# "Living on the edge" Fine-scale observations reveal distinct frontal phytoplankton communities

Laurina Oms, Andrea M. Doglioli, Monique Messié, Francesco d'Ovidio, Louise Rousselet, Xavier Capet, Lloyd Izard, Marina Lévy, Maristella Berta, Anne Petrenko, et al.

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Laurina Oms, Andrea M. Doglioli, Monique Messié, Francesco d'Ovidio, Louise Rousselet, et al. "Living on the edge" Fine-scale observations reveal distinct frontal phytoplankton communities. 2025. hal-05354625

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# "Living on the edge" Fine-scale observations reveal distinct frontal phytoplankton communities

Laurina Oms

laurina.oms@mio.osupytheas.fr

Aix Marseille Univ., Toulon University, CNRS, IRD, MIO UM 110

Andrea Doglioli

Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO https://orcid.org/0000-0003-1309-9954

Monique Messié

Monterey Bay Aquarium Research Institute https://orcid.org/0000-0002-4985-3413

Francesco d'Ovidio

Sorbonne Université,

Louise Rousselet

Laboratoire d'Océanographie et du Climat: Expérimentation et Approches Numériques-Institut Pierre Simon Laplace, Université Pierre et Marie Curie, BC 100

Xavier Capet

Laboratoire d'Océanographie et du Climat: Expérimentation et Approches Numériques (LOCEAN)

Lloyd Izard

Laboratoire d'Océanographie et du Climat: Expérimentation et Approches Numériques-Institut Pierre Simon Laplace, Université Pierre et Marie Curie, BC 100

Marina Levy

Sorbonne Université, LOCEAN-IPSL, CNRS/IRD/MNHN https://orcid.org/0000-0003-2961-608X

Maristella Berta

CNR-ISMAR (National Research Council- Institute of Marine Sciences)

Anne Petrenko

Aix Marseille Université, CNRS, Université de Toulon, IRD, OSU Pythéas, Mediterranean Institute of Oceanography (MIO), UM 110F https://orcid.org/0000-0002-2519-9285

Marco Bellacicco

ISMAR CNR

Stéphanie Barrillon

Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO

Gerald Gregori

Mediterranean Institute of Oceanography
Article

Keywords:

**Posted Date:** May 22nd, 2025

DOI: https://doi.org/10.21203/rs.3.rs-6412120/v1

**License:** ©① This work is licensed under a Creative Commons Attribution 4.0 International License.

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**Additional Declarations:** There is **NO** Competing Interest.
1

2

"Living on the edge"

3

**Fine-scale observations reveal distinct frontal phytoplankton communities**

5

6

7 Laurina Omsª, Andrea Doglioliª, Monique Messiéᵇ, Francesco d'Ovidioᶜ, Louise Rousseletᶜ, Xavier

8 Capetᶜ, Lloyd Izardᶜ, Marina Lévyᶜ, Maristella Bertaᵈ, Anne Petrenkoª, Marco Bellaciccoᵉ, Stéphanie

9 Barrillonª, Gérald Grégoriª

10

11 <sup>a</sup>Aix Marseille Univ., Toulon University, CNRS, IRD, MIO UM 110, Marseille, 13288, France;

12 <sup>b</sup>Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA;

13 <sup>c</sup>Laboratoire d'Océanographie et du Climat: Expérimentation et Approches Numériques-Institut

14 Pierre Simon Laplace, Université Pierre et Marie Curie, BC 100, 4 place Jussieu, 75005 Paris,

15 France;

16 <sup>d</sup>CNR-ISMAR (National Research Council- Institute of Marine Sciences), Lerici (SP), Italy;

17 <sup>e</sup>Institute of Marine Science, National Research Council of Italy, Via Fosso del Cavaliere, 100,

18 00133, Rome, Italy

19 **Corresponding author:** Laurina Oms

20 **Email:** laurina.oms@mio.osupytheas.fr

21 **Abstract**

22 Phytoplankton community composition plays a key role in oceanic productivity and in the biological

23 carbon pump. A few *in situ* surveys have shown the structuring role of fine-scale physical processes

24 (1-100 km, day-weeks), in particular fronts, on phytoplankton communities. Most of these studies

25 were conducted in highly productive and dynamic regions, while oligotrophic and moderate energy

26 conditions have been less explored, though very common in the global ocean. Guided by novel

27 SWOT altimetry, we used an adaptive and multidisciplinary strategy to conduct high-resolution

28 sampling of the North-Balearic Front in the oligotrophic Mediterranean Sea. An unprecedented

29 24-hour continuous sampling within the front was crucial to unveiling a distinct frontal community,

30 where the relative contribution of non-dominant phytoplankton groups increased relative to adjacent

31 water masses. This highlights the pivotal role of fine-scale features in increasing phytoplankton

32 heterogeneity, thus contributing to community diversity in oligotrophic and moderately energetic

33 regions. These findings represent a key step toward understanding the ecological response to

34 fine-scale structuring on a global scale.

35 **Main text**

36

37 **Introduction**

38

39 Understanding the intricate dynamics of physical and biological seascapes remains a fundamental

40 challenge, particularly because they are coupled over a wide range of spatial and temporal scales¹.

