# Microbial Remineralization Is a Depth-Varying Contributor to Particle Flux Attenuation in the Southern Ocean

L. Petiteau, P. Boyd, F. Le Moigne, M. Villa-Alfageme, I. Vioque, E.

Laurenceau-Cornec, K. Baker, L. Lacour, T. Rohr, R. Strzepek, et al.

► To cite this version:

L. Petiteau, P. Boyd, F. Le Moigne, M. Villa-Alfageme, I. Vioque, et al. Microbial Remineralization Is a Depth-Varying Contributor to Particle Flux Attenuation in the Southern Ocean. Global Biogeochemical Cycles, 2025, 39 (7), 10.1029/2025GB008498. hal-05184632

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# Global Biogeochemical Cycles®

## REVIEW ARTICLE

10.1029/2025GB008498

**Key Points:**

* Different export systems were dominated by fecal material in the subantarctic and phyto-aggregates in polar waters

* Sevenfold range in microbial remineralization rate was likely driven by the confounding effect of temperature and particle characteristics

* Multi-faceted observational, laboratory and modeling approaches reveal a depth-varying role of microbes in setting POC flux attenuation

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

L. Petiteau,
loouis.pteiteau@imev-mer.fr

**Citation:**

Petiteau, L., Boyd, P. W., Le Moigne, F. A. C., Villa-Alfageme, M., Vioque, I., Laurenceau-Cornec, E. C., et al. (2025). Microbial remineralization is a depth-varying contributor to particle flux attenuation in the Southern Ocean. *Global Biogeochemical Cycles*, 39, e2025GB008498. https://doi.org/10.1029/2025GB008498

Received 23 JAN 2025

Accepted 18 JUN 2025

© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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## Microbial Remineralization Is a Depth-Varying Contributor to Particle Flux Attenuation in the Southern Ocean

L. Petiteau¹ ID, P. W. Boyd²,³,⁴, F. A. C. Le Moigne⁵ ID, M. Villa-Alfageme⁶ ID, I. Vioque⁷ ID, E. C. Laurenceau-Cornec²,⁵ ID, K. Baker²,³ ID, L. Lacour¹,², T. Rohr²,⁸ ID, R. F. Strzepek²,³ ID, and M. Bressac¹ ID

¹Laboratoire d'Océanographie de Villefranche-sur-Mer (LOV), Sorbonne Université, CNRS, UMR, Villefranche-Sur-Mer, France, ²Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia, ³Australian Antarctic Program Partnership, University of Tasmania, Hobart, TAS, Australia, ⁴ARC Australian Centre for Excellence in Antarctic Sciences, University of Tasmania, Hobart, TAS, Australia, ⁵University Brest, CNRS, IRD, Ifremer, LEMAR, Plouzané, France, ⁶Department of Applied Physics II. E.T.S.I.E. Universidad de Sevilla, Sevilla, Spain, ⁷Department of Applied Physics II. E.T.S.A. Universidad de Sevilla, Sevilla, Spain, ⁸CSIRO Environment, Hobart, TAS, Australia

**Abstract** The biological carbon pump contributes to set the magnitude of carbon sequestration in the oceans' interior. Estimating the relative contribution of microbial versus zooplankton-mediated processes to particulate organic carbon (POC) flux attenuation provides insights into how this pump functions. Our study took place during the high productivity summer period in the Subantarctic and Polar Front Zone. In the upper mesopelagic (i.e., 180–300 m depth), we concurrently measured the downward POC flux, particle size and morphology, microbial remineralization rates and estimated size-specific sinking velocities. These concomitant measurements revealed two different export systems, dominated by fecal material in the Subantarctic, and phyto-aggregates in polar waters. These two systems were characterized by similar low particle sinking velocities (~10 m d⁻¹), while microbial remineralization rates differed by an order of magnitude. Higher microbial remineralization rates in the Subantarctic (0.11 d⁻¹), compared to polar waters (0.04 d⁻¹), were likely driven by the confounding effect of temperature and particle characteristics. Despite this difference in microbial remineralization rates, these two export systems were characterized by relatively similar transfer efficiencies, suggesting that microbes had differing influences. A comparison of microbially mediated (i.e., scaled using observed remineralization rates) with total POC flux attenuation (i.e., driven by the dual impact of microbes and flux-feeders) revealed a higher microbial contribution to the flux attenuation in the upper mesopelagic of the subantarctic compared to the polar region. This deconstruction of the flux attenuation revealed an increasing influence of microbes on POC degradation with depth to become the predominant actor in the lower mesopelagic.

**Plain Language Summary** In the surface ocean, plankton fix dissolved atmospheric CO₂ into particulate organic carbon (POC) that sinks into the ocean's interior, that is the POC flux. On its way down, POC is consumed by microbes and zooplankton, resulting in a depth-decrease of POC flux—the POC flux attenuation. The magnitude of the attenuation is set by the sinking rate and consumption rate of POC which varies widely throughout the ocean. Using multiple observational and modeling approaches at contrasting sites of the Southern Ocean, we specifically explore the role of microbes in the flux attenuation. We found that the relative role of microbes becomes increasingly important with depth (from ~120 to 500 m depth), to become the predominant actor deeper than 200 m depth. Our work supports the need to visualize the POC flux attenuation as a depth-varying interplay of zooplankton and microbes to understand what drives its magnitude. This would improve computer simulations that predict how much carbon is stored in the ocean's interior.

## 1. Introduction

The process of atmospheric CO₂ fixation to organic matter during primary production and its transfer to depth refers to the biological carbon pump (Volk & Hoffert, 1985). Between 5 and 15 Gt of carbon is annually transferred to the mesopelagic (DeVries & Weber, 2017; Henson et al., 2011; Wang et al., 2023) in the form of sinking organic aggregates, zooplankton fecal pellets and other detrital material (De La Rocha & Passow, 2014). In addition to this gravitational pathway, five injection pumps have been identified to contribute to the export of particulate organic carbon (POC) (Boyd et al., 2019), notably through vertical migration of zooplankton egesting
fecal pellets at depth (Steinberg, Cope, et al., 2008). Only ~10% of the POC leaving the euphotic zone reaches the bathypelagic realm due to multiple degradation processes remineralizing particles as they settle (Buesseler & Boyd, 2009; Giering et al., 2014; Steinberg, Van Mooy, et al., 2008). The vertical attenuation of POC flux—in the upper mesopelagic—is often parameterized with a power-law relationship, known as the Martin's curve (Martin et al., 1987). Martin's exponent b, which describes the magnitude of attenuation, integrates the complexity and diversity of biological processes degrading organic matter and altering particle sinking velocity. b value ranges from 0.4 to 1.5 with large spatial and seasonal variability (Guidi et al., 2015; Henson et al., 2023; Kienast & Torfstein, 2022), and no consensus has been found on the drivers of such variability (Henson et al., 2012; Marsay et al., 2015; Wiedmann et al., 2020), hindering model development (Wilson et al., 2022).

Removal of sinking POC in the mesopelagic is primarily driven by the dual action—and complex interplay—of zooplankton and heterotrophic microbes. Zooplankton directly alter the sinking particle flux through ingestion, respiration and assimilation of particles (Christiansen et al., 2018; Jackson, 1993; Steinberg & Landry, 2017). Concurrently, particle-attached heterotrophic bacteria convert POC to dissolved organic carbon via enzymatic solubilization or mechanical disaggregation (Smith et al., 1992). A proportion of the dissolved organic carbon is then respired by free-living and particle-attached microbes (Azam & Malfatti, 2007). The combined effect of solubilization and respiration is referred to as microbial remineralization (Herndl & Reinthaler, 2013) and has been identified as one of the main processes removing POC in the mesopelagic (Giering et al., 2014; Ploug et al., 1999). Sinking particles host a diverse microbe assemblage (Alldredge & Silver, 1988; Thiele et al., 2015) that display complex community dynamics as they sink (Baumas et al., 2021; Nguyen et al., 2022; Stephens et al., 2024) due to ongoing environmental changes (i.e., pressure, temperature, nutrient availability) and ecological interactions (i.e., competition and predation). The particle sinking velocity is also a key component of the biological carbon pump as it determines the time period available for degradation in the mesopelagic. The wide variety of particle shapes, structures and density results in velocities ranging from a few meters per day to several hundreds (Laurenceau-Cornec et al., 2020). Particle consumers that rely upon their motility/mobility to feed in the mesopelagic zone are sensitive to particle sinking velocity, since both interception capacity for zooplankton (Stukel et al., 2019) and colonization efficiency for microbes (Borer et al., 2023) increase for slowly sinking particles. Conversely, zooplankton can alter the particle sinking velocity through the fragmentation of large aggregates into smaller slower sinking particles (Briggs et al., 2020; Dilling & Alldredge, 2000; Goldthwait et al., 2004), increasing the particle surface area and the time that sinking particles remain in the upper mesopelagic, potentially promoting microbial colonization (Mayor et al., 2014).

Establishing the relative contributions of the various flux attenuation processes remains a challenge, given the extent of concomitant measurements required and their related uncertainties (Buesseler & Boyd, 2009; Giering et al., 2014). In this context, the inability to precisely and mechanistically deconstruct the particle flux attenuation limits our understanding of the biological pump in various ocean ecosystems and with depth. In this study, we used four interrelated approaches to dissect regional biological carbon pumps in the subantarctic and polar waters. In the upper mesopelagic, downward POC fluxes were measured using particle interceptor traps (PIT) mounted on a free-drifting array. To extend the vertical coverage, vertical profiles of particle abundance obtained with an underwater camera (UVP) were used—in conjunction with particle sinking rates and carbon content—to derive POC fluxes from the base of the primary production zone down to 500 m depth. Remineralization of sinking particles by particle-attached microbes was measured in situ using the C-RESPIRE particle collector/incubator (Boyd et al., 2015; Bressac et al., 2024). Size-specific sinking velocities were derived from simultaneous measurements of particle abundances from the UVP and particle flux from polyacrylamide gel traps (McDonnell & Buesseler, 2012). These approaches, used in the Subantarctic and Polar Front Zone, revealed two contrasting export systems in which we explored the key mesopelagic processes that control the magnitude and fate of the POC flux. In addition, we assessed the contribution of particle-attached microbes to the POC flux attenuation and how this contribution varies with depth and between export systems.

