Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv.

Zhang, Huihui; Zhang, Xinke; Zhang, Guoshuai; Sun, Xiao; Chen, Shilin; Huang, Linfang

doi:10.1016/j.jclepro.2024.142021

Cited by 1 publication

(1 citation statement)

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“…Climate change majorly affects on the distribution of medicinal plants. For example, the upslope distribution of many mountain biological communities has changed owing to climate warming, which was also one of the reasons for the changes in Eucommia ulmoides distribution 5 . The primary and secondary metabolic pathways of medicinal plants are affected by climate change.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chinese suitable habitats of Panax notoginseng under climate change based on MaxEnt and chemometric methods

Guo,

Zhang,

Ren

et al. 2024

Sci Rep

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Notoginseng saponin R1; ginsenosides Rg1, Re, Rb1, and Rd; the sum of the five saponins; and underground-part fresh weight (UPFW) of single plants were used as quality evaluation indices for Panax notoginseng (Burk.) F. H. Chen (P. notoginseng). Comprehensive evaluation of P. notoginseng samples from 30 production areas was performed using that MaxEnt model. Spatial pattern changes in suitable P. notoginseng habitats were predicted for current and future periods (2050s, 2070s, and 2090s) using SSP126 and SSP585 models. The results revealed that temperature, precipitation, and solar radiation were important environmental variables. Suitable habitats were located mainly in Yunnan, Guizhou, and Sichuan Provinces. The distribution core of P. notoginseng is predicted to shift southeast in the future. The saponin content decreased from the southeast to the northwest of Yunnan Province, which was contrary to the UPFW trend. This study provides the necessary information for the protection and sustainable utilization of P. notoginseng resources, and a theoretical reference for its application in the quality evaluation of Chinese medicinal products.

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Section: Introductionmentioning

confidence: 99%