Assessing the relative contribution of conservation areas to the protection of key biodiversity features in Mpumalanga, South Africa.

Karen Vickers, Percy Fitzpatrick Institute of African Ornithology, University of Cape Town, Private Bag, Rondebosch, 7701, South Africa. email: [email protected]

Supervisors: Mathieu Rouget, South African National Biodiversity Institute. Private Bag x101, Pretoria 0001

Morne du Plessis, Percy Fitzpatrick Institute of African Ornithology, University of Cape Town, Private Bag, Rondebosch, 7701, South Africa.

ABSTRACT Contemporary conservation planning emphasizes target driven approaches for systematic identification of priority areas for biodiversity conservation. Budgetary constraints facing conservation agencies, particularly in developing countries, requires that the maintenance of existing reserves and the delineation of new reserves occurs in the most cost-effective manner possible, but little is known about whether the resources allocated to conservation areas reflects their conservation importance. The aim of this study was to quantify the conservation importance of every existing conservation area within the South African province of Mpumalanga and relate their importance to their current protection status. Using geographic information systems and associated conservation planning software, I assessed the spatial distribution of 336 key biodiversity features and calculated conservation importance based on an area’s contribution to feature targets. The province has 161 conservation areas including the Kruger National Park, categorized into 3 conservation area types based on legal protection status. Results indicate that while these areas contribute more to provincial biodiversity targets than non-conservation areas, a large proportion of biodiversity is found in informally protected areas such as conservancies and heritage sites. Conservation importance (determined through site irreplaceability) was 0.18, 0.04, and 0.10 for informal Type 3, semi-formal Type 2, and formally protected Type 1 conservation areas, respectively. While Type 1 conservation areas achieved more targets, Type 3 areas contained 42 (13%) features not represented elsewhere in the conservation network. For conservation agencies to succeed in meeting explicit biodiversity goals it is imperative that the contribution of informal conservation areas be addressed, and that resources be redistributed toward priority areas.

Keywords: protected area design, irreplaceability, conservation targets, biodiversity conservation. 2

1. Introduction Faced with the task of mitigating the ongoing loss of natural habitat and biodiversity, the conservation community has identified the need for an effective, efficient global protected area network (Chape et al. 2005). Currently, over 12% of the earth’s terrestrial surface is protected in reserves of various types (World Database on Protected Areas; WDPA 2004). However, the often cited 10% and 12% targets lack scientific credibility and many studies indicate that setting protected area targets at a uniform 10% is insufficient and potentially damaging to the conservation movement (Soule & Sanjyan 1998, Brooks et al. 2004). As land use pressures continue to increase, there exists a need to find a defensible means for determining the degree of coverage required to ensure the future persistence of the planet’s biodiversity (Brooks et al. 2004).

While the reported global protected area coverage has more then tripled in the last 15 years (Ervin 2003), using protected area number and extent as an indicator of conservation success may be inappropriate. One problem is that political, social, and economic pressures have, in the past, led to ad hoc reserves, delineated for purposes other than the representation and persistence of biodiversity (Pressey 1994). This has resulted in a network of conservation areas located on lands that may be unproductive and unrepresentative (Scott et al. 2001). The consequence of promoting quantity rather than quality means that worldwide we are spending limited conservation funding on protected areas that are inadequate in design and coverage (Ervin 2003).

The process of reserve delineation will remain inefficient unless it is directed at meeting measurable conservation goals. Contemporary conservation planning emphasizes target driven approaches for systematic identification of priority areas for biodiversity conservation, and is commonly conducted via Geographic Information Systems (GIS) and associated conservation planning tools (Margules & Pressey 2000). Spatial analyses of species distributions and other features thought to play a crucial role in ecosystem functioning can determine where current gaps exist in the protection of key biodiversity, and aid in the design of an optimal reserve network. Numerous studies have indeed revealed that globally there are gaps in the representation of biodiversity (e.g. the US GAP analysis, Scott 1993, Brooks et al. 3

2004, Rodrigues et al. 2004) and prioritized areas based on the concepts of species richness, representativeness (making sure all species are represented in the network at least once), complimentarity (representing species not represented elsewhere in the network), efficiency (protecting the most species per unit area), or the use of rare, endemic, or threatened species hotspots (e.g. Lombard 1995, Kiester et al. 1996, Rodrigues et al. 1999 Cantú et al. 2001, Eeley et al. 2001). Others include measures of site vulnerability and conservation importance to select areas of highest conservation priority (Myers 1988, Margules & Pressey 2000).

These exercises are undeniably useful in identifying current shortfalls and making practical recommendations for how to move forward in protected area design and land-use planning. However, the real challenge faced by many conservation agencies is not where to establish new reserves, but how to find the financial means to maintain the existing ones. Therefore, it is imperative that agencies ensure that conservation area resource allocation is clearly aligned with conservation objectives. Insight into assessing the effectiveness of reserves for achieving biodiversity objectives can only be gained by investigating the extent to which specific resources are being protected within specific reserves (Scott & Csuti 1997, Pressey et al. 2003). Yet a fundamental understanding of the relative importance of existing conservation areas, and interrogation into whether they receive adequate protection and resources that reflect their biodiversity value, is surprisingly non-existent in the conservation literature.

Over 1000 terms exist globally to designate a conservation area’s status and these terms often reflect the objectives of, and legal commitment to, habitat protection as defined by a nation’s legislation (Chape et al. 2005). No standard status requirements exist, though the World Conservation Union’s (IUCN) World Commission on Protected Areas are attempting to calibrate globally registered protected areas to six different classes based on park objectives and management strategies (IUCN 2004). If the preservation of biodiversity is the principal objective of a reserve network, then it is vital ensure the future integrity of an area by allocating protection status in a manner that is reflective of its contribution to biodiversity conservation.

The aim of this paper is therefore, to address the above issues of conservation area effectiveness. Using a novel approach, which to my knowledge, has never been used 4

to assess a region’s conservation network, I quantified each conservation area’s contribution towards explicit biodiversity targets by looking at area irreplaceability. I compare these results in relation to a conservation area’s status as well as to the biodiversity value of the non-conservation estate in order to reveal how effective the current network is. The study does not attempt to select an optimal network within the planning region, but merely to assess the relative importance of each existing conservation area. The study site is the South African province of Mpumalanga, a biologically diverse region that is in the process of conducting a provincial conservation plan for land-use management purposes. This analysis is based on the data from Mpumalanga’s conservation plan, it can therefore be used to make recommendations to the conservation agency responsible for the protection of the region’s biodiversity. . 2.Methods 2.1 Study Area Mpumalanga is located in eastern South Africa bordering Mozambique and Swaziland to the east and south, respectively (Figure 1). At 8.75 million hectares it makes up 6.5% of South Africa’s land and has a population of just over 3.1 million people. Agriculture and mining are two of the most important contributors to the provincial economy and these, along with afforestation, are also the major transformers of most of Mpumalanga’s vegetation communities. Still, the province has high levels of plant diversity containing 21% of the country’s known plant species (Emery et al. 2002). Plant diversity is predominantly confined to four identified centres of endemism. Previous landscape analyses indicate that 62% of Mpumalanga's 20 broad vegetation types are under protected (i.e. <10% under formal protection; Emery et al. 2002). In addition, five have been transformed by more than 40% (the theoretical threshold beyond which ecological processes are significantly disrupted; Driver et al. 2005) and are largely located along the foothills and high escarpment that runs north-south through the province (Emery et al. 2002).

Over 1170 of the province’s known flora (n=1094) and fauna (n=76) species are of special concern due to their endemic or threatened status (Driver et al. 2005). Plant species are largely at risk due to the extraction for medicinal purposes from local impoverished communities (Emery et al. 2002). The province contains at least 71 5

bird, 86 reptile, and 8 amphibian species that are threatened or endangered. Invertebrate species are less well known, but at least 13 species occur on the red-list.

2.2 Approach This study was designed to augment a provincial conservation plan conducted by the Mpumalanga Parks Board (MPB) and the Department of Agriculture and Land Administration, known as the Mpumalanga Biodiversity Conservation Plan (MBCP). The MBCP objective was to prioritize lands outside conservation areas and the results are now in the process of being integrated into land-use planning. This current study was performed using the same data as the MBCP (sources detailed below). Through the use of Arc View GIS (v3.2., ESRI, Redlands, California) and C-Plan (v3.20, Pressey 1999), I quantified the conservation importance of lands based on their contribution to the conservation targets of mapped biodiversity features (e.g. vegetation types, bird species, plant species etc.).

