The wildlife values area and site datasets represent the consolidation of 13 wildlife data classes collected by the Ministry of Natural Resources. The data estimates locations used by wildlife for various reasons, including: * breeding * calving and fawning * denning * feeding * staging * nesting * wintering * general habitat areas * nurseries * travel corridors Locations are represented as points (site) or polygons (area) and may be related to a specific species or described more generally. Wildlife values data is most often used to support policy and legislation associated with the Crown Forest Sustainability Act. The data may also be used to inform a wide range of resource management activities and decisions. There are additional sensitive features related to provincially tracked species and species at risk that are not available as part of the open data package. Sensitive features are subject to licensing and approvals and may be requested by contacting [geospatial@ontario.ca](geospatial@ontario.ca).

This resource documents a dataset of epifauna occurrences collected in 2021 during The Knowledge and Ecosystem-Based Approach in Baffin Bay (KEBABB) program developed by the Department of Fisheries and Oceans Canada (DFO) in collaboration with university partners. The overall objective of KEBABB is to characterize the variability and trends in physical, chemical, and biological oceanographic conditions and food webs supporting fisheries in the connected ecosystems of western Baffin Bay and Lancaster Sound. In 2021, DFO expanded the KEBABB program to Barrow Strait (KEBABS-Knowledge and Ecosystem-Based Approach in Barrow Strait), a key productive area of the Tallurutiup Imanga National Marine Conservation Area. The study took place in the Eastern Canadian Arctic (mainly in Baffin Bay, Davis Strait and Barrow Strait). Sampling is done along transects at fixed stations in the study area. Catches are collected with a 1.5 m Agassiz trawl (5 mm mesh net) for 3 minutes bottom-contact time at a target speed of 1.5 knots and with a 3 m benthic beam trawl (6.4 mm mesh net) for 15 minutes bottom-contact time at a target speed of 3 knots. A total of 16 stations were sampled for epifauna in 2021 between 85-850 m depth. Epibenthic invertebrates are identified to the lowest possible taxonomic level and photographed. All unknown specimens are frozen. In the lab, the identifications are validated or refined with the photos and the frozen specimens.The data are presented in Darwin Core and are separated in two files:The “Activité_épifaune_KEBABB_epifauna_event_en” file which contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The “Occurrence_épifaune_KEBABB_epifauna_en” file that contains the taxonomic occurrences.Further details on sampling can be found in the following report: Pućko, M., Charette, J., Tremblay P., Brulotte S., St-Denis B., Ciastek S., Hedges, K., Kuzyk, Z., Roy V., and Michel, C. 2022. An ecosystem-based approach in the eastern Arctic: KEBABB/S (Knowledge and Ecosystem-Based Approach in Baffin Bay/Barrow Strait) 2021 expedition report. Can. Manuscr. Rep. Fish. Aquat. Sci. 3250: viii + 58 p. https://publications.gc.ca/collections/collection_2022/mpo-dfo/Fs97-4-3250-eng.pdfUSE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood.The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998).Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval.To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns.Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008).The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all.Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.

Zooplankton and ichthyoplankton data are archived in the Institute of Ocean Sciences (IOS) Zooplankton Database. The data available spans from 1980 to 2018 and is an extraction of vertical net hauls as biomass by major taxa collected during surveys conducted in the oceanic and coastal waters of the Northeast Pacific Ocean. The majority of vertical net hauls in this data set were collected from 10 metres above the sea floor or an approximate maximum depth of 250 metres. For further data requests, please use the contact information provided.

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

Zooplankton samples were collected at Ocean Station "P" (50.0000, -145.0000) from 1956 to 1980, and were analyzed to various levels of taxonomic resolution over the years. Although summaries of these data have been previously published ((LeBrasseur 1965) and (Fulton 1978, 1983)) the detailed species data have never been published. This detailed dataset contains total zooplankton wet weights/m3 for the whole period of 1956 to 1980, as well as densities (numbers/m3) for five major taxa (copepods, chaetognaths, euphausiids, amphipods, and Aglantha) from 1964 to 1967, species identifications, counts and lengths for many samples collected between 1968 to 1980. The attached supporting document (Ocean Station "Papa" detailed zooplankton data: 1956 – 1980) contains information on the methods used to collect and process the data along with descriptions of a number of fairly minor points about the data that were not resolved. It also describes, in detail, the format of the original data files, the corrections/changes that were made to these files in creating this version, and how these errors affect what was published in Fulton (1983).The purpose of this record is to make the detailed data available to the scientific community in an electronic format and to provide a convenient reference for citing the detailed data. Waddell, Brenda J., and Skip McKinnell. 1995. Ocean Station "Papa" detailed zooplankton data:1956 - 1980. Can. Tech. Rep. Fish. Aquat. Sci. 2056: 21 p.

The Wildlife Management Unit Biologist Contact Boundaries dataset is comprised of all the polygons that represent the Wildlife Management Unit Biologist Contact Boundaries within the Province of Alberta. The dataset is to help determine which Biologist is responsible for each Wildlife Management Unit. Please refer to the metadata included with the data for full entity attribute information.

Distribution of dabbling duck species habitat in coastal British Columbia showing relative abundance (RA) by season and overall relative importance (RI). RI is based on project region and not on the province as a whole. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.

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. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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)

Table containing information relevant to animal disease investigations in Manitoba from 2012 to present.This table contains information relevant to animal disease investigations in Manitoba from 2012 to present, conducted by the Chief Veterinary Office (CVO). Information includes year, number of sites, number of linked sites, animal species, disease types and results. Updated on a weekly basis.  It is important that users are aware of the following caveats when reviewing data presented in the Animal Disease Investigations Dashboard: 1. Each investigation can have one or more cases involved depending on the number of herds or animals exposed.  Not all disease investigations are handled the same due to a partnership approach.  Diseases can be detected via surveillance, ad hoc reporting, or through other programs. 2. Rabies is a separate program. Please see Manitoba's Provincial Rabies Management Program for data related to Rabies Surveillance.3. Certain zoonotic diseases, such as salmonella or influenza, are also captured in more detail through other means.  The total occurrence of a zoonotic disease represented in this dashboard reflects occurrences where risks or exposures were deemed significant enough to warrant further investigation.  4. Historically, One Health Investigations that were predominantly focused on Public Health issues rather than Animal Health concerns were not captured in this system and will be underrepresented here. Fields included ( Alias (Field Name): Field description.) Year (Year): Year of the disease investigation Number of Sites (Number_of_Sites): Number of investigation sites Number of Linked Sites (Number_of_Linked_Sites): Number of sites linked to investigation sites Species/Class (Species__Class): Group of animal species Disease Type (Disease_Type): The type of disease that is being investigated Result (Result): The outcome (positive/negative) for the corresponding animal disease investigation