Host-pathogen Dynamics Research Articles

Life history and population regulation shape demographic competence and influence the maintenance of endemic disease.

Host life history and demography play important roles in host-pathogen dynamics, by influencing the ability of hosts and their pathogens to coexist. We introduce the concept of demographic competence to describe the ability of host populations to sustain endemic infectious disease. Hosts with high demographic competence are more likely to act as keystone hosts and constitute reservoirs of infection that can spill over into other species. We propose that the pace of host life history will interact with pathogen life history to influence demographic competence. Our models demonstrate that slow-living hosts (with high survival and low recruitment rates) have greater demographic competence than fast-living hosts (with low survival and high recruitment rates) for susceptible-infected disease dynamics, although this difference is reduced when populations of slow hosts are age structured. Demographic competence is generally greater among hosts with populations regulated by survival compared to reproduction, but this difference is smallest among slow life histories and reversed for some pathogens with frequency-dependent transmission. An association between pathogen life history traits and the demographic competence of faster-living hosts also has implications for trade-offs between pathogen virulence and transmissibility. Overall, we demonstrate how host life history traits can help predict wildlife reservoirs of zoonoses and the vulnerability of populations to disease-induced extinction.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions.

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Leishmania infection triggers hepcidin-mediated proteasomal degradation of Nramp1 to increase phagolysosomal iron availability.

Natural resistance-associated macrophage protein 1 (Nramp1) was originally discovered as a genetic determinant of resistance against multiple intracellular pathogens, including Leishmania. It encodes a transmembrane protein of the phago-endosomal compartments, where it functions as an iron transporter. But the mechanism by which Nramp1 controls host-pathogen dynamics and determines final outcome of an infection is yet to be fully deciphered. Whether the expression of Nramp1 is altered in response to a pathogen attack is also unknown. To address these, Nramp1 status was examined in Leishmania major-infected murine macrophages. We observed that at 12 hrs post infection, there was drastic lowering of Nramp1 level accompanied by increased phagolysosomal iron content and enhanced intracellular parasite growth. Leishmania infection-induced Nramp1 downregulation was caused by ubiquitin-proteasome degradation pathway, which in turn was found to be mediated by the iron-regulatory peptide hormone hepcidin. Blocking of Nramp1 degradation with proteasome inhibitor or transcriptional agonist of hepcidin resulted in depletion of phagolysosomal iron pool that led to significant reduction of intracellular parasite burden. Interestingly, Nramp1 level was restored to normalcy after 30 hrs of infection with a concomitant drop in phagolysosomal iron, which is suggestive of a host counteractive response to deprive the pathogen of this essential micronutrient. Taken together, our study implicates Nramp1 as a central player in the host-pathogen battle for phagolysosomal iron. We also report Nramp1 as a novel target for hepcidin, and this 'hepcidin-Nramp1' axis may have a broader role in regulating macrophage iron homeostasis.

How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep.

The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring.

Personality affects dynamics of an experimental pathogen in little brown bats.

Host behaviour can affect host–pathogen dynamics and theory predicts that certain individuals disproportionately infect conspecifics during an epidemic. Consistent individual differences in behaviour, or personality, could influence this variation with the most exploratory or sociable individuals most likely to spread pathogens. We quantified exploration and sociability in little brown bats (Myotis lucifugus) and then experimentally manipulated exposure to a proxy pathogen (i.e. ultraviolet (UV) fluorescent powder) to test two related hypotheses: (i) more sociable and more exploratory individuals would be more likely to transmit infections to other individuals, and (ii) more sociable and more exploratory individuals uninfected with an invading pathogen would be more likely to acquire infections. We captured 10 groups of 16 bats at a time and held each group in an outdoor flight tent equipped with roosting-boxes. We used hole-board and Y-maze tests to quantify exploration and sociability of each bat and randomly selected one individual from each group for ‘infection’ with non-toxic, UV fluorescent powder. Each group of 10 bats was released into the flight tent for 24 h, which represented an experimental infection trial. After 24 h, we removed bats from the trial, photographed each individual under UV light and quantified infection intensity from digital photographs. As predicted, the exploratory behaviour of the experimentally infected individual was positively correlated with infection intensity in their group-mates, while more exploratory females had higher pathogen acquisition. Our results highlight the potential influence of host personality and sex on pathogen dynamics in wildlife populations.

