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\begin{document}

\title{Population genetic structure and evolutionary history of Psammochloa
villosa (Trin.) Bor (Poaceae) revealed by AFLP marker}



\author[1]{Xu Su}%
\author[2]{Ting Lv}%
\affil[1]{Qinghai Normal University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 03, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
We sought to generate a preliminary demographic framework for
Psammochloa villosa to support of future studies of this ecologically
important desert grass species, its conservation, and sustainable
utilization. Psammochloa villosa occurs in the Inner Mongolian Plateau
where it is frequently the dominant species and is involved in sand
stabilization and wind breaking. Here, we characterized the genetic
diversity and structure of 210 individuals from 43 natural populations
of P. villosa using amplified fragment length polymorphism (AFLP)
markers. We obtained 1728 well-defined amplified bands from eight pairs
of primers, of which 1654 bands (95.72\%) were polymorphic.All these
values indicate that there is abundant genetic diversity, but limited
gene flow in P. villosa. However, an analysis of molecular variance
(AMOVA) showed that genetic variation mainly exists within 43
populations of the species (64.16\%), and we found that the most
genetically similar populations were often not geographically adjacent.
Thus, this suggests that the mechanisms of gene flow are surprisingly
complex in the species and may occur over long distances. In addition,
we predicted the distribution dynamics of P. villosa based on the
spatial distribution modeling and found that its range has contracted
continuously since the last inter-glacial period. We speculate that dry,
cold climates have been critical in determining the geographic
distribution of P. villosa during the Quaternary period. Our study
provides new insights into the population genetics and evolutionary
history of P. villosa in the Inner Mongolian Plateau, which can be used
to design in-situ conservation actions and to prioritize sustainable
utilization of germplasm resources.%
\end{abstract}%



\sloppy


\textbf{Population genetic structure and evolutionary history of
\emph{Psammochloa villosa}(Trin.) Bor (Poaceae) revealed by AFLP marker}

\textbf{Running Head: Population genetic structure of \emph{P. villosa}}

Ting Lv\textsuperscript{1,2}, AJ Harris\textsuperscript{3}, Tao
Liu\textsuperscript{1,2}, Ruifang Liang\textsuperscript{4,5}, Zilan
Ma\textsuperscript{4,5}, Yuping Liu\textsuperscript{*4,5}, Xu
Su\textsuperscript{*1, 2, 4,5}

1 School of Geosciences, Qinghai Normal University, Xining 810008, P. R.
China

2 Key Laboratory of Education Ministry of Earth Surface Processes and
Ecological Conservation of the Qinghai-Tibet Plateau, Qinghai Normal
University, Xining 810008, P. R. China

3 Key Laboratory of Plant Resources Conservation and Sustainable
Utilization, South China Botanical Garden, Chinese Academy of Sciences,
Guangzhou 510650, P. R. China

4 Key Laboratory of Medicinal Plant and Animal Resources of the
Qinghai-Tibet Plateau in Qinghai Province, School of Life Science,
Qinghai Normal University, Xining 810008, P. R. China

5 Academy of Plateau Science and Sustainability, Xining 810016, P. R.
China

\textsuperscript{*} Correspondence to:

Xu Su - Phone: +86 971 6134763; fax: +86 971 6134763; E-mail address:
xusu8527972@126.com

OR

Yuping Liu - Phone: +86 971 6134763; fax: 86 971 6134763; E-mail
address: lyp8527970@126. com

AJ Harris contributed equally to this work.

\textbf{Abstract}

We sought to generate a preliminary demographic framework
for\emph{Psammochloa villosa} to support of future studies of this
ecologically important desert grass species, its conservation, and
sustainable utilization. \emph{Psammochloa villosa} occurs in the Inner
Mongolian Plateau where it is frequently the dominant species and is
involved in sand stabilization and wind breaking. Here, we characterized
the genetic diversity and structure of 210 individuals from 43 natural
populations of \emph{P. villosa} using amplified fragment length
polymorphism (AFLP) markers. We obtained 1728 well-defined amplified
bands from eight pairs of primers, of which 1654 bands (95.72\%) were
polymorphic. Results obtained from the AFLPs estimated a number of
effective alleles among populations of 1.3229, a Nei's standard genetic
distance value of 0.2056, a Shannon index of 0.3316, a coefficient of
gene differentiation (\emph{G} \textsubscript{ST}) of 0.4689, and a gene
flow parameter (\emph{N} m) of 0.5761. All these values indicate that
there is abundant genetic diversity, but limited gene flow in \emph{P.
villosa} . However, an analysis of molecular variance (AMOVA) showed
that genetic variation mainly exists within 43 populations of the
species (64.16\%), and we found that the most genetically similar
populations were often not geographically adjacent. Thus, this suggests
that the mechanisms of gene flow are surprisingly complex in the species
and may occur over long distances. In addition, we predicted the
distribution dynamics of \emph{P. villosa}based on the spatial
distribution modeling and found that its range has contracted
continuously since the last inter-glacial period. We speculate that dry,
cold climates have been critical in determining the geographic
distribution of \emph{P. villosa} during the Quaternary period. Our
study provides new insights into the population genetics and
evolutionary history of \emph{P. villosa} in the Inner Mongolian
Plateau, which can be used to design in-situ conservation actions and to
prioritize sustainable utilization of germplasm resources.

\emph{\textbf{Keywords}:} desert grasslands; population genetics;
SAMOVA; ecological niche modelling; Inner Mongolian Plateau;

\emph{Psammochloa villosa}

\textbf{Introduction}

The Quaternary period, comprising the Holocene and Pleistocene Epochs,
spanned the last \textasciitilde{}2.6 million years (Myr) and has been
characterized by distinct climatic oscillations, especially alternating
glacial and interglacial cycles in the Northern Hemisphere (Shackleton
\& Opdyke, 1973; Pillans \& Gibbard, 2012; Elias, 2013). The glacial
cycles co-varied with, and probably profoundly affected, other aspects
of the climate, including the intensity of the Asian monsoon, even in
unglaciated regions (An, Kutzbach, Prell, \& Porter, 2001; Jiang , Lang,
Tian, \& Guo, 2011; Liu et al., 2018). Climate fluctuations during the
Quaternary glaciations led to dramatic changes in the geographical
distribution, genetic structure, and population demography of plant
species (Hewitt, 2000; Wang et al., 2009; Jia et al., 2012; Liu et al.,
2018).

Quaternary climate change is known to have strongly affected the
distributions of plants in Northern China (Tian et al., 2009; Qiu, Fu,
\& Comes, 2011; Liu, Sun, Ge, Gao, \& Qiu, 2012). Northern China is
dominated by deserts, which developed in the Quaternary to due to
sustained orogenesis of the Qinghai-Tibetan Plateau and surrounding
areas that ultimately enhanced aridity within the region (Wu, 1992,
2002; Meng \& Zhang, 2011). The climatic processes that gave rise to the
deserts also shaped regional plant diversity and yielded highly complex
demographic histories of native species (Ge, Yu, Yuan, Huang, \& Yan,
2005; Ge et al., 2011; Yang, Fang, Dong, Peng, \& Li, 2006). In
particular, many plants within the deserts of Northern China underwent
adaptive and demographic change in response to cold, arid conditions,
which occurred during the intermittent glacial periods and which may be
primary mechanisms explaining modern distributions (e.g., Comes \&
Kadereit, 1998; Hewitt, 2000; Ge et al., 2011; Zhang \& Zhang, 2012; Su
\& Zhang, 2013; Zhang, Zhang, \& Williams, 2014; Xu \& Zhang, 2015;
Shuyskaya, Toderich, Gismatullina, Rajabov, \& Khohlov, 2017; Merklinger
et al., 2019; El-Tayeh, Galal, Soliman, \& Zaki, 2020). For example, Su
\& Zhang (2013) proposed that the onset of aridity during Quaternary
glacial periods was a primary driver of population processes and
structures in \emph{Nitraria sphaerocarpa} Maxim. (Nitrariaceae,
Sapindales), and Xu \& Zhang (2015) revealed that periods of cold, arid
conditions during the Pleistocene glaciations resulted in the genetic
differentiation and demographic structuring in \emph{Atraphaxis
frutescens} (L.) K.Koch (Polygonaceae).

Nevertheless, these prior studies on population histories of desert
plants of Northern China have focused primarily on woody species.
Studies on herbs of the region are largely lacking, and to our
knowledge, the only such study is on \emph{Delphinium naviculare} W. T.
Wang (Ranunculaceae) (Zhang \& Zhang, 2012), which is endemic at
mid-elevations within the Tianshan Mountains of Xinjiang Province of
China (Wang \& Warnock, 2001). Herbaceous plants of the deserts of
Northern China merit further study because they comprise a vital
component of desert plant communities, and studies focusing on dominant
grass species are especially warranted (Meng \& Zhang, 2011). Herbaceous
plants may have been more sensitive to Quaternary climatic oscillations
because they differ markedly from woody species in their response to
cold, often via death of aboveground biomass as either part of an
annulment or perennial life cycle. Moreover, dominant species likely
achieved their abundance due to their responses to the glacial cycles.

In modern times, deserts and semi-deserts, such as in Northern China,
are extremely fragile ecosystems, the stability of which impacts global
environmental conditions and influences climate change (Su, 2013).
Although deserts typically have sparse vegetation, plants are critical
for maintaining their integrity. In Northern China, the desert grassland
ecosystems in particular are becoming rapidly degraded due to long-term
overgrazing and desertification and, simultaneously, desert is
encroaching on arable land within the region (Li, Liu, \& Wang, 2004;
Hanafi \& Jauffret, 2008; Wang, 2009; Peters, Yao, Sala, \& Anderson,
2012; Deng, Zhang, \& Shangguan, 2014; State Forestry Administration,
2015; Wang \& Zhou, 2015; Wei, Wang, \& Niu, 2020). These desert
grasslands represent a large area within China and adjacent countries
and occur at both low and high elevations. Dominant plant species within
the grasslands are often psammophytes, which have special adaptations to
resist being buried by sand and to tolerate having periodically exposed
roots. Simultaneously, these plants help to anchor sands in place and
prevented wind erosion, and are thus critical for promoting
environmental stability within their ecosystems (Ma, 1991; Pan, 2006;
Zhou, Yuan, \& Jing, 2011).

In this study, we focus on one psammophyte, \emph{Psammochloa
villosa}(Trin.) Bor, which comprises a monotypic genus in tribe Stipeae
of Poaceae. This species, commonly called sand whip, is a perennial
rhizomatous herb that is primarily distributed in the desert grasslands
of northwestern China, especially the Inner Mongolian Plateau, the Hexi
Corridor of Gansu, central and Northern Ningxia, and Northern Shanxi
(Liu, 1985; Ma, 1994; Huang, 2003). It also occurs in Mongolia. The
flowering and fruiting period of \emph{P. villosa} is from September to
November, and the seeds are 5 - 7 mm long with an average weight of
5.507 \selectlanguage{ngerman}± 0.053 mg (Huang, 2003). Such light seeds are likely dispersed by
high winds that occur throughout much of its natural desert habitat.
Nevertheless, seedlings are rarely observed (Zhu,
2005).\emph{Psammochloa villosa} is known to have high resistance to
drought, cold, alkaline soils, disease, wind, and burial by sand, all of
which likely represent evolutionary adaptations and facilitate its
survival in grassland and dune areas (Lu, 1987; Huang, 1995, 1997; Wu \&
Phillips, 2006). Previous research on \emph{P. villosa} has been mainly
focused on studying its anatomy, embryology and microbiology (e.g., Dong
\& Alaten, 1999; Huang, Dong, \& Gutterman, 2004; Wang et al., 2011;
Zhang et al., 2017; Lv et al., 2018), with few studies so far focused on
molecular markers (e.g., Li \& Ge, 2001). The species is ecologically
widespread at low and high elevations (900-2900 m) (Kuo, 1987; Wu \&
Phillips, 2006).

