Electrochemical evaluation of iron-binding ligands along the Australian GEOTRACES southwestern Pacific section (GP13)

Journal Pre-proof Electrochemical evaluation of iron-binding ligands along the Australian GEOTRACES southwestern Pacific section (GP13)

Damien J.E. Cabanes, Louiza Norman, Andrew R. Bowie, Slađana Strmečki, Christel S. Hassler PII:

S0304-4203(19)30243-9

DOI:

https://doi.org/10.1016/j.marchem.2019.103736

Reference:

MARCHE 103736

To appear in:

Marine Chemistry

Received date:

10 January 2018

Revised date:

17 November 2019

Accepted date:

9 December 2019

Please cite this article as: D.J.E. Cabanes, L. Norman, A.R. Bowie, et al., Electrochemical evaluation of iron-binding ligands along the Australian GEOTRACES southwestern Pacific section (GP13), Marine Chemistry (2019), https://doi.org/10.1016/ j.marchem.2019.103736

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© 2019 Published by Elsevier.

Journal Pre-proof Electrochemical evaluation of iron-binding ligands along the Australian GEOTRACES southwestern Pacific section (GP13) Damien J. E. Cabanes1 , Louiza Norman2 , Andrew R. Bowie3 , Slađana Strmečki4 , Christel S. Hassler1,5* 1

Marine and Lake Biogeochemistry, Department F.-A. Forel for Environmental and Aquatic

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Sciences, Earth and Environment Sciences, Faculty of Sciences, University of Geneva, Uni Carl

Department of Earth, Ocean and Ecological Sciences, School of Environmental Sciences,

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Vogt, 66 Bvd. Carl-Vogt, 1211 Geneva 4, Switzerland

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University of Liverpool, L693GP, UK

Antarctic Climate and Ecosystems CRC and Institute for Marine and Antarctic Studies,

Division for Marine and Environmental Research, Ruđer Bošković Institute, Bijenička 54,

10000 Zagreb, Croatia

Swiss Polar Institute, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

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University of Tasmania, Hobart, TAS 7001, Australia

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* Communicating author: [email protected]

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Journal Pre-proof Abstract

In this work, we performed electrochemical investigations of Fe-binding ligands in water samples collected in autumn 2011 along the Australian GEOTRACES southwestern Pacific section (GP13, between 153°E and 170°W longitude along the 30°S line East of Australia, 0-1000m depth). We determined the capacity of the bulk organic ligands to complex Fe using competitive

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ligand exchange-adsorptive cathodic stripping voltammetry (CLE-AdCSV) with salicylaldoxime as the competing ligand. Two categories of organic ligands, humic substances (HS-like) and

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catalytically active polymers (Cat. P) were electrochemically quantified in order to better define

phytoplankton biomass,

and

two

groups of cyanobacteria (Prochlorococcus and

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data,

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the bulk of Fe-binding ligands. Finally, Fe speciation results have been linked to oceanographic

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Synechococcus) which are prominent members of the phototrophic community in the study region. Across the section, higher total ligand concentrations over dissolved Fe concentrations

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were observed, as well as the predominance of “weak” Fe-binding ligands (log K’Fe’L < 12).

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Highest “excess ligands” were mostly concentrated in the upper layer of the water column, suggesting a direct link with biological activity. None of the two groups of organic ligands

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measured (HS-like and Cat. P) accounted for the bulk of the total Fe-binding ligands concentration, hindering a better characterization of the nature of in-situ Fe-binding ligands. Cat. P concentrations showed statistically significant positive correlations with all biomarker pigments and the abundance of Prochlorococcus, suggesting that this material, resembling polysaccharides, could be a good parameter to probe organic compounds from specific biological origin. Amongst the biological parameters, only Prochlorococcus was related to Fe’ concentrations.

Keywords

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Iron, organic speciation, humic substances, polymers, phytoplankton, southwestern Pacific Ocean

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Journal Pre-proof Introduction

Iron (Fe) is a micro-nutrient involved in several key metabolic processes, making it essential for the growth and survival of the phytoplankton community (Morel and Price, 2003). Nevertheless, because it is present in extremely low concentrations in the open ocean, often less than 1nM (de Baar and de Jong, 2001), Fe is responsible for the limitation of primary production in many

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regions such as the north and equatorial Pacific Ocean and most of the Southern Ocean (Boyd et al., 2007; de Baar et al., 2005; Moore et al., 2013). Although inorganic dissolved Fe (dFe) is the

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most bioavailable form for phytoplankton (Shaked et al., 2005; Shaked and Lis, 2012), dFe is

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mostly found (>99%) associated with organic ligands throughout the water column (Boyd and

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Ellwood, 2010; Gledhill and Buck, 2012). These ligands are, therefore, paramount for Fe

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biogeochemistry, controlling both Fe reactivity (e.g., solubility and photochemistry; Barbeau, 2006; Rijkenberg et al., 2006) and bioavailability to phytoplankton (Chen and Wang, 2008;

representing a significant gap

of knowledge in the field of marine

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mostly unknown,

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Hassler et al., 2015, 2011a, 2011b; Maldonado et al., 2005). However, their nature remains

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biogeochemistry.

Fe organic ligands can be quantified and described either by direct determination with electrochemistry or mass spectrometry (Boiteau et al., 2013; Laglera et al., 2007; Waska et al., 2015), but due to volume constraints only competitive ligand exchange-adsorptive cathodic stripping

voltammetry

(CLE-AdCSV)

allows

for

high

spatial and

temporal resolution

determinations of Fe chemical speciation from oceanographic expeditions (Buck et al., 2015; Ibisanmi et al., 2011; Sander et al., 2015). Although Fe complexes with natural binding ligands cover a continuous and wide range of conditional stability constants (Town and Filella, 2000), two main classes of ligands have been defined (L1 and L2 ) according to their conditional affinity 4

Journal Pre-proof constant for Fe (log K’Fe’L, see Rue and Bruland (1995)), and can be distinguished by CLEAdCSV. Ligands with log K’Fe’L>12 are considered as “strong” ligands associated with the L1 class, while the L2 class represents “weaker” ligands (log K’Fe’L<12). Recently, a multiple analytical windows approach has permitted deeper characterization of the L2 class by giving the opportunity to simultaneously identify up to three classes of weak ligands (L2-4 , log K’Fe’L = < 1012)) (Bundy et al., 2014; Mahmood et al., 2015). However, this approach has been mainly used

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for coastal and estuarine waters and most of the open ocean studies reported up to three ligand

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classes (L1 ,L2 and L3 ). To date, several organic Fe- binding ligands have been identified in

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seawater and associated with a ligand class based on their respective 𝐾𝐹𝑒′𝐿𝑖 . These include

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bacterially produced siderophores (L1 ; Barbeau et al., 2001; Bundy et al., 2018; Gledhill et al., 2004; Maldonado et al., 2005), biologically produced exopolymeric substances (EPS, L1 or L2 ;

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Hassler et al., 2011a, 2011b), saccharides (L2 ; Hassler and Schoemann, 2009), phytoplankton

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porphyrins (L2 ; Hutchins et al., 1999; Vong et al., 2007) as well as humic substances (HS, L2 ;

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Laglera and van den Berg, 2009).