41 Fine-scale physical features (combined meso- and submeso-scale, 1–100 km, day to weeks) are

42 widespread in the ocean²,³,⁴. They create a variable and ephemeral environment for non-swimming
43 microorganisms such as phytoplankton cells, essential primary producers for marine ecosystems
44 and biogeochemical cycles⁵,⁶.

45

46 Phytoplankton life within fine-scale fronts is subject to peculiar dynamics due to sharp density
47 gradients<sup>7,8,9,10</sup> and similar timescales between phytoplankton blooms and fine-scale physical
48 forcings<sup>11</sup>. Remote sensing and modelling studies suggest that phytoplankton dynamics is greatly
49 influenced by fine-scale fronts through a combination of abiotic and biotic factors<sup>6,11,12,13</sup>. For
50 instance, stirring by horizontal currents deforms phytoplankton patches<sup>14,15</sup> while vertical velocities
51 induce change in phytoplankton growth rate by influencing nutrient fluxes and their availability<sup>4,16</sup>.

52 These two processes generate biological reactions and behavioral responses of higher trophic-level
53 organisms with a feedback on phytoplankton<sup>17</sup>. *In situ* studies have demonstrated the influence of
54 fine-scale fronts on biology, particularly their strong structuring effect on shaping phytoplankton
55 communities<sup>18,19,20,21</sup>.

56

57 However, fine-scale *in situ* surveys are challenging due to the difficulty of tracking in space and time
58 the physical features involved, and the complex dynamics of both fronts and phytoplankton
59 communities. High-resolution *in situ* measurements of phytoplankton properties (community
60 composition, biomass, growth and loss rates) over diel cycles, along with coupled physical and
61 biological data, remain therefore limited. Most past analyses focused on productive, dynamic
62 regions dominated by microphytoplankton, where fine-scale biophysical signals are stronger, such
63 as western boundary currents (WBC) and eastern boundary upwelling systems (EBUS)⁹,²⁰,¹⁸,²²,²³,²⁴.

64 By contrast, the majority of the world's ocean is oligotrophic<sup>25</sup> and characterized by lower energetic
65 levels<sup>26,27</sup> and picophytoplankton dominance<sup>28</sup>. Moreover, past *in situ* studies primarily relied on
66 measurements conducted across fronts, so that high-resolution measurements within fronts remain
67 lacking. Due to these limitations, the intricate interactions between fronts and phytoplankton
68 communities, which underpin the ecological response to front structuring, remain poorly
69 understood. This knowledge gap significantly hinders our ability to assess the importance of
70 fine-scale systems at the global scale.

71

72 The BioSWOT-Med cruise²⁹, conducted from 20 April to 15 May 2023 (Fig. 1), marks a key step in
73 addressing this gap. The cruise took place in the Mediterranean Sea, characterized by high
74 plankton diversity under oligotrophic and moderately energetic conditions³⁰,³¹, and targeted the
75 North-Balearic Front (hereafter NBF), an area marked by hydrological and hydrodynamical
76 contrasts influencing biological production³²,³³,³⁴,³⁵,³⁶,³⁷. Importantly, an adaptive sampling strategy
77 was specifically designed to provide high-resolution sampling of this frontal system. The cruise
78 strategy was designed to align with the first 90 days of the SWOT (Surface Water and Ocean
79 Topography) satellite mission, known as the fast-sampling phase, launched in December 2022.

80 During this phase, the satellite revisited specific areas, including the BioSWOT-Med region, once
81 per day for calibration and validation. The daily SWOT imagery, combined with underway *in situ*
82 salinity and temperature measurements and drifter trajectories, enabled fine-scale targeting and
83 continuous tracking of the front.

84

85 Prior to the SWOT mission, satellite data for fine-scale studies were either too low in resolution (for
86 altimetry) or hindered by cloud coverage (for ocean color and infrared sea surface temperature).
87 SWOT overcomes these limitations by providing observations of sea surface height unaffected by
88 clouds at high enough resolution to target 15-30 km structures³⁸,³⁹,⁴⁰,⁴¹. Advances in altimetry, along
89 with the unique opportunities provided by the SWOT fast-sampling phase and adaptive sampling
90 strategy, have yielded robust Lagrangian datasets to address this specific question: is the
91 phytoplankton community within frontal regions significantly distinct from those in surrounding water
92 masses? To investigate this, we analyzed the frontal region using two complementary Lagrangian
93 datasets (Fig. 1), with positions carefully selected based on SWOT and chlorophyll images: (1)
94 adaptive repeated transects across the frontal region (hereafter "cross-front transect", April 29-30)
95 and (2) 24-hour drifting stations within the front itself and the two adjacent regions defined by
96 distinct water masses (May 5-6-7) (Fig. 1). Our dataset includes high-resolution underway data of
97 phytoplankton biomass and community composition characterized by flow cytometry and of
98 hydrological properties (temperature, salinity) measured by the thermosalinograph (TSG). The
99 unprecedented resolution of the station dataset enables a robust statistical analysis of the
100 phytoplankton community composition of the fine-scale frontal region.

