Matthew J. Silk

The consequences of unidentifiable individuals for the analysis of an animal social network

Social network analysis is pervasive in understanding animal social systems, and provides information about how individuals vary in their social strategies. Many long-term studies comprising uniquely marked individuals use social network analysis as an analytical tool. However, the assumption that it is possible to make inferences using network metrics calculated using a subset of the population has yet to be investigated in an animal social network. We use a simulation study of networks derived from social interactions in a typical fluid fission–fusion social system to determine the precision and accuracy of measures of individual social position based on incomplete knowledge. We show that individual social positions measured in partial social networks correlate strongly with positions in the full social network. This correlation typically becomes stronger as the size of the simulated population is increased and is largely not affected by network density. The choice of network metric has an important effect on the precision of partial networks only when they include a small subset of the population and therefore caution is advised using some of these measures in small partial networks. This work demonstrates that valid inferences about individual social position and strategy can be made using partial networks in a wide range of animal social networks, highlighting the value of applying these methods in large long-term study populations.

The application of statistical network models in disease research

Summary Host social structure is fundamental to how infections spread and persist, and so the statistical modelling of static and dynamic social networks provides an invaluable tool to parameterise realistic epidemiological models. We present a practical guide to the application of network modelling frameworks for hypothesis testing related to social interactions and epidemiology, illustrating some approaches with worked examples using data from a population of wild European badgers Meles meles naturally infected with bovine tuberculosis. Different empirical network datasets generate particular statistical issues related to non-independence and sampling constraints. We therefore discuss the strengths and weaknesses of modelling approaches for different types of network data and for answering different questions relating to disease transmission. We argue that statistical modelling frameworks designed specifically for network analysis offer great potential in directly relating network structure to infection. They have the potential to be powerful tools in analysing empirical contact data used in epidemiological studies, but remain untested for use in networks of spatio-temporal associations. As a result, we argue that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio-logging studies. Furthermore, we encourage improved integration of statistical network approaches into epidemiological research to facilitate the generation of novel modelling frameworks and help extend our understanding of disease transmission in natural populations.

Using Social Network Measures in Wildlife Disease Ecology, Epidemiology, and Management

Abstract Contact networks, behavioral interactions, and shared use of space can all have important implications for the spread of disease in animals. Social networks enable the quantification of complex patterns of interactions; therefore, network analysis is becoming increasingly widespread in the study of infectious disease in animals, including wildlife. We present an introductory guide to using social‐network‐analytical approaches in wildlife disease ecology, epidemiology, and management. We focus on providing detailed practical guidance for the use of basic descriptive network measures by suggesting the research questions to which each technique is best suited and detailing the software available for each. We also discuss how using network approaches can be used beyond the study of social contacts and across a range of spatial and temporal scales. Finally, we integrate these approaches to examine how network analysis can be used to inform the implementation and monitoring of effective disease management strategies.

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

Summary 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.

Wildlife disease ecology from the individual to the population: Insights from a long‐term study of a naturally infected European badger population

Long-term individual-based datasets on host-pathogen systems are a rare and valuable resource for understanding the infectious disease dynamics in wildlife. A study of European badgers (Meles meles) naturally infected with bovine tuberculosis (bTB) at Woodchester Park in Gloucestershire (UK) has produced a unique dataset, facilitating investigation of a diverse range of epidemiological and ecological questions with implications for disease management. Since the 1970s, this badger population has been monitored with a systematic mark-recapture regime yielding a dataset of >15,000 captures of >3,000 individuals, providing detailed individual life-history, morphometric, genetic, reproductive and disease data. The annual prevalence of bTB in the Woodchester Park badger population exhibits no straightforward relationship with population density, and both the incidence and prevalence of Mycobacterium bovis show marked variation in space. The study has revealed phenotypic traits that are critical for understanding the social structure of badger populations along with mechanisms vital for understanding disease spread at different spatial resolutions. Woodchester-based studies have provided key insights into how host ecology can influence infection at different spatial and temporal scales. Specifically, it has revealed heterogeneity in epidemiological parameters; intrinsic and extrinsic factors affecting population dynamics; provided insights into senescence and individual life histories; and revealed consistent individual variation in foraging patterns, refuge use and social interactions. An improved understanding of ecological and epidemiological processes is imperative for effective disease management. Woodchester Park research has provided information of direct relevance to bTB management, and a better appreciation of the role of individual heterogeneity in disease transmission can contribute further in this regard. The Woodchester Park study system now offers a rare opportunity to seek a dynamic understanding of how individual-, group- and population-level processes interact. The wealth of existing data makes it possible to take a more integrative approach to examining how the consequences of individual heterogeneity scale to determine population-level pathogen dynamics and help advance our understanding of the ecological drivers of host-pathogen systems.