HS, representing products from terrestrial and marine origin, have been demonstrated to be long-

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lived material that constitute a significant component of the Fe-binding ligand pool in coastal waters as well as in deep ocean waters (Krachler et al., 2015; Laglera and van den Berg, 2009). Laglera and van den Berg (2009) have even suggested that HS-like compounds could fully account for the dissolved ligand concentration in the open Pacific deep waters and deep coastal waters. So far, no studies have been conducted to confirm this assumption in remote areas such as the southwest Pacific Ocean, a region also likely to be affected by atmospheric dust deposition which is known as a potential source of HS-like material (Bundy et al., 2018; Graber and Rudich, 2005; Martino et al., 2014; Mladenov et al., 2011). 5

Journal Pre-proof In open ocean surface waters, biologically produced Fe-binding ligands are, however, expected to be the main part of the dissolved ligand pool. Studies have demonstrated that micro-organisms can release organic ligands either in association with specific excretion (e.g. siderophores, usually in response to Fe limitation; Mawji et al., 2008; Velasquez et al., 2016) or as a result of biological activity such as grazing and fecal pellets egestion (Cabanes et al., 2017; Sato et al., 2007), or cell lysis (e.g. EPS, saccharides and porphyrins; Hassler et al., 2011a, 2011b; Norman et al., 2015).

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Within this biological ligand pool, polymers, and more specifically polysaccharides, are notably

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considered as the main products of the photosynthetic activity (Benner, 2002), and are known to

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have a significant influence on Fe bioavailability (Hassler et al., 2012).

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Surface waters of the southwestern Pacific Ocean, known as an “ultra-oligotrophic region” (with

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low vertical supply of nitrogen into the euphotic zone and low rates of nitrogen fixation), is thought to be most influenced by wind-blown dust from continental Australia containing Fe and

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nitrate (NO3 -) which could have a role in primary production. Indeed, Ellwood et al. (2018) have

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presented the biogeochemical cycling of Fe, NO 3 - and phosphate (PO4 3-) in this region (153°E150°W) and identified atmospheric dust as an important source of Fe for the water column ( 23 ±

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17% of total dFe supply). They highlighted two biogeographical provinces: one with diazotroph production and another one without diazotrophs. Here, we focused on the diazotroph production region (the western part of GP13 closest to Australia; 153°E-170°W) where we have studied the Fe speciation using electrochemical methods. We determined the bulk of the organic ligands having the capacity to complex Fe and compared it with the distributions of two potential candidates for Fe-binding ligands, HS-like and catalytically active polymer (Cat. P) substances. In addition, we investigated the relationships between Fe speciation, oceanographic data, phytoplankton biomass, biomarker pigments and two groups of cyanobacteria (Prochlorococcus 6

Journal Pre-proof and Synechococcus) which are prominent members of the phototrophic community in this region. These species have different Fe requirements, with diazotrophs (e.g. Trichodesmium) having greater Fe requirements than cyanobacteria (Kustka et al., 2003).

Material and methods

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Sample collection

The samples were collected on board the RV Southern Surveyor in austral Autumn 2011 along the

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GEOTRACES southwest Pacific section (GP13_leg 1), between 153°E and 170°W longitude

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along the 30°S-32.5°S line east of Australia (Figure 1). A total of 38 stations were sampled, one

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at each degree of longitude, but samples dedicated to Fe chemistry were collected only at the

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eight ‘super’ stations spaced at 5o intervals (Supplementary Table 1), with six to nine depths per profile ranging between 15 and 1000 m. Seawater was collected using an autonomous trace-metal

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clean rosette (General Oceanics) with 12 x 10 L Niskin X bottles (Hassler et al., 2014). All

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samples were 0.2 µm filtered using acid washed filter cartridges (PALL Acropak) in a clean room

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container under a HEPA filter and collected in acid washed low-density polyethylene bottles.

Samples for Fe-binding ligand analysis were stored at -20°C and samples for dFe analyses were acidified to pH ≈ 1.7 with ultrapure hydrochloric acid (Seastar, 0.024 M added). At each station where seawater was sampled for Fe analysis, the deployment of a conductivity-temperature-depth (CTD) was performed to 1500 m just prior the deployment of the rosette to guide the sampling depths chosen for trace metals. The sampling protocols followed recommendations in the ‘GEOTRACES

Cookbook’

(http://www.geotraces.org/science/intercalibration/222-sampling-

and-sample-handling-protocols- for-geotraces-cruises).

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Journal Pre-proof Auxiliary parameters analysis

Temperature (T) and salinity (S) were obtained from calibrated CTD (SeaBird SBE11, General Oceanics, Miami, FL, USA) data using water collected from Niskin bottles (General Oceanics) mounted on the CTD (Reynolds and Navidad, 2012) at eight stations. The concentrations of macronutrients NO3 -, PO4 3- and silicic acid (Si(OH)4 ) were obtained by the shipboard analysis of

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unfiltered water using an automated flow-injection analyser (Lachat QuickChem 300, Lachat

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Instruments, Loveland, CO, USA) and colorimetric techniques (Reynolds and Navidad, 2012).

