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\begin{document}

\title{Predicting the potential distribution of 12 threatened medicinal plants
on the Qinghai-Tibet Plateau (QTP), with a maximum entropy model}



\author[1]{Yang Lucun}%
\author[2]{Xiaofeng Zhu}%
\author[1]{Wenzhu Song}%
\author[3]{Xingping Shi}%
\author[4]{Xiaotao Hang}%
\affil[1]{Northwest Institute of Plateau Biology Chinese Academy of Sciences}%
\affil[2]{Gande County Animal Disease Prevention and Control Center}%
\affil[3]{ Qing Hai West Copper Co.，LTD}%
\affil[4]{School of Geographical Sciences and Tourism, Zhaotong University}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Climate change is a vital driver of biodiversity patterns and species
distributions, understanding how organisms respond to climate change
will shed light on the conservation of endangered species. In this
study, the MaxEnt model was used to predict the potential suitable area
of 12 threatened medicinal plants in the QTP (Qinghai-Tibet Plateau)
under the current and future (2050s, 2070s) three climate scenarios
(RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically
suitable habitats for the threatened medicinal plants were primarily
found in the eastern, southeast, southern and some part of the central
regions on the QTP. Moreover, 25\% of the threatened medicinal plants
would have reduced suitable habitat areas within the next 30-50 years in
the different future global warming scenario. Among these medicinal
plants, FP (Fritillaria przewalskii) would miss the most habitat
(97.1\%), while the RAN (Rhododendron anthopogonoides) would miss the
least habitat (0.30\%). Nevertheless, 41.6\% of the threatened medicinal
plants showed an increase in their future habitat area because of their
physiological characteristics which are more adaptable to a wide range
of climate. The climatic suitable habitat for 50\% of the threatened
medicinal plants would migrate to higher altitudes or higher latitudes
regions. This study provides a data foundation for the conservation of
biodiversity and wild medicinal plants on the QTP.%
\end{abstract}%



\sloppy


\textbf{Predicting the potential distribution of 12 threatened medicinal
plants on the Qinghai-Tibet Plateau (QTP), with a maximum entropy model}

Lucun Yang\textsuperscript{1}, Xiaofeng Zhu\textsuperscript{2}, Wenzhu
Song\textsuperscript{1}, Xingping Shi\textsuperscript{3}, Xiaotao
Huang\textsuperscript{4*}

\textsuperscript{1}Qinghai Province Key Laboratory of Qinghai-Tibet
Plateau Biological Resources, \emph{Northwest Institute of Plateau
Biology, Chinese Academy of Sciences, Xining 810008, China} .

\textsuperscript{2}\emph{Gande County Animal Disease Prevention and
Control Center} , \emph{Gande, Qinghai814100, China} .

\textsuperscript{3} \emph{Qing Hai West Copper Co.,LTD} , \emph{Maqin,
Qinghai 814000, China}

\textsuperscript{4}\emph{School of Geographical Sciences and Tourism,
Zhaotong University, Zhaotong, Yunnan 657000, China}

\textsuperscript{*}Correspondence: Xiaotao Huang xthuang@ztu.edu.cn

\textbf{Abstract}

Climate change is a vital driver of biodiversity patterns and species
distributions, understanding how organisms respond to climate change
will shed light on the conservation of endangered species. In this
study, the MaxEnt model was used to predict the potential suitable area
of 12 threatened medicinal plants in the QTP (Qinghai-Tibet Plateau)
under the current and future (2050s, 2070s) three climate scenarios
(RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically
suitable habitats for the threatened medicinal plants were primarily
found in the eastern, southeast, southern and some part of the central
regions on the QTP. Moreover, 25\% of the threatened medicinal plants
would have reduced suitable habitat areas within the next 30-50 years in
the different future global warming scenario. Among these medicinal
plants, FP (\emph{Fritillaria przewalskii} ) would miss the most habitat
(97.1\%), while the RAN (\emph{Rhododendron anthopogonoides} ) would
miss the least habitat (0.30\%). Nevertheless, 41.6\% of the threatened
medicinal plants showed an increase in their future habitat area because
of their physiological characteristics which are more adaptable to a
wide range of climate. The climatic suitable habitat for 50\% of the
threatened medicinal plants would migrate to higher altitudes or higher
latitudes regions.

This study provides a data foundation for the conservation of
biodiversity and wild medicinal plants on the QTP.

\textbf{Keywords:} threatened medicinal plants; climate change; suitable
habitat; barycenter migration; Qinghai-Tibet Plateau (QTP)

\textbf{Introduction}

The interaction and relationship between plants and climate are hot
topics in the fields of botany, ecology, and geography (Jiang \& Ni.
2005; Wang \& Ni. 2005). Over the past 100 years from the Industrial
Revolution to the present, human activities have led to drastic changes
in the global climate. According to the fifth report of IPCC (the
Intergovernmental Panel on Climate Change), human activities have
emitted a large amount of greenhouse gases such as CO\textsubscript{2},
leading to an increase in global temperature. From 1880 to 2012, the
average global surface temperature increased by 0.85 , and it is
projected to increase by 0.3 to 4.8 \selectlanguage{ngerman}° C by the end of the 21st century
(2081\selectlanguage{english}-2100) compared to the base period (1986\selectlanguage{english}-2005) (Hartmann et al.
2013). Under the background of global climate change, climate warming in
the coming decades or even centuries may alter the structure and
function of terrestrial ecosystems by affecting the distribution of
organisms and vegetation composition.

Previous studies have shown that the extinction of some species was
resulted from climate change (Sharma et al. 2009), which could lead to
the reduction of the suitable distribution area of some species, habitat
fragmentation, and accelerated global biodiversity loss (Thomas et
al.2004). Endangered plants are a major component of biodiversity in a
region, and rare and endangered plants have a high extinction rate and
may face the risk of extinction in the coming period if their species
numbers continue to decline (Menges 1990). The geographical distribution
of species is one of the key geographical characteristics of species,
and each species has a specific distribution range in the world. The
drawing of species distribution maps is an important means of studying
species distribution, and plays a crucial part in analyzing the origin
and distribution law of species populations (Zhang \& Chen 2003). In
order to further protect global biodiversity and prevent further
destruction of ecosystems, it is necessary to systematically understand
the relationship between the potential geographical distribution areas
of species and climate change, as well as the potential geographical
distribution areas of species in future climate change scenarios, and
propose adaptive protection strategies.

