Social networks and the spread of Salmonella in a sleepy lizard population

The ontogeny of social networks in wild great tits (Parus major)

Sociality impacts many biological processes and can be tightly linked to an individual's fitness. To maximize the advantages of group living, many social animals prefer to associate with individuals that provide the most benefits, such as kin, familiar individuals, or those of similar phenotypes. Such social strategies are not necessarily stable over time but can vary with changing selection pressures. In particular, young individuals transitioning to independence should continuously adjust their social behavior in light of developmental changes. However, social strategies exhibited during adolescence in animals are understudied, and the factors underlying social network formation during ontogeny remain elusive. Here, we tracked associations of wild great tits (Parus major) during the transition to independence and across their first year of life. Both spatial and social factors predicted dyadic associations. During the transition to independence in spring, fledglings initially preferred to associate with siblings and peers over non-parent adults. We found no evidence for preferred associations among juveniles of similar age or fledge weight during that time but weak evidence for some potential inheritance of the parental social network. By autumn, after juveniles had reached full independence, they exhibited social strategies similar to those of adults by establishing stable social ties based on familiarity that persisted through winter into the next spring. Overall, this research demonstrates dynamic changes in social networks during ontogeny in a species with a fast life history and limited parental care, which likely reflect changes in selective pressures. It further highlights the importance of long-term social bonds based on familiarity in this species.

Fission-fusion dynamics in the social networks of a North American pitviper

Many animal species exist in fission-fusion societies, where the size and composition of conspecific groups change spatially and temporally. To help investigate such phenomena, social network analysis (SNA) has emerged as a powerful conceptual and analytical framework for assessing patterns of interconnectedness and quantifying group-level interactions. We leveraged behavioral observations via radiotelemetry and genotypic data from a long-term (>10 years) study on the pitviper Crotalus atrox (western diamondback rattlesnake) and used SNA to quantify the first robust demonstration of social network structures for any free-living snake. Group-level interactions among adults in this population resulted in structurally modular networks (i.e., distinct clusters of interacting individuals) for fidelis use of communal winter dens (denning network), mating behaviors (pairing network), and offspring production (parentage network). Although the structure of each network was similar, the size and composition of groups varied among them. Specifically, adults associated with moderately sized social groups at winter dens but often engaged in reproductive behaviors-both at and away from dens-with different and fewer partners. Additionally, modules formed by individuals in the pairing network were frequently different from those in the parentage network, likely due to multiple mating, long-term sperm storage by females, and resultant multiple paternity. Further evidence for fission-fusion dynamics exhibited by this population-interactions were rare when snakes were dispersing to and traversing their spring-summer home ranges (to which individuals show high fidelity), despite ample opportunities to associate with numerous conspecifics that had highly overlapping ranges. Taken together, we show that long-term datasets incorporating SNA with spatial and genetic information provide robust and unique insights to understanding the social structure of cryptic taxa that are understudied.

Perturbation theory for evolution of cooperation on networks

Network structure is a mechanism for promoting cooperation in social dilemma games. In the present study, we explore graph surgery, i.e., to slightly perturb the given network, towards a network that better fosters cooperation. To this end, we develop a perturbation theory to assess the change in the propensity of cooperation when we add or remove a single edge to/from the given network. Our perturbation theory is for a previously proposed random-walk-based theory that provides the threshold benefit-to-cost ratio, [Formula: see text], which is the value of the benefit-to-cost ratio in the donation game above which the cooperator is more likely to fixate than in a control case, for any finite networks. We find that [Formula: see text] decreases when we remove a single edge in a majority of cases and that our perturbation theory captures at a reasonable accuracy which edge removal makes [Formula: see text] small to facilitate cooperation. In contrast, [Formula: see text] tends to increase when we add an edge, and the perturbation theory is not good at predicting the edge addition that changes [Formula: see text] by a large amount. Our perturbation theory significantly reduces the computational complexity for calculating the outcome of graph surgery.

Flocking dynamics mediated by weighted social networks

We study the effects of animal social networks with a weighted pattern of interactions on the flocking transition exhibited by models of self-organized collective motion. We consider variations of traditional models of collective motion in which interactions between individuals are mediated by static complex weighted networks, representing patterns of social interactions. For a model representing dynamics on a one-dimensional substrate, application of a heterogeneous mean-field theory provides a phase diagram as function of the heterogeneity of the network connections and the correlations between weights and degree. In this diagram we observe two phases, one corresponding to the presence of a transition and other to a transition suppressed in an always ordered system, already observed in the nonweighted case. Interestingly, a third phase, with no transition in an always disordered state, is also obtained. These predictions, numerically recovered in computer simulations, are also fulfilled for the more realistic Vicsek model, with movement in a two-dimensional space. Additionally, we observe at finite network sizes the presence of a maximum threshold for particular weight configurations, indicating that it is possible to tune weights to achieve a maximum resilience to noise effects. Simulations in real weighted animal social networks show that, in general, the presence of weights diminishes the value of the flocking threshold, thus increasing the fragility of the flocking state. The shift in the threshold is observed to depend on the heterogeneity of the weight pattern.

