1 Introduction
Large-scale spatial distribution patterns of species richness and their
formation mechanisms are central to ecology and biogeography. It is also
the basic scale for measuring regional diversity and the basis for
constructing evolutionary and ecological models and conservation
strategies (Gotelli & Colwell, 2001; Jenkins, Pimm & Joppa, 2013;
D’Antraccoli et al., 2019). The prediction of species ranges can usually
be achieved in three ways: collection or surveyed species distribution
record points, expert mapping of species distributions, and the ranges
inferred from species distribution models (Guisan & Thuiller, 2005). At
present, species distribution models (SDM) are frequently used in
studies on species distribution prediction because of their relative
flexibility and better discriminatory and predictive power. SDM can use
the relationship between species distribution points and local
environmental variables to predict the potential distribution areas of
species (Zhang et al., 2019; Abdulwahab, Hammill & Hawkins, 2022;
Sanczuk et al., 2022). China is a vast territory, and covering all
biological surveys is challenging. Therefore, SDM can guide future field
surveys to a certain extent, provide references for further exploration
and discovery of species distribution, and provide a scientific basis
for the formulation of species protection measures (Nguyen & Leung,
2022). Among the simulation methods of various distribution models, the
maximum entropy model uses environmental variables and species
distribution sites to calculate constraints in the case of a small
sample size. It explores the possible distribution of maximum entropy
under this constraint to predict the habitat suitability of species in
the study area, resulting in better simulation results than other models
(Wang et al., 2021).
How the interactions of the modern environment, evolutionary history,
and ecological processes shape the patterns of species richness remains
an interesting but controversial issue in biogeography. Ecologists have
been trying to determine the effects of various environmental variables
on the distribution and diversity of organisms in different ecological
regions. The factors determining richness patterns are critical for
understanding the structure and dynamics of organisms in an area (Holt
et al., 2018). Species are not randomly distributed over the land
surface; rather, their distribution patterns are based on climate,
topography, and anthropogenic influences in recent decades (Li et al.,
2015; Xu et al., 2019). Consequently, various theories and hypotheses
have been developed to explain how geographical patterns of species
richness are formed.
The energy-water hypothesis is the most common and discussed hypothesis
for explaining species richness patterns (Hawkins et al., 2003; Pandey
et al., 2020). This hypothesis states that the availability of energy
and water determines the total plant resources that control biological
activity and that total plant resources, in turn, determine changes in
biodiversity (Jimenez-Alfaro et al., 2016). Second, habitat
heterogeneity, another form of environmental variation that affects the
production and maintenance of diversity, is considered one of the most
important factors controlling species richness gradients. Increased
space and shelter and opportunities for isolation and adaptation enhance
species coexistence, persistence, and diversification (Stein, Gerstner
& Kreft, 2014; Stein et al., 2015). Third, seasonal changes in climate
and unsystematic changes in daily maximum and minimum temperatures
increase organisms’ tolerance levels by altering their thermal
environments, enabling them to become geographically widespread (Mi et
al., 2022). Finally, human-induced environmental changes, such as
habitat fragmentation, land-use changes, and disturbances, can lead to
habitat loss for species (Li et al., 2015; Xu et al., 2019). These
hypotheses explore the main factors influencing species richness
formation based on different influencing factors.
Some studies have tested a single hypothesis (Sun et al., 2020), whereas
others have tested multiple hypotheses (Gebauer et al., 2018; Ding et
al., 2019; Pandey et al., 2020). A single variable or hypothesis is
limited in its interpretation of species richness distribution patterns
because it is a multiple-complex phenomenon that determines species
richness distribution patterns. Thus, multiple modeling approaches are
best suited for quantifying the contribution of various hypotheses to
spatial richness distribution patterns. In the context of global
biodiversity loss and concomitant climate change, attempts have been
made to determine the relationships between species populations and
their determinants (Xu et al., 2019; Pandey et al., 2020; Sun et al.,
2020). Some studies have explained the distribution patterns of the
regional richness of rodents in China (Zhou, Ma & Ye, 2002; Xing, Zhou
& Ma, 2008) but have not considered the mechanisms that determine
richness patterns. Chi et al. (2020, 2021) studied the distribution
pattern of terrestrial mammal abundance in China and its relationship
with environmental factors. Such studies include rodents in mammals,
which inevitably do not fully consider their distribution pattern,
resulting in studies that do not fully reflect the distribution pattern
of rodents. Moreover, few studies have been on the richness distribution
patterns of endemic and non-endemic rodent groups in China. Endemic
species are those found only in specific locations or regions, not
anywhere else in the world. They are usually restricted to a limited
geographic range, with small ranges and population sizes, and sometimes
with low genetic diversity and specific habitat requirements (Myers et
al., 2000; Isik, 2011). Multiscale drivers and geographic distribution
patterns of endemic species are also important topics in conservation
biogeography because these species are particularly vulnerable to
climate change and habitat degradation (Wu et al., 2016). It has been
shown that there is a lack of consistency between all species richness
or non-endemic species richness and endemic species richness (Orme et
al., 2005; Lamoreux et al., 2006). Areas with high species richness may
have many endemic species but not necessarily coherent patterns (Vetaas
& Grytnes, 2002).
Therefore, we attempted to explore the geographic distribution pattern
of rodents (Rodentia and Lagomorpha) in China. We divided rodent species
into endemic and non-endemic species and assumed that the factors
affecting endemic and non-endemic species distribution are different. We
investigated the relative importance of energy-water, climatic
seasonality, habitat heterogeneity, and human factors that may
contribute to the distribution patterns of rodents in China. The main
objectives of this research were to (1) explore the distribution pattern
of rodents and their endemic and non-endemic species and (2) assess the
explanatory power of energy-water, habitat heterogeneity, climate
seasonality, and human factors for rodent distribution patterns in
China.