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.

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

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.

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.

This data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones.SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology.# Limitations1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates.2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates.3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version.4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions.Updates of this dataset will eventually be available on this metadata page.# Details on the product development and validation can be found in the following publication:Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118# Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available:• NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer• Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies• Height (meters): vegetation height• Crown closure (%): percentage of pixel covered by the tree canopy• Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)

This feature delineates forest health disturbances which includes Abiotic and Biotic forest health agents in the Yukon at a scale of 1:100,000. It is a management level forest health overview survey (as opposed to an operational level) - meaning that analysis and mapping are most effective close to the 1:100,000 scale and not larger. This Forest Health Overview surveys has been completed in various stages: 1 ) Starting with mapping the disturbance type, and severity from the Air using Fix wing aircraft on to hardcopy 1:100,000 scale maps ; 2 ) Transfering the Data to a clean Mylar for scanning and digitizing ; and, 3 ) Scanning and digitizing and populate data into GIS spatial database .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)

Bioterrain (TBT) contains polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. TBT divides the landscape into units using the Terrain Classification System for British Columbia and ecological criteria. Polygon attributes include (but are not limited to) surficial material, surface expression, geomorphological processes, drainage class and aspect. TBT methods include manual air photo interpretation supported by selective field checking. Bioterrain mapping is integral to ecosystem mapping and its derivative products. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: TEM, TEMNSS, TEMPRE, TEMSEI, TEMSET, TEMTSM, TBS, TBT, TEMWHR, TEMSDM, TEMPRW, and TEMSEW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

Yukon Biophysical Plot locations are derived from the Yukon Biophysical Information System (YBIS) database, which is the Government of Yukon's repository for storing biophysical data . Data contain a combination of site, soil and vegetation information which are collected by multiple agencies to support vegetation inventory, habitat assessment and baseline ecosystem products collected from 1975 - 2018. Data are collected and input into the database using standardized biophysical field forms as per the "Field Manual for Describing Yukon Ecosystems" data collection standards. Data contributors include Government of Yukon, Government of Canada, First Nations Governments, private contractors, academia and the public. Location accuracy of plot data may vary based on the project year and location collection method. YBIS is an active database, which is subject to periodic updates and 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 data, there still may be errors. Please report errors in the data to the Custodian.Contact Information:Ecological and Landscape Classification (ELC) Coordinator, elc@yukon.ca Ecological and Landscape Classification Program, Fish and Wildlife Branch, Department of Environment Government of Yukon, Box 2703, Whitehorse, Yukon Y1A 2C6 ph. (867) 667-3081Distributed 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 greenland shark (Somniosus microcephalus), is a species found in Atlantic Canadian waters which is occasionally encountered in commercial fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to greenland sharks from 2006 to 2009 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial vessels throughout the year and in Cumberland Sound (Pangirtung) on a scientific expedition in April 2008. A variety of tag models were deployed: PAT 4 (n=1) and Mk10 (N=15) and 14 of 16 tags reported. Greenland sharks tagged ranged in size from 250 cm to 549 cm Total Length (curved); 3 were female, 9 were male, and 4 were of unknown sex. Time at liberty ranged from 48 – 350 days and 9 tags remained on the sharks for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

This shapefile dataset was designed using polygons extracted from the Cartography of Coastal Ecosystems of Maritime Quebec geodatabase (2022, Laboratory for Dynamics and Integrated Management of Coastal Zones, Fisheries and Oceans Canada), described in the paragraph below. It consists of polygons with eelgrass and incorporates attributes describing the vegetation cover, the composition of the seagrass beds, the associated ecosystem name, the imagery data that allowed photo-interpretation and the presence or absence of field data. A unique sequence number associated with each polygon makes it possible to trace the paired polygon of the geodatabase of coastal ecosystems to attribute values not detailed in this shapefile. The study area includes all of the estuarine and maritime coasts of Quebec, with the exception of certain sectors, including most of the Lower North Shore and Anticosti Island, with the exception of villages of Kegaska, la Romaine, Chevery, Blanc-Sablon and Port-Menier. Some islands off the estuary and gulf coasts are part of the region covered, such as Île d'Orléans, Isle-aux-Coudres, Île Verte and Île Bonaventure.The Mapping of Coastal Ecosystems of Maritime Quebec was carried out jointly by the Laboratory for Dynamics and Integrated Coastal Zone Management (LDGIZC) of the University of Quebec at Rimouski as part of the Coastal Resilience Project; and by the Fisheries and Oceans Canada team, as part of the Integrated Marine Response Planning Program (IMRP). A classification of coastal ecosystems was carried out on more than 4,200 km of coastal corridor, focusing on estuarine and maritime coasts of Quebec located between the limit of the upper foreshore and the shallow infralittoral (about 10m deep). The mapping method developed is based on semi-automated segmentation and a photo-interpretation of coastal ecosystems, using very high resolution multispectral photographs (RBVI) acquired between 2015 and 2020 by DFO. The classification of polygons is based on the assignment of predefined value classes for the biological and physical attributes under study (e.g., substrates, plant type, vegetation cover, geosystem, etc. ). Helicopter-born oblique photographs and field data helped to reduce the uncertainty associated with photo-interpretation. UQAR and DFO conducted field sampling campaigns targeting the mediolittoral (4,390 stations) and the lower mediolittoral and infralittoral zones (2,959 stations), respectively , which validated some of the attributes identified by photo-interpretation and provided detailed information on community structure . The geodatabase of the Mapping of coastal ecosystems is hosted and managed by UQAR on their SIGEC-Web cartographic platform: https://ldgizc.uqar.ca/Web/sigecwebCredits © DFO (2023, Fisheries and Oceans Canada)Provencher-Nolet, L., Paquette, L., Pitre, L.D., Grégoire, B. and Desjardins, C. 2024. Cartographie des macrophytes estuariens et marins du Québec. Rapp. Tech. Can. Sci. halieut. Aquat. 3617 : v + 99 p.Grégoire, B., Pitre, L.D., Provencher-Nolet, L., Paquette, L. and Desjardins, C. 2024. Distribution d’organismes marins de la zone côtière peu profonde du Québec recensés par imagerie sous-marine de 2017 à 2021. Rapp. tech. can. sci. halieut. aquat. 3616 : v + 78 p.Grégoire, B. 2022. Biodiversité du relevé côtier Planification pour une intervention environnementale intégrée dans l’estuaire et le golfe du Saint-Laurent (2017–2021). Observatoire global du Saint-Laurent. [Jeu de données]Jobin, A., Marquis, G., Provencher-Nolet, L., Gabaj Castrillo. M. J., Trubiano C., Drouet, M., Eustache-Létourneau, D., Drejza, S. Fraser, C. Marie, G. et P. Bernatchez (2021) Cartographie des écosystèmes côtiers du Québec maritime — Rapport méthodologique. Chaire de recherche en géoscience côtière, Laboratoire de dynamique et de gestion intégrée des zones côtières, Université du Québec à Rimouski. Rapport remis au ministère de l’Environnement et de la Lutte contre les changements climatiques, septembre 2021, 98 p.