This product contains 23,887 digitally restored 1971 census enumeration area (EA) boundaries for the 33 census metropolitan areas (CMA) defined by the 2011 census. EAs are the smallest geographical unit for the release of 1971 census statistics. These EA boundaries can be aggregated to the census agglomeration (CA) or the CMA level.The restored EA boundaries also include 1971 population and dwelling statistics. The areas covered by the restored EA polygons account for 61.8% Canada’s total population in 1971. The purpose of the data set is to provide a historical geography in a digital format. It is intended to be used for reference, mapping and for spatial and time series analyses.These boundaries were produced by Statistics Canada, Environment, Energy and Transportation Statistic Division in 2017. The restored 1971 EA boundaries are provided as a single spatial layer. They are also available in Statistics Canada, 2017, “Restoration of the 1971 enumeration area polygons for Canada's largest cities,” Environmental Statistics: Boundary Files, Catalogue no. 16-510-X.

Phytoplankton counts (cell/L)) at the 3 fixed stations and some of the 46 stations grouped into Atlantic Zone Monitoring Program (AZMP) transects under Quebec region responsibility.Phytoplankton data counts at AZMP stations in June 2014, 2018 and 2019 are displayed as 5 layers: Diatoms, Dinoflagellates, Flagellates, Protozoans and Total Phytoplankton. Another layer displays the fixed stations Rimouski, Anticosti Gyre and Gaspe Current and the attached files contain the phytoplankton data acquired at those stations: a .png file for each one, showing time series of counts for the 5 groups, and a .csv file containing the data themselves (columns : Latitude,Longitude, Date(UTC), Depth_min/Profondeur_min(m), Depth_max/Profondeur_max(m), Diatoms/Diatomées(cells/L), Dinoflagellates/Dinoflagellés(cells/L), Flagellates/Flagellés(cells/L), Protozoans/Protozoaires(cells/L), Phytoplankton/Phytoplancton(cells/L)).PurposeThe Atlantic Zone Monitoring Program (AZMP) was implemented in 1998 with the aim of increasing the Department of Fisheries and Oceans Canada’s (DFO) capacity to detect, track and predict changes in the state and productivity of the marine environment.The AZMP collects data from a network of stations composed of high-frequency monitoring sites and cross-shelf sections in each following DFO region: Québec, Gulf, Maritimes and Newfoundland. The sampling design provides basic information on the natural variability in physical, chemical, and biological properties of the Northwest Atlantic continental shelf. Cross-shelf sections sampling provides detailed geographic information but is limited in a seasonal coverage while critically placed high-frequency monitoring sites complement the geography-based sampling by providing more detailed information on temporal changes in ecosystem properties.In Quebec region, two surveys (46 stations grouped into transects) are conducted every year, one in June and the other in autumn in the Estuary and Gulf of St. Lawrence. Historically, 3 fixed stations were sampled more frequently. One of these is the Rimouski station that still takes part of the program and is sampled about weekly throughout the summer and occasionally in the winter period.Annual reports (physical, biological and a Zonal Scientific Advice) are available from the Canadian Science Advisory Secretariat (CSAS), (http://www.dfo-mpo.gc.ca/csas-sccs/index-eng.htm).Devine, L., Scarratt, M., Plourde, S., Galbraith, P.S., Michaud, S., and Lehoux, C. 2017. Chemical and Biological Oceanographic Conditions in the Estuary and Gulf of St. Lawrence during 2015. DFO Can. Sci. Advis. Sec. Res. Doc. 2017/034. v + 48 pp.Supplemental InformationPhytoplankton samples are collected using Niskin bottles, preserved with acid Lugol solution and analysed according to AZMP sampling protocol:Mitchell, M. R., Harrison, G., Pauley, K., Gagné, A., Maillet, G., and Strain, P. 2002. Atlantic Zonal Monitoring Program sampling protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223: iv + 23 pp.

