A supervised learning algorithm to evaluate occurrence records in virtual species

Digital and open access of occurrence data have encouraged the development of tools to improve biodiversity conservation and management. In this study, we proposed a methodology to evaluate point-occurrence records based on expert knowledge. We firstly generated virtual data to test our methodology without confounding factors by simulating geographical distributions, virtual sampling, and expert checking of occurrence records. We used a set of non-linear bioclimatic variables and principal component analysis (PCA) to define a duality function between niche and biotope spaces. Subsequently, a supervised-learning model was fit to classify records between true and doubtful presence based on the virtual expert checking. We then tested our methodology using three virtual species and 10-fold cross validation. Also, we evaluated the prediction performance of the supervise model compared with the virtual observer using a virtual external database of occurrence data.