Theory holds that these large mammals partition food resources to coexist, which results in a distinct diet. Traditional theories categorize these animals along a spectrum from grass-eating grazers to non–grass-eating browsers. However, little is known about the specific plants that elephants, impalas, zebras and other large herbivores eat. Having a classification with just two categories based on broad plant types seems insufficient.
Researchers from Princeton University, the Smithsonian Institution and the Mpala Research Centre in Kenya used metabarcoding to analyze the feces of seven herbivore species, matching the sequences they found to a reference library of plant DNA which was a combination of EMBL data and the researchers own sequencing efforts.
By sequencing plant DNA from large mammal herbivore fecal samples, we analyzed the diets of an large mammal herbivore assemblage in Kenya. Diet composition was similar within species and strongly divergent across species, irrespective of feeding guild: Grazers ate similar total amounts of grass but different suites of grass species. Diet composition differed between all species—even pairs of grazers matched in size, digestive physiology, and location—and dietary similarity was sometimes greater across grazing and browsing guilds than within them.
This clear diet partitioning suggests that the coarse trophic categorizations used so far may rather generate misleading conclusions about competition and coexistence of large mammal herbivores. Their diversity might be much more tightly linked to plant diversity. The study results suggest that species-specific plant traits may actually be key to understanding dietary differences.
Our approach could be applicable to environmental management. Wildlife and livestock overlap in rangelands worldwide, and resource competition between them (both real and perceived) is a major source of human–wildlife conflict. However, the extent of dietary overlap is poorly resolved due to the difficulty of studying wildlife diets. Controlled studies using DNA metabarcoding could elucidate the mechanisms of facilitative and competitive interactions as well as identify important forage species, thereby informing management strategies.