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\begin{document}

\title{Potential distribution of the rare and endangered Satyrium Species in
China under climate change}



\author[1]{Xianheng Ouyang}%
\author[2]{Shihao Bai}%
\author[1]{Garry Brien Strachan}%
\author[1]{Yangjun Mao}%
\author[1]{Luying Zuo}%
\author[3]{Yang Yang}%
\author[1]{Anliang Chen}%
\affil[1]{Zhejiang A and F University}%
\affil[2]{Shanghai Jiao Tong University}%
\affil[3]{Hunan University of Medicine}%


\vspace{-1em}



  
  \date{May 16, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Satyrium is an endangered and rare genus of plant that has various
pharmacodynamic functions. In this study, MaxEnt default parameters were
adjusted by the ENMeval package. Optimized MaxEnt models were used in
analyzing potential geographical distributions under current and future
climatic conditions (the 2050s and 2070s) and dominant environmental
variables influencing their geographic distribution. The results
provided reference for implementation of long-term conservation and
management approaches for the species. The results showed the area of
the total suitable habitat for S. ciliatum in China is 32.51 \selectlanguage{ngerman}× 104 km2,
the total suitable habitat area for S. nepalense in China is 61.76 × 104
km2, and the area of the total suitable habitat for S. yunnanense in
China is 89.73 × 104 km2 under current climatic conditions. The
potential suitable habitat of Satyrium is mainly distributed in
Southwest China. The major environmental variables influencing the
geographical distribution of S. ciliatum were isothermality (bio3),
temperature seasonality (bio4), and mean temperature of coldest quarter
(bio11). Environmental variables such as isothermality (bio3),
temperature seasonality (bio4), and precipitation of coldest quarter
(bio19) affected the geographical distribution of S. nepalense; and
environmental variables such as isothermality (bio3), temperature
seasonality (bio4), and lower temperature of coldest month (bio6)
affected the geographical distribution of S. yunnanense. The
distribution range of Satyrium was extended as global warming increased,
showing emissions of greenhouse gases with lower concentration
(SSP1-2.6) and higher concentration (SSP5-8.5). According to the study,
the distribution of suitable habitat will shift with a change to higher
elevation areas and higher latitude areas in the future.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Potential distribution of the rare and endangered\emph{Satyrium}
species in China under climate change}

\textbf{Xianheng Ouyang \textsuperscript{a, 1}, Shihao
Bai\textsuperscript{b, 1}, Garry Brien Strachan}

\textsuperscript{\textbf{c}}\textbf{, Yangjun Mao \textsuperscript{a},
Luying Zuo\textsuperscript{a}, Yang Yang \textsuperscript{d}, Anliang
Chen\textsuperscript{a,} *}

\textsuperscript{\textbf{a}} \textbf{School of Forestry and
Biotechnology, Zhejiang A\&F University, Hangzhou 311300, China}

\textsuperscript{\textbf{b}} \textbf{Shanghai Center for Systems
Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China}

\textsuperscript{\textbf{c}} \textbf{School of Law and Humanities,
Zhejiang A\&F University, Hangzhou 311300, China}

\textsuperscript{\textbf{d}} \textbf{School of Pharmaceutical Sciences,
Hunan University of Medicine, Huaihua, 418000, China}

\textbf{Correspondence}

\textbf{Anliang Chen, School of Forestry and Biotechnology, Zhejiang
A\&F University, Hangzhou 311300, China}

\textbf{Email: anlchen@126.com}

\textbf{Acknowledgements}

\textbf{The work was supported by the Key Research and Development
Program of Zhejiang Province {[}grant numbers 2019C02024{]}.}

\textsuperscript{\textbf{1}} \textbf{These authors contributed equally
to this work}

\textbf{Abstract:} \emph{Satyrium} is an endangered and rare genus of
plant that has various pharmacodynamic functions. In this study, MaxEnt
default parameters were adjusted by the ENMeval package. Optimized
MaxEnt models were used in analyzing potential geographical
distributions under current and future climatic conditions (the 2050s
and 2070s) and dominant environmental variables influencing their
geographic distribution. The results provided reference for
implementation of long-term conservation and management approaches for
the species. The results showed the area of the total suitable habitat
for \emph{Satyrium ciliatum} (\emph{S. ciliatum} ) in China is 32.51 \selectlanguage{ngerman}×
10\textsuperscript{4}km\textsuperscript{2}, the total suitable habitat
area for\emph{Satyrium nepalense} (\emph{S. nepalense} ) in China is
61.76 × 10\textsuperscript{4} km\textsuperscript{2}, and the area of the
total suitable habitat for\emph{Satyrium yunnanense}(\emph{S.
yunnanense} ) in China is 89.73 ×
10\textsuperscript{4}km\textsuperscript{2} under current climatic
conditions. The potential suitable habitat of \emph{Satyrium} is mainly
distributed in Southwest China. The major environmental variables
influencing the geographical distribution of \emph{S. ciliatum} were
isothermality (bio3), temperature seasonality (bio4), and mean
temperature of coldest quarter (bio11). Environmental variables such as
isothermality (bio3), temperature seasonality (bio4), and precipitation
of coldest quarter (bio19) affected the geographical distribution of
\emph{S. nepalense} ; and environmental variables such as isothermality
(bio3), temperature seasonality (bio4), and lower temperature of coldest
month (bio6) affected the geographical distribution of \emph{S.
yunnanense} . The distribution range of\emph{Satyrium} was extended as
global warming increased, showing emissions of greenhouse gases with
lower concentration (SSP1-2.6) and higher concentration (SSP5-8.5).
According to the study, the distribution of suitable habitat will shift
with a change to higher elevation areas and higher latitude areas in the
future.

