Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) are measured on DFO cruises three times a year in February, June, and August/September along Line P in the northeast subarctic Pacific. Sampling for phytoplankton pigments started in 2006 at the five main Line P stations and was expanded to sample at all twenty seven stations along the transect in June 2010.

Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) have been measured on DFO La Perouse cruises since 2011. Surface samples are taken along a series of transects off the west coast of Vancouver Island twice a year usually in May/June and early September.

Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) are measured seasonally along a 20-station transect in the Juan de Fuca / Strait of Georgia Basin. Sampling was initiated in 2004, discontinued in 2012 and restarted in 2015. In addition, occasional sampling is carried out at several locations and times in inland waters.For data inquiries please contact: Angelica Pena for data from 2004 to 2015 and Nina Nemcek for data from 2015 to present.

Changes in phytoplankton abundance and community composition have the potential to impact the entire food web and alter ecosystem productivity and biogeochemical cycles. Recognizing its importance, Fisheries and Oceans Canada (DFO) established a phytoplankton monitoring program on the Pacific coast of Canada based on phytoplankton pigment measurements.Phytoplankton pigments (chlorophylls and carotenoids) and ancillary data are collected annually on DFO cruises at multiple locations in waters of the northeast subarctic Pacific and the west coast of Canada. Water samples are collected at discrete depths in the upper layer and analyzed by high-performance liquid chromatography (HPLC).

Cold-water corals are conspicuous in the waters off Eastern Canada. Despite that, there are few DNA sequence records from specimens collected in the region available in GenBank, and not all species recorded in the region have sequence data regardless of geographic origin. This can limit the use of eDNA techniques to detect and identify corals. Our objective was to sequence and publish sequences for two octocoral DNA barcoding markers: CO1 and MutS. We sequenced and deposited 36 sequences to GenBank from 19 specimens representing three sea pen taxa (Octocorallia: Pennatuloidea): Distichoptilum gracile, Pennatula aculeata, and Protoptilum carpenteri. Identification of all specimens was confirmed by B. M. Neves before submission. Specimens and DNA tissues were donated to the Canadian Museum of Nature, where they are currently stored. This publication is part 1 of a series of GenBank submissions by our lab.Specimens were collected from across the Northwest Atlantic and originate from depths ranging between 200-1924 meters. Specimens were collected as part of research vessel multispecies trawl surveys or remotely operated vehicle (ROV ROPOS) surveys. DNA was isolated and purified using the QIAgen DNeasy Blood and Tissue kit, with an initial overnight incubation with Proteinase K. Two commonly used octocoral barcoding regions were amplified using previously described primers: 1) COII8068F (McFadden et al., 2004) and COIOCTR (France and Hoover, 2002) for the CO1 gene, and 2) ND42599F (France and Hoover, 2002) and mut3458R (Sánchez et al., 2003) for the MutS gene. Amplifications were conducted using 12.5 µl of Green DreamTaq Master Mix (Thermo Fisher Scientific), 1 µl of template DNA, 0.5 µl of each 10 µM forward and reverse primers, 0.5 µl of 10 µM reverse primer, and 10.5 µl of water. Thermocycling was run as follows: 3 min of initial denaturation at 95 °C, followed by 40 cycles at 95 °C for 30 s, 30 s at annealing temperature of 48 °C, then 65 s at an extension temperature of 72 °C, and a final elongation at 72 °C for 4 min. PCR products were cleaned using Agencourt AMPure XP Beads (Beckman Coulter) and sent to The Center for Advanced Genomics, Toronto, Canada for Sanger sequencing. Sequences were visualized and aligned using Geneious Prime 2022.0.2. Obtained sequences have been deposited in GenBank under accession numbers OQ569768- OQ569784 and OQ420359- OQ420377. This work was funded by Fisheries and Oceans Canada under an Enhanced Regional Capacity grant (2020-2021) and the Marine Conservation Targets (MCT) program (2021-2024), Newfoundland and Labrador Region.

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are long-wave radiations emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product. Four types of products are currently generated from the GOES-West and GOES-East satellites: "NightIR" and "NightMicrophysics", at 2km resolution, are generated 24 hours a day with infrared channels, so are visible both night and day, and "NaturalColour" and "DayCloudConvection", at 1km resolution, which combine visible light channels with infrared channels; their higher resolution makes the latter two products more popular, but they are not available during most of the night (between 02UTC and 07UTC for GOES-Est, and between 06UTC and 11UTC for GOES-Ouest) given the absence of reflected sunlight. Other RGB products should be added gradually in the future to meet different needs.

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are short-wave and long-wave radiation emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product.Other products are based on the enhancement of channel data for a single wavelength, also aimed at highlighting meteorological features of the observed surface or clouds, but in a simpler way since only a single wavelength is involved. This older approach is still useful today, as its simplicity makes image interpretation easier in some cases.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001.How can this data be used?The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

The National Ecological Framework for Canada's "Surface Material by Ecodistrict” dataset provides surface material information within the ecodistrict framework polygon. It provides surface material codes and their English and French language descriptions as well as information about the percentage of the polygon that the component occupies. Surface material includes the abiotic material at the earth's surface. The materials can be: ICE and SNOW - Glacial ice and permanent snow ORGANIC SOIL - Contains more than 30% organic matter as measured by weight ROCK - Rock undifferentiated MINERAL SOIL - Predominantly mineral particles: contains less than 30% organic matter as measured by weight URBAN - Urban areas. Note that only a few major urban area polygons are included on SLC source maps, therefore, do not use for tabulating total urban coverage.

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001.How can this data be used?The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.