Int J Primatol DOI 10.1007/s10764-013-9669-9

The Spatial Distribution of Chacma (Papio ursinus) Habitat Based on an Environmental Envelope Model

Olivia M. L. Stone & Shawn W. Laffan & Darren Curnoe & Andy I. R. Herries

Received: 26 September 2012 /Accepted: 30 January 2013 # Springer Science+Business Media New York 2013

Abstract Predictive spatial modeling has become a key research tool for species distribution modeling where actual data are limited. Although qualitative maps and distribution descriptions for chacma (Papio ursinus) are freely available, quantitative data are limited and do not provide the empirical information required to make informed decisions about issues such as population assessment, conservation, and management. Here we present the first quantitative, repeatable, and detailed predicted spatial distribution of the across southern . Our distribution is at a finer level of detail than has previously been available. We used an environmental envelope model implemented within a geographic information system to achieve this. The model used environmental layers representing water availability, temperature and altitude, and model parameters determined from geore- ferenced observational data. The data extracted from the environmental layers sug- gest chacma baboons inhabit areas with mean minimum temperatures of the coolest month as low as −6.1 °C, mean maximum temperatures of the warmest month as high as 38.2 °C, mean annual rainfall up to 1,555 mm, and altitude up to 3,286 m. Our model demonstrates that the distribution of chacma baboons may be limited by temperature and rainfall, with the predicted northern extent of its range being temperature dependent. The model also implies that some areas well known for chacma baboon occupation today may in fact be marginal habitat. The resulting map highlights areas of suitable habitat in . In addition, a linear “patchy” corridor was identified following the East African Rift Valley connecting

Electronic supplementary material The online version of this article (doi:10.1007/s10764-013-9669-9) contains supplementary material, which is available to authorized users. O. M. L. Stone (*) : S. W. Laffan : D. Curnoe School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia e-mail: [email protected]

A. I. R. Herries Archaeology Program, School of Historical and European Studies, Faculty of Humanities and Social Sciences, La Trobe University, Bundoora, Melbourne, VIC 3086, Australia O.M.L. Stone et al. the southern habitat with northeast Africa. We find the greatest proportion of suitable habitat to be located in . This modeling approach is generic and would be suitable to analyze other species across similar geographic extents.

Keywords Chacma baboon . Distribution . Habitat . Papio . Southern Africa

Introduction

Population, distribution, and landscape models are becoming vital ecological tools in conservation and management (Dunning et al. 1995), providing ecological estimates both in the absence of, and in combination with, actual data. The rapid pace and widespread impacts of economic development in Africa have meant that in some regions conservation managers are turning their attention to non-threatened species in order to manage biodiversity (Ezemvelo KZN Wildlife 2008). Unfortunately, gaining basic ecological knowledge for common species is often overlooked in favor of protecting endangered or financially beneficial . This is despite the need for more information to provide insight into species distributions, habitat preferences, and potential migration patterns. Detailed distributions are needed to assess the effects of ecological change and species responses to anthropogenic and climatic effects, including the future impacts of global warming. Environmental envelope models (EEM) (Guisan and Zimmermann 2000) imple- mented within geographic information systems (GIS) can be used to predict species distributions. Both the environmental and the climatic limitations of the focal organ- ism constrain the EEMs. Many factors limit species distributions at a local scale, i.e., home ranges, day ranges, or foraging times (Altmann and Altmann 1970; Brain 1990; Hill 2005), but at a continental scale climatic effects are known to dominate species distributions (Pearson and Dawson 2003). Suitable habitat is considered to comprise the set of locations that fall within the defined envelopes for all environmental variables (Franklin 2009; Walker and Cocks 1991). This allows for an easily inter- preted model, as well as one that can be extrapolated to unsampled locations as a prediction. Researchers have used GIS-based approaches successfully to analyze primate habitats including habitat association, prediction, selection, ranging patterns, and zoonotic disease transfer (Harcourt 2000; Hoffman and O’Riain 2012; Hopkins and Nunn 2007; Junker et al. 2012; Ostro et al. 2000; Sleeman 2005; Stickler and Southworth 2008; Zinner and Torkler 1996). Chacma baboons are a widespread species, inhabiting much of southern Africa, though detailed distribution data are limited. Geographically, chacma baboons are the southernmost of five to six commonly recognized species (Groves 2001; Grubb et al. 2003; Zinner et al. 2009), inhabiting an area from immediately north of the Zambezi River to the (Jolly 1993). For the most part, chacma baboon distribution maps are generalized, with the finer details of its distribution being poorly understood. As a result, published estimates are spatially general, qualitative, and based mostly on nonrepeatable methods (Anderson 1982; Henzi and Barrett 2003; Hoffman and Hilton-Taylor 2008; Skinner and Chimimba 2005; Zinner et al. 2009). This is surprising considering the genus Papio is one of the most intensively studied of non-human primate taxa (Newman et al. 2004). Spatial Modeling for Distribution of Chacma Baboons

