In marine ecosystems, the combination of multiple, co-occurring human activities can degrade habitats, undermining the very ecosystem services on which human societies depend. To examine the combined risk to marine habitats from multiple, overlapping human activities and stressors in Atlantic Canadian waters, a cumulative habitat risk assessment was conducted across four bioregions: (1) Scotian Shelf-Bay of Fundy, (2) Northern Gulf of St. Lawrence (3) Southern Gulf of St. Lawrence and (4) Newfoundland and Labrador. This analysis combined spatial information for 20 different marine habitats and 36 human activities across five different sectors (climate change, land-based, marine, coastal, and commercial fishing) using a spatially explicit, exposure-consequence risk framework. This dataset provides the cumulative habitat risk map for Atlantic Canadian marine waters at a 1 km² resolution, as well as the habitat map used in the risk analysis. This project was funded by Fisheries and Oceans Canada through a Competitive Science Research Fund (CSRF) grant. For further information concerning specifics of the maps and methods, contact the data provider. References Arkema, K.K., Verutes, G., Bernhardt, J.R., et al. 2014. Assessing habitat risk from human activities to inform coastal and marine spatial planning: a demonstration in Belize. Environ. Res. Lett. 9(11): 114016. Cite this data as: Kelly, N.E. and Murray, C. Cumulative risk maps for marine habitats of Atlantic Canada. Published: April 2026. Coastal Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS.

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys related to UNEP-MARLIN data only. 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: - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Selection of cigarette related items only. The list of selected items is attached to this metadata. 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); - Selection of surveys referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others; - Normalization of survey lengths to 100m & 1 survey / year: in some case, 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 cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length) Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per 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. Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from MSFD monitoring data (UNEP-MARLIN protocol only) for all years. 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.

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

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

This product displays for Triphenyltin, median values of the last 6 available years that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2022. The median values ranges are derived from the following percentiles: 0-25%, 25-75%, 75-90%, >90%. Only "good data" are used, namely data with Quality Flag=1, 2, 6, Q (SeaDataNet Quality Flag schema). For water, only surface values are used (0-15 m), for sediment and biota data at all depths are used.

This gridded product visualizes 1960 - 2014 water body phosphate concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.9: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.

Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - St. Andrews 2011 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 - St. Andrews 2011. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

Significant Benthic Areas are defined in DFO's Ecological Risk Assessment Framework (ERAF) as "significant areas of cold-water corals and sponge dominated communities", where significance is determined "through guidance provided by DFO-lead processes based on current knowledge of such species, communities and ecosystems". Here we provide maps of the location of significant concentrations of corals and sponges on the east coast of Canada produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. We have conducted those analyses following a bio-regionalization approach in order to facilitate modelling of similar species, given that many of the multispecies surveys do not record coral and sponge catch at species level resolution. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). We applied kernel density estimation (KDE) to create a modelled biomass surface for each of those taxa, and applied an aerial expansion method to identify significant concentrations, following an approach first applied in 2010 to this region. We compared our results to those obtained previously. KDE uses only geo-referenced biomass data to identify "hot spots". The borders of the areas so identified can be refined using knowledge of null catches and species distribution models that predict species presence-absence and/or biomass, both incorporating environmental data.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7

The assessment of the status of eelgrass (Zostera marina) beds at the bay-scale in turbid, shallow estuaries is problematic. The bay-scale assessment (i.e., tens of km) of eelgrass beds usually involves remote sensing methods such as aerial photography or satellite imagery. These methods can fail if the water column is turbid, as is the case for many shallow estuaries on Canada’s eastern seaboard. A novel towfish package was developed for the bay-scale assessment of eelgrass beds irrespective of water column turbidity. The towfish consisted of an underwater video camera with scaling lasers, sidescan sonar and a transponder-based positioning system. The towfish was deployed along predetermined transects in three northern New Brunswick estuaries. Maps were created of eelgrass cover and health (epiphyte load) and ancillary bottom features such as benthic algal growth, bacterial mats (Beggiatoa) and oysters. All three estuaries had accumulations of material reminiscent of the oomycete Leptomitus, although it was not positively identified in our study. Tabusintac held the most extensive eelgrass beds of the best health. Cocagne had the lowest scores for eelgrass health, while Bouctouche was slightly better. The towfish method proved to be cost effective and useful for the bay-scale assessment of eelgrass beds to sub-meter precision in real time. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Using Sidescan and Video - Cocagne 2008. Published: November 2019. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/431c815e-65f0-477b-9389-060fa41ec955