Classical models of consumer-resource matching typically assume that individuals are either omniscient (e.g., most optimal foraging theory) or that they base movement decisions on purely local information (e.g., most spatial population models). Real biological organisms fall between these extremes. That is, individuals can often benefit by making movement decisions based on environmental conditions extending beyond their immediate location, but typically lack spatially complete information about those conditions. Behaviorists have worked on this topic for decades leading to recent papers linking perception to resource acquisition and the delineation of perceptual ranges. In this talk, I will bring together ideas from several different fields to explore how foragers can exploit non-local information to improve their spatiotemporal matching to resource peaks in dynamic (rather than static) landscapes. This process is mathematized using a continuous time / continuous space model in which organisms have explicit perceptual ranges, which they rely on to obtain information on the ‘non-local resource gradient’ in their vicinity. For foragers that move using both random (diffusive) movement and resource-following (advective) movement, we then characterize the optimal detection scales for foragers in different dynamic landscapes and with different perceptual abilities.
Results/Conclusions
We find that non-local information can be highly beneficial, increasing the spatiotemporal match between foragers and their resources up to twofold compared to movement based on purely local information. Our results further demonstrate that the spatial scale over which non-local information should be acquired depends on the foragers’ dispersal abilities. Specifically, non-local information is only useful when foragers possess both high advective movement (allowing them to react to transient resources) and low diffusive movement (preventing them from drifting away from resource peaks). Non-local information is particularly beneficial in landscapes with sharp (rather than gradual) patch edges and in landscapes with highly transient resources. This modeling work complements lessons from classical optimal foraging theory, providing a mathematical framework in which the behavioral concept of perceptual range can be represented explicitly. Future extensions will allow for even more increased biological realism at the interface of perception and movement.