2.3 Data Sources 2.3.1 Biodiversity To ascertain the true biodiversity value of a unit of land would require data on every biological feature of the unit in question, an impossible task considering most of the world’s species remain either unsurveyed or undescribed. To mitigate this problem, many conservation planners have undertaken assessments using either environmental land class information (such as continuous vegetation types) or partial datasets which act as surrogates for biodiversity as a whole (Hitt & Frissell 2004, Sarkar 2005). However, the appropriateness of using surrogates is widely debated. Studies reveal that outcomes will differ depending on which surrogate type is used (see Lombard 1995, Reyers et al. 2002, Moore et al. 2003) and hence, no consensus has emerged in the literature. Therefore, it is necessary to incorporate a broad range of data types such as both land class information and species data, and the dataset used in this analysis is powerful for this reason. By incorporating many different types of features, it was possible to encompass a hierarchy of biodiversity (i.e. from genes to ecosystems; see Noss 1990). Coarse scale continuous features, such as vegetation types, are useful for capturing many levels of biodiversity pattern which occur across the entire planning domain, while fine scale features, such as species localities, allow for the inclusion of features which are of specific conservation concern (Cowling et al. 2004). 6

For the purposes of this study, a biodiversity feature is defined as either a biotic or abiotic factor that plays a role in the structure, composition and/or function of an ecosystem. The 336 biodiversity features used in this analysis include vegetation types, ecosystem processes, species distributions, and species localities and represent a dataset of priority features for conservation to the MPB. As Mpumalanga is responsible for conserving provincial endemics, as well as nationally and globally rare and threatened species (Act 10: the Mpumalanga Nature Conservation Act of 1998), the majority of species included are critically endangered, threatened, or endemic. Because they are species of conservation concern their distribution throughout the province is well documented. Furthermore, strength is derived from the fact that the targets set for these features of conservation concern play a very influential role in MPB’s management strategies thereby ensuring this current assessment is aligned with the goals of the implementing conservation agency. I hereafter refer to these data as Key Biodiversity Features (KBF).

KBF were classified into the following eight groups: vegetation types (n=68), mammals (n=13), birds (n=17), amphibians (n=3), invertebrates (n=13), reptiles (n=10), plants (n=189), and surrogates for important ecological processes (n=14). Data on the KBF were obtained from a large number of sources and are outlined in Table 1. Species distributions were modelled from locality data using the software BioMapper (v1.0, Hirzel et al. 2001) and are based on a series of environmental variables (vegetation types, mean annual precipitation, elevation, mean annual temperature, slope, land-cover classes, seasonal or perennial streams and rivers). Mammal, bird, amphibian and reptile data were mapped as distributions while plant species largely remained as locality data. Invertebrates contained both modelled and locality features.

The mammal features used in this study are primarily small mammals. This has the potential to affect the outcome of measuring conservation importance of areas, particularly those like Kruger National Park, and private game reserves whose predominant value lies in their ability to protect large mammal populations. I attempted to collect and include large mammal data for the conservation areas 7

throughout Mpumalanga, however because this data is largely only available for the formally protected areas, it was not viable to include in this assessment.

The ecosystem processes represented in this study were chosen based on their importance to the persistence of biodiversity. While ecosystem processes themselves are dynamic and not appropriate for setting tangible conservation targets, surrogates which represent ecosystem processes can be used. For example, while one cannot isolate the process of migration, corridors are features that enable the process of migration to persist and hence can be used as a surrogate to represent such a process. Data for most process surrogates were collected by MPB personnel and include: 1) known cave localities with 250m buffer zones because of their importance for maintaining bio and geo-diversity. Caves incorporate entire subterranean ecosystems which are sites of important geological processes and provide roosting and breeding habitat for bats, 2) important pans and wetlands which act as surrogates for water processes, nutrient exchange and inter and intra-specific interactions (they were largely identified by experts based on their notable importance for breeding and foraging grounds for species), 3) summit corridors thought to be crucial for the movement of species along the highly diverse escarpment, 4) forest patch and river corridors which are interfaces for nutrient exchange and species interactions and also crucial for species movement 5) Centres of Plant Endemism which contain high concentrations of endemic species and are therefore thought to be sites of important evolutionary process and 6) cool mountain slopes thought to play a role in biodiversity persistence in the face of global climate change.

2.3.2 Conservation Areas A comprehensive conservation area layer for the province was compiled. It revealed that there are currently 161 areas receiving recognition for being naturally vegetated parcels with some form of conservation strategy occurring there. These 161 areas fall into 13 types (see Table 2) and range in size from 16 hectares (Thorncroft Nature Reserve) to 914,794 hectares (Kruger National Park). Because the sample sizes of many of the reserve types were very small (6 of the 13 types consisted of only one conservation area), coarser classification was necessary for comparison and statistical purposes. In a meeting of MPB personnel, each conservation area was assigned a legal protection status based on the ownership and formality of the boundaries of each 8

area. Type 1 conservation areas are the only lands with any form of legal protection (e.g. most are proclaimed in a government gazette) and are managed largely for the purpose of biodiversity protection by provincial and national conservation agencies. Type 2 conservation areas are private or public lands that have been registered as protected areas but do not necessarily contain management strategies and whose status is less secure. Type 3 conservation areas are informal areas such as conservancies, heritage sites and statelands that may exist for purposes other than that of biodiversity conservation. Statelands are not conservation areas per se but those included here are considered so for the purposes of this study as they were identified in a prior study as having conservation potential (L. Cohen, unpublished data). These areas have no legal status, but have the potential to become conservation areas in the future if they are recognized as having high biodiversity value.

2.3.3 Land Cover Lands that have been transformed to urban, cultivated, or mined areas result in the loss of natural habitat and species diversity in these areas is likely to have been lost or altered from its original composition. Therefore, the spatial distributions of KBF were mapped only in naturally vegetated areas that are not mined, urban or cultivated. Habitat transformation data were derived from the National Land-Cover database of South Africa (Thompson 1999) and is based on 1994-1995 LANDSAT Thematic Mapper ™ satellite imagery. However, these data were updated by the MPB for use in the MBCP and provides more detailed information about the extent of land transformation within Mpumalanga. This was done by merging the 1995 and 2000 National Land Cover transformation data. The new coverage was then overlayed on top of a false colour positive satellite image (made up of Landsat 7 bands 5,4 & 3), and manually checked for discrepancies. Errors were picked up and the transformation coverage manually edited.

2.3.4 Threats Mpumalanga is currently faced with many land use threats and pressures. In 2005 alone the province received over 400 mining applications, from which conservation areas are not necessarily exempt (M. Lötter, MPB, pers. comm.). Furthermore, due to the socio-economic status of many of the surrounding communities, and the current development rates in the country, resource extraction and urban sprawl are primary 9

concerns for biodiversity conservation. I obtained land-use pressure maps on resource extraction (for medicinal and subsistence purposes), alien invasion, urban potential, and mining potential, from the Mpumalanga Biobase (Emery et al. 2002) and used them to derive a vulnerability score per planning unit. Sources and methods used to derive threat maps are outlined in Table 3.

Not all threats have the same effect on biodiversity (Neke & du Plessis 2004). While resource extraction and alien invasion are likely the more immediate threats affecting land areas, they have considerably less effect on biodiversity patterns and processes then mining and urbanization which cause large scale habitat destruction. Therefore, in calculating vulnerability scores, the latter two threats were weighted twice as heavily as the former two.

2.4. Analysis The analysis was conducted by dividing the province into uniform planning units and overlaying spatial information on the biodiversity features to calculate the number or extent of each feature occurring in each planning unit. An irreplaceability analysis was then conducted and each unit assigned a protection status. The conservation importance of each unit was derived from irreplaceability information and measures of representativeness, effectiveness and conservation priority were also considered (see below). These variables were then analyzed by conservation area, status, bioregion, and feature subset type (e.g. by taxonomic group) and where appropriate contingency tables were performed in order to determine statistical differences at the α=0.05 level.

2.4.1 Designing Planning Units The province was partitioned into hexagonal planning units 118 hectares in size (n=77,400). This scale was appropriate for assessing conservation areas as many conservation areas are quite small and would otherwise not have been feasible to include. Additionally, these were the same size units utilized in the MBCP. Such fine detail was selected as connectivity was an important feature in the MBCP and hexagons allow for more efficient reserve design due to the increased connectivity created by using a six-sided rather than a four-sided unit. It was also chosen in order 10 to retain as much resolution on key biodiversity features as possible while still functioning within the capabilities of the software.

2.4.2 Irreplaceability Analysis The conservation importance of both conservation and non-conservation lands within the entire province of Mpumalanga was quantified by calculating site irreplaceability. Irreplaceability is defined as the likelihood that a given site will need to be included in a network of sites to ensure that specific conservation targets are achieved (Pressey et al. 1993). It is expressed as a proportion of the total number of representative combinations that require the inclusion of that site to meet the pre-defined targets (see Ferrier et al. 2000).

A quantitative target is the amount of a feature (expressed as an area or number of localities) that is required to achieve a conservation goal (e.g. maintaining a minimum viable population size of 50 for species X). Units that are absolutely necessary to include in a network in order to meet the set target will have an irreplaceability equal to one.

