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Conservation planning for the
endemic and endangered
medicinal plants under the
climate change and human
disturbance: a case study of
Gentiana manshurica in China
Hui Zou
1
, Bingrui Chen
1
, Boyan Zhang
1
, Xinyu Zhou
1
,
Xiyuan Zhang
1
, Xinxin Zhang
1
*and Jianwei Wang
2
*
1
Heilongjiang Research Center of Genuine Wild Medicinal Materials Germplasm Resources, School of
Life Sciences and Technology, Harbin Normal University, Harbin, China,
2
College of Basic Medicine,
Heilongjiang University of Chinese Medicine, Harbin, China
Human activities and climate change have significantly impacted the quantity
and sustainable utilization of medicinal plants. Gentiana manshurica Kitagawa, a
high-quality original species of Gentianae Radix et Rhizoma, has significant
medicinal value. However, wild resources have experienced a sharp decline
due to human excavation, habitat destruction, and other factors. Consequently, it
has been classified as an Endangered (EN) species on the IUCN Red List and is
considered a third-level national key-protected medicinal material in China. The
effects of climate change on G. manshurica are not yet known in the context of
the severe negative impacts of climate change on most species. In this study, an
optimized MaxEnt model was used to predict the current and future potential
distribution of G. manshurica. In addition, land use data in 1980, 2000, and 2020
were used to calculate habitat quality by InVEST model and landscape
fragmentation by the Fragstats model. Finally, using the above-calculated
results, the priority protection areas and wild tending areas of G. manshurica
were planned in ZONATION software. The results show that the suitable area is
mainly distributed in the central part of the Songnen Plain. Bio15, bio03, bio01,
and clay content are the environmental variables affecting the distribution. In
general, the future potential distribution is expected to show an increasing trend.
However, the species is expected to become threatened as carbon emission
scenarios and years increase gradually. At worst, the high suitability area is
expected to disappear completely under SSP585-2090s. Combined with the t-
test, this could be due to pressure from bio01. The migration trends of climate
niche centroid are inconsistent and do not all move to higher latitudes under
different carbon emission scenarios. Over the past 40 years, habitat quality in the
current potential distribution has declined yearly, and natural habitat has
gradually fragmented. Existing reserves protect only 9.52% of G. manshurica’s
Frontiers in Plant Science frontiersin.org01
OPEN ACCESS
EDITED BY
Sailesh Ranjitkar,
N.Gene Solution of Natural Innovation,
Nepal
REVIEWED BY
Eklabya Sharma,
Ashoka Trust for Research in Ecology and
the Environment (ATREE), India
Yunlong Yao,
Northeast Forestry University, China
*CORRESPONDENCE
Xinxin Zhang
hsdzxx2021@163.com
Jianwei Wang
wangjianweilikai@163.com
RECEIVED 12 March 2023
ACCEPTED 04 July 2023
PUBLISHED 26 July 2023
CITATION
Zou H, Chen B, Zhang B, Zhou X, Zhang X,
Zhang X and Wang J (2023) Conservation
planning for the endemic and endangered
medicinal plants under the climate change
and human disturbance: a case study of
Gentiana manshurica in China.
Front. Plant Sci. 14:1184556.
doi: 10.3389/fpls.2023.1184556
COPYRIGHT
© 2023 Zou, Chen, Zhang, Zhou, Zhang,
Zhang and Wang. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that
the original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
TYPE Original Research
PUBLISHED 26 July 2023
DOI 10.3389/fpls.2023.1184556
priority conservation area. To avoid extinction risk and increase the practicality of
the results, we clarified the hotspot counties of priority protection area gaps and
wild tending areas. These results can provide an essential reference and decision
basis for effectively protecting G. manshurica under climate change.
KEYWORDS
Gentiana manshurica, optimized MaxEnt model, potential distribution, environmental
variables, landscape pattern, habitat quality, conservation management
1 Introduction
Global climate change has long been a pressing research issue,
with the loss of biodiversity caused by climate change and human
activities being one of the most serious problems (Mantyka-pringle
et al., 2012). Understanding and predicting how species respond to
global climate change is crucial in biodiversity research (Lan et al.,
2022). Numerous studies have shown that the geographic
distribution patterns of species change in response to the impacts
of climate change (Yu et al., 2021). The rates of species change vary
widely in species characteristics and variational external drivers
(Chen et al., 2011). Most studies have pointed out the profound
effects of global warming on species distributions. Climate and
geology have shaped ecosystems and evolution in the past, while
human forces may now outweigh these across most of the Earth’s
land surface today (Yue et al., 2011). Landscape structures are
altered, and habitats are fragmented and degraded by changing land
use, so most of the natural landscape is now embedded within
anthropogenic land use and land cover mosaics (Ellis and
Ramankutty, 2008). Populations of species restricted to these
habitats are often spatially fragmented and threatened (Opdam
and Wascher, 2004). Most studies suggest that habitat loss and
fragmentation now outweigh the response of species and
ecosystems to climate change. However, the effects of climate
change are expected to increase over time and eventually
outweigh land use change in determining population trends
(Mantyka-pringle et al., 2012). Overall, there is growing evidences
that climate change will interact negatively with habitat loss and
fragmentation and contribute synergistically to biodiversity
degradation at the species, genetic, and/or habitat levels
(Mantyka-pringle et al., 2012).
The niche breadth-range size hypothesis states that by utilizing
a greater array of resources and maintaining viable populations
within a wider variety of conditions, a species should become more
widespread, leading to a positive correlation between niche breadth
and geographical range size (Brown, 1984). Most studies have tested
this hypothesis (Boulangeat et al., 2012;Slatyer et al., 2013).
Carscadden K. A. thus inferred that species with narrow
distribution ranges would further narrow their distribution ranges
under changed climate in the future, which also has been tested in
the majority studies (Beaumont and Hughes, 2002;Tang et al.,
2021a). However, the extent to which climate limits the distribution
of endemic species is unclear (Morueta-Holme et al., 2010). Human
activities have reduced the ranges of narrow-ranged species but
expanded those of widespread species in China, leading to rare and
distinct species being losers (Xu et al., 2019). Although narrow-
ranged species are known to be more vulnerable to climate change,
few studies have assessed climate change impacts (Dubos et al.,
2021). Therefore, to maintain sustainable use of endangered wild
resources, it is necessary to characterize the ecology of their
populations, investigate their current potential distribution,
determine the response of species to global warming, and then
identify sites for protection and conservation (Li et al., 2020;Yoo
et al., 2022). Strategically, protecting the natural environment (in
situ conservation) will be the most effective way to maintain
populations and promote gene flow (Hellmann et al., 2012;
Krishnan et al., 2013). However, the climatic conditions available
to species in static reserves will change as climate change intensifies,
and the positive effects of reserves on species conservation will
decline. That human activities cause structural disconnections in
protected area networks leads to habitat loss and fragmentation,
exacerbating this change (Asamoah et al., 2021). Therefore, when
constructing protected areas, it is necessary to consider the impact
of future climate and strong human activities on species distribution
(Platts et al., 2014).
