The kinematics of swimming and relocation jumps in copepod nauplii

Copepod nauplii move in a world dominated by viscosity. Their swimming-by-jumping propulsion mode, with alternating power and recovery strokes of three pairs of cephalic appendages, is fundamentally different from the way other microplankters move. Protozoans move using cilia or flagella, and copepodites are equipped with highly specialized swimming legs. In some species the nauplius may also propel itself more slowly through the water by beating and rotating the appendages in a different, more complex pattern. We use high-speed video to describe jumping and swimming in nauplii of three species of pelagic copepods: Temora longicornis, Oithona davisae and Acartia tonsa. The kinematics of jumping is similar between the three species. Jumps result in a very erratic translation with no phase of passive coasting and the nauplii move backwards during recovery strokes. This is due to poorly synchronized recovery strokes and a low beat frequency relative to the coasting time scale. For the same reason, the propulsion efficiency of the nauplii is low. Given the universality of the nauplius body plan, it is surprising that they seem to be inefficient when jumping, which is different from the very efficient larger copepodites. A slow-swimming mode is only displayed by T. longicornis. In this mode, beating of the appendages results in the creation of a strong feeding current that is about 10 times faster than the average translation speed of the nauplius. The nauplius is thus essentially hovering when feeding, which results in a higher feeding efficiency than that of a nauplius cruising through the water

Comparison of techniques used to count single-celled viable phytoplankton

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Journal of Applied Phycology 24 (2012): 751-758, doi:10.1007/s10811-011-9694-z.Four methods commonly used to count phytoplankton were evaluated based upon the precision of concentration estimates: Sedgewick Rafter and membrane filter direct counts, flow cytometry, and flow-based imaging cytometry (FlowCAM). Counting methods were all able to estimate the cell concentrations, categorize cells into size classes, and determine cell viability using fluorescent probes. These criteria are essential to determine whether discharged ballast water complies with international standards that limit the concentration of viable planktonic organisms based on size class. Samples containing unknown concentrations of live and UV-inactivated phytoflagellates (Tetraselmis impellucida) were formulated to have low concentrations (<100 ml-1) of viable phytoplankton. All count methods used chlorophyll a fluorescence to detect cells and SYTOX fluorescence to detect non-viable cells. With the exception of one sample, the methods generated live and non-viable cell counts that were significantly different from each other, although estimates were generally within 100% of the ensemble mean of all subsamples from all methods. Overall, percent coefficient of variation (CV) among sample replicates was lowest in membrane filtration sample replicates, and CVs for all four counting methods were usually lower than 30% (although instances of ~60% were observed). Since all four methods were generally appropriate for monitoring discharged ballast water, ancillary considerations (e.g., ease of analysis, sample processing rate, sample size, etc.) become critical factors for choosing the optimal phytoplankton counting method.This study was supported by the U.S. Coast Guard Research and Development Center under contract HSCG32-07- X-R00018. Partial research support to DMA and DMK was provided through NSF International Contract 03/06/394, and Environmental Protection Agency Grant RD-83382801-0

Velarium control and visual steering in box jellyfish

Directional swimming in the box jellyfish Tripedalia cystophora (cubozoa, cnidaria) is controlled by the shape of the velarium, which is a thin muscular sheet that forms the opening of the bell. It was unclear how different patterns of visual stimulation control directional swimming and that is the focus of this study. Jellyfish were tethered inside a small experimental tank, where the four vertical walls formed light panels. All four panels were lit at the start of an experiment. The shape of the opening in the velarium was recorded in response to switching off different combinations of panels. We found that under the experimental conditions the opening in the velarium assumed three distinct shapes during a swim contraction. The opening was (1) centred or it was off-centred and pocketed out either towards (2) a rhopalium or (3) a pedalium. The shape of the opening in the velarium followed the direction of the stimulus as long as the stimulus contained directional information. When the stimulus contained no directional information, the percentage of centred pulses increased and the shape of the off-centred pulses had a random orientation. Removing one rhopalium did not change the directional response of the animals, however, the number of centred pulses increased. When three rhopalia were removed, the percentage of centred pulses increased even further and the animals lost their ability to respond to directional information

Light in the Polar Night

How much light isa vailable for biological processes during Polar Night? This question appears simple enough. But the reality is that conventional light sen- sors for measuring visible light (~350 to ~700 nm) have not been sensitive enough to answer it. Beyond this technical challenge, “light” is a general term that must be qualified in terms of “light climate” before it has meaning for biological systems. In this chapter, we provide an answer to the question posed above and explore aspects of light climate during Polar Night with relevance to biology, specifically, how Polar Night is defined by solar elevation, atmospheric light in Polar Night and its propaga- tion underwater, bioluminescence in Polar Night and the concept of Polar Night as a deep-sea analogue, light pollution, and future perspectives. This chapter focuses on the quantity and quality of light present during Polar Night, while subsequent chapters in this volume focus on specific biological effects of this light for algae (Chap. “Marine Micro- and Macroalgae in the Polar Night”), zooplankton (Chaps.“Zooplankton in the Polar Night” and “Biological Clocks and Rhythms in Polar Organisms”), and fish (Chap. “Fish Ecology in the Polar Night”)

High Genetic Diversity and Fine-Scale Spatial Structure in the Marine Flagellate Oxyrrhis marina (Dinophyceae) Uncovered by Microsatellite Loci

Free-living marine protists are often assumed to be broadly distributed and genetically homogeneous on large spatial scales. However, an increasing application of highly polymorphic genetic markers (e.g., microsatellites) has provided evidence for high genetic diversity and population structuring on small spatial scales in many free-living protists. Here we characterise a panel of new microsatellite markers for the common marine flagellate Oxyrrhis marina. Nine microsatellite loci were used to assess genotypic diversity at two spatial scales by genotyping 200 isolates of O. marina from 6 broad geographic regions around Great Britain and Ireland; in one region, a single 2 km shore line was sampled intensively to assess fine-scale genetic diversity. Microsatellite loci resolved between 1–6 and 7–23 distinct alleles per region in the least and most variable loci respectively, with corresponding variation in expected heterozygosities (He) of 0.00–0.30 and 0.81–0.93. Across the dataset, genotypic diversity was high with 183 genotypes detected from 200 isolates. Bayesian analysis of population structure supported two model populations. One population was distributed across all sampled regions; the other was confined to the intensively sampled shore, and thus two distinct populations co-occurred at this site. Whilst model-based analysis inferred a single UK-wide population, pairwise regional FST values indicated weak to moderate population sub-division (0.01–0.12), but no clear correlation between spatial and genetic distance was evident. Data presented in this study highlight extensive genetic diversity for O. marina; however, it remains a substantial challenge to uncover the mechanisms that drive genetic diversity in free-living microorganisms