Since 2017, industry-supported scientific surveys targeting Atlantic halibut (Hippoglossus hippoglossus) have been conducted in the Gulf of St. Lawrence and parts of the estuary. The primary objective of the project is to gather data on the species' abundance, distribution, and biological characteristics. To achieve this, a stratified random longline survey is carried out, during which Atlantic halibut of all sizes are measured and tagged using two spaghetti tags. Beginning in 2024, tissue samples from the pelvic fin of each individual were also collected for genetic analysis.Atlantic halibut are captured using longlines equipped with 500 circle hooks. While the length of the leaders varies among vessels, the spacing between leaders ranges from 3.6 to 5.5 meters. Fork length is measured for each individual of commercial species caught - such as Atlantic halibut, cod, redfish, and Greenland halibut (turbot). Measurements are also occasionally taken for non-commercial species.This publication includes the file Donnees_Releve_Fletan_Halibut_Survey_Data, which provides information associated with each sampled station. It also contains the number of Atlantic halibut, cod, redfish and Greenland halibut (turbot) caught at each station. It is important to note that only beginning in 2019 were all commercial species systematically measured. The "NA" values in 2017–2018 do not indicate that no individuals were caught, but rather that they were not measured systematically.Note: There are missing data in various columns of the dataset - use data with caution - if you have any questions please contact the data management team gddaiss-dmsaisb@dfo-mpo.gc.ca.

Since 1979, Fisheries and Oceans Canada has conducted near-annual mackerel egg surveys in the southern Gulf of St. Lawrence using a standardized methodology. This survey typically takes place over approximately 10 days in June and aims to quantify mackerel eggs, thereby contributing to the stock assessment of the northern contingent. Sampling is conducted at 66 fixed stations arranged in a predefined grid (see included image for station locations and names). Additional stations—following the same sampling methodology—have also been surveyed off of southwestern Newfoundland, eastern Cape Breton and south of Prince Edward Island, as well as in other regions. The mackerel survey is often coordinated with the Atlantic Zone Monitoring Program (AZMP) for the Quebec region, which takes place around the same time using the same research vessel and similar equipment. While these two surveys are distinct, they are coordinated to complement each other.At each station, a tow using 61 cm bongo nets (333 µm mesh size) is towed following a saw-tooth profile through the upper 50 m of the water column. Tows last approximately 10 minutes at a speed of roughly 2.5 knots. These tows target mackerel eggs and larvae but collect other species and plankton stages. The taxonomic identification and classification of the developmental stages of the samples are then carried out in the laboratory. The number of taxon counted varies between survey years, based on new species encountered and identified. Egg development stages (I–V) are recorded only for mackerel.This dataset includes the records of all ichthyoplankton species sampled during the survey. It includes information for each sampling station, including gear specifications, species identifications, and life history stages. Each unique combination of COLLECTOR_STATION_NAME, COLLECTOR_EVENT_ID, START_DATE, START_LAT and START_LON represents a single sampling event, and all rows sharing this combination correspond to individual samples collected during that event.The dataset covers the period from 1983 to 2024 and is updated annually as new data become available.It is important to note that prior to 2023, nearly all species were systematically counted, with only a few exceptions where presence alone was recorded. However, since 2023, data collection has shifted to presence/absence for all species, except capelin larvae, herring larvae, and mackerel eggs and larvae, which continue to be counted.Note – raw data use with caution – please contact the author if you have any questions.The data processing methods used for the stock assessment are described in detail in the following publication:Lehoux, C., Van Beveren, E., and Plourde, S. 2024. Results of the Mackerel (Scomber scombrus L.) Egg Surveys Conducted in the Southern Gulf of St Lawrence from 1979 to 2022. DFO Can. Sci. Advis. Sec. Res. Doc. 2024/037. v + 47 p.https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2024/2024_037-eng.html

