1 INTRODUCTION
Anthropogenic activities have led to rapid global changes that are
creating unprecedented climatic conditions, which could compromise the
viability of many species (Walther et al. 2002; Walther et al. 2009).
Understanding how environmental conditions affect the survival of
species’ populations and shape their distributions is fundamental for
establishing conservation strategies (Bosch et al. 2018). However,
estimating the relationships between species and their environment is a
complex task, as it involves multiple factors (Gaston and Fuller 2009;
Sexton et al. 2009; Wiens 2011). These factors can be summarized into
three categories: 1) limiting or regulatory, which control the
ecophysiological responses of the species, 2) disturbance factors, which
describe the historical modifications in the occupation of habitats by
both natural and anthropogenic processes, and 3) resource factors, which
represent the supplies necessary for the survival of organisms (Guisan
and Thuiller 2005). Some causal relationships between these factors and
species’ attributes, such as abundance and distribution, have been
demonstrated through physiological experiments in laboratories and
wildlife demographic studies (Gause 1931; Hutchings and Myers 1994;
Kordas et al. 2011). However, carrying out laboratory experiments or
fieldwork to obtain demographic data has some drawbacks. First, both are
generally costly and time-consuming. Secondly, implementing
physiological experiments is not ethically feasible for many organisms
(Fraser 1999). Finally, some environmental variables are more relevant
at broad spatial scales (>200 km). For instance, limiting
or regulating factors associated with climate are considered to be the
most relevant in explaining the distribution of species across their
entire range (Contreras-Díaz et al. 2022; Gillespie et al. 2008; Pearson
and Dawson 2003).
An alternative approach to studying species-environment relationships in
a spatial context is the use of correlative methods, known generically
as ecological niche modeling (ENM) and species distribution modeling
(SDM) (Bentlage et al. 2013; Pearson and Dawson 2003; Peterson and
Soberón 2012). The inputs required by correlative methods are easier to
acquire since they are generally available in online repositories. These
inputs are sets of georeferenced localities where the presence of the
species in question has been observed (some methods also require points
that represent absence) and spatial surfaces that describe the
environmental conditions of the area of interest (Randin et al. 2009).
Thus, correlative methods are extensively used to model
species-environment relationships and estimate their actual and
potential distributions, both in terrestrial (Colwell and Rangel 2010;
Eme et al. 2014) and marine ecosystems (Bosch et al. 2018; Bradie and
Leung 2017). Although correlative methods have certain limitations
(e.g., it can be difficult to discern whether their results are mere
statistical associations or causal relationships; see Wiens et al.
2009), they allow us to recognize diverse ecological patterns (Ashcroft
et al. 2011). As a result, these methods have become popular for
analyzing the importance of environmental variables as limiting factors
of distribution ranges and various ecological aspects across multiple
taxa (Bosch et al. 2018; Bradie and Leung 2017; Lee-Yaw et al. 2016).
One of the most studied limiting factors is temperature, as it has a key
relevance for many physiological and ecological processes (Bicego et al.
2007; Clarke 2003; Kordas et al. 2011). Thus, studying the thermal niche
(TN) and realized thermal niche (TNR) of species has been a major focus
of ecological research (Gvoždík 2018; Lee-Yaw et al. 2016; Magnuson et
al. 1979; Stuart-Smith et al. 2017). According to Gvoždík (2018), TN
refers to the range of body temperature that individuals of a species
require for the population to experience positive growth, while TNR
refers to the environmental range of temperatures that individuals of a
species are exposed to (Collin et al. 2021; Stuart-Smith et al. 2017).
Based on the concepts of TN and TNR, two hypotheses have been proposed
to explain patterns of temperature tolerance and organismal response to
global warming. The first hypothesis suggests that tropical and polar
terrestrial species have lower TN than those from temperate climates
(Pörtner 2001; Pörtner and Farrell 2008). The second hypothesis is the
realized thermal limit asymmetry, which refers to significant
differences in the variation of upper limits in a species or taxon
compared to the variation of lower limits within the same taxon, with
the variation being greater in the lower limits. This means that under
climate change scenarios, high rates of global warming can severely
impact the conservation of most species (Collin et al. 2021;
Stuart-Smith et al. 2017). Ideally, evaluating these hypotheses requires
direct estimation of the species’ TN in question through physiological
population growth-mortality experiments (Dong and Somero 2009; Helmuth
et al. 2006; Sánchez-Fernández et al. 2012; Stillman and Somero 2000).
However, despite the high cost, time and ethical constrains, there are
global initiatives to analyze this patterns (Bennett et al. 2018).
Nonetheless, characterization of the TN for most taxa is still lacking.
Therefore, through the use of correlative methods, it is possible to
characterize a portion of the TN called TNR, which represents the range
of temperatures associated with the presence of a species (Descombes et
al. 2015; Gaüzère et al. 2015; Quintero and Wiens 2013). Since
correlative methods do not require experimentation and management of
organisms, they are a more feasible option for studying species TNs. As
a result, they have been commonly applied to many taxonomic groups (Huff
et al. 2005; Kuo and Sanford 2009; Sánchez-Fernández et al. 2012;
Sánchez-Fernández et al. 2016). Moreover, most TNs analysis have been
focused on species from terrestrial environments (Bennett et al. 2018),
leaving no information on many important marine groups, such as sea
snakes.
Sea snakes are an ecologically diverse and marine-adapted lineage of
elapids that includes two subfamilies, Hydrophiinae (true sea snakes)
and Laticaudinae (sea kraits). The group comprises >60
species widely distributed in the tropical and subtropical regions of
the Indian and Pacific Oceans (Sanders et al. 2013; Wallach et al.
2014). Sea snakes’ lineage has evolved with unique physiological
adaptations to survive in marine environments, being their ability to
thermoregulate a critical adaptation to adjust their metabolic rate and
behavior in response to water changes in temperature (Heatwole et al.
2012). Recent research has also shown that temperature plays a critical
role in the diving behavior and metabolism of sea snakes, as individuals
exhibit different diving behaviors and metabolic rates in response to
changes in water temperature (Dabruzzi et al. 2012; Udyawer et al.
2016). These findings highlight the complex ways in which temperature
influences the behavior and physiology of sea snakes and underscore the
importance of understanding the thermal biology of these reptiles for
conservation and management efforts. Nevertheless, most sea snake
research has focused on their toxicity, and their phylogenetic and
evolutionary relationships (Udyawer et al. 2018). However, to date,
there is no information on the factors that determine sea snakes’
distribution, nor the asymmetry of their realized thermal limits. In
this study, we used correlative ecological niche modeling (ENM) to
evaluate the relative importance of environmental variables in
explaining the distribution of almost all sea snake species on a global
scale. We also analyzed whether there is an asymmetry in the dispersion
of their lower and upper realized thermal limits. The results of our
research provide a better understanding of the biogeographical phenomena
that determine the distribution patterns in this group and will allow us
to infer their vulnerability to future ocean warming scenarios.