Kezia Manlove

Deer can transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to humans and harbour human-extinct variants. Despite this public health threat, ecological mechanisms driving exposure patterns in deer populations remain poorly understood. Our objective was to identify behavioural risk factors by linking serostatus of individual deer to their movement and space use using multilevel logistic regression. We examined serosurveillance data of white-tailed (Odocoileus virginianus) and mule deer (O. hemionus) wearing global positioning system devices across an urban–rural gradient in the United States (1151 individuals, 20 sites, 8 states). Seropositivity was positively associated with average human footprint index and negatively associated with average SARS-CoV-2 human wastewater concentration. Seropositivity was not associated with time spent in GAP-modelled deer habitat. Together, these findings provide evidence that human-to-deer transmission could be a primary source of deer SARS-CoV-2 exposure and deer populations may struggle to maintain transmission without repeated spillover from humans. Integrating human and environmental indices with movement behaviours and space use can thus clarify pathways that facilitate cross-species transmission in wildlife.

Over the past several decades, deer populations in North America have grown considerably, resulting in frequent contact with humans and livestock, and increased potential for pathogen spillover. Despite the importance of pathogen spillover among humans, wildlife, and livestock, the diversity of viruses present in deer remains largely unknown. Using a metagenomic high-throughput sequencing approach, we characterized viral communities in the upper respiratory tracts of live mule deer ( Odocoileus hemionus ) and white-tailed deer ( Odocoileus virginianus ) captured at fourteen study sites in nine states across the United States. We identified vertebrate-infecting viruses in both deer species at all but two of the study sites, located in Illinois and Utah. Viral richness did not vary among species or study sites. However, viral community composition was different across study sites but not among deer species. Amongst the detected viral sequences, several originate from or were closely related to viruses previously described in humans ( e.g. , severe acute respiratory syndrome coronavirus 2) and livestock ( e.g. , bovine-like coronavirus). We also documented viruses recently discovered in deer, such as CHeRI orbivirus 1. Finally, we identified several new putative viruses in the Picornaviridae , Rhabdoviridae and Tobaniviridae families, including a novel Aphthovirus related to bovine rhinitis A virus in both deer species, across seven states and nine study sites. Our findings expand the understanding of viral diversity in two deer species across the United States, providing important insights for managing pathogens at the wildlife, livestock, and human interface.

In this study, we used a multi-faceted approach to understand patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and persistence in a wild white-tailed deer (Odocoileus virginianus) population. Serology data indicated transmission of SARS-CoV-2 and persistence during the seven-month sampling period. Traditional disease modelling based on deer-to-deer transmission indicated relatively low prevalence with an R0 of 1.9 and recovery period of 7 days; however, individual-based modelling informed by GPS tracked-movement data captured a potential transmission event. Phylogenetic analyses revealed a recurring pattern of divergent groups of deer-derived sequences with human-derived sequences falling close to each deer-derived cluster. Further, human-derived sequences were frequently sampled months prior to the deer-derived sequences, indicating repeated human to deer spillover. Using multiple types of data as well as both fine and broad scale analyses, we have characterized a pattern of localized outbreaks of SARS-CoV-2 within white-tailed deer populations that are likely recurring due to frequent spillover events. Our results suggest that while deer-to-deer transmission occurs over small spatiotemporal scales, SARS-CoV-2 persistence over longer periods and across larger regions is likely driven by repeated spillover from human populations.

Wildlife disease surveillance helps in protecting public health, agriculture, and biodiversity. Planning effective surveillance involves strategic methods for identifying an effective sampling design for a program's objectives. Gaps in existing standards and complexity for wildlife surveillance justify a need for tools that can build statistically based intuition in wildlife professionals when designing surveillance systems. To address this need, we present the use of plug‐and‐play tools, specifically our surveillance analysis and sample size explorer (SASSE), to allow wildlife professionals to build intuition about the role of sample size vis‐à‐vis sampling design and diagnostic test performance in wildlife systems. SASSE uses open‐source software (R, R Shiny) to design an interactive, module‐based teaching tool to cover key surveillance objectives, including detection, prevalence, and epidemiological dynamics. Our tool fills the following gaps: (1) allows a broad audience to apply statistical sample size theory for designing disease surveillance, and (2) provides a simple statistical tool for addressing challenges with disease surveillance design in wildlife populations. Thus, the tool we present here can be used readily to identify efficient sampling designs for a surveillance objective across a wide variety of settings.

Given the importance of protected areas for biodiversity, the growth of visitation to many areas has raised concerns about the effects of humans on wildlife. In 2020, the COVID-19 pandemic led to temporary closure of national parks in the United States, offering a pseudonatural experiment to tease apart the effects of permanent infrastructure and transient human presence on animals. We compiled GPS tracking data from 229 individuals of 10 mammal species in 14 parks and used third-order hierarchical resource selection functions to evaluate the influence of the human footprint on animal space use in 2019 and 2020. Averaged across all parks and species, animals avoided the human footprint, whether the park was open or closed. However, although animals in remote areas showed consistent avoidance, on average those in more developed areas switched from avoidance to selection when protected areas were closed. Findings varied across species: some responded consistently negatively to the footprint (wolves, mountain goats), some positively (mule deer, red fox) and others had a strong exposure-mediated response (elk, mountain lion). Furthermore, some species responded more strongly to the park closure (black bear, moose). This study advances our understanding of complex interactions between recreation and wildlife in protected areas.

Efficient learning about disease dynamics in free‐ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape‐scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife‐livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape‐scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape‐scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape‐scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in two free‐ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS‐CoV‐2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape‐scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape‐scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design.

Wildlife disease surveillance helps in protecting public health, agriculture, and biodiversity. Planning effective surveillance involves strategic methods for identifying an effective sampling design for a program’s objectives. Gaps in existing standards and complexity for wildlife surveillance justify a need for tools that can build statistically-based intuition in wildlife professionals when designing surveillance systems. To address this need, we present the use of plug-and-play tools, specifically our Surveillance Analysis and Sample Size Explorer (SASSE), to allow wildlife professionals to build intuition about the role of sample size vis-a-vis sampling design and diagnostic test performance in wildlife systems. SASSE uses open-source software (R, R Shiny) to design an interactive, module-based teaching tool to cover key surveillance objectives including detection, prevalence, and epidemiological dynamics. Our tool fills the following gaps: 1) allows a broad audience to apply statistical sample size theory for designing disease surveillance, and 2) provides a simple statistical tool for addressing challenges with disease surveillance design in wildlife populations. Thus, the tool we present here can be used readily to identify efficient sampling designs for a surveillance objective across a wide variety of settings.

Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen’s environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host–pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource—access to receptive mates—produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance ‘foray’ movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.

All animals exhibit some combination of spatial and social behaviours. A diversity of interactions occurs between such behaviours, producing emergent phenomena at the spatial–social interface . Untangling and interrogating these complex, intertwined processes can be vital for identifying the mechanisms, causes and consequences of behavioural variation in animal ecology. Nevertheless, the integrated study of the interactions between spatial and social phenotypes and environments (at the spatial–social interface) is in its relative infancy. In this theme issue, we present a collection of papers chosen to expand the spatial–social interface along several theoretical, methodological and empirical dimensions. They detail new perspectives, methods, study systems and more, as well as offering roadmaps for applied outputs and detailing exciting new directions for the field to move in the future. In this Introduction, we outline the contents of these papers, placing them in the context of what comes before, and we synthesize a number of takeaways and future directions for the spatial–social interface. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.