Forecasting the potential distribution of species under current and
future climate conditions is mainly based on niche models (Leroy et al.
2013). Niche models can simulate the potential distribution of species
using the known distribution points and GIS-based environmental
variables, and predict the suitable distribution range and changes of
species under future climate change scenarios. At present, there are
various species distribution models based on ecological niches (Elith et
al. 2006), which have been widely used in predicting suitable
distribution areas for endangered plants. For example, the maximum
entropy model MaxEnt (Zhang et al. 2023; Zhang and Zhao 2001; Abdelaal
et al. 2019; Gao et al. 2022; Ma \& Zhang 2012), the ruleset genetic
algorithm model GARP (Li et al. 2023), and the BIOCLIM model (Semwal et
al. 2021) and so on. Among them, MaxEnt, the maximum entropy model, is
an intensively used prediction model in recent years, and its prediction
results are the most accurate. Even though the species distribution data
information and environmental variables in the distribution area are
incomplete, it can precisely predict the potential distribution area of
species (Phillips \& Dudik 2008; Warren \& Seifert 2011). In addition,
the stability of the model is good and the predicted results are
basically in line with the actual distribution of species (the average
AUC value is the largest).

The QTP (Qinghai-Tibetan Plateau) is of great significance for the
geographical environment pattern and climate change of China and Asia.
It is an important distribution area of terrestrial ecosystems in China
and also an important gene pool of biological species worldwide. The QTP
is recognized as the ''driving force'' and sensitive area of global
climate change, and its response to ''greenhouse gases'' is more
sensitive than other regions (Pan \& Li 1996; Yao et al.2020; Yao et
al.2012; Zhang et al. 2020). The QTP is one of the geographical regions
with the highest number of medicinal plants in China. There are over
2000 kinds of medicinal plants on the QTP, occupying approximately half
of the total number of medicinal plants in China. The unique
geographical location and climatic conditions of the QTP had actuated
the formation of endemic medicinal plants, and it also had considerably
enhanced the richness of medicinal species on the plateau. However, with
the continuous development of human society and the increase of
population, along with the vigorous development of Tibetan medicine, the
Tibetan medicine resources in the QTP have been arbitrarily exploited
and plundered for a long time, resulting in the destruction of medicinal
vegetation habitats, rapid shrinkage of medicinal plant resources, and
some species are on the brink of extinction. In addition, climate change
characterized by warming will have a significant impact on the natural
environment, ecosystems, and species distribution in this region
(Fitzpatrick et al. 2008; Descombes et al. 2015; Allen \& Lendemer
2016). In addition, climate change with warming as its main feature will
have a huge impact on the natural environment, ecosystem, and species
distribution in this region, and some endangered wild medicinal plant
resources will face greater threats. Therefore, understanding the
potential geographical distribution of threatened species and their
suitable habitat status is a prerequisite for effective conservation
work (Pearce \& Boyce 2006). However, in reality, the geographical
distribution data of many species is scarce, especially for endangered
medicinal plants in the QTP.

In this study, the distribution areas of suitable habitats for
threatened medicinal plants on the QTP were evaluated on spatial and
temporal scales by using the MaxEnt model. And the potential impacts of
global climate change on endangered medicinal plants on the QTP were
evaluated for the first time. The main purpose of this study were: (i)
identifying the key areas of suitable habitats for the threatened
medicinal plants on the QTP and priority areas for conservation; (ii)
estimating the influence of climate change on the suitable habitat of
the threatened medicinal plants on the QTP region; (iii) determining the
migration direction of suitable habitat for the threatened medicinal
plants on the QTP; (iv) comparing changes and trends in future suitable
habitat among the different threatened medicinal plants.

\textbf{2. Materials and Methods}

\textbf{2.1} \textbf{. Occurrence records of the threatened medicinal
plant on the QTP}

In this study, 12 native threatened medicinal plants on the QTP were
used by consulting the books and database such as ``Flora Qinghaiica'',
``Flora of Tibet'', ``List of rare and endangered plants in China'',
``Chinese Red List of species'', ``List of wild plants under State Key
Protection (the first batch)'', The IUCN red list of threatened species
(\emph{https://www.iucnredlist.org/}), China wild plant conservation
association,\emph{https://www.wpca.org.cn/bhml}. Then, these 12
medicinal plants were divided into Critically Endangered (CR),
Endangered (EN), Vulnerable (VU) and Near Threatened (NT) levels
according to the record of the International Union for Conservation of
Nature (IUCN) Red List of Endangered Species (Table 1). Since the NT
species is close to or likely to become a threatened category not long
in the future, it is included. Organism photographs in the flowering
stage of 12 native threatened medicinal plants on the QTP were shown in
Figure 1.

The date on the locations of 12 threatened medicinal plants were
acquired from field investigation reports from the last 30 years,
principally in the specimen libraries, e.g. Chinese Virtual Herbarium
(\emph{http://www.cvh.ac.cn/}), Global Biodiversity Information Facility
(https://www.gbif.org/), China National Specimen Information
Infrastructure (http://www.naii.org.cn), and the reports in literatures.
The total occurrence records for these 12 species are 38, 17, 34, 33,
110, 25, 59, 26,72, 64, 37 and 27, respectively (Table S1). We then used
ENMTools.pl (https://github.com/danlwarren/ENMTools) to clip occurrence
points in order to only one observation was reserved in each 30s grid
cell (according with the environment variable data below), to reduce the
sampling bias of the data. Finally, all the remaining occurrence records
were applied for the ecological niche modeling (ENM) (Table S2).

\textbf{2.2. Climate Data Collection and Environmental Variables}

The correlation and completeness of variables are the key elements of
constructing ENM. In this study, we chose three types of environmental
variables, including bioclimatic, soil variables and topographical
(Table 2). Nineteen bioclimatic and altitude variables were downloaded
from WorldClim 1.4 (https://world clim.org) for the current period
(1960-1990) at a spatial resolution of 30 arc-seconds (approximately 1
km). Another two topographical variables (slope and aspect) were
obtained from Digital Elevation Model using the 3D analyst tools in the
software ArcGIS 10.4. We downloaded the eight soil variables from the
National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/zh-hans/)
based on previous studies (Guo et al. 2019; Li et al. 2021; Ru 2006).
Furthermore, the old WorldClim version 1.4 (https://worldclim.org/) was
used to obtain projected future climate data. The average values of
representative concentration pathways (RCPs) under the future Community
Climate System Model (CCSM) were selected to predict the suitable area
changes in 2050 and 2070. RCPs include RCP4.5 and RCP6.0 (medium
greenhouse gas emissions), and RCP8.5 (high greenhouse gas emissions)
(Thomson et al. 2011). However, the available data on the other two
types of environmental variables (soil variables and topographical
variable) were lack for the future periods according with the same
periods. The 12 species are relatively less affected by topography and
soil because they are chiefly located in the mountain areas. Therefore,
we assumed that these two types of environment variables were constant,
as reported in previous studies (Evans et al. 2020; Lv et al. 2021;
Zhang et al. 2019).

