dataesr/scientific-paragraphs-categorization huggingface dataset:
We have followed the method described by  Rudnick (1996) , using the scripts freely downloadable from his web page at the address http://chowder. ucsd. edu/Rudnick/SIO_221B. html, last access: noise-to-signal ratio is assumed to be 0. 05 as in  Rudnick (1996). The interpolated fields are shown superposed to the in situ measurements in Fig. 2. The ageostrophic component of the velocity measured by the VMADCP is then removed and Eq. (  1 ) is solved with an iterative relaxation method and constrained by Dirichlet boundary conditions (w = 0) as in the case of the front studied by  Rudnick (1996). To minimize the effect of the imposed boundary conditions, only data with an error on the objective mapping of σ ≤ 0. 0025 have been then considered. 3 Results.
Statistical analysis Surface temperature and salinity data measured with the TSG are merged with the data of abundances of the 9 different groups of phytoplankton at each cytometry sampling point along the WE and NS hippodromes. Thus, the final data set consists of 11 variables, each variable containing 215 observations. The principal component analysis (PCA) consists of summarizing the information contained in the data set by replacing the initial variables with new synthetic variables (called principal components), linear combinations of the initial variables and uncorrelated two by two. Applied to our data set, the PCA points out that the first three components explain respectively 36. 7 %, 18. 1 % and 13 % of the total variance of the data. The following statistical analysis treats these three components, representing 67.
Description of the three clusters obtained with the K-medoid algorithm and represented on Fig. 15b, c and d. The local average (i. e. the average of each variables in a cluster) have been compared with the global average (i. e. the average of each variables for all the dataset) to highlight the contribution of each variable to a cluster. Note that only the five most discriminating variables, determined with the standard deviation are shown for each cluster. Absolute salinitiy is measured in gram per kilogram, conservative temperature in degree Celsius and the phytoplankton abundances in cells per cubic centimeter. The bold lines represent the variables with the most contribution of each cluster (i. e. with local average > global average).
Acknowledgements. This work was supported by the  CNES  in the framework of the project  BIOSWOT-AdAC , and by the  MIO Axes Transverses program  ( AT-COUPLAGE ). The chlorophyll a product is produced by  CLS. The authors thank the SHOM and the crew of the  RV Beautemps-Beaupré  for shipboard operations,  J. -L. Fuda  for his help in Seasoar data treatment,  L. Rousselet  for discussion about vertical velocity estimations, and  M. Goutx  for the early discussions about the glider FDOM data. The Cytobuoy® flow cytometer was funded by the  CHROME  project , Excellence Initiative of Aix-Marseille University -A*MIDEX , a  French "Investissements d'Avenir" program. SPASSO is operated and developed with the support of the  SIP (Service Informatique de Pythéas)  and in particular  C. Yohia ,  J. Lecubin.
Code availability. The LATEXtools can be openly available: https://people. mio. osupytheas. fr/~doglioli/latextools. htm, last access: 10 February 2021 (Doglioli et al. , 2013). The script for objective mapping can be openly available on Rudnick's web page: http://chowder. ucsd. edu/Rudnick/SIO_221B. html, last access: 10 February 2021. Data availability. The data can be openly available: https://www. seanoe. org/data/00512/62352/, last access: 10 February 2021  (Dumas et al. , 2018).
, 2019). Despite sampling morphologically similar colonies in French Polynesia and Oman, we got samples which corresponded to different mitochondrial haplotypes, and species hypotheses. In Oman several mitochondrial lineages were also sampled in sympatry. We therefore applied RAD-Seq to these samples to test species limits and genetic differentiation among sampling sites. We simulated data with different levels of clonal reproduction to help in the interpretation of results obtained with RAD-Seq.
After sampling, all colonies fragments were bleached with menthol according to previously detailed protocols  (Vidal-Dupiol et al. , 2019; Wang et al. , 2012). Total genomic DNA was extracted according to the protocol of  (Sambrook et al. , 1989) , followed by a purification using Qiagen DNeasy blood and tissue spin columns (Qiagen). Genomic DNA concentration was quantified using a Qubit 2. 0 Fluorometer (Life Technologies). Table 1: characteristics of sampling sites. For the final sampling sizes depending on datasets and mitochondrial lineages, see supplementary table  S1. The thermal regime gives a qualitative indication of temperature variability at the corresponding sampling site. sequencing in both directions. The GenBank accession numbers are indicated in  Gélin et al. , 2017. Protein-coding sequences were analysed using MEGA version 6  (Tamura et al.
