1. MISSION ATLANTIC

The MISSION ATLANTIC project is an EU-funded initiative that focuses on understanding the impacts of climate change and human activities on these ecosystems. The project aims to map and assess the current and future status of Atlantic marine ecosystems, develop tools for sustainable management, and support ecosystem-based governance to ensure the resilience and sustainable use of ocean resources. The project brings together experts from 33 partner organizations across 14 countries, including Europe, Africa, North, and South America.

MISSION ATLANTIC includes ten work packages. The present published dataset is included in WP3, which focuses on mapping the pelagic ecosystems, resources, and pressures in the Atlantic Ocean. This WP aims to collect extensive spatial and temporal data to create 3D maps of the water column, identify key vertical ecosystem domains, and assess the pressures from climate change and human activities. More specifically, the dataset corresponds to the fishing intensity presented in the Deliverable 3.2, which integrates data from various sources to map the distribution and dynamics of present ecosystem pressures over time, providing crucial insights for sustainable management strategies.

1. Data description

2.1. Data Source

Fishing intensity estimates from the Global Fishing Watch initiative (GFW) (Kroodsma et al. 2018), who applies machine learning algorithms to data from Automatic Identification Systems (AIS), Vessel Monitoring Systems (VMS), and vessel registries, has been used for the year 2020. This machine learning approach has been able to distinguish between fishing and routing activity of individual vessels, while using pattern recognition to differentiate seven main fishing gear types at the Atlantic Ocean scale (Taconet et al., 2019). The seven main fishing vessel types considered are: trawlers, purse seiners, drifting longliners, set gillnets, squid jiggers, pots and traps, and other. In this work we have aggregated these into pelagic, seabed and passive fishing activities to align with our grouping of ecosystem components.

The GFW data has some limitations:

AIS is only required for large vessels. The International Maritime Organization requires AIS use for all vessels of 300 gross tonnage and upward, although some jurisdictions mandate its use in smaller vessels. For example, within the European Union it is required for fishing vessels at least 15m in length. This means that in some areas the fishing intensity estimates will not include the activity of small vessels operating near shore.

AIS can be intentionally turned off, for example, when vessels carry out illegal fishing activities (Kurekin et al. 2019).

In the GFW dataset, vessels classified as trawlers include both pelagic and bottom trawlers. As trawlers are included in the bottom fishing category, it is highly likely that the data overestimates the effort on the seafloor and underestimates it on the water column.

2.2. Data Processing

1. Data download from the GFW portal.

2. Using R:

Add daily files and aggregate fishing hours by fishing gear and coordinates:

library(data.table)## Load data fileIdx = list.files(".../fleet-daily-csvs-100-v2-2020/", full.names = T)

Loop

colsIdx = c("geartype", "hours", "fishing_hours", "x", "y")

lapply(fileIdx, function(xx) { out = data.table (x = NA_real_, y = NA_real_, geartype = NA_character_) tmp = fread(xx) tmp[, ":=" (y = floor(cell_ll_lat * 10L) / 10L, x = floor(cell_ll_lon * 10L) / 10L)] tmp = tmp[, ..colsIdx] h = tmp[, c(.N, lapply(.SD, sum, na.rm = T)), by = .(x, y, geartype)] outh = data.table::merge.data.table(out, h, by = c("x", "y", "geartype"), all=TRUE) fwrite(outh, ".../GFW_2020_0.1_degrees_and_gear_all.csv", nThread = 14, append = T) })

Group fishing gears into main fishing groups:

library(dplyr)library(tidyr)## Load data fishing <- read.csv(".../GFW_2020_0.1_degrees_and_gear_all.csv", sep=",", dec=".", header=T, stringsAsFactors = FALSE)

Grouping fishing gears (fishing, pelagic, bottom, passive)

unique(fishing$geartype)

fishing$group <- NA fishing$group[which(fishing$geartype == "fishing")] = "fishing" # Unknown

fishing$group[fishing$geartype %in% c("trollers", "squid_jigger", "pole_and_line", "purse_seines", "tuna_purse_seines", "seiners", "other_purse_seines", "other_seines", "set_longlines", "drifting_longlines")] <- "pelagic"

fishing$group[fishing$geartype %in% c("trawlers", "dredge_fishing")] <- "bottom"

fishing$group[fishing$geartype %in% c("set_gillnets", "fixed_gear", "pots_and_traps")] <- "passive"

