The Sea Around Us is a research initiative at The University of British Columbia (located at the Institute for the Oceans and Fisheries, formerly Fisheries Centre) that assesses the impact of fisheries on the marine ecosystems of the world, and offers mitigating solutions to a range of stakeholders.

The Sea Around Us was initiated in collaboration with The Pew Charitable Trusts in 1999, and in 2014, the Sea Around Us also began a collaboration with The Paul G. Allen Family Foundation to provide African and Asian countries with more accurate and comprehensive fisheries data.

The Sea Around Us provides data and analyses through View Data, articles in peer-reviewed journals, and other media (News). The Sea Around Us regularly update products at the scale of countries’ Exclusive Economic Zones, Large Marine Ecosystems, the High Seas and other spatial scales, and as global maps and summaries.

The Sea Around Us emphasizes catch time series starting in 1950, and related series (e.g., landed value and catch by flag state, fishing sector and catch type), and fisheries-related information on every maritime country (e.g., government subsidies, marine biodiversity). Information is also offered on sub-projects, e.g., the historic expansion of fisheries, the performance of Regional Fisheries Management Organizations, or the likely impact of climate change on fisheries.

The information and data presented on their website is freely available to any user, granted that its source is acknowledged. The Sea Around Us is aware that this information may be incomplete. Please let them know about this via the feedback options available on this website.

If you cite or display any content from the Site, or reference the Sea Around Us, the Sea Around Us – Indian Ocean, the University of British Columbia or the University of Western Australia, in any format, written or otherwise, including print or web publications, presentations, grant applications, websites, other online applications such as blogs, or other works, you must provide appropriate acknowledgement using a citation consistent with the following standard:

When referring to various datasets downloaded from the website, and/or its concept or design, or to several datasets extracted from its underlying databases, cite its architects. Example: Pauly D., Zeller D., Palomares M.L.D. (Editors), 2020. Sea Around Us Concepts, Design and Data (seaaroundus.org).

When referring to a set of values extracted for a given country, EEZ or territory, cite the most recent catch reconstruction report or paper (available on the website) for that country, EEZ or territory. Example: For the Mexican Pacific EEZ, the citation should be “Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.”, which is accessible on the EEZ page for Mexico (Pacific) on seaaroundus.org.

To help us track the use of Sea Around Us data, we would appreciate you also citing Pauly, Zeller, and Palomares (2020) as the source of the information in an appropriate part of your text;

When using data from our website that are not part of a typical catch reconstruction (e.g., catches by LME or other spatial entity, subsidies given to fisheries, the estuaries in a given country, or the surface area of a given EEZ), cite both the website and the study that generated the underlying database. Many of these can be derived from the ’methods’ texts associated with data pages on seaaroundus.org. Example: Sumaila et al. (2010) for subsides, Alder (2003) for estuaries and Claus et al. (2014) for EEZ delineations, respectively.

The Sea Around Us data are (where not otherwise regulated) under a Creative Commons Attribution Non-Commercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/). Notices regarding copyrights (© The University of British Columbia), license and disclaimer can be found under http://www.seaaroundus.org/terms-and-conditions/. References:

Alder J (2003) Putting the coast in the Sea Around Us Project. The Sea Around Us Newsletter (15): 1-2.

Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.

Pauly D, Zeller D, and Palomares M.L.D. (Editors) (2020) Sea Around Us Concepts, Design and Data (www.seaaroundus.org)

Claus S, De Hauwere N, Vanhoorne B, Deckers P, Souza Dias F, Hernandez F and Mees J (2014) Marine Regions: Towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37(2): 99-125.

Sumaila UR, Khan A, Dyck A, Watson R, Munro R, Tydemers P and Pauly D (2010) A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics 12: 201-225.

Nearshore fisheries in the Main Hawaiian Islands encompass a diverse group of fishers using a wide array of gears and targeting many different species. Communities in Hawaii often rely on these fisheries for economic, social, and cultural services. However, the stress from overfishing can cause ecosystem degradation and long-term economic loss. This layer represents the average annual commercial catch of reef fish over the years 2003-2013 as reported in commercial catch data collected by the State of Hawaii Department of Aquatic Resources (DAR) Commercial Marine Landings Database (CML). Commercial catch is reported to DAR in large irregular reporting blocks by gear and by species. This layer is the sum of the three gear-specific Ocean Tipping Points (OTP) commercial fishing rasters (line, net, and spear). This layer's spatial footprint aligns with the inshore commercial reporting blocks from the shapefile served on the Hawaii Statewide GIS Program website (Fishchart2008.shp) (http://planning.hawaii.gov/gis/download-gis-data/). Data are filtered by DAR before release such that reporting blocks with less than three fishers reporting are excluded in order to protect fisher identities. It is not possible to explicitly distinguish between boat-based and shore-based fishing based on the gear types reported in CML data. OTP filtered the data for reef fish species only and calculated average annual catch in kilograms by reporting block and gear type to match with data from the Marine Recreational Information Program (MRIP): line, net, and spear. In marine protected areas (MPAs) where boat-based fishing is not allowed, catch was set to zero; and inside de facto MPAs with restricted access, catch was reduced according to expert input and local knowledge. Average annual commercial catch data were converted from polygon to raster for each gear type and then divided by the number of 100-m raster cells within each reporting block so that units are comparable to non-commercial fishing layers (kg/ha). The result assumes commercial catch is evenly distributed spatially across each reporting block.

