Toward the Quantification of Predation with Predator Gut Immunoassays: A New Approach Integrating Functional Response Behavior

Abstract

Abstract Immunological methods have been widely used to identify key predator species and qualitatively evaluate predation of target prey. However, despite the quantitative nature of many immunoassays, the translation to number of prey attacked has been problematic because of the many factors that confound interpretation of the strength of the immunoassay response. We developed a new predation model that couples the proportion of predators positive for prey remains determined by enzyme-linked immunosorbent assay (ELISA), predator density, and predator functional response to prey density for estimating total prey attacked. We used single cotton plant arenas in the greenhouse to develop functional response models for two generalist predators, Geocoris punctipes (Say) and Orius insidiosus (Say), preying on Pectinophora gossypiella (Saunders) eggs. The model was validated and compared with other immunologically based predation models in multiple plant/multiple predator arenas. Our predation model was relatively accurate in predicting the total number of prey attacked by both predator species and was a significant improvement over previous models that rely on simple assumptions regarding predator attack rates. The model also improves the predictive capacity of the functional response model alone by correcting for the number of predators actually consuming prey. Sensitivity analyses indicated that model performance was most sensitive to accurate measurement of input variables such as temperature and the proportion of individuals positive for prey antigens by ELISA and less sensitive to changes in estimates of prey density. Accurate estimation of the functional response parameters is also important, especially for the behavioral parameter defining the decline in plant leaf area searched with increases in prey density. Limitations of the model and application to the field are discussed.