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\begin{document}

\title{Potential geographical distribution and environmental explanations of
rare and endangered plant species through combined modelling: A case
study of Northwest Yunnan, China}



\author[1]{Pengcheng Ye}%
\author[2]{Guangfu Zhang}%
\author[2]{Xiao Zhao}%
\author[2]{Hui Chen}%
\author[2]{Qin Si}%
\author[1]{Jianyong Wu}%
\affil[1]{Nanjing Institute of Environmental Sciences}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 09, 2024}


\begingroup
\let\center\flushleft
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\maketitle
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\selectlanguage{english}
\begin{abstract}
In recent decades, due to the effect of climate change and the
interference of human activities, the species habitat index fallen 2\%.
Studying the geographical distribution pattern and predicting the
potential geographical distribution of species are of great significance
for developing scientific and effective biodiversity conservation
strategies. The purpose of this research is to predict the potential
geographical distribution of 25 rare and endangered plant species in
Northwest Yunnan, China on the grid map with a resolution of 0.05\selectlanguage{ngerman}° ×
0.05° and analyze the explanation capabilities of various environmental
factors on the potential geographical distribution patterns of these
species, and explore the main restrictive environmental factors.
Initially, we employed the ecological niche model MaxEnt to predict the
potential geographical distribution of target species. Following that,
we overlaid the potential geographical distribution of each species, and
we obtained the potential geographical distribution pattern of species
richness on the spatial scale of the ecological niche model with a
resolution of 0.05° × 0.05°. Ultimately, we also adopted geographically
weighted regression (GWR) model to investigate the explanation
capabilities of various environmental parameters on the potential
distribution patterns. The results showed that the average AUC value of
each species was between 0.80 and 1.00, which indicated that the
simulation precision of the MaxEnt model for each species was good or
excellent. Besides, the potential distribution areas of these species
were between 826.33 km2 and 44,963.53 km2. In addition, the average
contribution values of the annual precipitation (Bio12), precipitation
of coldest quarter (Bio19) and population density (Pop) were 25.92\%,
15.86\% and 17.95\%, respectively. Moreover, the goodness of fit R2 and
AIC value of the water model were 0.88 and 7,703.82, respectively, which
indicated the water factor largely influenced the potential distribution
of these species. The results of this study would be helpful for
implementing long-term conservation and reintroduction for these
species.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Potential geographical distribution andenvironmental
explanations of rare and endangered plant species through combined
modelling: A case study of Northwest Yunnan, China}

\textbf{Pengcheng Ye \textsuperscript{1, 2}, Guangfu
Zhang\textsuperscript{2}, Xiao Zhao \textsuperscript{1}, Hui
Chen\textsuperscript{1}, Qin Si \textsuperscript{1}, Jianyong Wu}

\textsuperscript{\textbf{1}}

\textsuperscript{1} Nanjing Institute of Environmental Science, Ministry
of Ecology and Environment of the People's Republic of China, Nanjing,
China

\textsuperscript{2} Jiangsu Key Laboratory of Biodiversity and
Biotechnology, School of Life Sciences, Nanjing Normal University,
Nanjing, China

\section*{Abstract}

{\label{abstract}}

In recent decades, due to the effect of climate change and the
interference of human activities, the species habitat index fallen 2\%.
Studying the geographical distribution pattern and predicting the
potential geographical distribution of species are of great significance
for developing scientific and effective biodiversity conservation
strategies. The purpose of this research is to predict the potential
geographical distribution of 25 rare and endangered plant species in
Northwest Yunnan, China on the grid map with a resolution of 0.05\selectlanguage{ngerman}° ×
0.05° and analyze the explanation capabilities of various environmental
factors on the potential geographical distribution patterns of these
species, and explore the main restrictive environmental factors.
Initially, we employed the ecological niche model MaxEnt to predict the
potential geographical distribution of target species. Following that,
we overlaid the potential geographical distribution of each species, and
we obtained the potential geographical distribution pattern of species
richness on the spatial scale of the ecological niche model with a
resolution of 0.05° × 0.05°. Ultimately, we also adopted geographically
weighted regression (GWR) model to investigate the explanation
capabilities of various environmental parameters on the potential
distribution patterns. The results showed that the average AUC value of
each species was between 0.80 and 1.00, which indicated that the
simulation precision of the MaxEnt model for each species was good or
excellent. Besides, the potential distribution areas of these species
were between 826.33 km\textsuperscript{2} and 44,963.53
km\textsuperscript{2}. In addition, the average contribution values of
the annual precipitation (Bio12), precipitation of coldest quarter
(Bio19) and population density (Pop) were 25.92\%, 15.86\% and 17.95\%,
respectively. Moreover, the goodness of fit \emph{R} \textsuperscript{2}
and AIC value of the water model were 0.88 and 7,703.82, respectively,
which indicated the water factor largely influenced the potential
distribution of these species. The results of this study would be
helpful for implementing long-term conservation and reintroduction for
these species.

\textbf{Keywords:} biodiversity conservation, environmental
explanations, geographically weighted regression (GWR), habitat
suitability, MaxEnt, Northwest Yunnan, potential geographical
distribution

\section*{1 INTRODUCTION}

{\label{introduction}}

The geographical distribution pattern of species and utilizing species
distribution models (SDMs) to predict the potential geographical
distribution of species are one of the hot research issues in the fields
of biogeography and biodiversity conservation (Gaston, 2000; Zhang \&
Ma, 2008; Ning et al., 2018; Tripathi et al., 2019a). Studying the
geographical distribution pattern and predicting the potential
geographical distribution of species are of great significance for
developing effective biodiversity conservation strategies (Zhang et al.,
2019; Lazo-Cancino et al., 2020), preventing and managing the spread of
invasive alien species (IAS) diffusion (Fernandes et al., 2019) and
assessing the impact of climate change on species distribution
(Lazo-Cancino et al., 2020), and they are also an effective means of
protecting and managing some rare, endangered and/or threatened species.

