Virtual plankton: A novel approach to the investigation of aquatic predator-prey interactions

Small-scale zooplankton swimming behaviors can affect aquatic predator-prey interactions. Difficulties in controlling prey swimming behavior however, have restricted the ability to test hypotheses relating differences in small-scale swimming behavior to frequency of predation by fish We report here a Virtual Plankton (VP) system that circumvents this problem by allowing the observation of fish ''preying'' on computer-generated prey images whose size, shape, color and swimming behavior can be precisely controlled. Two experiments were performed in which bluegill sunfish (Lepomis macrochirus) were given a choice of either two VP images, one of which moved twice as fast as the other, or six VP, one of which moved either faster (1.25 x, 1.5 x or 2 x) or slower (0.5 x) than the other five. Current predator-prey models based on encounter probabilities and prey visibility predict that moving faster increases predation risk and conversely, moving slower decreases predation risk. In agreement with existing predator-prey models, in both experiments, fish chose faster moving VP significantly more often than their slower moving neighbors. Contrary to the predictions of existing models, in the second experiment with six VP, the rate at which fish chose a prey image moving half as fast as the five surrounding images did not differ significantly from the rate predicted by chance (1/6). These results suggest that current fish-zooplankton predation models would benefit by the incorporation of small-scale swimming behavior and assessments of its influence on overall prey visibility.