The apportionment of dietary diversity in wildlife

Evaluating species’ roles in food webs is critical for advancing
ecological theories on competition, coexistence, and biodiversity, but
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-F4,498 = 14.7, R2 = 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 offer
promising new insights into wildlife diets, with potential applications in
resource management and environmental monitoring.