PURPOSE: To provide access to detailed stomach content data and associated metadata from Atlantic Bluefin Tuna sampled in the southern Gulf of St. Lawrence from 2018 to 2023. These data support fisheries science by contributing to analyses of predator–prey dynamics, diet composition, and ecosystem understanding, as well as informing stock assessment and fisheries management activities within Fisheries and Oceans Canada. DESCRIPTION: This dataset contains metadata and stomach content information collected from Atlantic Bluefin Tuna (ABFT) caught from mid-August to late September in the commercial fishery in the southern Gulf of St. Lawrence between 2018 and 2023. Stomach samples were primarily obtained from fish harvested near the eastern end of Prince Edward Island, with additional samples collected from the Miscou/Baie‑des‑Chaleurs area in 2018 and 2019. SAMPLING METHODS: Fish were measured to the nearest curved fork length (cm) and weighed to the nearest round weight (kg). Stomachs were obtained directly from harvesters or through a fish buyer and were stored at −20 ◦C before being processed in the laboratory. Stomachs identification numbers were cross-referenced with ABFT tag numbers recorded by fish provider in order to obtain logbook and port data (catch location, time, weight length, sex, gear, etc.) for each sample. Stomachs were thawed in the laboratory and the content was sorted and identified to the lowest possible taxonomic level. For each stomach, prey were weighed collectively as a taxonomic group and individually to the nearest 0.1 g. Dead bait used to capture ABFT, identified by cut marks, were recorded and weighed but excluded from the analysis. Live bait items cannot be identified from stomach content analyses. Only a few otoliths were found in 2018 and their degraded quality precluded performing ageing or species identification. Rare and small prey items such as algae and rocks were classified in the category “other”. Fish remains that could not be identified were classified in the category “Unidentified teleostei remains”. For 2019 to 2023, when stomach content items could not be visually identified and when tissue was available, tissue samples were collected and stored at −20 °C for DNA barcoding analysis. DNA extraction, mitochondrial cytochrome oxidase subunit 1 amplification, Sanger sequencing and species assignation were performed at the Plateforme d’Analyses Génomiques and Plateforme Bio-informatique of the Institut de Biologie Intégrative et des Systèmes (PAG-IBIS, Université Laval, Quebec city, QC, Canada, http://www.ibis.ulaval.ca/en/services-2/genomic-analysis-platform/). DNA was extracted from 20 mg of muscle tissue using the Omega Bio-tek E-Z-96 Tissue DNA Kit (Omega Bio-tek, Norcross GA, USA) following manufacturer instructions. The mitochondrial cytochrome oxidase subunit 1 region was amplified and sequenced as described in Hashemzadeh Segherloo et al., 2021). Sanger forward and reverse reads were analyzed independently using the Basic Local Alignment Search Tool against non-redundant sequences to identify the top hit for each sequence. When samples could not be identified by a top hit sequence they were classified as “unidentifiable fish”. Prey items that were successfully identified using DNA barcoding were incorporated into the stomach content analysis database and used in all subsequent diet analyses (abundance, occurrence and weight). The weight of the items used in the database was the weight of the remains as they were, and not reconstructed weights calculated for a live animal of the species identified by the barcoding. USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys related to UNEP-MARLIN data only. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Selection of cigarette related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Selection of surveys referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not exactly 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length) Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from MSFD monitoring data (UNEP-MARLIN protocol only) for all years. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

The assessment of the status of eelgrass (Zostera marina) beds at the bay-scale in turbid, shallow estuaries is problematic. The bay-scale assessment (i.e., tens of km) of eelgrass beds usually involves remote sensing methods such as aerial photography or satellite imagery. These methods can fail if the water column is turbid, as is the case for many shallow estuaries on Canada’s eastern seaboard. A novel towfish package was developed for the bay-scale assessment of eelgrass beds irrespective of water column turbidity. The towfish consisted of an underwater video camera with scaling lasers, sidescan sonar and a transponder-based positioning system. The towfish was deployed along predetermined transects in three northern New Brunswick estuaries. Maps were created of eelgrass cover and health (epiphyte load) and ancillary bottom features such as benthic algal growth, bacterial mats (Beggiatoa) and oysters. All three estuaries had accumulations of material reminiscent of the oomycete Leptomitus, although it was not positively identified in our study. Tabusintac held the most extensive eelgrass beds of the best health. Cocagne had the lowest scores for eelgrass health, while Bouctouche was slightly better. The towfish method proved to be cost effective and useful for the bay-scale assessment of eelgrass beds to sub-meter precision in real time. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Using Sidescan and Video - Cocagne 2008. Published: November 2019. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/431c815e-65f0-477b-9389-060fa41ec955