In our present study, we investigated the influence of climate
aridification and oscillations on the genetic structure and evolutionary
processes of \emph{P. villosa}during the Quaternary in north-western
China using amplified fragment length polymorphism (AFLP) markers and
ENM. We used AFLPs because they remain extremely efficient for
investigating genetic diversity, genetic structure, and population
demography due to their high levels of polymorphism (Wang, Wang, Liu,
Yang, \& Chen, 2008), their reproducible, reliable results that are
unaffected by the developmental stage of plant materials, and their
universality among plant species. In addition, they have been used to
resolve genetic structures and population demography in many diverse
grass species such as \emph{Oryza sativa} , \emph{Leymus racemosus} ,
\emph{Orinus thoroldii} and \emph{O. kokonoricus} (Zhang \& Jia, 2002;
Sim, 2005; Li, 2015; Cai, 2017; Liu, Harris, Gao, Su, \& Ren, 2019). Our
main objectives were to (1) analyze the genetic structure from 43
populations of \emph{P. villosa} from Inner Mongolian Plateau using an
AFLP dataset, (2) test whether historical genetic divergence occurred
among populations in response to Quaternary climate oscillations, and
(3) evaluate the abiotic factors that are most influential in driving
the distributions of \emph{P. villosa} . Moreover, because no assessment
of the conservation needs of \emph{P. villosa} had previously been
accomplished, we also performed a preliminary assessment based on Extent
of Occurrence (EOO) with interpretation according to guidelines of the
International Union for the Conservation of Nature (IUCN). We believe
that, taken together, our results can provide a scientific basis for
improved protection and sustainable utilization (e.g., as forage) of
\emph{P. villosa} within the fragile desert grassland ecosystems of
Northern China.

\textbf{2 Materials and Methods}

\textbf{2.1 Population sampling}

We collected a total of 210 individuals from 43 populations of \emph{P.
villosa} in the field throughout its natural range in China (Table S1 \&
(Figure S1) and randomly sampled five to ten individuals from each
population. We sampled individuals 20 m apart to avoid sampling a single
clone more than once. In the field, we immediately put the fresh leaves
into sealed bags filled with silica gel and then stored them in the
laboratory in a -20\selectlanguage{english} freezer until processing. We deposited one voucher
specimen representing each population in the Herbarium of Qinghai-Tibet
Plateau Museum of Biology (QTPMB), Northwest Institute of Plateau
Biology, Chinese Academy of Sciences, China.

\textbf{2.2 DNA extraction and AFLP genotyping}

We extracted total DNA from each sample according to a modification of
the CTAB procedure (Doyle \& Doyle, 1987) and accessed DNA quality using
1.0\% agarose gel electrophoresis and A260/A280 ratio determined on a
Nanodrop 2000c. Our procedure to obtain AFLPs was based on a
modification of the method in Vos et al. (1995). First, we digested the
genomic DNA with the restriction enzymes \emph{EcoR} I and
\emph{MseI}for 5 h at 37degC, and ligated the adaptors of \emph{EcoR} I
and\emph{Mse} I to the digestion products over night at 4degC (Beijing
Dingguo Biotechnology Co., Ltd). Using the digested products, we
performed a two-stage PCR amplification comprising pre-amplification and
selective amplification. The selective amplification was conducted in 25
\selectlanguage{greek}μ\selectlanguage{english}l volume of reaction mixture containing of 1.0 \selectlanguage{greek}μ\selectlanguage{english}l \emph{EcoR}
I/\emph{Mse} I primer combinations (AAC/CAA, AAG/CAC, ACA/CAG, ACT/CAT,
ACC/CTA, ACG/CTC, AGC/CTG, AGG/CTT; Table 1). Subsequently, we separated
the fluorescently-labeled fragments on an ABI PRISM 377 DNA Calibrator
(Applied Biosystems) using GeneScan ROX-500 with an internal size
standard, allowing visual inspection of all individual sites (Liu,
Harris, Gao, Su, \& Ren, 2019). We recorded the presence or absence of
AFLP amplification bands (Figure S2) in a binary matrix as 1 or 0,
respectively, based on interpretations from GeneScan 3.1 (Applied
Biosystems). In total, we assessed 1728 AFLP markers for the 210
individuals, and all interpretations were performed randomly.

\textbf{2.3 Genetic diversity and population genetic structure}

For each population, we evaluated genetic diversity and population
genetic structure according to standard metrics in POPGENE 1.32 (Wang,
1996; Yeh, Yang \& Boyle, 1999). These metrics included the number of
individuals (N), percentage of polymorphic loci (PPL), observed number
of alleles (\emph{N} a), effective number of alleles (\emph{Ne} ),
Shannon's information index (\emph{I} ; Lewontin, 1972), expected
heterozygosity (\emph{H} e; Kimura \& Crow, 1964), Nei's genetic
diversity (\emph{h} ), total gene diversity (\emph{H} t), the average
gene diversity within populations (\emph{H} s), and Nei's standard
genetic distance (GD). We also calculated the degree of genetic
differentiation between populations (\emph{G} \textsubscript{ST}) as
(\emph{H} t--\emph{H} s)/\emph{H} t (Nei, 1973) and the parameter of
gene exchange as \emph{N} m = 0.5(1- \emph{G}
\textsubscript{ST})/\emph{G} \textsubscript{ST} (McDermott \& McDonald,
1993). Similarly, we estimated parameters of genetic diversity,
coefficients of gene differentiation and gene flow for eight pairs of
AFLP primers.

In order to search for partitions of sampling sites genetically
homogenous but maximally differentiated from each other, we conducted a
spatial analysis of molecular variance using SAMOVA 1.0 (Dupanloup,
Schneider, \& Excoffier, 2002) based on AFLP datasets. Within SAMOVA, we
used a \emph{K} -means method to select the best clustering among groups
of populations based on genetic variation coefficients (\emph{F}
\textsubscript{CT}) (Li et al., 2020). For values of \emph{K}in the
range two to ten, we set simulated annealing processes to 100 with
10,000 steps each. We selected the value of \emph{K} that
maximized\emph{F} \textsubscript{CT} values as the optimal grouping of
populations. Using this optimal grouping, we evaluated the genetic
variation between populations within groups and between groups in SAMOVA
1.0 via an analysis of molecular variance (AMOVA, Excoffier, Smouse, \&
Quattro, 1992) in ARLEQUIN v3.01 (Excoffier, Laval, \& Schneider, 2005).
Neutrality tests, such as Tajima's \emph{D} and Fu's \emph{Fs} , were
also calculated with this program. Subsequently, we determined the
correlation between \emph{F} \textsubscript{ST} inferred from the binary
matrix of scored AFLPs and geographic distance of the populations via a
Mantel test (Mantel, 1967) in GenAlEx 6.5 (Peakall \& Smouse, 2012) with
9999 permutations to evaluate significance.

To further investigate the genetic associations among 43 populations
of\emph{P. villosa} , we used the SAHN module in NTSYS-pc 2.10e (Rohlf,
1997) to generate a UPGMA tree from the genetic distance matrix derived
from the binary AFLP dataset, and also carried out a principal
coordinate analysis (PCoA) based on the distance matrix. Moreover, we
constructed a similarity-based network in SplitsTree 4.13 (Huson \&
Bryant, 2006) to infer the relationships between individuals and
populations by applying the Neighbor-Net algorithm with the Jaccard's
measure of distance.

In addition, we inferred groupings and genetic structures of populations
of \emph{P. villosa} using STRUCTURE V2.2 (Pritchard, Stephens, \&
Donnelly, 2000; Falush, Stephens, \& Pritchard, 2007; Hubisz, Falush,
Stephens, \& Pritchard, 2009), which differs from SAMOVA by not
requiring that groupings be geographically adjacent. In STRUCTURE, we
performed the analyses using an admixture model with independent allele
frequencies for 90 independent runs for the number of clusters (\emph{K}
) ranging from one to ten. We applied 2\selectlanguage{ngerman}×10\textsuperscript{5}repetitions
of the Markov chain Monte Carlo with a burn-in of 25\%. To determine the
best value of \emph{K} for the STRUCTURE analyses, we used the \selectlanguage{greek}Δ\selectlanguage{english}\emph{K}
statistical method (Evanno, Regnaut, \& Goudet, 2005).

\textbf{2.4 Distribution modeling of \emph{P. villosa}}

In order to predict the impact that Quaternary climatic oscillation
might have on the geographic distribution change of \emph{P. villosa} ,
we employed an ecological niche modelling (ENM) approach to evaluate the
potential distribution of \emph{P. villosa} at the Last Inter-Glacial
(LIG, \textasciitilde{} 120,000 - 140,000 years before present), the
Last Glacial Maximum (LGM, \textasciitilde{} 21,000 years before
present), the present and future times (2050s and 2070s), respectively.
In addition to the distribution records of our field surveys, we also
collected GPS data from the Chinese Virtual Herbarium (CVH, http:
//www.cvh.ac.cn), Global Biodiversity Information Facility (http: //www.
gbif.org), China National Specimen Information Infrastructure
(http://www.nsii.org.cn) and Specimen Resources Sharing Platform for
Education (http://mnh.scu.edu.cn/main.aspx) for \emph{P. villosa} . In
total, after removing duplicate and ambiguous records, we used 155
localities to generate spatial distribution models for \emph{P.
villosa}(Table S2). To improve abilities in establishing high-resolution
predictions and identifying the critical factors influencing the
species' distribution, we obtained 19 bioclimatic variables and three
geographic factors, such as altitude, slope and aspect, at 2.5 arc-min
resolution from WorldClim database (Hijmans, Cameron , Parra, Jones, \&
Jarvis, 2005, www.worldclim.org). The future climate data involved in
two emission scenarios of representative concentration pathways (RCP8.5
and RCP2.6) with the CCSM4 model (Van et al., 2011). We excluded highly
correlated variables according to Spearman's correlation test (Peterson
\& Nakazawa, 2008). Specifically, we selected the variables with a
relative contribution score [?] 0.8 or a correlation of \textless{} 0.75
compared to other variables. Based on the outcome of Spearman's, we
retained the eleven variables with the lowest correlations to build a
maximum entropy model for the habitat of\emph{P. villosa} using.
Subsequently, we generated this model representing the potential
distribution of \emph{P. villosa} in environmental space in MaxEnt
3.3.3k (Phillips, Anderson, \& Schapire, 2006; Phillips \& Dudik, 2008).
Within MaxEnt, we performed modeling with 75\% of localities randomly
selected for training and 25\% selected for testing 500 times
independently to ensure reliable results, and we evaluated model
performance using the area under the curve (AUC) of receiver operating
characteristic (ROC). The value of AUC ranges between 0 (randomness) and
1 (exact match), and the value above 0.9 indicated good performance of
the model (Swets, 1988). Additionally, we projected the predicted
geographic ranges of species based on the ENMs using ArcGIS 10.2. In
particular, we divided suitable habitat into four classes: highly
suitable habitat (0.5 [?] \emph{P} [?] 1.0), moderately suitable habitat
(0.3 [?] \emph{P} \textless{} 0.5), poorly suitable habitat (0.1 [?]\emph{P}
\textless{} 0.3) and unsuitable habitat (0.0 [?] \emph{P}\textless{} 0.1).

