Contingent trade-off decisions with feedbacks in cyclical environments: testing alternative theories. (21st July 2020) - Railsback, Steven F
Abstract: Many animals make contingent decisions, such as when and where to feed, as trade-offs between growth and risk when these vary not only with activity and location but also 1) in cycles such as the daily light cycle and 2) with feedbacks due to competition. Theory can assume an individual decides whether and where to feed, at any point in the light cycle and under any new conditions, by predicting future conditions and maximizing an approximate measure of future fitness. We develop four such theories for stream trout and evaluate them by their ability to reproduce, in an individual-based model, seven patterns observed in real trout. The patterns concern how feeding in four circadian phases—dawn, day, dusk, and night—varies with predation risk, food availability, temperature, trout density, physical habitat, day length, and circadian cycles in food availability. We found that theory must consider the full circadian cycle: decisions at one phase must consider what happens in other phases. Three theories that do so could reproduce almost all the patterns, and their ability to let individuals adapt decisions over time produced higher average fitness than any fixed behavior cycle. Because individuals could adapt by selecting among habitat patches as well as activity, multiple behaviors produced similar fitness. Our most successful theories base selection of habitat and activity at each phase on memory of survival probabilities and growth rates experienced 1) in the three previous phases of the current day or 2) in each phase of several previous days. Abstract : Deciding where and when to feed or hide is a tough decision for animals—they must eat without getting eaten—and a tough decision to model. The decision is tougher when feeding success and predation risks depend on time of day and on how many other animals compete for feeding and hiding places. We can model such decisions well by assuming animals use simple predictions about future daily cycles and update decisions as conditions change.