The published literature suggests temperature, availability of water, and altitude are likely to be the main environmental variables affecting habitat selection for Papio (Altmann 1974; Biquand et al. 1992; Dunbar 1992; Henzi et al. 1992; Hill 2005). This is consistent with findings from a wide range of organisms (Cavagnaro 1988; Engelbrecht et al. 2007; Guisan and Thuiller 2005). Chacma baboons inhabit areas with temperatures ranging from <−10 °C to >40 °C (Anderson 1982; Cowlishaw 1997; Gaynor 1994; Henzi et al. 1992; McQualter 2005; Noser and Byrne 2007;Van der Weyde 2004). Extreme cold restricts baboon home ranges, with baboons avoiding areas until the ground temperature returns to >0 °C (Henzi et al. 1992). Although resting time —which is directly influenced by temperature variation— is an important determinant of primate distribution, Papio and another African genus, , are exceptions to this rule (Korstjens et al. 2010). Further, although high temperatures led to a decrease in foraging activity and the pursuit of shade to reduce the impact of solar radiation (Hill 2005, 2006), this held true only for locations where day length allowed the to rest without adversely affecting foraging. Baboons continued to forage regardless of temperature variation in areas with shorter day lengths. Interestingly, locations with longer summer day lengths were those with lower temperatures that, in theory, would be less detrimental to foraging. Although primate populations are known to suffer in drought conditions (Lemur catta: Gould et al. 1999; Cercopithecus aethiops: Struhsaker 1973; Semnopithecus entellus: Waite et al. 2007), Papio demonstrates marked interspecies variation in its responses to rainfall and water availability, ranging from more arid regions in Saudi Arabia (Biquand et al. 1992) to high rainfall regions in and Ethiopia (Dunbar and Dunbar, 1974; Popp 1979). Published data indicate that chacma baboons inhabit areas with annual rainfall ranging from <20 to >1,400 mm (Barrett and Henzi 1997; Hamilton et al. 1976). Both water availability and the distribution of drinking sites affect the day range and home range of Papio (Altmann and Altmann 1970; Barton et al. 1992; Hall 1963; Stoltz and Saayman 1970). The amount of time elapsed without water, and the distribution of water itself, appear to limit baboon distribution. Chacma baboons have been recorded to abstain from drinking for between 1 and >90 days, demon- strating a partial independence from drinking water (Brain 1988, 1990; Brain and Bohrmann 1992; Brain and Mitchell 1999). However, if food substitutes are not available, a single water source within a day range is required (Brain 1988; Hamilton 1985). Eight kilometers appears to be the farthest reported distance from which a troop may reside from a water source (Brain 1988). Overall, the distance from a water supply appears to restrict how far baboon troops traveled, with areas reportedly rich in food left unoccupied as they were beyond range from permanent water sources (Altmann 1974; Hamilton et al. 1976). A negative association exists between the altitude of the home range and group size for chacma baboons (Hall 1963), unlike hamadryas baboons, which tend to have larger groups at higher altitudes (Zinner et al. 2001). Dunbar (1992) suggested that Papio would not inhabit an altitude >3,000 m. However, olive baboons have been found at altitudes ranging from 3,200 to 3,600 m (Stephens et al. 2001)where conditions are less restrictive than in southern Africa. The highest altitude reported for the chacma baboon is 3,000 m (Whiten et al. 1987), and this altitude is likely to be close to the ecological threshold for habitation, as snow occurs at elevations >3,000 m O.M.L. Stone et al. in southern Africa (O’Brien 1993). This altitude would thus limit available food sources, at least seasonally. We aimed to develop a predictive distribution model that depicts the habitat of chacma baboons across southern Africa at a much finer resolution than has previously been available (Apps 2008; Estes 1992; Skinner and Chimimba 2005). Numerous species distribution modeling approaches are available (Franklin 2009); however, both the availability of data and required predictive precision limit many of these. For example, reliable absence data are not available for baboons; thus modeling techni- ques that require absence data are excluded from consideration, e.g., HABITAT (Walker and Cocks 1991). The current absence of baboons from some locations in southern Africa may result from considerable historical anthropogenic land distur- bance. Large areas of potential suitable baboon habitat may be affected in this way. Therefore, methods that use pseudo-absence data, e.g., Maxent (Phillips et al. 2006), will represent the realized niche including anthropogenic influences. Although po- tentially useful, we are instead interested here in an estimate closer to the potential historical distribution without such influences. This would allow us to draw a comparison with current populations to detect any change. We used an EEM to achieve this. EEMs have been criticized as being likely to lead to an over-estimation (Franklin 2009), but we consider this to be more acceptable than underestimation given data limitations and the mobility of baboon troops and individual animals (Brain 1990; Davidge 1978; Hall 1963; Henzi et al. 1992; Stoltz and Saayman 1970). This model allows for a comparison between the historic and present distribution of the species and enables us to make inferences about population size and dynamics. It will also provide the data required to identify possible historical trends in species distributions and to prioritize regions and populations for conserva- tion efforts. This information is timely given new evidence for significant population decline within parts of its range (Stone et al. 2012).

