**ATTENTION! The files in this dataset are designed for streaming, not downloading. For the best experience, please follow the instructions available in the resources.**In replacement of the former Canadian Digital Elevation Model (CDEM) that is no longer supported, the Medium Resolution Digital Elevation Model (MRDEM) product is a multi-source product that integrates elevation data from the Copernicus DEM** acquired during the TanDEM-X Mission (AIRBUS, 2022), and the High Resolution Digital Elevation Model data derived from airborne lidar. This product provides a complete, 30 meters resolution, nationwide coverage for Canada. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived products. The spatial coverage extends into the USA, where needed, to provide coverage for cross-border watersheds in support of hydrological studies and applications. The MRDEM-30-DSM is partially based on the GLO-30 version of the Copernicus DEM** (hereafter named GLO-30). Since elevation values from the GLO-30 are referenced to the EGM2008 geoid model, they were transformed to the Canadian Height Reference System of 2013 (CGVD2013), using the CGG2013 geoid model. Where available, the MRDEM-30-DSM integrates surface data from the lidar-derived HRDEM mosaic, resampled from 1 m to 30 m. The process to generate the MRDEM-30-DTM is more complex. Where available, the HRDEM Mosaic derived from lidar was used since it already provides reliable terrain elevation values. The HRDEM Mosaic data used was resampled from 1m to 30m. Elsewhere, the processing workflow combines a forest removal model and a settlement removal model that is applied to the GLO-30 values in order to estimate the terrain elevation values. Both datasets are projected to Canada Atlas Lambert NAD83 (CSRS) (EPSG:3979).The MRDEM is referenced to the CGVD2013 which is the reference standard for orthometric heights across Canada.The product Medium Resolution Digital Elevation Model (MRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan.** This product was in part produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014- 2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.The organisations in charge of the Copernicus program by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30.

Yukon Energy Corporation (YEC) Power S ubstations are the locations where electrical power is transformed, generally from high to low voltage. This data was provided by YEC and will be updated when new substations are constructed.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

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.

Description:Spatial information on the distribution of juvenile Pacific salmon is needed to support Marine Spatial Planning in the Pacific Region of Canada. Here we provide spatial estimates of the distribution of juvenile fish in the Strait of Georgia for all five species of Pacific salmon. These estimates were generated using a spatiotemporal generalized linear model and are based on standardized fishery-independent survey data from the Strait of Georgia mid-water juvenile salmon mid-water trawl survey from 2010 to 2020. We provide predicted catch per unit effort (CPUE), year-to-year variation in CPUE, and prediction uncertainty for both summer (June–July) and fall (September–October) at a 0.5 km resolution, covering the majority of the strait. These results show that the surface 75 m of the entire Strait of Georgia is habitat for juvenile salmon from June through early October, but that distributions within the strait differ across species and across seasons. While there is interannual variability in abundances and distributions, our analysis identifies areas that have consistently high abundances across years. The results from this study illustrate juvenile habitat use in the Strait of Georgia for the five species of Pacific salmon and can support ongoing marine spatial planning initiatives in the Pacific region of Canada.Methods:Juvenile Salmon Survey DataThis analysis is based on surveys conducted between 2010 and 2020. Sets that lasted between 12 and 50 minutes and at depths less than or equal to 60 m (head rope depth) were included. The resulting survey dataset consists of 1588 sets. The analysis included all five species of Pacific salmon. For pink salmon, only even year surveys were included as they have a two-year life cycle and are effectively absent from the Strait in odd years.Geostatistical model of salmon abundance and PredictionsWe estimated the spatial distribution and abundance of each species of Pacific Salmon using geostatistical models fit with sdmTMB (Anderson et al. 2022). For each species, we modelled the number of individuals caught in a set, at a location and time using a negative binomial observation model with a log link. Predictions were made for each survey season (summer and fall) in each year from 2010 to 2020 over a 500 m by 500 m grid based on a 3 km buffer around the outer concave hull of the trawl coordinates. The concave hull was calculated using the ‘sf_concave_hull’ function from the sf package using a concavity ratio of 0.3, and excluding holes. Predictions were made as catch per unit effort (CPUE, for 60 minutes) for tows conducted in the surface waters (i.e., head rope at 0 m). Continuous estimates are provided at a 0.5 km resolution throughout the Strait of Georgia. These estimates consist of 1) mean catch per unit effort (CPUE), 2) year-to-year coefficient of variation (CV) of CPUE as a measure of the temporal variability, 3) binned biscale measures of mean vs. CV of CPUE to distinguish areas where abundance is consistently high vs. areas where it is high on average, but with high year-to-year variability, and 4) mean standard error in CPUE as a measure of uncertainty.See Thompson and Neville for full method details.Uncertainties:Although the models had relatively low uncertainty and the estimated spatial patterns reflected the spatial and temporal variation in CPUE in the surveys, it is important to understand the limitations of these model predictions. Because juvenile salmon are often aggregated, there is high variability in the CPUE in the survey data. Our model predictions represent the geometric mean CPUE and so are an average expectation, but do not reproduce the high inter-tow variability that is present in the survey data. Spatially, our predictions have low uncertainty in areas that are central within the standard survey track line. However, uncertainty is higher on the margins of the survey area, where there are fewer sets to inform those predictions.Data Sources:Juvenile salmon survey database from Salmon Marine Interactions Program, REEFF, ESD, Pacific Biological Station.Data is also available through Canadian Data Report of Fisheries and Aquatic Sciences publications.