The maximum entropy (MaxEnt) model is a species distribution model based
on the environmental factors matching method, and is often used to
predict the potential geographical distribution of species. The MaxEnt
model employs the data of species distribution locations and
environmental variables to jointly simulate the potential geographical
distribution of species and has many advantages over other species
distribution models, including easy operation and use, high simulation
precision, good performance with incomplete datasets (Li et al., 2020).
Currently, the MaxEnt is the most widely used species distribution model
(Merow et al., 2013; Gong et al., 2015; Ning et al., 2018). The research
results of Hernandez et al. (2006) and Deb et al. (2017) both showed
that in the case of few geographical locations (\textless{} 10), even as
low as 4 or 5, the MaxEnt model can still produce effective prediction
results with high precision. At present, the model has been applied to
simulate and predict the potential geographical distribution of endemic
species (e.g.,\emph{Impatiens hainanensis} ; Ning et al., 2018),
national protected species (e.g., \emph{Phellodendron amurense} ; Huang
et al., 2018) and many other key species.

It is helpful to improve the scientific understanding of the spatial
relationship between species and environment by studying the impact of
different types of environmental factors on the potential geographical
distribution of species. The spatial distribution pattern of species
diversity is related to variations in environmental factors (e.g.,
Latitude, elevation and climate) (Tripathi et al., 2019a). However, the
relationship between them often has spatial non-stationarity (i.e.,
Relationship between independent and dependent variables will change
with geographical location) (Gouveia et al., 2013). Geographically
weighted regression (GWR) model, which is an extension of traditional
regression model (e.g., Ordinary least squares, OLS) (\selectlanguage{romanian}Ștef\selectlanguage{polish}ă\selectlanguage{english}nescu et al.,
2017; Tripathi et al., 2019a; Tripathi et al., 2019b; Xue et al., 2020),
has become one of the crucial spatial heterogeneity modeling tools (Lu
et al., 2020). In recent years, many domestic and foreign scholars have
carried out in-depth and extensive research in various fields by using
GWR model, including social environmental factors and regional economy,
regional house prices and pollution (McCord et al., 2018; Xu et al.,
2019), the impacts of environmental heterogeneity and land use change on
wild animal distribution (Liu et al., 2019; Wang et al., 2020; Xue et
al., 2020), vegetation activity and climate change (Gao et al., 2019).
However, there are few studies that in combination MaxEnt with GWR
models to analyze the potential geographical distribution and explore
environmental explanations for some key species, especially in
biodiversity hotspot areas.

According to the Living Planet Report 2020
(https://www.wwf.org.uk/press-release/living-planet-report-2020),
published by World Wide Fund for Nature (WWF) and Zoological Society of
London (ZSL), population sizes of mammals, birds, fish, amphibians and
reptiles have fallen an average of 68\% globally. Northwest Yunnan of
China is one of the global biodiversity hotspots (Myers et al., 2000; Ye
et al., 2020a; Ye et al., 2020b). Plenty of rare, endangered, threatened
and endemic species that need critical conservation are distributed in
this area (Yang et al., 2017). In recent decades, due to the
interference of human activities (e.g., Mining and collecting herbs) and
the influence of the external natural environment, especially climate
change, the populations and distribution area of some rare, endangered
and threatened species have been decreasing (Yu et al., 2014). Living
Planet Report 2020 also stated that from 2000 to 2018, the species
habitat index dropped by 2\%, which indicated that the habitat quality
of species shows a general trend of rapid decline. In addition, some
species have the characteristics of geographical isolation and narrow
distribution (Wang et al., 2013), as a result, their survival and
development are faced with severe threats. Therefore, it is urgent to
protect these key species and their suitable habitats in this area. In
order to protect these key species reasonably and effectively, their
geographical distribution must be clear first. However, at present, we
are still unclear about the potential geographical distribution and main
limited environmental factors for these key species in Northwest Yunnan.

Therefore, in this study, 25 key species were the focus of analysis,
including some rare, endangered and/or national protected key higher
plant species in Northwest Yunnan. Initially, we adopted the method of
combining the MaxEnt model together with GWR model to predict the
potential geographical distribution of each species on the grid map with
a resolution of 0.05\selectlanguage{ngerman}° × 0.05° (for more details about resolution, see Ye
et al., 2020a). Then, we overlaid the potential geographical
distribution of each species, and obtained the potential geographical
distribution pattern of species richness on the spatial scale of the
ecological niche model with a resolution of 0.05° × 0.05°. Ultimately,
we analyzed the explanation capabilities of various environmental
factors on the potential geographical distribution patterns of these key
species, and explored the main restrictive environmental factors. These
research results would contribute to a more comprehensive understanding
of the potential geographical distribution pattern of some key higher
plant species in Northwest Yunnan, and would provide a scientific basis
for the conservation and management of suitable habitat for rare,
endangered and many other key species in this region.

