Zooplankton and ichthyoplankton data are archived in the Institute of Ocean Sciences (IOS) Zooplankton Database. The data available spans from 1980 to 2018 and is an extraction of vertical net hauls as biomass by major taxa collected during surveys conducted in the oceanic and coastal waters of the Northeast Pacific Ocean. The majority of vertical net hauls in this data set were collected from 10 metres above the sea floor or an approximate maximum depth of 250 metres. For further data requests, please use the contact information provided.

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

The “Soil Landscapes of Canada (SLC) Version 2.2” dataset series provides a set of geo-referenced soil areas (polygons) that are linked to attribute data found in the associated Component Table (CMP), Landscape Table (LAT), Carbon Layer Table (CLYR), and Dom/Sub File (DOM_SUB). Together, these datasets describe the spatial distribution of soils and associated landscapes for Canada.

The Agri-Environmental Indicator of Risk of Water Contamination by Phosphorus dataset estimates the relative risk of phosphorus loss from Soil Landscapes of Canada agricultural areas to surface water. The data series for this indicator consists of four (4) datasets: Annual P-Balance, Soil-P-Source, Edge of Field and IROWC-P. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values.

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage. ‌ The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.

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; records were created for both 1 km and 4 km pixel resolutions to be consistent with other satellite products. Methods: MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from these datasets, 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, assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have final pixel resolutions of approximately 0.01 degrees and 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.

The health of individual amphibians, amphibian populations, and their wetland habitats are monitored in the oil sands region and at reference locations. Contaminants assessments are done at all sites. Amphibians developing near oil sands activities may be exposed to concentrations of oil sands-related contaminants, through air emissions as well as water contamination. The focus of field investigations is to evaluate the health of wild amphibian populations at varying distances from oil sands operations. Wood frog (Lithobates sylvaticus) populations are being studied in Alberta, Saskatchewan and the Northwest Territories in order to examine the relationship of proximity to oil sands activities and to prevalence of infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons.

30 Year Spatial Climate Averages are used to describe the average climatic conditions for an area and include variables for maximum temperature, minimum temperature, precipitation, and climate moisture index. At the end of each decade, scientists at Natural Resources Canada have been creating the newest models for as many climate variables as possible. Using a program called ANUSPLIN and climate data points, models for Canada and the United States are created. The NRCan Climate Averages are a large suite of datasets that can be used to compare weather of the past and present to help predict the future climate. The 30 year averages are computed for a uniform 30 year period and consists of the 12 monthly averages computed over the 30 year time period. The 30-year periods included in this series are: 1901-1930; 1921-1950; 1931-1960; 1951-1980; 1961-1990; 1971-2000; 1981-2010; 1991-2020. These are standard 30-year WMO (World Meteorological Organization) periods. Although this data has been processed successfully on a computer system at the Canadian Forest Service, no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general scientific purposes, nor shall the act of distribution constitute any such warranty. The disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that careful attention be paid to the contents of the metadata file associated with these data. The Canadian Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein.

Statistically downscaled multi-model ensembles of maximum temperature 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. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled maximum temperature (°C) are available for the historical time period, 1951-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. 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.