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个体感知和认知因素对数据驱动的鱼群模型中集体状态的影响。

The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model.

机构信息

School of Systems Science, Beijing Normal University, Beijing, China.

Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse Paul Sabatier, Toulouse, France.

出版信息

PLoS Comput Biol. 2022 Mar 2;18(3):e1009437. doi: 10.1371/journal.pcbi.1009437. eCollection 2022 Mar.

DOI:10.1371/journal.pcbi.1009437
PMID:35235565
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8932591/
Abstract

In moving animal groups, social interactions play a key role in the ability of individuals to achieve coordinated motion. However, a large number of environmental and cognitive factors are able to modulate the expression of these interactions and the characteristics of the collective movements that result from these interactions. Here, we use a data-driven fish school model to quantitatively investigate the impact of perceptual and cognitive factors on coordination and collective swimming patterns. The model describes the interactions involved in the coordination of burst-and-coast swimming in groups of Hemigrammus rhodostomus. We perform a comprehensive investigation of the respective impacts of two interactions strategies between fish based on the selection of the most or the two most influential neighbors, of the range and intensity of social interactions, of the intensity of individual random behavioral fluctuations, and of the group size, on the ability of groups of fish to coordinate their movements. We find that fish are able to coordinate their movements when they interact with their most or two most influential neighbors, provided that a minimal level of attraction between fish exist to maintain group cohesion. A minimal level of alignment is also required to allow the formation of schooling and milling. However, increasing the strength of social interactions does not necessarily enhance group cohesion and coordination. When attraction and alignment strengths are too high, or when the heading random fluctuations are too large, schooling and milling can no longer be maintained and the school switches to a swarming phase. Increasing the interaction range between fish has a similar impact on collective dynamics as increasing the strengths of attraction and alignment. Finally, we find that coordination and schooling occurs for a wider range of attraction and alignment strength in small group sizes.

摘要

在动物群体中,社会互动在个体实现协调运动的能力中起着关键作用。然而,大量的环境和认知因素能够调节这些互动的表达以及这些互动产生的集体运动的特征。在这里,我们使用数据驱动的鱼群模型来定量研究感知和认知因素对协调和集体游动模式的影响。该模型描述了 Hemigrammus rhodostomus 群体中爆发式游动的协调所涉及的相互作用。我们全面研究了两种基于选择最有或两个最有影响力的邻居的互动策略、社会互动的范围和强度、个体随机行为波动的强度以及群体大小,对鱼群协调其运动能力的各自影响。我们发现,当鱼与最有或两个最有影响力的邻居互动时,它们能够协调它们的运动,前提是鱼之间存在最小水平的吸引力以保持群体凝聚力。也需要最小的对齐度来允许形成编队和围捕。然而,增加社会互动的强度不一定会增强群体的凝聚力和协调性。当吸引力和对齐强度过高,或者当航向随机波动过大时,编队和围捕就无法维持,鱼群会切换到蜂拥状态。增加鱼之间的相互作用范围对集体动力学具有类似的影响,就像增加吸引力和对齐强度一样。最后,我们发现,在较小的群体规模中,协调和编队发生在更大的吸引力和对齐强度范围内。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/7ac7094d8e84/pcbi.1009437.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/de71a6ff5b86/pcbi.1009437.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/d90b8c7cfa12/pcbi.1009437.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/07036dab4151/pcbi.1009437.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/311ff07436b5/pcbi.1009437.g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/dd089edf326b/pcbi.1009437.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/b8d3dbb5cb1e/pcbi.1009437.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/7ac7094d8e84/pcbi.1009437.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/de71a6ff5b86/pcbi.1009437.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/d90b8c7cfa12/pcbi.1009437.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/07036dab4151/pcbi.1009437.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/311ff07436b5/pcbi.1009437.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/b65fed82f04e/pcbi.1009437.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/dd089edf326b/pcbi.1009437.g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c461/8932591/7ac7094d8e84/pcbi.1009437.g008.jpg

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3
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4
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Sci Rep. 2025 Jan 29;15(1):3709. doi: 10.1038/s41598-025-88440-3.
5
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J R Soc Interface. 2024 Mar;21(212):20230630. doi: 10.1098/rsif.2023.0630. Epub 2024 Mar 6.
6
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7
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8
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5
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Philos Trans R Soc Lond B Biol Sci. 2020 Sep 14;375(1807):20190380. doi: 10.1098/rstb.2019.0380. Epub 2020 Jul 27.
6
Multi-scale analysis and modelling of collective migration in biological systems.多尺度分析和生物系统中群体迁移的建模。
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7
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