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\begin{document}

\title{Distribution patterns and drivers of nonendemic and endemic glires
species in China}



\author[1]{Lei Meng}%
\author[1]{Lizhi Zhou}%
\affil[1]{Anhui University}%


\vspace{-1em}



  
  \date{March 12, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Species distribution patterns are essential for the conservation of
biodiversity. The aim of this study was to evaluate the influence of
multiple ecological hypotheses on the spatial patterns of rodent species
richness in China. First, we divided the geographic region of China into
80 \selectlanguage{ngerman}× 80 km2 grid cells and mapped the distribution ranges of the 237
rodent species. Rodent taxa were separated into three response variables
based on their distribution: (a) all species, (b) non-endemic species,
and (c) endemic species. The predictors were divided into four factor
sets: (a) energy-water, (b) climatic seasonality, (c) habitat
heterogeneity, and (d) human factors, which were used to represent four
different ecological hypotheses. We then performed multiple regression
analysis (OLS), spatial autoregressive models (SAR), and variation
partitioning analyses to determine the effects of predictors on the
spatial patterns of rodent species. The Hengduan Mountains and
surrounding mountains in southwest China showed the highest species
richness and endemism. Habitat heterogeneity is the most important
factor explaining the species richness distribution patterns across all
species and non-endemic species. Endemic species richness patterns are
most susceptible to seasonal changes in climate and least affected by
human factors. The effects of energy and water on the three response
variables showed consistent levels of importance.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Distribution patterns and drivers of non-endemic and endemic
rodent species in China}

Lei Meng\textsuperscript{1,2}, Lizhi Zhou\textsuperscript{1,2*}

1. School of Resources and Environmental Engineering, Anhui University,
Hefei China

2. Anhui Biodiversity Information Center, Anhui University, Hefei, China

\textbf{Correspondence}

Lizhi Zhou, School of Resources and Environmental Engineering, Anhui
University, Jiulong Road, Hefei, 230601, China.

Email: zhoulz@ahu.edu.cn

\textbf{Abstract}

Species distribution patterns are essential for the conservation of
biodiversity. The aim of this study was to evaluate the influence of
multiple ecological hypotheses on the spatial patterns of rodent species
richness in China. First, we divided the geographic region of China into
80 \selectlanguage{ngerman}× 80 km\textsuperscript{2} grid cells and mapped the distribution
ranges of the 237 rodent species. Rodent taxa were separated into three
response variables based on their distribution: (a) all species, (b)
non-endemic species, and (c) endemic species. The predictors were
divided into four factor sets: (a) energy-water, (b) climatic
seasonality, (c) habitat heterogeneity, and (d) human factors, which
were used to represent four different ecological hypotheses. We then
performed multiple regression analysis (OLS), spatial autoregressive
models (SAR), and variation partitioning analyses to determine the
effects of predictors on the spatial patterns of rodent species. The
Hengduan Mountains and surrounding mountains in southwest China showed
the highest species richness and endemism. Habitat heterogeneity is the
most important factor explaining the species richness distribution
patterns across all species and non-endemic species. Endemic species
richness patterns are most susceptible to seasonal changes in climate
and least affected by human factors. The effects of energy and water on
the three response variables showed consistent levels of importance.

\textbf{Keywords}

rodents, environmental gradients, endemic species, richness patterns,
spatial autoregressive model

\textbf{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.

\textbf{2 Materials and methods}

\textbf{2.1 Study area}

The study encompassed all of China's land mass, including Taiwan and
Hainan Island, but excluded the surrounding oceans and islets. Chinese
mammalian fauna belongs to the Palearctic and Oriental realms, which can
be further subdivided into seven biogeographic subregions (i.e.,
northeast China, north China, Inner Mongolia-Xinjiang region,
Qinghai-Tibet region, southwest China, central China, and south China).
These ecogeographic zones have high species diversity and many endemic
animal species, including rodents. The present study divided the Chinese
territory into a grid system of 80 × 80 km\textsuperscript{2} to
eliminate the influence of spatial scale or area on species distribution
patterns. Grids with less than 75\% of the complete grid included in the
study area were removed to prevent the incomplete grids in the coastline
and boundary areas from affecting the subsequent statistical analysis. A
total of 1672 grids were obtained.

\textbf{2.2 Distribution pattern calculation}

We compiled a database of rodent species distribution in China. Species
distribution data were obtained mainly from the following sources: 1)
the research results of Zhou et al. (2002) and Xing et al. (2008); 2)
National Zoological Museum of China, NZMC; 3) Global Biodiversity
Information Facility (GBIF); and 4) distribution and collection records
available in books or literature (Jiang et al., 2015; Ge et al., 2018;
Liu et al., 2019; Li et al., 2019; Cheng et al., 2021; Jackson et al.,
2022). After removing null values, offset values, and redundant data
from the distribution records, 237 species of rodents in two orders were
included in the analysis of this study. There were 67 endemic and 170
non-endemic species (Jiang et al., 2015; Wei et al., 2021) (Table S1).

MaxEnt (v3.4.1) was used for ecological niche modeling (ENM) of
potential rodent habitat areas in China. Considering that the MaxEnt
model requires at least five different coordinate values for each
species to produce more accurate results, six points were used as the
minimum criteria for calculating species distribution in this study. The
potential habitats of 210 rodent species with six or more distribution
points were simulated using ENM to determine the potential species
richness of rodents in China. Based on the characteristics of
distribution data and rodent habits, 26 environmental variables were
selected. The five categories of predictors were climate, topography,
vegetation, soil, and human activity intensity (Table S2). Chinese
administrative vector boundaries were obtained from the Data Center for
Resources and Environmental Sciences at the Chinese Academy of Sciences
(RESDC) (http://www.resdc.cn) and converted to 1
km\textsuperscript{2}resolution.

The correlation of environmental variables was detected using the
ENMTools (Warren et al., 2021) package in R 4.0.5
(http://www.r-project.org). The variables that were not highly
correlated (\emph{r} \textless{} 0.7) were used in the model prediction
to reduce the complexity of the model (Table S2). The percentage of
random test data was set to 25\%, 10 sub-models were generated using the
bootstrap function of the MaxEnt model, and the average of the output of
the 10 sub-models on each image element was calculated as the final
prediction result of the species. Because each species has a different
degree of tolerance to the environment, the suitable habitat threshold
for each species was divided based on the critical value of the
available distribution records. The growth suitability at each sampling
point was extracted from the plot of the calculated growth suitability.
The standard deviation \selectlanguage{greek}σ \selectlanguage{english}and mean value \selectlanguage{greek}μ \selectlanguage{english}were calculated according to
the theory of normal distribution, \selectlanguage{greek}μ-σ \selectlanguage{english}was selected as the threshold
value, transforming the species distribution probability maps into 0/1
binary distribution maps. The model accuracy was evaluated using
receiver operating characteristic (ROC) curves. The area enclosed by the
ROC curve and horizontal axis is the AUC value (Hanley \& McNeil, 1982),
which can be used to measure the strengths and weaknesses of the model.
For species with predicted AUC values less than 0.8, the MaxEnt model
was optimized using the ENMeveal package in R (Muscarella et al., 2014),
and the model was run again.

