Surveying coastal systems to estimate distribution and abundance of fish and benthic organisms is labor-intensive, often resulting in spatially limited data that are difficult to scale up to an entire reef or island. We developed a method that leverages the automation of a machine learning platform, CoralNet, to efficiently and cost-effectively allow a single observer to simultaneously generate georeferenced data on abundances of fish and benthic taxa over large areas in shallow coastal environments. Briefly, a researcher conducts a fish survey while snorkeling on the surface and towing a float equipped with a handheld GPS and a downward-facing GoPro, passively taking ~ 10 photographs per meter of benthos. Photographs and surveys are later georeferenced and photographs are automatically annotated by CoralNet. We found that this method provides similar biomass and density values for common fishes as traditional scuba-based fish counts on fixed transects, with the advantage of covering a larger area. Our CoralNet validation determined that while photographs automatically annotated by CoralNet are less accurate than photographs annotated by humans at the level of a single image, the automated approach provides comparable or better estimations of the percent cover of the benthic substrates at the level of a minute of survey (~ 50 m² of reef) due to the volume of photographs that can be automatically annotated, providing greater spatial coverage of the site. This method can be used in a variety of shallow systems and is particularly advantageous when spatially explicit data or surveys of large spatial extents are necessary.

中文翻译：

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利用机器学习对浅海珊瑚礁上的鱼类和底栖群落进行同步地理参考调查

调查沿海系统以估计鱼类和底栖生物的分布和丰度是一项劳动密集型工作，通常会导致数据空间有限，难以扩展到整个珊瑚礁或岛屿。我们开发了一种利用机器学习平台 CoralNet 自动化的方法，能够高效且经济地允许单个观察者同时生成浅海环境中大面积鱼类和底栖类群丰度的地理参考数据。简而言之，研究人员在水面浮潜并拖曳配备手持式 GPS 和朝下 GoPro 的浮标时进行鱼类调查，每米海底生物被动拍摄约 10 张照片。随后对照片和调查进行地理参考，并由 CoralNet 自动注释照片。我们发现，该方法为常见鱼类提供了与传统的基于水肺的鱼类在固定样带上计数相似的生物量和密度值，并具有覆盖更大区域的优点。我们的 CoralNet 验证确定，虽然 CoralNet 自动注释的照片在单个图像水平上不如人类注释的照片准确，但自动化方法可以在一分钟的水平上提供可比较或更好的底栖基质覆盖百分比估计。调查（~ 50 m²号珊瑚礁），因为可以自动注释大量照片，从而提供更大的场地空间覆盖范围。该方法可用于各种浅层系统，并且当需要空间明确的数据或大空间范围的调查时特别有利。