Anthony Vial

Cyntia Taveneau

Brieuc Luca Costa

Hussein Chauvin

Cédric Nasrallah

Patrice Godefroy

Hervé Dosset

Isambert

Xuan Kien

Stéphanie Ngo

Mangenot

Luca Costa

Brieuc Chauvin

Hussein Nasrallah

Cédric Godefroy

Patrice Dosset

Hervé Isambert

Kien Xuan Ngo

Stéphanie Mangenot

Daniel Levy

Aurélie Bertin
email: aurelie.bertin@curie.fr

Pierre-Emmanuel Milhiet

Correlative AFM and fluorescence imaging demonstrate nanoscale membrane remodeling and ring-like and tubular structure formation by septins

Keywords: Septins, membrane, reshaping, curvature, Atomic Force Microscopy, fluorescence microscopy

Introduction

Septins are ubiquitous conserved proteins present in eukaryotes and bound to the inner plasma membrane. From palindromic rod-like oligomers, they self-assemble into higher-ordered structures, including filaments, bundles and rings 1 . They participate in a large spectrum of cellular processes including cytokinesis 2,3 , ciliogenesis 4 or cell migration 5 . The poor regulation of septin expression is thus related to numerous pathologies from cancer diseases 6 to infections 7 or neurodegenerative diseases 8 . All septins are GTP-binding proteins that form hetero-oligomeric palindromic assemblies. Studies by Electron Microscopy (EM) and X-rays crystallography 9 have provided pieces of structural information arguing for the conservation of septin ultra-structures across species. Septins were first discovered in Saccharomyces cerevisiae. The S. cerevisiae septins are rod-like octameric palindromic units of 32-35 nm long and 4-5 nm in diameter in the presence of more than 150 mM monovalent salt 10 (Figure 1.A, insert). At ionic strength below 150 mM monovalent salt, septin rods self-associate to form paired filaments of several microns long (Figure 1.A, insert), suggesting that electrostatic interactions drive the assembly of filaments. GTP and/or GDP are necessary to ensure the self-association of stable octameric complexes 1 . However, the role of GTP hydrolysis is not required to ensure proper septin polymerization or promote their interaction with lipids. A recent report 11 suggests, using atomic force microscopy (AFM), that the mica surface is likely to mimic membranes and thus analyzes how septins self-assemble on mica. However, budding yeast septins interact specifically with phosphatidylinositol-4,5-bisphosphate (PIP2) and their polymerization is facilitated in its presence. Indeed, PIP2 is required to observe any budding yeast septins membrane interaction even in the presence of other negatively charged (DOPS) 12 . Using lipid monolayer assays combined with EM observations, PIP2 was shown to promote budding yeast septin filament assembly and organization 13 . Both their organization and functions are thus closely related to their interplay with lipids 13 . At the membrane, septins have a key role as scaffolds for protein recruitment in signaling pathways. Septins are involved in building diffusion barriers for membrane bound proteins in various tissues. Specifically, during cytokinesis, they have a compartmentalizing role around the cleavage site 14 . Septins also impact cell morphogenesis and can affect the membrane stability and rigidity of migrating cells 15 . In situ, septins have been shown to have a role in membrane remodeling, for instance in the context of endo lysosomal sorting 16 . Using in vitro bottom-up approaches, human septins from extracts were shown to reshape PIP2-containing membranes and induce tubular structures from giant unilamellar vesicles (GUVs) 17 . More recently, the curvature sensitivity of septins was analyzed 18,19 . In particular, we demonstrated that septin filaments can reshape liposomes and that the observed reshaping results from a septin filament curvature preference 20 . Bound to a lipid membrane covering an undulated solid surface, filaments can indeed adopt negative curvatures, while septins avoid interacting with highly positive curvatures and would rather lie flat on a biomimetic membrane. Here, to further understand the molecular mechanisms associated with septin-induced membrane remodeling, we have used AFM and fluorescence microscopy to analyze the self-oligomerization of budding yeast septins, both in the presence and absence of a supported lipid bilayer (SLB). We ensured that the lipid composition mimics the inner leaflet of the plasma membrane. We have thereby analyzed here, for the first time, the behavior of septins interacting with a flat supported lipid bilayer. We employed High-speed AFM (HS-AFM) to investigate the real-time protein oligomer self-assembly. Supporting previous observations suggesting that septins are able to reshape and remodel membranes spontaneously, we have demonstrated here that septin short rods (octamers) can dramatically disrupt flat 2D lipid bilayers to reshape them into peculiar three-dimensional structures.

Experimental

Protein purification

Yeast-septin complexes containing Cdc11, His6-Cdc12, Cdc10 (± GFP) and Cdc3 were co-expressed in Escherichia coli and purified as previously described 10 . Briefly, septins are purified by immobilized nickel affinity, size exclusion and ion exchange chromatography. A Cdc11_His6-Cdc12_Cdc3_Cdc10_ Cdc10_Cdc3-His6-Cdc12_Cdc11 or the version with GFP-tagged Cdc10 and palindromic complex are thus obtained.

Supported lipid bilayers

The lipid mixture was composed of EggPC (EPC), di-oleoyl-phosphatidylethanolamine (DOPE), dioleoyl-phosphatidylserine (DOPS), cholesterol (Chl), PIP2 at 55:10:10:15:10 ratios, as already described 20 , except in Figure 4 and S2 and Movies 2 and 6 where it was composed of palmitoyl-oleoylphosphatidylcholine (POPC), palmitoyl-oleoyl-phosphatidylethanolamine (POPE), palmitoyl-oleoylphosphatidylserine (POPS), and di-oleoyl-phosphatidyl-inositol (PIP2) at 50:35:10:5 molecular ratios. Fluorescence labeling of SLBs was performed using 0.1% GloPIP Bodipy C16 or 0.1% Rhodamine-DOPE. All the lipids were purchased from Avanti Polar lipids except the GloPIP Bodipy C16 that is from Echelon. Large unilamellar vesicles (100 nm diameter) are prepared in buffer A composed of HEPES 20 mM (pH 8) and NaCl 150 mM by extrusion through a polycarbonate filter. The vesicle solution is then added at 40°C for 40 min on top of the support in the presence of 10 mM CaCl2 to facilitate membrane fusion onto the support 21,22 . Vesicles then adsorbed on the substrate before rupturing and formed a continuous supported bilayer. The bilayer is rinsed with buffer A supplemented with EDTA in order to remove the excess of divalent cations that can reduce the in-plane diffusion of lipids within the membrane on mica 23 .