Mpumalanga’s conservation targets, like the data on which they are based, were determined by a variety of methods for the various features. They were set by the MPB for use in the MBCP and hence are not arbitrary, but realistic targets designed to be achievable for reserve network and land-use designs. Targets for critically endangered species were set to 100% as every unit that contains a critically endangered feature is crucial for the preservation of that species. Targets for vegetation types were adopted from the National Spatial Biodiversity Assessment (NSBA; Driver et al. 2005). South Africa’s NSBA applies the concept of species-area curves for formulating targets which describes the relative area required for the conservation of a certain percentage of species and differs for each vegetation type (Desmet & Cowling 2004). Formulation of plant, bird, and mammal species targets occurred through a series of target workshops held to assess minimum viable populations and attended by expert ecologists. Forest targets were determined by the Department of Water and Forestry and other targets were set by the MBCP team. The biodiversity features used in this assessment and their associated target information is outlined in Appendix 1. 11

C-Plan was used to calculate the irreplaceability (IRR) of each planning unit in the province. An IRR threshold value of 0.5 was chosen to represent importance with regards to KBF irreplaceability. Thus, if the probability that a unit needs to be included in the network in order to achieve conservation targets is greater than 50%, it is deemed “important”. The conservation importance of any category of lands could then be determined by calculating the proportion of units in that category that had

IRR>0.5 (PIRR>0.5). Conservation importance was determined for conservation and non-conservation areas, each conservation area status and legal type, and each bioregion.

2.4.3 Bioregional Analysis As biodiversity is not distributed evenly across the landscape I sought to compare the value of KBF within designated bioregions throughout the province. Maps of the various biomes occurring in South Africa were obtained from the NSBA (Mucina et al. 2004) and used to designate bioregions. Three major biomes are found in Mpumalanga: forest, grassland, and savanna. The savanna biome was partitioned into lowveld and central bushveld regions while the grassland biome was split into highveld grasslands and escarpment/montane grassland. Because of the importance of Mpumalanga’s extensive freshwater and wetland habitat to provincial biodiversity, a wetlands layer was also added to the bioregional map, created in Arc View from pre- existing spatial maps of the 4000-plus endorheic pans found in the province. The resulting six bioregions were used to conduct an analysis of the irreplaceability of lands within each region and the proportion of irreplaceable units was compared to each bioregion’s conservation area statistics.

2.4.4 Feature Group Analysis (e.g. birds, mammals, ecosystem processes) C-Plan allows users to analyze planning unit irreplaceability for a subset of features. Therefore, I calculated IRR separately for each feature group (i.e. vegetation types, mammals, birds, amphibians, reptiles, invertebrates, plant species, and ecosystem processes). Conservation importance of each conservation area for each feature group

was then determined by calculating PIRR>0.5. This provides information on whether there are certain feature groups whose targets are not being met and hence are of particular conservation concern. The proportion of features in each feature group that 12 had their targets fully achieved in conservation areas (i.e. conservation area effectiveness, see below) allowed me to determine which conservation areas are important for those priority feature groups whose targets are not being met.

2.4.5. Excluding non-conservation areas An additional analysis was run where non-conservation planning units were excluded and the IRR of conservation area units only was calculated. This enabled a comparison of the conservation importance of those lands that the MPB are most concerned with, and reveals the importance of these lands for meeting targets if non- conservation units cannot be considered for biodiversity protection. Subsequently, a similar IRR analysis was repeated by feature group to show the relative importance of each conservation area for each feature group.

2.4.6 Conservation Area Representativeness and Effectiveness Many protected area assessments focus on the concepts of representativeness, complimentarity, and efficiency (see Bedward et al. 1991, Rodrigues et al. 1999, and Margules & Pressey 2000). I established conservation area representativeness by calculating the total number of features found in each area and determining the proportion not found in any other conservation area type. Instead of efficiency which is difficult to discern without incorporating land values (Balmford et al. 2000), I measured conservation area effectiveness by summing the percent contribution to targets of each feature per conservation area. This determined how well each conservation area type currently achieves KBF targets.

2.4.7 Conservation Priority Vulnerability can be incorporated into landscape assessments by weighing threats from other land uses against biodiversity value data per unit of land. The resulting measure can be thought of as conservation priority (Pressey 1997). Areas of high conservation importance and high vulnerability are the highest priorities for conservation action and focusing resources on these areas will enhance the likelihood of achieving conservation goals on the ground (Cowling et al. 1999). Therefore, the conservation priority of each planning unit was calculated by multiplying IRR and vulnerability scores together. Conservation priority was then compared between conservation and non-conservation areas by bioregion in order to determine whether 13 those lands with the highest conservation priorities are receiving the highest level of protection.

3. Results 3.1 Mpumalanga’s current conservation area network Of the 8.7 million hectares of land in Mpumalanga, 1.9 million ha (23%) falls within conservation areas; 62% of all conservation area is classified as Type 1 legal status, 7% as Type 2, and 30% as Type 3. The extent of land conserved in Type 1 areas is largely due to the Kruger National Park which constitutes 76% of all Type 1 conservation lands. Type 2 areas are larger on average than Type 1 and Type 3 areas, with median scores and ranges of 1940 ha (16-914,794 ha), 2214 ha (675 – 77,255 ha), and 1639 ha (17 – 90,039 ha) for Type 1, 2, and 3 conservation areas, respectively.

3.2 Regional patterns of irreplaceability A large percentage of the province has low irreplaceability, with 78.5% of the province’s 77,400 planning scoring an IRR<0.2. Only 5.5% of the province’s units are completely irreplaceable, 38% of which are contained in conservation areas. While units with lower IRR tend to be scattered throughout the province the highly irreplaceable units are clustered, and largely occur on and around the escarpment that runs north-south through the province. A provincial irreplaceability map depicts important biodiversity areas in relation to the province’s conservation area network (Figure 2). Conservation areas contain a higher proportion of irreplaceable units than non-conservation areas (Figure 3).

The key biodiversity features used for this assessment are not distributed evenly across bioregions causing some bioregions to contain more important biodiversity sites than others (Table 4). Mpumalanga’s forests contain the highest proportion of conservation importance, and forests are the second most protected bioregion by area. In contrast, the bioregion with the lowest conservation importance score, the lowveld bioregion, is the most protected (61% protected, of which 74% is covered by Kruger NP). Highveld grassland is the second most important region with regards to its contribution to KBF targets but is severely under-represented in the network at less than 5% (of which 80% falls into Type 3 conservation areas). 14

3.3 The conservation importance of Conservation Areas Recalling that conservation importance is defined as the proportion of sites with IRR>0.5, 7.9% of Mpumalanga is defined as “important” for achieving conservation targets. Of these important biodiversity units, 40% are already receiving some form of protection. Overall, informally protected Type 3 areas have higher conservation 2 importance than any other area type (χ 0.05,1=184.0, p<0.0001). Surprisingly, Type 2 areas scored lower for conservation importance than non-conservation areas.

When non-conservation areas are excluded from the IRR analyses all but 5 of the province’s 17,782 conservation units become completely irreplaceable. This indicates that the current conservation network is not adequately meeting provincial targets, and every planning unit that falls within a Type 1, 2, or 3 area is important for protecting at least one key feature. Thus, none of Mpumalanga’s reserves are redundant. Conservation areas combined contain 273 of the 336 KBF, 50 of which are not found anywhere on non-conservation lands. Summed IRR, the sum of the likelihood a site would need to be included for each feature found in a unit (possibly >1), can be used to differentiate between units that are highly irreplaceable for more than one feature (see Ferrier et al. 2000), and was calculated to show the relative importance of each conservation area in the province for contributing to conservation targets (Figure 5). Type 3 areas had a higher summed IRR than the other conservation area types

(XSUMIRR(±SE) =2.71±0.02, 2.49±0.03, and 4.43±0.03 for Type 1, 2, and 3 areas, respectively).

3.4 Contribution by Feature group Each feature group did not contribute evenly to conservation importance (Figure 6a). In particular, bird features and ecosystem process features (and to a lesser extent, vegetation types) seem to be driving the IRR analysis results. Examining the IRR of planning units by feature group reveals that those groups containing more features were not necessarily the groups contributing more to IRR.

When non-conservation areas are excluded from the IRR analysis, reptiles, amphibians, invertebrates, and mammals had the highest percentage of their targets fully achieved in conservation areas. While bird features make almost all planning 15 units in conservation areas irreplaceable, more bird species had 100% of their targets met in Type 1 conservation areas than Type 2 and Type 3 areas, a trend that also exists for five of the other seven feature types (Figure 6b).

3.5 Representativeness and Effectiveness Representativeness of a conservation area for the key biodiversity features in question was determined by calculating the number of KBF found there. Of the 336 features assessed, the most found in any one conservation area is 79 (23.5% of 336, Blyde River Canyon National Park) while the least is 2 (0.6% of 336; Flora Reserve Nature Reserve Forest Act), both of these areas being Type 1 conservation areas. Provincial Nature Reserves are the most representative of the 13 conservation area status types containing 153 (45.5%) KBF. However, 63 KBF are not represented anywhere in Mpumalanga’s conservation area network. Type 3 conservation areas contain 181 KBF, 42 of which are not found in other conservation area types; these areas are more representative than Type 2 areas but not as representative as Type 1 areas which contain 221 KBF, 78 of which are not represented elsewhere in the conservation network.

Effectiveness was measured by looking at areas that met 100% of a feature’s target. These results are obviously largely affected by the amount of land contained within each area. Results are congruent with the feature group results and reveal that Type 1 areas are the most effective of the conservation area types (Fig. 6b), fully achieving the targets of 95 (28.0%) biodiversity features. All conservation areas combined effectively achieve the targets of 153 (45.5%) KBF.