Apathogenic proxies for transmission dynamics of a fatal virus

Identifying drivers of transmission-especially of emerging pathogens-is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission-even based on analogously-transmitted, apathogenic agents-in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

Mean-field theory for double-well systems on degree-heterogeneous networks

Many complex dynamical systems in the real world, including ecological, climate, financial and power-grid systems, often show critical transitions, or tipping points, in which the system’s dynamics suddenly transit into a qualitatively different state. In mathematical models, tipping points happen as a control parameter gradually changes and crosses a certain threshold. Tipping elements in such systems may interact with each other as a network, and understanding the behaviour of interacting tipping elements is a challenge because of the high dimensionality originating from the network. Here, we develop a degree-based mean-field theory for a prototypical double-well system coupled on a network with the aim of understanding coupled tipping dynamics with a low-dimensional description. The method approximates both the onset of the tipping point and the position of equilibria with a reasonable accuracy. Based on the developed theory and numerical simulations, we also provide evidence for multistage tipping point transitions in networks of double-well systems.

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Network models are widely used to understand and predict infectious disease spread in human and animal populations. However, the choice of network model often relies on subjective expert knowledge or disease transmission experiments that are time-consuming and difficult to perform. We developed a novel tool, called INoDS (Identifying contact Networks of infectious Disease Spread), that uses robust statistical approach to establish relevance of a network model in explaining transmission pathways of an infectious disease outbreak. We used computer simulations and real-world dataset to test the accuracy of our tool and robustness to missing data. We found that INoDS is robust to common data-collection constraints, broadly applicable and accurate compared to current approaches. The tool that we have developed can therefore provide immediate epidemiological insights in the event of an epidemic outbreak, and can be used to improve targeted disease control.

Respiratory Disease Risk of Zoo-Housed Bonobos Is Associated with Sex and Betweenness Centrality in the Proximity Network

Infectious diseases can be considered a threat to animal welfare and are commonly spread through both direct and indirect social interactions with conspecifics. This is especially true for species with complex social lives, like primates. While several studies have investigated the impact of sociality on disease risk in primates, only a handful have focused on respiratory disease, despite it being a major cause of morbidity and mortality in both wild and captive populations and thus an important threat to primate welfare. Therefore, we examined the role of social-network position on the occurrence of respiratory disease symptoms during one winter season in a relatively large group of 20 zoo-housed bonobos with managed fission-fusion dynamics. We found that within the proximity network, symptoms were more likely to occur in individuals with higher betweenness centrality, which are individuals that form bridges between different parts of the network. Symptoms were also more likely to occur in males than in females, independent of their social-network position. Taken together, these results highlight a combined role of close proximity and sex in increased risk of attracting respiratory disease, two factors that can be taken into account for further welfare management of the species.

The Potential for Physiological Performance Curves to Shape Environmental Effects on Social Behavior

As individual animals are exposed to varying environmental conditions, phenotypic plasticity will occur in a vast array of physiological traits. For example, shifts in factors such as temperature and oxygen availability can affect the energy demand, cardiovascular system, and neuromuscular function of animals that in turn impact individual behavior. Here, we argue that nonlinear changes in the physiological traits and performance of animals across environmental gradients—known as physiological performance curves—may have wide-ranging effects on the behavior of individual social group members and the functioning of animal social groups as a whole. Previous work has demonstrated how variation between individuals can have profound implications for socially living animals, as well as how environmental conditions affect social behavior. However, the importance of variation between individuals in how they respond to changing environmental conditions has so far been largely overlooked in the context of animal social behavior. First, we consider the broad effects that individual variation in performance curves may have on the behavior of socially living animals, including: (1) changes in the rank order of performance capacity among group mates across environments; (2) environment-dependent changes in the amount of among- and within-individual variation, and (3) differences among group members in terms of the environmental optima, the critical environmental limits, and the peak capacity and breadth of performance. We then consider the ecological implications of these effects for a range of socially mediated phenomena, including within-group conflict, within- and among group assortment, collective movement, social foraging, predator-prey interactions and disease and parasite transfer. We end by outlining the type of empirical work required to test the implications for physiological performance curves in social behavior.

