When Does Evolution Optimise?

Goal: Elucidating the role of the eco-evolutionary feedback loop in determining evolutionarily stable life histories, with particular reference to the methodological status of the optimisation procedures of classical evolutionary ecology. Conclusion: A pure optimisation approach holds water only when the eco-evolutionary feedbacks are of a particularly simple kind

When Does Evolution Optimize? On the Relation Between Types of Density Dependence and Evolutionarily Stable Life History Parameters

In this paper we (i) put forward a simple notational device clarifying the, undeniable but generally ignored, role of density dependence in determining evolutionarily stable life histories, (ii) use this device to derive necessary and sufficient conditions for (a) the existence of an evolutionary extremization principle, and (b) the reduction of such a principle to straight r- or RO-maximization, (iii) use the latter results to analyze a simple concrete example showing that the details of the population dynamical embedding may influence our evolutionary predictions to an unexpected extent

When does evolution optimize?

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. Conclusion: A pure optimization approach holds water only when the eco-evolutionary feedbacks are of a particularly simple kind

Even in the odd cases when evolution optimizes, unrelated population dynamical details may shine through in the ESS

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 rho of a type depends both on its strategy X and on the environment E, rho = rho(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 X_r: E = E_attr (X_r). 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 mu_A and maturation time T. When E influences only T, the ESS, T*, decreases with mu_A. When E influences juvenile mortality only or both juvenile and adult mortality in equal measure, T* increases with mu_A. When E influences the reproduction rate only, T* is independent of mu_A. 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 mu_A 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