The use of classification and regression algorithms using the random forests method with presence-only data to model species’ distribution

Abstract

Random forests (RF) is a powerful species distribution model (SDM) algorithm. This ensemble model by default can produce categorical and numerical species distribution maps based on its classification tree (CT) and regression tree (RT) algorithms, respectively. The CT algorithm can also produce numerical predictions (class probability). Here, we present a detailed procedure involving the use of the CT and RT algorithms using the RF method with presence-only data to model the distribution of species. CT and RT are used to generate numerical prediction maps, and then numerical predictions are converted to binary predictions through objective threshold-setting methods. We also applied simple methods to deal with collinearity of predictor variables and spatial autocorrelation of species occurrence data. A geographically stratified sampling method was employed for generating pseudo-absences. The detailed procedural framework is meant to be a generic method to be applied to virtually any SDM prediction question using presence-only data. • How to use RF as a standard method for generic species distributions with presence-only data • How to choose RF (CT or RT) methods for the distribution modeling of species • A general and detailed procedure for any SDM prediction question. Method name: Random forests models species distribution, Keywords: Binary prediction, Numerical prediction, Threshold, Machine learning, Species traits, Climate change, Forestation