Abstract

Population genetic models of evolution along linear environmental gradients cannot explain why adaptation stops at ecological margins. This is because, unless models impose reductions in carrying capacity at species’ edges, the dominant effect of gene flow is to increase genetic variance and adaptive potential rather than swamping local adaptation. This allows the population to match even very steep changes in trait optima. We extend our previous simulations to explore two nonlinear models of ecological gradients: (a) a sigmoid (steepening) gradient and (b) a linear gradient with a flat centre of variable width. We compare the parameter conditions that allow local adaptation and range expansion from the centre, with those that permit the persistence of a perfectly adapted population distributed across the entire range. Along nonlinear gradients, colonization is easier, and extinction rarer, than along a linear gradient. This is because the shallow environmental gradient near the range centre does not cause gene flow to increase genetic variation, and so does not result in reduced population density. However, as gradient steepness increases, gene flow inflates genetic variance and reduces local population density sufficiently that genetic drift overcomes local selection, creating a finite range margin. When a flat centre is superimposed on a linear gradient, gene flow increases genetic variation dramatically at its edges, leading to an abrupt reduction in density that prevents niche expansion. Remarkably local interruptions in a linear ecological gradient (of a width much less than the mean dispersal distance) can prevent local adaptation beyond this flat centre. In contrast to other situations, this effect is stronger and more consistent where carrying capacity is high. Practically speaking, this means that habitat improvement at patch margins will make evolutionary rescue more likely. By contrast, even small improvements in habitat at patch centres may confine populations to limited areas of ecological space.

Highlights

  • Why is local adaptation prevented in some ecological and genetic situations, meaning that populations cannot track changing environments, and so have finite ranges in space and time? Understanding when and where such limits to adaptation occur is critical for predicting species’ extinction rates in time, their geographical distributions in space, and the evolution of Evolutionary Applications. 2019;1–14.|2 ecological communities

  • Models exploring the effect of gene flow on local adaptation have focussed on either a few ecologically divergent patches, with varying levels of dispersal between them; a series of populations with stepping‐stone dispersal (e.g., Alleaume‐ Benharira, Pen, & Ronce, 2005); or the joint effect of gene flow and selection when individuals are distributed continuously across a linear ecological gradient in space (Barton, 2001; Bridle, Polechová, Kawata, & Butlin, 2010; Haldane, 1948; Kirkpatrick & Barton, 1997; Polechová, 2018; Polechová & Barton, 2015)

  • Where all populations match the local trait optima, gene flow has no effect on the mean phenotype because alleles arrive and leave all populations so that gene flow has no net effect on local allele frequencies (Felsenstein, 1975; Kirkpatrick & Barton, 1997)

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Summary

| INTRODUCTION

Why is local adaptation prevented in some ecological and genetic situations, meaning that populations cannot track changing environments, and so have finite ranges in space and time? Understanding when and where such limits to adaptation occur is critical for predicting species’ extinction rates in time, their geographical distributions in space, and the evolution of. A deterministic limit is reached when the variance generated by dispersal reduces population mean fitness (i.e., growth rate) sufficiently to cause extinction throughout the whole range, despite allowing evolution of the trait mean to match the local optimum everywhere. Polechová and Barton's (2015) model provides a general explanation for the failure for populations to adapt at a narrower range of parameter conditions (in terms of gradient steepness and population demography) than predicted by the deterministic limit, as observed by Bridle et al (2010) At their most realistic, linear models of adaptation along ecological gradients typically produce only two outputs in most regions of parameter space: unlimited species’ ranges (adaptation everyone), or extinction (adaptation nowhere). Since both represent smoothly increasing rates of environmental change, we do not expect this difference to influence the threshold gradient at which further adaptation is prevented

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| DISCUSSION
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