Assessing the Impact of Climate Change on Potential Distribution of Meconopsis punicea and Its Influence on Ecosystem Services Supply in the Southeastern Margin of Qinghai-Tibet Plateau

Abstract

Meconopsis punicea is an iconic ornamental and medicinal plant whose natural habitat has degraded under global climate change, posing a serious threat to the future survival of the species. Therefore, it is critical to analyze the influence of climate change on possible distribution of M. punicea for conservation and sustainable utilization of this species. In this study, we used MaxEnt ecological niche modeling to predict the potential distribution of M. punicea under current and future climate scenarios in the southeastern margin region of Qinghai-Tibet Plateau. Model projections under current climate show that 16.8% of the study area is suitable habitat for Meconopsis. However, future projections indicate a sharp decline in potential habitat for 2050 and 2070 climate change scenarios. Soil type was the most important environmental variable in determining the habitat suitability of M. punicea, with 27.75% contribution to model output. Temperature seasonality (16.41%), precipitation of warmest quarter (14.01%), and precipitation of wettest month (13.02%), precipitation seasonality (9.41%) and annual temperature range (9.24%) also made significant contributions to model output. The mean elevation of suitable habitat for distribution of M. punicea is also likely to shift upward in most future climate change scenarios. This study provides vital information for the protection and sustainable use of medicinal species like M. punicea in the context of global environmental change. Our findings can aid in developing rational, broad-scale adaptation strategies for conservation and management for ecosystem services, in light of future climate changes.

Full text

fpls-12-830119 January 7, 2022 Time: 14:11 # 1
ORIGINAL RESEARCH
published: 13 January 2022
doi: 10.3389/fpls.2021.830119
Edited by:
Jian Sun,
Institute of Tibetan Plateau Research,
Chinese Academy of Sciences (CAS),
China
Reviewed by:
Yafeng Wang,
Nanjing Forestry University, China
Su Xu,
Qinghai Normal University, China
*Correspondence:
Jinniu Wang
wangjn@cib.ac.cn
†These authors have contributed
equally to this work and share first
authorship
Specialty section:
This article was submitted to
Functional Plant Ecology,
a section of the journal
Frontiers in Plant Science
Received: 06 December 2021
Accepted: 22 December 2021
Published: 13 January 2022
Citation:
Shi N, Naudiyal N, Wang J,
Gaire NP, Wu Y, Wei Y, He J and
Wang C (2022) Assessing the Impact
of Climate Change on Potential
Distribution of Meconopsis punicea
and Its Influence on Ecosystem
Services Supply in the Southeastern
Margin of Qinghai-Tibet Plateau.
Front. Plant Sci. 12:830119.
doi: 10.3389/fpls.2021.830119
Assessing the Impact of Climate
Change on Potential Distribution of
Meconopsis punicea and Its
Influence on Ecosystem Services
Supply in the Southeastern Margin of
Qinghai-Tibet Plateau
Ning Shi1,2†, Niyati Naudiyal1†, Jinniu Wang1,3*, Narayan Prasad Gaire4,5, Yan Wu1,
Yanqiang Wei6, Jiali He1and Chunya Wang1
1Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China, 2College of Life Sciences, University
of Chinese Academy of Sciences, Beijing, China, 3Mangkang Ecological Station, Tibet Ecological Safety Monitor Network,
Chengdu, China, 4Key Lab of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden (XTBG), Chinese Academy
of Sciences, Mengla, China, 5Department of Environmental Science, Patan Multiple Campus, Tribhuvan University, Lalitpur,
Nepal, 6Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
Meconopsis punicea is an iconic ornamental and medicinal plant whose natural habitat
has degraded under global climate change, posing a serious threat to the future
survival of the species. Therefore, it is critical to analyze the influence of climate change
on possible distribution of M. punicea for conservation and sustainable utilization of
this species. In this study, we used MaxEnt ecological niche modeling to predict the
potential distribution of M. punicea under current and future climate scenarios in the
southeastern margin region of Qinghai-Tibet Plateau. Model projections under current
climate show that 16.8% of the study area is suitable habitat for Meconopsis. However,
future projections indicate a sharp decline in potential habitat for 2050 and 2070
climate change scenarios. Soil type was the most important environmental variable in
determining the habitat suitability of M. punicea, with 27.75% contribution to model
output. Temperature seasonality (16.41%), precipitation of warmest quarter (14.01%),
and precipitation of wettest month (13.02%), precipitation seasonality (9.41%) and
annual temperature range (9.24%) also made significant contributions to model output.
The mean elevation of suitable habitat for distribution of M. punicea is also likely to shift
upward in most future climate change scenarios. This study provides vital information for
the protection and sustainable use of medicinal species like M. punicea in the context
of global environmental change. Our findings can aid in developing rational, broad-scale
adaptation strategies for conservation and management for ecosystem services, in light
of future climate changes.
Keywords: Meconopsis punicea, MaxEnt modeling, habitat suitability, ecosystem service, climate change,
Qinghai-Tibet Plateau
Frontiers in Plant Science | www.frontiersin.org 1January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 2
Shi et al. Climate Change on Meconopsis punicea
INTRODUCTION
Climate not only plays a significant role in the growth and
reproduction of plants, but also governs factors concerning
the survival, development, and distribution of species (Kozak
et al., 2008;Wu et al., 2011;Ford and HilleRisLambers, 2019;
Criado et al., 2020;Dolezal et al., 2020;Wang et al., 2021).
Detrimental influences of rapid climatic warming on global
biodiversity include the changing the life history of species,
community composition, vegetation pattern, and ecosystem
function (Williams, 2000;Thomas C. D. et al., 2004;Thomas J. A.
et al., 2004;McKenney et al., 2007;Thuiller, 2007;Bellard et al.,
2012;Alberto et al., 2013;Al-Qaddi et al., 2017;Xu et al., 2018).
Among them, the variation of species distribution range, which
is a direct visible response of species toward climate change, is
a key issue that has garnered international attention (Mantyka-
pringle et al., 2012;Anderson, 2013;Pacifici et al., 2015). The
effects of climate change are more pronounced at high altitudes
than at low altitudes (Wilmking and Juday, 2005;Gou et al., 2007;
Peng et al., 2011). Higher elevations are warming faster than the
average (Diaz and Bradley, 1997;Diaz and Eischeid, 2007;Wang
Q. et al., 2014), and this has been confirmed and verified by many
researchers across different mountainous ecosystems around the
world (Wang Q. et al., 2014), including the Rocky Mountain
(Minder et al., 2018), the European Alps (Pauli et al., 2007), the
mountains of the western United States (Oyler et al., 2015), and
the Greater Alpine Region (Palazzi et al., 2019). Similarly, the
Qinghai-Tibet Plateau, also known as the world’s third pole, has
a much higher warming rate than the global average (Liu and
Chen, 2000;Niu et al., 2004;Pauli et al., 2007). The intensity
as well as the frequency of heat waves and droughts in alpine
regions have increased in the last decades (Easterling et al., 2000;
Orsenigo et al., 2014;Corona-Lozada et al., 2019;Francon et al.,
2020). Continued warming without any corresponding increase
in rainfall suggests that the future climate would be hotter and
drier (Zhang et al., 2020). Additionally, the frequency of extreme
climate events has also increased significantly in the past few
decades, which could trigger a multitude of biophysical and
economic impacts on the functioning of alpine ecosystems and
their associated services (Wester et al., 2019). The most alarming
direct evidence of climate change observed on the Qinghai-Tibet
Plateau over the past 35 years is the rapid early growth of alpine
grassland vegetation, shortened optimal growth times, increased
vegetation biomass in spring, and decreased vegetation biomass
in autumn (Wang et al., 2020). Changes in grassland vegetation
growth patterns directly affect the survival of millions of cattle
and sheep, as well as a large number of ungulates, and thus the
livelihoods of local pastoralists on the Qinghai-Tibet Plateau.
Climate change will lead to fragmentation of suitable habitats
for species, which may result in the decline of habitat quality
(Li et al., 2015). The composition of plant communities is also
expected to change, with a dominance of species adapted to
warmer climates, which in turn would lead to a decline in
the abundance of species suited for colder regional climates,
particularly in alpine and arctic regions (Bertrand et al., 2011;
Gottfried et al., 2012). Additionally, distributional shifts of plant
species and communities, to higher latitudes and/or altitudes are
expected under potential climate change scenarios (Bugmann,
2001;Hörsch, 2003;Wang et al., 2011). As a result, species
richness and diversity can potentially decrease in the lower
altitudes and latitudes, while endangered species present at higher
altitudes and latitudes face exacerbated risks of extinction due
to increased competition among species (Lotstein, 2013;Wang
et al., 2016). Alpine ecosystems are more sensitive and vulnerable
to climate change than vegetation in other regions due to
lack of plasticity, migration constraints, and insufficient genetic
variation to respond to novel selection pressures (KrÄuchi and
Kienast, 1993;Bugmann, 2001;Hörsch, 2003;Chauchard et al.,
2007;Wang et al., 2011, 2016). The Qinghai-Tibet Plateau region
has already witnessed the adverse impacts of climate change
on its diverse and unique flora and fauna with evidences of
change in growth and phenology of species and shifts in their
natural distribution range (Zhang et al., 2011;Zhao et al., 2011;
Schickhoff et al., 2016;Song et al., 2018;Singh et al., 2019;
Dorji et al., 2020). Many alpine species have high ornamental,
economic and medicinal value, and contribute to the livelihood
of local and ethnic communities. Even though this valuable flora
is under imminent threat from rapid climate change in the region,
there has been no systematic study that addresses the bioclimatic
factors controlling species growth and distribution, in the region.
Species Distribution Models (SDMs) typically construct a
mathematical relationship between known species occurrence
records and the corresponding environmental variables and
predicts the environmental conditions within which a species’
population can be maintained, thereby estimating the suitable
spatial distribution for that species across the study area (Pearson
et al., 2007). Among the many SDMs available, MaxEnt has
shown greater accuracy than other models, especially with
limited species occurrences data (Merow and Silander, 2014;
Radosavljevic and Anderson, 2014). It can visually provide the
size of the distribution area of species in different periods, and
through comparison, the response model of the same species
to different climate changes can be obtained. In this study, we
selected Meconopsis as a representative genus/species to study
the impact of climate and climate change on the distribution
of ecologically and socio-economically valuable sub-alpine and
alpine plants in the fragile and sensitive area in Qinghai-
Tibet Plateau.
Meconopsis, commonly known as blue poppy, belongs to
the Papaveraceae family with 79 species worldwide, about 80%
of which are in China, mainly distributed in the Himalayan-
Hengduan Mountain region (Grey-Wilson, 2014). Meconopsis
punicea Maxim (M. punicea) is a perennial herb with high
ornamental value that grows in alpine scrubs and meadows at
elevation of 3,000–4,800 m (Shang et al., 2015). As a Chinese
traditional Tibetan medicine, flowers of M. punicea have been
used for thousands of years by the local people to treat pain,
fever, cough, inflammation, liver and lung inflammation in
humans and animals (Shang et al., 2015). Meanwhile, the
beautiful flowers are also used as ornamental plants across the
Tibetan region. However, degradation of its natural habitat and
overexploitation are increasingly threatening the survival of wild
M. punicea, and it has been listed as an endangered species
on the China Species Red List in 1999 (Qu and Qu, 2012).