After the epizootic: Host–pathogen dynamics in montane tropical amphibian communities with high prevalence of chytridiomycosis

AbstractThe amphibian fungal disease chytridiomycosis, caused by Batrachochytrium dendrobatidis (Bd), poses a great threat to global amphibian biodiversity. In Peruvian cloud forests of the Kosñipata Valley of Manu National Park where chytrid infection is highly prevalent, we have monitored species‐rich amphibian communities since 1996. An epizootic of chytridiomycosis is thought to have caused the disappearance of 35% of species richness in the early 2000s. We investigated the post‐epizootic Bd prevalence and infection intensity within the remnant amphibian community from 2008 to 2015, and modeled Bd dynamics as a function of species, season, reproductive mode, life stage, and elevation. Prevalence was higher in 2012–2015 than in 2008–2009, but overall prevalence has remained fairly constant (~50%) post‐epizootic. We also found that while prevalence decreased with elevation during the wet season, it generally increased with elevation during the dry season, potentially due to seasonal changes in temperature and precipitation. In aquatic habitats, Bd is likely maintained through a single, stream‐breeding, putative reservoir species (which survived epizootics, in contrast to other aquatic‐breeding species). The now‐dominant terrestrial‐breeding species allow Bd to persist and spread in terrestrial habitats, possibly through individual dispersal into naïve areas. We conclude that Bd prevalence in the Kosñipata Valley has stabilized over time, suggesting that Bd is now enzootic. Long‐term monitoring of host infection is important because temporal changes in prevalence and infection intensity can cause changes in host species richness and abundance, which in turn may alter the trajectory of host–pathogen dynamics.Abstract in Spanish is available with online material.

Partitioning the environmental drivers of immunocompetence

By determining susceptibility to disease, environment-driven variation in immune responses can affect the health, productivity and fitness of vertebrates. Yet how the different components of the total environment control this immune variation is remarkably poorly understood. Here, through combining field observation, experimentation and modelling, we are able to quantitatively partition the key environmental drivers of constitutive immune allocation in a model wild vertebrate (three-spined stickleback, Gasterosteus aculeatus). We demonstrate that, in natural populations, thermal conditions and diet alone are sufficient (and necessary) to explain a dominant (seasonal) axis of variation in immune allocation. This dominant axis contributes to both infection resistance and tolerance and, in turn, to the vital rates of infectious agents and the progression of the disease they cause. Our results illuminate the environmental regulation of vertebrate immunity (given the evolutionary conservation of the molecular pathways involved) and they identify mechanisms through which immunocompetence and host-parasite dynamics might be impacted by changing environments. In particular, we predict a dominant sensitivity of immunocompetence and immunocompetence-driven host-pathogen dynamics to host diet shifts.

Importancia de la genómica en aves para entender su interacción con patógenos

Garcia-Longoria, L., Ruiz-Lopez, M.J. 2020. Relevance of genomics for a better understanding of the host-parasite interaction in birds. Ecosistemas 29(2):1969. https://doi.org/10.7818/ECOS.1969 Anthropogenic pressure and global change are favouring the increase of infectious diseases not only in humans but also in wildlife. The negative effects of pathogens can be devastating, affecting natural populations and even causing the extinction of species. Therefore, it is fundamental to understand the mechanisms and strategies that these agents develop during their life cycle, as well as the immunological and biochemical responses that natural hosts show during host-pathogen interaction. Development of new molecular tools such as genomics or the analysis of transcriptomes have provided the scientific community significant information about host-pathogen interactions, including the biochemical cascades and immunological responses that occur. This article focuses on showing the advances in this field in birds infected with avian Plasmodium and West Nile Virus. In addition, we also review the problems that scientists must face in order to better understand the host-pathogen dynamics.