Gentiana manshurica Kitagawa (Gentianaceae: Gentiana)isa
perennial herb (Editorial Board of Flora of China, 2016). It has great
medicinal value and is used as traditional Chinese medicine (TCM)
Gentianae Radix et Rhizoma for its dried roots and rhizomes. It is
widely used in Europe and Asia (Tanaka et al., 2014;Jiang et al.,
2021). Among the various original species, G. manshurica is the
mainstream of excellent-quality commercial products (Guo et al.,
2001;Meng et al., 2011). In recent decades, however, wild resources
have been severely damaged due to the biological characteristics and
the destruction of grasslands caused by human disturbance. It has
been classified as Endangered (EN) on the IUCN Red List and is the
third-level national key-protected wild medicinal materials species
in China. Some scholars have suggested changing it to the second
level considering the exhausted wild resources (Meng et al., 2011).
Undoubtedly, global warming and increased human disturbance
will be more detrimental to the growth of narrow species than
widespread species. However, the future survival potential of G.
manshurica, as an endemic species of the western meadow
grasslands in Northeast China, is unknown.
Therefore, we simulated the current and future potential
distribution (CPD and FPD) of G. manshurica and analyzed its
Zou et al. 10.3389/fpls.2023.1184556
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priority protection areas (PPAs) and wild tending areas (WTAs) to
provide theoretical guidance for conservation. The objectives of this
study were to (1) predict the CPD and FPD and analyze the changes
in the potential distribution under different periods and carbon
emission scenarios, (2) identify the key environmental variables
affecting the distribution of the G. manshurica and analyze the
environmental pressure on the potential distribution under the
climate change, (3) evaluate the dynamic changes in the landscape
pattern and habitat quality of the CPD, (4) based on the CPD, FPD,
natural habitat quality, and landscape fragmentation of CPD, apply
ZONATION model to analyze PPAs and WTAs for G. manshurica,
and (5) provide a reference for the protection of other rare and
endangered wild medicinal plants.
2 Materials and methods
2.1 Study area
Species identification is the key to species conservation (Trias-
Blasi and Vorontsova, 2015). It records that G. manshurica mainly
distributes in Northeast China, North China, East China, and South
China, etc. (Editorial Board of Flora of China, 2016). Scholars have
made in-depth studies on the G. manshurica distributed in southern
China, pointing out that it should be Gentiana scabra Bge. ssp.
australis, the southern subspecies of G. scabra Bge (Liu, 1981;Guo
et al., 2001;Wang, 2005). As a result, G. manshurica is a narrow-
distributed species in Northeast China.
Northeast China (115°05’~ 135°02’E, 38°40’~ 53°34’N) is a
geographical region of China, including Heilongjiang, Jilin, and
Liaoning provinces, as well as Hulunbeier, Xing’an Meng, Tongliao,
and Chifeng in Inner Mongolia (Figure 1A). The region has a
complex landform, including the Greater Khingan Range, Lesser
Khingan Range, Changbai Mountains, Songnen Plain, and Songliao
Plain. The region features a continental monsoon climate,
exhibiting chilly and arid winters alongside humid and sultry
summers. And the annual average temperature of 2.75 ~ 5.72°C.
From southeast to northwest, annual precipitation drops from 1,000
mm to 300 mm (Zhou and Zu, 1997).
2.2 Occurrence records
We obtained the occurrence records by three methods. First, 43
occurrence records were based on our field surveys from 2016 to
2022. Second, 16 occurrence records were obtained based on the
Fourth Chinese Materia Medica Resources Survey. Last, 12
occurrence records were investigated based on the Chinese
Virtual Herbarium (https://www.cvh.ac.cn/). All specimens were
carefully examined to ensure correct identification. Invalid,
duplicate, and non-natural records were removed. Then the data
were spatially filtered to retain only one point in each grid cell (1×1
km) using ENMtools. Finally, we obtained 71 occurrence records of
G. manshurica.
2.3 Environmental and geographic data
G. manshurica (Figures 1B,C) is mainly concentrated in the
seasonal water area in the semi-humid meadow grassland area.
Moreover, there are requirements for soil, such as the general
distribution of salinized meadow soil, and the pH value is
often greater than 8.0 (Liu, 1981). To accurately simulate
the potential geographic distribution of G. manshurica,we
comprehensively considered the habitat characteristics. Therefore,
31 environmental variables were collected to simulate the CPD,
including climate and soil (Table S1). Soil variables were from the
Dataset of soil properties for land surface modeling over China of
FIGURE 1
Occurrence records and morphological characteristics of G manshurica (A). occurrence records; (B) flower; (C) dry rhizoma.
Zou et al. 10.3389/fpls.2023.1184556
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National Tibetan Plateau Third Pole Environment Data Center with
a resolution of 30”(http://data.tpdc.ac.cn/)(Dai and Shangguan,
2019). Other data were from WorldClim database with the same
resolution (Version 2.1, https://worldclim.org/)(Fick and Hijmans,
2017). We used CMIP6 published by the IPCC organization for the
simulation of potential distribution in the future, including four
periods: 2021-2040 (2030s), 2041-2060 (2050s), 2061-2080 (2070s),
and 2081-2100 (2090s). Compared to CMIP5, CMIP6 improves the
ability to simulate and predict future models (Xin et al., 2020).
Published to date, the global Shared Socio-economic Pathways
(SSPs) represent the most comprehensive research on
environmental and sustainable development. The SSPs include
SSP126, SSP245, SSP370, and SSP585 (Jiang et al., 2020). It can
be used for climate change research and other areas, such as
biodiversity and sustainable development (Van Vuuren et al.,
2017). Thus, 16 combinations of future periods and climate
scenarios were used in this study. IPSL-CM6A-LR, MRI-ESM2-0,
and UKESM1-0-LL were selected, and the average values of each
climate variable of three general circulation models were input into
the model (Brambilla et al., 2022). Since soil conditions hardly
change quickly, we made these variables consistent under current
and future conditions (Yu et al., 2021).
To enhance the accuracy of the model simulation while
avoiding collinearity between different environmental variables,
the ENMtools correlation tools were employed to analyze highly
collinear variables (|r|≥0.8) (Figure S1)(Dormann et al., 2013;
Merow et al., 2013). Using the percent contribution metric, we
excluded environmental variables with a contribution rate of less
than 1%, and eliminated environmental variables with low
contributions among two highly collinear variables (Table S2).
Ultimately, nine environmental variables (bio15, bio03, bio01, CL,
PH, POR, TN, bio13, and bio14) were used in the MaxEnt model to
predict the CPD and FPD.
The China Administrative Division Maps and nature reserves
were obtained from the National Geomatics Center of China (https://
www.webmap.cn/). 1980, 2000, and 2020 land use data (the resolution
is 30”) and the Chinese Agricultural Natural Zoning were obtained
from the Resource and Environment Science and Data Center of the
Institute of Geographic Science and Natural Resource Research
(https://www.resdc.cn/). MaxEnt version 3.4.4 was downloaded from
the website (https://biodiversityinformatics.amnh.org/open_source/
maxent/). The version of ArcGIS software used in this study was
10.5, RStudio was 3.6.3, and the ZONATION model was 4.0.0
downloaded from the website (https://www.helsinki.fi/en).
