Coral genetic structure in the Western Indian Ocean mirrors ocean circulation and thermal stress

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Guillaume , View ORCID Profile Stéphane Joost , Sarvanen Curpen , Danishta Dumur Neelayya , Luxmibye Harree-Somah , Oocheetsing Sadasing , View ORCID Profile Luca Saponari , View ORCID Profile Charlotte Dale , View ORCID Profile Léo Barret , Nina Andrews , Sanjeev Kumar Leckraz , Ronnie François , Vasisht Seetapah , Vinayaganidhi Munusami , View ORCID Profile Suraj Bacha Gian , View ORCID Profile Reshad Jhangeer-Khan , Terence Mahoune , Pramod Kumar Chumun , View ORCID Profile Manuel Poretti , View ORCID Profile Véronique Berteaux-Lecellier , View ORCID Profile Gael Lecellier , View ORCID Profile Oliver Selmoni doi: https://doi.org/10.1101/2025.06.16.659828 Annie S. Guillaume 1 Geospatial Molecular Epidemiology group (GEOME), Laboratory for Biological Geochemistry (LGB), Ecole Polytechnique Fédérale de Lausanne (EPFL) , Station 2, CH-1015 Lausanne, CH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annie S. Guillaume For correspondence: annie.guillaume{at}alumni.epfl.ch oliver.selmoni{at}geo.uzh.ch Stéphane Joost 1 Geospatial Molecular Epidemiology group (GEOME), Laboratory for Biological Geochemistry (LGB), Ecole Polytechnique Fédérale de Lausanne (EPFL) , Station 2, CH-1015 Lausanne, CH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stéphane Joost Sarvanen Curpen 2 Mauritius Oceanography Institute , Albion, Rivière Noire District, MU Find this author on Google Scholar Find this author on PubMed Search for this author on this site Danishta Dumur Neelayya 2 Mauritius Oceanography Institute , Albion, Rivière Noire District, MU Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luxmibye Harree-Somah 2 Mauritius Oceanography Institute , Albion, Rivière Noire District, MU Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oocheetsing Sadasing 2 Mauritius Oceanography Institute , Albion, Rivière Noire District, MU Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luca Saponari 3 Nature Seychelles , Roche Caiman, Roche Caiman, SC Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luca Saponari Charlotte Dale 4 James Cook University , Townsville City, QLD, AU Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charlotte Dale Léo Barret 5 Bee Ecological Consulting , Victoria, Mahé, SC Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Léo Barret Nina Andrews 6 Marine Conservation Society Seychelles , Beau Vallon, SC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sanjeev Kumar Leckraz 7 Albion Fisheries Research Centre , Albion, Petite Rivière, Mauritius Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ronnie François 7 Albion Fisheries Research Centre , Albion, Petite Rivière, Mauritius Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vasisht Seetapah 8 Eco-Sud , Blue Bay, Mauritius Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vinayaganidhi Munusami 8 Eco-Sud , Blue Bay, Mauritius Find this author on Google Scholar Find this author on PubMed Search for this author on this site Suraj Bacha Gian 9 Reef Conservation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Suraj Bacha Gian Reshad Jhangeer-Khan 10 Shoals Rodrigues Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Reshad Jhangeer-Khan Terence Mahoune 11 Seychelles Parks and Gardens Authority Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pramod Kumar Chumun 12 United Nations Development Programme Mauritius and Seychelles , Port Louis, Mauritius Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manuel Poretti 13 University of Melbourne, School of Biological Sciences , Melbourne, VIC, AU Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel Poretti Véronique Berteaux-Lecellier 14 EMR9001 SantEco - UMR250 ENTROPIE (IRD-CNRS-UR-UNC) , Promenade Roger-Laroque, Noumea Cedex, New Caledonia, FR Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Véronique Berteaux-Lecellier Gael Lecellier 15 Institut des Sciences Exactes et Appliquées (ISEA) EA7484-UNC , 145, Avenue James Cook, BP R4 98 851, Nouméa, New Caledonia, FR Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gael Lecellier Oliver Selmoni 16 Department of Embryology, Carnegie Institution for Science , Baltimore, MD, USA 17 Department