\textbf{Keywords:} \emph{Satyrium} , MaxEnt, potential suitable habitat,
environmental variables.

\textbf{INTRODUCTION}

Global warming is one of the crucial environmental problems facing the
world today (Bayer et al., 2021). The global temperature has risen by
about 1\selectlanguage{english} in the past century, especially in the past 30 years, causing
some plant species to move to higher elevation and higher latitude
areas, as the Fifth Assessment Report (AR5) of the United Nations
Intergovernmental Panel on Climate Change reported (Allen et al., 2014).
Climate change in the future will affect the distribution range of
species, resulting in a loss of biodiversity and the extinction of
endangered species (Bellard et al., 2012). After climate change, the
prediction of suitable habitats of species will be considered important
in the future (Kumar et al., 2021).

In research on the geographical distribution range of plants affected by
climate change, the species distribution model (SDM) uses distribution
data of species and environmental variables in species habitats to
predict the basic niche of species (Elith \& Leathwick, 2009). These
data are combined with environmental data in different periods for
simulating the potential distribution areas of species in those periods
(Araujo \& Peterson, 2012). Among the many algorithms used in modeling
species distribution, the maximum entropy approach (MaxEnt) has
demonstrated fast modeling, wide use, high accuracy, and stability even
with small sample sizes (Merow et al., 2013; Pearson et al., 2007). It
can better anticipate the possible distribution of species compared with
other SDMs (Phillips et al., 2006). If the MaxEnt model and ArcGis
predict potential species distributions and biodiversity risks, relative
strategies can be established to reduce climate change's negative
influence on global biodiversity.

\emph{S} a\emph{tyrium} (Orchidaceae) is a rare and endangered plant
genus with about 92 species---mostly found in Africa, with five species
found in Madagascar alone, and four species found in Asian countries
(Mahendran \& Bai, 2009). Only three species have been found in China
alone (all of which are endemic in China) (Cun, 2005). They are mostly
distributed throughout Southwest China. \emph{Satyrium} is also used for
traditional herbal medicinal purposes; for example, Greek medics used
\emph{Satyrium} ~tubers as aphrodisiacs (Teoh, 2016). Traditional
health-care centers in India use the tubers of \emph{S} a\emph{tyrium
nepalense} (\emph{S. nepalense} ) to make energy tonics and cure
different types of fevers (Mishra et al., 2018). The number of
\emph{Satyrium} resources has been rapidly diminishing. Its population
has a fragmented distribution, and it is in a rare and endangered status
because of the value of herbal medicine and the serious deterioration of
the ecological environment in recent times (Mahendran \& Bai, 2009).
Changes in climate will result in changes in the biological phenology
period. This in turn will result in changes in the geographical
distribution of species and an acceleration in the rate of species
extinction. Therefore, it is necessary to adequately understand the
changing trends of the geographical distribution of species under
climate change conditions and to develop relative protection strategies.

Geographical distributions of three species from \emph{Satyrium} genus
were predicted by employing the MaxEnt model: (1) In current climatic
conditions, the geographical distributions of \emph{Satyrium} species in
China and the relationships between these distributions and
environmental variables were studied. (2) The main environmental
variables limiting their potential geographical distribution were
outlined. (3) The potential geographical distribution and shifting
trends of the centroid of suitable habitats in China were predicted in
future climate change scenarios. This study could offer a scientific
foundation for the suitable protection and use of \emph{Satyrium} .

\textbf{2 MATERIALS AND METHODS}

\textbf{2.1 Data of species occurrence}

\textbf{FIGURE 1} Distribution records of \emph{Satyrium ciliatum}
,\emph{Satyrium nepalense} , and \emph{Satyrium yunnanense} in China

Distribution records of \emph{Satyrium ciliatum} , \emph{Satyrium
nepalense} , and \emph{Satyrium yunnanense} in China were obtained from
the National Plant Specimen Information Infrastructure
(http://www.nsii.org.cn/), Chinese Virtual Herbarium
(http://www.cvh.ac.cn/), and the Global Biodiversity Information
Facility (https: www.gbif.org/). Geographical coordinates were not
recorded in detail at some distribution points. Thus, Google Earth
(http://ditu.google.cn/) was used to obtain the geographical coordinates
of the points. Points with repeated latitude and longitude and missing
points with latitude and longitude were deleted. A total of 44, 20, and
16 distribution records of \emph{S. ciliatum} , \emph{S. nepalense} ,
and\emph{S. yunnanense} were gathered and used to run the MaxEnt model
simulation (Figure 1)\emph{.}

