Journal of Sea Research

CORRIGENDUM. In version 1 of June 2019 an error has been made. The class boundary between shallow sublittoral and low intertidal was not set to 4% mean exposure but to 0.001%. This resulted in a too low cover of sublitoral areas. In Version 2 this is corrected. The data file is a zipped file "EctpWZ3L_gpkg.zip" containing a GeoPackage, holding the geographical data (polygons etc.): i. EcotopeWS3_mp = polygon geodata of ecotope map ii. bivbeds_focc = geodata for eco-element bivalve beds (polygon) iii. eelgrass_presence = geodata for eco-element seagrass aka eelgrass presence (polygon) iv. EcotopeWS3CodeInterpretation, table explaining how fields in ecotope map should be interpreted v. ZESsmpleCode2Legend, table expanding ZESsimple from code to full legend text. and includes: 2) a set of QML-files (symbology definition files for QGIS), 3) a set of SLD-files (symbology layer definition files for GeoServer), 4) three PNG-files to provide the different textures/patterns used in the symbolising the sediment types.

% Matlab script % Developed by Julio Antonio Lara-Hernandez (julioalh@atmosfera.unam.mx) % and Jorge Zavala-Hidalgo (jzavala@atmosfera.unam.mx). % Ocean-atmosphere interaction group, Center of Atmospheric Sciences, % National Autonomous University of Mexico (UNAM), Mexico. % % This script shows that a Turbulent-Difussion velocity (v') can be % equivalently constructed from random numbers of (1) uniform or (2) normal % distributions. It also shows that by setting a = b/sqrt(dt), the effect of % Turbulent-Difussion remains the same regardless the selected time-step (dt): % % (1) v' = Va * Ru % Where: v' = Turbulent-Difussion velocity % Va = Advective velocity (e.g. velocity of an ocean current) % Ru = Random number of uniform distribution from -a to a % a = b/sqrt(dt) % b = User-defined parameter modulating the degree of Turbulen-Difussion % dt = Time-step defined by the user % % (2) v' = Rn % Where: Rn = Random number of normal (Gaussian) distribution with a mean % equal to zero and a standard deviation std_Rn = Va*a/sqrt(3) % % In the resulting graphs after running the script, the central curve % indicates the mean, the upper curve is the mean+std, and the lower curve % is the mean-std. The overlapping of the curves from both approaches % indicates that they are equivalent. Reproducing the same curves by % considering different time-steps shows that setting a equal to b/sqrt(dt) % works to achieve the same effect of Turbulent-Difussion regardless the % selected time-step.

The data were collected through inspection of ROV videos by Floor M.F. Driessen [1]. 1.†††††† Bureau Waardenburg, Varkensmarkt 9, Postbus 365, 4100 AJ Culemborg, The Netherlands. EXPLANATION OF DATASET The data are formatted in a single csv file. It contains 30 columns with 5587 rows, including title row. Each row represents a separate observation of species abundance according to the Braun-Blanquet scale. The dataset contains null values which are indicated with NA. The first 21 columns contain sample information. Columns 22-29 contain information about taxonomic grouping and Column 30 contains the Braun-Blanquet abundance values. Explanation per column (1-30): Observation_number: Unique identification code for each observation. Include_YES_NO: indication whether or not the observation was included in data analysis: YES =included, NO= not included. Country: Country of operation of the offshore gas or oil platform: NL = The Netherlands, DK = Denmark Company: Company operating the offshore gas or oil platform: Maersk Oil or Petrogas E&P LLC. Platform_name: name of the offshore gas or oil platform. Latitude: latitudinal coordinate of the location of the platform in decimal degrees, WGS84. Longitude: longitudinal coordinate of the location of the platform in decimal degrees, WGS84. Distance from Dutch shore: shortest distance from platform to the Dutch shoreline as the crow flies (in km) Distance from Danish shore: shortest distance from platform to the Danish shoreline as the crow flies (in km) Year_of_installation: Year of installation of the gas or oil platform (OSPAR production start) Depth_platform: depth of platform at lowest astronomical tide (LAT) in meters Video_Name: unique video name Video_Date: date on which the ROV video was taken Video_quality: video quality as assessed by the video observer and relative to available video material. 0 = no visibility/black screen, 9 = very good visibility, no limitations for video analysis, NA = unknown. Depth_ROVsample: depth recorded by ROV at which the video was taken. Depth_Range: depth class, e.g. depth between 0 and 5 m corresponds to a depth range of 1. Substrate_type: type of substrate on which observation was made: anode material, concrete, debris, pebbles, rockdump, sand, shells, steel, or unknown (when video quality was very low) Sample_Size: area surveyed in m2. Standard surveyed area is 1x1m, but in some cases the ROV took more detailed (0,25 and 0,5 m2) or less detailed (2 m2) footage. Phylum: phylum of the observed organism. NA= unknown Class: taxonomic class of the identified organism. NA= unknown Order: taxonomic order of the identified organism. NA= unknown Species/taxon: species name of the identified organism. NA= unknown Sp.Indet.: species name or the closest recognizable taxonomic grouping of unidentified species or unknown Braun_Blanquet_Abundance: abundance according to an adapted version of the Braun-Blanquet scale (Schutter et al. 2018)

Meta_data provides the meta data for all samples (Sample Ids) and the corresponding barcodes for the *.fastq.gz files. For 16S data universal 16S bacteria primers were used, and for COI data universal fish/invertebrate primers.

The sheet 'LaboExpt' contains raw data of laboratory experiment on feeding and growth of juveniles. The sheet 'FieldSurvey' contains data on temporal changes in density and temperature and on body size of juveniles. The sheet 'GrowthRate' contains data on growth estimation of field-collected juveniles from their otoliths and on the maximum growth in the laboratory experiment.

Supplemenary files for "Fitting Multiple Models to Multiple Data" Goncalo M. Marques, Dina Lika, Starrlight Augustine, Laure Pecquerie and Sebastiaan A.L.M. Kooijman. Journal of Sea Research, 2018 compatible with DEBtool_M v.2017 https://github.com/add-my-pet/DEBtool_M/releases/tag/v.DEB2017 and are most likely compatible with the latest version. To use these files, dowload the latest version of DEBtool_M from GitHub and set the matlab path to it. Keep these files together in a separate directory. Excecute the run_test file to obtain parameter estimates and view model prediction VS data. We refer the reader to the study to read about how the data are generated. There are four files: run_test - this is the executable file mydata_test pars_init_test predict_test To understand the set-up of the files we refer the reader to the open access publication "The AmP project: Comparing species on the basis of dynamic energy budget parameters" https://doi.org/10.1371/journal.pcbi.1006100 The user should also consult the mediawiki powered AmP estimation manual: http://www.debtheory.org/wiki/index.php?title=Add-my-Pet_manual which provides technical information on the files (run, mydata, predict, pars_init).

Nitocra_spinipes_abp.zip & Nitocra_spinipes_sbp.zip These zip files contain all files that were used for the parametrization of the DEB models abp and sbp in the Matlab package DEBtool. Nitocra_spinipes_length_measurements.text This file contains all measured body size data obtained in the growth experiment with Nitocra spinipes. Nitocra_spinipes_DEB_model.xlsx This file contains a hands-on implementation (Excel spreadsheet) of the DEB models abp and sbp parametrized for Nitocra spinipes. The state variables body length, reserve density, maturity, and cumulative energy invested in reproduction are computed from the parameters in an ODE approach.

Environmental variables at the sampling stations. The gray background indicates the stations located in the inner basin of the port

3 sheets

Assessment of ROV videos of scour protection in offshore wind farms North Sea