Effects of sample size,number of markers,and allelic richness on the detection of spatial genetic pattern |
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Authors: | ERIN L LANDGUTH BRADLEY C FEDY SARA J OYLER‐McCANCE ANDREW L GAREY SARAH L EMEL MATTHEW MUMMA HELENE H WAGNER MARIE‐JOSÉE FORTIN SAMUEL A CUSHMAN |
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Institution: | 1. Division of Biological Sciences, University of Montana, Missoula, MT 59812, USA;2. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80526, USA;3. U.S. Geological Survey, Fort Collins Science Center, Fort Collins, CO 80526, USA;4. VCU Rice Center, Virginia Commonwealth University, Richmond, VA 23284, USA;5. School of Biological Sciences, Washington State University, Pullman, WA 99164, USA;6. Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844, USA;7. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, M5S 3G5 Canada;8. USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ 86001, USA |
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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. |
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Keywords: | causal modelling cdpop isolation‐by‐barrier isolation‐by‐distance isolation‐by‐landscape resistance partial Mantel test sampling simulation modelling |
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