Acoustic deterrents are recognized as a promising method to prevent the spread of invasive grass carp, Ctenopharyngodon idella (Valenciennes, 1844) and the negative ecological impacts caused by them. As the efficacy of sound barriers depends on the hearing capabilities of carp, it is important to identify whether carps can recognize acoustic signals and alter their swimming behavior. Our study focuses on quantifying the response of grass carp larvae when exposed to out-of-water acoustic signals within the range of 100–1000 Hz, by capturing their movement using particle-tracking velocimetry (PTV), a quantitative imaging tool often used for hydrodynamic studies. The number of responsive larvae is counted to compute response ratio at each frequency, to quantify the influence of sound on larval behavior. While the highest response occurred at 700 Hz, we did not observe any clear functional relation between frequency of sound and response ratio. Overall, 20–30% of larvae were consistently reacting to sound stimuli regardless of the frequency. In this study, we emphasize that larval behaviors when exposed to acoustic signals vary by individual, and thus a sufficient number of larvae should be surveyed at the same time under identical conditions, to better quantify their sensitivity to sound rather than repeating the experiment with individual specimens. Since bulk quantification, such as mean or quantile velocities of multiple specimens, can misrepresent larval behavior, our study finds that including the response ratio can more effectively reflect the larval response.

The wave wash hunting employed by Orcinus orca, also known as killer whales, is unique in that the prey is hunted outside of the water by generating waves. To quantitatively analyze the specific mechanism of the wave wash, data were obtained using computational fluid dynamics (CFD), and wave theory was introduced as the theoretical background to clarify the mechanism. The relationships between the swimming characteristics and wave parameters are defined in this paper. The results obtained by numerical investigation revealed that the wavelength increased with the swimming speed. Additionally, the wave height increased as the swimming speed increased and the swimming depth became shallower, and subsequently converged to a maximum of 2.42 m. The success of hunting is determined by two wave parameters, which indicate the intensity of the wave wash: the wave height and force exerted on the prey. The metabolic rate and the drag force are considered to evaluate the efficiency of the locomotion, which varied according to the swimming speed (V) and swimming depth (d) of the whales. To generate hunting waves efficiently, the optimal ranges of V and d were estimated to be 3 ~ 5 m/s and 0.5 m ~ 1.1 m respectively.

Transport of particles in rivers is a fundamental process in aquatic ecosystems relevant to various engineering and scientific problems, such as the removal of polluted particles, transport of organisms, optimal sampling of organic matter and sediment transport (McQueen et al., 2021;Prada et al., 2021;Shi et al., 2021). Since Elghobashi (1994) first defined the order of interaction between particle and turbulence based on volume fraction and time scales, flow physics became a significant aspect to understand the transport mechanism of particulate matter. For instance, microplastic pollution, which causes detrimental effect on aquatic biota and requires effective management strategies, can also be analyzed based on the close relation between flow and particulate matter (McCormick et al., 2014). Common obstacles in freshwater such as branches, logs, and hydraulic structures, are identified as local hotspots of particles as the obstacles create flow structures and influence their retention in water (Carmen et al., 2021;Kapp & Yeatman, 2018;Prada et al., 2020). Similarly, laboratory studies have been carried out to quantitively measure the transport and retention of drifting particles and devise effective removal techniques (Boos et al., 2021;Miehls et al., 2020;Palmer et al., 2004).Despite the wide application and numerous studies on particle transport, the knowledge about their interactions with aquatic ecosystems is still lacking due to the difficulties of analyzing continuous flow and discrete particles simultaneously. Immersed obstacles such as vegetation, bedforms, and hydraulic structures, complicate the analysis by introducing specific ranges of flow structures which affect transport of particles (Chung et al., 2021;Le Ribault et al., 2021;Soleimani & Ketabdari, 2020). Laboratory and numerical studies often simplify the complicated geometry and arrangement of such obstructions to understand how submerged obstacles disturb the flow, and questions remain on how to thoroughly account for the effects of transport mechanism of particles (Follett et al., 2021;Park & Hwang, 2019). Such simplifications include assumptions that replicate either cavity flow or porous flow media.Cavity flow is a classical problem that investigates how flow structures evolve in the presence of submerged obstacles with a simplified geometry. Depending on the aspect ratio of the cavity, the flow is categorized into: (a) closed cavity flow with a shear layer reattached to the bottom, (b) open cavity flow with the cutout bridged

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