# Response of phytoplankton to eddy dipole structure in the Mozambican channel: An automated underway evaluation

Jordan Toullec, Millat Blanc, Pierrick Penven, Alexandre Epinoux, Natasha

Buckiewicz, Emmanuelle Jaouen, Valentine Szrama, Margaux Noyon,

Jean-François Ternon, Luis Felipe Artigas

► To cite this version:

Jordan Toullec, Millat Blanc, Pierrick Penven, Alexandre Epinoux, Natasha Buckiewicz, et al. Re-sponse of phytoplankton to eddy dipole structure in the Mozambican channel: An automated underway evaluation. Deep Sea Research Part II: Topical Studies in Oceanography, 2025, 224, pp.105554. 10.1016/j.dsr2.2025.105554. hal-05371658

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https://hal.science/hal-05371658v1

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# Response of phytoplankton to eddy dipole structure in the Mozambican channel: An automated underway evaluation

Jordan Toullec<sup>a,b,*</sup>, Millat Blanc<sup>b</sup>, Pierrick Penven<sup>c</sup>, Alexandre Epinoux<sup>b,c</sup>,
Natasha Buckiewicz<sup>b,d</sup>, Emmanuelle Jaouen<sup>b</sup>, Valentine Szrama<sup>b</sup>, Margaux Noyon<sup>e</sup>,
Jean-François Ternon<sup>f</sup>, Luis Felipe Artigas<sup>b,**</sup>

## ARTICLE INFO

Handling Editor: Prof. J Aristegui

Keywords:

Eddy dipole structure

Mozambique channel (MZC)

Phytoplankton functional groups

## ABSTRACT

Fine-scale oceanographic structures such as eddies, fronts, and filaments strongly influence biogeochemical and ecological processes. So far, the characterization of phytoplankton communities of the Mozambique Channel (MZC) was limited to *in-situ* sampling and satellite monitoring of chlorophyll-α biomass estimation. Few studies have looked at phytoplankton community composition. During the RESILIENCE cruise in 2022, three structures were sampled; an anticyclonic eddy, a frontal area and a cyclonic eddy. Phytoplankton functional groups (PFGs) were studied at small-scale (1 km) in subsurface (about 5m depth) waters using automated underway measurements. A multispectral fluorometer (FLP) and an automated pulse shape-recording flow cytometer (AFCM). The oceanographic mesoscale features clearly structured the phytoplankton groups with distinct patterns observed in each of the three areas studied. A relatively high concentration of brown pigmentary group (groups containing xanthophyll and carotenoids-like pigments) was observed in the cyclonic eddy. High abundance of nano-microeukaryotes and of prokaryotic phytoplankton (*Synechococcus* spp. and *Prochlorococcus* spp.) were identified in the cyclonic eddy. These differences could potentially affect higher trophic levels such as zooplankton, micronekton, large pelagic fish, mammals, and seabirds. Finally, this phytoplankton community differentiation could also impact biogeochemical processes such as carbon sequestration and nutrient dynamics in the MZC.

## 1. Introduction

In the pelagic ocean realm, phytoplankton drive the primary pro-
duction which represents the basis of oceanic food webs and plays a key
role in most biogeochemical processes. Hydrodynamical features such as
upwellings (deep water rising), fronts (rapid change of surface water
properties), eddies (water mass vortex) are the main drivers of phyto-
plankton community composition by creating niches within the
euphotic layer through nutrients input and mixing (Gaube et al., 2019;
Lévy et al., 2001; Mahadevan et al., 2012; McGillicuddy, 2016;

McGillicuddy Jr. et al., 2003). By modulating the mixed layer depth, meso (~10–100 km) and submesoscale (~1–10 km) features create fine-scale spatial variability in nutrients concentration both within and around these structures (Gaube et al., 2019; McGillicuddy, 2016). Indian ocean eddies are known to enhance phytoplankton production and biomass, which subsequently supports the marine trophic food web (Barlow et al., 2014; Huggett, 2014).

Located between the coasts of Africa and Madagascar, the
Mozambique Channel (MZC) is characterized by high eddy kinetic en-
ergy and intense mesoscale eddy formation – both anticyclonic and

<footnote><sup>a</sup> Laboratoire des sciences de l'environnement marin, UMR 6539 LEMAR, Univ Brest, CNRS, IRD, Ifremer, IUEM, Brest, France</footnote>

<footnote><sup>b</sup> Laboratoire d'Océanologie et de Géosciences, UMR 8187 LOG, CNRS, Université du Littoral Côte d'Opale, Université de Lille, F-62930, Wimereux, France</footnote>

<footnote><sup>c</sup> Laboratoire d'Océanographie Physique et Spatiale, UMR 254 LOPS, Univ Brest, CNRS, IRD, Ifremer, IUEM, Brest, France</footnote>

<footnote><sup>d</sup> Institute for the Oceans and Fisheries, Faculty of Science, The University of British Columbia, 2202 Main Mall, V6T 1Z4, Vancouver, Canada</footnote>

<footnote><sup>e</sup> Institute for Coastal and Marine Research, Nelson Mandela University, Gqeberha, 6001, South Africa</footnote>

<footnote><sup>f</sup> MARBEC, Univ. Montpellier, CNRS, Ifremer, IRD, Sète, France</footnote>

<footnote>This article is part of a special issue entitled: IIOE2 V9 published in Deep-Sea Research Part II.</footnote>

<footnote>* Corresponding author. Laboratoire des sciences de l'environnement marin, UMR 6539 LEMAR, Univ Brest, CNRS, IRD, Ifremer, IUEM, Brest, France.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: Toullec.jordan@gmail.com (J. Toullec), felipe.artigas@univ-littoral.fr (L.F. Artigas).</footnote>
cyclonic - and circulation (Penven et al., 2006, 2025; Ternon et al., 2014). Mozambique Channel eddies have diameters typically ranging between 50 and 350 km (Biastoch and Krauss, 1999; de Ruijter et al., 2002; Ridderinkhof and de Ruijter, 2003). They mostly follow a well-defined path along the Mozambican shelf flowing southward (Halo et al., 2014), travelling up to thousands of kilometres over a few weeks to months (Chelton et al., 2011). Chapman (2003) suggested a significant horizontal mixing between eddies and surrounding waters, leading to horizontal transport of plankton/seston, essential lipids and nutrients (Becker et al., 2025; Morales et al., 2017; Uchiyama et al., 2017). In particular, chlorophyll-rich coastal waters can be transported to the central MZC (Quartly and Srokosz, 2004). Eddies can also induce vertical motions, resulting in the upwelling of nutrient-rich waters, favouring phytoplankton growth (Gangrade and Franks, 2023; McGillicuddy et al., 1998; Noyon et al., 2019). The stirring and straining induced by mesoscale eddies participate in the formation of filaments, which can evolve into submesoscale fronts visible on the ocean surface (Barton et al., 1998; Capet et al., 2008). These fronts are at the interface between water masses of different temperatures and/or salinities (Talley, 2011). MZC eddies are characterized by their greater size with radii exceeding 100 km (up to 350 km) and typical vorticity structure (Halo et al., 2014). Cyclonic eddies are also present, generally smaller in diameter (Halo et al., 2014; Penven et al., 2025). Anticyclonic and cyclonic eddies may occasionally pair, generating a dipole structure with a strong frontal zone separating the two eddies. Eddy dipoles are relatively common in the MZC (Ni et al., 2020; Penven et al., 2025; Roberts et al., 2014; Ternon et al., 2014).

Investigating phytoplankton community shift across physical struc-
ture thanks to discrete sampling is a challenge in oceanography due to
coarse sampling strategy and time consuming sample analysis. Tech-
nological improvements of high-frequency sampling of phytoplankton
communities are now making such investigations possible. Technologies
such as multispectral fluorometer (FLP), and automated pulse shape
recording flow cytometer (AFCM) address the technical issue to inves-
tigate submesoscale features at a higher resolution. Phytoplankton can
be described with size classes: picoplankton (<2 µm), nanoplankton
(2–20 µm) and microplankton (>20 µm). While AFCM allow identifying
phytoplankton functional groups (PFGs) that gather pigment composi-
tion and size classes based on single cell/colony, FLP allow to identify
pigmentary groups in a bulk of cell suspension.

