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Calicchia, M. A.

Publications and source records attributed to Calicchia, M. A..

2 recordsLinked to original sources

Collective movement of schooling fish reduces locomotor cost in turbulence

The ecological and evolutionary benefits of collective behaviours are rooted in the physical principles and physiological mechanisms underpinning animal locomotion. We propose a turbulence sheltering hypothesis that collective movements of fish schools in turbulent flow can reduce the total energetic cost of locomotion by shielding individuals from the perturbation of chaotic turbulent eddies. We test this hypothesis by quantifying energetics and kinematics in schools of giant danio (Devario aequipinnatus) compared to solitary individuals swimming under control and turbulent conditions over a wide speed range. We discovered that, when swimming at high speeds and high turbulence levels, fish schools reduced their total energy expenditure (TEE, both aerobic and anaerobic energy) by 63-79% compared to solitary fish. Solitary individuals spend [~]25% more kinematic effort (tail beat amplitude*frequency) to swim in turbulence at higher speeds than in control conditions. However, fish schools swimming in turbulence reduced their three-dimensional group volume by 41-68% (at higher speeds) and did not alter their kinematic effort compared to control conditions. This substantial energy saving highlighted a [~]261% higher TEE when fish swimming alone in turbulence are compared to swimming in a school. Schooling behaviour could mitigate turbulent disturbances by sheltering fish within schools from the eddies of sufficient kinetic energy that can disrupt the locomotor gaits. Providing a more desirable internal hydrodynamic environment could be one of the ecological drivers underlying collective behaviours in a dense fluid environment. One-Sentence SummaryThe collective movement of fish schools substantially reduces the energetic cost of locomotion in turbulence compared to that of swimming alone.

physiology↗

Reconstructing the pressure field around a swimming fish using a physics-informed neural network

Hydrodynamic pressure is a physical quantity that is utilized by fish and many other aquatic animals to generate thrust and sense the surrounding environment. To advance our understanding of how fish react to unsteady flows, it is necessary to intercept the pressure signals sensed by their lateral line system. In this study, the authors propose a new, non-invasive method for reconstructing the instantaneous pressure field around a swimming fish from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the velocity and pressure fields that satisfy in an [L]2 sense both the Navier Stokes equations and the constraints put forward by the measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to empirically obtained data of a turning zebrafish, Danio rerio. The results demonstrate that when compared to traditional methods that rely on directly integrating the pressure gradient field, the PINN is less sensitive to the spatio-temporal resolution of the velocity field measurements and provides a more accurate pressure reconstruction, particularly on the surface of the body.

animal behavior and cognition↗