AIM: The effective conservation and management of any given species frequently requires basic knowledge on its distribution, which is problematic for highly mobile, species‐ and ecologically‐rich groups, like bats. Moreover,… Click to show full abstract
AIM: The effective conservation and management of any given species frequently requires basic knowledge on its distribution, which is problematic for highly mobile, species‐ and ecologically‐rich groups, like bats. Moreover, to produce detailed distributions for continental‐sized, bat species‐rich countries, like Brazil, is difficult, especially considering that ~60% of the country has not a single formal bat record. Using Species Distribution Modelling (SDM), we assessed the potential distribution of >100 species of Brazilian bats. LOCATION: Brazil. METHODS: We (a) updated data on the distribution of 135 bat species, (b) generated SDMs for each species, (c) constructed species richness maps for Brazil, (d) determined areas of endemism and (e) identified areas harbouring threatened species. RESULTS: Amazonia harbours the highest bat species richness (76% of the species), followed by the Atlantic Forest (66%), Cerrado (65%) and the Caatinga (53%). Richness in 5 × 5 km grid cells varied between 23 and 117 species and 70% of Brazil's territory is predicted to have 50–90 species, and 25% >90 species. Coastal Atlantic forest, mainly in its north‐eastern region, and along its contact zone with the Caatinga biome had the highest potential richness. However, there is a severe regional sampling bias: 48% of the records available were from the Atlantic Forest and 21% were from along the Amazon River. MAIN CONCLUSIONS: Pantanal and Pampa are clearly under sampled, with <50% of their expected richness already assessed. Endemic and endangered bat species are in the Atlantic Forest, Cerrado and Caatinga, the most threatened biomes in Brazil. In addition to the seven nationally threatened bat species in Brazil, the situation for other species is worrisome: In a business‐as‐usual scenario, local extinctions in large portions of the country may happen before basic information gaps are filled.
               
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