Saskatchewan's woodland caribou range is divided into two conservation units, based on the ecozone boundaries of the boreal shield (SK1) and the boreal plain (SK2). The SK2 Caribou Conservation Unit is further divided into three administrative units: SK2 East, SK2 Central and SK2 West.The SK1 (Boreal Shield) Caribou Conservation Unit encompasses the rocky shield, sandy plains and many lakes of northern Saskatchewan. The SK2 (Boreal Plain) Caribou Conservation Unit encompasses the more productive mixed-wood forests and lakes of central Saskatchewan, including large areas of low-lying peatlands. While these two units represent important differences in ecological conditions (e.g., habitat types, fire regimes, landforms, etc.) and human land use and management (e.g., overall levels and types of land use, fire management, etc.), the boundary between SK1 and SK2 does not represent a population boundary, as caribou move freely between the two areas. The large size of the SK2 Caribou Conservation Unit (i.e., 109,717 km2) is not well suited for range assessment and range planning activities, given the large variation in ecological conditions, habitat types, land use, and natural disturbance regimes across the Boreal Plain of Saskatchewan. As a result, three smaller caribou administrative units within SK2 were developed: SK2 East, SK2 Central and SK2 West. SK2 West is further subdivided into two smaller management subunits. At present, the SK1 area has not been sub-divided into administrative units. Find out more about woodland caribou and what the province is doing to manage their habitat and protect their populations: https://www.saskatchewan.ca/business/environmental-protection-and-sustainability/wildlife-and-conservation/wildlife-species-at-risk/woodland-caribou-program

The Global Ice-Ocean Prediction System (GIOPS) produces global sea ice and ocean analyses and 10 day forecasts daily. This product contains time-mean sea ice and ocean forecast fields interpolated to two grids. One of the grids is a 0.2° resolution regular latitude-longitude grid covering the global ocean (north of 80° S). The other grid is in north-polar stereographic projection with a 5-km spacing at the standard parallel 60° N and covers the Arctic Ocean and the neighbouring sub-polar seas. Data is available for 50 depths. The data files are in netCDF format and comply with the Climate and Forecast Conventions.

Modeled data showing the likely distribution of Killer Whales. 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.

Description:These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia.Methods:A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record.References:Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300.Data Sources:Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3DihubUncertainties:The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

Spot height locations captured at 1:1,000,000 scale from Digital Chart of the World data for the Yukon and surrounding area.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 ASTER instrument that was launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic capability using two telescopes in its near infrared spectral band to acquire data from nadir and backward views. Over 1.2 million scenes (level-1A products) acquired between March 2000 and August 2008 were used to generate the ASTER Global DEM (ASTGTM) collection.  For more information on the ASTER Global DEM, please see the metadata link.

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.

Placer Lake Classification - 50kDistributed 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)

Modeled data showing the likely distribution of gray whales. 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.

Since 1985, research surveys targeting scallops—primarily the sea scallop (Placopecten magellanicus) and, to a lesser extent, the Icelandic scallop (Chlamys islandica)—have been conducted by Fisheries and Oceans Canada (DFO) at one- or two-year intervals around the Magdalen Islands (fishing area 20A). The main objective of this survey is to assess the status of sea scallop stocks. The study area is situated south of the Magdalen Islands, where scallop beds are typically sampled at depths ranging from approximately 25 to 35 m. Sampling stations are randomly selected from a predetermined fixed grid, with sampling conducted along transects at these randomly assigned locations within the study area. Each station is sampled using a lined Digby scallop dredge (20 mm mesh size), towed for roughly 500 m across the seabed.This publication includes three files: the file biometriePetoncle_20, which contains detailed biometric data (species, size, weights and sex) from 1998 to 2024; the file taillePetoncle_20, which provides the size of the individuals sampled from 2009 to 2024; and the file traitPetoncle_20 which contains the abundances and densities per tow from 2009 to 2024. Data on abundances and densities per tow from 1998-2008 is available upon request.This dataset is updated every one to two years as data becomes available. A cleaning of aberrant data has been carried out. However, there is missing data in various columns of the dataset – use the data with caution. If you have any questions please contact DFO.DataManagementSAISB-GestionDonneesDAISS.MPO@dfo-mpo.gc.ca or the author. For certain time periods, associated species are identified and semi-quantitatively counted directly on the sorting table, and the results are presented in the following publications: - https://open.canada.ca/data/en/dataset/6529a4b0-f863-4568-ac71-1fa26cf68679- https://open.canada.ca/data/en/dataset/71732ad5-5c70-4dbf-916d-a94e1380c53b