Programs that perform a laboratory analysis of body fluids and tissue to determine the presence of irregularities or to identify unknown substances submitted for analysis. Definition is protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

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

Consolidated Local Health Areas (CLHAs) Boundaries. B.C. Consolidated Local Health Areas (CLHAs) are a health geography standard that partitions the province into distinct areas that support coordination of specialized care between Health Authorities and Primary Care. The province is divided into five Health Authorities, which are subdivided into 41 Consolidated Local Health Areas (CLHAs), which are subdivided into 231 Community Health Service Areas (CHSAs). CLHAs encompass all of B.C., so the classification is exhaustive of all the land area in the province.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

This spatial layer displays stream inventory sample sites that have had full or partial surveys, and contains measurements or indicator information of the data collected at each survey site on each date.

This data is used for referencing spatial and tabular Forest Resource Processing Facility information. Each facility has one or more processing sites, each dedicated to processing resources for a specific purpose. For example, a pulp, paper and paperboard facility has one processing site to produce pulp, and another one dedicated to producing paper. All facilities that use 1,000 cubic metres or more of forest resources in one year must have a facility licence. This data class has been remodeled in 2014 to make it more flexible as a stand-alone product.

**Attention: there is a new version of this product (SCANFI v2)**SCANFI v2 can be found here: https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbfThis data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones.SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology.**Limitations**1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates.2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates.3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version.4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions.**Details on the product development and validation can be found in the following publication:**- Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118**Please cite this dataset as:**- Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc **The following raster layers are available:**- NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer- Live aboveground dry tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies- Height (meters): vegetation height- Crown closure (%): percentage of pixel covered by the tree canopy - Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: - Balsam fir tree cover in percentage (Abies balsamea) - Black spruce tree cover in percentage (Picea mariana) - Douglas fir tree cover in percentage (Pseudotsuga menziesii) - Jack pine tree cover in percentage (Pinus banksiana) - Lodgepole pine tree cover in percentage (Pinus contorta) - Ponderosa pine tree cover in percentage (Pinus ponderosa) - Tamarack tree cover in percentage (Larix laricina) - White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) - Broadleaf tree cover in percentage (PrcB) - Other coniferous tree cover in percentage (PrcC)

The Standardized Precipitation Index (SPI) is widely regarded as one of the most accessible tools for quantifying and reporting meteorological drought. At shorter timescales, SPI values are highly correlated with soil moisture, while at extended timescales, they reflect variations in groundwater and reservoir storage. The SPI model derives probability distributions from historical precipitation records, which are then normalized using an incomplete gamma function across multiple timescales. SPI values indicate the number of standard deviations the observed anomaly departs from the long-term period of record, with positive values corresponding to wetter-than-average conditions.Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980Indices values were calculated using the xclim python package The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

This dataset provides results obtained by Health Canada’s Canadian Radiological Monitoring Network (CRMN) for 90Sr activity in milk samples originating from various locations across Canada. More information about the CRMN network can be found on the Health Canada website (see link below). From 1984 to 1993, data was collected from 20 locations. After 1993, routine milk analysis for radio-strontium activity was discontinued for all monitoring stations, except the Ottawa location. The results provided here are 90Sr activity concentrations in units of becquerels per litre (Bq/L). Milk is an important matrix for environmental radioactivity assessment since many radionuclides of health concern exhibit efficient soil to milk uptake pathways. As such, the surveillance of milk provides a very good indication as to the severity of environmental impact following a nuclear event. Strontium-90 (90Sr) is a radionuclide of particular concern because it has a long radiological and biological half-life coupled with a high affinity for incorporation into human bone and teeth. Nuclear weapons testing and nuclear accidents have released such long-lived radionuclides into the atmosphere, which in turn have entered the biological life cycle. Nevertheless, a steady decline in the activity concentration of 90Sr in milk can be seen since most of the nuclear weapon testing was halted in the 1960s. The map shows the approximate sampling location for each monitoring station. Stations are found within the associated location range.

Hospitals and other health care facilities in BC that provide 24-hour diagnostic and treatment services for people who have acute, life-threatening injuries or severe illnesses. Definition is protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)