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      A network approach to zooarchaeological datasets and human-centered ecosystems in southwestern Florida

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      PLOS ONE
      Public Library of Science

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          Abstract

          Zooarchaeological datasets are often large, complex, and difficult to visualize and communicate. Many visual aids and summaries often limit the patterns that can be identified and mask interpretations of relationships between contexts, species, and environmental information. The most commonly used of these often include bar charts, pie charts, and other such graphs that aid in categorizing data and highlighting the differences or similarities between categories. While such simplification is often necessary for effective communication, it can also obscure the full range of complexity of zooarchaeological datasets and the human-environment dynamics they reflect. In this paper, we demonstrate the utility of formal network graphs to capturing the complexity of zooarchaeological datasets and to effectively highlighting the kinds of relationships between contexts, time, and faunal assemblages in which zooarchaeologists are primarily interested. Using a case study from southwestern Florida (USA), we argue that network graphs provide a quick solution to visualizing the structure of zooarchaeological datasets and serve as a useful aid in interpreting patterns that represent fundamental reflections of human-centered ecosystems.

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          Community structure in social and biological networks.

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
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            Toward a Network Perspective of the Study of Resilience in Social-Ecological Systems

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              Ecological network analysis: network construction

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 December 2023
                2023
                : 18
                : 12
                : e0295906
                Affiliations
                [001] Department of Anthropology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
                New York State Museum, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ IHL and JHL are joint senior authors on this work.

                Author information
                https://orcid.org/0000-0003-3326-5913
                https://orcid.org/0000-0002-0389-9846
                Article
                PONE-D-23-15391
                10.1371/journal.pone.0295906
                10729997
                38113235
                2e337d32-98fc-48f3-b184-3d1274d5d3b9
                © 2023 Holland-Lulewicz, Holland-Lulewicz

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 May 2023
                : 20 November 2023
                Page count
                Figures: 15, Tables: 2, Pages: 40
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Computer and Information Sciences
                Network Analysis
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Computer and Information Sciences
                Data Management
                Data Visualization
                Infographics
                Graphs
                Earth Sciences
                Geology
                Stratigraphy
                Social Sciences
                Archaeology
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Fish
                Chondrichthyes
                Elasmobranchii
                Sharks
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Fish
                Chondrichthyes
                Elasmobranchii
                Sharks
                Computer and Information Sciences
                Data Management
                Data Visualization
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Custom metadata
                All data is archived on Zenoto at https://doi.org/10.5281/zenodo.7948824.

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