AFM imaging

The setup was a combination of a Nanowizard 4 microscope (JPK, Berlin) together with a homemade objective-type TIRF inverted optical microscope (Zeiss, Le Pecq, France) equipped for single molecule localization microscopy with an oil-immersion objective (αPlanFluar 100x, 1.45 DIC, Zeiss)(see more details in 24 ). HS-AFM was performed with a prototype from Toshio Ando's laboratory and provided by RIBM Research Institute of Biomolecule Metrology Co., Ltd., Tsukuba, Japan). Imaging was performed in a Tris 50 mM pH 8 buffer supplemented with 50 or 300 mM NaCl.

Confocal fluorescence imaging and image analysis

Confocal experiments were performed on a Nikon Ti inverted microscope. The software EZ-C1 was used to acquire confocal images and a MatLab routine was used to view the stage in real time and acquire movies via the camera. The image J radial profile plugin was used to analyse the fluorescence signal on the GUVs.

Results

Septin oligomerization observed by AFM

We first characterized by AFM the different ultra-structures of septins and thus confirmed that the expected rods as well as paired filaments could be observed by atomic force microscopy. To this end, we have incubated septins in solution at low salt conditions and then deposited the solution on mica, a substrate that is atomically flat, allowing high resolution AFM imaging 25 (Figures 1A and1B). At low density obtained with ~ 2.6 nM (1 µg.mL -1 ) septin (Figure 1A), we observed individual octameric rods of 35 nm long and 5 nm diameter as well as longer filaments, with lengths up to several hundreds of nanometers (Figure S3), which likely corresponded to the association of small octameric rods as already observed by electron microscopy 10 . Isolated and round-shaped structures were also delineated by the AFM tip and probably from a partial dissociation of the septin complex that occurs in solution 10 . The mean value of the diameter of these small oligomers was 9.3 ± 4.3 nm (mean ± SD), ranging from 5.7 to 25 nm (data not shown). As already observed by EM 10 , fluorescence microscopy 18,26 , and FRET assays 27,28 , paired filaments were also observed, even at low density, suggesting long-range interactions between oligomerized rods. This tendency to associate along the long axis of filaments was more pronounced with 80 nM septin when the density of filaments coating the mica increased (Figure 1B). A network of disorganized septin filaments was observed with a high proportion of paired structures where the flexible coiled coils connecting paired filaments make the filament-filament distance variable 10 . We next analyzed the dynamics of septin oligomer self-assembly imaging in real-time with HS-AFM, i.e. 1-2 frames/s. The complexes were first equilibrated at high ionic strength where septins remain octameric. The buffer was subsequently exchanged with low ionic strength buffer during imaging to induce polymerization. Under these conditions, we observed the formation of filamentous septin oligomers under the AFM tip. Snapshots in Figure 1C and Movie 1 clearly demonstrate the nonpolar growth of septin filaments. Indeed, the oligomers were able to grow from both ends as expected from the palindromic molecular nature of the septin rod structure. In the course of movie acquisition, we observed both self-assembly (elongation as well as pairing, see black arrowheads in Figure 1C) and disassembly of septin filaments. However, their maximal length remained below 80 nm (Figure S3A, right panel) while septin filaments have previously been shown to reach several microns in length 13,26 . These results suggest that septin oligomerization is mitigated by forces below a few tens of pN, the force that we imposed during HS-AFM imaging. Septin scanning sensitivity and oligomerization fragility was also supported by observations carried out using conventional AFM where successive scans of the same area led to the disruption of filaments by pulling out portions of the filaments (Figure S1). A recent article using high-speed AFM reports that low ionic strengths do not promote any septin self-assembly into filaments 11 . Instead, the elongation of septins is facilitated in the presence of salt, suggesting that the charges present on the mica surface significantly alter the septin elongation mechanism that is observed in solution. Our current study along with others mentioned above using cryo-EM tends to show that polymerization indeed occurs at low ionic strengths, suggesting that the nature and state of the support surfaces can affect the polymerization of septins.

Septin induced membrane destabilization and remodeling

To mimic the lipid composition of the inner leaflet of the plasma membrane of eukaryotic cells we produced lipid bilayers, composed of a mixture of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), cholesterol, PIP2, and traces of fluorescent PIP2, supported on mica. SLBs were formed on freshly-cleaved thin mica glued on a coverslip allowing both AFM (Figure 2F) and fluorescence microscopy acquisitions (Figure 2A-D). 80 nM GFP-tagged septins were added to the buffer surrounding the AFM tip under non polymerizing high salt conditions. The buffer was then exchanged to a lower salt solution, thus enabling septin polymerization. The bare control membrane labeled with fluorescent PIP2 (red channel) was often non continuous with some defects (black patterns in the fluorescence micrograph shown in Figure 2A), allowing the measurement of the bilayer thickness by AFM (> 3.5 nm above the mica). Such defects are often observed for membranes containing negatively charged lipids. To minimize it, we add calcium to increase the yield of fusion of small vesicles used to form the SLBs 29 . After 15 min of incubation with GFP-septins, we observed a specific binding of proteins to mica, accessible at membrane holes (Figure 2C), suggesting a higher affinity for charged mica than for membranes. Additionally, the surface of some of the mica holes in the SLB increases from Figures 2A to Figure 2B , suggesting that septins induce membrane remodeling by pulling out the membrane. Septins have been indeed shown to be capable of membrane reshaping on inducing periodic "spikes" on giant unilamellar vesicules or by flattening large unilamellar vesicles 18 . Importantly, we observed septin and PIP2 co-localization close to the edge of the membrane (see the fluorescence profile in Figures 2E showing an overlap of the 2 channels at the edge of the hole) and also within clusters of PIP2 present before the addition of the protein (see the white circles in Figures 2B and2C and the merge image in 2D). This membrane remodeling was confirmed by AFM showing a corrugated pattern corresponding to septin oligomers surrounded by the smoother bilayer (Figures 2F and2G). Interestingly, the AFM resolution revealed that septins were forming clusters or assemblies on mica but also bound to the surface of the membrane (see empty circles in Figure 2F), with a high density at their edges. The presence of septins in these clusters, which vary in size, was confirmed by the GFP fluorescence channel (compare Figures 2C and2F and see empty circles used as fiducial marks to compare the 2 fluorescence channels and the AFM image). The layer of septins coated on mica was protruding 3-5 nm above the surrounding membrane, suggesting a 3D stacking of septin oligomers (Figure 2F and see a profile in Figure 2G). For longer incubation times (Movie 2 and two snapshots of the movie in Figure S2), the GFP-septin signal indicated that both the surface area and the density of septins bound to mica increased over time (white arrowheads in Figure S2). The thickness measured by AFM was up to 16 nm (data not shown), strongly suggesting membrane remodeling and 3D stacking of septins. Movie 2 also confirmed the presence of clusters of septin accumulated at the surface of the membrane. Interestingly, filamentous structures up to 2.5 µm in length could also be observed over time. Those filamentous structures were often anchored to the membrane through a single point co-localized with the clusters mentioned above, with the rest of the filament floating in the buffer (Figure S2B and snapshots in Figure S2C). However, such filaments could not be imaged using AFM, probably because they were swept away during tip scanning, thus reinforcing the idea that septin filaments are sensitive to the resulting scanning force when imaged directly on mica.

