Facteurs de Contrôle des Variations Saisonnières des Isotopes du Silicium

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Sample collection, spike and incubation conditions

The isotopic dilution technique adapted by Fripiat et al. (2009) from Corvaisier et al. (2005) aims at simultaneously determining the rates of Si uptake (i.e. silica production) and of biogenic silica dissolution in the same seawater sample. After spiking with a solution enriched in 30Si followed by incubation of the samples, the production rate is estimated from the change in isotopic composition of the particulate phase (increase in 30Si). Similarly, the isotopic dilution (increase in 28Si) in the 30Si enriched seawater, due to the dissolution of naturally 28Si enriched BSi initially present, is used to estimate the dissolution rate.
Production and dissolution rates were determined at 7 and 5 depths respectively, corresponding to different levels of Photosynthetically Active Radiation (PAR), from 75 % to 0.1 % of surface irradiance. Seawater was collected at defined depths in the euphotic layer using Niskin bottles mounted on a CTD-rosette. For each depth, 5 l of seawater were sampled. 1 l was subsampled to obtain a natural silicon isotopic standard (i.e. not spiked with 30Si) to be processed along with the samples to correct for the matrix effect and mass bias during isotopic analysis (Fripiat et al., 2009). These unspiked samples were immediately filtered on 0.8 µm Nuclepore polycarbonate membranes to separate biogenic silica from silicic acid. The membrane was dried at 50 °C overnight and the filtrate was directly preconcentrated (see section – Sample preparation and isotopic measurements) and stored at room temperature in the dark.
The remaining seawater volume was subsampled in 2 l aliquots spiked with H430SiO4-enriched solution (99 % 30Si). Aliquots devoted to production measurements were spiked with a spike contribution representing usually less than 10 % of natural concentrations to minimize the perturbation of the natural DSi contents (Nelson & Goering, 1977a). In order to improve the detection limit of the method for dissolution, a second 2 l aliquot was spiked by adding 30Si in the same amount as natural DSi (i.e. DSi spike addition at 100 % of the initial DSi). This provided sufficient sensitivity for the isotopic measurements of dissolution (see section – Accuracy of the model, detection limit and standard deviation).
Immediately after spike addition and gentle mixing, 1 l was filtered following the same procedure than for the unspiked standard, to determine the initial conditions (t0). The second half of the sample was poured into polycarbonate incubation-bottles and incubated under light conditions simulating those prevailing in situ for 24 h (10 % spiked samples) and for 48 h (100 % spiked samples). Deck-incubators were fitted with blue plastic optical filters to simulate the light attenuation of the corresponding sampling depths, and temperature was regulated by circulating surface seawater. At the end of the incubation period, samples were filtered and treated as described above to characterize the final conditions of the incubation (t24 or t48).

Sample preparation and isotopic measurements

Preconcentration of H4SiO4 in the seawater samples (for both production and dissolution measurements) was applied on-board to increase the Si:salinity ratio, because the maximum salinity of the solution that can be introduced in the mass spectrometer is about 2 ‰ (Fripiat et al., 2009). This step was achieved using a protocol adapted from the MAGIC method (Karl & Tien, 1992; Reynolds et al., 2006). The H4SiO4 in seawater was scavenged by the brucite precipitate (Mg(OH)2) obtained by adding 1 ml of 14 N NaOH to the 1 l of seawater sample and strong stirring. The precipitate was recovered by decantation and centrifugation, and was then dissolved in 3 ml of 3 N HCl.
In the shore based laboratory, polycarbonate membranes (t0, t24 and t48 for both production and dissolution measurements) were digested in one step using a protocol adapted from Ragueneau et al. (2005) with 4 ml of 0.2 N NaOH during 40 min at 100 °C to hydrolyse BSi. Samples were then neutralized with 1 ml of 1 N HCl to stop the reaction.
An aliquot of the solutions obtained after preconcentration and digestion was used to determine colorimetrically the DSi and BSi concentrations, following the method of Strickland and Parsons (1972). The remaining sample was diluted to 100 ppb Si in a 2 % HNO3 solution to determine the initial and final Si isotopic composition of the dissolved and particulate phases using a Element 2 (Thermo-Fischer) HR-SF-ICP-MS (High Resolution – Sector Field – Inductively Coupled Plasma – Mass Spectrometer) with the same configuration used by Fripiat et al. (2009). The sequence of analysis consists in: blank – natural standard – spiked sample 1 – natural standard – spiked sample 2 – natural standard – spiked sample 3 – natural standard – blank. The average of the two blanks were subtracted to each standard and sample. To test whether our dissolution measurements were biased by a 30Si contamination linked to a possible memory effect in the HR-SF-ICP-MS, we compared the average composition of the first natural standards (i.e., without contamination from memory effect, n = 55) with the composition of natural standards analyzed after a spiked sample (n = 102). There was no significant difference between natural standards passed before and after a 100 % DSi spiked sample (T-test, p-value <0.001). We can thus exclude significant memory effect when applying the analytical sequence described above.