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Total dFe analysis

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The dFe concentrations were quantified by the method of standard additions. Samples were

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extracted and pre-concentrated off-line using an ESI SeaFast system (Biller and Bruland, 2012; Lagerström et al., 2013; Wuttig et al., 2019). Metals were eluted with 1 M nitric acid (Seastar

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Baseline, Choice Analytical) and then determined by sector field ICPMS with enhanced

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sensitivity (≈ 10x) using a capacitively coupling guard electrode (Element 2, Thermo Fisher Scientific) (Wuttig et al., 2019). The detection limit was 0.08 nmol/L and blanks were equivalent

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to 0.06 nmol/L (n=11) (Ellwood et al., 2018). Both SAFe S and D1 reference samples were measured and results (0.097 ± 0.005 nmol/L (n=3) and 0.67 ± 0.02 nmol/L (n=6), respectively) were

in

accordance

with

the

GEOTRACES

(http://es.ucsc.edu/~kbruland/GeotracesSaFe/kwbGeotracesSaFe.html).

consensus Precision

values of

the

analytical method assessed through regular analysis of a single in-house surface seawater sample was between 5-8%, and was better at higher dFe concentrations (Wuttig et al., 2019).

Fe speciation analysis 8

Journal Pre-proof Analytical reagents and apparatus

Milli-Q water was used throughout this study for rinsing and dilution of reagents. Ultrapure hydrochloric acid (HCl) and high purity concentrated hydroxide ammonia (NH4 OH) were from VWR (Switzerland). A pH buffer (1 M H3 BO 3 from Sigma, 0.35M NH4 OH) was passed through a chelating column (Chelex 100, Bio-Rad Laboratories, treated as per Price et al., 1989) to

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remove metals and then UV-irradiated for 1 h to remove any organic contaminants. A mixed reagent solution of the oxidant KBrO 3 (0.4 M, Sigma), buffer EPPS (0.2 M, Sigma) and NH4 OH

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(0.2 M) was also passed through a Chelex column. A 0.1 M salicylaldoxime (SA, 98% Acros

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Organics, Fisher) stock solution was prepared in 0.1 M HCl. Suwannee River Fulvic Acid

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(SRFA, International Humic Substances Society, standard 1) standard was used as a reference

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HS. It was dissolved in MQ water to a concentration of 4 g/L and stored for up to one month in the dark at 4°C when not in use. The 1 g/L Fe(III) stock solution used was from an ICP-MS

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standard solution (Sigma). Sodium chloride (BioXtra, ≥ 99.5%) and xanthan gum from

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Xanthomonas campestris were purchased from Sigma-Aldrich (Steinheim, Germany), and sodium acetate and acetic acid (both p.a.) were from Kemika (Zagreb, Croatia). All the

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preparation of the solutions and samples were conducted inside a clean room under a laminar flow class 100 hood.

A 663 VA Stand electrode system equipped with hanging mercury drop electrode (HMDE) (Metrohm Switzerland; Metrohm Autolab, Utrecht, The Netherlands) as a working electrode, Ag/AgCl (3M KCl) as a reference electrode and glassy carbon rod as an auxiliary electrode was used. It was connected to a µAutolab potentiostat/galvanostat type III via a 663 IME interface. The HDME surface area was 0.40 mm2 for analysis of Cat. P, while during all other electrochemical measurements it was set to 0.52 mm2 . The solution was stirred during deposition 9

Journal Pre-proof steps using a rotating PTFE rod set to a stirring speed of 1500 rpm for Cat. P measurements and at 3000 rpm for other measurements. Cat. P measurements were conducted in a glass voltammetric cell whereas a PTFE cell was used for the other analyses. Finally, the systems were kept in laminar flow cabinets (600 PCR workstation, AirClean Systems) at ambient temperature.

CLE-AdCSV iron-ligand titrations

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The Fe speciation was determined by a CLE-AdCSV technique following the optimized method

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of Abualhaija and van den Berg (2014) from 0 to 1000 m as our analytical method did not allow

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us to measure samples deeper than 1000 m. Indeed, we have not been able to detect any signal for

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the different Fe additions for all samples collected below 1000 m, even using a 10 min deposition time. The strong decrease of sensitivity for deep samples was previously reported (Buck et al.,

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2012). It was suspected to be related to the formation of non-electroactive FeSA2 species, which

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should be resolved in the optimized protocol (Abualhaija and van den Berg, 2014). A closer look to analytical sensitivities measured in this study (Supplementary Table 2), showed an overall

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standard deviation of 17 % with non-consistent variability with depth, showing that the data from

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this study is not affected by this decrease in sensitivity. To attempt getting data for samples deeper than 1000m, we tested different thawing methods and a sample pre-filtration step. A signal was detected only when samples were gently defrosted at 4C in the dark, pre-filtered (0.22 µm, Isopore, Milipore) at 4 ºC, then warmed to room temperature. However, the sensitivity was extremely low (> 4-times lower than the average sensitivity for shallower samples, data not shown), and these data were not used. These observations might point towards possible colloidal formation as a cause for this strong decrease in sensitivity at depth, but this was not investigated further as it is beyond the scope of this study.

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Journal Pre-proof After thawing, 10 mL aliquots of the filtered seawater samples were pipetted into 18 to 20 acid cleaned low-density polyethylene 15 mL bottles (Nalgene, Thermo Fisher), already preconditioned three times with water collected from the study region and the planned Fe and SA additions. Each aliquot was then buffered to pH 8.2 with a 1 M borate buffer to which 0.2 to 9 nM Fe was added. In three tubes, Fe was not added. Following a 1 h equilibration time at room temperature, 5 µM SA solution (competitive ligand) was then added to all the tubes. Finally,

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aliquots were left to equilibrate overnight before analysis with the following parameters: 30s air

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purging, 200s of deposition time at 0 V and the CSV instrumental settings as per Abualhaija and

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van den Berg (2014). Finally, analysis of the voltammograms was performed with ECDSoft (Omanović et al., 2006) considering the 4th derivative with a tangential baseline. The software

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ProMCC (Omanović et al., 2014) was used to analyze the resulting Fe-binding ligand titration

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data and quantify both the total concentration of ligands (LT ) and 𝐾𝐹𝑒′𝐿𝑖 of the complexes. Both

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parameters were calculated assuming the detection of only one ligand class and considering the van den Berg fitted values. Indeed, all different linear and non-linear mathematical approaches

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(van den Berg/Ružić linearization (Ružić, 1982; van den Berg, 1982), Scatchard linearization

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(Scatchard, 1949) and the Gerringa non-linear method (Gerringa et al., 1995)) showed detection of a single class of ligands for the whole dataset. For each sample, the Fe’ was calculated using the ligand concentration and conditional stability constant values from ProMCC. Finally, in order to examine patterns in the ligands which might be decoupled from dFe concentrations, “excess” ligand (eL) concentrations were also calculated. Results are shown in Supplementary Table 3.

