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\begin{document}

\title{Predicting the potential distribution change of the endangered Francois'
langur (Trachypithecus francoisi) across its entire range in China under
climate change}



\author[1]{Yaqiong Wan}%
\author[1]{LuanXin Li}%
\author[2]{Jiang Zhou}%
\author[1]{Jiaqi  Li}%
\author[1]{Yan Liu}%
\author[1]{Yanjing Zhang}%
\author[1]{Yue Ma}%
\author[1]{Wei Liu}%
\affil[1]{MEE Nanjing Institute of Environmental Sciences}%
\affil[2]{Guizhou Normal University}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Francois' langur (Trachypithecus francoisi) is a rare and endangered
and the indicated primate species distributed in karst area in northern
Vietnam and southwestern China. However, holistic conservation
management has been hampered by research limited to specific nature
reserves or sites. Building a comprehensive map of potential
distribution for the Francois' langur is essential to advance
conservation efforts and ensure coordinated management across regions.
Here, we used 82 occurrence records of Francois' langur surveyed in
Guangxi, Guizhou, and Chongqing from 2017 to 2020, along with 12
environmental variables to build the potential habitat model under
current and future climate (for the periods 2021-2040, 2041-2060,
2061-2080, 2081-2100) using maximum entropy models (MaxEnt). Our results
indicated that 1) precipitation- and temperature-associated bioclimatic
variables made the greatest contributions to the distribution of
Francois' langur, vegetation, water source and anthropogenic variables
were also important factors affecting the distribution of Francois'
langur; 2) 144207.44 km2 potential Francois' langur suitable habitat
across the entire range in China was estimated by the current model,
moderate- and high-suitability habitat accounted for only 23.76\%
(34265.96 km2) of the predicted suitable habitat and mainly distributed
in southwest Guangxi, east of Chongqing and the border between Guizhou
and Chongqing; 3) the suitable habitats of Francois' langur will
dramatically contract under future climate change and the habitat
centroid will move in the southeast direction with the shifting distance
about 2.84km/year from current to 2100. The habitat prediction of
Francois' langur and related drivers proposed in this work will provide
essential insights for future conservation of this species, which is,
not only existing distribution areas should be monitored and protected,
but also conservation beyond existing habitats should be a focus of
effort, especially future expansion areas, to ensure effective and
timely protection under climate change and anthropogenic pressures.%
\end{abstract}%



\sloppy


\textbf{1 INTRODUCTION}

Climate change is one of the most significant contemporary threats to
biodiversity worldwide (Isaac \& Williams, 2007; Jing et al., 2022) and
is already affecting species and ecosystems on a global scale, and these
effects are projected to become more rapid and extensive (Field et al.,
2014). In particular, many species that are already vulnerable, such as
those with limited geographical range and/or restricted habitat
requirements, could find their available habitat area reduced or
eliminated (Hobbs et al., 2009). And these endangered species may be
threatened by a combination of disturbances associated with climate
change (such as temperature or precipitation) and ongoing anthropogenic
activities (such as habitat fragmentation, pollution, urbanization)
(Holling, 1973). For example, deforestation and forest degradation are
threatening the survival of about half of all bamboo species worldwide,
and climate change may present an additional significant threat,
especially, many bamboo species provide essential food and shelter for
wildlife species, including one of the most endangered species, the
giant panda (Tuanmu et al., 2013). Many species are responding to
changing environments by shifting their geographical ranges (Chen et
al., 2011; Parmesan, 2006).

Francois' langur (\emph{Trachypithecus francoisi} ) is an endangered
species endemic to the limestone forests of the tropical and subtropical
zones of northern Vietnam and southwestern China (Nadler et al., 2004),
with a current estimated global population of approximately 2,000
individuals (Wang et al., 2011; Zeng et al., 2013). In China, the
distribution of Francois' langur was largely restricted to areas
characterized by karst topography at altitudes of 120-1,800 m in
Guangxi, Guizhou and Chongqing (Han et al., 2021). These areas have some
unique features that differentiate them from mountain forests, such as
steep cliffs, which cover about 10-20\% of the area, lack of surface
water (Huang, 2002), and relatively poor, but more diverse vegetation
(Zhou et al., 2013). Due to forest loss, habitat destruction and
fragmentation, some Francois' langur populations have been locally
extirpated from some parts of their historic range (Li et al., 2007;
Zeng et al., 2013). For instance, this species has become restricted to
only five isolated sites in Guizhou (Hu et al., 2011). Although the
Francois' langur was listed as endangered on the Red List of the
International Union for Conservation of Nature (IUCN, 2020), and was
considered a first-grade protected wildlife species in China (Niu et
al., 2016; Wang et al., 2011; Wen et al., 2010), little was known about
its habitat use and factors affecting habitat selection or association,
except for some roost selection and behavioral ecology, moreover,
previous studies were limited to specific nature reserves or locations
(Hu et al., 2011; Li et al., 2020; Liu \& Bhumpakphan, 2020; Zhou et
al., 2013, 2018).

Climate change effects on distribution of species, particularly rare and
endangered ones, have attracted increasing attention, and predicting how
these species will react to climate change is a major current challenge
in ecology. Some species, including plants (Mahmoodi et al., 2022; Pan
et al., 2020; Wu et al., 2016; Zhang et al., 2020), freshwater fishes
(Chu et al. 2005), mammals (Wilkening et al., 2019), coral reefs (Sill
\& Dawson, 2021) and invertebrates (Mart\selectlanguage{ngerman}ín \& Abellán, 2022), have been
predicted distribution affected by the climate change with some model
methods, such as bioclimatic prediction system (BioCLIM), random forest
model (RFM) and maximum entropy models (MaxEnt) (Phillips et al., 2004,
2006). Among these models, MaxEnt is the most accurate and commonly used
model (Bellard et al., 2018; Tsoar et al., 2007). The MaxEnt is an
open-source software package for modeling species niches and
distributions through the application of a machine-learning technique.
It combines the current distribution data of the species with the
background environmental variables, associates the known occurrences
with a set of unique environmental conditions and then predicts the
probability of presence for unknown locations via the use of multiple
algorithms, and evaluates the importance of environmental variables
using jackknife (Yin et al., 2022). Thus, MaxEnt can be applied to well
predict the distribution of Francois' langur as well as habitat
selection.

In this study, MaxEnt was applied to construct the potential
distributions of Francois' langur. The primary objectives were as
follows: (1) to model current potential distribution of the Francois'
langur habitat across the entire range in China, (2)to explore the
relationships among the potential distribution patterns and environments
of the Francois' langur and determine relevant influencing variables,
(3)to predict potential distribution in the future different periods,
and (4)to evaluate how the climate change in the future affect the
potential distribution of this species. Additionally, the study aims to
provide suggestions and new insights for the biodiversity conservation
of rare species.