Description:In the coming decades, warming and deoxygenation of marine waters are anticipated to result in shifts in the distribution and abundance of fishes, with consequences for the diversity and composition of fish communities. Here, we combine fisheries-independent trawl survey data spanning the west coast of the USA and Canada with high-resolution regional ocean models to make projections of how 34 groundfish species will be impacted by changes in temperature and oxygen in British Columbia (BC) and Washington. In this region, species that are projected to decrease in occurrence are roughly balanced by those that are projected to increase, resulting in considerable compositional turnover. Many, but not all, species are projected to shift to deeper depths as conditions warm, but low oxygen will limit how deep they can go. Thus, biodiversity will likely decrease in the shallowest waters (less than 100 m), where warming will be greatest, increase at mid-depths (100–600 m) as shallow species shift deeper, and decrease at depths where oxygen is limited (greater than 600 m). These results highlight the critical importance of accounting for the joint role of temperature, oxygen and depth when projecting the impacts of climate change on marine biodiversity.The rasters available in this dataset project the occurrence of each of the 34 groundfish species in a 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models (BCCM and NEP36). Each projection layer is provided as the mean projected occurrence as well as the lower and upper 95% confidence interval of projected occurrence.Methods:Estimated species response curves:We estimated how the observed distribution of groundfish species is determined by temperature, dissolved oxygen and seafloor depth using data from fisheries-independent scientific research trawls spanning the entire American and Canadian west coast. We included data from 4 surveys (NOAA West Coast, NOAA Alaska, NOAA Bering or DFO Pacific) from 2000 to 2019. For each species, we modelled occurrences in the coastwide trawl dataset using a generalized linear model (GLM) using the sdmTMB package in R v. 4.0.2. The predictors were temperature, log dissolved oxygen, log depth and survey. We included quadratic terms for temperature and log depth to allow species occurrences to peak at intermediate values. We fitted a breakpoint function for log dissolved oxygen to reflect the fact oxygen is a limiting factor. We assessed the forecasting accuracy of the SDM by comparing how well a model fitted to only data from 2000 to 2010 could forecast species’ occurrences in trawls within our focal region for the period of 2011–2019. We assessed all 77 groundfish species that were present in the overall trawl dataset, however the final analysis included only the 34 species for which the models had adequate forecasting ability.Projecting groundfish biodiversity changes:We based our groundfish biodiversity change projections on two regional models that downscale climate projections: the British Columbia Continental Margin model (BCCM) and the North-Eastern Pacific Canadian Ocean Ecosystem model (NEP36-CanOE). We used a historical baseline of 1986–2005 and future projected values for 2046–2065 based on RCP 4.5 and 8.5 emissions scenarios. Using the models that we validated in our forecasting accuracy assessment, we projected the occurrence of each species in each 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models.Uncertainties:Source survey data was collected by consistent methods with survey-grade GPS for all years included. Data quality is expected to be high. Modeled data are at 3 km resolution. Outputs are as accurate as source input models and are deemed to be of high quality and accurate based upon the precision of model inputs.Projecting biodiversity responses to climate change involves considerable uncertainty and our approach allows us to quantify some aspects of this. Of the uncertainty that we could quantify, roughly half was due to uncertainty in our SDMs and the remainder was due to regional ocean model uncertainty or scenario uncertainty. This amount of uncertainty in the SDMs is typical, stemming from the fact that contemporary species distributions are also influenced by other factors that we have not included in our model. In addition, although oxygen demand is understood to vary with temperature, limitations in the implementation of breakpoint models prevented us from estimating a temperature-dependent oxygen breakpoint. However, although somewhat unrealistic, this limitation is unlikely to have greatly increased the uncertainty in our SDMs because low oxygen concentrations occurred almost exclusively at depths where temperature variation and projected change was small.To reduce uncertainty due to year-to-year variation in climate, our model projections are based on 20-year climatologies with a future period that is far enough ahead to ensure that changes are unambiguously due to greenhouse gases. We have made projections based on two different emissions scenarios, and two different regional ocean models that are both downscaled from the same global model, the second generation Canadian Earth System Model (CanESM2), using different downscaling techniques. While the BCCM model was run inter-annually and then averaged to produce the climatologies, the NEP36 model used atmospheric climatologies with augmented winds to force the ocean model and produce representative climatologies. Comparing these regional projections provides an estimate of the uncertainty across different regional downscaling models and methods. We find that the projected impacts of climate change on the groundfish community are more sensitive to the differences in the regional ocean models than they are to the emissions scenarios used. However, these differences are in magnitude (changes tend to be larger based on NEP36 compared with the BCCM) rather than in direction, with both models resulting in similar overall patterns of biodiversity change and turnover for the groundfish community. Over the 60-year time period (1986–2005 versus 2046–2065) used in our study, our projections suggest that groundfish community changes are similar regardless of the scenario used.

