Host-pathogen interactions, insect outbreaks, and natural selection for disease resistance

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.

The Evolution of the Age of Onset of Resistance to Infectious Disease

Many organisms experience an increase in disease resistance as they age, but the time of life at which this change occurs varies. Increases in resistance are partially due to prior exposure and physiological constraints, but these cannot fully explain the observed patterns of age-related resistance. An alternative explanation is that developing resistance at an earlier age incurs costs to other life-history traits. Here, we explore how trade-offs with host reproduction or mortality affect the evolution of the onset of resistance, depending on when during the host's life cycle the costs are paid (only when resistance is developing, only when resistant or throughout the lifetime). We find that the timing of the costs is crucial to determining evolutionary outcomes, often making the difference between resistance developing at an early or late age. Accurate modelling of biological systems therefore relies on knowing not only the shape of trade-offs but also when they take effect. We also find that the evolution of the rate of onset of resistance can result in evolutionary branching. This provides an alternative, possible evolutionary history of populations which are dimorphic in disease resistance, where the rate of onset of resistance has diversified rather than the level of resistance.

Meta-Analysis of the Effects of Insect Pathogens: Implications for Plant Reproduction

Despite extensive work on both insect disease and plant reproduction, there is little research on the intersection of the two. Insect-infecting pathogens could disrupt the pollination process by affecting pollinator population density or traits. Pathogens may also infect insect herbivores and change herbivory, potentially altering resource allocation to plant reproduction. We conducted a meta-analysis to (1) summarize the literature on the effects of pathogens on insect pollinators and herbivores and (2) quantify the extent to which pathogens affect insect traits, with potential repercussions for plant reproduction. We found 39 articles that fit our criteria for inclusion, extracting 218 measures of insect traits for 21 different insect species exposed to 25 different pathogens. We detected a negative effect of pathogen exposure on insect traits, which varied by host function: pathogens had a significant negative effect on insects that were herbivores or carried multiple functions but not on insects that solely functioned as pollinators. Particular pathogen types were heavily studied in certain insect orders, with 7 of 11 viral pathogen studies conducted in Lepidoptera and 5 of 9 fungal pathogen studies conducted in Hymenoptera. Our results suggest that most studies have focused on a small set of host-pathogen pairs. To understand the implications for plant reproduction, future work is needed to directly measure the effects of pathogens on pollinator effectiveness.

Host phenology regulates parasite-host demographic cycles and eco-evolutionary feedbacks

Parasite-host interactions can drive periodic population dynamics when parasites overexploit host populations. The timing of host seasonal activity, or host phenology, determines the frequency and demographic impact of parasite-host interactions, which may govern whether parasites sufficiently overexploit hosts to drive population cycles. We describe a mathematical model of a monocyclic, obligate-killer parasite system with seasonal host activity to investigate the consequences of host phenology on host-parasite dynamics. The results suggest that parasites can reach the densities necessary to destabilize host dynamics and drive cycling as they adapt, but only in some phenological scenarios such as environments with short seasons and synchronous host emergence. Furthermore, only parasite lineages that are sufficiently adapted to phenological scenarios with short seasons and synchronous host emergence can achieve the densities necessary to overexploit hosts and produce population cycles. Host-parasite cycles also generate an eco-evolutionary feedback that slows parasite adaptation to the phenological environment as rare advantageous phenotypes can be driven extinct due to a population bottleneck depending on when they are introduced in the cycle. The results demonstrate that seasonal environments can drive population cycling in a restricted set of phenological patterns and provide further evidence that the rate of adaptive evolution depends on underlying ecological dynamics.

Parasite dynamics in North American monarchs predicted by host density and seasonal migratory culling

Insect-pathogen dynamics can show seasonal and inter-annual variation that covaries with fluctuations in insect abundance and climate. Long-term analyses are especially needed to track parasite dynamics in migratory insects, in part because their vast habitat ranges and high mobility might dampen local effects of density and climate on infection prevalence. Monarch butterflies (Danaus plexippus) are commonly infected with the protozoan Ophryocystis elektroscirrha (OE). Because this parasite lowers monarch survival and flight performance, and because migratory monarchs have experienced declines in recent decades, it is important to understand patterns and drivers of infection. 3. Here we compiled data on OE infection spanning 50 years, from wild monarchs sampled in the USA, Canada, and Mexico during summer breeding, fall migrating, and overwintering periods. We examined eastern versus western North American monarchs separately, to ask how abundance estimates, resource availability, climate, and breeding season length impact infection trends. We further assessed the intensity of migratory culling, which occurs when infected individuals are removed from the population during migration. 4. Average infection prevalence was four times higher in western compared to eastern subpopulations. In eastern North America, the proportion of infected monarchs increased three-fold since the mid-2000s. In the western region, the proportion of infected monarchs declined sharply from 2000-2015, and increased thereafter. For both eastern and western subpopulations, years with greater summer adult abundance predicted greater infection prevalence, indicating that transmission increases with host breeding density. Environmental variables (temperature and NDVI) were not associated with changes in infected adults. We found evidence for migratory culling of infected butterflies, based on declines in parasitism during fall migration. We estimated that tens of millions fewer monarchs reach overwintering sites in Mexico as a result of OE, highlighting the need to consider the parasite as a potential threat to the monarch population. 5. Increases in infection among eastern North American monarchs post-2002 suggest that changes to the host's ecology or environment have intensified parasite transmission. Further work is needed to examine the degree to which human practices, such as mass caterpillar rearing and the widespread planting of exotic milkweed, have contributed to this trend.

