Using DNA Metabarcoding to Understand Individual Variation in Diets of Wild Baboons

Individual variation in diet composition is thought to play a causal role in organism health and fitness. Yet to date, research on individual diets is limited by observation and identification constraints. DNA metabarcoding offers a solution. In this thesis, I use DNA metabarcoding to identify plant species in fecal samples from baboons living in the Amboseli ecosystem, Kenya. Leveraging public databases, I characterize diet species composition of 84 fecal samples from 66 individuals living in two neighboring social groups over two different seasons in the same year. As expected, diet composition differed significantly for baboons sampled in different groups and seasons. However, the predictive power of other social variables was mixed: diet sometimes differs by rank, but not by social network proximity. These data represent an important contribution to the field of metabarcoding, and a new approach to measuring individual diet composition for the long-term Amboseli project. My research will lend new insights into baboon foraging ecology, and the relationship between diet and the social environment.