, Volume 34, Issue 2, pp 407-422
Date: 20 Mar 2013

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

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

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 baboons (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 chacma baboon across southern Africa. 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 georeferenced observational data. The data extracted from the environmental layers suggest 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 southern Africa. In addition, a linear “patchy” corridor was identified following the East African Rift Valley connecting the southern habitat with northeast Africa. We find the greatest proportion of suitable habitat to be located in South Africa. This modeling approach is generic and would be suitable to analyze other primate species across similar geographic extents.