bioRxivGeneticsPreprint
Genetic Dynamics of Mustang and Feral Horse Populations in the Western United States
E. Gus CothraniD, Anas M. Khanshour, Stephan Michael FunkiD, Eleanore Conant, Rytis JurasiD, Brian W. DavisiD
Abstract
The history and population dynamics of feral horse and wild mustang population in the Western United States has led to diverse populations of disparate ancestry. These iconic populations are currently managed by the Bureau of Land Management (BLM) and their genetic history is of great interest for both management and conservation purposes. We examined population genetic parameters using 12 well established microsatellite loci in nearly 8,500 horses representing 235 populations sampled across more than 20 years. Samples were collected by BLM or by members of other management agencies from 10 states. Genetic variability and genetic resemblance to domestic horse breeds using multiple methods were estimated. A wide range of variation levels were observed across the populations. In general, within-population variability was slightly lower than what has been found in domestic horse breeds, but still retains diversity. As expected, levels of population variation correlated to census size. Several populations were sampled longitudinally with intervals between sampling of about 5 years. For these longitudinal samples, there was no trend towards an increase or decline in diversity, indicating consistent management practices. Relationships between populations and domestic breeds ranged from close association to one or two specific breeds to extreme divergence of the feral horses to all breeds examined. Reasons for divergence are mainly related to the founding of the population and subsequent demographic history. Overall, there was a slight tendency for geographically close feral populations to be more similar to each other than to more distant populations. The results of this study show the feral horse populations in the western US have a considerable variation, though management practices can strongly influence variability levels.
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