Evolutionary rescue in host-pathogen systems

Natural populations encounter a variety of threats that can increase their risk of extinction. Populations can avoid extinction through evolutionary rescue (ER), which occurs when an adaptive, genetic response to selection allows a population to recover from an environmental change that would otherwise cause extinction. While the traditional framework for ER was developed with abiotic risk factors in mind, ER may also occur in response to a biotic source of demographic change, such as the introduction of a novel pathogen. We first describe how ER in response to a pathogen differs from the traditional ER framework; density-dependent transmission, pathogen evolution, and pathogen extinction can change the strength of selection imposed by a pathogen and make host population persistence more likely. We also discuss several variables that affect traditional ER (abundance, genetic diversity, population connectivity, and community composition) that also directly affect disease risk resulting in diverse outcomes for ER in host-pathogen systems. Thus, generalizations developed in studies of traditional ER may not be relevant for ER in response to the introduction of a pathogen. Incorporating pathogens into the framework of ER will lead to a better understanding of how and when populations can avoid extinction in response to novel pathogens.

Understanding biological control with entomopathogenic fungi-Insights from a stochastic pest-pathogen model

In this study, an individual-based model is proposed to investigate the effect of demographic stochasticity on biological control using entomopathogenic fungi. The model is formulated as a continuous time Markov process, which is then decomposed into a deterministic dynamics using stochastic corrections and system size expansion. The stability and bifurcation analysis shows that the system dynamic is strongly affected by the contagion rate and the basic reproduction number. However, sensitivity analysis of the extinction probability shows that the persistence of a biological control agent depends to the proportion of spores collected from insect cadavers as well as their ability to be reactivated and infect insects. When considering the migration of each species within a set of patches, the dispersion relation shows a Hopf-damped Turing mode for a threshold contagion rate. A large size population led to a spatial and temporal resonant stochasticity and also induces an amplification effect on power spectrum density.

Variation in intrinsic resistance of pea aphids to parasitoid wasps: A transcriptomic basis

Evolutionary interactions between parasitoid wasps and insect hosts have been well studied at the organismal level, but little is known about the molecular mechanisms that insects use to resist wasp parasitism. Here we study the interaction between a braconid wasp (Aphidius ervi) and its pea aphid host (Acyrthosiphon pisum). We first identify variation in resistance to wasp parasitism that can be attributed to aphid genotype. We then use transcriptome sequencing to identify genes in the aphid genome that are differentially expressed at an early stage of parasitism, and we compare these patterns in highly resistant and susceptible aphid host lines. We find that resistant genotypes are upregulating genes involved in carbohydrate metabolism and several key innate immune system genes in response to parasitism, but that this response seems to be weaker in susceptible aphid genotypes. Together, our results provide a first look into the complex molecular mechanisms that underlie aphid resistance to wasp parasitism and contribute to a broader understanding of how resistance mechanisms evolve in natural populations.

An Empirical Test of the Role of Small-Scale Transmission in Large-Scale Disease Dynamics

A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here, we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model's performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared with branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multiscale data are key to understanding these dynamics.

Understanding the Evolutionary Ecology of host--pathogen Interactions Provides Insights into the Outcomes of Insect Pest Biocontrol