In order to measure the niche similarity between populations occurring
in groups, we calculated Schoener's \emph{D} (Schoener, 1968) and
standardized Hellinger distance (calculated as \emph{I} ) in ENMTools
1.3 (Warren, Glor, \& Turelli, 2008, 2010). We obtained the null
distribution of niche models in the identity test based on 1000 pseudo
replicates generated by random sampling from the data points pooled for
each pair of cluster. We determined measures of niche similarity
(\emph{D} and \emph{I} ) by comparing with null distributions drawn from
pooled occurrences retaining original cluster size, and we drew
histograms of frequency distributions using R 2.13
(http://www.r-project.org/).

\textbf{2.5 Conservation assessment of \emph{P. villosa}}

We performed a conservation assessment using the Extent of Occurrence
(EOO) (Moat, 2007; Velzen \& Wieringa, 2014) and following guidelines
for their interpretation from IUCN (2001). EOO comprises the minimum
convex polygon covering all known or predicted sites for the species. It
is frequently used as a preliminary assessment tool, such as when a new
species is described or when populations of a species are found in new
places or were locally extirpated (e.g., Velzen \& Wieringa, 2014;
Lachenaud et al., 2013). In the case of \emph{P. villosa} , there had
been no prior conservation assessment for the species, and it status is
not presently included in the IUCN Red List of Threatened Species (IUCN,
2020). Therefore, we analyzed the EOO using the location data of 155
populations from ecological niche model (Table S2).

\textbf{3. Results}

\textbf{3.1 Polymorphism of AFLP markers}

The eight primer pairs yielded 1728 clearly identifiable amplified
bands, of which 1654 (95.72\%) were polymorphic (Table 2). Different
primers yielded different numbers of bands ranging from 214 (\emph{E}
-ACG/\emph{M} -CTC) to 199 (\emph{E} -AGC/\emph{M} -CTG and\emph{E}
-AAG/\emph{M} -CAC) with an average of 207. The highest rate of
polymorphism for an individual primer was 99.07\%, and the lowest was
92.13\%. Overall, these eight primers showed high levels of polymorphism
among individuals of \emph{P. villosa} .

The indices, \emph{N} a, \emph{N} e, \emph{h} , and \emph{I} were
important indicators of genetic diversity of populations. According to
the genetic diversity index of eight primer pairs, the indices of
\emph{N} a,\emph{N} e, \emph{h} and \emph{I} ranged from 1.9950 to
2.0000 with an average of 1.9994, 1.2898 to 1.3445 with an average of
1.3229, 0.1926 to 0.2131 with an average of 0.2056, and 0.3183 to 0.3379
with an average of 0.3316, respectively. Overall, these indices showed
the highest genetic diversity for the primer pair of \emph{E}
-AGG/\emph{M} -CAC, while the lowest was for \emph{E} -ACT/\emph{M}
-CTT. In general, each of eight primer pairs appeared to facilitate a
robust assessment of genetic diversity in \emph{P. villosa} .

\textbf{3.2 Population Grouping}

Based on SAMOVA, we found that the \emph{F} \textsubscript{CT} value was
the highest when \emph{K} = 3, which resulted in separation of
populations into three groups. Among them, both the first group (P1-6)
and the second group (P7-12) contained six populations, and the third
group consisted of all other populations. However, three populations
(P6-8) were highly similar in their geographic distributions and their
habitats according to our field surveys, and this was why we did not
adopt \emph{K} = 4. When \emph{K} was \textgreater{} 4, at least one
group consisted of a single population (Table S3), indicating
over-splitting and loss of geographic structural data. Thus, we thought
it was the most reasonable to divide 43 populations of \emph{P.
villosa}into two major groups.

The first group, Group 1, identified using SAMOVA, consisted of 12
populations (P1-12), which mainly occurred in the central and eastern
regions of the Inner Mongolian Plateau, while the second one, Group 2,
was composed all other populations, which distributed throughout the
range of the species in China. Notably, the populations of Group 1 tend
to be found at lower elevations compared to those from Group 2 (Table S1
\& Figure S1). Groupings of populations can facilitate detection of
finer scale geographic structures (i.e., within groups) juxtaposed with
broader, regional patterns (i.e., between groups) (Li et al., 2020).
Therefore, we used the recovered grouping scheme for downstream data
analysis in our present study.

\textbf{3.3 Genetic diversity of populations}

The percentage of polymorphic loci (PPL), observed number of alleles
(\emph{N} a), effective number of alleles (\emph{N} e), expected
heterozygosity (\emph{H} e) and Shannon information index (\emph{I} ) of
43 populations were from 19.23 to 54.29, 0.5012 to 1.1155, 1.1315 to
1.2908, 0.0791 to 0.1771 and 0.1163 to 0.2709, respectively. The
PPL,\emph{N} a, \emph{N} e, \emph{H} e, \emph{I} of populations from
Group 1 were 32.63, 0.7193, 1.1848, 0.1100 and 0.1667, respectively,
while those of Group 2 were 32.02, 0.7322, 1.1817, 0.1082 and 0.1640
(Table S1). The genetic diversity indices showed the populations of
Group 2 exhibited greater variability than those of Group 1. However,
the difference of genetic diversity between two groups was minimal.
Overall, the results of genetic diversity analyses from 43 populations
of \emph{P. villosa}suggested that there was abundant genetic diversity
of this species within the Inner Mongolian Plateau.

\textbf{3.4 Genetic differentiation and distance of populations}

The genetic differentiation analyses based on 43 populations of \emph{P.
villosa} revealed that the total gene diversity (\emph{H} t) was 0.2052,
the average gene diversity within populations (\emph{H} s) was 0.1088,
the genetic differentiation between populations (\emph{G}
\textsubscript{ST}) was 0.4689, and the gene flow (\emph{N} m) was
0.5761. The value of \emph{N} m indicated that a limited level of gene
exchange existed within the species (Table 3). With respect to
populations of Group 1, the values of \emph{H} t, \emph{H} s and\emph{G}
\textsubscript{ST} were 0.1668, 0.1102, and 0.3402, respectively, while
these were 0.1918, 0.1083, and 0.4349 for Group 2. Thus, the genetic
diversity of Group 1 was lower than that of Group 2, and the genetic
differentiation was smaller. The gene flow among populations within
Group 1 and Group 2 was 0.9996 and 0.6605, respectively, which revealed
that the gene exchange among populations within Group 1 was more
frequent.

\textbf{3.5 Population genetic structure}

Analysis of molecular variance (AMOVA) based on the AFLP dataset from 43
populations of \emph{P. villosa} showed that the proportion of genetic
variation among populations was 35.84\%, while that within populations
was 64.16\%, and the value of average pairwise\emph{F}
\textsubscript{ST} was 0.35841 (\emph{P} \textless{} 0.001) (Table 4),
which showed that the genetic variation of \emph{P. villosa}mainly
occurred within populations. More interestingly, when aggregating
populations into Groups 1 and 2, we found that 22.38\% of the genetic
variation occurred among populations within groups (\emph{F}
\textsubscript{CT} = 0.28501, \emph{P} \textless{} 0.001), while most of
genetic variation (56.14\%) existed within populations (\emph{F}
\textsubscript{ST} = 0.43865, \emph{P} \textless{} 0.001) (Table 4).
Overall, the genetic variation at the population- and local
geographic-scale was much higher than regionally in \emph{P. villosa} .
Additionally, the result of neutrality test suggested the value of
Tajima's \emph{D} and Fu's \emph{Fs} was positive, but non-significant
for all populations of \emph{P. villosa} (Table 3).

The Mantel test revealed that there was a significant positive
correlation between geographic distance and \emph{F}
\textsubscript{ST}for 43 populations (\emph{r} = 0.282, \emph{P}
\textless{} 0.05) (Figure 1). Similarly, we detected a strong,
significant, positive correlation between geographic distance and
\emph{F} \textsubscript{ST} for Group 1 (\emph{r} = 0.622, \emph{P}
\textless{} 0.05) and a weak but significant positive correlation for
Group 2 (\emph{r} = 0.372, \emph{P} \textless{} 0.05). Simultaneously,
results from UPGMA tree, SplitsTree network, PCoA, and STRUCTURE
suggested that 43 populations of \emph{P. villosa}were divided into two
groups, which were largely consistent with our assessment using SAMOVA
(Figure 2 - 5).

\textbf{3.6Distributional change of \emph{P. villosa}}

ENMs for \emph{P. villosa} yielded relatively high AUC, demonstrating
reliable model performance (AUC = 0.969, Figure S3). For the eleven
non-biological variables used for modeling, the most significant factor
for the spatial distribution pattern of \emph{P. villosa} was altitude
(Alt), followed by temperature annual range (bio 7) and precipitation of
warmest quarter (bio 18), whose contribution rates were 40.0\%, 17.2\%
and 16.7\%, respectively (Table 5). In comparison with the LIG, we
observed a contraction in highly suitable habitat during the LGM based
on the MaxEnt models (Table 6 \& Figure 6). Similarly, the spatial
distribution of the present was continuously shrinking compared to the
potential range during the LGM (Figure 7). The simulated distribution
based on present climate data was mostly congruent with the actual
distribution range of \emph{P. villosa} , which was mainly distributed
in the Inner Mongolia Plateau with an area of approximately 111.2450 \selectlanguage{ngerman}×
10\textsuperscript{4} km\textsuperscript{2} (Table 6 \& Figure7).
Simultaneously, we estimated the future changes in the potential spatial
distribution under the RCP 2.6 and RCP 8.5 scenarios for the 2050 s and
2070 s. According to the future model predictions, the areas of suitable
habitat is likely to remain stable under the climatic scenario of RCP
2.6 for the 2050s and 2070s, whereas there was an increase of highly
suitable areas based on RCP 8.5 (Table 7 \& Figure 8).

When we compared the niches of hypothesis of niche identity was rejected
when the empirically observed value for \emph{D} and/or \emph{I} was
significantly lower than the values expected from the pseudo-replicated
data sets. Therefore, identity tests between two groups indicated that
there was distinct niche differentiation (\emph{P} \textless{} 0.01)
(Figure 9). The niche of two groups differs mainly in that it was
characterized by high elevation and temperature.

\textbf{3.7 Conservation status of \emph{P. villosa}}

IUCN is a global classification standard system for threatened species.
Its main purpose is to provide a clear and objective framework for the
classification of species according to their extinction
risk.\emph{Psammochloa villosa} qualifies as a species of least concern
(LC) under the EOO criterion (EOO = 2,064,370 km²). However, after
observing populations at 43 sampling locations during our field work, we
noted that some populations of the species presently grew in severely
degraded habitat. Thus, while this species was probably not currently
threatened, decreases in its frequency within population and the
abundance of populations could jeopardize its vital ecological role.
Therefore, we advocate for continued ecological monitoring of this
dominant, key-stone desert grass species.