In order to decrease the autocorrelation between the variable data,
ArcGIS software was applied to upload all of the variable data and to
execute Pearson correlation analysis on variables by multivariate and
band collection statistics (Narkis et al. 2017). For the factor of phase
coefficient \textbar{}R\textbar{} \textgreater{} 0.8 between variables
(Figure 2), on the basis of the contribution rate of each environmental
variable to the MaxEnt model and the replacement important value, the
variables with a larger contribution rate and replacement important
value give preference to take part in MaxEnt modeling and prediction, to
avoid overfitting (Hill et al.2012). Therefore, different variables were
used as the environmental variables for building the distribution model
of 12 threatened medicinal plants (Table S3).

\textbf{2.3. Optimization of Model Parameters}

The regularization multiplier and feature class are the two most
important parameters for establishing the species distribution model
using the MaxEnt version 3.4.4 software. Therefore, we adopted the Kuenm
package (https://github.com/marlonecobos/kuenm) to optimize these two
parameters in the R version 3.6.3 software (Cobos et al.2019). In the
modeling, we used 75\% of the data as the training set. A total of 1240
candidate models, with parameters reflecting all combinations of 40
regularization multiplier settings (from 0.1 to 4, the interval is 0.1),
and 31 feature class combinations were evaluated (Phillips et al. 2006;
Phillips \& Dudik 2008).

Model selection was prioritized by statistical significance (partial
ROC), predictive power (low omission rate), and complexity (AICc value).
First, candidate models were screened and those with statistical
significance were retained; second, the set of models were simplified
using the omission rate criterion (i.e., \textless{}5\% when possible);
finally, we selected the models with lowest delta AICc values
(\textless{}2) among the significant and low-omission candidate models
(Cobos et al. 2019). The obtained model of each species had different
regularization multiplier and feature class parameters (Table 3).

\textbf{2.4. Species Distribution Model}

On the base of the relevant parameter ahead, we established the
distribution model of the 12 threatened medicinal plants on QTP with
MaxEnt version 3.4.4 and projected it to future periods. In present
study, we used 75\% of the geographic distribution data as train and
25\% of the geographic distribution data to validate the model. Our
analysis set 10 replicates and 5000 bootstrap iterations, with default
settings for the other parameters (Phillips et al. 2006; Hazzi et al.
2018; Morales et al. 2017; Radosavljevic \& Anderson 2014). We used the
area under the receiving operator curve (AUC) to estimate model
performance (Allouche et al.2006; Swets 1988). The AUC ranged from 0 to
1. The model with AUC value greater than 0.9 was regarded as excellent
(Guisan \& Thuiller 2005).

Subsequently, we loaded the arithmetic results from MaxEnt into
ArcGIS10.4 to execute suitability classification and visualization and
thereby generate the potential distribution of 12 threatened medicinal
plants. It was vital to select an applicable threshold when shifting the
continuous species suitability prediction results into a Boolean
classification of suitable and unsuitable habitats. The maximum test
sensitivity plus specificity logistic threshold approach was proved to
be superior to other threshold division methods (Poirazidis et al.
2019). Then we used the maximum test sensitivity plus specificity
threshold as a threshold or ``cutoff'' value for each scenario, and
habitat suitability was divided into four classes: high suitability,
moderate suitability, low suitability and unsuitability (Table S4).

\textbf{2.5. Distribution barycenter migration of threatened medicinal
plants}

In the case of further investigating the dynamic migration paths of the
12 threatened medicinal plants, we calculated the centroids of the 12
threatened medicinal plants from their current distribution to their
future distribution by using a Python-based SDM toolbox (Brown 2014).
The analysis centralized the species distribution into an independent
central point and established a vector file depicting the magnitude and
direction of changes over time (Hu et al. 2015). We observed the
distributional shifts by tracking the centroid changes among different
SDMs.

\textbf{3. Results}

\textbf{3.1. Habitat distribution models for the threatened medicinal
plants of the QTP}

The ROC results showed that the average training AUC value for the
10-replicate runs in current and future surpassed 0.9 in all the
threatened medicinal plants on the QTP. Furthermore, the standard
deviation (SD) values of 10 repetition runs remained below 0.06 in all
the threatened medicinal plants on the QTP (Figure 3). These results
indicated that the prediction results of MaxEnt model were accurate and
reliable. Thus, we could conduct follow-up analysis.

The models for the present scenario properly predicted the known
distribution range of all the threatened medicinal plants on the QTP
(Figure 4). As far as CR medicinal plants was concerned, present
distribution model predictions result demonstrated that the most
suitable habitats for RAL (\emph{Rhodiola alterna} ) were similar with
the GL (\emph{Gentiana lhassica} ), both of which were located in
southern and southeast region of QTP, including Shigatse city and
Shannan city in Tibet; however, for RAL it was not distributed in the
Changdu city of Sichuan Province on the QTP (Figure 4A-B). In addition,
the GL had a larger area of high and medium suitable habitat than the
RAL (Table S5). The high suitable habitats of the EN medicinal plants,
being different to the CR medicinal plants, preferred to inhabit the
eastern region on the QTP (Figure 4C-D). These areas include Garze
Tibetan Autonomous Prefecture, Ngawa Tibtean Autonomous Prefecture and
Maerkang city in Sichuan Province. The high suitable habitats of the VU
medicinal plants showed different distribution ranges: FP
(\emph{Fritillaria przewalskii} ) (Golog Tibtean Autonomous Prefecture
in Qinghai Province, Hezuo city in Gansu Province and Maerkang city in
Sichuan Province) and RT (\emph{Rheum tanguticum} ) were located in the
eastern region on the QTP (Golog Tibtean Autonomous Prefecture in
Qinghai Province) (Figure 4E-F), while ABR (\emph{Arenaria brevipetala}
) mainly distributed in southeast and southern region on the QTP(Shannan
and Changdu city in Tibet, Garze Tibetan Autonomous Prefecture,
Liangshan yi autonomous prefecture and Ngawa Tibtean Autonomous
Prefecture in Sichuan Province) (Figure 4G), and FD (\emph{Fritillaria
delavayi} ) was found in southern region on the QTP (Garze Tibetan
Autonomous Prefecture and Liangshan yi autonomous prefecture in Sichuan
Province) (Figure 4H). It was worth noting that ABR (\emph{A.
brevipetala} ) had the largest area, 34.5 x
10\textsuperscript{4}km\textsuperscript{2}, of high suitable habitat in
the threatened medicinal plants (Table S5). Similarly, it had the
largest combined area of high and medium suitable habitat. Notably, the
RAL (\emph{R. alterna} ) had the minimum area, only 8.2x
10\textsuperscript{4}km\textsuperscript{2} and 17.7 x
10\textsuperscript{4}km\textsuperscript{2}, of high and medium suitable
habitat among the threatened medicinal plants (Table S5).

