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dc.contributor.authorMims, Meryl C.en
dc.contributor.authorDay, C. C.en
dc.contributor.authorBurkhart, J. J.en
dc.contributor.authorFuller, M. R.en
dc.contributor.authorHinkle, J.en
dc.contributor.authorBearlin, A.en
dc.contributor.authorDunham, J. B.en
dc.contributor.authorDeHaan, P. W.en
dc.contributor.authorHolden, Z. A.en
dc.contributor.authorLandguth, E. E.en
dc.date.accessioned2019-04-04T18:51:53Zen
dc.date.available2019-04-04T18:51:53Zen
dc.date.issued2019-02-12en
dc.identifier.issn21508925en
dc.identifier.othere02589en
dc.identifier.urihttp://hdl.handle.net/10919/88826en
dc.description.abstractThe 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 dynamics—and their implications for management strategies—in fresh waters. © 2019 The Authors.en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherWiley-Blackwellen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectbull trouten
dc.subjectconservation geneticsen
dc.subjectdemogeneticen
dc.subjectfreshwater fishen
dc.subjectindividual-based modelsen
dc.subjectlandscape geneticsen
dc.subjectmetapopulationen
dc.subjectPend Oreille Riveren
dc.subjectpopulation geneticsen
dc.subjectriverscapeen
dc.subjectSalvelinus confluentusen
dc.subjectspatially explicit individual-based modelen
dc.titleSimulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened charen
dc.typeArticle - Refereeden
dc.contributor.departmentBiological Sciencesen
dc.description.notesWe thank Seattle City Light and West Fork Environmental for providing field data for this study. We would like to thank all participating members of the Distributed Graduate Course in Landscape Genetics 2014 for their constructive comments throughout this project. U.S. Fish and Wildlife Service genotyping efforts were funded by USFWS and the Kalispel Tribe. The Kalispel Tribe collected genetic samples from the Pend Oreille River. This research was supported in part by funds provided by Seattle City Light and NASA grant NNX14AC91G. MCM was funded in part by the Climate and Land Use Change Mission Area and Mendenhall Research Fellowship Program of the U.S. Geological Survey (USGS) and the USGS Forest and Rangeland Ecosystem Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.en
dc.title.serialEcosphereen
dc.identifier.doihttps://doi.org/10.1002/ecs2.2589en
dc.identifier.volume10en
dc.identifier.issue2en
dc.type.dcmitypeTexten


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International