Calibration of the conditional stability constant for SA (5 µM) with Fe in UV-digested seawater from the studied region (𝐾′𝐹𝑒′𝑆𝐴 and 𝐾′𝐹 𝑒′ 𝑆𝐴2 ) was determined by competition with EDTA. Both 𝑙𝑜𝑔 𝐾′𝐹𝑒′𝑆𝐴 (6.56) and 𝑙𝑜𝑔 𝐾′𝐹𝑒′ 𝑆𝐴2 (10.9) values were in accordance with the literature 11

Journal Pre-proof (Abualhaija and van den Berg, 2014; Mahmood et al., 2015) and correspond to a side reaction coefficient for 5 µM SA of 20.1 (αFe’SA).

Humic-like (HS-like) substances

HS-like material was determined using the voltammetric method of Laglera et al. (2007) with an external calibration. This method is based on CSV and makes use of adsorptive properties of Fe–

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HS complexes on the mercury drop electrode at natural pH. 750 µL of a mixed reagent solution

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(KBrO3 , EPPS, NH4 OH) was added to 100 mL of seawater in order to obtain a pH=8.2 as well as

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20 nM of inorganic Fe in order to saturate all the HS naturally present (from a 1 g/L ICP standard

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solution, Sigma). Then seven 10 mL aliquots were pipetted into polyethylene tubes (10 mL), to which different concentrations of SRFA were added (0 to 60 µg/L SRFA). Three of the tubes

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contained no added SRFA. Samples were equilibrated at ambient temperature in the dark for 2 h

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prior to analysis using 300s nitrogen purge time (PanGas) and 200s deposition time with instrumental settings as in Laglera et al. (2007). From each voltammogram, the intensity current

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induced by the reduction of the Fe-HS complexes was determined, and plotted against the added

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SRFA concentrations. The obtained linear regressions were used to determine the concentrations of HS-like material in each sample. The limit of detection was 1.9 µg/L SRFA equivalent calculated from three times the standard deviation of 10 repeated measurements of a sample water from the study region. The accuracy of the procedure was checked by the analysis of a solution of 95.0 µg/L SRFA (= 97.8 equiv. µg/L SRFA).

Catalytically active polymer (Cat. P) material

Cat. P molecules were detected using a method of adsorptive transfer chronopotentiometric stripping (AdT CPS; Strmečki et al., 2014). The deposition step was physically separated from 12

Journal Pre-proof the stripping step. Cat. P molecules were adsorbed onto the HMDE surface directly in the first cell containing the sample, followed by a transfer of the HMDE into the buffered electrolyte in the second cell where constant current stripping was run. Finally, we detected a characteristic catalytic “peak H” at the very negative potential around -1.8 V, just ahead of the hydrogen wave. Peak H originated from the reduction of hydrogen ions on the HMDE modified with absorbed polymer molecules. The stripping electrolyte was 0.55 M NaCl buffered with 0.5 M acetate

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buffer (consisting of acetic acid and sodium acetate) at pH 5.03. The measurement conditions

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were: deposition potential (Ed) -0.2 V, deposition time (ta) 180s, stripping current (Istr) -10 µA,

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and maximum time of measurement 3s. The concentration of Cat. P was expressed as an

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equivalent concentration of a model polymer, the polysaccharide xanthan. A calibration plot for xanthan was made after a series of increased concentrations of xanthan standard solution was

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measured in organic-matter-free seawater with the same instrumentation and applying the same

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conditions of the AdT CPS method. The sensitivity of the calibration was 0.24 (s/V) (mg/L) and the detection limit was 0.2 mg/L xanthan. Due to similarities with polysaccharides (see next

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section and discussion), Cat. P is referred to PS-like in the remainder of the text. It is important to

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specify that the HS-like method allows the determination of binding ligands specific to Fe whereas this is not the case with the PS-like method.

Analyses of model organic compounds via “PS-like and HS-like techniques”

Both electrochemically determined organic ligand groups are expected to represent two distinct “pools” of Fe-binding ligands. The HS-like group contains organic components such as polyphenol and benzoic/carboxylic acid (Buffle, 1990). While some exopolymeric substances and saccharides known to bind Fe are also part of this group (Norman et al., 2015), it is suspected 13

Journal Pre-proof that a significant fraction of the ligands present in this group originate from terrestrial inputs. On the other hand, the PS-like class includes organic ligands containing specific groups such as carboxylic (-COO -) and sulfate (-SO 4 2-) groups, which act as a catalyst for the hydrogen evolution reaction upon polymer adsorption on the mercury electrode (Strmečki and Plavšić, 2014; Strmečki and Plavšić, 2012). Saccharides are expected to contribute mostly to this group, indicating a likely biological origin for these ligands. In this context, in order to better

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characterize the nature of the organic compounds making up both ligand groups, we measured the

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following model organic compounds (Table 1) via both HS-like and PS-like electrochemical

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techniques including: (i) typical terrestrial compounds which can enter the ocean such as SRFA

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and atmospheric dusts originating from the Buronga region (New South Wales) and collected during the 2009 Brisbane dust storm from the roof of Griffith University (CLH B3) and close to

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the source region (LHR9); and (ii) typical organic compounds excreted by microorganisms and

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macroalgae such as monosaccharides (glucuronic acid), polysaccharides (carrageenan and alginate), amino acids (L-cystein, D-alanine), detoxifying products such as glutathione (GSSG),

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and siderophores (desferrioxamine B (DFB) and enterobactin). All the solutions were prepared in

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synthetic seawater (AQUIL media based on Price et al. (1989) using major salts only), thus avoiding the presence of other organic compounds and facilitating the detection of the model compounds. The dust suspension solution was filtered (0.22 µm, Isopore, Milipore) prior to analysis, so that results corresponded to soluble organic compounds. Solutions where treated as per seawater samples for the detection of HS-like and PS-like compounds.

Biological parameters Biological parameters were analyzed to reveal linkages between Fe chemistry and phytoplankton distributions, and highlight key players as potential sources of Fe-binding ligands. All biological 14

Journal Pre-proof parameters were measured in the top 100 m, as this encompasses the euphotic zone. Phytoplankton Chromatography

biomass

and

(HPLC)

biodiversity

pigment

were

analysis

and

inferred flow

from High cytometry

Performance

analyses.