Previous investigations in the MZC have focused on the physical characterization of eddies, but their small-scale biological implications have not yet been addressed. Several multi-disciplinary research cruises have been undertaken with physical, chemical, and biological sampling focused on mesoscale eddies and frontal regions (Ternon et al., 2014). A previous study in the MZC described the enhancement of chlorophyll-a concentration inside a cyclonic eddy, characterized by a higher proportion of diatoms and similar abundance of picoplankton, nanoplankton, ciliates, and dinoflagellates within and outside the eddy, which was not observed in the zooplankton distribution pattern (Noyon et al., 2019). This suggests complex interactions acting at different spatial and temporal scales, as well as at different trophic levels within eddies and fronts. Prior to the RESILIENCE cruise (fRonts, EddieS and marineLife in the wEstern Indian Ocean) and to the best of the authors' knowledge, no study sought to investigate shifts in phytoplankton communities at the submesoscale in relation to mesoscale eddies in the MZC.

The objective of this study was to characterize phytoplankton com-
munities in the MZC at the scale of a eddy dipole (including the frontal
area) by addressing the following question: How multispectral fluo-
rometer, and automated pulse shape recording flow cytometer, can be
used as descriptors of phytoplankton communities?

## 2. Materials and methods

2.1. Study area

The RESILIENCE cruise MD237 (https://doi.org/10.17600/1800
1917) took place in 2022, from the 19th of April to the 22nd of May,
on-board the R/V Marion Dufresne (Fig. 1a). The MD237 cruise left La
Réunion Island towards Bassas da India Island in the middle of the MZC,
before following a southward trajectory towards Durban, and an east-
ward return trajectory back to La Réunion Island (Fig. 1a). Shipboard
activities included underway recording (described further in this sec-
tion) and hydrographic profiling (CTD casts and water sampling). Our
study focuses only on the first leg (Leg 1) of the cruise which sampled at
mid and high spatial resolution (meso- and submesoscale) water masses
across a dipole made of an anticyclonic eddy and a cyclonic eddy
separated by a front, located northwest of Europa Island (38.4–39.1° E
and 20.6 to 22.2°S, Table S1; Figs. 1 and 2). Thirteen discrete sampling
stations were performed during Leg 1 (Supplementary Table S1), data
collected at these stations are not used in this study which only rely on
surface underway measurements.

## 2.2. Satellite altimetry/mesoscale eddies location

During Leg 1, the anticyclonic eddy (north), the front and the
cyclonic eddy (south) were characterized using sea surface elevation
satellite data (Roberts et al., 2014; Schouten et al., 2003). Altimetry was
used for the description at the mesoscale while on-board current meter
(S-ADCP 150 kHz) was used for finer structure characterization (Penven
et al., 2025). Sea surface elevation data (geostrophic altimetry-derived)
were retrieved on-board from a global ocean gridded L4 sea surface
heights and derived variables model processed by the Copernicus
Climate Service with a resolution of 0.25° × 0.25°. For this study,
altimetry data from 25 to April 30, 2022 (duration of Leg 1) were
retrieved from Copernicus Marine Service Information (https://doi.org/
10.48670/moi-00145) and are represented in Figs. 1 and 2.

2.3. Underway sampling

Sub-surface seawater was continuously pumped from a depth of 5–6
m entering the FerryBox and the medium flow-through system of the R/
V Marion Dufresne. Pressure was kept under 1 bar, and the flow rate
maintained at 60 dm³ min⁻¹. The thermosalinograph (TSG, SeaBird
SBE21) measured sea surface temperature (SST) and salinity (SSS) data
every 30 s. Automated underway in-vivo measurements of phyto-
plankton were carried out combining: (1) in-vivo total chlorophyll-a
fluorometry implemented in the FerryBox (FB, FerryBox 4H-Jena, Ger-
many), measured every 30 s, (2) Fluoroprobe (bbe Moldaenke, GmbH,
DE) in-vivo multispectral fluorometer (FLP), measured every 30 s, and
(3) CytoSense (Cytobuoy, b.v., NL) automated pulse shape recording
flow cytometer (AFCM) measured every hours. The AFCM and the FLP
were connected to the medium flow-through system of the vessel during
the whole cruise, except during sampling stations when they were used
for hydrographic profiling (FLP) and/or analysis of discrete samples
(FLP and AFCM).

2.3.1. TSG calibration and quality control

Sea surface temperature (SST) and salinity (SSS) were continuously
measured using a SBE21 thermosalinograph. Salinity data were regu-
larly controlled by daily samples taken at the entrance of the TSG, which
were analysed at the French Service Hydrographique de la Marine after the
cruise. The quality control was then performed using the “TSG-QC”
software (IRD; https://forge.ird.fr/us191/TSG-QC), and a minor
correction was applied to the salinity data. SST and SSS measurements’
accuracy are about 0.005 °C and 0.01, respectively.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (a) Ship track from 20th of April to May 21, 2022 in the Southwestern Indian Ocean and the MZC. Left: detailed track of Leg 1, the red/blue scale bar corresponding to the anomaly of sea surface elevation (cm) on (b) 25th of April and (c) April 30, 2022. White numbered points correspond to sampling stations (stations 1–13). For each panel, the x-axis corresponds to the longitude (°E) and the y-axis to the latitude (°S). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Ship track from 25th of April to 30th of April. (a) The x-axis corresponds to the longitude (°E) and the y-axis to the latitude (°S). The color gradient of ship track and isolines represents the day of the cruise. (b) x-axis and y-axis correspond to the dipole referential axis (km). White dots with number correspond to sampling station (stations 1–13). The solid, dashed and dotted isolines correspond respectively to 0, 16 and 24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). Adapted from Penven et al. (2025). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

### 2.3.2. Chlorophyll *a* determination for ferry box calibration

Regularly, seawater from the medium flow-through system of the vessel was sampled for chlorophyll-*a* (Chl *a*) *in-vivo* and *in-vitro* fluorescence measurements. These discrete samples were then compared with *in-vivo* Chl *a* fluorescence measured by the Ferrybox (FB) fluorometer. Every 2 h, 1–2 L of seawater was filtered onto 0.7 µm micro-glass fibre filters (MF300 Fisherbrand) and directly extracted into 4–6 mL of 100 % acetone for at least 12 h at 4 °C in the dark. When the extraction was not carried out directly after the filtration, filters were

stored at –20 °C until Chl *a* measurement. Following Welschmeyer (1994) non-acidification method, Chl *a* measurements were made on-board using the “blue” module of a Turner Designs (USA) Trilogy fluorometer. Chl *a* concentration values were then calculated and expressed as µg Chl *a* L⁻¹.

### 2.3.3. Multispectral Fluorometry: fluoroprobe (FLP)

The multispectral fluorometer FluoroProbe (FLP, bbe Moldaenke, Germany) has five light-emitting diodes (470, 525, 570, 590 and 610 nm) that sequentially excite in the range of Chl *a* and accessory pigments absorption bands. Relative fluorescence intensity of Chl *a* is measured between 690 and 710 nm, after each LED excitation, and then converted into Chl *a* equivalent concentration (µg Chl *a* eq L⁻¹). Estimated concentrations of each phytoplankton spectral (inferred as pigmentary) group is calculated by the bbe++ software applying the algorithm designed by Beutler et al. (2002) and expressed as equivalent amount of Chl *a* per liter (µg Chl *a* eq L⁻¹). The brown pigmentary group (BPG) is composed of part of the haptophyte group (Houliez et al., 2012), bac-ciliophyceae (diatoms), part of the dinoflagellates and chrysophyceae. The Green pigmentary group (GPG) corresponds to chlorophyceae, euglenophyceae, prasinophyceae but also part of the haptophyte group. The blue-green pigmentary group (BGPG) is composed mainly of phycocyanin-rich cyanobacteria whereas the mixed-red group (RPG) is represented by both phycoerythin-rich cyanobacteria and crypto-phytes. The definition of these groups was based on pre-calibrated excitation and emission spectra (termed “fingerprints”) obtained from the fluorometry analysis of pure laboratory cultures of phytoplankton species selected by the manufacturer, representative of the groups of interest (Beutler et al., 2002), and registered into the instrument. Further work established that these groups do not correspond to any taxonomical group or even to any strictly defined pigmentary group (Houliez et al., 2012). An additional diode (370 nm) is used for the excitation of humic-like DOM. Multispectral Fluorometry is commonly used to identify high-resolution phytoplankton diel variations (Liu et al., 2012), coastal marine phytoplankton assemblages (Kodama et al., 2022), lake vertical phytoplankton dynamic (Lofton et al., 2020) or also along mesoscale structure (Correa-Ramirez et al., 2018; Davila et al., 2021). Recently machine learning improved the analysis to reflect ecological and physiological features (Lafer et al., 2025).
### 2.3.4. Automated Pulse-Shape Recording Flow Cytometry: CytoSense (AFCM)

