Data set covers metrics and metadata related to wild collected copepods Calanus spp. (C. hyperboreus, C. glacialis, C. finmarchicus) and Metridia longa: - body size in prosome length [PL] - dry weight [DW] - lipid content (oil sac area [OSA] and oil sac volume [OSV]) Spatial coverage: North Atlantic sampling sites - Scotian Shelf (SS) - Gulf of Saint Lawrence (GSL) - Gulf of Maine-Georges Bank-Nantucket Shoals (GoM) - Newfoundland shelf (NFL) Cite this data as: Helenius LK, Head EJH, Jekielek P, Orphanides CD, Pepin P, Plourde S, Ringuette M, Walsh HJ, Runge JA, Johnson CL. Calanus spp. size and lipid content metrics in North Atlantic, 1977-2019. Published September 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/72e6d3a1-06e7-4f41-acec-e0f1474b555b

In Canada, DFO assessments have reported a high probability of significant climate change impacts in all marine and freshwater basins, with effects increasing over time (DFO 2012a, 2012b), while climate projections indicate that ecosystems and fisheries will be disrupted into the foreseeable future (Lotze et al. 2019b; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2022c). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021a; Pepin et al. 2022). The Climate Risk Index for Biodiversity was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable way to better support climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022a), regionally (Lewis et al. 2023), and for fisheries (Boyce et al. 2022c). These data present results from application of the CRIB framework to estimate average climate risks associated with sea surface warming across 2,959 species throughout the Canadian marine territory under contrasting future emission scenarios. In the Technical Report accompanying this data publication, we use Atlantic cod (Gadus morhua) as an example to describe the approach’s data, methods, and outputs, and to transparently and tangibly show how it quantifies risk and can inform and support climate-informed decision-making in Canada. Cite this data as: Boyce, D., Greenan, B., Shackell, N. Data of: A climate risk index for marine life across the Canadian exclusive economic zone. Published: January 2024. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/2a0b3298-2bcc-49a0-a745-af56ed0462f1

The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood. The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998). Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval. To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns. Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008). The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all. Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.

Contour Lines for the Pasqua, Crooked, Echo, and Round Lakes within the Qu’Appelle Valley river system in Saskatchewan

Spiny dogfish (Squlaus acanthias), is a species found in Atlantic Canadian waters which is encountered mostly in commercial fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to spiny dogfish from 2008 to 2009 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial fishing vessels from August to October. Wildlife Computers PSAT Mk10 (N=6) were used and 3 of 6 tags reported. One tag was found washed up on shore and was returned. The spiny dogfish tagged ranged in size from 80 cm to 96 cm Fork Length (curved); all 6 were female. Time at liberty ranged from 75 – 234 days and the 43 tags that reported remained on the sharks for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

50 cm Aerial photography for the Pasqua, Crooked, Echo, and Round Lakes within the Qu'Appelle Valley River system in Saskatchewan

The 2006 Derived Interpolated Census of Agriculture by Soil Landscapes of Canada takes a subset of attributes from the 2006 Agricultural Census and creates new derived attributes that show the proportionate contribution of a variable to the total.

The “Biomass Agriculture Inventory 1-in-20 Probability” dataset is a table that contains the estimated 1-in-20 year low for agricultural residue yield and crop production for each Biomass Report Framework. It provides the fifth percentile values for the years 1985-2016. The table includes straw or stover information for barley, wheat, flax, oats and corn, and crop information for barley, wheat, flax, oats, corn, canola and soybean. This dataset also includes information about the type of tillage used in the area and demand for straw used for cattle bedding and feed. These values are derived from Statistics Canada data. Additionally, the dataset includes the amount of agricultural residue calculated as necessary to remain on the field to prevent soil degradation. Soil degradation is determined by the type of tillage in use as well as the landscape type of the area.

The toponymic features of the CanVec series include proper nouns designating places and representations of the territory. This data come from provincial, territorial and Canadian toponymic databases. They are used in the CanVec Series for cartographic reference purposes and vary according to the scale of display. The toponymic features of the CanVec series can differ from the Canada's official geographical names. The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool. Related Products (Open Maps Links): [Topographic Data of Canada - CanVec Series](https://open.canada.ca/data/en/dataset/8ba2aa2a-7bb9-4448-b4d7-f164409fe056) Users can obtain information about Canada's official toponyms at: **[Geographical names in Canada](https://www.nrcan.gc.ca/maps-tools-publications/maps/geographical-names-canada/10786)**

The Turkey Lakes Watershed (TLW) Study was established in 1979 by several federal government departments (Natural Resources Canada, Environment and Climate Change Canada, and Fisheries and Oceans Canada) to study the impacts of acid rain on aquatic and forest ecosystems but has since expanded to include other research on toxic contaminants, forest harvesting, habitat modification, and climate change. The TLW is a mixed hardwood forest with a 10.5 km squared area in the Canada Shield that drains into Lake Superior, and has been the focus of many interdisciplinary, whole-ecosystem studies on environmental issues. Benthic invertebrates, or insects found at the bottom of waterbodies, can be key indicators of stream health and play important roles in nutrient cycling and the food chain. In 1995, a project within the TLW aimed to evaluate how stream ecosystems respond to tree harvesting, which involved long-term surveys of benthic invertebrate communities pre- and post-harvest. Three stream catchments (c31, c33, c34) were harvested at different intensities in the summer of 1997 and were compared to multiple undisturbed catchments. Alongside the invertebrate data, corresponding stream habitat surveys, streamside litterfall traps, leaf decomposition, deposition of stream particulate matter, and standing sediment bedload data was collected during the same period. The data was collected from 1995-2009 by researchers at the Canadian Forest Service (Great Lakes Forestry Centre). This dataset is a rare and valuable resource because there are very few existing long-term datasets of benthic invertebrates from the same locations, and because this dataset is integrated with other biological and chemical datasets within the broader TLW database stored at the Open Government data repository. For an overview of other types of data available from the TLW, see https://doi.org/10.1002/hyp.14109, and for a complete list of publications that have been produced from data collected at this site, see https://www.canada.ca/en/environment-climate-change/services/turkey-lakes-watershed-study/publications.html.