101

102 Results

103

104 Definition of the frontal region

105

106 We used surface absolute salinity [g/kg] from TSG data collected during the cross-front transect
107 (spatial resolution: 18 m at a typical speed of 6 knots) to identify the NBF, defined as a recurrent
108 salinity front between the warmer and fresher Atlantic Water spreading from the Algerian Basin and
109 the saltier and colder waters of the Liguro-Provençal basin⁴². Using a Gaussian Mixture Model
110 (GMM)⁴³,⁴⁴ applied to surface salinity distribution, we identified the two distinct water masses "A"
111 (saltier) and "B" (fresher) separated by the front "F" (Fig. 2a). The boundaries of F were defined by
112 salinity values corresponding to the lower and upper limits of the 1-sigma confidence intervals for
113 the A and B Gaussians, respectively. Region F is characterised by a limited number of
114 measurements when the strategy of the cross-front transect is applied, due to the crossing of a
115 fine-scale gradient. The superimposed histograms of station data show that stations A2, F2, B2
116 correctly targeted the different water masses. In particular, with the drifting station strategy, much
117 more data is collected within the front during station F2, which is clearly distinct from stations A2
118 and B2. The spatial distribution of A, F, and B water masses aligns with Finite Time Lyapunov
119 Exponent (FTLE) patterns derived from altimetric products including SWOT data, confirming the
120 consistency with fine-scale hydrodynamic features (Fig. 2b and Fig. A2). In particular, Fig. A2 and
121 A3 show that the frontal dynamics is characterized by intensified eastward displacements. Figures
122 2b,c and A2 both clearly illustrate the progress made by SWOT in revealing fine-scale frontal areas,
123 as investigated here, compared to previous Lagrangian diagnostics derived from DUACS 2023
124 satellite products (Fig. A1).

125

126 A distinct phytoplankton community at the front

127 Automated flow cytometry uniquely enables high-resolution assessments of community structure,
128 with sampling intervals as short as 15 minutes, by grouping cells with similar properties (size,
129 shape, pigment content) assuming comparable functional traits between them. Thus, cytometric
130 clusters have ecological relevance and serve as insightful proxies for phytoplankton functional
131 types⁴⁵. In this study, we optimized the flow cytometer setup to focus on pico- and nano-
132 phytoplankton (<10 µm), which dominate phytoplankton communities in the Mediterranean Sea⁴⁶.
133 Seven phytoplankton groups were identified: three nanophytoplankton; RedNano, HflrNano,
134 HsNano, HfNano, two picoeukaryotes; RedPico, HflrPico and one cyanobacteria; OraPicoProk (sp.
135 Synechococcus) (Fig. B1), named according to the standardized cytometric nomenclature⁴⁷. The
136 flow cytometer measures plankton cell size and concentrations, converted into carbon biomass
137 [mmolC m⁻³] using group-specific relationships.

138

139 Surface phytoplankton community composition along the cross-front transect and stations,
140 expressed in biomass percentage, highlights the spatial heterogeneity of communities within the
141 frontal area and their close association with physical patterns (Fig. 2c, B4). As expected,
142 phytoplankton communities are primarily dominated by the small-cell group OraPicoProk
143 (Synechococcus) throughout the region⁴⁸. However, some groups (such as HflrPico, Fig. 2c)
144 displayed higher biomass percentages in F than in A and B during the cross-front transect, and also
145 in F2 compared to A2 and B2. The cross-front transect provides a spatial overview of the frontal
146 region but lacks the resolution to characterize frontal communities due to limited sampling. To
147 overcome this, in the following we focus on stations A2, F2, and B2, where each water mass was
148 sampled comparably, allowing for the first time a more detailed phytoplankton community analysis
149 beyond the transect data alone.

150 A detailed analysis of community composition reveals distinct phytoplankton group assemblages
151 between stations (Fig. 3a). At station A2, the community is characterized by high percentages of
152 RedNano and HflrNano, whereas station B2 is characterized by a predominance of RedPico. In
153 station F2, intermediate percentages of specific phytoplankton groups are observed between
154 adjacent water masses, forming what we term "transient" groups. The transient group T1 includes
155 RedNano and HflrNano, which have higher percentages at station A2 compared to station B2, while
156 the transient group T2 comprises RedPico, which has higher percentages at station B2 than at
157 station A2 (Fig.3b). The key finding is that the relative biomass fraction of some phytoplankton
158 groups is higher or lower than it is in both A2 and B2 stations. We refer to these groups as
159 "edge-positive" (E+; HsNano, HfNano, and HflrPico) and "edge-negative" (E-; OraPicoProk),
160 respectively (Fig. 3b). The mean absolute biomass [mmolC m⁻³] of most groups is lower in station
161 F2 than expected under passive mixing, except for E+ (Fig. B3), suggesting frontal conditions favor
162 E+ over E- and T. The singularity of the phytoplankton community at the front is explored in more
163 details below.

164 Statistical evidence of the frontal community

165 We used two statistical methods to quantify the distinct features in the communities of stations A2,
166 F2, and B2 using the PRIMER software (Fig. 4b): an analysis of similarity (ANOSIM) to quantify
167 how different the three communities are, and a similarity percentages analysis (SIMPER) to quantify
168 the contribution of each phytoplankton group to these differences. ANOSIM uses the R statistic,
169 which compares dissimilarities between samples within a station to those between samples from
170 different stations. An R value close to 1 indicates high similarity among samples within a station and
171 strong differentiation from samples at other stations.

