Modeling infection transmission in primate networks to predict centrality-based risk

Epidemiological Consequences of Individual Centrality on Wild Chimpanzees

Disease outbreaks are one of the key threats to great apes and other wildlife. Because the spread of some pathogens (e.g., respiratory viruses, sexually transmitted diseases, ectoparasites) are mediated by social interactions, there is a growing interest in understanding how social networks predict the chain of pathogen transmission. In this study, we built a party network from wild chimpanzees (Pan troglodytes), and used agent-based modeling to test: (i) whether individual attributes (sex, age) predict individual centrality (i.e., whether it is more or less socially connected); (ii) whether individual centrality affects an individual's role in the chain of pathogen transmission; and, (iii) whether the basic reproduction number (R0) and infectious period modulate the influence of centrality on pathogen transmission. We show that sex and age predict individual centrality, with older males presenting many (degree centrality) and strong (strength centrality) relationships. As expected, males are more central than females within their network, and their centrality determines their probability of getting infected during simulated outbreaks. We then demonstrate that direct measures of social interaction (strength centrality), as well as eigenvector centrality, strongly predict disease dynamics in the chimpanzee community. Finally, we show that this predictive power depends on the pathogen's R0 and infectious period: individual centrality was most predictive in simulations with the most transmissible pathogens and long-lasting diseases. These findings highlight the importance of considering animal social networks when investigating disease outbreaks.

Female social dynamics as viewed from grooming networks in the Central Himalayan Langur (Semnopithecus schistaceus)

Enhanced survival and reproduction are associated with an individual's direct and indirect social connections with members of a group. Yet, the role of these connections is little known in a vast range of primate species. We studied female Central Himalayan Langur (CHL) to investigate the link between four specific attributes (dominance rank, age, genetic relatedness, and the presence of females carrying infants) and a female's direct and indirect social relationships. By analyzing grooming networks, we revealed different behavioral strategies: high-ranking females form relationships with many females (high degree), whereas females with dependent infants have strong relationships (high strength and eigenvector). Subadult females are important individuals that hold the social network together (high betweenness), while an immigrant female strategy is to integrate herself into the group by forming strong bonds with females who themselves have strong bonds (high eigenvector). Our study sheds light on how behavioral strategies shape female CHL grooming networks, which may help them to secure fitness and survival advantages.

Deep learning for automatic facial detection and recognition in Japanese macaques: illuminating social networks

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research has offered new methodological perspectives through the automatisation of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identification done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching an accuracy of 82.2% and (ii) the creation of an individual recogniser for the Kōjima Island macaque population (YOLOv8n model), reaching an accuracy of 83%. We also created a Kōjima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

What you have, not who you know: food-enhanced social capital and changes in social behavioural relationships in a non-human primate

Social network position in non-human primates has far-reaching fitness consequences. Critically, social networks are both heterogeneous and dynamic, meaning an individual's current network position is likely to change due to both intrinsic and extrinsic factors. However, our understanding of the drivers of changes in social network position is largely confined to opportunistic studies. Experimental research on the consequences of in situ, controlled network perturbations is limited. Here we conducted a food-based experiment in rhesus macaques to assess whether allowing an individual the ability to provide high-quality food to her group changed her social behavioural relationships. We considered both her social network position across five behavioural networks, as well as her dominance and kin interactions. We found that gaining control over a preferential food resource had far-reaching social consequences. There was an increase in both submission and aggression centrality and changes in the socio-demographic characteristics of her agonistic interaction partners. Further, we found that her grooming balance shifted in her favour as she received more grooming than she gave. Together, these results provide a novel, preliminary insight into how in situ, experimental manipulations can modify social network position and point to broader network-level shifts in both social capital and social power.

