Impact of moderately energetic fine-scale dynamics on the phytoplankton community structure in the western Mediterranean Sea

Model simulations and remote sensing observations show that ocean dynamics at fine scales (1–100 km in space, day–weeks in time) strongly influence the distribution of phytoplankton. However, only few in situ samplings have been performed and most of them in western boundary currents which may not be representative of less energetic regions. The PROTEVSMEDSWOT cruise took place in the moderately energetic waters of the western Mediterranean Sea, in the southern region of the 5 Balearic Islands. Taking advantage of near-real time satellite information, a sampling strategy was defined in order to cross a frontal zone separating different water masses. Multi-parametric in situ sensors mounted on the vessel, on a towed vehicle and on an ocean glider were used to sample at high spatial resolution both physical and biogeochemical variables. Particular attention was put in adapting the sampling route, in order to also estimate the vertical velocities in the frontal area. Such a strategy was successful in sampling quasi-synoptically an oceanic area characterized by the presence of a narrow front 10 with an associated vertical circulation. A multiparametric statistical analysis of the collected data identifies two water masses characterized by different abundances of several phytoplankton cytometric functional groups, as well as different contents in chlorophyll a and O2. With respect previous studies, here we focus on moderately energetic fronts induced by fine-scale circulation. Moreover, we explore physical-biological coupling in an oligotrophic region. Our results show that the fronts induced by the fine-scale circulation, even if weaker than the fronts occurring in energetic and nutrient-rich boundary current 15 systems, maintain a strong structuring effect on phytoplankton community by segregating different groups at the surface. Since oligotrophic and moderately energetic regions are representative of a very large part of the world ocean, our results can be extrapolated and have global significance.

regions are dominated by weak fronts, which are continuously created, moved, and dissipated, and separate water masses with similar properties. Whether fine-scale fronts maintain their driving role on phytoplankton diversity also in these weaker regions remains therefore an open question which has been largely neglected, due to the difficulty of performing in situ experiments 55 over these short lived features. In this study, we attempt to address this issue in a case study, focusing on a moderately energetic front, and to relate its role on the distribution of phytoplankton groups. Compared to more energetic regions, the effect of fine-scale on biogeochemical processes is non predictable here. This leads to the following question: is the physical forcing induced by horizontal and vertical motions sufficiently strong to segregate phytoplankton groups effectively in this moderately energetic region? 60 New sampling strategies were required to track the fine-scale ephemeral structures. Using remote sensing and numerical simulations to define the sampling strategy, field studies such as LatMix (Shcherbina et al., 2015), AlborEx (Pascual et al., 2017) and LATEX  have demonstrated that individual fine-scale features can be targeted experimentally.
While these past campaigns focused mainly on physical processes, recent progress in biogeochemical sensors now makes the 65 study of physical-biological coupling easier, including in moderately-energetic and oligotrophic areas. During the BLUE-FIN-13 cruise, Mena et al. (2016) studied the picophytoplankton distribution in a haline front formation area in the Balearic Sea.
More recently, the OSCAHR cruise in the Ligurian Sea was specially designed to combine high-resolution measurements of both physical and biological variables (Doglioli, 2015). Exploiting the large data set collected during this cruise, Marrec et al. (2018) and Rousselet et al. (2019) have shown the influence of physical dynamics in controlling the spatial distribution of 70 phytoplankton through an eddy structure, highlighting the close relationship between fine scale dynamics and the distribution of phytoplankton. In the southwest Pacific ocean, high-resolution biological sampling during OUTPACE (Moutin and Bonnet, 2015) and TONGA (Guieu and Bonnet, 2019) has shown the influence of fronts in controlling the spatial distribution of bacteria and phytoplankton (Rousselet et al., 2019;Benavides et al., 2021). Recently, the methodological developments in underway nitrogen (N 2 ) fixation measurements allowed to capture a rapid shift of the diazotrophic community in the North Atlantic ocean 75 (Tang et al., 2020).
Following a satellite-based adaptive and Lagrangian sampling strategy, a high-resolution coupled physical-biological sampling was performed during the PROTEVSMED-SWOT cruise in the southwestern Mediterranean Sea, south of the Balearic Islands (Dumas, 2018;Garreau et al., 2020). This cruise was operated in the context of the preparation of the new satellite mission Surface Water and Ocean Topography (SWOT; https://swot.jpl.nasa.gov/,https://swot.cnes.fr, last access: July 16, In this paper, we first describe the hydrodynamics of the area of study, with a focus on the vertical velocities estimated through the omega equation, and we identify the water masses residing in the region during the cruise. Then, the distribution of various groups of phytoplankton, and of fluorescent dissolved organic matter (FDOM), is presented in relation to the fine-scale dynamics. An advanced statistical analysis is performed to highlight the physical-biological coupling objectively. Environment Monitoring Service, https://marine.copernicus.eu, last access: July 16, 2021). Moreover ocean color composite maps were provided by CLS with the support from CNES. They are constructed with a simple weighted average over the five previous days of the data issued from the Suomi/NPP/VIIRS sensor. The altimetry-derived geostrophic velocities from 100 AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic) database were exploited to extract near realtime daily maps. These were also used to derive the finite size Lyapunov exponents (FSLEs). The FSLE analysis permits to identify biogeochemical regions of potential interest. Indeed, FSLEs values often form continuous lines, or ridges, which are used to identify regions of enhanced strain that are to be expected near frontal zones. The first study that showed the interest of using FSLE-derived fronts for biogeochemical studies was probably Lehahn et al. (2007). Before, Abraham and Bowen 105 (2002) have been the first to apply the Lyapunov exponent technique (although finite-time, not finite-size) to the ocean, in turn borrowing some ideas from dynamical system theory (see in particular Boffetta et al., 2001). A review on the FSLE and other satellite-based Lagrangian techniques can be also found in Lehahn et al. (2018); Hu and Zhou (2019). Since several years, this strategy has already been tested, either in post-cruise or real-time analysis, during many campaigns (e.g., Smetacek et al., 2012;d'Ovidio et al., 2015;Rousselet et al., 2018;De Verneil et al., 2019;Benavides et al., 2021). In the present work, FSLE  (Fig. 1). Consequently, these two regions have been sampled along a dedicated route of the ship, represented in purple and in orange on Fig. 1. A special attention was paid to adapt the temporal sampling in these different water masses to the biological time scales, i.e. trying to catch the diurnal cycle. Because of the shape of the ship track, in the following we refer to these areas as "West-East (WE) hippodrome" (in orange on Fig. 1) performed from 8 May 15:30 to 10 May 17:30 UTC, and "North-South (NS) hippodrome" (in purple on Fig. 1) performed between 11 May 02:00 and 13 May 08:30 UTC.