2.4 Prediction of the CPD and FPD
This study used the MaxEnt model to predict the CPD and FPD
of G. manshurica in Northeast China. It was shown that the
prediction results of the MaxEnt model were closely related to the
feature combination (FC) and regularization multiplier (RM). And
using default parameters to construct the MaxEnt model is prone to
overfitting, resulting in low model transfer ability and obtaining
prediction results that are not the best or even unreliable (Merow
et al., 2013;Morales et al., 2017). Therefore, we used the kuenm
package to adjust the FC and RM of the model to obtain the best-
fitting parameter combinations with the input data. First, the
package created many candidate models and evaluated and
selected the best model. Candidate models were obtained by
combinations of 17 regularized multiplier values (0.1 ~ 1 with
interval 0.1, 2 ~ 6 with interval 1, 8, and 10) and all 31 possible
combinations of feature classes (linear-L, quadratic-Q, hinge-H,
product-P, and threshold-T). Second, candidate models were
selected using partial ROC values to measure statistical
significance, omission rates to measure the predictive power of
the models, and AICc values to measure the complexity of the
models. The model parameter combination with the lowest AICc
value (delta.AICc=0) was selected as the best model (Cobos et al.,
2019). To improve the accuracy, 75% of the occurrence records
were randomly selected as the training data, and the remaining 25%
as the test data. Finally, we generated 10 bootstrap replicates for
each of the best calibration models with a Cloglog of the output
format. Other parameters were set as default.
The prediction results were examined using the area under the
curve (AUC) of the receiver operating characteristic (ROC). The
closer the AUC to 1, the more accurate the model is (Tang et al.,
2021b). This study used the maximum training sensitivity plus
specificity Cloglog threshold (P) to determine the optimal
conversion threshold, and P was 0.1899. The suitability of areas
was determined based on the probability of species presence (p)
being greater than or equal to P (suitable) or less than P (non-
suitable) (Liu et al., 2013). Combining the treatment of
uncertainties for the IPCC Fifth Assessment Report (Sun et al.,
2012), the suitability area was classified as: < 0.1899 is a non-suitable
area; 0.1899 ≤p < 0.33 is low suitability area; 0.33 ≤p < 0.66 is mid
suitability area; 0.66 < p is high suitability area.
We used SDMtoolbox written in python to binarize the
potential area of future climate scenarios, clarify the changes, and
obtain the expansion, stability, and contraction areas (Brown et al.,
2017). We could not interpret the adaptive potential of species, so
our interpretation of the following results is limited to the expected
changes in the distribution of environmental features associated
with the presence rather than the distribution of G. manshurica.
Finally, to assess the novelty of future climate conditions in the
calibration region compared to current conditions, we employed
the kuenm_mop function from the kuenm package to calculate the
mobility-oriented parity (MOP) metric. MOP analysis aids in
identifying where strict extrapolation occurs, with values of 0
corresponding to such regions.
2.5 Assessment of the importance of
environmental variables and their effect
on climate change
The first step in conservation is to understand the relationship
between the geographic distribution of taxa and environmental
conditions. And this assessment can help conserve and restore
endangered plants more scientifically and cost-effectively (Harapan
et al., 2022). In this study, the contribution of environmental
variables to the geographic distribution was determined by
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percent contribution and permutation importance. The percent
contribution is the contribution of each environmental variable to
the geographic distribution given by the MaxEnt model. And the
permutation importance is the reduction of the AUC value obtained
by randomly replacing the environmental variables in the training
data. The larger reduction indicates that the model depends on the
variable (Phillips, 2005). To understand how environmental
variable affects the distributions, the MaxEnt model gives a
response curve, showing the relationship between the probability
of species presence and environmental variables. In this study, the
environment suitable for the growth of G. manshurica was defined
as the presence probability is larger than P (0.1899 in this study).
To elucidate the impact of future climate change on climate
variables that affect the distribution of G. manshurica, the random
selection of 1,000 points within the CPD was performed, followed
by the generation of box plots for the relevant climate variables. In
addition, for climate variables used in the modeling, we used a t-test
to test whether the combination of SSP and the period is
significantly different (p < 0.01) from the current climate. In this
way, we can visualize what environmental stresses the CPD of G.
manshurica will face under climate change.
2.6 Future range shift of elevation, latitude,
and climate niche centroid
1,000 points were randomly selected in the FPD of G.
manshurica. And the changes in elevation and latitude under
climate change were calculated, and the ridge density map was
drawn using the Ridgeline R package for representation.
The abundant center hypothesis (ACH) considers species most
abundant close to the center of their geographic range. As the
distance from the geographic centroid increases, the abundance is
expected to decrease until reaching the distribution limit (Brown,
1984;Hengeveld, 1992). However, it has been demonstrated that
ACH negatively correlates with environmental distance at the
central condition of the climate niche centroid, rather than the
geographic centroid of the species distribution (Martı
nez-Meyer
et al., 2013). To reveal the influence of global warming on the G.
manshurica climate niche centroid, we utilized the modeling
climate factors used for PCA analytical modeling from sixteen
climate scenario combinations. We retained the first 2
components, which explained cumulatively ≥95% of the total
variance in the dataset. The climate niche centroid was derived
from the mean of the retained PC layers as suitable by the
thresholded MaxEnt result (Manthey et al., 2015).
2.7 Changes in landscape
structure, landscape indexes,
and landscape fragmentation
We extracted the land use data from the CPD. Then, we
counted the area and proportion of different landscape types to
analyze the changes in landscape structures in three years (1980,
2000, and 2020). Six indexes at the class level were calculated using
Fragstats software. The landscape indexes were selected from three
aspects. Patch area and number indexes: patch number (NP), patch
density (PD), mean patch area (MPS); shape indexes: area-weighted
mean of fractal dimension index (FRAC_AM); aggregation indexes:
landscape division index (DIVISION) and aggregation index (AI)
(Ran et al., 2019). The formulas of landscape indexes are described
in the literature (Wu, 2007). The main landscape type suitable for G.
manshurica under current climate conditions is grassland, so we
chose this type to calculate the landscape fragmentation (LF). The
moving window method was used, and the operation was referred
to Ran et al. (2019). The result was classified with the equidistant
method: extremely low fragmentation (0 ≤LF < 0.2), low
fragmentation (0.2 ≤LF < 0.4), mid fragmentation (0.4 ≤LF <
0.6), high fragmentation (0.6 ≤LF < 0.8), and extremely high
fragmentation (0.8 ≤LF < 1). We used a raster calculator to map the
spatial and temporal changes of landscape fragmentation in three
years to identify increased and stabilized or reduced landscape
fragmentation areas.
2.8 Analysis of habitat quality change
The InVEST 3.11.0-Habitat Quality model performed habitat
quality in the study area. Referring to related studies (Chen et al.,
2016;Yang et al., 2018b;Liang et al., 2020) and considering the
actual situation of the study area and the research content,
cultivated land, urban land, rural settlement, and other
construction land were selected as threats. The following data
were entered into the software: land use data in 1980, 2000, and
2020, threats factors layers, tables of threats factors (Table S3), and
the table of sensitivity of land use types to threats (Table S4). The
output results were the habitat quality in the study area in three
years. After that, we extracted the habitat quality (HQ) results of
three years based on the CPD of G. manshurica to obtain the habitat
quality of this region. Then, we classified them into three levels: low-
quality habitat (0 ≤HQ < 0.33), medium-quality habitat (0.33 ≤HQ
< 0.66), and high-quality habitat (0.66 ≤HQ < 1).
2.9 The determination of the PPAs
and WTAs
ZONATION software proposed by Moilanen was applied to
spatial priority conservation of species and large-scale spatial
conservation planning. The principle is to generate hierarchical
landscape priorities based on the raster’sbiologicalvalues,
considering the connectivity and priorities of biodiversity features
(species, land use types, etc.) (Di Minin et al., 2014;Li et al., 2020).