The distribution ranges for the 27 species with less than six recorded
distribution points defaults to the grids where the distribution points
were located. The distribution range layer was converted into a 0/1
binary distribution map. Finally, the binary distribution map of 237
species was superimposed on the grid map, and the number of species
appearing in a single grid was counted to obtain the species richness
distribution map.

\textbf{2.3 Analysis of influencing factors of the species richness
pattern}

2.3.1 Environment variables

Based on previous studies and the four hypotheses, we selected 12
environmental predictors in four categories to evaluate the hypotheses
explaining the distribution pattern of rodent species richness in China.
The hypotheses and their associated variables are

\begin{enumerate}
\tightlist
\item
  Energy-water: the availability of energy and water can be measured
  using many indicators, such as temperature, precipitation, and solar
  radiation (Pandey et al., 2020). We selected annual mean temperature
  (MAT), annual precipitation (DVL), potential evapotranspiration (PET),
  and actual evapotranspiration (AET) as substitute variables. We
  extracted MAT and DVL from the WorldClim (https://worldclim.org/)
  database as measures of temperature and water effectiveness variables,
  PET and AET were obtained from the Global Land Evaporation Amsterdam
  Model (GLEAM) (https://www.gleam.eu/).
\item
  Habitat heterogeneity: the mean elevation (MELV), elevation range
  (ELR), and the number of vegetation types (VEG) within a single grid,
  the most commonly used predictors to represent information on habitat
  heterogeneity, were selected as habitat heterogeneity factors. These
  values were obtained from the RESDC.
\item
  Climate seasonality: temperature seasonality (TES), annual temperature
  range (ART), and precipitation seasonality (PRS) were used as proxies
  for short-term climate seasonality. All factors were obtained from the
  WorldClim database download.
\item
  Human factors: we used the human impact index (HII) and human
  footprint index (HFI) as proxy variables representing human-induced
  effects. The HII and HFI data were downloaded from the archives of the
  Wildlife Conservation Society (http://sedac.ciesin.colum
  bia.edu/data/).
\end{enumerate}

2.3.2 Data analysis

Species richness was defined as the number of species in each cell grid.
Species richness data usually show non-normal distribution. The species
richness data were square root transformed before regression analysis to
avoid the impact of skewed data distribution on statistical analysis. We
used a simple regression analysis of all species richness, non-endemic
species richness, and endemic species richness for each environmental
variable to explore the potential mechanisms of individual factors in
explaining the distribution patterns of species richness.

To evaluate the relative importance of the predictive variables, we
separated the environmental factors into four distinct predictor sets
based on our main research objectives: (a) energy-water (EW), (b)
climatic seasonality (CS), (c) habitat heterogeneity (HH), and (d) human
factors (HE). Because all predictors were highly correlated, we
eliminated collinearity by performing a principal component analysis
(PCA) in each prediction set. The squared term of the predictor variable
was included in the principal component analysis, considering the
nonlinear relationship between the response variable and environmental
factors. We extracted the first two principal components of each
prediction set, which accounted for 94\% of energy-water, 87\% of
habitat heterogeneity, 99\% of climatic seasonality, and 99\% of human
factors (Table S3).

To make the model coefficients comparable, the principal components
extracted from all factor sets were standardized (standard deviation = 1
and mean = 0). Multiple linear regression used ordinary least squares
(OLS) to determine the most appropriate predictors that explain the
richness of the three response variables. We followed the variable
backward selection method to identify the optimal model. The optimal
linear regression model was determined using the stepAIC function in R
in combination with the Akaike Information Criterion (AIC). Variance
inflation factors (VIF) were used to test for multicollinearity between
predictor variables, and we selected predictors with VIF \textless{} 5
(Dormann et al., 2013) (Table S4). Because spatial autocorrelation
affects the explanatory power of regression models, the spatial
autocorrelation of the residuals of multiple regression models was
assessed using Moran's I method. Because the residuals of the multiple
regression models all had significant spatial autocorrelation
(\emph{p}\textless{} 0.001), the spatial linear simultaneous
autoregressive error model (SAR) was further developed using the
predictor variables from the optimal model. The explanatory power of the
predictor variables for species richness was measured using
Pseudo-\emph{R\textsuperscript{2}}(the square of the correlation
coefficient between the predicted and actual values of the model for the
non-spatial component) (Kissling \& Carl, 2007). The relative importance
between the predictor variables was also compared using standard
regression coefficients.

Finally, we performed variance partitioning to assess the pure effects
of the predictor variables and their joint contributions to better
explain the distribution patterns of species richness. Wayne diagrams
were used to show various factor sets' pure and shared effects.

Statistical analysis for this study was performed in R 4.0.5
(http://www. r-project.org). The ''psych'' package was used for
principal component analysis, the ''MASS'' package for optimal model
selection, the ''vegan'' package for variance partitioning, and the
''spedep'' package for spatial autoregressive model building.

\textbf{3 Results}

\textbf{3.1 Species richness patterns of all species of rodents}

Rodents are widely distributed in China and are recorded in almost every
grid; however, their spatial distribution is uneven. All species (n =
237) of rodents occurred in 1665 grids, and the species richness in each
grid was between 0 and 84 (mean: 31.39 \selectlanguage{ngerman}± 16.51 SD) species (Figure 1a;
Table S5). The highest abundance of rodent species in China was found in
the subtropical and tropical regions of the oriental realm, with the
Hengduan Mountains being the most abundant region, followed by the
Qilian Mountains and Tianshan Mountains regions. In addition, Taiwan and
Hainan Island also have high species richness. The species richness was
the lowest in the Qinghai-Tibet Plateau and Tarim Basin regions, with
only a few species in most grids.

The simple regression results showed that, when considering the effect
of individual factors, all species richness was highly significantly
correlated with each environmental factor (\emph{p} \textless{} 0.001).
The two most relevant variables were VEG and AET (Figure 2a, b; Table
S6).

