Abstract

Mule deer are an important rangeland grazer, large prey species, and game animal for the state of Utah, so herd size is monitored and managed actively. A significant cause of population decline is poor overwintering survival, including from the lack of available forage during winter months. Mule deer energy storage is correlated with greater overwintering success, so physical estimates of energy storage including body condition score (BCS), rump fat (RF), and loin thickness (LT), can be used to track and predict a herd's health. Current methods of collecting deer information are costly, time consuming, and cause physical stress to deer, so here we sought to test if a microbiome analysis could be used to predict deer overwintering success. We analyzed nearly 1000 fecal samples collected from deer in Utah over a five-year period. We found that the microbiome composition of these samples shared characteristics with published reports of other reported ruminant species. Also, the location and time when the samples were collected significantly influenced mule deer microbiota composition and abundance. We found that there is a relationship between microbes and health measures (BCS, RF, LT), including some microbial abundances that could predict the health measures of mule deer several months ahead of time. There were also microbial groups whose abundances were significantly correlated with the latitude and elevation of the deer. Finally, a longitudinal analysis on a subset of sampled deer produced slightly different results than the broad analysis of all samples, including suggesting that some of the differences in microbiota composition with time may have been related more to sampling distinct deer at different time points, rather than that the deer microbiota composition changed with time. . These results suggest possible candidate microbial taxa for use in developing assays to replace current methods of measuring and tracking deer health.

Degree

College and Department

Life Sciences; Plant and Wildlife Sciences

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