Rapid transmission of respiratory infections within but not between mountain gorilla groups

Minimizing disease transmission between humans and wild apes and controlling outbreaks in ape populations is vital to both ape conservation and human health, but information on the transmission of real infections in wild populations is rare. We analyzed respiratory outbreaks in a subpopulation of wild mountain gorillas (Gorilla beringei beringei) between 2004 and 2020. We investigated transmission within groups during 7 outbreaks using social networks based on contact and proximity, and transmission between groups during 15 outbreaks using inter-group encounters, transfers and home range overlap. Patterns of contact and proximity within groups were highly predictable based on gorillas' age and sex. Disease transmission within groups was rapid with a median estimated basic reproductive number (R0) of 4.18 (min = 1.74, max = 9.42), and transmission was not predicted by the social network. Between groups, encounters and transfers did not appear to have enabled disease transmission and the overlap of groups' ranges did not predict concurrent outbreaks. Our findings suggest that gorilla social structure, with many strong connections within groups and weak ties between groups, may enable rapid transmission within a group once an infection is present, but limit the transmission of infections between groups.

Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission

Enteric microparasites like Escherichia coli use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like E. coli spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal E. coli transmission experiments in two cattle (Bos taurus) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioural variables on weekly E. coli colonization risk in these populations. We found that sociobehavioural variables alone were ultimately poor predictors of E. coli colonization in feedlot cattle, but can have significant effects on colonization hazard rates (p ≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioural effects on microparasite transmission.

Towards a more healthy conservation paradigm: integrating disease and molecular ecology to aid biological conservation†

Parasites, and the diseases they cause, are important from an ecological and evolutionary perspective because they can negatively affect host fitness and can regulate host populations. Consequently, conservation biology has long recognized the vital role that parasites can play in the process of species endangerment and recovery. However, we are only beginning to understand how deeply parasites are embedded in ecological systems, and there is a growing recognition of the important ways in which parasites affect ecosystem structure and function. Thus, there is an urgent need to revisit how parasites are viewed from a conservation perspective and broaden the role that disease ecology plays in conservation-related research and outcomes. This review broadly focusses on the role that disease ecology can play in biological conservation. Our review specifically emphasizes on how the integration of tools and analytical approaches associated with both disease and molecular ecology can be leveraged to aid conservation biology. Our review first concentrates on disease-mediated extinctions and wildlife epidemics. We then focus on elucidating how host–parasite interactions has improved our understanding of the eco-evolutionary dynamics affecting hosts at the individual, population, community and ecosystem scales. We believe that the role of parasites as drivers and indicators of ecosystem health is especially an exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation. The review concludes with a broad overview of the current and potential applications of modern genomic tools in disease ecology to aid biological conservation.

Bidirectional interactions between host social behaviour and parasites arise through ecological and evolutionary processes

An animal's social behaviour both influences and changes in response to its parasites. Here we consider these bidirectional links between host social behaviours and parasite infection, both those that occur from ecological vs evolutionary processes. First, we review how social behaviours of individuals and groups influence ecological patterns of parasite transmission. We then discuss how parasite infection, in turn, can alter host social interactions by changing the behaviour of both infected and uninfected individuals. Together, these ecological feedbacks between social behaviour and parasite infection can result in important epidemiological consequences. Next, we consider the ways in which host social behaviours evolve in response to parasites, highlighting constraints that arise from the need for hosts to maintain benefits of sociality while minimizing fitness costs of parasites. Finally, we consider how host social behaviours shape the population genetic structure of parasites and the evolution of key parasite traits, such as virulence. Overall, these bidirectional relationships between host social behaviours and parasites are an important yet often underappreciated component of population-level disease dynamics and host-parasite coevolution.

Social Network Predicts Exposure to Respiratory Infection in a Wild Chimpanzee Group

Respiratory pathogens are expected to spread through social contacts, but outbreaks often occur quickly and unpredictably, making it challenging to simultaneously record social contact and disease incidence data, especially in wildlife. Thus, the role of social contacts in the spread of infectious disease is often treated as an assumption in disease simulation studies, and few studies have empirically demonstrated how pathogens spread through social networks. In July-August 2015, an outbreak of respiratory disease was observed in a wild chimpanzee community in Kibale National Park, Uganda, during an ongoing behavioral study of male chimpanzees, offering a rare opportunity to evaluate how social behavior affects individual exposure to socially transmissible diseases. From May to August 2015, we identified adult and adolescent male chimpanzees displaying coughs and rhinorrhea and recorded 5-m proximity data on males (N = 40). Using the network k-test, we found significant relationships between male network connectivity and the distribution of cases within the network, supporting the importance of short-distance contacts for the spread of the respiratory outbreak. Additionally, chimpanzees central to the network were more likely to display clinical signs than those with fewer connections. Although our analyses were limited to male chimpanzees, these findings underscore the value of social connectivity data in predicting disease outcomes and elucidate a potential evolutionary cost of being social.