The use of viral pathogens to control thepopulation size of pest insects has produced both successful and unsuccessful outcomes. Here, we investigate whether those biocontrol successes and failures can be explained by key ecological and evolutionary processes between hosts and pathogens. Specifically, we examine how heterogeneity inpathogen transmission, ecological and evolutionary tradeoffs, andpathogen diversity affect insect population density and thus successful control. Wefirst review theexisting literature and then use numerical simulations of mathematical models to further explore these processes. Our results show that thecontrol of insect densities using viruses depends strongly on theheterogeneity of virus transmission among insects. Overall, increased heterogeneity of transmission reduces theeffect of viruses on insect densities and increases thelong-term stability of insect populations. Lower equilibrium insect densities occur when transmission is heritable and when there is atradeoff between mean transmission and insect fecundity compared to when theheterogeneity of transmission arises from non-genetic sources. Thus, theheterogeneity of transmission is akey parameter that regulates thelong-term population dynamics of insects and their pathogens. Wealso show that both heterogeneity of transmission and life-history tradeoffs modulate characteristics of population dynamics such as thefrequency and intensity of ``boom--bust" population cycles. Furthermore, we show that because of life-history tradeoffs affecting thetransmission rate, theuse of multiple pathogen strains is more effective than theuse of asingle strain to control insect densities only when thepathogen strains differ considerably intheir transmission characteristics. By quantifying theeffects of ecology and evolution on population densities, we are able to offer recommendations to assess thelong-term effects of classical biocontrol.

Enhancing genetic disease control by selecting for lower host infectivity and susceptibility

Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R₀ from 7.64 to the critical threshold of <1, below which epidemics die out. Adding infectivity in the selection objective accelerated the decline towards R₀ < 1, to 3 generations. Our results show that although genetic selection on susceptibility reduces disease risk and prevalence, the additional gain from selection on infectivity accelerates disease eradication and reduces more efficiently the risk of new outbreaks, while it alleviates delays generated by unfavourable correlations. In conclusion, host infectivity was found to be an important trait to target in future genetic studies and breeding schemes, to help reducing the occurrence and impact of epidemics.

Virulence-driven trade-offs in disease transmission: A meta-analysis

The virulence-transmission trade-off hypothesis proposed more than 30 years ago is the cornerstone in the study of host-parasite co-evolution. This hypothesis rests on the premise that virulence is an unavoidable and increasing cost because the parasite uses host resources to replicate. This cost associated with replication ultimately results in a deceleration in transmission rate because increasing within-host replication increases host mortality. Empirical tests of predictions of the hypothesis have found mixed support, which cast doubt about its overall generalizability. To quantitatively address this issue, we conducted a meta-analysis of 29 empirical studies, after reviewing over 6000 published papers, addressing the four core relationships between (1) virulence and recovery rate, (2) within-host replication rate and virulence, (3) within-host replication and transmission rate, and (4) virulence and transmission rate. We found strong support for an increasing relationship between replication and virulence, and replication and transmission. Yet, it is still uncertain if these relationships generally decelerate due to high within-study variability. There was insufficient data to quantitatively test the other two core relationships predicted by the theory. Overall, the results suggest that the current empirical evidence provides partial support for the trade-off hypothesis, but more work remains to be done.

Bottom-up trait-mediated indirect effects decrease pathogen transmission in a tritrophic system

A plant's induction of secondary defenses helps to decrease herbivore damage by changing resource quality. While these chemical or physical defenses may directly decrease herbivory, they can also have indirect consequences. In a tritrophic system consisting of a plant, an insect herbivore, and an insect pathogen, plant based trait-mediated indirect effects (TMIEs) can alter host-pathogen interactions and, thereby, indirectly affect disease transmission. In a series of field experiments, individual soybean plants (Glycine max) were sprayed with either a jasmonic acid (JA) solution to trigger induction of plant defenses or a similar control compound. Fall armyworm (Spodoptera frugiperda) larvae along with varying amounts of a lethal baculovirus were placed on the plants to measure transmission. Induction of plant defenses decreased viral transmission due to increased population heterogeneity arising from changes in individual susceptibility. The change in susceptibility via TMIEs was driven by a decrease in feeding rates and an increase viral dose needed to infect larvae. While the induction against herbivore attack may decrease herbivory, it can also decrease the efficacy of the herbivore's pathogen potentially to the plant's detriment. While TMIEs have been well-recognized for being driven by top-down forces, bottom-up interactions can dictate community dynamics and, here, epizootic severity.

Effects of multiple sources of genetic drift on pathogen variation within hosts

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale-such as within hosts or between hosts-or by making drastic simplifying assumptions, for example, that host immune systems can be ignored or that transmission bottlenecks are complete. Moreover, previous studies have used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales-including within hosts, between hosts, and between years-it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. A de novo mutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

Modeling Marek's disease virus transmission: A framework for evaluating the impact of farming practices and evolution

Marek’s disease virus (MDV) is a pathogen of chickens whose control has twice been undermined by pathogen evolution. Disease ecology is believed to be the main driver of this evolution, yet mathematical models of MDV disease ecology have never been confronted with data to test their reliability. Here, we develop a suite of MDV models that differ in the ecological mechanisms they include. We fit these models with maximum likelihood using iterated filtering in ‘pomp’ to data on MDV concentration in dust collected from two commercial broiler farms. We find that virus dynamics are influenced by between-flock variation in host susceptibility to virus, shedding rate from infectious birds, and cleanout efficiency. We also find evidence that virus is reintroduced to farms approximately once per month, but we do not find evidence that virus sanitization rates vary between flocks. Of the models that survive model selection, we find agreement between parameter estimates and previous experimental data, as well as agreement between field data and the predictions of these models. Using the set of surviving models, we explore how changes to farming practices are predicted to influence MDV-associated condemnation risk (production losses at slaughter). By quantitatively capturing the mechanisms of disease ecology, we have laid the groundwork to explore the future trajectory of virus evolution.

