Sartoretto Stéphane
email: author.stephane.sartoretto@ifremer.fr

Jean-Baptiste Ledoux

Gueret Elise

Guillemain Dorian

Ravel Christophe

Moirand Laurine

Aurelle Didier

Running page head: Characterisation of Mesophotic gorgonian populations Ecological and genomic characterization of a remarkable natural heritage: a mesophotic 'giant' Paramuricea clavata forest

Keywords: Paramuricea clavata, Mediterranean Sea, Climate refugee, Environmental adaptation, transplant experiment, RAD-Seq

Paramuricea clavata is an ecosystem engineer, structuring hard bottom communities in the Mediterranean Sea, from 10 to 200m depth. For more than two decades, shallow populations of P. clavata have been impacted by marine heatwaves, resulting in massive mortality events. Recently, a new dense population characterised by the tallest colonies ever 1 recorded (up to 1.80m) in the Mediterranean Sea was discovered between 50 and 60m depth. Here, we analyzed the size and genetic structure of this "deep giant population" and conducted a reciprocal transplant experiment with a shallow population inhabiting the same area to test for local adaptation. The experiment showed no significant difference in the survival rate despite the high temperatures registered in the shallow area. Nevertheless, we observed a significant differentiation by depth in this area, with low estimates of gene flow. The particular local oceanographic conditions could lead to a relative isolation of the "deep giant colony" population. In conclusion, the reduced connectivity of this newly discovered giant populations with shallower ones, question its potential role as a climate refuge. Additionally, this population constitutes a unique natural heritage site that should be effectively protected from physical impacts and other direct consequences of anthropogenic activities.

INTRODUCTION

Gorgonians are present in the world's ocean, from the surface to the bathyal zones. These anthozoans are ecosystem engineers, structuring their habitats [START_REF] Boudouresque | Where Seaweed Forests Meet Animal Forests: the Examples of Macroalgae in Coral Reefs and the Mediterranean Coralligenous Ecosystem[END_REF]). Among the gorgonian species present in the Mediterranean Sea, the populations of red gorgonian Paramuricea clavata (Risso, 1826) constitute noteworthy submarine landscapes [START_REF] Carpine | Les Gorgonaires de la Méditerranée[END_REF], Grasshoff 1992[START_REF] Linares | Population ecology and conservation of a long-lived marine species: the red gorgonian Paramuricea clavata[END_REF]. This Mediterranean endemic species, living from 10m to 200m depth [START_REF] Boavida | Overlooked habitat of a vulnerable gorgonian revealed in the Mediterranean and Eastern Atlantic by ecological niche modelling[END_REF][START_REF] Coelho | Not out of the Mediterranean: Atlantic populations of the gorgonian Paramuricea clavata are a separate sister species under further lineage diversification[END_REF][START_REF] Fourt | Guide de la faune profonde de la mer Méditerranée. Exploration des roches et canyons sous-marins des côtes françaises[END_REF], has a long life span (decades) and a slow growth rate (mean values: from 1.8 to 3 cm/yr in colony height) (Mistri & Cecherelli, 1994;[START_REF] Coma | Growth in a Modular Colonial Marine Invertebrate[END_REF][START_REF] Coma | Seguiment temporal de la gorgònia Paramuricea clavata de les illes Medes. Exercici 2001. Seguiment temporal de la reserva marina de les Illes Medes[END_REF][START_REF] Cupido | Sexual structure of a highly reproductive, recovering gorgonian population: quantifying reproductive output[END_REF]. Paramuricea clavata is a gonochoric species with low re-cruitment rates [START_REF] Coma | Reproduction and cycle of gonadal development in the Mediterranean gorgonian Paramuricea clavata[END_REF][START_REF] Coma | Seguiment temporal de la gorgònia Paramuricea clavata de les illes Medes. Exercici 2001. Seguiment temporal de la reserva marina de les Illes Medes[END_REF]. Asexual reproduction is negligible and the maintenance of the population is based on the recruitment of sexually produced offspring [START_REF] Coma | Reproduction and cycle of gonadal development in the Mediterranean gorgonian Paramuricea clavata[END_REF], Pilczinska et al. 2017). A late first reproductive age depending to Mediterranean region (3 to 13 years old) characterizes this species [START_REF] Coma | Reproduction and cycle of gonadal development in the Mediterranean gorgonian Paramuricea clavata[END_REF][START_REF] Cupido | Sexual structure of a highly reproductive, recovering gorgonian population: quantifying reproductive output[END_REF]. Paramuricea clavata is a suspension feeder, feeding on a large range of particles and this species constitutes a major interface for the transfer of carbon between pelagic and benthic components [START_REF] Coma | Feedin and prey capture cycles in the aposymbiontic gorgonian Paramuricea clavata[END_REF], Gili & Coma 1998[START_REF] Linares | Life history and population viability of a long-lived marine invertebrate: the octocoral Paramuricea clavata[END_REF]. The presence of P. clavata enhances the hard bottom biodiversity and reduces the sedimentation impacting benthic habitats [START_REF] Ponti | The role of gorgonians on the diversity of vagile benthic fauna in Mediterranean rocky habitats[END_REF][START_REF] Valisano | Temporal variability of sedimentation rates and mobile fauna inside and outside a gorgonian garden[END_REF].

Paramuricea clavata is an emblematic species of the coralligenous habitat and its populations are of major interest for recreational scuba divers [START_REF] Coma | Long-term assessment of the patterns of mortality of a temperate octocoral in protected and unprotected areas: a contribution to conservation and management needs[END_REF], Linarès 2006).

They can be impacted by different physical pressures (anchoring, SCUBA diving, fishing nets) [START_REF] Ballesteros | Mediterranean coralligenous assemblages: a synthesis of present knowledge[END_REF][START_REF] Betti | Evidences of fishing impact on the coastal gorgonian forests inside the Portofino MPA (NW Mediterranean Sea)[END_REF]). In addition, shallow populations until 40 to 50 meters depth have been affected by several mass mortality events linked to marine heatwaves in recent decades (Perez et al. 2000, Garrabou et al. 2022). These thermal anomalies, which occur in late summer and autumn periods, are characterized by long periods of high seawater temperatures [START_REF] Romano | Anomalie thermique dans les eaux du golfe de Marseille durant l'été 1999. Une explication partielle de la mortalité d'invertébrés fixés ?[END_REF][START_REF] Crisci | Temperature Anomalies and Mortality Events in Marine Communities: Insights on Factors behind Differential Mortality Impacts in the NW Mediterranean[END_REF], Pairaud et al. 2014). The consequences are a partial or complete necrosis of living tissues (coenenchyme and polyps) surrounding the skeleton, which can result in the death of gorgonians [START_REF] Garrabou | Mass mortality in NW Mediterranean rocky benthic communities: effects of the 2003 heat wave[END_REF][START_REF] Garrabou | Sliding Toward the Collapse of Mediterranean Coastal Marine Rocky Ecosystems[END_REF]. Unfortunately, P. clavata has overall low resilience capacity [START_REF] Garrabou | Mass mortality event in red coral Corallium rubrum populations in the Provence region (France, NW Mediterranean)[END_REF][START_REF] Cerrano | Gorgonian population recovery after a mass mortality event[END_REF], Linarès et al. 2005;[START_REF] Garrabou | Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea[END_REF] even if locally this species seems to recover faster [START_REF] Cupido | Unexpected long-term population dynamics in a canopy-forming gorgonian coral following mass mortality[END_REF][START_REF] Santori | After the Fall: The Demographic Destiny of a Gorgonian Population Stricken by Catastrophic Mortality[END_REF]. Therefore, recurrent marine heat waves are a major threat for the sustainability of shallow gorgonian populations in the Mediterranean Sea (Pairaud et al. 2014).

Therefore, the study of the genetic diversity of P. clavata populations and of their thermotolerance is crucial in order to assess: (i) how unimpacted populations can contribute to the resilience of populations affected by mortality events through genetic connectivity and (ii) whether or not the newly established populations are adapted to the local thermal regime, and could cope with warming. These questions started to be addressed focusing on three Mediterranean gorgonians affected by thermal anomalies: the red coral (Corallium rubrum) [START_REF] Ledoux | Potential for adaptive evolution at species range margins: contrasting interactions between red coral populations and their environment in a changing ocean[END_REF], the white gorgonian (Eunicella singularis) (Pey et al. 2013) and the red gorgonian (Paramuricea clavata) (Arizmendi-Meija et al. 2015a). Regarding P. clavata, the genetic structure was studied in different regions and depths [START_REF] Mokhtar-Jamaï | From global to local genetic structuring in the red gorgonian Paramuricea clavata: the interplay between oceanographic conditions and limited larval dispersal[END_REF][START_REF] Padrón | Evaluation of conservation efficiency for gorgonian species at a regional scale based on an existing Marine Protected Area network and modelling scenarios PMCB[END_REF], Padrón & Guizen 2016, Arizmendi-Meija et al. 2015b, Pérez-Portela et al. 2016[START_REF] Ledoux | Post-glacial range expansion shaped the spatial genetic structure in a marine habitat-forming species: Implications for conservation plans in the Eastern Adriatic Sea[END_REF]. It has been shown that significant genetic structure exists at short distances (a few hundred meters) and, occasionally, differences between depths can be observed within the same site [START_REF] Mokhtar-Jamaï | From global to local genetic structuring in the red gorgonian Paramuricea clavata: the interplay between oceanographic conditions and limited larval dispersal[END_REF], Pilczynska et al. 2019). However, the limited number of genetic markers used in these studies did not provide a clear understanding of the adaptation of this species to local environmental conditions (but see [START_REF] Crisci | Regional and local environmental conditions do not shape the response to warming of a marine habitat-forming species[END_REF]. On the other hand, shallow populations of P. clavata (between 20 and 40m depth) have been more extensively studied than deeper ones, which remain poorly understood, apart from the study of Pérez-Portela et al. (2016).

