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swaRmverse: An R package for the comparative analysis of collective motion

Marina Papadopoulou Orcid Logo, Simon Garnier Orcid Logo, Andrew King Orcid Logo

Methods in Ecology and Evolution, Volume: 16, Issue: 1, Pages: 29 - 39

Swansea University Authors: Marina Papadopoulou Orcid Logo, Andrew King Orcid Logo

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DOI (Published version): 10.1111/2041-210x.14460

Abstract

1. Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self-organised properties of how a group moves influence its various behavioural and ecological processes, such as predator-prey dynamics...

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Published in: Methods in Ecology and Evolution
ISSN: 2041-210X 2041-210X
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68232
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The self-organised properties of how a group moves influence its various behavioural and ecological processes, such as predator-prey dynamics, social foraging, and migration. Little is, however, known about the inter- and intra-specific variation in collective motion. Despite the significant advancement in high-resolution tracking of multiple individuals within groups, providing collective motion data for animals in the lab and the field, a framework to perform quantitative comparisons across species and contexts is lacking.2. Here, we present the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables the identification and analysis of collective motion &#x2018;events&#x2019;, as presented in Papadopoulou et al. (2023a), creating a unit of comparison across datasets. We describe the package&#x2019;s structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent-based model.3. From positional time-series data for multiple individuals (x-y-t-id), swaRmverse identifies events of collective motion based on the distribution of polarisation and group speed. For each motion event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a &#x2018;swarm space&#x2019; through dimensional reduction techniques.4. Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. 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spelling 2025-01-15T14:32:16.5287743 v2 68232 2024-11-12 swaRmverse: An R package for the comparative analysis of collective motion a2fe90e37bd6b78c6fdb9e640057c0ea 0000-0002-6478-8365 Marina Papadopoulou Marina Papadopoulou true false cc115b4bc4672840f960acc1cb078642 0000-0002-6870-9767 Andrew King Andrew King true false 2024-11-12 BGPS 1. Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self-organised properties of how a group moves influence its various behavioural and ecological processes, such as predator-prey dynamics, social foraging, and migration. Little is, however, known about the inter- and intra-specific variation in collective motion. Despite the significant advancement in high-resolution tracking of multiple individuals within groups, providing collective motion data for animals in the lab and the field, a framework to perform quantitative comparisons across species and contexts is lacking.2. Here, we present the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023a), creating a unit of comparison across datasets. We describe the package’s structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent-based model.3. From positional time-series data for multiple individuals (x-y-t-id), swaRmverse identifies events of collective motion based on the distribution of polarisation and group speed. For each motion event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques.4. Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. Thepackage allows the calculation and use of 18 complex metrics for users without a strong quantitative background and will promote communication and data-sharing across disciplines, standardising the quantification of collective motion across species and promoting comparative investigations. Journal Article Methods in Ecology and Evolution 16 1 29 39 Wiley 2041-210X 2041-210X animal behaviour, collective motion, comparative analysis, dimensionality reduction, modelvalidation, R package, trajectory data 7 1 2025 2025-01-07 10.1111/2041-210x.14460 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Other Office of Naval Research. Grant Number: N629092112030 ; National Science Foundation. Grant Number: EF 2222418 2025-01-15T14:32:16.5287743 2024-11-12T15:15:23.8873131 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Marina Papadopoulou 0000-0002-6478-8365 1 Simon Garnier 0000-0002-3886-3974 2 Andrew King 0000-0002-6870-9767 3 68232__33341__e622c29a22574562a71c59300d93bf20.pdf 68232.VoR.pdf 2025-01-15T14:30:05.4724556 Output 3235586 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License. true eng http://creativecommons.org/licenses/by-nc/4.0/
title swaRmverse: An R package for the comparative analysis of collective motion
spellingShingle swaRmverse: An R package for the comparative analysis of collective motion
Marina Papadopoulou
Andrew King
title_short swaRmverse: An R package for the comparative analysis of collective motion
title_full swaRmverse: An R package for the comparative analysis of collective motion
title_fullStr swaRmverse: An R package for the comparative analysis of collective motion
title_full_unstemmed swaRmverse: An R package for the comparative analysis of collective motion
title_sort swaRmverse: An R package for the comparative analysis of collective motion
author_id_str_mv a2fe90e37bd6b78c6fdb9e640057c0ea
cc115b4bc4672840f960acc1cb078642
author_id_fullname_str_mv a2fe90e37bd6b78c6fdb9e640057c0ea_***_Marina Papadopoulou
cc115b4bc4672840f960acc1cb078642_***_Andrew King
author Marina Papadopoulou
Andrew King
author2 Marina Papadopoulou
Simon Garnier
Andrew King
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container_title Methods in Ecology and Evolution
container_volume 16
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container_start_page 29
publishDate 2025
institution Swansea University
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description 1. Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self-organised properties of how a group moves influence its various behavioural and ecological processes, such as predator-prey dynamics, social foraging, and migration. Little is, however, known about the inter- and intra-specific variation in collective motion. Despite the significant advancement in high-resolution tracking of multiple individuals within groups, providing collective motion data for animals in the lab and the field, a framework to perform quantitative comparisons across species and contexts is lacking.2. Here, we present the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023a), creating a unit of comparison across datasets. We describe the package’s structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent-based model.3. From positional time-series data for multiple individuals (x-y-t-id), swaRmverse identifies events of collective motion based on the distribution of polarisation and group speed. For each motion event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques.4. Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. Thepackage allows the calculation and use of 18 complex metrics for users without a strong quantitative background and will promote communication and data-sharing across disciplines, standardising the quantification of collective motion across species and promoting comparative investigations.
published_date 2025-01-07T20:45:03Z
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