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.

Influence of climate change and trophic coupling across four trophic levels in the Celtic Sea

Climate change has had profound effects upon marine ecosystems, impacting across all trophic levels from plankton to apex predators. Determining the impacts of climate change on marine ecosystems requires understanding the direct effects on all trophic levels as well as indirect effects mediated by trophic coupling. The aim of this study was to investigate the effects of climate change on the pelagic food web in the Celtic Sea, a productive shelf region in the Northeast Atlantic. Using long-term data, we examined possible direct and indirect ‘bottom-up’ climate effects across four trophic levels: phytoplankton, zooplankton, mid-trophic level fish and seabirds. During the period 1986–2007, although there was no temporal trend in the North Atlantic Oscillation index (NAO), the decadal mean Sea Surface Temperature (SST) in the Celtic Sea increased by 0.66±0.02°C. Despite this, there was only a weak signal of climate change in the Celtic Sea food web. Changes in plankton community structure were found, however this was not related to SST or NAO. A negative relationship occurred between herring abundance (0- and 1-group) and spring SST (0-group: p = 0.02, slope = −0.305±0.125; 1-group: p = 0.04, slope = −0.410±0.193). Seabird demographics showed complex species–specific responses. There was evidence of direct effects of spring NAO (on black-legged kittiwake population growth rate: p = 0.03, slope = 0.0314±0.014) as well as indirect bottom-up effects of lagged spring SST (on razorbill breeding success: p = 0.01, slope = −0.144±0.05). Negative relationships between breeding success and population growth rate of razorbills and common guillemots may be explained by interactions between mid-trophic level fish. Our findings show that the impacts of climate change on the Celtic Sea ecosystem is not as marked as in nearby regions (e.g. the North Sea), emphasizing the need for more research at regional scales.

Identifying four phytoplankton functional types from space: An ecological approach

Deriving maps of phytoplankton taxa based on remote sensing data using bio-optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio-optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio-temporal information and sea surface temperature. This approach can supply data for large-scale maps of predicted phytoplankton functional types, and an example is shown.

Permanent draft genomes of four new coccolithoviruses: EhV-18, EhV-145, EhV-156 and EhV-164

Coccolithoviruses infect the marine coccolithophorid microalga Emiliania huxleyi. Here, we describe the genomes of four new coccolithoviruses isolated from UK coastal locations. Of particular interest, EhV-18 and EhV-145 encode serine palmitoyltransferase function via two distinct genes, whereas all other coccolithoviruses have SPT as a gene fusion of LCB1/LCB2 domains.