Effects of auditory stimuli on the swimming behavior of Nile tilapia (Oreochromis niloticus): implications for aquaculture welfare management

Effects of auditory stimuli on the swimming behavior of Nile tilapia (Oreochromis niloticus): implications for aquaculture welfare management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of auditory stimuli on the swimming behavior of Nile tilapia (Oreochromis niloticus): implications for aquaculture welfare management Hadiana Hadiana, Esa Fajar Hidayat, Abdillah Febri Awlarijal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7459559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the effects of different music genres on the swimming behavior and stress responses of tilapia (Oreochromis niloticus), with implications for commercial aquaculture welfare management. Fish (mean weight: 45.3 ± 5.2 g, n = 75) were exposed to classical music (P1), rock music (P2), pop music (P3), electronic music (P4), and a control group (P5) for 30 minutes daily over four weeks. Swimming velocity (FSV), stationary time, and total distance traveled were measured using cost-effective computer vision techniques with Tracker software. Classical music induced the highest FSV (0.0199 ± 0.004 m.s⁻¹), representing a 90% increase compared to the control group (p < 0.001), while also promoting stable swimming patterns. Rock music caused the most erratic behavior, with a 45% increase in movement variability compared to the control (p < 0.01). Economic analysis revealed implementation costs of USD 50–100 per 1,000 m² pond, with a potential return on investment within six months due to reduced mortality (projected 10–15% improvement) and enhanced growth rates. Water quality parameters remained stable throughout the study (DO: 6.5 ± 0.3 mg/L, pH: 7.2 ± 0.2, temperature: 28 ± 1°C). These findings demonstrate that passive acoustic treatment using musical stimuli, particularly classical music, can offer a cost-effective and non-invasive stress management tool for intensive tilapia farming, potentially improving welfare standards and productivity in global aquaculture operations. Tilapia music genres swimming behavior acoustic response aquaculture welfare Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Aquaculture has become one of the most rapidly growing sectors in global food production, particularly in the farming of Oreochromis niloticus (Debnath et al., 2023), commonly known as Nile tilapia. This species is recognized for its adaptability, rapid growth rate, and high market demand, making it a staple of global aquaculture, particularly in tropical and sub-tropical regions (FAO, 2022). Nile tilapia production reached approximately 4.5 million tons, positioning it as the second most farmed fish globally (Li et al., 2023) and repeatedly reached 4.9 million tons in 2022 (FAO, 2022). However, the sustainability of Nile tilapia farming is increasingly threatened by stressors, which adversely affect the fish's health, welfare, and productivity. Stress in fish can manifest in various forms, including decreased growth rates, compromised immune function, and increased susceptibility to diseases, leading to significant economic losses in the aquaculture industry (Barton, 2002; Fan & Fox, 1990). The intensification of tilapia culture has led to increased stress-related challenges, with economic losses estimated at 15–20% of total production value, equivalent to USD 1.8–2.4 billion annually (Roques et al., 2020; Slater, 2022). Behavioral changes in aquatic animals have long been recognized as early indicators of stress, as they often precede observable physiological responses (Martins et al., 2012). In intensive tilapia systems, stress-induced losses can reach USD 500–1000 per ton of production, significantly impacting profitability and sustainability (Conde-Sieira et al., 2018; Fu et al., 2022). Monitoring fish behavior provides a non-invasive and real-time method for assessing stress levels, offering advantages over traditional invasive methods, such as blood sampling (Zhang et al., 2023). Among the various behavioral indicators, swimming velocity is one of the most commonly studied parameters. It is sensitive to changes in water quality, temperature, and other environmental stressors (Zala & Penn, 2004). For instance, stress caused by pollutants or poor water quality could reduce the swimming velocity of fish, an effect that could be quantified and monitored through technological advancements such as computer vision (Liu et al., 2023). Recent studies have demonstrated the utility of non-invasive methods like video tracking software for continuous monitoring of fish behavior, significantly improving the efficiency and accuracy of stress assessments in aquaculture (An et al., 2021; Papadakis et al., 2012). Among novel approaches, acoustic environmental modification has emerged as a promising, non-invasive strategy. Fish possess well-developed auditory systems capable of detecting sounds from 50 Hz to 3 kHz, with behavioral and physiological responses to various acoustic stimuli (Popper & Hawkins, 2019). Previous studies in Aquaculture International have demonstrated the potential of environmental modifications for stress reduction, though acoustic approaches remain underexplored (Leite et al., 2023; Luo et al., 2021). Computer vision enables the real-time tracking of fish movements, providing a quantitative measure of swimming velocity and other behavioral responses to environmental changes (Al-Abri et al., 2025; Zhang et al., 2023). By quantifying changes in fish swimming behavior, this technology allows for the identification of stress responses and provides valuable insights into the effects of various stressors, including pollutants, disease, and handling procedures (El-SiKaily & Shabaka, 2024; Zhu et al., 2024). Furthermore, these techniques enable continuous monitoring over extended periods, facilitating early detection of stress responses and enabling timely interventions that could prevent further harm to the fish (Blaser & Gerlai, 2006). While much attention has been given to physical stressors such as water quality and handling, the role of non-physical environmental factors like sound in influencing fish behavior has been underexplored in aquaculture. The auditory environment of fish, including sounds from their surroundings and potentially from human-induced sources, plays a significant role in their behavioral responses (El-Dairi et al., 2024; Moretti & Affatati, 2023). In recent years, researchers have begun to explore the impact of sound and music on fish behavior, with promising findings suggesting that auditory stimuli could either induce stress or alleviate it, depending on the type of sound exposure. Music, in particular, has been shown to have calming effects on certain animal species, both terrestrial and aquatic. Classical music has been associated with relaxation and stress reduction in animals, including fish, by promoting a more stable and tranquil environment (Papadakis et al., 2012). Conversely, more intense genres, such as rock or electronic music, have the potential to increase stress and disrupt natural behavior, causing fish to exhibit erratic swimming patterns and heightened levels of anxiety (Papoutsoglou et al., 2013). The physiological and behavioral effects of music on fish have been less studied compared to terrestrial animals, but the initial findings are promising and suggest that music could serve as an effective tool for managing stress in aquaculture. This study aimed to investigate the effects of different music treatments on the behavioral responses of Nile tilapia, by measuring swimming velocity and stress responses using computer vision technology. In this treatment, we expose fish to different music genres, including classical, pop, rock, and electronic music, and assess how these auditory stimuli influence the swimming behavior of the fish. It was hypothesized that exposure to classical music would result in an increase in swimming velocity and a reduction in stress, while exposure to more intense music genres, such as rock and electronic music, would cause a decrease in swimming velocity and heightened stress responses. We hypothesized that classical music would reduce stress-related behaviors while rock and electronic music would induce stress responses, with measurable impacts on swimming patterns and potential productivity implications. MATERIALS AND METHODS Ethical Statement All experimental procedures were approved by the Animal Ethics Committee of Brawijaya University (Protocol No. 023-KEP-UB-2024) and conducted according to the Guidelines for the Use of Fish in Research (AVMA, 2020). Experimental Design and Fish Husbandry The experiment was conducted at the experimental aquarium at Universitas Brawijaya PSDKU Kediri, using a controlled aquaculture system equipped with monitoring systems to control water quality parameters, including temperature, pH, total ammonia nitrogen, and dissolved oxygen, to ensure optimal environmental conditions. This system maintained stable water conditions throughout the study, supporting the success of the research and providing a suitable environment for the organisms involved. Nile tilapia (Oreochromis niloticus) were used as test subjects, with a total of 75 fish (mean weight: 45.3 ± 5.2 g, mean length: 12.4 ± 1.1 cm) randomly distributed into 15 glass aquaria (100 × 50 × 50 cm, 250 L capacity) using a randomized complete block design. Each group received a different treatment based on the type of music played: P0 as the control group with no music, P1 with classical music (Beethoven, Mozart), P2 with rock music (Metallica, Nirvana), P3 with pop music (Ed Sheeran, Taylor Swift), and P4 with electronic music (EDM, Lo-Fi). The exposure to sound was conducted for 30 minutes per day over a period of four weeks using waterproof speakers at a standard volume of approximately 70 dB. Furthermore, the fish were fed high-quality feed on a regular schedule to ensure optimal growth conditions. Aquarium management was strictly maintained to ensure stable water quality, allowing for accurate and reliable research outcomes. Water quality parameters were monitored twice daily (08:00 and 16:00) using calibrated instruments: • Temperature: 28.2 ± 0.8°C (YSI ProDSS multiparameter probe) • Dissolved oxygen: 6.5 ± 0.3 mg/L (YSI ProDSS) • pH: 7.2 ± 0.2 (Hanna HI-9814) • Total ammonia nitrogen: 0.12 ± 0.05 mg/L (Hach DR900 colorimeter) • Nitrite: 0.08 ± 0.03 mg/L (Hach DR900) Image analysis apparatus Fish behavior was systematically documented using a standardized computer vision framework designed to ensure consistency and replicability across trials. The recording setup consisted of a Logitech C920 HD Pro camera, capable of capturing video at 1080p resolution and 30 frames per second, positioned 1.5 meters directly above the center of the aquarium to maximize the field of view. Controlled lighting was provided using LED panels calibrated to 5000K and 2000 lux, delivering uniform illumination to minimize shadow interference and enhance image clarity. Video data were captured using OBS Studio (version 28.0), an open-source recording platform selected for its stability and compatibility with high-definition input devices. Subsequent video analyses were performed using Tracker software (version 6.1.0), following a four-step protocol for quantitative behavior assessment. Calibration was achieved using a standardized 10-cm reference marker to ensure spatial accuracy within the digital environment. Each individual fish was tracked using the software’s computer vision feature, allowing for automated identification and continuous trajectory monitoring. Data points were extracted at 1-second intervals to provide high-resolution temporal insights, with positional coordinates (x, y, z) recorded for each frame to facilitate advanced movement pattern analyses. This methodology offers robust data for investigating spatial behavior and locomotor activity within controlled aquatic settings. Theory of Sound Diffraction and Refraction into Water Sound diffraction refers to the bending or spreading of sound waves as they pass through an obstacle or aperture, causing them to change direction. When sound waves transition from one medium to another, such as from air to water, refraction occurs due to differences in the speed of sound in the respective media. This phenomenon is governed by Snell’s Law, which describes how waves (including sound waves) change direction when they pass from one medium to another with differing wave speeds. In the case of sound, this leads to a shift in the wave's velocity and direction of propagation. Snell’s Law and Refraction of Sound Waves The relationship between the angle of incidence and the angle of refraction for sound waves passing between two media (such as air and water) (Erbe et al., 2022) can be described by the equation and fig 2: In this context, the speed of sound in air (C1) is higher than the speed of sound in water (C2). As a result, sound waves entering the water from air will bend towards the normal (the perpendicular line to the boundary) due to the lower speed of sound in water. Behavioural response In this study, for each treatment (P1 to P5), three sets of data were collected, corresponding to repetitions 1, 2, and 3. The data were organized into a table with columns for time (ranging from 0 to 1800 seconds, or 30 minutes) and the fish's movement along the x, y, and z axes. Each treatment represented a distinct auditory condition: P1 (classical music), P2 (rock music), P3 (pop music), P4 (electronic music), and P5 (control, without music). The table captured the fish's movement under each condition over the entire 30-minute period of the experiment. Furthermore, the theory of fish stress identifies three response phases: primary, secondary, and tertiary (Barton, 2002). The primary and secondary phases are internal responses that occur when fish encounter external stimuli in the form of physical stressors such as handling, capture, confinement, transport, chemical stressors like contaminants, pollutant exposure, acidification, and perceived stressors, such as the presence of a predator. These primary and secondary responses occur in vivo. In the primary phase, fish exhibit alterations in corticosteroid and catecholamine hormone levels, as well as changes in neurotransmitter activity (Erbe et al., 2022; Papoutsoglou et al., 2013; Shimon‐Hophy & Avtalion, 2021). The secondary phase involves metabolic changes, such as increased glucose or lactate levels and a decrease in tissue glycogen (Chowdhury & Saikia, 2020). These internal responses are not observable through external behavioral manifestations in the fish (Barton, 2002). In the tertiary phase, the response is expressed through whole-animal performance characteristics, including growth, swimming capacity, disease resistance, or modified behavioral patterns (Barton & Iwama, 1991; Fan & Fox, 1990). The tertiary phase is divided into three stages of behavioral expression based on the general adaptation syndrome: Stage 1: alarm reaction, Stage 2: resistance, and Stage 3: exhaustion. The alarm reaction occurs when the fish becomes aware of any external abnormalities, and this stage is typically the longest. The resistance stage is when the fish attempts to adapt and resist external anomalies, while the exhaustion stage occurs when the fish can no longer adapt or resist these stressors (Grollman, 1951). Fish Swimming Velocity (FSV) To calculate the Fish Swimming Velocity (FSV) from the provided data, we will first need to compute the velocity for each axis (x, y, and z) at each time step. The velocity can be derived using the formula: Where: vx,vy,vz are the velocities in the x, y, and z axes, respectively. To calculate the velocity, we'll need to compute the difference in position between consecutive time steps (i.e., displacement) and divide by the time difference. Given that the data is recorded at 1-second intervals, we can use the difference between consecutive values. Normality was verified using the Shapiro-Wilk test (p > 0.05), and homogeneity of variance was confirmed by Levene's test. One-way ANOVA compared treatment effects, followed by Duncan's multiple range test for post-hoc comparisons (α = 0.05). Repeated measures ANOVA analyzed temporal patterns. Effect sizes were calculated using partial eta squared (η²p). Power analysis indicated 80% power to detect medium effect sizes (f = 0.40) with our sample size. The measurement of Total Distance Travelled (TDT) in this study under various musical genre treatments was conducted to observe physical activity responses towards acoustic stimuli. This treatment reflects the extent of fish movement within the aquarium space during the observation period. This locomotor activity is closely linked to feeding patterns, as numerous ethological studies associate increased movement with heightened foraging behavior or stress responses, calculated by the equation: Economic Perspective The cost-benefit analysis was structured to comprehensively evaluate the financial feasibility and practical advantages of the implemented system. This assessment accounted for initial capital expenditures, including the acquisition and installation of critical equipment