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The influence of landscape on gene flow in the eastern massasauga rattlesnake (Sistrurus c. catenatus): insight from computer simulations.

Byline: Michelle F. DiLeo, Jeremy D. Rouse, Jose A. Davila, Stephen C. Lougheed Keywords: anthropogenic disturbances; CDPOP ; DNA microsatellites; landscape genetics; replicate landscapes; simulations Abstract Understanding how gene flow shapes contemporary population structure requires the explicit... Full description

Journal Title: Molecular ecology September 2013, Vol.22(17), pp.4483-4498
Main Author: Dileo, Michelle F
Other Authors: Rouse, Jeremy D , Dávila, José A , Lougheed, Stephen C
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1365-294X ; DOI: 10.1111/mec.12411
Link: http://search.proquest.com/docview/1428772172/?pq-origsite=primo
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title: The influence of landscape on gene flow in the eastern massasauga rattlesnake (Sistrurus c. catenatus): insight from computer simulations.
format: Article
creator:
  • Dileo, Michelle F
  • Rouse, Jeremy D
  • Dávila, José A
  • Lougheed, Stephen C
subjects:
  • Animals–Genetics
  • Bayes Theorem–Genetics
  • Cluster Analysis–Genetics
  • Computer Simulation–Genetics
  • Crotalus–Genetics
  • Ecosystem–Genetics
  • Gene Flow–Genetics
  • Genetics, Population–Genetics
  • Geography–Genetics
  • Models, Genetic–Genetics
  • Ontario–Genetics
  • Cdpop
  • DNA Microsatellites
  • Anthropogenic Disturbances
  • Landscape Genetics
  • Replicate Landscapes
  • Simulations
ispartof: Molecular ecology, September 2013, Vol.22(17), pp.4483-4498
description: Byline: Michelle F. DiLeo, Jeremy D. Rouse, Jose A. Davila, Stephen C. Lougheed Keywords: anthropogenic disturbances; CDPOP ; DNA microsatellites; landscape genetics; replicate landscapes; simulations Abstract Understanding how gene flow shapes contemporary population structure requires the explicit consideration of landscape composition and configuration. New landscape genetic approaches allow us to link such heterogeneity to gene flow within and among populations. However, the attribution of cause is difficult when landscape features are spatially correlated, or when genetic patterns reflect past events. We use spatial Bayesian clustering and landscape resistance analysis to identify the landscape features that influence gene flow across two regional populations of the eastern massasauga rattlesnake, Sistrurus c. catenatus. Based on spatially explicit simulations, we inferred how habitat distribution modulates gene flow and attempted to disentangle the effects of spatially confounded landscape features. We found genetic clustering across one regional landscape but not the other, and also local differences in the effect of landscape on gene flow. Beyond the effects of isolation-by-distance, water bodies appear to underlie genetic differentiation among individuals in one regional population. Significant effects of roads were additionally detected locally, but these effects are possibly confounded with the signal of water bodies. In contrast, we found no signal of isolation-by-distance or landscape effects on genetic structure in the other regional population. Our simulations imply that these local differences have arisen as a result of differences in population density or tendencies for juvenile rather than adult dispersal. Importantly, our simulations also demonstrate that the ability to detect the consequences of contemporary anthropogenic landscape features (e.g. roads) on gene flow may be compromised when long-standing natural features (e.g. water bodies) co-exist on the landscape. Supporting information: Additional Supporting Information may be found in the online version of this article CAPTION(S): Appendix S1 Supplementary methods. Table S1 DNA microsatellite loci used in this study and the number of alleles found at each locus within each regional population (BP, Bruce Peninsula; EGB, Eastern Georgian Bay). Table S2 fragstats results. Table S3 Results from partial Mantel tests across three study regions after varying relative resistance values assi
language: eng
source:
identifier: E-ISSN: 1365-294X ; DOI: 10.1111/mec.12411
fulltext: fulltext
issn:
  • 1365294X
  • 1365-294X
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titleThe influence of landscape on gene flow in the eastern massasauga rattlesnake (Sistrurus c. catenatus): insight from computer simulations.