The impact of host metapopulation structure on short-term evolutionary rescue in the face of a novel pathogenic threat

While most evolutionary studies of host-pathogen dynamics consider pathogen evolution alone or host-pathogen coevolution, there is evidence that hosts can evolve more rapidly than their pathogen during initial outbreaks after disease introduction, e.g. evolutionary rescue in the short term. In these cases, spatial, temporal and epidemiological factors could all affect the evolutionary dynamics of the host population. To help inform potential conservation policies in the near-term, we considered the case of one pathogen introduced into a metapopulation of hosts with two genotypes: wild type and robust in which there is a tradeoff in disease-driven mortality and spatial mobility. We employed a classic Susceptible-Infected model and explored how differences in mortality and migration affect the representation of host genotypes and total host population persistence. We find that greater difference in disease-driven mortality between the two host types increases the probability of evolutionary rescue, but there is a point after which disease-driven mortality is so high as to drive the disease prevalence below the reproductive threshold, ending the outbreak and therefore benefitting the wild type. Migration reduces the chance of evolutionary rescue by reducing the competition between the two host genotypes when the difference in disease-driven mortality is sufficiently small, but at larger differences migration acts primarily as a facilitator of disease spread, increasing the probability of evolutionary rescue, though significantly decreasing the total size of the surviving population. This study reveals that both epidemiology and metapopulation ecology can play critical roles in host evolution during the emergence of a novel infection and provide guidance for host conservation and disease control.

Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospirain California sea lions (Zalophus californianus) as a case study.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a “host-pathogen space” by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual’s location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual’s time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Amphibian Disease Ecology: Are We Just Scratching the Surface?

Pathogen-induced population declines and extinction events have been recognized as main threats to amphibian species around the globe. However, the ecological drivers underlying epidemiological patterns are still poorly understood. In an attempt to assess the current knowledge on the ecological drivers of amphibian diseases, we identified 832 peer-reviewed publications on the ecology of amphibian pathogens and diseases published between 2009 and 2019. The vast majority of publications investigated either chytrid or ranavirus infections (79% of the articles), whereas other pathogens such as bacteria and helminths received considerably less attention. Just over half of the studies we reviewed included field research and 40% were experimental in nature, yet only 8% combined field and experimental approaches. More than half of the literature (56%) investigated postmetamorphic stages, whereas premetamorphic stages were considered in 23% of the reviewed studies, and only 13% included both life stages. Susceptibility and mortality have been assessed in almost every study (91%) whereas 37% of them tested for cellular, physiological, or immunological responses. However, other host characteristics such as growth/development, behavior, and specific mucosome/microbiome were considered in only one of four studies. Most research included at least one biotic factor (e.g., host and pathogen identity, species diversity, genetic adaptations), but only one-third considered environmental factors (e.g., temperature, landscape features, inorganic chemicals). Furthermore, there is no general consensus about the factors driving epidemiological patterns of pathogens in amphibian communities, and it is clear that the complexity and specificity of interactions between ecological factors and host–pathogen dynamics make conservation implications difficult and management decisions challenging. To this end, our review identifies some research gaps and proposes future directions to better understand one of the major threats to this class of vertebrates.

Heterogeneity in social network connections is density-dependent: implications for disease dynamics in a gregarious ungulate

