dataesr/scientific-paragraphs-categorization huggingface dataset:
Influence of temperature regime on evolutionary trajectories To investigate whether temperature (as a prevalent stressor of the coral metaorganism) was a potential driver of species divergence pattern in Pocillopora and Porites, we assessed whether SNPs that aligned with historical temperature regimes exist. Indeed, we could identify SNPs in Pocillopora and Porites whose local allelic frequencies could be predicted by historical temperature regime (i. e. , temperature outlier SNPs; Pocillopora -38,229 out of 461,989 SNPs (8. 27%); Porites -6,441 out of 57,966 SNPs (11. 11%); at q value <0. 1 from the unliked dataset, see Methods; Figure  6 ). To determine which of these SNPs were potentially involved in species divergence patterns we queried which of the SNPs originated from a set of genomic islands of differentiation (GID) defined as the 1% of the 500 bp bins with the highest mean F ST value among SSH for Pocillopora and Porites.
Reproductive mode could explain the differences in connectivity we observe here but has not been characterized for the corals sampled. Further, generalizations based on other members of the respective genera are difficult, as Pocillopora and Porites both contain species that broadcast spawn and internally fertilize (brood), with some species exhibiting locationdependent reproductive modes  (Brown et al. 2020; Richmond and Hunter 1990; Fadlallah 1985; Sier and Olive 1994; Stimson 1978; Schmidt-Roach et al. 2012). Independent of identifying a causal factor for differences in connectivity, it is clear that species-and even population-specific characterizations of connectivity are a necessity in designing effective strategies (and conservation areas/zones) for genetic diversity maintenance. Our dataset comprising samples of multiple species of Pocillopora and Porites across the same basin-wide range has enabled us to demonstrate contrasting evolutionary histories in these corals, despite exposure to the same past thermal, and arguably environmental, regimes.
SNP calling and filtering For Pocillopora and Porites colonies, we identified a set of genome-wide single nucleotide polymorphisms (SNPs) by mapping the metagenomic reads to the Pocillopora meandrina and Porites lobata genomic references generated by  (Noel et al. 2022 ) using the Genome Analysis Toolkit program (Van der Auwera and O'Connor 2020) (GATK, v3. 7. 0). We followed a modified version of the best practices protocol for variant discovery with GATK with manual filtering of the resulting variants. The following protocol was carried out for each species independently. We aligned Illumina-generated 150-bp paired-end metagenomic reads sequenced from each colony to the predicted coding sequences of the Pocillopora meandrina and Porites lobata host reference genomes using Burrows-Wheeler Transform Aligner (BWA-mem, v0.
7. 15) with the default settings (H. Li and Durbin 2009). A read was considered a host contig if its sequence aligned with ≥ 95% sequence identity and with ≥ 50% of the sequence aligned. Host-mapped reads were sorted and filtered to remove sequences which contained > 75% of low-complexity bases and < 30% high-complexity bases using SAMtools v1. 10. 2 (Heng  Li et al. 2009)  with the resultant bam files visualized using the Integrated Genomics Viewer  (Robinson et al. 2011). The reference genomes were indexed with picardtools' v2. 6. 0 (https://broadinstitute. github. io/picard/) CreateSequenceDictionary before performing local realignment around small insertions and deletions (indels) using GATK's RealignerTargetCreator and IndelRealigner to reduce false positive variant identification and represent indels more parsimoniously.
After sample pre-processing, we called DNA variants (SNPs and indels) individually for each colony (GATK, HaplotypeCaller), generating one Genomic Variant Call Format (GVCF) file per sample. We then combined all per-colony GVCF files for a given island (ca. 9 files per island) into a single, multi-sample GVCF file  (GATK, CombineGVCFs). This resulted in the generation of 11, island-specific combined GVCF files for each species. We then consolidated these 11 multi-sample GVCFs into a GenomicsDB database which allowed for subsequent variant calling across all island cohorts through a joint genotyping analysis (GATK, GenotypeGVCFs). Because the GenotypeGVCFs tool is capable of handling any ploidy level (or mix of ploidies) intelligently, we did not specify ploidy level in our function call. This analysis resulted in the generation of a single, "raw" Variant Call Format (VCF) file for each species in which all colonies were jointly genotyped.