\textbf{4. Discussion}

\textbf{4.1Genetic diversity of \emph{P. villosa}}

Genetic diversity refers to intraspecific genetic variation and is
closely linked to the evolutionary potential of a species to adapt to
adverse environments (Ma \& Qian, 1994). The study of genetic diversity
can facilitate understanding of spatial genetic patterns of a species,
and elucidate its evolutionary processes and adaptive mechanisms (Hao,
2005). In the present study, we observed high genetic diversity at
species-level in \emph{P. villosa} (\emph{P} =95.72\%, \emph{I} =
0.3316) and the population-level (\emph{P}= 32.02\%, \emph{I} = 0.1640)
(Table 2 \& Table S1). Compared to genetic diversity in other species of
Poaceae assessed using AFLPs, genetic diversity in \emph{P. villosa} was
slightly lower than that of\emph{Chascolytrum bulbosum} (\emph{P} =
98.2\%, \emph{h} = 0.1500; Silva, Essi, Welker, \& de, Souza-Chies,
2016), and was higher than that of \emph{Leymus chinensis} (\emph{P} =
16.53\%, \emph{I} = 0.0890),\emph{Leymus racemosus} (\emph{P} = 16.53\%,
\emph{I} = 0.0890), and\emph{Dactylis glomerata} (\emph{P} = 61.70\%,
\emph{I} = 0.2664) (Gong et al., 2007; Cai, 2016; Zhang, He, Zhao,
Zhang, \& Xu, 2017).

The underlying drivers of genetic diversity within species are generally
a combination of biological properties, such as dispersal abilities and
life history, and environmental factors, such as climate and
anthropogenic activities (Loveless \& Hamrick, 1984; Hamrick \& Godt,
1996; Wang \& Hu, 1996; Wen, Han, \& Wu, 2010). The life history
of\emph{P. villosa} frequently involves clonal reproduction via its
rhizomes under harsh environmental conditions, although the species
reproduces sexually by seed following wind pollination (Wang, Ge, \&
Dong, 1999; Li \& Ge, 2001). In comparison with \emph{L. chinensis}
,\emph{L. racemosus} , and \emph{D. glomerata} , the relatively high
genetic diversity of \emph{P. villosa} might be explained by one or more
of several factors. Among these, our study design comprised more
populations, which might lead to greater accuracy. However, biological
explanations are more likely and include possible higher clonal fitness
due to \emph{P. villosa} having extremely robust, hardy rhizomes; high
rates of seed production; and seedling regeneration, while rare within
any one growing season, occurring often over the long lifetime of the
species (Eriksson \& Bremer, 1993; Helena \& Mikko, 1996; Shimizu et
al., 1998).

\textbf{4.2 Genetic differentiation and genetic structure}

Genetic structure of a species is effectively the sum of genetic
differentiation among and within populations (Hamrick \& Godt, 1989).
Overall, genetic structure occurring among populations results from the
evolutionary history of the species in question; natural selection;
genomic factors (e.g., mutations, reorganization, and genetic drift);
and biological characteristics, including gene flow, mating system, mode
of reproduction, and seed dispersal mechanisms (Slatkin, 1987; Zhen,
2010). Genetic differentiation is primarily controlled by aspects of
gene flow, such as its rate and directionality (Hamrick \& Godt, 1989).
In plants, gene flow occurs primarily via the transmission of pollen and
seeds during sexual reproduction. However, for clonal species, such
as\emph{P. villosa} , asexual propagules often have limited dispersal
distance, and this restricts gene flow among populations (Xia, Li, \&
Li, 2002).

For \emph{P. villosa} , we inferred that more than 56\% of the genetic
variation existed within populations, with average pairwise\emph{F}
\textsubscript{ST} of 0.35841 for all 43 population and gene flow
(\emph{N} m) of 0.5761 (Table 4). According to Wright's (1978) theory,
genetic differentiation among populations might be large when\emph{F}
\textsubscript{ST} \textgreater{} 0.25, but this can be mitigated by
gene flow of \emph{N} m \textgreater{} 1, which can reduce the effects
of genetic drift and prevent genetic differentiation among populations.
In \emph{P. villosa} , we found high\emph{F} \textsubscript{ST} but
limited gene flow, which should yield high rates of between population
differentiation. However, we found higher rates of within population
differentiation for the species. This differs from findings in other
studies of \emph{P. villosa} that revealed greater genetic variation
among populations, such as in Li \& Ge (2001), who studied genetic
diversity in \emph{P. villosa} using and seven populations, of which two
were from Shihuimiao Ecological Station and five were from the
Shilongmiao Ecological Station. Similarly, Wang et al. (1999) assessed
the genetic diversity of four populations of\emph{P. villosa} from
mobile and fixed sand dunes in the Shihuimiao Ecological Station (two
populations) and the Shilongmiao Ecological Station (two populations)
and found greater genetic variation among populations. Nevertheless,
population genetic structure was a comprehensive result of a variety of
factors that were related to the evolutionary origins and modes of
dispersal and sexual reproduction, which all might be unique to each
population within a species (Wang, Ge, \& Dong, 1999). Accordingly, we
inferred this inconsistency may be related to the numbers of populations
and geographic location. Our sampling, which included a larger number of
populations and a greater portion of the geographic range of\emph{P.
villosa} , might have yielded results that more robustly detect patterns
among unique populations. In addition, the efficiency and accuracy of
gene identification are also related to the number of polymorphic bands,
and the results obtained by different experimental methods are
different.

In this study, our SAMOVA analysis revealed two well-defined groups
corresponding to Group1 and Group2 (Table S3). These groups are also
consistent with our analyses using UPGMA, STRUCTURE, SplitsTree, and
PCoA (Figure 2-5). Within populations of Groups 1 and 2, the average
genetic variation was 64\% with an average pairwise\emph{F}
\textsubscript{ST} value of 0.43865. The gene flow (\emph{N} m) in
Groups 1and 2 was 0.9996, 0.6605, respectively (Table 4). In addition,
the UPGMA tree revealed that populations with closer geographic
distances did not always cluster together, and we observed a similar
pattern in the results from STRUCTURE and SplitsTree. Similarly, the
PCoA showed that individuals from the same population did not always
group together, further indicating that, while gene flow may be limited
in \emph{P. villosa} , it occurs over long distances more often than
between adjacent populations. This may suggest that some critical
dispersal vector, such as birds, is yet-unknown for the species.
Overall, \emph{P. villosa} has undergone considerable genetic divergence
and has a high level of genetic structure based on the combined results
from our population genetics analyses.

Based on a Mantel test, we found that there was significant correlation
between genetic distance and geographic distance, indicating that
geographic distance is an important factor affecting the genetic
structure of \emph{P. villosa} . Therefore, we inferred that genetic
structure might have been resulted mainly from geographic isolation
imposed by mountains (e.g., Yin Mountains; Helan Mountains) and large
deserts in northwestern China (e.g. Tengger Desert; Mu Us Sandy Land) as
well as range contraction and population fragmentation induced by
climatic oscillations (e.g., \emph{Gymnocarpos przewalskii}
Maxim.;\emph{Helianthemum songaricum} Schrenk) (Liu, 1995; Su, Zhang, \&
Sanderson, 2011; Ma, Zhang, \& Sanderson, 2012; Meng, Gao, Huang, \&
Zhang, 2014). In addition, founder effects and population bottlenecks
might have also contributed to the genetic structures of the species
(Birky, Fuerst, \& Maruyama, 1989; Liu et al., 2015).

\textbf{4.3 Demographic historyof\emph{P. villosa}}

The genetic diversity within the Group 1, identified according to
SAMOVA, was lower than that of the populations in Group 2 (Table~3).
Based on this, extant populations of this species originated from the
genetic stock of Group 2, as geographic areas with both high genetic
diversity and frequency of dominant genes usually represent centers of
origins for source populations (Vavilov, 1926). However, our study
design and results cannot discern the exact center of origin for the
species nor the main migrational patterns of \emph{P. villosa} , and
accomplishing this will require additional molecular data and
informatics approaches.

Climate oscillation during the Quaternary has often been hypothesized to
be an important factor in influencing the current geographical
distribution and demographic history of plant species (Hewitt, 2004; Su
\& Zhang, 2013). One widely utilized approach to comparing past and
future distributions of plant species and determining the primary
environment factors driving them is via ENM (e.g., Nabout, Magalhães,
Ma, \& Da, 2016; Bai et al., 2017; Huang et al., 2017; Noulèkoun, Chude,
Zenebe, \& Birhane, 2017; Swanti, Kusum, Dhruval, \& Rajkanti, 2018;
Wei, Wang, Hou, Wang, \& Wu, 2018). Based on the neutral test, we found
that the species did not expand its range during the Quaternary.
Although these results were not statistically significant (Table 3), our
ENMs, in general, show that the range of \emph{P. villosa} contracted
from the LGM to the present (Figure 6-7). Specifically, our models show
that the range of \emph{P. villosa} was the most extensive during the
LIG period and included the northeast edge of the Qinghai-Tibet Plateau,
Tarim Basin, Tianshan Mountains, Inner Mongolia Plateau, and the western
regions of DaXinggan Ling. The range became limited to the Inner
Mongolia Plateau, Ordos Plateau, and the Yinshan-Helanshan area during
the LGM. The contraction of the range is likely the result of glaciation
and climatic shifts within the Tianshan Mountains and Tarim Basin, where
temperatures dropped significantly as glaciation developed on a large
scale in the Northern Hemisphere during the early-Middle Pleistocene
(Williams, Dunkerley, De, Dekker, Kershaw, \& Stokes, 1993; Yi et al.,
2004; Shi, Cui, \& Su, 2005; Lehmkuhl \& Owen, 2005; Xu et al., 2010;
Meng, Gao, Huang, \& Zhang, 2014). Nevertheless, it is surprising that
the species range did not rebound as temperatures grew warmer following
the LGM. This may be because of the onset of extreme aridity within the
region during the Quaternary period, as this is widely-known to have
played a significant role in determining the geographic distribution and
evolutionary history of many plant species (Meng \& Zhang, 2011; Su,
Zhang, \& Sanderson, 2011; Su \& Zhang, 2013). For example, in a
previous study of \emph{Helianthemum songaricum} (Cistaceae), which
occurs in Northern China and adjacent desert areas of central Asia (Yang
\& Gilbert, 2007), the worsening of the dry climate restricted the
distribution range, and acceptable habitats for the species gradually
became reduced and isolated (Su, Zhang, \& Sanderson, 2011). Besides, it
needs to explain that AFLP dataset used in this manuscript do not reveal
too many informative sites, so more markers should be selected to
discuss the evolutionary history of \emph{P. villosa} in the future.

\textbf{4.4 Germplasm conservation of \emph{P. villosa}}

\emph{Psammochloa villosa} is a dominant species in its desert habitat
and sometimes the only herbaceous species occurring within its plant
community. It helps to maintain a fragile desert ecosystem by preventing
wind erosion, development of quicksand, and further desertification
(Cai, 2016). While we found that it is a species of least concern (LC)
based on EOO, we believe that it can only continue to perform its vital
ecosystem services if its populations remain large and abundant.

This species may have great potential for sustainable utilization as a
forage plant for livestock. The sand whips have relatively long
inflorescences with large spikes that make it suitable for forage.
Moreover, its adaptations to drought may make it a valuable source of
genetic resources for molecular breeding of other crop and forage
species as, presently, it is one of few forage species that can
withstand the intensifying long-term drought conditions in northwest
China. Developing a sustainable use strategy for \emph{P. villosa} will
also help to ensure its continued availability as a keystone species
within desert communities of the Inner Mongolian Plateau and adjacent
areas.

\textbf{Conflict of interest}

None declared.

