Search

Background: Despite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory is needed that quantifies the contributions of both fine-scale host space use and non-independent, correlated host movements to epidemiological dynamics., Methods: We developed and applied new theory that quantifies relative contributions of fine-scale space use and non-independent host movements to spatio-temporal transmission risk. Our theory decomposes pairwise spatio-temporal transmission risk into two components: (i) spatial overlap of hosts-a classic metric of spatial transmission risk - and (ii) pairwise correlations in space use - a component of transmission risk that is almost universally ignored. Using analytical results, simulations, and empirical movement data, we ask: under what ecological and epidemiological conditions do non-independent movements substantially alter spatio-temporal transmission risk compared to spatial overlap?, Results: Using theory and simulation, we found that for directly transmitted pathogens even weak pairwise correlations in space use among hosts can increase contact and transmission risk by orders of magnitude compared to independent host movements. In contrast, non-independent movements had reduced importance for transmission risk for indirectly transmitted pathogens. Furthermore, we found that if the scale of pathogen transmission is smaller than the scale where host social decisions occur, host movements can be highly correlated but this correlation matters little for transmission. We applied our theory to GPS movement data from white-tailed deer (Odocoileus virginianus). Our approach predicted highly seasonally varying contributions of the spatial and social drivers of transmission risk - with social interactions augmenting transmission risk between hosts by greater than a factor of 10 in some cases, despite similar degrees of spatial overlap. Moreover, social interactions could lead to a distinct shift in the predicted locations of transmission hotspots, compared to joint space use., Conclusions: Our theory provides clear expectations for when non-independent movements alter spatio-temporal transmission risk, showing that correlated movements can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap., Competing Interests: Declarations. Ethics approval and consent to participate: Deer capture was performed under the IACUC permit # 2850-1021 issued by the University of Tennessee. Consent for publication: Not applicable Competing interests: The authors declare that they have no Conflict of interest., (© 2025. The Author(s).)

Management of large herbivores often involves increasing availability of forages sufficient in nutrient density to allow animals to meet dietary demands. Nutritional carrying capacity (NCC) models commonly are used to compare plant communities and management strategies, but failure to use the most limiting nutrient could result in overestimating NCC. Moreover, the relationship between limiting nutrients often is not considered, which may influence the utility of NCC models based on a single nutrient, especially when herbivores must simultaneously meet multiple constraints. We examined crude protein and phosphorus concentrations in 131 plant species commonly eaten by white-tailed deer (Odocoileus virginianus) to determine whether they would meet a minimum 14% crude protein and 0.3% phosphorus constraint for a lactating female. Crude protein and phosphorus demands were met in 43.9% and 18.8% of sampled forages, respectively. Concentrations of crude protein and phosphorus were greatest in young forb tissue, with an average of 18.6% crude protein and 0.28% phosphorus. Protein and phosphorus concentrations were positively correlated, but not all plants which met protein requirements simultaneously provided sufficient phosphorus. We created NCC models using crude protein and phosphorus and documented phosphorus tended to be more limiting, but variation existed among sites. Given that limiting nutrients may vary spatiotemporally, focusing conservation efforts on providing a diversity of plants, particularly to include forbs that simultaneously meet multiple nutritional demands, is likely the most practical management approach., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Vast alteration of the biosphere by humans is causing a sixth mass extinction, driven in part by an increase in infectious diseases. The emergence of the lethal fungal pathogen Batrachochytrium dendrobatidis (Bd) has devastated global amphibian biodiversity. Given the lack of any broadly applicable methods to reverse these impacts, the future of many amphibians appears grim. The Sierra Nevada yellow-legged frog (Rana sierrae) is highly susceptible to Bd infection and most R. sierrae populations are extirpated following disease outbreaks. However, some populations persist and eventually recover, and frogs in these recovering populations have increased resistance against infection. Here, we conduct a 15-year reintroduction study and show that frogs collected from recovering populations and reintroduced to vacant habitats can reestablish populations despite the presence of Bd. In addition, the likelihood of establishment is influenced by site, cohort, and frog attributes. Results from viability modeling suggest that many reintroduced populations have a low probability of extinction over 50 years. These results provide a rare example of how reintroduction of resistant individuals can allow the landscape-scale recovery of disease-impacted species, and have broad implications for amphibians and other taxa that are threatened with extinction by novel pathogens., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

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.

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.

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.