\textbf{2 MATERIAL AND METHODS}

2.1 Occurrence data of Francois' langur

A total of 171 occurrence records of Francois' langur were obtained from
our survey data. From 2017 to 2020, we conducted field surveys in all
known distribution areas of the species in Guangxi, Guizhou, and
Chongqing provinces, which are located in the southwest China with
special ecological environment of karst ecosystem (Figure 1). We
recorded the occurrence of species based on direct observations, fecal
remains and infrared cameras. To avoid overfitting in the models (Li et
al., 2022) and to prevent spatial auto-correlation of raster data, we
used the SDM Toolbox v2.4 (spatially rarefy occurrence data) of ArcGIS
10.2 to ensure that there was only one occurrence record for the species
in each 1 × 1 km resolution grid cell (Tu et al., 2021). After
selection, 82 occurrence records of Francois' langur were identified for
modelling analysis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

FIGURE 1 Study areas and occurrence records of Francois' langur in the
present study

2.2 Environmental variables and data processing

A total of 26 environmental variables, including climate (19 bioclimatic
variables), topography (elevation, slope, aspect), vegetation
(Normalized Difference Vegetation Index (NDVI)), water sources (distance
to river) and human activities (distance to road, distance to housing),
were provisionally selected to build the initial models based on
previous research on the habitat of Francois' langur.

19 grid-based bioclimatic variable data were downloaded from the
WorldClim (www.worldclim.com) dataset. The current climate data were
obtained with a spatial resolution of 30 arc-second (approximately
1km\textsuperscript{2}) (Li et al., 2022). Future climate data were
obtained from the Beijing Climate Center Climate System Model (BCC-CSM)
under the Coupled Model Intercomparison Project Phase 6 (CMIP6) global
climate model in four periods of 2021-2040, 2041-2060, 2061-2080,
2081-2100 with the shared socio-economic pathways 245 (SSP 245 for
moderate scenario). These climatic variables represent annual trends
(mean annual temperature and precipitation), seasonality (annual range
of temperature and precipitation), and limiting environmental factors
(temperature and precipitation of a certain quarter) (Hijmans et al.,
2005; Thapa et al., 2018). As the 19 bioclimatic variables were all
extended from temperature and precipitation based on different
calculations, to avoid the effects of model overfitting caused by
multicollinearity of these variables (Zhao et al., 2020), the 19
predictor climatic variables used in this study were tested using
Pearson correlation analysis with the 'Cor' function in R 4.3.0 (Team,
2014) (Appendix 1), and variables with high correlation coefficients
(\textbar{} r \textbar{}[?]0.8) and relatively low contribution rates in
the initial models were removed to improve model predictability (Ji et
al., 2020; Liu et al., 2022).

Three topography variables including elevation, slope and aspect were
derived from Digital Elevation Model (DEM) data, which was obtained from
geospatial data cloud (http://www.gscloud.cn) with a resolution of 30m
using ArcGIS 10.2. NDVI was downloaded from the Resources Environment
Data Center, Chinese Academy of Sciences (http://www.resdc.cn) with a
resolution of 500m. River, road and housing data were obtained from the
National Basic Geographic Information System, and the Euclidian distance
tool of the ArcGIS 10.2 was used to calculate the distance between each
grid cell layer and the nearest river, road and housing.

Consequently, five predictor climate variables were retained, namely,
the Mean Diurnal Range (Mean of monthly (max temp-min temp)) (Bio2), Min
Temperature of Coldest Month (Bio6), Temperature Annual Range (Bio7),
Precipitation Seasonality (Coefficient of Variation) (Bio15),
Precipitation of Coldest Quarter (Bio19), additionally, elevation,
slope, aspect, NDVI, distance to river, distance to road, distance to
housing were also used in the model prediction (Table 1). To facilitate
spatial analysis within China, all variables selected for modelling were
projected to WGS\_1984\_UTM\_Zone\_48N and resampled to 30 arc-second
(approximately 1 km\textsuperscript{2}) using the spatial analysis tools
in ArcGIS 10.2.

TABLE 1 Environmental variables included in the analysis and their
percentage contribution and permutation importance in predicting the
current distribution of Francois' langur\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Variables & Description & Percent contribution (\%) & Permutation
importance (\%)\tabularnewline
Bio2 () & Mean of monthly (max temp-min temp) & 15.6 &
9.5\tabularnewline
Bio6 () & Min temperature of coldest month & 20.4 & 7.1\tabularnewline
Bio7 () & Temperature annual range & 7.4 & 15.3\tabularnewline
Bio15 (mm) & Precipitation seasonality (coefficient of variation) & 5.1
& 10.7\tabularnewline
Bio19 (mm) & Precipitation of coldest quarter & 14.9 & 34\tabularnewline
Ele & Elevation & 4.4 & 2.3\tabularnewline
Slop & Slope & 2.3 & 4.1\tabularnewline
Asp & Aspect & 0.7 & 0.3\tabularnewline
NDVI & Normalized difference vegetation index & 3.2 & 1.2\tabularnewline
Dis\_river & Distance to nearest river & 2 & 2.5\tabularnewline
Dis\_road & Distance to nearest road & 20.6 & 11.7\tabularnewline
Dis\_house & Distance to nearest housing & 3.4 & 1.4\tabularnewline
\bottomrule
\end{longtable}

2.3 MaxEnt modelling

We used the open source software package MaxEnt v.3.4.4, which can be
downloaded from http://biodiversity
informatics.amnh.org/open\_source/maxent/ (Sun et al., 2020). Francois'
langur occurrence records and environmental variables were imported into
MaxEnt. All models were set random seeds and used 75\% of the record
data as the training set and the remaining 25\% as the test set with 10
replicates and bootstrap as replicated run type (Wang et al., 2020),
while the number of modelling iterations was set at 10000 to allow the
models to have adequate time for convergence (Young et al., 2011). The
jackknife test procedure was used to evaluate the relative importance of
each environmental variable. The response curves of environmental
variables were used to test the correlation between the variables and
the occurrence probability of Francois' langur. Logistic was selected to
output results. The default options were used for the other parameters.

2.4 Model evaluation and validation

Receiver operating characteristic (ROC) curve analysis is an effective
method for evaluating the accuracy of species distribution models (Liu
et al., 2022; Wang et al., 2007). We used the area under the ROC curve
(AUC), which is a threshold-independent discrimination metric that
represents the probability that a random presence or absence is
correctly assigned by the model (Phillips et al., 2006). The AUC values
range from 0.5 to 1.0, with AUC \textgreater{} 0.9 indicating excellent
performance (Janitza et al., 2013).