\textbf{Author contributions}

\textbf{Ting Lv:}Investigation (equal); Conceptualization (equal); Data
curation (equal); Methodology (lead); Formal analysis (equal);
Writing\selectlanguage{english}-original draft (lead); Writing\selectlanguage{english}-review \& editing (equal).
\textbf{AJ Harris:} Conceptualization (equal); Writing\selectlanguage{english}-original draft
(lead); Writing\selectlanguage{english}-review \& editing (equal). \textbf{Tao Liu:}
Investigation (equal); Formal analysis (equal); Methodology
(supporting). \textbf{Ruifang Liang:} Data curation (equal); Formal
analysis (equal); Methodology (supporting).\textbf{Zilan Ma:}
Investigation (equal); Formal analysis (equal); Methodology
(supporting). \textbf{Yuping Liu:} Conceptualization (equal); Formal
analysis (equal); Methodology (supporting); Funding acquisition (lead);
Writing\selectlanguage{english}-review \& editing (equal). \textbf{Xu Su:} Investigation
(equal); Conceptualization (equal); Formal analysis (equal);
Writing\selectlanguage{english}-original draft (supporting); Project administration
(supporting); Supervision (lead); Writing\selectlanguage{english}-review \& editing (equal).

\textbf{Data accessibility}

All tables and figures supporting the results and conclusions were
included in the article, except for the binary scoring of AFLP bands,
which we have submitted to the Dryad Digital Repository
(https://doi.org/10.5061/dryad.dbrv15f0v) and provided an available link
for review at
https://datadryad.org/stash/share/aepU6ms8Yp7kveC0mQTnYjxptp2GhAr3OUCteVtm5B4.

\textbf{ORCID}

Ting Lv: https://orcid.org/0000-0002-4751-8750

\textbf{Acknowledgements}

We greatly appreciate Dr. Jiabin Zou helping to improve an earlier draft
of our manuscript. This work was financially supported by the National
Natural Science Foundation of China (Grant Nos. 41761009 and 31800310),
the Natural Science Foundation of Qinghai Province (Grant No.
2019\selectlanguage{english}-ZJ\selectlanguage{english}-7011), The Dawn of West China'' Talent Training Program of the
Chinese Academy of Sciences (2019-1-4) via grants made to XS.

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\textbf{Table 1} Adapters and primer combination sequences used in the
present study\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Primer & Name & Sequence\tabularnewline
\midrule
\endhead
Adapters & Adapters & Adapters\tabularnewline
E-L & \emph{Eco}R I-adapter & 5'-CTCGTAGACTGCGTACC-3'\tabularnewline
E-R & \emph{Eco}R I-adapter & 5'-AATTGGTACGCAGTCTAC-3'\tabularnewline
M-L & \emph{Mse} I-adapter & 5'-GACGATGAGTCCTGAG-3'\tabularnewline
M-R & \emph{Mse} I-adapter & 5'-TACTCAGGACTCAT-3'\tabularnewline
Preamplification primer & Preamplification primer & Preamplification
primer\tabularnewline
P01 & \emph{Eco}R I & 5'-GACTGCGTACCAATTCA-3'\tabularnewline
P02 & \emph{Mse} I & 5'-GATGAGTCCTGAGTAAC-3'\tabularnewline
Selective amplification primer & Selective amplification primer &
Selective amplification primer\tabularnewline
A-1 & \emph{Eco}R I-AAC & 5'-GACTGCGTACCAATTCAAC-3'\tabularnewline
& \emph{Mse} I-CAA & 5'-GATGAGTCCTGAGTAACAA-3'\tabularnewline
A-2 & \emph{Eco}R I-AAG & 5'-GACTGCGTACCAATTCAAG-3'\tabularnewline
& \emph{Mse} I-CAC & 5'-GATGAGTCCTGAGTAACAC-3'\tabularnewline
A-3 & \emph{Eco}R I-ACA & 5'-GACTGCGTACCAATTCACA-3'\tabularnewline
& \emph{Mse} I-CAG & 5'-GATGAGTCCTGAGTAACAG-3'\tabularnewline
B-1 & \emph{Eco}R I-ACT & 5'-GACTGCGTACCAATTCACT-3'\tabularnewline
& \emph{Mse} I-CAT & 5'-GATGAGTCCTGAGTAACAT-3'\tabularnewline
B-2 & \emph{Eco}R I-ACC & 5'-GACTGCGTACCAATTCACC-3'\tabularnewline
& \emph{Mse} I-CTA & 5'-GATGAGTCCTGAGTAACTA-3'\tabularnewline
C-1 & \emph{Eco}R I-ACG & 5'-GACTGCGTACCAATTCACG-3'\tabularnewline
& \emph{Mse} I-CTC & 5'-GATGAGTCCTGAGTAACTC-3'\tabularnewline
D-1 & \emph{Eco}R I-AGC & 5'-GACTGCGTACCAATTCAGC-3'\tabularnewline
& \emph{Mse} I-CTG & 5'-GATGAGTCCTGAGTAACTG-3'\tabularnewline
D-2 & \emph{Eco}R I-AGG & 5'-GACTGCGTACCAATTCAGG-3'\tabularnewline
& \emph{Mse} I-CTT & 5'-GATGAGTCCTGAGTAACTT-3'\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} Summary statistics for eight selective primer
combinations of amplified fragment length polymorphism (AFLP) in the
present study\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
Selective nuclear & Polymorphism band & Amplification band & PPL (\%) &
Size range (bp) & \emph{N}a & \emph{N}e & \emph{h} & \emph{I} &
\emph{H}t & \emph{H}s & \emph{G}st & \emph{N}m\tabularnewline
\midrule
\endhead
E-AAC/M-CAA & 201 & 216 & 93.06 & 69.5-501.5 & 1.9950 & 1.3397 & 0.2118
& 0.3377 & 0.2101 & 0.1173 & 0.4415 & 0.6326\tabularnewline
E-AAG/M-CAC & 199 & 216 & 92.13 & 69.5-501.5 & 2.0000 & 1.3261 & 0.2030
& 0.3251 & 0.2033 & 0.1127 & 0.4457 & 0.6219\tabularnewline
E-ACA/M-CAG & 206 & 216 & 95.37 & 69.5-501.5 & 2.0000 & 1.3201 & 0.2079
& 0.3367 & 0.2073 & 0.1133 & 0.4532 & 0.6033\tabularnewline
E-ACT/M-CAT & 211 & 216 & 97.69 & 69.5-501.5 & 2.0000 & 1.3259 & 0.2084
& 0.3369 & 0.2073 & 0.0903 & 0.5645 & 0.3857\tabularnewline
E-ACC/M-CTA & 211 & 216 & 97.69 & 69.5-501.5 & 2.0000 & 1.3060 & 0.1988
& 0.3243 & 0.1992 & 0.1122 & 0.4370 & 0.6442\tabularnewline
E-ACG/M-CTC & 214 & 216 & 99.07 & 69.5-501.5 & 2.0000 & 1.2898 & 0.1926
& 0.3183 & 0.1922 & 0.1132 & 0.4111 & 0.7162\tabularnewline
E-AGC/M-CTG & 199 & 216 & 92.13 & 69.5-501.5 & 2.0000 & 1.3445 & 0.2131
& 0.3379 & 0.2133 & 0.1075 & 0.4958 & 0.5085\tabularnewline
E-AGG/M-CTT & 213 & 216 & 98.61 & 69.5-501.5 & 2.0000 & 1.3311 & 0.2091
& 0.3358 & 0.2085 & 0.1039 & 0.5020 & 0.4961\tabularnewline
Total & 1654 & 1728 & - & - & - & - & & - & - & - & & -\tabularnewline
Average & 207 & 216 & 95.72 & - & 1.9994 & 1.3229 & 0.2056 & 0.3316 &
0.2052 & 0.1088 & 0.4689 & 0.5761\tabularnewline
\bottomrule
\end{longtable}

Note. PPL, percentage of polymorphic loci; \emph{N} a, observed number
of alleles; \emph{N} e, effective number of alleles; \emph{h} , Nei's
genetic diversity; \emph{I} , Shannon's information index. \emph{H} t,
total gene diversity; \emph{H} s, the average gene diversity within
populations;\emph{G} \textsubscript{ST}, the genetic differentiation
between populations; \emph{N} m, gene flow.

\textbf{Table 3} Genetic diversity, differentiation parameters, and
neutrality test for 43 populations of \emph{P. villosa} in the present
study\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
Population group & \emph{N}a & \emph{N}e & \emph{h} & \emph{I} &
\emph{H}t & \emph{H}s & \emph{G}\textsubscript{ST} & \emph{N}m & Fu's
\emph{Fs} (\emph{P}-Value) & Tajima's \emph{D}
(\emph{P}-Value)\tabularnewline
\midrule
\endhead
Populations of Group1 (1-12) & 1.7645 & 1.2681 & 0.1668 & 0.2656 &
0.1668 & 0.1102 & 0.3402 & 0.9996 & 3.2491 (0.5701) & 0.1134
(0.5885)\tabularnewline
Populations of Group2 (13-43) & 1.9710 & 1.3001 & 0.1921 & 0.3116 &
0.1918 & 0.1083 & 0.4349 & 0.6605 & 3.3578 (0.5738) & 0.0592
(0.5806)\tabularnewline
All populations & 1.9994 & 1.3229 & 0.2056 & 0.3316 & 0.2052 & 0.1088 &
0.4689 & 0.5761 & 3.3274 (0.5702) & 0.0744 (0.5804)\tabularnewline
\bottomrule
\end{longtable}

\emph{N} a, observed number of alleles; \emph{N} e, effective number of
alleles; \emph{h} , Nei's genetic diversity; \emph{I} , Shannon's
information index. \emph{H} t, total gene diversity; \emph{H} s, the
average gene diversity within populations; \emph{G} \textsubscript{ST},
the genetic differentiation between populations; \emph{N} m, gene flow.

\textbf{Table 4} Results of analyses of molecular variance (AMOVAs)
based on amplified fragment length polymorphism markers for \emph{P.
villosa}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Grouping & Source of variation & \emph{df} & SS & VC & Percent variation
(\%) & Fixation index\tabularnewline
\midrule
\endhead
Total populations & Among populations & 42 & 20586.257 & 73.45887 &
35.84\% & \emph{F}\textsubscript{ST}=0.35841**\tabularnewline
& Within populations & 167 & 21960.467 & 131.49980 & 64.16\%
&\tabularnewline
& Total & 209 & 42546.724 & 204.95867 & &\tabularnewline
SAMOVA groups & Among groups & 1 & 4707.100 & 50.33831 Va & 21.49\% &
\emph{F}\textsubscript{CT}=0.21489**\tabularnewline
& Among populations within groups & 41 & 15879.157 & 52.41736 Vb &
22.38\% & \emph{F}\textsubscript{SC}=0.28501**\tabularnewline
& Within populations & 167 & 21960.467 & 131.49980 Vc & 56.14\% &
\emph{F}\textsubscript{ST}=0.43865**\tabularnewline
& Total & 209 & 42546.724 & 234.25546 & &\tabularnewline
\bottomrule
\end{longtable}

Note. \emph{df} , degrees of freedom; SS, sum of squares; VC, variance
components; \emph{F} \textsubscript{CT}, variance among groups relative
to total variance; \emph{F} \textsubscript{SC}, variance among
populations within groups; \emph{F} \textsubscript{ST}, variance among
populations; Significant level:**\emph{p} \textless{} 0.001.

