Fishing vessel presence, measured in hours per square km. Each asset is the vessel presence for a given flag state and day, with one band for the presence of each gear type. See sample Earth Engine scripts. Also see the main GFW site for program information, fully interactive visualization maps, …

Overview

This dataset contains version 3.0 (March 2025 release) of the Global Fishing Watch apparent fishing effort dataset. Data is available for 2012-2024 and based on positions of >190,000 unique automatic identification system (AIS) devices on fishing vessels, of which up to ~96,000 are active in a given year. Fishing vessels are identified via a machine learning model, vessel registry databases, and manual review by GFW and regional experts. Vessel time is measured in hours, calculated by assigning to each AIS position the amount of time elapsed since the previous AIS position of the vessel. The time is counted as apparent fishing hours if the GFW fishing detection model - a neural network machine learning model - determines the vessel is engaged in fishing behavior during that AIS position.

Data are spatially binned into grid cells that measure 0.01 or 0.1 degrees on a side; the coordinates defining each cell are provided in decimal degrees (WGS84) and correspond to the lower-left corner. Data are available in the following formats:

1. Daily apparent fishing hours by flag state and gear type at 100th degree resolution
2. Monthly apparent fishing hours by flag state and gear type at 10th degree resolution
3. Daily apparent fishing hours by MMSI at 10th degree resolution

The fishing effort dataset is accompanied by a table of vessel information (e.g. gear type, flag state, dimensions).

File structure

Fishing effort and vessel presence data are available as .csv files in daily formats. Files for each year are stored in separate .zip files. A README.txt and schema.json file is provided for each dataset version and contains the table schema and additional information. There is also a README-known-issues-v3.txt file outlining some of the known issues with the version 3 release.

Files are names according to the following convention:

• Daily file format:

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[year].zip

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[date].csv

• Monthly file format:

• fleet-monthly-csvs-10-v3-[year].zip

• fleet-monthly-csvs-10-v3-[date].csv

• Fishing vessel format: fishing-vessels-v3.csv

• README file format: README-[fleet/mmsi/fishing-vessels/known-issues]-v3.txt

File identifiers:

• [fleet/mmsi]: Data by fleet (flag and geartype) or by MMSI

• [100/10]: 100th or 10th degree resolution

• [year]: Year of data included in .zip file

• [date]: Date of data included in .csv files. For monthly data, [date]corresponds to the first date of the month

Examples: fleet-daily-csvs-100-v3-2020.zip; mmsi-daily-csvs-10-v3-2020-01-10.csv; fishing-vessels-v3.csv; README-fleet-v3.txt; fleet-monthly-csvs-10-v3-2024.zip; fleet-monthly-csvs-10-v3-2024-08-01.csv

Key documentation

• For an overview of how GFW turns raw AIS positions into estimates of fishing hours, see this page.

• The models used to produce this dataset were developed as part of this publication: D.A. Kroodsma, J. Mayorga, T. Hochberg, N.A. Miller, K. Boerder, F. Ferretti, A. Wilson, B. Bergman, T.D. White, B.A. Block, P. Woods, B. Sullivan, C. Costello, and B. Worm. "Tracking the global footprint of fisheries." Science 361.6378 (2018). Model details are available in the Supplementary Materials.

• The README-known-issues-v3.txt file describing this dataset's specific caveats can be downloaded from this page. We highly recommend that users read this file in full.

• The README-mmsi-v3.txt file, the README-fleet-v3.txt file, and the README-fishing-vessels-v3.txt files are downloadable from this page and contain the data description for (respectively) the fishing hours by MMSI dataset, the fishing hours by fleet dataset, and the vessel information file. These readmes contain key explanations about the gear types and flag states assigned to vessels in the dataset.

• File name structure for the datafiles are available below on this page and file schema can be downloaded from this page.

• A FAQ describing the updates in this version and the differences between this dataset and the data available from the GFW Map and APIs is available here.

