The Canadian Hydrospatial Network (CHN) is an analysis-ready geospatial network of features that help enable the modelling of surface water flow in Canada. The six main layers and feature types are: flowlines, waterbodies, catchments, catchment aggregates, work units, and hydro nodes. Where possible the CHN is derived from high resolution source data such as Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and aerial imagery, to name a few. If existing provincial or territorial hydrographic networks meet the standards, they are incorporated into the CHN, otherwise automatic extraction methods are used on the high-resolution source data. To provide full network connectivity, if neither of these methods is possible in a region, the NHN is converted into the CHN until higher-resolution source data is available.Additional value-added attributes are included in the CHN to aid modelling, such as stream order and reach slope. The CHN physical model and features are also closely aligned and harmonized with the USGS 3DHP hydrographic network, which aids trans-border modelling. Where possible geonames (i.e. toponyms) are also added.The CHN is produced and disseminated by hydrologically connected geographic areas called work units. Work units can contain just one watershed, several small adjacent watersheds outletting into a large body of water, or be one of many parts of a larger watershed. In all cases, the features of a work unit are hydrologically connected. This is a more natural approach to data delivery, in comparison to data that is split into tiles. A generalized work unit index file is provided in the downloads to help users decide which files to download.For more information on the CHN please visit the project webpage: https://natural-resources.canada.ca/canadian-hydrospatial-network

Description:These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia.Methods:A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record.References:Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300.Data Sources:Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3DihubUncertainties:The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

The Canadian Geographical Names Data Base (CGNDB) is the authoritative national database of Canada's geographical names. The purpose of the CGNDB is to store place names and their attributes that have been approved by the Geographical Names Board of Canada (GNBC), the national coordinating body responsible for standards and policies on place names. The CGNDB is maintained by Natural Resources Canada, through the Canada Centre for Mapping and Earth Observation. The geographic extent of the CGNDB is the Canadian landmass and water bodies; the temporal extent is from 1897 to present. This dataset is extracted from the CGNDB on a weekly basis, and consists of current officially approved names, feature type, coordinates of the feature, decision date, source, and other attributes. The output file formats for this product are: text (CSV), Shape (SHP), and Keyhole Markup Language (KML).Content advisory: The Canadian Geographical Names Database contains historical terminology that is considered racist, offensive and derogatory. Geographical naming authorities are in the process of addressing many offensive place names, but the work is still ongoing. For more information, please contact the GNBC Secretariat.

Radiocarbon dates are derived from organic samples collected through marine and coastal expeditions of the Geological Survey of Canada Atlantic and Pacific. These efforts were conducted primarily to better understand the spatial and temporal coverage of sediments and seabed-fast marine ice during the last deglaciation. The quality of these data varies - ranging from imprecise bulk samples and more accurate AMS estimates derived from single shell fragments. These data are ordered in the menu in 1000 year divisions. By default, only conventional radiocarbon ages are displayed, and reservoir-corrected and measured ages are hidden.

General distribution of Humpback Whales in the Estuary and Gulf of St. Lawrence based on all identified whales from the entire MICS database (Mingan Island Cetacean Study).Additional informationThe MICS (Mingan Island Cetacean Study) has been collecting and compiling in a photo-identification catalogue, blue whale (and other type of whales) sightings for the western North Atlantic since 1979. Since 1987, the material and sampling protocol has been being relatively stable, neither random nor systematic. Field work is conducted abord inflatable boats. Because the whole Gulf ot St. Lawrence cannot be thoroughly studied, MCIS conducts surveys in known whale aggregation areas. The spatial effort is also weather dependent and is mainly constrained by wind direction and strength. Therefore, the study area is not homogeneously covered.Each whale observation is associated with a picture that allows individual identification based on the animal's pigmentation pattern. For more details consult the mentionned report:Gagné, J.A., Ouellet, P., Savenkoff, C., Galbraith, P.S., Bui, A.O.V. et Bourassa, M.-N. Éd. 2013. Rapport intégré de l’initiative de recherche écosystémique (IRÉ) de la région du Québec pour le projet : les espèces fourragères responsables de la présence des rorquals dans l’estuaire maritime du Saint-Laurent. Secr. can. de consult. sci. du MPO. Doc. de rech. 2013/086. vi + 181 p.

This dataset contains digital data files on transects flown and reported in Harwood, L.A. and P. Norton (1996). Aerial survey data from the southeast Beaufort Sea, Mackenzie River estuary and west. Amundsen Gulf, July 1992. Canadian Data Report of Fisheries and Aquatic Sciences No. 964

PURPOSE:The focus of this research is on changes in the distribution of killer whales in the Canadian Arctic, which is within the field of marine biogeography and marine megafauna. Our research details change in killer whale presence and ties it to changes in sea ice coverage. These are novel results, presenting trends in the arrival and departure dates of killer whales into the eastern Canadian Arctic for the first time. We go on to discuss the impacts of these changes on other aspects of Arctic ecosystems and how increasing in killer whale presence might affect other species and the management of those species in Canada. Killer whales are a widespread species of interest, especially in the Canadian Arctic as their presence is tied to multiple aspects of a region rapidly changing from the effects of climate change. DESCRIPTION:This study examines 20 years of killer whale (Orcinus orca) sightings in the eastern Canadian Arctic, drawing from a comprehensive sighting database spanning 1850-2023. Despite inherent biases favoring data collection near communities and coastal areas, spatiotemporal analyses reveal significant shifts in killer whale distribution linked to changing sea ice conditions. We developed a clustering metric representing the mean distance to the five nearest sightings and results show that killer whales are progressively moving away from historically high-use areas and that sighting locations are becoming more dispersed over time. A significant year × sea ice interaction indicates observations occur earlier during their arrival period at lower sea ice concentrations over time, suggesting that declining sea iceconcentration contributes to earlier arrival. Conversely, for departure periods, killer whales are observed farther south later in the year, likely linked to earlier freeze-up at higher latitudes, and are overall observed later into the year over time. This trend has led to a near doubling of their average presence from 26 days in 2002 to 48 days in 2023 (27 July to 13 September) reflecting an extended open-water season. These findings underscore the prolonged seasonal use of Arctic regions by killer whales, driven by diminishing sea ice and expanding openwater habitat. Such shifts highlight potential implications for Arctic marine ecosystems as killer whales increasingly overlap with endemic species.

Rouyn-Noranda cycling network**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The residual materials collection calendar distributed according to the partners' standard.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**