithmic model (recording success = [regression coefficient * loge of record number] + constant). Both models were highly significant and explained nearly 90 % of the variance (Table 6-9). The models predicted that recording success was improved by only using data from Chapter 6: Mapping the distribution of Maputaland's bird species 127 distinctive species due to a consistently higher recording success rate (Table 6-10, Figure 6-16). For example, the distinctive species model predicted that 15 556 records were needed for a recording success of 95 %, whereas the all species model would need 21 078 to achieve the same level of success.

Table 6-9: Descriptions of the two recording success models

% Variance Model Coefficient Constant Sig explained Distinctive species 0.1360 -0.3627 < 0.001 89.1 All species 0.1386 -0.4299 < 0.001 88.3

Table 6-10: A comparison of predicted recording success for the two models

Recording success of the Recording success of the all Record number distinctive species model species model 2000 0.671 0.624 4000 0.765 0.720 8000 0.860 0.816 16000 0.954 0.912

Figure 6-16: A comparison of the two recording success models

Chapter 6: Mapping the distribution of Maputaland's bird species 128 6.6.2.3 Finding whether distinctive species had unrepresentative habitat associations There was no significant difference in the frequencies of the broad habitat associations of the distinctive species when compared with the frequencies for all of the species (Χ2 = 4.33, df = 6, n = 0.633). The ranking of these habitats according to the number of associated species was identical for the two groups (Table 6-11).

Table 6-11: A comparison of the habitat associations for species used in the two models

Reed Bushveld Forest Grassland Marsh Water Woodland beds Distinct. spp 25 44 37 8 12 61 67 All spp407069141890137 Distinct prop. 0.091 0.160 0.158 0.032 0.041 0.205 0.313 All prop. 0.098 0.173 0.146 0.031 0.047 0.240 0.264

6.6.3 Discussion These results show that using the distinctive species as a surrogate for all the bird species would reduce the effects of sampling bias in the Maputaland SABAP data set. They also show the habitat associations of these distinctive birds are not significantly different from those of all the species. This suggests that this method could be widely applied, especially as it does not require any complicated mathematical manipulation of the data. Problems may occur when deciding objective criteria for judging whether a species is distinctive or not but this was relatively simple for Maputaland’s birds and could probably be repeated for other groups. It is also important that the surrogate group contains many species, so it may not be suitable for some taxa.

6.7 Chapter summary

• The SABAP data set for Maputaland contained almost 105 000 records and described the distribution of 452 species. Species density in the SABAP quarter degree grid squares was determined by CSIR land-cover type density and record density. This meant that the recorded distributions of Maputaland’s bird species were affected by sampling bias and could not be used as a basis for modelling their distributions at a finer scale.

• A land-cover type/bird species association matrix was used to model the distribution of Maputaland’s bird species at a 1 km resolution. The modelled number of species associated with each land-cover type varied between 21 for commercial agriculture and 181 for riverine forest. The matrix predicted that 441 bird species were present in the region and the mean total habitat for these species was 1910.3 km2.

Chapter 6: Mapping the distribution of Maputaland's bird species 129 • By comparing the recorded and modelled distribution of bird species in the SABAP grid squares, it was possible to identify characteristics that determined the recorded accuracy of species’ distributions. It was found that herbivorous species with a distinctive appearance and song were recorded with greatest accuracy. This recording success also depended on the broad habitat type with which a species was associated.

• Birds with a distinctive appearance and/or song were less affected by sampling bias in the SABAP data set when compared to other species. Despite this difference, their habitat associations were not significantly different from those of the whole group. Therefore, it is suggested that distinctive species should be used as a surrogate for all species when using data sets affected by sampling bias.

The 1 km resolution bird distribution data set described above, together with the land-cover and transformation risk data described in previous chapters, provide the information needed to assess the efficacy of the present PAs in Maputaland. This is discussed in the following chapter, together with analyses that use the concept of complementarity to develop a more efficient PA system.

Chapter 6: Mapping the distribution of Maputaland's bird species 130 Chapter 7: A complementarity analysis of Maputaland

7.1 Introduction

The conservation importance of Maputaland has been recognised for more than a century (Bruton et al., 1980) and this has recently been confirmed by several global priority setting initiatives (Van Wyk, 1994; Stattersfield et al., 1998 Olson & Dinerstein, 1998). This has led to the creation of 17 PAs in the area (Figure 2-8), which were generally established to ensure that the biodiversity of the region was adequately protected (Tinley & van Riet, 1981). However, lack of data restricted the efficacy of this conservation strategy because the biodiversity of the region had not been mapped. This project aimed to address these deficiencies and chapters four and six describe the methods that were used to produce land-cover and bird distribution coverages for Maputaland. Therefore, this chapter uses the available data to identify priority conservation sites based on the concept of complementarity (Vane-Wright et al., 1991), so that each biodiversity element is adequately represented in the PA system.

This planning exercise is particularly pertinent because Maputaland is the focus of several important conservation projects and land-use issues. The region is part of a proposed TFCA and the focus of a Spatial Development Initiative (SDI) that aims to increase eco-tourism and commercial agriculture. In addition, many of the PAs in Maputaland are the subject of land-claims from communities that were removed from the land during the apartheid era. Therefore, one aim of this chapter is to provide information that can be used by the NCS to determine their land-use policies in the region. In addition, the Maputaland data set will be used to investigate whether methods of area selection that were generally developed for use with coarse-scale data are relevant when applied at the much finer scales needed for this type of analysis.

Section 7.2 describes the PAs of Maputaland and the protection they give to the land-cover types and bird species of the region. Section 7.3 assesses the value of using hotspots to identify priority sites. Sections 7.4 and 7.5 use gap analysis techniques to identify priority sites that would increase the protection given to biodiversity elements under-protected by the existing PA system and also test the relative merits of three different biodiversity surrogates. Section 7.6 uses the same three surrogates to identify the near-minimum number of grid squares needed to achieve different conservation targets. The final analysis in section 7.7 uses the same near-minimum set strategy to identify the conservation value of the different areas that have been identified in land-claims and the chapter is summarised in section 7.8.

Chapter 7: A complementarity analysis of Maputaland 131 7.2 The PA status of Maputaland’s bird species and vegetation types

The first PA in Maputaland was created in 1897. However, the criteria used to establish new PAs have changed dramatically in the last 100 years. The first PAs were chosen to protect the region’s over-hunted large mammal species but many aspects of biodiversity remained unprotected (Mountain, 1990). Thus a second wave of PA creation included these unprotected habitat types, such as woody grassland and Lebombo forest (Tinley & van Riet, 1981). In addition, PAs have been established to protect marine resources and recreational and subsistence fishing sites (Kyle, 1997a). These different policies have produced a PA network that covers a large and probably unrepresentative proportion of Maputaland. This section describes how well these PAs protect the region’s biodiversity.

7.2.1 Methods The area of each land-cover type and each bird species habitat in each 1 km x 1km grid square was determined in chapters four and six. Therefore, this resolution was also used for determining the PA levels for each of these biodiversity elements. A grid square was assumed to have PA status if more than 25 % of its area fell within a PA. This was determined by using the clip function in ArcView’s “Geoprocessing wizard” to cut the PA vector coverage into a series of pieces based on the boundaries of the grid squares. The “X-Tools” extension was then used to calculate the area of each these PA fragments and to identify which grid squares had more than 25 % of their area covered by a PA. The boundaries of Maputaland were also re-defined to have the same resolution as the 1 km grid. It was assumed that any square with at least 10 % of its area within Maputaland would be considered as part of the study area used for the analysis.

Once the PA grid squares had been identified, it was then possible to use MS Excel to calculate the area of each land-cover type and bird species habitat found within these squares and so calculate the proportion of each with PA status. Linear regression tests were used both for the land-cover and bird habitat data to find whether there was a relationship between the log10 land-cover or log10 habitat area and the arcsine transformed proportion with PA status.

7.2.2 Results Reducing the scale of the analysis from the vector coverages (digitised from 1:10 000 orthophotos) to the 1 km resolution changed the area of Maputaland from 9372 to 9549 km2. This 1 km resolution Maputaland boundary contained 77 km2 of sea so the land area in the 1 km resolution coverage was 9472 km2. The total area of land and freshwater in the 1km grid squares falling within PAs was 2669 km2. Hence, the percentage of Maputaland with PA status was 28.2 % based on the grid squares, compared with 25.4 % when calculated using the original vector coverages.

Chapter 7: A complementarity analysis of Maputaland 132 The proportion of the natural land-cover types with PA status ranged between 11.4 % for Lebombo aquatic and 100 % for mangroves, dune thicket and beach, with a mean of 54.0 % (Table 7-1;

Figure 7-1). There was a significant negative relationship between the log10 of a land-cover type’s area and the proportion of this area with PA status (n = 29, t = -2.291, p = 0.030, adj. R2 = 0.132) (Figure 7-2).

The mean percentage of the bird species’ habitats with PA status ranged between 4.5 % for the rock martin (Hirundo fuligula) and 100 % for the sanderling (Calidris alba), with a mean of 41.3 %. A histogram of these results showed that the PA status of these habitats was quite variable, although most species had between 20 and 40 % of their habitat protected (Figure 7-3). There was a significant negative relationship between the log10 of a bird’s habitat and the proportion of this area with PA status (n = 438, t = -16.71, p < 0.001, adj. R2 = 0.393) (Figure 7-4).

Table 7-1: PA status of the natural land-cover types of Maputaland

% in % in Land cover type Land cover type PAs PAs Lebombo aquatic 11.4 Sedge & grass swamp 55.6 Rock face 17.9 Hygrophilous grasslands 55.1 Lebombo grassland 12.5 Woody grassland 32.8 Lebombo woodland 13.7 Terminalia woodland 20.6 Lebombo thicket 11.9 Woodland on red sands 77.7 Lebombo forest 59.2 Sand thicket 34.7 Sand forest 49.3 Acacia tortilis woodland 53.2 Inland evergreen forest 69.3 Acacia nigrescens woodland 40.3 Swamp forest 77.4 Acacia grandicornuta bushland 71.3 Mangroves 100.0 Acacia luederitzii thicket 20.0 Beach 100.0 Floodplain grassland 41.4 Dune thicket 100.0 Reed beds 61.7 Dune forest 99.8 Riverine thicket 35.1 Riverine forest 82.0 Open water 90.7 Mud flats 70.2

Chapter 7: A complementarity analysis of Maputaland 133 Figure 7-1: The PA status of Maputaland’s natural land-cover types

Figure 7-2: The relationship between land-cover type area and PA status

Chapter 7: A complementarity analysis of Maputaland 134 Figure 7-3: The PA status of the habitats of Maputaland’s bird species

Figure 7-4: The relationship between bird habitat area and PA status

7.2.3 Discussion More than 25 % of Maputaland had PA status, with a mean percentage protection for natural land- cover types of 54 % and a mean percentage of 41 % for bird habitats. These mean values are generally inflated by the relationship between percentage under protection and total area, which was significant and negative for both vegetation types and bird species. This relationship probably

Chapter 7: A complementarity analysis of Maputaland 135 arose because it is generally recognised that biodiversity elements with a limited distribution are more susceptible to extirpation, assuming threat levels are equal. In addition, these elements can be given high levels of protection without the need for large and expensive PAs. These results also show that there is a large range of protection given to different vegetation types and bird species and so the following four sections describe the results of using several different strategies to produce a more balanced PA network.

7.3 Identifying biodiversity hotspots in Maputaland

The term “hotspot” is applied to a geographical area that ranks particularly highly on one or more axes of species richness, levels of endemism, numbers of rare or threatened species and intensity of threat (Reid, 1998). The concept attracted interest because of the suggestion that protecting species richness hotspots for one indicator group might also provide protection for many other biodiversity elements, as well as rare or threatened species belonging to the indicator group (Prendergast et al. 1993). However, these relationships were weak when comparing intra-group hotspots for species richness and endemism (Williams et al., 1996), inter-group species richness (Prendergast et al. 1993) and inter-group threatened species richness (Troumbis & Dimitrakopoulos, 1998). These results were probably scale dependent, as work from Australia found that a higher proportion of threatened bird species were protected in 100 km x 100 km species richness hotspots than by the 10 x 10 km hotspots used in Britain (Curnutt et al., 1994).

The Maputaland bird habitat data set gives an opportunity to test the relevance of the hotspot approach using an even finer resolution of 1 x 1 km. It also allows a comparison between using presence/absence and continuous data because the area of each bird habitat in each grid square was known. This is particularly important because previous attempts to identify hotspots have only used binary data, which may only identify areas that contain small patches of many different habitat types. There are two ways that the habitat area data could be used to identify hotspots. The first would involve summing the habitat areas of all the birds found in a grid square but this would produce a bias towards species with a large range in Maputaland. The alternative is to calculate the proportion of total habitat area occurring in a grid square for each bird species and to sum these results for all the species present. However, this produces a bias towards species with small ranges but these hotspots would have more conservation relevance, as they would protect a larger percentage of the habitat of their associated species. Hence, this method is used in this section.

7.3.1 Methods The Excel spreadsheet containing the habitat area for each bird species in each grid square was used to identity species richness, endemism and threat hotspots. Three new spreadsheets were created, one containing all the data, one containing data from species that are endemic to South or Southern Africa (Table 7-2) and one containing species that were listed as endangered or

Chapter 7: A complementarity analysis of Maputaland 136 vulnerable in the South African Red Data book (Table 7-3). The number of species found in each grid square with a habitat area greater than zero was found using the COUNTIF option in Excel. Excel was also used to calculate for each species in each square the proportion of the total habitat in Maputaland that the grid square contained. The total proportional richness was then found by summing all the proportional values in each row for the relevant species. Each grid square was then ranked according to its species richness and proportional richness values.

The total, endemic and threatened species richness and proportional richness data for each grid square were then imported into ArcView and used to produce six raster coverages. ArcView was also used to identify those grid squares that ranked as having one of the 300 highest species or proportional richness values. Three hundred was chosen as the standard number of grid squares to be chosen for all of the relevant analyses in chapters seven and eight, as it was seen as a large enough number to identify patterns and similarities between different methods but small enough to be a feasible target for conservation policy in Maputaland.

Many of the squares shared similar species richness results, so it proved impossible to choose the 300 squares with the highest values without excluding other squares that had the same rank. Therefore, all those squares that had a rank value of less than 301 were included in the hotspot analysis, even though this selected more than 300 squares. ArcView was then used to determine how many of these hotspot squares had PA status and were identified as hotspots using more than one criterion. Chi-squared tests were used to test whether coincidence between hotspots chosen using different methods were significantly different from what would be expected from random.