172 The ANOSIM analysis (Fig. 4a, dashed black arrows) demonstrates significant statistical differences
173 (R>>0, p-value<1‰) for each pair of stations (A2-B2, A2-F2, B2-F2), confirming the presence of
174 three distinct community structures each associated with stations A2, F2 and B2. As expected, the
175 most pronounced differences are observed between stations A2 and B2 (R=0.79) representing the
176 water masses separated by the front. The difference between stations F2 and A2 (R=0.74) is
177 greater than between stations F2 and B2 (R=0.46). The total biomass [mmolC m⁻³] is also more
178 similar between stations F2 and B2 than between stations F2 and A2 (Fig. B2a).

179 The SIMPER analysis (Fig. 4a, pie charts) shows that various phytoplankton groups contribute to
180 the differences between the three communities. Differences between stations A2 and B2 are
181 primarily driven by RedNano, HflrNano and RedPico (T groups, together explaining 49% of the
difference). Differences between stations F2 and both A2 and B2 are strongly driven by the three
182 E+ groups (HsNano, HfNano, HflrPico), together explaining 54% of the difference between stations
183 F2 and A2 and 58% of the difference between stations F2 and B2, despite contributing only 18% of
184 the difference between stations A2 and B2. The frontal community differences are also attributed to
185 OraPicoProk (E-) for stations F2 vs B2, and to RedPico (T2) for stations F2 vs A2.

187

188 Discussion

189

190 Over the past decade, there has been a wide consensus that ocean fronts exert a major role in
191 shaping phytoplankton community structure at the so-called fine-scales⁴⁹. However, *in situ* studies
192 of this process have been confined to energetic and productive oceanic regions and have been
193 hindered by the challenge of obtaining high-resolution observations of concomitant fine-scale
194 features and plankton dynamics. Our work, to the best of our knowledge, is the first attempt to
195 extend these observations to oligotrophic and moderately energetic current systems, while also
196 addressing the resolution challenge.

197 A key ingredient has been the design of a multi-platform sampling strategy capable of dealing with
198 the weaker biophysical signals characteristics of these regions as well as with the high
199 spatio-temporal frequencies of fine-scales. This sampling strategy, combining high-resolution
200 underway measurements, drifters, and near-real-time Lagrangian analysis of multi-satellite data,
201 including the high-resolution SWOT altimetric mission, enables accurate positioning of cross-front
202 transects and stations as well as a robust frontal biophysical dataset in the North Balearic Frontal
203 region. This sampling strategy is designed for fine-scale biophysical measurements across and
204 within a front and will enable advances in understanding front-phytoplankton dynamics in any
205 oceanic condition.

206 The synoptic situation encountered during our cruise showcased the typical conditions for
207 front-phytoplankton interaction, displaying two contrasted water masses separated by a meandering
208 frontal zone which pre-existed and stayed in place for the entire duration of our cruise. Our results
209 reveal that, beyond representing a physical barrier, the front harbors a distinct phytoplankton
210 community, reflecting unique ecological conditions.

211 *In situ* studies within energetic and productive regions suggest that fronts are associated with a
212 biomass peak of larger cells, which often lead to an increase in total biomass²⁰,²¹. Surprisingly,
213 within the NBF, no frontal increase in phytoplankton group biomass or total cytometric biomass was
214 observed (Fig. B2). However, we identify distinct relative biomass signals among the constituent
215 cytometry groups within the front, consistent with taxon-dependent responses observed in the
216 California Current Ecosystem (CCE)²⁰.

217 Phytoplankton taxa responding to fronts can be categorized as “winners” and “losers”⁵⁰. In the CCE,
218 diatoms emerged as the primary “winners”, exhibiting biomass peaks at fronts, whereas other taxa
219 showed lower biomass (“losers”). In contrast, within the NBF the E+ group did not exhibit a biomass
220 peak but was the only group not negatively impacted by frontal conditions (Fig. 3b and B3b). This
221 group, composed of both pico- and nanophytoplankton, is likely distinct from diatoms observed in
222 the CCE, which typically belong to the microphytoplankton size class. This difference may be
223 attributed to contrasting underlying mechanisms between the dynamic, nutrient-rich conditions of
224 the CCE and the less dynamic, oligotrophic conditions of the NBF. Within productive and dynamic
225 regions, chlorophyll-a is more evenly distributed, potentially due to the dominance of fast-growing
226 phytoplankton species, such as microphytoplankton diatoms whereas oligotrophic and less dynamic
227 regions exhibit a patchier chlorophyll-a distribution⁵¹. This suggests that plankton communities
228 composition in oligotrophic regions are more sensitive to subtle environmental or biological forcings.
229 Here, we propose a framework where dominant groups tend to be the “losers” (T and E- groups),
230 while non-dominant groups tend to be the “winners” at the front (E+ groups) (Fig. 4b).

231 Gaining insight into the processes through which phytoplankton composition is modified at fronts
232 including in oligotrophic and moderately energetic regions could potentially be a rich source of
233 general knowledge on plankton dynamics. A conceptual framework is already in place⁶ that
234 distinguishes passive, active, and reactive scenarios (i.e., respectively, spatial redistribution of
235 biogeochemical properties by advection; physics affecting phytoplankton growth rates via
236 modulations of nutrient enrichment or light availability; and more complex ecological and
237 biogeochemical feedbacks mediated by physics). The relevance of these scenarios to
238 BioSWOT-Med are briefly examined in sequence.

