1
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Stranks J, Heistermann M, Sangmaneedet S, Schülke O, Ostner J. The dynamics of sociality and glucocorticoids in wild male Assamese macaques. Horm Behav 2024; 164:105604. [PMID: 39013354 DOI: 10.1016/j.yhbeh.2024.105604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 05/22/2024] [Accepted: 07/09/2024] [Indexed: 07/18/2024]
Abstract
For males of gregarious species, dominance status and the strength of affiliative relationships can have major fitness consequences. Social dynamics also impose costs by affecting glucocorticoids, mediators of homeostasis and indicators of the physiological response to challenges and within-group competition. We investigated the relationships between dominance, social bonds, seasonal challenges, and faecal glucocorticoid metabolite (fGC) measures in wild Assamese macaques (Macaca assamensis) at Phu Khieo Wildlife Sanctuary, Thailand, combining behavioural data with 4129 samples from 62 adult males over 15 years. Our previous work on this population suggested that increased competition during the mating season was associated with elevated fGC levels and that, unusually for male primates, lower rank position correlated with higher fGC levels. With a much larger dataset and dynamic measures of sociality, we re-examined these relationships and additionally tested the potentially fGC-attenuating effect of social support. Contrary to our previous study, yet consistent with the majority of work on male primates, dominance rank had a positive relationship with fGC levels, as high status correlated with elevated glucocorticoid measures. fGC levels were increased at the onset of the mating season. We demonstrated an fGC-reducing effect of supportive relationships in males and showed that dynamics in affiliation can correlate with dynamics in physiological responses. Our results suggest that in a system with intermediate contest potential, high dominance status can impose physiological costs on males that may potentially be moderated by social relationships. We highlight the need to consider the dynamics of sociality and competition that influence hormonal processes.
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Affiliation(s)
- James Stranks
- Behavioral Ecology Department, University of Goettingen, Goettingen, Germany; Primate Social Evolution Group, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany; Leibniz ScienceCampus Primate Cognition, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany.
| | - Michael Heistermann
- Endocrinology Laboratory, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany
| | - Somboon Sangmaneedet
- Department of Pathobiology, Faculty of Veterinary Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Oliver Schülke
- Behavioral Ecology Department, University of Goettingen, Goettingen, Germany; Primate Social Evolution Group, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany; Leibniz ScienceCampus Primate Cognition, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany
| | - Julia Ostner
- Behavioral Ecology Department, University of Goettingen, Goettingen, Germany; Primate Social Evolution Group, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany; Leibniz ScienceCampus Primate Cognition, German Primate Center, Leibniz Institute for Primate Research, Goettingen, Germany
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2
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Social consequences of rapid environmental change. Trends Ecol Evol 2023; 38:337-345. [PMID: 36473809 DOI: 10.1016/j.tree.2022.11.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 12/05/2022]
Abstract
While direct influences of the environment on population growth and resilience are well studied, indirect routes linking environmental changes to population consequences are less explored. We suggest that social behavior is key for understanding how anthropogenic environmental changes affect the resilience of animal populations. Social structures of animal groups are evolved and emergent phenotypes that often have demographic consequences for group members. Importantly, environmental drivers may directly influence the consequences of social structure or indirectly influence them through modifications to social interactions, group composition, or group size. We have developed a framework to study these demographic consequences. Estimating the strength of direct and indirect pathways will give us tools to understand, and potentially manage, the effect of human-induced rapid environmental changes.
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3
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Perry SE, Carter A, Foster JG, Nöbel S, Smolla M. What Makes Inventions Become Traditions? ANNUAL REVIEW OF ANTHROPOLOGY 2022. [DOI: 10.1146/annurev-anthro-012121-012127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Although anthropology was the first academic discipline to investigate cultural change, many other disciplines have made noteworthy contributions to understanding what influences the adoption of new behaviors. Drawing on a broad, interdisciplinary literature covering both humans and nonhumans, we examine ( a) which features of behavioral traits make them more transmissible, ( b) which individual characteristics of inventors promote copying of their inventions, ( c) which characteristics of individuals make them more prone to adopting new behaviors, ( d) which characteristics of dyadic relationships promote cultural transmission, ( e) which properties of groups (e.g., network structures) promote transmission of traits, and ( f) which characteristics of groups promote retention, rather than extinction, of cultural traits. One of anthropology's strengths is its readiness to adopt and improve theories and methods from other disciplines, integrating them into a more holistic approach; hence, we identify approaches that might be particularly useful to biological and cultural anthropologists, and knowledge gaps that should be filled.
