Predicting predator-prey interactions in terrestrial endotherms using random forest

Abstract

AbstractSpecies interactions play a fundamental role in ecosystem function and stability. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all potential interactions in a community, various methods have been developed to infer interactions. Random forest—a machine learning technique—is emerging as one of the most frequently used methods for making interaction predictions, but its performance in inferring predator-prey interactions in terrestrial vertebrates and its sensitivity to variation in quality of training data remain untested. We examined predator-prey interactions within and between two diverse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forests predict predator-prey interactions for mammals and birds using species’ ecomorphological and phylogenetic traits. We also tested how variation in training data quality—manipulated by removing records and switching interaction records to non-interactions—affected model performance. We found that random forests could predict predator-prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88% and 67% of interactions and non-interactions in the global and community-specific datasets, respectively. These predictions were accurate even when there were no records in the training data for the focal predator or prey species. In contrast, false non-interactions for focal predators in the training data strongly degraded model performance. Our results demonstrate that random forests can identify predator-prey interactions for birds and mammals that have few or no trophic interaction records. Furthermore, our study provides a roadmap for predicting interactions using machine learning, which might help ecologists (i) address knowledge gaps and explore network-related questions in data-poor situations, and (ii) predict interactions for range-expanding species.