Most hosts, including humans, are simultaneously or sequentially infected with several parasites. A key question is whether patterns of coinfection arise because infection by one parasite species affects susceptibility to others or because of inherent differences between hosts. We used timeseries data from individual hosts in natural populations to analyze patterns of infection risk for a microparasite community, detecting large positive and negative effects of other infections. Patterns remain once variations in host susceptibility and exposure are accounted for. Indeed, effects are typically of greater magnitude, and explain more variation in infection risk, than the effects associated with host and environmental factors more commonly considered in disease studies. We highlight the danger of mistaken inference when considering parasite species in isolation rather than parasite communities.Macroparasites (helminths and arthropods) and microparasites (viruses, bacteria, and protozoa) are integral components of the ecological communities that include their hosts (1), and it is likely that most hosts, most of the time, are infected with more than one parasite species (2). Interactions between parasites in natural populations, however, have been studied only rarely. A community ecology perspective is particularly relevant for studies of coinfection, as parasites may interact directly by competing for resources or indirectly via the host immune system (3). Interactions may be antagonistic to at least one of the parasites, either as a result of resource shortage or where there are cross-effective immune responses, or they may be beneficial to one or both parasites, as a result of parasite-induced immunosuppression or down-regulation of all or part of the immune system (1). Table 1 for details of their biology). Infection risk will depend on both the probability of encountering an infectious dose and the probability of infection given exposure (host susceptibility). We aimed to determine whether susceptibility to infection by a microparasite was influenced by other microparasites. Therefore, for each microparasite, we investigated whether the other microparasites influenced the probability that a susceptible animal became infected at a given time point (t 0 ), adding infection status for these other microparasites as explanatory variables to baseline statistical models that accounted for environmental and individual variables (e.g., sex and season) (12) (table S1). Thus, we guard against detecting spurious associations, which, in reality, reflect correlated exposure risk (e.g., a positive association simply because both parasites are most prevalent in late summer). For parasites causing self-limiting infections, infection status at both t 0 and/ or the previous month (t −1 ) were considered as explanatory variables, whereas for B. microti infections, which are chronic, a three-level covariate was used: uninfected, newly infected (infected at t 0 but not before), and chronically infected (first infected prior ...

Summary1. We review our ecological understanding of wildlife infectious diseases from the individual host to the ecosystem scale, highlighting where conceptual thinking lacks verification, discussing difficulties and challenges, and offering potential future research directions. 2. New molecular approaches hold potential to increase our understanding of parasite interactions within hosts. Also, advances in our knowledge of immune systems makes immunological parameters viable measures of parasite exposure, and useful tools for improving our understanding of causal mechanisms. 3. Studies of transmission dynamics have revealed the importance of heterogeneity in host behaviour and physiology, and of contact processes operating at different spatial and temporal scales. An important future challenge is to determine the key transmission mechanisms maintaining the persistence of different types of diseases in the wild. 4. Regulation of host populations is too complex to consider parasite effects in isolation from other factors. One solution is to seek a unified understanding of the conditions under which (and the ecological rules determining when) population scale impacts of parasites can occur. 5. Good evidence now shows that both direct effects of parasites, and trait mediated indirect effects, frequently mediate the success of invasive species and their impacts on recipient communities. A wider exploration of these effects is now needed. 6. At the ecosystem scale, research is needed to characterize the circumstances and conditions under which both fluxes in parasite biomass, and trait mediated effects, are significant in ecosystem processes, and to demonstrate that parasites do indeed increase 'ecosystem health'. 7. There is a general need for more empirical testing of predictions and subsequent development of theory in the classic research cycle. Experimental field studies, meta-analyses, the collection and analysis of long-term data sets, and data constrained modelling, will all be key to advancing our understanding. 8. Finally, we are only now beginning to understand the importance of cross-scale interactions associated with parasitism. Such interactions may offer key insights into bigger picture questions such as when and how different regulatory factors are important, when disease can cause species extinctions, and what characteristics are indicative of functionally resilient ecosystems.

A key aim in epidemiology is to understand how pathogens spread within their host populations. Central to this is an elucidation of a pathogen's transmission dynamics. Mathematical models have generally assumed that either contact rate between hosts is linearly related to host density (density-dependent) or that contact rate is independent of density (frequency-dependent), but attempts to confirm either these or alternative transmission functions have been rare. Here, we fit infection equations to 6 years of data on cowpox virus infection (a zoonotic pathogen) for 4 natural populations to investigate which of these transmission functions is best supported by the data. We utilize a simple reformulation of the traditional transmission equations that greatly aids the estimation of the relationship between density and host contact rate. Our results provide support for an infection rate that is a saturating function of host density. Moreover, we find strong support for seasonality in both the transmission coefficient and the relationship between host contact rate and host density, probably reflecting seasonal variations in social behavior and/or host susceptibility to infection. We find, too, that the identification of an appropriate loss term is a key component in inferring the transmission mechanism. Our study illustrates how time series data of the hostpathogen dynamics, especially of the number of susceptible individuals, can greatly facilitate the fitting of mechanistic disease models.cowpox ͉ disease ͉ population cycles ͉ Markov chain Monte Carlo T he seminal studies of Anderson and May (1, 2) introduced a framework for modeling the dynamics of pathogens and their hosts that has since underpinned most predictive models of host-pathogen dynamics. It has been standard practice when modeling the dynamics of host-microparasite interactions (viral and bacterial infections) to represent the rate of change of infected hosts I(t) at time t by dI͑t͒ dt ϭ transmission rate ͑infection͒ Ϫ loss rate ͑death ϩ recovery͒.[1]However, empirically based identification of appropriate functional forms for the ''transmission rate'' and ''loss rate'' terms has not generally been possible for systems with host dynamics because of a lack of sufficient data (although refs. 3 and 4 have recently done this for infectious diseases of human populations).To date, most studies have used transmission rate terms that are either density-dependent or frequency-dependent (5, 6). The underlying difference between these is the assumption about how host contact rate c, varies with host density [(N(t))/A], where N(t) is host abundance and A, the area occupied by the population, is usually assumed constant and omitted from the equations (6). For density-dependent transmission, host contact rate varies linearly with density [typically adopted for directly transmitted diseases such as measles (7) and foot and mouth disease (8)], whereas for frequencydependent transmission it is constant [typically adopted for sexually transmitted diseases such as HIV in ...

The fundamental role of the major histocompatibility complex (MHC) in immune recognition has led to a general consensus that the characteristically high levels of functional polymorphism at MHC genes is maintained by balancing selection operating through host-parasite coevolution. However, the actual mechanism by which selection operates is unclear. Two hypotheses have been proposed: overdominance (or heterozygote superiority) and negative frequency-dependent selection. Evidence for these hypotheses was evaluated by examining MHC-parasite relationships in an island population of water voles (Arvicola terrestris). Generalized linear mixed models were used to examine whether individual variation at an MHC class II DRB locus explained variation in the individual burdens of five different parasites. MHC genotype explained a significant amount of variation in the burden of gamasid mites, fleas (Megabothris walkeri ) and nymphs of sheep ticks (Ixodes ricinus). Additionally, MHC heterozygotes were simultaneously co-infected by fewer parasite types than homozygotes. In each case where an MHC-dependent effect on parasite burden was resolved, the heterozygote genotype was associated with fewer parasites, and the heterozygote outperformed each homozygote in two of three cases, suggesting an overall superiority against parasitism for MHC heterozygote genotypes. This is the first demonstration of MHC heterozygote superiority against multiple parasites in a natural population, a mechanism that could help maintain high levels of functional MHC genetic diversity in natural populations.