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Abstract 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. However, little is 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 laboratory and the field, a framework to perform quantitative comparisons across species and contexts is lacking.Here, we present theswaRmversepackage. Building on two existing R packages,trackdfandswaRm,swaRmverseenables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023), 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.From positional time‐series data for multiple individuals (x‐y‐t‐id),swaRmverseidentifies events of collective motion based on the distribution of polarisation and group speed. For each event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques.Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. The package allows the calculation and use of 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.
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Portable locomotion activity monitor ( pLAM ): A cost‐effective setup for robust activity tracking in small animals
Abstract Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging, is often needed to get a complete picture of a species' ecology. Automated trackers are an invaluable tool for high‐throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial activity monitors can be expensive, closed source and generally limited to laboratory settings.Here, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity. Our setup uses inexpensive components, builds upon open‐source motion tracking software, and is easy to assemble and use in the field. It runs off‐grid, supports low‐light tracking with infrared lights and can implement arbitrary light cycle colours and brightnesses with programmable LEDs. We provide a user‐friendly guide to assembling pLAM hardware, accessing its pre‐configured software and guidelines for using it in other systems.We benchmarked pLAM for insects under various laboratory and field conditions, then compared results to a commercial activity detector. They offer broadly similar activity measures, but our setup captures flight and bouts of motion that are often missed by beam breaking activity detection.pLAM can automate laboratory and field monitoring of activity and timing in a wide range of biological processes, including circadian rhythm, eclosion and diapause timing, pollination and flower foraging, or pest feeding activity. This low cost and easy setup allows high‐throughput animal behaviour studies for basic and applied ecology and evolution research.
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- Award ID(s):
- 1750833
- PAR ID:
- 10419434
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 805-812
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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