Commensal bacterial sharing does not predict host social associations in kangaroos

Social network analysis has been postulated as a tool to study potential pathogen transmission in wildlife but is resource-intensive to quantify. Networks based on bacterial genotypes have been proposed as a cost-effective method for estimating social or transmission network based on the assumption that individuals in close contact will share commensal bacteria. However, the use of network analysis to study wild populations requires critical evaluation of the assumptions and parameters these models are founded on. We test (a) whether networks of commensal bacterial sharing are related to hosts' social associations and hence could act as a proxy for estimating transmission networks, (b) how the parameters chosen to define host associations and delineate bacterial genotypes impact inference and (c) whether these relationships change across time. We use stochastic simulations to evaluate how uncertainty in parameter choice affects network structure. We focused on a well-studied population of eastern grey kangaroos (Macropus giganteus), from Sundown National Park, Australia. Using natural markings, each individual was identified and its associations with other kangaroos recorded through direct field observations over 2 years to construct social networks. Faecal samples were collected, Escherichia coli was cultured and genotyped using BOX-PCR, and bacterial networks were constructed. Two individuals were connected in the bacterial network if they shared at least one E. coli genotype. We determined the capacity of bacterial networks to predict the observed social network structure in each year. We found little support for a relationship between social association and dyadic commensal bacterial similarity. Thresholds to determine host associations and similarity cut-off values used to define E. coli genotypes had important ramifications for inferring links between individuals. In fact, we found that inferences can show opposite patterns based on the chosen thresholds. Moreover, no similarity in overall bacterial network structure was detected between years. Although empirical disease transmission data are often unavailable in wildlife populations, both bacterial networks and social networks have limitations in representing the mode of transmission of a pathogen. Our results suggest that caution is needed when designing such studies and interpreting results.

Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal

Many animals socialize in two or more major ecological contexts. In nature, these contexts often involve one situation in which space is more constrained (e.g. shared refuges, sleeping cliffs, nests, dens or burrows) and another situation in which animal movements are relatively free (e.g. in open spaces lacking architectural constraints). Although it is widely recognized that an individual's characteristics may shape its social life, the extent to which architecture constrains social decisions within and between habitats remains poorly understood. Here we developed a novel, automated-monitoring system to study the effects of personality, life-history stage and sex on the social network structure of a facultatively social mammal, the California ground squirrel (Otospermophilus beecheyi) in two distinct contexts: aboveground where space is relatively open and belowground where it is relatively constrained by burrow architecture. Aboveground networks reflected affiliative social interactions whereas belowground networks reflected burrow associations. Network structure in one context (belowground), along with preferential juvenile-adult associations, predicted structure in a second context (aboveground). Network positions of individuals were generally consistent across years (within contexts) and between ecological contexts (within years), suggesting that individual personalities and behavioural syndromes, respectively, contribute to the social network structure of these free-living mammals. Direct ties (strength) tended to be stronger in belowground networks whereas more indirect paths (betweenness centrality) flowed through individuals in aboveground networks. Belowground, females fostered significantly more indirect paths than did males. Our findings have important potential implications for disease and information transmission, offering new insights into the multiple factors contributing to social structures across ecological contexts.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

Assessing Behavioral Associations in a Hybrid Zone through Social Network Analysis: Complex Assortative Behaviors Structure Associations in a Hybrid Quail Population

Behavior can strongly influence rates and patterns of hybridization between animal populations and species. Yet few studies have examined reproductive behaviors in natural hybrid zones within the fine-scale social context in which they naturally occur. We use radio-frequency identification tags with social network analyses to test whether phenotypic similarity in plumage and mass correlate with social behavior throughout a breeding season in a California and Gambel's quail hybrid zone. We use a novel approach to partition phenotypic variation in a way that does not confound differences between sexes and species, and we illustrate the complex ways that phenotype and behavior structure the social environment, mating opportunities, and male-male associations. Associations within the admixed population were random with respect to species-specific plumage but showed strong patterns of assortment based on sexually dimorphic plumage, monomorphic plumage, and mass. Weak behavioral reproductive isolation in this admixed population may be the result of complex patterns of phenotypic assortment based on multiple traits rather than a lack of phenotypic discrimination. More generally, our results support the utility of social network analyses for analyzing behavioral factors affecting genetic exchange between populations and species.