As a consequence we chose to use different assembling strategies leading to different datasets. Following preliminary assembly analyses, we removed individuals with less than 900 000 reads: their inclusion led to datasets with very low numbers of SNPs. The resulting dataset comprised 140 individuals: 104 from Oman and 36 from Polynesia. Then we used the module populations of stacks to assemble three datasets : 1) one considering all 140 individuals grouped by mitochondrial lineage ("All" dataset), 2) one with only samples from Oman and separation based on mitochondrial lineage, and 3) one with only samples from Polynesia and a separation based on sampling sites (apart from outgroups, all individuals from Polynesia shared the same mitochondrial haplotype; see results). At that stage, the individuals of the All and Oman datasets for which we did not get any mitochondrial sequence were put in an additional group for assembly ("unknown").
For these three datasets, we retained the first SNP of each RAD locus and we removed sites with minor allele frequency lower than 0. 01. Second we used Tassel 5. 0  (Bradbury et al. , 2007)  to filter the corresponding three VCF files according to missing data, with two strategies for each dataset : in one case we retained only loci present in at least 75 % of the individuals and individuals with data for 75 % of the loci (i. e. less than 25 % of missing data ; hereafter strategy 75-75), in the other case we retained loci present in at least 95 % of the individuals, and individuals with data for 75 % of the loci (strategy 95-75). These filtering led to the removal of the less informative samples. The 75-75 strategy allowed to retain more loci with less individuals compared to the 95-75 strategy (Table  2 ).
We used the R package poppr to analyse MLGs and MLLs  (Kamvar et al. , 2015). The choice of thresholds to delineate MLLs was made according to two criteria: first we used MLGs obtained with microsatellite loci (data not shown) for a subset of individuals to define an MLL threshold. We also used the distribution of genetic distances among individuals to look for lowly differentiated individuals that could correspond to the same MLL. The genetic distances among individuals were measured by the percentage of nucleotidic divergence, and computed with poppr. According to their respective levels of diversity, the retained MLL threshold was different for the different datasets (see results). Networks based on the aforementioned distances were built with the NeighborNet option of SPLITSTREE 4. 14.
8  (Huson & Bryant, 2006). One representative of each MLL was kept for clustering and F ST analyses. We analysed the genetic disequilibrium among loci by computing the modified index of association rd defined by  (Agapow & Burt, 2001)  with the poppr R package. To keep reasonable computing time, we first randomly subsampled the All_75_75 and Oman_75_75 to 25 000 SNPs. Then we computed rd on datasets comprising randomly subsampled 200 SNPs (this number allowed enough different resampling with the smallest dataset), and with 10 000 repetitions of this subsampling. With this approach we can analyse the linkage disequilibrium in datasets with a high number of SNPs, where pairwise methods would be difficult to analyse. To take into account the impact of Wahlund effect on this analysis, we performed it at two levels: first at the level of the whole corresponding dataset, and second at the level of mitochondrial lineages for the All and Oman datasets, or of sampling sites for the Polynesia datasets (i.
e. the "strata" levels used in poppr). Genetic differentiation among populations was measured with the F ST estimator of  Weir & Cockerham (1984)  as computed with VCFTOOLS. The differentiation among individuals was visualised thanks to a Principal Component Analysis (PCA) with the R package adegenet  (Jombart, 2008). Missing data were replaced by the mean allele frequency. As a complementary analysis to PCA, in order to identify the main genetic groups in the dataset, we analysed the partition in K independent units with the snmf function of the R package LEA  (Frichot & François, 2015). This approach performs a least squares estimates of ancestry proportions  (Frichot et al. , 2014). We tested K values from 1 to 10, with ten replicates for each K value.