Methods

Data and Model Parameters

We used 459 georeferenced baboon locations (Fig. 1), derived from published data, museum records, and our own field observations, to identify inhabited areas. We included records of baboon sightings only if they could be accurately located to within a distance of 1 arc minute (approximately 1.6 km). We discarded coordinates that were less precise. Spatially dense samples can potentially bias the calculation of the percen- tiles used in the EEM. To reduce this effect, we used random selection to thin samples from northern Limpopo (Stoltz and Keith 1973) and KwaZulu-Natal to a spatial density consistent with the sample density of all other points from South Africa. We placed a buffer around each datum to incorporate movement around sighting locations and to account for positional error in our data. We estimate the mean day range for the chacma baboon from published data to be approximately 5 km (max=8.0 km, min=2.2 km, N=16; Anderson 1982;Brain1990;Davidge1978;Gaynor1994;Hall 1962; Hamilton 1986; Marais 2005; Stoltz and Saayman 1970; Van der Weyde 2004; Whiten et al. 1987). Therefore, we used a 2.5-km buffer distance as the approximate Spatial Modeling for Distribution of Chacma Baboons

Fig. 1 Georeferenced locations of baboon sightings used for developing the model (including published data from Anderson 1982; Barrett et al. 2004; Barrett and Henzi 1997; Bateman 1961; Bielert and Busse 1983; Bolwig 1959; Butler et al. 2004; Cheney et al. 2004; Codron et al. 2006; Cowlishaw 1997; Dunham 1990; Gaynor 1994; Geostratics CC 2006; Gow 1973; Hall 1962; Hamilton et al. 1976; Katsvanga et al. 2006; Keller et al. 2010; Marais 1969, 2005; McGrew et al. 2003; McQualter 2005; Noser and Byrne 2007; Pienaar 1969; Radloff and Du Toit 2004; Sithaldeen et al. 2009; Stoltz and Keith 1973; Stoltz and Saayman 1970; Van der Weyde 2004; Whiten et al. 1987; Zinner et al. 2011).

mean distance a baboon troop could travel to gain access to a water source and safely return to its sleeping site in the evening. We then converted the data buffers to a set of point locations at a spacing of 3 arc seconds (resulting in >800,000 points) from which to sample the environmental variable layers. Then we exported and summarized the sampled data to determine the environmental envelope bounds. We used the NASA Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM, resolution 3 arc seconds, approximately 90 m) (Jarvis et al. 2006) for altitude measurements (meters) and the Worldclim data set for climatic variables (resolution 30 arc seconds, approximately 800–900 m) (Hijmans et al. 2005). The Worldclim layers used were the mean minimum temperature of the coldest month and mean maximum temperature of the warmest month (°C) and mean annual rainfall (mm). We obtained river data from Hoogeveen (2000). These layers are all freely available, have a geographical extent covering southern Africa, and are of sufficiently fine spatial resolution to represent baboon day ranges. We stored the environmental data layers in raster format and manipulated them using ArcGIS version 10.0. All analyses were run at the spatial resolution of the SRTM DEM, as it had the finest resolution (a 3 arc second cell size, approximately 80–90 m) and ensured data were not lost due to aggregation to a coarse resolution. However, the Worldclim data sets (30 arc second cell size, approximately 800–900 m) did constrain the true resolution of the analysis.