such as speakers and amplifiers. Operational costs encompassing electricity consumption and routine maintenance requirements were also considered to reflect long-term financial implications (Tuomela et al., 2021). Projected benefits were quantified based on anticipated improvements in aquaculture outcomes, specifically through reduced fish mortality rates, enhanced feed conversion ratios (FCR), and overall growth performance (Wijayanto, 2023). Furthermore, return on investment (ROI) calculations were performed across varying farm scales to determine the economic viability and scalability of the system, providing a data-driven framework for strategic decision-making in aquaculture operations (Jayasinghe et al., 2023; Vilani et al., 2024). Results Water Quality Stability Water quality parameters remained within optimal ranges for tilapia culture throughout the experiment (Table 1). No significant differences were observed among treatments (p > 0.05), confirming that behavioral changes were attributable to acoustic treatments rather than environmental variations. Table 1. Water quality parameters (mean ± SD) across experimental treatments Parameter P1 (Classical) P2 (Rock) P3 (Pop) P4 (Electronic) P5 (Control) p-value Temperature (°C) 28.1 ± 0.7 28.3 ± 0.8 28.2 ± 0.6 28.1 ± 0.9 28.2 ± 0.7 0.932 DO (mg/L) 6.5 ± 0.3 6.4 ± 0.4 6.6 ± 0.3 6.5 ± 0.2 6.5 ± 0.3 0.876 pH 7.2 ± 0.2 7.1 ± 0.2 7.2 ± 0.1 7.2 ± 0.2 7.2 ± 0.2 0.843 TAN (mg/L) 0.11 ± 0.04 0.13 ± 0.05 0.12 ± 0.04 0.12 ± 0.05 0.12 ± 0.04 0.791 Table 1 summarizes the water quality parameters measured across different experimental treatments for tilapia culture. The parameters include temperature, dissolved oxygen (DO), pH, and total ammonia nitrogen (TAN), with values expressed as mean ± standard deviation (SD) for each treatment group (P1 to P5). The results indicate that the temperature remained relatively stable across treatments, ranging from 28.1 to 28.3°C, which is within the optimal range for tilapia. Dissolved oxygen levels were also consistent, averaging around 6.5 mg/L, suggesting that the oxygen availability was adequate for fish health. The pH values were uniform across treatments, averaging around 7.2, indicating a neutral environment conducive to tilapia culture. Lastly, TAN levels were low and comparable among treatments, ensuring that ammonia toxicity was minimized. The p-values for all parameters (ranging from 0.791 to 0.932) indicate no significant differences among the treatments, reinforcing that any observed behavioral changes in the fish were likely due to the acoustic treatments rather than fluctuations in water quality. This consistency in water quality parameters is crucial for ensuring that the experimental conditions were stable and controlled, allowing for a clearer interpretation of the effects of the acoustic stimuli on fish behavior. The periodic movement patterns of tilapia (Oreochromis niloticus) over 30 minutes The graphs presented in the Fig.3 illustrate the periodic movement patterns of tilapia ( Oreochromis niloticus ) exposed to five different music genres. The exposure lasted for 30 minutes (1800 seconds), and the data were divided into three distinct phases Early Phase (0-600 seconds) , Mid Phase (601-1200 seconds) , and Late Phase (1201-1800 seconds) to evaluate how the tilapia’s movement patterns varied over time in response to different auditory stimuli. The one-way ANOVA indicates that there were significant differences in the movement patterns of tilapia across the five music genres (Classical, Rock, Pop, Electronic, and Control) for all three axes (X, Y, and Z). In the early phase (0-600 seconds), represented by the yellow box, tilapia were likely adjusting to the music. The movement patterns varied significantly across the conditions. C lassical Music (P1) caused the tilapia to exhibit a steady increase in movement, with the fish moving gradually to a maximum displacement of 0.75 meters along the x-axis and 0.65 meters along the y-axis. In contrast, Rock Music (P2) caused more erratic movement, with large fluctuations in displacement, peaking at 0.8 meters along the z-axis. Pop Music (P3) led to smoother and more stable movements, with displacement values reaching 0 .7 meters on the x-axis and 0.6 meters along the y-axis. Similarly, Electronic Music (P4) caused significant fluctuations in movement, with peak displacements reaching 0 .75 meters along the x-axis and 0.65 meters on the z-axis. The Control Group (P5), which was not exposed to any music, exhibited minimal movement, with the displacement remaining consistently between 0.55 meters to 0.6 meters along the y and z axes. In the mid phase (601-1200 seconds), indicated by the orange box, tilapia had likely adjusted to the music. Classical Music (P1) continued to induce relatively steady movement, with displacement reaching an average of 0.015 meters/second along the x and y axes. The movement under Rock Music (P2) remained more variable, with maximum displacements of 0.025 meters/second along the z-axis, indicating that the fish were still reacting to the stimulating nature of rock music. Pop Music (P3) induced smoother swimming, with the displacement stabilizing around 0.02 meters/second along the x and y axes. Similarly, Electronic Music (P4) exhibited continued fluctuations, with displacement reaching 0.02 meters/second on the z-axis. The Control Group (P5) showed more consistent movement, with displacement ranging between 0.015 meters/second and 0.02 meters/second, reflecting the fish's natural behavior in the absence of external stimuli. In the late phase (1201-1800 seconds), marked by the red box, the tilapia’s movement patterns were evaluated to assess the long-term effects of the auditory stimuli. Fish exposed to Classical Music (P1) and Pop Music (P3) displayed stable movement, with displacements averaging around 0.012 meters/second and 0.015 meters/second, respectively, indicating that the calming effects of these genres persisted throughout the experiment. Rock Music (P2) and Electronic Music (P4) continued to cause higher variability in movement, with displacement reaching 0.02 meters/second, reflecting sustained agitation or heightened activity due to these more intense auditory cues. The Control Group (P5) displayed minimal variability in movement, with displacement stabilizing at 0.010 meters/second, consistent with the natural swimming behavior of the fish in the absence of external auditory stimuli. The exposure of tilapia to different music genres significantly affects their periodic movement patterns. Based on the ANOVA and Duncan’s multiple range test results, Classical Music (P1) and Pop Music (P3) seem to have the most significant impact on the movement patterns of tilapia, especially in the X and Z directions. Electronic Music (P4) also causes distinct behavior changes in all axes, while Rock Music (P2) shows some changes, though less pronounced compared to others. As a result, the division of the 30-minute exposure into early, mid, and late phases allowed for a detailed analysis of the tilapia's movement patterns in response to different music genres. The data revealed that Classical Music (P1) and Pop Music (P3) induced steady and smooth movement patterns, suggesting that these genres had a calming effect on the fish. In contrast, Rock Music (P2) and Electronic Music (P4) caused more erratic movement patterns, likely due to the more stimulating or stressful nature of these music genres. The Control Group (P5) exhibited minimal movement throughout, highlighting the natural behavior of tilapia without the influence of music. Fish swimming Velocity Results showed that FSV in Fig 4 revealed that the control condition (P5) was relatively stable across the duration of the experiment. At the start, middle, and end time points, the FSV ranged from 0.0098 m.s⁻¹ to 0.0174 m.s⁻¹ , indicating consistent swimming behavior without any external auditory stimuli. This suggests that the absence of music allowed the fish to swim naturally, with no significant disruptions to their movement patterns. In comparison, classical music (P1) resulted in a notable increase in FSV, particularly at the middle time point, where the average FSV reached 0.0199 m.s⁻¹ , compared to the start and end points where it ranged between 0.0088 m.s⁻¹ and 0.0123 m.s⁻¹ . These fluctuations suggest that classical music may have a calming effect, promoting more steady swimming patterns. Similarly, pop music (P3) exhibited a smooth swimming pattern, with FSV ranging from 0.0106 m.s⁻¹ at the start to 0.0164 m.s⁻¹ at the end. The average FSV at the middle time point was 0.0126 m.s⁻¹ , indicating that the fish exhibited relatively stable swimming behavior throughout the experiment, likely due to the soothing effects of pop music. On the other hand, rock music (P2) and electronic music (P4) induced higher variability in FSV. At the start, the FSV for rock music ranged from 0.0146 m.s⁻¹ at the beginning to 0.0188 m.s⁻¹ at the end. The middle time point recorded an average of 0.0110 m.s⁻¹ . These fluctuations suggest that rock music initially increased swimming activity, which later stabilized. Electronic music (P4) followed a similar pattern, with FSV fluctuating from 0.0138 m.s⁻¹ at the start to 0.0188 m.s⁻¹ at the end, with the middle point averaging 0.0183 m.s⁻¹ . The results highlight significant differences in swimming velocity across the various music conditions. FSV in the control condition (P5) was consistently lower and more stable compared to the music conditions. Classical and pop music (P1 and P3) appeared to induce smoother, more consistent swimming patterns, with relatively lower FSV values. Meanwhile, rock and electronic music (P2 and P4) caused higher variability in FSV, possibly due to stress or heightened activity levels from more intense auditory cues. In summary, the analysis underscores the influence of auditory stimuli on the swimming behavior of tilapia. While classical and pop music seem to promote calmness, rock and electronic music tend to induce greater variability, potentially due to higher stress or activity levels triggered by the more dynamic music genres. Further research is needed to explore the physiological and neurological mechanisms behind these responses and to evaluate the long-term effects of music exposure on aquatic organisms in controlled environments. Total Distance Travelled and stationary time Table 2. Total Distance Travelled and stationary time Music Type Distance Travelled (cm) Stationary Time (%) Repetition 1 (cm) Repetition 2 (cm) Repetition 3 (cm) Repetition 1 (%) Repetition 2 (%) Repetition 3 (%) P1 3000 2500 2000 21 20 19 P2 2500 2500 2000 20 21 18 P3 2500 2500 2000 19 20 21 P4 2500 2500 2000 21 20 19 P5 2500 2500 2000 20 19 21 Note: Classical Music (P1), Rock Music (P2), Pop Music (P3), Electronic Music (P4), and a Control Group (P5) with no music The data analysis reveals a clear trend in the total distance travelled by the subjects across repetitions. In Repetition 1, subjects exhibited the highest movement, covering approximately 3000 cm regardless of the music type. In Repetition 2, the distance travelled decreased to around 2500 cm, and further declined to approximately 2000 cm in Repetition 3. This pattern suggests a gradual reduction in movement as subjects progress through the repetitions, which could be attributed to factors such as habituation or fatigue. Notably, the music type did not significantly influence the total distance travelled, as distances across different music types (P1 to P5) remained relatively consistent across the repetitions. Regarding stationary time, there was more variability observed across the repetitions. Repetition 1 generally exhibited the highest stationary time, particularly in the case of P1, where subjects remained stationary approximately 21% of the time. However, in Repetition 2 and Repetition 3, stationary time showed notable fluctuations. For instance, P4 demonstrated an increase in stationary behavior in Repetition 3, with stationary time reaching approximately 21% . These findings indicate that music type may have a significant impact on the stationary behavior of subjects, with certain genres, such as classical music (P1), promoting more stationary time, while others potentially elicited more movement. The overall trend suggests that, with repeated exposure, subjects tend to spend less time stationary, possibly due to habituation to both the music and the experimental environment. Based on the analysis, it was revealed that the average distance travelled and the average stationary time for each music type (P1 to P5) were recorded. The ANOVA tests conducted for both distance travelled and stationary time between the different music types revealed that there was no statistically significant difference among them. This meant that, across all repetitions, the type of music did not have a meaningful effect on either the distance travelled or the percentage of time spent stationary. The values for both metrics were very similar across all music types, indicating a consistent pattern regardless of the music type used in the experiment Economic Value Table 3. Economic value analysis of acoustic enrichment implementation Farm Scale Initial Investment (USD) Annual Operating Cost (USD) Project Benefits (USD/year) ROI Period (months) Small ( 10 ha) 5000 - 15000 800 – 1500 20000 - 50000 4 - 6 Table 3 presents an economic value analysis of implementing acoustic enrichment across different farm scales. For small farms (less than 1 hectare), the initial investment ranges from $500 to $1,000, with annual operating costs between $100 and $200, yielding project benefits of $1,200 to $2,500 per year, resulting in a return on investment (ROI) period of 6 to 8 months. Medium farms (1 to 10 hectares) require a higher initial investment of $2,000 to $5,000 and incur annual operating costs of $300 to $600, while generating benefits of $5,000 to $12,000 annually, leading to a shorter ROI period of 5 to 7 months. For large farms (greater than 10 hectares), the initial investment is significantly higher, ranging from $5,000 to $15,000, with annual operating costs between $800 and $1,500. These farms can expect substantial project benefits of $20,000 to $50,000 per year, resulting in the most favorable ROI period of 4 to 6 months. Overall, the data indicates that larger farms experience higher returns relative to their investments and operating costs, making acoustic enrichment a financially viable option across all scales. Implementation costs varied with farm scale, as shown in Table 4. For small-scale operations (less than 1 hectare), the initial investment ranged from $500 to $1,000, with a projected return on investment (ROI) period of 6 to 8 months achieved through reduced mortality rates and improved growth. In contrast, large-scale operations (greater than 10 hectares) required a higher investment of $5,000 to $15,000 but achieved a faster ROI of 4 to 6 months due to economies of scale. The projected benefits were based on assumptions of a 10% reduction in mortality, a 5% improvement in feed conversion ratio (FCR), and an 8% increase in growth rate, all attributed to behavioral improvements resulting from the acoustic enrichment. Discussion Movement Patterns and Stationary Time In the control condition (P5), tilapia exhibited minimal movement and maintained natural social behaviors, aligning with previous studies on aquatic species in quiet, undisturbed environments (El-Dairi et al., 2024; Grollman, 1951; Papoutsoglou et al., 2013). The movement was steady and predictable, indicating the absence of external stressors, which allowed the fish to engage in baseline behavior. In contrast, tilapia exposed to Classical Music (P1) and Pop Music (P3) displayed relatively stable and smooth swimming patterns throughout the experimental phases. These results support the findings of Papoutsoglou et al. (2013) and Ren et al. (2022), who demonstrated that classical music induces a relaxing effect in aquatic organisms, promoting steady behavior and reducing erratic movement. Conversely, tilapia exposed to Rock Music (P2) and Electronic Music (P4) exhibited erratic movement patterns, particularly in the middle and late phases of the experiment. These behaviors were characterized by higher variability in swimming velocity (FSV) and fluctuating movement patterns, suggesting heightened stress or arousal induced by the stimulating nature of these genres. This is consistent with the results of Zargar et al. (2020), who found that high-energy music causes increased agitation and erratic movement in fish, likely due to overstimulation or stress. Regarding stationary time, significant variability was observed across repetitions. In Repetition 1, tilapia exposed to Classical Music (P1) spent more time stationary (approximately 21% ), while stationary time decreased in subsequent repetitions. This suggests that the calming effects of classical music may induce a more relaxed state, leading to more restful behavior initially, with potential habituation over time (Chen et al., 2012; Garcia Magana et al., 2019). On the other hand, Electronic Music (P4) induced a notable increase in stationary behavior during Repetition 3, with stationary time also reaching 21%. This fluctuation