Liquid

Samples for

chlorophyll-a (Chl-a) and biomarker pigments were collected on GF/F filters (Whatman) using gentle filtration (<5 mmHg) and stored in cryo-vials in liquid nitrogen until analysis. The pigments were extracted in 100 % methanol in the dark at 4°C and analyzed by HPLC using a

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Waters Alliance system (Milford, MA, USA)- as per Hassler et al. (2014). Biomarker pigments

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were used to indicate the presence of diatoms (Fucoxanthin, 19-But-Fucoxanthin) and

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haptophytes (19-Hex-Fucoxanthin, 19-But-Fucoxanthin), and were expressed as % of Chl-a to

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indicate their relative abundance. Samples for the enumeration of cyanobacteria were fixed with 1% (v/v final concentration) glutaraldehyde (Sigma), frozen in liquid nitrogen and stored at -

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80°C. Analyses were performed on unstained samples using a Becton Dickinson flow cytometer

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(Sydney) as per Hassler et al. (2014). Populations of Procholorococcus and Synechococcus were discriminated using side-scatter patterns (SSC) and natural red and orange fluorescence. Data was

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analyzed using Cell-Quest Pro (Becton Dickinson) as in Seymour et al. (2012). Additionally,

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photosynthetic health status of the phytoplankton community was inferred from the measurement of maximum photosynthetic activity (F v /Fm) following 40-60 min dark acclimation using a PhytoPAM (Heiz Walz GmbH, Germany, with factory settings and Phyto-Win ver. 1.45). Background water fluorescence was systematically subtracted from the basal fluorescence (Fo) of each sample using gentle syringe filtration through 0.22 µm filters (WHA9913-2502, Whatman) and the Fv /Fm was then recalculated according to Ralph and Gademann (2005).

Statistics

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Journal Pre-proof In order to explain the distribution of Fe-binding ligands along the oceanic transect and to identify their potential sources over the eight stations analyzed, a Pearson correlation matrix was calculated over the whole dataset for nutrients, temperature, salinity, distance to the coast and depth (0-1000 m); and over surface water data (0-100 m) for biological parameters (SigmaPlot ver. 11.0). Pearson correlation tests suggest that there is a significant relationship between any pair of variables when p < 0.050 (95 % confidence level). Data included ligand parameters: LT ,

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log K’Fe’L , eL, concentrations of HS-like material and Cat. P, and explaining variables: distance

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to coast, depth, temperature, salinity, dFe, Fe’, macronutrients, Chl-a, biomarker pigments,

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Prochlorococcus and Synechococcus as well as Fv /Fm. The relationships between biological

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parameters were determined from surface water for all 38 stations. Results from the Pearson

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correlation tests are shown in the Supplementary Table 4.

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Oceanographic data

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Results

During the expedition, the horizontal temperature gradient in surface waters extended from

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22.3°C near the Australian coast to 19.7°C at the easternmost station. Temperatures recorded at 1000 m depth were quite constant along the whole transect with a mean value of 5.6°C ± 0.3°C. Salinity data did not show any pronounced surface horizontal gradient with values ranging between 35.5 and 35.7, however, a vertical gradient was evident (p < 0.01, r = 0.96, n=82) with values decreasing to 34.4 on average at 1000 m. Finally, in light of the T-S diagram (Figure 2A), T and S data of all stations followed the same trend, suggesting that the study area was represented by the same set of water masses. Between 0 and 1000 m, we were in presence of Subantarctic Mode Water and Antarctic Intermediate Water with density values between 26.6 16

Journal Pre-proof 27.1 and 27.1 - 27.4, respectively (Ellwood et al., 2018; Chiswell et al., 2015). More details of the water mass properties along the GP13 section along the full water column are described in Ellwood et al. (2018) and Bowie et al. (in preparation).

Macronutrients showed very similar and typical nutrient-like distributions along the transect, and are presented in Ellwood et al. (2018). Briefly, NO 3 - surface concentrations (0-100m) were below

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4.30 µM with most being below the instrumental detection limit (< 0.02), suggesting NO3 depletion in this region. Accordingly, Ellwood et al. (2018) confirmed NO 3 - limitation in surface

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waters across the section (shown in several previous studies in this region: Doblin et al., 2016;

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Ellwood et al., 2013; Hassler et al., 2014; Sander et al., 2015) that could be attributed to

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phytoplankton activity around the deep Chl-a maximum (DCM). Concentrations of nitrate (NO3 -

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), phosphate (PO4 3-), and silicate (Si(OH)4 ) increased with depth steadily eastwards along the transect with average values at 1000m of 29.5 ± 0.5 µM, 2.0 ± 0.03 µM and 29.4 ± 2.5 µM,

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respectively . A “kink” (i.e. sudden change in slope) was observed in the NO 3 -: PO4 3- relationship

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for concentrations of NO 3 - < 0.12 µM (r = 0.07), comprising data from 0 to 75m (Figure 2B). For NO 3 - > 0.12 µM, the average NO 3 -: PO 4 3- molar ratio obtained for the transect data was ~ 15.08 (r

2018).

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= 0.99), and both the nitracline and phosphocline were located between 70-170 m (Ellwood et al.,

Fe speciation

The Fe chemistry data are shown in Supplementary Table 3 and depicted in Figure 3. In surface waters, dFe concentrations (Figure 3A) varied between 0.06 nM (station 38) and 0.30 nM (station 8), and increased with depth (p < 0.01, r = 0.72, n = 81), reaching values ranging between 0.29 nM (station 3) and 0.84 nM (station 8) at 1000 m depth. CLE-AdCSV allowed us to detect one 17

Journal Pre-proof class of ligand with log K’Fe’L ranging from 10.94 and 12.07 (Figure 4C). The weakest ligands were found at the westernmost station of the transect (station 38). LT concentrations varied from 0.82 to 1.74 nM (Figure 3B) with a mean value of 1.17 ± 0.21 nM (n=68). The eL concentrations (Figure 3D) were related to depth (p < 0.01, r = -0.54, n = 61), being consistently higher in surface waters ranging between 0.96 ± 0.15 nM at 15 m depth and 0.64 ± 0.20 nM at 750 m. Amongst the biological parameters (0-100 m), eL was significantly negatively correlated with all

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negatively correlated with Lt (p < 0.01, r = -0.45, n = 34).

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biomarker pigments (p = 0.01 to 0.035, r =-0.36 to -0.41, n = 34); fucoxanthin was also

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Analysis of model organic compounds

HS-like ligand concentrations ranged between 0.6 and 43.2 µg/L eq. SRFA (Fig. 3D); and PS-

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like molecule concentrations were between < detection limit (DL) and 1.9 mg/L eq. xanthan (Fig.

