Approximately 25% of bats globally are threatened, but limited data on African bats, which account for 20% of bat species, hinders our understanding of their conservation status across this ecologically diverse continent. This study combined: modelling techniques, to predict current species distributions for 58 southern African bat species and project past, current and future distributions of 22 endemic and near-endemic species; bat acoustic surveys, to assess landscape features influencing bat activity in arid and semi-arid regions; and conservation planning software to design a large-scale monitoring network for bats across this subcontinent. Species distribution models were employed using a robust and well established presence-only modelling technique (Maximum Entropy - Maxent) to model the current distributions of 58 species in southern Africa. Although the important eco-geographical variables were species- or in some cases family-specific, overall water availability (both temporary and permanent), seasonal precipitation, vegetation and karst (caves/limestone) areas were the most important factors associated with distribution patterns. These species distributions were then used to identify range-restricted and narrow niche breadth species, alongside other life-history strategies considered to put species at risk, such as Old World pteropodids and cave-dwelling bats to identify species most at risk. Nine of the 58 species in this study were identified as 'at risk'. Considering range-restriction and endemism separately, the results showed that range-restricted species were a higher proportion (50%) of 'at risk' species than endemics (41%) but six of the nine identified species were endemic and range restricted (67%). If only areas of high species richness are prioritised, important areas with low species richness but rare, 'at risk' or endemic species would be excluded. Species distributions are not fixed but may shift due to changes in environmental conditions. Accurately predicting changes in species' distributions due to anthropogenic climate change remains a fundamental challenge for conservation biologists, and this is amplified when dealing with taxa such as bats that are inherently difficult to study and in areas, such as Africa, with sparse ecological data. To better understand endemic bat species risk to climate change in southern Africa and to highlight historical and future likely refugia, Maxent was employed to forecast range-shifts for 22 southern African endemic or near-endemic species. Species distributions were projected during the Last Glacial Maximum (LGM ~22,000 BP), present (1950-2000) and future (2070: averaged 2061-2080, using IPCC5 scenarios) climatic conditions. Keywords: Chiroptera, bats, species distribution modelling, southern Africa, cave-dwelling, conservation, marxan, maxent, monitoring network, call analysis, Bats-Conservation-Africa, Southern.