239 Regarding passive transport, a water mass with a distinct salinity signature is observed within the
240 front on the western side of the cross-front transect (Fig. B2a). FTLE patterns (Fig. 2b,c and A2)
241 and convergent drifter trajectories (Fig. A3) suggest this water filament was trapped in the eastward
242 frontal current. This implies that the frontal community, with a higher proportion of E+, may have
243 originated from a distinct water source advected by the convergent flow.
244 Conversely, since we did not observe an increase in total cytometric biomass or in E+ biomass we 245 suggest that active processes could not have acted in isolation. On the other hand, there are 246 various candidate forms of interplay between active and reactive factors that could have contributed 247 to producing advantageous (resp. disadvantageous) conditions for E+ (resp. E-) groups at station 248 F2, e.g., physiological ability to utilize buoyancy to remain at surface despite subduction, capacity to 249 cope with front-specific nutrient availability or grazing pressure. These considerations imply that E+ 250 species may be selected based on physiological traits suited to the local frontal conditions.

251 This work demonstrates the existence of a distinct frontal phytoplankton community in the North 252 Balearic Front. Our results extend our understanding of the specificity of phytoplankton 253 communities at fronts and on their ability to "live on the edge" by providing a first investigation of 254 front-phytoplankton association in moderately energetic and oligotrophic conditions, largely 255 neglected in the past but characteristic of a substantial part of the world ocean. Adding information 256 on vertical processes, nutrient fields, growth and grazing rates, plankton species taxonomy, and a 257 Lagrangian modeling framework⁵² into the sampling strategy developed here will help disentangle 258 passive/active/reactive scenarios in contrasted regions of the world ocean.

259

260 Materials and Methods

261 Cruise strategy

262

263 The BioSWOT-Med cruise²⁹ took place from April 21 to May 15, 2023 aboard the R/V L'Atalante, in 264 the North Balearic Islands region of the northwestern Mediterranean Sea. The timing, location, and 265 strategy of the cruise were carefully designed to leverage data from the novel SWOT satellite 266 mission, to better resolve fine-scale oceanic features. Specifically, the BioSWOT-Med cruise was 267 scheduled to match the SWOT "fast sampling phase," characterized by high spatial resolution and a 268 1-day revisit period over ~150 km-wide oceanic regions, in the framework of the international 269 SWOT AdAc (Adopt-A-Crossover, https://www.swot-adac.org/) Consortium. An adaptive 270 multidisciplinary approach was employed by combining daily SWOT images and environmental 271 bulletins provided daily by the SPASSO toolbox (https://spasso.mio.osupytheas.fr/), and in situ 272 measurements using a suite of instruments to capture physical, biological, and chemical properties. 273 This strategy enabled the targeting of fine-scale features, especially the NBF. Continuous underway 274 systems (here, TSG and automated flow cytometer) revealed a sharp salinity gradient of 0.2-0.3 275 g/kg across a few kilometers associated with distinct biological patterns (Fig. 2). Surface drifters 276 deployed along the converging front, in close spatial agreement with the front derived by SWOT 277 maps, revealed an eastward frontal jet characterized by an average speed of 30 cm/s and local 278 intensifications up to approximately 70 cm/s (Fig. A3).

279

280 Satellite products for front detection

281

282 Circulation patterns of the NBF are evidenced using three different types of satellite products. The 283 European Seas DUACS Level-4 gridded product is used as a reference for conventional 284 multi-mission altimetry-derived ADT over the western Mediterranean Sea 285 (https://doi.org/10.48670/moi-00141). For this work, the 2023 version of the DUACS product is used 286 (i.e. not including SWOT nadir) to evidence the contribution of the SWOT mission over traditional 287 (pre-SWOT) multi-mission altimetry products. The 2-km ADT derived from SWOT observations 288 allows for the precise identification of the NBF location. Version v1.0.2 of SWOT Level-3 product is 289 used in this study. The SWOT_L3LR_SSH product, derived from the L2 SWOT KaRIn low rate 290 ocean data products (NASA/JPL and CNES), is produced and made freely available by AVISO and 291 DUACS teams as part of the DESMOS Science Team project". AVISO/DUACS. 2024. SWOT 292 Level-3 KaRIn Low Rate ADT Expert (v1.0.2) [Data set]. CNES. 293 https://doi.org/10.24400/527896/A01-2023.018). To extend high-resolution SWOT observations
294 beyond SWOT swaths, we used a joint-reconstruction of gridded ADT over the western
295 Mediterranean Sea using all available altimetry missions including SWOT data (hereafter referred to
296 as the VarDyn product; LeGuillou2024). VarDyn was chosen as the best representation of fine-scale
297 circulation in the studied area and was used to compute Lyapunov exponents, a Lagrangian
298 diagnostic detecting frontal areas¹¹. The Lagrangian Manifolds and Trajectories Analyzer (LAMTA)
299 computed thousands of numerical particle trajectories advected backward-in-time for 15 days to
300 derive Finite Time Lyapunov Exponents (FTLEs) and advected longitude both with DUACS and
301 VarDyn velocity fields⁵³. Ocean color data from Sentinel-3 product distributed by EUMETSAT (OLCI
302 Level 2 CHL Concentration - Sentinel-3A: https://navigator.eumetsat.int/product/EO:EUM:DAT:0178)
303 is also used to identify the front on May 5.

