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The unaccounted dissolved iron (II) sink: Insights from dFe(II) concentrations in the deep Atlantic Ocean

Abstract

Hydrothermal vent sites found along mid-ocean ridges are sources of numerous reduced chemical species and trace elements. To establish dissolved iron (II) (dFe(II)) variability along the Mid Atlantic Ridge (between 39.5°N and 26°N), dFe(II) concentrations were measured above six hydrothermal vent sites, as well as at stations with no active hydrothermal activity. The dFe(II) concentrations ranged from 0.00 to 0.12 nmol L−1 (detection limit = 0.02 ± 0.02 nmol L−1) in non-hydrothermally affected regions to values as high as 12.8 nmol L−1 within hydrothermal plumes. Iron (II) in seawater is oxidised over a period of minutes to hours, which is on average two times faster than the time required to collect the sample from the deep ocean and its analysis in the onboard laboratory. A multiparametric equation was used to estimate the original dFe(II) concentration in the deep ocean. The in-situ temperature, pH, salinity and delay between sample collection and its analysis were considered. The results showed that dFe(II) plays a more significant role in the iron pool than previously accounted for, constituting a fraction >20 % of the dissolved iron pool, in contrast to <10 % of the iron pool formerly reported. This discrepancy is caused by Fe(II) loss during sampling when between 35 and 90 % of the dFe(II) gets oxidised. In-situ dFe(II) concentrations are therefore significantly higher than values reported in sedimentary and hydrothermal settings where Fe is added to the ocean in its reduced form. Consequently, the high dynamism of dFe(II) in hydrothermal environments masks the magnitude of dFe(II) sourced within the deep ocean.

Highlights

  • Considering oxidation, open ocean iron (II) concentrations are below 0.2 nmol L−1.
  • The highest measured iron (II) concentration was 69.6 nmol L−1 at the Rainbow vent.
  • In the open ocean iron (II) account for 20 % of the dissolved iron pool.
  • Oxygen variations within OMZ account for 60 % of iron(II) oxidation variability.

Reference

Gonzalez-Santana, D.; Lough, A. J. M.; Planquette, H.; Sarthou, G.; Tagliabue, A.; Lohan, M. C. The Unaccounted Dissolved Iron (II) Sink: Insights from DFe(II) Concentrations in the Deep Atlantic Ocean. Sci. Total Environ. 2023, 862, 161179.

https://doi.org/10.1016/j.scitotenv.2022.161179.

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Spatial distribution of tropical fish assemblages

Sea bottom

Comprehensive spatial distribution of tropical fish assemblages from multifrequency acoustics and video fulfils the island mass effect framework.

Describing fish distribution and associated environmental features is the first step toward understanding how fish communities are spatially structured and is a necessary step to conduct Marine Spatial Planning (MSP) and operate relevant protection policies.

Abstract

Tropical marine ecosystems are highly biodiverse and provide resources for small-scale fisheries and tourism. However, precise information on fish spatial distribution is lacking, which limits our ability to reconcile exploitation and conservation. We combined acoustics to video observations to provide a comprehensive description of fish distribution in a typical tropical environment, the Fernando de Noronha Archipelago (FNA) off Northeast Brazil. We identified and classified all acoustic echoes into ten fish assemblage and two triggerfish species. This opened up the possibility to relate the different spatial patterns to a series of environmental factors and the level of protection. We provide the first biomass estimation of the black triggerfish Melichthys niger, a key tropical player. By comparing the effects of euphotic and mesophotic reefs we show that more than the depth, the most important feature is the topography with the shelf-break as the most important hotspot. We also complete the portrait of the island mass effect revealing a clear spatial dissymmetry regarding fish distribution. Indeed, while primary productivity is higher downstream, fish concentrate upstream. The comprehensive fish distribution provided by our approach is directly usable to implement scientific-grounded Marine Spatial Planning..

Synthetic representation of the island mass effect as illustrated by the case of Fernando de Noronha.

Synthetic representation of the island mass effect as illustrated by the case of Fernando de Noronha.

