Extinction rates of established spatial populations

This paper deals with extinction of an isolated population caused by intrinsic noise. We model the population dynamics in a "refuge" as a Markov process which involves births and deaths on discrete lattice sites and random migrations between neighboring sites. In extinction scenario I the zero population size is a repelling fixed point of the on-site deterministic dynamics. In extinction scenario II the zero population size is an attracting fixed point, corresponding to what is known in ecology as Allee effect. Assuming a large population size, we develop WKB (Wentzel-Kramers-Brillouin) approximation to the master equation. The resulting Hamilton's equations encode the most probable path of the population toward extinction and the mean time to extinction. In the fast-migration limit these equations coincide, up to a canonical transformation, with those obtained, in a different way, by Elgart and Kamenev (2004). We classify possible regimes of population extinction with and without an Allee effect and for different types of refuge and solve several examples analytically and numerically. For a very strong Allee effect the extinction problem can be mapped into the over-damped limit of theory of homogeneous nucleation due to Langer (1969). In this regime, and for very long systems, we predict an optimal refuge size that maximizes the mean time to extinction.Comment: 26 pages including 3 appendices, 16 figure

Myths and Legends of the Baldwin Effect

This position paper argues that the Baldwin effect is widely misunderstood by the evolutionary computation community. The misunderstandings appear to fall into two general categories. Firstly, it is commonly believed that the Baldwin effect is concerned with the synergy that results when there is an evolving population of learning individuals. This is only half of the story. The full story is more complicated and more interesting. The Baldwin effect is concerned with the costs and benefits of lifetime learning by individuals in an evolving population. Several researchers have focussed exclusively on the benefits, but there is much to be gained from attention to the costs. This paper explains the two sides of the story and enumerates ten of the costs and benefits of lifetime learning by individuals in an evolving population. Secondly, there is a cluster of misunderstandings about the relationship between the Baldwin effect and Lamarckian inheritance of acquired characteristics. The Baldwin effect is not Lamarckian. A Lamarckian algorithm is not better for most evolutionary computing problems than a Baldwinian algorithm. Finally, Lamarckian inheritance is not a better model of memetic (cultural) evolution than the Baldwin effect