Description:Seasonal sigma-t climatology of the Northeast Pacific Ocean was computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period.Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. 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 raster layers of seasonal sigma-t climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.References:Foreman, M. G. G., W. R. Crawford, J. Y. Cherniawsky, and J. Galbraith (2008). Dynamic ocean topography for the northeast Pacific and its continental margins, Geophys. Res. Lett., 35, L22606, doi: 10.1029/2008GL035152Data Sources:NOAA, MEDS and IOS observational dataUncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

AIS NL Biofouling Species Fisheries and Oceans Canada's (DFO) National Marine Biofouling Monitoring Program conducts annual field surveys to monitor the introduction, establishment, spread, species richness, and relative abundance of native and some non-native species in Newfoundland and Labrador (NL) Region since 2006. Standardized monitoring protocols employed by DFO's NL, Maritimes, Gulf, and Quebec regions include biofouling collector plates deployed from May to October at georeferenced intertidal and shallow subtidal sites, including public docks, and public and private marinas and nautical clubs. Initially, (2006-2017), the collectors consisted of three 10 cm by 10 cm PVC plates deployed in a vertical array and spaced approximately 40 cm apart, with the shallowest plate suspended at least 1 m below the surface to sample subtidal and shallow intertidal species (McKenzie et al 2016a). Three replicate arrays were deployed at least 5 m apart per site. Since 2018, collector networks have been modified to improve statistical replication, including up to 10 individual collectors deployed per site at 1 m depth and at least 5 m apart (as above) from May to October. Since 2006, seven invasive biofouling organisms have been detected in Newfoundland and Labrador harbours, marinas and coastal areas.Should be cited as follows: DFO Newfoundland and Labrador Region Aquatic Invasive Species Marine Biofouling Monitoring Program. Published March 2024. Coastal and Freshwater Ecology, Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador.Reference:TunicatesViolet tunicate (Botrylloides violaceus) 2007The violet tunicate was first detected in NL waters in 2007 in Belleoram, Fortune Bay on wharf structures and vessels (McKenzie et al. 2016b). This colonial tunicate forms irregular shaped colonies usually of a solid color (orange, purple, yellow or cream). It is currently found in relatively small colonies in four harbours in NL; Placentia Bay (1), Fortune Bay (1), Conception Bay (1) and the west coast of NL (2). The data provided here indicates the detections of this AIS in coastal NL.From 2018-2022, the Coastal Environmental Baseline Program provided additional support to enhance sampling efforts in Placentia Bay.

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.

AIS NL Biofouling Species Fisheries and Oceans Canada's (DFO) National Marine Biofouling Monitoring Program conducts annual field surveys to monitor the introduction, establishment, spread, species richness, and relative abundance of native and some non-native species in Newfoundland and Labrador (NL) Region since 2006. Standardized monitoring protocols employed by DFO's NL, Maritimes, Gulf, and Quebec regions include biofouling collector plates deployed from May to October at georeferenced intertidal and shallow subtidal sites, including public docks, and public and private marinas and nautical clubs. Initially, (2006-2017), the collectors consisted of three 10 cm by 10 cm PVC plates deployed in a vertical array and spaced approximately 40 cm apart, with the shallowest plate suspended at least 1 m below the surface to sample subtidal and shallow intertidal species (McKenzie et al 2016a). Three replicate arrays were deployed at least 5 m apart per site. Since 2018, collector networks have been modified to improve statistical replication, including up to 10 individual collectors deployed per site at 1 m depth and at least 5 m apart (as above) from May to October. Since 2006, seven invasive biofouling organisms have been detected in Newfoundland and Labrador harbours, marinas and coastal areas.Should be cited as follows: DFO Newfoundland and Labrador Region Aquatic Invasive Species Marine Biofouling Monitoring Program. Published March 2024. Coastal and Freshwater Ecology, Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador.Reference:TunicatesGolden star tunicate (Botryllus schlosseri) 2006 The Golden star tunicate was the first invasive tunicate detected in NL waters. It was reported in Argentia by the US Navy around 1945. It was found in 2006 on wharf structures in Argentia, Placentia Bay during the first AIS survey (Callahan et al 2010). This colonial tunicate is recognized by it star shaped grouping of individuals within the colony. It is currently found in Placentia Bay, Fortune Bay, St. Mary’s Bay, Conception Bay and the west coast of NL. The data provided here indicates the detections of this AIS in coastal NL.From 2018-2022, the Coastal Environmental Baseline Program provided additional support to enhance sampling efforts in Placentia Bay.