To assess the dynamics of both septin oligomerization and its ability to remodel membranes, we imaged SLBs with HS-AFM (Figure 3 and Movie 3). Under these conditions, for short incubation times (Figure 3A), we imaged dynamic septin assemblies, directly interacting with mica, with a size in the range of 10 nm and protruding 2 to 5 nm above the mica. We also observed round-shaped assemblies at the surface of the lipid bilayer, protruding up to 10 nm above the membrane (white arrow in Fig. 3A). In addition, we sometimes pinpointed larger patches protruding ~ 2 nm above the membrane (white arrowhead in Figure 3A) that appeared to be very dynamic, as shown by the diffusion within the patch, likely septins (Movie 3). The lateral dimensions of such patches could reach several hundreds of nanometers (Figure 3 and Movie 3). Because of the sensitivity of septin filaments to the force applied by the AFM tips as mentioned above, we never observed the formation of filaments below the AFM tip in the course of imaging. However, when scanning a neighboring area which had not been imaged yet, longer filaments at the top of mica were clearly observed (Figure 3B), reinforcing the idea that the mechanical stress imposed by the AFM tip prevents septin polymerization. The length of these filaments varied, from a few tens to a few hundreds of nanometers (see the scatter plot in Figure S3). We observed the Brownian movement of these filaments with an amplitude inversely proportional to their length, probably due to their attachment to the substrate that is stronger for a long filament. Interestingly, imaging the same area after a few minutes (Figure 3C) highlighted the membrane disruption induced by septin oligomerization (see the black arrows and compare with Figure 3B). Real time membrane disruption that occurs within the intervals of a few minutes could also be observed in movies 4 and 5.

3D-organization of septin-lipid assemblies HS-AFM imaging was able to capture 3D remodeling of membrane/septin assemblies (Figure 4). The presence of both interacting partners in these arrangements was captured by fluorescence imaging of GFP-septins and red rhodamine-labelled lipids. We captured in real-time the spontaneous formation of doughnut-shaped structures from 2D lipid bilayer organization (Figure 4A and Movie 6). The starting structure in the first example was a membrane protruding 6 nm above the SLB underneath (see Movie 6) that first bent along the main axis (white arrowhead in Figure 4A.1) and then reshaped to produce a doughnut/ring shape of 1.1 µm diameter and 10-20 nm thickness (measured at different places on the ring as compared to the membrane underneath) within a few tens of seconds. The width of the loop varied between 150 nm and 250 nm. Importantly, following longer septin incubation times, we observed an increase in the number of doughnut-shaped structures. Some of them were smaller and thus probably resulted from different types of membrane organizations. We also frequently imaged "fried egg-shaped" structures lying on the lipid bilayer (see Figure 4B1 andB2). They could be reshaped by increasing the force during imaging into a "doughnut" shaped structure in Figure 4B3 (350 nm protruding 5 nm above the bilayer). At higher forces, the doughnut was swept away, allowing the AFM tip to image mica (dark area in Figure 4B4) that was rapidly covered by septins forming a thick layer protruding from the lipid membrane (Figure 4B5). We confirmed the presence of doughnutshaped structures on larger areas inaccessible with HS-AFM. Indeed, similar structures were also visualized using TIRF fluorescent microscopy (Figures 4C and4D) both in the PIP2 and septin fluorescent channels and ring-like structures of different diameters were also observed (Figure 4C), the largest having diameters in the micrometer range, in good agreement with the rings observed in Figure 4A with AFM. The dimensions of the smallest ones were difficult to estimate because of fluorescence microscopy resolution limitations. These observations are consistent with the spontaneous curvature of membrane-bound septin filaments recently reported on giant unilamellar vesicles (GUVs) as well as with supported bilayer membranes on wavy substrates with locally varying curvature 18 . Such a curvature affinity stems from an increase in surface contact of septin filaments binding along their stretched (rather than compressed) side to the membrane 18 . This corresponds to a spontaneous negative curvature of membrane-bound septin filaments and filament bundles. The balance between binding energy gain and bending energy cost of membrane-bound septin filaments then leads to an optimum radii of curvature in the micrometric range, as well as a minimum radii in the sub-micrometric range, depending on filament bundle sizes 18 . Such a model is in good agreement with the ring like structures surrounded by membrane observed here (Figure 4) and reminiscent of the ring-like structures observed in vivo at sites of constriction in living cells. Finally, to address the effect of varying septin concentrations, we performed similar fluorescence microscopy experiments using higher 300 nM septin concentrations (Figure 5 and Movie 7). Immediately upon septin addition, a few thin and long filaments were observed (Figure 5A) and the lipid bilayer appeared highly disrupted, with holes through which the the surface of mica was apparent (black areas growing over time in Movie 7 and Figure S4 A and B, white asterisk). In addition, septins accumulated at the lipid bilayer-mica interfaces and, after a few tens of seconds, tubes started arising from the surface of the bilayer, generally from a septin cluster (Figures S4A andS4D, white arrowhead). The tube diameters were measured between 1 and 2 µm. As tubular structures formed, the surface of the bilayer continually decreased, suggesting that most of the bilayer, upon its interaction with a high density of septins, is largely altered and recycled within the visualized tubes (easily observable in Movie 7). After about five minutes, the process seemed to reach an equilibrium. Hence, upon a higher septin density, the observed doughnuts reshape the membranes, converting them into tubes. In addition, both septins and PIP2 seem to colocalize along these tubes (Figures 5D, 5E and5F). A few rings and filaments were also observed in Figure S4B (white arrow) and in Movie 7. Interestingly, this behavior was not observed when similar bilayers were deposited on glass surfaces. When supported by glass, bilayers were not altered by septin addition likely due to a stronger interaction with the surfaces. Indeed, FRAP (Fluorescence Recovery After Photobleaching) indicated that PIP2 lipids move freely within a bilayer deposited on mica, while the diffusion of PIP2 molecules is constrained when embedded within a bilayer deposited on glass (data not shown).