\textbf{2.2 Environmental variables}

The World Climate Database (http://www.worldclim.org/) was used to
download current (1970-2000) and future climate data (2050: 2041-2060,
2070: 2061-2080), including 19 bioclimatic variables with a resolution
of 2.5'. Future climate data (2050: 2041-2060, 2070: 2061-2080) were
carried out by the BCC-CSM2-MR climate system model, which the National
Climate Center developed (Zhou et al., 2021). New future pathways based
on socioeconomic assumptions are called shared socioeconomic pathways
(SSPs), which describe the various levels of socioeconomic development
used in the study (Riahi et al., 2017). The SSPs include the
high-forcing scenario (SSP5-8.5), medium-forcing scenario (SSP2-4.5),
and low-forcing scenario (SSP1-2.6). High and low emission scenarios
were represented by SSP5-8.5 and SSP1-2.6 in the study, respectively.
Topographic parameters (altitude, slope, and aspect) were downloaded
from World Climate data (http://www.worldclim.org/) with a resolution of
2.5'.

\textbf{TABLE 1 Environmental variables selected in the MaxEnt model}

It is necessary to analyze environmental variables before they can be
used for niche simulation calculations to prevent the multicollinearity
of the variables from causing overfitting of the model, because many
bioclimatic variables are spatially related (Graham, 2003). ArcGIS10.2
was employed to examine the relationships among all the environmental
variables. If the correlation coefficient of both variables was greater
than or equal to 0.8, we removed the relevant environment variables with
a lower contribution rate (Pan et al., 2020). A total of 10, 10, and 9
environmental variables based on the MaxEnt model analysis and the
Spearman coefficient method were used to predict \emph{S. ciliatum}
,\emph{S. nepalense} , and \emph{S. yunnanense} distributions in China,
respectively (Table 1).

\textbf{2.3 Optimization of model parameters and model building}

The feature combination (FC) and regularization multiplier (RM) were
adjusted by the ENMeval package in R 4.0.2 software (Yan et al., 2021).
There are five FCs in the MaxEnt model: linear, quadratic, product,
threshold, and hinge (Phillips et al., 2006). The MaxEnt default
parameters are RM = 1, FC = LQHPT. To optimize the model, we set RM to
0.5-4, increased 0.5 each time, and adopted 8 regularization and 6 FCs,
namely, L, LQ, H, LQH, LQHP, and LQHP (Li et al., 2020). The ENMeval
data package was used to test the 48 parameter combinations and the
model's fitting and complexity according to the Delta in Akaike
information criterion models Delta AICc and AUC.diff (Zhao et al, 2021).

Data of \emph{Satyrium} species occurrence and each environmental
variable were imported into the MaxEnt model, and MaxEnt software
(http://www.cs.princeton.edu/, version 3.4.1) was employed for
simulation. To validate the model's overall predictive performance, 75\%
of occurrence records were used for training and the remaining 25\% were
used for testing (Fielding \& Bell, 1997). An average of 10 repetitions
was the final output result. In the end, the main environmental
variables affecting \emph{Satyrium} species' distribution were evaluated
based on the jackknife test results and the contribution rate of
environmental variables. Values from the receiver operating
characteristic (ROC) and true skill statistic (TSS) were applied to
evaluate the model's accuracy (Jiang et al., 2018; Xu et al., 2019). It
has been shown that model simulation results are excellent when the
values of the area under the curve (AUC) and TSS are \textgreater{} 0.9
(Janitza et al., 2013; McIntyre et al., 2017; Phillips et al., 2006).

\textbf{2.4 Classification of suitable habitat}

We imported the MaxEnt model outputs into ArcGIS v.10.6 to convert them
into raster data using the conversion tool. Additionally, suitable
\emph{Satyrium} species habitats were determined by a reclassification
tool (Yan et al., 2021) and placed into four groups: highly suitable
habitat (0.60-1.00), moderately suitable habitat (0.40-0.60), poorly
suitable habitat (0.20-0.40), and unsuitable habitat (0-0.20) (Yang et
al., 2013).

\textbf{2.5 Core distributional shifts}

Under current and future climate circumstances, the SDM toolbox was used
to calculate shifting trends in the area of suitable habitat based on
Python, as well as changes in the centroid of the area of suitable
habitat (Etherington, 2011). The toolbox is a Python-based GIS
application (Brown et al., 2017). Using the toolbox, the researchers
calculated shifting trends in areas of suitable habitats and compared
the centroids of the regions between current and future climate
conditions. The study provides information about the core shifts and
distributions of \emph{S. ciliatum} , \emph{S. nepalense} , and \emph{S.
yunnanense} . These species' distributional changes were reduced to a
single centroid (central) point, and a vector file was constructed to
show the amount and direction of expected change over time. Finally, we
examined how the centroid changed with different SDMs to determine
whether there were any distribution shifts.

