Water_body_dissolved_oxygen_concentration - Monthly Climatology for the European Seas for the period 1960-2023 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: umol/l. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

This dataset provides 30-year, 50-year, and 100 year return levels for small craft harbours in British Columbia, relative to the mean sea level over 1993-2020. The return levels are derived from coastal sea levels for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

Water_body_ammonium - Monthly Climatology for the European Seas for the period 1960-2023 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: umol/l. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

Moving 6-year analysis of dissolved oxygen concentration in the Arctic Ocean, for each season in the period 1965-2024. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.13, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.125 degrees. Signal-to-noise ratio was fixed to 3.0, horizontal correlation length varying from 45 km near the coast to 150 km, and vertical correlation length varying between 25 and 1000 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: analysis with signal-to-noise ratio = 20, horizontal correlation length 50-200 km, and vertical correlation length 25-1000 m.

Trends in biomass or diversity of benthic Focal Ecosystem Components across each Arctic Marine Area. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - Chapter 4 - Page 179 - Figure 4.3

This product displays for Cadmium, positions with percentages of all available data values per group of animals that are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.

This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach, per 100m & to 1 survey aggregated over the period 2001 to 2020 from Marine Strategy Framework Directive (MSFD) monitoring surveys. 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; - 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 (total abundance list). This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Normalization of survey lengths to 100m & 1 survey / year: in some cases, the survey length was not exactly 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, a median is calculated over the entire period among all these total numbers of litter per 100m calculated for each survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. The size of each circle on this map increases with the calculated median number of marine litter per beach, per 100m & to 1 survey. The median litter abundance values displayed in the legend correspond to the 50 and 99 percentiles and the maximum value. 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 map was created to give an idea of the distribution of beach litter between 2001 and 2021 in a synthetic manner. NOT ALL BEACHES MAY HAVE DATA FOR THE ENTIRE PERIOD, SO IT IS NOT POSSIBLE TO MAKE A COMPARISON BETWEEN BEACHES.

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.

Vinsamlega hafið samband við Fiskistofu vegna nánari upplýsinga.

Description: In the coming decades, warming and deoxygenation of marine waters are anticipated to result in shifts in the distribution and abundance of fishes, with consequences for the diversity and composition of fish communities. Here, we combine fisheries-independent trawl survey data spanning the west coast of the USA and Canada with high-resolution regional ocean models to make projections of how 34 groundfish species will be impacted by changes in temperature and oxygen in British Columbia (BC) and Washington. In this region, species that are projected to decrease in occurrence are roughly balanced by those that are projected to increase, resulting in considerable compositional turnover. Many, but not all, species are projected to shift to deeper depths as conditions warm, but low oxygen will limit how deep they can go. Thus, biodiversity will likely decrease in the shallowest waters (less than 100 m), where warming will be greatest, increase at mid-depths (100–600 m) as shallow species shift deeper, and decrease at depths where oxygen is limited (greater than 600 m). These results highlight the critical importance of accounting for the joint role of temperature, oxygen and depth when projecting the impacts of climate change on marine biodiversity. The rasters available in this dataset project the occurrence of each of the 34 groundfish species in a 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models (BCCM and NEP36). Each projection layer is provided as the mean projected occurrence as well as the lower and upper 95% confidence interval of projected occurrence. Methods: Estimated species response curves: We estimated how the observed distribution of groundfish species is determined by temperature, dissolved oxygen and seafloor depth using data from fisheries-independent scientific research trawls spanning the entire American and Canadian west coast. We included data from 4 surveys (NOAA West Coast, NOAA Alaska, NOAA Bering or DFO Pacific) from 2000 to 2019. For each species, we modelled occurrences in the coastwide trawl dataset using a generalized linear model (GLM) using the sdmTMB package in R v. 4.0.2. The predictors were temperature, log dissolved oxygen, log depth and survey. We included quadratic terms for temperature and log depth to allow species occurrences to peak at intermediate values. We fitted a breakpoint function for log dissolved oxygen to reflect the fact oxygen is a limiting factor. We assessed the forecasting accuracy of the SDM by comparing how well a model fitted to only data from 2000 to 2010 could forecast species’ occurrences in trawls within our focal region for the period of 2011–2019. We assessed all 77 groundfish species that were present in the overall trawl dataset, however the final analysis included only the 34 species for which the models had adequate forecasting ability. Projecting groundfish biodiversity changes: We based our groundfish biodiversity change projections on two regional models that downscale climate projections: the British Columbia Continental Margin model (BCCM) and the North-Eastern Pacific Canadian Ocean Ecosystem model (NEP36-CanOE). We used a historical baseline of 1986–2005 and future projected values for 2046–2065 based on RCP 4.5 and 8.5 emissions scenarios. Using the models that we validated in our forecasting accuracy assessment, we projected the occurrence of each species in each 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models. Uncertainties: Source survey data was collected by consistent methods with survey-grade GPS for all years included. Data quality is expected to be high. Modeled data are at 3 km resolution. Outputs are as accurate as source input models and are deemed to be of high quality and accurate based upon the precision of model inputs. Projecting biodiversity responses to climate change involves considerable uncertainty and our approach allows us to quantify some aspects of this. Of the uncertainty that we could quantify, roughly half was due to uncertainty in our SDMs and the remainder was due to regional ocean model uncertainty or scenario uncertainty. This amount of uncertainty in the SDMs is typical, stemming from the fact that contemporary species distributions are also influenced by other factors that we have not included in our model. In addition, although oxygen demand is understood to vary with temperature, limitations in the implementation of breakpoint models prevented us from estimating a temperature-dependent oxygen breakpoint. However, although somewhat unrealistic, this limitation is unlikely to have greatly increased the uncertainty in our SDMs because low oxygen concentrations occurred almost exclusively at depths where temperature variation and projected change was small. To reduce uncertainty due to year-to-year variation in climate, our model projections are based on 20-year climatologies with a future period that is far enough ahead to ensure that changes are unambiguously due to greenhouse gases. We have made projections based on two different emissions scenarios, and two different regional ocean models that are both downscaled from the same global model, the second generation Canadian Earth System Model (CanESM2), using different downscaling techniques. While the BCCM model was run inter-annually and then averaged to produce the climatologies, the NEP36 model used atmospheric climatologies with augmented winds to force the ocean model and produce representative climatologies. Comparing these regional projections provides an estimate of the uncertainty across different regional downscaling models and methods. We find that the projected impacts of climate change on the groundfish community are more sensitive to the differences in the regional ocean models than they are to the emissions scenarios used. However, these differences are in magnitude (changes tend to be larger based on NEP36 compared with the BCCM) rather than in direction, with both models resulting in similar overall patterns of biodiversity change and turnover for the groundfish community. Over the 60-year time period (1986–2005 versus 2046–2065) used in our study, our projections suggest that groundfish community changes are similar regardless of the scenario used.