Use Cases

The apparent fishing hours dataset is intended to allow users to analyze patterns of fishing across the world’s oceans at temporal scales as fine as daily and at spatial scales as fine as 0.1 or 0.01 degree cells. Fishing hours can be separated out by gear type, vessel flag and other characteristics of vessels such as tonnage.

Potential applications for this dataset are broad. We offer suggested use cases to illustrate its utility. The dataset can be integrated as a static layer in multi-layered analyses, allowing researchers to investigate relationships between fishing effort and other variables, including biodiversity, tracking, and environmental data, as defined by their research objectives.

A few example questions that these data could be used to answer:

• What flag states have fishing activity in my area of interest?

• Do hotspots of longline fishing overlap with known migration routes of sea turtles?

• How does fishing time by trawlers change by month in my area of interest? Which seasons see the most trawling hours and which see the least?

Caveats

This global dataset estimates apparent fishing hours effort. The dataset is based on publicly available information and statistical classifications which may not fully capture the nuances of local fishing practices. While we manually review the dataset at a global scale and in a select set of smaller test regions to check for issues, given the scale of the dataset we are unable to manually review every fleet in every region. We recognize the potential for inaccuracies and encourage users to approach regional analyses with caution, utilizing their own regional expertise to validate findings. We welcome your feedback on any regional analysis at research@globalfishingwatch.org to enhance the dataset's accuracy.

Caveats relating to known sources of inaccuracy as well as interpretation pitfalls to avoid are described in the README-known-issues-v3.txt file available for download from this page. We highly recommend that users read this file in full. The issues described include:

• Data from 2024 should be considered provisional, as vessel classifications may change as more data from 2025 becomes available.

• MMSI is used in this dataset as the vessel identifier. While MMSI is intended to serve as the unique AIS identifier for an individual vessel, this does not always hold in practice.

• The Maritime Identification Digits (MID), the first 3 digits of MMSI, are the only source of information on vessel flag state when the vessel does not appear on a registry. The MID may be entered incorrectly, obscuring information about an MMSI’s flag state.

• AIS reception is not consistent across all areas and changes over time.

Alternative ways to access

1. Query using SQL in the Global Fishing Watch public BigQuery dataset: global-fishing-watch.fishing_effort_v3

2. Download the entire dataset from the Global Fishing Watch Data Download Portal (https://globalfishingwatch.org/data-download/datasets/public-fishing-effort)

This layer is part of SDGs Today. Please see sdgstoday.orgToday, the health of our ocean is under immense pressure from both intensive human activity and climate change. One-third of global fish stocks are overfished and two-thirds of the ocean has been significantly altered by human actions. Despite the threats it faces, the ocean remains the least observed part of our planet. This lack of visibility allows illicit activity to thrive. Global Fishing Watch (GFW) is advancing ocean governance through increased transparency of human activity at sea. By creating and publicly sharing map visualizations, data and analysis tools, we enable scientific research and drive a transformation in how we manage our ocean. In 2018, GFW published the first-ever global assessment of commercial fishing activity (2012-2016) in Science. The updated 2021 version of this published dataset contains the GFW AIS-based fishing effort and vessel presence from 2012-2020, which includes over 328 million hours of fishing effort across the globe from over 117,000 unique maritime mobile service identity (MMSI) numbers. The new API Portal provides access to near real-time data on fishing vessel activity and identity.Fishing vessels are identified via a neural network classifier, vessel registry databases, and manual review by GFW and regional experts. Data are binned into grid cells 0.01 (or 0.1) degrees on a side and measured in units of hours. The time is calculated by assigning an amount of time to each AIS detection (which is the time to the previous position) and then summing all positions in each grid cell.Learn more about Global Fishing Watch technology here.