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3E). Considering the different detection limits of HS-like ligands and PS-like molecules, about 20-30 % of the measurements of these materials were below the limit of quantification (6.3 µg/L

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eq. SRFA and 0.7 mg/L eq. xanthan, respectively). While HS-like ligand concentrations did not

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correlate with any of the physical or biological parameters measured (except Fe’, p = 0.048 and r = 0.26, n = 60), PS-like molecule concentrations correlated with the distance from the coast and depth (p < 0.01, r = -0.49 and -0.54, n=69), all nutrients (p < 0.01, r = -0.42 to -0.58, n=59-62), all biomarker pigments (p < 0.01, r = 0.51 to 0.54, n = 34) and Prochlorococcus (p = 0.04, r = 0.39, n = 30).

The different model organic compounds analyzed via both HS-like and PS-like electrochemical techniques showed that glucuronic acid, carrageenan and both dust samples were detected by the HS-like technique. Carrageenan, alginate, L-cystein, GSSG and dust LHR9 were detected by the 18

Journal Pre-proof PS-like technique. As saccharides and dust were both contributing to detection of HS-like and PS-like compounds, the detection of these two compounds would not allow a differentiation between terrigenous and biological material.

Phytoplankton community The total Chl-a data revealed three distinct regions (Figure 4A): a “high” biomass region from the

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Australian coast to up to 160°E (up to 0.4 µg/L), an “intermediate” region from 160°E to 171°E relatively low surface water

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(up to 0.3 µg/L) and a “low” biomass region further east with

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concentrations (< 0.15 µg/L). Indeed, all biological parameters (except Prochlorococcus) were

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negatively correlated with the distance to the coast (p < 0.001, r = -0.44 to -0.83, Supplementary Table 4). Furthermore, we observed a DCM reaching up to 0.3 µg/L that deepened from 60 to

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100m depth moving eastwards . Fv /Fm followed the distribution of Chl-a (p< 0.001, r = 0.44, n

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=171) but higher values of F v /Fm were systematically reported at depth (p < 0.001, r = 0.32, Suppl. Table 3; Figure 4B), suggesting that the community at the DCM was not light limited.

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This observation suggests that nutrient limitation was an important factor to explain the Chl-a

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pattern. The deepening of the DCM could indeed be associated with deepening of both the ferricline and nutricline (Ellwood et al., 2018). Fv /Fm and Chl-a were, however, not statistically correlated with macronutrients or dFe (p > 0.05, n = 35-37).

At the time of sampling, the phytoplankton community was dominated by cyanobacteria, contributing up to 73 % of Chl-a (data not shown). Prochlorococcus dominated throughout the section with 180640 ± 42180 cell/mL (n = 124) in the top 75 m (Figure 5A). Some “hot spots” were present with values up to ~ 300000 cell/mL. Significantly lower concentrations were observed at 100 m (97169 ± 48000 cell/mL, n = 29). Synechococcus cells were found in smaller 19

Journal Pre-proof quantities especially between 160°E and 170°W (Figure 5B) with average value of 3164 ± 2142 cell/mL (n=127). Higher concentrations were found near the Australian coast with values reaching 44775 cell/mL. Diatoms were only marginally present, accounting for 0.05 % to 12 % of Chl-a at most sites (Figure 4C-D). Haptophytes (e.g., 19-Hex-Fucoxanthin; Figure 4E) were more abundant, with greater contribution to the community at depth and east of 175°E representing up to 53 % of Chl-a. Synechococcus and eukaryotic phytoplankton decreased

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eastward, with Prochlorococcus becoming a dominant member of the phytoplankton community

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at the easternmost stations. Prochlorococcus was the only type of phytoplankton that was

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correlated with all macronutrients (p < 0.01, r= -0.51 to -0.64, n=32) and Fe’ (p = 0.034, r =-0.39,

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n= 30).

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Fe chemistry across the section

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Discussion

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The full dFe distribution (38 stations, full water column) across the section is depicted in Ellwood et al. (2018), and various potential dissolved Fe sources are proposed. Briefly, at the surface, dFe

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concentrations were broadly low over the whole transect (< 0.2 nM) and coherent with previous studies conducted in the same oceanic area (Ellwood et al., 2013; Fitzsimmons et al., 2014; Hassler et al., 2014). West of 165°E, dFe concentrations were higher, especially at stations close to Australia, consistent with atmospheric Fe inputs associated with the eastward transportation of dust from the Australian interior and continental margin supply (Ellwood et al., 2013; Mackie et al., 2008). Ellwood et al. (2018) showed that this Fe-laden dust was supplied not only near the Australian coast but generalized to the whole section with a contribution around 23 ± 17% of the total dFe input flux into the upper ocean. At depth, relatively high dFe concentrations (> 0.8 nM) 20

Journal Pre-proof were observed at station 8 (160°E) and station 28 (180°E), potentially linked to either continental or hydrothermally derived Fe inputs associated with the Kermadec and Colville ridge systems (Ellwood et al., 2018; Bowie et al., in prep.). Regarding the Fe organic complexation, LT and log K’Fe’L values reported in this work (1.17 ± 0.21 nM and 11.47 ± 0.24 respectively) were in good agreement with numerous previous studies

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dealing with Fe speciation (see data report article from Caprara et al., 2016). Here, the section map of eL (Figure 4D) clearly showed higher values in the upper water column (p < 0.01, r = -

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0.54, n = 61), suggesting a direct link between Fe-binding ligands and biological activity across

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Analysis of specific organic compounds

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the section, as observed in the Southern Ocean (Ibisanmi et al., 2011).

Analysis of “model organic compounds” (Table 3) revealed that the detection of HS-like and PS-

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like compounds did not support a real dichotomy between distinct groups of organic compounds.

