Growing Degree Days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Insect and plant development are very dependent on temperature and the daily accumulation of heat. The amount of heat required to move a plant or pest to the next development stage remains constant from year to year. However, the actual amount of time (days) can vary considerably from year to year because of weather conditions. Base temperatures are a point below which development does not occur for the organism in question. Base 0 temperatures are commonly used for cereals. These values are calculated across Canada in 10x10 km cells.

Climate Normals and Averages are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its Climate Normals for as many locations and as many climatic characteristics as possible. The Climate Normals, Averages and Extremes offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

Magnitude 5.7 earthquake scenario located directly southeast of Ladysmith Town Centre. This fault is not known to be active, but this scenario represents a small but damaging event near Ladysmith and Burleith Arm.

The Agri-Environmental Indicator Agricultural Greenhouse gas Budget datasets provide estimated net greenhouse gas emissions due to agricultural activities per hectare of Soil Landscapes of Canada agricultural areas. 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.

The Canadian Wildlife Service - Ontario Region Biodiversity Atlas represents the Canadian Wildlife Service biodiversity portfolio across the Ontario portions of the Boreal Hardwood Shield (Bird Conservation Region 12) and Mixedwood Plains (Bird Conservation Region 13) ecozones. These data are the derived product from an extensive landscape assessment that assessed the Canadian Wildlife Service biodiversity portfolio (Species at Risk, migratory birds, habitat) at various resolutions. Biodiversity is mapped by forest, grassland (open country) and wetland quality and quantity, and then progressively combined to identify local High Value Biodiversity Areas. At the finest resolution, scores were applied to each unit of analysis (5 hectare hexagon in Bird Conservation Region 12; 2 hectare hexagon in Bird Conservation Region 13), based on over 30 criteria for landscape habitat condition, Species at Risk and migratory birds. Habitat condition scores were derived from guidance in Environment and Climate Change Canada's existing How Much Habitat is Enough? and in Bird Conservation Region 12, where the landscape is less fragmented, habitat was also based on draft guidance in How Much Disturbance is too Much? Individual scores were summed and various combinations (e.g. top 25% of forest scores + top 25% of Species at Risk (SAR) scores) were calculated to identify areas with multiple conservation value. For each habitat type (forest, grassland and wetland), study units with more than one conservation value were aggregated into High Value Habitat which were subsequently aggregated into High Value Biodiversity Areas (HVBA). The results are areas on the landscape that have high value from a Canadian Wildlife Service specific lens; that is, they are high quality habitats that are important for Species at Risk and/or migratory birds. High value habitats are those forests, grasslands and wetlands with potential high conservation value (PHCV). They contain at least 1 of a possible 3 potential high conservation values: top 25% of overall habitat scores, top 25% of Species at Risk (SAR) scores, and/or top 25% of relevant migratory bird scores. High value forest, grassland and wetland were derived by combining landscape, Species at Risk (SAR) and migratory bird elements (see Table 1). Overall habitat scores were assigned to each study unit based on the combined scores for each forest, grassland and wetland. These overall habitat scores were divided into quartiles, and the top 25% of each total score (overall forest, overall grassland and overall wetland) are considered to be potential high conservation value. Similarly, SAR scores were assigned for each study unit, totalled and broken into quartiles. The top 25% of SAR scores that intersect each of forest, grassland and wetland are considered to be the highest quality habitats important to SAR and have potential high conservation value. Finally, relevant migratory bird scores were totalled within each study unit, divided into quartiles and the top 25% of migratory bird scores that intersect each of forest, grassland and wetland are considered to be the highest quality habitats important to migratory birds and have potential high conservation value. Study units with a PHCV greater than 0 (i.e., contains at least 1 of the possible 3 potential high conservation values) were aggregated together by 750 m to create High Value Habitats. High value biodiversity areas (HVBAs) are those study units that contain multiple high value habitats (high value forest and/or high value grassland and/or high value wetland). High value biodiversity areas (HVBA) were derived by aggregating high value forest, grassland and wetland. Study units with a potential high conservation value greater than 1 were aggregated together by 750 m. Biodiversity sites are areas greater than 20 ha, and secondary biodiversity sites are areas less than 20 ha in area.

Benthic invertebrates monitoring includes both lotic (rivers/streams) and lentic (wetlands) ecosystems. Aquatic biomonitoring provides a direct measure of change in biotic populations and communities in relation to benchmark or reference conditions and can help identify the ecological effects of cumulative stressors. Used together with the water chemical and physical monitoring components, this program uses an integrated approach to assess whether ecological affects are occurring in response to OS developments. Sampling can include the collection of invertebrates, algal biomass, water chemistry, and appropriate supporting habitat information and is conducted during periods of high abundance and diversity of macroinvertebrates. Sampling focuses on near-shore gravel and sand habitats on the Athabasca River, erosional habitats on major tributaries and in wadable areas in deltaic wetlands within the Expanded Geographical Area. As of October 2012, over 80 locations have been visited.

Statistically downscaled multi-model ensembles of minimum 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 minimum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded minimum 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 minimum 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.

CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

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.

In 2015, a magnitude 4.7 earthquake occurred 60 km beneath Sidney, BC. This scenario visualizes the effects of that event if it had a magnitude of 7.1.