This layer represents important areas for the Harp seal (Pagophilus groenlandicus). It includes the three main pupping areas for this species and migratory pathways used by Harp seals to migrate between its summering (Baffin Bay) and wintering (Gulf of St. Lawrence and Newfoundland and Labrador coasts) areas. Note that this dataset do not represent the Harp seal distribution.Reference:DFO. 2020. 2019 Status of Northwest Atlantic Harp Seals, Pagophilus groenlandicus. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2020/020.

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).

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).

Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression.The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C.Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: the fire season length across Canada for a reference period (1981-2010).

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in maximum temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in maximum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected maximum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of maximum temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled maximum temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

In 2018, Fisheries and Oceans Canada initiated the Multidisciplinary Arctic Program (MAP) – Last Ice, the first ecosystem study of the poorly characterized region of Tuvaijuittuq, where multiyear ice still resides in the Arctic Ocean. The program MAP-Last Ice takes a coordinated approach to integrate the physical, biochemical, and ecological components of the sea ice-ocean connected ecosystem and its response to climate and ocean forcings. This program provides baseline ecological knowledge for Tuvaijuittuq and, in particular, for its unique multiyear ice ecosystem. The database provides baseline data on fatty acid composition and stable isotopes signatures of sea ice communities in multi- and first-year ice in Tuvaijuittuq. The data were collected during the 2018 spring field campaign of the MAP-Last Ice Program, offshore of Canadian Forces Station (CFS) Alert, in the Lincoln Sea.

The number of days in the forecast period with a minimum temperature below the frost temperature. It is -15°C for herbaceous crops over the dormant period (ifd_herb_dorm).Week 1 and week 2 forecasted index is available daily from November 1 to March 31.Week 3 and week 4 forecasted index is available weekly (Thursday) from November 1 to March 31.Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

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

The number of days in the forecast period with a minimum temperature below the frost temperature. It is -15°C for herbaceous crops over the dormant period (ifd_wood_nogrow).Week 1 and week 2 forecasted index is available daily from November 1 to March 31.Week 3 and week 4 forecasted index is available weekly (Thursday) from November 1 to March 31.Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

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.