Literature DB >> 24433364

Chaos and unpredictability in evolution.

Michael Doebeli1, Iaroslav Ispolatov.   

Abstract

The possibility of complicated dynamic behavior driven by nonlinear feedbacks in dynamical systems has revolutionized science in the latter part of the last century. Yet despite examples of complicated frequency dynamics, the possibility of long-term evolutionary chaos is rarely considered. The concept of "survival of the fittest" is central to much evolutionary thinking and embodies a perspective of evolution as a directional optimization process exhibiting simple, predictable dynamics. This perspective is adequate for simple scenarios, when frequency-independent selection acts on scalar phenotypes. However, in most organisms many phenotypic properties combine in complicated ways to determine ecological interactions, and hence frequency-dependent selection. Therefore, it is natural to consider models for evolutionary dynamics generated by frequency-dependent selection acting simultaneously on many different phenotypes. Here we show that complicated, chaotic dynamics of long-term evolutionary trajectories in phenotype space is very common in a large class of such models when the dimension of phenotype space is large, and when there are selective interactions between the phenotypic components. Our results suggest that the perspective of evolution as a process with simple, predictable dynamics covers only a small fragment of long-term evolution.
© 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Keywords:  Adaptive dynamics; chaos; complex dynamics; high-dimensional phenotype space; logistic competition models

Mesh:

Year:  2014        PMID: 24433364     DOI: 10.1111/evo.12354

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  12 in total

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2.  Epistasis can accelerate adaptive diversification in haploid asexual populations.

Authors:  Cortland K Griswold
Journal:  Proc Biol Sci       Date:  2015-03-07       Impact factor: 5.349

3.  Predicting metabolic adaptation from networks of mutational paths.

Authors:  Christos Josephides; Peter S Swain
Journal:  Nat Commun       Date:  2017-09-25       Impact factor: 14.919

4.  Chaos is not rare in natural ecosystems.

Authors:  Tanya L Rogers; Bethany J Johnson; Stephan B Munch
Journal:  Nat Ecol Evol       Date:  2022-06-27       Impact factor: 19.100

5.  Chaos and the (un)predictability of evolution in a changing environment.

Authors:  Artur Rego-Costa; Florence Débarre; Luis-Miguel Chevin
Journal:  Evolution       Date:  2018-01-10       Impact factor: 3.694

6.  The ecological forecast horizon, and examples of its uses and determinants.

Authors:  Owen L Petchey; Mikael Pontarp; Thomas M Massie; Sonia Kéfi; Arpat Ozgul; Maja Weilenmann; Gian Marco Palamara; Florian Altermatt; Blake Matthews; Jonathan M Levine; Dylan Z Childs; Brian J McGill; Michael E Schaepman; Bernhard Schmid; Piet Spaak; Andrew P Beckerman; Frank Pennekamp; Ian S Pearse
Journal:  Ecol Lett       Date:  2015-05-07       Impact factor: 9.492

7.  Adaptive introgression from distant Caribbean islands contributed to the diversification of a microendemic adaptive radiation of trophic specialist pupfishes.

Authors:  Emilie J Richards; Christopher H Martin
Journal:  PLoS Genet       Date:  2017-08-10       Impact factor: 5.917

8.  Towards a mechanistic foundation of evolutionary theory.

Authors:  Michael Doebeli; Yaroslav Ispolatov; Burt Simon
Journal:  Elife       Date:  2017-02-15       Impact factor: 8.140

9.  Chaos in high-dimensional dissipative dynamical systems.

Authors:  Iaroslav Ispolatov; Vaibhav Madhok; Sebastian Allende; Michael Doebeli
Journal:  Sci Rep       Date:  2015-07-30       Impact factor: 4.379

10.  A phase transition induces chaos in a predator-prey ecosystem with a dynamic fitness landscape.

Authors:  William Gilpin; Marcus W Feldman
Journal:  PLoS Comput Biol       Date:  2017-07-05       Impact factor: 4.475

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