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Affiliation(s)
- Susan E. Perry
- Evolution and Culture Program, Department of Anthropology and Behavior, University of California, Los Angeles, California, USA
| | - Alecia Carter
- Department of Anthropology, University College London, London, United Kingdom
| | - Jacob G. Foster
- Department of Sociology, University of California, Los Angeles, California, USA
| | - Sabine Nöbel
- Université Toulouse 1 Capitole and Institute for Advanced Study in Toulouse, Toulouse, France
- Laboratoire Évolution et Diversité Biologique, CNRS, UMR 5174, IRD, Université de Toulouse, Toulouse, France
| | - Marco Smolla
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
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4
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Social Support and Network Formation in a Small-Scale Horticulturalist Population. Sci Data 2022; 9:570. [PMID: 36109560 PMCID: PMC9477840 DOI: 10.1038/s41597-022-01516-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/29/2022] [Indexed: 11/11/2022] Open
Abstract
Evolutionary studies of cooperation in traditional human societies suggest that helping family and responding in kind when helped are the primary mechanisms for informally distributing resources vital to day-to-day survival (e.g., food, knowledge, money, childcare). However, these studies generally rely on forms of regression analysis that disregard complex interdependences between aid, resulting in the implicit assumption that kinship and reciprocity drive the emergence of entire networks of supportive social bonds. Here I evaluate this assumption using individual-oriented simulations of network formation (i.e., Stochastic Actor-Oriented Models). Specifically, I test standard predictions of cooperation derived from the evolutionary theories of kin selection and reciprocal altruism alongside well-established sociological predictions around the self-organisation of asymmetric relationships. Simulations are calibrated to exceptional public data on genetic relatedness and the provision of tangible aid amongst all 108 adult residents of a village of indigenous horticulturalists in Nicaragua (11,556 ordered dyads). Results indicate that relatedness and reciprocity are markedly less important to whom one helps compared to the supra-dyadic arrangement of the tangible aid network itself.
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5
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Bonnell TR, Henzi SP, Barrett L. Using network synchrony to identify drivers of social dynamics. Proc Biol Sci 2022; 289:20220537. [PMID: 35765841 PMCID: PMC9240667 DOI: 10.1098/rspb.2022.0537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Social animals frequently show dynamic social network patterns, the consequences of which are felt at the individual and group level. It is often difficult, however, to identify what drivers are responsible for changes in these networks. We suggest that patterns of network synchronization across multiple social groups can be used to better understand the relative contributions of extrinsic and intrinsic drivers. When groups are socially separated, but share similar physical environments, the extent to which network measures across multiple groups covary (i.e. network synchrony) can provide an estimate of the relative roles of extrinsic and intrinsic drivers. As a case example, we use allogrooming data from three adjacent vervet monkey groups to generate dynamic social networks. We found that network strength was strongly synchronized across the three groups, pointing to shared extrinsic environmental conditions as the driver. We also found low to moderate levels of synchrony in network modularity, suggesting that intrinsic social processes may be more important in driving changes in subgroup formation in this population. We conclude that patterns of network synchronization can help guide future research in identifying the proximate mechanisms behind observed social dynamics in animal groups.
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Affiliation(s)
- Tyler R. Bonnell
- Department of Psychology, University of Lethbridge, Lethbridge, Alberta, Canada,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Pretoria, 0002, South Africa
| | - S. Peter Henzi
- Department of Psychology, University of Lethbridge, Lethbridge, Alberta, Canada,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Pretoria, 0002, South Africa
| | - Louise Barrett
- Department of Psychology, University of Lethbridge, Lethbridge, Alberta, Canada,Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Pretoria, 0002, South Africa
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Hobson EA, Silk MJ, Fefferman NH, Larremore DB, Rombach P, Shai S, Pinter-Wollman N. A guide to choosing and implementing reference models for social network analysis. Biol Rev Camb Philos Soc 2021; 96:2716-2734. [PMID: 34216192 PMCID: PMC9292850 DOI: 10.1111/brv.12775] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/29/2022]
Abstract
Analysing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.
