Tags

Type your tag names separated by a space and hit enter

Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment.
Mol Ecol. 2008 Sep; 17(17):3901-16.ME

Abstract

Disentangling the relative contributions of selective and neutral processes underlying phenotypic and genetic variation under natural, environmental conditions remains a central challenge in evolutionary ecology. However, much of the variation that could be informative in this area of research is likely to be cryptic in nature; thus, the identification of wild populations suitable for study may be problematic. We use a landscape genetics approach to identify such populations of three-spined stickleback inhabiting the Saint Lawrence River estuary. We sampled 1865 adult fish over multiple years. Individuals were genotyped for nine microsatellite loci, and georeferenced multilocus data were used to infer population groupings, as well as locations of genetic discontinuities, under a Bayesian model framework (geneland). We modelled environmental data using nonparametric multiple regression to explain genetic differentiation as a function of spatio-ecological effects. Additionally, we used genotype data to estimate dispersal and gene flow to parameterize a simple model predicting adaptive vs. plastic divergence between demes. We demonstrate a bipartite division of the genetic landscape into freshwater and maritime zones, independent of geographical distance. Moreover, we show that the greatest proportion of genetic variation (31.5%) is explained by environmental differences. However, the potential for either adaptive or plastic divergence between demes is highly dependent upon the strength of migration and selection. Consequently, we highlight the utility of landscape genetics as a tool for hypothesis generation and experimental design, to identify focal populations and putative selection gradients, in order to distinguish between phenotypic plasticity and local adaptation.

Links

Publisher Full Text (DOI)

Authors+Show Affiliations

McCairns RJ
Département de biologie, Québec Océan, Université Laval, Québec, Canada.
Bernatchez L
No affiliation info available

MeSH

Adaptation, BiologicalAnalysis of VarianceAnimal MigrationAnimalsDNAEcologyEcosystemEvolution, MolecularFishesGene FlowGenetic VariationGenetics, PopulationGenotypeMarkov ChainsMicrosatellite RepeatsModels, GeneticMonte Carlo MethodPhenotypePopulation DensityRiversSelection, Genetic

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18662229

Citation

McCairns, R J Scott, and Louis Bernatchez. "Landscape Genetic Analyses Reveal Cryptic Population Structure and Putative Selection Gradients in a Large-scale Estuarine Environment." Molecular Ecology, vol. 17, no. 17, 2008, pp. 3901-16.
McCairns RJ, Bernatchez L. Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment. Mol Ecol. 2008;17(17):3901-16.
McCairns, R. J., & Bernatchez, L. (2008). Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment. Molecular Ecology, 17(17), 3901-16. https://doi.org/10.1111/j.1365-294X.2008.03884.x
McCairns RJ, Bernatchez L. Landscape Genetic Analyses Reveal Cryptic Population Structure and Putative Selection Gradients in a Large-scale Estuarine Environment. Mol Ecol. 2008;17(17):3901-16. PubMed PMID: 18662229.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment. AU - McCairns,R J Scott, AU - Bernatchez,Louis, Y1 - 2008/07/24/ PY - 2008/7/30/pubmed PY - 2008/10/22/medline PY - 2008/7/30/entrez SP - 3901 EP - 16 JF - Molecular ecology JO - Mol Ecol VL - 17 IS - 17 N2 - Disentangling the relative contributions of selective and neutral processes underlying phenotypic and genetic variation under natural, environmental conditions remains a central challenge in evolutionary ecology. However, much of the variation that could be informative in this area of research is likely to be cryptic in nature; thus, the identification of wild populations suitable for study may be problematic. We use a landscape genetics approach to identify such populations of three-spined stickleback inhabiting the Saint Lawrence River estuary. We sampled 1865 adult fish over multiple years. Individuals were genotyped for nine microsatellite loci, and georeferenced multilocus data were used to infer population groupings, as well as locations of genetic discontinuities, under a Bayesian model framework (geneland). We modelled environmental data using nonparametric multiple regression to explain genetic differentiation as a function of spatio-ecological effects. Additionally, we used genotype data to estimate dispersal and gene flow to parameterize a simple model predicting adaptive vs. plastic divergence between demes. We demonstrate a bipartite division of the genetic landscape into freshwater and maritime zones, independent of geographical distance. Moreover, we show that the greatest proportion of genetic variation (31.5%) is explained by environmental differences. However, the potential for either adaptive or plastic divergence between demes is highly dependent upon the strength of migration and selection. Consequently, we highlight the utility of landscape genetics as a tool for hypothesis generation and experimental design, to identify focal populations and putative selection gradients, in order to distinguish between phenotypic plasticity and local adaptation. SN - 1365-294X UR - https://www.unboundmedicine.com/medline/citation/18662229/Landscape_genetic_analyses_reveal_cryptic_population_structure_and_putative_selection_gradients_in_a_large_scale_estuarine_environment_ L2 - https://doi.org/10.1111/j.1365-294X.2008.03884.x DB - PRIME DP - Unbound Medicine ER -