Discussion

Until recently, septins have been considered primarily as scaffolding proteins enabling the establishment of diffusion barriers and recruiting different factors. However, our observations unambiguously demonstrate that septins can remodel and reshape membranes by remodeling supported lipid bilayers and generating peculiar deformations in 3D. Our findings are in agreement with previous bottom-up experiments where membrane deformations were directly induced by septins. Human septins were shown to generate tubulations from giant unilamellar vesicles 30 . Additionally, we have demonstrated that budding yeast septins can either trigger the formation of "spiky" structures on GUVs or flatten unilamellar vesicles 18 . Taken together, these observations suggest that septins might have a crucial and underestimated role in membrane reshaping in different cellular contexts and functions. Indeed, septins have been shown to be implicated in the formation of membrane protrusions in detached tumor cells 31 . Septins have also been shown to be involved in preserving the rigidity of the plasma membrane and tuning the plasma membrane morphology by reducing spontaneous membrane blebbing during cell motility 32 . Notably, the dimensions of the ring or tubular shapes observed in this report correspond to the dimensions of ring-like architectures of septins visualized at the constriction sites of budding yeast during cell division (hundreds of nanometers to 1-2 µm). Moreover, these micrometric curvatures are also found within metazoa at the base of cilia 33 , at the annulus of spermatozoa 34 or even at the base of dendrites 35 where septins localize. Hence, the spontaneous reshaping of membranes within tubular or ring (doughnut-like) architectures reflects the localization of septins in situ and a specific sensitivity to micrometric curvatures (see 18,19 ). Even though the concentrations of septins used for bottom-up assays might be higher than septin densities in situ, we are nevertheless convinced that the observed reshaping is relevant. The concentration of septin in cell is not known. However, septin concentrate at the plama membrane and self-assemble. By analogy, the concentration of septin in our bottom assays reproduce this high concentration at the proximity of the membrane. Furthermore, in our hands, no other protein has ever displayed such an uncommon behavior. Importantly, septin and PIP2 are co-localized in both rings and tubes, questioning the driving force involved in both membrane remodeling and the formation of 3D structures. Septin subunits contain several basic residues, e.g. at the N-terminal that are candidates to interact with PIP2 36 . Such residues would concentrate locally PIP2 forming seeds for the filaments and larger assemblies of septin. In budding yeast, at the constriction site, the density of PIP2 is indeed enhanced 37 . This is in good agreement with small septin oligomers that were observed diffusing at the SLB surface and that immobilized over time (data not shown). In addition, as shown in Movie 7 and Figure S4, it is clear that the formation of tubes emerged from PIP2enriched areas. As observed by AFM, septin filaments are rather fragile and sensitive to piconewton forces applied by the AFM tip, either directly bound to mica (Figure S1) or in interaction with SLBs (septin swept away during scanning can be easily observed using correlative AFM-fluorescence microscopy (Movie 8). The action of the AFM tip is indeed capable of displacing long septin filaments, fragmenting them and, to a certain extent, preventing septins from polymerizing. The polymerization of septins is thus highly sensitive to mechanical stress, suggesting that septin ultrastructures can be easily remodeled in vivo as well to undergo major conformational remodeling. However, septins are able to collectively remodel membranes in a spectacular fashion. Not only are membranes reshaped by long filamentous septins as described in several papers (see introduction) but it appears that short filaments or even septin protomeric rods can also disrupt membranes, as visualized by high-speed AFM. Small rods can enlarge holes by destabilizing the lipid bilayer (Movies 4 and 5). Once a hole or a defect within the bilayer emerges, septin filaments seem sometimes to grow beneath the lipid bilayer to induce its detachment from the mica surface (data not shown). This does not exclude the possibility that septin-lipid interactions destabilize the lipid bilayer by perturbing membrane line tension. The visualized growing holes imaged by AFM are most likely coupled to the generation of 3D structures. However, the AFM tip certainly disrupts the tubes which are displaced and thereby cannot be detected by AFM. It is then tempting to propose that long septin filaments role may have been overestimated in membrane destabilization, at least at early stages of the process. Indeed, they were removed by tip scanning and were not visualized associated to doughnuts and tubes using either AFM or fluorescence microscopy, though we cannot completely exclude their presence in 3D structures. Octameric rods or small septin assemblies could be functional units involved in membrane remodeling, with the cellular cytosol acting as a reservoir of septins (cytosolic septins are in small complexes 26 ). This assumption is supported by our previous work showing septin-induced reshaping of GUVs, where no long filaments were observed to interact with the vesicle 18 . Our results also indicate that small oligomers are not sensitive to the curvature, whereas septin filaments sense nanometer or micrometer-scale curvature 19 . Indeed, we clearly observed that filaments can interact with flat membranes, and the preferential interaction of septin with curved membranes is probably valid only when proteins are organized in long filaments.

Conclusion

Taken together, our results clearly demonstrate that small assemblies/rods of septin can destabilize supported lipid bilayers and reshape the membrane to form 3D structures, reinforcing their role as membrane organizers when interacting with PIP2. This phenomenon, which is not energy-dependent, could be explained by the propensity of septins to form and/or bind PIP2-enriched clusters, inducing membrane bending through an increase in membrane pressure that cannot be counterbalanced by the tension in the plane of the membrane 38 .

Figure captions

Figure 1 -AFM imaging of purified septin coated on mica A and B are AFM height images recorded in liquid at low and high density, respectively. Black arrows indicate septin oligomers of different lengths, 36 nm and 76 nm respectively for 1 and 2). The arrow 3 points out a smaller and globular structure (in the range of 10 nm). Black arrowheads in B point out disordered filaments (one can observe that some filaments are at the top of others with different orientation). Scale bars are 100 nm (A) and 50 nm (B) and z scales are 10 nm. The inset in A shows a cartoon of the octameric rod that is the elementary bricks of filaments. Its length is 35 nm 10 . C corresponds to snapshots of Movie 1 recorded with HS-AFM with the number of frames indicated. The 4 height images show the growth of septin filaments over time. The green parallelogram indicates a filament that is growing overtime in both directions. The last frame (274) highlights the fact that the filament could be disrupted during imaging (part of the filament that is perpendicular to the scan axis). Black arrowheads point out paired filaments. The scale bar is 50 nm.

