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Niche breadth coevolution between biotic partners underpins theories of diversity and co-existence and influences patterns of disease emergence and transmission in host-parasite systems. Despite these broad implications, we still do not fully understand how the breadth of parasites' infectivity evolves, the nature of any associated costs, or the genetic basis of specialization. Here, we serially passage a granulosis virus on multiple inbred populations of its Plodia interpunctella host to explore the dynamics and outcomes of specialization. In particular, we collect time series of phenotypic and genetic data to explore the dynamics of host genotype specialization throughout the course of experimental evolution and examine two fitness components. We find that the Plodia interpunctella granulosis virus consistently evolves and increases in overall specialization, but that our two fitness components evolve independently such that lines can specialize in productivity or infectivity. Furthermore, we find that specialization in our experiment is a highly polygenic trait best explained by a combination of evolutionary mechanisms. These results are important for understanding the evolution of specialization in host-parasite interactions and its broader implications for co-existence, diversification, and infectious disease management.

Host diversity and spatial heterogeneity are both predicted to impact parasite evolution, and these processes may interact in nature to impact local adaptation. Here, we experimentally evolve granulosis virus in microcosms of itsPlodia interpunctellahost with varied spatial structure and host genetic diversity. We find that virus evolves specific interactions with its locally familiar host genotype in both homogeneous and spatially heterogeneous host populations, but that the impact of local adaptation depends on the spatial structure. In one treatment, exploitation rates are higher on the familiar host, while, in the other, they are lower. While we find that food viscosity had unexpected impacts on the spatial structuring of contacts, spatially structured transmission may have led to the virus evolving more prudent exploitation rates on only the familiar host, leading to locally maladapted high exploitation rates on foreign hosts. Additionally, we also find that virus in heterogeneous microcosms is more locally adapted when there are higher contact rates between the hosts. Our experiment demonstrates that trade-offs optimizing exploitation rates at intermediate values, like those governing pathogen infectivity in spatial structure, may interact with trade-offs determining niche breadth in ways that can reverse the impact of local adaptation on pathogen phenotypes. [200/200]

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. 'Oligomorphic dynamics' can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.

Theoretical models suggest that infectious diseases could play a substantial role in determining the spatial extent of host species, but few studies have collected the empirical data required to test this hypothesis. Pathogens that sterilize their hosts or spread through frequency-dependent transmission could have especially strong effects on the limits of species' distributions because diseased hosts that are sterilized but not killed may continue to produce infectious stages and frequency-dependent transmission mechanisms are effective even at very low population densities. We collected spatial pathogen prevalence data and population abundance data for alpine carnations infected by the sterilizing pathogen M. violaceum, a parasite that is spread through both frequency-dependent (vector-borne) and density-dependent (aerial spore transmission) mechanisms. Our 13-year study reveals rapid declines in population abundance without a compensatory decrease in pathogen prevalence. We apply a stochastic, spatial model of parasite spread that accommodates spatial habitat heterogeneity to investigate how the population dynamics depend on multimodal (frequency-dependent and density-dependent) transmission. We found that the observed rate of population decline could plausibly be explained by multimodal transmission, but is unlikely to be explained by either frequency-dependent or density-dependent mechanisms alone. Multimodal pathogen transmission rates high enough to explain the observed decline predicted that eventual local extinction of the host species is highly likely. Our results add to a growing body of literature showing how multimodal transmission can constrain species distributions in nature.

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. “Oligomorphic dynamics” can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feed-backs, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.

Hosts can often evolve resistance to parasites (and other stressors), but such resistance is generally thought to be constrained by trade-offs with other traits. These trade-offs determine the host’s optimal resistance strategy and whether resistance cycles, diversifies, and/or is maintained in the absence of parasite. However, trade-offs are often inconsistently measured across experiments and can depend on environmental conditions. Here, we extend a selection experiment evolving resistance to viral infection under variable resource quality in the Plodia interpunctella model system to explore the evolutionary conditions leading to an incongruent earlier measurement of costless resistance. We find that environmental resource quality, historical contingency, and the time scale of selection all affect trade-offs in our long-term selection experiment. Specifically, populations selected for resistance with the dual stressor of low resource quality are slowed, but not prevented, from evolving resistance. Second, variation in starting populations or early sampled adaptations led to contingency towards context-dependent resistance. Finally, some costs to resistance observed at early time points were compensated over longer evolutionary time scales. Our work therefore informs perspectives for the predictability of adaptation and how variation in specific evolutionary conditions can alter the evolutionary trajectories of a population towards costly or costless resistance strategies.

