DOI: 10.5061/dryad.tdz08kq4j
title: Data from: Future climatically suitable areas for bats in South Asia

description: 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 &gt;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.
description: There are 150 recognized bat species in South Asia (Srinivasulu
 et al., 2023) but we <em>a priori</em> 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.
description: 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).
description: # **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:
 [https://www.worldclim.org/data/bioclim.html](https://www.worldclim.org/data/bioclim.html) ## Sharing/access Information Bioclimatic variables for the world are also available at the WorldClim 2.1 website: [https://www.worldclim.org/data/worldclim21.html](https://www.worldclim.org/data/worldclim21.html)