This dataset contains Manitoba Agriculture soil survey data at various scales ranging from highly detailed to broader reconnaissance level information.Soil is essential to human survival. We rely on it for the production of food, fibre, timber and energy crops. Together with climate, the soil determines which crops can be grown, where, and how much they will yield. In addition to supporting our agricultural needs, we rely on the soil to regulate the flow of rainwater and to act as a filter for drinking water. With such a tremendously important role, it is imperative that we manage our soils for their long-term productivity, sustainability and health. The first step in sustainable soil management is ensuring that the soil will support the land use activity. For example, only the better agricultural soils in Manitoba will support grain and vegetable production, while more marginal agricultural soils will support forage and pasture-based production. For this reason, agricultural development should only occur in areas where the soil resource will support the agricultural activity. The only way to do this is to understand the soil resource that is available. Soil survey information is the key to understanding the soil resource. Soil survey is an inventory of the properties of the soil (such as texture, internal drainage, parent material, depth to groundwater, topography, degree of erosion, stoniness, pH, and salinity) and their spatial distribution over a landscape. Soils are grouped into similar types and their boundaries are delineated on a map. Each soil type has a unique set of physical, chemical and mineralogical characteristics and has similar reactions to use and management. The information assembled in a soil survey can be used to predict or estimate the potentials and limitations of the soils’ behaviour under different uses. As such, soil surveys can be used to plan the development of new lands or to evaluate the conversion of land to new uses. Soil surveys also provide insight into the kind and intensity of land management that will be needed. The survey scale of soils data for Manitoba ranges from 1:5,000 to 1:126,720, as identified in the 'SCALE' column. This file contains soils data that has been collected at a survey intensity level of the first order. This includes data collected at a scale of 1:5,000. The survey objective at this scale is to collect high precision field scale data and it is mostly used in research plots and other highly intensive areas. It is also applicable to agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Profile descriptions and samples are collected for all soils. At least one soil inspection exists per delineation and the minimum size delineation is 0.25 acres. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:5,000 or 12.7 in/ mile. This file also contains soils data that has been collected in Manitoba at a survey intensity level of the second order. This includes data collected at a scale of 1:20,000. The survey objective at this scale is to collect field scale data and it is mostly used in agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally about 200 metres apart and are dug along transects which are about 500 metres apart. This translates to about 32 inspections sites per section(640 acres). The soils in each delineation are identified by field observations and remotely sensed data. Boundaries are verified at closely spaced intervals. Profile descriptions are collected for all major named soils and 10 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in over 90% of delineations and the minimum size delineation is generally about 4 acres at 1:20,000. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:20,000 or 3.2 inch/ mile. This file also contains data that has been collected at the third order. This includes scales of 1:40,000 and 1:50,000.The survey objective at this scale is to collect field scale or regional data. If the topography is relatively uniform, appropriate interpretations include agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally dug adjacent to section perimeters. This translates to about 16 inspection sites per section(640 acres). Soil boundaries are plotted by observation and remote sensed data. Profile descriptions exist for all major named soils and 2 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in 60-80% of delineations and the minimum size delineation is generally in the 10 to 20acre range. The soil taxonomy is generally Series or Phases of Soil Series. The mapping scale is 1:40,000 or 2 inch/ mile; 1:50,000 or 1.5 inch/mile.This file also contains soils data that has been collected at a survey intensity level of the fourth order. This includes scales of 1:63,360, 1:100,000, 1:125,000, and 1:126,720.The survey objective is to collect provincial data and to provide general soil information about land management and land use. The number of soil pits dug averaged to about 6 inspections per section (640 acres). Soil boundaries are plotted by interpretation of remotely sensed data and few inspections exist. Profile descriptions are collected for all major named soils. At least one soil inspection exists in 30-60% of delineations and the minimum size delineation is 40 acres (1:63,360), 100 acres (1:100,000), 156 acres (126,700) and 623 acres (250,000). The soil taxonomy is generally phases of Subgroup or Association.As of 2022, soil survey field work and reports are still currently being collected in certain areas where detailed information does not exist. This file will be updated as more information becomes available. Typically, this is conducted on an rural municipality basis.In some areas of Manitoba, more detailed and historical information exists than what is contained in this file. However, at this time, some of this information is only available in a hard copy format. This file will be updated as more of this information is transferred into a GIS format.This file has an organizational framework similar to the original SoilAID digital files and a portion of this geographic extent was originally available on the Manitoba Land Initiative (MLI) website.Domains and coded values have also been integrated into the geodatabase files. This allows the user to view attribute information in either an abbreviated or a more descriptive manner. Choosing to display the description of the coded values allows the user to view the expanded information associated with the attribute value (reducing the need to constantly refer to the descriptions within the metadata). To change these settings in ArcCatalog, go to Customize --> ArcCatalog Options --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'.To change these settings in ArcMap, go to Customize --> ArcMapOptions --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'. This setting can also be changed by opening the attribute table, then Table Options (top left) --> Appearance --> check or uncheck 'Display coded value domain and subtype descriptions'. The file also contains field aliases, which can also be turned on or off under Table Options.This same capability is available in ArcGIS Pro.For more info:https://www.gov.mb.ca/agriculture/soil/soil-survey/importance-of-soil-survey-mb.html#