Incorporating genomic methods into contact networks to reveal new insights into animal behavior and infectious disease dynamics

Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behavior. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behavior and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behavior and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

Assessing Transmission of Antimicrobial-Resistant Escherichia coli in Wild Giraffe Contact Networks

There is growing evidence that anthropogenic sources of antibiotics and antimicrobial-resistant bacteria can spill over into natural ecosystems, raising questions about the role wild animals play in the emergence, maintenance, and dispersal of antibiotic resistance genes. In particular, we lack an understanding of how resistance genes circulate within wild animal populations, including whether specific host characteristics, such as social associations, promote interhost transmission of these genes. In this study, we used social network analysis to explore the forces shaping population-level patterns of resistant Escherichia coli in wild giraffe (Giraffa camelopardalis) and assess the relative importance of social contact for the dissemination of resistant E. coli between giraffe. Of 195 giraffe sampled, only 5.1% harbored E. coli isolates resistant to one or more tested antibiotics. Whole-genome sequencing on a subset of resistant isolates revealed a number of acquired resistance genes with linkages to mobile genetic elements. However, we found no evidence that the spread of resistance genes among giraffe was facilitated by interhost associations. Giraffe with lower social degree were more likely to harbor resistant E. coli, but this relationship was likely driven by a correlation between an individual's social connectedness and age. Indeed, resistant E. coli was most frequently detected in socially isolated neonates, indicating that resistant E. coli may have a selective advantage in the gastrointestinal tracts of neonates compared to other age classes. Taken together, these results suggest that the maintenance of antimicrobial-resistant bacteria in wild populations may, in part, be determined by host traits and microbial competition dynamics within the host.IMPORTANCE Antimicrobial resistance represents a significant threat to human health, food security, and the global economy. To fully understand the evolution and dissemination of resistance genes, a complete picture of antimicrobial resistance in all biological compartments, including natural ecosystems, is required. The environment and wild animals may act as reservoirs for anthropogenically derived resistance genes that could be transferrable to clinically relevant bacteria of humans and domestic animals. Our study investigated the possible transmission mechanisms for antimicrobial-resistant bacteria within a wild animal population and, more broadly, contributes to our understanding of how resistance genes are spread and maintained in natural ecosystems.

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

Landscape genetics has provided many insights into how heterogeneous landscape features drive processes influencing spatial genetic variation in free-living organisms. This rapidly developing field has focused heavily on vertebrates, and expansion of this scope to the study of infectious diseases holds great potential for landscape geneticists and disease ecologists alike. The potential application of landscape genetics to infectious agents has garnered attention at formative stages in the development of landscape genetics, but systematic examination is lacking. We comprehensively review how landscape genetics is being used to better understand pathogen dynamics. We characterize the field and evaluate the types of questions addressed, approaches used and systems studied. We also review the now established landscape genetic methods and their realized and potential applications to disease ecology. Lastly, we identify emerging frontiers in the landscape genetic study of infectious agents, including recent phylogeographic approaches and frameworks for studying complex multihost and host-vector systems. Our review emphasizes the expanding utility of landscape genetic methods available for elucidating key pathogen dynamics (particularly transmission and spread) and also how landscape genetic studies of pathogens can provide insight into host population dynamics. Through this review, we convey how increasing awareness of the complementarity of landscape genetics and disease ecology among practitioners of each field promises to drive important cross-disciplinary advances.

Exploratory behavior is linked to stress physiology and social network centrality in free-living house finches (Haemorhous mexicanus)

Animal personality has been linked to individual variation in both stress physiology and social behaviors, but few studies have simultaneously examined covariation between personality traits, stress hormone levels, and behaviors in free-living animals. We investigated relationships between exploratory behavior (one aspect of animal personality), stress physiology, and social and foraging behaviors in wild house finches (Haemorhous mexicanus). We conducted novel environment assays after collecting samples of baseline and stress-induced plasma corticosterone concentrations from a subset of house finches. We then fitted individuals with Passive Integrated Transponder tags and monitored feeder use and social interactions at radio-frequency identification equipped bird feeders. First, we found that individuals with higher baseline corticosterone concentrations exhibit more exploratory behaviors in a novel environment. Second, more exploratory individuals interacted with more unique conspecifics in the wild, though this result was stronger for female than for male house finches. Third, individuals that were quick to begin exploring interacted more frequently with conspecifics than slow-exploring individuals. Finally, exploratory behaviors were unrelated to foraging behaviors, including the amount of time spent on bird feeders, a behavior previously shown to be predictive of acquiring a bacterial disease that causes annual epidemics in house finches. Overall, our results indicate that individual differences in exploratory behavior are linked to variation in both stress physiology and social network traits in free-living house finches. Such covariation has important implications for house finch ecology, as both traits can contribute to fitness in the wild.