Regarding the relationship between the predictor set and all species
richness, the best model was explained by a set of five variables
(EW1+CS1+HH1+HE1+HE2). The multiple regression model (OLS) explained
71\% of the total variation in rodent species richness. Due to the
removal of the effect of spatial autocorrelation by the spatial
autoregressive model (SAR), the degree of explanation was reduced to
64\% (Table 1). As shown in Table 1, the importance of each variable in
explaining the species richness pattern varied slightly across the
regression models, but the difference was not significant. Both the
regression models showed that HH1 and HE1 were the most important
predictors. The variance partitioning results showed that the four
predictor sets explained 71.7\% of the variance in the total species
richness. The habitat heterogeneity predictor set explained 48.94\% of
the variation in all species richness patterns, followed by human
factors (42.30\%), energy-water (28.79\%), and climatic seasonality
(18.30\%) (Figure 3a, b; Table S7)

\textbf{3.2 Patterns of non-endemic species richness}

Non-endemic species (n = 170) accounted for 71.7\% of all species and
were found in 1,659 grids. Their distribution in each grid ranges from
0--58 (mean: 25.65 ± 12.06 SD) species (Figure 1b; Table S5). The
species richness distribution pattern of non-endemic species was similar
to that of all rodent species. The Southern Hengduan Mountains, Qilian
Mountains, Tianshan Mountains, and Altai Mountains had high non-endemic
species richness. Similar to all species, species richness was low in
the Tibetan Plateau and Tarim Basin regions.

The analysis of non-endemic species richness and individual
environmental factors showed that VEG and AET had the greatest impact on
the non-endemic species richness pattern. This result was the same for
all the species (Figure 2c, d; Table S6).

The best model for predicting non-endemic species richness comprised
three predictor sets of five variables (EW1+ HH1+ HH2+ HE1+ HE2). OLS
and SAR explained 70\% and 61\% of the non-endemic species richness
(Table 1), respectively. As with the results for all species, both
regression models showed that HH1 and HE1 were the most important
predictors. However, OLS showed that, in addition to the two factors
mentioned above, EW1 is equally important. In variance partitioning, all
four predictor sets explained 70.10\% of the variance in defining the
richness patterns of non-endemic species. Non-endemic species richness
was significantly correlated with the habitat heterogeneity predictor
set, explaining 51.21\% of the variation. This was followed by human
factors (50.42\%), the energy-water predictor set (27.67\%), and the
climate seasonality predictor set (13.14\%) (Figure 3a, c; Table S7).

\textbf{3.3 Patterns of endemic species richness}

Endemic (n = 67) species were found in 1430 grids, approximately 28.27\%
of all species. Endemic species richness in each grid ranged from 0--33
(mean: 5.75 ± 6.54 SD) species (Figure 1c; Table S5). The distribution
of endemic species is mainly concentrated in the Hengduan Mountains and
the surrounding areas. However, their distribution in the north is rare,
and only a few endemic species have adapted to high-latitude
environments.

Unlike all species and non-endemic species, when a single factor acted,
TES and ART had the highest effect on endemic species richness (Figure
2e, f; Table S6).

Finally, regarding the relationship between the distribution pattern of
endemic species richness and predictor sets, the best prediction model
contained seven variables (EW1+EW2+CS1+CS2+HH1+HE1+HE2). OLS and SAR
explained 65\% and 53\% of the total variation. The importance of the
variables in explaining the endemic species richness pattern differed
significantly between the two regression models (Table 1). In OLS, CS1
dominates overwhelmingly and is positively correlated with CS2. However,
HH1 and CS1 were equally important and negatively correlated with CS2 in
SAR. In variance partitioning, 65.58\% of the variance was explained by
the four predictor sets. The largest variation in endemic species
richness was the total effect of the climatic seasonal predictor set,
explaining 45.89\% of the variation. The habitat heterogeneity set and
energy-water predictor sets explained 32.44\% and 15.14\% of the
variation in the endemic species richness pattern, respectively. The
human factor predictor set explained the lowest variance (6.03\%)
(Figure 3a, d; Table S7).

\textbf{4 Discussion}

Species richness, including that of rodents, is characterized by spatial
heterogeneity (Gaston, 2000). The results of this study indicate that
there is a wide variety of rodents, including endemic species, in the
Hengduan Mountains and surrounding areas of Southwest China. The
Hengduan Mountain area is considered an ecological corridor between the
Palaearctic and Oriental realms, connecting the northern and southern
fauna. It is an important center for the origin of species (Wu et al.,
2016). This may explain why southwest China has the highest level of
endemism. Southwest China is rich in plant resources and has a complex
topography and a high degree of habitat heterogeneity. The area's
climate ranges from tropical to temperate and is dominated by high
temperatures and abundant precipitation (Liu et al., 2015; Shrestha et
al., 2017). These findings may contribute to the diversity of rodents in
China. In comparison, the extreme climatic conditions of the
Qinghai-Tibet Plateau, Tarim Basin, and northern China may limit the
distribution of species intolerant to climatic factors (Hu et al., 2017;
Ding et al., 2019). The results show that the border areas of China's
Yunnan, Sichuan, and Tibet autonomous regions, especially the Qionglai,
Yunling, Minshan, and Gaoligong mountain systems in the Hengduan
Mountains, have the highest richness values of all studied areas due to
their diverse topography, hydrothermal conditions, and habitats. The
distribution pattern of rodent species richness may be explained by a
combination of habitat heterogeneity, climate seasonality, energy-water,
and human factors (Figure 3).

The close relationship between species richness and habitat
heterogeneity and the variance partitioning results showed that habitat
heterogeneity is the most influential predictor variable for describing
the species richness of all and non-endemic rodent species in China. It
is also an important factor in the richness distribution pattern of
endemic species. Habitat heterogeneity is considered the most critical
factor in shaping biological distribution patterns. Habitat variation
creates a microhabitat for species reproduction by creating dramatic
changes in climate and habitat differentiation on a small scale,
enabling species to coexist locally (Carmignotto et al., 2022).
Southwest China has a variety of hydrothermal conditions and habitats
that result from a combination of topographic changes caused by
elevation differentiation (Huang et al., 2011; Liu et al., 2015). As a
result, the site had the highest richness values for rodent species. In
this study, we used the elevation range as one of the main predictors of
habitat heterogeneity, arguably the best indicator of topographic
variation. Meanwhile, southwest China is also the richest region in
terms of plant species (Shrestha et al., 2017; Dakhil et al., 2019; Sun
et al., 2020; Pandey et al., 2020). Abundant plant resources provide a
large amount of food for rodents and a suitable shelter for unfavorable
climatic conditions (Barreto, et al., 2019). In addition, the
Qinghai-Tibet Plateau's uplift has profoundly changed the geomorphology
of mainland China, mountains, ravines, and canyons, resulting in huge
mountain height differences. Quaternary ice sheet intrusion drives
rodent migration from higher to lower elevations, and the cyclical
effects of warming cause rodents to return to higher elevations (Barreto
et al., 2019). Faunas that cannot migrate can only adapt to the
differentiation of low- and medium-altitude environments, thus causing
high endemic species richness in rodents. In addition, highly
heterogeneous tropical and subtropical mountain ranges may be cradles of
biodiversity and thus dominate in terms of species richness and
concentrations of endemic species with narrow distributions.

