Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

Social inheritance of avoidances shapes the structure of animal social networks

Social structure can have significant effects on selection, affecting both individual fitness traits and population-level processes. As such, research into its dynamics and evolution has spiked in the last decade, where theoretical and computational advances in social network analysis have increased our understanding of its ecological and inheritance underpinnings. Yet, the processes that shape the formation of structure within social networks are poorly understood and the role of social avoidances unknown. Social avoidances are an alternate of social affiliation in animal societies, which, although invisible, likely play a role in shaping animal social networks. Assuming social avoidances evolve under similar constraints as affiliative behavior, we extended a previous model of social inheritance of affiliations to investigate the impact of social inheritance of avoidances on social network structure. We modeled avoidances as relationships that individuals can copy from their mothers or from their mother's social environment and varied the degrees to which individuals inherit social affiliates and avoidances to test their combined influence on social network structure. We found that inheriting avoidances via maternal social environments made social networks less dense and more modular, thereby demonstrating how social avoidance can shape the evolution of animal social networks.

Individual-level patterns of resource selection do not predict hotspots of contact

Contact among animals is crucial for various ecological processes, including social behaviors, disease transmission, and predator-prey interactions. However, the distribution of contact events across time and space is heterogeneous, influenced by environmental factors and biological purposes. Previous studies have assumed that areas with abundant resources and preferred habitats attract more individuals and, therefore, lead to more contact. To examine the accuracy of this assumption, we used a use-available framework to compare landscape factors influencing the location of contacts between wild pigs (Sus scrofa) in two study areas in Florida and Texas (USA) from those influencing non-contact space use. We employed a contact-resource selection function (RSF) model, where contact locations were defined as used points and locations without contact as available points. By comparing outputs from this contact RSF with a general, population-level RSF, we assessed the factors driving both habitat selection and contact. We found that the landscape predictors (e.g., wetland, linear features, and food resources) played different roles in habitat selection from contact processes for wild pigs in both study areas. This indicated that pigs interacted with their landscapes differently when choosing habitats compared to when they encountered other individuals. Consequently, relying solely on the spatial overlap of individual or population-level RSF models may lead to a misleading understanding of contact-related ecology. Our findings challenge prevailing assumptions about contact and introduce innovative approaches to better understand the ecological drivers of spatially explicit contact. By accurately predicting the spatial distribution of contact events, we can enhance our understanding of contact based ecological processes and their spatial dynamics.

Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model

Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.

Drivers of longevity of wild-caught Aedes albopictus populations

BACKGROUND

Age structure and longevity constitute fundamental determinants of mosquito populations' capacity to transmit pathogens. However, investigations on mosquito-borne diseases primarily focus on aspects such as abundance or dispersal rather than survival and demography. Here, we examine the post-capture longevity of wild-caught populations of the Asian tiger mosquito Aedes albopictus to investigate the influence of environmental factors and individual frailty on longevity.

METHODS

We captured females of Ae. albopictus from June to November 2021 in a vegetated and an urban area by two methods of capture (BG traps and Human Landing catch). They were kept in semi-controlled conditions in the field, and survival was monitored daily across the 859 individuals captured. We studied the differences in longevity per capture method and location and the influence on longevity of seasonal, climatic and individual factors.

RESULTS

Photoperiod, GDD, minimum and maximum temperature and relative humidity showed an effect on the risk of death of females in the field. Females captured in urban area with Human Landing catch methods had greater longevity than females captured in non-urban areas with BG traps. Individual variance, reflecting individual frailties, had an important effect on the risk of death: the greater the frailty, the shorter the post-capture longevity. Overall, longevity is affected not only by climate and seasonal drivers like temperature and photoperiod but also by the individual frailty of mosquitoes.

CONCLUSION

This work unravels environmental drivers of key demographic parameters such as longevity, as modulated by individual frailty, in disease vectors with strong seasonal dynamics. Further demographic understanding of disease vectors in the wild is needed to adopt new surveillance and control strategies and improve our understanding of disease risk and spread.