\textbf{Table 5} Environmental variables used for modeling and percent
contribution of \emph{P. villosa}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Code & Environment variable & Contribution/\%\tabularnewline
\midrule
\endhead
Alt & Altitude & 40\tabularnewline
Bio 7 & Temperature annual range (bio5-bio6) & 17.2\tabularnewline
Bio 18 & Precipitation of warmest quarter & 16.7\tabularnewline
Slop & Slope & 8.3\tabularnewline
Bio 19 & Precipitation of coldest quarter & 7.2\tabularnewline
Bio 15 & Precipitation seasonality (coefficient of variation) &
4.2\tabularnewline
Bio 6 & Min temperature of coldest month & 2.4\tabularnewline
Asp & Aspect & 1.7\tabularnewline
Bio 5 & Max temperature of warmest month & 1\tabularnewline
Bio 2 & Mean diurnal range (mean of monthly (max temp-min temp)) &
0.9\tabularnewline
Bio 3 & Isothermality (Bio 2/Bio 7) (\selectlanguage{ngerman}×100) & 0.3\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 6} Prediction of potential suitable distribution areas
of\emph{P. villosa} in different periods\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Period & Prediction area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2})
& Prediction area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2}) &
Prediction area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2}) &
Prediction area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2}) &
Prediction area (\selectlanguage{ngerman}×10\textsuperscript{4}
km\textsuperscript{2})\tabularnewline
\midrule
\endhead
& Unsuitable habitat & Poorly suitable habitat & Moderately suitable
habitat & Highly suitable habitat & Total suitable
habitat\tabularnewline
LIG & 532.4994 & 85.2601 & 54.3909 & 282.0408 & 421.6918\tabularnewline
LGM & 724.5804 & 111.8953 & 45.7149 & 74.2954 & 231.9056\tabularnewline
Present & 845.8872 & 43.8207 & 30.2673 & 37.1570 &
111.2450\tabularnewline
2050s-2.6 & 804.7547 & 65.9706 & 25.4776 & 60.2943 &
151.7425\tabularnewline
2050s-8.5 & 793.9596 & 72.6263 & 29.3144 & 60.5905 &
162.5312\tabularnewline
2070s-2.6 & 804.7547 & 65.9706 & 25.4776 & 60.2943 &
151.7425\tabularnewline
2070s-8.5 & 710.9724 & 112.0121 & 47.4650 & 86.0193 &
245.4964\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 7} Dynamic changes in the suitable habitat area
for\emph{P. villosa} under different climate scenarios\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Climate scenario & Area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2} )
& Area (\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2} ) & Area
(\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2} ) & Area
(\selectlanguage{ngerman}×10\textsuperscript{4} km\textsuperscript{2} ) & Proportion of area
(\%) & Proportion of area (\%) & Proportion of area (\%) & Proportion of
area (\%)\tabularnewline
\midrule
\endhead
& Loss & Gain & Stable & Total & Loss & Gain & Stable &
Total\tabularnewline
Last Inter Glacial (LIG) & 1.2193 & 270.3439 & 66.2606 & 269.1247 & 1.10
& 243.02 & 59.56 & 241.92\tabularnewline
Last Glacial Maximum (LGM) & 0.0449 & 52.2913 & 67.6280 & 52.2464 & 0.04
& 47.01 & 60.79 & 46.97\tabularnewline
Representative concentration pathway (RCP) 2.6-2050 & 0.0905 & 17.9933 &
67.5910 & 17.9028 & 0.08 & 16.17 & 60.76 & 16.09\tabularnewline
Representative concentration pathway (RCP) 2.6-2070 & 0.0261 & 22.0181 &
67.6961 & 21.9921 & 0.02 & 19.79 & 60.85 & 19.77\tabularnewline
Representative concentration pathway (RCP) 8.5-2050 & 0.0905 & 17.9933 &
67.5910 & 17.9028 & 0.08 & 16.17 & 60.76 & 16.09\tabularnewline
Representative concentration pathway (RCP) 8.5-2070 & 0.2209 & 65.9030 &
67.5632 & 65.6820 & 0.20 & 59.24 & 60.73 & 59.04\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S1} Localities and genetic diversity indices for samples
of \emph{P.villosa} collected in the present study\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
Population code & Location & Latitude (N) & Longitude (E) & Altitude (m)
& Voucher & N & Polymorphic loci (\%) & \emph{N}a & \emph{N}e & \emph{I}
& \emph{H}e\tabularnewline
\midrule
\endhead
Populations of Group1 & Populations of Group1 & Populations of Group1 &
Populations of Group1 & Populations of Group1 & Populations of Group1 &
Populations of Group1 & Populations of Group1 & Populations of Group1 &
Populations of Group1 & Populations of Group1 & Populations of
Group1\tabularnewline
1 & Uxin Banner, NMG & 38\selectlanguage{ngerman}°52\selectlanguage{english}'18.9'' & 109\selectlanguage{ngerman}°10\selectlanguage{english}'18.2'' & 1301 & X. Su, 16,
013 & 5 & 32.22 & 0.7146 & 1.1827 & 0.1646 & 0.1086\tabularnewline
2 & Hanggin Banner, NMG & 39\selectlanguage{ngerman}°20\selectlanguage{english}'37.8'' & 109\selectlanguage{ngerman}°00\selectlanguage{english}'5.9'' & 1314 & X. Su,
16, 017 & 5 & 31.74 & 0.6983 & 1.1811 & 0.1626 & 0.1074\tabularnewline
3 & Hanggin Banner, NMG & 40\selectlanguage{ngerman}°01\selectlanguage{english}'59.2'' & 108\selectlanguage{ngerman}°28\selectlanguage{english}'34.6'' & 1236 & X. Su,
16, 021 & 5 & 29.08 & 0.6505 & 1.1725 & 0.1519 & 0.1010\tabularnewline
4 & Hanggin Banner, NMG & 40\selectlanguage{ngerman}°12\selectlanguage{english}'19.5'' & 108\selectlanguage{ngerman}°29\selectlanguage{english}'11.4'' & 1229 & X. Su,
16, 022 & 5 & 31.98 & 0.7092 & 1.1843 & 0.1646 & 0.1089\tabularnewline
5 & Hanggin Banner, NMG & 40\selectlanguage{ngerman}°26'4.6'' & 108\selectlanguage{ngerman}°37'52.9'' & 1114 & X. Su,
16, 023 & 5 & 27.69 & 0.6378 & 1.1703 & 0.1477 & 0.0988\tabularnewline
6 & Dalad Banner, NMG & 40\selectlanguage{ngerman}°20\selectlanguage{english}'25.8'' & 109\selectlanguage{ngerman}°31\selectlanguage{english}'21.5'' & 1057 & X. Su, 16,
025 & 5 & 25.82 & 0.6270 & 1.1496 & 0.1335 & 0.0884\tabularnewline
7 & Dalad Banner, NMG & 40\selectlanguage{ngerman}°15\selectlanguage{english}'39.1'' & 110\selectlanguage{ngerman}°00\selectlanguage{english}'43.1'' & 1126 & X. Su, 16,
026 & 5 & 33.31 & 0.7340 & 1.1819 & 0.1671 & 0.1096\tabularnewline
8 & Dalad Banner, NMG & 40\selectlanguage{ngerman}°11'57.4'' & 111\selectlanguage{ngerman}°07'21.4'' & 1047 & X. Su, 16,
029 & 5 & 36.52 & 0.7836 & 1.1937 & 0.1808 & 0.1180\tabularnewline
9 & Sonid Left Banner, NMG & 43\selectlanguage{ngerman}°40\selectlanguage{english}'36.5'' & 113\selectlanguage{ngerman}°26\selectlanguage{english}'57.9'' & 1010 & X.
Su, 16, 043 & 5 & 35.61 & 0.7678 & 1.2044 & 0.1836 &
0.1214\tabularnewline
10 & Sonid Left Banner, NMG & 43\selectlanguage{ngerman}°26\selectlanguage{english}'4.1'' & 114\selectlanguage{ngerman}°18\selectlanguage{english}'57.8'' & 1112 & X.
Su, 16, 039 & 5 & 31.98 & 0.7080 & 1.1831 & 0.1644 &
0.1087\tabularnewline
11 & Sonid Left Banner, NMG & 43\selectlanguage{ngerman}°16\selectlanguage{english}'49.7'' & 114\selectlanguage{ngerman}°26\selectlanguage{english}'13.1'' & 1035 & X.
Su, 16, 038 & 5 & 36.64 & 0.7751 & 1.2097 & 0.1886 &
0.1246\tabularnewline
12 & Zhengxiangbai Banner, NMG & 42\selectlanguage{ngerman}°33\selectlanguage{english}'20.9'' & 114\selectlanguage{ngerman}°49\selectlanguage{english}'0.7'' & 1211 & X.
Su, 16, 030 & 5 & 39.00 & 0.8259 & 1.2039 & 0.1913 &
0.1245\tabularnewline
Average & & & & & & & 32.63 & 0.7193 & 1.1848 & 0.1667 &
0.1100\tabularnewline
Populations of Group2 & Populations of Group2 & Populations of Group2 &
Populations of Group2 & Populations of Group2 & Populations of Group2 &
Populations of Group2 & Populations of Group2 & Populations of Group2 &
Populations of Group2 & Populations of Group2 & Populations of