In situ measurements
Physical and biological variables (horizontal velocities, temperature, salinity, and abundances of the different phytoplankton functional groups) were measured at high frequency all along the route. In situ systems include a Seasoar deployed at sea, a vessel-mounted Acoustic Doppler Current Profiler (VMADCP), a thermosalinograph (TSG) and a flow cytometer installed on 125 board. The Seasoar is a towed undulating vehicle capable of achieving undulations from surface down to 400 m. Two Sea-Bird SBE-9 (with SBE-3 temperature and SBE-4 conductivity sensors) instruments mounted on either side of the Seasoar enables the simultaneous measurements of temperature, salinity (from conductivity) and pressure. The conductivity and temperature data were lag-corrected to reduce salinity spiking with the methodology developed by Lueck and Picklo (1990), Morison et al. (1994) and Mensah et al. (2009), before conversion to absolute salinity S A and conservative temperature Θ, in accordance 130 with TEOS-10 standards (McDougall et al., 2012). In the following, temperature and salinity refer to absolute salinity and conservative temperature. Horizontal velocities between 19 m and 253 m have been measured with a VMADCP operating at 150 kHz. VMADCP data treatment was performed with the Matlab software Cascade V.7 (https://www.umr-lops.fr/en/ Technology/Software/CASCADE-7.2, last access: July 16, 2021). The sea surface temperature and salinity have been measured continuously along the ship route by the underway TSG. The TSG was equipped with two sensors: i) a CTD sensor SeaBird 135 Electronics SBE 45, installed in the wet lab, connected to the surface water, and pumping continuously seawater at 3 m depth.
ii) a temperature sensor SBE 38 installed at the entry of the water intake.
An automated CytoSense flow cytometer (CytoBuoy b.v.) was installed on board and connected to the seawater circuit of the TSG, to perform scheduled automated sampling and analysis of phytoplankton (Thyssen et al., 2009(Thyssen et al., , 2015. The instrument contains a sheath fluid made of 0.1 µm filtered seawater which stretches the sample in order to separate, align and drive the 140 individual particles (i.e. cells) through a light source. This light source is made of a 488 nm laser beam. When the particles cross the laser beam, they interact with the photons. Several optical signals are recorded for each single particle: the forward angle light scatter (FWS) and 90°side-ward angle scatter (SWS), related to the size and the structure (granularity) of the particles. Two signals of fluorescence induced by the light excitation were also recorded, a red fluorescence (FLR) induced by chlorophyll a and an orange fluorescence (FLO) induced by the phycoerythrin pigment. Two distinct protocols have been 145 run sequentially every 30 min, in order to process the 1164 samples. The first protocol (FLR6) has a FLR trigger threshold fixed at 6 mV and is able to analyze a volume of 1.5 cm 3 . It was dedicated to the analysis of the smaller phytoplankton.
For instance, Synechococcus were optimally resolved and counted with this protocol. The second protocol (FLR25), targeted nanophytoplankton and microphytoplankton with FLR trigger level fixed at 25 mV and an analyzed volume of 4 cm 3 . The data were acquired thanks to the USB software (Cytobuoy b.v.) but analyzed with the CytoClus software (Cytobuoy b.v.).

