Deriving spatially explicit direct and indirect interaction networks from animal movement data.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Behavioural ecology at the spatial-social interface.

Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the 'spatial-social interface' as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial-social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial-social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial-social interface.

ABORTION AND NEONATAL MORTALITY DUE TO TOXOPLASMA GONDII IN BIGHORN SHEEP (OVIS CANADENSIS).

Low lamb recruitment can be an obstacle to bighorn sheep (Ovis canadensis) conservation and restoration. Causes of abortion and neonate loss in bighorn sheep, which may affect recruitment, are poorly understood. Toxoplasma gondii is a major cause of abortion and stillbirth in domestic small ruminants worldwide, but no reports exist documenting abortion or neonatal death in bighorn sheep attributable to toxoplasmosis. Between March 2019 and May 2021, eight fetal and neonatal bighorn lamb cadavers from four western US states (Idaho, Montana, Nebraska, and Washington) were submitted to the Washington Animal Disease Diagnostic Laboratory for postmortem examination, histologic examination, and ancillary testing to determine the cause of abortion or neonatal death. Necrotizing encephalitis characteristic of toxoplasmosis was identified histologically in six of eight cases, and T. gondii infection was confirmed by PCR in five cases with characteristic lesions. Other lesions attributable to toxoplasmosis were pneumonia (3/5 cases) and myocarditis (2/5 cases). Protozoal cysts were identified histologically within brain, lung, heart, skeletal muscle, adipose tissue, or a combination of samples in all five sheep with PCR-confirmed T. gondii infections. Seroprevalence of T. gondii ranged from 40-81% of adult females sampled in the Washington population in October and November 2018-2021, confirming high rates of exposure before detection of Toxoplasma abortions in this study. Of 1,149 bighorn sheep postmortem samples submitted to Washington Animal Disease Diagnostic Laboratory between January 2000 and May 2021, 21 of which were from fetuses or neonates, a single case of chronic toxoplasmosis was diagnosed in one adult ewe. Recent identification of Toxoplasma abortions in bighorn sheep suggests that toxoplasmosis is an underappreciated cause of reproductive loss. Abortions and neonatal mortalities should be investigated through postmortem and histologic examination, particularly in herds that are chronically small, demographically stagnant, or exhibit reproductive rates lower than expected.

Using transcriptomics to predict and visualize disease status in bighorn sheep (Ovis canadensis).

Increasing risk of pathogen spillover coupled with overall declines in wildlife population abundance in the Anthropocene make infectious disease a relevant concern for species conservation worldwide. While emerging molecular tools could improve our diagnostic capabilities and give insight into mechanisms underlying wildlife disease risk, they have rarely been applied in practice. Here, employing a previously reported gene transcription panel of common immune markers to track physiological changes, we present a detailed analysis over the course of both acute and chronic infection in one wildlife species where disease plays a critical role in conservation, bighorn sheep (Ovis canadensis). Differential gene transcription patterns distinguished between infection statuses over the course of acute infection and differential correlation (DC) analyses identified clear changes in gene co-transcription patterns over the early stages of infection, with transcription of four genes-TGFb, AHR, IL1b and MX1-continuing to increase even as transcription of other immune-associated genes waned. In a separate analysis, we considered the capacity of the same gene transcription panel to aid in differentiating between chronically infected animals and animals in other disease states outside of acute disease events (an immediate priority for wildlife management in this system). We found that this transcription panel was capable of accurately identifying chronically infected animals in the test dataset, though additional data will be required to determine how far this ability extends. Taken together, our results showcase the successful proof of concept and breadth of potential utilities that gene transcription might provide to wildlife disease management, from direct insight into mechanisms associated with differential disease response to improved diagnostic capacity in the field.

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life.

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an 'epidemiological landscape' connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a 'movement-pathogen pace of life' heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

Bighorn sheep show similar in-host responses to the same pathogen strain in two contrasting environments.

Ecological context-the biotic and abiotic environment, along with its influence on population mixing dynamics and individual susceptibility-is thought to have major bearing on epidemic outcomes. However, direct comparisons of wildlife disease events in contrasting ecological contexts are often confounded by concurrent differences in host genetics, exposure histories, or pathogen strains. Here, we compare disease dynamics of a Mycoplasma ovipneumoniae spillover event that affected bighorn sheep populations in two contrasting ecological contexts. One event occurred on the herd's home range near the Rio Grande Gorge in New Mexico, while the other occurred in a captive facility at Hardware Ranch in Utah. While data collection regimens varied, general patterns of antibody signal strength and symptom emergence were conserved between the two sites. Symptoms appeared in the captive setting an average of 12.9 days postexposure, average time to seroconversion was 24.9 days, and clinical signs peaked at approximately 36 days postinfection. These patterns were consistent with serological testing and subsequent declines in symptom intensity in the free-ranging herd. At the captive site, older animals exhibited more severe declines in body condition and loin thickness, higher symptom burdens, and slower antibody response to the pathogen than younger animals. Younger animals were more likely than older animals to clear infection by the time of sampling at both sites. The patterns presented here suggest that environment may not be a major determinant of epidemiological outcomes in the bighorn sheep-M. ovipneumoniae system, elevating the possibility that host- or pathogen-factors may be responsible for observed variation.