Among the NT medicinal plants, the largest combine area of high and
medium suitable habitat was found in RAN (\emph{Rhododendron
anthopogonoides} ), with an area of 395.3 x
10\textsuperscript{4}km\textsuperscript{2} (Figure 4K \& Supplementary
Table 5). And it was mainly found in the eastern, southeastern and
southern regions of the QTP, containing Yushu Tibet Autonomous
Prefecture in Qinghai Province, Shangri-la city in Yunnan Province,
Changdu city and Linzhi city in Tibet and most of region in Sichuan
Province. NI (\emph{Notopterygium incisum} ) had the second largest
combined area of high and medium suitable habitat, with an area of 293.5
x 10\textsuperscript{4} km\textsuperscript{2}, which mainly distributed
in the eastern and southeastern regions of the QTP, including Haidong
city, Haibei city, Huangnan Tibetan Autonomous Prefecture and Golog
Tibetan Autonomous Prefecture in Qinghai Province, Gannan Tibetan
Autonomous Prefecture~in Gansu Province and Garze Tibetan Autonomous
Prefecture, Ngawa Tibetan Autonomous Prefecture in Sichuan Province. The
high and medium suitable habitats of AB (\emph{Aconitum brunneum} ) were
located at eastern region of the QTP, including Gannan Tibetan
Autonomous Prefecture~in Gansu Province and Ngawa Tibetan Autonomous
Prefecture in Sichuan Province. The combined area of high and medium
suitable habitat of AE (\emph{Androsace elatior} ) was the smallest,
only 25.7 x 10\textsuperscript{4} km\textsuperscript{2}, and it was
found sporadically on Garze Tibetan Autonomous Prefecture, Ngawa Tibetan
Autonomous Prefecture in Sichuan Province.

\textbf{3.2. Future suitable habitat fluctuations}

The results of future climate model prediction showed that the high and
medium suitable habitats of GL, RAL, FU, NI and AB expanded in all
future climate scenarios with global warming by comparison with
contemporary prediction results (Figure 5, Supplementary Figure 1-7 and
Supplementary Table 5). Among these medicinal plants, the maximum
increasing of high and medium suitability habitats were found in RAL,
and the fluctuation ranges were 231.95\% \textasciitilde{} 740.04\% and
584.78\% \textasciitilde{} 810.43\%, respectively (Figure 5 and Figure
S1), both of which reached a maximum in the RCP 8.5 climate scenario of
2070 (Figure 5 and Figure S1). Notably, the high suitability habitats of
LA decreased 48.60\% in the RCP8.5 climate scenario of 2070, however, in
other future climate scenarios it increased, and reached a maximum of
83.66\% in the RCP6.0 in 2050 (Figure 5-B). In addition, high
suitability habitats for 25\% threatened medicinal plants had a
decreased in all future climate scenarios, including FP, RT and RAN
(Figure 5). Among these medicinal plants, the high suitable habitat of
FP had the largest decrease, and it achieved a maximum of 97.1\% in the
RCP 8.5 climate scenario of 2070 (Figure 5-F). The medium suitable
habitats only for LA had a decreased in all future climate scenarios,
with the maximum decrease area of habitat of 74.7\% in RCP8.5 of 2070
climate scenarios (Figure S1-F). The high and medium suitable habitat of
the remaining medicinal plants decreased or increased in different
future climate scenarios (Figure 5 and Figure S1-S7).

\textbf{3.3. Future migratory trends of the threatened medicinal plants}

Barycenter results of the threatened medicinal plants indicated that the
GL, LA, FU, RT, FD and NI were likely to migrate northward in the future
climatic scenario of global climate change (Figure 6A, C, D, F, H and
I). On the contrary, the FP, AB and RAN were inclined to migrate
southward in the future distribution model. And, the barycenter of RAL
had a trend of eastward migration (Figure 6B), whereas the barycenter of
ABR tend to migrate westward in the future climatic scenario of global
climate change (Figure 6G). Distance of barycenter migration results
indicated that the maximum migration distance of CR medicinal plants
were 82 Km and 193 Km, respectively (Table S6). The maximum migration
distance of EN medicinal plants were 93 Km and 127 Km, separately (Table
S6). As far as VU medicinal plants were concerned, the maximum migration
distance of FP, RT, ABR and FD were 83 Km, 166 Km, 68 Km and 69 Km,
respectively. Furthermore, the NI, AB, RAN and AE in NT medicinal plants
had a maximum migration distance of 378 Km, 121 Km, 367 Km and 101 Km,
separately (Table S6). In addition, the elevation of barycenter of LA,
FP, RT, ABR, FD, AB and AE were persistently increased due to future
climate change (Figure 7). And the FU had a consistently decrease of
elevation of barycenter in all future climate scenarios. The elevation
of barycenter of the remaining threatened medicinal plants showed
increase and decrease in different future climate scenario (Figure 7).

\textbf{4. Discussion}

\textbf{4.1. Threatened medicinal plants distribution and habitat
fluctuations}

Combined Maxent model with GIS technology, we predicted potential
distribution areas of 12 endangered medicinal plants, and we obtained
the spatial distribution pattern and distribution suitability of 12
endangered medicinal plants endemic to the QTP. The AUC values of the
models were all more than 0.9, which proved that the prediction results
were accurate and could accurately reflect the potential geographical
distribution of 12 endangered medicinal plants endemic to the QTP. The
results of present suitable habitat distribution models indicated that
the low suitability and unsuitability habitats of the threatened
medicinal plants were mainly found in the western, northern,
northwestern and most part of the central regions on the QTP, while the
habits with high and medium suitability were mainly located at the
eastern, southeast, southern and some part of the central regions on the
QTP. Previous research indicated that the diversity of medicinal plants
on the QTP was mainly concentrated in the eastern and southeastern
regions, including northwest Yunnan, western Sichuan, and eastern and
southeastern Tibet (Fan \& Bai 2021; Xing et al. 2023; Song et al. 2023;
Chen et al. 2022), which was basically consistent with our results. The
rich medicinal plant diversity resources in the eastern and southeastern
Tibetan Plateau can be attributed to the complex topographic and
geomorphic features (Wu et al. 2022; Li et al. 2015), adequate
hydrothermal conditions (Favre et al. 2015), and the relatively stable
environment during the ice age in this region compared to the inland
regions. Therefore, we proposed to establish protected areas in eastern,
southeast and southern regions on the QTP, using the limited protected
areas to maximize the protection of more species.

