Tropical forests and the biodiversity within them are rapidly declining in the face of increasing human encroachment. Resource management and conservation of endangered species requires an understanding of how species perceive and respond to their environment so efforts can focus on areas of highest importance for the species of concern. Species Distribution Modeling (SDM) is an appropriate tool for identifying conservation priority areas. In this study, maximum entropy SDM was used to identify areas of suitable chimpanzee (Pan troglodytes verus) habitat within the Mont Nimba Strict Nature Reserve of Guinea, Africa. Suitable habitat was predicted based on the location of chimpanzee nest sites and the spatial distribution of 16 biophysical variables within the study area.
Results/Conclusions
The accuracy of the model was assessed by examining the area under the curve (AUC). The overall accuracy of the model was 0.754 (random expectation results in an AUC = 0.5). In addition to a map showing suitable chimpanzee habitat, the model identified the biophysical variables contributing most to habitat suitability (percent permutation importance). The variables most influencing habitat suitability for chimpanzees in the study area were elevation (26.1%), wetness index (18.5%), distance to the Strict Nature Reserve boundary (15.7%), distance to rivers (8.5%), the normalized difference vegetation index (6.5%), and topographic position index (6.3%). Understanding the factors influencing chimpanzee behavior, specifically the biophysical variables considered in this study, will greatly contribute to efforts to conserve endangered chimpanzees. SDMs such as this should be used to identify priority areas and more effectively conserve chimpanzees.