Table 7-2: Endemic bird species found in Maputaland (Barnes, 1998)

Endemic to South Africa Endemic to southern Africa Roberts Roberts Common name Common name No No 92 Bald ibis 602 Whitethroated robin 122 Cape vulture 616 Brown robin 152 Jackal buzzard 649 Rudd's apalis 370 Knysna lourie 700 Cape batis 581 Cape rock thrush 727 Orangethroated longclaw 598 Chorister robin 736 Southern boubou 698 Fiscal flycatcher 782 Neergaard's sunbird 742 Southern tchagra 838 Pinkthroated twinspot 796 Cape white-eye 850 Swee waxbill

Chapter 7: A complementarity analysis of Maputaland 137 Table 7-3: Endangered and vulnerable bird species found in Maputaland (Barnes, 2000)

Roberts Roberts Common name Status Common name Status No No 88 Saddlebilled stork Endangered 146 Bateleur Vulnerable 578 Spotted thrush Endangered 165 African marsh harrier Vulnerable 50 Pinkbacked pelican Vulnerable 211 Corncrake Vulnerable 77 Whiteb’d night heron Vulnerable 229 African finfoot Vulnerable 92 Bald ibis Vulnerable 231 Stanley's bustard Vulnerable 122 Cape vulture Vulnerable 322 Caspian tern Vulnerable 123 Whitebacked vulture Vulnerable 351 Delegorgue's pigeon Vulnerable 124 Lappetfaced vulture Vulnerable 403 Pel's fishing owl Vulnerable 125 Whiteheaded vulture Vulnerable 407 Natal nightjar Vulnerable 132 Tawny eagle Vulnerable 434 Mangrove kingfisher Vulnerable 140 Martial eagle Vulnerable 463 Ground hornbill Vulnerable 144 S. banded snake eagle Vulnerable 724 Shorttailed pipit Vulnerable

7.3.2 Results Areas with high species richness were widely distributed throughout Maputaland (Figure 7-5) but tended to be near rivers, lakes and wetlands (Figure 2-2). High species proportional richness was much more restricted (Figure 7-6) and was concentrated in the fig forest area of Mkhuze GR and the Muzi swamps, an area to the north of Lake St Lucia. There were similar trends with the endemic species and threatened species, with the proportional data showing a much more distinct cluster of hotspots. Most of the areas with high endemic proportional values were found in Hlatikhulu FR and the areas with high threatened proportional values were in Mkhuze GR (Figure 7-8; Figure 7-10; Figure 2-8).

The number of 1 km2 hotspots identified for species richness, endemic species richness and threatened species was 301, 578 and 400 respectively. In contrast, it was always possible to identify the preferred number of 300 for the three proportional hotspots. The percentage of these hotspots that fell within PAs varied between 12.3 % for endemic species and 76.3 % for species proportional richness (Table 7-4). There was only one non-significant result when using paired comparisons to find whether different methods identified the same hotspots. More than half (57 %) of the species richness hotspots were also threatened species hotspots and all of the proportional methods shared more hotspots than would be expected from a random distribution (Table 7-5).

Chapter 7: A complementarity analysis of Maputaland 138 Species richness Species proportions 377 0.364

20 km 20 km

21 0

Figure 7-5: Bird species richness Figure 7-6: Species proportional richness

Species richness Species proportions 15 0.135

20 km 20 km

0 0

Figure 7-7: Endemic bird species richness Figure 7-8: Endemic species proportional richness

Chapter 7: A complementarity analysis of Maputaland 139 Species richness Species proportions 19 0.079

20 km 20 km

0 0

Figure 7-9: Threatened bird species richness Figure 7-10: Threatened species proportional richness

There was also a significant number of shared hotspots when comparing endemic species richness with endemic proportional richness (Table 7-6). Perhaps more importantly, there were fewer shared hotspots than expected when comparing: species richness with endemic species richness; endemic species richness with threatened species richness; and species richness with species proportional richness (Table 7-5; Table 7-6). The number of these hotspots found in PAs varied between 12.3 % for endemic species richness and 76.3 % for species proportional richness (Table 7-4; Figure 7-11; Figure 7-12).

Table 7-4: Details of hotspots in Maputaland

Proportion Category Richness % in PAs % in PAs richness All species 301 56.8 300 76.3 Endemic species 578 12.3 300 39.0 Threatened species 400 66.0 300 72.7

Chapter 7: A complementarity analysis of Maputaland 140 #S#S#S #S#S #S #S#S#S#S #S#S #S #S #S #S#S #S#S #S#S #S#S#S #S #S#S#S#S #S #S#S#S#S#S $T#S$T $T #S #S#S#S#S #S$T #S $T$T $T$T #S #S#S#S#S #S$T #S#S $T$T$T$T $T#S#S#S #S#S #S#S#S #S$T #S $T$T #S #S #S#S #S #S#S#S $T $T$T #S #S #S $T #S#S #S #S #S #S #S $T#S#S$T #S $T #S #S#S #S#S $T #S #S#S #S #S #S #S #S#S #S #S #S#S #S #S #S #S#S #S #S #S #S #S#S #S #S #S #S #S #S #S#S $T#S #S #S#S#S #S #S #S #S#S #S #S #S#S #S #S #S #S #S#S#S $T$T #S #S#S #S $T #S#S $T #S #S #S#S #S $T #S#S #S#S#S #S #S #S#S$T $T #S #S #S#S#S #S #S #S #S#S#S #S #S #S #S#S#S #S $T #S$T #S#S#S #S#S #S #S #S #S #S#S #S #S #S $T #S#S #S $T #S#S $T $T #S#S #S#S #S $T $T#S #S#S#S#S$T #S$T #S#S #S#S#S #S #S$T #S#S#S #S #S#S#S#S#S#S #S#S #S#S$T #S#S #S #S #S #S#S#S $T #S#S#S $T#S #S#S#S #S #S #S#S#S#S $T#S #S#S#S#S #S #S #S $T$T #S#S#S#S #S#S#S #S #S #S#S#S#S#S#S #S #S #S #S#S $T#S #S#S#S#S #S #S #S #S #S#S#S $T #S#S#S#S#S #S #S #S #S #S#S$T #S #S #S #S#S#S #S #S #S#S#S#S#S#S #S#S #S #S #S#S#S #S#S #S #S #S#S#S #S#S #S #S#S #S#S#S#S#S #S#S #S #S #S #S$T$T #S #S#S#S#S#S#S#S#S $T$T $T #S#S#S #S#S#S#S #S #S $T#S$T #S#S #S#S#S#S#S#S#S $T$T $T #S#S #S#S #S#S#S $T #S #S#S#S#S#S#S#S#S $T $T$T $T#S #S #S#S#S#S#S $T$T$T$T #S #S#S#S#S $T$T #S #S#S#S#S#S#S $T$T #S #S #S#S#S#S#S $T $T#S #S #S#S #S#S#S #S #S $T $T $T$T $T$T $T#S #S #S#S#S#S#S#S#S $T $T $T#S#S$T #S #S#S #S#S#S#S#S $T #S#S$T#S #S#S#S #S#S#S#S #S $T #S#S #S#S#S#S#S $T $T #S #S #S#S#S#S #S$T #S #S#S#S#S #S #S #S#S#S $T #S#S #S#S #S#S #S $T$T $T$T$T$T #S#S#S#S #S#S#S #S$T $T$T #S#S #S#S#S #S#S #S#S #S #S $T#S #S#S#S#S#S#S#S #S #S#S#S#S#S#S#S #S #S #S #S #S $T #S#S#S#S #S $T #S#S #S #S #S #S#S#S #S#S #S #S#S#S #S #S#S#S $T$T #S #S#S#S#S #S$T$T #S #S#S#S#S $T #S#S#S#S#S #S #S #S#S#S#S#S#S #S $T $T$T #S #S#S#S#S#S #S#S #S#S #S#S#S#S#S #S#S#S #S #S#S#S #S #S $T #S #S#S #S #S #S#S #S #S #S#S #S#S $T #S #S #S #S#S #S#S #S #S #S#S#S#S #S #S#S#S#S#S #S #S$T #S #S $T #S #S$T#S #S #S#S#S#S#S#S#S#S $T #S#S#S#S #S #S$T #S#S #S#S#S#S#S#S#S#S#S$T#S #S$T$T #S$T $T #S#S#S#S #S#S#S #S#S $T $T #S#S#S #S#S#S#S#S#S $T #S#S#S #S#S#S#S#S#S $T $T$T #S#S#S #S #S#S#S#S#S#S#S $T$T #S #S#S#S #S#S #S #S#S#S#S#S#S $T$T #S #S#S#S #S $T #S#S#S#S#S#S $T $T #S$T#S #S#S #S#S #S$T$T #S#S#S#S $T #S $T#S#S#S#S#S $T #S#S #S #S#S#S#S#S#S #S#S#S $T #S $T #S #S#S#S#S#S#S $T$T$T#S $T$T #S$T #S #S #S#S#S#S#S#S#S $T$T$T #S #S #S #S#S#S#S#S#S $T#S #S#S#S#S #S #S#S#S#S#S#S #S#S #S#S #S#S#S #S#S#S#S#S#S #S $T #S #S#S#S #S#S#S#S#S#S#S #S #S $T$T$T #S #S #S#S#S#S#S#S #S #S#S#S #S#S#S#S#S #S#S#S #S#S#S #S#S #S#S#S#S #S #S #S#S#S#S#S #S #S#S#S#S#S#S#S#S #S#S#S#S#S#S #S #S#S #S#S#S #S #S #S #S#S #S #S Hotspots

All spp Endemic spp

#S$T #S#S #S$T#S #S#S Threatened spp #S#S #S All & threatened #S #S#S#S #S #S$T#S $T#S #S $T #S #S $T$T#S #S#S $T #S $T #S 20 km #S PA #S #S #S#S #S#S #S#S

#S #S #S #S #S#S #S #S

#S#S #S #S

Figure 7-11: Bird species hotspots in Maputaland

Chapter 7: A complementarity analysis of Maputaland 141 $T #S $T#S $T#S $T$T$T$T $T $T#S #S#S#S $T$T$T$T$T #S #S #S #S #S $T$T#S #S #S$T #S#S #S $T #S $T$T #S#S #S #S$T #S#S #S#S #S #S #S $T#S #S #S #S #S#S

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$T#S#S #S$T$T#S #S#S#S

Figure 7-12: Bird species proportional hotspots

Chapter 7: A complementarity analysis of Maputaland 142 Table 7-5: Coincidence within richness and proportional richness hotspots

% in Proportion % in Categories Richness Sig Sig PAs richness PAs All and endemic 0 - <0.001 16 81.3 0.032 All and threatened 172 55.8 <0.001 68 98.5 < 0.001 Endemic and threatened 0 - <0.001 93 52.7 < 0.001

Table 7-6: Coincidence between richness and proportional richness hotspots

Categories Coincidence Sig

All spp richness and all proportional richness 0 0.002 All endemic richness and endemic proportional richness 106 < 0.001 All threatened richness and threatened proportional richness 13 ns

7.3.3 Discussion The results described above are a fairly basic analysis of the efficacy of using hotspots to identify conservation priorities. The data set could be used to measure how well the region’s bird species would be protected by using a hotspot approach and which of the methods of identifying hotspots was the most efficient. Despite this, it was felt that there was little reason for such a rigorous analysis because the use of hotspots for conservation planning has been largely discredited in the literature (Prendergast et al., 1993; Hacker et al, 1998; Harcourt, 2000). The more important aspect of this analysis is the comparison achieved between using species richness and species proportional richness.

The use of proportional richness has its disadvantages because it reduces the influence of wide- ranging species. This is particularly important for large raptors, many of which have threatened status (Table 7-3). However, using proportional data is more relevant to conservation planning and these results show that richness hotspots may be a very poor surrogate for proportional hotspots. The exception to this was when using data for endemic species, which was probably because both sets of hotspots were generally restricted to the Lebombo Mountains (Figure 7-11; Figure 7-12). In contrast, the results for threatened species were no different from random and the results for all the species were actually significantly worse than random. It is intuitive that species richness hotspots will generally contain a range of habitat types but these results suggest that many of these will contain small, fragmented patches of these habitats with little conservation value.

Chapter 7: A complementarity analysis of Maputaland 143 7.4 A gap analysis of Maputaland

It is generally agreed that the most effective land-use for most of Maputaland is eco-tourism and sustainable harvesting because the agricultural value of the region is so poor (Bruton, 1980b). The NCS support this strategy and would like to focus some of their future resources on protecting biodiversity elements that are under-represented in the present PA system. Any new conservation programmes would almost certainly not include the creation of any new formal reserves because these would be costly and unpopular. Instead, the NCS would aim to help establish projects that would create revenue for the communities involved whilst preserving biodiversity. These PAs would differ from formal reserves in that the land would not be identified by the state as being for biodiversity conservation. Therefore, this section describes a gap analysis of Maputaland that identifies the under-represented biodiversity elements and determines which new PAs best address this imbalance (Scott et al., 1993).

The first stage in this process was to identify surrogates for biodiversity (Margules & Pressey, 2000) and land-cover types, bird species and distinctive bird species were already chosen for this study (chapters four and six). The use of distinctive birds followed on from results from chapter six that found that this group was less affected by sampling bias in data sets based on field observations. Therefore, it was felt important to test whether this surrogate group produced similar results to those produced using data on all the region’s bird species. The next stage was to define a specific conservation target and it was decided to identify the 300 1 km x 1km grid squares that had the highest ranking in the gap analysis. Three hundred was an arbitrary number used to identify groups of high ranking areas and allowing for the likelihood that some areas may be unsuitable for political or socio-economic reasons.

The methods used to identify conservation value then had to be decided, based on the data available. Most species data used in conservation planning is in the form of species lists for each of the units used in the analysis. The Maputaland data sets described the area of each biodiversity element in each grid square. Hence, the first decision was whether to set conservation goals based on maximising the area or on the percentage of each biodiversity element with conservation status. For the reasons described in section 7.3, it was decided to maximise the percentage of each biodiversity element, a policy that is widely used in conservation planning (IUCN, 1992).

Many different approaches have been described in the literature that identify optimal or near- optimal solutions to the problem of maximising biodiversity protection given a set limit on the area of land available. The only way to guarantee producing an optimal solution is to use linear programming techniques and several authors have proposed these methods and suggested suitable models (Underhill, 1994; Church et al., 1996). Despite this efficiency, most authors have used a rule-based iterative approach to identify priority areas, as is illustrated by an algorithm used in the WORLDMAP software (Williams, 1998). This “near-maximum-coverage” set algorithm involves Chapter 7: A complementarity analysis of Maputaland 144 six major steps that select areas based on their complementarity value for a set of species. The algorithm uses presence/absence data and first identifies those areas that contain species that are found nowhere else. It then uses a subset of the rarest species, based on range, to identify squares with the highest complementarity value and adds these to the selection. This process is then repeated using decreasingly rare species until the conservation target is met and the final step removes any redundant areas.

This is only one of many heuristic reserve selection algorithms that have been developed in the previous ten years (Bedward et al., 1992; Lombard et al., 1995; Williams, 1998), although rarity based methods have generally been shown to be the most effective (Csuti et al., 1997). The results of these algorithms are generally less efficient than those obtained using linear programming methods but they do have advantages. This is because these algorithms are generally much faster, allowing conservation planners to test different scenarios and identify areas that were not classed as priority sites but should be included if the preferred sites are not available for conservation. In addition, PA selection involves a range of factors that are not related to biodiversity and so the small increase in efficiency produced by using linear programming tends to have a negligible effect on the final results (Pressey et al., 1996).

For these reasons, it was decided to use a heuristic algorithm to analyse the Maputaland land-cover and bird data sets but suitable software was not available in the public domain. Hence, a computer program was developed that would select priority sites based on the following three criteria:

• Squares containing a large number of biodiversity elements would have higher scores than squares containing a small number of biodiversity elements. • Squares containing elements that were under-represented in the PA network would have higher scores than squares containing over-represented elements. • Squares containing a large proportion of an element’s range would have a higher score than squares containing a small proportion.

