Imaging sonars, such as the Adaptive Resolution Imaging Sonar (ARIS), provide high-resolution sonar data that are used in fisheries research and management. While sonar methods have enormous potential for making population estimates, species identification via sonar remains an unresolved challenge. One method that may overcome this challenge involves measuring tailbeat frequencies to guide species differentiation. The tailbeat frequencies of three commonly sympatric anadromous fish species of eastern North America, Atlantic Salmon Salmo salar, Striped Bass Morone saxatilis, and American Shad Alosa sapidissima, were measured from imaging sonar data collected in an experimental low-flow, short-range setup. The frequencies were significantly different between the species (mean ± 1 SD beats/s: 0.6 ± 0.3 [Atlantic Salmon], 0.9 ± 0.2 [Striped Bass], and 1.4 ± 0.3 [American Shad]) when measured using a previously established manual method. Building on this, an automated method was developed and tested, and the method showed promising results. However, when compared to manually identified number of beats the error was large (on average, 1.1 [Atlantic Salmon], 4.8 [Striped Bass], and −0.4 [American Shad] beats in a fish track), especially in high fish densities. Despite the limitations, the automated method has utility in fisheries management when high-quality data can be collected for species with differing tailbeat frequencies.

中文翻译：

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从自适应分辨率成像声纳数据测量三种鱼类的尾拍频率

成像声纳，例如自适应分辨率成像声纳 (ARIS)，提供用于渔业研究和管理的高分辨率声纳数据。虽然声纳方法在进行种群估计方面具有巨大潜力，但通过声纳进行物种识别仍然是一个悬而未决的挑战。可以克服这一挑战的一种方法是测量尾拍频率以指导物种分化。的北美东部的三个常用同域溯河产卵鱼类的tailbeat频率，大西洋鲑鱼大西洋鲑，条纹鲈诸根黑眉，和美国鲥鱼鲱鲥鱼，是根据在实验性低流量、短程设置中收集的成像声纳数据进行测量的。使用先前建立的手动方法测量时，物种之间的频率显着不同（平均 ± 1 SD 节拍/秒：0.6 ± 0.3 [大西洋鲑鱼]、0.9 ± 0.2 [条纹鲈鱼] 和 1.4 ± 0.3 [美国鲱鱼]） . 在此基础上，开发并测试了一种自动化方法，该方法显示出有希望的结果。然而，与手动识别的节拍数相比，误差很大（平均而言，1.1 [Atlantic Salmon]、4.8 [Striped Bass] 和 -0.4 [American Shad] 在鱼的轨迹中节拍），尤其是在高鱼密度的情况下。尽管存在局限性，当可以为具有不同尾拍频率的物种收集高质量数据时，自动化方法在渔业管理中具有实用性。