\textbf{3 Results}

\textbf{3.1 Model optimization and its accuracy}

\textbf{TABLE S1 Evaluation results of MaxEnt model under different
parameter setting of \emph{Satyrium ciliatum}, \emph{Satyrium
nepalense}, \emph{Satyrium yunnanense}}

For \emph{S. ciliatum} , under the MaxEnt default parameter, the RM = 1,
FC = LQPHT, and Delta AICc = 26.990. When RM = 0.5, FC = LQ, and Delta
AICC = 0, the model is optimal (Table S1). Therefore, RM = 0.5 and FC =
LQ were selected as the model's final parameters (Figure 2). Under
modified parameters, the average values of AUC and TSS indicated an
accurate prediction result with values of 0.994 and 0.939 (Figure 3).
For \emph{S. nepalense} , under the MaxEnt default parameter, RM = 1, FC
= LQPHT, and Delta AICc = 217.062. When RM = 0.5, FC = LQH, Delta AICC =
0, the model is optimal (Table S1). Therefore, RM = 0.5 and FC = LQH
were selected as the model's final parameters (Figure 2). Under the
modified parameters, the average values of AUC and TSS indicated an
accurate prediction result with values of 0.941 and 0.890 (Figure 3).
For \emph{S. yunnanense} , under the MaxEnt default parameter, RM = 1,
FC = LQPHT, and Delta AICc = 40.356. When RM = 1.5, FC = LQ, Delta AICC
= 0, the model is optimal (Table S1). Therefore, RM = 1.5 and FC = LQ
were selected as the final parameters of the model (Figure 2). Under the
modified parameters, the average values of AUC and TSS indicated an
accurate prediction result with values of 0.981 and 0.894 (Figure 3).

\textbf{FIGURE 2 Evaluation metrics of MaxEnt generated by ENMeval}

\textbf{FIGURE 3 Model evaluation results of AUC value and TSS value}

\textbf{3.2 Important environmental variables}

\textbf{FIGURE S1} Jackknife test of important environment variables
for\emph{Satyrium ciliatum} , \emph{Satyrium nepalense} , and
\emph{Satyrium yunnanense}

The jackknife test results indicated that the top three environmental
variables with the largest effect on regularized training gains were as
follows: isothermality (bio3), temperature seasonality (bio4), and mean
temperature of coldest quarter (bio11) for \emph{S. ciliatum} ;
isothermality (bio3), temperature seasonality (bio4), and precipitation
of the coldest quarter (bio19) for \emph{S. nepalense} ; isothermality
(bio3), temperature seasonality (bio4), and lower temperature of coldest
month (bio6) for \emph{S. yunnanense} when modeling with a single
environmental variable (Figure S1). The contribution rate of
environmental variables could also be used to evaluate each
environment's importance. The contribution rate of each variable was
combined with the distribution of \emph{Satyrium} species (Table 1).
Isothermality (bio3, 52\%) and temperature seasonality (bio4, 12.7\%)
for \emph{S. ciliatum} reached 64.7\%. Isothermality (bio3, 61.8\%) and
temperature seasonality (bio4, 14\%) for \emph{S. nepalense} reached
75.8\%, much greater than the other contribution rates. Isothermality
(bio3, 28.6\%) and lower temperature of coldest month (bio6, 54.3\%) for
\emph{S. yunnanense}reached 82.9\%. The major environmental variables
influencing the geographical distribution of \emph{S. ciliatum} were
isothermality (bio3), temperature seasonality (bio4), and mean
temperature of coldest quarter (bio11). Environmental variables like
isothermality (bio3), temperature seasonality (bio4), and precipitation
of coldest quarter (bio19) affected the geographical distribution of
\emph{S. nepalense} ; and environmental variables like isothermality
(bio3), temperature seasonality (bio4), and lower temperature of coldest
month (bio6) affected the geographical distribution of \emph{S.
yunnanense} .

\textbf{FIGURE S2} Probability of response curves for presence
of\emph{Satyrium ciliatum, Satyrium nepalense,} and \emph{Satyrium
yunnanense}

The response curve was used to calculate the thresholds (existence
probability \textgreater{} 0.5) for the main bioclimatic parameters
(Figure S2) (Guo et al., 2017). For \emph{S. ciliatum} , isothermality
(bio3) ranged from 44 to 51.5, temperature seasonality (bio4) ranged
from 420 to 580, and mean temperature of coldest quarter (bio11) ranged
from 1\selectlanguage{english} to 13\selectlanguage{english}. For \emph{S. nepalense} , isothermality (bio3) ranged
from 44 to 53, temperature seasonality (bio4) ranged from 400 to 580,
and precipitation of coldest quarter (bio19) ranged from 25 to 80 mm.
For \emph{S. yunnanense} , isothermality (bio3) ranged from 41 to 53,
temperature seasonality (bio4) ranged from 300 to 600, and lower
temperature of coldest month (bio6) ranged from -6 to 6\selectlanguage{english}.

