Biology subjects

Eskew, E. A.

Publications and source records attributed to Eskew, E. A..

12 recordsLinked to original sources

bioRxiv2024

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.

ecology↗

bioRxiv2023

Ongoing amphibian trade into the United States threatens salamander biodiversity

The fungal pathogen Batrachochytrium salamandrivorans (Bsal) is a major potential threat to salamander biodiversity in North America, where it is not yet known to occur. In the United States, a 2016 policy restricted the trade in 20 salamander genera in attempts to prevent Bsal introduction. However, little comprehensive data is available to evaluate the impact of this policy action. Here, we collated a dataset of United States amphibian imports from 1999 to 2021 and show that reported legal trade in the targeted taxa was effectively reduced by the ban. Unfortunately, amphibian trade into the United States continues to risk Bsal introduction given that other species and genera now known to carry Bsal are still traded in large quantities (millions of live individuals annually). Additional policy responses focused on Bsal carrier taxa, especially frogs in the genus Rana, could help mitigate the impact of Bsal on North American salamanders.

ecology↗

bioRxiv2022

Viral diversity and zoonotic risk in endangered species

A growing body of evidence links zoonotic disease risk, including pandemic threats, to biodiversity loss and other upstream anthropogenic impacts on ecosystem health. However, there is little current research assessing viral diversity in endangered species. Here, combining IUCN Red List data on 5,876 mammal species with data on host-virus associations for a subset of 1,273 extant species, we examine the relationship between endangered species status and viral diversity, including the subset of viruses that can infect humans (zoonotic viruses). We show that fewer total viruses and fewer zoonotic viruses are known to infect more threatened species. After correcting for sampling effort, zoonotic virus diversity is mostly independent of threat status, but endangered species--despite a higher apparent research effort--have a significantly lower diversity of viruses, a property that is not explained by collinearity with host phylogeography or life history variation. Although this pattern could be generated by real biological processes, we suspect instead that endangered species may be subject to additional sampling biases not captured by the total volume of scientific literature (e.g., lower rates of invasive sampling may decrease viral discovery). Overall, our findings suggest that endangered species are no more or less likely to host viruses that pose a threat to humans, but future zoonotic threats might remain undiscovered in these species. This may be concerning, given that drivers of endangered species vulnerability such as habitat disturbance, wildlife trade, or climate vulnerability may increase virus prevalence in reservoirs and risk of spillover into humans.

ecology↗

bioRxiv2021

Mammal virus diversity estimates are unstable due to accelerating discovery effort

Host-virus association data form the backbone of research into eco-evolutionary drivers of viral diversity and host-level zoonotic risk. However, 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 6,571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are still either constant or accelerating, with little evidence of declines towards viral richness asymptotes in even 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 show 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 our findings suggest that an analysis conducted today could feasibly draw quite different conclusions than one conducted only a decade ago.

ecology↗

bioRxiv2021

The Global Virome in One Network (VIRION): an atlas of vertebrate-virus associations

Data cataloguing viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open collation, 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 datasets 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 tenth 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.

microbiology↗

bioRxiv2021

Data proliferation, reconciliation, and synthesis in viral ecology

The fields of viral ecology and evolution have rapidly expanded in the last two decades, driven by technological improvements, and motivated by efforts to discover potentially zoonotic wildlife viruses under the rubric of pandemic prevention. One consequence has been a massive proliferation of host-virus association data, which comprise the backbone of research in viral macroecology and zoonotic risk prediction. These data remain fragmented across numerous data portals and projects, each with their own scope, structure, and reporting standards. Here, we propose that synthesis of host-virus association data is a central challenge to improve our understanding of the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open reconciled mammal-virus database from four key published datasets, applying a standardized taxonomy and metadata. We show that reconciling these datasets provides a substantially richer view of the mammal virome than that offered by any one individual database. We argue for a shift in best practice towards the incremental development and use of synthetic datasets in viral ecology research, both to improve comparability and replicability across studies, and to facilitate future efforts to use machine learning to predict the structure and dynamics of the global virome.

