Rethinking animal social complexity measures with the help of complex systems concepts

Hobson, Elizabeth A.; Ferdinand, Vanessa; Kolchinsky, Artemy; Garland, Joshua

doi:10.1016/j.anbehav.2019.05.016

Cited by 40 publications

(47 citation statements)

References 77 publications

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“…2012 ). Multilayer networks, however, can contain an even wider range of social units—they can simultaneously represent micro-level interactions, higher-level patterning of interactions into relationships and individual roles, macro scale structure across the whole group such as dominance hierarchies, and finally the entire group structure across all social domains, all of which are important components of sociality ( Hobson et al. 2019 ).…”

Section: Measuring Social Structurementioning

confidence: 99%

“…If uniquely assigned to individuals, interlayer edges can help preserve more detail about how individuals are likely to behave and represent individual differences in a more detailed way. Researchers can represent “types” of individuals in ways that match the differentiation a species is able to make among group members given their cognitive abilities ( Hobson et al. 2019 ), or in ways that reflect functionally different contributions to group dynamics.…”

Section: The Functional Parts Of a Multilayer Social Networkmentioning

confidence: 99%

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Multilayer network analyses as a toolkit for measuring social structure

Finn

2021

Current Zoology

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The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses—some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.

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Section: Measuring Social Structurementioning

confidence: 99%

Section: The Functional Parts Of a Multilayer Social Networkmentioning

confidence: 99%

Multilayer network analyses as a toolkit for measuring social structure

Finn

2021

Current Zoology

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“…When individuals interact in a group, their previous experience, motivation and physiological state (termed here as internal state) affect their action selection, giving rise to diverse activity levels, behavioral responses, and engagement with others [6][7][8] . This results in a highly complex and ever changing environment, where each interaction can change the social context of subsequent interactions, leading to a variety of behavioral outcomes from what seem to be identical starting conditions 7,9 . The complex nature of this environment imposes conceptual challenges in the quantification and analysis of group behavior 10 .…”

Section: Introductionmentioning

confidence: 99%

Social interaction and network structure in groups ofDrosophilamales are shaped by prior social experience and group composition

Bentzur

Ben-Shaanan

Benishou

et al. 2020

Preprint

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Living in a group creates a complex and dynamic environment in which the behavior of the individual is influenced by and affects the behavior of others. Although social interactions and group living are fundamental adaptations exhibited by many organisms, relatively little is known about how prior social experience, internal states and group composition shape behavior in a group, and the neuronal and molecular mechanisms that mediate it. Here we present a practical framework for studying the interplay between social experience and group interaction in Drosophila melanogaster and show that the structure of social networks and group interactions are sensitive to group composition and individuals' social history. We simplified the complexity of interaction in a group using a series of experiments in which we controlled the social experience and motivational states of individuals to dissect patterns that represent distinct structures and behavioral responses of groups under different social conditions. Using high-resolution data capture, machine learning and graph theory, we analyzed 60 distinct behavioral and social network features, generating a comprehensive representation ("group signature") for each condition. We show that social enrichment promotes the formation of a distinct group structure that is characterized by high network modularity, high interindividual and inter-group variance, high inter-individual synchrony, and stable social clusters. Using environmental and genetic manipulations, we show that this structure requires visual and pheromonal cues. Particularly, the male specific pheromone cVA and Or65a sensory neurons are necessary for the expression of different aspects of social interaction in a group. Finally, we explored social interactions in heterogenous groups and identified clusters of features that are sensitive to increasing ratios of aggressive flies, some of which reveal that inter-individual synchronization depends on group composition. Our results demonstrate that fruit flies exhibit complex and dynamic social structures that are modulated by the experience and composition of different individuals within the group.This paves the path for using simple model organisms to dissect the neurobiology of behavior in complex environments associated with living in a group.Many species have adapted to living in groups, from simple organisms, such as nematodes, to humans. Group living takes different forms with various levels of complexity, from almost random interactions to fully synchronized collective behavior 1-5 , and can be described by measuring the behavior of individuals, the interaction between individuals and the resulting social network, altogether defined here as "group behavior". When individuals interact in a group, their internal motivational state, previous memories and other physiological processes affect their action selection, giving rise to diverse activity levels, behavioral responses, and engagement with others 6 . This results in a highly complex and continuously changing envi...

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“…A great many studies on a variety of species, ranging from insects to nonhuman primates demonstrated the complexity of social structures in the animal kingdom 13 , 14 . Particularly for nonhuman primates, who are living in complex social societies, using a variety of strategies and behaviours to interact and connect with each other 15 , it seems a promising approach to implement a more realistic framework in order to explore their social structures.…”

Section: Introductionmentioning

confidence: 99%

Assessing the sociability of former pet and entertainment chimpanzees by using multiplex networks

Crailsheim

Romani

Llorente

et al. 2020

Sci Rep

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Advances in the field of social network analysis facilitate the creation of multiplex networks where several interaction types can be analysed simultaneously. In order to test the potential benefits of this approach, we investigated the sociability of atypically raised chimpanzees by constructing and analysing 4-layered multiplex networks of two groups of former pet and entertainment chimpanzees (Pan troglodytes). These networks are based on four social interaction types (stationary vicinity, affiliative behaviour, allogrooming, passive close proximity) representing low- to high-level interaction types in terms of sociability. Using the tools provided by the MuxViz software, we could assess and compare the similarity and information gain of each these social interaction types. We found some social interaction types to be more similar than other ones. However, each social interaction type imparted different information. We also tested for a possible impact of the chimpanzees’ biographical background on the social interaction types and found affiliative behaviour as well as allogrooming to be affected by adverse early life experiences. We conclude that this multiplex approach provides a more realistic framework giving detailed insight into the sociability of these chimpanzees and can function as a tool to support captive care management decisions.

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Rethinking animal social complexity measures with the help of complex systems concepts

Cited by 40 publications

References 77 publications

Multilayer network analyses as a toolkit for measuring social structure

Multilayer network analyses as a toolkit for measuring social structure

Social interaction and network structure in groups ofDrosophilamales are shaped by prior social experience and group composition

Assessing the sociability of former pet and entertainment chimpanzees by using multiplex networks

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