Frontiers in Plant Science | www.frontiersin.org 2January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 3
Shi et al. Climate Change on Meconopsis punicea
Once a species is extinct, it cannot be regenerated, let alone
used by other organisms in the ecosystem or for human well-
being as an ecosystem service. Changes in temperature and
precipitation patterns caused by climate change affect individual
species and the way they interact with their habitats, resulting
in changes in the structure and function of ecosystems and,
ultimately, in biodiversity and ecosystem services (Nogués-Bravo
et al., 2007;Locatelli et al., 2008;Shaw et al., 2011;Díaz
et al., 2019). Moreover, the disappearance of one species can
have a cascading effect, leading to the threat or extinction of
another 10–30 species (Lv, 2009). Diverse biomes and well-
functioning ecosystems are essential for maintaining ecosystem
services that support human well-being (Díaz et al., 2019).
Therefore, to avoid further habitat fragmentation and loss of
species diversity, it is critical to develop adequate conservation
strategies and measures. However, the development of strategies
requires a comprehensive understanding of the relationship
between the geographical distribution of species and climate
change, as well as a reasonable understanding of the distribution
range of species under future climatic conditions. Hence,
analyzing the relationship between the distribution of species
and climate factors to reasonably predict the impact of climate
change on the species distribution and proposing conservation
countermeasures, has a very important theoretical and practical
significance for future biodiversity conservation. A widely used
research tool in this context is MaxEnt model that enables us
to identify land cover change changes under future climate
change scenarios (Faleiro et al., 2013) which can be used to
predict biodiversity loss (Bertrand et al., 2012), assess the risk
of biological invasion (Gallagher et al., 2010), cultivate rare
medicinal materials (Lu et al., 2012;Guo et al., 2016), and manage
and protect endangered species (Li et al., 2013). It is also one
of the most effective and extensively used approach for studying
the impact of climate change on the suitability of species habitats
(Araújo et al., 2019).
In this study, we use MaxEnt modeling to explore the influence
of climate change on potential distribution of wild M. punicea
by (1) identifying the most significant environmental factors
influencing the potential distribution of M. punicea in the study
area; (2) evaluating the potential changes in its distribution; and
(3) assessing the implications for its critical ecosystem services
The results of the present study could aid in conservation and
management of M. punicea in headwater region of Min River and
other habitats in China.
MATERIALS AND METHODS
Study Area
The study was conducted in the headwater region of Min River,
situated at the southeast edge of the Qinghai-Tibet Plateau in
Songpan County of Sichuan Province in China. The distribution
of specimen data recorded in the Chinese Virtual Herbarium
(CVH1) indicates that this region is one of the hotspots for
the distribution of M. punicea (Figure 1). Most parts of this
1https://www.cvh.ac.cn/
region is above 3,400 m in elevation. The region experiences a
typical alpine climate with large variability in temperature day
and night time, with an annual average temperature of 2.8◦C. The
average monthly temperature ranges from −7.6◦C in January to
9.7◦C in July. The annual precipitation of 700–800 mm is mainly
concentrated in the months of May and August (Guan et al.,
2002;Wang J. et al., 2014;Naudiyal et al., 2021), and the region
experiences an average of 1,827.5 h of sunshine per year (Chen,
2004;Wang J. et al., 2014). The soil is approximately 60 cm deep
and classified as mountain brown meadow soil.
The vegetation in headwater region of Min River has distinct
altitudinal zonation and horizontal distribution with strong
floristic transition and abundant plant species. Due to the
complex undulating terrain solar incident radiation does not
illuminate the entire region uniformly creating comparatively
shaded and sun-facing aspects, which act like ecological islands
creating habitats that support a wide variety of species with
different ecological requirements. The sunny slopes are mostly
covered by Sabina chinensis with Spirea,Sibiraea, and Berberis
patched bushes, while the vegetation on shaded slopes mainly
comprises of Picea and Abies forests with an understory of
Rhododendron,Salix, and Lonicera shrubs (Chen, 2004). Most
of the M.punicea population grows in shaded and semi-shaded
mountain slopes with shrub or grassland (Shang et al., 2015;
Wang et al., 2016). Above the treeline, mosaics of Rhododendron,
Salix,Spiraea,Sibiraea, and Dasiphora form shrub belt for nearly
200 m vertical distance followed upslope by alpine meadows,
dominated by species from genus Compositae, Cyperaceae,
and Gentianaceae.
Species Characteristics and Occurrence
Records
Meconopsis punicea is a monocarpic perennial, with a fibrous
root system, usually growing 30–75 cm tall. All leaves are
basal and form a rosette, the leaf shape is oblanceolate
or narrowly obovate, and both surfaces have dense setae.
Its remarkable carmine flowers, of high ornamental value,
usually have four petals arranged in an elliptic formation. The
species is naturally distributed in the southwestern Gansu,
southeastern Qinghai, northwestern Sichuan and northeastern
Tibet (Zhang and Christopher, 2008).
The occurrence data for M. punicea was collated from
primary and secondary sources which included field surveys,
Global Biodiversity Information Facility2database and the CVH.
Primary data collection was done between July and September in
2019. All presence data points were carefully evaluated to remove
duplicate data points and secondary data points that lacked
detailed location information. Presence locations collected from
secondary sources were carefully validated with Google Earth to
eliminate possible errors. We ensured that the selected presence
points of M. punicea were spatially isolated and evenly distributed
throughout the study area to avoid sampling bias (Veloz, 2009)
while ensuring the inclusion of occurrence data across the area
where the species is known to occur in the available literature.
Based on this selection and elimination criteria, resulting in 100
2https://www.gbif.org
Frontiers in Plant Science | www.frontiersin.org 3January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 4
Shi et al. Climate Change on Meconopsis punicea
FIGURE 1 | Distribution map of herbarium sampling and map of the study region.
spatially separated presence records were retained to simulate
habitat suitability.
Environmental Variables
Environmental variables included bioclimatic, topographic and
soil variables. Bioclimatic variables were obtained with 1 km
spatial resolution from WorldClim dataset (Hijmans et al.,
2005),3which generated using monthly temperature and rainfall
records from 1950 to 2000. The dataset is commonly used in
species distribution modeling studies around the world because
it provides biologically meaningful information on climate trends
and seasonality (Kumar, 2012). Our projections included current
and two future periods (2050s and 2070s) under current and
four Representative Concentration Pathways (R) scenarios from
most optimistic to most pessimistic, these include: RCP 2.6 (most
optimistic scenario), RCP 4.5 (intermediate scenario), RCP 6.0
(pessimistic scenario) and RCP 8.5 (highly pessimistic scenario).
RCP represents a representative pathway for greenhouse
gas emissions and atmospheric concentrations, air pollutant
emissions and land use in the 21st century (Vuuren et al., 2011).
Two general circulation models [BCC-CSM1.1 (Beijing Climate
Center, China Meteorological Administration, China) and
HadGEM2-ES (Met Office Hadley Centre, United Kingdom)],
which are commonly used for species distribution modeling in
the Hindu-Kush Himalaya and Qinghai-Tibet Plateau regions
(Kumar, 2012;He et al., 2019;Zhang et al., 2019), were
used in this study.
Among topographic information variables, elevation data as
was downloaded from digital elevation model (DEM) datasets
with a spatial resolution of 30 m, slope and aspect data were
extracted from the elevation data using ARC-GIS ver. 10.1. The
3www.worldclim.org
soil data were derived from Soil and Terrain Database of China
(SOTER China), which provides detailed spatial information on
topographic attributes and basic soil characteristics (e.g., organic
carbon content and pH) of an area that has been widely used for
agroecological assessments and climate studies.
All environmental variables (climatic, topographic, and soil)
were resampled and brought to the same spatial resolution (30
arc-seconds) using ArcGIS for before further analysis.
MaxEnt Modeling Procedures
We used a presence only niche model, MaxEnt (Maximum
Entropy) to predict the potential distribution of M. punicea under
current and future climate scenarios. MaxEnt is a robust species
distribution model that has been extensively used for species
distribution modeling across the globe (He et al., 2019;Xu et al.,
2019;Zhang et al., 2019) and works well with small sample sizes
as compared to other modeling methods (Khanum et al., 2013).
Selection of Predictor Variables
Since bioclimatic variables are often highly correlated to each
other (Feilhauer et al., 2012;Dormann et al., 2013), all
environmental variables used in in this study were tested for
multicollinearity using Pearson product-moment correlation
(Yang et al., 2013). ENM tools, a Perl script based graphical user
interface that provides a table of cross-correlation values (−1 to
1) between input variables was used to compute the correlations
between ASCII raster grid layers of input variables. Based on
the results of this analysis, from each set of highly correlated
variables (r= 0.7 or r≤ −0.7) only one was included in the final
model, reducing the total number of variables used for modeling
to 12 (Table 1), that included eight bioclimatic variables, one soil
variable, and three topographic variables.
Frontiers in Plant Science | www.frontiersin.org 4January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 5
Shi et al. Climate Change on Meconopsis punicea
TABLE 1 | Environmental variables used in the study and their percent contribution to the model output for Meconopsis punicea.
Abbreviation Description Unit % contribution Cumulative contributions
Soil Soil type – 27.75 27.75
Bio 4 Temperature seasonality – 16.41 44.16
Bio 18 Precipitation of warmest quarter mm 14.01 58.17
Bio 13 Precipitation of wettest month mm 13.02 71.19
Bio 15 Precipitation seasonality – 9.41 80.6
Bio 7 Annual temperature range ◦C 9.24 89.84
Slo Slope ◦5.12 94.96
Asp Aspect ◦3.43 98.39
Bio 8 Mean temperature of wettest quarter ◦C 0.83 99.22
Bio 2 Mean diurnal range [mean of monthly (max temp/min temp)] ◦C 0.78 100
Bio 3 Isothermality (Bio2/Bio7) (×100) – 0 100
Ele Elevation m 0 100
Model Implementation and Accuracy Assessment
Processing parameters were kept consistent for all model runs
with the regularization multiplier set at “2” to reduce overfitting.
A maximum of 1,000 iterations were allowed for each model
run to ensure sufficient time for model convergence. The model
estimates a probability distribution of species based on its current
presence points and associated environmental variables and
provides a spatial representation of habitat suitability varying
from 0 (lowest suitability) to 1 (highest suitability). The model
thus trained using current climate data was projected on future
climate change bioclimatic datasets for all RCP scenarios to
identify potential species habitat of species in 2050s and 2070s.
The Jackknife test was used to assess the relative importance of
environmental variables in determining the potential distribution
of species (Kumar, 2012;Yang et al., 2013;Kumar et al.,
2014). Species response curves were created to investigate
the relationship between habitat suitability and environmental
factors. Model accuracy was evaluated through area under
receiver operating characteristics (ROC) curve. The AUC (Area
Under Curve) is capable of evaluating the ability of a model to
discriminate presence from absence (Elith et al., 2006;Fourcade
et al., 2014), with values range from 0 to 1, where 0 represents
least perfect and 1 represents most perfect discrimination
between sites (Phillips et al., 2006). Models with mean AUC
scores between 0.7 and 0.8 are considered “fair,” between 0.8 and
0.9 are “good,” and greater than 0.9 are “excellent” (Swets, 1988).
In general, we consider that all models with AUC values greater
than 0.75 are acceptable (Elith et al., 2006).
Analysis of Model Predictions
Model outputs were exported to ArcGIS platform for post
processing and analysis of model predictions. Species distribution
prediction was reclassified into three arbitrary habitat suitability
categories: low (25–50% probability of occurrence), medium (50–
75% probability of occurrence) and high (75% probability of
occurrence) with values below 25% omitted as unsuitable habitat
based on logical thresholds (Chakraborty et al., 2016). Area under
each habitat suitability category was calculated to assess potential
area changes from distribution under current climate to future
climate change scenarios.
The influence of climate change on elevational shifts in
M. punicea distribution was assessed through 100 random points
generated in regions with medium and high suitability for
occurrence. The elevation of each randomly chosen point was
obtained from the digital elevation model of the region, based on
which the minimum, maximum, and mean elevations at which
the species is expected to exist was calculated for each RCP.
