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 p of a type depends both on its strategy X and on the environment E, p = p(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 sub(r): E = E sub(attt)(X sub(r)). Procedures: Use the ideas developed in the companion paper (Metz et at., 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 sub(A) and maturation time T. When E influences only T, the ESS, T*, decreases with mu sub(A). When E influences juvenile mortality only or both juvenile and adult mortality in equal measure, T* increases with mu sub(A). When E influences the reproduction rate only, T* is independent of mu sub(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 sub(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. 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.