Group2\tabularnewline
13 & Zhenglan Banner, NMG & 42\selectlanguage{ngerman}°41\selectlanguage{english}'0.0'' & 115\selectlanguage{ngerman}°59\selectlanguage{english}'0.0'' & 1321 & X. Su,
18, 026 & 5 & 28.60 & 0.6820 & 1.1707 & 0.1504 & 0.1001\tabularnewline
14 & Xilingol League, NMG & 43\selectlanguage{ngerman}°38\selectlanguage{english}'0.0'' & 116\selectlanguage{ngerman}°39\selectlanguage{english}'0.0'' & 1183 & X. Su,
18, 029 & 5 & 32.10 & 0.7310 & 1.1891 & 0.1675 & 0.1113\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S1} (Continued)\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
15 & Xilin Hot, NMG & 43\selectlanguage{ngerman}°39\selectlanguage{english}'0.0'' & 116\selectlanguage{ngerman}°10\selectlanguage{english}'0.0'' & 1111 & X. Su, 18, 031
& 5 & 25.70 & 0.6360 & 1.1604 & 0.1378 & 0.0924\tabularnewline
\midrule
\endhead
16 & Abag Banner, NMG & 43\selectlanguage{ngerman}°23\selectlanguage{english}'0.0'' & 115\selectlanguage{ngerman}°00\selectlanguage{english}'0.0'' & 1056 & X. Su, 18,
036 & 5 & 34.04 & 0.7757 & 1.1964 & 0.1758 & 0.1164\tabularnewline
17 & Abag Banner, NMG & 43\selectlanguage{ngerman}°18\selectlanguage{english}'0.0'' & 114\selectlanguage{ngerman}°46\selectlanguage{english}'0.0'' & 1079 & X. Su, 18,
038 & 5 & 31.74 & 0.7328 & 1.1846 & 0.1650 & 0.1093\tabularnewline
18 & Sonid Right Banner, NMG & 43\selectlanguage{ngerman}°07\selectlanguage{english}'11.8'' & 112\selectlanguage{ngerman}°54\selectlanguage{english}'23.5'' & 1072 & X.
Su, 16, 048 & 5 & 29.63 & 0.6892 & 1.1624 & 0.1484 &
0.0973\tabularnewline
19 & Zhenglan Banner, NMG & 42\selectlanguage{ngerman}°42\selectlanguage{english}'0.0'' & 108\selectlanguage{ngerman}°56\selectlanguage{english}'0.0'' & 1323 & X. Su,
18, 027 & 5 & 27.33 & 0.6663 & 1.1557 & 0.1399 & 0.0924\tabularnewline
20 & Sonid Right Banner, NMG & 41\selectlanguage{ngerman}°41\selectlanguage{english}'38.0'' & 107\selectlanguage{ngerman}°00\selectlanguage{english}'7.9'' & 1111 & X.
Su, 16, 049 & 3 & 24.49 & 0.5979 & 1.1442 & 0.1273 &
0.0845\tabularnewline
21 & Alxa Zuoqi, NMG & 40\selectlanguage{ngerman}°18\selectlanguage{english}'39.0'' & 105\selectlanguage{ngerman}°53\selectlanguage{english}'35.9'' & 1211 & X. Su, 16,
050 & 2 & 21.83 & 0.5320 & 1.1315 & 0.1189 & 0.0791\tabularnewline
22 & Alxa Zuoqi, NMG & 40\selectlanguage{ngerman}°37\selectlanguage{english}'0.0'' & 104\selectlanguage{ngerman}°35\selectlanguage{english}'0.0'' & 1276 & X. Su, 18,
046 & 5 & 36.70 & 0.8144 & 1.2039 & 0.1860 & 0.1224\tabularnewline
23 & Alxa Youqi, NMG & 39\selectlanguage{ngerman}°59\selectlanguage{english}'0.0'' & 104\selectlanguage{ngerman}°12\selectlanguage{english}'0.0'' & 1243 & X. Su, 18,
011 & 5 & 32.47 & 0.7515 & 1.1862 & 0.1677 & 0.1109\tabularnewline
24 & Alxa Youqi, NMG & 40\selectlanguage{ngerman}°07'49.5'' & 103\selectlanguage{ngerman}°58'20.7'' & 1430 & X. Su, 16,
054 & 5 & 32.53 & 0.7213 & 1.1709 & 0.1601 & 0.1043\tabularnewline
25 & Alxa Youqi, NMG & 40\selectlanguage{ngerman}°01\selectlanguage{english}'0.0'' & 103\selectlanguage{ngerman}°53\selectlanguage{english}'0.0'' & 1403 & X. Su, 18,
008 & 5 & 37.00 & 0.8295 & 1.2092 & 0.1885 & 0.1244\tabularnewline
26 & Alxa Youqi, NMG & 39\selectlanguage{ngerman}°24\selectlanguage{english}'26.3'' & 102\selectlanguage{ngerman}°22\selectlanguage{english}'1.8'' & 1457 & X. Su, 16,
057 & 5 & 30.53 & 0.6911 & 1.1759 & 0.1567 & 0.1037\tabularnewline
27 & Alxa Youqi, NMG & 39\selectlanguage{ngerman}°22'11.8'' & 102\selectlanguage{ngerman}°12'52.1'' & 1570 & X. Su, 16,
058 & 5 & 28.54 & 0.6560 & 1.1751 & 0.1519 & 0.1016\tabularnewline
28 & Alxa Youqi, NMG & 39\selectlanguage{ngerman}°21'24.6'' & 102\selectlanguage{ngerman}°06'58.9'' & 1560 & X. Su, 16,
059 & 5 & 30.29 & 0.7104 & 1.1728 & 0.1544 & 0.1020\tabularnewline
29 & Liangzhou District, GS & 38\selectlanguage{ngerman}°06\selectlanguage{english}'0.0'' & 102\selectlanguage{ngerman}°59\selectlanguage{english}'0.0'' & 1498 & X. Su,
18, 002 & 5 & 30.83 & 0.7195 & 1.1744 & 0.1572 & 0.1037\tabularnewline
30 & Liangzhou District, GS & 38\selectlanguage{ngerman}°13\selectlanguage{english}'0.0'' & 103\selectlanguage{ngerman}°18\selectlanguage{english}'0'' & 1459 & X. Su,
18, 004 & 5 & 38.63 & 0.8507 & 1.2138 & 0.1952 & 0.1283\tabularnewline
31 & Minqin County, GS & 39\selectlanguage{ngerman}°08\selectlanguage{english}'0.0'' & 103\selectlanguage{ngerman}°40\selectlanguage{english}'0.0'' & 1311 & X. Su, 18,
006 & 5 & 33.62 & 0.7787 & 1.1896 & 0.1719 & 0.1133\tabularnewline
32 & Alxa Zuoqi, NMG & 39\selectlanguage{ngerman}°16\selectlanguage{english}'0.0'' & 104\selectlanguage{ngerman}°57\selectlanguage{english}'0.0'' & 1241 & X. Su, 18,
013 & 5 & 32.47 & 0.7557 & 1.1890 & 0.1689 & 0.1120\tabularnewline
33 & Alxa Zuoqi, NMG & 39\selectlanguage{ngerman}°40\selectlanguage{english}'1.2'' & 105\selectlanguage{ngerman}°42\selectlanguage{english}'12.8'' & 1025 & X. Su, 16,
051 & 5 & 19.23 & 0.5012 & 1.1359 & 0.1163 & 0.0796\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S1} (Continued)\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
34 & Alxa Zuoqi, NMG & 38\selectlanguage{ngerman}°57\selectlanguage{english}'43.2'' & 105\selectlanguage{ngerman}°39\selectlanguage{english}'16.3'' & 1458 & X. Su, 16,
052 & 5 & 25.45 & 0.6161 & 1.1523 & 0.1338 & 0.0891\tabularnewline
\midrule
\endhead
35 & Pingluo Xian, NX & 38\selectlanguage{ngerman}°47\selectlanguage{english}'0.0'' & 105\selectlanguage{ngerman}°31\selectlanguage{english}'0.0'' & 1179 & X. Su, 18,
015 & 5 & 34.40 & 0.7878 & 1.1977 & 0.1774 & 0.1173\tabularnewline
36 & Zhongwei City, NX & 37\selectlanguage{ngerman}°25\selectlanguage{english}'29.1'' & 104\selectlanguage{ngerman}°40\selectlanguage{english}'5.0'' & 1707 & X. Su, 16,
001 & 5 & 43.35 & 0.9256 & 1.2226 & 0.2102 & 0.1364\tabularnewline
37 & Lingwu County, NX & 38\selectlanguage{ngerman}°07\selectlanguage{english}'9.8'' & 106\selectlanguage{ngerman}°30\selectlanguage{english}'57.8'' & 1227 & X. Su, 16,
003 & 5 & 37.61 & 0.8150 & 1.2046 & 0.1878 & 0.1230\tabularnewline
38 & Ordos, NMG & 37\selectlanguage{ngerman}°46'46.0'' & 108\selectlanguage{ngerman}°08'31.4'' & 1344 & X. Su, 16, 005 &
5 & 38.15 & 0.8277 & 1.1990 & 0.1860 & 0.1210\tabularnewline
39 & Jingbian County, SX & 37\selectlanguage{ngerman}°40\selectlanguage{english}'52.2'' & 108\selectlanguage{ngerman}°50\selectlanguage{english}'23.5'' & 1328 & X. Su,
16, 007 & 5 & 35.49 & 0.7696 & 1.1984 & 0.1793 & 0.1180\tabularnewline
40 & Uxin Banner, NMG & 37\selectlanguage{ngerman}°47\selectlanguage{english}'56.7'' & 108\selectlanguage{ngerman}°42\selectlanguage{english}'39.3'' & 1254 & X. Su, 16,
008 & 5 & 26.78 & 0.6348 & 1.1438 & 0.1326 & 0.0867\tabularnewline
41 & Uxin Banner, NMG & 38\selectlanguage{ngerman}°22'8.1'' & 108\selectlanguage{ngerman}°40'6.3'' & 1286 & X. Su, 16,
010 & 5 & 26.00 & 0.6288 & 1.1436 & 0.1307 & 0.0858\tabularnewline
42 & Otog Banner, NMG & 38\selectlanguage{ngerman}°53\selectlanguage{english}'0.0'' & 108\selectlanguage{ngerman}°18\selectlanguage{english}'0.0'' & 1371 & X. Su, 18,
019 & 5 & 32.95 & 0.7545 & 1.1877 & 0.1686 & 0.1113\tabularnewline
43 & Otog Banner, NMG & 39\selectlanguage{ngerman}°01\selectlanguage{english}'0.0'' & 108\selectlanguage{ngerman}°00\selectlanguage{english}'0.0'' & 1358 & X. Su, 18,
017 & 5 & 54.29 & 1.1155 & 1.2908 & 0.2709 & 0.1771\tabularnewline
Average & & & & & & & 32.02 & 0.7322 & 1.1817 & 0.1640 &
0.1082\tabularnewline
\bottomrule
\end{longtable}

