dataesr/scientific-paragraphs-categorization huggingface dataset:
SNPs were filtered using VCFtools (v0. 1. 12)  88 to include only biallelic SNPs with a quality score ≥30 and a coverage ≥4. The frequencies of these SNPs were then clustered using the Hclust function of the stats package (v4. 2. 2) in R with the completeLinkage method. The optimal number of clusters was determined with the gap statistics method  89  (fviz_nbclust function in the R package factoextra v1. 0. 7). We then compared the Bray-Curtis-derived pairwise distances (based on ITS2 type profiles) of the Cladocopium-containing Pocillopora colonies to the SNP-based clustering of Cladocopium populations. In general, we observed a high degree of overlap between the two identification methods across Cladocopium lineages. The ITS2 profile distances indicate that Cladocopium lineages which were undefined in the SNP clustering method belong to L5 (Supplementary Fig.
2b ). Finally, we extracted reads matching the non-coding region of the psbA gene (psbA ncr ) from metagenomes to assign Cladocopium lineages to Cladocopium species previously identified  29, 33. For this analysis, metagenomic reads of the 82 Pocillopora corals containing Cladocopium symbionts were aligned simultaneously on three psbA ncr sequences (C. latusorum MW819767. 1, C. pacificum MW861717, and C. goreaui KF572161. 1) with bwa-mem (v2. 2. 1)  90. Full-length reads aligned with more than 90% of identity were kept. The reads aligned on the psbA ncr reference sequence covered by the highest number of reads were selected to build a consensus sequence for each sample (samtools mpileup). We selected two psbA ncr sequences for each Cladocopium clade identified in ref. 29 and aligned them with the 82 consensus sequences with clustalW (MegaX software)  91.
Procrustean analysis of Cophylogeny (PACo) To test for phylogenetic congruence between Pocillopora hosts and Cladocopium photosymbionts we used the R package paco (v0. 4. 2) to conduct a Procrustean analysis of cophylogeny (PACo)  94. The PACo approach uses a distance-based global fit to quantify the topological congruence between two phylogenetic trees and identifies the associations contributing most strongly to the cophylogenetic structure  29. For Cladocopium, we used the SNP frequency clustering described above. For Pocillopora, we used a subsetted phylogenetic tree generated from the SVD quartet phylogeny of Hume et al. 27  generated by selecting a single representative for each of the five host lineages. The host and photosymbiont phylogenetic trees were aligned with the untangle function (step2side method) of the R package dendextend (v1.
In the first approach, we used the variancePartition package in R (v 1. 21. 2)  99, 100  to partition the variance attributable to the environment, the host genotype, and the symbiont genotype in the Pocillopora and Cladocopium expression datasets (35,066 genes for Pocillopora and 24,160 genes for Cladocopium). Prior to performing the variance partitioning analysis, a correction was applied to remove a bias due to the two different library preparation methods used for RNA sequencing following the procedure described in ref. 99. Three variables were defined as random effects: Pocillopora genetic lineage, Cladocopium genetic lineage, and the island of sampling. In order to account for any potential biases in our estimation of gene expression variance introduced by var-iancePartition's random sub-sampling of colonies we repeated the variance partitioning analysis 100 times with a random removal of two samples during each repetition.
A higher proportion of colonies correctly re-assigned to their pre-defined groups indicates a greater power to discriminate between divergent expression profiles and, therefore, a greater influence of that grouping factor on gene expression. The input for these analyses were the normalized expression data (i. e. , variance stabilized transformed counts obtained from the package DESeq2, v1. 28. 1) for all genes with 10 or more counts in at least 90% of colonies with lineage assignations (n = 80 colonies; host n = 28,972 genes; symbiont n = 20,517 genes). We began each model run with an ordination analysis to extract Principal Components (PCs) of gene expression. We then computed a-scores to determine the optimal number of PCs to retain under each clustering model for subsequent cluster identification using discriminant analysis  102.
