Models for managing wildlife disease

Rodent control strategies and Lassa virus: some unexpected effects in Guinea, West Africa

The Natal multimammate mouse (Mastomys natalensis) is the host of Lassa mammarenavirus, causing Lassa haemorrhagic fever in West Africa. As there is currently no operational vaccine and therapeutic drugs are limited, we explored rodent control as an alternative to prevent Lassa virus spillover in Upper Guinea, where the disease is highly endemic in rural areas. In a seven-year experiment, we distributed rodenticides for 10-30 days once a year and, in the last year, added intensive snap trapping for three months in all the houses of one village. We also captured rodents both before and after the intervention period to assess their effectiveness by examining alterations in trapping success and infection rates (Lassa virus RNA and IgG antibodies). We found that both interventions reduced the rodent population by 74-92% but swiftly rebounded to pre-treatment levels, even already six months after the last snap-trapping control. Furthermore, while we observed that chemical control modestly decreased Lassa virus infection rates annually (a reduction of 5% in seroprevalence per year), the intensive trapping unexpectedly led to a significantly higher infection rate (from a seroprevalence of 28% before to 67% after snap trapping control). After seven years, we conclude that annual chemical control, alone or with intensive trapping, is ineffective and sometimes counterproductive in preventing Lassa virus spillover in rural villages. These unexpected findings may result from density-dependent breeding compensation following culling and the survival of a small percentage of chronically infected rodents that may spread the virus to a new susceptible generation of mice.

Wildlife vaccination strategies for eliminating bovine tuberculosis in white-tailed deer populations

Many pathogens of humans and livestock also infect wildlife that can act as a reservoir and challenge disease control or elimination. Efficient and effective prioritization of research and management actions requires an understanding of the potential for new tools to improve elimination probability with feasible deployment strategies that can be implemented at scale. Wildlife vaccination is gaining interest as a tool for managing several wildlife diseases. To evaluate the effect of vaccinating white-tailed deer (Odocoileus virginianus), in combination with harvest, in reducing and eliminating bovine tuberculosis from deer populations in Michigan, we developed a mechanistic age-structured disease transmission model for bovine tuberculosis with integrated disease management. We evaluated the impact of pulse vaccination across a range of vaccine properties. Pulse vaccination was effective for reducing disease prevalence rapidly with even low (30%) to moderate (60%) vaccine coverage of the susceptible and exposed deer population and was further improved when combined with increased harvest. The impact of increased harvest depended on the relative strength of transmission modes, i.e., direct vs indirect transmission. Vaccine coverage and efficacy were the most important vaccine properties for reducing and eliminating disease from the local population. By fitting the model to the core endemic area of bovine tuberculosis in Michigan, USA, we identified feasible integrated management strategies involving vaccination and increased harvest that reduced disease prevalence in free-ranging deer. Few scenarios led to disease elimination due to the chronic nature of bovine tuberculosis. A long-term commitment to regular vaccination campaigns, and further research on increasing vaccines efficacy and uptake rate in free-ranging deer are important for disease management.

The evolution of parasite virulence under targeted culling and harvesting in wildlife and livestock

There is a clear need to understand the effect of human intervention on the evolution of infectious disease. In particular, culling and harvesting of both wildlife and managed livestock populations are carried out in a wide range of management practices, and they have the potential to impact the evolution of a broad range of disease characteristics. Applying eco-evolutionary theory we show that once culling/harvesting becomes targeted on specific disease classes, the established result that culling selects for higher virulence is only found when sufficient infected individuals are culled. If susceptible or recovered individuals are targeted, selection for lower virulence can occur. An important implication of this result is that when culling to eradicate an infectious disease from a population, while it is optimal to target infected individuals, the consequent evolution can increase the basic reproductive ratio of the infection, R 0 , and make parasite eradication more difficult. We show that increases in evolved virulence due to the culling of infected individuals can lead to excess population decline when sustainably harvesting a population. In contrast, culling susceptible or recovered individuals can select for decreased virulence and a reduction in population decline through culling. The implications to the evolution of virulence are typically the same in wildlife populations, that are regulated by the parasite, and livestock populations, that have a constant population size where restocking balances the losses due to mortality. However, the well-known result that vertical transmission selects for lower virulence and transmission in wildlife populations is less marked in livestock populations for parasites that convey long-term immunity since restocking can enhance the density of the immune class. Our work emphasizes the importance of understanding the evolutionary consequences of intervention strategies and the different ecological feedbacks that can occur in wildlife and livestock populations.

Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species

Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.

A microfluidic biosensor for the diagnosis of chronic wasting disease

Cervids are affected by a neurologic disease that is always fatal to individuals and has population effects. This disease is called chronic wasting disease (CWD) and is caused by a misfolded prion protein. The disease is transmitted via contact with contaminated body fluids and tissue or exposure to the environment, such as drinking water or food. Current CWD diagnosis depends on ELISA screening of cervid lymph nodes and subsequent immunohistochemistry (IHC) confirmation of ELISA-positive results. The disease has proven to be difficult to control in part because of sensitivity and specificity issues with the current test regimen. We have investigated an accurate, rapid, and low-cost microfluidic microelectromechanical system (MEMS) biosensing device for the detection of CWD pathologic prions in retropharyngeal lymph nodes (RLNs), which is the current standard type of CWD diagnostic sample. The device consists of three novel regions for concentrating, trapping, and detecting the prion. The detection region includes an array of electrodes coated with a monoclonal antibody against pathologic prions. The experimental conditions were optimized using an engineered prion control antigen. Testing could be completed in less than 1 hour with high sensitivity and selectivity. The biosensor detected the engineered prion antigen at a 1:24 dilution, while ELISA detected the same antigen at a 1:8 dilution. The relative limit of detection (rLOD) of the biosensor was a 1:1000 dilution of a known strong positive RLN sample, whereas ELISA showed a rLOD of 1:100 dilution. Thus, the biosensor was 10 times more sensitive than ELISA, which is the currently approved CWD diagnostic test. The biosensor's specificity and selectivity were confirmed using known negative RPLN samples, a negative control antibody (monoclonal antibody against bovine coronavirus BCV), and two negative control antigens (bluetongue virus and Epizootic hemorrhagic disease virus). The biosensor's ability to detect pathogenic prions was verified by testing proteinase-digested positive RLN samples.

Climate change and infectious disease: a review of evidence and research trends

BACKGROUND

Climate change presents an imminent threat to almost all biological systems across the globe. In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission. Many of these publications focus on simulations based on in silico data, shadowing empirical research based on field and laboratory data. A synthesis work of empirical climate change and infectious disease research is still lacking.

METHODS

We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research. Literature was sourced from Web of Science and PubMed literary repositories using a key word search, and was reviewed using a delineated inclusion criteria by a team of reviewers.

RESULTS

Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research, specifically with regard to types of disease transmission and localities studied. Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature. Furthermore, demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate, high-income countries. We also identified key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may reflect current systemic inequities in the scientific field.

CONCLUSIONS

Future research lines on climate change and infectious diseases should considered diseases of direct transmission (non-vector-borne) and more research effort in the tropics. Inclusion of local research in low- and middle-income countries was generally neglected. Research on climate change and infectious disease has failed to be socially inclusive, geographically balanced, and broad in terms of the disease systems studied, limiting our capacities to better understand the actual effects of climate change on health.

Projecting the impact of an ebola virus outbreak on endangered mountain gorillas

Ebola virus is highly lethal for great apes. Estimated mortality rates up to 98% have reduced the global gorilla population by approximately one-third. As mountain gorillas (Gorilla beringei beringei) are endangered, with just over 1000 individuals remaining in the world, an outbreak could decimate the population. Simulation modeling was used to evaluate the potential impact of an Ebola virus outbreak on the mountain gorilla population of the Virunga Massif. Findings indicate that estimated contact rates among gorilla groups are high enough to allow rapid spread of Ebola, with less than 20% of the population projected to survive at 100 days post-infection of just one gorilla. Despite increasing survival with vaccination, no modeled vaccination strategy prevented widespread infection. However, the model projected that survival rates greater than 50% could be achieved by vaccinating at least half the habituated gorillas within 3 weeks of the first infectious individual.

