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\begin{document}

\title{The wild tending framework of medicinal plants constructed based on
Biomod2 and PLUS model: A case study of Thesium chinense Turcz. in China}



\author[1]{Boyan Zhang}%
\author[1]{Bingrui Chen}%
\author[1]{Xinyu Zhou}%
\author[1]{Hui Zou}%
\author[1]{Detai Duan}%
\author[1]{Xiyuan Zhang}%
\author[1]{Xin xin Zhang}%
\affil[1]{Harbin Normal University}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
\let\center\flushleft
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\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Wild medicinal plants dominate the market of Traditonal Chinese Medicine
(TCM). However, the intensification of human activities and ecological
deterioration have caused a gradual depletion or extinction of wild
medicinal plant resources in China. Scientific planning of wild tending
areas is a priority to realize the sustainable utilization of wild
medicinal plant resources. Thesium chinense, a known ``plant
antibiotic'', has been overharvested in recent years, resulting in a
sharp reduction in its wild resources. In this study, we combined three
atmospheric circulation models and four common socio-economic approaches
(SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to explore the main
environmental factors affecting the distribution of T. chinense and the
changes in the suitable area under the complete niche based on the
Biomod2 package. The PLUS model was used to predict and analyze the land
use change trend in the climate-stable areas of T. chinense in the
future. And the wild tending areas of T. chinense were planned using
ZONATION software. In the next hundred years, the climate-stable areas
of T. chinense in China will mainly be distributed in humid and subhumid
area, and the natural habitat areas of T. chinense in this region will
decrease year by year. Hot spot analysis showed that Qiqihar, Chifeng,
Zunyi, and other counties were the most suitable for the wild tending of
T. chinense. These results can provide a comprehensive research
framework for wild tending planning of T. chinense and other medicinal
plants.%
\end{abstract}%



\sloppy


\textbf{The wild tending framework of medicinal plants constructed based
on Biomod2 and PLUS model: A case study of \emph{Thesium chinense}
Turcz. in China}

Boyan Zhang \textsuperscript{1}, Bingrui Chen \textsuperscript{1}, Xinyu
Zhou \textsuperscript{1}, Hui Zou \textsuperscript{1}, Detai
Duan\textsuperscript{1}, Xiyuan Zhang \textsuperscript{1}, Xinxin
Zhang\textsuperscript{1}

\textsuperscript{\textbf{1}} Heilongjiang Genuine Wild Medicinal
Materials Germplasm Resources Research Center, School of Life Sciences
and Technology, Harbin normal university, Harbin, China

Correspondence

Xinxin Zhang, Heilongjiang Genuine Wild Medicinal Materials Germplasm
Resources Research Center, School of Life Sciences and Technology,
Harbin normal university, Harbin 150025, China. E-mail addresses:
hsdzxx2021@163.com

\textbf{Abstract}

Wild medicinal plants dominate the market of Traditonal Chinese Medicine
(TCM). However, the intensification of human activities and ecological
deterioration have caused a gradual depletion or extinction of wild
medicinal plant resources in China. Scientific planning of wild tending
areas is a priority to realize the sustainable utilization of wild
medicinal plant resources. \emph{Thesium chinense} , a known ''plant
antibiotic'', has been overharvested in recent years, resulting in a
sharp reduction in its wild resources. In this study, we combined three
atmospheric circulation models and four common socio-economic approaches
(SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to explore the main
environmental factors affecting the distribution of \emph{T.
chinense}and the changes in the suitable area under the complete niche
based on the Biomod2 package. The PLUS model was used to predict and
analyze the land use change trend in the climate-stable areas of
\emph{T. chinense}in the future. And the wild tending areas of \emph{T.
chinense} were planned using ZONATION software. In the next hundred
years, the climate-stable areas of \emph{T. chinense} in China will
mainly be distributed in humid and subhumid area, and the natural
habitat areas of\emph{T. chinense} in this region will decrease year by
year. Hot spot analysis showed that Qiqihar, Chifeng, Zunyi, and other
counties were the most suitable for the wild tending of \emph{T.
chinense} . These results can provide a comprehensive research framework
for wild tending planning of \emph{T. chinense} and other medicinal
plants.

\textbf{Keywords:} \emph{Thesium chinense} ; climate change; land use
change; species distribution model; wild tending

\textbf{1. Introduction}

Climate is one of the most important factors in determining the
distribution of species. The response of vegetation to climate change
and the regional changes of plant distribution under the influence of
climate are hot topics in biogeography, macroecology, and other
disciplines (Y. Chen, Le, et al., 2022; O'Connor et al., 2020). With the
continuous intensification of global warming, the global average surface
temperature will rise by 1.1 to 6.4 by the end of the 21st century (P.
Gao et al., 2023). Global warming is likely to significantly alter the
structure and function of terrestrial ecosystems and increase the risk
of species extinction (Thomas et al., 2004). To adapt to these changing
conditions, plants can modify their ecological niche and distribution
areas to match the new environment (Tang et al., 2021; Moran \&
Alexander, 2014). Climate change will pose significant uncertainty for
future rational cultivation of medicinal plants (Higa et al., 2013).
Blind introduction and expansion of medicinal plants without scientific
guidance not only jeopardizes their quality but also misallocates
resources (He et al., 2021). Long-term field introduction trials have
traditionally been the most reliable way to identify suitable areas for
herbal plant cultivation. However, it requires substantial resources and
observation of several growth cycles (F. Li et al., 2019). Therefore, it
is crucial to predict the potential suitable areas of medicinal plants
under future climate change, which facilitates a better understanding of
the cultivation potential of medicinal plants in new habitats.