3.6 Conservation Priority The proportion of units with high conservation priority was similar between conservation and non-conservation areas. Priority areas are those with high conservation importance and high vulnerability. Throughout the province, 2.9% of the planning units are of intermediate priority (priority score >16 and <30), while 0.6% had a priority score >30 and are thus considered high priority (half of which fall into conservation areas; Figure 7). Of the 0.3% high priority sites occurring in conservation areas, Type 1 and Type 3 areas contained the highest proportion. In 16

addition, priority was not distributed evenly across each bioregion, as Mpumalanga’s forests contained a much higher proportion of the provinces high priority sites than any other bioregion (30%; Table 4).

4. Discussion 4.1 The Conservation importance of Mpumalanga’s Conservation Areas In a time when the impact of conservation investment on biodiversity status is being questioned by donors and policy-makers, agencies must be held accountable for the way resources are being allocated throughout the conservation network, a difficult task considering few parks have established systems to evaluate their own effectiveness (Parrish et al. 2003). This study is one of a small handful aimed at discerning the relative importance of each conservation area within a network. It has revealed that conservation areas are more irreplaceable, and contribute more to targets than non-conservation areas. Furthermore, even though not all targets are achieved in conservation areas, all areas in the current network are equally irreplaceable if biodiversity targets are to be met. This suggests that regardless of the motivation behind reserve delineation in Mpumalanga, the current network is not arbitrary, and conservation areas are not duplicating one another by representing the same features.

Type 1 areas are the most representative and effective of the three area types because they contain the most features and fully achieve a higher proportion of KBF targets. However, this result is likely linked to the extent they make up in the conservation area network. Despite their smaller total area, Type 3 conservation areas also scored highly for representativeness and effectiveness indicating that their contribution towards the protection of biodiversity is disproportionate to their total area. In addition, a large amount of the priority diversity which is necessary for meeting conservation targets is contained inside informally conserved Type 3 conservation areas which have not, in the past, been widely recognized as priority areas for biodiversity protection. This indicates that while, as stated above, reserves are not arbitrary, they are not currently maximizing the protection of important biodiversity features. If they were, then areas receiving highest conservation priority in terms of formal legislative protection should contain the most important biodiversity sites.

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This study highlights the value of private untransformed conservation areas and draws attention to the issue of how to maintain the integrity of biodiversity contained on such lands. Many of Mpumalanga’s Type 3 areas are relatively new (i.e. post 1980) and the unexpectedly high contribution of these areas to targets may reflect a more recent appreciation of the potential contribution of private lands to the conservation of species (Young et al. 1996, Botha 2001). However, it is not enough to simply have them exist on paper without management or future security. Furthermore, conservation areas contained on private lands are likely more vulnerable to extrinsic threats and future land-use change, which is supported by the high conservation priority score of Type 3 areas in this assessment. Because private lands dominate the South African landscape (a situation that is not likely to change in the near future), if representative and effective biodiversity conservation is to succeed in the future conservation agencies must begin to include the procurement of these areas in their management objectives.

4.2 Strengths and weaknesses of the present analysis The dataset used for this analysis has practical applications due to it’s origins in the MPB, but should be applied with caution. Distributional data are not indicative of population numbers, and population viability into the future is ultimately what will determine the success of conservation. Although the dataset captures a broad scale of biodiversity features, it is lacking in representation of a number of Mpumalanga’s priority features (e.g. only 22 of the province’s 71 red-listed bird species were used), and is also problematic in the number of data points used to model species distributions (i.e. lacking in survey extent, and survey intensity). Both of these factors could greatly alter the outcomes of such an analysis if improved (as shown by Freitag & van Jaarsveld 1998). Still, complete datasets for studies of this type are virtually non-existent and we cannot delay conservation assessments while biodiversity loss continues at current rates. Improvements on this dataset will make outcomes more defensible and can and should be made.

Some of the MPB’s conservation strategies will be based on large mammals and their distributions in protected areas, data that is lacking in our assessment due to conflicts of scale. In particular this may have influenced the weak performance of Type 2 conservation areas in our assessment. Of the 14 Type 2 areas analyzed in this study, 18

11 were private nature reserves, which are largely managed as private games farms and stocked with large mammals. Inclusion of large mammal data would likely boost the importance of Type 1 and 2 areas and should be a priority for future analysis in Mpumalanga.

The science of irreplaceability is limited by its inability to incorporate species ecology. Because the distribution of species is correlated across the landscape, targeting individual units for conservation action could have serious implications. If a population is distributed across more than one planning unit, and C-Plan meets the target by selecting two unconnected sites, there is a chance that the sub-populations selected will not be viable. Realistically, information is required on minimum viable population sizes in order to determine whether a feature can persist as two separate populations. However, this scenario was hopefully mitigated as much as possible by using planning units large enough in size, considering that no large mammal features were included.

Spatial autocorrelation also affects the probability that two neighbouring planning units have similar IRR values (as opposed to two unconnected units) which in turn affects the conservation importance score of a conservation area. This should not be seen as an issue because it is a natural property of species distributions across the landscape. This analysis was designed only to quantify a conservation area’s conservation importance regardless of the reason or history behind current biodiversity distributions.

Conservation area size was not included as a measure of conservation importance in this study. This has ramifications for an area like Kruger National Park where there are many options for achieving targets (and some targets are over achieved) which results in low irreplaceability values. However, conservation area size can be a proxy for a number of processes such as the maintenance of disturbance regimes, inter- specific interactions, faunal movements, distribution of biomass, and moisture and water dynamics (Cowling et al. 1999, Parrish et al. 2003).

Due to the above issues of size and correlation, caution must be exercised so that the quantitative value obtained for each planning unit in the province is not the only 19

measure upon which conservation decisions are based and that ecological considerations are included in reserve design. Nonetheless, our small planning units have benefits as coarse scale conservation assessments are generally not applicable to conservation plans on the ground due to their incongruency with the units that may be actually used in conservation such as cadastral units (Rodrigues et al. 1999, Reyers et al. 2002). Using quarter degree grid cells may have allowed for a more comprehensive dataset as a large amount of data is available for South Africa at this scale (see Lombard 1995). However, many of Mpumalanga’s reserves are significantly smaller than a quarter degree and hence, the outcomes would not have been practical for assessing individual reserves.

Lastly, while the predictive modelling should have minimized the bias in the spatial distributions of species diversity, it is possible that formal conservation areas such as national parks, are sampled more than private lands and non-conservation areas, due to their accessibility, infrastructure, and perception as efficient places to collect species information. While such a bias may have inflated the conservation importance of conservation areas as compared to non-conservation areas, it would have resulted in an under-representation of the conservation importance of Type 3 conservation areas and only strengthen the finding that these informal reserves are under- recognized for their contribution to biodiversity targets.

4.3 Patterns of conservation importance My results not only demonstrate discrepancies in conservation importance between conservation area type, but also between bioregions. It reveals further inconsistency in the representation of conservation area type within each bioregion. Of greatest concern is the lack of formal protection in two of the province’s most irreplaceable bioregions (i.e. highveld and escarpment/montane grasslands). Only 1% of the province provides formal protection for the highveld grasslands. However, this bioregion contains the second highest proportion of irreplaceable sites and has been identified as a critically endangered biome due to intensive land-use practices (Neke & du Plessis 2004). This has serious implications for the future persistence of the species dependent upon this bioregion. Therefore, either designating formal reserves in the highveld grassland should be a priority for the MPB or, at the very least, 20 management strategies need to be placed in the Type 3 reserves which form 80% of the protected areas in the highveld grasslands.

If agencies do not have the means to ensure that 100% of their conservation targets are contained in conservation areas, they should start by allocating resources to those areas that have high conservation importance but are most vulnerable to threats (priority) or to those areas where the most feature targets are achieved (effectiveness). Land-use pressures pose the highest risk to informal conservation areas whose boundaries are likely to be more provisional, but even formal reserves are vulnerable to some of these pressures, especially in developing countries where the likelihood of exploitation is greater due to poor management capacity (Bruner et al. 2004).

4.4 Future conservation strategies Resource allocation should be aligned with conservation importance if conserving biodiversity is the main objective of a conservation agency. Depending on the financial situation and purchasing options, redesigning a reserve network may not be feasible for a conservation agency. Particularly in developing countries, inadequate financial support limits the likelihood of expanding conservation area systems, where the combined costs of purchase, compensation, and management could equal $9 billion a year for the first 10 years (Bruner et al. 2004). However the fact that many of the highly irreplaceable sites in the province are found in areas that have already been identified for their conservation potential (even if it is only informally), gives Mpumalanga agencies a head start. The MPB can use the results obtained in this assessment to ensure that certain informal reserves become a substantial component of provincial biodiversity strategies. Without any added financial burdens, this can be achieved by focussing attentions on building positive relationships with private landowners and discussing the potential for formalizing some of these crucial areas. Other viable options that should be considered are implementing stewardship programs on private lands, or providing incentives for land owners to manage their conservation areas more formally (see Norton & Miller, 2002), programs that are already underway in other South African provinces (Botha 2001). These goals should be addressed beginning with priority areas in the bioregions that are currently under- represented, as well as in those areas where conservation targets for certain features (e.g. birds in Mpumalanga) are not being met. 21

4.5 Conclusion Every conservation area has a role to play in the global contribution to biodiversity protection whether managed as strict nature reserves, national parks, community conserved areas or private conservancies (Chape et al. 2005). This study does not attempt to denigrate the value of those conservation areas that do not score highly in my KBF assessment. However, it does attempt to highlight the discrepancy currently occurring in the allocation of scarce conservation resources to areas that are not necessarily deserving in the interests of biodiversity conservation. Agencies must be held accountable for decisions regarding where reserves are delineated and how scarce conservation resources are being spent. Setting targets and working towards achieving them are one of the only ways parks can measure their own effectiveness.

