Biology subjects

Farrell, M. J.

Publications and source records attributed to Farrell, M. J..

8 recordsLinked to original sources

bioRxiv2025

Experimental infections reveal unexceptional viral tolerance in bats

A common explanation for bats being a conspicuous source of zoonotic viruses is their purported ability to coexist with viruses without suffering overt disease. This belief has catalyzed the discovery of unique features of bat immune systems which may have translational value as broad-spectrum antivirals, particularly if they evolved as a byproduct of bats' unique life history rather than through conventional co-evolutionary processes. Surprisingly, whether bats compared to other host groups suffer less disease from co-evolved viruses or from viruses generally has not been formally assessed. Here, synthesizing eighty-six years of experimental infections, involving 54 viruses, 85 host species, and over 5,600 animals, we document disease in bats following inoculation by taxonomically diverse viruses, including ones that are relatively benign in humans. The occurrence of overt disease, the likelihood of mortality, and the severity of disease in bats were indistinguishable from those experienced by rodents, another group associated with many zoonotic viruses. These patterns were consistent when considering only bat-associated viruses inoculated into bats and rodent-associated viruses inoculated into rodents and among inoculations which lacked shared co-evolutionary history. Instead, disease outcomes following infection were shaped by experimental design, viral host range, and evolutionary context. Unexceptional disease in bats from novel or co-evolved infections suggests that order-level host life history traits such as flight have inconsistent consequences for antiviral immunity and highlights the need to evaluate the functional properties of putatively unique features of bat immunity in vivo. These results do not exclude the possibility of developing broader-acting or more potent antivirals from bat immune systems nor do they diminish the potential value of mechanistic insights into bat immunity. However, they do not support the premise underlying the idea that bats will be an unusually rich source of future biomedical breakthroughs.

evolutionary biology↗

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↗

bioRxiv2020

Environmental DNA for tracking waterhole visitation in savanna ecosystems

Game parks are the last preserve of many large mammals, and in savanna ecosystems, management of surface waters poses a conservation challenge. In arid and semi-arid regions, water can be a scarce resource during dry seasons and drought. Artificial waterholes are common in parks and reserves across Africa, but can alter mammal community composition by favoring drought intolerant species, with consequences for disease dynamics, and population viability of drought-tolerant species. Analysis of waterborne environmental DNA (eDNA) is increasingly used to inform conservation of rare and invasive species, and conduct large-scale biodiversity assessments. To explore the reliability of eDNA as an indicator of mammal waterhole use in savannas, we compare eDNA metabarcoding and camera traps for documenting artificial waterhole use in the Kruger National Park, South Africa, a global hotspot for mammal diversity. We show that eDNA metabarcoding can recover the majority of mammal species detected by camera traps, including a number of endangered species, but DNA signatures of mammal visitation are temporally limited, with best performance when tracking water-dependent large bodied mammals visiting within two days of sampling. Our results highlight limitation of eDNA based monitoring in these systems, including the lack of long-term eDNA persistence in small and highly utilized waterholes, and variability in detection rates among species. However, we demonstrate that eDNA-based approaches can be used to track mammals of conservation concern, and reflect patterns of recent waterhole use and co-occurrence across water-dependent species, both of which are crucial for making evidence-based decisions regarding water management and provisioning.

ecology↗

bioRxiv2020

Opposing macroevolutionary and trait-mediated patterns of threat and naturalization in flowering plants

Due to expanding global trade and movement, new plant species are establishing in exotic ranges at increasing rates while the number of native species facing extinction from multiple threats grows. Yet, how species losses and gains globally may together be linked to traits and macroevolutionary processes is poorly understood. Here we show that, adjusting for diversification rate and age, the proportion of threatened species across flowering plant families is negatively related to the proportion of naturalized species. Moreover, naturalization is positively associated with climate variability, short generation time, autonomous seed production, and interspecific hybridization, but negatively with age and diversification; whereas threat is negatively associated with climate variability and hybridization, and positively with biotic pollination, age and diversification. Such a pronounced signature of naturalization and threat across plant families suggests that both trait syndromes have coexisted over deep evolutionary time and that neither strategy is necessarily superior to the other.

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

Predicting missing links in global host-parasite networks

O_LIParasites that infect multiple species cause major health burdens globally, but for many, the full suite of susceptible hosts is unknown. Predicting undocumented host-parasite associations will help expand knowledge of parasite host specificities, promote the development of theory in disease ecology and evolution, and support surveillance of multi-host infectious diseases. Analysis of global species interaction networks allows for leveraging of information across taxa, but link prediction at this scale is often limited by extreme network sparsity, and lack of comparable trait data across species. C_LIO_LIHere we use recently developed methods to predict missing links in global mammal-parasite networks using readily available data: network properties and evolutionary relationships among hosts. We demonstrate how these link predictions can efficiently guide the collection of species interaction data and increase the completeness of global species interaction networks. C_LIO_LIWe amalgamate a global mammal host-parasite interaction network (>29,000 interactions) and apply a hierarchical Bayesian approach for link prediction that leverages information on network structure and scaled phylogenetic distances among hosts. We use these predictions to guide targeted literature searches of the most likely yet undocumented interactions, and identify empirical evidence supporting many of the top "missing" links. C_LIO_LIWe find that link prediction in global host-parasite networks can accurately predict parasites of humans, domesticated animals, and endangered wildlife, representing a combination of published interactions missing from existing global databases, and potential but currently undocumented associations. C_LIO_LIOur study provides further insight into the use of phylogenies for predicting host-parasite interactions, and highlights the utility of iterated prediction and targeted search to efficiently guide the collection of host-parasite interaction. These data are critical for understanding the evolution of host specificity, and may be used to support disease surveillance through a process of predicting missing links, and targeting research towards the most likely undocumented interactions. C_LI

ecology↗

bioRxiv2018

Disease mortality in domesticated animals is predicted by host evolutionary relationships

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 multi-host 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 multi-host parasites. Here, using a global dataset of over 4000 case-fatality rates for 65 infectious diseases (caused by micro and macro-parasites) 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 phy-1 logenetic 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.

evolutionary biology↗