Literature DB >> 24921607

The evolution of bet hedging in response to local ecological conditions.

Etienne Rajon1, Emmanuel Desouhant, Mathieu Chevalier, François Débias, Frédéric Menu.   

Abstract

Genotypes that hedge their bets can be favored by selection in an unpredictably varying environment. Bet hedging can be achieved by systematically expressing several phenotypes, such as one that readily attempts to reproduce and one that procrastinates in a dormant stage. But how much of each phenotype should a genotype express? Theory predicts that evolving bet-hedging strategies depend on local environmental variation, on how the population is regulated, and on exchanges with neighboring populations. Empirically, however, it remains unknown whether bet hedging can evolve to cope with the ecological conditions experienced by populations. Here we study the evolution of bet-hedging dormancy frequencies in two neighboring populations of the chestnut weevil, Curculio elephas. We estimate the temporal distribution of demographic parameters together with the form of the relationship between fecundity and population density and use both to parameterize models that predict the bet-hedging dormancy frequency expected to evolve in each population. Strikingly, the observed dormancy frequencies closely match predictions in their respective localities. We also found that dormancy frequencies vary randomly across generations, likely due to environmental perturbations of the underlying physiological mechanism. Using a model that includes these constraints, we predict the whole distribution of dormancy frequencies whose mean and shape agree with our observed data. Overall, our results suggest that dormancy frequencies have evolved according to local ecological conditions and physiological constraints.

Entities:  

Mesh:

Year:  2014        PMID: 24921607     DOI: 10.1086/676506

Source DB:  PubMed          Journal:  Am Nat        ISSN: 0003-0147            Impact factor:   3.926


  6 in total

Review 1.  Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution.

Authors:  Bram Van den Bergh; Toon Swings; Maarten Fauvart; Jan Michiels
Journal:  Microbiol Mol Biol Rev       Date:  2018-07-25       Impact factor: 11.056

2.  Evolutionary learning of adaptation to varying environments through a transgenerational feedback.

Authors:  BingKan Xue; Stanislas Leibler
Journal:  Proc Natl Acad Sci U S A       Date:  2016-09-19       Impact factor: 11.205

3.  Individual variation in reproductive behaviour is linked to temporal heterogeneity in predation risk.

Authors:  Miguel Barbosa; Amy E Deacon; Maria Joao Janeiro; Indar Ramnarine; Michael Blair Morrissey; Anne E Magurran
Journal:  Proc Biol Sci       Date:  2018-01-10       Impact factor: 5.349

4.  Facing environmental predictability with different sources of epigenetic variation.

Authors:  Christelle Leung; Sophie Breton; Bernard Angers
Journal:  Ecol Evol       Date:  2016-06-28       Impact factor: 2.912

5.  Maternal response to environmental unpredictability.

Authors:  Miguel Barbosa; Isabel Lopes; Catia Venâncio; Maria João Janeiro; Michael Blair Morrisey; Amadeu M V M Soares
Journal:  Ecol Evol       Date:  2015-10-05       Impact factor: 2.912

6.  Evolution of alternative insect life histories in stochastic seasonal environments.

Authors:  Sami M Kivelä; Panu Välimäki; Karl Gotthard
Journal:  Ecol Evol       Date:  2016-07-15       Impact factor: 2.912

  6 in total

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