DOI: 10.5061/dryad.931zcrjxv
title: Data and analysis scripts to accompany: Coral genetic structure in the Western Indian Ocean mirrors ocean circulation and thermal stress history

description: With ever-growing concerns over the conditions of coral reefs under
 warming oceans comes a need to better understand the connectivity and
 adaptive capacities of reef-building corals from across large oceanic
 regional extents. This dataset accompanies the published article by
 Guillaume et al (2026) in Evolutionary Applications, where we applied a
 seascape genomics approach to model (i) population connectivity and (ii)
 thermal adaptive potentials for two keystone coral species across the West
 Indian Ocean (WIO). Specifically, we sampled 345 Acropora
 muricata and 403 Pocillopora damicornis individuals
 around islands of three WIO regions–Seychelles, Mauritius, and
 Rodrigues–as part of a UN development programme (UNDP; funding information
 below). Genomic data were obtained from DNA extracted from sampled coral
 tissue and sequenced with DArT-seq. We used a bioinformatic pipeline to
 process and filter reads, which were then used to assess population
 structure and connectivity, identify putative genomic regions under
 thermal selection, and produce  maps of adaptive potential across
 the WIO. This repository hosts one pdf per species that steps the user
 through all the main analyses of the publication. These pdfs have been
 produced using the Rmarkdown script provided, which will run when all the
 accompanying data (sample information, environmental variables, genomic
 data) are downloaded into one folder. 
description: Sample design
 and study species: Two keystone reef-building coral
 species, <em>Acropora muricata</em> and <em>Pocillopora
 damicornis</em>, were sampled from 15 reefs with contrasting
 environmental conditions around the Seychelles, Mauritius, and Rodrigues
 islands in the WIO in 2022. These species have distinct life history
 strategies, warranting independent investigations of population structure
 and thermal tolerance. <em>A. muricata</em> is predominately a
 broadcast spawning coral with synchronised reproduction in November to
 January, followed by larvae that generally settle within 10–14 days of
 fertilisation. <em>P. damicornis</em> has a mixed reproductive
 modes, reproducing sexually via year-round broadcast spawning or by
 producing brooding planula larvae with rapid settlement rates. Clonal
 propagation is also common, with the production of asexual larvae or
 polyps that can disperse over large distances (&gt;50km). Overall, 345
 <em>A. muricata</em> and 403 <em>P.
 damicornis</em> colonies were sampled for DNA extraction and single
 nucleotide polymorphism (SNP) genotyping. All analyses
 were performed in the R environment, following the script
 attached. Environmental
 variables: We used 10 uncorrelated environmental and
 geomorphic variables to characterise the seascape at the study sites
 (details of variables
 in <strong>Environmental_variable_description.docx</strong>).
 We further use sSST, mDHW and BackReef as predictors in
 genotype–environment association (GEA) analyses. 
 Genomic
 data: DNA extraction and SNP genotyping followed
 methods of Selmoni et al. (2021), where extracted DNA was sent to
 Diversity Arrays Technology (Canberra, Australia) for quality check
 screening and genotype-by-sequencing using the DArT-sequencing method
 (DArT-seq). Here, we provide the metadata for the FASTQ files uploaded to
 GEOME
 and NCBI (BioProject PRJNA1277000;
 <strong>Metadata_DARTinfo_<em>species</em>.csv</strong>). The Dar-T-seq analytical pipeline resulted in 73,253 bi-allelic SNPs genotyped for 345 <em>A. muricata</em> individuals and 65,708 SNPs for 403 <em>P. damicornis</em> individuals (<strong>SNPS_RAW_<em>species</em>.rda</strong>). These SNPs were called against the chromosome-level reference genome of <em>Acropora millepora</em> (v2.1; GCF_013753865.1; Fuller et al., 2020) for <em>A. muricata</em>, and the scaffold-level reference genome assembly of <em>P. damicornis</em> (v1; GCF_003704095.1; Cunning et al., 2018) for <em>P. damicornis</em>. After filtering (detailed below) our final genotype matrix comprised of 211 <em>A. muricata</em> individuals with 7663 SNPs and 97 <em>P. damicornis</em> individuals with 1319 (<strong>SNPS_FILT_<em>species</em>.rda</strong>). <strong>SNP filtering:</strong> SNP filtering was performed for each species separately. We