Abstract
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.
Highlights
Submesoscale features like eddies, fronts or filaments structure the pelagic realm at spatial scales of order (1–10 km) (Lévy et al, 2012; Shulman et al, 2015; Buckingham et al, 2016) and temporal scales that range from several hours to a few days (Baschek and Maarten Molemaker, 2010; Thompson et al, 2016)
What is well known and trivial in landscape ecology can be quite challenging in seascape ecology
While it remains difficult to segregate pelagic habitats, which exhibit no clear boundaries (Hinchey et al, 2008; Pittman et al, 2011; Wedding et al, 2011) and can be quite dynamic on a wide range of scales, benthic habitat maps can give an impression of physically distinct areas that consistently occur together with particular species communities (Harris and Baker, 2012)
Summary
Submesoscale features like eddies, fronts or filaments structure the pelagic realm at spatial scales of order (1–10 km) (Lévy et al, 2012; Shulman et al, 2015; Buckingham et al, 2016) and temporal scales that range from several hours to a few days (Baschek and Maarten Molemaker, 2010; Thompson et al, 2016). Despite the growing awareness of the importance of spatial structure for ecology and management (Pittman et al, 2011; Wedding et al, 2011), there is still a lack of concepts and techniques applicable to characterize the spatial structure of the seascape in pelagic environments (Alvarez-Berastegui et al, 2014). Some machine learning techniques are designed to identify and characterize features in a “sea of data,” which makes it very promising to apply them in this challenging field of research
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