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\begin{document}

\title{The presence of transparent exopolymer particles rather than
ballast determines sinking of small particles during late summer in the
Northeast Pacific Ocean}



\author[1]{Elisa Romanelli}%
\author[1]{Julia Sweet}%
\author[1]{Sarah Lou Carolin Giering}%
\author[1]{David Siegel}%
\author[1]{Uta Passow}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 16, 2024}


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Elisa Romanelli \textsuperscript{1}, Julia Sweet \textsuperscript{2,3},
Sarah Lou Carolin Giering \textsuperscript{4}, David A.
Siegel\textsuperscript{1}, Uta Passow \textsuperscript{2,5}

\textsuperscript{1} Earth Research Institute, University of California,
Santa Barbara, CA, United States

\emph{{elisa.romanelli@lifesci.ucsb.ed\emph{u}}},~\emph{\href{mailto:david.siegel@ucsb.edu}{david.siegel@ucsb.ed\emph{u}}}

\textsuperscript{2} Marine Science Institute, University of California,
Santa Barbara, CA, United States

\textsuperscript{3} University of Louisiana at Lafayette, LA, United
States

\emph{julia.sweet1@louisiana.edu}

\textsuperscript{4} National Oceanography Centre, Southampton, United
Kingdom

\emph{s.giering@noc.ac.uk}

\textsuperscript{5} Memorial University, St John's, Canada

\emph{uta.passow@mun.ca}

\textbf{Corresponding author}

Elisa Romanelli

Interdepartmental Graduate Program in Marine Science

University of California

Santa Barbara, CA

\emph{elisa.romanelli@lifesci.ucsb.edu}

\section*{Abstract}

{\label{abstract}}

Gravitational sinking of particles is a key pathway for the transport of
particulate organic carbon (POC) to the deep ocean. Particle size and
composition influence particle sinking velocity and thus play a critical
role in controlling particle flux. Canonically, sinking particles that
reach the mesopelagic are expected to be either large, or ballasted by
minerals. However, the presence of transparent exopolymer particles
(TEP), which are positively buoyant, may also influence particle sinking
velocity. We investigated the relationship between particle composition
and sinking velocity during the EXPORTS campaign (Export Processes in
the Ocean from RemoTe Sensing) in the Northeast Pacific Ocean using
Marine Snow Catchers. Suspended and sinking particles were sized using
the FlowCam and their biogeochemical composition was assessed by
measuring the concentration of particulate organic carbon (POC) and
nitrogen, particulate inorganic carbon, biogenic and lithogenic silica
and TEP. Sinking fluxes were also calculated. Overall, suspended and
sinking particles were small (\textless{} 51 \selectlanguage{greek}μ\selectlanguage{english}m, diameter) in this late
summer, oligotrophic system. Contrary to expectation, the ratio of
ballast minerals to POC was higher for suspended particles than sinking
particles. Further, suspended particles showed TEP-to-POC ratios three
times higher than sinking particles. This suggests that TEP content
dictated the sinking behavior of the two particle fractions. Fluxes of
POC averaged to 4.3 \selectlanguage{ngerman}± 2.5 mmol C
m\textsuperscript{-2}d\textsuperscript{-1} at 50 m and decreased to 3.1
± 1.1 mmol C m\textsuperscript{-2} d\textsuperscript{-1} at 300--500 m.
These flux estimates were slightly higher than fluxes measured during
EXPORTS with drifting sediment traps and Thorium-234. A comparison
between these approaches illustrates that small sinking particles were
an important component of the POC flux in the mesopelagic of this late
summer oligotrophic system.

\section*{1. Introduction}

{\label{introduction}}

The biological carbon pump (BCP) comprises the processes that mediate
the transfer of organic carbon from the euphotic zone, where it is
produced, to the deep ocean (Volk and Hoffert, 1985). Without the BCP
the concentrations of atmospheric CO\textsubscript{2} would be
\textasciitilde{} 200 ppm higher than present (Parekh et al., 2006). The
efficiency of the BCP depends primarily on the gravitational settling of
particles, physical advection and mixing of particles, and the active
vertical transport of particles due to migrating zooplankton and fish
(Boyd et al., 2019). These processes shape the flux of carbon and other
elements through the mesopelagic and abyssopelagic zones, where carbon
can be locked away from the atmosphere, impacting atmospheric
CO\textsubscript{2} concentrations over climatologically relevant
timescales (Kwon et al., 2009; DeVries et al., 2012). Accurately
predicting the response of the ocean carbon storage to already underway
and future climate changes requires a mechanistic knowledge of the
processes making up the BCP (e.g., Siegel et al., 2023).

The largest component of the BCP is settling particles (Boyd et al.,
2019; Nowicki et al., 2022). Particles in the open ocean are mainly
produced in the surface ocean and consist of living and dead
phytoplankton cells, detritus, carcasses, fecal material, and minerals.
Their size range spans between less than a micrometer (typical threshold
defined as 0.7 \selectlanguage{greek}µ\selectlanguage{english}m in diameter) to many millimeters, with those with a
diameter of \textgreater{} 0.5 mm being referred to as ``marine snow''
(Alldredge and Silver, 1988). The fate and distribution of particles are
influenced by the transformation processes that change a particle's
size, composition and, consequently, sinking velocity. Particles can
aggregate, disaggregate, solubilize or be remineralized back to
inorganic forms via mechanical forcing, bacterioplankton activity, and
interaction with zooplankton (Stemmann et al., 2004; Burd et al., 2010;
Giering et al., 2014; Collins et al., 2015). When particles aggregate or
are repackaged into fecal matter, they have the potential to sink
rapidly (\textasciitilde{}50 to [?] 2,000 m d\textsuperscript{-1}) and
hence more easily escape consumption, fragmentation, and dissolution at
shallow depths; thus, bringing organic matter to the deep ocean more
efficiently than smaller particles (Alldredge and Silver, 1988;
Ebersbach and Trull, 2008).

Particle sinking velocities are typically thought to be largely
determined by size. Stokes' Law, which quantifies the sinking velocity
of spherical solid particles under laminar flow conditions, assumes that
particle sinking velocity increases as the product of the spherical
particle's diameter squared and the excess density with respect to
seawater. The implication for sinking particles in the ocean is that
large particles should sink fast and are hence effective vectors for
carbon transport to depth, whereas small particles sink slow and are
remineralized within the upper mesopelagic, contributing little to
BCP-mediated ocean carbon storage (Kriest, 2002; Marsay et al., 2015;
Cavan et al., 2017). Nevertheless, the presence of small particles
(0.2--20 \selectlanguage{greek}μ\selectlanguage{english}m) has been observed at great depths (\textgreater{} 1000 m)
(e.g., Dall'Olmo and Mork, 2014; Briggs et al., 2020). Furthermore,
recent studies have shown that the downward flux of particulate organic
carbon (POC) via small particles (\textless{} 100 \selectlanguage{greek}μ\selectlanguage{english}m) can be significant
in specific ecosystems and seasons, at times constituting the bulk of
the total POC flux through the mesopelagic (e.g., Durkin et al., 2015;
Giering et al., 2016; Bisson et al., 2020; Dever et al., 2021).

The presence of ballast minerals (biogenic silica from diatoms,
particulate inorganic carbon from coccolithophores and foraminifera, and
lithogenic material from aeolian and riverine inputs) is also thought to
increase particle sinking velocity by increasing particle excess density
in respect to seawater (Armstrong et al., 2002; Passow and De La Rocha,
2006; Laurenceau-Cornec et al., 2019; Iversen and Lampitt, 2020;
Iversen, 2023). However, cause and effect in the relationship between
organic matter and minerals are not clear, and sinking aggregates
originating from biological activity in the mixed layer could scavenge
and subsequently transport small, suspended mineral particles to depth
(Passow, 2004).

The presence of transparent exopolymer particles (TEP; Alldredge et
al.,1993) also has the potential to influence the sinking of particles.
TEP are largely composed of polysaccharides released by phytoplankton
and bacteria as extracellular surface-active exopolymers (Passow, 2002),
especially under nutrient-limited conditions (Obernosterer and Herndl,
1995). TEP may act as biological glue and thus enhance particle
coagulation rate by increasing particle ''stickiness'' (Passow et al.,
1994; Jackson, 1995). However, by being positively buoyant, TEP may also
reduce aggregates' sinking velocities especially when aggregates are
characterized by high TEP-to-solid particles ratio (Engel and Schartau,
1999; Azetsu-Scott and Passow, 2004). Although TEP has the potential to
play an important role in controlling the downward transport of
particles, we lack a robust mechanistic understanding of this process
(e.g., Mari et al., 2017; Nagata et al., 2021).

Our hypothesis is that TEP play a critical role in determining the
sinking velocity of particles, potentially outcompeting the role of
ballast minerals. We assess how particle size and composition regulates
the partitioning between sinking and suspended particles within the
upper mesopelagic in an iron-limited region of the Northeast Pacific
Ocean. Particles were collected during the EXPORTS (Export Processes in
the Ocean from RemoTe Sensing) field campaign using Marine Snow Catchers
(MSC). Finally, we discuss the possible mechanisms driving the formation
of small sinking particles in the mesopelagic and highlight the
importance of studying small particle characteristics and particle
patchiness to enhance our understanding of the functioning of the
biological carbon pump.

\section*{2. Materials and Methods}

{\label{materials-and-methods}}

\subsection*{2.1 Ocean Station Papa}

{\label{ocean-station-papa}}

This study is part of the first EXPORTS field campaign, which took place
in late summer 2018 (August 14 to September 9) at Ocean Station Papa
(`Station P'). Station P is a time-series site located in the Northeast
Pacific Ocean (nominally 50\selectlanguage{ngerman}°N 145°W) that has been regularly monitored
since 1949 (e.g., Tabata 1965). It is a high-nutrient and
low-chlorophyll system with a `muted' spring bloom owing to persistently
low concentrations of iron (Martin and Fitzwater, 1988; Boyd et al.,
1996). During our study, Station P was characterized by a shallow mixed
layer (average: 29 \(\pm\ \)4 m), and strong vertical and weak
horizontal gradients in hydrographic properties (Siegel et al., 2021).
Mixed layer macronutrient concentrations were elevated and
chlorophyll\emph{a} concentrations were low with a mean of 0.21 \selectlanguage{greek}μ\selectlanguage{english}g
L\textsuperscript{-1}, which was less than typical August values
(2000--2017) for Station P (Siegel et al., 2021). This condition
resulted in an isolume depths of the 1\% photosynthetically available
radiation (PAR) between 70 to 90 m (mean: 78 \(\pm\) 6 m)
(Siegel et al., 2021), which was slightly deeper than climatological
conditions (Siegel et al., 2021). The site was characterized by a highly
recycled food web in the mixed layer (Meyer et al., 2020; McNair et al.,
2023), with low POC fluxes (Buesseler et al., 2020; Estapa et al.,2021),
which were in line with previous observations performed at Station P
(Charette et al., 1999; Wong et al., 2002; Kawakami et al., 2010;
Timothy et al., 2013; Mackinson et al., 2015). An in-depth discussion of
how POC fluxes measured during EXPORTS compare to previous POC flux
measurements is provided in Buesseler et al., (2020).

The observations presented here were conducted from the R/V \emph{Roger
Revelle} , which sampled ecological and biogeochemical properties
following an instrumented Lagrangian Float drugged at
\textasciitilde{}100 m depth. A detailed operational description of the
EXPORTS North Pacific field campaign can be found in Siegel et al.,
(2021).

\subsection*{2.2 Sample collection with the Marine Snow
Catchers}

{\label{sample-collection-with-the-marine-snow-catchers}}

Profiles of suspended and sinking particles were collected using three
Marine Snow Catchers (MSC; Lampitt et al., 1993, OSIL, UK). The MSC is a
large volume (volume: V\textsubscript{MSC} \(=\ \)89.8 L,
height: h\textsubscript{MSC}\(\ =\) 1.5 m) water sampler with
a removable base section (\textasciitilde{} 8 L) that enables the
partitioning of particles according to a predefined settling time (Riley
et al., 2012; Giering et al., 2016). The MSCs were deployed at two to
three depths ranging between 20 and 500 m at 13 stations (\textbf{Table
S1} ). Deployment depths were chosen to include (1) just below the mixed
layer depth (20--65 m), (2) at \textasciitilde{}100 m (Lagrangian float
depth), and (3) between 100--500 m. Depths were adjusted to match where
particle maxima were detected immediately before the MSC deployments,
either via chlorophyll fluorescence profile, or particle counts from the
Underwater Vision Profiler-5 (Picheral et al., 2010, Hydroptic, FR). MSC
casts were conducted in the afternoons on days 1, 3, and 6 of each of
the three 8-day sampling epochs, with additional two-depths casts on
days 5 of epochs 2 and 3 (\textbf{Table S1} ).

