This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations. 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 processings 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 surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata). - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. Finally, the median abundance for each beach and year is calculated from these normalized abundances per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account other sources data for all years. More information is available in the attached documents. 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.

It has not been possible to identify available trend data for Arctic Ocean sea surface temperatures because there is not enough data to calculate reliable long-term trends for much of the Arctic marine environment (IPCC 2013, NOAA 2015). Here, sea surface temperature for July 2015 is shown from CAFF’s Land Cover Change Index. MODIS Sea Surface Temperature (SST) provided a four-kilometre spatial resolution monthly composite snapshot made from night-time measurements from the NASA Aqua Satellite. The night-time measurements are used to collect a consistent temperature measurement that is unaffected by the warming of the top layer of water by the sun. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/marine" target="_blank">Chapter 2</a> - Page 25 - Figure 2.3

The Coastal Infrastructure Vulnerability Index (CIVI) was jointly developed by DFO Science Branch, Small Craft Harbours (SCH) Program and the Economic Analysis and Statistics Directorate. The CIVI was designed with the intent of developing a climate change adaptation tool that would support management decisions regarding the long-term infrastructure planning for SCH sites. The CIVI provides a numerical indication of the relative vulnerability of small craft harbour sites to the effects of climate change and was designed with three component sub-indices: Environmental Exposure (natural forces), Infrastructure, and Socio-economic. The spatial component for the coastline was derived from the CanVec 1:50,000 hydrographic layer (https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b). This layer combines the 1:50,000 CanVec coastline of Canada with the following CIVI environmental exposure variables: - projected sea level rise (for the decades 2030, 2040,...2100) in meters - wave height (metres) and wind speed (metres/second) - change in sea ice coverage in Atlantic Canada from the 1970s to the 2000s Sea level change: Data for relative sea level change (SLC) were derived from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2014, AR5). The projected relative sea level change under the high emission scenario (RCP8.5) was calculated for all years between 2006 and 2100. Sea level change for the years 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100 were used. Wind Speed and Wave Height Modelled hindcasts of yearly maximum wind speed (1990 - 2012) and wave height (1990- 2014) were used. This dataset was generated from IFREMER wave hindcasts using the WAVEWATCH III model with wind data from NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010). Two high resolution (10 minute) grids of Atlantic and Pacific maximum modeled wind speeds and maximum significant wave height were used for southern Canadian coastal areas while a coarser (30 minute) worldwide grid was used for the Arctic areas. From these datasets the mean annual maximum wind speed over 23 years and the mean maximum significant wave height over 25 years were calculated. Change in sea ice coverage: Sea ice data from the Canadian Ice Service were acquired for Atlantic and Arctic Canada, representing percent ice coverage for each week over four decades (1970s, 1980s, 1990, 2000s). For each decade a single dataset was calculated to represent the sum of all weeks with ice coverage in excess of 50%, with a maximum possible score of 52 weeks for each decade. To measure change in ice duration, the summary mapsheet from the 2000s was subtracted from the 1970s summary mapsheet. The final dataset represents the change between the 1970s and 2000s in the number of weeks with ice concentrations greater than 50%. A positive number indicates a reduction in weeks of ice coverage, a negative number an increase in ice coverage. The data for individual small craft harbours included here contains predicted sea level change for the decades between 2030 and 2100, wave height, windspeed, change in sea ice coverage, population, and the final environmental exposure sub-index value (ESI). The population for each harbour is derived from the 2016 Census of Canada data for the Census subdivision (CSD) geographic unit. Reference: Relative sea-level projections for Canada based on the IPCC Fifth Assessment Report and the NAD83v70VG national crustal velocity model https://doi.org/10.4095/327878 IPCC, 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp. Cite this data as: Greenan B. and Greyson P. Coastal Environmental Exposure Layer. Published March 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

This visualization product displays the marine litter material categories percentage 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 fishing bottom trawl surveys. Unlike other EMODnet seafloor litter products, all trawls surveyed since 2007 are included in this map even if the wingspread and/or the distance are unknown. Only surveys with an unknown number of items were excluded from this product. Harmonization of the material categories between ICES and MEDITS lists has been performed and the following calculation has been applied: Material % = (∑Number of items of each material category*100)/(∑Number of items of all material categories) More information on data processing and calculation are detailed in the document attached. 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.

The analysis was performed per season using DIVA software tool (Data-Interpolating Variational Analysis). The analyses products are stored as NetCDF CF files and made available as WMS layers for easy browsing and adding. Every step of the time dimension corresponds to a 6-year moving average from 1983 to 2016. The depth dimension spans from surface to 1000 m, with 21 vertical levels. The boundaries and overlapping zones between these regions were filtered to avoid any unrealistic spatial discontinuities. This combined water body dissolved oxygen concentration product is masked using the relative error threshold 0.5. Units: µmol/l Created by 'University of Liège, GeoHydrodynamics and Environment Research (ULiège-GHER)'. The data used as input for DIVA have been extracted from the EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search Intermediate regional data products: Mediterranean Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1990/2017 v2018, Arctic Ocean - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018, Black Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1990/2016 v2018, North East Atlantic Ocean - DIVA 4D 6-year analysis of Water body chlorophyll-a 1960/2017 v2018, North Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018, Baltic Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2016 v2018

Envision Mapping was sub-contracted by Heriot Watt University for Scottish Natural Heritage (SNH) to undertake broad scale subtidal biotope mapping of Sullom Voe cSAC. Sullom Voe in the Shetland Isles is the most northerly site in the UK to be selected as a representative of large shallow inlets and bays, and within the site series it is the only Scottish example of a ria (known locally as a ?voe?). The boreal-arctic (northern) species-rich communities of Sullom Voe are restricted to Shetland voes and are not represented elsewhere in the SAC series. The purpose was to map the main features and biota using acoustic remote sensing techniques combined with grab and video sampling.

Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Whycocomagh 2007 data is part of the publication Bay Scale Assessment of Nearshore Habitat Bras d'Or Lakes. A history of nearshore benthic surveys of Bras d’Or Lake from 2005 – 2011 is presented. Early work utilized drop camera and fixed mount sidescan. The next phase was one of towfish development, where camera and sidescan were placed on one platform with transponder-based positioning. From 2009 to 2011 the new towfish was used to ground truth an echosounder. The surveys were performed primarily in the northern half of the lake; from 10 m depth right into the shallows at less than 1 m. Different shorelines could be distinguished from others based upon the relative proportions of substrate types and macrophyte canopy. The vast majority of macrophytes occurred within the first 3 m of depth. This zone was dominated by a thin but consistent cover of eelgrass (Zostera marina L.) on almost all shores with a current or wave regime conducive to the growth of this plant. However, the eelgrass beds were frequently in poor shape and the negative impacts of commonly occurring water column turbidity, siltation, or possible localized eutrophication, are suspected. All survey data were placed into a Geographic Information System, and this document is a guide to that package. The Geographic Information System could be used to answer management questions such as the placement and character of habitat compensation projects, the selection of nearshore protected areas or as a baseline to determine long term changes. Vandermeulen, H. 2016. Video-sidescan and echosounder surveys of nearshore Bras d’Or Lake. Can. Tech. Rep. Fish. Aquat. Sci. 3183: viii + 39 p. Cite this data as: Vandermeulen H. Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Whycocomagh 2007. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

The Atlantic dataset is part of Environment and Climate Change Canada’s Shoreline Classification and Pre-Spill database. Shoreline classification data has been developed for use by the Environmental Emergencies Program of Environment and Climate Change Canada for environmental protection purposes. Marine and estuarine shorelines are classified according to the character (substrate and form) of the upper intertidal (foreshore) or upper swash zone (Sergy, 2008). This is the area where oil from a spill usually becomes stranded and where treatment or cleanup activities take place. The basic parameter that defines the shoreline type is the material that is present in the intertidal zone. The presence or absence of sediments is a key factor in determining whether oil is stranded on the surface of a substrate or can penetrate and/or be buried. This dataset contains thousands of linear shoreline segments ranging in length from 200 m and 2 km long. The entities represent the location of the segments and their geomorphological description. There exist further fields in the attribute table for this dataset. We are currently working on standardizing our shoreline segmentation datasets and the updated data will soon be uploaded to the catalog. Sergy, G. (2008). The Shoreline Classification Scheme for SCAT and Oil Spill Response in Canada. Proceedings of the 31stArctic and Marine Oil Spill Program Technical Seminar.Environment Canada, Ottawa, ON, Pp. 811-819.

Megafauna distribution of biomass (g/15 min trawling) in the Barents Sea in 2007, 2011 and 2015. The green circles show the distribution of the snow crab as it spreads from east to west, and the blue triangles show the invasion of king crab along the coast of the southern Barents Sea. Data from Institute of Marine Research, Norway and the Polar Research Institute of Marine Fisheries and Oceanography, Murmansk, Russia. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/benthos" target="_blank">Chapter 3</a> - Page 95 - Figure 3.3.2 The annual joint Norwegian–Russian Ecosystem Survey provides from more than 400 stations and during extensive cruise tracks covering more or less the whole Barents Sea in August– September. The sampling is based on a regular grid spanning about 1.5 millionkm2 with fixed positions of stations which make it possible to measure changes in spatial distribution over time. The trawl is a Campelen 1800 bottom trawl rigged with rock-hopper groundgear and towed on double Warps. The mesh size is 80 mm (stretched) in the front and 16–22 mmin the cod end, allowing the capture and retention of smaller fish and the largest benthos from the seabed (benthic megafauna). The horizontal opening was 11.7 m, and the vertical opening 4–5 m (Teigsmark and Øynes, 1982). The trawl configuration and bottom contact was monitored remotely by SCANMAR trawl sensors. The standard distance between trawl stations was 35 nautical miles (65 km), except north and west of Svalbard where a stratified sampling was adapted to the steep continental shelve. The standard procedure was to tow 15 min after the trawl had made contact with the bottom, but the actual tow duration ranged between 5 min and 1 h and data were subsequently standardized to 15 min trawl time. Towing speed was 3 knots, equivalent to a towing distance of 0.75 nautical miles (1.4 km) during a 15 min tow. The trawl catches were recorded using the same procedures on the Russian and the Norwegian Research vessels to ensure comparability across Barents Sea regions. The benthic megafauna was separated from the fish and shrimp catch, washed, and sorted to lowest possible taxonomic level, in most cases to species, on-Board the vessel. Species identification was standardized between the researcher teams by annually exchanging the benthic expert’s among the vessels and taxon names were fixed each year according toWORMSwhen possible.This resulted in an Electronic identification manual and photo-compendium as a tool to standardize taxon identifications, in addition to various sources of identification literature. Difficult taxa were photographed and, in some cases, brought back as preserved voucher specimens for further identification. Wet-weight biomass was recorded with electronic scales in the ship laboratories for each taxon.The biomass determination included all fragments.

Map of contemporary marine fish data sources. Green squares indicate data from benthic trawl monitoring efforts, blue squares indicate data from benthic trawl surveys, while red triangles indicate data from pelagic trawl monitoring efforts. Red line indicates the CAFF boundary. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/marine-fishes" target="_blank">Chapter 3</a> - Page 112 - Figure 3.4.1