The results showed that climatic seasonality is the dominant factor in
the distribution patterns of endemic species of rodents in China.
Conversely, it explained the least of all the species and non-endemic
species richness patterns. This suggested that seasonal temperature
changes have a profound effect on endemic species richness (Penjor et
al., 2022). Previous research has also identified a significant role of
climate seasonality in maintaining patterns of species richness in
endemic species of small mammals (Amori et al., 2011; Wu et al., 2013;
Hu et al., 2017). The temperature in China fluctuates widely, with more
extreme temperatures in the north than in the south. This might be an
important factor in northern China's restricted distribution of endemic
species. Our findings support the theory of tropical niche conservation
and the inability of endemic rodent species to adapt to northern
environments. Their ability to migrate from south to north is consistent
with our findings (Romdal et al., 2013). This seasonality in southwest
China creates stable climatic conditions for endemic species to survive
under harsh conditions. Araújo et al. (2008) and Dakhil et al. (2019)
reported the significant role of climate stability during the warmest
season of the Quaternary glaciation. Climatic stability is an ecological
indicator of the range stability of subtropical fauna in the
high-altitude regions of southwest China, including the eastern Tibetan
Plateau (Thuiller, 2004; Huang et al., 2011). Due to climate variability
and the unsystematic variation in daily maximum and minimum
temperatures, modifying the thermal environment experienced by rodents
to increase the body's tolerance level enables them to be widely
distributed geographically.

Our results also revealed the importance of anthropogenic variables in
the distribution patterns of total and non-endemic species. However, its
effect on endemic species richness was minimal. Thus, it can be expected
that anthropogenic activities will significantly impact the distribution
of rodents. Anthropogenic activities can drive the contraction and
expansion of species. Many species have lost significant areas of
distribution owing to increased land use and other human activities
(Ceballos \& Ehrlich, 2002; Di Marco \& Santini, 2015). Human activities
have also contributed to the range expansion of many other species, and
many regional native species have experienced increased range through
human-mediated dispersal and the ability to thrive in anthropogenic
landscapes (Li et al., 2015). However, endemic species have more
specialized habitat requirements, are concentrated in specific
geographic areas, and may deviate ecologically from a wide range of
species (Toma\selectlanguage{polish}š\selectlanguage{english}ov\selectlanguage{ngerman}ých \& Jablonski, 2016). Therefore, the anthropogenic
impact on rodent endemics is limited. However, endemic species are
confined to limited areas, and their habitats are often fragile and
highly susceptible to reduction or even disappearance by external
factors. Therefore, we cannot ignore human-interference scenarios. In
southwest China, anthropogenic disturbances, such as natural resource
exploitation, tourism, and land use change, are gradually increasing,
accelerating the impact of human activities on wildlife. Therefore,
protecting this center of species richness and endemism is a big
challenge.

On a large scale, energy-water did not have a significant direct
relationship with the distribution pattern of rodent species richness in
China. This finding differs slightly from Hu et al. (2017) and Wu et al.
(2013) at the local scale. The spatial pattern of species richness and
the importance of biotic and abiotic predictor variables in influencing
species richness may depend to a large extent on the spatial scale of
the sampling unit (Qian \& Kissling, 2010). In some cases, species
richness patterns and potential drivers can be reasonably explained by
ecological requirements and the evolutionary history of species grouping
(Wu et al., 2013). Rodents are phytophagous or omnivorous; therefore,
the pattern of species richness may be more closely related to plant
species richness (Hawkins \& Pausas, 2004). At a large spatial scale,
the direct impact of energy-water on plants is often more significant
(Zhang et al., 2015). Although the effect of energy-water on the
distribution pattern of rodent species richness is not very significant
at large scales, on a smaller scale, rodent dispersal behavior is
temperature oriented, and species may spread more rapidly when
temperature changes (Wu et al., 2013). Therefore, temperature and
precipitation may strongly influence rodent species richness at a
regional scale.

In summary, we against using all species or non-endemic species richness
as proxies for endemic species richness. This is consistent with the
results of previous studies (Orme et al., 2005; Isik, 2011). The reasons
for the formation of endemic and widespread species differ. Endemic
species formation is often closely related to environmental and
anthropogenic changes. Some endemic species may have been widely
distributed; however, due to climate change during the geological and
historical period, their habitats have shrunk or even disappeared,
forcing them to confine their distribution to a narrow range; or newly
evolved species have not yet spread to a larger geographic area; or
destruction of the environment limits their ranges. In addition, we
found that the reasons for determining the distribution of non-endemic
and endemic species richness differed.

Although this study contributes to a more nuanced understanding of
rodent species richness patterns and their drivers in China, it has
several limitations. For example, there are 271 species of rodents in
China (Wei et al., 2021), however, only the 237 species with relatively
complete data were analyzed in this study, which does not provide a
complete picture of the distribution pattern of rodents in China. The
variables used in MaxEnt partially overlap with the factors involved in
the regression model, which will inevitably affect the regression
results. In addition, the species distribution model assumes that
species distribution is influenced only by environmental variables,
without interactions between organisms and biological dispersal
constraints. In practice, species distribution is also influenced by
biological factors such as competition, predation, and disease. A more
sophisticated and integrated SDM for species range prediction is
required to consider these factors, which is an important direction for
future model development (Kearney \& Porter, 2009).