Climate change plays an important role in species distribution.
Temperature and precipitation are the main factors that determine the
geographical distribution, growth and reproduction of species. Rising
temperatures, changes in water conditions, and extreme climate events
have caused widespread impacts on biodiversity and species distribution
patterns (Wu 2011; Lenoir et al. 2008; Hu et al. 2022; Ma \& Sun 2018).
At different time periods in the future, the distribution pattern of
most plants and animals in the world show a clear trend of migration
from low latitude and low altitude areas to high latitude and
high-altitude areas, accompanied by varying degrees of reduction or
expansion (Parmesan \& Yohe 2003; Root et al. 2003). Compared with the
present, the potential habitat areas of GL, RAL, FU, NI and AB on the
QTP show an overall trend of expansion in the future. By comparing the
terrain characteristics, it was found that the expanded suitable
habitats were all located in higher altitude mountainous areas. This may
be due to climate warming increasing the species richness in the
original habitats of these five medicinal plants, resulting in niche
overlap and interspecific competition promoting their migration to
higher mountainous areas. This is similar to the research results of
Jump et al. (2005) and Engler et al. (2011). In addition, under future
climate change scenarios, the potential suitable habitats for FP, RT,
and RAN are generally showed a decreasing trend, indicating that climate
warming will pose a threat to the habitats of these species in the
future. Consequently, we considered that the FP, RT and RAN deserved
more protection and attention under the global change.

\textbf{4.2. Trend in migration of barycenter}

The results of migration of barycenter showed that about 50\% of the
threatened medicinal plants on the QTP had a northward migration trend
in response to climate change. Meanwhile, the results of barycenter
elevation indicated that 58.33\% of the threatened medicinal plants on
the QTP had a tendency to migrate to higher altitudes in order to
alleviate the pressure of climate change. Under different climatic
scenarios, the FP and AN of threatened medicinal plants on the QTP
tended to continually migrate to lower latitudes. However, in different
climate scenarios both of them had an increasing elevation barycenter.
We hypothesize that the FP and AN were mainly mitigating the effects of
global warming on themselves by moving to higher altitudes. In addition,
in the future scenario, the LA, RT and FD have a tendency to migrate to
high latitude and high altitude. More and more studies have shown that
the distribution pattern of most plants and animals in the world would
obviously move from low latitude and low altitude areas to high latitude
and high-altitude areas in different time periods in the future. Our
findings on endangered medicinal plants on the QTP confirm this
perspective. Climate warming has obviously affected the number of plant
populations, and the migration of species distribution has led to a
decrease in biodiversity at low altitudes and low latitudes, increasing
the competition among species at high altitudes and high latitudes, and
further increasing the risk of extinction of endangered plants.

In summary, the determination of potential habitats for endangered
medicinal plants is of great significance for the collection of wild
excellent medicinal provenances and their biological research, and
provides basic data for an accurate and comprehensive understanding of
the distribution and resource reserves of endangered medicinal plants.
The high suitability and suitability areas of 12 endangered medicinal
plants can be used as key planning and development areas for the
cultivation and planting of endangered medicinal plants in the future.
However, this article only used climate factors, soil variables and
topographical to simulate the potential geographical distribution areas
of the species, and the results obtained may not be suitable for the
survival of the species in some areas. In the actual application
process, guidance should still be given based on the local hydrological
and geological conditions. In future research, more environmental
factors such as human interference factors, soil physical and chemical
properties, and vegetation type data can be attempted to be added, then
conduct on-site experiments at a small scale to better guide its
protection, management, and introduction and cultivation. In addition,
this article only selected 12 endangered plants as the research object.
It is not yet known whether the potential geographical distribution
trend of all endangered plants under future climate change conforms to
this law. In the future, more rare and endangered plants need to be
added for verification to confirm the universality of the research
results.

\textbf{5. Conclusion}

The conservation of endangered species is based on a systematic
understanding of the relationship between the potential geographical
range of species and climate change, and the potential geographical
range of species under future climate change scenarios. In this study,
the suitable habitats for the threatened medicinal plants on the QTP
were found in the eastern, southeast, southern and some part of the
central regions on the QTP, containing southeastern region in Tibet
Province, eastern and central regions in Qinghai Province and
northwestern region in Sichuan Province. In addition, 25\% of threatened
medicinal plants showed a reduction of the area of suitable habitat in
different future scenarios. However, the area of suitable habitat of the
41.6\% threatened medicinal plants showed an increase on different
degrees due to their more responsive ability to respond to environmental
stresses. Among these medicinal plants, the most negatively influenced
by climate change is FP, which will miss 97.1\% of suitable habitat in
2070 when the carbon emissions are at high (RCP8.5). And, the least
negative affected by climate change is RAN, whose area of suitable
habitat will increase by 740.04\% in 2070 when the carbon emissions are
at high (RCP8.5). Then, the suitable habitat for 50\% of the threatened
medicinal plants would migrate to higher altitudes or higher latitudes
regions in the climate change future. Based on these findings, in
addition to controlling greenhouse gas emissions, we appealed that the
endangered species on the QTP with the reduction of the area of suitable
habitat should be given more attention in the conservation measures.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Lucun Yang} : Methodology, Software, Investigation, Writing --
original draft, Writing -- review \& editing. \textbf{Xiaofeng Zhu} :
Software, Investigation. \textbf{Wenzhu Song} :
Investigation.\textbf{Xingping Shi} : Investigation. \textbf{Xiaotao
Huang} : Supervision, Writing -- review \& editing

\textbf{ACKNOWLEDGEMENTS}

This research was financially supported by the Qinghai Basic research
Program {[}Grant No. 2023-ZJ-775{]}.

\textbf{CONFLICT OF INTEREST STATEMENT}

The authors declare there are no conflicts of interest.

\textbf{DATA AVAILABILITY STATEMENT}

We used open-access data from the Global Biodiversity Information
Facility database (GBIF, https://www.gbif.org/), Chinese Virtual
Herbarium (\emph{http://www.cvh.ac.cn/})\selectlanguage{english},China National Specimen
Information Infrastructure (\emph{http://www.naii.org.cn})\selectlanguage{english}, WorldClim
(http://world clim.org), and National Tibetan Plateau Data Center
(https://data.tpdc.ac.cn/zh-hans/).