In the last decade, new populations of P. clavata have been discovered between 50 and 60 m depth in the Natura 2000 site "Côte Bleue marine" (north of Marseille) [START_REF] Astruch | Inventaires biologiques et Analyse écologique de l'existant, Natura 2000 en mer[END_REF]). These populations settle on rocky outcrops sparsely covering 2300 hectares ("Catchoffs" site). This site is influenced by the flow of the Rhône River and remarkable seasonal upwelling [START_REF] Fraysse | Rôle du forçage physique sur l'écosystème à l'est du Golfe du Lion : modulation de[END_REF]. By their exceptional size (up to 1.80m high) and density, the gorgonians that form the Catchoffs site stand out from the colonies usually observed (see Results). Such gigantic morphologies could correspond to a phenotypic plas-ticity linked to environmental conditions and/or particular genetic characteristics. Finally, unlike the shallow populations, these deep populations do not appear to be affected by marine heat waves. In this context, the main objective here is to test whether these deep populations of gigantic P. clavata constitute potential climatic refuge populations in the face of current global warming. Could they contribute to the resilience of neighboring and shallower populations impacted by mass mortalities? To answer this question, the environmental conditions and size structure of P. clavata populations in the Catchoffs site were characterized and compared to a shallower population from the same geographical area.

Next, we analyzed the differentiation between this deep population and neighboring shallower populations at the genomic level using restriction site associated DNA sequencing (RAD-Seq). Finally, the local adaptation signals of the populations to their local environment (especially to the thermal regime) were tested using experimental (reciprocal in situ transplantation) and genomic (RAD-Seq) approaches.

MATERIAL AND METHODS

Studied area

Two populations of P. clavata were considered along the "Côte Bleue", north of Marseille (France), near the Rhône Delta. The first one (Catchoffs -COF) is located two nautical miles south of Carry-le-Rouet (Fig. 1). This population is included in a large Natura 2000 site [START_REF] Leleu | Métiers, effort and catches of a Mediterranean small-scale coastal fishery: The case of the Côte Bleue Marine Park[END_REF]). This site is characterized by rocky outcrops emerging from the sediment on a surface covering 2300 ha. Giant colonies of P. clavata (between 1.5 and 1.8m high) can be observed on these outcrops [START_REF] Astruch | Inventaires biologiques et Analyse écologique de l'existant, Natura 2000 en mer[END_REF]. Other gorgonian species also occur at this site: Eunicella cavolini (Koch, 1887), Eunicella singularis (Esper, 1791), Eunicella verrucosa (Pallas, 1766), Leptogorgia sarmentosa (Esper, 1791), and Corallium rubrum (Linnaeus, 1758). The marine habitat is characterized by a high turbidity of water and by sandy-muddy sediments around rocky outcrops due to the proximity of the Rhône Delta and the impact of associated river plume [START_REF] Fraysse | Rôle du forçage physique sur l'écosystème à l'est du Golfe du Lion : modulation de[END_REF]. In this Natura 2000 site, two stations were selected at: (i) 60 m (COF-station A) and (ii) 58 m depth (COF-station B) (Fig. 1a; Table 1). A third station was studied at 30 m depth (Yeux de chat -ODC), 4,500 m away from COF, it is an inshore site characterized by a rocky peak colonized by a dense population of P. clavata with some large isolated colonies of Eunicella cavolini. As for the previous COF sites, the ODC station is strongly impacted by the Rhône River (Fig. 1a; Table 1).

Characterization of marine environment and gorgonian populations

We monitored the temperature and current regime of both habitats (ODC station and COFstation A) continuously for 13 months between the 1 st of July 2019 and the 31 st of July 2020. Temperature was recorded every 30 minutes with a HOBO water temp pro V2 sensor (@Prosensor). A Tilt Current Meter Model 1 (TCM-1) (@Lowell Instruments) was used to characterize the current regime (speed and orientation of current) among the giant colony populations (60 m depth) (Fig. S1). Measurements were also made every 30 minutes. We also characterized the density and size structure of the P. clavata populations studied (COF-site A, B and ODC station) (Table 2). According to [START_REF] Linares | Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis[END_REF], we used 20 open quadrats (50x50 cm) per station (total surface: 5m²) (Fig. S2). In each quadrat, the number of colonies was counted and their height was measured with centimeter precision. In gorgonians, the characterization of the demographic structure of a population is challenging. Indeed, the annual growth rate decreases with the age of the considered colonies [START_REF] Coma | Growth in a Modular Colonial Marine Invertebrate[END_REF]Sartoretto & Francour, 2012;[START_REF] Viladrich | Fast growth rate in a young colony of the Mediterranean gorgonian Eunicella singularis[END_REF]. In P.

clavata, annual growth rates values were almost exclusively estimated on small and young colonies (e.g. Mistri & Cecherelli, 1994;[START_REF] Coma | Growth in a Modular Colonial Marine Invertebrate[END_REF][START_REF] Cupido | Sexual structure of a highly reproductive, recovering gorgonian population: quantifying reproductive output[END_REF]. In this context and considering the maximal size of P. clavata observed in Catchoff site (180cm high), we only focused on the characterization of the size structure of the populations. Following [START_REF] Linares | Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis[END_REF], we defined 16 size classes, each corresponding to a 10 cm range.

Experimental design

To test the potential for local adaptation of P. clavata populations according to depth, we performed a reciprocal transplant experiment between the two localities (COF-station A and ODC station) from 1 st July 2019 to 3 rd June 2020. The transplant experiment included one control (colonies transplanted at their native depth) and one transplant (colonies transplanted at the foreign depth) in both locations. Sixty 10 cm long apical tips (two per colony) were randomly sampled with scuba diving from each population. For each population, 30 fragments were attached to experimental plates (Fig. S2) and installed at their original depth (control transplants). Plates with the remaining 30 fragments were installed at a foreign depth (reciprocal transplants; i.e. 30 m to 60 m and 60 m to 30 m). Each sampled colony was genotyped as explained below. Photographic monitoring of each transplant was carried out every two months depending on meteorological conditions.

Genomic analysis

We aimed to characterize the genetic make-up of the COF deep site and to involve these results in a wider context. Therefore, we sampled P. clavata colonies in different areas:

Northern Marseille Bay (COF station A and ODC), Calanques (Grand Congloue (GOC) and Impérial (IMP) stations) and Porquerolles (Sec du Petit Langoustier (SPL) and Cap d'Armes (CAA) stations), at increasing distance from COF (from 5 km up to 94 km) (Fig. 1b). In each area we sampled sites corresponding to different depths: around 30 m deep (hereafter "shallow"), and below 50-55 m ("deep"). Details of the sampling sites are pre -sented in Table 2. It should be noted that we analyzed more individuals from the Northern Bay of Marseille (including the deep COF site) which was our main study area.

2.3.1. Library preparation and sequencing DNA from the samples was extracted using the Macherey-Nagel NucleoSpin DNA RapidLyse extraction kit. After DNA quantification, the extracted DNA samples were sent to the MGX platform (https://www.mgx.cnrs.fr/) for RAD library preparation and sequencing. We included in the batch of individuals four replicates with two DNA extractions from the same colony. Library preparation followed the protocol of [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF] with the PstI restriction enzyme. The library was sequenced on an Illumina NovaSeq 6000. We obtained a mean of 6,426,012 raw sequences per sample (minimum: 618,725; maximum: 9,181,838; Fig. S3).

Analysis of RAD sequencing data

We checked the quality of sequences with FastQC (Andrews 2019). In a preliminary analysis we included an individual of the congeneric species Paramuricea placomus (Linnaeus, 1758) from Tjärnö (Sweden). A first Principal Component Analysis on this dataset led to the main separation of P. placomus and P. clavata (Fig. S4). As this analysis did not lead us to suspect the presence of different cryptic lineages among the P. clavata samples, we removed the P. placomus sample for further analyses. This allowed us to increase the number of SNP loci (from 45,251 to 65,726 SNPs before further filtering), and to improve the characterization of the genetic structure within P. clavata. At that stage we also removed a P. clavata individual with a very low number of reads (618,725). We then removed potential contaminant sequences with Kraken 2 [START_REF] Wood | Improved metagenomic analysis with Kraken 2[END_REF] with the stan-dard database including references from archae, bacteria, human, plasmid, and viral sequences. The sequences identified by Kraken 2 were filtered with KrakenTools [START_REF] Lu | Kraken Tools[END_REF].

The assembly of RAD loci was done with ipyrad 0.9.79 on these filtered sequences (Eaton & Overcast, 2020). We used the genome of P. clavata as a reference [START_REF] Ledoux | The Genome Sequence of the Octocoral Paramuricea clavata -A Key Resource to Study the Impact of Climate Change in the Mediterranean[END_REF].

We then performed a first PCA which showed the main separation of two samples of COF with high rates of heterozygous loci. The inbreeding coefficient F computed with VCFTOOLS 0.1.15 [START_REF] Danecek | The variant call format and VCFtools[END_REF]) for these two samples was -0.24 and -0.08, compared to a mean value of 0.13 and a minimum of -0.03 for other samples. We removed these two samples for further analyses. We used the output of ipyrad to build a matrix of pairwise p-distances among samples with VCF2Dis 1.45 (https://github.com/BGIshenzhen/VCF2Dis.git). We then compared the pairwise distance observed among different samples with those observed among sequencing replicates. This last analysis led us to identify four sample pairs which showed the same distance than the distance observed between replicates (Table S1). For each pair, we removed the sample with the lowest number of reads, resulting in a total of 82 individuals. We then filtered SNPs with the following filters: two alleles, a maximum of 25% of missing data, in Hardy-Weinberg equilibrium, not in linkage disequilibrium, and one SNP per locus. Filtering on the basis of Hardy-Weinberg equilibrium is useful to remove problematic markers, such as paralogous loci for example. and previous studies based on microsatellites did not suspect any asexual reproduction in this species (Mokhtar-Jamai et al., 2011;2013). The filter based on linkage disequilibrium aims at retaining only independent loci. The filtering was performed with vcftools 0.1.16 and the GBS_SNP_filter 1.17 pipeline (Alexander 2020, [START_REF] Danecek | The variant call format and VCFtools[END_REF].

We used the GENEPOP R package [START_REF] Rousset | Genepop'007: A complete re-implementation of the genepop software for Windows and Linux[END_REF] to compute gene diversity within individuals (1-Qintra; corresponding to observed heterozygosity) and among individuals within sampling sites (1-Qinter; corresponding to expected heterozygosity), and F statistics [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF]. We tested the pairwise differentiation among sampling sites with 1,000 permutations with Arlequin 3.5 [START_REF] Excoffier | Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows[END_REF]. We analysed the genetic structure among samples with a Principal Component Analysis [START_REF] Jombart | Discriminant analysis of principal components: A new method for the analysis of genetically structured populations[END_REF], and a clustering analysis followed by a Discriminant Analysis of Principal Components (DAPC; [START_REF] Jombart | Discriminant analysis of principal components: A new method for the analysis of genetically structured populations[END_REF], with the adegenet 2.1.5 R package [START_REF] Jombart | Adegenet 1.3-1: New tools for the analysis of genome-wide SNP data[END_REF]. We tested for the presence of loci potentially involved in local adaptation with three approaches: Arlequin [START_REF] Excoffier | Detecting loci under selection in a hierarchically structured population[END_REF][START_REF] Excoffier | Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows[END_REF], OUTFLANK [START_REF] Whitlock | Reliable detection of loci responsible for local adaptation: Inference of a null model through trimming the distribution of F ST[END_REF], and PCADAPT [START_REF] Luu | Pcadapt: An R package to perform genome scans for selection based on principal component analysis[END_REF].