Phytoplankton communities were characterized at the single-cell/ colony level using a CytoSense (Cytobuoy b.v., NL), which is an in-vivo automated pulse shape-recording flow cytometer (AFCM). Particles are aligned by a 0.2 µm filtered sheath fluid driven by a calibrated peristaltic pump (up to 20 µL s⁻¹) and then cross a 5 µm-wide laser beam. The detection limit is set by the user to reduce the amount of non-target events recorded, while retaining relevant information. Forward scatter (FWS) is recorded by a photodiode, while sideward scatter (SWS), red and yellow fluorescence (FLR and FLY) are amplified by photomultiplier tubes (PMTs) before being recorded. For each of these recordings, several features are calculated for each particle, which allow to optically characterize relevant particles. The signal-to-noise ratio limits the lower size-range to approximately 1 µm. The CytoSense has an improved discriminatory power due to its "scanning" method: instead of recording each event as a single pulse, it records and provides a one-dimensional scan of every event, for every optical channel. This detailed in-silico image (pulse-shape) is a function of the cell features (including size and morphology from FWS, external and internal structures from SWS, and fluorescence). As the mismatch between the refractive indices of the sample and the sheath fluid can lead to the accumulation of noise in the scatter channels (Cucci and Sieracki, 2001), the system is filled with 0.2 µm-filtered seawater from the sampling area at the beginning of each cruise. After each measurement, the sample is filtered again onto two 0.1 µm filters and is incorporated into the sheath fluid, so that its composition is constantly adjusted to the environment (Dubelaar et al., 1999). Each particle of the core sample is excited by a laser beam (operating at 488 nm and 120 mW) and produces scattered light and fluorescence that are recorded electronically. Flow cytometry is frequently used to identify phytoplankton community variations along surface physical features (Mangolte et al., 2023), hydrophysical event (Barrillon et al., 2023) or during time series (Hubert et al., 2025). Automated Flow cytometry is being frequently used to characterize identify phytoplankton community variations along surface physical features (Barrillon et al., 2023; Louchart et al., 2020; Mangolte et al., 2023; Oms et al., 2024; Tzortzis et al., 2021), hydrophysical events (Barrillon et al., 2023) or during time series (Hubert et al., 2025).

### 2.3.5. Characterization of phytoplankton Functional groups from AFCM data

Based on their optical features as recorded by the flow cytometer, particles are assigned to clusters that represent phytoplankton functional groups (PFGs). A gating is done using the CytoClus4 software (Cytobuoy b.v., NL) that allows plotting cytograms, i.e., two-dimensional representations of the signals' parameters, on which clusters are manually drawn (Fuchs et al., 2022; Thyssen et al., 2022a). Indeed, optical traits can be interpreted as morpho-physiological traits and be used to discriminate phytoplankton cells (Fragoso, 2019; Pomati et al., 2013). PFGs are usually characterized using several cytograms as they are based on cell optical features related to size, structure, fluorescence type and intensity (Supplementary Fig. 1). A standardized nomenclature of PFGs was implemented (Thyssen et al., 2022b) and was used in this work. *Prochlorococcus* (RedPicoProk), *Synechococcus* (OraPicoProk, RedRedPico), picoeukaryotes (RedPico) and picocryptophytes (OraPico) are targeted by the "Pico" protocol (high PMT amplification, and low detection threshold and volume sampled) and nanophytoplankton (RedNano, RedRedNano, HSNano, OraNano and coccolithophores-like) and microphytoplankton (RedMicro, OraMicro) groups by the "Micro" protocol (with a low PMT amplification, and high detection threshold and volume sampled). Coccolithophores-like group was discriminated according to the side scatter depolarization (Von Dassow et al., 2012).

### 2.3.6. Characterization of phytoplankton communities

All analyses were made using R (version 4.3.3). The characterization

of phytoplankton communities was made using a hierarchical clustering method. PFGs abundance and average red fluorescence per cell (hereafter FLR<sub>cell</sub>) were Hellinger-transformed according to Legendre and Gallagher (2001), prior to computing distance matrices using the Euclidean distance, suited for hierarchical clustering and ordination methods of ecological data (Borcard et al., 2011). Abundance and FLR<sub>cell</sub> were used as communities' descriptors since they respectively provide information at the PFGs level and at the cellular level. This avoids redundancy in the distance matrix since fluorescence is normalized by abundance to obtain FLR<sub>cell</sub>. Considering non-photochemical quenching mechanisms that can influence phytoplankton fluorescence on surface (Müller et al., 2001), the impact of the diel cycle was assessed using a Kruskal-Wallis test (kruskal.test() from stats package version 4.3.1) as assumptions for ANOVA were not satisfied (Borcard et al., 2011). As FLR<sub>cell</sub> was independent from the moment of the day for all PFGs, the clustering was based on an average distance matrix built from Hellinger-transformed abundance and FLR<sub>cell</sub> distance matrices. Ward's minimum variance clustering was applied to the distance matrix using the hclust() function with the argument method = "ward.D2", that implements Ward's criterion on distance matrices not previously squared (Murtagh and Legendre, 2014). The Ward's method is an ascending hierarchical clustering method that detects, at each step, the homogeneity of clusters and aggregates the two ones with the minimum within-cluster inertia (Ward Jr, 1963). The optimal number of clusters was determined using the NbClust() function from the NbClust package (version 3.0.1) that uses 30 indices to propose the best clustering (Charrad et al., 2014). A secondary clustering was made using a higher number of clusters than the optimal one to account for the remaining intra-cluster variability. Cluster assignation was used as a factor to look for differences in PFGs abundance and FLR<sub>cell</sub> across communities using Kruskal-Wallis tests (kruskal.test() from stats package version 4.3.1) as assumptions for ANOVA were not satisfied (Borcard et al., 2011). When significant differences were detected, post hoc Dunn tests were performed using the dunnTest function from the FSA package (version 0.9.4) to identify which clusters differed from each other in terms of abundance and FLR<sub>cell</sub>.

## 2.4. Physical parameters

### 2.4.1. Dipole referential

The RESILIENCE cruise strategy was adaptative, following in real time, from satellite and *in-situ* observations, the physical structures while in transit. Altimetry observations showed that for the duration of Leg 1 (26 April–1 May) the dipole moved in a straight line at almost a constant rate of 9 km day⁻¹ southeasterly (bearing: 115°). To unambiguously separate the specific roles played by the different elements of the dipole (Mozambican eddies, frontal system, filament, cyclonic eddy) on marine life and to address the submesoscale processes, the observations were relocated on a moving frame centred on the structure and moving with it (Fig. 2). Since the dipole moved at almost a constant velocity over a straight line during Leg1, the use of such a frame did not introduce any spurious deformation in the representation of the structure. The frame was centred so that the dipole centre was located at X = 0 km and Y = 0 km and the dipole axis coincided with the x-axis. To help visualization, an objective analysis was performed on the observations in this frame, using a decorrelation scale of 100 km on a regular grid at 5 km resolution (Penven et al., 2025). Fig. 2 shows that we principally sampled the eastern side of the central region of the dipole.

