Fish Schools Research Articles

Unraveling hydrodynamic interactions in fish schools: A three-dimensional computational study of in-line and side-by-side configurations

We numerically investigate the hydrodynamic interactions between a pair of three-dimensional (3D) fish-like bodies arranged in both in-line and side-by-side configurations. The morphology and kinematics of these fish-like bodies are modeled on a live rainbow trout (Oncorhynchus mykiss) observed during steady swimming in the laboratory. An immersed-boundary-method-based incompressible Navier–Stokes flow solver is employed to capture the flow dynamics around the fish-like bodies accurately. Our findings indicate that hydrodynamic performance of individual fish in both arrangements is influenced by their spatial separation when in close proximity as well as by the relative phase difference between the two fish. In the case of in-phase in-line schools, the leading fish experiences up to 5.3% increase in propulsive efficiency, attributed to the water blockage effect caused by the following fish. In comparison, the following fish experiences an increase in drag and power consumption along its body. Detailed analysis reveals that this rise in drag primarily results from an increase in friction drag (89%), driven by the amplified velocity field around the fish's body. Furthermore, altering the phase difference between the fish can help reduce pressure drag on the following fish by affecting the interaction between incoming vortex rings and its trunk. In side-by-side schools with in-phase swimming, a reduction of 6.8% in power consumption on the caudal fin is achieved for each fish when the transverse distance is maintained at 0.25 body lengths. Flow analysis reveals that the decrease in power usage is attributed to a diminished velocity field between the caudal fins, facilitating flow separation and subsequently reducing energy expenditure required for generating comparative thrust. For the out-of-phase swimming, the side-by-side school system experiences enhanced thrust production, owing to a wake energy recapture mechanism. The degree of enhancement varies for each fish and is determined by the specific phase difference. These insights obtained from our study hold the potential to inform the design and navigation strategies of underwater robotic swarms.

Hidden rivals: the negative impacts of dolphinfish on seabird foraging behaviour.

Marine predators often aggregate at the air-sea boundary layer to pursue shared prey. In such scenarios, seabirds are likely to benefit from underwater predators herding fish schools into tight clusters thereby enhancing seabirds' prey detectability and capture potential. However, this coexistence can lead to competition, affecting not only immediate foraging strategies but also their distribution and interspecies dynamics. We investigated both the longitudinal relationships and instantaneous interactions between streaked shearwaters (Calonectris leucomelas) and common dolphinfish (Coryphaena hippurus), both preying on Japanese anchovy (Engraulis japonicus). Using GPS data from 2011 to 2021, we calculated behavioural parameters for streaked shearwaters as an index of time spent and distance travelled. Despite the abundance of Japanese anchovies, we found that streaked shearwaters might increase their foraging time in the presence of underwater predators. Moreover, video loggers provided direct evidence of streaked shearwaters and common dolphinfish attacking the same fish schools, potentially interfering with bird foraging by dolphinfish. Our results suggest that the presence of underwater predators in a given patch might increase the time spent by seabirds foraging without affecting the distance travelled. This highlights the need for future studies that consider the potential adverse effects of other top predators on seabird prey availability.

Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems

Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems

Various and orderly formations in the hydrodynamic schooling of multiple flapping swimmers

The fluid mechanics underlying the collective motion of fish schools and bird flocks still lack full understanding. In this paper, the collective motion of multiple asynchronous flapping foils is numerically studied. It is found that various and orderly formations are achieved by multiple foils only via hydrodynamic interactions. Three distinct states have been verified according to the equilibrium distance between adjacent foils, i.e., the sparse state, the compact state, and the combined state. The “head goose effect” is found in the combined state, and the significant speed enhancement can be observed in both the compact and combined states, except when the first subgroup of the combined group is isolated. The obvious energy savings can be observed in most cases examined in the current work, no matter which state occurs. Moreover, for a given phase difference, the compact group has the highest propulsive efficiency, while the sparse group has the lowest. In addition, the fluid mechanics by which the multiple-foil system achieves speed enhancement and energy savings are analyzed. The results obtained here may shed some light on understanding the collective motion of fish schools and bird flocks.

