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

Adaptive mechanisms of social and asocial learning in immersive collective foraging

Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories. Our behavioral analyses capture both the structure and temporal dynamics of social interactions, which are then directly tested using computational models sequentially predicting each foraging decision. These results reveal that adaptation mechanisms of both asocial foraging and selective social learning are driven by individual foraging success (rather than social factors). Furthermore, it is the degree of adaptivity-of both asocial and social learning-that best predicts individual performance. These findings not only integrate theories across asocial and social domains, but also provide key insights into the adaptability of human decision-making in complex and dynamic social landscapes.

The dynamics of sociality and glucocorticoids in wild male Assamese macaques

For males of gregarious species, dominance status and the strength of affiliative relationships can have major fitness consequences. Social dynamics also impose costs by affecting glucocorticoids, mediators of homeostasis and indicators of the physiological response to challenges and within-group competition. We investigated the relationships between dominance, social bonds, seasonal challenges, and faecal glucocorticoid metabolite (fGC) measures in wild Assamese macaques (Macaca assamensis) at Phu Khieo Wildlife Sanctuary, Thailand, combining behavioural data with 4129 samples from 62 adult males over 15 years. Our previous work on this population suggested that increased competition during the mating season was associated with elevated fGC levels and that, unusually for male primates, lower rank position correlated with higher fGC levels. With a much larger dataset and dynamic measures of sociality, we re-examined these relationships and additionally tested the potentially fGC-attenuating effect of social support. Contrary to our previous study, yet consistent with the majority of work on male primates, dominance rank had a positive relationship with fGC levels, as high status correlated with elevated glucocorticoid measures. fGC levels were increased at the onset of the mating season. We demonstrated an fGC-reducing effect of supportive relationships in males and showed that dynamics in affiliation can correlate with dynamics in physiological responses. Our results suggest that in a system with intermediate contest potential, high dominance status can impose physiological costs on males that may potentially be moderated by social relationships. We highlight the need to consider the dynamics of sociality and competition that influence hormonal processes.

Social consequences of rapid environmental change

While direct influences of the environment on population growth and resilience are well studied, indirect routes linking environmental changes to population consequences are less explored. We suggest that social behavior is key for understanding how anthropogenic environmental changes affect the resilience of animal populations. Social structures of animal groups are evolved and emergent phenotypes that often have demographic consequences for group members. Importantly, environmental drivers may directly influence the consequences of social structure or indirectly influence them through modifications to social interactions, group composition, or group size. We have developed a framework to study these demographic consequences. Estimating the strength of direct and indirect pathways will give us tools to understand, and potentially manage, the effect of human-induced rapid environmental changes.

What Makes Inventions Become Traditions?

Although anthropology was the first academic discipline to investigate cultural change, many other disciplines have made noteworthy contributions to understanding what influences the adoption of new behaviors. Drawing on a broad, interdisciplinary literature covering both humans and nonhumans, we examine ( a) which features of behavioral traits make them more transmissible, ( b) which individual characteristics of inventors promote copying of their inventions, ( c) which characteristics of individuals make them more prone to adopting new behaviors, ( d) which characteristics of dyadic relationships promote cultural transmission, ( e) which properties of groups (e.g., network structures) promote transmission of traits, and ( f) which characteristics of groups promote retention, rather than extinction, of cultural traits. One of anthropology's strengths is its readiness to adopt and improve theories and methods from other disciplines, integrating them into a more holistic approach; hence, we identify approaches that might be particularly useful to biological and cultural anthropologists, and knowledge gaps that should be filled.