first removed putatively cryptic individuals likely present for our sampled species, where ‘cryptic’ is defined as genetically distinct groups among sets of colonies identified in-situ as the same species (Grupstra et al., 2024; Riginos et al., 2024). To this end, we performed a soft filtering for loci and individual missingness (50% threshold each), then ran a principal coordinate analysis (PCoA) on Euclidean distances of the genotype matrix (an ordination-based method that can handle missing data). We used PCoA axes to visually identify clusters of genetically distinct individuals co-occurring with most of the sampled individuals. Individuals from these clusters are putatively cryptic and were removed, as recommended by standard guidelines. We then identified and removed clonal genotypes on a hard filtered dataset (loci and individuals each pruned for 80% missingness). Clones were identified as those sharing over 95% or 90% of their genotypes for <em>A. muricata</em> and <em>P. damicornis</em>, respectively, using the <em>gl.report.replicates</em> function in the <em>dartR.base</em> package (v. 1.0.5; Mijangos et al., 2022). The choice of threshold corresponds to the separation between first-degree relatives and replicated individuals in the histogram of pairwise relatedness values per species. For each group of putative clones, we retained the one individual with the least missing SNP data when genetic similarity was found. We identified and removed another cluster of putatively cryptic individuals for <em>P. damicornis</em> as per the first cryptic filtering step. Finally, we performed a hard filtering on the genotype matrix pruned for cryptic and clonal individuals, applying a missingness threshold of 80% for loci and individuals, before excluding rare alleles with a MAF &lt;5%. Global MAF thresholds were applied to retain population-specific SNP variants for downstream population structure and genotype–environment analyses. Neutral genetic structure: Neutral genetic structure was assessed for each species using complementary methods: i) Principal Coordinate Analysis (PCoA) and ii) Sparse Nonnegative Matrix Factorization (sNMF), where we used a putatively neutral genomic dataset (hereafter termed ‘neutral genotype matrix’) by removing outlier loci identified using a genome scan of the filtered genotype matrix with <em>pcadapt</em>. Isolation-by-Distance vs Isolation-by-Resistance: Patterns of Isolation-by-Distance (IBD) and Isolation-by-Resistance (IBR) were identified between reefs by regressing linearised genetic distance (i.e., F<sub>ST</sub>/1-F<sub>ST</sub>; Rousset, 1997) against Euclidean or sea distances, respectively. IBD was calculated from the log<sub>10</sub> of Euclidean distances obtained by converting degree latitude and longitude to cartesian coordinates. IBR was calculated from the log<sub>10</sub> of sea distances derived from ocean current data for each calendar month and annually. Genotype–Environment Associations (GEA): We performed GEAs using multivariate redundancy analyses (RDA) at the site-level following methods of Capblancq and Forester (2021). For each sampling site, we converted individual genotypes of the filtered dataset to allele frequencies per locus by averaging observed genotypes across individuals, ignoring missing data. We imputed allele frequencies for loci missing information at the site-level using the median allele frequency from the other sites in the same region (i.e., Seychelles, Mauritius or Rodrigues). We chose explanatory environmental variables for the RDA using a bidirectional stepwide model selection procedure (<em>ordistep)</em>, which retained mDHW (mean degree heating week) and sSST (sd of sea surface temperature) for both species, as well as BackReef (proportion of backreef around site) for <em>P. damicornis</em>.  