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 in pathogen recognition. Here we have tested the arms-race model in Arabidopsis thaliana by analysing sequences flanking Rpm1, a gene conferring the ability to recognize Pseudomonas pathogens carrying AvrRpm1 or AvrB. We reject the arms-race hypothesis: resistance and susceptibility alleles at this locus have co-existed for millions of years. To account for the age of alleles and the relative levels of polymorphism within allelic classes, we use coalescence theory to model the long-term accumulation of nucleotide polymorphism in the context of the short-term ecological dynamics of disease resistance. This analysis supports a 'trench warfare' hypothesis, in which advances and retreats of resistance-allele frequency maintain variation for disease resistance as a dynamic polymorphism.

Most mathematical models of disease assume that transmission is linearly dependent on the densities of host and pathogen. Recent data for animal diseases, however, have cast doubt on this assumption, without assessing the usefulness of alternative models. In this article, we use a combination of laboratory dose-response experiments, field transmission experiments, and observations of naturally occurring populations to show that virus transmission in gypsy moths is a nonlinear function of virus density, apparently because of heterogeneity among individual gypsy moth larvae in their susceptibility to the virus. Dose-response experiments showed that larvae from a laboratory colony of gypsy moths are substantially less heterogeneous in their susceptibility to the virus than are larvae from feral populations, and field experiments showed that there is a more strongly nonlinear relationship between transmission and virus density for feral larvae than for lab larvae. This nonlinearity in transmission changes the dynamics of the virus in natural populations so that a model incorporating host heterogeneity in susceptibility to the virus gives a much better fit to data on virus dynamics from large-scale field plots than does a classical model that ignores host heterogeneity. Our results suggest that heterogeneity among individuals has important effects on the dynamics of disease in insects at several spatial and temporal scales and that heterogeneity in susceptibility may be of general importance in the ecology of disease.

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 of outbreaks. Field biologists counter that outbreaks occur when generalist predators fail, because predation in low-density defoliator populations is usually high enough to prevent outbreaks. Neither explanation is sufficient, however, because the time between outbreaks in the data is far more variable than in host-pathogen and host-parasitoid models, and far shorter than in generalist-predator models. Here we show that insect outbreaks can be explained by a model that includes both a generalist predator and a specialist pathogen. In this host-pathogen-predator model, stochasticity causes defoliator densities to fluctuate erratically between an equilibrium maintained by the predator, and cycles driven by the pathogen. Outbreaks in this model occur at long but irregular intervals, matching the data. Our results suggest that explanations of insect outbreaks must go beyond classical models to consider interactions among multiple species.

Although coevolution is complicated, in that the interacting species evolve in response to each other, such evolutionary dynamics are amenable to mathematical modeling. In this article, we briefly review models and data on coevolution between plants and the pathogens and herbivores that attack them. We focus on "arms races," in which trait values in the plant and its enemies escalate to more and more extreme values. Untested key assumptions in many of the models are the relationships between costs and benefits of resistance in the plant and the level of resistance, as well as how costs of virulence or detoxification ability in the enemy change with levels of these traits. A preliminary assessment of these assumptions finds only mixed support for the models. What is needed are models that are more closely tailored to particular plant-enemy interactions, as well as experiments that are expressly designed to test existing models.

