Cartoon by First Dog on the Moon in The Guardian

April 17, 2020
By Dr Tigga Kingston

Bats have earned an unwarranted reputation as disease spreaders since the Covid-19 outbreak. With April 17 marking world Bat Appreciation Day, Dr Tigga Kingston sets out to provide the full picture on the misunderstood mammals

It’s 7.20pm at the edge of the rainforest of Krau Wildlife Reserve in Peninsular Malaysia. Plaintive calls of nightjars herald the transition from day to night as silhouettes of bats flitter in the twilight. It is time to head into the forest. 

There are five of us tonight; I’m joined by my two PhD students Juliana Senawi (Julie) and Nurul Ain Elias (Ain) and two assistants, Rahman and Amri, from the nearby Jah Hut indigenous village. 

During the day Rahman and Amri have spaced out ten harp traps along the network of forest trails. Harp traps, made up of metal rectangular frames about the size of a vertical window with fishing line stretched across, are set on legs across forest trails. 

Bats that forage for insects inside the forest understory often follow trails for part of their journey, and although they have very sophisticated echolocation to find their way around and hunt insects, they have trouble detecting the fine fishing lines. They hit the lines and slide down into a long collecting bag where they can roost and rest before we come and get them. 

While bats have earned an unwarranted reputation as spreaders of disease, particularly as a result of the Covid-19 outbreak, we have been studying these forest specialists for over two decades and how human activity is impacting their habitats.

Our research shows that because so many are adapted to forest life and the stable resources it provides, they are unable to adjust to landscapes dominated by agriculture and non-timber plantations like rubber and oil palm. We aren’t the only ones to find this, forest-dwelling bats across Southeast Asia are being lost as humans change the landscape. 

The problem on everyone’s mind right now is of course Covid-19. Bats do not spread Covid-19, you can only catch it from other people. So why all the talk about bats? 

The first trap starts us off well. It has two of my favourites – the trefoil horseshoe bat and clear-winged woolly bat. The trefoil horseshoe bat is a very handsome fellow – bats of this species roost on their own under palm or rattan leaves and have tan-coloured wings, long fluffy grey fur, yellow ears, elbows and knees and wonderful yellow noseleaf. 

Horseshoe bats blast out their echolocation calls through their nostrils and use their noseleaf, a structure around their nose, to help focus the sound beam as they search for insects. 

Julie carefully gets the bat out of the trap. He will be a good candidate for the obstacle course she has devised to see how well different bat species fly through the dense forest of the understory. This helps us understand how different species will respond to changes in forest structure that result in fragmentation and timber extraction. 

When Ain goes to get the woolly bat there is a surprise in store – it’s not one bat but three clustered together to make a fluffy ball with the odd foot and wing sticking out! We’ve seen this before – the species lives in small social groups and roosts in clusters of dead leaves or old hanging bird nests.  

In the middle of the ball of fluff, noses, feet and wings is a pup, firmly attached to the mother’s nipple. Ain is thrilled, mothers need a lot of energy to produce milk for their pups, so it’s important that they give birth when there are many insects around. We are worried that climate change may alter patterns in insect availability, depriving mothers of food when they need it most, and reducing breeding success. 

By the time we have finished checking all 10 traps we have captured 30 bats of 15 species. All told, the rainforest of Krau Wildlife Reserve is home to the highest known diversity of bats in the Old World with at least 72 species. About 60 of the species are strictly insect-eating, and many of them are forest specialists. 

The problem on everyone’s mind right now is of course Covid-19. Bats do not spread Covid-19, you can only catch it from other people. So why all the talk about bats? 

SARS-CoV-2 is the virus that causes the disease Covid-19 in people. A virus related to SARS-CoV-2, called RatG2013, was isolated from an intermediate horseshoe bat from China in 2013, and that led to suggestions that perhaps the bat virus jumped from bats to people. 

There has been a flurry of research since, and now we know this is extremely unlikely for a couple of reasons. 

First, the key part of the virus that enables it to infect people is not from bats. The closest match identified so far is from a pangolin, but other wildlife may be involved. Second, although the human virus (SARS-CoV-2) and bat virus (RatG2013) are very closely related, recent work suggests they separated from one another 40-70 years ago. This means that it is not possible for the human virus to have come directly from the virus circulating in the intermediate horseshoe bats in 2013, other hosts must be involved.  

A great diversity of viruses can be found in all wildlife, but spillovers – in which the virus jumps from animals to humans – have historically been rare because many conditions must align for spillover to be possible. 

What is becoming very clear is that human activities are making these conditions possible more often. Habitat disturbance and destruction stress animals making them susceptible to viruses and more likely to produce high numbers of viruses.

Humans are encroaching on wildlife habitat or trading, butchering and consuming wildlife, all of which increases human exposure. Humans bring species together in markets and farmlands increasing opportunities for cross-species transmission that is often a key step towards human infection. 

As a result of these human activities, we are seeing more spillover events, sometimes with deadly pandemic consequences. The solutions are simple and familiar – we must protect and restore habitats, stop exploitation of wildlife, and keep the worlds’ wildlife and ecosystems healthy. 

As mentioned, loss of habitat is just one challenge facing Southeast Asia’s bats. About half the region’s bats roost in caves, but these are not the peaceful refuges they once were. Tourism, harvesting of guano and cave swiftlet nests, people just hanging out– all can disturb bats at roosts.  

Many of the cave systems in Southeast Asia are made up of limestone and quarried by the cement industry resulting in total loss of cave roosts. Intense hunting of some species, particularly the large fruit bats, or “flying foxes”, for consumption or a perceived medicinal use is pushing many species to the brink of extinction. 

Demand for large fruit bats is so great in Indonesia’s North Sulawesi that populations have been wiped out in the north and bats are being hunted and shipped from other provinces of Sulawesi and nearby islands to meet demand– as many as 500 tonnes per year. Fifty years ago, similar hunting levels on the Pacific island of Guam drove the island’s flying fox species, found nowhere else in the world, to extinction and decimated populations of another nine species from nearby islands.  

The reason that hunting has such a big impact is because bats are very like us in many ways. They are long-lived mammals, particularly for their size, with some species living more than 30 years. Most species only give birth to one pup a year, and large species like the flying foxes typically don’t start to breed until their second or even third year. This means that populations are very slow to recover losses and can’t withstand sustained disturbance or intense hunting. 

Land-use change, widespread hunting and destruction of roosts threaten 24% of Southeast Asia’s species but the conservation status and population trends for many species are unknown. This is tragic because Southeast Asia is such an extraordinary region for bats – a bat diversity hotspot home to over 400 species. That’s about 28% of all the world’s 1400 bat species in just 11 countries! 

For a long time, very little was known about the region’s bat diversity, with just a handful of dedicated researchers active, but over the last 10-15 years more researchers are training in bats and have come together to form a multinational network – the Southeast Asian Bat Conservation Research Unit (SEABCRU) – to exchange information and work together across borders. 

There have been lots of exciting discoveries as a result, including the description of 40 species new to science in just the last 12 years – a rate of discovery that suggests that there are lots more species still to describe. How tragic it would be to lose species because of habitat loss before we have even found and described them. 

Researchers in the SEABCRU have shown just how important bats are for humans, particularly for ecosystem health and human food security. Bats are important pollinators and seed dispersers of hundreds of ecologically and economically important plants. Most famously they are the key pollinator of the King of Fruits itself, durian. Durian is an $18 billion dollar industry, but no bats means no durian!  

Dr Sara Bumrungsi and his students at Prince of Songkla University in Thailand have calculated that in southern Thailand alone, the services of just one species, the dawn cave bat, in pollinating durian and petai (another regional favourite) is worth around USD $12 million a year.

As their name suggests, cave dawn bats roost in colonies of thousands to tens of thousands in caves, but there are disturbing reports that numbers across Southeast Asia are declining as caves are lost to disturbance and limestone extraction, and populations hunted. 

The wrinkle-lipped free-tailed bat is another colonial species that roosts in caves in the thousands and even millions. Each night, thousands of bats stream out of their caves and zoom out across the landscape, travelling tens of kilometres on some nights in pursuit of insects. Among their favorite foods are plant-hoppers – one of the biggest pests of rice crops in Southeast Asia. 

One colony in Thailand of 2.6 million bats eats about four tonnes of these pests in a single night, and across Thailand this one species saves the country about 2892 tonnes of rice annually – that’s 26,000 rice meals!  Of course, everything that bats are doing that benefits us, they also do in the forests and natural landscapes of the region, ensuring that bat-dependent trees are pollinated, seeds are dispersed, and insect populations suppressed. 

It’s 11pm and Julie, Ain and I have just finished processing our 30 bats. Each bat was identified to species, sexed, and the length of its forearm and body mass recorded. We put numbered bands on their forearms so we can recognise individuals if we catch them again in coming years and learn how long they live, and how they move around the forest. 