\textbf{3.3 Current potential distributionsof \emph{S. ciliatum, S.
nepalense,} and \emph{S. yunnanense}}

\textbf{FIGURE 4} Potential current and suitable habitats for
\emph{Satyrium ciliatum} ,\emph{Satyrium nepalense} , and \emph{Satyrium
yunnanense} in China

As Figure 4 shows, \emph{S. ciliatum} is mainly distributed in Southwest
China. Highly suitable habitats are located at the border of Yunnan and
Sichuan. Eastern Yunnan and Southern Sichuan have moderately suitable
habitats. Poorly suitable habitats cover a broad area, primarily in
Northern and Central Yunnan, Southern Tibet, Western Sichuan, and
Western Guizhou. There is also a sporadic distribution in Western
Guangxi. \emph{S. nepalense} is mainly distributed in Southwest China.
Highly suitable habitats are distributed in Central Yunnan. Western and
Eastern Yunnan, as well as Southern Sichuan, have moderately suitable
habitats. Poorly suitable habitats are located in Southern Yunnan,
Western and Southern Sichuan, Southern Tibet, Western Sichuan, and
Western Guangxi. \emph{S. yunnanense} is mainly distributed in Southwest
China. Highly suitable habitats are distributed in Yunnan. Moderately
suitable habitats are located at the border of Yunnan, Guizhou, and
Sichuan. Poorly suitable habitats are distributed in Western Guizhou,
Central Sichuan, Southern Shaanxi, Western Henan, Southern Fujian,
Southern Tibet, and Western Guangxi. The total suitable area for\emph{S.
yunnanense} is more than that of \emph{S. nepalense} and\emph{S.
ciliatum} according to the mentioned climatic conditions, and these
areas account for 9.35\%, 6.43\% and 3.39\%, respectively, of the total
land area of China (960x10\textsuperscript{4}km\textsuperscript{2}).

\textbf{3.4 Potentially suitable climatic distributions of \emph{S.
ciliatum, S. nepalense,} and \emph{S. yunnanense} in the future}

\textbf{FIGURE 5} Potential distributions of \emph{Satyrium ciliatum}
,\emph{Satyrium nepalense} , and\emph{Satyrium yunnanense} under future
climatic scenarios in China

Figure 5 shows the potential distributions of \emph{S. ciliatum}
,\emph{S. nepalense} , and \emph{S. yunnanense} in 2050 and 2070 based
on two different shared SSPs (SSP1 - 2.6 and SSP5 - 8.5). A suitable
habitat for \emph{S. ciliatum} will be found in parts of Sichuan, parts
of Yunnan, Southern Tibet, and the border between Chongqing and Sichuan.
The order of total suitable area of different climate models of \emph{S.
ciliatum} was as follows: 2070SSP5 - 8.5 (69.46 x
10\textsuperscript{4}km\textsuperscript{2}) \textgreater{} 2050SSP5 -
8.5 (51.83 x 10\textsuperscript{4} km\textsuperscript{2}) \textgreater{}
2050SSP1 - 2.6 (49.36 x 10\textsuperscript{4} km\textsuperscript{2})
\textgreater{} 2070SSP1 - 2.6 (48.1 x 10\textsuperscript{4}
km\textsuperscript{2}) \textgreater{} Current (32.51 x
10\textsuperscript{4}km\textsuperscript{2}). The area of suitable
habitat in the future was increased compared with the area of highly
suitable habitat in current climatic conditions. The order of highly
suitable area of different climate models of \emph{S. ciliatum} was as
follows: 2070SSP5 - 8.5 (21.94 x 10\textsuperscript{4}
km\textsuperscript{2}) \textgreater{} 2050SSP5 - 8.5 (12.19 x
10\textsuperscript{4} km\textsuperscript{2}) \textgreater{} 2050SSP1 -
2.6 (9.92 x 10\textsuperscript{4}km\textsuperscript{2}) \textgreater{}
2070SSP1 - 2.6 (9.74 x 10\textsuperscript{4}km\textsuperscript{2})
\textgreater{} Current (3.27 x
10\textsuperscript{4}km\textsuperscript{2}).