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Affiliation(s)
- Elizabeth A Hobson
- Department of Biological Sciences, University of Cincinnati, 318 College Drive, Cincinnati, OH, 45221, U.S.A
| | - Matthew J Silk
- Centre for Ecology and Conservation, University of Exeter Penryn Campus, Treliever Road, Penryn, Cornwall, TR10 9FE, U.K
| | - Nina H Fefferman
- Departments of Ecology and Evolutionary Biology & Mathematics, University of Tennessee, 569 Dabney Hall, Knoxville, TN, 37996, U.S.A
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO, 80309, U.S.A.,BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave,, Boulder, CO, 80303, U.S.A
| | - Puck Rombach
- Department of Mathematics & Statistics, University of Vermont, 82 University Place, Burlington, VT, 05405, U.S.A
| | - Saray Shai
- Department of Mathematics and Computer Science, Wesleyan University, Science Tower 655, 265 Church Street, Middletown, CT, 06459, U.S.A
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 612 Charles E. Young Drive South, Los Angeles, CA, 90095, U.S.A
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Pacheco XP, Madden JR. Does the social network structure of wild animal populations differ from that of animals in captivity? Behav Processes 2021; 190:104446. [PMID: 34147575 DOI: 10.1016/j.beproc.2021.104446] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/16/2020] [Accepted: 06/14/2021] [Indexed: 12/27/2022]
Abstract
The social behaviour of wild animals living in groups leads to social networks with structures that produce group-level effects and position individuals within them with differential consequences for an individual's fitness. Social dynamics in captivity can differ greatly from those in wild conspecifics given the different constraints on social organization in wild populations, e.g. group size, predation pressure, distribution of resources (food, mates), which are all regulated by human carers in captive populations. The social networks of animals in zoos is expected to differ from those of free-living conspecifics. While many studies have described the social networks of a wide diversity of wild and captive animals, none has directly compared the networks of multiple groups of a single species both in the wild and in captivity. Meerkats, Suricata suricatta, are an excellent species to compare the social networks of wild and captive groups. We replicated the methods of Madden et al. (2009, 2011), who studied eight groups in the wild, in fifteen captive groups. We tested how network structures and individual positions in grooming, foraging competition and dominance networks differed between wild and captive groups. Groups of wild and captive meerkats differed in various aspects of their social network structure. Differences in the network may be due to individuals occupying different network positions and the difference in the number and strength of their connections to other individuals. This distinct way of interacting and associating could be a result of group specific attributes, such as group size, and/or the attributes of the donor and recipient, including sex, status or age. Critically, the differences may be explained by the dissimilar living environment that each encounters.
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Affiliation(s)
- Xareni P Pacheco
- Centre for Research in Animal Behaviour, Psychology, Washington Singer Building, University of Exeter, Perry Road, Exeter EX4 4QG, UK; Centre for Research in Applied Biological Sciences, Autonomous University of the State of Mexico, Instituto Literario 100, Centro, 50000 Toluca, Mexico.
| | - Joah R Madden
- Centre for Research in Animal Behaviour, Psychology, Washington Singer Building, University of Exeter, Perry Road, Exeter EX4 4QG, UK.
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Fisher DN, Pinter-Wollman N. Using multilayer network analysis to explore the temporal dynamics of collective behavior. Curr Zool 2021; 67:71-80. [PMID: 33654492 PMCID: PMC7901757 DOI: 10.1093/cz/zoaa050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/27/2020] [Indexed: 01/12/2023] Open
Abstract
Social organisms often show collective behaviors such as group foraging or movement. Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals. When social interactions change over time, collective behaviors may change because these behaviors emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behavior is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modeled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.
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Affiliation(s)
- David N Fisher
- Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, ON L8S 4K1, Canada
- School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3FX, UK
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
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9
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Bonnell TR, Vilette C. Constructing and analysing time‐aggregated networks: The role of bootstrapping, permutation and simulation. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13351] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tyler R. Bonnell
- Department of Psychology University of Lethbridge Lethbridge Alberta Canada
- Applied Behavioural Ecology and Ecosystems Research Unit University of South Africa Florida Gauteng South Africa
| | - Chloé Vilette
- Department of Psychology University of Lethbridge Lethbridge Alberta Canada
- Applied Behavioural Ecology and Ecosystems Research Unit University of South Africa Florida Gauteng South Africa
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10
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Webber QM, Schneider DC, Vander Wal E. Is less more? A commentary on the practice of ‘metric hacking’ in animal social network analysis. Anim Behav 2020. [DOI: 10.1016/j.anbehav.2020.08.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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11
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Evans JC, Fisher DN, Silk MJ. The performance of permutations and exponential random graph models when analyzing animal networks. Behav Ecol 2020. [DOI: 10.1093/beheco/araa082] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.
