Reservoir displacement by an invasive rodent reduces Lassa virus zoonotic spillover risk

The black rat (Rattus rattus) is a globally invasive species that has been widely introduced across Africa. Within its invasive range in West Africa, R. rattus may compete with the native rodent Mastomys natalensis, the primary reservoir host of Lassa virus, a zoonotic pathogen that kills thousands annually. Here, we use rodent trapping data from Sierra Leone and Guinea to show that R. rattus presence reduces M. natalensis density within the human dwellings where Lassa virus exposure is most likely to occur. Further, we integrate infection data from M. natalensis to demonstrate that Lassa virus zoonotic spillover risk is lower at sites with R. rattus. While non-native species can have numerous negative effects on ecosystems, our results suggest that R. rattus invasion has the indirect benefit of decreasing zoonotic spillover of an endemic pathogen, with important implications for invasive species control across West Africa.

A global-scale dataset of bat viral detection suggests that pregnancy reduces viral shedding

Understanding viral infection dynamics in wildlife hosts can help forecast zoonotic pathogen spillover and human disease risk. Bats are particularly important reservoirs of zoonotic viruses, including some of major public health concern such as Nipah virus, Hendra virus, and SARS-related coronaviruses. Previous work has suggested that metapopulation dynamics, seasonal reproductive patterns, and other bat life history characteristics might explain temporal variation in spillover of bat-associated viruses into people. Here, we analyze viral dynamics in free-ranging bat hosts, leveraging a multi-year, global-scale viral detection dataset that spans eight viral families and 96 bat species from 14 countries. We fit hierarchical Bayesian models that explicitly control for important sources of variation, including geographic region, specimen type, and testing protocols, while estimating the influence of reproductive status on viral detection in female bats. Our models revealed that late pregnancy had a negative effect on viral shedding across multiple data subsets, while lactation had a weaker influence that was inconsistent across data subsets. These results are unusual for mammalian hosts, but given recent findings that bats may have high individual viral loads and population-level prevalence due to dampening of antiviral immunity, we propose that it would be evolutionarily advantageous for pregnancy to either not further reduce immunity or actually increase the immune response, reducing viral load, shedding, and risk of fetal infection. This novel hypothesis would be valuable to test given its potential to help monitor, predict, and manage viral spillover risk from bats.

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Climate change increases cross-species viral transmission risk

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals^1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife^3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Climate change increases cross-species viral transmission risk

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals^1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife^3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Assessing the risk of human-to-wildlife pathogen transmission for conservation and public health

The SARS‐CoV‐2 pandemic has led to increased concern over transmission of pathogens from humans to animals, and its potential to threaten conservation and public health. To assess this threat, we reviewed published evidence of human‐to‐wildlife transmission events, with a focus on how such events could threaten animal and human health. We identified 97 verified examples, involving a wide range of pathogens; however, reported hosts were mostly non‐human primates or large, long‐lived captive animals. Relatively few documented examples resulted in morbidity and mortality, and very few led to maintenance of a human pathogen in a new reservoir or subsequent “secondary spillover” back into humans. We discuss limitations in the literature surrounding these phenomena, including strong evidence of sampling bias towards non‐human primates and human‐proximate mammals and the possibility of systematic bias against reporting human parasites in wildlife, both of which limit our ability to assess the risk of human‐to‐wildlife pathogen transmission. We outline how researchers can collect experimental and observational evidence that will expand our capacity for risk assessment for human‐to‐wildlife pathogen transmission.

Mammal virus diversity estimates are unstable due to accelerating discovery effort

Host-virus association data underpin research into the distribution and eco-evolutionary correlates of viral diversity and zoonotic risk across host species. However, current knowledge of the wildlife virome is inherently constrained by historical discovery effort, and there are concerns that the reliability of ecological inference from host-virus data may be undermined by taxonomic and geographical sampling biases. Here, we evaluate whether current estimates of host-level viral diversity in wild mammals are stable enough to be considered biologically meaningful, by analysing a comprehensive dataset of discovery dates of 6571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are either constant or accelerating, with little evidence of declines towards viral richness asymptotes, even in highly sampled hosts. Consequently, inference of relative viral richness across host species has been unstable over time, particularly in bats, where intensified surveillance since the early 2000s caused a rapid rearrangement of species' ranked viral richness. Our results illustrate that comparative inference of host-level virus diversity across mammals is highly sensitive to even short-term changes in sampling effort. We advise caution to avoid overinterpreting patterns in current data, since it is feasible that an analysis conducted today could draw quite different conclusions than one conducted only a decade ago.