Multi-group analysis of grooming network position in a highly social primate

Individual variation in complex social behavioral traits, like primate grooming, can be influenced by the characteristics of the individual and those of its social group. To better grasp this complexity, social network analysis can be used to quantify direct and indirect grooming relationships. However, multi-group social network studies remain rare, despite their importance to disentangle individual from group-level trait effects on grooming strategies. We applied social network analysis to grooming data of 22 groups of zoo-housed bonobos and investigated the impact of three individual (sex, age, and rearing-history) and two group-level traits (group size and sex ratio) on five social network measures (out-strength, in-strength, disparity, affinity, and eigenvector centrality). Our results showed age-effects on all investigated measures: for females, all measures except for affinity showed quadratic relationships with age, while in males, the effects of age were more variable depending on the network measure. Bonobos with atypical rearing histories showed lower out-strength and eigenvector centrality, while in-strength was only impacted by rearing history in males. Group size showed a negative association with disparity and eigenvector centrality, while sex ratio did not influence any of the investigated measures. Standardization for group size did not impact the effects of sex and age, indicating the robustness of these findings. Our study provides comprehensive insights into the complexity of grooming behavior in zoo-housed bonobos, and underlines the importance of multi-group analyses for the generalizability of social network analysis results for species as a whole.

Familiarity, age, weaning and health status impact social proximity networks in dairy calves

Social network analysis in dairy calves has not been widely studied, with previous studies limited by the short study duration, and low number of animals and replicates. In this study, we investigated social proximity interactions of 79 Holstein-Friesian calves from 5 cohorts for up to 76 days. Networks were computed using 4-day aggregated associations obtained from ultrawideband location sensor technology, at 1 Hz sampling rate. The effect of age, familiarity, health, and weaning status on the social proximity networks of dairy calves was assessed. Networks were poorly correlated (non-stable) between the different 4-day periods, in the majority of them calves associated heterogeneously, and individuals assorted based on previous familiarity for the whole duration of the study. Age significantly increased association strength, social time and eigenvector centrality and significantly decreased closeness and coefficient of variation in association (CV). Sick calves had a significantly lower strength, social time, centrality and CV, and significantly higher closeness compared to the healthy calves. During and after weaning, calves had significantly lower closeness and CV, and significantly higher association strength, social time, and eigenvector centrality. These results indicate that age, familiarity, weaning, and sickness have a significant impact on the variation of social proximity interaction of calves.

Pathogen transmission modes determine contact network structure, altering other pathogen characteristics

Pathogen traits can vary greatly and heavily impact the ability of a pathogen to persist in a population. Although this variation is fundamental to disease ecology, little is known about the evolutionary pressures that drive these differences, particularly where they interact with host behaviour. We hypothesized that host behaviours relevant to different transmission routes give rise to differences in contact network structure, constraining the space over which pathogen traits can evolve to maximize fitness. Our analysis of 232 contact networks across mammals, birds, reptiles, amphibians, arthropods, fish and molluscs found that contact network topology varies by contact type, most notably in networks that are representative of fluid-exchange transmission. Using infectious disease model simulations, we showed that these differences in network structure suggest pathogens transmitted through fluid-exchange contact types will need traits associated with high transmissibility to successfully proliferate, compared to pathogens that transmit through other types of contact. These findings were supported through a review of known traits of pathogens that transmit in humans. Our work demonstrates that contact network structure may drive the evolution of compensatory pathogen traits according to transmission strategy, providing essential context for understanding pathogen evolution and ecology.

Linking parasitism to network centrality and the impact of sampling bias in its interpretation

Group living is beneficial for individuals, but also comes with costs. One such cost is the increased possibility of pathogen transmission because increased numbers or frequencies of social contacts are often associated with increased parasite abundance or diversity. The social structure of a group or population is paramount to patterns of infection and transmission. Yet, for various reasons, studies investigating the links between sociality and parasitism in animals, especially in primates, have only accounted for parts of the group (e.g., only adults), which is likely to impact the interpretation of results. Here, we investigated the relationship between social network centrality and an estimate of gastrointestinal helminth infection intensity in a whole group of Japanese macaques (Macaca fuscata). We then tested the impact of omitting parts of the group on this relationship. We aimed to test: (1) whether social network centrality -in terms of the number of partners (degree), frequency of interactions (strength), and level of social integration (eigenvector) -was linked to parasite infection intensity (estimated by eggs per gram of faeces, EPG); and, (2) to what extent excluding portions of individuals within the group might influence the observed relationship. We conducted social network analysis on data collected from one group of Japanese macaques over three months on Koshima Island, Japan. We then ran a series of knock-out simulations. General linear mixed models showed that, at the whole-group level, network centrality was positively associated with geohelminth infection intensity. However, in partial networks with only adult females, only juveniles, or random subsets of the group, the strength of this relationship - albeit still generally positive - lost statistical significance. Furthermore, knock-out simulations where individuals were removed but network metrics were retained from the original whole-group network showed that these changes are partly a power issue and partly an effect of sampling the incomplete network. Our study indicates that sampling bias can thus hamper our ability to detect real network effects involving social interaction and parasitism. In addition to supporting earlier results linking geohelminth infection to Japanese macaque social networks, this work introduces important methodological considerations for research into the dynamics of social transmission, with implications for infectious disease epidemiology, population management, and health interventions.