Accuracy of the model, detection limit and standard deviation

To estimate the production and dissolution of biogenic silica (ρSi and ρDiss, respectively), two different models are available: the linear one-compartmental model described by Nelson and Goering (1977a, b) and the non-linear two-compartmental model described in de Brauwere et al. (2005) and Elskens et al. (2007). In the latter, both isotopic composition and concentration changes occurring during the incubation time are taken into account to estimate production and dissolution rates simultaneously. Lack of consideration of these changes could induce significant biases in the estimated fluxes (Elskens et al., 2007).
The best solution is found numerically by optimizing parameter values (ρSi and ρDiss) and minimizing the cost function (weighted sum of squared differences between calculated and measured variables, [BSi], [H4SiO4], æBSi and æDSi for the four equations simultaneously; de Brauwere et al., 2005; Elskens et al., 2007).
The relevance of the 2 models against a given data set has already been discussed by Elskens et al. (2007) and Fripiat et al. (2011b). Taking into account these considerations, and after testing the accuracy and the sensitivity of each model, we use preferentially the non-linear 2 compartmental model to estimate the biogenic silica production and dissolution during KEOPS-2. This model was tested according to the four criteria and the residual of the cost function was checked to follow a Chi² distribution as detailed in Elskens et al. (2007). Due to unexpected sampling problems on-board, we were not able to measure [DSi]t. Thus, in addition to the biogenic silica production and dissolution rates, this variable was also estimated by the model (Eqs. 1.1. to 1.4.). Under these conditions, one degree of freedom is lost but the system remains identifiable with 3 unknowns and 4 equations.
KEOPS-2 took place during the onset of the blooms, the biogenic silica production rates were quite high and far above the detection limit, except for 3 depths of the HNLC reference station (R-2) and for the deepest value at each station (0.01 % PAR attenuation depth, 8 samples). However, since biogenic silica dissolution rates were expected to be low in early spring, it is essential to determine the limit of detection for the 30Si isotopic dilution.
In most cases, final æDSi were significantly different from initial æDSi (paired T-test, p-value < 0.001). The detection limit for isotopic dilution was then estimated as being the lowest difference between initial and final 30Si isotopic abundances (ΔæDSi) measurable by the instrument. Every æDSi solutions have been analyzed in duplicates with a pooled standard deviation of 0.32 % (n = 35). In addition, we analyzed the same in-house standard several times during every analytical session. This solution was a 10 % spiked seawater from Southern Ocean analyzed since several years with a æDSi at 11.83 ± 0.43 % (n = 40). The relative standard deviation (RSD) on æDSi of this standard solution is 0.43% (n = 40) and represents the long-term reproducibility of HR-SF-ICP-MS measurements. Therefore, each KEOPS-2 incubation with a ΔæDSi between t0 and t48 higher than this RSD was considered to be significantly different from zero, and hence above the detection limit. This was the case for almost all the KEOPS-2 dataset (see e.g. Fig. 1.3.), except for 7 values showing a change in 30Si abundance below the detection limit. This included 4 samples from the HNLC reference station R-2 where biological activity was extremely low.
Figure 1.3. Comparison of changes in 30Si-abundance of seawater for each incubation (symbols) with detection limit of the 30Si-isotopic dilution method (plain line) estimated from the reproductibility of an internal standard (0.43%, n = 40). The dotted line represents the detection limit obtained from the average reproductibility of all dissolution duplicates (0.32%, n = 35).
Due to time and sampled water volume constraints, the sampling strategy adopted for KEOPS-2 gave the priority to highest vertical resolution instead of replicate incubations. Since only the analytical reproducibility was taken into account in the model, the standard deviations on Si uptake and dissolution rates were likely to be underestimated. Therefore we will use a theoretical relative precision for the whole incubation experiments of 10 %, as estimated for Si uptake rates by Fripiat et al. (2009).

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Physical, chemical and biological parameters

