PURPOSE:Understanding and predicting species range shifts is crucial for conservation amid global warming. This study analyzes life-history traits of four seal species (ringed (Pusa hispida Schreber, 1775), bearded (Erignathus barbatus Pallas, 1811), harp (Pagophilus groenlandicus Erxleben, 1777), and harbour (Phoca vitulina Linnaeus, 1758) seals) in the Canadian Arctic using data from Inuit subsistence harvests. Bearded seals are largest, followed by harp seals, harbour seals, and ringed seals. Seasonal blubber depth patterns show minimal variation in bearded seals, whereas harbour and ringed seals accumulate fat in open-water seasons and use it during ice-covered seasons. Endemic Arctic seals (ringed and bearded) exhibit greater longevity and determinate body growth, reaching maximum size by 5 years, while harbour and harp seals grow indeterminately, physically maturing around 10-15 years. Age of maturation varies, with ringed and harbour seals being more sensitive to environmental fluctuations. Most bearded seals reproduce successfully each year, while ringed seals exhibit more variability in their annual reproductive success. Analysis of isoprenoid lipids in liver tissue indicates that ringed and bearded seals rely on ice-algal production, whereas harp and harbour seals depend on open-water phytoplankton production. Bearded seals appear more specialized and potentially face less competition, while harp seals may adapt better to changing habitats. Despite expected range shifts to higher latitudes, all species exhibit tradeoffs, complicating predictions for the evolving Arctic environment. DESCRIPTION:This dataset contains the data reported in Steven H. Ferguson, Jeff W. Higdon, Brent G. Young, Stephen D. Petersen, Cody G. Carlyle, Ellen V. Lea, Caroline C. Sauvé, Doreen Kohlbach, Aaron T. Fisk, Gregory W. Thiemann, Katie R. N. Florko, Derek C. G. Muir, Charmain D. Hamilton, Magali Houde, Enooyaq Sudlovenick, and David J. Yurkowski. 2024. A comparative analysis of life-history features and adaptive strategies of Arctic and subarctic seal species - who will win the climate change challenge? Canadian Journal of Zoology 2024-0093.R1The data set includes species, location, harvest date, sex, age, standard length, girth, fat depth, teste size, parity status, pregnancy status, corpora lutea (n), corpus albicans (n), follicles (n). This dataset includes raw, unfiltered, and unprocessed historical data provided by harvesters that have not been screened for outliers. Individual users should screen the data for their specific use.Cite these data as:Steven H. Ferguson, Jeff W. Higdon, Brent G. Young, Stephen D. Petersen, Cody G. Carlyle, Ellen V. Lea, Caroline C. Sauvé, Doreen Kohlbach, Aaron T. Fisk, Gregory W. Thiemann, Katie R. N. Florko, Derek C. G. Muir, Charmain D. Hamilton, Magali Houde, Enooyaq Sudlovenick, and David J. Yurkowski. 2024. Arctic and Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, MB. https://open.canada.ca/data/en/dataset/ea9ff038-8b16-11ef-8cce-55cc7f028297

A relative exposure index (REI), unweighted fetch, effective fetch, and other fetch-based indices (i.e., sum, minimum) were calculated for the Newfoundland and Labrador (NL) Shelves bioregion. Due to the extensive coastline of the study region, this analysis was conducted for a 5km buffered region along the coast at a spatial resolution of 250m. Detailed methods on the selection of input points for the NL bioregion are included below.MethodsPreprocessing and input point selection:Land boundary files were obtained for Eastern Canada and the Canadian Arctic (NrCan 2017) at a scale of 1:50,000 as well as for Saint Pierre and Miquelon (Hijmans 2015), and the New England states (GADM 2012) however the scale at which these layers were produced is unknown. Land boundary files were merged into a single land polygon layer and watercourses reaching for in-land and/or above sea level were clipped from this polygon layer (Greyson 2021). A 5km buffer was generated around the NL provincial boundary. This buffer was then clipped by all land polygons to remove areas overlapping land polygons within the study area. All buffer segments intersecting the NAFO divisions within the NL bioregion were selected and the Union tool in ArcGIS Pro (v. 2.7.2) was used to fill-in gaps within the buffered area, creating a more continuous polygon. The buffered layer was then dissolved, and the NL provincial boundary polygon was erased from the buffered layer to create the study area polygon. A 250m fishnet was created and clipped to the study area (5km buffer layer) and the feature to point tool was used (with the “inside parameter checked”) to convert this grid into a point layer (approx. 1,000,000 points). The spatial resolution for all subsequent analyses was matched to the fishnet grid at 250m.ReferencesGADM database of Global Administrative Areas (2012). Global Administrative Areas, version 2.0. (accessed 2 December 2020). www.gadm.orgGreyson, P (2021) Land boundary file for Eastern Canada, the Canadian Arctic, the New England States and Saint Pierre and Miquelon. [shapefile]. Unpublished data.Hijmans, R. and University of California, Berkeley, Museum of Vertebrate Zoology. (2015). First-level Administrative Divisions, Saint Pierre and Miquelon, 2015. UC Berkeley, Museum of Vertebrate Zoology. Available at: http://purl.stanford.edu/bz573nv9230Natural Resources Canada (2017) Administrative Boundaries in Canada - CanVec Series - Administrative Features - Open Government Portal. (accessed 2 December 2020). https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97.

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).

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

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

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).

In this dataset, we share maps of annual dominant tree species (also known as leading tree species) from 1984-2022 covering the entirety of Canada's 650 Mha forested ecosystems using Landsat time-series imagery at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Classifications are based on regionally representative Random Forests model using local training samples from Canada's National Forest Inventory (Hermosilla et al., 2024). Descriptive metrics provide information on spectral, geographic, climatic, and topographic characteristics. Initial annual tree species classifications were subjected to a time series post-classification process using the forward-backward Hidden Markov Model to improve the temporal consistency of tree species transitions within the time series. Assessment of the annual species maps using independent validation data resulted in an overall accuracy of 86.1% ± 0.14% (95%-confidence interval). These data allow consistent comparison of trends and rates of change in tree species composition nationally and across regions using a common time frame, spatial resolution, and analytical approach.Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Bater, C.W., Hobart, G.W., 2024. Characterizing long-term tree species dynamics in Canada's forested ecosystems using annual time series remote sensing data. Forest Ecology and Management, 122313. https://doi.org/10.1016/j.foreco.2024.122313 (Hermosilla et al. 2024)

Description:Seasonal climatologies for Zooplankton biomass in seven size categories were calculated for the period 1990-2019. The data used were a subset of the Fisheries and Oceans Canada, Institute of Ocean Sciences Zooplankton Database. This dataset is incomplete and is regularly updated as analysis takes place. Methods:Data-Interpolating Variational Analysis (DIVA) was used for spatial. Seasonal averages were computed as the mean of yearly seasonal means. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal Zooplankton climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), with high spatial resolution of 1/300 degree.Uncertainties:Uncertainties are introduced when observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values. Caution should be used for data in inlets and nearshore as the interpolation does not perform well in these areas, data in some inlets have been masked.

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 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.