To detect putative genomic regions under selection, we performed a multivariate GEA using a partial RDA (pRDA) on the entire genotype matrix, with the <em>ordistep</em> selected variables as predictors. Reef-level population structure was used to condition the RDA, limiting false positives while potentially reducing power to detect true outlier loci along neutral gradients Gene Ontology (GO) enrichment analysis: Gene Ontology (GO) enrichment analyses were used to assess the putative molecular function(s) of significant outlier SNPs (i.e., <em>q</em>-value&lt;0.05) from the pRDA. Estimating the Adaptive Seascape: We estimated the adaptive seascape across the WIO to identify reefs potentially harbouring adaptive genetic variation linked to thermal gradients. For each species, we performed an RDA to quantify genotype–environment interactions using site-level allele frequencies as the response matrix, using the <em>ordistep</em> selected environmental variables. We then projected environmental conditions from all mapped WIO reefs into the RDA ordination space to derive a genetic-based index of adaptation (Adaptive Index) for each environmental pixel of the seascape. This Adaptive Index reflects how genetic variation is structured relative to thermal gradients, allowing us to map the spatial distribution of reefs where allelic compositions are most consistent with thermal adaptive potential across the WIO seascape Funding statement and acknowledgements This study was funded by the Adaptation Fund (AF), who financed the “Restoring Marine Ecosystem Services by Rehabilitating Coral Reefs to Meet a Changing Climate Future” project (PIMS No. 5736), implemented by United Nations Development Programme (UNDP) Mauritius and Seychelles, with the support of the Ministry of Agro-Industry, Food Security, Blue Economy, and Fisheries, and in the Republic of Seychelles with the support of the Ministry of Agriculture, Climate Change and Environment. They were sampled under permits SPA 30 and A0157. We gratefully acknowledge the contributions of the following institutions to field sampling efforts: Albion Fisheries Research Center, Eco-Sud, Reef Conservation, Marine Conservation Society Seychelles, Mauritius Oceanographic Institute, Nature Seychelles, Seychelles National Park Authority, Rodrigues Regional Assembly and Shoals Rodrigues. We thank the UNDP Mauritius and Seychelles for their support in coordinating activities throughout the project. We also thank Katherine Prata and Zoe Meziere for discussions regarding genomic analyses. ASG acknowledges support from the Swiss National Science Foundation (ASG acknowledges support from the Swiss National Science Foundation (SNSF; Postdoc Mobility Fellowship P500PB_230450). The data were used in the following scientific publication: Guillaume, A.S., Joost, S., Curpen, S., Dumur Neelayy, D., Harree-Somah, L., Sadasing, O., Saponari, L., Dale, C., Barret, L., Andrews, N., Leckraz, S.K., François, R., Seetapah, V., Munusami, V., Bacha Gian, S., Jhangeer-Khan, R., Mahoune, T., Chumun, P.K., Poretti, M., Berteaux-Lecellier, V., Lecellier, G., Selmoni, O. (2026). Coral genetic structure in the Western Indian Ocean mirrors ocean circulation and thermal stress history. <em>Evolutionary Applications.</em> https://doi.org/10/1111/eva.70206 A.S.G. and O.S. wrote the scripts for these analyses, with final versioning and archiving by A.S.G.
description: # Data and analysis scripts to accompany: Coral genetic structure in the
 Western Indian Ocean mirrors ocean circulation and thermal stress history
 ## Description of the data and file structure The following data are
 provided to replicate analyses and main figures in the article ‘*Coral
 genetic structure in the Western Indian Ocean mirrors ocean circulation
 and thermal stress history’* (citations below). These data and Rmarkdown
 script will reproduce the pdf tutorial files for both species
 (**WIOcoral_seascapegenomics_*species*.pdf**) Briefly, the population
 connectivity and adaptive potentials of two reef building coral species
 (*Acropora muricata* and *Pocillopora damicornis)* were assessed by
 applying seascape genomic approaches. We sampled 345 *A. muricata* and
 403 *P. damicornis* around islands of three West Indian Ocean (WIO)
 regions: Seychelles, Mauritius, and Rodrigues. Download all files into one
 folder with the Rmarkdown to run the script. Simply change the `SP`
 variable in the first chunk to either `Acropora` or `Pocillopora` to
 perform analyses of one or the other species 
 
 # Files and variables #### File: WIOcoral_seascapegenomics_Acropora.pdf
 & File: WIOcoral_seascapegenomics_Pocillopora.pdf **Description:** The
 resulting pdf output when the Rmarkdown script is knitted in R. These pdfs
 act as tutorials to follow the primary analyses of the manuscript,
 outlining the key methods and analyses undertaken in the manuscript, and
 replicating the results figures and tables. One pdf is provided for each
 species, which can be replicated using
 the *WIOcoral_seascapegenomics_script.Rmd* script (information below).