Ain found that most of the females were either pregnant or lactating – we are in the breeding season. The trefoil horseshoe bat did a so-so job on Julie’s flight obstacle course (a series of vertical strings), but the woolly bats skipped through as though there was nothing there. We take each bat back to the trap it was caught in for release. 

This is one of the best parts of the night, so we share the bats out evenly, although, as the boss, I always get the trefoil horseshoe bats. I gently put my hand at the edge of the trefoil’s bag and open it up. After a bit of scrabbling, his feet latch onto my finger and I draw him out.

He hangs from my finger and scans the area with echolocation calls too high for us to hear. He’s not blind but lives in a world made of echoes. Satisfied, he silently launches from my finger and flies into the night. 

Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts

by Nardus Mollentze

View ORCID ProfileNardus Mollentze and View ORCID ProfileDaniel G. Streicker PNAS first published April 13, 2020 https://doi.org/10.1073/pnas.1919176117

1. Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved March 9, 2020 (received for review November 6, 2019)

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Significance

Identifying whether novel human viruses disproportionately originate from certain animal groups could inform risk-based allocations of research and surveillance effort. Whether such “special reservoirs” exist remains controversial. We show that the proportion of viruses that infect humans varies minimally across reservoir taxonomic orders. Instead, the number of human-infecting viruses increases proportionately to the total number of viruses maintained by each reservoir group, which is in turn explained by the number of animal species within each group. This supports a host-neutral explanation for observed variation in the number of zoonoses among animal groups, such that traits of animal orders are unlikely to produce viruses that disproportionately threaten humans. These findings refine strategies to identify high-risk viruses prior to their emergence.

Abstract

The notion that certain animal groups disproportionately maintain and transmit viruses to humans due to broad-scale differences in ecology, life history, and physiology currently influences global health surveillance and research in disease ecology, virology, and immunology. To directly test whether such “special reservoirs” of zoonoses exist, we used literature searches to construct the largest existing dataset of virus–reservoir relationships, consisting of the avian and mammalian reservoir hosts of 415 RNA and DNA viruses along with their histories of human infection. Reservoir host effects on the propensity of viruses to have been reported as infecting humans were rare and when present were restricted to one or two viral families. The data instead support a largely host-neutral explanation for the distribution of human-infecting viruses across the animal orders studied. After controlling for higher baseline viral richness in mammals versus birds, the observed number of zoonoses per animal order increased as a function of their species richness. Animal orders of established importance as zoonotic reservoirs including bats and rodents were unexceptional, maintaining numbers of zoonoses that closely matched expectations for mammalian groups of their size. Our findings show that variation in the frequency of zoonoses among animal orders can be explained without invoking special ecological or immunological relationships between hosts and viruses, pointing to a need to reconsider current approaches aimed at finding and predicting novel zoonoses.

• infectious disease
• reservoir
• surveillance
• generalized additive model

Most emerging infectious diseases of humans are viruses that originate from nonhuman animals via “zoonotic” transmission (1⇓–3). Given the diversity of animals and viruses in nature, targeting virus discovery, surveillance, and research toward the taxonomic groups with the highest propensity to infect humans would benefit attempts to mitigate future disease outbreaks (4⇓⇓–7). Identifying these groups remains a major challenge. Viruses with RNA genomes are overrepresented as zoonoses and certain families including Filoviridae, Orthomyxoviridae, and Togaviridae contain a large proportion of zoonotic species (8, 9). Among hosts, however, patterns are less clear. Large-scale comparative studies have suggested that barriers to cross-species transmission increase with evolutionary divergence from humans, implying heightened zoonotic risk from closely related nonhuman primates (9, 10). Yet, other animal groups including bats, rodents, and ungulates are also frequently associated with zoonoses despite their evolutionary distance from humans (9, 11⇓–13). A popular explanation (here, the “special reservoir hypothesis”) is that physiological or ecological traits of these taxa make them more likely to maintain zoonotic viruses or transmit them to humans. For example, ungulates and rodents include domesticated or anthropophilic species whose high ecological overlap with humans could facilitate pathogen exchange (13, 14). In contrast, the unique life history of bats has been hypothesized to create an evolutionarily distinct immunological environment that selects for viral traits that favor human infection (15⇓⇓⇓⇓–20). An alternative to the special reservoir hypothesis is that host species maintain a similar number of viruses with a similar per-virus risk of zoonotic transmission. Variation in the number of zoonoses among animal groups therefore arises as a consequence of their species richness (here, the “reservoir richness hypothesis”). For example, the preponderance of rodent- or bat-associated zoonoses could reflect the large number of rodent and bat species relative to other mammalian groups (21). These hypotheses imply different management strategies. The special reservoir hypothesis would advocate fundamental research to define the ecological or physiological trait profiles that explain the propensity of certain hosts to harbor zoonoses, followed by targeted surveillance or virus discovery in hosts with high-risk trait profiles. In contrast, the reservoir richness hypothesis would advocate broader surveillance and virus discovery, possibly proportionate to the local species richness of different animal groups, and would imply the need for deeper understanding of which features of virus biology enhance zoonotic transmission.

Identifying patterns in the animal origins of zoonoses has been frustrated by disconnects between the key outcome of interest, the likelihood of zoonotic transmission, and currently available data which document viral infection in human and nonhuman hosts but provide limited information on the origins of these infections. For example, humans may infect nonhuman hosts, rather than the other way around, but only the latter direction of transmission is pertinent to zoonotic origins. A greater challenge is that the reservoir host species that maintain and transmit viruses to humans can rarely be distinguished from species that form a larger community of “dead-end” hosts that are inconsequential to transmission (22, 23). Rather than reflecting genuine imbalances in the animal origins of human infections, associations between certain animal groups and zoonotic viruses based on shared detections might instead emerge from heightened surveillance for or susceptibility to zoonotic viruses in these groups even if they are not key components of natural transmission cycles. As the known diversity of viruses expands, the increasing difficulty of differentiating related viruses in different host species from the same virus transmitted between these host species would exacerbate this effect by overestimating viral sharing, particularly if relying on serological or PCR data alone (24).

Given this uncertainty in the reservoir hosts of most viruses at the species level, an alternative approach to resolving whether animal host associations influence zoonotic risk would be to assess reservoir associations at lower taxonomic resolutions. Such analyses would take advantage of the tendency for viruses to be maintained by host species within the same taxonomic order, a phenomenon which arises from more frequent host shifting among closely related species and the cospeciation of hosts and viruses (25⇓–27). At this coarser taxonomic level, transmission cycles may be accurately defined for hundreds of viruses, enabling a test of host effects predicted by the special reservoir hypothesis (e.g., applying to animal orders such as Chiroptera or Rodentia) that is robust to reservoir species uncertainty. Further, condensing reservoir–virus associations to a single observation per virus would enable statistical approaches that separate confounding effects of virus and host taxonomy that arise from the nonrandom distribution of virus families with differing zoonotic propensity among reservoir groups. These models could also account for additional factors including differential research effort among host and virus groups and the phylogenetic proximity of animal hosts to humans that are suspected to vary with zoonotic transmission (9, 28).

Results

We used literature searches to expand an existing dataset of the maintenance cycles of single-stranded RNA viruses (27) to include all 35 RNA and DNA virus families infecting birds and mammals, the hosts that account for the vast majority of zoonoses (8). From a total of 673 virus species, 415 species from 30 families had compelling evidence for transmission by one or more of the 11 taxonomic orders of nonhuman reservoirs (three avian, eight mammalian) which met our inclusion criteria (Materials and Methods). When possible, viral species linked to more than one reservoir group were subdivided into reservoir order-associated viral clades (n = 11 species), and otherwise excluded (n = 2), creating a final dataset of 429 reservoir–virus associations. Data on zoonotic status (i.e., whether a virus had been reported to transmit from nonhumans to humans) were obtained from Olival et al. (9), Woolhouse and Brierley (29), and primary literature searches. We found dramatic variation in the number and diversity of viruses across reservoir groups (Fig. 1). Cetartiodactyls and rodents were the most common reservoirs in our dataset, together accounting for half of the included viruses (50.6%), while anseriforms (waterfowl) were the most poorly represented, with only 1.4% of viruses (Fig. 1A). We quantified the degree of skew in viral diversity among groups of reservoirs using a sliding metric which places varying amounts of emphasis on rare viral lineages (30), here defined as those virus taxonomic lineages that were rarely found in the focal reservoir order. Despite harboring substantial viral species richness, the taxonomic distribution of viruses was notably skewed for some host groups (Fig. 1B). The majority of primate and rodent viruses fell in two to three viral families and the remaining viruses were scattered across families that contained few other species infecting these host groups (on average, two species; Fig. 1 A and B). Other groups including carnivores and odd-toed ungulates had smaller, but more balanced, viral communities (Fig. 1B). Both the number and the proportion of zoonotic virus species appeared to differ among reservoir groups, although the 95% CI of proportions overlapped in most cases (Fig. 1C). The taxonomic distribution of zoonoses within host groups was highly skewed (Fig. 1A). For example, 50% of zoonotic bat viruses were rhabdoviruses and 53.7% of zoonotic rodent viruses were hantaviruses or arenaviruses, reinforcing the need to account for virus taxonomy to avoid detecting effects of reservoirs on zoonotic status that would be unlikely to generalize across bat or rodent viruses (SI Appendix, Fig. S1).