Mean elevations of potential species occurrence under future
climate conditions were compared with mean elevation of species
distribution under current climate to estimate the net elevational
shift of species.
RESULTS
Model Evaluation and Contribution of
Environmental Variables
The model for current and future climate change scenarios
performed well with AUC values >0.9. The mean AUC value
for model performance under current climatic condition was 0.92
over 10 replicates with a standard deviation of 0.045, indicating
that the predicted distribution model is better than the random
model, with robust stability between each repetition. Therefore,
the model performance for this study can be considered good-
excellent (Swets, 1988).
Percentage contribution values of each predictor variable
are average values established over 10 replicate runs (Table 1).
Results reveal that soil type (14 different types of soils, including
mollic leptosols, and haplic luvisols, etc.) has the most significant
contribution (27.75%) to model output under current climate.
In addition to soil, climate variables also play a key role
in determining the distribution of M. punicea. The major
contributing climate variables were temperature seasonality
(Bio 4, 16.41%), precipitation of warmest quarter (Bio 18,
14.01%), and precipitation of wettest month (Bio 13, 13.02%),
precipitation seasonality (Bio 15, 9.41%), annual temperature
range (Bio 7, 9.24%), accounting for 62.09% of variation in total.
Since the cumulative contribution rate of these six environmental
variables is 89.84%, it is reasonable to say that they contain the
most significant and useful information for predicting species
Frontiers in Plant Science | www.frontiersin.org 5January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 6
Shi et al. Climate Change on Meconopsis punicea
distribution as compared to another environmental variables
included in this study. Besides, soil type, temperature seasonality
and precipitation of warmest quarter were observed to have
a crucial role in estimating the potential distribution pattern
of M. punicea. However, isothermality and elevation have little
influence on model output.
According to the response curves of the dominant
environmental variables (Figure 2), it can be observed that
the soil types most suitable for the survival of M. punicea
are mollic leptosols and eutric leptosols. Meanwhile the most
conducive annual temperature for species growth is close to
34◦C, accompanied by approximately 130 mm of precipitation
in the wettest month. However, with increase of temperature
seasonality and precipitation of warmest quarter, the range
suitable for species distribution is not obvious from the curve,
which indicates these response curves of two variables cannot be
referred to assess the most suitable range of M. punicea.
Potential Distribution of Meconopsis
punicea Under Multiple Climate Change
Scenarios
Based on the known distribution data and environmental
variables, the potential geographic distribution map of
M. punicea was constructed and its survival area was predicted
in the headwater region for Minjiang River. The potential
distribution of M. punicea for two GCMs (BCC and HADGEM2-
ES) in near (2050s) and distant (2070s) future climate change
scenarios is represented in Figure 3. The suitable habitats of
M. punicea are relatively fragmented, with high suitable habitats
mainly limited to the northern part of the study area under
current climate. Based on model outputs the regions which have
high, medium, and low suitability of M. punicea occupy an area
of 269.7, 428, and 733 km2, respectively. In total, suitable habitat
occupies 16.8% of the study area.
Compared with the area of total suitable habitat under current
climate scenarios, the predicted future ranges of habitat showed
a sharp decline for both 2050 and 2070 (Figures 3,4). However,
the magnitude of impact is different for each general circulation
model (GCM) and RCPs within those GCMs. The area of suitable
habitat of M. punicea under the RCP 2.6, RCP 4.5, RCP 6.0,
and RCP 8.5 scenarios for 2050 decreased by 35.60, 36.60, 74.00,
and 50.90% in BCC model, and decreased 64.80, 73.60, 72.20,
and 61.60% in HAD model predicted, respectively. By the 2070,
the area of suitable habitat of M. punicea is likely to decline by
64, 65.3, 60.9, and 92.2% (BCC data), and 60.6, 67.3, 70.6, and
77.7% (HADGEM2-ES data), for RCP 2.6, RCP 4.5, RCP 6.0,
and RCP 8.5, respectively. For the 2050 climate scenario, the
suitable habitats declined the most under the RCP 6.0 (BCC data).
However, for the 2070 climate scenario, the suitable habitats have
the greatest reduction under the RCP 8.5 (BCC data). In most of
future climate scenarios, habitat loss of M. punicea exceeds 60%
in comparison to its current potential distribution.
Model outputs reveal that changes in species distribution vary
amongst all habitat suitability classes. The changes in distribution
areas of regions with low, medium, and high habitat suitability is
represented in Figure 4. The results highlight those regions with
high habitat suitability were most severely influenced by climate
change, regardless of the time and emission scenario. However,
areas of with medium probability of species occurrence decreased
more than those with low probability, except for RCP 2.6 scenario
in BCC model (2050) and RCP 6.0 and RCP 8.5 scenarios in HAD
model (2050). The distribution of M. punicea in the short-term
(2050) has been less affected by climate change in comparison
with that in the long-term (2070).
Climate Change Induced Changes in
Elevational Range of Meconopsis
punicea
In most future climate change scenarios, the mean elevation of
suitable habitat for distribution of M. punicea is likely to shift
upward (Table 2). Upward movement of M. punicea distribution
is likely to be accompanied with a reduction in its overall
elevational range. In most of the future climate scenarios analyzed
in this study, we find that M. punicea is likely to inhabit a
narrower elevational range as compared to its distribution under
current climate scenario (Table 2). The reduction in range implies
that there would be a net loss in area conducive for M. punicea
occurrence. These results show that in future we can expect a
potential decrease in the climatic niches of M. punicea, the extent
of which would depend on the GHG emission scenario. Given
that other environmental factors and human interference in this
region have not been taken into account in the modeling exercise,
the potential impact is expected to be much higher than current
model predictions.
DISCUSSION
Factors Controlling the Distribution of
Meconopsis punicea
Habitat suitability of a species is influenced by environmental
factors that play a key role in driving biological processes
of the species growth, which is a critical aspect in modeling
(Beaumont et al., 2005;Gavilán, 2005). Our study found
that the distribution of M. punicea was mainly controlled
by three precipitation-related bioclimatic variables (Bio 18:
precipitation of warmest quarter; Bio 13: precipitation of wettest
month; Bio 15: precipitation seasonality), two temperature-
related bioclimatic variables (Bio 4: temperature seasonality;
Bio 7: annual temperature range), and soil type. Changes in
precipitation patterns due to climate change can affect plant
physiological and ecological processes at different scales (Barker
et al., 2006;Tognetti et al., 2007), including plasticity response of
vegetative growth (Heisler-white et al., 2009) and reproductive
growth (Molina-Montenegro et al., 2010;Pol et al., 2010)
characteristics of plants. In addition, precipitation can affect soil
moisture and nutrient availability, thus regulating plant growth
and development (Brant and Chen, 2015). For instance, the
influence of increasing temperature and precipitation on the
greening and average flowering of Kobresia pygmaea has been
observed in the Qinghai-Tibet Plateau (Ganjurjav et al., 2020).
Frontiers in Plant Science | www.frontiersin.org 6January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 7
Shi et al. Climate Change on Meconopsis punicea
FIGURE 2 | Response curves for dominant environmental predictors in the species distribution model for Meconopsis punicea.
In addition to precipitation, temperature is another key
environment variable that directly influences plant growth and
distribution by maintaining plant physiological and biochemical
activities such as photosynthesis, respiration, and material
transfer (Henry and Molau, 1997;Arft et al., 1999;Klanderud
and Totland, 2005;Walther et al., 2005). Experimental study
has shown that temperature changes can directly influence
the photosynthetic capacity and growth rate of plants (Jarvis
et al., 2004), while indirectly affecting the soil moisture
content and plant nutrient uptake and utilization (Shah and
Paulsen, 2003). Our findings are in concordance with previous
research on Meconopsis distribution, emphasizing precipitation
and temperature as key climatic factors influencing species
distribution (He et al., 2019;Li et al., 2020).
Frontiers in Plant Science | www.frontiersin.org 7January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 8
Shi et al. Climate Change on Meconopsis punicea
FIGURE 3 | Distribution of varying habitat suitability for M. punicea under different climate change scenarios.
FIGURE 4 | Changes of distribution areas for M. punicea under different climate change scenarios.
Soil type is another crucial variable to determine the
distribution of M. punicea in our study area. Soil is the
product of a combination of soil forming parent material,
climate, biology, topography and time, among which climate
and biology are more active factors (Huang and Xu, 2010).
Each species undergoes its own specific biogeochemical
cycle, resulting in soil properties that are most favorable
for its growth and establishment (Naudiyal et al., 2021).
Due to the interaction between environmental variables
and organisms, changes in temperature and precipitation
may cause changes in soil biogeochemical cycles (Huang
and Xu, 2010;Naudiyal et al., 2021), thus changing soil
types. Studies have shown that plants are quite sensitive to
changes in soil properties driven by temperature changes
(Sullivan, 2016;Ma and Chang, 2019). Similarly, this trend
is likely to exist in our study area, which undoubtedly
Frontiers in Plant Science | www.frontiersin.org 8January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 9
Shi et al. Climate Change on Meconopsis punicea
TABLE 2 | The four scenarios of BCC-CSM1-1 (BC) and HadGEM2-ES (HAD) models projections for distribution in elevation (m ±standard deviation) between the
current time period, the year 2050 and 2070 for M. punicea (Based on randomly chosen sampling points in regions with medium to high probability of species
occurrence in each climate change scenario).
Minimum Maximum Mean Std. deviation
Current climate 2,627 4,170 3,451 467
2050 BCC-CSM1-1 RCP 2.6 2,658 4,095 3,559 311
RCP 4.5 2,373 4,151 3,483 314
RCP 6.0 2,551 4,151 3,556 331
RCP 8.5 2,331 4,159 3,539 328
HadGEM2-ES RCP 2.6 2,551 4,152 3,482 319
RCP 4.5 2,609 4,211 3,482 298
RCP 6.0 2,920 4,203 3,502 298
RCP 8.5 2,647 4,126 3,538 299
2070 BCC-CSM1-1 RCP 2.6 2,581 4,045 3,469 665
RCP 4.5 2,857 4,151 3,458 431
RCP 6.0 2,678 4,212 3,525 316
RCP 8.5 2,373 4,170 3,538 294
HadGEM2-ES RCP 2.6 2,418 4,203 3,467 343
RCP 4.5 2,618 4,055 3,478 300
RCP 6.0 2,618 4,199 3,488 290
RCP 8.5 2,803 4,131 3,512 459
Narrower range as compared with current range.
Increase in elevation compared with current mean elevation.
further increases the vulnerability of M. punicea to
persistent climate change.
This modeling provided strong statistical validation and
robust maps of the potential distribution of M. punicea based on
existing data. However, accurate spatial data on variables such as
biotic interactions, anthropogenic disturbance, and land use/land
cover change were not included in this study due to a lack of
accurate data on these variables. If spatial information on these
variables were available, it would help to build a more robust
model for better prediction of species distribution. Limitations
in spatial data (Rocchini et al., 2011) and the assumption that
species can migrate to climate-friendly areas under climate
change (Engler et al., 2009) have led to uncertainty in species
distribution projections. The actual movement of species in a
changing climate may be fraught with numerous challenges such
as competition, predation, physical barriers, and lack of dispersal
vectors among others. Despite the predictions made by species
distribution models have certain limitations, they remain an
important data source for future suitability predictions to assess
scientific adaptation strategies at the community and ecosystem
levels to offset the effects of future warming on biota (Wiens et al.,
2009;Ackerly et al., 2010).