\textbf{5 Conclusion}

This study investigated the spatial patterns of rodent species richness
in China and possible interpretations of the results based on multiple
environmental and human factors. This study found that the highest
species richness values of non-endemic and endemic species of rodents in
China were in and around the Hengduan Mountains in southwest China. The
lowest number of rodent-endemic species was in the Qinghai-Tibet Plateau
and northern China. Habitat heterogeneity was the most effective
variable to reflect changes in the potential distribution of all rodents
and non-endemic species, as well as species richness in China. Endemic
species richness patterns are most vulnerable to seasonal changes in
climate, they are least influenced by human factors, and the effect of
energy-water on the three response variables showed a consistent level
of importance.

\textbf{References:}

Abdulwahab, U. A., Hammill, E. \& Hawkins, C. P. (2022). Choice of
climate data affects the performance and interpretation of species
distribution models., \emph{Ecological Modelling} , \emph{471} , 110042.
https://doi.org/10.1016/j.ecolmodel.2022.110042

Amori, G., Chiozza, F., Rondinini, C. \& Luiselli, L. (2011).
Country-based patterns of total species richness, endemicity, and
threatened species richness in African rodents and
insectivores,\emph{Biodiversity and Conservation} , \emph{20} (6),
1225-1237. https://doi.org/10.1007/s10531-011-0024-1

Araújo, M. B., Nogués-Bravo, D., Diniz-Filho, J. A. F., Haywood, A. M.,
Valdes, P. J. \& Rahbek, C. (2008). Quaternary climate changes explain
diversity among reptiles and amphibians, \emph{Ecography} , \emph{31}
(1), 8-15. https://doi.org/10.1111/j.2007.0906-7590.05318.x

Barreto, E., Graham, C. H. \& Rangel, T. F. (2019). Environmental
factors explain the spatial mismatches between species richness and
phylogenetic diversity of terrestrial mammals, \emph{Global Ecology and
Biogeography} , \emph{28} (12), 1855-1865.
https://doi.org/10.1111/geb.12999

Carmignotto, A. P., Pardini, R. \& de Vivo, M. (2022). Habitat
heterogeneity and geographic location as major drivers of Cerrado small
mammal diversity across multiple spatial scales, \emph{Frontiers in
Ecology and Evolution} , \emph{9} , 1-20.
https://doi.org/10.3389/fevo.2021.739919

Ceballos, G. \& Ehrlich, P. R. (2002). Mammal population losses and the
extinction crisis, \emph{Science} , \emph{296} (5569), 904-907.
https://doi.org/10.1126/science.1069349

Cheng, J., Xia, L., Feijó, A., Shenbrot, G. I., Wen, Z., Ge, D., Lu, L.
\& Yang, Q. (2021). Phylogeny, taxonomic reassessment and 'ecomorph'
relationship of the \emph{Orientallactaga sibirica} complex (Rodentia:
Dipodidae: Allactaginae), \emph{Zoological Journal of the Linnean
Society} , \emph{192} (1), 185-205.
https://doi.org/10.1093/zoolinnean/zlaa102

Chi, Y., Qian, T., Sheng, C., Xi, C. \& Wang, J. (2021). Analysis of
differences in the spatial distribution among terrestrial mammals using
Geodetector---A case study of China, \emph{ISPRS International Journal
of Geo-Information} , \emph{10} (1), 21.
https://doi.org/10.3390/ijgi10010021

Chi, Y., Wang, J., Xi, C., Qian, T. \& Sheng, C. (2020). Spatial pattern
of species richness among terrestrial mammals in
China,\emph{Diversity-Basel} , \emph{12} (3), 96.
https://doi.org/10.3390/d12030096

Dakhil, M. A., Xiong, Q., Farahat, E. A., Zhang, L., Pan, K., Pandey,
B., Olatunji, O. A., Tariq, A., Wu, X., Zhang, A., Tan, X. \& Huang, D.
(2019). Past and future climatic indicators for distribution patterns
and conservation planning of temperate coniferous forests in
southwestern China, \emph{Ecological Indicators} , \emph{107} , 105559.
https://doi.org/10.1016/j.ecolind.2019.105559

D'Antraccoli, M., Roma-Marzio, F., Carta, A., Landi, S., Bedini, G.,
Chiarucci, A. \& Peruzzi, L. (2019). Drivers of floristic richness in
the Mediterranean: a case study from Tuscany, \emph{Biodiversity and
Conservation} ,\emph{28} (6), 1411-1429.
https://doi.org/10.1007/s10531-019-01730-x

Di Marco, M. \& Santini, L. (2015). Human pressures predict species'
geographic range size better than biological traits, \emph{Global Change
Biology} , \emph{21} (6), 2169-2178. https://doi.org/10.1111/gcb.12834

Ding, Z., Liang, J., Hu, Y., Zhou, Z., Sun, H., Liu, L., Liu, H., Hu, H.
\& Si, X. (2019). Different responses of avian feeding guilds to spatial
and environmental factors across an elevation gradient in the central
Himalaya, \emph{Ecology and Evolution} , \emph{9} (7), 4116-4128.
https://doi.org/10.1002/ece3.5040

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré,
G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J.,
Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder,
B., et al. (2013). Collinearity: a review of methods to deal with it and
a simulation study evaluating their performance, \emph{Ecography}
,\emph{36} (1), 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x

Gaston, K. J. (2000). Global patterns in biodiversity, \emph{Nature}
,\emph{405} (6783), 220-227. https://doi.org/10.1038/35012228

Ge, D., Lu, L., Xia, L., Du, Y., Wen, Z., Cheng, J., Abramov, A. V. \&
Yang, Q. (2018). Molecular phylogeny, morphological diversity, and
systematic revision of a species complex of common wild rat species in
China (Rodentia, Murinae), \emph{Journal of Mammalogy} , \emph{99} (6),
1350-1374. https://doi.org/10.1093/jmammal/gyy117

Gebauer, R., Diví\selectlanguage{polish}š\selectlanguage{english}ek, J., Bu\selectlanguage{polish}ř\selectlanguage{english}i\selectlanguage{polish}č, \selectlanguage{english}M., Ve\selectlanguage{polish}č\selectlanguage{english}e\selectlanguage{polish}ř\selectlanguage{english}a, M., Kouba, A. \& Drozd, B.
(2018). Distribution of alien animal species richness in the Czech
Republic,\emph{Ecology and Evolution} , \emph{8} (9), 4455-4464.
https://doi.org/10.1002/ece3.4008

Gotelli, N. J. \& Colwell, R. K. (2001). Quantifying biodiversity:
procedures and pitfalls in the measurement and comparison of species
richness,\emph{Ecology Letters} , \emph{4} (4), 379-391.
https://doi.org/10.1046/j.1461-0248.2001.00230.x

Guisan, A. \& Thuiller, W. (2005). Predicting species distribution:
offering more than simple habitat models, \emph{Ecology Letters}
,\emph{8} (9), 993-1009.
https://doi.org/10.1111/j.1461-0248.2005.00792.x

Hanley, J. A. \& McNeil, B. J. (1982). The meaning and use of the area
under a receiver operating characteristic (ROC) curve, \emph{Radiology}
,\emph{143} (1), 29-36.