As threats to amphibian health increase, there is a growing need for diagnostic tools to assess and monitor their health status. Plasma protein electrophoresis has proven to be useful in other nonmammalian species. It enables quantification of protein fractions in plasma that may be altered in various disease processes, and is therefore useful in narrowing down differential diagnoses and detecting inflammation, in combination with other modalities such as biochemical and hematologic testing. The amphibian electrophoretogram must be defined before baseline reference intervals are obtained across species. Agarose gel electrophoresis was performed on plasma samples collected from presumed clinically normal individuals of one anuran and six urodelans: Osteopilus septentrionalis (n=2), Gyrinophilus porphyriticus (n=1), Notophthalmus viridescens (n=1), Eurycea guttolineata (n=2), Amphiuma tridactylum (n=2), Cryptobranchus alleganiensis (n=5), and Siren lacertina (n=6). The electrophoretograms varied in number of fractions between each species; however, the number of fractions was consistent within a species. An albumin migrating fraction was consistently observed in all species. A prealbumin migrating fraction was identified in species that primarily use organs other than skin for respiration. This study provides preliminary examples of a normal plasma protein electrophoretogram for seven amphibian species. Further studies quantifying reference intervals and identification of protein fractions will help establish protein electrophoresis as a useful tool in amphibian health investigations., (© Wildlife Disease Association 2023.)

Batrachochytrium salamandrivorans (Bsal) is a fungal pathogen of amphibians that is emerging in Europe and could be introduced to North America through international trade or other pathways. To evaluate the risk of Bsal invasion to amphibian biodiversity, we performed dose-response experiments on 35 North American species from 10 families, including larvae from five species. We discovered that Bsal caused infection in 74% and mortality in 35% of species tested. Both salamanders and frogs became infected and developed Bsal chytridiomycosis. Based on our host susceptibility results, environmental suitability conditions for Bsal, and geographic ranges of salamanders in the United States, predicted biodiversity loss is expected to be greatest in the Appalachian Region and along the West Coast. Indices of infection and disease susceptibility suggest that North American amphibian species span a spectrum of vulnerability to Bsal chytridiomycosis and most amphibian communities will include an assemblage of resistant, carrier, and amplification species. Predicted salamander losses could exceed 80 species in the United States and 140 species in North America., (© 2023. The Author(s).)

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., Competing Interests: All authors declare no conflict of interest., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Introduction: One of the most important emerging infectious diseases of amphibians is caused by the fungal pathogen Batrachochytrium salamandrivorans (Bsal) . Bsal was recently discovered and is of global concern due to its potential to cause high mortality in amphibians, especially salamander species. To date, little has been reported on the pathophysiological effects of Bsal ; however, studies of a similar fungus, B. dendrobatidis (Bd) , have shown that electrolyte losses and immunosuppression likely play a key role in morbidity and mortality associated with this disease. The goal of this study was to investigate pathophysiological effects and immune responses associated with Bsal chytridiomycosis using 49 rough-skinned newts ( Taricha granulosa ) as the model species., Methods: Taricha granulosa were exposed to a 1 × 107 per 10 mL dose of Bsal zoospores and allowed to reach various stages of disease progression before being humanely euthanized. At the time of euthanasia, blood was collected for biochemical and hematological analyses as well as protein electrophoresis. Ten standardized body sections were histologically examined, and Bsal -induced skin lesions were counted and graded on a scale of 1-5 based on severity., Results: Results indicated that electrolyte imbalances and dehydration induced by damage to the epidermis likely play a major role in the pathogenesis of Bsal chytridiomycosis in this species. Additionally, Bsal -infected, clinically diseased T. granulosa exhibited a systemic inflammatory response identified through alterations in complete blood counts and protein electrophoretograms., Discussion: Overall, these results provide foundational information on the pathogenesis of this disease and highlight the differences and similarities between Bsal and Bd chytridiomycosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sheley, Gray, Wilber, Cray, Carter and Miller.)

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., (© 2022 The Authors. Journal of Animal Ecology © 2022 British Ecological Society.)

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., (© 2022 John Wiley & Sons Ltd.)

The emerging fungal amphibian pathogen, Batrachochytrium salamandrivorans ( Bsal ), is currently spreading across Europe and given its estimated invasion potential, has the capacity to decimate salamander populations worldwide. Fungicides are a promising in situ management strategy for Bsal due to their ability to treat the environment and infected individuals. However, antifungal drugs or pesticides could adversely affect the environment and non-target hosts, thus identifying safe, effective candidate fungicides for in situ treatment is needed. Here, we estimated the inhibitory fungicidal efficacy of five plant-derived fungicides (thymol, curcumin, allicin, 6-gingerol, and Pond Pimafix ® ) and one chemical fungicide (Virkon ® Aquatic) against Bsal zoospores in vitro. We used a broth microdilution method in 48-well plates to test the efficacy of six concentrations per fungicide on Bsal zoospore viability. Following plate incubation, we performed cell viability assays and agar plate growth trials to estimate the minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) of each fungicide. All six fungicides exhibited inhibitory and fungicidal effects against Bsal growth, with estimated MIC concentrations ranging from 60 to 0.156 μg/mL for the different compounds. Allicin showed the greatest efficacy (i.e., lowest MIC and MFC) against Bsal zoospores followed by curcumin, Pond Pimafix ® , thymol, 6-gingerol, and Virkon ® Aquatic, respectively. Our results provide evidence that plant-derived fungicides are effective at inhibiting and killing Bsal zoospores in vitro and may be useful for in situ treatment. Additional studies are needed to estimate the efficacy of these fungicides at inactivating Bsal in the environment and treating Bsal -infected amphibians.

