Detecting social (in)stability in primates from their temporal co-presence network

Modelling animal network data in R using STRAND

There have been recent calls for wider application of generative modelling approaches in applied social network analysis. At present, however, it remains difficult for typical end users-for example, field researchers-to implement generative network models, as there is a dearth of openly available software packages that make application of such models as simple as other, permutation-based approaches. Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of animal social network data that can be implemented using simple, base R syntax. To facilitate ease of use, we provide a tutorial demonstrating how STRAND can be used to model proportion, count or binary network data using stochastic block models, social relation models or a combination of the two modelling frameworks. STRAND facilitates the application of generative network models to a broad range of data found in the animal social networks literature.

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.

Reconstructing signed relations from interaction data

Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in five communities. We then show that these relations correspond to the ones reported by the individuals themselves. Additionally, using inferred relations, we study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. Finally, we study group cohesion in the analyzed communities by evaluating triad statistics in the reconstructed signed network.

The "Second Life" of laboratory rats (Rattus norvegicus): Assessment of social behavior of a colony of rats based on social network analysis

Rattus norvegicus is a social animal and holds a significant economic value, considering its use in scientific research. Here, we use the Social Network Analysis (SNA) approach to study the social interactions of a group of rats held in a post-laboratory animal care facility. We collected interaction data during four study periods, for a total of 60 days. At the group level, rats presented two communities for each study period, consisting mainly of littermates. At individual level, we found that the rats preferred to interact with individuals of the same strain and laboratory of origin and with littermates. At temporal level, we studied how stable social interactions were over time. During the first study period, we found high social stability, whereas the introduction of new individuals in the subsequent period caused social rearrangements; however, the initial social stability was restored. Our findings have shown that studying the social behavior of rats using SNA is a valuable tool for advancing our understanding of the social system of this species, which has the potential to enhance management and welfare practices.

The evolution of social timing

Sociality and timing are tightly interrelated in human interaction as seen in turn-taking or synchronised dance movements. Sociality and timing also show in communicative acts of other species that might be pleasurable, but also necessary for survival. Sociality and timing often co-occur, but their shared phylogenetic trajectory is unknown: How, when, and why did they become so tightly linked? Answering these questions is complicated by several constraints; these include the use of divergent operational definitions across fields and species, the focus on diverse mechanistic explanations (e.g., physiological, neural, or cognitive), and the frequent adoption of anthropocentric theories and methodologies in comparative research. These limitations hinder the development of an integrative framework on the evolutionary trajectory of social timing and make comparative studies not as fruitful as they could be. Here, we outline a theoretical and empirical framework to test contrasting hypotheses on the evolution of social timing with species-appropriate paradigms and consistent definitions. To facilitate future research, we introduce an initial set of representative species and empirical hypotheses. The proposed framework aims at building and contrasting evolutionary trees of social timing toward and beyond the crucial branch represented by our own lineage. Given the integration of cross-species and quantitative approaches, this research line might lead to an integrated empirical-theoretical paradigm and, as a long-term goal, explain why humans are such socially coordinated animals.

Modeling framework unifying contact and social networks

Temporal networks of face-to-face interactions between individuals are useful proxies of the dynamics of social systems on fast timescales. Several empirical statistical properties of these networks have been shown to be robust across a large variety of contexts. To better grasp the role of various mechanisms of social interactions in the emergence of these properties, models in which schematic implementations of such mechanisms can be carried out have proven useful. Here, we put forward a framework to model temporal networks of human interactions based on the idea of a coevolution and feedback between (i) an observed network of instantaneous interactions and (ii) an underlying unobserved social bond network: Social bonds partially drive interaction opportunities and in turn are reinforced by interactions and weakened or even removed by the lack of interactions. Through this coevolution, we also integrate in the model well-known mechanisms such as triadic closure, but also the impact of shared social context and nonintentional (casual) interactions, with several tunable parameters. We then propose a method to compare the statistical properties of each version of the model with empirical face-to-face interaction data sets to determine which sets of mechanisms lead to realistic social temporal networks within this modeling framework.

Dynamics of cooperative networks associated with gender among South Indian Tamils

Helping behaviour is thought to play a major role in the evolution of group-living animals. Yet, it is unclear to what extent human males and human females use the same strategies to secure support. Accordingly, we investigate help-seeking over a 5-year period in relation to gender using data from virtually all adults in two Tamil villages (N = 782). Simulations of network dynamics (i.e. stochastic actor-oriented models) calibrated to these data broadly indicate that women are more inclined than men to create and maintain supportive bonds via multiple mechanisms of cooperation (e.g. reciprocity, kin bias, friend bias, generalized exchange). However, gender-related differences in the simulated dynamics of help-seeking are modest, vary based on structural position (e.g. out-degree), and do not appear to translate to divergence in the observed structure of respondents' egocentric networks. Findings ultimately suggest that men and women in the two villages are similarly social but channel their sociality differently. This article is part of the theme issue 'Cooperation among women: evolutionary and cross-cultural perspectives'.

