Abstract
The diversity of life on our planet has produced a remarkable variety of biological traits that characterize different species. Such traits are widely employed instead of taxonomy to increase our understanding of biodiversity and ecosystem functioning. However, for species’ trophic niches, one of the most critical aspects of organismal ecology, a paucity of empirical information has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the same species. Even when broad categories are assigned, 60% of the evaluated trait schemes disagree on the attribution of trophic categories for at least 20% of the species. This disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, unbiased, and fully reproducible framework to define species’ trophic guilds based on empirical data. First, we synthesize data from community-wide visual gut content analysis of tropical coral reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish species across all ocean basins. We then use network analysis to cluster the resulting global bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body size. Our model achieved a misclassification error of 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, which can be updated as new information becomes available. Although our case study is for reef fishes, the most diverse vertebrate consumer group, our approach can be applied to other organismal groups to advance reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in our changing world.
