Evolutionary optimization and neural network models of behavior

Local orientation and the evolution of foraging: changes in decision making can eliminate evolutionary trade-offs

Information processing is a major aspect of the evolution of animal behavior. In foraging, responsiveness to local feeding opportunities can generate patterns of behavior which reflect or "recognize patterns" in the environment beyond the perception of individuals. Theory on the evolution of behavior generally neglects such opportunity-based adaptation. Using a spatial individual-based model we study the role of opportunity-based adaptation in the evolution of foraging, and how it depends on local decision making. We compare two model variants which differ in the individual decision making that can evolve (restricted and extended model), and study the evolution of simple foraging behavior in environments where food is distributed either uniformly or in patches. We find that opportunity-based adaptation and the pattern recognition it generates, plays an important role in foraging success, particularly in patchy environments where one of the main challenges is "staying in patches". In the restricted model this is achieved by genetic adaptation of move and search behavior, in light of a trade-off on within- and between-patch behavior. In the extended model this trade-off does not arise because decision making capabilities allow for differentiated behavioral patterns. As a consequence, it becomes possible for properties of movement to be specialized for detection of patches with more food, a larger scale information processing not present in the restricted model. Our results show that changes in decision making abilities can alter what kinds of pattern recognition are possible, eliminate an evolutionary trade-off and change the adaptive landscape.

Human birthweight evolution across contrasting environments

We explore from both theoretical and empirical perspectives the hypothesis that a significant part of the worldwide variability in human birthweight results from adaptive responses to local selective pressures. We first developed an agent-based model to simulate the process of evolutionary selection on life history strategy, and then we performed a comparative analysis across 89 countries worldwide. The model illustrates that optimal birthweight depends on which fitness-reducing risk locally predominates (somatic diseases, parasitic diseases or adverse environmental conditions). When fitness variations between individuals mainly result from somatic diseases (e.g. industrialized countries), or conversely from infectious and parasitic diseases (e.g. developing countries), selection is expected to favour individuals producing larger children. Conversely, when environmental risks increase in relative importance, selective pressures for producing children with high birthweight are reduced. The comparative analysis supports these theoretical expectations, in particular the finding that birthweight is higher than predicted in highly parasitized countries.