We performed demographic inferences to estimate effective size and gene flow among populations in COF and ODC populations for which we obtained the highest sample sizes. We used the dataset obtained from the assembly of these two populations only. We used fastsimcoal2 for demographic inferences [START_REF] Excoffier | Robust demographic inference from genomic and SNP data[END_REF][START_REF] Excoffier | fastsimcoal2: demographic inference under complex evolutionary scenarios[END_REF], under a model of divergence with gene flow (Fig. S5). The site frequency spectrum (SFS) was computed with easySFS (https://github.com/isaacovercast/easySFS). The inference was first performed on the basis of 100 replicated analyses of this initial dataset, and the run with the highest estimated likelihood was retained to estimate demographic parameters. We performed 50 non-parametric bootstraps of the original dataset: each bootstrapped dataset was analysed with 10 replicates and we retained the best run to get a confidence interval of the estimated parameters.

RESULTS

Characterization of the marine environment

Temperature

At the ODC station (-30 m), the range of values of seawater temperature values extended from 23.2°C during autumn 2019 to 13.1°C during winter 2019-2020. In summer and autumn, temperature trend showed successions of warm water peaks (21-23°C at 30 m depth). These peaks were observed during short time periods (less than a week) in July and August (Fig. 2). Between September and November 2019, two longer periods (two and three weeks) were observed with temperatures reaching 20°C and 23°C. In the COF station (-60 m), temperature values ranged from 22.54°C in autumn 2019 to 13.40°C in winter 2019-2020. During the summer, the same sequence of temperature increases as at the ODC station was observed with lower maximum values (16-17°C). During the autumn, the warm water periods were also the same as at the ODC station with the same maximum values (20°C to 23°C) but of a shorter duration (two weeks) (Fig. 2).

Current regime

The current regime in the COF station is characterized by low speed values (< 0.09 m.s -1 ).

During spring and summer, we observed a maximal period of time with a current speed of less than 0.03 to 0.04 m.s -1 . (Fig. 3A). During autumn and winter, the current speed values are more unstable, ranging from 0.01 to 0.09 m.s -1 . The current orientation shows a unimodal pattern in spring and summer with a predominance of the southeast direction (bearing: 220°). In autumn and winter, this pattern is bimodal with: (i) southeast predominance (bearing: 230°) and (ii) to a lesser extent northeast predominance (bearing: 50°) (Fig. 3B).

Size structure

The shallow population studied (ODC station, -30m) is characterized by a density of 26 ± 7.3 colonies.m -2 . The size structure of this population shows a unimodal distribution of size classes. The small (10 to 20 cm) and average sized gorgonians (20 to 50 cm) represent 67 % of the total (Fig. 4). The tallest colonies (50-110 cm) represent 23 % with only 1 % of the colonies belonging to the class size 110-120 cm.

Compared to the ODC station, the COF-station A (-60m) shows a lower value for density with 18.05 ± 7.29 colonies.m -2 . The gorgonian population is characterized by a bimodal size structure with a predominance of average sized colonies (20 to 50 cm) representing 50 % of total and high colonies (100-130 cm), representing 23 % of the colonies. Tall colonies (130 to 150 cm) and giant (>150 cm with a maximum of 180 cm) represent respectively 9 % and 6 % of the total (Fig. 4). In the COF-station B (-58m), the density value is 23.6 ± 7.4 colonies.m -2 , with small (10-20 cm) and average sized colonies (20 to 50 cm) representing 17% and 39% of the total number of colonies. Tall colonies (130-150 cm) and giant (>150 cm with a maximum of 160 cm) represent 13 % and 2 % of the observed colonies.

Transplantation experiments and genomic analysis

Monitoring of transplants

At 60 m depth (COF-Station A), 24 fragments from the control transplant (60 to 60 m) died one month after their installation. Ten months later, all fragments were dead. Regarding the reciprocal transplants from ODC (30 to 60 m), the survival rate was higher than for the control transplant with 11 dead fragments 5 months after the beginning of the experiment.

One year later, 14 fragments were dead (47 %) and the remaining ones showed between 25 % and 75 % of tissue necrosis. At 30 m depth (ODC station), after four months the survival rate reached 96.67 % for the control transplants (30 to 30 m), and 83.33 % for the reciprocal transplants from COF (60 to 30 m). After 12 months, these values respectively reached 60 % and 40 % (Fig. 5). At the end of the experiment, no significant differences (Wilconson Rank Sum Test, p =1) in final necrosis levels was observed between control transplants and reciprocal transplants coming from COF. The majority of colonies presented a mean necrosis level lower or equal to 50 %. In June 2020 (end of experiment), 90 % of COF transplants and 92 % ODC transplants presented fertile polyps. Finally, the keptoparasitic annelid Haplosyllis chamaeleon [START_REF] Pola | Updating the current knowledge on the relationships between Haplosyllis chamaeleon Laubier, 1960 (Annelida, Syllidae) and Paramuricea clavata (Risso, 1826) (Cnidaria, Plexauridae) in the Mediterranean Sea[END_REF]) was observed on 28.6 % of the reciprocal transplants from COF and 22 % of the control transplants (ODC).

Genetic diversity and genetic structure

After the different filtering steps, statistical analyses were performed with 82 individuals and 49,215 SNPs. The number of loci retained at each stage of the analysis is shown in Tables S2 andS3. The levels of diversity were roughly similar between samples, with 1-Qintra around 0.06 and 1-Qinter around 0.07 (see Table 2). We did not observe any regular increase or decrease of genetic diversity along depth. The FIS estimates indicated a significant heterozygous deficit for all populations, varying from 0.08 for SPL to 0.13 for CAA.

When computed over all sites and loci, the observed F statistics were 0.094 for FIS, 0.044 for FST, and 0.135 for FIT. The pairwise FST, averaged over all loci, are presented in Table 3.

The highest FST value (0.053) was observed for the comparison between the two sites in the northern bay of Marseille, Catchoffs (COF) and Yeux de Chat (ODC), and between COF and GCO (different depths, Marseille North and Calanques). The mean FST value for the comparisons between depths within a given area was 0.032. Between areas, the mean FST value was 0.038, 0.044 and 0.030 considering all sites, shallow sites and deep sites, respectively. All pairwise differentiation tests were significant according to the permutation tests (highest p-value: 0.014 for the CAA/IMP comparison).

The result of the PCA analysis on all loci is presented in Fig. 6A. The first axis mainly separated populations COF and ODC (2.1 % of variance), while the second axis was driven by a few individuals from SPL (2 % of variance). The DAPC analysis did not indicate a clear cut-off for the choice of the number K of clusters (Fig. S6). The clustering at K= 2 separated ODC from other populations, and the clustering at K= 3 further separated COF from ODC and other populations (Fig. S7). The clustering at K= 4 separated COF, ODC, the two Calanques populations (GCO and IMP), and the two Porquerolles populations (SPL and CAA; Fig. 7) with high membership probabilities (near 1 in all cases). One can note that one individual from Porquerolles was assigned to the Calanques cluster, and one individual from Calanques was assigned to the Porquerolles cluster.

We identified 267 potentially selected outlier loci with PCADAPT, 25 with Arlequin and none with OUTFLANK. None of the outliers has been identified with both methods simultaneously.

We performed a separate PCA analysis with the outlier loci identified by each method.

With PCADAPT, the first axis of this last PCA separated a small group of individuals from various populations, whereas the second axis mainly separated ODC from other populations (Fig. 6B). The third axis separated the COF population, and the fourth axis mainly separated the two Porquerolles populations (CAA and SPL; Fig. 6C). With the Arlequin outliers, there was no clear signal of differentiation in the PCA (results not shown).

The results of demographic inferences are presented in Table S4. Estimates of effective size indicate a much higher population size for the 30 m population (ODC) compared to the 58 m population (COF), with non-overlapping distributions based on bootstraps. Gene flow estimates indicate very low levels of connectivity between these two populations, but gene flow from ODC to COF appears higher than in the reverse direction (0.0041 compared to less than 0.0001; non-overlapping distributions).

DISCUSSION

Size structure

At the two stations of COF site, mean values for colony height of Paramuricea clavata reached 71.08 ± 47.9 cm (site A) and 57.39 ± 39.1 cm (site B), respectively. These values are higher than those reported in the ODC station (32.44 ± 22.54 cm), and, more generally, higher than those reported from the same depth in the northwestern Mediterranean basin [START_REF] Linares | Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis[END_REF], Pérez-Portela et al. 2016). Surprisingly, the presence of tall (1.25-1.50 m) and giant colonies (>1.50 m) is associated with density values close to those noted in shallower sites with smaller colonies [START_REF] Harmelin | Suivi d'une population de Paramuricea clavata (Risso, 1826) (Cnidaria, Octocorallia, Gorgonacea) dans le parc national de Port-Cros (Méditerranée, France) : Comparaison des états 1992 et 2004 sur le site de la Galère[END_REF], Linarès 2008, Ibora et al. 2022). By referring to the growth rate value estimated by Mistri & Ceccherelli (1994), [START_REF] Cerrano | Gorgonian population recovery after a mass mortality event[END_REF] and [START_REF] Cupido | Sexual structure of a highly reproductive, recovering gorgonian population: quantifying reproductive output[END_REF] on small colonies (2.7 to 3.7 cm.yr - 1 ), the highest P. clavata (1.8 m) could be 50 to 70 years old. Nevertheless, the smallest (and youngest) gorgonians usually show hight values of annual growth rate [START_REF] Coma | Growth in a Modular Colonial Marine Invertebrate[END_REF][START_REF] Viladrich | Fast growth rate in a young colony of the Mediterranean gorgonian Eunicella singularis[END_REF]. Based on the growth rate value calculated by Coma et al.