We next used generalized additive mixed models with zoonotic status as a binary response variable to identify and rank the host and/or viral traits that influenced the zoonotic status of viruses. When ranking models using the Akaike information criterion (AIC), the top model explained a total of 52.3% of deviance in zoonotic status (Fig. 2A). This model contained several previously reported effects of viral biology, including transmission by arthropod vectors and exclusively cytoplasm-based replication, which increased the odds of being zoonotic 4.1-fold (log odds = 1.40) and 15.7-fold (log odds = 2.75), respectively (Fig. 2C). However, neither explained >2.2% of the deviance (Fig. 2A). Zoonotic viruses also tended to be more studied, with the number of publications associated with each virus species explaining 15.2% of the deviance (Fig. 2B).

Among effects related to reservoir hosts, zoonotic risk declined with greater phylogenetic distance from reservoirs to primates, but this effect explained only 0.7% of the deviance in the top model and was absent from other competitive models (Fig. 2 A and E). Effects of reservoir order or phylogeny (either alone or crossed with an effect of virus family-level taxonomy) did not occur until the 23rd-ranked model, where they explained <0.01% of the deviance (ΔAIC from top model >16.9; Fig. 2A). The only strongly supported models containing an effect of reservoir host restrained the effect to certain viral families (i.e., a random effect of reservoir nested within virus family, here termed the “virus family-specific reservoir” effect). This effect explained 20.7% of the deviance in zoonotic status in the top model. In particular, primate adenoviruses and retroviruses, bat rhabdoviruses, and rodent picornaviruses were more likely to be zoonotic relative to viruses from the same families associated with other reservoirs (Fig. 2F). Critically however, none of these reservoir groups increased zoonotic propensity across additional viral families, suggesting the absence of generalizable effects of reservoir group on the likelihood of zoonotic transmission (Fig. 2A and SI Appendix, Fig. S2).

We further evaluated whether effects of reservoir hosts on zoonotic status might have been missed due to the inclusion of potentially collinear variables or insufficient statistical power to fit complex models. First, given that study effort understandably increased for zoonotic viruses, including this factor might have disguised true reservoir effects. However, models which did not adjust for virus-related publication counts failed to support reservoir effects; the best of such models achieved similar AIC values with and without reservoir effects (models 177 to 182, Fig. 2A). Second, including virus taxonomy might have reduced the power of our models. However, reservoir effects remained negligible or absent in models which excluded viral taxonomy (SI Appendix, Fig. S3). In contrast, viral family effects were robust to inclusion of general reservoir effects (i.e., those testing for consistent reservoir effects across viral families). When the family-specific reservoir effect was not present, viral rather than reservoir taxonomy was the stronger predictor of zoonotic status (models 23 to 28 in Fig. 2A and SI Appendix, Fig. S3). Thus, our results provide no support for the special reservoir hypothesis, which predicted that viruses associated with certain reservoir groups would be more likely to be zoonotic.

Instead, both the total number of viruses associated with each reservoir group (“viral richness”) and the number of zoonoses matched expectations under the reservoir richness hypothesis. The absence of reservoir effects meant that viral richness was strongly correlated with the number of zoonotic species (R² = 0.88, P < 0.001; Fig. 3), consistent with a similar per-virus zoonotic risk across reservoirs. Notably, a similar correlation was previously observed when measuring viral richness at the host species level (9). A series of negative-binomial generalized linear models showed that—after controlling for greater viral richness in mammals compared to birds—reservoir species richness was correlated with both the number of zoonotic viruses (65.5% of deviance) and the viral richness (56.4% of deviance) of different reservoir orders (Fig. 4 and SI Appendix, Fig. S4). These models outcompeted those including previously identified effects of virus-related research effort and the phylogenetic distance of each reservoir group from primates (9), which when included explained a maximum of 3.1% additional deviance (ΔAIC ≥ 1.09; models 2 to 4 in Fig. 4A). Exchanging the reservoir class effect (i.e., birds versus mammals) for phylogenetic distance to primates performed considerably worse than the top model (ΔAIC = 4.72; model 5 in Fig. 4A). Similar results were observed for models predicting total viral richness (SI Appendix, Fig. S4).

We next assessed how well the predictions of the reservoir richness model matched observations in individual reservoir groups. Highly recognized mammalian reservoir groups including bats and rodents hosted close to the number of zoonoses expected from their species richness (bats: 22 observed and 28 predicted [95% CI: 10.7 to 45.7]; rodents: 41 observed and 42 predicted [95% CI: 10.2 to 72.9]; Fig. 4E). In contrast, lagomorphs (rabbits, hares, and pikas) and diprotodonts (an order of marsupials) hosted both fewer viruses and fewer zoonoses than predicted; however, these were among the least-studied animal groups (Fig. 4E and SI Appendix, Fig. S4E). The only potentially “special” reservoirs identified by this analysis were the cetartiodactyls (even-toed ungulates and whales) and primates, which hosted more zoonoses than would be predicted for mammalian groups with their species richness (Fig. 4E). In the case of primates, this likely reflects the higher zoonotic propensity of primate-borne adeno- and retroviruses identified above (24 zoonotic viruses observed, of which 3 are adenoviruses and 7 are retroviruses; 15 predicted [95% CI: 7.8 to 22.4]). The mismatch was greater for cetartiodactyls (28 observed, 12 predicted [95% CI: 6.1 to 17.3]). However, perhaps due to their close association with humans as domestic livestock, this group also had considerably higher total viral richness than predicted (112 observed and 45 predicted [95% CI: 25.3 to 65.5]; SI Appendix, Fig. S4E). Consequently, the proportion of cetartiodactyl viruses which were zoonotic was unexceptional, inconsistent with the special reservoir hypothesis (Figs. 1 and 3). Strikingly, although mammals had elevated viral and zoonosis richness compared to birds, the proportion of zoonotic viruses was similar between classes, with 9/30 of avian and 115/387 of mammalian virus species being zoonotic (30% and 28.8%, respectively), emphasizing that—regardless of the cause—it is the underlying number of virus species which differs between mammals and birds, not the zoonotic component.

Discussion

Animal groups have been proposed to differ in their propensity to transmit viruses to humans as a consequence of variation in their life history, physiology, or ecology. By combining a large dataset of reservoir host–virus associations with records describing their histories of human infection, our analysis found no evidence that the taxonomic identity of reservoirs affects the probability that the viruses they harbor are zoonotic. Instead, variation in the number of zoonoses maintained by each reservoir group was consistent with a largely host-neutral model, whereby more species-rich reservoir groups host more virus species and therefore a larger number of zoonotic species. These findings imply the need to reconsider some current approaches to virus discovery, surveillance, and research.

The absence of effects of reservoir host associations on the zoonotic status of viruses is at odds with the idea that conserved traits of certain host groups alter the zoonotic potential of their viruses. If host traits predictably altered zoonotic risk, these effects would be expected to act on multiple viral groups. Instead, we found that reservoir effects were isolated within individual virus families, such that no reservoir group altered the zoonotic risk of viruses across a broad range of viral families. For example, bats are widely considered special reservoirs due to their association with several high-profile zoonoses, including Severe acute respiratory syndrome-related coronavirus (Coronaviridae), Nipah henipavirus (Paramyxoviridae), and Ebola viruses (Filoviridae) (17, 19, 31). However, virus species within those families with a bat reservoir were no more likely to be zoonotic than those transmitted by other hosts, although we acknowledge that this effect would have been difficult to detect for filoviruses given the small number of virus species in this family. While our results do not dispute the existence of distinct features of bat immunity or life history which have been hypothesized to influence viral communities in bats (16, 18, 19, 32, 33), they provide no compelling evidence that these traits translate into an increased probability of bat-associated viruses infecting humans.