### 2.4.2. Ocean currents

Ocean currents were continuously measured by a RDI Ocean Surveyor 150 kHz hull mounted S-ADCP (Shipboard Acoustic Doppler Current Profilers). S-ADCP velocity data were processed and analysed by the CASCADE v7.2 software (Kermabon et al., 2023), using a temporal sampling rate of 1 min and a vertical resolution of 8 m. The mesoscale dipole structure was represented by current velocities averaged over the
first 240 m (u for the northward component, v for the eastward component). Before vertical integration, S-ADCP velocities were first gridded over the dipole using a vectorial objective analysis with a 100 km horizontal decorrelation scale (Arhan and Verdière, 1985).

Mesoscale eddies natures and intensities were underlined by vorticity (the amount of local rotation), defined as $\xi = \frac{\partial v}{\partial x} - \frac{\partial u}{\partial y}$, divided by the absolute value of the Coriolis parameter (f). Here, $\xi = \frac{\partial v}{\partial f} > 0$ account for an anticyclone and $\xi = \frac{\partial u}{\partial f} < 0$ account for a cyclone. $\xi$ is also used to define the Okubo-Weiss parameter ($\omega$), which separates the regions dominated by rotation (here the eddies) from the regions dominated by

deformation (here the dipole axis): $\omega = \left(\frac{\partial u}{\partial x} - \frac{\partial v}{\partial y}\right)^2 + \left(\frac{\partial v}{\partial x} + \frac{\partial u}{\partial y}\right)^2 - \xi^2$.

If vorticity (for example rotation in an eddy) dominates, the Okubo-Weiss parameter is negative, whereas if deformation (for example straining in a front) dominates, the Okubo-Weiss parameter is positive. The Okubo-Weiss parameter was used for eddy detection in the Mozambique Channel (Halo et al., 2014). For readability, we have scaled the Okubo-Weiss parameter by $f^2$.

# 3. Results

## 3.1. Physical and Hydrological description

Along the dipole structure (northeast to southwest, which corresponds to the y-axis in the dipole referential) we observed slightly warmer and saltier waters in the anticyclonic eddy (northeast: SSS >35.25 and SST >28 °C) compared to the cyclonic eddy (south: SSS <35 and SST <27.5 °C), clearly visible along the y-axis (Fig. 3a and b). The y-axis also characterized anomalies in sea surface height (relative sea surface elevation in cm represented by black isolines). Altimetry isolines at y-axis >0 represent positive sea surface elevation and at y-axis <0 negative sea surface elevation. Vorticity/f ratio values in the dipole showed a transition across the front, from positive values at y-axis >0 and negative values at y-axis <0 km (at y-axis = 0 km, altimetry anomaly = 0 cm). Okubo-Weiss parameter within the dipole referential highlights three distinct structures: two with negative values on either side of the front, and an isolated one at x-axis >45 km and y-axis <0 km (under the dipole front system). Vorticity/f ratio and Okubo-Weiss parameters allow us to clearly separate the structure into three regions:

1. Inside the cyclonic eddy (negative vorticity and negative Okubo-Weiss)

2. Inside the anticyclonic eddy (positive vorticity and negative Okubo-Weiss)

3. The frontal separation region between the eddies where deformation dominates (positive Okubo-Weiss) – this accounts for the centre of the dipole (X = 0 km, Y = 0 km), but also for the east (right) of the dipole.

## 3.2. Phytoplankton Pigmentary Groups (PG)

During Leg 1, we obtained 54,020 FLP spectral measurements (from underway measures), with values of total Chl $a_{eq}$ ranging from 0 (detection limit) to 6.20 µg Chl $a_{eq}$ L⁻¹ (mean = 0.91; SD = 0.47). The concentrations of brown and blue-green phytoplankton groups (BPG and BGPG, respectively) were in similar ranges with 0–2.68 µg Chl $a_{eq}$ L⁻¹ (mean = 0.19; SD = 0.27) and 0–1.29 µg Chl $a_{eq}$ L⁻¹ (mean = 0.25; SD = 0.14), respectively. The range of the green phytoplankton group (GPG) was slightly higher with concentrations ranging from 0 to 5.68 µg Chl $a_{eq}$ L⁻¹ (mean = 0.42; SD = 0.32) while the red-mixed (“cryptophyceae”) phytoplankton group (RPG) concentration ranged only from 0 to 1.04 µg Chl $a_{eq}$ L⁻¹ (mean = 0.04; SD = 0.07).

Higher total Chl $a_{eq}$ concentrations measured with FLP spectral measurements (Total pigment concentration) were mainly located in the

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** (a) Sea surface temperature (SST, °C); (b) Sea surface salinity (SSS, PSU); (c) Surface vorticity/f ratio (no dimension) and (d) Surface Okubo-Weiss (no dimension) mapped (interpolation) in the dipole referential. x-axis and y-axis correspond to the axes in the dipole referential (km). Grey lines correspond to the ship track. The solid, dashed, and dotted black isolines correspond respectively to 0, ±16, and ±24 km anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period).</figcaption></figure>

cyclonic eddy south of the frontal zone, which is congruent with the FB total Chl *a* in-vivo fluorescence track (Fig. 4a). Both coincided with patches of high concentrations of BPG and RPG Chl $a_{eq}$ (Fig. 4c and f) and low values of GPG Chl $a_{eq}$ concentration (Fig. 4d).

The ratios of brown/green groups and blue-green/green groups presented a patchy distribution (Fig. 5). High brown/green ratios (>0.5) and blue-green were located close to y-axis = 0 km; x-axis = 10 km, around the front. Low brown/green ratios were located to the north and south of the front. The pattern of blue green/green ratio was more contrasted regarding the dipole structure.

## 3.3. Phytoplankton Functional Groups (PFGs): CytoSense (AFCM)

Twelve PFGs were identified from the 81 underway measurements carried out with the CytoSense, including two groups of microphytoplankton (RedMicro, OraMicro), three groups of nanophytoplankton (RedNano, HsNano and OraNano), seven groups of picophytoplankton (four groups of picoeukaryotes (3 RedPico and OraPico) and three groups of picoprokaryotes (2 OraPicoProk, redPicoProk). AFCM cell abundance (cell mL⁻¹), red fluorescence concentration (FLR A.U. mL⁻¹), red fluorescence per cell (FLR A.U. cell⁻¹), and the contribution of each PFGs to the respective totals are presented in
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Spatial distribution of: (a) FerryBox total Chl *a* concentration (µg Chl *a*eq L⁻¹). (b) Fluoroprobe (FLP) total Chl *a* concentration (µg Chl *a*eq L⁻¹). (c) Brown pigmentary group (BPG) (d) Green pigmentary group (GPG) (e) Bluegreen pigmentary group (BGPG) and (f) Red mixed pigmentary group (RPG), the four expressed as Chl *a* concentration (µg Chl *a*eq L⁻¹). For each panel x-axis and y-axis correspond to the axes in the dipole referential (km). The solid, dashed and dotted white or red isolines correspond respectively to 0, ±16 and ±24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). Note that the scale of the color bars varies in each subplot. The background colors correspond to the mapping (interpolation) of each variable in the dipole referential. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

**Table 1.** The PFGs were determined on a consensus vocabulary for the most common marine microbial groups (see Thyssen et al., 2022).

For both FLR and total abundance, significant correlations were observed with FB Chl *a* concentration (Supplementary Fig. 2). Higher total FLR concentration and total cell abundance were measured in the southern part of the dipole structure, corresponding to the cyclonic eddy (Fig. 6).

Picoprokaryote groups (*Synechococcus* ecotypes and *Prochlorococcus*) contributed the most to total abundance, while nanoeukaryotes contributed the most to total FLR. Nanoeukaryotes FLR were positively correlated with FB total *in-vivo* Chl *a* concentration (Supplementary Fig. 2c). No variation of relative PFGs' FLRcell was observed with the increase in FB total *in-vivo* Chl *a* fluorescence (Supplementary Fig. 2d).

