“Summer” missions occur in June, July and August and these focus on the Scotian Shelf and Bay of Fundy (i.e. 4VWX 5Yb, expanding recently to include the Laurentian Channel and Georges Bank (5Zc). Collected data includes total catch in numbers and weights by species. Length frequency data is available for most species, as are the age, sex, maturity and weight information for a subset of the individual animals. Other data such as ageing material, genetic material, and stomach contents are often also collected, but are stored elsewhere.“Summer” cruises occur in May, June, July and August and these focus on the Scotian Shelf and Bay of Fundy (i.e. 4VWX).Cite this data as: Clark, D., Emberley, J. Data of MARITIMES SUMMER RESEARCH VESSEL SURVEYS. Published January 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1366e1f1-e2c8-4905-89ae-e10f1be0a164

Industrial sectors.attributs:ID - Unique identifierName - Industrial park name**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

An exploratory project on the taxonomic and genetic diversity of decapods in three ocean subregions (Northeast Pacific, Canadian Arctic, and Northwest Atlantic), which were sampled in 2022, was undertaken by the Arctic Working Group under the Canada-U.S. Fisheries and Climate Collaboration between Fisheries and Oceans Canada (DFO) and the National Marine Fisheries Service (NMFS) of the National Oceanic and Atmospheric Administration (NOAA). This collaboration framework aims to pool Canadian and U.S. data to explore the impacts of broad-scale climate change on marine biodiversity. In early summer 2022, a sampling protocol with the selection of targeted decapods was provided to DFO and NOAA collaborators. Targeted genera were collected from a total of 10 research programs across three ocean subregions and four marine regions. The Northeast Pacific samples were collected in the Bering Sea during the Northern Bering Sea Ecosystem and Surface Trawl Survey, and the Eastern and Northern Bering Sea Continental Shelf Bottom Trawl Survey of Groundfish and Invertebrate Fauna onboard the F/V Northwest Explorer, F/V Alaska Knight and F/V Vesteraalen. In the Western Canadian Arctic (mainly from Beaufort Sea and Amundsen Gulf), specimens were collected during DFO’s Canadian Beaufort Sea – Marine Ecosystem Assessment (CBS-MEA) survey onboard the F/V Frosti. In Eastern Canadian Arctic (mainly from Baffin Bay and Davis Strait), specimens were collected during DFO’s Knowledge and Ecosystem-Based Approach in Baffin Bay (KEBABB) survey onboard the CCGS Amundsen and DFO’s North Atlantic Fisheries Organization (NAFO) Subarea 0B survey onboard the R/V Tarajoq. In the Estuary and Gulf of St. Lawrence (EGSL), specimens were collected from coastal surveys (scallops, sea cucumber, snow crab, and whelk surveys) onboard the CCGS Leim and offshore during the Ecosystemic Survey onboard the CCGS Teleost. Decapods were collected from various sampling gears (benthic beam trawl, modified Atlantic Western IIA otter trawl, Bacalao trawl, shrimp trawl, Digby scallop dredge, or modified sea cucumber dredge) and identified to the lowest possible taxonomic level and photographed, when possible. All specimens were frozen at sea (n = 995). In the lab, the identifications were validated or refined with the photos and the frozen specimens. DNA was extracted for 87 specimens and a section of COI gene was amplified in order to be sequenced using Sanger method. Sequences were compared with existing data using The Basic Local Alignment Search Tool (BLAST) in the National Center for Bio-technology Information Nucleotide database (NCBI-nt, including the GenBank database) to compare scientific names, where available.The present dataset includes 391 decapod species occurrences. DNA was extracted for a subset of 87 specimens (COI gene); sequences are publicly available on BOLD data portal under project code DDAO (see supporting document "citations_references.csv" for more information).The data are presented in Darwin Core format and are separated in three files:The "Activité_décapodes_DDAO_decapods_event_en" file contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The "Occurrence_décapodes_DDAO_decapods_en" file contains the taxonomic occurrences.The "ADN_décapodes_DDAO_decapods_DNA_en" file contains the DNA derived data.For further details, please refer to the technical report available in the supporting document named "citations_references.csv". USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

"Fall" missions occur primarily in October and November, but sets from September and December are also present in the data. Collected data includes total catch in numbers and weights by species. Length frequency data is available for most species, as are the age, sex, maturity and weight information for a subset of the individual animals. Other data such as ageing material, genetic material, and stomach contents are often also collected, but are stored elsewhere."Fall" cruises occur in September, October, November and December.Cite this data as: Clark, D., Emberley, J. Data of Maritimes Fall Research Vessel Survey. Published January 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/5f82b379-c1e5-4a02-b825-f34fc645a529