(1998) on higher colonies (from 3 to 55cm), they could be around 100 years, old even though the authors underline the great variability of the growth rate within and between the colonies of P. clavate. This age is well above the upper limit of this gorgonian's previously estimated life span (50 years). Nevertheless, this astonishing size could be also due to a growth rate boosted by environmental conditions. Indeed, the Catchoff study area is characterized by a low current and the proximity of the Rhône River induces high input of organic matter with a high productivity (chlorophyll a) potentially beneficial to the growth of colonies (Cocito et al. 1997[START_REF] Linares | Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis[END_REF][START_REF] Fraysse | Rôle du forçage physique sur l'écosystème à l'est du Golfe du Lion : modulation de[END_REF]. Further studies are needed to formally estimate the age of the highest colonies of P. clavata. To date, it should be noted that these giant deep-sea populations of P. clavata are not only unique but also constitute a natural heritage in the north-western Mediterranean basin deserving special attention in terms of conservation and management.

Transplant experimentation

At 30 m, the survival rate of transplants was similar to the percentage obtained in other experiments with different methods and gorgonian species [START_REF] Weinberg | Transplantation experiments with Mediterranean gorgonians[END_REF][START_REF] Fava | Survival, growth and regeneration in explants of four temperate gorgonian species in the Mediterranean Sea[END_REF][START_REF] Villechanoux | Testing Transplantation Techniques for the Red Coral Corallium rubrum[END_REF]. The presence of the kleptoparasite Haplosyllis chamaeleon after one year did not cause injuries or any malformation on transplants as usually observed on colonies of P. clavata [START_REF] Pola | Updating the current knowledge on the relationships between Haplosyllis chamaeleon Laubier, 1960 (Annelida, Syllidae) and Paramuricea clavata (Risso, 1826) (Cnidaria, Plexauridae) in the Mediterranean Sea[END_REF]. A majority of fertile polyps at the end of the experiment was a sign of a good adaptation of reciprocal and control transplants to the 30 m habitat. On the contrary, the high mortality rate at 60 m was surprising.

The factors explaining this mortality are probably of different origins such as handling during experimental set up or environmental conditions. At 60 m, the high level of sediments falling on the experimental disposal could strongly impact transplants [START_REF] Cocito | Zonation of a suspension-feeder assemblage on a temperate rocky shoal: the influence of water current and bottom topography[END_REF][START_REF] Rowley | Gorgonian Responses to Environmental Change on Coral Reefs in SE Sulawesi, Indonesia[END_REF]. Identifying their relative impacts on the mortality rates is challenging, yet the high level of sediments on experimental disposal observed at 60 but not at 30 m may be a prominent factor.

Genetic originality of the deep population of Paramuricea clavata

With nearly 50,000 SNPs genotyped, our results confirm the interest of RAD-sequencing for population genetics in non-model species. This number of loci is comparable to results with the same restriction enzyme by [START_REF] Quattrini | Phylogeography of Paramuricea: The Role of Depth and Water Mass in the Evolution and Distribution of Deep-Sea Corals[END_REF] on different Paramuricea species (almost 30,000 SNPs), and by [START_REF] Pratlong | Separate the wheat from the chaff: genomic scan for local adaptation in the red coral Corallium rubrum[END_REF] on C. rubrum with more distant populations (more than 25,000 SNPs). Within P. clavata, we observed significant differentiation between sampling sites. When considering our three replicate comparisons between depths, clustering analysis indicated grouping first by sampling area and then by depth. This is consistent with the results obtained with microsatellites by Pérez-Portela et al. (2016) focusing on populations around 50-60 m depth, and by [START_REF] Mokhtar-Jamaï | From global to local genetic structuring in the red gorgonian Paramuricea clavata: the interplay between oceanographic conditions and limited larval dispersal[END_REF] for shallower populations (mainly above 40 m). There is then a local differentiation along depth, rather than the presence of deep and shallow lineages, which would be shared among sites. The highest FST value (0.053) was observed for the comparison between the deep population of giant colonies and the nearest population at around 30 m depth (30 m). This differentiation also corresponded to the first axis of the PCA. It can be noted that with microsatellite loci, Mokthar-JamaÏ et al. ( 2011) also observed a clear differentiation with a shallow population from the Côte Bleue where the ODC site is located. The particular oceanographic conditions of the Côte Bleue [START_REF] Fraysse | Rôle du forçage physique sur l'écosystème à l'est du Golfe du Lion : modulation de[END_REF]) could lead to a relative isolation of this area explaining our results. The deep population of giant colonies (COF) therefore corresponds to a unique population well differentiated from shallower populations from the same area, as well as from the populations of the Southern Calanques area. The results of demographic inferences support this distinction with very low gene flow between COF and ODC, but higher from COF and ODC. Indeed, these populations are 4,500 m apart and currents are very low (<0.1 m.s -1 ), limiting the dispersal of planula with a short life span before their transformation into new colonies (around 48h, see [START_REF] Linares | Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis[END_REF][START_REF] Coma | Reproduction and cycle of gonadal development in the Mediterranean gorgonian Paramuricea clavata[END_REF]. This may explain the relatively high FST observed between these two spatially close sites. The observed FST between COF and ODC is also similar to the FST estimated between P. clavata populations below 50 m and above 30 m with microsatellites by Perez-Portela et al. (2016; FST 0.049-0.066). The FST observed here is nevertheless lower than most pairwise FST observed for C. rubrum in the Marseille area using RAD-sequencing (variation between 0.03 and 0.10; [START_REF] Pratlong | Separate the wheat from the chaff: genomic scan for local adaptation in the red coral Corallium rubrum[END_REF]): this would indicate either lower gene flow or higher drift for C. rubrum compared to P. clavata. For P. clavata, with respect to effective population size, the estimated value with RAD-Seq, is much higher for the shallow ODC population compared to the deep COF population. This result is in contradiction with the analyses of Pilczynska et al. (2019), who observed an increase of genetic diversity with depth, a pattern not observed here. To take demographic inference analyses further, it would be necessary to test the impact of additional connectivity with other neighboring populations, and of variations in census population size on the inferred patterns of effective population size.

Local adaptation to depth

The P. clavata populations from COF and ODC occur under contrasting ecological conditions, including different thermal regimes with higher and more variable temperatures in ODC compared to COF. Nevertheless, it can be noted that the deep population in COF was exposed to temperatures above 22°C during our survey. We could not formally test the hypothesis of local adaptation in our reciprocal transplant experiment due to the loss of the samples at the COF site. Indeed, a full test of local adaptation would require a comparison of fitness in both habitats [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF], Ledoux et al. 2015). Nevertheless, the treatment at 30 m depth including reciprocal transplantation (from COF) and control transplantation (ODC) can be considered as a common garden experiment. Interestingly, we did not observe any significant difference in necrosis rates between colonies from COF and ODC, despite the high temperatures in this shallower habitat (especially in July-August 2019). This result would indicate a lack of local adaptation, which is at odds with the differences in tolerance capacity towards warm water, observed between 20 and 40 m depth in C. rubrum (Oriol Torrent 2007, Ledoux et al. 2015) and E. cavolini (Pivotto et al. 2015). In an ex-situ common garden, [START_REF] Crisci | Regional and local environmental conditions do not shape the response to warming of a marine habitat-forming species[END_REF] observed no difference in thermotolerance between P. clavata colonies of different depths (20 m vs 40 m) from the Marseille area (Calanques). The same lack of phenotypic differences was observed in Corsica and Catalonia when comparing gorgonians populations along a depth gradient. This result could be the consequence of genetic drift blurring the result of local selection [START_REF] Crisci | Regional and local environmental conditions do not shape the response to warming of a marine habitat-forming species[END_REF]. In line with this apparent lack of local adaptation at the phenotypic level, the search for outlier loci has identified loci that confirm the geographical separation of populations, but without a clear link to environmental variables and local adaptation. Such a RAD-sequencing based approach may miss locally selected alleles (e.g., due to reduced genome representation), or may be limited especially when local adaptation is based on a combination of loci with small effect [START_REF] Hoban | Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions[END_REF].

Conservation aspects

The results of demographic inferences indicate that the deep population of giant P. clavata is isolated and unique with a reduced effective size compared to the shallower populations. Our results, and previous ones on such deep populations (Pérez-Portela et al. 2016) do not support the hypothesis of deep refugia for P. clavata, at least for the studied areas.

This also strengthens the need to protect such a deep population which is original, both from a genetic and demographic point of view. This need is reinforced by the major impact of anchoring and use of fishnets on the giant populations of P. clavata in a Marine Protected Area, with up to 100 boats per day [START_REF] Leleu | Métiers, effort and catches of a Mediterranean small-scale coastal fishery: The case of the Côte Bleue Marine Park[END_REF][START_REF] Charbonnel | Suivis de la fréquentation de la pêche récréative embarquée sur les roches du large durant la période automnale dans le Parc Marin de la Côte Bleue. Bilan de onze ans de suivis 2010-2020[END_REF], PMCB 2022) (Fig. S8). In this, the Marine Park of the 'Côte Bleue' (manager of the Natura 2000 site) has proposed a new technical disposal to the recreational fishermen in order to prevent the impact of anchoring (Fig. S8). Despite the fact that the deeper populations are less impacted by warming [START_REF] Cupido | Response of a gorgonian (Paramuricea clavata) population to mortality events: recovery or loss?[END_REF], proper management of this human activity is strongly needed to help preserve these populations [START_REF] Betti | Evidences of fishing impact on the coastal gorgonian forests inside the Portofino MPA (NW Mediterranean Sea)[END_REF]. Indeed, gorgonian forests enhance α and β diversity, and their loss replaces a structurally complex habitat dominated by long-lived species with a simplified habitat (turf forming species) with a loss of biodiversity [START_REF] Verdura | Biodiversity loss in a Mediterranean ecosystem due to an extreme warming event unveils the role of an engineering gorgonian species[END_REF], Piazzi et al. 2021).