A full description of the MSC and its sampling methodology has been
published by Riley at al. (2012) and Giering et al. (2016). We modified
the protocol slightly as follows: During deployment, the terminal
apertures of the top and base sections of the MSC were kept open and
were closed at the target depth through a wire-guided messenger trigger
mechanism. A tray (height: h\textsubscript{tray} = 4.4 cm, area:
A\textsubscript{tray} \(=\) 0.028 m\textsuperscript{2},
approximate volume: \textasciitilde{} 1 L) was placed at the bottom of
the MSC base section prior to each deployment. Immediately following
deployment and retrieval, the MSC was secured in an upright position,
the initial bulk population of particles was sampled from the MSC's
central tap (time zero, \emph{T\textsubscript{0}} ), and particles were
allowed to settle for 2 hours. We operationally defined three particle
fractions,\emph{top} (\emph{t} ), \emph{base} (\emph{b} ) and
\emph{tray}(\emph{tr} ) according to their sampling location within the
MSC after the 2-hour settling period. The \emph{top} fraction was
collected from the central tap in the top section of the MSC and
represents the suspended particle pool, i.e., particles that did not
sink during the 2 hours settling period. Subsequently, the water
contained in the upper part of the MSC was gently drained, and the upper
part of the MSC removed. The water in the base section overlying the
tray was siphoned off, constituting the \emph{base} fraction (volume:
V\textsubscript{base;} 4.5--5.5 L). Water in the tray was considered as
the \emph{tray} fraction (volume: V\textsubscript{tray} 0.33--0.98 L).
The \emph{base} and \emph{tray} sampled volumes varied across
deployments depending on sampling efficiency (i.e., how much volume was
lost during sampling). In the laboratory, the tray was visually
investigated for the presence of marine-snow-sized particles. In two
instances (settling time experiments, see section 2.6) the
\emph{tray}and \emph{base} fraction were combined before sampling
(referred to as\emph{bottom} fraction).

All three fractions (\emph{top} , \emph{base} , \emph{tray} ) were
subsampled for various parameters (\textbf{Table S1} ). Subsamples for
particulate organic carbon and nitrogen (POC and PON) analysis and for
counts and identification (particle imaging via the FlowCam) were
collected at all stations, except on August 17 when no samples were
fixed. Subsamples for the analysis of total particulate carbon (TC),
from which we obtained particulate inorganic carbon estimates (PIC) by
difference (PIC = TC - POC), biogenic and lithogenic silica (bSi and
lSi, respectively) were taken on days 3 and 5 of each epoch. Subsamples
for the determination of TEP were collected on days 1 and 5 of each
epoch, and the abiotic aggregation potential was determined on seawater
collected on day 1 of each epoch.

\subsection*{2.3 Particle imaging and sizing with the
FlowCam}

{\label{particle-imaging-and-sizing-with-the-flowcam}}

Particle size distribution of suspended and sinking particles was
assessed using particle imaging. Subsamples (50 mL) were fixed with 37\%
formalin (hexamethylenetetramine buffered) to a final concentration of
1--2\% and stored in dark at 4\selectlanguage{ngerman}°C until analysis. Subsamples were
analyzed using a FlowCam 8000 Series (Sieracki et al., 1998, Yokogawa
Fluid Imaging Technologies, US) in auto-image mode (particles imaged at
a pre-defined flow rate) (\textbf{Table S2} ). To assess relevant
particle sizes, we first analyzed subsamples using a x40 objective
combined to a 300x1500-\selectlanguage{greek}μ\selectlanguage{english}m flow cell and then a x200 objective with a
50x300-\selectlanguage{greek}μ\selectlanguage{english}m flow cell. Note that 13 out of 30 suspended particle samples
were not analyzed with the x40 objective due to time constraints. The
minimum sizes reliably resolved by the objectives were 30 \selectlanguage{greek}μ\selectlanguage{english}m and 3 \selectlanguage{greek}μ\selectlanguage{english}m,
respectively. The maximum size was dictated by the flow cell size and
was 300 \selectlanguage{greek}μ\selectlanguage{english}m for the x40 objective and 50 \selectlanguage{greek}μ\selectlanguage{english}m for the x200
objective.\emph{Base} and \emph{tray} fractions were pre-filtered using
a 44-\selectlanguage{greek}μ\selectlanguage{english}m mesh filter to avoid clogging the 50x300-\selectlanguage{greek}μ\selectlanguage{english}m flow cell when using
the x200 objective.

Based on the size of the particles and the FlowCam resolution, we chose
to restrict our analysis to particles with an Equivalent Spherical
Diameter (ESD) between 4 and 128 \selectlanguage{greek}μ\selectlanguage{english}m. We calculated the minimum number of
particles required to estimate a robust particle size spectrum using the
empirical relationship given by Blanco et al. (1994) and assuming that a
size bin is well represented if there are at least 10 particles in it
(\selectlanguage{ngerman}Álvarez et al., 2011). Thus, we imaged around 1,300 particles (i.e.,
minimum desired count and lowest number of counted particles) to cover a
size range of 4 to \textless{} 32 \selectlanguage{greek}μ\selectlanguage{english}m at x200 and a size range of [?] 32 to
128 \selectlanguage{greek}μ\selectlanguage{english}m at x40.

Imaged particles were sized using FlowCam's VisualSpreadsheet\selectlanguage{ngerman}© software
version 4.15.1. and grouped in the five size bins based on their ESD.
The complete method settings and example images are presented
in\textbf{Table S2} . Particle concentrations of \emph{top} ,
\emph{base}and \emph{tray} were calculated by dividing the counted
number of particles in each size bin by the sample volume imaged.
Suspended and sinking particle abundances were calculated as explained
in section 2.7. Particle size distributions (PSDs) were estimated as
differential number size distributions N(D) (\# particle
mL\textsuperscript{-1}\selectlanguage{greek}μ\selectlanguage{english}m\textsuperscript{-1}) for \emph{top} and
\emph{tray} fractions. The slope of the PSDs was calculated as the slope
of a one-degree polynomial fit\emph{~} to~the
\emph{log\textsubscript{10}} -transformed PSD
and\emph{log\textsubscript{10}} -transformed arithmetic mean of each
size bin (\selectlanguage{greek}μ\selectlanguage{english}m) over the entire measured size range (polyfit function,
Matlab~R2021b).

\subsection*{2.4 Biogeochemical analyses}

{\label{biogeochemical-analyses}}

Typically, 1 L of the \emph{T\textsubscript{0}} , \emph{top}
and\emph{base} fractions and \textasciitilde{} 0.1 L of the
\emph{tray}fraction were filtered each for analysis of POC and PON, TC,
bSi and lSi, and TEP. For the occasions when we carried out the settling
time methodological test, between 0.5--1.5 L of the \emph{top}
and\emph{bottom} subsamples were filtered instead.

Concentrations of POC and PON were determined by filtering subsamples
onto two replicate pre-combusted (450 \selectlanguage{ngerman}°C, 30 minutes) GF/F filters (25
mm, Whatmann, UK). The filters were dried at 60°C and stored at room
temperature until analysis. Filters were analyzed using a CEC 44OHA
elemental analyzer (Control Equipment, US) after treatment with 10\%
HCl. Replicates were averaged (see average of the relative standard
deviations of the filter replicates in \textbf{Table S3} ). Detection
limit of POC and PON ranged between 0.8--16 \selectlanguage{greek}μ\selectlanguage{english}g and 0.2--3.9 \selectlanguage{greek}μ\selectlanguage{english}g,
respectively. All POC values were above the detection limit of the
instrument. However, for PON, 24 of 237 values were below detection,
seven of which were negative and therefore substituted with zero (\selectlanguage{greek}μ\selectlanguage{english}g
L\textsuperscript{-1}). Values were corrected to account for non-target
carbon on the filter using the average POC and PON mass (12.6 \selectlanguage{greek}μ\selectlanguage{english}g for
POC; 2.1 \selectlanguage{greek}μ\selectlanguage{english}g for PON) of 28 blanks obtained using a multiple volume (0.5,
1 and \textasciitilde{} 2L) regression approach (Moran et al., 1999) of
water collected with Niskin Bottles from the CTD-rosette system within
the mixed layer (campaign-wide correction). A detailed explanation of
the blank correction method and a reconciliation of all the POC and PON
measurements obtained using different methodologies during the EXPORTS
field campaign is presented by Graff et al., (\emph{in review} ).

Total particulate carbon (TC) was measured following procedures similar
to POC but without prior acidification. Only one filter per fraction was
generated due to volume constraints. No measured values fell below the
instrument detection limit (1.2 to 4.5 \selectlanguage{greek}μ\selectlanguage{english}g). Particulate inorganic carbon
(PIC) was calculated by subtracting the uncorrected POC from TC.

Biogenic and lithogenic silica concentrations were determined by
filtering samples onto 0.6-\selectlanguage{greek}μ\selectlanguage{english}m pore size polycarbonate membrane filters
(47 mm diameter, Isopore, Millipore), which were dried at 60\selectlanguage{ngerman}°C and
stored at room temperature until analysis. Filters were digested in
Teflon tubes by adding 4 mL of 0.2 N (normal) NaOH (95°C, 40 minutes)
and cooled immediately afterwards. The resulting solutions were
neutralized by adding 1 mL of 1 M (molar) HCl and centrifuged (10
minutes, 2500 rpms) to separate lSi from bSi. 4 mL of the solution was
diluted with 6 mL of Milli-Q water and assessed via the molybdosilicic
acid spectrophotographic method to measure bSi (Strickland and Parsons,
1968). The remaining 1 mL of solution, which was left at the bottom of
the Teflon tube together with the filter, was rinsed using Milli-Q
water, left to dry, and cooled. To extract lSi, 0.25 mL of 2.5 M (molar)
hydrofluoric acid were added. After 48 hours 9.75 mL 1 M (molar)
saturated boric acid solution were added, and filters were centrifuged.
8 mL of the resulting solution was added to 2 mL 1 M (molar) saturated
boric acid solution and assessed spectrophotometrically as
aforementioned.

Concentrations of TEP were determined colorimetrically on triplicate
samples. Subsamples were filtered onto 0.4-\selectlanguage{greek}μ\selectlanguage{english}m pore size polycarbonate
filters (25 mm, Whatmann, UK), stained with Alcian blue, and stored
frozen until analysis. Filter blanks, prepared by staining and rinsing
wet filters, were processed like the samples. Stained filters were
soaked for at least 2 hours in 80\% sulfuric acid (Fisher Scientific;
95\% w/w), and the absorption at 787 nm was measured
spectrophotometrically (Thermo Scientific GENESYS 10S UV- VIS; Passow
and Alldredge, 1995). The stained filters were compared to a standard
curve developed using Gum Xanthan (Sigma-Aldrich) and hence TEP
determinations were expressed as standardized Gum Xanthan equivalents
(GXeq) (Bittar et al., 2018). TEP determinations were considered above
detection, if the absorbance value of a sample at 787 nm was at least
twice the absorbance value of the blank at 787 nm (Passow and Alldredge,
1995). A total of 28 of 112 TEP determinations measured from the MSC
were below this detection limit. TEP associated with sinking particles
between 300 and 500 m was always at or below detection limit. In
addition to the MSC samples, we also determined water column TEP
concentration on water collected using Niskin bottles fitted to a CTD
rosette (depth range: 5--500 m; n = 153). A total of 22 of 153 TEP
measured on the water column sampled with the Niskin bottles were at or
below the detection limit. We did not remove TEP values at or below the
limit of detection but consider these observations an upper bound
estimate of the real in situ concentrations. We expressed TEP in carbon
units (TEP-C) using a conversion factor of 0.75 \selectlanguage{greek}μ\selectlanguage{english}g C \selectlanguage{greek}µ\selectlanguage{english}g
L\textsuperscript{-1}/ GXeq L\textsuperscript{-1} (Engel and Passow,
2001).

Several values of the biogeochemical measurements were considerably
higher than the average concentrations at their collection depth. This
occurred for \textasciitilde{}5\% of the total number of observations
and these samples are referred to here as \emph{outliers} . Outliers
were defined as observations that were at least twice the average
concentration for that depth layer. We refer to background
concentrations when these outliers were removed to assess the baseline
biogeochemical composition of suspended and sinking particles. The
nature of the outliers and the implication of their presence is
discussed in section 4.5 below.

\subsection*{2.5 Abiotic aggregation potential
experiments}

{\label{abiotic-aggregation-potential-experiments}}

The potential for the abiotic formation of aggregates was evaluated
using rolling tanks experiments. Four experiments were conducted using
MSC \emph{T\textsubscript{0}} (August 25 at 95 m), \emph{top} (August 25
at 95 m) or \emph{base} (August 17 at 55 m and August 19 at 55 and 95 m)
sample fractions. Each experiment was conducted for 36 hours in 1.1-L
rolling tanks at near \emph{in situ} temperature (13\selectlanguage{ngerman}°C or 4°C) in the
dark. Aggregate formation was monitored by visually checking every 6
hours for the appearance of marine snow-sized particles (ESD
\textgreater{} 0.5 mm).

\subsection*{2.6 Settling time experiments for
MSC}

{\label{settling-time-experiments-for-msc}}

Methodological experiments to determine the effect of settling time on
particle fractions partitioning were conducted twice (on August 20 and
September 03, \textbf{Table S1} ) from MSC deployments at 60 and 80 m
depth, respectively. Each time, all three MSCs were deployed at the same
depth, but settling times after retrieval varied: particles were allowed
to settle for 1, 2 and 4 hours after recovery of the MSCs. Subsamples
of\emph{top} and \emph{bottom} fractions were analyzed for POC and PON,
bSi and TEP.

\subsection*{2.7 Calculations of particle concentrations and
fluxes}

{\label{calculations-of-particle-concentrations-and-fluxes}}

Following Riley et al. (2012) and Giering et al. (2016), concentrations
of suspended (c\textsubscript{NS}), slow-sinking (c\textsubscript{SS})
and fast-sinking (c\textsubscript{FS}) particles were calculated as
follows:

c\textsubscript{NS} = \emph{top} concentration (\emph{2} )

c\textsubscript{SS} = (\emph{base} -- \emph{top}
concentrations)\(\times\ \)V\emph{\textsubscript{base}} /
V\textsubscript{MSC}(\emph{3} )

c\textsubscript{FS} = (\emph{tray} -- \emph{base}
concentrations)\(\times\ \) V\emph{\textsubscript{tray}} /
(A\emph{\textsubscript{tray}} \(\times\ \)h\textsubscript{MSC})
(\emph{4} )

Occasionally, after applying the corrections we obtained negative values
i.e., the \emph{top} concentration exceeded the
\emph{base}concentration, or the \emph{base} concentration exceeded the
\emph{tray}concentration. We assigned a zero concentration to the
specific observations. The negative values could reflect a lack of
sinking particles, the presence of ascending particles and/or actively
moving zooplankton. In the analyses that follow, little differences were
found between slow- and fast-sinking particle concentrations (see
section 3.2). Hence, these two sinking particle fractions are combined.
In four deployments, TEP and TC were measured only in the \emph{top}
and\emph{tray} fractions. In these cases, sinking TEP and TC
concentrations were estimated following equation (4) but subtracting the
\emph{top}fraction from the \emph{tray} fraction.