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The combination of the various variables recorded by the flow cytometer exhibits various clusters of particles (cells), which abundance (cells per cubic centimetre) and average variable intensities are provided by the CytoCLUS software. The latter generates several two-dimensional cytograms (e.g. Fig. 6, see section 3.3 for explanation of the group identification) of retrieved information from the 4 pulse shapes curves (FWS, SWS, FLO, FLR) obtained for every single cell.
On 5 May 2018, a SeaExplorer glider, manufactured by Alseamar (codename: SEA003), was deployed at sea. After a short 155 transit, it performed a route approximately parallel to the NS hippodrome (red track on Fig. 1). This glider, set to dive down to 650 m depth, was equipped with a pumped conductivity-temperature-depth sensor (Seabird's GPCTD) from which the conservative temperature (Θ), the absolute salinity (S A ) and the density anomaly referenced to the surface (σ 0 ) were derived using TEOS-10 toolbox (McDougall and Barker, 2011). This GPCTD was also equipped with a dissolved oxygen (O 2 ) sensor (Seabird's SBE-43F) to measure oxygen concentrations. The glider also embarked a WET Labs ECO Puck FLBBCD for 160 measurements of i) [Chla] fluorescence (targeting excitation and emission wavelengths at λEx/λEm: 470/695 nm), converted into Chla concentrations (in microgram per litre), ii) backscattering at 700 nm (BB700), and iii) a FDOM fluorophore, namely the humic-like fluorophore or peak C in the Coble (1996)'s classification (λEx/λEm: 370/460 nm), expressed in microgram per litre equivalent quinine sulfate units (microgram per litre QSU). Finally, the SeaExplorer was also equiped with two MiniFluo-UV fluorescence sensors (hereafter called MiniFluo) for the detection of various FDOM fluorophores (Cyr et al., 165 2017(Cyr et al., 165 , 2019. In this study, the MiniFluo-1 was used for the detection of tryptophan-like fluorophore (λEx/λEm: 275/340 nm), while the MiniFluo-2 was used for the detection of tyrosine-like fluorophore (λEx/λEm: 260/315 nm). Tryptophan-and tyrosine-like fluorophores, referred to as peak T and peak B, respectively, in the Coble (1996)'s classification, are amino acidlike components commonly found in the marine environment, and generally associated with autochthonous biological processes (see review by Coble et al. (2014)). Here, fluorescence intensities of tryptophan-and tyrosine-like fluorophores are provided 170 in relative unit (RU) and are not converted into mass concentration (microgram per litre) (Cyr et al., 2017(Cyr et al., , 2019. Glider observations were processed with the Socib glider toolbox (Troupin et al., 2015) for cast identification and geo-referencing.

Vertical velocity estimation
The vertical velocity has been diagnosed solving the so-called quasi-geostrophic (QG) omega equation (Hoskins et al., 1978;Tintoré et al., 1991;Allen and Smeed, 1996, Eq. (1)): where w is the vertical component of the velocity field and Q is the vector determined by horizontal derivatives of water density and horizontal velocity (Hoskins et al., 1978;Giordani et al., 2006, Eq. (2)): with V g the geostrophic horizontal velocity vector, ρ the density, ρ 0 a reference density equal to 1025 kg m −3 , g the gravi-High-resolution in situ data are necessary to solve Eq. (1). In this work, σ is obtained from Seasoar CTD measurements ( Fig.   A1) and the geostrophic component of the horizontal velocity has been estimated from the measurements performed with the VMADCP as in the work of Barceló-Llull et al. (2017). Following Allen et al. (2001)'s suggestions to preserve as much as 185 possible synopticity, four transects of the NS hippodrome (see Fig. 1) have been selected between 11 May and 12 May 2018, to obtain a "butterfly" design as in Cotroneo et al. (2016) and Rousselet et al. (2019). The σ, u and v fields have been interpolated onto a 3D grid, using objective analysis (Le Traon, 1990;Rudnick, 1996). The horizontal grid resolution is 0.9 km × 0.9 km and the vertical resolution is 6 m (from 19 m to 253 m depth). We have followed the method described by Rudnick (1996), using the scripts freely downloadable from his web page at the address http://chowder.ucsd.edu/ Rudnick/SIO_221B.html,last access: 190 10 February 2021. The in situ data are considered to be composed of a mean value, a fluctuation and some noise including both smaller scale variability and instrumental error. The fluctuation part of the field's statistics is assumed to have a decorrelation length scale of 20 km in both the x and y directions, with a structure orientation of 18.4°from north. The correlation length scale has been chosen by analyzing the auto-covariance matrix of the σ field and performing several sensitivity tests. The noise-to-signal ratio is assumed to be 0.05 as in Rudnick (1996). The interpolated fields are shown superposed to the in situ 195 measurements (see Fig. A1 in Appendices). The ageostrophic component of the velocity measured by the VMADCP is then removed and Eq. (1) is solved with an iterative relaxation method and constrained by Dirichlet boundary conditions (w = 0) as in the case of the front studied by Rudnick (1996). To minimize the effect of the imposed boundary conditions, only data with an error on the objective mapping of σ ≤ 0.0025 have been then considered.

