Data Sheet 1_Avoidance behaviours of farmed Atlantic salmon (Salmo salar L.) to artificial sound and light: a case study of net-pen mariculture in Norway.pdf

Intensive finfish aquaculture is increasingly relying on enabling technologies and solutions such as sensor systems, robotics, and other machinery. Together with conventional farming equipment, these systems may emanate acoustic noise and artificial light, impacting the pen environment. Farmed fish have been observed to respond behaviourally and/or physiologically to anthropogenic sounds and lights, indicating a stress reaction that could impair welfare and health. This study aimed to investigate how farmed Atlantic salmon respond to such stimuli, with direct implications for the design and operation of robotic and mechanised systems in sea pens. We conducted experiments where we systematically exposed adult farmed Atlantic salmon in commercial net pens to sounds of frequencies within the range common to farm equipment (100–1,000 Hz), and submerged lights at 8 and 12 m with four different intensities (600 lx–14,500 lx). Data was analysed using sonar data and a deep learning (DL) based method for processing that automatically identified fish distribution patterns and estimated the average avoidance distance to the sound/light source. The fish fled from the sound source while playing sounds of 400 Hz, while sounds at other frequencies did not elicit a response. The response to light intensity depended on deployment depth, with the fish moving closer to the source when intensity was increased at 8 m depth, but conversely moving further away with increasing density when it was placed at 12 m. These outcomes are important inputs for the design of equipment, autonomous vehicles, robotic interventions and operations at commercial farms to ensure that their sound and light emissions have minimal impact on the fish, thereby reducing the potential of induced stress.

集约化海水鱼类养殖正日益依赖传感器系统、机器人技术及其他机械设备等赋能技术与解决方案。此类系统与传统养殖设备协同工作时，可能会产生声学噪声与人工光照，进而对网箱养殖环境造成影响。已有研究观察到，养殖鱼类会对人为产生的声响与光照产生行为学及/或生理学层面的响应，这表明其出现了应激反应，可能会损害鱼类的福利与健康状况。本研究旨在探究养殖大西洋鲑对这类刺激的响应规律，其研究结果对海上网箱内机器人与机械化系统的设计与运维具有直接指导意义。我们开展了相关实验：将商业养殖网箱中的成年养殖大西洋鲑，系统性暴露于养殖设备常见频段（100~1000赫兹）的声响，以及布放于8米、12米水深、四种不同光照强度（600勒克斯~14500勒克斯）的水下光照环境中。研究采用声纳（sonar）数据与基于深度学习（DL）的处理方法对数据进行分析，该方法可自动识别鱼类分布模式，并估算出鱼类与声响/光源之间的平均避离距离。当播放400赫兹的声响时，鱼类会逃离声源；而其他频率的声响则未引发鱼类的响应。鱼类对光照强度的响应取决于布放水深：当布放于8米水深时，随着光照强度提升，鱼类会愈发靠近光源；而当布放于12米水深时，情况则相反，鱼类会随着光照强度提升而愈发远离光源。上述研究结果可为商业养殖场的设备、自动驾驶运载工具、机器人干预作业及运维流程的设计提供重要参考，确保其声、光排放对鱼类的影响降至最低，从而降低诱导应激反应的潜在风险。