Nearshore marine construction activities often involve projects conducted directly in or adjacent to eelgrass beds and can have detrimental effects on eelgrass health, through physical destruction of beds, smothering of plants by sediment, and light reduction from turbidity. A liquefied natural gas (LNG) marine terminal is proposed to be constructed near Goldboro in Isaacs Harbour on the Eastern shore of Nova Scotia in an area where sediments are contaminated with heavy metals from historical goldmining tailings. We conducted a pre-impact assessment of the eelgrass beds in Isaacs Harbour and in adjacent contaminated and non-contaminated harbours. We used underwater video to precisely map the eelgrass bed in the direct construction footprint in Isaacs Harbour. We surveyed 169 stations along ~40 km of coastline from Wine Harbour to New Harbour to identify eelgrass presence or absence in the nearby region and provide data on the distribution and abundance of other sensitive fish habitat such as kelp and other macrophytes. Sediment samples were collected and analyzed for grain size, organic matter content and heavy metal contamination. We also collected eelgrass plants to assess plant condition using morphological and physiological metrics, and heavy metal contamination in plant tissues. The overall condition of eelgrass plants in the surveyed area fell within the range of healthy plant characteristics (morphometrics and carbohydrates reserves) seen elsewhere along the Atlantic coast. However, a few stations displayed high arsenic and mercury contamination in sediments, which translated in some cases to high contamination in eelgrass rhizomes and leaves. There would be significant risk of impact on benthic habitat and contamination of marine biota from resuspension of sediments during a construction and operation of a ship terminal in Isaacs Harbour. This pre-impact assessment will allow DFO to assess the LNG terminal construction proposal and develop appropriate mitigation and monitoring procedures. Collected data will also be used for habitat-forming species distribution modeling to inform marine spatial and conservation planning. Vercaemer, B., O’Brien, J. M., Guijarro-Sabaniel, J. and Wong, M. C. 2022. Distribution and condition of eelgrass (Zostera marina) in the historical goldmining region of Goldboro, Nova Scotia. Can. Tech. Rep. Aquat. Sci. 3513: v + 67 p. Cite this data as: Vercaemer, B., O’Brien, J. M., Guijarro-Sabaniel, J., Wong, M. Data of: Eelgrass (Zostera marina) study in the historical goldmining region of Goldboro, Nova Scotia (2020). Published: February 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/ee88aa17-fd30-4d4a-8924-897fd47cf560

Data layers show commercial fishery footprints for directed fisheries using bottom and pelagic longlines for groundfish and large pelagics respectively, and traps for hagfish, LFA 41 and Grey Zone lobster, snow crab, and other crab on the Scotian Shelf, the Bay of Fundy, and Georges Bank in NAFO Divisions 4VWX and Canadian portions of 5Y and 5Z. Bottom longline and trap fishery maps aggregate commercial logbook effort (bottom longline soak time and logbook entries) per 2-minute grid cell using 2002–2017 data. Pelagic longline maps aggregate speed-filtered vessel monitoring system (VMS) track lines as vessel minutes per km2 on a base-10 log scale using 2003–2018 data. The following data layers are included in the mapping service for use in marine spatial planning and ecological risk assessment: 1) multi-year and quarterly composite data layers for bottom longline and trap gear, and 2) multi-year and monthly composite data layers for pelagic longline gear. Additional details are available online: S. Butler, D. Ibarra and S. Coffen-Smout, 2019. Maritimes Region Longline and Trap Fisheries Footprint Mapping for Marine Spatial Planning and Risk Assessment. Can. Tech. Rep. Fish. Aquat. Sci. 3293: v + 30 p. http://publications.gc.ca/collections/collection_2019/mpo-dfo/Fs97-6-3293-eng.pdf

A towfish containing sidescan and video hardware was used to map eelgrass in two shallow northern New Brunswick estuaries. The sidescan and video data were useful in documenting suspected impacts of oyster aquaculture gear and eutrophication on eelgrass. With one boat and a crew of three, the mapping was accomplished at a rate of almost 10 km2 per day. That rate far exceeds what could be accomplished by a SCUBA based survey with the same crew. Moreover, the towfish survey applied with a complementary echosounder survey is potentially a more cost effective mapping method than satellite based remote sensing. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Beds Using Sidescan and Video - Shippagan 2007. Published: November 2019. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/6454594e-c8f9-41c4-801a-db125b8a8875

This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.