### 3.4. Characterization of phytoplankton Communities (AFCM)

From CytoSense measurements, two parameters (abundance and FLRcell) were used to estimate the structure of phytoplankton

assemblages. FLRcell was used for clustering because it was estimated to be independent of the time of the day, showing no significant effect from quenching mechanisms on the variability of phytoplankton cell fluorescence (all Kruskal-Wallis tests with *p* > 0.05). Based on a distance matrix using the Euclidean distance (see in the method part 2.3.6), the hierarchical clustering and ordination displayed two main communities. The two communities were mainly distributed in the cyclonic (south of the front) and anticyclonic (north of the front) part of the dipole (Figs. 7–8a). Choosing a lower dissimilarity coefficient (below 0.5), 6 sub-communities were identified (Fig. 7), which reveal a sequential organization along the dipole structure, congruent with vorticity/f ratio isolines (Fig. 8b). Indeed, clusters 3, 4, 5 and 6 are located in the anticyclonic eddy (represented by positive vorticity/f ratio), while cluster 2 is located in the cyclonic eddy (represented by negative vorticity/f ratio). Clusters 1 and 6 however are spread out within the cyclonic eddy, without clear pattern underlining some patchiness (Fig. 8b).
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** (a) Ratios of brown over green pigmentary groups concentration (log₁₀(brown/green)). (b) Ratio of blue-green over green pigmentary groups concentration (log₁₀(blue-green/green)). For each panel x-axis and y-axis correspond to the axes in the dipole referential (km). The solid, dashed and dotted red isolines correspond respectively to 0, ±16 and ±24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<caption>**Table 1**</caption>

<table><caption>Table of AFCM parameters (Cell abundance, FLR per cell and Total FLR) per cytometric classes and PFGs.</caption><thead><tr><th>Cytometry common vocabulary</th><th>PFGs</th><th>Cell abundance</th><th>FLR per cell</th><th>Total FLR</th></tr></thead><tbody><tr><td rowspan="2">RedMicro, OraMicro</td><td>Microcryptophytes</td><td>1-6</td><td>0,02 %</td><td>1-399</td></tr><tr><td>Microeukaryotes</td><td>0-6</td><td>0,01 %</td><td>0-40.124</td></tr><tr><td rowspan="3">RedNano, OraNano, HS Nano</td><td>Nanococcolithales like</td><td>14-324</td><td>0,60 %</td><td>449 - 6639</td></tr><tr><td>Nanocryptophytes</td><td>50-282</td><td>1,03 %</td><td>227 - 1693</td></tr><tr><td>Nanoeukaryotes</td><td>186 - 2742</td><td>6,76 %</td><td>1836 - 19,980</td></tr><tr><td rowspan="4">RedPicoEuk/OraPico</td><td>Picocryptophytes</td><td>2-256</td><td>0,26 %</td><td>0-57</td></tr><tr><td>Picoeukaryotes</td><td>2 - 2823</td><td>0,95 %</td><td>1-608</td></tr><tr><td>Picoeukaryotes</td><td>0-335</td><td>0,81 %</td><td>0-257</td></tr><tr><td>'Bright'</td><td></td><td></td><td></td></tr><tr><td rowspan="4">OraPicoProk/RedPicoProk/RedRed PicoProk</td><td>Picoeukaryotes 'Dim'</td><td>14 - 2242</td><td>5,90 %</td><td>8 - 1323</td></tr><tr><td>Prochlorococcus</td><td>229 - 32,750</td><td>24,13 %</td><td>2-299</td></tr><tr><td>Synechococcus</td><td>117 - 32,100</td><td>36,01 %</td><td>1-575</td></tr><tr><td>Synechococcus 'Bright'</td><td>7 - 44,610</td><td>23,52 %</td><td>1 - 2130</td></tr></tbody></table>

# 4. Discussion

## 4.1. Synergistic effects of different phytoplankton underway measurements

Even though patchy distribution of phytoplankton biomass has been highlighted in meso- and submesoscale oceanic structures, few studies have focused on small-scale phytoplankton shifts along such structures in the open ocean (Gaube et al., 2019; Lévy et al., 2001; Mahadevan et al., 2012; McGillicuddy, 2016; McGillicuddy Jr. et al., 2003). The FerryBox Chl *a* fluorometer (FB), CytoSense (AFCM) and multispectral fluorometer (FLP) measurements are shown here to be consistent, considering the congruent results obtained for phytoplankton chlorophyll *a* distribution with the three devices (estimated from FB total *in-vivo* Chl *a* fluorescence, FLP total *in-vivo* Chl *a*eq, AFCM total FLR) and that was compared to total abundance. Zubkov and Quartly (2003) had already explored the correlation between different assessment methods of phytoplankton biomass in the area, including the comparison between AquaModis satellite estimations of total Chl *a* and in situ

measurements. They described a strong correlation between satellite ocean colour and *Synechococcus* abundance, but a lower correlation with *Prochlorococcus* and eukaryotes groups abundance (Zubkov and Quartly, 2003). Here, we highlight a strong positive correlation between prokaryote groups (*Synechococcus* and *Prochlorococcus*) both in terms of abundance and total FLR and the FB total *in-vivo* Chl *a*, which is also correlated with the brown pigmentary group concentration. The study of Zubkov and Quartly (2003) based on benchtop flow cytometry in the MZC found that *Prochlorococcus* dominated phytoplankton assemblages with abundance ranging from $17 \times 10^3$ to $160 \times 10^3$ cell mL$^{-1}$ (0.2–32 $\times 10^3$ cell mL$^{-1}$ in our dataset), *Synechococcus* concentration from $45 \times 10^3$ to $57 \times 10^3$ cell mL$^{-1}$ (0.4–76 $\times 10^3$ cell mL$^{-1}$ in our dataset), and eukaryotic algae concentration from 0.6 to $4 \times 10^3$ cell mL$^{-1}$ (0.7–7 $\times 10^3$ cell mL$^{-1}$ in our dataset). The results of Zubkov and Quartly (2003) are comparable to our results.

Despite both FLP Pigments phytoplankton groups and AFCM PFGs were correlated with FB fluorescence (Table 1, Supplementary Figs. 2 and 3), FLP Pigments phytoplankton groups and AFCM PFGs had not correlated each other for any specific phytoplankton groups
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** (a) Total cell abundance (10⁴ cell mL⁻¹); (b) Total FLR (10⁸ FLR A.U. mL⁻¹) measured by the CytoSense (AFCM). For each panel x-axis and y-axis correspond to the axes in the dipole referential (km). The solid, dashed and dotted black isolines correspond respectively to 0, ±16 and ±24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). The background colors correspond the mapping (interpolation) of each variable in the dipole referential. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Dendrogram obtained by the clustering of all AFCM measurements (N = 81) using Ward's dissimilarity on PFGs abundance and FLRcell. Stack plots give for each sample the contribution of each PFGs to total abundance and total FLR.</figcaption></figure>

(Supplementary Fig. 4). The respective sampling frequency of the two approaches were different (FLP measures the water sampled every 30sec whereas AFCM measures the water sampled every hour). Moreover, the time of data acquisition is also different for each instrument, which is instantly for FLP, and about 40min for AFCM because of the different gating protocols applied. This discrepancy induces a gap in data

matching regarding the sampled water mass. FLP spectral/pigmentary groups cannot be taxonomic nor ecologically functional (size classes), because the bulk of fluorescence is retrieved on a mix of phytoplankton groups in suspension. The discrimination amongst the contribution of target phytoplankton spectral groups to total chlorophyll a is based on their accessory pigment composition and content and their response to
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Maps of the surface vorticity/f ratio (red/blue scale). a) AFCM-based clusters, considering two groups (cf. Fig. 7). b) AFCM-based sub-clusters, considering six groups (cf. Fig. 7). For each panel x-axis and y-axis correspond to the axes in the dipole referential (km). The blue/red scale (background map) represents the surface vorticity/f. The solid, dashed and dotted black isolines correspond respectively to 0, ±16 and ±24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

multispectral excitation, no matter their size. On the other hand, PFGs gating is manually performed from AFCM data, which could induce subjectivity in PFGs determination for the totality of the cytograms (see Supplementary Fig. 1). The bias of subjectivity in AFCM gating does not concern FLP spectral/pigmentary groups. Finally, the mismatch between FLP spectral/pigmentary phytoplankton groups and AFCM PFGs (Supplementary Fig. 4), could also being explained by pigments overlapping during specific LED excitation. Indeed, relatively variable pigments content per groups (e.g: Phycocyanin and phycoerythrin respectively represented by Blue Green and Red mixed pigment groups, are present in cyanobacteria). Moreover, higher brown pigment (carotenoid, xanthophyll) in the community could not necessary reflect specific PFGs determined by AFCM (e.g: Diatoms). Finally, the algorithm used by the FLP raw data processing program have been proven to have limitations, especially when discriminating Haptophytes (nanophytoplankton) which are present in both “green” and “brown” signal and that need to be detected and discriminated from “brown” group by a dedicated fingerprint (Houliez et al., 2012).

