This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed).The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling. Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore.Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.

The Physical Exposure component of the National Human Settlement Layer (NHSL), defined here as the ‘Physical Exposure Model’, includes a delineation of settled areas and related land use across Canada, as well as information about buildings, persons, and building replacement values (structure and contents) within those areas.Buildings within the inventory are classified using a combination of occupancy types, engineering-based construction types adopted for Canada, and design levels representing the approximate building code requirements at the time of construction. The inventory is derived from detailed housing statistics provided at the dissemination area level as part of the 2016 national census and from georeferenced business listings. Building populations at different times of day are estimated for standard daytime hours (9am-5pm); for morning and evening commute hours (7am-9am; 5pm-7pm), and; for nighttime hours when the majority of people are home (7pm-7am). Replacement values are provided for structural, nonstructural, and contents components of buildings, based on industry replacement costs for representative regions across Canada.The physical exposure model is provided in two formats: (1) According to settled areas (i.e., polygons), which are areas that approximately delineate clusters of buildings across Canada. Summary statistics about buildings and populations within each settled area boundary are provided. (2) According to building archetypes (i.e., points) within settled areas. These are represented as point locations at the centroid of the corresponding settled area, and each settled area can have multiple point features corresponding to different building archetypes present within that area. In total, the model characterizes 35.2 million people in 9.7 million buildings across 390,000 locations with a total approximate replacement value of $8.2 trillion (2019 CAD) including contents.

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Air quality indicators track ambient concentrations of fine particulate matter, ground-level ozone, sulphur dioxide, nitrogen dioxide, and volatile organic compounds at the national, regional and urban levels and at local monitoring stations. The national and regional indicators are presented with their corresponding Canadian Ambient Air Quality Standard when available. Canadians are exposed to air pollutants on a daily basis, and this exposure can cause adverse health and environmental effects. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators

TOPEX (acronym which means “topographical exposure”) is an index of topographic exposure. It reflects the influence of local topography on the degree of exposure to wind and on its behavior (Ruel et al. 2002). TOPEX data are presented in the form of a matrix file (raster) whose spatial resolution is 50 m with a projection in Nad 1983 Quebec Lambert. This raster covers the entire territory of Quebec located approximately south of 52°40' and west of 61°10' and extends beyond the borders of Quebec by 75 to 125 m (in order to have values on the entire Quebec territory covered).TOPEX values were calculated using the Digital Terrain Model (DTM) from the NASA Shuttle Radar Topography Mission (SRTM). This MNT is provided in a WGS 84 projection (EPSG: 4326) with a resolution of one arcsecond (+/- 30 m). A mosaic of the SRTM tiles was created to then project the MNT into Lambert and resample it at 50 m.TOPEX can be used as an input in a windfall vulnerability assessment system. However, it should be borne in mind that this index does not take into account the wind channeling effect that may occur in certain places and that can influence windfall. In addition, other factors must be taken into consideration in order to carry out a complete analysis of the risk of windfall. RUEL, J.-C., S. J. MITCHELL and M. DORNIER, 2002. A GIS based approach to map wind exposure for windthrow hazard rating. Northern Journal of Applied Forestry, 19 (4): 183-187.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Exposure to wind-driven waves forms a key physical gradient in nearshore environments influencing both ecological communities and human activities. We calculated a relative exposure index (REI) for wind-driven waves covering the coastal zone of the Scotian Shelf-Bay of Fundy bioregion. We derived REI and two other fetch-based indices (sum fetch, minimum fetch) from two formulations of wind fetch (unweighted and effective fetch) for input points in an evenly spaced fishnet grid (50-m resolution) covering a buffered area within 5 km from the coastline and shallower than 50 m depth. We calculated unweighted fetch lengths (m) for 32 compass headings per input point (11.25° intervals), and effective fetch lengths for 8 headings per point (45° intervals). Unweighted fetch is the distance along a given heading from a point in coastal waters to land. Effective fetch is a directionally weighted average of multiple fetch measures around a given heading that reduces the influence of irregular coastline shape on exposure estimates. For fetch calculations, we used land features at a 1:50,000 scale for Canadian administrative boundaries (NrCan 2017), and unknown resolution for St. Pierre and Miquelon, and US states bordering the Gulf of Maine (GADM 2012). The summed and minimum unweighted fetch lengths for each point provide coarse summaries of wave exposure and distance to land, respectively. The relative exposure index (REI) gives a more accurate metric of exposure by combining effective fetch with modelled wind speeds (m s-1) and frequency data. We provide the original calculations of unweighted fetch, effective fetch, and other fetch-based indices (i.e., sum, minimum) in csv format along with the REI layer (GeoTIFF format) resampled to 35-m resolution. With broad spatial coverage and high resolution, these indices can support regional-scale distribution modelling of species and biological assemblages in the coastal zone as well as marine spatial planning activities.When using data please cite following:O'Brien JM, Wong MC, Stanley RRE (2022) A relative wave exposure index for the coastal zone of the Scotian Shelf-Bay of Fundy Bioregion. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5433567ReferencesGADM database of Global Administrative Areas (2012). Global Administrative Areas, version 2.0. (accessed 2 December 2020). www.gadm.orgNatural 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 Coastal Infrastructure Vulnerability Index (CIVI) was jointly developed by DFO Science Branch, Small Craft Harbours (SCH) Program and the Economic Analysis and Statistics Directorate. The CIVI was designed with the intent of developing a climate change adaptation tool that would support management decisions regarding the long-term infrastructure planning for SCH sites.The CIVI provides a numerical indication of the relative vulnerability of small craft harbour sites to the effects of climate change and was designed with three component sub-indices: Environmental Exposure (natural forces), Infrastructure, and Socio-economic. The spatial component for the coastline was derived from the CanVec 1:50,000 hydrographic layer (https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b).This layer combines the 1:50,000 CanVec coastline of Canada with the following CIVI environmental exposure variables:- projected sea level rise (for the decades 2030, 2040,...2100) in meters- wave height (metres) and wind speed (metres/second)- change in sea ice coverage in Atlantic Canada from the 1970s to the 2000sSea level change:Data for relative sea level change (SLC) were derived from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2014, AR5). The projected relative sea level change under the high emission scenario (RCP8.5) was calculated for all years between 2006 and 2100. Sea level change for the years 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100 were used.Wind Speed and Wave HeightModelled hindcasts of yearly maximum wind speed (1990 - 2012) and wave height (1990- 2014) were used. This dataset was generated from IFREMER wave hindcasts using the WAVEWATCH III model with wind data from NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010). Two high resolution (10 minute) grids of Atlantic and Pacific maximum modeled wind speeds and maximum significant wave height were used for southern Canadian coastal areas while a coarser (30 minute) worldwide grid was used for the Arctic areas. From these datasets the mean annual maximum wind speed over 23 years and the mean maximum significant wave height over 25 years were calculated.Change in sea ice coverage:Sea ice data from the Canadian Ice Service were acquired for Atlantic and Arctic Canada, representing percent ice coverage for each week over four decades (1970s, 1980s, 1990, 2000s). For each decade a single dataset was calculated to represent the sum of all weeks with ice coverage in excess of 50%, with a maximum possible score of 52 weeks for each decade. To measure change in ice duration, the summary mapsheet from the 2000s was subtracted from the 1970s summary mapsheet. The final dataset represents the change between the 1970s and 2000s in the number of weeks with ice concentrations greater than 50%. A positive number indicates a reduction in weeks of ice coverage, a negative number an increase in ice coverage.The data for individual small craft harbours included here contains predicted sea level change for the decades between 2030 and 2100, wave height, windspeed, change in sea ice coverage, population, and the final environmental exposure sub-index value (ESI). The population for each harbour is derived from the 2016 Census of Canada data for the Census subdivision (CSD) geographic unit.Reference:Relative sea-level projections for Canada based on the IPCC Fifth Assessment Report and the NAD83v70VG national crustal velocity modelhttps://doi.org/10.4095/327878 IPCC, 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.Cite this data as: Greenan B. and Greyson P. Coastal Environmental Exposure Layer. Published March 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

