Broad Ecosystem Units were mapped using predictive modeling methods from various data sources (ranging from 1:50,000 to 1:250,000 in scale) and are referenced to the CanVec digital spatial framework (1:50,000). Broad Ecosystem Units (BEU) are a level in the Yukon bioclimate ecosystem classification system that represents areas with similar broad vegetation communities, terrain type (soils and topography) within bioclimate zones. Broad Ecosystem Units are described in the accompanying report "Regional Ecosystems of West-Central Yukon, Part 1: Ecosystem descriptions ".The intended application for mapped broad ecosystem units is 1:100,000 or smaller (1:100,000 - 1:250,000 scale) - interpretations derived from the map products should not be applied at more detailed scales, even though the resultant 30m raster map allows users to view results at more detailed resolutions. With new information, boundaries and designations of Broad Ecosystem Units can change. Updates to Broad Ecosystem Units occur only periodically. For the most current information, or if you have questions, please contact the Ecological and Landscape Classification Program (ELC@yukon.ca).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Effective fisheries and habitat management processes require knowledge of the distribution of areas of high ecological or biological significance. On the Scotian Shelf and Slope, a number of benthic ecologically or biologically significant areas consisting of habitat-forming species such as sponges and deep-water corals have been identified. However, knowledge of their spatial distribution is largely based on targeted surveys that are limited in their spatial extent. We used a species distribution modelling approach called random forest (RF) to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals across the entire spatial extent of Fisheries and Oceans Canada’s (DFO) Maritimes Region. We also modelled the rare sponge Vazella pourtalesi, which forms the largest known aggregation of its kind on the Scotian Shelf. We utilized a number of data sources including DFO multispecies trawl catch data and in situ benthic imagery observations. Most models had excellent predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.760 to 0.977. Areas of suitable habitat were identified for each taxon and were contrasted against their known distribution and when applicable, the location of closure areas designated for their protection. Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. The RF and GAM models provided comparable results, although GAMs provided superior predictions of biomass along the continental slope for some taxonomic groups. In the absence of data observations, the results of this study could be used to identify the potential distribution of sensitive benthic taxa for use in fisheries and habitat management applications. These results could also be used to refine significant concentrations of these taxa as identified through the kernel density analyses.Cite this data as: Beazley, Lindsay; Kenchington, Ellen; Murillo-Perez, Javier; Lirette, Camille; Guijarro-Sabaniel, Javier; McMillan, Andrew; Knudby, Anders (2019). Species Distribution Modelling of Corals and Sponges in the Maritimes Region for Use in the Identification of Significant Benthic Areas. Published July 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/356e92f3-5bf3-4810-98b1-3e10cd7742aa

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Leaf area index (LAI) quantifies the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of time series of LAI observed between 2016 and 2025 over reclamation sites in Alberta, Canada May and October. The temporal frequency depends on cloud cover.

Wooded areas of interest and particular ecosystems or habitats covered by the Interim Control Regulation (RCI) Nature plan amended by Regulation 1274-2.attributs:mb_ID - Identifier of the wooded environmentCOHABparticular - Presence of a particular ecosystem or habitat - Presence of a particular ecosystem or habitat (Yes/No) INFORCI - Additional information on the Interim Control Regulation**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Landscape Units (Subdivisions) were developed as a part of the 2002 Northern Caribou Strategy and are used for landscape level planning within Wildlife Habitat Area 5-086. For details please see: [Apps, C. D., T. A. Kinley, and J. A. Young. 2001. Multi-scale habitat modeling for woodland caribou in the Itcha, Ilgachuz, and Rainbow mountains of west-central British Columbia. Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada.](http://www.env.gov.bc.ca/cariboo/env_stewardship/wildlife/inventory/caribou/northcar/hmi/hsi06-01.pdf)

STE_TEI_ATTRIBUTE_POLYS_SP contains Terrestrial Ecosystem Information (TEI) polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. These describe the physical and biological characteristics of ecosystems at a landscape level. TEI currently includes Terrestrial Ecosystem Mapping (TEM), Predictive Ecosystem Mapping (PEM), Sensitive Ecosystems Inventory (SEI), Terrain Mapping (TER), Wetland Mapping (WET) and Soil Mapping (SOIL). Mapping methods include manual air photo interpretation and modeling supported by limited field checking. **Current version**: v12 (2026-06-22)

Dominant Species Map 2015The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada.BC Species Likelihood 2015The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero.When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).

In permafrost dominated regions, a gap persists in our understanding of water resources, the influence of groundwater, and the impact of climate change at the regional scale. Regional scale modelling can help to advance the understanding of these impacts by integrating with regional climate models. For regional modelling to be tenable, ongoing development of modelling methods and conceptualizations is required. By developing a fully integrated numerical groundwater-surface water climate model using HydroGeoSphere (HGS) (Aquanty 2021) for a gauged basin within the discontinuous permafrost zone, this dataset allows the verification of existing numerical methods and the testing of various conceptualizations of integrated groundwater-surface water flow in permafrost regions at the regional scale. This work informs future modelling and forecasting of regional water resources in permafrost regimes.

Mapping of exceptional forest ecosystems (EFE) in the urban planning code (CDU) on the territory of Laval.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**