The combined application of MaxEnt and ZONATION has been
extensively utilized in biodiversity conservation planning for both
plants (Wang et al., 2015;Gouwakinnou et al., 2019) and animals
(Yang et al., 2019). The input layers included the biotic factor layers,
the landscape fragmentation layer, and the habitat quality layer in
this study. The current and future results generated by the MaxEnt
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model constituted the biotic factor layers. The landscape
fragmentation layer resulted from 2020 in Section 2.7 computed
by Raster Calculator. The higher the value, the lower the degree of
landscape fragmentation. And the habitat quality layer was the
result of 2020 in Section 2.8. The cultivated and construction land in
the 19 layers were removed because China is practicing the policy of
preventing Non-Grain Production. We used the core-area zonation
(CAZ) cell-removal rule to retain the core area of species
distribution. And edge removal was used to promote the
maintenance of structural habitat continuity in the removal
process (Moilanen et al., 2009). The warp factor was set to 1 to
remove one grid at a time for optimal running results. The weights
of input layers were all set to 1. Other parameters were model
default. Protecting 5% ~ 20% of the habitat has been shown to
achieve more than 50% of species conservation (Margules and
Pressey, 2000). According to the target, we set the top 20% of the
result as the PPAs for practical purposes. Then, we graded the
output results: the top 5% of the results were considered mandatory
protection areas, 5% ~ 10% negotiated protection areas, and 10% ~
20% partial protection areas (Yang et al., 2018a). We also collected
data on national nature reserves in the study area to reveal the gap
between PPAs and existing reserves.
China has vigorously advocated wild medicine materials
tending in recent years to improve the quality of wild TCM and
ensure the healthy development of the TCM industry. In the general
rules, WTAs should be in the TCM primitive environment, with no
pollution, good environmental quality, and business potential.
Therefore, the remaining 80% of results were set as WTAs.
County-level governments in China serve as crucial management
units for conservation efforts (Yang et al., 2018a). The Getis-Ord
Gi* was used to analyze the hotspots of the PPAs gap and WTAs for
G. manshurica at the county level.
3 Result
3.1 The CPD of G. manshurica
In this study, we used the MaxEnt model to predict the potential
distribution of G. manshurica. After kuenm package optimization,
the best parameters were FC=QT and RM=2. The mean of training
AUC and test AUC for the CPD and FPD under this parameter
condition were greater than 0.9 (Figure S2), which can be used for
the prediction.
The CPD of G. manshurica is mainly distributed in the central
part of the Songnen Plain (Figure 2), with an area of 8.83×10
4
km
2
(accounting for 7.19% of the study area). It is mainly distributed in
the semi-humid area (97.64%), only a small part of the low and high
suitability areas are distributed in the semi-arid area (2.36%). The
high suitability area covers 1.97×10
4
km
2
(1.61%), mainly in
Lindian, Anda, and Dorbod Mongol Autonomous County
(Dorbod) of Daqing City. The mid suitability area is centered on
the high suitability area and distributed radially outward, covering
3.31×10
4
km
2
(2.70%), mainly in Dorbod and Qiqihar. The low
suitability area is centered on the mid suitability area and
distributed sporadically outward, with an area of 3.54×10
4
km
2
(2.89%), mainly in Zalaite Banner, Nong’an, and Zhaodong.
3.2 Result of the changes in landscape
structure, landscape indexes, and
landscape fragmentation
We analyzed landscape structure changes in the study area from
1980 to 2020. Currently (2020), the main landscape type in the CPD
is cultivated land (56.25%), which is much larger than the
FIGURE 2
Current potential distribution of G. manshurica (A). Current potential distribution of G. manshurica; (B). Area I of current potential distribution of G.
manshurica;(C). Area II of current potential distribution of G. manshurica).
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proportion of other landscape types. The proportion decreases as
the level of the suitability area increases (Figures 3A,B). The trend
of the change in the CPD in the last 40 years is that grassland and
waters have decreased, while cultivated land, construction land, and
forest land have increased, and unused land has no significant
change. Compared with 1980, the grassland area decreased
significantly by 22.25% in 2000 and 27.08% in 2020 (Figure 3A).
Combined with the landscape structure shift matrix, it is clear that
the intensified decline in grassland over the 40 years is mainly due
to the significant expansion of cultivated land and the degradation
of partly grassland to unused land (mainly marshland and saline
land), followed by the conversion of forests, construction land and
waters (Figure 3C;Tables S5,S6).
Dynamic analysis of landscape indexes in the CPD showed that
during 1980-2020, NP and PD decreased and then increased, while
MPS decreased yearly, indicating that the patch area gradually
decreased. Regarding the landscape shape index, FRAC_AM
decreased yearly, indicating that humans have increasingly
disturbed the grassland landscape. From the perspective of the
aggregation indexes, DIVISION showed an increasing trend over
the years, while AI showed an increasing trend, indicating that
landscape aggregation decreased yearly and its integrity decreased
(Table S7). In the past 40 years, the landscape fragmentation index
increased and intensified everywhere. From 1980 to 2000, the area
of each fragmentation level decreased due to the sharp decrease in
grassland. The proportion of mid, high, and extremely high
fragmentation increased, and the other decreased. From 2000 to
2020, the area of low and extremely high fragmentation increased,
while the other decreased (Table S8). The spatial-temporal change
analysis of the landscape fragmentation index of grassland types in
CPD from 1980 to 2020 showed that the increased fragmentation
mainly occurred in the high and mid suitability areas, mainly in
Dorbod, Jalaid Banner, Lindian, and Horqin Right Front Banner
(Figure 4D). Currently (2020), the area with extremely low
fragmentation is generally in the center of the patch. Extremely
high fragmentation is generally in the periphery. The area with
extremely low and low fragmentation accounts for a relatively large
proportion (61.62%) (Figures 4A,C). From the perspective of
different levels of suitability area, with the increase of suitability,
the proportion of the mid, high, and extremely high fragmentation
showed a decreasing trend, while the other was contrary
(Figure 4B). In summary, in the past 40 years, the grassland
A
B
C
FIGURE 3
Landscape structure change map (A). The proportion of landscape structure in the study area in 1980, 2000, and 2020; (B) The proportion of
landscape structure in each grade of the CPD in 2020; (C) Landscape structure transformation matrix of the study area in 1980, 2000, and 2020.
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landscape in CPD decreased, and the fragmentation trend
intensified. Although the area with low fragmentation in the high
suitability area is relatively large at present, the intensified
fragmentation mainly occurs in the high suitability area from the
dynamic point of view, which is still a challenge for the population
survival of the G. manshurica.
3.3 Results of habitat quality
In 1980 and 2000, the average habitat quality index in the CPD
was 0.28 ± 0.42 and 0.22 ± 0.39, decreased by 21.41%. High-quality
and mid-quality habitat areas decreased by 22.73% and 14.37%,while
low-quality habitat increased by 10.01%. The average habitat quality
index in 2020 was 0.21 ± 0.38, representing a 3.51% decrease
compared to the index in 2000. The high-quality and low-quality
habitats decreased by 22.87% and 0.18%. Conversely, mid-quality
habitats increased significantly by 71.26% (Figure 5C). In 2020, high-
quality and mid-quality habitats were 1.25×10
4
km
2
and 1.19×10
4
km
2
, accounting for 14.14% and 13.40%, mainly distributed in Anda,
Jalaid Qi, and Dorbod. Low-quality habitat accounted for 72.12%,
mainly distributed in the Mongolian Autonomous County of Qian
Gorlos and Dorbod. This result showed that low-quality habitat
accounted for a large proportion, and the habitat quality of each level
was embedded with each other, which was not conducive to the
growth of species (Figure 5A). It can be seen that with the increase in
habitat suitability, high-quality and mid-quality habitats gradually
increased, while low-quality habitats gradually decreased (Figure 5B).