The joint Natural Resources Canada/Department of Fisheries and Oceans Marine Spatial Planning Program requires the highest resolution marine based bathymetric elevation data and adjacent land based topographic elevation data that are available. This digital elevation model of Canada's west coast compiles the best data available from multiple government agencies to create a regional model gridded at 10 meter spacing. The transitions between the marine and terrestrial areas are seamless creating a continuous surface of elevations for scientific research and mapping.

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding.Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status.NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest.NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding.Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status.NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest.NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Research surveys targeting Icelandic scallops (Chlamys islandica) have been conducted by Fisheries and Oceans Canada (DFO) at one- or two-year intervals in the Mingan Archipelago (since 1990 in fishing area 16E and since 2004 in fishing area 16F). The main objective of this survey is to assess the status of Icelandic scallop stocks. The study area is situated around the Mingan Archipelago, where scallop beds are sampled at depths ranging from approximately 8 to 136 m, but typically around 40 to 60 m. Sampling is conducted along transects at fixed stations in the study area. Each station is sampled using a lined Digby scallop dredge (20 mm mesh size), towed for roughly 150 m across the seabed.This publication includes three files: the file biometriePetoncle_16, which contains detailed biometric data (species, size, weights and sex) from 2001 to 2025; the file taillePetoncle_16, which provides the size of the individuals sampled from 2010 to 2024; and the file traitPetoncle_16 which contains the abundances and densities per tow from 2004 to 2024. Data on abundances and densities per tow from 2001-2003 is available upon request.This dataset is updated every one to two years as data becomes available. A cleaning of aberrant data has been carried out. However, there is missing data in various columns of the dataset – use the data with caution. If you have any questions please contact DFO.DataManagementSAISB-GestionDonneesDAISS.MPO@dfo-mpo.gc.ca or the author. For certain time periods, associated species are identified and semi-quantitatively counted directly on the sorting table, and the results are presented in the following publication: - https://open.canada.ca/data/en/dataset/7d29bdb1-7e39-43ee-9fb1-bbf33cc9c9b9

This dataset contains parks and protected areas managed for important conservation values and are dedicated for the preservation of their natural environments for the inspiration, use and enjoyment of the public. Places of special ecological importance are designated as ecological reserves for scientific research and educational purposes. Source data is Tantalis. *April 18, 2018: Prior to this date this dataset had one spatial boundary per park per survey plan that intersected the boundary of that park. This resulted in multiple identical boundaries for each park that had more than one survey plan overlapping it’s boundaries. The change aggregated the park data so that there is just one boundary per park with the plan numbers concatenated into a single column where each different plan number is separated by a comma.

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.

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.