 #### File: WIOcoral_seascapegenomics_script.Rmd **Description:** Rmarkdown
 script to follow the primary analyses of the manuscript, outlining the key
 methods and analyses undertaken in the manuscript, and replicating the
 results figures and tables. Simply change the '`SP`' variable in
 the first chunk to modify the species modelled. Variables can be either
 '`Acropora`' or '`Pocillopora`'. The script can then
 be knitted together in R to reproduce the pdfs (above), or individual
 chunks can be run for each analysis. Basic interpretations of analyses are
 included; see the published manuscript (link at bottom) for detailed
 information. 
 
 ## Sample information #### File: Sample_ID_location_Acropora.csv &
 File: Sample_ID_location_Pocillopora.csv **Description:** Location details
 for each genotype sample of *Acropora* and *Pocillopora* ##### Variables *
 SampleID: genotype name, corresponds to genomic rda files * SS: sample
 site identifier, starting with ‘S’ * Reef: unique reef name *
 SubRegion: One of five subregions: PRA = Praslin, MAH = Mahé, IPL = Ile
 Plate, MW = West Mauritius, ME = East Mauritius, ROD = Rodrigues *
 Region: One of three regions: MAU = Mauritius, ROD = Rodrigues, SEY =
 Seychelles * LAT: decimal latitude * LON: decimal longitude * depth: range
 of depths between which coral samples were taken  *
 BioProject_PRJNA1277000_Accession: Genotype’s Sample ID in the BioProject
 PRJNA1277000 * FASTQ.ID: Genotype’s Sample ID in the BioProject
 PRJNA1277000 * Final_dataset: binary YES/NO retained for the final
 analyses 
 
 ## Environment We provide values of 10 uncorrelated **environmental and
 geomorphic variables** at each of the 15 reefs used to characterise their
 environments, where sSST (sd of sea surface temperature), mDHW (mean
 degree heating week) and BackReef (proportion of backreef) were used as
 predictors in genotype–environment association (GEA) analyses. #### File:
 Environmental_variable_description.csv **Description:** Environmental
 variable names used to characterise the seascape of 15 reefs sampled for
 this research, where this file is not needed to run the script. Variable
 names are provided with the acronyms used in analyses, alongside
 informaiton on the original source of the data, spatial and temporal
 resolutions. Please see the main manuscript of Guillaume et al (2026)
 Evolutionary Applicaitons for more specific details regarding these
 variables. ##### Variables * Variable: Name of the variable * Acronym:
 Acronym of the variable used in the script and analyses * Source: Open
 access source of the enviornmental variable. Either RECIFS or ACA. See
 'Access information' below for more information on these two
 open-access data sources * Spatial resolution: spatial extent of raster
 cells for each variable. Either 5x5 km or 10x 10m * Temporal resolution:
 temporal extent used to calculate variable values for each variable. Value
 is 'na' when only one time point is used, otherwise the years of
 measurement are listed. #### File: Site_coordinates_environment.csv
 **Description:** Information regarding the 15 sample sites (reefs) where
 coral colonies were collected. This file contains site coordinates and
 environmental variable values extracted from raster files for the 15
 environmental variables (see Environmental_variable_description.csv above
 for more information regarding data sources). ##### Variables * SS: Unique
 site identifier, staring with ‘S’ * Reef: Unique reef name * Region: One
 of three regions: MAU = Mauritius, ROD = Rodrigues, SEY = Seychelles *
 SubRegion: One of five subregions: PRA = Praslin, MAH = Mahé, IPL = Ile
 Plate, MW = West Mauritius, ME = East Mauritius, ROD = Rodrigues * LAT:
 decimal latitude * LON: decimal longitude * depth: range of depths between
 which coral samples were taken  * ReefSlope: Proportion of reef slope
 around sample site * ReefFlat: Proportion of reef flat around sample site
 * BackReef: Proportion of back reef around sample site * mDHW: mean degree
 heating week (°C-week) * sSST: sd of the sea surface temperature (°C) *
 CRO: Proportion of cropland around sample site (cropland proportion in
 5km^2^) * LAN: Proportion of land around sample site (land proportion in
 5km^2^) * HPO: Human population density around sample site (human density
 per 5km^2^) * VBD: Boat density around sample site (boat counts per
 5km^2^) * DEP: Depth of sample site, derived from bathymetry maps (meters
 below sea level) #### File: SeaCurrents_TM.zip **Description:** We provide
 the transition matrices (a** **square array of
 numbers representing probabilities of moving from one reef to another) of
 sea distances between reefs in our study, derived from sea current data,
 following methods of Selmoni et al. (2020a). Briefly, we retrieved maps
 describing monthly average direction and strength of surface sea currents
 throughout the WIO. These maps are available at a spatial resolution of
 0.083° across 30 years as satellite-derived reconstructions of sea
 currents (from the ‘GLOBAL_REANALYSIS_PHY_001_030_104’ dataset, accessed
 on 03-04-2014; E.U. Copernicus Marine Service Information (CMEMS), 2024),
 which are publicly available via RECIFS (Selmoni et al., 2023; citation
 below). For each pixel, we calculated the cumulative speed towards each of
 the eight neighbouring pixels and divided this by the total speed to
 obtain a probability of transition in each direction (the conductance). We
 then calculated the dispersal costs as the inverse of the square
 conductance to obtain transition matrices using the gdistance R package
 (v1.6.4, van Etten, 2017). **Upon unzipping:** files are rda:
 **transition_matrix_01.rda** , where one transition matrix is provided
 per month (denoted with '01'-'12') alongside an annual
 average ('13'). 