Impacts of Climate Change on
Meconopsis punicea
Model projections suggest that climate change will reduce
suitable habitat for M. punicea (Figure 3), which is consistent
with the findings of He et al. (2019) and Li et al. (2020). The
effects of climate change on the distribution of alpine species
does not follow a fixed pattern, with impacts varying with the
species’ life history and resource requirements. While several
studies predict a dramatic decline in suitable habitat of alpine
floral species with climate change (Frishkoff et al., 2016;Fragnière
et al., 2020), there are some species that also benefit from it
and may cover larger area in the future (Wang et al., 2019).
For instance, the distribution of alpine herb specie Pedicularis
kansuensis is expected to expand with climate change, under
RCP scenarios RCP 2.6 and RCP 8.5, with a northward shift in
distribution (Wang et al., 2019). Meanwhile suitable area for the
alpine plant Papaver occidentaleis would decrease considerably
in the coming decades (Fragnière et al., 2020). Climate warming
is expected to promote seedling emergence phenology in other
alpine herbs such as Primula alpicola,Pedicularis fletcheri,
Meconopsis integrifolia and M. racemose (Wang et al., 2018),
while species such as Canacomyrica monticola and Fritillaria
cirrhosa face threats of rapid habitat loss and extinction (Davies
et al., 2009;Kumar and Stohlgren, 2009;Barnosky et al., 2011;
Wang J. J. et al., 2014).
With varied responses of species to climate change it is hard
to discern one specific pattern. In such situations, geographical
distribution of a species can be an indicator of its ecological
resilience to an extent. We find that M. punicea covers a small
geographical range and has limited distribution in the study
area under current climate, which is expected to decline further
with climate change. Species with smaller geographical ranges,
such as Meconopsis, can be regarded as more vulnerable to
extinction (Davies et al., 2009) since their limited distribution
is typically considered an indicator for low ecological tolerance
and high sensitivity to environmental changes (Murray et al.,
2011). Such species are therefore vulnerable and are more
likely to face local extinction due to habitat loss under climate
change scenarios (Staude et al., 2020). Generally, small ranged
species also tend to have smaller local populations (Brown, 1984;
Frontiers in Plant Science | www.frontiersin.org 9January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 10
Shi et al. Climate Change on Meconopsis punicea
Gaston et al., 2000), with the decrease of population size,
the anti-interference ability to the environment is weakened
(Schoener and Spiller, 1987). Moreover, species with small
populations are often characterized by increased inbreeding,
decreased population fitness and decreased genetic diversity due
to a bottleneck effect, and therefore face a higher risk of extinction
than other species (Hoffmann et al., 2010;Nayak and Davidar,
2010;Delmas et al., 2014;Theodorou and Couvet, 2015). With
global climate warming, it has been estimated that species with
narrow ecological ranges occupying cold climate niches are likely
to be extinct, while other species that occupy warm climate
niches could benefit (Frishkoff et al., 2016). While global warming
promotes the growth of vegetation (Cao et al., 2004;Xu et al.,
2015) the continuous increase in temperature has had an adverse
impact on alpine plants (Xu and Xuan, 2013). Over the past two
decades, soil water content in inland areas of East Asia has shown
a significant drying trend as temperatures have risen (Zhang et al.,
2020). Field warming experiments of alpine meadow ecosystems
on the Qinghai-Tibet Plateau show that there is a threshold of
soil water content (SWC) that promotes net carbon uptake by
terrestrial ecosystems (Quan et al., 2019). Since temperature is
one critical factor in determining the distribution of M. punicea,
an increase in temperature might trigger a habitat shift from a
lower elevation to a higher elevation. According to the trend of
precipitation, the climate of Qinghai-Tibet Plateau will become
warmer and drier in future (Wei et al., 2021), which may intensify
the vulnerability of M. punicea to the new climate regime.
Climate warming affects not only the distribution of
alpine herbs but also other aspects. Studies have proven that
temperature stress affects the secondary metabolites of plants,
which are usually the basis of their medicinal activity (Schär
et al., 2004). Metabolic components vary with habitat quality
and are directly or indirectly influenced by factors such as
growing environment, climate and soil conditions. This implies
that the efficacy of traditional medicines may be affected by
climate change. The influence of abiotic factors on phyto-
chemistry have been documented by some researchers. Zhang
(2009) found that altitude significantly influenced the amount
of luteolin in Meconopsis quintuplinervia. Similarly, a study by
Liu et al. (2014) showed that total alkaloids content of Coptis
chinensis was significantly affected by topographic factors such as
slope, slope direction and altitude. Previous studies found that
the concentrations of berberine and palmatine in the rhizomes
of Coptis chinensis increased with elevation over a range of
elevations (Zhang et al., 2008), which was also observed in a
similar experimental study by Li et al. (2020). Therefore, as the
distribution area of M. punicea changes under global climate
change, we expect that the medicinal properties of this species
will also change.
This study conducted with two GCMs and all four RCP
scenarios will help to better understand of the vulnerability and
sensitivity of M. punicea to climate change. Moreover, since
this study focused on a relatively smaller geographical region,
we expect to have more accurate results as compared to other
maximum entropy modeling case for predicting the distribution
of M. punicea. Thus, for a more comprehensive assessment of
future changes, not only a wider range of GCMs and RCP
scenarios need to be considered, but additional regional or
local climate change model also be incorporated to make a
reliable prediction.
Influence on Climate Change on
Ecosystem Services From Meconopsis
punicea
Ecosystem services are the environmental basis for human
survival (Boyd and Banzhaf, 2007), through products and services
obtained directly or indirectly from ecosystems (Costanza
et al., 1997). Millennium Ecosystem Assessment (2005) divides
ecosystem services into provisioning, regulation, cultural and
supporting services. Provisioning services refer to material goods
provided by ecosystem like food. Regulating services refer to
indirect benefits such as climate regulation. Cultural services
refer to immaterial services such as tourism and esthetic value
of an ecosystem. Meanwhile supporting services maintain overall
ecosystem functioning for providing all aforementioned services
(Tian et al., 2015). As an organism in the ecosystem, M. punicea
can provide diversified ecosystem services (Figure 5).
Over the past three decades, the global loss of species
diversity due to climate warming has had a serious direct
impact on ecosystem functions (Dib et al., 2020), which could
cause significant damage to the ecosystem products and services
on which human survival depends on (Cardinale et al., 2012;
Hooper et al., 2012;Isbell et al., 2013). Human-driven habitat
loss and fragmentation has been occurring for thousands of
years, leading to biodiversity loss and extinction in many areas,
which has accelerated significantly in recent decades (Dawson
et al., 2011). Biodiversity is linked to many ecosystem services
that are critical for human well-being and its change can affect
ecological stability and the sustainability of ecosystem functions
and services (Pennekamp et al., 2018). It is known that species
loss in ecosystems may alter overall ecosystem structure and
function by influencing the physical formation of habitats,
biogeochemical cycles, and ecosystem productivity (Jones et al.,
1996;Power et al., 1996). As a higher plant, M. punicea plays
a unique role in maintaining biodiversity and participating in
primary production and primary productivity is critical to all
ecosystem services and products (Srivastava and Vellend, 2005;
Duffy et al., 2017).
As an ornamental plant unique to alpine habitat, M. punicea
has captured the attention of several European explorers and
horticulturalists. Most notable amongst them was E. H. Wilson, a
British plant collector and explore, who wrote extensively about
the beauty of Meconopsis and is credited with introducing this
plant to the western world (Xu, 2016). Based on his works several
beautiful hybrids of the species have been created at the Scottish
Rock Garden, including Meconopsis ×Cookei “Old Rose,” a
hybrid of M. punicea and M. quintuplinervia (Christie, 2007). In
Tibetan Buddhism, the prototype of the ubala flower held by the
Green Tara is Meconopsis. The flowers can also be seen in various
frescoes and thangkas, and are believed to relieve suffering. In
addition to its cultural and supporting services, M. punicea is
also essential as a resource for direct utilization by the local
Tibetan population. It is a traditional Tibetan medicinal plant,
Frontiers in Plant Science | www.frontiersin.org 10 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 11
Shi et al. Climate Change on Meconopsis punicea
FIGURE 5 | Synthetical assessing ecosystem services of M. punicea under frameworks of international importance.
Frontiers in Plant Science | www.frontiersin.org 11 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 12
Shi et al. Climate Change on Meconopsis punicea
whose efficiency has been well documented in classic Tibetan
medicine works such as Yueangyaozhen and Jingzhubencao (Liu
et al., 2019). Local populations living on the Qinghai-Tibet
Plateau thousands of years ago mastered its usage for health
benefits (Shang et al., 2015). Meconopsis punicea has been used
by local people to treat pain, fever, cough, inflammation, liver
fever and lung fever in humans and animals for millennia, and
there are five medicinal preparations containing M. punicea that
have been listed in Drug Standard of the Ministry of Public
Health, People’s Republic of China (Tibetan medicine volume)
(Shang et al., 2015).
With climate change and ever-increasing human disturbance,
the ecosystem services provided by M. punicea are also changing.
In particular, there is a decline in supporting and regulating
services due to habitat loss. The changes in cultural services
are difficult to quantify since they are largely subjective and
based on individual associations to nature (Figure 5). This
study indicates a sharp decline in the area of suitable habitat
of M. punicea in the future climate change scenarios, coupled
with the disturbance of human activities, the population of
M. punicea will experience the crisis of shrinking, which will
affect the primary production, carbon sequestration, hydrological
functions, biogeochemical cycles and other supporting services
provided by M. punicea. A decline in supporting services,
which are the basis of other ecosystem services, will inevitably
lead to a decline of diverse services provided, such as medical
supplies, the provision of horticultural materials, and gene banks
to store germplasm resources; regulating services like soil and
water conservation, controlling human disease will be affected
consequently. Meanwhile, even though the influence on cultural
services, such as spiritual and religious value, medicinal use, and
esthetic value, if often not explicitly noticeable, it is a critical
ecosystem service and should also be acknowledged (Figure 5).
Today, society recognizes the need for an objective and
scientific assessment of climate change impacts on the
ecosystem (Siebenhüner, 2002). Ecosystem assessments
provide useful knowledge for stakeholder decision-making,
strategy formulation, and ecosystem management. Assessment
reports by the Intergovernmental Panel on Climate Change
(IPCC) and Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services (IPBES) have time and
again highlighted the risks of climate change and need for timely
action toward conservation and mitigation. The latest IPCC
report (Climate Change 2021: the Physical Science Basis) shows
that human-induced extreme climate change has intensified
since the Fifth Assessment Report. The Glasgow Climate Pact
was adopted at the 26th Conference (COP26) of the Parties to
the United Nations Framework Convention on Climate Change
(UNFCCC) in 2021, aims to limit global warming to 1.5◦C and
thus preserve the chance of saving the world from catastrophic
climate change. Millenium Development Goals and Sustainable
Development Goals put forward by the United Nations also
explicitly address the need to reduce biodiversity loss and protect
mountain ecosystems. The Kunming Declaration adopted at
the 15th Conference of the Parties (COP15) to the United
Nations Convention on Biological Diversity (CBD) in 2021 calls
for action to halt biodiversity loss, enhance human well-being
and achieve sustainable development. IPBES links Biodiversity
and Ecosystem Services in its assessment report (Díaz et al.,
2019), which noted that species distribution, population size
and migration time in the Asia-pacific region will be affected
under the influence of climate change and extreme events. Since
the novel Coronavirus pandemic in 2019, serious damage has
been done. In response to the link between biodiversity and
pandemics, the report of IPBES workshop on biodiversity and
pandemics was published, which states that the emergence
of COVID-19 is entirely driven by human activities driving
climate change and biodiversity loss. The risk of pandemics
can be significantly reduced by reducing human activities that
contribute to biodiversity loss, enhancing protected areas, and
taking measures to reduce unsustainable exploitation of high
biodiversity areas. A series of changes caused by climate change
could have serious implications for biodiversity and the goods
and services derived from ecosystems (Chettri and Sharma,
2016). Climate change caused by human activities threaten the
survival and development of the wild population of M. punicea,
which calls for immediate measures to protect and conserve the
species in its natural habitat.