The vertical structure of upper layer waters in the area was characteristic of the Antarctic Surface Water in the vicinity of the Polar Front (Park et al., 1998, 2008). The Winter Water (WW), identified by the minimum of temperature centered around 200 m, was capped by a homogeneous mixed layer (ML) induced by seasonal stratification. The boundary between the surface ML and the WW is usually marked by a strong seasonal pycnocline. However, at some stations, the stratification of the surface layer was relatively complex and showed two successive discontinuities evidenced by two different density gradients as indicated in figure 1.4.
Figure 1.4. Vertical distribution of chlorophyll a (continuous-black line; estimated from CTD fluorescence), biogenic silica concentration ([BSi], light dots) and H4SiO4 concentration ([DSi], dark dots). Dotted lines show the bottom of the euphotic layer (1% of Photosynthetically Active Radiation, Ze) for each station. Dark dashed lines represent the Mixed Layer Depth (MLD; estimated by Park et al., in prep.) and grey dashed lines correspond to a 2nd density gradient identify from the density CTD-profile.
During KEOPS-2, the surface ML depth, defined by the density difference of 0.02 from the surface (Park et al, in prep.), showed a large variability between stations (Fig. 1.4.). A strong and shallow stratification was measured north of the polar front, while wind events induced weak stratification and deep ML in the stations above the plateau and in the HNLC area. Stations in the recirculation zone (E-1 to E-5) supported a complex stratification due to their highly spatial and temporal dynamic and were characterized by 2 distinct density discontinuities.
All the stations located south of the Polar Front had quite homogeneous Chl-a, BSi and DSi stocks from the surface to the deepest density discontinuity (below the so-called ML; Fig. 1.4.). North of the Polar Front, these stocks were higher at the surface and decreased with depth (Fig. 1.4c.). Stations A3-2 and E-4W present similar BSi and DSi surface concentrations (Fig. 1.4e., 1.4g.). At these 2 stations, DSi concentrations increase gradually while Chl-a and BSi decrease drastically below the deepest density discontinuity. Station R-2 contrasted from the latter stations by its low BSi, low Chl-a content and relatively high DSi concentrations, confirming its HNLC character (Fig. 1.4a.). During the lagrangian survey (stations E-1, E-3, E-4E and E-5), we observed a DSi depletion from ≈15 to ≈10 µmol l-1 in surface waters, an increase of Chl-a concentrations from < 1 to > 1 mg m-3 and a doubling of the BSi content from ≈1.5 to > 3 µmol l-1 (Fig. 1.4b., 1.4d., 1.4f., 1.4h.). Such temporal variations were mainly driven by diatom production as described below.

Biogenic silica production and dissolution rates

Silica production rates were quite homogeneously distributed in the euphotic layer with an exception for the station F-L located north of the Polar Front where it decreases progressively with depth (Fig. 1.5a.). Surface ρSi varied from 0.036 ± 0.003 µmol l-1 d-1 (R-2 in the HNLC area) to 1.28 ± 0.12 µmol l-1 d-1 (A3-2, above the Plateau). All over the study area, Si uptake rates reached very low values at the base of the euphotic layer. Note that the same decreasing trend was also observed in primary production experiments performed in parallel (see e.g. Cavagna et al., in prep.).
BSi dissolution rates were considerably lower than Si uptake rates except in the HNLC area (R-2) and at station E-3 where ρSi was in the lower range of the KEOPS-2 dataset. Vertical profiles of ρDiss (Fig. 1.5b.) were quite homogeneous from the surface to the base of the euphotic layer and did not increase at depth. This indicates that, the physical and biogeochemical processes affecting BSi dissolution did not vary significantly over the water column. This is also consistent with the low accumulation of biogenic silica observed at depth in spring (Lasbleiz et al., 2014) which contrasts with the occurrence of deep BSi maxima at the end of summer (Mosseri et al., 2008). Moreover, silica dissolution rates were not significantly different between bloom stations, and were comparable to those measured by Brzezinski et al. (2001) for the same season in the Pacific sector and by Beucher et al. (2004) and Fripiat et al. (2011b) for the end of summer in the Australian sector.

Table of contents :

1. L’Océan Austral et son rôle dans le climat
2. Facteurs de contrôle de la fixation et de l’export de carbone dans l’Océan Austral
3. Le cycle biogéochimique du silicium dans l’Océan Austral
4. Les diatomées : acteurs majeurs de la pompe à silicium dans l’Océan Austral
5. Fonctionnement du système isotopique du silicium
6. Objectifs de la thèse
7. Références bibliographiques
CHAPITRE I : Evolution Saisonnière de la Production de Silice Biogénique dans l’Océan de Surface
1. Préambule
2. Article 1 : Seasonal evolution of net and regenerated silica production around a natural Fefertilized area in the Southern Ocean estimated from Si isotopic approaches
CHAPITRE 2 : Facteurs de Contrôle des Variations Saisonnières des Isotopes du Silicium
1. Préambule
2. Article 2 : Controls on seasonal variations of Si isotopic composition of diatoms and seawater related to iron supply and mesoscale activity in the naturally Fe-fertilized Kerguelen area (Southern Ocean)
CHAPITRE 3 : Variations Saisonnières, Origine et Devenir du Flux de Diatomées dans la Colonne d’eau
1. Préambule
2. Article 3 : Seasonal variations, origin and fate of settling diatoms in the Southern Ocean tracked by silicon isotope records in deep sediment traps
1. Synthèse des principaux résultats
2. Développement analytique
3. Conclusion générale
4. Perspectives
5. Références bibliographiques
Annexes A
Supplementary Method B
Annexes B
Supplementary Method C
Annexes C
Article Annexe : Seasonal dynamics in diatom and particulate export fluxes to the deep sea in the Australian sector of the southern Antarctic Zone


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