description: These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the south at Pigeon Point to the north at Alder Creek. These data depict the spatial change in the stated relative value of commercial fishing grounds. The data were created by calculating the difference between data collected in the summer of 2007 (pre-MPAs) and in the spring of 2010 (post-MPAs). Specifically, the data were produced by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surfaces (also known as a 'heat map') were used in this analysis. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight the aggregated individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery and not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing relative values are greater than the pre-MPA relative values, thus an increase in relative importance and value between the 2007 survey to the 2010 survey. This dataset represents the spatial change in relative value for Nearshore finfish--live--fixed gear commercial fishing grounds for the North Central Coast Region.; abstract: These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the south at Pigeon Point to the north at Alder Creek. These data depict the spatial change in the stated relative value of commercial fishing grounds. The data were created by calculating the difference between data collected in the summer of 2007 (pre-MPAs) and in the spring of 2010 (post-MPAs). Specifically, the data were produced by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surfaces (also known as a 'heat map') were used in this analysis. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight the aggregated individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery and not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing relative values are greater than the pre-MPA relative values, thus an increase in relative importance and value between the 2007 survey to the 2010 survey. This dataset represents the spatial change in relative value for Nearshore finfish--live--fixed gear commercial fishing grounds for the North Central Coast Region.

This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation. The Central Coast study region extends from the north at Pigeon Point to the south at Point Conception. These data depict the spatial change in the stated relative value of commercial fishing grounds difference for spatial fisheries data collected in the spring of 2012 (post-MPAs) and summer of 2005 (pre-MPAs). Specifically, the data were calculated by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surface (also known as a 'heat map') that was developed for the pre and post MPA data sets were used in these analyses. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight and the aggregation of individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery?not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing values are greater than the pre-MPA values, thus an increase in relative importance and value between the 2005 survey to the 2012 survey. This dataset represents the spatial change in relative value for Nearshore finfish-live--hook and line commercial fishing grounds for the Central Coast Region.

This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation. The Central Coast study region extends from the north at Pigeon Point to the south at Point Conception. These data depict the spatial change in the stated relative value of commercial fishing grounds difference for spatial fisheries data collected in the spring of 2012 (post-MPAs) and summer of 2005 (pre-MPAs). Specifically, the data were calculated by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surface (also known as a 'heat map') that was developed for the pre and post MPA data sets were used in these analyses. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight and the aggregation of individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery?not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing values are greater than the pre-MPA values, thus an increase in relative importance and value between the 2005 survey to the 2012 survey. This dataset represents the spatial change in relative value for Nearshore finfish-live--trap commercial fishing grounds for the Central Coast Region.

Data has been processed by NODC to the NODC standard Fish/Shell-fish Surveys (F123) format. The F123 format is used for data from field sampling of marine fish and shellfish. The data derive from analysis of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include meteorological conditions, surface and bottom temperature and salinity, and current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/ prey information (from stomach contents analysis), and growth data may also be included. A text record is available for comment.

description: This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation. The Central Coast study region extends from the north at Pigeon Point to the south at Point Conception. These data depict the spatial change in the stated relative value of commercial fishing grounds difference for spatial fisheries data collected in the spring of 2012 (post-MPAs) and summer of 2005 (pre-MPAs). Specifically, the data were calculated by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surface (also known as a 'heat map') that was developed for the pre and post MPA data sets were used in these analyses. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight and the aggregation of individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery?not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing values are greater than the pre-MPA values, thus an increase in relative importance and value between the 2005 survey to the 2012 survey. This dataset represents the spatial change in relative value for Market squid--seine commercial fishing grounds for the Central Coast Region.; abstract: This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation. The Central Coast study region extends from the north at Pigeon Point to the south at Point Conception. These data depict the spatial change in the stated relative value of commercial fishing grounds difference for spatial fisheries data collected in the spring of 2012 (post-MPAs) and summer of 2005 (pre-MPAs). Specifically, the data were calculated by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surface (also known as a 'heat map') that was developed for the pre and post MPA data sets were used in these analyses. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight and the aggregation of individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery?not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing values are greater than the pre-MPA values, thus an increase in relative importance and value between the 2005 survey to the 2012 survey. This dataset represents the spatial change in relative value for Market squid--seine commercial fishing grounds for the Central Coast Region.

