Foraging to balance conflicting demands: novel insights from grasshoppers under predation risk

Animal foraging may be influenced by multiple demands simultaneously (e g., nutrient gain and predator avoidance). Conventional approaches to understand the trade-offs between these demands require cramming them in similar currencies, which is impractical in many field situations. We introduce a new method, called multiobjective programming, as a framework to explore how animals balance conflicting demands. Multiobjective programming allows one to explore the influence of foraging demands directly, without explicit assumptions about how they enter into fitness and without conversion to some common currency. Using multiobjective programming, we show that, as foraging demands change, animals may adaptively adjust their behavior, even if the constraints on feasible behavior are unaffected (contrary to the predictions of the conventional models). Hence, we may see a variable response in foraging that is consistent with adaptive behavior. We used an empirical test with herbivore grasshoppers and predator spiders to evaluate the utility of multiobjective programming Our experiments show that grasshoppers are able to optimally balance the foraging objectives of energy intake and vigilance under changing levels of predation risk. The multiobjective model is used both to evaluate the biological significance of the broad variation that was observed in the grasshoppers' foraging behavior and to quantify explicitly the trade-off between energy intake and predator avoidance.