Idiosyncratic family-specific reservoir effects could reflect especially zoonotic clades within viral families that are strongly host-associated, sampling biases, or genuine interactions between the biological features of specific viral groups and specific reservoirs that increase zoonotic capability (e.g., differences in viral shedding or tissue tropism). Among rhabdoviruses, a significantly elevated proportion of bat-associated species were zoonotic, but this effect was driven by the rabies-causing genus Lyssavirus, which contained 73% of the 25 zoonotic rhabdovirus species and is strongly bat-associated (35). However, Rabies lyssavirus is also zoonotic when associated with carnivores and bat-associated Rhabdoviruses outside of the Lyssaviruses are not known to be zoonotic, suggesting that traits of this viral genus rather than its reservoirs are the underlying driver of the detected bat effect (SI Appendix, Fig. S3). The putatively heightened frequency of zoonotic rodent-associated picornaviruses might conceivably arise if transmission by rodents selects for viral traits that disproportionately promote zoonotic capability or if picornaviruses have similar zoonotic capability across reservoir groups but heightened ecological overlap favors zoonotic transmission from rodents. Yet, given that our dataset contained only three rodent-associated picornaviruses (all zoonotic) and primates and cetartiodactyls also hosted zoonotic picornaviruses (three out of four and one out of six zoonotic, respectively), a statistical artifact of low sample size cannot be ruled out. In contrast, effects of nonhuman primate reservoirs on the zoonotic potential of adenoviruses and retroviruses may have a biological basis. All primate-associated adenoviruses (three out of three) and 7 out of 12 primate-associated retroviruses were zoonotic, while none of the 15 adenoviruses and 16 retroviruses associated with other reservoirs have been reported to infect humans. This suggests that evolutionarily conserved similarities between nonhuman primates and humans may facilitate zoonotic transmission in these viral families. Yet, our results also highlight limitations of host phylogeny as a generalizable predictor of zoonotic capability since primate associations did not affect the zoonotic status in most of the viral families maintained by nonhuman primates. We hypothesize that the advantages conferred by phylogenetic relatedness are crucial for viral families that are inherently host-restricted (e.g., retroviruses, which must integrate into host genomes) but are less influential for viruses with less specialized infection and replication mechanisms. More generally, the relatively poor performance of phylogenetic distance in predicting either the proportion or the number of zoonoses from different reservoir groups (Figs. 2E and 4 A and B) suggests that the evolutionarily conserved factors that facilitate cross-species transmission within animal orders (25, 36, 37) are unlikely to extend over deeper evolutionary distances.

Our modeling framework allowed us to distinguish the relative contribution of host and virus features to zoonotic status. Consistent effects of viral taxonomy support the conclusion that zoonotic ability is a feature of viral clades rather than host associations (9, 28); however, the underlying viral trait determinants remain unresolved. As shown here (Fig. 2) and elsewhere, transmission by arthropod vectors and replication in the cytoplasm of host cells were linked to zoonotic transmission (9, 38). These findings are potentially explainable by selection for viral generalism arising from the need to replicate in both vertebrate and invertebrate hosts and the avoidance of the additional barriers associated with entering and replicating in the nucleus in novel host species, respectively. Yet, the site of viral replication within cells is invariant within viral families and arthropod-borne transmission is also strongly evolutionarily conserved (27, 39). Therefore, these traits have limited utility for explaining large variation in zoonotic risk within viral families. Identifying whether factors beyond ecological opportunity predict zoonotic status is vitally important to anticipate zoonotic transmission and to estimate the number of viruses which may emerge in the future.

The number of zoonoses scaled positively with the host species richness of animal orders (Fig. 4). Since reservoir species richness did not affect the probability that viruses were zoonotic (Fig. 2A), direct effects of species richness—which might occur if viral maintenance by more species rich host orders facilitates zoonotic transmission by selecting for broader host range—are unlikely (2). Instead, our results support a numerical, host species-neutral explanation: more species-rich animal orders maintain more viruses and hence more zoonoses. Indeed, after controlling for diminished viral richness in birds compared to mammals, key groups including bats and rodents harbored close to the number of zoonoses expected from their species richness (Fig. 4). These results mirror—and potentially explain—observations that the risk of zoonotic emergence is elevated in regions with high species richness (3, 40), since more virus species and hence zoonoses would be expected in species-rich habitats. More generally, our results suggest that the processes which shape viral richness (i.e., extinction, codivergence, and host shifts) do not vary enough among animal orders to create significant differences in the average number of viruses per species across reservoir orders. One implication of the relationship between reservoir species richness and the number of zoonoses is that surveillance efforts aimed at finding potential zoonoses should scale with the species richness of reservoir groups. This recommendation differs from the current practice which is based on the a priori expectation that certain groups (e.g., bats, rodents, and primates) are more likely to maintain zoonotic viruses (7). Analogously, our results underscore the challenges of identifying unknown reservoirs for viruses of importance to human health, since sampling effort would need to scale with local biodiversity, which itself may be uncertain.

Our dataset and analytical approach differed in several epidemiologically important ways from previous studies which suggested variation in viral zoonotic propensity across animal groups (9, 12). First, restricting virus–reservoir associations to the order level allowed us to test hypotheses on the role of broad animal groups on zoonotic origins in the face of widespread uncertainty in the identity of viral reservoirs at the host species level (22, 23, 27). This meant excluding human viruses that occasionally infect nonhumans and all associations between viruses and nonhuman hosts which are not currently considered to be important in natural transmission cycles. While it is conceivable that some excluded host–virus associations have an unrecognized role in transmission, adding 302 previously reported associations which did not meet our criteria did not qualitatively change our results (SI Appendix, Fig. S6). Second, we considered a single reservoir order for most viruses rather than modeling every association of each virus with each infected host species within that order independently. This was critical to avoid potentially spurious effects driven by surveillance effort and multiple observations of the same virus in closely related species. For example, Dengue virus (DENV) has been detected in at least 20 bat species, which comprise 76% of DENV–host associations (9, 12), but nonhuman primates, not bats, are the currently accepted reservoirs of zoonotic DENV outbreaks (41⇓–43). Our dataset therefore included a single primate reservoir since the majority of DENV–host associations would have obscured conclusions on zoonotic origins. Finally, unlike previous studies, we included avian viruses; however, restricting our analysis to mammalian viruses failed to recover reservoir host effects on zoonotic risk (SI Appendix, Fig. S7). One possible explanation for the apparent discrepancy between our findings and those based on shared virus detections could be that heightened surveillance for or susceptibility to zoonoses has led to elevated detections of spillover infections in previously identified “special hosts.” Our results highlight the importance of separating exposure and infection from transmission in future studies which involve reservoirs of infection.

A clear limitation of our approach was that we were unable to consider traits that vary within reservoir groups (e.g., reproductive rates, population size, and geographic range size) which may moderate the baseline zoonotic risk by altering viral richness, transmission dynamics, or ecological contacts of hosts with humans (44). In addition, even at the broad level of host taxonomy we used, knowledge of virus–reservoir relationships and zoonotic capability is incomplete and unevenly distributed among host groups (45). Analogous analyses of the capability of viruses to infect nonhuman hosts could also enable broader understanding of the determinants of cross-species transmission but will require constructing comprehensive datasets of infection histories in these alternative species. Finally, we evaluated only viral richness and whether viruses were reported to infect humans. Whether viruses from different reservoirs differ systematically in their pathogenicity, capacity to transmit among humans, or in their frequency of zoonotic transmission cannot be assessed from our data.

In summary, our analysis suggests that variation in the frequency of zoonoses among major bird and mammal reservoir groups is an emergent property of variation in host and virus species richness. We find no evidence that intrinsic or ecological differences among animal groups increases the number of viruses they maintain or the likelihood that any given virus is zoonotic. Basing public health surveillance and research strategies aiming to identify high-risk viruses on the assumption that some taxonomic orders of hosts are disproportionate sources of zoonoses risks missing important zoonotic viruses while simultaneously reenforcing patterns that may reflect detection biases rather than zoonotic risk.

Materials and Methods

Database Construction.

We studied 35 virus families listed as infecting animals in either Fields Virology or the ViralZone web resource (Adenoviridae, Anelloviridae, Arenaviridae, Arteriviridae, Asfarviridae, Astroviridae, Birnaviridae, Bornaviridae, Caliciviridae, Circoviridae, Coronaviridae, Filoviridae, Flaviviridae, Genomoviridae, Hantaviridae, Hepadnaviridae, Hepeviridae, Herpesviridae, Nairoviridae, Orthomyxoviridae, Papillomaviridae, Paramyxoviridae, Parvoviridae, Peribunyaviridae, Phenuiviridae, Picobirnaviridae, Picornaviridae, Pneumoviridae, Polyomaviridae, Poxviridae, Reoviridae, Retroviridae, Rhabdoviridae, Tobaniviridae, and Togaviridae) (39, 46). For all data-collection steps below, virus names were matched to the latest accepted taxonomy according to version 2018b of the ICTV Master Species List by referring to historical taxonomic releases (47).