Habitat loss caused by land use change will exacerbate the impact of
climate change on species and ecological dynamics globally (Pant et al.,
2021). Most studies that explore changes in plant species distribution
and their future distribution only consider climate factors, with little
attention paid to the synergistic effects of habitat and climate change
(Sirami et al., 2017). However, in exploring changes in plant
distribution, climate change-driven range expansion occurs in a spatial
context. Yet each species' habitat is unique. Thus, although many
regions are suitable for survival, they may not be suitable as natural
population habitats due to land use limitations. With increasing human
activity, it is expected that 10\% to 20\% of natural grasslands and
forests will be replaced by agriculture and urban infrastructure by
2050, leading to substantial habitat loss for most wild species (Y.
Chen, Li, et al., 2022; X. Li et al., 2016). In this sense, it is
necessary to explore the impact of land use change on species' habitat.
Integrating both climate and land use changes is essential for gaining a
more accurate understanding of species' distribution range under future
climate and land use change.

\emph{Thesium chinense} Turcz. is a semi-parasitic perennial herb of the
genus Thesium in the Santalaceae. Its wild population is distributed in
China, Japan, Korea, and other places (Chinese botanical society, 1988).
As an important medicinal plant in China, dried whole grass of \emph{T.
chinense} has significant anti-inflammatory and analgesic effects. It is
often used to prevent and treat all kinds of acute inflammation, so it
has the reputation of a ''plant antibiotic'' (Luo \& Guo, 2011). In
recent years, due to the deterioration of the ecological environment
caused by excessive harvesting and the intensification of human
activities, the population of \emph{T. chinense} has sharply decreased
throughout China and even disappeared in some ecological areas,
seriously reducing the production of \emph{T. chinense} (J. Gao et al.,
2023). The supply-demand contradiction has become more prominent with
the increasing demand from medicinal herb manufacturers (Luo et al.,
2012). But at present, the system of artificial breeding of \emph{T.
chinense}is not fully mature, which can not meet the rapid development
of the\emph{T. chinense} industry (J. Gao et al., 2023). Wild tending is
a way to artificially or naturally increase the population in its native
or similar environment according to the growth characteristics of the
target species and its requirements for ecological environmental
conditions. The significance of this method is to make the amount of
resources available for people to collect and use, and to maintain the
balance of the community (S. Chen et al., 2004, 2016). Therefore,
planning a wild tending area for \emph{T. chinense} is very important.

In this study, Biomod2 based ensemble species distribution model was
used to investigate the relationship between population distribution and
environmental factors in the complete ecological niche, and to predict
and analyze the changes of population distribution under climate warming
conditions in the century and the changes of natural habitat areas in
the climate stable areas of China. In addition, we planned the wild
tending areas of \emph{T. chinense} in China based on ZONATION software
(integrating future climate and land use change results). Furthermore,
the synergistic effects of climate and land use change were considered,
aiming to provide a more effective reference for the sustainable use
of\emph{T. chinense} in China under the land use and climate change. The
purpose of this study is: (1) To determine the main environmental
factors limiting the distribution of \emph{T. chinense} ; (2) To
investigate the effects of climate change on the population distribution
of \emph{T. chinense} ; (3) To identify habitat changes within the
climate-stable areas of \emph{T. chinense} in China; (4) To plan wild
tending areas for \emph{T. chinense} in China and put forward management
strategies.

\textbf{2 Materials and Methods}

\textbf{2.1 Source and processing of species occurrence data}

The geographical occurrence data of \emph{T. chinense} were obtained by
three methods: (1) Field investigation. An extensive survey was
conducted from May 2018 to July 2022. (2) Network database. The Global
Biodiversity Information Facility (GBIF, https://www.gbif.org), the
Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn), and the National
Specimen Information Infrastructure (NSII, http://www.nsii.org.cn) and
the Teaching Specimen Resource sharing platform (http://mnh.scu.edu.cn)
have collected geographic location information with precise latitude and
longitude and time range from 2000 to the present. (3) Literature
search. Through the above methods, a total of 271 geographic occurrence
data were obtained. Used Google earth pro software (Google Earth USA) to
reject geographical occurrence data of \emph{T. chinense} in
construction land, cultivated land, and waters. To further eliminate the
spatial autocorrelation of occurrence data, the ''spThin'' package was
used to remove the occurrence data gathered within 10 km (Aiello-Lammens
et al., 2015). Finally, 174 occurrence data were retained for modeling
(Figure 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1} Morphological characteristics and occurrence data
of\emph{T. chinense} . (A): Occurrence data; (B): Plants of \emph{T.
chinense} ; (C): Flowers of \emph{T. chinense} ; (D): Haustoriums
of\emph{T chinense} .

\textbf{2.2 Environmental data sources and processing}

\textbf{2.2.1 Environmental factors}

Using 22 environmental factors (including climate, soil and biology) to
study the changes in the distribution of \emph{T. chinense} (Table S1).
In order to match the environmental factors with the current climate
scenario, based on the monthly temperature and precipitation data
provided by the Worldclimate database (v2.1), we calculated 19 climate
data in the time range of 2000-2018 through the ``biovar'' package. The
two soil factors (T\_OC and T\_pH\_H2O) were downloaded from Harmonized
World Soil Database (HWSD, http://www.fao.org). As a semi-parasitic
plant, vegetation (host) is the prerequisite for the growth of \emph{T.
chinense} . Therefore, we included NDVI factors that can reflect
vegetation growth in the ensemble species modeling. NDVI data was
downloaded in MODIS (https://www.earthdata.nasa.gov). We averaged the
NDVI data from 2000 to 2020 according to the month of the growth period
(May to July) of \emph{T. chinense} , and finally obtained layer was
required for the species distribution model modeling.

The high correlation between environmental factors may lead to the
species distribution model overfitting. Therefore, we first built the
initial model (10 repeated modeling) with MAXENT 3.4.4 software without
adjusting the parameters, and removed the environmental factors that
contributed less than 1\%. The correlation function in ENMTools software
was then used to analyze the remaining environmental factors'
correlation (Warren et al., 2010). The two environmental factors with
\textbar{} r \textbar{} [?] 0.8 were screened, and the one with relatively
small contribution rate was removed. After the above steps, we reserved
11 environmental factors for the final modeling.