Catch data conducted on hook and line along Pt. Lobos shore in California from 2007 to 2012. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=This dataset contains fish hook-and-line census data that were collected by hook-and-line fishing in a cooperative data collection effort on fishing boats offshore of Pt. Lobos California.\u00a0Full\u00a0description of details is provided in Starr et al. (2015). awards_0_award_nid=542383 awards_0_award_number=OCE-1435473 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1435473 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Hook and line survey data Collected by Rick Starr J. W. White, M. Carr, and R. Starr, PIs Version 4 August 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.712899.1 infoUrl=https://www.bco-dmo.org/dataset/712899 institution=BCO-DMO instruments_0_acronym=Hook and line instruments_0_dataset_instrument_description=Used to collect fish census data instruments_0_dataset_instrument_nid=712910 instruments_0_description=A type of fishing methodology sometimes used to collect fish census data. It is a hook at the end of a line. instruments_0_instrument_name=Hook and line instruments_0_instrument_nid=712909 instruments_0_supplied_name=Hook and line metadata_source=https://www.bco-dmo.org/api/dataset/712899 param_mapping={'712899': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/712899/parameters people_0_affiliation=Oregon State University people_0_affiliation_acronym=OSU people_0_person_name=J Wilson White people_0_person_nid=516429 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of California-Santa Cruz people_1_affiliation_acronym=UC Santa Cruz people_1_person_name=Mark Carr people_1_person_nid=51504 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Moss Landing Marine Laboratories people_2_affiliation_acronym=MLML people_2_person_name=Dr Rick Starr people_2_person_nid=712778 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Oregon State University people_3_affiliation_acronym=OSU people_3_person_name=J Wilson White people_3_person_nid=516429 people_3_role=Contact people_3_role_type=related people_4_affiliation=Woods Hole Oceanographic Institution people_4_affiliation_acronym=WHOI BCO-DMO people_4_person_name=Hannah Ake people_4_person_nid=650173 people_4_role=BCO-DMO Data Manager people_4_role_type=related project=Goby size-selection projects_0_acronym=Goby size-selection projects_0_description=Description from NSF award abstract: Many marine fish species change sex during their lifetimes, and many of them are targets of commercial and recreational fishing. The timing of sex change in these animals is often related to body size, so populations typically consist of many small fish of the initial sex (usually female) and few large fish of the other sex (usually male). In nature, smaller fish are at a greater risk of mortality due to predation, but fishermen tend to seek larger fish. Thus fishing that targets larger individuals may skew sex ratios, removing enough of the larger sex to hinder reproduction. However, the extent to which size-selective mortality affects sex-changing fishes is poorly understood. This research will explore the effects of size-selective mortality on the population dynamics of sex-changing species using an integrated set of field experiments and mathematical models. It will provide the first experimental exploration of the sensitivity of different sex-change patterns and reproductive strategies to selective mortality. The results will advance our knowledge of the susceptibility and resilience of sex-changing organisms to different types of size-selective mortality and will reveal how sex-changing species can recover after size-selection ceases, as in populations within marine reserves where fishing is suddenly prohibited. The findings will inform fisheries management policies, which do not currently consider the ability of a species to change sex in setting fisheries regulations. This project will consist of a three-year study of the effects of size-specific mortality on sex-changing fishes. Field experiments will use three closely related rocky-reef fishes that differ in sex-change pattern and are amenable to field manipulation and direct measurement of reproductive output. The species include a protogynous hermaphrodite (a female-to-male sex-change pattern common among harvested species) and two simultaneous hermaphrodites that differ in their ability to switch between male and female. Two types of experiments will be conducted on populations established on replicate patch reefs at Santa Catalina Island, California: (1) sex ratios will be manipulated to determine when the scarcity of males limits population-level reproductive output; and (2) experiments cross-factoring the intensity of mortality with the form of size-selection (i.e., higher mortality of large or small individuals) will test the demographic consequences of size-selective mortality. In concert with the field experiments, size- and sex-structured population models (integral projection models) will be developed for use in three ways: (1) to evaluate how different types of selective mortality should affect population dynamics; (2) to predict outcomes of the field experiments, testing/validating the model and allowing direct prediction of the ecological significance of short-term selection; and (3) to fit to existing survey data for a fourth species, a widely fished, sex-changing fish, inside and outside of marine reserves. Part (3) will evaluate whether and how quickly the mating system and reproductive output of that species (not directly measurable in the field) is recovering inside reserves. This integrated set of field experiments and models will yield novel insight into the effects of size-selective mortality on the population dynamics of sex-changing marine species. projects_0_end_date=2018-02 projects_0_geolocation=Southern California, Santa Catalina Island projects_0_name=Impacts of size-selective mortality on sex-changing fishes projects_0_project_nid=516431 projects_0_start_date=2015-03 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=Area,Common_Name version=1 xml_source=osprey2erddap.update_xml() v1.3