The standard particle sinking velocity for the MSC is assumed to be 18 m
d\textsuperscript{-1} as determined geometrically by dividing the
sinking distance (height of the MSC; h\textsubscript{MSC} =
V\textsubscript{MSC}/ A\textsubscript{MSC} = 1.5 m) by the settling time
(t = 2 hours). We assumed that the 18 m d\textsuperscript{-1} sinking
velocity estimate for sinking particles represents a lower bound
estimate because the sinking particles could have reached the bottom of
the MSC much sooner (Giering et al., 2016).

\subsection*{2.8 Statistical analyses}

{\label{statistical-analyses}}

The relative uncertainties in the calculated concentrations and fluxes
were determined by using a Monte Carlo error propagation with mean
values obtained by averaging the replicate filters and the precision
estimated by calculating the average of the relative standard deviations
of the filter replicates (\textbf{Table S3} ). A 1\% uncertainty was
assumed for the measured values (V\textsubscript{MSC},
A\emph{\textsubscript{tray}} , h\textsubscript{MSC},
V\emph{\textsubscript{base}} , V\textsubscript{tray},
h\textsubscript{MSC} and sinking time). Final estimates of the averages
and standard deviations of particle concentrations and fluxes were then
calculated from the simulation results. Simple linear regression was
used to test (Student's t-test, 95\% confidence) for statistically
significant temporal and depth trends displayed by the biogeochemical
content of suspended, sinking particles and their partitioning to total.
The linear analyses were performed using the
function~fitlm~in~Matlab~(R2021b). The same approach was performed to
test for significant temporal trends displayed by the biogeochemical
content of suspended and sinking particles during the settling tests.
Paired t-test was used to test for differences among particle fractions.
The data presented in this study, and all the data generated during the
first EXPORTS field campaign can be found at NASA SeaBASS data
repository (https://seabass.gsfc.nasa.gov/cruise/EXPORTSNP).

\section*{3. Results}

{\label{results}}

\subsection*{3.1 Marine snow and abiotic aggregation
potential}

{\label{marine-snow-and-abiotic-aggregation-potential}}

No visible sinking marine snow particles (diameter \textgreater{} 0.5
mm) were found in the tray from any of the MSCs deployments. This
observation suggests that the concentration of sinking marine-snow-sized
particles was less than 0.02 per L. This finding is consistent with
particle size distribution data from the UVP (pers. comm. Andrew
McDonnell, 2021). Furthermore, during the four aggregation potential
experiments, no visible marine snow-sized aggregates formed in the
rolling tanks regardless of depth (55 and 95 m) and sample fraction
(T\textsubscript{0}, \emph{top} or \emph{base} ).

\subsection*{3.2 Particle concentrations and
size}

{\label{particle-concentrations-and-size}}

Most of the imaged particles (were smaller than 16 \selectlanguage{greek}μ\selectlanguage{english}m in diameter (ESD),
whether suspended or sinking (\textbf{Figure 1} ). Furthermore, particle
concentrations measured in the tray were equal or lower than the ones
measured in the base for 20 out of 30 MSC deployments. These
observations suggest that the concentration differences
between\emph{base} and \emph{tray} particle fractions were too small to
be detected with this method. We hence summed the concentrations of
slow- and fast-sinking particles into a single sinking particle
fraction. This decision is additionally supported by the lack of marine
snow, which would have contributed appreciably to the fast-sinking
particle fraction.

Our results, based on the analysis of particles with diameters (ESD)
between 4 and 128 \selectlanguage{greek}μ\selectlanguage{english}m, suggest that \textgreater{} 70\% of suspended and
sinking particles had a diameter of 4--8 \selectlanguage{greek}μ\selectlanguage{english}m, and that particles larger
than 32 \selectlanguage{greek}μ\selectlanguage{english}m were at the detection limit of our method (i.e., too rare to
be quantified). The partitioning of particles across the size bins was
consistent among particle fractions (paired t-test, p = 0.33--0.63, n =
17), indicating that suspended and sinking particles were similarly
sized (\textbf{Figure 1} and \textbf{Table S4} ).

Additionally, we calculated the particle size distribution (PSD, 4--128
\selectlanguage{greek}μ\selectlanguage{english}m) of suspended particles and of sinking (`\emph{tray} ') particles
(uncorrected fraction) to assess differences in slopes among fractions
with depth. Overall, throughout the entire water column, PSD slopes of
suspended particles were similar (-3.3 \selectlanguage{ngerman}± 0.3) to those of sinking
particles (-3.5 ± 0.2\emph{;} \textbf{Figure S1} ). Yet below 100 m,
PSDs of sinking particles were significantly steeper than those of
suspended particles (paired t-test, p = 0.03, n = 5\emph{;}
\textbf{Figure S1} ). Furthermore, the slope of suspended PSD became
significantly shallower with depth between 20 and 500 m
(R\textsuperscript{2} = 0.3, p \textless{} 0.02, n = 17), while the
slopes of sinking PSDs remained constant (R\textsuperscript{2} = 0; p =
0.79, n = 17; \textbf{Figure S1} ).

It is important to note that the determination of particle size using
this approach is associated with uncertainties due to samples handling.
We cannot exclude the possibility that fragile and larger aggregates
(ESD \textless{} 0.5 mm) may have broken up due to physical disturbance
developed during the sampling and partitioning of the three particle
fractions and during injection of samples in the FlowCam flowcell used
in combination with the x40 objective (minimum size of 300 \selectlanguage{greek}μ\selectlanguage{english}m).
Therefore, the size characterization performed with the FlowCam must be
taken with caution. Lastly, particle type and shape could not be
reliably resolved with the FlowCam due to the small size of the
particles.

\subsection*{3.3 Biogeochemical partitioning by particle sinking
fraction}

{\label{biogeochemical-partitioning-by-particle-sinking-fraction}}

The background suspended POC concentrations decreased significantly with
depth in the euphotic zone between 20 and 65 m (R\textsuperscript{2} =
0.43, p = 0.03, n = 11) and in the upper mesopelagic between 95 and 500
m (R\textsuperscript{2} = 0.59, p = 0.001, n = 14) (\textbf{Figure 2A}
), whereas background sinking POC concentrations did not significantly
attenuate with depth (R\textsuperscript{2} = 0, p = 0.61, n = 24). The
relative contribution of background suspended POC to total background
POC decreased significantly in the upper mesopelagic
(R\textsuperscript{2} = 0.70, p \textless{} 0.001, n = 13), resulting in
a concomitant increase in the contribution of the background sinking POC
(R\textsuperscript{2} = 0.70, p \textless{} 0.001, n = 13)
(\textbf{Figure 2A} ). Nearly all the background POC was found in
suspended particles, whose contribution to the total POC was on average
88 \selectlanguage{ngerman}± 9\%. In respect to outliers, in five out of 29 MSC deployments, the
measured POC of either suspended or sinking particle fractions was at
least twice higher than POC concentrations measured at other stations in
the same depth layer (\textbf{Table S5} ).

Patterns of PON (\textbf{Figure S2} ) were similar to those of POC, with
molar POC-to-PON ratios of the suspended fraction on average slightly
higher than the Redfield ratio of 6.6 (average: 7.9 ± 3.9). The molar
POC-to-PON ratios did not show any trend with depth
(R\textsuperscript{2} = 0.06, p = 0.25, n = 24) and were in general
agreement with molar POC-to-PON ratios measured during the EXPORTS field
campaign (Graff et al., in review). Molar POC-to-PON for sinking
particles varied between 3 and 16 (average: 7.2 ± 3.8) and did not show
any significant trend with depth (R\textsuperscript{2}= 0.11, p = 0.1, n
= 26).

PIC was below detection in 20\% of the suspended particle fractions, and
in 60\% of sinking particle fractions analyzed. Suspended PIC
concentrations increased significantly with depth (R\textsuperscript{2}=
0.40, p = 0.05, \emph{n} = 10) (\textbf{Figure 2B} ). Background sinking
PIC was on average 0.4 ± 0.3 \selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1} and remained
constant with depth (R\textsuperscript{2} = 0.05, p = 0.59, n = 8).
Background molar PIC-to-POC ratios calculated for suspended particles
increased significantly with depth (R\textsuperscript{2} = 0.80, p
\textless{} 0.001, n = 10) and were significantly higher than PIC-to-POC
ratios of sinking particles in the mesopelagic (paired t-test, p = 0.01,
n = 5) (\textbf{Figure 3A} ).

Background suspended and sinking bSi concentrations and their
contribution to total bSi concentrations did not significantly change
with depth between 50 and 500 m (R\textsuperscript{2} = 0.06--0.18, p =
0.17--0.49, n = 8--11) (\textbf{Figure 2C} ). Suspended particles stored
on average 83 \selectlanguage{ngerman}± 9\% of the total background bSi pool. Molar bSi-to-POC
ratios associated with background suspended particles increased
significantly with depth between 50 and 500 m (R\textsuperscript{2} =
0.84, p \textless{} 0.001, n = 8), whereas ratios of sinking particles
remained constant (R\textsuperscript{2} = 0.16, p = 0.32, n = 8).
Furthermore, molar bSi-to-POC ratios of sinking particles were on
average higher than the ratios associated with suspended particles above
95 m (n = 3), and lower between 350 and 500 m (n = 4). Ratios above and
below 95 m were statistically different (paired t-test, p = 0.01 and p =
0.008, respectively) (\textbf{Figure 3B} ).

Suspended and sinking lSi concentrations and relative contribution to
the total lSi pool did not display a significant trend with depth and
showed high variability (R\textsuperscript{2} = 0.004--0.07 p =
0.39--0.85, n = 11-13) (\textbf{Figure 2D} ). Suspended lSi accounted,
on average, for 83 ± 19\% of the total lSi pool\textbf{.} Molar
lSi-to-POC ratios associated with suspended particles increased
significantly with depth between 50 and 350 m depth
(R\textsuperscript{2} = 0.78, p = 0.02, n = 6). For sinking particles,
molar lSi-to-POC ratios displayed the tendency of decreasing with depth
between 60 and 500 m, although the trend was not statistically
significant (R\textsuperscript{2} = 0.05, p = 0.58, n = 9)
(\textbf{Figure 3C} ). Ratios between suspended and sinking particles
were statistically different below the euphotic zone (paired t-test, p
\textless{} 0.01) with suspended particles characterized by consistently
higher ratios than sinking particles.

Suspended TEP decreased significantly with depth (R\textsuperscript{2} =
0.53, p =0.001, n = 16) and like POC, displayed the sharpest decrease
between 50 and 95 m. On average, 95 ± 4\% of TEP was suspended, and
sinking TEP was often at or near detection (average = 0.3 ± 0.2 \selectlanguage{greek}µ\selectlanguage{english}g GXeq
L\textsuperscript{-1}, n = 15) (\textbf{Figure 2E} ). TEP concentrations
measured in the MSCs were in line with the concentrations measured in
the water collected with the CTD rosette (\textbf{Figure S3} ). After
converting TEP to carbon concentration of TEP, we estimated, in 11 out
of 15 deployments, that 15 \selectlanguage{ngerman}± 5\% of suspended POC was TEP and only 3 ±
1\% of sinking POC was TEP. In 4 observations collected above 100 m,
TEP-C accounted for 13\% of the sinking POC (\textbf{Figure 3D} ).
TEP-C-to-POC ratios associated with suspended and sinking particles were
statistically different (paired t-test, p \textless{} 0.001, n = 15).

Background POC, TEP, bSi and lSi concentrations in the suspended and
sinking particle pools did not display a significant variability over
time at 50--65 m, 95 m or 300--500 m (t-test, 95\% confidence). Temporal
trend for PIC could not be tested owing to the limited number of
observations available.

To provide an overall assessment of the relative changes in the measured
biogeochemical composition of suspended and sinking particles with
depth, we summed the background cruise-wide averaged concentrations of
organic TEP-C, POC and PON and ballast minerals and evaluated the
differences in their relative contributions (\textbf{Figure 4} ). We
found that the composition of suspended particles was dominated by POC
(64\%) in the euphotic zone and by POC (34 ± 11\%), lSi (30 ± 7\%) and
PIC (16 ± 8\%) in the upper mesopelagic, whereas sinking particles were
on average composed mainly by POC (50 ± 10\%), followed by lSi (20 ±
7\%), bSi (14 ± 7\%) and PON (9 ± 3\%) throughout the water column. The
relative contribution of TEP-C in suspended particles was on average
twice the one in sinking particles (\textbf{Figure 4B} ).