Disease Ecology of a Low-Virulence Mycoplasma ovipneumoniae Strain in a Free-Ranging Desert Bighorn Sheep Population.

Infectious pneumonia associated with the bacterial pathogen Mycoplasma ovipneumoniae is an impediment to bighorn sheep (Ovis canadensis) population recovery throughout western North America, yet the full range of M. ovipneumoniae virulence in bighorn sheep is not well-understood. Here, we present data from an M. ovipneumoniae introduction event in the Zion desert bighorn sheep (Ovis canadensis nelsoni) population in southern Utah. The ensuing disease event exhibited epidemiology distinct from what has been reported elsewhere, with virtually no mortality (0 adult mortalities among 70 animals tracked over 118 animal-years; 1 lamb mortality among 40 lambs tracked through weaning in the two summers following introduction; and lamb:ewe ratios of 34.9:100 in the year immediately after introduction and 49.4:100 in the second year after introduction). Individual-level immune responses were lower than expected, and M. ovipneumoniae appeared to fade out approximately 1.5 to 2 years after introduction. Several mechanisms could explain the limited burden of this M. ovipneumoniae event. First, most work on M. ovipneumoniae has centered on Rocky Mountain bighorn sheep (O. c. candensis), but the Zion bighorns are members of the desert subspecies (O. c. nelsoni). Second, the particular M. ovipneumoniae strain involved comes from a clade of strains associated with weaker demographic responses in other settings. Third, the substructuring of the Zion population may have made this population more resilient to disease invasion and persistence. The limited burden of the disease event on the Zion bighorn population underscores a broader point in wildlife disease ecology: that one size may not fit all events.

A model for leveraging animal movement to understand spatio-temporal disease dynamics.

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

Natural history of a bighorn sheep pneumonia epizootic: Source of infection, course of disease, and pathogen clearance.

A respiratory disease epizootic at the National Bison Range (NBR) in Montana in 2016-2017 caused an 85% decline in the bighorn sheep population, documented by observations of its unmarked but individually identifiable members, the subjects of an ongoing long-term study. The index case was likely one of a small group of young bighorn sheep on a short-term exploratory foray in early summer of 2016. Disease subsequently spread through the population, with peak mortality in September and October and continuing signs of respiratory disease and sporadic mortality of all age classes through early July 2017. Body condition scores and clinical signs suggested that the disease affected ewe groups before rams, although by the end of the epizootic, ram mortality (90% of 71) exceeded ewe mortality (79% of 84). Microbiological sampling 10 years to 3 months prior to the epizootic had documented no evidence of infection or exposure to Mycoplasma ovipneumoniae at NBR, but during the epizootic, a single genetic strain of M. ovipneumoniae was detected in affected animals. Retrospective screening of domestic sheep flocks near the NBR identified the same genetic strain in one flock, presumptively the source of the epizootic infection. Evidence of fatal lamb pneumonia was observed during the first two lambing seasons following the epizootic but was absent during the third season following the death of the last identified M. ovipneumoniae carrier ewe. Monitoring of life-history traits prior to the epizootic provided no evidence that environmentally and/or demographically induced nutritional or other stress contributed to the epizootic. Furthermore, the epizootic occurred despite proactive management actions undertaken to reduce risk of disease and increase resilience in this population. This closely observed bighorn sheep epizootic uniquely illustrates the natural history of the disease including the (presumptive) source of spillover, course, severity, and eventual pathogen clearance.

Impact of Brown Marmorated Stink Bug (Hemiptera: Pentatomidae) Feeding on Tart Cherry (Rosales: Rosaceae) Quality and Yield in Utah.

Brown marmorated stink bug (Halyomorpha halys Stål) is an invasive and economically important agricultural and ornamental insect pest now established in 46 U.S. states. It was first detected in Utah in 2012 and began causing agricultural damage in 2017. Tart cherry (Prunus cerasus Linnaeus) is a major processed agricultural commodity in Utah; yet, its susceptibility to brown marmorated stink bug is unstudied. Limb cages with six brown marmorated stink bug adults, nymphs, or no brown marmorated stink bug were established in a randomized complete block design in a tart cherry orchard to determine feeding impact on different fruit developmental stages. After 1 wk of feeding, half of the fruits in each cage were removed to assess feeding intensity, and the remainder left through maturity to assess marketability and quality. Feeding by adults and nymphs between petal fall and fruit pit hardening, even at feeding pressures as low as 1.7-4.0 feeding sites per fruit, caused 100% abscission of fruits, significantly reducing marketability when compared with the control treatment. For fruits that escaped abscission and matured, few quality differences were detected among treatments, indicating that brown marmorated stink bug feeding caused minimal detectable quality loss to this processed tree fruit crop. We conclude that tart cherries are at risk of abscission with short-term brown marmorated stink bug feeding between petal fall and pit hardening when overwintered adults or F1 nymphs are present in orchards, and suggest that longer-term feeding may be necessary to cause quality and yield reductions after pit hardening.

