Predicted sand percentage (%) at a defined depth range.

The initial objective of this dataset was to study the seasonal movement patterns of harbour seals (Phoca vitulina) in the St. Lawrence Estuary and Sable Island. This study was part of a larger program that studied the foraging behavior of the species.Ten harbour seals were captured using gillnets from 1994 to 1998 at three sites in the St. Lawrence Estuary (Bic, n=1 individual; île Blanche, n=1; Métis-sur-Mer, n=5) and one site on Sable Island (n=3 individuals). The individuals were equipped with a satellite-linked time-depth recorder (Type3.10, Wildlife Computers) equipped with an Argos tag and placed on the back of the neck. For most individuals, satellite tracking began in September and continued until the following spring.The dataset consists of series of geographic locations of ten harbor seals with associated dates and times and movement speeds calculated from successive locations.The location data were only filtered based on the validity class provided by Argos. Class Z locations were excluded.

This map illustrates the distribution of soil parent material textures in the agricultural region of Alberta. Soil texture is defined by the relative proportions of the sand, silt and clay particles present. Soil textures are identified by classes using the Soil Texture Triangle illustrated below. The Soil Texture Triangle identifies the textural class of a soil at the intersection of the percent sand (x-axis) and the percent clay (y-axis). The percent silt of the soil is the remainder to add up to 100 percent. This resource was created in 2002 using ArcGIS.

Past wind directions are mapped from stabilized sand dunes in Canada and the northern United States. The map shows the near-surface wind directions responsible for transporting sand when the dunes were active. The directions were mapped by interpreting the orientation of parabolic dunes from open-sourced Lidar (light detection and ranging) derived digital terrain models. The map also shows new dune areas that add to the existing knowledge of dune fields in North America. The interpreted wind directions provide insight into the past atmospheric circulation patterns that occurred during the deglaciation of North America and the transition to modern circulation patterns that occur today.

Areas of non-productive forest land in the Cariboo Region. Includes icefields, alpine areas, rock, gravel pits, sand, clay, non-productive brush, etc. From FC1 circa 2002

The land features of the CanVec series contains landscape features of Canada such as islands, shoreline delineation, wooded areas, saturated soil features, landform features (esker, sand, etc.).The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool.Related Products (Open Maps Links):[Topographic Data of Canada - CanVec Series](https://open.canada.ca/data/en/dataset/8ba2aa2a-7bb9-4448-b4d7-f164409fe056)

Grain size is the most fundamental physical property of sediment, and these data are widely used in a variety of applications in science. Marine expeditions of the Geological Survey of Canada have been collecting grain size information on seabed and sub-seabed samples for over 50 years. Results have been recorded at 5th phi midpoints since the early 1990's in contrast to the earlier full, half or quarter phi interval end point values. Users of high resolution data must note that the sum of %Silt and %Clay equals the total %Mud makeup and that %Gravel, %Sand, %Silt and %Clay sum to 100%. Summary statistics include percentages of gravel, sand, silt, clay and mud as well as mean, kurtosis, skewness and standard deviation. The quality of these data varies. Results should be used with some caution as they may not be fully representative of seabed grainsize, particularly in areas of sandy and coarser sediment (e.g., sand and mud can leak out of the sampler during recovery). Canada makes no representation or warranty of any kind with respect to the accuracy, usefulness, novelty, validity, scope, completeness or currency of the data and expressly disclaims any implied warranty of merchantability or fitness for a particular purpose of the data. For the purpose of the web mapping service, grain size data are sorted by the expedition id. Coarse and detailed grain size distribution plots are shown when a point is chosen. If the sample contains more that one sub-sample ( e.g., as with a piston core sequence), the grain size plots are stacked in the display window from the top of the core downwards.

The data in this layer represents habitat suitability of soft-shelled clams (Mya arenaria) in the DFO Maritimes region, and was developed using an interdepartmental approach. Substrate classification data as well as bathymetric data for the Region were used to identify potential habitat for soft-shelled clams. Substrates identified as suitable included: sand, mud, sand and mud (Greenlaw, 2022). Contours (0m and 70m) from GEBCO bathymetric data were used to isolate depths between which soft-shelled clams are present. At this stage, a polygon reflecting soft substrates from 0-70m was created as "Suitable". A "Not Suitable" layer was similarly created using the substrates: boulders, continuous bedrock, discontinuous bedrock, gravel, mixed sediment, sand and gravel.To digitally validate the model, the Regional shoreline was divided into subsectors (developed by Environment and Climate Change Canada for the Canadian Shellfish Sanitation Program). Data from DFO (clam harvesting intensity) as well as Conservation and Protection (clam harvesting infraction locations) were used to established species presence within each sub-sector. If there had been any harvesting activity, legal or illegal, in an individual subsector, it was considered "Suitable and Validated". Merged into one final product, the model includes areas that are "Not Suitable", "Suitable", as well as "Suitable and Validated" for soft-shelled clam habitat.Cite this data as: Harvey, C., Vincent, M., Greyson, P., Hamer, A. (2024) Data of: A Soft-Shelled Clam (Mya arenaria) Habitat Suitability Model For The DFO Maritimes Region.Published: January 2024. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/c76f7813-d802-4b31-8ebe-476f8a7cacf2

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat in the Bay of Fundy (Northwest Atlantic, Canada). This method involves separating environmental variables derived from multibeam bathymetry (slope, bathymetric position index), backscatter, and oceanographic information (wave-shear current velocity) into spatial units (i.e. image objects) and classifying the acoustically and oceanographically separated units into 7 habitat classes (Bedrock and Boulders, Mixed Sediments, Gravelly Sand, Sand, Silty Gravel with Anemones, Silt, and Tidal Scoured Mixed Sediments) using in-situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically and oceanographically distinguishable. Reference:Wilson, B.R., Brown, C.J., Sameoto, J.A., Lacharite, M., Redden, A. (2021). Mapping seafloor habitats in the Bay of Fundy to assess macrofaunal assemblages associated with Modiolus modiolus beds. Estuarine, Coastal and Shelf Science, 252. https://doi.org/10.1016/j.ecss.2021.107294Cite this data as: Wilson, B.R., Brown, C.J., Sameoto, J.A., Lacharite, M., Redden, A. Bay of Fundy Benthoscape. Published May 2023. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/dbabd17a-a2c7-4b3f-9bd8-a77a9c7f9c1c