\subsection*{3.4 Constraining the particle sinking
velocity}

{\label{constraining-the-particle-sinking-velocity}}

The settling time experiments found no consistent differences in the
fraction of sinking particles to the whole particle population
(suspended plus sinking) as a function of settling time (t-test, 95\%
confidence). After 1, 2 and 4 hours of settling, sinking POC made up 10,
11 and 10\% and sinking PON 6, 7 and 8\% of the whole particle
population (\textbf{Table S6} ). Sinking bSi made up 23, 25 and 25\%,
whereas sinking TEP made up 6, 3 and 5\%. However, our experiment was
limited to only two casts within the euphotic zone; hence, we cannot
assume that all sinking particles collected had the potential to sink
within one hour at 36 m d\textsuperscript{-1}. We therefore present
fluxes calculated assuming the standard sinking velocity of 18 m
d\textsuperscript{-1}, and we reinforce that these fluxes represent a
lower bound as suggested by our settling time experiments
(\textbf{Figure 5} ). Background POC fluxes averaged to 4.2 ± 2.6, 3.1 ±
1.5 and 3.1 ± 1.1 mmol C m\textsuperscript{-2} d\textsuperscript{-1} at
50--65 m, 95 m and 300--500 m, respectively. When including the
outliers, the estimates increased up to 7 ± 10, 9 ± 18 and 4 ± 2 mmol C
m\textsuperscript{-2} d\textsuperscript{-1}, at these same depth
intervals. Fluxes of bSi were on average 0.2, 0.2 and 0.1 mmol Si
m\textsuperscript{-2} d\textsuperscript{-1} and lSi fluxes were 0.3, 0.4
and 0.2 mmol Si m\textsuperscript{-2} d\textsuperscript{-1} at 50--65 m,
95 m and 300--500 m, respectively.

\section*{4. Discussion}

{\label{discussion}}

In this discussion, we first put our results into the overall cruise
context and then focus on the causes that drive the differences between
the suspended and sinking particles as measured with the MSC.
Specifically, we discuss why -- despite all particles being small --
some particles sank while others remained suspended. Furthermore, we
discuss the potential mechanisms that can explain the presence of small,
slow sinking particles in the mesopelagic. We finally address two
aspects of the methodology: (1) comparison of sinking particle fluxes
derived from different methods, and (2) interpretation and implications
of the observed outliers in our data set including the potential role of
`patchiness' on ocean particle distributions.

4.1 Comparison with other particulate biochemical measurements performed
during EXPORTS: consistency in responses by size

{\label{comparison-with-other-particulate-biochemical-measurements-performed-during-exports-consistency-in-responses-by-size}}

A variety of methods and sampling equipment were used to measure the
biogeochemical composition of particulate matter during the first
EXPORTS field campaign (Siegel et al., 2021). Values of POC and PON
measured in the MSC time zero fraction were slightly higher than those
obtained from the 12-L Niskin bottles sampled from the CTD/rosette
system from the same ship (Graff et al., in review). The differences
between the two measurements may be explained by a combination of the
following: (1) under sampling of sinking particles by Niskin bottles
compared to the MSC, (2) slight overestimation of MSC POC values due to
a possibly higher POC blank than the one used to correct the measured
POC masses (see section 2.4), and (3) spatiotemporal differences in
sampling (Graff et al., in review).

Our PIC concentrations were low, but consistent with low productivity
regimes in the global ocean (Mitchell et al., 2017) and complimentary
measurements made at Station P during EXPORTS (Roca-Martí et al., 2021),
suggesting a limited presence of calcifying microorganisms. In situ pump
observations showed low PIC concentrations and small contributions to
particle stocks during EXPORTS (Roca-Martí et al., 2021). Our PIC
estimates were higher than the in situ pump observations, which may be
attributed to the use of larger filter pore size with in situ pumps
(QMA; \textasciitilde{} 1.0 \selectlanguage{greek}μ\selectlanguage{english}m nominal pore size) compared to the
smaller pore size of glass fiber filters used with Niskin bottles and
MSC analyses (GF/F; \textasciitilde{} 0.3 \selectlanguage{greek}µ\selectlanguage{english}m nominal pore size after
combustion, Nayar and Chou, 2003) (Graff et al., in review). Indeed,
during EXPORTS, most of the PIC was associated with the smallest size
particle fraction measured by in situ pumps (i.e., in 1--5 \selectlanguage{greek}µ\selectlanguage{english}m fraction
rather than in the 5--51 \selectlanguage{greek}µ\selectlanguage{english}m or \textgreater{} 51 \selectlanguage{greek}µ\selectlanguage{english}m fractions;
Roca-Mart\selectlanguage{ngerman}í et al., 2021).

Our measurements of bSi standing stocks were in good agreement with
measurements performed on seawater collected with Niskin bottles
(Brzezinski et al., 2022) and measurements obtained with in situ pumps
(Roca-Martí et al., 2021), which used similar pore sizes (0.6, 0.6 and
0.8 \selectlanguage{greek}µ\selectlanguage{english}m, respectively). Our molar bSi-to-POC ratios associated with
suspended and sinking particles were in the same range as the 1--51 \selectlanguage{greek}μ\selectlanguage{english}m
particle size fractions (bSi:POC = 0.03--0.16, Roca-Mart\selectlanguage{ngerman}í et al., 2021),
except for one outlier (0.35 at 500 m; see Section 4.5). Molar
bSi-to-POC ratios measured from larger sized particles with in situ
pumps and drifting sediment traps were higher (Roca-Martí et al., 2021;
Estapa et al., 2021). This comparison suggests that the MSC sampled on
average small particles (\textless{} 51 \selectlanguage{greek}μ\selectlanguage{english}m), under-sampled large
particles, and that using GF/F filters allowed us to better represent
particles \textless{} 1 \selectlanguage{greek}μ\selectlanguage{english}m in respect to in situ pumps. Overall, the
concentrations and flux values (see also section 4.4) obtained by the
MSCs agreed with other measurements obtained during the cruise, and we
are therefore confident that the MSC observations are representative of
the conditions present in situ during our visit.

\subsection*{4.2 Partitioning of TEP and ballast minerals in suspended
and sinking
particles}

{\label{partitioning-of-tep-and-ballast-minerals-in-suspended-and-sinking-particles}}

When comparing sinking to suspended particles, we expected to find
either larger particles in the sinking fraction compared with the
suspended fraction, and/or a higher ratio of ballast materials-to-POC.
However, neither expectation was met (\textbf{Figure. 1, 3 and 4} ).
Both fractions were characterized by small particles, which - in a
simplistic view - would suggest that sinking particles contained a
higher fraction of ballast material than suspended particles. Contrary
to this idea, however, suspended particles were characterized by a
higher ratio of ballast material in the mesopelagic. This begs the
question why the small non-ballasted particles sank (and, why the
ballasted particles did not sink).

The partitioning of particles into suspended and sinking pools must have
been controlled by another factor. So far, we looked at the composition
in context of the traditional constituents: POC, PIC and silicate.
However, the molecular makeup of POC can have a large effect on particle
density. For example, diatoms have been shown to be positively buoyant
(i.e., float) (Villareal, 1988; Moore et al., 1996; Woods et al., 2008).
However, the phytoplankton community consistent predominantly of very
small non-diatom cells (McNair et al., 2021). Alternatively, TEP are
positively buoyant and thus could reduce the sinking velocity of
aggregates (Engel and Schartau, 1999; Azetsu-Scott and Passow, 2004).
Overall, we observed low concentrations of water column TEP between 5
and 500 m (17.3--1.2 \selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}). This result is in
line with low productivity systems such as the Southern Ocean (from
undetectable values to 48 \selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}; Ortega-Retuerta
et al., 2009), the tropical western Pacific Ocean (5.3--40 \selectlanguage{greek}µ\selectlanguage{english}g GXeq
L\textsuperscript{-1}; Yamada et al., 2017), the Eastern Mediterranean
Sea (5.7--25.1 \selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}; Ortega-Retuerta et al.,
2019) and the Indian and the subtropical South West Pacific (\textless{}
20 \selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}, Engel et al., 2020). Despite the
observed low TEP concentrations, TEP-C accounted for approx. 15\% of the
suspended POC pool. According to theoretical calculations, a 1-mm
aggregate composed solely of TEP and diatoms can sink only if less than
5\% of its carbon content consists of TEP (Mari et al., 2017). Here we
show that the relative contribution of TEP-C was on average three times
that threshold (15\%) in suspended particles while in sinking particles
on average only 3\% of the POC was TEP. While the model used diatom as
primary particle pool, in our case most of the suspended particle pool
consisted of very small cells (Synechococcus spp, picoeukaryotes and
nanoeukaryotes; McNair et al., 2021), and possibly fragments of old
cells, minerals, and detritus. Nonetheless, our data support the model
implication (Mari et al., 2017) and provide evidence that the chemical
composition of particulate organic carbon, and especially the
contribution of exudates, is critical to understand particle sinking
velocity.

Hence, the higher TEP-to-POC ratio in suspended particles compared to
sinking particles may have led to the difference in the partitioning of
the two particle fractions as collected by the MSC by reducing the
sinking velocity of small, ballasted particles that - in the absence of
TEP - would have been part of the collected sinking fraction. Whereas
the suspended POC was associated with ballasting material and consisted
of a large fraction of TEP, the sinking POC consisted of relatively
small fractions of ballasting material and TEP. This result suggests
that the ratio of ballasting material-to-POC cannot be used as a sole
predictor for sinking velocity, but that organic carbon composition
needs to be considered.

The importance of TEP for potentially reducing sinking velocity and thus
flux was first suggested by Mari et al. (2017), and here we present the
first in situ measurements suggesting that high TEP concentrations
reduce particle sinking velocities. Yet, our observations are limited to
one field campaign only, and hence we cannot generalize. We observed
this phenomenon in a low productivity system where particle
concentrations were low and TEP concentrations were high. Our dataset
was collected during late summer, when the system was characterized by
low concentrations of chlorophyll \emph{a} , nutrients, particles, and
ballast minerals (Siegel et al., 2021; Roca-Mart\selectlanguage{ngerman}í et al., 2021;
Brzezinski et al., 2022). The TEP-to-particle ratio may have been higher
compared to other seasons, due to stressful conditions for phytoplankton
growth and high light levels (Ortega-Retuerta et al. 2009), implying
that the observed mechanisms could be seasonal (Mari et al., 2017).
Regardless, our data suggests that future biological pump studies should
characterize POC composition, including TEP, to help gain a mechanistic
understanding of how these materials influence particle sinking
velocities and, in turn, the role of particle dynamics in ocean
biogeochemical cycles across different productivity systems and seasons.

\subsection*{4.3 Potential mechanisms explaining small particles at
depth}

{\label{potential-mechanisms-explaining-small-particles-at-depth}}

Our observations suggest that suspended particles were increasingly
reworked with depth, e.g., a larger fraction of POC was remineralized
with depth, leading to the relative increase in the ratio between
ballasting material and POC with depth. Sinking POC did not decrease
with depth, suggesting that either the attenuation of the sinking
particle population was negligible, or an input of sinking material
balanced the expected losses. If we assume that sinking particles
collected at 500 m were produced at 30 m (within the mixed layer) and
sank at 18 m d\textsuperscript{-1}, those particles would require 26
days to reach 500 m. We can estimate the loss rate assuming a
temperature-dependent remineralization rate of 0.04
d\textsuperscript{-1} measured experimentally using a temperature of 7
°C (average temperature between 30 and 500 m from CTD) and a
Q\textsubscript{10} of 3.5 (Iversen and Ploug, 2013; Giering et al.,
2017). If particles were to sink undisturbed, 30\% of the POC would have
been remineralized by bacteria during that time window (26 days). Yet,
such decrease was not observed.

One theoretical explanation for a lack of flux attenuation of sinking
particles would be rapid physical transport processes (Omand et al.,
2015; Giering et al., 2016; Siegel et al., 2023). Particle subduction or
convective mixing can, however, be excluded as during our visit density
stratification beneath the mixed layer was strong and consistent and
lateral density gradients were weak (Siegel et al., 2021).

Alternatively, an input of sinking particles at depth may have balanced
their loss. Large sinking phytodetrital aggregates were rare, implying
that disaggregation of sinking aggregates was not the prevailing source
of small sinking particles. However, if suspended POC existed as loosely
packed aggregates within a buoyant TEP matrix (e.g., low-density
aggregates; Mari et al., 2017), their disaggregation would allow dense
components to sink, albeit at a relatively slow velocity. Such
disaggregation could be caused by zooplankton (Passow and Alldredge,
1999) or by microbially mediated degradation processes (Passow, 2002).
During microbially mediated degradation, lighter C-components may be
consumed preferentially, leaving denser and more recalcitrant material
in the sinking fraction (Hamanaka et al., 2002). In fact, extracellular
particulate carbohydrates released by phytoplankton (i.e., TEP) are
remineralized by bacteria at a faster rate than non-TEP carbon (i.e.,
0.53 and 0.21 d-1, respectively; Harvey et al., 1995; Mari et al.,
2017).