Incorporating host behavioral variation into epidemiological models is important for predicting host-pathogen dynamics. Animals living at high densities or with many strong social connections are predicted to have greater risk of acquiring pathogens. Using social network analysis, we tested the hypothesis that variation in the strength of social connections would influence simulated elk (Cervus canadensis) pathogen dynamics. We quantified fine-scale social connections for captive elk at three experimentally manipulated densities and wild elk at two natural densities. We applied susceptible-infected epidemiological models to networks to infer the relationship between fine-scale host sociality and simulated pathogen dynamics. Networks were filtered based on four association thresholds to determine how variation in the strength of social connections influenced pathogen dynamics. Our simulations suggest that social behavior interacts with population density to predict pathogen dynamics, but this effect was sex-specific. For both males and females at higher density, elk had strong social connections, resulting in higher number of infected individuals. We observed differences in social connections across density, and these results translated to our simulations, which predicted density-dependent pathogen dynamics for captive and wild elk networks. Our results highlight host social behavior as a potential mechanism driving variation in the relationship between population density and pathogen dynamics. Elk are reservoir hosts for numerous emerging infectious diseases, and our models suggest that density-dependent host social behavior could influence pathogen dynamics in elk social networks. Animal population density can influence transmission of parasites and pathogens, but variation in social connections at different densities could impact this relationship. Using social network analysis and epidemiological simulations, we tested the hypothesis that density-dependent variation in social connections would influence pathogen dynamics using captive and free-ranging populations of elk (Cervus elaphus). For all elk groups, simulated number of infected individuals increased with social connectedness. In addition, our simulations suggest that social connectedness interacts with population density to predict number of infected individuals, but this effect was sex-specific. For males and females at high density, elk had strong social connections, resulting in more infected individuals; this relationship was linear for males and non-linear for females. Taken together, our results suggest fine-scale measures of social behavior vary with population density, a result which could have implications for pathogen dynamics.

Identifying research needs to inform white‐nose syndrome management decisions

AbstractEcological understanding of host–pathogen dynamics is the basis for managing wildlife diseases. Since 2008, federal, state, and provincial agencies and tribal and private organizations have collaborated on bat and white‐nose syndrome (WNS) surveillance and monitoring, research, and management programs. Accordingly, scientists and managers have learned a lot about the hosts, pathogen, and dynamics of WNS. However, effective mitigation measures to combat WNS remain elusive. Host–pathogen systems are complex, and identifying ecological research priorities to improve management, choosing among various actions, and deciding when to implement those actions can be challenging. Through a cross‐disciplinary approach, a group of diverse subject matter experts created an influence diagram used to identify uncertainties and prioritize research needs for WNS management. Critical knowledge gaps were identified, particularly with respect to how WNS dynamics and impacts may differ among bat species. We highlight critical uncertainties and identify targets for WNS research. This tool can be used to maximize the likelihood of achieving bat conservation goals within the context and limitations of specific real‐world scenarios.

Genomic heterogeneity of Dichelobacter nodosus within and between UK sheep flocks and between age groups within a flock

BackgroundFootrot and interdigital dermatitis are endemic infectious diseases in all sheep farming regions, impairing welfare and production. The development of efficacious vaccines against the primary causative pathogen has been hampered by the extensive antigenic diversity of Dichelobacter nodosus. Understanding the heterogeneity of the pathogen within and between flocks is essential if the feasibility of bespoke vaccine production is to be assessed for use in the U.K.ResultsIn this study 56 ewe and lamb isolates from 9 flocks were compared by D. nodosus serogroup and Multi Locus Sequence Type which provides significantly enhanced discriminatory power for molecular epidemiology. Serogroup heterogeneity between flocks ranged from two to five unique serogroups per flock. Three flocks contained isolates of two serogroups, two flocks contained isolates of three serogroups and one flock included isolates of five serogroups. Analysis of 25 isolates from one flock with high prevalence of lameness, identified that serogroup and sequence type was significantly correlated with age. Significantly higher proportion of lambs were infected with serogroup B (principally ST85) as opposed to serogroup H (principally ST86), which predominated amongst adult sheep.ConclusionsGenomic heterogeneity of the pathogen was significantly lower within flock compared to heterogenicity observed between flocks. Furthermore, this study indicates that within a flock, the host-pathogen dynamics and susceptibility to particular D. nodosus strains may be age dependent.

Vaginal Microbiota and HPV Infection: Novel Mechanistic Insights and Therapeutic Strategies.

