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