\section*{2. MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsection*{2.1 Study area and species
data}

{\label{study-area-and-species-data}}

Northwest Yunnan is located in the mountains of southwest China, and
lies to the Northwestern part of Yunnan Province (Figure 1). The area is
situated on the transition zone between the Qinghai-Tibetan Plateau
(QTP) and the Yunnan\selectlanguage{english}-Guizhou Plateau (YGP), and is seated in the
Longitudinal Range-Gorge Region (LRGR) of the Hengduan Mountains in the
east of the Himalayas, and is simultaneously located in the Three
Parallel Rivers Region (TPRR) (i.e., Jinsha River, Lancang River, and
Nujiang River) (Yang, 2002). Massive variations in terrain and climate
in Northwest Yunnan (for more details, see Ye et al., 2020a), as well as
the resulting rich and diverse special habitats, provide habitats for
special species, especially for rare and endangered species. In
particular, alpine ecosystem and plateau lake ecosystem that contain a
large number of rare and endangered species (Tao et al., 2016). The
special terrain and diverse climate make this area become one of the
most concentrated and abundant regions of biodiversity in China (Xue \&
Wu, 2016; Ye et al., 2020b). In addition, Northwest Yunnan is also
considered to be one of the world's 36 biodiversity hotspots
(Conservation International,
https://www.conservation.org/priorities/biodiversity-hotspots). The
spatial variation of environmental factors make the distribution pattern
of species diversity more complicated. Therefore, Northwest Yunnan has
become an ideal region to discuss the distribution pattern of species
diversity and simulate the distribution of potential suitable habitats.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1 } Map of the study area. (a) The location of Northwest
Yunnan in China; (b) The topographic map of Northwest Yunnan and the
distribution of national nature reserves (NNRs) and provincial nature
reserves (PNRs) in this region

Combined with the results of our previous research, a total of 114 key
higher plant species in Northwest Yunnan were suggested (Ye et al.,
2020a). The information used to construct the dataset (e.g., Taxonomic
level, threatened level and geo-referenced records) were obtained from
field survey in nearly a decade and main virtual herbarium in China (for
more details, see Ye et al., 2020a). In this study, we selected a total
of 25 species (including 314 distribution records) from 114 key higher
plant species (comprises 941 geo-referenced records) (see selection
standards below). The selection was based on the combination of the
following standards: (1) occurrence records: in order to improve the
accuracy of the MaxEnt model prediction as much as possible, the number
of species distribution records should not less than four; (2)
simulation accuracy: MaxEnt model should have good or excellent
simulation accuracy for included species (for more details, see section
2.4.1).

\subsection*{2.2 Environmental variables}

{\label{environmental-variables}}

Based on previous studies (Nieto et al., 2015; \selectlanguage{romanian}Ștef\selectlanguage{polish}ă\selectlanguage{english}nescu et al., 2017;
Zhang et al., 2019; Liu et al., 2019), we initially selected 24
environmental variables that may affect species distribution to model
the current potential geographical distribution patterns (Table 1).
Above all, we divided these variables into five groups according to
their categories. After that, 24 environmental factors were resampled
and reprojected to an equal-area grid system with the same spatial
resolution (0.05\selectlanguage{ngerman}° × 0.05°) as species richness (Wang et al., 2018). Then
we employed the ArcGIS 10.4 software (Esri; Redlands, California, USA)
to extract the raster data of environmental variables.

In order to avoid multicollinearity of environmental parameters that
might result in model over-fitting, we calculated Pearson correlation
coefficient between each variable with the help of R 3.5.2 software
(https://www.r-project.org/). After performed a multicollinearity test,
we finally obtained 11 independent environmental variables
(\emph{r}\textless{} 0.8) to model the potential geographical
distribution of each species (Zhang et al., 2019; Mukherjee et al.,
2020) (Figure 2).

\subsection*{2.3 Model construction}

{\label{model-construction}}

We constructed two models in this research: one was for predicting the
potential geographical distribution area of species; the other was for
analyzing the main environmental factors influencing the potential
distribution of species.

\subsection*{2.3.1 Construction of the MaxEnt
model}

{\label{construction-of-the-maxent-model}}

Phillips et al. (2004) developed the MaxEnt model based on the theory of
maximum entropy, which was applied to the study of simulating species
distribution (Phillips et al., 2006; Zhang et al., 2011). The model can
combine species occurrence localities and environmental parameters to
predict the habitat suitability, and then explores the possible
distribution of the species in the study area (Phillips et al., 2006;
Phillips \& Dudík, 2008; Zhang et al., 2019).

In this study, the latitude and longitude of species distribution sites
and the independent environmental factors in Northwest Yunnan were
simultaneously imported into the MaxEnt model (Version 3.3.3k) to
construct the correlation function between species and the environment.
Usually, the prediction results of the MaxEnt model are related to some
set parameters, such as the max number of background points (BC),
regularization multiplier (RM), and feature combination (FC) (Zhu et
al., 2018). MaxEnt is applied to run with the following modeling
regulations: (1) for species with \textless{} 10 distribution records
linear features were applied; (2) for species with 10-14 records
quadratic features were utilized, and; (3) species with \textgreater{}
15 records hinge features were employed (Zhang et al., 2012; Zhang et
al., 2017). In this research, we set the RM value to {[}0.5, 3{]}, the
step size was 0.5; the BC value set as {[}5000, 15000{]}, the step size
was 5000. After that, we applied the linear, quadratic and hinge
features to construct the MaxEnt model, respectively. In addition, 75\%

of species distribution locations were randomly selected as training
data to build the model, and the remaining 25\% of the species
distribution locations were used as testing data for model validation
(Guan et al., 2018; Zhang et al., 2019). The maximum iterations was set
as 500, and the number of replicate runs was set as 10 or 20.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2 } Independent environmental variables used to model the
potential geographical distribution of each species in Northwest Yunnan