## 2. Materials and Methods

### 2.1. Study Site and Deployments

The biological carbon pump was studied in the Southern Ocean during the SOLACE (Southern Ocean Large Areal Carbon Export) voyage onboard the R/V Investigator (IN2020_V08) during the austral summer (4 Dec 2020–15 Jan 2021). Two distinct oceanic provinces of the Southern Ocean were investigated (Figure 1a). The first
<figure><img src="image_3.png" /><figcaption>**Figure 1.** (a) Locations of the sites where the free-drifting surface-tethered array was deployed (colored circles) overlaid on a map of surface chlorophyll-a concentration averaged over a 48-day period (25 Nov. 2020 – 12 Jan. 2021; MODIS Aqua). Subtropical Front (STF), Subantarctic Front (SAF), and Polar Front (PF) are represented by black dashed lines. (b–d) Temporal evolution of CTD-derived chlorophyll-a concentrations at (b) SOTS, (c) PF1, and (d) PF2. CTD casts used to derive the interpolated chlorophyll-a time-series are indicated by black triangles at the top of each plot. The base of the primary production zone and the mixed layer depth are delimited by the white and black lines, respectively. White dashed lines represent the depths and periods of the PIT deployment.</figcaption></figure>

site, in the Subantarctic Zone, southwest of Tasmania (47.0°S, 141.3°E), is co-located with the Australian Southern Ocean Time Series (SOTS), a long-term particle flux time-series observatory with deep-moored sediment traps at 1,000, 2,000, and 3,800 m depth (Trull et al., 2019). Two other polar sites, PF1 (55.9°S, 138.8°E) and PF2 (58.0°S, 141.3°E), were located south of the Polar Front (PF).

A total of 82 vertical profiles were made using a conductivity–temperature–depth (CTD) unit mounted with an Underwater Vision Profiler (UVP) version 5 (Picheral et al., 2010). A free-drifting surface-tethered array, mounted with 3 particle interceptor traps (PIT) and 3 C-RESPIREs (i.e., dual particle interceptor/incubator; Boyd et al., 2015), was deployed for a period of 3 days, on 3 consecutive deployments at SOTS and 2 consecutive deployments at the 2 polar sites. The vertical array also comprised a 25-m long bungee cord to reduce the vertical motion of the mooring line, and temperature/pressure sensors (RBR, Canada) to determine the exact deployment depths. These instruments were deployed below the mixed layer depth (MLD) and primary production zone (PPZ) in the upper mesopelagic (180–300 m depth) where the particle flux attenuation is maximum. MLD was calculated as the mean of the density threshold (0.03 kg m⁻³ difference criteria) and density gradient methods (0.0005 kg m⁻³ dbar⁻¹ criteria). The base of the PPZ (zₚ) is defined as the depth where chlorophyll fluorescence drops to 10% of its maximum (Owens et al., 2015). The net primary production (NPP) was obtained from 24 hr deckboard incubations using the 14C method with water samples collected at 6 depths in the euphotic zone and incubated under 6 irradiances, each corresponding to the % of incoming solar radiation across the upper water column as detailed in Boyd et al. (2022).

## 2.2. Particle Flux Collection and In Situ Particle Incubation

The PITs were deployed at 3 depths between ~180 and 280 m (Table 1). At each depth, the PIT consisted of 4 cylindrical polycarbonate tubes with a cross-sectional area of 0.007 m² and an aspect ratio of 6.7. Each tube was equipped with a honeycomb baffle. The first tube was filled with 2.5 L of 50 ppt brine poisoned with 0.75% formaldehyde concentration overlain with 6 L of <0.2-µm filtered seawater. This tube was used to quantify the downward particle flux. A second tube, used to characterize the nature and size distribution of the sinking
<table><caption>**Table 1**

*Deployments of the Free-Drifting Surface-Tethered Array Mounted With the PIT and C-RESPIRE Traps*</caption><thead><tr><th>Site</th><th></th><th>Longitude<br>°E</th><th>Latitude<br>°S</th><th>Deployment date<br>UTC</th><th>Depth<sup>a</sup><br>m</th><th>Drift<br>km</th></tr></thead><tbody><tr><td rowspan="3">SOTS</td><td>depl. 1</td><td>141.4</td><td>47.1</td><td>08 Dec 2020 23:45</td><td>179, 229, 272</td><td>12.1</td></tr><tr><td>depl. 2</td><td>141.4</td><td>47.1</td><td>12 Dec 2020 23:15</td><td>179, 229, 275</td><td>29.5</td></tr><tr><td>depl. 3</td><td>141.4</td><td>47.1</td><td>16 Dec 2020 20:00</td><td>185, 232, 277</td><td>28.6</td></tr><tr><td rowspan="2">PF1</td><td>depl. 1</td><td>139.1</td><td>55.9</td><td>23 Dec 2020 23:50</td><td>189, 237, 282</td><td>16.4</td></tr><tr><td>depl. 2</td><td>138.6</td><td>55.8</td><td>27 Dec 2020 21:45</td><td>189, 236, 282</td><td>15.7</td></tr><tr><td rowspan="2">PF2</td><td>depl. 1</td><td>141.3</td><td>58.1</td><td>31 Dec 2020 22:30</td><td>192, 239, 285</td><td>11.2</td></tr><tr><td>depl. 2</td><td>141.4</td><td>58.0</td><td>04 Jan 2021 22:15</td><td>191, 239, 284</td><td>14.9</td></tr></tbody></table>

particles, contained a polyacrylamide gel (Ebersbach & Trull, 2008) and was filled with 2.5 L of 50 ppt brine overlain with 6 L of <0.2-µm filtered seawater. The two last tubes were filled with (a) a homemade RNA later solution (Fontanez et al., 2015) for metagenomic analysis, and (b) with <0.2-µm filtered seawater for onboard incubations.

Three dual particle interceptors/incubators C-RESPIRE were deployed 20 m below each PIT between ~200 and 300 m depth (this spacing was required due to the ships freeboard—to enable the RESPIREs to be underwater when the PITS were added to the mooring). Briefly, C-RESPIRE is a titanium cylinder with a cross-sectional area of 0.0124 m² housing an indented rotating sphere and an inner chamber with an oxygen optode (Aanderaa 4831 series) (Boyd et al., 2015). Prior to deployment, C-RESPIREs are gently filled with <0.2-µm filtered seawater collected at each deployment depth. A lagged delay of 3 hr is pre-programmed in order to avoid collection of particles above the programmed deployment depth. After this delay, C-RESPIRE intercepts sinking particles and transfers them in a 1.4 L incubation chamber, while the rotating sphere ensures the exclusion of mesozooplankton. After this initial step, the sphere stops rotating, ensuring the isolation of the inner chamber and allowing in situ incubation of the collected sinking particles along with their attached microbial consortia. Dissolved oxygen consumption is monitored all along this incubation phase. During the SOLACE voyage, collection and incubation phases each lasted ~36 hr (Table S1 in Supporting Information S1).

After recovery of the free-drifting array, the seawater overlying the brine layer of the PITs was removed using a peristaltic pump and the brine layer containing the collected sinking particles was screened through a 350-µm nylon mesh to remove potential swimmers. Seawater above the indented rotating sphere of the C-RESPIREs was siphoned off and the inner chamber was sampled. Particle-enriched samples collected with the PIT and C-RESPIRE were split into 6 equal fractions using a peristaltic pump and a magnetic stirrer. Subsamples were used to measure the elemental flux of POC, particulate nitrogen (PN), biogenic silica (BSi), pigments, and for metagenomic analysis. POC- and PN-designated subsamples were filtered through pre-combusted QMA filters, and stored at -20°C until processing and analysis. Filters were acidified overnight with 2 M HCl to remove inorganic C, and then dried at 60°C for 2 days. Samples were then analyzed on a CHN analyzer (Thermo Finnigan EA 1112 Series Flash Elemental Analysis). Pigments samples were filtered through 25-mm GF/F filters, flash frozen in liquid nitrogen and stored in the dark at -80°C, and were analyzed according to the method described by Zapata et al. (2004). BSi samples were filtered through 25-mm polycarbonate filters, stored at -20°C and analyzed according to Boyd et al. (2024).

