This map data displays environmental discharges (spills) in Saskatchewan with location, confirmed substance and quantity. This data is limited to events that occurred between January 1, 2015 to present.The Ministry of Environment is responsible for responding to incidents where a substance of potential concern has been discharged into the environment. The Environmental Management and Protection Act, 2010 defines this as a discharge, drainage, deposit, release or emission into the environment.We are currently improving how this information is displayed. This map provides information on more recent discharges that were reported to the ministry. For older Spills, please go to our new GeoHub page: Discharge Cases (Spills) historic incidents”Please note: information may take up to 30 days to be updated and is subject to change at any time.For further information, please contact the Ministry of Environment Inquiry Centre (Toll Free): 800-567-4224, email: centre.inquiry@gov.sk.ca or visit: Hazardous Spills Reporting.

This record contains two datasets: 1. Raw unfiltered geographic coordinates and accuracy estimates of ringed seals tagged in the Western Canadian Arctic and 2. The location estimate from state-space models using a 12-hr time step. In total, 17 ringed seals were captured, measured, weighed, and tagged with satellite-linked transmitters (SDR-10, SDR-16, SPLASH) in June and July of 1999, 2000, and 2010. The tags, manufactured by Wildlife Computers Ltd. (Redmond, Washington, USA), sent data to polar orbiting satellites. Data were then retrieved via the Argos system (Harris et al., 1990). Tags collected and relayed information on movement (geographic positions) and diving data of the instrumented animals.

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

Biological samples of Geoduck Clams have been collected during surveys in British Columbia as part of the broader survey objectives of determining Geoduck density, distribution and population structure. Samples of Geoducks were collected from 41 locations throughout British Columbia between 1993 and 2002. Clams were measured for total weight, shell length, shell weight and were aged. Biological parameters are presented here for individual clams sampled. See Bureau D., W. Hajas, N.W. Surry, C.M. Hand, G. Dovey and A. Campbell. 2002. Age, size structure and growth parameters of Geoducks (Panopea abrupta, Conrad 1849) from 34 locations in British Columbia sampled between 1993 and 2000. Can Tech. Rep. Fish. Aquat. Sci. 2413: 84 p. and Bureau D., W. Hajas, C.M. Hand and G. Dovey. 2003. Age, size structure and growth parameters of Geoducks (Panopea abrupta, Conrad 1849) from seven locations in British Columbia sampled in 2001 and 2002. Can. Tech. Rep. Fish. Aquat. Sci. 2494: 29 p.

TA_CROWN_REV_SHARE_AGRMNTS_SVW contains the spatial representation (polygon) of active and applied for Crown Land Revenue Sharing Agreements. A Revenue Sharing Agreement is made between the crown and one or more parties to share revenue. The view was created to provide a simplified presentation of this single tenure type from the disposition information in the Tantalis operational system. The same content could be derived from the TA_CROWN_TENURES_SVW by filtering to this tenure type only. It’s possible that this dataset may contain few or no records, depending on the current number of active tenures or applications.

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.

Modeled data showing the likely distribution of humpback 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.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001.How can this data be used?The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