The conservation value of a grid square for a biodiversity element would then be calculated using the following equation:

Conservation Area of element in grid square * Area of element unprotected in Maputaland value Area of element in Maputaland Area of element in Maputaland

The total conservation value for a square would be calculated by summing all the values for each associated biodiversity element. The algorithm would then be used to identify the highest ranking grid square and to give it PA status. This would remove the chosen grid square from further analysis and change the unprotected area of each associated biodiversity element. The iterative process would then recalculate conservation scores and identify the highest ranking square and Chapter 7: A complementarity analysis of Maputaland 145 repeat this process until the required number of grid squares were identified. This section describes the software that was used in more detail and describes the 300 grid squares it identified using the three biodiversity surrogates described above.

7.4.1 Methods The software used to identify the important grid squares was written by Dr Michael Fischer using the Java programming language. It used two input tables called the “Grid table” and the “Element details table”. Rows in the “Grid table” represented each grid square and columns represented each biodiversity element (either a land-cover type or bird species). A cell contained proportional data for the corresponding element in the corresponding grid square, where the proportion was calculated as the area of land-cover type or bird species habitat found in the grid square divided by its total area found in Maputaland. The “Element details table” listed the elements to be used in the analysis, together with data on the total area of that element in Maputaland and the unprotected area. Some of the bird species were associated with transformed land-cover types and so it was decided to assume that these types had PA status by default.

The program used an iterative approach and the various steps are described below:

1) Calculate the unprotected proportional area for each element by using data in the “Element details table” to divide the unprotected area by the total area. 2) Multiply the proportional data in each cell by its corresponding unprotected proportional area. 3) Sum the resultant data for each grid square that does not have PA status. 4) Rank the grid squares according to their proportional data score and identify the square with the highest rank. 5) Reclass the highest ranking square as having PA status. 6) Multiply the proportional data for each species found in the highest ranking square by the total area data to calculate the area of each element found in the selected square. 7) Subtract the area of each element found in the selected grid square from the unprotected area data stored in the “Element details table”. 8) Return to stage 1.

The proportional data had already been calculated for section 7.3 and the area of each biodiversity element in Maputaland and in the PAs was calculated for section 7.2. Therefore, these data were exported from MS Excel into the comma-delimited text files that the software required. Three different analyses were carried out using different surrogates for biodiversity. The first used the untransformed land-cover types, including open water and mud flats. The second used all the bird species and the third only used bird species that had been identified as distinctive in the previous chapter. The software was used to carry out 300 iterations for each analysis to identify the 300 grid

Chapter 7: A complementarity analysis of Maputaland 146 squares that would most increase the PA status of the biodiversity elements that were under- represented in the Maputaland PA system.

7.4.2 Results Most of the priority sites identified using all three surrogates were found in the Lebombo Mountains (Figure 7-13). Land-cover priority sites were also found along the Pongolo River and in sand thicket, whereas bird and distinctive bird priority sites were generally found in the Muzi swamps and around Sileza NR. There was significant overlap when using the three surrogates and this ranged between 34 % for land-cover and birds and 81 % for birds and distinctive birds (Table 7-7). Eighty-eight of these sites were identified by all three of the surrogates.

Table 7-7: Results from the gap analysis

Category Frequency Sig

Coincidence between land-cover & birds 102 p < 0.001 Coincidence between land-cover & distinctive birds 107 p < 0.001 Coincidence between birds & distinctive birds 243 p < 0.001

Coincidence between land-cover, birds & distinctive birds 88 p < 0.001

7.4.3 Discussion The results described above should be used only as an indicator of future conservation priorities, as there were several limitations with the methods used. Firstly, the software was only recently developed for this research and the algorithms have not been subjected to peer review. It is possible that other, more efficient algorithms will be developed in the future (Ferrier et al., 2000). In addition, the software also did not include rules for favouring the selection of adjoining grid squares, which is an important element in conservation planning (Lombard et al., 1997). It is expected that this feature will be included in subsequent versions of the software but it was felt that even without this feature it was possible to use the results to identify clusters of priority sites.

The results also showed that there was a relatively low coincidence between sites chosen using land-cover and birds as a biodiversity surrogate. This was probably because of differences in the number of bird species associated with each land-cover type and illustrates the importance of using a surrogate group whose constituent elements are independently defined. There was a much higher overlap between sites chosen using birds and distinctive birds as a surrogate. This was to be expected, given that one was a sub-group of the other but it does suggest that distinctive birds could act as an effective surrogate when the distribution data are affected by sampling bias.

Chapter 7: A complementarity analysis of Maputaland 147 $T ÊÚ$T$T$T $T #S #S $T#S$T $TÊÚÊÚ$T$T $T$T$T$T$T $T$T$T $T ÊÚ$T $T$T$T$T #S $T $TÊÚ#S $T ÊÚÊÚ #S #S#S#S#SÊÚ $T$T$T$T $T#S#S$T #S $T $T $T #S #S #S#SÊÚ$T#S $TÊÚ #S#S #S ÊÚ $T$T$T$T#S #S #S $T#S$T$T #S #S$T $T #S #S $T #S$T$T$T $T $T #S $TÊÚ$T #S$T $T #S #S #S ÊÚ $T #S #S #S ÊÚ#S #S$T #S #S #S#S #S$T$T#S #S #S#S $T $T$T$T$T $T $T ÊÚ$T #S $T $T #S ÊÚ$T #S$T $T$T $T$T $T ÊÚ $T $T #SÊÚ$T #S #S #S $T$T#S #S#S #S #SÊÚ$T $T #S#S $T #S #S #S $T#S #S#S #S #SÊÚ#S$T $T #S #S #S $T $T #S #S #S $T$T$T #S $T$T $T$T #S#S #S $T $T #S #S #S $T$T$T ÊÚ #S #S $T #S$TÊÚÊÚ $T #S ÊÚÊÚ#S #S #S ÊÚÊÚ$TÊÚ $T ÊÚÊÚ$T$T #S #S$T #S $T #S$T $T$T $TÊÚÊÚ #S #S#S $T$T #S #S $T#S #S#S #S#S$T#S$T #S #S $T $T ÊÚ #S #S $T #S #S$TÊÚÊÚ$T #S #S #S ÊÚÊÚ#S #S #S ÊÚÊÚ#S #S #S #S ÊÚ #S#S$T$T #S#S$T #S #S ÊÚÊÚ #S $T ÊÚÊÚ #S ÊÚÊÚ #S ÊÚÊÚÊÚ #S #S $TÊÚ #S $T #S #S #S ÊÚÊÚ ÊÚ$T$TÊÚ #S ÊÚÊÚÊÚÊÚ $T#S #S$T $T #S #S #S #S $T #S

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#S #S Land-cover & distinctive #S 20 km All & distinctive birds Land-cover, all & distinctive birds

PA

Figure 7-13: Gap analysis priority sites using three different surrogates for biodiversity

Chapter 7: A complementarity analysis of Maputaland 148 Most of the priority sites chosen using all three surrogates were found in the Lebombo Mountains, which illustrates two problems with this method. The first is that aiming to protect a certain proportion of each biodiversity element based on present distributions inevitably under-represents those elements that have been affected by previous habitat transformation. For example, much of the natural vegetation found in the Cretaceous and alluvial ecological zones have been cleared for agriculture and so most of the remaining areas are found in PAs. One solution to this problem would be to model past vegetation but this is often inaccurate and difficult to verify (Eeley et al., 1999). Another approach is to set targets based on protecting equal percentages of the different ecological zones. However, this method was not used here because it was felt that it would over- represent those remaining areas in the highly transformed zones that probably had relatively low agricultural value.

The second related problem was that results described in chapter five showed that most of the vegetation types in the Lebombo Mountains have little risk of transformation and so may not be seen as a priority. This is a reflection of the conservation planning strategies of the state conservation organisations that established PAs partly as a response to perceived risk. Therefore, the next section describes the results of allowing for transformation risk in a gap analysis.

7.5 A gap analysis of Maputaland allowing for risk

Risk of agricultural transformation in Maputaland is not uniform and tends to be highest on rich soils, in low-lying, flat areas that are close to existing agriculture (section 5.3). Therefore, any conservation planning exercise should account for these patterns when selecting new PAs. It could be argued that the concept of triage should be adopted, so that conservation priority should be given to those areas with a medium level of threat (Myers, 1979). However, this assumes that the most threatened areas are impossible to save or that cost of protection is proportional to level of risk. This relationship is unlikely to be so simple as under-financed PAs have been shown to be effective at reducing habitat transformation (Bruner et al., 2001). In addition, the PAs proposed in this planning exercise should be relatively cheap to maintain because they would benefit the communities involved and hopefully encourage them to adopt sustainable land-use practices.

Therefore, it was decided to adopt a different approach when allowing for transformation risk by instead assuming that those grid squares with low risk levels were acting as PAs. This assumption is probably inaccurate because the biodiversity in these squares is still at risk from over- exploitation of plants and animals and it is not suggested that low risk vegetation types and their associated biodiversity require no formal protection. However, given that most biodiversity elements in Maputaland are well represented in the PA system, any new PA should be chosen based on the assumption that low-risk grid squares have PA status. However, to avoid over- emphasising the role of these squares, low risk was defined as having a modelled risk of

Chapter 7: A complementarity analysis of Maputaland 149 transformation of less than 0.05. This section describes how this affected which squares were identified as priority sites.

7.5.1 Methods The mean risk of transformation for each 1 km grid square was calculated using the “Summarize zones” option in ArcView. This found the identifier code of all the grid squares that had a mean risk of transformation probability of ≥ 0.05. This value was chosen because it is commonly used significance level in statistical tests. The area of each biodiversity elements in these “low risk” squares was calculated in Excel and these values were subtracted from the unprotected area values stored in the “Element details table” of the Java software. Excel was also used to remove these low risk squares from the “Grid table” and the program was used to carry out the same three types of analysis described in section 7.4, using land-cover type, all bird species and distinctive bird species.

7.5.2 Results Most of the 1260 low-risk grid squares were found in the Lebombo Mountains, to the west and south of TEP and on the southerly borders of Mkhuze GR. Many of the priority sites were still found in the Lebombo Mountains but there was an increase in the number of sites in the west of Maputaland and in the Cretaceous and alluvial zones (Figure 7-14). There was a significant coincidence in the sites chosen by the three surrogates, although the number was lower for all of the pairwise comparisons when compared with the initial gap analysis (Table 7-7; Table 7-8). Sixty-seven sites were identified as priorities by all three methods (Table 7-8). There was also a significant coincidence between priority sites chosen with the same surrogate when using the methods described in section 7.4 and the methods described in this section (Table 7-9).

Table 7-8: Results from the gap analysis allowing for habitat transformation risk

Category Frequency Sig

Coincidence between land-cover & birds 77 p < 0.001 Coincidence between land-cover & distinctive birds 105 p < 0.001 Coincidence between birds & distinctive birds 198 p < 0.001 Coincidence between land-cover, birds & distinctive birds 67 p < 0.001

Table 7-9: Coincidence sites chosen using gap analysis and gap analysis allowing for risk

Category Frequency Sig

Land-cover 141 p < 0.001 Birds 67 p < 0.001 Distinctive birds 71 p < 0.001

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$T ÊÚ $T$T $T $T #S ÊÚ #S$T#S #S#S #S $T#S$T #S #S#S#S $T#S ÊÚ #S #S #S #S #S ÊÚ #S#S#S #S$T #S #S#S $T$T #S #S #S $T ÊÚÊÚ ÊÚÊÚ ÊÚÊÚ #S$T $T$T ÊÚ #S #S $TÊÚ $T #S ÊÚ ÊÚÊÚÊÚ $T ÊÚÊÚ ÊÚ $T ÊÚ $T ÊÚ $TÊÚÊÚ ÊÚ ÊÚ#S ÊÚ $TÊÚ#S Gap analysis priority sites ÊÚÊÚ#S$T #S $TÊÚÊÚ $T $T$TÊÚ#S $TÊÚÊÚÊÚ $T ÊÚ#S $T#S ÊÚ#S $T #S ÊÚÊÚ$T $TÊÚ$T $T#S ÊÚÊÚ Land-cover types $T#SÊÚ $T$T ÊÚ All birds Distinctive birds Land-cover & all birds

ÊÚ #S #S$T ÊÚ Land-cover & distinctive $T #S 20 km All & distinctive birds Land-cover, all & distinctive birds

PA Low risk of transformation

Figure 7-14: Priority sites allowing for habitat transformation risk

Chapter 7: A complementarity analysis of Maputaland 151 7.5.3 Discussion It is important to include transformation risk in gap analyses. Coincidence of priority sites chosen using this method and the one described in section 7.4 was only 22.3 % when using bird species as a surrogate and 47 % when using land-cover types (Table 7-9). The analysis described in this section is also more relevant because most of the grid squares excluded from the analysis that are found to the south of Mkhuze GR are privately owned game reserves. These reserves were excluded from the PA coverage because their future conservation status may change but they do help protect the region’s biodiversity.

The coincidence between priority sites chosen using different biodiversity surrogates was also lower for all three pairwise comparisons (Table 7-7; Table 7-8). This was probably because any inherent differences between the surrogates were masked in the original gap analysis because the Lebombo Mountains were so under-represented in the PA system. This led to most of the priority sites being located there and produced high levels of coincidence.

7.6 Identifying near-minimum sets for PA targets

More than a quarter of Maputaland has PA status and this is much larger than the recommendation of the IUCN that 10 % of each biome should be protected (IUCN, 1992). Therefore, it might be argued that the NCS could deproclaim some of these PAs and not identify further sites for conservation. However, others factors need to be considered when deciding the proportion of the region that should be protected. The first is that the NCS is responsible for biodiversity management of the whole province and only 8 % of KwaZulu-Natal has PA status. The largest of these PAs are found in Maputaland and the Drakensberg Mountains, both of which have low agricultural potential (Fairbanks et al., 2001). It would be very difficult to create new PAs in other parts of the province and so it could be argued that it is better to have a large proportion of Maputaland with PA status. This is because the resultant PA network is relatively well connected and contains high levels of biodiversity and endemism.

Another more fundamental argument is that 10 % protection is a relatively arbitrary figure that has gained authority from repetition and that 50 % would be a more appropriate conservation target (Soulé & Sanjayan, 1998). This larger amount has been criticised as being highly unrealistic for conservation in Africa (Musters et al., 2000) but this section uses the software described in section 7.4 to determine how much of Maputaland would need PA status to meet different conservation targets. It also compares results when using different surrogates for biodiversity and identifies the proportion of the chosen sites falling within existing PAs.

Chapter 7: A complementarity analysis of Maputaland 152 7.6.1 Methods The Java program was modified to carry out this near-minimum set analysis so that the user could input the minimum percentage of protection required. Hence, once a biodiversity element had been protected to the required level, it was not included in further iterations. The output of the program listed the chosen squares in sequence and calculated the percentage protection given to each element once the minimum target had been reached. The analyses used all of Maputaland’s grid squares and initially assumed that none of these had PA status. However, it was still assumed that transformed land-cover had PA status by default and so was subtracted from the unprotected area values of associated bird species. The software was used to identify the near-minimum sets for the three biodiversity surrogates for three conservation targets. These specified that at least 10 %, 20 % or 50 % of each biodiversity element, should have PA status.