The mean value of the 10 replicates from the model was taken as the
distribution result, and the value represent habitat suitability index
(HSI), which was between 0 and 1. The ASCII output file from MaxEnt was
transformed into a raster layer, which was reclassified using the
Spatial Analyst tool. At present, the classification of suitable habitat
areas is largely based on experience, and there is no unified standard
(Sun et al., 2020). We used the Jenks' natural breaks classification in
ArcGIS combined with maximum training sensitivity plus specificity (Max
TSS), balance training omission, predicted area and threshold value
(average TPT) in the MaxEnt output to reclassify the data into four
suitability areas for Francois' langur: high habitat suitability,
moderate habitat suitability, low habitat suitability, and unsuitable
habitat. The spatial analysis tool in ArcGIS 10.2 was used to calculate
the areas and proportions of each suitability area.

2.5 Changes in potential distribution and centroid shifts

To construct the core distribution shifts and potential area changes
under each future climate model, binary suitable or unsuitable maps were
produced using ArcGIS 10.2 to analyse the changes in centroid and
potential distributions based on the threshold of unsuitable area. By
comparing the suitable/unsuitable potential distributions in different
time periods, a total of four types of distribution changes were
determined, namely, contraction, expansion, no change and no occupancy
(Jose \& Po, 2020; Liu et al., 2022). Moreover, because the potential
distribution of Francois' langur was scattered and its outline was
irregular, centroid shift analysis was conducted based on the potential
distribution under different time periods using SDMtoolbox v2.4 (Brown,
2014) to visually show the changing trends of the potential core
distribution of Francois' langur.

\textbf{3 RESULTS}

3.1 Model performance

Predicting suitable habitat for Francois' langur with MaxEnt resulted in
high model performance. The AUC metric for the ten replicate runs was
0.973\selectlanguage{ngerman}±0.006 (mean±SD), indicating a high level of predictive performance
in the current period. For future climate models of the Francois' langur
distribution, the obtained AUC values in 2021-2040, 2041-2060,
2061-2080, 2081-2100 were 0.977, 0.977, 0.974, 0.975, respectively, all
of which were greater than the 0.9 AUC threshold, indicating good model
performance.

3.2 Environmental variable contributions

The Jackknife analysis showed the contribution of each variable and
their permutation importance in predicting the habitat of Francois'
langur. The current prediction was mainly affected by Dis\_res (20.6\%),
Bio6 (20.4\%), Bio2 (15.6\%), Bio19 (14.9\%), Bio7 (7.4\%) and Bio15
(5.1\%) in decreasing percentage of their contribution towards the
prediction model. All other variables contributed less than 16\% (Table
1). When considering the permutation importance, Bio19 (34\%), Bio7
(15.3\%), Dis\_road (11.7\%), Bio15 (10.7\%), Bio2 (9.5\%) showed the
highest percentage. The Jackknife test also revealed the importance of
the variables when used in isolation as well as when each variable was
excluded. The variables Bio6, Bio7, Bio2, Bio15, Bio19 registered the
highest gain when the variables were evaluated individually, whereas the
variables aspect and slop showed the negative gain (Figure 2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

FIGURE 2 The relative predictive power of different environmental
variables based on the jackknife test and permutation importance in
MaxEnt for Francois' langur

3.3 Response of environmental variables

Species response curves revealed the relationship between environmental
variables and the probability of species presence, indicating biological
tolerances for target species and habitat preferences. Based on the
acquired species response curves (Appendix 2), Francois' langur prefers
habitats with mean diurnal range (mean of monthly (max temp-min temp))
(bio2) less than 6*C, min temperature of coldest month (bio6) of 9-11*C,
temperature annual range (bio7) of 21-23*C, precipitation seasonality
(coefficient of variation) (bio15) less than 65mm, precipitation of
coldest quarter (bio19) from 70 to 80mm. Meanwhile, the probability of
Francois' langur presence increases with increasing the distance to road
in the range of 0-15km, distance to housing in the range of 0-30km and
NDVI from 0 to 0.8. The probability of Francois' langur presence
decreases as the distance to river increases.

3.4 Current potential distribution

A map of current habitat suitability for Francois' langur based on
MaxEnt model predictions is shown in Figure 3; it is based on the 12
environmental variables and the current occurrence data of Francois'
langur. All 82 occurrences of Francois' langur fell within the predicted
suitable area, again indicating that the model performed well and
produced excellent evaluation. The total suitable habitat area was
predicted to be 144207.44 km\textsuperscript{2} (29.09\% of the study
area), of which the highly suitable area (0.45-1) covered 13108.55
km\textsuperscript{2} (9.09\% of the total suitable area); the
moderately suitable area (0.25-0.45) covered 21157.41
km\textsuperscript{2} (14.67\% of the total suitable area) and the
poorly suitable area (0.04-0.25) covered 109941.49
km\textsuperscript{2}(76.24\% of the total suitable area). The highly
suitable areas were located in a small part of southwest Guangxi, east
of Chongqing and on the border between Guizhou and Chongqing. The
moderately suitable habitats were close to the highly suitable ones with
different expansion. Less suitable areas, which were fragmented, were
mainly located in Guizhou, some parts of Chongqing except the northern
area and southwest of Guangxi (Figure 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

FIGURE 3 The current potential distribution map of Francois' langur

3.5 Future changes in suitable habitat

Under future climate conditions (2021-2040, 2041-2060, 2061-2080, and
2081-2100) in the SSP245 climate scenario, the area of the potential
geographical distribution of Francois' langur was predicted to decrease
compared to the current situation. Habitat suitability maps for future
climate models are shown in Figure 4. By 2021-2040, the high potential
suitable area for Francois' langur would decrease by 76.73\%. Similarly,
the moderately suitable area would decrease by 50.87\% and the low
suitable area would decrease by 46.22\%. By 2041-2060, however, the
highly, moderately and poorly suitable areas for Francois' langur would
only decrease by 20.47\%, 21.42\% and 40.81\% respectively, revealing an
increase in the suitable area for Francois' langur compared to the
period of 2021-2040. By 2061-2080, the high suitable area for Francois'
langur would decrease by 83.46\%, the medium suitable area would
decrease by 54.56\% and the low suitable area would decrease by 47.39\%.
A similar pattern was also confirmed for 2081-2100, where the highly,
moderately and poorly suitable areas for Francois' langur would decrease
by 76.85\%, 61.73\% and 51.62\% respectively (Figure 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

FIGURE 4 Predicted future distributions of periods 2021-2040, 2041-2060,
2061-2080 and 2081-2100 for Francois' langur\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

FIGURE 5 Suitable habitat areas of Francois' langur in all different
periods

3.6 The centroid distribution shifts

The change in the potential distribution of Francois' langur from 2021
to 2100 is shown in Figure 6. These models predicted that future global
change would promote the contraction of the potentially suitable
habitats for Francois' langur, with the total suitable habitats
decreasing. In particular, most of the suitable habitat in Chongqing and
Guizhou would contract sharply. Francois' langur would expand slightly
in southwestern Guangxi and western Chongqing. The potential core
distribution of Francois' langur would move southeast from southern
Guizhou to northern Guangxi under future climate conditions.
Furthermore, the distance of the centroid shifts were 109.43km,
145.99km, 82.26km, and 227.09km in 2021-2040, 2041-2060, 2061-2080, and
2081-2100, respectively, compared to the current (Figure 7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

FIGURE 6 Potential distribution changes in the future (2021-2040,
2041-2060, 2061-2080, 2081-2100) compared to current for Francois'
langur\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

FIGURE 7 Centroid change in the potential distribution of Francois'
langur from 2021 to 2100. The distance of the centroid shifts were
109.43km, 145.99km, 82.26km, and 227.09km in 2021-2040, 2041-2060,
2061-2080, and 2081-2100, respectively, compared to the current.

