Fisheries and Oceans Canada (DFO) has been conducting surface water trawl surveys since 1992 in the coastal waters of British Columbia, Washington, Oregon and Alaska and in the high seas of the Gulf of Alaska. These surveys initially focused on determining the migratory patterns (1992-2002) and on the growth and physiology (2003-2016) of juvenile Pacific Salmon. Since 2016, the focus has been expanded to include all components of the pelagic ecosystem while retaining a strong focus on juvenile Pacific Salmon. Given the change in research priorities, there are differences between years in location and timing. The survey series are provided based on large marine ecosystems, so data will vary in availability. These survey data contain fishing and catch information along with biological information recorded. Surveys available here have published reports that outline overall operations and any oceanographic data, zooplankton and additional samples collected.

Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The maximum daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

The Agri-Environmental Indicator Risk of Water Contamination by Coliforms provides two variables including the Soil Coliform Load and the Coliform Risk to Water. The Soil Coliform Load indicator is the estimated accumulation of coliforms on the soil and the Coliform Risk to Water indicator is the relative risk of coliforms getting into the waterways. 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.

Monthly 30-year Average Mean Temperature represents the average monthly mean temperature calculated at a given location averaged across a 30 year period (1961-1991, 1971-2000, 1981-2010, 1991-2020). These values are calculated across Canada in 10x10 km cells.

Description: Seasonal mean total phytoplankton at the surface from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean surface climatology of the Canadian Pacific Exclusive Economic Zone. Methods: Total phytoplankton is the sum of diatoms and flagellates concentration. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal surface phytoplankton climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution. Uncertainties: Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

The Canadian major and minor crop field trial regions were developed following extensive stakeholder consultation and have been harmonized between the Pest Management Regulatory Agency (PMRA) and the Environmental Protection Agency of the USA. The Canadian major and minor crop field trial regions were delineated, using the geographic information system (GIS) data processing hardware and software facilities in Spatial Analysis and Geomatics Applications (SAGA), Agriculture Division, Statistics Canada. In general, the delineation process involved integration, evaluation and reference to numerous geographic data sources in a GIS to determine the best sources for the delineation. There are seven major and four minor field trial regions. Each of these regions recognizes physical characteristics, such as soils, and crops and climate, that make the region unique within the Canadian agricultural landscape. The subzones address differences within a region, generally reflected in the types of crops grown in that region. The Canadian regions, as much as possible, correspond to the U.S. regions

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.

Zooplankton samples were collected at Ocean Station "P" (50.0000, -145.0000) from 1956 to 1980, and were analyzed to various levels of taxonomic resolution over the years. Although summaries of these data have been previously published ((LeBrasseur 1965) and (Fulton 1978, 1983)) the detailed species data have never been published. This detailed dataset contains total zooplankton wet weights/m3 for the whole period of 1956 to 1980, as well as densities (numbers/m3) for five major taxa (copepods, chaetognaths, euphausiids, amphipods, and Aglantha) from 1964 to 1967, species identifications, counts and lengths for many samples collected between 1968 to 1980. The attached supporting document (Ocean Station "Papa" detailed zooplankton data: 1956 – 1980) contains information on the methods used to collect and process the data along with descriptions of a number of fairly minor points about the data that were not resolved. It also describes, in detail, the format of the original data files, the corrections/changes that were made to these files in creating this version, and how these errors affect what was published in Fulton (1983). The purpose of this record is to make the detailed data available to the scientific community in an electronic format and to provide a convenient reference for citing the detailed data. Waddell, Brenda J., and Skip McKinnell. 1995. Ocean Station "Papa" detailed zooplankton data: 1956 - 1980. Can. Tech. Rep. Fish. Aquat. Sci. 2056: 21 p.

Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 103 sites. From these sites 70% were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 81% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 6. Tracadie This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.

Aquatic bird eggs are being collected for contaminants analysis. Egg collections in the Peace-Athabasca Delta area support Parks Canada’s activities at Wood Buffalo National Park and the multi-stakeholder Peace-Athabasca Ecosystem Monitoring Program. This monitoring activity employs repeated censuses of birds and builds on initial egg collections made in 2009 from Egg Island (Lake Athabasca) and Wood Buffalo National Park, with the goal of evaluating contaminant burdens, contaminant sources and changes in sources through time. Egg samples are collected from colonial waterbirds California Gulls (Larus californicus), Herring Gulls (Larus argentatus), Ring-billed Gulls (Larus delawarensis), Caspian Terns (Hydroprogne caspia) and Common Terns (Sterna hirundo) and insectivorous birds Bank Swallows (Riparia riparia), Cliff Swallows (Petrochelidon pyrrhonota) and Tree Swallows (Tachycineta bicolor) to monitor health and contaminant levels of aquatic and terrestrial birds in the oil sands region and in reference areas. The samples collected are analysed for oil sands-related contaminants including polycyclic aromatic hydrocarbons (PAHs) and metals such as mercury (Hg) and arsenic (As).