Host species that can independently maintain a pathogen in a host community and contribute to infection in other species are important targets for disease management. However, the potential of host species to maintain a pathogen is not fixed over time, and an important challenge is understanding how within- and across-season variability in host maintenance potential affects pathogen persistence over longer time scales relevant for disease management (e.g., years). Here, we sought to understand the causes and consequences of seasonal infection dynamics in leopard frogs (Rana sphenocephala and Rana pipiens) infected with the fungal pathogen Batrachochytrium dendrobatidis (Bd). We addressed three questions broadly applicable to seasonal host-parasite systems. First, to what degree are observed seasonal patterns in infection driven by temperature-dependent infection processes compared to seasonal host demographic processes? Second, how does seasonal variation in maintenance potential affect long-term pathogen persistence in multi-host communities? Third, does high deterministic maintenance potential relate to the long-term stochastic persistence of pathogens in host populations with seasonal infection dynamics? To answer these questions, we used field data collected over 3 years on >1400 amphibians across four geographic locations, laboratory and mesocosm experiments, and a novel mathematical model. We found that the mechanisms that drive seasonal prevalence were different from those driving seasonal infection intensity. Seasonal variation in Bd prevalence was driven primarily by changes in host contact rates associated with breeding migrations to and from aquatic habitat. In contrast, seasonal changes in infection intensity were driven by temperature-induced changes in Bd growth rate. Using our model, we found that the maintenance potential of leopard frogs varied significantly throughout the year and that seasonal troughs in infection prevalence made it unlikely that leopard frogs were responsible for long-term Bd persistence in these seasonal amphibian communities, highlighting the importance of alternative pathogen reservoirs for Bd persistence. Our results have broad implications for management in seasonal host-pathogen systems, showing that seasonal changes in host and pathogen vital rates, rather than the depletion of susceptible hosts, can lead to troughs in pathogen prevalence and stochastic pathogen extirpation., (© 2022 The Ecological Society of America.)

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., (© 2022 The Authors. Journal of Animal Ecology © 2022 British Ecological Society.)

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., (© 2022 John Wiley & Sons Ltd.)

Transmission is the fundamental process whereby pathogens infect their hosts and spread through populations, and can be characterized using mathematical functions. The functional form of transmission for emerging pathogens can determine pathogen impacts on host populations and can inform the efficacy of disease management strategies. By directly measuring transmission between infected and susceptible adult eastern newts (Notophthalmus viridescens) in aquatic mesocosms, we identified the most plausible transmission function for the emerging amphibian fungal pathogen Batrachochytrium salamandrivorans (Bsal). Although we considered a range of possible transmission functions, we found that Bsal transmission was best explained by pure frequency dependence. We observed that >90% of susceptible newts became infected within 17 days post-exposure to an infected newt across a range of host densities and initial infection prevalence treatments. Under these conditions, we estimated R 0  = 4.9 for Bsal in an eastern newt population. Our results suggest that Bsal has the capability of driving eastern newt populations to extinction and that managing host density may not be an effective management strategy. Intervention strategies that prevent Bsal introduction or increase host resistance or tolerance to infection may be more effective. Our results add to the growing empirical evidence that transmission of wildlife pathogens can saturate and be functionally frequency-dependent., (© 2021 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.)

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.

Batrachochytrium salamandrivorans is an emerging fungus that is causing salamander declines in Europe. We evaluated whether an invasive frog species (Cuban treefrog, Osteopilus septentrionalis) that is found in international trade could be an asymptomatic carrier when exposed to zoospore doses known to infect salamanders. We discovered that Cuban treefrogs could be infected with B. salamandrivorans and, surprisingly, that chytridiomycosis developed in animals at the two highest zoospore doses. To fulfill Koch's postulates, we isolated B. salamandrivorans from infected frogs, exposed eastern newts (Notophthalmus viridescens) to the isolate, and verified infection and disease by histopathology. This experiment represents the first documentation of B. salamandrivorans chytridiomycosis in a frog species and substantially expands the conservation threat and possible mobilization of this pathogen in trade., (© Wildlife Disease Association 2021.)

