Landguth2012276

Référence

Landguth, E.L., Fedy, B.C., Oyler-Mccance, S.J., Garey, A.L., Emel, S.L., Mumma, M., Wagner, H.H., Fortin, M.-J., Cushman, S.A. (2012) Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Molecular Ecology Resources, 12(2):276-284. (Scopus )

Résumé

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals. © 2011 Blackwell Publishing Ltd.

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@ARTICLE { Landguth2012276,
    AUTHOR = { Landguth, E.L. and Fedy, B.C. and Oyler-Mccance, S.J. and Garey, A.L. and Emel, S.L. and Mumma, M. and Wagner, H.H. and Fortin, M.-J. and Cushman, S.A. },
    TITLE = { Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern },
    JOURNAL = { Molecular Ecology Resources },
    YEAR = { 2012 },
    VOLUME = { 12 },
    NUMBER = { 2 },
    PAGES = { 276-284 },
    NOTE = { cited By 64 },
    ABSTRACT = { The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals. © 2011 Blackwell Publishing Ltd. },
    AFFILIATION = { Division of Biological Sciences, University of Montana, Missoula, MT 59812, United States; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80526, United States; U.S. Geological Survey, Fort Collins Science Center, Fort Collins, CO 80526, United States; VCU Rice Center, Virginia Commonwealth University, Richmond, VA 23284, United States; School of Biological Sciences, Washington State University, Pullman, WA 99164, United States; Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844, United States; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada; USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ 86001, United States },
    AUTHOR_KEYWORDS = { Causal modelling; Cdpop; Isolation-by-barrier; Isolation-by-distance; Isolation-by-landscape resistance; Partial Mantel test; Sampling; Simulation modelling },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/j.1755-0998.2011.03077.x },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856760886&doi=10.1111%2fj.1755-0998.2011.03077.x&partnerID=40&md5=28a546bb48de205fc5708e38d4ec2d69 },
}

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