Determining Successful Translocation of Large Mammalian Carnivores Using Random Forest Classification

During the past century, scientists across the globe have attempted to reintroduce species to areas where they previously thrived, but are now extinct. These reintroduction efforts are attempts to reverse the human carelessness of the past and redress the ecological balance. From a project and process point of view, these undertakings involve considerable complexity. In light of this complexity, there has been minimal effort to access, evaluate, or predict the specific factors that may contribute to the success of such ventures. In the current study, machine learning was used to classify the outcome of large carnivore translocations, and to predict the probability of success. Using the Random Forest method, a model was developed to classify success and failure of these translocations with a high degree of accuracy. In addition, specific contributing, and controllable factors that increase the probability of a successful reintroduction of species were identified.