Rapid Evolution of Resistance and Tolerance Leads to Variable Host Recoveries following Disease-Induced Declines.

AbstractRecoveries of populations that have suffered severe disease-induced declines are being observed across disparate taxa. Yet we lack theoretical understanding of the drivers and dynamics of recovery in host populations and communities impacted by infectious disease. Motivated by disease-induced declines and nascent recoveries in amphibians, we developed a model to ask the following question: How does the rapid evolution of different host defense strategies affect the transient recovery trajectories of hosts following pathogen invasion and disease-induced declines? We found that while host life history is predictably a major driver of variability in population recovery trajectories (including declines and recoveries), populations that use different host defense strategies (i.e., tolerance, avoidance resistance, and intensity-reduction resistance) experience notably different recoveries. In single-species host populations, populations evolving tolerance recovered on average four times slower than populations evolving resistance. Moreover, while populations using avoidance resistance strategies had the fastest potential recovery rates, these populations could get trapped in long transient states at low abundance prior to recovery. In contrast, the recovery of populations evolving intensity-reduction resistance strategies were more consistent across ecological contexts. Overall, host defense strategies strongly affect the transient dynamics of population recovery and may affect the ultimate fate of real populations recovering from disease-induced declines.

One Health Approach to Globalizing, Accelerating, and Focusing Amphibian and Reptile Disease Research-Reflections and Opinions from the First Global Amphibian and Reptile Disease Conference.

The world's reptiles and amphibians are experiencing dramatic and ongoing losses in biodiversity, changes that can have substantial effects on ecosystems and human health. In 2022, the first Global Amphibian and Reptile Disease Conference was held, using One Health as a guiding principle. The conference showcased knowledge on numerous reptile and amphibian pathogens from several standpoints, including epidemiology, host immune defenses, wild population effects, and mitigation. The conference also provided field experts the opportunity to discuss and identify the most urgent herpetofaunal disease research directions necessary to address current and future threats to reptile and amphibian biodiversity.

Capturing complex interactions in disease ecology with simplicial sets.

Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.

A model for leveraging animal movement to understand spatio-temporal disease dynamics.

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when using surveillance data.

Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. However, we lack a systematic understanding of (i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models (ii) what types of biases may be associated with these conclusions and (iii) how explicitly invoking causal or mechanistic modelling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference about pathogen persistence and spillover in multihost-parasite systems, with clear implications for human and wildlife health. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed multihost-parasite models to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model-driven inference of maintenance species can minimize and eliminate emergent biases. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modelling. Using our model, we demonstrate how the community, spatial and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model-driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. Model-driven inference is an essential, yet underused, component of multidisciplinary studies that make inference about host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.

Host density has limited effects on pathogen invasion, disease-induced declines and within-host infection dynamics across a landscape of disease.

1. Host density is hypothesized to be a major driver of variability in the responses and outcomes of wildlife populations following pathogen invasion. While the effects of host density on pathogen transmission have been extensively studied, these studies are dominated by theoretical analyses and small-scale experiments. This focus leads to an incomplete picture regarding how host density drives observed variability in disease outcomes in the field. 2. Here, we leveraged a dataset of hundreds of replicate amphibian populations that varied by orders of magnitude in host density. We used these data to test the effects of host density on three outcomes following the arrival of the amphibian-killing fungal pathogen Batrachochytrium dendrobatidis (Bd): the probability that Bd successfully invaded a host population and led to a pathogen outbreak, the magnitude of the host population-level decline following an outbreak and within-host infection dynamics that drive population-level outcomes in amphibian-pathogen systems. 3. Based on previous small-scale transmission experiments, we expected that populations with higher densities would be more likely to experience Bd outbreaks and would suffer larger proportional declines following outbreaks. To test these predictions, we developed and fitted a Hidden Markov Model that accounted for imperfectly observed disease outbreak states in the amphibian populations we surveyed. 4. Contrary to our predictions, we found minimal effects of host density on the probability of successful Bd invasion, the magnitude of population decline following Bd invasion and the dynamics of within-host infection intensity. Environmental conditions, such as summer temperature, winter severity and the presence of pathogen reservoirs, were more predictive of variability in disease outcomes. 5. Our results highlight the limitations of extrapolating findings from small-scale transmission experiments to observed disease trajectories in the field and provide strong evidence that variability in host density does not necessarily drive variability in host population responses following pathogen arrival. In an applied context, we show that feedbacks between host density and disease will not necessarily affect the success of reintroduction efforts in amphibian-Bd systems of conservation concern.

