This dataset contains the data reported in Wesley R Ogloff, Randi A Anderson, David J Yurkowski, Cassandra D Debets, W Gary Anderson, Steven H Ferguson, Spatiotemporal variation of ringed seal blubber cortisol levels in the Canadian Arctic, Journal of Mammalogy, 2022;, gyac047, https://doi.org/10.1093/jmammal/gyac047Cite this data as:Wesley R Ogloff, Randi A Anderson, David J Yurkowski, Cassandra D Debets, W Gary Anderson, Steven H Ferguson. 2022 Spatiotemporal variation of ringed seal blubber cortisol levels in the Canadian Arctic. Arctic and Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, MB. https://open.canada.ca/data/en/dataset/e1c6b350-0159-11ed-8212-1860247f53e3

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

This dataset identifies locations of wilderness tourism trails. This is not a complete or up to date dataset.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)

Contains the boundaries of current British Columbia Strategic Land and Resource Plans (SLRPs). The plans can be accessed [here](https://www2.gov.bc.ca/gov/content/industry/crown-land-water/land-use-planning/regions). SLRPs provide direction for Crown land use through the establishment of broad land use goals, planning zone designations, objectives and strategies. This layer represents an integrated regional consensus-based process, which requires public and First Nations participation to produce a SLRP for review and approval by government. SLRPs establish direction on land and resource use and specify broad resource management objectives and strategies. Historical plan types include SRMPs, LRMPs, RLUPs and coastal plans. Current, non-retired SLRP boundaries are included in this layer, where RETIREMENT_DATE is blank. RETIREMENT_DATE is the field that stores the retirement date. If RETIREMENT_DATE is empty, the feature is the current shape. All SLRP shapes (past/retired and present/current) are in the layer [Strategic Land and Resource Plans - All](https://catalogue.data.gov.bc.ca/dataset/298d1034-c1be-4fd1-ad4b-d00ad5ab4b88). ** Please review the Data Quality section below.**

This dataset represent the Ice Flow Indicator compilation for the province of Saskatchewan.This dataset represents glacial ice flow indicators compiled from recent and historical bedrock and surficial geology maps and digital datasets. The data was created as file geodatabase feature class and output for public distribution. **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse.  They are therefore identical and share the same refresh schedule. 

This data set contains Manitoba market cattle prices and volumes sold at local auction marts.Description of Cattle Prices -source

DescriptionConservation of marine biodiversity requires understanding the joint influence of ongoing environmental change and fishing pressure. Addressing this challenge requires robust biodiversity monitoring and analyses that jointly account for potential drivers of change. Here, we ask how demersal fish biodiversity in Canadian Pacific waters has changed since 2003 and assess the degree to which these changes can be explained by environmental change and commercial fishing. Using a spatiotemporal multispecies model based on fisheries independent data, we find that species density (number of species per area) and community biomass have increased during this period. Environmental changes during this period were associated with temporal fluctuations in the biomass of species and the community as a whole. However, environmental changes were less associated with changes in species’ occurrence. Thus, the estimated increases in species density are not likely to be due to environmental change. Instead, our results are consistent with an ongoing recovery of the demersal fish community from a reduction in commercial fishing intensity from historical levels. These findings provide key insight into the drivers of biodiversity change that can inform ecosystem-based management.The layers provided represent three community metrics: 1) species density (i.e., species richness), 2) Hill-Shannon diversity, and 3) community biomass. All layers are provided at a 3 km resolution across the study domain for the period of 2003 to 2019. For each metric, we provide layers for three summary statistics: 1) the mean value in each grid cell over the temporal range, 2) the probability that the grid cell is a hotspot for that metric, and 3) the temporal coefficient of variation (i.e., standard deviation/mean) across all years.Methods:The analysis that produced these layers is presented in Thompson et al. (2022). The analysis uses data from the Groundfish Synoptic Bottom Trawl Research surveys in Queen Charlotte Sound (QCS), Hecate Strait (HS), West Coast Vancouver Island (WCVI), and West Coast Haida Gwaii (WCHG) from 2003 to 2019. Cartilaginous and bony fish species caught in DFO groundfish surveys that were present in at least 15% of all trawls over the depth range in which they were caught were included. This depth range was defined as that which included 95% of all trawls in which that species was present. The final dataset used in our analysis consisted of 57 species (Table S1 in Thompson et al. 2022).The spatiotemporal dynamics of the demersal fish community were modeled using the Hierarchical Modeling of Species Communities (HMSC) framework and package (Tikhonov et al. 2021) in R. This framework uses Bayesian inference to fit a multivariate hierarchical generalized mixed model. We modeled community dynamics using a hurdle model, which consists of two sub models: a presence-absence model and a biomass model that is conditional on presence. Our list of environmental covariates included bottom depth, bathymetric position index (BPI), mean summer tidal speed, substrate muddiness, substrate rockiness, whether the trawl was inside or outside of the ecosystem-based trawling footprint, and survey region (QCS & HS vs. WCVI & WCHG)), mean summer near-bottom temperature deviation, mean summer near-bottom dissolved oxygen deviation, mean summer cross-shore and along-shore current velocities near the seafloor, mean summer depth-integrated primary production, and local-scale commercial fishing effort.Layers are provided for three community metrics. All metrics should be interpreted as the value that would be expected in the catch from an average tow in the Groundfish Synoptic Bottom Trawl Research Surveys taken in a given 3 km grid cell. Species density (sometimes called species richness) should be interpreted as the number of the 57 species that would be caught in a trawl. Hill-Shannon diversity is a measure of diversity that gives greater weight to communities where biomass is spread equally across species. Community biomass is the total biomass across all 57 species that would be expected to be caught per square km in an average tow. Data Sources:Research data was provided by Pacific Science's Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2019 that occurred in four regions: Queen Charlotte Sound, Hecate Strait, West Coast Haida Gwaii, and West Coast Vancouver Island. Our analysis excludes species that are rarely caught in the research trawls and so our estimates would not include the occurrence or biomass of these rare species.Commercial fishing data was accessed through a DFO R script detailed here: https://github.com/pbsassess/gfdata. Local scale commercial fishing effort was calculated from this data. The substrate layers were obtained from a substrate model (Gregr et al. 2021). The oceanographic layers (bottom temperature, dissolved oxygen, tidal and circulation speeds, primary production) were obtained from a hindcast simulation of the British Columbia continental margin (BCCM) model (Peña et al. 2019).Uncertainties:Species that are not well sampled by the trawl surveys may not be accurately estimated by our model. The model did not include spatiotemporal random effects, which likely underestimates spatiotemporal variability in the region. It is also important to underline covariate uncertainty and model uncertainty. The hotspot estimates provide one measure of model uncertainty/certainty.