To estimate the distribution changes of \emph{T. chinense} in different
periods in the future (2050s: 2041-2060, 2070s: 2061-2080, 2090s:
2081-2100), we selected three widely used atmospheric circulation models
(MIROC-ES2L, CNRM-CM6-1, MRI-ESM2-0) to build species distribution model
in the future. Each atmospheric circulation model includes four shared
socio-economic pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5,
with 12 climate scenario combinations. Among the four selected shared
socio-economic pathways, the low to high radiative forcing scenarios
range from SSP1-2.6 to SSP5-8.5 (Jiang et al., 2020). ArcMap10.5
software was used to average the data of three climate models in the
same carbon emission scenario and year. The spatial resolution of
environmental data was 2.5 min.

\textbf{2.2.2 Land use and driver data}

In the simulation of future land use in China, the land use data were
downloaded from the Resource and Environment Science and Data Center
(RESDC), including 2010 and 2015. Referring to the classification system
of China's National Land Use and Cover Change (CNLUCC) (Table S2), the
land use data in 2015 were reclassified by ArcMap10.5 software,
including cultivated land, forest, grassland, waters, construction land,
and unused land. The region expressed by the classified grassland (all
kinds of grasslands with herbaceous plants and coverage of more than
5\%, including shrub grassland dominated by grazing and sparse forest
grassland with canopy density of less than 10\%) is consistent with the
habitat previously recorded by \emph{T. chinense} , so we regarded the
grassland as the natural habitat of \emph{T. chinense} . The driving
factors were divided into physical geography, human disturbance, and
bioclimate (Figure S1). Physical geographic factors included elevation
(WorldClim Database v2.1), soil erosion (RESDC), and distance from
water. Distance from the water was calculated based on water system data
(RESDC) using the EuclideanDistance tool in ArcMap10.5. Human
disturbance factors included the population data and GDP data of 2015
were downloaded from the RESDC and the distance from road and railway
(DRA) calculated by EuclideanDistance tool based on the road data and
railway data of RESDC. Bioclimatic factors included annual precipitation
and mean temperature data from WorldClim Database v2.1 in 2015. All the
above data layers used the Resample tool in ArcMap10.5 to unify the data
resolution to 2.5 min.

\textbf{2.3 Construction of species distribution model}

Ten modeling (GLM, GBM, CTA, RF, GAM, ANN, SRE, FDA, MARS and MAXENT)
algorithms provided by ''Biomod2'' package were used to predict the
potential distribution of \emph{T. chinense} . All models use default
parameters except the MAXENT model.

The prediction accuracy of MAXENT model is affected by parameter
settings. We tested the complexity and performance of the MAXENT model
under different settings of regularization multiplier (RM) and feature
class (FC) used the kuenm package in R 3.6.3 (Cobos et al., 2019).
Candidate models were created by combining 17 RM values and all 31
possible combinations of five FC (L: linear feature, Q: secondary
feature, H: fragmentation feature, P: product feature and T: threshold
feature). According to Akaike information criterion (AICc) model of the
delta on the choice of the optimal model, when the minimum value AICc
(deltaAICc = 0), it is considered to be the optimal model (Cobos et al.,
2019). The optimized MAXENT software parameters were RM=3 and FC=LQPT.

In species distribution modeling using the Biomod2 package, 70\% of
occurrence data was selected as training data, and the rest was used as
testing data. The above process has been carried out five times. In
order to reduce spatial bias and better simulate the actual distribution
of species, we created 5,000 pseudo-absence points, repeated 3 times and
modeled. In the end, 150 layers were generated. We evaluated each model
using the true skill statistic (TSS) and the area under the receiver
operating characteristic curve (AUC) (Bucklin et al., 2015; X. Zhang et
al., 2020). The closer the TSS value and AUC value are to 1, the more
reliable the prediction will be (Zhao et al., 2021; Freitas et al.,
2019). We used the model with large average TSS ([?] 0.8) and AUC ([?] 0.9)
values to calculate the final species distribution layer.

\textbf{2.4 Division of suitable areas and climatic characteristics}

When converting a continuous prediction result into the Boolean form of
''suitable habitat'' and ''unsuitable habitat'', it is important to
select the appropriate threshold (Chen et al., 2022). We used the
maximum training sensitivity plus specificity threshold, which maximizes
the TSS value to create binary maps (de Andrade et al., 2020;
Jim\selectlanguage{ngerman}énez-Valverde \& Lobo, 2007). The threshold value (P) of the suitable
areas of\emph{T. chinense} was 0.275. In addition, the suitable areas
of\emph{T. chinense} also included three levels of suitable areas, which
were low suitable areas (0.275 \selectlanguage{english}[?] P \textless{} 0.5), medium suitable
areas (0.5 \selectlanguage{english}[?] P \textless{} 0.7), and high suitable areas (P \selectlanguage{english}[?] 0.7). We
used SDMtools to compare the spatial changes of the suitable areas
of\emph{T. chinense} under different climate scenarios.

To analyze the change of environmental characteristics in the suitable
areas of \emph{T. chinense} , we randomly selected 5000 points in the
current suitable areas of \emph{T. chinense} . We extracted the
corresponding values from the 5000 points in the layers of different
climatic conditions corresponding to the dominant environmental factors.
R 4.2.0 software was used to calculate the 95\% quantile and average
value of the extracted values, and what kind of environmental pressure
will be faced by \emph{T. chinense} in the future was intuitively
verified (Tagliari et al., 2021).

