The interaction between a consumer and its resource is the fundamental building block of community structure; understanding the nature of this interaction is essential to almost every aspect of community ecology. As a consequence, substantial theory exists for consumer resource interactions, and there is a large body of empirical research documenting significant impacts of consumers on vital rates of the resource. One of the latest challenges is to bridge empirical and theoretical research by constructing mathematical models that are tailored to specific systems. These models can synthesize a large body of empirical data, incorporating key life history strategies, to quantify the effect of consumers on resource growth rates, as well as aid management of both consumer and resource. My research synergistically combines theoretical and empirical work on consumer-resource interactions. I am providing modeling expertise when collaborating with empirical scientists and ecological expertise when collaborating with mathematicians. The majority of my work focuses on insect-plant systems to address these questions in basic and applied ecology.
My work spans four broad areas of inquiry:
- Quantifying biological drivers of plant and insect population dynamics: I use stage structured population models to identify key factors shaping population dynamics.
- Optimal decisions in behavior and life history: I use mathematical models predicting ultimate causes for the evolution of specific phenotypes.
- Transient dynamics: Traditional models focus on long term (stable) dynamics, and ignore the often dramatically different transient dynamics that happen before the system stabilizes. Recently there is an increasing recognition that frequent disturbances like fire or drought prevent populations in many systems from reaching stable dynamics.
- Population management: My research evaluates alternative perturbation methods for stage structured population models; perturbation analysis is the corner stone for identifying which life history stage should be targeted to most effectively reach ones management goal.