creatorDileo, Michelle F ; Rouse, Jeremy D ; Dávila, José A ; Lougheed, Stephen C
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ispartofMolecular ecology, September 2013, Vol.22(17), pp.4483-4498
identifierE-ISSN: 1365-294X ; DOI: 10.1111/mec.12411
subjectAnimals–Genetics ; Bayes Theorem–Genetics ; Cluster Analysis–Genetics ; Computer Simulation–Genetics ; Crotalus–Genetics ; Ecosystem–Genetics ; Gene Flow–Genetics ; Genetics, Population–Genetics ; Geography–Genetics ; Models, Genetic–Genetics ; Ontario–Genetics ; Cdpop ; DNA Microsatellites ; Anthropogenic Disturbances ; Landscape Genetics ; Replicate Landscapes ; Simulations
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descriptionByline: Michelle F. DiLeo, Jeremy D. Rouse, Jose A. Davila, Stephen C. Lougheed Keywords: anthropogenic disturbances; CDPOP ; DNA microsatellites; landscape genetics; replicate landscapes; simulations Abstract Understanding how gene flow shapes contemporary population structure requires the explicit consideration of landscape composition and configuration. New landscape genetic approaches allow us to link such heterogeneity to gene flow within and among populations. However, the attribution of cause is difficult when landscape features are spatially correlated, or when genetic patterns reflect past events. We use spatial Bayesian clustering and landscape resistance analysis to identify the landscape features that influence gene flow across two regional populations of the eastern massasauga rattlesnake, Sistrurus c. catenatus. Based on spatially explicit simulations, we inferred how habitat distribution modulates gene flow and attempted to disentangle the effects of spatially confounded landscape features. We found genetic clustering across one regional landscape but not the other, and also local differences in the effect of landscape on gene flow. Beyond the effects of isolation-by-distance, water bodies appear to underlie genetic differentiation among individuals in one regional population. Significant effects of roads were additionally detected locally, but these effects are possibly confounded with the signal of water bodies. In contrast, we found no signal of isolation-by-distance or landscape effects on genetic structure in the other regional population. Our simulations imply that these local differences have arisen as a result of differences in population density or tendencies for juvenile rather than adult dispersal. Importantly, our simulations also demonstrate that the ability to detect the consequences of contemporary anthropogenic landscape features (e.g. roads) on gene flow may be compromised when long-standing natural features (e.g. water bodies) co-exist on the landscape. Supporting information: Additional Supporting Information may be found in the online version of this article CAPTION(S): Appendix S1 Supplementary methods. Table S1 DNA microsatellite loci used in this study and the number of alleles found at each locus within each regional population (BP, Bruce Peninsula; EGB, Eastern Georgian Bay). Table S2 fragstats results. Table S3 Results from partial Mantel tests across three study regions after varying relative resistance values assigned to landscape features. Table S4 Results from partial Mantel tests for sub-samples of 29 individuals south of Parry Sound. Table S5 Pairwise Fst between diagnosed clusters. Table S6 Results from the second step of two-step causal modeling for empirical data north and south of Parry Sound in Eastern Georgian Bay. Table S7 Mantel correlations between forest, water, roads on the Bruce Peninsula (above the diagonal) and eastern Georgian Bay (below the diagonal). Fig. S1 Barplot representing the q matrix from a global spatial assignment test in tess. Fig. S2 Plot showing the detection of IBD across simulated generations. Fig. S3 Plot of partial Mantel r-values across all sampled generations, demonstrating step 2 in the Wasserman et al. ()two-step causal modeling approach, where IBRwater|IBRwater + roads indicates a partial Mantel test between genetic distance and IBRwater partialling out the effects of IBRwater + roads, and IBRwater + roads|IBRwater indicates a partial Mantel test between genetic distance and IBRwater + roads partialling out the effects of IBRwater. Fig. S4 Estimates of Ne with 95% confidence intervals from sibship assignment for 3 sampling locations. Fig. S5 Correlations between empirical (gen.emp) and simulated (gen.sim) genetic distance matrices for a subset of individuals (n = 53) south of Parry Sound.
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