Demographic turnover can be a leading driver of hierarchy dynamics, and social inheritance modifies its effects

Individuals and societies are linked through a feedback loop of mutual influence. Demographic turnover shapes group composition and structure by adding and removing individuals, and social inheritance shapes social structure through the transmission of social traits from parents to offspring. Here I examine how these drivers of social structure feedback to influence individual outcomes. I explore these society-to-individual effects in systems with social inheritance of hierarchy position, as occur in many primates and spotted hyenas. Applying Markov chain models to empirical and simulated data reveals how demography and social inheritance interact to strongly shape individual hierarchy positions. In hyena societies, demographic processes-not status seeking-account for the majority of hierarchy dynamics and cause an on-average lifetime decline in social hierarchy position. Simulated societies clarify how social inheritance alters demographic effects-demographic processes cause hierarchy position to regress to the mean, but the addition of social inheritance modifies this pattern. Notably, the combination of social inheritance and rank-related reproductive success causes individuals to decline in rank over their lifespans, as seen in the hyena data. Further analyses explore how 'queens' escape this pattern of decline, and how variation in social inheritance generates variability in reproductive inequality. This article is part of the theme issue 'Evolutionary ecology of inequality'.

Group-level differences in social network structure remain repeatable after accounting for environmental drivers

Individuals show consistent between-individual behavioural variation when they interact with conspecifics or heterospecifics. Such patterns might underlie emergent group-specific behavioural patterns and between-group behavioural differences. However, little is known about (i) how social and non-social drivers (external drivers) shape group-level social structures and (ii) whether animal groups show consistent between-group differences in social structure after accounting for external drivers. We used automated tracking to quantify daily social interactions and association networks in 12 colonies of zebra finches (Taeniopygia guttata). We quantified the effects of five external drivers (group size, group composition, ecological factors, physical environments and methodological differences) on daily interaction and association networks and tested whether colonies expressed consistent differences in day-to-day network structure after controlling for these drivers. Overall, we found that external drivers contribute significantly to network structure. However, even after accounting for the contribution of external drivers, there remained significant support for consistent between-group differences in both interaction (repeatability R: up to 0.493) and association (repeatability R: up to 0.736) network structures. Our study demonstrates how group-level differences in social behaviour can be partitioned into different drivers of variation, with consistent contributions from both social and non-social factors.

Generation and applications of simulated datasets to integrate social network and demographic analyses

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social network and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships, it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects into CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers testing other sampling considerations in social network studies.

Sex-biased infections scale to population impacts for an emerging wildlife disease

Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

Strategies for managing marine disease

The incidence of emerging infectious diseases (EIDs) has increased in wildlife populations in recent years and is expected to continue to increase with global environmental change. Marine diseases are relatively understudied compared with terrestrial diseases but warrant parallel attention as they can disrupt ecosystems, cause economic loss, and threaten human livelihoods. Although there are many existing tools to combat the direct and indirect consequences of EIDs, these management strategies are often insufficient or ineffective in marine habitats compared with their terrestrial counterparts, often due to fundamental differences between marine and terrestrial systems. Here, we first illustrate how the marine environment and marine organism life histories present challenges and opportunities for wildlife disease management. We then assess the application of common disease management strategies to marine versus terrestrial systems to identify those that may be most effective for marine disease outbreak prevention, response, and recovery. Finally, we recommend multiple actions that will enable more successful management of marine wildlife disease emergencies in the future. These include prioritizing marine disease research and understanding its links to climate change, improving marine ecosystem health, forming better monitoring and response networks, developing marine veterinary medicine programs, and enacting policy that addresses marine and other wildlife diseases. Overall, we encourage a more proactive rather than reactive approach to marine wildlife disease management and emphasize that multidisciplinary collaborations are crucial to managing marine wildlife health.

