Foraging challenges: Unsuitable prey and limited information

Food acquisition is a complicated task. The profitability of potential food items depends on numerous factors, including the spatial distribution, probability of detection and capture, and suitability of the food. Animals faced with such challenges can use relatively simple mechanisms to maximize foraging efficiency. However, mechanisms that maximize foraging efficiency under some ecological conditions (e.g., prey scarcity) may produce ostensibly suboptimal behavior under different ecological conditions (e.g., prey abundance). In the work presented here, we explore two facets of foraging: (1) consuming unsuitable prey, and (2) searching for resources with limited information about resource location. To explore the consequences of consuming unsuitable prey on predator behavior, we first measured the suitability of two aphid species, black bean aphids and pea aphids, for a native predatory insect, the convergent ladybird beetle. Ladybird larvae had lower larval survival, longer developmental times, and lower adult weights on a diet of bean than pea aphids. We found that ladybird larvae killed bean aphids even if pea aphids were abundant, presumably because bean aphids were easier to capture than the pea aphids. Consumption of even a single bean aphid had pronounced short-term (\u3c 1 day) effects on predator behavior. Ladybird larvae had longer handling times, longer bouts of inactivity, shorter bouts of intensive search, and lower patch-leaving tendencies after eating a bean aphid than after eating a pea aphid. The general lethargy from eating bean aphids may reduce the foraging efficiency of ladybird larvae. We built a simulation model to explore the performance of composite search strategies on landscapes where resource distributions ranged from dispersed to clumped. The search strategies involved switching between intensive and extensive modes based on either resource encounters or sensory cues. We found that the search strategy based on sensory cues outperformed the search strategy based on resource encounters across all resource distributions and was more robust to changes in the resource distribution