Following Babayan et al. (27), data on the reservoirs known to maintain the included virus species were obtained by searching common virology textbooks, such as Fields Virology (39), followed by in-depth literature searches until at least one reservoir or consistent evidence that the reservoir is currently considered as unknown was found. We summarized viral maintenance to the level of taxonomic orders of reservoirs because 1) the special reservoir hypothesis is generally articulated in terms of taxonomic order (e.g., applying to Chiroptera or Rodentia) and 2) order-level analyses constituted a reasonable compromise between taxonomic resolution and sample size, maximizing the number of virus species which could be included (27). We supplemented the single-stranded RNA virus dataset of Babayan et al. (27) with 344 additional records representing all recognized species in the families above for which reservoirs could be found. When there was evidence for independent maintenance by multiple reservoir orders, all known virus species–reservoir order combinations were recorded. For example, Rabies lyssavirus is maintained by both Carnivora and Chiroptera, but never as part of the same transmission cycle (48). Nineteen such virus species were detected, associated with 2.5 reservoir orders on average. Viruses known to be maintained by humans were retained if there was evidence for independent maintenance of a separate viral lineage in nonhuman hosts (n = 25) and otherwise discarded (n = 72). Yellow fever virus, for example, can be maintained by humans but also has an independent nonhuman primate reservoir community, and lineages from such maintenance cycles are zoonotic (39). Two virus species, Mammalian orthoreovirus and Usutu virus, had reservoir communities currently thought to span multiple taxonomic orders and were excluded from further analysis. Finally, the dataset was restricted to contain only viruses associated with mammalian and avian reservoir groups for which at least five viruses were known. The zoonotic status of individual virus species was obtained by combining records of detected human infections from Olival et al. (9) (n = 342 virus species) and Woolhouse and Brierley (29) (n = 70) with additional literature searching (n = 3). Only detections where the identity of the virus infecting humans was confirmed to species level by PCR or sequencing were considered.

Statistical Modeling.

We used logistic regression models to assess the association between reservoir group and zoonotic status (a binary response variable). Because at least one previously reported predictor of zoonotic status—publication count—was expected to be nonlinear (9), we used generalized additive mixed models (48). Three possible representations of a reservoir host effect were included (described below in more detail) and combined with all possible combinations of additional variables that were previously reported to predict zoonotic status. These included measures of research effort, the phylogenetic distance between each reservoir taxonomic order and primates, the species richness of each reservoir order, whether or not the virus was transmitted by arthropods or replicated exclusively in the cytoplasm, and a hierarchical representation of virus taxonomy. Previously reported variables relating to host species, such as geographic overlap with humans, could not be included when summarizing reservoirs at the level of taxonomic order.

The simplest reservoir effect was a random intercept for each reservoir taxonomic order, with all reservoir orders assumed independent. This represented the typically assumed special reservoir effect, in which some reservoirs are more prone to maintain zoonotic viruses than others. A second representation allowed clustering on the reservoir phylogeny to represent the hypothesis that related reservoir groups have correlated associations with zoonotic viruses. For example, mammalian reservoirs may be associated with a larger fraction of zoonoses than avian reservoirs, or related mammalian clades may share phylogenetically conserved features which shape their association with zoonoses. This phylogenetic random effect was implemented as a multivariate normal distribution, where phylogenetic relationships among taxa determined the amount of correlation between randomly sampled intercepts for different reservoir taxonomic orders (51). The variance–covariance matrix of this distribution took the form σ2Aσ2A, where σ2σ2 is a variance parameter to be estimated and A is a known variance–covariance matrix derived from a phylogeny (51, 52). We used a composite time-scaled reservoir phylogeny representing the mean divergence dates for all clades estimated across multiple studies, as contained in the TimeTree database (51). Following Longdon et al. (36), this phylogeny was converted to a variance–covariance matrix by assuming a Brownian motion model of trait evolution, using the vcv.phylo function in the APE package in R version 3.5.1 (52, 53). The third representation of a reservoir effect allowed independent random intercepts for each combination of reservoir order and virus family (i.e., the random effect of reservoir order was nested within virus family). This represented the hypothesis that specific reservoirs influence the propensity for viruses to be zoonotic for only some virus families, or that the identity of special reservoirs differs between families.

We used two measures to correct for the effects of variation in research effort on observations of zoonotic transmission: the numbers of results matching PubMed Central queries related to 1) virus species and 2) reservoir group (on 1 November 2019). For each virus species, this query was “<taxid>[Organism:noexp],” where “<taxid>” was replaced by the NCBI Taxonomy ID corresponding to each virus species. Viruses with no entry in the NCBI taxonomy database were removed from all analyses (this affected Cercopithecine gammaherpesvirus 14 and Hare fibroma virus, neither of which have publicly available sequence data in GenBank, meaning their reported reservoir associations are questionable). To capture a measure of the virus-related research effort in each reservoir group, a set of queries of the form “<taxid>[Organism:noexp] AND virus” were constructed, replacing “<taxid>” with the NCBI Taxonomy ID of each taxonomic order in turn. For nonhuman primates, this query was modified to “(txid9443[Organism:noexp] NOT txid9606[Organism:noexp]) AND virus,” where “txid9443” is the Taxonomy ID for the order Primates, and “txid9606” is the Taxonomy ID specific to Homo sapiens. Both variables were included as the natural logarithm of the respective publication counts, reflecting our prior belief that the effect of increasing numbers of publications would become saturated at high values. Nevertheless, we also allowed for additional nonlinear effects by fitting both variables as thin-plate smooths with 10 and 8 knots for virus and reservoir publication counts, respectively (54).

Phylogenetic distance was calculated as the cophenetic distance between each reservoir order and primates, using the same time-scaled reservoir phylogeny as above. Following Olival et al. (9), this effect was log(x + 1)-transformed in all models. The species richness represented by each reservoir order was derived from the Catalogue of Life using version 0.9.6 of the taxize library in R (55, 56). Both variables were fit as a thin-plate smooths with 6 and 10 knots, respectively.

The taxonomic random effect of virus family used the same specification as the reservoir phylogenetic effect above. Because the included virus families were too diverse to be represented in a single phylogeny (and might not share a common ancestor), a variance–covariance matrix representing currently accepted virus taxonomic relationships was generated. This matrix reflects the proportion of the taxonomy that is shared between virus families. Specifically, pairs of viruses were assigned a similarity score N/max(N), where N is the number of taxonomic levels they share, and max(N) the total number of taxonomic levels. This calculation was performed considering all taxonomic levels from realm to family. Thus, viruses in the same virus family received a similarity value of 1, while viruses from different families in the same order (e.g., Bunyavirales) received a value of 0.8. Virus species sharing no taxonomic levels were treated as independent (similarity = 0). Missing taxonomic assignments were interpolated to retain a comparable number of levels across all viruses, ensuring that all viruses remained independent at all levels not supported by the official ICTV taxonomy. For example, since many viruses are not classified using suborders a gap between order and family was bridged by creating a new level, assigning each family to its own unique suborder. This approach generated several free-floating branches, which are assumed independent in our models, but this is consistent with current virus taxonomy (ICTV Master Species List 2018b, https://ictv.global/files/master-species-lists/; SI Appendix, Fig. S3).

All models were fit using restricted maximum likelihood in the mgcv library in R and subsequently ranked by AIC (57, 58). The validity of models was checked using both standard methods implemented in the mgcv library and by inspecting simulated residuals generated using the DHARMa library in R (59). To calculate the proportion of the deviance explained by each term, each model was compared to submodels fit in the absence of individual terms, fixing the values for the smoothing parameters of the remaining terms to those estimated in the full model. The proportion of the deviance explained was calculated as (Di−DF)/D0(Di−DF)/D0, where DiDi is the deviance of model i, DFDF is the deviance of the full model, and D0D0 is the deviance of an intercept-only model.

To investigate potential explanations for differences in either the number of virus species or the number of zoonotic species associated with each reservoir order, two independent sets of negative binomial generalized linear models (GLMs) were fit to the counts of each virus type using the MASS library in R (60). Both sets of models contained all possible combinations of variables describing the log number virus-related publications associated with each reservoir, log phylogenetic distance to primates, log species richness in each reservoir, and a binary variable describing whether the reservoir is mammalian. We considered simpler Poisson GLMs instead of the negative binomial GLMs, but these showed strong overdispersion in simulated residuals using the DHARMa library.

Diversity Calculations.

The distribution of viral diversity among reservoir groups was quantified by calculating profiles of normalized alpha diversity using the rdiversity library in R (30). These calculations incorporated a species-level taxonomic similarity matrix, reflecting the proportion of the taxonomy that is shared between virus species and calculated as described for the family-level similarity matrix above.

Data Availability.

Data and code used in this manuscript are available at https://doi.org/10.5281/zenodo.3516613.

