Sediment microbial communities at CO2 seeps in Papua New Guinea

To understand how ocean acidification (OA) influences sediment microbial communities, naturally CO2-rich sites are increasingly being used as OA analogues. However, the characterization of these naturally CO2-rich sites is often limited to OA-related variables, neglecting additional environmental variables that may confound OA effects. Here, we used an extensive array of sediment and bottom water parameters to evaluate pH effects on sediment microbial communities at hydrothermal CO2 seeps in Papua New Guinea. The geochemical composition of the sediment pore water showed variations in the hydrothermal signature at seep sites with comparable pH, allowing the identification of sites that may better represent future OA scenarios. At these sites, we detected a 60% shift in the microbial community composition compared with reference sites, mostly related to increases in Chloroflexi sequences. pH was among the factors significantly, yet not mainly, explaining changes in microbial community composition. pH variation may therefore often not be the primary cause of microbial changes when sampling is done along complex environmental gradients. Thus, we recommend an ecosystem approach when assessing OA effects on sediment microbial communities under natural conditions. This will enable a more reliable quantification of OA effects via a reduction of potential confounding effects. This pangaea entry contains the data on the microbial community structure and bottom water parameters.

Retreat of top cliff of Kurungnakh Island, Lena Delta, Siberia, Russia, 2010-2014, with links to shapefiles

Thawing-induced cliff top retreat in permafrost landscapes is mainly due to thermo-erosion. Ground-ice-rich permafrost landscapes are specifically vulnerable to thermo-erosion and may show high degradation rates. Within the HGF Alliance Remote Sensing and the FP7 PAGE21 permafrost programs we investigated how SAR and optical remote sensing can contribute to the monitoring of erosion rates of ice-rich cliffs in Arctic Siberia (Lena Delta, Russia). We produced two different vector products: i) Intra-annual cliff top retreat based on TerraSAR-X (TSX) satellite data (2012-2014): High-temporal resolution time series of TSX satellite data allow the inter-annual and intra-annual monitoring of the upper cliff-line retreat also under bad weather conditions and continuous cloud coverage. This published SAR product contains the retreating upper cliff lines of a 1.5 km long part of eroding ice-rich coast of Kurungnakh Island in the central Lena Delta. The upper cliff line was mapped using a thresholding approach for images acquired in the years 2012, 2013 and 2014 for the months June (2013, 2014), July (2013, 2014), August (2012, 2013, 2014) and September (2013, 2014). The cliff top retreat vector product is called 'upper_cliff_TerraSAR-X'. While the 2014 cliff lines show a clear retreat of 2 to 3 m/month, the cliff top lines for 2012 and 2013 are not chronologically ordered. However, lines from the end of the season of a year are always close to the lines from the beginning of the next summer season, indicating low cliff retreat in winter. ii) 4-year cliff top retreat based on optical satellite data (2010-2014): Long-term cliff top retreat could be assessed with two high-spatial resolution optical satellite images (GeoEye-1, 2010-08-05 and Worldview-1, 2014-08-19). The cliff top retreat vector product is called 'upper_cliff_optical'. Results: The long-term cliff top retreat derived from optical satellite data are 35 m cliff retreat within 4 years. The higher-temporal resolution SAR data equivalently show long-term rates of 18 m within 2 years and nearly now degradation activities in winter but maximum erosion rates in summer months.The Intra-seasonal cliff top retreat lines from 2014 show a rate of 2 to 3 m per month.

Abundance and biomass of mesozooplankton in the Levantine Basin during the Bilim 2 cruise in April 2008

The Sesame dataset contains mesozooplankton data collected during April 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layers at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling biomass was measured by weighing filters and then determined by sampling volume. The samples were sieved sequentially through meshes of 500 and 200 micron to separate the mesozooplankton into size fractions. The entire sample (1/2) or an aliquot of the taxon-specific mesozooplankton abundance and the total abundance of the mesozooplankton were was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak.

Abundance of mesozooplankton in the Levantine Basin during the Bilim 2 cruise in October 2008

The Sesame dataset contains mesozooplankton data collected during October 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layer at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. The entire sample (1/2) or an aliquot was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak Örek. Specification via marine planktonic copepods database (http://copepodes.obs-banyuls.fr/en/).

Abundance of mesozooplankton in the Cilician Basin during the Bilim 2 cruise in March 2008

The Sesame dataset contains mesozooplankton data collected during March 2008 in the Cilician Basin (between between 35.40'- 36.79 N latitude and 33.19- 36.07 E ). Mesozooplankton samples were collected by using a WP-2 closing net with 200 micron mesh size during day hours (07:00-18:00). Samples were taken in the 0-50, 50-100, 100-200 m layer at 6 stations in the Cilician Basin. The dataset includes samples analyzed for mesozooplankton species composition, abundance and total biomass (Dry weight(mg/m**3)). Taxon-specific mesozooplankton abundance: 1/2 sample or an aliquot was analyzed under the binocular microscope. Copepod species were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Taxonomic identification was done at the METU-Institute of Marine Sciences by Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton total abundance: 1/2 sample or an aliquot was analyzed under the binocular microscope. Copepod species were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Taxonomic identification was done at the METU-Institute of Marine Sciences using the relevant taxonomic literatures