In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities

Bacillus anthracis, the causative agent of anthrax in humans, livestock, and wildlife, exists in a community with hundreds of other species of bacteria in the environment. Work on the genetics of these communities has shown that B. anthracis shares a high percentage of chromosomal genes with both B. thuringiensis and B. cereus, and that phenotypic differences among these bacteria can result from extra-chromosomal DNA in the form of plasmids. We developed a simple hypothetical individual-based model to simulate the likelihood of detecting plasmids with genes encoding anthrax toxins within bacterial communities composed of B. anthracis, B. thuringiensis, and B. cereus, and the surrounding matrix of extra-cellular polymeric substances. Simulation results suggest the horizontal transfer of plasmids with genes encoding anthrax toxins among Bacillus species persisting outside the host could function as a proximate factor triggering anthrax outbreaks.

Contributions of distemper control and habitat expansion to the Amur leopard viability

The Amur leopard (Panthera pardus orientalis) is a critically endangered top predator that struggles on the brink of extinction due to threats such as canine distemper virus (CDV), habitat loss, and inbreeding depression. Here we develop a viability analysis metamodel that combines a traditional individual-based demographic model with an epidemiological model to assess the benefits of alternative population management actions in response to multiple distinct threats. Our results showed an extinction risk of 10.3%-99.9% if no management actions were taken over 100 years under different levels of inbreeding depression. Reducing the risk of CDV infection in Amur leopards through the low-coverage vaccination of leopards and the management of sympatric domestic dogs could effectively improve the survival probability of the leopard population, and with habitat expansion added to these management measures, the population expanded further. Our findings highlight that protecting the Amur leopard necessitates a multifaceted synergistic effort, and controlling multiple threats together may significantly escalate overall viability of a species, especially for small-isolated threatened population. More broadly, our modeling framework could offer critical perspectives and scientific support for conservation planning, as well as specific adaptive management actions for endangered species around the world.

In the absence of management strategies, canine distemper virus threatens the future existence of the endangered Amur leopard.

Ranavirus Amplification in Low-Diversity Amphibian Communities

In an era where emerging infectious diseases are a serious threat to biodiversity, epidemiological patterns need to be identified, particularly the complex mechanisms driving the dynamics of multi-host pathogens in natural communities. Many amphibian species have faced unprecedented population declines associated with diseases. Yet, specific processes shaping host-pathogen relationships within and among communities for amphibian pathogens such as ranaviruses (RV) remain poorly understood. To address this gap, we conducted a comprehensive study of RV in low-diversity amphibian communities in north-western Canada to assess the effects of biotic factors (species identity, species richness, abundance) and abiotic factors (conductivity, pH) on the pathogen prevalence and viral loads. Across 2 years and 18 sites, with communities of up to three hosts (wood frog, Rana sylvatica; boreal chorus frog, Pseudacris maculata; Canadian toad, Anaxyrus hemiophrys), we observed that RV prevalence nearly doubled with each additional species in a community, suggesting an amplification effect in aquatic, as well as terrestrial life-history stages. Infection intensity among infected wood frogs and boreal chorus frogs also significantly increased with an increase in species richness. Interestingly, we did not observe any effects of host abundance or abiotic factors, highlighting the importance of including host identity and species richness when investigating multi-host pathogens. Ultimately, only such a comprehensive approach can improve our understanding of complex and often highly context-dependent host-pathogen interactions.