Medium resolution digital elevation model - 30 meters (MRDEM-30). **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This lithium ground- and formation-water geochemical dataset will enable present and future companies to better evaluate their targets and characterize their resource estimates by being able to distinguish between background and anomalous concentrations of lithium throughout Alberta. The dataset comprises lithium geochemical data from ground and formation water in Alberta and near the Alberta border. The data were captured from several databases, including those from Alberta Geological Survey (oil and gas wells database, AERI and Beaver River Basin projects), and the Alberta Research Council. In total there are 1,511 records, of which 48 records have >75 mg/L Li. Nineteen analyses have >100 mg/L Li (up to 140 mg/L) and occur within the Middle to Upper Devonian Beaverhill Lake Formation and Woodbend and Winterburn groups of west-central to northwestern Alberta. Economic concentrations of lithium are known to form in either lithium pegmatite or in high-lithium brine and clay. A few of the world's oil-field waters also have medium to high lithium content. During the mid-1990s, several government reports showed that lithium values of up to 140 mg/L Li occurred in west-central Alberta formation waters of the Beaverhill Lake and Woodbend groups. however, minimal data were publicly released.

High-Resolution Digital Elevation Model (HRDEM) generated from LiDAR. This data collection includes a Digital Terrain Model (DTM) and a Digital Surface Model (DSM). The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013). Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The data in this collection have been reprojected from the source reference system to the Canada Atlas Lambert projection (EPSG:3979). **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The High Resolution Digital Elevation Model (HRDEM) Mosaic represents the current and continous coverage of high-resolution elevation data available in Canada. This version of the mosaic, available at a spatial resolution of 1 meter, is a combination of DEM data generated from airborne LiDAR data and available at this resolution. This data collection includes a digital terrain model (DTM) and a digital surface model (DSM) for the entire coverage. The mosaic product is referenced to the Canadian Geodetic Vertical Datum 2013 (CGVD2013). The data in this collection have been reprojected from the source reference system to the Canada Atlas Lambert projection (EPSG:3979). In case of overlap between data sources, pixel values are set according to the most recent source. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The High Resolution Digital Elevation Model (HRDEM) Mosaic represents the current and continous coverage of high-resolution elevation data available in Canada. This version of the mosaic, available at a spatial resolution of 1 meter, is a combination of DEM data generated from airborne LiDAR data and available at this resolution. This data collection includes a digital terrain model (DTM) and a digital surface model (DSM) for the entire coverage. The mosaic product is referenced to the Canadian Geodetic Vertical Datum 2013 (CGVD2013). The data in this collection have been reprojected from the source reference system to the Canada Atlas Lambert projection (EPSG:3979). In case of overlap between data sources, pixel values are set according to the most recent source. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**