Multidimensional (co)evolutionary stability

The American Naturalist
Florence DébarreMichael Doebeli

Abstract

The complexity of biotic and abiotic environmental conditions is such that the fitness of individuals is likely to depend on multiple traits. Using a synthetic framework of phenotypic evolution that draws from adaptive dynamics and quantitative genetics approaches, we explore how the number of traits under selection influences convergence stability and evolutionary stability in models for coevolution in multidimensional phenotype spaces. Our results allow us to identify three different effects of trait dimensionality on stability. First are (i) a "combinatorial effect": without epistasis and genetic correlations, a higher number of trait dimensions offers more opportunities for equilibria to be unstable; and (ii) epistatic interactions, that is, fitness interactions between traits, which tend to destabilize evolutionary equilibria; this effect increases with the dimension of phenotype space. These first two effects influence both convergence stability and evolutionary stability, while (iii) genetic correlations (due, e.g., to pleiotropy or linkage disequilibrium) can affect only convergence stability. We illustrate the general prediction that increased dimensionality destabilizes evolutionary equilibria using examples drawn fro...Continue Reading

Associated Datasets

Apr 23, 2014·Scott L. NuismerMichael Doebeli

References

Aug 1, 1982·Theoretical Population Biology·S W Pacala, J Roughgarden
Jun 1, 1981·Theoretical Population Biology·I Eshel, U Motro
Jan 1, 1996·Journal of Mathematical Biology·Ulf Dieckmann, R Law
Jan 11, 2007·Journal of Evolutionary Biology·E D Brodie, J W McGlothlin
Apr 24, 2010·Science·Michael Doebeli, Iaroslav Ispolatov
Nov 26, 2010·Journal of Mathematical Biology·Akira Sasaki, Ulf Dieckmann
Aug 1, 1995·Trends in Ecology & Evolution·E D BrodieF J Janzen
Apr 5, 2011·The American Naturalist·Florence Débarre, Sylvain Gandon
Oct 2, 2012·Journal of Evolutionary Biology·Florence Débarre

Citations

Jan 17, 2016·Theoretical Population Biology·Florence Débarre, Sarah P Otto
Aug 5, 2014·Evolution; International Journal of Organic Evolution·Hannes SvardalMichael Doebeli
Nov 25, 2015·Journal of Theoretical Biology·Iaroslav IspolatovMichael Doebeli
Aug 6, 2015·The New Phytologist·Michael P SpeedMichael A Brockhurst
Jun 10, 2017·Nature Communications·Vincent CalcagnoPatrice David
Nov 22, 2017·Journal of Biological Dynamics·Benjamin J Ridenhour, Jerry R Ridenhour
Jul 6, 2019·Evolution; International Journal of Organic Evolution·Nathan W Bailey, Mathias Kölliker
Jan 30, 2015·Proceedings. Biological Sciences·Ailene MacPhersonScott L Nuismer
Jul 21, 2019·Evolution; International Journal of Organic Evolution·Charles Mullon, Laurent Lehmann
Dec 14, 2017·Evolution; International Journal of Organic Evolution·Artur Rego-CostaLuis-Miguel Chevin
Oct 24, 2019·Ecology and Evolution·Trey J Scott, David C Queller
Sep 25, 2020·Royal Society Open Science·Jan Martin NordbottenNils Chr Stenseth

Related Concepts

Adaptation, Physiological
Impacts, Environmental
Deviation, Epistatic
Biological Evolution
Genetics, Population
Selection, Genetic
Environment
Enzyme Stability
Epistasis, Genetic
Biological Evolution

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