The science of the host-virus network

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Synzootics

Ecologists increasingly recognise coinfection as an important component of emergent epidemiological patterns, connecting aspects of ecoimmunology, behaviour, ecosystem function and even extinction risk. Building on syndemic theory in medical anthropology, we propose the term 'synzootics' to describe co-occurring enzootic or epizootic processes that produce worse health outcomes in wild animals. Using framing from syndemic theory, we describe how the synzootic concept offers new insights into the ecology and evolution of infectious diseases. We then recommend a set of empirical criteria and lines of evidence that can be used to identify synzootics in nature. We conclude by exploring how synzootics could indirectly drive the emergence of novel pathogens in human populations.

Host gene expression in wildlife disease: making sense of species-level responses

Emerging infectious diseases are significant threats to wildlife conservation, yet the impacts of pathogen exposure and infection can vary widely among host species. As such, conservation biologists and disease ecologists have increasingly aimed to understand species-specific host susceptibility using molecular methods. In particular, comparative gene expression assays have been used to contrast the transcriptomic responses of disease-resistant and disease-susceptible hosts to pathogen exposure. This work usually assumes that the gene expression responses of disease-resistant species will reveal the activation of molecular pathways contributing to host defence. However, results often show that disease-resistant hosts undergo little gene expression change following pathogen challenge. Here, we discuss the mechanistic implications of these "null" findings and offer methodological suggestions for future molecular studies of wildlife disease. First, we highlight that muted transcriptomic responses with minimal immune system recruitment may indeed be protective for nonsusceptible hosts if they limit immunopathology and promote pathogen tolerance in systems where susceptible hosts suffer from genetic dysregulation. Second, we argue that overly narrow investigation of responses to pathogen exposure may overlook important, constitutively active molecular pathways that underlie species-specific defences. Finally, we outline alternative study designs and approaches that complement interspecific transcriptomic comparisons, including intraspecific gene expression studies and genomic methods to detect signatures of selection. Collectively, these insights will help ecologists extract maximal information from conservation-relevant transcriptomic data sets, leading to a deeper understanding of host defences and, ultimately, the implementation of successful conservation interventions.

Data Proliferation, Reconciliation, and Synthesis in Viral Ecology

The fields of viral ecology and evolution are rapidly expanding, motivated in part by concerns around emerging zoonoses. One consequence is the proliferation of host–virus association data, which underpin viral macroecology and zoonotic risk prediction but remain fragmented across numerous data portals. In the present article, we propose that synthesis of host–virus data is a central challenge to characterize the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open database of mammal host–virus associations that reconciles four published data sets. We show that this offers a substantially richer view of the known virome than any individual source data set but also that databases such as these risk becoming out of date as viral discovery accelerates. We argue for a shift in practice toward the development, incremental updating, and use of synthetic data sets in viral ecology, to improve replicability and facilitate work to predict the structure and dynamics of the global virome.

Predicting the global mammalian viral sharing network using phylogeography

Understanding interspecific viral transmission is key to understanding viral ecology and evolution, disease spillover into humans, and the consequences of global change. Prior studies have uncovered macroecological drivers of viral sharing, but analyses have never attempted to predict viral sharing in a pan-mammalian context. Using a conservative modelling framework, we confirm that host phylogenetic similarity and geographic range overlap are strong, nonlinear predictors of viral sharing among species across the entire mammal class. Using these traits, we predict global viral sharing patterns of 4196 mammal species and show that our simulated network successfully predicts viral sharing and reservoir host status using internal validation and an external dataset. We predict high rates of mammalian viral sharing in the tropics, particularly among rodents and bats, and within- and between-order sharing differed geographically and taxonomically. Our results emphasize the importance of ecological and phylogenetic factors in shaping mammalian viral communities, and provide a robust, general model to predict viral host range and guide pathogen surveillance and conservation efforts.

Prior studies have investigated macroecological patterns of host sharing among viruses, although certain mammal clades have not been represented in these analyses, and the findings have not been used to predict the true network. Here the authors model the species level traits that predict viral sharing across all mammal clades and validate their predictions using an independent dataset.

United States wildlife and wildlife product imports from 2000-2014

The global wildlife trade network is a massive system that has been shown to threaten biodiversity, introduce non-native species and pathogens, and cause chronic animal welfare concerns. Despite its scale and impact, comprehensive characterization of the global wildlife trade is hampered by data that are limited in their temporal or taxonomic scope and detail. To help fill this gap, we present data on 15 years of the importation of wildlife and their derived products into the United States (2000–2014), originally collected by the United States Fish and Wildlife Service. We curated and cleaned the data and added taxonomic information to improve data usability. These data include >2 million wildlife or wildlife product shipments, representing >60 biological classes and >3.2 billion live organisms. Further, the majority of species in the dataset are not currently reported on by CITES parties. These data will be broadly useful to both scientists and policymakers seeking to better understand the volume, sources, biological composition, and potential risks of the global wildlife trade.

