Abstract

Estimations of invertebrate predation parameters usually rely on behavioral observations and on feeding rates in single, arbitrary prey densities. In this study, predation parameters were estimated directly from functional response data. This type of analysis relies on many prey densities and therefore should lead to more widely applicable parameter values. Functional response curves of third—and fourth—instar Chaoborus americanus preying on five size—classes of Daphnia pulex in the laboratory were examined. Data for each size—class were fitted to Rogers' random predator equation, a modification of Holling's type II functional response equation. The data fit the model well. The parameters a' (instantaneous attack rate) and Th (handling time) were calculated for each prey size from these curves. Attack rate reached a maximum, indicating highest vulnerability, at an intermediate size. Deduced handling times were greater than simple observation would suggest and were highly correlated with prey dry mass, suggesting digestion as the primary component. Results of mixed—size—class experiments were compared to predictions based on the parameters from single—size—class experiments. The ratio of predation—caused mortality rates for prey size—classes was consistent with the predictions, suggesting that no active behavioral selection was occurring. Feeding rates in mixed—sized—class experiments were underestimated when experiments were run a month later than the single—size—class experiments during the 1st yr of this study. However, when single— and mixed—size—class experiments were run simultaneously during the 2nd yr, predictions were very close to observed feeding rates. This indicates the possibility that predator age and seasonal physiological changes affect functional response parameters. Attack rate increased with temperature, probably due to increased encounter rates as Daphnia swimming speed increased. Handling time decreased with increased temperature, probably due to increased digestive rate. The biological significance of Holling's parameters and those of other more behaviorally based models is discussed.

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