description: These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the south at Pigeon Point to the north at Alder Creek. These data depict the spatial change in the stated relative value of commercial fishing grounds. The data were created by calculating the difference between data collected in the summer of 2007 (pre-MPAs) and in the spring of 2010 (post-MPAs). Specifically, the data were produced by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surfaces (also known as a 'heat map') were used in this analysis. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight the aggregated individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery and not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing relative values are greater than the pre-MPA relative values, thus an increase in relative importance and value between the 2007 survey to the 2010 survey. This dataset represents the spatial change in relative value for Urchin--dive commercial fishing grounds for the North Central Coast Region.; abstract: These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the south at Pigeon Point to the north at Alder Creek. These data depict the spatial change in the stated relative value of commercial fishing grounds. The data were created by calculating the difference between data collected in the summer of 2007 (pre-MPAs) and in the spring of 2010 (post-MPAs). Specifically, the data were produced by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surfaces (also known as a 'heat map') were used in this analysis. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight the aggregated individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery and not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing relative values are greater than the pre-MPA relative values, thus an increase in relative importance and value between the 2007 survey to the 2010 survey. This dataset represents the spatial change in relative value for Urchin--dive commercial fishing grounds for the North Central Coast Region.

Standardized test-fishing with Nordic multi-mesh gillnets is a widely used method in Sweden to investigate the fish fauna in a lake. In 2005 the method became an European standard (EN 14757). Standardized test-fishing catch most of the Swedish species in a representative way, giving a good estimate of species abundance and size distribution. The results are generally used for purposes of environmental protection and fishery management.

NORS consists of thousands of test-fishing occasions back to the 1950’s. The Department of Aquatic Resources (SLU Aqua) at the Swedish University of Agricultural Sciences is responsible of collecting and checking test-fishing data generated in national and regional environmental programs, on behalf of the Swedish Agency for Marine and Water Management. SLU Aqua also collect test-fishing data from several other types of investigations in order to create a database as representative as possible. The purpose is to facilitate obtaining data of high quality for research, national investigations and reports. The database also serves as a reference for local and regional investigations. All data is available for the public.

Data provided by the Marine Institute, and may also incorporate data from other agencies and bodies. This dataset shows the distribution of fishing effort by fishing vessels according to the gear type used. Fishing effort is defined as the time spent engaged in fishing operations or time spent at sea, this time may be multiplied by a measure of fishing capacity, e.g. engine power. In this dataset fishing effort is measured as average hours spent actively fishing per kilometre square, per year. Data from years 2014 to 2018 was used to produce this data product for the Marine Institute publication the “Atlas of Commercial Fisheries around Ireland, third edition“ (https://oar.marine.ie/handle/10793/1432). Effort for offshore fisheries is based on the following 2 primary data types - data on vessel positioning and data on gear types used: Vessel Monitoring Systems (VMS) supplied by the Irish Naval Service provide geographical position and speed of vessel at intervals of two hours or less (Commission Regulation (EC) No. 2244/2003). The data are available for all EU vessels of 12m and larger, operating inside the Irish EEZ; outside this zone only Irish VMS data are routinely available. VMS do not record whether a vessel is fishing, steaming or inactive. Logbooks collected by the Sea-Fisheries Protection Authority and supplied by the Department of Agriculture, Food & the Marine were the primary data source for information on landings and gear types used by Irish vessels. EU Fleet Register obtained from the EU fleet register provides information for non-Irish vessels and for Irish vessels for which the gear was not known from the logbooks. Note that if vessels use more than one gear, it is possible that the gear type assigned to them was not the one that was actually used. The fishing gear data was classified into eight main groups: demersal otter trawls; beam trawls; demersal seines; gill and trammel nets; longlines; dredges; pots and pelagic trawls. The VMS data was analysed using the approach described by Gerritsen and Lordan (IJMS 68(1)). This approach assigns effort to each of the VMS data points. The effort of a VMS data point is defined as the time interval since the previous data point. Next the data are filtered for fishing activity using speed criteria, vessels were assumed to be actively fishing if their speed fell within a certain range (depending on the fishing gear used). The points that remain are then aggregated into a spatial grid to produce a raster dataset showing fishing effort (in hours) per kilometre square per year for each gear type group. The data is available for all countries combined and for Irish vessels only.