A Web-Based Interactive Application to Simulate and Correct Distortion in Multibeam Sonars

Multibeam sonars are advanced scientific tools for estimating fish school volume and density, using multiple beams to provide comprehensive size measurements of detected targets. However, challenges remain in accurately estimating target dimensions due to beam geometric expansion and overlap, particularly in athwart-beam measurements, which tend to be overestimated with increasing distance from the transducer. We present an interactive web application that simulates distortion caused by beam overlap and expansion in multibeam sonars using simple geometric equations. Users can define sonar characteristics, such as the number of beams, swath opening, or degree of overlap, as well as specify an elliptical target’s dimensions, orientation, and distance from the transducer. The application estimates and visualises the true and distorted shapes of the target, calculating the level of distortion. It can run simulations in both forward and inverse directions, either simulating the distortion of a true school or correcting the shape of a distorted school. This tool aims to enhance the interpretation of multibeam sonar signals and improve the accuracy of target dimension estimates, facilitating more effective use of these sonars in scientific research.

Development of neural circuits for social motion perception in schooling fish

The collective behavior of animal groups emerges from the interactions among individuals. These social interactions produce the coordinated movements of bird flocks and fish schools, but little is known about their developmental emergence and neurobiological foundations. By characterizing the visually based schooling behavior of the micro glassfish Danionella cerebrum, we found that social development progresses sequentially, with animals first acquiring the ability to aggregate, followed by postural alignment with social partners. This social maturation was accompanied by the development of neural populations in the midbrain that were preferentially driven by visual stimuli that resemble the shape and movements of schooling fish. Furthermore, social isolation over the course of development impaired both schooling behavior and the neural encoding of social motion in adults. This work demonstrates that neural populations selective for the form and motion of conspecifics emerge with the experience-dependent development of collective movement.

Structure size may affect fish density around oil platforms

Abstract Thousands of offshore oil and gas platforms have been installed worldwide and are known to act as artificial reefs. Many platforms are nearing the end of their operational lives and will soon require decommissioning, but uncertainty remains about the impacts of these structures, and their removal, on the environment. Fish aggregate at platforms, but little is known about the extent of these effects in the North Sea and the causes of variability in these associations. Here, an uncrewed surface vessel (USV) was used to collect fisheries acoustic data on distributions of schooling and non-schooling fish around six oil platforms, collecting data within tens of metres of four of the surveyed platforms. In areas with more platforms, more non-schooling fish were found, and the probability of detecting fish schools was higher. Interplatform variability was found in trends in non-schooling fish density with increasing distance from platform, but the relationship was found to be strongest and most negative at the larger platforms. These findings may influence future management decisions around the decommissioning of these platforms, particularly if some structure is to be left in place to maximize the potential benefits associated with these artificial reef effects.

Monitoring tropical freshwater fish with underwater videography and deep learning

Context The application of deep learning to monitor tropical freshwater fish assemblages and detect potential anthropogenic impacts is poorly understood. Aims This study aimed to compare the results between trained human observers and deep learning, using the fish monitoring program for impact detection at Ranger Uranium Mine as a case study. Methods Fish abundance (MaxN) was measured by trained observers and deep learning. Microsoft’s Azure Custom Vision was used to annotate, label and train deep learning models with fish imagery. PERMANOVA was used to compare method, year and billabong. Key results Deep learning model training on 23 fish taxa resulted in mean average precision, precision and recall of 83.6, 81.3 and 89.1%, respectively. PERMANOVA revealed significant differences between the two methods, but no significant interaction was observed in method, billabong and year. Conclusions These results suggest that the distribution of fish taxa and their relative abundances determined by deep learning and trained observers reflect similar changes between control and exposed billabongs over a 3-year period. Implications The implications of these method-related differences should be carefully considered in the context of impact detection, and further research is required to more accurately characterise small-growing schooling fish species, which were found to contribute significantly to the observed differences.

Sailfish generate foraging opportunities for seabirds in multi-species predator aggregations.

While various marine predators form associations, the most commonly studied are those between subsurface predators and seabirds, with gulls, shearwaters or terns frequently co-occurring with dolphins, billfish or tuna. However, the mechanisms underlying these associations remain poorly understood. Three hypotheses have been proposed to explain the prevalence of these associations: (1) subsurface predators herd prey to the surface and make prey accessible to birds, (2) subsurface predators damage prey close to the surface and thereby provide food scraps to birds, and (3) attacks of underwater predators lower the cohesion of prey groups and thereby their collective defences making the prey easier to be captured by birds. Using drone footage, we investigated the interaction between Indo-Pacific sailfish (Istiophorus platypterus) and terns (Onychoprion sp.) preying on schooling fish off the eastern coast of the Malaysian peninsula. Through spatio-temporal analysis of the hunting behaviour of the two predatory species and direct measures of prey cohesion we showed that terns attacked when school cohesion was low, and that this decrease in cohesion was frequently caused by sailfish attacks. Therefore, we propose that sailfish created a by-product benefit for the bird species, lending support to the hypothesis that lowering cohesion can facilitate associations between subsurface predators and seabirds.

Decision-Making in Healthcare Resources Optimal Management: the Case of Inpatient Bed Occupancy and Associated Costs

The aim of this paper is to explore how efficient is the \textit{Fish School Search} (FSS) algorithm in building a \textit{Decision Support System (DSS)} for healthcare facility managers in order to optimize both the number of beds in their facilities and their budget. We have used a finite capacity queueing model with phase-type service distribution combined with a compartmental model and associated cost model. The \textit{Fish School Search} algorithm was used to optimize the queueing model. To illustrate the proposed approach, we used the available data reported by a department of geriatric medicine of a hospital. Our findings showed that by encoding the whole information provided by the queueing system and the cost model in a fish, the Fish School Search is a potentially beneficial tool for optimizing healthcare resources and the facility's budget. The model can be extended to different medical facilities with respect to the objective parameters.

Discovering dynamic laws from observations: The case of self-propelled, interacting colloids.

Active matter spans a wide range of time and length scales, from groups of cells and synthetic self-propelled colloids to schools of fish and flocks of birds. The theoretical framework describing these systems has shown tremendous success in finding universal phenomenology. However, further progress is often burdened by the difficulty of determining forces controlling the dynamics of individual elements within each system. Accessing this local information is pivotal for the understanding of the physics governing an ensemble of active particles and for the creation of numerical models capable of explaining the observed collective phenomena. In this work, we present ActiveNet, a machine-learning tool consisting of a graph neural network that uses the collective motion of particles to learn active and two-body forces controlling their individual dynamics. We verify our approach using numerical simulations of active Brownian particles, active particles undergoing underdamped Langevin dynamics, and chiral active Brownian particles considering different interaction potentials and values of activity. Interestingly, ActiveNet can equally learn conservative or nonconservative forces as well as torques. Moreover, ActiveNet has proven to be a useful tool to learn the stochastic contribution to the forces, enabling the estimation of the diffusion coefficients. Therefore, all coefficients of the equationof motion of Active Brownian Particles are captured. Finally, we apply ActiveNet to experiments of electrophoretic Janus particles, extracting the active and two-body forces controlling colloids' dynamics. On the one side, we have learned that the active force depends on the electric field and area fraction. On the other side, we have also discovered a dependence of the two-body interaction with the electric field that leads us to propose that the dominant force between active colloids is a screened electrostatic interaction with a constant length scale. We believe that the proposed methodological tool, ActiveNet, might open a new avenue for the study and modeling of experimental suspensions of active particles.

OPERATION TECHNIQUE AND OPERATIONAL TIME CALCULATION OF PURSE SEINE IN FISH CAPTURE: A CASE STUDY ON KM. MUTIARA SEJATI

A purse seine is an active fishing gear operated by encircling schools of fish. KM. Mutiara Sejati is one of the fishing vessels based in PPP Bajomulyo, Central Java, employing purse seine for small pelagic fisheries. That research aimed to explore the operational techniques of purse seine fishing gear and determine the time calculation for its operation. The study was conducted over five months, from October 2022 to March 2023, utilizing observation, interviews, and documentation as research methods. The operational area of the vessel was in the WPP 712 (Java Sea) with PPP Bajomulyo serving as the fishing base. Based on the research findings, the operational process of small pelagic purse seine began with a series of steps, including the deployment of fish aggregating devices, activation of vessel lights, deactivation of lights except for those at the stern, lowering of the boom lights, total shutdown of vessel lights, setting of the net, purse line retrieval, retrieval of the boom lights, net hauling process, catch retrieval, and ultimately, the re-arrangement of fishing gear. Calculation results indicated that the average operational time for the fishing gear during the nighttime was 87 minutes, while during the early morning hours, it was 83 minutes.

Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.