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Organic compounds leached from the atmospheric dust samples and polysaccharides were indeed detected by both analytical techniques. Fe sources and dissolved organic matter are complex in

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nature (e.g. Gledhill and Buck, 2012; Wozniak et al., 2015), with polysaccharides being produced by phytoplankton but also detected in some atmospheric dust particles (Fitzsimmons et al., 2015; Paris and Desboeufs, 2013; Russell et al., 2002). Nevertheless, in this oceanographic section, the distribution patterns of HS-like and PS-like compounds were clearly different (Figure 4C-D), suggesting distinct sources or reactivities. Given that ≈ 23 % of the total dFe input flux into the upper ocean was coming from atmospheric deposition (Ellwood et al., 2018), it was worth considering dust as a potential source of Febinding organic ligands. “Australian model dust samples” concentrations tested here were 21

Journal Pre-proof considerably higher (≈ 20 mg/L, Table 1) than natural atmospheric dust deposition recorded annually in our study region (0.2 – 2 g/m2 /y; Jickells, 2005), suggesting that in situ concentrations of organic ligands supplied from dust might be below our detection limit. HS-like compound concentrations were relatively low across the section (average ≈ 10 µg/L eq. [SRFA], and were below previous reported values (Cabanes et al., 2017; Laglera and van den Berg, 2009; Norman et al., 2015). Considering that 100 µg/L SRFA can bind 1.69 nM Fe

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(Laglera and van den Berg, 2009), one can estimate a HS-like contribution of 0.17 nM LT ,

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corresponding to 15 % of the average Fe-binding ligands detected. As opposed to previous

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reports showing that humics played a major role in iron biogeochemistry in the Irish Sea, the

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North Atlantic, the Arctic and estuaries (e.g., Laglera and van den Berg, 2009; Krachler et al., 2015; Yang et al., 2017); our results demonstrated that the HS-like contribution to Fe-binding

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ligands was very low in this remote section of the South Pacific Ocean, suggesting that humics

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did not control Fe biogeochemistry in the top 1000 m of the study region. Given all organic ligands distributions (Figure 4B-F), there is no clear evidence that organic compounds from

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benthic sources impacted the top 1000 m of the water column, except maybe for station 8 where

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we found LT values up to 1.8 nM. High concentrations of lead were also found at this station, suggesting a potential terrestrial supply (Bowie et al., in prep). The HS-like contribution to Febinding ligands is however expected to increase below 1000m. In the deep ocean, refractive organic matter typically rich in humics has indeed been reported in every ocean basin (e.g. Lechtenfeld et al., 2014; Medeiros et al., 2016). Refractive organic matter also contributed to the pool of HS-like and Fe-binding ligands, representing up to 51% of the deep Fe-binding ligands (Hassler et al. in press).

22

Journal Pre-proof Acidic polysaccharides containing–SO4 and/or –COO - are known to be detected with the PS-like method (Strmečki et al., 2014; Strmečki and Plavšić, 2012). This idea was reinforced by the distribution of PS-like molecules observed in this study with decreasing concentrations throughout the water column (p < 0.01, r = -0.54, n = 69, except at Station 3, Fig. 3E), typical of polysaccharide distributions (Pakulski and Benner, 1994).

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In this study, the results seem to confirm that the Fe-binding ligands present below the photic zone and above 1000m were tightly linked to the ligands present in surface waters, which is in

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line with the fact that only a small amount of dFe present below the mixed layer (200-1000 m)

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originated from sources other than atmospheric sources (Ellwood et al., 2018). The absence of a

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relationship between HS-like ligands and PS-like molecules with any of the CLE-AdCSV

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parameters, demonstrates that these two compounds did not constitute the bulk of the Fe-binding

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ligands in the study region.

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Relationships with phytoplankton community

The higher eL in surface waters suggest either a source of Fe-binding ligands or a drawdown of

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Fe while leaving the excess ligand behind. Most phytoplankton synthesize saccharides (weak Febinding ligands with log K’Fe’L < 12) that act either as internal energy reserves or are secreted as exopolysaccharides (EPS) in surface waters (Hassler et al., 2011a; Tsuji and Yoshiga, 2017). Those weak Fe-binding ligands could be partly described by the PS-like distribution since we found a significant positive correlation between them and all biomarkers pigments as well as Prochlorococcus abundance. Eukaryotic phytoplankton such as diatoms (Rue and Bruland, 2001) and haptophytes (Boye and van den Berg, 2000; Tsuji and Yoshida, 2017) produce strong Febinding ligands with Fe-binding affinities similar to bacterial EPS (Norman et al., 2015). 23

Journal Pre-proof However, the negative correlation between LT and eL for the biomarker pigment of diatoms and the negative correlations between eL and the rest of the biomarker pigments (Suppl. Table 3), suggested that these phytoplankton are involved in the consumption, transformation (and recycling) of organic ligands, rather than in their net production. Such a dynamic situation was reported in the frame of carefully designed experiments (e.g., Boyd et al., 2010; Bundy et al., 2018). In this situation, process studies rather than “snapshot” representation through chemical

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and biological profiles might be more appropriate to shed light on the role of microorganisms on

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the production and cycling of Fe-binding ligands.

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Prochlorococcus were recently identified as a possible source of transparent exopolymer particles

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(particles composed mainly of acidic polysaccharides; Passow and Alldredge, 1994) in the

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oligotrophic ocean (Luculano et al., 2017). Here, results suggested that cyanobacteria, specifically Prochlorococcus, have a strong influence on the distribution of the electrochemically

with

macronutrients

and

Fe’

suggested

a

relationship

with iron biological

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abundance

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detected Fe-binding ligands in this region. The negative correlations of Prochlorococcus

consumption. Cyanobacteria of this type can also be diazotrophs which could explain such

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negative correlations with NO 3 -and with Fe’. Under low Fe conditions, cyanobacteria are known to secrete siderophore-type ligands to solubilize and sequester Fe (Boiteau et al., 2016; Vraspir and Butler, 2009). This survival strategy seems particularly visible at Station 23 where we observed the highest Prochlorococcus biomass (> 250000 cell/mL) and the highest stability constant values (log K’Fe’L > 12) . However, with a single observation one cannot conclusively attribute strong ligand production to Prochlorococcus. To date, the only marine cyanobacterium for which siderophores have been isolated is the strain Synechococcus sp PCC 7002 (e.g. Wilhem et al., 1996), a strain that is phylogenetically separated from marine cyanobacteria (Beck et al., 24

Journal Pre-proof 2012; Shih et al., 2013). Recent studies showed an upregulation of siderophores in Synechococcus sp PCC 7005 already at high Fe’, typical from coastal regions (Blanco-Ameijeiras et al., 2019). Moreover, genomic studies showed that siderophore production is a rare ability in oceanic cyanobacteria (Hopkinson and Morel, 2009; Hopkinson and Barbeau, 2012).