Multilayer Networks Assisting to Untangle Direct and Indirect Pathogen Transmission in Bats

The importance of species that connect the different types of interactions is becoming increasingly recognized, and this role may be related to specific attributes of these species. Multilayer networks have two or more layers, which represent different types of interactions, for example, between different parasites and hosts that are nonetheless connected. The understanding of the ecological relationship between bats, ectoparasites, and vector-borne bacteria could shed some light on the complex transmission cycles of these pathogens. In this study, we investigated a multilayer network in Brazil formed by interactions between bat-bacteria, bat-ectoparasite, and ectoparasite-bacteria, and asked how these interactions overlap considering different groups and transmission modes. The multilayer network was composed of 31 nodes (12 bat species, 14 ectoparasite species, and five bacteria genera) and 334 links, distributed over three layers. The multilayer network has low modularity and shows a core-periphery organization, that is, composed of a few generalist species with many interactions and many specialist species participating in few interactions in the multilayer network. The three layers were needed to accurately describe the multilayer structure, while aggregation leads to loss of information. Our findings also demonstrated that the multilayer network is influenced by a specific set of species that can easily be connected to the behavior, life cycle, and type of existing interactions of these species. Four bat species (Artibeus lituratus, A. planirostris, Phyllostomus discolor, and Platyrrhinus lineatus), one ectoparasite species (Steatonyssus) and three bacteria genera (Ehrlichia, hemotropic Mycoplasma and Neorickettsia) are the most important species for the multilayer network structure. Finally, our study brings an ecological perspective under a multilayer network approach on the interactions between bats, ectoparasites, and pathogens. By using a multilayer approach (different types of interactions), it was possible to better understand these different ecological interactions and how they affect each other, advancing our knowledge on the role of bats and ectoparasites as potential pathogen vectors and reservoirs, as well as the modes of transmission of these pathogens.

Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among wildlife populations

Pandemics caused by pathogens that originate in wildlife highlight the importance of understanding the behavioral ecology of disease outbreaks at human–wildlife interfaces. Specifically, the relative effects of human–wildlife and wildlife-wildlife interactions on disease outbreaks among wildlife populations in urban and peri-urban environments remain unclear. We used social network analysis and epidemiological Susceptible-Infected-Recovered models to simulate zooanthroponotic outbreaks, through wild animals’ joint propensities to co-interact with humans, and their social grooming of conspecifics. On 10 groups of macaques (Macaca spp.) in peri-urban environments in Asia, we collected behavioral data using event sampling of human–macaque interactions within the same time and space, and focal sampling of macaques’ social interactions with conspecifics and overall anthropogenic exposure. Model-predicted outbreak sizes were related to structural features of macaques’ networks. For all three species, and for both anthropogenic (co-interactions) and social (grooming) contexts, outbreak sizes were positively correlated to the network centrality of first-infected macaques. Across host species and contexts, the above effects were stronger through macaques’ human co-interaction networks than through their grooming networks, particularly for rhesus and bonnet macaques. Long-tailed macaques appeared to show intraspecific variation in these effects. Our findings suggest that among wildlife in anthropogenically-impacted environments, the structure of their aggregations around anthropogenic factors makes them more vulnerable to zooanthroponotic outbreaks than their social structure. The global features of these networks that influence disease outbreaks, and their underlying socio-ecological covariates, need further investigation. Animals that consistently interact with both humans and their conspecifics are important targets for disease control.

Social information use shapes the coevolution of sociality and virulence

Social contacts can facilitate the spread of both survival-related information and infectious diseases, but little is known about how these processes combine to shape host and parasite evolution. Here, we use a theoretical model that captures both infection and information transmission processes to investigate how host sociality (contact effort) and parasite virulence (disease-associated mortality rate) (co)evolve. We show that selection for sociality (and in turn, virulence) depends on both the intrinsic costs and benefits of social information and infection as well as their relative prevalence in the population. Specifically, greater sociality and lower virulence evolve when the risk of infection is either low or high and social information is neither very common nor too rare. Lower sociality and higher virulence evolve when the prevalence patterns are reversed. When infection and social information are both at moderate levels in the population, the direction of selection depends on the relative costs and benefits of being infected or informed. We also show that sociality varies inversely with virulence, and that parasites may be unable to prevent runaway selection for higher contact efforts. Together, these findings provide new insights for our understanding of group living and how apparently opposing ecological processes can influence the evolution of sociality and virulence in a range of ways.