Hydrodynamics
At first, we describe the hydrodynamic conditions encountered during PROTEVSMED-SWOT in order to characterize the area.
For simplicity, two transects only, representative of each of the two hippodromes, are described: a first transect, on the WE hippodrome, performed from 9 May 16:50 to 23:45 UTC is referred to as the WE transect (Fig. 2a). The second one performed on the NS hippodrome, from 11 May 02:00 to 08:40 UTC, is referred to as the NS transect ( Fig. 2b). Note that we obtained 205 similar results for the other transects (see Fig. A2 in Appendices located between 38°N 30' and 38°N 36'. Another smaller downwelling patch is present in the south-east of the sampling area. The intensities of these vertical motions, ranging from 2 to 8 10 −5 m s −1 (corresponding to 1.7 to 6.9 meters per day), are stronger in the intermediate layer.

Hydrology
The typical southwestern Mediterranean water masses are observed in the Θ-S A diagrams of the Seasoar CTD data of the 220 WE and NS transects ( Fig. 4a and Fig. 4c, respectively). A clear separation in two different water masses appears at the surface. In order to identify where these two water masses separate objectively, we first distinguish surface from intermediate waters by the isopycnal at 28.45 kg m −3 on the Θ-S A diagrams. Then, an iterative method with a granularity of 0.05°in longitude(latitude) along the WE(NS) transect has been used to calculate the means of the two surface water masses in terms of salinity and temperature, (S A1 , Θ 1 ) and (S A2 , Θ 2 ), and the difference in terms of mean salinity, |S A1 − S A2 |. Then, the best 225 separation between the two surface water masses corresponds to the longitude(latitude) along the WE(NS) transect where the  4d). Note that for the other meridional transects the estimated separation varies of few minutes only (Table 1) using the same color code as in the Θ-S A diagrams. Interpretation of these data is the following. The surface layer is occupied 235 by Atlantic Waters (AW) with different residence times in the Mediterranean Sea. We refer to them as "younger AW" (in light blue) and "older AW" (in dark blue). The "younger AW" corresponds to AW entered more recently in the Mediterranean basin and characterized by a salinity between 37 g kg −1 and 38 g kg −1 , while the "older AW" is characterized by a higher salinity.
Other authors refer to this water as "local AW" (Barceló-Llull et al., 2019) or "resident AW" (Balbín et al., 2012). The "younger AW" is located at the west and the south of the WE and NS transects, respectively. Moreover, the separation between the two 240 types of AW is in agreement with the localisation of the front identified by the FSLE and the change in the current direction ( Fig. 2). Below the surface layer, the intermediate water is more homogeneous and is characterized by a temperature of 13-14°C and a salinity of 38-38.5 g kg −1 (in pink on Fig. 4a and Fig. 4c).
The SeaExplorer glider has also performed temperature and salinity measurements along a transect parallel and slightly west of the NS hippodrome (see Fig. 1). The Θ-S A diagrams of the glider data ( Fig. A3a and Fig. A3b) confirm the presence 245 of the two surface water masses mentioned above, in particular during the outward route (Fig. A3a). During the glider return route (Fig. A3b) the surface water masses begin to be more homogeneous than during the outward route and the NS Seasoar transect (Fig. 4c). This fact can be explained because the transects completed with the Seasoar were realized within a few hours, while the glider transects lasted several days. Moreover, the deeper glider sampling has allowed to detect another thermohaline signature, with temperature values about 13°C and salinity about 38.5 g kg −1 , corresponding to the Western Mediterranean
The vertical extension of the surface and intermediate water masses can be observed plotting the Seasoar data (conservative temperature, absolute salinity and density) as vertical sections along the WE and NS transects (Fig. 5). The warm surface layer with temperature greater than 15°C extends until about 100 m, on Fig. 5a and Fig. 5d. This layer is also characterized by a salinity between 37.5 g kg −1 and 38 g kg −1 (Fig. 5b and Fig. 5e) and, as a consequence, by the lowest density ( Fig. 5c and Fig.   255 5f). Note that this surface layer is more apparent along the transects of the WE (Fig. 5a, b and c), than along the NS transect.
Below 100 m depth there is the intermediate water layer. Looking at gradients along the temperature and salinity transects, we can clearly identify the separation between the two types of AW at longitude 4°E for the WE transect ( Fig. 5a and 5b) and latitude 38°N 30' for the NW transect ( Fig. 5d and Fig. 5e), as obtained with the iterative method. However, this separation is less apparent along the two density transects ( Fig. 5c and Fig. 5f).