This is time-series layer of monthly fishing activity for the year 2020. This layer shows the amount of fishing activity recorded by Automatic Identification System (AIS) broken out by month. Each pixel value represents the number of hours per square kilometer of fishing activity.Fishing activity can be described using the two available variables:Hours - Identification of fishing vessels in the AIS data.Fishing Hours (displayed by default) - Detection of fishing activity.Using cloud computing, machine learning and public vessel registry information, Global Fishing Watch (GFW) analyze tens of millions of AIS positions each day to map global apparent fishing effort. Producing such a map involves two key steps: Identification of fishing vessels in the AIS data (Hours) and Detection of fishing activity (Fishing Hours).This annual summary is produced from the daily GFW AIS-based apparent fishing effort data. The daily fishing activity made available by GFW was rasterized at 100th degree resolution then aggregated to produce an annual summary for the given year. The maps show areas where likely fishing activity occurred in the year 2020 and the estimated level of fishing intensity. This information can help understand areas where fishing activity might be considered in a marine spatial planning application.More information: https://globalfishingwatch.org/dataset-and-code-fishing-effort/Dataset summaryVariable Mapped: Occupancy (hours) and effort (fishing hours) in hours per sq/kmDimension: Time – 12Months (Year 2020)Data Projection: GCS WGS84Service Projection: Web MercatorExtent: GlobalCell Size: (~1km)Source Type: Scientific/DoubleData Source: Global Fishing WatchData Accessed Date: September 23, 2023Version: 2.0, released 18 March 2021What can you do with this layer?The layer can be used in analysis and visualization. This layer can be used to summarize the values within a polygon (using zonal statistics). Fishing effort can be used to understand the areas impacted by fishing and designate marine protection if needed. This layer allows use of the time dimension to help understand locations and months/seasons when fishing intensity is higher or lower.See companion layers in this ArcGIS Online Group: Global Fishing Watch

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.

2. 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:

1. 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.
2. AIS can be intentionally turned off, for example, when vessels carry out illegal fishing activities (Kurekin et al. 2019).
3. 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

3. 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 map is part of SDGs Today. Please see sdgstoday.orgToday, the health of our ocean is under immense pressure from both intensive human activity and climate change. One-third of global fish stocks are overfished and two-thirds of the ocean has been significantly altered by human actions. Despite the threats it faces, the ocean remains the least observed part of our planet. This lack of visibility allows illicit activity to thrive. Global Fishing Watch (GFW) is advancing ocean governance through increased transparency of human activity at sea. By creating and publicly sharing map visualizations, data and analysis tools, we enable scientific research and drive a transformation in how we manage our ocean.In 2018, GFW published the first-ever global assessment of commercial fishing activity (2012-2016) in Science. The updated 2021 version of this published dataset contains the GFW AIS-based fishing effort and vessel presence from 2012-2020, which includes over 328 million hours of fishing effort across the globe from over 117,000 unique maritime mobile service identity (MMSI) numbers. Fishing vessels are identified via a neural network classifier, vessel registry databases, and a manual review by GFW and regional experts. Data are binned into grid cells 0.01 (or 0.1) degrees on a side and measured in units of hours. The time is calculated by assigning an amount of time to each AIS detection (which is the time to the previous position) and then summing all positions in each grid cell.For more information, contact Global Fishing Watch at research@globalfishingwatch.org.

This map is part of SDGs Today. Please see sdgstoday.orgToday, the health of our ocean is under immense pressure from both intensive human activity and climate change. One-third of global fish stocks are overfished and two-thirds of the ocean has been significantly altered by human actions. Despite the threats it faces, the ocean remains the least observed part of our planet. This lack of visibility allows illicit activity to thrive.Global Fishing Watch (GFW) is advancing ocean governance through increased transparency of human activity at sea. By creating and publicly sharing map visualizations, data and analysis tools, we enable scientific research and drive a transformation in how we manage our ocean.In 2018, GFW published the first-ever global assessment of commercial fishing activity (2012-2016) in Science. The updated 2021 version of this published dataset contains the GFW AIS-based fishing effort and vessel presence from 2012-2020, which includes over 328 million hours of fishing effort across the globe from over 117,000 unique maritime mobile service identity (MMSI) numbers. Fishing vessels are identified via a neural network classifier, vessel registry databases, and a manual review by GFW and regional experts. Data are binned into grid cells 0.01 (or 0.1) degrees on a side and measured in units of hours. The time is calculated by assigning an amount of time to each AIS detection (which is the time to the previous position) and then summing all positions in each grid cell.For more information, contact Global Fishing Watch at research@globalfishingwatch.org.