Conservation Priorities and Outlook
Meconopsis punicea is an endangered indigenous plant species
(Qu and Qu, 2012) with multiple functions, values, and services.
Detailed knowledge of its distribution is a prerequisite for
the recovery and sustainable use of this species. Our model
projections indicate that M. punicea will be at high-risk of
habitat loss in response to climate change. Without timely
conservation measures, the stability and sustainability of the
habitat of M. punicea habitat may decline rapidly due to
habitat specificity. This decline is likely to be accelerated
further by anthropogenic disturbances (Ghimire et al., 2008).
The headwater region of Min River is located right on the
famous Jiuzhai-Huanglong tourist circuit and is inhabited by
different ethnic groups including Tibetans, Qiang, Hui, and
Han Chinese. Human disturbances in M. punicea habitat comes
not only from local community, but also from high tourism
flows at high environmental costs. These chronic anthropogenic
disturbances, combined with climate change, can lead to severe
habitat destruction of endemic mountain plant species (Naudiyal
et al., 2021). Thus, it is important to protect habitats of
this species for the benefit to the local communities whose
diverse cultures and traditional knowledge are rooted in the
principles of environmental protection and harmony between
humans and nature.
One of the key aspects for handling this impending climatic
crisis is monitoring through long-term observational data, which
is currently lacking. Localized long-term hydrometeorological
monitoring need to be improved for robust climate change
analysis and adaptation. Even though elevation-dependent
warming has been verified by many studies (Pepin et al.,
2015;Minder et al., 2018), at a local level there are multiple
feedback mechanisms underlying climate change such as snow-
albedo interactions, which can also affect local climates. Policies
and planning should focus on improved disaster warning
Frontiers in Plant Science | www.frontiersin.org 12 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 13
Shi et al. Climate Change on Meconopsis punicea
systems, management and mitigation measures to address
hydrometeorological extremes (Wester et al., 2019). Creating
nature reserves is another effective in situ strategy for the
protection of biodiversity and ecosystem services. We suggest
that future adaptation management strategies should consider
the impacts of climate change on the distribution of M. punicea
in particular and other alpine flora in general and ecosystem-
based solutions for its ecological conservation. An integrated
habitat conservation plan for M. Punicea should be developed
and implemented with grassroots assistance in these fragile
environmental areas, applying the principles of “preparing for
the worst” in the decision-making process (Wright et al.,
2015). Since, rainfall is one of the main factors influencing the
distribution of M. punicea, implementing integrated programs
aimed at soil and water conservation will increase M. punicea
habitat suitability, which may not just support M. punicea but
several other important endemic species playing critical role
in maintaining the ecosystem functioning of the study area.
Appropriate conservation strategies based on a socio-ecological
framework for landscape planning are the need of the hour
(Virapongse et al., 2016).
In this study we attempted to highlight the possible influence
of climate change on M. punicea distribution. However, like
other modeling studies, our inferences are subject to some
limitations. Therefore, further research is required to improve
the data availability and modeling precision by integrating other
important factors except bioclimatic variables into the models or
developing an integration model across multi-scales.
CONCLUSION
The models presented here predict the potential impacts of
current and future climate on the distribution of M. punicea.
The results suggest that the potential suitable distribution
areas of M. punicea would decrease and shift toward higher
elevation. To protect the ecological niche, this study suggests
incorporating future climate scenarios into current restoration
and conservation policies to protect ecologically sensitive species
in current habitats. Furthermore, the findings of this study are
helpful to better understand the influence of environmental
factors on the current distribution of M. punicea. At the same
time, based on the geographical range of M. punicea, further
detailed study is needed to evaluate the conservation status and
provide reference for the future protection and management of
M. punicea in headwater region of Min River.
This study not only provides solid baseline information on
the impact of climate change on M. punicea in ecotone of sub-
alpine forests and alpine grassland on the southeastern margin
of Qinghai-Tibet Plateau, but may also help in developing
rational broad-scale adaptation strategies for forest conservation
and management for ecosystem services, in the face of future
climate changes.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
AUTHOR CONTRIBUTIONS
NS: investigation, data curation, formal analysis, and writing –
original draft. NN: conceptualization, methodology, software,
and writing – review and editing. JW: funding acquisition,
investigation, project administration, resources, supervision, and
writing – review and editing. NG and YWe: validation and
writing – review and editing. YWu: resources, supervision,
validation, and writing – review and editing. JH: investigation
and data curation. CW: graphing. All authors contributed to the
article and approved the submitted version.
FUNDING
This research was funded by National Natural Science
Foundation of China (31971436 and 41661144045), Talented
Young Scientist Program (Indian-18-008) by China Science
and Technology Exchange Centre, Ministry of Science and
Technology, and State Key Laboratory of Cryospheric Science,
Northwest Institute of Eco-Environment and Resources, Chinese
Academy Sciences (SKLCS-OP-2018-07).
ACKNOWLEDGMENTS
Special thanks to Ji Suonan from Qinghai Normal University for
providing us with the photos.
REFERENCES
Ackerly, D. D., Loarie, S. R., Cornwell, W. K., Weiss, S. B., Hamilton, H.,
Branciforte, R., et al. (2010). The geography of climate change: implications for
conservation biogeography. Divers. Distrib. 16, 476–487. doi: 10.1111/j.1472-
4642.2010.00654.x
Alberto, F. J., Aitken, S. N., Alía, R., González-Martínez, S. C., Hänninen, H.,
Kremer, A., et al. (2013). Potential for evolutionary responses to climate change-
evidence from tree populations. Glob. Change Biol. 19, 1645–1661. doi: 10.1111/
gcb.12181
Al-Qaddi, N., Vessella, F., Stephan, J., Al-Eisawi, D., and Schirone, B. (2017).
Current and future suitability areas of kermes oak (Quercus coccifera L.) in
the Levant under climate change. Region. Environ. Change 17, 143–156. doi:
10.1007/s10113-016- 0987-2
Anderson, R. P. (2013). A framework for using niche models to estimate impacts
of climate change on species distributions. Ann. N.Y. Acad. Sci. 1297, 8–28.
doi: 10.1111/nyas.12264
Araújo, M. B., Anderson, R. P., Márcia Barbosa, A., Beale, C. M., Dormann,
C. F., Early, R., et al. (2019). Standards for distribution models in biodiversity
assessments. Sci. Adv. 5, 1–10. doi: 10.1126/sciadv.aat4858
Arft, A. M., Walker, M. D., Gurevitch, J., Alatalo, J. M., Bret-Harte, M. S., Dale,
M., et al. (1999). Responses of tundra plants to experimental warming: meta-
analysis of the international tundra experiment. Ecol. Monogr. 69, 491–511.
doi: 10.1007/s004420000544
Frontiers in Plant Science | www.frontiersin.org 13 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 14
Shi et al. Climate Change on Meconopsis punicea
Barker, D. H., Vanier, C., Naumburg,E., Charlet, T. N., Nielsen, K. M., Newingham,
B. A., et al. (2006). Enhanced monsoon precipitation and nitrogen deposition
affect leaf traits and photosynthesis differently in spring and summer in the
desert shrub Larrea tridentata.New Phytol. 169, 799–808. doi: 10.1111/j.1469-
8137.2006.01628.x
Barnosky, A. D., Matzke, N., Tomiya, S., Wogan, G. O. U., Swartz, B., Quental,
T. B., et al. (2011). Has the Earth’s sixth mass extinction already arrived? Nature
471, 51–57. doi: 10.1038/nature09678
Beaumont, L. J., Hughes, L., and Poulsen, M. (2005). Predicting species
distributions: use of climatic parameters in BIOCLIM and its impact on
predictions of species’ current and future distributions. Ecol. Model. 186,
251–270. doi: 10.1016/j.ecolmodel.2005.01.030
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., and Courchamp, F. (2012).
Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377.
doi: 10.1111/j.1461-0248.2011.01736.x
Bertrand, R., Lenoir, J., Piedallu, C., Riofrío-Dillon, G., de Ruffray, P., Vidal, C.,
et al. (2011). Changes in plant community composition lag behind climate
warming in lowland forests. Nature 479, 517–520. doi: 10.1038/nature10548
Bertrand, R., Perez, V., and Gégout, J. C. (2012). Disregarding the edaphic
dimension in species distribution models leads to the omission of crucial spatial
information under climate change: the case of Quercus pubescens in France.
Glob. Change Biol. 18, 2648–2660. doi: 10.1111/j.1365-2486.2012.02679.x
Boyd, J., and Banzhaf, S. (2007). What are ecosystem services? The need for
standardized environmental accounting units. Ecol. Econ. 63, 616–626. doi:
10.1016/j.ecolecon.2007.01.002
Brant, A. N., and Chen, H. Y. H. (2015). Patterns and mechanisms of nutrient
resorption in plants. Crit. Rev. Plant Sci. 34, 471–486. doi: 10.1080/07352689.
2015.1078611
Brown, J. H. (1984). On the relationship between abundance and distribution of
species. Am. Nat. 124, 255–279.
Bugmann, H. (2001). A review of forest gap models. Clim. Change 51, 259–305.
doi: 10.1023/A:1012525626267
Cao, M., Prince, S. D., Small, J., and Goetz, S. J. (2004). Remotely sensed
interannual variations and trends in terrestrial net primary productivity 1981-
2000. Ecosystems 7, 233–242. doi: 10.1007/s10021-003-0189-x
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P.,
et al. (2012). Biodiversity loss and its impact on humanity. Nature 486, 59–67.
doi: 10.1038/nature11148
Chakraborty, A., Joshi, P. K., and Sachdeva, K. (2016). Predicting distribution of
major forest tree species to potential impacts of climate change in the central
Himalayan region. Ecol. Eng. 97, 593–609. doi: 10.1016/j.ecoleng.2016.10.006
Chauchard, S., Carcaillet, C., and Guibal, F. (2007). Patterns of land-use
abandonment control tree-recruitment and forest dynamics in Mediterranean
mountains. Ecosystems 10, 936–948. doi: 10.1007/s10021-007- 9065-4
Chen, W. (2004). A comparison of species diversity between the shady-slopes and
the sunny slopes in the sources of the Minjiang river. J. Neijiang Norm. Univ.
19, 31–34.
Chettri, N., and Sharma, E. (2016). Reconciling mountain biodiversity
conservation and human wellbeing: drivers of biodiversity loss and new
approaches in the Hindu-Kush Himalayas. Proc. Indian Natl. Sci. Acad. 82,
53–73. doi: 10.16943/ptinsa/2016/v82i1/48378
Christie, I. (2007). Meconopsis x cookei ‘Old Rose.’. Scotland: Scottish Rock Garden
Club, 64–69.
Corona-Lozada, M. C., Morin, S., and Choler, P. (2019). Drought offsets the
positive effect of summer heat waves on the canopy greenness of mountain
grasslands. Agric. For. Meteorol. 276-277:107617. doi: 10.1016/j.agrformet.2019.
107617
Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., et al.
(1997). The value of the world’s ecosystem services and natural capital. Nature
387, 253–260. doi: 10.1038/387253a0
Criado, M. G., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R., and
Stevens, N. (2020). Woody plant encroachment intensifies under climate
change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29, 925–943.
doi: 10.1111/geb.13072
Davies, T. J., Purvis, A., and Gittleman, J. L. (2009). Quaternary climate change and
the geographic ranges of mammals. Am. Nat. 174, 297–307. doi: 10.1086/603614
Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C., and Mace, G. M. (2011).
