Export of algal biomass from the melting Arctic sea ice

In the Arctic, under-ice primary production is limited to summer months and is not only restricted by ice thickness and snow cover but also by the stratification of the water column, which constrains nutrient supply for algal growth. RV Polarstern visited the ice-covered Eastern Central basins between 82 to 89°N and 30 to 130°E in summer 2012 when Arctic sea ice declined to a record minimum. During this cruise, we observed a widespread deposition of ice algal biomass of on average 9 g C per m**2 to the deep-sea floor of the Central Arctic basins. Data from this cruise will contribute to assessing the impact of current climate change on Arctic productivity, biodiversity, and ecological function.

Maps of coastal and marine geomorphology between Albenga and Savona (NW Mediterranean Sea, Italy)

In this paper, we present a map describing the main geomorphological features of the coastal and marine area between the towns of Albenga and Savona (Ligurian Sea, NW Mediterranean) corresponding to a coastal stretch of ~40 km. To produce this map, we collated data from the literature, orthophotos, perspective photos, multibeam and side scan sonar data, and undertook direct surveys to ground truth data obtained using indirect techniques. We divided the information into nine thematic layers, including bathymetry, natural coastal types, geomorphological elements, seafloor coverage (both geological and biological), coastal and nearshore dynamics, human influence on coastal and marine environments, coastal occupation and protected areas.

Terrain model of the eastern slope of the Porcupine Seabight (50 m grid size)

The topography of the eastern margin of the Porcupine Seabight was surveyed in June 2000 utilizing swath bathymetry. The survey was carried out during RV Polarstern cruise ANT XVII/4 as part of the GEOMOUND project. The main objective was to map and investigate the seafloor topography of this region. The investigated area contains a variability of morphological features such as deep sea channels and giant mounds. The survey was planned and realized on the basis of existing data so as to guarantee the complete coverage of the margin. In order to achieve a resolution of the final digital terrain model (DTM) that meets the project demands, data processing was adjusted accordingly. The grid spacing of the DTM was set to 50 m and an accuracy better than 1% of the water depth was achieved for 96% of the soundings.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

Benthic and substrate cover data derived from photo-transect surveys in Heron Reef

Underwater georeferenced photo-transect survey was conducted on September 23 - 27, 2007 at different sections of the reef flat, reef crest and reef slope in Heron Reef. For this survey a snorkeler or diver swam over the bottom while taking photos of the benthos at a set height using a standard digital camera and towing a surface float GPS which was logging its track every five seconds. A standard digital compact camera was placed in an underwater housing and fitted with a 16 mm lens which provided a 1.0 m x 1.0 m footprint, at 0.5 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey diver/snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry-bag and logged its position as it floated at the surface while being towed by the photographer. A total of 3,586 benthic photos were taken. A floating GPS setup connected to the swimmer/diver by a line enabled recording of coordinates of each benthic. Approximation of coordinates of each benthic photo was done based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the gps coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count excel program (Kohler and Gill, 2006). Each point was then assigned to 1 out of 80 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to gps coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 56 South.