Results Assignment to species hypotheses according to mitochondrial sequences and microsatellite genotypes: Out of the 140 individuals retained in the final RAD-Seq dataset, we did not get any usable mitochondrial sequence for 18 individuals (three from Polynesia and 15 from Oman). We indicate in Supplementary Table  S1  the correspondence between the nomenclature of mitochondrial lineages used in main text, the ORF haplotype number and the PSH and SSH as defined in  Gélin et al. , 2017. The sequences obtained for the other individuals allowed a clear assignment to previously defined sequence groups and corresponding primary species hypotheses. In Polynesia, two individuals sampled as outgroups on the basis of morphology were highly divergent from other ones with RAD-Seq, and corresponded to mitochondrial lineages type 1a (PSH09 and SSH09c) and type 2 (PSH01).
Genetic differences among individuals and repeated MLLs The networks based on pairwise distances among individuals are presented in Figure  1  for the 95_75 datasets which had the highest number of individuals. The corresponding histograms of the distribution of genetic distances are presented in Supplementary Material (Figure  S1 ). The All_95_75 network shows a clear distinction between Oman and Polynesia individuals, which is correlated to a difference in mitochondrial lineage: type 5a versus all other lineages, with one exception of type 5a sampled in Oman and grouping with other individuals from Oman. This distinction is also visible in the bimodal distribution of genetic distances (Figure  S1 ). In the separate analysis of Oman and Polynesia, no clear sub-grouping appeared according to sampling location or mitochondrial lineages (Figure  1 ).
Similar results were obtained for the networks based on the 75_75 datasets, except in Polynesia where we observed small clusters of individuals from the same site, albeit without clear differentiation from other groups (Supplementary Figure  S2 ). The histograms of pairwise differences among individuals in Oman, and to a lesser extent in Polynesia, showed a peak of low value distances, potentially reflecting repeated MLLs. We used the distance observed with RAD-Seq among repeated microsatellite MLGs to define MLLs in the Oman dataset: we analysed the distance among all individuals corresponding to identical microsatellite MLGs. We estimated the distance (percentage of differences) with SNPs for individuals with identical microsatellite MLG. We retained the second highest of these distance as a threshold to define MLLs with SNPs, because the highest distance among individuals with identical microsatellite MLGs seemed too high to be used to define MLLs with SNPs.
We could not use a single threshold for all datasets because the levels of divergence were very different between the datasets (95_75 vs 75_75, and Polynesia vs Oman), and we had no repeated microsatellite MLG to be used as a reference in some datasets (see above). Therefore, in cases where no threshold could be defined on the basis of microsatellites, we used a threshold allowing the removal of the closest individuals, as indicated by preliminary tests and by the observation of the distribution of pairwise distances among individuals. The number of individuals for each corrected dataset is given in Table  2 , and the corresponding thresholds are indicated below the distribution of individual distances (Figure  S1 ).
Genetic diversity and differentiation The parameters of genetic diversity for the different datasets are presented in Table  3. Separate estimates of genetic diversity per lineage or site are presented in Supplementary Material (Table  S4 ). The estimates of genetic diversity were higher in the 75_75 datasets compared to the 95_75 ones. The mean genetic diversity within individuals ('1-Qintra') varied between 0. 04 in Polynesia_95_75 and 0. 20 in Oman_75_75. The mean genetic diversity among individuals ('1-Qinter') varied between 0. 04 in Polynesia_95_75 and 0. 24 in Oman_75_75. The F IS were mainly positive in the 75_75 datasets and negative to positive in the 95_75 ones, but with important variations among lineages or sites, especially in Polynesia (Table  S4 ). The estimates of individual inbreeding coefficient F gave highly variable and extreme values (Table  3 ): the lowest minimum values were observed in the 95_75 datasets, and in Polynesia (down to -2.
733 in Polynesia_95_75), which indicated the presence of individuals with an excess of hetezygous loci. The maximum F value was observed in the All_75_75 dataset (0. 732). The distributions of the F estimates illustrate this wide dispersion, and the shift to more positive values from 95_75 to 75_75 datasets (Figure  S3 ). In the All_95_75 dataset, the F values in Oman (from -0. 278 to 0,546) were higher than in Polynesia (from -1,306 to -0. 303). We analysed whether the individuals involved in potential MLLs (i. e. individuals involved in the closest pairwise relationships in the different datasets) corresponded to particularly high or low F values (Figure  S3 ). In the All datasets, the closest individuals showed among the highest F values for the 95_75 dataset, whereas for the 75_75 dataset they also included the lowest F values.