Efficient recognition of fish feeding behavior: A novel two-stage framework pioneering intelligent aquaculture strategies

In the domain of aquaculture, the act of feeding fish is a pivotal factor influencing both the growth of the fish and the associated cultivation costs. The implementation of intelligent feeding strategies is a crucial prerequisite for maintaining fish health and minimizing costs, with the accurate discernment of fish feeding behavior serving as the fundamental basis for the realization of such strategies. Addressing the issues of high data redundancy and substantial noise content inherent in the datasets utilized by existing identification models, as well as the intricate design and suboptimal execution efficiency of the model structures, this study introduces a two-stage framework for discriminating fish feeding behavior. In the initial stage, a re-parameterizable multi-scale object detection model is established, facilitating the acquisition of the spatial distribution of fish schools. This process effectively eliminates redundant data and noise, decouples the training and inference processes of the model, equivalently transforms the complex network training weights, and simplifies the inference process of the model. An analysis of the dataset characteristics is conducted, leading to the optimization of the model detector’s design and a reduction in both the model parameters and computational requirements. In the subsequent stage, responding to the key data characteristics of the spatial distribution of fish schools, a lightweight behavior recognition model is designed. This model enables the rapid and accurate identification of fish feeding behavior. A plethora of experimental results demonstrate that the proposed method can achieve a high recognition accuracy (Acc 83.33%) while operating under the constraints of minimal model parameters and computations (6.45M Params, 8.135G FLOPs). This provides a robust model foundation for the industrial application of the algorithm, underscoring the significant potential of the proposed method in advancing intelligent feeding strategies in aquaculture.

Behavioral transition of a fish school in a crowded environment.

In open water, social fish gather to form schools, in which fish generally align with each other. In this work, we study how this social behavior evolves when perturbed by artificial obstacles. We measure the behavior of a group of zebrafish in the presence of a periodic array of pillars. When the pillar density is low, the fish regroup with a typical interdistance and a well-polarized state with parallel orientations, similarly to their behavior in open-water conditions. Above a critical density of pillars, their social interactions, which are mostly based on vision, are screened and the fish spread randomly through the aquarium, orienting themselves along the free axes of the pillar lattice. The abrupt transition from natural to artificial orientation happens when the pillar interdistance is comparable to the social distance of the fish, i.e., their most probable interdistance. We develop a stochastic model of the relative orientation between fish pairs, taking into account alignment, antialignment, and tumbling, from a distribution biased by the environment. This model provides a good description of the experimental probability distribution of the relative orientation between the fish and captures the behavioral transition. Using the model to fit the experimental data provides qualitative information on the evolution of cognitive parameters, such as the alignment or the tumbling rates, as the pillar density increases. At high pillar density, we find that the artificial environment imposes its geometrical constraints to the fish school, drastically increasing the tumbling rate.

Sonar Fish School Detection and Counting Method Based on Improved YOLOv8 and BoT-SORT

Fish object detection and counting in pelagic fisheries face many challenges in complex environments. Sonar imaging technology offers a solution because it generates high-resolution images underwater. In this paper, we propose a sonar-based fish object detection and counting method using an improved YOLOv8 combined with BoT-SORT to address issues such as missed detection, false detection, and low accuracy caused by complex factors such as equipment motion, light changes, and background noise in pelagic environments. The algorithm utilizes the techniques of lightweight upsampling operator CARAFE, generalized feature pyramid network GFPN, and partial convolution. It integrates with the BoT-SORT tracking algorithm to propose a new region detection method that detects and tracks the schools of fish, providing stable real-time fish counts in the designated area. The experimental results indicate that while focusing on maintaining a lightweight design, the improved algorithm achieved a 3.8% increase in recall and a 2.4% increase in mAP0.5 compared to the original algorithm. This significantly impacts scientific and rational fishery planning, marine resource protection, and improved productivity. At the same time, it provides important data support for marine ecological monitoring, environmental protection, and fishery management, contributing to sustainable fishery development and marine ecology preservation.

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

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

SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

Compatibilities of Cyprinus carpio with Varied Colors of Robotic Fish

Visual selection plays a fundamental role in various aspects of animal behavior, such as colony formation, maintenance, defense, and courtship. This study investigated the effect of bionic robot fish color on carp behavior based on physiological characteristics that were observed during behavioral experiments. Through computer image processing and analysis of light attenuation, we observed changes in the number and positioning of carp with bionic robotic fish of different colors (white, red, blue, green, and yellow). The results indicated that (1) the attenuation coefficient of visible light in freshwater was red > yellow > green > blue; (2) the order of the average change in the number of carp responding to different colors of robotic fish was white > red > green > yellow > blue, and carp were more sensitive and responsive to white and red robotic fish; and (3) the order of the distances between different colors of robotic fish and carp was white < yellow < blue < green < red, and white and yellow robotic fish were more attractive to carp. Therefore, the use of white or yellow robotic fish for relevant operations can reduce disturbance to fish schools.

The weird and wonderful world of physics

Featuring the noise produced by a school of fish, a quantum-inspired escape room and a new coffee lab, Michael Banks picks his favourite stories and quotes from the past month.