Seasonality and the Coexistence of Pathogen Strains.

AbstractHost-pathogen models usually explain the coexistence of pathogen strains by invoking population structure, meaning host or pathogen variation across space or individuals; most models, however, neglect the seasonal variation typical of host-pathogen interactions in nature. To determine the extent to which seasonality can drive pathogen coexistence, we constructed a model in which seasonal host reproduction fuels annual epidemics, which are in turn followed by interepidemic periods with no transmission, a pattern seen in many host-pathogen interactions in nature. In our model, a pathogen strain with low infectiousness and high interepidemic survival can coexist with a strain with high infectiousness and low interepidemic survival: seasonality thus permits coexistence. This seemingly simple type of coexistence can be achieved through two very different pathogen strategies, but understanding these strategies requires novel mathematical analyses. Standard analyses show that coexistence can occur if the competing strains differ in terms of R0, the number of new infections per infectious life span in a completely susceptible population. A novel mathematical method of analyzing transient dynamics, however, allows us to show that coexistence can also occur if one strain has a lower R0 than its competitor but a higher initial fitness λ0, the number of new infections per unit time in a completely susceptible population. This second strategy allows coexisting pathogens to have quite similar phenotypes, whereas coexistence that depends on differences in R0 values requires that coexisting pathogens have very different phenotypes. Our novel analytic method suggests that transient dynamics are an overlooked force in host-pathogen interactions.

Can Eco-Evo Theory Explain Population Cycles in the Field?

AbstractEfforts to explain animal population cycles often invoke consumer-resource theory, which has shown that consumer-resource interactions alone can drive population cycles. Eco-evo theory instead argues that population cycles are partly driven by fluctuating selection for resistance in the resource, but support for eco-evo theory has come almost entirely from laboratory microcosms. Here we ask, Can eco-evo theory explain population cycles in the field? We compared the ability of eco-evo models and classical "eco-only" models to explain data on cycles in the insect Lymantria dispar, in which outbreaks of the insect are terminated by a fatal baculovirus. We carried out a statistical comparison of the ability of eco-only and eco-evo models to explain combined data from L. dispar outbreak cycles and baculovirus epizootics (epidemics in animals). Both models require high host variation in resistance to explain the epizootic data, but high host variation in the eco-evo model leads to consistently accurate predictions of outbreak cycles, whereas in the presence of high host variation the eco-only model can explain outbreak cycles only by invoking high levels of stochasticity, which leads to highly variable and often inaccurate predictions of outbreak cycles. Our work provides statistically robust evidence that eco-evo models can explain population cycles in the field.

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.

Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen.

In deterministic models of epidemics, there is a host abundance threshold above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, Entomophaga maimaiga, that infects the gypsy moth, Lymantria dispar. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on E. maimaiga epizootics (epidemics in animals). We then used Akaike information criterion analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects or only for density-dependent effects do a poor job of explaining the data. Density-dependent transmission in our best model produces a host density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.

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.

Recurring infection with ecologically distinct HPV types can explain high prevalence and diversity.

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or "homologous" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterward. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the 1-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher 2 years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from autoinoculation, episodic reactivation of latent virus, or both. Overall, our results suggest that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Because of the high risk of reinfection, vaccinating boys who have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits to vaccinating previously infected individuals.

Combined Effects of Natural Enemies and Competition for Resources on a Forest Defoliator: A Theoretical and Empirical Analysis.

Explanations for the dynamics of insect outbreaks often focus on natural enemies, on the grounds that parasitoid and pathogen attack rates are high during outbreaks. While natural enemy models can successfully reproduce outbreak cycles, experiments have repeatedly demonstrated the importance of resource quality and abundance. Experiments, however, are rarely invoked in modeling studies. Here we combine mechanistic models, observational data, and field experiments to quantify the roles of parasitoid attacks and resource competition on the jack pine budworm, Choristoneura pinus. By fitting models to a combination of observational and experimental data, we show that parasitoid attacks are the main source of larval budworm mortality at low and intermediate budworm densities but that resource competition is the main source of mortality at high densities. Our results further show that the effects of resource competition become more severe with increasing host tree age and that the effects of parasitoids are moderated by strong competition between parasitoids for hosts. Allowing for these effects in a model of insect outbreaks leads to realistic outbreak cycles, while a host-parasitoid model without resource competition produces an unrealistic stable equilibrium. The effects of resource competition are modulated by tree age, which in turn depends on fire regimes. Our model therefore suggests that increases in fire frequency due to climate change may interact in complex ways with budworm outbreaks. Our work shows that resource competition can be as important as natural enemies in modulating insect outbreaks, while demonstrating the usefulness of high-performance computing in experimental field ecology.