Cervical cancer is a global public health concern. The complex interaction of genetic and environmental factors is critical for the progress of cervical cancer. Growing evidence suggests that microbes, human papillomavirus (HPV), and the immune system interact closely with each other to govern homeostasis of the vaginal environment and the health of the lower genital tract of females. Certain vaginal microbial strains may play either a protective or a pathogenic role in carcinogenesis of the cervix after HPV persistent infection. Probiotics can therefore present a putative therapeutic approach for cervical cancer. However, work in this field remains limited. Recent technological developments have allowed us to identify microbes and their products using culture-independent molecular detection techniques. In this review, we discuss the composition of the vaginal bacterial community, its commensal flora and the protective impact this has on the health of the female genital tract. This review will also describe critical immune factors in lower genital tract health and summarize the role of the vaginal microbiota in cervical carcinogenesis. Knowledge in this field has provided researchers with the clues and tools to propose the use of probiotics as a potential line of treatment for cervical cancer and has provided valuable insights into host–pathogen interaction dynamics within the female genital tract.

Detailed Phenotypic and Functional Characterization of a Rare, Antibody-Dependent SLAM-Associated Protein Expression Pattern.

SLAM-associated protein (SAP) is an adaptor molecule that facilitates critical effector functions in immune cells, and its deficiency causes X-linked lymphoproliferative disease type 1 in which effector responses directed against EBV are severely compromised. The primary objective of this study was to phenotypically and functionally characterize a rare, CD8 T cell-restricted bimodal SAP expression pattern observed in healthy, human donors with the widely used 1C9-SAP mAb clone. We initially observed this pattern during the clinical validation of our flow cytometry-based assay to diagnose X-linked lymphoproliferative disease type 1 in our laboratory. For this validation study, we used multiparameter flow cytometry to identify cytosolic SAP expression in lymphocyte subsets, and CD8 T cells from the donors displaying the rare SAP expression pattern mentioned above were separately further evaluated by intracellular cytokine and CD107a staining to examine polyfunctionality following PMA/ionomycin and HLA class I allele-restricted EBV peptide epitope-induced T cell activation. Our data revealed that SAP 1C9-hi CD8 T cells clearly displayed higher polyfunctional responses versus SAP 1C9-lo CD8 T cells following PMA/ionomycin stimulation. Furthermore, polyfunctional EBV-specific CD8 T cell responses segregated with the SAP 1C9-hi CD8 T cells and not the SAP 1C9-lo CD8 T cells. Additionally, and rather intriguingly, short- and long-term T cell stimulation selectively diminished the signal for the 1C9-hi subset. Overall, our data suggest that although rare, this unique SAP expression pattern merits further evaluation as it has the potential to provide some insight into fundamental processes as they might relate to host-pathogen dynamics.

The ecology and evolution of wildlife cancers: Applications for management and conservation.

Ecological and evolutionary concepts have been widely adopted to understand host–pathogen dynamics, and more recently, integrated into wildlife disease management. Cancer is a ubiquitous disease that affects most metazoan species; however, the role of oncogenic phenomena in eco‐evolutionary processes and its implications for wildlife management and conservation remains undeveloped. Despite the pervasive nature of cancer across taxa, our ability to detect its occurrence, progression and prevalence in wildlife populations is constrained due to logistic and diagnostic limitations, which suggests that most cancers in the wild are unreported and understudied. Nevertheless, an increasing number of virus‐associated and directly transmissible cancers in terrestrial and aquatic environments have been detected. Furthermore, anthropogenic activities and sudden environmental changes are increasingly associated with cancer incidence in wildlife. This highlights the need to upscale surveillance efforts, collection of critical data and developing novel approaches for studying the emergence and evolution of cancers in the wild. Here, we discuss the relevance of malignant cells as important agents of selection and offer a holistic framework to understand the interplay of ecological, epidemiological and evolutionary dynamics of cancer in wildlife. We use a directly transmissible cancer (devil facial tumour disease) as a model system to reveal the potential evolutionary dynamics and broader ecological effects of cancer epidemics in wildlife. We provide further examples of tumour–host interactions and trade‐offs that may lead to changes in life histories, and epidemiological and population dynamics. Within this framework, we explore immunological strategies at the individual level as well as transgenerational adaptations at the population level. Then, we highlight the need to integrate multiple disciplines to undertake comparative cancer research at the human–domestic–wildlife interface and their environments. Finally, we suggest strategies for screening cancer incidence in wildlife and discuss how to integrate ecological and evolutionary concepts in the management of current and future cancer epizootics.

Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes

Understanding host–pathogen dynamics requires realistic consideration of transmission events that, in the case of directly transmitted pathogens, result from contacts between susceptible and infected individuals. The corresponding contact rates are usually heterogeneous due to variation in individual movement patterns and the underlying landscape structure. However, in epidemiological models, the roles that explicit host movements and landscape structure play in shaping contact rates are often overlooked.We adapted an established agent‐based model of classical swine fever (CSF) in wild boar Sus scrofa to investigate how explicit representation of landscape heterogeneity and host movement between social groups affects invasion and persistence probabilities. We simulated individual movement both phenomenologically as a correlated random walk (CRW) and mechanistically by representing interactions of the moving individuals with the landscape and host population structure.The effect of landscape structure on the probability of invasion success and disease persistence depended remarkably on the way host movement is simulated and the case fatality ratio associated with the pathogen strain. The persistence probabilities were generally low with CRW which ignores feedbacks to external factors. Although the basic reproduction number R0, a measure of the contagiousness of an infectious disease, was kept constant, these probabilities were up to eight times higher under mechanistic movement rules, especially in heterogeneous landscapes. The increased persistence emerged due to important feedbacks of the directed movement on the spatial variation of host density, contact rates and transmission events to distant areas.Our findings underscore the importance of accounting for spatial context and group size structures in eco‐epidemiological models. Our study highlights that the simulation of explicit, mechanistic movement behaviour can reverse predictions of disease persistence in comparison to phenomenological rules such as random walk approaches. This can have severe consequences when predicting the probability of disease persistence and assessing control measures to prevent outbreaks.

A potential tradeoff between feeding rate and aversive learning determines intoxication in a Caenorhabditis elegans host-pathogen system

Despite being the first line of defense against infection, little is known about how host-pathogen interactions determine avoidance. Caenorhabditis elegans can become infected by chemoattractant-producing bacteria through ingestion. The worms can learn to associate these chemoattractants with harm through aversive learning. As a result, the worms will avoid the pathogen. Evolutionary constraints have likely shaped the attraction, intoxication and learning dynamics between bacteria and C. elegans, but these have not been explored. Using bacteria engineered to express an acylhomoserine lactone chemoattractant and a nematicidal protein, we explored how manipulating the amount of attractant produced by the bacteria affects learning and intoxication in mixed stage populations of C. elegans. We found that increasing the production rate of the chemoattractant increased the feeding rate in C. elegans, but decreased the time required for C. elegans to learn to avoid the chemoattractant. Learning generally coincided with a decreased feeding rate. We also observed that the percentage of intoxicated worms was maximized at intermediate production rates of the attractant. We propose that interactions between attractant driven feeding rate and aversive learning are likely responsible for this trend. Our results increase our understanding of behavioral avoidance in C. elegans and have implications in understanding host-pathogen dynamics that shape avoidance.

Eco-evolutionary agriculture: Host-pathogen dynamics in crop rotations.

Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. Temporal rotations of host and non-host crops have helped farmers to control epidemics among other utilities, but further efforts for strategy assessment are needed. Here, we present a methodology for developing crop rotation strategies optimal for control of pathogens informed by numerical simulations of eco-evolutionary dynamics in one field. This approach can integrate agronomic criteria used in crop rotations—soil quality and cash yield—and the analysis of pathogen evolution in systems where hosts are artificially selected. Our analysis shows which rotation patterns perform better in maximising crop yield when an unspecified infection occurs, with yield being dependent on both soil quality and the strength of the epidemic. Importantly, the use of non-host crops, which both improve soil quality and control the epidemic results in similar rational rotation strategies for diverse agronomic and infection conditions. We test the repeatability of the best rotation patterns over multiple decades, an essential end-user goal. Our results provide sustainable strategies for optimal resource investment for increased food production and lead to further insights into the minimisation of pesticide use in a society demanding ever more efficient agriculture.