Theoretical models suggest that infectious diseases could play a substantial role in determining species’ ranges, but few studies have collected the empirical data required to test this hypothesis. Pathogens that sterilize their hosts or spread through frequency-dependent transmission could have especially strong effects on the limits of species’ distributions because sterilized hosts can serve as long-lived disease reservoirs and frequency-dependent transmission mechanisms are effective even at very low population densities. We collected spatial disease prevalence data and population abundance data for alpine carnations infected by the sterilizing pathogen M. violaceum, a disease that is spread through both frequency-dependent (vector-borne) and density-dependent (aerial spore transmission) mechanisms. Our 13-year study reveals rapid declines in population abundance without a compensatory decrease in disease prevalence. We apply a stochastic, spatial model of disease spread that accommodates spatial habitat heterogeneity to investigate how the population dynamics depend on multimodal (frequency-dependent and density-dependent) transmission. We found that the observed rate of population decline can be readily explained by multimodal transmission, but is unlikely to be explained by either frequency-dependent or density-dependent mechanisms alone. Multimodal disease transmission rates high enough to explain the observed decline predicted that eventual local extinction of the host species is highly likely. Our results add to a growing body of literature showing how multimodal transmission can constrain species distributions in nature.Open ResearchAll scripts associated with the analyses in this manuscript, as well as the data we collected, are available at https://github.com/uricchio/antherSmutDis.

Significance The clear need to mitigate zoonotic risk has fueled increased viral discovery in specific reservoir host taxa. We show that a combination of viral and reservoir traits can predict zoonotic virus virulence and transmissibility in humans, supporting the hypothesis that bats harbor exceptionally virulent zoonoses. However, pandemic prevention requires thinking beyond zoonotic capacity, virulence, and transmissibility to consider collective “burden” on human health. For this, viral discovery targeting specific reservoirs may be inefficient as death burden correlates with viral, not reservoir, traits, and depends on context-specific epidemiological dynamics across and beyond the human–animal interface. These findings suggest that longitudinal studies of viral dynamics in reservoir and spillover host populations may offer the most effective strategy for mitigating zoonotic risk.

Despite the propensity for complex and non-equilibrium dynamics in nature, eco-evolutionary analytical theory typically assumes that populations are at equilibria. In particular pathogens often show antigenic escape from host immune defense, leading to repeated epidemics, fluctuating selection and diversification, but we do not understand how this impacts the evolution of virulence. We model the impact of antigenic drift and escape on the evolution of virulence in a generalized pathogen and apply a recently introduced oligomorphic methodology that captures the dynamics of the mean and variance of traits, to show analytically that these non-equilibrium dynamics select for the long-term persistence of more acute pathogens with higher virulence. Our analysis predicts both the timings and outcomes of antigenic shifts leading to repeated epidemics and predicts the increase in variation in both antigenicity and virulence before antigenic escape. There is considerable variation in the degree of antigenic escape that occurs across pathogens and our results may help to explain the difference in virulence between related pathogens including, potentially, human influenzas. Furthermore, it follows that these pathogens will have a lower R(0) with clear implications for epidemic, endemic behavior and control. More generally our results show the importance of examining the evolutionary consequences of non-equilibrium dynamics.

Evolutionary theory has typically focused on pairwise interactions, such as those between hosts and parasites, with relatively little work on more complex interactions including hyperparasites: parasites of parasites. Hyperparasites are common in nature, with the chestnut blight fungus virus CHV-1 a well-known natural example, but also notably include the phages of important human bacterial diseases. Theory on hyperparasitism has mostly focused on their impact on the evolution of virulence of their parasite host and relatively little is known about evolutionary trajectories of hyperparasites themselves. Our general modeling framework highlights the central role the that ability of a hyperparasite to be transmitted with its parasite plays in their evolution. Hyperparasites which transmit with their parasite hosts (hitchhike) will be selected for lower virulence, trending towards hypermutualism or hypercommensalism and select against causing a reduction in parasite virulence (hypovirulence). We examine the impact on the evolution of hyperparasite systems a of a wide range of host and parasite traits showing, for example, that high parasite virulence selects for higher hyperparasite virulence feeding back into selection for hypovirulence in the parasite. Our results have implications for hyperparasite research, both as biocontrol agents and for understanding of how hyperparasites shape community ecology and evolution.

