Probing the Bioavailability of Dissolved Iron to Marine Eukaryotic Phytoplankton Using In Situ Single Cell Iron Quotas

Shaked, Yeala; Twining, Benjamin S.; Tagliabue, Alessandro; Maldonado, María T.

doi:10.1029/2021gb006979

Cited by 14 publications

(17 citation statements)

References 106 publications

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“…TAC detected 55 %-70 % of FA and HA in contrast to an early report that TAC could not detect any portion of IHSS humic reference material (Laglera et al, 2011). Our FA and HA results are in line with the partial detection of HS by TAC that was observed already by several field studies, where an increase in [L] TAC correlated with an increase in natural humics (Gerringa et al, 2017;Dulaquais et al, 2018;Slagter et al, 2017Slagter et al, , 2019Laglera et al, 2019). HS showed the largest deviations from the expected (literature) results of all tested ligands in [L].…”

Section: Humic Substancessupporting

confidence: 88%

“…Humic substances are ubiquitous, they form large diverse molecules and they are broadly recognized as Fe-binding ligands (Krachler et al, 2015;Su et al, 2018;Laglera et al, 2019;Whitby et al, 2020;Yamashita et al, 2020). According to other work, TAC is able to detect at least some humics as Fe-binding organic ligands (Batchelli et al, 2010;Slagter et al, 2017;Dulaquais et al, 2018). The SA25 application has been criticized for not meeting assumption 1; SA25 has a waiting time of 15 to 20 min in contrast with the overnight equilibration used for the TAC and SA5 applications (Abualhaija and van den Berg, 2014;Abualhaija et al, 2015;Slagter et al, 2019).…”

Section: Introductionmentioning

confidence: 97%

“…Four different ALs have been reported as forming effective electro-active complexes for the purposes of CLE-AdSCV: 1-nitroso-2-napthol (NN) (Gledhill and van den Berg, 1994), salicylaldoxime (SA) (Rue and Bruland, 1995), TAC 2-(2thiazolylazo)-p-cresol (Croot and Johansson, 2000) and 2,3dihydroxynaphthalen (DHN) (van den Berg, 2006). The two ALs, SA and TAC, are the usual selection in field studies Bruland, 1995, 1997;Croot and Johansson, 2000;Croot et al, 2004;Boye et al, 2005;Thuróczy et al, 2011aThuróczy et al, , b, 2012Kondo et al, 2012;Bundy et al, 2014Bundy et al, , 2015Buck et al, 2015Buck et al, , 2018Gerringa et al, 2015Gerringa et al, , 2017Abualhaija et al, 2015;Kleint et al, 2016;Slagter et al, 2017Slagter et al, , 2019, and basin-scale data sets now exist for K cond FeL and [L] obtained using these two ALs (Caprara et al, 2016;Cabanes et al, 2020;Schlitzer et al, 2018), which provide an important resource for our understanding of iron biogeochemistry in the ocean (Boyd and Ellwood, 2010;Boyd and Tagliabue, 2015;Völker and Tagliabue, 2015;Tagliabue et al, 2016;Lauderdale et al, 2020). However, results of intercomparisons of field data suggest that although trends may be similar for different ALs, the different methods may not be directly intercomparable as conditional [L] differed significantly, with SA giving higher [L] and often identifying more than one ligand group compared with TAC .…”

Section: Introductionmentioning

confidence: 99%

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Comparing CLE-AdCSV applications using SA and TAC to determine the Fe-binding characteristics of model ligands in seawater

et al. 2021

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Abstract. Competitive ligand exchange–adsorptive cathodic stripping voltammetry (CLE-AdCSV) is used to determine the conditional concentration ([L]) and the conditional binding strength (logKcond) of dissolved organic Fe-binding ligands, which together influence the solubility of Fe in seawater. Electrochemical applications of Fe speciation measurements vary predominantly in the choice of the added competing ligand. Although different applications show the same trends, [L] and logKcond differ between the applications. In this study, binding of two added ligands in three different common applications to three known types of natural binding ligands is compared. The applications are (1) salicylaldoxime (SA) at 25 µM (SA25) and short waiting time, (2) SA at 5 µM (SA5), and (3) 2-(2-thiazolylazo)-ρ-cresol (TAC) at 10 µM, the latter two with overnight equilibration. The three applications were calibrated under the same conditions, although having different pH values, resulting in the detection window centers (D) DTAC > DSA25 ≥ SA5 (as logD values with respect to Fe3+: 12.3 > 11.2 ≥ 11). For the model ligands, there is no common trend in the results of logKcond. The values have a considerable spread, which indicates that the error in logKcond is large. The ligand concentrations of the nonhumic model ligands are overestimated by SA25, which we attribute to the lack of equilibrium between Fe-SA species in the SA25 application. The application TAC more often underestimated the ligand concentrations and the application SA5 over- and underestimated the ligand concentration. The extent of overestimation and underestimation differed per model ligand, and the three applications showed the same trend between the nonhumic model ligands, especially for SA5 and SA25. The estimated ligand concentrations for the humic and fulvic acids differed approximately 2-fold between TAC and SA5 and another factor of 2 between SA5 and SA25. The use of SA above 5 µM suffers from the formation of the species Fe(SA)x (x>1) that is not electro-active as already suggested by Abualhaija and van den Berg (2014). Moreover, we found that the reaction between the electro-active and non-electro-active species is probably irreversible. This undermines the assumption of the CLE principle, causes overestimation of [L] and could result in a false distinction into more than one ligand group. For future electrochemical work it is recommended to take the above limitations of the applications into account. Overall, the uncertainties arising from the CLE-AdCSV approach mean we need to search for new ways to determine the organic complexation of Fe in seawater.