For \emph{S. nepalense} , the suitable habitat is mostly located in
parts of Yunnan, parts of Sichuan, Southern Tibet, and along the border
between Sichuan and Chongqing. The order of total suitable area of
different climate models of \emph{S. nepalense} was as follows: 2070SSP5
- 8.5 (97.52 x 10\textsuperscript{4} km\textsuperscript{2})
\textgreater{} 2050SSP1- 2.6 (76.42 x 10\textsuperscript{4}
km\textsuperscript{2}) \textgreater{} 2070SSP1 - 2.6 (75.15 x
10\textsuperscript{4} km\textsuperscript{2}) \textgreater{} 2050SSP5 -
8.5 (74.75 x 10\textsuperscript{4}km\textsuperscript{2}) \textgreater{}
Current (61.76 x 10\textsuperscript{4}km\textsuperscript{2}). The area
of suitable habitat in the future was increased compared with the area
of highly suitable habitat under current climatic conditions. The order
of highly suitable area of different climate models of \emph{S.
nepalense} was as follows: 2070SSP5 - 8.5 (16.60 x 10\textsuperscript{4}
km\textsuperscript{2}) \textgreater{} 2050SSP1 - 2.6 (13.07x
10\textsuperscript{4} km\textsuperscript{2}) \textgreater{} 2050SSP5 -
8.5 (11.95x 10\textsuperscript{4} km\textsuperscript{2}) \textgreater{}
2070SSP1 - 2.6 (11. 51x 10\textsuperscript{4}km\textsuperscript{2})
\textgreater{} Current (8.76x 10\textsuperscript{4}
km\textsuperscript{2}).

For \emph{S. yunnanense} , the suitable habitat is mostly located in
Yunnan, the middle of Sichuan, Southern Tibet, and the border between
Sichuan and Chongqing. The order of total suitable area of different
climate models of \emph{S. yunnanense} was as follows: 2070SSP5 - 8.5
(163.3 x 10\textsuperscript{4} km\textsuperscript{2}) \textgreater{}
2050SSP5 - 8.5 (124.33 x 10\textsuperscript{4}km\textsuperscript{2})
\textgreater{} 2050SSP1 - 2.6 (123.37 x
10\textsuperscript{4}km\textsuperscript{2}) \textgreater{} 2070SSP1 -
2.6 (111.33 x 10\textsuperscript{4} km\textsuperscript{2})
\textgreater{} Current (89.73 x 10\textsuperscript{4}
km\textsuperscript{2}). The area of suitable habitat in the future was
increased compared with the area of highly suitable habitat under
current climatic conditions. The order of highly suitable area of
different climate models of \emph{S. yunnanense} was as follows:
2070SSP5 - 8.5 (25.12 x 10\textsuperscript{4} km\textsuperscript{2})
\textgreater{} 2050SSP1 - 2.6 (20.91 x 10\textsuperscript{4}
km\textsuperscript{2}) \textgreater{} 2070SSP1 - 2.6 (19.58 x
10\textsuperscript{4} km\textsuperscript{2}) \textgreater{} 2050SSP5 -
8.5 (18.25 x 10\textsuperscript{4}km\textsuperscript{2}) \textgreater{}
Current (16.64 x 10\textsuperscript{4} km\textsuperscript{2}).

\textbf{3.5 Future changes in suitable habitat areas in highly suitable
centroid distributions.}

\textbf{FIGURE 6 Alterations to possible geographical distributions
of\emph{Satyrium ciliatum}, \emph{Satyrium nepalense}, and
\emph{Satyrium yunnanense} in climate change scenarios}

In regard to future changes in suitable habitat areas, both the gained
and lost areas of \emph{Satyrium} will increase in emission
concentrations; however, the gained area will be larger than the lost
area (Figure 6). According to SSP5 - 8.5, the number of increased and
decreased areas were the highest compared with the current simulation
results. For \emph{S. ciliatum} , by the 2050s, the area of total
suitable habitat would increase to 9.65 x
10\textsuperscript{4}km\textsuperscript{2} (SSP1 - 2.6) and 12.63 x
10\textsuperscript{4}km\textsuperscript{2} (SSP5 - 8.5), and the gained
territory would be in Southern Tibet and Northwest Sichuan. By the
2070s, the areas would increase to 9.56 x 10\textsuperscript{4}
km\textsuperscript{2} (SSP1 - 2.6) and 26.33 x 10\textsuperscript{4}
km\textsuperscript{2} (SSP5 - 8.5), and the new territory would be in
Southern Tibet, Northwest Sichuan, and Northern Yunnan. The decreased
habitat would mainly be distributed in Tibet.

For \emph{S. nepalense} , by the 2050s, the area of total suitable
habitat would increase to 13.43 x
10\textsuperscript{4}km\textsuperscript{2} (SSP1 - 2.6) and 12.42 x
10\textsuperscript{4}km\textsuperscript{2} (SSP5 - 8.5), and the gained
territory would be in Southern Tibet and Northwest Sichuan. By the
2070s, the areas would increase to 12.70 x 10\textsuperscript{4}
km\textsuperscript{2} (SSP1 - 2.6) and 36.86 x 10\textsuperscript{4}
km\textsuperscript{2} (SSP5 - 8.5), and the gained territory would be in
Southern Tibet, Northwest Sichuan, and Southwest Gansu. The decreased
areas would be fewer, and they would mainly be located in parts of Tibet
and Yunnan.