Niche breadth coevolution between biotic partners underpins theories of diversity and co-existence and influences patterns of disease emergence and transmission in host-parasite systems. Despite these broad implications, we still do not fully understand how the breadth of parasites’ infectivity evolves, the nature of any associated costs, or the genetic basis of specialization. Here, we serially passage a granulosis virus on multiple inbred populations of its Plodia interpunctella host to explore the dynamics and outcomes of specialization. In particular, we collect time series of phenotypic and genetic data to explore the dynamics of host genotype specialization throughout the course of experimental evolution and examine two fitness components. We find that the Plodia interpunctella granulosis virus consistently evolves increases in overall specialization, but that our two fitness components evolve independently such that lines specialize in either productivity or infectivity. Furthermore, we find that specialization in our experiment is a highly polygenic trait best explained by a combination of evolutionary mechanisms including conditionally positive fitness asymmetries and mutation accumulation. These results are important for understanding the evolution of specialization in host-parasite interactions and its broader implications for co-existence, diversification, and infectious disease management.

The risk of a zoonotic pandemic disease threatens hundreds of millions of people. Emerging infectious diseases also threaten livestock and wildlife populations around the world and can lead to devastating economic damages. China and the USA—due to their unparalleled resources, widespread engagement in activities driving emerging infectious diseases and national as well as geopolitical imperatives to contribute to global health security—play an essential role in our understanding of pandemic threats. Critical to efforts to mitigate risk is building upon existing investments in global capacity to develop training and research focused on the ecological factors driving infectious disease spillover from animals to humans. International cooperation, particularly between China and the USA, is essential to fully engage the resources and scientific strengths necessary to add this ecological emphasis to the pandemic preparedness strategy. Here, we review the world’s current state of emerging infectious disease preparedness, the ecological and evolutionary knowledge needed to anticipate disease emergence, the roles that China and the USA currently play as sources and solutions to mitigating risk, and the next steps needed to better protect the global community from zoonotic disease.

It is generally thought that the intensification of farming will result in higher disease prevalences, although there is little specific modelling testing this idea. Focussing on honeybees, we build multi‐colony models to inform how “apicultural intensification” is predicted to impact honeybee pathogen epidemiology at the apiary scale.We used both agent‐based and analytical models to show that three linked aspects of apicultural intensification (increased population sizes, changes in population network structure and increased between‐colony transmission) are unlikely to greatly increase disease prevalence in apiaries. Principally this is because even low‐intensity apiculture exhibits high disease prevalence.The greatest impacts of apicultural intensification are found for diseases with relatively low R0 (basic reproduction number), however, such diseases cause little overall disease prevalence and, therefore, the impacts of intensification are minor. Furthermore, the smallest impacts of intensification are for diseases with high R0 values, which we argue are typical of important honeybee diseases. Policy Implications: Our findings contradict the idea that apicultural intensification by crowding honeybee colonies in large, dense apiaries leads to notably higher disease prevalences for established honeybee pathogens. More broadly, our work demonstrates the need for informative models of all agricultural systems and management practices in order to understand the implications of management changes on diseases.

There is increasing interest in the role that evolution may play in current and future pandemics, but there is often also considerable confusion about the actual evolutionary predictions. This may be, in part, due to a historical separation of evolutionary and medical fields, but there is a large, somewhat nuanced body of evidence-supported theory on the evolution of infectious disease. In this review, we synthesize this evolutionary theory in order to provide a framework for clearer understanding of the key principles. Specifically, we discuss the selection acting on zoonotic pathogens' transmission rates and virulence at spillover and during emergence. We explain how the direction and strength of selection during epidemics of emerging zoonotic disease can be understood by a three Ts framework: trade-offs, transmission, and time scales. Virulence and transmission rate may trade-off, but transmission rate is likely to be favoured by selection early in emergence, particularly if maladapted zoonotic pathogens have ‘no-cost’ transmission rate improving mutations available to them. Additionally, the optimal virulence and transmission rates can shift with the time scale of the epidemic. Predicting pathogen evolution, therefore, depends on understanding both the trade-offs of transmission-improving mutations and the time scales of selection.