Oral disease is linked to low nestling condition and brood size in a raptor species living in a highly modified environment

Anthropogenic stressors can favor the occurrence of non-infectious disease that can be worsened by the impact of opportunistic pathogens, making the epizootiology of environmental diseases difficult to unravel. The incidence and impact of oral lesions in nestlings of a facultative scavenger species, the black kite Milvus migrans, were examined over seven breeding seasons in the highly degraded environment close to Madrid, Spain. We found an overall prevalence of 31% of nestlings with oral lesions, with no clear spatial pattern in nests with affected and unaffected individuals. The occurrence and number of oral lesions was negatively associated with nestling body condition and brood size. Broods where all siblings had oral lesions were smaller than those where some or all siblings were apparently healthy, suggesting that oral disease could be causing nestling mortality and, consequently, brood size reduction. In turn, nestling body condition was negatively affected by lesion occurrence, brood size and laying date. Although these relationship were bidirectional, piecewise structural equation modeling analyses showed a greater negative effect of body condition on lesion occurrence than vice versa, indicating that nestlings in poorer body condition were more likely to develop oral lesions (which could contribute to aggravate their state of deterioration) than those in better condition. Nestlings from small broods were also more likely to have oral disease (directly or indirectly through their lower body condition) than nestlings from large broods. Nestlings that hatched last in the broods showed greater development stress than those that hatched first. Anthropogenic stressors could trigger poor body condition, and contribute to microbiota dysbiosis-related diseases. Although further research is needed to determine the consequences for the long-term fitness of individuals, actions should be taken to mitigate adverse conditions that may favor the appearance of environmental diseases associated with peri-urban areas, given their rapid expansion over natural areas.

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

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

Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles

Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioural ecology is the expectation that the distribution of resources shapes patterns of social interactions.

We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions.

We experimentally manipulated the distribution of an essential food and reproductive resource in semi‐natural populations of forked fungus beetles Bolitotherus cornutus. We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half‐way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioural differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments.

We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length and global clustering coefficient) did not differ between environments with dramatically different distributions of resources.

Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggest that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.

Sociability as a personality trait in animals: methods, causes and consequences

Within animal populations there is variation among individuals in their tendency to be social, where more sociable individuals associate more with other individuals. Consistent inter-individual variation in 'sociability' is considered one of the major axes of personality variation in animals along with aggressiveness, activity, exploration and boldness. Not only is variation in sociability important in terms of animal personalities, but it holds particular significance for, and can be informed by, two other topics of major interest: social networks and collective behaviour. Further, knowledge of what generates inter-individual variation in social behaviour also holds applied implications, such as understanding disorders of social behaviour in humans. In turn, research using non-human animals in the genetics, neuroscience and physiology of these disorders can inform our understanding of sociability. For the first time, this review brings together insights across these areas of research, across animal taxa from primates to invertebrates, and across studies from both the laboratory and field. We show there are mixed results in whether and how sociability correlates with other major behavioural traits. Whether and in what direction these correlations are observed may differ with individual traits such as sex and body condition, as well as ecological conditions. A large body of evidence provides the proximate mechanisms for why individuals vary in their social tendency. Evidence exists for the importance of genes and their expression, chemical messengers, social interactions and the environment in determining an individual's social tendency, although the specifics vary with species and other variables such as age, and interactions amongst these proximate factors. Less well understood is how evolution can maintain consistent variation in social tendencies within populations. Shifts in the benefits and costs of social tendencies over time, as well as the social niche hypothesis, are currently the best supported theories for how variation in sociability can evolve and be maintained in populations. Increased exposure to infectious diseases is the best documented cost of a greater social tendency, and benefits include greater access to socially transmitted information. We also highlight that direct evidence for more sociable individuals being safer from predators is lacking. Variation in sociability is likely to have broad ecological consequences, but beyond its importance in the spread of infectious diseases, direct evidence is limited to a few examples related to dispersal and invasive species biology. Overall, our knowledge of inter-individual variation in sociability is highly skewed towards the proximate mechanisms. Our review also demonstrates, however, that considering research from social networks and collective behaviour greatly enriches our understanding of sociability, highlighting the need for greater integration of these approaches into future animal personality research to address the imbalance in our understanding of sociability as a personality trait.