Species Distribution Models (SDM) were used to predict and identify priority areas for enhanced monitoring of cetaceans in eastern Canadian waters off Nova Scotia, Newfoundland and Labrador. This data set represents information presented in Gomez et al. (2020) and includes sighting records and SDM outputs for ten cetacean species with sufficient records (n > 450) and sightings only for an additional six species. For more information about sighting records including which were included in each SDM, please see Gomez et al. 2020. This study used a compilation of aerial- and vessel-based cetacean sightings data from 1975-2015 assembled in Gomez et al. (2017) from variety of sources. Note that sightings data from many of these sources are not effort-corrected and apparent distribution patterns based on these opportunistic sightings data are biased by when and where survey activities were conducted. Unfavorable weather and reduced visual effort in winter, spring, and autumn likely account for the fewer sighting records in these seasons compared to summer. The dataset does not include dead animal, stranding, entanglement or entrapment data. While some of the databases include records obtained during the whaling period (catches or sightings recorded prior to 1975), for all analyses/modelling conducted in this study, only sightings of free-swimming whales obtained during the post-whaling period (1975-2015) were used. Quality control checks included discarding all records outside of our study area and removing redundant records (identical species, day, month, latitude and longitude).The data used do not reflect any updates or corrections to the databases that have occurred since the data were compiled in 2016. Sightings are not available for download here, please contact the original data sources listed below to obtain raw sightings data. This study represents an important initiative in eastern Canada to highlight key areas for cetacean monitoring in waters off Nova Scotia, Newfoundland and Labrador. Habitats with high suitability are interpreted as areas where cetacean monitoring efforts may be prioritized, and results can help direct future survey efforts. These model outputs used cetacean sightings from several decades and dynamic environmental predictors that used seasonal averages across multiple years. As proxies for prey availability, five predictor environmental variables were selected for the SDM: ocean depth, compound topographic index, sea surface temperature, areas of persistently high chlorophyll-a concentration, and regional chlorophyll-a magnitude. See Gomez et al. (2020) for further details on modelling methods. Persistent patterns over time (between 1975-2015) are the main patterns expected to be captured by these models. Further, SDM results presented here are not the same as species density maps; rather, they portray predicted suitable habitat based on environmental characteristics and sightings data that were not always derived from effort-based surveys. Consequently, the use of these models in marine spatial planning processes should be accompanied by complimentary approaches such as acoustic and visual validation of the SDM results as well as additional monitoring and modeling efforts. Please refer to Gomez et al. (2020) for examples on how to best use these data outputs. Future efforts will focus on using more recent data and improving these models to facilitate the inclusion of cetaceans in marine spatial planning processes that are currently underway. Data sources: Fisheries and Oceans Canada Maritimes region and Newfoundland and Labrador region (Whale Sightings Database, Ocean and Ecosystem Sciences Division, Dartmouth, NS; http://www.inter.dfo-mpo.gc.ca/Maritimes/SABS/popec/sara/Database, MacDonald et. al. 2017) Ocean Biogeographic Information System (OBIS; http://www.iobis.org/), North Atlantic Right Whale Consortium (NARWC; http://www.narwc.org/) Whitehead Lab at Dalhousie University (http://whitelab.biology.dal.ca/) Environment and Climate Change Canada’s (Canadian Wildlife Service) Eastern Canada Seabirds at Sea (ECSAS) program (Gjerdrum et al. 2012). References: Gomez, C., Konrad, C.M., Vanderlaan, A., Moors-Murphy, H.B., Marotte, E., Lawson, J.,Kouwenberg, A-L., Fuentes-Yaco, C., Buren, A. 2020. Identifying priority areas toenhance monitoring of cetaceans in the Northwest Atlantic Ocean. Can. Tech. Rep.Fish. Aquat. Sci. 3370: vi + 103 p. http://waves-vagues.dfo-mpo.gc.ca/Library/40869155.pdfGomez C, Lawson J, Kouwenberg A, Moors-Murphy H, Buren A, Fuentes-Yaco C, Marotte E, Wiersma YF, Wimmer T. 2017. Predicted distribution of whales at risk: identifying priority areas to enhance cetacean monitoring in the Northwest Atlantic Ocean. Endangered Species Research 32:437-458 https://www.int-res.com/abstracts/esr/v32/p437-458/Gjerdrum, C., D.A. Fifield, and S.I. Wilhelm. 2012. Eastern Canada Seabirds at Sea (ECSAS) standardized protocol for pelagic seabird surveys from moving and stationary platforms. 31 Canadian Wildlife Service Technical Report Series No. 515. Atlantic Region. vi + 37 p. MacDonald, D., Emery, P., Themelis, D., Smedbol, R.K., Harris, L.E., and McCurdy, Q. 2017. Marine mammal and pelagic animal sightings (Whalesightings) database: a user’s guide. Can. Tech. Rep. Fish. Aquat. Sci. 3244: v + 44 p.

Modeled data showing the likely distribution of dall's porpoises. 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.