Identifying virus characteristics associated with the largest public health impacts on human populations is critical to informing zoonotic risk assessments and surveillance strategies. Efforts to assess “zoonotic risk” often use trait-based analyses to identify which viral and reservoir host groups are most likely to source zoonoses but have not fully addressed how and why the impacts of zoonotic viruses vary in terms of disease severity (‘virulence’), capacity to spread within human populations (‘transmissibility’), or total human mortality (‘death burden’). We analyzed trends in human case fatality rates, transmission capacities, and total death burdens across a comprehensive dataset of mammalian and avian zoonotic viruses. Bats harbor the most virulent zoonotic viruses even when compared to birds, which alongside bats, have been hypothesized to be “special” zoonotic reservoirs due to molecular adaptations that support the physiology of flight. Reservoir host groups more closely related to humans—in particular, Primates—harbor less virulent, but more highly transmissible viruses. Importantly, disproportionately high human death burden, arguably the most important metric of zoonotic risk, is not associated with any animal reservoir, including bats. Our data demonstrate that mechanisms driving death burdens are diverse and often contradict trait-based predictions. Ultimately, total human mortality is dependent on context-specific epidemiological dynamics, which are shaped by a combination of viral traits and conditions in the animal host population and across and beyond the human-animal interface. Understanding the conditions that predict high zoonotic burden in humans will require longitudinal studies of epidemiological dynamics in wildlife and human populations.Significance statementThe clear need to mitigate zoonotic risk has fueled increased viral discovery in specific reservoir host taxa. We show that a combination of viral and reservoir traits can predict zoonotic virus virulence and transmissibility in humans, supporting the hypothesis that bats harbor exceptionally virulent zoonoses. However, pandemic prevention requires thinking beyond zoonotic capacity, virulence, and transmissibility to consider collective ‘burden’ on human health. For this, viral discovery targeting specific reservoirs may be inefficient as death burden correlates with viral, not reservoir, traits, and depends on context-specific epidemiological dynamics across and beyond the human-animal interface. These findings suggest that longitudinal studies of viral dynamics in reservoir and spillover host populations may offer the most effective strategy for mitigating zoonotic risk.

Our understanding of the evolution of quantitative traits in nature is still limited by the challenge of including realistic trait distributions in the context of frequency-dependent selection and ecological feedbacks. We develop a theoretical framework to analyse the dynamics of populations composed of several morphs and structured into distinct classes (e.g. age, size, habitats, infection status, species...). Our approach extends to class-structured populations a recently introduced "oligomorphic approximation" which bridges the gap between adaptive dynamics and quantitative genetics approaches and allows for the joint description of the dynamics of ecological variables and of the moments of multimodal trait distributions. We also introduce a new approximation to simplify the eco-evolutionary dynamics using reproductive values. This effectively extends Lande9s univariate theorem not only to frequency- and density-dependent selection but also to multimodal trait distributions. We illustrate the effectiveness of this approach by applying it to the important conceptual case of two-habitat migration-selection models. In particular, we use our approach to predict the equilibrium trait distributions in a local adaptation model with asymmetric migration and habitat-specific mutational variance. We discuss the theoretical and practical implications of our results and sketch perspectives for future work.

Significance In many important human diseases, a large proportion of infections do not lead to symptoms, but we typically have little understanding of the cause, impact, and dynamics of such inapparent infections. Our data from the Nicaragua Pediatric Cohort show that the dynamics of both symptomatic and inapparent infections are complex, such that in some years there are many symptomatic cases, but in others the cases are predominantly inapparent. Using epidemiologic models, we show that boosting the immune system is a parsimonious mechanism that can explain these patterns. This is important because it suggests that boosting is an important to the epidemiology of dengue and moreover that immune mechanisms can lead to complex inapparent infection dynamics., Dengue is the most prevalent arboviral disease worldwide, and the four dengue virus (DENV) serotypes circulate endemically in many tropical and subtropical regions. Numerous studies have shown that the majority of DENV infections are inapparent, and that the ratio of inapparent to symptomatic infections (I/S) fluctuates substantially year-to-year. For example, in the ongoing Pediatric Dengue Cohort Study (PDCS) in Nicaragua, which was established in 2004, the I/S ratio has varied from 16.5:1 in 2006–2007 to 1.2:1 in 2009–2010. However, the mechanisms explaining these large fluctuations are not well understood. We hypothesized that in dengue-endemic areas, frequent boosting (i.e., exposures to DENV that do not lead to extensive viremia and result in a less than fourfold rise in antibody titers) of the immune response can be protective against symptomatic disease, and this can explain fluctuating I/S ratios. We formulate mechanistic epidemiologic models to examine the epidemiologic effects of protective homologous and heterologous boosting of the antibody response in preventing subsequent symptomatic DENV infection. We show that models that include frequent boosts that protect against symptomatic disease can recover the fluctuations in the I/S ratio that we observe, whereas a classic model without boosting cannot. Furthermore, we show that a boosting model can recover the inverse relationship between the number of symptomatic cases and the I/S ratio observed in the PDCS. These results highlight the importance of robust dengue control efforts, as intermediate dengue control may have the potential to decrease the protective effects of boosting.

How social behaviours evolve remains one of the most debated questions in evolutionary biology. An important theoretical prediction is that when organisms interact locally due to limited dispersal or strong social ties, the population structure that emerges may favour cooperation over antagonism. We carry out an experimental test of this theory by directly manipulating population spatial structure in an insect laboratory model system and measuring the impact on the evolution of the extreme selfish behaviour of cannibalism. We show that, as predicted by the theory, Indian meal moth larvae that evolved in environments with more limited dispersal are selected for lower rates of cannibalism. This is important because it demonstrates that local interactions select against selfish behaviour. Therefore, the ubiquitous variation in population structure that we see in nature is a simple mechanism that can help to explain the variation in selfish and cooperative behaviours that we see in nature.