\textbf{4 DISCUSSION}

4.1 Influence of environmental variables

In the field of Francois' langur ecology, habitat selection and use have
been an important research topic for researchers. The factors of habitat
selection have been divided into biological, geographical, and
anthropogenic factors (Han et al., 2023). Our results suggested that
bioclimatic variables made the largest contribution to the distribution
of Francois' langur, vegetation, water source and anthropogenic
variables were also important factors influencing the distribution of
Francois' langur. As demonstrated by numerous previous studies,, the
distribution of Francois' langur was largely restricted to areas
characterized by karst topography, limestone cliffs, and caves with
mixed conifer-broadleaf forests as they provided quality food resources
and cover for predator avoidance (Wang et al., 2011; Wen et al., 2010;
Zeng et al., 2013). Additionally, its distribution was concurrently
influenced by various factors, including temperature, precipitation and
vegetation (Enstam \& Isbell, 2004; Han et al., 2011).

We used the MaxEnt model to construct patterns of habitat suitability
for the Francois' langur in China. According to our model prediction,
temperature-associated bioclimatic variables were of great importance in
predicting habitat suitability for Francois' langur, while
precipitation-associated bioclimatic variables were the most important
habitat predictor for Francois' langur. Numerous studies have shown that
in recent years the frequency of precipitation in southwest China has
decreased, while the intensity, total precipitation, and occurrence of
extreme rainstorm have increased (Yan et al., 2023; Lu et al., 2022;
Ding, 2014; Feng et al., 2012). As a result, river water level have
risen, and the original valley area with food and habitat caves for
Francois' langur have been flooded, posing a direct threat to the living
habitat of Francois' langur. In addition, temperature and precipitation
have a great influence on the growth rates of plant species, and fruits,
seeds, and especially young leaves are the main food of Francois'
langur. Therefore, temperature and precipitation indirectly determined
the distribution of Francois' langur by affecting the availability of
potential food resources (Huang et al., 2008; Zhou et al., 2011; Zhou et
al., 2006). Based on the response curves in our study, the most suitable
distribution of Francois' langur exhibited the following temperature
characteristics: temperature annual range of 21-23, mean of monthly
temperature range (max temp-min temp) of \textless{} 6, and minimum
temperature of coldest month of 9-11. The precipitation seasonality
(coefficient of variation) and precipitation of coldest quarter of
Francois' langur's most suitable distribution were \textless{} 65 mm and
70-80 mm, respectively. This result is consistent with the climatic
conditions of the extant range of Francois' langur (Han \& Hu, 2010; Li
et al., 2019; Niu et al., 2016).

Vegetation-related variable, the normalized difference vegetation index,
was another important variable. Francois' langur prefers areas with
relatively high NDVI, which is consistent with Han et al. (2023), Niu et
al. (2016), Zeng et al. (2013) and Zeng (2011), who showed that
Francois' langur habitat was mainly in the valley areas on both sides of
the rivers, covered by evergreen broad-leaf forests with higher tree
canopy density and vegetation cover, which provided availability of
better food resources and suitable cover for predator avoidance.
Meanwhile, distance to rivers and altitude were the less important
variables. Previous studies have indicated that Francois' langur was
primarily distributed in valleys characterized by dominant evergreen
broadleaf forests. These areas offer abundant food and water sources,
and culverts and karst caves along rivers provide enough suitable
sleeping sites for Francois' langur (Han et al., 2011; Hu, 2011; Wang et
al., 2011). In addition, the elevation of Francois' langur distribution
ranges from 300 to 1800m, with seasonal changes in climate and food (Li
et al., 2019; Li et al., 2015).

For the anthropogenic variables, distance to roads (Including railways)
was found to be the more important variable compared to distance to
housing. Most previous studies have assessed the impact of linear
infrastructure on wild animals and have typically shown that
distribution decreases as road density increases (C\selectlanguage{ngerman}éline Clauzel et al.,
2013; Clauzel et al., 2015). Roads are likely to act as barriers to
wildlife movement (Coffin, 2007; Zhu et al., 2011). Therefore, roads may
have negative impact on the activity and distribution of Francois'
langur. In this study, distance to roads was positively correlated with
the occurrence probability of Francois' langur. However, distance to
housing had a limited influence on the distribution prediction of
Francois' langur. This finding aligns with Han et al. (2023), Zeng et
al. (2013), Wang et al. (2011), who reported that Francois' langur has
become accustomed to human disturbances, especially, crops grown on
agricultural land may supply the food resources for Francois' langurs
(Han et al., 2023).

4.2 Characteristics of the current potential distribution

Our model estimated potential suitable habitat of 144207.44
km\textsuperscript{2} for Francois' langur across the range, based on
the Max TSS and average TPT thresholds in the MaxEnt output. Hu et al.
(2011) showed that Francois' langur was restricted to only five isolated
sites (Dashahe, Baiqing, Mayanghe, Kuankuoshui and Yezhong) with a total
area of about 912 km\textsuperscript{2} in Guizhou\selectlanguage{english}.Chen (2006)
estimated potential Francois' langur habitat in Guangxi at approximately
3216.23 km\textsuperscript{2}. Han and Hu (2010) confirmed that the
distribution area of Francois' langur in Chongqing Jinfo Mountain was
about 50 km\textsuperscript{2}. Our model predicted the largest
proportion of Francois' langur habitat in China than the total habitat
in three provinces (Guizhou, Guangxi, and Chongqing) in previous
studies, which were based on survey records or estimates at a small
scale, such as one or more nature reserves. Importantly, our model
analysis was based on a machine-learning algorithm, species records from
the entire range in China, and a set of environmental variables
including climatic factors. Furthermore, our model prediction for the
Francois' langur was based on a larger and more comprehensive scale than
prior methods (Chen, 2006; Han et al., 2011, 2023; Hu et al., 2011).