It is likely that the outcomes of this assessment are not unique to Mpumalanga but applicable across the world. Globally, conservation planners need to move one step beyond assessing what species are and aren’t protected and address the contribution that each protected area in a network makes to explicit conservation goals so that conservation resources are focussed in a consistent, effective manner.

Acknowledgements This study was funded by the Mpumalanga Parks Board/DALA and the South African National Biodiversity Institute. However, it would not have been possible without input from numerous individuals. Thank you to my supervisors, Mathieu Rouget (SANBI) and Morne du Plessis (University of Cape Town) for their much valued advice and comments. Mervyn Lötter from the Mpumalanga Parks Board provided valuable help with obtaining and utilizing the data. Many thanks to other MPB personnel who contributed: Tony Ferrar, Wilma Drodsky, Johan Eksteen, Frik Bronkhorst, and others.

22

Table 1. A list of the feature types and their respective data sources used to determine the Irreplaceability of planning units throughout the province of Mpumalanga, South Africa.

No. of Feature Type Sources features Vegetation Types 68 Modified from Mucina & Rutherford (2004) and the National Spatial Biodiversity Assessment (Driver et al. 2005) Mammals 13 Northern Flagship Institution (NFI); MPB mammal database; South African Natural Heritage Programme (du Preez 2000); Ecoplan Monograph No. 1 (Rautenbach 1982); MONDI; SAPPI; specialists in the respective fields; private individuals Birds 22 MPB Biobase (Emery et al. 2002); University of Cape Town Avian Demography Unit; NFI; TOTAL CWAC Report 1992- 97; South African Crane Working Group; Natural Heritage Sites Programme 2000/2001; Important Bird Areas of Southern Africa; The Water Research Commission Report TT82/96; Expert ornithologists (Dr. Warwick Tarboton; Mr. John McAllister; Mr. Kotie Herholdt); SAPPI; MONDI; private landowners Amphibians 3 Jacobsen (1989); Transvaal Museum records; MPB biobase Invertebrates 17 Transvaal Museum records; Agricultural Research Council; University of Pretoria Conservation Planning Unit Department of Zoology and Entemology; Lepidopterist Society Reptiles 10 Jacobsen (1989); expert herpetologists Plants 189 Distributional data from the former Transvaal Threatened Plant Program (Fourie 1986), MPB Biobase (Emery et al. 2002), Expert botanists; Protea Atlas Project Ecosystem 14 MPB Biobase (Emery et al. 2002) and regional experts for Processes important wetlands and pans; Centres of endemism, Van Wyk & Smith (2001); expert on phytochoria (Prof. Braam van Wyk)

23

Table 2. The status, number and extent of protected areas in the South African Province of Mpumalanga. Protected Amount % of PA

Protected Area Status Areas (n) (Ha.) estate Type 1 National Park 2 967,145 50.4 Provincial Nature Reserve 30 186,464 9.7 MPB Flora Reserve 1 32 <0.1 Primary Conservation Area 6 10,313 0.5 Nature Reserve Forest Act 4 1,431 0.1 Joint Management Area 1 9,191 0.5 Type 2 Municipal Nature Reserve 1 2,220 0.1 Private Nature/Game Reserve 11 121,434 6.3 Leased Area 1 675 <0.1 MPB Cooperative Conservancy 1 12,429 0.6 Type 3 SA Heritage Site 60 121,184 6.3 Conservancy 26 449,049 23.4 Stateland 16 24,475 1.3 Unknown 1 12,779 0.7 PA TOTAL 161 1,918,819 100.0

Non- Unprotected - 6,285,745 conservation GRAND TOTAL 161 8,204,564

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Table 3. A summary of the threats used to determine conservation priority and including the sources and methodology used to obtain the 4 treat layers. Threat Weight Source Methodology Resource 1 MPB† Scores assigned based on proximity to rural Extraction Biobase villages, settlements, towns and roads (Emery et al. 2002) Urban Potential 2 MPB† Buffer zones placed around established urban Biobase centres. Urban threat score decreased as distance (Emery et from cities and towns increased. al. 2002) †† Alien Plant 1 NSBA Climatic envelope modelling was used to derive Invasion (Rouget et climatic suitability surfaces for each major plant al. 2005) invader. A potential distribution surface was created for 71 major plant invaders and the number of plant invaders that could potentially invade per pixel was summarized.

†† Mining Potential 2 NSBA Areas of high mining potential include large (Driver et al. deposits or mineralised fields for 13 2005) economically important commodities (gold, platinum, group metals, diamonds, chromites, manganese, vanadium, titanium, zirconium, antimony, aluminium silicates, coal, fluorspar and vermiculite). Mining potential was determined based on the accuracy of the deposit map and it’s size. Mines and deposits were buffered by 500m and mineralised layers by 1000 m.

† Mpumalanga Parks Board †† National Biodiversity Spatial Assessment

25

Table 4. A summary of the protected area status, conservation importance, and conservation priority of each bioregion found within the province of Mpumalanga. Conservation importance is measured as the proportion of planning units with an irreplaceability value >0.5 while conservation priority is determined as a product of a planning unit’s conservation importance score and its vulnerability score.

High Conservation Amount Protected (%) priority Bioregion Size (ha.) importance units Type Type Type (Pirr>0.5) Total (%) 1 2 3 Central Bushveld 140,193 0.04 1.1 6.8 0.3 4.4 11.6 Escarpment Grasslands 302,068 0.22 5.4 5.3 0.7 14.6 20.6 Forests 95,035 0.65 30.0 21.4 1.1 20.6 43.1 Highveld Grasslands 143,337 0.22 2.9 0.4 0.1 4.2 4.8 Lowveld 1,203,338 0.04 1.6 49.6 5.8 6.2 61.5 Wetlands 34,578 0.18 4.1 1.9 1.1 7.8 10.8

26

Figure 1. Provincial map of South Africa showing the location of Mpumalanga, where the relative contribution of conservation and non-conservation lands to the conservation of key biodiversity features was assessed.

27

A)

B)

C)

Figure 2. Results of an irreplaceability analysis conducted using C-Plan for the province of Mpumalanga with a) Type 1 b) Type 2 and c) Type 3 conservation areas. Red areas indicate full irreplaceability meaning that these sites are necessary to conserve if conservation targets are to be met.

28

0.80

Protected 0.75 Non-protected

0.70 0.15

0.10

Frequency units) (proportion of planning 0.05

0.00 0 0.01-0.25 0.26-0.5 0.51-0.75 0.76-0.99 1.0

Irreplaceability

Figure 3. Frequency distribution of the irreplaceability values of conservation and non-conservation areas, displayed as a proportion of planning units. There are 17,783 conservation area, and 54,724 untransformed non-conservation, planning units of 118 ha within Mpumalanga province.

29

0.20

0.18

0.16 0.14

0.12 0.10

0.08

0.06 0.04

0.02 (Pirr>0.5) Importance Conservation 0.00 Type 1 Type 2 Type 3 non-protected

Figure 4. The relative conservation importance (displayed as a proportion of planning units with IRR>0.5) of conservation areas and non-conservation areas in Mpumalanga when these areas are classified into Type 1, 2, or 3 conservation areas based on their legal protection status. The dotted line indicates the provincial proportion of planning units with an IRR>0.5.

30

Figure 5. Results of an irreplaceability analysis that excludes non-conservation areas (grey area on map) showing summed irreplaceability values for the conservation area planning units. Because almost all planning units were completely irreplaceable in this analysis, summed irreplaceability was used to distinguish between areas that contribute a great deal to the targets of more than one feature (by summing each feature’s IRR in that particular unit).

31

A) 1 Type 1 0.9 Type 2 Type 3 0.8 all conservation areas 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Conservation Importance (Pirr>0.5) 0 VEG MAM BIR AMP INV REP PLA PRO ALL FEATURES

B) 100

90 Type 1 Type 2 80 Type 3 70 all conservation areas 60

50

40

30

Targets fully achieved (%) fully Targets 20

10

0 VEG MAM BIR AMP INV REP PLA PRO ALL FEATURES

Figure 6. Irreplaceability analysis for each feature type to reveal A) conservation importance (as a proportion of units with irreplaceability>0.5) of each feature subset in Type 1, 2, and 3 conservation areas and B) the quantity of feature targets (n=336) that are fully achieved for each feature subset in Type 1, 2, and 3 conservation areas. The black bars denote all conservation areas and are not necessarily the sum of Types 1, 2, and 3, as a portion of targets achieved in each of these conservation area types can sum to more than 100%. The feature subsets analyzed are Vegetation types (VEG, n=68), Mammals (MAM, n=13), Birds (BIR, n=22), Amphibians (AMP, n=3), Invertebrates (INV, n=17), Reptiles (REP, n=10), Plants (PLA, n=189), and Ecosystem processes (PRO, n=11).