Because well-curated filtering resources necessary for GATK's Variant Quality Score Recalibration (VQSR) tool were not available for either coral species, we filtered the raw VCF files manually using VCFtools  (Danecek et al. 2011)  v0. 1. 12 keeping only biallelic sites with minor allele frequencies ≥ 0. 05, site-quality scores ≥ 30, and no missing data across colonies. This produced the "linked" datasets (5,937,714 and 1,971,638 variant sites with minimum read depth coverages ≥ 16 and 17 for Pocillopora and Porites, respectively) from which the SNPs (i. e. , discounting indels) were used for the identification of genomic islands of differentiation and of the genomic signature of selection. In order to avoid variant site linkage effects in downstream genetic analyses, we additionally filtered variants on linkage disequilibrium.
We used the 'prune' add-on feature in BCFtools  (Danecek et al. 2021; H. Li 2011)  v1. 11 to discard variants with a linkage disequilibrium (r 2 ) ≥ 0. 2 in a window of 1,000 sites. This resulted in the "unlinked" datasets  (347,243 and 183,222  variant sites for Pocillopora and Porites, respectively). The SNPs from this curated set were used to identify Secondary Species Hypotheses (SSH) using individual based phylogenies, genetic hierarchical clustering and species tree, and to test for population structure, admixture and Genotype Environment associations, as described below. For the detection of clonal lineages, we had to further reduce the size of these unlinked datasets to avoid unreasonable computing times. This was performed in Pocillopora by first further filtering on linkage disequilibrium (r2 ≥ 0.
02) by BCFtools to a 27,382 variant site dataset for a first round of clone detection, and then by partitioning the individuals by SVDquartet lineages (see below). The coalescent testing of SSH needed a further size reduction of the datasets, again on linkage disequilibrium and keeping only one individual per putative genetic lineage per island (two replicates of 25 individuals and 636 variant sites, and of 24 individuals and 1,032 variant sites for Pocillopora and Porites, respectively). Because no reference genome was available at the time for SNP calling in Millepora cf. platyphylla, a 'target gene' approach was followed. Trimmed RNA-seq metatranscriptomic sequences were subjected to de novo whole transcriptome assembly using Trinity  (Grabherr et al. 2011) , after being further reduced to 23,755,336 reads by in silico normalization.
Trinity assembly produced 302,299 transcripts, which were then clustered into 40,560 unigenes (i. e. uniquely assembled transcripts) with N50 of 1,053 bp. Based on BUSCO, the assembled transcriptome was highly complete with 85. 6% of the ortholog genes from the Eukaryota database being present with low fragmentation (3. 6%), missing (10. 8%), and duplication (16. 7%) metrics. Within this transcriptome we identified Millepora orthologs for a set of arbitrary stress and/or environment response genes from various cnidarian: stress response genes from scleractinians and actinarians, or genomic fragments from Pocillopora and Porites that contain temperature outlier SNPs obtained within the present study. The Millepora orthologs were obtained using QuickParanoid that takes a collection of files produced by InParanoid  (Sonnhammer and Östlund 2015)  as input and finds ortholog clusters among multiple species.
The protocol used strictly followed the instructions published in http://pl. postech. ac. kr/QuickParanoid/. Briefly, for a given set of "target genes", QuickParanoid first preprocesses each InParanoid output file by using Blastall and then computes ortholog clusters. As for the previous coral genera, metagenomic reads were aligned on this set of target genes using Burrows-Wheeler Transform Aligner (BWA-mem, v0. 7. 15) with the default settings  (Li and Durbin 2009). Host-mapped reads were then sorted and processed using SAMtools v1. 10. 2  (Li et al. 2009 ) to generate respective bam files and SNPs were identified as previously with GATK. The list of orthologs for the Millepora transcriptome contigs that contained SNPs is part of the resource files and scripts deposited in  (Hume et al. 2022 ).
The SNP dataset was then filtered for biallelic status, SNPs only, quality, missingness and minimum allele frequency with the same thresholds as for the other corals resulting in a final set of 446 variant sites. One individual with an excess of missing data (I09S03C026) was not included in the sNMF analyses. Filtering on linkage disequilibrium (r 2 >0. 2) for further genetic analyses reduced this dataset to 243 'unlinked" variant sites in 56 individuals.
