This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.

This GIS dataset depicts the result of a geostatistical model of the bedrock topography in Alberta. This model is based on water-well litholog data, stratigraphic picks made on oil and gas petrophysical logs, data derived from digitized contour interpretations on bedrock topography maps, including bedrock outcrop locations and the 60 m grid-spaced Shuttle Rader Topography Mission digital elevation model. These data comprise the raster surface of Alberta Geological Survey Map 550, Bedrock Topography of Alberta, Canada.

This GIS dataset depicts a geostatistical model of the thickness of sediment overlying bedrock in Alberta. The sediment thickness surface was generated to assist the construction of a new geological framework for Alberta. The surface was derived by subtracting the bedrock topography of Alberta (Alberta Geological Survey Map 550) from the 60 m Shuttle Radar Topography Mission digital elevation model. These data comprise the raster surface of Alberta Geological Survey Map 551, Thickness of Quaternary and Neogene Sediment in Alberta, Canada.

Monthly mean currents from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request.The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process.When using data please cite following:Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p

Monthly mean temperature from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request.The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process.When using data please cite following:Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p

Summer, Winter Alpine, and Winter Forest-Dwelling habitat model for caribou in the Itcha, Ilgachuz, and Rainbow Mountains of West-Central BC. This habitat model was developed using telemetry from the Itcha-Ilgachuz, Rainbow, and Charlotte Alplands Herds. [Season] field should be used to split the data out into separate summer, winter alpine, and winter forest-dwelling habitat models. Model development is detailed in _Apps, C. D., T. A. Kinley, and J. A. Young. 2001. Multi-scale habitat modeling for woodland caribou in the Itcha, Ilgachuz, and Rainbow mountains of west-central British Columbia.Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada_. See also: https://catalogue.data.gov.bc.ca/dataset/caribou-habitat-model-for-the-western-cariboo-region-2017-. __Note: The 2017 habitat model covers a similar area, but does not supersede the 2001 habitat model.__

Monthly mean salinity from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request.The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process.When using data please cite following:Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p

To support improved groundwater geoscience knowledge for southern Ontario, a regional 3-D model of the surficial geology of southern Ontario has been developed as a part of a collaboration between the Ontario Geological Survey and the Geological Survey of Canada. Covering approximately 66,870 km2 in area, the model is a synthesis of existing geological models, surficial geology mapping, and subsurface data. The model is a simplified 9-layer reclassification of numerous mapped local surficial sediment formations in places over 200 m thick with a total volume of approximately 2,455 km3. The model integrates 1:50,000 scale surficial geology mapping with 90 m bathymetrically corrected topographic digital elevation model (DEM) and 8 existing local 3-D models. Archival subsurface data include 10,237 geotechnical and stratigraphic boreholes, 3,312 picks from geophysical surveys, 15,902 field mapping sites and sections, 537 monitoring and water supply wells and 282,995 water well records. Roughly corresponding to regional aquifer and aquitard layers, primary model layers are (from oldest to youngest): Bedrock, Basal Aquifer, Lower Sediment, Regional Till, Post Regional Till Channel Fill, Glaciofluvial Sediment, Post Regional Till Mud, Glaciolacustrine Sand and Recent Sediment / Organics. Modelling was completed using an implicit modelling application (LeapFrog®) complemented by an expert knowledge approach to data classification and rules-based Expert System procedure for data interpretation and validation. An iterative cycle of automated data coding, intermediate model construction and manual data corrections, expert evaluations, and revisions lead to the final 3-D model. A semi-quantitative confidence assessment has been made for each model layer surface based on data quality, distribution and density. This surficial geology model completes the development of a series of regional 3-D geological and hydrogeological models for southern Ontario.

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.

Prospectivity model highlights areas of Canada with the greatest potential for magmatic nickel deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.