Acknowledgments

We thank Dan Haydon and Roman Biek for helpful comments on earlier versions of this manuscript, as well as two anonymous reviewers, whose suggestions greatly improved the clarity of this publication. D.G.S. was supported by a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and Royal Society (102507/Z/13/Z) and a Wellcome Senior Research Fellowship (217221/Z/19/Z). Additional funding was provided by the Medical Research Council through programme grants MC_UU_12014/8 and MC_UU_12014/12. Reservoir silhouettes were obtained from phylopic.org and were created by Matt Martyniuk (Anseriformes), Brian Gratwicke and T. Michael Keesey (Carnivora), and Sarah Werning (Lagomorpha), used with permission (https://creativecommons.org/licenses/by/3.0). Additional silhouettes by Andrew Butko (Passeriformes; https://creativecommons.org/licenses/by-sa/3.0) and Tamara L. Clark (Perissodactyla; https://creativecommons.org/licenses/by-nc-sa/3.0). All other silhouettes are in the public domain.

Footnotes

• ↵¹To whom correspondence may be addressed. Email: nardus.mollentze@glasgow.ac.uk or daniel.streicker@glasgow.ac.uk.

• Author contributions: N.M. and D.G.S. designed research; N.M. and D.G.S. performed research; N.M. analyzed data; and N.M. and D.G.S. wrote the paper.
• The authors declare no competing interest.
• This article is a PNAS Direct Submission.
• Data deposition: The data and analysis code reported in this manuscript have been deposited in Zenodo (https://doi.org/10.5281/zenodo.3516613).
• This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1919176117/-/DCSupplemental.

• Copyright © 2020 the Author(s). Published by PNAS.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts | PNAS

References

1. ↵
   1. L. H. Taylor,
   2. S. M. Latham,
   3. M. E. Woolhouse
   , Risk factors for human disease emergence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 983–989 (2001).OpenUrlCrossRefPubMedGoogle Scholar
2. ↵
   1. M. E. J. Woolhouse,
   2. S. Gowtage-Sequeria
   , Host range and emerging and reemerging pathogens. Emerg. Infect. Dis. 11, 1842–1847 (2005).OpenUrlCrossRefPubMedGoogle Scholar
3. ↵
   1. K. E. Jones et al
   ., Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).OpenUrlCrossRefPubMedGoogle Scholar
4. ↵
   1. D. A. King,
   2. C. Peckham,
   3. J. K. Waage,
   4. J. Brownlie,
   5. M. E. J. Woolhouse
   , Infectious diseases: Preparing for the future. Science 313, 1392–1393 (2006).OpenUrlAbstract/FREE Full TextGoogle Scholar
5. ↵
   1. S. S. Morse et al
   ., Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).OpenUrlCrossRefPubMedGoogle Scholar
6. ↵
   1. D. Carroll et al
   ., The global virome project. Science 359, 872–874 (2018).OpenUrlAbstract/FREE Full TextGoogle Scholar
7. ↵
   1. D. Carroll et al
   ., Building a global atlas of zoonotic viruses. Bull. World Health Organ. 96, 292–294 (2018).OpenUrlCrossRefGoogle Scholar
8. ↵
   1. M. Woolhouse,
   2. E. Gaunt
   , Ecological origins of novel human pathogens. Crit. Rev. Microbiol. 33, 231–242 (2007).OpenUrlCrossRefPubMedGoogle Scholar
9. ↵
   1. K. J. Olival et al
   ., Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).OpenUrlCrossRefPubMedGoogle Scholar
10. ↵
    1. N. Cooper et al
    ., Phylogenetic host specificity and understanding parasite sharing in primates. Ecol. Lett. 15, 1370–1377 (2012).OpenUrlCrossRefPubMedGoogle Scholar
11. ↵
    1. S. Cleaveland,
    2. M. K. Laurenson,
    3. L. H. Taylor
    , Diseases of humans and their domestic mammals: Pathogen characteristics, host range and the risk of emergence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 991–999 (2001).OpenUrlCrossRefPubMedGoogle Scholar
12. ↵
    1. A. D. Luis et al
    ., A comparison of bats and rodents as reservoirs of zoonotic viruses: Are bats special? Proc. Biol. Sci. 280, 20122753 (2013).OpenUrlCrossRefPubMedGoogle Scholar
13. ↵
    1. B. A. Han,
    2. A. M. Kramer,
    3. J. M. Drake
    , Global patterns of zoonotic disease in mammals. Trends Parasitol. 32, 565–577 (2016).OpenUrlGoogle Scholar
14. ↵
    1. N. D. Wolfe,
    2. C. P. Dunavan,
    3. J. Diamond
    , Origins of major human infectious diseases. Nature 447, 279–283 (2007).OpenUrlCrossRefPubMedGoogle Scholar
15. ↵
    1. G. Zhang et al
    ., Comparative analysis of bat genomes provides insight into the evolution of flight and immunity. Science 339, 456–460 (2013).OpenUrlAbstract/FREE Full TextGoogle Scholar
16. ↵
    1. T. J. O’Shea et al
    ., Bat flight and zoonotic viruses. Emerg. Infect. Dis. 20, 741–745 (2014).OpenUrlCrossRefPubMedGoogle Scholar
17. ↵
    1. C. E. Brook,
    2. A. P. Dobson
    , Bats as ‘special’ reservoirs for emerging zoonotic pathogens. Trends Microbiol. 23, 172–180 (2015).OpenUrlCrossRefPubMedGoogle Scholar
18. ↵
    1. P. Zhou et al
    ., Contraction of the type I IFN locus and unusual constitutive expression of IFN-α in bats. Proc. Natl. Acad. Sci. U.S.A. 113, 2696–2701 (2016).OpenUrlAbstract/FREE Full TextGoogle Scholar
19. ↵
    1. M. Ahn et al
    ., Dampened NLRP3-mediated inflammation in bats and implications for a special viral reservoir host. Nat. Microbiol. 4, 789–799 (2019).OpenUrlGoogle Scholar
20. ↵
    1. C. E. Brook et al
    ., Accelerated viral dynamics in bat cell lines, with implications for zoonotic emergence. eLife 9, e48401 (2020).OpenUrlGoogle Scholar
21. ↵
    1. K. J. Olival,
    2. J. H. Epstein,
    3. L.-F. Wang,
    4. H. E. Field,
    5. P. Daszak
    , “Are bats exceptional viral reservoirs?” in New Directions in Conservation Medicine: Applied Cases of Ecological Health, A. A. Aguirre, R. Ostfeld, P. Daszak, Eds. (Oxford University Press, 2012), pp. 195–212.Google Scholar
22. ↵
    1. D. T. Haydon,
    2. S. Cleaveland,
    3. L. H. Taylor,
    4. M. K. Laurenson
    , Identifying reservoirs of infection: A conceptual and practical challenge. Emerg. Infect. Dis. 8, 1468–1473 (2002).OpenUrlCrossRefPubMedGoogle Scholar
23. ↵
    1. M. Viana et al
    ., Assembling evidence for identifying reservoirs of infection. Trends Ecol. Evol. 29, 270–279 (2014).OpenUrlCrossRefPubMedGoogle Scholar
24. ↵
    1. P. Simmonds et al
    ., Consensus statement: Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).OpenUrlCrossRefPubMedGoogle Scholar
25. ↵
    1. D. G. Streicker et al
    ., Host phylogeny constrains cross-species emergence and establishment of rabies virus in bats. Science 329, 676–679 (2010).OpenUrlAbstract/FREE Full TextGoogle Scholar
26. ↵
    1. A. Kitchen,
    2. L. A. Shackelton,
    3. E. C. Holmes
    , Family level phylogenies reveal modes of macroevolution in RNA viruses. Proc. Natl. Acad. Sci. U.S.A. 108, 238–243 (2011).OpenUrlAbstract/FREE Full TextGoogle Scholar
27. ↵
    1. S. A. Babayan,
    2. R. J. Orton,
    3. D. G. Streicker
    , Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).OpenUrlAbstract/FREE Full TextGoogle Scholar
28. ↵
    1. A. D. Washburne et al
    ., Taxonomic patterns in the zoonotic potential of mammalian viruses. PeerJ 6, e5979 (2018).OpenUrlCrossRefGoogle Scholar
29. ↵
    1. M. E. J. Woolhouse,
    2. L. Brierley
    , Epidemiological characteristics of human-infective RNA viruses. Sci. Data 5, 180017 (2018).OpenUrlGoogle Scholar
30. ↵
    1. R. Reeve et al
    ., How to partition diversity. arXiv:14046520v3 (8 December 2016).Google Scholar
31. ↵
    1. S. Dorai-Raj
    , binom: Binomial confidence intervals for several parameterizations (R package, Version 1.1-1, 2014).Google Scholar
32. ↵
    1. C. H. Calisher,
    2. J. E. Childs,
    3. H. E. Field,
    4. K. V. Holmes,
    5. T. Schountz
    , Bats: Important reservoir hosts of emerging viruses. Clin. Microbiol. Rev. 19, 531–545 (2006).OpenUrlAbstract/FREE Full TextGoogle Scholar
33. ↵
    1. A. D. Luis et al
    ., Network analysis of host-virus communities in bats and rodents reveals determinants of cross-species transmission. Ecol. Lett. 18, 1153–1162 (2015).OpenUrlCrossRefGoogle Scholar
34. 1. U. Karakus et al
    ., MHC class II proteins mediate cross-species entry of bat influenza viruses. Nature 567, 109–112 (2019).OpenUrlCrossRefGoogle Scholar
35. ↵
    1. C. Rupprecht,
    2. I. Kuzmin,
    3. F. Meslin
    , Lyssaviruses and rabies: Current conundrums, concerns, contradictions and controversies. F1000 Res. 6, 184 (2017).OpenUrlGoogle Scholar
36. ↵
    1. B. Longdon,
    2. J. D. Hadfield,
    3. C. L. Webster,
    4. D. J. Obbard,
    5. F. M. Jiggins
    , Host phylogeny determines viral persistence and replication in novel hosts. PLoS Pathog. 7, e1002260 (2011).OpenUrlCrossRefPubMedGoogle Scholar
37. ↵
    1. S. Huang,
    2. O. R. Bininda-Emonds,
    3. P. R. Stephens,
    4. J. L. Gittleman,
    5. S. Altizer
    , Phylogenetically related and ecologically similar carnivores harbour similar parasite assemblages. J. Anim. Ecol. 83, 671–680 (2014).OpenUrlGoogle Scholar
38. ↵
    1. J. R. C. Pulliam,
    2. J. Dushoff
    , Ability to replicate in the cytoplasm predicts zoonotic transmission of livestock viruses. J. Infect. Dis. 199, 565–568 (2009).OpenUrlCrossRefPubMedGoogle Scholar
39. ↵
    1. D. M. Knipe,
    2. P. Howley
    , Eds., Fields Virology (Lippincott Williams & Wilkins, ed. 6, 2013).Google Scholar
40. ↵
    1. T. Allen et al
    ., Global hotspots and correlates of emerging zoonotic diseases. Nat. Commun. 8, 1124 (2017).OpenUrlCrossRefPubMedGoogle Scholar
41. ↵
    1. L. Perea-Martínez et al
    ., Experimental infection of Artibeus intermedius bats with serotype-2 dengue virus. Comp. Immunol. Microbiol. Infect. Dis. 36, 193–198 (2013).OpenUrlCrossRefPubMedGoogle Scholar
42. ↵
    1. S. Cabrera-Romo et al
    ., Experimental inoculation of Artibeus jamaicensis bats with dengue virus serotypes 1 or 4 showed no evidence of sustained replication. Am. J. Trop. Med. Hyg. 91, 1227–1234 (2014).OpenUrlAbstract/FREE Full TextGoogle Scholar
43. ↵
    1. A. Vicente-Santos et al
    ., Neotropical bats that co-habit with humans function as dead-end hosts for dengue virus. PLoS Negl. Trop. Dis. 11, e0005537 (2017).OpenUrlGoogle Scholar
44. ↵
    1. B. A. Han,
    2. J. P. Schmidt,
    3. S. E. Bowden,
    4. J. M. Drake
    , Rodent reservoirs of future zoonotic diseases. Proc. Natl. Acad. Sci. U.S.A. 112, 7039–7044 (2015).OpenUrlAbstract/FREE Full TextGoogle Scholar
45. ↵
    1. Y.-Z. Zhang,
    2. M. Shi,
    3. E. C. Holmes
    , Using metagenomics to characterize an expanding virosphere. Cell 172, 1168–1172 (2018).OpenUrlCrossRefGoogle Scholar
46. ↵
    1. C. Hulo,
    2. P. Masson,
    3. E. de Castro,
    4. P. Le Mercier
    , ViralZone. https://viralzone.expasy.org/. Accessed 21 October 2019.Google Scholar
47. ↵
    1. E. J. Lefkowitz et al
    ., Virus taxonomy: The database of the international committee on taxonomy of viruses (ICTV). Nucleic Acids Res. 46, D708–D717 (2018).OpenUrlCrossRefGoogle Scholar
48. ↵
    1. N. Mollentze,
    2. R. Biek,
    3. D. G. Streicker
    , The role of viral evolution in rabies host shifts and emergence. Curr. Opin. Virol. 8, 68–72 (2014).OpenUrlCrossRefGoogle Scholar
49. 1. N. Mollentze,
    2. D. G. Streicker
    , Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3516613. Deposited 23 October 2019.Google Scholar
50. 1. S. N. Wood
    , Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).OpenUrlCrossRefPubMedGoogle Scholar
51. ↵
    1. J. D. Hadfield,
    2. S. Nakagawa
    , General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).OpenUrlCrossRefPubMedGoogle Scholar
52. ↵
    1. S. N. Wood
    , Fast stable direct fitting and smoothness selection for generalized additive models. J. R. Stat. Soc. Series B Stat. Methodol. 70, 495–518 (2008).OpenUrlGoogle Scholar
53. ↵
    1. S. Kumar,
    2. G. Stecher,
    3. M. Suleski,
    4. S. B. Hedges
    , TimeTree: A resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).OpenUrlCrossRefGoogle Scholar
54. ↵
    1. E. Paradis,
    2. J. Claude,
    3. K. Strimmer
    , APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).OpenUrlCrossRefPubMedGoogle Scholar
55. ↵
    1. R Core Team
    , R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Google Scholar
56. ↵
    1. S. N. Wood
    , Thin-plate regression splines. J. R. Stat. Soc. B 65, 95–114 (2003).OpenUrlCrossRefGoogle Scholar
57. ↵
    1. S. A. Chamberlain,
    2. E. Szöcs
    , Taxize: Taxonomic search and retrieval in R. F1000 Res. 2, 191 (2013).OpenUrlGoogle Scholar
58. ↵
    1. Y. Roskov et al
    ., Eds., Species 2000 & ITIS Catalogue of Life, 2015 Annual Checklist (Naturalis, 2015).Google Scholar
59. ↵
    1. H. Akaike
    , A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).OpenUrlCrossRefGoogle Scholar
60. ↵
    1. S. N. Wood,
    2. N. Pya,
    3. B. Säfken
    , Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).OpenUrlCrossRefGoogle Scholar
61. 1. F. Hartig
    , DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models (R package, Version 0.2.4, 2019).Google Scholar
62. 1. W. N. Venables,
    2. B. D. Ripley
    , Modern Applied Statistics with S (Springer, ed. 4, 2002).Google Scholar