 
 ## Genomic files We provide genomic files for each species, where SNPs
 (single nucleotide polymorphisms) were genotyped from individual coral
 colonies collected at the sample sites (reefs). Information on all the
 samples collected and sequenced in this study are available on GEOME at
 [https://n2t.net/ark:/21547/R2651](https://n2t.net/ark:/21547/R2651). Raw
 sequences can be downloaded from NCBI (BioProject PRJNA1277000). Some
 files are provided as rda objects, which can be read into R using the
 `load()` base R function (eg: `load(file= SNPS_RAW_Acropora.rda)`).
 ‘SNPS_RAW’ and ‘SNPS_FILT’ are `genlight` objects from the *adegenet* R
 package (Jombart et al 2008), which are a specialised formal S4 class
 designed to store and manage genotype data, particularly for binary SNPs
 in a memory-efficient manner. They store data about the loci associated
 with each genotyped individual. These objects can be manipulated using the
 *dartR* R package (Mijangos et al., 2022). #### File:
 SNPS_RAW_Acropora.rda & File: SNPS_RAW_Pocillopora.rda
 **Description:** RDA object called as`SNPS_RAW`. This is a DARTR object
 that contains the raw SNPs from the DArT-seq analytical pipeline,
 resulting in 73,253 bi-allelic SNPs genotyped for 345 *A. muricata*
 individuals and 65,708 SNPs for 403 *P. damicornis* individuals. This file
 is provided for completeness of the repository and interest, but is not
 required in the Rscript. Only the filtered DART-seq rda file is needed
 (see following file entry). #### File: SNPS_FILT_Acropora.rda & File:
 SNPS_FILT_Pocillopora.rda **Description:** RDA object called as
 `SNPS_FILT`. The SNPs retained after stringent filtering of the
 SNPS_RAW_*species*.rda to produce SNPS_FILT_*species*.rda, which includes
 filtering for quality control, and removing clones and putatively cryptic
 individuals (refer to the main manuscript for detailed methods of
 filtering steps). This is a DARTR object that contains the final genotype
 matrix comprised of 211 *A. muricata* individuals with 7,663 SNPs and 97
 *P. damicornis* individuals with 13,190 SNPs. #### File:
 BLAST_REF_Acropora.rda & File: BLAST_REF_Pocillopora.rda
 **Description:** RDA object called as `BLAST_REF`. The BLAST_REF dataframe
 files were created to align raw DArT-seq loci to each species’ reference
 genomes using BLAST (Basic Local Alignment Search Tool) to retain only the
 SNPs associated with the coral hosts (filtering thresholds: &gt;70%
 percentage identity, &gt;80% overlap identity, and &gt;50 bitscore). The
 reference genome for *A. muricata* was the chromosome-level assembly of
 *Acropora millepora*
 ([v2.1](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_013753865.1/);
 GCF_013753865.1; Fuller et al., 2020), and the reference genome for *P.