## 2.3. Particle Imaging From Polyacrylamide Gel Traps and UVP Profiles

One PIT tube per deployment depth contained a polyacrylamide gel jar (~3 cm thick) that was used to collect and image intact sinking particles. Particles within the gels were photographed using a Zeiss Stemi 2000-CS stereo microscope coupled to a Leica DFC-280 1.5MP digital camera with a magnification of ×6.5. A laser-etched glass grid of 36 cells (each 14 mm × 12.5 mm) was used for subsequent size calibration. The images from the gel jars were analyzed for particle counting, size, and shape evaluation using ImageJ, public domain Java image processing program developed by the U.S. National Institute of Health (Schindelin et al., 2012). For each gel, approximately

<footnote><sup>a</sup>Deployment depths of the 3 Particle Interceptor Traps. C-RESPIREs were deployed 20 m below each PIT. Deployment durations were all exactly 3 days.</footnote>
<caption>**Table 2**</caption>

Symbols and Units Used in This Study (M is Mass, L is Length, and T is Time)

<table><thead><tr><th>Symbol</th><th>Related section</th><th>Description</th><th>Dimension</th></tr></thead><tbody><tr><td>w</td><td>Sinking velocity</td><td>Sinking velocity</td><td>L T<sup>-1</sup></td></tr><tr><td>F<sub>i</sub><sup>gel</sup></td><td></td><td>Numerical particle flux</td><td>L<sup>-2</sup> T<sup>-1</sup></td></tr><tr><td>n<sub>i</sub><sup>UVP</sup></td><td></td><td>Particle concentration</td><td>L<sup>-3</sup></td></tr><tr><td>a</td><td></td><td>Empirical constant for porosity calculation</td><td>L<sup>D3-3</sup></td></tr><tr><td>D<sub>3</sub></td><td></td><td>3-dimensional fractal number</td><td>—</td></tr><tr><td>l</td><td></td><td>Largest particle diameter</td><td>L</td></tr><tr><td>P</td><td></td><td>Porosity</td><td>—</td></tr><tr><td>c</td><td>Carbon content</td><td>Particle carbon content</td><td>M</td></tr><tr><td>A</td><td></td><td>Volumetric carbon content</td><td>M L<sup>-3 B</sup></td></tr><tr><td>B</td><td></td><td>Exponent for carbon content calculation</td><td>—</td></tr><tr><td>V</td><td></td><td>Particle Volume</td><td>L<sup>3</sup></td></tr><tr><td>F</td><td>POC flux</td><td>POC flux</td><td>M L<sup>-2</sup> T<sup>-1</sup></td></tr><tr><td>F<sub>tot</sub></td><td></td><td>POC flux modeled based on UVP particle concentration</td><td>M L<sup>-2</sup> T<sup>-1</sup></td></tr><tr><td>F<sub>microb</sub></td><td></td><td>POC flux modeled assuming microbial degradation is the only attenuation process</td><td>M L<sup>-2</sup> T<sup>-1</sup></td></tr><tr><td>z<sub>top</sub></td><td></td><td>Depth of the upper layer boundary</td><td>L</td></tr><tr><td>z<sub>p</sub></td><td></td><td>Base of the primary production zone</td><td>L</td></tr><tr><td>C<sub>remin</sub></td><td>Remineralization rate</td><td>Microbial remineralization rate</td><td>T<sup>-1</sup></td></tr><tr><td>ΔDO</td><td></td><td>Oxygen consumption in incubation chamber</td><td>mol</td></tr><tr><td>POC<sub>remin</sub></td><td></td><td>Quantity of POC remineralized in RESPIRE trap during incubation phase</td><td>mol</td></tr><tr><td>POC<sub>resid</sub></td><td></td><td>Quantity of POC in RESPIRE trap chamber after incubation</td><td>mol</td></tr><tr><td>RQ</td><td></td><td>Respiratory quotient</td><td>mol mol<sup>-1</sup></td></tr><tr><td>t<sub>incub</sub></td><td></td><td>Incubation duration</td><td>T</td></tr></tbody></table>

Note. Symbols indexed by *i* in the main text refer to the *i*th size class.

50% of the trap collection area was examined. Each identified particle was individually determined using ImageJ and the Equivalent Spherical Diameter (ESD) was calculated based on the projected area. Further details on the methods used to photograph, count and size particles are provided in Text S1 of Supporting Information S1. Based on their ESD, particles imaged in the polyacrylamide gels were binned into logarithmically spaced size classes. For further comparison with the UVP, we used the same size bins as the UVP (i.e., 24 size classes for particles larger than 0.1 mm; Picheral et al., 2010). Size class bins with less than 5 particles counted were considered undersampled and removed from the data set (Jackson et al., 2005). Numerical particle fluxes were then calculated as the number of particles in each size class divided by the deployment duration and trap collection area (Ebersbach & Trull, 2008). The particle volume flux was obtained by multiplying the numerical particle flux by the particle volume (based on the ESD with the assumption of spherical particles). Concurrently to the free-drifting array deployments, particle abundance and biovolume were measured in the water column using the UVP. Similarly to the calculation of particle volume flux, the particle biovolume was calculated as the multiplication of particle concentration and particle volume for each size class. Particle biovolume was calculated based on particle size imaged by the UVP (Picheral et al., 2010). For each deployment, 5 to 8 vertical profiles were performed and averaged to obtain a single vertical profile and standard deviation. Following the approach of Jackson et al. (2005), particle abundance, numerical flux and volume flux are presented as size spectra based on the ESD. Units and definition of the terms used to construct the spectra are provided in Table 2. LabelImg graphical image annotation tool was used to manually label particles collected in gels. This classification was only performed on a subsample of images and was rather used qualitatively than quantitatively to characterize the export flux.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Overview of (a) the instruments and (b) derived metrics used to describe the regional biological carbon pumps at the 3 sites. Arrows indicate the data processing steps. UVP-derived POC flux profiles and the microbially mediated component of the flux attenuation are conceptually represented in panel (c). Note that PIT-derived POC fluxes were not used to calculate the UVP-derived POC flux, but were used to assess the model performances and bias (as indicated by the dashed line).</figcaption></figure>

## 2.4. Microbial Remineralization Rates

The method to measure microbial respiration rates and derive carbon-specific remineralization rates from the C-RESPIRE particle interceptor/incubator is fully described in Bressac et al. (2024). Briefly, the measured dissolved oxygen drawdown during incubations yielded oxygen consumption (ΔDO, mmol) which was attributed to particle-attached heterotrophic microbes as they dominate the respiration signal relative to that of free-living heterotrophic bacteria (not concentrated in the C-RESPIRE) (Boyd et al., 2015). Dissolved oxygen concentrations were corrected for pressure, temperature and salinity according to Thierry et al. (2022). A respiratory quotient (RQ; see below) is used to convert an oxygen-based to a carbon-based remineralization (POC<sub>remin</sub>, mmol). The carbon-specific remineralization rate C<sub>remin</sub> (d<sup>-1</sup>) was calculated as the proportion of POC remineralized per day relative to the initial quantity of POC (Table S1 in Supporting Information S1), as follows:

$$C_{\text{remin}} = \frac{\text{POC}_{\text{remin}}}{(\text{POC}_{\text{remin}} + \text{POC}_{\text{resid}}) \cdot t_{\text{incub}}} = \frac{\Delta\text{DO} \cdot \text{RQ}}{(\Delta\text{DO} \cdot \text{RQ} + \text{POC}_{\text{resid}}) \cdot t_{\text{incub}}} \quad (1)$$

POC<sub>resid</sub> (mmol) corresponds to the quantity of POC present in the incubation chamber after incubation (i.e., not remineralized by microbes during incubation). The quantity of POC initially incubated is calculated as the sum of POC<sub>resid</sub> and remineralized POC during incubation (POC<sub>remin</sub>). Marine particles are complex environments colonized by dynamic microbial communities (Datta et al., 2016; Nguyen et al., 2022) and characterized by changing substrate lability as they settle to depth. As a consequence, a large range of RQ's has been reported (Robinson, 2019). In this study, we selected a fixed RQ of 0.70 (Anderson & Sarmiento, 1994; Tanioka & Matsumoto, 2020). Propagation of uncertainties related to the choice of a fixed RQ, dissolved oxygen measurements, particle sample splitting and elemental analysis gave a total relative error on C<sub>remin</sub> of 30% (Bressac et al., 2024).

## 2.5. Particle Sinking Velocities

We derived size-specific sinking velocities between 150 and 300 m depth for particles ranging from 0.1 to 2.6 mm by comparing sinking particles collected in the gel traps with particle populations imaged in the water column by the UVP at similar depth horizons following the method described in McDonnell and Buesseler (2012) (Figure 2a). For the t<sup>th</sup> size class, the sinking velocity w<sub>i</sub> (m d<sup>-1</sup>) can be inferred using the numerical particle flux (F<sub>i</sub><sup>gel</sup>) and the particle abundance (n<sub>i</sub><sup>UVP</sup>), as follows:
$$w_i = \frac{F_i^{\text{gel}}}{n_i^{\text{UVP}}} \qquad (2)$$

Numerical particle fluxes were compared to particle abundances measured within 50 m depth intervals centered on PIT deployment depths. The error in $w_i$ was calculated by propagating the particle abundance uncertainty (see Section 2.3).

Following Laurenceau-Cornec et al. (2020), the relationships between particle sinking velocities measured in this study and their ESD were compared with two theoretical predictions, using the Stokes' law and the general Navier-Stokes' drag equation (hereafter referred as Model 1 and Model 2, respectively). Stokes' law assumes that at low Reynolds number (Re < 0.5), inertial forces can be neglected compared to viscous forces, thus allowing the easy calculation of particle sinking velocities based only on their size, density, and fluid properties (Stokes, 1851). However, in most cases large marine aggregates are too complex in shape, structure, and heterogeneous in composition to allow such assumption. For large marine aggregates the original Navier-Stokes' drag equation must be used. To account for the increase of aggregate porosity P (i.e., the volume of the particle filled with water) with size due to their fractal nature (Laurenceau-Cornec et al., 2020; Logan & Wilkinson, 1990; Meakin, 1988), these two theoretical sinking velocity models have been used to estimate the porosity using the following equation (Logan & Wilkinson, 1990):

$$P = 1 - a \times l^{D_3 - 3} \qquad (3)$$

where a is an empirical constant, l is the largest particle diameter, and $D_3$ is the 3-dimensional fractal number. The value of the fractal number describes the rate at which individual primary particles fill the empty space during the assembly of an aggregate. For fractal aggregates $D_3 < 3$, primary particles fill the available space slower than particle size increases. In some cases, $D_3$ can be negative (Kilps, 1993; Mandelbrot, 1990), illustrating an extremely inefficient space filling during the particle assembly and leading to very loose and porous particles. Parameterization of the models is available in Text S2 of Supporting Information S1).