Operationalizing "One Health" as "One Digital Health" Through a Global Framework That Emphasizes Fair and Equitable Sharing of Benefits From the Use of Artificial Intelligence and Related Digital Technologies

The operationalization of One Health (OH) through digitalization is a means to deploy digital technologies (including Artificial Intelligence (AI), big data and related digital technologies) to better capacitate us to deal with growing climate exigency and related threats to human, animal and plant health. With reference to the concept of One Digital Health (ODH), this paper considers how digital capabilities can help to overcome ‘operational brakes’ in OH through new and deeper insights, better predictions, and more targeted or precise preventive strategies and public health countermeasures. However, the data landscape is fragmented and access to certain types of data is increasingly restrictive as individuals, communities and countries seek to assert greater control over data taken from them. This paper proposes for a dedicated global ODH framework—centered on fairness and equity—to be established to promote data-sharing across all the key knowledge domains of OH and to devise data-driven solutions to challenges in the human-animal-ecosystems interface. It first considers the data landscape in relation to: (1) Human and population health; (2) Pathogens; (3) Animal and plant health; and (4) Ecosystems and biodiversity. The complexification from the application of advance genetic sequencing technology is then considered, with focus on current debates over whether certain types of data like digital (genetic) sequencing information (DSI) should remain openly and freely accessible. The proposed ODH framework must augment the existing access and benefit sharing (ABS) framework currently prescribed under the Nagoya Protocol to the Convention on Biological Diversity (CBD) in at least three different ways. First, the ODH framework should apply to all genetic resources and data, including DSI, whether from humans or non-humans. Second, the FAIRER principles should be implemented, with focus on fair and equitable benefit-sharing. Third, the ODH framework should adopt multilateral approaches to data sharing (such as through federated data systems) and to ABS. By operationalizing OH as ODH, we are more likely to be able to protect and restore natural habitats, secure the health and well-being of all living things, and thereby realize the goals set out in the post-2020 Global Biodiversity Framework under the CBD.

Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas (Gorilla beringei beringei) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Hierarchical networks of food exchange in the black garden ant Lasius niger

In most eusocial insects, the division of labor results in relatively few individuals foraging for the entire colony. Thus, the survival of the colony depends on its efficiency in meeting the nutritional needs of all its members. Here, we characterize the network topology of a eusocial insect to understand the role and centrality of each caste in this network during the process of food dissemination. We constructed trophallaxis networks from 34 food-exchange experiments in black garden ants (Lasius niger). We tested the influence of brood and colony size on (i) global indices at the network level (i.e., efficiency, resilience, centralization, and modularity) and (ii) individual values (i.e., degree, strength, betweenness, and the clustering coefficient). Network resilience, the ratio between global efficiency and centralization, was stable with colony size but increased in the presence of broods, presumably in response to the nutritional needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some domestics acted as intermediaries between foragers and other domestics. Networks appeared to be hierarchical rather than random or centralized exclusively around foragers. Finally, our results suggested that networks emerging from social insect interactions can improve group performance and thus colony fitness.