7.6.2 Results The number of 1 km2 grid squares needed to protect 10 % of each biodiversity element ranged between 663 for land-cover types and 694 for bird species (Table 7-11). There was a linear relationship between the number of squares needed and the conservation target, with the 20 % target sets containing approximately double the number of squares in the 10 % sets and the 50 % sets containing five times the number (Table 7-11). The squares chosen using the three different surrogates showed similar patterns, with the 50 % target squares being located throughout the Lebombo Mountains, in and around Ndumo GR and TEP and a large block that included Mkhuze GR and an area to the south, Sodwana State Forest, False Bay Park and the northern part of Lake St Lucia (Figure 7-19 to Figure 7-21). The 10 % target squares were much more widely scattered, with the largest patches occurring in Lake Sibaya and the Kosi lake system.

The coincidence of squares chosen using the different surrogates varied between 69 % for the 20 % target using land-cover types and all bird species and 93.2 % for the 50 % target using all bird species and distinctive bird species (Table 7-12). The percentage of chosen squares that fell within PAs varied between 47.3 % and 58.4 % and this generally diminished as the target percentage increased (Table 7-11). A similar pattern was shown by those grid squares that were selected as priority sites by more than one surrogate type (Table 7-12).

The near-minimum set to protect at least 10 % of each biodiversity element protected a mean of 11.7 % of the land-cover types, 18.3 % of bird species when using all the species as a surrogate and 18 % of bird species when only using the distinctive species (Table 7-10; Figure 7-15 to Figure 7-17). Only one bird species had less than 10 % of its range protected when using distinctive birds as a surrogate (Figure 7-17). However, 34 of the bird species had less than 10 % of their habitat protected by the land-cover type minimum set, with two of these having less than 5 % protection (Figure 7-18).

Chapter 7: A complementarity analysis of Maputaland 153 Table 7-10: Protection given to biodiversity elements based on 10 % conservation target

Mean Minimum Maximum Analysis protection (%) protection (%) protection (%) Land-cover types 11.7 10.0 28.7 All bird species 18.3 10.0 100.0 Distinctive bird species 18.0 5.7 100.0

Table 7-11: Results from near-minimum set analysis using different conservation targets

10 % 20 % 50 % No. of % in No. of % in No. of % in Analysis sites PAs sites PAs sites PAs Land-cover type 663 55.4 1325 50.9 3374 47.3 All bird species 694 58.4 1385 53.7 3444 50.6 Distinctive bird species 685 58.2 1358 53.8 3400 50.5

Table 7-12: Coincidence of priority sites chosen using different biodiversity surrogates

10 % target 20 % target 50 % target Mean % of Mean % of Mean % of Analysis coinc. coinc in coinc coinc in coinc coinc in (%) PAs (%) PAs (%) PAs Land-cover & 71.1 55.4 69.0 51.6 80.7 49.1 all bird spp. Land-cover & 70.5 56.0 70.3 51.9 81.3 48.7 distinctive bird spp. All bird spp. & 84.8 58.5 85.7 53.4 93.2 50.3 distinctive bird spp.

Chapter 7: A complementarity analysis of Maputaland 154 Figure 7-15: Protection given to land-cover types based on 10 % conservation target

Figure 7-16: Protection given to all bird species based on 10 % target

Figure 7-17: Protection given to all bird species based on 10 % target for distinctive birds

Chapter 7: A complementarity analysis of Maputaland 155 Figure 7-18: Protection given to bird species based on 10 % target for land-cover types

10 % 10 % 20 km 20 km 20 % 20 %

50 % 50 %

PA and Maputaland PA and Maputaland boundary boundary

Figure 7-19: Set for land-cover types Figure 7-20: Set for distinctive bird spp

Chapter 7: A complementarity analysis of Maputaland 156 10 % 20 km 20 %

50 %

PA and Maputaland boundary

Figure 7-21: Near-minimum set for three conservation targets using bird species distributions

Chapter 7: A complementarity analysis of Maputaland 157 7.6.3 Discussion These results show the near-minimum sets based on three different conservation targets and using three different surrogates for biodiversity. The software identified grid squares by using a heuristic method and so it is likely that the actual minimum set of squares would be smaller. In addition, the number of sets of squares that would meet a particular conservation target without adding many more grid squares is probably huge. This is because habitat transformation in Maputaland has been generally localised and so the remaining natural vegetation is generally found in large patches. This means that many grid squares still contain large percentages of important habitat and could replace squares identified as the near-minimum set with a negligible loss of overall protection. This probably explains why there was an almost linear relationship between the percentage target and the number of grid squares in a near-minimum set.

The coincidence between grid squares identified using the different surrogates were generally similar, varying between 69 and 93 % (Table 7-12). The highest coincidence was between the all bird and distinctive bird surrogates, which again suggests that distinctive species could be used as an effective alternative when the data are affected by sampling bias. This is emphasised by the finding that only one non-distinctive bird species had less than 10 % of its habitat protected by the distinctive species 10 % target near-minimum set (Figure 7-17). The land-cover type 10 % target near-minimum set was less effective, as the selected grid squares did not protect 10 % of the habitat of 34 bird species. However, only two of these species had less than 5 % protection, so using land- cover types as a surrogate would have been acceptable if no other data were available.

These results were very different to those produced using presence/absence data at a coarser scale. Identifying minimum sets for these data is usually based on first identifying squares that contain biodiversity elements that are only found in one or several squares and choosing other squares that represent the remaining species. The Maputaland data sets do not follow this pattern and future software for conservation planning needs to address these changes. One suggested solution is to use the concept of irreplaceability to distinguish between the many squares that could be included in the minimum-set (Ferrier et al., 2000). However, it is likely that many of the grid squares would still have similar irreplaceability scores because they contain similar amounts of different biodiversity elements.

Therefore, the only way to produce more conclusive results is to improve the software in two specific ways. The first is to adapt the algorithms so that the squares they choose are based on other factors, such as proximity to previously chosen squares. Secondly, it is vital that the software can be easily understood and used by conservation planners so that they can allow for real-world constraints and compare results from different scenarios. One such scenario is based on the issue of land-claims on PAs in Maputaland by communities that were moved forcibly from the land during the apartheid era and this is discussed in the next section.

Chapter 7: A complementarity analysis of Maputaland 158 7.7 Assessing the biodiversity value of land-claim areas

Despite arguments in the literature, the issue of what percentage of each biodiversity element should be protected in Maputaland is largely irrelevant. Biodiversity conservation, together with eco-tourism and sustainable harvesting, has been identified as the most effective land-use and the one with the highest potential profit margin. A far more important issue is land-ownership and this has been brought into focus by recent land-claims. The South African government aims to redress the apartheid policies that forced many black people from their land for the establishment of PAs. They have set up the Restitution of Land Claims Commission (RLCC) to investigate and settle these disputes and there are 21 such land-claims under consideration in Maputaland (Figure 2-9).

More than 70 % of the area with PA status in Maputaland is part of a land-claim, which could be a huge threat to biodiversity conservation in the region if they were transformed to other uses. However, those land-claims that have been settled or are close to resolution suggest that the RLCC have generally decided that the land should continue to be used for conservation, with the communities involved sharing any profits and being involved in the management of the land. Therefore, the Maputaland biodiversity data sets can be used to provide information for several planning scenarios that arise from the land-claim issue.

The first is to identify the conservation value of the different land-claim areas based on information provided from the analysis described in section 7.6. The second scenario is based on the assumption that management of the PAs will probably change in line with views of the communities who own the land and so there may be an increase in sustainable harvesting of resources. However, the NCS may feel that it is important that the PAs remaining under its control still fulfil the IUCN target of giving full protection to 10 % of each habitat type. Therefore, the second part of this section describes a method that uses a near-minimum set to identify which of the grid squares with PA status would be needed to achieve a target of 10 % protection for Maputaland’s bird species.

7.7.1 Methods The land-claim coverage was converted to a 1 km vector coverage by using the “Select by theme” option in ArcView to identify those 1 km grid squares that contained part of the land-claim polygons. The dissolve option of the “Geoprocessing Wizard” was then used to produce the final coverage and the “Spatial join” option was used to produce a coverage that listed whether each square belonged to a minimum set for the bird data and whether it also fell within one of the land- claim areas and the “Summarize” option was used to produce the required descriptive statistics.

Chapter 7: A complementarity analysis of Maputaland 159 The rank of each grid square was based on using all the bird species data in the Java program. The minimum set criterion was entered as 100 % so that all of the squares in Maputaland would be selected. The software output listed the order in which these squares were chosen and these ranks were exported into Excel and joined to the 1 km grid square coverage in ArcView. This was converted to raster format and the “Summarize zones” option was used to determine the mean rank values of the different land-claim areas.

7.7.2 Results The 21 areas under land-claims varied in size between 4 km2 for Manguzi Forest and Sodwana State Forest & Triangle and 574 km2 for the Sodwana State Forest (Table 7-13). The four different methods used to identify land-claim areas with high conservation value gave different results, although there was some overlap between the high ranking areas.

The five highest scoring land-claim areas based on the percentage of their grid squares that belonged to the 10 % near-minimum set for bird species were Lake Sibaya, the Mbangweni Corridor/Ndumo, Ndumo, Mkhuze and the Sodwana State Forest. The percentage ranged between 20.2 and 34.8 %, with a mean of 26.6 % (Table 7-13).

The five highest scoring land-claim areas based on the percentage of their grid squares that belonged to the 20 % near-minimum set for bird species were Cape Vidal, the Sodwana State Forest, Lake Sibaya, Ubombo Mountain and Ndumo. The percentage ranged between 35.3 and 45.5 %, with a mean of 39.1 % (Table 7-13).

The five highest scoring land-claim areas based on the percentage of their grid squares that belonged to the 50 % near-minimum set for bird species were Cape Vidal, Ubombo Mountain, the Sodwana State Forest, Hlatikhulu and Tembe Elephant Park. The percentage ranged between 77.1 and 100 %, with a mean of 83.44 % (Table 7-13).

The five highest scoring land-claim areas based on the mean rank of their grid squares, as assigned in the 100 % near-minimum set for bird species were Hlatikhulu, Cape Vidal, Ubombo Mountain, the Sodwana State Forest, and Mkhuze. Their mean ranks ranged between 167.4 and 1994.6, with a mean of 1366.0 (Table 7-13).

The near-minimum set to protect at least 10 % of each bird species’ habitat using only those squares with PA status contained 696 grid squares. The squares that were chosen mostly fell within TEP, Lebombo Mountain NR, the east and west of Mkhuze GR and large patches of the Sodwana State Forest (Figure 7-22).

Chapter 7: A complementarity analysis of Maputaland 160 Table 7-13: Conservation importance of land-claim areas

Area 50 Mean Analysis 10 % 20 % (km2) % rank Cape Vidal 11 18.2 45.5* 100.0* 1215.0* Coastal Forest Reserve 264 12.5 17.8 49.6 3501.5 Eastern Shores State Forest 9 0.0 11.1 33.3 4841.4 False Bay 45 0.0 22.2 57.8 3732.7 Hlatikhulu 9 11.1 33.3 77.8* 167.4* Lake Sibaya 23 34.8* 39.1* 65.2 3934.8 Lower Link Properties 37 0.0 2.7 43.2 3451.3 Makhasa 27 3.7 25.9 44.4 4650.1 Manguzi Forest 4 0.0 0.0 0.0 6101.3 Manzengwenya Plantation 214 4.2 5.6 8.4 7556.3 Mbangweni Corridor/Ndumo 32 34.4* 34.4 56.3 3966.1 Mkhuze 372 21.0* 34.7 76.3 1994.6* Ndumo 133 22.6* 35.3* 66.9 2414.5 Sileza 22 9.1 9.1 63.6 2856.5 Sodwana State Forest 574 20.2* 39.2* 80.5* 1740.6* Sodwana State Forest & Triangle 4 0.0 0.0 25.0 4687.0 Sodwana Triangle North 11 0.0 0.0 27.3 2564.6 St Lucia Park 11 0.0 0.0 54.5 3630.6 Tembe Elephant Park 323 19.2 30.3 77.1* 2998.4 Ubombo Mountain 11 18.2 36.4* 81.8* 1712.6* Western Shores 312 4.5 9.6 22.4 5654.0 (The five most important areas based on the scores in each column are marked with *)

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Figure 7-22: Minimum set for conservation target of 10 % using bird species and restricting the choice to grid squares with PA status.

Chapter 7: A complementarity analysis of Maputaland 162 7.7.3 Discussion There was some variability in the identification of priority land-claim areas for biodiversity conservation based on the different methods. This was to be expected but there was general consensus in identifying the importance of areas in the Lebombo Mountains, together with Mkhuze GR, TEP, Sodwana State Forest and Lake Sibaya. Manguzi FR and Western Shores had particularly low scores, partly because the latter contained large areas of pine and eucalyptus plantations. The near-minimum set identified areas that could be managed solely for biodiversity conservation as part of a mixed-utilisation strategy for Maputaland’s PAs (Inamdar et al., 1999). However, the more important aspects of these results is that they illustrate the huge potential flexibility of any near-minimum set. It was found that restricting the 10 % near-minimum set to grid squares found within PAs only increased the area needed by 2 km2. This was despite reducing the number of grid squares used in the analysis from 9549 to 2742 squares and the findings that only 58% of the original near-minimum set had PA status (Table 7-11).

Once again, this emphasises that results from the literature based on coarse-scale, presence/absence data have little relevance to conservation planning in Maputaland. In practice, each biodiversity element is represented in many different grid squares and there is a huge amount of flexibility when choosing near-minimum sets based on broad or unfocussed conservation targets. The only way to improve the relevance of these methods is to ensure that conservation planners can manipulate the area-selection software to investigate particular planning scenarios. It is hoped that the heuristic algorithm developed for this project can be adapted for incorporation into existing GIS software.

Another result of using fine-scale data for conservation planning is the need to include factors that will ensure the long term persistence of the selected biodiversity elements and maintain ecological processes. The importance of these factors has long been long recognised and widely discussed but they are rarely relevant to conservation planning exercises where the selection units are larger than many existing and potential PAs. Instead, it is implicitly assumed that areas will contain sufficient habitat to protect the targeted biodiversity elements or that selecting two neighbouring areas will inevitably produce an interconnected PA system. This does not necessarily happen, as is illustrated by TEP and Ndumo GR (Figure 2-8), and is a particular problem when dealing with 1 km2 grid squares.

7.8 Chapter summary

• The PAs of Maputaland contained more than 10 % of each land-cover type found in the region, ranging from 11.4 % for Lebombo aquatic and 100 % for mangroves, dune thicket and beach. They also protected 10 % or more of all but one of the region’s bird species.

Chapter 7: A complementarity analysis of Maputaland 163 • The number of species richness and proportional richness hotspots with PA status varied between 12.3 % for endemic species richness and 76 % for all species proportional richness. Species richness hotspots tended to occur in areas with high land-cover type diversity, whereas proportional richness was highest in grid squares with large patches of land-cover types with a large number of associated species.

• The coincidence between priority sites identified in a gap analysis using land-cover types, bird species and distinctive bird species ranged between 34 % for land-cover and birds and 81 % for birds and distinctive birds. Most of the priority sites were located in the Lebombo Mountains.

