Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health

Risk of invasive waterfowl interaction with poultry production: Understanding potential for avian pathogen transmission via species distribution models

Recent outbreaks of highly pathogenic avian influenza have devastated poultry production across the United States, with more than 77 million birds culled in 2022-2024 alone. Wild waterfowl, including various invasive species, host numerous pathogens, including highly pathogenic avian influenza virus (HPAIV), and have been implicated as catalysts of disease outbreaks among native fauna and domestic birds. In major poultry-producing states like Arkansas, USA, where the poultry sector is responsible for significant economic activity (>$4 billion USD in 2022), understanding the risk of invasive waterfowl interactions with domestic poultry is critical. Here, we assessed the risk of invasive waterfowl-poultry interaction in Arkansas by comparing the density of poultry production sites (chicken houses) to areas of high habitat suitability for two invasive waterfowl species, (Egyptian Goose [Alopochen aegyptiaca] and Mute Swan [Cygnus olor]), known to host significant pathogens, including avian influenza viruses. The percentage of urban land cover was the most important habitat characteristic for both invasive waterfowl species. At the 95% confidence interval, chicken house densities in areas highly suitable for both species (Egyptian Goose = 0.91 ± 0.11 chicken houses/km2; Mute Swan = 0.61 ± 0.03 chicken houses/km2) were three to five times higher than chicken house densities across the state (0.17 ± 0.01 chicken houses/km2). We show that northwestern and western Arkansas, both areas of high importance for poultry production, are also at high risk of invasive waterfowl presence. Our results suggest that targeted monitoring efforts for waterfowl-poultry contact in these areas could help mitigate the risk of avian pathogen exposure in Arkansas and similar regions with high poultry production.

Patterns and correlates of potential range shifts of bat species in China in the context of climate change

Climate change may diminish biodiversity; thus, it is urgent to predict how species' ranges may shift in the future by integrating multiple factors involving more taxa. Bats are particularly sensitive to climate change due to their high surface-to-volume ratio. However, few studies have considered geographic variables associated with roost availability and even fewer have linked the distributions of bats to their thermoregulation and energy regulation traits. We used species distribution models to predict the potential distributions of 12 bat species in China under current and future greenhouse gas emission scenarios (SSP1-2.6 and SSP5-8.5) and examined factors that could affect species' range shifts, including climatic, geographic, habitat, and human activity variables and wing surface-to-mass ratio (S-MR). The results suggest that Ia io, Rhinolophus ferrumequinum, and Rhinolophus rex should be given the highest priority for conservation in future climate conservation strategies. Most species were predicted to move northward, except for I. io and R. rex, which moved southward. Temperature seasonality, distance to forest, and distance to karst or cave were the main environmental factors affecting the potential distributions of bats. We found significant relationships between S-MR and geographic distribution, current potential distribution, and future potential distribution in the 2050s. Our work highlights the importance of analyzing range shifts of species with multifactorial approaches, especially for species traits related to thermoregulation and energy regulation, to provide targeted conservation strategies.

Advances in understanding bat infection dynamics across biological scales

Over the past two decades, research on bat-associated microbes such as viruses, bacteria and fungi has dramatically increased. Here, we synthesize themes from a conference symposium focused on advances in the research of bats and their microbes, including physiological, immunological, ecological and epidemiological research that has improved our understanding of bat infection dynamics at multiple biological scales. We first present metrics for measuring individual bat responses to infection and challenges associated with using these metrics. We next discuss infection dynamics within bat populations of the same species, before introducing complexities that arise in multi-species communities of bats, humans and/or livestock. Finally, we outline critical gaps and opportunities for future interdisciplinary work on topics involving bats and their microbes.