Social networks: a tool for assessing the impact of perturbations on wildlife behaviour and implications for pathogen transmission

Wildlife are increasingly subject to perturbations, which can impact pathogen transmission and lead to disease emergence. While a myriad of factors influence disease dynamics in wildlife, behaviour is emerging as a major influence. In this review, we examine how perturbations alter the behaviour of individuals and how, in turn, disease transmission may be impacted, with a focus on the use of network models as a powerful tool. There are emerging hypotheses as to how networks respond to different types of perturbations. The broad effects of perturbations make predicting potential outcomes and identifying mitigation opportunities for disease emergence critical; yet, the current paucity of data makes identification of underlying trends difficult. Social network analysis facilitates a mechanistic approach to how perturbation-induced behavioural changes result in shifts in pathogen transmission. However, the field is still developing, and future work should strive to address current deficits. There is particular need for empirical data to support modelling predictions and increased inclusion of pathogen monitoring in network studies.

Advances from the nexus of animal behaviour and pathogen transmission: new directions and opportunities using contact networks

Contact network models have enabled significant advances in understanding the influence of behaviour on parasite and pathogen transmission. They are an important tool that links variation in individual behaviour, to epidemiological consequences at the population level. Here, in our introduction to this special issue, we highlight the importance of applying network approaches to disease ecological and epidemiological questions, and how this has provided a much deeper understanding of these research areas. Recent advances in tracking host behaviour (bio-logging: e.g., GPS tracking, barcoding) and tracking pathogens (high-resolution sequencing), as well as methodological advances (multi-layer networks, computational techniques) started producing exciting new insights into disease transmission through contact networks. We discuss some of the exciting directions that the field is taking, some of the challenges, and importantly the opportunities that lie ahead. For instance, we suggest to integrate multiple transmission pathways, multiple pathogens, and in some systems, multiple host species, into the next generation of network models. Corresponding opportunities exist in utilising molecular techniques, such as high-resolution sequencing, to establish causality in network connectivity and disease outcomes. Such novel developments and the continued integration of network tools offers a more complete understanding of pathogen transmission processes, their underlying mechanisms and their evolutionary consequences.

Infectious Disease in Wild Animal Populations: Examining Transmission and Control with Mathematical Models

The mathematical modeling of ecological interactions is an essential tool in predicting the behavior of complex systems across landscapes. The scientific literature is growing with examples of models used to explore predator-prey interactions, resource selection, population growth, and dynamics of disease transmission. These models provide managers with an efficient alternative means of testing new management and control strategies without resorting to empirical testing that is often costly, time-consuming, and impractical. This chapter presents a review of four types of mathematical models used to understand and predict the spread of infectious diseases in wild animals: compartmental, metapopulation, spatial, and contact network models. Descriptions of each model’s uses and limitations are used to provide a look at the complexities involved in modeling the spread of diseases and the trade-offs that accompany selecting one modeling approach over another. Potential avenues for the improvement and use of these models in future studies are also discussed, as are specific examples of how each type of model has improved our understanding of infectious diseases in populations of wild animals.

Hierarchical social networks shape gut microbial composition in wild Verreaux's sifaka

In wild primates, social behaviour influences exposure to environmentally acquired and directly transmitted microorganisms. Prior studies indicate that gut microbiota reflect pairwise social interactions among chimpanzee and baboon hosts. Here, we demonstrate that higher-order social network structure-beyond just pairwise interactions-drives gut bacterial composition in wild lemurs, which live in smaller and more cohesive groups than previously studied anthropoid species. Using 16S rRNA gene sequencing and social network analysis of grooming contacts, we estimate the relative impacts of hierarchical (i.e. multilevel) social structure, individual demographic traits, diet, scent-marking, and habitat overlap on bacteria acquisition in a wild population of Verreaux's sifaka (Propithecus verreauxi) consisting of seven social groups. We show that social group membership is clearly reflected in the microbiomes of individual sifaka, and that social groups with denser grooming networks have more homogeneous gut microbial compositions. Within social groups, adults, more gregarious individuals, and individuals that scent-mark frequently harbour the greatest microbial diversity. Thus, the community structure of wild lemurs governs symbiotic relationships by constraining transmission between hosts and partitioning environmental exposure to microorganisms. This social cultivation of mutualistic gut flora may be an evolutionary benefit of tight-knit group living.