Our model predicted a total of 34265.96 km\textsuperscript{2} of
moderate- and high-suitability habitat for Francois' langur, which only
accounted for 23.76\% of predicted suitable habitat. The moderate- and
high-suitability habitats, primarily located in southwest Guangxi, east
of Chongqing and the Guizhou-Chongqing border, was nearly 10 times the
area of previous surveys (Chen, 2006; Han \& Hu, 2010; Hu et al., 2011).
However, the low suitable habitat was distributed over a larger area as
fragmentation, which is consistent with Guan et al. (2022) and Niu et
al. (2016), who suggested that the Francois' langur habitats presented
severe fragmentation, complex patch shape, weak patch aggregation, and
decentralization, and typically showed that habitat area decreased and
further fragmented with increasing human activities. The current
potential distribution in our study basically covered and was larger
than the historical distribution areas of Francois' langur recorded in
``A guide to the mammals of China'' (Smith et al., 2010) and ``China' s
mammal diversity and geographic distribution'' (Jiang et al., 2015). Our
results can provide a scientific reference for further field survey on
the distribution of Francois' langur in China.

4.3 Distribution change in the future

Our results indicate that, compared to the current potential habitat,
the suitable habitats of Francois' langur will decrease in all future
periods under climate change. In the future climate prediction, the
temperature in southwest China are expected to continue increasing, and
show a more obvious difference in the increase between the northern and
southern regions (Yang et al., 2022; Zhang, 2020).Climate change will
lead to changes in species habitat, such as increased precipitation and
more extreme precipitation events (Feng et al., 2023; Yue, 2022). From
2021 to 2100, the area of suitable habitat will greatly contracted,
especially in the medium- and high-suitability areas along the border
between Guizhou and Chongqing. Moreover, most of the patchy areas of
low-suitability will cease to exist. The centroid of habitat shifted to
the southeast at a rate of approximately 2.84km/year from current to
2100. Previous studies have indicated that changes in annual
precipitation, annual mean temperature, precipitation and temperature
seasonality, as well as land use/ land cover changes, could reduce
suitable habitat for the large mammals such as Asiatic black bear
(\emph{Ursus thibetanus} ), Asian elephant (\emph{Elephas maximus} ) and
western hoolock gibbon (\emph{Hoolock hoolock} ), and thus increase
their extinction risk (Deb, 2016). In addition, our results are
consistent with the description that primates and other large mammals
are highly dependent on a complete forest ecosystem for their food and
security requirements, and habitat fragmentation will accelerate local
population extinctions (Guan et al., 2022). For Francois' langur,
habitat fragmentation caused by climate change and anthropogenic
disturbance would be the most important factor in changing the
distribution of this species.

4.4 Conservation recommendations

Our results showed that the current predicted habitats of Francois'
langur were over more than the history areas, but the distribution
habitats of Francois' langur would be threatened to contract and
fragment dramatically in the future. We suggest to expand the survey
range of their habitat, reduce human disturbance and enhance habitat
connectivity by establishing ecological corridor especially the border
of Chongqing and Guizhou and further protection on these areas.
Moreover, conservation of the Francois' langur should not be based only
on the existing distribution areas, but should also focus on
conservation beyond existing habitats, especially the expansion areas in
the study, which should be considered to join the national programme as
protected areas.

AUTHOR CONTRIBUTIONS

\textbf{Yaqiong Wan:} Conceptualization (lead); data curation (equal);
formal analysis (equal); writing - original draft (lead); writing -
review and editing (equal). \textbf{Luanxin Li:} Data curation (equal);
writing - original draft (equal). \textbf{Jiang Zhou:} Investigation
(equal); writing - review and editing (equal). \textbf{Yue Ma:} Data
curation (equal); writing - original draft (equal). \textbf{Yanjing
Zhang:} data curation (equal); methodology (equal). \textbf{Yan
Liu:}writing - review and editing (equal). \textbf{Jiaqi Li:}
Supervision (equal); writing - review and editing (equal). \textbf{Wei
Liu:}Supervision (equal); writing - review and editing (equal).

ACKNOWLEDGMENTS

We thank the nature reserves for their support to collect data: Guangxi
Xialei Nature Reserve, Guangxi Encheng Nature Reserve, Guangxi Chongzuo
White-Headed Langur Nature Reserve, Guangxi Nonggang National Nature
Reserve, Guizhou Dashahe Nature Reserve, Guizhou Yezhong Nature Reserve,
Guizhou Mayanghe National Nature Reserve, Guizhou Kuankuoshui National
Nature Reserve, and Chongqing Jinfoshan National Nature Reserve.

APPENDIXES

APPENDIX 1: Pearson' s correlation coefficients between 19 climate
variables, and intensity of color represents greater positive (blue) or
negative correlation (orange).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

APPENDIX 2: Response curves of environmental variables in the potential
distribution model of Francois' langur. Note: The red curves represent
average value over 10 replicate runs, while blue margins represented \selectlanguage{ngerman}±
SD calculated for 10 replicates\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

REFERENCES

Bellard, C., Jeschke, J. M., Leroy, B., \& Mace, G. M. (2018). Insights
from modeling studies on how climate change affects invasive alien
species geography. \emph{Ecology and Evolution,} 8(11), 5688-5700.

\emph{https://doi.org/10.1002/ece3.4098}

Brown, J. L. (2014). SDMtoolbox: a python-based GIS toolkit for
landscape genetic, biogeographic and species distribution model
analyses. \emph{Methods in Ecology and Evolution} .

Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B., \& Thomas, C. D.
(2011). Rapid range shifts of species associated with high levels of
climate warming. \emph{Science,} 333(6045), 1024-1026.

\emph{https://doi.org/10.1126/science.1206432}

Chen, Z. (2006). Research on habitat fragmentation of Francois' Langur
based on 3S technology\emph{.} (Master's thesis), Guangxi Normal
University, China.

Chu, C., Mandrak, N. E., \& Minns, C. K. (2005). Potential impacts of
climate change on the distributions of several common and rare
freshwater fishes in Canada. \emph{Diversity and Distributions,} 11(4),
299-310. \emph{https://doi.org/10.1111/j.1366-9516.2005.00153.x}

Clauzel, C., Girardet, X., \& Folt\selectlanguage{ngerman}ête, J.-C. (2013). Impact assessment
of a high-speed railway line on species distribution: Application to the
European tree frog (\emph{Hyla arborea} ) in Franche-Comté.
\emph{Journal of Environmental Management,} 127, 125-134.

\emph{https://doi.org/10.1016/j.jenvman.2013.04.018}

Clauzel, C., Xiqing, D., Gongsheng, W., Giraudoux, P., \& Li, L. (2015).
Assessing the impact of road developments on connectivity across
multiple scales: Application to Yunnan snub-nosed monkey
conservation.\emph{Biological Conservation,} 192, 207-217.

\emph{https://doi.org/10.1016/j.biocon.2015.09.029}

Coffin, A. W. (2007). From roadkill to road ecology: A review of the
ecological effects of roads. \emph{Journal of Transport
Geography,}15(5), 396-406.
\emph{https://doi.org/10.1016/j.jtrangeo.2006.11.006}

Deb, J. C. (2016). Assessing species vulnerability to climate change in
tropical Asia: Implications for biodiversity conservation and forest
management. (PhD Thesis), The University of Queensland.