304

305 A, F, B identification

306

307 High-resolution underway TSG data (time resolution: 6 s) show strong horizontal salinity patterns
308 between the north and south parts of the front. To accurately and objectively identify the boundaries
309 of the frontal area we used a Gaussian Mixture Model (GMM) on the cross-front transect dataset.

310 GMM is a probabilistic approach for describing and classifying data. It attempts to fit data as a linear
311 combination of multidimensional gaussian distributions with unknown means and unknown standard
312 deviations⁵⁴. X is the array of n samples (here, n=26,229), and p(X) be the probability distribution
313 function (PDF) representing the entire dataset. GMM provides the PDF as a weighted sum of K
314 gaussian classes, indexed by k; that is,

315

$$
316 \quad p(X) = \sum_{k=1}^{K} \lambda_k N(X; \mu_{k'}, \Sigma_k).
$$

317

318 Here N(X; μ̂_k, Σ_k) is the multidimensional Gaussian PDF with a vector of means μ_k and covariance
319 matrix Σ_k, λ_k represents the probability associated with class k. In our case, we supposed k = 2, for
320 A and B water masses. The Expectation-Maximization (EM) algorithm is employed to estimate the
321 unknown means and unknown standard deviations of gaussian distributions. In the E-step, the
322 algorithm computes the probability that each data point belongs to each gaussian component, while
323 in the M-step, it maximizes the likelihood function by adjusting the parameters of the gaussians.
324 This algorithm is repeated until the convergence of the solution. We estimated boundaries of the
325 front F as the position of isohalines corresponding to the lower and upper limits of the 1-sigma
326 confidence intervals for the A and B gaussians, respectively.

327

328 Automated flow cytometry

329

330 An automated CytoSense flow cytometer (CytoBuoy b.v.) was connected to the seawater circuit of
331 the TSG to perform scheduled automated sampling and analysis of phytoplankton at high frequency
332 during the cross-front transect and stations. Three distinct analyses using the same instrument
333 set-up (protocol) have been run sequentially every 15 min. The protocol named FLR8 had a trigger
334 threshold fixed at 8 mV on the red fluorescence signal (FLR8) and analyzed a volume of 0.5 cm³. It
335 was dedicated to the analysis of the smaller phytoplankton (picoplankton and nanophytoplankton),
336 which are dominant in the Mediterranean Sea. We did not take into consideration the other protocol
337 (FLR25) which is used to run to detect larger cells such as microphytoplankton for two main
338 reasons: first, the analyses performed at the beginning of the cruise confirmed that the oligotrophic
339 Mediterranean ecosystem is predominantly characterized by small phytoplankton (pico- and
340 nanoplankton); second, flow cytometry has inherent limitations in accurately detecting larger cells
341 when its occurrence is too low in the small volume analysed⁵⁵,⁵⁶. The data were acquired by using
342 the USB software (Cytobuoy b.v.) and were analyzed with the CytoClus 4 software (Cytobuoy b.v.).
343 We determined and measured the abundance of 7 phytoplankton clusters named following the
344 standardized cytometric nomemclature⁴⁷ expect for those written in quotes: HsNano (averaging
345 2.10⁸ cell cm⁻³ over the entire cruise), "HfNano" (2.10⁸ cell cm⁻³), RedNano (7x10⁸ cell cm⁻³),
346 "HflrNano" (2x10⁸ cell cm⁻³), RedPico (1x10⁹ cell cm⁻³), "HflrPico" (1x10⁸ cell cm⁻³), OraPicoProk
347 (Synechococcus) (1x10¹⁰cell cm⁻³) (Fig. B1).

348

349 **Biomass estimations**

350

351 Phytoplankton abundances measured by flow cytometry were converted in terms of carbon
352 biomass. Cell carbon content can be estimated from cell biovolume (corresponding to the 3D space
353 occupied by a cell), enabling measured abundances [cell m⁻³] to be converted into biomass [mmolC
354 m⁻³]. The biovolume (BioV [um³ cell⁻¹]) was calculated by converting optical measurements by flow
355 cytometry according to Eq. (1a)⁵⁷,⁵⁸,⁵⁹. The carbon content (Qc [mmolC cell⁻¹]) was calculated
356 according to Eq. (1b)⁶⁰. Coefficients β₀ and β₁ represent, respectively, the y-intercept and the slope
357 of the log-log regression describing the relationship between the size of silica beads and their FWS.

358 Coefficients α₀ and α₁ are identical to those previously applied to a similar dataset¹³. The biomass
359 (BioM [mmolC m⁻³]) was then calculated for each sample by multiplying phytoplankton measured
360 abundances by their estimated average carbon content.

