Northeast Atlantic Ocean, Loire River, DIVAnd 4D seasonal analysis of Water body phosphate 1950/2021 v2023 [time][lat][lon], 1985

Summary:
Northeast Atlantic Ocean - Loire River - DIVAnd 4D seasonal analysis of Water body phosphate 1950/2021 v2023. Seasonal climatology of Water body phosphate for Loire river for the period 1950-2021 and for the following seasons:
 - winter: January-March,
 - spring: April-June,
 - summer: July-September,
 - autumn: October-December.
Observation data span from 1950 to 2021.
Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0].
Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network.
Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 230000 (winter), 264000 (spring), 140000 (summer), 135000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no.
Units: umol/l.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Water_body_phosphate_deepest (Deepest values of Water body phosphate, umol/l)


Northeast Atlantic Ocean, Loire River, DIVAnd 4D seasonal analysis of Water body silicate 1950/2021 v2023 [time][depth][lat][lon], 1985

Summary:
Northeast Atlantic Ocean - Loire River - DIVAnd 4D seasonal analysis of Water body silicate 1950/2021 v2023. Seasonal climatology of Water body silicate for Loire river for the period 1950-2021 and for the following seasons:
 - winter: January-March,
 - spring: April-June,
 - summer: July-September,
 - autumn: October-December.
Observation data span from 1950 to 2021.
Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0].
Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network.
Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 133000 (winter), 180000 (spring), 150000 (summer), 210000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no.
Units: umol/l.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
Water_body_silicate_L1 (Water body silicate masked using relative error threshold 0.3, umol/l)
Water_body_silicate_L2 (Water body silicate masked using relative error threshold 0.5, umol/l)
Water_body_silicate_relerr (Relative error of Water body silicate, 1)

IFREMER, SISMER, Scientific Information Systems for the SEA


Northeast Atlantic Ocean, Loire River, DIVAnd 4D seasonal analysis of Water body silicate 1950/2021 v2023 [time][lat][lon], 1985

Summary:
Northeast Atlantic Ocean - Loire River - DIVAnd 4D seasonal analysis of Water body silicate 1950/2021 v2023. Seasonal climatology of Water body silicate for Loire river for the period 1950-2021 and for the following seasons:
 - winter: January-March,
 - spring: April-June,
 - summer: July-September,
 - autumn: October-December.
Observation data span from 1950 to 2021.
Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0].
Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network.
Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 133000 (winter), 180000 (spring), 150000 (summer), 210000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no.
Units: umol/l.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Water_body_silicate_deepest (Deepest values of Water body silicate, umol/l)


Phytoplankton alpha diversity in the greater Baltic Sea area

Summary:
This dataset compiles monthly Shannon diversity index (H') and species richness (n taxa) of phytoplankton data in the greater Baltic Sea area between years 2000-2021, calculated from abundance data originating from the Swedish National phytoplankton monitoring programme. Samples have been rarified before calculating alpha diversity measures, and nearby spatial data points (stations) have been clustered together

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
shannon (Shannon index)
richness (Species richness (n taxa))

Swedish Meteorological and Hydrological Institute


Potential biomass catches (tons/year) per surface unit (0.25º latitude x 0.25º longitude) based on 3-D probability of occurrence for the main commercial fish species of the Atlantic

Summary:
Potential catch value of each cell integrates the catches along the water column (from surface until 1000 m depth). See Valle et al. (2024) in Ecological Modelling 490:110632 (https://doi.org/10.1016/j.ecolmodel.2024.110632), for more details

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [aphiaid][latitude][longitude]):
potential_catches (Potential biomass catch, tons year-1)


Presence/Absence maps of phytoplankton in the Greater Baltic Sea

Summary:
The project aims to produce comprehensive data product of the occurence and absence of (phyto)plankton species. As a basis, data from EMODnet Biology are used. The selection of relevant datasets is optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurences from EMODnet Biology were complemenented with absence data assuming fixed species lists within each dataset and year. The products are presented as maps of the distribution of the 20 most common species of (phyto)plankton in the Greater Baltic Sea.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [aphiaid][latitude][longitude]):
probability_occurrence (Probability of occurrence of biological entity)