Constrained correspondence analysis of environmental data. We also used an automatic stepwise model-building approach for constrained correspondence analysis (CCA) between the host/symbiont expression data and the environmental data in order to visualize how expression data were structured with respect to potential environmental drivers. To avoid model overfitting we first identified environmental variables that were highly correlated with one another at an r 2 ≥ 0. 7 using the corclust function of the klaR package (v1. 7. 1) in R. From this, we identified clusters of correlated variables (Supplementary Fig. 8 ) and selected one representative variable from each cluster for further analysis. We then calculated multiple regressions of each environmental variable with the CCA ordination axes of the gene expression data for both the host and photosymbiont separately using the envfit function of the vegan package (v2.
Functional annotations Pocillopora genes were obtained from the genomic reference provided in ref. 84. These annotations were obtained using the Interproscan tool for each coding sequence  105, 106. Gene Ontology (GO) annotations were attributed with the Inter-pro2GO correspondence table (v2020/06/13)  105. Functional annotations of Cladocopium goreaui genes were recovered from the published genome  32. Functional enrichment among top variant (variance partitioning) and discriminant (DAPC) genes were analyzed in the host using the goseq (v1. 40. 0) 107 package in R. For top variant genes, the number of GO annotations assigned to genes within that interest group was compared to the number of annotations assigned to the rest of the dataset, to evaluate whether any ontologies were more highly represented within the module than expected by chance (i.
e. , Fisher's exact test in goseq). Cladocopium functional enrichment analyses were performed using independent Fisher's exact tests for each Pfam (protein family) domain between genes present in each gene-of-interest category (top variant, discriminant, etc. ) as compared to all expressed genes of Cladocopium. P values were corrected for multiple testing with the Benjamini & Hochberg method implemented in the p. adjust function in R. For each Pfam term, the number of annotations assigned to genes within an interest group (i. e. , top variant and discriminant genes) was compared to the number of annotations assigned to the genome, to evaluate whether any ontologies were more highly represented within the module than expected by chance (Fisher's exact test).
Acknowledgements The Tara Pacific expedition would not have been possible without the participation and commitment of over 200 scientists, the  Tara Ocean Foundation  and its sailors, artists, staff and partners, the  R/V Tara crew , and the  Tara Pacific Expedition Participants  (https://doi. org/10. 5281/zenodo. 3777760). We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique  Tara Pacific Expedition possible: le Centre national de la recherche scientifique (CNRS) , l' Université Paris Sciences & Lettres (PSL), le Centre Scientifique de Monaco (CSM), l'École Pratique des Hautes Études (EPHE) ,  le Centre National de Séquençage -Genoscope , le Commissariat à l'énergie atomique et aux énergies alternatives (CEA),  Inserm , l' Université Côte d'Azur , l' Agence Nationale de la Recherche (ANR) ,  Agnès Troublé  said agnès b.
,  UNESCO-IOC , the  Veolia Foundation , the  Prince Albert II de Monaco Foundation ,  Région Bretagne, Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm ,  France Collectivités ,  Fonds Français pour l'Environnement Mondial (FFEM) ,  Etienne Bourgois , and the  Tara Ocean Foundation  teams. Tara Pacific  would not exist without the continuous support of the participating institutes. The authors also particularly thank  Serge Planes ,  Denis Allemand , and the  Tara Pacific consortium. This project was funded by France Génomique  ( ANR-10-INBS-09 ). We also acknowledge  C. Scarpelli  for support in highperformance computing. Computations were performed using the cobalt machine at the  TGCC (CEA). This is publication #27 of the  Tara Pacific Consortium.
Data availability The genomic data used in this study have been deposited in the European Nucleotide Archive database under accession code PRJEB47249. The ITS2 data used in this study have been deposited in the European Nucleotide Archive database under accession code PRJEB52458. The metatranscriptomic data generated in this study have been deposited in the European Nucleotide Archive database under accession code PRJEB52301. The metagenomic used generated in this study have been deposited in the European Nucleotide Archive database under accession code PRJEB52368. The read count and Cladocopium metaTderived filtered SNP data generated in this study have been deposited in a Zenodo repository accessible at: https://doi. org/10. 5281/zenodo. 7398767  98. Several, publicly available genomic resources were used for mapping purposes in this study including an Enterobacteria phage PhiX174 genome (GenBank accession code NC_001422.
