Editorial: Social networks analyses in primates, a multilevel perspective

Puga-Gonzalez, Ivan; Sosa, Sebastiàn; Sueur, Cédric

doi:10.1007/s10329-019-00720-5

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…We found support for our prediction that social networks along the road would be less cohesive than social networks within the forest. Regardless of network type, social networks had lower density— i.e., were less cohesive 32,78 —along the road than in the forest. The loss of cohesion is further demonstrated by the greater cluster modularity score Q in the road affiliative network compared to the forest affiliative network.…”

Section: Discussionmentioning

confidence: 99%

“…To examine how metrics in forest networks differed from metrics in road networks, we used randomization tests, a common approach to compare observed and simulated social networks 32,34,69 . For both interaction and proximity-based networks, and for each network metric analysed, we first subtracted each individuals’ metric value in the road network from their metric value in the estimated forest network.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, association patterns within a social group can influence group movement patterns 25–28 , individuals’ risk-taking behaviour 17,24 , and group cohesion and stability 12,29 . Social network analysis (SNA) offers a powerful tool to investigate animal sociality and explore how changes in environmental and social context shape group-wide patterns 30–32 . Using social network analysis to examine the causes and effects of human-wildlife interaction represents a promising application of this tool that has yet to be explored fully 17 , 33,34 .…”

Section: Introductionmentioning

confidence: 99%

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Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura)

Morrow

Glanz

Ngakan

et al. 2019

Sci Rep

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Human-wildlife encounters are becoming increasingly frequent across the globe, often leading people to interact with and feed wild animals and impacting animal behaviour and ecology. Although the nature of human-wildlife interactions has been well documented across a number of species, we still have limited understanding as to why some individual animals interact more frequently with humans than others. Additionally, we lack a comprehensive understanding of how these interactions influence animal social networks. Using behavioural data from a group of moor macaque monkeys (Macaca maura), we used permutation-based linear regression analyses to understand how life history and social network factors jointly explain interindividual variation in tendency to interact with humans along a provincial road in South Sulawesi, Indonesia. As our study group spent only a portion of their time in proximity to humans, we also examined how social network structure changes in response to human presence by comparing social networks in the forest to those along the road. We found that sex, individual network position, and associate network position interact in complex ways to influence individual behaviour. Individual variation in tendency to be along the road caused social networks to become less cohesive when in proximity to humans. This study demonstrates that nuanced intragroup analyses are necessary to fully understand and address conservation issues relating to human-wildlife interactions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura)

Morrow

Glanz

Ngakan

et al. 2019

Sci Rep

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“…These links are quantified and graphically represented through social network analysis (SNA; Wasserman & Faust, 1994). The application of SNA within primatology has a long history (Beisner, Jackson, Cameron, & McCowan, 2011; Flack, Girvan, De Waal, & Krakauer, 2006; McCowan, Anderson, Heagarty, & Cameron, 2008; McCowan et al, 2011; Sade, 1972; Sade, Altmann, Loy, Hausfater, & Breuggeman, 1988), but only adopted new software platforms for complex network analytics within the last decade (Brent, Lehmann, & Ramos‐Fernández, 2011; Puga‐Gonzalez, Sosa, & Sueur, 2019). The application of SNA within areas of primatology has included documenting patterns of disease transmission (Gómez, Nunn, & Verdú, 2013; Griffin & Nunn, 2012; MacIntosh et al, 2012; Nunn, 2012; Rimbach et al, 2015; Rushmore et al, 2013), characterizing the structure of adult social interactions (Barrett, Henzi, & Lusseau, 2012; Kasper & Voelkl, 2009; Lehmann & Ross, 2011; Sueur, Jacobs, Amblard, Petit, & King, 2011), modeling fission–fusion dynamics (Ramos‐Fernández & Morales, 2014; Ramos‐Fernández, Boyer, Aureli, & Vick, 2009; Shimooka, 2015; Smith‐Aguilar, Aureli, Busia, Schaffner, & Ramos‐Fernández, 2019; Wakefield, 2013), and assessing structure of captive social groups (Clark, 2011; Dufour, Sueur, Whiten, & Buchanan‐Smith, 2011; Levé, Sueur, Petit, Matsuzawa, & Hirata, 2016; Rodrigues & Boeving, 2019; Schel et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Network analysis as a tool to understand social development in spider monkeys

Boeving

Rodrigues

Nelson

2020

American J Primatol

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The emerging field of network science has demonstrated that an individual's connectedness within their social network has cascading effects to other dimensions of life. Like humans, spider monkeys live in societies with high fission-fusion dynamics, and are remarkably social. Social network analysis (SNA) is a powerful tool for quantifying connections that may vary as a function of initiating or receiving social behaviors, which has been described as shifting social roles. In primatology, the SNA literature is dominated by work in catarrhines, and has yet to be applied to the study of development in a platyrrhine model. Here, SNA was utilized in combination with R-Index social role calculation to characterize social interaction patterns in juvenile and adult Colombian spider monkeys (Ateles fusciceps rufiventris). Connections were examined across five behaviors: embrace, face-embrace, grooming, agonism, and tailwrapping from 186 hr of observation and four network metrics. Mann-Whitney U tests were utilized to determine differences between adult and juvenile social network patterns for each behavior. Face-embrace emerged as the behavior with different network patterns for adults and juveniles for every network metric. With regard to social role, juveniles were receivers, not initiators, for embrace, faceembrace, and grooming (ps < .05). Network and social role differences are discussed in light of social development and aspects of the different behaviors.

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“…The recent update of computational models for social network analysis (SNA) enables quantitative evaluation of complex processes of social networks based using big data. The basic framework of SNA envisions multiple nodes as animals, viruses, people, or any other agents within the network and at its edges as a dyadic relation mathematically defined between the two nodes (e.g., [3][4][5]). The SNA aims to statistically link network structures with bottom-up rules defining dyadic relations, e.g., affiliations, friendships, proximities, co-occurrences, follower/followee, signal sender/receiver, or communications [6].…”

Section: Introductionmentioning

confidence: 99%

Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

2020

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Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data—serialized order movements generated by simple bottom-up rules of collective movements—are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations—a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

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Editorial: Social networks analyses in primates, a multilevel perspective

Cited by 10 publications

References 28 publications

Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura)

Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura)

Network analysis as a tool to understand social development in spider monkeys

Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

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