Description:Data on recreational boating are needed for marine spatial planning initiatives in British Columbia (BC). Vessel traffic data are typically obtained by analyzing automatic identification system (AIS) vessel tracking data, but recreational vessels are often omitted or underrepresented in AIS data because they are not required to carry AIS tracking devices. Transport Canada’s National Aerial Surveillance Program (NASP) conducted aerial surveys to collect information on recreational vessels along several sections of the BC coast between 2018 and 2022. Recreational vessel sightings were modeled against predictor variables (e.g., distance to shore, water depth, distance to, and density of marinas) to predict the number of recreational vessels along coastal waters of BC.The files included here are:--A Geodatabase (‘Recreational_Boating_Data_Model’), which includes: (1) recreational vessel sightings data collected by NASP in BC and used in the recreational vessel traffic model (‘Recreational_Vessels_PointData_BC’); (2) aerial survey effort (or number of aerial surveys) raster dataset (‘surveyeffort’); and (3) a vector grid dataset (2.5 km resolution) containing the predicted number of recreational vessels per cell and predictor variables (‘Recreational_Boating_Model_Results_BC).--Scripts folder which includes R Markdown file with R code to run the modelling analysis (‘Recreational_Boating_Model_R_Script’) and data used to run the code.Methods:Data on recreational vessels were collected by NASP during planned aerial surveys along pre-determined routes along the BC coast from 2018 to 2022. Data on non-AIS recreational vessels were collected using video cameras onboard the aircraft, and data on AIS recreational vessels using an AIS receiver also onboard the aircraft. Recreational boating predictors explored were: water depth, distance to shore, distance to marinas, density of marinas, latitude, and longitude. Recreational vessel traffic models were fitted using Generalized Linear Models (GLM) R packages and libraries used here include: AED (Roman Lustrik, 2021) and MASS (Venables, W. N., Ripley, 2002), pscl package (Zeileis, Kleiber, and Jackman, 2008) for zeroinfl() and hurdle() function. Final model was selected based on the Akaike’s information criterion (AIC) and the Bayes’ information criterion (BIC). An R Markdown file with code use to run this analysis is included in the data package in a folder called Script. Spatial Predictive Model: The selected model, ZINB, consist of two parts: one with a binomial process that predicts the probability of encountering a recreational vessel, and a second part that predicts the number of recreational vessels via a count model. The closer to shore and to marinas, and the higher the density of marinas, the higher the predicted number of recreational vessels. The probability of encountering recreational vessels is driven by water depth and distance to shore. For more information on methodology, consult metadata pdf available with the Open Data record.References:Serra-Sogas, N. et al. 2021. Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning. Marine Policy 133: 104765. https://doi.org/10.1016/j.marpol.2021.104765Data Sources:Recreational vessel sightings and survey effort: Data collected by NASP and analyzed by Norma Serra to extract vessel information and survey effort (more information on how this data was analyzed see SerraSogas et al, 2021). Bathymetry data for the whole BC coast and only waters within the Canadian EEZ was provided by DFO – Science (Selina Agbayani). The data layer was presented as a raster file of 100 meters resolution. Coastline dataset used to estimate distance to shore and to clip grid was provided by DFO – Science (Selina Agbayani), created by David Williams and Yuriko Hashimoto (DFO – Oceans). Marinas dataset was provided by DFO – Science (Selina Agbayani), created by Josie Iacarella (DFO – Science). This dataset includes large and medium size marinas and fishing lodges. The data can be downloaded from here: Floating Structures in the Pacific Northwest - Open Government Portal (https://open.canada.ca/data/en/dataset/049770ef-6cb3-44ee-afc8-5d77d6200a12)Uncertainties:Model results are based on recreational vessels sighted by NASP and their related predictor variables and not always might reflect real-world vessel distributions. Any biases caused by the opportunistic nature of the NASP surveys were minimized by using survey effort as an offset variable.

The Okanagan Lake kokanee shore spawner data set is comprised of multiple combined data sets. The historical data sets for the years 1974, 77, 78, 79 and 80 and more recent data sets collected from 2001 to 2016, and 2018. The historical data was derived from information collected in the field and hand drawn onto air photographs. Ministry staff circled Okanagan Lake in a boat one time each year and recorded fish numbers and spawner locations onto air photographs that were digitized in 2006 to make up the historical data set. This data set may not capture the peak reach count for these years. The data collected from 2001 to 2018 was derived from boat counts undertaken along the shoreline of Okanagan, Wood and Kalamalka Lakes. A GPS was used to record shore spawner locations and numbers. Multiple counts were undertaken over the entire spawning cycle and covered the peak spawning period for each year of data provided. The data collected for Christina Lake began in 2003 and ended in 2006. Christina Lake kokanee spawn at night in late December and early January. Kokanee spawning redd locations are available for the 2003/2004 count. Kokanee enumerations were undertaken at night for the 2004/2005 and 2005/2006 seasons and spawning redds were counted at the end of spawning cycle. For these two years there is both spawning and redd count data available.

The wetland year count data included in this product is national in scope (entire forested ecosystem) and represents a wall to wall wetland characterization for 1984-2016 (Wulder et al. 2018). This product was generated using both annual gap free composite reflectance images and annual forest change maps following the Virtual Land Cover Engine (VLCE) process (see Hermosilla et al. 2018), over the 650 million ha forested ecosystems of Canada. Elements of the VLCE classification approach are inclusion of disturbance information in the processes as well as ensuring class transitions over time are logical. Further, a Hidden Markov Model is implemented to assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). The values can range from 0 to 33 denoting the number of years between 1984 and 2016 that a pixel was classified as wetland or wetland-treed in the VLCE data cube.For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018). The focused wetland analyses can be found described in Wulder et al (2018).Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

PURPOSE:To track juvenile Atlantic salmon densities.DESCRIPTION:Indices of freshwater production are derived annually from electrofishing surveys in the rivers of Nova Scotia flowing into the Gulf of St. Lawrence. Fixed site sampling for juvenile salmon has been conducted most consistently since the mid-1980s for these rivers. Juvenile salmon abundances at sites, in terms of number of fish per habitat area sampled by age or size group (densities), are obtained using successive removal sampling or catch per unit effort sampling calibrated to densities. Sampling intensities vary among years and among rivers.PARAMETERS COLLECTED:distribution (ecological); species counts (ecological); point (spatial)USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The distribution of nesting areas for bird colonies in coastal British Columbia showing relative abundance (RA) by season and overall relative importance (RI). RI is based on project region and not on the province as a whole. Number counts for various species in the colony location are provided. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.

This dataset is comprised of locations and current information for all schools for Kindergarten to Grade 12 in British Columbia. Indicators are included for schools that offer French programs including: Core French, Early French Immersion, Late French Immersion and Francophone Program.

Watershed subdistricts of Manitoba

Breakdown of the electoral districts of the City of Rouyn-Noranda**This third party metadata element was translated using an automated translation tool (Amazon Translate).**