Gregariousness is associated with parasite species richness in a community of wild chimpanzees

Increased risk of pathogen transmission through proximity and contact is a well-documented cost of sociality. Affiliative social contact, however, is an integral part of primate group life and can benefit health. Despite its importance to the evolution and maintenance of sociality, the tradeoff between costs and benefits of social contact for group-living primate species remains poorly understood. To improve our understanding of this interplay, we used social network analysis to investigate whether contact via association in the same space and/or physical contact measured through grooming were associated with helminth parasite species richness in a community of wild chimpanzees (Pan troglodytes schweinfurthii). We identified parasite taxa in 381 fecal samples from 36 individuals from the Kasekela community of chimpanzees in Gombe National Park, Tanzania, from November 1, 2006 - October 31, 2012. Over the study period, eight environmentally transmitted helminth taxa were identified. We quantified three network metrics for association and grooming contact, including degree strength, betweenness, and closeness. Our findings suggest that more gregarious individuals - those who spent more time with more individuals in the same space - had higher parasite richness, while the connections in the grooming network were not related to parasite richness. The expected parasite richness in individuals increased by 1.13 taxa (CI: 1.04, 1.22; p = 0.02) per one standard deviation increase in degree strength of association contact. The results of this study add to the understanding of the role that different types of social contact plays in the parasite richness of group-living social primates.

The consistency of individual centrality across time and networks in wild vervet monkeys

Previous primate social network studies largely limited their focus to grooming and/or aggression networks, particularly among adult females. In addition, the consistency of individuals' network centrality across time and/or different networks has received little attention, despite this being critical for a global understanding of dynamic social structure. Here, we analyzed the grooming, aggression, and play social networks of a group of 26-28 wild vervet monkeys (Chlorocebus pygerythrus), including adults and juveniles, over two periods of 6 months. We collected data on grooming, play, and aggression using focal animal sampling with instantaneous recording and ad libitum sampling. We examined whether individuals' network centralities were consistent over the two periods and across networks, as well as the effect of age, sex, and dominance rank on three individual centrality metrics in each network and within each study period. We found that individuals were quite consistent in their network position from 1 year to the next despite changes in group composition. However, their network centralities were not correlated across networks, except for Strength and weighted Eigenvector centrality between grooming and aggression networks. We also found that in the aggression network, high-rankers showed the highest centrality in most network metrics (e.g., Degree, Strength, and Eigenvector centrality) and compared to males, females were most central in 2017 but not in 2018. In the grooming network, high-ranking females had the highest Eigenvector centrality, whereas in the play network, juvenile males had the highest Eigenvector centrality. Our findings corroborate previous findings on vervet monkeys. In addition, they show that individuals' network centralities may vary among networks and over time; thus highlighting the effect of sociodemographics and behaviors' functions on the group level dynamics of social behavior.

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.

How socio-ecological factors influence the differentiation of social relationships: an integrated conceptual framework

The extent of differentiation of social relationships within groups is a means to assess social complexity, with greater differentiation indicating greater social complexity. Socio-ecological factors are likely to influence social complexity, but no attempt has been made to explain the differentiation of social relationships using multiple socio-ecological factors. Here, we propose a conceptual framework based on four components underlying multiple socio-ecological factors that influence the differentiation of social relationships: the extent of within-group contest competition to access resources, the extent to which individuals differ in their ability to provide a variety of services, the need for group-level cooperation and the constraints on social interactions. We use the framework to make predictions about the degree of relationship differentiation that can be expected within a group according to the cumulative contribution of multiple socio-ecological factors to each of the four components. The framework has broad applicability, since the four components are likely to be relevant to a wide range of animal taxa and to additional socio-ecological factors not explicitly dealt with here. Hence, the framework can be used as the basis for the development of novel and testable hypotheses about intra- and interspecific differences in relationship differentiation and social complexity.

A multilevel analytical framework for studying cultural evolution in prehistoric hunter-gatherer societies

Over the past decade, a major debate has taken place on the underpinnings of cultural changes in human societies. A growing array of evidence in behavioural and evolutionary biology has revealed that social connectivity among populations and within them affects, and is affected by, culture. Yet the interplay between prehistoric hunter-gatherer social structure and cultural transmission has typically been overlooked. Interestingly, the archaeological record contains large data sets, allowing us to track cultural changes over thousands of years: they thus offer a unique opportunity to shed light on long-term cultural transmission processes. In this review, we demonstrate how well-developed methods for social structure analysis can increase our understanding of the selective pressures underlying cumulative culture. We propose a multilevel analytical framework that considers finer aspects of the complex social structure in which regional groups of prehistoric hunter-gatherers were embedded. We put forward predictions of cultural transmission based on local- and global-level network metrics of small-scale societies and their potential effects on cumulative culture. By bridging the gaps between network science, palaeodemography and cultural evolution, we draw attention to the use of the archaeological record to depict patterns of social interactions and transmission variability. We argue that this new framework will contribute to improving our understanding of social interaction patterns, as well as the contexts in which cultural changes occur. Ultimately, this may provide insights into the evolution of human behaviour.