This mismatch between phytoplankton community evaluation (AFCM vs FLP) is an interesting finding for the scientific community. Indeed, a different conclusion could be made regarding which instrument is used. Our case study shows that combining both instruments revealed that AFCM is more powerful to evaluate ecological gradient along mesoscale features (Figs. 7 and 8). While FLP display rapid change of pigment composition across fronts (e.g. the central jet, see Fig. 5).

In the dipole studied here, higher phytoplankton Chl *a* concentration were located inside the cyclone (in the south) compared to the anticyclone (in the north). We observed a quick shift in pigmentary groups (discriminated by FLP multispectral analysis) across the front (Figs. 4 and 5). The distribution of the brown algae group in particular seemed to be linked to the front, which indicates a potential enrichment in brownish pigments (carotenoids, xanthophyll) that could either result from the production/accumulation of specific phytoplankton groups (as

characterized by AFCM).

## 4.2. Uneven phytoplankton distribution through a dipole system

Eddies are known to convert physical energy (stirring) into phytoplankton biomass production indirectly by the transportation of nutrients, which support biological processes through trophic transfer (Barlow et al., 2014; Huggett, 2014). During their spin-up phase, cyclonic eddies create upwards transport of cold, nutrient-rich waters, and also a downward transport when the cyclonic eddies relax (McGillicuddy et al., 1998; Oschlies and Garçon, 1998). These stirring (horizontal transport) and upwelling (vertical transport) processes contribute to increased productivity in the Eastern African coastal province (Kolasinski et al., 2012). Additionally, the interactions of mesoscale eddies with the continental shelf drives the transport of phytoplankton-rich coastal waters into the MZC (Kolasinski et al., 2012; Tew-Kai and Marsac, 2009; Zubkov and Quartly, 2003). Hence, high phytoplankton concentration in the MZC could either be due to upwelling, horizontal advection, or likely a combination of both processes. This study highlights contrasting surface ocean dynamics at a small spatial scale, which creates physical downwelling leading to a deep chlorophyll maximum (DCM) and thermocline (anticyclonic eddy) but also upwelling with opposite effects. Generally, mesoscale structures are associated with small scale variability such as stirring and variation in vertical velocities. At a larger scale, anticyclonic eddy in their spinning up phase generally push the DCM and phytoplankton biomass deeper in the water column (Stramma and Lutjeharms, 1997).

One of the most important findings of this study is that we provide insights into phytoplankton composition of a Chl a filament observed by remote sensing (see Fig. 1 in Penven et al., 2025), in-vivo total Chl a fluorometry (FerryBox), multispectral fluorometry (Fluoroprobe) and Automated Flow Cytometry in the MZC. The dipole structure can be characterized by surface waters (sampled from a depth of 5–6 m) during
the cruise, regarding the SST and SSS (Fig. 3). Hence, underway analysis of phytoplankton was relevant regarding the mesoscale structure sampled. As presented in the study of Tew-Kai and Marsac (2009), Chl *a* is lower in anticyclonic than in cyclonic eddies. These authors suggested that the phytoplankton biomass originated from the Mozambique shelf and was then transported at the periphery of anticyclones by horizontal transport. Eddy-mediated transport of phytoplankton from the south Madagascar upwelling area was also found in MZC by Noyon et al. (2019). The authors described a vertically homogeneous distribution of Chl *a* concentration inside young eddies. They were characterized by a higher proportion of diatoms, despite abundance of picoplankton, nanoplankton, ciliates and dinoflagellates being similar within and outside the eddies (Noyon et al., 2019). A modelling approach of the time required for horizontal transport of the coastal waters (Supplementary Fig. 5, Penven et al., 2025) shows a patchy surface water mass age distribution. Water mass less than a month old in the cyclonic eddy, younger water mass (less than 10 days) on the central jet (eddy frontal area) and water mass older than a month in the anticyclonic eddy. The patchiness of the water mass age distribution (time since continent shelf, from days to months) suggests that the high biomass and specific community structure found in the cyclonic eddy reflect a horizontal mixing of phytoplankton biomass (continental shelf biomass in offshore surface waters).

Satellite-estimated sea surface elevation allowed us to determine the structure of the dipole and its location with an anticyclonic eddy in the north (up to 21.4 °S) and a cyclonic eddy in the south (down to 21.4 °S). This dipole was relatively stable during Leg 1 (25th of April–1st of May 2022), with a south-eastern displacement of about 50 km over the sampling time. The use of a referential centred on the dipole allowed to account for ship and eddies movements for the duration of Leg 1 (6 days), and thus to focus on spatial patterns of phytoplankton distribution. Lamont et al. (2014) observed in the MZC that phytoplankton biomass in cyclonic and anticyclonic eddies was relatively low and not significantly different. Our results show, on the contrary, a higher phytoplankton biomass inside the cyclonic eddy and at the front within water masses characterized by lower sea surface temperature and salinity. Moreover, those water masses can be attributed to shelf-offshore transport (low salinity). Turbulence and isopycnal mixing can also support production at edges of eddies (i.e., in frontal systems, Falkowski et al., 1991; Swart et al., 2010). Indeed fronts between eddies have been identified as preferred foraging areas for top predators such as pelagic predators (e.g. tuna) and seabirds (Tew-Kai and Marsac, 2009; Weimerskirch et al., 2004), which suggests a high primary production and standing stock in those areas. Moreover, submesoscale structuring is also evidenced from AFCM data, with four isolated statistical cluster (based on biomass and fluorescence) in the anticyclonic eddy (Figs. 7

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Map of the PFGs (see Table 2) overlayed on the surface vorticity/f ratio (red/blue scale in the background). For each PFG, cell abundance (cell mL⁻¹) is depicted by the color scale, dot size represents the cell abundance sum of prokaryotic groups (a, b & c), and eucaryotic groups (d, e & f): a) Synechococcus, b) Synechococcus bright, c) *Prochlorococcus* d) Picoeukaryotes. e) Nanoeukaryotes f) Microeukaryotes. For each panel x-axis and y-axis correspond to the axes in the dipole referential (km). The solid, dashed and dotted black isolines correspond respectively to 0, 16 and 24 cm anomalies of sea surface height (composite sea surface height in the dipole reference frame during the sampling period). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
and 8). Considering that the anticyclonic eddy is charaterized by an offshore water mass without recent mixing with continental plateau surface water, we hypothesize that those communities could be based on re-generated production (secondary production).

The phytoplankton assemblage associated with the front was identified (cluster 1, 5 and 6, Fig. 8). The dominance of *Synechococcus* (in terms of abundance in most areas of both systems) and *Prochlorococcus* (mainly in clusters 2, 3 and 5) abundances are coherent with previous assessments of phytoplankton community composition within oligotrophic marine systems, usually representing (combined) 50–90 % of the phytoplankton community (e.g., Zubkov et al., 1998; Agawin et al., 2000). In other studies, a dominance of *Prochlorococcus* was observed in cooler waters while *Synechococcus* was more prominent in warmer waters (e.g., Maranon et al., 2003; Lamont et al., 2014; Marrec et al., 2018). In the present study, *Prochlorococcus* were indeed more abundant in the cooler waters of the cyclone as well as the bright *Synechococcus* (Fig. 9). Pulses of prokaryote abundance have only been observed for bright *Synechococcus* detected in cyclonic eddies associated with the upwelling

process (Li et al., 2017). Pigment content (FLR per cell) is linked to division cycle and physiological changes, such as it size (e.g., Sournia, 1975), and caution for this intrinsic variability should be taken when integrating these traits for the analysis of their spatio-temporal dynamics (discussed in the section below). The bright ecotype contains more pigment (FLR per cell) for an equivalent size, and could be associated with a higher physiological state in the cyclonic eddy (healthiest condition).