• Results from a gap analysis that assumed that grid squares with a low risk of transformation had PA status differed from those of the first gap analysis. This was because many of these squares were located in the Lebombo Mountains and so their associated land-cover types became less under-represented. This reduced the coincidence levels of sites chosen using the three different surrogates to 26 % for land-cover and birds and 66 % for birds and distinctive birds.

• The number of 1 km2 grid squares needed to achieve the conservation target of protecting 10 % of each biodiversity element ranged between 663 for land-cover types and 694 for bird species. There was a near-linear relationship between the percentage conservation target and the number of grid squares in the selected set, suggesting that there are a large number of squares with similar conservation value.

• The different methods used to measure the conservation value of the land-claim areas identified different priorities. However, Cape Vidal, Hlatikhulu, Lake Sibaya, Mkhuze, Ndumo, Sodwana State Forest and Ubombo Mountain were identified as being important by at least two different methods.

• Only 696 grid squares were needed to protect 10 % of the habitat of each bird when the choice was restricted to those grid squares with PA status. This was an increase of two squares on the original analysis, despite reducing the number of squares available from 9549 to 2742. This supports the idea that many of the squares have similar conservation value and could be replaced in the selected set by others with minimal biodiversity loss.

All of these results assume that each biodiversity element, whether land-cover type or bird species, is equally important. Conservation planning is a value-based process and the next chapter describes how these values, together with issues of ecological viability, were incorporated in the priority site selection process. Chapter 7: A complementarity analysis of Maputaland 164 Chapter 8: Identifying areas of high conservation value in Maputaland

8.1 Introduction

Many of the early attempts to identify areas with high conservation value were based on combinatorial scoring systems for different criteria (Usher, 1986). A wide range of criteria were used but some of the most common were diversity, rarity, naturalness, area, threat of human interference, representativeness, research and educational value, recorded history and potential value (Margules & Usher, 1981). Each area would be given a score for each criterion, which would either be interval or ratio values, for criteria such as species diversity, or an ordinal value like 1, 2 or 3 for low, medium or high, for criteria such as education value. A composite final score was then determined for each site by combining the information from each criterion (Bedward et al., 1991).

This approach was easy to use and understand but it has been criticised for two main reasons. The most obvious of these is that combining category scores has no theoretical basis (Smith & Theberge, 1987) and this has largely stopped the widespread use of this specific approach. A more fundamental argument is that any approach that combines scores from a series of categories produces results that are difficult to understand and justify (Williams, 1998). When using a large combinatorial equation, it may not be obvious why a particular area is given a high score. In addition, these scoring systems have to be based on subjective weightings given to each category and so a different person may be equally justified in giving high scores to different areas based on different preferences.

It could be argued that these subjective scoring systems have been made redundant by the development of selection algorithms (chapter seven). These clearly specify the selection goals and identify the algorithms used at the beginning of the process. Hence, the results are more transparent and easier to justify. They also tend to be more efficient, as they are based on complementarity. Obviously, these algorithms are still chiefly concerned with measures of biodiversity but others factors may be included by restricting the analysis to areas that fulfil a certain criterion (Bedward et al., 1992) or by ordering the complementarity results by modelled threat (Williams, 1998). In addition, the representation target of a biodiversity element could be based on its perceived vulnerability or conservation status (Kirkpatrick & Brown, 1991).

It is important to realise, however, that these methods increase the transparency of the process but not their objectivity. All conservation planning involves setting conservation goals and these are inherently subjective, requiring considerable value judgment. For example, the philosophy behind gap analysis is to give equal protection to each vertebrate species but this is in direct contrast to the Chapter 8: Identifying areas of high conservation value in Maputaland 165 Endangered Species Act, another mainstay of conservation policy in the United States (Kohm, 1991). It is for this reason that it is still common for conservation planners to give greater weight to some biodiversity elements in their selection methods (Given & Norton, 1993; Howard et al., 1998; Dunn et al., 1999).

In addition, the method of pre-selection to remove unsuitable areas from a reserve-selection exercise can be compared to criticisms of the use of a Boolean approach in GIS to identify suitable areas (Eastman, 1999). These methods often use a subjective cut-off point to identify suitable areas, for example by stating that any area with a slope of more than 5 º is unsuitable for locating a factory. This would exclude a site that was highly suitable based on the range of other important factors but had a slope of 5.001 º. A more relevant example might be a grid square with a low complementarity value that would make an excellent corridor between two existing PAs. This grid square would either have to be excluded by pre-selection or the cut-off point for pre-selection would have to be set so low that this initial process loses its relevance.

Taking these points together, there is a need for incorporating different factors into the same analysis, but any analysis must overcome the hurdles of combinatorial scoring systems. It must do this by having a sound theoretical basis with a transparent methodology but other factors are also important. In particular, it is vital that the final scoring system reflects the views of a range of people so that it is less susceptible to criticisms based on the subjectivity of results. In addition, it is important to carry out a sensitivity analysis of the final results to determine whether the priority sites identified by the process are likely to be affected by changes in the weightings given.

One method that fulfils all these criteria is the Analytical Hierarchy Process (AHP). This allows decision makers to define suitability in terms of constituent factors that are standardised and individually weighted based on a series of pairwise comparisons (Saaty, 1980). In addition, these factors can also be combined to form sub-components that are also individually weighted to produce the final scoring system. AHP has been used in a variety of fields, including nuclear waste management (Saaty & Gholamnezhad, 1982) and urban planning (Anselin & Arias, 1983) and has since been adapted for use in GIS to identify preferred sites based on multi-criteria evaluation (Pereira & Duckstein, 1993; Eastman et al., 1995). Despite its suitability, these methods are little used by conservation biologists (Anselin et al., 1989; Kuusipalo & Kangas, 1994; Li et al., 1999), although it was used to identify potential locations for water-points in Mkhuze GR (Woods, 1997).

This chapter describes how the AHP process was used to identify the ideal location of PAs by integrating data on complementarity, risk and ecological viability based on the opinions of key NCS staff. Section 8.2 describes how the structure of the conservation value scoring system was derived and section 8.3 describes how the GIS coverages needed for this system were derived. Sections 8.4 and 8.5 describe how each of the sub-components and their constituent factors were

Chapter 8: Identifying areas of high conservation value in Maputaland 166 weighted. The results of using the AHP to identify priority sites are compared with those from the gap analysis in section 8.6 and the chapter is summarised in section 8.7.

8.2 Deciding the structure of the conservation scoring system

The structure of any conservation planning system is enormously important, so it should ideally be based on the opinions of a range of stakeholders (Hannah et al., 1998; Kremen et al., 1999). The AHP methodology allows varied opinions to be easily incorporated and it permits different factors to be combined to form functional groups that can also be individually weighted. For example, the scoring system used to decide the width of a buffer zone of a Biosphere Reserve in China was based on two groups of directly influential and indirectly influential factors (Li et al., 1999a). Furthermore, the system used to determine the best location for an artificial water point in Mkhuze GR grouped the factors according to whether they influenced environmental impact, poaching risk, tourist opportunities or cost (Woods, 1997).

Another important consideration is that most people cannot compare and consistently rank more than nine factors. Hence, combining them into functional groups also makes the scoring system easy to understand (Anselin et al., 1989). Therefore, this section describes the methods that were used to identify which factors should be used in the scoring and how they should be grouped.

8.2.1 Methods The first stage in this process was a literature review to identify broad elements that were seen as important in determining conservation importance. These ideas were developed as part of informal discussions with NCS staff and considerations were made as to whether potential elements could be measured using the available data. This was followed by a workshop attended by ten NCS staff, where the structure of the conservation scoring system and the factors to be used were formally discussed.

Each attendee was sent a document before the meeting that described the purpose of the conservation scoring system and the methods used to produce the available GIS coverages. The first part of the workshop involved an introduction where I went through this document and suggested some factors for consideration. The attendees then split into three working groups with a designated group leader. After an hour of discussion, each group leader explained their conclusions. This was followed by a final discussion where the views of each working group were integrated to produce a final proposed scoring system.

8.2.2 Results The group decided that the units of the conservation planning exercise should be a series of 1 x 1 km grid squares. Squares were chosen because they are easily represented in GIS coverages and the

Chapter 8: Identifying areas of high conservation value in Maputaland 167 1 km2 resolution was seen as the smallest viable conservation unit. Any selected areas would probably be larger than 1 km2 but would consist of a cluster of high-ranking grid squares.

It was decided that calculating the conservation value system should be a two stage process. The first stage would use factor data, where each factor was derived directly from the GIS coverages. These factors would be assigned to one of three groups, depending on their characteristics. The factors in each group would then be individually weighted and combined to form a sub-component. The second stage would then involve individually weighting and combining the three sub-components to produce the final conservation value score. These sub-components, together with their constituent factors, are described below:

8.2.2.1 Complementarity sub-component Complementarity was seen as an extremely important part of the conservation scoring system, which explains why this issue was investigated in chapter seven. However, its inclusion in this planning exercise differed from the method described in chapter seven because the AHP methodology allowed the group to identify aspects of biodiversity that they felt were particularly important and to incorporate information from different levels of biodiversity (Noss, 1990). These levels were the bird species and land-cover types data described in chapters four and six, as well as data derived from the KwaZulu-Natal landscape coverage (Fairbanks & Benn, 2000).

The bird species and land-cover type data were both divided into three different groups. These groups (endemic, threatened and other) were based on conservation status. The methodology developed in chapter seven was also adopted to calculate a complementarity value for each grid square. Therefore, the value of a particular grid square for a particular biodiversity element was calculated using the following equation:

(Area in grid square / Area in Maputaland) * (Area outside PAs / Area in Maputaland)

The score for each complementarity factor was then found by summing the scores of each biodiversity element that the factor comprised. These factors, together with a description of the biodiversity elements they contained, are listed below:

A1. Endemic birds This category consists of all of the bird species that are endemic to South or Southern Africa (Table 7-1). Maputaland shares zoological affinities with neighbouring areas in Moçambique and Swaziland (Van Wyk, 1994), so it was important to include species that were endemic to these regions.

Chapter 8: Identifying areas of high conservation value in Maputaland 168 A2. Threatened birds This category consists of all of those bird species that are found in Maputaland that are classified as endangered or vulnerable in the South African Red Data book (Table 7-2), yet are not endemic to South or southern Africa. Both the bald ibis (Geronticus calvus) and Cape vulture (Gyps coprotheres) are endemic and threatened species, so it was decided to class them as endemic for this analysis as the working groups generally considered that endemic birds had a higher conservation importance than threatened species.

A3. Other birds This category consists of the bird species that are neither endemic nor threatened.

A4. Endemic land-cover types Sand forest and woody grassland were identified as being endemic to Maputaland, containing species and vegetation communities that are restricted to Greater Maputaland.

A5. Threatened land-cover types There is no equivalent to the Red Data book for threatened land-cover types and so eight land- cover types were identified as being locally and regionally restricted and at risk of agricultural transformation. The identified land-cover types were Lebombo grassland, riverine forest, reed beds, floodplain grassland, riverine thicket, hygrophilous grassland, swamp forest and inland evergreen forest.

A6. Other land-cover types This category consists of the untransformed land-cover types that are neither endemic nor threatened.

A7. Landscape types This category consists of the different landscape types that are found in Maputaland (Figure 8-1) based on a 1 km2 coverage that was produced for KwaZulu-Natal. This consisted of 20 landscape types that were classified based on elevation, topographic classification and plant growth days (Fairbanks & Benn, 2000).

8.2.2.2 Risk sub-component Risk of agricultural transformation plays a large role in determining conservation priorities, for reasons discussed in chapters five and seven. This risk can be measured in two ways that are described below:

Chapter 8: Identifying areas of high conservation value in Maputaland 169 B1. Risk of transformation The most obvious threat to biodiversity conservation comes from the natural vegetation in each grid being cleared for agriculture and this was measured as the mean modelled risk of all the pixels of untransformed vegetation found in a grid square.

B2. Risk of isolation Another risk is that the natural vegetation in a grid square may become isolated from other patches because its neighbouring grid squares are cleared for agriculture. This would increase levels of habitat fragmentation and reduce the biodiversity value of a grid square even if it remained untransformed (Saunders et al., 1991). Therefore, it was felt that the risk of isolation should be included as a factor that should be measured for each grid square as the mean of the transformation risk of the entire set of grid squares that share a boundary.

Key

Lowlands mountainous/hilly moderately dry Coastal hinterland undulating/flat moderately dry Mid-lowlands undulating/flat moderately dry Coastal hinterland mountainous/hilly mod. dry Mid-lowlands mountainous/hilly moderately dry Lowlands undulating/flat dry Coastal plain undulating/flat moderately dry Coastal hinterland undulating/flat dry Coastal hinterland mountainous/hilly dry Coastal hinterland mountainous/hilly mod. moist Coastal plain undulating/flat dry Lowlands mountainous/hilly dry Coastal hinterland mountainous/hilly moist Lowlands mountainous/hilly moist Mid-lowalnds mountainous/hilly dry Coastal plain mountainous/hilly dry

20 km Coastal plain undulating/flat moderately moist Coastal plain undulating/flat moist Mid-lowlands undulating/flat dry Coastal plain undulating/flat wet

Figure 8-1: A landscape coverage of Maputaland (Fairbanks & Benn, 2000)

Chapter 8: Identifying areas of high conservation value in Maputaland 170 8.2.2.3 Viability sub-component The importance of including factors related to complementarity and risk has already been discussed in detail but other issues need to be addressed when identifying areas with high conservation value. Many of these include socio-economic and political factors that are outside the scope of this study and need to be addressed on a case-by-case basis once the initial set of sites have been identified. However, another important set of factors relate to the ecological viability of any potential PAs and hence these were included as a third sub-component.

These factors fall in to two main groups, based on ecological integrity and connectivity. It is obvious that grid squares with high levels of transformation will have little conservation value and neither will highly fragmented squares that contain small habitat patches, which explains the inclusion of the first four factors described below. The final three factors described below are related to whether a grid square would increase connectivity levels between the existing PAs or help link patches of the same vegetation type

C1. Transformation This describes how affected the square has been by agricultural transformation and is measured as the proportion of pixels of each square that consist of transformed land-cover types.

C2. Isolation This follows the same logic as the risk of isolation described above and is calculated as the mean of the proportion of transformed land in the bordering grid squares.

C3. Untransformed patch size Larger patches of natural vegetation can be expected to contain a larger number of associated biodiversity elements (Diamond, 1975). Hence, these were given a higher conservation value. Each pixel belonged to a patch of pixels that share the same land-cover category and so it is possible to give the pixel the value of the total area of the patch to which it belongs. Therefore, the untransformed patch size score was calculated as the mean of these area values for all of the pixels that contain untransformed land-cover types.

C4. Edge score Another measure of fragmentation in a grid square, other than isolation, is the amount of edge between transformed and untransformed habitats. Two squares may have the same level of agricultural transformation but the one with the larger amount of edge would contain smaller patches of habitat. These would be more prone to edge effects, which affect species composition and communities and disrupt energy and nutrient cycles (Yahner, 1988; Young et al., 1996). The edge score was calculated as the length of edge between any transformed and any untransformed land-cover type found in each grid square.