{\label{alternatively-an-input-of-sinking-particles-at-depth-may-have-balanced-their-loss.-large-sinking-phytodetrital-aggregates-were-rare-implying-that-disaggregation-of-sinking-aggregates-was-not-the-prevailing-source-of-small-sinking-particles.-however-if-suspended-poc-existed-as-loosely-packed-aggregates-within-a-buoyant-tep-matrix-e.g.-low-density-aggregates-mari-et-al.-2017-their-disaggregation-would-allow-dense-components-to-sink-albeit-at-a-relatively-slow-velocity.-such-disaggregation-could-be-caused-by-zooplankton-passow-and-alldredge-1999-or-by-microbially-mediated-degradation-processes-passow-2002.-during-microbially-mediated-degradation-lighter-c-components-may-be-consumed-preferentially-leaving-denser-and-more-recalcitrant-material-in-the-sinking-fraction-hamanaka-et-al.-2002.-in-fact-extracellular-particulate-carbohydrates-released-by-phytoplankton-i.e.-tep-are-remineralized-by-bacteria-at-a-faster-rate-than-non-tep-carbon-i.e.-0.53-and-0.21-d-1-respectively-harvey-et-al.-1995-mari-et-al.-2017.}}

Particle flux was, in fact, dominated by fecal pellets during EXPORTS
(\textasciitilde{}80\%; Durkin et al., 2021; Steinberg et al., 2023).
Specifically, mini-pellets (ESD \textless{} 100 \selectlanguage{greek}μ\selectlanguage{english}m) contributed up to
46\% of the total carbon flux in the upper mesopelagic (Durkin et al.,
2021), with mesozooplankton and salp pellets contributing the majority
of the sinking flux (Durkin et al., 2021; McNair et al., 2023).
Fragmentation of fecal pellets, e.g., due to sloppy feeding or swimming
motions (Steinberg and Landry, 2017) could have produced small and dense
particles, which may have constituted the bulk of the small sinking
particle pool we collected. Such pellet fragments would likely contain a
high percentage of organic carbon and a low percentage of TEP, similar
to our sinking particle pool. Though we did not observe large fast
sinking pellets in the MSCs, this absence could have been caused by an
overall low abundance of pellets or because the MSCs were deployed
during the daytime when a large fraction of the zooplankton community
likely resided at depth (e.g., Steinberg et al., 2023).

{\label{particle-flux-was-in-fact-dominated-by-fecal-pellets-during-exports-80-durkin-et-al.-2021-steinberg-et-al.-2023.-specifically-mini-pellets-esd-100-ux3bcm-contributed-up-to-46-of-the-total-carbon-flux-in-the-upper-mesopelagic-durkin-et-al.-2021-with-mesozooplankton-and-salp-pellets-contributing-the-majority-of-the-sinking-flux-durkin-et-al.-2021-mcnair-et-al.-2023.-fragmentation-of-fecal-pellets-e.g.-due-to-sloppy-feeding-or-swimming-motions-steinberg-and-landry-2017-could-have-produced-small-and-dense-particles-which-may-have-constituted-the-bulk-of-the-small-sinking-particle-pool-we-collected.-such-pellet-fragments-would-likely-contain-a-high-percentage-of-organic-carbon-and-a-low-percentage-of-tep-similar-to-our-sinking-particle-pool.-though-we-did-not-observe-large-fast-sinking-pellets-in-the-mscs-this-absence-could-have-been-caused-by-an-overall-low-abundance-of-pellets-or-because-the-mscs-were-deployed-during-the-daytime-when-a-large-fraction-of-the-zooplankton-community-likely-resided-at-depth-e.g.-steinberg-et-al.-2023.}}

Microzooplankton grazing of suspended POC, and the resulting production
of mini-pellets (Stemmann et al., 2004), may have also contributed to
the production of small, dense sinking particles. Consumption of small
particles by zooplankton and incorporation of their carbon and biogenic
silica into sinking particles (Dagg et al., 2003) is especially
important for carbon flux if it occurs within relatively short-path food
webs (Richardson, 2019). This mechanism could also have been important
during our study: Compound-specific isotope analysis (CSIA-AA) performed
during our field campaign (Connor Shea, 2023, pers. commun.) found that
the mesopelagic zooplankton food web at Station P was mainly (72--96 \%)
based on small particles (\textless{} 6 \selectlanguage{greek}µ\selectlanguage{english}m).

{\label{microzooplankton-grazing-of-suspended-poc-and-the-resulting-production-of-mini-pellets-stemmann-et-al.-2004-may-have-also-contributed-to-the-production-of-small-dense-sinking-particles.-consumption-of-small-particles-by-zooplankton-and-incorporation-of-their-carbon-and-biogenic-silica-into-sinking-particles-dagg-et-al.-2003-is-especially-important-for-carbon-flux-if-it-occurs-within-relatively-short-path-food-webs-richardson-2019.-this-mechanism-could-also-have-been-important-during-our-study-compound-specific-isotope-analysis-csia-aa-performed-during-our-field-campaign-connor-shea-2023-pers.-commun.-found-that-the-mesopelagic-zooplankton-food-web-at-station-p-was-mainly-7296-based-on-small-particles-6-uxb5m.}}

Although we cannot say with certainty why sinking flux of POC did not
decrease with depth, likely a series of biologically mediated
aggregation or fragmentation processes led to a replenishment of the
small sinking particle pool with depth. The described mechanisms are
potentially significant contributors in dictating the efficiency of the
biological carbon pump, especially in stable low-productivity systems,
characterized by small particles in low concentrations.

\subsection*{4.4 Comparison of fluxes derived from different methods
during
EXPORTS}

{\label{comparison-of-fluxes-derived-from-different-methods-during-exports}}

Our background POC fluxes compared to the cruise mean POC fluxes
obtained using both neutrally buoyant and surface-tethered sediment
traps (Estapa et al., 2021) and water column Thorium-234 assessment
(Buesseler et al., 2020) were similar in the upper 100 m, but larger
below 100 m (\textbf{Figure 6} ). These differences between the
estimates for depths \textgreater{} 100 m existed even when the
lower-bound estimate (assuming a sinking velocity of 18 m
d\textsuperscript{-1}; Section 3.4) was used. In contrast, bSi flux
estimates obtained from the MSC were in good agreement with those
derived from the traps, but lower than the Thorium-234-based values
(Roca-Mart\selectlanguage{ngerman}í et al., 2021) (\textbf{Figure 6} ).

These three approaches for determining sinking particle fluxes differ in
the design, spatiotemporal resolutions, and size of collected particles;
thus, may not be directly comparable and comparisons should be made
cautiously. The approaches mainly diverge in: (1) Temporal coverage: the
MSC provided virtually instantaneous estimates, whereas drifting
sediments trap (Gardner, 1977) and water column Thorium-234 carbon flux
determinations (Buesseler et al., 1992) integrate over periods of 3--6
days (time to collect sinking particles) and 24 days (the half-life
of\textsuperscript{234}Th), respectively. (2) Spatial coverage: the MSC
deployments have the smallest spatial resolution among these methods
with 39 discrete deployments as each MSC deployment assesses the flux
from a single \textasciitilde{} 100 L sample. Drifting sediment traps
sampled sinking particles from an area of \selectlanguage{english}[?] 10
km\textsuperscript{2}(Siegel et al., 2008). Due to the long integration
times and abundance of samples (nearly 1000), \textsuperscript{234}Th
particle fluxes average over even larger spatial scales. (3) Minimum
size of collected particles: differences in pore sizes of filters can
lead to differences in measured biogeochemical concentrations. Here we
measured POC using filters with a pore size of \textasciitilde{} 0.3 \selectlanguage{greek}μ\selectlanguage{english}m,
while POC from sediment traps (Estapa et al., 2021) and water column
Thorium-234 samples (Buesseler et al., 2020) were determined using
filters with a pore size of \textasciitilde{} 1 \selectlanguage{greek}μ\selectlanguage{english}m. The filter pore
sizes used to estimate bSi were 0.6 \selectlanguage{greek}μ\selectlanguage{english}m, \textasciitilde{} 1 \selectlanguage{greek}μ\selectlanguage{english}m and 0.8
\selectlanguage{greek}μ\selectlanguage{english}m for MSC, sediment traps and Thorium-234, respectively. Additionally,
drifting sediment traps often underestimate small particles due to
hydrodynamics (Buesseler et al., 2007). (4) Maximum size of collected
particles: the MSC likely under samples rare particles, such as large
particles, as it collects a limited amount of seawater. During EXPORTS,
sediment trap samples were highly impacted by zooplankton ``swimmers''
and fluxes were corrected to account for the removed biomass (Estapa et
al., 2021).

The potential sources of discrepancies between fluxes measured from
sediment traps and water column Thorium-234 were addressed in Estapa et
al. (2021). Briefly, Thorium-234 fluxes measured in sediment traps were
roughly three-fold smaller than water column Thorium-234 fluxes. The
different temporal coverages did not play a major role due to the
stability in production and exports before the EXPORTS cruise (McNair et
al., 2023). The mismatch was largely attributed to under-sampling of
both small particles (\textless{} 32 \selectlanguage{greek}μ\selectlanguage{english}m) due hydrodynamic biases and
rare, large particles (\textgreater{} 1 mm) and zooplankton active
migrant flux by the sediment traps (Estapa et al., 2021). The size of
particles collected in the polyacrylamide gel traps, which ranged from
75 to 1461 \selectlanguage{greek}μ\selectlanguage{english}m (median of 282 \selectlanguage{greek}μ\selectlanguage{english}m), supported this hypothesis (Estapa et
al., 2021). Estimates from the MSC account for the contribution of those
small particles close to \textasciitilde{} 0.3 \selectlanguage{greek}μ\selectlanguage{english}m. Thus, the discrepancy
between MSC and sediment traps may be partially explained by an
under-characterization of very small (\textless{} 1 \selectlanguage{greek}µ\selectlanguage{english}m) particles by the
traps. This statement is also valid when comparing Thorium-234-based POC
fluxes if we consider the pore size of the filters used and that
Thorium-based fluxes were estimated assuming the POC/Th ratio of
mid-sized particles (5--51 \selectlanguage{greek}μ\selectlanguage{english}m) (Buesseler et al., 2020).

However, the MSCs did not capture large, rare particles such as
aggregates, salp fecal pellets nor the active flux due to migration. We
would therefore have expected to see the opposite trend: higher flux
measured using Thorium-234 and traps, if large, rare particles played an
important role in overall flux at the site. This comparison suggests
that POC flux from the MSCs is complementary to those measured from
Thorium-234 and traps. Most importantly, it suggests that small sinking
particles were a relevant part of the sinking POC flux in the upper
mesopelagic zone, likely exceeding the contribution of rare, large
sinking particles. The fact that the MSCs and sediment traps produced
similar estimates of bSi fluxes reinforced our finding that the
population of sinking particles collected by the MSCs included small
sinking POC missed by the other methods, whereas bSi, which was mostly
associated with slightly larger particles (Roca-Mart\selectlanguage{ngerman}í et al., 2021;
Estapa et al., 2021; Brzezinski et al., 2022), was equally sampled by
all three methods.

\subsection*{4.5 Outliers -- Patchiness or
artifacts?}

{\label{outliers-patchiness-or-artifacts}}

The dataset obtained from the MSC holds the potential of highlighting
the ecosystem's patchiness through the collection of spatially
heterogeneous features. Patchiness in the ocean exists both vertically
and horizontally spanning from the microscale to the mesoscale (Cassie,
1962; Siegel, 1998; Robinson et al., 2021) and is in general difficult
to map with most oceanographic approaches (McNair et al., 2023). Several
of our biogeochemical measurements resulted in values considerably
higher than average concentrations for their collection depth
(\textbf{Table S5} ). These ``outliers'' may be attributed to patchiness
or could be due to methodological artifacts. Methodological artifacts
could be caused by misfiring of the MSCs at a shallower depth than
intended or by contamination of the filter. The latter is improbable as
replicate filters and comparisons between time zero and suspended plus
sinking particle fractions would have indicated ``contamination''.
Additionally, filters were always visually inspected for visible
particles, such as macrozooplankton, which, if present, were removed.
These outliers were specific to the MSC, no outliers were detected by
the 12-Liter Niskin bottles of the CTD on board \emph{R/V Roger Revelle}
.

Three of the five outliers in POC were observed on August 16, at 55 m,
95 m, 195 m, one on August 21 at 195 m, and one on September 1 at 350 m.
The outlier concentrations of sinking POC measured at 55 and 95 m, which
were an order of magnitude higher than any other sinking POC values, are
likely reflective of in situ patchiness: the respective time zero and
suspended POC concentrations were also within the upper range of the
values measured at those depths. The POC measured in the time zero
fraction at 55 m equals the sum of POC measured in the suspended and
sinking particle fraction, confirming that the MSC samples were
uncontaminated. Associated sinking PON values were also marked as
outliers and the molar C-to-N ratios were reasonable (10.4 and 12.5 at
55 and 95 m, respectively), albeit somewhat higher than those of the
time zero and suspended fractions (7.3 and 6.4 and 8.3 and 9.7 at 55 and
95 m, respectively). Lastly, while the associated sinking TEP were high
(with the observation at 95 m being an outlier), suspended TEP
concentrations agreed with TEP measured from water collected with Niskin
bottles at those depths (\textbf{Figure S3} ). These observations and
the fact that the MSC deployment depths were chosen to specifically
target ``interesting'' areas as seen by the CTD or UVP profiles, make us
believe that the 100-L sample we collected may have included patches of
unusually high biochemical concentrations.

The three POC outlier concentrations of suspended POC at 195, 195 and
350 m were associated with outlier values of PON, and could reflect POC
and PON concentrations measured at 50--65 m. However, the associated
suspended TEP concentrations (2.8, and 3.1 \selectlanguage{greek}µ\selectlanguage{english}g GXan equiv
L\textsuperscript{-1}) suggest samples stemmed from below 50--65 m
(\textbf{Figure S3} ). Although uncertain, these too could reflect
patchiness.