These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the south at Pigeon Point to the north at Alder Creek. These data depict the spatial change in the stated relative value of commercial fishing grounds. The data were created by calculating the difference between data collected in the summer of 2007 (pre-MPAs) and in the spring of 2010 (post-MPAs). Specifically, the data were produced by subtracting the post-MPA relative value surface data from the pre-MPA relative value surface data (x = pre-MPA - post-MPA). It is important to emphasize that only the relative value surfaces (also known as a 'heat map') were used in this analysis. In other words, ex-vessel revenue was not applied to the 'heat map' value surfaces, however ex-vessel revenue was used to weight the aggregated individual fishing grounds. This was done to reduce the number of variables which would influence analysis results. Thus, results are simply an examination of changes in the stated values/importance of fishing areas to a fishery and not spatial changes in revenue across the two datasets. The data ranges from negative to positive values on a relative scale. In this dataset the areas with negative values are areas where the post-MPA fishing relative values are greater than the pre-MPA relative values, thus an increase in relative importance and value between the 2007 survey to the 2010 survey. This dataset represents the spatial change in relative value for Dungeness crab--trap commercial fishing grounds for the North Central Coast Region.

This layer is the output of spatial modeling developed by Ecotrust in 2021. The goal of this modeling effort was to create a spatial dataset that provides a better understanding of fishing patterns in and around California state MPAs in the years 2005-2020. Model outputs were created for nearshore finfish group including individual species as follows: Black Rockfish, Black-Yellow Rockfish, Blue Rockfish, Brown Rockfish, Cabezon, California Scorpionfish, California Sheephead, China Rockfish, Copper Rockfish, Gopher Rockfish, Grass Rockfish, Kelp Greenling, Kelp Rockfish, Monkeyface Prickleback, Olive Rockfish, Quillback Rockfish, Treefish Rockfish. Layers were also created for spiny lobster and red urchin. To complete this spatial modeling Ecotrust used two data sources, commercial fishing landings receipts from California Department of Fish and Wildlife (CDFW) and spatial layers of fishing areas developed by Ecotrust based on in-person interviews to support the MPA network development and implementation. The data were summarized to the California Recreational Fisheries Survey (CRFS) micro-blocks. See below for more information about CRFS micro-blocks. For decades California fishery reporting and management has been organized and documented using a 10 minute lat-long (10') grid, known colloquially as 'California Trawl Blocks'. In recent years, however, a need has developed for finer (smaller area) grid blocks to record catch and develop habitat maps and models, especially in coastal nearshore waters and for residential and sessile species. This shapefile of 1 minute lat-long (1') blocks was created to nest within the original 10' lat-long blocks, thus allowing the transfer of 10' historic and current catch data to each of the 100 1' cells within. The 1' blocks in this shapefile extend inland to include coastal areas, estuaries and bays. This shapefile is a polygon file, not a raster file, therefore, area for each 1' cell may be unique.10/27/2012 - Caltrawl blocks 538,547,548,549,550, 553, 554,555,556, & 557 contained incorrect lables for index (used old index starting at 1). Correction made redindexing blocks to start with 0.

This dataset was managed for the aims of the project ECOSISTER (Ecosystem for Sustainable Transition in Emilia-Romagna), WP3 of Spoke 5 and provides the CPUE (Catch Per Unit of Effort) of fish species sampled in Valle Fattibello, a brackish (salinity ca. 13–31) lagoon in the north-western shore of the Adriatic Sea. Depth varies from a minimum of a few centimetres on the shallowest sandbars to a maximum of 3 m in the deepest channels (average depth 1 m).

The lagoon substrate is also very variable, including muds and silts as well as sands and harder substrates (mussel beds, shell hash). Water currents reshape the softer sediments, sometimes creating temporally shifting patterns of sandbars.

The dataset is provided in two formats:

• Microsoft Excel XLSX file, including 3 sheets (Dataset, Fields and units, Taxonomy)

• CSV files (UTF-8), 3 files corresponding to the 3 sheets of the Excel file

The dataset includes total CPUE data for 32 fish species collected during the sampling as also their functional guild related to the use of the habitat.

Fish sampling was performed in 2009-2014 period at a fixed location on the banks of the Logonovo Channel, at the entrance of the lagoon, using stationary lift nets, a traditional fishing method in the area adapted to capture also juvenile individuals. Each species was assigned to a category based on their prevalent functional use of the lagoon for a total of four different categories, with the last one having three subcategories: Catadromous, Anadromous, Brackish (lagoon resident) and Marine (divided into feeding ground, nursery and stragglers subcategories). Marine species that enter the lagoon regularly to feed were categorized under ‘‘feeding ground,’’ those that live predominantly in the lagoon as juveniles were categorized under ‘‘nursery’’ and finally those that enter the lagoon only occasionally as ‘‘stragglers.’’ The fields are described in Table 1.