\subsection*{2.3.2 Construction of the GWR
model}

{\label{construction-of-the-gwr-model}}

Fotheringham et al. (1996) proposed the GWR model based on the thought
of local smoothing and the summary of local regression analysis and
variable parameter research. GWR is an extension and improvement of the
traditional global linear regression (e.g., OLS) which adds geographical
locations (i.e., Spatial factors) to the regression parameters, and also
considers the spatial weights of adjacent points (Han et al., 2016). In
addition, GWR model is a local regression model, which can profoundly
explain the spatial non-stationarity relationship between response
variables and explanatory variables by decomposing global parameters
into local parameters (Tripathi et al., 2019a). The regression equation
can be developed as (Han et al., 2016; Tripathi et al., 2019b):

\begin{equation}
\mathrm{y}_{\mathrm{i}}\mathrm{\ }\mathrm{=}\mathrm{\ }\mathrm{\beta}_{\mathrm{i}\mathrm{0}}\mathrm{\ }\mathrm{(}\mathrm{u}_{\mathrm{i}}\mathrm{,}\mathrm{v}_{\mathrm{i}}\mathrm{)}\mathrm{\ }\mathrm{+}\mathrm{\ }\sum_{\mathrm{k}\mathrm{\ }\mathrm{=}\mathrm{\ }\mathrm{1}}^{\mathrm{p}}{\mathrm{\beta}_{\mathrm{\text{ik}}}\mathrm{\ }\mathrm{(}\mathrm{u}_{\mathrm{i}}\mathrm{,}\mathrm{v}_{\mathrm{i}}\mathrm{)}\mathrm{\ }\mathrm{x}_{\mathrm{\text{ik}}}\mathrm{\ }\mathrm{+}\mathrm{\ }\mathrm{\varepsilon}_{\mathrm{i}}}\nonumber \\
 \end{equation}

Where \emph{k} = 1, \emph{p} explanatory
variables,\selectlanguage{greek}\emph{ε\textsubscript{i}}\selectlanguage{english} denotes the random error term at
position\emph{i} . In addition, (\emph{u\textsubscript{i}}
,\emph{v\textsubscript{i}} ) represents the geographic coordinate or
spatial location the of each observation,\selectlanguage{greek}\emph{β\textsubscript{i}}\selectlanguage{english}
\textsubscript{0} (\emph{u\textsubscript{i}} ,\emph{v\textsubscript{i}}
) is the intercept at position \emph{i} ,\selectlanguage{greek}\emph{β\textsubscript{ik}}\selectlanguage{english}
(\emph{u\textsubscript{i}} ,\emph{v\textsubscript{i}} ) denotes the
local regression coefficient at position \emph{i} . When\selectlanguage{greek}\emph{β}
\selectlanguage{english}\textsubscript{1\emph{k}} = \selectlanguage{greek}\emph{β} \selectlanguage{english}\textsubscript{2\emph{k}} =
\ldots{} = \selectlanguage{greek}\emph{β\textsubscript{nk}}\selectlanguage{english} , it indicates that the GWR model
is transformed into an ordinary linear regression model. In this study,
the potential species richness within each grid were used as dependent
variables and environmental factors were used as independent variables
to investigate the explanation capabilities of different categories of
environmental parameters on the potential geographical distribution
patterns of species.

According to the Tobler's first law (TFL) of geography (Tobler, 1970),
the basic principle of the GWR model to calculate the weight is ``the
closer the distance, the higher the assigned weight; on the contrary,
the lower the assigned weight (Fotheringham et al., 2002)''. Therefore,
the weight can be calculated by a monotonically decreasing function in
space distance with {[}0, 1{]} as the value range. This type of function
is called as kernel function (Lu et al., 2020). The GWR method usually
employs a Gaussian model as a weight function, where bandwidth is a
function that describes the weight and the distance and is considered as
an important control parameter in weight calculation (Gao et al., 2019).
The function is expressed as (Wang et al., 2020):

\selectlanguage{greek}\emph{ω\textsubscript{ij}}\selectlanguage{english} = exp\(\left(-\frac{\mathrm{d}_{\mathrm{\text{ij}}}^{\mathrm{\ }\mathrm{2}}}{\mathrm{b}^{\mathrm{2}}}\right)\)

Where \selectlanguage{greek}\emph{ω\textsubscript{ij}}\selectlanguage{english} denotes the distance weight of
observation location \emph{i} and \emph{j} ,
\emph{d\textsubscript{ij}}is the Euclidean distance between location
\emph{i} and \emph{j} , and b represents the bandwidth. When the
distance between location \emph{i}and \emph{j} is larger than b,
\selectlanguage{greek}\emph{ω\textsubscript{ij}}\selectlanguage{english} is equal to 0; when the distance between
location \emph{i} and \emph{j} is equal to 0,\selectlanguage{greek}\emph{ω\textsubscript{ij}}\selectlanguage{english}
is equal to 1.

\subsection*{2.4 Model evaluation}

{\label{model-evaluation}}

\subsection*{2.4.1 Evaluation of the MaxEnt
model}

{\label{evaluation-of-the-maxent-model}}

We adopted AUC value to evaluate the fitting accuracy of the MaxEnt
model. The model fitting precision can be evaluated as failed if AUC
value is between 0.50 and 0.60, poor if AUC value is between 0.60 and
0.70, fair if AUC value is between 0.70 and 0.80, good if AUC value is
between 0.80 and 0.90, and excellent if AUC value is between 0.90 and
1.00 (Phillips et al., 2006; Zhang et al., 2019). In addition, the
suitability maps were calculated employing the logistic output of the
Maxent, and the range of habitat suitability index (HSI) value we had
obtained was {[}0, 1{]}. According to a large number of previous studies
and the expert experience method, we reclassified HSI value into four
grades by Natural Breaks in ArcGIS 10.4 software: 0-0.20 is low,
0.20-0.40 is medium, 0.40-0.60 is high, and 0.60-1.00 is optimal
(Convertino et al., 2014; Yi et al., 2017). In order to conservatively
estimate the suitable potential geographical distribution area of
species, we considered grids with the HSI value larger than or equal to
0.40 as the suitable potential distribution area.