## 2.6. UVP-Derived POC Flux

In order to extend the depth range considered beyond the upper mesopelagic where the mooring array only sampled the upper 300 m, POC flux profiles over the upper mesopelagic ($z_p$-500 m depth) were derived from the UVP particle size spectrum (Figure 2a). Previous studies suggested that the bulk POC flux $F$ (g m$^{-2}$ d$^{-1}$) can be approximated as the sum of carbon fluxes of a finite number of logarithmically spaced intervals of particles ESD (Guidi et al., 2008) ranging from hundreds of micrometers to several millimeters:

$$F \approx F_{\text{tot}}(z) = \sum_{i=1}^{N} n_i(z) w_i c_i \qquad (4)$$

where $n_i$ is the particle abundance, $w_i$ the sinking velocity, and $c_i$ the carbon content of the particles in the $i^{th}$ size class. In this study, the missing part of the equation was the size-specific carbon content. We converted the particle volume $V_i$ (µm$^{-3}$) into carbon content $c_i$ (mg C) using a power relationship broadly used (All-dredge, 1998; Durkin et al., 2021; Ebersbach & Trull, 2008), as follows:

$$c_i = A V_i^B \qquad (5)$$

We assumed spherical particles to calculate their respective volume from their ESD. Two different parameterizations for $c_i$ were used. A scaling factor $A = 0.1 \pm 0.04 \times 10^{-9}$ mg C µm$^{-3}$, and an exponent $B = 1$ (Durkin et al., 2021) were used for the smaller size fraction (ESD < 0.3 mm) mostly composed of fecal pellets (see later). A scaling factor $A = 0.99 \pm 0.12 \times 10^{-3}$ mg C mm$^{-3}$, and an exponent $B = 0.52 \pm 0.03$ (All-dredge, 1998) were used for the larger size fraction (ESD > 0.3 mm) dominated by porous phyto-aggregates (see later). The abundance of particle ESD ranging from 0.01 to 2.6 mm measured with the UVP was used to estimate POC flux profiles $F_{tot}$, from $z_p$ down to 500 m depth for each of the seven deployments using Equation 4. $F_{tot}$ uncertainty was computed using a Monte Carlo simulation method. We generated 10,000 data sets from the combination of A, B, $n_i$ and $w_i$ within the normal distribution of their uncertainty to calculate the mean and standard deviation of the POC flux.
UVP-derived POC fluxes were on average 1.3 times higher than PIT-derived POC fluxes (range 0.9–1.7). However, the linear regression between the UVP-derived and PIT-derived POC fluxes has a low statistical significance ($r^2 = 0.4$), most likely due to the narrow range (5.5–11.7 mmol m⁻² d⁻¹) and the stacked distribution of the PIT-derived POC fluxes (see Figure S1 in Supporting Information S1). The main limitations of this POC flux model reside in (a) the particle size range considered (i.e., excluding ESD > 2.6 mm), (b) the choice of a size-specific carbon content parameterization, and (c) the assumption that size-specific sinking velocities and carbon contents are constant with depth. The range of particle ESD was motivated by the limited contribution of particles >2.6 mm to the volume flux at SOTS (<8% during deployment 1%–2% and 34% during deployment 3). At the polar sites, large and fluffy particles were collected in the gel traps (see later) and greatly increased the volume flux. However their porous nature suggested low carbon content and low SV, thus were probably not significantly contributing to the POC flux. The choice of carbon content parameterization was motivated by the observed differences in particle types between the two size fractions (Text S2 in Supporting Information S1) and the best fit with the observed POC flux it provided compared to other parameterizations (Figure S1 in Supporting Information S1). The assumption of constant sinking velocity and carbon content parameterization with depth could not be tested and validation of the model performance was only possible at PITs deployment depths (180–280 m depth). That is why we restrained the use of the UVP-derived POC flux to the $z_p$-500 m depth range, as particle properties may drastically change deeper in the water column.

## 2.7. Microbially Mediated Component of the POC Flux Attenuation

To explore the microbial contribution to the POC flux attenuation (Section 4.5), we calculated the vertical attenuation of the POC flux driven only by microbial remineralization (Figure 2b). We considered two depth intervals below the PPZ. The upper one corresponds to the observed high POC flux attenuation layer (HAL) and ranges from $z_p$ to $z_p + 50$ m (see later). The underlying one corresponds to the low attenuation layer (LAL) and ranges from $z_p + 50$ m to $z_p + 350$ m. The distinction between these two layers was motivated by the significantly different attenuation slopes of the POC flux profiles (Figure 3). When assuming constant SV and remineralization rate within the layers (HAL or LAL), the theoretically derived POC flux attenuation attributed to microbes follows an exponential function (Banse, 1990). Based on our measurements, we were able to compute the POC flux at depth z if microbes were the only contributor to the attenuation ($F_{microb}(z)$). To do so, we used the measured carbon-specific microbial remineralization rate $C_{remin}$ and the average sinking velocity of POC $w_{avg}$ (Figure 2a), as follows:

$$F_{microb}(z) = F_{tot}(z_{top}) \exp\left(-\frac{(z - z_{top}) C_{remin}}{w_{avg}}\right) \quad (6)$$

With $z_{top}$ the upper boundary of the depth interval considered. The weighted average sinking velocity, $w_{avg}$, is calculated so that the weight of a size class corresponds to its relative contribution to the POC flux. In Equation 6, $C_{remin}$ measured at the shallowest depth was used for the HAL, and the average of the 3 depths was used for the LAL.

# 3. Results

## 3.1. Surface Production and Hydrography

NPP ranged from 556 to 2,688 mg m⁻² d⁻¹ with the highest rates measured at SOTS and the lowest at PF1 (Table 3). Integrated Chlorophyll-a (Chl-a) varied little over time at the 2 polar sites, whereas it increased by 40% during the 13 days of occupation at SOTS (Figures 1b–1d). This increase in Chl-a was associated with a shallowing of the mixed layer from 102 to 22 m depth, while the MLD was stable at the 2 polar sites (57–68 m). The base of the PPZ ranged from 108 to 142 m depth and was stable at the 3 sites (Table 3). Chl-a was homogeneously distributed within the PPZ at SOTS and PF2, but displayed a marked deep chlorophyll maxima at PF1 (93 ± 6 m; Figure 1c). Satellite Chl-a from the MODIS AQUA product suggested that the 3 sites were sampled during—or close to—their summer productivity maxima (Boyd et al., 2024). In the upper mesopelagic, the temperature differed significantly between sites, with temperatures 3.5–5 fold higher at SOTS than at the polar sites (Table 3).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** UVP-derived POC flux obtained using Equation 4 at (a) SOTS, (b) PF1, and (c) PF2. Shaded colored area represents the 95% confidence interval of the UVP-derived POC flux computed from the Monte Carlo bootstrap method (see Section 2.6). PIT-derived POC fluxes are indicated with squares, diamonds and circles for the first, second and third deployments, respectively. Horizontal dashed lines indicate the upper and lower limits of the High Attenuation Layer and Low Attenuation Layer at the base of the primary production zone ($z_p$), $z_p + 50$ m and $z_p + 350$ m.</figcaption></figure>

## 3.2. Particle Flux Magnitude and Composition

POC fluxes measured with the PITs ranged from 5.6 to 11.7 mmol m⁻² d⁻¹, and did not monotonically decrease with depth (Figure 3, *p* > 0.16). At the shallowest deployment depth (~180 m), average POC fluxes were 8.9 ± 2.4, 9.3 ± 1.2 and 6.6 ± 0.6 mmol m⁻² d⁻¹ at SOTS, PF1 and PF2, respectively (Table 4).

In order to improve the vertical resolution and increase the depth range considered, POC fluxes were also estimated from the base of the PPZ to 500 m depth based on UVP-derived particle abundance (Figure 2). UVP-derived POC flux profiles exhibited rapid attenuation in the HAL (6–21 mmol m⁻² d⁻¹), while attenuation in the underlying LAL was significantly lower (3–8 mmol m⁻² d⁻¹; Figure 3, *p* < 0.005 Student *t*-test). At all sites, UVP-derived POC flux reached local maximums around 230–310 m depth (Figure 3), suggesting particle injection at depth likely due to fecal pellet production by resident and/or vertically migrating zooplankton (Baker et al., 2025).

The elemental flux composition differed strongly between SOTS and the 2 polar sites. At SOTS, the POC/PN molar ratio ranged between 7.4 and 10.7, while it was stable at the two polar sites, slightly higher than the canonical Redfield ratio (7.4 ± 0.4) (Table S2 in Supporting Information S1). Similarly, POC/BSi and POC/Chl-a ratios were significantly higher at SOTS than at the polar sites (Table S2 in Supporting Information S1, *p* < 0.02 Student *t*-test). POC/BSi and POC/Chl-a ratios are used as proxies of the contribution of diatom-derived material and fresh phytoplankton cells in the particle flux, respectively—higher values indicating a lower contribution of diatoms and

<table><caption>**Table 3**

Characteristics of the Epipelagic and Mesopelagic Layers</caption><thead><tr><th>Site</th><th></th><th>MLD<sup>a</sup><br>m</th><th>NPP<sup>b</sup><br>mgC m<sup>-2</sup> d<sup>-1</sup></th><th>z<sub>p</sub><sup>c</sup><br>m</th><th>Temperature<sup>d</sup><br>°C</th></tr></thead><tbody><tr><td rowspan="3">SOTS</td><td>depl. 1</td><td>102 ± 37</td><td>2,688 ± 600</td><td>142 ± 14</td><td>9.8 ± 0.2</td></tr><tr><td>depl. 2</td><td>48 ± 11</td><td></td><td></td><td></td></tr><tr><td>depl. 3</td><td>22 ± 10</td><td></td><td></td><td></td></tr><tr><td>PF1</td><td></td><td>57 ± 8</td><td>556 ± 122</td><td>122 ± 12</td><td>2.6 ± 0.2</td></tr><tr><td>PF2</td><td></td><td>68 ± 9</td><td>1,498 ± 405</td><td>108 ± 8</td><td>1.4 ± 0.4</td></tr></tbody></table>

