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

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

Vast alteration of the biosphere by humans is causing a sixth mass extinction. Adaptation to modified environments often is the only means for species to persist, but the extent to which such “evolutionary rescue” can prevent or reverse biodiversity loss is largely unknown. Us- ing results from genomic analyses, a 15-year reintroduction effort, and population modeling, we provide a compelling example of evolution reversing the declines of an imperiled amphibian and allowing the reestablishment of extirpated populations. The once-common mountain yellow-legged (MYL) frog is threatened with extinction by the human-facilitated emergence of a lethal fungal pathogen (Batrachochytrium dendrobatidis; “Bd”). Although most MYL frog populations are extirpated following disease outbreaks, some persist and eventually begin to recover. Using an exome capture approach, we show that MYL frogs have undergone substantial evolutionary change following disease outbreaks. Moreover, in naturally recovering populations observed changes in immune function loci are associated with increased resis- tance/tolerance to Bd infection. Large-scale reintroduction of frogs from rescued populations resulted in the establishment of reproducing populations despite ongoing disease. In addition, results from viability modeling suggest that established populations have a low prob- ability of extinction over 50 years. Collectively, these results provide a rare example of how evolutionary rescue and the reintroduction of resistant/tolerant individuals can allow the landscape-scale recovery of disease-impacted species. This example has potentially broad implications for the many taxa worldwide that are threatened with extinction by anthropogenic stressors.

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

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

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

Host species that can independently maintain a pathogen in a host community and contribute to infection in other species are important targets for disease management. However, the potential of host species to maintain a pathogen is not fixed over time, and an important challenge is understanding how within- and across-season variability in host maintenance potential affects pathogen persistence over longer time scales relevant for disease management (e.g., years). Here, we sought to understand the causes and consequences of seasonal infection dynamics in leopard frogs (Rana sphenocephala and Rana pipiens) infected with the fungal pathogen Batrachochytrium dendrobatidis (Bd). We addressed three questions broadly applicable to seasonal host-parasite systems. First, to what degree are observed seasonal patterns in infection driven by temperature-dependent infection processes compared to seasonal host demographic processes? Second, how does seasonal variation in maintenance potential affect long-term pathogen persistence in multi-host communities? Third, does high deterministic maintenance potential relate to the long-term stochastic persistence of pathogens in host populations with seasonal infection dynamics? To answer these questions, we used field data collected over 3 years on1400 amphibians across four geographic locations, laboratory and mesocosm experiments, and a novel mathematical model. We found that the mechanisms that drive seasonal prevalence were different from those driving seasonal infection intensity. Seasonal variation in Bd prevalence was driven primarily by changes in host contact rates associated with breeding migrations to and from aquatic habitat. In contrast, seasonal changes in infection intensity were driven by temperature-induced changes in Bd growth rate. Using our model, we found that the maintenance potential of leopard frogs varied significantly throughout the year and that seasonal troughs in infection prevalence made it unlikely that leopard frogs were responsible for long-term Bd persistence in these seasonal amphibian communities, highlighting the importance of alternative pathogen reservoirs for Bd persistence. Our results have broad implications for management in seasonal host-pathogen systems, showing that seasonal changes in host and pathogen vital rates, rather than the depletion of susceptible hosts, can lead to troughs in pathogen prevalence and stochastic pathogen extirpation.

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

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

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

Transmission is the fundamental process whereby pathogens infect their hosts and spread through populations, and can be characterized using mathematical functions. The functional form of transmission for emerging pathogens can determine pathogen impacts on host populations and can inform the efficacy of disease management strategies. By directly measuring transmission between infected and susceptible adult eastern newts (Notophthalmus viridescens) in aquatic mesocosms, we identified the most plausible transmission function for the emerging amphibian fungal pathogen Batrachochytrium salamandrivorans (Bsal). Although we considered a range of possible transmission functions, we found that Bsal transmission was best explained by pure frequency dependence. We observed that >90% of susceptible newts became infected within 17 days post-exposure to an infected newt across a range of host densities and initial infection prevalence treatments. Under these conditions, we estimated R0 = 4.9 for Bsal in an eastern newt population. Our results suggest that Bsal has the capability of driving eastern newt populations to extinction and that managing host density may not be an effective management strategy. Intervention strategies that prevent Bsal introduction or increase host resistance or tolerance to infection may be more effective. Our results add to the growing empirical evidence that transmission of wildlife pathogens can saturate and be functionally frequency-dependent.