\subsection*{2.4.2 Evaluation of the GWR
model}

{\label{evaluation-of-the-gwr-model}}

In this study, we used the package `spgwr' of R software to select
bandwidth by adopting Gaussian function and employed Akaike Information
Criterion (AIC) to confirm the optimal bandwidth. Generally, regression
residual is an evaluation value of the fitting goodness of the model,
including residual sum of squares (RSS) and residual standard deviation
(Sigma), and these two values should be as small as possible. In
addition, \emph{R} \textsuperscript{2} and AIC value can also reflect
fitting goodness of the model. The higher \emph{R} \textsuperscript{2},
and the lower AIC value, indicating the better fitting effect of the
model (Li et al., 2017; Liu et al., 2019). When the difference in the
AIC value ([?]AIC) of the two models is greater than three, then the model
with smaller AIC value has better fitting effect (Han et al., 2016; Xue
et al., 2020).

\section*{3 RESULTS}

{\label{results}}

\subsection*{3.1 Species distribution
records}

{\label{species-distribution-records}}

On the basis of the selection criteria in section 2.1, we finally
identified 25 rare, endangered and/or national protected species with
high simulation accuracy, belonging to 23 genera and 19 families (Table
2). We had organized the collected species distribution data and
obtained a total of 314 distribution records of species, and further
understood the distribution status of each species in Northwest Yunnan.
The result suggested that there were at least four distribution records
for each species, as a result, detailed distribution records were shown
in Table 2. Among them, \emph{Dipentodon sinicus} and \emph{Psammosilene
tunicoides} had more occurrence records, with a total of 59 and 41,
respectively. They were mainly distributed in the vicinity of the
Gaoligong Mountain National Nature Reserve and several PNRs (i.e.,
Napahai, Bitahai, Haba Snow Mountain and Yulong Snow Mountain). However,
\emph{Anisodus tanguticus} , \emph{Camellia reticulata} and \emph{Paris
rugosa} owned few distribution records, with four for each. They were
scattered in the vicinity of Gaoligong Mountain, Baima Snow Mountain and
Cangshan Erhai NNRs.

\textbf{TABLE 2 } The checklist of species used in this study was
according to IUCN Red List criteria and The National Key Protected Wild
Plants List, specifically: 1) Threat level: referenced IUCN Red List
criteria, Vulnerable (VU), Endangered (EN), Critically Endangered (CR);
2) Protection level: 1 represents The National Key Protected Wild Plants
List (the first), 2 represents The National Key Protected Wild Plants
List (the second), I denotes national first-level protected species, II
denotes national second-level protected species. Samples: the
distribution records of the 25 rare, endangered and/or national
protected species as the input of the MaxEnt modelling\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Family & Species & Threat level & Protection level &
Samples\tabularnewline
\midrule
\endhead
Actinidiaceae & \emph{Actinidia pilosula} & VU & 2(II) &
9\tabularnewline
Solanaceae & \emph{Anisodus acutangulus} & CR & --- & 14\tabularnewline
Solanaceae & \emph{Anisodus tanguticus} & --- & 1(II) & 4\tabularnewline
Aristolochiaceae & \emph{Aristolochia delavayi} & EN & --- &
7\tabularnewline
Orchidaceae & \emph{Bulleyia yunnanensis} & EN & 2(II) &
10\tabularnewline
Theaceae & \emph{Camellia reticulata} & VU & 2(II) & 4\tabularnewline
Ranunculaceae & \emph{Coptis teeta} & CR & 2(II) & 5\tabularnewline
Orchidaceae & \emph{Cypripedium guttatum} & EN & 2(I) & 9\tabularnewline
Celastraceae & \emph{Dipentodon sinicus} & --- & 1(II) &
59\tabularnewline
Orchidaceae & \emph{Diphylax uniformis} & VU & 2(II) & 6\tabularnewline
Campanulaceae & \emph{Echinocodon lobophyllus} & CR & 2(II) &
7\tabularnewline
Liliaceae & \emph{Fritillaria delavayi} & VU & --- & 8\tabularnewline
Orchidaceae & \emph{Gymnadenia crassinervis} & VU & 2(II) &
5\tabularnewline
Magnoliaceae & \emph{Magnolia rostrata} & VU & 1(II) & 5\tabularnewline
Compositae & \emph{Nouelia insignis} & VU & 2(II) & 25\tabularnewline
Hydrocharitaceae & \emph{Ottelia acuminata} & VU & --- &
7\tabularnewline
Liliaceae & \emph{Paris dulongensis} & CR & 2(II) & 7\tabularnewline
Liliaceae & \emph{Paris rugosa} & EN & 2(II) & 4\tabularnewline
Caryophyllaceae & \emph{Psammosilene tunicoides} & EN & 1(II) &
41\tabularnewline
Crassulaceae & \emph{Rhodiola atuntsuensis} & EN & 2(II) &
8\tabularnewline
Berberidaceae & \emph{Sinopodophyllum hexandrum} & --- & 2(II) &
26\tabularnewline
Dryopteridaceae & \emph{Sorolepidium glaciale} & --- & 1(I) &
25\tabularnewline
Taxodiaceae & \emph{Taiwania cryptomerioides} & VU & 1(II) &
4\tabularnewline
Combretaceae & \emph{Terminalia myriocarpa} & VU & 1(II) &
5\tabularnewline
Tetracentraceae & \emph{Tetracentron sinense} & EN & 1(II) &
10\tabularnewline
\bottomrule
\end{longtable}

\subsection*{3.2 MaxEnt model
performance}

{\label{maxent-model-performance}}

The average AUC value of each species was between 0.80 and 1.00,
indicating that the simulation effect of the model on each species was
good or excellent. Among them, the minimum average AUC value was 0.80
and the maximum was 0.99 (Figure 3), which demonstrated that the
constructed MaxEnt model could be used to predict and analyze the
potential geographical distribution of 25 rare, endangered and/or
national protected species in Northwest Yunnan.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3 } The AUC value calculated and output by the Maxent
model