Why disease ecology needs life-history theory: a host perspective.

When facing an emerging infectious disease of conservation concern, we often have little information on the nature of the host-parasite interaction to inform management decisions. However, it is becoming increasingly clear that the life-history strategies of host species can be predictive of individual- and population-level responses to infectious disease, even without detailed knowledge on the specifics of the host-parasite interaction. Here, we argue that a deeper integration of life-history theory into disease ecology is timely and necessary to improve our capacity to understand, predict and mitigate the impact of endemic and emerging infectious diseases in wild populations. Using wild vertebrates as an example, we show that host life-history characteristics influence host responses to parasitism at different levels of organisation, from individuals to communities. We also highlight knowledge gaps and future directions for the study of life-history and host responses to parasitism. We conclude by illustrating how this theoretical insight can inform the monitoring and control of infectious diseases in wildlife.

Integrating Infection Intensity into Within- and Between-Host Pathogen Dynamics: Implications for Invasion and Virulence Evolution.

AbstractInfection intensity can dictate disease outcomes but is typically ignored when modeling infection dynamics of microparasites (e.g., bacteria, virus, and fungi). However, for a number of pathogens of wildlife typically categorized as microparasites, accounting for infection intensity and within-host infection processes is critical for predicting population-level responses to pathogen invasion. Here, we develop a modeling framework we refer to as reduced-dimension host-parasite integral projection models (reduced IPMs) that we use to explore how within-host infection processes affect the dynamics of pathogen invasion and virulence evolution. We find that individual-level heterogeneity in pathogen load-a nearly ubiquitous characteristic of host-parasite interactions that is rarely considered in models of microparasites-generally reduces pathogen invasion probability and dampens virulence-transmission trade-offs in host-parasite systems. The latter effect likely contributes to widely predicted virulence-transmission trade-offs being difficult to observe empirically. Moreover, our analyses show that intensity-dependent host mortality does not always induce a virulence-transmission trade-off, and systems with steeper than linear relationships between pathogen intensity and host mortality rate are significantly more likely to exhibit these trade-offs. Overall, reduced IPMs provide a useful framework to expand our theoretical and data-driven understanding of how within-host processes affect population-level disease dynamics.

Effects of social structure and management on risk of disease establishment in wild pigs.

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group and spatial distribution (monthly home-range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modelling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. We demonstrate an approach for understanding the relative role of spatial, social and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially and socially structured wildlife species.

Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome.

Symbiotic microbial communities are important for host health, but the processes shaping these communities are poorly understood. Understanding how community assembly processes jointly affect microbial community composition is limited because inflexible community models rely on rejecting dispersal and drift before considering selection. We developed a flexible community assembly model based on neutral theory to ask: How do dispersal, drift and selection concurrently affect the microbiome across environmental gradients? We applied this approach to examine how a fungal pathogen affected the assembly processes structuring the amphibian skin microbiome. We found that the rejection of neutrality for the amphibian microbiome across a fungal gradient was not strictly due to selection processes, but was also a result of species-specific changes in dispersal and drift. Our modelling framework brings the qualitative recognition that niche and neutral processes jointly structure microbiomes into quantitative focus, allowing for improved predictions of microbial community turnover across environmental gradients.

Disease hotspots or hot species? Infection dynamics in multi-host metacommunities controlled by species identity, not source location.

Pathogen persistence in host communities is influenced by processes operating at the individual host to landscape-level scale, but isolating the relative contributions of these processes is challenging. We developed theory to partition the influence of host species, habitat patches and landscape connectivity on pathogen persistence within metacommunities of hosts and pathogens. We used this framework to quantify the contributions of host species composition and habitat patch identity on the persistence of an amphibian pathogen across the landscape. By sampling over 11 000 hosts of six amphibian species, we found that a single host species could maintain the pathogen in 91% of observed metacommunities. Moreover, this dominant maintenance species contributed, on average, twice as much to landscape-level pathogen persistence compared to the most influential source patch in a metacommunity. Our analysis demonstrates substantial inequality in how species and patches contribute to pathogen persistence, with important implications for targeted disease management.