of Plant Biology, Carnegie Institution for Science , Stanford, CA, USA 18 Department of Geography, University of Zurich , CH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oliver Selmoni For correspondence: annie.guillaume{at}alumni.epfl.ch oliver.selmoni{at}geo.uzh.ch Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Global warming and rising sea temperatures are pushing many reef-building coral species towards extinction. As thermal tolerance in corals is partially heritable, identifying genes under thermal selection is critical for targeted biodiversity management. However, it remains unclear how large breaks in connectivity (>100 km of open sea) affect the spread of adaptive alleles for different coral species in discontinuous reef networks such as the West Indian Ocean (WIO). To address this, we applied a seascape genomics approach to model (i) population connectivity and (ii) thermal adaptive potentials for two keystone coral species, Acropora muricata and Pocillopora damicornis , across the WIO. For both species, corals from the Seychelles were predominantly genetically isolated from corals in Rodrigues and Mauritius, putatively an effect of regional oceanographic barriers. Furthermore, sea currents during reproductive periods better predicted genetic connectivity than did Euclidean distances for both species, highlighting that connectivity models can serve as proxies to understand dispersal potential depending on reproductive strategies. Spatial patterns of neutral genetic variation were best explained by sea surface temperature variability and mean degree heating weeks. When used in genotype– environment association (GEA) analyses, we identified hundreds of loci under putative thermal selection from linked to known heat stress responses. In A. muricata , five Sacsin genes—co- chaperones of the Hsp70 heat-shock protein involved in thermal stress response—were identified, alongside genes related to immune defence, antioxidant response, signalling, and protein folding. In contrast, only the centromere protein V, involved in mitosis, was enriched in P. damicornis . By integrating patterns of gene flow with molecular adaptations to estimate species-specific adaptive potentials, we found that large sea distances and strong oceanographic barriers inhibit the genetic exchange of adapted genotypes across the WIO, providing valuable insights to guide local and regional biodiversity management in this region. 1. Introduction Covering less than 1% of the ocean floor, coral reefs are biodiversity hotspots hosting approximately a third of all marine species ( Moberg and Folke, 1999 ). These dynamic ecosystems provide valuable habitat for a variety of taxa, shaping food chains and biogeochemical cycles while also providing important ecosystem services for humans, including food, medicine, coastal protection, and tourism ( Costanza et al., 2014 ; Hughes et al., 2017a ; Moberg and Folke, 1999 ; Roff et al., 2016 ). The foundation of these ecosystems are reef-building scleractinian corals, whose calcareous skeletons construct the reef’s physical structure ( Hughes et al., 2017a ; Moberg and Folke, 1999 ). Coral reefs are under threat of intensifying anthropogenic-induced stressors, particularly from global warming and elevated sea temperatures ( Bozec et al., 2025 ; Hughes et al., 2020 , 2017b ; Ortiz et al., 2021 ; Otto, 2018 ; Virgen-Urcelay and Donner, 2023 ). Under such stressful conditions, corals can lose their symbiotic algae in a reversible process known as coral bleaching ( Helgoe et al., 2024 ; Hoegh- Guldberg et al., 2007). As corals rely on their symbionts for metabolic inputs, persistent bleaching can result in widespread coral deaths ( Sully et al., 2022 ). An estimated 14% of the world’s coral cover has already been lost between 2009 and 2018 ( Souter et al., 2021 ), and with bleaching-induced coral mortality on the rise ( Bozec et al., 2025 ; Hughes et al., 2020 , 2017b ; Virgen-Urcelay and Donner, 2023 ) there is concern for the impact of climate change on coral reef structures, biodiversity, functioning and productivity globally ( Graham et al., 2015 ; Hughes et al., 2019 ; Morais et al., 2022 ; Sully et al., 2022 ). Despite increased frequency and intensity of mass bleaching events, corals appear to