Measurement(s)	Import • wildlife • wildlife product
Technology Type(s)	digital curation
Sample Characteristic - Environment	wildlife trade network
Sample Characteristic - Location	United States of America

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11439471

Influence of damming on anuran species richness in riparian areas: A test of the serial discontinuity concept

Almost all large rivers worldwide are fragmented by dams, and their impacts have been modeled using the serial discontinuity concept (SDC), a series of predictions regarding responses of key biotic and abiotic variables. We evaluated the effects of damming on anuran communities along a 245-km river corridor by conducting repeated, time-constrained anuran calling surveys at 42 locations along the Broad and Pacolet Rivers in South Carolina, USA. Using a hierarchical Bayesian analysis, we test the biodiversity prediction of the SDC (modified for floodplain rivers) by evaluating anuran occupancy and species diversity relative to dams and degree of urbanized land use. The mean response of the anuran community indicated that occupancy and species richness were maximized when sites were farther downstream from dams. Sites at the farthest distances downstream of dams (47.5 km) had an estimated ~3 more species than those just below dams. Similarly, species-specific occupancy estimates showed a trend of higher occupancy downstream from dams. Therefore, using empirical estimation within the context of a 245-km river riparian landscape, our study supports SDC predictions for a meandering river. We demonstrate that with increasing distance downstream from dams, riparian anuran communities have higher species richness. Reduced species richness immediately downstream of dams is likely driven by alterations in flow regime that reduce or eliminate flows which sustain riparian wetlands that serve as anuran breeding habitat. Therefore, to maintain anuran biodiversity, we suggest that flow regulation should be managed to ensure water releases inundate riparian wetlands during amphibian breeding seasons and aseasonal releases, which can displace adults, larvae, and eggs, are avoided. These outcomes could be achieved by emulating pre-dam seasonal discharge data, mirroring discharge of an undammed tributary within the focal watershed, or by basing real-time flow releases on current environmental conditions.

Gene expression differs in susceptible and resistant amphibians exposed to Batrachochytrium dendrobatidis

Chytridiomycosis, the disease caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), has devastated global amphibian biodiversity. Nevertheless, some hosts avoid disease after Bd exposure even as others experience near-complete extirpation. It remains unclear whether the amphibian adaptive immune system plays a role in Bd defence. Here, we describe gene expression in two host species-one susceptible to chytridiomycosis and one resistant-following exposure to two Bd isolates that differ in virulence. Susceptible wood frogs (Rana sylvatica) had high infection loads and mortality when exposed to the more virulent Bd isolate but lower infection loads and no fatal disease when exposed to the less virulent isolate. Resistant American bullfrogs (R. catesbeiana) had high survival across treatments and rapidly cleared Bd infection or avoided infection entirely. We found widespread upregulation of adaptive immune genes and downregulation of important metabolic and cellular maintenance components in wood frogs after Bd exposure, whereas American bullfrogs showed little gene expression change and no evidence of an adaptive immune response. Wood frog responses suggest that adaptive immune defences may be ineffective against virulent Bd isolates that can cause rapid physiological dysfunction. By contrast, American bullfrogs exhibited robust resistance to Bd that is likely attributable, at least in part, to their continued upkeep of metabolic and skin integrity pathways as well as greater antimicrobial peptide expression compared to wood frogs, regardless of exposure. Greater understanding of these defences will ultimately help conservationists manage chytridiomycosis.

Are disease reservoirs special? Taxonomic and life history characteristics

Pathogens that spill over between species cause a significant human and animal health burden. Here, we describe characteristics of animal reservoirs that are required for pathogen spillover. We assembled and analyzed a database of 330 disease systems in which a pathogen spills over from a reservoir of one or more species. Three-quarters of reservoirs included wildlife, and 84% included mammals. Further, 65% of pathogens depended on a community of reservoir hosts, rather than a single species, for persistence. Among mammals, the most frequently identified reservoir hosts were rodents, artiodactyls, and carnivores. The distribution among orders of mammalian species identified as reservoirs did not differ from that expected by chance. Among disease systems with high priority pathogens and epidemic potential, we found birds, primates, and bats to be overrepresented. We also analyzed the life history traits of mammalian reservoir hosts and compared them to mammals as a whole. Reservoir species had faster life history characteristics than mammals overall, exhibiting traits associated with greater reproductive output rather than long-term survival. Thus, we find that in many respects, reservoirs of spillover pathogens are indeed special. The described patterns provide a useful resource for studying and managing emerging infectious diseases.

Infection risk decreases with increasing mismatch in host and pathogen environmental tolerances

The fungal pathogen Batrachochytrium dendrobatidis (Bd) has caused the greatest known wildlife pandemic, infecting over 500 amphibian species. It remains unclear why some host species decline from disease-related mortality whereas others persist. We introduce a conceptual model that predicts that infection risk in ectotherms will decrease as the difference between host and pathogen environmental tolerances (i.e. tolerance mismatch) increases. We test this prediction using both local-scale data from Costa Rica and global analyses of over 11 000 Bd infection assays. We find that infection prevalence decreases with increasing thermal tolerance mismatch and with increasing host tolerance of habitat modification. The relationship between environmental tolerance mismatches and Bd infection prevalence is generalisable across multiple amphibian families and spatial scales, and the magnitude of the tolerance mismatch effect depends on environmental context. These findings may help explain patterns of amphibian declines driven by a global wildlife pandemic.

