Predicting bat distributions and diversity hotspots in southern Africa
 
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1
Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Scotland and Bats without Borders, Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Scotland
2
CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto. Campus Agrário de Vairão; CEABN/InBio, Centro de Ecologia Aplicada, Instituto Superior de Agronomia, Universidade de Lisboa; School of Biological Sciences, Life Sciences Building, University of Bristol, 24 Tyndall Ave, Bristol, BS8 1TH, UK and Bats without Borders, Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Scotland, UK
3
School of Biological Sciences, Life Sciences Building, University of Bristol, 24 Tyndall Ave, Bristol, BS8 1TH
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African Earth Observatory Network (AEON), Geoecodynamics Research Hub, c/o Department of Botand and Zoology, University of Stellenbosch, Stellenbosch, 7602
5
All Out Africa Research Unit, Department of Biological Sciences, University of Swaziland, Private Bag 4, Kwaluseni, Swaziland, and Mammal Research Institute, Department of Zoology & Entomology, University of Pretoria, Private Bag 20, Hatfield, 0028, Pretoria
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School of Life Sciences, University of KwaZulu Natal, Westville Campus, Durban, 4000
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South African Research Chair on Biodiversity Value and Change, School of Mathematical and Natural Sciences, University of Venda, Thohoyandou, 0950
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Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Scotland, and Bats without Borders, Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Scotland 3
Publish date: 2016-06-29
 
Hystrix It. J. Mamm. 2016;27(1)
KEYWORDS
ABSTRACT

Species distribution models were used to predict bat species richness across southern Africaand 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 9,781,840 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 eco-geographic 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.

eISSN:1825-5272
ISSN:0394-1914