Abstract
To predict suitable growing regions for Leonurus japonicus and to
provide scientific sopport for the habitat conservation and the
exploitation and utilization of germplasm resources under climate change
conditions, this study combined niche and priority conservation models
to assess the future potential distribution of L. japonicus in
China. To this end, distribution points and samples of L.
japonicus were gathered through online and field surveys. The Maxent
model with optimized parameters was used for predicting the suitable
habitats of L. japonicus at different stages, and the Marxan
model was used to determine the priority of protected areas. The results
showed that the highest temperature in the hottest month, the lowest
temperature in the coldest month, the precipitation in the wettest
month, the precipitation in the driest month, and altitude were the main
environmental factors influencing the distribution of L.
japonicus . Under the three climate change scenarios, the centroid of
the suitable area of L. japonicus migrated northward, and the
migration position tended to expand further northwest. In the future,
there would be no significant niche differentiation of L.
japonicus ; the Marxan results showed that priority protected areas forL. japonicus were in southwestern central China, Lingnan,
southern east China, and Guizhou. Overall, the results of this research
can provide a strategy for the determination of priority protection
areas for Leonurus japonicus in China.
Keywords: Leonurus japonicus ; Maxent model; ENMeval
packet; Marxan model; suitable area
prediction
1. Introduction
In the sixth assessment report of the Intergovernmental Panel on Climate
Change (IPCC), it was pointed out that the impact and risk of climate
warming are becoming increasingly complex, and this has produced a
series of irreversible effects on ecosystems and human societies. Global
warming will outweigh habitat destruction as the greatest threat to
biodiversity in the coming decades (Li et al., 2021). In terms of
species distributions, climate change will have a driving effect on the
distributions of plants, so that the suitable area of migration (Li et
al., 2021) and the changes in species distribution ranges will have a
certain risk to human beings. Therefore, to help human society cope with
changes in the distribution of species in response to climate change,
suitable habitat distribution prediction is particularly important, and
current research reports show that the predictions of climate change
scenarios concerning species’ suitable area have become a focus in plant
protection research, especially in the study of medicinal plants (Zhang
et al., 2019; Zhang et al., 2020; Gupta et al., 2021).
There are many habitat suitability models, including mechanism models,
regression models, machine learning models, and niche models; however,
the niche model has become the main application model used to study the
suitability distributions of species. Among these, the Maxent
model has the highest feasibility
and prediction accuracy (Li et al., 2021) and thus is considered to be
the most effective prediction method (Zhan et al., 2022). This model can
not only integrate multiple environmental variables when predicting
potential distributions but also obtain the main influencing factors and
adaptation range that affect the growth of the species. It has been
widely used to predict the possible distribution of species and to study
ecological characteristics (Zeng et al., 2021).
Leonurus japonicus is an annual or biennial herb of the family
Labiatae. It is distributed throughout China and grows in a variety of
habitats, especially in areas with the sun exposure, at altitudes of up
to 3400 meters. Leonurus japonicus prefers a warm and humid
climate, and the plants can grow in most soils. Leonurus
japonicus is a traditional Chinese herbal medicine. Its fresh or dry
aboveground parts can be used as a medicine. It has the effects of
regulating menstruation, diuresis, and swelling; removing blood stasis;
promoting blood circulation and hemostasis; and preventing miscarriage
(Wang et al., 2022; Zhang et al., 2000). It was used as a gynecological
drug in traditional Chinese medicine during past dynasties. It is also
widely used in the production of gynecological Chinese patent medicines
today, and the market demand is high (Jiang, H., 2009). Environmental
conditions such as light, temperature, and water affect the growth and
accumulation of total alkaloids in plants.
At present, the research on Leonurus japonicus primarily focuses
on its pharmacological aspects and chemical composition, while the
prediction of potential distribution areas under future climate change
scenarios has not been considered. Maxent has been used to predict
suitable areas of endangered species, biological invasive species,
crops, and medicinal plants (Zeng et al., 2021; Li et al., 2021). To
clarify the distribution of the suitable area of the medicinal plantLeonurus japonicus and its response to future climate change,
this study employed the ENMeval package to optimize the parameter
settings in the Maxent model (Muscarella et al., 2015), and the
optimized model was used to predict and analyze the suitable
distribution of L. japonicus . On this basis, according to the
potentially suitable area under the current climatic conditions, the
Marxan model was used to derive the priority protection area ofLeonurus japonicus . The appropriate ecological environment for
planting can be determined to avoid the loss caused by blind
introduction. The results can provide a reference for the rational
conservation of Chinese herbal medicines.
2. Research methods
2.1. Collection of species geographical distribution data
Through the retrieval from plant
herbaria such as the Chinese Virtual Herbarium (CVH) and the Global
Biodiversity Information Facility (GBIF), any repeated or incorrect
distribution point data were eliminated. For the distribution points
with detailed information, the longitude and latitude were conformed by
the Baidu coordinate system, and a total of 489 distribution data points
of Leonurus japonicus specimens were collected (Figure 1).
2.2. Selection and processing of environmental variables
Species niches are affected by climate, topography, biology, and other
factors. After considering the comprehensiveness and complexity of
ecological factors, 34 environmental variables that could reflect
species niches were selected. The list included 19 bioclimatic factors,
14 soil factors, and one topographic factor (altitude).
The current (1970–2000), 2050s (2041–2060), and 2090s (2081–2100)
bioclimatic factor data used in this research were derived from the
world climate database Worldclim (http://www.worldclim.Org), and the
spatial sampling rate of the data was 2.5 arc-minutes
(~5 km). The
climate data of the 2050s and 2090s were obtained from the Beijing
Climate Center-Climate System Model-Medium Resolution
(BCC-CSM2-MR), one of the Coupled
Model Inter-Comparison Project Phase 6
(CMIP6) datasets, which included
three scenarios: sustainable development (ssp126), intermediate
development (ssp245), and conventional development (ssp585). Ssp data
have a high accuracy and separation rate and can integrate local
development factors, and so are more convincing than CMIP5 data. The
data of soil factors and topographic factors were from the World Soil
Database (HWSD) of the FAO
(http://www.fao.org/faostat/en/#data),
and the map data were from China’s Ministry of Natural Resources
(http://www.mnr.gov.cn/).
In the application of species distribution models, the accuracy of the
Maxent model can be improved by using the R language, variance inflation
factor (VIF)-based environmental variable screening, and Spearman
correlation to reduce the multicollinearity between multiple
environmental variables. In this study, the R language package was used
to preliminarily screen the factors with correlations less than 0.7, and
then on this basis, the factors with variance inflation factor VIF less
than 5 were selected. The R language was used for Pearson correlations,
and factors with correlation
coefficients less than 0.7 were retained. At the same time, factors with
extremely high ecological significance were retained among the factors
with correlation coefficients
greater than 0.7 (Zhang et al.,
2017). Finally, six climatic factors, one topographic factor, and five
soil factors were selected, for a total of 12 environmental factors
(Table 1).
Table 1 The environmental aspects used in the modeling