### 4.3. Characterization of phytoplankton Functional groups associated with the dipole

#### 4.3.1. Vorticity/f ratio and pigmentary groups

It is noticeable that areas with high brown pigmentary group concentration (observed in correlation with FB patches of high total chlorophyll fluorescence) are in areas with vorticity/f ratio inferior or equal to zero (mostly in the cyclonic eddy). The accumulation of brown/orange pigmented phytoplankton at the front could be related to the

<table><caption>**Table 2**
Summary of statistical correlation between Fluorescence per excitation Led (λ<sub>nm</sub>), pigmentary groups and FerryBox total Chl a fluorescence.</caption><thead><tr><th>Fluorescence per excitation Led (λ<sub>nm</sub>)</th><th>ρ Spearman correlation with FerryBox total chlorophyll a fluorescence<sup>a</sup></th><th>Pigment targetted (λ<sub>nm</sub> absorption range)</th><th>Reference (λ<sub>nm</sub> absorption range)</th></tr></thead><tbody><tr><td>370 nm</td><td>0.662***</td><td>Colored dissolved organic matter</td><td>Bricaud et al. (1995)</td></tr><tr><td>470 nm</td><td>0.809***</td><td>CDOM</td><td>Beutler et al. (2002)</td></tr><tr><td rowspan="10">525 nm</td><td rowspan="10">0.649***</td><td rowspan="10">Chlorophyll a, b and c</td><td>Bricaud et al. (2004)</td></tr><tr><td>Seppälä (2003)</td></tr><tr><td>Zang et al. (1997)</td></tr><tr><td>Hoepffner and Sathyendranath (1993)</td></tr><tr><td>Hoepffner and Sathyendranath (1993)</td></tr><tr><td>Bricaud et al. (2004)</td></tr><tr><td>Seppälä (2003)</td></tr><tr><td>Zang et al. (1997)</td></tr><tr><td>Seppälä (2003)</td></tr><tr><td>Fucoxanthin</td><td>Mangolte et al. (2023)</td><td></td></tr><tr><td>Phaeophorbide a</td><td>Mangolte et al. (2023)</td><td></td></tr><tr><td rowspan="4">570 nm</td><td rowspan="4">0.545***</td><td>Carotenoids<sup>b</sup></td><td>Hoepffner and Sathyendranath (1993)</td></tr><tr><td>Carotenoids</td><td>Seppälä (2003)</td></tr><tr><td>Fucoxanthin</td><td>Deepika et al. (2022)</td></tr><tr><td>Phycocyanin</td><td>Seppälä (2003)</td></tr><tr><td rowspan="4">590 nm</td><td rowspan="4">0.270***</td><td>Phycocyanin</td><td>Deepika et al. (2022)</td></tr><tr><td>Phaeophytin a &amp; b</td><td>Mangolte et al. (2023)</td></tr><tr><td>Phycocyanin</td><td>Seppälä (2003)</td></tr><tr><td>Phycocyanin</td><td>Deepika et al. (2022)</td></tr><tr><td rowspan="6">610 nm</td><td rowspan="6">0.294***</td><td>Phaeophytin a &amp; b</td><td>Seppälä (2003)</td></tr><tr><td>Phycocyanin</td><td>Mangolte et al. (2023)</td></tr><tr><td>Phaeophytin c1 &amp; c2</td><td>Seppälä (2003)</td></tr><tr><td>Phycocyanin</td><td>Mangolte et al. (2023)</td></tr><tr><td>Chlorophyll b &amp; c</td><td>Mangolte et al. (2023)</td></tr><tr><td>Phaeophytin b</td><td>Seppälä (2003)</td></tr><tr><td rowspan="2">Pigmentary group</td><td rowspan="2">ρ Spearman correlation with FerryBox fluorescence</td><td>Phycocyanin</td><td>Deepika et al. (2022)</td></tr><tr><td>Phaeophytin a &amp; b</td><td>Mangolte et al. (2023)</td></tr><tr><td></td><td></td><td>Phytochlorophyll a</td><td>Phytoplankton group (Beutler et al., 2002; MacIntyre et al., 2010)</td></tr><tr><td>Total chlorophyll a concentration</td><td>0.666***</td><td>Sum</td><td>Total phytoplankton biomass</td></tr><tr><td>Brown pigment group concentration (in chl a eq. units)</td><td>0.522***</td><td>Xanthophyll</td><td>Haptophyceae<br>Baccilariophyceae<br>Dinophyceae<br>Chrysophyceae</td></tr><tr><td>Green pigment group concentration, Rich Chlorophyll a,b (in chl a eq. units)</td><td>0.116***</td><td>Chlorophyll a and b</td><td>Chlorophyceae<br>Euglenophyceae<br>Prasinophyceae<br>Haptophytes</td></tr><tr><td>Bluegreen pigment group concentration (in chl a eq. units)</td><td>0.092***</td><td>Phycocyanin</td><td>Phycocyanin-rich cyanobacteria</td></tr><tr><td>Mix red pigment group concentration (in chl a eq. units)</td><td>0.350***</td><td>Phycoerythrin</td><td>Phycoerythrin-rich Cyanobacteria<br>Cryptophyceae</td></tr></tbody></table>

<footnote>*** correspond to p-value <0.001.</footnote>

<footnote>a comparision between 2496 points (comparaison with FB in vivo fluo).</footnote>

<footnote>b (e.g: synechoxanthin, canthaxanthin, caloxanthin, echinenone, myxoxanthophyll, nostoxanthin, zeaxantin, diadinoxanthin, alloxanthin, Lutein, Peridin & α/β-carotene).</footnote>
presence of diatoms and haptophytes (xanthophyll-rich microphytoplankton cells) (Catherine et al., 2012; Garrido et al., 2019). This phytoplankton assemblage is common in coastal waters or during phytoplankton bloom events. Here, the presence of such groups at the dipole's front probably results from the transport of coastal surface phytoplankton by the filament as the frontal jet mostly transport recent coastal surface waters (Supplementary Fig. 5). Even though relatively low (non significant) microphytoplankton abundance was measured with the CytoSense, the high contribution (up to almost 57 % of mean total value, see Fig. 9, Table 1) of this minor PFG to the total FLR supports the hypothesis of a local accumulation of diatoms and haptophytes, especially if we include RedNano representing also a great part (67 %) of total FLR (Fig. 9, Table 1). In addition to enhanced abundance of Micro and Nanoeukaryotes (RedNano and RedMicro FPGs), we observed high abundance of cyanobacteria (*Synechocococcus* and *Prochlorococcus*) in the areas of accumulation of brown/orange pigmented phytoplankton (Fig. 9), despite their low contribution to the total FLR (from AFCM). Those prokaryotic photosynthetic organisms, as well as Nano and Microeukaryotes of the brown pigmentary group, are known to produce a large variety of pigments, including chlorophylls, carotenoids and phycobiliproteins (Deepika et al., 2022). The similar distribution of cyanobacteria, picoeukaryotes, nanoeukaryotes and microeukaryotes and brown/orange pigments possibly reflect not only a change in abundance but also an accumulation of carotenoid pigments. Because FLP only detects Chl *a* fluorescence, the carotenoids enhance-ment can only be related to FLR and not to abundance by AFCM. PFG distribution can only be compared to total FLR per PFG (and not to abundance). In cyanobacteria, the most abundant carotenoids are carotenes (e.g., β-carotene) and various types of xanthophylls (synechoxanthin, canthaxanthin, caloxanthin, echinenone, myxoxanthophyll, nostoxanthin, zeaxanthin), which are oxygenated derivatives of the carotenes (Domonkos et al., 2013; Takaichi and Mochimaru, 2007; Tóth et al., 2015). We make the hypothesis that these accessory pigments (see Table 2) and potential associated protein complex (Bersanini et al., 2014) could have been excited at 470, 525 and 570 nm wavelength by the FLP LEDs, in addition to the excitation of carotenoids and xanthophylls in nano- and microeukaryotes, constituting the brown/orange pigment signal detected at the front. Although this hypothesis cannot be confirmed by our data, these results highlight the importance of comparing the distribution of FLP-based phytoplankton spectral groups with other descriptors of phytoplankton communities in oligotrophic waters (AFCM in this study). Moreover, the characterization of phytoplankton pigmentary groups by FLP does not take into account the physiological state of micro-algae nor the photochemical state of accessory pigments and associated proteins complex. In addition to interspecific variability, this can lead to biased assignation of phytoplankton to pigmentary groups (see Supplementary Fig. 4).