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

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

Individual‐ and species‐level heterogeneity in contact rates can alter the ability of a pathogen to invade a host community. Many pathogens have multiple modes of transmission—by direct or indirect contact. It is important to identify the role of heterogeneity in different types of transmission when managing the risk of disease spillover at the interface among different host species. We developed a network‐based analysis to explore how individual‐ and species‐level heterogeneity shape multi‐mode contact networks. We applied this network‐based approach to contact data from proximity loggers collected in a multi‐species host community that contributes to the spillover of the disease bovine tuberculosis (bTB) to cattle populations in Michigan, USA. We used this approach to (a) quantify how individual‐ and species‐level heterogeneity influence direct and indirect contacts in this system, (b) explore how management interventions to control spillovers, such as the installation of deer fences, can alter observed contact networks and c) predict the role that wildlife species have in maintaining bTB in the community. We found that individual‐ and species‐level heterogeneity disproportionately influenced indirect and direct contact networks, with individual‐level heterogeneity having a greater effect on indirect contact networks and species‐level heterogeneity having a greater effect on direct contact networks. We also found that the installation of deer fences significantly reduced deer‐specific indirect contacts. We used the results from our network analysis to show that white‐tailed deer (Odocoileus virginianus) could act as the sole reservoir host for bTB in this community with important implications for understanding past bTB dynamics and managing the persistence of bTB in the future. Synthesis and applications. Analyses of epidemiological networks rarely account for multiple modes of contact, which can lead to an incomplete understanding of how individual‐ and species‐level heterogeneity affect disease transmission. The multi‐mode, multi‐species network analysis we develop in this study illustrates that individual‐ and species‐level heterogeneity can play significantly different roles depending on the type of contact network considered. This has important implications when managing disease at the wildlife–livestock interface, where strategies may need to be multi‐pronged to account for the variable role of heterogeneity on different modes of contact.

Author(s): Newman, Erica A; Wilber, Mark Q; Kopper, Karen E; Moritz, Max A; Falk, Donald A; McKenzie, Don; Harte, John | Abstract: Macroecological studies have established widespread patterns of species diversity and abundance in ecosystems but have generally restricted their scope to relatively steady‐state systems. As a result, how macroecological metrics are expected to scale in ecosystems that experience natural disturbance regimes is unknown. We examine macroecological patterns in a fire‐dependent forest of Bishop pine (Pinus muricata ). We target two different‐aged stands in a stand‐replacing fire regime: a mature stand with a diverse understory and with no history of major disturbance for at least 40 yr, and one disturbed by a stand‐replacing fire 17 yr prior to measurement. We compare properties of these stands with macroecological predictions from the Maximum Entropy Theory of Ecology (METE ), an information entropy‐based theory that has proven highly successful in predicting macroecological metrics in multiple ecosystems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the more recently disturbed, mid‐seral stage stand. This suggests METE 's predictions are more robust in late‐successional, slowly changing, or steady‐state systems than those in rapid flux with respect to species composition, abundances, and organisms’ sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance, perturbations, and ecological dynamics into its predictive capabilities, because most natural systems are not in a steady state.

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

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

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

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

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

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

We present case examples of foreshock density holes and results from a statistical survey, which provide additional characterizations of these recently-described structures. Specific effort is made to place these objects into context with well-studied foreshock phenomena, such as hot flow anomalies (HFAs) and large-amplitude magnetic pulsations (SLAMS). Density holes are observed during higher-than-average solar wind speeds (~620 km s−1), have well-correlated density and magnetic field intensities, and anti-correlated density and temperature variations. Like HFAs, these structures occur over a wide range of foreshock geometries, suggesting that this is not a determining factor. They are embedded within IMF current sheets, but their cross-structure magnetic shears are considerably lower than for HFAs. When the Cluster spacecraft are widely separated, they are able to measure structure time development, with substantial changes occurring over 10s of seconds, confirming an earlier case study, and possibly indicating short lifetimes as well. We find that density holes can occur in the absence of strong upstream magnetic pulsations and/or density enhancements, which rules out a "wake effect" as the sole explanation for their formation. Most important is the observation that the observed solar wind motional electric fields tend to have components pointing away from the embedding IMF current sheets. Density holes have no connection with magnetic holes and foreshock cavities, and appear not to be early-stage or weakly-formed HFAs.

Almost 80% of the 4 billion projected increase in world population by 2100 comes from 37 Mid-African Countries (MACs), caused mostly by slow declines in Total Fertility Rate (TFR). Historically, TFR has declined in response to increases in wellbeing associated with economic development. We show that, when Infant Survival Rate (ISR, a proxy for wellbeing) has increased, MAC fertility has declined at the same rate, in relation to ISR, as it did in 61 comparable Other Developing Countries (ODCs) whose average fertility is close to replacement level. If MAC ISR were to increase at the historic rate of these ODCs, and TFR declined correspondingly, then the projected world population in 2100 would be decreasing and 1.1 billion lower than currently projected. Such rates of ISR increase, and TFR decrease, are quite feasible and have occurred in comparable ODCs. Increased efforts to improve the wellbeing of poor MAC populations are key.

Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METE’s predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms’ sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.

macroeco is a Python package that supports the analysis of empirical macroecological patterns and the comparison of these patterns to theoretical predictions. Here we describe the use of macroeco and the various functions that it contains. We also highlight a unique high-level interface included with the package, MacroecoDesktop, that allows non-programmers to access the functionality of macroeco. MacroecoDesktop takes simple text-based metadata and parameter files as inputs and generates both tabular and graphical outputs, supporting users in creating reproducible workflows that follow the principles of simplicity, provenance, and automation. Both macroeco and MacroecoDesktop provide case studies for developers of analytically-focused scientific software packages who wish to better support the reproducible use of their tools.

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

Emerging infectious diseases pose an important threat to wildlife. While established protocols exist for combating outbreaks of human and agricultural pathogens, appropriate management actions before, during, and after the invasion of wildlife pathogens have not been developed. We describe stage-specific goals and management actions that minimize disease impacts on wildlife, and the research required to implement them. Before pathogen arrival, reducing the probability of introduction through quarantine and trade restrictions is key because prevention is more cost effective than subsequent responses. On the invasion front, the main goals are limiting pathogen spread and preventing establishment. In locations experiencing an epidemic, management should focus on reducing transmission and disease, and promoting the development of resistance or tolerance. Finally, if pathogen and host populations reach a stable stage, then recovery of host populations in the face of new threats is paramount. Successful management of wildlife disease requires risk-taking, rapid implementation, and an adaptive approach. "Funding was provided by the US National Science Foundation (grants EF-0914866, DGE-0741448, DEB-1115069, DEB-1336290) and the National Institutes of Health (grant 1R010AI090159)." https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/140241

Aim The recently proposed maximum entropy theory of ecology predicts that all nested species–area relationships (SARs) will collapse to a single, universal curve that exhibits a decreasing log–log slope with increasing scale, suggesting that the power law form of the SAR is invalid at any scale. In this analysis we test the generality of this scale collapse behaviour and determine the scale at which approximate power law behaviour of the SAR is predicted to occur. Location Global. Methods We use common SAR models to recursively upscale SARs to (1) look for scale collapse of SARs built from different community assumptions and (2) identify the scale at which approximate power law SARs are recovered. We explore four SAR models in which species abundance distributions and the spatial aggregation of species vary within and across scales according to observed patterns. Results We show that scale collapse is a property of nested SARs, not of maximum entropy theory, and that many types of SARs can exhibit scale-collapsed curves that follow the general pattern of empirical communities. Moreover, we use the scale-collapsed curves to show that power law behaviour is not predicted to occur at the scale of most SAR studies and that variation in species spatial aggregation or the scaling of species abundance distributions are needed to produce power law SARs at realistic scales. Main conclusions Our findings show that power law behaviour of nested SARs is rare and that communities displaying approximate power law SARs may exhibit predictable characteristics within and across scale, such as interspecific variation in aggregation. Our findings regarding the general rarity of power law behaviour suggests that careful consideration of the curvature of a SAR and the scale of an ecological community are necessary before using power law SARs to predict biodiversity across scales.

Emerging infectious diseases (EIDs) are on the rise due to multiple factors, including human facilitated movement of pathogens, broad-scale landscape changes, and perturbations to ecological systems (Jones et al. 2008; Fisher et al. 2012). Epidemics in wildlife are problematic because they can lead to pathogen spillover to new host organisms, erode biodiversity and threaten ecosystems that sustain human societies (Fisher et al. 2012; Kilpatrick 2011). There have been recent calls for large-scale research approaches to combat the threats EIDs pose to wildlife (Sleeman 2013). While it is true that developing new analytical models, diagnostic assays and molecular tools will significantly advance our abilities to respond to disease threats, we also propose that addressing difficult problems in EIDs will require considerable shifts in international health policy and infrastructure. While there are currently international organizations responsible for rapidly initiating and coordinating preventative measures to control infectious diseases in human, livestock, and arable systems, there are few comparable institutions that have the authority to implement transnational responses to EIDs in wildlife. This absence of well-developed infrastructure hampers the rapid responses necessary to mitigate international spread of EIDs.