Vaccine Effects on Heterogeneity in Susceptibility and Implications for Population Health Management

Heterogeneity in host susceptibility is a key determinant of infectious disease dynamics but is rarely accounted for in assessment of disease control measures. Understanding how susceptibility is distributed in populations, and how control measures change this distribution, is integral to predicting the course of epidemics with and without interventions. Using multiple experimental and modeling approaches, we show that rainbow trout have relatively homogeneous susceptibility to infection with infectious hematopoietic necrosis virus and that vaccination increases heterogeneity in susceptibility in a nearly all-or-nothing fashion. In a simple transmission model with an R₀ of 2, the highly heterogeneous vaccine protection would cause a 35 percentage-point reduction in outbreak size over an intervention inducing homogenous protection at the same mean level. More broadly, these findings provide validation of methodology that can help to reduce biases in predictions of vaccine impact in natural settings and provide insight into how vaccination shapes population susceptibility.

Differences among individuals influence transmission and spread of infectious diseases as well as the effectiveness of control measures. Control measures, such as vaccines, may provide leaky protection, protecting all hosts to an identical degree, or all-or-nothing protection, protecting some hosts completely while leaving others completely unprotected. This distinction can have a dramatic influence on disease dynamics, yet this distribution of protection is frequently unaccounted for in epidemiological models and estimates of vaccine efficacy. Here, we apply new methodology to experimentally examine host heterogeneity in susceptibility and mode of vaccine action as distinct components influencing disease outcome. Through multiple experiments and new modeling approaches, we show that the distribution of vaccine effects can be robustly estimated. These results offer new experimental and inferential methodology that can improve predictions of vaccine effectiveness and have broad applicability to human, wildlife, and ecosystem health.

Cannibalism and Infectious Disease: Friends or Foes?

Cannibalism occurs in a majority of both carnivorous and noncarnivorous animal taxa from invertebrates to mammals. Similarly, infectious parasites are ubiquitous in nature. Thus, interactions between cannibalism and disease occur regularly. While some adaptive benefits of cannibalism are clear, the prevailing view is that the risk of parasite transmission due to cannibalism would increase disease spread and, thus, limit the evolutionary extent of cannibalism throughout the animal kingdom. In contrast, surprisingly little attention has been paid to the other half of the interaction between cannibalism and disease, that is, how cannibalism affects parasites. Here we examine the interaction between cannibalism and parasites and show how advances across independent lines of research suggest that cannibalism can also reduce the prevalence of parasites and, thus, infection risk for cannibals. Cannibalism does this by both directly killing parasites in infected victims and by reducing the number of susceptible hosts, often enhanced by the stage-structured nature of cannibalism and infection. While the well-established view that disease should limit cannibalism has held sway, we present theory and examples from a synthesis of the literature showing how cannibalism may also limit disease and highlight key areas where conceptual and empirical work is needed to resolve this debate.

Eco-Evolutionary Theory and Insect Outbreaks

Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that the theory explains cycles in nature, however, is almost nonexistent. Experimental tests of model assumptions are logistically impractical for most organisms, while for others, evidence that population cycles occur in nature is lacking. For insect baculoviruses in contrast, tests of model assumptions are straightforward, and there is strong evidence that baculoviruses help drive population cycles in many insects, including the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovirus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles: that reduced host infection risk is heritable and that it is costly. Our experiments confirm both assumptions, and inserting parameters estimated from our data into eco-evolutionary insect-outbreak models gives cycles closely resembling gypsy moth outbreak cycles in North America, whereas standard models predict unrealistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection on defoliators in nature and of heritable and costly resistance in defoliators in the lab together suggest that eco-evolutionary dynamics may play a general role in defoliator outbreaks.