Model Implementation

We defined the environmental envelope as the central 95 % of the total range of samples from each environmental variable within the 2.5 km buffer distance of baboon sighting locations. This was done to exclude outliers and to decrease the effect of populations living in marginal areas. We also incorporated proximity to O.M.L. Stone et al. rivers in arid areas to include locations with a water source within half the day range of the baboons, again using the 2.5-km buffer distance. As troops are not migratory and require a constant water source, we included only major waterways. We assigned all cells within the 95 % envelope for each environmental variable a value of 1 and assigned all other locations the value 0. However, we reassigned areas with insufficient rainfall with a value of 1 if they were ≤2.5 km of a river. The product of the resulting rasters was then calculated to identify the cells that occur within the environmental envelope for all variables considered. Finally, we applied the model to all cells across the continental extent to identify the northern limits of the predicted habitat.

Results

Table I lists the environmental limitations and the established parameters used in the EEM. The resulting predicted distribution of the habitat of the chacma baboon is given in Fig. 2. Our model predicted approximately 3,600,000 km2 of land as suitable habitat from a total of approximately 6,000,000 km2 in southern Africa (Fig. 2). This is a mostly contiguous area, approximately three times the size of South Africa. The countries of , , Lesotho, Malawi, , , South Africa, Swaziland, , and all contain potential chacma baboon habitat. Our model predicts that the largest proportion of habitat is within South Africa, with 92 % of South Africa’s land area included (approximately 1,100,000 km2). Three large areas known to contain baboons appear to fall outside the 95 % envelope (highlighted on Fig. 2). These are 1) the Zimbabwean/Zambian border (Zambezi River) extending west to the Caprivi Strip, 2) the area immediately inland from the coast of Namibia (within the Namib Desert), and 3) the area adjacent to the Orange River on the southern border of Namibia. River systems provide a suitable predicted habitat and explain the Namibian data. However, the Zambezi and Orange River regions remained outside the 95 % envelope owing to the high temperatures that exceeded the upper bound for the mean maximum temperature of the warmest month. Our model predicted that much of the Mountain range within KwaZulu-Natal, South Africa would be excluded as habitat. However, this area is known to contain chacma baboons (Stone et al. 2012), yet sits outside the 95 %

Table I The parameters (enve- Environmental variable Limits of extracted Envelope lope bounds) used to model the data bounds distribution of chacma baboons (central 95 %) (Papio ursinus) Minimum Maximum Lower Upper

Altitude (m) −6 3,286 64 2,232 Max. temp. (°C) 14.4 38.2 21.9 35.1 Min. temp. (°C) −6.1 17.3 −3.5 10.8 Rainfall (mm) 15 1,555 99 1,081 Spatial Modeling for Distribution of Chacma Baboons

Fig. 2 The predicted spatial distribution of chacma baboons across southern Africa based on environmental variables. Large areas known to currently contain baboons that were not predicted as suitable habitat are circled. environmental envelope of the model (Fig. 3). This is because the mean maximum temperatures for the hottest month and the mean minimum temperatures for the coldest month are below the modeled lower bounds, whereas rainfall is above the modeled upper bound. When applied to the whole of Africa, the model identified a linear corridor of patches following the Rift Valley as far north as Yemen and Saudi Arabia (Fig. 4).

Discussion

Chacma baboons show considerable intraspecific variation in environment conditions within habitats. Data extracted from the environmental layers identified the temperature limitations of −6.1 °C to 38.2 °C (Table I). Previous research suggests that time budget constraints (Dunbar 1992) would prevent Papio from inhabiting areas with ambient temperatures <10 °C and >35 °C. The actual extremes will exceed our values, as Worldclim data are averaged over 10 yr. Reports of chacma baboon populations living in areas with maximum temperatures of ≥40 °C support our findings (Cowlishaw 1997; Gaynor 1994; Noser and Byrne 2007). In one example, mean maximum temperature reported was 39.5 °C (Dunham 1990). This is not surprising considering that other Papio sp. have also been reported in locations with maximum and mean maximum temperatures >40 °C (Biquand et al. 1992; Norton et al. 1987). The model suggests chacma baboons will not inhabit areas with an altitude higher than 3286 m. Although this is slightly higher than the 3000 m previously reported (Whiten et al. 1987), this datum was sampled from altitude data in areas that are known to be inhabited by baboons. Similarly, our lowest annual rainfall of 15 mm/yr (sampled from the environmental data sets) was below the lowest published value of 18.1 mm/yr (Hamilton et al. 1976). As previously noted, the Worldclim layer used to O.M.L. Stone et al.