All the dataset fields are described in the Excel sheet/CSV file “Fields and units”, while the taxonomic related information for each taxon is provided in the Excel sheet/CSV file “Taxonomy”. Information extracted from the World Register of Marine Species (WoRMS; https://marinespecies.org/) is provided here (see details in Table 2). ESRI Shapefile provides users with direct upload in any Geographic Information System (GIS). Nevertheless, due to this file format limitations, dataset field names have been truncated and/or renamed to fit 10 characters.

Table 1. Fields in the dataset (NA=not available).

Field

Darwin Core term

Unit

Precision

Note

decimalLatitude

decimalLatitude

decimal degrees

± 0.00001

WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position

decimalLongitude

decimalLongitude

decimal degrees

± 0.00001

WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position

dateIdentified

dateIdentified

YYYY-YYYY

NA

The year on which the sampling was performed

SamplingGear

NA

NA

NA

stationary lift nets, a traditional fishing method called "bilancione" in Italian

Location

NA

NA

NA

Samplig location

Depth

maximumDepthInMeters

m

± 0.1

Mean Lower Low Water - measured with ecosaunder or depth gauge corrected by tide gauge of Porto Corsini (RA)

ScientificName

ScientificName

NA

NA

The combination of genus and first (species) epithet of the scientificName

HabitatGuild

NA

NA

NA

Prevalent functional use of the lagoon of fish species

Total CPUE

organismQuantity

CPUE, based on number of net

lifts

NA

Catch per Unit Effort based on total fish biomass and based on number of net lifts

Table 2. Provided taxonomic information.

Taxon

the name used in the dataset

ScientificName_accepted

the name accepted according WoRMS

AphiaID

Unique identifier in WoRMS

Kingdom

Taxonomic level

Phylum

Taxonomic level

Class

Taxonomic level

Order

Taxonomic level

Family

Taxonomic level

Genus

Taxonomic level

Species

Taxonomic level

A total of 33 fish species to the family of Alosidae (1), Anguillidae (1), Atherinidae (1), Belonidae (1), Blenniidae (1), Clupeidae (1), Cyprinodontidae (1), Engraulidae (1), Gobiidae (6), Triglidae (1), Moronidae (1), Mugilidae (5), Soleidae (4), Syngnathidae (2), Mullidae (1), Pleuronectidae (1), Sciaenidae (1), Scombridae (1), Scophthalmidae (1) were collected.

This dataset permitted to investigate fish movements between inner and outer habitats in a Northern Mediterranean coastal lagoon trough the publication of 3 scientific work Lanzoni, M., Gaglio, M., Gavioli, A., Anna Fano, E., & Castaldelli, G. (2021). Seasonal variation of functional traits in the fish community in a brackish lagoon of the Po River Delta (northern Italy). Water (Switzerland), 13(5). https://doi.org/10.3390/w13050679; Milardi, M., Gavioli, A., Lanzoni, M., Fano, E. A., & Castaldelli, G. (2019). Meteorological factors influence marine and resident fish movements in a brackish lagoon. Aquatic Ecology, 53(2), 251–263. https://doi.org/10.1007/s10452-019-09686-4-; Milardi, M., Lanzoni, M., Gavioli, A., Fano, E. A., & Castaldelli, G. (2018). Tides and moon drive fish movements in a brackish lagoon. Estuarine, Coastal and Shelf Science, 215, 207–214. https://doi.org/10.1016/j.ecss.2018.09.016. The information provided could improve the understanding of the drivers in these ecosystem as well as potentially provide useful insights for improved fisheries management.

The dataset provides information on the fish community and the environmental conditions that regulate their movement within a lagoon ecosystem. In fact, Valle Fattibello is a lagoon that, due to its ecological characteristics, provides information on the species composition and the relative stages of development of the fish community informative of the coastal area of the Emilia-Romagna region, from the Po Delta to Ravenna.

This dataset was provided by the Recreational Fisheries Information Network (RecFIN), and contained GPS coordinates for 680 CPFV trips during 1999 and 2001 at depths ranging from 0 - 2200 m. Fishers targeted specific species during each trip, and visited between 1 and 22 locations. Each trip/location combination was considered a unique site and was used as a sample unit in analyses. RecFIN provided information on four hook and line fishing methods: free drift, stationary drift, anchor, and troll. The trolling trips were removed before analysis because they targeted specific pelagic species and therefore provide limited information about diversity. In addition, fish that were not present in at least 5% of the trawls were also removed. The final data set analyzed contained 18 fish species captured by hook and line at 2967 trip/location combinations. Site groups were calculated using the Bray-Curtis dissimilarity coefficient with average means clustering (Romesburg, 1991).