*Note.* Metrics for the SOTS site were separated for deployments 1–3 due to abrupt changes in the mixed layer depth (Figure 1b). <sup>a</sup>Mixed layer depth (MLD) averaged over the deployment periods of the free-drifting array. <sup>b</sup>Net Primary Production (NPP) averaged over the deployment periods of the free-drifting array. <sup>c</sup>Average depth of the base of the primary production zone ($z_p$; depth where chlorophyll fluorescence drops to 10% of its maximum). <sup>d</sup>Temperature averaged over the 180–300 m depth layer.
<table><caption>**Table 4**
Metrics Characterizing the 3 Regional Biological Carbon Pumps</caption><thead><tr><th>Site</th><th>POC flux<sup>a</sup><br>mmol m<sup>-2</sup> d<sup>-1</sup></th><th>PIT-derived<sup>b</sup> e-ratio<br>—</th><th>UVP-derived<sup>c</sup> e-ratio<br>—</th><th>PIT-derived<sup>d</sup> T<sub>eff</sub><br>—</th><th>UVP-derived<sup>e</sup> T<sub>eff</sub><br>—</th><th>w<sub>avg</sub><sup>f</sup><br>m d<sup>-1</sup></th><th>C<sub>remin</sub><sup>g</sup><br>d<sup>-1</sup></th></tr></thead><tbody><tr><td>SOTS</td><td>8.9 ± 2.4</td><td>0.03 ± 0.01</td><td>0.12 ± 0.03</td><td>0.85 ± 0.18</td><td>0.47 ± 0.13</td><td>8 ± 3</td><td>0.11 ± 0.05</td></tr><tr><td>PF1</td><td>9.3 ± 1.2</td><td>0.21 ± 0.07</td><td>0.39 ± 0.21</td><td>0.74 ± 0.05</td><td>0.53 ± 0.06</td><td>11 ± 3</td><td>0.04 ± 0.01</td></tr><tr><td>PF2</td><td>6.6 ± 0.6</td><td>0.06 ± 0.02</td><td>0.22 ± 0.06</td><td>0.78</td><td>0.37 ± 0.04</td><td>8 ± 2</td><td>0.04 ± 0.01</td></tr></tbody></table>

*Note.* These values correspond to the averages and standard deviations of the values obtained from the 2–3 deployments carried out at each site (no standard deviation indicates that only one deployment was used). <sup>a</sup>Site-averaged POC flux from the shallowest PIT (~180 m depth). <sup>b</sup>Export efficiency calculated as POC flux from the shallowest PIT/NPP. <sup>c</sup>Export efficiency calculated as UVP-derived POC flux at the base of the PPZ (108–142 m; Table 3)/NPP. <sup>d</sup>Transfer efficiency calculated as the POC flux of the deepest PIT (~280 m depth)/shallowest PIT (~180 m depth). <sup>e</sup>Transfer efficiency calculated as the UVP-derived POC flux at $z_p$ + 100 m/UVP-derived POC flux at $z_p$. <sup>f</sup>Weighted average sinking velocity (i.e., the weight of each size class corresponds to its relative contribution to the POC flux; see Section 2.7). <sup>g</sup>Site-averaged C-specific remineralization rates measured with the C-RESPIREs.

phytoplankton cells. Together, these ratios indicate a higher contribution of fresh organic matter (including diatom cells) to the particle flux at the polar sites than at SOTS.

### 3.3. Particles From Imaging Systems

Polyacrylamide gels collected a wide variety of particles from ~30 µm to several millimeters including phytoplankton cells, intact and fragmented fecal pellets, phytoplankton and fecal aggregates, encompassing various shapes (Figures 4a and 4b). Particle type and size fractionated volume flux differed between sites. At SOTS, the particles were mostly composed of fecal matter as intact or fragmented fecal pellets and fecal aggregates (Figure 4a). At the PF sites, large particles were mostly phytodetritus aggregates (Figure 4b). At SOTS, the volume flux was carried by mid-size particles (0.15–1.6 mm, contributing to 54%–82% of total volume flux; Figure 4c). At PF sites, the volume flux was dominated by larger size fractions (Figure 4d), especially at PF1 where rare but large particles reaching up to 5 mm were collected (Figure 4b). At PF1, the volume flux was 10- and 4-fold higher than at SOTS and PF2, respectively. Qualitative classification of particles suggested that particles <0.3 mm were dominated by fecal pellets while particles ESD >0.3 mm were dominated by aggregates (See Text S2 in Supporting Information S1).

In addition to this characterization of the sinking particles collected in gel traps, abundance and biovolume of particles from 0.1 to 10 mm were also measured with the UVP. At the PF sites, size-specific biovolume peaked around 2 mm whereas at SOTS, biovolume was carried by the smallest size fractions (Figures 5c and 5d). At the PF sites and during deployment 3 at SOTS, the biovolume peak was due to ~2 mm large aggregates (Figures 5a and 5b). For a given aggregate size, a wide range of gray levels were observed, from almost completely transparent (presumably porous and slow sinking) to very dark (presumably dense). At SOTS, these aggregates were less abundant and their gray level range was more uniform (Figure 5a).

### 3.4. Particle Sinking Velocity

Particle sinking velocities, computed for ESD < 2.6 mm, ranged from 1 to 21 m d⁻¹ (Figure 6). Figure 6 shows the SV versus ESD relationships and results from the two sinking velocity models. The SV spectrum suggests the existence of two distinct particle sinking populations. Particles smaller than 0.3 mm exhibited a SV versus size relationship that clearly follows a Stokes' behavior (Model 1), indicating near spherical shapes and a compact structure as suggested by the high value of the $D_3$ fractal number ($D_3$ = 2.5) close to the $D_3$ = 3 for the euclidean geometry (i.e., null porosity). Above 0.3 mm, sinking velocity decreased with increasing particle size (Model 2). This more complex and counter-intuitive relationship required a model parameterization with negative $D_3$ fractal dimensions ($D_3$ = −1 and −2).

For large marine snow aggregates, $D_3$ fractal numbers are often <3 and indicate that the rate at which primary particles assemble during aggregation processes is slower than the rate at which the size increases (Jackson, 1998; Laurenceau-Cornec et al., 2015; Logan & Wilkinson, 1990). This results in particle pore volume (i.e., porosity) increasing as particle size increases, thus decreasing the particle density and explaining how large particles can sink slower than small ones. In our study, particles >0.3 mm featured a very sharp decrease of the sinking velocity
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Images from the polyacrylamide gels (grid cell 14 × 12.5 mm) highlighting differences in size and nature of the sinking particles collected at (a) SOTS and (b) PF sites. Particles include small-sized fecal pellets (white stars) and fecal aggregates (white triangle). At the PF sites, large aggregates (likely phyto-detritus; white circle) were notably more fluffy than at SOTS. At SOTS, abundant and long cylindrical particles were likely euphausiid fecal pellets (white asterisk). The size scale is indicated at the lower right end of each panel. Particle counting from the gels provide particle volume flux spectra at (c) SOTS and (d) PF1 (in blue) and PF2 (in green). Particle volume fluxes were averaged over the three deployment depths (180–300 m depth), and are used to estimate size-specific sinking velocities (see Figure 2a). Squares, diamonds and circles correspond to deployments 1, 2 and 3, respectively. Note the logarithmic scale for both axes in (c, d).</figcaption></figure>

with size. The best model fits are obtained for negative $D_3$ fractal numbers that are theoretically valid (Mandelbrot, 1990) and have already been measured for artificially made particles (Kilps, 1993). While we have no prior knowledge of such low fractal number values documented for natural marine particles, it can inform us on the general processes potentially implied. Applications of negative fractal numbers are to diffusion-limited aggregation (Logan & Wilkinson, 1990; Mandelbrot, 1990) and turbulence.

## 3.5. Microbial Remineralization

Carbon-specific microbial remineralization rates ($C_{remin}$) ranged from 0.03 to 0.21 $d^{-1}$ across sites and depths (Figure 7). $C_{remin}$ rates showed no significant differences ($p$ > 0.61, Student t-test) between the two PF sites, averaging 0.04 ± 0.01 $d^{-1}$, but was 3-fold higher at SOTS (0.11 ± 0.05 $d^{-1}$; Table 4). At SOTS, $C_{remin}$ rates measured at 250 and 290 m depth were 2-fold higher during the second deployment than rates measured at the same depths during deployments 1 and 3. Conversely, no clear vertical trend was observed at PF1 and PF2 ($p$ > 0.32). However, a 4-fold higher $C_{remin}$ rate was measured at PF2 at the shallowest depth during deployment 2 (0.13 $d^{-1}$) compared to other PF observations that ranged from 0.03 to 0.05 $d^{-1}$.

# 4. Discussion

## 4.1. Distinct Export Systems in the Subantarctic and Polar Waters

The three study sites were sampled during the onset (SOTS; Figure 1b; Trail et al., 2025), at the peak (PF2; Boyd et al., 2024), and after the peak (PF1; Boyd et al., 2024) of the summer productivity maxima. Community structure at SOTS was characterized by a mixed assemblage of phytoplankton groups, and calanoid copepods. Mesozooplankton populations mostly present in the upper 200 m exhibited little change in biomass between night and day, indicating a non-migratory behavior (Baker et al., 2025), and suggesting a high contribution to POC recycling within the epipelagic zone and an important contribution to the gravitational pump. In contrast, the 2 polar sites were characterized by the presence of a deep chlorophyll and biomass maximum dominated by large healthy diatoms (many >200 µm in length; Boyd et al., 2024). Higher mesozooplankton biomass was observed at the polar sites compared to SOTS. These populations were migrating daily between the surface to deep scattering layers at ~350–550 m depth (Baker et al., 2025). Despite this higher mesozooplankton biomass, herbivory was reduced and dominated by patchy distributed salp organisms (Boyd et al., 2024).

