Identification of senescence and death in Emiliania huxleyi and Thalassiosira pseudonana: Cell staining, chlorophyll alterations, and dimethylsulfoniopropionate (DMSP) metabolism

We measured membrane permeability, hydrolytic enzyme, and caspase-like activities using fluorescent cell stains to document changes caused by nutrient exhaustion in the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana, during batch-culture nutrient limitation. We related these changes to cell death, pigment alteration, and concentrations of dimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP) to assess the transformation of these compounds as cell physiological condition changes. E. huxleyi persisted for 1 month in stationary phase; in contrast, T. pseudonana cells rapidly declined within 10 d of nutrient depletion. T. pseudonana progressively lost membrane integrity and the ability to metabolize 5-chloromethylfluorescein diacetate (CMFDA; hydrolytic activity), whereas E. huxleyi developed two distinct CMFDA populations and retained membrane integrity (SYTOX Green). Caspase-like activity appeared higher in E. huxleyi than in T. pseudonana during the post-growth phase, despite a lack of apparent mortality and cell lysis. Photosynthetic pigment degradation and transformation occurred in both species after growth; chlorophyll a (Chl a) degradation was characterized by an increase in the ratio of methoxy Chl a : Chl a in T. pseudonana but not in E. huxleyi, and the increase in this ratio preceded loss of membrane integrity. Total DMSP declined in T. pseudonana during cell death and DMS increased. In contrast, and in the absence of cell death, total DMSP and DMS increased in E. huxleyi. Our data show a novel chlorophyll alteration product associated with T. pseudonana death, suggesting a promising approach to discriminate nonviable cells in nature

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.info:eu-repo/semantics/publishedVersio

Benthic silicon cycling in the Arctic Barents Sea: a reaction–transport model study

Over recent decades the highest rates of water column warming and sea ice loss across the Arctic Ocean have been observed in the Barents Sea. These physical changes have resulted in rapid ecosystem adjustments, manifesting as a northward migration of temperate phytoplankton species at the expense of silica-based diatoms. These changes will potentially alter the composition of phytodetritus deposited at the seafloor, which acts as a biogeochemical reactor and is pivotal in the recycling of key nutrients, such as silicon (Si). To appreciate the sensitivity of the Barents Sea benthic system to the observed changes in surface primary production, there is a need to better understand this benthic–pelagic coupling. Stable Si isotopic compositions of sediment pore waters and the solid phase from three stations in the Barents Sea reveal a coupling of the iron (Fe) and Si cycles, the contemporaneous dissolution of lithogenic silicate minerals (LSi) alongside biogenic silica (BSi), and the potential for the reprecipitation of dissolved silicic acid (DSi) as authigenic clay minerals (AuSi). However, as reaction rates cannot be quantified from observational data alone, a mechanistic understanding of which factors control these processes is missing. Here, we employ reaction–transport modelling together with observational data to disentangle the reaction pathways controlling the cycling of Si within the seafloor. Processes such as the dissolution of BSi are active on multiple timescales, ranging from weeks to hundreds of years, which we are able to examine through steady state and transient model runs. Steady state simulations show that 60 % to 98 % of the sediment pore water DSi pool may be sourced from the dissolution of LSi, while the isotopic composition is also strongly influenced by the desorption of Si from metal oxides, most likely Fe (oxyhydr)oxides (FeSi), as they reductively dissolve. Further, our model simulations indicate that between 2.9 % and 37 % of the DSi released into sediment pore waters is subsequently removed by a process that has a fractionation factor of approximately −2 ‰, most likely representing reprecipitation as AuSi. These observations are significant as the dissolution of LSi represents a source of new Si to the ocean DSi pool and precipitation of AuSi an additional sink, which could address imbalances in the current regional ocean Si budget. Lastly, transient modelling suggests that at least one-third of the total annual benthic DSi flux could be sourced from the dissolution of more reactive, diatom-derived BSi deposited after the surface water bloom at the marginal ice zone. This benthic–pelagic coupling will be subject to change with the continued northward migration of Atlantic phytoplankton species, the northward retreat of the marginal ice zone and the observed decline in the DSi inventory of the subpolar North Atlantic Ocean over the last 3 decades

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano- and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite ocean-colour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19’-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano- and pico-phytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure ̶ designed to quantify the relationship between PSC and TCHLa ̶ to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a time-series of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme

Data sheets aiding identification of phytoplankton carotenoids and chlorophylls

9 pagesSince the publication of 47 key phytoplankton pigment data sheets in the volume by Jeffrey et al. (1997b), several new algal groups and pigments have been reported. To reflect this and the increased use of mass spectrometry for phytoplankton pigment characterisation we have compiled revised and expanded data sheets documenting 47 carotenoids and 21 chlorophylls. These new data sheets complement the ones produced for the 1997 volume. They are also available online, at www.cambridge.org/phytoplankton, for ease of consultation. We do not include data sheets for the many chlorophyll transformation products found particularly in sediments; for information on these pigments readers should refer to the comprehensive review by Keely (2006). Similarly, our coverage of pigments contained in phototrophic bacteria is limited mostly to cyanobacteria found in the water column of freshwater and marine environments (see Chapter 1, this volume), hence we exclude the newly discovered chlorophyll f in stromatolites (Chen et al., 2010). Readers interested in anoxygenic phototrophic bacteria should consult the reviews by Takaichi (1999) and Scheer (2006)N

Abundance of a chlorophyll a precursor and the oxidation product hydroxychlorophyll a during seasonal phytoplankton community progression in the Western English Channel

This study presents the first in-situ measurements of the chlorophyll a oxidation product, hydroxychlorophyll a as well as the chlorophyll a precursor, chlorophyll aP276 conducted over an annual cycle. Chlorophyll a oxidation products, such as hydroxychlorophyll a may be associated with the decline of algal populations and can act as an initial step in the degradation of chlorophyll a into products which can be found in the geochemical record, important for studying past climate change events. Here, hydroxychlorophyll a and chlorophyll aP276 were measured at the long-term monitoring station L4, Western Channel Observatory (UK, www.westernchannelobservatory.org) over an annual cycle (2012). Weekly measurements of phytoplankton species composition and abundance enabled detailed analysis of possible sources of hydroxychlorophyll a. Dinoflagellates, 2 diatom species, the prymnesiophyte Phaeocystis spp. and the coccolithophorid Emiliania huxleyi were all associated with hydroxychlorophyll a occurrence. However, during alternate peaks in abundance of the diatoms, no association with hydroxychlorophyll a occurred, indicating that the oxidation of chlorophyll a was dependant not only on species but also on additional factors such as the mode of mortality, growth limiting factor (i.e. nutrient concentration) or phenotypic plasticity. Surface sediment samples contained 10 times more hydroxychlorophyll a (relative to chlorophyll a) than pelagic particulate samples, indicating that more chlorophyll a oxidation occurred during sedimentation or at the sediment-water interface, than in the pelagic environment. In addition, chlorophyll aP276 correlated with chl-a concentration, thus supporting its assignment as a chl-a precursor

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90ĝ€¯d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and "hotspots"of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question

Liquid chromatography-mass spectrometry for pigment analysis

29 pages, 13 figures,1 tableBecause of improvements in HPLC methods for the analysis of phytoplankton pigments, pigments that are unidentified but spectrally related to known compounds are frequently reported in microalgal cultures as well as in natural distributions (Jeffrey and Wright, 1994; Vaulot et al., 1994; Garrido and Zapata, 1998; Airs et al., 2001a; Carreto et al., 2001; Zapata et al., 2004). Distinctions between common chlorophylls and carotenoids can be ascertained during HPLC from on-line UV/visible (UV/Vis) spectra, or co-elution with authentic standards. Several chlorophyll types however occur as suites of compounds. A diverse array of chlorophylls c have been observed in marine microalgal cultures as free acids or esterified by a range of non-polar groups (Garrido and Zapata, 1998; Garrido et al., 2000; Zapata et al., 2001, 2006). Photosynthetic bacteria belonging to the genera Chlorobiaceae produce suites of bacteriochlorophylls differing both in the degree of alkylation at positions C-8 and/or C-12 (Smith and Bobe, 1987; Airs and Keely, 2002), and/or the alcohol esterified to the propionic acid group at C-17 (Caple et al., 1978; Otte et al., 1993; Airs et al., 2001b). Furthermore, chlorophyll a undergoes a range of transformation and alteration reactions when the phytoplankton cell is compromised and as detrital material is transported through the water column; these reactions are potentially indicative of specific environmental conditions or processes (Head and Horne, 1993; Head et al., 1994; Veldhuis et al., 2001; Walker and Keely, 2004). Several carotenoid types also exist as suites of compounds, for example fucoxanthin esters (Airs and Llewellyn, 2006). Characterisation of novel compounds involves rigorous chemical and analytical techniques following preparative isolation of individual components (Egeland et al., 2000). Such an approach can be impractical when unknowns are present in low relative abundance. Liquid chromatography-mass spectrometry (LC-MS) permits acquisition of structural data during a single chromatographic run. Molecular mass information, used in conjunction with PDA UV/vis spectra is often sufficient for the assignment of components. HPLC coupled to tandem MS (LC-tandem MS) adds a further dimension and can be used to identify structural differences that do not affect the UV/Vis absorption properties, or to distinguish ions with the same mass-to-charge ratio (isobaric ions). Furthermore, LC-tandem MS is particularly powerful if MS/MS spectra of an unknown compound are compared with a structurally related, identified compoundN