Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char

• Published in: Ecosphere (Washington, D.C) D.C), 2019-02, Vol.10 (2), p.n/a
• Main Authors: Mims, Meryl C., Day, Casey C., Burkhart, Jacob J., Fuller, Matthew R., Hinkle, Jameson, Bearlin, Andrew, Dunham, Jason B., DeHaan, Patrick W., Holden, Zachary A., Landguth, Erin E.
• Format: Article
• Language: English
• Subjects: bull trout; Climate change; conservation genetics; Creeks & streams; demogenetic; Demography; Endangered & extinct species; Fish populations; freshwater fish; Genetic diversity; Genetic drift; Genetic factors; Genetics; individual‐based models; Landscape; landscape genetics; Life history; metapopulation; Metapopulations; Pend Oreille River; Population; population genetics; Reintroduction; riverscape; Salvelinus confluentus; Simulation; spatially explicit individual‐based model; Studies; Success; Threatened species; Topology; Trends; Trout; Watersheds
• ISSN: 2150-8925; 2150-8925
• DOI: 10.1002/ecs2.2589

The success of species reintroductions can depend on a combination of environmental, demographic, and genetic factors. Although the importance of these factors in the success of reintroductions is well‐accepted, they are typically evaluated independently, which can miss important interactions. For species that persist in metapopulations, movement through and interaction with the landscape is predicted to be a vital component of persistence. Simulation‐based approaches are a promising technique for evaluating the independent and combined effects of these factors on the outcome of various reintroduction and associated management actions. We report results from a simulation study of bull trout (Salvelinus confluentus) reintroduction to three watersheds of the Pend Oreille River system in northeastern Washington State, USA. We used an individual‐based, spatially explicit simulation model to evaluate how reintroduction strategies, life history variation, and riverscape structure (e.g., network topology) interact to influence the demographic and genetic characteristics of reintroduced bull trout populations in three watersheds. Simulation scenarios included a range of initial genetic stocks (informed by empirical bull trout genetic data), variation in migratory tendency and life history, and two landscape connectivity alternatives representing a connected network (isolation‐by‐distance) and a fragmented network (isolation‐by‐barrier, using the known existing barriers). A novel feature of these simulations was the ability to consider the interaction of both demographic and genetic (i.e., demogenetic) factors in riverscapes with implicit asymmetric movement probabilities across the barriers. We found that connectivity (presence or absence of barriers) had the largest effect on demographic and genetic outcomes over 200 yr, with a greater effect than both initial genetic diversity and life history variation. We also identified regions of the study system in which bull trout populations persisted across a wide range of demographic, life history, and environmental connectivity parameters. Finally, we found no evidence that initial neutral genetic diversity influenced genetic diversity and structure after 200 yr; instead, genetic drift due to stray rate and population isolation dominated and erased any initial differences in genetic diversity. Our results highlight the utility of spatially explicit demogenetic approaches in exploring and understanding population dynami