Characterization and distribution of phytoplankton by flow cytometry
Up to nine phytoplankton groups were optically resolved by flow cytometry (Fig. 6), thanks to their light scatter (forward scatter, FWS, and sideward scatter, SWS) and their red (FLR) and orange (FLO) fluorescence intensities, as in the work of Thyssen et al. (2015) and Marrec et al. (2018). We have called these groups with the conventional names used by flow cytometrists, i.e., some groups related to the taxonomy (Synechococcus, Cryptophytes) while the others related to a range of size (picoeukary-265 otes, nanoeukaryotes) according to Sieburth et al. (1978). Indeed, the first group corresponds to Synechococcus (Syn on Fig.   6c), which is a prokaryotic picophytoplankton. We distinguish it from the other picophytoplanktonic groups, because it was unambiguously put in evidence by flow cytometry thanks to its higher FLO intensity compared to the FLR intensity (Fig. 6c), induced by the presence of phycoerythrin pigments. A first eukaryotic picophytoplankton group (Pico1) shows lower FLR and FLO intensities than Synechococcus (Fig. 6c). Two other groups of picophytoplankton (Pico2 and Pico3) exhibit higher 270 FWS, SWS and FLR intensities than Pico1 (Fig. 6d). The last group of picophytoplankton (PicoHFLR) is characterized by a high FLR signal induced by Chla. Two distinct nanophytoplankton groups (SNano) and (RNano) were defined according to their high FLR and FLO intensities ( Fig. 6a and Fig. 6c). SNano have a high SWS/FWS ratio and higher SWS intensities than RNano ( Fig. 6b) and Fig. 6a). Finally, microphytoplankton (Micro) and Cryptophytes (Crypto) exhibit high FLR and FLO intensities (Fig. 6c). Cryptophytes can belong to the pico-or nanoeukaryotes but have also been discriminated from the red-only 275 fluorescing pico-or nanoeukaryotes based on their orange fluorescence induced by phycoerythrin, like Synechococcus. The size and the abundances of these nine groups have been summarized in Table 2. and ∼ 900 cells cm −3 for Pico1 and Pico2, respectively). RNano abundances ( Fig. 7g and Fig. 8g) present a similar distribu-tion with these groups along the NS transect, with high abundances (450-500 cells cm −3 ) located at the southern part of the front. However, the distribution of this latter is less clear along the WE transect. Pico3 ( Fig. 7d and Fig. 8d), Microphytoplank-285 ton ( Fig. 7f and Fig. 8f), and SNano ( Fig. 7h and Fig. 8h) abundances vary between 100-500 cells cm −3 , 8-18 cells cm −3 , and 300-500 cells cm −3 , respectively, and present an opposite distribution compared to the other previous groups. Indeed, higher abundances are found in the eastern and northern parts of the front. PicoHFLR (Fig. 7e and Fig. 8e) and Cryptophytes abundances ( Fig. 7i and Fig. 8i) ranged from 160-280 cells cm −3 and 10-30 cells cm −3 respectively. However, these latter exhibited a less obvious pattern between the two sides of the front. Overall, the distribution of the abundances of the various 290 phytoplankton groups evidenced by flow cytometry on either side of the front (except for these two last groups) fits well with the hydrodynamic and hydrological observations.