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.

Primate Infectious Disease Ecology: Insights and Future Directions at the Human-Macaque Interface

Global population expansion has increased interactions and conflicts between humans and nonhuman primates over shared ecological space and resources. Such ecological overlap, along with our shared evolutionary histories, makes human-nonhuman primate interfaces hot spots for the acquisition and transmission of parasites. In this chapter, we bring to light the importance of human-macaque interfaces in particular as hot spots for infectious disease ecological and epidemiological assessments. We first outline the significance and broader objectives behind research related to the subfield of primate infectious disease ecology and epidemiology. We then reveal how members of the genus Macaca, being among the most socioecologically flexible and invasive of all primate taxa, live under varying degrees of overlap with humans in anthropogenic landscapes. Thus, human-macaque interfaces may favor the bidirectional exchange of parasites. We then review studies that have isolated various types of parasites at human-macaque interfaces, using information from the Global Mammal Parasite Database (GMPD: http://www.mammalparasites.org/). Finally, we elaborate on avenues through which the implementation of both novel conceptual frameworks (e.g., Coupled Systems, One Health) and quantitative network-based approaches (e.g., social and bipartite networks, agent-based modeling) may potentially address some of the critical gaps in our current knowledge of infectious disease ecology at human-primate interfaces.

Its all about connections: hubs and invasion in habitat networks

During the early stages of invasion, the interaction between the features of the invaded landscape, notably its spatial structure, and the internal dynamics of an introduced population has a crucial impact on establishment and spread. By approximating introduction areas as networks of patches linked by dispersal, we characterised their spatial structure with specific metrics and tested their impact on two essential steps of the invasion process: establishment and spread. By combining simulations with experimental introductions of Trichogramma chilonis (Hymenoptera: Trichogrammatidae) in artificial laboratory microcosms, we demonstrated that spread was hindered by clusters and accelerated by hubs but was also affected by small-population mechanisms prevalent for invasions, such as Allee effects. Establishment was also affected by demographic mechanisms, in interaction with network metrics. These results highlight the importance of considering the demography of invaders as well as the structure of the invaded area to predict the outcome of invasions.

Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size

The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, "enss." Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.

The One Health Concept: 10 Years Old and a Long Road Ahead

Over the past decade, a significant increase in the circulation of infectious agents was observed. With the spread and emergence of epizootics, zoonoses, and epidemics, the risks of pandemics became more and more critical. Human and animal health has also been threatened by antimicrobial resistance, environmental pollution, and the development of multifactorial and chronic diseases. This highlighted the increasing globalization of health risks and the importance of the human-animal-ecosystem interface in the evolution and emergence of pathogens. A better knowledge of causes and consequences of certain human activities, lifestyles, and behaviors in ecosystems is crucial for a rigorous interpretation of disease dynamics and to drive public policies. As a global good, health security must be understood on a global scale and from a global and crosscutting perspective, integrating human health, animal health, plant health, ecosystems health, and biodiversity. In this study, we discuss how crucial it is to consider ecological, evolutionary, and environmental sciences in understanding the emergence and re-emergence of infectious diseases and in facing the challenges of antimicrobial resistance. We also discuss the application of the "One Health" concept to non-communicable chronic diseases linked to exposure to multiple stresses, including toxic stress, and new lifestyles. Finally, we draw up a list of barriers that need removing and the ambitions that we must nurture for the effective application of the "One Health" concept. We conclude that the success of this One Health concept now requires breaking down the interdisciplinary barriers that still separate human and veterinary medicine from ecological, evolutionary, and environmental sciences. The development of integrative approaches should be promoted by linking the study of factors underlying stress responses to their consequences on ecosystem functioning and evolution. This knowledge is required for the development of novel control strategies inspired by environmental mechanisms leading to desired equilibrium and dynamics in healthy ecosystems and must provide in the near future a framework for more integrated operational initiatives.

