Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning

Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety services to humans. Biodiversity is one structural features dynamic complex forest ecosystems. One most challenging issues assessing understanding relationship between biodiversity environmental factors. The aim this study was investigate effect biotic abiotic factors on tree diversity Hyrcanian forests northern Iran. For purpose, we analyzed 8 sites locations from east west Caspian Sea. 15,988 trees were measured 655 circular permanent sample plots (0.1 ha). A combination machine learning methods used for modeling investigating Machine models included generalized additive (GAMs), support vector (SVM), random (RF) K-nearest–neighbor (KNN). To determine related variables such as average diameter at breast height (DBH) plot, basal area largest (BAL), (BA), number per hectare, species, slope, aspect elevation. comparison RMSEs, relative coefficients determination methods, showed that method resulted best among all those tested. Based results RF method, elevation, BA BAL recognized influential defining variation diversity.