\subsection*{3.3 Environmental variable
contribution}

{\label{environmental-variable-contribution}}

The percentage contribution values of environmental variables to the
predicted results of the MaxEnt model were defined through heuristics,
and depended on the specific path that MaxEnt codes used to provide best
solutions (Li et al., 2020). In this research, the percentage
contribution values were the average contribution values established
over cross-validation and repeated runs for each species (Li et al.,
2020). The results showed that among the 11 independent environmental
variables, annual precipitation (Bio12), precipitation of coldest
quarter (Bio19) and population density (Pop) had higher average
contribution rates to the species distribution model than other
environmental variables. Their average contribution values to the model
were 25.92\%, 15.86\% and 17.95\%, respectively, and the cumulative
contribution value accounted for 59.73\% of the total contribution value
of all environmental factors to the model (Figure 4). However, compared
with other environmental factors, mean diurnal range (Bio2) and altitude
(Alt) had the lowest average contribution values to the species
distribution model. Their average contribution values to the model were
1.07\% and 1.64\%, respectively, and the accumulated contribution value
accounted for 2.71\% of the total contribution value of all
environmental variables to the model (Figure 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 4 } Contribution value of environmental variables to
predicting result of the MaxEnt model

\subsection*{3.4 Potential geographical distribution of
species}

{\label{potential-geographical-distribution-of-species}}

HSI is an important or key factor that affects the survival and
development of species. It refers to the potential ability of a habitat
to support the survival of a particular species. The results of the
study revealed that potential distribution areas of species appears to
be decreasing with the improvement of habitat suitability (Figure 5),
which indicated that the remaining natural habitat suitable for species
was becoming less and less. In addition, the research results also
indicated that the species with a larger potential distribution areas
were \emph{Echinocodon lobophyllus} , \emph{Gymnadenia crassinervis}
,\emph{Rhodiola atuntsuensis} and \emph{Aristolochia delavayi} . Their
potential distribution areas were 44,963.53 km\textsuperscript{2},
30,830.51 km\textsuperscript{2}, 22,481.77 km\textsuperscript{2} and
22,396.28 km\textsuperscript{2}, respectively, accounting for 56.36\%,
38.64\%, 28.18\% and 28.07\% of the total area of Northwest Yunnan
(Table 3; Figure 6). The species with smaller potential distribution
areas were \emph{Diphylax uniformis} , \emph{Paris dulongensis}
and\emph{Terminalia myriocarpa} . Their potential distribution areas
were 2,137.05 km\textsuperscript{2}, 1,054.28 km\textsuperscript{2} and
826.33 km\textsuperscript{2}, respectively, occupying only 2.68\%,
1.32\% and 1.04\% of the total area of Northwest

Yunnan (Table 3; Figure 6). Generally, the potential distribution areas
of each species were relatively concentrated. The HSI values of the
mid-western, mid-eastern and northern regions of the study area were
between 0.40 and 1.00, which were deemed the main concentration area of
the potential distribution of species. Furthermore, the area of high
suitability plaques (i.e., Sum area of high suitability level habitats,
where 0.40 [?] HIS \textless{} 0.60) was larger than the area of optimal
suitability plaques (i.e., Sum area of optimal suitability level
habitats, where 0.60 [?] HIS \textless{} 1.00) (Table 3; Figure 6).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{FIGURE 5 } Potential distribution area of habitat for various
suitability levels\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{FIGURE 6 } Potential distribution area of each species predicted
by the MaxEnt model. The habitat suitability will be evaluated as high,
if HIS value is between 0.40 and 0.60, and optimal if HIS value is
between 0.60 and 1.00

In addition, we adopted ArcGIS 10.4 software to superimpose the
appropriate potential distribution areas of each species (HSI [?] 0.4) to
obtain the potential distribution pattern of species richness (Figure
7). The results showed that on the grid scale with a resolution of 0.05\selectlanguage{ngerman}°
× 0.05°, the potential species richness of each grid ranged from 0 to
12. Through a combination of the distribution of the existing national
and provincial nature reserves in Northwest Yunnan together with the
administrative division of this region (for more details, see Ye et al.,
2020a), we found that part of the grids with the highest potential
species richness were located near Baima Snow Mountain and Gaoligong
Mountain NNRs, while the other part of the grids with the highest
potential species richness were situated in Shangri-La County (Figure
7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{FIGURE 7 } Distribution pattern of potential species richness of
25 rare, endangered and/or national protected key higher plant species
in Northwest Yunnan

\subsection*{3.5 Environmental explanations for potential distribution of
species}

{\label{environmental-explanations-for-potential-distribution-of-species}}

According to the category of environmental factors, we employed the
package `spgwr' of the software R and constructed six different types of
environmental factor models to explain the potential distribution of
species through the GWR model (Table 4). The research results indicated
that the human activity model had a lower interpretation rate than other
models, which appeared a goodness of fit \emph{R} \textsuperscript{2}
and AIC value of 0.83 and 8,850.15, respectively. However, the goodness
of fit \emph{R} \textsuperscript{2} and AIC value of the water model
were 0.88 and 7,703.82, respectively, which gave a higher explanation
rate than other models, indicating that the water factor largely
influenced the potential distribution of species. In addition, the
goodness of fit\emph{R} \textsuperscript{2} and AIC value of the
comprehensive model were 0.92 and 6,785.26, respectively, and the
difference in the AIC value ([?]AIC) with other models was greater than
three, demonstrating that the comprehensive model had a better
simulation effect on the potential distribution of species. The results
also revealed that multiple environmental variables were not mutually
exclusive, and the potential distribution of species was the result of
the combined effects of various environmental factors.