Statistical analysis
Surface temperature and salinity data measured with the TSG were merged with abundance data of the 9 phytoplankton groups at each cytometry sampling point along the WE and NS hippodromes. Thus, the final data set consists of 11 variables, each 295 variable containing 215 observations. To deal with this large multivariate data set a principal component analysis (PCA) was applied.
The PCA consists of summarizing the information contained in the data set by replacing the initial variables with new synthetic variables (called principal components), linear combinations of the initial variables and uncorrelated two by two.
Applied to our data set, the PCA points out that the first three components explain 36.7 %, 18.1 % and 13 % of the total 300 variance of the data, respectively. The following statistical analysis focuses on these three components, representing 67.8 % of the total variance of the data. The K-medoid algorithm, described by Hartigan and Wong (1979) and Kaufman and Rousseeuw (1987), is an-other method to represent the various aspects of the data structure. This algorithm divides M points in N dimensions into K groups (or clusters). A cluster is an object for which the average dissimilarity to all the data is minimal. In our case of study, the K-medoid algorithm splits the 215 points (i.e., observations) into three clusters (Fig. 9b). Each point of a cluster shows a high degree of 310 similarity with the others points of the same cluster. The three clusters are well separated from each other. Only a few points of the black and red clusters are difficult to disentangle. Finally, the average of each variable called local average and the global average have been calculated for each cluster to show the contribution of each variable to a cluster. The most discriminating variables for each cluster (Table 3) have also been determined with the standard deviation. Figure 9c is a spatio-temporal representation of the three main clusters obtained by the K-medoid algorithm. The WE 315 hippodrome from 8 to 10 May is characterized by the presence of the black cluster in the east and the red cluster in the west.
The NS hippodrome starts the 11 May with the red cluster present in the south and the black cluster in the north. The latter remains dominant in the north for the remaining of the sampling. In the south, the red cluster is gradually replaced by the blue cluster, except for a few points on the 13 May. During the PROTEVSMED-SWOT cruise, the satellite-derived surface [Chla] showed contrasted values between the southwest and the north-east of the studied area (Fig. 1). The analysis of the ocean color images combined with the altimetry-derived FSLE field, have been associated with the strong variation of current direction observed in the horizontal velocities measured by VMADCP data, allowing the identification of a frontal area located at the latitude of about 38°N 30' and between 3°E and 4°E of longitude (Fig. 2).

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Our estimation of vertical velocities with the omega equation method allowed to investigate the vertical dynamics in the frontal area (Fig. 3). Several previous studies have shown the importance of fine-scale dynamics in generating vertical velocities (e.g., Rudnick, 1996;Lapeyre and Klein, 2006;Mahadevan and Tandon, 2006;Balbín et al., 2012). Although the ship route was designed mainly for the cytometry sampling across the front, we obtained a spatial grid sufficiently regular to apply the omega equation method, as recommended by Allen et al. (2001). In the area where our sampling with the Seasoar overlaps The data from the CTD sensors mounted on the Seasoar towed fish and on the SeaExplorer ocean glider identify a rapid shift between two different types of surface water masses on the Θ-S A diagrams ( Fig. 4 and Fig. A3). These water masses are AW 350 at different stages of modification. Indeed, the AW enters into the Mediterranean Sea through the Strait of Gibraltar and then forms an anticlockwise circulation along the continental slope of the western Mediterranean basin caused by the combination of the Coriolis effect and the topographical forcing (Millot, 1999;Millot and Taupier-Letage, 2005;Millot et al., 2006). In the south-west of the basin, this circulation is dominated by the Algerian Current (AC), which can form meanders and mesoscale eddies due to baroclinic and barotropic instabilities (e.g., Millot, 1999). These eddies spread over the basin and join the region 355 south of Balearic Islands, carrying with them the AW recently entered in the Mediterranean that we define "younger AW". In this region, the "younger AW" encounters the "older AW", i.e. the AW modified by cooling and evaporation while progressing along the northern part of the western Mediterranean basin (Millot, 1999;Millot and Taupier-Letage, 2005). The presence of this water has already observed by Balbín et al. (2012) and Barceló-Llull et al. (2019) which refers to it as "resident" or "local" AW, respectively, to indicate the colder and saltier AW of the Balearic Sea.

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During our cruise, the "older and younger AW" are separated by a frontal area (Fig. 4). This separation between these two AWs is also clearly visible in the first 100 m of the water column, on the depth sections of temperature and salinity (Fig. 5).
López-Jurado et al. (2008) (Coble et al., 2014), being produced through the activity of autotrophic and heterotrophic plankton organisms, in particular phytoplankton and heterotrophic bacteria (Stedmon and Cory, 2014), and are known to be indicators of bioavailable/labile 380 DOM (C and N) (Hudson et al., 2008;Fellman et al., 2009). Even though phytoplankton activity is considered a source of tryptophan-and tyrosine-like fluorophores (Determann et al., 1998;Stedmon and Markager, 2005;Romera-Castillo et al., 2010), bacterial degradation appears to be a source, but also a sink for these fluorophores, depending on the availability in nutrients (Cammack et al., 2004;Nieto-Cid et al., 2006;Biers et al., 2007). These similarities in the profiles of [Chla] and between [Chla] and tyrosine-, and between [O 2 ] and tryptophan-like fluorophores when considering all glider data for the two transects from 5 to 200 m depth (r = 0.88 and 0.84, n ≈ 32595, p < 0.0001). The fact that tyrosine-like fluorophore was rather associated with [Chla] and tryptophan-like with [O 2 ] reveal that these two fluorophores were probably not issued from the same phytoplankton groups. Moreover, it seems that tryptophan would be more susceptible to be released by heterotrophic bacteria (in addition to be released by phytoplankton) than would be tyrosine-like material (Hudson et al., 2008;Tedetti et al., 2012;390 Stedmon and Cory, 2014). The high contents in tryptophan-and tyrosine-like fluorophores found in the northern part of the transect ("older" AW) must be correlated with the abundances of Microphytoplankton at this place. Indeed, it has been recently shown that various groups of microphytoplankton might produce tryptophan-and tyrosine-like fluorophores (Romera-Castillo et al., 2010;Fukuzaki et al., 2014;Retelletti Brogi et al., 2020).