 damicornis* was the scaffold-level assembly of *P. damicornis*
 ([v1](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_003704095.1/);
 GCF_003704095.1; Cunning et al., 2018). See the [BLAST help
 page](https://www.metagenomics.wiki/tools/blast/blastn-output-format-6)
 for full details on variable descriptions. #### File: GENES_Acropora.rda
 & File: GENES_Pocillopora.rda **Description:** RDA object called
 as `GENES`. RDA object that stores information regarding genes alongside
 their chromosome location, created from a BLAST against the reference
 genomes (NCBI downloads GCF_013753865.1 for *Acropora muricata* and
 GCF_003704095.1 for *Pocillopora damicornis* ##### Variables * Gene_name:
 Name of the gene/locus identifier (e.g. LOC IDs) * V1: Reference sequence
 accession for the genomic region (e.g. chromosome or scaffold ID) * V2:
 Annotation source; indicates the gene prediction pipeline used for
 annotation. * V3: Feature type = “gene” * V4: Start genomic coordinate
 (integer); leftmost position of the gene on the reference sequence. *
 V5: End genomic coordinate (integer); rightmost position of the gene on
 the reference sequence. * V6: Score field from GFF format. A dot (“.”)
 indicates no score was provided. * V7: Strand orientation. “+” for forward
 strand and “−” for reverse strand. * V8: Phase/frame field from GFF
 format. (For gene features this is typically “.”) * V9: Attribute field
 (GFF-style metadata). Contains semicolon-separated values of: gene ID,
 cross-references (e.g. GeneID), gene name, gene biotype (e.g.
 protein_coding, lncRNA) * UP_ID: UniProt accession ID corresponding to the
 gene product (if available). NA = no mapped UniProt entry. *
 UP_Protein_name: Full protein name from UniProt, including alternative
 names and EC numbers where applicable. * GO_bp: Gene Ontology (GO)
 Biological Process annotations associated with the protein, including GO
 term names and IDs. * GO_mf: Gene Ontology Molecular Function annotations
 associated with the protein. * GO_cc: Gene Ontology Cellular Component
 annotations indicating subcellular localisation. #### File:
 MF_gsc_Acropora.rda & File: MF_gsc_Pocillopora.rda
 **Description:** RDA object called as `MF_gsc`. Stores gene set
 collections (gsc) pertaining to molecular functions (MF) used in Gene
 Ontology (GO) enrichment analyses. ##### Variables * maxSetSize: The
 maximum allowed number of genes for a GO term set to be included in the
 analysis. * referenceSet: A character vector containing all gene
 identifiers used as the background or universe for the analysis * sets: A
 list of GO term–specific gene sets. Each element corresponds to one
 Molecular Function (MF) GO term and contains the genes annotated to that
 term. * g: The total number of genes in the reference set (i.e. the size
 of the gene universe). * bigSets: A named logical vector indicating
 whether each GO term exceeds the maximum set size threshold. `TRUE`
 denotes sets considered too large; `FALSE` denotes retained sets. *
 intersection.p.cutoff: The significance threshold used to retain pairwise
 intersections between GO term sets (p-value cut-off). * intersections: A
 data table containing statistically significant pairwise overlaps between
 GO term sets, including the two term IDs (`setA`, `setB`) and their
 associated p-value. * iMatrix: A binary incidence matrix (genes × sets)
 indicating gene membership in GO term sets (1 = gene belongs to the set, 0
 = not a member). #### File: genomic_Acropora.gff & File:
 genomic_Pocillopora.gff **Description:** The reference genome files
 downloaded from NCBI alongside fna files (see description above for
 reference genome information). The .gff objects are formal class
 'GRanges' from the R package "GenomicRanges", with 7
 slots ##### Variables * seqnames: An `Rle` (run-length encoded) object
 indicating the sequence (e.g. chromosome or scaffold) on which each
 genomic feature is located. The `values` correspond to chromosome
 identifiers (e.g. `"NC_058066.1"`), and `lengths` reflect
 consecutive runs of identical sequence names. * ranges: An `IRanges`
 object defining the genomic coordinates of each feature. It contains the
 start position and the width (feature length), from which end positions
 can be derived. * strand: An `Rle` object specifying the strand
 orientation of each feature: `"+"` (forward), `"-"`
 (reverse), or `"*"` (unstranded/unknown). * seqinfo: A `Seqinfo`
 object holding metadata about the reference sequences, including sequence
 names, sequence lengths, circularity status, and genome build information.
