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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'. [ABSTRACT FROM AUTHOR]

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'. [ABSTRACT FROM AUTHOR]

Human disturbance is contributing to widespread, global changes in the distributions and densities of wild animals. These anthropogenic impacts on wildlife arise from multiple bottom-up and top-down pathways, including habitat loss, resource provisioning, climate change, pollution, infrastructure development, hunting and our direct presence. Animal behaviour is an important mechanism linking these disturbances to population outcomes, although these behavioural pathways are often complex and can remain obscured when different aspects of behaviour are studied in isolation from one another. The spatial–social interface provides a lens for understanding how an animal's spatial and social environments interact to determine its spatial and social phenotype (i.e. measurable characteristics of an individual), and how these phenotypes interact and feed back to reshape environments. Here, we review studies of animal behaviour at the spatial–social interface to understand and predict how human disturbance affects animal movement, distribution and intraspecific interactions, with consequences for the conservation of populations and ecosystems. By understanding the spatial–social mechanisms linking human disturbance to conservation outcomes, we can better design management interventions to mitigate undesired consequences of disturbance. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

Epizootic events of pneumonia, presumably caused by Mycoplasma ovipneumoniae, in bighorn sheep (Ovis canadensis) have been observed in the western United States and Canada. Until recently, it was thought that populations of Mexican (O. c. mexicana) and Nelson's (O. c. nelsoni) desert bighorn sheep in Texas, USA, had not been exposed to Mycoplasma. Evidence of disease and potential population decline from outbreaks of M. ovipneumoniae are now known from several populations across the Trans‐Pecos Ecoregion with documented instances of pneumonia and bluetongue in desert bighorn sheep from the Van Horn Mountains and Black Gap Wildlife Management Area. These disease events, especially those in 2019–2021, may be a result of increasing populations of aoudad (Ammotragus lervia), an introduced and invasive ungulate, in the region. With large population sizes and similar movement patterns as desert bighorn sheep, aoudad potentially are the reservoirs for bacterial and viral diseases, such as pneumonia and bluetongue, and are possibly contributing to the decline of desert bighorn sheep. Herein, we optimized the multi‐locus sequence typing (MLST) with modifications in the Taq polymerase and annealing temperatures to determine the genetic identity of Mycoplasma strains or species within the nasal passages of desert bighorn sheep and aoudad in the Trans‐Pecos Ecoregion of Texas. Four loci (small ribosomal unit, 16S; 16S‐23S intergenic spacer region, IGS; RNA polymerase B, rpoB; gyrase B, gyrB) were characterized using MLST. Based on results from the modified MLST technique, we identified 9 desert bighorn sheep and 5 aoudad with M. ovipneumoniae, 9 aoudad with bacterial sequences genetically similar to M. conjunctivae, and 10 aoudad with bacterial sequences genetically similar M. hyopneumoniae. Of these, 9 aoudad possessed bacterial sequences genetically similar to both M. conjunctivae and M. hyopneumoniae. Among the 4 diagnostic loci, genetic divergence of M. ovipneumoniae ranged from 0.00–0.90% among desert bighorn sheep and aoudad. Future sampling efforts of seemingly asymptomatic aoudad, and asymptomatic, visibly sick, or deceased desert bighorn sheep, are important to monitor the spread of disease in desert bighorn sheep populations across mountain ranges in western Texas. It is imperative that aoudad removal plans are implemented to reduce and eliminate current infections and putative transmission of M. ovipneumoniae, prevent future disease outbreaks of pneumonia, and ultimately conserve desert bighorn sheep for future generations. [ABSTRACT FROM AUTHOR]

Non‐native species can affect ecosystems by influencing native predator‐prey dynamics. Therefore, management interventions designed to remove non‐natives may inadvertently lead to increased predation on native species. Feral horses are widely distributed throughout the arid parts of western North America. A growing body of research indicates that horses can be an important prey species to mountain lions in ecosystems where they overlap. In December 2020, the Bureau of Land Management removed 455 horses from the Delamar Mountains, Nevada, USA. We leveraged this management intervention to implement a before–after–control–impact study to test hypotheses about predation on horses and native ungulates. We predicted (1) that horses would comprise an important part of the diet in this mixed‐prey community, (2) following removal, the proportion of horses in the diet would decrease and native ungulates would increase, and (3) mountain lion home ranges overlapping the treatment areas would increase in response to decreased prey availability. From 2018 to 2022, we investigated 1360 clusters from 29 GPS‐collared lions and identified 1056 prey items. To model the probability of a predation event (a kill), we fit a mixed‐effects logistic regression model for ungulate prey as a function of lion sex, treatment area (in/out), and treatment period (pre‐/post‐removal). We used a log‐linear regression model to evaluate changes in home range size. The most common prey were mule deer (55%), feral horses (32%), and coyotes (4%). Twenty‐two of 29 lions consumed horses, although the rate of horse consumption was highly variable across individuals. Horses of both sexes and all age classes were predated. In contrast to predictions, our models detected no effect of removals on diet composition (βinteraction = 0.30 ± 1.1), nor did the removal influence home range size (βinteraction = 0.02 ± 0.02). Despite a 46% reduction in horse abundance, we found no evidence for prey‐switching following the horse removal treatment. Removal magnitude, rapid horse immigration, and/or behavioral specialization of individual mountain lions may help explain these results. Our findings have important implications for mountain lion and feral horse management in arid environments characterized by high prey diversity, but low prey abundance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Controlling persistent infectious disease in wildlife populations is an ongoing challenge for wildlife managers and conservationists worldwide, and chronic diseases in particular remain a pernicious problem.Here, we develop a dynamic pathogen transmission model capturing key features of Mycoplasma ovipneumoniae infection, a major cause of population declines in North American bighorn sheep Ovis canadensis. We explore the effects of model assumptions and parameter values on disease dynamics, including density‐ versus frequency‐dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease‐induced mortality and recovery rates and the epidemic growth rate. Along the way, we estimate the basic reproductive ratio, R0, for M. ovipneumoniae in bighorn sheep to fall between approximately 1.36 and 1.74.We apply the model to compare efficacies across a suite of management actions following an epidemic, including test‐and‐remove, depopulation‐and‐reintroduction, range expansion, herd augmentation and density reduction.Our results suggest that test‐and‐remove, depopulation‐and‐reintroduction and range expansion could help persistently infected bighorn sheep herds recovery following an epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Other management actions that improve host survival or reduce disease‐induced mortality are also likely to improve population size and persistence of chronically infected herds.Synthesis and applications. Dynamic transmission models like the one employed here offer a structured, logical approach for exploring hypotheses, planning field experiments and designing adaptive management. We find that management strategies that removed infected animals or isolated them within a structured metapopulation were most successful at facilitating herd recovery from a low‐prevalence, chronic pathogen. Ideally, models like ours should operate iteratively with field experiments to triangulate on better approaches for managing wildlife diseases. [ABSTRACT FROM AUTHOR]