Beyond predictions: biodiversity conservation in a changing climate. Science
332, 53–58. doi: 10.1126/science.1200303
Delmas, C. E. L., Cheptou, P. O., Escaravage, N., and Pornon, A. (2014). High
lifetime inbreeding depression counteracts the reproductive assurance benefit
of selfing in a mass-flowering shrub. BMC Evol. Biol. 14:7. doi: 10.1186/s12862-
014-0243- 7
Diaz, H. F., and Bradley, R. S. (1997). Temperature variations during the last
century at high elevation sites. Clim. Change 36, 253–279. doi: 10.1023/A:
1005335731187
Diaz, H. F., and Eischeid, J. K. (2007). Disappearing “alpine tundra” Köppen
climatic type in the western United States. Geophys. Res. Lett. 34:L18707. doi:
10.1029/2007GL031253
Díaz, S., Settele, J., Brondizio, E. S., Ngo, H. T., Guèze, M., and Agard, J.
(2019). IPBES: Summary for Policymakers of the Global Assessment Report on
Biodiversity and Ecosystem Services. Bonn: Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services.
Dib, V., Pires, A. P. F., Casa Nova, C., Bozelli, R. L., and Farjalla, V. F. (2020).
Biodiversity-mediated effects on ecosystem functioning depend on the type and
intensity of environmental disturbances. Oikos 129, 433–443. doi: 10.1111/oik.
06768
Dolezal, J., Kurnotova, M., Stastna, P., and Klimesova, J. (2020). Alpine plant
growth and reproduction dynamics in a warmer world. New Phytol. 228,
1295–1305. doi: 10.1111/nph.16790
Dorji, T., Hopping, K. A., Meng, F., Wang, S., Jiang, L., and Klein, J. A. (2020).
Impacts of climate change on flowering phenology and production in alpine
plants: the importance of end of flowering. Agric. Ecosyst. Environ. 291:106795.
doi: 10.1016/j.agee.2019.106795
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., et al.
(2013). Collinearity: a review of methods to deal with it and a simulation study
evaluating their performance. Ecography 36, 1–20. doi: 10.1111/j.1600-0587.
2012.07348.x
Duffy, J. E., Godwin, C. M., and Cardinale, B. J. (2017). Biodiversity effects in
the wild are common and as strong as key drivers of productivity. Nature 549,
261–264. doi: 10.1038/nature23886
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and
Mearns, L. O. (2000). Climate extremes: observations, modeling, and impacts.
Science 289, 2068–2074. doi: 10.1126/science.289.5487.2068
Elith, J., Graham, C., Anderson, R., Dudík, M., Ferrier, S., Guisan, A., et al. (2006).
Novel methods improve prediction of species’ distributions from occurrence
data. Ecography 29, 129–151. doi: 10.1111/j.2006.0906-7590.04596.x
Engler, R., Randin, C. F., Vittoz, P., Czáka, T., Beniston, M., Zimmermann, N. E.,
et al. (2009). Predicting future distributions of mountain plants under climate
change: does dispersal capacity matter? Ecography 32, 34–45. doi: 10.1111/j.
1600-0587.2009.05789.x
Faleiro, F. V., Machado, R. B., and Loyola, R. D. (2013). Defining spatial
conservation priorities in the face of land-use and climate change. Biol. Conserv.
158, 248–257. doi: 10.1016/j.biocon.2012.09.020
Feilhauer, H., He, K. S., and Rocchini, D. (2012). Modeling species distribution
using niche-based proxies derived from composite bioclimatic variables
and MODIS NDVI. Remote Sens. 4, 2057–2075. doi: 10.3390/rs407
2057
Ford, K. R., and HilleRisLambers, J. (2019). Soil alters seedling establishment
responses to climate. Ecol. Lett. 23, 140–148. doi: 10.1111/ele.13416
Fourcade, Y., Engler, J. O., Rödder, D., and Secondi, J. (2014). Mapping species
distributions with MaxEnt using a geographically biased sample of presence
data: a performance assessment of methods for correcting sampling bias. PLoS
One 9:e97122. doi: 10.1371/journal.pone.0097122
Fragnière, Y., Pittet, L., Clément, B., Sébastien, B., Gerber, E., Ronikier, M., et al.
(2020). Climate change and alpine screes: no future for glacial relict Papaver
occidentale (Papaveraceae) in Western Prealps. Diversity 2020:346. doi: 10.3390/
d12090346
Francon, L., Corona, C., Till-Bottraud, I., Carlson, B. Z., and Stoffel, M. (2020).
Some (do not) like it hot: shrub growth is hampered by heat and drought at the
alpine treeline in recent decades. Am. J. Bot. 107, 607–617. doi: 10.1002/ajb2.
1459
Frontiers in Plant Science | www.frontiersin.org 14 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 15
Shi et al. Climate Change on Meconopsis punicea
Frishkoff, L. O., Karp, D. S., Flanders, J. R., Zook, J., Hadly, E. A., and Daily, G. C.
(2016). Climate change and habitat conversion favour the same species. Ecol.
Lett. 19, 1081–1090. doi: 10.1111/ele.12645
Gallagher, R. V., Hughes, L., Leishman, M. R., and Wilson, P. D. (2010).
Predicted impact of exotic vines on an endangered ecological community under
future climate change. Biol. Invas. 12, 4049–4063. doi: 10.1007/s10530-010-
9814-8
Ganjurjav, H., Gornish, E. S., Hu, G., Schwartz, M. W., Wan, Y., and Li, Y. (2020).
Warming and precipitation addition interact to affect plant spring phenology in
alpine meadows on the central Qinghai-Tibetan Plateau. Agric. For. Meteorol.
287:107943. doi: 10.1016/j.agrformet.2020.107943
Gaston, K. J., Blackburn, T. M., Greenwood, J. J. D., Gregory, R. D., Quinn, R. M.,
and Lawton, J. H. (2000). Abundance-occupancy relationships. J. Appl. Ecol. 37,
39–59. doi: 10.1046/j.1365-2664.2000.00485.x
Gavilán, R. G. (2005). The use of climatic parameters and indices in vegetation
distribution. A case study in the Spanish Sistema Central. Int. J. Biometeorol.
50, 111–120. doi: 10.1007/s00484-005- 0271-5
Ghimire, S. K., Sapkota, I. B., and Oli, B. R. (2008). Non-Timber Forest Products of
Nepal Himalaya: Database of Some Important Species Found in the Mountain
Protected Areas, and Surrounding Regions. Kathmandu: WWF Nepal, 1–4.
Gottfried, M., Pauli, H., Futschik, A., Akhalkatsi, M., Baranˇ
cok, P., Benito Alonso,
J. L., et al. (2012). Continent-wide response of mountain vegetation to climate
change. Nat. Clim. Change 2, 111–115. doi: 10.1038/nclimate1329
Gou, X., Chen, F., Jacoby, G., Cook, E., Yang, M., and Peng, J. (2007). Rapid
tree growth with respect to the last 400 years in response to climate warming,
northeastern Tibetan Plateau. Int. J. Climatol. 27, 1497–1503. doi: 10.1002/joc.
1480
Grey-Wilson, C. (2014). The Genus Meconopsis: Blue Poppies, and Their Relatives.
Kew: Kew Publishing, 1–5.
Guan, W., Wu, J., Liang, G., Wang, B., Ma, K., and Liu, G. (2002). “Vegetation
classification and the main types in the headwater area of the Minjiang river.
Biodiversity conservation and research in China,” in Proceedings of the 5th
National Conference on Biodiversity Conservation and Sustainable Utilization,
(Beijing: China Meteorological Press), 296–308.
Guo, Y., Wei, H., Lu, C., Gao, B., and Gu, W. (2016). Predictions of potential
geographical distribution and quality of Schisandra sphenanthera under climate
change. PeerJ 4:e2554. doi: 10.7717/peerj.2554
He, X., Burgess, K. S., Yang, X. F., Ahrends, A., Gao, L. M., and Li, D. Z. (2019).
Upward elevation and northwest range shifts for alpine Meconopsis species
in the Himalaya-Hengduan Mountains region. Ecol. Evol. 9, 4055–4064. doi:
10.1002/ece3.5034
Heisler-white, J. L., Blair,J. M., Kelly, E. F., Harmoney, K., and Knapp, A. K. (2009).
Contingent productivity responses to more extreme rainfall regimes across a
grassland biome. Glob. Change Biol. 15, 2894–2904. doi: 10.1111/j.1365-2486.
2009.01961.x
Henry, G. H. R., and Molau, U. (1997). Tundra plants and climate change: the
International Tundra Experiment (ITEX). Glob. Change Biol. 3, 1–9. doi: 10.
1111/j.1365-2486.1997.gcb132.x
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005).
Very high resolution interpolated climate surfaces for global land areas. Int. J.
Climatol. 25, 1965–1978. doi: 10.1002/joc.1276
Hoffmann, M., Hilton-Taylor, C., Angulo, A., Böhm, M., Brooks, T., Butchart, S.,
et al. (2010). The impact of conservation on the status of the world’s vertebrates.
Science 330, 1503–1509. doi: 10.1126/science.1194442
Hooper, D. U., Adair, E. C., Cardinale, B. J., Byrnes, J. E. K., Hungate, B. A.,
Matulich, K. L., et al. (2012). A global synthesis reveals biodiversity loss
as a major driver of ecosystem change. Nature 486, 105–108. doi: 10.1038/
nature11118
Hörsch, B. (2003). Modelling the spatial distribution of montane and subalpine
forests in the central Alps using digital elevation models. Ecol. Model. 168,
267–282. doi: 10.1016/S0304-3800(03)00141- 8
Huang, C. Y., and Xu, J. M. (2010). Soil Science. Beijing: China Agriculture Press,
80–88.
Isbell, F., Reich, P. B., Tilman, D., Hobbie, S. E., Polasky, S., and Binder, S.
(2013). Nutrient enrichment, biodiversity loss, and consequent declines in
ecosystem productivity. PNAS 110, 11911–11916. doi: 10.1073/pnas.131088
0110
Jarvis, A. J., Stauch, V. J., Schulz, K., and Young, P. C. (2004). The seasonal
temperature dependency of photosynthesis and respiration in two deciduous
forests. Glob. Change Biol. 10, 939–950. doi: 10.1111/j.1529-8817.2003.00743.x
Jones, C. G., Lawton, J. H., and Shachak, M. (1996). “Organisms as ecosystem
engineers,” in Ecosystem Management, eds F. B. Samson and F. L. Knopf
(New York, NY: Springer), 130–147. doi: 10.1353/pbm.2003.0003
Khanum, R., Mumtaz, A. S., and Kumar, S. (2013). Predicting impacts of climate
change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecol.
49, 23–31. doi: 10.1016/j.actao.2013.02.007
Klanderud, K., and Totland, Ø (2005). Simulated climate change altered
dominance hierarchies and diversity of an alpine biodiversity hotspot. Ecology
86, 2047–2054. doi: 10.1890/04-1563
Kozak, K. H., Graham, C. H., and Wiens, J. J. (2008). Integrating GIS-based
environmental data into evolutionary biology. Trends Ecol. Evol. 23, 141–148.
doi: 10.1016/j.tree.2008.02.001
KrÄuchi, N., and Kienast, F. (1993). Modelling subalpine forest dynamics as
influenced by a changing environment. Water Air Soil Pollut. 68, 185–197.
doi: 10.1007/BF00479402
Kumar, P. (2012). Assessment of impact of climate change on Rhododendrons
in Sikkim Himalayas using Maxent modelling: limitations and challenges.
