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Data from: Future climatically suitable areas for bats in South Asia

Cite this dataset

Srinivasulu, Aditya et al. (2024). Data from: Future climatically suitable areas for bats in South Asia [Dataset]. Dryad.


Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well‐studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near‐future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970–2000). We then extrapolated near future (2041–2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle‐of‐the‐road and SSP5: fossil‐fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.

README: Varied projected changes in near-future climatically suitable areas for bats in South Asia

This dataset contains ocurrence localities and bioclimatic variables used in an ensemble ecological niche modelling analysis of bats in South Asia.

Data and file structure

Data is split into two parts. The occurrence localities for each species are provided in a CSV file with the following columns: Family, Species, Latitude, Longitude, Location, Source. Locations have been corrected for consistency with nearest landmarks where necessary (for instance, if the coordinates point to a spot in a forest, the nearest settlement and the distance may be given as a location).

Bioclimatic variables are provided as a layered TIFF file. This file can be opened in any GIS software, and each layer of the file is one bioclimatic variable (labelled BIOX, where X is 1 - 19). For reference to the names and definitions of bioclimatic variables, visit:

Sharing/access Information

Bioclimatic variables for the world are also available at the WorldClim 2.1 website:


There are 150 recognized bat species in South Asia (Srinivasulu et al., 2023) but we a priori excluded the four species endemic to the Andaman and Nicobar Islands given the geographic isolation of the islands from the rest of the South Asian landmass. Species were identified based on current taxonomic information at the time of analysis (Srinivasulu et al., 2021). We also limited our study to species for which we could gather five or more occurrence localities across South Asia, with a minimum distance between occurrences of 5 km. Presence-only occurrence data for these species were collected from published (Bates & Harrison, 1997; Srinivasulu & Srinivasulu, 2012; Srinivasulu et al., 2021), unpublished sources (records collected during field surveys conducted in India between 2002 and 2022, and records communicated by collaborators and citizen scientists in the region confirmed by photographic or other evidence), and GBIF records [accessed July 2022]. Records of specimens housed in museums including the Natural History Museum (London, UK), Harrison Institute (Sevenoaks, United Kingdom), American Museum of Natural History (New York City, USA), Zoological Survey of India (Kolkata, India), and Natural History Museum, Osmania University (Hyderabad, India), were also included.


Yearly and seasonal patterns in temperature and precipitation are known to influence behaviour and life history in bats around the world (Conenna et al., 2019; Gorman et al., 2021; Kohyt et al., 2021; Weinberg et al., 2022). To capture these conditions the 19 standard current bioclimatic variables were sourced at 2.5 arc-minute resolution from the WorldClim v2.1 database for the period of 1970 to 2000 (Fick & Hijmans, 2017). Future climate predictions were obtained from two different models and two different scenarios to capture uncertainty in future climates. We considered the Canadian Earth System Model 5 (CanESM5; Swart et al., 2019) and Hadley Centre Global Environment Model 3 (HadGEM3; Good, 2019; Good, 2020) available in the Coupled Model Intercomparison Project 6 (CMIP6) from which we obtained 2.5 arc-minute climate predictions for a near-future time (2050, averaged from 2041-2060). From each model, data were obtained for the shared socioeconomic pathways 2 and 5 (SSP2, equivalent to Representative Concentration Pathway RCP4.5; and SSP5, equivalent to Representative Concentration Pathway RCP8.5). These pathways, used by the International Panel on Climate Change, characterise an optimistic ‘middle of the road’ socioeconomic scenario representing an ideology towards sustainable development, and a pessimistic ‘fossil-fuelled development’ scenario of climate change based on development almost entirely based on fossil fuels in the future, and little development towards sustainability and an emphasis on resource- and energy-intensive lifestyles (Kriegler et al., 2017). The two CanESM5 models are hereafter referred to as Can2-45 for SSP2-RCP4.5 and Can5-85 for SSP5-RCP8.5, and the two HadGEM3 models as Had2-45 for SSP2-RCP4.5 and Had5-85 for SSP5-RCP8.5.

Usage notes

Occurrence localities for each species are given in a CSV (comma separated values) file. These files can be accessed using any spreadsheet software (we use Microsoft Excel but Google Sheets and OpenOffice Calc are also usable).

Bioclimatic variables are uploaded as layered TIFF (Tag Image File Format) files for each climate scenario. These files can be accessed using any GIS software (we use QGIS and R).

The results of this analysis are also provided as XLSX files (which can also be opened in any spreadsheet software).


University of Reading, Award: GS21-026