Secondary sexual traits (e.g., horns and antlers) have ecological and evolutionary importance and are of management interest for game species. Yet, how these traits respond to emerging threats like infectious disease remains underexplored. Infectious pneumonia threatens bighorn sheep (Ovis canadensis) populations across North America and we hypothesized it may also reduce horn growth in male sheep. We assess the effect of pneumonia on horn size in male bighorn sheep using 12 herd datasets from across the western United States that had horn growth and disease data. Disease resulted in 12–35% reduction in increment (yearly) length and 3–13% reduction in total horn length in exposed individuals. The disease effect was prolonged when pathogens continued to circulate in sheep populations. Further, disease likely delays the age at which horns reach ¾‐curl and prevents achievement of full‐curl. This is further evidenced with 6 of the 12 herds experiencing an increase in average age at harvest following die‐off events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Peer-reviewed publication volume and caliber are widely-recognized proxies for academic merit, and a strong publication record is essential for academic success and advancement. However, recent work suggests that publication productivity for particular author groups may also be determined in part by implicit biases lurking in the publication pipeline. Here, we explore patterns of gender, geography, and institutional rank among authors, editorial board members, and handling editors in high-impact ecological publications during 2015 and 2016. A higher proportion of lead authors had female first names (33.9%) than editorial board members (28.9%), and the proportion of female first names among handling editors was even lower (21.1%). Female editors disproportionately edited publications with female lead authors (40.3% of publications with female lead authors were handled by female editors, though female editors handled only 34.4% of all studied publications). Additionally, ecological authors and editors were overwhelmingly from countries in the G8, and high-ranking academic institutions accounted for a large portion of both the published work, and its editorship. Editors and lead authors with female names were typically affiliated with higher-ranking institutions than their male peers. This description of author and editor features provides a baseline for benchmarking future trends in the ecological publishing culture. [ABSTRACT FROM AUTHOR]

Superspreading, the phenomenon where a small proportion of individuals contribute disproportionately to new infections, has profound effects on disease dynamics. Superspreading can arise through variation in contacts, infectiousness or infectious periods. The latter has received little attention, yet it drives the dynamics of many diseases of critical public health, livestock health and conservation concern. Here, we present rare evidence of variation in infectious periods underlying a superspreading phenomenon in a free-ranging wildlife system. We detected persistent infections of Mycoplasma ovipneumoniae, the primary causative agent of pneumonia in bighorn sheep ( Ovis canadensis), in a small number of older individuals that were homozygous at an immunologically relevant genetic locus. Interactions among age-structure, genetic composition and infectious periods may drive feedbacks in disease dynamics that determine the magnitude of population response to infection. Accordingly, variation in initial conditions may explain divergent population responses to infection that range from recovery to catastrophic decline and extirpation. [ABSTRACT FROM AUTHOR]

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains. [ABSTRACT FROM AUTHOR]

Checkpoint blockade-based immunotherapies are effective in cancers with high numbers of nonsynonymous mutations. In contrast, current paradigms suggest that such approaches will be ineffective in cancers with few nonsynonymous mutations. To examine this issue, we made use of a murine model of BCR-ABL+ B-lineage acute lymphoblastic leukemia. Using a principal component analysis, we found that robust MHC class II expression, coupled with appropriate costimulation, correlated with lower leukemic burden. We next assessed whether checkpoint blockade or therapeutic vaccination could improve survival in mice with pre-established leukemia. Consistent with the low mutation load in our leukemia model, we found that checkpoint blockade alone had only modest effects on survival. In contrast, robust heterologous vaccination with a peptide derived from the BCR-ABL fusion (BAp), a key driver mutation, generated a small population of mice that survived long-term. Checkpoint blockade strongly synergized with heterologous vaccination to enhance overall survival in mice with leukemia. Enhanced survival did not correlate with numbers of BAp:I-Ab-specific T cells, but rather with increased expression of IL-10, IL-17, and granzyme B and decreased expression of programmed death 1 on these cells. Our findings demonstrate that vaccination to key driver mutations cooperates with checkpoint blockade and allows for immune control of cancers with low nonsynonymous mutation loads. [ABSTRACT FROM AUTHOR]

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'.

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'.

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'.

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'.