There are many ways to quantify fish movement through shallow‐water habitats, but most noninvasive methods (e.g., visual counts) are not effective in turbid coastal wetland waters of the Great Lakes. Dual‐frequency identification sonar (DIDSON) technology (Sound Metrics) offers a noninvasive, hydroacoustic‐based approach to characterize fish movement in wetlands and other habitats by collecting highly detailed fish movement data regardless of light and water quality conditions. High‐resolution data can be analyzed to estimate fish movement in areas where visual observations are difficult. However, enumerating a complex mix of fish sizes by manually counting fish visible in echogram files requires training and is very time consuming. Therefore, four counting techniques were tested to estimate fish abundance from DIDSON echograms that were collected at a hydrologically reconnected coastal wetland in the Great Lakes. Briefly, the four counting methods were (1) manually viewing the entire length of the echogram (full‐hour manual count), (2) manually viewing subsections of the echogram before generating fish estimates by per‐minute average (subsample manual count), (3) using Echoview automated software to generate automated estimates, and (4) using DIDSON viewer software to generate automated estimates. Over 800 echogram‐hours were recorded over a 9‐month period at an open‐flow water control structure connecting a coastal wetland to a tributary to Lake Erie. Commercial fish tracking software (Echoview) and custom software scripts from Milne Technologies were used to semi‐automate fish count estimates for a small subset of data. Semi‐automated software counts were compared to manual counts of identical data files to assess differences in accuracy, cost, processing time, and counter effort. Semi‐automated fish count estimates using Echoview and custom pre‐ and postprocessing software scripts did not differ from baseline manual counts, suggesting that the semi‐automated count process could be a reliable tool to increase efficiency when processing large DIDSON data sets.

中文翻译：

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改进的鱼类计数方法可准确量化沿海湿地环境双频识别声纳数据文件中的高密度鱼类运动

有许多方法可以量化鱼类在浅水生境中的活动，但是大多数非侵入性方法（例如视觉计数）在大湖的混浊沿海湿地水域中无效。双频识别声纳（DIDSON）技术（Sound Metrics）提供了一种非侵入性的，基于水声技术的方法，可通过收集高度详细的鱼类运动数据来表征湿地和其他栖息地中鱼类的运动，而不受光照和水质条件的影响。可以对高分辨率数据进行分析，以估计难以目视观察的区域中的鱼类运动。但是，通过手动计算在回波图文件中可见的鱼来枚举鱼的大小的复杂组合需要训练，并且非常耗时。因此，测试了四种计数技术，以根据DIDSON回波图估计鱼类的丰度，DIDSON回波图是在大湖区水文重连的沿海湿地收集的。简而言之，四种计数方法是（1）手动查看回波图的全长（全小时手动计数），（2）手动查看回波图的各个部分，然后按每分钟平均值生成鱼估计值（子样本手动计数）， （3）使用Echoview自动化软件生成自动估算，以及（4）使用DIDSON Viewer软件生成自动估算。在一个连接沿海湿地与伊利湖支流的开放水控结构中，在9个月内记录了800多个回声小时。商业鱼类追踪软件（Echoview）和Milne Technologies的定制软件脚本用于半自动化一小部分数据的鱼类计数估计。将半自动软件计数与相同数据文件的手动计数进行比较，以评估准确性，成本，处理时间和对策方面的差异。使用Echoview以及自定义的预处理和后处理软件脚本进行的半自动鱼类计数估计与基准人工计数没有区别，这表明半自动计数过程可能是处理大型DIDSON数据集时提高效率的可靠工具。