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The co-evolutionary 'arms race' is a widely accepted model for the evolution of host-pathogen interactions. This model predicts that variation for disease resistance will be transient, and that host populations generally will be monomorphic at disease-resistance (R-gene) loci. However, plant populations show considerable polymorphism at R-gene loci involved(More)
The economic damage caused by episodic outbreaks of forest-defoliating insects has spurred much research, yet why such outbreaks occur remains unclear. Theoretical biologists argue that outbreaks are driven by specialist pathogens or parasitoids, because host-pathogen and host-parasitoid models show large-amplitude, long-period cycles resembling time series(More)
Many mobile organisms exhibit resource-dependent movement in which movement rates adjust to changes in local resource densities through changes in either the probability of moving or the distance moved. Such changes may have important consequences for invasions because reductions in resources behind an invasion front may cause higher dispersal while(More)
Estimates of a disease's basic reproductive rate R0 play a central role in understanding outbreaks and planning intervention strategies. In many calculations of R0, a simplifying assumption is that different host populations have effectively identical transmission rates. This assumption can lead to an underestimate of the overall uncertainty associated with(More)
Stochastic models are of increasing importance in ecology but are usually only applied to observational data. Here we use a stochastic population model to combine experimental and observational data to understand the colonization of old fields by monarch butterflies Danaus plexippus. We experimentally tested for density dependence in oviposition rates when(More)
In many forest insects, subpopulations fluctuate concurrently across large geographical areas, a phenomenon known as population synchrony. Because of the large spatial scales involved, empirical tests to identify the causes of synchrony are often impractical. Simple models are, therefore, a useful aid to understanding, but data often seem to contradict(More)
When Markov chain Monte Carlo (MCMC) algorithms are used with complex mechanistic models, convergence times are often severely compromised by poor mixing rates and a lack of computational power. Methods such as adaptive algorithms have been developed to improve mixing, but these algorithms are typically highly sophisticated, both mathematically and(More)
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