TY - JOUR
TI - Automatic Segregation of Pelagic Habitats
AU - Plonus, R-M
AU - Vogl, S
AU - Floeter, J
T2 - Frontiers in Marine Science
AB - 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.
DA - 2021/10//
PY - 2021
VL - 8
SP - 11
UR - https://www.frontiersin.org/articles/10.3389/fmars.2021.754375/full
DO - 10.3389/fmars.2021.754375
LA - English
KW - Marine Energy
KW - Wind Energy
KW - Fixed Offshore Wind
KW - Floating Offshore Wind
KW - Habitat Change
KW - Physical Environment
KW - Human Dimensions
ER -