Spatial mapping Zataria multiflora using different machine-learning algorithms

Abstract

Understanding the relationships between environmental factors that influence the distribution of medicinal plants is crucial to identifying suitable habitats for them. Studies have shown that the responses of such species to environmental influences is unclear. A region known as one of the most important areas for Zataria multiflora is Fars Province in southwestern Iran. This study determines the importance of 13 environmental factors (elevation, distance from rivers, distance from roads, pH, electrical conductivity, mean annual temperature, mean annual rainfall, slope angle, slope aspect, plan curvature, and soil properties) to the distribution of Zataria multiflora. The suitable habitats for Zataria multiflora are distinguished by the species-variable relationships and the application of five machine-learning techniques (MLTs): generalized linear model (GLM), generalized boosting model (GBM), boosted regression tree (BRT), functional discrimination analysis (FDA), and mixture discriminant analysis (MDA). The partial least-squares algorithm was used to determine the rank of importance of each variable. The results reveal that the most important factors influencing Zataria multiflora distribution are slope, elevation, EC, and mean annual temperature. The MLTs were applied, and the predictions were classified into four classes (very high, high, moderate, and low). Results indicate that the region of low habitat suitability is in the central portion of the study area and the percentages of the study area classified as having low potential are: 62.01% for GLM, 74.51% for MDA, 63.56% for FDA, 70.14% for GBM, and 23.19% for BRT. The models accuracies as indicated by AUC are MDA (93.6%), GBM (90.3%), GLM (89.1%), FDM (88.1%), and (83.4%). Modeling of habitat suitability can improve farmers’ and managers’ decisions regarding the protection of medicinal plants.