In general, the habitat quality of CPD was very low, showing a
declining trend. Moreover, the habitat quality decreased significantly
from 1980 to 2000 compared with 2000 to 2020.
3.4 Dynamic change of spatial
pattern of FPD
A comparative analysis of the CPD and FPDs is expected to
show an overall increasing trend in the FPDs (Figures 6,7). For the
FIGURE 4
Landscape fragmentation of grassland in CPD (A). landscape fragmentation in 2020; (B). the proportion of different levels of landscape
fragmentation; (C). the proportion of different levels of landscape fragmentation in different levels of suitability area; (D). landscape fragmentation
changes in three periods.
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low suitability areas, mid suitability areas, and total suitability areas,
increasing trends are expected to be generally consistent under
different SSPs in the 2030s, with growth rates between 52.82% and
76.23%. With the increase of SSPs, the area growth rate is expected
to increase in the 2050s and 2070s. And in the 2090s, the growth
rate is expected to increase first and then decrease. Note that in
SSP585-2090s, the area is no longer increased. And the mid
suitability area is expected to be 1.45×10
4
km
2
, decreased by
56.35%. And the total suitability area is expected to be 7.21×10
4
km
2
, decreased by 18.30%. For the high suitability area, with the
increase of SSPs in the same year, the growth rate is expected to
increase first and then decrease. At the same time, it is worth noting
that in SSP370-2090s, the area is expected to stop growing and
begin to decline, decreasing by 27.73%. During the SSP585-2090s,
the high suitability area might completely disappear. In general,
global warming is favorable to the growth of G. manshurica at the
beginning. However, as the increase of years and SSPs, it poses an
increasing threat to growth (Figures 6,7).
A more detailed map analysis showed that the central Songnen
Plain is a stable potential distribution for G. manshurica. And the
expansion will mainly be expected in the northwest and south of the
CPD. With the increase of SSPs and years, the FPD in Liaohe Plain
and Hulunbuir Plateau will be expected to increase year by year
(except SSP585-2090s). The only shrinkage is expected in the
western Songnen Plain (Figure S3). From the perspective of the
division of dry and wet areas in China, generally speaking,
compared with the CPD, the semi-arid area in the FPDs of
different suitable levels will increase significantly, the humid area
will increase slightly, and the area of the sub-humid area will
decrease (Figure S4). The MOP analysis of the current climate
conditions between the accessible region “M”and the projected area
shows the strict extrapolation in the eastern, southern and central
parts of the study area (Figure S5).
3.5 Environmental variables related
to G. manshurica
As can be seen from Table S9, bio15 (precipitation seasonality
(CV)), bio03 (isothermality), bio01 (annual mean temperature),
and CL (clay content) are the environmental variables affecting G.
manshurica.Thecumulativecontribution percent of these
environmental factors reached 88.2%, and the cumulative
permutation importance reached 93.5%. Combined with the
A
BC
FIGURE 5
Habitat quality results (A). habitat quality of CPD; (B). statistical results of habitat quality at different levels; (C). habitat quality indexes of CPD in 1980,
2000, and 2020.
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response curve given by the MaxEnt model, it can be known that
there is a relationship between environmental variables and the
presence probability of G. manshurica. Bio01 is -8 ~ 13°C, whereas
the optimum temperature is 2.71 ~ 6.06°C (Figure 8A). The suitable
range of bio03 is between 18.28 and 25.09. And there is a turning
point at 21.16. From 18.28 to 21.16, habitat suitability decreased
with increasing isothermality. While the value of isothermality from
21.16 to 25.09, habitat suitability suddenly increases, then gradually
descends (Figure 8B). The optimum growth condition for G.
manshurica in bio15 is between 114.63 and 130.05 (Figure 8C).
The clay content of 18.88 ~ 48.02 g/100g is suitable for the growth of
G. manshurica (Figure 8D).
Three climate variables dominating the distribution of G.
manshurica are affected differently by climate change. Bio01 and
bio15 have extremely significant differences with the current climate
conditions (p-value < 0.01), while bio03 is basically consistent
(Table S10). With the increase of year and SSPs, the average
bio01 in the CPD is expected to increase from 4.18°C to 13.51°C
by the end of this century. Bio01, in the future, will gradually deviate
from the suitable range in the CPD based on the response curve of
environmental factors (Figures 8,9). Although bio15 under future
climate conditions significantly differs from current climate
conditions, its average value is still in the appropriate range
(Figures 8,10). In conclusion, with climate change, the future
pressure on the CPD of G. manshurica is due to the growth of bio01.
Bio01 in the study area decreases with increasing latitude.
Climatechangesignificantly affects it and has obvious changes
with increasing SSPs. At the end of this century of the SSP126 and
SSP585, the mean values increase to 6.59°C and 12.29°C
(Figure 9),whicharebothhigherthanthetoleranceofG.
manshurica for bio01. Bio15 shows a downward trend with the
Songnen Plain as the center. And it also shows a trend affected by
climate warming. At the end of this century of the SSP126 and
SSP585, the mean value increases to 114.82 and 115.29
(Figure 10), which is still within the suitable range. Therefore,
we believe that the expansion of the suitable area is also due to
increasing bio01.
3.6 Future range shift of elevation, latitude,
and climate niche centroids
In the future, the average elevation of the FPD of G. manshurica
will increase. The current average elevation is 176.99 m. Under low
carbon emission scenarios (SSP126 and SSP245), the average
elevation is expected to increase yearly. In SSP245-2090s, the
average elevation is expected to change the most, rising to
415.68 m. However, under the high carbon emission scenario
(SSP370 and SSP585), the average elevation is expected to
increase in the first three periods and decrease in the 2090s.
There is no apparent change in the mean latitude. Under the
same SSPs, the mean shows an increasing and then decreasing
trend. The mean latitude is lower than the current in the SSP370-
2090s, SSP585-2050s, and SSP585-2090s (Figure S6).
FIGURE 6
Suitability area of G. manshurica in Northeast China under different SSPs and periods. Blue in the cell represents an increase in the area of FPD
compared to CPD. The bluer the color, the bigger the increase. Red represents a decrease in the area of FPDs compared to CPD. The more red the
color, the larger the reduction.
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Currently, the climate niche centroid is 124.27° E, 46.27° N (in
Dorbod County). Climate niche centroid migrates to the northwest
under the low carbon emission scenarios (SSP126 and SSP245). In
the SSP245-2070s, the climate niche centroid is expected to shift the
farthest to the northwest (150.67 km) to 123.09° E, 47.36° N (in
Longjiang County). In SSP370, the migration trend is first to the
northwest and then to the southwest over time (Figure 11). Under
extreme climatic conditions (SSP585), with the increase of years, the
climate niche centroid is expected to migrate to the northwest, then
to the southwest, and finally return to Dorbod County (124.10° E,
46.06° N).