32

Figure 7. A priority map of Mpumalanga in relation to current conservation areas of formal and informal status. Priority was determined by identifying areas with high conservation importance and high vulnerability to threats. Resource extraction and alien plant invasion were single weighted threats, while urban potential and mining potential were double-weighted due to their potential effects on biodiversity. Priority scores are the product of threat scores and irreplaceability scores.

33

Literature Cited

Balmford, A., K.J. Gaston, A.S.L. Rodrigues and A. James. 2000. Integrating Costs of Conservation into International Priority Setting. Conservation Biology 14(3): 597-605.

Bedward, M., R.L Pressey and A.O. Nicholls. 1991. Scores and score classes for evaluation criteria: A comparison based on the cost of reserving all natural features. Biological Conservation 56: 281-294.

Botha, M. 2001. Incentives for conservation on private land: Options and opportunities. Botanical Society – Cape Conservation Unit Summary Report 02/2001. Cape Town, South Africa.

Brooks, T.M., M.I. Bakarr, T. Boucher, G.A.B. Da Fonseca, C. Hilton-Taylor, J.M. Hoekstra, T. Moritz, S. Olivieri, J. Parrish, R.L. Pressey, A.S.L. Rodrigues, W. Sechrest, A. Stattersfield, W. Strahm and S.N. Stuart. 2004. Coverage provided by the global protected-area system: Is it enough? BioScience 54(12): 1081-1091.

Bruner, A.G., R.E. Gullison, and A. Balmford. 2004. Financial costs and shortfalls of managing and expanding protected-area systems in developing countries. Bioscience 54(12): 1119-1126.

Cantu, C., R. G. Wright, J. M. Scott and E. Strand. 2001 Assessment of current and proposed nature reserves of Mexico based on their capacity to protect geophysical features and biodiversity. Biological Conservation 115: 411-417.

Chape, S., J. Harrison, M. Spalding and I. Lysenko. 2005. Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Phil. Trans. R. Soc. B. 360: 443-455.

Cohen, L. 2004. Prioritizing statelands for conservation action in Mplumalanga. Unpublished report. Nelspruit, South Africa.

Cowling R.M., R.L. Pressey, A.T. Lombard, P.G. Desmet and A.G. Ellis. 1999. From representation to persistence: Requirements for a sustainable system of conservation areas in the species-rich Mediterranean-climate desert of southern Africa. Diversity and Distributions 5(1): 51-71.

Cowling, R.M., A. Knight, D.P. Faith, S. Ferrier, A. Lombard, A. Driver, M. Rouget, K. Maze and P. Desmet. 2004. Nature conservation requires more than a passion for species. Conservation Biology 18(6): 1674-1676

Desmet, P. and R. Cowling. 2004. Using the species–area relationship to set baseline targets for conservation. Ecology and Society 9(2): 11-34

Driver, A., K. Maze, M. Rouget, A.T. Lombard, J. Nel, J.K. Turpie, R.M. Cowling, P. Desmet, P. Goodman, J. Harris, Z. Jonas, B. Reyers, K. Sink, and T Strauss. 2005. 34

South African National Spatial Biodiversity Assessment 2004: Priorities for biodiversity conservation in South Africa. Strelitzia 17. South African National Biodiversity Institute, Pretoria.

Du Preez, C. 2000. South African Natural Heritage Programme. Annual Report 2000/2001. Department of Environmental Affairs & Tourism. Midrand.

Eeley, H.A.C., M.J. Lawes and B. Reyers. 2001. Priority areas for the conservation of subtropical indigenous forest in southern Africa: a case study from KwaZulu Natal. Biodiversity and Conservation 10(8): 1221-1246.

Eken, G., L. Bennun, T.M. Brooks, W. Darwall, L.D.C. Fishpool, M. Foster, D. Knox, P. Langhammer, P. Matiku, E. Radford, P. Salaman, W. Sechrest, M.L. Smith, S. Spector and A. Tordoff. 2004. Key biodiversity areas as site conservation targets. BioScience 54(12): 1110-1118.

Emery, A.J., M. Lötter and S.D. Williamson. 2002. Determining the conservation value of land in Mpumalanga. Department of Water Affairs and Forestry/Department for International Development Strategic Environmental Assessment. Mpumalanga Parks Board, Nelspruit, South Africa.

Ervin J. 2003. Protected area assessments in perspective. BioScience, 53(9): 819-822.

ESRI, 1999. Arc View GIS version 3.2. Environmental Systems Research Institute, Redlands, CA, USA.

Ferrier, S., R.L. Pressey, and T.W. Barrett. 2000. A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real- world planning, and a research agenda for further refinement. Biological Conservation 93: 303-325.

Fourie, S.P. 1986. The Transvaal, South Africa, Threatened Plants Programme. Biological Conservation 37: 23-42

Freitag S. and A.S. van Jaarsveld. 1998. Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledge. Proceedings of the Royal Society of London, 265: 1475-1482.

Hirzel, A.H., V. Helfer and F. Métral. 2001. Assessing habitat-suitability models with a virtual species. Ecological Modelling 145: 111-121.

Hitt, N.P. and C.A. Frissell. 2004. A case study of surrogate species in aquatic conservation planning. Aquatic conservation –Marine and Freshwater Ecosystems 14(6): 625-633.

IUCN – The World Conservation Union. 2004. Guidelines for Protected Area Management Categories. Gland, Switzerland:IUCN.

Jacobsen, N.H.G. 1989. A Herpetological Survey of the Transvaal. Ph.D. Thesis, University of Pretoria. 35

Kiester, A.R., J.M. Scott, B. Csuti, R.F. Noss, B. Butterfield, K. Sahr and D. White. 1996. Conservation prioritization using GAP data. Conservation Biology 10(5): 1332-1342.

Lombard, A.T. 1995. The problems with multi-species conservation: do hotspots, ideal reserves and existing reserves coincide? South African Journal of Zoology, 30(3): 145-163.

Lombard, A.T., R. M. Cowling, R.L. Pressey and A.G. Rebelo. 2003. Effectiveness of land classes as surrogates for species in conservation planning for the Cape Floristic Region. Biological Conservation, 112: 45-62.

Margules, C.R. and R.L. Pressey. 2000. Systematic conservation planning. Nature 405: 243-253.

Moore, J.L., A. Balmford, T. Brooks, N.D. Burgess, L.A. Hansen, C. Rahbek and P. Williams. 2003. Performance of sub-Saharan vertebrates as indicator groups for identifying priority areas for conservation. Conservation Biology 17(1):207-218.

Mucina, L. and Rutherford, M.C. (eds.) 2004 Vegetation Map of South Africa, Lesotho and Swaziland: Shapefiles of basic mapping units. Beta version 4.0, February 2004, National Botanical Institute, Cape Town.

Myers, N. 1988. Threatened biotas: hotspots in tropical forests. The Environmentalist 8: 178-208.

Neke K.S. and M.A. du Plessis. 2004. The threat of transformation: Quantifying the vulnerability of grasslands in South Africa. Conservation Biology 18(2): 466-477.

Norton, D.A. and C.J. Miller. 2000. Some issues for the conservation of native biodiversity in rural New Zealand. Ecological Management and Restoration 1: 26- 33.

Noss, R.F. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4: 355-364.

Noss, R.F. 2004. Conservation targets and information needs for regional conservation planning. Natural Areas Journal 24(3): 223-231.

Noss, R.F., C. Carroll, K. Vance-Borland and G. Wuerthner. 2002. A multicriteria assessment of the irreplaceability and vulnerability of sites in the greater Yellowstone ecosystem. Conservation Biology 16: 895-908.

Parrish, J.D., D.P. Braun and R.S. Unnasch. 2003. Are we conserving what we say we are? Measuring ecological integrity within protected areas. BioScience 53(9): 851- 860.

Pressey, R.L. 1994. Ad Hoc Reservations: Forward or backward steps in developing representative reserves systems? Conservation Biology 8(3): 662-668. 36

Pressey, R.L. 1999. Applications of irreplaceability analysis to planning and management problems. Parks 9(1): 42-51.

Pressey, R.L., C.J. Humphries, C.R. Margules, R.I. Vane-Wright and P.H. Williams. 1993. Beyond opportunism: Key principles for systematic reserve selection. TREE 8(4): 124-128.

Pressey, R.L., R.M. Cowling and M. Rouget. 2003. Formulating conservation targets for biodiversity pattern and process in the Cape Floristic Region, South Africa. Biological Conservation, 112: 99-127.

Rautenbach, I.L. 1982. Mammals of the Transvaal. Ecoplan Monographs 1: 1-211.