Genomic Epidemiology and Management of Salmonella in Island Ecosystems Used for Takahe Conservation

Translocation and isolation of threatened wildlife in new environments may have unforeseen consequences on pathogen transmission and evolution in host populations. Disease threats associated with intensive conservation management of wildlife remain speculative without gaining an understanding of pathogen dynamics in meta-populations and how location attributes may determine pathogen prevalence. We determined the prevalence and population structure of an opportunistic pathogen, Salmonella, in geographically isolated translocated sub-populations of an endangered New Zealand flightless bird, the takahe (Porphyrio hochstetteri). Out of the nine sub-populations tested, Salmonella was only isolated from takahe living on one private island. The apparent prevalence of Salmonella in takahe on the private island was 32% (95% CI 13-57%), with two serotypes, Salmonella Mississippi and Salmonella houtenae 40:gt-, identified. Epidemiological investigation of reservoirs on the private island and another island occupied by takahe identified environmental and reptile sources of S. Mississippi and S. houtenae 40:gt- on the private island. Single nucleotide polymorphism analysis of core genomes revealed low-level diversity among isolates belonging to the same serotype and little differentiation according to host and environmental source. The pattern observed may be representative of transmission between sympatric hosts and environmental sources, the presence of a common unsampled source, and/or evidence of a recent introduction into the ecosystem. This study highlights how genomic epidemiology can be used to ascertain and understand disease dynamics to inform the management of disease threats in endangered wildlife populations.

A Review of Salmonella and Squamates (Lizards, Snakes and Amphisbians): Implications for Public Health

Globally, there has been an increase in squamates (particularly lizards and snakes) being kept as pets. Additionally, urban spread has resulted in greater human encroachment and interaction with the natural habitat of wild squamates. A potential consequence of increasing human interaction with squamates is the increased potential for disease transfer. This review collates the literature describing clinical salmonellosis cases that were definitively linked to a squamate through testing of the animal and population-based studies which investigate the risk of salmonellosis linked to pet squamates. It was demonstrated that although squamate-acquired salmonellosis accounted for a small percentage of total cases, children under five were at greatest risk, with the clinical manifestations tending to be more severe. In many cases, it was noted that the patient was unaware of the risks associated with keeping squamates and did not practice proper hand hygiene after handling the animals or cleaning cages. This highlights the need for more education focused on informing the general public of ways to reduce the risk of salmonellosis from pet squamates. There is also the need for future research into the role of wild squamates in the spread of human salmonellosis, both directly and indirectly through cross contamination.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Analysing animal social network dynamics: the potential of stochastic actor-oriented models

Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic.

There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks.

We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study.

SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships.

Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.

Pair Housing of Dairy Calves and Age at Pairing: Effects on Weaning Stress, Health, Production and Social Networks

The early social environment can influence the health and behaviour of animals, with effects lasting into adulthood. In Europe, around 60% of dairy calves are reared individually during their first eight weeks of life, while others may be housed in pairs or small groups. This study assessed the effects of varying degrees of social contact on weaning stress, health and production during pen rearing, and on the social networks that calves later formed when grouped. Forty female Holstein-Friesian calves were allocated to one of three treatments: individually housed (I, n = 8), pair-housed from day five (P5, n = 8 pairs), and pair-housed from day 28 (P28, n = 8 pairs). From day 48, calves were weaned by gradual reduction of milk over three days, and vocalisations were recorded as a measure of stress for three days before, during and after weaning. Health and production (growth rate and concentrate intakes) were not affected by treatment during the weaning period or over the whole study. Vocalisations were highest post-weaning, and were significantly higher in I calves than pair-reared calves. Furthermore, P28 calves vocalised significantly more than P5 calves. The social network of calves was measured for one month after all calves were grouped in a barn, using association data from spatial proximity loggers. We tested for week-week stability, social differentiation and assortment in the calf network. Additionally, we tested for treatment differences in: coefficient of variation (CV) in association strength, percentage of time spent with ex-penmate (P5 and P28 calves only) and weighted degree centrality (the sum of the strength of an individual’s associations). The network was relatively stable from weeks one to four and was significantly differentiated, with individuals assorting based on prior familiarity. P5 calves had significantly higher CV in association strength than I calves in week one (indicating more heterogeneous social associations) but there were no significant treatment differences in week four. The mean percentage of time that individuals spent with their ex-penmate after regrouping decreased from weeks 1–4, though treatment did not affect this. There were also no significant differences in weighted degree centrality between calves in each rearing treatment. These results suggest that early pair-rearing can allow calves the stress buffering benefits of social support (and that this is more effective when calves are paired earlier) without compromising health or production, and sheds light on the early development of social behaviour in cattle.

