Ecological indicators of mammal exposure to Ebolavirus

Identifying life-history patterns along the fast-slow continuum of mammalian viral carriers

Life-history traits have been identified as major indicators of mammals' susceptibility and exposure to viruses due to evolutionary constraints that link life-history speed with species' ecology and immunity. Nonetheless, it is unclear where along the fast-slow continuum of mammalian life-history lies the greatest diversity of host species. Consequently, life-history patterns that govern host-virus associations remain largely unknown. Here we analyse the virome of 1350 wild mammals and detect the characteristics that drive species' compatibility with different groups of viruses. We highlight that mammals with larger body size and either very rapid or very slow life histories are more likely to carry different groups of viruses, particularly zoonotic ones. While some common life-history patterns emerge across carriers, eco-evolutionary characteristics of viral groups appear to determine association with certain carrier species. Our findings underline the importance of incorporating both mammals' life-history information and viruses' ecological diversity into surveillance strategies to identify potential zoonotic carriers in wildlife.

Using serosurveys to optimize surveillance for zoonotic pathogens

Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. Here we develop a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data sets we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. We then apply our method to two putative Ebolavirus reservoirs, straw-colored fruit bats (Eidolon helvum) and hammer-headed bats (Hypsignathus monstrosus) to predict when these species should be sampled to maximize the probability of detecting active infections. In addition to guiding future sampling of these species, our method yields predictions for the times of year that are most likely to produce future spillover events. The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to predictable, but potentially short-lived, pulses of pathogen prevalence.

Robust evidence for bats as reservoir hosts is lacking in most African virus studies: a review and call to optimize sampling and conserve bats

Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoir and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for Orthoebolavirus versus Orthomarburgvirus as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.

Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals

We explore how animal host traits, phylogenetic identity and cell receptor sequences relate to infection status and mortality from ebolaviruses. We gathered exhaustive databases of mortality from Ebolavirus after exposure and infection status based on PCR and antibody tests. We performed ridge regressions predicting mortality and infection as a function of traits, phylogenetic eigenvectors and separately host receptor sequences. We found that mortality from Ebolavirus had a strong association to life history characteristics and phylogeny. In contrast, infection status related not just to life history and phylogeny, but also to fruit consumption which suggests that geographic overlap of frugivorous mammals can lead to spread of virus in the wild. Niemann Pick C1 (NPC1) receptor sequences predicted infection statuses of bats included in our study with very high accuracy, suggesting that characterizing NPC1 in additional species is a promising avenue for future work. We combine the predictions from our mortality and infection status models to differentiate between species that are infected and also die from Ebolavirus versus species that are infected but tolerate the virus (possible reservoirs of Ebolavirus). We therefore present the first comprehensive estimates of Ebolavirus reservoir statuses for all known terrestrial mammals in Africa.

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.

Mammals, wildlife trade, and the next global pandemic

Most new infectious diseases emerge when pathogens transfer from animals to humans.^1,2 The suspected origin of the COVID pandemic in a wildlife wet market has resurfaced debates on the role of wildlife trade as a potential source of emerging zoonotic diseases.^3-5 Yet there are no studies quantitatively assessing zoonotic disease risk associated with wildlife trade. Combining data on mammal species hosting zoonotic viruses and mammals known to be in current and future wildlife trade,⁶ we found that one-quarter (26.5%) of the mammals in wildlife trade harbor 75% of known zoonotic viruses, a level much higher than domesticated and non-traded mammals. The traded mammals also harbor distinct compositions of zoonotic viruses and different host reservoirs from non-traded and domesticated mammals. Furthermore, we highlight that primates, ungulates, carnivores, and bats represent significant zoonotic disease risks as they host 132 (58%) of 226 known zoonotic viruses in present wildlife trade, whereas species of bats, rodents, and marsupials represent significant zoonotic disease risks in future wildlife trade. Thus, the risk of carrying zoonotic diseases is not equal for all mammal species in wildlife trade. Overall, our findings strengthen the evidence that wildlife trade and zoonotic disease risks are strongly associated, and that mitigation measures should prioritize species with the highest risk of carrying zoonotic viruses. Curbing the sales of wildlife products and developing principles that support the sustainable and healthy trade of wildlife could be cost-effective investments given the potential risk and consequences of zoonotic outbreaks.