CHRONIC WASTING DISEASE MODELING: AN OVERVIEW

Chronic wasting disease (CWD) is an infectious and fatal prion disease occurring in the family Cervidae. To update the research community regarding the status quo of CWD epidemic models, we conducted a meta-analysis on CWD research. We collected data from peer-reviewed articles published since 1980, when CWD was first diagnosed, until December 2018. We explored the analytical methods used historically to understand CWD. We used 14 standardized variables to assess overall analytical approaches of CWD research communities, data used, and the modeling methods used. We found that CWD modeling initiated in the early 2000s and has increased since then. Connectivity of the research community was heavily reliant on a cluster of CWD researchers. Studies focused primarily on regression and compartment-based models, population-level approaches, and host species of game management concern. Similarly, CWD research focused on single populations, species, and locations, neglecting modeling using community ecology and biogeographic approaches. Chronic wasting disease detection relied on classic diagnostic methods with limited sensitivity for most stages of infection. Overall, we found that past modeling efforts generated a solid baseline for understanding CWD in wildlife and increased our knowledge on infectious prion ecology. Future analytical efforts should consider more sensitive diagnostic methods to quantify uncertainty and broader scale studies to elucidate CWD transmission beyond population-level approaches. Considering that infectious prions may not follow biological rules of well-known wildlife pathogens (i.e., viruses, bacteria, fungi), assumptions used when modeling other infectious disease may not apply for CWD. Chronic wasting disease is a new challenge in wildlife epidemiology.

Using Stochastic Modeling to Predict the Effect of Culling and Colony Dispersal of Bats on Zoonotic Viral Epidemics

Frequent outbreaks of emerging infectious diseases originating from wild animals have highlighted the necessity of managing wildlife populations to prevent zoonotic spillover, and the appropriate development of management protocols required attention on gaining a better understanding of viral dynamics in wild animal populations. In east Australia, there have been outbreaks of Hendra virus (HeV) infection in horses and humans following spillover from the virus's reservoir hosts, flying foxes (family Pteropodidae), and bat culling and colony dispersal have been proposed as appropriate management strategies. A key factor relating to flying fox population structure that influences HeV dynamics is that these bats form metapopulations, and consequently, to assess this factor, we designed an epidemic dynamics model of HeV transmission, focusing on bat metapopulation dynamics. Specifically, using flying fox movement data, we stochastically simulated models for a hypothetical metapopulation of flying foxes to examine the impact of metapopulation-related parameters, and subsequently simulated probable scenarios of culling and colony dispersal to estimate their effects on the probability of epidemic occurrence. Modeling of the hypothetical metapopulation revealed that a reduction in the number of large-sized colonies would lead to an increase in the probability of epidemic occurrence within the bat population, whereas the strong spatial coupling among colonies was found to dilute the effects of altering the number of colonies and the number of bats in each colony through culling or colony dispersal of bats on the probability that an epidemic within the bat population would occur. Culling and colony dispersal scenarios showed no significantly beneficial effect with respect to reducing the probability of an HeV epidemic occurring in flying foxes, and may indeed prove counterproductive. In conclusion, the modeling results indicate that bat culling and colony dispersal may not be an effective strategy to control HeV epidemics.

Frequency-dependent transmission of Batrachochytrium salamandrivorans in eastern newts

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 R₀ = 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.

Significant reductions of host abundance weakly impact infection intensity of Batrachochytrium dendrobatidis

Infectious diseases are considered major threats to biodiversity, however strategies to mitigate their impacts in the natural world are scarce and largely unsuccessful. Chytridiomycosis is responsible for the decline of hundreds of amphibian species worldwide, but an effective disease management strategy that could be applied across natural habitats is still lacking. In general amphibian larvae can be easily captured, offering opportunities to ascertain the impact of altering the abundance of hosts, considered to be a key parameter affecting the severity of the disease. Here, we report the results of two experiments to investigate how altering host abundance affects infection intensity in amphibian populations of a montane area of Central Spain suffering from lethal amphibian chytridiomycosis. Our laboratory-based experiment supported the conclusion that varying density had a significant effect on infection intensity when salamander larvae were housed at low densities. Our field experiment showed that reducing the abundance of salamander larvae in the field also had a significant, but weak, impact on infection the following year, but only when removals were extreme. While this suggests adjusting host abundance as a mitigation strategy to reduce infection intensity could be useful, our evidence suggests only heavy culling efforts will succeed, which may run contrary to objectives for conservation.