Total fishing hours (by fishing and position)

fish_gr <- fishing %>% group_by(x, y, group) %>% summarise(gfishing_hours = sum(fishing_hours))

Pivot table in order to have fishing groups in columns. Each row corresponds to the coordinates of the left corner of the grid cell (0.1 decimal degrees):

Pivoting table (fishing groups in columns)

fish_gr3 <- fish_gr %>% pivot_wider(names_from = "group", values_from = "gfishing_hours", values_fill = 0)

Saving data (to import in PostgreSQL)

write.csv(fish_gr3, ".../fishing.csv"), row.names = FALSE)

Export the table in our PostGIS spatial database using QGis

1. Using PostgreSQL:

Create grid cell identifiers (gid):

-- Generating a gid ALTER TABLE public.fishing ADD COLUMN gid uuid PRIMARY KEY DEFAULT uuid_generate_v4();

Estimate the centroid of each grid cell:

-- Create columns ALTER TABLE public.fishing ADD COLUMN cen_lat float; ALTER TABLE public.fishing ADD COLUMN cen_lon float;

-- Calculate the grid centroid UPDATE public.fishing SET cen_lat = y + 0.05; UPDATE public.fishing SET cen_lon = x + 0.05;

Create the geometry column based on the estimated centroids to provide the spatial component:

-- (if necessary) SELECT AddGeometryColumn ('public','fishing','geom', 4326,'POINT',2); UPDATE public.fishing SET geom = ST_SetSRID(ST_MakePoint(cen_lon, cen_lat), 4326); ALTER TABLE public.fishing RENAME COLUMN geom TO geom_point;

Expand a bounding box in all directions from the centroid geometry to estimate the grid cell (from point to polygon):

-- Expand a bounding box in all directions from the centroid geometry SELECT AddGeometryColumn ('public','fishing','geom', 4326,'POLYGON', 2); UPDATE public.fishing SET geom = St_Expand(geom_point, 0.05);

-- Drop deprecated columns ALTER TABLE public.fishing DROP COLUMN geom_point; ALTER TABLE public.fishing DROP COLUMN cen_lat; ALTER TABLE public.fishing DROP COLUMN cen_lon;

-- Create a spatial index CREATE INDEX ON public.fishing USING gist (geom);

Estimate the fishing hours per square kilometre by fishing group in each grid cell:

-- Create columns to estimate fishing hours per km2 ALTER TABLE public.fishing ADD COLUMN pelagic_km numeric, ADD COLUMN bottom_km numeric, ADD COLUMN fishing_km numeric, ADD COLUMN passive_km numeric;

-- Estimate fishing hours per km2 UPDATE public.fishing SET pelagic_km = pelagic / (ST_Area(geom::geography)/1000000); UPDATE public.fishing SET bottom_km = bottom / (ST_Area(geom::geography)/1000000); UPDATE public.fishing SET fishing_km = fishing / (ST_Area(geom::geography)/1000000); UPDATE public.fishing SET passive_km = passive / (ST_Area(geom::geography)/1000000);

Select only the Atlantic Ocean area (we have used the boundaries of the Atlantic Ocean to select only the data that fall within it, joining both tables and using ST_Contains() function)

2.3. Data Output

The Fishing_Intensity_Mission_Atlantic table corresponds to fishing hours per square kilometre estimated by grid cell (0.1 degree) of the Atlantic Ocean in 2020, and spatially identified by geometry (Spatial Reference System 4326). The attributes associated are:

gid: grid cell identifier [data type: UUID]

name: name of the Atlantic Ocean area [data type: character]

pelagic_km: Pelagic fishing hours per square kilometre [data type: numeric]

bottom_km: Seabed fishing hours per square kilometre [data type: numeric]

fishing_km: Unknown fishing hours per square kilometre [data type: numeric]

passive_km: Passive fishing hours per square kilometre [data type: character]

geom: grid cell geometry (EPSG: 4326) [data type: geometry]

This dataset was compiled for the Regional Seabed Monitoring Plan (RSMP) baseline assessment reported in Cooper & Barry (2017).

