Predicted Soil Great Group class as defined by the The Canadian System of Soil Classification (third edition).

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

High-resolution map of leading tree species distribution for Canada’s forested ecosystems (2019). Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada’s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).The data represents leading tree species of Canada's forested ecosystems in 2019. An image compositing window of August 1 ± 30 days was used to generate the best-available-pixel (BAP) image composites utilized as source data for the classification.The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada’s forests, were led by Canadian Forest Service of Natural Resources Canada, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS), partnered with the University of British Columbia, augmented by processing capacity from Digital Research Alliance of Canada.For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276When using this data, please cite as: Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment 282, 113276.

The “Soil Landscapes of Canada V.2.2/V.3.1 - Soil Order” displays the highest (most general) level of soil classification. Within the Canadian System of Soil Classification there are ten recognized soil orders (Soil Classification Working Group 1998). This system is hierarchical (from general to specific). Soil orders are further subdivided to great groups, subgroups, families, and series.

Snowpack classification for the Cariboo Region (based on 2014 biogeoclimatic zones)

Ecologically and Biologically Significant Areas (EBSAs) are areas within Canada's oceans that have been identified through formal scientific assessments as having special biological or ecological significance when compared with the surrounding marine ecosystem.Failure to define an area as an EBSA does not mean that it is unimportant ecologically. All areas serve ecological functions to some extent and require sustainable management. Rather, areas identified as EBSAs should be viewed as the most important areas where, with existing knowledge, regulators and marine users should be particularly risk averse to ensure ecosystems remain healthy and productive.Why are EBSAs identified?EBSA information is used to inform marine planning, including environmental assessment and the siting of marine-based activities, by:- Informing and guiding project-specific or regional environmental assessments;- Informing and guiding industries and regulators in their planning and operations, for example: EBSAs have been acknowledged and referred to (often as "Special Areas" or "Potentially Sensitive Areas") in oil and gas related assessments;- EBSA information has been provided to proponents of submarine cable projects to be used for route planning purposes;- Informing and guiding Integrated Oceans Management (IOM) process within five Large Ocean Management Areas (LOMAs) and twelve marine bioregions;- Serving as a basis for the identification of Areas of Interest (AOIs) and of Marine Protected Areas (MPAs) (individually and in the context of planning bioregional networks of MPAs).How are EBSAs identified?The process used to identify EBSAs is generally comprised of two phases. The first phase involves compiling scientific data and knowledge of a marine area's ecosystems - notably fish species, marine mammals, sea birds, marine flora, marine productivity, physical and chemical conditions and geology. "Knowledge" includes experiential knowledge of long-time uses of the areas. In some cases (e.g., in the Arctic), substantial efforts are taken to collect traditional knowledge on ecosystems and environmental conditions from community members, fish harvests, hunters and individuals whose knowledge of the study area complement often helps fill scientific data gaps.In the second phase, the available information for a marine area (e.g. a bioregion) is assessed against five nationally-established science-based criteria including:- Uniqueness: How distinct is the ecosystem of an area compared to surrounding ones?- Aggregation: Whether or not species populate or convene to the study area?- Fitness consequence: How critical the area is to the life history of the species that use it (e.g. is it a spawning or feeding ground)?- Naturalness: How pristine or disturbed by human activities is the study area?- Resilience: What is the ability of the ecosystem to bounce back if it is disturbed?Progress to date and next stepsEBSAs have been identified for large portions of Canada's Atlantic and Pacific Oceans as well as most of the Arctic oceans. EBSAs will continue to be identified in priority areas as resources become available to carry out the process. The boundaries or locations of existing EBSAs may be modified to reflect both new knowledge and changing environmental conditions.

