As the global populace continues to expand, the imperative of food security and sustainable development is gaining paramount importance (Godfray et al., 2010). Aquaculture, an escalating segment of food production, is playing an increasingly significant role in maintaining global food security and bolstering economic growth (Subasinghe et al., 2009). As per a United Nations Food and Agriculture Organization (FAO) report, nearly half of the world’s fishery yield is derived from aquaculture, with the prospect of this fraction growing in the foreseeable future (Canton, 2021). Aquaculture not only offers an abundant supply of animal and plant protein but also contributes to mitigating the overexploitation of marine resources and environmental degradation induced by conventional fishing methods (Naylor et al., 2000; Diana, 2009). In light of technological advances and improved aquaculture management, the industry is progressively adopting more ecofriendly and resource-efficient practices.

For instance, through deploying recirculating aquaculture systems (RAS), multitrophic aquaculture models, and precision feeding strategies, we can alleviate environmental pollution associated with aquaculture while augmenting resource utilization efficiency (Buck et al., 2018; Tang and Liu, 2018; Naylor et al., 2021). Amidst rapid advancements in aquaculture technology, the deployment of aquaculture equipment has become increasingly prevalent. While such equipment is instrumental in enhancing aquaculture productivity, promoting environmental quality, and ensuring product integrity, the escalating prevalence of noise pollution within the industry necessitates heightened attention (Bart et al., 2001; Slabbekoorn et al., 2010; Todd et al., 2021).

Noise emissions from various mechanical activities can have detrimental effects on animal health (Craven et al., 2009; New et al., 2014; Kunc et al., 2016; Slabbekoorn et al., 2019), compromising auditory abilities, inducing physiological stress (Popper, 2003; Halvorsen et al., 2012; Mancera et al., 2017), disrupting communication and behavior (Sarà et al., 2007; Gil et al., 2015; Gendron et al., 2020), and increasing stress-response hormones (Wale et al., 2013a; Hawkins and Chapman, 1975; Smith et al., 2004; Duarte et al., 2015), potentially impacting animal health and survival (Popper, 2003; Purser and Radford, 2011), especially in captive settings like farms and reserves (Popper and Hawkins, 2019; Sherwen and Hemsworth, 2019; Duarte et al., 2023). These noise-induced effects may manifest as reduced feeding behavior due to heightened vigilance (Evans et al., 2018; Giordano et al., 2022), leading to diverted attention (Mendl, 1999; Chan and Blumstein, 2011), potentially causing misjudgments in prey identification and feeding responses (Purser and Radford, 2011; Holles et al., 2013). Additionally, noise could obscure feeding-related acoustic signals (Brumm and Slabbekoorn, 2005; Zhang et al., 2021), resulting in missed feeding opportunities (Schaub et al., 2008; Siemers and Schaub, 2011) and altering food detection and classification during foraging (Lupien and McEwen, 1997; De Kloet et al., 1999).

Noise interference can disrupt animals’ ability to accurately discern food quantity and quality (Kight and Swaddle, 2011), potentially leading to reduced foraging efficiency (Schaub et al., 2008), contrary to optimal foraging theory (MacArthur and Pianka, 1966). Alternatively, this could also be attributed to noise-induced stress, suppressing appetite and activity, and consequently reducing foraging behavior (Mendl, 1999; Charmandari et al., 2005; Williams et al., 2015; Zhang et al., 2022). For instance, Gendron et al. (2020) discovered that noise-exposed Pseudopleuronectes americanus larvae exhibited significantly shorter feeding durations and smaller stomach capacities than controls. Purser and Radford (2011) observed decreased foraging performance in Gasterosteus aculeatus due to increased feeding errors. Likewise, Voellmy et al. (2014) reported on the impact of noise on Phoxinus phoxinus feeding success. These investigations collectively suggest that noise pollution can significantly affect animal feeding behavior, consequently influencing their growth and health indices.

