In general, I work in theoretical evolutionary ecology. In particular, I'm investigating the sources and consequences of spatial heterogeneity.

Even though my training is in pure mathematics, I was a postdoctoral research associate in the Webb lab and with PRIMES, the program for interdisciplinary mathematics, ecology and statistics. This article in Nature says a tiny bit about how I came to be there.

- Using
network properties to predict disease dynamics on human contact
networks
Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

(Greg Ames, Dylan George, Christian Hampson, Andrew Kanarek, Cayla McBee, Dale Lockwood, Jeff Achter, and Colleen Webb, Proceedings of the Royal Society B,

**278**(1724), 3544-3550, 7 December 2011.) - Mixed dispersal strategies and response to
disturbance
How does selection mediated by a spatially spreading disturbance affect individual-level dispersal and population-level spatial structure? We investigate this question with a birth-death process on a lattice on which a spatially spreading disturbanvce is imposed. We consider three different scenarios affecting whether an individual recovers from disturbance, corresponding to different trade-offs between short- and long-distance dispersal. In addition to computer simulation, we use a pair approximation to conduct an invasion analysis of different dispersal strategies. Of the trade-offs we examined, only a context-sensitive recovery, in which an individual's survival probability is enhanced by the presence of weaker neighbors, results in a mixed dispersal strategy. (With Colleen Webb. Evolutionary Ecology Research,

**8**(8), December 2006, 1377-1392.) - Pair statistics clarify percolation properties of spatially explicit simulations
We consider a spatially-explicit birth death process in which organisms can reproduce using local dispersal, long-distance dispersal, or a mixed strategy. We approximate the system with a pair of equations which, empirically, does an excellent job of capturing the dynamics of the system. We use this approximation to view spatial aggregation through the lens of percolation theory. In doing so, we correct a pervasive mistake in the literature. (With Colleen Webb. Theoretical Population Biology,

**69**(2), March 2006, 155-164.) - Comment on "Families and clustering in a
natural numbers network"
This isn't, strictly speaking, a paper about biology; but my interest in complex networks is grounded in biology.

For a given natural number

*N*, Corso constructs a graph with vertices {2,...,*N*}. He analyzes this family of graphs with computer calculations; I get exact answers using number theory. (Physical Review E,**70**(5), 2004.)