PDF(Pubmed)
Abstract：
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 in situ species-sediment interactions, opening new opportunities to quantify species-specific contributions to ecosystems.
摘要：
海底居住着大量的底栖无脊椎动物,它们在介导碳矿化和生物地球化学循环中的重要性得到了认可。然而,大多数动物生活在沉积物表面以下，因此，大多数调查手段都依赖于破坏性的采样方法，这些方法仅限于记录物种的存在，而不是事件驱动的活动和物种行为的功能重要方面。我们已经开发并测试了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。这里,我们提供了重建的沉积物剖面的三维声学图像，强烈的反向散射揭示了单个底栖生物的存在和位置。这些数据用于训练三维卷积神经网络模型，使用数据增强和数据校正技术的组合，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，可以提供有关原位物种-沉积物相互作用的详细机理信息。为量化物种对生态系统的贡献开辟了新的机会。