\textbf{TABLE 4 } Fitting effects of different models on the potential
distribution of species\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Model (0.05\selectlanguage{ngerman}° × 0.05°)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Bandwidth (km)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Sigma\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
RSS\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
AIC\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
[?]AIC\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\emph{R}\textsuperscript{2}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Temperature model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.37\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.04\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2,372.18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7,939.56\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1,154.30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.88\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Water model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.45\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2,229.88\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7,703.82\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
918.56\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.88\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Productive model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6.72\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2,729.22\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8,382.65\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1,597.39\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.86\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Topographical model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.04\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2,376.74\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7,900.40\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1,115.14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.88\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Human activity model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6.64\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.24\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3,253.97\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8,850.15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2,064.89\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.83\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Comprehensive model\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
14.96\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.86\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1,528.70\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6,785.26\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.92\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\section*{4 DISCUSSION}

{\label{discussion}}

\subsection*{4.1 The potential geographical distribution of
species}

{\label{the-potential-geographical-distribution-of-species}}

Habitat is a vital place for survival, reproduction and population
development of species. Its quality can directly influence the
distribution, quantity and survival rate of species (Hall et al., 1997;
Zhang et al., 2019). We utilized the MaxEnt model to predict the
potential geographical distribution of 25 rare, endangered and/or
national protected plant species in Northwest Yunnan. The results showed
that the potential distribution area (i.e., Region where HSI [?] 0.4) of
each species was between 826.33 km\textsuperscript{2} and 44,963.53
km\textsuperscript{2}, which indicated that these species had obvious
differences in their adaptability to environmental factors such as the
topography and climate in Northwest Yunnan. The potential distribution
area of these target species were mainly concentrated in the
mid-western, mid-eastern and northern parts of the study area, which was
in good agreement with the prediction results of the potential
distribution area which studied by Zhuang et al. (2018a). The prediction
results of this study will provide possible new areas for species
distribution and field investigation. In addition, the potential
distribution area of species overlapped greatly in the mid-western and
northern parts of Northwest Yunnan. We roughly divided them into two
parts. One part was close to the Gaoligong Mountain and Baima Snow
Mountain NNRs, which were almost consistent with the hotspot
distribution of key higher plant species in Northwest Yunnan (Ye et al.,
2020a). Similarly, the other part was located in Shangri-La County,
which might be related to the fact that Shangri-La is situated at the
core area of the Three Parallel Rivers World Natural Heritage Site (Yang
et al., 2013), with abundant landscape types, vegetation types, and
ecosystem types. Consequently, the results reflected that these regions
mentioned above will play a crucial role in biodiversity conservation in
the future.

\subsection*{4.2 A model used to predict the distribution of
species}

{\label{a-model-used-to-predict-the-distribution-of-species}}

The model prediction method can help to compensate for the difficulty in
field investigation. The vertical peaks and horizontal valleys in
Northwest Yunnan greatly limit the accessibility of field surveys.
Hence, by combining the model prediction with field investigation, the
field survey can be carried out in the order of habitat suitability,
with priority given to the field investigation in the optimal
suitability distribution area and the potential distribution area that
has not been studied. In addition, the completeness, accuracy and
reliability of species geographical distribution data are important and
key links of division of key biodiversity areas. Meanwhile, the accuracy
of the data and the precision of the model prediction are also mutually
promoted (Zhang et al., 2016; Wu et al., 2016; Zhuang et al., 2018a). In
this study, we tried our best to collect a lot of species distribution
data through multiple approaches, but there are still some species with
relatively less distribution data. For example, \emph{Coptis teeta} is
also distributed in Myanmar and other countries. Therefore, more species
distribution data must be collected to construct a more accurate species
distribution model. In addition, the distribution of species is not only
determined by topography, climate and edaphic factors, but also
influenced by social and economic structure, land use type, human
disturbance and other social factors. In some cases, due to the
influence of the local microenvironment, the areas predicted to be of
lower suitability levels are actually the distribution areas of species.
Therefore, the results obtained in this study are only advisory. A more
precise and accurate potential distribution prediction needs to be
supported by more comprehensive social and environmental factors and
more precise and more reliable species distribution information.

\subsection*{4.3 Environmental explanations for the potential
distribution of
species}

{\label{environmental-explanations-for-the-potential-distribution-of-species}}

From an ecological point of view, environmental factors can affect the
spatial distribution of species, as well as their habitat suitability
(Zhang et al., 2019). In this study, we found that the cumulative
contribution rate of Bio12 (25.92\%), Bio19 (15.86\%) and Pop (17.95\%)
to the MaxEnt model prediction results reached 59.73\%. Furthermore, the
contribution rate of Bio12 was the highest, which was consistent with
the results obtained by Zhuang et al. (2018). In addition, the \emph{R}
\textsuperscript{2} values of the GWR model were all above 0.80,
indicated that the model has a reliable goodness of fit for explaining
the potential distribution of species. Moreover, the water model
(\emph{R} \textsuperscript{2} = 0.88, AIC = 7703.82) showed a higher
explanation rate compared to other single models, followed by the
temperature model (\emph{R} \textsuperscript{2} = 0.88, AIC = 7939.56)
and topographical model (\emph{R} \textsuperscript{2} = 0.88, AIC =
7900.40), which were second only to the water model in their
interpretation effect on the potential distribution of species.
Accordingly, this result suggested that climate (temperature and water
factor) and habitat heterogeneity (topographical factor) could play an
important role in the prediction of potential distribution areas of
species, which was coincide with the research results of \selectlanguage{romanian}Ștef\selectlanguage{polish}ă\selectlanguage{english}nescu et
al. (2017). However, compared with all single models, the comprehensive
model (\emph{R} \textsuperscript{2} = 0.92, AIC = 6785.26) that combines
all environmental factors had a better goodness of fit, which indicated
that the potential distribution of species was the result of the
combined effects of various environmental factors (Wang et al., 2018).
The main thing worth mentioning is that, rare and endangered species are
usually restricted to specialized edaphic or topographic or other
environmental conditions which occupy a quite fraction of their
climatically suitable range. Therefore, on the basis of this study, it
is necessary to study the effects of other relevant environmental
factors on the potential distribution of species.