, 1979) , asymmetrical boudinage  (Lacassin, 1988) , delta and sigma porphyroclasts  (Hanmer & Passchier, 1991) , and lattice preferred orientation in quartz  (Gapais & Barbarin, 1986; Passchier & Trouw, 2005). Field data  (observations, measurements, samples, photos, etc. )  are managed in a relational database, DataGeol, that we specifically designed for the processing of structural geology field data. DataGeol efficiently organizes, stores, and archives the data into different tables that are standardized with vocabularies, structured with constraints (primary keys), and linked together with foreign keys. The database is coded with the Structured Query Language (SQL) through the open-source system PostgreSQL with PGadmin graphical interface, which is a powerful programming language for object-relational databases.
The use of a database and the SQL language allows interrogations and queries of the data for different applications purpose either for mapping in a GIS, structural analysis, 3D modeling, or any other export tables. Note that DataGeol is fully compatible with the vocabularies of the Open Geospatial Consortium (OGC) and the data model standard of the Darwin Core. Pfiffner et al. , (2010). HGTx, TBx, PMszx, FFx, FBx are acronyms for sites corresponding to several locations (HBT: Haut-Giffre Décollement, TB: Le Tour back thrust, PMsz: Pormenaz-Merlet shear zone, FF: Forclaz fault, FB: Finhaut back thrust). Samples and datations references are (1:  Boutoux et al. , 2016; 2: Rolland et al. , 2008 and 3: Leloup et al. , 2005). ZHe : U/Th/He technique on Zircons; Ar/Ar: 40 Ar/ 39 Ar method. Bt. : Biotite; Kf.
8a ), with transport direction towards ∼N310° (Fig. S7b ) for the bottom one and ∼N285° for the top one (Fig. S7a ). These faults most likely connect with the Haut-Giffre thrust located less than 2. 5 km away (Fig. 3 ). Because the peak is in a high-mountain area, access is quite challenging. Therefore, we acquired high-resolution drone images, to build a 3D photogrammetric digital elevation model (DEM). Fig. 8a  is a screenshot of the model. See data Availability for more information and links to visualize and download the model. Seven kilometers southwest of the Aiguille du Belvédère, another buttress of limestones atop the basement is described at the Crête Blanche de Pormenaz (site HGT7, Fig. 3a ). At this location, the Triassic sandstones rest on dark Carboniferous series and are overlain by white limestones (Fig.
Acknowledgments This study was partly supported by  INSU-CNRS  through the " SYSTER-FEI-UMR5276-Dotation Performance 2022 " and the  RGF "Alpes et bassins périphériques " from the  French Geological Survey. It forms part of the Ph. D. project of  Antoine Mercier  at  Université Claude Bernard Lyon 1 , supported by the  French Ministry of Research and Higher Education. The authors thank  B. Boulicaut  and  J. Jacquod  from the  Emosson SA company  for providing the geological sections along the water penstock pipes. We also thank  Clémentine Fellah ,  Cyril Langlois , and  Thierry Douillard  from the  INSA Lyon  laboratory that performed the EBSD experiment for the quartz LPO analysis. Finally, we thank the conservatory of natural areas (ASTER), for authorizing the drone surveying (n°DDT- 2020-1166 ) above the  Aiguille du Belvedere in the Aiguilles Rouges massif , and especially the guard  Laurent Delomez  for coming us in the field.
Data availability GeoModeller software used in this research is available for download at https:// www. intrepidgeophysics. com/product/geomodeller/ with a valid commercial or research Licence. 3D Geological models are provided in GeoModeller native format (. xml). The models are also provided in a 3D portable document format (. pdf) that can be opened with free Acrobat Reader software. See data repository. Samples are identified with a persistent identifier of the International Geo Sample Number (IGSN:https://www. igsn. org/): these numbers are indicated in the Samples. csv table in the data repository. Stereographic plots were performed using the Stereonet v. 11 programs of  Allmendinger et al. (2011)  and  Cardozo and Allmendinger (2013) , freely available at https://www. rickallmendinger.
