Predicting predator–prey interactions in terrestrial endotherms using random forest

Abstract

Species interactions play a fundamental role in ecosystems. 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 interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator‐prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator–prey interactions in 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 forest predicted 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 forest 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 focal species. In contrast, false non‐interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator–prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine learning classifiers for predicting species interactions, which could help ecologists 1) address knowledge gaps and explore network‐related questions in data‐poor situations, and 2) predict interactions for range‐expanding species.