The Morina genus has 13 species in the world, out of which only M. persica L. is found to be growing wild in Iran. The aim of this research is to predict the spatial distribution and model the habitat suitability for M. persica species using four data mining models: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), and boosted regression trees (BRT). A total of 404 M. persica locations were identified during extensive field surveys, and their geographical locations were recorded using a global positioning system (GPS) device. Furthermore, seventeen environmental predictors including topographical, geological, climatic, and edaphic factors were selected, and their thematic layers were mapped in ArcGIS. Lastly, habitat suitability was modeled using data mining techniques. The validity of the results was assessed using the area under the receiver operating characteristic curve (AUROC). Moreover, three cutoff-dependent metrics, Cohen’s kappa, sensitivity, and specificity, were used for more scrutinized performance assessment. The results revealed that the highest effects on M. persica distribution were mostly associated with edaphic factors, followed by climatic, lithological, and topographical factors. The results showed that MaxEnt with an AUROC value of 95% showed an outstanding performance in terms of prediction power and generalization capacity, followed by SVM (94.1%), GLM (87.4%), and BRT (84.7%). Comparing the AUROC values, MaxEnt was selected as the premier model with the best performance for M. persica distribution modelling across the study area. Cutoff-dependent metrics were also in line with AUROC values; however, the latter made a more discernible distinction between the performance of SVM and MaxEnt. The GLM, BRT, SVM, and MaxEnt models classified 37.37%, 27.28%, 23.31%, and 6.51% of the study area as high and very high suitable habitats for M. persica, respectively. The inferences of this research would be of interest to authorities in the natural resources sector, the research community, local stakeholders, and biodiversity conservation agencies for use in conserving and reclaiming M. persica habitats in the study area.

该刺参属有13种在世界上，其中只有M.桃L.被发现是在伊朗日益疯狂。此研究的目的是预测的空间分布和用于栖息地适宜性模型M.桃使用四个数据挖掘模型物种：最大熵（最大墒），支持向量机（SVM），广义线性模型（GLM）和升压回归树木（BRT）。总计404  M. persica在广泛的实地调查中确定了位置，并使用全球定位系统（GPS）设备记录了它们的地理位置。此外，还选择了17个环境预测因子，包括地形，地质，气候和水生因子，并在ArcGIS中映射了它们的主题层。最后，使用数据挖掘技术对栖息地的适宜性进行了建模。使用接收器工作特性曲线（AUROC）下的面积评估结果的有效性。此外，使用了三个与临界值相关的指标，即Cohen的kappa，敏感性和特异性，来进行更详细的绩效评估。结果表明，对百日草的影响最大分布主要与海相因素有关，其次是气候，岩性和地形因素。结果表明，AUROC值为95％的MaxEnt在预测能力和泛化能力方面表现出出色的性能，其次是SVM（94.1％），GLM（87.4％）和BRT（84.7％）。比较AUROC值，最大墒被选为与最佳性能首屈一指的模型M.桃在研究区域内的分布模型。与临界值相关的指标也符合AUROC值；但是，后者在SVM和MaxEnt的性能之间做出了更明显的区分。GLM，BRT，SVM和MaxEnt模型将研究区域的37.37％，27.28％，23.31％和6.51％归类为高分布，高适应性的M. persica，分别。自然资源部门的主管部门，研究界，地方利益相关者和生物多样性保护机构都应进行这项研究的推论，以保护和垦殖研究区的多年生红豆杉生境。