Presence/Absence maps of phytoplankton in the Greater North Sea

Summary:
The project aims to produce comprehensive data product of the occurence and absence of (phyto)plankton species. As a basis, data from EMODnet Biology are used. The selection of relevant datasets is optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurences from EMODnet Biology were complemenented with absence data assuming fixed species lists within each dataset and year. The products are presented as maps of the distribution of the 100 most common species of (phyto)plankton in the Greater North Sea. This product then is also used for interpolated maps, using the DIVA software.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [aphiaid][season][latitude][longitude]):
probability_occurrence (Probability of occurrence of biological entity)


Probability maps for different benthos species in the North Sea

Summary:
Probability of occurrence of different macroinvertebrate benthic species in the North Sea. This product was created using DIVAnd, an interpolation method that takes into account several environmental variables and physical coastlines.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [aphiaid][latitude][longitude]):
probability (Probability of occurrence of biological entity)
probability_error (Error on Probability of occurrence of biological entity)

GHER - University of Liege, Deltares, VLIZ


Probability of occurrence 3D map

Summary:
3-D habitat suitability maps (HSMS) or probability of occurrence maps for main commercial fish species, built using Shape-Constrained Generalized Additive Models (SC-GAMs)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [depth][aphiaid][latitude][longitude]):
occurrence_probability (Probability of occurrence of biological entity)


Spatio-Temporal Interpolation of Zooplankton Alpha Diversity in the Greater Baltic Sea Region

Summary:
Uses attributes recommended by https://cfconventions.org

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
shannon (Shannon index)


Summary presence/absence maps of macro-endobenthos in the greater North Sea, based on nearly 100,000 samples from 65 assembled monitoring data sets

Summary:
The large databases of EMODNET Biology only store confirmed presences of species. However, when mapping species distribution, it is also important where the species did not occur: there is at least as much information in absences as in presences. Inferring absences from presence-only databases is difficult and always involves some guesswork. In this product we have used as much meta-information as possible to guide us in inferring absences.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [aphiaid][latitude][longitude]):
probability_occurrence (Probability of occurrence of biological entity)


Temporal turnover of macrobenthos in European seas

Summary:
Eight measures of changes in species compositions of European macrobenthic communities between different time periods based on presence / absence in  on a 1 degree grid

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
beta_total_pre1990_v_1990s (all_diversity_measures.nc beta_total_pre1990_v_1990s)
beta_total_pre1990_v_2000_2004 (all_diversity_measures.nc beta_total_pre1990_v_2000_2004)
beta_total_pre1990_v_2005_2009 (all_diversity_measures.nc beta_total_pre1990_v_2005_2009)
beta_total_pre1990_v_2012_2014 (all_diversity_measures.nc beta_total_pre1990_v_2012_2014)
beta_total_pre1990_v_post2015 (all_diversity_measures.nc beta_total_pre1990_v_post2015)
beta_total_1990s_v_2000_2004 (all_diversity_measures.nc beta_total_1990s_v_2000_2004)
beta_total_1990s_v_2005_2009 (all_diversity_measures.nc beta_total_1990s_v_2005_2009)
beta_total_1990s_v_2012_2014 (all_diversity_measures.nc beta_total_1990s_v_2012_2014)
beta_total_1990s_v_post2015 (all_diversity_measures.nc beta_total_1990s_v_post2015)
beta_total_2000_2004_v_2005_2009 (all_diversity_measures.nc beta_total_2000_2004_v_2005_2009)
beta_total_2000_2004_v_2012_2014 (all_diversity_measures.nc beta_total_2000_2004_v_2012_2014)
beta_total_2000_2004_v_post2015 (all_diversity_measures.nc beta_total_2000_2004_v_post2015)
beta_total_2005_2009_v_2012_2014 (all_diversity_measures.nc beta_total_2005_2009_v_2012_2014)
beta_total_2005_2009_v_post2015 (all_diversity_measures.nc beta_total_2005_2009_v_post2015)
beta_total_2012_2014_v_post2015 (all_diversity_measures.nc beta_total_2012_2014_v_post2015)
beta_repl_pre1990_v_1990s (all_diversity_measures.nc beta_repl_pre1990_v_1990s)
beta_repl_pre1990_v_2000_2004 (all_diversity_measures.nc beta_repl_pre1990_v_2000_2004)
beta_repl_pre1990_v_2005_2009 (all_diversity_measures.nc beta_repl_pre1990_v_2005_2009)
beta_repl_pre1990_v_2012_2014 (all_diversity_measures.nc beta_repl_pre1990_v_2012_2014)
beta_repl_pre1990_v_post2015 (all_diversity_measures.nc beta_repl_pre1990_v_post2015)
beta_repl_1990s_v_2000_2004 (all_diversity_measures.nc beta_repl_1990s_v_2000_2004)
beta_repl_1990s_v_2005_2009 (all_diversity_measures.nc beta_repl_1990s_v_2005_2009)
beta_repl_1990s_v_2012_2014 (all_diversity_measures.nc beta_repl_1990s_v_2012_2014)
beta_repl_1990s_v_post2015 (all_diversity_measures.nc beta_repl_1990s_v_post2015)
beta_repl_2000_2004_v_2005_2009 (all_diversity_measures.nc beta_repl_2000_2004_v_2005_2009)
beta_repl_2000_2004_v_2012_2014 (all_diversity_measures.nc beta_repl_2000_2004_v_2012_2014)
... (94 more variables)