Active responses to outbreaks of infectious wildlife diseases: objectives, strategies and constraints determine feasibility and success

Emerging wildlife diseases are taking a heavy toll on animal and plant species worldwide. Mitigation, particularly in the initial epidemic phase, is hindered by uncertainty about the epidemiology and management of emerging diseases, but also by vague or poorly defined objectives. Here, we use a quantitative analysis to assess how the decision context of mitigation objectives, available strategies and practical constraints influences the decision of whether and how to respond to epidemics in wildlife. To illustrate our approach, we parametrized the model for European fire salamanders affected by Batrachochytrium salamandrivorans, and explored different combinations of conservation, containment and budgetary objectives. We found that in approximately half of those scenarios, host removal strategies perform equal to or worse than no management at all during a local outbreak, particularly where removal cannot exclusively target infected individuals. Moreover, the window for intervention shrinks rapidly if an outbreak is detected late or if a response is delayed. Clearly defining the decision context is, therefore, vital to plan meaningful responses to novel outbreaks. Explicitly stating objectives, strategies and constraints, if possible before an outbreak occurs, avoids wasting precious resources and creating false expectations about what can and cannot be achieved during the epidemic phase.

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.

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.

Host density drives viral, but not trypanosome, transmission in a key pollinator

Supplemental feeding of wildlife populations can locally increase the density of individuals, which may in turn impact disease dynamics. Flower strips are a widely used intervention in intensive agricultural systems to nutritionally support pollinators such as bees. Using a controlled experimental semi-field design, we asked how density impacts transmission of a virus and a trypanosome parasite in bumblebees. We manipulated bumblebee density by using different numbers of colonies within the same area of floral resource. In high-density compartments, slow bee paralysis virus was transmitted more quickly, resulting in higher prevalence and level of infection in bumblebee hosts. By contrast, there was no impact of density on the transmission of the trypanosome Crithidia bombi, which may reflect the ease with which this parasite is transmitted. These results suggest that agri-environment schemes such as flower strips, which are known to enhance the nutrition and survival of bumblebees, may also have negative impacts on pollinators through enhanced disease transmission. Future studies should assess how changing the design of these schemes could minimize disease transmission and thus maximise their health benefits to wild pollinators.

Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations

The emergence and spread of infections can contribute to the decline and extinction of populations, particularly in conjunction with anthropogenic environmental change. The importance of heterogeneity in processes of transmission, resistance and tolerance is increasingly well understood in theory, but empirical studies that consider both the demographic and behavioural implications of infection are scarce. Non-random mixing of host individuals can impact the demographic thresholds that determine the amplification or attenuation of disease prevalence. Risk assessment and management of disease in threatened wildlife populations must therefore consider not just host density, but also the social structure of host populations. Here we integrate the most recent developments in epidemiological research from a demographic and social network perspective, and synthesize the latest developments in social network modelling for wildlife disease, to explore their applications to disease management in populations in decline and at risk of extinction. We use simulated examples to support our key points and reveal how disease-management strategies can and should exploit both behavioural and demographic information to prevent or control the spread of disease. Our synthesis highlights the importance of considering the combined impacts of demographic and behavioural processes in epidemics to successful disease management in a conservation context. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

The Critical Role of Infectious Disease in Compensatory Population Growth in Response to Culling

Despite the ubiquity of disease in nature, the role that disease dynamics play in the compensatory growth response to harvesting has been ignored. We use a mathematical approach to show that harvesting can lead to compensatory growth due to a release from disease-induced mortality. Our findings imply that culling in systems that harbor virulent parasites can reduce disease prevalence and increase population density. Our models predict that this compensation occurs for a broad range of infectious disease characteristics unless the disease induces long-lasting immunity in hosts. Our key insight is that a population can be regulated at a similar density by disease or at reduced prevalence by a combination of culling and disease. We illustrate our predictions with a system-specific model representing wild boar tuberculosis infection, parameterized for central Spain, and find significant compensation to culling. Given that few wildlife diseases are likely to induce long-lived immunity, populations with virulent diseases may often be resilient to harvesting.