Social Support and Network Formation in a Small-Scale Horticulturalist Population

Evolutionary studies of cooperation in traditional human societies suggest that helping family and responding in kind when helped are the primary mechanisms for informally distributing resources vital to day-to-day survival (e.g., food, knowledge, money, childcare). However, these studies generally rely on forms of regression analysis that disregard complex interdependences between aid, resulting in the implicit assumption that kinship and reciprocity drive the emergence of entire networks of supportive social bonds. Here I evaluate this assumption using individual-oriented simulations of network formation (i.e., Stochastic Actor-Oriented Models). Specifically, I test standard predictions of cooperation derived from the evolutionary theories of kin selection and reciprocal altruism alongside well-established sociological predictions around the self-organisation of asymmetric relationships. Simulations are calibrated to exceptional public data on genetic relatedness and the provision of tangible aid amongst all 108 adult residents of a village of indigenous horticulturalists in Nicaragua (11,556 ordered dyads). Results indicate that relatedness and reciprocity are markedly less important to whom one helps compared to the supra-dyadic arrangement of the tangible aid network itself.

Using network synchrony to identify drivers of social dynamics

Social animals frequently show dynamic social network patterns, the consequences of which are felt at the individual and group level. It is often difficult, however, to identify what drivers are responsible for changes in these networks. We suggest that patterns of network synchronization across multiple social groups can be used to better understand the relative contributions of extrinsic and intrinsic drivers. When groups are socially separated, but share similar physical environments, the extent to which network measures across multiple groups covary (i.e. network synchrony) can provide an estimate of the relative roles of extrinsic and intrinsic drivers. As a case example, we use allogrooming data from three adjacent vervet monkey groups to generate dynamic social networks. We found that network strength was strongly synchronized across the three groups, pointing to shared extrinsic environmental conditions as the driver. We also found low to moderate levels of synchrony in network modularity, suggesting that intrinsic social processes may be more important in driving changes in subgroup formation in this population. We conclude that patterns of network synchronization can help guide future research in identifying the proximate mechanisms behind observed social dynamics in animal groups.

A guide to choosing and implementing reference models for social network analysis

Analysing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.

Does the social network structure of wild animal populations differ from that of animals in captivity?

The social behaviour of wild animals living in groups leads to social networks with structures that produce group-level effects and position individuals within them with differential consequences for an individual's fitness. Social dynamics in captivity can differ greatly from those in wild conspecifics given the different constraints on social organization in wild populations, e.g. group size, predation pressure, distribution of resources (food, mates), which are all regulated by human carers in captive populations. The social networks of animals in zoos is expected to differ from those of free-living conspecifics. While many studies have described the social networks of a wide diversity of wild and captive animals, none has directly compared the networks of multiple groups of a single species both in the wild and in captivity. Meerkats, Suricata suricatta, are an excellent species to compare the social networks of wild and captive groups. We replicated the methods of Madden et al. (2009, 2011), who studied eight groups in the wild, in fifteen captive groups. We tested how network structures and individual positions in grooming, foraging competition and dominance networks differed between wild and captive groups. Groups of wild and captive meerkats differed in various aspects of their social network structure. Differences in the network may be due to individuals occupying different network positions and the difference in the number and strength of their connections to other individuals. This distinct way of interacting and associating could be a result of group specific attributes, such as group size, and/or the attributes of the donor and recipient, including sex, status or age. Critically, the differences may be explained by the dissimilar living environment that each encounters.

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.

The performance of permutations and exponential random graph models when analyzing animal networks

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

Understanding Allogrooming Through a Dynamic Social Network Approach: An Example in a Group of Dairy Cows

