Evol Ecol DOI 10.1007/s10682-016-9833-8 ORIGINAL PAPER The chacma baboon (Papio ursinus) through time: a model of potential core habitat regions during a glacial–interglacial cycle 1 2,3 4,5 Olivia M. L. Stone • Andy I. R. Herries • James S. Brink • Shawn W. Laffan1 Received: 10 November 2015 / Accepted: 11 April 2016 Ó Springer International Publishing Switzerland 2016 Abstract The aim of this research is to understand changes in the biogeography of the chacma baboon (Papio ursinus) through time, by modelling potential habitat changes through the last glacial–interglacial cycle (last interglacial, glacial maximum and current conditions). An environmental envelope model in a geographic information system is used to produce a range of habitat distribution models for the chacma baboon. Initially it is modelled as a single taxon, following which the data are further divided and explored to model predicted habitats for the chacma clades during different stages of the glacial– interglacial cycle. An area of approximately 1,044,000 km2 was identified as potentially having been within the environmental core habitat at some stage of the glacial–interglacial cycle. Of this, 63,700 km2 of land was predicted to have been core habitat regardless of the stage of the glacial–interglacial cycle. Additionally, rainfall appears to be the environ- mental variable with the most limiting effect on habitat size. This is true for the current, last glacial maximum and last interglacial environmental conditions. The largest area that remains within the core habitat throughout the last glacial–interglacial cycle is found in the northern provinces of South Africa. The chacma clades appear to interact in a cyclic pattern of expansion and contraction, each clade being more prominent under different environmental conditions. It appears that grey footed chacma habitat periodically extends Electronic supplementary material The online version of this article (doi:10.1007/s10682-016-9833-8) contains supplementary material, which is available to authorized users. & Olivia M. L. Stone [email protected] 1 School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia 2 Palaeoscience Laboratories, Department of Archaeology and History, La Trobe University, Melbourne Campus, Bundoora, Melbourne, VIC 3086, Australia 3 Centre for Anthropological Research, University of Johannesburg, Gauteng, South Africa 4 Florisbad Quaternary Research Department, National Museum, Bloemfontein, South Africa 5 Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa 123 Evol Ecol northward towards the yellow baboon. These findings suggest dynamic and varied pro- gression of the chacma baboon palaeo-distributions. Keywords Papio ursinus Á Palaeobiogeography Á GIS Á Biogeography Á Core habitat Á Refugia Introduction Quantifying changes in spatial distribution through time allows us to infer estimates of population expansion, contraction and interactions, and allows us to estimate current population trends and/or patterns (Stigall and Lieberman 2006; Stigall Rode 2005). These predicted species palaeo-distributions and palaeo-habitats provide information otherwise not available. They can lead to estimates of diversification and speciation or suggest dispersal as habitats shift (Stigall and Lieberman 2006). Ultimately, ancient species dis- tributions and habitat predictions can help us understand species dynamics through time and how distributions become what we see today. They can even allow us to track potential changes resulting from the effects of time or climate change and more fully assess palaeobiogeography of particular species more effectively than simple fossil distribution maps. Geographic information system (GIS) modelling, including environmental envelope models (EEMs), can be an effective way to assess habitat and by inference species dis- tributions in differing environmental conditions (Chatterjee et al. 2012; Hijmans and Graham 2006). The repeatability of such methodologies means it is easy to integrate new data as it becomes available. This is valuable in fields such as palaoebiogeography, where knowledge is constantly expanding and evolving. GIS has been used in a wide range of research in primatology, ranging from small localised analyses (Guy et al. 2012; Hoffman and O’Riain 2011; Ostro et al. 1999) to larger national or continental scales (Chatterjee et al. 2012; Junker et al. 2012; Sleeman 2005). GIS based modelling has already been successfully used to model and assess primate biogeography and palaeobiogeography (Chatterjee et al. 2012; Stone et al. 2012, 2013). The ability to assess species dynamics through time can be invaluable when trying to predict the effects of and prepare for future environmental and