Dynamics of Hybridization and Introgression in Red Wolves and Coyotes

Publisher Website
Google Scholar
Abstract
Abstract:  Hybridization and introgression are significant causes of endangerment in many taxa and are considered the greatest biological threats to the reintroduced population of red wolves (Canis rufus) in North Carolina (U.S.A.). Little is known, however, about these processes in red wolves and coyotes (C. latrans). We used individual‐based simulations to examine the process of hybridization and introgression between these species. Under the range of circumstances we considered, red wolves in colonizing and established populations were quickly extirpated, persisted near the carrying capacity, or had intermediate outcomes. Sensitivity analyses suggested that the probabilities of quasi extinction and persistence of red wolves near the carrying capacity were most affected by the strength of two reproductive barriers: red wolf challenges and assortative mating between red wolves and coyotes. Because model parameters for these barriers may be difficult to estimate, we also sought to identify other predictors of red wolf population fate. The proportion of pure red wolves in the population was a strong predictor of the future probabilities of red wolf quasi extinction and persistence. Finally, we examined whether sterilization can be effective in minimizing introgression while allowing the reintroduced red wolf population to grow. Our results suggest sterilization can be an effective short‐term strategy to reduce the likelihood of extirpation in colonizing populations of red wolves. Whether red wolf numbers are increased by sterilization depends on the level of sterilization effort and the acting reproductive barriers. Our results provide an outline of the conditions likely required for successful reestablishment and long‐term maintenance of populations of wild red wolves in the presence of coyotes. Our modeling approach may prove generally useful in providing insight into situations involving complex species interactions when data are few.