SummaryRegular surveillance testing of asymptomatic individuals for SARS-CoV-2 has played a vital role in SARS-CoV-2 outbreak prevention on college and university campuses. Here we describe the voluntary saliva testing program instituted at the University of California, Berkeley during an early period of the SARS-CoV-2 pandemic in 2020. The program was administered as a research study ahead of clinical implementation, enabling us to launch surveillance testing while continuing to optimize the assay. Results of both the testing protocol itself and the study participants’ experience show how the program succeeded in providing routine, robust testing capable of contributing to outbreak prevention within a campus community and offer strategies for encouraging participation and a sense of civic responsibility.

Understanding the evolutionary drivers determining the transmission rate and virulence of pathogens remains an important challenge for evolutionary theory with clear implications to the control of human, agricultural and wildlife infectious disease. Although disease is often very dynamic, classical theory examines the long-term outcome of evolution at equilibrium and, in simple models, typically predicts that R0 is maximized. For example, immune escape may lead to complex disease dynamics including repeated epidemics, fluctuating selection and diversification. Here we model the impact of antigenic drift and escape on the evolution of virulence and show analytically that these non-equilibrium dynamics select for more acute pathogens with higher virulence. Specifically, under antigenic drift and when partial cross immunity leads to antigenic escape, our analysis predicts the long-term maximization of the intrinsic growth rate of the parasite resulting in more acute and virulent pathogens than those predicted by classic R0 maximization. Furthermore, it follows that these pathogens will have a lower R0 leading to implications for epidemic, endemic behavior and control. Our analysis predicts both the timings and outcomes of antigenic shifts leading to repeated epidemics and predicts the increase in variation in both antigenicity and virulence before antigenic escape. There is considerable variation in the degree of antigenic escape that occurs across pathogens and our results may help to explain the difference in virulence between related pathogens most clearly seen in the human A, B and C influenzas. More generally our results show the importance of examining the evolutionary consequences of non-equilibrium dynamics.

Parasites impose strong selection on their hosts, but the level of any evolved resistance may be constrained by the availability of resources. However, studies identifying the genomic basis of such resource-mediated selection are rare, particularly in nonmodel organisms. Here, we investigated the role of nutrition in the evolution of resistance to a DNA virus (PiGV), and any associated trade-offs in a lepidopteran pest species (Plodia interpunctella). Through selection experiments and whole-genome resequencing, we identify genetic markers of resistance that vary between the nutritional environments during selection. We do not find consistent evolution of resistance in the presence of virus but rather see substantial variation among replicate populations. Resistance in a low-nutrition environment is negatively correlated with growth rate, consistent with an established trade-off between immunity and development, but this relationship is highly context dependent. Whole-genome resequencing of the host shows that resistance mechanisms are likely to be highly polygenic and although the underlying genetic architecture may differ between high and low-nutrition environments, similar mechanisms are commonly used. As a whole, our results emphasize the importance of the resource environment on influencing the evolution of resistance.

Many of our theories for the generation and maintenance of diversity in nature depend on the existence of specialist biotic interactions which, in host–pathogen systems, also shape cross-species disease emergence. As such, niche breadth evolution, especially in host–parasite systems, remains a central focus in ecology and evolution. The predominant explanation for the existence of specialization in the literature is that niche breadth is constrained by trade-offs, such that a generalist is less fit on any particular environment than a given specialist. This trade-off theory has been used to predict niche breadth (co)evolution in both population genetics and eco-evolutionary models, with the different modelling methods providing separate, complementary insights. However, trade-offs may be far from universal, so population genetics theory has also proposed alternate mechanisms for costly generalism, including mutation accumulation. However, these mechanisms have yet to be integrated into eco-evolutionary models in order to understand how the mechanism of costly generalism alters the biological and ecological circumstances predicted to maintain specialism. In this review, we outline how population genetics and eco-evolutionary models based on trade-offs have provided insights for parasite niche breadth evolution and argue that the population genetics-derived mutation accumulation theory needs to be better integrated into eco-evolutionary theory.

