Average grid cell density is a polygon feature class containing the average density value for each grid cell per species/groups and season.

Rate of mineralization and vegetation of surfaces in the territory of the agglomeration of Montreal represented by polygons and based on the data [Mineral and vegetable surfaces of 2016] (https://donnees.montreal.ca/dataset/surfaces-minerales-vegetales) from the Geomatics Division of the City of Montreal. The data was calculated at the district level and at the level of the distribution islands of Statistics Canada. The data can also be consulted on the [interactive climate change vulnerability map] (https://experience.arcgis.com/experience/944e0b7104bd491591ccca829da24670/page/Page/).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data set presents the places of interest in the City of Montreal according to the classification carried out as part of the Montreal on Foot (MàP) initiative in 2020. The Montréal à Pied project aims to improve orientation and pedestrian paths throughout Montreal. Although the data concern the territory of the boroughs, places of interest may be located on the territory of linked cities for a better coherence of geographic information.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This collection is a legacy product that is no longer maintained. It may not meet current government standards. Users of Atlas of Canada National Scale Data 1:5,000,000 (release of May 2017) should plan to make the transition towards the new CanVec product. The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

The Ontario Raw Point Cloud (Imagery-Derived) is elevation point cloud data created from aerial photography from the Geospatial Ontario (GEO) imagery program. It was created using a pixel-autocorrelation process based on aerial photography collected by the imagery contractor for the GEO imagery program. The dataset consists of overlapping tiles in LAZ format and is 6.29 terabytes in size. Tiles are overlapping because the pixel-autocorrelation process extracts elevation values from overlapping stereo photo strips. No classification has been applied to the point cloud, however they are encoded with colour (RGB) values from the source photography. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis. __Related data__ For a product in non-overlapping tiles with a ground classification applied, see the [Ontario Classified Point Cloud (Imagery-Derived)](https://geohub.lio.gov.on.ca/datasets/febf17330adb4100a22738e1684b5feb). Raster derivatives have been created from the point clouds for some imagery projects. These products may meet your needs and are available for direct download. For a representation of bare earth, see [Ontario Digital Elevation Model (Imagery-Derived)](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-elevation-model-imagery-derived/about). For a model representing all surface features, see the [Ontario Digital Surface Model (Imagery-Derived)](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-surface-model-imagery-derived/about).

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)

Sidney Island Shorebird Surveys transects area feature.

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.

PURPOSE: As a part of a two-decade series of research, this study aims to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake. The main objectives are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. DESCRIPTION: Great Bear Lake, one of the largest lakes in North America, contains culturally and recreationally important fish species. Great Bear Lake is located in the sub-Arctic and Arctic Circle. As part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake, the main objectives of this study are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. From 2012 to 2019, surface water temperature data was collected at depths of 0.1 to 1.0 meters using a Hydrolab Series 5 Data Sonde Multiparameter instrument through partnered community-led and community/Fisheries and Oceans Canada/university partners collaborative sampling. The project has strong community involvement, including youth through the Guardian Program, to facilitate capacity building and community leadership in the long-term monitoring of Great Bear Lake fisheries and the aquatic ecosystem. This data is an extension of baseline data sets on water quality on the lake. These data will contribute to a better understanding cumulative impacts of climate change on the functioning of large northern lake ecosystems and provide a benchmark for monitoring further change. This data will be important for developing effective strategies for maintaining community-led aquatic monitoring and managing natural resources, particularly fish, which are expected to be increasingly important to communities with declines in other country foods such as caribou. We acknowledge the data were collected in the Sahtú Settlement Area and are made publicly available with the agreement of the Délı̨nę Renewable Resources Council (Délı̨nę Ɂehdzo Got’ı̨nę (Renewable Resources Council)). Collaborators include: the Community of Délı̨nę partners (data collection), Délı̨nę Renewable Resource Council, University of Manitoba, University of Queens, University of British Columbia, University of Alberta, Environment and Climate Change Canada, and Great Lakes Fisheries Commission, Sahtú Renewable Resource Board. Community of Délı̨nę partners and field workers that participated in data collection include Daniel Baton, Morris Betsidea, Joey Dillion, Jade English, Stanley Ferdanan, Bruce Kenny, Elaine Kenny, Darren Kenny, Greg Kenny, Joseph Kenny, Rocky Kenny, Ted Mackienzo, George Menacho, Bobby Modeste, Gina Nyelle, Brent Taniton, Allison Tatti, Gerald Tutcho, Archie Vital, Barbara Yukon, Caroline Yukon, Chris Yukon, and Cyre Yukon. Funding and logistical support was provided by: Northwest Territories Cumulative Impact Monitoring, Natural Sciences and Engineering Research Council of Canada, Environment and Climate Change Canada, Sahtú Renewable Resource Board, the Polar Continental Shelf Program, and Fisheries and Oceans Canada.

__The link: *Access the data directory* is available in the section*Dataset Description Sheets; Additional Information*__. **The forest maps in the second inventory** are available at a scale of 1/20,000. They cover almost all of the territory south of the 52nd parallel. Each file covers an area of approximately 250 km2. These digital cards correspond to the black and white paper cards with a dimension of 125 cm X 75 cm that have been scanned. They illustrate forest stands. They were prepared from the photo-interpretation of aerial photos on a scale of 1/15,000. Main components: * outline of forest stands; * sub-groupings of species in all stands; * type of vegetation (forest species, density, height and stage of development, origin); * age class; * disturbances; * nature of the land (peatlands, gravel, etc.); * territorial subdivisions; * hydrography; * transport network and bridges; * topography (level curves); * slope classes; * defoliation class.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**