Microbiome Analysis Enables Non-invasive Monitoring of Rocky Mountain
Elk Populations
Abstract
Rocky Mountain elk (Cervus elaphus nelsoni) seasonal migration,
body-condition and sex ratios are important parameters for
characterizing elk populations but have thus far been outside the scope
of non-invasive methods. Fecal microbiomes can be surveyed
non-invasively from scat samples and are associated with changes in
diet, stress, age, disease and physical condition of the host, as well
as differences between sexes. With this in mind, we surveyed the fecal
microbiome of Montana elk that varied geographically (i.e. populations),
by body condition, age and by sex. Our goal was to explore an approach
for evaluating linkages between the host animal and its microbiome
composition, and to develop bioinformatic techniques useful for
characterizing host categories and population parameters based on
microbiome analysis. We built a supervised-machine learning classifier
based on bacterial taxa with cross validation to predict each fecal
microbiome’s affiliation to known host categories. The microbiome
classifier predicted host population, sex, age and body-condition with
promising cross validation results. Monitoring wildlife microbiomes
represents a breakthrough for non-invasive conservation biology, and we
provide proof of concept for obtaining low cost, fine scale,
management-relevant information from scat samples.