2021.00855.CEECIND through national funds provided by FCT -Fundação para a Ciência e a Tecnologia. We also acknowledge the staff of the "Cluster de calcul intensif HPC" Platform of the OSU Institut Pythéas (Aix-Marseille Université, INSU-CNRS) for providing the computing facilities. We gratefully acknowledge Julien Lecubin and Christophe Yohia from the Informatic Service of Pythéas Institute (SIP) for their technical assistance. We are grateful to the Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.5572369328961167E12) for providing help, computing and storage resources. We thank Frédéric Austerlitz for helpful discussions on data analysis. We blocker can be installed by a person. In a first step, the mooring with the sliding buoy is droped until the anchor touch the bottom. In the next step, the mooring is pulled. When the anchor begins to take off from the bottom, the blocker is fixed on the rope keeping away the chain from the rocky substrate.

  acknowledge the use of the computing cluster of MNHN (Plateforme de Calcul Intensif et Algorithmique PCIA, Muséum national d'histoire naturelle, Centre national de la recherche scientifique, UAR 2700 2AD, CP 26, 57 rue Cuvier, F-75231 Paris Cedex 05, France). MGX acknowledges financial support from France Génomique National infrastructure, funded as part of "Investissement d'Avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09). We would also like to thank Anouchka Krygelmans for proofreading the article. LITERATURE CITED Alexander A (2020) GBS_SNP_filter v1.17. https://github.com/laninsky/GBS_SNP_filter.git Andrews S (2019) A quality control tool for high throughput sequence data. https:// www.bioinformatics.babraham.ac.uk/projects/fastqc/ Arizmendi-Mejía R, Ledoux JB, Civit S, Agostinho A, Thanopoulou Z, Garrabou J, Linares C (2015a) Demographic responses to warming: reproductive maturity and sex influence vulnerability in an octocoral. Coral Reefs 34: 1207-1216 accounting for hydrodynamics. PhD University of Pierre and Marie Curie, University of Bologna, Bologna Padrón M, Guizien K (2016) Modelling the effect of demographic traits and connectivity on the genetic structuration of marine metapopulations of sedentary benthic invertebrates. ICES J Mar Sci 73(7): 1935-1945 Pairaud IL, Bensoussan N, Garreau P, Faure V, Garrabou J (2014) Impacts of climate change on coastal benthic ecosystems: assessing the current risk of mortality outbreaks associated with thermal stress in NW Mediterranean coastal areas. Ocean Dyn 64: 103-105 Pérez-Portela R, Cerro-Gálvez E, Taboada S, Tidu C, Campillo-Campbell C, Mora J, Riesgo A (2016) Lonely populations in the deep: genetic structure of red gorgonians at the heads of submarine canyons in the north-western Mediterranean Sea. Coral Reefs, 35:1013-1026 Pey A, Catanéo J, Forcioli D, Merle PL, Furla P (2013) Thermal threshold and sensitivity of the only symbiotic Mediterranean gorgonian Eunicella singularis by morphometric and genotypic analyses. C R Biol 336(7): 331-341 Piazzi L, Atzori F, Cadoni N, Cinti MF, Frau F, Pansini A, Pinna F, Stipcich P, Ceccherelli G (2021) Animal Forest Mortality: Following the Consequences of a Gorgonian Coral Loss on a Mediterranean Coralligenous Assemblage. Diversity 13(3), 133 DOI 10.3390/d13020133 Pilczynska J, Cocito S, Boavida J, Serrão EA, Assis J, Fragkopoulou E, Queiroga H (2019) Genetic diversity increases with depth in red gorgonian populations of the Mediterranean Sea and the Atlantic Ocean. PeerJ 7: e6794 Pivotto ID, Nerini D, Masmoudi M, Kara H, Chaoui L, Aurelle D (2015) Highly contrasted responses of Mediterranean octocorals to climate change along a depth gradient. R Soc Open Sci 2(5): 140493

Fig. 1 .Fig. 2 .

 12 Fig. 1. Location of studied Paramuricea clavata populations. A: Catchoff-station A, Yeux de Chat (temperature and current monitoring, transplant experiment, density and size structure) and Catchoff-station B (density and size structure). B: Additionnal sites selected for the genomic analysis of Paramuricea clavata populations.

Fig. 4 .

 4 Fig. 4. Characterisation of Paramuricea clavata populations: size structure (up) and density of colonies (down).

Fig. 5 .

 5 Fig. 5. Survival rate of Paramuricea clavata transplants after one year of experiment. COF: Catchoff-Station A; ODC: Yeux de Chat station.

Fig. 7 .

 7 Fig. 7. Barplot of the results of the DPAC analysis for K= 4 clusters. ODC: Yeux de Chat; COF: Catchoff -Station A; GCO: Grand Congloue; IMP: Impérial du large; SPL: Sec du petit langoustier; CAA: Cap d'Arme.

Fig. S5 .

 S5 Fig. S5. Model of evolution used for demographic inferences with fastsimcoal2. This is is a model of divergence with gene flow. T_DIV indicates the time of divergence, M_ODC_COF and M_COF_ODC the gene flow (forward in time) between the two populations, N_COF and N_ODC the effective size of populations COF and ODC respectively.

Fig. S6 .

 S6 Fig. S6. Evolution of the BIC parameter according to the number of clusters for the DAPC analysis.

TABLES Table 1 .

 TABLES1 Studied sites with tasks performed. TM: temperature monitoring, CM: current monitoring, TRE: Transplant experimentation, DS: Density of colonies and size structure, G: Genomic analysis.

	94
	Area	Station	Geographical coordinates	Depth	Task
	Marseille north	Catchoff -Station A	43°17.640 N/05 11.151 E	60m	TM, CM, DS, TRE, G
	Marseille north	Catchoff -Station B	43°17.840 N/05 11.009 E	58m	DS
	Marseille north	Yeux de Chat	43°11.640 N/05°11.151 E	30m	TM, CM, DS, TRE, G
	Calanques	Grand Congloue	43°10.582 N/05°24.185 E	33m	G
	Calanques	Impérial du large	43°10.177 N/05°23.694 E	54m	G
	Porquerolles	Sec Petit Langoustier	42°59.895 N/06°09.612 E	30m	G
	Porquerolles	Cap d'Arme	42°58.692 N/06°12.250 E	54m	G
	95				32

Table 2 .

 2 Characteristics of the sampling sites for the genetic analyses. N initial, N all and N Marseille North indicate the initial number of samples, the number of samples retained for the complete analysis and the number of samples for the analysis focused on the two sites in the North of the Bay of Marseille respectively. The last columns present the estimates of gene diversity within ( 1-Qintra) and among individuals (1-Qinter), and of FIS computed with all samples and averaged over 49 215 loci. The lines with a grey background indicate the populations around 30 m depth.

	Station	Code	Sampling date	N initial	N all N Marseille North 1-Qintra 1-Qinter	FIS
	Yeux de chat	ODC	01/07/19	32	27	27	0.060	0.066	0.091
	Catchoff -Station A	COF	01/07/19	32	28	28	0.061	0.067	0.087
	Grand Congloue	GCO	30/04/19	7	6	-	0.058	0.064	0.099
	Impérial du large	IMP	30/04/19	7	6	-	0.059	0.067	0.115
	Sec Petit Langoustier	SPL	22/05/19	8	8	-	0.063	0.068	0.080
	Cap d'Arme	CAA	22/05/19	8	7	-	0.058	0.066	0.127

Table 3 .

 3 Pairwise FST for the whole dataset averaged over all loci. Grey areas highlight the comparisons between depths in the same area. ODC: Yeux de chat; COF: Catchoff -Station A; GCO: Grand Congloue; IMP: Imperial du large, SPL: Sec Petit Langoustier; CAA:

	Cap d'Arme.
		ODC	COF	GCO	IMP	SPL	CAA
	ODC	-
	COF	0.053	-
	GCO	0.046	0.053	-
	IMP	0.039	0.030	0.024	-
	SPL	0.044	0.041	0.043	0.027	-
	CAA	0.050	0.039	0.023	0.020	0.020	-

Table S1 .

 S1 Pairs of sequencing replicates (with grey background) and of samples separated by p distance similar or lower than distances observed for replicates (on the basis of 51 439 RAD loci obtained with ipyrad; see tableS3). The read numbers correspond to the number of reads after the Kraken analysis. For each sample pair, we retained the sample with the highest number of reads for further analyses. See main text for details. PCCOF: Catchoff -Station A; PCODF: Yeux de chat; PCIMP: Impérial du large; PCGCO: Grand

	Congloue.
	Sample 1	Reads	Sample 2	Reads	p distance
	PCCOF58-17	7708166	PCCOF58-17-2	6,380,908	0.018
	PCCOF58-8	6733912	PCCOF58-8-2	9,117,315	0.022
	PCODC30-25	5057861	PCODC30-25-2	4,444,312	0.027
	PCODC30-9	6613213	PCODC30-9-2	4,153,968	0.022
	PCODC30-21-2	4365254	PCODC30-5	6,556,789	0.020
	PCODC30-18	9148556	PCODC30-20-2	5,220,776	0.018
	PCIMP54-18	6264317	PCIMP54-13	5,090,699	0.020
	PCGCO33-8	6548623	PCGCO33-4	6,513,661	0.025

Table S2 .

 S2 Number of loci retained at each filtering step for all populations, with the software used at each step. The final dataset included 49,215 SNPs for 82 samples. North of the Bay of Marseille, with the software used at each step. The last two lines correspond to the dataset Mars_1 and Mars_2 respectively, with 55 samples in both

	the cases.	retained loci	software
	total_prefiltered_loci	109,1960	ipyrad
	filtered_by_rm_duplicates	109,1960 retained loci	ipyrad software
	filtered_by_max_indels total_prefiltered_loci	109,1960 757,308	ipyrad ipyrad
	filtered_by_max_SNPs filtered_by_rm_duplicates	109,1081 757,308	ipyrad ipyrad
	filtered_by_max_shared_het filtered_by_max_indels	108,7331 757,308	ipyrad ipyrad
	filtered_by_min_sample filtered_by_max_SNPs	51,439 756,695	ipyrad ipyrad
	max 75 % of missing data, 2 alleles filtered_by_max_shared_het per SNP, no LD, one SNP per locus filtered_by_min_sample	49,215 751,168 62,313	ipyrad vcftools and GBS_SNP_filter ipyrad
	max 75% missing data per locus, 2 locus alleles per SNP, no LD, one SNP per	49,215	vcftools and GBS_SNP_filter
	no missing data per locus, 2 alleles perSNP, no LD, one SNP per locus	7,653	Vcftools and GBS_SNP_filter

Acknowledgements

The project leading to this publication has received funding from EC2CO (CNRS) fund and from European FEDER Fund under project 1166-39417. The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University -A*MIDEX, a French "Investissements d'Avenir" programme. We also obtained a technical and financial support from Marine Park of the "Côte Bleue" (program GIGOR). A special thanks to the SCUBA diver team of INSU/OSU Pytheas (Deny Malengro and Sandrine Chenesseau) and Septentrion Environment for their help during the fieldwork. JBL's research was supported by the strategic funding UIDB/04423/2020, UIDP/04423/2020 and

Table S4. Demographic inferences for the ODC (Yeux de Chat) and COF (Catchoffs -Station A) populations. The model used for inferences is presented in Figure S2. The estimated parameters are effective sizes N_COF and N_ODC for COF and ODC respectively, the gene flows M_ODC_COF (from ODC to COF in forward time) and M_COF_ODC (from COF to ODC), and the divergence time (TDIV in generations). The first line presents the value estimated for the best run (over 100 replicates) for the analysis of observed data.

