Measuring social networks in primates: wearable sensors vs. direct observations

Abstract

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: behavioral 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. Sensors data yield moreover a reliable social network already over short timescales and can be used for long term campaigns, showing their important potential for detailed studies of the evolution of animal social groups. Introduction 1 Interactions between individuals are the foundation of complex social structures in 2 human and other animal societies. Network analysis represents a valuable framework to 3 understand the structure and evolution of these interactions, as it encodes a whole 4 hierarchy of patterns, from individual-level interactions to complex population-level 5 social structures. [1-3, 3-8].6 With the increasing deployment of digital devices, new ways of collecting data, 7 combined with new network analysis tools, have made possible the development of 8 quantitative measures of these relationships and patterns in modern human societies, 9 January 17, 2020 1/20leading to the emergence of computational social science in the last decades [9]. For 10 instance, social relationships have been inferred and studied using various data sources 11 ranging from phone calls [10], e-mails [11,12], online interactions [13], to face-to-face 12 interactions measured by wearable sensors [14][15][16][17][18][19][20]. 13The availability of large volumes of data with high temporal resolution has thus 14 contributed to the rapid expansion of data-driven computational studies of human 15 relationships and human social networks. On the contrary, the data collection remains 16 more challenging in the field of animal studies, because the data on animal interactions 17 are still largely obtained from direct observations [6,21]. Data resulting from such 18 observations are extremely valuable as they often include detailed information about the 19 nature, duration and location of the interactions between individuals. They thus allow 20 researchers to grasp and investigate complex social patterns in animal groups. 21 Unfortunat...

The Future of Artificial Intelligence in Monitoring Animal Identification, Health, and Behaviour

“…Evidently, a variety of technologies are available, such as wearable sensors, described above for livestock, that can track individuals and monitor changes [33,34]. However, it is important to note that, as the title suggests, these devices are quite obtrusive and sometimes invasive.…”