The Regional Ice Ocean Prediction System (RIOPS) provides ice and ocean forecasts up to 84 hours, four times per day on a 1/12° resolution grid (3-8 km). RIOPS is initialized using analyses from the Global Ice-Ocean Prediction System (GIOPS). Atmospheric fluxes up to 84 hours forecasts are calculated using fields from a component of the Global Deterministic Prediction System (GDPS) at 10km horizontal resolution

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

Description: The Regional Freshwater Index Layers dataset is composed of five single-band raster layers in GeoTIFF format. Each layer corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_WCVI, and Shelf_SalishSea. The main purpose of the dataset is to supplement existing layers that are used for species distribution modelling in the Pacific nearshore marine environment. Each regional freshwater index layer has the same spatial resolution and extent as other predictor layers for the corresponding region. While salinity layers exist from oceanographic models, they may not capture local difference from smaller scale rivers and streams entering the marine environment. Therefore, these layers are meant to complement salinity layers and are not suitable as a replacement for salinity data in species modelling. Methods: The cell values represent an estimate of freshwater influence on a 0-1 scale, where a higher value represents a greater level of freshwater influence. Details on how these values are determined is described in the supplemental information section of the metadata. The main data source for these derived products is the B.C. Freshwater Atlas, including the stream network and river polygons layers. Uncertainties: The values in the rasters are not a measure of salinity. The units are an index representing the level of freshwater influence weighted by the stream order and rescaled across regions on a 0-1 scale where only the region with the greatest value has a range of values 0-1 and the other regions are scaled relatively. This is done to ensure that values in one region can be compared to values in another region. As a result, some regions have very small values because the Salish Sea with the Fraser River is dominant, even after applying a rescale factor to the data.

Marine Ecosection classification for coastal and offshore British Columbia. The Marine Ecosections are: Johnstone Strait; Continental Slope; Dixon Entrance; Hecate Strait; Queen Charlotte Strait; Juan de Fuca Strait; North Coast Fjords; Queen Charlotte Sound; Strait of Georgia; Subarctic Pacific; Transitional Pacific; and Vancouver Island Shelf. The British Columbia Marine Ecological Classification (BCMEC) is a hierarchical classification that delineates Provincial marine areas into Ecozones, Ecoprovinces, Ecoregions and Ecosections. The classification was developed from previous Federal and Provincial marine ecological classifications which were based on 1:2,000,000 scale information. The BCMEC has been developed for marine and coastal planning, resource management and a Provincial marine protected areas strategy. A new, smaller level of classification termed ecounits developed using 1:250,000 scale depth, current, exposure, subsurface relief and substrate was created to verify the larger ecosections, and to delineate their boundaries. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.

This product displays for Cadmium, median values since 2012 that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2024. The median values ranges are derived from the following percentiles: 0-25%, 25-75%, 75-90%, >90%. Only "good data" are used, namely data with Quality Flag=1, 2, 6, Q (SeaDataNet Quality Flag schema). For water, only surface values are used (0-15 m), for sediment and biota data at all depths are used.

The Coastal Ice Ocean Prediction System (CIOPS) provides a 48 hour ocean and ice forecast over different domains (East, West, Salish Sea) four times a day at 1/36° resolution. A pseudo-analysis component is forced at the ocean boundaries by the Regional Ice Ocean Prediction System (RIOPS) forecasts and spectrally nudged to the RIOPS solution in the deep ocean. Fields from the pseudo-analysis are used to initialize the 00Z forecast, whilst the 06, 12 and 18Z forecasts use a restart files saved at hour 6 from the previous forecast. The atmospheric fluxes for both the pseudo-analysis and forecast components are provided by the High Resolution Deterministic Prediction System (HRDPS) blended both spatially and temporally with either the Global Deterministic Prediction System (GDPS) (for CIOPS-East) or an uncoupled component of the Global Deterministic Prediction System (GDPS) at 10km horizontal resolution (for CIOPS-West) for areas not covered by the HRDPS.

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

Mean zooplankton biomass (g/m³) at the 46 stations grouped into Atlantic Zone Monitoring Program (AZMP) transects under Quebec region responsibility. Mean zooplankton wet weights of the last ten years are displayed as 4 layers in june (2013-2022, 2020 not sampled) and 4 layers in november (2013-2022). The 4 layers stand for total zooplankton, mesozooplankton, macrozooplankton and euphausiids. The attached files contain the biomass data: a .png file for each station, showing time series of biomass for the total zooplankton and the euphausiids, and a .csv file containing the data themselves (columns : Station,Date(UTC), Latitude, Longitude, Sounding(m), Depth_max/Profondeur_max(m), Depth_min/Profondeur_min(m), Mesozooplankton/Mésozooplancton(g/m³), Macrozooplankton/Macrozooplancton(g/m³), Zooplankton/Zooplancton(g/m³), Euphausiids/Euphausides(g/m³)). Purpose The Atlantic Zone Monitoring Program (AZMP) was implemented in 1998 with the aim of increasing the Department of Fisheries and Oceans Canada’s (DFO) capacity to detect, track and predict changes in the state and productivity of the marine environment. The AZMP collects data from a network of stations composed of high-frequency monitoring sites and cross-shelf sections in each following DFO region: Québec, Gulf, Maritimes and Newfoundland. The sampling design provides basic information on the natural variability in physical, chemical, and biological properties of the Northwest Atlantic continental shelf. Cross-shelf sections sampling provides detailed geographic information but is limited in a seasonal coverage while critically placed high-frequency monitoring sites complement the geography-based sampling by providing more detailed information on temporal changes in ecosystem properties. In Quebec region, two surveys (46 stations grouped into transects) are conducted every year, one in June and the other in autumn in the Estuary and Gulf of St. Lawrence. Historically, 3 fixed stations were sampled more frequently. One of these is the Rimouski station that still takes part of the program and is sampled about weekly throughout the summer and occasionally in the winter period. Annual reports (physical, biological and a Zonal Scientific Advice) are available from the Canadian Science Advisory Secretariat (CSAS), (http://www.dfo-mpo.gc.ca/csas-sccs/index-eng.htm). Devine, L., Scarratt, M., Plourde, S., Galbraith, P.S., Michaud, S., and Lehoux, C. 2017. Chemical and Biological Oceanographic Conditions in the Estuary and Gulf of St. Lawrence during 2015. DFO Can. Sci. Advis. Sec. Res. Doc. 2017/034. v + 48 pp. Supplemental Information Zooplankton is sampled by bottom-surface vertical net tow with a conic 202 µm net and preserved in a 4% solution of buffered formaldehyde according to AZMP sampling protocol: Mitchell, M. R., Harrison, G., Pauley, K., Gagné, A., Maillet, G., and Strain, P. 2002. Atlantic Zonal Monitoring Program sampling protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223: iv + 23 pp.

Description: These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia. Methods: A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record. References: Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300. Data Sources: Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3Dihub Uncertainties: The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

This product displays for Benzo(a)pyrene, positions with values counts that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.