The dataset comprises of 33,198 macrofaunal samples (83% with associated data on sediment particle size composition) covering large parts of the UK continental shelf. Whilst most samples come from existing datasets, also included are 2,500 new samples collected specifically for the purpose of this study. These new samples were collected during 2014-2016 from the main English aggregate dredging regions (Humber, Anglian, Thames, Eastern English Channel and South Coast) and at four individual, isolated extraction sites where the RSMP methodology is also being adopted (e.g. Area 457, North-West dredging region; Area 392, North-West dredging region; Area 376, Bristol Channel dredging region; Goodwin Sands, English Channel). This work was funded by the aggregates industry, and carried out by contractors on their behalf. Samples were collected in accordance with a detailed protocols document which included control measures to ensure the quality of faunal and sediment sample processing. Additional samples were acquired to fill in gaps in spatial coverage and to provide a contemporary baseline for sediment composition.

Sources of existing data include both government and industry, with contributions from the marine aggregate dredging, offshore wind, oil and gas, nuclear and port and harbour sectors. Samples have been collected over a period of 48 years from 1969 to 2016, although the vast majority (96%) were acquired since 2000. Samples have been collected during every month of the year, although there is a clear peak during summer months when weather conditions are generally more favourable for fieldwork.

The DOI includes multiple files for use with the R script that accompanies the paper: Cooper, K. M. & Barry, J. A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed. Scientific Reports 7, doi: 10.1038/s41598-017-11377-9 (2017). Files include:

1. C5922 FINAL SCRIPTV91.R
2. C5922DATASET13022017REDACTED.csv (Raw data)*
3. Dataset description.xlsx (Description of data in C5922DATASET13022017.csv)
4. PARTBAGG05022017.csv (Faunal Aggregation data)
5. EUROPE.shp (European Coastline)
6. EuropeLiteScoWal.shp (European Coastline with UK boundaries)
7. Aggregates_Licence_20151112.shp (Aggregates Licensed extraction areas)
8. Aggregates_Application_20150813.shp (Aggregates Application areas)
9. HUMBERLICANDAPP.shp (Licensed Extraction and Application Areas - Humber)
10. H_SIZ_PSD_POLYGONS_UNION_2014.shp (Humber SIZs)
11. H_492_PIZ_APP.shp (Area 492 Application Area)
12. ANGLIANLICANDAPP.shp (Licensed Extraction and Application Areas - Anglian)
13. A_SIZ_PSD_POLYGONS_UNION.shp (Anglian SIZs)
14. THAMESLICANDAPP.shp (Licensed Extraction and Application Areas - Thames)
15. T_SIZ_PSD_POLYGONS_UNION_REV_2014.shp (Thames SIZs)
16. T_501_1_2_SIZ_PSD.shp (Area 501 1/2 SIZ)
17. EECLICANDAPP.shp (Licensed Extraction and Application Areas-East Channel)
18. EC_SIZ_PSD_POLYGONS_UNION_REV.shp (East Channel SIZs)
19. SCOASTLICANDAPP.shp (Licensed Extraction and Application Areas - South Coast)
20. SC_SIZ_PSD_POLYGONS_UNION.shp (South Coast SIZs)
21. BRISTOLCHANNELLICANDAPP.shp (Licensed Extraction and Application Areas - Bristol Channel)
22. BC_SIZ2.shp (Bristol Channel/Severn Estuary SIZs)
23. NORTHWESTLICANDAPP.shp(Licensed Extraction and Application Areas - North West)
24. NW_392_SIZ_PSD_LICENCE_EXISTING.shp (Area 392 SIZ)
25. AREA_457_PSD.shp (Area 457 SIZ)
26. GOODWIN LICENCE FINAL POLYGON.shp (Goodwin Sands Extraction area)
27. GoodwinSIZ.shp (Goodwin Sands SIZ)
28. DEFRADEMKC8.shp (Seabed bathymetry)

*At the request of data owners, macrofaunal abundance and sediment particle size data have been redacted from 13 of the 777 surveys (1.7%) in the dataset. Note that metadata and derived variables are still included. Surveys with redacted data include:

SurveyName

1. TRIKNOOWF2008,
2. EAOWF (Owner: East Anglia Offshore Wind Limited),
3. Wight Barfleur_cSAC_infauna,
4. MPAFORTH2011,
5. Hinkely point 108 benthos survey (BEEMS-WP2),
6. Hinkely point 208 benthos survey (BEEMS-WP2),
7. Hinkely point 408 benthos survey (BEEMS-WP2),
8. Hinkely point 308 benthos survey (BEEMS-WP2),
9. BEEMS WP2 Hinkley Point Q2 2009,
10. BEEMS WP5 Hinkley Point Infauna,
11. Hinkley Point 510 benthic survey (WP2-BEEMS),
12. Hinkley Point benthos survey June 2011 (BEEMS-WP2),
13. Hinkley Point benthos survey Feb 2010 (BEEMS-WP2)

Cefas will only make redacted data available where the data requester can provide written permission from the relevant data owner(s) - see below. Note that it is the responsibility of the data requester to seek permission from the relevant data owners.