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests.

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental-scale movement data within agro-ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems.

Disease's hidden death toll: Using parasite aggregation patterns to quantify landscape-level host mortality in a wildlife system.

World-wide, infectious diseases represent a major source of mortality in humans and livestock. For wildlife populations, disease-induced mortality is likely even greater, but remains notoriously difficult to estimate-especially for endemic infections. Approaches for quantifying wildlife mortality due to endemic infections have historically been limited by an inability to directly observe wildlife mortality in nature. Here we address a question that can rarely be answered for endemic pathogens of wildlife: what are the population- and landscape-level effects of infection on host mortality? We combined laboratory experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude of mortality induced by an endemic, virulent trematode parasite (Ribeiroia ondatrae) on hundreds of amphibian populations spanning four native species. We developed a flexible statistical model that uses patterns of aggregation in parasite abundance to infer host mortality. Our model improves on previous approaches for inferring host mortality from parasite abundance data by (i) relaxing restrictive assumptions on the timing of host mortality and sampling, (ii) placing all mortality inference within a Bayesian framework to better quantify uncertainty and (iii) accommodating data from laboratory experiments and field sampling to allow for estimates and comparisons of mortality within and among host populations. Applying our approach to 301 amphibian populations, we found that trematode infection was associated with an average of between 13% and 40% population-level mortality. For three of the four amphibian species, our models predicted that some populations experienced >90% mortality due to infection, leading to mortality of thousands of amphibian larvae within a pond. At the landscape scale, the total number of amphibians predicted to succumb to infection was driven by a few high mortality sites, with fewer than 20% of sites contributing to greater than 80% of amphibian mortality on the landscape. The mortality estimates in this study provide a rare glimpse into the magnitude of effects that endemic parasites can have on wildlife populations and our theoretical framework for indirectly inferring parasite-induced mortality can be applied to other host-parasite systems to help reveal the hidden death toll of pathogens on wildlife hosts.

Resistance, tolerance and environmental transmission dynamics determine host extinction risk in a load-dependent amphibian disease.

While disease-induced extinction is generally considered rare, a number of recently emerging infectious diseases with load-dependent pathology have led to extinction in wildlife populations. Transmission is a critical factor affecting disease-induced extinction, but the relative importance of transmission compared to load-dependent host resistance and tolerance is currently unknown. Using a combination of models and experiments on an amphibian species suffering extirpations from the fungal pathogen Batrachochytrium dendrobatidis (Bd), we show that while transmission from an environmental Bd reservoir increased the ability of Bd to invade an amphibian population and the extinction risk of that population, Bd-induced extinction dynamics were far more sensitive to host resistance and tolerance than to Bd transmission. We demonstrate that this is a general result for load-dependent pathogens, where non-linear resistance and tolerance functions can interact such that small changes in these functions lead to drastic changes in extinction dynamics.

Biological and statistical processes jointly drive population aggregation: using host-parasite interactions to understand Taylor's power law.

The macroecological pattern known as Taylor's power law (TPL) represents the pervasive tendency of the variance in population density to increase as a power function of the mean. Despite empirical illustrations in systems ranging from viruses to vertebrates, the biological significance of this relationship continues to be debated. Here we combined collection of a unique dataset involving 11 987 amphibian hosts and 332 684 trematode parasites with experimental measurements of core epidemiological outcomes to explicitly test the contributions of hypothesized biological processes in driving aggregation. After using feasible set theory to account for mechanisms acting indirectly on aggregation and statistical constraints inherent to the data, we detected strongly consistent influences of host and parasite species identity over 7 years of sampling. Incorporation of field-based measurements of host body size, its variance and spatial heterogeneity in host density accounted for host identity effects, while experimental quantification of infection competence (and especially virulence from the 20 most common host-parasite combinations) revealed the role of species-by-environment interactions. By uniting constraint-based theory, controlled experiments and community-based field surveys, we illustrate the joint influences of biological and statistical processes on parasite aggregation and emphasize their importance for understanding population regulation and ecological stability across a range of systems, both infectious and free-living.

When can we infer mechanism from parasite aggregation? A constraint-based approach to disease ecology.