Fig. 3 Predicted distribution of chacma baboons in KwaZulu-Natal based on environmental variables. sample rainfall is itself averaged from a minimum of 10 yr of data; therefore, baboons may be inhabiting areas with rainfall of <15 mm/yr. The locations (baboon sightings) that we used to extract the environmental data have a bias toward South Africa and Namibia (Fig. 1). This is not ideal, but difficulties with data collection and the greater research attention given to baboons in these countries made it unavoidable. Although we would expect our predicted models to be most accurate for South Africa and Namibia, the environmental ranges of Zimbabwe and Botswana fall within those of South Africa. Thus, predictions based on South African data alone (which is not the case in this study) are likely to be applicable for these countries also. Small sample sizes prevented comparisons with other southern African countries. The parameters also have the potential to introduce error into our models. Of most concern are errors relating to the current anthropogenic impact on populations from which location data were collected. These populations will already have reacted to anthropogenic change. Therefore, it seems likely that some populations will be living in marginal habitat, with all data collected reflecting the current realized niche rather Spatial Modeling for Distribution of Chacma Baboons

Fig. 4 Extrapolation of the predicted habitat for chacma baboons to all of Africa identifies a putative corridor of predicted suitable habitat leading northward up eastern Africa. than the fundamental niche. This does, however, support the use of an EEM, as an EEM does not exclude locations from the model because they are uninhabited. The predicted habitat covers an extensive area. As a result, there will be errors at local scales. This model is the first attempt to define the entire distribution with a repeatable and quantifiable approach. Thus, the margins to the distribution may change with additional data and refinements to the model. Nonetheless, the overall pattern can be considered reliable. The maps presented here are likely to be realistic representations of the broad scale distribution of chacma baboons. Importantly, this technique can be applied to other primate species across similar geographic extents. Large proportions of most southern African countries were predicted as habitat. However, the model indicates that Mozambique contains the largest area of marginal habitat (Fig. 2). The majority of land in Mozambique does not fall within the 95 % environmental envelope, despite the fact that baboons are known to occur there. The underlying reason for this is that the minimum temperature exceeds the maximum envelope bound in the model (see Electronic Supplementary Material, Fig. 1). It is important to note that a region predicted as marginal habitat is one that is at an extreme of their environmental niche, and not that it is uninhabitable. Northern Mozambique is also known to be within the range of the (Papio cynocephalus) (Kingdon et al. 2008), a species that is likely to occupy a different environmental niche from that of chacma baboons. Species interaction and competi- tion are not incoporated into this model. Although the Drakensberg Mountains of South Africa do contain baboon troops (Kingdon et al. 2008; Whiten et al. 1987), our model excluded much of this region from predicted habitat based on environmental factors alone. This region was found O.M.L. Stone et al. to have both maximum and minimum temperatures below their respective lower envelope bounds and high rainfall exceeding the upper bound. Potentially, a colder wetter environment could place more physiological stress on the baboons; thus the exclusion of this area based on environmental variables alone is not surprising. Although there are patches extending into the Drakensberg, this area is predicted mostly as marginal habitat. It seems possible that their occupation of this area may result from recent anthropogenic influences such as large-scale land disturbance (Stone et al. 2012). Recent displacement may explain why the body mass of male chacma baboons and mean annual rainfall were found to form a positive linear correlation when the Drakensberg populations were removed, although this did not hold true for female chacma baboons (Jolly 2011). Harsh environmental conditions explain the predicted absence of baboons from the southern region of the Kalahari Desert (southwest Botswana, southeast Namibia, and the adjacent north and northwest region of South Africa). Although a solitary male baboon has been sighted crossing the Central Kalahari Game Reserve >80 km from the nearest troop (Hamilton and Tilson 1982), these conditions are unlikely to sustain an entire troop. The southern Namibian habitat is isolated from the habitat directly to the south in northwest South Africa due to both temperature and rainfall limitations. In support of this idea, Sithaldeen et al. (2009) suggested that these northwestern (Namibian) baboons form a genetically distinct lineage. The Zambezi River region is known as the transition zone between chacma baboons and yellow baboons and is predicted to be unsuitable habitat (Fig. 2) for chacma baboons. Although the Zambezi River itself is often considered to be the northern border of the chacma baboon distribution (Groves 2001; Hall 1963), our findings suggest that hotter temperatures in this area may be the key parameter that defines the northern extent of chacma baboons. The areas north of the Zambezi could be considered a putative transition zone between the southernmost baboon species. Our prediction depicts a northern border extending centrally through western Zambia, arching northward, becoming increasingly fragmented and diminishing in eastern Zambia and Malawi. This description corresponds with the contact zone between kinda baboons (Papio kindae), yellow baboons, and chacma baboons identified by others (Jolly et al. 2011; Keller et al. 2010). However, confirmation of a possible historic transition zone would require an equivalent model and distri- bution descriptions for the more northerly species. High rainfall limits the northern border from Angola eastward, whereas low rainfall defines the western border through Namibia. The eastern border, unlike the northern and western borders, is an amalgam of maximum and minimum temperatures and rainfall that exceed the model upper bounds. Therefore Mozambique, which encompasses and aligns with the majority of this eastern border, is climatically different from the rest of the inhabited southern African countries. Not surprisingly, it thus contains the least predicted habitat. Chacma baboons appear to be strongly influenced by temperature. Fig. 5 depicts the mean minimum temperature of the coolest month, overlain by locations of chacma baboons collected by Stoltz and Keith (1973). The sample locations suggest that baboons are selecting cooler regions. Although there is a potential bias toward higher altitude regions if the location data are focused on sleeping sites, the method- ology (Stoltz and Keith 1973) reports data collection from throughout the baboons’ Spatial Modeling for Distribution of Chacma Baboons