For gregarious species such as domestic cattle, the social environment is a very important determinant of their welfare and fitness. Understanding the complexity of cows' relationships can assist the development of management practices that are more integrated with the cows' social behavioral processes. The two aims of this study were: (1) to determine the dynamics of affiliative relationships, as indicated by allogrooming, by means of stochastic actor-oriented modeling, in dairy cows during early lactation; (2) to explore the underlying processes and the individual attributes, such as age, social rank and reproductive state, that could shape network pattern changes in grooming contacts between individual. We observed the allogrooming behavior of a dynamic group of 38 dairy cows for 4 h per day for 30 days. Using stochastic actor-oriented models, we modeled the dynamics of weekly contacts and studied how structural processes (e.g., reciprocity, transitivity, or popularity) and individual attributes (i.e., age, social rank, and reproductive state) influence network changes. We found that cows tended to groom individuals that had previously groomed them, implying a possible cooperation. Cows that groomed more actively did not appear to have a preference for specific individuals in the herd, and in return, tended to be groomed by fewer cows over time. Older individuals groomed more cows than younger ones, indicating that allogrooming could be related to seniority. Cows groomed mainly individuals of similar age, suggesting that familiarity and growing up together enhanced social grooming. Over time, cows with higher social rank were groomed by fewer cows and individuals recently reintroduced to the group groomed more herdmates. The study of social network dynamics can be used to better understand the complexity and non-linearity of cow relationships. Our findings, along with further research, can complement and strengthen the design of improved management practices that are more in line with the natural social behavior of cows.

How demographic processes shape animal social networks

Demographic processes play a key role in shaping the patterns of social relations among individuals in a population. Social network analysis is a powerful quantitative tool for assessing the social structure formed by associations between individuals. However, demographic processes are rarely accounted for in such analyses. Here, we summarize how the structure of animal social networks is shaped by the joint effects of social behavior and turnover of individuals and suggest how a deeper understanding of these processes can open new, exciting avenues for research. Death or dispersal can have the direct effect of removing an individual and all its social connections, and can also have indirect effects, spurring changes in the distribution of social connections between remaining individuals. Recruitment and integration of juveniles and immigrant into existing social networks are critical to the emergence and persistence of social network structure. Together, these behavioral responses to loss and gain of social partners may impact how societies respond to seasonal or catastrophic turnover events. The fitness consequences of social position (e.g., survival and reproductive rates) may also create feedback between the social network structure and demography. Understanding how social structure changes in response to turnover of individuals requires further integration between long-term field studies and network modeling methods. These efforts will likely yield new insights into the connections between social networks and life history, ecological change, and evolutionary dynamics.

Dynamic Relationships between Information Transmission and Social Connections

Understanding the drivers of sociality is a major goal in biology. Individual differences in social connections determine the overall group structure and have consequences for a variety of processes, including if and when individuals acquire information from conspecifics. Effects in the opposite direction, where information acquisition and transmission have consequences for social connections, are also likely to be widespread. However, these effects are typically overlooked. We propose that individuals who successfully learn about their environment become valuable social partners and become highly connected, leading to feedback-based dynamic relationships between social connections and information transmission. These dynamics have the potential to change our understanding of social evolution, including how selection acts on behavior and how sociality influences population-level processes.

The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.

Social interactions shape individual and collective personality in social spiders

The behavioural composition of a group and the dynamics of social interactions can both influence how social animals work collectively. For example, individuals exhibiting certain behavioural tendencies may have a disproportionately large impact on the group, and so are referred to as keystone individuals, while interactions between individuals can facilitate information transmission about resources. Despite the potential impact of both behavioural composition and interactions on collective behaviour, the relationship between consistent behaviours (also known as personalities) and social interactions remains poorly understood. Here, we use stochastic actor-oriented models to uncover the interdependencies between boldness and social interactions in the social spider Stegodyphus dumicola We find that boldness has no effect on the likelihood of forming social interactions, but interactions do affect boldness, and lead to an increase in the boldness of the shyer individual. Furthermore, spiders tend to interact with the same individuals as their neighbours. In general, boldness decreases over time, but once an individual's boldness begins to increase, this increase accelerates, suggesting a positive feedback mechanism. These dynamics of interactions and boldness result in skewed boldness distributions of a few bold individuals and many shy individuals, as observed in nature. This group behavioural composition facilitates efficient collective behaviours, such as rapid collective prey attack. Thus, by examining the relationship between behaviour and interactions, we reveal the mechanisms that underlie the emergence of adaptive group composition and collective behaviour.