ecology↗

bioRxiv2021

Urban-adapted mammal species have more known pathogens

The world is rapidly urbanising, inviting mounting concern that urban environments will experience increased zoonotic disease risk. Urban animals could have more frequent contact with humans, and therefore may transmit more zoonotic parasites; however, these animals have a specific set of underlying traits that may determine their parasite burdens while predisposing them to urban living, and they may be subject to more intense research effort, both of which could complicate our ability to reliably identify the role of urbanisation in driving zoonotic risk. Here, we test whether urban mammal species host more known zoonotic parasites, investigating the potential underlying drivers while accounting for a correlated suite of phenotypic, taxonomic, and geographic predictors. We found that urban-adapted mammals have more documented parasites, and more zoonotic parasites specifically: despite comprising only 157 of the 2792 investigated species (6%), urban mammals provided 39% of known host-parasite combinations and showed consistently higher viral discovery rates throughout the last century. However, contrary to predictions, much of the observed effect was attributable to research effort rather than to urban adaptation status itself, and urban-adapted species in fact hosted fewer zoonoses than expected given their total observed parasite richness. We conclude that extended historical contact with humans has had a limited impact on the number of observed zoonotic parasites in urban-adapted mammals; instead, their greater observed zoonotic richness likely reflects sampling bias arising from proximity to humans, which supports a near-universal underlying pattern of conflation between zoonotic risk, research effort, and synanthropy. These findings underscore the need to resolve the ecological mechanisms underlying links between anthropogenic change, sampling bias, and observed wildlife disease dynamics.

ecology↗

bioRxiv2020

Using publicly available data to conduct rapid assessments of extinction risk

The IUCN Red List plays a key role in setting global conservation priorities. Species are added to the Red List through a rigorous assessment process that, while robust, can be quite time-intensive. Here, we test the rapid preliminary assessment of plant species extinction risk using a single Red List metric: Extent of Occurrence (EOO). To do so, we developed REBA (Rapid EOO-Based Assessment), a workflow that harvests and cleans data from the Global Biodiversity Information Facility (GBIF), calculates each species EOO, and assigns Red List categories based on that metric. We validated REBA results against 1,546 North American plant species already on the Red List and found ~90% overlap between REBAs rapid classifications and those of full IUCN assessments. Our preliminary workflow can be used to quickly evaluate data deficient Red List species or those in need of reassessment, and can prioritize unevaluated species for a full assessment.

ecology↗

bioRxiv2020

Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization

Despite global investment in One Health disease surveillance, it remains difficult--and often very costly--to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs and potential bridge hosts for SARS-CoV-2. Over more than a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of horseshoe bats (Rhinolophus spp.) are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to demonstrate through systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

ecology↗

bioRxiv2020

Climate change will drive novel cross-species viral transmission

At least 10,000 species of mammal virus are estimated to have the potential to spread in human populations, but the vast majority are currently circulating in wildlife, largely undescribed and undetected by disease outbreak surveillance 1-3 . In addition, changing climate and land use are already driving geographic range shifts in wildlife, producing novel species assemblages and opportunities for viral sharing between previously isolated species 4, 5 . In some cases, this will inevitably facilitate spillover into humans 6, 7 --a possible mechanistic link between global environmental change and emerging zoonotic disease 8 . Here, we map potential hotspots of viral sharing, using a phylogeographic model of the mammal-virus network, and projections of potential geographic range shifts for 3,139 mammal species under climate change and land use scenarios for the year 2070. Range-shifting mammal species are predicted to aggregate at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, driving the novel cross-species transmission of their viruses an estimated 4,000 times. Counter to expectations, holding warming under 2{degrees}C within the century does not reduce new viral sharing, due to greater potential range expansions-- highlighting the need to invest in surveillance even in a low-warming future. Most projected viral sharing is driven by diverse hyperreservoirs (rodents and bats) and large-bodied predators (carnivores). Because of their unique dispersal capacity, bats account for the majority of novel viral sharing, and are likely to share viruses along evolutionary pathways that could facilitate future emergence in humans. Our findings highlight the urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking species range shifts, especially in tropical countries that harbor the most emerging zoonoses.

ecology↗

bioRxiv2019

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 conservation, 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. 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.

ecology↗

bioRxiv2019

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 work has demonstrated that macroecological factors drive viral sharing in some mammalian groups, but analyses have never attempted to predict viral sharing in a pan-mammalian context. Here we show 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 across 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 that within- and between-order sharing differs geographically and taxonomically. Our results emphasize the importance of macroecological factors in shaping mammalian viral communities, and provide a robust, general model to predict viral host range and guide pathogen surveillance and conservation efforts.

ecology↗