Data owners for the redacted surveys listed above are:

1. Triton Knoll Offshore Wind Farm Limited
2. East Anglia Offshore Wind Limited
3. Joint Nature Conservation Committee (JNCC)
4. Joint Nature Conservation Committee (JNCC)
5. EDF Energy
6. EDF Energy
7. EDF Energy
8. EDF Energy
9. EDF Energy
10. EDF Energy
11. EDF Energy
12. EDF Energy
13. EDF Energy

Description of the C5922DATASET13022017.csv/ C5922DATASET13022017REDACTED.csv (Raw data)

A variety of gear types have been used for sample collection including grabs (0.1m2 Hamon, 0.2m2 Hamon, 0.1m2 Day, 0.1m2 Van Veen and 0.1m2 Smith McIntrye) and cores. Of these various devices, 93% of samples were acquired using either a 0.1m2 Hamon grab or a 0.1m2 Day grab. Sieve sizes used in sample processing include 1mm and 0.5mm, reflecting the conventional preference for 1mm offshore and 0.5mm inshore (see Figure 2). Of the samples collected using either a 0.1m2 Hamon grab or a 0.1m2 Day grab, 88% were processed using a 1mm sieve.

Taxon names were standardised according to the WoRMS (World Register of Marine Species) list using the Taxon Match Tool (http://www.marinespecies.org/aphia.php?p=match). Of the initial 13,449 taxon names, only 4,248 remained after correction. The output from this tool also provides taxonomic aggregation information, allowing data to be analysed at different taxonomic levels - from species to phyla. The final dataset comprises of a single sheet comma-separated values (.csv) file. Colonials accounted for less than 20% of the total number of taxa and, where present, were given a value of 1 in the dataset. This component of the fauna was missing from 325 out of the 777 surveys, reflecting either a true absence, or simply that colonial taxa were ignored by the analyst. Sediment particle size data were provided as percentage weight by sieve mesh size, with the dataset including 99 different sieve sizes. Sediment samples have been processed using sieve, and a combination of sieve and laser diffraction techniques. Key metadata fields include: Sample coordinates (Latitude & Longitude), Survey Name, Gear, Date, Grab Sample Volume (litres) and Water Depth (m). A number of additional explanatory variables are also provided (salinity, temperature, chlorophyll a, Suspended particulate matter, Water depth, Wave Orbital Velocity, Average Current, Bed Stress). In total, the dataset dimensions are 33,198 rows (samples) x 13,588 columns (variables/factors), yielding a matrix of 451,094,424 individual data values.

NCEI is the long-term archive for all NOAA Deep-ocean Assessment and Reporting of Tsunamis (DART) ocean bottom pressure data. Ocean bottom pressure data also undergo quality control and harmonic analysis at NCEI. The raw data and products are discoverable via the Map, Timelines, and THREDDS Data Server links. Data are provided as netCDF and as gzipped comma-separated-values (CSV). Background InformationIn the 1980s, NOAA's Pacific Marine Environmental Laboratory (PMEL) developed deep ocean tsunameters for the early detection, measurement, and real-time reporting of tsunamis in the open ocean. The PMEL's Project Deep-ocean Assessment and Reporting of Tsunamis (DART®) developed the tsunameters. A DART® system consists of a seafloor bottom pressure recorder (BPR) capable of detecting tsunamis as small as 1 centimeter, and a moored surface buoy for real-time communications. In 2003, operational responsibility of DART® transitioned from PMEL to the National Data Buoy Center (NDBC). There are currently 39 U.S. owned and operated DART® buoys installed throughout the Pacific and Atlantic oceans. This completes the current requirements for the DART® array. NOAA has installed DART® systems in the Indian Ocean in partnership with several international organizations (Owned Operated DART Buoys Data Available at NDBC). Upon recovery from the seafloor BPR, 15-second-resolution data undergo quality control and harmonic analysis at NOAA NCEI. Please contact haz.info@noaa.gov if you have questions. More information about DART Ocean Bottom Pressure Data at NCEI