Research and Development, Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USA
Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA.
J Exp Biol. 2019 Aug 23;222(Pt 16):jeb207654. doi: 10.1242/jeb.207654.
Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals Here, we combine biologging with supervised machine learning (ML) to propose a pipeline for studying behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on eight in Monterey Bay, using the tether method for retrieval. By analyzing simultaneous video footage of the tagged jellyfish, we developed ML methods to: (1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and (2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and we conclude that it is essential to develop behavioral classifiers on rather than laboratory data.
浮游动物在海洋生态系统中起着至关重要的作用，但由于研究个体的难度，它们的细微行为仍未被充分了解。在这里，我们将生物标记与监督机器学习 (ML) 相结合，提出了一种研究大型浮游动物（如水母）行为的管道。我们在蒙特雷湾部署了 ITAG，这是一个带有高分辨率运动传感器的生物标记包，专为软体无脊椎动物设计，使用系绳法进行检索。通过分析标记水母的同步视频片段，我们开发了 ML 方法来：（1）识别系绳法损坏标签数据的时间段，这可能会影响先前的研究结果；（2）对水母行为进行分类。我们的工具可长时间对水母的细微活动和方向进行特征描述，我们得出的结论是，在野外而不是实验室数据上开发行为分类器至关重要。