For the Oman datasets, these individuals showed a wide range of F negative and positive values. For the Polynesia datasets, these individuals were among the highest F values. The results of the analysis of linkage disequilibrium with the rd index are presented in supplementary material (Table  S5  and Figure  S4 ). For the analyses at the level of the whole datasets, the highest rd values were obtained in the All_75_75 dataset, followed by the Polynesia_95_75 and All_95_75 datasets. The values obtained in Polynesia were higher than those in Oman for the 95_75 and the 75_75 datasets. When the analysis was performed at the level of mitochondrial lineages or sites, the highest values were observed for the MH site in Polynesia with mean values of 0. 278 for the 95_75 dataset, and 0. 235 for the 75_75 dataset (Table  S4 , Figure  S4 ).
S5. The distribution of F ST estimates was very different between the two datasets, despite a peak around 0 in both cases: for All_75_75, there was an important proportion of loci with F ST above 0. 2, and a peak at F ST = 1, whereas the distribution was mainly restricted to values below 0. 2 for All_95_75. For Oman, the 75_75 F ST estimates were slightly higher than the 95_75 ones, but in all cases (75_75 or 95_75, population or mitochondrial lineages comparisons) these estimates indicated very low levels of differentiation in Oman, with values ranging from -0. 006 (Oman_95_75 for the comparison of mitochondrial lineages, with MLL correction) to 0. 016 (Oman_75_75 for the comparison of populations, no correction; Table  4 ). The Polynesia F ST estimates also indicated very low levels of differentiation among sampling sites.
Regarding multivariate analyses, the PCA of the All_95_75 and All_75_75 datasets separated the samples from Polynesia and Oman, but with individuals from Polynesia (type 5a lineage) more spread apart than those from Oman (Figure  S6 ). The separate PCAs on the Oman and Polynesia datasets did not reveal any clear structure according to sampling site nor mitochondrial lineage, whatever the filtering strategy. The analysis of genetic structure with the snmf method did not give a clear signal for an informative K value on the basis of cross-entropy plots, apart for the All_95_75 dataset where a first minimum value was observed at K = 2 (and a second at K = 5), and for Polynesia_95_75 where a minimum was observed at K = 2 (data not shown). We also analysed the results corresponding to the number.
Simulations The distribution of the individual inbreeding coefficients F, of the indices of association rd , and of the estimates of F ST for the different simulation configurations are presented in Supplementary Material. The average and F values were higher for the simulations including selfing. The maximum F values were highly variable and tended to be higher for the configurations with the highest clonality rates (from 0. 5), but were much higher for the simulations including selfing. Regarding the minimum F value, a decrease in the distribution was observed for the highest levels of clonality compared to other configurations. When comparing these results with observed data, one should note that for the All dataset, the F estimates were based on the pool of Oman and Polynesia samples, whereas for the simulations we analysed the two simulated populations separately.
The average F obtained in the All_75_75 and Polynesia_75_75 was higher than all values obtained with simulations. Regarding the maximum F values, the highest values observed in All_75_75 and Oman_95_75 appeared only compatible with simulations integrating selfing. For the minimum F values, no configuration allowed to recover such highly negative F as those observed in Polynesia (both datasets), nor in All_95_75. We analysed the linkage disequilibrium within each of the two simulated populations with the rd index. An increase in rd was observed with increasing clonality rate, mainly for the highest clonality rate tested here (0. 9). A much higher increase both in mean and standard deviation was observed for a combination of selfing and a 0. 9 clonality rate. We compared these values to the highest observed values obtained with the 75_75 datasets.