Physical-biological coupling in the frontal area 395
The distribution of phytoplankton groups showed contrasted abundances across the front. Figure 10 summarized our results, providing a view of the physical forcing occurring in the frontal area and its effect on the distribution of phytoplankton groups.
The south-west of the front corresponding to the localisation of the "young AW" is characterized by high abundances of Synechococcus, Pico1, Pico2 and RNano groups, whereas the north-east of the front associated with the "old AW" is dominated by the other phytoplankton groups, i.e. Pico3, Microphytoplankton and SNano ( Fig. 7 and Fig. 8).

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Although some studies led in western boundary currents have shown that fronts are sites of elevated phytoplankton diversity (Barton et al., 2010;Clayton et al., 2014), it is still unclear how the physical processes impact the phytoplankton distribution.
That is why three mechanisms, i.e., advection, mingling and fertilization may be advanced as possible explanation for the distribution of phytoplankton abundances, as observed during our cruise.
First, the dominance of certain groups of phytoplankton appears well related with the type of AW. The statistical analysis 405 proved very useful in synthesizing the physical and biological information and in identifying the relationship between some groups of phytoplankton and the hydrological water conditions objectively. For instance, on the principal component analysis (PCA), Synechoccoccus, salinity and temperature are well correlated (Fig. 9). This correlation is in accordance with the study of Mena et al. (2016), which have already found higher abundances of Synechococcus in the "new" AW less salty than the "resident AW", and this is also in agreement with Marrec et al. (2018) who have shown a dominance of Synechococcus in 410 warm boundary waters. Previous studies have shown that fine-scale can form ecological niches, by segregating water masses long enough and thus creating favourable conditions for some phytoplankton groups locally well adapted which will become dominant (d'Ovidio et al., 2010;Perruche et al., 2011). d'Ovidio et al. (2010 have also showed that these fluid ecological niches can be advected and then can transport over a distance far apart from their origin the phytoplankton cells, creating complex community distributions by enhancing the mingling of populations of phytoplankton. This theory has been deepened 415 by Clayton et al. (2013) by global numerical simulations. According to them, dominant phytoplankton very well "locally" adapted can emerge in hotspot regions, whereas other phytoplankton called "immigrants" are sustained by physical transport.
The Mediterranean spans a much reduced latitudinal gradient. In our study, the intensity of currents allows a slower advection than the one by western boundary currents, and our cytometry measurements do not show presence or absence of specific groups. Nevertheless, our results highlight that fine-scale dynamics modulate the relative abundance of phytoplankton groups. 420 We can assume for instance that the "young" AW could have transported some phytoplankton cells from the Algerian basin to the Balearic Islands. However, the front observed during our cruise can not be defined as an "hotspot" of diversity as in the work of Clayton et al. (2014Clayton et al. ( , 2017, where fine-scale features advect phytoplankton ecotypes that are absent in an oceanic region to an other. In future cruises, we plan to perform taxonomic analyses such as metagenomics, that will allow us to detail the structure of the communities further and tell more about competition and immigration processes.

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Second, we can point out the role of vertical velocity (see Fig. 3). Clayton et al. (2014Clayton et al. ( , 2017 have highlighted the role of fine-scale circulation in regulating phytoplankton diversity, in particular for opportunistic species (as diatoms) fertilized by an important nutrient enrichment induced by a very intense vertical transport in boundary currents and coastal upwelling regions. In our case, we found vertical velocities associated to the front less intense than in the work of Clayton et al. (2014Clayton et al. ( , 2017. This vertical transport can advect a sufficient quantity of nutrients in the euphotic layer, creating favorable conditions for 430 phytoplancton groups well adapted to the oligotrophic conditions of the Mediterranean Sea. Indeed, previous modelling study of Lévy et al. (2001), have shown the significant impact of the enhancement of nutrient on the production of phytoplankton in an oligotrophic regime. In addition to a role on nutrient distribution, the vertical velocity can also directly convey phytoplankton cells and, as a consequence, play a key role in their distributions (Marrec et al., 2018). In order to disentangle these two mechanisms, additional both high-resolution and, since the oligotrophic Mediterranean conditions, high-precision nutrient 435 measurements need.