PURPOSE:To provide demographic parameters and relative indices of abundance for Atlantic herring in NAFO Division 4T.DESCRIPTION:The experimental gillnet survey is conducted in collaboration with Atlantic herring harvesters and uses standardized multi-mesh gillnets. Gillnets are set on spawning grounds in the spring and fall. Data (fish and sample) are collected to provide: (1) relative age-disaggregated indices of abundance, (2) an indicator of relative changes in fishery selectivity over time, and (3) information on the demographic composition of herring on the spawning grounds. PARAMETERS COLLECTED:Sample: sample location; sample weight; mesh size; length frequencyBiological: length; weight; gonad weight; maturity stage (biological); sex (biological); ring count (biological)USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Identification of ecological and biological significant areas (EBSA) in the Estuary and the Gulf of St. Lawrence according to six groups of the food chain : primary production (Lavoie et al, 2007), secondary production (Plourde et McQuinn, 2010), meroplankton (Ouellet, 2007), benthic invertebrates (Chabot et al, 2007), demersal fishes (Castonguay et Valois, 2007) and pelagic fishes (McQuinn et al, 2012). The distribution area of each group has been evaluated using five criteria in order to determine the EBSA (DFO, 2004):1. Uniqueness: Ranked from areas whose characteristics are unique, rare, distinct, and for which alternatives do not exist to areas whose characteristics are widespread with many areas which are similar.2. Aggregation: Ranked from areas where most individuals of a species are aggregated to areas where individuals of the species are widespread3. Fitness consequence: Ranked from areas where the life history activity(ies) undertaken make a major contribution to the fitness of the population or species present to areas where the life history activity(ies) undertaken make only marginal contributions to fitness.4. Resilience: Ranked from areas where the habitat structures or species are highly sensitive, easily perturbed, and slow to recover to areas where the habitat structures or species are robust, resistant to perturbation, or readily return to the pre-perturbation state.5. Naturalness: Ranked from areas which are pristine and characterized by native species to areas which are highly perturbed by anthropogenic activities and/or with high abundances of introduced or cultured species.Castonguay, M. and Valois, S. 2007. Zones d’importance écologique et biologique pour les poissons démersaux dans le nord du Golfe du Saint-Laurent. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/014. iii + 34 p.Chabot, D., Rondeau A., Sainte-Marie B., Savard L., Surette T. et Archambault P. 2007. Distribution des invertébrés benthiques dans l’estuaire et le golfe du Saint-Laurent. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/018. iii + 118 p.DFO, 2004. Identification of Ecologically and Biologically Significant Areas. DFO Can. Sci. Advis. Sec. Ecosystem Status Rep. 2004/006. Lavoie, D., Starr, M., Zakardjian, B. and Larouche, P. 2007. Identification of ecologically and biologically significant areas (EBSA) in the Estuary and Gulf of St. Lawrence: Primary production. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/079. iii + 29 p. McQuinn, I.H., Bourassa, M-N., Tournois, C., Grégoire, F., and Baril, D. 2012. Ecologically and biologically significant areas in the Estuary and Gulf of St. Lawrence: small pelagic fishes. DFO Can. Sci. Advis. Sec. Res. Doc. 2012/087. iii + 76 p.Ouellet P. 2007. Contribution à l’identification de zones d’importance écologique et biologique (ZIEB) pour l’estuaire et le golfe du Saint-Laurent : La couche des oeufs et des larves de poissons et de crustacés décapodes. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/011. iii + 76 p. (Mise à jour novembre 2010)Plourde, S. et McQuinn, I.A. 2010. Zones d’importance écologique et biologique dans le golfe du Saint-Laurent : zooplancton et production secondaire. DFO Can. Sci. Advis. Sec. Res. Doc. 2009/104. iv + 27 p.

This dataset is produced for the Government of Alberta and is available to the general public. Please consult the Distribution Information of this metadata for the appropriate contact to acquire this dataset. Physical Land Classification (PLC) is a mapping system that was designed to describe the landscape in terms of landform, soils, drainage and slope. It is a hierarchical system that captures physiographic information at the following levels: Region - 1:3 000 000 or smaller Section - 1:1 000 000 to 1:3 000 000 District - 1:500 000 to 1:1 000 000 Geomorphic System - 1:100 000 (can range from 1:50 000 to 1:250 000) Geomorphic Unit - 1:10 000 to 1:50 000 There are some variations in this hierarchy for individual study areas. The Land Classification Group (Resource Inventory Section), Alberta Energy and Natural Resources, adopted the initial Physical Land Classification methodology in 1977 to meet the needs of resource planning and management agencies. Many aspects of the methodology were developed from landform mapping schemes used by the System of Soil Classification for Canada (1976). The PLC system is essentially a geomorphic interpretation and classification system based on the principles of the inherent properties of the land and its forms. Physical Land Classification (PLC) maps have been created largely during the 1980s and 1990s as part of a program to acquire background information for Integrated Resource Plans along the eastern slopes and across northern Alberta. The data were generally mapped at the geomorphic unit level using the 1:50 000 scale National Topographic System maps as a base. The PLC hardcopy maps were scanned, georeferenced, rectified, cleaned, vectorized, merged and attributed to form GIS polygons. The polygons are attributed for parent geologic material, landform / surface expression, modifying process, slope, texture, soil taxonomy and soil drainage. This classification system was designed to enhance and replace the Canada Land Inventory (CLI) and Alberta Landform Inventory (ALI) Landform classification systems. There is more attribution associated with PLC mapping than with ALI / CLI Landform mapping. There is some overlap with the ALI / CLI Landform maps but much of the PLC mapping was conducted in areas not covered by ALI / CLI Landform maps. PLC mapping is considered to be more reliable than ALI / CLI Landform mapping as field checking was more extensive.