Information cues in the physical environment have taken on an unprecedented role in contemporary ecological analysis. Their biological processing and triggered responses together make up the pipeline that connects individual behavior to population structure. Referred to by Maynard Smith as the defining feature of biological systems, information, under rigorous consideration, allows the unification of multiple scales of biological functions and interactions. Here, we generate numerical solutions that specifically assess the significance of environmental information in the context of spatial ecology. Building upon modern mechanistic home range models, we approximate the space use patterns and subsequent ecological consequences of N-body systems in which the constituents move according to information noise. From the result, we ultimately suggest that actively acquired ecological information encodes behavior-based algorithms that convert environmental conditions to spatio-temporal dynamics at the population level. To determine the effect of information in a typical, exogenously-driven ecological system, we refer to recent developments in behavioral syndrome (behavioral correlation across situations), in particular, the idea that uncertainty in predation risk differentiates foraging intensity within conspecifics of different state variables. We re-frame the previous model in a spatial context, focusing on the frequency distribution of home-range sizes reported for coyotes, red foxes, and wolves populations. A generalized form of noise-dependent spatial dynamics is then analytically formulated using advection-diffusion equations before simulation within a two-dimensional, radially symmetric landscape of “zero-at-infinity” boundary conditions.
Results/Conclusions
The results show that selective pressure on transient dispersal can dichotomize the species' collective space-use pattern, converging on some individuals being significantly more sedentary than others in response to the reliability of environmental information. From there, we explore the results' ecological consequence through predator-prey interactions under seasonal cues. This step is done by first introducing an additional variable of predator density whose values are likewise seasonally determined. We then borrow first-passage time models from statistical physics to translate relocation distances into duration of exposure to predation, thereby ultimately bridging periodic information noise with long-term viability of the population. These interactive processes can easily generate complex dynamics that in turn may reshape our ecological understanding.