Organic contaminants in western pond turtles in remote habitat in California

Remote aquatic ecosystems are exposed to an assortment of semivolatile organic compounds (SOCs) originating from current and historic uses, of local and global origin. Here, a representative suite of 57 current- and historic-use pesticides, polychlorinated biphenyls, and polycyclic aromatic hydrocarbons were surveyed in the plasma of the western pond turtle (Emys marmorata) and their potential prey items and habitat. California study sites included Sequoia National Park, Whiskeytown National Recreation Area, and Six Rivers National Forest. Each was downstream of undeveloped watersheds and varied in distance from agricultural and urban pollution sources. SOCs were detected frequently in all sites with more found in turtle plasma and aquatic macroinvertebrates in the two sites closest to agricultural and urban sources. Summed PCBs were highest in Whiskeytown National Recreation Area turtle plasma (mean; 1.56 ng/g ww) compared to plasma from Sequoia National Park (0.16 ng/g ww; p = 0.002) and Six Rivers National Forest (0.07 ng/g ww; p = 0.001). While no current-use pesticides were detected in turtle plasma at any site, both current- and historic-use pesticides were found prominently in sediment and macroinvertebrates at the Sequoia National Park site, which is immediately downwind of Central Valley agriculture. SOC classes associated with urban and industrial pollution were found more often and at higher concentrations at Whiskeytown National Recreation Area. These findings demonstrate a range of SOC exposure in a turtle species with current and proposed conservation status and shed additional light on the fate of environmental contaminants in remote watersheds.

American Bullfrogs (Lithobates catesbeianus) Resist Infection by Multiple Isolates of Batrachochytrium dendrobatidis, Including One Implicated in Wild Mass Mortality

The emerging amphibian disease chytridiomycosis varies in severity depending on host species. Within species, disease susceptibility can also be influenced by pathogen variation and environmental factors. Here, we report on experimental exposures of American bullfrogs (Lithobates catesbeianus) to three different isolates of Batrachochytrium dendrobatidis (Bd), including one implicated in causing mass mortality of wild American bullfrogs. Exposed frogs showed low infection prevalence, relatively low infection load, and lack of clinical disease. Our results suggest that environmental cofactors are likely important contributors to Bd-associated American bullfrog mortality and that this species both resists and tolerates Bd infection.

Parallels in amphibian and bat declines from pathogenic fungi

Pathogenic fungi have substantial effects on global biodiversity, and 2 emerging pathogenic species—the chytridiomycete Batrachochytrium dendrobatidis, which causes chytridiomycosis in amphibians, and the ascomycete Geomyces destructans, which causes white-nose syndrome in hibernating bats—are implicated in the widespread decline of their vertebrate hosts. We synthesized current knowledge for chytridiomycosis and white-nose syndrome regarding disease emergence, environmental reservoirs, life history characteristics of the host, and host–pathogen interactions. We found striking similarities between these aspects of chytridiomycosis and white-nose syndrome, and the research that we review and propose should help guide management of future emerging fungal diseases.

Effects of river-flow regulation on anuran occupancy and abundance in riparian zones

The natural flow regimes of rivers worldwide have been heavily altered through anthropogenic activities, and dams in particular have a pervasive effect on riverine ecosystems. Flow-regulation effects of dams negatively affect species diversity and abundance of a variety of aquatic animals, including invertebrates and fishes. However, the effects on semiaquatic animals are relatively unknown. We conducted anuran calling surveys at 42 study locations along the Broad and Pacolet Rivers in South Carolina to address the potential effects of flow regulation by damming on anuran occupancy and abundance. We estimated occupancy and abundance with Program PRESENCE. Models incorporated distance upstream and downstream from the nearest dam as covariates and urbanization pressure as an alternative stressor. Distance from dam was associated with occupancy of 2 of the 9 anuran species in our analyses and with abundance of 6 species. In all cases, distance downstream from nearest dam was a better predictor of occupancy and abundance than distance upstream from nearest dam. For all but one species, distance downstream from nearest dam was positively correlated with both occupancy and abundance. Reduced occupancy and abundance of anurans likely resulted from downstream alterations in flow regime associated with damming, which can lead to reduced area of riparian wetlands that serve as anuran breeding habitat. Our results showed that damming has a strong negative effect on multiple anuran species across large spatial extents and suggest that flow regulation can affect semiaquatic animals occupying riparian zones.