The Olympia oyster (Ostrea lurida Carpenter, 1864) is one of four species of oysters established in British Columbia (BC), Canada, and the only naturally occurring oyster in BC (Bourne 1997; Gillespie 1999, 2009). O. lurida reaches the northern limit of its range in the Central Coast of British Columbia at Gale Passage, Campbell Island, approximately 52°12’N, 128°24’W (Gillespie 2009).First Nations historically utilized Olympia oysters for food and their shells for ornamentation (Ellis and Swan 1981; Harbo 1997). European settlers harvested Olympia oysters commercially from the early 1800s until the early 1930s when stocks became depleted and the industry moved towards other larger, introduced oyster species (Bourne 1997; Quayle 1988). Since that time, Olympia oysters have likely maintained stable populations in BC, but have not recovered to abundance levels observed prior to the late 1800s (Gillespie 1999, 2009).Olympia oysters were designated a species of Special Concern by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) in 2000 and 2010 and listed under the Species at Risk Act (SARA) in 2003 (DFO 2009; COSEWIC 2011). A management plan was developed and posted to the SARA Public Registry in 2009 (DFO 2009). One of the objectives of this plan was to ensure maintenance of the relative abundance (density) of Olympia oyster at index sites. The plan also recommended development of a survey protocol for determining relative abundance (density) estimates. In response, a Canadian Science Advisory Secretariat (CSAS) Research Document was completed recommending a survey method for Olympia oysters (Norgard et al. 2010); a CSAS Science Advisory Report (DFO 2010) for selection of index sites was also completed.Thirteen index sites were chosen from a mixture of previously surveyed sites, and by random site selection. In 2014, a fourteenth site was added at Joes Bay in the Broken Group area in partnership with Parks Canada. The selected sites provided a representative sample of Olympia oyster populations in different geographic zones in the Pacific region and span the much of the range of Olympia oysters in BC. The number of sites was reduced to six in 2018 so that annual surveys could be completed to better understand population dynamics and identify long-term trends.

Description:This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021.Methods:This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans.The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone.Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes.While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016).Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditionsData Sources:Model outputUncertainties:The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse and satellite SST, and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions.

Locations are indicated based on the information available. If coordinates were not available, the approximate location is indicated using the description associated with the record. Note that effort is not accounted for in this dataset, nor is effort equally distributed throughout the area captured. Data derived from satellite or acoustic tagging are not included in this dataset. Note that not all records are confirmed. DFO Science reviews records and reports and classifies them as either confirmed or unconfirmed, based on the available information (e.g., pictures, videos, descriptions).

This service provides a compilation of Electronic Navigational Charts (ENC) produced by the Canadian Hydrographic Service. All information provided by this service conforms to the International Hydrographic Organization (IHO) Publication S-57. An ENC contains all the chart information useful for navigation, and may contain supplementary information in addition to that contained in the paper chart. This service is not for navigation.

Get data on fish stocking records from 1900 - 2012. This dataset is the most complete collection of historical fish stocking records available for the time period 1900 to 2012. It includes: * data source * species * rearing location * developmental stage * stock strain * spawning year * stocking date Related datasets: * [Ontario waterbody location identifer](https://data.ontario.ca/dataset/ontario-waterbody-location-identifier)

This dataset contains information on fish stockings since 1917 in Manitoba waterbodies with details on date of stocking, amount of fish, size of fish and species of fish.Stocking Data is a point layer representing individual stocking events dating back to 1917. Each record contains information such as waterbody name, fish species, fish size and quantities, as well as the fishing division of the waterbody. Stocking data is updated multiple times per year as the Province of Manitoba Hatchery conducts stocking. The stocking data layer, along with related information, can be found at the Manitoba Lake Information For Anglers app. The project was initiated by Manitoba Wildlife and Fisheries Branch with funding from the Fish and Wildlife Enhancement Fund. Data is provided by Manitoba Wildlife and Fisheries Branch, Swan Valley Sport Fishing and Manitoba Watershed Districts. For additional information visit Manitoba Fisheries. The dataset includes the following fields (Alias (Name): Description) Waterbody ID (WATERBODY_ID): Unique identifier for an individual waterbody. Waterbody Name (WATERBODY_NAME): Name of the waterbody that was stocked. Fishing Division (FISHING_DIVISION): Indicates which fishing division the waterbody is located in and where stocking event took place. Year (YEAR): Year of the stocking event. Date (DATE): Date of the stocking event. Species (SPECIES): Species that was stocked. Size (SIZE): Size of the fish that were stocked. Quantity (QUANTITY): Approximate number of fish stocked. Lat (DD) (LAT_DD): Latitudinal coordinates of the stocking event (if recorded, otherwise the representative centroid of the waterbody) in decimal degrees. Long (DD) (LONG_DD): Longitudinal coordinates of the stocking event (if recorded, otherwise the representative centroid of the waterbody) in decimal degrees. Waterbody Lat (WATERBODY_LAT): Latitudinal coordinates of the representative centroid of the waterbody in decimal degrees. Waterbody Long (WATERBODY_LONG): Longitudinal coordinates of the representative centroid of the waterbody in decimal degrees.