Wildfire change magnitude 1985-2022. Spectral change magnitude for wildfires that occurred from 1985 to 2022. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. Higher dNBR values are related to higher burn severity. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2022 for Canada's 650 million-hectare forested ecosystems.When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. (Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. (Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. (Hermosilla et al. 2015b).

Harvest changes occurred from 1985 to 2022 displaying the year of greatest harvest disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of harvest activity in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by harvest for the period 1985-2022 for Canada's 650-million-hectare forested ecosystems.When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. https://doi.org/10.1080/17538947.2016.1187673 ( Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution, and time series change detection methods applied, as well as information on independent accuracy assessment of the data.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. ( Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. ( Hermosilla et al. 2015b).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017)

Wildfire change year 1985-2022.Wildfire changes occurred from 1985 to 2022 displaying the year of greatest wildfire disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of wildfires in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2022 for Canada's 650-million-hectare forested ecosystems.When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. https://doi.org/10.1080/17538947.2016.1187673 (Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution, and time series change detection methods applied, as well as information on independent accuracy assessment of the data..Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. https://doi.org/10.1016/j.rse.2014.11.005 (Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. https://doi.org/10.1016/j.rse.2015.09.004 (Hermosilla et al. 2015b).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017).

As part of the development of a nationally-consistent sampling design within the Aquaculture Monitoring Program (AMP), this data reports mesozooplankton assemblages observed at nine coastal shellfish aquaculture sites, located across four DFO regions, with sampling across months, tide phases, and sampling locations. In most sites, strong spatial effects were observed, while tide effects were generally less important for structuring the mesozooplankton communities. Seasonality emerged as an essential factor to design an efficient monitoring program. This dataset represents the first large-scale Canadian coastal study using imaging technology for plankton taxonomic Identification.Cite this data as: Finnis, S., Guyondet, T., McKindsey, C.W., Arseneau, J., Barrell, J., Duhaime, J., Filgueira, R., Gallardi, D., Gaspard, D., Gibb, O., Goodwin, C., Hua, K., Macdonald, T., Milne, R., Lacoursière-Roussel, A. 2023. Guidance on sampling effort to monitor mesozooplankton communities at Canadian bivalve aquaculture sites using an optical imaging system. Can. Tech. Rep. Fish. Aquat. Sci. 3581: vii + 101 p

Greenland halibut (Reinhardtius hippoglossoides) occupy deep waters off Newfoundland and Labrador and are a commercially important species. The Groundfish Section, Fisheries and Oceans Canada – Newfoundland and Labrador Region implemented an acoustic tagging program to better understand the movement and habitat use for this species. This program began in 2021, and was further developed as a results of two DFO Competitive Science Research Fund project between 2022-2026.Fish were caught via trawl or longline, VEMCO acoustic transmitters surgically implanted, and released at site of capture. Detections occurred from acoustic receivers deployed by the Groundfish section and/or from receiver arrays associated with the Oceans Tracking Network (e.g. Northern Cod Acoustic Telemetry Array [NCAT]). Raw detection data are downloaded from receivers and sent to the Ocean Tracking Network for formatting and archiving. Data provided here summarize reported detections from 2021-2024 as part of a master’s project at Memorial University of Newfoundland. Release locations, number and location of detections, defined movement class, and detection duration are summarized by individual. Location information includes the position of the receiver the fish was detected at, and the North Atlantic Fisheries Organization (NAFO) Divisions where fish were detected in.

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)