Social network community structure and the contact-mediated sharing of commensal E. coli among captive rhesus macaques (Macaca mulatta)

In group-living animals, heterogeneity in individuals' social connections may mediate the sharing of microbial infectious agents. In this regard, the genetic relatedness of individuals' commensal gut bacterium Escherichia coli may be ideal to assess the potential for pathogen transmission through animal social networks. Here we use microbial phylogenetics and population genetics approaches, as well as host social network reconstruction, to assess evidence for the contact-mediated sharing of E. coli among three groups of captively housed rhesus macaques (Macaca mulatta), at multiple organizational scales. For each group, behavioral data on grooming, huddling, and aggressive interactions collected for a six-week period were used to reconstruct social network communities via the Data Cloud Geometry (DCG) clustering algorithm. Further, an E. coli isolate was biochemically confirmed and genotypically fingerprinted from fecal swabs collected from each macaque. Population genetics approaches revealed that Group Membership, in comparison to intrinsic attributes like age, sex, and/or matriline membership of individuals, accounted for the highest proportion of variance in E. coli genotypic similarity. Social network approaches revealed that such sharing was evident at the community-level rather than the dyadic level. Specifically, although we found no links between dyadic E. coli similarity and social contact frequencies, similarity was significantly greater among macaques within the same social network communities compared to those across different communities. Moreover, tests for one of our study-groups confirmed that E. coli isolated from macaque rectal swabs were more genotypically similar to each other than they were to isolates from environmentally deposited feces. In summary, our results suggest that among frequently interacting, spatially constrained macaques with complex social relationships, microbial sharing via fecal-oral, social contact-mediated routes may depend on both individuals' direct connections and on secondary network pathways that define community structure. They lend support to the hypothesis that social network communities may act as bottlenecks to contain the spread of infectious agents, thereby encouraging disease control strategies to focus on multiple organizational scales. Future directions includeincreasing microbial sampling effort per individual to better-detect dyadic transmission events, and assessments of the co-evolutionary links between sociality, infectious agent risk, and host immune function.

Network size, structure, and pathogen transmission: a simulation study comparing different community detection algorithms

Social substructure can influence pathogen transmission. Modularity measures the degree of social contact within versus between “communities” in a network, with increasing modularity expected to reduce transmission opportunities. We investigated how social substructure scales with network size and disease transmission. Using small-scale primate social networks, we applied seven community detection algorithms to calculate modularity and subgroup cohesion, defined as individuals’ interactions within subgroups proportional to the network. We found larger networks were more modular with higher subgroup cohesion, but the association’s strength varied by community detection algorithm and substructure measure. These findings highlight the importance of choosing an appropriate community detection algorithm for the question of interest, and if not possible, using multiple algorithms. Disease transmission simulations revealed higher modularity and subgroup cohesion resulted in fewer infections, confirming that social substructure has epidemiological consequences. Increased subdivision in larger networks could reflect constrained time budgets or evolved defences against disease risk.

Implications of Tourist-Macaque Interactions for Disease Transmission

During wildlife tourism, proximity or actual contact between people and animals may lead to a significant risk of anthropozoonotic disease transmission. In this paper, we use social network analysis, disease simulation modelling and data on animal health and behaviour to investigate such risks at a site in Morocco, where tourists come to see wild Barbary macaques (Macaca sylvanus). Measures of individual macaques' network centrality-an index of the strength and distribution of their social relationships and thus potentially their ability to spread disease-did not show clear and consistent relationships with their time spent in close proximity to, or rate of interacting with, tourists. Disease simulation modelling indicated that while higher-ranked animals had a significantly greater ability to spread disease within the group, in absolute terms there was little difference in the size of outbreaks that different individuals were predicted to cause. We observed a high rate of physical contact and close proximity between humans and macaques, including during three periods when the macaques were coughing and sneezing heavily, highlighting the potential risk of disease transmission. We recommend that general disease prevention strategies, such as those aimed at reducing opportunities for contact between tourists and macaques, should be adopted.