Figure 2 -AFM-Fluorescence correlative microscopy of supported lipid membranes incubated with septin SLB composed of EPC, DOPE, DOPS, Chl, PIP2 at 55:10:10:15:10 ratios supplemented with 0.1% GloPIP Bodipy C16 (PIP2 labeling in red) were observed using correlative TIRF-AFM microscopy before (A) or after (B to G) incubation with 80 nM septin GFP-septin. White empty circles are used as fiducial marks in the different images of the figure, including the control A. The white doted cercles are fiducial marks only observed after the addition of septin. Scale bar, 5 µm. A) Fluorescence imaging of the SLB labelled with fluorescent PIP2 lipids (red signal) before septin incubation. The black holes correspond to membrane defects where mica is accessible. B to D) Fluorescence imaging of the same area of the SLB after 15 min of incubation with GFP-septin. B and C respectively correspond to the red (PIP2) and green (septin) channels and D is the merge image. The majority of GFP-septin are bound to mica, accessible at membrane holes. In addition, GFP-septin molecules were also detected in interaction with the lipid membrane. Importantly, comparing the surface of the fluorescent patch of septins in C and the area of the holes in B indicates that septins and PIP2 lipids overlap and co-localize at the edge of the membrane (see the intensity profile of the green and red channels in E). After incubation with septin, an increase of the size of the holes into the SLB was observed (see the holes indicated by white arrowheads in A and B). F) Height AFM image revealing that septins were forming assemblies on mica but also bound to the surface of the membrane (white arrows in F), especially at the edge of holes (membrane defects). All the circles are localized at the top or within the SLB. The white line indicates the profile that is shown in G. Height scale, 70 nm. G) Profile of image F (blue line). The difference between the 2 red lines used as marks is 3.8 nm, suggesting 3D stacking of septin oligomers. The lipid bilayer supported on mica is remodeled leading to the formation of tubes (white arrowhead) at the expense of the membrane, suggesting that lipids are recycled within the visualized tubes (formation of holes within the SLB indicated by a white star). D, E, F: Fluorescence imaging of PIP2 (D, red channel), GFP-septin (E, green channel) and the merge image of the 2 channels (F). Both septins and PIP2 seem to colocalize along those tubes. The inset in F is a zoom as indicated by the black rectangle.

Figure 3 -

 3 Figure 3 -Septin induces destabilization and remodeling of model membranes SLB composed of EPC, DOPE, DOPS, Chl, PIP2 at 55:10:10:15:10 ratios were observed using HS-AFM in the presence of 80 nM septin in low salt buffer. A) Height image (snapshot of Movie 3) recorded at short incubation time (< 15 min). The white asterisk indicates a hole (defect) within the bilayer covered by septin assemblies. The white arrowhead and white arrow point out assemblies protruding ~ 2 nm and ~ 10 nm above the membrane, respectively. Scale bar, 100 nm.

B

  and C) Height images recorded for long incubation (>15 min). Septin filaments were observed at the top of mica. C has been recorded 220 s after B and SLB remodeling can be easily observed (black arrows). Scale bar, 50 nm.

Figure 4 -

 4 Figure 4 -Septin-induced reshaping of membrane forms doughnut-shaped structures AFM imaging of SLB composed of POPC, POPE, POPS, PIP2 at 50:35:10:5 ratios after incubation with 80 nM septin. A and B) HS-AFM height images of two different areas of the sample (A1 to A3, Movie 6, and B1 to B5, movie not shown). Acquisition times of HS-AFM images are indicated at the upper right of each picture. The white arrow indicates bending of the membrane patch along the main axis (white arrowhead in Figure 5A.1) and its reshaping to produce a doughnut/ring shape of 1.1 µm diameter and 10-20 nm thickness within a few tens of seconds. Scan size 2 µm, Z scale, 20 nm (A) and (B). C and D) TIRF fluorescent images of SLB supplemented with 0.1% GloPIP Bodipy C16; C and D respectively correspond to PIP2 and septin labeling after 60 min incubation. Images are z projections of 50 frames. The dashed circles indicate doughnut-shaped assemblies similar to A and B. Scale bar, 5 µm.

Figure 5 -

 5 Figure 5 -Formation of membrane tubes from supported membranes induced by septin. Incubation of membranes composed of EPC, DOPE, DOPS, Chl, PIP2 at 55:10:10:15:10 ratios supplemented with 0.1% GloPIP Bodipy C16 with 300 nM septin and observed using confocal microscopy. Scale bar, 10 µm. A, B, C: fluorescence imaging (PIP2 red channel) upon septin incubation for 1, 3 and 7 min (A, B, C).The lipid bilayer supported on mica is remodeled leading to the formation of tubes (white arrowhead) at the expense of the membrane, suggesting that lipids are recycled within the visualized tubes (formation of holes within the SLB indicated by a white star). D, E, F: Fluorescence imaging of PIP2 (D, red channel), GFP-septin (E, green channel) and the merge image of the 2 channels (F). Both septins and PIP2 seem to colocalize along those tubes. The inset in F is a zoom as indicated by the black rectangle.

Acknowledgements

We acknowledge the support from FranceBioImaging (FBI, ANR10INSB04), the Agence Nationale pour la Recherche (ANR-13-JSV8-0002-01 Septime, ANR-16-CE11-0004-01 Neopore), and the EpiGenMed Labex (ANR-10-LABX-12-01) and the GIS IBISA (Infrastructures en Biologie Santé et Agronomie). AV is a recipient of the Fondation pour la Recherche sur le Cancer (ARC) and CT has been supported by Labex CelTisPhyBio (ANR-11-LABX0038). We are indebted to the Bio-AFM Summer School organized by the Nano Life Science Institute (WPI NanoLSI) in Kanazawa (Japan) led by Pr Toshio Ando, especially to Motonori Imamura (current address: Bio-AFM-Lab, Weill Cornell Medicine, New York, USA).

Conflict of interest

The authors declare that they have no conflicts of interest with the contents of this article.


Marie Lamouret

Pierre Boissery

Christophe Viala

Nadège Thirion-Moreau

Arnaud Abadie
email: abadie@seaviews.fr

Coupling the map of marine habitats and fish accumulation zones : a three dimensional spatial approach for the management of halieutic resources

The spatialisation of halieutic data is an essential element to define and create efficient protected and managed areas. Moreover, the distribution of fish schools is not homogeneous in the water column and is strongly linked with marine habitats. It is thus necessary to develop techniques allowing a spatial evaluation of halieutic resources. Multibeam echo sounders (MBES) provide acoustic data of the seafloor and the water column with a high accuracy and resolution. A single acquisition gives the bathymetry, a backscatter mosaic of the sea bottom and an acoustic imagery of the water column. The bathymetric data processing highlights the seafloor rugosity using several metric indices. A semi-automated classification including the depth, the rugosity indices and the backscatter values provides maps of marine habitats which are finally validated with ground truth. Data from the water column are analysed using an algorithm that detects acoustic targets corresponding to fishes. A georeferenced scatter graph of fish schools is thus automatically created. The 3D model of the seafloor obtained from the bathymetry is textured with the map of marine habitats. Points corresponding to fish detection are then added on the 3D model to provide a complete map. Through this process managers can access to a clear visualization of fish accumulations and the key marine habitats within their areas of interest.