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Affiliation(s)
- Julian C Evans
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse, Zurich, Switzerland
| | - David N Fisher
- School of Biological Sciences, University of Aberdeen, King’s College, Aberdeen, UK
| | - Matthew J Silk
- Centre for Ecology and Conservation, University of Exeter Penryn Campus, Treliever Road, Penryn, Cornwall, UK
- Environment and Sustainability Institute, University of Exeter Penryn Campus, Penryn, Cornwall, UK
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12
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de Freslon I, Peralta JM, Strappini AC, Monti G. Understanding Allogrooming Through a Dynamic Social Network Approach: An Example in a Group of Dairy Cows. Front Vet Sci 2020; 7:535. [PMID: 32851054 PMCID: PMC7417353 DOI: 10.3389/fvets.2020.00535] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 07/09/2020] [Indexed: 11/13/2022] Open
Abstract
For gregarious species such as domestic cattle, the social environment is a very important determinant of their welfare and fitness. Understanding the complexity of cows' relationships can assist the development of management practices that are more integrated with the cows' social behavioral processes. The two aims of this study were: (1) to determine the dynamics of affiliative relationships, as indicated by allogrooming, by means of stochastic actor-oriented modeling, in dairy cows during early lactation; (2) to explore the underlying processes and the individual attributes, such as age, social rank and reproductive state, that could shape network pattern changes in grooming contacts between individual. We observed the allogrooming behavior of a dynamic group of 38 dairy cows for 4 h per day for 30 days. Using stochastic actor-oriented models, we modeled the dynamics of weekly contacts and studied how structural processes (e.g., reciprocity, transitivity, or popularity) and individual attributes (i.e., age, social rank, and reproductive state) influence network changes. We found that cows tended to groom individuals that had previously groomed them, implying a possible cooperation. Cows that groomed more actively did not appear to have a preference for specific individuals in the herd, and in return, tended to be groomed by fewer cows over time. Older individuals groomed more cows than younger ones, indicating that allogrooming could be related to seniority. Cows groomed mainly individuals of similar age, suggesting that familiarity and growing up together enhanced social grooming. Over time, cows with higher social rank were groomed by fewer cows and individuals recently reintroduced to the group groomed more herdmates. The study of social network dynamics can be used to better understand the complexity and non-linearity of cow relationships. Our findings, along with further research, can complement and strengthen the design of improved management practices that are more in line with the natural social behavior of cows.
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Affiliation(s)
- Inés de Freslon
- Faculty of Veterinary Sciences, Preventive Veterinary Medicine Institute, Universidad Austral de Chile, Valdivia, Chile
| | - J M Peralta
- College of Veterinary Medicine, Western University of Health Sciences, Pomona, CA, United States
| | - Ana C Strappini
- Faculty of Veterinary Sciences, Animal Science Institute, Universidad Austral de Chile, Valdivia, Chile
| | - Gustavo Monti
- Faculty of Veterinary Sciences, Preventive Veterinary Medicine Institute, Universidad Austral de Chile, Valdivia, Chile
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13
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Hart EE, Fennessy J, Rasmussen HB, Butler-Brown M, Muneza AB, Ciuti S. Precision and performance of an 180g solar-powered GPS device for tracking medium to large-bodied terrestrial mammals. WILDLIFE BIOLOGY 2020. [DOI: 10.2981/wlb.00669] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Emma E. Hart
- E. E. Hart (https://orcid.org/0000-0002-5622-2089) ✉ , Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, Univ. College Dublin, Dublin, Ireland
| | - Julian Fennessy
- J. Fennessy, Giraffe Conservation Foundation, Windhoek, Namibia
| | | | - Michael Butler-Brown
- M. Butler-Brown, Dept of Biological Sciences Graduate Program in Ecology, Evolution Ecosystems and Society, Dartmouth College, Hanover, NH, USA
| | - Arthur B. Muneza
- A. B. Muneza, Giraffe Conservation Foundation, Windhoek, Namibia
| | - Simone Ciuti
- S. Ciuti, Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Science, Univ. College Dublin, Dublin, Ireland
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14
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Franks DW, Weiss MN, Silk MJ, Perryman RJY, Croft DP. Calculating effect sizes in animal social network analysis. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13429] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Daniel W. Franks
- Departments of Biology and Computer Science The University of York York UK
| | - Michael N. Weiss
- Centre for Research in Animal Behaviour College of Life and Environmental Sciences University of Exeter Exeter UK
| | - Matthew J. Silk
- Environment and Sustainability Institute University of Exeter Penryn UK
| | - Robert J. Y. Perryman
- Department of Biological Sciences Macquarie University Sydney NSW Australia
- Marine Megafauna Foundation Truckee CA USA
| | - Darren P. Croft
- Centre for Research in Animal Behaviour College of Life and Environmental Sciences University of Exeter Exeter UK