Noise disturbances can not only alter fish feeding behavior but also significantly impact their swimming behavior and spatial distribution (De Vincenzi et al., 2015; Sabet et al., 2016; Simpson et al., 2016; Mickle et al., 2019; Hang et al., 2021). For instance, Hanache et al. (2020) reported that Phoxinus phoxinus, when exposed to noise, allocated less time to foraging in low food density conditions, exhibited changes in spatial distribution, and demonstrated decreased cohesiveness in their swimming. Similarly, noise-affected tuna schools have been observed to alter their coordinated unidirectional swimming structure, increase vertical movements towards the water surface or bottom, and exhibit uncoordinated swimming behavior in more dispersed groups (Sarà et al., 2007).

Largemouth bass (Micropterus salmoides), an economically significant freshwater species known for its fast growth rate, high nutritional value, and strong environmental adaptability (Bai and Li, 2018), holds global economic importance in aquaculture. While previous research has explored the effects of noise on fish feeding and swimming behavior, this study takes approach by introducing the Shannon-Wiener Diversity Index (SWDI) analysis method, extending investigation to encompass group behavior. Specifically, this study will assess the impact of oxygenation equipment noise on feeding and swimming behaviors of largemouth bass school, and quantify the differences in noise impacts on fish school behavior. Through a comprehensive analysis of the findings, this study aims to offer scientific insights to the aquaculture industry, mitigate adverse environmental impacts, and bolster the sustainable development of the industry.

In this investigation, the profound influence of aeration equipment noise on the swimming patterns, feeding habits, and growth of largemouth bass was distinctly observed. Evidently, compared to those in the ambient group, the fish in the noise group exhibited substantial differences in swimming behavior, feeding vigor, and quantity of food consumed. The escalated swimming expenditure and diminished feeding intensity and quantity within a noisy environment adversely affected the growth performance of the largemouth bass in the noise group.

Acoustic signals constitute one of the primary channels through which fish garner information about their environment, with aquatic species relying on auditory data for communication, navigation, orientation, evasion, and foraging (Popper and Hawkins, 2019). Noise acts as a stimulus source that disrupts fish attention and interferes with fish behavior by masking acoustic information (Slabbekoorn et al., 2010). In terms of swimming behavior observed in this study, significant disparities arose between the ambient and aerator noise groups. The average distance and angle between each fish in the noise group and its nearest neighbor were greater, implying that the noise environment might have impeded the orientation navigation information of the largemouth bass or induced discomfort, thus compelling them to maintain a more significant distance and angle from other individuals.

The distance and angle between the closest individuals in a fish school can serve as metrics of the school’s clustering strategy, which enhances group collaboration and reduces swimming energy expenditure (Chen et al., 2016; Kent et al., 2019). In this study, the noise group fish had larger average distances and angles, indicative of lower fish school cohesion and polarity. This observation aligns with previous research findings. For instance, Sarà et al. (2007) found that noise could alter the swimming direction of tuna, leading to a relatively dispersed cluster structure and more chaotic swimming behavior in the school. Similarly, De Vincenzi et al. (2015) demonstrated that noise impacted the swimming activity and behavior distribution of the lesser spotted dogfish (Scyliorhinus canicula) in aquaculture ponds. McLaughlin and Kunc (2015) reported that noise exposure caused the european minnow (Phoxinus phoxinus) to modify its spatial distribution, with marked increases in social interaction and swimming distance. These findings resonate with the present observations: under the influence of the noise environment, the distribution of largemouth bass in the culture pond becomes more dispersed.