PURPOSE:Given the paucity of information on Arctic char along western Hudson Bay, in 2018, Fisheries and Oceans Canada (DFO) hosted an Arctic char workshop in Rankin Inlet, Nunavut, bringing together local resource users, knowledge holders, and co-management groups (e.g., Hunters and Trappers Organizations, Regional Wildlife Organization) to identify and discuss community-based Arctic char research priorities across the Kivalliq region of Nunavut. Communities were especially interested in examining “what Arctic char were eating” and “why the colour of their muscle is different” along the western Hudson Bay coastline, and in the summer of 2018, a regional community-based Arctic char monitoring program was implemented across the region. DESCRIPTION:Climate-induced alterations to Arctic sea ice dynamics are influencing the availability and distribution of resources, and in turn, the nutrient and energy intake of opportunistic predators across the food web. These temporal changes in local prey communities likely influence the availability of carotenoid-rich prey types, as well as the foraging ecology of opportunistic predators that forage in the marine environment, such as anadromous Arctic char (Salvelinus alpinus). Despite its socioeconomic importance across its range, anadromous Arctic char foraging ecology and its influence on muscle pigmentation, particularly in relation to sea ice dynamics, remains understudied. Here, over two years (2021, 2022) with contrasting sea ice dynamics, we investigated the foraging ecology of anadromous Arctic char and its influence on their muscle pigmentation at a southern (Rankin Inlet) and northern (Naujaat) location along western Hudson Bay using a combination of stomach contents, stable isotopes (δ¹³C and δ¹⁵N), highly branched isoprenoids, carotenoid spectrophotometry, and a standard muscle colour scale (DSM SalmoFan). Spatiotemporal variation in Arctic char diet occurred, where Rankin Inlet Arctic char generally consumed more fish and phytoplankton-based carbon sources, occupied a higher trophic position, and displayed a similar isotopic niche breadth compared to Arctic char in Naujaat. Invertebrates were higher in carotenoid concentration than fishes, and in association with a more invertebrate-based diet, Arctic char in Naujaat contained higher muscle carotenoid concentrations (e.g., astaxanthin) compared to Rankin Inlet Arctic char in 2021. In 2022, however, muscle carotenoid concentrations in Naujaat and Rankin Inlet Arctic char were more similar, as the diet of Arctic char in both locations was largely fish-based despite muscle colour remaining redder in Naujaat Arctic char. Overall, the observed plastic foraging ecology of Arctic char highlights this species' ability to adjust to inter-annual variability in environmental changes, which then impacts their muscle carotenoid concentration. Such inter-annual variation in Arctic char foraging ecology is anticipated to increase with unpredictable climate-driven environmental changes in the region, which could therefore negatively affect local resource users over the long term, resulting in socioeconomic impacts across the Arctic.Collection/sampling methodology:Arctic char were collected by angling and gillnetting (5.5” mesh, regularly checked) between June and August in the estuarine and marine environments near the communities of Rankin Inlet and Naujaat, Nunavut. In 2021, Naujaat Arctic char were collected by community fishers as part of a community-based sampling program. Concurrently, invertebrate prey types were opportunistically collected in the vicinity of Arctic char sampling sites using a conical zooplankton net (200-μm mesh; 10-minute tows) or obtained fresh from Arctic char stomachs. Additionally, marine fishes were opportunistically collected by angling or obtained fresh from Arctic char stomachs over both years in Rankin Inlet, while samples from the Naujaat area were collected in 2018 and 2019.The Kivalliq Wildlife Board (Rankin Inlet, NU) and Arviq Hunters and Trappers Association (Naujaat, NU) each supported this community-formulated research project and assisted with sample collections throughout the duration of the project. We would like to recognize and thank Sonny Ittinuar (Kivalliq Wildlife Board/Rankin Inlet Local Resource User), Clayton Tartak (Kivalliq Wildlife Board), Vincent L’Herault (ArctiConnexion), and Gail Davoren (University of Manitoba MSc co-supervisor) for their participation in the project. We would also like to thank Sonny Ittinuar, Poisey (Adam) Alogut, John-El, Peter, Quassa, and Goretti Tinashlu, who assisted in field work. USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products.Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope.The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada.Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners.Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.