Introduction
Climate change is a major threat to biodiversity (Hughes et al., 2003; Araújo & Rahbek, 2006), and its impacts are predicted to accelerate towards the end of the century (Urban, 2015). The known consequences of climate change include modification of species biology, ecology, distribution, and ultimately, increased extinction risk across the world (Parmesan & Yohe, 2003; Thomas et al., 2004; Walther et al., 2005; Schmittner & Galbraith, 2008; Wolkovich et al., 2014). Loss of species diversity and reduced distribution ranges are expected in response to climate change (Malcom et al., 2006; Midgley et al., 2006; Jetz et al., 2007), particularly among taxa with behaviour and lifecycles closely influenced by climatic conditions (Brook, 2009; Sherwin et al., 2012). Climate change currently affects and will continue to impact many areas of the world including South Asia, which is considered one of the most vulnerable regions to climate change impacts (World Bank Group, 2022). This region hosts a wide and diverse range of biotic and abiotic conditions with spatial variation in climate and vegetation that have resulted in high degrees of diversity, richness, and endemism (Srinivasulu & Srinivasulu, 2016) and four recognized global biodiversity hotspots: Himalaya, Indo-Burma, Western Ghats & Sri Lanka, and Sundaland (Olson & Dinerstein, 1998; Myers et al., 2000). This biodiversity is likely to be threatened by climate change, but few studies have investigated the potential impacts of future climate scenarios in this region.
South Asia hosts over 500 species of mammals, of which 151 species, in nine families, are bats (Srinivasulu, 2018; Srinivasulu et al., 2023). Unfortunately, in most regions in South Asia, bats are often perceived negatively (Frembgen, 2006), and are not considered to be of conservation value - only six species are specifically protected by the Indian Wildlife (Protection) Act, 1972. Bats can be important as indicator species (Jones et al., 2009), ecological service providers, and keystone species (Kalka et al., 2008; Williams-Guillén et al., 2008; Altringham, 2011; Hughes et al., 2012; Raman et al., 2023). Globally bats have been identified as particularly susceptible to climate change (Sherwin et al., 2013; Festa et al., 2022) due to their high risk of dehydration caused by their high surface-to-volume ratios (as a result of their relatively smaller bodies and larger wing and tail membranes; Korine et al., 2016; Salinas-Ramos et al., 2023), and their slower reproductive strategies (Frick et al., 2019). In addition, bat behaviour and ecology are often driven by climate-based cues (Bates & Harrison, 1997), and due to lacking an effective evaporative cooling body mechanism, bats are especially sensitive to heat (Salinas-Ramo et al., 2023). Climate extremes like heat waves, increasing in frequency due to anthropogenic climate change (Sippel et al., 2015; Vogel et al., 2019), are known to cause mass-mortality events in bats across the world (O’Shea et al., 2016). Overall, bats are likely to be impacted by predicted climate changes in South Asia; however, how changes could affect the current hotspots of bat diversity and species distribution ranges in this region remains unclear, and yet must be understood to develop much-needed conservation strategies.
Ecological niche modelling (ENM) is a set of techniques widely used to model potential climatic suitability in a spatial context by extrapolating from the abiotic and/or biotic ecological niche conditions present within a species’ current distribution (Pearson & Dawson, 2003; Araújo et al., 2006) and define climatic suitability envelopes that approximate the fundamental niche (Soberón & Arroyo-Peña, 2017). This climatic suitability is subsequently used to evaluate changes in climatically suitable locations into the future based on modelled climate scenarios, as an assessment of the effect of climate change on the study species (Guisan & Thuiller, 2005). However, due to uncertainty in data acquisition and generation, modelling methodology, assumptions of statistical analyses, reproducibility of analytical methods, and other limitations, ENM requires careful consideration and application (Feng et al., 2019). This has resulted in the development of various robust statistical applications, algorithms, and frameworks for ENM (Hijmans et al., 2005; Pearson et al., 2006; Araújo & New, 2007; Elith et al., 2011; Drake, 2014; Breiner et al., 2018), and a rise in the use of these modelling methods in ecology, conservation, and policymaking (Araújo et al., 2019).
In this study, we investigate the predicted impact of climate change on bat species in South Asia using geographic occurrence data and bioclimatic variables describing current climates and four near future (2041-2060) scenarios. We used ensemble ENM and carefully constructed sets of simulated pseudoabsences that incorporate uncertainty in the data and considered biological and environmental factors. The consensus output was then evaluated to characterise changes in the size and location of climatically suitable areas for all studied bats and to identify hotspots of diversity based on climate suitability. These results provide information of value for conservation planning, prioritisation, and policymaking.