3.7 Planing the PPAs and WTAs
According to the results of the ZONATION, the PPAs under
climate change are 3,823 km
2
(Figure 12A). Only 364 km
2
of PPAs
are protected, accounting for 9.52% of PPAs, and only 0.41% of
CPD. It is mainly distributed in Songnen Plain (Area I) and
Hulunbuir Plateau (Area II) (Figures 12B,C). Mandatory and
negotiated protection areas are 956 km
2
, and partial protection
areas are 1,911 km
2
. Five protected areas contribute to the
conservation of G. manshurica, and Heilongjiang Zhalong
National Nature Reserve has the highest contribution, accounting
for 46.43%. Inner Mongolia Hulun Lake National Nature Reserve
followed, accounting for 27.20% (Figure 12E). Simultaneously, these
two reserves provide the largest mandatory and negotiated
protection area, accounting for 88.82% and 81.01% of the total
area of the mandatory and negotiated protection area. We used
Getis-Ord Gi* hotspot analysis for PPAs gaps to delimit county-
level protection. The results showed 4 hotspots in PPAs, including
two core hotspots (Lindian and Qinggang County), one sub-hotspot
(Sartu), and one marginal hotspot (Lanxi) (Figure 12D). The area of
WTAs is 15,291 km
2
, and there are 11 hot spots in WTAs. Seven
core hotspots (Lindian, Datong, Jalaid Banner, Ranghulu, Zhenlai,
Tailai, and Dorbod), two sub-hotspots (Ulanhot and Tiefeng), and
two marginal hotspots (Zhaoyuan and Zhaozhou) are planned to as
the site for the wild tending of G. manshurica (Figures 12F,G).
FIGURE 7
FPDs under different periods and SSPs for G. manshurica.
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4 Discussion
4.1 Effects of environmental variables
on the distribution of suitable areas
of G. manshurica
The first step in conservation is to understand the relationship
between the geographical distribution of taxa and environmental
conditions (Harapan et al., 2022). The results show that
precipitation (bio15) and temperature (bio01 and bio03) are the
main factors limiting the distribution of G. manshurica. Under the
background of climate change, precipitation, and temperature
changes will play an important role in the dynamic evolution of
plant communities.
The change in precipitation pattern significantly affects plant
growth and regeneration. Growing evidence suggests that
precipitation variability and extremes exert a more significant
influence on ecosystem processes than the average precipitation
level (Zeppel et al., 2014). Bio15 is a measure of the variation in
monthly precipitation totals over the course of the year, which affect
phenology, such as fruit, leaf, and early/late wood development.
Thus, bio15 is important for species growth (Misson et al., 2011).
The results show that bio15 positively affects the growth of G.
manshurica. The seasonal rainfall in Northeast China varies greatly,
mainly in summer and fall and least in winter (Yao et al., 2017). As a
narrow species in Northeast China, bio15 in the suitable area is
higher than in the unsuitable area, indicating that precipitation
distribution in a year in the suitable area is more uneven, and the
precipitation in summer and fall would be more concentrated. This
result is consistent with our field survey results and literature
records that G. manshurica is mainly concentrated in seasonally
waterlogged areas of local higher elevation (Liu et al., 1995).
Clay content is also a key factor for G. manshurica,whichworks
by coupling with precipitation. The change in precipitation pattern
has an important effect on soil water. The size and distribution of
precipitation events further interact with local topography and soil,
affecting the extent and depth of soil water replenishment (Reynolds
et al., 2004). Soils with high clay content generally have a great water-
holding capacity and increase the utilization efficiency of precipitation,
which positively affects water absorption, plant water status,
evaporation, cooling, and carbon gain (Ismail and Ozawa, 2007;
Maharjan et al., 2022). Seed germination of G. manshurica requires
a high-humidity environment. In the early stage of individual
development, G. manshurica seeds have some characteristics of
original aquatic macrophytes, such as radicle hysteresis and
hypocotyl hair at the root tip. Therefore, long-term moist
conditions are required. At the same time, within one month after
germination, the seedlings will be easily exposed to dry soil and die due
to the lack of water in the shallow soil layer (Meng et al., 2011).
Therefore, soil with high clay content is conducive to seed germination
and promotes the establishment and continuation of population.
Temperature is a significant factor in controlling plant growth and
development, influencing plant metabolism, regulating nutrient
uptake, and determining the biogeographic distribution of
organisms. Bio03 is the diurnal temperature range vs. the annual
temperature range, which reflects the time and amplitude of
temperature change. And it is related to the length of the growing
season and affects the distribution of species (O’donnell and Ignizio,
2012;Rawat et al., 2022). The suitable range of G. manshurica is 18.28
~ 25.09 (less than 100), indicating that the annual temperature range is
FIGURE 10
Bio15 value in SSP126 and SSP585 and box plots of bio15 change within CPD.
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more drastic than the diurnal temperature range in the suitable area.
Bio01 is one of the most important environmental variables affecting
above-ground biomass and species richness. The earliest seed at the
top of the stem of G. manshurica is usually fully ripe in late September.
Then the above-ground part of the plant grows until it dies from frost.
In Northeast China, the first frost date in Songnen Plain is in late
September, which is later than that in the Greater and Lesser Khingan
Range, Changbai Mountains, and other Northeast China. The late first
frost date is beneficial to the seed fruiting of G. manshurica. A good
deal of high-maturity seeds obtained can ensure the natural
regeneration of the population. Therefore, the four dominant
environmental variables can explain why G. manshurica only
distributes in the narrow area of Songnen Plain in Northeast China.
4.2 Distribution pattern and change of
suitable areas of G. manshurica
G. manshurica has very strict requirements for its growth
environment (climate and soil), with narrow environmental
tolerance. And it is a typical grassland plant. It often occurs in
local highlands in seasonally flooded areas (Liu, 1988). At the same
time, it requires a high temperature and high humidity
environment during seed germination. The seed germination and
seedling growth process is extremely slow, and such environmental
conditions must be maintained for one month afterward (Meng
et al., 2011). Few areas in the Songnen Plain can simultaneously
meet such habitats (Liu, 1988). The results of this study show that
soil factors (such as clay content and pH) are also environmental
factors limiting the distribution of G. manshurica. The Songnen
Plain is one of the world’s three major saline-alkali lands, and the
soil is severely saline-alkali and desertified (Yang and Zhang, 2010).
The salinization of soil will reduce the suitability value of G.
manshurica in the region. At the same time, land use changes will
also affect the climate (Yin, 2023). And our research results also
show that a large amount of cultivated land has replaced grassland
areas. These results make it even more difficult to meet the optimal
climate conditions (high temperature and high humidity) for the
growth of G. manshurica. Thus, it is plausible that the limited
spatial extent of optimal environmental conditions for G.
FIGURE 11
Climatic niche centroid of G. manshurica in FPDs.
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manshurica growth in Northeast China may explain the small
proportion of high suitability areas.
G. manshurica is confined to the Songnen Plain in Northeast
China, an area vulnerable to the impacts of climate change
characterized by the high frequency and intensity of extreme
weather events (Ji et al., 2006). Recent studies indicate that the
region has experienced a significant increase in average temperature
and a corresponding decrease in annual precipitation over the last
50 years, posing a significant threat to the area’s biodiversity (Wang
et al., 2022b). Our study found that global warming is initially
expected to favor G. manshurica, but as years and carbon emissions
increase, it begins to threaten growth. Our t-test seems to provide a
plausible explanation for this trend. Bio01 and bio15, affecting the
distribution of G. manshurica, have significant differences between
the future and the current, while bio03 has no significant difference.
Bio15 values have been in the suitable range in all climate scenarios.