net/stereonet. 3D photogrammetric digital elevation model (DEM) of the Aiguille du Belvédère is built from 260 shots with a spatial resolution of about 6 cm, acquired by drone, assembled with Agistoft Metashape 1. 7. 3 software. The model is visible online with annotations and detailed images in the Sketchfab platform at the following link: https://skfb. ly/o9Wsw. The high-resolution source of the model is retrievable in the data repository. All data used in this publication: location of observations, measurements, samples, GeoModels source format, and 3D photogrammetric models are stored in a repository with the following link: https://data. mendeley. com/datasets/snx9zkrvts/1.
| MATERIAL S AND ME THODS We collated data from genus-level macroalgae benthic surveys conducted by the authors (Supporting Information: Data Sources) from 1205 individual tropical and subtropical coral reef study sites across the Indian and Pacific Oceans between 2004 and 2020, covering a period of 16 years. This dataset provides a snapshot of each site at a single time point and we did not investigate temporal changes in macroalgal communities. As discussed below, we considered temporal and methodological variables to account for differences across sites and surveys (Table  S1 ). Of the 1205 sites, 1145 identified all macroalgae to the genus level, while 60 surveys only identified macroalgae of the genus Halimeda. We did not include these Halimeda-only surveys in the investigation of community drivers of macroalgae, but we included them in the genus-specific analyses described below.
All statistical modeling, figures, and plots were done using R Statistical Software version 4. 4. 1 (R Core  Team, 2021) , R Studio version 2021. 09. 0 Build 351 (RStudio Team, 2020), the R package ggplot2  (Wickham, 2016) , and InkScape (Inkscape Project, 2020). The R code is available on GitHub (https://github. com/secan no/Canno netal 2023_Macro algae). We created the map in QGIS version 3. 24 (QGIS Development Team, 2022) using a base map from OpenStreetMap (OpenStreetMap Foundation, 2021). To limit the ability of confounding factors to obscure potential relationships between macroalgae and local human disturbance, we identified and calculated 45 site-specific variables (in addition to 15 variables representing human disturbance), representing drivers known to influence macroalgae growth and distribution at multiple resolutions.
These variables encompassed eight categories: connectivity with other reefs, heat stress, human disturbance, methodological and site descriptive variables, net primary productivity, seasonality, storms, and wind and wave exposure. Because the estimates of these variables cover a wide geographic area, we conducted the analysis for the entire dataset, and also separated the sites into marine biogeographic realms  (Costello et al. , 2017)  to test whether the macroalgae communities within realms were explained by different variables. One of the realms, the offshore Indian Ocean, included just 12 sites, which we added to the Indo-Pacific Seas and Indian Ocean realm. We also considered data contributors and the survey methodologies as explanatory variables to account for differences in site selection and sampling methodologies.
Due to spatial constraints, we are unable to detail the methods and justifications for each of the 60 explanatory variables that we considered in the main text of the manuscript. Instead, Table  S1  contains a table describing each variable, including its definition, source, spatial resolution, and justification for inclusion in the analysis. We assessed multicollinearity during variable selection at two steps in the analysis. First, we used the R package Hmisc  (Harrell, 2021)  to calculate the Pearson's r correlation coefficients for all possible pairs of variables and eliminated any with r correlation values of greater than 0. 7 within each of the eight covariate categories. When multiple variables were correlated within a given category, we chose those with the lowest summed Pearson's r coefficient, eliminating 33 variables (Table  S2 ).
Then, following  Borcard et al. (2011) , to select variables that best explained the macroalgae community compositions, we conducted CCA and stepwise variable selection using the R package vegan  (Oksanen et al. , 2020)  for all sites combined and independently for each of the six realms. We addressed multicollinearity in this second step by eliminating any variables with a variable inflation factor (VIF) > 10 (Table  1 ;  Borcard et al. , 2011). We detail the variables selected by the CCAs and their VIF in Table  S3. Using the R package vegan  (Oksanen et al. , 2020) , we ran SIMPER (999 permutations) to identify the macroalgae taxa driving differences across biogeographic realms. We also identified variables with strong correlations to macroalgal community composition with principal component analysis and by fitting seven PERMANOVAs: one for all the data combined and for each of the six realms independently.