be adapting to repeated exposure of elevated temperatures (e.g., Bozec et al., 2025 ; Drury and Lirman, 2021 ; Louis et al., 2016 ; Palumbi et al., 2014 ; Schoepf et al., 2015 ). For example, increased thermal tolerance of coral communities has been observed around Palau in the Pacific Ocean in recent years following multiple acute bleaching events across the last few decades ( Bruno et al., 2001 ; Lachs et al., 2023 ). While these trends could be attributed to acclimatisation, shifts in community structure or changes in the holobiont associations ( Gouezo et al., 2019 ), contemporary within-population variation in coral heat tolerance indicates the possibility of an underlying genetic adaptation to elevated temperatures ( Lachs et al., 2023 ; Mumby and Van Woesik, 2014 ). Indeed, studies using a variety of experimental methods (summarised in Selmoni et al., 2024 ) found that within-species thermal variation is heritable, with evidence arising from the Pacific Ocean (e.g., Bay and Palumbi, 2014 ; Selmoni et al., 2021 , 2020a ), the Great Barrier Reef of eastern Australia (e.g., Cooke et al., 2020 ; Dixon et al., 2015 ; Elder et al., 2022 ; Fuller et al., 2020 ; Jin et al., 2016; Lundgren et al., 2013 ; Quigley et al., 2020 ), the East Indian Ocean along the west Australian coast (e.g., Thomas et al., 2022 , 2017 ), the Persian Gulf in the Middle East (e.g., Howells et al., 2021 , 2016 ; Kirk et al., 2018 ; Smith et al., 2022 ), and the Caribbean Sea (e.g., Drury and Lirman, 2021 ; Dziedzic et al., 2019 ). As the heritability of thermal variation implies the existence of alleles that can promote tolerance to anticipated increased thermal regimes, identifying candidate genes and populations adapted to thermal stresses can provide valuable information for improving spatial mapping of reef vulnerability to inform reef restoration efforts ( Selmoni et al., 2024 ). Seascape genomics provides a promising framework for investigating adaptation to environmental stressors in in-situ populations located across diverse geographic and environmental ranges ( Riginos et al., 2016 ; Selmoni et al., 2021 , 2020a ). Within this framework, neutral population structuring and connectivity across large spatial extents can provide key insights for understanding how sea currents might affect the spread of adaptive alleles within and between coral populations ( De Mita et al., 2013 ; Lotterhos and Whitlock, 2015 ; Matz et al., 2020 ; Selkoe et al., 2016 ; Selmoni et al., 2020a ; Weersing and Toonen, 2009 ). Seascape genomics also provides a framework for correlating whole genome variation with environmental factors using genotype–environment associations (GEA). In this way, genes putatively involved in adaptation are identified while simultaneously providing insights into the environmental conditions that might be driving genetic differences ( Rellstab et al., 2015 ). Significant associations from GEA correlations suggest candidate loci under selection, which, if reference genomes are available, can be used to highlight markers near or within a gene of interest to infer molecular functions under selection ( Storfer et al., 2018 ). This is important, as long-term coral cover is expected to benefit from genetic influxes of thermally-adapted coral recruits originating from reefs experiencing temperatures 0.5°C higher than the local average ( Matz et al., 2020 ). To assist the long-term adaptive capacities of coral populations, it is imperative to understand connectivity between reefs and the genes involved in adaptation. While strides are being made to understand coral thermal adaptations and thermal heritability globally, research gaps remain. First, research is predominantly concentrated around reef systems in the Indo-Pacific region, notably the Great Barrier Reef in Australia (summarised in Selmoni et al., 2024 ). For instance, early GEA methods successfully detected candidate genes under thermal selection in the Great Barrier Reef ( Lundgren et al., 2013 ). However, large stretches of coral reef regions remain understudied despite coral bleaching responses varying within and among oceans, attributable to differences in regional thermal regimes, evolutionary histories, and past disturbances (Shlesinger and van Woesik, 2023). Notably, coral