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Section: Humic Substancessupporting

confidence: 88%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Comparing CLE-AdCSV applications using SA and TAC to determine the Fe-binding characteristics of model ligands in seawater

et al. 2021

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“…Abundances reflect flow cytometry measurements except for Crocosphaera (estimated from Wilson et al 2017) and dust (from particulate Al). The surface area normalized k in value of 3.16 Â 10 À10 L μm À2 d À1 originates from Shaked et al (2021). Constraints from an upper limit of a representative concentration of 3 μM particulate carbon is estimated using C : biovolume power law relationship of Menden-Deuer and Lessard (2000) derived for all phytoplankton except diatoms.…”

Section: Biological Turnover Of Dissolved Iron In the Euphotic Zonementioning

confidence: 99%

Recycling of dissolved iron in the North Pacific Subtropical Gyre

Hawco

Yang

Pinedo‐González

et al. 2022

Limnology & Oceanography

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The importance of iron as a limiting nutrient in the open ocean is widely recognized, but there is substantial uncertainty about the rate that it cycles in the marine environment. Here, we combine measurements from the water column, sediment traps, and incubations to constrain Fe turnover during summer at Station ALOHA in the North Pacific Subtropical Gyre. Using low levels of 57 Fe-58 Fe double spike, measured with high precision by multicollector inductively coupled plasma mass spectrometry, we find Fe uptake rates of 30-60 pM d À1 throughout the euphotic zone. Dissolved Fe turnover times are estimated at 10-15 d in the mixed layer and 1-3 d near the deep chlorophyll maximum. Aerosol Fe supply inferred from a thorium isotope mass balance indicates that the dissolved Fe residence time is approximately 6 months in the upper euphotic zone (0-75 m), relative to external sources, and 2 months in the lower euphotic zone (75-150 m). To reconcile these observations, the average Fe atom must be recycled over 25 times at Station ALOHA in both the upper and lower euphotic zones (an "Fe ratio" equal to 0.04 and 0.03, respectively), a level of conservation that has only been documented in Fe-limited regions thus far. At steady state, this scenario requires an aerosol Fe solubility of 4.5%, which is similar to dissolution experiments from Pacific Ocean aerosols. Our results suggest that the oligotrophic ocean is capable of recycling iron efficiently even when these ecosystems are not demonstrably iron-limited.

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“…These contigs were paired with their corresponding TARA-generated gene coverage data to calculate nitrogen assimilation and utilization gene copy numbers per genome equivalents. The ratios at each station were then compared to surface nitrate and dissolved iron concentrations (N:Fe ratios) predicted by the PISCES global ocean biogeochemical model (52, 53). Station P Synechococcus narB genome copies were depleted compared to most TARA stations (Fig.…”

Section: Resultsmentioning

confidence: 99%

Synechococcusnitrogen gene loss in iron-limited ocean regions

Sharpe

Meyer

Gong

et al. 2022

Preprint

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Synechococcus are the most abundant cyanobacteria in high latitude regions and are responsible for an estimated 17% of annual marine primary productivity. Despite their biogeochemical importance, Synechococcus populations have been unevenly sampled across the ocean, with most studies focused on low-latitude strains. In particular, the near absence of Synechococcus genomes from high-latitude, High Nutrient Low Chlorophyll (HNLC) regions leaves a gap in our knowledge of picocyanobacterial adaptation to iron limitation and their influence on carbon, nitrogen, and iron cycles. We examined Synechococcus populations from the subarctic North Pacific, a well characterized HNLC region, with quantitative metagenomics. Assembly with short and long reads produced two near complete Synechococcus metagenome-assembled genomes (MAGs). Quantitative metagenome-derived abundances of these populations matched well with flow cytometry counts, and the Synechococcus MAGs were estimated to comprise >99% of the Synechococcus at Station P. Whereas the Station P Synechococcus MAGs contained multiple genes for adaptation to iron limitation, both genomes lacked genes for uptake and assimilation of nitrate and nitrite, suggesting a dependence on ammonium, urea, and other forms of recycled nitrogen leading to reduced iron requirements. A global analysis of Synechococcus nitrate reductase abundance in the TARA Oceans dataset found nitrate assimilation genes are also lower in other HNLC regions. We propose nitrate and nitrite assimilation gene loss in Synechococcus represents an adaptation to severe iron limitation in high-latitude regions where ammonium availability is higher. Our findings have implications for models that quantify the contribution of cyanobacteria to primary production and subsequent carbon export.

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Probing the Bioavailability of Dissolved Iron to Marine Eukaryotic Phytoplankton Using In Situ Single Cell Iron Quotas

Cited by 14 publications

References 106 publications

Comparing CLE-AdCSV applications using SA and TAC to determine the Fe-binding characteristics of model ligands in seawater

Comparing CLE-AdCSV applications using SA and TAC to determine the Fe-binding characteristics of model ligands in seawater

Recycling of dissolved iron in the North Pacific Subtropical Gyre

Synechococcusnitrogen gene loss in iron-limited ocean regions

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