One value of biogenic silica (176 nmol Si L\textsuperscript{-1}, 500 m)
was more than a factor of 3 higher than the bSi measured on other days
at that depth either with the Niskin bottles or the MSC (57 and 45 nmol
Si L\textsuperscript{-1}, respectively). However, associated POC
concentrations were consistent with the deployment depth and the molar
bSi-to-C ratio of this observation (0.35) matched the one measured for
particles \textgreater{} 5 \selectlanguage{greek}μ\selectlanguage{english}m (bSi:POC = 0.16--0.94 ) collected with in
situ pumps (Roca-Mart\selectlanguage{ngerman}í et al., 2021). Thus, we assume that this sample
included a higher-than-average presence of diatoms or rhizaria. Hot
spots of bSi accumulation have previously been found in low productivity
systems (Crombet et al., 2010) and this depth was targeted because the
UVP profile directly before the MSC deployment indicated a Rhizaria
maximum. Similarly, the elevated PIC concentration associated with
sinking particles (21.5 \selectlanguage{greek}μ\selectlanguage{english}g PIC L\textsuperscript{-1}) could potentially
be attributed to the presence of foraminifera test fragments or
coccoliths.

In summary, the ``outliers'' measured with the MSC are likely, at least
in part, reflective of patchiness in concentrations of small particles
in this low productivity system. Patchiness in the distribution of salp
swarms was also a prevalent feature during this study (Steinberg et al.,
2023). The sporadic nature of patchiness is challenging to quantify and
its importance with respect to standing stocks and fluxes of an entire
ecosystem hard to assess, especially in a low productivity system.

\section*{5. Summary and Conclusions}

{\label{summary-and-conclusions}}

Small, slow sinking particles are usually under-sampled by canonical
particle collection methods and thus have been poorly characterized.
Hence, little is known about their origins, biogeochemical composition,
and role in the ocean biogeochemical cycles.

Our study shows that suspended and sinking particles were small and
similarly sized. Unexpectedly, sinking particles were not characterized
by a higher contribution of ballast materials (bSi, lSi and PIC), which
would have justified their sinking behavior. However, they displayed low
TEP-to-POC ratios in comparison to suspended particles. This observation
suggests that high TEP contribution may have increased the residence
time of suspended POC and thus reduced sinking particle fluxes impacting
the efficiency of the biological carbon pump. Hence, we suggest that the
composition of POC (i.e., presence of gels like TEP) should be routinely
measured to assess how it affects the sinking of particles.

Our data together with findings from the field campaign hypothesize that
the pool of small sinking particles in the mesopelagic formed via
fragmentation of sinking fecal pellets, and zooplankton-mediated
repackaging of suspended particles into fecal pellets. A smaller
contribution to the small sinking particulate pool may have been
provided via the disaggregation of low-density suspended aggregates.
Such mechanisms may be especially important in low productivity systems
during late summer months.

A comparison between particle fluxes calculated from the MSC with fluxes
measured with drifting sediment traps and Thorium-234, suggests that
small slow sinking particles exceeded the contribution of rare, large
sinking particles to POC flux in the upper mesopelagic. Our data
suggests that such flux of small sinking particles tends to be
underestimated by trap and Thorium approaches. Potentially this flux of
small sinking particles may contribute towards resolving the budget
discrepancies between sinking flux and metabolic carbon requirements in
the mesopelagic (Burd et al., 2010).

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\section*{Contributions}

{\label{contributions}}

Contributed to conception and design: UP, DAS

Contributed to acquisition of data: UP, JS, ER, SLG

Contributed to analysis and interpretation of data: ER, UP, DAS, SLG

Drafted and/or revised the article: ER, UP, DAS, SLG, JS

Approved the submitted version for publication: All

\section*{Acknowledgements}

{\label{acknowledgements}}

The authors are grateful to the EXPORTS science team for the
collaborative effort, data and ideas exchange and scientific production
that acted as a baseline for the present work. We would like to
acknowledge support from the NASA Ocean Biology and Biogeochemistry
program. Thanks go to the captain and crew of the R/Vs\emph{Revelle} and
to the Ship Operations staff of the Scripps Institution of Oceanography.
We also thank Stuart Halewood and Nils Ha\selectlanguage{ngerman}ëntjens who provided support at
sea and Marianne Pelletier who helped with the analysis of the silica
samples. Finally, we thank Dr. Margaret Estapa, Dr. Montserrat Roca
Martí and Dr. Carolina Cisternas-Novoa for their valuable feedback on
the preliminary draft of the manuscript.

{\label{the-authors-are-grateful-to-the-exports-science-team-for-the-collaborative-effort-data-and-ideas-exchange-and-scientific-production-that-acted-as-a-baseline-for-the-present-work.-we-would-like-to-acknowledge-support-from-the-nasa-ocean-biology-and-biogeochemistry-program.-thanks-go-to-the-captain-and-crew-of-the-rvs-revelle-and-to-the-ship-operations-staff-of-the-scripps-institution-of-oceanography.-we-also-thank-stuart-halewood-and-nils-hauxebntjens-who-provided-support-at-sea-and-marianne-pelletier-who-helped-with-the-analysis-of-the-silica-samples.-finally-we-thank-dr.-margaret-estapa-dr.-montserrat-roca-martuxed-and-dr.-carolina-cisternas-novoa-for-their-valuable-feedback-on-the-preliminary-draft-of-the-manuscript.}}

\textbf{Funding information}

DAS, UP, ER: NASA 80NSSC17K0692

ER: Supported by the UCSB Earth Research Institute (150RE1)

\textbf{Competing interests}

The authors declare no competing interests.

\textbf{Data accessibility statement}

All data presented here are publicly available on the NASA data
repository, SeaBASS along with all other EXPORTS data. The data can be
accessed here: https://seabass.gsfc.nasa.gov/cruise/EXPORTSNP.

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} \selectlanguage{greek}\textbf{Relative contribution of particle abundances
per size (4 to \textgreater{} 32 μm) versus depth.}\selectlanguage{english}

Bar plots display the contribution in \% of suspended (A) and sinking
(B) particle concentrations in 4 size bins based on the octave scale
(4--8, 8--16, 16--32 and \textgreater{} 32 \selectlanguage{greek}μ\selectlanguage{english}m) and on the particle
equivalent spherical diameter (ESD). Concentrations are calculated as
cruise-wide averages for 6 depth layers (20--25, 50--65, 95, 195,
300--350 and 500 m). Numbers of stations per depth layer are 2, 10, 10,
2, 4 and 2, respectively for bins between 4 and 32 \selectlanguage{greek}μ\selectlanguage{english}m and 2, 4, 5, 2, 2
and 1 for the largest size bin (\textgreater{} 32 \selectlanguage{greek}μ\selectlanguage{english}m). Sinking particles
(\textgreater{} 32 \selectlanguage{greek}μ\selectlanguage{english}m) were at the detection limit of our method.

A)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

B)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

C)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

D)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

E)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 2. Time-averaged concentrations of biogeochemical
components in suspended and sinking particles and the partition of
sinking particles to total.}

Bar graphs display background suspended (left) and sinking (middle)
particles and the fractional contribution of sinking particle
concentrations to the total (in \%; right) for (A) POC, (B) PIC, (C)
bSi, (D) lSi, (E) TEP concentrations. Outliers are represented as red
triangles. The values are cruise-wide averages calculated for six depth
layers (20--25, 50--65, 95, 195, 300--350 and 500 m). Error bars are the
standard deviations of the concentrations averaged within each depth
layer and thus are reflective of the dataset variability rather than the
propagated uncertainty of each datapoint. Numbers on the right of each
bar plot are representative of the total number of observations per
depth layer. Note that the differences in the number of observations
between sinking and suspended particles is reflective of loss of samples
during analysis. Note differences in scales.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.63\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.63\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 3. Molar ratios of ballast minerals and TEP to POC in
suspended and sinking particles versus depth.}

Scatter plots display the depth profile of molar PIC:POC ratios (A),
molar bSi:POC ratios (B), molar lSi:POC ratios (C) and molar TEP-C:POC
ratios (D) calculated for suspended (white triangles) and sinking (black
circles) particles. Dash and continuous line are smoothing spline used
to enhance trends visualization and thus do not have statistical
significance. Outliers of suspended molar bSi-POC ratio (0.35, 500 m)
and sinking PIC-to-POC ratios (12.4, 350 m) have been removed to better
display depth trends, which are not affected by their exclusion.

A)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

B)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 4. Concentrations of organic and inorganic elements in
suspended and sinking particles and their contribution to the total
measured elements.}

Bar plots display the summed biogeochemical concentrations (in \selectlanguage{greek}µ\selectlanguage{english}g
L\textsuperscript{-1}) (A), and their relative contributions (in \%) (B)
of the measured organic (TEP-C, POC, PON) and inorganic (PIC, bSi and
lSi) components of suspended (left) and sinking particles (right) at
five depth layers (50--65, 95, 300--350 and 500 m). Biogenic and
lithogenic silica were converted to mass units using a molar mass of 61
g mol\textsuperscript{-1}. Here we display POC (grey) as total POC minus
TEP-C. The compositional plots display background concentrations and
reflect the measured particle composition.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.77\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 5. Background fluxes of particulate organic carbon,
biogenic and lithogenic silica versus depth.}

Plots represent fluxes of POC (grey), bSi (green) and lSi (brown)
calculated using the background biogeochemical concentrations and a
conservative sinking velocity of 18 m d\textsuperscript{-1}. The filled
area represents the upper bound fluxes up to values estimated using a
sinking velocity of 36 m d\textsuperscript{-1}. The values are
cruise-wide averages calculated for five depth layers (50--65, 95, 195,
300--350 and 500 m). Error bars are calculated as standard deviations
averaged within each depth layer and thus are reflective of the dataset
variability rather than the propagated uncertainty of each datapoint.
Note that we have one observation for all biogeochemical components at
195 m and one for bSi and lSi at 20 m. Hence, these depths are not well
represented by our dataset.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.56\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 6. Comparison of particulate organic carbon and biogenic
silica fluxes estimated using different approaches during EXPORTS.}

Depth profiles of POC (left) and bSi (right) fluxes obtained from the
MSC assuming a sinking velocity of 18 m d\textsuperscript{-1} (black
triangles) to 36 m d\textsuperscript{-1} (filled area), Thorium-234
assessment (pink circles, Buesseler et al., 2020) and drifting sediment
traps (orange squares, Estapa et al., 2021). The MSC fluxes are
calculated using the background biogeochemical concentrations. Note that
we do not consider the 195 m depth to be well represented due to there
being only a single POC and bSi observation at that depth.

\textbf{Supplemental material}

\textbf{Figure S1.} \textbf{Particle size distribution (PSDs)
of\emph{top} and \emph{tray} particle fractions.}

Panel A shows PSDs calculated using particle concentrations of\emph{top}
and \emph{tray} fractions with Equivalent Spherical Diameter (ESD)
between 4 and 128 \selectlanguage{greek}μ\selectlanguage{english}m. Panel B display PSDs slopes versus depth
of\emph{top} (white squares) and \emph{tray} (black circles) particle
fractions.

A)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

B)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.35\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}

\textbf{Figure S2. Time-averaged concentration of PON in suspended and
sinking particles and the partition of sinking particles to total.}

Bar graphs display background suspended (left) and sinking (middle) and
the fractional contribution of sinking particle concentrations to the
total (in \%; right) for PON concentrations. Outliers are represented as
red triangles. The values are cruise-wide averages calculated for six
depth layers (20--25, 50--65, 95, 195, 300--350 and 500 m). Error bars
are the standard deviations of the concentrations averaged within each
depth layer and thus are reflective of the dataset variability rather
than the propagated uncertainty of each datapoint. Numbers on the right
are representative of the total number of observations per depth layer.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}

\textbf{Figure S3. Comparison of TEP concentrations measured on seawater
collected with Niskin bottles and the MSCs.}

Scatter plot displays a comparison of TEP concentrations estimated in
the whole water column collected with the Niskin bottles (green), by
summing suspended and sinking TEP collected with the MSC (red) and in
the MSC time zero fraction (lilac).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.35\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}

\textbf{Table S1. Summary of Marine Snow Catcher's deployments ordinated
through time.}

