Asymmetric Patch Size Distribution Leads to Disruptive Selection on Dispersal

Massol, François; Duputié, Anne; David, Patrice; Jarne, Philippe

doi:10.1111/j.1558-5646.2010.01143.x

Cited by 55 publications

(108 citation statements)

References 62 publications

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“…We built a metacommunity model including dispersal evolution which reproduces the central result obtained by Massol et al [33] at the metapopulation scale: increasing ) and large (bars labelled 'K 2 ') communities for metacommunity 1 (dark grey) and 2 (light grey). 95% CI of these average value based on central limit theorem approximation are reported for each bar.…”

Section: Discussion (A) Dispersal In Metacommunitiesmentioning

confidence: 99%

“…Keeping K and N constant yields the same overall number of individuals in the metacommunity, which implies the same rate of occurrence of new species and the same singular value of dispersal d Ã ¼ min( 1=ð Kðc þ g 2 ÞÞ, 1Þ in the two metacommunities (i.e. the value at which branching or evolutionary stability may occur, see [33]). We set the mean carrying capacity of communities to K ¼ 8 and the number of communities in the metacommunity to N ¼ 1 110 (see electronic supplementary material, appendix S1), so that both metacommunities contained J ¼ 8 880 individuals.…”

Section: Methods (A) Model With Dispersal Evolution (Model M 1 )mentioning

confidence: 99%

“…Following [33] (equations 4 and 5), the mean K, squared coefficient of variation g 2 ¼ E½ðK À KÞ 2 = K 2 , and standardized…”

Section: Methods (A) Model With Dispersal Evolution (Model M 1 )mentioning

confidence: 99%

“…This can be intuitively explained by the fact that, in more asymmetric metacommunities, drift is stronger, because more individuals belong to small communities. Here, we focused on the prediction about asymmetry as it is the feature of carrying capacity distribution that discriminates dispersal evolution scenarios [33]. Yet, the analytical results derived from our neutral model may allow a more comprehensive exploration of the effects of carrying capacity distribution on species g-diversity (electronic supplementary material, figure A4.3) and thus contribute to current research in conservation biology, in particular with respect to reserve design [49].…”

Section: (B) Neutral Predictions On Species Diversity Patternsmentioning

confidence: 99%

“…spatial and temporal fluctuations of environmental conditions [30][31][32]). In particular, Massol et al [33] showed that variation in the strength of kin selection owing to variation in carrying capacity among populations is sufficient to generate disruptive selection on dispersal and EB, without calling for fluctuations. Their model includes both an explicit metapopulation structure and a competition-dispersal trade-off.…”

Section: Introductionmentioning

confidence: 99%

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The evolution of the competition–dispersal trade-off affects α- and β-diversity in a heterogeneous metacommunity

et al. 2016

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Difference in dispersal ability is a key driver of species coexistence in metacommunities. However, the available frameworks for interpreting species diversity patterns in natura often overlook trade-offs and evolutionary constraints associated with dispersal. Here, we build a metacommunity model accounting for dispersal evolution and a competition-dispersal trade-off. Depending on the distribution of carrying capacities among communities, species dispersal values are distributed either around a single strategy (evolutionarily stable strategy, ESS), or around distinct strategies (evolutionary branching, EB). We show that limited dispersal generates spatial aggregation of dispersal traits in ESS and EB scenarios, and that the competition-dispersal trade-off strengthens the pattern in the EB scenario. Importantly, individuals in larger (respectively (resp.) smaller) communities tend to harbour lower (resp. higher) dispersal, especially under the EB scenario. We explore how dispersal evolution affects species diversity patterns by comparing those from our model to the predictions of a neutral metacommunity model. The most marked difference is detected under EB, with distinctive values of both aand b-diversity (e.g. the dissimilarity in species composition between small and large communities was significantly larger than neutral predictions). We conclude that, from an empirical perspective, jointly assessing community carrying capacity with species dispersal strategies should improve our understanding of diversity patterns in metacommunities.

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Section: Discussion (A) Dispersal In Metacommunitiesmentioning

confidence: 99%

Section: Methods (A) Model With Dispersal Evolution (Model M 1 )mentioning

confidence: 99%

“…Following [33] (equations 4 and 5), the mean K, squared coefficient of variation g 2 ¼ E½ðK À KÞ 2 = K 2 , and standardized…”

Section: Methods (A) Model With Dispersal Evolution (Model M 1 )mentioning

confidence: 99%

Section: (B) Neutral Predictions On Species Diversity Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The evolution of the competition–dispersal trade-off affects α- and β-diversity in a heterogeneous metacommunity

et al. 2016

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Dispersal evolution in temporally variable environments: implications for plant range dynamics

Oldfather

Elzen

Heffernan

et al. 2021

American J of Botany

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Dispersal—the movement of an individual from the site of birth to a different site for reproduction—is an ecological and evolutionary driver of species ranges that shapes patterns of colonization, connectivity, gene flow, and adaptation. In plants, the traits that influence dispersal often vary within and among species, are heritable, and evolve in response to the fitness consequences of moving through heterogeneous landscapes. Spatial and temporal variation in the quality and quantity of habitat are important sources of selection on dispersal strategies across species ranges. While recent reviews have evaluated the interactions between spatial variation in habitat and dispersal dynamics, the extent to which geographic variation in temporal variability can also shape range‐wide patterns in dispersal traits has not been synthesized. In this paper, we summarize key predictions from metapopulation models that evaluate how dispersal evolves in response to spatial and temporal habitat variability. Next, we compile empirical data that quantify temporal variability in plant demography and patterns of dispersal trait variation across species ranges to evaluate the hypothesis that higher temporal variability favors increased dispersal at plant range limits. We found some suggestive evidence supporting this hypothesis while more generally identifying a major gap in empirical work evaluating plant metapopulation dynamics across species ranges and geographic variation in dispersal traits. To address this gap, we propose several future research directions that would advance our understanding of the interplay between spatiotemporal variability and dispersal trait variation in shaping the dynamics of current and future species ranges.

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Eco-Evolutionary Dynamics of Agricultural Networks

Loeuille¹,

Barot²,

Georgelin³

et al. 2013

Ecological Networks in an Agricultural World

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Asymmetric Patch Size Distribution Leads to Disruptive Selection on Dispersal

Abstract: Numerous models have been designed to understand how dispersal ability evolves when organisms live in a fragmented landscape. Most of them predict

Cited by 55 publications

References 62 publications

The evolution of the competition–dispersal trade-off affects α- and β-diversity in a heterogeneous metacommunity

The evolution of the competition–dispersal trade-off affects α- and β-diversity in a heterogeneous metacommunity

Dispersal evolution in temporally variable environments: implications for plant range dynamics

Eco-Evolutionary Dynamics of Agricultural Networks

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