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.

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.

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.

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.

Effects of forest spatial structure on insect outbreaks: insights from a host-parasitoid model.

Understanding how cycles of forest-defoliating insects are affected by forest destruction is of major importance for forest management. Achieving such an understanding with data alone is difficult, however, because population cycles are typically driven by species interactions that are highly nonlinear. We therefore constructed a mathematical model to investigate the effects of forest destruction on defoliator cycles, focusing on defoliator cycles driven by parasitoids. Our model shows that forest destruction can increase defoliator density when parasitoids disperse much farther than defoliators because the benefits of reduced defoliator mortality due to increased parasitoid dispersal mortality exceed the costs of increased defoliator dispersal mortality. This novel result can explain observations of increased outbreak duration with increasing forest fragmentation in forest tent caterpillar populations. Our model also shows that larger habitat patches can mitigate habitat loss, with clear implications for forest management. To better understand our results, we developed an approximate model that shows that defoliator spatial dynamics can be predicted from the proportion of dispersing animals that land in suitable habitat. This approximate model is practically useful because its parameters can be estimated from widely available data. Our model thus suggests that forest destruction may exacerbate defoliator outbreaks but that management practices could mitigate such effects.

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.

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.

Cheating, trade-offs and the evolution of aggressiveness in a natural pathogen population.

The evolutionary dynamics of pathogens are critically important for disease outcomes, prevalence and emergence. In this study we investigate ecological conditions that may promote the long-term maintenance of virulence polymorphisms in pathogen populations. Recent theory predicts that evolution towards increased virulence can be reversed if less-aggressive social 'cheats' exploit more aggressive 'cooperator' pathogens. However, there is no evidence that social exploitation operates within natural pathogen populations. We show that for the bacterium Pseudomonas syringae, major polymorphisms for pathogenicity are maintained at unexpectedly high frequencies in populations infecting the host Arabidopsis thaliana. Experiments reveal that less-aggressive strains substantially increase their growth potential in mixed infections and have a fitness advantage in non-host environments. These results suggest that niche differentiation can contribute to the maintenance of virulence polymorphisms, and that both within-host and between-host growth rates modulate cheating and cooperation in P. syringae populations.

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.

The combined effects of pathogens and predators on insect outbreaks.

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.

Use of a mechanistic growth model in evaluating post-restoration habitat quality for juvenile salmonids.

Individual growth data are useful in assessing relative habitat quality, but this approach is less common when evaluating the efficacy of habitat restoration. Furthermore, available models describing growth are infrequently combined with computational approaches capable of handling large data sets. We apply a mechanistic model to evaluate whether selection of restored habitat can affect individual growth. We used mark-recapture to collect size and growth data on sub-yearling Chinook salmon and steelhead in restored and unrestored habitat in five sampling years (2009, 2010, 2012, 2013, 2016). Modeling strategies differed for the two species: For Chinook, we compared growth patterns of individuals recaptured in restored habitat over 15-60 d with those not recaptured regardless of initial habitat at marking. For steelhead, we had enough recaptured fish in each habitat type to use the model to directly compare habitats. The model generated spatially explicit growth parameters describing size of fish over the growing season in restored vs. unrestored habitat. Model parameters showed benefits of restoration for both species, but that varied by year and time of season, consistent with known patterns of habitat partitioning among them. The model was also supported by direct measurement of growth rates in steelhead and by known patterns of spatio-temporal partitioning of habitat between these two species. Model parameters described not only the rate of growth, but the timing of size increases, and is spatially explicit, accounting for habitat differences, making it widely applicable across taxa. The model usually supported data on density differences among habitat types in Chinook, but only in a couple of cases in steelhead. Modeling growth can thus prevent overconfidence in distributional data, which are commonly used as the metric of restoration success.

Untangling the dynamics of persistence and colonization in microbial communities.

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.