Plankton and particles from Seaexplorer glider and UVP6 across the Ligurian Front

We targeted a permanent mesoscale front in the Ligurian Sea (NW Mediterranean) that we repeatedly sampled between January and June 2021 using a SeaExplorer glider equipped with a UVP6, a versatile in situ imager. We aimed to resolve plankton and particle distribution during the spring bloom, to assess whether the front was a location of increased concentration of zooplankton, and if it constrained the distribution of particles. During the 5 months, the glider did more than 5,000 dives and the UVP6 collected 1.1 million images.

Images captured by the UVP6 during cruising (n = 785,405) were imported into the Morphocluster application to quickly detect large clusters of similar objects (e.g. marine snow aggregates). In a second step, images collected during back transects (n = 434,129, on which we focused our analyses) were imported onto the EcoTaxa web application with their Morphocluster label in order to be sorted at a finer scale into taxonomic or morphological groups (marine snow, artefact, badfocus, reflection or unidentifiable) with the help of a supervised machine learning algorithm. As sorting all 400k+ images would have required a multiple months effor, we instead decided to rely on the prediction of a Random Forest classifier fed with both handcrafted and deep features generated by a MobileNet V2 feature extractor previously finetuned on UVP6 data. We selected a RF classifier for the following reasons: RFs tend to produce good classification probability estimates (Niculescu-Mizil and Caruana 2005), they are faster to train than a full CNN stack and, when trained with deep features, they perform as well as a full CNN.

The dataset thus contains the following elements:

  • CTD data, some collected by the glider payload, and other collected by a SMRU
  • particles data, exported from Ecopart
  • plankton data, exported from Ecotaxa. Validated objects were either individually inspected by an operator, or batch validated in the morphocluster application. Predicted classifications were not reviewed.

We targeted a permanent mesoscale front in the Ligurian Sea (NW Mediterranean) that we repeatedly sampled between January and June 2021 using a SeaExplorer glider equipped with a UVP6, a versatile in situ imager. We aimed to resolve plankton and particle distribution during the spring bloom, to assess whether the front was a location of increased concentration of zooplankton, and if it constrained the distribution of particles. During the 5 months, the glider did more than 5,000 dives and the UVP6 collected 1.1 million images.

Images captured by the UVP6 during cruising (n = 785,405) were imported into the Morphocluster application to quickly detect large clusters of similar objects (e.g. marine snow aggregates). In a second step, images collected during back transects (n = 434,129, on which we focused our analyses) were imported onto the EcoTaxa web application with their Morphocluster label in order to be sorted at a finer scale into taxonomic or morphological groups (marine snow, artefact, badfocus, reflection or unidentifiable) with the help of a supervised machine learning algorithm. As sorting all 400k+ images would have required a multiple months effor, we instead decided to rely on the prediction of a Random Forest classifier fed with both handcrafted and deep features generated by a MobileNet V2 feature extractor previously finetuned on UVP6 data. We selected a RF classifier for the following reasons: RFs tend to produce good classification probability estimates (Niculescu-Mizil and Caruana 2005), they are faster to train than a full CNN stack and, when trained with deep features, they perform as well as a full CNN.

The dataset thus contains the following elements:

  • CTD data, some collected by the glider payload, and other collected by a SMRU
  • particles data, exported from Ecopart
  • plankton data, exported from Ecotaxa. Validated objects were either individually inspected by an operator, or batch validated in the morphocluster application. Predicted classifications were not reviewed.

Disciplines

Biological oceanography, Physical oceanography

Keywords

in situ imaging, glider, plankton, particles, spring bloom

Location

43.6597N, 43.36335S, 7.233087E, 7.92215W

Data

FileSizeFormatProcessingAccess
CTD data, either recorded by the payload or by the SMRU
312 MoTEXTRaw data
Particles data on 5 meter bins, exported from ecopart
80 MoTSVQuality controlled data
Plankton data as individual observations
93 MoTSVRaw data
How to cite
Panaïotis Thelma, Poteau Antoine, Diamond Riquier Émilie, Catalano Camille, Courchet Lucas, Motreuil Solène, Coppola Laurent, Picheral Marc, Irisson Jean-Olivier (2023). Plankton and particles from Seaexplorer glider and UVP6 across the Ligurian Front. SEANOE. https://doi.org/10.17882/95806

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