Sea level rise increases coastal flooding in many areas of Canada. The Canadian Extreme Water Level Adaptation tool has been developed to accommodate sea level rise. The infrastructure needs to be built higher in order to reduce the risk of flooding. The vertical allowance is the recommended height that the infrastructure to be raised in future years relative to year 2010. The vertical allowance depends on (1) statistics of historical storm surge and tides, and (2) the best estimate and associated uncertainty of future sea level rise. The vertical allowance preserves the frequency of flooding events at some future time under uncertain sea level rise. Vertical allowances are provided for scenarios based on the fifth assessment report (AR5) of IPCC for the period of 2020-2100 and the sixth assessment report (AR6) of IPCC for the period of 2020-2150. Cite this data as: Zhai, L., Greenan, B., Perrie, W. Data of: Vertical allowance gridded dataset for Canada. Published: February 2024. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/5c164079-9785-42fa-8fa5-d886ccbae3b3

This dataset accompanies the open access article "Improving satellite chlorophyll-a retrieval in the turbid waters of the Bay of Fundy, Canada" published in Estuaries and Coasts (https://doi.org/10.1007/s12237-024-01334-x). A full methods description is provided in the article. Briefly, we processed daily satellite data from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from 2003 to 2021 at 300 m resolution to understand and quantify spatial and temporal trends in chlorophyll-a concentration (chl-a, a measure of phytoplankton biomass), and suspended particulate matter concentration (SPM) in the Bay of Fundy surface waters. This dataset provides the median yearly and seasonal climatology of chl-a (mg m-3 ) and SPM (g m-3) from 2003 to 2021 as geotiff layers. Here winter is defined as January to March, spring as April to June, summer as July to September, and fall as October to December. Chl-a was calculated with the OCX-SPMCor algorithm and SPM was calculated with the Nechad et al 2010 algorithm. Cite this data as: Wilson, K., Hilborn, A., Clay, S., Devred, E. Data of Satellite derived surface chlorophyll-a and suspended particulate matter in the Bay of Fundy from 2003 to 2021. Published February 2024. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/272f5cf1-52bb-416b-b92a-8bc9384fc24d

The blue shark (Prionace glauca), is a species found in Atlantic Canadian waters which is commonly encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) and Smart Position and Temperature tag (SPOT) from Wildlife Computers were applied to blue sharks from 2004 to 2008 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial and recreational vessels from mid-August to early October, but mostly in September. A variety of tag models were deployed: PAT 4 (n=16), Mk10 (N=28), and SPOT3 (N=2) and 39 of 46 tags reported. The blue sharks tagged ranged in size from 124 cm to 251 cm Fork Length (curved); 30 were female, 15 were male and 1 was unknown sex. Time at liberty ranged from 4 – 210 days and 16 tags remained on for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

The SiIGsol-100m maps make it possible to document the spatial evolution of the properties of the mineral part of the soil at a spatial resolution of 100 m. These values were simulated by combining point observation data and spatially explicit covariates, derived from remote sensing data, using automated learning methods. These values should not be interpreted as an observation, but rather as the most likely value for a given distribution of observations and set of predictor variables. Soil property values are simulated for all non-urbanized land surfaces. All attributes whose value is likely to change with the depth are available in 6 layers, which make it possible to translate the evolution of properties with depth. The depth limits used come from the GlobalSoilMap.net project mapping standard: 00-05 cm, 05-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Map products are currently divided into 6 variables; the sand fraction, the silt fraction, the clay fraction, the pH, the cation exchange capacity and the proportion of organic matter. They cover the entire territory of the Province of Québecois located south of the 55th parallel.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The ‘Circa 1995 Landcover of the Prairies’dataset is a geospatial raster data layer portraying the rudimentaryland cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer.