\textbf{2.5 Future land use simulation}

PLUS model is a new land use simulation model based on cellular
automata, which has advantages in studying the causes of land use change
and dynamically simulating the changes of various land uses, especially
forest and grassland patches (Sun et al., 2023; J. Wang et al., 2022).
By extracting the samples of the mutual transformation of various types
of land use between the two time periods of land use data for training,
the future land use is simulated based on the transformation
probability. The random forest algorithm calculated the expansion and
driving factors of various land use types to obtain the development
probability and the contribution of driving factors to the expansion of
various land use types in this period. And we combined with the
generation of random patches and the transition matrix, determined
future land use (Sun et al., 2023; Qiao et al., 2013). The PLUS Model
mainly includes Land Expansion Analysis Strategy (LEAS) and Cellular
Automata Model Based on Muiti-type Random Patch Seeds (CARS). The module
calculation formula can be found in relevant literature (Ji et al.,
2023; J. Wu \& Wang, 2022; Liang et al., 2021).

\textbf{2.6 Planning of wild tending area}

To serve the wild tending action of \emph{T. chinense} under climate
change and land use change, the grassland types in 2015, 2050, 2070, and
2090 were superimposed to obtain grass stability region this century.
Considering the prohibition of the development of nature reserves, the
existing protection areas in China were removed from the current high
suitable areas. By using the core area zonation algorithm in ZONATION4.0
software (https://www.helsinki.fi/), the layers of the high suitable
areas in different climate scenarios in the grass stability region were
superimposed. The weight of each layer was set to 1, and other
parameters were the default values (B. Chen et al., 2023; B. Zhang et
al., 2023). The natural discontinuity method was used to classify the
output layers from 0 to 1, and the wild tending areas with low, medium,
and high suitability of \emph{T. chinense} were obtained.

\textbf{3.Result}

\textbf{3.1 Accuracy of ensemble species distribution model}

The ensemble species distribution model results showed differences in
the prediction accuracy of the 10 models (Figure 2). Among them, RF,
GBM, and MARS models have the highest prediction accuracy, and the
repeated modeling is stable which the TSS and ROC met the standards of
establishing the ensemble models. MAXENT and GLM also had high
prediction precision, and only part of repeats did not reach the
standard. In addition, the performance of ANN and SRE models was the
worst. All replicates of the SRE model failed to meet the specified TSS
and ROC value standards, and only a few replicates of ANN reached the
standard. Therefore, these two models were not involved in ensemble
models.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2} Prediction accuracy evaluation of different models.

\textbf{3.2 Environmental factors affecting the distribution of \emph{T.
chinense}}

After screening, the 11 environmental factors were used to establish the
model and their importance were shown in Table S5. Among them, Bio18
(precipitation of warmest quarter) is the most important environmental
factor, followed by Bio11 (mean temperature of coldest quarter) and
Bio08 (mean temperature of wettest quarter). These three factors are the
main environmental factors that affect the distribution of \emph{T.
chinense} . The suitable range of main environmental factors in the
current suitable area of \emph{T. chinense} was shown in Table S6.
Specifically, the suitable range of Bio18 is 188.98 mm \textasciitilde{}
763.52 mm. The suitable value distribution of Bio11 was -26.47 to
13.22. The lowest suitable value for Bio08 was 9.08, and the highest
value was 26.38. In the future climate scenario, the value ranges of
Bio18, Bio11, and Bio08 show an increasing trend in the current suitable
area. With the increase of carbon emission concentration, the increase
rate will increase year by year (Table S6). It is expected that under
the climate scenario combination of 2090s-SSP5-8.5, Bio11 and Bio08 will
rise to the highest level, with an increase of 6.32 and 6.24 compared
with the current climate scenario. The highest value of Bio18 appears in
the 2090s-SSP1-2.6 climate scenario combination, increasing by
approximately 36.10 mm (Figure 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3} Changes of the dominant environmental factors in the
current suitable area of \emph{T. chinense} under different carbon
emission scenarios in the 2090s\emph{.}

\textbf{3.3 Distribution range of the suitable areas of \emph{T.
chinense} under current climate conditions}

Under the current climate conditions, the suitable area of the complete
ecological niche of \emph{T. chinense} is mainly distributed in three
vegetation areas (Boreal forest, Temperate seasonal forest, and
Shrubland) in the eastern of Asia (Figure 4A). The total area of
suitable areas is about 734.53 * 104 km2, among which the high, medium,
and low suitable areas are about 280.89 * 104 km2, 174.02 * 104 km2, and
279.62 * 104 km2, respectively. In China, the total area of the suitable
areas of \emph{T. chinense} is 509.26 * 104 km2, mainly distributed in
the humid and semi-humid areas, with a small amount of distribution in
the semi-arid area of northeast China (Figure 4B). The high suitable
areas of \emph{T. chinense} in China is approximately 273.72 * 104 km2,
mainly distributed in the subtropical evergreen broad-leaved forest
region, the warm temperate deciduous broad-leaved forest region, the
temperate grassland region and the temperate coniferous and deciduous
broad-leaved mixed forest region, accounting for 99.82\% of the total
area of high suitable areas, and a small amount of the tropical monsoon
forests, rainforest region and the cold temperate coniferous forest
region; The medium suitable areas are distributed around the high
suitable areas, from the south to the subtropical evergreen broad-leaved
forest region from the north to the cold temperate coniferous forest
region were distributed in large numbers, with an area of about 131.00 *
104 km2; The low suitable areas is about 104.54 * 104 km2, which is
mainly distributed in the subtropical evergreen broad-leaved forest
region, the temperate grassland region and the high cold vegetation
region on the Qinghai Tibet Plateau, accounting for about 36.82\%,
27.99\% and 13.17\%, respectively (Figure 4C).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 4} Distribution of potential suitable areas of \emph{T.
chinense} . (A): Current distribution range ; (B): The distribution
range and dry and wet areas of \emph{T. chinense} in China, among which
1-4 represent: 1: humid area, 2: sub-humid area, 3: semi-arid area, 4:
arid area; (C): The proportion of suitable areas of different grades
of\emph{T. chinense} in each vegetation areas in China, where the
abbreviations represent: CTCFR: Cold temperate coniferous forest region,
TCADBMFR: Temperate coniferous and deciduous broad-leaved mixed forest
region, TGR: Temperate grassland region, TDR: Temperate desert region,
WTDBFR: Warm temperate deciduous broad-leaved forest region, HCVRQTP:
High cold vegetation region on the Qinghai Tibet Plateau, SEBFR:
Subtropical evergreen broad-leaved forest region, TMFRR: Tropical
monsoon forests, rainforest region.