Table shows the type of sampling performed for each MSC deployment. The
symbol - is reflective of lack of sampling.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Date in 2018 (UTC) & Epoch & Depth & Lat & Lon & BGC measurements &
FlowCam & Settling Time Test\tabularnewline
& & m & \selectlanguage{ngerman}°N & \selectlanguage{ngerman}°E & & &\tabularnewline
August 16, 04:24 & 1 & 55 & 50.107 & -145.071 & POC/N, TEP & X &
-\tabularnewline
August 16, 03:56 & 1 & 95 & 50.107 & -145.071 & POC/N, TEP & X &
-\tabularnewline
August 16, 03:03 & 1 & 195 & 50.107 & -145.071 & POC/N, TEP & - &
-\tabularnewline
August 17, 23:42 & 1 & 195 & 50.200 & -145.041 & POC/N, TC, bSi/lSi & -
& -\tabularnewline
August 18, 00:44 & 1 & 55 & 50.200 & -145.041 & POC/N, TC, bSi/lSi & - &
-\tabularnewline
August 18, 00:17 & 1 & 95 & 50.200 & -145.041 & TC, bSi/lSi & X &
-\tabularnewline
August 20, 04:38 & 1 & 80 & 50.320 & -145.073 & POC/N, bSi & - & 4
hours\tabularnewline
August 20, 04:59 & 1 & 80 & 50.320 & -145.073 & POC/N, bSi, TEP & - & 2
hours\tabularnewline
August 20, 05:19 & 1 & 80 & 50.320 & -145.073 & POC/N, bSi, TEP & - & 1
hour\tabularnewline
August 20, 23:43 & 1 & 195 & 50.366 & -145.063 & POC/N & X &
-\tabularnewline
August 21, 00:48 & 1 & 65 & 50.366 & -145.063 & POC/N & X &
-\tabularnewline
August 21, 00:22 & 1 & 95 & 50.366 & -145.063 & POC/N & X &
-\tabularnewline
August 24, 01:12 & 2 & 63 & 50.412 & -145.125 & POC/N, TEP & X &
-\tabularnewline
August 24, 00:45 & 2 & 95 & 50.412 & -145.125 & POC/N, TEP & X &
-\tabularnewline
August 24, 00:08 & 2 & 350 & 50.412 & -145.125 & POC/N, TEP & X &
-\tabularnewline
August 25, 23:54 & 2 & 350 & 50.461 & -145.005 & POC/N, TC, bSi/lSi & X
& -\tabularnewline
August 26, 00:54 & 2 & 60 & 50.461 & -145.005 & POC/N, TC & X &
-\tabularnewline
August 26, 00:32 & 2 & 95 & 50.461 & -145.005 & POC/N, TC, bSi/lSi & X &
-\tabularnewline
August 28, 01:22 & 2 & 60 & 50.476 & -144.898 & POC/N, TC, TEP & X &
-\tabularnewline
August 28, 01:52 & 2 & 500 & 50.476 & -144.898 & POC/N, TC, bSi/lSi, TEP
& - & -\tabularnewline
August 29, 01:07 & 2 & 60 & 50.489 & -144.838 & POC/N, TEP & X &
-\tabularnewline
August 29, 00:44 & 2 & 95 & 50.489 & -144.838 & POC/N, TEP & X &
-\tabularnewline
August 29, 00:11 & 2 & 300 & 50.489 & -144.838 & POC/N, TEP & X &
-\tabularnewline
September 01, 03:58 & 3 & 55 & 50.584 & -144.863 & POC/N, TEP & X &
-\tabularnewline
September 01, 06:10 & 3 & 95 & 50.584 & -144.863 & POC/N, TEP & X &
-\tabularnewline
September 01, 06:41 & 3 & 350 & 50.584 & -144.863 & POC/N, TEP & X &
-\tabularnewline
September 02, 22:58 & 3 & 350 & 50.577 & -144.748 & POC/N, TC, bSi/lSi &
X & -\tabularnewline
September 03, 00:54 & 3 & 50 & 50.577 & -144.748 & POC/N, TC, bSi/lSi &
X & -\tabularnewline
September 03, 00:30 & 3 & 95 & 50.577 & -144.748 & POC/N, TC, bSi/lSi &
X & -\tabularnewline
September 05, 00:55 & 3 & 20 & 50.534 & -144.694 & POC/N, TC, bSi/lSi,
TEP & X & -\tabularnewline
September 05, 00:14 & 3 & 500 & 50.534 & -144.694 & POC/N, TC, bSi/lSi,
TEP & X & -\tabularnewline
September 05, 23:59 & 3 & 95 & 50.487 & -144.717 & POC/N & X &
-\tabularnewline
September 06, 00:48 & 3 & 25 & 50.487 & -144.717 & POC/N & X &
-\tabularnewline
September 06, 00:24 & 3 & 60 & 50.487 & -144.717 & POC/N & X &
-\tabularnewline
September 08, 19:35 & 3 & 60 & 50.558 & -144.677 & POC/N, bSi, TEP & - &
4 hours\tabularnewline
September 08, 20:04 & 3 & 60 & 50.558 & -144.677 & POC/N, bSi, TEP & - &
2 hours\tabularnewline
September 08, 20:30 & 3 & 60 & 50.558 & -144.677 & POC/N, bSi, TEP & - &
1 hour\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2. FlowCam technical specification and detailed methods
explanation.}

Table show the protocol implemented to image and count marine particles
using the FlowCam. The template we used is a modification of the
template offered in Owen et al., 2022.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
FlowCam technical specifications & FlowCam technical
specifications\tabularnewline
FlowCam model number & 8410\tabularnewline
FlowCam unit serial number & 10446\tabularnewline
Camera resolution & \emph{1920 x 1200 pixels}\tabularnewline
Camera & Color\tabularnewline
Fluidics & Syringe pump (0.5 mL for 20x, 5.0 mL for 4x)\tabularnewline
Software details & VisualSpreadsheet\selectlanguage{ngerman}© software version
4.15.1\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Sample details & Sample details\tabularnewline
Preservation methods & Seawater samples preserved with 37\% buffered
formaldehyde to a final concentration of 1--2\%\tabularnewline
Pre-filtration details & Subsamples of the \emph{base} and \emph{tray}
fractions were pre-filtered before analysis at 20x using a 44 \selectlanguage{greek}μ\selectlanguage{english}m mesh
filter\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
FlowCam setup details for analysis with the 20x objective\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
FlowCam setup details for analysis with the 20x objective\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Flow cell sizes and types used, and objectives used for each flow
cell\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
50x300 \selectlanguage{greek}μ\selectlanguage{english}m flow cell (FOV), 20x objective\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Calibration factor\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
0.3380\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Image acquisition mode\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Auto-image\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Flow rate\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
0.03 mL min\textsuperscript{-1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Example of full context settings for 20180816\_0356\_001\_NSP\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Run: Mode: AutoImage Priming Method: machine prime Flow Rate: 0.030
ml/min Recalibrations: 1 Stop Reason: Sample Volume Processed Sample
Volume Aspirated: 1.0000 ml Sample Volume Processed: 0.9990 ml Fluid
Volume Imaged: 0.3388 ml Efficiency: 33.9\% Particle Count: 1239 Images:
Total: 38201 Used: 1219 Percentage Used: 3.19\% Particles Per Used
Image: 1.02 Particles Per Image: 0.03 Particles Per Image: 0.03 Frame
Rate: 19.57 fps Intensity Mean: 177.09 Intensity Min: 174.78 Intensity
Max: 177.72 Date/Time: Start: 2019-08-22 10:48:26 End: 2019-08-22
11:21:52 Sampling Time: 00:32:31 Environment: Software:
VisualSpreadsheet 4.15.1 Magnification: 20X Calibration Factor: 0.3380
GumStix Firmware: 23 Calibration Factor: 0.3380 SerialNo: 10446 Number
of Processors: 8 Pump: C80 Syringe Syringe Size: 0.50 ml Capture
Parameters: Distance To Neighbor=30 Threshold Dark=15.0000 Threshold
Light=18.0000 Close Holes=0 Capture Dark or Light Pixels=2\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
FlowCam setup details for analysis with the 4x objective\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
FlowCam setup details for analysis with the 4x objective\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Flow cell sizes and types used, and objectives used for each flow
cell\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
300x1500 \selectlanguage{greek}μ\selectlanguage{english}m flow cell (FOV), 4x objective\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Calibration factor\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
1.8130\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Image acquisition mode\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Auto-image\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Flow rate\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
0.9 mL min\textsuperscript{-1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Example of full context settings for 20180817\_2205\_001\_NSP\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Run: Mode: AutoImage Priming Method: machine prime Flow Rate: 0.900
ml/min Recalibrations: 0 Stop Reason: Sample Volume Processed Sample
Volume Aspirated: 9.9892 ml Sample Volume Processed: 9.9544 ml Fluid
Volume Imaged: 6.9242 ml Efficiency: 34.9\% Particle Count: 1542 Images:
Total: 2648 Used: 1166 Percentage Used: 44.03\% Particles Per Used
Image: 1.32 Particles Per Image: 0.58 Particles Per Image: 0.58 Frame
Rate: 3.99 fps Intensity Mean: 168.49 Intensity Min: 167.68 Intensity
Max: 169.85 Date/Time: Start: 2019-09-10 11:04:32 End: 2019-09-10
11:16:10 Sampling Time: 00:11:03 Environment: Software:
VisualSpreadsheet 4.15.1 Magnification: 4X Calibration Factor: 1.8130
GumStix Firmware: 23 Calibration Factor: 1.8130 SerialNo: 10446 Number
of Processors: 8 Pump: C80 Syringe Syringe Size: 5.00 ml Capture
Parameters: Distance To Neighbor=50 Threshold Dark=15.0000 Threshold
Light=18.0000 Close Holes=0 Capture Dark or Light Pixels=2\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Measurement outputs\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Measurement outputs\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Measurement type(s) used\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Equivalent Spherical Diameter (ESD)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Justification for this choice\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Measurement type chosen based on particles type: particles were very
small, mostly round, and transparent (see example of images below).
VisualSpreadsheet\selectlanguage{ngerman}© software version 4.15.1. identifies pixels that
comprise a particle by distinguishing them from the image background
taken automatically at each run. Among other parameters, the software
estimates the Area-Based Diameter (hereafter ABD) and the Equivalent
Spherical Diameter (hereafter ESD). ABD is the diameter calculated based
on a circle with an area equal to the ABD area. The ABD area is
determined by the number of dark pixels in the binary imaged particle,
excluding inner light pixels. ESD is the mean value of 36 feret
measurements. Feret measurements are the perpendicular distance between
parallel tangents touching opposite sides of the particle
(VisualSpreadsheet 5 Particle Analysis Software manual). ABD would be
expected to underestimate the size of particles with internal
transparent components, while ESD would be expected to overestimate the
size of particles with protrusions or flagella (Owen et al.,
2022).\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

Examples of suspended particles imaged at x200 (left) and x40 (right):\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.46\columnwidth]{figures/image17/image17}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.46\columnwidth]{figures/image18/image18}

\end{center}
\end{figure}

\textbf{Table S3. Relative standard deviations (}
\(\mathbf{\sigma}^{\mathbf{2}}\)\textbf{) used as part of the Monte Carlo method to
account for uncertainties propagation} .\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Biogeochemical component & \(\sigma^{2}\) of suspended particles
(\%) & \(\sigma^{2}\)of slow-sinking particles (\%) &
\(\sigma^{2}\)of fast-sinking particles (\%)\tabularnewline
POC & 17 & 6 & 28\tabularnewline
PON & 20 & 14 & 28\tabularnewline
TEP & 11 & 13 & 19\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{greek}\textbf{Table S4. Particle concentrations estimated within 5 size bins
based on particle Equivalent Spherical Diameters (ESD) spanning from 4
to 128 μm.}\selectlanguage{english}

The table shows suspended and sinking particle concentration (particles
mL\textsuperscript{-1}) grouped in size bins according to their
equivalent spherical diameter (ESD). The abbreviation ``na'' is
reflective of unmeasured values. Zeros are reflective of negative or
null concentrations.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& & Suspended particles & Suspended particles & Suspended particles &
Suspended particles & Suspended particles & Sinking particles & Sinking
particles & Sinking particles\tabularnewline
Date in 2018 (UTC) & Depth & 4-8 \selectlanguage{greek}μ\selectlanguage{english}m & 8-16 \selectlanguage{greek}μ\selectlanguage{english}m & 16-32 \selectlanguage{greek}μ\selectlanguage{english}m & 32-64 \selectlanguage{greek}μ\selectlanguage{english}m &
64-128 \selectlanguage{greek}μ\selectlanguage{english}m & 4-8 \selectlanguage{greek}μ\selectlanguage{english}m & 8-16 \selectlanguage{greek}μ\selectlanguage{english}m & 16-32 \selectlanguage{greek}μ\selectlanguage{english}m\tabularnewline
& m & particles mL\textsuperscript{-1} & particles
mL\textsuperscript{-1} & particles mL\textsuperscript{-1} & particles
mL\textsuperscript{-1} & particles mL\textsuperscript{-1} & particles
mL\textsuperscript{-1} & particles mL\textsuperscript{-1} & particles
mL\textsuperscript{-1}\tabularnewline
August 16, 03:56 & 95 & 741 & 413 & 251 & na & na & 144 & 17 &
5\tabularnewline
August 16, 04:24 & 55 & 3036 & 1273 & 359 & na & na & 125 & 7 &
0\tabularnewline
August 17, 23:42 & 195 & 720 & 233 & 71 & 16 & 2 & 126 & 41 &
11\tabularnewline
August 18, 00:17 & 95 & 540 & 142 & 27 & 8 & 1 & 1373 & 150 &
7\tabularnewline
August 18, 00:44 & 55 & 1032 & 305 & 80 & 9 & 1 & 65 & 16 &
2\tabularnewline
August 20, 23:43 & 195 & 469 & 165 & 50 & 10 & 2 & 254 & 35 &
4\tabularnewline
August 21, 00:22 & 95 & 484 & 109 & 21 & 9 & 0 & 68 & 22 &
4\tabularnewline
August 21, 00:48 & 65 & 723 & 136 & 30 & 8 & 1 & 74 & 26 &
8\tabularnewline
August 24, 00:08 & 350 & 1025 & 373 & 151 & na & na & 142 & 43 &
3\tabularnewline
August 24, 00:45 & 95 & 941 & 263 & 62 & na & na & 16 & 3 &
0\tabularnewline
August 24, 01:12 & 63 & 2143 & 686 & 90 & na & na & 768 & 201 &
69\tabularnewline
August 25, 23:54 & 350 & 1982 & 497 & 92 & na & na & 297 & 26 &
5\tabularnewline
August 26, 00:32 & 95 & 1702 & 463 & 144 & na & na & 226 & 38 &
3\tabularnewline
August 26, 00:54 & 60 & 528 & 201 & 44 & na & na & 410 & 60 &
18\tabularnewline
August 28, 01:33 & 60 & 1183 & 298 & 67 & na & na & 144 & 33 &
12\tabularnewline
August 28, 01:53 & 500 & 1344 & 331 & 71 & na & na & 24 & 7 &
3\tabularnewline
August 29, 00:11 & 95 & 1082 & 238 & 23 & na & na & 152 & 72 &
33\tabularnewline
August 29, 00:44 & 95 & 629 & 165 & 21 & na & na & 142 & 26 &
6\tabularnewline
August 29, 01:07 & 60 & 2391 & 343 & 29 & na & na & 351 & 114 &
26\tabularnewline
September 01, 03:58 & 55 & 5115 & 1615 & 190 & 21 & 3 & 56 & 21 &
6\tabularnewline
September 01, 06:10 & 95 & 900 & 274 & 41 & 9 & 2 & 84 & 26 &
6\tabularnewline
September 01, 06:41 & 350 & 880 & 183 & 52 & 13 & 3 & 142 & 23 &
7\tabularnewline
September 02, 23:58 & 350 & 508 & 200 & 30 & 11 & 3 & 345 & 56 &
12\tabularnewline
September 03, 00:30 & 95 & 521 & 164 & 40 & 9 & 2 & 148 & 47 &
9\tabularnewline
September 03, 00:54 & 50 & 982 & 497 & 97 & 24 & 4 & 35 & 20 &
6\tabularnewline
September 05, 00:14 & 500 & 865 & 316 & 68 & 17 & 3 & 57 & 15 &
3\tabularnewline
September 05, 00:55 & 20 & 714 & 375 & 103 & 10 & 2 & 255 & 58 &
18\tabularnewline
September 05, 23:59 & 95 & 2745 & 736 & 84 & 18 & 2 & 165 & 27 &
4\tabularnewline
September 06, 00:24 & 60 & 1327 & 402 & 48 & 6 & 1 & 180 & 61 &
13\tabularnewline
September 06, 00:48 & 25 & 1441 & 455 & 68 & 14 & 3 & 63 & 38 &
16\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S5. Concentrations of suspended and sinking particulate
biogeochemical elements.}