However, the research further points out that the noise environment not only affects the clustering behavior of largemouth bass schools but also affects the diversity of their swimming behavior. It was discovered that, compared to the ambient group, the diversity of swimming behavior among the largemouth bass in the noise group was higher. This could be due to the largemouth bass being distracted by the disruption of aeration equipment noise, or the occlusion of orientation and navigation acoustic information by noise. Additionally, this might be a response strategy of the largemouth bass to the noise environment, in which behavioral diversity is increased to adapt to the noisy conditions. A higher SWDI may indicate a more diversified distance and angle between fish in a school and more irregular formations during swimming. In some instances, a greater diversity of swimming behavior may correlate with increased swimming energy expenditure (Webb, 1984). For example, irregular formations that lead to larger distances between individuals could cause fish to expend more energy while adjusting their position and speed during swimming. Maintaining this state of elevated energy expenditure over an extended period could potentially affect individual growth performance within the fish school (Jobling et al., 1993). The application of the SWDI to assess the behavior of largemouth bass schools is a novel contribution of this study. Although this index is often used to evaluate the diversity of biological communities (Spellerberg and Fedor, 2003; Thukral, 2017), it is infrequently applied within behavioral ecology. The study’s results indicate that this index effectively captures the diversity of swimming behavior in largemouth bass schools, thereby introducing a novel research tool in behavioral ecology. The reliability of the findings was ensured through meticulous experimental design and data analysis methodologies. For example, the swimming behavior of fish schools was continuously monitored throughout the experiment, encompassing comprehensive classification of angle-distance combinations and rigorous statistical analyses. Additionally, regular inspection and maintenance of experimental equipment and data training models were conducted to ensure the stability of the noise level and the accuracy of the algorithm. As a result, the credibility of the findings is held with a high level of confidence.

While this study primarily aimed to investigate the distinct impact of noise generated by oxygenation equipment on largemouth bass behavior, an additional insight emerged regarding the general behavioral patterns of fish schools. By analyzing the diversity of fish school behavior at different times throughout the day, the present study observed that fish schools maintain a more regular swimming formation prior to feeding times (6:00, 18:00). Post-feeding, fish schools may exhibit a greater diversity of behavior. Once satiated, they might be more inclined to engage in other activities such as reproduction, territorial protection, and social interactions (Ward et al., 2006). At this juncture, the fish school may become more dispersed, reducing the need for foraging, and individuals within the school may have greater freedom of movement. Under conditions of scarcity, the behavior of fish schools may be more uniform. This is due to the need for cooperation in the search for food in the face of reduced availability, causing the fish to form more compact groups. This scenario leads to an increase in fish school cohesion, with the tighter assembly being more effective in food foraging (Keenleyside, 2012). Starving fish schools might rely more heavily on the guidance of a leader (Reebs, 2000), subsequently reducing behavioral diversity.

In this study, it was observed that largemouth bass in the ambient group displayed a stronger feeding inclination, while those in the noise group demonstrated a significantly diminished appetite. This discrepancy might be attributed to noise distractions affecting the largemouth bass’s focus, indirectly impacting their feeding desire and resulting in decreased food consumption. These findings align with those of Voellmy et al. (2014), who verified the disruptive influence of sound on fish feeding, showing that significantly more individuals in the control group obtained food, indicating that fish foraging activities were hindered under experimental conditions. Purser and Radford (2011) demonstrated that the stickleback fish, Gasterosteus aculeatus, exposed to noise, increased the frequency of attacks on non-food items due to misidentifying them as food, leading to a decline in its foraging performance. Gendron et al. (2020) found that the winter flounder, Pseudopleuronectes americanus, preyed less frequently and had a relatively reduced stomach capacity under noise conditions. Wale et al. (2013b) reported that noise affected the feeding behavior and performance of crabs in a tank-based controlled experiment. While the mentioned studies involve different fish species, the present study focus on largemouth bass contributes to understanding of how noise pollution can impact feeding behavior in this particular species.

It’s worth noting that this research also observed that the feeding kinetic energy in both groups was initially low, but increased as the experiment progressed with the growth and increased food consumption of the largemouth bass. In the noise group, the increase in feeding kinetic energy may reflect an adaptive response, suggesting that, after a period of acclimation to the noise environment, largemouth bass could adjust their behavior to cope with the noise conditions (Dooling et al., 2015). Nonetheless, despite these adaptive shifts, the feeding performance of the largemouth bass in the noise-exposed group continued to be significantly inferior compared to the ambient group. This diminished performance might be attributed to the persistent interference and suppression induced by the noisy environment on the largemouth bass’s feeding conduct and appetite.

