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We present an individual-based model that uses artificial evolution to predict fit behavior and life-history traits on the basis of environmental data and organism physiology. Our main purpose is to investigate whether artificial evolution is a suitable tool for studying life history and behavior of real biological organisms. The evolutionary adaptation is founded on a genetic algorithm that searches for improved solutions to the traits under scrutiny. From the genetic algorithm's "genetic code," behavior is determined using an artificial neural network. The marine planktivorous fish Müller's pearlside (Maurolicus muelleri) is used as the model organism because of the broad knowledge of its behavior and life history, by which the model's performance is evaluated. The model adapts three traits: habitat choice, energy allocation, and spawning strategy. We present one simulation with, and one without, stochastic juvenile survival. Spawning pattern, longevity, and energy allocation are the life-history traits most affected by stochastic juvenile survival. Predicted behavior is in good agreement with field observations and with previous modeling results, validating the usefulness of the presented model in particular and artificial evolution in ecological modeling in general. The advantages, possibilities, and limitations of this modeling approach are further discussed.

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The search strategies dispersers employ to search for new habitat patches affect individuals' search success and subsequently landscape connectivity and metapopulation viability. Some evidence indicates that individuals within the same species may display a variety of behavioural patch searching strategies rather than one species-specific strategy. This may result from landscape heterogeneity. We modelled the evolution of individual patch searching strategies in different landscapes. Specifically, we analysed whether evolution can favour different, co-existing, behavioural search strategies within one population and to what extent this coexistence of multiple strategies was dependent on landscape configuration. Using an individual-based simulation model, we studied the evolution of patch searching strategies in three different landscape configurations: uniform, random and clumped. We found that landscape configuration strongly influenced the evolved search strategy. In uniform landscapes, one fixed search strategy evolved for the entire spatially structured population, while in random and clumped landscapes, a set of different search strategies emerged. The coexistence of several search strategies also strongly depended on the dispersal mortality. We show that our result can affect landscape connectivity and metapopulation dynamics.