For \emph{S. yunnanense} , by the 2050s, the area of total suitable
habitat would increase to 34.15 x
10\textsuperscript{4}km\textsuperscript{2} (SSP1 - 2.6) and 41.42 x
10\textsuperscript{4}km\textsuperscript{2} (SSP5 - 8.5), and Southern
Shaanxi, Southern Shanxi, Northern Henan, and Central Shandong would be
the new provinces. By the 2070s, the areas would increase to 26.87 x
10\textsuperscript{4}km\textsuperscript{2} (SSP1 - 2.6) and 89.29 x
10\textsuperscript{4}km\textsuperscript{2} (SSP5 - 8.5), and
Southwestern Xinjiang, Southern Tibet, Northern Sichuan, Southern
Shaanxi, Southern Gansu, Northern Henan, and Central Shandong would gain
new territory. The decreased areas would mainly be distributed in parts
of Guangxi, Fujian, Chongqing, and Yunnan.

\textbf{3.6 Core distributional shifts}

\textbf{FIGURE 7 The centroid distributional shifts for \emph{Satyrium
ciliatum}, \emph{Satyrium nepalense}, and \emph{Satyrium yunnanense}}

Figure 7 shows that the centroid of \emph{S. ciliatum} 's current
habitat is located in Central Yunnan Province at 99deg84'E and 27deg27'N.
Under SSP1-2.6, the centroid of suitable habitat will shift to 99deg35'E
and 27deg87'N in the 2050s, and 98deg69'E and 27deg94'N in the 2070s. Under
SSP5-8.5, the centroid of the future suitable region will be located at
98deg86'E and 28deg01'N in the 2050s. Under SSP5-8.5, the acceptable area's
centroid will relocate to 98deg15'E and 28deg91'N in the 2070s. In summary,
we can see that the core distribution of \emph{S. ciliatum} will shift
to the northwest under both future emission trajectories (SSP1-2.6,
SSP5-8.5). The centroid of the existing habitat for \emph{S.
nepalense}is located in Southern Yunnan Province at 99deg28'E and 27deg15'N.
Under SSP1-2.6, the centroid of the suitable region will shift to
98deg79'E and 27 deg69'N in the 2050s and 98deg38'E and 27 deg69'N in the 2070s.
Under SSP5-8.5, the centroid of the future suitable region will be
located at 98deg82'E and 27deg69'N in the 2050s. The centroid of the
suitable region will relocate to 97deg67'E and 28 deg56'N in the 2070s under
SSP5-8.5. The centroid of the current habitat for \emph{S. yunnanense}
is located in south Sichuan province at 102deg67'E and 27deg75'N. Under
SSP1-2.6, the centroid of the suitable area will shift to 103deg73'E and
29 deg02'N in the 2050s, and 102deg22'E and 28 deg94'N in the 2070s. According
to SSP5-8.5, the suitable area's centroid will relocate to 101deg91'E and
29 deg68'N by the 2050s, then to 100deg05'E and 30 deg94'N in the 2070s.

\textbf{4 DISCUSS}

The \emph{Satyrium} plant species are important resources, but they are
endangered and rare (Mishra \& Saklani, 2012). MaxEnt models were
applied to predict current and future potential distribution areas
of\emph{S. ciliatum} , \emph{S. nepalense} , and \emph{S.
yunnanense.}Generally, the evaluation index of prediction model accuracy
is the ROC curve, which has the following advantage: the AUC value is
not affected by the threshold and can be used for the comparison of
various models (Fielding et al., 1997). However, further research shows
that it is inaccurate to evaluate accuracy only by AUC value because it
cannot reflect wrong spatial distribution information, and calculate the
omission rate and error rate by a single method (Lobo et al., 2008;
Peterson et al., 2008). Nowadays, the information measurement index AUC
is used to select the ideal setting parameters of MaxEnt (Muscarella et
al., 2014); the method of combining omission rate and AUC value is used
to obtain the best parameters (Radosavljevic \& Anderson., 2014); TSS
value is used to evaluate accuracy (Bedia et al., 2011); and multiple
parameters are set to select the optimal result (Elith et al., 2010;
Warren et al., 2011). TSS can accurately correct the overall accuracy of
the model without being affected by the size of the species validation
data set and without depending on the model threshold, therefore, AUC
and TSS values are used as evaluation indexes in this prediction (Bedia
et al., 2011).

