Description:Seasonal mean primary production from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period and depth-integrated to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods:Total primary production is the sum of diatoms and flagellates production. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal depth-integrated primary production climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Description:Seasonal mean primary production from the British Columbia continental margin model (BCCM) were averaged over the 1993 to 2020 period and depth-integrated to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods:Total primary production is the sum of diatoms and flagellates production. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal depth-integrated primary production climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

This layer represents areas where primary production is considered to be high. Primary production includes microscopic algal blooms, named phytoplankton, a food resource at the base of the food web of marine ecosystems. The knowledge of these zones can serve as a proxy to identify areas of the St. Lawrence where productivity is higher at different times of the year. Impacting his component may influence the rest of the life cycle in the affected area. Data were generated from the Gulf of St. Lawrence Biogeochemical Model (GSBM) developed by Dr. Diane Lavoie. This model makes it possible to calculate, using 10 variables, the primary production in each cell of the grid of the model. This calculation was done at a monthly resolution and a threshold was then applied to the data to keep only those cells where the estimated concentrations exceeded 20 mg C / m-2. This level of primary production is considered high.Additional InformationMonthly mean primary production (mg C m-2) in the first 50 meters of the simulated surface with the three-dimensional CANOPA-GSBM numerical model over a period of 13 years (1998-2010).The Gulf of St. Lawrence Biogeochemical Model (GSBM) simulates biogeochemical cycles of oxygen, carbon and nitrogen, and the biological components that determine the dynamics of the planktonic ecosystem. The model has 10 state variables. The NPZD (nutrients, primary production, zooplankton, detritus) model includes both simplified herbivorous and microbial food chains typical of bloom and post-bloom conditions. The export of biogenic matter at depth is mediated by the herbivorous food web (nitrate, large phytoplankton (diatoms), mesozooplankton, particulate organic matter), while the microbial food web (ammonium, small phytoplankton, microzooplankton, dissolved organic matter) is mainly responsible for nutrient recycling in the euphotic zone. Nitrate is also supplied by rivers. The tight coupling between small phytoplankton growth and microzooplankton grazing, autochtonous nitrogen release and (dissolved organic nitrogen) DON remineralization to ammonium (NH4+) is used to represent the dynamic of the microbial food chain. Biological transfer functions are derived from bulk formulations using mean parameters found in the literature. Biological variables are calculated in nitrogen units and algal biomass and production converted to Chl a and carbon units using fixed stoichiometric ratios. Detrital particulate organic nitrogen (PON) gets fragmented to dissolved organic nitrogen (DON) as it sinks toward the bottom. The phytoplankton growth rate is a function of light and nutrient availability. The available light for phytoplankton growth is a function of sea-ice cover, Chl a and colored dissolved organic matter (CDOM). The GSBM biogeochemical model, coupled with the CANOPA regional circulation model, was used to produce the Chl a layer. The grid of the model is 1/12° horizontally (about 6 x 8 km), 46 layers vertical and covers the Gulf of St. Lawrence, Scotian Shelf and Gulf of Maine regions. The vertical resolution is variable (between 6 m close to the surface to 90 m at depths of about 500 m). This model includes tidal forcing and the freshwater supply of the St. Lawrence River and the many rivers in the region, as well as atmospheric forcing (temperature, wind, etc.) produced by an independent model (National Center for Environmental Prediction (NCEP) Climate Forecast System Version 2). In addition, the circulation model is coupled with a model of sea ice that reproduces the seasonality of the ice cover in the region. The temperature and salinity fields are produced freely by the model and only constrained by monthly climatologies of these conditions at the boundaries of the model domain. The simulation was carried out over a part of the period covering the Zonal Monitoring Program (AZMP) from 1998 to 2010.

The data represents the relative amount of manure production in the agricultural area of Alberta. It is an estimate of the degree to which livestock production may contribute to nutrient loading, pathogens and odour. The classes shown on the map are ranked between 0 (lowest) and 1 (highest). This resource was created in 2002 using ArcGIS.

To estimate Grey Seal (Halichoerus grypus) pup production, photographic aerial surveys were conducted of the major Grey Seal breeding colonies in Canadian waters. The last survey was completed in January 2021. A total of 72,209 pups were counted on digital imagery from Sable Island, the largest breeding colony. Reconnaissance flights found no new colonies along the Atlantic coast of Nova Scotia and New Brunswick. Pup developmental stage surveys were undertaken on the ground or from helicopter at the seven largest breeding colonies to describe the birth distribution and correct the pup count for the estimate of pups born after the aerial photographic survey. The estimated number of pups born on Sable Island was 76,600 (SE = 2,900) and for Coastal Nova Scotia was 4,700 (SE = 550). For Sable Island, this is the first estimate of pup production since the 1960s that has not been a significant increase. Sable Island accounts for 77.5% of total pup production in Canada, and the change in trend in pup production on Sable Island is reflected in the trend in total pup production. Pup production in the Gulf of St. Lawrence continues to fluctuate with little evidence of trend over the past several decades, while at the more recently-established breeding colonies in southwest Nova Scotia, pup production continues to increase.In February 2026, the time series of grey seal pup production estimates for Maritimes Region was made open source. The open data was set up with no abbreviations or codes and restricted to just Maritimes Region. Notably there are counts in the dataset provided not linked to specific breeding colonies, for the miscellaneous locations the latitude and longitude are for roughly center of the colonies or region.Cite this data as: den Heyer., C. Data of Grey Seal Pup Production in Canadian Water. Published: April 2026. Ocean Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS. https://open.canada.ca/data/en/dataset/ea8962b2-0d75-4500-a3de-d631a1e5308f

The Oil and Gas Rights dataset contains the digital boundaries for existing exploration licences, significant discovery licences, production licences, former permits, former leases and the Norman Wells Proven Area. These boundaries are available for download on the Northern petroleum pesources Website at https://www.rcaanc-cirnac.gc.ca/eng/1100100036087/1538585604719.The Oil and Gas Rights dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC) primary source for northern petroleum titles geographic location on maps.

The objective of this project was to gather data to develop a model of the food web of the lower trophic levels of the nearshore area of the Beaufort Sea. Sampling took place from 2005 to 2008 using the CCGS Nahidik. The multidisciplinary character of the Nahidik program produced measurements of biology/ecology (primary production, phytoplankton, zooplankton, benthos, fish), chemical and physical oceanography, contaminants, geology and hydro acoustics. The data were collected in July and August of each year. The Nahidik program provided data to provide a baseline for future studies as well as an information source for environmental assessment.

The objective of this project was to gather data to develop a model of the food web of the lower trophic levels of the nearshore area of the Beaufort Sea. Sampling took place from 2005 to 2008 using the CCGS Nahidik. The multidisciplinary character of the Nahidik program produced measurements of biology/ecology (primary production, phytoplankton, zooplankton, benthos, fish), chemical and physical oceanography, contaminants, geology and hydro acoustics. The data were collected in July and August of each year. The Nahidik program provided data to provide a baseline for future studies as well as an information source for environmental assessment. This record contains the geographic coordinates and station names from 2005 to 2008.

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage.‌The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.

Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).