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

The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing.

Microbial ecology is plagued by problems of an abstract nature. Cell sizes are so small and population sizes so large that both are virtually incomprehensible. Niches are so far from our everyday experience as to make their very definition elusive. Organisms that may be abundant and critical to our survival are little understood, seldom described and/or cultured, and sometimes yet to be even seen. One way to confront these problems is to use data of an even more abstract nature: molecular sequence data. Massive environmental nucleic acid sequencing, such as metagenomics or metatranscriptomics, promises functional analysis of microbial communities as a whole, without prior knowledge of which organisms are in the environment or exactly how they are interacting. But sequence-based ecological studies nearly always use a comparative approach, and that requires relevant reference sequences, which are an extremely limited resource when it comes to microbial eukaryotes. In practice, this means sequence databases need to be populated with enormous quantities of data for which we have some certainties about the source. Most important is the taxonomic identity of the organism from which a sequence is derived and as much functional identification of the encoded proteins as possible. In an ideal world, such information would be available as a large set of complete, well curated, and annotated genomes for all the major organisms from the environment in question. Reality substantially diverges from this ideal, but at least for bacterial molecular ecology, there is a database consisting of thousands of complete genomes from a wide range of taxa, supplemented by a phylogeny-driven approach to diversifying genomics [2]. For eukaryotes, the number of available genomes is far, far fewer, and we have relied much more heavily on random growth of sequence databases, raising the question as to whether this is fit for purpose

A compilation of global bio-optical in situ data for ocean colour satellite applications – version three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)

Low-shot learning of plankton categories

International audienceThe size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few examples. We employ the recently introduced weight imprinting technique in order to use the available training data to train accurate classifiers in absence of enough examples for some classes. The model architecture used in this work succeeds in the identification of plankton using machine learning with its unique challenges, i.e. a limited number of training examples and a severely skewed class size distribution. Weight imprinting enables a neural network to recognize small classes immediately without retraining. This permits the mining of examples for novel classes

Efficient CO2 fixation by surface Prochlorococcus in the Atlantic Ocean

Nearly half of the Earth’s surface is covered by the ocean populated by the most abundant photosynthetic organisms on the planet—Prochlorococcus cyanobacteria. However, in the oligotrophic open ocean, the majority of their cells in the top half of the photic layer have levels of photosynthetic pigmentation barely detectable by flow cytometry, suggesting low efficiency of CO2 fixation compared with other phytoplankton living in the same waters. To test the latter assumption, CO2 fixation rates of flow cytometrically sorted 14C-labelled phytoplankton cells were directly compared in surface waters of the open Atlantic Ocean (30°S to 30°N). CO2 fixation rates of Prochlorococcus are at least 1.5–2.0 times higher than CO2 fixation rates of the smallest plastidic protists and Synechococcus cyanobacteria when normalised to photosynthetic pigmentation assessed using cellular red autofluorescence. Therefore, our data indicate that in oligotrophic oceanic surface waters, pigment minimisation allows Prochlorococcus cells to harvest plentiful sunlight more effectively than other phytoplankton