Homogenous mixing and network approximations in discrete-time formulation of a SIRS model

A discrete-time deterministic epidemic model is proposed to better understand the contagious dynamics and the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model both in a random-mixing approach and in a small-world network formulation. The models include the basic parameters that characterize an epidemic: infection and recovery times, as well as mechanisms of contagion. Depending on the parameters, the random-mixing model has different types of behaviour of an epidemic: pathogen extinction; endemic infection; sustained oscillations and dynamic extinction. Spatial effects are included in our network-based approach, where each individual of a population is represented by a node of a small-world network. Our network-based approach includes rewiring connections to account for time-varying network structure, a consequence of the natural response to the emergence of an epidemic (e.g. avoiding contacts with infected individuals). Random and adaptive rewiring conditions are analysed and numerical simulation are made. A comparison of model predictions with the actual effects of COVID-19 infection on population that occurred in Italy and France is produced. Results of the time series of infected people show that our adaptive evolving networks represent effective strategies able to decrease the epidemic spreading.

The Role of Movement Patterns in Epidemic Models on Complex Networks

In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Isotopic niche variation in Tasmanian devils Sarcophilus harrisii with progression of devil facial tumor disease

Devil facial tumor disease (DFTD) is a transmissible cancer affecting Tasmanian devils Sarcophilus harrisii. The disease has caused severe population declines and is associated with demographic and behavioral changes, including earlier breeding, younger age structures, and reduced dispersal and social interactions. Devils are generally solitary, but social encounters are commonplace when feeding upon large carcasses. DFTD tumors can disfigure the jaw and mouth and so diseased individuals might alter their diets to enable ingestion of alternative foods, to avoid conspecific interactions, or to reduce competition. Using stable isotope analysis (δ13C and δ15N) of whiskers, we tested whether DFTD progression, measured as tumor volume, affected the isotope ratios and isotopic niches of 94 infected Tasmanian devils from six sites in Tasmania, comprising four eucalypt plantations, an area of smallholdings and a national park. Then, using tissue from 10 devils sampled before and after detection of tumors and 8 devils where no tumors were detected, we examined whether mean and standard deviation of δ13C and δ15N of the same individuals changed between healthy and diseased states. δ13C and δ15N values were generally not related to tumor volume in infected devils, though at one site, Freycinet National Park, δ15N values increased significantly as tumor volume increased. Infection with DFTD was not associated with significant changes in the mean or standard deviation of δ13C and δ15N values in individual devils sampled before and after detection of tumors. Our analysis suggests that devils tend to maintain their isotopic niche in the face of DFTD infection and progression, except where ecological conditions facilitate a shift in diets and feeding behaviors, demonstrating that ecological context, alongside disease severity, can modulate the behavioral responses of Tasmanian devils to DFTD.

Network ecology in dynamic landscapes

Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the 'ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.

Multiple spatial behaviours govern social network positions in a wild ungulate

The structure of wild animal social systems depends on a complex combination of intrinsic and extrinsic drivers. Population structuring and spatial behaviour are key determinants of individuals' observed social behaviour, but quantifying these spatial components alongside multiple other drivers remains difficult due to data scarcity and analytical complexity. We used a 43-year dataset detailing a wild red deer population to investigate how individuals' spatial behaviours drive social network positioning, while simultaneously assessing other potential contributing factors. Using Integrated Nested Laplace Approximation (INLA) multi-matrix animal models, we demonstrate that social network positions are shaped by two-dimensional landscape locations, pairwise space sharing, individual range size, and spatial and temporal variation in population density, alongside smaller but detectable impacts of a selection of individual-level phenotypic traits. These results indicate strong, multifaceted spatiotemporal structuring in this society, emphasising the importance of considering multiple spatial components when investigating the causes and consequences of sociality.

