Bridle JR, Polechová J, Kawata M, Butlin RK.. Why is adaptation prevented at ecological margins? New insights from individual-based simulations. Ecol Lett 13: 485-494

Institute of Zoology, Regent's Park, London, UK. <>
Ecology Letters (Impact Factor: 10.69). 04/2010; 13(4):485-94. DOI: 10.1111/j.1461-0248.2010.01442.x
Source: PubMed


All species are restricted in their distribution. Currently, ecological models can only explain such limits if patches vary in quality, leading to asymmetrical dispersal, or if genetic variation is too low at the margins for adaptation. However, population genetic models suggest that the increase in genetic variance resulting from dispersal should allow adaptation to almost any ecological gradient. Clearly therefore, these models miss something that prevents evolution in natural populations. We developed an individual-based simulation to explore stochastic effects in these models. At high carrying capacities, our simulations largely agree with deterministic predictions. However, when carrying capacity is low, the population fails to establish for a wide range of parameter values where adaptation was expected from previous models. Stochastic or transient effects appear critical around the boundaries in parameter space between simulation behaviours. Dispersal, gradient steepness, and population density emerge as key factors determining adaptation on an ecological gradient.

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    • "Understanding the underlying genetic architecture of thermal responses across populations and species can provide critical information on this debate given that genetic correlations between traits and spatial distribution of genetic variance (Lavergne et al., 2010) may limit local adaptation to thermal challenges. Models of adaptation to either continuously varying environmental optima (Kirkpatrick and Barton, 2006; Bridle et al., 2010) or local adaptation to two contrasting environments (Yeaman and Whitlock, 2011) vary in their predictions about clines of allelic frequencies and whether evolution tends (or not) towards few genes of large effect. The clinal work looking at parallel differentiation in D. melanogaster cannot test for such patterns given that only the ends of the clines were examined. "
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    • "Here, we explore the effects of environmental heterogeneity , dispersal ability and selection on the strength of local adaptation through simulations. A simulation framework is valuable because it allows for stochastic demography and evolution, and also makes the study of complex landscapes more tractable compared with analytical models (Bridle et al. 2010). We test a suite of GEA methods in the simulation parameter space to determine how these factors impact our ability to detect patterns of local adaptation. "
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    ABSTRACT: Local adaptation is strongly shaped by the interaction of selection and gene flow. Because the spatial configuration of the landscape can affect the probability of gene flow between habitats, landscape heterogeneity also has important implications for local adaptation. Here, we explore the effects of landscape heterogeneity, dispersal ability, and selection on the strength of local adaptation in a simulation framework. We test a suite of genotype-environment association (GEA) methods to determine how these factors impact our ability to detect local adaptation. The strength of local adaptation was positively correlated with the degree of spatial heterogeneity of selection regimes. For instance, continuous gradients of selection generally produced stronger signatures of selection than discrete selection landscapes, even when those discrete landscapes were highly aggregated. However, strong signatures of local adaptation developed in highly patchy landscapes under moderate and strong selection. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. The univariate GEA method we tested had high false positive rates (up to 55%) under limited dispersal scenarios, likely as a result of strong isolation by distance (IBD) in these simulated populations. In contrast, multivariate, ordination-based approaches had uniformly low false positive rates (0-2%) across the simulation parameter space, suggesting these methods are able to effectively control for IBD and population structure. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these impact our ability to detect selection in wild populations.
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    • "Pollen-and seed-mediated gene flow can facilitate adaptation to new environmental conditions by replenishing population genetic variation (Le Corre and Kremer, 2003; Polechova et al., 2009; Bridle et al., 2010), and by reducing the effects of genetic "
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