Fig. 5 Locations of baboon sightings (Stoltz and Keith, 1973) compared to the mean minimum temper- ature of the coolest month. day range. It may be that the baboons in the Limpopo region are merely utilizing higher (therefore cooler) ground as a result of anthropogenic pressures. However, the same may not be true for the Zambezi region, where the inhabited cooler area is lower in altitude than surrounding areas. With this selection of cooler areas considered, the use of the Drakensberg Mountains for a refuge may be a reasonable compromise for a primate that is more suited to cooler temperatures. Our data suggest the baboons may be avoiding areas with the warmest minimum temperatures, as well as areas with highest maximum temperatures. Finally, the model shows regions of predicted habitat outside of southern Africa. Some of these form a corridor within the Rift valley. This is interesting because genetic and palaeontological studies have suggested that the genus Papio may have O.M.L. Stone et al. evolved in southern Africa (Delson 1984; Herries et al. 2009; Newman et al. 2004; Sithaldeen et al. 2009; Zinner et al. 2009). If this is correct, our model suggests the Rift valley could have offered a dispersal route. A similar corridor has also been proposed for hominins and other dispersals between eastern and southern Africa (Grubb et al. 1999). The current environmental conditions (cooling from an interglacial maximum) used to predict these regions would have recurred periodically through the glacial–interglacial cycling of the Quaternary, notably during Marine IsotopeStages5,9,and11(Berger2008; Chapman et al. 1999;Emilianiand Shackleton 1974).

Conclusions

This is the first quantitative predictive model for the distribution of chacma baboons across southern Africa based on the major environmental parameters known to influence their distribution. It predicts the historical habitat and therefore likely distribution before significant recent anthropogenic land alteration. Our model marks the first step in providing a baseline for future quantitative modeling needed for more detailed population and distribution assessments as well as analyses of long-term trends. It predicts that many areas presently known to be inhabited by baboons are unsuitable or marginal habitat, although it is calibrated using contemporary popula- tion distributions. Not surprisingly, South Africa contains the largest area of predicted habitat for chacma baboons, whereas most of Mozambique falls outside of the 95 % range limit. The model implies that hotter temperatures are more limiting for this species than cooler temperatures, with chacma baboons utilizing the cooler temper- ature refuge areas. Our model suggests that occupation of areas such as the Drakensburg Mountains may result from recent anthropogenic impact, with the primates selecting the colder largely undisturbed areas rather than occupying regions of prime habitat extensively altered by humans.

Acknowledgments Amathole Museum, Ezemvelo Wildlife, Iziko South African Museum, National Museum Bloemfontein, and the Ditsong National Museum of Natural History (formerly the Transvaal Museum) provided location data. We thank two anonymous reviewers, whose comments improved this manuscript. A. I. R. Herries was supported by an Australian Research Fellowship as part of ARC Discovery Grant DP0877603.

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