The population dynamical implications of covert infections in host–microparasite interactions

Abstract

Summary The persistence of parasites that are only seen in intermittent outbreaks has intrigued ecologists. Long the subject of speculation, there is now evidence that viral insect diseases can cause covert as well as overt infections. Typically, infection leads to overtly diseased individuals that are infectious and die as a result of the disease, but it can also produce other individuals that show a covert, non‐infectious pathology, which is typically asymptomatic. Covertly infected individuals do not die as a result of infection, although they may be affected sublethally such that their reproductive output falls, they may transmit the covert infection vertically and, crucially, they may convert at some rate to overt infectious individuals. In this way the parasites are able to persist in host populations and can be detected only by the use of molecular techniques. Here, we apply novel analytical techniques to a general host–parasite modelling framework and examine the population dynamical consequences of such covert infections. The interplay between various stabilizing and destabilizing forces leads to a highly complex pattern of host dynamics, including a theoretically intriguing pinch point. Covert infections may be stabilizing or destabilizing depending on the specific characteristics of the interaction, particularly in relation to the rate of conversion from covert to overt infection. In general terms, intermediate rates of conversion are stabilizing, while high and low rates may each destabilize the host population. In contrast, there is no consistent pattern for the role of vertical transmission because, in some cases, high levels are stabilizing while in others they are destabilizing. Thus, our results demonstrate that relatively subtle pathogen effects such as covert infections can have important and novel dynamical impacts on the host–pathogen interaction. It also suggests that the effects of covert infection are likely to be system‐specific and information may be required on a system‐by‐system basis in order to make predictions.