Description:This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution.Methods:These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset.Abstract from Thompson et al. 2023:Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys.Data Sources:Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available.Uncertainties:These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.

The data shared are spatially explicit projections of wildfire burn probability across Canada’s forested ecozones under multiple future climate scenarios at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Four future climate scenarios were used to examine the spatiotemporal distribution of burn probability in the 21st century based on climate, vegetation, and topographic conditions ( Mulverhill et al. 2024). Projected burn probability is provided for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods, including 2021-2040, 2041-2060, 2061-2080, and 2081-2100, along with a baseline period representing average climate conditions and burn probability between 1991 and 2020. Outputs represent the probability that the conditions (climate, vegetation, topography) of a given pixel resemble those of historically burned areas. All non-climate variables were held static; therefore, projections represent burn probability under future climate scenarios given contemporary (2020) forest conditions. When using this dataset, please cite Mulverhill et al. (2025), as below.Mulverhill, C., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & Bater, C. W. (2025). Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios. Canadian Journal of Remote Sensing, 51(1). https://doi.org/10.1080/07038992.2025.2560347(Mulverhill et al. 2025).For a detailed description of the source data and methods applied to the baseline period to enable the Mulverhill et al. (2025) projections, see:Mulverhill, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., and Bater, C.W. 2024. “Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 209 pp. 279–295. https://doi.org/10.1016/j.isprsjprs.2024.02.006(Mulverhill et al. 2024).

The “Annual Unit Runoff (dam3/km2) for a 70% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 70% Probability of exceedence annual unit runoff.

The “Annual Unit Runoff (dam3/km2) for a 75% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 75% Probability of exceedence annual unit runoff.

The “Annual Unit Runoff (dam3/km2) for a 25% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 25% Probability of exceedence annual unit runoff.

The “Annual Unit Runoff (dam3/km2) for a 50% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 50% Probability of exceedence annual unit runoff.

The “Annual Unit Runoff (dam3/km2) for a 10% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 10% Probability of exceedence annual unit runoff.

This model is derived from geological, geophysical and other forms of geodata. Feature extraction used deep learning. Predictive modelling made use of the deep ensemble method. Displayed is a Pan-Canadian probability map of mineral potential of graphite. This map was generated using known graphite deposits and occurrences and their associated features. Higher probability values highlight areas with an increased probability of graphite mineral systems.

The data layer (.tif) presented are the results of using MaxEnt to produce a single species habitat map for Sea Scallop (Placopecten magellanicus) on German Bank (off South West Nova Scotia, Canada). Presence data derived from videos and still images were compared against environmental variables derived from multibeam bathymetry (Slope, Curvature, Aspect and Bathymetric Position Index (BPI)), and backscatter data (principal components: Q1, Q2, and Q3). Results represent a probability of habitat suitability for Sea Scallop on German Bank.Probability of suitability: The probability that a given habitat is suitable for a species based on presence data and underlying environmental variables (i.e. probability of species occurrence).Reference:Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Distribution of Sea Scallop on German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/2bb98a09-5daf-42c4-94e8-e5de718b821d

To ensure informed decision-making, management, and conservation of marine ecosystems along the British Columbia (BC) coastline, a comprehensive understanding of marine communities and habitats is necessary. Marine planning initiatives, including environmental incident response planning and the development of a marine protected area network, have long been challenged by significant gaps in our knowledge of the distribution of many nearshore marine species. This lack of critical information can impede our ability to effectively manage and protect vulnerable species and ecosystems.   We developed a set of species distribution models (single-SDMs) using data collected from the benthic habitat mapping survey. These models correlate species occurrence data with environmental variables. We used these models to make predictions of the probability of occurrence of each species across the entire BC coast. A cluster analysis of the single-SDM predictions was then used to determine biotopes, which are distinct combinations of physical and chemical water properties and associated biological communities.   We defined and mapped the spatial distribution and probability of nine nearshore macrofauna epibenthic biotopes along with three physiotopes based on environmental conditions. The definition of the biotopes includes correlated species, the type of substrate, and range of environmental conditions (slope, depth, current, temperature, salinity, and tidal current) occurring within each biotope. The biotopes defined here are a first, but important, step in quantitative bioregionalization for the Pacific region as the resolution of nearshore marine community information has been lacking in BC and has hindered marine spatial initiatives.  This dataset contains 71 of the 72 single-SDMs (which excludes Northern abalone, an endangered species), the spatial distribution of 9 biotopes and 3 physiotopes, and the species richness calculated as the sum of occurrence probabilities across all 72 species.