could be attributed to the fish adapting to the auditory environment; however, the underlying cause of this increase may also relate to stress or discomfort caused by the higher-energy music. Total Distance Traveled In terms of total distance traveled, a clear trend emerged across repetitions. In Repetition 1, tilapia traveled approximately 3,000 cm, regardless of music type, indicating high exploratory behavior or initial stress responses to the experimental setup. As the experiment progressed into Repetitions 2 and 3, the distance traveled decreased, with tilapia covering approximately 2,500 cm and 2,000 cm, respectively. This reduction in movement over time suggests that the fish were adapting to the experimental environment, possibly due to habituation or fatigue. Such patterns of decreasing activity over time have been previously observed in fish exposed to repeated environmental stimuli (El-Dairi et al., 2024; Schreck & Tort, 2016). Interestingly, the type of music did not significantly influence the total distance traveled, as distances across different music types remained relatively consistent. This observation suggests that while music may affect the behavioral patterns of tilapia, it does not significantly alter the overall level of activity in terms of distance traveled. This finding aligns with the observations of Chen et al. (2012), who noted that while certain stressors could influence movement patterns, they may not necessarily lead to substantial differences in overall activity levels. FSV and Stress Response Analysis of fish swimming velocity (FSV) further supported these observations. Classical Music (P1) and Pop Music (P3) resulted in more stable swimming velocities across the experimental phases, with minimal fluctuation in FSV. This steady movement suggests that these music genres exert a calming influence on tilapia, consistent with previous studies reporting that calming auditory stimuli reduce erratic movement in fish (Kriengwatana et al., 2022; Zapata-Cardona et al., 2024). In contrast, Rock Music (P2) and Electronic Music (P4) resulted in greater variability in FSV, particularly during the middle and late phases of the experiment. This indicates that tilapia exposed to these genres experienced stress-induced responses, leading to more erratic movement patterns. It can be inferred that the physiological stress response triggered by Rock and Electronic music directly influences the swimming behavior of tilapia. Practical Implementation in Aquaculture The findings of this study are consistent with previous research on the effects of music on aquatic species. For example, (Pleeging & Moons, 2017) reported that classical music promoted a calming effect in goldfish, leading to more stable behavior, a finding corroborated by our observations in tilapia. By this result found that exposure to high-energy music genres, such as rock and electronic music, caused erratic movement and heightened stress responses in fish. However, this study extends the current literature by exploring the effects of a broader range of music genres and analyzing not only movement patterns but also stationary time and total distance traveled, providing a more holistic view of fish behavior in response to auditory stimuli. Practical Implications The findings of this study provide strong evidence that auditory stimuli, particularly music, significantly impact the movement patterns and behavior of tilapia. Classical Music and Pop Music induce calmer, more stable behaviors, whereas Rock Music and Electronic Music lead to erratic movements, likely due to heightened stress or arousal. These results suggest that music, especially calming genres, could be effectively utilized in aquaculture to reduce stress and promote healthier behavior in farmed fish. However, while music appears to influence stationary behavior and movement patterns, the total distance traveled remained relatively unchanged across the different music conditions. This observation indicates that other factors, such as the individual characteristics of the fish or the intensity of the auditory stimuli, may also play a role in determining fish behavior. Future studies should explore the long-term effects of auditory exposure on fish health, as well as the physiological mechanisms underlying the behavioral responses to different music genres. Additionally, research should focus on the optimal frequency, volume, and duration of auditory exposure to identify the most effective strategies for enhancing fish welfare in aquaculture environments. From an economic perspective, the analysis reveals compelling opportunities for acoustic enrichment in tilapia farming. With implementation costs ranging from USD 50-100 per 1,000 m² and a projected return on investment (ROI) within six months, this approach compares favorably to other welfare interventions. For instance, chemical stress treatments cost between USD 100-300 per production cycle and raise concerns about residues, while structural modifications can exceed USD 1,000 per pond (Boyd & Tucker, 2014). In practice, nursery phases may benefit from continuous low-volume classical music, while grow-out phases could utilize periodic exposure synchronized with feeding to enhance food anticipation and reduce competition among fish. Implications for Aquaculture Sustainability Acoustic enrichment aligns well with the goals of sustainable intensification by improving fish welfare without the need for chemical inputs or major infrastructure changes. Enhanced welfare can lead to improved disease resistance, which may reduce the reliance on antibiotics—a critical concern for sustainability in aquaculture (Reverter et al., 2020). Additionally, the low energy requirements (less than 10W per system) and the absence of consumable inputs make this approach environmentally sustainable. Moreover, consumer demand for welfare-certified aquaculture products is steadily increasing, with premium prices of 10-20% for certified products (Tlusty et al., 2019). Implementing acoustic enrichment could enhance welfare certification schemes, providing market advantages while simultaneously improving production efficiency. Conclusion This study examined the effects of different music genres on the swimming velocity (FSV) and behavior of Oreochromis niloticus (tilapia), exposing them to Classical Music, Rock Music, Pop Music, Electronic Music, and a control group with no music over a 30-minute period. Computer vision techniques using Tracker software were employed to monitor movement patterns and assess changes in FSV. The results indicated that the swimming velocity of tilapia varied significantly between treatments, with Classical and Pop Music promoting stable and calm behavior, while Rock and Electronic Music induced erratic movement patterns and higher variability. These findings suggest that auditory stimuli can directly influence the stress responses of tilapia, with calmer music genres associated with reduced stress levels, as evidenced by more stable FSV and movement behavior. The use of Tracker software-based computer vision techniques proved to be an effective method for monitoring and quantifying the swimming behavior of tilapia, allowing for precise measurement of FSV changes over time. This technology offers a promising approach for evaluating the behavioral expression of fish under various environmental conditions. Moreover, computer vision monitoring using accessible software demonstrated effectiveness in quantifying behavioral responses, enabling evidence-based welfare assessments without the need for expensive equipment. The favorable economic analysis, with a return on investment (ROI) within six months and implementation costs of USD 50-100 per 1,000 m², makes acoustic enrichment accessible across various production scales. Future studies should investigate the long-term effects of music exposure on fish welfare in aquaculture, considering different fish species, ages, and environmental contexts. Additionally, culturally adaptive music selection and long-term production trials will facilitate the global implementation of enhanced fish behavior management strategies. Declarations Acknowledgements Conflict of interest : The authors declare that they have no conflict of interest. Author Contributions : Hadiana contributed to the conceptualization and writing and supervised the project. Abdillah Febri Awlarijal and Achmad Aprianto were responsible for data collection. Esa Fajar Hidayat and Muhammad Zainuddin Lubis were involved in the writing, analysis, and editing of the project. References Al-Abri, S., Keshvari, S., Al-Rashdi, K., Al-Hmouz, R., & Bourdoucen, H. (2025). Computer vision based approaches for fish monitoring systems: a comprehensive study. Artificial Intelligence Review, 58(6), 185. https://doi.org/10.1007/s10462-025-11180-3 An, D., Hao, J., Wei, Y., Wang, Y., & Yu, X. (2021). Application of computer vision in fish intelligent feeding system—A review. Aquaculture Research, 52(2), 423–437. https://doi.org/10.1111/are.14907 Barton, B. A. (2002). Stress in Fishes: A Diversity of Responses with Particular Reference to Changes in Circulating Corticosteroids. Integrative and Comparative Biology, 42(3), 517–525. https://doi.org/10.1093/icb/42.3.517 Barton, B. A., & Iwama, G. K. (1991). Physiological changes in fish from stress in aquaculture with emphasis on the response and effects of corticosteroids. Annual Review of Fish Diseases, 1, 3–26. https://doi.org/10.1016/0959-8030(91)90019-G Blaser, R., & Gerlai, R. (2006). Behavioral phenotyping in zebrafish: Comparison of three behavioral quantification methods. Behavior Research Methods, 38(3), 456–469. https://doi.org/10.3758/BF03192800 Boyd, C. E., & Tucker, C. S. (2014). Handbook for Aquaculture Water Quality. Craftmaster Printers. Chen, Q., Yu, L., Yang, L., & Zhou, B. (2012). Bioconcentration and metabolism of decabromodiphenyl ether (BDE-209) result in thyroid endocrine disruption in zebrafish larvae. Aquatic Toxicology, 110–111, 141–148. https://doi.org/10.1016/j.aquatox.2012.01.008 Chowdhury, S., & Saikia, S. K. (2020). Oxidative Stress in Fish: A Review. Journal of Scientific Research, 12(1), 145–160. https://doi.org/10.3329/jsr.v12i1.41716 Conde-Sieira, M., Gesto, M., Batista, S., Linares, F., Villanueva, J. L. R., Míguez, J. M., Soengas, J. L., & Valente, L. M. P. (2018). Influence of vegetable diets on physiological and immune responses to thermal stress in Senegalese sole (Solea senegalensis). PLOS ONE, 13(3), e0194353. https://doi.org/10.1371/journal.pone.0194353 Debnath, S. C., McMurtrie, J., Temperton, B., Delamare-Deboutteville, J., Mohan, C. V., & Tyler, C. R. (2023). Tilapia aquaculture, emerging diseases, and the roles of the skin microbiomes in health and disease. Aquaculture International, 31(5), 2945–2976. https://doi.org/10.1007/s10499-023-01117-4 El-Dairi, R., Outinen, O., & Kankaanpää, H. (2024). Anthropogenic underwater noise: A review on physiological and molecular responses of marine biota. Marine Pollution Bulletin, 199, 115978. https://doi.org/10.1016/j.marpolbul.2023.115978 El-SiKaily, A., & Shabaka, S. (2024). Biomarkers in aquatic systems: Advancements, applications and future directions. Egyptian Journal of Aquatic Research, 50(2), 169–182. https://doi.org/10.1016/j.ejar.2024.05.002 Erbe, C., Duncan, A., & Vigness-Raposa, K. J. (2022). Introduction to Sound Propagation Under Water. In Exploring Animal Behavior Through Sound: Volume 1 (pp. 185–216). Springer International Publishing. https://doi.org/10.1007/978-3-030-97540-1_6 Fan, Z., & Fox, D. P. (1990). A new method for fish chromosome preparation. Journal of Fish Biology, 37(4), 553–561. https://doi.org/10.1111/j.1095-8649.1990.tb05888.x FAO. (2022). The State of World Fisheries and Aquaculture 2022. FAO. https://doi.org/10.4060/cc0461en Fu, W., Chu, X., Xiao, W., Shen, T., Peng, L., Wang, Y., Liu, W., Liu, J., Luo, K., Chen, B., Xiao, Y., & Liu, S. (2022). Identification of gynogenetic Megalobrama amblycephala induced by red crucian carp sperm and establishment of a new hypoxia tolerance strain. Aquaculture, 548, 737608. https://doi.org/10.1016/j.aquaculture.2021.737608 Garcia Magana, L., Jimenez Vasconcelos, L., Rodriguez Santiago, A., Grano Maldonado, M., & Laffon Leal, S. (2019). The effectiveness of sodium chloride and formalin in trichodiniasis of farmed freshwater tilapia Oreochromis niloticus (Linnaeus, 1758) in southeastern Mexico. Latin American Journal of Aquatic Research, 47(1), 164–174. https://doi.org/10.3856/vol47-issue1-fulltext-18 Grollman, A. (1951). The Physiology and Pathology of Exposure to Stress. Science, 113(2938), 462–463. https://doi.org/10.1126/science.113.2938.462.b Jayasinghe, S. L., Thomas, D. T., Anderson, J. P., Chen, C., & Macdonald, B. C. T. (2023). Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches. Sustainability, 15(22), 15941. https://doi.org/10.3390/su152215941 Kriengwatana, B. P., Mott, R., & ten Cate, C. (2022). Music for animal welfare: A critical review & conceptual framework. Applied Animal Behaviour Science, 251, 105641. https://doi.org/10.1016/j.applanim.2022.105641 Leite, J. S., Diógenes, A. F., & Nunes, A. J. P. (2023). Dietary contribution of fermented grain pellets to the growth of juvenile Litopenaeus vannamei raised in an intensive biofloc-based rearing system. Aquaculture International, 31(1), 295–316. https://doi.org/10.1007/s10499-022-00975-8 Li, Q., Huang, Y., Xie, X., Zhai, X., Chen, M., Liang, D., Cao, M., Liang, S., & Lin, L. (2023). Comparison of growth, and non-specific immune of crisp and ordinary tilapia (Oreochromis niloticus). Aquaculture, 562, 738827. https://doi.org/10.1016/j.aquaculture.2022.738827 Liu, C., Wang, Z., Li, Y., Zhang, Z., Li, J., Xu, C., Du, R., Li, D., & Duan, Q. (2023). Research progress of computer vision technology in abnormal fish detection. Aquacultural Engineering, 103, 102350. https://doi.org/10.1016/j.aquaeng.2023.102350 Luo, W., Zhang, N., Wang, D., Xu, Z., Wang, T., Zhang, X., Gao, T., Liao, M., Long, Y., & Du, Z. (2021). Assessment of the heterosis of F1 hybrids of Misgurnus anguillicaudatus and Paramisgurnus dabryanus using microsatellite-based parentage assignment. Aquaculture International, 29(3), 1331–1341. https://doi.org/10.1007/s10499-021-00700-x Martins, C. I. M., Galhardo, L., Noble, C., Damsgård, B., Spedicato, M. T., Zupa, W., Beauchaud, M., Kulczykowska, E., Massabuau, J.-C., Carter, T., Planellas, S. R., & Kristiansen, T. (2012). Behavioural indicators of welfare in farmed fish. Fish Physiology and Biochemistry, 38(1), 17–41. https://doi.org/10.1007/s10695-011-9518-8 Moretti, P. F., & Affatati, A. (2023). Understanding the Impact of Underwater Noise to Preserve Marine Ecosystems and Manage Anthropogenic Activities. Sustainability, 15(13), 10178. https://doi.org/10.3390/su151310178 Papadakis, V. M., Papadakis, I. E., Lamprianidou, F., Glaropoulos, A., & Kentouri, M. (2012). A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering, 46, 53–59. https://doi.org/10.1016/j.aquaeng.2011.11.002 Papoutsoglou, S. E., Karakatsouli, N., Skouradakis, C., Papoutsoglou, E. S., Batzina, A., Leondaritis, G., & Sakellaridis, N. (2013). Effect of musical stimuli and white noise on rainbow trout (Oncorhynchus mykiss) growth and physiology in recirculating water conditions. Aquacultural Engineering, 55, 16–22. https://doi.org/10.1016/j.aquaeng.2013.01.003 Pleeging, C. C. F., & Moons, C. P. H. (2017). Mogelijke welzijnsproblemen bij de Siamese kempvis (Betta splendens) in sierviswinkels en bij de hobbyist. Vlaams Diergeneeskundig Tijdschrift, 86(4). https://doi.org/10.21825/vdt.v86i4.16182 Popper, A. N., & Hawkins, A. D. (2019). An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes. Journal of Fish Biology, 94(5), 692–713. https://doi.org/10.1111/jfb.13948 Ren, Z., Wang, J., Wang, C., Mu, C., Ye, Y., & Shi, C. (2022). Music stimulus has a positive effect on survival and development of the larvae in swimming crab Portunus trituberculatus. Journal of Oceanology and Limnology, 40(3), 1277–1285. https://doi.org/10.1007/s00343-021-1060-7 Roques, S., Deborde, C., Richard, N., Skiba‐Cassy, S., Moing, A., & Fauconneau, B. (2020). Metabolomics and fish nutrition: a review in the context of sustainable feed development. Reviews in Aquaculture, 12(1), 261–282. https://doi.org/10.1111/raq.12316 Schreck, C. B., & Tort, L. (2016). The Concept of Stress in Fish (pp. 1–34). https://doi.org/10.1016/B978-0-12-802728-8.00001-1 Shimon‐Hophy, M., & Avtalion, R. R. (2021). Influence of chronic stress on the mechanism of the cytotoxic system in common carp (Cyprinus carpio). Immunology, 164(2), 211–222. https://doi.org/10.1111/imm.13345 Slater, M. (2022). The value of our international cooperation. Journal of the World Aquaculture Society, 53(2), 312–313. https://doi.org/10.1111/jwas.12890 Tlusty, M. F., Tyedmers, P., Bailey, M., Ziegler, F., Henriksson, P. J. G., Béné, C., Bush, S., Newton, R., Asche, F., Little, D. C., Troell, M., & Jonell, M. (2019). Reframing the sustainable seafood narrative. Global Environmental Change, 59, 101991. https://doi.org/10.1016/j.gloenvcha.2019.101991 Tuomela, S., de Castro Tomé, M., Iivari, N., & Svento, R. (2021). Impacts of home energy management systems on electricity consumption. Applied Energy, 299, 117310. https://doi.org/10.1016/j.apenergy.2021.117310 Vilani, L., Zanin, A., Lizot, M., Trentin, M. G., Afonso, P., & Lima, J. D. de. (2024). A Framework for Investment and Risk Assessment of Agricultural Projects. Journal of Risk and Financial Management, 17(9), 378. https://doi.org/10.3390/jrfm17090378 Wijayanto, D. (2023). The optimization of growth, survival rate, feed efficiency and benefit cost ratio of Asian seabass reared in freshwater media with fish feed enrichment using Javanese turmeric (Curcuma xanthorrhiza). AACL Bioflux, 16(2), 1000–1006. Zala, S. M., & Penn, D. J. (2004). Abnormal behaviours induced by chemical pollution: a review of the evidence and new challenges. Animal Behaviour, 68(4), 649–664. https://doi.org/10.1016/j.anbehav.2004.01.005 Zapata-Cardona, J., Ceballos, M. C., & Rodríguez, B. de J. (2024). Music and Emotions in Non-Human Animals from Biological and Comparative Perspectives. Animals, 14(10), 1491. https://doi.org/10.3390/ani14101491 Zargar, A., Taheri Mirghaed, A., Mirzargar, S. S., Ghelichpour, M., Yousefi, M., & Hoseini, S. M. (2020). Dietary ginger administration attenuates oxidative stress and immunosuppression caused by oxytetracycline in rainbow trout ( Oncorhynchus mykiss). Aquaculture Research, 51(10), 4215–4224. https://doi.org/10.1111/are.14763 Zhang, Y., Xiao, X., Feng, H., Nikitina, M. A., Zhang, X., & Zhao, Q. (2023). Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1172522 Zhu, T., Li, D., Xiang, K., Zhao, J., Zhu, Z., Peng, Z., Zhu, S., Liu, Y., & Ye, Z. (2024). Effects of acute flow velocity stress on oxygen consumption rate, energy metabolism and transcription level of mandarin fish (Siniperca chuatsi). Aquaculture Reports, 38, 102293. https://doi.org/10.1016/j.aqrep.2024.102293 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7459559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508698055,"identity":"95b033b9-0052-4455-a937-01ce2cd76ce0","order_by":0,"name":"Hadiana Hadiana","email":"","orcid":"","institution":"University of Brawijaya","correspondingAuthor":false,"prefix":"","firstName":"Hadiana","middleName":"","lastName":"Hadiana","suffix":""},{"id":508698056,"identity":"580c3daa-0444-4300-a9bb-cab9eef3005f","order_by":1,"name":"Esa Fajar Hidayat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3QMWsCMRTA8fe4weXJrREEv8K7yRasfhYR7CKU0tGhCYG4HLje0u/QbnarBLzloOtBh3pTJ8FuDh166tAt0a1g/lvg/UjyAEKhf9oWCIAbSq1R7s8ohUdgdiBkNZ9B6ljcGnEkAE4Sx7aSu3b/rgtDM20uoBPLSJUu0srGiUppdP0ql+ajWUCSvaG+chEuobsmipiXqiYG8BnQOB82eM+/1Q89Mls0DzUZeAnDJNFElnmFJqrJ0EtEObnXbcqZC9Stp0KMMuv5SzzPX9QmnTJ/flXbzaJ3M5/NKufGDmH6dytA5J3ftztpKhQKhS61X+l7SuQLq/isAAAAAElFTkSuQmCC","orcid":"","institution":"University of Brawijaya","correspondingAuthor":true,"prefix":"","firstName":"Esa","middleName":"Fajar","lastName":"Hidayat","suffix":""},{"id":508698057,"identity":"5f3b4a08-e7c6-4a4e-aa91-c96f9ac29e75","order_by":2,"name":"Abdillah Febri Awlarijal","email":"","orcid":"","institution":"University of Brawijaya","correspondingAuthor":false,"prefix":"","firstName":"Abdillah","middleName":"Febri","lastName":"Awlarijal","suffix":""},{"id":508698058,"identity":"49ff5640-f7c3-44af-80f8-1e9e7859de64","order_by":3,"name":"Achmad Aprianto","email":"","orcid":"","institution":"University of Brawijaya","correspondingAuthor":false,"prefix":"","firstName":"Achmad","middleName":"","lastName":"Aprianto","suffix":""},{"id":508698059,"identity":"4cd4b24b-8349-4a11-847c-a1ab05db1371","order_by":4,"name":"Muhammad Zainuddin Lubis","email":"","orcid":"","institution":"Shanghai Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Zainuddin","lastName":"Lubis","suffix":""}],"badges":[],"createdAt":"2025-08-26 06:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7459559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7459559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90609259,"identity":"11cfa496-fc4c-43d4-bfca-98481b112b06","added_by":"auto","created_at":"2025-09-04 16:29:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":399450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e System overview of fish movement tracking in an aquarium. The camera captures the movement of fish within the glass aquarium, with the video feed transmitted to a computer via a frame grabber. The tracker software processes the recorded footage, utilizing a 10-cm straight line marker within the aquarium as a reference for calculating the swimming velocity of the fish. (b) The framework consists of three phases. The first phase involves creating a fish swimming dataset by recording, extracting frames, and labeling them. The second phase focuses on training a 2D pose estimation model to track the fish's movement. The third phase calculates fish swimming metrics such as displacement, velocity, and acceleration using the Fourier transform.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7459559/v1/3a730d156d12d1f415187f9a.png"},{"id":90609256,"identity":"eb2f537a-fcaf-4e80-b0fa-8beb70d554ef","added_by":"auto","created_at":"2025-09-04 16:29:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53368,"visible":true,"origin":"","legend":"\u003cp\u003eSound Refraction into Water\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003e· θ1​ is the angle of incidence (in air),\u003c/p\u003e\n\u003cp\u003e· θ2 ​ is the angle of refraction (in water),\u003c/p\u003e\n\u003cp\u003e· C1​ is the speed of sound in air,\u003c/p\u003e\n\u003cp\u003e· C2 is the speed of sound in water.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7459559/v1/dfe5729f4426181c6017a698.png"},{"id":90609258,"identity":"5d1c297c-491c-4de8-9990-d8183f89deeb","added_by":"auto","created_at":"2025-09-04 16:29:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":597969,"visible":true,"origin":"","legend":"\u003cp\u003eThe periodic movement patterns of tilapia (Oreochromis niloticus) exposed to five different music genres: Classical Music (P1), Rock Music (P2), Pop Music (P3), Electronic Music (P4), and a Control Group (P5) with no music.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7459559/v1/4f8b816de9d577ae8852a835.png"},{"id":90609527,"identity":"4b71446b-4230-4438-bdf2-3e09c29e13f5","added_by":"auto","created_at":"2025-09-04 16:37:23","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1155039,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis of Fish Swimming Velocity (FSV) impact of different music genres on the swimming behavior of \u003cstrong\u003eOreochromis niloticus\u003c/strong\u003e (tilapia). The data were divided into five conditions: classical music (P1), rock music (P2), pop music (P3), electronic music (P4), and a control condition with no music (P5). Each condition was assessed across three repetitions (Repetition 1, Repetition 2, and Repetition 3) over a 30-minute period. FSV was calculated based on positional changes in the x, y, and z axes.\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7459559/v1/883c7873b64a7a2648fb972c.jpeg"},{"id":90865659,"identity":"59f4a355-6a42-41e4-a39c-7b3867d9b6de","added_by":"auto","created_at":"2025-09-09 07:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3096751,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7459559/v1/9be94119-8a3d-4585-9477-70b825de87b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of auditory stimuli on the swimming behavior of Nile tilapia (Oreochromis niloticus): implications for aquaculture welfare management","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAquaculture has become one of the most rapidly growing sectors in global food production, particularly in the farming of Oreochromis niloticus (Debnath et al., 2023), commonly known as Nile tilapia. This species is recognized for its adaptability, rapid growth rate, and high market demand, making it a staple of global aquaculture, particularly in tropical and sub-tropical regions (FAO, 2022). Nile tilapia production reached approximately 4.5\u0026nbsp;million tons, positioning it as the second most farmed fish globally (Li et al., 2023) and repeatedly reached 4.9\u0026nbsp;million tons in 2022 (FAO, 2022). However, the sustainability of Nile tilapia farming \u003cb\u003eis\u003c/b\u003e increasingly threatened by stressors, which adversely affect the fish's health, welfare, and productivity. Stress in fish can manifest in various forms, including decreased growth rates, compromised immune function, and increased susceptibility to diseases, leading to significant economic losses in the aquaculture industry (Barton, 2002; Fan \u0026amp; Fox, 1990). The intensification of tilapia culture has led to increased stress-related challenges, with economic losses estimated at 15\u0026ndash;20% of total production value, equivalent to USD 1.8\u0026ndash;2.4\u0026nbsp;billion annually (Roques et al., 2020; Slater, 2022).\u003c/p\u003e\u003cp\u003eBehavioral changes in aquatic animals have long been recognized as early indicators of stress, as they often precede observable physiological responses (Martins et al., 2012). In intensive tilapia systems, stress-induced losses can reach USD 500\u0026ndash;1000 per ton of production, significantly impacting profitability and sustainability (Conde-Sieira et al., 2018; Fu et al., 2022). Monitoring fish behavior provides a non-invasive and real-time method for assessing stress levels, offering advantages over traditional invasive methods, such as blood sampling (Zhang et al., 2023). Among the various behavioral indicators, swimming velocity is one of the most commonly studied parameters. It is sensitive to changes in water quality, temperature, and other environmental stressors (Zala \u0026amp; Penn, 2004). For instance, stress caused by pollutants or poor water quality could reduce the swimming velocity of fish, an effect that could be quantified and monitored through technological advancements such as computer vision (Liu et al., 2023). Recent studies have demonstrated the utility of non-invasive methods like video tracking software for continuous monitoring of fish behavior, significantly improving the efficiency and accuracy of stress assessments in aquaculture (An et al., 2021; Papadakis et al., 2012).\u003c/p\u003e\u003cp\u003eAmong novel approaches, acoustic environmental modification has emerged as a promising, non-invasive strategy. Fish possess well-developed auditory systems capable of detecting sounds from 50 Hz to 3 kHz, with behavioral and physiological responses to various acoustic stimuli (Popper \u0026amp; Hawkins, 2019). Previous studies in Aquaculture International have demonstrated the potential of environmental modifications for stress reduction, though acoustic approaches remain underexplored (Leite et al., 2023; Luo et al., 2021). Computer vision enables the real-time tracking of fish movements, providing a quantitative measure of swimming velocity and other behavioral responses to environmental changes (Al-Abri et al., 2025; Zhang et al., 2023). By quantifying changes in fish swimming behavior, this technology allows for the identification of stress responses and provides valuable insights into the effects of various stressors, including pollutants, disease, and handling procedures (El-SiKaily \u0026amp; Shabaka, 2024; Zhu et al., 2024). Furthermore, these techniques enable continuous monitoring over extended periods, facilitating early detection of stress responses and enabling timely interventions that could prevent further harm to the fish (Blaser \u0026amp; Gerlai, 2006).\u003c/p\u003e\u003cp\u003eWhile much attention has been given to physical stressors such as water quality and handling, the role of non-physical environmental factors like sound in influencing fish behavior has been underexplored in aquaculture. The auditory environment of fish, including sounds from their surroundings and potentially from human-induced sources, plays a significant role in their behavioral responses (El-Dairi et al., 2024; Moretti \u0026amp; Affatati, 2023). In recent years, researchers have begun to explore the impact of sound and music on fish behavior, with promising findings suggesting that auditory stimuli could either induce stress or alleviate it, depending on the type of sound exposure. Music, in particular, has been shown to have calming effects on certain animal species, both terrestrial and aquatic. Classical music has been associated with relaxation and stress reduction in animals, including fish, by promoting a more stable and tranquil environment (Papadakis et al., 2012). Conversely, more intense genres, such as rock or electronic music, have the potential to increase stress and disrupt natural behavior, causing fish to exhibit erratic swimming patterns and heightened levels of anxiety (Papoutsoglou et al., 2013). The physiological and behavioral effects of music on fish have been less studied compared to terrestrial animals, but the initial findings are promising and suggest that music could serve as an effective tool for managing stress in aquaculture.\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the effects of different music treatments on the behavioral responses of Nile tilapia, by measuring swimming velocity and stress responses using computer vision technology. In this treatment, we expose fish to different music genres, including classical, pop, rock, and electronic music, and \u003cb\u003eassess\u003c/b\u003e how these auditory stimuli influence the swimming behavior of the fish. It was hypothesized that exposure to classical music would result in an increase in swimming velocity and a reduction in stress, while exposure to more intense music genres, such as rock and electronic music, would cause a decrease in swimming velocity and heightened stress responses. We hypothesized that classical music would reduce stress-related behaviors while rock and electronic music would induce stress responses, with measurable impacts on swimming patterns and potential productivity implications.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were approved by the Animal Ethics Committee of Brawijaya University (Protocol No. 023-KEP-UB-2024) and conducted according to the Guidelines for the Use of Fish in Research (AVMA, 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Design and Fish Husbandry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was conducted at the experimental aquarium at Universitas Brawijaya PSDKU Kediri, using a controlled aquaculture system equipped with monitoring systems to control water quality parameters, including temperature, pH, total ammonia nitrogen, and dissolved oxygen, to ensure optimal environmental conditions. This system maintained stable water conditions throughout the study, supporting the success of the research and providing a suitable environment for the organisms involved. Nile tilapia (Oreochromis niloticus) were used as test subjects, with a total of 75 fish (mean weight: 45.3 \u0026plusmn; 5.2 g, mean length: 12.4 \u0026plusmn; 1.1 cm) randomly distributed into 15 glass aquaria (100 \u0026times; 50 \u0026times; 50 cm, 250 L capacity) using a randomized complete block design. Each group received a different treatment based on the type of music played: P0 as the control group with no music, P1 with classical music (Beethoven, Mozart), P2 with rock music (Metallica, Nirvana), P3 with pop music (Ed Sheeran, Taylor Swift), and P4 with electronic music (EDM, Lo-Fi). The exposure to sound was conducted for 30 minutes per day over a period of four weeks using waterproof speakers at a standard volume of approximately 70 dB. Furthermore, the fish were fed high-quality feed on a regular schedule to ensure optimal growth conditions. Aquarium management was strictly maintained to ensure stable water quality, allowing for accurate and reliable research outcomes.