\textbf{References}

Abdelaal, M., Fois, M., Fenu, G., Bacchetta, G. (2019). Using MaxEnt
modeling to predict the potential distribution of the endemic plant Rosa
arabica Crep. in Egypt. \emph{Ecological Informatics} , \emph{50} :
68-75.\emph{https://doi.org/10.1016/j.ecoinf.2019.01.003}

Allouche, O., Tsoar, A., Kadmon, R. (2006). Assessing the accuracy of
species distribution models: prevalence, kappa and the true skill
statistic (TSS). \emph{Journal of Applied Ecology} , \emph{43} (6),
1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x

Allen, J.L., Lendemer, J.C. (2016). Climate change impacts on endemic,
high-elevation lichens in a biodiversity hotspot. \emph{Biodiversity and
Conservation} , \emph{25} (3),
555-568.~https://doi.org/10.1007/s10531-016-1071-4

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} , \emph{5} (7),
694-700. https://doi.org/10.1111/2041-210X.12200

Chen, K.Y., Wang, B., Chen, C., Zhou, G.Y. (2022). MaxEnt Modeling to
Predict the Current and Future Distribution of \emph{Pomatosace
filicula} under Climate Change Scenarios on the Qinghai-Tibet
Plateau.\emph{Plants} , \emph{11} : 670. https://doi.org/
10.3390/plants11050670.

Cobos, M.E., Peterson, A.T., Barve, N., Osorio-Olvera, L. (2019). Kuenm:
a R package for detailed development of ecological niche models using
Maxent. \emph{PeerJ} , \emph{7} , e6281.
https://doi.org/\emph{10.7717/peerj.6281}

Descombes, P., Wisz, M.S., Leprieur, F., Parravicini, V., Heine, C.,
Olsen, S., Swingedouw, D., Kulbicki, M., Mouillot, D., Pellissier, L.
(2015). Forecasted coral reef decline in marine biodiversity hotspots
under climate change. \emph{Global Change Biology} , \emph{21} (7),
2479-2487.\emph{https://doi.org/10.1111/gcb.12868}

Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan,
A., Hijmans, R.J.,~Huettmann, F.,~Leathwick, J.R.,~Lehmann, A.,~Li,
J.,~Lohmann, L.G.,~Loiselle, B.A.,~Manion, G.,~Moritz, C.,~Nakamura,
M.,~Nakazawa, Y.,~Overton, J.M.M.,~Peterson, A.T.,~Phillips,
S.J.,~Richardson, K.,~Scachetti-Pereira, R.,~Schapire, R.E.,~Soberon,
J.,~Williams, S.,~Wisz, M.S.,~Zimmermann, N.E. (2006). Novel methods
improve prediction of species' distributions from occurrence
data.\emph{Ecography} , \emph{29} (2), 129-151.
https://doi.org/10.1111/j.2006.0906-7590.04596.x

Engler, R., Randin, C.F., Thuiller, W., et al. (2011). 21st century
climate change threatens mountain flora unequally across
Europe.\emph{Global Change Biology} , \emph{17} (7),
2330-2341\selectlanguage{english}.https://doi.org/10.1111/j.1365-2486.2010.02393.x

Evans, A., Janssens, S. \& Jacquemyn, H. (2020). Impact of climate
change on the distribution of four closely related Orchis (Orchidaceae)
species. \emph{Diversity} , \emph{12} (8),
312-324.\emph{https://doi.org/10.3390/d1208 0312}

Fitzpatrick, M.C., Gove, A.D., Sanders, N.D., Dunn, R.R. (2008) Climate
change, plant migration, and range collapse in a global biodiversity
hotspot: The Banksia (Proteaceae) of Western Australia. \emph{Global
Change Biology} , \emph{14} ,
1337-1352.\emph{http://doi.org/10.1111/j.1365-2486.2008.01559.x}\selectlanguage{english}.

Fan, Z., Bai, X. (2021). Scenarios of potential vegetation distribution
in the different gradient zones of Qinghai-Tibet Plateau under future
climate change. \emph{Science of the Total Environment} , 796, 148918.
https://doi.org/\emph{10.1016/j.scitotenv.2021.148918}

Favre, A., Packert, M., Pauls, S.U., Jahnig, S.C., Uhl, D., Michalak,
I., Muellner-Riehl, A.N. (2015). The role of the uplift of the
Qinghai-Tibetan Plateau for the evolution of Tibetan
biotas.\emph{Biology of Reproduction} , \emph{90} , 236--253.
https://doi.org/10.1111/brv.12107

Gao, X.X., Liu, J., Huang, Z.H. (2022). The impact of climate change on
the distribution of rare and endangered tree \emph{Firmiana
kwangsiensis} using the Maxent modeling. \emph{Ecology and Evolution} ,
12(8), e9165. ~\emph{https://doi.org/10.1002/ece3.9165}

Guo, Y., Li, X., Zhao, Z. \& Nawaz, Z. (2019). Predicting the impacts of
climate change, soils and vegetation types on the geographic
distribution of \emph{Polyporus umbellatus} in China. \emph{Science of
the Total Environment} , 648, 1-11.\emph{https://doi.org/10.1016/j.scito
tenv.2018.07.465}

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

Hartmann, D.L., Klein-Tank, A.M.G., Rusticucci, M. (2013). Chapter 2:
observations: atmosphere and surface{[}R{]}//Stocker, T.F., Qin, D.,
Plattenr, G-K\selectlanguage{english},et al. IPCC, 2013: climate change 2013: the physical
science basis. Contribution of Working Group \selectlanguage{english}I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge:
Cambridge University Press 39.

Hazzi, N.A., Moreno, J.S., Ortiz-Movliav, C., Palacio, R.D. (2018).
Biogeographic regions and events of isolation and diversification of the
endemic biota of the tropical Andes. \emph{Proceedings of the National
Academy of Sciences} , \emph{115} (31),
7985-7990.\emph{https://doi.org/10.1073/pnas.1803908115}

Hill, M.P., Hoffmann, A.A., Mccoll, S.A., Umina, P.A. (2012).
Distribution of cryptic blue oat mite species in Australia: Current and
future climate conditions. \emph{Agricultural and Forest Entomology} ,
14, 127-137.\emph{https://doi.org/10.1111/j.1461-9563.2011.00544.x}

Hu, X.G., Jin, Y.Q., Wang, X.R., Mao, J.F., Li, Y. (2015). Predicting
impacts of future climate change on the distribution of the widespread
conifer \emph{platycladus orientalis} . \emph{PloS One} , \emph{10} (7),
e0132326. https://doi.org/10. 1371/journal.pone.0132326

Hu, H.W., Wei, Y.Q., Wang, W.Y., Suonan, J., Wang, S.X., Chen, Z., Guan,
J.H., Deng, Y.F. (2022). Richness and distribute on of endangered orchid
species under different climate scenarios on the Qinghai-Tibetan
Plateau. \emph{Frontiers in Plant Science} , \emph{13} , 948189.
~\emph{https://doi.org/10.3389/fpls.2022.948189}

Jiang, X., Ni, J. (2005). Species-climate relationships of 10 desert
plant species and their estimated potential distribution range in the
arid lands of northwestern China. \emph{Chinese Journal of Plant
Ecology, 29} (1): 98-107. https://doi.org/\emph{10.17521/cjpe.2005.0013}

