Sightings data were collected by the Mingan Island Cetacean Study (MICS) from 1980 to 2008 with annual surveys realised in the Gulf of St. Lawrence between the end of may and early november. Surveys were conducted using inflatable boats enabling the close approaches necessary to photograph and biopsy blue whales.The aim of this project was to provide additional information for designating blue whale critical habitat as required under the Canadian Species at Risk Act.For more details consult the following report:Ramp, C. and Sears, R. 2013. Distribution, densities, and annual occurrence of individual blue whales (Balaenoptera musculus) in the Gulf of St. Lawrence, Canada from 1980-2008. DFO Can. Sci. Advis. Sec. Res. Doc. 2012/157. vii + 37 p.http://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2012/2012_157-eng.htmlData of blue whale sightings, collected by the MICS, have been analysed per km of effort in 3 x 3 km grid cells in the Gulf of St. Lawrence for the 2000-2008 period.

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.

A modelling analysis conducted by Fisheries and Oceans Canada (DFO) identified these areas as the most suitable habitat for Blue whales: Gulf of St. Lawrence, waters off the southern coast of Newfoundland, the region of Mecatina Trough, the Esquiman Channel and the continental shelf margin off Nova Scotia. They represent important areas for foraging, feeding and socializing for Blue whales. The sources of data used to determine these important areas (by the enclosing boxes method) and the annual and seasonal cycles of Blue whale travel patterns include, but are not limited to, radio and satellite telemetry, passive acoustic monitoring, line-transect aerial surveys, anecdotal reports of observations and modelling.This layer does not represent the general distribution of the Blue whale. Important areas have been identified by reviewing several sources of information and to the best of researchers' knowledge. Several information about Blue whales, their behaviour and habitat use are still unknown. Data is scarce in some areas during winter periods. Observation efforts mostly occur during the summer period, however, data sources can validate their presence during seasons when the observation effort is lower. The Mecatina trough region represents an important area based on historical and non-current data. The presence data per month refers strictly to the information available in the cited research document, and does not express the absence of the species outside the months when a presence was validated. The presented information is valid until the following research survey.Reference:Lesage, V., J.-F. Gosselin, J. W. Lawson, I. McQuinn, H. Moors-Murphy, S. Plourde, R. Sears. and Y. Simard. 2018. Habitats important to blue whales (Balaenoptera musculus) in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/080: iv + 50 p.

The St. Lawrence Estuary is known as a summer foraging area for several species of marine mammals, including several species of rorquals. Among these is the blue whale, which feeds almost exclusively on euphausiids. Therefore, the abundance, distribution and local density of krill should logically be a strong explanatory variable for the distribution of blue whales. However little is known about the spatial association of blue whales with the aggregation dynamics of krill in eastern Canada. Six years of acoustic surveys, conducted in August from 2009 to 2014, were undertaken to study the medium- and small-scale distribution of krill within the northwestern Gulf of St. Lawrence and estuary. The data shows a mosaic of the maximum annual density of arctic krill (T. raschii) made from these surveys.McQuinn, I.H., Gosselin, J.-F., Bourassa, M.-N., Mosnier, A., St-Pierre, J.-F., Plourde, S., Lesage, V., Raymond, A. 2016. The spatial association of blue whales (Balaenoptera musculus) with krill patches (Thysanoessa spp. and Meganyctiphanes norvegica) in the estuary and northwestern Gulf of St. Lawrence. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/104. iv + 19 p.

11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

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.

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 objective of the study was to describe the spatial distribution of krill in eastern Canadian waters using a statistical modelling approach in support of the identification of important habitat for the western North Atlantic (WNA) blue whale (Balaenoptera musculus). Generalized Additive Models (GAMs) were used to predict ‘Significant Aggregations of Krill’ (SAK), i.e., areas where dense krill aggregations would have a greater probability of occurring. SAK cover less than 2% of the entire spatial domain and their location varied among krill categories and seasons. These SAK are interpreted as areas where environmental conditions promote krill aggregation on a regular basis and therefore are potentially important for WNA blue whale foraging in eastern Canadian waters.Plourde, S., Lehoux, C., McQuinn, I.H., and Lesage, V. 2016. Describing krill distribution in the western North Atlantic using statistical habitat models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/111. v + 34 p.

This record contains data on bowhead whale locations 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

Coastal vegetated ecosystems are recognized for their role in cycling and storing carbon in the world’s oceans (i.e., blue carbon); however, high uncertainty in carbon sequestration rates is partly due to an absence of studies estimating carbon export to the deep sea. We modeled export from nearshore kelp forests and seagrass beds, showing variability by orders of magnitude across spatial scales (3 to hundreds of kilometers), kelp and seagrass species, seasons, and carbon forms, raising caution in using generalized export rates in blue carbon accounting. Our results also show rapid (20 to 30 days) and extensive export of neutrally buoyant dissolved organic carbon particles to the shelf break (up to 44% within 90 days), contrasting sinking particulate organic carbon particles that largely remained within 100-meter water depth in the nearshore. These results improve estimates of carbon sequestration by blue carbon ecosystems and reveal contrasting patterns of export relative to other regions of the globe. Cite this data as: Kira A. Krumhansl et al., Pathways of blue carbon export from kelp and seagrass beds along the Atlantic coast of Nova Scotia.Sci. Adv.11,eadw1952(2025).DOI: 10.1126/sciadv.adw1952