Abstract
Evaluating species' roles in food webs is critical for advancing ecological theories on competition, coexistence, and biodiversity but is complicated by pronounced dietary variability within species and overlap across species. We combined dietary DNA metabarcoding, GPS tracking, and a machine-learning algorithm to cluster and compare dietary profiles within and among five migratory large-herbivore species from Yellowstone National Park. Interspecific niche partitioning was weak, but statistically significant (PERMANOVA: pseudo-F(4,498) = 14.7, R(2) = 0.11, P ≤ 0.001), such that some diet profiles from different species were as similar as those from within one species. Instead of affirming species' identity as a primary determinant of diet composition, we found three statistically different clusters of diet profiles-one concentrated on graminoids and forbs, another on forbs and deciduous shrubs, and a third on gymnosperms-each including samples from all herbivore species. Clusters did not reflect traditional diet classification schemes such as the grazer-browser continuum that is often used to distinguish species by percent grass consumption or use of grassland habitat in African savannas. Instead, clusters in Yellowstone reflected seasonal dietary variation within species that often equaled or exceeded niche differences between species, contributing to our growing understanding of why environmental variability may favor generalist foraging strategies at temperate latitudes, whereas specialized grazer and browser guilds appear to predominate in tropical savannas. Data-driven strategies that untangle complex trophic networks without relying on a priori groupings can offer new insights into wildlife diets, with potential applications in resource management and environmental monitoring.