Moving 6-year analysis of dissolved oxygen concentration in the Arctic Ocean, for each season in the period 1965-2022. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.1 degrees. Signal-to-noise ratio was fixed to 3.0, horizontal correlation length varying from 45 km near the coast to 150 km, and vertical correlation length varying between 25 and 1000 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: analysis with signal-to-noise ratio = 10, horizontal correlation length 60-200 km, and vertical correlation length 25-1000 m.

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

A novel towfish incorporating sidescan and video hardware was used to ground truth echosounder data for the nearshore of Halifax Harbour. The resulting sampling grid extended from the shoreline to a depth of 10 m, including Bedford Basin through the Inner Harbour to the Outer Harbour. Each of these three zones could be distinguished from the others based upon combinations of substrate type, benthic invertebrates, and macrophyte canopy. Bedford Basin had a relative lack of macrophytes and evidence of intense herbivory. The Inner Harbour was characterized by shoreline hardening due to anthropogenic activities. The Outer Harbour was the most “natural” nearshore area with a mix of bottom types and a relatively abundant and diverse macrophyte canopy. All survey data were placed into a GIS, which could be used to answer management questions such as the placement and character of habitat compensation projects in the harbour. Future surveys utilizing similar techniques could be used to determine long term changes in the nearshore of the harbour. Cite this data as: Vandermeulen H. Data of: A Video, Sidescan and Echosounder Survey of Nearshore Halifax Harbour. Published: September 2021. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/9122c3e2-3cfc-45d0-ac36-aecb306130f6

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 dataset provides 30-year, 50-year, and 100 year return levels for small craft harbours in British Columbia, relative to the mean sea level over 1993-2020. The return levels are derived from coastal sea levels for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

This product displays for DDT, DDE, and DDD, positions with percentages of all available data values per group of animals that are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.

Sea ice provides a wide range of microhabitats for diverse biota including microbes, single-celled eukaryotes (labelled algae), multicellular meiofauna, larger under-ice fauna (represented by amphipods), as well as polar cod (Boreogadus saida). Modified from Bluhm et al. (2017). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/sea-ice-biota" target="_blank">Chapter 3</a> - Page 35 - Figure 3.1.1

Bathymetric features, warm currents (red arrows), cold currents (blue arrows) and riverine inflow in the Arctic. Adapted from Jakobsen et al. (2012). Simplified Arctic Ocean currents (Fig. 2.1) show that the main circulation patterns follow the continental shelf breaks and margins of the basins in the Arctic Ocean. Different global models predict different types of changes, which can cause changes to Arctic ecosystems (AMAP 2013, Meltofte 2013). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/marine" target="_blank">Chapter 2</a> - Page 22 - Figure 2.1

Bay Scale Assessment of Nearshore Habitat Bras dOr Lake - Malagawash 2007 2008 data is part of the publication Bay Scale Assessment of Nearshore Habitat Bras d'Or Lakes. A history of nearshore benthic surveys of Bras d’Or Lake from 2005 – 2011 is presented. Early work utilized drop camera and fixed mount sidescan. The next phase was one of towfish development, where camera and sidescan were placed on one platform with transponder-based positioning. From 2009 to 2011 the new towfish was used to ground truth an echosounder. The surveys were performed primarily in the northern half of the lake; from 10 m depth right into the shallows at less than 1 m. Different shorelines could be distinguished from others based upon the relative proportions of substrate types and macrophyte canopy. The vast majority of macrophytes occurred within the first 3 m of depth. This zone was dominated by a thin but consistent cover of eelgrass (Zostera marina L.) on almost all shores with a current or wave regime conducive to the growth of this plant. However, the eelgrass beds were frequently in poor shape and the negative impacts of commonly occurring water column turbidity, siltation, or possible localized eutrophication, are suspected. All survey data were placed into a Geographic Information System, and this document is a guide to that package. The Geographic Information System could be used to answer management questions such as the placement and character of habitat compensation projects, the selection of nearshore protected areas or as a baseline to determine long term changes. Vandermeulen, H. 2016. Video-sidescan and echosounder surveys of nearshore Bras d’Or Lake. Can. Tech. Rep. Fish. Aquat. Sci. 3183: viii + 39 p. Cite this data as: Vandermeulen H. Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Malagawash 2007 - 2008. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on eutrophication and acidity, and covers the Greater North Sea and Celtic Seas. Data were aggregated and quality controlled by 'Aarhus University, Department of Bioscience, Marine Ecology Roskilde' in Denmark. ITS-90 water temperature and water body salinity variables have also been included ('as are') to complete the eutrophication and acidity data. If you use these variables for calculations, please refer to SeaDataNet for the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets. Regional datasets concerning eutrophication and acidity are automatically harvested, and the resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.13120/8xm0-5m67). Parameter names are based on P35 vocabulary, which relates to EMODnet Chemistry aggregated parameter names and is available at: https://vocab.nerc.ac.uk/search_nvs/P35/. When not present in original data, water body nitrate plus nitrite was calculated by summing all nitrate and nitrite parameters. The same procedure was applied for water body dissolved inorganic nitrogen (DIN), which was calculated by summing all nitrate, nitrite, and ammonium parameters. Concentrations per unit mass were converted to a unit volume using a constant density of 1.025 kg/L. The aggregated dataset can also be downloaded as an ODV collection and spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV).