Department of Biology, Wake Forest University, Winston-Salem, NC, United States of America.
Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC, United States of America.
PLoS One. 2022 Feb 2;17(2):e0263377. doi: 10.1371/journal.pone.0263377. eCollection 2022.
Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques' use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras' potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.
了解海洋生态系统的长期趋势需要在空间和时间尺度上进行准确且可重复的鱼类和其他水生生物计数，这在潜水员调查中是困难或不可能实现的。由于水生环境的限制，长期、空间分布的摄像机，如在陆地摄像机诱捕中使用的摄像机，尚未在海洋系统中成功应用。在这里，我们开发了一种低成本、长期摄像机（分散式环境水下摄像机）系统的方法，该系统可在远程海洋环境中大规模部署。我们使用机器学习来对摄像机收集的大量图像进行分类。我们通过解决与光环有关的鱼类运动和摄食行为的案例研究，介绍了这些综合技术的使用情况，光环是浅热带珊瑚礁中一种有充分记录的底栖模式。摄像机能够在部署深度（高达 7 米，后来的版本部署到 40 米）下连续水下运行，无需维护或监测，超过五个月，并在白天以定时模式（每 15 分钟）总共收集了超过 10 万张图像。我们基于 ResNet-50 的深度学习模型在有鱼和无鱼的图像分类中达到了 92.5%的总体准确性，潜水员调查显示摄像机图像准确地代表了当地的鱼类群落。这些摄像机和基于机器学习的分类代表了大规模水下摄像机诱捕部署的第一个成功方法，我们的案例研究展示了摄像机在解决海洋动物行为、分布和大规模空间模式问题方面的潜力。
