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Raj U, Banerjee A, Ray S, Bhattacharya S. Structure of higher-order interactions in social-ecological networks through Q-analysis of their neighbourhood and clique complex. PLoS One 2024; 19:e0306409. [PMID: 39186487 PMCID: PMC11346666 DOI: 10.1371/journal.pone.0306409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/17/2024] [Indexed: 08/28/2024] Open
Abstract
This paper studies higher-order interactions in social-ecological networks, which formally represent interactions within the social and ecological units of an ecosystem. Many real-world social ecosystems exhibit not only pairwise interactions but also higher-order interactions among their units. Therefore, the conventional graph-theoretic description of networks falls short of capturing these higher-order interactions due to the inherent limitations of the graph definition. In this work, a mathematical framework for capturing the higher-order interactions of a social-ecological system has been given by incorporating notions from combinatorial algebraic topology. In order to achieve this, two different simplicial complexes, the clique and the neighbourhood complex, have been constructed from a pairwise social-ecological network. As a case study, the Q-analysis and a structural study of the interactions in the rural agricultural system of southern Madagascar have been done at various structural levels denoted by q. The results obtained by calculating all the structural vectors for both simplicial complexes, along with exciting results about the participation of facets of the clique complex at different q-levels, have been discussed. This work also establishes significant theorems concerning the dimension of the neighbourhood complex and clique complex obtained from the parent pairwise network.
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Affiliation(s)
- Udit Raj
- Department of Mathematics, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India
| | - Arnab Banerjee
- Department of Zoology, School of Life Sciences, Sikkim University (A Central University), Gangtok, Sikkim, India
| | - Santanu Ray
- School of Informatics, Kerala University of Digital Sciences, Innovation and Technology, Veiloor, Kerala, India
| | - Sudeepto Bhattacharya
- Department of Mathematics, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India
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Iacopini I, Foote JR, Fefferman NH, Derryberry EP, Silk MJ. Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230190. [PMID: 38768202 DOI: 10.1098/rstb.2023.0190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/10/2023] [Indexed: 05/22/2024] Open
Abstract
Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalization. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g. hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: (i) alter predictions made about the outcome of vocally coordinated group departures; (ii) generate different patterns of song synchronization from models that only include dyadic interactions; and (iii) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions that these techniques could help answer. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.
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Affiliation(s)
- Iacopo Iacopini
- Network Science Institute, Northeastern University London , London, E1W 1LP, UK
- Department of Physics, Northeastern University , Boston, MA 02115, USA
| | | | - Nina H Fefferman
- Department of Ecology and Evolutionary Biology, University of Tennessee , Knoxville, TN, USA
- Department of Mathematics, University of Tennessee , Knoxville, TN, USA
- NIMBioS, University of Tennessee , Knoxville, TN, USA
| | - Elizabeth P Derryberry
- Department of Ecology and Evolutionary Biology, University of Tennessee , Knoxville, TN, USA
| | - Matthew J Silk
- CEFE, Univ Montpellier, CNRS, EPHE, IRD , Montpellier, France
- Institute of Ecology and Evolution, University of Edinburgh , Edinburgh, UK
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Albery GF, Bansal S, Silk MJ. Comparative approaches in social network ecology. Ecol Lett 2024; 27:e14345. [PMID: 38069575 DOI: 10.1111/ele.14345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 01/31/2024]
Abstract
Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
| | - Matthew J Silk
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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Pritchard AJ, Carter AJ, Palombit RA. Individual differences in coping styles and associations with social structure in wild baboons (Papio anubis). Anim Behav 2023. [DOI: 10.1016/j.anbehav.2023.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Silk MJ, Wilber MQ, Fefferman NH. Capturing complex interactions in disease ecology with simplicial sets. Ecol Lett 2022; 25:2217-2231. [PMID: 36001469 DOI: 10.1111/ele.14079] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/21/2022] [Accepted: 06/29/2022] [Indexed: 11/28/2022]
Abstract
Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.
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Affiliation(s)
- Matthew J Silk
- NIMBioS, University of Tennessee, Knoxville, Tennessee, USA.,CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Mark Q Wilber
- Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, Tennessee, USA
| | - Nina H Fefferman
- NIMBioS, University of Tennessee, Knoxville, Tennessee, USA.,Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA.,Department of Mathematics, University of Tennessee, Knoxville, Tennessee, USA
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