Hawkins, B. A. \& Pausas, J. G. (2004). Does plant richness influence
animal richness? the mammals of Catalonia (NE Spain), \emph{Diversity
and Distributions} , \emph{10} (4), 247-252.
https://doi.org/10.1111/j.1366-9516.2004.00085.x

Hawkins, B. A., Field, R., Cornell, H. V., Currie, D. J., Gu\selectlanguage{ngerman}égan, J.,
Kaufman, D. M., Kerr, J. T., Mittelbach, G. G., Oberdorff, T., O'Brien,
E. M., Porter, E. E. \& Turner, J. R. G. (2003). Energy, water, and
broad-scale geographic patterns of species richness, \emph{Ecology}
,\emph{84} (12), 3105-3117. https://doi.org/10.1890/03-8006

Holt, B. G., Costa, G. C., Penone, C., Lessard, J. P., Brooks, T. M.,
Davidson, A. D., Blair Hedges, S., Radeloff, V. C., Rahbek, C.,
Rondinini, C. \& Graham, C. H. (2018). Environmental variation is a
major predictor of global trait turnover in mammals, \emph{Journal of
Biogeography} , \emph{45} (1), 225-237.
https://doi.org/10.1111/jbi.13091

Hu, Y., Jin, K., Huang, Z., Ding, Z., Liang, J., Pan, X., Hu, H. \&
Jiang, Z. (2017). Elevational patterns of non-volant small mammal
species richness in Gyirong Valley, Central Himalaya: Evaluating
multiple spatial and environmental drivers, \emph{Journal of
Biogeography} ,\emph{44} (12), 2764-2777.
https://doi.org/10.1111/jbi.13102

Huang, Y., Dai, Q., Chen, Y., Wan, H., Li, J. \& Wang, Y. (2011). Lizard
species richness patterns in China and its environmental
associations,\emph{Biodiversity and Conservation} , \emph{20} (7),
1399-1414. https://doi.org/10.1007/s10531-011-0033-0

Isik, K. (2011). Rare and endemic species: why are they prone to
extinction? \emph{Turkish Journal of Botany} , \emph{35} (4), 411-417.
https://doi.org/10.3906/bot-1012-90

Jackson, S. M., Li, Q., Wan, T., Li, X., Yu, F., Gao, G., He, L.,
Helgen, K. M. \& Jiang, X. (2022). Across the great divide: revision of
the genus \emph{Eupetaurus} (Sciuridae: Pteromyini), the woolly flying
squirrels of the Himalayan region, with the description of two new
species, \emph{Zoological Journal of the Linnean Society} ,\emph{194}
(2), 502-526. https://doi.org/10.1093/zoolinnean/zlab018

Jenkins, C. N., Pimm, S. L. \& Joppa, L. N. (2013). Global patterns of
terrestrial vertebrate diversity and conservation, \emph{Proceedings of
the National Academy of Sciences} , \emph{110} (28), E2602-E2610.
https://doi.org/10.1073/pnas.1302251110

Jiang, Z., Ma, Y., Wu, Y., Wang, Y., Zhou, K. \& Liu, S. (2015), China's
Mammal Diversity and Geographic Distribution, Beijing: China Science
Publishing.

Jimenez-Alfaro, B., Chytry, M., Mucina, L., Grace, J. B. \& Rejmanek, M.
(2016). Disentangling vegetation diversity from climate-energy and
habitat heterogeneity for explaining animal geographic patterns,
\emph{Ecology and Evolution} , \emph{6} (5), 1515-1526.
https://doi.org/10.1002/ece3.1972

Kearney, M. \& Porter, W. (2009). Mechanistic niche modelling: combining
physiological and spatial data to predict species' ranges, \emph{Ecology
Letters} , \emph{12} (4), 334-350.
https://doi.org/10.1111/j.1461-0248.2008.01277.x

Kissling, W. D. \& Carl, G. (2007). Spatial autocorrelation and the
selection of simultaneous autoregressive models, \emph{Global Ecology
and Biogeography} , \emph{17} (1), 59-71.
https://doi.org/10.1111/j.1466-8238.2007.00334.x

Lamoreux, J. F., Morrison, J. C., Ricketts, T. H., Olson, D. M.,
Dinerstein, E., McKnight, M. W. \& Shugart, H. H. (2006). Global tests
of biodiversity concordance and the importance of endemism,\emph{Nature}
, \emph{440} (7081), 212-214. https://doi.org/10.1038/nature04291

Li, Q., Li, X., Jackson, S. M., Li, F., Jiang, M., Zhao, W., Song, W. \&
Jiang, X. (2019). Discovery and description of a mysterious Asian flying
squirrel (Rodentia, Sciuridae, \emph{Biswamoyopterus} ) from Mount
Gaoligong, southwest China, \emph{ZooKeys} , \emph{864} , 147-160.
https://doi.org/10.3897/zookeys.864.33678

Li, X., Jiang, G., Tian, H., Xu, L., Yan, C., Wang, Z., Wei, F. \&
Zhang, Z. (2015). Human impact and climate cooling caused range
contraction of large mammals in China over the past two millennia,
\emph{Ecography} ,\emph{38} (1), 74-82.
https://doi.org/10.1111/ecog.00795

Liu, B., Chen, C., Lian, Y., Chen, J. \& Chen, X. (2015). Long-term
change of wet and dry climatic conditions in the southwest karst area of
China, \emph{Global and Planetary Change} , \emph{127} , 1-11.
https://doi.org/10.1016/j.gloplacha.2015.01.009

Liu, S., Chen, S., He, K., Tang, M., Liu, Y., Jin, W., Li, S., Li, Q.,
Zeng, T., Sun, Z., Fu, J., Liao, R., Meng, Y., Wang, X. \& Jiang, X., et
al. (2019). Molecular phylogeny and taxonomy of
subgenus\emph{Eothenomys} (Cricetidae: Arvicolinae: \emph{Eothenomys} )
with the description of four new species from Sichuan, China,
\emph{Zoological Journal of the Linnean Society} , \emph{186} (2),
569-598. https://doi.org/10.1093/zoolinnean/zly071