Jump, A.S.\selectlanguage{english},Penuelas, J. (2005). Running to stand still: adaptation and
the response of plants to rapid climate change. \emph{Ecology Letters}
,\emph{8} (9), 1010-1020.
~\emph{https://doi.org/10.1111/j.1461-0248.2005.00796.x}

Leroy, B., Paschetta, M., Canard, A., Bakkenes, M., Isaia, M., Ysnel, F.
(2013). First assessment of effects of global change on threatened
spiders: potential impacts on \emph{Dolomedes plantarius} (Clerck) and
its conservation plants. \emph{Biological Conservation} , \emph{161}
(5), 155-163\selectlanguage{english}.\emph{https://doi.org/10.1016/j.biocon.2013.03.022}

Lenoir, J., Ge\selectlanguage{english}gout, J.C., Marquet, P.A., Ruffray, P.D., Brisse, H.
(2008). A Significant Upward Shift in Plant Species Optimum Elevation
During the 20th Century. \emph{Science} , 320, 1768-1771.
https://doi.org/10.1126/science.1156831

Li, G.D., Xiong, Y., Zhong, X.M., Song, D.D., Kang, Z.J., Li, D.J.,
Tang, J.H., Wang, Y.P., Wu, L. (2023). Changes in overwintering ground
of small yellow croaker\emph{~} (\emph{Larimichthys~polyactis} ) based
on MaxEnt and~GARP~models: A case study of the southern Yellow Sea
stock.\emph{Journal of Fish Biology} , \emph{102} (6), 1358-1372.
https://doi.org/10.1111/jfb.15383.

Li, K.D., Zhang, G.F., Zhang, Y. \& Griffith, M.P. (2021). A noteworthy
case of rewilding Chinese yew from a garden population in eastern
China.\emph{PeerJ} , 9, e12341.https://doi.org/10.7717/peerj.12341

Li, S., Currie, B.S., Rowley, D.B., Ingalls, M. (2015). Cenozoic
paleoaltimetry of the SE margin of the Tibetan Plateau: constraints on
the tectonic evolution of the region. \emph{Earth And Planetary Science
Letters} , \emph{432} , 415e424.

Lv, T., Harris, A.J., Liu, Y., Liu, T., Liang, R., Ma, Z. \& Su, X.
(2021). Population genetic structure and evolutionary history
of\emph{Psammochloa villosa} (Trin.) Bor (Poaceae) revealed by AFLP
marker. \emph{Ecology and Evolution} ,\emph{11} (15),
10258-10276.\emph{https://doi.org/10.1002/ece3.7831}

Ma, S.M., Zhang, M.L. (2012). Phylogeography and conservation genetics
of the relic \emph{Gymnocarpos przewalskii} (Caryophyllaceae) restricted
to northwestern China.\emph{Conservation Genetics} , 13,
1531--1541.~\emph{http://doi.10.1007/s10592-012-0397-z}.

Ma, B., Sun, J. (2018). Predicting the distribution of \emph{Stipa
purpurea} across the Tibetan Plateau via the MaxEnt model. \emph{BMC
Ecology} , 18, 10. http://doe.10.1186/s12898-018-0165-0.

Menges, E.S. (1990) Population viability analysis for an endangered
plant. \emph{Conservation Genetics} , \emph{4} (1):
52-62.\emph{https://doi.org/10.1111/j.1523-1739.1990.tb00267.x}

Morales, N.S., Fern\selectlanguage{ngerman}ández, I.C., Baca-González, V. (2017). MaxEnt's
parameter configuration and small samples: are we paying attention to
recommendations? A systematic review. \emph{PeerJ} , 5,
e3093.\emph{https://doi.org/10.7717/}p eerj.3093

Narkis, M.S., Fernandes, C.I., Victoria, B-G. (2017). MaxEnt's parameter
configuration and small samples: Are we paying attention to
recommendations? A systematic review. \emph{PeerJ} , 5, e3093.
https://doi.org/10.7717/peerj.3093

Pan, B.T., Li, J.J. (1996). Qinghai-Tibetan Plateau: A Driver and
Amplifier of the Global Climatic Change. III. The effects of the uplift
of Qinghai-Tibetan Plateau on Climatic Changes. \emph{Journal of Lanzhou
University (Med Sci)} , \emph{32} (1), 108-115.
http://doi.org/10.13885/j.issn.0455-2059.1996.01.024

Parmesan, C., Yohe, G. (2003). A globally coherent fingerprint of
climate change impacts across natural systems. \emph{Nature} ,\emph{421}
(6918), 37-42. https://doi.org/10.1038/nature01286.

Pearce, J., Boyce, M.~(2006).~Modelling distribution and abundance with
presence-only data.\emph{~Journal of Applied Ecology} , 43, 405-412.
https://doi.org/10.1111/j.1365-2664.2005.01112.x

Phillips, S.J., Dudík, M. (2008). Modeling of species distributions with
Maxent: new extensions and a comprehensive evaluation. \emph{Ecography}
,\emph{31}
(2):161-175.\emph{https://doi.org/10.1111/j.0906-7590.2008.5203.x}

Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum entropy
modeling of species geographic distributions. \emph{Ecological
Modelling} , \emph{190} (3-4),
231-259.\emph{https://doi.org/10.1016/j.ecolmodel.2005.03.026}

Phillips, S.J., Dudík, M. (2008). Modeling of species distributions with
Maxent: new extensions and a comprehensive evaluation. \emph{Ecography}
,\emph{31} (2), 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

Poirazidis, K., Bontzorlos, V., Xofis, P., Zakkak, S., Xirouchakis, S.,
Grigoriadou, E., Kechagioglou, S., Gasteratos, I., Alivizatos, H.,
Panagiotopoulou, M. (2019). Bioclimatic and environmental suitability
models for capercaillie (Tetrao urogallus) conservation: Identification
of optimal and marginal areas in Rodopi Mountain-Range National Park
(Northern Greece). \emph{Global Ecology and Conservation} , 17, e00526.
https://doi.org/10.1016/j.gecco.2019.e00526

Radosavljevic, A., Anderson, R.P. (2014). Making better MAXENT models of
species distributions: complexity, overfitting and
evaluation.\emph{Journal of Biogeography} , \emph{41} (4), 629-643.
https://doi.org/10.1111/jbi.12227

Ru, W.M. (2006). Study on the ecology of endangering plant \emph{Taxus
chinensis} var. \emph{mairei} . Shanxi University.

Root, T.L., Price, J., Hall, K.R., Schneider, S.H., Rosenzweig, C.,
Pounds, A. (2003). Fingerprints of global warming on wild animals and
plants. \emph{Nature} , 421, 57-60. https://doi.org/10.1038/nature01333.