Discovery and Partial Genomic Characterisation of a Novel Nidovirus Associated with Respiratory Disease in Wild Shingleback Lizards (Tiliqua rugosa)

A respiratory disease syndrome has been observed in large numbers of wild shingleback lizards (Tiliqua rugosa) admitted to wildlife care facilities in the Perth metropolitan region of Western Australia. Mortality rates are reportedly high without supportive treatment and care. Here we used next generation sequencing techniques to screen affected and unaffected individuals admitted to Kanyana Wildlife Rehabilitation Centre in Perth between April and December 2015, with the resultant discovery of a novel nidovirus significantly associated with cases of respiratory disease according to a case definition based on clinical signs. Interestingly this virus was also found in 12% of apparently healthy individuals, which may reflect testing during the incubation period or a carrier status, or it may be that this agent is not causative in the disease process. This is the first report of a nidovirus in lizards globally. In addition to detection of this virus, characterisation of a 23,832 nt segment of the viral genome revealed the presence of characteristic nidoviral genomic elements providing phylogenetic support for the inclusion of this virus in a novel genus alongside Ball Python nidovirus, within the Torovirinae sub-family of the Coronaviridae. This study highlights the importance of next generation sequencing technologies to detect and describe emerging infectious diseases in wildlife species, as well as the importance of rehabilitation centres to enhance early detection mechanisms through passive and targeted health surveillance. Further development of diagnostic tools from these findings will aid in detection and control of this agent across Australia, and potentially in wild lizard populations globally.

Feeder use predicts both acquisition and transmission of a contagious pathogen in a North American songbird

Individual heterogeneity can influence the dynamics of infectious diseases in wildlife and humans alike. Thus, recent work has sought to identify behavioural characteristics that contribute disproportionately to individual variation in pathogen acquisition (super-receiving) or transmission (super-spreading). However, it remains unknown whether the same behaviours enhance both acquisition and transmission, a scenario likely to result in explosive epidemics. Here, we examined this possibility in an ecologically relevant host-pathogen system: house finches and their bacterial pathogen, Mycoplasma gallisepticum, which causes severe conjunctivitis. We examined behaviours likely to influence disease acquisition (feeder use, aggression, social network affiliations) in an observational field study, finding that the time an individual spends on bird feeders best predicted the risk of conjunctivitis. To test whether this behaviour also influences the likelihood of transmitting M. gallisepticum, we experimentally inoculated individuals based on feeding behaviour and tracked epidemics within captive flocks. As predicted, transmission was fastest when birds that spent the most time on feeders initiated the epidemic. Our results suggest that the same behaviour underlies both pathogen acquisition and transmission in this system and potentially others. Identifying individuals that exhibit such behaviours is critical for disease management.

Wild cricket social networks show stability across generations

BACKGROUND

A central part of an animal's environment is its interactions with conspecifics. There has been growing interest in the potential to capture these interactions in the form of a social network. Such networks can then be used to examine how relationships among individuals affect ecological and evolutionary processes. However, in the context of selection and evolution, the utility of this approach relies on social network structures persisting across generations. This is an assumption that has been difficult to test because networks spanning multiple generations have not been available. We constructed social networks for six annual generations over a period of eight years for a wild population of the cricket Gryllus campestris.

RESULTS

Through the use of exponential random graph models (ERGMs), we found that the networks in any given year were able to predict the structure of networks in other years for some network characteristics. The capacity of a network model of any given year to predict the networks of other years did not depend on how far apart those other years were in time. Instead, the capacity of a network model to predict the structure of a network in another year depended on the similarity in population size between those years.

CONCLUSIONS

Our results indicate that cricket social network structure resists the turnover of individuals and is stable across generations. This would allow evolutionary processes that rely on network structure to take place. The influence of network size may indicate that scaling up findings on social behaviour from small populations to larger ones will be difficult. Our study also illustrates the utility of ERGMs for comparing networks, a task for which an effective approach has been elusive.

Social inheritance can explain the structure of animal social networks

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

Microbial Genomics of a Host-Associated Commensal Bacterium in Fragmented Populations of Endangered Takahe

Isolation of wildlife into fragmented populations as a consequence of anthropogenic-mediated environmental change may alter host-pathogen relationships. Our understanding of some of the epidemiological features of infectious disease in vulnerable populations can be enhanced by the use of commensal bacteria as a proxy for invasive pathogens in natural ecosystems. The distinctive population structure of a well-described meta-population of a New Zealand endangered flightless bird, the takahe (Porphyrio hochstetteri), provided a unique opportunity to investigate the influence of host isolation on enteric microbial diversity. The genomic epidemiology of a prevalent rail-associated endemic commensal bacterium was explored using core genome and ribosomal multilocus sequence typing (rMLST) of 70 Campylobacter sp. nova 1 isolated from one third of the takahe population resident in multiple locations. While there was evidence of recombination between lineages, bacterial divergence appears to have occurred and multivariate analysis of 52 rMLST genes revealed location-associated differentiation of C. sp. nova 1 sequence types. Our results indicate that fragmentation and anthropogenic manipulation of populations can influence host-microbial relationships, with potential implications for niche adaptation and the evolution of micro-organisms in remote environments. This study provides a novel framework in which to explore the complex genomic epidemiology of micro-organisms in wildlife populations.