Risk landscapes such as the landscape of fear or disgust, describe either the perceived risk-reward trade-offs present in an animal’s environment or a pattern of behavioral responses to an environment with measurable risk-reward trade-offs/ However, analyzing a risk landscape in a natural system is exceedingly difficult. First, significant effort is required to track natural animal movements or behavior. Second, sampling the relative risk-reward trade-offs within that environment at high resolution is exhaustive and requires empirical justification of how that trade-off is measured. Third, determining perceived risk and reward in natural systems can only be inferred by behavior. Fourth, while several examples have demonstrated perceived risk can drive behavioral response, a lack of response is ambiguous; was a risk ignored or overlooked? As a result, landscapes of fear are inferred by correlation (via animal presence and absence, giving up densities, etc) rather than explained by mechanism.
We (and others) suggest that perceptual landscapes in movement ecology would benefit from a stronger theoretical base. Previous work has shown that individual-based models that explicitly account for nervous system, physiological, and environmental dynamics can be used as a framework for studying the perceptual trade-offs that underlie adaptive movement behavior by real organisms. We generate solutions to a given trade-off scenario using genetic algorithms that shape the organism-environment coupling via altering parameters of the model nervous system. Evolutionary approaches of this kind help avoid assumptions about what the optimal solution to a task should look like, instead exploring the full space of possible solutions afforded by the prescribed task.
Going forward, we intend to employ this approach to create a more robust theory underlying the concept of perceptual landscapes that become popular in contemporary ecology. Ongoing work includes (1) exploring how signal reliability in time and space alters perceptual trade-offs and movement behavior, (2) exploring how different perceptual modalities shape perceptual trade-offs and movement behavior, and (3) applying field methods from telemetry studies to our models to bring the developing theory closer to empirical practice.
Investigators
Eden Forbes
Collaborators
Allegra Love, University of Guelph; (Randall Beer, Indiana University?)
Resources
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