Dispersal of optimal foragers in a patchy environment: Simulations with a mathematical model and tests of predictions in field experiments

To predict the effect of density on the dispersal of foraging parasitoids, we developed a spatially explicit individual-based model in which parasitoids move among host patches at random but use an optimal decision-rule about when to leave these patches. We used a simple decision rule where an individual forager exploits a patch as long as the instantaneous attack rate is higher than the average attack rate it has experienced in the environment. Such a rule implies that foragers compute and remember attack rates as they forage. Simulations with different combinations of patch distribution, host density, mutual interference, and parasitoid density predicted that dispersal distance should increase with parasitoid density. To test this prediction, we used data from two field experiments in which we released either few or many adults of the aphid parasitoid Aphelinus asychis in replicated sites, and subsequently assessed dispersal from the spatial distribution of parasitoid offspring. In the first experiment, we did not to detect a relation between the number released and dispersal distance, but in the second experiment, dispersal distance increased with initial density, as predicted by our model. We propose two hypotheses to explain the discrepancy between the experiments. Different levels of environmental variability among experiments, resulting from differences in experimental designs, could cause differences in statistical conclusions. However, there could be a threshold density below which dispersal is not density-dependent, and this threshold may have been exceeded in the second experiment where large releases involved many more individuals than in the first experiment. In any case, our approach linked individual behavior and spatial distribution, and our results show that the insect distributions in the field can be predicted, qualitatively, from theory about individual behavior.