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.