Self-organization and information transfer in Antarctic krill swarms

Abstract

Antarctic krill swarms are one of the largest known animal aggregations, and yet, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. Understanding the local interactions between individuals that provide the basis for these swarms is fundamental to knowing how swarms arise in nature, and what potential factors might lead to their breakdown. Here, we analysed the trajectories of captive, wild-caught krill in 3D to determine individual-level interaction rules and quantify patterns of information flow. Our results demonstrate that krill align with near neighbours and that they regulate both their direction and speed relative to the positions of groupmates. These results suggest that social factors are vital to the formation and maintenance of swarms. Furthermore, krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. This research represents a vital step in understanding the fundamentally important swarming behaviour of krill.

An examination of force maps targeted at orientation interactions in moving groups

“…For the simple alignment-only model examined here, confinement within a bounded region resulted in a much clearer alignment response in our force maps than in an unbounded case, but this was coupled with overestimation or distortion of the region over which alignment occurred. A large portion of studies that have used force mapping have been of fish, or other aquatic species, confined to swim in laboratory aquaria (see for example [ 2 , 8 – 11 , 17 , 37 ]. It might be reasonable to expect that for such aquaria studies that alignment interactions sought by force maps should clearly show such interactions if they are present, but that the force maps may overestimate the spatial range of alignment interactions.…”

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior

“…The clustering law of krill groups is very different from that of general marine cluster organisms. The swimming speed of krill groups is adjusted according to the speed of the individuals in front of them, but the direction of advance is adjusted according to other individuals in the vertical dimension; in addition, their speed and direction are closer to those of individuals in front and below but far away from individuals in the front and rear [ 28 ], as shown in Figure 2 c. The main reason why krill use more vertical dimension information is that the krill’s eyes are on the top of their heads and can only look up, not down, which creates partial visual blindness below and horizontally. Second, when krill are frightened, their abdomens glow, a message that can be transmitted to other individuals.…”

“…Antarctic krill generally live in colonies, and this feature is considered one of the factors that contribute to the reproductive success of their species [ 25 ]. Researchers have conducted many studies on krill populations in laboratory and field environments to understand the structure and function of krill populations [ 26 , 27 , 28 ], focusing on the movement mode of krill populations in 3D marine environments, their ability to track nutrients in the water, flexible avoidance of enemy hazards, and efficient long-distance swimming [ 29 ]. The main characteristics of the krill swarm movement strategy are that it is simple and efficient and can successfully complete path planning in a 3D water space with a simple and effective method.…”

Mechanisms of group‐hunting in vertebrates

“…Methods include: biologgers or mounted video to record changes in animal behaviour indicative of predator selection or prey detection (Watanabe & Takahashi, 2013; Lynch et al ., 2015; Williams et al ., 2017; Watanabe et al ., 2019; Wilson et al ., 2020; Ryan et al ., 2022 a ); computer vision‐tracking software (Dell et al ., 2014; Couzin & Heins, 2022; Koger et al ., 2023); and unmanned aerial vehicles (UAVs), global positioning system (GPS) and accelerometers to record spatiotemporal data (Shubkina et al ., 2010; Strandburg‐Peshkin et al ., 2015, 2017; Christie et al ., 2016; Harvey et al ., 2016; Hodgson & Koh, 2016; Hubel et al ., 2016 b , a ; Marras et al ., 2015 b ; Jackson et al ., 2016; Handley et al ., 2018; Torney et al ., 2018; Westley et al ., 2018; Wilson et al ., 2018; Hughey et al ., 2018; King et al ., 2018; Couzin & Heins, 2022; Hansen et al ., 2022; Koger et al ., 2023). Differences in predator locomotion and within‐group position may indeed correspond to changes in informational state (Bode et al ., 2010; Shubkina et al ., 2010, 2012; Herbert‐Read et al ., 2019) and network‐based diffusion analysis (Franz & Nunn, 2009; Hoppitt, 2017) or methodology borrowed from information theory and tested in laboratory animals (Ward et al ., 2018; Hansen et al ., 2021; Burns et al ., 2022) can then assess if these state changes are transferred socially (Fig. 4B).…”