Ding, W. R. (2014). Spatial and temporal variability of the extreme
daily precipitation in southwest China. \emph{Resources and Environment
in the Yangtze Basin} , 2014, 23(7): 9.

\emph{https://doi.org/10.11870/cjlyzyyhj201407019}

Enstam, K. L., \& Isbell, L. A. (2004). Microhabitat preference and
vertical use of space by patas monkeys (\emph{Erythrocebus patas} ) in
relation to predation risk and habitat structure. \emph{Folia
Primatologica,} 75(2), 70-84. \emph{https://doi.org/10.1159/000076265}

Feng, J., Chen, B. F., Wang, K., Hu, Y. (2012). Typical characteristics
of rainfall evolution in southwest China: A case study of meitan county
in Guizhou province. \emph{Journal of Yangtze River Scientific Research
Institute} , 29(12):1-4.

\emph{https://doi.org/10.3969/j.issn.1001-5485.2012.12.001}

Feng, Y., He, Z., Jiao, S., \& Liu, W. (2023). Scenario prediction of
extreme precipitation in Guizhou province based on CMIP6 climate
model.\emph{Research of Soil and Water Conservation,} 30(1), 282-290.

\emph{https://doi.org/10.13869/j.cnki.rswc.20220621.004}

Field, C. B., Barros, V. R., \& Change, I. P. C. (2014). IPCC, 2014:
Climate change 2014: Impacts, adaptation, and vulnerability. Part A:
Global and sectoral aspects. Contribution of working group II to the
fifth assessment report of the intergovernmental panel on climate
change. \emph{guangdong agricultural sciences} .

Guan, C., Chen, Z., Huang, C., \& Zhou, Q. (2022). Analysis of
fragmentation in landscape pattern for the Franois' langur's habitats in
Guangxi and its influence on population. \emph{Acta Ecologica
Sinica,}42(3), 1203-1212.
\emph{https://doi.org/10.5846/stxb202101010003}

Han, J., Liu, G., Bai, W., Zou, Q., Cao, Y., Zhou, C., \& Williams, G.
M. (2021). Relationship between human disturbance and habitat use by the
endangered François' Langur (\emph{Trachypithecus francoisi} ) in
Mayanghe Nature Reserve, China. \emph{Pakistan Journal of
Zoology,}54(1).
\emph{https://doi.org/10.17582/journal.pjz/20200704020744}

Han, J., Williams, G. M., Zou, Q., \& Dong, B. (2023). The range and
habitat suitability of Franois' langur (\emph{Trachypithecus francoisi}
) in Mayanghe Nature Reserve, China. \emph{Environmental Science and
Pollution Research,} 30(14), 40952-40960.

\emph{https://doi.org/10.1007/s11356-023-25170-x}

Han, Z. X., \& Hu, J. C. (2010). Food selection by Francois' Langur
(\emph{Trachypithecus francoisi francoisi} ) in the spring at Chongqing
Jinfo Mountain National Nature Reserve. \emph{Sichuan Journal of
Zoology,} 29(6), 857-861.

Han, Z., Wang, W., \& Hu, J. (2011). Habitat selection by Francois'
Langur in Jinfo Mountain in spring. \emph{Scientia Silvae
Sinicae,}47(4), 121-128.

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., \& Jarvis,
A. (2005). Very high resolution interpolated climate surfaces for global
land areas. \emph{International Journal of Climatology,} 25(15),
1965-1978. \emph{https://doi.org/10.1002/joc.1276}

Hobbs, R. J., Higgs, E., \& Harris, J. A. (2009). Novel ecosystems:
Implications for conservation and restoration. \emph{Trends Ecol
Evol,}24(11), 599-605. \emph{https://doi.org/10.1016/j.tree.2009.05.012}

Holling, C. S. (1973). Resilience and stability of ecological
systems.\emph{Annual Review of Ecology and Systematics,} 4(1), 1-23.

\emph{https://doi.org/10.1146/annurev.es.04.110173.000245}

Hu, G. (2011). Dietary breadth and resource use of Francois' Langur in a
seasonal and disturbed habitat. \emph{American journal of
primatology,}73(11), 1176-1187. \emph{https://doi.org/10.1002/ajp.20985}

Hu, G., Dong, X., Luo, H., Su, X., \& Zhou, C. (2011). The distribution
and population dynamics of Franois' Iangur over the past two decades in
Guizhou, China and threats to its survival. \emph{Acta Theriologica
Sinica,} 31(3), 306-311.

\emph{https://doi.org/10.16829/J.SLXB.2011.03.013}

Huang, C. (2002). White-headed Langur in China. \emph{Guilin: Guangxi
Normal University Press} .

Huang, C., Wu, H., Zhou, Q., Li, Y., \& Cai, X. (2008). Feeding strategy
of François' langur and white-headed langur at Fusui, China. \emph{Am J
Primatol,} 70(4), 320-326. \emph{https://doi.org/10.1002/ajp.20490}

Isaac, J. L., \& Williams, S. E. (2007). Climate change and extinctions.
In S. A. Levin (Ed.), \emph{Encyclopedia of Biodiversity} (pp. 1-7). New
York: Elsevier.

The IUCN Red List of Threatened Species. (2020). Retrieved from

\emph{https://www.iucnredlist.org}

Janitza, S., Strobl, C., \& Boulesteix, A. L. (2013). An AUC-based
permutation variable importance measure for random forests. \emph{BMC
Bioinformatics,} 14(1), 119.

\emph{https://doi.org/10.1186/1471-2105-14-119}

Ji, W., Han, K., Lu, Y., \& Wei, J. (2020). Predicting the potential
distribution of the vine mealybug, Planococcus ficus under climate
change by MaxEnt. \emph{Crop Protection} , 105268.

Jiang, Z., Ma, Y., Wu, Y., Wang, Y., Zhou, K., Liu, S., \& Feng, Z.
(2015). China's mammal diversity and geographic distribution. Beijing:
Science Press.

Jing ,X., Jiang, S. J., Liu, H. Y., Li, Y., He, J. S. (2022). Complex
relationships and feedback mechanisms between climate change and
biodiversity. \emph{Biodiversity Science} , 2022,
30(10):19.DOI:10.17520/biods.2022462.

Jose, S., \& Po, N. P. (2020). The expanding distribution of the Indian
Peafowl (\emph{Pavo cristatus} ) as an indicator of changing climate in
Kerala, southern India: A modelling study using MaxEnt. \emph{Ecological
Indicators,} 110. \emph{https://doi.org/10.1016/j.ecolind.2019.105930}

Li, W., Song, Q., Huang, R., Zhao, J., Li, Y., \& Zhou, Q. (2019).
Current status and conservation of Francois' langurs
(\emph{Trachypithecus francoisi} ) in Encheng National Nature Reserve in
Guangxi, China. \emph{Acta Theriologica Sinica,} 39(6), 623-629.

