Yukon Bioclimate Zones and Subzones Version 1.0 is derived from a 30 m Digital Elevation Model (DEM) and a set of "rule-polygons". Each rule-polygon contains attributes that define upper and lower elevation limits of the bioclimate zone/subzone(s) that occur within the rule-polygon. Rule-polygon attributes and extent is defined by field data, expert observation and/or available imagery. Where available, rule-polygons were derived from plot data representative of climate (i.e. reference site). Yukon Bioclimate Zones and Subzones Version 1.0 may be used at scales larger than 1:250,000 with caution. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of the user's choice. A map legend and map for this version is published in Southern Lakes Boreal Low Subzone (BOLsl): A Field Guide to Ecosite (Environment Yukon 2017). The Yukon Bioclimate Classification and Mapping project is ongoing, and subject to periodic updates or revisions. Because of this, the onus is on the end-user to ensure that they are using the most current version of the data. Although every effort has been made to ensure the correctness of the report and spatial products, there still may be errors. Please report errors in the data to the Custodian.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)

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)

The Canadian Aquatic Biomonitoring Network (CABIN) is an aquatic biomonitoring program for assessing the health of fresh water ecosystems in Canada. Benthic macroinvertebrates are collected at a site location and their counts are used as an indicator of the health of that water body. CABIN is based on the network of networks approach that promotes inter-agency collaboration and data-sharing to achieve consistent and comparable reporting on fresh water quality and aquatic ecosystem conditions in Canada. The program is maintained by Environment and Climate Change Canada (ECCC) to support the collection, assessment, reporting and distribution of biological monitoring information. A set of nationally standardized CABIN protocols are used for field collection, laboratory work, and analysis of biological monitoring data. A training program is available to certify participants in the standard protocols. There are two types of sites in the CABIN database (reference and test). Reference sites represent habitats that are closest to “natural” before any human impact. The data from reference sites are used to create reference models that CABIN partners use to evaluate their test sites in an approach known as the Reference Condition Approach (RCA). Using the RCA models, CABIN partners match their test sites to groups of reference sites on similar habitats and compare the observed macroinvertebrate communities. The extent of the differences between the test site communities and the reference site communities allows CABIN partners to estimate the severity of the impacts at those locations. CABIN samples have been collected since 1987 and are organized in the database by study (partner project). The data is delineated by the 11 major drainage areas (MDA) found in Canada and each one has a corresponding study, habitat and benthic invertebrate data file. Links to auxiliary water quality data are provided when available. Visits may be conducted at the same location over time with repeat site visits being identified by identical study name / site code with different dates. All data collected by the federal government is available on Open Data and more partners are adding their data continually. The csv files are updated monthly. Contact the CABIN study authority to request permission to access non open data.

The Shorezone Biobanding Polygons are an area representation of the various types of biota (flora and fauna) and their distribution, or lack thereof, found in the shoreunit.

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

A point file showing a collection of specific GPS spatial points recorded during the video taping of the shoreline. The points are represented by a specific latitude and longitude taken at a specific date and time. Each are associated with a specific BIOSLIDE at a specific SHOREUNIT in the Shorezone data

Description:This dataset consists of monthly mean simulation results from Canada's three Oceans: the Atlantic, Pacific and Arctic from 2015 to 2017.Abstract from the report:A numerical ocean model with biogeochemistry has been developed for a domain that spans Canada's three oceans: the Atlantic, Pacific and Arctic. The domain extends to 26°N in the Atlantic and 44°N in the Pacific, and spans the full width of each basin as well as the whole of the Arctic Ocean. The resolution is moderate to high (≈0.25°, 75 levels). A series of simulations was conducted to assess the best choices for biogeochemical model parameters across the diverse regions, using a variety of validation data sets including satellite ocean colour (surface chlorophyll and particulate organic carbon, integrated primary production), surface underway pCO2, and depth profiles of oxygen and nitrate concentration from ships and Argo floats. In addition to parameter values, processes examined include interactive sediments, fluvial nutrients, light attenuation by fluvial coloured dissolved organic matter (CDOM), and iron limitation. The results indicate that the optimal parameter set is one that limits phytoplankton losses to grazing and other processes so as to ensure strong biological drawdown of dissolved inorganic carbon and nutrients in spring and summer; among the parameter sets tested both insufficient and excessive drawdown were observed. Sensitivity to other processes such as interactive sediments, fluvial nutrients or CDOM attenuation was weak in most regions. In some regions, attenuation by CDOM or sequestration of nutrients in the sediment can substantially reduce primary production and zooplankton biomass, and fluvial nutrients can cause localized reduction of pCO2 by as much as 60 μatm. Iron limitation has an effect on the model solution in regions generally considered iron-replete; building a model that successfully spans iron-limited and non-iron-limited domains will require complete and accurate specification of iron sources and sinks.

In 2020, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-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 in Alberta, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Charlottetown, Fredericton, and Guelph. Due to COVID-19 travel restrictions, complete sampling coverages in NL, NS, NB and BC were not possible, as a result the general agriculture class (120) is found in these provinces in areas where there was no ground data collected.

This dataset is used is used to determine the significance or status of wetland classes and certain other natural heritage features. It is also used to set targets for Wilderness Class Provincial parks, State of the Forest reporting and to study natural disturbance regimes.

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m. Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management.For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018).Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697-714. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)