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: Animal groups arise from individuals’ choices about the number, characteristics, and identity of associates. Individuals make these choices to gain benefits from their associations. As the needs of an individual change with its phenotype, so too we expect the nature of its associations to vary. In this paper, we investigate how the social priorities of male plains zebra (Equus burchelli) depend on reproductive state. An adult male is either a bachelor, and lacking mating access, or a stallion defending a harem. Multiple harems and bachelor males aggregate in larger herds. Herds frequently split and merge, affording males opportunities to change associates. Over a 4-year period, we sampled the herd associations in a population of 500–700 zebras. To isolate the effects of reproductive state on male social behavior, we account for potential confounding factors: changes in population size, grouping tendencies, and sampling intensity. We develop a generally applicable permutation procedure, which allows us to test the null hypothesis that social behavior is independent of male status. Averaging over all individuals in the population, we find that a typical bachelor is found in herds containing significantly more adults, bachelors, and stallions than the herds of a typical stallion. Further, bachelors’ bonds with each other are more persistent over time than those among stallions. These results suggest that bachelors form cohesive cliques, in which we may expect cooperative behaviors to develop. Stallion–stallion associations are more diffuse, and less conducive to long-term cooperation.

Abstract: Social interactions are critical to the organization of worker activities in insect colonies and their consequent ecological success. The structure of this interaction network is therefore crucial to our understanding of colony organization and functioning. In this paper, I study the properties of the interaction network in the colonies of the social wasp Ropalidia marginata. I find that the network is characterized by a uniform connectivity among individuals with increasing heterogeneity as colonies become larger. Important network parameters are found to be correlated with colony size and I investigate how this is reflected in the organization of work in colonies of different sizes. Finally, I test the resilience of these interaction networks by experimental removal of individuals from the colony and discuss the structural properties of the network that are related to resilience in a social network.

Abstract: We use two novel techniques to analyze association patterns in a group of wild spider monkeys (Ateles geoffroyi) studied continuously for 8 years. Permutation tests identified association rates higher or lower than chance expectation, indicating active processes of companionship and avoidance as opposed to passive aggregation. Network graphs represented individual adults as nodes and their association rates as weighted edges. Strength and eigenvector centrality (a measure of how strongly linked an individual is to other strongly linked individuals) were used to quantify the particular role of individuals in determining the network's structure. Female–female dyads showed higher association rates than any other type of dyad, but permutation tests revealed that these associations cannot be distinguished from random aggregation. Females formed tightly linked clusters that were stable over time, with the exception of immigrant females who showed little association with any adult in the group. Eigenvector centrality was higher for females than for males. Adult males were associated mostly among them, and although their strength of association with others was lower than that of females, their association rates revealed a process of active companionship. Female–male bonds were weaker than those between same-sex pairs, with the exception of those involving young male adults, who by virtue of their strong connections both with female and male adults, appear as temporary brokers between the female and male clusters of the network. This analytical framework can serve to develop a more complete explanation of social structure in species with high levels of fission–fusion dynamics.

Abstract: Abstract  SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.