Parallel computing on 50 threads was performed to analyze all possible quartets and 100 bootstrap replicates. The bootstrap consensus tree was then transferred to Mega X for graphical annotation. To select which of the SVDquartet clades formed pertinent SSH, a hierarchical genetic clustering was also performed using the program sNMF  (Frichot et al. 2014)  with the snmf function from the LEA (Frichot and François 2015) R package. However, as this analysis is sensitive to clonality (B. Porro pers. comm. ), we first identified clonal lineages in the Pocillopora, Porites and Millepora "unlinked" datasets using the R package Rclone  (Bailleul, Stoeckel, and Arnaud Haond 2016)  to identify the pairwise threshold distance between clonal individuals, and used mlg. filter function from the R package poppr  (Kamvar, Tabima, and Grünwald 2014; Kamvar, Brooks, and Grünwald 2015)  to assign individuals to clonal, or, more properly, multilocus lineages (MLL).
We ran this clonal analysis on all samples for each of the 3 genera. In Pocillopora and Porites, due to a considerable lineage-correlating variation in pairwise distances, we also ran further analyses on SVD-grouping-defined subsets of samples. The subsequent sNMF analyses were performed keeping only one ramet per clonal genet with the exception of the 3 M. intricata samples that were identified as being a single MLL. sNMF determines the optimal number of ancestral populations from which the actual dataset could be issued through admixture. These analyses were performed on 50 cores and 10 repetitions for a number of possible ancestral populations that varied from 1 to 20, with an alpha parameter value of 10. The best number of ancestral clusters was determined by the entropy criterion and a bar chart representing the individual admixture coefficient from each of these ancestral clusters was produced.
To test for possible sub clustering, a second round of snmf analysis was performed separately on each of the ancestral clusters identified in the first round (with the same analysis parameters). As a further visualization of the genetic diversity within each of the 3 genera, we performed principal component analysis (PCA) based on individual genome wide genotypes using the glPca function of the adegenet R package  (Jombart and Ahmed 2011; Jombart 2008). For Pocillopra and Porites, sNMF clustering and SVDquartet species tree topology then guided the construction of secondary species hypotheses to be tested by coalescent analysis using Bayes factor delimitation with genomic data (BFD*)  (Grummer, Bryson, and Reeder 2014; Bryant et al. 2012). BFD analyses were not conducted in Millepora due to the low number of SNPs.
Morphology analysis To investigate the degree to which morphology may support or confound the sofar geneticsbased SSH we categorized the samples into morphotypes through morphological analysis and contrasted these groupings with the genetic resolutions. The M. intricata samples were excluded from the analysis. Photographs of entire colonies were taken for all samples using an underwater camera with 20 x 20 cm quadrats as a scale (photographs available online at https://store. pangaea. de/Projects/TARA-PACIFIC/Images/). Collection of morphological parameters was conducted using Imagej  (Schindelin et al. 2012)  , the image annotation plugin objectj (https://sils. fnwi. uva. nl/bcb/objectj/), and customs macros with the photos corrected for perspective using the "interactive perspective" plugin with the quadrat as a reference.
The Gini coefficient was tested to measure the contribution of morphological variables in the homogeneity of the nodes of the decision trees. The proximity matrix was used as input to generate a principal coordinate analysis ordination (PCoA; stats::cmdscale) to visualize variance in morphology across the samples in accordance with the genetic lineage assignments. Additionally, PERMANOVA (pairwiseAdonis::pairwise. adonis2; 999 permutations; p-adjusted Bonferroni correction applied; https://github. com/pmartinezarbizu/pairwiseAdonis) was used to test whether variance in morphology could be significantly explained by the genetic delineations.
