Abstract: Close associations between adult males and lactating females and their dependent infants are not commonly described in non-monogamous mammals. However, such associations [sometimes called friendships (Smuts 1985)] are regularly observed in several primate species in which females mate with multiple males during the fertile period. The absence of mating exclusivity among friends suggests that males should invest little in infant care, raising questions about the adaptive significance of friendship bonds. Using data from genetic paternity analyses, patterns of behavior, and long-term demographic and reproductive records, we evaluated the extent to which friendships in four multi-male, multi-female yellow baboon (Papio cynocephalus) groups in Amboseli, Kenya represent joint parental care of offspring or male mating effort. We found evidence that mothers and infants benefited directly from friendships; friendships provided mother–infant dyads protection from harassment from other adult and immature females. In addition, nearly half of all male friends were the genetic fathers of offspring and had been observed mating with mothers during the days of most likely conception for those offspring. In contrast, nearly all friends who were not fathers were also not observed to consort with the mother during the days of most likely conception, suggesting that friendships between mothers and non-fathers did not result from paternity confusion. Finally, we found no evidence that prior friendship increased a male’s chances of mating with a female in future reproductive cycles. Our results suggest that, for many male–female pairs at Amboseli, friendships represented a form of biparental care of offspring. Males in the remaining friendship dyads may be trading protection of infants in exchange for some resources or services not yet identified. Our study is the first to find evidence that female primates gain social benefits from their early associations with adult males.

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.

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: I use 10 years of data from a long-term study of lek-mating long-tailed manakins to relate the social network among males to their spatial and genetic structure. Previously, I showed that the network connectivity of young males predicts their future success. Here, I ask whether kinship might shape the organization of this young-boy network . Not surprisingly, males that were more socially distant (linked by longer network paths) were affiliated with perch zones (lek arenas) that were further apart. Relatedness (r) among males within the network decreased as social distance increased, as might be expected under kin selection. Nevertheless, any role for indirect inclusive fitness benefits is refuted by the slightly negative mean relatedness among males at all social distances within the network (overall mean r = -0.06). That is, relatedness ranged from slightly negative (-0.04) to more negative (-0.2). In contrast, relatedness in dyads for which at least one of the males was outside the social network (involving at least one blood-sampled male not documented to have interacted with other banded males) was slightly above the random expectation (mean r = 0.05). The slight variations around r = 0 among males of different categories likely reflect dispersal dynamics, rather than any influence of kinship on social organization. Relatedness did not covary with the age difference between males. These results, together with previous results for lack of relatedness between alpha and beta male partners, refute any role for kin selection in the evolution of cooperative display in this lek-mating system.

Abstract: There is an established and very influential view that primate societies have identifiable, persistent social organizations. It assumes that association patterns reflect long-term strategic interests that are not qualitatively perturbed by short-term environmental variability. We used data from two baboon troops in markedly different habitats over three consecutive seasons to test this assumption. Our results demonstrate pronounced cyclicity in the extent to which females maintained differentiated relationships. When food was plentiful, the companionships identified by social network analysis in the food-scarce season disappeared and were replaced by casual acquaintanceships more representative of mere gregariousness. Data from the fourth, food-scarce, season at one site indicated that few companions were re-united. It is likely that this reflected stochastic variation in individual circumstances. These results suggest that attention could profitably be paid to the effects of short-term local contingencies on social dynamics, and has implications for current theories of primate cognitive evolution.