Apart from one value in Oman, these mean observed values were similar or higher to those obtained with simulations at clonal rates of 0. 9. The standard deviation for the observed estimates were usually very high, and in most cases these values were only approached by simulations with selfing and a 0. 9 clonal rate. Regarding the average F ST , without any variation neither in census size nor migration rate, the resulting values were mostly similar among simulation configurations. A slight decrease and higher variance was nevertheless observed for the highest clonality rate (0. 9). The observed F ST for the Oman and Polynesia datasets were lower than those obtained in all simulations, while the F ST for the All datasets were higher than those obtained in almost all simulations, except a for a few simulations performed with the highest clonality rates.
Impact of filtering strategies on RAD-Seq data We obtained very uneven levels of missing data in our results. High levels of missing data, if not accounted for, can lead to incorrect conclusions regarding genetic structure for example  (Larson et al. , 2021). Missing data in RAD-Seq can have several origins including mutations in enzymecutting sites, technical problems linked to library preparation, uneven amplification or sequencing, or errors in the in silico identification of homologous sites  (Eaton et al. , 2017; O'Leary et al. , 2018). Here, the lowest read numbers were obtained for outgroup samples, corresponding here to species hypotheses PSH01 and SSH09c (as opposed to PSH05, PSH12 and SSH13a which were the most frequent in our datasets): for example seven of these samples had only around 20 000 reads or less and were not retained (data not shown).
Some results were stable among the different datasets: there was a marked differentiation between samples from Oman and Polynesia, correlated with the species sampled in each region, and a lack of genetic structure inside each region. Conversely the estimates of genetic diversity and structure differed among datasets. The genetic distances among individuals were much higher with more loci in the All and Oman datasets. If part of missing data are linked to mutations in the cutting site, filtering loci according to their rates of missing data is expected to reduce the frequency of loci with high mutation rates  (Huang & Knowles, 2016) , which would agree well with our observation of a lower diversity with more stringent filtering. Allele dropout, which corresponds to the non-observation of a SNP linked to a mutated restriction site, should be more frequent in the 75_75 datasets (with low filters on SNP missing data).
Allele dropout is expected to lead to an overall underestimate of genetic diversity, increasing with the level of polymorphism  (Arnold et al. , 2013; Cariou et al. , 2016). Contrarily to these expectations, we observed more diversity with less filtering on SNPs. For the retained variable loci, an overestimation of heterozygosity can be expected with allele dropout if they concern more ancestral allelic states and therefore lead to an increase in minimum allele frequencies  (Gautier et al. , 2013) ; accordingly we observed an increase in heterozygosity for the 75_75 compared to the 95_75 datasets. We also observed an increase of x for the 75_75 compared to the 95_75 datasets, but the reason of this effect remains to be studied: one possible explanation could be a difference in the sampling of clonal lineages depending on datasets (see below).
Finally, allele dropout can overestimate F ST  (Gautier et al. , 2013)  which seems coherent with our results (with higher F ST for the 75_75 compared to the 97_75 All datasets). Regarding linkage disequilibrium, the increase in rd observed for All75_75 compared to All_95_75 can result from a combination of higher Wahlund effect (with the inclusion of more differentiated loci), and of a higher number of physically linked loci. Importantly, the potential impact of missing data and allele dropout on our results is potentially blurred by the fact that these filtering led to different numbers of retained individuals. Retaining less variable loci in the 95_75 datasets may have favored the retention of less divergent individuals which can impact estimates of relatedness or clonality. Another question is whether the variance of polymorphism among loci is high enough to explain the observed differences through the aforementioned effects.
, 2003). Though, the scope of our simulations is limited on several points. First, we did not explore the impact of sampling scheme on the estimates of genetic diversity and genetic structure. Second, Pocillopora corals show overlapping generations, and a given clone may persist over several generations, which was not possible in our simulation framework. Third, selective effects can lead to the expansion of one clone (see  Gélin et al. , 2017)  and references therein), and modify the distribution of clones. These limits evidently hinder estimates of clonal rates based on these simulations. This would require simulations taking into account these limits, for example coupled with Approximate Bayesian Computation  (Csilléry et al. , 2010). We should also take into account the robustness of these results to varying sample sizes: for example rd is based on the pairwise distances between individuals, therefore it may be sensitive to low sample sizes (as was the case here for some empirical datasets).