Among and within species genetic differentiation To further resolve evolutionary history in the Pocillopora and Porites species designations, we investigated introgression and pairwise divergence for the SSH. Following  Louis et al. 2021 (Louis et al. 2021) , introgression among lineages was computed with TreeMix  (Pickrell and Pritchard 2012). The unlinked SNP dataset vcf was converted to TreeMix format using vcftools  (Danecek et al. 2011 ) and PLINK  (Purcell et al. 2007 ). The individuals were regrouped in populations according to their SSH and islands of origin, and the optimal number of admixture events was estimated at this level. For this we ran TreeMix 10 times for 0 to 10 events and estimated the optimal number of events using the optM R packages  (Fitak 2021). We then ran TreeMix 100 times for 0 to this optimal number of admixture/introgression events to produce a consensus tree and bootstrap values using the BITE R package  (Milanesi et al.
2017 ). The residual covariance matrix was estimated for the optimal number of admixture/introgression events and the consensus tree using TreeMix. The occurrence of the admixture/introgression events were verified by computing the relevant f 4 indices  (Reich et al. 2009 ) and correlation in genetic content was further quantified by calculating pairwise f 2 distances both using the AdmixTools2 R package (https://uqrmaie1. github. io/admixtools/index. html) after converting the unlinked SNP dataset vcf to the BED format using vcftools  (Danecek et al. 2011 ) and PLINK  (Purcell et al. 2007 ). To quantify and characterize genomic divergence between the SSH within each of Pocillopora and Porites we computed pairwise Weir and Cockerham  (Weir and Cockerham 1984 ) F ST along the genome (500 bp sliding window) with vcftools  (Danecek et al.
Correlation of genetic lineages with geographic distance and historical thermal regime To assess for isolation-by-distance and a possible effect of temperature regime on the evolutionary trajectories of the species, we conducted correlation analyses sensu  Rousset 1997 (Rousset 1997). For Pocillopora and Porites, samples were again grouped according to their SSH and islands of origin (i. e. SVD4_I01, SVD4_I02… etc. ) while Millepora samples were grouped according to island of orgin. Within each genus, pairwise group F ST distances were generated using vcftools  (Danecek et al. 2011 ) and used to generate pairwise matrices of F ST /(1-F ST ). These distances, representative of genetic dissimilarity, were then tested for correlation against distance matrices representing geographic and historical temperature (environmental) distances using the mantel function from the vegan R package  (Oksanen et al.
2020). Geographic distances were based on GPS coordinates of islands (estimated by an average of the site coordinates). Euclidean historical temperature distances were generated from a multiparameter historical temperature dataset collected for each site as part of the Tara Pacific consortium (as detailed in  Lombard et al. 2022 )  (Lombard et al. 2022 ) and used in a sitewise manner after conducting PCA to reduce its dimensionality from 63 initial parameters to the 5 highest scoring PCs that cumulatively accounted for >80% of the observed variance (using R package FactoMineR)  (Lê, Josse, and Husson 2008).
Effect of environment on SSH divergence To assess the influence of environment (using historical temperature as a proxy) on divergence between the SSH in Pocillopora and Porites we first used the reduced dimension historical temperature dataset (see above) to identify temperature outlier SNPs whose allelic frequencies were linked to historical temperature variation using redundancy analysis (RDA) following the exact protocol of  Capblancq and Forester (Capblancq and Forester 2021). For this, the unlinked SNP dataset had first to be converted to ped format using vcftools. From this RDA, we identified temperature outlier SNPs, i. e. the SNPs whose allelic frequencies distribution among populations were best explained by the local historical temperature variation, as the 100 SNPs with the associated lowest p-values (respectively p < 2.
15 10 -4 in Pocillopora and p < 1. 72 10 -3 in Porites). We then defined a set of genomic islands of differentiation (GID) as the 1% of the 500bp bins with the highest mean F ST value after conducting a further pairwise sliding window F ST analysis as detailed above but with samples grouped according to SSH for each of the genera. We compared the proportion of these temperature outlier SNPs that were found in GIDs (see above) between the two genera. Finally, we investigated the proportion of the temperature outlier SNPs, and in particular those located in GIDs, that were predicted to be under divergent selection using the program Flink  (Galimberti et al. 2020). Flink operates on a dataset of linked loci and is based on the F-model which measures differences in allele frequencies (F ST ) that are partitioned into locus-and population-specific components reflecting selection and drift, respectively.
