What are social networks, and how is Encounternet used to study them?

All societies (human and animal) can be viewed as networks of interconnected individuals, linked by social, spatial, temporal, and other relationships. By studying network structure (the links between individuals) we can derive unique insights into the workings of the society, and better understand the behavioral strategies that individuals use to enhance their success. Social network analysis has been extensively developed and used for studying human societies and human behavior. Animals are also social, and researchers have applied social network analysis to animal societies and behaviors as well. To date, surprisingly complex social networks have been described in a wide range of species and taxa, including primates, cetaceans, ungulates, rodents, birds, fish, and insects.

Although there are unique insights to be gained by studying animal social networks, there are also unique problems. In particular, unlike humans, animals will not tell us where they have been and who they associate with, information that is key to studying a social network. Direct observation methods are usually used study animal social associations and movement patterns. However, in many cases observation is not possible, especially when animals must be continuously monitored for long periods of time, when animals behave cryptically (as they often do), or when animals are out of sight of observers (as with nocturnal, arboreal, or fossorial species).

For these reasons, new tools have been developed to allow researchers to monitor social associations on a continuous basis no matter where the animal subjects are. These wireless devices, variously called encounter or proximity logging tags, are worn by all study subjects and can detect and log the presence of other individuals wearing similar tags. When two such tags come to within a preset range, they exchange their unique ID codes and store the event as an “encounter” in a log file. Later, the tags are retrieved, and encounter logs are downloaded and analyzed to determine the association network structure.

The goal of the Encounternet project is to construct such an encounter monitoring tag system that is approximately 100X smaller than the smallest commercially available tag system with similar functionality. For behavioral recording, Encounternet tags will interface with a variety of sensors, including accelerometers, microphones, and neural recording electrode amplifiers. Sensor-equipped tags can be configured to wirelessly stream sensor data to a receiver, or store it to onboard nonvolatile memory for later retrieval. The Encounternet system will use small, portable wireless fixed position collection stations to collect encounter and sensor data autonomously, and monitor the locations of tagged animals. Encounternet tags will be reconfigurable via a wireless link, eliminating the need to recapture tagged animals. Finally, we are exploring energy harvesting technology, such as solar cells, that will allow tags to operate on animals indefinitely without a battery.

Encounternet is targeted to weight approximately 1 gram, while allowing features and capabilities are currently only available in devices several orders of magnitude larger and heavier. Encounternet will thus, for the first time, provide a powerful behavior monitoring tool for small animal research.