The OSPAR (https://www.ospar.org/convention) fish biodiversity assessment concerns sensitive and often rare species in the northeast Atlantic Ocean and marginal seas. All fish species captured during fishing on scientific groundfish (otter and beam trawl) surveys are recorded but not necessarily weighed and measured for length. The biodiversity assessment for sensitive and often rare species thus makes use of the occurrences (presence and absences) of a species only.

A list of sensitive fish species was developed to guide the work for the OSPAR fish biodiversity assessment. The list was created by first collating all sensitive fish species recorded on international and national hard law lists, Regional Seas Conventions lists, International Agreement Lists, relevant IUCN Red List species (classified as EX, CR, VU or EN) and all ICES and academic work to identify sensitive fish species. From this extensive list of species, species were removed if they did not occur in the OSPAR area of the Northeast Atlantic Ocean. Remaining species were divided by region; Bay of Biscay and Iberian Coast, Celtic Seas, Greater North Sea, Norwegian Sea and parts of Macaronesia where data was available. Those species whose normal distribution was at the very edge of an OSPAR region were not assessed within the region.

Species where ICES or ICCAT quantitative stock assessments are conducted in all or part of their distribution were retained on the list, but highlighted so as to avoid duplication of work. The list was then cross-referenced with the ICES WKCOFIBYC (ICES, 2021a) and WKABSENS (ICES, 2021b) sensitive species lists and reviewed by expert members of the OSPAR Fish subgroup to ensure all sensitive species were included.

To overcome the potential for species misidentification for those that are difficult to identify beyond the genus level, some species were grouped by genus for the assessment. These include Hippocampus spp. (combining Hippocampus hippocampus with H. guttulatus), Alosa spp. (combining Alosa alosa and A. fallax), Dipturus spp. (combining Dipturus batis complex, D. batis, D. flossada and D. intermedia), Mustelus spp. (combining Mustelus mustelus and M. asterias), Sebastes spp. (combining Sebastes marinus, S. mentella and S. norvegicus), Dasyatis spp. (combining Dasyatis pastinaca and D. tortonesei), Galeus spp. (combining Galeus melastomus and G. atlanticus), Coregonus spp. (combining Coregonus maraena and Coregonus oxyrinchus), Raja brachyura (including Bathyraja brachyurops) and Deania calcea (including D. profundorum). After grouping, a total of 102 unique taxonomic groups were retained on the OSPAR sensitive fish species list for four OSPAR regions (Greater North Sea, Celtic Sea, Bay of Biscay and Wider Atlantic).

Data file names reflect the OSPAR region sampled, country conducting the sampling, fishing gear and time of years of sampling (as defined by Greenstreet and Moriarty 2017), e.g.: BBICFraBT4 refers to Bay of Biscay and Iberian Coast data from France by a Beam Trawl survey in quarter 4 of the year andGNSIntOT3 refers to Greater North Sea data from International (multiple countries) sampling by an Otter Trawl survey in quarter 3 of the year etc.

Scientific trawl survey data are submitted to the ICES Database of Trawl Surveys (DATRAS):http://www.ices.dk/marine-data/data-portals/Pages/DATRAS.aspx The DATRAS reporting format is detailed online: https://datras.ices.dk/Data_products/ReportingFormat.aspx The metadata relating to the ICES co-ordinated surveys are available here: http://www.ices.dk/marine-data/data-portals/Pages/DATRAS-Docs.aspx

ICES Data Centre host the database of trawl surveys (DATRAS) for groundfish and beam trawl data. DATRAS has an integrated quality check utility. All data, before entering the database, have to pass an extensive quality check. Despite this errors and missing data arise, which are subsequently dealt with by the data submitters from the contributing countries as required. However, this screening process was implemented in 2009 for data from 2004 onwards. Since some survey time-series extend back to the 1960s, historic data (unless re-evaluated and re-submitted by contributing countries) may not have been subject to the same level of quality control as these more recent data. Furthermore, the type of information collected, the level of detail and resolution in the data, has gradually evolved over time. In order to derive a single format, quality assured monitoring programme data product covering the entire Northeast Atlantic region inconsistencies in the datasets required resolution. These corrections are detailed in ICES 2021a,b:Biological data for trawl surveys are downloaded directly from DATRAS in raw exchange format (known as “HL data”). Ancillary data were processed by ICES 2021a,b to create the "SweptAreaAssessmentOuput" (which replaces the “HH data”) and these were downloaded from the same location: https://datras.ices.dk/Data _ products/Download/Download _ Data _ public.aspx) For the biodiversity assessment of sensitive species, the data are processed to create a standalone dataproduct on species occurrence (presence and absence) and haul location. Initially, valid hauls are subset to determine the Standard Monitoring Programme (i.e. excluding hauls of duration shorter than 13 minutes or longer than 66 minutes) and these hauls are used to define the Standard Survey Area (excluding areas sampled infrequently over time) following the methods detailed in Greenstreet and Moriarty 2017). Biological data were accepted with ICES SpecVal of 1, 4, 5, 6, 7, 10 (see http://vocab.ices.dk/ for further information on SpecVal categories). Additional QA/QC is made at this step to determine if species identification issues are present in the raw biological data.