The influence of phylogeny, social style, and sociodemographic factors on macaque social network structure

Among nonhuman primates, the evolutionary underpinnings of variation in social structure remain debated, with both ancestral relationships and adaptation to current conditions hypothesized to play determining roles. Here we assess whether interspecific variation in higher-order aspects of female macaque (genus: Macaca) dominance and grooming social structure show phylogenetic signals, that is, greater similarity among more closely-related species. We use a social network approach to describe higher-order characteristics of social structure, based on both direct interactions and secondary pathways that connect group members. We also ask whether network traits covary with each other, with species-typical social style grades, and/or with sociodemographic characteristics, specifically group size, sex-ratio, and current living condition (captive vs. free-living). We assembled 34-38 datasets of female-female dyadic aggression and allogrooming among captive and free-living macaques representing 10 species. We calculated dominance (transitivity, certainty), and grooming (centrality coefficient, Newman's modularity, clustering coefficient) network traits as aspects of social structure. Computations of K statistics and randomization tests on multiple phylogenies revealed moderate-strong phylogenetic signals in dominance traits, but moderate-weak signals in grooming traits. GLMMs showed that grooming traits did not covary with dominance traits and/or social style grade. Rather, modularity and clustering coefficient, but not centrality coefficient, were strongly predicted by group size and current living condition. Specifically, larger groups showed more modular networks with sparsely-connected clusters than smaller groups. Further, this effect was independent of variation in living condition, and/or sampling effort. In summary, our results reveal that female dominance networks were more phylogenetically conserved across macaque species than grooming networks, which were more labile to sociodemographic factors. Such findings narrow down the processes that influence interspecific variation in two core aspects of macaque social structure. Future directions should include using phylogeographic approaches, and addressing challenges in examining the effects of socioecological factors on primate social structure.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes"

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics.

Making New Connections: Insights from Primate-Parasite Networks

Social interactions are important in everyday life for primates and many other group-living animals; however, these essential exchanges also provide opportunities for parasites to spread through social groups. Network analysis is a unique toolkit for studying pathogen transmission in a social context, and recent primate-parasite network studies shed light on linkages between behavior and infectious disease dynamics, providing insights for conservation and public health. We review existing literature on primate-parasite networks, examining determinants of infection risk, issues of network scale and temporal dynamics, and applications for disease control. We also discuss analytical and conceptual gaps that should be addressed to improve our understanding of how individual and group-level factors affect infection risk, while highlighting interesting areas for future research.

Intergroup Variation of Social Relationships in Wild Vervet Monkeys: A Dynamic Network Approach

Social network analysis is a powerful tool that enables us to describe and quantify relationships between individuals. So far most of the studies rely on the analyses of various network snapshots, but do not capture changes over time. Here we use a stochastic actor-oriented model (SAOM) to test both the structure and the dynamics of relationships of three groups of wild vervet monkeys. We found that triadic closure (i.e., the friend of a friend is a friend) was significant in all three groups while degree popularity (i.e., the willingness to associate with individuals with high degree of connections) was significant in only two groups (AK, BD). The structure and dynamics of relationships according to the attributes of sex, matrilineand age differed significantly among groups. With respect to the structure, when analyzing the likelihood of bonds according to the different attributes, we found that individuals associate themselves preferably to individuals of the same sex only in two groups (AK, NH), while significant results for attachment to individuals of the same matriline were found also in two groups (BD, NH). With respect to the dynamics, i.e., how quickly relationships are modified, we found in two groups (AK, BD) that females' relationships were more prone to variation than males.' In the BD group, relationships within high-ranking matrilines were less stable than low-ranking ones while in the NH group, juveniles' relationships were also less stable than adults' ones. The intergroup variation indicates that establishing species-specific or even population specific characteristics of social networks for later between-species comparisons will be challenging. Although, such variation could also indicate some methodological issue, we are quite confident that data was collected similarly within the different groups. Our study therefore provides a potential new method to quantify social complexity according to natural demographic variation.