\emph{https://doi.org/10.16829/j.slxb.150289}

Li, X., Ru, D., Garber, P. A., Zhou, Q., Li, M., \& Zhao, X. (2022).
Climate change and human activities promoted speciation of two
endangered langurs (François' langur and white-headed
langur).\emph{Global Ecology and Conservation,} 38, e02185.

\emph{https://doi.org/10.1016/j.gecco.2022.e02185}

Li, Y., Huang, C., Ding, P., Tang, Z., \& Wood, C. (2007). Dramatic
decline of François' langur \emph{Trachypithecus francoisi} in Guangxi
Province, China. \emph{Oryx,} 41(1), 38-43.

\emph{https://doi.org/10.1017/S0030605307001500}

Li, Y., Huang, X., \& Huang, Z. (2020). Behavioral adjustments and
support use of François' langur in limestone habitat in Fusui, China:
Implications for behavioral thermoregulation. \emph{Ecology and
Evolution,} 10(11), 4956-4967. \emph{https://doi.org/10.1002/ece3.6249}

Li, Y., LU, S., \& SU, L. (2015). Dietary diversity and monthly overlap
of Francois' langur in a fragmented habitat in Fusui, Guangxi,
China.\emph{Acta Ecologica Sinica,} 35(7), 2360-2365.

\emph{https://doi.org/10.5846/stxb201307011809}

Liu, J., \& Bhumpakphan, N. (2020). Comparison of activity budgets,
diet, and habitat utilization between provisioned and wild groups of the
François' Langur (\emph{Trachypithecus francoisi} ) in Mayanghe National
Nature Reserve, China. \emph{Folia Primatol (Basel),} 91(1), 15-30.

\emph{https://doi.org/10.1159/000500444}

Liu, T., Liu, H., Tong, J., \& Yang, Y. (2022). Habitat suitability of
neotenic net-winged beetles (Coleoptera: Lycidae) in China using
combined ecological models, with implications for biological
conservation. \emph{Diversity and Distributions,} 28(12), 2806-2823.

\emph{https://doi.org/10.1111/ddi.13545}

Lu, F., Liu, Y. L., Zhu, S. J., Wang, G. Q., Jin, J. L., He, R. M., Liu,
C. S. (2022). Analysis and preliminary thinking of rainstorm and flood
in China in the flood period of 2020. \emph{Water Resources and Power
,}2022(005):040.

Mahmoodi, S., Heydari, M., Ahmadi, K., Khwarahm, N. R., Karami, O.,
Almasieh, K., Naderi, B., Bernard, P., \& Mosavi, A. (2022). The current
and future potential geographical distribution of Nepeta crispa Willd.,
an endemic, rare and threatened aromatic plant of Iran: Implications for
ecological conservation and restoration. \emph{Ecological
Indicators,}137, 108752.
\emph{https://doi.org/10.1016/j.ecolind.2022.108752}

Martín\selectlanguage{english}-Velez, V., \& Abellan, P. (2022). Effects of climate change on
the distribution of threatened invertebrates in a Mediterranean
hotspot.\emph{Insect Conservation and Diversity,} 15(3), 370-379.

\emph{https://doi.org/10.1111/icad.12563}

Nadler, T., Streicher, U., \& Ha, T. L. (2004). Conservation of primates
in Vietnam.

Niu, K., Xiao, Z., Wang, B., Yang, D., Tan, C. L., Zhang, P., Yan, X.
G., Wang, H., Bo, Y., \& Yang, T. (2016). Population estimates and
distribution of Francois\selectlanguage{english}'Langurs (\emph{Trachypithecus francoisi} ) in
Mayanghe National Nature Reserve, China. \emph{Chinese Journal of
Zoology} . \emph{https://doi.org/10.13859/j.cjz.201606001}

Pan, J., Fan, X., Luo, S., Zhang, Y., Yao, S., Guo, Q., \& Qian, Z.
(2020). Predicting the potential distribution of two varieties of Litsea
coreana (Leopard-Skin Camphor) in China under climate
change.\emph{Forests,} 11(11), 1159.
\emph{https://doi.org/10.3390/f11111159}

Parmesan, C. (2006). Ecological and evolutionary responses to recent
climate change. \emph{Annual Review of Ecology, Evolution, and
Systematics,} 37(1), 637-669.

\emph{https://doi.org/10.1146/annurev.ecolsys.37.091305.110100}

Phillips, S. J., Anderson, R. P., \& Schapire, R. E. (2006). Maximum
entropy modeling of species geographic distributions. \emph{Ecological
Modelling,} 190(3), 231-259.

\emph{https://doi.org/10.1016/j.ecolmodel.2005.03.026}

Phillips, S. J., Dudik, M., \& Schapire, R. E. (2004). A maximum entropy
approach to species distribution modeling. Paper presented at the
Proceedings of the twenty-first international conference on Machine
learning, Banff, Alberta, Canada.

\emph{https://doi.org/10.1145/1015330.1015412}

Sill, S. R., \& Dawson, T. P. (2021). Climate change impacts on the
ecological dynamics of two coral reef species, the humphead wrasse
(\emph{Cheilinus undulatus} ) and crown-of-thorns starfish
(\emph{Ancanthaster planci} ). \emph{Ecological Informatics,} 65,
101399.

\emph{https://doi.org/10.1016/j.ecoinf.2021.101399}

Smith, A. T., Xie, Y., Hoffmann, R. S., Lunde, D., MacKinnon, J.,
Wilson, D. E., \& Wozencraft, W. C. (2010). A guide to the mammals of
China. \emph{Princeton University Press} .

Sun, X., Xu, Q., \& Luo, Y. (2020). A maximum entropy model predicts the
potential geographic distribution of sirex noctilio.
\emph{Forests,}11(2), 175. \emph{https://doi.org/doi:10.3390/f11020175}

Team, R. C. (2014). R: A language and environment for statistical
computing. \emph{MSOR connections,} 1.

Thapa, A., Wu, R., Hu, Y., Nie, Y., Singh, P. B., Khatiwada, J. R., Yan,
L., Gu, X., \& Wei, F. (2018). Predicting the potential distribution of
the endangered red panda across its entire range using MaxEnt
modeling.\emph{Ecology and Evolution,} 8(21), 10542-10554.

\emph{https://doi.org/10.1002/ece3.4526}

Tsoar, A., Allouche, O., Steinitz, O., Rotem, D., \& Kadmon, R. (2007).
A comparative evaluation of presence-only methods for modelling species
distribution. \emph{Diversity and Distributions,} 13(4), 397-405.

\emph{https://doi.org/10.1111/j.1472-4642.2007.00346.x}

Tu, W., Xiong, Q., Qiu, X., \& Zhang, Y. (2021). Dynamics of invasive
alien plant species in China under climate change
scenarios.\emph{Ecological Indicators,} 129, 107919.