Genotype-by-genotype interactions between an insect and its pathogen

Genotype-by-genotype (G×G) interactions are an essential requirement for the coevolution of hosts and parasites, but have only been documented in a small number of animal model systems. G×G effects arise from interactions between host and pathogen genotypes, such that some pathogen strains are more infectious in certain hosts and some hosts are more susceptible to certain pathogen strains. We tested for G×G interactions in the gypsy moth (Lymantria dispar) and its baculovirus. We infected 21 full-sib families of gypsy moths with each of 16 isolates of baculovirus and measured the between-isolate correlations of infection rate across host families for all pairwise combinations of isolates. Mean infectiousness varied among isolates and disease susceptibility varied among host families. Between-isolate correlations of infection rate were generally less than one, indicating nonadditive effects of host and pathogen type consistent with G×G interactions. Our results support the presence of G×G effects in the gypsy moth-baculovirus interaction and provide empirical evidence that correlations in infection rates between field-collected isolates are consistent with values that mathematical models have previously shown to increase the likelihood of pathogen polymorphism.

Ecology and evolution of pathogens in natural populations of Lepidoptera

Pathogens are ubiquitous in insect populations and yet few studies examine their dynamics and impacts on host populations. We discuss four lepidopteran systems and explore their contributions to disease ecology and evolution. More specifically, we elucidate the role of pathogens in insect population dynamics. For three species, western tent caterpillars, African armyworm and introduced populations of gypsy moth, infection by nucleopolyhedrovirus (NPV) clearly regulates host populations or reduces their outbreaks. Transmission of NPV is largely horizontal although low levels of vertical transmission occur, and high levels of covert infection in some cases suggest that the virus can persist in a nonsymptomatic form. The prevalence of a mostly vertically transmitted protozoan parasite, Ophryocystis elektroscirrha, in monarch butterflies is intimately related to their migratory behaviour that culls highly infected individuals. Virulence and transmission are positively related among genotypes of this parasite. These systems clearly demonstrate that the interactions between insects and pathogens are highly context dependent. Not only is the outcome a consequence of changes in density and genetic diversity: environmental factors, particularly diet, can have strong impacts on virulence, transmission and host resistance or tolerance. What maintains the high level of host and pathogen diversity in these systems, however, remains a question.

Effects of host heterogeneity on pathogen diversity and evolution

Phenotypic variation is common in most pathogens, yet the mechanisms that maintain this diversity are still poorly understood. We asked whether continuous host variation in susceptibility helps maintain phenotypic variation, using experiments conducted with a baculovirus that infects gypsy moth (Lymantria dispar) larvae. We found that an empirically observed tradeoff between mean transmission rate and variation in transmission, which results from host heterogeneity, promotes long-term coexistence of two pathogen types in simulations of a population model. This tradeoff introduces an alternative strategy for the pathogen: a low-transmission, low-variability type can coexist with the high-transmission type favoured by classical non-heterogeneity models. In addition, this tradeoff can help explain the extensive phenotypic variation we observed in field-collected pathogen isolates, in traits affecting virus fitness including transmission and environmental persistence. Similar heterogeneity tradeoffs might be a general mechanism promoting phenotypic variation in any pathogen for which hosts vary continuously in susceptibility.

Phenotypic Variation in Overwinter Environmental Transmission of a Baculovirus and the Cost of Virulence

A pathogen's ability to persist in the environment is an ecologically important trait, and variation in this trait may promote coexistence of different pathogen strains. We asked whether naturally occurring isolates of the baculovirus that infects gypsy moth larvae varied in their overwinter environmental transmission and whether this variation was consistent with a trade-off or an upper limit to virulence that might promote pathogen diversity. We used experimental manipulations to replicate the natural overwinter infection process, using 16 field-collected isolates. Virus isolates varied substantially in the fraction of larvae infected, leading to differences in overwinter transmission rates. Furthermore, isolates that killed more larvae also had higher rates of early larval death in which no infectious particles were produced, consistent with a cost of high virulence. Our results thus support the existence of a cost that could impose an upper limit to virulence even in a highly virulent pathogen.

Effects of pathogen exposure on life-history variation in the gypsy moth (Lymantria dispar)

Investment in host defences against pathogens may lead to trade-offs with host fecundity. When such trade-offs arise from genetic correlations, rates of phenotypic change by natural selection may be affected. However, genetic correlations between host survival and fecundity are rarely quantified. To understand trade-offs between immune responses to baculovirus exposure and fecundity in the gypsy moth (Lymantria dispar), we estimated genetic correlations between survival probability and traits related to fecundity, such as pupal weight. In addition, we tested whether different virus isolates have different effects on male and female pupal weight. To estimate genetic correlations, we exposed individuals of known relatedness to a single baculovirus isolate. To then evaluate the effect of virus isolate on pupal weight, we exposed a single gypsy moth strain to 16 baculovirus isolates. We found a negative genetic correlation between survival and pupal weight. In addition, virus exposure caused late-pupating females to be identical in weight to males, whereas unexposed females were 2-3 times as large as unexposed males. Finally, we found that female pupal weight is a quadratic function of host mortality across virus isolates, which is likely due to trade-offs and compensatory growth processes acting at high and low mortality levels, respectively. Overall, our results suggest that fecundity costs may strongly affect the response to selection for disease resistance. In nature, baculoviruses contribute to the regulation of gypsy moth outbreaks, as pathogens often do in forest-defoliating insects. We therefore argue that trade-offs between host life-history traits may help explain outbreak dynamics.