Many fundamental properties of ecological systems and interactions are tied to body size and a related metric, the metabolic rate distribution, both within and across species. A previously proposed maximum entropy theory of ecology (METE) predicts numerous interrelated macroecological patterns, including spatial distributions of individuals within species, abundance distributions across species, species area relationships, and distributions of metabolic rates of all individuals within a community. Extensive tests of METE's macroecological predictions generally support the theory, but two related predictions have not been evaluated against full community census data: the distribution of metabolic rates of individuals within species as a function of the abundance of the species and the distribution of average individual metabolic rates across species. We test the metabolic predictions of METE for herbaceous plants in a subalpine meadow and show that while this theory realistically predicts the distribution of individual metabolic rates across the entire community, the within- and across-species predictions generally fail. We also test the energy-equivalence type prediction that arises as a consequence of the prediction for the distribution of average individual metabolic rates across species. We suggest several possible explanations for the empirical deviations from theory, and distinguish between the expected deviations caused by ecological disturbance and those deviations that might be corrected within the theory.

[1] The parallel and perpendicular reduced distribution functions of field-aligned beams (FABs) observed upstream of the Earth's bow shock using the Cluster spacecrafts are examined. A previous study revealed that FABs, observed in oblique shock geometries, exhibit reduced distribution functions with high-energy tails. A selection of FABs with weak-energy tails are considered, and the associated reduced distributions are fit with Maxwellians. First, we have found that the FABs full width at half maximum (FWHM), σ∥ and σ⊥ derived from the fit, are linearly correlated with the solar wind speed (or equivalently to solar wind temperature). Moreover, the parallel beam σ∥ has a very weak dependence upon the beam parallel speed which reflects the shock geometry; we have found that σ∥∼0.23Vsw. In contrast, we have found that the perpendicular beam σ⊥, in the range of beam speeds investigated, depends on the shock geometry. These new results indicate that the parallel σ∥ is essentially controlled by the solar wind while the shock geometry plays, along with the solar wind, a role in the perpendicular σ⊥. These results also put some strong constraints on theoretical models as far as field-aligned beam production mechanisms are concerned. One potential explanation for the significant perpendicular broadening of the FAB distribution reported in this study could be the presence of kinetic Alfven (or/and whistler) turbulence at the shock.

Freshwater biodiversity is imperiled across the globe, and multiple stressors such as habitat alteration, non-native species invasion, disease, and climate change can act in concert to threaten vulnerable taxa. The amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd), which causes the disease chytridiomycosis, is one of the causative factors of severe amphibian declines. The foothill yellow-legged frog (Rana boylii) is a stream-breeding anuran endemic to California and Oregon (USA) that has declined precipitously in recent decades, yet there is little information on its susceptibility to Bd. In the fall of 2013, we observed dead and dying juvenile R. boylii in a San Francisco Bay Area watershed where annual amphibian breeding censuses have been conducted since 1997 in a free-flowing reach and since 2003 in an anthropogenically modified stream reach. High pathogen loads on R. boylii and histologic lesions observed on a dead R. boylii metamorph collected from the site were consistent with lethal chytridiomycosis. The outbreak coincided with extremely low stream flows in autumn that concentrated frogs in drying pools and the absence of high peak flows in winter that allowed non-native American bullfrogs (Rana catesbeiana) to expand their spatial distribution in the stream network. Following the outbreak, we surveyed R. boylii and sympatric anurans at the site for the next two years to determine Bd trends within the population. Using mixed-effects models, we found that bullfrog presence was a positive predictor of both Bd prevalence and Bd load in R. boylii. Prevalence was also influenced by sex and life stage: Adult males were more likely to be infected than either females or juveniles. Moreover, we found that stream flow volume was negatively associated with Bd load. These results indicate that disease, drought, and flow regulation may interact synergistically to impact amphibians in ways not previously recognized, informing stream flow management strategies for native aquatic taxa.

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

We present Wind spacecraft observations of ion distributions showing field-aligned beams (FABs) and large-amplitude magnetic fluctuations composed of a series of shocklets and short large-amplitude magnetic structures (SLAMS). We show that the SLAMS are acting like a local quasi-perpendicular shock reflecting ions to produce the FABs. Previous FAB observations reported the source as the quasi-perpendicular bow shock. The SLAMS exhibit a foot-like magnetic enhancement with a leading magnetosonic whistler train, consistent with previous observations. The FABs are found to have T_b ~ 80-850 eV, V_b/V_sw ~ 1-2, T_{b,perp}/T{b,para} ~ 1-10, and n_b/n_i ~ 0.2-14%. Strong ion and electron heating are observed within the series of shocklets and SLAMS increasing by factors \geq 5 and \geq 3, respectively. Both the core and halo electron components show strong perpendicular heating inside the feature.