This investigation underscores the significant influence of aeration equipment noise on largemouth bass growth performance. Largemouth bass in the noise-exposed group exhibited a considerably lower average weight and slower growth rate compared to the ambient group. This discrepancy may stem from the noise-induced reduction in feeding desire and intake, leading to decreased energy consumption. Additionally, the less cohesive and polarized swimming patterns observed in the noise group could contribute to heightened energy expenditure during swimming activities. These findings align with prior research. Lagardčre (1982) reported elevated noise levels in aquaculture ponds leading to increased metabolic rates in prawns and reduced growth performance. Banner and Hyatt (1973) observed decreased SR and growth performance in carp exposed to noise. Similarly, Wysocki et al. (2007) and Davidson et al. (2009) noted short-term negative effects of noise on rainbow trout growth performance in RAS, with potential adaptability over time. Hang et al. (2021) also explored the negative effects of noise on largemouth bass growth performance in RAS systems, this study uniquely focuses on analyzing the specific impact of aeration equipment noise, which plays a prominent role in the soundscape of intensive aquaculture settings, on the diversity of fish school behavior, providing additional evidence to better assess the impact of noise on fish ecology and welfare. Furthermore, this research expands the understanding by directly linking noise to the total feeding quantity of largemouth bass schools, providing concrete evidence for the reduction in feeding desire and intake caused by noise.

However, the study presents certain limitations. For instance, the sample size employed is relatively modest, potentially lacking comprehensive representation of the entire largemouth bass population. Furthermore, the experiment’s duration is comparatively brief, and variations between the experimental conditions and actual production environments may impact the validity of the results. It is also important to acknowledge the influence of the 24-hour photoperiod with full-spectrum LED lights on fish behavior. While this lighting condition was chosen to ensure consistent visibility for our observations, it may have disrupted the natural diurnal rhythms of the tested fish. This is a notable limitation as it could have affected the baseline behavior of largemouth bass, potentially confounding the results. Additionally, the methodologies and instruments implemented for sound environment assessment may inherently possess constraints. While sound pressure was utilized to characterize the noise environment, measurement of the particle motion component remained beyond the scope of this study. Notwithstanding these limitations, the research elucidates the noteworthy impact of noise on the behavior and growth of largemouth bass within the specific experimental framework. Such insights are crucial for understanding the repercussions of noise pollution on fish ecology, contributing to the enhancement of fish welfare and potentially influencing fish production. These findings also provide a foundation for future investigations into the impacts of noise on largemouth bass and other fish species.

This investigation has illuminated the influence of aeration equipment noise on the swimming behavior, feeding habits, and growth progression of largemouth bass populations. The results from the experimental trials indicate that largemouth bass residing in noisy environments, when contrasted with the ambient group, display substantial disparities in swimming behavior, characterized by larger average angles and distances from the nearest neighbor fish. Moreover, these bass experience diminished appetite, decreased food consumption, and compromised growth performance. Additionally, the present study has pioneered the use of the SWDI to appraise the diversity of fish swimming behavior, thereby offering a novel evaluation instrument for fish behavior studies. Going forward, it is crucial to delve deeper into the precise mechanisms by which noise impacts largemouth bass and other aquatic organisms, aiming to provide scientific substantiation for the mitigation and regulation of noise pollution. Simultaneously, these research findings assume paramount importance in addressing the challenges posed to fish welfare within aquaculture settings. Efforts to minimize noise pollution within aquacultural settings hold the potential to elevate farming efficiency, foster animal welfare, and establish a robust framework for the sustainable advancement of aquaculture practices.

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

The animal study was approved by Experimental Animal Welfare Ethics Committee of Zhejiang University. The study was conducted in accordance with the local legislation and institutional requirements.

YZ conceived the study, carried out field work, analyzed data, and wrote the majority of the manuscript. AS, SH, HZ carried out filed work. WX, JZ, SZ conceived the study, analyzed data and revised the manuscript All authors contributed to the article and approved the submitted version.