Cruise report r/v "Alkor" [Alkor AL357] Cruise-No. 06AK/10/03 Monitoring Cruise 16 July – 25 July 2010 Kiel Bight to Northern Baltic Proper

Dates and names of ports of call 18./19.06.2010: Saßnit

Phytoplankton stimulation in frontal regions of Benguela Upwelling filaments by internal factors

Filaments are intrusions of upwelling water into the sea, separated from the surrounding water by fronts. Current knowledge explains the enhanced primary production and phytoplankton growth found in frontal areas by external factors like nutrient input. The question is whether this enhancement is also caused by intrinsic factors, i.e., simple mixing without external forcing. In order to study the direct effect of frontal mixing on organisms, disturbing external influx has to be excluded. Therefore, mixing was simulated by joining waters originating from “inside” and “outside” the filament in mesocosms (“tanks”). These experiments were conducted during two cruises in the northern Benguela upwelling system in September 2013 and January 2014. The mixed waters reached a much higher net primary production and chlorophyll a (chla) concentration than the original waters already 2–3 days after their merging. The peak in phytoplankton biomass stays longer than the chla peak. After their maxima, primary production rates decreased quickly due to depletion of the nutrients. The increase in colored dissolved organic matter (CDOM) may indicate excretion and degradation. Zooplankton is not quickly reacting on the changed conditions. We conclude that already simple mixing of two water bodies, which occurs generally at fronts between upwelled and ambient water, leads to a short-term stimulation of the phytoplankton growth. However, after the exhaustion of the nutrient stock, external nutrient supply is necessary to maintain the enhanced phytoplankton growth in the frontal area. Based on these data, some generally important ecological factors are discussed as for example nutrient ratios and limitations, silicate requirements and growth rates

Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea

Almost every summer, dense blooms of filamentous cyanobacteria are formed in the Baltic Sea. These blooms may cause problems for tourism and ecosystem services, where surface accumulations and beach fouling are commonly occurring. Future changes in environmental drivers, including climate change and other anthropogenic disturbances, may further enhance these problems. By compiling monitoring data from countries adjacent to the Baltic Sea, we present spatial and temporal genus-specific distribution of diazotrophic filamentous cyanobacteria (Nostocales) during four decades (1979–2017). While the summer surface salinity decreased with a half up to one unit, the surface temperature in July-August increased with 2–3 °C in most sub-basins of the Baltic Sea, during the time period. The biovolumes of the toxic Nodularia spumigena did not change in any of the sub-basins during the period. On the other hand, the biovolume of the non-toxic Aphanizomenon sp. and the potentially toxic Dolichospermum spp. increased in the northern parts of the Baltic Sea, along with the decreased salinity and elevated temperatures, but Aphanizomenon sp. decreased in the southern parts despite decreased salinity and increased temperatures. These contradictory changes in biovolume of Aphanizomenon sp. between the northern and southern parts of the Baltic Sea may be due to basin-specific effects of the changed environmental conditions, or can be related to local adaptation by sub-populations of the genera. Overall, this comprehensive dataset presents insights to genus-specific bloom dynamics by potentially harmful diazotrophic filamentous cyanobacteria in the Baltic Sea. Highlights • Biovolumes of bloom-forming cyanobacteria during four decades in the Baltic Sea. • Aphanizomenon sp. has increased with decreased salinity in the Bothnian Sea. • Dolichospermum spp. has increased with temperature in Bothnian Sea. • The total biovolume of Nostocales has decreased in the Southern Baltic Proper. • The biovolume of the toxic Nodularia spumigena has not changed since the 1980s

Cyanobacteria net community production in the Baltic Sea as inferred from profiling pCO(2) measurements

Organic matter production by cyanobacteria blooms is a major environmental concern for the Baltic Sea, as it promotes the spread of anoxic zones. Partial pressure of carbon dioxide (pCO(2)) measurements carried out on Ships of Opportunity (SOOP) since 2003 have proven to be a powerful tool to resolve the carbon dynamics of the blooms in space and time. However, SOOP measurements lack the possibility to directly constrain depth-integrated net community production (NCP) in moles of carbon per surface area due to their restriction to the sea surface. This study tackles the knowledge gap through (1) providing an NCP best guess for an individual cyanobacteria bloom based on repeated profiling measurements of pCO(2) and (2) establishing an algorithm to accurately reconstruct depth-integrated NCP from surface pCO(2) observations in combination with modelled temperature profiles.Goal (1) was achieved by deploying state-of-the-art sensor technology from a small-scale sailing vessel. The low-cost and flexible platform enabled observations covering an entire bloom event that occurred in July-August 2018 in the Eastern Gotland Sea. For the biogeochemical interpretation, recorded pCO(2) profiles were converted to C-T*, which is the dissolved inorganic carbon concentration normalised to alkalinity. We found that the investigated bloom event was dominated by Nodularia and had many biogeochemical characteristics in common with blooms in previous years. In particular, it lasted for about 3 weeks, caused a C-T* drawdown of 90 mu mol kg(-1), and was accompanied by a sea surface temperature increase of 10 degrees C. The novel finding of this study is the vertical extension of the C-T* drawdown up to the compensation depth located at around 12 m. Integration of the C-T* drawdown across this depth and correction for vertical fluxes leads to an NCP best guess of similar to 1:2 mol m(-2) over the productive period.Addressing goal (2), we combined modelled hydrographical profiles with surface pCO(2) observations recorded by SOOP Finnmaid within the study area. Introducing the temperature penetration depth (TPD) as a new parameter to integrate SOOP observations across depth, we achieve an NCP reconstruction that agrees to the best guess within 10 %, which is considerably better than the reconstruction based on a classical mixed-layer depth constraint.Applying the TPD approach to almost 2 decades of surface pCO(2) observations available for the Baltic Sea bears the potential to provide new insights into the control and long-term trends of cyanobacteria NCP. This understanding is key for an effective design and monitoring of conservation measures aiming at a Good Environmental Status of the Baltic Sea

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p