Abstract: Abstract  Unshared consensus decision-making processes, in which one or a small number of individuals make the decision for the rest of a group, are rarely documented. However, this mechanism can be beneficial for all group members when one individual has greater knowledge about the benefits of the decision than other group members. Such decisions are reached during certain activity shifts within the population of bottlenose dolphins residing in Doubtful Sound, New Zealand. Behavioral signals are performed by one individual and seem to precipitate shifts in the behavior of the entire group: males perform side flops and initiate traveling bouts while females perform upside-down lobtails and terminate traveling bouts. However, these signals are not observed at all activity shifts. We find that, while side flops were performed by males that have greater knowledge than other male group members, this was not the case for females performing upside-down lobtails. The reason for this could have been that a generally high knowledge about the optimal timing of travel terminations rendered it less important which individual female made the decision.

Abstract: Self‐organizing‐system approaches have shed significant light on the mechanisms underlying synchronized movements by large groups of animals, such as shoals of fish, flocks of birds, or herds of ungulates. However, these approaches rarely consider conflicts of interest between group members, although there is reason to suppose that such conflicts are commonplace. Here, we demonstrate that, where conflicts exist, individual members of self‐organizing groups can, in principle, increase their influence on group movement destination by strategically changing simple behavioral parameters (namely, movement speed, assertiveness, and social attraction range). However, they do so at the expense of an increased risk of group fragmentation and a decrease in movement efficiency. We argue that the resulting trade‐offs faced by each group member render it likely that group movements are led by those members for which reaching a particular destination is most crucial or group cohesion is least important. We term this phenomenon leading according to “need” or “social indifference,” respectively. Both kinds of leading can occur in the absence of knowledge of or communication about the needs of other group members and without the assumption of altruistic cooperation. We discuss our findings in the light of observations on fish and other vertebrates.

Abstract: Controversy exists regarding the nature of primate social relationships. While individual primates are frequently hypothesized to form enduring social bonds with conspecifics, recent studies suggest that relationships are labile, with animals interacting only over short periods to satisfy their immediate needs. Here I use data collected over 10 years on a community of chimpanzees, Pan troglodytes, at Ngogo, Kibale National Park, Uganda, to investigate whether male chimpanzees establish long-term social relationships and to determine the factors that affect variation in relationship quality and the stability of social bonds. Kinship and dominance rank influenced the quality of relationships. Maternal brothers and males of the same dominance rank class groomed each other more equitably than did unrelated males and males that were dissimilar in rank. In addition, males that formed strong social bonds groomed more equitably than did males that displayed weaker bonds. Social bonds were stable over time, with relationships in one year predicting those in subsequent years. Kinship and the quality of social relationships affected bond stability. Maternal half siblings and males that groomed each other equitably maintained longer-lasting bonds than did nonkin and males that groomed each other unevenly. Virtually all of the males established at least one enduring relationship with another individual. The most enduring bonds formed between a few pairs of maternal brothers and dyads that maintained balanced grooming interactions. These results indicate that male chimpanzees maintain long-lasting and equitable social bonds whose formation is affected by maternal kinship and the quality of social relationships.

Abstract: Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a behavior space spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research.

Abstract: Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network.