Hojati Mahboobe, Naderi Ruhollah, Edalat Mohsen, Pourghasemi Hamid Reza
Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran.
Department of Soil Science, School of Agriculture, Shiraz University, Shiraz, Iran.
PLoS One. 2025 Jul 11;20(7):e0322442. doi: 10.1371/journal.pone.0322442. eCollection 2025.
The increasing demand for natural medicine has increased the significance of Silybum marianum as a valuable medicinal plant. It is used to restore liver cells; reduce blood cholesterol; prevent prostate, skin, and breast cancer; and protect cervical cells and kidneys. To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms including boosted regression trees (BRT), random forest (RF), and support vector machines (SVM) have been applied in Fars Province, Iran. Fourteen factors affecting S. marianum growth and development were determined and subsequently converted into raster maps for the modeling phase using a Geographic Information System (GIS). Subsequently, the Receiver Operating Characteristic (ROC) curve and random forest algorithm were used to evaluate the models and the significance of the factors, respectively. Results showed that The RF (ROC: 0.99), BRT (ROC: 0.98), and SVM (ROC: 0.96) models were highly accurate in predicting the habitat suitability of S. marianum. The results of the RF algorithm also revealed that factors such as distance from roads, elevation, and mean annual rainfall had the most significant influence on the habitat suitability of S. marianum. In addition, the mean annual rainfall, mean annual temperature, and elevation had the highest effects on silymarin accumulation. In general, the northern and northwestern regions of the Fars Province offer optimal environmental conditions for the growth of S. marianum. The southern and southwestern regions of Fars Province, characterized by higher temperatures and lower precipitation, are suitable for the enhanced biosynthesis of silymarin and expansion of its cultivation and production. This study provides a robust framework for understanding the ecological preferences of S. marianum and optimizing its cultivation and management for pharmaceutical applications. By identifying the most influential environmental variables, this research has the potential for the sustainable utilization of this species, enhancing both its conservation and use as a medicinal resource.
对天然药物日益增长的需求提升了水飞蓟作为一种珍贵药用植物的重要性。它被用于修复肝细胞；降低血液胆固醇；预防前列腺癌、皮肤癌和乳腺癌；以及保护宫颈细胞和肾脏。为了确定影响水飞蓟中水飞蓟素分布和含量的生态因素，在伊朗法尔斯省应用了三种机器学习算法，包括增强回归树（BRT）、随机森林（RF）和支持向量机（SVM）。确定了影响水飞蓟生长和发育的14个因素，随后使用地理信息系统（GIS）将其转换为栅格地图用于建模阶段。随后，分别使用接收器操作特征（ROC）曲线和随机森林算法来评估模型和因素的重要性。结果表明，RF（ROC：0.99）、BRT（ROC：0.98）和SVM（ROC：0.96）模型在预测水飞蓟的栖息地适宜性方面具有很高的准确性。RF算法的结果还表明，距离道路的远近、海拔和年平均降雨量等因素对水飞蓟的栖息地适宜性影响最为显著。此外，年平均降雨量、年平均温度和海拔对水飞蓟素积累的影响最大。总体而言，法尔斯省的北部和西北部地区为水飞蓟的生长提供了最佳环境条件。法尔斯省的南部和西南部地区温度较高且降水较少，适合水飞蓟素的增强生物合成及其种植和生产的扩大。本研究为理解水飞蓟的生态偏好以及优化其药用栽培和管理提供了一个强大的框架。通过识别最具影响力的环境变量，本研究具有对该物种进行可持续利用的潜力，既能加强其保护又能将其用作药用资源。
