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Integrated evaluation of habitat suitability and spatial prediction of medicinal quality for Bletilla striata using species distribution models and machine learning approaches

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Název: Integrated evaluation of habitat suitability and spatial prediction of medicinal quality for Bletilla striata using species distribution models and machine learning approaches
Autoři: Kai-lang Mu, Fei Ran, Yun-qian Feng, Chang-mao Guo, Qiu-mei Luo, Tian-jian Wang, Rui-qi Liao, Yu-chen Liu, Gang Liu, Yu-xin Pang
Zdroj: Industrial Crops and Products, Vol 236, Iss , Pp 121937- (2025)
Informace o vydavateli: Elsevier, 2025.
Rok vydání: 2025
Sbírka: LCC:Plant culture
Témata: Bletilla striata, Ecological niche modeling, Medicinal quality, Multivariate statistical analysis, Soil factors, Climate change, Plant culture, SB1-1110
Popis: Bletilla striata (Thunb.) Rchb.f. holds significant value in traditional Chinese medicine and is widely applied in pharmaceuticals, healthcare, and cosmetics. Its unique medicinal properties and increasing market demand have led to extensive artificial cultivation; however, this has also resulted in pronounced spatial variations in medicinal quality, coupled with the decline of wild resources. Addressing this challenge necessitates a systematic assessment framework. This study integrates species distribution models (Maxent and biomod2), active constituent quantification, principal component analysis (PCA), partial least squares regression (PLSR), and over ten machine learning algorithms including XGBoost to evaluate B.striata from three dimensions: ecological suitability, phytochemical variability, and environmental driving mechanisms. Results indicate that the biomod2 model outperforms Maxent in predicting the current and future potential habitat suitability, identifying southwestern regions such as Guizhou and Sichuan as core high-suitability zones. Quantitative analysis of cultivated samples from different regions revealed substantial variation in phenolic glycosides and polysaccharide content, with coefficients of variation reaching up to 48.31 %. Pearson correlation and PLSR analyses revealed that soil factors including selenium content, pH, exchangeable sodium, soil bulk density, gravel proportion, and silt content play dominant roles in influencing the accumulation of active compounds. An XGBoost-based quality classification model was constructed and overlaid with suitability maps to delineate five distinct zones: core, enhancement, consolidation, buffering, and general zones. The integrated framework provides scientific guidance for the conservation, high-quality cultivation, and sustainable utilization of B.striata. More broadly, this innovative framework, combining ecological modeling with machine learning for quality assessment, offers a valuable methodological reference for the sustainable development and quality management of other medicinal plants.
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 1872-633X
Relation: http://www.sciencedirect.com/science/article/pii/S0926669025014839; https://doaj.org/toc/1872-633X
DOI: 10.1016/j.indcrop.2025.121937
Přístupová URL adresa: https://doaj.org/article/a3fc69a482124e1f872f65abbe8c0d6f
Přístupové číslo: edsdoj.3fc69a482124e1f872f65abbe8c0d6f
Databáze: Directory of Open Access Journals
Popis
Abstrakt:Bletilla striata (Thunb.) Rchb.f. holds significant value in traditional Chinese medicine and is widely applied in pharmaceuticals, healthcare, and cosmetics. Its unique medicinal properties and increasing market demand have led to extensive artificial cultivation; however, this has also resulted in pronounced spatial variations in medicinal quality, coupled with the decline of wild resources. Addressing this challenge necessitates a systematic assessment framework. This study integrates species distribution models (Maxent and biomod2), active constituent quantification, principal component analysis (PCA), partial least squares regression (PLSR), and over ten machine learning algorithms including XGBoost to evaluate B.striata from three dimensions: ecological suitability, phytochemical variability, and environmental driving mechanisms. Results indicate that the biomod2 model outperforms Maxent in predicting the current and future potential habitat suitability, identifying southwestern regions such as Guizhou and Sichuan as core high-suitability zones. Quantitative analysis of cultivated samples from different regions revealed substantial variation in phenolic glycosides and polysaccharide content, with coefficients of variation reaching up to 48.31 %. Pearson correlation and PLSR analyses revealed that soil factors including selenium content, pH, exchangeable sodium, soil bulk density, gravel proportion, and silt content play dominant roles in influencing the accumulation of active compounds. An XGBoost-based quality classification model was constructed and overlaid with suitability maps to delineate five distinct zones: core, enhancement, consolidation, buffering, and general zones. The integrated framework provides scientific guidance for the conservation, high-quality cultivation, and sustainable utilization of B.striata. More broadly, this innovative framework, combining ecological modeling with machine learning for quality assessment, offers a valuable methodological reference for the sustainable development and quality management of other medicinal plants.
ISSN:1872633X
DOI:10.1016/j.indcrop.2025.121937