The Eurasian wild boar (Sus scrofa) is the main wild reservoir of the Mycobacterium tuberculosis complex in Mediterranean woodlands and a key risk factor for cattle tuberculosis (TB) breakdowns. Wild boar vaccination therefore has the potential to be a valuable tool for TB control. We tested two orally delivered vaccines, heat-inactivated Mycobacterium bovis (IV) and BCG, in four sites (two per vaccine type: one Managed and one Natural or unmanaged) during four years. TB was also monitored in 15 unvaccinated sites (spatial control), as well as in all sites from one year prior to intervention (temporal control). The rationale is that by vaccinating 2–6 month old wild boar piglets we can reduce disease at the population level during the study period. This is achievable due to the fast turnover of wild boar populations. Vaccine baits were deployed using selective piglet feeders and this method proved highly successful with uptake rates of 50 to 74% in Natural sites and 89 to 92% in Managed sites. This is relevant for the potential delivery of vaccines to control other diseases, too. Local wild boar TB prevalence at the beginning of the study was already high ranging from 50 to 100%. TB prevalence increased in unvaccinated sites (6%), while a significant decline occurred in the Managed IV site (34%). Changes recorded in the remaining sites were not significant. The short-term impact of vaccination observed in the field was complemented by mathematical modelling, representative of the field system, which examined the long-term impact and showed that vaccination of piglets reduced prevalence and increased abundance at the population level. We conclude that IV could become part of integrated TB control schemes, although its application must be tailored for each specific site., This is a contribution to Plan Nacional I+D+i AGL2014-56305 from MINECO and EU FEDER, and to the EU–FP7 grant WildTBVac. E. Tanner was supported by The Maxwell Institute Graduate School in Analysis and its Applications, a Centre for Doctoral Training funded by the UK Engineering and Physical Sciences Research Council (grant EP/L016508/01), the Scottish Funding Council, Heriot-Watt University and the University of Edinburgh. I. Díez-Delgado was supported by a predoctoral grant from MINECO.

Honey bee declines are a serious threat to global agricultural security and productivity. While multiple factors contribute to these declines, parasites are a key driver. Disease problems in honey bees have intensified in recent years, despite increasing attention to addressing them. Here we argue that we must focus on the principles of disease ecology and evolution to understand disease dynamics, assess the severity of disease threats, and manage these threats via honey bee management. We cover the ecological context of honey bee disease, including both host and parasite factors driving current transmission dynamics, and then discuss evolutionary dynamics including how beekeeping management practices may drive selection for more virulent parasites. We then outline how ecological and evolutionary principles can guide disease mitigation in honey bees, including several practical management suggestions for addressing short- and long-term disease dynamics and consequences.

Summary Host social structure is fundamental to how infections spread and persist, and so the statistical modelling of static and dynamic social networks provides an invaluable tool to parameterise realistic epidemiological models. We present a practical guide to the application of network modelling frameworks for hypothesis testing related to social interactions and epidemiology, illustrating some approaches with worked examples using data from a population of wild European badgers Meles meles naturally infected with bovine tuberculosis. Different empirical network datasets generate particular statistical issues related to non-independence and sampling constraints. We therefore discuss the strengths and weaknesses of modelling approaches for different types of network data and for answering different questions relating to disease transmission. We argue that statistical modelling frameworks designed specifically for network analysis offer great potential in directly relating network structure to infection. They have the potential to be powerful tools in analysing empirical contact data used in epidemiological studies, but remain untested for use in networks of spatio-temporal associations. As a result, we argue that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio-logging studies. Furthermore, we encourage improved integration of statistical network approaches into epidemiological research to facilitate the generation of novel modelling frameworks and help extend our understanding of disease transmission in natural populations.

Slow bee paralysis virus (SBPV)—previously considered an obligate honeybee disease—is now known to be prevalent in bumblebee species. SBPV is highly virulent in honeybees in association with Varroa mites, but has been considered relatively benign otherwise. However, condition-dependent pathogens can appear asymptomatic under good, resource abundant conditions, and negative impacts on host fitness may only become apparent when under stressful or resource-limited conditions. We tested whether SBPV expresses condition-dependent virulence in its bumblebee host, Bombus terrestris, by orally inoculating bees with SBPV and recording longevity under satiated and starvation conditions. SBPV infection resulted in significant virulence under starvation conditions, with infected bees 1.6 times more likely to die at any given time point (a median of 2.3 h earlier than uninfected bees), whereas there was no effect under satiated conditions. This demonstrates clear condition-dependent virulence for SBPV in B. terrestris. Infections that appear asymptomatic in non-stressful laboratory assays may nevertheless have significant impacts under natural conditions in the wild. For multi-host pathogens such as SBPV, the use of sentinel host species in laboratory assays may further lead to the underestimation of pathogen impacts on other species in nature. In this case the impact of ‘honeybee viruses’ on wild pollinators may be underestimated, with detrimental effects on conservation and food security. Our results highlight the importance of multiple assays and multiple host species when testing for virulence, in order for laboratory studies to accurately inform conservation policy and mitigate disease impacts in wild pollinators. Electronic supplementary material The online version of this article (doi:10.1007/s00442-017-3851-2) contains supplementary material, which is available to authorized users.

