Evolution of condition-dependent dispersal: A genetic-algorithm search for the ESS reaction norm.

摘要

Condition-dependent dispersal of winged and wingless insect offspring was studied. The model is also applicable to annual plants that produce two types of seeds differing in dispersal rates. The model assumptions are: the population is composed of anumber of sites each occupied by a single adu and the total number of offspring produced depends on the environmental quality of the site that varies over the years and between sites. The evolutionarily stable state (ESS) fraction of dispersing typeas a function of the quality of the habitat (or ESS reaction norm) states that dispersers should not be produced if habitat quality m is smaller than a critical value k. If m is larger than k, the number of dispersers should increase with m and that of nondispersers should be kept constant. Second, an alternative way of searching for the ESS was developed: the reaction norm is represented as a three-layered neural network, and the parameters (weights and biases) are chosen by genetic algorithm. This method can be extended easily to the cases of multiple environmental factors. There was an optimal (relatively wide) range of mutation rates for weights and biases, outside of which the convergence of the network to the valid ESS was likely to fail. Recombination, or crossing-over, was not effective in improving the success rate. The learned network often showed several characteristic ways of deviation from the ESS. The case in which the quality of different sites was correlated was also examined. In thiscase the ESS fraction of dispersers increased both with the quality of the site and with the average quality of the whole population in that year.