Global Fishing Watch is working across the globe to provide governments and authorities with actionable reports and capacity building to help strengthen fisheries monitoring and compliance. Our global team of experts produce analyses to inform monitoring, control and surveillance of fisheries in five key areas: - Illegal, unreported and unregulated fishing - Transshipment - Port controls - Marine protected areas - Operation support

Collaboration and information sharing are integral to achieving well-managed fisheries. By working with stakeholders and making analyses available to national, regional and intergovernmental partners, Global Fishing Watch is enabling fisheries agencies to make more informed and cost-efficient decisions.

Topics: - Commercial fishing, Global Fishing Watch is harnessing innovative technology to turn transparent data into actionable information and drive tangible change in the way that fisheries are governed. - Transshipment, Through publicly sharing map visualisations and creating data and analysis tools, we seek to inform management and policy efforts and provide a more complete picture of transshipment at sea. - Marine protected areas, Global Fishing Watch is harnessing the data and technology revolution to support the effective design, management and monitoring of marine protected areas.

מאמצי הדיג, שנמדדים בשעות של פעילות דיג משוערת. כל נכס הוא המאמץ במדינה נתונה עם דגל מסוים וביום נתון, עם פס אחד לפעילות הדיג של כל סוג ציוד. סקריפטים לדוגמה ב-Earth Engine באתר הראשי של GFW אפשר למצוא מידע על התוכנית, מפות אינטראקטיביות מלאות של התצוגה החזותית, …

Financial overview and grant giving statistics of Global Fishing Watch Inc

This layer shows the amount of fishing activity recorded by Automatic Identification System (AIS) for the year 2020.This layer has two variables:Hours - Identification of fishing vessels in the AIS data.Fishing Hours (displayed by default) - Detection of fishing activity.Using cloud computing, machine learning and public vessel registry information, Global Fishing Watch (GFW) analyze tens of millions of AIS positions each day to map global apparent fishing effort. Producing such a map involves two key steps: Identification of fishing vessels in the AIS data (Hours) and Detection of fishing activity (Fishing Hours).

This annual summary is produced from the daily GFW AIS-based apparent fishing effort data. The daily fishing activity made available by GFW was rasterized at 100th degree resolution then aggregated to produce an annual summary for the given year. The maps show areas where likely fishing activity occurred in the year 2020 and the estimated level of fishing intensity. This information can help understand areas where fishing activity might be considered in a marine spatial planning application.More information: https://globalfishingwatch.org/dataset-and-code-fishing-effort/Dataset SummaryVariable Mapped: Occupancy (hours) and effort (fishing hours) in hours per sq/kmData Projection: GCS WGS84Service Projection: Web MercatorExtent: GlobalCell Size: (~1km)Source Type: Scientific/DoubleData Source: Global Fishing WatchData Accessed Date: September 23, 2023Version: 2.0, released 18 March 2021What can you do with this layer?The layer can be used in analysis and visualization. This layer can be used to summarize the values withing a polygon (using zonal statistics). Fishing effort can be used to understand the areas impacted by fishing and designate marine protection if needed.See companion layers in this ArcGIS Online Group: Global Fishing Watch

Abbreviations: N, number of vessels; Cells, number of grid cells (0.01 decimal degrees) associated with fishing activity; Sum, the quantity of fishing hours per category; Mean, the arithmetic mean; sd, the standard deviation. Only the flag state category contains information on ‘Unknown flags’ (UNK).

Fishing effort data derived from Automatic Identification System (AIS) in 2012 -2020, Global fishing watch Data were obtained from https://globalfishingwatch.org/

TW logbook data are available from the corresponding author, [Y, Chang], upon reasonable request. *yichang@mail.nsysu.edu.tw

Esforço de pesca, medido em horas de atividade de pesca inferida. Cada recurso é o esforço para um determinado estado e dia da sinalização, com uma faixa para a atividade de pesca de cada tipo de equipamento. Confira exemplos de scripts do Earth Engine. Consulte também o site principal da GFW para informações sobre o programa, mapas de visualização totalmente interativos, etc.

Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada’s static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA’s regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation’s full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting. Methods See Iacarella, J. C., Burke, L., Clyde, G., Wicks, A., Clavelle, T., Dunham, A., Rubidge, E., & Woods, P. (2023). Application of AIS- and flyover-based methods to monitor illegal and legal fishing in Canada's Pacific marine conservation areas. Conservation Science and Practice, 5(6), e12926 and Iacarella, J. C., Burke, L., Clyde, G., Wicks, A., Clavelle, T., Dunham, A., Rubidge, E., & Woods, P. (2023). Monitoring temporal and spatial trends of illegal and legal fishing in marine conservation areas across Canada's three oceans. Conservation Science and Practice, 5(6), e12919.

Mức độ đánh bắt, được đo bằng số giờ hoạt động đánh bắt được suy luận. Mỗi thành phần là nỗ lực cho một ngày và trạng thái cờ nhất định, với một dải cho hoạt động đánh bắt cá của mỗi loại thiết bị. Xem các tập lệnh mẫu của Earth Engine. Ngoài ra, hãy xem trang web chính của GFW để biết thông tin về chương trình, bản đồ trực quan tương tác đầy đủ, v.v.

This data package includes 10 child items with data about the distribution, abundance, and morphology of forage fish, zooplankton, and predators, and oceanographic conditions during surveys in Prince William Sound, Alaska. Child Item 1: "Forage Fish Catch Data from Prince William Sound, Alaska". Child Item 2: "Forage Fish Morphology Data from Prince William Sound, Alaska". Child Item 3: "Forage Fish Size, Age, and Energy Density Data from Prince William Sound, Alaska". Child Item 4: "Forage Fish Aerial Validation Data from Prince William Sound, Alaska". Child Item 5: "Marine Bird and Mammal Survey Data from Prince William Sound, Alaska". Child Item 6: "Zooplankton Biomass Data from Prince William Sound, Alaska". Child Item 7: "Macrozooplankton Hydroacoustic Index Data from Prince William Sound, Alaska". Child Item 8: "Hydroacoustic Survey Data from Prince William Sound, Alaska". Child Item 9: "Nutrient Depth Profile Data from Prince William Sound, Alaska". Child Item 10: "Conductivity, Temperature, Depth Profile Data from Prince William Sound, Alaska"

Percent of overlap of core effort areas (50% kernels) and spatial extent of fishing activities (95% kernels) by seasons.

1. Understanding encounters between marine predators and fisheries across national borders and outside national jurisdictions offers new perspectives on unwanted interactions to inform ocean management and predator conservation. Although seabird-fisheries overlap has been documented at many scales, remote identification of vessel encounters has lagged because vessel movement data often is lacking.

2. Here, we reveal albatross-fisheries associations throughout the North Pacific Ocean. We identified commercial fishing operations using Global Fishing Watch data and algorithms to detect fishing vessels. We compiled GPS tracks of adult black-footed (Phoebastria nigripes) and Laysan (P. immutabilis) albatrosses, and juvenile short-tailed albatrosses (P. albatrus). We quantified albatross-vessel encounters based on the assumed distance that birds perceive a vessel (≤30km), and associations when birds approached vessels (≤3km). For each event we quantified bird behavior, environmental condit...

These data are part of the Gulf Watch Alaska (GWA) long term monitoring program, pelagic monitoring component. This dataset consists of one table, providing fish morphology data from summer surveys in Prince William Sound, Alaska. Data includes: date, time, latitude, longitude, fishing method, fish common name, total length, fork length, weight, and whether or not that fish was saved. Various catch methods were used to obtain fish samples for aerial and hydroacoustic validations, including: modified herring trawl, purse seine, beach seine, gillnet, cast net, dip net, and jig.