Semwal, D.P., Pandey, A., Gore, P.G., Ahlawat, S.P., Yadav, S.K., Kumar,
A.V. (2021). Habitat prediction mapping using BioClim model for
prioritizing germplasm collection and conservation of an aquatic cash
crop `\emph{makhana'} ~(\emph{Euryale ferox} ~Salisb.) in
India.~\emph{Genetic Resources and Crop Evolution} , 68,
3445-3456.\emph{https://doi.org/10.1007/s10722-021-01265-7}

Sharma, E., Chettri, N., Tsering, K,. et al. Climate change impacts and
vulnerability in the eastern Himalayas. Kathmandu: International Centre
for Integrated Mountain Development, 2009.

Song, D., Li, Z., Wang, T., Qi, Y., Han, H., Chen, Z. (2023). Prediction
of Changes to the Suitable Distribution Area of \emph{Fritillaria
przewalskii} Maxim. in the Qinghai-Tibet Plateau under Shared
Socioeconomic Pathways (SSPs). \emph{Sustainability} , 15, 2833.
https://doi.org/10.3390/su15032833

Swets, J.A. (1988). Measuring the accuracy of diagnostic
systems.\emph{Science} , \emph{240} (4857),
1285-1293.\emph{https://doi.org/10.1126/science.3287615}

Thomas, C.D.,~Cameron, A.,~Green, R.E.,~Bakkenes, M., Beaumont, L.J.,
Collingham, Y.C., Erasmus, B.F.N., Siqueira, M.F., Grainger, A., Hannah,
L., Hughes, L., Huntley, B., Jaarsveld, A.S., Midgley, G.F., Miles, L.,
Ortega-Huerta, M.A., Peterson, A.T., Phillips, O.L. \& Williams, S.E.
(2004). Extinction risk from climate change.~\emph{Nature} , \emph{427}
, 145-148. https://doi.org/10.1038/nature02121

Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel,
P., Delgado-Arias, S. \& Bond-Lamberty, B. (2011). RCP4.5: a pathway for
stabilization of radiative forcing by 2100. \emph{Climatic Change} ,
109, 77-94. https://doi.org/10.1007/s1058 4-011-0151-4

Warren, D.L., Seifert, S.N. (2011). Ecological niche modeling in Maxent:
the importance of model complexity and the performance of model
selection criteria. \emph{Ecological Applications} , 21(2),
335-342.\emph{https://doi.org/10.1890/10-1171.1}.

Wang, J., Ni, J. (2006). Review of modelling the distribution of plant
species. \emph{Chinese Journal of Plant Ecology} , \emph{30} (6),
1040-1053. https://doi.org/\emph{10.17521/cjpe.2006.0133}

Wu, S.D., Wang, Y., Wang, Z.F., Shrestha, N. \& Liu, J.Q. (2022).
Species divergence with gene flow and hybrid speciation on the
Qinghai--Tibet Plateau. \emph{New Phytologist} , \emph{234} , 392-404.
https://doi.org/ 10.1111/nph.17956

Wu, J.G. (2011). The potential effects of climate change on the
distributions of 7 plants in China. \emph{Guihaia} , 31(5), 595-607.
http://doi.org/10.3969/j.issn.1000-3142.2011.05.008

Xing, Y., Shi, J., De, K., Wang, X., Wang, W., Ma, Y., Zhang, H., He,
M., Liu, Q. (2023). The Current Distribution of \emph{Carex
alatauensis}in the Qinghai--Tibet Plateau Estimated by MaxEnt.
\emph{Agronomy} ,\emph{13} , 564. https://doi.org/10.3390/
agronomy13020564

Yao, T., Thompson, L., Chen, D., Zhang, Y., Wang, N., Zhao, L., Che, T.,
Xu, B., Wu, G., Zhang, F.J.Bn. (2020). Third Pole climate warming and
cryosphere system changes. 69, 1.

Yao, T., Thompson, L.G., Mosbrugger, V., Zhang, F., Ma, Y., Luo, T., Xu,
B., Yang, X., Joswiak, D.R., Wang, W.J.E.D. (2012). Third pole
environment (TPE). \emph{Environmental Development} , 3,
52-64.\emph{https://doi.org/10.1016/j.envdev.2012.04.002}

Zhang, W.J., Chen, J.K. (2003). Research progress of species
distribution. \emph{Biodiversity} , \emph{11} (005), 364-369.

Zhang, Q, Shen, X., Jiang, X., Fan, T., Liang, X., Yan, W. (2023).
MaxEnt Modeling for Predicting Suitable Habitat for Endangered
Tree\emph{Keteleeria davidiana} (Pinaceae) in China. \emph{Forests}
,\emph{14} , 394.\emph{https://doi.org/10.3390/f14020394}.

Zhang, H., Zhao, H.X. (2001). Study on rare and endangered plants under
climate: maxent modeling for identifying hot spots in northwest
China.\emph{Cerne} , \emph{27} , e-102667.
~\emph{https://doi.org/10.1590/01047760202127012667}

Zhang, G.Q., Yao, T.D., Xie, H.J., Yang, K., Zhu, L.P., Shum, C.K.,
Bolch, T., Yi, S., Allen, S., Jiang, L.G., Chen, W.F., Ke, C.Q. (2020).
Response of Tibetan Plateau lakes to climate change: trends, patterns,
and mechanisms. \emph{Earth-Science Reviews} , \emph{208} , 103269.
https://doi.org/10.1016/j.earscirev.2020.103269

Zhang, Z.X., Capinha, C., Usio, N., Weterings, R., Liu, X., Li, Y.M.,
Landeria, J.M., Zhou, Q. \& Yokota, M. (2019). Impacts of climate change
on the global potential distribution of two notorious invasive cray
fishes. \emph{Freshwater Biology} , 65(3), 353-365.
https://doi.org/10.1111/ fwb.13429\selectlanguage{english}
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\textbf{Hosted file}

\verb`Table1.docx`  available at \url{https://authorea.com/users/753642/articles/723819-predicting-the-potential-distribution-of-12-threatened-medicinal-plants-on-the-qinghai-tibet-plateau-qtp-with-a-maximum-entropy-model}

\textbf{Hosted file}

\verb`Table2.docx`  available at \url{https://authorea.com/users/753642/articles/723819-predicting-the-potential-distribution-of-12-threatened-medicinal-plants-on-the-qinghai-tibet-plateau-qtp-with-a-maximum-entropy-model}

\textbf{Hosted file}

\verb`Table3.docx`  available at \url{https://authorea.com/users/753642/articles/723819-predicting-the-potential-distribution-of-12-threatened-medicinal-plants-on-the-qinghai-tibet-plateau-qtp-with-a-maximum-entropy-model}

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