Introduction

Marine ecosystems are among the most rich and complex biologic systems of our planet but remain difficult to study when compared with terrestrial habitats (Appeltans et al., 2012). To integrate and assess marine ecosystems' complexity in governmental efforts to protect them against anthropogenic influences, an ecosystem-based management theory was produced [START_REF] Slocombe | Implementing ecosystem-based management: Development of theory, practice, and research for planning and managing a region[END_REF]. More and more applications based on this approach are currently developed to take into account the resilience and robustness of marine systems [START_REF] Curtin | Understanding marine ecosystem based management: A literature review[END_REF]. These approaches rely on the understanding of ecosystems functioning by associating marine species in functional groups according to their ecosystemic roles [START_REF] Hooper | Species diversity, functional diversity and ecosystem functioning[END_REF]. They are especially suitable to respond Finished writting in January 2020. Reviewed in June 2020. Published in Vie et Milieu -Life andEnvironment 70(3-4), 2020 1/11 to the European directives, such as the Marine Strategy Framework Directive (MSFD), aiming to reach a good ecological status of marine areas.

Due to complexity and the difficult access to the marine environment, most of the survey techniques classically used are based on discrete samplings and observations focusing on a single or few functional compartments. A good example of the paradigm evolution concerning the survey of marine ecosystems is the study of Posidonia oceanica (L.) Delile seagrass meadows that previously focused on the plant morphology [START_REF] Pergent-Martini | Descriptors of posidonia oceanica meadows: Use and application[END_REF] while, nowadays, ecosystem-based approaches are developed [START_REF] Personnic | An ecosystem-based approach to assess the status of a mediterranean ecosystem, the posidonia oceanica seagrass meadow[END_REF]. Although this shift in paradigm provides a better evaluation of ecosystems status, the data on which it relies are still discrete and random measurements upscaled to large areas. Complementary spatial data are thus required to fulfil an effective ecosystem-based management of the marine environment.

The most effective tool to acquire spatial information on the seafloor and the water column above -with a high resolution and positioning accuracy -is currently the multibeam echo sounder (MBES) (Abadie and Viala, 2018). This type of acoustic probe is able to provide simultaneously bathymetric data, backscatter images and the water column imagery (WCI) on a large swath (increasing with the depth). Recent signal processing methods for bathymetric data allow to generate maps of marine habitats using various rugosity indices (Abadie et al., 2018). Likewise, innovative algorithms are able to detect various acoustic targets on the WCI among which the fish schools and the individuals that are composing them [START_REF] Lamouret | Measuring fish activities as additional environmental data during a hydrographic survey with a multi-beam echo sounder[END_REF].

In order to pave the way for a spatial approach of the ecosystem-base management, we investigated the capacity of MBES to provide exploitable two and three-dimensional information on several functional compartments of key marine ecosystems of the Mediterranean Sea. With this main target in mind, we studied the inter-seasonal and inter-annual characteristics of fish accumulations on various habitats. We also experimented different environmental indicators in an attempt to describe the link between marine habitats' features and fish distribution.

Materials and Methods

Study site and data acquisition

This study took place in the Bay of La Ciotat in the south of France off the Ile Verte (Fig. 1) on a site renowned for the richness of its habitats and biodiversity. The site covers an area of 0.83 km². An exhaustive acoustic data acquisition was performed two times in 2016 in June and August to take into account the increase of the water temperature linked with fish observations. These two acquisitions were realised in the same conditions: a two hours work done in the morning to collect the data along the same lines north-south oriented. Another acoustic dataset was obtained in June 2019, allowing an inter-annual comparison. Acoustic data were acquired by using a R2Sonic 2022 MBES fixed on the hull of a 6 m long survey boat. Position and attitude were recorded by an Applanix I2NS, an inertial system equipped with a RTK GNSS positioning device providing 0.015°roll/pitch precision as well as a horizontal accuracy of 1 cm and a vertical one of 1.5 cm. Acoustic data were acquired at a frequency of 450 kHz with an indi-2/11 vidual beam width of 0.9°x 0.9°for a maximum swath sector of 160°and 1 024 soundings per swath. Transects were defined prior to the data acquisition and the navigation was operated by a Raymarine ACU 200 autopilot synchronised with the RTK GNSS using ViewMap, a Geographic Information System (GIS) and navigation software developed by [START_REF] Viala | Viewmap and viewsmf software[END_REF] . The underwater sound velocity was constantly checked using a Valeport Ltd miniSVS sound velocity sensor mounted on the MBES. Additional underwater sound velocity profiles were performed with another miniSVS to detect the possible presence of a thermocline or fresh water layers impacting the sound propagation. Water temperature profiles were computed from the sound velocity data. Ground truth data were performed by scuba diving to validate the seafloor classification.

Acoustic data treatment and generation of habitat maps

R2Sonic 2022 bathymetric soundings were processed using the ViewSMF computer program developed by [START_REF] Viala | Viewmap and viewsmf software[END_REF] for the visualisation and processing (automatic or manual) of MBES acoustic data and metadata. False echoes were removed using filters to isolate one or several soundings. A rugosity index, named Bathymetric Automated Treatment for the Classification of the Seafloor (BATCLAS), is then computed from bathymetric soundings to highlight the underwater landscape according to the method developed by Abadie et al. (2018). The noise on the backscatter imagery was reduced using a time variable gain and snippets. A digital elevation model (DEM) encompassing a bathymetric map, the treated backscatter imagery and the BATCLAS index was generated. Finally, data from the DEMs and ground truthing were computed in ViewMap using a decision tree to classify marine habitats and build polygons exploitable in Geographical Information System (GIS) for further analysis. The final maps takes the shape of 2D and 3D maps of marine habitats.

Water Column Processing and Environmental indicators

The WCI was analysed following the technique developed by [START_REF] Lamouret | Measuring fish activities as additional environmental data during a hydrographic survey with a multi-beam echo sounder[END_REF]. This method utilises an algorithm that automatically detect and identify acoustic targets corresponding to fishes on the WCI. For each detection, the localisation, the dimensions and the energy are computed and stored in computer files. The halieutic data set is finally exported under the shape of a scatter plot for 2D and 3D analyses, as well as for investigating the relationships between fish distribution and marine habitats.

In order to compare the density between the different dataset, the fish density is computed from the scatter plot for the whole area and for several sub-areas of interest of the marine habitat map. The density is given in fishes/m², corresponding to the number of fishes in a column of 1 m² and of height given by the bathymetry. The density by subzone is then easily comparable through times on 2D maps. The study area was divided in three main sub-areas: (1) the Ile Verte walls, (2) the rocky reefs, (3) the sedimentary plains. Moreover, the rocky reefs were subdivided one by one.