Exposure to wind-driven waves manifests an important physical gradient in the coastal zone that influences a variety of physical and biological processes (i.e., species distribution). Fetch, the unobstructed distance over which wind-driven waves can build, is a popular proxy for wave exposure at a given location commonly used for site-specific evaluations. Here, we provide two measures of fetch (unweighted fetch, effective fetch) and three fetch-derived indices of wave exposure (sum fetch, minimum fetch, and a relative exposure index) covering the coastal zones of two Canadian bioregions (Scotian Shelf-Bay of Fundy, Newfoundland-Labrador Shelves). For each region, we calculated fetch and exposure indices for input points in an evenly spaced fishnet grid (see linked records below for datasets by region). We provide unweighted fetch lengths (m) for 32 compass headings per input point (11.25° intervals), and effective fetch lengths for 8 headings per point (45° intervals). Effective fetch is a weighted average of multiple fetch measures around a given heading that reduces the influence of irregular coastline shape on exposure estimates. We also include calculations of the summed and minimum unweighted fetch lengths for each point that provide coarse proxies of exposure and distance to land, respectively. The relative exposure index (REI), provided as regional spatial layers in raster format, provides a more accurate metric of exposure by combining effective fetch with modelled wind speeds (m s-1) and frequency data. Users may also use fetch calculations to derive their own exposure layers using alternative sources of wind data, indices, or formulations. Detailed methodology on the calculations for fetch, effective fetch and REI are outlined in the Supplementary Information below. Citation information and differences in data pre-processing methods and spatial resolution of the regional analyses are described in their respective data records. The broad spatial coverage and high resolution offered by these indices are suitable to support regional-scale modelling and planning exercises. In particular, these indices will be of value to ongoing Marine Spatial Planning efforts, which includes regional conservation planning, that seek to evaluate the distribution of coastal species and overlap with human activities.

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 Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

Table of animal samples requested for rabies testing in Manitoba since the province took over the rabies program on April 1, 2014.This table contains information on animal samples requested for rabies testing in Manitoba since the province took over the rabies program on April 1, 2014.  As of that date, the Canadian Food Inspection Agency (CFIA) is no longer involved with managing rabies cases in Manitoba. The provincial Rabies Management Program is now coordinated by Manitoba Rabies Central, which is a collaborative effort involving the Manitoba departments of Health, and Environment, Climate and Parks. Samples for rabies testing are requested by Manitoba Health in cases of human exposure to a suspected rabid animal, and by Agriculture in the case of animal exposure to another suspect rabid animal. Collection of a sample from the suspect rabid animal is coordinated by Agriculture.  This information will be updated on a quarterly basis. This data is used in the Manitoba Rabies Surveillance Dashboard. Fields included (Alias (Field Name): Field Description.) Region (Region): Regional health authority where the affected person or animal was exposed Fiscal Year (Fiscal_Year): Government of Manitoba fiscal year (April 1-March 31) when the rabies testing was requested Date (Date): Date when the sample for rabies testing was requested Year (Year): Calendar year (January 1-December 31) when the sample for rabies testing was requested Species (Species): Animal species of the sample requested for rabies testing Result (Result): Laboratory result of the sample tested for rabies.