Conclusion and perspectives
In conclusion, our adaptive Lagrangian strategy and high-resolution coupled physical-biological sampling has allowed to detect a fine-scale frontal structure and has highlighted its structuring effect on the surface phytoplankton community, in accordance with previous modelling studies. The originality of our work resides in the fact that we are able to show that less energetic fronts 440 than those found in western boundary currents have also an impact on the phytoplankton distribution. This suggests that the physico-chemical contrasts induced by the horizontal stirring processes in moderately energetic fronts are sufficiently strong to spatially reflect into phytoplanktonic communities with different structures. Furthermore, our work offers an interesting explication concerning the fact that despite the Mediterranean Sea is an oligotrophic regime, this region is also characterized by a high diversity of phytoplankton. Because fine-scale processes and oligotrophic regions are predominant in ocean, our 445 results shed a new light on the functioning of the global ocean.
In the future, a better understanding of the biogeochemical processes generating this observed fine-scale physical-biological coupling is needed. In particular, we plan to estimate and compare the growth rates of the various phytoplankton groups in the different water masses thanks to the data collected in a Lagrangian manner and applying a method similar to the one by Marrec et al. (2018). Moreover, the role of nutrient supply (bottom-up) and of zooplankton grazing (top-down) are also key 450 factors to be considered in explaining the differences in abundances of the different phytoplankton groups in the different areas visited during the cruise. To address these latter points, future experiments will need high-resolution (and also highprecision, considering the oligotrophy of the Mediterranean Sea) nutrient measurements coupled with zooplankton sampling and dedicated experiment about its grazing on the different phytoplankton groups. Finally, this study also shows how the satellite information is extremely useful for the design of the cruise sampling strategy and, then, for the on-shore post-cruise 455 interpretation of the data.
The new satellite SWOT (https://swot.jpl.nasa.gov/, last access: July 16, 2021; https://swot.cnes.fr, last access: July 16, 2021) will provide ocean topography and surface current with a resolution one order of magnitude higher than present altimeters (Morrow et al., 2019). During the few months after its launch, the period called "fast sampling phase", the satellite will be on a special orbit that will overfly a portion of the global ocean, called "cross-overs", where high spatial resolution will be associated 460 with high temporal resolution (d'Ovidio et al., 2019). The large data set collected during PROTEVSMED-SWOT represents precious new information on the SWOT cross-over area located in the south of the Balearic Islands. The work presented here thus paves the way to future cruises to be planned in this area during the SWOT period, which will be a unique opportunity to study the physical-biological fine-scale coupling in more detail.   d'Ovidio, F., Della Penna, A., Trull, T. W., Nencioli, F., Pujol, M.-I., Rio, M.-H., Park, Y.-H., Cotté, C., Zhou, M., and Blain, S.: The biogeochemical structuring role of horizontal stirring: Lagrangian perspectives on iron delivery downstream of the Kerguelen Plateau, Biogeosciences, 12, 5567-5581, https://doi.org/10.5194/bg-12-5567-2015, 2015     (a)     Figure A1. Objective mapping of potential density anomaly σ at 25 (a) and 85 m (b). The circles represent σ as measured by the Seasoar, along the transects used for the interpolation. Black lines represent σ contours. The data have been selected where the error on the objective mapping of σ is ≤ 0.0025. The lines in bold correspond to the WE and the NS transects described in the manuscript, and also represented in Fig. 4. In our study, we have chosen to select the transect (c) for the WE hippodrome, because we deplore a lack of temperature and salinity data for the other transects of the WE hippodrome, due to technical problems with the Seasoar.  Figure 4, the "younger" AW is represented in light blue whereas the "older" AW is represented in dark blue. Intermediate water and deeper water are represented in pink and black, respectively. The isopycnals 28.2 and 28.9 kg m −3 , separating surface waters from the deeper ones, are also shown. percentile (the median), and the cross is the mean. The ends of the error bars correspond to the 10th percentile (bottom) and to 90th percentile (top). All the SeaExplorer glider transects are considered here (all data acquired from 6 to 15 May 2018). "Younger AW" correspond to samples showing a salinity between 37.20 and 37.84, and a temperature between 14.5 and 17.4°C (n = 1657). "Older AW" correspond to samples displaying a salinity between 37.82 and 38.14, and a temperature between 14.0 and 18.3°C (n = 11760). Mean values of Chla, Peak B and Peak T of "older AW" are significantly higher than those of "younger AW" (t test, p inf 0.0001).