Chapter 8: Identifying areas of high conservation value in Maputaland 171 C5. Distance to same land-cover type Many species are able to move between patches of the same habitat type (Hill, 1995; Schultz, 1998) but this ability may diminish as the patches become more dispersed (Schippers et al., 1996). Therefore, grid squares containing patches of untransformed land-cover types that were close to other patches of the same type were given a higher conservation value. This factor was calculated as the mean distance of the untransformed pixels in a grid square to the nearest patch of the same land-cover type.

C6. Distance to PA Networks of areas supporting biodiversity conservation should ideally be connected for ecological and financial reasons. Joining these different areas will maintain dispersal routes and ecological processes but it also allows adjoining areas to be jointly managed, reducing fencing, transport and administration costs. It was decided to calculate this factor as the mean distance of the pixels in a grid square to the nearest PA.

C7. Connectivity value Grid squares can either act as corridors to join existing PAs or as “stepping stones” that can be used by mobile species to cross large patches of unsuitable habitats (Date et al., 1991). It was assumed that the connectivity value of a grid square would be highest when lying on the shortest line between a pair of PAs that were already close together. However, it was also felt that one grid square could provide connectivity for several different PAs.

Therefore, the connectivity value of a grid square with regards to one pair of PAs was calculated as “Shortest distance / Actual distance2”, where the shortest distance is the shortest distance between the pair of PAs and the actual distance is the shortest route between the pair of PAs that includes the centre of the grid square. The final connectivity value was then calculated as the sum of the connectivity values for all the possible PA pair combinations.

Chapter 8: Identifying areas of high conservation value in Maputaland 172 8.2.2.4 Final score The final conservation scoring system was based on 16 factors grouped into three sub-components, as is illustrated below:

(a1 x Complementarity score) + (a2 x Risk score) + (a3 x Viability score)

(b1 x Endemic bird score) (c1 x Risk of trans. score) (d1 x Transformation score) + + + (b2 x Threatened bird score) (c2 x Risk of isolation score) (d2 x Isolation score) + + (b3 x Other bird score) (d3 x Patch size score) + + (b4 x Endemic land-cover score) (d4 x Edge score) + + (b5 x Threaten’d land-cover score) (d5 x Dist. to same land-c. type score) + + (b6 x Other land-cover score) (d6 x Distance to nearest PA) + + (b7 x Landscape unit score) (d7 x Connectivity value score)

Where a1-3, b1-7, c1-2 and d1-7 are the associated factor weightings

8.2.3 Discussion The aim of this process was to include all those people who would be involved in deciding NCS conservation policy in Maputaland and to ensure that the final scoring system reflected their opinions. However, there were several reasons why this was not fully achieved. Firstly, it proved impossible to arrange a date for the workshop so that all of the people invited could attend. The most serious consequence of this was that no-one from the management divisions of the NCS was present at the workshop. Another possible limitation was that most of the factors that were used in the final scoring system were suggested by myself. This was probably inevitable given that I had spent months working on this project developing ideas, whereas most of the NCS staff only had time to consider the exercise for two days at most.

Despite these limitations the NCS staff were able to provide extremely useful feedback based on their experience of conservation planning. Many of their comments were in response to direct questions but they identified several factors that they felt should be excluded, made changes to other factors and determined the spatial scale of the analysis. Their views on the 1 km x 1km resolution of the conservation scoring system were mirrored by the views of other conservation biologists in KwaZulu-Natal (Maddock & Samways, 2000) and by others who argue that small reserves can play an important role in conserving some elements of biodiversity (Cowling & Bond, 1991; Turner & Corlett, 1996). However, it was generally agreed that PAs would probably consist of a cluster of high-scoring squares but that using this finer resolution would increase the accuracy of the process (Pressey & Logan, 1997). Chapter 8: Identifying areas of high conservation value in Maputaland 173 8.3 Producing the factor data

Once the scoring system had been decided, the next step was to produce a 1 km resolution coverage for each of the factors described in section 8.2. Each coverage only described the score for grid squares found within Maputaland but outside the existing PA system.

8.3.1 Methods The methods used to produce each factor are described and grouped by the sub-component to which they belong. The Idrisi modules used in the AHP analysis require all of the coverages to be in byte binary format (integer values between 0 and 255) and so each set of factor data were manipulated using the same methods. This involved finding the highest scoring grid value for a particular factor, dividing all the grid scores by the highest score, multiplying the result by 255 and rounding the result to the nearest integer. This was done in MS Excel and the results were joined to the vector 1 km grid coverage in ArcView and converted to raster format using the “Convert to grid” option. There were some differences in how the data were derived for each factor and these are described below:

8.3.1.1 The complementarity sub-component factors As was described in section 8.2.2, a complementarity factor score of a grid square was based on the number of biodiversity elements the square contained, how under-represented those elements were in the PA system and how restricted the range of those elements were in Maputaland. Therefore, it was necessary to calculate the area of each biodiversity element in each grid square, its area in Maputaland and its area in the Maputaland PAs.

The area of each land-cover type and bird species habitat in Maputaland and the PAs had already been calculated in chapter seven. This information was calculated for each landscape category by multiplying the landscape coverage by a 1 km resolution PA coverage mask using the OVERLAY module in Idrisi. The AREA module was then used to calculate the area of each category in the landscape coverage and in the new landscape types in the PAs coverage.

The next stage was to produce a spreadsheet for each of the seven complementarity factors, which listed the area of each biodiversity element (whether bird species habitat, land-cover type or landscape type) found in each grid square. Each column represented an element and each row represented a grid square. Each cell was then divided by the total area found in Maputaland of the corresponding element using the “Paste special” option in Excel. The same option was also used to multiply each cell by the elements unprotected area proportion. The factor score for each row was then calculated by summing all the cell scores for its constituent elements. This process was completed and used to produce a new coverage for each of the seven factors. Chapter 8: Identifying areas of high conservation value in Maputaland 174 8.3.1.2 The risk sub-component factors The risk score was based on the 25 m resolution risk coverage that was described in chapter five. This, together with the 1 km grid coverage, was imported into ArcView and the “Summarize zones” option was used to sum the risk values of all the pixels in each grid square that were threatened by transformation. The risk coverage was then reclassed so that all these pixels were given a value of 1 and the “Summarize zones” option was then used to count the number of “threatened pixels” in each grid square. It was then possible to calculate the mean risk of each square by dividing the summed risk by the number of threatened pixels. These data were then assigned to the vector 1 km grid coverage to produce the 1 km risk coverage.

The risk of isolation coverage was based on the 1 km resolution risk coverage, which was imported into Idrisi. The FILTER module was used to identify each pixel in the coverage and change its value to the sum of its eight neighbouring pixels, producing a new coverage. This module was also used to produce a coverage that described for each pixel the number of these eight neighbouring pixels that contained number that were greater than zero. This summed risk coverage was then divided by this second coverage using the OVERLAY module to produce the 1 km resolution risk of isolation coverage.

8.3.1.3 The viability sub-component factors The viability sub-component contains factors that are derived using several different methods, so these are described below either individually or in pairs.

C1 & C2. Transformation and isolation The area of each land-cover type in each 1 km grid square was calculated in chapter seven. These data were then used to calculate the proportion of transformed land-cover types in each square and converted to a raster in ArcView. The isolation coverage was calculated from the transformation coverage using the same methods that were used to produce the risk of isolation score from the risk of transformation score.

C3. Natural land-cover patch size The 25 m resolution land-cover coverage was converted into vector format using the POLYVEC module in Idrisi. The resultant coverage was imported into ArcView and the area of each polygon of natural land-cover type was calculated. These polygons were then converted back to a 25 m resolution raster coverage, with each pixel being given the value of the area of the polygon to which it belonged. The “Summarize zones” option was then used to calculate the mean value of

Chapter 8: Identifying areas of high conservation value in Maputaland 175 these pixels in each grid square. These data were then used to produce a 1 km resolution coverage using the standard methodology.

C4. Edge score The 25 m resolution land-cover coverage was manipulated using the RECLASS module in Idrisi to combine all the natural land-cover classes in one new category and all the transformed classes into another. This coverage was exported as a bitmap file using the BMPIDRIS module and the PhotoPaint software was used to extend the boundaries of each patch of land-cover type that crossed the artificially set boundaries of Maputaland. This ensured that none of the artificial edges, produced at the boundaries of the mapped region, fell within the 1 km grid squares used in the analysis. The bitmap was then re-imported into Idrisi and the POLYVEC was used to covert this into vector format.

The vector polygons were then imported into ArcView and converted to lines using the X-Tools extension. The “clip” option in the Geoprocessing wizard was used to cut these lines whenever they crossed a grid square boundaries and the “spatial join” option was used to label each line fragment according to the identifier code of the grid square that surrounded it. The length of each line fragment was then calculated and the length of lines found in each square was calculated using the “Summarize” option. These data were then used to produce a 1 km resolution edge coverage using the standard methodology.

C5. Distance to same land-cover type The vectorised 25 m land-cover coverage used for the patch size coverage was converted into 29 vector files, each containing polygons belonging to the same natural land-cover category. The “Nearest Features v.3” extension was then used to calculate the distance between each polygon in a vector file and its nearest neighbour. These vector files were then recombined using the “merge” option of the Geoprocessing wizard and converted to a 25 m raster coverage, where each pixel was given the distance value of the polygon to which it belonged. The mean values of the pixels found in each grid square were then calculated and these data were used to produce the 1 km resolution distance to same land-cover type coverage.

C6. Distance to PA ArcView was used to rasterise the PA vector file to produce a 25 m resolution raster coverage and the “Find distance” option was used to calculate the distance of each pixel from the nearest PA. The mean distance for each grid square was then calculated using the “Summarize zones” option and these data were used to produce the 1 km resolution distance to PA coverage.

Chapter 8: Identifying areas of high conservation value in Maputaland 176 C7. Connectivity value The mean distance of each grid square from each PA was calculated using the methods described for producing the distance to PA coverage. The distances between the PAs (including those NCS PAs that were found outside Maputaland but were within 25 km of the region) were calculated using the “Nearest Features v3” extension and all of the resultant data were entered into an Excel spreadsheet. This information was used to calculate the connectivity score for each grid square for all the pairs of PAs and the final score for each square was found by summing these results. These data were imported into ArcView and used to produce the 1 km resolution connectivity coverage.

8.3.2 Results The sixteen coverages for the different factors are displayed below (Figure 8-2 to Figure 8-4) and the number of grid squares that had high values varied dramatically. Most of the complementarity factors contained a few patches of high ranking squares (Figure 8-2) but the risk factors contained a much larger number (Figure 8-3). The viability factors showed a range of patterns, from the small patch of high values between Lebombo Mountain NR and Mkhuze GR in the connectivity value coverage to the large number of high-scoring squares found in the edge value coverage (Figure 8-4).

Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Endemic bird score Threatened bird score

Chapter 8: Identifying areas of high conservation value in Maputaland 177 Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Other birds score Endemic land-cover score

Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Threatened land-cover score Other land-cover score

Chapter 8: Identifying areas of high conservation value in Maputaland 178 Suitability 1

20 km

0

PA

Landscape score

Figure 8-2: The complementarity factors

Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Risk of transformation score Risk of isolation score

Figure 8-3: The risk factors Chapter 8: Identifying areas of high conservation value in Maputaland 179 Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Transformation score Isolation score

Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Land-cover patch size score Edge score

Chapter 8: Identifying areas of high conservation value in Maputaland 180 Suitability Suitability 1 1

20 km 20 km

0 0

PA PA

Distance to same land-cover type score Distance to nearest PA score

Suitability 1

20 km

0

PA

Connectivity value score

Figure 8-4: The viability factors

Chapter 8: Identifying areas of high conservation value in Maputaland 181 8.3.3 Discussion Some of the factors described above have similar patterns and it could be argued that this unnecessarily exaggerates the importance of some grid squares. This is because some factors are actually measuring the same phenomenon. Hence, for example, grid squares with little agricultural transformation had high complementarity and viability scores. One solution would possibly be to manipulate these factors, perhaps by using a principal component analysis, to produce a much small number of coverages that had the same information content (Eastman, 1999). However, the original factor coverages were still used in the analysis because it would be much more difficult to assign weightings to the manipulated factors. In addition, the areas that would be most affected by these correlations would be the highly transformed squares and so they were unlikely to be identified as priority sites for PAs.

8.4 Producing the factor weights

The AHP decision making process has a well defined methodology that has been incorporated into Idrisi (Eastman, 1999). Therefore, it is a relatively easy process to use the information provided by the decision makers to produce the required weighting information. However, it proved difficult to organise workshops to ensure that all of the people involved were consulted at the same time. Instead, it was decided to divide the decision makers into groups of three and to use the mean weightings given by the groups as the final weighting values for each factor. This had the additional benefit of allowing comparisons to be made between the final results and those based on the different group weightings.

The weighting process in AHP is based on a series of pairwise comparisons made between each factor in the different sub-components (Saaty, 1980). The weightings are then derived by calculating the principal eigenvector of the pairwise comparison matrix (Eastman et al., 1995). Idrisi also calculates a consistency ratio for each set of weightings to determine whether any of the individual pairwise comparisons contradict patterns shown by the rest of the data. This section describes the methods that were used to determine the factor weightings in more detail and compares the results from the different groups.

8.4.1 Methods The factor weighting was carried out by two groups of three NCS staff that worked independently. Both groups were sent a document that explained the methodology and details of how each factor was derived. I was present when the first group (Group A) went through this process but not the second (Group B), although I was able to answer any questions that group B had by e-mail and one of the members of group B had been involved in a previous AHP project. The first stage was for the group to draw a table with each factor represented as a row and column (Table 8-1).

Chapter 8: Identifying areas of high conservation value in Maputaland 182 Table 8-1: An example of the table used to determine factor weightings

Factor 1 Factor 2 Factor 3

Factor 1 - Factor 2 - Factor 3 -

Each factor was then compared with every other factor by using a series of pairwise comparisons and given a score. The score was based on comparing the factor in the row with the factor in the column and used the following system:

1/91/71/51/313579

extremely very strongly moderately equally moderately strongly very extremely less important more important

Therefore if a group member thought that: • Factor 2 was moderately less important than Factor 1 • Factor 3 was moderately more important than Factor 1 • Factor 3 was strongly more important than Factor 2 then the example table should be filled in as follows:

Factor 1 Factor 2 Factor 3

Factor 1 - Factor 2 1/3 - Factor 3 3 5 -

It was explained that the numbers used in the pairwise comparisons did not need to be odd numbers or integers and that the phrases, such as “moderately more important” that were used in this scoring system were only a guide. It was also stressed that giving a pairwise comparison a value of 1 did not mean that the two factors were important or unimportant when compared to other factors, only that their importance was equally high or low.

The results from the two groups were entered into Idrisi and the WEIGHT module was used to calculate the weighting value of each factor. This module also calculated the consistency ratio for each set of factor. As this was below 0.1 in each case, it was considered acceptable (Eastman, 1999). The weightings from both groups were used to calculate the mean values that were used to produce the final set of weightings. The group and final weightings were then used in MS Excel to

Chapter 8: Identifying areas of high conservation value in Maputaland 183 calculate sub-component scores for each grid square and these were imported into ArcView. ArcView was used to identify the 300 highest ranking squares for each sub-component using the group and final weightings and to determine the levels of coincidence between the squares identified using the different methods.

