Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach

Webber, Quinn M. R.; Wal, Eric Vander

doi:10.1016/j.anbehav.2019.01.010

Cited by 84 publications

(107 citation statements)

References 64 publications

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“…Social structure, defined as the patterning of repeated interactions between individuals (Hinde 49 1976), represents a fundamental characteristic of many animal populations with far-reaching 50 consequences for ecology and evolution, including for gene-flow, social evolution, pathogen 51 transmission, and the emergence of culture (Kurvers et al, 2014). The last two decades have seen 52 widespread adoption of social network methods in animal behaviour research to quantify social 53 structure (Webber & vander Wal, 2019). The network framework is appealing because it explicitly 54 represents the relationships between social entities from which social structure emerges (Hinde, 55 1976), and thus allows tests of hypotheses about social structure at a variety of scales (individual, 56 dyadic, group, population).…”

Section: Introduction 48mentioning

confidence: 99%

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

Weiss

Franks

Brent

et al. 2020

Preprint

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22 1. Social network methods have become a key tool for describing, modelling, and testing 23 hypotheses about the social structures of animals. However, due to the non-independence 24 of network data and the presence of confounds, specialized statistical techniques are often 25 needed to test hypotheses in these networks. Datastream permutations, originally 26 developed to test the null hypothesis of random social structure, have become a popular 27 tool for testing a wide array of null hypotheses. In particular, they have been used to test 28 whether exogenous factors are related to network structure by interfacing these 29 permutations with regression models. 30 2. Here, we show that these datastream permutations typically do not represent the null 31 hypothesis of interest to researchers interfacing animal social network analysis with 32 regression modelling, and use simulations to demonstrate the potential pitfalls of using this 33 methodology. 34 3. Our simulations show that utilizing common datastream permutations to test the 35 coefficients of regression models can lead to extremely high type I (false-positive) error rates 36 (> 30%) in the presence of non-random social structure. The magnitude of this problem is 37 primarily dependent on the degree of non-randomness within the social structure and the 38 intensity of sampling 39 4. We strongly recommend against utilizing datastream permutations to test regression models 40 in animal social networks. We suggest that a potential solution may be found in regarding 41 the problems of non-independence of network data and unreliability of observations as 42 separate problems with distinct solutions. 43 44

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Section: Introduction 48mentioning

confidence: 99%

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

Weiss

Franks

Brent

et al. 2020

Preprint

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“…For example, data collected using passive‐integrated transponders (e.g. Aplin et al, ) is increasingly being used to generate animal social networks (Webber & Vander Wal, ) and spatsoc could represent a novel and computationally efficient way to generate social networks for large PIT‐tag datasets. The basic principles of spatsoc and grouping functions can be applied to other data types, including PIT‐tags, as long as both spatial and temporal information are known.…”

Section: Future Directionsmentioning

confidence: 99%

“…These include dynamic interaction networks (Long, Nelson, Webb, & Gee, 2014), PBSNs (Spiegel, Sih, Leu, & Bull, 2017) and the development of traditional randomization techniques to assess non-random structure of PBSNs constructed using animal telemetry data (Spiegel et al, 2016). Despite the recent increase in the number of studies using animal telemetry data and GPS relocation data (Webber & Vander Wal, 2019), there is no comprehensive r package that generates PBSNs using animal telemetry data.…”

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confidence: 99%

Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

2019

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We present spatsoc, an r package for conducting social network analysis with animal telemetry data. Animal social network analysis is a method for measuring relationships between individuals to describe social structure. Proximity‐based social networks are generated from animal telemetry data by grouping relocations temporally and spatially, using thresholds that are informed by the characteristics of the species and study system. spatsoc fills a gap in r packages by providing flexible functions, explicitly for animal telemetry data, to generate edge lists and gambit‐of‐the‐group data, perform data‐stream randomization, and generate group by individual matrices. The implications of spatsoc are that current users of animal telemetry or otherwise georeferenced data for movement or spatial analyses will have access to efficient and intuitive functions to generate social networks.

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“…By evaluating every interaction between pairs of individuals in a group, network analysis can be used to represent integrated systems such as social groups, providing insights into the formation, dynamics, and function of group structure 24,[32][33][34] . This type of analysis can be employed to investigate transmission processes in groups as a basis for understanding complex phenomena such as microbe transmission, social grooming, decision making, and hierarchy 3,32,[35][36][37][38][39][40][41][42][43][44][45][46][47] . Although analysis of individual behaviors and social networks highlight different aspects of social interaction, they are complementary for understanding complex emergent phenomena such as group behavior.…”

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

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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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Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach

Cited by 84 publications

References 64 publications

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

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

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