\textbf{3.4 Changes in the suitable areas of \emph{T. chinense} under
future climatic conditions}

Figure S2 and S3 showed the spatial distribution pattern and area change
of suitable areas at each level of the complete ecological niche
of\emph{T. chinense} under future climate conditions. In China, the
suitable areas will still be mainly distributed in the eastern and
southern of China under future climatic conditions. As climate warming
intensifies (increase of year and carbon emissions), the suitable areas
will gradually expand to the northern part of the current suitable range
and Tibet, and only slightly decrease in the southern part of the
suitable areas (Figure 5A). Under future climate change, it is expected
that \emph{T. chinense} will move towards higher altitude and higher
latitude areas in China (Figure 5B). Compared with the current
distribution of suitable areas, the total area of suitable areas
of\emph{T. chinense} in China will increase year by year with the
increase of carbon emissions, and the total area of \emph{T. chinense}
will expand to the maximum (557.82 * 104 km2) under the climate scenario
of 2050s-SSP3-7.0 (Figure 6). In all levels of suitable areas, the areas
of high suitable areas show a decreasing trend, while the areas of low
suitable areas and medium suitable areas show an increasing trend.
Moreover, the degree of change is gradually obvious with the increase of
year and carbon emissions. In the 2090s-SSP5-8.5, the area of high
suitable areas is reduced to the smallest, which decreases by 36.76\%
compared with the current situation. Under the 2090s-SSP5-8.5 scenario,
the area of low suitable areas expands to the largest, about 159.95 *
104 km2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{FIGURE 5} Spatial pattern, altitude, and latitude variations
of\emph{T. chinense} in China under different climatic scenarios. (A):
Spatial pattern changes; (B): Changes in altitude and latitude of the
suitable area for \emph{T. chinense} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{FIGURE 6} Changes in the areas of suitable areas of different
grades of \emph{T. chinense} under different climatic scenarios.

\textbf{3.5 Distribution and change of land use pattern in
climate-stable areas of \emph{T. chinense} in China}

Kappa coefficient was used to verify the simulation accuracy between the
simulation results in 2015 and the actual land use data in 2015. The
results showed that the overall accuracy of the simulation results of
land use change in 2015 was 0.992 and Kappa=0.990, indicating that the
PLUS model was reliable and could predict future land use distribution.
The contribution degree of driving factors of each land use type change
was shown in Table S7. The largest contribution of driving factor of
grassland areas change was distance from water, followed by annual
precipitation (Figure. 7B).

Under different climate scenarios, the total area of climate-stable
areas of \emph{T. chinense} in China is about 480.01 * 104 km2 (Figure
7A). According to the expansion and contraction trends of land use in
2010 and 2015, the distribution pattern of land use in 2050, 2070 and
2090 within the climate-stable areas is predicted (Figure S4). Among
them, the cultivated land, grassland, and unused land in the
climate-stable areas show a downward trend. And by 2090, they will
decrease by 1.63\%, 13.84\%, and 10.67\%, respectively, compared with
2015 (Figure 7C). In the future, the land use types transferred to
grassland types mainly are forest, unused land, and construction land.
From 2050 to 2070, the areas will transfer from forest, unused land, and
construction land to grassland is about 1.83 * 104 km2, 0.90 * 104 km2,
and 0.70 * 104 km2. With the increase of the years, the transferred
areas gradually increase, and it is estimated that the transferred areas
from 2070 to 2090 are about 3.00 * 104 km2, 0.97 * 104 km2, and 1.08 *
104 km2. In the future, grassland will mainly transfer to forest and
construction land, and the transferred areas will increase from 4.65 *
104 km2 and 1.98 * 104 km2 in 2050 and 2070 to 5.80 * 104 km2 and 2.26 *
104 km2 in 2070 to 2090 (Figure 7C and Table S8). In the future, the
area of forest, waters, and construction land in this region will show
an increasing trend, especially the construction land. By 2090, the area
of forest and water will increase by 5.51\% and 1.49\%, respectively.
Construction land will increase to 25.71 * 104 km2, expanded by about
46.94\%.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{FIGURE 7} Map of the climate-stable areas and land use change
of\emph{T. chinense} in China. (A): Climate-stable areas of \emph{T.
chinense} under different climate scenarios in China; (B): Contribution
of driving factors to the change of grassland; (C): Conversion and
change of land use type area in 2015, 2050, 2070, and 2090 within the
climate-stable areas of China.