Each PERMANOVA included the variables selected by the CCA, excluding those with VIFs >10 (Table  S3 ). Last, we evaluated how local human disturbance, represented by variables detailed in Table  S1 , influenced the distribution of the most common genera of macroalgae. To estimate the effects of five categories of local human disturbance on the most common macroalgae taxa, their divisions, and for all macroalgae combined, we fit generalized linear mixed models using the R package glmmTMB  (Bolker et al. , 2009)  for the following equation: We considered each of the five human disturbance variables fixed effects, the interaction between latitude and longitude a random effect to account for spatial autocorrelation across sites. The most common genera were defined as those comprising more than 1% of the total macroalgae cover, either across the entire dataset or within one of the realms.
We used diagnostic plots to test for normal distribution and equal variance of residuals with the R package DHARMA  (Hartig, 2022) , and Moran's tests to test for spatial autocorrelation with the package spdep  (Bolker et al. , 2009, Supporting Information S4). We also calculated the R 2 values (marginal R 2 , which represents only the fixed effects, and conditional R 2 , which measures the fit of the entire model) using the Nakagawa method  (Nakagawa et al. , 2017). Finally, to enable comparing the model results across taxa, we used the R package ggeffects to calculate the adjusted marginal effects for each of the explanatory variables  (Lüdecke, 2018).
This indicates that taxon-specific responses to individual human pressures should be considered when evaluating local anthropogenic impacts on coral reefs. The individual traits of the macroalgae genera may explain their relationships with the various disturbance metrics, each of which represents a different form of localized disturbance. The genus Halimeda was present at almost 70% of the sites and was the most common macroalga in our dataset. As the most common calcifying alga on tropical reefs globally, Halimeda, produce sediment on coral reefs and play an important role in reef accretion  (Hillis-Colinvaux, 1980). Our results suggest that Halimeda cover will increase with increasing cumulative human impacts but will decline with increasing market gravity and nutrients from agriculture.
, 2019; McCook, 1999; Vroom, 2011)  with potentially negative implications for management  (McCook, 1999; Vroom, 2011) , and that has criticized the widespread reliance on macroalgae as an indicator of reef health or degradation  (Bruno et al. , 2009; Vroom, 2011). Despite these critiques, researchers and managers continue to use total macroalgae cover to provide proxy estimates on the health of coral reefs and how they are affected by people  (Bruno & Valdivia, 2016; Smith et al. , 2016). A key limitation to this and other research on macroalgae distribution  (Keith et al. , 2014; Tebbett et al. , 2023)  is the lack of available survey data identifying macroalgae at the genus level. Most of the survey data we analyzed were collected to investigate the status and/or health of coral reefs, and site selection may have excluded parts of the reef with higher macroalgae cover.
In addition, the sampling was uneven across realms, and the reliance on largescale, low-resolution global databases to calculate site-specific independent variables may have affected our ability to account for local drivers of macroalgal communities because of differences in scale. Despite these limitations, this study demonstrates that the links between macroalgae cover and human disturbance are uncertain, which undermines the usefulness of total macroalgae cover as a way of estimating local, human-driven degradation. Strategic management of coral reefs is increasingly vital as the climate continues to warm  (Darling et al. , 2019). Evaluating how coral reefs are being affected by disturbance is an indispensable part of research and management, but the most common metrics used in that work are based on an oversimplified and poorly tested paradigm.
Pseudomonas aeruginosa clinical isolates encode an abundance of ICEclc-like elements To describe the distribution of ICEs and, in particular, of ICEclc-type elements, we analyzed 181 P. aeruginosa genomes, obtained from strains isolated between 2003 and 2022 in Switzerland and France from the Lausanne University Hospital (CHUV), Geneva University Hospital (HUG), University Hospital of Montpellier (CHU Montpellier), and Besançon Regional University Hospital Center (CHU Besançon). The strains were isolated either from the hospital environment (e. g. , contaminated hand soaps, door handles) or from infected patients (Table  S1 ). Draft genomes were constructed by de novo assembly followed by reference-guided contigs scaffolding and patching (deploying the reference P. aeruginosa strain H26027 genome; NCBI accession number: CP033684).