Sarcoptic mange severity is associated with reduced genomic variation and evidence of selection in Yellowstone National Park wolves (Canis lupus)

Population genetic theory posits that molecular variation buffers against disease risk. Although this "monoculture effect" is well supported in agricultural settings, its applicability to wildlife populations remains in question. In the present study, we examined the genomics underlying individual-level disease severity and population-level consequences of sarcoptic mange infection in a wild population of canids. Using gray wolves (Canis lupus) reintroduced to Yellowstone National Park (YNP) as our focal system, we leveraged 25 years of observational data and biobanked blood and tissue to genotype 76,859 loci in over 400 wolves. At the individual level, we reported an inverse relationship between host genomic variation and infection severity. We additionally identified 410 loci significantly associated with mange severity, with annotations related to inflammation, immunity, and skin barrier integrity and disorders. We contextualized results within environmental, demographic, and behavioral variables, and confirmed that genetic variation was predictive of infection severity. At the population level, we reported decreased genome-wide variation since the initial gray wolf reintroduction event and identified evidence of selection acting against alleles associated with mange infection severity. We concluded that genomic variation plays an important role in disease severity in YNP wolves. This role scales from individual to population levels, and includes patterns of genome-wide variation in support of the monoculture effect and specific loci associated with the complex mange phenotype. Results yielded system-specific insights, while also highlighting the relevance of genomic analyses to wildlife disease ecology, evolution, and conservation.

Behavioral Causes, Ecological Consequences, and Management Challenges Associated with Wildlife Foraging in Human-Modified Landscapes

Humans have altered up to half of the world's land surface. Wildlife living within or close to these human-modified landscapes are presented with opportunities and risks associated with feeding on human-derived foods (e.g., agricultural crops and food waste). Understanding whether and how wildlife adapts to these landscapes is a major challenge, with thousands of studies published on the topic over the past 10 years. In the present article, we build on established theoretical frameworks to understand the behavioral causes of crop and urban foraging by wildlife. We then develop and extend this framework to describe the multifaceted ecological consequences of crop and urban foraging for the individuals and populations in which they arise, with emphasis on social species for which interactions with people are, on balance, negative (commonly referred to as raiding species). Finally, we discuss the management challenges faced by urban and rural land managers, businesses, and government organizations in mitigating human-wildlife conflicts and propose ways to improve the lives of both wildlife and humans living in human-modified landscapes and to promote coexistence.

The role of social structure and dynamics in the maintenance of endemic disease

Social interactions are required for the direct transmission of infectious diseases. Consequently, the social network structure of populations plays a key role in shaping infectious disease dynamics. A huge research effort has examined how specific social network structures make populations more (or less) vulnerable to damaging epidemics. However, it can be just as important to understand how social networks can contribute to endemic disease dynamics, in which pathogens are maintained at stable levels for prolonged periods of time. Hosts that can maintain endemic disease may serve as keystone hosts for multi-host pathogens within an ecological community, and also have greater potential to act as key wildlife reservoirs of agricultural and zoonotic diseases. Here, we examine combinations of social and demographic processes that can foster endemic disease in hosts. We synthesise theoretical and empirical work to demonstrate the importance of both social structure and social dynamics in maintaining endemic disease. We also highlight the importance of distinguishing between the local and global persistence of infection and reveal how different social processes drive variation in the scale at which infectious diseases appear endemic. Our synthesis provides a framework by which to understand how sociality contributes to the long-term maintenance of infectious disease in wildlife hosts and provides a set of tools to unpick the social and demographic mechanisms involved in any given host-pathogen system.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00265-021-03055-8.

Life history and population regulation shape demographic competence and influence the maintenance of endemic disease

Host life history and demography play important roles in host-pathogen dynamics, by influencing the ability of hosts and their pathogens to coexist. We introduce the concept of demographic competence to describe the ability of host populations to sustain endemic infectious disease. Hosts with high demographic competence are more likely to act as keystone hosts and constitute reservoirs of infection that can spill over into other species. We propose that the pace of host life history will interact with pathogen life history to influence demographic competence. Our models demonstrate that slow-living hosts (with high survival and low recruitment rates) have greater demographic competence than fast-living hosts (with low survival and high recruitment rates) for susceptible-infected disease dynamics, although this difference is reduced when populations of slow hosts are age structured. Demographic competence is generally greater among hosts with populations regulated by survival compared to reproduction, but this difference is smallest among slow life histories and reversed for some pathogens with frequency-dependent transmission. An association between pathogen life history traits and the demographic competence of faster-living hosts also has implications for trade-offs between pathogen virulence and transmissibility. Overall, we demonstrate how host life history traits can help predict wildlife reservoirs of zoonoses and the vulnerability of populations to disease-induced extinction.