Mi, C., Huettmann, F., Li, X., Jiang, Z., Du, W. \& Sun, B. (2022).
Effects of climate and human activity on the current distribution of
amphibians in China, \emph{Conservation Biology} .
https://doi.org/10.1111/cobi.13964

Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass,
J. M., Uriarte, M. \& Anderson, R. P. (2014). ENMeval: An R package for
conducting spatially independent evaluations and estimating optimal
model complexity for Maxent ecological niche models, \emph{Methods in
Ecology and Evolution} , \emph{5} (11), 1198-1205.
https://doi.org/10.1111/2041-210X.12261

Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B.
\& Kent, J. (2000). Biodiversity hotspots for conservation
priorities,\emph{Nature} , \emph{403} (6772), 853-858.
https://doi.org/10.1038/35002501

Nguyen, D. \& Leung, B. (2022). How well do species distribution models
predict occurrences in exotic ranges? \emph{Global Ecology and
Biogeography} , \emph{31} (6), 1051-1065.
https://doi.org/10.1111/geb.13482

Orme, C. D. L., Davies, R. G., Burgess, M., Eigenbrod, F., Pickup, N.,
Olson, V. A., Webster, A. J., Ding, T., Rasmussen, P. C., Ridgely, R.
S., Stattersfield, A. J., Bennett, P. M., Blackburn, T. M., Gaston, K.
J. \& Owens, I. P. F. (2005). Global hotspots of species richness are
not congruent with endemism or threat, \emph{Nature} , \emph{436}
(7053), 1016-1019. https://doi.org/10.1038/nature03850

Pandey, B., Khatiwada, J. R., Zhang, L., Pan, K., Dakhil, M. A., Xiong,
Q., Yadav, R. K. P., Siwakoti, M., Tariq, A., Olatunji, O. A., Justine,
M. F., Wu, X., Sun, X., Liao, Z. \& Negesse, Z. T. (2020). Energy--water
and seasonal variations in climate underlie the spatial distribution
patterns of gymnosperm species richness in China, \emph{Ecology and
Evolution} , \emph{10} (17), 9474-9485.
https://doi.org/10.1002/ece3.6639

Penjor, U., Cushman, S. A., Kaszta, Z. M., Sherub, S. \& Macdonald, D.
W. (2022). Effects of land use and climate change on functional and
phylogenetic diversity of terrestrial vertebrates in a Himalayan
biodiversity hotspot, \emph{Diversity and Distributions} .
https://doi.org/10.1111/ddi.13613

Qian, H. \& Kissling, W. D. (2010). Spatial scale and cross-taxon
congruence of terrestrial vertebrate and vascular plant species richness
in China, \emph{Ecology} , \emph{91} (4), 1172-1183.
https://doi.org/10.1890/09-0620.1

Romdal, T. S., Araújo, M. B. \& Rahbek, C. (2013). Life on a tropical
planet: niche conservatism and the global diversity gradient,
\emph{Global Ecology and Biogeography} , \emph{22} (3), 344-350.
https://doi.org/10.1111/j.1466-8238.2012.00786.x

Sanczuk, P., De Lombaerde, E., Haesen, S., Van Meerbeek, K., Van Der
Veken, B., Hermy, M., Verheyen, K., Vangansbeke, P. \& De Frenne, P.
(2022). Species distribution models and a 60\selectlanguage{english}-year\selectlanguage{english}-old transplant
experiment reveal inhibited forest plant range shifts under climate
change,\emph{Journal of Biogeography} , \emph{49} (3), 537-550.
https://doi.org/10.1111/jbi.14325

Shrestha, N., Su, X., Xu, X. \& Wang, Z. (2017). The drivers of
high\emph{Rhododendron} diversity in south-west China: Does seasonality
matter? \emph{Journal of Biogeography} , \emph{45} (2), 438-447.
https://doi.org/10.1111/jbi.13136

Stein, A., Beck, J., Meyer, C., Waldmann, E., Weigelt, P. \& Kreft, H.
(2015). Differential effects of environmental heterogeneity on global
mammal species richness, \emph{Global Ecology and Biogeography}
,\emph{24} (9), 1072-1083. https://doi.org/10.1111/geb.12337

Stein, A., Gerstner, K. \& Kreft, H. (2014). Environmental heterogeneity
as a universal driver of species richness across taxa, biomes and
spatial scales, \emph{Ecology Letters} , \emph{17} (7), 866-880.
https://doi.org/10.1111/ele.12277

Sun, S., Zhang, Y., Huang, D., Wang, H., Cao, Q., Fan, P., Yang, N.,
Zheng, P. \& Wang, R. (2020). The effect of climate change on the
richness distribution pattern of oaks (\emph{Quercus} L.) in
China,\emph{Science of the Total Environment} , \emph{744} , 140786.
https://doi.org/10.1016/j.scitotenv.2020.140786

Thuiller, W. (2004). Patterns and uncertainties of species' range shifts
under climate change, \emph{Global Change Biology} , \emph{10} (12),
2020-2027. https://doi.org/10.1111/j.1365-2486.2004.00859.x

Toma\selectlanguage{polish}š\selectlanguage{english}ov\selectlanguage{ngerman}ých, A. \& Jablonski, D. (2016). Decoupling of latitudinal
gradients in species and genus geographic range size: a signature of
clade range expansion, \emph{Global Ecology and Biogeography} ,
\emph{26} (3), 288-303. https://doi.org/10.1111/geb.12533

Vetaas, O. R. \& Grytnes, J. (2002). Distribution of vascular plant
species richness and endemic richness along the Himalayan elevation
gradient in Nepal, \emph{Global Ecology and Biogeography} , \emph{11}
(4), 291-301. https://doi.org/10.1046/j.1466-822x.2002.00297.x

Wang, C., Jiang, F., Liu, B., Shen, Z., Yang, G., Wang, C., Hu, H.,
Wang, M., Hu, J., Lu, X. \& Li, Q. (2021). Prediction of the potential
distribution of the predatory mite \emph{Neoseiulus
californicus}McGregor in China using MaxEnt, \emph{Global Ecology and
Conservation} ,\emph{29} , e1733.
https://doi.org/10.1016/j.gecco.2021.e01733

Warren, D. L., Matzke, N. J., Cardillo, M., Baumgartner, J. B.,
Beaumont, L. J., Turelli, M., Glor, R. E., Huron, N. A., Simoes, M.,
Iglesias, T. L., Piquet, J. C. \& Dinnage, R. (2021). ENMTools 1.0: an R
package for comparative ecological biogeography, \emph{Ecography}
,\emph{44} (4), 504-511. https://doi.org/10.1111/ecog.05485