361

$$
362 \text{BioV} = \text{FWS}^{\beta_1} \cdot e^{-\beta_0} \quad (1a)
$$

$$
363 \quad Q_c = \alpha_0 \cdot \text{BioV}^{\alpha_1} \qquad (1b)
$$

$$
364 \quad \text{BioM} = \text{abundance} \cdot \bar{Q}_c \quad (1c)
$$

365

366 Statistical analysis of cytometric samples

367

368 A total of 451 cytometric samples were collected during the cross-front transect and at the stations.
369 The front is under-represented in the cross-front transect dataset due to its small spatial footprint:
370 26 samples vs 79 for A and 66 for B. As a consequence, to ensure the robustness of the dataset,
371 only the station data are presented here for statistical analysis. The number of samples per station
372 included in the statistical analyses is as follows: 101 for station A2, 85 for station F2 (more than
373 three times more than for F), and 94 for station B2.

374

375 We conducted a Mann-Whitney test to statistically evaluate differences in the biomass proportions
376 of phytoplankton groups between stations. Specifically, we aimed to determine whether significant
377 differences exist in station F2 compared to other stations. The Mann-Whitney test is a
378 non-parametric statistical method used to assess whether two independent samples come from
379 distributions with different medians. It evaluates the probability that a randomly chosen value from
380 one sample (X) is greater than a randomly chosen value from the other sample (Y). In our case, X
381 and Y represent the biomass proportions of a given phytoplankton group *i* at two stations (e.g.,
382 station *i* versus station *y*). We performed 18 tests in total: 9 phytoplankton groups × 2 station
383 comparisons (F2 vs. A2 and F2 vs. B2). Additionally, we conducted the same analysis to compare
384 transient (T1, T2) and edge (E+, E−) groups within each station. This resulted in 8 additional tests:
385 4 groups × 2 station comparisons (F2 vs. A2 and F2 vs. B2).

386

387 We also conducted two other statistical analyses using PRIMER 8
388 (https://www.primer-e.com/software) to investigate the community structures in A, F and B regions.

389 For these analyses, we used normalised biomass proportions for each phytoplankton group to
390 minimise bias due to significant differences between dominant (RedNano, RedPico, OraPicoprok)
391 and non-dominant (HflrNano, HsNano, HfNano, HflrPico) phytoplankton groups.

392

393 First, ANOSIM analysis⁶¹ was conducted to statistically test the existence of the A, F, and B
394 communities. ANOSIM evaluates differences between categories (A2, F2, B2) by calculating the R
395 statistic, which represents the scaled difference between average dissimilarity ranks within and between categories,

397

398 $R = \frac{\bar{r}_B - \bar{r}_W}{M/2}$ with $M = \frac{N(N-1)}{2}$,

399 $\bar{r}_B$ and $r_W$ represent the average rank similarities between and within categories, respectively, calculated here using Euclidean distance matrices. $N$ is the total number of samples. An $R$ value near zero indicates no group differences, while higher $R$ values signify greater differences between categories.

403

404 Second, SIMPER analysis<sup>62</sup> was conducted to identify which phytoplankton groups are responsible for differences between A, F and B communities. SIMPER evaluates the relative contribution of species to dissimilarities between categories (A2, F2, B2) by calculating the percentage of contribution $\delta_{jk}(i)$ of $i$th species to Eulerian distance dissimilarity between one pair of samples, $j$ and $k$,

409

410 $\delta_{jk}(i) = \frac{100|r_{ij}-r_{ik}|}{\sum_{j} (r_{ij}+r_{ik})}$,

411

412 This calculation is repeated for each pair (all combinations of $j$ and $k$ from A2, F2, and B2), and the contribution of the $i$th species to overall dissimilarity is represented as the mean of all $\delta_{jk}(i)$.

414 Acknowledgments

415

416 The authors acknowledge the CNES TOSCA funding the project BIOSWOT-ADAC and the ANR –

417 FRANCE (French National Research Agency) for its financial support of the BIOSWOT project

418 ANR-23-CE01-0027, the International Cooperation Project DYF2M (CNRS-CAS), the State-Region

419 Planning Contract (CPER) 2007-2013. The French oceanographic fleet and in particular the captain

420 Gilles Ferrand and the crew of the R/V L'Atalante are acknowledged for their precious support

421 during the BioSWOT-Med cruise. Dr. Maristella Berta contribution was supported by the

422 ITINERIS Project (IR0000032-Italian Integrated Environmental Research Infrastructures

423 System-CUP B53C22002150006) and by CNR-ISMAR (Lerici, Italy) dedicated fundings.

424 We thank the entire BioSWOT-Med research team and all contributors, with special

425 appreciation to Thierry Moutin, John Ryan, Riccardo Martellucci, and Karine Leblanc for

426 their precious scientific insights.

427 Author contributions

428 L.O. performed the data analysis and led the manuscript writing. A.D. and M.M. contributed equally

429 to the guidance and interpretation of results, as well as to the manuscript writing. F.d'O. and X.C.

430 contributed equally to the manuscript writing. L.R. assisted in satellite data acquisition and

431 contributed to the analysis of satellite data. L.I. contributed to the guidance and interpretation of

432 results and performed manuscript proofreading. M.L. helped conceive the figure designs and

433 contributed to the manuscript structure. M.B. assisted in the deployment and acquisition of drifter

434 data and provided results on drifter trajectories. M.B., A.P., and S.B. contributed equally to the

435 manuscript conception and proofreading. G.G. enabled the installation of the automated flow

436 cytometer aboard R/V L'Atalante and contributed to the manuscript conception and writing.