8.4.2 Results Both groups decided that land-cover types should have higher weightings than bird species and that these two should have higher weightings than landscape types (Table 8-2). In addition, they agreed that endemic biodiversity elements were more important than threatened elements and both were more important than elements that were neither threatened or endemic. The final weightings ranked the factors in the same order and gave a weighting to endemic land-cover types that was more than twice that of endemic bird species. There was a coincidence of 85 % between the 300 grid squares identified as having the highest complementarity sub-component score by the two different groups (Table 8-3).

Table 8-2: Weightings given to complementarity factors

Group A Group B Final

Factor Score Rank Score Rank Score Rank

Endemic birds 0.165 3 0.143 4 0.154 3 Threatened birds 0.103 4 0.169 3 0.136 4 Other birds 0.022 7 0.027 7 0.025 7 Endemic land-cover type 0.406 1 0.296 1 0.351 1 Threatened land-cover types 0.230 2 0.273 2 0.252 2 Other land-cover types 0.029 6 0.044 6 0.037 6 Landscape types 0.045 5 0.048 5 0.047 5

Table 8-3: Coincidence between the different complementarity sub-component results

Category Percentage coincidence

A & B 85.3 A & Final 91.3 B & Final 94.0

Both groups felt that risk of transformation was a much more important factor than risk of isolation (Table 8-4) and there was a coincidence of 96 % between the 300 grid squares identified as having the highest risk sub-component score by the two different groups (Table 8-5).

Chapter 8: Identifying areas of high conservation value in Maputaland 184 Table 8-4: Weightings given to risk factors

Group A Group B Final

Factor Score Rank Score Rank Score Rank

Risk of transformation 0.800 1 0.758 1 0.779 1 Risk of isolation 0.200 2 0.242 2 0.221 2

Table 8-5: Coincidence between the different risk sub-component results

Category Percentage coincidence

A & B 96.0 A & Final 98.3 B & Final 97.7

There was more disagreement over the weightings given to the viability factors by the two groups. Both felt that the transformation and patch size scores were the most important. However, group A considered isolation to be the next most important, whereas group B thought it was connectivity value (Table 8-6). The final ranking identified isolation as the third most important and gave a weighting to the transformation factor that was more than ten times higher than the edge factor, the lowest ranking factor in all three weighting systems. As a result of these differences, the coincidence between the 300 highest scoring grid squares was only 80 % (Table 8-7).

Table 8-6: Weightings given to viability factors

Group A Group B Final

Factor Score Rank Score Rank Score Rank

Transformation 0.337 1 0.403 1 0.370 1 Isolation 0.169 3 0.117 4 0.143 3 Land-cover patch size 0.284 2 0.196 2 0.240 2 Edge 0.019 7 0.041 7 0.030 7 Distance to same land-cover type 0.126 4 0.083 5 0.105 4 Distance to nearest PA 0.029 6 0.041 6 0.035 6 Connectivity value 0.037 5 0.118 3 0.078 5

Chapter 8: Identifying areas of high conservation value in Maputaland 185 Table 8-7: Coincidence between the different viability sub-component results

Category Percentage coincidence

A & B 79.7 A & Final 90.7 B & Final 89.0

The complementarity sub-component scores were highest in small patches close to Hlatikhulu FR, TEP, Mkuze GR and False Bay Park (Figure 2-8, Figure 8-5 to Figure 8-7). Large numbers of grid squares had high risk sub-component scores and these were mostly found in the Cretaceous and alluvial ecological zones and around Kosi Bay (Figure 8-8 to Figure 8-10). There were also a large number of grid squares with high viability sub-component scores and these were found in large patches in the Lebombo Mountains, and around TEP, Mkhuze GR, Sileza NR and Lake Sibaya (Figure 8-11 to Figure 8-13).

Chapter 8: Identifying areas of high conservation value in Maputaland 186 Suitability Suitability 1 1

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Figure 8-7: Final complementarity sub-component score Chapter 8: Identifying areas of high conservation value in Maputaland 187 Suitability Suitability 1 1

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Chapter 8: Identifying areas of high conservation value in Maputaland 188 Suitability Suitability 1 1

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Figure 8-13: Final viability sub-component score

Chapter 8: Identifying areas of high conservation value in Maputaland 189 8.4.3 Discussion These results emphasised that the NCS staff do not feel that each biodiversity element should be given equal weighting in conservation planning exercises. One possible reason for the low weightings given to landscape complementarity was that this coverage had a much lower resolution than the other data. In addition, it is likely that the landscape coverage was seen to give particular emphasis to areas in the Lebombo Mountains that may be an artefact of the methods used in its development. These weightings were only based on the opinions of six people and it would have been preferable to consult more. However, there were still high levels of agreement between the two groups and the final sub-component scores can be used with some confidence.

Both groups of NCS staff also considered land-cover types to be much more important than bird species, presumably because of the assumption that protecting these broad habitat types would also protect a wide range of other biodiversity elements. This contradicts the findings of the previous chapter, where protecting 10 % of each land-cover type did not protect 10 % of each bird species habitat. However, these weightings are still reasonable because protecting land-cover types may be more important for the protection of other species and the land-cover coverage has to be more accurate than the bird distribution coverages that were derived from it.

In general, the two groups of NCS staff gave similar weightings to each of the factors but there were discrepancies. This was especially the case for the viability sub-component where there was even disagreement with the ranking given to each factor. However, the coincidence of the 300 highest scoring grid squares chosen by groups A and B for the sub-components was generally high. The final sub-component scores were calculated using the mean of the group weightings and results from groups A and B had coincidence percentages of between 80 and 98 %.

8.5 Producing the sub-component weights

The production of the sub-component score data allows these to be weighted and combined to produce the final conservation score. This involves the same process described for weighting the factors, so this section briefly explains how this was achieved and compares the results produced by the different groups.

8.5.1 Methods Three groups of three NCS staff were used to produce the sub-component weightings. The third group (Group C) were given the same background information and training as the other two groups and so the results that they gave were expected to be the same as if they had also taken part in the factor-weighting process. The scores that each group gave for the different pairwise comparisons between the sub-components were again analysed in Idrisi using the WEIGHT module. The

Chapter 8: Identifying areas of high conservation value in Maputaland 190 resultant weightings were then applied to the final sub-component scores, which had been modified using a linear transformation so that all of the scores within a sub-component fell between 1 and 255 and had an integer format. Once again, these results were imported into ArcView and used to calculate the percentage coincidence between the 300 highest ranking grid squares identified using the different group weightings and the final weighting that was the mean of the group results.

A sensitivity analysis was carried out on the final weightings to investigate whether the chosen 300 grid squares would be affected by changes in the sub-component weightings. This was done for each sub-component by using weightings values that ranged between 20 % lower and 20 % higher than the value used in the final scoring system. One hundred different sets of weightings were used to test the effects of these changes, with each successive step increasing the sub-component weighting by 0.4 %. Obviously, increasing the weighting of one sub-component reduced that of the other two but the ratio between the other two sub-components was kept constant.

The effect of changing the weighting given to each sub-component was determined using MS Excel. A spreadsheet was designed that allowed a new set of weightings to be inputted and then found the unique grid identifier code of the 300 squares with highest conservation score values. This process was repeated using the 100 different weighting combinations for each sub-component and these results were combined to give a score between 0 and 300 for each grid square, depending on the number of times it had been identified as one of the 300 potential PAs.

8.5.2 Results There was some disagreement about the relative importance given to the three sub-components by the different groups. All three groups considered the risk sub-component to be the least important but only group B thought that viability was more important than complementarity (Table 8-8). The final results gave complementarity a weighting that was more than 50 % higher than the viability subcomponent. The coincidence between the 300 highest scoring squares identified using the three group weightings ranged between 82 and 94 % (Table 8-9).

Table 8-8: Weightings given to sub-components

Group A Group B Group C Final

Factor Score Rank Score Rank Score Rank Score Rank

Complementarity 0.577 1 0.371 2 0.655 1 0.534 1 Risk 0.081 3 0.130 3 0.095 3 0.102 3 Viability 0.342 2 0.499 1 0.250 2 0.364 2

Chapter 8: Identifying areas of high conservation value in Maputaland 191 Table 8-9: Coincidence between the different conservation score results

Category Percentage coincidence

A & B 85.7 A & C 93.7 B & C 81.7 A & Final 97.7 B & Final 87.7 C & Final 93.0

The grid squares with high conservation value scores identified by the three different groups tended to be similar (Figure 8-15 to Figure 8-17). However, the scoring system of group B tended to increase the number of grid squares with high conservation value (Figure 8-16).

The sensitivity analysis found that 348 grid squares were identified as being one of the 300 priority sites by at least one of the different weighting combinations. More than 85 % of the grid squares identified in the final analysis were selected each time in the sensitivity analysis (Figure 8-14). The grid squares that were affected by changing the weightings were scattered throughout Maputaland, although many were at the periphery of clusters of unaffected squares (Figure 8-19).

The 300 most important grid squares identified by the final scoring system tended to be found around TEP, Sileza NR, Hlatikhulu FR, Lake Sibaya, Mkhuze GR and between False Bay Park and the Sodwana State Forest (Figure 8-18).

Figure 8-14: Grid squares chosen in sensitivity analysis Chapter 8: Identifying areas of high conservation value in Maputaland 192 Suitability Suitability 1 1

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Figure 8-17: Group C final score

Chapter 8: Identifying areas of high conservation value in Maputaland 193 S

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Chapter 8: Identifying areas of high conservation value in Maputaland 194 #S

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Figure 8-19: A sensitivity analysis of the final AHP conservation scoring system

Chapter 8: Identifying areas of high conservation value in Maputaland 195 8.5.3 Discussion The decision making process and the conservation value scoring system it produced identified several parts of Maputaland that would be suitable for the establishment of new PAs. Most of the chosen grid squares formed clusters, showing that using a fine resolution in this planning exercise did not produce a set of priority sites that were widely dispersed and so difficult to manage. However, this process should only be seen as a first step because many other factors will play a role in the final conservation land-use plan. However, it is important that this first stage in priority setting was based on accurate ecological assessments and on the opinions of key stakeholders.

These methods are still open to the criticism that they are unrealistic when dealing with legal disputes over land-use. Hence, anyone who wishes to develop a piece of land that has been identified as having high conservation importance using AHP methods could make a case for a different set of weightings. However, in Maputaland the NCS would only create a new PA if the local community gave their full backing and so these reservations are not relevant. In addition, the sensitivity analysis found that the chosen grid squares were relatively unaffected by changes in the sub-component weightings (Figure 8-19). This shows that the results of the selection process were robust and would put the onus on anyone disputing the results to find a justifiable set of weightings, based on the opinions of a group of experts, which would select different areas.

8.6 Comparing the results from the AHP with the gap analysis

The methods used in the AHP had great advantages because they included the opinions of a range of NCS stakeholders in the priority site selection process. However, this was a time consuming and costly process and so it is important to judge whether its results were a significant improvement on the gap analysis methods described in the previous chapter. One way to determine this is based on the extent to which the sites identified by the two methods coincided, as high coincidence levels would suggest that the extra effort was not needed. This section measures the coincidence between the two methods and suggests new approaches that would integrate the advantages of AHP and iterative reserve selection algorithms.

8.6.1 Methods and results The results from the gap analysis and the AHP exercise were imported into ArcView, which was used to identify the number of grid squares that were identified as being one of the 300 most important using both methods. A Chi squared test was used to find whether the number of sites chosen by both methods was significantly different from random. There were 52 grid squares that were identified as being one of the 300 most important by both the gap and the AHP analysis. This is a percentage coincidence of 17.3 %, which was significantly higher than would expect from a random distribution. Most of the coinciding squares were found in the Lebombo Mountains, either bordering Mkhuze GR or Hlatikhulu FR (Figure 8-20).

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Figure 8-20: Priority sites chosen by gap analysis and AHP methods

Chapter 8: Identifying areas of high conservation value in Maputaland 197 8.6.2 Discussion There was some overlap between sites identified using the two different methods but the coincidence percentage was only 17 %. This shows that incorporating information about ecological viability can have a dramatic effect on the decision making process and that it may not be enough to use the heuristic reserve selection algorithms described in the previous chapter. The results also show that the weightings given to the different biodiversity elements had a large effect on the grid squares that were identified as priority sites. In particular, the high weighting given to endemic vegetation types resulted in the selection of large patches of woody grassland between the Sodwana State Forest and Lake Sibaya. This land-cover type contains many endemic plant species and is seen as a high conservation priority but more than 30 % of its distribution has PA status, so it was not identified as a high conservation priority in the gap analysis.

When choosing sites for potential PAs it appears that the AHP methodology is better than the gap analysis. This is because the methods described in this chapter incorporated issues of ecological viability and allowed the NCS staff to identify particular conservation priorities. However, this decision-making process was unusual because it aimed to supplement an extensive PA network and so there was little need for an iterative approach to site selection. A more common approach is to plan a de novo PA system or supplement an area with little protection (Lombard et al., 1995; Hacker et al., 1998). This would have meant that many of the factors used in this analysis could not be calculated and that any factors based on biodiversity value would have acted to identify hotspots that might not represent biodiversity as a whole.

Therefore, a new approach is needed that uses AHP and an iterative approach in conservation planning. The most obvious way to achieve this would be to use the AHP methodology to set conservation targets, so for example it might be decided that their should be a 10 % conservation target for vegetation types and 25 % for endemic or threatened species (Maddock & Benn, 2000). At present, most work in the literature gives equal weighting to different biodiversity elements and so this approach has been widely adopted (Bedward et al., 1992; Kershaw et al., 1995; Csuti et al., 1997; Hopkinson et al., 2001). This ubiquity is probably an artefact of two major influences on the reserve selection literature, one of which is that equal-weighting simplifies the selection process and allows comparisons between different methods.

The second influence is more worrying because it derives from the lack of interaction between academics and conservation planners. This leads to reserve-selection methods that use equal- weightings because they have had no input from those directly involved in designing conservation networks. Hence, these methods will have little influence on the final planning process because there has been no buy in by stakeholders who will have to implement additions or changes to the PA network. Obviously, there are many examples of joint initiatives between the two groups and

Chapter 8: Identifying areas of high conservation value in Maputaland 198 the role of university academics has been very important in providing a theoretical base and developing new approaches (Vane-Wright et al., 1991; Scott et al., 1993; Margules & Pressey, 2000). However, this project has highlighted a variety of issues that are relevant to conservation planning at a fine scale that could serve as a focus for further work. This chapter has shown that one of the most pressing of these is the development of methods to allow for differential weightings of biodiversity elements. It is only by explicitly recognising and integrating the role of value judgements that any conservation planning exercise will reflect the goals of the people involved.

8.7 Chapter summary

• Conservation planning is a values-based process and so needs to incorporate the views of relevant stakeholders. One way to achieve this is to use the Analytical Hierarchy Process (AHP) methodology, which allows different factors to be weighted and combined in a transparent, mathematically valid manner.

• The AHP methodology was used to produce a conservation value scoring system based on factor data derived directly from the Maputaland GIS coverages. The factors were then classed, depending on their characteristics, as belonging to a complementarity, risk or viability sub-component.

• Maputaland was divided into a series of 1 km2 grid squares and the GIS was used to produce a factor score for each square. Two groups of NCS ecologists were asked to produce a weighting value for each factor by using a series of pairwise comparisons and three groups were asked to weight the sub-components. The different groups generally gave similar weightings to each factor and sub-component, with coincidence levels for the 300 most important sites identified by different groups ranging between 80 % and 98 %.