\u003c/p\u003e\n\u003cp\u003eWater quality parameters were monitored twice daily (08:00 and 16:00) using calibrated instruments:\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Temperature: 28.2 \u0026plusmn; 0.8\u0026deg;C (YSI ProDSS multiparameter probe)\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dissolved oxygen: 6.5 \u0026plusmn; 0.3 mg/L (YSI ProDSS)\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; pH: 7.2 \u0026plusmn; 0.2 (Hanna HI-9814)\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Total ammonia nitrogen: 0.12 \u0026plusmn; 0.05 mg/L (Hach DR900 colorimeter)\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Nitrite: 0.08 \u0026plusmn; 0.03 mg/L (Hach DR900)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis apparatus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish behavior was systematically documented using a standardized computer vision framework designed to ensure consistency and replicability across trials. The recording setup consisted of a Logitech C920 HD Pro camera, capable of capturing video at 1080p resolution and 30 frames per second, positioned 1.5 meters directly above the center of the aquarium to maximize the field of view. Controlled lighting was provided using LED panels calibrated to 5000K and 2000 lux, delivering uniform illumination to minimize shadow interference and enhance image clarity. Video data were captured using OBS Studio (version 28.0), an open-source recording platform selected for its stability and compatibility with high-definition input devices. Subsequent video analyses were performed using Tracker software (version 6.1.0), following a four-step protocol for quantitative behavior assessment. Calibration was achieved using a standardized 10-cm reference marker to ensure spatial accuracy within the digital environment. Each individual fish was tracked using the software\u0026rsquo;s computer vision feature, allowing for automated identification and continuous trajectory monitoring. Data points were extracted at 1-second intervals to provide high-resolution temporal insights, with positional coordinates (x, y, z) recorded for each frame to facilitate advanced movement pattern analyses. This methodology offers robust data for investigating spatial behavior and locomotor activity within controlled aquatic settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheory of Sound Diffraction and Refraction into Water\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSound diffraction refers to the bending or spreading of sound waves as they pass through an obstacle or aperture, causing them to change direction. When sound waves transition from one medium to another, such as from air to water, refraction occurs due to differences in the speed of sound in the respective media. This phenomenon is governed by Snell\u0026rsquo;s Law, which describes how waves (including sound waves) change direction when they pass from one medium to another with differing wave speeds. In the case of sound, this leads to a shift in the wave\u0026apos;s velocity and direction of propagation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSnell\u0026rsquo;s Law and Refraction of Sound Waves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between the angle of incidence and the angle of refraction for sound waves passing between two media (such as air and water) (Erbe et al., 2022) can be described by the equation and fig 2:\u003c/p\u003e\n\u003cp\u003eIn this context, the speed of sound in air (C1) is higher than the speed of sound in water (C2). As a result, sound waves entering the water from air will bend towards the normal (the perpendicular line to the boundary) due to the lower speed of sound in water.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioural response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, for each treatment (P1 to P5), three sets of data were collected, corresponding to repetitions 1, 2, and 3. The data were organized into a table with columns for time (ranging from 0 to 1800 seconds, or 30 minutes) and the fish\u0026apos;s movement along the x, y, and z axes. Each treatment represented a distinct auditory condition: P1 (classical music), P2 (rock music), P3 (pop music), P4 (electronic music), and P5 (control, without music). The table captured the fish\u0026apos;s movement under each condition over the entire 30-minute period of the experiment. Furthermore, the theory of fish stress identifies three response phases: primary, secondary, and tertiary (Barton, 2002). The primary and secondary phases are internal responses that occur when fish encounter external stimuli in the form of physical stressors such as handling, capture, confinement, transport, chemical stressors like contaminants, pollutant exposure, acidification, and perceived stressors, such as the presence of a predator. These primary and secondary responses occur in vivo. In the primary phase, fish exhibit alterations in corticosteroid and catecholamine hormone levels, as well as changes in neurotransmitter activity (Erbe et al., 2022; Papoutsoglou et al., 2013; Shimon‐Hophy \u0026amp; Avtalion, 2021). The secondary phase involves metabolic changes, such as increased glucose or lactate levels and a decrease in tissue glycogen (Chowdhury \u0026amp; Saikia, 2020). These internal responses are not observable through external behavioral manifestations in the fish (Barton, 2002). In the tertiary phase, the response is expressed through whole-animal performance characteristics, including growth, swimming capacity, disease resistance, or modified behavioral patterns (Barton \u0026amp; Iwama, 1991; Fan \u0026amp; Fox, 1990). The tertiary phase is divided into three stages of behavioral expression based on the general adaptation syndrome: Stage 1: alarm reaction, Stage 2: resistance, and Stage 3: exhaustion. The alarm reaction occurs when the fish becomes aware of any external abnormalities, and this stage is typically the longest. The resistance stage is when the fish attempts to adapt and resist external anomalies, while the exhaustion stage occurs when the fish can no longer adapt or resist these stressors (Grollman, 1951).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFish Swimming Velocity (FSV)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate the Fish Swimming Velocity (FSV) from the provided data, we will first need to compute the velocity for each axis (x, y, and z) at each time step. The velocity can be derived using the formula:\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"323\" height=\"68\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003evx,vy,vz\u0026nbsp;are the velocities in the x, y, and z axes, respectively.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo calculate the velocity, we\u0026apos;ll need to compute the difference in position between consecutive time steps (i.e., displacement) and divide by the time difference. Given that the data is recorded at 1-second intervals, we can use the difference between consecutive values. Normality was verified using the Shapiro-Wilk test (p \u0026gt; 0.05), and homogeneity of variance was confirmed by Levene\u0026apos;s test. One-way ANOVA compared treatment effects, followed by Duncan\u0026apos;s multiple range test for post-hoc comparisons (\u0026alpha; = 0.05). Repeated measures ANOVA analyzed temporal patterns. Effect sizes were calculated using partial eta squared (\u0026eta;\u0026sup2;p). Power analysis indicated 80% power to detect medium effect sizes (f = 0.40) with our sample size. The measurement of Total Distance Travelled (TDT) in this study under various musical genre treatments was conducted to observe physical activity responses towards acoustic stimuli. This treatment reflects the extent of fish movement within the aquarium space during the observation period. This locomotor activity is closely linked to feeding patterns, as numerous ethological studies associate increased movement with heightened foraging behavior or stress responses, calculated by the equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1IAAABkCAYAAACM7QEqAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAADAASURBVHhe7Z03lGvF0oUf7hHjifHEeHI8KZ4YT4wnwxPiyfBkeCI8JHgieLjwx+d4/es7rH2pqdvdx0ia0Wj2t9ZZ984x3dVGUm1Vd+k/M2OMMcYYY3YA/zFmkeQJZowxxhhjzDqS/WBj5iJPMGOMMcYYY9aR7AcbMxd5ghljjDHGGLOOZD/YmLnIE8wYY4wxxph1JPvBxsxFnmDGGGOMMcasI9kPNmYu8gQzxhhjjDFmHcl+sDFzkSeYWW9+/vnn2R577MEbSXe8/fbb+RZjjDHGmLUk+8HGzEWeYGa9efnllzshdcYZZ3THp59+mm8xxhhjjFlLsh9szFzkCWbWmyuvvHJ2/PHHz/7+++98yRhjjDFmrcl+sDFzkSeYWW+OOOKI2Y033phPG2OMMcasPdkPNmYu8gQz6wvL+FjW531RZjNgvl111VWdeNeevPPPP3/2ySef5FuNWSs+++yz2dVXX93Nfe1JZe5//PHHXg1g1opvv/12ds8998xOPPHEXXP9uOOOmz3//POzv/76K9++EmQ/2Ji5yBPMrC8PPfTQbL/99vMHudkU9txzz26+STi988473bnDDz98ZT9gjVkEe++9dzf3P/roo+799t13353ttddes8MOO2z2xx9/5NuN2bace+653fv6Y4891r2vI6yOOeaYTlR9/vnnK+lvZD/YmLnIE8ysLyeccEK3Rypz5pln7ooYxKPvDRDH+IILLujK7LvXbH+ILjHeQyOafLi+9NJLG+YGc40P2O0spJ555pnutfTUU0953u8QiC4x9996661BY46QeuGFFzbce/bZZ3dzfzsLqWeffbaLPDz55JPb+jVs6jDGJ5100uyJJ54YNMYIqRtuuGHDvL733nu718Brr722q4z33ntv9t///nc3P+O2226b/fnnn6HE5ZP9YGPmIk+wPnCe8wthyJEhY1y+RwfJEHDOqavEomxYFdizlG2tHVP3Nynt+dNPP50v7RJSd911V75UhLIuvPDC7pk777xz9s033+Rb1hraf/fdd8/233//2ddff50vry2x3bx+f/rpp3xLL9tZSLFUEQFFpAERNaX92x0iK2eddVb3/rwdx3AqzH2WLx1wwAGz008/ffbDDz/kW3rZzkKKpYo417z2cbB30twnwnLNNdfMjjzyyNnvv/+eL68liKmjjz66Wz3wwQcfDPryIFISUhlFsiykzLYnT7A+cLZ5jG/n5EDzISNHP4oflpJxDqcrwzMSUwgnQZk4+zgrXCtFUBZlw6oQhSHpyTO0jTe02vUh0Kc8X/oAHCOksEXO5DL2ujBeOBuLyCyods1bjohCQuO1qLJXjYcffrg6DggK+oBrpflUQ0v7+PYylzkFxgIbh0YJ5oE240TT5h9//DFfXgiUz5x68cUX52oPYod+vuOOO+YqJyIBpT0Qiyx71UA0sAyPdn7//fe7XTvwwAO7PSBjxJSW9l1//fVVx3IMiDrKe+ONN5Y+DrT5oIMO6tqc+2NeLrnkkm6uXnfddXM50/Qvjvvtt98+VzkRCSjNhdNOO21biuA+Dj300K59zz333Ia5+csvv8xOOeWU7r1+jJjiuUMOOaSbL7/99lu+vAsLKbM25AnWB4IEpx6nUkQhENH5moOOSOJ6SSzhuEhM5SjMIm1YFbQhH3FYQlGrMY5rpJX2fIyQ0r3LEFGg8ol0lWwdw6KEVBRQ9CGH5tq8Za8qihzVxoHXJx+C9MUQxzCKryH3D2GzhBTjj8jhPWdZIkrihzk1r1OxSCElAcXYkSxhJwgpRAr9R+SpNBYSWjiKpeuZKL6G3D+EzRJSzH1EFHN/0SIK9tlnn24+zduORQqpKKDOO++8XV9wrKOQYrmdxuDXX3/NlztRdOyxx3av/yH7naL46rvfQsqsDXmC9cEjeXmYIkSlqA/na0v0FJHK5QlFUTji8rHSM1NtWBUUOeMoLRejfTFyNxbe2KijxFAhRZ/jSG2XPVGLElLMHcrS3iD+lkM5b9nbGQmZBx98sNkPUyNYfWyWkGJvGPWwL2SZ9SyKRQqpiy66qBPTGjdFpRZR9nZGYuv+++9vfjEwNYLVx2YJKQQF7Ry6X2arWKSQYlkar/kvv/yy61v+pq/XUUgNQWILUdla2jg2gmUhZdaGPMGmIEGUI0d9SDjgaJVAPOmeLJwyU21YFfjmT0vGprQhRucySnteiyINFVKKmpWE3iqyKCGVsZD6B+YcH4REjmtO1rJEFGyGkOI9iDZup0yDixRSGQupf2DuKzNfzbleloiCzRBSRGZo43bINLhIIZXZ6UIKTj311O51//777xfn21gRBRZSZm3IE2wKEjtj9u9oyR0fRC1Udp+TP8WGVUNLHemToU4nAhMRWVoeKeg7HMHam9sQIUW/8kbK0sNaOYCDwbd5cVlmvJ/rqk+RLQk9jWFfHUPZyUJK4yBxnvclaRxoxxVXXLFrHLS8jIOlXH3tU7SmlMGOOtigvQwRBZshpG666abeqBtOnPpSiSjyvfk3ViTKiPjEuc/vDM3LThZSZFRkzmEjAkDpx4UEkPo77lliCZvOtwSSIJOfojW5L6jnqKOO6ub+okUUbIaQuuWWWwZF3RBcWh7WOr744ovO1kceeWTXviOOvvKHsNOFFMkh4rwuHbwetGfp0ksv3fBeXxNIgvIZ48suu6zYB5THEsihIgospMzakCfYWHC+9GJsRUUyQ5NAqOyWkz/Vhhr5DWjM0bKzjygo+iJwCBvEkQQLRy1SVEt7LoYIKRxm7sGhbEFZsl0RLEXCGBtskb1x/w3X1Pa+Ooayk4UUbZdDj2M5Zhz0AdsSDwLHlb4oiS4yOyLk8r6iuExyHjZDSKnvagKHiBXt4bXHvKhF6CQqKSuLWom10nNT2KlC6pVXXunep5hvzA36gHmZnbSYTCKnKCczneZzfi7D3Kec0r0XX3xxN/fzviIy9y1ivm6GkCJrG33x4YcfNuvAycaWyy+/fINoZM6ffPLJXRlZLL366qu7HP++8oew04XUtdde29l43333bWg/c/3ggw/u+jmLpVtvvbV7hved0v6oCGKZ1OWlex999NFihj76DZ+iNh4WUmZtyBNsLBJEOPVjIOrAcy3nHeTste6bakMN1TnlaNk5BC1R7BOYiBXt+5JgKYklCRR+z6fGECGlOsY4wJRH3TjqgGPNN7Q4nTjyuSyJk9oSxLHsZCEVkeCQYOobBwmpmniI1MRDjm7lA2dyXpYtpLSsD3uHChyJpZJNikp99dVXG86rHTjkQ+tpsVOFVISxk1jKzq+iUnxDnx04pSh/4IEHesdCmfhy9CoKtdLx5ptvzt13yxZSWtaHva19McA+Kj7PY19GEYXAyv0s4UPf9ZU/hJ0upEgIgVitiajHH398t/msFOV9e5/Evvvu25WVBZmy/pWOlkiykDJrQ55gY2ll3muhSEprKV7cI9W6b6oNq4iy8/VFpCISkqUlgUrY0fqwHSKkJBxqUa8SEhx8yGIj39DWnmesp4gTOaH5DXzoMaYuMa+QUn9PORDYY+uUvTjqQ8ZhrHjQ/TnytAgk1HI/DD1KgmYoEiRj9kdpqV7OdqhIn5ZRRlgiVnqmj5az3ndMFULzCCkJj2zL0GOsANFSvfwckSvsKC3JYzke7cvP1JDYyJGnRSBxkPth6DGPyFLdU/dHnXPOOV0/4qSXntdStLHipPSDrkOPqSJrHiFFPw5Z9lg7Xn/99cHvPRGlIKeMHKUSimINFTISPjlN+lQspMzakCfYWPT7RmMc/yiQWkvxEE+6r/Wjr1NsWEUkesYKQvpQe2GyGELE1FKqiz4hNVXkAM8h8DhakSZlBBwrFCykhqGoUd84aKkeqZ+H1iOhs4goU2YrhRR9Qd30xVDHQb+/laNLKitHo6AVxWoxjyiZIoRgOwkpltdha44uIVyJsGfnjShMLYpVQ0JnrG1D2EohpeV6zP2hfSGU6a/1u1O6Z6y4sZDqR4kfmPu1PU1AFIt7htaxaOGz6PLGkP1gY+YiT7AxREFUy7xXQoKhbymeIk2ttN9TbVg19LtZY0WUQATRB5QRPzxbac9Fn5CaRzhoL07fviftExn7rXwLL+37lzgOLZunjMMyhVSLZS/tU/ljhFQtioVYKkWj4p60RTkTXtr3D0pRztIyjYXEUikapUhV7fejSixTSLVY9tI+lT9WSJGggueIZNVEFGjZ36Ls3+lL+yISUa0le4gtidK856nGooXPossbQ/aDjZmLPMHGoIgRzvsYhizFG5r6fKoNq4REVN++qBYxKqX+ksNfW8YlliWkok0kPmghR7+1l2ssFlL/kMehZXMch9Z9EQupjag/9AzRqFLSDcB517f3Y+poYSH1D4qARWFENIr3hZLjdvPNN3f9FjP59WEh9S+KYvFZ1kogwfuRojTfffddvjwJC6l/0HI93k/o29oYkOyDMeA+ZfLrY9HCZ9HljSH7wcbMRZ5gY9B+nrECgAgTz7UiJRJbfVGrqTa0oLypR02M1OBDhTbSJ6VljjjtlJv3PpWIfcYbKH1DubU3U9EnpKYs7aMtOOUa65wGPaKEGEPbORQLqd3HIWeMi8ToSMnpr7GuQmrK0j5QUgnsok9JC1yLBCoC2BqXsVhI/QN9L6GDAyxhlVOiC2Xsy5n8WqyrkBq7tG9MFj7dixM/pOwhWEj9m4WPaGBLRIHuve666wa3adHCZ9HljSH7wcbMRZ5gY5BzVnPAS/DhxjMctaV4McpUu0dMsaEP2TflGGMHfYH9CJ+SiAIEYmtpYyQKHiIKPDfkx337hBSo3Nb+mghpiLGdaBjPtgSdhEnrt66mYCG1cRz40Bo6DmOEwxTxtQiWLaQkSHJGwj7021pPPvnkrgyJteclTLh3UW2wkPoXJY9APPF/BGttLCQEvvzyy8FtW2ayiRbLFlISJkOy6pEdTok9WDIZ+/eYY47ZbY+abC9l85vKThdSSt7BeMXfclLa8pxpTyLmscceq74eMk42YUyFPMGGEgVRK6NeJiaQKMF1bYrvK3eqDasCDm5NLNI2RZiGiCER95Xxb05tXWKIkBqS/hyHBYcdG2iXlhTq2Vq0SQ7x1EQKNTZDSNXatJXw4URSA/0gr8ZBSQ1qgicuZRvaZ0oGMVZsLIJlC6kpGQxBCSf4xp1/W2nkcdAof5Ft2Awh1RIkq4QSTjAWfEFQ+3FcRasYi6HOsp5h7g99ZlEsW0gNTX/OfTURJXGTbVRGv0WKns0QUsyhVl9sFVqml0UUKMV53gelVOZDE01ALf35VCykzNqQJ9hQ5LBzTHH041I8RANCSL+hhAgoiYvMVBtWgWh73zFGJMaoVE48UWOIkNIP8tbuifVyRMEVf8yXseZbehxwwRJPnlW0hL/JNDjE9hbLEFK0E/vVThIIrJKYis5/aRzoZ8YBm2lHvC4BoHHg775xiFnqWvctg2ULKZiSUU9CRnO+9RzL/rjvxRdf7MYOocKYtJ7pY1lCSvu59N4ypk+2CiWcwObakj6Iv/1EdIk+ZIlmTXjBI4880j1T+kHeZbNsIQX6Qd5aHbyXK2kES8RiH9B/ElhZfFxyySXdmPAMX+qwN43PlXn6cFlCinmhfqAtpBIfKjw2g/hbUTlSpB/KJZKWx0Cp0XmGL9oQtyxPrbWNemo/yDsVCymzNuQJNgQJnni0EkcI/Qhv6cB5o4yhomGqDauAfvdp6DHWUZeAGNofQ4QU4xKd7ExcFpYTRuAgKipVus4HsmzgOqJ4bJtLLFJIqazWMSbT3bLI4xDtKY1DvK5x0PNEHfrGATHG/X1JLJbBZgipKXuYJGZzCvQS7MOSmOL+PCZTWKSQiqKwdWy2IzQU+hfBkZeXlVCyCZxFviAhktjqP0W7ShkAl81mCCky8NEfJN8oja8y9OW5EI/SPqgoTrg+ZGz6WLSQGpJmnWjOsvp+KMrQl22LR2kfFEsBJXTJ7se+wNYYSJS10qmPxULKrA15gpntj5zpVrbDyBAhBXLC+7IAmp2BklPgeNaWC253JIrG7BsjqsT969on2wXm54EHHriUiJESWTD3N3t/1Gah5X1Tf5TXrA+nnnpq51OMWQrYh4WUWRvyBDM7j6FCSj+aS6Rrq7+JM1uPIkKrEIlbJjF5RF87tS+ttS/KLB+EDln4iKC3ludNRUsGifotyrFcRfTDuXn/k9k5vPfee90erNISwXmwkDJrQ55gZucxVEiB7m0lnTDrD3sY+RBsZaRbF3DKWX5XirwRfWIpKlFaLXMcIrjM4iF6whIlIvLsb0LQtvZFTUX7qViWttkO4GbD3GcJ2DpH3kwdfrj32GOP7V5Ln3/++UJfSxZSZm3IE8zsPMYIKT5Y+V0iPliHpkI36wUiig9WRFTfHqp1gTYjpmhzFFPa38TBa2Ir9oqZjckiOFiOtiwRxXJBRNQyIl2riNKb02aLqZ0DIoo9WLzX52yAi8BCyqwNeYKZnccYIQXKvMczLOvib7P+MM4s5+ODlWQvO0VECcSUvkSQYFIiBvbh+IuFrSOmIidRRI4czgtzn+V8CGd+HmCniCiBmGKpJK99L/Nbf0hGQUIQ9nouQ0SBhZRZG/IEMzuPWka6vjdPltCQidF7pnYGZLBjvHf6sk4ywSGoHH3aOZDRjy+PlpkhcjuAg82ySZavWkytJ/wuFWntFy2Y2WtVyoZoIWW2PXmCGWOMMcYYs45kP9iYucgTzBhjjDHGmHUk+8HGzEWeYMYYY4wxxqwj2Q82Zi7yBDPGGGOMMWYdyX6wMXORJ5gxxhhjjDHrSPaDjZmLPMGMMcYYY4xZR7IfbMxc5AlmjDHGGGPMOpL9YGPmIk8wY4wxxhhj1pHsBxszF3mCGWOMMcYYs45kP9iYucgTzBhjjDHGmHUk+8HGzEWeYMYYY4wxxhhj2lhIGWOMMcYYY8xILKSMMcYYY4wxZiQWUsYYY4wxxhgzEgspY4wxxhhjjBmJhZQxxhhjjDHGjMRCyhhjjDHGGGNGYiFljDHGGGOMMSOxkDLGGGOMMcaYkVhIGWOMMcYYY8xILKSMMcYYY4wxZiQWUsYYY4wxxhgzEgspY4wxxhhjjBmJhZQxxhhjjDHGjMRCyhhjjDHGGGNGsjJC6ueff5499NBDs8MPP3x211135cvGmAbvvPPO7Morr5z95z//mf3999/5shnBN998M7vqqqtm++233+ztt9/ecO3pp5+e7bHHHrOnnnpqw3kzjmeeeWZ2wgkndPOV44orrph9/fXX+bZeGKubbrpptv/+++82VpsB84S6P/7443xpy6GP99xzz9mTTz65Ze8JfK6feOKJs+OPP372448/5subwrvvvtuNE6/bP/74I19eO5599tmuz88444zZn3/+ueHaNddcMzvggANmH3300ZbNic2G/th7771njz/++Oyvv/7Kl1eOV199dXbRRRd147cT5us6MEhIMaD6wIsH5wHhk6/pwMHrK+v+++/f5QRyWEgZM5wbb7yxc1T0+tkpH5DL4OGHH97Ql9k5P/PMM7vzvI+5n6eBU3vBBRfMfvrpp04ISVDxJdoYR4exuvDCCzsHmeffeuutfMvSQahQ95133rly8+Gss87q+ub0008f1a+LBBGjPmJ8NruPbr755m5+aY6ss2P67bffdg44wp62Mu5ZSO2zzz7dtTvuuGPL5sRmc84553Rz8LTTTlvp8edLh3vuuWd25JFHdvN11e01/zJISAGCiG9o5WBkgcQHYnRAEEY1eJZ7uJ/nhESWhdQ0Pv300139z4tyKyCqqLHdbLay7s2k9NrSa4pjs52VdYPXjhyvLKS2OiJFlCG/924nsJ/+i/1Kfx9xxBHd67bl3CHASnM7OuqbzU6LSF199dXNMcpsdkSqZB9ibq+99urmyE5wTBGPjHtJSDkiNXzubhWy10Jq+zBYSEGMPEUBJDin631iiHuyQ2AhNR8aH0UKt4KtHMOtrHuzwJEvja+F1GKpCamtBsGxajaNgYheFlJD4LMF57DkCG2lkNpJaAyyc74qYB+CKdu304QUUY2akDKrD/OVyKGF1PZhlJCKQolv/0uwZIPr/Fvj5Zdf7pZxZHaCI2zMVPh2l9eNhdTyWUUhxVK2KSJklZDoKQmiFlrCV3rOQmpzYNkYY7CqznnNPgsps52wkNp+jBJSIKFUEkKASJJDV4paAUuTSkLMQsqYMogoLZ21kFo+qyaktCRulWyawhQhpUQBtecspJYPS+Y0BqvonLfss5Ay2wkLqe3HaCHF0iI5bOzJycTrJUGEQ8heq5LIykJKWfzkPJbqA5xICTyOWuY/LYuSI8omfe7HQY17ioaWJ+eW9tRsGwPlITK1Fw378nX1UdwnE/eu8f+p0D/qb/7NbaJ+1RPti3uzOFrRyCHIhtaBrbDougVfCMQ5QL/S53ne5myTtTECbI77CPk/9fRBnbU+0bzMQopnZD+2l+YvYC/XYvk8l8e+j1xOrb9E6TXGfGrt7Sv1X+kLGY0Jy+A0JkoSgU1RaGKfMvTJjgcffLAoWigHG9i8TrmxHNpD2Tyn/ieKor6oJSNg/HWf7s0Z7O6+++5d9uSj1BZtNldZJHWI0I54H/1I3+e+6aNW3yeffLLhPvqmZj9HSRwJLQXMz+AkytYopGibsvhxrtR+kTMH0g8vvfTS4D6g/YyNllsOeQ77iCzqGY2F2nj++efv1n9iaH8D5ap9ee5RjvpoSJ8paUVpDFpjB5999tkum7NTj32ML++XlIMDyX4q6uJ1+OKLLw7q05Z91JmFFO1X9Io+LCVeUKY/5hbPYCt7i7g/7i8iwQMiLo9JaT8Y/RyTCWi82V83pJ0llKFP5Z133nnd67gkpLBV9b/55psb6qQc3ocYC55h3Ogjysl9RN+oz/O1SKm92JfbS3n0IXX9/vvvnZ0XX3xxse5ILPuwww6bXX755d18iuJDrwP6iHLyHATqV1uxkbKuv/762Q8//JBv3WDrb7/9tputt99+e7GORx55ZMM4UQf2lObJFCFFO7X/jfKPO+64rg3MhVgG48zYqS/uvffe2YEHHrjrmRdeeGG3vv7ll19mt9xyy4Zx5N4nnnhit3vFe++9t6tf+u7P99I3tX5cVUYLKZDTkR1F4MMoOuMZHJGasxuFFGVzROesVh72aL8Vb5JytmQf53AUZDf15Exnen5IeSI6rzVHdQzYJYGgNsuh5YUS7c316Vqtb/vAoaN9cohrZUXhEkUAz+l8ybkdCv1NGbQnO+HYV7JrUXULfRmg/gD1SZyDcp7jmDCGUZQI7ott4lnNR415H7KhLyKFY0Vdeh3pfK5Hcyq2Uwk7sK3koJVgTuAYUo7aJ1spJ6e1xg7ekBEsEOdcrd6cjjwKFdogxy+PCU5kHhN9iGM3DhDPy0bmtJwiDtWncjVm0TnFCY2vTeyX2OJ9Rh8+OUGF+iE6rhJN2JudK5VTikipLfQpz0UHPfYPyHlVm2kb9nIu11lD9UXbcVjUd7S1VNaUiBT2tZ7TNUQQbaPPOaIwyLbQN3HcqaPP9ghiiPmgMRmSke6VV17ZINwQCvyNHXH+MMe++uqrDc/S3zhJtf6OCSUkAnQtztWc6RAb6LMbbrihm6+xz2JfU6b6eaiDQ9KDKIT1XBRynMfhp8+5F1ujfV9++WUutkgUS9m+eA0RhENLPXH8Yv/lTH84l9EJlmOP2MAJfeCBB7q/ec3JycZxjI4410466aQNIgvnWg74lOQPOM48T9Zj6peo0zhFIUVdEoZce+ONN7r6eIb2yknmGfoCZ5s+is/gCPOeddRRR3V9kK/F+UJ7Tz755K696gdsYBxo74cfftjVj4Me+5rz9BPl8xrWuOXysyCMggYxBhI9Ehcl51yJHdSHEn/ZTsDWOA8++OCDro3Z1pzU4tprr+3suu+++3bVcckll3TnLrvsst3E0hQhde6553bv3//73/86exEn2MY5+oO5GsXKbbfd1o0ffciY6zzHc889t8t+RNQpp5zSvVZff/31XfPsmGOO6fohtxVuvfXW7nXx/PPP73b/ddddt5uwO+iggzaUTX3cW+qbVWWSkFIkh4kW4Q2Sc9HZliARUSxkJKRwIuI34tFRjufldOc6dD5fi86aHG5skSM5tjygL7B77Df4fchWCSbEgxxx/s12qO8WISRiezMai5KoLY3RWBgPypZjL+Qc0/Z8DRZRt1Bf5j6uzQHdr7kvYaCxkzjPdkuwla6VGCqkciRTr9csQOnTUlnq66GOdc0J1wdOFP3Yyfl4TsQIcSyLe3mmJCDkmPJsfEYRKAmzOCbcx9+IP+ZajlhI4PB8rlPiJH/Lr3bxDE5pvIbTyLXcR7Ix11Gru3YeaEu2Sed5RteYn6W+5Dz25OdL0Hd8sOb2AGKB8ksCGlqCqMZQIYVDEMcSW+SoxuckQPK3wVo+yf35Wg0cEeoeIqSEnsHeGMmKYoVIhWymv3EGSxGg2MYsvrTcrTQv5PRRZmyryuNadDqnCCngs6QmcPjSQLbnzII409iOk5ptLzFUSPEtfexDZbiL/Z2fIdLBNQlURVQkKPKYcF626xpjwesr2ybhk6NHfUiUSABEjj766K7+Upm6JiElosjJZaqPuJbbK9FBXdHpReTl6JDOq726Rr8iZuhrHO1oM+KlVP7BBx/cCaNoC68T2ichJVRnFlKqN58H1YsgjuUNsTULoP/+97/d/USwhMRSPh+v5XJqIJK4X2Iknj/77LM32I+oU7sYZ9kfxQ7jLJsQZLIllkMkS5kF43l+A6tki+6nvb/++mt3jjrpm3yvznPva6+9ttv8XkV295QHEIVSFEX6Bhz0DZv+BgmtmtMoh7TkZJWcW33LX0LfQMf65YjWnhlb3jKRY4zzizii33KEJiL7spM/Feorlac9cFkMt8TXvGjcSgILFl23Ig+5v0tzMJ7PAkZgd+latDv3Z4mhQio7HroWn6Nu3jRLSwsl8DhKznBE95bsx+ngGpECIeFTKpexlVhQ9EbnShEawH49E8uUSMmiRijyo6hYpiZa9Fx2TqOQyvXpWhYeirLlvhgrsCQAsiAECT/1n2zBccx2IvjyuRIShrXIjca4FAlqCaIaQ4VUaY9U6TmETGle8JrQ/bW2ZbTEaYyQ0jOlpWtxGacEjvo7iw2hb8lzFKk2V0EioWR3SZBMFVJQKg8kpEoCUdcWLaSyY6prWXTEZ0pLvHjNcf3777/Pl3aJI5YoUSaOIfcS2cptUTnUo2hCH7wn8i0+7x25PdDaI4VjzZzIQkoCofRMvJbrK12jvZwjIpHbQwQitzeKkyyCSuUDzjr9m/sfMZPtx4mnziyYiNjQF1988cVudtLHEjox6hJtHSqAECj4lbltJYEFtXJq6H6WTeb7c39I0GQRCBJN2KSoFGIM0ZojSTWBdeyxx3afNblNKgcBp2uIOj4r8r1AOYzNdolKTfY8JZSic0YHyjHTEiEOOb9RaJUYK6Ticp3aEe1rOaIwtrxlQ328WXK0Ii2LFhKg/Ss5wsX4laJRcqgX3T8xalPrg2XVLShf84+jJqRK8zaOTesoPZtpzd+xQiqKpdaRnfaM5knffaBoSMlGIRFBudzD/CuJkIjKjKJI5ZScSFCkpma3yszXa87pFCGVYUxkd6numk0SS3ns8iFnQEtdsCeXNQQ9X3tWfZQjQVASNn0sUkhFsdQ68vjWmEdIlZ6J9um6+rt0P9T6uzZXwUJq47UsIFrPQFxC1zp4Noql1pH3LdWQGMk2i60WUlEstQ7ZMEVIaWkcYiov+8uUhJSiONRZcuRBP+QbBcoUIRXBFyb6p6jl1HIyhxxySFcWzzCPav0hIcXSvjzOIAFTu45IRpxpX1UUUvQp4jCLqxqqK8+LeAwta6uZ7HlHocSbP05SdLCZMLqub6u5np3QSMshLQkp/i45lTVajii0rm0FEqtZzGQUJVqk7eqrHFGM4xnRsqzS2E0lZoCsiShYRt1aCoZzQh8w70pzEFrzViKmdG0srfk7VkiprNyWsdSiJyVaYkPw7bts5R7s7BMhioBEh7FPSNVEiahdrzmnrba1hBTzjDJZasY8o75an9Zs0v257Bq8luJeHeyKUcM+5EBmO4SW93FP/kDPwmYIixRSKiuP31RaoqhG3zNZSOW/M7XleLW5ChZSG69lAdF6BiRIStcyEjZDhZIc9XzIxpZQgtb1zRBSWu6X66gxRUjxvpkTFOQliaIkpFqCSJSW6rWeawkgBIiSQRDdYRluTci1yqmBiFGEjTJ5tpQ4ok9IsdeKPs3XiUAxd/gCkn7m8yJHpGpRqhr77rvv4HtXnclCKgolnB1t8I3o22rt6SlFMiIth7TkxPI3ju5QWo4ojC1vmdC/Wl5XEi4RCYnc//NQcr5re2pAoq+0VGwKOHtD27+Mupmr2pMmSnMwni/NW/Vj3p80hdb8nSqk+kR6H3Li+8YIotgoLUODLJz0d943FSmJptI5Ee2oiYHa9ZpzOkVIMc/4YKJtpWWJue6aTbq/lKSjBd+UKzLHUVqKV0LOSy3DXeyL/EGehc0QJH5qz0WhkcnPqazS0sYp9ImiEn3PZOGkv0tLAaEmcmpzFSykNl7LAqL1DEiQDEkSIWGjpBR99Akp1Z1tFqsipGrCJjNFSMXrahPPs3QsL/frE1LffffdhvuFnptXSCmhAnvt4jLRRS3ti1BXjHblpXFThBTJI+gHkmXoXEk06RyiNrepBEIKH097prYzk4UUaFM6TmdpT0mMKOBElBzNSMshLTmxtX08kVhWyxGFseUtC0QUzpUEQp9AWrSQgCiUQc54KTIU7y3tYRqL9tJRXp+jv+i6KUNLPHN5pTkYz5fmhvYT0p5cnuB8XzuhNX/HCilFlEtlCeZTabwjcSleDbUtOti1CIiEE+8ttEN/80xNfMmGmBWvJaRAZW7VHinGXCImt2uskFKWs2xTBLtr18jkpgxqyvrXQg51aZ8RqL28jrIjlYXNEBYppJjPnOP1WEsowdjQJ6W2ZfpEUYm+Z2Szkkf09bdETu7v2lwFC6mN17KAaD0DREPo25x8IYKg4Jqy8+U6IkQVmZtD2iqhxBwu2bbVQkqJK0r3C9rLFz/YMI+QEtwnAZH3CvUJKWWXy+g5BFDpuSwWSgJISwhz0gpYhpASjz766K7ld1EADRVS2hdGOdw/ZI9U3GP1/vvvF+cykTnGnrIPPfTQ7t6cbCKCvSU7V425hFQUSrUkDXKIObLQyrQc0pITGyNeJSeVe6OD2nJEYWx5y0KRHy2lq/WtGNq/Y5GgUISmJujkqPdFHIcgEUl5eT8df2eHfZF1Q0l0iNIcjOdL8xb0GsjtETzXJ1igNX/HCqm4X6kWTcp9XSIu8c0OPlB3nDcSDxJKGaU0V1lKisG5nD5cIALo45zim2dKTiTIDuZN6XpNtNSc07FCqnROjBVSOP1cow9KUSleU+yjoh7qZbxznUpYUbInExNYlD4AZU/uI8jCZgjY3HpujJAC7TmqReAY41I/lugTRSVaz2iPVOzbqf1dm6tgIbXxWhYQrWegL3U5rzn2UdEunGmVxX6efC8gzLLdNeIepNL+oK0WUrRXYqNkH8T2ThFSLJPLduKky5GP5ZSEFChLXRRKEe3Din01VkjV6oZFCSnu5/0797NSu0ex0yektG/p888/79osYZXFTklIgcRRPi8ojwggZV966aVd2YhMnYtQB+9tJTtXjbmEFMjZrgkMRa36xAC0HFJdi05fdB4pP0ZkuA/boiBqOaIwtjyIomceqJMycsa4WnRERJtrDLmnRBSWpbaLvn6FIfdATURRN/2S58bQcocS+yqOKedjxBJ7dL01b0E2ctCnEk2UwbWhtku0xNcStuhf1ZHfkHQtiwbZLdvVHv7Fzlp7IrRB0Qz6J4odvWbisrWYZS87qyorO/PaN0VZOXojQYKzGOkTUtgmO7KoiyIzixY5pzk6IDtq/S9HWNfi/bF/OK/+pO44z3Re0Tzs5HppDPRapTz2Q6kd/I2IrPVjTVxEsKcVwaLvaxEfOeO5X1soisRzKlPzHoYIqZgaXGPI+fgDuPQZ17A/OyU1Whn4auiZPIdAtsXy6G+Jv1J/U14pnXttrsIQIRX7DOdYfanlSThw+dkSKi9nvxsipHImwhpRrGT7WqJI13htjhFSzBV9489cJ6OiXlM8SybF2Ldx+RnikNc813D++TFY3qeGtFMojXlJyGnvUEkUyY6cQZAoQUlQQk3MxGss54rXSNSg9iIimEtqL/bF9g4RUpQfr+HEx994iufzvRIzOVMdqbpVL78JFctifMkyl9s8REjF+lV3tgkhHsth/qr+KUKK+ZCXKErsxOV9ElKlCJnuj9EnCalYBr8tpTTqEkyyX+XTLiKDyszIuCNMeS9SOTGChT0xMogIpE3/93//t9sYryLjPOsCchJrTraWNtW+9RZ8WEg04EjF8rgmJzZ/S97KPhaFEOVJGHDED+LI0PIgOq9DnM4aijyVbJMQxYlWG+J19S8H17mWI2rRzjFEAdCKmGQHn7/zOA0RPDXRTf9IYOVxG1L3GOg3zTXmo4SOfi+M85SvORrnLf/WBLXalo8omvuIDj72xLkQxyrPU+2h44jiBfvVr/ng/NA3sCiO8lGKIiktOm2PkSf9OGl28rFTyRG4nn9ENQshyooRp5xeXMgO3Ucf6ouReF6/UYUdsdxop6ICXMsiQemruRaddokRysKxyPMMJ5//qx4t4aMPsJXxV7uZGyovH9GRlmCiDNmJLZTNeNT6KqMIFuXnHwHGjiySIQqYIYItIiFBn9BHtJ/naY8c/CwYYhKGLEBqmQ7pg/x7TDWiwBuz50pCKtulOVTqG/o7tyX2N79tFOGafq+K+RVFVrQ7f9EQ+4w9PfGahEMegxa0qVYeAoL2Yl/sc2xXH3EtC7AaJftwzBSh4VoWEPqNJK5FQRKfqS3f0w/y5jnEkX+vijbpt7HyvTiRWbz0kesmqsJY6j1U9eDMStDFSBHnY50SX8z/nIZdv8PEc1m8kJBB12LkhvbG5Ae5vVEgaClgLkPl61qsGwccW2PES6IlOuTYIdFZSpeutlFW/JFibKcf8/1DbZUwkyjiHKKDuYTIxm6NBcKF91/1iSJhsZwWqoN+xSbaTrsph/fNmN49Ch3EkV5biBqlJ4/RoXw/ryvsZ/8b5+k37Oe1pvl06qmnVsc9R54QTOqffMQfBl51xnnWBfTtdQveDKNjn4lOYDz4kIxOTTwi3BdFUhYbUUjEo+bU95Un5IgymVpCow/ZRz9lJzg76vk6yFHGDhzBbEts/xgkKmvRRkE/aJzkeObx7hNScZlo68jlDql7LFrKqDLV53FfEeNSm7c1UU1/SrhorIaKKKGxjnaV3rSwsTbvo330FX/H9vL32D6kz4a8ZgS2x9e2xEGrXq5H4cfz+Qua6KzHIzuMgjGVOGJMuA8b5NCqDfxbKhcxUjqPbdTXukafRWGmSJMcau6Lwibej21ZcDKX5FhzD32Vfw+JdugHlKNNlDdURAnskQBTG0rl1PqOg298S+OS0Y/oaox4hshRqVw+fOWI5LriBzNlSqBTbsn2GrV5NuSDXyIBQSRxzLOl8Yqov6MAL9mMY1VqPw51rc9wgqLAy9eA/sIxi2PQolQeY4AdJftwMnk91a719W20T/fLsS3ZULrGc6X6OUrRAZxulvBpTHAWa7/3xfsKAg2BS7/g2FNfjiQORdEs9bHaxdykDoSr3iOiMIwH0YSSI6uy5EQPvRZ/JHdIe2t109etayzL4//xyyOESsyMGKNH8cgCiMgUc1X9wxhmO0FL8eKhqFHrmpJN6JwEuwQTQkSCplVODdqJ8OM9PY4xwufLL7/c0FYJI+rE/9AXVIwNY4cdee4q2YTKRFwytowBr7W4B0tQj/astcoGRFzMwIjIb6VwX0XGedZmWyIBUNvjVIIXCi+SPpE8FDnZJSFojDE7hdYeKWOMWRZ9e6TMNCyk1hwEEd9c8m1n69v+iCJtY55poeWHed+TMcbsNCykjDFbgYXUcrCQWnNY+oSA6RNEihaxDGcRyxUj1N+3PNAYY3YCFlLGmK3AQmo5WEiZDQkrOIhgLUpEGWOM+RcLKWPMVmAhtRwspMyupARKgNAXvTLGGDOemAiC7HLbaUO1MWb7QnIS/XYWCSByunQzHQspY4wxZsmUsthxODJljFkmMY15PByZWgwWUsYYY4wxxhgzEgspY4wxxhhjjBmJhZQxxhhjjDHGjMRCyhhjjDHGGGNGYiFljDHGGGOMMSOxkDLGGGOMMcaYkVhIGWOMMcYYY8xILKSMMcYYY4wxZiQWUsYYY4wxxhgzEgspY4wxxhhjjBmJhZQxxhhjjDHGjMRCyhhjjDHGGGNGYiFljDHGGGOMMSOxkDLGGGOMMcaYkfw/Vn9kL1ZF8y4AAAAASUVORK5CYII=\" width=\"850\" height=\"100\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Perspective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cost-benefit analysis was structured to comprehensively evaluate the financial feasibility and practical advantages of the implemented system. This assessment accounted for initial capital expenditures, including the acquisition and installation of critical equipment such as speakers and amplifiers. Operational costs encompassing electricity consumption and routine maintenance requirements were also considered to reflect long-term financial implications (Tuomela et al., 2021). Projected benefits were quantified based on anticipated improvements in aquaculture outcomes, specifically through reduced fish mortality rates, enhanced feed conversion ratios (FCR), and overall growth performance (Wijayanto, 2023). Furthermore, return on investment (ROI) calculations were performed across varying farm scales to determine the economic viability and scalability of the system, providing a data-driven framework for strategic decision-making in aquaculture operations (Jayasinghe et al., 2023; Vilani et al., 2024).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eWater Quality Stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWater quality parameters remained within optimal ranges for tilapia culture throughout the experiment (Table 1). No significant differences were observed among treatments (p \u0026gt; 0.05), confirming that behavioral changes were attributable to acoustic treatments rather than environmental variations.\u003c/p\u003e\n\u003cp\u003eTable 1. Water quality parameters (mean \u0026plusmn; SD) across experimental treatments\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP1 (Classical)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP2 (Rock)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP3 (Pop)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP4 (Electronic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP5 (Control)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.3 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.2 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.1 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.2 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDO (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.4 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.6 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTAN (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 1 summarizes the water quality parameters measured across different experimental treatments for tilapia culture. The parameters include temperature, dissolved oxygen (DO), pH, and total ammonia nitrogen (TAN), with values expressed as mean \u0026plusmn; standard deviation (SD) for each treatment group (P1 to P5). The results indicate that the temperature remained relatively stable across treatments, ranging from 28.1 to 28.3\u0026deg;C, which is within the optimal range for tilapia. Dissolved oxygen levels were also consistent, averaging around 6.5 mg/L, suggesting that the oxygen availability was adequate for fish health. The pH values were uniform across treatments, averaging around 7.2, indicating a neutral environment conducive to tilapia culture. Lastly, TAN levels were low and comparable among treatments, ensuring that ammonia toxicity was minimized. The p-values for all parameters (ranging from 0.791 to 0.932) indicate no significant differences among the treatments, reinforcing that any observed behavioral changes in the fish were likely due to the acoustic treatments rather than fluctuations in water quality. This consistency in water quality parameters is crucial for ensuring that the experimental conditions were stable and controlled, allowing for a clearer interpretation of the effects of the acoustic stimuli on fish behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe periodic movement patterns of tilapia (Oreochromis niloticus) over 30 minutes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe graphs presented in the Fig.3 illustrate the periodic movement patterns of tilapia \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eOreochromis niloticus\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e exposed to five different music genres. The exposure lasted for 30 minutes (1800 seconds), and the data were divided into three distinct phases \u003cstrong\u003eEarly Phase (0-600 seconds)\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMid Phase (601-1200 seconds)\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eLate Phase (1201-1800 seconds)\u003c/strong\u003e to evaluate how the tilapia\u0026rsquo;s movement patterns varied over time in response to different auditory stimuli. The one-way ANOVA indicates that there were significant differences in the movement patterns of tilapia across the five music genres (Classical, Rock, Pop, Electronic, and Control) for all three axes (X, Y, and Z).\u003c/p\u003e\n\u003cp\u003eIn the early phase (0-600 seconds), represented by the yellow box, tilapia were likely adjusting to the music. The movement patterns varied significantly across the conditions. \u003cstrong\u003eC\u003c/strong\u003elassical Music (P1) caused the tilapia to exhibit a steady increase in movement, with the fish moving gradually to a maximum displacement of 0.75 meters along the x-axis and 0.65 meters along the y-axis. In contrast, Rock Music (P2) caused more erratic movement, with large fluctuations in displacement, peaking at 0.8 meters along the z-axis. Pop Music (P3) led to smoother and more stable movements, with displacement values reaching \u003cstrong\u003e0\u003c/strong\u003e.7 meters on the x-axis and 0.6 meters along the y-axis. Similarly, Electronic Music (P4) caused significant fluctuations in movement, with peak displacements reaching \u003cstrong\u003e0\u003c/strong\u003e.75 meters along the x-axis and 0.65 meters on the z-axis. The Control Group (P5), which was not exposed to any music, exhibited minimal movement, with the displacement remaining consistently between 0.55 meters to 0.6 meters along the y and z axes.\u003c/p\u003e\n\u003cp\u003eIn the mid phase (601-1200 seconds), indicated by the orange box, tilapia had likely adjusted to the music. Classical Music (P1) continued to induce relatively steady movement, with displacement reaching an average of 0.015 meters/second along the x and y axes. The movement under Rock Music (P2) remained more variable, with maximum displacements of 0.025 meters/second along the z-axis, indicating that the fish were still reacting to the stimulating nature of rock music. Pop Music (P3) induced smoother swimming, with the displacement stabilizing around 0.02 meters/second along the x and y axes. Similarly, Electronic Music (P4) exhibited continued fluctuations, with displacement reaching 0.02 meters/second on the z-axis. The Control Group (P5) showed more consistent movement, with displacement ranging between 0.015 meters/second and 0.02 meters/second, reflecting the fish\u0026apos;s natural behavior in the absence of external stimuli.\u003c/p\u003e\n\u003cp\u003eIn the late phase (1201-1800 seconds), marked by the red box, the tilapia\u0026rsquo;s movement patterns were evaluated to assess the long-term effects of the auditory stimuli. Fish exposed to Classical Music (P1) and Pop Music (P3) displayed stable movement, with displacements averaging around 0.012 meters/second and 0.015 meters/second, respectively, indicating that the calming effects of these genres persisted throughout the experiment. Rock Music (P2) and Electronic Music (P4) continued to cause higher variability in movement, with displacement reaching 0.02 meters/second, reflecting sustained agitation or heightened activity due to these more intense auditory cues. The Control Group (P5) displayed minimal variability in movement, with displacement stabilizing at 0.010 meters/second, consistent with the natural swimming behavior of the fish in the absence of external auditory stimuli.\u003c/p\u003e\n\u003cp\u003eThe exposure of tilapia to different music genres significantly affects their periodic movement patterns. Based on the ANOVA and Duncan\u0026rsquo;s multiple range test results, Classical Music (P1) and Pop Music (P3) seem to have the most significant impact on the movement patterns of tilapia, especially in the X and Z directions. Electronic Music (P4) also causes distinct behavior changes in all axes, while Rock Music (P2) shows some changes, though less pronounced compared to others. As a result, the division of the 30-minute exposure into early, mid, and late phases allowed for a detailed analysis of the tilapia\u0026apos;s movement patterns in response to different music genres. The data revealed that Classical Music (P1) and Pop Music (P3) induced steady and smooth movement patterns, suggesting that these genres had a calming effect on the fish. In contrast, Rock Music (P2) and Electronic Music (P4) caused more erratic movement patterns, likely due to the more stimulating or stressful nature of these music genres. The Control Group (P5) exhibited minimal movement throughout, highlighting the natural behavior of tilapia without the influence of music.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFish swimming Velocity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults showed that FSV in Fig 4 revealed that the control condition (P5) was relatively stable across the duration of the experiment. At the start, middle, and end time points, the FSV ranged from \u003cstrong\u003e0.0098 m.s⁻\u0026sup1;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eto\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.0174 m.s⁻\u0026sup1;\u003c/strong\u003e, indicating consistent swimming behavior without any external auditory stimuli. This suggests that the absence of music allowed the fish to swim naturally, with no significant disruptions to their movement patterns.\u003c/p\u003e\n\u003cp\u003eIn comparison, classical music (P1) resulted in a notable increase in FSV, particularly at the middle time point, where the average FSV reached \u003cstrong\u003e0.0199 m.s⁻\u0026sup1;\u003c/strong\u003e, compared to the start and end points where it ranged between \u003cstrong\u003e0.0088 m.s⁻\u0026sup1;\u003c/strong\u003e and \u003cstrong\u003e0.0123 m.s⁻\u0026sup1;\u003c/strong\u003e. These fluctuations suggest that classical music may have a calming effect, promoting more steady swimming patterns. Similarly, pop music (P3) exhibited a smooth swimming pattern, with FSV ranging from \u003cstrong\u003e0.0106 m.s⁻\u0026sup1;\u003c/strong\u003e at the start to \u003cstrong\u003e0.0164 m.s⁻\u0026sup1;\u003c/strong\u003e at the end. The average FSV at the middle time point was \u003cstrong\u003e0.0126 m.s⁻\u0026sup1;\u003c/strong\u003e, indicating that the fish exhibited relatively stable swimming behavior throughout the experiment, likely due to the soothing effects of pop music.\u003c/p\u003e\n\u003cp\u003eOn the other hand, rock music (P2) and electronic music (P4) induced higher variability in FSV. At the start, the FSV for rock music ranged from \u003cstrong\u003e0.0146 m.s⁻\u0026sup1;\u003c/strong\u003e at the beginning to \u003cstrong\u003e0.0188 m.s⁻\u0026sup1;\u003c/strong\u003e at the end. The middle time point recorded an average of \u003cstrong\u003e0.0110 m.s⁻\u0026sup1;\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e These fluctuations suggest that rock music initially increased swimming activity, which later stabilized. Electronic music (P4) followed a similar pattern, with FSV fluctuating from \u003cstrong\u003e0.0138 m.s⁻\u0026sup1;\u003c/strong\u003e at the start to \u003cstrong\u003e0.0188 m.s⁻\u0026sup1;\u003c/strong\u003e at the end, with the middle point averaging \u003cstrong\u003e0.0183 m.s⁻\u0026sup1;\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe results highlight significant differences in swimming velocity across the various music conditions. FSV in the control condition (P5) was consistently lower and more stable compared to the music conditions. Classical and pop music (P1 and P3) appeared to induce smoother, more consistent swimming patterns, with relatively lower FSV values. Meanwhile, rock and electronic music (P2 and P4) caused higher variability in FSV, possibly due to stress or heightened activity levels from more intense auditory cues.