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15
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Scherer C, Radchuk V, Franz M, Thulke H, Lange M, Grimm V, Kramer‐Schadt S. Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. OIKOS 2020. [DOI: 10.1111/oik.07002] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cédric Scherer
- Leibniz Inst. for Zoo and Wildlife Research (IZW) Alfred‐Kowalke‐Str. 17 DE‐10315 Berlin Germany
| | - Viktoriia Radchuk
- Leibniz Inst. for Zoo and Wildlife Research (IZW) Alfred‐Kowalke‐Str. 17 DE‐10315 Berlin Germany
| | - Mathias Franz
- Leibniz Inst. for Zoo and Wildlife Research (IZW) Alfred‐Kowalke‐Str. 17 DE‐10315 Berlin Germany
| | | | - Martin Lange
- Helmholtz Centre for Environmental Research–UFZ Leipzig Germany
| | - Volker Grimm
- Helmholtz Centre for Environmental Research–UFZ Leipzig Germany
| | - Stephanie Kramer‐Schadt
- Leibniz Inst. for Zoo and Wildlife Research (IZW) Alfred‐Kowalke‐Str. 17 DE‐10315 Berlin Germany
- Dept of Ecology, Technische Univ. Berlin Berlin Germany
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16
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Fisher DN, Rodríguez-Muñoz R, Tregenza T. Dynamic networks of fighting and mating in a wild cricket population. Anim Behav 2019. [DOI: 10.1016/j.anbehav.2019.05.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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17
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Evans JC, Morand-Ferron J. The importance of preferential associations and group cohesion: constraint or optimality. Behav Ecol Sociobiol 2019. [DOI: 10.1007/s00265-019-2723-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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18
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Abstract
Abstract
Demographic processes play a key role in shaping the patterns of social relations among individuals in a population. Social network analysis is a powerful quantitative tool for assessing the social structure formed by associations between individuals. However, demographic processes are rarely accounted for in such analyses. Here, we summarize how the structure of animal social networks is shaped by the joint effects of social behavior and turnover of individuals and suggest how a deeper understanding of these processes can open new, exciting avenues for research. Death or dispersal can have the direct effect of removing an individual and all its social connections, and can also have indirect effects, spurring changes in the distribution of social connections between remaining individuals. Recruitment and integration of juveniles and immigrant into existing social networks are critical to the emergence and persistence of social network structure. Together, these behavioral responses to loss and gain of social partners may impact how societies respond to seasonal or catastrophic turnover events. The fitness consequences of social position (e.g., survival and reproductive rates) may also create feedback between the social network structure and demography. Understanding how social structure changes in response to turnover of individuals requires further integration between long-term field studies and network modeling methods. These efforts will likely yield new insights into the connections between social networks and life history, ecological change, and evolutionary dynamics.
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Affiliation(s)
| | - Allison E Johnson
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
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19
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Kulahci IG, Quinn JL. Dynamic Relationships between Information Transmission and Social Connections. Trends Ecol Evol 2019; 34:545-554. [PMID: 30902359 DOI: 10.1016/j.tree.2019.02.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 01/21/2019] [Accepted: 02/13/2019] [Indexed: 11/19/2022]
Abstract
Understanding the drivers of sociality is a major goal in biology. Individual differences in social connections determine the overall group structure and have consequences for a variety of processes, including if and when individuals acquire information from conspecifics. Effects in the opposite direction, where information acquisition and transmission have consequences for social connections, are also likely to be widespread. However, these effects are typically overlooked. We propose that individuals who successfully learn about their environment become valuable social partners and become highly connected, leading to feedback-based dynamic relationships between social connections and information transmission. These dynamics have the potential to change our understanding of social evolution, including how selection acts on behavior and how sociality influences population-level processes.
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Affiliation(s)
- Ipek G Kulahci
- School of Biological, Earth, and Environmental Sciences, Distillery Fields, North Mall Campus, University College Cork, Cork, Ireland.
| | - John L Quinn
- School of Biological, Earth, and Environmental Sciences, Distillery Fields, North Mall Campus, University College Cork, Cork, Ireland; Environmental Research Institute, University College Cork, Cork, Ireland
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20
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Abstract
Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.
