Mighty small: Observing and modeling individual microbes becomes big science

Progress in microbiology has always been driven by technological advances, ever since Antonie van Leeuwenhoek discovered bacteria by making an improved compound microscope. However, until very recently we have not been able to identify microbes and record their mostly invisible activities, such as nutrient consumption or toxin production on the level of the single cell, not even in the laboratory. This is now changing with the rapid rise of exciting new technologies for single-cell microbiology (1, 2), which enable microbiologists to do what plant and animal ecologists have been doing for a long time: observe who does what, when, where, and next to whom. Single cells taken from the environment can be identified and even their genomes sequenced. Ex situ, their size, elemental, and biochemical composition, as well as other characteristics can be measured with high-throughput and cells sorted accordingly. Even better, individual microbes can be observed in situ with a range of novel microscopic and spectroscopic methods, enabling localization, identification, or functional characterization of cells in a natural sample, combined with detecting uptake of labeled compounds. Alternatively, they can be placed into fabricated microfluidic environments, where they can be positioned, exposed to stimuli, monitored, and their interactions controlled “in microfluido.” By introducing genetically engineered reporter cells into a fabricated landscape or a microcosm taken from nature, their reproductive success or activity can be followed, or their sensing of their local environment recorded.

Covariance among North Sea ecosystem state indicators during the past 50 years - contrasts between coastal and open waters

Regime shift and principal component analysis of a spatially disaggregated database capturing time-series of climatic, nutrient and plankton variables in the North Sea revealed considerable covariance between groups of ecosystem indicators. Plankton and climate time-series span the period 1958–2003, those of nutrients start in 1980. In both regions, the period from 1989 to 2001 identified in principal component 1 had warmer surface waters, higher Atlantic inflow and stronger winds, than the periods before or after. However, it was preceded by a regime shift in both open (PC2) and coastal (PC3) waters during 1977 towards more hours of sunlight and higher water temperature, which lasted until 1997. The relative influence of nutrient availability and climatic forcing differed between open and coastal North Sea regions. Inter-annual variability in phytoplankton dynamics of the open North Sea was primarily regulated by climatic forcing, specifically by sea surface temperature, Atlantic inflow and co-varying wind stress and NAO. Coastal phytoplankton variability, however, was regulated by insolation and sea surface temperature, as well as Si availability, but not by N or P. Regime shifts in principal components of hydrographic and climatic variables (explaining 55 and 61% of the variance in coastal and open water variables) were detected using Rodionov's sequential t-test. These shifts in hydroclimatic variables which occurred around 1977, 1989, 1997 and 2001, were synchronized in open and coastal waters, and were tracked by open water chlorophyll and copepods, but not by coastal plankton. North–central–south or open-coastal spatial breakdowns of the North Sea explained similar amounts of variability in most ecosystem indicators with the exception of diatom abundance and chlorophyll concentration, which were clearly better explained using the open-coastal configuration.

ALES: a multi-mission adaptive sub-waveform retracker for coastal and open ocean altimetry

Satellite altimetry has revolutionized our understanding of ocean dynamics thanks to frequent sampling and global coverage. Nevertheless, coastal data have been flagged as unreliable due to land and calm water interference in the altimeter and radiometer footprint and uncertainty in the modelling of high-frequency tidal and atmospheric forcing. Our study addresses the first issue, i.e. altimeter footprint contamination, via retracking, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to retrack both open ocean and coastal data with the same accuracy using just one algorithm. ALES selects part of each returned echo and models it with a classic ”open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder-Mead nonlinear optimization technique. By avoiding echoes from bright targets along the trailing edge, it is capable of retrieving more coastal waveforms than the standard processing. By adapting the width of the estimation window according to the significant wave height, it aims at maintaining the accuracy of the standard processing in both the open ocean and the coastal strip. This innovative retracker is validated against tide gauges in the Adriatic Sea and in the Greater Agulhas System for three different missions: Envisat, Jason-1 and Jason-2. Considerations of noise and biases provide a further verification of the strategy. The results show that ALES is able to provide more reliable 20-Hz data for all three missions in areas where even 1-Hz averages are flagged as unreliable in standard products. Application of the ALES retracker led to roughly a half of the analysed tracks showing a marked improvement in correlation with the tide gauge records, with the rms difference being reduced by a factor of 1.5 for Jason-1 and Jason-2 and over 4 for Envisat in the Adriatic Sea (at the closest point to the tide gauge).