• The final conservation value score was based on the mean weightings given by the different groups. Most of the 300 most important grid squares identified by this system were found around TEP, Sileza NR, Hlatikhulu FR, Lake Sibaya, Mkhuze GR and the Sodwana State Forest. A sensitivity analysis showed that this system was robust and identified many of the same sites when the sub-component weightings were changed.

• The coincidence between the 300 priority sites chosen using the gap analysis and AHP methodology was only 17 %. This showed that standard methods, based on complementarity, did not reflect the views of NCS staff because they gave an equal weighting to each biodiversity element and did not allow for ecological viability.

Chapter 8: Identifying areas of high conservation value in Maputaland 199 Chapter 9: Conclusions

9.1 Introduction

This project is the first to use a GIS to analyse the effectiveness of a regional PA system at a spatial resolution that is relevant to conservation planning. It did this by combining a range of biodiversity and ecological data to identify conservation priorities based on different planning scenarios. It found that the PAs of Maputaland adequately represented the biodiversity of the region but that the area with PA status could be reduced from 2742 to 694 km2 and still contain at least 10 % of each bird species habitat. It also found that habitat types in the Lebombo Mountains were the least well represented but these had the lowest risk of being destroyed by subsistence agriculture. Finally, it was shown that incorporating data on ecological viability in the planning process can affect the outcome, as can changing the weightings given to different biodiversity elements as a result of value judgements on the part of conservation planners.

This project involved a series of stages that used methods associated with different disciplines, such as land-cover mapping, threat calculation, habitat modelling and conservation planning. Despite the disparate nature of these subjects, several common themes emerged and these will be discussed in the following sections. One of the most important aspects was the applicability of the scale used in this project and this will be discussed in section 9.2. The suitability of using the methods described in this thesis for other projects is discussed in section 9.3 and the effectiveness of using birds as a surrogate for biodiversity is discussed in section 9.4. Section 9.5 argues the importance of developing new conservation planning software and section 9.6 suggests a new model for conservation planning that combines complementarity and land-ownership issues.

9.2 Scale and conservation planning

Conservation planning for PA networks needs to be undertaken at two different spatial scales. The first is the broad scale approach that identifies countries or regions that should be the focus of further investigation. Most of these priorities have already been identified and there is a general coincidence between areas identified using different methods and different biodiversity surrogates (Davis et al., 1994; Olson & Dinerstein, 1998; Stattersfield et al., 1998; Myers et al., 2000; Williams et al., 2000). This scale of analysis is the most commonly used in the literature to identify priority sites and develop more sophisticated selection methods.

The second approach uses fine scale data to identify the approximate location of new PAs or additions to existing PAs. This can either follow on from the broad scale approach or be used by national or provincial conservation organisations to decide priorities. This study divided Maputaland into 1 km2 grid squares and so typifies the fine scale approach. This resolution was

Chapter 9: Conclusions 200 chosen so that any resultant PA would consist of a cluster of grid squares, all of which would have high biodiversity value. This avoided the problem of coarse-scale selection units that may be selected, despite containing large patches of agriculture or human settlements.

Work still needs to be done to refine large-scale priorities but there is a much greater need to develop practical PA planning strategies in those coarse-scale priority areas. For example, it is important to know that Madagascar is a biodiversity hotspot (Myers et al., 2000). However, Madagascar is a huge area that contained 14 million people in 1995 and where habitat conversion proceeds at a rapid rate (Green & Sussman, 1990; Cincotta et al., 2000). Hence deciding where to locate new PAs or expand existing PAs to protect this biodiversity is a much more pressing problem than further refining its hotspot status. Despite this, coarse-scale analyses predominate in the literature for reasons already described in chapter one. This is probably inevitable and will still produce important insights for conservation planning. However, this predominance will also have the following negative effects that need to be recognised and mitigated:

• The first problem is based on the finding that advances described in the literature have had little influence on most conservation planners (Prendergast et al., 1999). Part of the reason for this may be that most of these results do not address real-world problems and so seem less relevant to conservation managers than to the academics who generate these studies.

• The second problem is that the emphasis placed on the importance of some approaches may not be relevant to fine-scale analysis. For example, most of the literature on complementarity describes the importance of irreplaceable and flexible selection units and identifying selected-area sets (Williams, 1998a). However, results from chapter seven suggest that there would be a huge number of possible sets, containing no irreplaceable units. Therefore, any attempt to find a definitive selected-area set is probably irrelevant, as huge numbers of combinations would achieve the same level of representativeness with little loss in efficiency. Instead, conservation planners need to bring in a whole series of political, socio-economic and ecological factors to identify a manageable number of PA network choices.

• The final problem is the development of increasingly sophisticated methods in the literature to identify and solve problems that are actually an artefact of the coarse-scale approach. For example, the problem of choosing grid squares that are not ecologically viable or contain species at the edge of their range is mostly a by-product of using species list data (Branch et al., 1995; Nicholls, 1998; Williams, 1998a). This problem was overcome in this project by using habitat area data instead and this could be reduced in any future analysis by using selection units that are larger than the resolution of the surrogate data.

Chapter 9: Conclusions 201 The apparent effectiveness of a surrogate group may also depend on the scale used. For example, bird species have been shown to be an effective surrogate at the global or continental scale (Stattersfield et al., 1998). Results from Maputaland also show that birds can be effective at the fine resolution scale, as they show distinct land-cover type preferences. However, this relationship is much less obvious at the quarter degree grid square resolution, as 36 species were recorded in all of the 17 SABAP squares found in Maputaland, while 170 species were recorded in 13 squares or more. This explains the findings from an analysis of the KwaZulu-Natal SABAP data, which found that bird species were a poor surrogate (Fairbanks et al., 2001). Instead, these authors suggested that a much more complicated method was needed that used species data, together with environmental variables, to identify avian community assemblages and use them as a surrogate.

In some cases, such suggestions could lead to the rejection of a valuable surrogate even though the decision was made based on coarse-scale resolution research that had little relevance to conservation planning. Therefore, a better strategy would be devote some of the resources that are currently used to re-analyse existing coarse scale data sets to producing fine-scale information. This would then allow a parallel set of methods to be developed that are relevant to real-world conservation planning. Such a data set was developed for this project and the next section discusses its general applicability.

9.3 Applicability of methods for general use

Many conservation planning exercises are hampered by the lack of available data and so new methods need to be developed to obtain this information as cheaply as possible. This project developed a methodology that was relatively cheap, as the Landsat TM image cost US $600 and fieldwork costs were less than US $500. However, there were three factors that hid the actual costs of this exercise that need to be discussed. The first was that the data were collected as part of a PhD project and so there were no up-front analysis costs, despite the months it took to produce the land- cover coverage and analyse the data. The second factor was that the vegetation of Maputaland had already been described by a number of authors, several of whom work for the NCS and were consulted without charge for this project. Hence, this avoided the need for any fieldwork-based analysis to develop the land-cover classification scheme. A similar situation existed for the avifauna and some of the NCS staff have expertise on both groups. This allowed the land-cover type/bird species association matrix to be constructed without the need for further fieldwork, thus masking the actual cost of this exercise.

It should be remembered, however, that the association matrix, although supported by several published descriptions of Maputaland’s avifauna, had not previously been tested. Therefore, there is still a need to conduct more field surveys, as these can be cost effective if they significantly

Chapter 9: Conclusions 202 improve the representativeness of the final conservation network (Balmford & Gaston, 1999). However, it would probably not be feasible to survey an area as large as Maputaland and so a better strategy would be to model distributions based on original survey data (Nicholls, 1988). Such an analysis would have to take into account a range of factors, including land-cover type and pattern, climatic conditions and topography but the results could be sufficiently accurate for a biogeographically coherent area such as Maputaland.

Surveying could be even more effective if coupled with existing data and an analysis to identify areas that most need to be surveyed. Data recorded by volunteers also could be extremely important if collected in the appropriate manner. For example, the usefulness of the SABAP data could be improved enormously if the observers recorded their locations, either with a GPS unit or on a 1 km2 paper map. Thus with little extra effort the 105 000 Maputaland records could have been used to test the land-cover type/bird species association matrix. Hence, with some minor changes, the methods described above could act as a base for a cost effective strategy to produce the distributional data needed for conservation planning. Another important factor in this process is choosing an appropriate biodiversity surrogate and this is described in the next section.

9.4 Choosing biodiversity surrogates

Three types of surrogate could have been used in this project, with each representing a different level in the biodiversity hierarchy (Noss, 1990). The broadest surrogate could have been based on geological and topographical data, supplemented with climatic information. However, there are problems with this approach that are illustrated by the landscape coverage of KwaZulu-Natal (Figure 8-1; Fairbanks & Benn, 2000). This is a useful source of information for conservation planning at the broad, provincial scale but not for any finer scale analysis. This is because the derived data had a coarse scale and the landscape types were not derived independently. This is a particular problem for Maputaland because biodiversity patterns are partly determined by subtle differences in rainfall, geology and hydrology and these were masked when dealing with the KwaZulu-Natal data set (Fairbanks & Benn, 2000).

Therefore, the land-cover coverage developed for this project was a much more appropriate surrogate because it had a more relevant scale and the land-cover types were based on expert knowledge of the region’s biodiversity. In data-poor regions this could be the most useful surrogate because the information can be derived cheaply from satellite imagery. Higher costs may be incurred if fieldwork is needed to develop the classification system. However, efforts should be made to ensure that the resultant system is not too elaborate, in part because it is difficult to distinguish many similar land-cover types from satellite imagery but also because such complicated land-cover coverages are less useful in conservation planning. This is because any overly-detailed

Chapter 9: Conclusions 203 coverages would contain many different vegetation patches and the resultant selected-area sets would be widely dispersed.

Despite these advantages, using land-cover types as a surrogate is affected by the same problem as described for environmental variables, namely the classification system is based on the perceptions of the developers. This is why this project used bird species as the main surrogate because different species identified different combinations of land-cover types as suitable habitats. Hence, results showed that protecting 10 % of each land-cover type failed to protect the same target percentage for 34 bird species (chapter seven). This illustrates the effectiveness of the approach used in this project because it combines land-cover information that is accurate and cost-effective with species data, ensuring that the surrogate elements are independently derived.

There are many examples, however, where species data based on direct sampling is used for conservation planning. These data sets are often affected by sampling bias and so it would be preferable to model the species distributions instead. However, in some cases the necessary supplemental data or expertise may not be available as it was in Maputaland. In these cases, the approach described in chapter seven should be used, which involves identifying which bird species were least affected by sampling bias and using them as a surrogate for the whole taxon. In Maputaland, it was found that birds with a distinctive appearance or song were least affected by sampling bias and it was easy to classify these birds based on the descriptions from bird identification books. Identifying such criteria for other species groups may be more difficult but suitable distinctive species could be identified as those that were recorded first in each selection unit after a predetermined sampling effort. This approach of using distinctive species could be widely adopted because results showed that protecting at least 10 % of each distinctive bird’s habitat also protected the same target for all but one of the whole taxon.

9.5 Increasing the applicability of conservation planning methods

Geographic information systems have been used to design individual PAs based on biotic, abiotic and geographical features (Smith, et al., 1997; Kremen et al., 1999; Li et al., 1999b). This has allowed planners to set goals and integrate conflicting demands to produce PAs that are more likely to protect biodiversity and minimise conflict with local communities. This progress in PA design has been matched by those developing methods for selecting priority sites for the location of new PAs but these developments are much less widely implemented by the relevant conservation organisations (Prendergast et al., 1999). There are several reasons for this problem, one of which is that some planners think that the advantages are far out-weighed by the costs involved (Pressey, 1999a), despite the proven benefits (Pressey, 1994). Another is that many practitioners are not aware of these recent advances (Prendergast et al., 1999).

Chapter 9: Conclusions 204 There are other important obstacles, however, that need to be overcome before progress can be made. One of these is that many of the conservation planning scenarios described in the literature seem to have little practical relevance. The value of choosing a suitable scale has already been described in section 9.2. Another important problem is that researchers often produce spurious results because they do not follow steps that are usually implemented by conservation practitioners. This is illustrated by recent work that found that PA networks based on political units often give high ranking to species found at the edge of their range that are well protected in neighbouring provinces or countries (Erasmus et al., 1999).

Such a result is of academic interest but conservation planners tend to use expert review to identify these species and ensure that their influence in the planning process is reduced. For example, there are occasional records of Jackass penguins (Spheniscus demersus) being washed up on the beaches of Maputaland but no-one would use this information to determine where to locate a PA. More importantly, it would be difficult to convince someone with an ambivalent attitude to use these methods if their value was illustrated with a selected-area set that contained a site based on its marginal role as a penguin sanctuary.

Another very important barrier is the lack of freely available software that incorporates complementarity and irreplaceability into conservation planning. Excellent software does exist but these are either not well known, are quite expensive or are not generally available. If these ideas are to become widely applied, then there is a need for free software that is available on the Internet and has excellent support facilities. One way to achieve this would be to develop an extension for ArcView, a piece of software that is available without charge to conservation organisations. This would overcome problems of developing and supporting a de novo computer programme and would allow people familiar with the software to use it immediately. Such a system would allow people directly involved in conservation planning to investigate different scenarios and increase the flexibility and relevance of these approaches. One such strategy is to use complementarity to produce an integrated PA system that includes different management and ownership types and this is described in the next section.

9.6 PA planning and land-ownership

Future conservation strategies for PAs must involve local communities and the public and private sector (James et al., 2000). Such an approach is being developed in Maputaland, where community involvement is set to increase as part of land-claim settlements. In addition, there are several privately owned game reserves and the government is encouraging more private investment as part of the Maputaland Spatial Development Initiative. As well as different ownership status, these PAs are managed in different ways and zonation based on land-use is already practiced. For example, Mkhuze GR has one area devoted to general tourism and one to trophy hunting and it is likely that

Chapter 9: Conclusions 205 the amount of land where sustainable use is practiced will increase with local community involvement.

This means that the Maputaland data set could be used to plan land-use and land-ownership strategies for the region based on complementarity of state, private and communal ownership. This would involve setting conservation targets for each surrogate in each land-use/ownership category and using an algorithm that forced adjacency to provide large clusters of selected squares. The risk of agricultural transformation could also be incorporated by excluding squares with little risk from the PA system but ensuring that the selected grid squares bordered these low risk areas. This would increase the effective size of the areas under conservation management and possibly reduce access to the low risk grid squares (Peres & Terborgh, 1995), thereby reducing levels of unsustainable harvesting.

Such a strategy would have two great advantages. Firstly, it could be adopted widely as a model for conservation land-use planning. It was argued in sections 9.3 and 9.4 that the methods described in this project could be widely applied and so it would also be relatively straightforward to also adopt this planning system. Secondly, it would allow comparisons to be made between the cost and success of the different ownership and use types. This would provide valuable information on the relative advantages of the different methods and help ensure that the most successful management techniques are more widely adopted. It has been argued that the future success of biodiversity conservation is dependent on the “mundane” strategy of “rationalizing expenditure and increasing productivity” (Inamdar et al., 1999). It is hoped that the approach developed in this project will help achieve this by producing a sound methodological base for PA planning and assessment, both in Maputaland and more widely.

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