Particulate organic carbon and nitrogen (POC and PON), particulate
inorganic carbon (PIC), biogenic and lithogenic silica (bSi and lSi) and
transparent exopolymer particles (TEP) concentrations associated with
suspended (SU) and sinking (S) particles. Values in bold are the
outliers discussed in paragraph 4.5. The abbreviation ``na'' is
reflective of values that were either not collected or lost during
analysis. The propagated uncertainty is denoted as ``sd''.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllllllll@{}}
\toprule
Date in 2018 (UTC) & Depth (m) & POC (\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & POC
(\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & POC (\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & POC
(\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & PON (\selectlanguage{greek}µ\selectlanguage{english}g N L\textsuperscript{-1}) & PON
(\selectlanguage{greek}µ\selectlanguage{english}g N L\textsuperscript{-1}) & PON (\selectlanguage{greek}µ\selectlanguage{english}g N L\textsuperscript{-1}) & PON
(\selectlanguage{greek}µ\selectlanguage{english}g N L\textsuperscript{-1}) & PIC (\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & PIC
(\selectlanguage{greek}µ\selectlanguage{english}g C L\textsuperscript{-1}) & bSi (\selectlanguage{greek}µ\selectlanguage{english}mol Si L\textsuperscript{-1}) & bSi
(\selectlanguage{greek}µ\selectlanguage{english}mol Si L\textsuperscript{-1}) & lSi (\selectlanguage{greek}µ\selectlanguage{english}mol Si L\textsuperscript{-1}) &
lSi (\selectlanguage{greek}µ\selectlanguage{english}mol Si L\textsuperscript{-1}) & TEP (\selectlanguage{greek}µ\selectlanguage{english}g GXeq
L\textsuperscript{-1}) & TEP (\selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}) & TEP (\selectlanguage{greek}µ\selectlanguage{english}g
GXeq L\textsuperscript{-1}) & TEP (\selectlanguage{greek}µ\selectlanguage{english}g GXeq
L\textsuperscript{-1})\tabularnewline
& & SU & SUsd & S & Ssd & SU & SUsd & S & Ssd & SU & S & SU & S & SU & S
& SU & SUsd & S & Ssd\tabularnewline
September 05, 00:55 & 20 & 74.2 & 12.4 & 3.1 & 1.5 & 13.9 & 2.6 & 0.6 &
0.3 & 0.0 & 0.0 & 0.14 & 0.02 & 0.1 & 0.02 & 12.3 & 1.4 & 0.2 &
0.2\tabularnewline
September 06, 00:48 & 25 & 78.1 & 13.0 & 1.5 & 1.1 & 14.8 & 2.7 & 0.5 &
0.3 & na & na & na & na & na & na & na & na & na & na\tabularnewline
September 03, 00:54 & 50 & 45.9 & 7.7 & 4.0 & 1.2 & 8.8 & 1.6 & 1.0 &
0.3 & 1.0 & 0.9 & 0.05 & na & 0.1 & 0.0 & na & na & na &
na\tabularnewline
August 16, 04:24 & 55 & 58.7 & 9.8 & \textbf{21.3} & 4.7 & 10.7 & 2.0 &
\textbf{2.0} & 0.5 & na & na & na & na & na & na & na & 1.4 & 0.7 &
0.1\tabularnewline
August 18, 00:44 & 55 & na & na & na & na & na & na & na & na & na & na
& 0.04 & 0.02 & 0.1 & 0.01 & na & na & na & na\tabularnewline
September 01, 03:58 & 55 & 37.8 & 6.3 & 5.0 & 1.2 & 10.3 & 1.9 & 1.2 &
0.4 & na & na & na & na & na & na & 8.3 & 0.9 & 0.2 & 0.1\tabularnewline
August 26, 00:54 & 60 & 46.0 & 7.7 & 1.9 & 0.6 & 4.6 & 0.8 & 0.3 & 0.1 &
3.0 & na & 0.06 & 0.01 & 0.2 & 0.01 & na & na & na & na\tabularnewline
August 28, 01:33 & 60 & 63.9 & 10.7 & 2.4 & 1.1 & 5.8 & 1.1 & 0.4 & 0.1
& 0.0 & 0.6 & 0.05 & 0.01 & 0.1 & 0.04 & 11.3 & 1.2 & 0.4 &
0.2\tabularnewline
August 29, 01:07 & 60 & 71.2 & 11.9 & 1.2 & 1.0 & 6.4 & 1.2 & 0.7 & 0.3
& na & na & na & na & na & na & 9.4 & 1.0 & 0.2 & 0.1\tabularnewline
September 06, 00:24 & 60 & 44.4 & 7.4 & 2.2 & 0.8 & 10.9 & 2.0 & 0.5 &
0.2 & na & na & na & na & na & na & na & na & na & na\tabularnewline
August 24, 01:12 & 63 & 48.3 & 8.1 & 5.2 & 1.4 & 8.9 & 1.6 & 0.6 & 0.2 &
na & na & na & na & na & na & 9.6 & 1.1 & 0.6 & 0.0\tabularnewline
August 21, 00:48 & 65 & 48.0 & 8.0 & 0.4 & 0.5 & 7.9 & 1.5 & 0.1 & 0.1 &
na & na & na & na & na & na & na & na & na & na\tabularnewline
August 16, 03:56 & 95 & 35.0 & 5.8 & \textbf{35.1} & 7.5 & 4.2 & 0.8 &
\textbf{4.0} & 0.9 & na & na & na & na & na & na & 4.7 & 0.5 &
\textbf{0.8} & 0.0\tabularnewline
August 18, 00:17 & 95 & na & na & na & na & na & na & na & na & na & na
& 0.04 & 0.02 & 0.0 & 0.04 & na & na & na & na\tabularnewline
August 21, 00:22 & 95 & 12.9 & 2.2 & 0.9 & 0.2 & 4.1 & 0.8 & 0.1 & 0.1 &
na & na & na & na & na & na & na & na & na & na\tabularnewline
August 24, 00:45 & 95 & 23.5 & 3.9 & 3.8 & 0.9 & 3.8 & 0.7 & 0.4 & 0.1 &
na & na & na & na & na & na & 3.8 & 0.4 & 0.1 & 0.1\tabularnewline
August 26, 00:32 & 95 & 18.6 & 3.1 & 2.5 & 0.7 & 0.2 & 0.0 & 0.3 & 0.1 &
2.5 & 0.0 & 0.05 & 0.00 & na & 0.00 & na & na & na & na\tabularnewline
August 29, 00:44 & 95 & 19.5 & 3.3 & 2.4 & 0.5 & 1.7 & 0.3 & 0.7 & 0.2 &
na & na & na & na & na & na & 3.8 & 0.4 & 0.1 & 0.1\tabularnewline
September 01, 06:10 & 95 & 18.7 & 3.1 & 1.3 & 0.4 & 5.2 & 1.0 & 0.4 &
0.1 & na & na & na & na & na & na & 3.0 & 0.3 & 0.3 & 0.1\tabularnewline
September 03, 00:30 & 95 & 14.6 & 2.4 & 2.3 & 0.6 & 4.3 & 0.8 & 0.5 &
0.2 & 6.7 & 0.0 & 0.05 & 0.01 & 0.3 & 0.02 & na & na & na &
na\tabularnewline
September 05, 23:59 & 95 & 20.7 & 3.5 & 1.1 & 0.4 & 6.6 & 1.2 & 0.3 &
0.1 & na & na & na & na & na & na & na & na & na & na\tabularnewline
August 20, 23:43 & 195 & \textbf{43.9} & 7.3 & 2.0 & 0.5 & \textbf{5.4}
& 1.0 & 0.5 & 0.1 & na & na & na & na & na & na & na & na & na &
na\tabularnewline
August 16, 03:03 & 195 & \textbf{32.2} & 5.4 & na & na & 3.8 & 0.7 & na
& na & na & na & na & na & na & na & 2.8 & 0.3 & na & na\tabularnewline
August 17, 23:42 & 195 & na & na & na & na & na & na & na & na & na & na
& 0.08 & 0.02 & 0.1 & 0.05 & na & na & na & na\tabularnewline
August 29, 00:11 & 300 & 9.6 & 1.6 & 2.2 & 0.6 & 0.0 & 0.0 & 0.0 & 0.0 &
na & na & na & na & na & na & 2.0 & 0.2 & 0.1 & 0.0\tabularnewline
August 24, 00:08 & 350 & 13.1 & 2.2 & 2.0 & 0.4 & 0.8 & 0.1 & 0.1 & 0.0
& na & na & na & na & na & na & 2.3 & 0.2 & 0.1 & 0.0\tabularnewline
August 25, 23:54 & 350 & 4.3 & 0.7 & 1.7 & 0.3 & 0.0 & 0.1 & 0.1 & 0.0 &
3.0 & \textbf{21.5} & 0.04 & 0.00 & 0.1 & 0.02 & na & na & na &
na\tabularnewline
September 01, 06:41 & 350 & \textbf{32.4} & 5.4 & \textbf{5.5} & 0.9 &
\textbf{8.5} & 1.6 & \textbf{1.2} & 0.3 & na & na & na & na & na & na &
3.1 & 0.3 & 0.1 & 0.0\tabularnewline
September 02, 23:58 & 350 & 5.6 & 0.9 & 1.2 & 0.2 & 0.7 & 0.1 & 0.3 &
0.1 & 2.9 & 0.0 & 0.03 & 0.00 & 0.2 & 0.00 & na & na & na &
na\tabularnewline
August 28, 01:53 & 500 & 6.0 & 1.0 & 2.0 & 0.3 & 0.0 & 0.1 & 0.2 & 0.0 &
4.9 & 0.4 & \textbf{0.18} & 0.01 & na & 0.01 & 2.0 & 0.2 & 0.1 &
0.0\tabularnewline
September 05, 00:14 & 500 & 5.6 & 0.9 & 3.4 & 0.6 & 0.9 & 0.2 & 0.7 &
0.2 & 8.3 & 0.0 & 0.04 & 0.01 & 0.1 & 0.00 & 1.5 & 0.2 & 0.1 &
0.0\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S6. Concentrations of biogeochemical components of sinking
particles after 1, 2 and 4 hours of settling.}

Table illustrates the results of the settling time test. POC, PON, bSi
and TEP concentrations (average of replicate filters) and associated
uncertainties (standard deviations) measured in sinking particles after
1, 2 and 4 hours of settling. The three marine snow catchers were
deployed at two stations at 80 and 60 m, respectively.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Date in 2018 (UTC)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Depth\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Settling time\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sinking POC\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sinking PON\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sinking bSi\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sinking TEP\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
m\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
hours\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\selectlanguage{greek}µ\selectlanguage{english}g L\textsuperscript{-1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\selectlanguage{greek}µ\selectlanguage{english}g L\textsuperscript{-1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
nmol L\textsuperscript{-1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\selectlanguage{greek}µ\selectlanguage{english}g GXeq L\textsuperscript{-1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
August 20\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
80\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.4 \selectlanguage{ngerman}± 1.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4 \selectlanguage{ngerman}± 0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8 \selectlanguage{ngerman}± 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4 \selectlanguage{ngerman}± 0.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
August 20\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
80\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.1 \selectlanguage{ngerman}± 0.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.5 \selectlanguage{ngerman}± 0.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8 \selectlanguage{ngerman}± 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.2 \selectlanguage{ngerman}± 0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
August 20\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
80\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.6 \selectlanguage{ngerman}± 0.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.7 \selectlanguage{ngerman}± 0.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8 \selectlanguage{ngerman}± 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3 \selectlanguage{ngerman}± 0.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
September 08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.9 \selectlanguage{ngerman}± 0.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.5 \selectlanguage{ngerman}±0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12 \selectlanguage{ngerman}± 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4 \selectlanguage{ngerman}± 0.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
September 08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.4 \selectlanguage{ngerman}± 0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4 \selectlanguage{ngerman}± 0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10 \selectlanguage{ngerman}± 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.1 \selectlanguage{ngerman}± 0.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
September 08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.6 \selectlanguage{ngerman}± 0.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.6 \selectlanguage{ngerman}± 0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15 \selectlanguage{ngerman}± 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.2 \selectlanguage{ngerman}± 0.1\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\selectlanguage{english}
\FloatBarrier
\end{document}