The following lines indicate the statistics describing the distribution of the parameters over 50 non-parametric bootstraps: median, standard deviation, minimum and maximum values. The computation was performed with a mutation rate of 2.9 x 10 -8 / site / generation as estimated in Acropora corals by [START_REF] Mao | The roles of introgression and climate change in the rise to dominance of Acropora corals[END_REF]


Hito Sepulveda

Queffeulou Pierre
email: pierre.queffeulou@ifremer.fr

Ardhuin Fabrice

Pierre Queffeulou

Assessment of SARAL/AltiKa Wave Height Measurements Relative to Buoy, Jason-2, and Cryosat-2 Data

Keywords: Radar altimeter, significant wave height, satellite, SARAL, AltiKa, validation

SARAL/AltiKa GDR-T are analyzed to assess the quality of the significant wave height (SWH) measurements. SARAL along-track SWH plots reveal cases of erroneous data, more or less isolated, not detected by the quality flags. The anomalies are often correlated with strong attenuation of the Kaband backscatter coefficient, sensitive to clouds and rain. A quality test based on the 1Hz standard deviation is proposed to detect such anomalies. From buoy comparison, it is shown that SARAL SWH is more accurate than Jason-2, particularly at low SWH, and globally does not require any correction. Results are better with open ocean than with coastal buoys. The scatter and the number of outliers are much larger for coastal buoys. SARAL is then compared with Jason-2 and Cryosat-2. The altimeter data are extracted from the global altimeter SWH Ifremer data base, including specific corrections to calibrate the various altimeters. The comparison confirms the high quality of SARAL SWH. The 1Hz standard deviation is much less than for Jason-2 and Cryosat-2, particularly at low SWH. Furthermore, results show that the corrections applied to Jason-2 and to Cryosat-2, in the data base, are efficient, improving the global agreement between the three altimeters.

Introduction Introduction Introduction

The AltiKa radar altimeter was launched on February 25th, 2013, on the SARAL mission, a cooperative project between the Indian Space Research Organization (ISRO) and the French space agency Centre National d'Etudes Spatiale (CNES). Technical description of the altimeter and of the mission can be found in Verron et al. (this issue). AltiKa is operated at Ka-band (35 GHz, 0.8 cm wavelength) a higher frequency than all previous satellite altimeters, operating at Ku-band (13,6 GHZ, 2.2 cm) and using secondary frequency at C-band or S-band to correct for ionospheric attenuation. Using Ka-band frequency enables a low ionospheric attenuation and better altitude and along-track spatial resolutions than at Ku-band [START_REF] Vincent | AltiKa: a Ka-band Altimetry Payload and System for Operational Altimetry during the GMES Period[END_REF].

Using a higher frequency, jointly with technological innovations, lead to several improvements of Altika performances, relative to conventional Ku-band altimeter, for both sea level and significant wave height (SWH) measurements (Vincent at al. 2006, Raney and[START_REF] Raney | The future of coastal altimetry[END_REF]. First, increasing the frequency bandwidth results in a better height resolution (31 cm instead of 45 cm at Ku-band) and a smaller pulse limited footprint. Second, a smaller antenna beam-width reduces the measurement footprint to 8 km in diameter, instead of 21 km. Third, a higher pulse repetition frequency (4 kHz) increases the number of individual waveforms for averaging, with improvement of SWH estimate accuracy. Fourth, the tracking performances are improved, with a resulting better monitoring of changing surface elevations, in particular at coastal transitions. Past altimeter missions have largely demonstrated the accuracy [START_REF] Queffeulou | Long term validation of wave height measurements from altimeters[END_REF], Zieger et al. 2009[START_REF] Durant | Validation of Jason-1 and Envisat remotely sensed wave heights[END_REF], Ray and Beckley 2012) and the usefulness of satellite altimeter wave height measurements in various research areas like wave observation and climate [START_REF] Cooper | The use of satellite altimeter data to estimate the extreme wave climate[END_REF]Forristall 1997, Young et al. 2011), and numerical wave modelling (Rascle and Ardhuin 2013et al. 2008, Skandrani et al. 2009[START_REF] Abdalla | Jason-2 OGDR wind and wave products: monitoring, validation and assimilation[END_REF][START_REF] Ardhuin | Observation and parameterization of small icebergs : drifting breakwaters in the Southern Ocean[END_REF].

Up to now, coastal altimeter SWH measurements were not considered because of land contamination of the altimeter signal. Up to now, very coastal altimeter SWH measurements were not considered. In coastal waters the altimeter signal is contaminated by land because of the large antenna footprint and sidelobes. Coastal measurement performance depends also on the agility of the tracker to recover the signal, at the transition from the land to the sea. The height scale of the coastal topography has a significant impact on the coastal measurement perturbation. Indeed, cComparisons with coastal buoy measurements indicate dramatic biases and errors in the altimeter measurements, relative to the coastal ones [START_REF] Shanas | Comparison of gridded multi-mission and along-track mono-mission satellite altimletry wave heughts with in situ near-shore buoy data[END_REF], and this is mainly because in such comparisons, in order to be free from land contamination, altimeter data are necessarily limited to offshore areas (at least 50 km), where sea state conditions can be very different from the coastal ones. With SARAL one can expect improvements in wave height measurement at low sea state because of better height resolution, and in coastal areas because smaller footprint and better tracking.

Nevertheless the main and serious drawback of Ka-band is a larger signal attenuation by the atmospheric vapour and liquid water content (clouds and rain). In strong rain events, the signal attenuation results in distortion of the return waveform [START_REF] Tournadre | Cloud and rain effects on ALTIKA/SARAL Ka band radar altimeter. Part I: modeling and mean annual data availability[END_REF], inducing erroneous wave height estimates.

First SARAL Intermediate Geophysical Data Records (IGDR) were made available rapidly after launch, and preliminary wave height validation studies have shown the global good accuracy of SARAL significant wave height (SWH) measurements, but have also revealed the effects of rain attenuation on SWH measurement quality (Queffeulou 2013-a). After the commissioning phase a new reprocessed data set, Geophysical Data Records in version "T" (GDR-T), was made available (GDR-T) from the beginning of the mission.

The aim of the present paper is to validate the SARAL GDR-T SWH data, firstly to assess the quality and particularities of the SARAL AltiKa measurements, and, secondly, with the goal of implementing the SARAL wave height measurements in the global merged altimeter wave height data set, developed at Ifremer. For this purpose, SARAL SWH measurements are compared with buoy data, and with altimeter measurements from Jason-2 and Cryosat-2. Section 2 describes the various data set used in the study, and the quality test processing, when needed. Section 3 shows the data screening and quality tests performed over SARAL SWH data before comparison with other sources. Results of comparisons are shown and discussed in section 4, for buoy data, and in section 5 for Jason-2 and Cryosat-2 comparisons. Jason-2 and Cryosat-2 data are from the merged SWH altimeter data base developed at Ifremer [START_REF] Queffeulou | Global altimeter SWH data set, version 10[END_REF] -described here under, and also distributed within the Globwave project (www.globwave.org). In the data base, Jason-2 data are the GDR-D provided by AVISO [START_REF] Picot | AVISO and PODAAC User Handbook. IGDR and GDR Jason Products[END_REF], and Cryosat-2 data [START_REF] Francis | CryoSat Mission and Data Description[END_REF] shown to be of good quality and consistent with other observations [START_REF] Hanafin | Phenomenal sea states and swell from a North Atlantic Storm in February 2011: a comprehensive analysis[END_REF].

To set up the merged data base, basic quality flags are tested (land, ice), and specific flags are also considered for some altimeters. After this step, many erroneous data still remain, due to land contamination, strong rain attenuation, and sigma0 blooms, for example ... The rms level depends also on SWH, due to both instrument and geophysical sea surface height variability (at the wave height scale). For a given narrow SWH bin range the distribution of the rms is not Gaussian, but the distribution of the logarithm is generally more Gaussian. This observation property can be used to estimate a maximum threshold value for the logarithm of SWH rms as the sum of the mean value and twice or 3 timesthrice the standard deviationthis factor is adjusted empirically for each altimeter -the standard deviation. From that, a maximum threshold is then estimated for the SWH rms itself. This technique was applied for the data base, producing a specific SWH rms threshold for each altimeter.

Analysis

Applying this threshold, very few isolated spurious data still remain and are eliminated using the following specific filter. For each altimeter measurement, a neighbouring data set is constructed in selecting all the along-track data over 100 km, 50 km each side of the considered point, resulting in a 14 to 17 measurement data set, depending on the satellite. Then the two extreme values of this data set are discarded for computing mean value and standard deviation. Lastly the considered measurement is tested relatively to the range defined by the mean value plus or minus four times (can be adjusted empirically, depending on altimeter) the standard deviation.

The measurement is discarded if outside this range.

After this cleaning procedure, biases and trends are analysed for the various satellites. To calibrate the data, buoy comparisons, and cross-altimeter comparisons are generally performed.

Numerical wave models are also used for quality assessment [START_REF] Abdalla | Jason-2 OGDR wind and wave products: monitoring, validation and assimilation[END_REF]. Dedicated studies proposed various altimeter SWH corrections (Zieger et al. 2009[START_REF] Durant | Validation of Jason-1 and Envisat remotely sensed wave heights[END_REF]). In the data base, specific SWH corrections [START_REF] Queffeulou | Global altimeter SWH data set, version 10[END_REF] are applied, and updated on a regular basis.

For Jason-2 (GDR-D), the correction is linear, and rather small (a few cm to 20 cm ): It was not possible to control the quality of each buoy record as the provided data were in raw format. Buoy satellite collocated pairs whose difference was outside the range defined by the mean value plus or minus twice standard deviation, were identified as possible outliers. These outliers represent about 4% of the total for both SARAL and Jason-2. This approach is helpful to discard not only data from buoys with possible instrumental errors but also data from buoys whose measurements are not representative of local surrounding wave environment, such as buoys in protected areas of a harbour. In order to understand the impact of this quality control step, the statistical parameters were calculated with and without the excluded data pairs (Table 1). A more refined approach would require to inspect the data set and to select the buoys to be excluded individually.

