Rapid succession drives spring community dynamics of small protists at Helgoland Roads, North Sea

The dynamics of diatoms and dinoflagellates have been monitored for many decades at the Helgoland Roads Long-Term Ecological Research site and are relatively well understood. In contrast, small-sized eukaryotic microbes and their community changes are still much more elusive, mainly due to their small size and uniform morphology, which makes them difficult to identify microscopically. By using next-generation sequencing, we wanted to shed light on the Helgoland planktonic community dynamics, including nano- and picoplankton, during a spring bloom. We took samples from March to May 2016 and sequenced the V4 region of the 18S rDNA. Our results showed that mixotrophic and heterotrophic taxa were more abundant than autotrophic diatoms. Dinoflagellates dominated the sequence assemblage, and several small-sized eukaryotic microbes like Haptophyta, Choanoflagellata, Marine Stramenopiles and Syndiniales were identified. A diverse background community including taxa from all size classes was present during the whole sampling period. Five phases with several communities were distinguished. The fastest changes in community composition took place in phase 3, while the communities from phases 1 to 5 were more similar to each other despite contrasting environmental conditions. Synergy effects of next-generation sequencing and traditional methods may be exploited in future long-term observations

FROM FRESH- TO MARINE WATERS: THE FATE OF DISSOLVED ORGANIC MATTER IN THE LENA DELTA REGION, SIBERIA

The connectivity between the terrestrial and marine environment in the Artic is changing as a result of climate change. This is influencing both freshwater budgets and the supply of carbon to sea. This study characterizes the composition of dissolved organic matter (DOM) within the Lena Delta region across the fresh water-marine gradient. Six fluorescent components (four humic-like; one marine humic-like; one protein-like) were identified by Parallel Factor Analysis, with a clear dominance of humic-like signals in fresh waters. At higher salinities there was an increased autochthonous contribution. Colored DOM (CDOM) and dissolved organic carbon (DOC) were highly correlated and, as a response to the hydrographical forcing, the region displayed a pseudo-conservative behavior of DOM in relation to salinity at marine-influenced sites; and a non-conservative behavior with evidence of considerable removal of DOM (up to 54%), likely driven by photodegradation and sorption/flocculation, at sites influenced by the Lena River plume. The latter mixing curve was split into three mixing regimes with regard to different amount and reactivity degree of DOM and to the factors driving DOM variability: 1) the low salinity regime (salinity>10) with high concentrations of DOM, dominated by highly reactive terrigenous contribution and characterized by rapid removal; 2) the intermediate regime (1025) showing the lowest DOM and an increased contribution of less reactive compounds, displaying a pseudo-conservative behavior, with relatively low removal/addition processes controlling the dilution of DOM

Molecular analyses of protists in long-term observation programmes—current status and future perspectives

Protists (microbial eukaryotes) are diverse, major components of marine ecosystems, and are fundamental to ecosystem services. In the last 10 years, molecular studies have highlighted substantial novel diversity in marine systems including sequences with no taxonomic context. At the same time, many known protists remain without a DNA identity. Since the majority of pelagic protists are too small to identify by light microscopy, most are neither comprehensively or regularly taken into account, particularly in Long-term Ecological Research Sites. This potentially undermines the quality of research and the accuracy of predictions about biological species shifts in a changing environment. The ICES Working Group for Phytoplankton and Microbial Ecology conducted a questionnaire survey in 2013–2014 on methods and identification of protists using molecular methods plus a literature review of protist molecular diversity studies. The results revealed an increased use of high-throughput sequencing methods and a recognition that sequence data enhance the overall datasets on protist species composition. However, we found only a few long-term molecular studies and noticed a lack of integration between microscopic and molecular methods. Here, we discuss and put forward recommendations to improve and make molecular methods more accessible to Long-term Ecological Research Site investigators

Ocean data product integration through innovation-the next level of data interoperability

In the next decade the pressures on ocean systems and the communities that rely on them will increase along with impacts from the multiple stressors of climate change and human activities. Our ability to manage and sustain our oceans will depend on the data we collect and the information and knowledge derived from it. Much of the uptake of this knowledge will be outside the ocean domain, for example by policy makers, local Governments, custodians, and other organizations, so it is imperative that we democratize or open the access and use of ocean data. This paper looks at how technologies, scoped by standards, best practice and communities of practice, can be deployed to change the way that ocean data is accessed, utilized, augmented and transformed into information and knowledge. The current portal-download model which requires the user to know what data exists, where it is stored, in what format and with what processing, limits the uptake and use of ocean data. Using examples from a range of disciplines, a web services model of data and information flows is presented. A framework is described, including the systems, processes and human components, which delivers a radical rethink about the delivery of knowledge from ocean data. A series of statements describe parts of the future vision along with recommendations about how this may be achieved. The paper recommends the development of virtual test-beds for end-to-end development of new data workflows and knowledge pathways. This supports the continued development, rationalization and uptake of standards, creates a platform around which a community of practice can be developed, promotes cross discipline engagement from ocean science through to ocean policy, allows for the commercial sector, including the informatics sector, to partner in delivering outcomes and provides a focus to leverage long term sustained funding. The next 10 years will be “make or break” for many ocean systems. The decadal challenge is to develop the governance and co-operative mechanisms to harness emerging information technology to deliver on the goal of generating the information and knowledge required to sustain oceans into the future

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

Time is an affliction: why ecology cannot be as predictive as physics and why it needs time series

Ecological systems depend on both constraints and historical contingencies, both of which shape their present observable system state. In contrast to ahistorical systems, which are governed solely by constraints (i.e. laws), historical systems and their dynamics can be understood only if properly described, in the course of time. Describing these dynamics and understanding long-term variability can be seen as the mission of long time series measuring not only simple abiotic features but also complex biological variables, such as species diversity and abundances, allowing deep insights in the functioning of food webs and ecosystems in general. Long time-series are irreplaceable for understanding change, and cruicially inherent system variability and thus envisaging future scenarios. This nonewithstanding, current policies in funding and evaluating scientific research discourage the maintenance of long termseries, despite a clear need for long-term strategies to cope with climate change. Timeseries are cruicial for a pursuit of the much invoked Ecosystem Approach and to the passage from simple monitoring programmes large-scale and long-term Earth observatories – thus promoting a better understanding of the causes and effects of change in ecosystems. The few ongoing long timeseries in European waters must be integrated and networked so as to facilitate the formation of nodes of a series of observatories which, together, should allow the long-term management of the features and characteristics of European waters. Human capacity building in this region of expertise and a stronger societal involvement are also urgently needed, since the expertise in recognizing and describing species and therefore recording them reliably in the context of timeseries is rapidly vanishing from the European Scientific community