While bio01 is only at the tolerance threshold in the SSP126, it
adversely affected G. manshurica in the other three climate
scenarios. Therefore, it is speculated that bio01 and bio15 may
determine the future distribution of G. manshurica. Specifically, the
combination of bio15 and bio01 is conducive to G. manshurica
under SSP126, SSP245, and SSP370. Even though bio01 is
unfavorable to growth, its minor negative effects may be covered
by bio15 with more positive effects. As a result, under these three
climate scenarios, the suitable area is expected to show an increasing
trend, expanding from Songnen Plain to Liaohe Plain and
FIGURE 12
PPAs and WTAs of G. manshurica (A). PPAs compared with existing nature reserves; (B). PPAs compared with existing nature reserves of Area I;
(C). PPAs compared with existing nature reserves of Area II; (D). hotspots of PPAs; (E). contribution of existing nature reserves to different levels of
PPAs; (F). WTAs of G manshurica;(G). hotspots of the WTAs). 1: Heilongjiang Zhalong National Nature Reserve; 2: Inner Mongolia Hulun Lake
National Nature Reserve; 3: Inner Mongolia Tumuji National Nature Reserve; 4: Jilin Chagan Lake National Nature Reserve; 5: Inner Mongolia Huihe
National Nature Reserve.
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Hulunbuir Plateau. However, in the SSP585, the value of bio01 will
increase abnormally, which greatly exceeds the thermal tolerance of
G. manshurica. The negative effects will gradually exceed the
positive effects, resulting in the growth rate of the suitable area
decreasing year by year, and the high suitability area will be
expected to disappear completely in the SSP585-2090s.
4.3 Future range shift of elevation, latitude,
and climate niche centroid
The interplay of adaptation and migration has played a central
role in biological responses to climate change (Davis and Shaw,
2001). Most species are projected to shift towards higher latitudes
and elevations in response to climatic shifts. However, this response
can vary significantly within taxonomic groups (Chen et al., 2011).
According to a global literature survey, until the early 21st century,
nearly 20% of species altered their ranges towards lower elevations
and/or southern latitudes in response to rising temperatures
(Parmesan and Yohe, 2003;Lenoir et al., 2010).
For G. manshurica, the elevation and climate niche centroid are
projected to respond to warming, while latitude trends are less
significant. Under various climate scenarios, there is a trend for G.
manshurica to move to higher elevations, albeit with a weak
elevation increase trend in the later stages of high carbon emission
scenarios. Moreover, the migration trend for the climatic niche
centroid indicates inconsistency under different climate scenarios.
Under low carbon emission scenarios, the centroid migrates
northwestward with increased years. In contrast, under high
carbon emission scenarios, the centroid migrates exceptionally,
shifting first to the northwest, then moving southwestward before
finally returning to the original administrative region of the current
centroid position. This phenomenon may be due to a decrease in the
total suitable habitat area of G. manshurica under extreme climate
scenarios. Thus, the remaining populations may face significant
threats to their reproduction and survival and may only be
distributed within CPD. Northeast China is greatly affected by
climate warming (Zhou, 2015). The reason for the shifting of G.
manshurica’s climate ecological niche centroid may be that as
temperatures rise, potential evapotranspiration will increase, water
consumption will become more significant, and the phenomenon of
warm-drying will become more apparent (Lian et al, 2001). Under
future climate change scenarios, the current semi-humid areas in
Northeast China are likely to become semi-arid regions (Ma et al.,
2019). The shift of climatic niche centroid reflects core habitat shifts.
That implies increasing protection of the CPD is necessary and
critical for the conservation of G. manshurica and the ecosystem
structure. While it is unlikely that a few dominant environmental
variables and their tolerance thresholds will explain the aspects of
species distribution, some experience supports a strong influence of
these variables in determining geographic distribution (Hoffmann
et al., 2005;Markle and Kozak, 2018;Tagliari et al., 2021).
It is worth noting that the climatic niche centroid we used
differs from the geographical centroid in previous studies. By
comparing the migration results of the two centroids, we can see
that the migration trend is basically the same, but the migration of
the geographical centroid is larger and farther away (Figure S7).
Specifically, the geographical centroid only represents the shape
center of the suitable area, which has no ecological significance. The
climate niche centroid is the weighted average center of key climatic
variables that affect the distribution of G. manshurica in the suitable
areas, representing the highest region in the climate-suitable area.
Moreover, the population in the climate niche centroid has the
largest genetic variation, which is conducive to improving the
adaptation potential to climate change (Hoffmann et al., 2005;
Leimu et al., 2010;Lira‐Noriega and Manthey, 2014). Therefore,
it is more reasonable to use climate niche centroid to study the
migration trend of G. manshurica under climate change.
4.4 Effects of landscape pattern and
habitat changes on the distribution
of G. manshurica
Climate change is not the only factor threatening the survival of
G. manshurica. The disturbance from humans is also increasing.
Previous studies showed that G. manshurica, originally distributed
throughout Northeast Plain, was only maintained for half a century.
In the 1990s, only a few individuals were available in the Songnen
Plain of Heilongjiang province, and nowhere else can be found (Liu,
1988). In the past 40 years, the landscape structure of CPD changed.
Cultivated landscapes replaced many grassland landscapes, and the
habitat was degraded. Moreover, the habitat quality was extremely
low and developed in a lower direction. These results will lead
directly to a decrease in suitable habitats. Another reason for the
decline in grassland landscapes is degrading into marshland and
saline-alkali land. Songnen Plain is one of the three saline-alkali
regions in the world. In addition to natural factors, human factors
such as unreasonable wasteland reclamation, grazing, and mowing
will also cause secondary salinization, accelerating the alkalinization
process again (Wang et al., 2009). G. manshurica can’t survive on
salinized land. Thus, the increase in salinized land will further
reduce its potential habitat. Therefore, the loss of habitat caused by
human disturbance has an extremely adverse effect on the
reproduction of the population, which would further aggravate
the decline of the species.
Human disturbance has also increased habitat fragmentation
and this ecological process is increasingly becoming a major threat
to biodiversity (Fahrig, 2003;Fischer and Lindenmayer, 2007;Wu
and Lv, 2008). From 1980 to 2020, CPD was affected by habitat
fragmentation. Habitat fragmentation in synergy with climate
change produces more severe negative effects, reducing the ability
of species to track rapid climate change (Leimu et al., 2010;Meng
et al., 2011). There are two reasons to interpret this phenomenon.
First, the range shift to habitats with optimal climate conditions is
impeded in a fragmented landscape. Second, the reduction of
genetic variation in fragmented populations is predicted to reduce
the adaptive potential of species under climate change (Leimu et al.,
2010). For plants, migration occurs in the form of propagules and
pollens, which requires habitat patches to be tightly connected
enough to allow genes to flow between populations (Davis and
Shaw, 2001).
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The seeds of G. manshurica are small and light, belonging to the
short-distance seed. Under natural conditions, the longevity of
seeds is up to two years, which is short-lived (Liu et al., 1995). It
isn’t easy to realize natural population regeneration by soil seed
banks, which can only rely on the seed rain of plants. Moreover, its
seeds mature in late September at the earliest. However, mowing
begins mid to late August, making it impossible to form mature
seeds due to the developed animal husbandry in the Songnen Plain
(Sun et al., 2003). The natural regeneration of the population will be
limited to some extent because the amount of seed rain is negatively
affected by habitat fragmentation and the direct human factor, thus
affecting the propagation, spread, and continuation of species (Li
et al., 2007;Jesus et al., 2012;Liao et al., 2013).