The EMODnet Grid

Summary:
The data in the EMODnet Grid should not be used for navigation or any purpose relating to safety at sea.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
elevation (Elevation relative to sea level, m)
value_count (Number of values used to compute the resulting elevation over the cell)
cdi_index (CDI index of this cell. matching CDI information is retrieved from cdi_reference variable)
interpolation_flag (Indicator of cell processed as extrapolation of the neighbouring cells (absence of real soundings data).)
elevation_max (max elevation value over a cell, relative to Lowest Astronomical Tide, m)
elevation_min (min elevation value over a cell, relative to Lowest Astronomical Tide, m)
stdev (Standard Deviation of elevation data over cell, m)

On behalf of the EMODnet project, http://www.emodnet-bathymetry.eu/.


The EMODnet Grid


The EMODnet Grid

Summary:
The data in the EMODnet Grid should not be used for navigation or any purpose relating to safety at sea.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
elevation (Elevation relative to Lowest Astronomical Tide datum, m)
value_count (Number of values used to compute the resulting elevation over the cell)
cdi_index (CDI index of this cell. matching CDI information is retrieved from cdi_reference variable)
interpolation_flag (Indicator of cell processed as extrapolation of the neighbouring cells (absence of real soundings data))
elevation_max (max elevation value over a cell, relative to Lowest Astronomical Tide, m)
elevation_min (min elevation value over a cell, relative to Lowest Astronomical Tide, m)
stdev (Standard Deviation of elevation data over cell, m)

On behalf of the EMODnet project, https://emodnet.ec.europa.eu


Vessel Density

Summary:
Vessel Density. Cogea data from a local source.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
vd (Vessel density, seconds)


Vessel Density

Summary:
Vessel Density. Flanders Marine Institute data from a local source.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
vd (Vessel density, seconds)


Vessel traffic density, 2019, All


Vessel traffic density, 2019, Cargo


Vessel traffic density, 2019, Dredging or underwater ops


Vessel traffic density, 2019, Fishing


Vessel traffic density, 2019, High Speed craft


Vessel traffic density, 2019, Miliary and law enforcement


Vessel traffic density, 2019, Other


Vessel traffic density, 2019, Passenger


Vessel traffic density, 2019, Pleasure craft


Vessel traffic density, 2019, Sailing


Vessel traffic density, 2019, Service


Vessel traffic density, 2019, Tanker


Vessel traffic density, 2019, Tug and Towing


Vessel traffic density, 2019, Unknown