climatic change. However, palaeobiogeographic predictions are fraught with problems. Fossil data are often too sparsely distributed to reliably calibrate predictive models, leaving modern data as the best available source. In the case of fossil Papio, specimens have been recovered from a range of fossil sites in southern Africa (Delson 1984; Freedman 1970; Gilbert 2013). However, the majority of these have come from a restricted area of karst (480 km2) near Johan- nesburg, South Africa and satellite sites near Taung (Buxton-Norlim Limeworks) and at Makapansgat near Mokopane (designated as the UNESCO World Heritage ‘Fossil Hominid Sites of South Africa’) (Delson 1984; Freedman 1976; Jablonski and Frost 2010). There is a major geological bias evident in southern Africa, with other early fossil primates also being found in caves in restricted karst areas in northern Namibia, southern Angola and western Botswana (Pickford et al. 1994). Thus data for the inhabited palaeo landscapes and environments is similarly restricted and remains largely unknown beyond basic models from faunal data (Herries et al. 2010). The limited data that exists for the Plio-Pleistocene suggests increasing aridity from *2 million years ago in southern Africa (Doran et al. 123 Evol Ecol 2015; Dupont et al. 2005; Hopley et al. 2007), and major faunal turnover (Herries et al. 2010). However, detailed palaeoenvironmental records do not exist for the region at this time. Moreover, over the last 4.0–1.5 million years, since the fossils were deposited, significant erosion has taken place (Dirks et al. 2010). Thus the paucity of fossil data means predictions based on fossils alone are problematic. This research aims to predict the potential distribution of fossil southern African Papio by extrapolating suitable past environmental conditions (thus suitable habitat) derived from current and historic chacma baboon (Papio ursinus) observation data. The modern chacma baboon is the best available analogue for southern African Papio, including its extinct lineages. This is because the Papio genus is believed to have a southern African origin (Newman et al. 2004; Zinner et al. 2009) and the chacma baboon is likely to be the oldest clade within Papio (Newman et al. 2004; Sithaldeen et al. 2009). As shown by the fossil record, the chacma baboon has evolved as a distinct taxon within the geographic region of its ancestral populations (Jablonski and Frost 2010). This, in addition to the likelihood of shared evolutionary traits, means that both the current and ancestral populations are most likely to survive within similar environmental conditions. Understandably, there are numerous issues with using modern population data to predict an ancient biogeography. These include: 1. The improbability that two species, separated by geological time, will have identical niche requirements. Assemblages attributed to a single species of Papio (P. angusticeps) have been shown to have wide ranging isotopic values (Adams et al. 2013). This suggests niche separation due to time, or one species with wide ranging diets across C3 and C4 environments, or two separate species that have not previously been identified. 2. The probability that current primate distributions will have been affected by anthropogenic alteration of the landscape. 3. Past environmental conditions are, at best, only estimates. Such issues must be considered, however, as the extinct species were within the same geographic area, it is also highly unlikely that the descendant species will have evolved to require and inhabit an entirely different environment. More likely, there would be some form of niche differentiation, such as variation in habitat types (i.e. forest cover, savannah etc.) within similar environmental bounds. Therefore, we would expect to observe some degree of habitat overlap. We do not know the direction of any likely shifts, expansions or reductions that would have occurred to this habitat, but the central part of the distribution is likely to be the least affected. For this reason the central niche, used to predict the environmental core habitat, becomes of particular interest. The central niche is the environmental zone within an animal’s environmental niche that encompasses the central 50 % of observations for each environmental variable that affects that specific animal’s distribution (Stone et al. 2015). For example, if rainfall was deemed influential then it would include the areas that fell within the central part of that species’ rainfall distribution. The central niche is the most moderate part of an animal’s environ- mental range; as such, it affords less environmental stress and is likely to be stable given changes in the environmental limits