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Huabin Zhao Department of Ecology, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei 430072, China.

Letter to the editor Science Magazine 27Mar2020

In China, bats are traditionally symbols of good luck and happiness (1). There are more than 1400 species of bats worldwide, but more than half of them have unknown or decreasing population trends (2). Unfortunately, the suggestion that coronavirus disease 2019 (COVID-19) may have originated in bats (3) is putting them at increased risk. As COVID-19 has spread, people in China have started requesting that hibernating bats in or near their houses be expelled (4, 5). Disturbing hibernating bats causes abnormal arousal patterns (6), which could lead to high bat mortality and potentially to the spread of other viruses. Moreover, the captured bats are being released into the wild, which is not their native habitat (4), posing further threats to their survival. These decisions are not based in fact; COVID-19 was linked to horseshoe bats (3), which do not hibernate in cities in China (7). The reputation of bats as virus carriers has even led to extreme suggestions of mass slaughter to protect public health (8). The exaggeration of bats’ negative traits without regard for their positive ones could ultimately lead to their needless and intentional elimination. Bats serve many critical roles for the ecosystem. They are biological—and economical—pesticides (9), and they contribute to the pollination and seed dispersal for many important plants (10). They are also excellent subjects for studies on healthy aging, cancer prevention, disease defense, biomimetic engineering, ecosystem functioning, and adaptive evolution (11). The need for public education about bats, including their positive and negative impacts, is urgent and vital to their conservation.