437 Competing interests

438 The authors declare no competing interests.
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696 **Figures**

<figure><img src="image_3.png" /></figure>

697

698

699 **Fig. 1.** Study area and sampling strategy. **a** BioSWOT-Med sampling region. The black line 700 represents the ship's track during the entire cruise, while the red box delineates the area presented 701 in panels **b**, **c**, and **d**. Panels **b** and **c** display respectively the Absolute Dynamic Topography (ADT) 702 from DUACS 2023 and SWOT on May 5 (date for which satellite image of chlorophyll is available). 703 Current streamlines are shown in white. Panel **d** displays the surface chlorophyll concentration 704 (CHL) from OLCI/Sentinel-3A/B on May 5. The purple line indicates the adaptive cross-front 705 transect strategy performed on April 29–30 using daily SWOT imagery, which provided a finer-scale 706 view of the front, combined with *in situ* measurements. The eastward-moving frontal jet (Fig. A2 and 707 A3) guided the alignment of the cross-front transect with a specific angular delay to follow its 708 evolution (black triangles mark the transect direction). Additionally, three 24h drifting stations were 709 performed (green, red, and blue stars and lines): B2 (May 5), F2 (May 6), and A2 (May 7). Notably, 710 at the F2 station located within the front, the ship drifted eastward with the frontal jet aligning with 711 current streamlines and chlorophyll distribution (star positions indicate the beginning of stations). 712 The stations are named according to the cruise convention.
<figure><img src="image_4.png" /><figcaption>713 **Fig. 2.** Identification of the front. **a** Frequency histograms of the absolute *in situ* salinity along the 714 cross-front transect (purple bars) and at each station: A2 (blue bars), F2 (red bars), and B2 (green 715 bars). The width of the bars was adjusted to reflect the larger number of data collected during the 716 stations. The purple curve represents the fitted salinity distribution for the cross-front transect, 717 modeled as a Gaussian mixture. The vertical red dashed lines represent the salinity values at the 718 boundaries of the front (F), corresponding to the lower and upper limits of the 1-sigma confidence 719 interval for the A and B Gaussians, respectively. 720 **b** Resulting map of the cross-front transect regions, colored according to the salinity criterion and stations, overlaid on SWOT FTLE on April 29 721 (the first day of the cross-front transect). The three stations are also shown (star positions indicate 722 the beginning of stations). 723 **c** Example of relative biomass percentage for one cytometric phytoplankton group: HflrPico, along the cross-front transect and at stations, overlaid on the same 724 SWOT FTLEs.</figcaption></figure>

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<figure><img src="image_5.png" /></figure>

730 **Fig. 3.** Phytoplankton community composition. **a** Relative biomass proportions of each cytometric phytoplankton group averaged for stations A2 ($p_A$), F2 ($p_F$), and B2 ($p_B$) (vertical color spans indicate the classification of phytoplankton groups into transient and edge groups). Transient groups 731 (T: RedNano, HfIrNano, RedPico) are those with $p_F$ values between $p_B$ and $p_A$ (T1: if $p_B<p_A$, T2: if $p_B>p_A$), while edge groups (E: OraPicoProk, HsNano, HfNano, HfIrPico) are characterized by $p_F$ 732 values either lower (E-) or higher (E+) than both $p_A$ and $p_B$. **b** Relative proportions of transient (T) and edge (E) groups across the three stations. A Mann-Whitney test was conducted with 101 733 samples for station A2, 85 for station F2, and 94 for station B2. Stars indicated the p-value of the 734 Mann-Whitney statistics (n.s (non-significant) for p-value>0.1, ** for p-value<0.1, *** for 735 p-value<0.01), indicating if $p_F$ is significantly different from $p_A$ and $p_B$. There are always significant 736 differences in frontal proportions, except for the OraPicoprok. 737
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<figure><img src="image_6.png" /></figure>

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743 **Fig. 4.** Frontal phytoplankton community characterization. **a** Results of the ANOSIM (dashed black arrow) and SIMPER (pie charts) statistical analyses of station data. The R statistical values from ANOSIM are shown on the dashed black arrows for each pairwise comparison (contrasting stations A2-B2, A2-F2, B2-F2), indicating the separation of three distinct phytoplankton communities corresponding to regions A, F, and B. Pie charts display the percentage contributions of phytoplankton groups to the differences between these communities, as determined by SIMPER analysis. **b** Schematic conclusion of the eastward frontal system sampled during BioSWOT-Med. The A community (Ac) is found in the saltier water mass (A region, S+), the B community (Bc) in the fresher water mass (B region, S-), and the F community (Fc) in the haline gradient between A and B (F region). The A and B communities differ primarily due to opposite trends in T1 and T2 groups. In contrast, the F community is characterized by a higher proportion of E+, encompassing diverse non-dominant phytoplankton groups, and a lower representation of E-, the most dominant group. The E- curve is represented by a dashed line due to the non-significance of the Mann-Whitney statistics.

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**Supplementary Files**

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* supplementary.pdf