In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

The greenland shark (Somniosus microcephalus), is a species found in Atlantic Canadian waters which is occasionally encountered in commercial fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to greenland sharks from 2006 to 2009 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial vessels throughout the year and in Cumberland Sound (Pangirtung) on a scientific expedition in April 2008. A variety of tag models were deployed: PAT 4 (n=1) and Mk10 (N=15) and 14 of 16 tags reported. Greenland sharks tagged ranged in size from 250 cm to 549 cm Total Length (curved); 3 were female, 9 were male, and 4 were of unknown sex. Time at liberty ranged from 48 – 350 days and 9 tags remained on the sharks for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

Description: This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Data Sources: Model output Uncertainties: The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse and satellite SST, and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions.

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 - Richibucto 2007. Published: October 2017. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/ca7af8ba-8810-4de5-aa91-473613b0b38d

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of Saint Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of these taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.

Layer that includes the known information on the atlantic sturgeon breeding, feeding and concentration areas in the St. Lawrence River and Estuary according to a literature review of documents produced between 1993 and 2003. Additional Information Atlantic sturgeon's breeding, feeding and concentration areas were produced according to a literature review of the following documents: Communication personnelle par Hatin. D. 2003. Gagnon, M., Y. Ménard et J.-F. La Rue. 1993. Caractérisation et évaluation des habitats du poisson dans la zone de transition saline du Saint-Laurent. Rapp. tech. can. sci. halieut. aquat. 1920: viii + 104 p. Hatin. D., F. Caron et R. Fortin. 1999. Rapport d'opération : Déplacement et caractérisation du stock reproducteur d'esturgeon noir (Acipenser oxyrinchus) dans l'estuaire du fleuve Saint-Laurent. Faune et Parcs Québec, Direction de la faune et des habitats. 91 p. Hatin, D. et F. Caron. 2002. Déplacement et caractéristiques des esturgeons noirs (Acipencer oxyrinchus) adultes dans l'estuaire du fleuve Saint-Laurent en 1998 et 1999. Société de la Faune et des Parcs du Québec, Direction de la recherche sur la faune. 151 p. Naturam Environnement. 1995. Les possibilités de fraie de l'esturgeon noir dans l'estuaire de la rivière Manicouagan. Rapport présenté au Comité ZIP et à la Corporation d'amélioration et de protection de l'environnement (CAPE). Dossier 95-869. 75 p. Therrien, J. 1998. Rapport sur la situation de l'esturgeon noir (Acipenser oxyrinchus) au Québec. Ministère de l'Environnement et de la Faune, Direction de la faune et des habitats, Service de la faune aquatique. 45 p. Société de la Faune et des Parcs du Québec. 2000. Atlas des habitats critiques connus ou d'intérêt particulier pour les poissons du fleuve Saint-Laurent entre le port de Montréal et l'Île aux Coudres. Direction du développement de la faune. Trencia, G. Communication personnelle.

The contaminants in fish database is a compilation of contaminant data analysed from fish tissue at the Fresh Water Institute from 1970 to 2005. Data include lab number, region, analysis, organs, species, lake, form (whole fish, headon dressed, headless dressed), weight, and length and contaminant concentrations. Total mercury was the predominant contaminant measured. Results were expressed as ppm or ppb based on the parameter analyzed. Concentrations are expressed based on wet weight.

This dataset is no longer maintained by Agriculture and Agri-Food Canada and should be considered as an archived product. For current estimates of the agricultural extent in Canada please refer to the Agricultural Ecumeme produced by Statistics Canada. https://www150.statcan.gc.ca/n1/en/catalogue/92-639-X The Agriculture Extents of Canada derived from the 2001 census of agriculture, based upon soil landscape of Canada polygons (Version 3).

Data relating to the quality of water in the St. Lawrence River resulting from the special program planned and carried out by the Aquatic Environment Monitoring Network as part of the work that required the closure of the south-east interceptor in November 2015.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Towfish (sidescan and video) and echo sounder surveys were utilized to examine bottom type and macrophyte cover within the area of two coastal marine finfish aquaculture sites, one in New Brunswick (Welch Cove) and one in Nova Scotia (Jordan Bay). Both towfish and echo sounder data could be used independently of one another. However, the towfish data were very useful for ground truthing echo sounder based classifications. All survey data were placed into a GIS which could be used to answer management questions such as the placement of cages at sites, benthic impacts and baseline conditions to determine long term changes. Cite this data as: Vandermeulen H. Data of: Exploratory Video-Sidescan and Echosounder Survey of Welch Cove. Published: June 2021. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/0083e317-8bb5-492a-8348-c021e183f307