The effects of the avoidance of infectious hosts on infection risk in an insect-pathogen interaction

In many animal host-pathogen interactions, uninfected hosts either avoid or are attracted to infected conspecifics, but understanding how such behaviors affect infection risk is difficult. In experiments, behaviors are often eliminated entirely, which allows demonstration that a behavior affects risk but makes it impossible to quantify effects of individual behaviors. In models, host behaviors have been studied using ordinary differential equations, which can be easily analyzed but cannot be used to relate individual behaviors to risk. For many insect baculoviruses, however, quantifying effects of behavior on risk is straightforward because transmission occurs when host larvae accidentally consume virus-contaminated foliage. Moreover, increases in computing power have made it possible to fit complex models to data. We therefore used experiments to quantify the behavior of gypsy moth larvae feeding on oak leaves contaminated with virus-infected cadavers, and we tested for effects of cadaver-avoidance behavior by fitting stochastic simulation models to our data. The models that best explain the data include cadaver avoidance, and comparison of models that do and do not include cadaver avoidance shows that this behavior substantially reduces infection risk. Our work demonstrates that host behaviors that affect exposure risk play a key role in baculovirus transmission and adds to the growing consensus that host behavior can strongly alter pathogen transmission rates.

Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response-time data

Pathogen population dynamics within individual hosts can alter disease epidemics and pathogen evolution, but our understanding of the mechanisms driving within-host dynamics is weak. Mathematical models have provided useful insights, but existing models have only rarely been subjected to rigorous tests, and their reliability is therefore open to question. Most models assume that initial pathogen population sizes are so large that stochastic effects due to small population sizes, so-called demographic stochasticity, are negligible, but whether this assumption is reasonable is unknown. Most models also assume that the dynamic effects of a host's immune system strongly affect pathogen incubation times or "response times," but whether such effects are important in real host-pathogen interactions is likewise unknown. Here we use data for a baculovirus of the gypsy moth to test models of within-host pathogen growth. By using Bayesian statistical techniques and formal model-selection procedures, we are able to show that the response time of the gypsy moth virus is strongly affected by both demographic stochasticity and a dynamic response of the host immune system. Our results imply that not all response-time variability can be explained by host and pathogen variability, and that immune system responses to infection may have important effects on population-level disease dynamics.

Population-level differences in disease transmission: a Bayesian analysis of multiple smallpox epidemics

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 R0, which, due to the non-linearity of epidemic processes, may result in a mis-estimate of epidemic intensity and miscalculated expenditures associated with public-health interventions. In this paper, we utilize a Bayesian method for quantifying the overall uncertainty arising from differences in population-specific basic reproductive rates. Using this method, we fit spatial and non-spatial susceptible-exposed-infected-recovered (SEIR) models to a series of 13 smallpox outbreaks. Five outbreaks occurred in populations that had been previously exposed to smallpox, while the remaining eight occurred in Native-American populations that were naïve to the disease at the time. The Native-American outbreaks were close in a spatial and temporal sense. Using Bayesian Information Criterion (BIC), we show that the best model includes population-specific R0 values. These differences in R0 values may, in part, be due to differences in genetic background, social structure, or food and water availability. As a result of these inter-population differences, the overall uncertainty associated with the "population average" value of smallpox R0 is larger, a finding that can have important consequences for controlling epidemics. In general, Bayesian hierarchical models are able to properly account for the uncertainty associated with multiple epidemics, provide a clearer understanding of variability in epidemic dynamics, and yield a better assessment of the range of potential risks and consequences that decision makers face.

Induced plant defenses, host-pathogen interactions, and forest insect outbreaks

Cyclic outbreaks of defoliating insects devastate forests, but their causes are poorly understood. Outbreak cycles are often assumed to be driven by density-dependent mortality due to natural enemies, because pathogens and predators cause high mortality and because natural-enemy models reproduce fluctuations in defoliation data. The role of induced defenses is in contrast often dismissed, because toxic effects of defenses are often weak and because induced-defense models explain defoliation data no better than natural-enemy models. Natural-enemy models, however, fail to explain gypsy moth outbreaks in North America, in which outbreaks in forests with a higher percentage of oaks have alternated between severe and mild, whereas outbreaks in forests with a lower percentage of oaks have been uniformly moderate. Here we show that this pattern can be explained by an interaction between induced defenses and a natural enemy. We experimentally induced hydrolyzable-tannin defenses in red oak, to show that induction reduces variability in a gypsy moth's risk of baculovirus infection. Because this effect can modulate outbreak severity and because oaks are the only genus of gypsy moth host tree that can be induced, we extended a natural-enemy model to allow for spatial variability in inducibility. Our model shows alternating outbreaks in forests with a high frequency of oaks, and uniform outbreaks in forests with a low frequency of oaks, matching the data. The complexity of this effect suggests that detecting effects of induced defenses on defoliator cycles requires a combination of experiments and models.

Navigating the devious course of evolution: the importance of mechanistic models for identifying eco-evolutionary dynamics in nature

In proposing his genetic feedback mechanism, David Pimentel was one of the first biologists to argue that the reciprocal interplay of ecological and evolutionary dynamics is an important process regulating population dynamics and ultimately affecting community composition. Although the past decade has seen an increase in research activity on these so-called eco-evolutionary dynamics, there remains a conspicuous lack of compelling natural examples of such feedback. Here we argue that this lack may be due to an inherent difficulty in detecting eco-evolutionary dynamics in nature. By examining models of virulence evolution, host resistance evolution, and antigenic evolution, we show that the influence of evolution on ecological dynamics can often be obscured by other ecological processes that yield similar dynamics. We then show, however, that mechanistic models can be used to navigate this, in Pimentel's words, "devious" course of evolution when effectively combined with empirical data. We argue that these models, improving upon Pimentel's original mathematical models, will therefore play an increasingly important role in identifying more subtle, but possibly ubiquitous, eco-evolutionary dynamics in nature. To highlight the importance of identifying these potentially subtle dynamics in nature, we end by considering our ability to anticipate the effect of population control strategies in the presence of these eco-evolutionary feedbacks.

Pathogen persistence in the environment and insect-baculovirus interactions: disease-density thresholds, epidemic burnout, and insect outbreaks

Classical epidemic theory focuses on directly transmitted pathogens, but many pathogens are instead transmitted when hosts encounter infectious particles. Theory has shown that for such diseases pathogen persistence time in the environment can strongly affect disease dynamics, but estimates of persistence time, and consequently tests of the theory, are extremely rare. We consider the consequences of persistence time for the dynamics of the gypsy moth baculovirus, a pathogen transmitted when larvae consume foliage contaminated with particles released from infectious cadavers. Using field-transmission experiments, we are able to estimate persistence time under natural conditions, and inserting our estimates into a standard epidemic model suggests that epidemics are often terminated by a combination of pupation and burnout rather than by burnout alone, as predicted by theory. Extending our models to allow for multiple generations, and including environmental transmission over the winter, suggests that the virus may survive over the long term even in the absence of complex persistence mechanisms, such as environmental reservoirs or covert infections. Our work suggests that estimates of persistence times can lead to a deeper understanding of environmentally transmitted pathogens and illustrates the usefulness of experiments that are closely tied to mathematical models.

Heterogeneous host susceptibility enhances prevalence of mixed-genotype micro-parasite infections

Dose response in micro-parasite infections is usually shallower than predicted by the independent action model, which assumes that each infectious unit has a probability of infection that is independent of the presence of other infectious units. Moreover, the prevalence of mixed-genotype infections was greater than predicted by this model. No probabilistic infection model has been proposed to account for the higher prevalence of mixed-genotype infections. We use model selection within a set of four alternative models to explain high prevalence of mixed-genotype infections in combination with a shallow dose response. These models contrast dependent versus independent action of micro-parasite infectious units, and homogeneous versus heterogeneous host susceptibility. We specifically consider a situation in which genome differences between genotypes are minimal, and highly unlikely to result in genotype-genotype interactions. Data on dose response and mixed-genotype infection prevalence were collected by challenging fifth instar Spodoptera exigua larvae with two genotypes of Autographa californica multicapsid nucleopolyhedrovirus (AcMNPV), differing only in a 100 bp PCR marker sequence. We show that an independent action model that includes heterogeneity in host susceptibility can explain both the shallow dose response and the high prevalence of mixed-genotype infections. Theoretical results indicate that variation in host susceptibility is inextricably linked to increased prevalence of mixed-genotype infections. We have shown, to our knowledge for the first time, how heterogeneity in host susceptibility affects mixed-genotype infection prevalence. No evidence was found that virions operate dependently. While it has been recognized that heterogeneity in host susceptibility must be included in models of micro-parasite transmission and epidemiology to account for dose response, here we show that heterogeneity in susceptibility is also a fundamental principle explaining patterns of pathogen genetic diversity among hosts in a population. This principle has potentially wide implications for the monitoring, modeling and management of infectious diseases.

