Predicting bat distributions and diversity hotspots in southern Africa

Show simple item record Cooper-Bohannon, Rachael Rebelo, Hugo Jones, Gareth Cotterill, Fenton (Woody) Monadjem, Ara Schoeman, M. Corrie Taylor, Peter Park, Kirsty 2016-12-21T09:55:05Z 2016-12-21T09:55:05Z 2016-07-19
dc.description Table S1 Seventy-six eco-geographical variables trialled to build Maxent model for focal bat species in southern Africa. en_ZA
dc.description Table S2 Species information and modelling prediction results. en_ZA
dc.description Figure S3 Species distribution maps: Pteropodidae, Hipposideridae. en_ZA
dc.description Figure S4 Species distribution maps: Rhinolophidae. en_ZA
dc.description Figure S5 Species distribution maps: Emballonuridae, Nycteridae. en_ZA
dc.description Figure S6 Species distribution maps: Molossidae. en_ZA
dc.description Figure S7 Species distribution maps: Miniopteridae, Vespertilionidae. en_ZA
dc.description Figure S8 Species distribution maps: Vespertilionidae (cont.). en_ZA
dc.description Figure S9 Species distribution maps: Vespertilionidae (cont.). en_ZA
dc.description.abstract Species distribution models were used to predict bat species richness across southern Africa and to identify potential drivers of these spatial patterns. We also identified species richness within each biotic zone and the distributions of species considered of high conservation priority. We used this information to highlight conservation priorities for bats in southern Africa (defined here as between the latitudes of 8° S, slightly north of Zambia, to the southern tip of Africa 34° S, an area of approximately 9781840 km2). We used maximum entropy modelling (Maxent) to model habitat suitability for 58 bat species in order to determine the key eco-geographical variables influencing their distributions. The potential distribution of each bat species was affected by different ecogeographic variables but in general, water availability (both temporary and permanent), seasonal precipitation, vegetation, and karst (caves/limestone) areas were the most important factors. The highest levels of species richness were found mainly in the eastern dry savanna area and some areas of wet savanna. Of the species considered to be of high priority due to a combination of restricted distributions or niches and/or endemism (7 fruit bats, 23 cave-dwellers, 18 endemic and near-endemic, 14 niche-restricted and 15 range-restricted), nine species were considered to be at most risk. We found that range-restricted species were commonly found in areas with low species richness; therefore, conservation decisions need to take into account not only species richness but also species considered to be particularly vulnerable across the biogeographical area of interest. en_ZA
dc.description.department Zoology and Entomology en_ZA
dc.description.librarian am2016 en_ZA
dc.description.sponsorship Rachael Cooper-Bohannon was funded under a Horizon grant at the University of Stirling and by the Rufford Foundation. Hugo Rebelo was funded by Fundação para a Ciência e Tecnologia, Portugal (contract IF/00497/2013). en_ZA
dc.description.uri en_ZA
dc.identifier.citation Cooper-Bohannon, R, Rebelo, H, Jones. G, Cotterill, F, Monadjem, A, Schoeman, MC, Taylor, P & Park, K 2016, 'Predicting bat distributions and diversity hotspots in southern Africa', Hystrix, the Italian Journal of Mammalogy, vol. 27, no. 1, pp. 1-11. en_ZA
dc.identifier.issn 0394-1914 (print)
dc.identifier.issn 1825-5272 (online)
dc.identifier.other 10.4404/hystrix-27.1-11722
dc.language.iso en en_ZA
dc.publisher Associazione Teriologica Italiana en_ZA
dc.rights Article is published under a Creative Commons Attribution Non-Commercial No Derivatives License 3.0. en_ZA
dc.subject Biogeographical strata en_ZA
dc.subject Chiroptera en_ZA
dc.subject Conservation priorities en_ZA
dc.subject Maxent en_ZA
dc.subject Species distribution modelling en_ZA
dc.subject Southern Africa en_ZA
dc.title Predicting bat distributions and diversity hotspots in southern Africa en_ZA
dc.type Article en_ZA

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