Despite these large differences in surface production, community structures, particle characteristics, and hydrodynamical conditions (Table 4), the 3 sites exhibited only modest differences in upper mesopelagic POC flux. Overall, POC flux at 180 m depth was higher at PF1 (9.3 ± 1.2 mmol $m^{-2} d^{-1}$) and SOTS (8.9 ± 2.4 mmol $m^{-2} d^{-1}$), than at PF2 (6.6 ± 0.6 mmol $m^{-2} d^{-1}$). On the other hand, the composition and nature of the particle flux strongly differed between the subantarctic and polar sites. At SOTS, the particle flux was dominated by fecal material (Figure 4a), in agreement with previous summer observations (e.g., Ebersbach et al., 2011). Higher POC/PN and POC/Chl-a ratios at SOTS than at the PF sites confirm this important contribution of fecal material to the export flux (Figure 8); the C/N ratio of fecal pellets being usually higher than the corresponding zooplankton biomass (Doherty et al., 2021) and Redfield ratio (Morales, 1987). This major contribution of zooplankton to the export flux is in agreement with the non-migratory behavior of the mesozooplankton populations (see above) and with previous observations of a high grazing pressure of zooplankton on primary production in the subantarctic (Halfter et al., 2020; Pearce et al., 2011). However, the grazing pressure during summer is focused mostly on pico phytoplankton (Halfter et al., 2020), leaving large sinking particles intact (Figure 4c).
<figure><img src="image_7.png" /></figure>

**Figure 5.** Representative subsample of the ~2 mm particles imaged by the UVP at the (a) SOTS and (b) PF sites, and contributing to the biovolume peak observed in panels (c, d). The size scale is indicated at the lower right end of each panel. The resolution of the UVP images is not designed to identify small particles, but to count them (Picheral et al., 2010). Only images of particles >1 mm were explored, and the subsamples chosen here represent the main particle shapes and gray levels observed between 180 and 300 m depth. This includes aggregates of various gray levels and shapes at all stations and large fecal pellets at SOTS during first deployment. Light colors correspond to transparent matter and dark colors correspond to matter that strongly reflects light such as dense organic or mineral material. Particle biovolume spectra measured with the UVP and averaged over the three deployment depths (180–300 m depth) at (c) SOTS, (d) PF1 and PF2. Particle biovolume spectra are used to estimate size-specific sinking velocities (see Figure 2a). Squares, diamonds and circles correspond to deployments 1, 2 and 3, respectively. Note the logarithmic scale for both axes in (c, d).

At the polar sites, the analysis of the gels revealed that sinking aggregates were larger and fluffier than those observed at SOTS (Figures 4b–4d). The particle flux was mainly composed of phytodetritus aggregates, while a fraction of the flux probably also originated from the fragmentation and degradation of salp fecal pellets (Iversen et al., 2017; Pauli et al., 2021). These fluffy aggregates were notably more abundant and larger than that reported by Ebersbach et al. (2011). Lower POC/BSi and POC/Chl-a ratios compared to SOTS (Figure 8) confirm the important contribution of fresh phytoplankton—including diatoms (the dominant phytoplankton group at the polar sites; Boyd et al., 2024)—to the export flux. Together, the surface community observations, particle flux images, and the elemental flux composition reveal two distinct export systems: (a) a fecally dominated export system at SOTS, and (b) a phyto-aggregate dominated export system at the PF sites.

## 4.2. Export and Transfer Efficiencies

How do these major differences in export pathways translate into export and transfer efficiencies? Interpreting and comparing multi-day snapshot measurements of the e-ratio is challenging for two reasons: the choice of a reference depth for measuring POC flux (Buesseler et al., 2020), and the timing of measurements regarding the seasonality of NPP and POC export (Laurenceau-Cornec et al., 2023). Two different reference depths were used to estimate the e-ratio: (a) ~180 m depth using the POC fluxes collected by the shallowest PIT, and (b) the base of the PPZ, $z_p$ (~110–140 m depth), using the UVP-derived POC fluxes (Table 4). Note that these reference depths were both located in waters overlying the injection depth of the mesopelagic migrant pump (Baker et al., 2025). As the e-ratio trends are similar for the 2 reference depths (Table 4), we subsequently discuss the e-ratio based on $z_p$ which is closer to the 100 m reference depth used by the majority of e-ratio studies in the Southern Ocean. At SOTS, the e-ratio of 0.12 ± 0.03 obtained in our study was similar to previous summer observations (Ebersbach et al., 2011; Jacquet et al., 2011), and close to the annual mean of ~0.1 obtained for similar export systems from biogeochemical models (Laurenceau-Cornec et al., 2023). In the subantarctic, the slow build up of small phytoplankton stocks and high grazing pressure often results in low retention of surface biomass and rapid export of POC through fecal pellets and fecal aggregates (Ebersbach et al., 2011; Eriksen et al., 2018; Trull et al., 2019), and thus little variation in the e-ratio during the year.

Two to four fold higher e-ratio values (0.22–0.39) were obtained at the 2 polar sites. Evidence of low but constant export flux (from fluorescence and backscatter time-series) from January to April 2021 (Boyd et al., 2024) suggests a close coupling between the seasonal cycles of NPP and POC export. Therefore, a time lag between these 2 cycles cannot be evoked to explain these higher e-ratios—as is often the case at high latitudes (Henson et al., 2019; Laurenceau-Cornec et al., 2023; Laws & Maiti, 2019). On the other hand, the significant contribution of heavily silicified diatom frustules (as revealed by the low POC/BSi ratio; Figure 8) and large salp fecal pellets to the export flux (Figure S2 in Supporting Information S1) could explain these higher e-ratio. Indeed, by repackaging small particles into large, dense and carbon-rich fecal pellets (Andersen, 1998; Bruland & Silver, 1981), salps can make a large contribution to episodes of export production in the Southern Ocean (Decima et al., 2023; Steinberg et al., 2023), and thus efficiently transfer a large proportion of the surface primary production to the mesopelagic.

The transfer efficiencies, calculated from the PIT-derived fluxes (~180–280 m depth), ranged between 0.78 and 0.85 at the 3 sites (Table 4). Ebersbach et al. (2011) reported a lower transfer efficiency at SOTS (0.79) but a higher efficiency in the polar frontal zone (0.88) using a free-drifting array of gel traps deployed at a comparable depth range. At the PF sites, the larger contribution of fluffy and fragile aggregates observed in our study could explain these transfer efficiencies lower than previously reported. Export and transfer efficiencies normalized to
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Size-specific sinking velocities (SV) obtained from Equation 2. SV at SOTS were averaged over the three deployments. Triangles and squares correspond to ESD < 0.3 mm and ESD > 0.3 mm, respectively. The size limit of 0.3 mm was set to distinguish between the small-sized particles dominated by fecal pellets, and large-sized particles dominated by aggregates (see Figure 4 caption). Full lines correspond to the Stokes' model 1 (see Section 2.5) that best fits the SV versus ESD relationship for ESD < 0.3 mm. Dashed and dotted line correspond to the general Navier-Stokes model 2 (see Section 2.5) that best fits the SV versus ESD relationship for ESD > 0.3 mm.</figcaption></figure>

the depth of the base of the productive layer are better suited to capture the distinct dynamics of regional biological carbon pumps (Buesseler et al., 2020), as they focus on the maximum attenuation layer. To target this layer, high-resolution vertical profiles of particle concentrations from the UVP were used to complement sediment trap measurements (Figure 3). Lower transfer efficiencies were obtained when considering depths between $z_P$ and $z_P + 100$ m (using UVP-derived POC fluxes). These transfer efficiencies were relatively similar between the 3 sites, ranging between 0.37 and 0.53 (Table 4), but decreased at SOTS from 0.57 to 0.32 over a 2-week period due to the temporal increase in the PPZ-integrated Chl-a stock (Figure 1b). In the next sections, we explore trends in key mesopelagic parameters and processes that could explain these similar transfer efficiencies in such different export systems.

### 4.3. Particle Sinking Velocity

We estimated size-specific sinking velocities by comparing particle fluxes collected in the gel traps with particle abundances measured in the water column by the UVP at similar depth ranges (McDonnell & Buesseler, 2012). Size-specific SV ranged from 1 to 21 m $d^{-1}$ (Figure 3c), and the average sinking velocity of POC ($w_{avg}$; see Section 2.7) ranged from 8 to 11 m $d^{-1}$ (Table 4). This range is 9–16 fold lower than average SV estimated at SOTS from polyacrylamide gels (100–130 m $d^{-1}$; Ebersbach et al., 2011) and in the Polar Front Zone from deep sediment traps (35 m $d^{-1}$; Closset et al., 2015). Although similarly low average SV have been obtained from radioactive tracers (15 ± 9 m $d^{-1}$; Yang et al., 2022) or from floats equipped with optical sensors (10 ± 19 m $d^{-1}$; Estapa et al., 2019), for example, our estimates are at the lower-end of previous measurements on similarly sized particles (Bach

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Vertical profiles of the C-specific microbial remineralization rate ($C_{remin}$) measured in situ at our 3 sites using the C-RESPIRE. $C_{remin}$ was obtained from the oxygen drawdown converted into carbon using a conversion factor (RQ = 0.70) and normalized to the POC flux (intercepted and incubated) and incubation time (see Equation 1 in Section 2.4). Table S1 in Supporting Information S1 details the parameters used to calculate $C_{remin}$. Error bars correspond to ± the standard deviation derived using uncertainty propagation laws (see Section 2.4). The color code is similar to Figure 3.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Carbon-specific remineralization rate as a function of (a) net primary production (NPP), (b) POC flux, (c) in situ mesopelagic temperature, (d) molar POC/PN, (e) molar POC/BSi, and (f) mass POC/Chl-a ratios. Ratios and POC flux were calculated from the PIT, corresponding to the pre-incubation particle flux magnitude and composition.</figcaption></figure>

et al., 2012, 2019; Iversen & Ploug, 2010; Laurenceau-Cornec et al., 2020; Ploug et al., 2008). Such discrepancy could be explained by the methodological bias in measuring SV ex situ or in situ, the latter often resulting in lower sinking velocities (Williams & Giering, 2022). In particular, our approach to estimating sinking velocities takes into account all the particles seen by the UVP in the water column. This includes the sinking and suspended fractions, the latter tending to decrease SV and $w_{avg}$ (McDonnell & Buesseler, 2010).