Reference

Salvetat, J., Bez, N., Habasque, J., Lebourges-Dhaussy, A., Lopes, C., Roudaut, G., Simier, M., Travassos, P., Vargas, G., and Bertrand, A. 2022. Comprehensive spatial distribution of tropical fish assemblages from multifrequency acoustics and video fulfils the island mass effect framework. Scientific Reports 12(1): 8787. Nature Portfolio, Berlin. doi:10.1038/s41598-022-12409-9.
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Immune defense response of the king scallop

Alexandrium minutum(c) Gert Hansen - Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License

Physiological and comparative proteomic analyzes reveal immune defense response of the king scallop Pecten maximus in presence of paralytic shellfish toxin (PST) from Alexandrium minutum

Abstract

The king scallop, Pecten maximus is a highly valuable seafood in Europe. Over the last few years, its culture has been threatened by toxic microalgae during harmful algal blooms, inducing public health concerns. Indeed, phycotoxins accumulated in bivalves can be harmful for human, especially paralytic shellfish toxins (PST) synthesized by the microalgae Alexandrium minutum. Deleterious effects of these toxic algae on bivalves have also been reported. However, its impact on bivalves such as king scallop is far from being completely understood. This study combined ecophysiological and proteomic analyzes to investigate the early response of juvenile king scallops to a short term exposure to PST producing A. minutum. Our data showed that all along the 2-days exposure to A. minutum, king scallops exhibited transient lower filtration and respiration rates and accumulated PST. Significant inter-individual variability of toxin accumulation potential was observed among individuals. Furthermore, we found that ingestion of toxic algae, correlated to toxin accumulation was driven by two factors: 1/ the time it takes king scallop to recover from filtration inhibition and starts to filtrate again, 2/ the filtration level to which king scallop starts again to filtrate after inhibition. Furthermore, at the end of the 2-day exposure to A. minutum, proteomic analyzes revealed an increase of the killer cell lectin-like receptor B1, involved in adaptative immune response. Proteins involved in detoxification and in metabolism were found in lower amount in A. minutum exposed king scallops. Proteomic data also showed differential accumulation in several structure proteins such as β-actin, paramyosin and filamin A, suggesting a remodeling of the mantle tissue when king scallops are subjected to an A. minutum exposure.

Toxin accumulation linked to feeding behavior. (a). Individual toxin concentration in scallop digestive gland (DG) at the end of the exposure (day 4, μg STX 100 g−1 DG) against the total numbers of A. minutum cells consumed for each scallop on days 3&4 per g of scallop (number of cells g−1) for all assays (n=18). The line indicates the adjusted type II regression model. (b). Graph shows clearance rates (L h−1) from TC-A assays (days 1&2 exposition to T. lutea and C. muelleri and days 3&4 exposition to A. minutum) measured from day 1 to day 4 and standardized for a 7g scallop in total mass. Data have been highlighted according to the 3 clusters (low, medium and high accumulation potential), each empty shape representing an individual from the low □, mean △ or high ○ accumulation cluster and filled shapes corresponding to the average values for 5h from the low ■, mean ▲ and high ●.

Highlights

  • Harmful microalgae A. minutum transiently inhibits king scallop filtration and respiration activities.
  • The A. minutum paralytic shellfish toxin accumulation in king scallop is highly variable between individuals.
  • Proteomic analysis of toxic algae A. minutum effect on king scallop: immune response and detoxication.

Reference

Even, Y., Pousse, E., Chapperon, C., Artigaud, S., Hegaret, H., Bernay, B., Pichereau, V., Flye-Sainte-Marie, J., and Jean, F. 2022. Physiological and comparative proteomic analyzes reveal immune defense response of the king scallop Pecten maximus in presence of paralytic shellfish toxin (PST) from Alexandrium minutum. Harmful Algae 115: 102231. doi:10.1016/j.hal.2022.102231.

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Influence of strong iron-binding ligands on cloud water oxidant capacity

Abstract

Iron (Fe) plays a dual role in atmospheric chemistry: it is involved in chemical and photochemical reactivity and serves as a micronutrient for microorganisms that have recently been shown to produce strong organic ligands. These ligands control the reactivity, mobility, solubility and speciation of Fe, which have a potential impact on Fe bioavailability and cloud water oxidant capacity.

