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.

Urban-adapted mammal species have more known pathogens.

The world is rapidly urbanizing, inviting mounting concern that urban environments will experience increased zoonotic disease risk. Urban animals could have more frequent contact with humans, therefore transmitting more zoonotic parasites; however, this relationship is complicated by sampling bias and phenotypic confounders. Here we test whether urban mammal species host more zoonotic parasites, investigating the underlying drivers alongside a suite of phenotypic, taxonomic and geographic predictors. We found that urban-adapted mammals have more documented parasites and more zoonotic parasites: despite comprising only 6% of investigated species, urban mammals provided 39% of known host-parasite combinations. However, contrary to predictions, much of the observed effect was attributable to parasite discovery and research effort rather than to urban adaptation status, and urban-adapted species in fact hosted fewer zoonotic parasites than expected on the basis of their total parasite richness. We conclude that extended historical contact with humans has had a limited impact on zoonotic parasite richness in urban-adapted mammals; instead, their greater observed zoonotic richness probably reflects sampling bias arising from proximity to humans, supporting a near-universal conflation between zoonotic risk, research effort and synanthropy. These findings underscore the need to resolve the mechanisms linking anthropogenic change, sampling bias and observed wildlife disease dynamics.

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 mammals1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife3,4. 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.

The Global Virome in One Network (VIRION): an Atlas of Vertebrate-Virus Associations.

Data that catalogue viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open sharing, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static data sets and integration of dynamically updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus "species" (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically valid organisms. Together, these data cover roughly a quarter of mammal diversity, a 10th of bird diversity, and ∼6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to test hypotheses about microbiology, ecology, and evolution and make suggestions for best practices that address the unique mix of evidence that coexists in these data. IMPORTANCE Animals and their viruses are connected by a sprawling, tangled network of species interactions. Data on the host-virus network are available from several sources, which use different naming conventions and often report metadata in different levels of detail. VIRION is a new database that combines several of these existing data sources, reconciles taxonomy to a single consistent backbone, and reports metadata in a format designed by and for virologists. Researchers can use VIRION to easily answer questions like "Can any fish viruses infect humans?" or "Which bats host coronaviruses?" or to build more advanced predictive models, making it an unprecedented step toward a full inventory of the global virome.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.