Wei, F., Yang, Q., Wu, Y., Jiang, X., Liu, S., Li, B., Yang, G., Li, M.,
Zhou, J., Li, S., Hu, Y., Ge, D., Li, S., Yu, W., Cheng, B., et al.
(2021). Catalogue of mammals in China (2021), \emph{Acta Theriologica
Sinica} , \emph{41} (5), 487-501. https://doi.org/10.16829/j.slxb.150595

Wu, Y., DuBay, S. G., Colwell, R. K., Ran, J. \& Lei, F. (2016). Mobile
hotspots and refugia of avian diversity in the mountains of south-west
China under past and contemporary global climate change, \emph{Journal
of Biogeography} , \emph{44} (3), 615-626.
https://doi.org/10.1111/jbi.12862

Wu, Y., Yang, Q., Wen, Z., Xia, L., Zhang, Q. \& Zhou, H. (2013). What
drives the species richness patterns of non-volant small mammals along a
subtropical elevational gradient? \emph{Ecography} , \emph{36} (2),
185-196. https://doi.org/10.1111/j.1600-0587.2011.07132.x

Xing, Y., Zhou, L. \& Ma, Y. (2008). Geographical distribution pattern
of glires species in the humid and semi-humid region of
China,\emph{Chinese Journal of Zoology} , \emph{41} (5), 51-61.

Xu, W., Svenning, J. C., Chen, G., Zhang, M., Huang, J., Chen, B.,
Ordonez, A. \& Ma, K. (2019). Human activities have opposing effects on
distributions of narrow-ranged and widespread plant species in
China,\emph{Proceedings of the National Academy of Sciences} ,
\emph{116} (52), 26674-26681. https://doi.org/10.1073/pnas.1911851116

Zhang, J., Jiang, F., Li, G., Qin, W., Li, S., Gao, H., Cai, Z., Lin, G.
\& Zhang, T. (2019). Maxent modeling for predicting the spatial
distribution of three raptors in the Sanjiangyuan National Park,
China,\emph{Ecology and Evolution} , \emph{9} (11), 6643-6654.
https://doi.org/10.1002/ece3.5243

Zhang, Z., Yan, Y., Tian, Y., Li, J., He, J. \& Tang, Z. (2015).
Distribution and conservation of orchid species richness in
China,\emph{Biological Conservation} , \emph{181} , 64-72.
https://doi.org/10.1016/j.biocon.2014.10.026

Zhou, L., Ma, Y. \& Ye, X. (2002). Distribution of glires in arid
regions of China, \emph{Current Zoology} , \emph{48} (2), 183-194.

\textbf{Table}

Table 1 Results of multiple linear regression (OLS) and spatial
autoregressive model (SAR) analysis of rodent species richness\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Variable & All species & All species & Non-endemic & Non-endemic &
Endemic & Endemic\tabularnewline
& OLS & SAR & OLS & SAR & OLS & SAR\tabularnewline
EW1 & 0.15*** & 0.011 & 0.23*** & 0.017* & -0.16*** &
-0.025**\tabularnewline
EW2 & -- & -- & -- & -- & -0.13*** & -0.018*\tabularnewline
CS1 & -0.18*** & -0.032*** & -- & -- & -0.73*** &
-0.080***\tabularnewline
CS2 & -- & -- & -- & -- & -0.08*** & 0.0062\tabularnewline
HH1 & 0.55*** & 0.19*** & 0.43*** & 0.18*** & 0.36*** &
0.11***\tabularnewline
HH2 & -- & -- & -0.12*** & -0.0038 & -- & --\tabularnewline
HE1 & 0.40*** & 0.070*** & 0.38*** & 0.084*** & 0.14*** &
0.017*\tabularnewline
HE2 & -0.12*** & -0.018** & -0.11*** & -0.0067 & -0.10*** &
-0.026***\tabularnewline
AIC & 2687.71 & 411.79 & 2745.89 & 695.88 & 2997.49 &
392.16\tabularnewline
R\textsuperscript{2} & 0.71 & 0.64 & 0.70 & 0.61 & 0.65 &
0.53\tabularnewline
Moran's \emph{I} & 0.69*** & 0.068*** & 0.67*** & 0.07*** & 0.75*** &
0.02\tabularnewline
\bottomrule
\end{longtable}

EW, CS, HH, and HE refer to variables based on the first two axes of the
PCA using the energy-water, climatic seasonality, habitat heterogeneity,
and human factors variables, respectively. Moran's \emph{I} is the
Moran's index of the regression model residuals. *\emph{p} \textless{}
0.05; **\emph{p} \textless{} 0.01; ***\emph{p} \textless{} 0.001.



\textbf{Figure legends}

Figure 1 Spatial distribution of rodent species in China across all (a),
non-endemic (b), and endemic (c) species.

Figure 2 Relationship between all, non-endemic, and endemic species
richness and the most relevant environmental variables. VEG = Vegetation
Types, AET = Actual Evapotranspiration, TES = Temperature Seasonality,
ART = Temperature Annual Range. Shaded area shows 95\% confidence
interval.

Figure 3 Results of variance partitioning explained by environmental
variables. (a) Schematic diagram of variance partitioning, each letter
in the figure represents a part of the variance partitioning analysis.
Results of variance partitioning for (b) all species, (c) non-endemic
species and (d) endemic species.



\textbf{DATA AVAILABILITY STATEMENT}

The data that supports the findings of this study are available in the
supplementary material of this article.

\textbf{CONFLICT OF INTEREST}

All authors state that there is no conflict of interest.

\textbf{AUTHOR CONTRIBUTION}

\textbf{Lei Meng:} Data curation (lead); Formal analysis (equal);
Methodology (equal); Visualization (equal); Writing-original draft
(lead); Writing-review \& editing (lead).

\textbf{Lizhi Zhou:} Conceptualization (lead); Resources (lead);
Supervision (equal); Visualization (equal); Writing-review \& editing
(equal).

\textbf{ACKNOWLEDGMENTS}

This work was supported by the National Science \& Technology
Fundamental Resources Investigation Program of China (``Comprehensive
scientific study of biodiversity in the Dabie Mountains'' project,
``Scientific Study of Small Mammals Diversity'' topic) (Grant No.
2019FY101804). We express appreciation to Wenfeng Lai, Guanghua Zhao,
and Xiang Jia for their help in providing R code.

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\selectlanguage{english}
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