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      Chimpanzees employ context-specific behavioral strategies within fission–fusion societies

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          Abstract

          Fission–fusion social systems allow individuals to make flexible choices about where, with whom, and in what contexts to spend their time in response to competing social and ecological pressures. The ability for fission–fusion societies to support individual behavioral strategies that vary across contexts has been suggested, but the potential function of such context-specific social choices remains largely understudied. We adopted the concept of social niche construction to explore possible differences in social complexity at the individual and group level across feeding contexts. Specifically, we examined patterns of co-attendance across two common ecological contexts in wild Central African chimpanzees ( Pan troglodytes troglodytes) in the Goualougo Triangle, Republic of Congo. From data compiled over 6 years, we used multidimensional social network analysis to study the patterns of co-attendance generated from 436 group scans at Ficus and 4527 visits to termite mounds. These two contexts were chosen, because they are both fixed spatial features across the landscape that serve as well-defined points to compare association patterns. We identified context-specific social niche construction in a fission–fusion chimpanzee society that produce different patterns of relationships and social complexity that are consistent in their expression over many years, and offer functional benefits. While enhancing our understanding of chimpanzee behavioral strategies, culture, and conservation, our investigation also indicates that the social niche construction framework aids in elucidating the evolutionary advantages of fission–fusion sociality by accounting for intra- and interindividual variability, cognition, and choice in newfound ways.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10329-024-01165-1.

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          Most cited references134

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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            Constructing, conducting and interpreting animal social network analysis

            Summary Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.
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              The architecture of complex weighted networks.

              Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.
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                Author and article information

                Contributors
                jake.funkhouser@uzh.ch , jakefunkhouser@wustl.edu
                Journal
                Primates
                Primates
                Primates; Journal of Primatology
                Springer Nature Singapore (Singapore )
                0032-8332
                1610-7365
                19 October 2024
                19 October 2024
                2024
                : 65
                : 6
                : 541-555
                Affiliations
                [1 ]Department of Anthropology, Washington University in Saint Louis, ( https://ror.org/01yc7t268) One Brookings Drive, Saint Louis, MO 63130 USA
                [2 ]Department of Evolutionary Anthropology, University of Zurich, ( https://ror.org/02crff812) Winterthurerstrasse 190, 8057 Zurich, Switzerland
                [3 ]Department of Anthropology, University of Miami, ( https://ror.org/02dgjyy92) 5202 University Drive, Coral Gables, FL 33146 USA
                [4 ]Fisher Center for the Study and Conservation of Apes, Lincoln Park Zoo, 2001 N. Clark Street, Chicago, IL 60614 USA
                [5 ]Wildlife Conservation Society, Congo Program, ( https://ror.org/04avnsc24) B.P. 14537, Brazzaville, Republic of Congo
                Author information
                http://orcid.org/0000-0002-3132-9744
                Article
                1165
                10.1007/s10329-024-01165-1
                11561109
                39427097
                2b4ea0a3-fc06-4a1a-abab-6df53a650853
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 May 2024
                : 7 October 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100016681, Arcus Foundation;
                Funded by: FundRef http://dx.doi.org/10.13039/100001560, Conservation, Food and Health Foundation;
                Funded by: Indianapolis Zoo
                Funded by: Cincinnati Zoo and Botanical Garden
                Funded by: Saint Louis Zoo
                Funded by: University of Zurich
                Categories
                Special Issue: Original Article
                Custom metadata
                © Japan Monkey Centre 2024

                Animal science & Zoology
                niche construction,multidimensional social network analyses,behavioral ecology,behavioral flexibility,agency

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