 * elementMetadata: A `DFrame` (DataFrame) containing feature-level
 annotations (one row per genomic range). This corresponds to the attribute
 columns typically present in a GFF/GTF file (e.g. source, type, gene ID,
 transcript ID, biotype, product, etc.). * elementType: A character string
 defining the type of elements stored in the object (here
 `"ANY"`), describing the general class of the contained ranges.
 * metadata: A list for storing additional, object-level metadata that
 apply to the entire `GRanges` object rather than to individual features. 
 
 ## West Indian Ocean regional variables #### File: allDB_area.rda
 **Description:** RDA object called as `DB_area` that stores the area of
 all reef cells in RECIFS in the WIO. This named numeric vector contains
 the grid ID as the column names and the associated area in row 1.  ####
 File: WIO_reefs_coord.rda **Description:** RDA object called as
 `WIO_reefs_coord`that stores latitude and longitude coordinates for all
 reef raster cells across the WIO. ##### Variables * Row names: Grid ID
 that correspond to the column names in `DB_area` * lon: decimal longitude
 * lat: decimal latitude #### File: WIO_reefs_ENV.rda **Description:** RDA
 object called as `WIO_reefs_ENV`that stores values of environmental
 variables for all reef raster cells across the WIO, extracted from RECIFS
 using coordinates of *WIO_reefs_coord.rda*. ##### Variables * Row names:
 Grid ID that correspond to the column names in `DB_area` * ReefSlope:
 Proportion of reef slope in grid area * ReefFlat: Proportion of reef flat
 in grid area * BackReef: Proportion of back reef in grid area * mDHW: mean
 degree heating week of grid area (°C-week) * sSST: sd of the sea surface
 temperature of grid area (°C) * CRO: Proportion of cropland in grid area *
 LAN: Proportion of land in grid area * HPO: Human population density in
 grid area * VBD: Boat density in grid area * DEP: Depth derived from
 bathymetry maps of grid area (meters below sea level) #### File:
 WIO_reefs_coord_samplesites.csv **Description:** The grid values
 associated with each sample site to link to the
 *WIO_reefs_coord.rda* file  ##### Variables * SS: sample site ID * Grid:
 grid name that corresponds to the *WIO_reefs_coord.rda* file  
 
 # Code/software The R script provided
 (*WIOcoral_seascapegenomics_script.Rmd* ) contains code for the following
 analyses, where the user only needs to change the object '`SP`'
 to either '`Acropora`' or '`Pocillopora`' in the first
 line of code to run analyses for each. The script requires SetRank v1.0.0
 from
 [https://cran.r-project.org/src/contrib/Archive/SetRank/](https://cran.r-project.org/src/contrib/Archive/SetRank/) The R script follows this workflow: * Map of sample sites in the WIO * Population structure assessment using PCoA and sNMF * Connectivity using Fst to calculate Isolation-by-Distance (IBD) and Isolation-by-Resistance (IBR) * Selection of predictor variables * Genotype-Environment Associations (GEA) using Redundancy Analyses (RDA) to identify putative loci under selection, including GO term analyses * Adaptive Seascape projections across the WIO The R script was produced using **R version 4.4.1** (2024-06-14) with the following packages: **Attached base packages:**\ stats4, stats, graphics, grDevices, utils, datasets, methods, base **Other attached packages:**\ marmap_1.0.12, rnaturalearth_1.1.0, sf_1.0-24, viridis_0.6.5, viridisLite_0.4.2, ggh4x_0.3.1, ggrepel_0.9.6, ggpubr_0.6.2, gridExtra_2.3, SetRank_1.0.0, gdistance_1.6.5, Matrix_1.7-4, igraph_2.2.1, raster_3.6-32, sp_2.2-0, sphereplot_1.5.1, rgl_1.3.31, poppr_2.9.8, bigutilsr_0.3.11, vegan_2.7-2, permute_0.9-8, LEA_3.16.0, qqman_0.1.9, qvalue_2.36.0, pcadapt_4.4.1, rtracklayer_1.64.0, GenomicRanges_1.56.2, GenomeInfoDb_1.40.1, IRanges_2.38.1, S4Vectors_0.42.1, BiocGenerics_0.50.0, stringr_1.6.0, reshape2_1.4.5, tibble_3.3.1, tidyr_1.3.2, dartR_2.9.9.5, dartR.data_1.0.8, dplyr_1.1.4, ggplot2_4.0.1, adegenet_2.1.11, ade4_1.7-23 
 
 ## Access information Other publicly accessible locations of the data: *
 **FASTQ files**: Raw sequence data as FASTQ files have been deposited in
 the NCBI BioProject database under accession number PRJNA1277000. The
 corresponding [metadata from GEOME](https://n2t.net/ark:/21547/R2651) are
 included: *Metadata_DARTinfo_species.csv* Environmental data (described in
 *Environmentalvariabledescription.docx*) were obtained from the following
 open access sources: * **Reef Environment Centralized InFormation (RECIFS)
 at** **[recifs.epfl.ch](https://recifs.epfl.ch/)**\ *Reference:* Selmoni,
 O., Lecellier, G., Berteaux-Lecellier, V., Joost, S., 2023. The Reef
 Environment Centralized InFormation System (RECIFS): An integrated
 geo-environmental database for coral reef research and conservation. Glob.