Fast-lived Hosts and Zoonotic Risk

Because most emerging human pathogens originate in mammals, many studies aim to identify host traits that determine the risk of sourcing zoonotic outbreaks. Studies regularly assert that 'fast-lived' mammal species exhibiting greater fecundity and shorter lifespans tend to host more zoonoses; however, the causes of this association remain poorly understood and they cover a range of immune and nonimmune mechanisms. We discuss these drivers in the context of evolutionary ecology and wildlife-human interactions. Ultimately, differentiating these mechanisms will require linking interspecific variation in life history with immunity, pathogen diversity, transmissibility, and zoonotic risk, and critical data gaps currently limit our ability to do so. We highlight sampling and analytical frameworks to address this gap and to better inform zoonotic reservoir prediction.

Population structure and diet generalism define a preliminary ecological profile of zoonotic virus hosts in the Western Ghats, India

The rainforests of the Western Ghats exhibit some of the highest biodiversity on the planet, and yet are undergoing rapid land use change due to the expansion of agriculture and other industries. As the landscape of the region is transformed, more people are coming into conflict with wildlife and becoming exposed to pathogens that previously circulated beyond the boundaries of human incursion. Despite an ecological knowledge imperative, this emerging landscape is ill-defined with respect to the ecology of zoonotic viruses and their mammalian wildlife hosts. Without a better understanding of the underlying infection ecology, the epidemiology of viral spillover will remain elusive and unsuited to the task of predicting and preventing outbreaks. The current investigation explored the association between mammalian zoonotic virus richness and species-level landscape, life-history, and dietary traits to describe an initial ecological profile of zoonotic virus hosts in the Western Ghats. Social group composition and dietary forage were both non-linearly associated with greater zoonotic viral richness among these species, whereby species active in smaller social groups, albeit in higher population densities, and exhibiting a tendency toward a generalist diet hosted more zoonotic viruses. While these findings provide no definitive ecological demarcation of zoonotic virus hosts or their contribution to viral maintenance or amplification, it is expected that this preliminary profile can help to develop targeted wildlife pathogen surveillance programs and to expand the current approach to epidemiological modelling of emerging zoonoses in the region, which typically do not account for the macroecological parameters of infection transmission.

A preliminary ecological profile of Kyasanur Forest disease virus hosts among the mammalian wildlife of the Western Ghats, India

Kyasanur Forest disease (KFD) virus is one of India's severe arboviruses capable of causing prolonged debilitating disease. It has been expanding beyond its historical endemic locus at an alarming rate over the last two decades. The natural nidus of this zoonosis is located in the monsoon rainforest of the Western Ghats, India, which is one of the world's most important biodiversity hotspots. Definitive reservoir hosts for KFD virus (KFDV) have yet to be delineated, and thus much of the infection ecology of this virus, and its consequent transmission dynamics, remains uncertain. Given its unique biogeographical context, identifying ecological parameters of KFDV relevant to the virus' epidemiology has been complex and challenging. The challenge has been exacerbated by diminished research efforts in wildlife surveillance over the last two decades, coinciding with the expansion of the range of KFD across the region. The current investigation sought to define a preliminary ecological profile of KFDV hosts based on their life history and feeding traits to aid in re-establishing targeted wildlife surveillance and to discern those ecological traits of wildlife hosts that may improve our understanding of KFD epidemiology. The importance of fast-living among KFDV hosts was of special interest with respect to the latter aim. We compared mammalian traits between host and non-host species using general additive models and phylogenetic generalised linear models. This study found that both body mass and forest forage were strongly associated with mammalian host infection status, but that reproductive life history traits were not. These findings will help in structuring ecologically based wildlife surveillance and field investigations, while also helping to parameterise novel epidemiological models of zoonotic infection risk that incorporate species functional traits in a region where biogeography, landscape ecology, and community ecology manifest extraordinary complexity, particularly under growing anthropogenic pressure.

The problem of scale in the prediction and management of pathogen spillover

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.