11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

Identification of significant concentrations of sea pens in the Gulf of St. Lawrence biogeographic unit using Kernel density estimation (KDE).This method was applied to create a modelled biomass surface for each taxa and an aerial expansion method was permitted to identify significant concentrations. Only geo-referenced biomass data have been used to identify the “hot spots”. The borders of the areas were refined using knowledge of null catches and species distribution models. Predictive models were produced using a random forest machine-learning technique. For more details, please refer to this report: Kenchington, E., L. Beazley, C. Lirette, F.J. Murillo, J. Guijarro, V. Wareham, K. Gilkinson, M. Koen Alonso, H. Benoît, H. Bourdages, B. Sainte-Marie, M. Treble, and T. Siferd. 2016. Delineation of Coral and Sponge Significant Benthic Areas in Eastern Canada Using Kernel Density Analyses and Species Distribution Models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/093. vi + 178 p.http://waves-vagues.dfo-mpo.gc.ca/Library/40577806.pdfThe present layer only contains the analysis results for sea pens. Purpose:As part of the Canada's commitment to the identification and protection of sensitive benthic marine ecosystems, maps of the location of significant concentrations of corals and sponges on the east coast of Canada were produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). However, only the sponges (Porifera) and sea pens (Pennatulacea) have been considered in the analysis concerning the Gulf of St. Lawrence biogeographic unit.

Identification of significant concentrations of sponges in the Gulf of St. Lawrence biogeographic unit using Kernel density estimation (KDE).This method was applied to create a modelled biomass surface for each taxa and an aerial expansion method was permitted to identify significant concentrations. Only geo-referenced biomass data have been used to identify the “hot spots”. The borders of the areas were refined using knowledge of null catches and species distribution models. Predictive models were produced using a random forest machine-learning technique. For more details, please refer to this report: Kenchington, E., L. Beazley, C. Lirette, F.J. Murillo, J. Guijarro, V. Wareham, K. Gilkinson, M. Koen Alonso, H. Benoît, H. Bourdages, B. Sainte-Marie, M. Treble, and T. Siferd. 2016. Delineation of Coral and Sponge Significant Benthic Areas in Eastern Canada Using Kernel Density Analyses and Species Distribution Models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/093. vi + 178 p.http://waves-vagues.dfo-mpo.gc.ca/Library/40577806.pdfThe present layer only contains the analysis results for sponges. Purpose:As part of the Canada's commitment to the identification and protection of sensitive benthic marine ecosystems, maps of the location of significant concentrations of corals and sponges on the east coast of Canada were produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). However, only the sponges (Porifera) and sea pens (Pennatulacea) have been considered in the analysis concerning the Gulf of St. Lawrence biogeographic unit.

Administrative and territorial subdivisions of the City of Sherbrooke.attributs:ID - Unique identifierNumero - District numberName - Borough name - Borough name**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The data represents the relative expense of fertilizer and lime in the agricultural area of Alberta. It is an estimate of the degree to which agriculture may affect nutrient levels in surface and groundwater. The classes shown on the map are ranked between 0 (lowest) and 1 (highest).Mapping the relative values of fertilizer expenses by SLC polygon area is useful as an indication of where more fertilizer is applied in the province and as a proxy indicator for crop production.It also suggests the relative agricultural intensity in various parts of the province. This resource was created in 2002 using ArcGIS.

This dataset is comprised of locations and current information for all schools for Kindergarten to Grade 12 in British Columbia. Indicators are included for schools that offer French programs including: Core French, Early French Immersion, Late French Immersion and Francophone Program.

Timing windows are the period(s) during the year when work may be carried out in and about water bodies with the lowest risk to fish and wildlife species and habitat. Timing windows and terms and conditions vary based on regional differences in fish and wildlife species and habitat, and geography. The timing window of least risk to fish and fish habitat must be applied to all activities in water bodies, as well as tributaries that have a risk of depositing sediment into water bodies. Windows of least risk are designed to protect all fish species known to occur in a water body.

This record contains satellite-sensed chlorophyll-a concentration images of the Canadian Beaufort Sea at 1.1 km resolution. The dataset consists of 276 images, aggregated into two-week composites by calculating the mean value at each pixel, comprising years 1998 through 2020.The dataset spans two ocean colour sensors, MODIS-Aqua and SeaWiFS. The Arctic Ocean Empirical algorithm was used to calculate chlorophyll-a concentration, after images were corrected for atmospheric effects using the NIR-SWIR switching algorithm, and Remote Sensing Reflectance (Rrs) were produced. A linear transform in log-10 space was applied to the chlorophyll-a concentration measured by SeaWiFS to improve its correlation with chlorophyll-a concentration measured by MODIS-Aqua.The months of October through February were excluded from these datasets as the sun angle in winter is too low (e.g., polar night) for reliable data to be acquired, and the region is mostly covered in sea ice. For further details, see Galley et al., 2022.

Offshore wind represents a potentially significant source of low-carbon energy for Canada, and ensuring that relevant, high-quality data and scientifically sound analyses are brought forward into decision-making processes will increase the chances of success for any future deployment of offshore wind in Canada. To support this objective, CanmetENERGY-Ottawa (CE-O), a federal laboratory within Natural Resources Canada (NRCan), completed a preliminary analysis of relevant considerations for offshore wind, with an initial focus on Atlantic Canada. To conduct the analysis, CE-O used geographic information system (GIS) software and methods and engaged with multiple federal government departments to acquire relevant data and obtain insights from subject matter experts on the appropriate use of these data in the context of the analysis. The purpose of this work is to support the identification of candidate regions within Atlantic Canada that could become designated offshore wind energy areas in the future.The study area for the analysis included the Gulf of St. Lawrence, the western and southern coasts of the island of Newfoundland, and the coastal waters south of Nova Scotia. Twelve input data layers representing various geophysical, ecological, and ocean use considerations were incorporated as part of a multi-criteria analysis (MCA) approach to evaluate the effects of multiple inputs within a consistent framework. Six scenarios were developed which allow for visualization of a range of outcomes according to the influence weighting applied to the different input layers and the suitability scoring applied within each layer.This preliminary assessment resulted in the identification of several areas which could be candidates for future designated offshore wind areas, including the areas of the Gulf of St. Lawrence north of Prince Edward Island and west of the island of Newfoundland, and areas surrounding Sable Island. This study is subject to several limitations, namely missing and incomplete data, lack of emphasis on temporal and cumulative effects, and the inherent subjectivity of the scoring scheme applied. Further work is necessary to address data gaps and take ecosystem wide impacts into account before deployment of offshore wind projects in Canada’s coastal waters. Despite these limitations, this study and the data compiled in its preparation can aid in identifying promising locations for further review.A description of the methodology used to undertake this study is contained in the accompanying report, available at the following link: https://doi.org/10.4095/331855. This report provides in depth detail into how these data layers were compiled and details any analysis that was done on the data to produce the final data layers in this package.

Oceanographic data from stationary moorings deployed as part of the Centre for Marine Applied Research's (CMAR) Coastal Monitoring Program.