Hierarchical movement decisions in predators: effects of foraging experience at more than one spatial and temporal scale.

Animal movements in heterogeneous environments play a crucial role in a variety of ecological processes. Although a hierarchical structure to the scale of movements has been observed in many animal species, few studies to date have revealed what causes such multi-spatial scale movements within the framework of optimal theories. Using detailed information on movement paths and prey captures by Intermediate Egrets in rice fields, this study explored the effects of individual experience at multi-temporal scales on subsequent movement parameters. The results supported three predictions based on optimal foraging theories: (1) movement distances at small and large spatial scales are determined by past foraging experiences over short (one minute) and long (seven to eight minutes) temporal scales, respectively; (2) responses to prey encounters vary between temporal scales, e.g., prey attacks in the previous one minute caused egrets to walk away (i.e., area-avoided search), whereas those in the preceding eight minutes caused egrets to stay around (i.e., area-restricted search); and (3) the probability of patch departure by flying increases with decreasing intake rates in the previous seven minutes, and the quitting intake rate is mostly lower than the long-term average, not including travel time. These results suggest that egrets make different adaptive decisions at different spatial scales: in response to a clumped prey distribution at a large spatial scale, and to a dispersed distribution or behavioral depression of prey at a small spatial scale. This study showed that the framework of optimal foraging theories is useful for comprehensively explaining the different movement modes in animals. It should also be emphasized that considering both area-avoided and restricted searches at multi-temporal scales is important for understanding movement decisions, particularly in organisms searching for hierarchically distributed resources.