No abstract

Myxoma virus was released into Australia to control the introduced European rabbit, Oryctolagus cuniculus. Within a few years after introduction, the virulence of the virus had declined to an intermediate level, while the resistance of field rabbits and increased sharply. In the nearly 40 yr since the disease was introduced, host resistance has continued to increase, while viral virulence has only recently begun to show signs of counter—increases in some areas. The two questions of interest are thus: Is this system in a coevolutionary arms race (Dawkins and Krebs 1979); that is, will both host and pathogen continue to evolve antagonistically? Will the virus continue to control the rabbit in the future? We present a simulation model based loosely on previous host–pathogen models (Anderson and May 1979), but with detailed accounting of the virus titer in infected hosts, and using realistic estimates of the demographic parameters of the rabbit, including age structure and seasonally varying reproduction. For a single virulence grade, by varying the non—disease (or "natural") mortality of the rabbit, the age at first reproduction of the rabbit, and the virulence grade of the virus, we explored the parameter range for which the rabbit population is controlled. For the most prevalent grades of the virus, grades IIIB and IV, the virus can control the rabbit for most realistic values of natural mortality and age at first reproduction. However, control is dependent on both natural mortality and virus virulence. Since natural mortality varies both geographically and seasonally, the usefulness of the virus may vary geographically and seasonally, and management policies must be sensitive to this variation. When competing against several virus strains that together encompass the complete range of virulence seen in the field, a strain of grade IV virulence competitively excludes strains of all other grades. This competitively dominant grade is close to the most prevalent virulence grades seen in the field. We discuss possible mechanisms of coexistence, including local competitive exclusion with global persistence, variability in host resistance, high mutation rates, and trade—offs between within—host and between—host competitive ability. By examining the effects of flea transmission efficiency, we are able to show that, contrary to commonly held belief, whatever effect fleas have upon the outcome of selection on virulence cannot be due to differences in transmission efficiency between fleas and mosquitoes. Finally, by including host resistance, we improve our prediction of the most prevalent grade of virulence. We conclude that control of the rabbit by the virus is likely for the near future, but that until we understand the genetics of resistance in the rabbit and the relationship between resistance and virulence for different grades of virulence, for different grades of virulence, we cannot make a useful prediction of the long—term state of this system.

The theory of insect population dynamics has shown that heterogeneity in natural-enemy attack rates is strongly stabilizing. We tested the usefulness of this theory for outbreaking insects, many of which are attacked by infectious pathogens. We measured heterogeneity among gypsy moth larvae in their risk of infection with a nucleopolyhedrovirus, which is effectively heterogeneity in the pathogen's attack rate. Our data show that heterogeneity in infection risk in this insect is so high that it leads to a stable equilibrium in the models, which is inconsistent with the outbreaks seen in North American gypsy moth populations. Our data further suggest that infection risk declines after epidemics, in turn suggesting that the model assumption of constant infection risk is incorrect. We therefore constructed an alternative model in which natural selection drives fluctuations in infection risk, leading to reductions after epidemics because of selection for resistance and increases after epidemics because of a cost of resistance. This model shows cycles even for high heterogeneity, and experiments confirm that infection risk is indeed heritable. The model is very general, and so we argue that natural selection for disease resistance may play a role in many insect outbreaks.

Although the importance of insect viruses in the population dynamics of their hosts is widely acknowledged, ecologists are still relatively ignorant of the factors determining the rate of transmission of insect viruses in the field. I performed a series of field experiments in which I investigated the transmission dynamics of the nuclear poly— hedrosis virus (NPV) of Douglas—fir tussock moth, Orgyia pseudotsugata (Lepidoptera: Lymantriidae), in northern Idaho, USA. In these experiments, I reared healthy and infected healthy larvae that became infected as a measure of transmission. I explored the influences of density, stage structure, and spatial structure on transmission by manipulating the density and stage distribution of healthy and infected hosts, and the spatial distribution of infected hosts. The experiments indicate that transmission is strongly affected by the densities of both healthy and infected hosts, but the effect depends on the instar of each. Late instars are both more infectious and more likely to become infected than are early instars, so that the NPV is more likely to spread in populations of late—instar tussock moth larvae. I also found that transmission is affected by the spatial distribution of infected hosts, and this effect also depends on the instar of healthy hosts. That is, transmission to healthy early instars decreases with increasing patchiness of infected hosts, but transmission to healthy late instars is essentially unaffected by patchiness. I discuss how these results can be in— terpreted in terms of behavioral differences among instars, and relate the results to the mathematical theory of disease and the use of viruses in biological pest control.