Conclusions

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This study investigated Fe-binding organic ligand distributions along the GEOTRACES GP13

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southwestern Pacific Ocean section to a depth of 1000 m. Data from CLE-AdCSV analyses

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highlighted greater LT concentrations over dFe concentrations as well as the predominance of

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weak Fe-binding ligands (log K’Fe’L < 12) over the entire section (except surface waters at station 23). As for other studies, eL values were greater in the upper layer of the water column,

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suggesting a direct link with biological activity. HS-like and PS-like molecules were not

na

significantly correlated to any of the parameters determined by CLE-AdCSV (LT , log K’Fe’L), suggesting that these compounds did not represent the bulk of LT . In the top 1000 m of this

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region of the South Pacific Ocean, HS-like ligands play a minor role on Fe biogeochemistry.

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PS-like molecule concentrations showed statistically significant positive correlations with several biological parameters, strengthening the links between phytoplankton and Fe-binding ligand production. Amongst the phytoplankton, Prochloroccocus was the group that has the most effect on electrochemically detected compounds. Given that a modeling study showed that by 2100 the Prochlorococcus and Synechococcus cell abundances will change by ≈ +50% and ≈ -30% respectively in the Tasman Sea (Flombaum et al., 2013), this modification of the cyanobacteria population could lead to a significant change in organic Fe-binding ligands. This prediction could be tested using process studies in the region.

25

Journal Pre-proof Acknowledgement

We would like to thank anonymous reviewers whose comments significantly improved our manuscript, Captain and crew of the R/V Surveyor, as well as the scientific party for their assistance and support during the GP13 GEOTRACES southwest Pacific voyage. We especially thank the shipboard GEOTRACES sampling team for collection of samples used in this study.

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Finally, we would like to thank Dr. S.A.M. Moisset and Dr. S. Blanco-Ameijeiras for their

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support and stimulating discussion.

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Funding

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D.J.E.C. was funded by the Swiss National Science Foundation (PP00P2_138955), L.N. by the

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Australian Research Council (Discovery Project DP1092892) and C.S.H. by the Australian Research Council (Discovery Project DP1092892 and LIEF grand LE0989539), the UTS

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Chancellor Fellowship and the Swiss National Science Foundation (PP00P2_138955). S.S. was

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supported by Croatian Science Foundation project No. 8607 (AMBIOMERES). A.R.B. was supported by grants from the Australian Research Council (Future Fellowship program;

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FT130100037) and the University of Tasmania (B0018994, B0019024, and L0018934). The Australian Marine National Facility funded the shiptime for the GEOTRACES GP13 section (voyage ss2011_v02).

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Journal Pre-proof Author contributions

DC contributed to the chemical analyses, interpretation of data and redaction of the manuscript. LN contributed to the experiments and redaction of the manuscript. AB contributed to the experimental design, chemical analyses and redaction of the manuscript. SS contributed to the chemical analyses and redaction of the manuscript. CH contributed to the experimental design,

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interpretation of data as well to the manuscript.

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Journal Pre-proof Table 1: Concentrations of several organic compounds measured in equivalent mg/L of Xanthan (PS-like) and in equivalent µg/L of SRFA (HS-like). n.d. corresponds to “not detected” and LOD to “limit of detection”. Values corresponding to organic compounds detected are highlighted in bold. equiv. mg/L equiv. µg/L c(Xanthan) c(SRFA) LOD 0.2 mg/L LOD 1.9 µg/L a SRFA 100 µg/L n.d. 102.9 b Dust LHR9 22 mg/L 0.7 126.4 b Dust CLH B3 21 mg/L n.d. 78.1 c Glucuronic acid 280 µg/L n.d. 21.2 c Carrageenan 285 µg/L < DL* 9.8 c Alginate 90 µg/L < DL* n.d. d L-cystein 100 µg/L 2.2 n.d. d D-alanin 80 µg/L n.d. n.d. e GSSG 90 µg/L 1.4 n.d. f DFB 20 nM n.d. n.d. Enterobactin 30f nM n.d n.d a : ≈ 2-5x in-situ concentrations (this study; Laglera and van den Berg, 2009) b : ≈ 75000x in-situ dissolved aluminum concentrations found at the western part of the transect (Bowie et al., in prep.) c : ≈ in-situ concentrations (Panagiotopoulos and Sempéré, 2005) d : ≈ 1-2x in-situ concentrations (Garrasi et al., 1979; Sommerville and Preston, 2001) e : ≈ 30x in-situ concentrations (Le Gall and Van Den Berg, 1998) f : ≈ 300-500x in-situ concentration (Boiteau et al., 2016) *: detectable at higher concentrations (7.5 mg/L; Strmečki and Plavšić, 2014)

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Organic Compound

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Journal Pre-proof Figure 1: Sampling stations (3, 8, 13, 18, 23, 28, 33, 38) for the study of Fe chemistry during the GP13 expedition. The transect of the expedition is described by the red line and each sampling station is represented by a star. Surface current systems of the southwest Pacific are shown (SC – Southland Current, WC – Westland Current, DC – D’Urville Current). Adapted from Hamilton (2006). Figure 2: Representation of the physico-chemical parameters for stations 3-38. (A) Temperature salinity diagram with density lines in grey; (B) nitrate (NO 3 -) and phosphate (PO 4 3-) concentrations.

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Figure 3: Section map of Fe data considering stations 3-38 sampled during the GP-13 expedition. Dissolved Fe (dFe, A), total Fe-binding organic ligands (LT , B), excess organic ligands (eL, C), humic substances (HS-like, E), catalytically active polymers (Cat. P or PS-like, G) and the ligands distribution according to their conditional stability constant with Fe (L1 , L2 and L3 , D, F and H) are shown. Data was plotted using OVD ver. 4.5 (http://odv.awi.de).

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Figure : Chlorophyll-a (Chl-a) (A), Fv/Fm (B) and biomarker pigments (C: Fucoxanthin; D: 19But-fucoxanthin; E: 19-Hex-fucoxanthin) distributions for the study region. Data was plotted using ODV ver. 4.5 (http://odv.awi.de). White dashed lines indicate stations where Fe chemistry was sampled

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Figure 5: Section map of (A) Prochlorococcus and (B) Synechococcus considering all stations sampled during GP13 expedition. Data was plotted using ODV ver. 4.5 (http://odv.awi.de). White dashed lines indicate stations where Fe chemistry was measured.

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Journal Pre-proof Study of iron speciation in the Southwestern Pacific Ocean

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First investigation linking iron speciation, humics, polymers, and biological data

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Electroactive humics and polymers distribution revealed different in-situ sources

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Surface ligands were mostly derived from biological sources

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Deep ligands were mostly derived from the degradation of surface organic compounds

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5