Results

Depths varied from a few meters (<10m) on the shallowest cost of the Ile Verte to 66m in the south-east corner (Fig. 2A). In the extension of the island towards the south-east an uneven seascape was clearly visible, composed of vertical walls and plateaus. The backscatter imagery (Fig. 2B) highlights the relief seen on bathymetric data. Apart from these irregularities, the seabed was even with two ranges of backscatter intensity: a high value on the northern part and a lower one in the south, indicating two types of sediments with contrasted granularities. The unevenness were highlighted again and well 3/11 delimited on the rugosity index map (Fig. 2C). The rest of the seabed appeared smooth, except for small spots in the north east of the zone corresponding to known ship wrecks. Ground truthing confirmed the existence of the spots that were three very dilapidated wrecks -a wooden trawler and two sailing ships -playing the role of artificial reefs on the sandy plain. Ground truths also established the main habitats, i.e. rocky substrate with algal cover (39 151 m²), Posidonia oceanica (L.) Delile meadows (7 225 m²), coralligenous communities (27 838 m²) and soft sediments (624 502 m²; Table 1). The P. oceanica meadows were not found on sediments, but rather on hard substrates (Fig. 3). Sparse meadows were observed on the top of the rocky reefs too, however they were not represented due to their small size. The coralligenous communities were present on each rocky substrate from around 25 m depth while rocks covered by algal communities were found above this limit. The temperature profiles of the two June acquisitions did not present a clear thermocline, but rather two main temperature gradients (Fig. 4). The greatest temperature decreasing of 1. For each acquisition, fish accumulations were well focused on the Ile Verte walls and on the rocky reefs (Fig. 5). They appeared as large continuous and dense schools rather than 6/11 numerous medium schools. The accumulation on the wrecks were worth more noteworthy than the surroundings, except in August 2019 where a large fish school was observed (Fig. 5C). With the exception of the north west boundary of the site, there were no major fish schools on the sedimentary plains (Fig. 5). Numerous single detections were pointed on this habitat however. Fish detection were more numerous in June 2016 (38 498 detections) than in August (23 268 detections), while the number of fishes in August 2019 reached 57 472 detections (Table 2). Concerning fish densities, soft sediments showed low fish densities (from 0.01 fishes/m² in June and August 2016 to 0.03 fishes/m² in June 2019), however close to the average value (Fig. 6). On average through the three acquisitions, the Ile Verte walls had a higher fish abundance (0.31, 0.14 and 0.38 fishes/m² in June, August 2016 and June 2019 respectively) than the rocky reefs (0.25, 0.15 and 0.24 fishes/m² in June, August 2016 and June 2019 respectively; Fig. 6). Among the reefs, Rousteau Nord and Pierre du Jas showed the highest fish abundances, while the largest reefs -Rousteau and Levant -were the among the poorest (Fig. 6). Whatever the time acquisition, the fishes were mainly detected in the twenty first metres, more than 50 % of all detections in general and up to 66.8 % in June 2016 (Table 2). By averaging the three acquisitions, about a third of detections were situated in the mid-depth waters, between 20 and 40 m. Finally, less than 10 % of the fishes detected were found in the deepest waters of the study site (Table 2).

Discussion

This work was aiming at studying the capacity of a compact MBES to provide a precise map of marine habitats along with the fish accumulation, with the final purpose of providing a spatial ecosystem based approach to managers and stakeholders.

MBES advantages and operability

The main original outcome of this research effort is to put into light the possibility to have a global spatial review of an area with a single acoustic acquisition. Modern MBES are able to collect both bathymetry, backscatter and WCI without compensation of data quality and quantity. The WCI alone is used in various fields of research, such as biology, archeology, physical oceanology [START_REF] Colbo | A review of oceanographic applications of water column data from multibeam echosounders[END_REF], and reveals its full potential when combined with the other MBES outputs. The versatility of the MBES used in this study (a R2Sonic 2022) is an advantage, on the condition of having the capacity and to properly collect, store and process all the acoustic data, without forgetting GPS and Navigation information. However, this MBES is commonly dedicated to bathymetry and seafloor imagery acquisition, it is not a fishery-dedicated tool. Moreover, the system is adjusted and calibrated for the seafloor measurement and not for the water column observation. This implies several consequences: (1) acoustic noises can alter the WCI and hinder their processing; (2) only the pelagic fishes can be seen, the demersal and benthic ones are mingled with the seabed noise; (3) When compared to fishery MBES, the one used in this work is installed under the hull at mid-length of the boat and not in the bow as a 7/11 forward-looking MBES. Thus, the WCI is a vertical cut of the fish schools, and not a fish school seen as a whole; (4) as the MBES is used to map the seabed, the acoustic signals are emitted towards the nadir. There is also a good cover of the seabed, but the water column is not entirely scanned. This explains why the scatter plot is composed of bands North-South oriented.

The place on the hull and the looking direction of the MBES become important when the avoiding-boat behaviour of schools is considered. In a previous study on fish behaviours regarding MBES acquisition, [START_REF] Soria | Analysis of vessel influence on spatial behaviour of fish schools using a multi-beam sonar and consequences for biomass estimates by echo-sounder[END_REF] explained that a fish school feels the vessel coming far away. From this moment, a first part of the school avoids laterally the vessel and is not seen. Then, when the disturbance, i.e. the boat, arrives above the school, another part of the school also avoids it laterally and can only be seen on the edge of the WCI. What remains of the school is the little part recorded by the MBES while fishes are avoiding the disturbance by diving. That is why [START_REF] Soria | Analysis of vessel influence on spatial behaviour of fish schools using a multi-beam sonar and consequences for biomass estimates by echo-sounder[END_REF] and [START_REF] Paramo | Three dimensional structure and morphology of pelagic fish schools issue[END_REF] used a MBES with a 45°tilt from the nadir. Nevertheless, it is hard to say how many fishes are missed in the detection process.

Processing of the WCI

Although the algorithm developed to extract the fish information from the WCI is able to automatically detect fish targets without human intervention, this type of processing has currently several drawbacks. In this line of thought, wherever in the area, several fishes are not detected because they do not pass the filters. Some false alarms exist too. This is not disturbing the scatter plot and it does not influence so much the local density as well. However, it is more troublesome on the vast soft sedimentary seafloor where it seems that too much detections were performed. Thus, all these points are lonely, close to the seabed and around the nadir and could correspond to some acoustic noise. However, when looking the WCI at great depths, these points look like fishes for the algorithm as well as the human eyes. On the one hand, should these detections might be noises, then the sedimentary seafloor are really deserted by pelagic fishes. On the other hand, they could be true detections and these vast areas might be more populated than expected, while remaining very sparsely populated. We decided to display them, at least so that the reader may view the boat trajectories and assess the difficulty to validate fish detections.