Biodivers. Conserv. 21, 1251–1266. doi: 10.1007/s10531-012-0279-1
Kumar, S., Graham, J., West, A. M., and Evangelista, P. H. (2014). Using district-
level occurrences in MaxEnt for predicting the invasion potential of an exotic
insect pest in India. Comput. Electron. Agric. 103, 55–62. doi: 10.1016/j.compag.
2014.02.007
Kumar, S., and Stohlgren, T. (2009). Maxent modeling for predicting suitable
habitat for threatened and endangered tree Canacomyrica monticola in New
Caledonia. J. Ecol. Nat. Environ. 1, 94–98.
Li, J., Fan, G., and He, Y. (2020). Predicting the current and future distribution
of three Coptis herbs in China under climate change conditions, using the
MaxEnt model and chemical analysis. Sci. Tot. Environ. 698:134141. doi: 10.
1016/j.scitotenv.2019.134141
Li, R., Xu, M., Wong, M. H. G., Qiu, S., Li, X., Ehrenfeld, D., et al. (2015). Climate
change threatens giant panda protection in the 21st century. Biol. Conserv. 182,
93–101. doi: 10.1016/j.biocon.2014.11.037
Li, X., Tian, H., Wang, Y., Li, R., Song, Z., Zhang, F., et al. (2013). Vulnerability of
208 endemic or endangered species in China to the effects of climate change.
Region. Environ. Change 13, 843–852. doi: 10.1007/s10113-012- 0344-z
Liu, M., Li, Q., Ji, D., and Lu, Y. (2019). Determination of total flavonoids in
Meconopsis punicea Maxim and optimization of extraction technology. J. Seed
Ind. Guide 09, 8–11.
Liu, X., Huang, H., Yang, Y., and Wu, H. (2014). Analysis of effect of topographical
conditions on content of total alkaloid in Coptidis Rhizoma in Chongqin,China.
China J. Chinese Mater. Med. 39, 4748–4753. doi: 10.4268/cjcmm20142410
Liu, X. D., and Chen, B. D. (2000). Climatic warming in the Tibetan Plateau
during recent decades. Int. J. Climatol. 20, 1729–1742. doi: 10.1002/1097-
0088(20001130)20:14<1729::aid- joc556>3.0.co;2- y
Locatelli, B., Kanninen, M., Brockhaus, M., Colfer, C. J. P., Murdiyarso, D., and
Santoso, H. (2008). Facing an uncertain future: how forest and people can adapt
to climate change. Center Int. For. Res. 28:2600. doi: 10.17528/cifor/002600
Lotstein, E. L. (2013). Global climate change impacts in the United States: a state
of knowledge report from the U.S. global climate change research program.
J. Geogr. 40:178. doi: 10.1080/00221341.2013.770905
Lu, C. Y., Gu, W., Dai, A. H., and Wei, H. Y. (2012). Assessing habitat suitability
based on geographic information system (GIS) and fuzzy: a case study of
Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China. Ecol.
Model. 242, 105–115. doi: 10.1016/j.ecolmodel.2012.06.002
Lv, Z. H. (2009). The Impacts of Climate Change on the Distribution of Rare
or Endangered Species in China and Adaptations Strategies. Beijing: Chinese
Research Academy of Environmental Sciences.
Ma, M., and Chang, R. (2019). Temperature drive the altitudinal change in soil
carbon and nitrogen of montane forests: implication for global warming.
Catena 182:104126. doi: 10.1016/j.catena.2019.104126
Mantyka-pringle, C. S., Martin, T. G., and Rhodes, J. R. (2012). Interactions
between climate and habitat loss effects on biodiversity: a systematic review
and meta-analysis. Glob. Change Biol. 18, 1239–1252. doi: 10.1111/j.1365-2486.
2011.02593.x
Frontiers in Plant Science | www.frontiersin.org 15 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 16
Shi et al. Climate Change on Meconopsis punicea
McKenney, D. W., Pedlar, J. H., Lawrence, K., Campbell, K., and Hutchinson,
M. F. (2007). Potential impacts of climate change on the distribution of North
American trees. Bioscience 57, 939–948. doi: 10.1641/B571106
Merow, C., and Silander, J. A. Jr. (2014). A comparison of Maxlike and MaxEnt for
modelling species distributions. Methods Ecol. Evol. 5, 215–225. doi: 10.1111/
2041-210X.12152
Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-Being:
Synthesis. Washington, DC: Island Press, 39–40.
Minder, J. R., Letcher, T. W., and Liu, C. (2018). The character and causes
of elevation-dependent warming in high-resolution simulations of Rocky
Mountain climate change. J. Clim. 31, 2093–2113. doi: 10.1175/JCLI-D-17-
0321.1
Molina-Montenegro, M. A., Atala, C., and Gianoli, E. (2010). Phenotypic plasticity
and performance of Taraxacum officinale (dandelion) in habitats of contrasting
environmental heterogeneity. Biol. Invas. 12, 2277–2284. doi: 10.1007/s10530-
009-9638- 6
Murray, K. A., Rosauer, D., McCallum, H., and Skerratt, L. F. (2011). Integrating
species traits with extrinsic threats: closing the gap between predicting and
preventing species declines. Proc. R. Soc. B Biol. Sci. 278, 1515–1523. doi: 10.
1098/rspb.2010.1872
Naudiyal, N., Wang, J. N., Wu, N., Gaire, N. P., Shi, P. L., Wei, Y. Q., et al. (2021).
Potential distribution of Abies, Picea, and Juniperus species in the sub-alpine
forest of Minjiang headwater region under current and future climate scenarios
and its implications on ecosystem services supply. Ecol. Indic. 121:107131.
doi: 10.1016/j.ecolind.2020.107131
Nayak, K. G., and Davidar,P. (2010). Pollinator limitation and the effect of breeding
systems on plant reproduction in forest fragments. Acta Oecol. 36, 191–196.
doi: 10.1016/j.actao.2009.12.004
Niu, T., Chen, L., and Zhou, Z. (2004). The characteristics of climate change over
the Tibetan Plateau in the last 40 years and the detection of climatic jumps. Adv.
Atmos. Sci. 21, 193–203. doi: 10.1007/BF02915705
Nogués-Bravo, D., Araújo, M. B., Errea, M. P., and Martínez-Rica, J. P. (2007).
Exposure of global mountain systems to climate warming during the 21st
century. Glob. Environ. Change 17, 420–428. doi: 10.1016/j.gloenvcha.2006.11.
007
Orsenigo, S., Mondoni, A., Rossi, G., and Abeli, T. (2014). Some like it hot and
some like it cold, but not too much: plant responses to climate extremes. Plant
Ecol. 215, 677–688. doi: 10.1007/s11258-014- 0363-6
Oyler, J. W., Dobrowski, S. Z., Ballantyne, A. P., Klene, A. E., and Running,
S. W. (2015). Artificial amplification of warming trends across the mountains
of the western United States. Geophys. Res. Lett. 42, 153–161. doi: 10.1002/
2014GL062803
Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E. M., Butchart, S. H. M., Kovacs,
K. M., et al. (2015). Assessing species vulnerability to climate change. Nat. Clim.
Change 5, 215–224. doi: 10.1038/nclimate2448
Palazzi, E., Mortarini, L., Terzago, S., and von Hardenberg, J. (2019). Elevation-
dependent warming in global climate model simulations at high spatial
resolution. Clim. Dyn. 52, 2685–2702. doi: 10.1007/s00382-018- 4287-z
Pauli, H., Gottfried, M., Reiter, K., Klettner, C., and Grabherr, G. (2007). Signals
of range expansions and contractions of vascular plants in the high Alps:
observations (1994-2004) at the GLORIA∗master site Schrankogel, Tyrol,
Austria. Glob. Change Biol. 13, 147–156. doi: 10.1111/j.1365-2486.2006.01282.x
Pearson, R. G., Raxworthy, C. J., Nakamura, M., and Townsend Peterson, A. (2007).
Predicting species distributions from small numbers of occurrence records:
a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117. doi:
10.1111/j.1365-2699.2006.01594.x
Peng, C., Ma, Z., Lei, X., Zhu, Q., Chen, H., Wang, W., et al. (2011). A drought-
induced pervasive increase in tree mortality across Canada’s boreal forests. Nat.
Clim. Change 1, 467–471. doi: 10.1038/nclimate1293
Pennekamp, F., Pontarp, M., Tabi, A., Altermatt, F., Alther, R., Choffat, Y., et al.
(2018). Biodiversity increases and decreases ecosystem stability. Nature 563,
109–112. doi: 10.1038/s41586-018- 0627-8
Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe, N., et al.
(2015). Elevation-dependent warming in mountain regions of the world. Nat.
Clim. Change 5, 424–430. doi: 10.1038/nclimate2563
Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006). Maximum entropy
modeling of species geographic distributions. Ecol. Model. 190, 231–259. doi:
10.1016/j.ecolmodel.2005.03.026
Pol, R. G., Pirk, G. I., and Marone, L. (2010). Grass seed production in the central
Monte desert during successive wet and dry years. Plant Ecol. 208, 65–75.
doi: 10.1007/s11258-009- 9688-y
Power, M., Tilman, D., Estes, J., Menge, B., Bond, W., Mills, L., et al. (1996).
Challenges in the quest for keystones: identifying keystone species is difficult-
but essential to understanding how loss of species will affect ecosystems.
Bioscience 46, 609–620. doi: 10.2307/1312990
Qu, Y., and Qu, Z. (2012). The research advancement on the genus Meconpsis.
Northern Hortic. 02, 191–194.
Quan, Q., Tian, D., Luo, Y., Zhang, F., Crowther, T. W., Zhu, K., et al. (2019).
Water scaling of ecosystem carbon cycle feedback to climate warming. Sci. Adv.
5:eaav1131. doi: 10.1126/sciadv.aav1131
Radosavljevic, A., and Anderson, R. P. (2014). Making better MaxEnt models of
species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41,
629–643. doi: 10.1111/jbi.12227
Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A., Ricotta, C.,
et al. (2011). Accounting for uncertainty when mapping species distributions:
the need for maps of ignorance. Prog. Phys. Geogr. Earth Environ. 35, 211–226.
doi: 10.1177/0309133311399491
Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M. A., et al. (2004).
The role of increasing temperature variability in European summer heatwaves.
Nature 427, 332–336. doi: 10.1038/nature02300
Schickhoff, U., Bobrowski, M., Böhner, J., Bürzle, B., Chaudhary, R. P., Gerlitz,
L., et al. (2016). “Climate change and treeline dynamics in the Himalaya,” in
Climate Change, Glacier Response, and Vegetation Dynamics in the Himalaya,
eds R. B. Singh, U. Schickhoff, and S. Mal (Switzerland: Springer International
Publishing), 271–306.
Schoener, T. W., and Spiller, D. A. (1987). High population persistence in a system
with high turnover. Nature 330, 474–477. doi: 10.1038/330474a0
Shah, N. H., and Paulsen, G. M. (2003). Interaction of drought and high
temperature on photosynthesis and grain-filling of wheat. Plant Soil 257,
219–226. doi: 10.1023/A:1026237816578
Shang, X., Wang, D., Miao, X., Wang, Y., Zhang, J., Wang, X., et al. (2015).
Antinociceptive and anti-tussive activities of the ethanol extract of the flowers
of Meconopsis punicea Maxim. BMC Complement. Altern. Med. 15:154. doi:
10.1186/s12906-015- 0671-y
Shaw, M. R., Pendleton, L., Cameron, D. R., Morris, B., Bachelet, D.,
Klausmeyer, K., et al. (2011). The impact of climate change on California’s
ecosystem services. Clim Change 109, 465–484. doi: 10.1007/s10584-011-
0313-4
Siebenhüner, B. (2002). How do scientific assessments learn?: Part 1. Conceptual
framework and case study of the IPCC. Environ. Sci. Policy 5, 411–420. doi:
10.1016/S1462-9011(02)00050- 3
Singh, S. P., Sharma, S., and Dhyani, P. P. (2019). Himalayan arc and treeline:
distribution, climate change responses and ecosystem properties. Biodivers.