Reyers, B., D.H.K. Fairbanks, K.J. Wessels and A.S. van Jaarsveld. 2002. A multicriteria approach to reserve selection: addressing long-term biodiversity maintenance. Biodiversity and Conservation 11: 769-793.

Rodrigues, A.S.L., R. Tratt, B.D. Wheeler and K.J. Gaston. 1999. The performance of existing networks of conservation areas in representing biodiversity. Proceedings of the Royal Society of London 266: 1453-1460.

Rodrigues, A.S.L., S.J. Andelman, M.I. Bakarr, L. Boitani, T.M. Brooks, R.M. Cowling, L.D.C. Fispool, G.A.B. da Fonseca, K.J. Gaston, M. Hoffmann, J.S. Long, P.A. Marquet, J.D. Pilgrim, R.L. Pressey, J. Schipper, W. Sechrest, S.N. Stuart, L.G. Underhill, R.W. Waller, M.E.J. Watts and X. Yan. 2004. Effectiveness of the global protected area network in representing species diversity. Nature 428: 640-643.

Rouget, M., D. M. Richardson, J. A. Nel, D. C. Le Maitre, B. Egoh, and T. Mgidi. 2004. Mapping the potential ranges of major plant invaders in South Africa, Lesotho and Swaziland using climatic suitability. Diversity and Distributions 10:475-484.

Sarkar, S., J. Justus, T. Fuller, C. Kelley, J. Garson and M. Mayfield. 2005. Effectiveness of environmental surrogates for the selection of conservation area networks. Conservation Biology 19(3): 815-825.

Scott, J.M. and B. Csuti. 1997. Noah worked two jobs. Conservation Biology 11: 1255-1257.

Scott, J.M., F. Davis, B. Csuti, R. Noss, B. Butterfield, C. Groves, H. Anderson, S. Caicco, F. Derchia, T.C. Edwards, J. Ulliman and R.G. Wright. 1993. Gap Analysis – A geographic approach to protection of biological diversity. Wildlife Monographs 123: 1-41.

Scott, J.M., R.J.F. Abbitt and C.R. Groves. 2001. What are we protecting? The U.S. conservation portfolio. Conservation Biology in Practice 2(1): 18-19. 37

Soule, M.E. and M.A. Sanjyan. 1998. Conservation targets: do they help? Science 279: 2060.

Thompson, M.W. 1999. South African National Land Cover database project data users manual final report (phase1, 2 and 3). CSIR ARC Report ENV/P/C 98136 version 3.1.

Van Wyk, A.E. and G.F. Smith . 2001. Regions of floristic endemism in Southern Africa: a review with emphasis on succulents. Umdaus Press, Hatfield, South Africa

WDPA Consortium - World Database on Protected Areas Consortium. 2004 World Database on protected areas. Accessed on Jan 5, 2006 at http://sea.unep- wcmc.org/wdbpa.

Young, M.D., N. Gunningham, J. Elix , J. Lambert, B. Howard, P. Grabosky and E. Macrone. 1996. Reimbursing the future: An evaluation of motivational, voluntary, price-based, property-right, and regulatory incentives for the conservation of biodiversity. Biodiversity Paper 9, Biodiversity Unit, Dept of Environment, Canberra.

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Appendix 1. A list of the 336 Biodiviersity features used to analyze the conservation importance of conservation areas in Mpumalanga, South Africa. Each feature is listed either as continuous distributional data or as locality data and the corresponding conservation target that was set for that feature is given (either in hectares or as a number of localities). Feature Name Target Vegetation Features - Continuous distributional data hectares Malelane Mountain Bushveld 15130.70 Granite Lowveld 135853.10 Delagoa Lowveld 13272.30 Ohrigstad Mountain Bushveld 36781.80 Rand Highveld Grassland 141404.60 Central Sandy Bushveld 87139.40 Carletonville Dolomite Grassland 1243.10 Northern Escarpment Quartzite Sourveld 28382.20 Gold Reef Mountain Bushveld 2485.90 Andesite Mountain Bushveld 10133.10 Gabbro Grassy Bushveld 14101.90 Makuleke Sandy Bushveld 669.00 Southern Lebombo Bushveld 3275.80 Northern Lebombo Bushveld 21287.70 Leolo Summit Sourveld 28.10 Loskop Mountain Bushveld 42804.90 Frankfort Highveld Grassland 11933.80 Legogote Sour Bushveld 66937.40 Tzaneen Sour Lowveld 39.20 Lydenburg Thornveld 37247.20 Pretoriuskop Sour Bushveld 17915.40 Kaalrug Mountain Bushveld 11172.40 Sekhukhune Montane Grassland 33157.30 Sekhukhune Mountain Bushveld 38401.70 Sekhukhune Plains Bushveld 18936.20 Mopane Basalt Shrubland 2065.40 KaNgwane Montane Grassland 145118.90 Swaziland Sour Bushveld 16617.50 Sweet Arid Basalt Lowveld 53537.70 Amersfoort Highveld Clay Grassland 67496.00 Eastern Highveld Grassland 298223.20 Springbokvlakte Thornveld 24075.10 Eastern Temperate Freshwater Wetlands 3812.30 Soweto Highveld Grassland 234163.00 Lebombo Summit Sourveld 225.90 KwaZulu-Natal Highland Thornveld 21.10 Lowveld Riverine Forest 116.90 Northern Mistbelt Forest 110.50 Zululand Lowveld 7309.00 Subtropical Freshwater Wetlands 170.80 Barberton Montane Grassland 29555.90 Poung Dolomite Mountain Bushveld 8141.10 Northern Escarpment Dolomite Grassland 25302.60 Northern Escarpment Afromontane Fynbos 230.00 Barberton Serpentine Sourveld 2630.00 Steenkampsberg Montane Grassland 104140.00 39

Low Escarpment Moist Grassland 3920.00 Paulpietersburg Moist Grassland 30924.30 Ithala Quartzite Sourveld 6399.30 Wakkerstroom Montane Grassland 60839.20 Loskop Thornveld 14447.40 Lowveld Rugged Mopaneveld 2809.40 Phalaborwa-Timbavati Mopaneveld 8478.80 Nwambyia-Pumbe Sandy Bushveld 1773.30 Northern Free State Shrubland 180.20 Marikana Thornveld 2856.60 Subtropical Salt Pans 26.60 Tsakane Clay Grassland 5043.40 Mopane Gabbro Shrubland 633.80 Long Tom Pass Montane Grassland 28347.50 Croc Gorge Mountain Bushveld 12960.50 Northern KZN Misltbelt Forest 2053.90 Subtropical Afromontane Forest 3242.40 Mpumalanga Afromontane Forest 9975.70 Maputaland Scarp Forest 2280.40 Dry Scarp Forest 439.80 Dry Afromantane Forest 6487.70 Barberton Scarp Forest 2206.60 Mammal Features - Species distribution data hectares Amblysomus robustus 2689.00 Amblysomus hottentotus meesteri 489.00 Cloeotis percivali australia 2689.00 Chrysospalax villosus 1324.00 Neamblysomus julianae Kruger subpop 1984.80 Georychus capensis (yatesi) 2394.00 Rhinolophus blasii empusa 5802.00 Rhinolophus swinnyi 1342.00 Neamblysomus gunningi (Sekukuni) 929.85 Myotis welwitschii 14800.00 Miniopterus natalensis 3594.00 Ourebia ouribi 7500.00 Mammal Features - Species locality data localities Neamblysomus julianae Pretoria subpop 9.00 Bird Features - Species known distribution data hectares Rudd's Lark known 47332.00 White-winged Flufftail 12845.43 Botha's Lark 92964.00 Saddle-billed Stork 800000.00 Cape Vulture 625.16 Southern Bald Ibis 7560.69 Southern Ground Hornbill 797500.00 Striped Flufftail 30429.00 Lappet-faced Vulture 12929.00 White-headed Vulture 9251.00 Yellowbreasted pipit farm 21928.00 Bird Features - Species modelled distribution data hectares Blue Korhaan mod 320000.00 Rudd's Lark modelled 161705.00 Bird Features - Species known nesting distribution data hectares Blue Swallow (nesting) 4215.38 40

