Evol Ecol Res 10: 655-666 (2008) Full PDF if your library subscribes.
Even in the odd cases when evolution optimizes, unrelated population dynamical details may shine through in the ESS
J.A.J. Metz,1,2,3* S.D. Mylius4 and O. Diekmann5
1Institute of Biology and Mathematical Institute, Section of Theoretical Biology, Leiden University, Leiden, Netherlands, 2International Institute for Applied Systems Analysis, Evolution and Ecology Program, Laxenburg, Austria, 3Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland, 4RIVM, National Institute for Public Health and the Environment, Expertise Centre for Methodology and Information Services, Bilthoven, Netherlands and 5Department of Mathematics, University of Utrecht, Utrecht, Netherlands
Address all correspondence to J.A.J. Metz, Institute of Biology, Leiden University, PO Box 9561, 2300 RA Leiden, Netherlands.
Aim: To elucidate the role of the eco-evolutionary feedback loop in determining evolutionarily stable life histories, with particular reference to the methodological status of the optimization procedures of classical evolutionary ecology.
Key assumptions: The fitness ρ of a type depends both on its strategy X and on the environment E, ρ = ρ(X, E ), where E comprises everything, biotic and abiotic, outside an individual that may influence its population dynamically relevant behaviour. Through the community dynamics, this environment is determined (up to non-evolving external drivers) by the resident strategy Xr: E = Eattr(Xr).
Procedures: Use the ideas developed in the companion paper (Metz et al., 2008) to rig simply analysable – as they have an optimization principle – eco-evolutionary scenarios to explore the potential of the environmental feedback to influence evolutionary predictions, and to determine in what ways the predictions relate to the tools.
Results: Equipping the classical model for the evolution of maturation time with various possible feedback loops leads to different optimization principles as well as qualitatively different predicted relations between the field values of adult mortality µA and maturation time T. When E influences only T, the ESS, T *, decreases with µA. When E influences juvenile mortality only or both juvenile and adult mortality in equal measure, T * increases with µA. When E influences the reproduction rate only, T * is independent of µA. When E influences adult mortality only, the environmental feedback loop fixes adult mortality at a constant level so that there is no relationship between T * and µA to speak of. These six cases are subject to three different optimization principles. There turns out to be no relationship between an optimization principle and its predicted features.
Conclusions: Even in cases where an optimization principle exists, the evolutionary outcomes can be largely determined by other aspects of the population dynamical embedding. The existence of an optimization principle is technically helpful, biologically very restrictive, and has in general no further biological relevance.
Keywords: eco-evolutionary feedback, evolutionary optimization, life-history theory, maturation age.
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