### 4.3.2. Primary production and carbon export in the MZC

Phytoplankton primary production is strongly enhanced by meso- and submesoscale dynamics, up to two times more important than in surrounding waters according to modelling studies (Couespel et al., 2021; Lévy et al., 2001). Characterizing mechanisms that influence primary production in eddies would allow to appropriately represent the impact of eddies at a global scale on marine species biomass. Enhance-ment in primary production (not measured in the present study) can impact the balance between organic carbon production and remineral-ization as heterotrophic activities have also been shown to be enhanced within cyclonic eddies (Devresse et al., 2022). The distribution of autotrophic and heterotrophic activities within cyclonic eddies being highly variable, differences in carbon trapping can occur at the sub-mesoscale between eddies' core and edges. Indeed, Omand et al. (2015) investigated the transport of particulate organic carbon (POC) to depth associated to the North Atlantic spring bloom at fine-scale, to highlight that submesoscale, eddy-driven downward flux of POC can contribute up to half of the total export in this highly productive ecosystem.

Recently frontogenesis-driven eddies has been suggested to enhance the biological carbon pump by increasing the depth at which carbon is injected into the water column (Accardo et al., 2025). Phytoplankton biomass associated with POC transfer by zooplankton grazing at greater depths (active and passive flux) could result from higher carbon export efficiency within the cyclonic eddy. However, stirring and upwelling induced by cyclonic eddies may have attenuated POC aggregate sinking.

## 5. Conclusion

Deploying a diverse array of sensors for underway measurements during the RESILIENCE cruise enabled high-resolution spatial and temporal coverage, facilitating the measurement of subtle changes in the composition of phytoplankton. In general, cross-validation among sen-sors enhances data reliability, and multi-parametric analysis reveals more reliably the complex relationships within the phytoplankton community. Leveraging technological synergies maximizes the range of phytoplankton attributes captured including size structure, pigment composition, and photo-physiology (unfortunately lacking in the pre-sent study, see Aardema et al., 2019, for the use of an FRRf sensor). Integration with satellite-sensed data of sea surface elevation allows for a broader understanding of how physical oceanographic processes in-fluence phytoplankton distribution and abundance, down to the sub-mesoscale. Here, this led to insights in the physical mechanisms responsible for the formation and spread of chlorophyll filaments in the MZC. Three structures were sampled during the cruise: an anticyclonic eddy, a frontal area and a cyclonic eddy. In this study, we presented two different and complementary methods for addressing Phytoplankton Functional Groups shift across physical structures: a bulk spectral approach (multispectral fluorometry - Fluoroprobe-FLP) and a single-cell approach by automated pulse shape-recording flow cytom-etry (AFCM). Mesoscale structures were evidenced in the three areas using hierarchical statistical clustering. Higher phytoplankton biomass was observed in the cyclonic eddy and frontal area. Patchy phyto-plankton pigmentary groups (mostly brown pigmentary groups) were observed in the cyclonic eddy, associated with horizontal physical dy-namic pattern (Vorticity/f ratio and Okubo-Weiss). The higher carot-enoids concentration, which are represented by brown pigmentary groups in the cyclonic eddy, could be due not only to structural acces-sory pigments of brown algae (mostly nano and microeukaryotes as diatoms, haptophytes and dinoflagellates) but also to cyanobacteria (e. g: *Synechococcus* and *Synechococcus* "bright", Fig. 7), produced in response to oxidative stress caused by primary production. However, this statement remains hypothetical because no ROS, RNS measure-ments, nor HPLC validation were carried out on samples collected during the cruise. This process deserved to be study in future to validate or not this hypothesis. This higher primary production could be the result of stirring induced by the cyclonic eddy. Finally, our observations highlight the importance of coupling different automated devices to address high resolution distribution and changes in phytoplankton functional diversity and to explain ecological process involving phyto-plankton dynamics at sub-mesoscale.

## CRediT authorship contribution statement

**Jordan Toullec:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Millat Blanc:** Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation. **Pierrick Penven:** Writing – review & editing, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. **Alexandre Epinoux:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Data curation. **Natasha Buckiewicz:** Writing – review & editing, Investigation, Formal analysis. **Emmanuelle Jaouen:** Data curation.
**Valentine Szrama:** Data curation. **Margaux Noyon:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
**Jean-François Ternon:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. **Luis Felipe Artigas:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Key points

* Phytoplankton assemblage and biomass shifts along a dipole structure made of anticyclonic and cyclonic eddies separated by a frontal area, according to both multispectral fluorometry (FLP) and automated flow cytometry (AFCM) characterization.

* Observed phytoplankton patchiness could be related to the stirring induced by the cyclonic eddy.

* The variability in the distribution of optically defined phytoplankton functional groups (PFGs) highlights potential structural changes of composition as well as potential changes in pigments composition, related to cell size and/or physiological state.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements and Data Availability Statement

The RESILIENCE oceanographic campaign has been supported by the French National Oceanographic Fleet, by the Belmont Forum Ocean Front Change project, implemented through the French National Research Agency (ANR-20-BFOC-0006-04), by the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015) and co-funded by a grant from the French government under the program "Investissements d'Avenir" embedded in Fragoso, 2019), and by the French National program LEFE (Les Enveloppes Fluides et l'Environnement). The CytoSense on-board the R/V Marion Dufresne belongs to the MAP-IO observatory of the ocean-atmosphere processes in the Indian Ocean supported by the European Union (FEDER), "Région Réunion", SGAR-Réunion, Université de La Réunion, CNRS, French Oceanographic Fleet and Météo-France (https://lacy.univ-reunion.fr/map-io). Melilotus Thyssen (MIO) and Léa Gest (ULR) are thanked for their support in the use of the MAP-IO CytoSense. MAP-IO is a scientific programme led by the LACy/La Réunion University and was funded by the European Union through the ERDF programme, the University of Reunion, the SGAR-Réunion, the région Réunion, the CNRS, the TAAF, the IFREMER and the Flotte Océanographique Française. The authors thank the technical team of the LACy engaged in the data acquisition and the maintenance of the instruments of the MAP-IO program. This study has been conducted using E.U. Copernicus Marine Service Information. Copernicus Climate Change Service, Climate Data Store (2018): Sea level daily gridded data from satellite observations for the global ocean from 1993 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.4c328c78 (Accessed on 04-April-2023). We thank the crew of the R/V 'Marion Dufresne' (LDA, French Oceanographic Fleet) for their help and assistance, as well as the scientific partners on board, including the Master's students of the Floating University (Université de Bretagne Occidentale, Université du Littoral Côte d'Opale, Université Côte d'Azur), its coordinators on board and the funding institutions (ISBLUE, ULCO). AE was supported by a CNRS Engineer contract and a post-doctoral fellowship of the University of Littoral Côte d'Opale (ULCO) and by the graduate school IFSEA that benefits from a Fragoso,

2019 grant (ANR-21-EXES-0011) operated by the French National Research Agency. MB was supported by an IRD-LOG funding during her Master's 2nd year degree.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.dsr2.2025.105554.

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