Lack of detection of SARS-CoV-2 in wildlife from Kerala, India in 2020-21

Spillover of SARS-CoV-2 into a variety of wild and domestic animals has been an ongoing feature of the human pandemic. The establishment of a new reservoir in white-tailed deer in North America and increasing divergence of the viruses circulating in them from those circulating in the human population has highlighted the ongoing risk this poses for global health. Some parts of the world have seen more intensive monitoring of wildlife species for SARS-CoV-2 and related coronaviruses but there are still very large gaps in geographical and species-specific information. This paper reports negative results for SARS-CoV-2 PCR based testing using a pan coronavirus end point RDRP PCR and a Sarbecovirus specific E gene qPCR on lung and or gut tissue from wildlife from the Indian State of Kerala. These animals included: 121 Rhinolophus rouxii (Rufous Horsehoe Bat), six Rhinolophus bedommei (Lesser Woolly Horseshoe Bat), 15 Rossettus leschenaultii (Fulvous Fruit Bat), 47 Macaca radiata (Bonnet macaques), 35 Paradoxurus hermaphroditus (Common Palm Civet), five Viverricula indica (Small Indian Civet), four Herpestes edwardsii (Common Mongoose), ten Panthera tigris (Bengal Tiger), eight Panthera pardus fusca (Indian Leopard), four Prionailurus bengalensis (Leopard cats), two Felis chaus (Jungle cats), two Cuon alpinus (Wild dogs) and one Melursus ursinus (sloth bear).

Developing One Health surveillance systems

The health of humans, domestic and wild animals, plants, and the environment are inter-dependent. Global anthropogenic change is a key driver of disease emergence and spread and leads to biodiversity loss and ecosystem function degradation, which are themselves drivers of disease emergence. Pathogen spill-over events and subsequent disease outbreaks, including pandemics, in humans, animals and plants may arise when factors driving disease emergence and spread converge. One Health is an integrated approach that aims to sustainably balance and optimize human, animal and ecosystem health. Conventional disease surveillance has been siloed by sectors, with separate systems addressing the health of humans, domestic animals, cultivated plants, wildlife and the environment. One Health surveillance should include integrated surveillance for known and unknown pathogens, but combined with this more traditional disease-based surveillance, it also must include surveillance of drivers of disease emergence to improve prevention and mitigation of spill-over events. Here, we outline such an approach, including the characteristics and components required to overcome barriers and to optimize an integrated One Health surveillance system.

Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots

The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.

Sarbecoviruses of British horseshoe bats; sequence variation and epidemiology

Horseshoe bats are the natural hosts of the Sarbecovirus subgenus that includes SARS-CoV and SARS-CoV- 2. Despite the devastating impact of the COVID-19 pandemic, there is still little known about the underlying epidemiology and virology of sarbecoviruses in their natural hosts, leaving large gaps in our pandemic preparedness. Here we describe the results of PCR testing for sarbecoviruses in the two horseshoe bat species (Rhinolophus hipposideros and R. ferrumequinum) present in Great Britain, collected in 2021-22 during the peak of COVID-19 pandemic. One hundred and ninety seven R. hipposideros samples from 33 roost sites and 277 R. ferrumequinum samples from 20 roost sites were tested. No coronaviruses were detected in any samples from R. ferrumequinum whereas 44 and 56 % of individual and pooled (respectively) faecal samples from R. hipposideros across multiple roost sites tested positive in a sarbecovirus-specific qPCR. Full genome sequences were generated from three of the positive samples (and partial genomes from two more) using Illumina RNAseq on unenriched samples. Phylogenetic analyses showed that the obtained sequences belong to the same monophyletic clade, with >95 % similarity to previously-reported European isolates from R. hipposideros. The sequences differed in the presence or absence of accessory genes ORF 7b, 9b and 10. All lacked the furin cleavage site of SARS-CoV-2 spike gene and are therefore unlikely to be infective for humans. These results demonstrate a lack, or at least low incidence, of SARS-CoV-2 spill over from humans to susceptible GB bats, and confirm that sarbecovirus infection is widespread in R. hipposideros. Despite frequently sharing roost sites with R. ferrumequinum, no evidence of cross-species transmission was found.

Human disturbance increases coronavirus prevalence in bats

Human land modification is a known driver of animal-to-human transmission of infectious agents (zoonotic spillover). Infection prevalence in the reservoir is a key predictor of spillover, but landscape-level associations between the intensity of land modification and infection rates in wildlife remain largely untested. Bat-borne coronaviruses have caused three major disease outbreaks in humans: severe acute respiratory syndrome (SARS), Middle East respiratory syndrome, and coronavirus disease 2019 (COVID-19). We statistically link high-resolution land modification data with bat coronavirus surveillance records and show that coronavirus prevalence significantly increases with the intensity of human impact across all climates and levels of background biodiversity. The most significant contributors to the overall human impact are agriculture, deforestation, and mining. Regions of high predicted bat coronavirus prevalence coincide with global disease hotspots, suggesting that infection prevalence in wildlife may be an important factor underlying links between human land modification and zoonotic disease emergence.

A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia

Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence.

Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.