Predicting bat distributions and diversity hotspots in southern Africa
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Date
Authors
Cooper-Bohannon, Rachael
Rebelo, Hugo
Jones, Gareth
Cotterill, Fenton (Woody)
Monadjem, Ara
Schoeman, M. Corrie
Taylor, Peter
Park, Kirsty
Journal Title
Journal ISSN
Volume Title
Publisher
Associazione Teriologica Italiana
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.
Description
Table S1 Seventy-six eco-geographical variables trialled to build Maxent model for
focal bat species in southern Africa.
Table S2 Species information and modelling prediction results.
Figure S3 Species distribution maps: Pteropodidae, Hipposideridae.
Figure S4 Species distribution maps: Rhinolophidae.
Figure S5 Species distribution maps: Emballonuridae, Nycteridae.
Figure S6 Species distribution maps: Molossidae.
Figure S7 Species distribution maps: Miniopteridae, Vespertilionidae.
Figure S8 Species distribution maps: Vespertilionidae (cont.).
Figure S9 Species distribution maps: Vespertilionidae (cont.).
Table S2 Species information and modelling prediction results.
Figure S3 Species distribution maps: Pteropodidae, Hipposideridae.
Figure S4 Species distribution maps: Rhinolophidae.
Figure S5 Species distribution maps: Emballonuridae, Nycteridae.
Figure S6 Species distribution maps: Molossidae.
Figure S7 Species distribution maps: Miniopteridae, Vespertilionidae.
Figure S8 Species distribution maps: Vespertilionidae (cont.).
Figure S9 Species distribution maps: Vespertilionidae (cont.).
Keywords
Biogeographical strata, Chiroptera, Conservation priorities, Maxent, Species distribution modelling, Southern Africa
Sustainable Development Goals
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.