Thursday, August 6, 2009: 3:40 PM
Brazos, Albuquerque Convention Center
Michael McCoy, Department of Biology, East Carolina University, Greenville, NC, James R. Vonesh, Department of Biology, Virginia Commonwealth University, Richmond, VA, Karen M. Warkentin, Department of Biology, Boston University, Boston, MA and Ben Bolker, Department of Zoology, University of Florida, Gainesville, FL
Background/Question/Methods Consumer - resource/predator - prey interactions defines trophic structure in food webs. Although consumption rates for predators are often nonlinear functions of prey density (i.e., functional response) or prey phenotype (e.g., size, size structure, inducible defenses), the design of many predator-prey experiments are often insufficient to characterize the functional form of predation with respect to these factors. Indeed, the failure to appreciate the importance of quantifying functional forms of consumer resource interactions has been a major disjunct between empirical and theoretical studies. In this talk we focus on the design and analysis of response surface experiments as an alternative to more traditional approaches and for developing a more complete understanding of the context dependence of consumer - resource interactions.
Results/Conclusions
We illustrate this approach with data from two empirical studies. In the first we quantify predation rates as a function of prey size, density and predator-induced phenotype to better understand the context-dependence of the consequences and trade-offs associated with phenotypically plastic responses to predators. We show that predator induced plasticity in response to two different, but functionally similar, predators improves prey survival by changing the size-refuge effect and by differentially affecting the shapes of the predators’ functional responses. In the second example, we evaluate the consequences of cohort size structure and predator behavior on predation rates. We show that cohort size structure can have important effects on predation rates not predicted by homogeneous cohort experiments. In both examples, using a response surface approach allows us to attain a more comprehensive understanding of consumer – resource interactions and to estimate parameters that enable us to better elucidate and compare the mechanisms driving rates of consumption, providing a natural link to theoretical models.