At each site, SV increased with ESD for particles from 0.1 to 0.3 mm following Stokes' law (Figure 6). This Stokes-like relationship is coherent with our observations of small dense fecal pellets dominating this size fraction (Text S2 in Supporting Information S1). At the opposite, the larger size classes (i.e., ESD > 0.3 mm), dominated by fecal aggregates at SOTS and phytodetritus aggregates at the polar sites (see Figure 4 caption), displayed an inverse size-SV relationship. The biovolume peak observed for particles ~2 mm by the UVP (Figures 5c and 5d) was not observed in the particle volume flux (Figures 4c and 4d) indicating that a large proportion of these particles do not sink or sink at <10 m d⁻¹. Such low sinking velocities were unexpected considering many published reports of sinking rates >50 m d⁻¹ for similar particle size classes (e.g., Iversen & Ploug, 2010; Ploug et al., 2008). On the other hand, these different trends observed for our 2 size ranges are in agreement with a recent study showing increasing porosity and decreasing size-specific sinking velocities for particles >0.5 mm (Flintrop et al., preprint). Negative fractal numbers must be used to model this inverse size-SV relationship observed for ESD > 0.3 mm (Figure 6). Diffusion-limited aggregation and turbulence are two processes that can lead to the formation of these very loose and highly porous aggregates (Laurenceau-Cornec et al., 2015) that can be obtained under very efficient sticking conditions of primary particles. However, these negative fractal numbers are theoretically valid but have never been reported for natural marine particles (see Section 3.4). In addition, because sediment traps undersample the rare and large particles, their estimated sinking velocity is expected to be underestimated.
An alternative hypothesis can be proposed to explain how large particles can sink slower than small ones. Diatoms can modify their density as a response to environmental stress such as light, nutrients availability, or hydro-mechanical stress (e.g., Arrieta et al., 2020). This mechanism has been proposed to explain the vertical decoupling observed between POC and BSi fluxes in different areas of the subpolar Southern Ocean (Williams et al., 2025). This buoyancy regulation is expected to be more efficient for large diatoms (for which the volume of the vacuole is larger) such as those dominating the deep chlorophyll maximum at the 2 polar sites (Boyd et al., 2024). Moreover, herbivory data obtained at the polar sites revealed that ingested diatoms present in salp fecal pellets were still intact (Boyd et al., 2024), and thus potentially active. Therefore, active buoyancy regulation could—partly—explain how diatom-loaded aggregates sink at <10 m d⁻¹.

## 4.4. Microbial Remineralization

The carbon-specific remineralization rates—C<sub>remin</sub>—measured with the C-RESPIRE (0.03–0.21 d<sup>-1</sup>) fell in the range of values reported for different particle types, including fecal pellets (e.g., Belcher, Iversen, Giering, et al., 2016; Cavan et al., 2021; Iversen et al., 2017), aggregates (e.g., Bach et al., 2019; Belcher, Iversen, Manno, et al., 2016; Iversen & Ploug, 2010) and zooplankton carcasses (e.g., Halfter et al., 2022). C-RESPIRE enables the collection and in situ incubation of natural particle populations representing an assemblage of these different particle types. Thus, the comparison with microbial remineralization rates obtained from the same approach appears more relevant. A recent study reported C-RESPIRE measurements performed at upper mesopelagic depths in 6 contrasting oceanic regimes (Bressac et al., 2024). Three high-latitude regions with similarities to our study sites were sampled: a highly productive area downstream of South Georgia dominated by large diatoms and characterized by low mesopelagic temperatures (0.8–1.8°C); and 2 high-nutrient, low-chlorophyll regions in the northeast subarctic Pacific and subantarctic zone, the latter corresponding to our SOTS site but sampled at the end of the productive season (March 2019). Comparison of C-RESPIRE measurements performed at SOTS reveals a modest variability in C<sub>remin</sub> between the onset (0.11 ± 0.05 d<sup>-1</sup>; this study) and end of the productive season (0.16 ± 0.05 d<sup>-1</sup>; Bressac et al., 2024). The authors also reported a systematic vertical increase in C<sub>remin</sub> in these high-latitude regions where vertical temperature gradients are low. This feature was also observed at SOTS in December 2020 but was absent at PF1 and PF2 (Figure 7). At these polar sites, the deeper depth range considered (relative to the base of the PPZ; Table 3) may partly explain the narrow range in C<sub>remin</sub> and the absence of any vertical trend.

Drivers of the microbial remineralization are multiple (Bressac et al., 2024), and include temperature (e.g., Cavan & Boyd, 2018; Iversen & Ploug, 2013), particle composition and morphology (e.g., Boyd & Kennedy, 2021), microbial ecology and physiology (e.g., Datta et al., 2016; Enke et al., 2018), and oxygen concentrations (e.g., Le Moigne et al., 2017; Robinson, 2019). At first sight, the 7–8°C temperature difference between SOTS and the polar sites seems to explain the spatial pattern in C<sub>remin</sub> observed in our study (Figure 8). However, a simple comparison of C<sub>remin</sub> rates obtained at our polar sites (0.04 ± 0.01 d<sup>-1</sup>, i.e., the lower end of C-RESPIRE measurements) with rates twice as high measured in the ~1°C colder waters downstream of South Georgia (0.08 ± 0.04 d<sup>-1</sup>; Bressac et al., 2024) confirms that temperature is not the only controlling factor at play (Bressac et al., 2024). Indeed, it is likely that the sevenfold range in C<sub>remin</sub> (0.03–0.21 d<sup>-1</sup>) results from the confounding effect of the temperature and substrate characteristics (i.e., composition, origin, structure). Indeed, the elemental composition of the particle flux differed strongly between the 2 different export systems (Figure 8). These differences in flux composition explain an important part of the spatial and vertical variability in C<sub>remin</sub> (Figure 8). Together, these relationships suggest that fecal material—dominating the export flux at SOTS—represents a more accessible substrate for particle-attached microbes than phyto-aggregates dominating the export flux at the polar sites and loaded with highly silicified diatoms (Boyd et al., 2024). However, higher POC/PN ratio measured in fecal pellets suggested more degraded organic matter. This counterintuitive result could be explained by different microbial abundance and/or diversity between sites.

In conclusion, the 2 export systems are characterized by similar transfer efficiencies and sinking velocities, while C<sub>remin</sub> differed by an order of magnitude. These counter-intuitive observations suggest that the dominant attenuation pathways differ between the two export systems, at least in the upper mesopelagic where the flux attenuation is maximal (Figure 3). Therefore, in the next section, we explore the microbial contribution to the flux attenuation in the upper and lower mesopelagic, and how this may vary between our 3 sites.
<figure><img src="image_11.png" /><figcaption>**Figure 9.** POC flux profiles in the high attenuation (HAL) and low attenuation (LAL) layers at (a–c) SOTS, (d) PF1 and (e) PF2. Full line corresponds to the UVP-derived POC flux normalized to a POC flux of 1 at the base of the primary production zone ($z_p$). Dashed line corresponds to the microbially mediated component of the POC flux attenuation (i.e., by considering microbial remineralization as the only attenuation pathway). These attenuation profiles were obtained from the UVP-derived POC flux at the top of each depth layer, the average sinking velocity ($w_{avg}$), and $C_{remin}$ (Equation 6). The microbially mediated component of the POC flux attenuation was computed in the HAL and LAL independently, such that the flux at the top of each layer is equal to the UVP-derived one (colored dot). Shaded area corresponds to the 95% confidence interval computed with a Monte Carlo simulation method. We generated 10,000 data sets from the combination of $w_{avg}$ and $C_{remin}$ within the normal distribution of their uncertainty to derive the confidence interval of the microbially mediated component of the POC flux attenuation. Horizontal dashed lines indicate the upper and lower bounds of the HAL ($z_p$ and $z_p + 50$ m, respectively) and LAL ($z_p + 50$ m and $z_p + 350$ m, respectively).</figcaption></figure>

4.5. Microbial Contribution to the POC Flux Attenuation

The contribution of particle-attached microbes to the vertical attenuation of the POC flux was investigated by comparing the effect of microbial remineralization only to the cumulative effect of all degradation processes (Figure 9), including microbial remineralization, particle fragmentation and flux-feeding (Iversen, 2023). This deconstruction of the flux attenuation was possible thanks to the concomitant in situ measurements of microbial remineralization rates and estimates of the size-specific sinking velocities. It was done for 2 distinct layers: the high attenuation layer (HAL) and low attenuation layer (LAL), defined as the first 50 m below the base of the PPZ and the 300 m deep underlying layer, respectively (see Section 2.7). This microbial contribution was obtained by dividing the microbial-mediated POC loss by the POC sources (see Table 5). Lateral inputs were considered negligible due to the weak horizontal currents in the upper mesopelagic (Wynn-Edwards et al., 2020). POC injection within the 200–1,000 m depth interval by vertically migrating zooplankton and micronekton were obtained from Baker et al. (2025) and ranged from 0.2 to 1.2 mmol m⁻² d⁻¹, while it was considered negligible above 200 m depth in the HAL. This assumption is supported by the increases in POC flux observed at the 3 sites below 200 m depth only (Figure 3), these increases at discrete depths corresponding to the main scattering layers (~350–550 m depth; Baker et al., 2025) most likely being the signature of this active transport of particles.