Huabin Zhao Department of Ecology, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei 430072, China. Email: huabinzhao@whu.edu.cn

REFERENCES AND NOTES 1. V. Sung, Five-Fold Happiness: Chinese Concepts of Luck, Prosperity, Longevity, Happiness, and Wealth (Chronicle Books, 2002). 2. W. F. Frick, T, Kingston, J. Flanders, Ann. N.Y. Acad. Sci. 10.1111/nyas.14045 (2019). 3. P. Zhou et al., Nature 579, 270 (2020). 4. “Beijing residents call police to remove bats from their houses” (2020); https://wxn.qq.com/ cmsid/20200211A06BJG00 [in Chinese]. 5. “Shanghai residents call Wildlife Conservation Department to expel bats around their houses” (2020); https://sh.qq. com/a/20200208/014581.htm [in Chinese]. 6. J. R. Speakman et al., J. Appl. Ecol. 28, 1087 (1991). 7. N. M. Furey, P. A. Racey, in Bats in the Anthropocene: Conservation of Bats in a Changing World, C. Voigt, T. Kingston, Eds. (Springer, Cham, 2016). 8. “Ecological killing is under heated debate—revision of wildlife protection law must involve experts from all related fields” (2020); https://xw.qq.com/ cmsid/20200214A0JB1X00 [in Chinese]. 9. J. G. Boyles et al., Science 332, 41 (2011). 10. T. H. Kunz et al., Ann. N.Y. Acad. Sci. 1223, 1 (2011). 11. E.C. Teeling et al., Annu. Rev. Anim. Biosci. 6, 23 (2018).
10.1126/science.abb3088

Hongxin Wang
School of Government, Beijing Normal University, Beijing 100875, China

Letter to the editors Science Magazine 27Mar2020

On 24 February, China’s top legislature comprehensively prohibited the consumption of terrestrial wildlife to protect public health (1). The ban was enacted in response to the outbreak of coronavirus disease 2019 (COVID-19), which is considered to be linked to wildlife consumption (2). However, a total ban on the consumption of terrestrial wildlife alone is not enough to effectively protect public health from wildlife-associated diseases
China’s wildlife farming industry includes 6.3 million direct practitioners and a total output value of $18 billion (3). Curtailing this activity in a short period of time will be difficult. Conflicts may occur between the private interests of farmers and public health. It is also unclear how to dispose of the farmed animals. Killing them would be inhumane and could pose new risks to human health. Releasing them into unknown habitats in the wild could threaten ecosystem stability. Furthermore, given that banning the wildlife farming industry would threaten economic growth in many regions, implementation will be challenging. Meanwhile, myriad traditional Chinese medicines are made from wildlife products, such as pangolin scales (4), snake bile (5), and bat feces (6), yet medicinal use of wildlife is not covered by the ban. Disease transmission risks exist during the process of hunting, storing, and transporting such wildlife for medicinal purposes, activities that will continue (6). Even if the ban could be effectively implemented, the traditional medicine industry would continue to threaten wildlife. In addition to enacting a ban, the Chinese government should manage public health risks caused by wildlife-associated diseases by working together with wildlife protection and animal health agencies and making decisions about wildlife policies based on scientific evidence. Subsidies and financial support should be arranged to facilitate the transformation of the wildlife farming industry required by the ban, as well as made available to help transition away from the production of traditional Chinese medicine. As changes are made, the government should keep information timely and transparent so as to encourage public participation in the reform of the wildlife protection system.

Hongxin Wang1*, Junlin Shao1, Xi Luo2, Ziang Chuai1, Shengyue Xu1, Mingxia Geng3, Zhouyi Gao1 1School of Government, Beijing Normal University, Beijing 100875, China. 2School of Global Affairs, Kings College London, Strand London WC2R 2LS, UK. 3College of Chinese Language and Literature, Beijing Normal University, Beijing 100875, China. *Corresponding author. Email: wanghongxin@bnu.edu.cn

REFERENCES AND NOTES 1. “The Decision of the Standing Committee of the National People’s Congress on comprehensively prohibiting the illegal trade of wildlife, eliminating the bad habits of wildlife consumption, and protecting the health and safety of the people,” Xinhua.net (2020); www.xinhuanet.com/ politics/2020-02/24/c_1125620762.htm [in Chinese]. 2. J. Li et al., Lancet Infect. Dis., 10.1016/S14733099(20)30063-3 (2020). 3. “Report on sustainable development strategy of China’s wildlife farming industry” (Consulting Research Project of Chinese Academy of Engineering, 2017) [in Chinese]. 4. R. W. Byard, Forensic Sci. Med. Pathol. 12, 125 (2016). 5. J. Still, Complement Ther. Med. 11, 118 (2003). 6. T. M. Wassenaar, Y. Zou, Lett. Appl. Microbiol., 10.1111/ lam.13285 (2020). 10.1126/science.abb6463

Nian Yang Key Laboratory for Biodiversity and Ecological Engineering of Ministry of Education, College of Life Sciences, Beijing Normal University, Beijing 100875, China

Letter to the editor Science Magazine 27Mar2020

Although the origin of severe acute respiratory syndrome–coronavirus 2 (SARSCoV-2)—the virus that causes coronavirus disease 2019 (COVID-19)—has not been identified, it is clear that China’s wildlife market played an important role in the early spread of the disease (“Mining coronavirus genomes for clues to the outbreak’s origins,” J. Cohen, News, 31 January, https://scim.ag/ COVID-19genomeclues). On 24 February, China’s National People’s Congress adopted legislation banning the consumption of any field-harvested or captive-bred wildlife in an effort to prevent further public health threats until a revised wildlife protection law can be introduced (1). We argue that China needs to seize this opportunity and permanently ban wildlife consumption. Since the 2003 outbreak of zoonotic SARS, China has established several management policies and regulations to control wildlife markets (2, 3). However, the vague definition of “wildlife” in the current policies and regulations results in enforcement confusion and loopholes. The current laws protect species of terrestrial and aquatic wildlife that are rare, beneficial, or economically or scientifically valuable (4), but they fail to differentiate captive-bred and wild populations. The sika deer (Cervus nippon), for instance, is a national, first-class protected species (5) and is also on the commercial breeding list (2). The indistinguishable differences between wild and captive populations provide opportunities for illegal bushmeat to be blended into exotic livestock and flow into the market (6). Meanwhile, the protected species list has not been updated for nearly 30 years and covers only approximately two-thirds of the native wild species (2, 4, 5). The critically endangered spoon-billed sandpiper (Calidris pygmaea) (7) is still listed as a second-class protected animal (5). More than 1000 native species are absent from the protected list, including bats, which means that illegal hunting or trading of these species might not be punished and could threaten public health (4, 5). Furthermore, penalties for illegal wildlife distribution and consumption are not sufficiently severe. Wildlife consumption is not restricted. Therefore, the demand for wildlife products remains high, with high profits and mild punishments driving the dealers (8, 9). In 2018, a man who poached about 8000 birds, including the critically endangered yellow-breasted bunting (Emberiza aureola) (10), was sentenced to pay only a US$10,000 fine (11). The Chinese legislature should revise the wildlife protection law to ensure the effectiveness of the legislation. The definition of wildlife should be clarified as distinct from captive exotic populations. Meanwhile, a more stringent management plan for exotic livestock should be established, including an individual identification system, to increase the traceability of the exotic
livestock products. The ability to technically distinguish captive from wild individuals will strengthen law enforcement. The list of protected species should be updated regularly, and all native wild species should be protected. The penalty for violating behavior should be increased, and wildlife consumption and possession should be treated as criminal offenses. Both the supply and demand sections of the wildlife trading chain should be strictly monitored and contained. China must act to permanently ban wildlife consumption in order to prevent future public health risks.

Nian Yang1, Peng Liu1,2, Wenwen Li1, Li Zhang1* 1Key Laboratory for Biodiversity and Ecological Engineering of Ministry of Education, College of Life Sciences, Beijing Normal University, Beijing 100875, China. 2School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China. *Corresponding author. Email: asterzhang@bnu.edu.cn

REFERENCES AND NOTES 1. CGTN, “China’s top legislature approves decision to ban illegal wildlife trade” (2020); https://news.cgtn.com/ news/2020-02-24/China-s-top-legislature-approves-toban-illegal-wildlife-trade-OlrtorGko0/index.html. 2. National Forestry and Grassland Administration, “Notice on issuing the list of 54 species of terrestrial wildlife such as sika deers with mature domestication and reproduction technologies for commercial operation,” (2003); www. forestry.gov.cn/main/4818/20030805/796749.html [in Chinese]. 3. National Forestry and Grassland Administration, “Notice on strictly prohibiting illegal hunting, and operating terrestrial wildlife,” (2003); [in Chinese]. www.forestry.gov. cn/main/4818/content-796881.html. 4. National People’s Congress Standing Committee, “The law of the People’s Republic of China on the protection of wildlife” (2017). 5. National Forestry and Grassland Administration, “Lists of wildlife under special national protection” (1989); www. forestry.gov.cn/main/3954/content-1063883.html [in Chinese]. 6. B. Gratwicke et al., PLOS One 3, e2544 (2008). 7. BirdLife International 2018, Calidris pygmaea (The IUCN Red List of Threatened Species, 2018). 8. V. Nijman, M. X. Zhang, C. R. Shepherd, Glob. Ecol. Conserv. 5, 118 (2016). 9. L. Zhang, F. Yin, Biodivers. Conserv. 23, 2371 (2014). 10. BirdLife International 2017, Emberiza aureola (The IUCN Red List of Threatened Species, 2017). 11. Anhui Province Wuhe County People’s Procuratorate, “Anhui province Wuhe County court verdict” (2018); http://wenshu.court.gov.cn/website/ wenshu/181107ANFZ0BXSK4/index.html?docId=f1adbb 9b475f404185c1a93801552c64 [in Chinese].
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