Description: Seasonal climatologies (temperature, salinity, and sigma-t) of the Northeast Pacific Ocean were 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 in spatial resolution ranging from approximately 100m to 70km. 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. Uncertainties: 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.

In 2017 the SAMBR synthesized data about biodiversity in Arctic marine ecosystems around the circumpolar Arctic.. SAMBR highlighted observed changes and relevant monitoring gaps. This 2021 update provides information on the status of marine mammals in the Arctic from 2015–2020: More detail can be found in the Marine Mammals 2021 Technical report. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT

This visualization product displays the number of Marine Strategy Framework Directive (MSFD) monitoring surveys and the associated temporal coverage per beach. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays plastic bags density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. In cases where the wingspread and/or number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ∑Number of plastic bags related items / Swept area (km²) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is detailed in the attached methodology document. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

Trends in kittiwake colonies 2001-2010, based on linear regression with year as the explanatory variable. Slope of the regression is red = negative trend, blue = positive trend; shaded circle = significant trend (at p<0.05), open circle = non-significant trend. Non-significant deviation from zero could imply a stable population, but in some cases was due to low sample size and low power. Provided with permission from Descamps et al. (in prep). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/seabirds" target="_blank">Chapter 3</a> - Page 135 - Figure 3.5.3 This figure is compiled from data from researchers working throughout circumpolar regions, primarily members of the Circumpolar Seabird Group, an EN of CAFF/seabirds. Dr. Sebastien Decamps conducted the analysis and produced the original figure; the full results will be available in an article in prep titled: “Descamps et al. in prep. Circumpolar dynamics of black-legged kittiwakes track large-scale environmental shifts and oceans' warming rate.” [expected submission spring 2016]. Colony population trends were analyzed using a linear regression with the year as explanatory variable. Based on slope of the regression (which cannot be exactly 0) colonies are either Declining (Slope of the regression <0) or Increasing (Slope of the regression >0). (Colonies may have had a negative but not significant slope, and could be stable but for some others, the slope is not significant due to small sample size / low power; thus we cannot say that all colonies with a non- significant slope are stable. The threshold was put at 5% to assess the significance of the trend.

Trends in biomass of marine fish Focal Ecosystem Components across each Arctic Marine Area STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - Chapter 4 - Page 180 - Figure 4.4

Polar cod in the Barents Sea. Acoustic estimate of polar cod 1-year-old and older (green) and pelagic trawl index of age 0-group abundance (yellow). Source: Joint IMR-PINRO ecosystem survey (Prozorkevich 2016). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/marine-fishes" target="_blank">Chapter 3</a> - Page 116 - Figure 3.4.3

PURPOSE: This product serves a public facing webpage for the Canadian public to download Atlantic Bluefin Tuna stomach content data. DESCRIPTION: Metadata and stomach content from fish caught in the commercial fishery. SAMPLING METHODS: Stomachs were collected from Atlantic Bluefin Tuna (ABFT) caught from mid-August to late September over six years (2018–2023). Most samples originated from ABFT caught around the eastern end of Prince-Edward Island, which reflects the dominant ABFT fishing area, while a few samples were obtained from the Miscou/Baie-des-Chaleurs area in 2018 and 2019. Fish were measured to the nearest curved fork length (cm) and weighed to the nearest round weight (kg). Stomachs were obtained directly from harvesters or through a fish buyer and were stored at −20 ◦C before being processed in the laboratory. Stomachs identification numbers were cross-referenced with ABFT tag numbers recorded by fish provider in order to obtain logbook and port data (catch location, time, weight length, sex, gear, etc.) for each sample. Stomachs were thawed in the laboratory and the content was sorted and identified to the lowest possible taxonomic level. For each stomach, prey were weighed collectively as a taxonomic group and individually to the nearest 0.1 g. Dead bait used to capture ABFT, identified by cut marks, were recorded and weighed but excluded from the analysis. Live bait items cannot be identified from stomach content analyses. Only a few otoliths were found in 2018 and their degraded quality precluded performing ageing or species identification. Rare and small prey items such as algae and rocks were classified in the category “other”. Fish remains that could not be identified were classified in the category “Unidentified teleostei remains”. For 2019 to 2023, when stomach content items could not be visually identified and when tissue was available, tissue samples were collected and stored at −20 °C for DNA barcoding analysis. DNA extraction, mitochondrial cytochrome oxidase subunit 1 amplification, Sanger sequencing and species assignation were performed at the Plateforme d’Analyses Génomiques and Plateforme Bio-informatique of the Institut de Biologie Intégrative et des Systèmes (PAG-IBIS, Université Laval, Quebec city, QC, Canada, http://www.ibis.ulaval.ca/en/services-2/genomic-analysis-platform/). DNA was extracted from 20 mg of muscle tissue using the Omega Bio-tek E-Z-96 Tissue DNA Kit (Omega Bio-tek, Norcross GA, USA) following manufacturer instructions. The mitochondrial cytochrome oxidase subunit 1 region was amplified and sequenced as described in Hashemzadeh Segherloo et al., 2021). Sanger forward and reverse reads were analyzed independently using the Basic Local Alignment Search Tool against non-redundant sequences to identify the top hit for each sequence. When samples could not be identified by a top hit sequence they were classified as “unidentifiable fish”. Prey items that were successfully identified using DNA barcoding were incorporated into the stomach content analysis database and used in all subsequent diet analyses (abundance, occurrence and weight). The weight of the items used in the database was the weight of the remains as they were, and not reconstructed weights calculated for a live animal of the species identified by the barcoding. USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Trends in abundance of plankton Focal Ecosystem Components across each Arctic Marine Area. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - Chapter 4 - Page 178 - Figure 4.2

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.