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Rhizoma Paridis, a medicinal plant material with significant economic value, is derived from two species: Paris polyphylla var. chinensis (Pc) and var. yunnanensis (Py). However, the challenge in accurately distinguishing these two morphologically similar taxa, combined with uncertainty regarding their optimal cultivation zones under changing environmental conditions, creates significant obstacles for sustainable cultivation and quality control of this valuable medicinal resource. Climate change and human activities are major factors influencing suitable habitats for these species. Under different greenhouse gas emission scenarios (SSP1-2.6 and SSP5-8.5 representing low and high emissions respectively), climate impacts on species distributions will vary significantly. To address these challenges and support evidence-based conservation and cultivation strategies, this study utilized an ensemble forecasting machine learning approach to model current and future distribution scenarios under varying levels of greenhouse gas emissions. Comparative genomic techniques for species-specific barcoding were employed to analyze the impacts of anthropogenic activity and climatic changes on the distributions of the plants and species identification. Our findings indicated that the ecological niches of Pc and Py varied significantly: Pc is predominantly suited to southern China, while Py would thrive in Yunnan Province. Annual precipitation was the principal determinant of the distribution of Pc, whereas the annual temperature range held greater significance for Py. Human activity exerts a more substantial impact on the distribution of Pc than on Py. Under low-emission scenarios, Pc is projected to significantly expand its ultrahigh-suitability range by the end of the century, whereas the range of Py will remain relatively stable. Conversely, under high emissions, the distribution of Pc would initially expand before contracting, and that of Py would significantly decline. These shifts highlight the critical importance of maintaining low emissions for species conservation and sustainable use and help us delineate long-term cultivation areas. Our newly developed mini-barcodes were effective in distinguishing between Pc and Py. This comprehensive analysis provides essential insights for the sustainable cultivation and optimized yield of the two plant species, thereby providing a foundation for enhanced economic returns.
Wang et al. (Wed,) studied this question.