Understanding animal social structure: exponential random graph models in animal behaviour research

The social environment is a pervasive influence on the ecological and evolutionary dynamics of animal populations. Recently, social network analysis has provided an increasingly powerful and diverse toolset to enable animal behaviour researchers to quantify the social environment of animals and the impact that it has on ecological and evolutionary processes. However, there is considerable scope for improving these methods further. We outline an approach specifically designed to model the formation of network links, exponential random graph models (ERGMs), which have great potential for modelling animal social structure. ERGMs are generative models that treat network topology as a response variable. This makes them ideal for answering questions related directly to how and why social associations or interactions occur, from the modelling of population level transmission, through within-group behavioural dynamics to social evolutionary processes. We discuss how ERGMs have been used to study animal behaviour previously, and how recent developments in the ERGM framework can increase the scope of their use further. We also highlight the strengths and weaknesses of this approach relative to more conventional methods, and provide some guidance on the situations and research areas in which they can be used appropriately. ERGMs have the potential to be an important part of an animal behaviour researchers toolkit and fully integrating them into the field should enhance our ability to understand what shapes animal social interactions, and identify the underlying processes that lead to the social structure of animal populations.

Can Multilayer Networks Advance Animal Behavior Research

Interactions among individual animals - and between these individuals and their environment - yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and its relation to eco-evolutionary dynamics.

Seasonal variation in daily patterns of social contacts in the European badger Meles meles

Abstract Social interactions among hosts influence the persistence and spread of infectious pathogens. Daily and seasonal variation in the frequency and type of social interactions will play an important role in disease epidemiology and, alongside other factors, may have an influence on wider disease dynamics by causing seasonal forcing of infection, especially if the seasonal variation experienced by a population is considerable. We explored temporal variation in within‐group contacts in a high‐density population of European badgers Meles meles naturally infected with Mycobacterium bovis (the causative agent of bovine tuberculosis). Summer contacts were more likely and of longer duration during the daytime, while the frequency and duration of winter contacts did not differ between day and night. In spring and autumn, within‐group contacts peaked at dawn and dusk, corresponding with when they were of shortest duration with reduced potential for aerosol transmission of pathogens. Summer and winter could be critical for transmission of M. bovis in badgers, due to the high frequency and duration of contacts during resting periods, and we discuss the links between this result and empirical disease data. This study reveals clear seasonality in daily patterns of contact frequency and duration in species living in stable social groups, suggesting that changes in social contacts could drive seasonal forcing of infection in wildlife populations even when the number of individuals interacting remains similar.

Climate, landscape, habitat, and woodland management associations with hazel dormouse Muscardinus avellanarius population status

Although strictly protected, populations of the hazel dormouse Muscardinus avellanarius in the UK declined by 72% from 1993 to 2014. Using National Dormouse Monitoring Programme data from 300 sites throughout England and Wales, we investigated variation in hazel dormouse population status (expressed as Indices of Abundance, Breeding, and population Trend) in relation to climate, landscape, habitat, and woodland management. Dormice were more abundant and produced more litters on sites with warmer, sunnier springs, summers, and autumns. Dormouse abundance was also higher on sites with consistently cold local climate in winter. Habitat connectivity, woodland species composition, and active site management were all correlated with greater dormouse abundance and breeding. Abundances were also higher on sites with successional habitats, whereas the abundance of early successional bramble Rubus fruticosus habitat, woodland area, and landscape connectivity were important for population stability. Diversity in the structure of woodlands in Europe has decreased over the last 100 years, and the habitats we found to be associated with more favourable dormouse status have also been in decline. The conservation status of the hazel dormouse, and that of woodland birds and butterflies, may benefit from reinstatement or increased frequency of management practices, such as coppicing and glade management, that maintain successional and diverse habitats within woodland.

Contact networks structured by sex underpin sex-specific epidemiology of infection

Abstract Contact networks are fundamental to the transmission of infection and host sex often affects the acquisition and progression of infection. However, the epidemiological impacts of sex‐related variation in animal contact networks have rarely been investigated. We test the hypothesis that sex‐biases in infection are related to variation in multilayer contact networks structured by sex in a population of European badgers Meles meles naturally infected with Mycobacterium bovis. Our key results are that male‐male and between‐sex networks are structured at broader spatial scales than female‐female networks and that in male‐male and between‐sex contact networks, but not female‐female networks, there is a significant relationship between infection and contacts with individuals in other groups. These sex differences in social behaviour may underpin male‐biased acquisition of infection and may result in males being responsible for more between‐group transmission. This highlights the importance of sex‐related variation in host behaviour when managing animal diseases.