\subsection*{4.4 Suggestions for the protection of rare and endangered
plant
species}

{\label{suggestions-for-the-protection-of-rare-and-endangered-plant-species}}

In recent decades, due to the disturbance of human activities and the
impacts of the external natural environment, especially climate change,
the population size and distribution area of some rare, endangered
and/or threatened species had been declining (Yu et al., 2014).
Therefore, understanding the habitat suitability of species and its
influencing factors is the basis of protecting rare and endangered plant
species (Zhang et al., 2019). Habitat suitability plays an important
role in the survival and development of species. Hence, habitat
suitability assessment is the first step of effective conservation and
scientific management of species, and can provide scientific basis for
relevant departments to formulate valid conservation strategies. It has
become a resultful method to protect rare and endangered species by
scientifically predicting the potential distribution areas and habitat
suitability levels of species and planning wild nature reserve in the
best suitable areas (Xiao et al., 2011; Xu et al., 2015). The results of
this study revealed that the habitat suitability of species near
Gaoligong Mountain and Baima Snow Mountain NNRs and Shangri-La Country
was relatively high, and these areas were also the core regions for the
distribution of rare, endangered and/or national protected species in
Northwest Yunnan. For this reason, it is suggested that to strengthen
the conservation of these areas. In addition, Shangri-La Country had
more habitat suitability distribution areas for species, but there were
only three PNRs with a small area, namely, Napahai, Bitahai and Haba
Snow Mountain PNRs (Ye et al., 2020a). It is recommended that
appropriate expansion of the nature reserve should be carried out, with
measures combining \emph{in-situ} and \emph{ex-situ} conservation to
strengthen the protection of species in a state of isolation and/or
fragmentation. In addition, the species distribution model is an
estimate of the potential distribution of species, and its essence is
prediction research. Therefore, the results of the model simulation
cannot be used as the only basis for formulating strategies of species
conservation, and corresponding field surveys should be carried out
according to the simulation prediction results, so as to formulate more
scientific and reasonable strategies for species conservation (Renner \&
Warton, 2013; Wen et al., 2019). Correspondingly, by simulating the
potential distribution area of species, it can also provide some basic
and scientific evidence for species reintroduction.

\section*{5 CONCLUSIONS}

{\label{conclusions}}

In this study, we finally identified 25 rare, endangered and/or national
protected species with high simulation accuracy, belonging to 23 genera
and 19 families. The average AUC value of each species was from 0.80 to
1.00, which confirmed that the simulation precision of the MaxEnt model
on each species was good or excellent. On the whole, the potential
distribution area for each species was relatively concentrated, mainly
distributed in the central-western, central-eastern and northern regions
of Northwest Yunnan. Besides, the potential distribution areas of these
species were between 826.33 km\textsuperscript{2} and 44963.53
km\textsuperscript{2}. Additionally, the annual precipitation (Bio12),
precipitation of coldest quarter (Bio19) and population density (Pop)
had a greater contribution to the species distribution model, and their
contribution values were 25.92\%, 15.86\% and 17.95\%, respectively.
Furthermore, the goodness of fit \emph{R} \textsuperscript{2} and AIC
value of the water model were 0.88 and 7703.82, respectively, which
demonstrated the water factor largely influenced the potential
distribution of these target species. These results would contribute to
a more comprehensive understanding of the potential geographical
distribution pattern of some key higher plant species in Northwest
Yunnan, and would be helpful for implementing long-term conservation and
reintroduction for these species.

\section*{ACKNOWLEDGMENTS}

{\label{acknowledgments}}

This research was supported by the ``Biodiversity Survey, Observation
and Assessment Program of Ministry of Ecology and Environment of the
People's Republic of China''. We would like to thank the respectable
reviewers for their constructive suggestions.

\section*{CONFLICT OF INTEREST}

{\label{conflict-of-interest}}

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.

\section*{AUTHOR CONTRIBUTIONS}

{\label{author-contributions}}

Jianyong Wu and Guangfu Zhang conceived the ideas. Jianyong Wu and
Pengcheng Ye collected the data. Pengcheng Ye analyzed the data.
Pengcheng Ye drafted the first version of the manuscript. Jianyong Wu
and Guangfu Zhang led manuscript writing. All co-authors contributed to
the scientific discussions and commented on the manuscript.

\section*{DATA AVAILABILITY STATEMENT}

{\label{data-availability-statement}}

Detailed experimental data and results of each grid are available on
Dryad. The spreadsheet contains the environment variable value, habitat
suitability index (HSI) value and potential abundance of species of each
grid. In addition, we also used open-access data from Worldclim version
2-Bioclimatic variables (http://worldclim.com/), Resource and
Environment Data Cloud Platform (http://www.resdc.cn/Default.aspx) and
National Earth System Science Data Center (http://www.geodata.cn/).

\section*{ORCID}

{\label{orcid}}

Pengcheng Ye https://orcid.org/0000-0001-6245-3281

Jianyong Wu https://orcid.org/0000-0001-6887-3164

Guangfu Zhang https://orcid.org/0000-0001-8120-4838

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{\label{references}}

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\section*{SUPPORTING INFORMATION}

{\label{supporting-information}}

Additional supporting information may be found online in the Supporting
Information section at the end of the article.

\textbf{Hosted file}

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