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Automatic Segregation of Pelagic Habitats

  • 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.

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Metadaten
Author:Rene-Marcel Plonus, Stefanie VoglORCiDGND, Jens Floeter
DOI:https://doi.org/10.3389/fmars.2021.754375
Parent Title (English):Frontiers in Marine Science
Publisher:Frontiers
Document Type:Article
Language:English
Year of first Publication:2021
Release Date:2024/04/26
Tag:North Sea; machine learning; pelagic habitats; plankton patchiness; submesoscale
Edition:8
Pagenumber:11 S.
Institutes:Fakultät Informatik und Mathematik
Publication:Externe Publikationen
research focus:Digitalisierung
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International