According to the four different future scenarios, the suitable habitat
area for \emph{Satyrium} in China will be extended in the 2050s and
2070s, moving slowly to higher altitude areas in the northwest side of
China, where the humidity and habitat are suitable for the growth of
the\emph{Satyrium} species. These scenarios are consistent with the
results of a study on the \emph{Houttuynia cordata} Thunb (Ceercao)
habitat suitability. In the study, the suitable distribution area in the
high emission scenario was greater than that in the low emission
scenario (Liu et al., 2021). The increased precipitation in China in the
SSP5 - 8.5 emission scenario was higher than in the low concentration
emission scenario (Yue et al., 2021). This shows the increased
precipitation in the high concentration emission scenario can solve the
limitations of precipitation on distribution of species, and the
increased precipitation in the low concentration emission scenario
cannot reduce or solve the limitations of precipitation on the
distribution of species. This may also be the reason the most suitable
habitat area and the increasing area of the \emph{Satyrium} species are
the largest in the high concentration emission scenario, particularly
the SSP5-8.5 scenario. The prediction shows that the upcoming future
will be the most suitable for the growth of the \emph{Satyrium} species.
It shows that, generally, the suitable habitat is stable and the species
will not face extinction due to climate change. This study shows the new
areas for species distribution. The findings are in agreement with those
of previous studies, indicating that in some regions, habitat
suitability of plant species improves under climate change conditions
(Feng et al., 2020; Li et al., 2019). The distribution of species under
future climatic scenarios predicts that the area of species covering
20\% of the earth's surface will face the risk of extinction, and about
15\%--37\% of the species will be endangered (Thomas et al., 2004). One
of the reasons for the endangered status of \emph{Satyrium} is that
most\emph{Satyrium} species are hermaphroditic. Some species rely on
birds and other animals for long-distance transmission, the species
population is small, and the quality of seed development is low because
of long-distance pollination (Johnson et al., 2011). This study's
results provide a reliable basis for protecting \emph{Satyrium} . People
should effectively protect its native habitat and population. Nature
reserves for \emph{Satyrium} should be set up now according to the
prediction results, and they should be strengthened to minimize
human-made damage.

The results show that isothermality (bio3) and temperature seasonality
(bio4) are the common factors in the three species, indicating that the
main environmental variable affecting the appropriate distribution
of\emph{Satyrium} is temperature. Zhao et. al. (2021) used the MaxEnt
model to predict the potential suitable habitat of Chinese fir in China.
Their results showed that temperature factors are more important than
precipitation factors influencing habitat suitability for Chinese fir.
Lou et al. (2015) used regression models to predict tea crop yield
responses to climate change, finding that temperature plays a
significant role in tea production and quality. Environmental variables
like temperature can estimate plant distribution patterns (Deblauwe et
al., 2016; Wang, R. et al., 2021). In regard to \emph{S. nepalense} ,
precipitation is one of the environment variables influencing its
geographic distribution.\emph{Satyrium} is a perennial terrestrial
orchid distributed in Southwest China (Deng et al., 2019). Terrestrial
orchids are typically cold tolerant, live in low-temperature
environments, and are highly distributed in areas with large amounts of
annual rainfall (Phillips et al. 2011; Poff et al., 2016). More studies
have shown that the dominant variables restricting geographical
distribution of plants are energy supply, plenty of water, and cold
tolerance (Zhou \& Wang, 2000). Based on the prediction of the MaxEnt
model, the area of the current potential suitable habitat of
\emph{Satyrium} is mainly located in Southwest China, which has a
typical subtropical monsoon climate (Luo et al., 2021). Previous studies
have indicated that precipitation is the main variable affecting plant
growth, regeneration, nutrient cycles, and community productivity in
different habitats (Miranda et al., 2011). Climate change can determine
geographical distribution of species, and geographical distribution of
species can respond to changes in climate (Warren et al., 2021). Global
warming has changed the structures of terrestrial ecosystems, which have
changed the habitats and geographical distribution functions of species
in turn (Ghini et al., 2012). For instance, climate, topography, soil,
human disturbance, and spatial constraints are significant to the
distribution of many different spatial scales (Eiserhardt et al., 2011;
Parisien et al., 2009). The environmental data used in the study
included bioclimatic variables and topographic variables in WorldClim.
We did not study soil type, land use, human activities, biological
interaction, and other factors that influence the distribution
of\emph{Satyrium} . The more factors that are included, the more
accurate the prediction will be. Therefore, other factors affecting the
potential suitable habitat of \emph{Satyrium} should be studied in the
future, with different niche prediction models predicting areas of
potential suitable habitats. This will make the prediction of results
more reliable.

\textbf{5 Conclusion}

In the study, the potential suitable \emph{Satyrium} habitat in China
was predicted based on the MaxEnt model optimized by the ENMeval
package. Temperature was the common variable in the three species. In
regard to \emph{S. nepalense} , precipitation is one of environment
variables influencing its geographic distribution. The potential
geographical distribution of \emph{Satyrium} under current climatic
conditions is primarily on the southwest side of China.\emph{Satyrium}
's highly suitable habitat area, moderately suitable habitat area, and
total suitable habitat area were predicted to increase under future
climate change scenarios. The highly suitable habitat area of
\emph{Satyrium} showed a tendency to shift to areas with higher
elevation. In regard to future changes in suitable habitat areas, both
the gained and lost areas of\emph{Satyrium} will increase in emission
concentrations; however, the gained area will be larger than the lost
area. These results can provide theoretical guidance for protecting
endangered plants in China and offer further reference for biodiversity
protection in China.

\textbf{Competing Interests Statement}

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

\textbf{Data Availability Statement}

All authors agreed to deposit data from this manuscript in a public
repository. Data were submitted to Dryad (the DOI number is
https://doi.org/10.5061/dryad. k98sf7m6v).

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