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.

Data from the analysis of sea surface temperature, sea surface salinity, bottom temperature, and bottom salinity, over the Gulf of Maine and Scotian Shelf, for 23 CMIP6 models. The analysis includes an evaluation of CMIP6 model performance for the CMIP6 historical (1950-2014) experiment. Future projections are summarized for CMIP6 scenarios SSP245 and SSP370 with the calculation of relative annual and seasonal changes between the historical period (1950-2014) and three future periods (2030-2039, 2040-2049, 2030-2049).Wang, Z., DeTracey, B., Maniar, A., Greenan, B., Gilbert, D. and Brickman, D., Future hydrographic state of the Scotian Shelf and Gulf of Maine from 23 CMIP6 models. Can. Tech. Rep. Hydrogr. Ocean. Sci. XXX: vii + XXXp.Cite this data as: Wang, Z., DeTracey, B., Maniar, A., Greenan, B., Gilbert, D. and Brickman, D. Future hydrographic state of the Scotian Shelf and Gulf of Maine from 23 CMIP6 Models. Published July 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/6247bb5a-14b3-461d-9ed3-b42553107bbc

1999 to 2023 surface temperature and salinity measured along the track of commercial ships, mostly between Montreal (Quebec) and St. John's (Newfoundland).Monitoring of surface water conditions in the Estuary and Gulf of St. Lawrence is carried out with different complementary methods such as thermosalinographs (TSG) installed on commercial ships. These ships are sailing all year long from Montreal to St. John’s, one round trip per week, and are sampling water near the surface (3 to 8 meters deep) to determine the temperature and salinity all along the route.PurposeThe recorded data are used as input to numerical forecasting models for sea ice conditions and as a monitoring tool for the Gulf of St. Lawrence.Annual reports are available at the Canadian Science Advisory Secretariat (CSAS), (http://www.dfo-mpo.gc.ca/csas-sccs/index-eng.htm). Galbraith, P.S., Chassé, J., Caverhill, C., Nicot, P., Gilbert, D., Lefaivre, D. and Lafleur, C. 2018. Physical Oceanographic Conditions in the Gulf of St. Lawrence during 2017. DFO Can. Sci. Advis. Sec. Res. Doc. 2018/050. v + 79 p.

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020).Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological InvasionsCite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

Hillshade created from the Swift Current LiDAR Project 2009 – DEM. The hillshade provides a 3 D effect for the landscape covered by this project.

Monitoring data from DFO invasive species monitoring programs, along with occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict present-day and project future distributions of 24 non-indigenous species (NIS) on North America`s east coast, and 31 NIS on its west coast. Future distributions were predicted for 2100, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) have been estimated by summing the occurrence probabilities of NIS. This data set includes the present-day and year 2100 species distribution modeling results for each species, and the estimated species richness.Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Updated species distribution models for marine invasive species hotspot identification. Published: November 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1439dcb3-82a6-40fd-a9a4-8f045b20ff5b

Summer, Spring, Early Winter, and Late Winter multi-scale habitat model for mountain caribou in the Western Cariboo Region / Columbia Highlands / Northern Columbia Mountains. [Season] field should be used to split the data out into separate summer, spring, early winter, and late winter habitat models. [Model development is detailed in _Apps, C. D. and T. A. Kinley. 2000. Multiscale Habitat Modeling for Mountain Caribou in the Columbia Highlands and Northern Columbia Mountains Ecoregions, British Columbia.Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada](http://www.env.gov.bc.ca/cariboo/env_stewardship/wildlife/inventory/caribou/mtncar/hmi/habitatmod04-00.pdf).

