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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'.

Illuminating the ecological and evolutionary dynamics of parasites is one of the most pressing issues facing modern science, and is critical for basic science, the global economy, and human health. Extremely important to this effort are data on the disease-causing organisms of wild animal hosts (including viruses, bacteria, protozoa, helminths, arthropods, and fungi). Here we present an updated version of the Global Mammal Parasite Database, a database of the parasites of wild ungulates (artiodactyls and perissodactyls), carnivores, and primates, and make it available for download as complete flat files. The updated database has more than 24,000 entries in the main data file alone, representing data from over 2700 literature sources. We include data on sampling method and sample sizes when reported, as well as both \"reported\" and \"corrected\" (i.e., standardized) binomials for each host and parasite species. Also included are current higher taxonomies and data on transmission modes used by the majority of species of parasites in the database. In the associated metadata we describe the methods used to identify sources and extract data from the primary literature, how entries were checked for errors, methods used to georeference entries, and how host and parasite taxonomies were standardized across the database. We also provide definitions of the data fields in each of the four files that users can download.

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. 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.

Metawebs (networks of potential interactions within a species pool) are a powerful ion to understand how large‐scale species interaction networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.

Infectious diseases of domesticated animals impact human well-being via food insecurity, loss of livelihoods, and human infections. While much research has focused on parasites that infect single host species, most parasites of domesticated mammals infect multiple species. The impact of multihost parasites varies across hosts; some rarely result in death, whereas others are nearly always fatal. Despite their high ecological and societal costs, we currently lack theory for predicting the lethality of multihost parasites. Here, using a global dataset of >4,000 case-fatality rates for 65 infectious diseases (caused by microparasites and macroparasites) and 12 domesticated host species, we show that the average evolutionary distance from an infected host to other mammal host species is a strong predictor of disease-induced mortality. We find that as parasites infect species outside of their documented phylogenetic host range, they are more likely to result in lethal infections, with the odds of death doubling for each additional 10 million years of evolutionary distance. Our results for domesticated animal diseases reveal patterns in the evolution of highly lethal parasites that are difficult to observe in the wild and further suggest that the severity of infectious diseases may be predicted from evolutionary relationships among hosts.

Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host–parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host–parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host–parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

Determining the factors that influence the transmission of parasites among hosts is important for directing surveillance of animal parasites before they successfully emerge in humans, and increasing the efficacy of programs for the control and management of zoonotic diseases. Here we present a review of recent advances in the study of parasite sharing, wildlife ecology, and epidemiology that could be extended and incorporated into proactive surveillance frameworks for multi-host infectious diseases. These methods reflect emerging interdisciplinary techniques with significant promise for the identification of future zoonotic parasites and unknown reservoirs of current zoonoses, strategies for the reduction of parasite prevalence and transmission among hosts, and decreasing the burden of infectious diseases.

A growing body of research is focused on the extinction of parasite species in response to host endangerment and declines. Beyond the loss of parasite species richness, host extinction can impact apparent parasite host specificity, as measured by host richness or the phylogenetic distances among hosts. Such impacts on the distribution of parasites across the host phylogeny can have knock-on effects that may reshape the adaptation of both hosts and parasites, ultimately shifting the evolutionary landscape underlying the potential for emergence and the evolution of virulence across hosts. Here, we examine how the reshaping of host phylogenies through extinction may impact the host specificity of parasites, and offer examples fromhistorical extinctions, present-day endangerment, and future projections of biodiversity loss. We suggest that an improved understanding of the impact of host extinction on contemporary host–parasite interactionsmay shed light on core aspects of disease ecology, including comparative studies of host specificity, virulence evolution in multihost parasite systems, and future trajectories for host and parasite biodiversity. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

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.