Regarding genetic variation, habitat fragmentation increases the
chance of random genetic drift and inbreeding rates and reduces inter-
specificgeneflow (Yu et al., 2019). The reproductive structure of G.
manshurica shapes its self-pollination mode (Sun et al., 2003). Species
predominantly self-interbreeding tend to have low levels of
intraspecific genetic variation, affecting the gene flow between
individuals and populations (Durka et al., 2013). In addition, as a
narrow-range species, its genetic diversity at the population level may
be significantly lower than its widespread counterparts. It may be more
sensitive to the loss of variation due to genetic bottlenecks, resulting in
its poor ability to adapt to new environments (Lavergne et al., 2004;
Willis and MacDonald, 2011). Thus, it may not cope with future drastic
climate changes. In particular, G. manshurica has the characteristics of
rarity and specialization, which may increase the risk of species
extinction. At worst, these two characteristics have a synergistic effect
leading to more likely than common species to disappear from the
regional species pool (Davies et al., 2004;Platts et al., 2014).
When plants face new selective pressures, such as climate
change, there are three ways to respond: death, migration, and
adaption (Leimu et al., 2010). Genetic limitations to adaptation
combined with land use changes, which will hinder gene flow, may
reduce the rate of adaptation significantly below the rate required
for climate change (Davis and Shaw, 2001;Opdam and Wascher,
2004). Furthermore, the population tolerance and resilience of G.
manshurica may be reduced under climate change. Under the
double pressure of climate change and human disturbance, its
migration may become more difficult. These factors may work
alone or together, leading to an extinction vortex and the extinction
of the population ultimately. Therefore, measuring the relationship
between product development and wild conservation is necessary.
4.5 Identification of PPAs and WTAs under
climate change
Although FPD is expected to increase, part of CPD may fall
outside the climatic niche under the high carbon emission scenario. At
the same time, land use change, low habitat quality, and poor-
connectivity landscape will pose significant obstacles to the
migration of the population. If facing the pressures from prevented
migration, the inability to adapt to climate, and constant and
destructive digging, the population of G. manshurica will suffer a
great calamity. Therefore, our study provides a theoretical framework
for conserving the wild resources of G. manshurica.Ifreservesare
managedwell,theycouldbeanefficient and effective means to address
biodiversity loss, which can buffer communities from the effects of
climate change (He and Cliquet, 2020). The existing reserves provide
only 9.52% of PPAs. Heilongjiang Zhalong National Nature Reserve
and Inner Mongolia Hulun Lake National Nature Reserve, a stable
climate refuge under climate change, contribute a large area of
protection to the PPAs of G. manshurica, which can provide a good
environment for population reproduction. But there are still gaps in
the PPAs. Due to the difficulty of establishing a large reserve, further
efforts are needed to extend the existing reserves (Wang et al., 2022a).
In China, local governments are the main participants and law
enforcers in biological protection. We analyzed biological protection
hotspots at the county level to fill the protection gap. The results
showed that Lindian, Qinggang, Sartu, and Lanxi are the hotspots of
the PPAs. These cities with high-quality habitats and high-connected
landscapesaresuitableforG. manshurica and still provide a stable
refuge in climate change. Therefore, the strict conservation of the wild
population and natural habitat of G. manshurica in these cities should
be needed to achieve sustainable development of the species.
On the other hand, as one of the original species of Gentianae
Radix et Rhizoma, G. manshurica is of the best quality. However,
with the increasing use of medicine, the limited wild resources are in
danger. The domestication of G. manshurica has not been
successful (Wang, 2005). The most suitable cultivation method
for the species is to carry out wild tending in its native natural
ecological communities because of the high susceptibility to pests
and diseases and low survival rate planted out of its native habitat.
The hotspot analysis showed 11 counties where wild cultivation
could be carried out, including Lindian, Datong, Jalaid Banner, etc.
Under the conditions of the natural ecological environment, wild
tending can balance the contradiction between short supply and
increasing demand of the TCM, maintain the expansion and
protection of populations, solve the contradiction between
medicinal plant production and ecological diversity conservation,
maximize the best combination of yield and quality, and promote
the sustainable utilization of the resources.
There is no doubt that the establishment of PPAs and WTAs is
not enough to conserve G. manshurica. In addition, we propose the
following recommendations. First, to dynamically monitor changes
in the distribution, it is necessary to conduct regular surveys and
threat assessments of its resources. Also, conducting this work will
improve the prediction result by reducing sampling bias since model
accuracy improves with increasing sample size, making conservation
planning and other applications more scientificandrational
(Hernandez et al., 2006). Second, based on the results of this study,
germplasm collection should be conducted within the high suitability
area. Given that germplasm resources cannot be recreated once they
disappear, retaining as much genetic diversity as possible to mitigate
the impact of climate change. Last, strengthen the research on the
biology, cultivation, domestication, and wild tending of G.
manshurica. In particular, the significant effects of climate change
should be considered when carrying out related work. In these ways,
we provide a reference for formulating related policies and taking
countermeasures for adapting to climate change and human
disturbances, ultimately achieving the conservation of this species.
Zou et al. 10.3389/fpls.2023.1184556
Frontiers in Plant Science frontiersin.org17
5 Conclusion
In this study, we used the optimized MaxEnt model to
successfully predict the current and future suitable areas of
Gentiana manshurica. And we analyzed the habitat quality and
landscape fragmentation based on land use data. Finally, the above
results were used to construct a protective planning framework.
This work is time-consuming and complicated due to the
endangered state of the wild resource and the difficulty of
identifying. Moreover, improved distribution of information can
facilitate and promote the sustainable utilization of G. manshurica
resources, which can lead to salvation in the knowledge gap, and
budgetary costs associated with field surveys. Additionally, accurate
information can enable evidence-based initiatives to strategize
against environmental disturbance and climate change. The
results show that although the potential suitability area of G.
manshurica is relatively optimistic in the low carbon emission
scenario, the worse results obtained in the SSP585 may be more
reasonable and realistic because this scenario is most consistent
with the recent trends in China. At the same time, human activities
impact on the species and habitat cannot be ignored. Thus, our
research is a reminder of the urgent need to reduce global carbon
emissions to reduce the negative effects of climate change. In
addition, human beings should also carry out protective activities
and make multi-pronged efforts to maximize the stability and
reproduction of the population. Finally, our results support the
suggestions of relevant scholars, believing that it is necessary to
upgrade G. manshurica to the second-level national key-protected
medicinal material in China.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary Material. Further inquiries can be
directed to the corresponding authors.
Author contributions
HZ, BC, BZ, XYZho, XYZha, XXZ, and JW participated in the
study design and analysis of the manuscript. HZ wrote the
manuscript. BC, BZ, XYZho, and XYZha revised and processed
the manuscript. XZ and JW gave valuable comments in writing the
manuscript, and supervision and financial support. All authors
contributed to the article and approved the submitted version.
Funding
This work was supported by the Fourth National Survey of
Traditional Chinese Medicine Resources Heilongjiang Special
Project (grant number 2018Hljzyzypc-12).
Acknowledgments
We gratefully acknowledge the support of Prof. Chen Wang
(Harbin Normal University), Senior experimentalist Hongfeng
Wang (Northeast Forestry University), and Prof. Yan Sun
(Heilongjiang University), which offered great help in the
identification of Gentiana manshurica specimens.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1184556/
full#supplementary-material
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