\textbf{3.6 Wild tending areas of \emph{T. chinense}}

Under the current climate situation, the habitat of about 17.69 * 104
km2 in the high suitable areas will not be affected by future land use
change, which will be the stable habitat areas of the \emph{T.
chinense}in hundred years (Figure 8A). High, medium, and low suitable
wild tending areas were obtained by superposing the suitable layers
of\emph{T. chinense} under current and future climate scenarios in the
region. Guizhou Province has the largest area of high suitable wild
tending area, followed by Inner Mongolia Autonomous Region, Hebei and
Heilongjiang (Figure 8B). Based on the hot spot analysis of the area of
the high suitable wild tending areas (administrative units), the results
showed that there are 23 cities that are most suitable for planning the
wild tending areas of \emph{T. chinense} , such as Qiqihar City, Chifeng
City, Zunyi City (Figure 8B, S5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{FIGURE 8} The distribution of grassland-stable areas and wild
tending areas in the current high suitable areas of \emph{T. chinense}in
China. (A): The grassland-stable areas within the potential high
suitable areas of \emph{T. chinense} under the current climate scenario
in China; (B): Analysis on the provincial area and hot spots at city
level of high suitable wild tending area, where the abbreviations
represent: GXZ: Guangxi Zhuang Autonomous Region, GS: Gansu Province,
CQ: Chongqing City, BJ: Beijing, GZ: Guizhou Province, AH: Anhui
Province, ZJ: Zhejiang Province, YN: Yunnan Province, TA: Tibet
Autonomous Region, SC: Sichuan Province, SP: Shanxi Province, SD:
Shandong Province, SX: Shaanxi Province, HU: Hunan Province, HN: Henan
Province, HLJ: Heilongjiang Province, HB: Hebei Province, IM: Inner
Mongolia Autonomous Region, LN: Liaoning Province, JL: Jilin Province,
JX: Jiangxi Province.

\textbf{4. Discussion}

\textbf{4.1 The influence of environmental factors on \emph{T.
chinense}'s distribution}

Among the many environmental factors that affect the growth and
development of \emph{T. chinense} , only a few major environmental
factors affect its distribution. Under the current climate conditions,
water-related environmental factors (Bio18) have the highest impact on
the distribution of \emph{T. chinense} , which is basically consistent
with the research of Gao et al. (P. Gao et al., 2023). \emph{T.
chinense} is mostly found in shaded or humid areas, and has poor drought
resistance. If there is insufficient rainfall, its growth and
development will be inhibited (Tang et al., 2021; Chinese botanical
society, 1988). In the summer, excessive moisture in the soil or water
on the surface can lead to root rot and hinder its growth (Tang et al.,
2021; Yanar et al., 1997). In addition to precipitation, temperature
also has an important impact on the distribution of \emph{T. chinense} .
Among them, Bio11 and Bio08 play an important role in the distribution
of \emph{T. chinense} . The suitable value range of Bio11 is wide
(-26.47\textasciitilde{} 13.22), and it could be seen that \emph{T.
chinense} has high cold resistance. In cold winter, in the process of
overwintering and dormancy of \emph{T. chinense} , starch may be
degraded into soluble sugar mainly through starch phosphorylation to
ensure that there are sufficient carbohydrates to maintain its growth
and improve its stress resistance in low-temperature environment during
dormancy, so that it can overwinter smoothly (Song et al., 2015). At the
same time, the soluble sugar forms small molecular solutes under
low-temperature stress, which further improves its ability to resist
severe cold (Blasing et al., 2005). Bio08 represents the mean
temperature of wettest quarter. The spatial and temporal distribution of
precipitation in Asia is extremely uneven. There is more precipitation
in summer, and the wettest quarter is mainly concentrated in summer
(Zhan, 2013). When the summer temperature is greater than 30, seedling
collapse occurs in the\emph{T. chinense} . And the mortality rate of
annual individual seedling collapse is relatively high, which is
basically unable to survive (Song et al., 2015). This seriously affects
the renewal of the \emph{T. chinense} population and the continuation of
its species. The range of Bio08 (9.08 \textasciitilde{} 26.38) well
reflects this phenomenon (Luo \& Guo, 2012).

By the 2090s, the Bio11 and Bio08 within the suitable area of \emph{T.
chinense} have risen by 6.32\selectlanguage{ngerman}°C and 6.42°C, respectively. For
high-latitude areas within the suitable area, the increased winter
temperature may prevent \emph{T. chinense} from entering its hibernation
period, leading to continuous nutrient consumption and potentially
adverse effects on the growth and reproductive capacity of the following
year. For low-latitude areas in the suitable area, high temperatures may
not only exacerbate the lodging of \emph{T. chinense} , but also
accelerate leaf senescence, reduce the reuptake rate of N and P, and
weaken its ability to adapt to the environment (X. Li et al., 2023;
Vergutz et al., 2012). Therefore, in a century of climate change,
temperature increasing may be detrimental to the growth of \emph{T.
chinense} , reducing its ability to adapt to the environment and further
threatening the population. By the end of this century, the Bio18 may
increase to 438.51 mm. It is speculated that, with increasing summer
precipitation, the negative effect of the single temperature factor (the
increase in soil evaporation) on \emph{T. chinense} may be
counterbalanced by the increase in precipitation (the increase in soil
moisture) to reach an equilibrium state. However, supposing there is
further increase in precipitation or temperature, the balance may be
disrupted, creating water or temperature stress. In that case, it may
limit the growth of \emph{T. chinense} .

\textbf{4.2 Spatial transformation characteristics of \emph{T.
chinense}under climate change scenarios}