 Ecol. Biogeogr. 32, 622–632.
 [https://doi.org/10.1111/geb.13657](https://doi.org/10.1111/geb.13657) *
 **Allen Coral Atlas (ACA) at allencoralatlas.org**\ *Reference:* Allen
 Coral Atlas (2022) Imagery, maps and monitoring of the world’s tropical
 coral reefs.
 [https://doi.org/10.5281/zenodo.3833242](https://doi.org/doi.org/10.5281/zenodo.3833242) Beaman, R.J. (2010). 
 
 ## Referencing this work We welcome the script and data files provided
 for use in similar research elsewhere, and we are open to collaborations -
 our contact details are below. We do ask that if this script and
 associated data are used in your research, please cite the following
 publication and datasets: **Publication:** Guillaume, A.S., Joost, S.,
 Curpen, S., Dumur Neelayy, D., Harree-Somah, L., Sadasing, O., Saponari,
 L., Dale, C., Barret, L., Andrews, N., Leckraz, S.K., François, R.,
 Seetapah, V., Munusami, V., Bacha Gian, S., Jhangeer-Khan, R., Mahoune,
 T., Chumun, P.K., Poretti, M., Berteaux-Lecellier, V., Lecellier, G.,
 Selmoni, O. (2026) Coral genetic structure in the Western Indian Ocean
 mirrors ocean circulation and thermal stress history. *Evolutionary
 Applications* DOI: [10/1111/eva.70206](https://doi.org/10/1111/eva.70206)
 **Data set and scripts:** Guillaume, A.S., Joost, S., Curpen, S., Dumur
 Neelayy, D., Harree-Somah, L., Sadasing, O., Saponari, L., Dale, C.,
 Barret, L., Andrews, N., Leckraz, S.K., François, R., Seetapah, V.,
 Munusami, V., Bacha Gian, S., Jhangeer-Khan, R., Mahoune, T., Chumun,
 P.K., Poretti, M., Berteaux-Lecellier, V., Lecellier, G., Selmoni, O.
 (2026). Data and analysis scripts to accompany: Coral genetic structure in
 the Western Indian Ocean mirrors ocean circulation and thermal stress
 history [Dataset]. *Dryad* DOI:
 [10.5061/dryad.931zcrjxv](https://doi.org/10.5061/dryad.931zcrjxv) **Raw
 FASTQ sequences:** All DArT-seq genotyped individuals (*Acropora* spp.:
 515 FASTQ files for 345 individuals; *Pocillopora* spp.: 517 FASTQ files
 for 403 individuals) are available from **NCBI BioProject
 PRJNA1277000** with associated metadata also available on
 [GEOME](https://n2t.net/ark:/21547/GQW2) ##### Contact us at: Dr Annie
 Guillaume: annie.guillaume (at) alumni.epfl.ch ;
 [ORCID](https://orcid.org/0000-0002-2188-2861) Dr Oliver Selmoni:
 oliver.selmoni (at) geo.uzh.ch ;
 [ORCID](https://orcid.org/0000-0003-0904-5486) Find A.S. Guillaume on
 [github](https://github.com/as-guillaume) ####