Understanding the drivers of parasite evolution and in particular disease virulence remains a major focus of evolutionary theory. Here, we examine the role of resource quality and in particular spatial environmental heterogeneity in the distribution of these resources on the evolution of virulence. There may be direct effects of resources on host susceptibility and pathogenicity alongside effects on reproduction that indirectly impact host-parasite population dynamics. Therefore, we assume that high resource quality may lead to both increased host reproduction and/or increased disease resistance. In completely mixed populations there is no effect of resource quality on the outcome of disease evolution. However, when there are local interactions higher resource quality generally selects for higher virulence/transmission for both linear and saturating transmission-virulence trade-off assumptions. The exception is that in castrators (i.e., infected hosts have no reproduction), higher virulence is selected for both low and high resource qualities at mixed local and global infection. Heterogeneity in the distribution of environment resources only has an effect on the outcome in castrators where random distributions generally select for higher virulence. Overall, our results further underline the importance of considering spatial structure in order to understand evolutionary processes.

Bats host virulent zoonotic viruses without experiencing disease. A mechanistic understanding of the impact of bats’ virus hosting capacities, including uniquely constitutive immune pathways, on cellular-scale viral dynamics is needed to elucidate zoonotic emergence. We carried out virus infectivity assays on bat cell lines expressing induced and constitutive immune phenotypes, then developed a theoretical model of our in vitro system, which we fit to empirical data. Best fit models recapitulated expected immune phenotypes for representative cell lines, supporting robust antiviral defenses in bat cells that correlated with higher estimates for within-host viral propagation rates. In general, heightened immune responses limit pathogen-induced cellular morbidity, which can facilitate the establishment of rapidly-propagating persistent infections within-host. Rapidly-transmitting viruses that have evolved with bat immune systems will likely cause enhanced virulence following emergence into secondary hosts with immune systems that diverge from those unique to bats., eLife digest Bats can carry viruses that are deadly to other mammals without themselves showing serious symptoms. In fact, bats are natural reservoirs for viruses that have some of the highest fatality rates of any viruses that people acquire from wild animals – including rabies, Ebola and the SARS coronavirus. Bats have a suite of antiviral defenses that keep the amount of virus in check. For example, some bats have an antiviral immune response called the interferon pathway perpetually switched on. In most other mammals, having such a hyper-vigilant immune response would cause harmful inflammation. Bats, however, have adapted anti-inflammatory traits that protect them from such harm, include the loss of certain genes that normally promote inflammation. However, no one has previously explored how these unique antiviral defenses of bats impact the viruses themselves. Now, Brook et al. have studied this exact question using bat cells grown in the laboratory. The experiments made use of cells from one bat species – the black flying fox – in which the interferon pathway is always on, and another – the Egyptian fruit bat – in which this pathway is only activated during an infection. The bat cells were infected with three different viruses, and then Brook et al. observed how the interferon pathway helped keep the infections in check, before creating a computer model of this response. The experiments and model helped reveal that the bats’ defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not. The findings may help to explain why bats are often the source for viruses that are deadly in humans. Learning more about bats' antiviral defenses and how they drive virus evolution may help scientists develop better ways to predict, prevent or limit the spread of viruses from bats to humans. More studies are needed in bats to help these efforts. In the meantime, the experiments highlight the importance of warning people to avoid direct contact with wild bats.

Typically, pathogens infect multiple host species. Such multihost pathogens can show considerable variation in their degree of infection and transmission specificity, which has important implications for potential disease emergence. Transmission of multihost pathogens can be driven by key host species and changes in such transmission networks can lead to disease emergence. We study two viruses that show contrasting patterns of prevalence and specificity in managed honeybees and wild bumblebees, black queen cell virus (BQCV) and slow bee paralysis virus (SBPV), in the context of the novel transmission route provided by the virus‐vectoring Varroa destructor. Our key result is that viral communities and RNA virus genetic variation are structured by location, not host species or V. destructor presence. Interspecific transmission is pervasive with the same viral variants circulating between pollinator hosts in each location; yet, we found virus‐specific host differences in prevalence and viral load. Importantly, V. destructor presence increases the prevalence in honeybees and, indirectly, in wild bumblebees, but in contrast to its impact on deformed wing virus (DWV), BQCV and SBPV viral loads are not increased by Varroa presence, and do not show genetic evidence of recent emergence. Effective control of Varroa in managed honeybee colonies is necessary to mitigate further disease emergence, and alleviate disease pressure on our vital wild bee populations. More generally, our results highlight the over‐riding importance of geographical location to the epidemiological outcome despite the complexity of multihost‐parasite interactions.