To check SARAL's measurement quality in coastal waters, the location of each buoy was visually inspected and then buoys were separated into coastal, those within less than 50 km from the coast, and oceanic buoys. This threshold filter is applied to the SARAL GDR-T before comparisons with buoy data, in section 4, and with Jason-2 and Cryosat-2 altimeter data in section 5. the closest approach). Individual 1 Hz altimeter wave height measurements are obtained every 7 km for SARAL and every 5.8 km for Jason-2, so within this 50 km distance range it is possible to find up to 7 and 9 individual measurements for SARAL and Jason-2, respectively. Analysis of the dependence of the bias, of the root mean square error (RMSE) and of the scatter index (SI), on the minimum number of along-track averaged observations indicates an optimalPlots (not shown) of the root mean square error (RMSE), scatter index (SI) and number of collocated data

show that these three parameters decrease with the number of along-track averaged

observations. An arbitrary trade-off between scatter and number of collocated data consists in selecting a number of at least 5 valid data for SARAL, and 6 for Jason-2.

Figure 3 shows the comparison scatter plots for SARAL (left) and Jason-2 (right). Statistics in the gure are given when the outliers (in grey) are not taken into account. The RMSE is less for SARAL (20 cm) than for Jason-2 (24 cm). Scatter indexes are almost identical (0.13) for both altimeters. The slope of the regression line is very close to the unity, for SARAL. The SARAL observed mean bias (8 cm) is slightly larger than the Jason-2 one (5 cm). The main impact of the outliers is, of course, an increase of RMSE and SI, as shown in columns W and W/O in Table 1.

The dependence of the bias and RMSE on SWH, for both SARAL and Jason-2, is shown in Above statistical results are obtained over the whole buoy data set, with no information on the quality of measurements of each particular buoy. An idea of the distribution of the quality of the various buoys can be obtained in calculating the statistical parameters for each buoy. The bias, RMSE, SI and regression slope were calculated for each buoy having more than 6 collocation data. The histograms for the four parameters are shown in Figure 5. When comparing buoy and satellite data, the different errors should be kept in mind. In particular, short wind-waves sampling error already leads to uncertainties on SWH that are larger than 10 % for 17-minute long record [START_REF] Donelan | The sampling variability of estimates of spectra of windgenerated gravity waves[END_REF]. Many other factors can be at the origin of the observed outliers. A main one is the individual buoy instrumental quality.

An other one, observed in the present data set, is the relative location of satellite track, buoy and coastal line. Some cases were observed (not shown) of along-track sampling on the other side of a small island, relative to the buoy location. The 40 Hz high along-track resolution of SARAL is an opportunity for future investigation of such cases. One second measurement cells correspond to along-track distance of the order of 7 km for SARAL, 5.8 km for Jason-2, and 6.4 km for Cryosat-2. The small size of the 1 Hz "footprint", of the order of ten to twenty km, imposes to consider a short time window, to assume stationary sea state. Enlarging the time window width also increases the impact of the error induced by the time and space variability of sea state. To compensate for that a third collocated data set is constructed in averaging along-track altimeter measurements over 50 km, i.e. 25 km each side of the crossing point. Furthermore, for better reliability, data are selected when almost all the 50 km 1

Hz measurements are valid i.e. when the number of 1 Hz valid measurements over 50 km, is equal to 6 or 7 for SARAL, to 6, 7 or 8 for Cryosat-2, and to 8 or 9 for Jason-2.

The impact of the time measurement difference is illustrated in Figure 7. showing tThe bias, the root mean square error and the scatter index for 50 km along-track averaged , SARAL Jason-2 collocated data, are shown over successive time windows, 1 hour width: time measurement differences between 0 and 1 h, 1 h and 2 h, 2 h and 3 h... up to 12 h. Each time window is one hour wide but the range of the absolute value of the difference between time measurements increases, one hour step, from 0-1h to 11h-12h. The contribution of the time variability is a clear increase of the root mean square error (circle) and of the scatter index (square). Relative to the first time window (plus or minus 1h), the RMSE increases by a factor 5 at 6 h, and 8 at 12 h. The bias (dot) is almost constant, the number of data points number (star), called data number in the following, being uniform (about 5000) over the various time windows. So, for a given space sampling (i.e. 50 km average) a minimum time difference is suitable, in conjunction with a minimum data number, to eliminate errors due to the time sea state variability.

Comparison results are shown in scatter plots of Figures 8 and9, and in Tables 2 and3, for Jason-2 and Cryosat-2, respectively.

Figure 8 shows comparison with Jason-2 corrected SWH, for 1 Hz (left), and 50 km average (right) data set. The agreement is very good, over the whole SWH range. Above 8 m For SWH larger than 8 m, the number of data points is poorsmall and the scatter is larger than at lower SWH. Averaging over 50 km (right) decreases significantly the scatter: the RMSE decreases from about 20 cm to 10 cm, and the scatter index from 7 % to 3 %; the average distance to the regression line decreases from 14 cm to 7 cm. Table 2 gives the statistical comparison parameters for the 3 collocated data sets (1 Hz and 30 min, 1 Hz and 1 hour, 50 km average and 1 hour). A first remark is that increasing the time window width from 30 min to 1 h, increases the number of 1 Hz collocated data by a factor two (two first columns in the Table 2), but has no significant impact on the results: biases, RMSE, scatter indexes, slopes and intercepts are almost the same for the two data set. This is also observed in Table 3 for Cryosat-2 comparisons.

Though the Jason-2 SWH correction is linear and relatively small, it improves the results of the comparison, for all the statistical parameters in Table 2 (for each collocated data set, the second value of the statistical parameters is given for Jason-2 corrected data), with the exception of the intercept, which slightly increases.

In summary, the SARAL SWH is in very good agreement with Jason-2. The mean bias is less than 2 cm, the root mean square errors are 21 cm and 10 cm, and scatter index 7 % and 3%, for 1

Hz and 50 km average data, respectively. The slope of the regression line is almost equal to the unity.

SARAL Cryosat-2 comparisons are shown in Figure 9. Plots are given for both corrected (right)

and non corrected (left) Cryosat-2 data, because of the non linear correction required for

Cryosat-2 at low SWH, as indicated above. At very low SWH, Cryosat-2 exhibits some negative values (Figure 9,left). This is feasible because in the waveform processing, SWH is estimated as the root square of a quantity deduced from the return waveform data fitting analysis. At very low sea state, this quantity can be negative, due to the noise. In this case SWH is calculated as the root square of the absolute value, and a negative sign is added to SWH. One of the effects of the proposed correction is to increase this negative values to positive (compare left and right graphs at low SWH). Note that when estimating the correction to Cryosat-2 (Queffeulou 2013b) the negative values of SWH were discarded from the data set, so that the proposed correction could be not be well adapted to the negative occurrences. and the scatter index from 8 % to 5%, relative to the 1 Hz data. These comparison results are a bit worse than those obtained with Jason-2.

Variation of the bias and RMSE as a function of SARAL SWH is shown for the various collocated data set, in Figure 10. Upper left graph shows the 1 Hz SARAL Jason2 comparison. The Jason-2 SWH correction (black curves) reduces the bias and the RMSE, relative to the non corrected data (grey curves), over the whole SWH range. Over the 0.5 m -6.5 m SWH range, the bias is very low. Above 6.5 m SWH, the bias increases with some scatter, that could be due to the relatively low data number small number of data points combined with a larger time variability of SWH at high sea state. The bias variation over the whole SWH range, is much less for the 50 km average data set (black circle curve in top right graph), space averaging compensating probably for the time variability. This last graph shows a very good agreement between SARAL and Jason-2 corrected data, with RMSE increasing gently from 10 cm or less, for SWH under 2.5 m, up to 25 cm for 9 m SWH.

Results are not so good relative to Cryosat-2 (Figure 10, bottom graphs). The correction reduces the bias for SWH above 1.5 m, resulting bias being less than 10 cm, and the RMSE being of the same order as with Jason-2. But at low SWH, under 1.5 m, the bias increases (the correction changes the sign of the bias) and the RMSE is also higher. This probably results from the inaccuracy of the proposed correction at low negative Cryosat-2 SWH.

The better height measurement resolution of SARAL improves the 1 Hz SWH accuracy, as

shown by the buoy comparison results in section 4, and must also have some impact on the 1 Hz SWH rms level, which depends on the instrumental noise measurement, and on the geophysical variability of SWH. Comparing density plots of (SWH,rms) pairs for SARAL (Figure 2) with similar plots obtained with other Ku altimeters (not shown here) indicates that the SARAL SWH rms level is much less smaller than for the other altimeters. This is confirmed by the plots of the 1 Hz SWH rms as a function of SWH at the collocated crossing points (Figure 11). Jason-2 and Cryost-2 exhibit much larger rms than SARAL, and particularly at low SWH, where a significant increase is observed on Cryosat-2.

----Conclusion Conclusion Conclusion Conclusion

The successful launch of the SARAL mission has raised the quality of the available satellite measurements of significant wave height (SWH), once specific flags are applied to the measurements. These flags, essentially based on the standard deviation of SWH estimated from the 40 Hz data, are designed to exclude specific events due to land or rain contamination. As expected from the high frequency in Ka-band, compared to previous satellite missions, the AltiKa instrument performs better, and particularly for lower sea states, thanks to its narrower range gates.

The buoy comparison shows that SARAL is more accurate than Jason-2 at low SWH. The root mean square error relative to buoys is about 20 cm for SWH up to 4 m, with no increase at low wave heights, contrary to Jason-2. Global regression analysis also shows that SARAL SWH does not require any correction. Considering uncertainty factors such as the buoy data quality, the relative poor sampling at high and low SWH, or impact of the outliers, present results can be considered as very good. Results from open ocean and coastal buoy comparisons show that SARAL SWH is of better quality than Jason-2, for both situations. The high 40 Hz along-track resolution of SARAL represents a real opportunity for future investigation of sea state in coastal areas.

The comparison with other altimeters confirms the high quality of the SARAL SWH. The standard deviation of the 1 Hz measurement is much less than for other altimeters, and particularly at low SWH. It also demonstrates that the corrections applied to Jason-2 and to Cryosat-2 are efficient, improving the comparison results, with the exception of Cryosat-2 at very low SWH. The Cryosat-2 behaviour at low sea state has to be investigated.