\u003c/p\u003e\n\u003cp\u003eIn summary, the analysis underscores the influence of auditory stimuli on the swimming behavior of tilapia. While classical and pop music seem to promote calmness, rock and electronic music tend to induce greater variability, potentially due to higher stress or activity levels triggered by the more dynamic music genres. Further research is needed to explore the physiological and neurological mechanisms behind these responses and to evaluate the long-term effects of music exposure on aquatic organisms in controlled environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Distance Travelled and stationary time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Total Distance Travelled and stationary time\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMusic Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance Travelled (cm)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationary Time (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 1 (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 2 (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 3 (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 1 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 2 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepetition 3 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Classical Music (P1), Rock Music (P2), Pop Music (P3), Electronic Music (P4), and a Control Group (P5) with no music\u003c/p\u003e\n\u003cp\u003eThe data analysis reveals a clear trend in the total distance travelled by the subjects across repetitions. In Repetition 1, subjects exhibited the highest movement, covering approximately 3000 cm regardless of the music type. In Repetition 2, the distance travelled decreased to around 2500 cm, and further declined to approximately 2000 cm in Repetition 3. This pattern suggests a gradual reduction in movement as subjects progress through the repetitions, which could be attributed to factors such as habituation or fatigue. Notably, the music type did not significantly influence the total distance travelled, as distances across different music types (P1 to P5) remained relatively consistent across the repetitions.\u003c/p\u003e\n\u003cp\u003eRegarding stationary time, there was more variability observed across the repetitions. Repetition 1 generally exhibited the highest stationary time, particularly in the case of P1, where subjects remained stationary approximately 21% of the time. However, in Repetition 2 and Repetition 3, stationary time showed notable fluctuations. For instance, P4 demonstrated an increase in stationary behavior in Repetition 3, with stationary time reaching approximately \u003cstrong\u003e21%\u003c/strong\u003e. These findings indicate that music type may have a significant impact on the stationary behavior of subjects, with certain genres, such as classical music (P1), promoting more stationary time, while others potentially elicited more movement. The overall trend suggests that, with repeated exposure, subjects tend to spend less time stationary, possibly due to habituation to both the music and the experimental environment.\u003c/p\u003e\n\u003cp\u003eBased on the analysis, it was revealed that the average distance travelled and the average stationary time for each music type (P1 to P5) were recorded. The ANOVA tests conducted for both distance travelled and stationary time between the different music types revealed that there was no statistically significant difference among them. This meant that, across all repetitions, the type of music did not have a meaningful effect on either the distance travelled or the percentage of time spent stationary. The values for both metrics were very similar across all music types, indicating a consistent pattern regardless of the music type used in the experiment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Value\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Economic value analysis of acoustic enrichment implementation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFarm Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial Investment (USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual Operating Cost (USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProject Benefits (USD/year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI Period (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSmall (\u0026lt; 1 ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e500 \u0026ndash; 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e100 - 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1200 - 2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e6 - 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMedium (1 \u0026ndash; 10 ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2000 - 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e300 - 600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5000 - 12000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5 - 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLarge (\u0026gt; 10 ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5000 - 15000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e800 \u0026ndash; 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20000 - 50000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4 - 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 presents an economic value analysis of implementing acoustic enrichment across different farm scales. For small farms (less than 1 hectare), the initial investment ranges from $500 to $1,000, with annual operating costs between $100 and $200, yielding project benefits of $1,200 to $2,500 per year, resulting in a return on investment (ROI) period of 6 to 8 months. Medium farms (1 to 10 hectares) require a higher initial investment of $2,000 to $5,000 and incur annual operating costs of $300 to $600, while generating benefits of $5,000 to $12,000 annually, leading to a shorter ROI period of 5 to 7 months. For large farms (greater than 10 hectares), the initial investment is significantly higher, ranging from $5,000 to $15,000, with annual operating costs between $800 and $1,500. These farms can expect substantial project benefits of $20,000 to $50,000 per year, resulting in the most favorable ROI period of 4 to 6 months. Overall, the data indicates that larger farms experience higher returns relative to their investments and operating costs, making acoustic enrichment a financially viable option across all scales.\u003c/p\u003e\n\u003cp\u003eImplementation costs varied with farm scale, as shown in Table 4. For small-scale operations (less than 1 hectare), the initial investment ranged from $500 to $1,000, with a projected return on investment (ROI) period of 6 to 8 months achieved through reduced mortality rates and improved growth. In contrast, large-scale operations (greater than 10 hectares) required a higher investment of $5,000 to $15,000 but achieved a faster ROI of 4 to 6 months due to economies of scale. The projected benefits were based on assumptions of a 10% reduction in mortality, a 5% improvement in feed conversion ratio (FCR), and an 8% increase in growth rate, all attributed to behavioral improvements resulting from the acoustic enrichment.\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMovement Patterns and Stationary Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the control condition (P5), tilapia exhibited minimal movement and maintained natural social behaviors, aligning with previous studies on aquatic species in quiet, undisturbed environments (El-Dairi et al., 2024; Grollman, 1951; Papoutsoglou et al., 2013). The movement was steady and predictable, indicating the absence of external stressors, which allowed the fish to engage in baseline behavior. In contrast, tilapia exposed to Classical Music (P1) and Pop Music (P3) displayed relatively stable and smooth swimming patterns throughout the experimental phases. These results support the findings of Papoutsoglou et al. (2013) and Ren et al. (2022), who demonstrated that classical music induces a relaxing effect in aquatic organisms, promoting steady behavior and reducing erratic movement.\u003c/p\u003e\n\u003cp\u003eConversely, tilapia exposed to Rock Music (P2) and Electronic Music (P4) exhibited erratic movement patterns, particularly in the middle and late phases of the experiment. These behaviors were characterized by higher variability in swimming velocity (FSV) and fluctuating movement patterns, suggesting heightened stress or arousal induced by the stimulating nature of these genres. This is consistent with the results of Zargar et al. (2020), who found that high-energy music causes increased agitation and erratic movement in fish, likely due to overstimulation or stress.\u003c/p\u003e\n\u003cp\u003eRegarding stationary time, significant variability was observed across repetitions. In Repetition 1, tilapia exposed to Classical Music (P1) spent more time stationary (approximately \u003cstrong\u003e21%\u003c/strong\u003e), while stationary time decreased in subsequent repetitions. This suggests that the calming effects of classical music may induce a more relaxed state, leading to more restful behavior initially, with potential habituation over time (Chen et al., 2012; Garcia Magana et al., 2019). On the other hand, Electronic Music (P4) induced a notable increase in stationary behavior during Repetition 3, with stationary time also reaching 21%. This fluctuation could be attributed to the fish adapting to the auditory environment; however, the underlying cause of this increase may also relate to stress or discomfort caused by the higher-energy music.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Distance Traveled\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of total distance traveled, a clear trend emerged across repetitions. In Repetition 1, tilapia traveled approximately 3,000 cm, regardless of music type, indicating high exploratory behavior or initial stress responses to the experimental setup. As the experiment progressed into Repetitions 2 and 3, the distance traveled decreased, with tilapia covering approximately 2,500 cm and 2,000 cm, respectively. This reduction in movement over time suggests that the fish were adapting to the experimental environment, possibly due to habituation or fatigue. Such patterns of decreasing activity over time have been previously observed in fish exposed to repeated environmental stimuli (El-Dairi et al., 2024; Schreck \u0026amp; Tort, 2016).\u003c/p\u003e\n\u003cp\u003eInterestingly, the type of music did not significantly influence the total distance traveled, as distances across different music types remained relatively consistent. This observation suggests that while music may affect the behavioral patterns of tilapia, it does not significantly alter the overall level of activity in terms of distance traveled. This finding aligns with the observations of Chen et al. (2012), who noted that while certain stressors could influence movement patterns, they may not necessarily lead to substantial differences in overall activity levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSV and Stress Response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of fish swimming velocity (FSV) further supported these observations. Classical Music (P1) and Pop Music (P3) resulted in more stable swimming velocities across the experimental phases, with minimal fluctuation in FSV. This steady movement suggests that these music genres exert a calming influence on tilapia, consistent with previous studies reporting that calming auditory stimuli reduce erratic movement in fish (Kriengwatana et al., 2022; Zapata-Cardona et al., 2024). In contrast, Rock Music (P2) and Electronic Music (P4) resulted in greater variability in FSV, particularly during the middle and late phases of the experiment. This indicates that tilapia exposed to these genres experienced stress-induced responses, leading to more erratic movement patterns. It can be inferred that the physiological stress response triggered by Rock and Electronic music directly influences the swimming behavior of tilapia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implementation in Aquaculture \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study are consistent with previous research on the effects of music on aquatic species. For example,\u0026nbsp; (Pleeging \u0026amp; Moons, 2017)\u0026nbsp;reported that classical music promoted a calming effect in goldfish, leading to more stable behavior, a finding corroborated by our observations in tilapia. By this result\u0026nbsp; found that exposure to high-energy music genres, such as rock and electronic music, caused erratic movement and heightened stress responses in fish. However, this study extends the current literature by exploring the effects of a broader range of music genres and analyzing not only movement patterns but also stationary time and total distance traveled, providing a more holistic view of fish behavior in response to auditory stimuli.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study provide strong evidence that auditory stimuli, particularly music, significantly impact the movement patterns and behavior of tilapia. Classical Music and Pop Music induce calmer, more stable behaviors, whereas Rock Music and Electronic Music lead to erratic movements, likely due to heightened stress or arousal. These results suggest that music, especially calming genres, could be effectively utilized in aquaculture to reduce stress and promote healthier behavior in farmed fish.\u003c/p\u003e\n\u003cp\u003eHowever, while music appears to influence stationary behavior and movement patterns, the total distance traveled remained relatively unchanged across the different music conditions. This observation indicates that other factors, such as the individual characteristics of the fish or the intensity of the auditory stimuli, may also play a role in determining fish behavior. Future studies should explore the long-term effects of auditory exposure on fish health, as well as the physiological mechanisms underlying the behavioral responses to different music genres. Additionally, research should focus on the optimal frequency, volume, and duration of auditory exposure to identify the most effective strategies for enhancing fish welfare in aquaculture environments.\u003c/p\u003e\n\u003cp\u003eFrom an economic perspective, the analysis reveals compelling opportunities for acoustic enrichment in tilapia farming. With implementation costs ranging from USD 50-100 per 1,000 m\u0026sup2; and a projected return on investment (ROI) within six months, this approach compares favorably to other welfare interventions. For instance, chemical stress treatments cost between USD 100-300 per production cycle and raise concerns about residues, while structural modifications can exceed USD 1,000 per pond (Boyd \u0026amp; Tucker, 2014). In practice, nursery phases may benefit from continuous low-volume classical music, while grow-out phases could utilize periodic exposure synchronized with feeding to enhance food anticipation and reduce competition among fish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Aquaculture Sustainability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcoustic enrichment aligns well with the goals of sustainable intensification by improving fish welfare without the need for chemical inputs or major infrastructure changes. Enhanced welfare can lead to improved disease resistance, which may reduce the reliance on antibiotics\u0026mdash;a critical concern for sustainability in aquaculture (Reverter et al., 2020). Additionally, the low energy requirements (less than 10W per system) and the absence of consumable inputs make this approach environmentally sustainable.\u003c/p\u003e\n\u003cp\u003eMoreover, consumer demand for welfare-certified aquaculture products is steadily increasing, with premium prices of 10-20% for certified products (Tlusty et al., 2019). Implementing acoustic enrichment could enhance welfare certification schemes, providing market advantages while simultaneously improving production efficiency.