Local knowledge and species distribution models’ contribution towards mammalian conservation

Landscape-scale studies facilitate species diversity analysis according to environmental heterogeneity and human activity. This study was aimed at using local knowledge as a tool for testing predictive models’ validity for assessing the spatial distribution of medium-sized mammalian richness, identifying local patterns of species richness and evaluating local protected areas’ role in the conservation of mammals. Distribution maps were generated for historically recorded species using genetic algorithm for rule-set prediction (GARP). The landscape was reclassified as habitat, hospitable matrix and inhospitable matrix in the second scenario and a third scenario was generated limiting species distribution by using the home range. The local richness predicted by all scenarios varied from 1 to 32 species per cell while gamma diversity was 34. The 72 structured interviews led to recording 3–17 species (a total of 27). There have been no reports of nine wild species over the last 2 years. Currently protected areas cannot support viable populations of the species so recorded so shade coffee plantations must adopt conservation strategies. Historical inventories overestimate expected richness; however, combining GARP-generated models with the information obtained from local inhabitants and experts allows rapid regional evaluation of medium-sized mammalian richness and the identification of extinct species, declining populations and abundant species.