Collective movement of schooling fish reduces the costs of locomotion in turbulent conditions.

The ecological and evolutionary benefits of energy-saving in collective behaviors 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) and compared that to solitary individuals swimming under laminar 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% to 79% compared to solitary fish (e.g., 228 versus 48 kj kg-1). Solitary individuals spend approximately 22% more kinematic effort (tail beat amplitude•frequency: 1.7 versus 1.4 BL s-1) to swim in turbulence at higher speeds than in laminar conditions. Fish schools swimming in turbulence reduced their three-dimensional group volume by 41% to 68% (at higher speeds, approximately 103 versus 33 cm3) and did not alter their kinematic effort compared to laminar conditions. This substantial energy saving highlights that schooling behaviors can mitigate turbulent disturbances by sheltering fish (within schools) from the eddies of sufficient kinetic energy that can disrupt locomotor gaits. Therefore, providing a more desirable internal hydrodynamic environment could be one of the ecological drivers underlying collective behaviors in a dense fluid environment.

Energy conservation by collective movement in schooling fish.

Many animals moving through fluids exhibit highly coordinated group movement that is thought to reduce the cost of locomotion. However, direct energetic measurements demonstrating the energy-saving benefits of fluid-mediated collective movements remain elusive. By characterizing both aerobic and anaerobic metabolic energy contributions in schools of giant danio (Devario aequipinnatus), we discovered that fish schools have a concave upward shaped metabolism-speed curve, with a minimum metabolic cost at ~1 body length s-1. We demonstrate that fish schools reduce total energy expenditure (TEE) per tail beat by up to 56% compared to solitary fish. When reaching their maximum sustained swimming speed, fish swimming in schools had a 44% higher maximum aerobic performance and used 65% less non-aerobic energy compared to solitary individuals, which lowered the TEE and total cost of transport by up to 53%, near the lowest recorded for any aquatic organism. Fish in schools also recovered from exercise 43% faster than solitary fish. The non-aerobic energetic savings that occur when fish in schools actively swim at high speed can considerably improve both peak and repeated performance which is likely to be beneficial for evading predators. These energetic savings may underlie the prevalence of coordinated group locomotion in fishes.

Schools of fish, flocks of birds flying in a V-formation and other collective movements of animals are common and mesmerizing behaviours. Moving as a group can have many benefits including helping the animals to find food and reproduce and protecting them from predators. Collective movements may also help animals to save energy as they travel by altering the flow of air or water around individuals. Computational models based on the flow of water suggest several possible mechanisms for how fish swimming in schools may use less energy compared to fish swimming on their own. However, few studies have directly measured how much energy fish schools actually use while they swim compared to a solitary individual. Zhang and Lauder used a device called a respirometer to directly measure the energy used by small tropical fish, known as giant danio, swimming in schools and on their own in an aquatic treadmill. The experiments found that the fish swimming in schools used 53% less energy compared with fish swimming on their own, and that fish in schools recovered from a period of high-speed swimming 43% quicker than solitary fish. By adjusting the flow of the water in the tanks, the team were able to study the fish schools swimming at different speeds. This revealed that the fish used more energy when they hovered slowly, or swam fast, than when they swam at a more moderate speed. Previous studies have found that many fish tend to swim at a moderate speed of around one body length per second while they travel long distances. Zhang and Lauder found that the giant danio used the least energy when they swam at this 'migratory' speed. These findings show that swimming in schools can help fish save energy compared with swimming alone. Along with furthering our understanding of how collective movement benefits fish and other animals, this work may help engineers to design robots that can team up with other robots to move more efficiently through the water.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamic (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

Hydrodynamic function of the slimy and scaly surfaces of teleost fishes.

The scales and skin mucus of bony fishes are both proposed to have a role in beneficially modifying the hydrodynamics of water flow over the body surface. However, it has been challenging to provide direct experimental evidence that tests how mucus and fish scales change the boundary layer in part due to the difficulties in working with live animal tissue and difficulty directly imaging the boundary layer. In this manuscript we use direct imaging and flow tracking within the boundary layer to compare boundary layer dynamics over surfaces of fish skin with mucus, without mucus, and a flat control surface. Our direct measurements of boundary layer flows for these three different conditions are repeated for two different species, bluegill sunfish (Lepomis macrochirus) and blue tilapia (Oreochromis aureus). Our goals are to understand if mucus and scales reduce drag, shed light on mechanisms underlying drag reduction, compare these results between species, and evaluate the relative contributions to hydrodynamic function for both mucus and scales. We use our measurements of boundary layer flow to calculate shear stress (proportional to friction drag), and we find that mucus reduces drag overall by reducing the velocity gradient near the skin surface. Both bluegill and tilapia showed similar patterns of surface velocity reduction. We also note that scales alone do not appear to reduce drag, but that mucus may reduce friction drag up to 50% compared to scaled surfaces without mucus or flat controls.

Fish robotics: multi-fin propulsion and the coupling of fin phase, spacing, and compliance.

Fish coordinate the motion of their fins and body to create the time-varying forces required for swimming and agile maneuvers. To effectively adapt this biological strategy for underwater robots, it is necessary to understand how the location and coordination of interacting fish-like fins affect the production of propulsive forces. In this study, the impact that phase difference, horizontal and vertical spacing, and compliance of paired fins had on net thrust and lateral forces was investigated using two fish-like robotic swimmers and a series of computational fluid dynamic simulations. The results demonstrated that the propulsive forces created by pairs of fins that interact through wake flows are highly dependent on the fins' spacing and compliance. Changes to fin separation of less than one fin length had a dramatic effect on forces, and on the phase difference at which desired forces would occur. These findings have clear implications when designing multi-finned swimming robots. Well-designed, interacting fins can potentially produce several times more propulsive force than a poorly tuned robot with seemingly small differences in the kinematic, geometric, and mechanical properties.