Few hosts have many parasites while many hosts have few parasites. This axiom of macroparasite aggregation is so pervasive it is considered a general law in disease ecology, with important implications for the dynamics of host-parasite systems. Because of these dynamical implications, a significant amount of work has explored both the various mechanisms leading to parasite aggregation patterns and how to infer mechanism from these patterns. However, as many disease mechanisms can produce similar aggregation patterns, it is not clear whether aggregation itself provides any additional information about mechanism. Here we apply a "constraint-based" approach developed in macroecology that allows us to explore whether parasite aggregation contains any additional information beyond what is provided by mean parasite load. We tested two constraint-based null models, both of which were constrained on the total number of parasites P and hosts H found in a sample, using data from 842 observed amphibian host-trematode parasite distributions. We found that constraint-based models captured ~85% of the observed variation in host-parasite distributions, suggesting that the constraints P and H contain much of the information about the shape of the host-parasite distribution. However, we also found that extending the constraint-based null models can identify the potential role of known aggregating mechanisms (such as host heterogeneity) and disaggregating mechanisms (such as parasite-induced host mortality) in constraining host-parasite distributions. Thus, by providing robust null models, constraint-based approaches can help guide investigations aimed at detecting biological processes that directly affect parasite aggregation above and beyond those that indirectly affect aggregation through P and H.

Do fungi look like macroparasites? Quantifying the patterns and mechanisms of aggregation for host-fungal parasite relationships.

Most hosts contain few parasites, whereas few hosts contain many. This pattern, known as aggregation, is well-documented in macroparasites where parasite intensity distribution among hosts affects host-parasite dynamics. Infection intensity also drives fungal disease dynamics, but we lack a basic understanding of host-fungal aggregation patterns, how they compare to macroparasites, and if they reflect biological processes. To address these gaps, we characterized aggregation of the fungal pathogen Batrachochytrium dendrobatidis (Bd) in amphibian hosts. Utilizing the slope of Taylor's Power Law, we found Bd intensity distributions were more aggregated than macroparasites, conforming closely to lognormal distributions. We observed that Bd aggregation patterns are strongly correlated with known biological processes operating in amphibian populations, such as epizoological phase-invasion, post-invasion, and enzootic-and intensity-dependent disease mortality. Using intensity-dependent mathematical models, we found evidence of evolution of host resistance based on aggregation shifts in systems persisting with Bd following disease-induced declines. Our results show that Bd aggregation is highly conserved across disparate systems and is distinct from aggregation patterns in macroparasites, and contains signatures of potential biological processes of amphibian-Bd systems. Our work lays a foundation to unite host-fungal dynamics under a common theoretical framework and inform future modeling approaches that may elucidate host-fungus interactions.

Diverse Relationships between Batrachochytrium Infections and Antimicrobial Peptide Defenses Across Leopard Frog Populations.

Antimicrobial peptides (AMPs) play a fundamental role in the innate defense against microbial pathogens, as well as other immune and non-immune functions. Their role in amphibian skin defense against the pathogenic fungus Batrachochytrium dendrobatidis (Bd) is exemplified by experiments in which depletion of host's stored AMPs increases mortality from infection. Yet, the question remains whether there are generalizable patterns of negative or positive correlations between stored AMP defenses and the probability of infection or infection intensity across populations and species. This study aims to expand on prior field studies of AMP quantities and compositions by correlating stored defenses with an estimated risk of Bd exposure (prevalence and mean infection intensity in each survey) in five locations across the United States and a total of three species. In all locations, known AMPs correlated with the ability of recovered secretions to inhibit Bd in vitro. We found that stored AMP defenses were generally unrelated to Bd infection except in one location where the relative intensity of known AMPs was lower in secretions from infected frogs. In all other locations, known AMP relative intensities were higher in infected frogs. Stored peptide quantity was either positively or negatively correlated with Bd exposure risk. Thus, future experiments coupled with organismal modeling can elucidate whether Bd infection affects secretion/synthesis and will provide insight into how to interpret amphibian ecoimmunology studies of AMPs. We also demonstrate that future AMP isolating and sequencing studies can focus efforts by correlating mass spectrometry peaks to inhibitory capacity using linear decomposition modeling.

Deriving spatially explicit direct and indirect interaction networks from animal movement data.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Corrigendum: Electrolyte imbalances and dehydration play a key role in Batrachochytrium salamandrivorans chytridiomycosis.

[This corrects the article DOI: 10.3389/fvets.2022.1055153.].