Within the HAL, microbial remineralization was estimated to contribute to 31%–64% of the POC flux attenuation (Table 5 and Figure 9). Microbes were the main contributor to the flux attenuation at SOTS (64 ± 17%), while their contribution was more modest at the 2 polar sites (31%–38%). In the upper mesopelagic, other studies estimated slightly lower microbial contribution ranging from 12% to 42% (Belcher, Iversen, Giering, et al., 2016, Belcher, Iversen, Manno, et al., 2016; Collins et al., 2015; Van der Jagt et al., 2020), noting that these studies used degradation or sinking rates from the literature for their calculations or used only one pool of particles making absolute comparisons challenging. Similarly, recent estimates based on C-RESPIRE measurements did not exceed 30% (Bressac et al., 2024). However, this difference may be partly explained by the difficulty in modeling
<table><caption>**Table 5**
POC Sources and Sinks in the High Attenuation (HAL) and Low Attenuation (LAL) Layers of the Upper Mesopelagic (See Section 2.7 for Definition of These Layers), and Estimates of the Contribution of Particle-Attached Microbes to the POC Flux Loss in These Two Layers</caption><thead><tr><th></th><th>SOTS</th><th>PF1</th><th>PF2</th></tr></thead><tbody><tr><td colspan="4">Sinks (mmol m<sup>-2</sup> d<sup>-1</sup>)</td></tr><tr><td colspan="4">Microbial remineralization</td></tr><tr><td>HAL</td><td>9.9 ± 2.4</td><td>2.8 ± 0.9</td><td>4.7 ± 1.4</td></tr><tr><td>LAL</td><td>10.3 ± 0.8</td><td>6.9 ± 1.3</td><td>8.9 ± 1.6</td></tr><tr><td colspan="4">Sources (mmol m<sup>-2</sup> d<sup>-1</sup>)</td></tr><tr><td colspan="4">Sinking POC<sup>a</sup></td></tr><tr><td>HAL</td><td>14.2 ± 4.2</td><td>7.3 ± 2.0</td><td>15.0 ± 3.5</td></tr><tr><td>LAL</td><td>3.7 ± 0.8</td><td>3.6 ± 1.1</td><td>7.8 ± 1.8</td></tr><tr><td colspan="4">Diel vertical migration<sup>b</sup></td></tr><tr><td>HAL</td><td>0</td><td>0</td><td>0</td></tr><tr><td>LAL</td><td>0.2 ± 0.1</td><td>1.2 ± 0.2</td><td>0.8 ± 0.1</td></tr><tr><td colspan="4">Total<sup>c</sup></td></tr><tr><td>HAL</td><td>14.2 ± 4.2</td><td>7.3 ± 2.0</td><td>15.0 ± 3.5</td></tr><tr><td>LAL</td><td>3.9 ± 0.8</td><td>4.8 ± 1.1</td><td>8.6 ± 1.8</td></tr><tr><td colspan="4">Microbial contribution to POC flux loss<sup>d</sup></td></tr><tr><td>HAL</td><td>64 ± 17%</td><td>38 ± 12%</td><td>31 ± 9%</td></tr><tr><td>LAL</td><td>266 ± 20%</td><td>145 ± 37%</td><td>103 ± 21%</td></tr></tbody></table>

Note. The depth range considered for the estimation of migratory input (200–1,000 m depth) is wider than the LAL, leading to an overestimation of the POC input through vertical migration in that layer. Standard deviations are calculated following the Monte-Carlo approach (see Figure 9 caption). <sup>a</sup>Calculated as the difference between the POC fluxes obtained at the upper and lower bounds of each layer. <sup>b</sup>POC injection by migrating zooplankton in the 200–1,000 m depth range (Baker et al., 2025). Note that carbon injection as DIC from respiration is not taken into account. These estimates are based on day/night zooplankton net samples. POC injection is considered negligible in the HAL (Baker et al., 2025). <sup>c</sup>Calculated as the sum of gravitational settling input and migratory input. <sup>d</sup>Calculated as the ratio between the sink (i.e., microbial remineralization) and total sources.

the POC flux at the base of the PPZ, resulting in large uncertainties in our study (Figure 3). Moreover, Bressac et al. (2024) used a slightly different method in which sinking velocities were not constrained, and considered a shallowest but wider depth interval (80–290 m depth) than the 50 m deep HAL layer. At the two polar front sites, contributions in the HAL were estimated based on remineralization rates measured in the upper LAL (due to the absence of observations in the HAL), possibly explaining the lower microbial contributions. One might expect different remineralization rates in the HAL due to depth variability in particle lability and microbial community (Bressac et al., 2024).

The variability of these contributions across sites likely reflects differences in planktonic community and export flux compositions (see Section 4.1), mesopelagic temperatures and zooplankton vertical distributions (Baker et al., 2025). Thus, the larger abundance of fragile and fluffy phytoaggregates prone to fragmentation (Briggs et al., 2020) exported at the polar sites, compared with SOTS (Figure 4b), could explain the lower contribution of microbes to the flux attenuation at these sites. We infer (indirectly) that the remaining 36%–69% of the POC flux loss observed in the HAL was driven by zooplankton-mediated transformations (i.e., fragmentation, coprorhexy, coprophagy). This is within the 8%–70% range estimated for zooplankton flux-feeding for similar depth ranges (Mayor et al., 2014; Steinberg, Van Mooy, et al., 2008; Stukel et al., 2019; Van der Jagt et al., 2020).

In the LAL, the microbial remineralization was clearly the dominant degradation process with a contribution to the flux attenuation close to 100% at the polar sites (103%–145%; Table 5), and >200% at SOTS. This contribution well above 100% highlights potential limitations of our approach, such as the use of constant $w_{avg}$ and $C_{remin}$ with depth, or an inaccurate UVP-derived POC flux model at this site. The depth-increase—between the HAL and LAL—of the microbial contribution to the flux attenuation has already been suggested from in situ observations (e.g., Bressac et al., 2024) and modeling studies (e.g., Stemmann et al., 2004) as the consequence of the decreasing abundance of flux feeders with depth. This vertical trend highlights a fundamental difference between microbial and zooplankton-mediated degradation processes: microbial remineralization is a continuous process while fragmentation and flux-feeding by zooplankton occur at discrete depths where flux-feeders reside.

## 5. Conclusions and Perspectives

Vertical attenuation of POC fluxes displays contrasting patterns throughout the ocean (Henson et al., 2015), and the variability and controls of the processes driving this attenuation is still not well understood. In this study, we dissected regional biological carbon pumps in the subantarctic and polar front regions. These 2 regions were characterized by different export efficiencies but similar transfer efficiencies. Particle images and elemental flux composition reveals two different export systems: a fecally dominated export system in the subantarctic (SOTS), and a phyto-aggregate dominated export system in polar waters (PF sites). While particle sinking velocities were similar, microbial remineralization rates differed by an order of magnitude—this difference being likely driven by the confounding effect of temperature and substrate characteristics.

Multiple studies have challenged the use of a single Martin *b* value across the water column (Marsay et al., 2015), biogeochemical provinces or seasons (Guidi et al., 2015; Henson et al., 2015, 2023). We therefore deconstructed the Martin Curve by assessing the role of microbial remineralization in two distinct layers of the mesopelagic (HAL and LAL). In the high attenuation layer, POC degradation was driven by the combined action of microbes and probably zooplankton. While the role of attached microbes was preponderant in the subantarctic (SOTS), they
had only a minor influence at the PF sites, suggesting that particle characteristics (i.e., slow sinking porous aggregates) enabled higher rates of fragmentation and/or particle ingestion by zooplankton. Deeper in the mesopelagic, in the low attenuation layer, flux attenuation was clearly driven by particle-attached microbes. The variability of this microbial contribution to the flux attenuation between these two layers and across contrasting sites confirms the need to construct more complete descriptions of POC flux attenuation shapes.

A variety of mechanistic particle flux attenuation models have been proposed to capture the variations of attenuation rates with depth (Cael & Bisson, 2018), for instance the double exponential model representing rapidly and slowly attenuating fluxes (i.e., composed of a labile and a more refractory particle pools). However, these models aim at representing the vertical changes in particle characteristics rather than the diversity of processes shaping the attenuation, and the concept of lability is highly dependent on the degradation process considered. Particle flux attenuation models should demarcate the roles of zooplankton and particle-attached microbes in attenuating the POC flux. Given our current state of knowledge, such mechanistic conception addresses two challenges: (a) to better understand the multiple drivers of the microbial- and zooplankton-mediated degradation processes, and (b) to resolve their coupling with depth (e.g., through microbial gardening; Mayor et al., 2014). The recent efforts in deconstructing the Martin curve provides important insights into the mechanistic functioning of particle flux attenuation (Bressac et al., 2024; this study). These insights should stimulate new representations of flux attenuation in biogeochemical models to better capture the diversity of regional biological carbon pumps across the ocean.

## Data Availability Statement

Most of the data used in this study are available in Tables 2, 4 and 5, and in Tables S1 and S2 of supporting information S1. NPP data have been published and are available in Boyd et al. (2024). Gridded Chlorophyll product is the 8-day MODIS record and can be found at The OSU Ocean Productivity Site (2021). CTD data from the cruise can be found at the Commonwealth Science and Industrial Research Organisation Marine division (2021). Particle profiles obtained with the UVP5 are available in Boyd and Lacour (2021). The PITs (including polyacrylamide gels), C-RESPIREs, UVP-derived POC fluxes, and sinking velocities data used in the study are available under the Creative Commons Attribution 4.0 International licence in Petiteau et al. (2025). Data analysis and figures were made with MATLAB version R2021b (https://www.mathworks.com/). Requests for the codes should be addressed to L. Petiteau.

## Acknowledgments

This study was funded by the Australian Research Council by a Laureate awarded to P.W.B. (FL160100131). TR is an ARC DECRA Recipient (DE240100115) funded by the Australian Government. The authors wish to thank the CSIRO Marine National Facility (MNF) for its support in the form of sea time on RV Investigator, support personnel, scientific equipment and data management. We thank the captain and crew of the RV Investigator as well as the efforts of the full SOLACE team. We extend particular thanks to C. Young, P. Waller, M. Lewis, P. Strutton, C. Wynn-Edwards. F. Lombard, L. Guidi and J.O. Irisson.

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