Monitoring the prevalence and abundance of parasites over time is important for addressing their potential impact on host life histories, immunological profiles and their influence as a selective force. Only long-term ecological studies have the potential to shed light on both the temporal trends in infection prevalence and abundance and the drivers of such trends, because of their ability to dissect drivers that may be confounded over shorter time scales. Despite this, only a relatively small number of such studies exist. Here, we analysed changes in the prevalence and abundance of gastrointestinal parasites in the wild Soay sheep population of St. Kilda across 31 years. The host population density (PD) has increased across the study, and PD is known to increase parasite transmission, but we found that PD and year explained temporal variation in parasite prevalence and abundance independently. Prevalence of both strongyle nematodes and coccidian microparasites increased during the study, and this effect varied between lambs, yearlings and adults. Meanwhile, abundance of strongyles was more strongly linked to host PD than to temporal (yearly) dynamics, while abundance of coccidia showed a strong temporal trend without any influence of PD. Strikingly, coccidian abundance increased 3-fold across the course of the study in lambs, while increases in yearlings and adults were negligible. Our decades-long, intensive, individual-based study will enable the role of environmental change and selection pressures in driving these dynamics to be determined, potentially providing unparalleled insight into the drivers of temporal variation in parasite dynamics in the wild.

Within-host interactions among coinfecting parasites are common and have important consequences for host health and disease dynamics. However, these within-host interactions have traditionally been studied in laboratory mouse models, which often exclude important variation and use unnatural host–parasite combinations. Conversely, the few wild studies of within-host interactions often lack knowledge of parasite exposure and infection history. Here we exposed laboratory-reared wood mice (Apodemus sylvaticus) that were derived from wild-caught animals to two naturally-occurring parasites (nematode: Heligmosomoides polygyrus, coccidia: Eimeria hungaryensis) to investigate the impact of coinfection on parasite infection dynamics, and to determine if the host immune response mediates this interaction. Coinfection led to delayed worm expulsion and prolonged egg shedding in H. polygyrus infections and lower peak E. hungaryensis oocyst burdens. By comparing antibody levels between wild and colony-housed mice, we also found that wild mice had elevated H. polygyrus-IgG1 titres even if currently uninfected with H. polygyrus. Using this unique wild-laboratory system, we demonstrate, for the first time, clear evidence for a reciprocal interaction between these intestinal parasites, and that there is a great discrepancy between antibody levels measured in the wild vs those measured under controlled laboratory conditions in relation to parasite infection and coinfection.

The field of disease ecology – the study of the spread and impact of parasites and pathogens within their host populations and communities – has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard ‘one-host-one-parasite’ paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.

Parasitic helminths are ubiquitous in most host, including human, populations. Helminths often alter the likelihood of infection and disease progression of coinfecting microparasitic pathogens (viruses, bacteria, protozoa), and there is great interest in incorporating deworming into control programmes for many major diseases (e.g. HIV, tuberculosis, malaria). However, such calls are controversial; studies show the consequences of deworming for the severity and spread of pathogens to be highly variable. Hence, the benefits of deworming, although clear for reducing the morbidity due to helminth infection per se, are unclear regarding the outcome of coinfections and comorbidities. I develop a theoretical framework to explore how helminth coinfection with other pathogens affects host mortality and pathogen spread and evolution under different interspecific parasite interactions. In all cases the outcomes of coinfection are highly context-dependent, depending on the mechanism of helminth-pathogen interaction and the quantitative level of helminth infection, with the effects of deworming potentially switching from beneficial to detrimental depending on helminth burden. Such context-dependency may explain some of the variation in the benefits of deworming seen between studies, and highlights the need for obtaining a quantitative understanding of parasite interactions across realistic helminth infection ranges. However, despite this complexity, this framework reveals predictable patterns in the effects of helminths that may aid the development of more effective, integrated management strategies to combat pathogens in this coinfected world.

Predator-prey models are often applied to the interactions between host immunity and parasite growth. A key component of these models is the immune system's functional response, the relationship between immune activity and parasite load. Typically, models assume a simple, linear functional response. However, based on the mechanistic interactions between parasites and immunity we argue that alternative forms are more likely, resulting in very different predictions, ranging from parasite exclusion to chronic infection. By extending this framework to consider multiple infections we show that combinations of parasites eliciting different functional responses greatly affect community stability. Indeed, some parasites may stabilize other species that would be unstable if infecting alone. Therefore hosts' immune systems may have adapted to tolerate certain parasites, rather than clear them and risk erratic parasite dynamics. We urge for more detailed empirical information relating immune activity to parasite load to enable better predictions of the dynamic consequences of immune-mediated interspecific interactions within parasite communities.

Individuals are typically co-infected by a diverse community of microparasites (e.g. viruses or protozoa) and macroparasites (e.g. helminths). Vertebrates respond to these parasites differently, typically mounting T helper type 1 (Th1) responses against microparasites and Th2 responses against macroparasites. These two responses may be antagonistic such that hosts face a ‘decision’ of how to allocate potentially limiting resources. Such decisions at the individual host level will influence parasite abundance at the population level which, in turn, will feed back upon the individual level. We take a first step towards a complete theoretical framework by placing an analysis of optimal immune responses under microparasite-macroparasite co-infection within an epidemiological framework. We show that the optimal immune allocation is quantitatively sensitive to the shape of the trade-off curve and qualitatively sensitive to life-history traits of the host, microparasite and macroparasite. This model represents an important first step in placing optimality models of the immune response to co-infection into an epidemiological framework. Ultimately, however, a more complete framework is needed to bring together the optimal strategy at the individual level and the population-level consequences of those responses, before we can truly understand the evolution of host immune responses under parasite co-infection.