reefs in the West Indian Ocean (WIO) have been largely overlooked ( Carr et al., 2025 ), despite hosting approximately 5% of global coral reefs affected by recurrent short- and long-term bleaching events (Shlesinger and van Woesik, 2023; van Woesik and Kratochwill, 2022 ). With most WIO ecosystems labelled as vulnerable (e.g., Seychelles) to critically endangered (e.g., Mauritius Islands) due to human activities and global warming ( Obura et al., 2022 ), more research is necessary in this region for informed biodiversity management. Second, research typically focuses either on contiguous reef systems or at local isolated reefs ( Selmoni et al., 2024 ). Though marine environments are highly connected in general, where local stressors can have regional-level effects on community structures ( Green et al., 1987 ), it remains unclear of how breaks in connectivity (>100 km of open sea) affect the spread of adaptive alleles between reefs of a network. Last, research into coral thermal adaptation is typically conducted on a single study species at a time ( Selmoni et al., 2024 ; but see Selmoni et al., 2021 ). Diversity of morphologies and habitat preferences results in differential responses of corals to selection pressures and stressful events ( Guest et al., 2012 ; Johnston et al., 2021 ; Yuen et al., 2023 ). For example, corals with branched morphologies are generally more sensitive to heat stress than those with massive morphologies ( Loya et al., 2001 ). As evolutionary trajectories are species-specific, it seems that patterns of genomic diversity observed in one species cannot necessarily be extrapolated to others, yet conservation strategies must consider the needs of multiple species simultaneously ( Voolstra et al., 2023 ). Despite a growing body of research investigating coral connectivity between islands of the WIO (e.g., Carr et al., 2025 ; Gélin et al., 2018 ; Kusumoto et al., 2020 ; Oury et al., 2024 ; Vogt-Vincent et al., 2024 ), studies have yet to incorporate genetic data for investigating region-wide connectivity between Seychelles and the Mascarene Islands, with little investigation into the adaptive potentials of corals across the WIO region. Here, we aim to address these research gaps to better understand coral adaptive potential across large, non-contiguous reef networks. Specifically, we investigate (i) the genetic connectivity of two keystone coral species with contrasting reproductive strategies across the WIO, and (ii) their molecular adaptations to thermal conditions. To this end, we applied a seascape genomics framework to detect coral reefs that putatively host thermal stress-adapted coral genotypes, to ultimately provide information that will support local stakeholders in developing regional biodiversity management plans in the WIO ( Donovan et al., 2021 ; Goetze et al., 2021 ; Pittman et al., 2021 ; Stefanoudis et al., 2023 ). 2. Materials and Methods We focus on two common WIO reef-building coral species, Acropora muricata and Pocillopora damicornis , sampled at reefs around the Seychelles in the northern WIO and Mauritius and Rodrigues of the Mascarene Islands group in the southern WIO ( Fig. 1a ). We first characterised the seascape conditions around each island using open-access satellite and geomorphic data, from which 15 reefs with contrasting environmental conditions were selected for coral sampling and genotyping. Our framework follows a seascape genomics approach ( Selmoni et al., 2020a ) to (i) map the connectivity of populations across the WIO region and (ii) identify putatively adaptive coral genotypes using genotype–environment association (GEA) analyses ( Suppl. Fig. 1 ). By coupling these analyses, we then infer the spatial distribution of WIO reefs that potentially carry genotypes underpinning local adaptation to thermal stress for informed reef management planning. All analyses were performed in the R environment ( R Core Team, 2024 ). Download figure Open in new tab Figure 1. a) Map of the study area in the West Indian Ocean (WIO; map insert, top right) indicating the 15 sampling sites distributed around the Seychelles (Mahé: dark red, Praslin: red), and two of the Mascarene Islands of Mauritius (blue) and Rodrigues (green). Names and site identifiers (S#) used in this study are