Contiunous surface water uncorrected and quench-corrected active fluorescence and derived chlorophyll a concentration using calibration by HPLC pigments during POLARSTERN cruise ANT-XXV/1

The relationship between phytoplankton assemblages and the associated optical properties of the water body is important for the further development of algorithms for large-scale remote sensing of phytoplankton biomass and the identification of phytoplankton functional types (PFTs), which are often representative for different biogeochemical export scenarios. Optical in-situ measurements aid in the identification of phytoplankton groups with differing pigment compositions and are widely used to validate remote sensing data. In this study we present results from an interdisciplinary cruise aboard the RV Polarstern along a north-to-south transect in the eastern Atlantic Ocean in November 2008. Phytoplankton community composition was identified using a broad set of in-situ measurements. Water samples from the surface and the depth of maximum chlorophyll concentration were analyzed by high performance liquid chromatography (HPLC), flow cytometry, spectrophotometry and microscopy. Simultaneously, the above- and underwater light field was measured by a set of high spectral resolution (hyperspectral) radiometers. An unsupervised cluster algorithm applied to the measured parameters allowed us to define bio-optical provinces, which we compared to ecological provinces proposed elsewhere in the literature. As could be expected, picophytoplankton was responsible for most of the variability of PFTs in the eastern Atlantic Ocean. Our bio-optical clusters agreed well with established provinces and thus can be used to classify areas of similar biogeography. This method has the potential to become an automated approach where satellite data could be used to identify shifting boundaries of established ecological provinces or to track exceptions from the rule to improve our understanding of the biogeochemical cycles in the ocean.

(Table 1) Mineralogical composition of sediments from DSDP Sites 69-504 and 69-505

We discuss the provenance of minerals detected by X-ray-diffraction analyses of sediments of Sites 504 and 505 of Deep Sea Drilling Project Leg 69. These are X-ray-amorphous material, opal-CT, calcite, quartz, feldspar, apatite, smectite, illite, kaolinite, magnetite, maghemite, pyrite, marcasite, barite, sepiolite, and clinoptilolite. Authigenic marcasite and clinoptilolite together with opal-CT are restricted to Site 504, indicating the special diagenetic conditions related to relatively high sediment temperatures at this site. Marcasite formation is likely dependent on the relatively low pH values of <7.1 found in interstitial waters of Site 504 sediments below 50 meters sub-bottom. Clinoptilolite evidently was formed by diagenetic alteration of rhyolitic volcanic glass or smectite plus biogenic silica within the chalk-limestone-chert sequence of Site 504, where opal-CT also reflects a high degree of silica dissolution and reprecipitation. This was a consequence of high temperatures (50-55 °C) at the base of the sediment column.

Phycoerythrin concentration during POLARSTERN cruises ANT-XXIV/4, ANT-XXV/1, and ANT-XXVI/4

Phycobiliproteins are a family of water-soluble pigment proteins that play an important role as accessory or antenna pigments and absorb in the green part of the light spectrum poorly used by chlorophyll a. The phycoerythrins (PEs) are one of four types of phycobiliproteins that are generally distinguished based on their absorption properties. As PEs are water soluble, they are generally not captured with conventional pigment analysis. Here we present a statistical model based on in situ measurements of three transatlantic cruises which allows us to derive relative PE concentration from standardized hyperspectral underwater radiance measurements (Lu). The model relies on Empirical Orthogonal Function (EOF) analysis of Lu spectra and, subsequently, a Generalized Linear Model with measured PE concentrations as the response variable and EOF loadings as predictor variables. The method is used to predict relative PE concentrations throughout the water column and to calculate integrated PE estimates based on those profiles.