Kevin
Gross
PhD 2003
Advisor: Tony Ives / Rick Nordheim
Ph.D. Thesis: The aphid, the wasp, adn the matrix: aspects
of modeling hostparasitoid and singlespecies dynamics
In southern Wisconsin, pea aphids (Acyrthosiphon pisum) in alfalfa
provide a model system for studying biological control. Aphid populations
appear to be regulated by a suite of natural enemies, including
the specialist parasitoid wasp Aphidius ervi and several species
of generalist predators (e.g., coccinellids, nabids). This dissertation
contains four projects motivated by the population regulation of
pea aphids.
Chapter 1 proposes a method for estimating timevarying vital rates
from observational timeseries data. The model is used to analyze
monitoring data from the pea aphid system, with the goal of understanding
how parasitism impacts aphid dynamics. We find that although parasitism
reduces aphid population growth rates substantially, this reduction
is not density dependent, suggesting that the decreases in aphid
densities observed in some cutting cycles are not the beginning
of hostparasitoid cycles. In a larger context, this method provides
a way to estimate the fluctuations in vital rates that produced
observed dynamics, without requiring strong assumptions about dynamic
feedback.
Chapters 2 and 3 study aspects of demographic matrix models, common
tools for analyzing singlespecies dynamics. Chapter 2 uses Bayesian
techniques to estimate the parameters in a demographic matrix model
from time series of stagespecific abundances. We use the method
to analyze pea aphid data collected by previous researchers, before
Aphidius ervi had established widely. Chapter 3 derives an approximation
for the sampling variance in population growth rates estimated from
a matrix model, as a function of the number of individuals monitored
in each stage. This approximation can be used to design more efficient
data collection protocols.
Chapter 4 reexamines the practice of using the heterogeneity in
parasitism among host patches to infer stabilizing aggregation of
risk among hosts. The project shows that multiple parasitoid foraging
behaviors can produce aggregated patterns of observed parasitism,
but not all of these behaviors generate the aggregation of risk
that stabilizes hostparasitoid dynamics.
