Mizuno Katsunori, Terayama Kei, Ishida Shoichi, Godbold Jasmin A, Solan Martin
Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa, Chiba 277-8561, Japan.
Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan.
R Soc Open Sci. 2024 Jun 19;11(6):240042. doi: 10.1098/rsos.240042. eCollection 2024 Jun.
The seafloor is inhabited by a large number of benthic invertebrates, and their importance in mediating carbon mineralization and biogeochemical cycles is recognized. However, the majority of fauna live below the sediment surface, so most means of survey rely on destructive sampling methods that are limited to documenting species presence rather than event driven activity and functionally important aspects of species behaviour. We have developed and tested a laboratory-based three-dimensional acoustic coring system that is capable of non-invasively visualizing the presence and activity of invertebrates within the sediment matrix. Here, we present reconstructed three-dimensional acoustic images of the sediment profile, with strong backscatter revealing the presence and position of individual benthic organisms. These data were used to train a three-dimensional convolutional neural network model and, using a combination of data augmentation and data correction techniques, we were able to identify individual species with an 88% accuracy. Combining three-dimensional acoustic coring with deep learning forms an effective and non-invasive means of providing detailed mechanistic information of species-sediment interactions, opening new opportunities to quantify species-specific contributions to ecosystems.
海底栖息着大量底栖无脊椎动物，它们在介导碳矿化和生物地球化学循环中的重要性已得到认可。然而，大多数动物生活在沉积物表面以下，因此大多数调查方法依赖于破坏性采样方法，这些方法仅限于记录物种的存在，而无法记录事件驱动的活动以及物种行为的功能重要方面。我们开发并测试了一种基于实验室的三维声学取芯系统，该系统能够非侵入性地可视化沉积物基质中无脊椎动物的存在和活动。在此，我们展示了沉积物剖面的重建三维声学图像，强烈的反向散射揭示了单个底栖生物的存在和位置。这些数据被用于训练一个三维卷积神经网络模型，并且通过结合数据增强和数据校正技术，我们能够以88%的准确率识别单个物种。将三维声学取芯与深度学习相结合，形成了一种有效且非侵入性的手段，可提供物种与沉积物相互作用的详细机制信息，为量化物种对生态系统的特定贡献开辟了新机会。