Conserv. 28, 1997–2016. doi: 10.1007/s10531-019- 01777-w
Song, Y., Jin, L., and Wang, H. (2018). Vegetation changes along the qinghai-tibet
plateau engineering corridor since 2000 induced by climate change and human
activities. Remote Sens. 10:95. doi: 10.3390/rs10010095
Srivastava, D. S., and Vellend, M. (2005). Biodiversity-ecosystem function research:
is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294. doi:
10.1146/annurev.ecolsys.36.102003.152636
Staude, I. R., Navarro, L. M., and Pereira, H. M. (2020). Range size predicts the
risk of local extinction from habitat loss. Glob. Ecol. Biogeogr. 29, 16–25. doi:
10.1111/geb.13003
Sullivan, P. F. (2016). Evidence of soil nutrient availability as the proximate
constraint on growth of treeline trees in northwest Alaska: reply. Ecology 97,
803–808. doi: 10.1890/15-1734.1
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science 240,
1285–1293. doi: 10.1126/science.3287615
Theodorou, K., and Couvet, D. (2015). The efficiency of close inbreeding to reduce
genetic adaptation to captivity. Heredity 114, 38–47. doi: 10.1038/hdy.2014.63
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J.,
Collingham, Y. C., et al. (2004). Extinction risk from climate change. Nature
427, 145–148. doi: 10.1038/nature02121
Thomas, J. A., Telfer, M. G., Roy, D. B., Preston, C. D., Greenwood, J. J. D., Asher, J.,
et al. (2004). Comparative losses of british butterflies, birds, and plants and the
global extinction crisis. Science 303, 1879–1881. doi: 10.1126/science.1095046
Frontiers in Plant Science | www.frontiersin.org 16 January 2022 | Volume 12 | Article 830119

fpls-12-830119 January 7, 2022 Time: 14:11 # 17
Shi et al. Climate Change on Meconopsis punicea
Thuiller, W. (2007). Climate change and the ecologist. Nature 448, 550–552. doi:
10.1038/448550a
Tian, N., Poudyal, N. C., Hodges, D. G., Young, T. M., and Hoyt, K. P. (2015).
Understanding the factors influencing nonindustrial private forest landowner
interest in supplying ecosystem services in Cumberland Plateau, Tennessee.
Forests 6, 3985–4000. doi: 10.3390/f6113985
Tognetti, R., Cherubini, P., Marchi, S., and Raschi, A. (2007). Leaf traits and tree
rings suggest different water-use and carbon assimilation strategies by two co-
occurring Quercus species in a Mediterranean mixed-forest stand in Tuscany,
Italy. Tree Physiol. 27, 1741–1751. doi: 10.1093/treephys/27.12.1741
Veloz, S. D. (2009). Spatially auto-correlated sampling falsely inflates measures
of accuracy for presence-only niche models. J. Biogeogr. 36, 2290–2299. doi:
10.1111/j.1365-2699.2009.02174.x
Virapongse, A., Brooks, S., Metcalf, E. C., Zedalis, M., Gosz, J., Kliskey, A., et al.
(2016). A social-ecological systems approach for environmental management.
J. Environ. Manage. 178, 83–91. doi: 10.1016/j.jenvman.2016.02.028
Vuuren, D. P., Van Edmonds, J., Kainuma, M., Riahi, K., Nakicenovic, N., Smith,
S. J., et al. (2011). The representative concentration pathways: an overview.
Clim. Change 109, 5–31. doi: 10.1007/s10584-011- 0148-z
Walther, G. R., Beißner, S., and Burga, C. A. (2005). Trends in the upward shift of
alpine plants. J. Veg. Sci. 16, 541–548. doi: 10.1111/j.1654-1103.2005.tb02394.x
Wang, B., Chen, T., Xu, G., Liu, X., Wang, W., Wu, G., et al. (2016). Alpine
timberline population dynamics under climate change: a comparison between
Qilian juniper and Qinghai spruce tree species in the middle Qilian Mountains
of northeast Tibetan Plateau. Boreas 45, 411–422. doi: 10.1111/bor.12161
Wang, D., Cui, B., Duan, S., Chen, J., Fan, H., Lu, B., et al. (2019). Moving north
in China: the habitat of Pedicularis kansuensis in the context of climate change.
Sci. Total Environ. 697:133979. doi: 10.1016/j.scitotenv.2019.133979
Wang, G., Baskin, C. C., Baskin, J. M., Yang, X., Liu, G., Ye, X., et al. (2018). Effects
of climate warming and prolonged snow cover on phenology of the early life
history stages of four alpine herbs on the southeastern Tibetan Plateau. Am. J.
Bot. 105, 1–10. doi: 10.1002/ajb2.1104
Wang, J., Shi, F., Xu, B., Wang, Q., Wu, Y., and Wu, N. (2014). Uptake and recovery
of soil nitrogen by bryophytes and vascular plants in an alpine meadow.
J. Mountain Sci. 11, 475–484. doi: 10.1007/s11629-013-2707-4
Wang, J. J., Cao, B., Bai, C. K., Zhang, L. L., and Che, L. (2014). Potential
distribution prediction and suitability evaluation of Fritillaria cirrhosa D.don
based on MaxEnt modeling and GIS. Bull. Bot. Res. 34, 642–649. doi: 10.7525/j.
issn.1673-5102.2014.05.010
Wang, Q., Fan, X., and Wang, M. (2014). Recent warming amplification over high
elevation regions across the globe. Clim. Dyn. 43, 87–101. doi: 10.1007/s00382-
013-1889- 3
Wang, S., Zhang, Y., Ju, W., Jing, M. C., Ciais, P., Cescatti, A., et al. (2020). Recent
global decline of CO2 fertilization effects on vegetation photosynthesis. Science
370, 1295–1300. doi: 10.1126/science.abb7772
Wang, X., Cheng, G., and Zhong, X. (2011). Assessing potential impacts of climatic
change on subalpine forests on the eastern Tibetan Plateau. Clim. Change 108,
225–241. doi: 10.1007/s10584-010- 0008-2
Wang, Y., Liang, E., Lu, X., Camarero, J. J., Babst, F., Shen, M., et al. (2021).
Warming-induced shrubline advance stalled by moisture limitation on the
Tibetan Plateau. Ecography 44, 1631–1641. doi: 10.1111/ecog.05845
Wei, Y., Zhang, L., Wang, J., Wang, W., Niyati, N., and Guo, Y. (2021). Chinese
caterpillar fungus (Ophiocordyceps sinensis) in China: current distribution,
trading, and futures under climate change and overexploitation. Sci. Total
Environ. 755:142548. doi: 10.1016/j.scitotenv.2020.142548
Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B. (2019). The Hindu Kush
Himalaya Assessment. Kathmandu: Springer, 144–151.
Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A., and Snyder, M. A.
(2009). Niches, models, and climate change: assessing the assumptions and
uncertainties. PNAS 106, 19729–19736. doi: 10.1073/pnas.0901639106
Williams, J. E. (2000). The biodiversity crisis and adaptation to climate change:
a case study from Australia’s forests. Environ. Monit. Assess 61, 65–74. doi:
10.1023/A:1006361917359
Wilmking, M., and Juday, G. P. (2005). Longitudinal variation of radial growth
at Alaska’s northern treeline-recent changes and possible scenarios for the
21st century. Glob. Planet. Change 47, 282–300. doi: 10.1016/j.gloplacha.2004.
10.017
Wright, A. N., Hijmans, R. J., Schwartz, M. W., and Shaffer, H. B. (2015). Multiple
sources of uncertainty affect metrics for ranking conservation risk under
climate change. Divers. Distrib. 21, 111–122. doi: 10.1111/ddi.12257
Wu, J., Xu, H. G., and Chen, L. (2011). A review of impacts of climate change on
species. J. Ecol. Rural Environ. 27, 1–6.
Xu, B. (2016). Meconopsis, the legend of the Himalayas. Hum. Kind 4, 14–29.
Xu, D., Zhuo, Z., Wang, R., Ye, M., and Pu, B. (2019). Modeling the distribution of
Zanthoxylum armatum in China with MaxEnt modeling. Glob. Ecol. Conserv.
19:e00691. doi: 10.1016/j.gecco.2019.e00691
Xu, H., Wang, X., Zhao, C., and Yang, X. (2018). Diverse responses of vegetation
growth to meteorological drought across climate zones and land biomes in
northern China from 1981 to 2014. Agric. For. Meteorol. 262, 1–13. doi: 10.1016/
j.agrformet.2018.06.027
Xu, M., Peng, F., You, Q., Guo, J., Tian, X., Xue, X., et al. (2015). Year-round
warming and autumnal clipping lead to downward transport of root biomass,
carbon and total nitrogen in soil of an alpine meadow. Environ. Exp. Bot. 109,
54–62. doi: 10.1016/j.envexpbot.2014.07.012
Xu, M., and Xuan, X. (2013). Analysis on the effects of climate warming on growth
and phenology of alpine plants. J. Arid Land Resour. Environ. 27, 137–141.
doi: 10.13448/j.cnki.jalre.2013.03.026
Yang, X. Q., Kushwaha, S. P. S., Saran, S., Xu, J., and Roy, P. S. (2013). MaxEnt
modeling for predicting the potential distribution of medicinal plant, Justicia
adhatoda L. in lesser Himalayan foothills. Ecol. Eng. 51, 83–87. doi: 10.1016/j.
ecoleng.2012.12.004
Zhang, C. (2009). Studies on Chemical Constituents of Alkaloids and Flavonoids
of Meconopsis Quintuplinervia Regel. Xining: Northwest Institute of Plateau
Biology.
Zhang, J., Cai, C. T., Cai, Z. Q., Liu, G. Z., Luo, Y., and Yang, Z. X. (2008). Variation
patterns of Coptis teeta biomass and its major active compounds along an
altitude gradient. Chinese J. Appl. Ecol. 7, 1465–1461.
Zhang, K., Zhang, Y., Zhou, C., Meng, J., Sun, J., Zhou, T., et al. (2019). Impact of
climate factors on future distributions of Paeonia ostii across China estimated
by MaxEnt. Ecol. Inform. 50, 62–67. doi: 10.1016/j.ecoinf.2019.01.004
Zhang, M. L., and Christopher, G. W. (2008). Flora of China. Beijing: Science Press.
Zhang, P., Jeong, J. H., Yoon, J. H., Kim, H., Wang, S. Y. S., Linderholm, H. W.,
et al. (2020). Abrupt shift to hotter and drier climate over inner East Asia
beyond the tipping point. Science 370, 1095–1099. doi: 10.1126/science.abb
3368
Zhang, Y., Shao, X., and Wilmking, M. (2011). Dynamic relationships between
Picea crassifolia growth and climate at upper treeline in the Qilian Mts.,
Northeast Tibetan Plateau, China. Dendrochronologia 29, 185–199. doi: 10.
1016/j.dendro.2010.11.001
Zhao, D., Wu, S., Yin, Y., and Yin, Z. Y. (2011). Vegetation distribution on Tibetan
Plateau under climate change scenario. Region. Environ. Change 11, 905–915.
doi: 10.1007/s10113-011- 0228-7
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.
Copyright © 2022 Shi, Naudiyal, Wang, Gaire, Wu, Wei, He 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.
Frontiers in Plant Science | www.frontiersin.org 17 January 2022 | Volume 12 | Article 830119