Food for contagion: synthesis and future directions for studying host-parasite responses to resource shifts in anthropogenic environments

Human-provided resource subsidies for wildlife are diverse, common and have profound consequences for wildlife-pathogen interactions, as demonstrated by papers in this themed issue spanning empirical, theoretical and management perspectives from a range of study systems. Contributions cut across scales of organization, from the within-host dynamics of immune function, to population-level impacts on parasite transmission, to landscape- and regional-scale patterns of infection. In this concluding paper, we identify common threads and key findings from author contributions, including the consequences of resource subsidies for (i) host immunity; (ii) animal aggregation and contact rates; (iii) host movement and landscape-level infection patterns; and (iv) interspecific contacts and cross-species transmission. Exciting avenues for future work include studies that integrate mechanistic modelling and empirical approaches to better explore cross-scale processes, and experimental manipulations of food resources to quantify host and pathogen responses. Work is also needed to examine evolutionary responses to provisioning, and ask how diet-altered changes to the host microbiome influence infection processes. Given the massive public health and conservation implications of anthropogenic resource shifts, we end by underscoring the need for practical recommendations to manage supplemental feeding practices, limit human-wildlife conflicts over shared food resources and reduce cross-species transmission risks, including to humans.This article is part of the theme issue 'Anthropogenic resource subsidies and host-parasite dynamics in wildlife'.

Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling

The Natal multimammate mouse (Mastomys natalensis) is the reservoir host of Lassa virus, an arenavirus that causes Lassa haemorrhagic fever in humans in West Africa. Because no vaccine exists and therapeutic options are limited, preventing infection through rodent control and human behavioural measures is currently considered to be the only option. In order to assess the efficacy of rodent control, we performed a 4-year field experiment in rural Upper Guinea and developed a mathematical model to simulate different control strategies (annual density control, continuous density control, and rodent vaccination). For the field study, rodenticide baits were placed each year in three rural villages, while three other villages were used as controls. Rodents were trapped before and after every treatment and their antibody status and age were determined. Data from the field study were used to parameterize the mathematical model. In the field study, we found a significant negative effect of rodent control on seroprevalence, but this effect was small especially given the effort. Furthermore, the rodent populations recovered rapidly after rodenticide application, leading us to conclude that an annual control strategy is unlikely to significantly reduce Lassa virus spillover to humans. In agreement with this finding, the mathematical model suggests that the use of continuous control or rodent vaccination is the only strategy that could lead to Lassa virus elimination. These field and model results can serve as a guide for determining how long and frequent rodent control should be done in order to eliminate Lassa virus in rural villages.

Assessing the contributions of intraspecific and environmental sources of infection in urban wildlife: Salmonella enterica and white ibis as a case study

Conversion of natural habitats into urban landscapes can expose wildlife to novel pathogens and alter pathogen transmission pathways. Because transmission is difficult to quantify for many wildlife pathogens, mathematical models paired with field observations can help select among competing transmission pathways that might operate in urban landscapes. Here we develop a mathematical model for the enteric bacteria Salmonella enterica in urban-foraging white ibis ( Eudocimus albus) in south Florida as a case study to determine (i) the relative importance of contact-based versus environmental transmission among ibis and (ii) whether transmission can be supported by ibis alone or requires external sources of infection. We use biannual field prevalence data to restrict model outputs generated from a Latin hypercube sample of parameter space and select among competing transmission scenarios. We find the most support for transmission from environmental uptake rather than between-host contact and that ibis-ibis transmission alone could maintain low infection prevalence. Our analysis provides the first parameter estimates for Salmonella shedding and uptake in a wild bird and provides a key starting point for predicting how ibis response to urbanization alters their exposure to a multi-host zoonotic enteric pathogen. More broadly, our study provides an analytical roadmap to assess transmission pathways of multi-host wildlife pathogens in the face of scarce infection data.

Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

Using stochastic epidemiological models to evaluate conservation strategies for endangered amphibians

Recent outbreaks of chytridiomycosis, the disease of amphibians caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), have contributed to population declines of numerous amphibian species worldwide. The devastating impacts of this disease have led researchers to attempt drastic conservation measures to prevent further extinctions and loss of biodiversity. The conservation measures can be labour-intensive or expensive, and in many cases have been unsuccessful. We developed a mathematical model of Bd outbreaks that includes the effects of demographic stochasticity and within-host fungal load dynamics. We investigated the impacts of one-time treatment conservation strategies during the disease outbreak that occurs following the initial arrival of Bd into a previously uninfected frog population. We found that for all versions of the model, for a large fraction of parameter space, none of the one-time treatment strategies are effective at preventing disease-induced extinction of the amphibian population. Of the strategies considered, treating frogs with antifungal agents to reduce their fungal load had the greatest likelihood of a beneficial outcome and the lowest risk of decreasing the persistence of the frog population, suggesting that this disease mitigation strategy should be prioritized over disinfecting the environment or reducing host density.

Prediction and attenuation of seasonal spillover of parasites between wild and domestic ungulates in an arid mixed-use system

Transmission of parasites between host species affects host population dynamics, interspecific competition, and ecosystem structure and function. In areas where wild and domestic herbivores share grazing land, management of parasites in livestock may affect or be affected by sympatric wildlife due to cross-species transmission.We develop a novel method for simulating transmission potential based on both biotic and abiotic factors in a semi-arid system in Botswana. Optimal timing of antiparasitic treatment in livestock is then compared under a variety of alternative host scenarios, including seasonally migrating wild hosts.In this region, rainfall is the primary driver of seasonality of transmission, but wildlife migration leads to spatial differences in the effectiveness of treatment in domestic animals. Additionally, competent migratory wildlife hosts move parasites across the landscape.Simulated transmission potential matches observed patterns of clinical disease in livestock in the study area. Increased wildlife contact is correlated with a decrease in disease, suggesting that non-competent wild hosts may attenuate transmission by removing infective parasite larvae from livestock pasture.Optimising the timing of treatment according to within-year rainfall patterns was considerably more effective than treating at a standard time of year. By targeting treatment in this way, efficient control can be achieved, mitigating parasite spillover from wildlife where it does occur. Synthesis and applications. This model of parasite transmission potential enables evidence-based management of parasite spillover between wild and domestic species in a spatio-temporally dynamic system. It can be applied in other mixed-use systems to mitigate parasite transmission under altered climate scenarios or changes in host ranges.

Infectious Disease in Wild Animal Populations: Examining Transmission and Control with Mathematical Models

The mathematical modeling of ecological interactions is an essential tool in predicting the behavior of complex systems across landscapes. The scientific literature is growing with examples of models used to explore predator-prey interactions, resource selection, population growth, and dynamics of disease transmission. These models provide managers with an efficient alternative means of testing new management and control strategies without resorting to empirical testing that is often costly, time-consuming, and impractical. This chapter presents a review of four types of mathematical models used to understand and predict the spread of infectious diseases in wild animals: compartmental, metapopulation, spatial, and contact network models. Descriptions of each model’s uses and limitations are used to provide a look at the complexities involved in modeling the spread of diseases and the trade-offs that accompany selecting one modeling approach over another. Potential avenues for the improvement and use of these models in future studies are also discussed, as are specific examples of how each type of model has improved our understanding of infectious diseases in populations of wild animals.