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’.

Parasites impose strong selection on their hosts, but the level of resistance evolved may be constrained by the availability of resources. However, studies identifying the genomic basis of such resource mediated selection are rare, particularly in non-model organisms. Here, we investigated the role of nutrition in the evolution of resistance to a DNA virus (PiGV), and associated trade-offs, in a lepidopteran pest species (Plodia interpunctella). Through selection experiments and whole genome sequencing we identify putative mechanisms of resistance that depend on the nutritional environment during selection. We find that the evolution of resistance is specific to diet, with adaptation to a low nutrition diet constraining resistance when challenged with the pathogen on a high nutrition diet. Resistance in a low nutrition environment is negatively correlated with growth rate, consistent with an established trade-off between immunity and development. Whole genome resequencing of the host shows that resistance mechanisms are highly polygenic and suggests evidence for trade-offs at the genetic level. Critically when populations evolve in high resource conditions, resistance is linked to metabolic and immune pathways, however it is more closely associated with cytoskeleton organisation when selected under low nutrition. Our results emphasise the importance of resources on the evolution of resistance.

S. aureus is a leading cause of bacterial infection. Macrophages, the first line of defence in the human immune response, phagocytose and kill S. aureus but the pathogen can evade these responses. Therefore, the exact role of macrophages is incompletely defined. We develop a mathematical model of macrophage - S. aureus dynamics, built on recent experimental data. We demonstrate that, while macrophages may not clear infection, they significantly delay its growth and potentially buy time for recruitment of further cells. We find that macrophage killing is a major obstacle to controlling infection and ingestion capacity also limits the response. We find bistability such that the infection can be limited at low doses. Our combination of experimental data, mathematical analysis and model fitting provide important insights in to the early stages of S. aureus infections, showing macrophages play an important role limiting bacterial replication but can be overwhelmed with large inocula.

A substantial body of work has shown that local transmission selects for less acute, ‘prudent’ parasites that have lower virulence and transmission rates. This is because parasite strains with higher transmission rates ‘self-shade’ due to a combination of genetic correlations (self: clustered related parasite strains compete for susceptible individuals) and ecological correlations (shade: infected individuals clustering and blocking transmission). However, the interaction of ecological and genetic correlations alongside higher order ecological effects such as patch extinctions means that spatial evolutionary effects can be nuanced; theory has predicted that a relatively small proportion of local infection can select for highest virulence, such that there is a humped relationship between the degree of local infection and the harm that parasites are selected to cause. Here, we examine the separate roles of the interaction scales of reproduction and infection in the context of different degrees of pathogenic castration in determining virulence evolution outcomes. Our key result is that, as long as there is significant reproduction from infected individuals, local infection always selects for lower virulence, and that the prediction that a small proportion of local infection can select for higher virulence only occurs for highly castrating pathogens. The results emphasize the importance of demography for evolutionary outcomes in spatially structured populations, but also show that the core prediction that parasites are prudent in space is reasonable for the vast majority of host-parasite interactions and mixing patterns in nature.

Infectious diseases are a major threat to both managed and wild pollinators. One key question is how the movement or transplantation of honeybee colonies under different management regimes affects honeybee disease epidemiology. We opportunistically examined any persistent effect of colony management history following relocation by characterising the virus abundances of honeybee colonies from three management histories, representing different management histories: feral, low-intensity management, and high-intensity "industrial" management. The colonies had been maintained for one year under the same approximate 'common garden' condition. Colonies in this observational study differed in their virus abundances according to management history, with the feral population history showing qualitatively different viral abundance patterns compared to colonies from the two managed population management histories; for example, higher abundance of sacbrood virus but lower abundances of various paralysis viruses. Colonies from the high-intensity management history exhibited higher viral abundances for all viruses than colonies from the low-intensity management history. Our results provide evidence that management history has persistent impacts on honeybee disease epidemiology, suggesting that apicultural intensification could be majorly impacting on pollinator health, justifying much more substantial investigation.

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., This is a contribution to Ministerio de Economía y Competitividad (MINECO) Plan Nacional (grant WILD DRIVER ref. CGL2017-89866 and the European Regional Development Fund [EU-FEDER]). E.T. was supported by the Maxwell Institute Graduate School in Analysis and its Applications, a Centre for Doctoral Training funded by the United Kingdom Engineering and Physical Sciences Research Council (grant EP/L016508/01), the Scottish Funding Council, Heriot-Watt University, and the University of Edinburgh