DOI: 10.5061/dryad.34tmpg4s4
title: Data from: Identification of plankton habitats in the North Sea

description: The definition of an ecological niche makes it possible to anticipate the
 responses of a species to changing environmental conditions. Broad
 tolerance limits and a paucity of readily observable niches in the pelagic
 zone make it difficult to anticipate responses of the plankton community
 related to anthropogenic or environmental changes. Plankton distributions
 are closely linked to climate change and shape the seascape for higher
 trophic levels, so monitoring plankton distributions and defining
 ecological niches will help to understand and predict ecosystem responses.
 Here we apply a machine learning autoencoder and a density‐based
 clustering algorithm to high‐frequency datasets sampled with a ROTV
 Triaxus in the North Sea. The results indicate that in this highly dynamic
 environment, local hydrography prevents niche‐based separation of plankton
 species at the sub‐mesoscale, despite the availability of different
 habitats. Plankton patches were associated with naturally occurring
 frontal systems and anthropogenically induced upwelling‐downwelling
 dipoles in the vicinity of offshore wind farms (OWFs).
description: Physical and biological oceanographic measurements were recorded
 on different North Sea surveys with the RV Heincke (Knust <em>et
 al.</em>, 2017) using a MacArtney TRIAXUS ROTV, complemented by a
 Video Plankton Recorder (VPR). The TRIAXUS was towed behind the research
 vessel in an undulating fashion between the sea surface and
 bottom. Data was processed using a machine learning
 Autoencoder and a density-based clustering algorythm HDBSCAN. Analysis and
 data handling were handled with the statistical software R4.4.0 and Python
 3.7. A detailed description can be found in
 'Identification of plankton habitats in the North Sea' (the DOI
 can be found at 'Related works'). Knust, R.,
 Nixdorf, U. and Hirsekorn, M. 2017 ‘Research vessel HEINCKE operated by
 the alfred-wegener-institute’, <em>Journal of large-scale research
 facilities JLSRF</em>, 3, pp. A120–A120. doi: 10.17815/jlsrf-3-164.
description: # Identification of plankton habitats in the North Sea
 \[[https://doi.org/10.5061/dryad.34tmpg4s4\](Dataset](https://doi.org/10.5061/dryad.34tmpg4s4]\(Dataset) DOI link) This dataset includes environmental variables sampled using a ROTV TRIAXUS during various cruises in multiple years. The TRIAXUS is towed behind a research vessel but can move through the water column. Next to sensors for temperature, oxygen, salinity, and chlorophyll it was equipped with a Video Plankton Recorder (VPR). The plankton abundances presented here are based on the automatic classification of the VPR images using the model presented in Plonus et al 2021 [[https://doi.org/10.1002/lom3.10413](https://doi.org/10.1002/lom3.10413)]. The data is provided as .rda-Files and can easily be loaded into the statistical software R: load('filename.rda') #assuming the file 'filename' is in the current working directory of your R session, use getwd() to print the current working directory to your R console. ## Description of the data and file structure ### File: OWF\_positions.rda **Description:** The file contains the postional data of wind turbines in two Offshore Wind Parks BARD Offshore 1 (BARD) and Global Tech I (GTI). *Variables:* WEA.positionsLAT: Latitudinal Coordinate. WEA.positionsLON: Longitudinal Coordinate. ### File: history\_AE\_190922\_4nodes.csv **Description:** Documentation of the trainings process. Generated using AE_euclidean.py. *Variables:* Epoch: Each Epoch represents one iteration during which all the training data is presented to the model once in machine learning. RMSE: The Root Mean Squared Error between the original Input and the model generated output during the training. Further information can be found in [[https://doi.org/10.3389/fmars.2021.754375](https://doi.org/10.3389/fmars.2021.754375)]. Val_RMSE: The RMSE during the validation process that follows the training phase in each Epoch. LR: The current learning rate used to adjust the model weights during this specific Epoch. ### File: trainAE4.rda **Description:** Contains the data used to train the Autoencoder. The original data of multiple transects was combined in one file for reasons of accessability during training. Physical variables (temperature - chlorophyll) and plankton abundances (appendicularia - marinesnow) were calculated using Ocean Data View with the embedded spatial interpolation software DIVA. *Variables:* Section_Distance_grid_km [km]: The distance along the transect. Depth_grid_m [m]: The position in the water column. day_in_seconds [s]: The time of day in seconds since midnight. temperature [°C]: Ambient water temperature. oxygen [mol/l]: Ambient oxygen concentration. salinity [psu]: Ambient salinity. density_kg_m [kg/qm]: Ambient density calculated using temperature and salinity in ODV. chlorophyll [rfu]: Ambient chlorophyll concentration. Lat_N [°N]: Current position of the ship. Lon_E [°E]: Current position of the ship. appendicularia [N/l]: Abundance of appendicularia estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. copepoda [N/l]: Abundance of copepoda estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. dinoflagelattes [N/l]: Abundance of dinoflagellates estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. pluteus [N/l]: Abundance of pluteus larvae estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. marinesnow [N/l]: Abundance of marine snow particles estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. ### Folder: checkpoint **Description:** This folder was created by a tensorflow manager and contains the weights of the last 3 Epochs before the best Val_RMSE was reached. Do not change any files since they were created automatically (see: AE_euclidean.py). ### File: sobol\_HE446\_H06T1.txt (in Folder: sensitivity) **Description:** All files in the sensitivity folder are identic and contain the results of a sobol sensitivity analysis for the respective transect indicated by the file name. Generated with sensitivity.py. *Variables:* ST: The total Sobol sensitivity index includes the sensitivity of both first order effects as well as the sensitivity due to interactions (covariance) between a given parameter Qi and all other parameters. ST_conf: The confidence interval for ST. S1: The first order Sobol sensitivity index tells us the expected reduction in the variance of the model when we fix parameter Qi. The sum of the first order Sobol sensitivity indices can not exceed one. S1_conf: The confidence interval for S1. dim: The output dimension of the Autoencoder (d1-d4). ### File: resultsHEshar.rda (in Folder: shar) **Description:** Contains the results of the species-habitat-association analysis performed using the r-Package 'shar'. Generated with shar_analysis.R. *Variables:* cruise: Indicates cruise number (HEXXX), houl number (HXX) and transect (TX) within the houl. species: Indicates the analysed plankton group. habitat: The macro-habitat estimated for this specific cell. breaks: NA. count: The actual abundance of the respective group in this specific habitat. lo: The lower threshold below which an avoidance of the habitat by the plankton group is detected. hi: The upper threshold above which a cummulation within the habitat by the plankton group is detected. significance: The nature of the habitat-species-associations. Either 'negative' (count &lt; lo), 'positive' (count &gt; hi), or 'n.s.' (lo &lt; count &lt; hi). ### File: sharHE.rda (in Folder: shar) **Description:** The randomly generated maps for the species-habitat-associations analysed using 'shar'. Since generating 100 randomized maps was computationally expensive we saved the once generated maps to save time and improve the repeatability and reproducibility of the analysis. Generated with shar_analysis.R. *Variables:* file: Indicates cruise number (HEXXX), houl number (HXX) and transect (TX) within the houl. data: The original habitat map *Columns:* Depth_grid_m [m]: The depth in the water column. Section_Distance_grid_km [km]: The distance along the trnasect. Appendicularia [N/l]: Abundance of appendicularia estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Copepods [N/l]: Abundance of copepoda estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Pluteus [N/l]: Abundance of pluteus larvae estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. my_mh: The macro-habitat estimated for this specific grid cell. random_maps: The 100 randomly generated habitat maps used to perform the shar species-habitat-association analysis. Created using the 'randomize_raster' function. ### File: HE466\_H05T1.rda (in Folder: HE466) **Description:** This file contains all data accumulated for transect 1 (T1) of haul 5 (H05) from our cruise in June 2016 (HE466). The other files in the same folder contain the data for the respective transects while the other similar folders contain the data for the respective cruises. *Variables:* Section_Distance_grid_km [km]: Distance alonge the transect. Depth_grid_m [m]: Position in the water column. day_in_seconds [s]: The time of day in seconds since midnight. temperature_degc [°C]: Ambient water temperature. oxygen_mol_l [mol/l]: Ambient oxygen concentration. salinity_psu [psu]: Ambient salinity. density_kg_m [kg/qm]: Ambient density calculated using temperature and salinity in ODV. chlorophyll_a_rfu [rfu]: Ambient chlorophyll concentration. Lat_N [°N]: Current position of the ship. Lon_E [°E]: Current position of the ship. Appendicularia [N/l]: Abundance of appendicularia estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Copepods [N/l]: Abundance of copepoda estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Dinoflagelattes [N/l]: Abundance of dinoflagellates estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Pluteus [N/l]: Abundance of pluteus larvae estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. Snow [N/l]: Abundance of marine snow particles estimated using the classifications of a machine learning classifier using the plankton images derived by the VPR mounted on the TRIAXUS. euclidean: The euclidean distance calculated between the first row (Appendicularia - Snow) and all following rows to indicate the similarity of the plankton community between the respective grid cells. cluster: The cluster assigned to the grid cell after both Autoencoder and HDBScan were applied to the data. -1 indicates that the cell belongs to no specific cluster. probabilities [0-1]: The probability for this grid cell to belong to the assigned cluster, provided by the HDBScan algorithm. d1: The first output of the final encoding layer. d2: The second output of the final encoding layer. d3: The third output of the final encoding layer. d4: The fourth output of the final encoding layer. my_mh: The final (manuall) classification into across-transect consistent macro-habitats. ### File: grid\_HE466\_H05T1.rda (in Folder: HE466) **Description:** This is an exemplary file containing only the original data after it was exported as a grid from Ocean Data View (ODV). All variables are identical to the ones explained in the previous section. *Variables:* Section_Distance_grid_km, Depth_grid_m, day_in_seconds, temperature_degc, oxygen_mol_l, salinity_psu, density_kg_m, chlorophyll_a_rfu, Lat_N, Lon_E, Appendicularia, Copepods, Pluteus, Snow, Dinoflagelattes **Note:** **The content of the other folders is similar to HE466** ## Code/Software All files necessary to repeat the analysis are provided in scripts.zip [[https://doi.org/10.5281/zenodo.13375879](https://doi.org/10.5281/zenodo.13375879)]. The folder also includes a README which explains the tasks of the single files.