In this study, Biomod2 was used to predict the distribution of \emph{T.
chinense} in Asia, and the results were not completely consistent with
the regional niche results predicted by Tang et al. and Gao et al. using
the MAXENT model (P. Gao et al., 2023; Tang et al., 2021). It is
speculated that there are four reasons for this result. The first point
is the choice of modeling scope. In the existing studies, the methods of
constructing species distribution models are divided into complete and
regional niche models. Complete niche models are more reliable models
for describing the climatic niche of species because they can calibrate
the presence and capture a wider range of occupied environmental
conditions (Pearson et al., 2004). However, if unoccupied but
environmentally suitable areas for the species are considered for model
training, then the capacity to predict the species' potential
distribution will be reduced (Acevedo et al., 2012). Barve et al. argued
that the appropriate geographical background for model training,
validation, and comparison should comprise the set of localities that a
species has over its history (Barve et al., 2011). In this study, the
geographical distribution of \emph{T. chinense} was reviewed, and the
region with the highest accessibility (Asia) was selected as the study
area. In addition, the use of ensemble species distribution model can
reduce the instability of a single model (Goldsmit et al., 2020; Araújo
\& New, 2007), and can more accurately predict the range of suitable
areas of \emph{T. chinense} . Secondly, there is a serious bias in the
geographical distribution of the occurrence data used by Tang and Gao et
al. in constructing the model (P. Gao et al., 2023; Tang et al., 2021).
Through our field investigation, we found \emph{T. chinense} populations
also distribute in the high-latitude areas in the northern of China.
However, in the studies of Tang et al and Gao et al., there are few or
even no occurrence data of \emph{T. chinense} in high latitude areas,
relatively concentrated data in low latitude in the southern of China,
which is biased from the real occurrence data of \emph{T. chinense} .
This biased regional data cannot objectively identify the niche of
species (El\selectlanguage{english}-Gabbas \& Dormann, 2018). In addition to the above factors,
we speculate that the choice of factors is also one of the reasons for
the different results. As a semi-parasitic plant, vegetation (host) is
the premise for the good growth of \emph{T. chinense} , so biological
factors play a certain role in the distribution modeling of the species.
At present, the inclusion of interspecific interactions is considered to
be the main challenge for species distribution modeling (Y. Wang et al.,
2022). Considering that there are many host species of \emph{T.
chinense} , mainly Asteraceae, Fabaceae, and Poaceae, and the host
distribution is extensive (Guo \& Luo, 2011), we included NDVI data
reflecting vegetation growth in the species distribution model. Although
NDVI data can not describe the specific conditions of vegetation
classification groups in the land acquisition table, we believe that the
inclusion of NDVI data may make the modeling results closer to its real
niche for \emph{T. chinense} .

Compared with the current, the suitable areas of \emph{T. chinense} show
an increasing trend under the combination of future climate scenarios.
Spatially, consistently with the migration direction of most plant
species (B. Chen, Zou, et al., 2022; W. Li et al., 2019; Yin et al.,
2022; L. Zhang et al., 2022), the overall distribution areas gradually
move to the northwest of the current suitable areas (high latitude and
high altitude areas). In the future, the suitable areas of \emph{T.
chinense} in China will mainly expand to Tibet. The expansion area is
currently located in the semi-arid area, and its soil moisture content
can meet the needs of the growth of \emph{T. chinense} . However, due to
the high altitude, its temperature is lower than other semi-arid areas
suitable for the growth of \emph{T. chinense} . Therefore, this area is
not suitable for the growth of \emph{T. chinense} in the current
climate. In the future, the plateau temperate zone in the southern of
China will expand northward, gradually replacing the plateau sub-cold
zone in western China, and gradually increasing the temperature in this
region (S. Wu et al., 2010). Therefore, the climate conditions in this
area may be similar to those in the current suitable areas of \emph{T.
chinense} in the future, which may be one of the reasons for the
expansion of the suitable areas in this area. In the future, the
disappearing areas of suitable areas of \emph{T. chinense} are mainly
located in the southern of China, which will change from the subtropical
zone to the tropical zone with climate warming in the future (S. Wu et
al., 2010). At the same time, the increasing temperature will reduce the
moisture of the surface soil, increase evaporation, reduce the Climate
Moisture Index, and is not conducive to the absorption of soil fertility
(Michaelian et al., 2011; Peng et al., 2011). This will make the areas
no longer suitable for the growth of \emph{T. chinense} .

\textbf{4.3 Land use conversion characteristics and wild tending areas
planning in the climate-stable areas of \emph{T. chinense}}

Human activities are a major factor leading to global change, which
overwhelm the natural changes brought about by climate change in the
past few thousand years (Houghton et al., 1990). Human activities such
as agriculture, forestry, and other land management have changed the
entire landscape, thus affecting the flora and fauna communities of many
ecosystems worldwide (Ojima et al., 1994). In China, the climate-stable
areas of \emph{T. chinense} are mainly distributed in humid and
semi-humid areas, and the major changes of land use types in this area
in the future are mainly construction land and grassland. It is
predicted that the construction land in the region will expand rapidly
in the next hundred years, an increase of about 46.94\% compared with
2015. The distribution of construction land is mainly related to
population density. Future population projections indicate that although
the proportion of the population in the southeast of China will decrease
over the next 20 years, the change will only be between 0.1\% and 0.3\%
(L. Wang et al., 2014). Although the overall pattern of spatial
population distribution in China will not change fundamentally, with the
southeast being densely populated and the northwest being sparsely
populated, the scale and degree of population agglomeration in urban
agglomerations are gradually increasing, and the trend of population
agglomeration is more obvious (L. Wang et al., 2014) Therefore, this
study predicted that the rapid growth of the construction land
of\emph{T. chinense} in the future climate-stable areas in China is in
line with the actual situation.

In addition to the rapidly expanding construction land, the grassland
areas in the climate-stable areas have also changed significantly, and
their areas have decreased by 15.18\% compared with 2015. Grassland
ecosystem is the largest ecosystem in land surface types and occupies an
extremely important role in the terrestrial ecosystem. The impact of
human activities on the grassland ecological system in recent years
growing (X. Zhang et al., 2022). Since the 21st century, the Chinese
government has invested vigorously in ecological restoration projects
and carried out ecological restoration work, such as returning farmland
to grassland, which significantly affects the restoration of grassland
soil nutrients (carbon) (C. Chen et al., 2019). In the climate-stable
areas of \emph{T. chinense} in the next hundred years, there will be no
such transfer from 2050 to 2090, except that a small amount of
cultivated land will be transferred to grassland from 2015 to 2050.
Therefore, the soil nutrients restored by returning cultivated land to
grassland in the next hundred years will be very low. In the future, the
areas of construction land transferred to grassland in the region will
increase yearly. It is estimated that by 2090, the areas of construction
land transferred from 2070 will account for about 1.22\% of the
grassland areas in 2090. Large scale construction projects have not only
seriously damaged grassland vegetation and topsoil, resulting in a sharp
decline in grassland carbon storage, which will take many years to
recover, but also exacerbated climate change and indirectly affected
grassland ecosystem, causing a certain impact (X. Chen \& Shang, 2011).
Therefore, in the context of future climate change and land use change,
the natural habitat suitable for the growth of \emph{T. chinense} will
face a huge threat.