\emph{https://doi.org/10.1016/j.ecolind.2021.107919}

Tuanmu, M.-N., Vina, A., Winkler, J. A., Li, Y., Xu, W., Ouyang, Z., \&
Liu, J. (2013). Climate-change impacts on understorey bamboo~species and
giant pandas in China's~Qinling~Mountains. \emph{Nature Climate
Change,}3(3), 249-253. \emph{https://doi.org/10.1038/nclimate1727}

Wang, S., Luo, Y., \& Cui, G. (2011). Sleeping site selection of
Francois's langur (\emph{Trachypithecus francoisi} ) in two habitats in
Mayanghe National Nature Reserve, Guizhou, China. \emph{Primates,}52(1),
51-60. \emph{https://doi.org/10.1007/s10329-010-0218-2}

Wang, T., Zhu, Q., Heller, K. G., Zhou, Z., \& Shi, F. (2020).
Phylogenetic relationships and phylogeography of the genus Sinocyrtaspis
Liu, 2000 (Orthoptera: Tettigoniidae: Meconematinae) reveal speciation
processes related to climate change. \emph{Systematic Entomology,}45(1),
144-159. \emph{https://doi.org/10.1111/syen.12384}

Wang, Y., Xie, B., Wan, F., Xiao, Q., \& Dai, L. (2007). Application of
ROC curve analysis in evaluating the performance of alien species'
potential distribution models. \emph{Biodiversity science,} 15(4),
365-372. \emph{https://doi.org/10.1360/biodiv.060280}

Wen, L. I., Aiwei, G., Bin, W., Ankang, W. U., Ning, L., \& Gang, H. U.
(2010). Nutritional contents of winter food and its influences on food
choice of Francois' langur at Mayanghe Natur Reserve. \emph{Acta
Theriologica Sinica,} 30(2), 151-156.

Wilkening, J., Pearson\selectlanguage{english}-Prestera, W., Mungi, N. A., \& Bhattacharyya, S.
(2019). Endangered species management and climate change: When habitat
conservation becomes a moving target. \emph{Wildlife Society
Bulletin,}43(1), 11-20. \emph{https://doi.org/10.1002/wsb.944}

Wu, X., Nan, C., Tang, G., Li, Y., Mao, L., \& Zhang, Z. (2016). Impact
of climate change on potential distribution range and spatial pattern of
Phoebe chekiangensis. \emph{Journal of Nanjing Forestry University
(Natural Sciences Edition)} .

Yan, C. X., Zeng, B., Dong, Y. C., Tie, Y. B., Fan, Y. Y. (2023).
Analysis on spatial-temporal variation characteristics of heavy rain
over western China. \emph{Journal of Southwest China Normal University
(Natural Science Edition)} .

Yang, M., Xiao, T., Li, Y., \& Hu, T. (2022). Evaluation and projection
of climate change in southwest China using CMIP6 models. \emph{Plateau
Meteorology,} 41(6), 1557-1571.

\emph{https://doi.org/10.7522/j.issn.1000-0534.2021.00119}

Yin, Y., He, Q., Pan, X., Liu, Q., Wu, Y., \& Li, X. (2022). Predicting
current potential distribution and the range dynamics of pomacea
canaliculata in China under global climate change. \emph{Biology,}11(1),
110. \emph{https://doi.org/10.3390/biology11010110}

Young, N., Carter, L., Evangelista, P., \& Jarnevich, C. (2011). A
MaxEnt model v3.3.3e tutorial (ArcGIS v10).

Yue, Y. (2022). The changes of future runoff and drought and flood in
the Yangtze River Basin under climate change. (PhD thesis), \emph{East
China Normal University,} China.

Zeng, Y. (2011). Habitat suitability of Francois' Langur
(\emph{Trachypithecus francoisi francoisi} ) and its application in
setting minimum area of Mayanghe National Nature
Reserve\emph{.}(Master's thesis), \emph{Beijing Forestry University} ,
China.

Zeng, Y., Xu, J., Wang, Y., \& Zhou, C. (2013). Habitat association and
conservation implications of endangered Francois' Langur
(\emph{Trachypithecus francoisi} ). \emph{PLOS ONE,} 8(10), e75661.

\emph{https://doi.org/10.1371/journal.pone.0075661}

Zhang, J. (2020). Preliminary assessment of climate change in the
lancang river basin based on CMIP6 data set\emph{.} (Master's
thesis),\emph{Nanjing University of Information Science and Technology,}
China.

Zhang, X., Wei, H., Zhang, X., Liu, J., Zhang, Q., \& Gu, W. (2020).
Non-pessimistic predictions of the distributions and suitability of
Metasequoia glyptostroboides under climate change using a random forest
model. \emph{Forests,} 11(1), 62.

\emph{https://doi.org/doi:10.3390/f11010062}

Zhao, R., Chu, X., He, Q., Tang, Y., \& Zhu, Z. (2020). Modeling current
and future potential geographical distribution of Carpinus tientaiensis,
a critically endangered species from China. \emph{Forests,} 11(7), 774.

Zhou, Q., Huang, C., Ming, L. I., \& Wei, F. (2011). Ranging behavior of
the Franois' langur (\emph{Trachypithecus francoisi} ) in limestone
habitats of Nonggang, China. \emph{Integrative Zoology,} 6(2), 157-164.

\emph{https://doi.org/10.1111/j.1749-4877.2011.00239.x}

Zhou, Q., Luo, B., Wei, F., \& Huang, C. (2013). Habitat use and
locomotion of the Francois' langur (\emph{Trachypithecus francoisi} ) in
limestone habitats of Nonggang, China. \emph{Integrative Zoology,} 8(4),
346-355. \emph{https://doi.org/10.1111/j.1749-4877.2012.00299.x}

Zhou, Q., Wei, F., Li, M., Huang, C., \& Luo, B. (2006). Diet and food
choice of the Francois' langur (\emph{Trachypithecus francoisi} ) in the
Nonggang Nature Reserve, China. \emph{International Journal of
Primatology,} 27(5), 1441-1460.

\emph{https://doi.org/10.1007/s10764-006-9082-8}

Zhou, Q. H., Huang, Z. H., Wei, H., \& Huang, C. M. (2018). Variations
in diet composition of sympatric Trachypithecus francoisi and Macaca
assamensis in the limestone habitats of Nonggang, China. \emph{Zool
Res,} 39(4), 284-290.

\emph{https://doi.org/10.24272/j.issn.2095-8137.2018.046}

Zhu, L., Zhang, S., Gu, X., \& Wei, F. (2011). Significant genetic
boundaries and spatial dynamics of giant pandas occupying fragmented
habitat across southwest China. \emph{Mol Ecol,} 20(6), 1122-1132.

\emph{https://doi.org/10.1111/j.1365-294X.2011.04999.x}

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