Multilayer and Multiplex Networks: An Introduction to Their Use in Veterinary Epidemiology

Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders.

Personality affects dynamics of an experimental pathogen in little brown bats

Host behaviour can affect host-pathogen dynamics and theory predicts that certain individuals disproportionately infect conspecifics during an epidemic. Consistent individual differences in behaviour, or personality, could influence this variation with the most exploratory or sociable individuals most likely to spread pathogens. We quantified exploration and sociability in little brown bats (Myotis lucifugus) and then experimentally manipulated exposure to a proxy pathogen (i.e. ultraviolet (UV) fluorescent powder) to test two related hypotheses: (i) more sociable and more exploratory individuals would be more likely to transmit infections to other individuals, and (ii) more sociable and more exploratory individuals uninfected with an invading pathogen would be more likely to acquire infections. We captured 10 groups of 16 bats at a time and held each group in an outdoor flight tent equipped with roosting-boxes. We used hole-board and Y-maze tests to quantify exploration and sociability of each bat and randomly selected one individual from each group for 'infection' with non-toxic, UV fluorescent powder. Each group of 10 bats was released into the flight tent for 24 h, which represented an experimental infection trial. After 24 h, we removed bats from the trial, photographed each individual under UV light and quantified infection intensity from digital photographs. As predicted, the exploratory behaviour of the experimentally infected individual was positively correlated with infection intensity in their group-mates, while more exploratory females had higher pathogen acquisition. Our results highlight the potential influence of host personality and sex on pathogen dynamics in wildlife populations.

How disease constrains the evolution of social systems

Animal populations are occasionally shocked by epidemics of contagious diseases. The ability of social systems to withstand epidemic shocks and mitigate disruptions could shape the evolution of complex animal societies. We present a mathematical model to explore the potential impact of disease on the evolutionary fitness of different organizational strategies for populations of social species whose survival depends on collaborative efficiency. We show that infectious diseases select for a specific feature in the organization of collaborative roles-cohort stability-and that this feature is costly, and therefore unlikely to be maintained in environments where infection risks are absent. Our study provides evidence for an often-stated (but rarely supported) claim that pathogens have been the dominant force shaping the complexity of division of labour in eusocial societies of honeybees and termites and establishes a general theoretical approach for assessing evolutionary constraints on social organization from disease risk in other collaborative taxa.

Predicting the global mammalian viral sharing network using phylogeography

Understanding interspecific viral transmission is key to understanding viral ecology and evolution, disease spillover into humans, and the consequences of global change. Prior studies have uncovered macroecological drivers of viral sharing, but analyses have never attempted to predict viral sharing in a pan-mammalian context. Using a conservative modelling framework, we confirm that host phylogenetic similarity and geographic range overlap are strong, nonlinear predictors of viral sharing among species across the entire mammal class. Using these traits, we predict global viral sharing patterns of 4196 mammal species and show that our simulated network successfully predicts viral sharing and reservoir host status using internal validation and an external dataset. We predict high rates of mammalian viral sharing in the tropics, particularly among rodents and bats, and within- and between-order sharing differed geographically and taxonomically. Our results emphasize the importance of ecological and phylogenetic factors in shaping mammalian viral communities, and provide a robust, general model to predict viral host range and guide pathogen surveillance and conservation efforts.

Prior studies have investigated macroecological patterns of host sharing among viruses, although certain mammal clades have not been represented in these analyses, and the findings have not been used to predict the true network. Here the authors model the species level traits that predict viral sharing across all mammal clades and validate their predictions using an independent dataset.