Under land use and climate change, although climate change increases the
total suitable areas of \emph{T. chinense} , it will seriously threaten
the high suitable areas. In the climate-stable area of \emph{T.
chinense} under different climate scenarios, the habitat areas suitable
for the growth of \emph{T. chinense} will gradually reduce due to the
change in land use. These future changes may limit the expansion of wild
populations. Furthermore, combined with excessive harvesting, wild
resources are increasingly depleted, making it difficult for the species
to continue. At present, wild resources can not meet the needs of
medicine, but its artificial breeding technology is not mature. And
according to the Non-grain Production policy in China, cultivated land
is forbidden to grow economic crops. Therefore, it is necessary to carry
out wild tending of \emph{T. chinense} , which can promote the recovery
and sustainable utilization of \emph{T. chinense} population. Wild
tending is an available approach to solve the contradiction between the
shortage of wild resources of medicinal plants and the large market
demand. It is a production mode of artificially or naturally increasing
the population of target medicinal materials, which can not only greatly
increase the available resources, but also maintain the community
balance after over-collecting, and continuously supply genuine-quality
medicinal materials (S. Chen et al., 2004). The wild tending area has
the characteristics of primitive environment, less human intervention
and far away from pollution sources. It is very important to predict the
change range of the suitable habitat of \emph{T. chinense} for
formulating agricultural policies and planting planning (Shen et al.,
2021). Based on the current high suitable areas of \emph{T. chinense} in
China, combined with the future climate and land use change, this study
planned the wild tending areas of \emph{T. chinense} for hundred years.
This can effectively avoid the huge economic losses caused by blind
planting. According to the results of the wild tending area, we made the
following recommendations: (1) The wild tending areas of \emph{T.
chinense} in China are mainly distributed in the north, northeast, and
southwest of China. In the future, most of the areas will be high
suitable area, and only a few areas will be transformed into medium or
low suitable areas. It is suggested that germplasm collection and
diversity evaluation of wild plants should be carried out in the climate
transformation areas, which is conducive to the conservation and
domestication of the core germplasm of \emph{T. chinense} . (2) The
local government should take more actions to protect the natural
ecology, enhance the awareness of the protection and restoration of
local natural land types, and avoid irreversible damage to them. (3) The
planned wild tending area of \emph{T. chinense} should be combined with
local policies, integrate ecological, economic, and social benefits, and
strengthen the establishment of wild tending medicinal material bases.
This will effectively solve the contradiction between \emph{T.
chinense}ecological protection and biodiversity, resources and supply
demand. (4) The precondition of artificial breeding and wild tending is
to master the basic characteristics of growth and development of TCM.
Therefore, we should strengthen the research on population ecology and
developmental biology, especially on the influence of different hosts on
the composition and the mixed cultivation mode with other crops. This
can not only maintain the stability of \emph{T. chinense} population,
but also improve the quality and yield of \emph{T. chinense} . At the
same time, the mixed cultivation mode can effectively make full use of
land resources and make up for the shortage of grassland resources in
some areas due to the dense population.

\textbf{5 Conclusions}

High-quality TCM should be planted in specific natural conditions and
ecological environments. Therefore, the prediction and suitability
evaluation of potential distribution areas of TCM have important guiding
significance for scientific planning of wild tending areas. This study
analyzed the change in the distribution range of \emph{T. chinense} in
China under future climate and land use changes. Although the stable
areas suitable for the growth of \emph{T. chinense} under climate change
are still large, the grassland in this area is decreasing year by year,
which may pose a threat to the population of \emph{T. chinense} in
China. To solve the contradiction between the supply and demand
of\emph{T. chinense} , we used ZONATION software and hot spot analysis
to determine the counties in China that are suitable for the wild
tending of \emph{T. chinense} . This will provide a scientific and
effective reference for the relevant government departments at all
levels when planning the wild tending area of \emph{T. chinense} , and
also provide an important reference for the diversity protection,
restoration and sustainable development of other wild medicinal species.
From the macro-view, the distribution of \emph{T. chinense} under the
future climate and land use change was explored. In future, we will
continue to explore other factors that affect the change of the wild
population structure of \emph{T. chinense} , and find the reasons for
the change of distribution pattern from a microscopic perspective. We
should strengthen the research on the correlation between the content of
effective components of \emph{T. chinense} and environmental factors and
hosts, and construct the spatial distribution layer of effective
components of \emph{T. chinense} , to provide a more accurate and
scientific theoretical basis for more proper planning of the wild
tending areas of \emph{T. chinense} population.

\textbf{AUTHOR CONTRIBUTIONS}

Boyan Zhang: Methodology (lead); software (lead); formal analysis
(lead); data curation (equal); writing---original draft preparation
(lead); writing---review and editing (lead). Bingrui Chen: Methodology
(equal); formal analysis (equal); writing---review and editing (lead).
Xinyu Zhou: Validation (lead); investigation (equal). Hui Zou:
Validation (equal); writing---review and editing (equal). Detai Duan:
Investigation (equal). Xiyuan Zhang: Validation (equal). Xinxin Zhang:
Conceptualization (lead); methodology (equal); writing---review and
editing (equal); supervision (lead).

\textbf{ACKNOWLEDGEMENTS}

This work was supported by the Fourth National Survey of Traditional
Chinese Medicine Resources Heilongjiang Special Project (grant number
2018Hljzyzypc-12). We are deeply grateful.

\textbf{DATA AVAILABILITY STATEMENT}

The original contributions presented in this study are included in the
article/Supplementary material, further inquiries can bedirected to the
corresponding author.

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