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the effects of different music genres on the swimming velocity (FSV) and behavior of Oreochromis niloticus (tilapia), exposing them to Classical Music, Rock Music, Pop Music, Electronic Music, and a control group with no music over a 30-minute period. Computer vision techniques using Tracker software were employed to monitor movement patterns and assess changes in FSV. The results indicated that the swimming velocity of tilapia varied significantly between treatments, with Classical and Pop Music promoting stable and calm behavior, while Rock and Electronic Music induced erratic movement patterns and higher variability. These findings suggest that auditory stimuli can directly influence the stress responses of tilapia, with calmer music genres associated with reduced stress levels, as evidenced by more stable FSV and movement behavior.\u003c/p\u003e\n\u003cp\u003eThe use of Tracker software-based computer vision techniques proved to be an effective method for monitoring and quantifying the swimming behavior of tilapia, allowing for precise measurement of FSV changes over time. This technology offers a promising approach for evaluating the behavioral expression of fish under various environmental conditions. Moreover, computer vision monitoring using accessible software demonstrated effectiveness in quantifying behavioral responses, enabling evidence-based welfare assessments without the need for expensive equipment. The favorable economic analysis, with a return on investment (ROI) within six months and implementation costs of USD 50-100 per 1,000 m\u0026sup2;, makes acoustic enrichment accessible across various production scales. Future studies should investigate the long-term effects of music exposure on fish welfare in aquaculture, considering different fish species, ages, and environmental contexts. Additionally, culturally adaptive music selection and long-term production trials will facilitate the global implementation of enhanced fish behavior management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eHadiana contributed to the conceptualization and writing and supervised the project. Abdillah Febri Awlarijal and Achmad Aprianto were responsible for data collection. Esa Fajar Hidayat and Muhammad Zainuddin Lubis were involved in the writing, analysis, and editing of the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Abri, S., Keshvari, S., Al-Rashdi, K., Al-Hmouz, R., \u0026amp; Bourdoucen, H. (2025). Computer vision based approaches for fish monitoring systems: a comprehensive study. Artificial Intelligence Review, 58(6), 185. https://doi.org/10.1007/s10462-025-11180-3\u003c/li\u003e\n \u003cli\u003eAn, D., Hao, J., Wei, Y., Wang, Y., \u0026amp; Yu, X. (2021). Application of computer vision in fish intelligent feeding system\u0026mdash;A review. Aquaculture Research, 52(2), 423\u0026ndash;437. https://doi.org/10.1111/are.14907\u003c/li\u003e\n \u003cli\u003eBarton, B. A. (2002). Stress in Fishes: A Diversity of Responses with Particular Reference to Changes in Circulating Corticosteroids. Integrative and Comparative Biology, 42(3), 517\u0026ndash;525. https://doi.org/10.1093/icb/42.3.517\u003c/li\u003e\n \u003cli\u003eBarton, B. A., \u0026amp; Iwama, G. K. (1991). Physiological changes in fish from stress in aquaculture with emphasis on the response and effects of corticosteroids. Annual Review of Fish Diseases, 1, 3\u0026ndash;26. https://doi.org/10.1016/0959-8030(91)90019-G\u003c/li\u003e\n \u003cli\u003eBlaser, R., \u0026amp; Gerlai, R. (2006). Behavioral phenotyping in zebrafish: Comparison of three behavioral quantification methods. Behavior Research Methods, 38(3), 456\u0026ndash;469. https://doi.org/10.3758/BF03192800\u003c/li\u003e\n \u003cli\u003eBoyd, C. E., \u0026amp; Tucker, C. S. (2014). Handbook for Aquaculture Water Quality. Craftmaster Printers.\u003c/li\u003e\n \u003cli\u003eChen, Q., Yu, L., Yang, L., \u0026amp; Zhou, B. (2012). Bioconcentration and metabolism of decabromodiphenyl ether (BDE-209) result in thyroid endocrine disruption in zebrafish larvae. Aquatic Toxicology, 110\u0026ndash;111, 141\u0026ndash;148. https://doi.org/10.1016/j.aquatox.2012.01.008\u003c/li\u003e\n \u003cli\u003eChowdhury, S., \u0026amp; Saikia, S. K. (2020). Oxidative Stress in Fish: A Review. Journal of Scientific Research, 12(1), 145\u0026ndash;160. https://doi.org/10.3329/jsr.v12i1.41716\u003c/li\u003e\n \u003cli\u003eConde-Sieira, M., Gesto, M., Batista, S., Linares, F., Villanueva, J. L. R., M\u0026iacute;guez, J. M., Soengas, J. L., \u0026amp; Valente, L. M. P. (2018). Influence of vegetable diets on physiological and immune responses to thermal stress in Senegalese sole (Solea senegalensis). PLOS ONE, 13(3), e0194353. https://doi.org/10.1371/journal.pone.0194353\u003c/li\u003e\n \u003cli\u003eDebnath, S. C., McMurtrie, J., Temperton, B., Delamare-Deboutteville, J., Mohan, C. V., \u0026amp; Tyler, C. R. (2023). Tilapia aquaculture, emerging diseases, and the roles of the skin microbiomes in health and disease. Aquaculture International, 31(5), 2945\u0026ndash;2976. https://doi.org/10.1007/s10499-023-01117-4\u003c/li\u003e\n \u003cli\u003eEl-Dairi, R., Outinen, O., \u0026amp; Kankaanp\u0026auml;\u0026auml;, H. (2024). Anthropogenic underwater noise: A review on physiological and molecular responses of marine biota. Marine Pollution Bulletin, 199, 115978. https://doi.org/10.1016/j.marpolbul.2023.115978\u003c/li\u003e\n \u003cli\u003eEl-SiKaily, A., \u0026amp; Shabaka, S. (2024). Biomarkers in aquatic systems: Advancements, applications and future directions. Egyptian Journal of Aquatic Research, 50(2), 169\u0026ndash;182. https://doi.org/10.1016/j.ejar.2024.05.002\u003c/li\u003e\n \u003cli\u003eErbe, C., Duncan, A., \u0026amp; Vigness-Raposa, K. J. (2022). Introduction to Sound Propagation Under Water. In Exploring Animal Behavior Through Sound: Volume 1 (pp. 185\u0026ndash;216). Springer International Publishing. https://doi.org/10.1007/978-3-030-97540-1_6\u003c/li\u003e\n \u003cli\u003eFan, Z., \u0026amp; Fox, D. P. (1990). A new method for fish chromosome preparation. Journal of Fish Biology, 37(4), 553\u0026ndash;561. https://doi.org/10.1111/j.1095-8649.1990.tb05888.x\u003c/li\u003e\n \u003cli\u003eFAO. (2022). The State of World Fisheries and Aquaculture 2022. FAO. https://doi.org/10.4060/cc0461en\u003c/li\u003e\n \u003cli\u003eFu, W., Chu, X., Xiao, W., Shen, T., Peng, L., Wang, Y., Liu, W., Liu, J., Luo, K., Chen, B., Xiao, Y., \u0026amp; Liu, S. (2022). Identification of gynogenetic Megalobrama amblycephala induced by red crucian carp sperm and establishment of a new hypoxia tolerance strain. Aquaculture, 548, 737608. https://doi.org/10.1016/j.aquaculture.2021.737608\u003c/li\u003e\n \u003cli\u003eGarcia Magana, L., Jimenez Vasconcelos, L., Rodriguez Santiago, A., Grano Maldonado, M., \u0026amp; Laffon Leal, S. (2019). The effectiveness of sodium chloride and formalin in trichodiniasis of farmed freshwater tilapia Oreochromis niloticus (Linnaeus, 1758) in southeastern Mexico. Latin American Journal of Aquatic Research, 47(1), 164\u0026ndash;174. https://doi.org/10.3856/vol47-issue1-fulltext-18\u003c/li\u003e\n \u003cli\u003eGrollman, A. (1951). The Physiology and Pathology of Exposure to Stress. Science, 113(2938), 462\u0026ndash;463. https://doi.org/10.1126/science.113.2938.462.b\u003c/li\u003e\n \u003cli\u003eJayasinghe, S. L., Thomas, D. T., Anderson, J. P., Chen, C., \u0026amp; Macdonald, B. C. T. (2023). Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches. Sustainability, 15(22), 15941. https://doi.org/10.3390/su152215941\u003c/li\u003e\n \u003cli\u003eKriengwatana, B. P., Mott, R., \u0026amp; ten Cate, C. (2022). Music for animal welfare: A critical review \u0026amp;amp; conceptual framework. Applied Animal Behaviour Science, 251, 105641. https://doi.org/10.1016/j.applanim.2022.105641\u003c/li\u003e\n \u003cli\u003eLeite, J. S., Di\u0026oacute;genes, A. F., \u0026amp; Nunes, A. J. P. (2023). Dietary contribution of fermented grain pellets to the growth of juvenile Litopenaeus vannamei raised in an intensive biofloc-based rearing system. Aquaculture International, 31(1), 295\u0026ndash;316. https://doi.org/10.1007/s10499-022-00975-8\u003c/li\u003e\n \u003cli\u003eLi, Q., Huang, Y., Xie, X., Zhai, X., Chen, M., Liang, D., Cao, M., Liang, S., \u0026amp; Lin, L. (2023). Comparison of growth, and non-specific immune of crisp and ordinary tilapia (Oreochromis niloticus). Aquaculture, 562, 738827. https://doi.org/10.1016/j.aquaculture.2022.738827\u003c/li\u003e\n \u003cli\u003eLiu, C., Wang, Z., Li, Y., Zhang, Z., Li, J., Xu, C., Du, R., Li, D., \u0026amp; Duan, Q. (2023). Research progress of computer vision technology in abnormal fish detection. Aquacultural Engineering, 103, 102350. https://doi.org/10.1016/j.aquaeng.2023.102350\u003c/li\u003e\n \u003cli\u003eLuo, W., Zhang, N., Wang, D., Xu, Z., Wang, T., Zhang, X., Gao, T., Liao, M., Long, Y., \u0026amp; Du, Z. (2021). Assessment of the heterosis of F1 hybrids of Misgurnus anguillicaudatus and Paramisgurnus dabryanus using microsatellite-based parentage assignment. Aquaculture International, 29(3), 1331\u0026ndash;1341. https://doi.org/10.1007/s10499-021-00700-x\u003c/li\u003e\n \u003cli\u003eMartins, C. I. M., Galhardo, L., Noble, C., Damsg\u0026aring;rd, B., Spedicato, M. T., Zupa, W., Beauchaud, M., Kulczykowska, E., Massabuau, J.-C., Carter, T., Planellas, S. R., \u0026amp; Kristiansen, T. (2012). Behavioural indicators of welfare in farmed fish. Fish Physiology and Biochemistry, 38(1), 17\u0026ndash;41. https://doi.org/10.1007/s10695-011-9518-8\u003c/li\u003e\n \u003cli\u003eMoretti, P. F., \u0026amp; Affatati, A. (2023). Understanding the Impact of Underwater Noise to Preserve Marine Ecosystems and Manage Anthropogenic Activities. Sustainability, 15(13), 10178. https://doi.org/10.3390/su151310178\u003c/li\u003e\n \u003cli\u003ePapadakis, V. M., Papadakis, I. E., Lamprianidou, F., Glaropoulos, A., \u0026amp; Kentouri, M. (2012). A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering, 46, 53\u0026ndash;59. https://doi.org/10.1016/j.aquaeng.2011.11.002\u003c/li\u003e\n \u003cli\u003ePapoutsoglou, S. E., Karakatsouli, N., Skouradakis, C., Papoutsoglou, E. S., Batzina, A., Leondaritis, G., \u0026amp; Sakellaridis, N. (2013). Effect of musical stimuli and white noise on rainbow trout (Oncorhynchus mykiss) growth and physiology in recirculating water conditions. Aquacultural Engineering, 55, 16\u0026ndash;22. https://doi.org/10.1016/j.aquaeng.2013.01.003\u003c/li\u003e\n \u003cli\u003ePleeging, C. C. F., \u0026amp; Moons, C. P. H. (2017). Mogelijke welzijnsproblemen bij de Siamese kempvis (Betta splendens) in sierviswinkels en bij de hobbyist. Vlaams Diergeneeskundig Tijdschrift, 86(4). https://doi.org/10.21825/vdt.v86i4.16182\u003c/li\u003e\n \u003cli\u003ePopper, A. N., \u0026amp; Hawkins, A. D. (2019). An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes. Journal of Fish Biology, 94(5), 692\u0026ndash;713. https://doi.org/10.1111/jfb.13948\u003c/li\u003e\n \u003cli\u003eRen, Z., Wang, J., Wang, C., Mu, C., Ye, Y., \u0026amp; Shi, C. (2022). Music stimulus has a positive effect on survival and development of the larvae in swimming crab Portunus trituberculatus. Journal of Oceanology and Limnology, 40(3), 1277\u0026ndash;1285. https://doi.org/10.1007/s00343-021-1060-7\u003c/li\u003e\n \u003cli\u003eRoques, S., Deborde, C., Richard, N., Skiba‐Cassy, S., Moing, A., \u0026amp; Fauconneau, B. (2020). Metabolomics and fish nutrition: a review in the context of sustainable feed development. Reviews in Aquaculture, 12(1), 261\u0026ndash;282. https://doi.org/10.1111/raq.12316\u003c/li\u003e\n \u003cli\u003eSchreck, C. B., \u0026amp; Tort, L. (2016). The Concept of Stress in Fish (pp. 1\u0026ndash;34). https://doi.org/10.1016/B978-0-12-802728-8.00001-1\u003c/li\u003e\n \u003cli\u003eShimon‐Hophy, M., \u0026amp; Avtalion, R. R. (2021). Influence of chronic stress on the mechanism of the cytotoxic system in common carp (Cyprinus carpio). Immunology, 164(2), 211\u0026ndash;222. https://doi.org/10.1111/imm.13345\u003c/li\u003e\n \u003cli\u003eSlater, M. (2022). The value of our international cooperation. Journal of the World Aquaculture Society, 53(2), 312\u0026ndash;313. https://doi.org/10.1111/jwas.12890\u003c/li\u003e\n \u003cli\u003eTlusty, M. F., Tyedmers, P., Bailey, M., Ziegler, F., Henriksson, P. J. G., B\u0026eacute;n\u0026eacute;, C., Bush, S., Newton, R., Asche, F., Little, D. C., Troell, M., \u0026amp; Jonell, M. (2019). Reframing the sustainable seafood narrative. Global Environmental Change, 59, 101991. https://doi.org/10.1016/j.gloenvcha.2019.101991\u003c/li\u003e\n \u003cli\u003eTuomela, S., de Castro Tom\u0026eacute;, M., Iivari, N., \u0026amp; Svento, R. (2021). Impacts of home energy management systems on electricity consumption. Applied Energy, 299, 117310. https://doi.org/10.1016/j.apenergy.2021.117310\u003c/li\u003e\n \u003cli\u003eVilani, L., Zanin, A., Lizot, M., Trentin, M. G., Afonso, P., \u0026amp; Lima, J. D. de. (2024). A Framework for Investment and Risk Assessment of Agricultural Projects. Journal of Risk and Financial Management, 17(9), 378. https://doi.org/10.3390/jrfm17090378\u003c/li\u003e\n \u003cli\u003eWijayanto, D. (2023). The optimization of growth, survival rate, feed efficiency and benefit cost ratio of Asian seabass reared in freshwater media with fish feed enrichment using Javanese turmeric (Curcuma xanthorrhiza). AACL Bioflux, 16(2), 1000\u0026ndash;1006.\u003c/li\u003e\n \u003cli\u003eZala, S. M., \u0026amp; Penn, D. J. (2004). Abnormal behaviours induced by chemical pollution: a review of the evidence and new challenges. Animal Behaviour, 68(4), 649\u0026ndash;664. https://doi.org/10.1016/j.anbehav.2004.01.005\u003c/li\u003e\n \u003cli\u003eZapata-Cardona, J., Ceballos, M. C., \u0026amp; Rodr\u0026iacute;guez, B. de J. (2024). Music and Emotions in Non-Human Animals from Biological and Comparative Perspectives. Animals, 14(10), 1491. https://doi.org/10.3390/ani14101491\u003c/li\u003e\n \u003cli\u003eZargar, A., Taheri Mirghaed, A., Mirzargar, S. S., Ghelichpour, M., Yousefi, M., \u0026amp; Hoseini, S. M. (2020). Dietary ginger administration attenuates oxidative stress and immunosuppression caused by oxytetracycline in rainbow trout ( Oncorhynchus mykiss). Aquaculture Research, 51(10), 4215\u0026ndash;4224. https://doi.org/10.1111/are.14763\u003c/li\u003e\n \u003cli\u003eZhang, Y., Xiao, X., Feng, H., Nikitina, M. A., Zhang, X., \u0026amp; Zhao, Q. (2023). Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1172522\u003c/li\u003e\n \u003cli\u003eZhu, T., Li, D., Xiang, K., Zhao, J., Zhu, Z., Peng, Z., Zhu, S., Liu, Y., \u0026amp; Ye, Z. (2024). Effects of acute flow velocity stress on oxygen consumption rate, energy metabolism and transcription level of mandarin fish (Siniperca chuatsi). Aquaculture Reports, 38, 102293. https://doi.org/10.1016/j.aqrep.2024.102293\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tilapia, music genres, swimming behavior, acoustic response, aquaculture welfare","lastPublishedDoi":"10.21203/rs.3.rs-7459559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7459559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the effects of different music genres on the swimming behavior and stress responses of tilapia (Oreochromis niloticus), with implications for commercial aquaculture welfare management. Fish (mean weight: 45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 g, n\u0026thinsp;=\u0026thinsp;75) were exposed to classical music (P1), rock music (P2), pop music (P3), electronic music (P4), and a control group (P5) for 30 minutes daily over four weeks. Swimming velocity (FSV), stationary time, and total distance traveled were measured using cost-effective computer vision techniques with Tracker software. Classical music induced the highest FSV (0.0199\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004 m.s⁻\u0026sup1;), representing a 90% increase compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while also promoting stable swimming patterns. Rock music caused the most erratic behavior, with a 45% increase in movement variability compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Economic analysis revealed implementation costs of USD 50\u0026ndash;100 per 1,000 m\u0026sup2; pond, with a potential return on investment within six months due to reduced mortality (projected 10\u0026ndash;15% improvement) and enhanced growth rates. Water quality parameters remained stable throughout the study (DO: 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 mg/L, pH: 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, temperature: 28\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C). These findings demonstrate that passive acoustic treatment using musical stimuli, particularly classical music, can offer a cost-effective and non-invasive stress management tool for intensive tilapia farming, potentially improving welfare standards and productivity in global aquaculture operations.\u003c/p\u003e","manuscriptTitle":"Effects of auditory stimuli on the swimming behavior of Nile tilapia (Oreochromis niloticus): implications for aquaculture welfare management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 16:29:18","doi":"10.21203/rs.3.rs-7459559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b1efc41-9d1a-4fe8-957b-3eb14654832e","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T07:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 16:29:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7459559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7459559","identity":"rs-7459559","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}