Automatic Segregation of Pelagic Habitats
Automatic Segregation of Pelagic Habitats
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DOI：
10.3389/fmars.2021.754375
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Rene Plonus;S. Vogl;J. Floeter
中科院分区：
文献类型：
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作者：
Rene Plonus;S. Vogl;J. Floeter
由于构造过程在大范围内是动态的，并且公海中不存在明确的边界，因此隔离中上层栖息地仍然很困难。然而，为了提高我们对现有生态位和塑造海洋浮游生物巨大多样性的过程的了解，我们需要更好地了解浮游生物斑块背后的驱动力。在这里，我们描述了一种新的机器学习方法，用于根据水文测量来检测和量化远洋栖息地。自动编码器学习高维微生境的二维、有意义的表示，其特征是高速 ROTV 的各种生物和非生物测量。随后，我们应用基于密度的聚类算法将类似的微生境分组为北海德国湾相关的中上层宏观生境。一致地确定了三个不同的宏观栖息地，即“表面混合层”、“底层”和异常“生产层”，每个栖息地都有其独特的浮游生物群落。我们提供的证据表明，该模型检测到了相关特征，例如海上风电场内温跃层的隆起或潮汐混合锋的存在。
It remains difficult to segregate pelagic habitats since structuring processes are dynamic on a wide range of scales and clear boundaries in the open ocean are non-existent. However, to improve our knowledge about existing ecological niches and the processes shaping the enormous diversity of marine plankton, we need a better understanding of the driving forces behind plankton patchiness. Here we describe a new machine-learning method to detect and quantify pelagic habitats based on hydrographic measurements. An Autoencoder learns two-dimensional, meaningful representations of higher-dimensional micro-habitats, which are characterized by a variety of biotic and abiotic measurements from a high-speed ROTV. Subsequently, we apply a density-based clustering algorithm to group similar micro-habitats into associated pelagic macro-habitats in the German Bight of the North Sea. Three distinct macro-habitats, a “surface mixed layer,” a “bottom layer,” and an exceptionally “productive layer” are consistently identified, each with its distinct plankton community. We provide evidence that the model detects relevant features like the doming of the thermocline within an Offshore Wind Farm or the presence of a tidal mixing front.