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Affiliation(s)
- Kelly R. Finn
- Animal Behavior Graduate Group, University of California, Davis, U.S.A
| | - Matthew J. Silk
- Environment and Sustainability Institute, University of Exeter, U.K
| | - Mason A. Porter
- Department of Mathematics, University of California, Los Angeles, U.S.A
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, U.S.A
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21
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Lutz MC, Ratsimbazafy J, Judge PG. Use of social network models to understand play partner choice strategies in three primate species. Primates 2019; 60:247-260. [DOI: 10.1007/s10329-018-00708-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 12/12/2018] [Indexed: 12/26/2022]
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22
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Hunt ER, Mi B, Fernandez C, Wong BM, Pruitt JN, Pinter-Wollman N. Social interactions shape individual and collective personality in social spiders. Proc Biol Sci 2018; 285:20181366. [PMID: 30185649 PMCID: PMC6158534 DOI: 10.1098/rspb.2018.1366] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 08/09/2018] [Indexed: 11/12/2022] Open
Abstract
The behavioural composition of a group and the dynamics of social interactions can both influence how social animals work collectively. For example, individuals exhibiting certain behavioural tendencies may have a disproportionately large impact on the group, and so are referred to as keystone individuals, while interactions between individuals can facilitate information transmission about resources. Despite the potential impact of both behavioural composition and interactions on collective behaviour, the relationship between consistent behaviours (also known as personalities) and social interactions remains poorly understood. Here, we use stochastic actor-oriented models to uncover the interdependencies between boldness and social interactions in the social spider Stegodyphus dumicola We find that boldness has no effect on the likelihood of forming social interactions, but interactions do affect boldness, and lead to an increase in the boldness of the shyer individual. Furthermore, spiders tend to interact with the same individuals as their neighbours. In general, boldness decreases over time, but once an individual's boldness begins to increase, this increase accelerates, suggesting a positive feedback mechanism. These dynamics of interactions and boldness result in skewed boldness distributions of a few bold individuals and many shy individuals, as observed in nature. This group behavioural composition facilitates efficient collective behaviours, such as rapid collective prey attack. Thus, by examining the relationship between behaviour and interactions, we reveal the mechanisms that underlie the emergence of adaptive group composition and collective behaviour.
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Affiliation(s)
- Edmund R Hunt
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
| | - Brian Mi
- BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Camila Fernandez
- BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Brandyn M Wong
- BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan N Pruitt
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USA
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
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23
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Sih A, Spiegel O, Godfrey S, Leu S, Bull CM. Integrating social networks, animal personalities, movement ecology and parasites: a framework with examples from a lizard. Anim Behav 2018. [DOI: 10.1016/j.anbehav.2017.09.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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24
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Bani-Yaghoub M, Reed A. A methodology to quantify the long-term changes in social networks of competing species. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2017.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Fisher DN, McAdam AG. Social traits, social networks and evolutionary biology. J Evol Biol 2017; 30:2088-2103. [DOI: 10.1111/jeb.13195] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 10/08/2017] [Accepted: 10/12/2017] [Indexed: 01/20/2023]
Affiliation(s)
- D. N. Fisher
- Department for Integrative Biology; University of Guelph; Guelph Ontario Canada
| | - A. G. McAdam
- Department for Integrative Biology; University of Guelph; Guelph Ontario Canada
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26
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Silk MJ, Fisher DN. Understanding animal social structure: exponential random graph models in animal behaviour research. Anim Behav 2017. [DOI: 10.1016/j.anbehav.2017.08.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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27
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Silk MJ, Croft DP, Delahay RJ, Hodgson DJ, Weber N, Boots M, McDonald RA. The application of statistical network models in disease research. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12770] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Matthew J. Silk
- Environment and Sustainability Institute University of Exeter Penryn TR10 9FE UK
| | - Darren P. Croft
- Centre for Research in Animal Behaviour University of Exeter Exeter EX4 4QJ UK
| | - Richard J. Delahay
- National Wildlife Management Centre Animal and Plant Health Agency Woodchester Park, Nympsfield, Stonehouse GL10 3UJ UK
| | - David J. Hodgson
- Centre for Ecology and Conservation University of Exeter Penryn TR10 9FE UK
| | - Nicola Weber
- Centre for Ecology and Conservation University of Exeter Penryn TR10 9FE UK
| | - Mike Boots
- Centre for Ecology and Conservation University of Exeter Penryn TR10 9FE UK
- Department of Integrative Biology University of California Berkeley CA 94720‐3140 USA
| | - Robbie A. McDonald
- Environment and Sustainability Institute University of Exeter Penryn TR10 9FE UK
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