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., (© 2020 British Ecological Society.)

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., (© 2021 John Wiley & Sons Ltd.)

Pigs (Sus scrofa) may be important surveillance targets for risk assessment and risk-based control planning against emerging zoonoses. Pigs have high contact rates with humans and other animals, transmit similar pathogens as humans including CoVs, and serve as reservoirs and intermediate hosts for notable human pandemics. Wild and domestic pigs both interface with humans and each other but have unique ecologies that demand different surveillance strategies. Three fundamental questions shape any surveillance program: where, when, and how can surveillance be conducted to optimize the surveillance objective? Using theory of mechanisms of zoonotic spillover and data on risk factors, we propose a framework for determining where surveillance might begin initially to maximize a detection in each host species at their interface. We illustrate the utility of the framework using data from the United States. We then discuss variables to consider in refining when and how to conduct surveillance. Recent advances in accounting for opportunistic sampling designs and in translating serology samples into infection times provide promising directions for extracting spatio-temporal estimates of disease risk from typical surveillance data. Such robust estimates of population-level disease risk allow surveillance plans to be updated in space and time based on new information (adaptive surveillance) thus optimizing allocation of surveillance resources to maximize the quality of risk assessment insight., (Published by Elsevier B.V.)

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., (© 2020 British Ecological Society.)

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., (© 2019 by the Ecological Society of America.)

Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and improve understanding of the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies (serosurveillance), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock., (© 2019 by the Ecological Society of America.)

Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM-SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January-March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.)

Almost 80% of the 4 billion projected increase in world population by 2100 comes from 37 Mid-African Countries (MACs), caused mostly by slow declines in Total Fertility Rate (TFR). Historically, TFR has declined in response to increases in wellbeing associated with economic development. We show that, when Infant Survival Rate (ISR, a proxy for wellbeing) has increased, MAC fertility has declined at the same rate, in relation to ISR, as it did in 61 comparable Other Developing Countries (ODCs) whose average fertility is close to replacement level. If MAC ISR were to increase at the historic rate of these ODCs, and TFR declined correspondingly, then the projected world population in 2100 would be decreasing and 1.1 billion lower than currently projected. Such rates of ISR increase, and TFR decrease, are quite feasible and have occurred in comparable ODCs. Increased efforts to improve the wellbeing of poor MAC populations are key., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2017 The Author(s).)

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., (© 2016 by the Ecological Society of America.)

Host parasite models are typically constructed under either a microparasite or macroparasite paradigm. However, this has long been recognized as a false dichotomy because many infectious disease agents, including most fungal pathogens, have attributes of both microparasites and macroparasites.We illustrate how Integral Projection Models (IPM)s provide a novel, elegant modeling framework to represent both types of pathogens. We build a simple host-parasite IPM that tracks both the number of susceptible and infected hosts and the distribution of parasite burdens in infected hosts.The vital rate functions necessary to build IPMs for disease dynamics share many commonalities with classic micro and macroparasite models and we discuss how these functions can be parameterized to build a host-parasite IPM. We illustrate the utility of this IPM approach by modeling the temperature-dependent epizootic dynamics of amphibian chytrid fungus in Mountain yellow-legged frogs ( Rana muscosa ).The host-parasite IPM can be applied to other diseases such as facial tumor disease in Tasmanian devils and white-nose syndrome in bats. Moreover, the host-parasite IPM can be easily extended to capture more complex disease dynamics and provides an exciting new frontier in modeling wildlife disease.

Parasites can significantly impact animal populations by changing host behaviour, reproduction and survival. Detecting and quantifying these impacts is critical for understanding disease dynamics and managing wild animal populations. However, for wild hosts infected with macroparasites, it is notoriously difficult to quantify the fatal parasite load and number of animals that have died due to disease. When ethical or logistical constraints prohibit experimental determination of these values, examination of parasite intensity and distribution data may offer an alternative solution. In this study we introduce a novel method for using intensity data to detect and quantify parasite-induced mortality in wildlife populations. We use simulations to show that this method is more reliable than previously proposed methods while providing quantitative estimates of parasite-induced mortality from empirical data that are consistent with previously published qualitative estimates. However this method, and all techniques that estimate parasite-induced mortality from intensity data alone, have several important assumptions that must be scrutinised before applying those to real-world data. Given that these assumptions are met, our method is a new exploratory tool that can help inform more rigorous studies of parasite-induced host mortality., (Copyright © 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.)