Potential Distribution Modelling and mapping using Random Forest method: An example of Yukar？g？kdere District

Reasons such as climate abnormalities and anthropogenic effects on ecosystems threat the existence of plant and animal species. In this case, it is important to be more effective and functional planning studies for ecosystems and target species. In order to achieve the mentioned effectiveness and functionality, creating of potential distribution maps for the target species are of crucial importance. There are many methods (discriminant analysis, logistic regression analysis, generalized addicted model, classification tree technique, maximum entropy, the genetic algorithm for rule-set prediction etc.) used in the process of creating potential distribution maps. These methods process with different algorithms and can produce different results. The Random Forest (RF) method, which is one of the mentioned methods and is frequently preferred in recent years, is used in this study. RF method was run via RStudio. In the present, AUC value of the potential distribution model obtained by RF method was determined to be 0.978. The variables creating to the model are respectively elevation (elvn), topographic position index (tpi), bedrock (rock), radiation index (ri) and slope (slope)