The Coastal Infrastructure Vulnerability Index (CIVI) was developed by Fisheries and Oceans Canada (DFO) to provide a standardized, evidence‑based approach for assessing the relative vulnerability of Small Craft Harbours (SCH) infrastructure to climate change. Building on the first‑generation CIVI released in 2018, this report presents the second‑generation framework, CIVI 2.0, which incorporates updated climate projections and additional engineering information. CIVI 2.0 adopts the widely used vulnerability framework of sensitivity, exposure, and adaptive capacity, enabling greater alignment with current climate‑risk science and enhancing interpretability for decision‑makers. The methodology integrates national datasets on coastal geomorphology, relative sea‑level change, ice‑climate projections, harbour condition, facility protection, replacement cost, harbour utilization, and geographic proximity. Indicators are normalized and combined using a geometric‑mean approach to reduce the influence of outliers while preserving regional variability. CIVI 2.0 also incorporates new contextual information—including fishery‑reliant communities, Indigenous communities, commercial landings, and species‑level climate‑risk estimates—to support more holistic decision‑making, while maintaining a strictly indicator‑based index for vulnerability scoring. Results highlight substantial regional variation in infrastructure vulnerability across Canada. Marine sites in the Gulf of St. Lawrence generally exhibit high exposure due to projected increases in relative sea level and reductions in ice days, whereas many British Columbia and inland sites exhibit lower overall vulnerability. The updated framework improves national consistency, enhances transparency, and provides a reproducible methodology to support adaptive planning and prioritization under changing climate conditions. CIVI 2.0 is intended as an operational tool for SCH program managers, engineers, and policy analysts, guiding capital planning, risk reduction, and long‑term climate‑resilience strategies across Canada’s fishing harbour network. Cite this data as: Greenan, B., Shackell, N., Daigle, R., Harbin, J., Ferguson, K., Brickman, D., Boyce, D. Coastal Infrastructure Vulnerability Index 2.0. Published August 2026. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/38923004-919c-4446-9824-fb99fd1a4368

The Coastal Environmental Baseline Program is a multi-year Fisheries and Oceans Canada initiative designed to work with Indigenous and local communities and other key parties to collect coastal environmental data at six pilot sites across Canada (Port of Vancouver, Port of Prince Rupert, Lower St. Lawrence Estuary, Port of Saint John, Placentia Bay, and Iqaluit). The goal of the Program is to gather local information in these areas in effort to build a better understanding of marine ecological conditions. The Maritimes region has developed a physical oceanography program to align with the oceanographic interests and data needs of local communities and stakeholders, with the goal of sharing this information via open data. Starting in 2019, oceanographic parameters including temperature, salinity, depth, turbidity and currents have been continuously monitored at a series of locations covering a broad range of environments in the Port of Saint John and approaches vicinity, including the lower Saint John and Kennebecasis rivers, coastal fringe marshes and embayments, as well as the Musquash estuary Marine Protected Area (MPA). This dataset includes CTD data starting in 2019 and turbidity data from August 2020. Data collection methods range from bottom-mounted instruments in water depths of 10-50 meters, buoyant surface moorings, and hard-mounted instruments in intertidal zones. Intertidal data is interrupted during some low tide events, where the water level drops below the sensor, resulting in loss of functionality for periods up to 1-2 hours. Overall this dataset captures a dynamic balance between salt and fresh water on the highly tidal lower Saint John river, coastal seasonal dynamics in near-shore marine environments in the Musquash MPA, and the constant fluctuations of intertidal creeks and marshes. Update 2 - April 2025: included 2023-24 data Update 1 - Nov 2023: included 2022 data; removed daylight savings errors from 2019, 2020 and 2021; updated position for Evandale surface mooring.

The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) Mud The categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.