High-resolution tracking of hyrax social interactions highlights nighttime drivers of animal sociality

Network structure is a key driver of animal fitness, pathogen transmission, information spread, and population demographics in the wild. Although a considerable body of research applied network analysis to animal societies, only little effort has been devoted to separate daytime and nighttime sociality and explicitly test working hypotheses on social structures emerging at night. Here, we investigated the nighttime sociality of a wild population of rock hyraxes (Procavia capensis) and its relation to daytime social structure. We recorded nearly 15,000 encounters over 27 consecutive days and nights using proximity loggers. Overall, we show that hyraxes are more selective of their social affiliates at night compared to daytime. We also show that hyraxes maintain their overall network topology while reallocating the weights of social relationships at the daily and monthly scales, which could help hyraxes maintain their social structure over long periods while adapting to local constraints and generate complex social dynamics. These results suggest that complex network dynamics can be a by-product of simple daily social tactics and do not require high cognitive abilities. Our work sheds light on the function of nighttime social interactions in diurnal social species.

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.

From temporal network data to the dynamics of social relationships

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group's evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates' datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.

Assessing the reliability of an automated method for measuring dominance hierarchy in non-human primates

Among animal societies, dominance is an important social factor that influences inter-individual relationships. However, assessing dominance hierarchy can be a time-consuming activity which is potentially impeded by environmental factors, difficulties in the recognition of animals, or disturbance of animals during data collection. Here we took advantage of novel devices, machines for automated learning and testing (MALT), designed primarily to study non-human primate cognition, to additionally measure the dominance hierarchy of a semi-free-ranging primate group. When working on a MALT, an animal can be replaced by another, which could reflect an asymmetric dominance relationship. To assess the reliability of our method, we analysed a sample of the automated conflicts with video scoring and found that 74% of these replacements included genuine forms of social displacements. In 10% of the cases, we did not identify social interactions and in the remaining 16% we observed affiliative contacts between the monkeys. We analysed months of daily use of MALT by up to 26 semi-free-ranging Tonkean macaques (Macaca tonkeana) and found that dominance relationships inferred from these interactions strongly correlated with the ones derived from observations of spontaneous agonistic interactions collected during the same time period. An optional filtering procedure designed to exclude chance-driven displacements or affiliative contacts suggests that the presence of 26% of these interactions in data sets did not impair the reliability of this new method. We demonstrate that this method can be used to assess the dynamics of both individual social status, and group-wide hierarchical stability longitudinally with minimal research labour. Further, it facilitates a continuous assessment of dominance hierarchies in captive groups, even during unpredictable environmental or challenging social events, which underlines the usefulness of this method for group management purposes. Altogether, this study supports the use of MALT as a reliable tool to automatically and dynamically assess dominance hierarchy within captive groups of non-human primates, including juveniles, under conditions in which such technology can be used.

Computerized assessment of dominance hierarchy in baboons (Papio papio)

Dominance hierarchies are an important aspect of Primate social life, and there is an increasing need to develop new systems to collect social information automatically. The main goal of this research was to explore the possibility to infer the dominance hierarchy of a group of Guinea baboons (Papio papio) from the analysis of their spontaneous interactions with freely accessible automated learning devices for monkeys (ALDM, Fagot & Bonté Behavior Research Methods, 42, 507-516, 2010). Experiment 1 compared the dominance hierarchy obtained from conventional observations of agonistic behaviours to the one inferred from the analysis of automatically recorded supplanting behaviours within the ALDM workstations. The comparison, applied to three different datasets, shows that the dominance hierarchies obtained with the two methods are highly congruent (all rs ≥ 0.75). Experiment 2 investigated the experimental potential of inferring dominance hierarchy from ALDM testing. ALDM data previously published in Goujon and Fagot (Behavioural Brain Research, 247, 101-109, 2013) were re-analysed for that purpose. Results indicate that supplanting events within the workstations lead to a transient improvement of cognitive performance for the baboon supplanting its partners and that this improvement depends on the difference in rank between the two baboons. This study therefore opens new perspectives for cognitive studies conducted in a social context.

Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Neural computation is associated with the emergence, reconfiguration, and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatiotemporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and entorhinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve through time and as a function of changing global brain state (theta vs. slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units links to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and entorhinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity.

It is generally thought that computations performed by local brain circuits rely on complex neural processes, associated with the flexible waxing and waning of cell assemblies, that is, an ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time-averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness.” Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists.” Finally, by avoiding averages over preimposed states, we show that within each global oscillatory state rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

Measuring social networks in primates: wearable sensors versus direct observations

Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.