In this work, the concentrations of Fe-binding ligands and the conditional stability constants were experimentally measured for the first time by Competitive Ligand Exchange-Adsorptive Cathodic Stripping Voltammetry (CLE-ACSV) technique in cloud water samples collected at puy de Dôme (France). The conditional stability constants, which indicate the strength of the Fe-ligand complexes, are higher than those considered until now in cloud chemistry (mainly Fe-oxalate). To understand the effect of Fe complexation on cloud water reactivity, we used the CLEPS cloud chemistry model. According to the model results, we found that Fe complexation impacts the hydroxyl radical formation rate: contrary to our expectations, Fe complexation by natural organic ligands led to an increase in hydroxyl radical production. These findings have important impacts on cloud chemistry and the global iron cycle.

Highlights

  • 95% of iron is complexed by strong organic ligands, likely produced by microorganisms.
  • Fe complexes stability constants are much higher than those used in cloud chemistry.
  • The presence of strong organic ligands induces an increase in hydroxyl radical production.
  • The analysis of sources and sinks of radical dotOH highlighted that complexed iron does not deplete HO2/O2−radical dot.


Reference

Aridane G. González, Angelica Bianco, Julia Boutorh, Marie Cheize, Gilles Mailhot, Anne-Marie Delort, Hélène Planquette, Nadine Chaumerliac, Laurent Deguillaume, Geraldine Sarthou. Influence of strong iron-binding ligands on cloud water oxidant capacity, Science of The Total Environment, Volume 829, 2022, 154642, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2022.154642.

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Revisiting tolerance to ocean acidification: Insights from a new framework combining physiological and molecular tipping points of Pacific oyster

Abstract

Studies on the impact of ocean acidification on marine organisms involve exposing organisms to future acidification scenarios, which has limited relevance for coastal calcifiers living in a mosaic of habitats. Identification of tipping points beyond which detrimental effects are observed is a widely generalizable proxy of acidification susceptibility at the population level. This approach is limited to a handful of studies that focus on only a few macro-physiological traits, thus overlooking the whole organism response. Here we develop a framework to analyze the broad macro-physiological and molecular responses over a wide pH range in juvenile oyster. We identify low tipping points for physiological traits at pH 7.3–6.9 that coincide with a major reshuffling in membrane lipids and transcriptome. In contrast, a drop in pH affects shell parameters above tipping points, likely impacting animal fitness. These findings were made possible by the development of an innovative methodology to synthesize and identify the main patterns of variations in large -omic data sets, fitting them to pH and identifying molecular tipping points. We propose the broad application of our framework to the assessment of effects of global change on other organisms.

Graphical abstract

Tipping points of oyster transcriptome. (a–c) Frequency distribution of tipping point for piecewise linear relationships (left side). Linear and log-linear models (no tipping point) are under “Lin” name. Genes are grouped into three clusters of genes that co-vary together. The gray line indicates the distribution frequency of all genes irrespective of clusters. Groups of genes that exhibit neighboring tipping points with distribution frequencies >5% (shown as a dotted line), were grouped together. The segments above the bars indicate the groups of genes on which GO analyses were conducted. In each case, the gene that best represents the cluster according to module membership, gene significance for pH and R2 is presented as a function of pH as an example (right side). Tipping points and their 95% confidence intervals are shown in gray. The significance levels of the slopes are presented using stars (p < .001***, p < .01**, p < .05*). Gene names are as follows: LOC117690205: monocarboxylate transporter 12-like, LOC105317113: 60S ribosomal protein L10a, LOC105331560: protocadherin Fat 4

Reference

Lutier, M., Di Poi, C., Gazeau, F., Appolis, A., Le Luyer, J., & Pernet, F. (2022). Revisiting tolerance to ocean acidification: Insights from a new framework combining physiological and molecular tipping points of Pacific oyster. Global Change Biology, 00, 1– 16. https://doi.org/10.1111/gcb.16101
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