References: Greenstreet S P R and M Moriarty 2017. Manual for Version 3 of the Groundfish Survey Monitoring and Assessment Data Product. Scottish Marine and Freshwater Science Vol 8 No 18. Published by Marine Scotland. ISSN: 2043-7722. DOI: 10.7489/1986-1 ICES. 2021a. Workshop on Fish of Conservation and Bycatch Relevance (WKCOFIBYC). ICES Scientific Reports. 3:57. 125 pp. https://doi.org/10.17895/ices.pub.8194 ICES. 2021b. Workshop on the production of abundance estimates for sensitive species (WKABSENS). ICES Scientific Reports. 3:96. 128 pp. https://doi.org/10.17895/ices.pub.8299

FishMap Môn is a collaborative project between Natural Resources Wales (NRW), recreational and commercial fishing sectors, and the Welsh Government. The project sets out to inform the sustainable management of the Welsh marine environment by collecting and mapping fishing activity and combining it with information on seabed habitats and their sensitivity to such activity.

Fishing Intensity is described as the effort of a fishing activity in a given area. For example, this could be the number of pots hauled per day per hectare. It is also described as one of three levels, but the actual intensity value is derived differently for each fishing type. The purpose of this data capture was to examine the spatial extent of fishing activities and the determine the habitats affected by different fishing gear types in an area around Anglesey.

The data consists of output generated for nine different gear types - King Scallop Dredging, Queen Scallop Dredging, Mussel Seed Dredging, Light Otter Trawling, Bottom Set Nets, Potting, Sea Angling and Casual and Professional Hand Gathering. There are multiple outputs for Potting and Sea Angling for commercial and recreational sectors. From the attribute _overlaps" it is possible to determine the number of vessels/ fishers operating in a particular area.

Due to the sensitivity of fishing intensity related data two versions of the FishMap Môn intensity level data were created both a detailed and generalised version. This detailed version of the dataset shows the precise locations of the fishing activities and the data also contains specific information about the fishing activities and so is restricted to the FishMap Môn Team and certain partners - contact the dataset owner for more information. "

Estimates of home range sizes for marine fishes are essential for designing and assessing the effects of spatial wildlife conservation policies and management interventions. However, in situ studies of marine species movement are challenging and often expensive, resulting in a paucity of data on the home range size of the vast majority of marine fishes. Here, we develop a set of new datasets, which we have collectively named Marine Fish Movement, that synthesises published empirically evaluated home ranges reported for adult marine fishes that interact with fisheries and leverage these data to estimate home range sizes for unstudied species. The empirical data contain estimated home range sizes (km2) for 193 species across 63 family groups from 179 studies published between 1971 and 2022. We use a Random Forest regression model to estimate home range sizes (km2) for 664 fished marine species currently lacking home range estimates. Marine Fish Movement can inform spatial interventions i..., , All data files are CSV (comma-separated value) files and can be opened in any program (e.g., Microsoft Excel, OpenOffice Calc, Google Sheets)., ---

GENERAL INFORMATION

Title of Dataset: Marine Fish Movement

Author Information:

Darcy Bradley*, Alicia M. Caughman*, Sandra A. Fogg, Reniel B. Cabral, Juan Mayorga, Whitney Goodell, Katherine D. Millage, Timothy D. White

*Co-first authors. Corresponding author: Darcy Bradley (darcybradley@ucsb.edu)

Funding: National Geographic Society's Pristine Seas Program

PROJECT INFORMATION

Project name: Marine Fish Movement: home range sizes for commercially relevant species

Project description: Estimates of home range sizes for marine fishes are essential for designing and assessing the effects of spatial wildlife conservation policies and management interventions. However, in situ studies of marine species are challenging and often expensive, resulting in a paucity of data on the home range size of the vast majority of marine fishes. Here, we develop a set of new datasets, which we collectively refer to as Marine Fish Movement, that synthesis...

Landing data covers total catches of a sampled fishing vessel that also includes fishing gear, fishing ground, and supporting devices. The variables of these data are: organization/project; ID trip; surveyor; landing site/collector; WPP; landing time; commodity; vessel name/owner; catcher; container; number of fishing vessels; fishing gear; FAD; light; fishing areas; number of days per trip; number of days per catch; number of settings; number of hooks; total catch in volume (Kg); total catch in unit (Ind); local Name; common name; species name; haul (Kg); units (Ind). The landing data can be used to estimate catch per unit effort (CPUE) of the fisheries and socioeconomics of the fisheries including associated cost.