Flink structures samples in a hierarchical model with the level of 'population' nested in 'group' and 'group' nested in 'higher hierarchy'. We equated 'group' to the SSH, and 'population' to island of collection for the samples and generated allele counts from the 'linked SNPs' dataset as input using ATLAS  (Link et al. 2017). To reduce computational complexity, the target genome can be subdivided with individual Flink analysis run across each subdivision  (Galimberti et al. 2020 ). However, each subdivision must have a suitable number of variant sites to enable effective inference of demographic parameters with 10,000 suggested as a minimum by the authors. As such we first ran one instance of Flink for every contig in the Pocillopora and Porites genomes with at least 10,000 variants (subsampling using BCFtools  (Danecek et al.
2021)  v1. 9) using 4 cores per instance and a total of 800,000 MCMC iterations (300,000 burnin). Analysis showed that the demographic parameters converged at similar values across contigs within each of the respective genomes. To identify loci under selection in the remaining contigs (i. e. those with < 10,000 variants) we therefore ran one instance of Flink per contig with parameters fixed according to the converged demographic parameters of the previous runs. Contig-wise results were consolidated and loci under divergent selection at the higher hierarchy level (i. e. between lineages; output file 'Posterior_A. txt') were assessed by calculating a false discovery rate (FDR) from the posterior alpha distributions following the tool's documentation using a custom python script. The Nextflow (Di  Tommaso et al.
Zenodo deposition (Hume et al. 2022). We would like to thank  Hans-Joachim Ruscheweyh  as well for lending us computing time at  ETH  for some of the BFD* replicate runs. Work from  BP ,  ER , DF is supported by the  French Government (National Research Agency, ANR)  through the grant " Coralgene "  ANR-17-CE02-0020  as well as the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-0001. The Tara Pacific expedition would also not have been possible without the participation and commitment of over 200 scientists, sailors, artists and citizens (see https://zenodo. org/record/3777760#. YfEEsfXMLjB).
Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.
, 2019). Of the 57,501 markers from the original chip, 50,820 are uniquely localized on the Swanson reference genome  (Pearse et al. , 2019) , and in the remaining number, only 38,332 markers pass the control quality filters [no primer polymorphism, call rate > 97%, Minor Allele Frequency (MAF) >0. 001 over 3,000 fish from five French lines]. To overcome these limitations as well as to get access to a more powerful tool for GWAS and population genetics studies in rainbow trout, the aim of our study was to develop a highdensity SNP array. To develop this resource for rainbow trout, we made use of a large set of resequencing data from 31 doubled haploid (DH) lines from Washington State University (WSU) and Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE).
MATERIALS AND METHODS Use of the USDA Database for Initial SNP Detection  Gao et al. (2018)  constituted a first large SNP database (USDA1) by performing high coverage whole genome resequencing (WGS) with 61 unrelated samples, representing a wide range of rainbow trout and steelhead populations. Of the 61 samples, 11 were doubled-haploid lines from Washington State University (WSU), 12 were aquaculture samples from AquaGen (Norway), 38 were from wild and hatchery populations from a wide range of geographic distribution (Califormia, Oregon, Washington and Idaho states in the United States; Canada; Kamtchatka Penninsula in Russia). Overall, 31,441,105 SNPs were identified with 30,302,087 SNPs located on one of the 29 chromosomes of the Swanson reference genome assembly (Omyk_1. 0; GenBank, assembly accession GCA_002163495.
1)  (Pearse et al. , 2019). A second database (USDA2) with 17,889,078 SNPs coming from resequencing of 24 USDA samples was added to the initial USDA1 database. The samples were composed of 12 representatives from the USDA-NCCCWA odd-year class and 12 from the even-year class as previously described (NCBI BioProject PRJNA681179;  Liu et al. , 2021). The SNP discovery analysis followed the methods of  (Gao et al. , 2018). By merging these two databases using BCFtools  (Danecek et al. , 2021) , we constituted a single USDA database that contained 35,732,342 distinct SNPs, with 34,170,401 placed on the 29 chromosomes or mitochondrial chromosome of the Swanson reference genome. SNP filtering was performed to remove non bi-allelic variants and SNPs with MAF <1% using a vcf_filter. py homemade script (Supplementary File S1).