EppersonMcRaeScribnerEtAl2010

Référence

Epperson, B.K., McRae, B.H., Scribner, K., Cushman, S.A., Rosenberg, M.S., Fortin, M.-J., James, P.M.A., Murphy, M., Manel, S., Legendre, P., Dale, M.R.T. (2010) Utility of computer simulations in landscape genetics. Molecular Ecology, 19(17):3549-3564. (Scopus )

Résumé

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection. © 2010 Blackwell Publishing Ltd.

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@ARTICLE { EppersonMcRaeScribnerEtAl2010,
    AUTHOR = { Epperson, B.K. and McRae, B.H. and Scribner, K. and Cushman, S.A. and Rosenberg, M.S. and Fortin, M.-J. and James, P.M.A. and Murphy, M. and Manel, S. and Legendre, P. and Dale, M.R.T. },
    TITLE = { Utility of computer simulations in landscape genetics },
    JOURNAL = { Molecular Ecology },
    YEAR = { 2010 },
    VOLUME = { 19 },
    PAGES = { 3549-3564 },
    NUMBER = { 17 },
    ABSTRACT = { Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection. © 2010 Blackwell Publishing Ltd. },
    COMMENT = { Cited By (since 1996): 32 Export Date: 15 May 2012 Source: Scopus CODEN: MOECE doi: 10.1111/j.1365-294X.2010.04678.x },
    ISSN = { 09621083 (ISSN) },
    KEYWORDS = { individual-based models, landscape ecology, population genetics, simulations, spatial statistics, article, biological model, computer simulation, demography, ecology, environment, gene flow, genetic selection, geography, methodology, population genetics, statistical model, statistics, uncertainty, Computer Simulation, Demography, Ecology, Environment, Gene Flow, Genetics, Population, Geography, Models, Genetic, Models, Statistical, Selection, Genetic, Stochastic Processes, Uncertainty },
    OWNER = { Luc },
    TIMESTAMP = { 2012.05.15 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-77952095479&partnerID=40&md5=a0edf41ff4b5535b1835731f5fe307fb },
}

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