What elements are indispensable in the description of the most basic host-pathogen interactions? The simplest models of infection generally fail to predict how many host plants or animals will become infected, and which virus genotypes will be present in these infected hosts. These simple models of infection are the building blocks for more complicated models of epidemiology and disease dynamics and diversity, making it important to identify the reasons for failure. We developed four probabilistic models of infection incorporating different mechanisms that could potentially explain and overcome this failure. We obtained experimental data to test these models by exposing Lepidopteran larvae to different genotypes of an insect DNA virus, and determining which virus genotypes had infected them. The model which best described the data added only one element: variation in the susceptibility of individual caterpillars to the virus. Host variation in susceptibility is known to affect transmission of viruses between hosts, but here we show it is inextricably linked to infection biology and indispensable for understanding pathogen diversity in host populations.

Variation in resource acquisition and use among host clones creates key epidemiological trade‐offs

Parasites can certainly harm host fitness. Given such virulence, hosts should evolve strategies to resist or tolerate infection. But what governs those strategies and the costs that they incur? This study illustrates how a fecundity‐susceptibility trade‐off among clonally reared genotypes of a zooplankton (Daphnia dentifera) infected by a fungal parasite (Metschnikowia) arises due to variation in resource acquisition and use by hosts. To make these connections, we used lab experiments and theoretical models that link feeding with susceptibility, energetics, and fecundity of hosts. These feeding‐based mechanisms also produced a fecundity‐survivorship trade‐off. Meanwhile, a parasite spore yield–fecundity trade‐off arose from variation in juvenile growth rate among host clones (another index of resource use), a result that was readily anticipated and explained by the models. Thus, several key epidemiological trade‐offs stem from variation in resource acquisition and use among clones. This connection should catalyze the creation of new theory that integrates resource‐ and gene‐based responses of hosts to disease.

Reduction of adaptive genetic diversity radically alters eco-evolutionary community dynamics

Adaptive variation in the traits determining ecological interactions can lead to evolution so rapid that ecological dynamics change course while in progress (i.e., 'eco-evolutionary dynamics'). However, little is known about how the qualitative properties of eco-evolutionary dynamics (e.g., cycling, equilibrium, etc.) are affected by the amount of heritable variation present. Here, we show that a change in the range of variation in a heritable prey defense trait determines what dynamics are observed in an experimental predator-prey system. We combine modelling and laboratory experiments to show that initial defense trait variation determines whether populations exhibit eco-evolutionary cycles in which heritable variation is maintained, or converge to an equilibrium at which the prey population becomes monomorphic. Our results show how small changes in the amount of adaptive genetic variance initially present can radically alter eco-evolutionary dynamics, and can ultimately determine whether heritable variation is maintained or lost.

Host behaviour and exposure risk in an insect-pathogen interaction

1. Studies of variability in host resistance to disease generally emphasize variability in susceptibility given exposure, neglecting the possibility that hosts may vary in behaviours that affect the risk of exposure. 2. In many insects, horizontal transmission of baculoviruses occurs when larvae consume foliage contaminated by the cadavers of virus-infected conspecific larvae; so, host behaviour may have a strong effect on the risk of infection. 3. We studied variability in the behaviour of gypsy moth (Lymantria dispar) larvae, which are able to detect and avoid virus-contaminated foliage. 4. Our results show that detection ability can be affected by the family line that larvae originate from, even at some distance from a virus-infected cadaver, and suggest that cadaver-detection ability may be heritable. 5. There is thus the potential for natural selection to act on cadaver-detection ability, and thereby to affect the dynamics of pathogen-driven cycles in gypsy moth populations. 6. We argue that host behaviour is a neglected component in studies of variability in disease resistance.

Rapid contemporary evolution and clonal food web dynamics

Character evolution that affects ecological community interactions often occurs contemporaneously with temporal changes in population size, potentially altering the very nature of those dynamics. Such eco-evolutionary processes may be most readily explored in systems with short generations and simple genetics. Asexual and cyclically parthenogenetic organisms such as microalgae, cladocerans and rotifers, which frequently dominate freshwater plankton communities, meet these requirements. Multiple clonal lines can coexist within each species over extended periods, until either fixation occurs or a sexual phase reshuffles the genetic material. When clones differ in traits affecting interspecific interactions, within-species clonal dynamics can have major effects on the population dynamics. We first consider a simple predator-prey system with two prey genotypes, parametrized with data from a well-studied experimental system, and explore how the extent of differences in defence against predation within the prey population determine dynamic stability versus instability of the system. We then explore how increased potential for evolution affects the community dynamics in a more general community model with multiple predator and multiple prey genotypes. These examples illustrate how microevolutionary 'details' that enhance or limit the potential for heritable phenotypic change can have significant effects on contemporaneous community-level dynamics and the persistence and coexistence of species.