\emph{N} , number of individuals; polymorphic loci, the percentage of
loci that are polymorphic out of total 1654 loci; \emph{N} a, observed
number of alleles; \emph{N} e, effective number of alleles; \emph{I} ,
Shannon's information index; \emph{H} e, expected heterozygosity.

NX, Ningxia; NMG, Neimenggu; SX, Shaanxi. GS, Gansu.

\textbf{Table S2} Localities of 155 records used for niche modeling\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Population code & Latitude (N) & Longitude (E) & Population code &
Latitude (N) & Longitude (E) & Population code & Latitude (N) &
Longitude (E)\tabularnewline
\midrule
\endhead
P1 & 49.2126 & 119.7292 & P23 & 43.3833 & 115.0000 & P45 & 42.6736 &
115.9457\tabularnewline
P2 & 45.5126 & 116.9779 & P24 & 43.3500 & 114.8500 & P46 & 42.6363 &
114.7415\tabularnewline
P3 & 44.0894 & 113.9219 & P25 & 43.3000 & 114.7667 & P47 & 42.6167 &
115.8167\tabularnewline
P4 & 43.9390 & 116.0704 & P26 & 43.2973 & 113.0510 & P48 & 42.5558 &
114.8169\tabularnewline
P5 & 43.8576 & 113.6554 & P27 & 43.2909 & 116.6604 & P49 & 42.5167 &
115.5833\tabularnewline
P6 & 43.7481 & 113.7815 & P28 & 43.2805 & 114.4370 & P50 & 42.3414 &
115.9130\tabularnewline
P7 & 43.7000 & 117.0000 & P29 & 43.2500 & 113.0000 & P51 & 42.3375 &
115.0207\tabularnewline
P8 & 43.6833 & 116.3167 & P30 & 43.2122 & 112.9788 & P52 & 42.3289 &
115.0315\tabularnewline
P9 & 43.6768 & 113.4494 & P31 & 43.1523 & 114.4681 & P53 & 40.6167 &
104.5833\tabularnewline
P10 & 43.6749 & 113.9785 & P32 & 43.1199 & 112.9065 & P54 & 40.4358 &
106.4846\tabularnewline
P11 & 43.6667 & 113.4500 & P33 & 43.0613 & 114.5072 & P55 & 42.2390 &
115.9972\tabularnewline
P12 & 43.6500 & 116.1667 & P34 & 43.0119 & 115.7574 & P56 & 42.2153 &
111.8059\tabularnewline
P13 & 43.6460 & 111.9699 & P35 & 42.9256 & 114.5646 & P57 & 42.2025 &
116.5047\tabularnewline
P14 & 43.6333 & 116.6500 & P36 & 42.8632 & 89.1928 & P58 & 41.9912 &
116.2064\tabularnewline
P15 & 43.6281 & 113.3760 & P37 & 42.8345 & 114.5676 & P59 & 41.9569 &
101.0528\tabularnewline
P16 & 43.6167 & 115.3333 & P38 & 42.7698 & 114.6434 & P60 & 41.6939 &
107.0022\tabularnewline
P17 & 43.6000 & 115.6500 & P39 & 42.7329 & 112.6404 & P61 & 41.0834 &
107.0604\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2} (Continued)\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
P18 & 43.5833 & 115.7500 & P40 & 42.7293 & 112.6501 & P62 & 40.8836 &
107.1434\tabularnewline
\midrule
\endhead
P19 & 43.5491 & 113.2569 & P41 & 42.7167 & 116.0833 & P63 & 40.7791 &
107.4050\tabularnewline
P20 & 43.5154 & 114.1718 & P42 & 42.7028 & 114.7319 & P64 & 40.7619 &
107.4222\tabularnewline
P21 & 43.4667 & 115.0833 & P43 & 42.7000 & 108.9333 & P65 & 40.7458 &
104.5031\tabularnewline
P22 & 43.4345 & 114.3161 & P44 & 42.6833 & 115.9833 & P66 & 40.7418 &
107.3821\tabularnewline
P67 & 40.3882 & 110.0001 & P92 & 39.8000 & 108.7000 & P117 & 38.8535 &
105.7036\tabularnewline
P68 & 40.3833 & 104.7167 & P93 & 39.6670 & 105.7036 & P118 & 38.8380 &
105.6981\tabularnewline
P69 & 40.3817 & 109.3359 & P94 & 39.6630 & 108.7794 & P119 & 38.8367 &
99.6144\tabularnewline
P70 & 40.3405 & 109.5226 & P95 & 39.6281 & 103.0403 & P120 & 38.8330 &
105.6476\tabularnewline
P71 & 40.3366 & 106.9958 & P96 & 39.4333 & 102.7500 & P121 & 38.7833 &
105.5167\tabularnewline
P72 & 40.3256 & 107.0045 & P97 & 39.4073 & 102.3672 & P122 & 38.7566 &
110.1787\tabularnewline
P73 & 40.3252 & 107.0029 & P98 & 39.3771 & 99.8179 & P123 & 38.7392 &
109.1013\tabularnewline
P74 & 40.3108 & 105.8933 & P99 & 39.3616 & 110.1672 & P124 & 38.6650 &
105.8017\tabularnewline
P75 & 40.3055 & 109.9368 & P100 & 39.3476 & 102.0119 & P125 & 38.6500 &
108.9333\tabularnewline
P76 & 40.2884 & 109.9427 & P101 & 39.3438 & 109.0016 & P126 & 38.6410 &
108.9262\tabularnewline
P77 & 40.2609 & 110.0120 & P102 & 39.3333 & 104.9000 & P127 & 38.6269 &
106.5652\tabularnewline
P78 & 40.2431 & 109.9507 & P103 & 39.2667 & 104.9500 & P128 & 38.6106 &
108.8343\tabularnewline
P79 & 40.2379 & 105.9169 & P104 & 39.2167 & 103.7000 & P129 & 38.6000 &
108.7667\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2} (Continued)\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
P80 & 40.2054 & 108.4865 & P105 & 39.2078 & 101.6601 & P130 & 38.5304 &
105.6553\tabularnewline
\midrule
\endhead
P81 & 40.1975 & 110.7404 & P106 & 39.1333 & 103.6667 & P131 & 38.3667 &
103.2833\tabularnewline
P82 & 40.1333 & 110.5000 & P107 & 39.1129 & 109.0366 & P132 & 38.3282 &
109.7580\tabularnewline
P83 & 40.1167 & 104.0500 & P108 & 39.0962 & 108.0956 & P133 & 38.3000 &
109.7000\tabularnewline
P84 & 40.0943 & 109.0196 & P109 & 39.0167 & 108.0000 & P134 & 38.2167 &
103.3000\tabularnewline
P85 & 40.0833 & 103.9500 & P110 & 38.9620 & 105.6545 & P135 & 38.1799 &
109.0588\tabularnewline
P86 & 40.0634 & 103.9141 & P111 & 38.9463 & 107.8719 & P136 & 38.1194 &
106.5161\tabularnewline
P87 & 40.0331 & 108.4763 & P112 & 38.9333 & 108.1333 & P137 & 38.1167 &
103.1667\tabularnewline
P88 & 40.0167 & 103.8833 & P113 & 38.9167 & 105.5167 & P138 & 38.1000 &
102.9833\tabularnewline
P89 & 39.9833 & 104.2000 & P114 & 38.8833 & 108.3000 & P139 & 38.0333 &
102.8667\tabularnewline
P90 & 39.8203 & 109.9558 & P115 & 38.8719 & 109.1717 & P140 & 38.0306 &
104.8136\tabularnewline
P91 & 39.8141 & 109.9729 & P116 & 38.8642 & 105.7319 & P141 & 37.9761 &
106.3286\tabularnewline
P142 & 37.9567 & 108.7709 & P147 & 37.6812 & 108.8399 & P152 & 37.4248 &
104.6681\tabularnewline
P143 & 37.9318 & 102.6068 & P148 & 37.6789 & 108.3090 & P153 & 37.3636 &
102.8363\tabularnewline
P144 & 37.9212 & 107.9943 & P149 & 37.5000 & 102.9000 & P154 & 36.3000 &
98.1000\tabularnewline
P145 & 37.7991 & 108.7109 & P150 & 37.4445 & 104.9394 & P155 & 35.9944 &
97.8869\tabularnewline
P146 & 37.7769 & 107.3913 & P151 & 37.4426 & 104.9383 & &
&\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S3} \emph{F} \textsubscript{CT} values for different
numbers of population groups (\emph{K} ) inferred by SAMOVA algorithm
based on the AFLP dataset\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
K & \textbf{Population grouping} & \emph{F}\textsubscript{\textbf{CT}} &
\emph{P}\textbf{\_value}\tabularnewline
\midrule
\endhead
\emph{K}\textbf{=2} & \textbf{(1, 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12)
(13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)} & \textbf{0.21489}
& \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=3} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8, 9, 10, 11, 12)
(13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)} & \textbf{0.23429}
& \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=4} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8) (9, 10, 11, 12)
(13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)} & \textbf{0.23078}
& \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=5} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8) (9, 10, 11, 12)
(13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43) (15)} & \textbf{0.22777}
& \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=6} & \textbf{(1, 2, 3, 4, 5, 6 ) (7, 8, 9, 10, 11, 12)
(13) (14, 15, 16, 17) (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)} &
\textbf{0.22549} & \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=7} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8, 9, 10, 11, 12)
(13) (14, 15, 16, 17) (18, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31,
33, 34, 36, 37, 38, 39, 40, 41) (19) (23, 32, 35, 42, 43)} &
\textbf{0.22780} & \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=8} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8, 9, 10, 11, 12)
(13) (14, 15, 16, 17, 18, 19, 22 ) (20, 21, 24, 26, 33, 34) (23, 32, 35,
42, 43) (25, 27, 28, 29, 30, 31) (36, 37, 38, 39, 40, 41)} &
\textbf{0.23024} & \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=9} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8, 9, 10, 11, 12)
(13) (14, 15, 16, 17) (23, 32, 35) (18, 20,21, 22, 24, 25, 26, 27, 28,
29, 30, 31, 33, 34 ) (19) (36, 37, 38, 39, 40, 41) (42, 43)} &
\textbf{0.23153} & \textbf{0.00000}\tabularnewline
\emph{K}\textbf{=10} & \textbf{(1, 2, 3, 4, 5, 6) (7, 8, 9, 10, 11, 12)
(13) (14, 15, 16, 17) (18, 20, 21, 24, 26, 33) (19, 22) (23, 32, 35, 42,
43) (25, 27, 28, 29, 30, 31) (34) (36, 37, 38, 39, 40, 41)} &
\textbf{0.23612} & \textbf{0.00000}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1} Correlation of Mantel test between geographic distance
and \emph{F} \textsubscript{ST}. \textbf{A,} Mantel test from 43
populations of \emph{P. villosa} ; \textbf{B,} Mantel test from
populations of Group 1; \textbf{C,} Mantel test from populations of
Group 2

\textbf{Figure 2} Dendrogram of \emph{P. villosa} generated by
unweighted pair group method analysis (UPGMA) cluster analysis from the
genetic similarity matrix obtained using amplified fragment length
polymorphism genetic distance (see Figure S1 for population codes)

\textbf{Figure 3} Neighbor-Net split network of \emph{P. villosa} based
on amplified fragment length polymorphism dataset using Jaccard's
distances. Lines of red and yellow represent Group 1 and Group 2,
respectively

\textbf{Figure 4} A two-dimensional plot of the principal coordinate
analysis (PCoA) based on variation of amplified fragment length
polymorphism markers for \emph{P. villosa} (see Figure S1 for population
codes)

\textbf{Figure 5} Results of the Bayesian clustering analysis in
STRUCTURE of 210 individuals representing \emph{P. villosa.}
\textbf{A,}^\emph{K} values from the mean log-likelihood probabilities
through STRUCTURE runs where inferred cluster (\emph{K} ) ranged from
one to ten;\textbf{B,} Estimated genetic clustering for \emph{K} = 2,
where unique colors correspond to assignment at different clusters;
\textbf{C,}Geographic origin from 43 populations of \emph{P. villosa}
and their color-coded grouping according to the structure analysis for
the model with \emph{K} = 2

\textbf{Figure 6} Potentially suitable climatic distribution of \emph{P.
villosa} under different climate change scenarios in China

\textbf{Figure 7} Present suitable climatic distribution of \emph{P.
villosa} in China

\textbf{Figure 8} Spatial shifts for \emph{P. villosa} under climate
change scenarios

\textbf{Figure 9} Results of the niche-identity test.
\textbf{A,}Schoener's \emph{D} ; \textbf{B,} Warren's \emph{I} . The
arrow in each panel represents the observed niche similarity between
occurrence points for the corresponding pair of clusters. The histograms
represent the distribution of niche similarities obtained from pairs of
pseudo niches constructed by random resampling of occurrence points of
the two clusters

\textbf{Figure S1} Localities of Group 1 (red), Group 2 (yellow) sampled
of \emph{P. villosa} in the present study

\textbf{Figure S2} Fluorescently-labeled AFLPs generated using different
primer combinations. \textbf{A,} \emph{E} -AAC/\emph{M} -CAA; B,\emph{E}
-AAG/\emph{M} -CAC; C, \emph{E} -ACA/\emph{M} -CAG; D,\emph{E}
-ACT/\emph{M} -CAT; E, \emph{E} -ACC/\emph{M} -CTA; \textbf{F,}\emph{E}
-ACG/\emph{M} -CTC; \textbf{G,} \emph{E} -AGC/\emph{M} -CTG;\textbf{H,}
\emph{E} -AGG/\emph{M} -CTT

\textbf{Figure S3} The mean AUC of the test samples of \emph{P. villosa}
based on the MaxEnt model\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture-1/Figture-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--2/Figture--2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--3/Figture--3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--4/Figture--4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture-5/Figture-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--6/Figture--6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--7/Figture--7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--8/Figture--8}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--9/Figture--9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--S1/Figture--S1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture--S2/Figture--S2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figture-S3/Figture-S3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