Considering above good results, SARAL GDR-T SWH data are now included in the Ifremer merged altimeter wave height data base, with no correction, but using the specific SWH flag.

The high 40 Hz along-track resolution of SARAL and the improved accuracy at low SWH represent a real opportunity for future investigation of sea state in coastal areas. The high along-track sampling enables to get measurements closer to the coast, using a running average window over less than one second of data. Nevertheless, the random nature of the specular reflection on the sea surface and the statistical definition of SWH imply that the altimeter SWH measurement is typically noisy, and 40 Hz SWH are very noisy. SARAL data will be useful for

  data are the GDR in version T (SARAL/AltiKa Products Handbook 2013), collected on the AVISO ftp, for cycle number 1 to 12, covering the 14 month time period from March 14th,

  of the data reveals that the standard deviation of SWH values over 1 s is the most relevant parameter to detect erroneous values of SWH. For most altimeters the most widely used SWH data is estimated over 1 s along-track measurements (called 1 Hz data) as the mean value of a number of individual SWH estimated from the analysis of the return waveform. The number of individual measurements over 1 s depends on the altimeter technology and processing: 20 for Jason-1&2 and 40 for SARAL. Together with the mean value, the 1 Hz standard deviation (hereinafter called the rms) is available, and is a good indicator of the SWH measurement quality.

  Figure1) is very sensitive to the anomalies, while the Jason-2 SWH rms (grey circle), at Kuband, is less perturbed thant at Ka-band.

  -2 measurements were collocated with the selected buoys data. For each buoy observation, altimeter measurements within a 30 minute time window from the buoy measurement, and 50 km from the buoy location were selected. The collocated data set is obtained by averaging the altimeter measurements, along-track over 50 km (25 km each side of

Figure 4 .

 4 Figure 4. Bias and RMSE are estimated for buoy SWH bins, 0.1 m wide. Better results are obtained with SARAL. The bias is lower for SARAL (top), for SWH less than 0.5 m. The SARAL RMSE is about 20 cm, and also much less than Jason-2 for SWH less than 1 m. The better quality of SARAL SWH is also clearly shown when the results are normalized by the mean SWH of the bin (not shown).

  are compared to Jason-2 and Cryosat-2 data, at the ground track crossing points. Three different collocated data set are analysed. The fist one consists in the closest 1 Hz measurement cells at the crossing ground tracks, within a 30 minute time window.

Figure 1 :

 1 Figure captions

Figure 2 :

 2 Figure 2: density plot (log10) of the distribution of SARAL 1 Hz SWH, SWH rms data, for 0.01 m x 0.01 m bins, over cycle number 4 to 8. Solid red line is the estimated maximum SWH rms threshold.

Figure 3 :

 3 Figure 3: SARAL (left) and Jason-2 (right) SWH comparison with buoy data. Discarded outliers in grey. Orthogonal regression line in grey, and perfect line in dashed grey.

Figure 4 :

 4 Figure 4: SARAL and Jason-2 buoy comparison. Bias and RMSE as a function of SWH.

Figure 5 :

 5 Figure 5: SARAL individual buoy comparisons. Histograms of statistical parameters: bias, root mean square error, scatter index and slope coefficient, estimated from individual buoy comparisons. Median value reported (grey) in the graph for each parameter.

Figure 6 :

 6 Figure 6: SARAL (left) and Jason-2 (right) SWH comparison with buoy data, for open ocean buoys (top) and coastal buoys (bottom). Discarded outliers in grey. Orthogonal regression line in grey, and perfect line in dashed grey.

Figure 7 :

 7 Figure 7: statistical parameters for SARAL Jason-2 50 km along-track averaged collocated data over successive time windows, 1 hour width, ranging from 0-1h to 11h-12.

Figure 8 :

 8 Figure 8 : SARAL Jason-2 SWH comparison: 1 Hz (left) and 50 km along-track average (right) collocated data within 1 hour time window.

Figure 9 :

 9 Figure 9: SARAL Cryosat-2 SWH comparison: 1 Hz (top) and 50 km along-track average

Figure 10 :

 10 Figure 10: statistical parameters as a function of SARAL SWH (0.5 m bins) for 1 Hz (left) and 50 km average (right) collocated data, for Jason-2 (top) and Cryosat-2 (bottom) comparisons. Bias (circles), root mean square error (squares), and number of data points (dots). Jason-2 or Cryosat-2 corrected data in black , non corrected data in grey.

Figure 11 :

 11 Figure 11: comparison of 1 Hz SWH rms as a function of SWH, for collocated data set: SARAL Jason-2 (top), SARAL Cryosat-2 (bottom). Mean value of rms per SWH 0.1 m bins, in grey.

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 6 :Figure 7 :Figure 8 :Figure 9 :

 12346789 Figure 1: measurements along SARAL (pass 954, cycle 2) and Jason-2 (pass 244, cycle 179) collocated tracks, between 2° S and 4° N: 1 Hz SWH, SWH rms, and SARAL atmospheric backscatter coefficient correction (shifted by 3 dB). 118x70mm (300 x 300 DPI)

  outliers. The median values are slightly smaller than the mean values reported combining all the collocated data (Figure3, Table1).Separating open ocean and coastal buoys provides interesting results. Figure6shows the scatter plots for both SARAL (left) and Jason-2 (right) comparisons with oceanic (top) and coastal (bottom) buoys. Comparison statistical results are also given in Table1. Fist, all results indicate an increase of the scatter (both RMSE and SI) for coastal buoys. Second, the RMSE is less for SARAL than for Jason-2, for both open ocean and coastal buoys. Without considering outliers, the RMSE increases from 18 cm (oceanic) to 22 cm (coastal) for SARAL, and from 21 cm to 26 cm for Jason-2.

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Only very few buoys lead to extreme results. The median value of the parameter is reported and plotted in the graphs. The median value, rather than the mean, can be preferred as it is a robust indicator, not sensitive to

  Half an hour is an usual value, as in buoy wave measurement technology. Twenty to SWH ranges, over 6 m, isare poorly sampled. To increase the size of the data set the time window width is increased up to 1 hour, producing the second collocated data set.

	Marine Geodesy
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forty minutes is a usually required buoy measurement record length to estimate sea state parameters. Yet, over the 12 first SARAL cycles (about 14 months), the resulting collocated data set consists in 2657 pairs for Jason-2 and 1625 for Cryosat-2, and low and high (over 6 m) Page 11 of 36 URL: http:/mc.manuscriptcentral.com/umgd Email: marinegeodesy@osu.edu

Table 3

 3

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shows that, globally, the correction improves the agreement between SARAL and Cryosat-2, in term of bias, RMSE and scatter index (compare the two values given for each parameter). Along-track averaging over 50 km decreases the RMSE from 22.6 cm to 13.3 cm

Page 22 of 36 URL: http:/mc.manuscriptcentral.com/umgd Email: marinegeodesy@osu.edu

	Marine Geodesy
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Table 1 :

 1 statistical parameters for SARAL and Jason-2 SWH measurements, versus buoy observations. Data number, bias (cm), RMSE (cm) and scatter index, for a) all pairs of data, b) for data pairs from coastal buoys, c) for data pairs from oceanic buoys. In each case, results are given with outliers (column W) and without outliers (column W/O).

	Page 23 of 36						Marine Geodesy
					SARAL					JASON 2
		All		Coastal	Oceanic	All		Coastal	Oceanic
		W	W/O W	W/O W	W/O W	W/O W	W/O W	W/O
	n	6718 6390 3425 3197 3194 3106 7843 7504 3727 3271 4272 4096
	bias 8	8	10	8	6	7	3	5	11	7	2	3
	rmse 30	20	34	22	23	18	38	24	44	26	28	21
	si	0.18 0.13 0.23 0.16 0.13 0.10 0.19 0.13 0.25 0.16 0.13 0.10

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Table 2 :

 2 Saral Jason-2 SWH comparison for 3 collocated data set: 1 Hz measurement cells within 30 min or 1 hour time window, and 50 km along-track averages within 1 hour time window. Data number, SWH bias, root mean square error, scatter index, orthogonal regression slope and intercept. For each parameter the second value is obtained when the Jason-2 SWH is corrected.

				Marine Geodesy		Page 24 of 36
	Comparison of Saral and Jason-2 SWH for 3 collocated data sets: 1 Hz measurement cells at
	crossover within 30 min or 1 hour time window, and 50 km along-track averages within 1 hour time
	window. Number of data points, SWH bias, root mean square error, scatter index, orthogonal
	regression slope and intercept, are given for Jason-2 non corrected (NC) and corrected (C) data.
		Crossover 30 min	Crossover 1 hour	50 km average 1 hour
	n	2657		5272		4849
		NC	C	NC	C	NC	C
	bias (cm)	5.5	-1.7	5.4	-1.8	5.7	-1.7
	rmse (cm)	20.7	19.8	21.6	20.9	11.8	10.1
	si	0.07	0.06	0.07	0.07	0.04	0.03
	slope	0.9710	0.9855	0.9739	0.9884	0.9789	0.9935
	intercept (cm) 3.25	6.07	2.51	5.32	0.90	3.68

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Table 3 :

 3 as Table2, for Saral Cryosat-2 collocated data. Comparison of Saral and Cryosat-2 SWH for 3 collocated data sets: 1 Hz measurement cells at crossover within 30 min or 1 hour time window, and 50 km along-track averages within 1 hour time window. Number of data points, SWH bias, root mean square error, scatter index, orthogonal regression slope and intercept, are given for Cryosat-2 non corrected (NC) and corrected (C) data.

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		Crossover 30 min	Crossover 1 hour	50 km average 1 hour
	n	1625		3206		2841
		NC	C	NC	C	NC	C
	bias (cm)	-8.9	-5.9	-8.5	-5.5	-9.03	-4.95
	rmse (cm)	24.7	21.4	25.6	22.6	16.2	13.3
	si	0.09	0.08	0.09	0.08	0.06	0.05
	slope	1.0250	0.9861	1.0175	0.9815	1.0063	0.9782
	intercept (cm)	2.19	9.65	3.79	10.52	7.27	11.12

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Acknowledments Acknowledments Acknowledments Acknowledments

H.H. Sepulveda has been supported by the "Universidad de Concepcion" Fellowship while at the Laboratoire d'Oceanographie Spatiale, at Ifremer. Part of this work has been supported by the Centre National d'Etudes Spatiales in the frame of the TOSCA program.