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 \emph{multilayer network analysis}, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as 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 organisation. 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.Comment: Thoroughly revised; title changed slightl

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. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks

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

Spatial encoding in primate hippocampus during free navigation.

The hippocampus comprises two neural signals-place cells and θ oscillations-that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)-rather than continuously-and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals

The use of multilayer network analysis in animal behaviour

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We gratefully acknowledge the 806 supporters of MX16: the UC Davis Institute for Social Sciences, the U.S. Army Research Office 807 under Multidisciplinary University Research Initiative Award No. W911NF-13-1-0340, the UC 808 Davis Complexity Sciences Center, the UC Davis Anthropology Department, the UC Davis 809 Graduate Student Association, the UC Davis Department of Engineering, and the UC Davis 810 Office of Research.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.Natural Environment Research Council (NERC)National Science Foundation (NSF) Graduate Research FellowshipNFS IOS grantNIH R01NERC standard gran

The brain: What is critical about it?

We review the recent proposal that the most fascinating brain properties are related to the fact that it always stays close to a second order phase transition. In such conditions, the collective of neuronal groups can reliably generate robust and flexible behavior, because it is known that at the critical point there is the largest abundance of metastable states to choose from. Here we review the motivation, arguments and recent results, as well as further implications of this view of the functioning brain.Comment: Proceedings of BIOCOMP2007 - Collective Dynamics: Topics on Competition and Cooperation in the Biosciences. Vietri sul Mare, Italy (2007