Sex, synchrony, and skin contact: integrating multiple behaviors to assess pathogen transmission risk

Direct pathogen and parasite transmission is fundamentally driven by a population’s contact network structure and its demographic composition and is further modulated by pathogen life-history traits. Importantly, populations are most often concurrently exposed to a suite of pathogens, which is rarely investigated, because contact networks are typically inferred from spatial proximity only. Here, we use 5 years of detailed observations of Indo-Pacific bottlenose dolphins (Tursiops aduncus) that distinguish between four different types of social contact. We investigate how demography (sex and age) affects these different social behaviors. Three of the four social behaviors can be used as a proxy for understanding key routes of direct pathogen transmission (sexual contact, skin contact, and aerosol contact of respiratory vapor above the water surface). We quantify the demography-dependent network connectedness, representing the risk of exposure associated with the three pathogen transmission routes, and quantify coexposure risks and relate them to individual sociability. Our results suggest demography-driven disease risk in bottlenose dolphins, with males at greater risk than females, and transmission route-dependent implications for different age classes. We hypothesize that male alliance formation and the divergent reproductive strategies in males and females drive the demography-dependent connectedness and, hence, exposure risk to pathogens. Our study provides evidence for the risk of coexposure to pathogens transmitted along different transmission routes and that they relate to individual sociability. Hence, our results highlight the importance of a multibehavioral approach for a more complete understanding of the overall pathogen transmission risk in animal populations, as well as the cumulative costs of sociality.

Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation

The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the 'adaptationist' paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive 'big-picture' approaches to priority setting. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Using social network analysis of mixed-species groups in African savannah herbivores to assess how community structure responds to environmental change

The dynamics of wildlife populations often depend heavily on interspecific interactions and understanding the underlying principles can be an important step in designing conservation strategies. Behavioural ecological studies can here provide useful insights into the structure and function of communities and their likely response to environmental changes. In this study of the Masai Mara herbivore community, we use a social network approach to investigate social affinities between species and how these change over the year in response to seasonal changes in ecological conditions. We find that even though social networks were correlated across different ecological conditions, for half the species dyads in the community, the strength of social affinities responded to changes in rainfall and/or the presence of migratory wildebeest. Several species consequentially adopted more or less central positions in the network depending on the ecological conditions. The findings point out interspecific social links that are likely to be attenuated or strengthened as a consequence of human-induced environmental changes and therefore call for particular attention from conservation managers. The eco-evolutionary ramifications of the perturbations of social affinities still require further study.

This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

Invisible barriers: anthropogenic impacts on inter- and intra-specific interactions as drivers of landscape-independent fragmentation

Anthropogenically induced fragmentation constitutes a major threat to biodiversity. Presently, conservation research and actions focus predominantly on fragmentation caused directly by physical transformation of the landscape (e.g. deforestation, agriculture, urbanization, roads, etc.). While there is no doubt that landscape features play a key role in fragmenting populations or enhancing connectivity, fragmentation may also come about by processes other than the transformation of the landscape and which may not be readily visible. Such landscape-independent fragmentation (LIF) usually comes about when anthropogenic disturbance alters the inter- and intra-specific interactions among and within species. LIF and its drivers have received little attention in the scientific literature and in the management of wildlife populations. We discuss three major classes of LIF processes and their relevance for the conservation and management of species and habitats: (i) interspecific dispersal dependency, in which populations of species that rely on other species for transport and propagation become fragmented as the transporting species declines; (ii) interspecific avoidance induction, where species are excluded from habitats and corridors owing to interspecific interactions resulting from anthropogenically induced changes in community structure (e.g. exclusions by increased predation pressure); and (iii) intraspecific behavioural divergence, where populations become segregated owing to anthropogenically induced behavioural differentiation among them.

This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Integrating models of human behaviour between the individual and population levels to inform conservation interventions

Conservation takes place within social-ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of individuals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showing how combining two modelling approaches (social network analysis, SNA, and agent-based modelling, ABM) could lead to more robust insights into a particular type of conservation intervention. We use our simple model, which simulates knowledge of ranger patrols through a hunting community and is based on empirical data from a Cambodian protected area, to highlight the complex, context-dependent nature of outcomes of information-sharing interventions, depending both on the configuration of the network and the attributes of the agents. We conclude by reflecting that both SNA and ABM, and many other modelling tools, are still too compartmentalized in application, either in ecology or social science, despite the strong methodological and conceptual parallels between their uses in different disciplines. Even a greater sharing of methods between disciplines is insufficient, however; given the impact of conservation on both the social and ecological aspects of systems (and vice versa), a fully integrated approach is needed, combining both the modelling approaches and the disciplinary insights of ecology and social science. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Behavioural ecology and infectious disease: implications for conservation of biodiversity

Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.