Bird Features - Species known breeding distribution data hectares Wattled Crane (breeding) 50341.29 Blue Crane (breeding) 80823.17 Grey Crowned Crane (breeding) 793.13 Bird Features - Species known foraging distribution data hectares Wattled Crane (feeding) 94248.00 Yellowbreasted pipit (foragaing) 101075.00 Grey Crowned Crane (foraging) 374696.00 Blue Crane (foraging) 506120.00 Blue Swallow (foraging) 11647.00 Amphibian Features - Species distribution data hectares Breviceps sopranus 300.00 Bufo gariepensis nubicolus 480.00 Pyxicephalus adspersus 6689.50 Invertebrate Features - Species locality data localities Aloeides dentatis maseruna 1.00 Aloeides nubilus 1.00 Aloeides sp nr pierus 1.00 Chrysoritis aureus 3.00 Chrysoritis sp nr aethon 1.00 Metisella meninx 2.00 Orachrysops warreni 1.00 Serradinga clarki amissivallis 1.00 Serradinga clarki ocra 1.00 Dingana alaedeus 5.00 Aloeides barbarae 2.00 Aloeides titei 5.00 Invertebrate Features - Species distribution data hectares Aloeides merces 520.00 Aloeides rossouwi 6658.00 Dingana fraterna 6658.00 Lepidochrysops jefferyi 11554.00 Lepidochrysops swanepoeli 11554.00 Reptile Features - Species distribution data hectares Afroedura sp. nov. (able-erasmus) 1496.00 Afroedura sp. nov. (rondavels) 5.28 Aspedilaps scutatus intermedius 1500.00 Bradypodion transvaalense 1000.00 Cordylus giganteus 400.00 Cordylus warreni barbertonensis 400.00 Cordylus warreni warreni 934.00 Lamprophis swazicus 500.00 Platysaurus wilhelmi 1000.00 Afroedura haackei 400.00 Plant Features - Species locality data localities Acacia ebutsiniorum 1.00 Acacia welwitschii subsp. delagoensis 10.00 Acampe praemorsa 2.00 Acridocarpus natalitius var. natalitius 5.00 Adenia wilmsii 1.00 Adenium swazicum_ 7.00 Alepidea amatymbica var. amatymbica_ 10.00 Allophylus chaunostachys 4.00 Aloe albida 8.00 41

Aloe craebii 2.00 Aloe dewetii 2.00 Aloe fourei 5.00 Aloe hlangapies 7.00 Aloe integra 4.00 Aloe kniphofiodes 15.00 Aloe modesta 10.00 Aloe reitzii reitzii 9.00 Aloe reitzii vernalis 1.00 Aloe simii_ 9.00 Aloe thorncroftii 7.00 Aloe vryheidensis 2.00 Asparagus clareae 3.00 Asparagus 'elephantipes' 3.00 Asparagus fourei 2.00 Asparagus lynnetteae 3.00 Asplenium stolinerferum 1.00 Barleria mackenii 2.00 Boophane disticha 3.00 Boweia volubilis 2.00 Brachystelma chlorozonum 5.00 Cassipourea swaziensis 1.00 Brownleea recurvata 4.00 Calanthe sylvatica 2.00 Lydenburgia cassinioides 10.00 Centrostigma occultans 1.00 Ceropegia distincta 5.00 Cineraria hederifolia 2.00 Clivia miniata 5.00 Combretum sp. nov. (hairy plant but with sticky fruit) 1.00 Commiphora zanzibarica 1.00 Crocosmia mathewsiana 5.00 Cryptocarya transvaalensis 20.00 Curtisia dentata 20.00 Cyathea capensis 5.00 Cyrtanthus bicolor 5.00 Cyrtanthus epiphyticus 2.00 Cyrtanthus huttonii 2.00 Cyrtanthus thorncroftii 2.00 Cytinus sp. nov. 2.00 Delosperma deilanthoides 2.00 Diosorea ebutsiniorum 1.00 Disa alticola 5.00 Disa amoena 5.00 Disa cf. kluegi (D. bicolour) 1.00 Disa extinctoria 5.00 Disa hircicornis 1.00 Disa maculomarronina 4.00 Disa sp nov. (aff. D. montana) 2.00 Aspidoglossum xanthospaerum 1.00 Disa rungweensis 2.00 Disperis concinna 3.00 Disperis stenoplectron 5.00 Disperis tysonii 3.00 42

Disperis wealii 2.00 Duvernoia aconitiflora 2.00 Elephantorrhiza obliqua var. glabra 2.00 Elephantorrhiza praetermissa 10.00 Encephalartos cupidus 30.00 Encephalartos heenanii 90.00 Encephalartos humilis 75.00 Encephalartos laevifolius 57.00 Encephalartos lanatus 81.00 Encephalartos lebomboensis 11.00 Encephalartos middelburgensis 52.00 Encephalartos paucidentatus 167.00 Encephalartos sp. nov. (Lebomboensis complex) 15.00 Erica atherstonei 5.00 Erica revoluta 5.00 Erica rivularis 9.00 Erica subverticillaris 3.00 Eriosema naviculare 3.00 Eucomis autumnalis 20.00 Eucomis montana 20.00 Eucomis pole-evansii 6.00 Eucomis vandermerwei 10.00 Eulophia leachii 5.00 Eulophia meleagris 1.00 Eulophia parvilabris 2.00 Eulophia zeyheriana 2.00 Euphorbia sekukuniensis 10.00 Faurea macnaughtonii 5.00 Frithia humilis 10.00 Gladiolus appendiculatus (barberton) 4.00 Gladiolus appendiculatus (LongTomPass) 1.00 Gladiolus calcaratus 6.00 Gladiolus cataractarum 10.00 Gladiolus macneilii 5.00 Gladiolus malvinus 4.00 Gladiolus rufomarginatus 5.00 Gladiolus saxatilus 3.00 Gladiolus serpenticola 1.00 Gladiolus varius 10.00 Gladiolus vernus 5.00 Habenaria ciliosa 2.00 Haworthia koelmaniorum 9.00 Haworthia macmurtyi 3.00 Haworthia limifolia var. arcana 1.00 Haworthia limifolia var. limifolia 4.00 Helichrysum calocephalum 1.00 Helichrysum ephelos 3.00 Helichrysum lesliei 2.00 Helichrysum milleri 1.00 Helichrysum summo-montanum 2.00 Hesperantha saxicola 2.00 Holothrix villosa var. villosa 1.00 Hymenodictyon parvifolium subsp. parvifolium 3.00 Hypoxis hemerocallidea 3.00 43

Haworthia glaucophylla 2.00 Ceropegia decidua subsp pretoriensis 5.00 Khadia alticola 1.00 Delosperma leendertziae conf 2.00 Khadia carolinensis 3.00 Kniphofia splendida 3.00 Kniphofia triangularis subsp. obtusiloba 5.00 Ledebouria mokobulaanensis 3.00 Ledebouria purpurea (Andrew's manuscript name) 1.00 Ledebouria rupestris 1.00 Euphorbia barnardii 6.00 Leucospermum gerrardii 5.00 Leucospermum saxosum 5.00 Lycopodium saururus 1.00 Moerella microbracteata 2.00 Moraea robusta 2.00 Mossia intervallaris 2.00 Nerine gracilis 3.00 Nerine platypetala 3.00 Nervilia kotschyi var. purpurata 2.00 Ocotea bullata 6.00 Ocotea kenyensis 6.00 Orbea maculata 1.00 Orbea paradoxa 2.00 Orbea hardyi 3.00 Orbea gerstneri subsp. elongata 1.00 Platycoryne medocris 1.00 Polygala nodiflora 1.00 Polygala serpentaria 1.00 Protea comptonii 10.00 Protea curvata 10.00 Protea laetans 10.00 Protea roupelliae subsp. hamiltonii 1.00 Protea subvestita 8.00 Resnova megaphylla 7.00 Rhus batophylla 10.00 Rhus pygmaea 1.00 Ruspolia hypocrateriformis var. australis 1.00 Satyrium microrrhynchum 4.00 Trachyandra erythrorrhiza 2.00 Schizochilus crenulatus 5.00 Schizochilus lilacinus 5.00 Schotia latifolia 3.00 Scilla natalensis 15.00 Siphonochilus aethiopicus 5.00 Streptocarpus cyaneus subsp. long-tommi 4.00 Streptocarpus decipiens 5.00 Streptocarpus denticulatus 3.00 Streptocarpus fasciatus 4.00 Streptocarpus fenestra-dei 3.00 Streptocarpus grandis subsp. grandis (Mpum form) 2.00 Streptocarpus hilburtii 2.00 Streptocarpus latens 5.00 Streptocarpus meyeri 1.00 44

Streptocarpus occultus 1.00 Streptocarpus pogonites 5.00 Syzygium sp A (S. fourceidi) 3.00 Thesium davidsonae 2.00 Thorncroftia thorncroftii 2.00 Tulbaghia coddii 3.00 Urginea altissima 5.00 Warburgia salutaris 20.00 Watsonia latifolia 10.00 Watsonia occulta 5.00 Watsonia wilmsii 10.00 Zantedeschia pentlandii 10.00 Plant Features - Species distribution data hectares Eulophia coddii model confirmed 300.00 Gladiolus appendicu (wakkerstroom)mod 223.60 Euclea sekhukuniensis mod 261.00 Adenium swazicum_mod 1395.54 Aloe burgersfortensis mod 254.60 Aloe simii_mod 685.00 Ecosystem Process Surrogate Features - locality data localities NB pans and wetlands 25.00 Ecosystem Process Surrogate Features - distribution data hectares Cave localities two fifty m buffer 1406.54 Cool Mountain Slopes 180932.00 Summit escarp one 61273.41 Summit escarp two 83251.19 Summit escarp three 108605.42 Summit escarp four 52488.38 Wolkberg Centre of Endemism 85724.00 Sekhukune Centre of Endemism 147463.00 Lydenburg Centre of Endemism 263290.00 Barberton Centre of Endemism 109603.00 Important forest patches 22330.21 Important forest grassland 90744.74 Important forest river corridors 19637.98