Despite all these drawbacks, the processing method is fast enough to provide quick results, meaning with a mid-powerful computer, one hour of acquisition is processed in one hour of computer calculation. Moreover, improvement are under study in order to obtain a higher precision in target detection while decreasing the processing time. Another enhancement under progress concerns the pre-processing of the WCI to reduce the noise or calibrate the background noise.

Contribution to the ecosystem based management

This research work suggests a new approach to evaluate the ecological status of an area of interest with different levels of analysis that can be adapted according the characteristics of the managed zone (e.g. large areas, complex patchwork of marine habitats, extensive seagrass meadows), and the final aim of the study (e.g. MSFD, seascape analysis, halieutic research). It also allows to obtain spatial data for several functional compartments of the Mediterranean ecosystems P. oceanica meadows, algae-dominated rock reefs and coralligenous communities (Table 3), according to the conceptual representation of [START_REF] Personnic | An ecosystem-based approach to assess the status of a mediterranean ecosystem, the posidonia oceanica seagrass meadow[END_REF], [START_REF] Thibaut | An ecosystem-based approach to assess the status of mediterranean algae-dominated shallow rocky reefs[END_REF] and [START_REF] Ruitton | An ecosystem-based approach to assess the status of the mediterranean coralligenous habitat[END_REF] respectively. Obviously this spatial approach does not replace the qualitative and quantitative assessment made by scuba diving but rather intervenes as a complementary tool allowing cross-validation. It may also be used to produce a first investigation of an area with few data on fish accumulations and benthic habitats for a more efficient scuba diving evaluation later.

Fish number

One of the limits of a spatial approach through discrete acquisitions relies on the instantaneousness of the maps produced. Moreover, the pelagic fish distribution depends of numerous biotic and abiotic parameters that are virtually impossible to wholly assess such as marine habitats, sea temperature, salinity currents, day period, light intensity [START_REF] Saraux | Spatial structure and distribution of small pelagic fish in the northwestern mediterranean sea[END_REF]. This difficulty is illustrated in this study where the combination of the sea temperature, the seascape and the marine habitats are not sufficient to explain the fish distribution observed. In fact, if the fish biomass is increasing with sea temperatures, the August 2016 acquisition should have been the survey with the most detections in total and especially within the twenty first metres where temperatures were the highest. On the contrary, this acquisition shows about two times less fish detections than in June 2016 and 2019 (Table 2). The same observation is made for the June acquisitions, due to the sea surface temperature, the one of 2019 should have been more fish abundant than the one of 2016.

Although this first application is promising for an effective evaluation in a management purpose, further developments are still required for a deeper investigation of the link between the marine habitats and the fish accumulations. Moreover, the anthropogenic impacts and abiotic factors must be included in the analysis for a sharper ecological assessment. For instance, the fish densities need to be calculated per habitats and not only by area. Likewise, the vertical dimension should be more exploited with volumetric analysis rather than the only study of the vertical repartition of fish schools. An important research effort is also required to link the size of the WCI acoustic targets with the one of actual fishes to produce an evaluation of the biomass per surface and/or volume (even with a relatively large margin of error). At last, an ecological index can be built on the comparison between different sites (impacted and protected) at various seasons to link anthropogenic impacts with the ecological status. This research effort clearly highlights the capacity of spatial acoustic data obtained with a MBES to provide quantitative information on the marine habitats and fish distribution. This work aimed at paving the way to further developments to provide managers with effective spatial tools to evaluate the ecological status of key Mediterranean marine ecosystems. If coupled with in situ underwater observations, this spatial approach has the potential to give a complete view of underwater key biological systems as never before. 9/11

Figure 1 .

 1 Figure 1. Study site (red frame) in the Bay of La Ciotat.

Figure 2 .

 2 Figure 2. MBES data products.A, Bathymetry; B Backscatter imagery, C, Rugosity.

Figure 3 .

 3 Figure 3. 3D representation of the seabed topology with marine habitats and details of the three batches studied and the reefs names for the study of the fish density.

  5°C was found in the twenty first metres in June 2016 (from 19.0 °C to 17.5 °C), and in the ten first metres in June 2019 (from 23 °C to 21.5 °C). Beyond 20 m depth, the temperatures went down more slowly to stabilise at 17.2 °C and 19.0 °C in June 2016 and June 2019 respectively. The August 2016 profile showed a thermocline at 5-10 m depth where temperatures dropped from 25 °C to 21 °C and then declined more slowly to 19 °C deeper (Fig. 4).

Figure 4 . 11 Figure 5 .

 4115 Figure 4. Temperature profile in June 2016 (black); August 2016 (red); June 2019 (blue).

Figure 6 .

 6 Figure 6. Local fish densities in fishes/m² in A, June 2016; B August 2016; C, June 2019. The red value corresponds to the mean number of fishes/m².

Table 1 .

 1 Areas covered by each marine habitats and their proportion.

	Habitat	Area (m 2 ) Proportion (%)
	Coralligenous communities	27 838	2.9
	P. oceanica meadows	7 225	1.0
	Algal cover on rocky substrate	39 151	5.7
	Soft sediments	624 502	89,4
	Total	698 716	100
			4/11

Table 2 .

 2 Number of fish detections and proportions per depth categories.

	Position in the water column
	and depth range (m)	June 2016	August 2016	June 2019
	Near surface [0, 20[	25 695 66,8 % 14 316 61,5 % 29 590 51,5 %
	Mid-depth [20, 40[	10 892 28,3 % 6 979 30,0 % 23 653 41,1%
	Deep [40, max]	1 903	4,9 %	1 971	8,5 %	4 253	7,4 %
	Total	38 492	23 268	57 472

Table 3 .

 3 Functional compartments of the Mediterranean ecosystems investigated by the coupling of marine habitat maps and fish detection in the water column.

	Ecosystem	Functional compartments	Type of data
		Posidonia leaves	Area covered
	Posidonia oceanica meadows	Planktivorous teleosts Herbivores 1 Piscivorous teleosts Predatory teleosts	Fish number
		Multicellular photosynthetic organisms Area covered
		Herbivorous teleosts
	Algae-	Piscivorous teleosts
	dominated	Omnivorous teleosts	Fish number
	rock reefs	Invertivorous teleosts
		Planktivorous teleosts
		Builders	Area covered
	Coralligenous communities	High-level predators Predatory teleosts Planktivorous teleosts

Acknowledgments

This work was made possible thanks to the Agence de l'Eau RMC that financed the hydrographic surveys in 2016 and 2019, as well as the first research works on the analysis of the WCI. The first author acknowledges a PhD fund from the Association Nationale de la Recherche et de la Technologie (ANRT).