The assessment of the status of eelgrass (Zostera marina) beds at the bay-scale in turbid, shallow estuaries is problematic. The bay-scale assessment (i.e., tens of km) of eelgrass beds usually involves remote sensing methods such as aerial photography or satellite imagery. These methods can fail if the water column is turbid, as is the case for many shallow estuaries on Canada’s eastern seaboard. A novel towfish package was developed for the bay-scale assessment of eelgrass beds irrespective of water column turbidity. The towfish consisted of an underwater video camera with scaling lasers, sidescan sonar and a transponder-based positioning system. The towfish was deployed along predetermined transects in three northern New Brunswick estuaries. Maps were created of eelgrass cover and health (epiphyte load) and ancillary bottom features such as benthic algal growth, bacterial mats (Beggiatoa) and oysters. All three estuaries had accumulations of material reminiscent of the oomycete Leptomitus, although it was not positively identified in our study. Tabusintac held the most extensive eelgrass beds of the best health. Cocagne had the lowest scores for eelgrass health, while Bouctouche was slightly better. The towfish method proved to be cost effective and useful for the bay-scale assessment of eelgrass beds to sub-meter precision in real time. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Using Sidescan and Video - Cocagne 2008. Published: November 2019. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/431c815e-65f0-477b-9389-060fa41ec955

In marine ecosystems, the combination of multiple, co-occurring human activities can degrade habitats, undermining the very ecosystem services on which human societies depend. To examine the combined risk to marine habitats from multiple, overlapping human activities and stressors in Atlantic Canadian waters, a cumulative habitat risk assessment was conducted across four bioregions: (1) Scotian Shelf-Bay of Fundy, (2) Northern Gulf of St. Lawrence (3) Southern Gulf of St. Lawrence and (4) Newfoundland and Labrador. This analysis combined spatial information for 20 different marine habitats and 36 human activities across five different sectors (climate change, land-based, marine, coastal, and commercial fishing) using a spatially explicit, exposure-consequence risk framework. This dataset provides the cumulative habitat risk map for Atlantic Canadian marine waters at a 1 km² resolution, as well as the habitat map used in the risk analysis. This project was funded by Fisheries and Oceans Canada through a Competitive Science Research Fund (CSRF) grant. For further information concerning specifics of the maps and methods, contact the data provider. References Arkema, K.K., Verutes, G., Bernhardt, J.R., et al. 2014. Assessing habitat risk from human activities to inform coastal and marine spatial planning: a demonstration in Belize. Environ. Res. Lett. 9(11): 114016. Cite this data as: Kelly, N.E. and Murray, C. Cumulative risk maps for marine habitats of Atlantic Canada. Published: April 2026. Coastal Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS.

The Prince Edward Island Detailed Soil Survey is a dataset series describing the spatial distribution of soils and associated landscapes in the Canadian province of Prince Edward Island. Soil landscape information compiled and published over the previous several decades provided the basis for the development of this relational database. The graphic soil landscape polygons are intended to be represented at a scale of 1:75,000. The associated soil landscape information and soil characteristics are described in a standard format in the Component (CMP), Soil Name File (SNF) and Soil Layer File (SLF) tables.

Significant climate change impacts are highly likely in all Canadian marine and freshwater basins, with effects increasing over time (DFO 2012). Climate models project that ecosystems and fisheries across Canada will be disrupted into the foreseeable future (Lotze et al. 2019; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2024). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021; Pepin et al. 2022). The Climate Risk Index for Biodiversity (CRIB) was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable manner, supporting climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022), regionally (Lewis et al. 2023; Boyce et al. 2024; Keen et al. 2023), for fisheries (Boyce et al. 2024), and in support of spatial conservation planning (Keen et al. 2023). This dataset contains climate vulnerability and risk estimates from the CRIB framework adapted to consider warming at both the sea surface and its bottom for 145 marine species of conservation or fisheries interest across Canada’s marine territory. Climate risk is available at a 0.25-degree resolution under two contrasting emission scenarios to 2100. For each species, location, and scenario, 12 climate indexes, three vulnerability dimensions, and an overall vulnerability and risk score are provided. The accompanying report describes the data, methods, and workflow used to calculate risk. This report also guides the interpretation of these data to inform and support climate-informed decision-making in Canada.

In 2011, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) expanded the process of generating annual crop inventory digital maps using satellite imagery to include British Columbia, Ontario, Quebec, and the Maritime provinces, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-5, DMC) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.