Author Correction: Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States.

Spillover diseases have significant consequences for human and animal health, as well as wildlife conservation. We examined spillover and transmission of the pneumonia-associated bacterium Mycoplasma ovipneumoniae in domestic sheep, domestic goats, bighorn sheep, and mountain goats across the western United States using 594 isolates, collected from 1984 to 2017. Our results indicate high genetic diversity of M. ovipneumoniae strains within domestic sheep, whereas only one or a few strains tend to circulate in most populations of bighorn sheep or mountain goats. These data suggest domestic sheep are a reservoir, while the few spillovers to bighorn sheep and mountain goats can persist for extended periods. Domestic goat strains form a distinct clade from those in domestic sheep, and strains from both clades are found in bighorn sheep. The genetic structure of domestic sheep strains could not be explained by geography, whereas some strains are spatially clustered and shared among proximate bighorn sheep populations, supporting pathogen establishment and spread following spillover. These data suggest that the ability to predict M. ovipneumoniae spillover into wildlife populations may remain a challenge given the high strain diversity in domestic sheep and need for more comprehensive pathogen surveillance.

Percolation models of pathogen spillover.

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Ecological interventions to prevent and manage zoonotic pathogen spillover.

Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface.

Managing pathogen spillover at the wildlife-livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife-livestock interface. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory.

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of 'permeability' from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Sampling to elucidate the dynamics of infections in reservoir hosts.

The risk of zoonotic spillover from reservoir hosts, such as wildlife or domestic livestock, to people is shaped by the spatial and temporal distribution of infection in reservoir populations. Quantifying these distributions is a key challenge in epidemiology and disease ecology that requires researchers to make trade-offs between the extent and intensity of spatial versus temporal sampling. We discuss sampling methods that strengthen the reliability and validity of inferences about the dynamics of zoonotic pathogens in wildlife hosts. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Risk factors and productivity losses associated with Mycoplasma ovipneumoniae infection in United States domestic sheep operations.

Association of Mycoplasma ovipneumoniae with pneumonia in domestic small ruminants has been described in Europe, Asia, and New Zealand but has received less attention in the United States. In 2011, the US Department of Agriculture's National Animal Health Monitoring System detected M. ovipneumoniae shedding in 88% of 453 domestic sheep operations tested in 22 states that accounted for 85.5% of US ewe inventory in 2001. We evaluated factors associated with M. ovipneumoniae infection presence and prevalence, and we compared health, lamb production, and ewe losses in infected and uninfected operations. M. ovipneumoniae detection was more common in larger operations than in smaller operations. Both likelihood of detection (at the operation level) and within-operation prevalence were higher in operations with more open management practices than in operations with more closed management practices. M. ovipneumoniae-positive operations showed significantly lower lambing rates and lower rates of lamb survival to weaning after accounting for differences in operation size and management practice. While its effect on any single rate was not particularly large, in aggregate we estimated that M. ovipneumoniae presence was associated with an approximately 4.3% reduction in annual lamb production.

A pilot study of the effects of Mycoplasma ovipneumoniae exposure on domestic lamb growth and performance.

Mycoplasma ovipneumoniae is a globally distributed pathogen that has been associated with pneumonia in both domestic and wild Caprinae. It is closely related to M. hyopneumoniae, a respiratory pathogen of swine that is associated with decreased growth rates of pigs as well as clinical respiratory disease. In order to assess the effects of M. ovipneumoniae on lamb performance, we generated a cohort of lambs free of M. ovipneumoniae by segregation of test negative ewes after lambing, then compared the growth and carcass quality traits of M. ovipneumoniae-free and -colonized lambs from weaning to harvest. Some signs of respiratory disease were observed during the feeding trial in both lamb groups, but the M. ovipneumoniae-exposed group included more affected lambs and higher average disease scores. At harvest, lungs of lambs in both groups showed few grossly visible lesions, although the M. ovipneumoniae-exposed group did exhibit increased microscopic lung lesions (P<0.05). In addition, M. ovipneumoniae exposed lambs produced lower average daily gains (P<0.05), and lower yield grade carcasses (P<0.05) compared to those of non-exposed lambs. The results demonstrated the feasibility of test and segregation for elimination of M. ovipneumoniae from groups of sheep and suggested that this pathogen may impair lamb growth and productivity even in the absence of overt respiratory disease.

The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts.

Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.