This dataset contains observations of species occurrences from seafloor imagery collected by the autonomous underwater vehicle (AUV) during the 2012 Expedition to Cobb Seamount. The National Oceanographic and Atmospheric Administration-operated SeaBED-class AUV which collected photographic images from 4 transects ranging from 436 m to 1154 m in depth.

The Ontario Forest Biomonitoring Network (OFBN) monitors the health of mixed hardwood forests across southern and central Ontario. The data set includes: * Individual Tree Data: Decline Index and other measurements of visual stress symptoms of each tree within 111 plots * Decline Index: The Decline Index is a weighted average of tree stress symptoms (percent dead branches, percent slight or strong chlorosis (pale green-yellow leaves) and percent undersized leaves). Averaged for hardwood trees found within each of the 111 plots in each year. * Invasive Plant Species Presence Data * Salamander Data: numbers of individuals of salamander and other animal species in 14 plots * Tree Regeneration Data: monitors numbers of tree seedlings/saplings in 102 plots * Woody Debris Data: amount of woody debris on ground in 102 plots

Polygons denoting concentrations of sea pens, small and large gorgonian corals and sponges on the east coast of Canada have been identified through spatial analysis of research vessel survey by-catch data following an approach used by the Northwest Atlantic Fisheries Organization (NAFO) in the Regulatory Area (NRA) on Flemish Cap and southeast Grand Banks. Kernel density analysis was used to identify high concentrations and the area occupied by successive catch weight thresholds was used to identify aggregations. These analyses were performed for each of the five biogeographic zones of eastern Canada. The largest sea pen fields were found in the Laurentian Channel as it cuts through the Gulf of St. Lawrence, while large gorgonian coral forests were found in the Eastern Arctic and on the northern Labrador continental slope. Large ball-shaped Geodia spp. sponges were located along the continental slopes north of the Grand Banks, while on the Scotian Shelf a unique population of the large barrel-shaped sponge Vazella pourtalesi was identified. The latitude and longitude marking the positions of all tows which form these and other dense aggregations are provided along with the positions of all tows which captured black coral, a non-aggregating taxon which is long-lived and vulnerable to fishing pressures. These polygons identify large gorgonian coral fields from the broader distribution of large gorgonian corals in the region as sampled by Campelen trawl gear in the Eastern Arctic biogeographic zone. A 15 kg minimum threshold for the large gorgonian coral catch was identified as the weight that separated the large gorgonian field habitat from the broader distribution of large gorgonian corals with these research vessel tow data and gear type.

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two SSP scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts. This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections. - [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc) - [Historic - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77) - [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)

Watercourses are line features - natural or manmade - that represent the location of flowing surface water. This product requires the use of geographic information system (GIS) software.

In 2021, the Canada Coast Guard (CCG) and Fisheries and Oceans Canada updated its administrative boundaries following the creation a new Arctic region. There are now 4 administrative regions in CCG (Western, Arctic, Central and Atlantic). DFO and Coast Guard Arctic Regions developed these regions in partnership with the people they serve; this important decision will lead to stronger programs and services to better meet the unique needs of our Arctic communities. DFO and CCG operations and research cover Canada's land and waters to the international boundaries (EEZ) and are in no way limited to the boundaries drawn in the map.

The land cover classes consist of vegetation types (such as forest, wetlands and agricultural crops or pasture) and categories of non-vegetated surface (such as water bodies, bedrock outcrops or settlements). These classes reflect the nature of the land surface rather than actual or potential land use. The 2000 Edition of the Ontario Land Cover Data Base is the Second Edition of this provincial land cover classification. The coverage is derived wholly from Landsat-7 Thematic Mapper (TM) satellite data frames recorded between 1999 and 2002, most from 2000 onward. The Provincial Land Cover (2000) Data Base is divided into 4 individual Universal Transverse Mercator (UTM) grid zone tiles (15, 16, 17, and 18) and is distributed in Tagged Image File Format (TIFF) format. Documentation is provided with this database in the form of a user's guide and general use caveats.

The “Canadian Agricultural Crop Water Balance – Watershed Aggregates” provides watershed-scale summaries of agricultural crop water balance variables for Canada derived from DNDC crop simulations linked to Soil Landscapes of Canada (SLC) polygons. The monthly outputs have been aggregated and released as annual and growing season summaries to support national water accounting and watershed-scale analysis. Crop water variables, originally expressed as depth (millimetres), are converted to volumes using simulated crop area and spatially aggregated to drainage regions through polygon–watershed intersection and area apportionment. Aggregated volumes are then converted back to area-weighted depths to ensure water quantities are preserved during spatial aggregation. Released datasets include annual and growing season estimates of evapotranspiration, precipitation, irrigation application, leaching, and irrigated-field runoff associated with agricultural crop production. Supporting tables provide total and irrigated crop area contributing to each watershed estimate. Spatial processing is performed using the Statistics Canada Albers Equal Area projection (EPSG:3347), with raster products aligned to the Statistical Ecosystem Register 250m grid.

Description: Chlorophyll-a concentration (a proxy for phytoplankton biomass) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020 and this record includes data at 4 km pixel resolution. Methods: MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-3 (version 2018), 4-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The monthly geometric mean value at all pixels was calculated for individual years, then the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to the geometric mean and geometric standard deviation factor the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.0417 degrees. The monthly geometric mean, monthly geometric standard deviation factor, and number of occurrences for all pixels are provided. Data Sources: NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018 Uncertainties: Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

## GIS data containing the boundaries of Provincially Significant Employment Zones in the Greater Golden Horseshoe as identified by the Minister of Municipal Affairs and Housing As areas of high economic output, provincially significant employment zones are strategically located to provide stable, reliable employment across the Greater Golden Horseshoe region. They provide opportunities to improve coordination between land use planning, economic development, and infrastructure investments to support investment and job creation over the longer-term.