ERBB3: A potential serum biomarker for early detection and therapeutic target for devil facial tumour 1 (DFT1)

Devil Facial Tumour 1 (DFT1) is one of two transmissible neoplasms of Tasmanian devils (Sarcophilus harrisii) predominantly affecting their facial regions. DFT1's cellular origin is that of Schwann cell lineage where lesions are evident macroscopically late in the disease. Conversely, the pre-clinical timeframe from cellular transmission to appearance of DFT1 remains uncertain demonstrating the importance of an effective pre-clinical biomarker. We show that ERBB3, a marker expressed normally by the developing neural crest and Schwann cells, is immunohistohemically expressed by DFT1, therefore the potential of ERBB3 as a biomarker was explored. Under the hypothesis that serum ERBB3 levels may increase as DFT1 invades local and distant tissues our pilot study determined serum ERBB3 levels in normal Tasmanian devils and Tasmanian devils with DFT1. Compared to the baseline serum ERBB3 levels in unaffected Tasmanian devils, Tasmanian devils with DFT1 showed significant elevation of serum ERBB3 levels. Interestingly Tasmanian devils with cutaneous lymphoma (CL) also showed elevation of serum ERBB3 levels when compared to the baseline serum levels of Tasmanian devils without DFT1. Thus, elevated serum ERBB3 levels in otherwise healthy looking devils could predict possible DFT1 or CL in captive or wild devil populations and would have implications on the management, welfare and survival of Tasmanian devils. ERBB3 is also a therapeutic target and therefore the potential exists to consider modes of administration that may eradicate DFT1 from the wild.

Thermal Performance Curves and the Metabolic Theory of Ecology-A Practical Guide to Models and Experiments for Parasitologists

Climate change will affect host-parasite dynamics in complex ways. The development of forecast models is necessary for proactive disease management, but past studies have frequently reported thermal performance data in idiosyncratic ways that have limited use for parameterizing thermal host-parasite models. Development of improved forecast models will require strong collaborations between experimental parasitologists and disease modelers. The purpose of this article is to facilitate such collaborations by reviewing practical considerations for describing thermal performance curves of parasite and host performance traits, and using them to predict climate change impacts on host-parasite systems. In the first section, we provide an overview of how thermal performance curves can be embedded in life-cycle-based dynamical models of parasitism, and we outline how such models can capture the net effect of multiple nonlinear temperature dependencies affecting the host-parasite dynamics. We also discuss how macroecological generalities based on the metabolic theory of ecology (MTE) can be used to determine a priori parameter estimates for thermal performance curves to derive null models for data-deficient species, but we note that most of the generalities suggested by MTE remain to be tested for parasites. In the second section, we discuss empirical knowledge gaps for the temperature dependence of parasite and host performance traits, and we outline the types of data that need to be collected to inform MTE-based models for data-deficient species. We specifically emphasize the importance of (1) capturing the entire thermal response of performance traits, including lower and upper temperature thresholds, and (2) experimentally or statistically separating out the thermal responses of different performance traits (e.g., development and mortality) rather than only reporting composite measures (e.g., apparent development). Not adhering to these principles can lead to biased climate change impact predictions. In the third section, we provide a practical guide outlining how experimentalists can contribute to fill data gaps by measuring the temperature dependence of host and parasite performance traits in ways that are systematic, statistically rigorous, and consistent with the requirements of life cycle-based host-parasite models. This guide includes recommendations and practical examples illustrating (1) the use of perturbation analyses to determine experimental priorities, (2) experimental design tips for quantifying thermal response curves, and (3) statistical methods for estimating the parameters of thermal performance curves. Our hope is that this article helps researchers to maximize the value and use of future data collections for both empirical and modelling studies investigating the way in which temperature influences parasitism.

Editorial: Mathematical modelling of infectious diseases

The field of disease ecology - the study of the spread and impact of parasites and pathogens within their host populations and communities - has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R 0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard 'one-host-one-parasite' paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.