Moving 6-year analysis of phosphate 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.

Moving 6-year analysis and visualization of Water body silicate in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.12. results were subjected to the minfield option in DIVAnd 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. The depth dimension allows visualizing the gridded field at various depths.

Running 6-year analysis of Water body silicate in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1966-2023. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis in n dimensions) tool. GEBCO_08 Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the horizontal correlation length is fixed to 80 km and decreasing towards the coastline, the vertical correlation length is varying with depth. Signal to noise ratio is fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is umol/l.

Sea ice meiofauna composition (pie charts) and total abundance (red circles) across the Arctic, compiled by the CBMP Sea Ice Biota Expert Network from 27 studies between 1979 and 2015. Scaled circles show total abundance per individual ice core while pie charts show average relative contribution by taxon per Arctic Marine Area (AMA). Number of ice cores for each AMA is given in parenthesis after region name. Note that studies were conducted at different times of the year, with the majority between March and August (see 3.1 Appendix). The category ‘other’ includes young stages of bristle worms (Polychaeta), mussel shrimps (Ostracoda), forams (Foraminifera), hydroid polyps (Cnidaria), comb jellies (Ctenophora), sea butterflies (Pteropoda), marine mites (Acari) and unidentified organisms. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/sea-ice-biota" target="_blank">Chapter 3</a> - Page 40 - Figure 3.1.4 From the report draft: "Here, we synthesized 19 studies across the Arctic conducted between 1979 and 2015, including unpublished sources (B. Bluhm, R. Gradinger, UiT – The Arctic University of Norway; H. Hop, Norwegian Polar Institute; K. Iken, University of Alaska Fairbanks). These studies sampled landfast sea ice and offshore pack ice, both first- and multiyear ice (Appendix 3.1). Meiofauna abundances reported in individual data sources were converted to individuals m-2 of sea ice assuming that ice density was 95% of that in melted ice. Due to the low taxonomic resolution in the reviewed studies, ice meiofauna were grouped into: Copepoda, nauplii (for copepods as well as other taxa with naupliar stages), Nematoda, Polychaeta (mostly juveniles, but also trochophores), flatworms (Acoelomorpha and Platyhelminthes; these phyla have mostly been reported as one category), Rotifera, and others (which include meroplanktonic larvae other than Polychaeta, Ostracoda, Foraminifera, Cnidaria, Ctenophora, Pteropoda, Acari, and unidentified organisms). Percentage of total abundance for each group was calculated for each ice core, and these percentages were used for regional averages. Maximum available ice core length was used in data analysis, but 50% of these ice cores included only the bottom 10 cm of the ice, 12% the bottom 5 cm, 10% the bottom 2 cm, and 11% the entire ice-thickness. Data from 617 cores were used."

Routes used for hunting polar bear in Ittoqqoortoormiit, East Greenland before 1999 (red line), and in 2012 (yellow), 2013 (blue) and 2014 (green). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/marine-mammals" target="_blank">Chapter 3</a> - Page 159 - Box figure 3.6.1

Moving 6-year analysis of Water body silicate in the NorthEast Atlantic for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span from 1950/1955 until 2016/2021. Observation data span from 1950 to 2021. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Descrption of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 300km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

Temperature and copepod abundance in Zackenberg, northeastern Greenland. Temperature is measured at 80 m for Microcalanus and 5 m for Pseudocalanus (Arendt et al. 2016). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/plankton" target="_blank">Chapter 3</a> - Page 76 - Figure 3.2.7

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

Ocean physical conditions in the Maritimes Region in 2019 were characterized by cooler surface temperatures, continued warmer bottom temperatures and weaker stratification compared to recent years. Deep nutrient inventories were lower than normal over most of the region, with the exception of the Cabot Strait section where deep nutrients were near or higher than normal during the spring sampling and associated with record-warm water. Anomalies of surface nutrients were negative across the region, with the exception of positive anomalies observed at the deep shelf and offshore stations of the Louisbourg section. The spring phytoplankton bloom was near or slightly earlier than normal across the Scotian Shelf (SS) with near-normal duration. Peak chlorophyll a concentrations during the spring bloom occurred within a narrow time window across the SS. At Halifax-2 (HL2), the spring bloom was characterized by a high amplitude, and a rapid progression and decline. Plankton community changes persisted in 2019 with lower abundance of large phytoplankton (diatoms), mainly lower-than-normal biomass of zooplankton and abundance of Calanus finmarchicus, and higher-than-normal abundance of non-copepods. Arctic Calanus and warm-shelf copepods showed mixed abundance anomalies in 2019, reversing the pattern of 2018. Above-normal abundances of Oithona atlantica, especially at HL2, suggest a greater influence of offshore waters in recent years. Surface temperature in the Bedford Basin was near normal in 2019 with mainly cooler-than-normal temperatures from January to June and near- or slightly-above-normal temperatures from July to December. Bottom temperature and salinity were below normal in 2019 with near- or slightly-above-normal conditions at the start of the year and progressing toward cooler and fresher water from February to December. Surface and deep nitrate, phosphate and silicate were near or below normal, with surface phosphate reaching a record low in 2019. The 2018 Continuous Plankton Recorder data indicated an annual abundance of diatoms close to normal for the Eastern (ESS) and Western Scotian Shelf (WSS), while the abundance of dinoflagellates and the Phytoplankton Colour Index values were near (WSS) or above (ESS) normal. The annual abundance of Calanus CI-IV was near normal (ESS) or slightly below normal (WSS), while C. finmarchicus CV-VI levels were slightly below (ESS) or below (WSS) normal. The abundance of Calanus glacialis (ESS, WSS) and Para/Pseudocalanus and Limacina spp. (WSS) were lower than normal, while that of coccolithphore (ESS, WSS), and copepod nauplii and foraminifera (ESS) was higher than normal. "

Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Chapel Island 2008 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 - Chapel Island 2008. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.