Description:Spatial information on ecologically important species is needed to support marine spatial planning initiatives in British Columbia’s (BC) marine environment. For data deficient taxa, such as shark species, species distribution models that integrate presence-absence data from different sources can be used to predict their coastwide distributions. Here we provide spatial estimates of the distribution of Blue Shark (Prionace glauca), Salmon Shark (Lamna ditropis), Pacific Sleeper Shark (Somniosus pacificus) and Bluntnose Sixgill Shark (Hexanchus griseus). These estimates were generated using spatial generalized linear mixed effects models and are based on data from two scientific surveys and the commercial hook and line, midwater trawl and bottom trawl fisheries. For each species, we provide predicted probability of occurrence and prediction uncertainty at a 3 km resolution for the British Columbia coast, and parameter estimates for model covariates (depth, slope, year, data source). Results show variable predicted distributions across species, with Blue Shark and Pacific Sleeper Shark showing higher probability of presence along the continental slope, while Salmon Shark show low probability of occurrence coastwide and Bluntnose Sixgill Shark show the highest probability of occurrence in the Strait of Georgia. The results from this study can support ongoing marine spatial planning initiatives in the BC and support the conservation and management of these important species.Methods:Data Sources The species distribution models (SDMs) are based on data from two fishery independent scientific surveys and from the commercial hook and line fishery, which are all conducted within Canadian Pacific waters. The scientific surveys include the Fisheries and Oceans Canada (DFO) hard bottom longline surveys and the International Pacific Halibut Commission (IPHC) fishery-independent setline survey. The study area is bound by the outer convex hull of these three data sources. Other DFO research surveys, such as the groundfish synoptic bottom trawl surveys, midwater trawl surveys and sablefish trap surveys were investigated as potential data sources, but were found to have insufficient presence observations for the species of interest to warrant their inclusion in the analysis. For more information on the details of the source data please refer to Proudfoot et al. 2024.Modelling Approach and Comparison For each species, we fit a suite of generalized linear mixed effects models (GLMMs) using the sdmTMB package (Anderson et al. 2022). For each species, we fit four models, each with a different set of fixed effects/environmental predictors. Additionally, we compared the predictive power of four models for each species, with each model having a different combination of environmental predictors (i.e., slope, depth, slope + depth, none). A summary of the candidate models is provided in Table 2 of Proudfoot et al. 2024. For each species, we selected the model with the highest predictive accuracy (assessed using the predicted log likelihood based on the cross-validation) as the best fit.Spatial Species Distribution Predictions We made predictions of species occurrence using the selected model and a 3 km resolution spatial prediction grid. Our predictions were made for the entire BC coast, and species distribution predictions were made using models fit to the full dataset, as opposed to models fit using cross-validation. We made predictions with year set to 2014 (the approximate midpoint of the dataset) and type set to IPHC (the dataset with the most even spatial distribution of data points).Uncertainties:Because limited survey and commercial catch data exists for deep areas off the continental shelf, predictions in these areas are likely more uncertain than predictions on the shelf. To illustrate this, uncertainty (standard deviation derived from the 500 simulated values from the joint precision matrix of selected models) was mapped across the full study area for each species. Additionally, because these models are based on data that likely do not span the full spatiotemporal extent of the species’ habitat (i.e., mid depths, surface waters, and data across all seasons may not be captured), these results illustrate a snapshot of occurrence but do not account for more complex migration and movement patterns undertaken by these species.

This dataset maps the thickness of the Lacombe aquitard as defined by outputs of a 3-D model of sandiness in the Paskapoo Formation. The Lacombe aquitard consists of >65% non-sandy material in stacked, 25 m thick slices above the base of the Paskapoo Formation.

Fish Habitat Assessment Output: 3 of 16High Water Level (75.4m ASL) - Nursery Habitat - High Vegetation Association Species (All Temperature Windows)Habitat suitability was assessed for the Bay of Quinte Area of Concern, at a 3 m grid resolution, using the Habitat Ecosystem Assessment Tool (HEAT), temperature algorithms, vegetation models, and water level input. Habitat classifications were based on three variables: depth (elevation), vegetation, and substrate; and modified by temperature suitabilities. The final suitability maps were based on documented habitat and temperature associations for the fish in the area. Different life stages (spawning requirements, nursery habitat, adult habitat) were modeled for the years of 1972-2011. Suitability values were scaled from 0 (not suitable) to 1 (highly suitable) and converted to suitability classes of very low, low, medium, and high. The final maps for each guild – life stage combination are maximum suitability values from the 39-year period modelled.