provided for each reef. The number of genotyped individuals retained after quality filtering are indicated at each sampling site per species, with values first for Acropora muricata then Pocillopora damicornis . b) Environmental characterisation of the study reefs using 10 uncorrelated environmental variables ( Table 1 ), where abbreviations are as follows: sSST = standard deviation of sea surface temperature; mDHW = mean degree heating week; HPO = human population density. View this table: View inline View popup Download powerpoint Table 1. Summary of the uncorrelated environmental variables used to characterise the seascape of 15 reefs sampled for this research. 2.1 Study species We selected A. muricata and P. damicornis for the present study as two common reef-building corals of the WIO that represent keystone species with high ecological relevance ( Bridge et al., 2023 ; Cunning et al., 2018 ). Both species show evidence of population recovery after severe bleaching events ( Bruno et al., 2001 ; Lachs et al., 2023 ), which could be attributed to thermal tolerance ( Ainsworth et al., 2016 ; Carr et al., 2025 ; Gouezo et al., 2019 ; Humanes et al., 2022 ), such that they are interesting subjects for investigating adaptation to thermal stressors. Furthermore, these species have distinct life history strategies that warrant independent investigations of thermal tolerance and connectivity. A. muricata is a predominately broadcast spawning coral, releasing gametes into the water column in synchronised events at the beginning of the Austral summer (October to December) when sea temperatures increase ( Wijayanti et al., 2019 ). P. damicornis has mixed reproduction, with sexual reproduction via year-round broadcast spawning ( Gélin et al., 2018 , 2017 ; Schmidt-Roach et al., 2012 ; Smith et al., 2019 ; Ward, 1992 ). 2.2 Sampling strategy Fifteen reefs were selected for sampling across three regions of the WIO: six sites around Mauritius, six sites around the Seychelles (three each at Mahé and Praslin) and three sites around Rodrigues ( Fig. 1a ; Suppl. Table 1 ). Reefs were selected following the methods of Selmoni et al. (2020b) to maximise environmental contrast for improved power in seascape genomic analyses, while simultaneously considering logistical constraints in collaboration with local stakeholders, including site accessibility and weather conditions during sampling. Full details are provided in the Supplementary Material ( Suppl. Methods: Selection of sample sites ). The field sampling campaign took place with the assistance of scientists and technical staff of the Mauritius Oceanography Institute (MOI) in January and February 2022 for Mauritius and Seychelles, and in May 2022 for Rodrigues. This sampling campaign was performed within the framework of the Coral Reef Restoration Project of the United Nations Development Programme Mauritius (PIMS 5736, sampling permit in Rodrigues SPA 30) and the Government of Seychelles (sampling permit A0157). Colonies identified as A. muricata and P. damicornis were sampled for genotyping within a radius of 250m from the coordinates of the sample site at depths between 2-10m, with up to 30 colonies sampled per site per species. Each sample consisted of a 2cm branch collected with pliers and bagged underwater, before being transferred to 80% ethanol and stored at -20°C. A total of 345 A. muricata and 403 P. damicornis colonies were sampled for DNA extraction and single nucleotide polymorphism (SNP) genotyping ( Suppl. Table 1 ). 2.3 Environmental characterisation The seascapes at the 15 study reefs were characterised using 17 environmental and geomorphic variables that might be exerting selection pressures on corals. These variables were accessed from two publicly available online archives. Environmental variables pertaining to climatic conditions and proximity to anthropogenic activities covering the whole study extent were extracted from the Reef Environment Centralized Information System (RECIFS; Selmoni et al., 2023 ). These variables were accessed at 5 km 2 spatial resolutions and described as month- by-month environmental variation covering a temporal range of a minimum of 20 years up to December 2021 (i.e., when sampling took place; full details on variables are summarised in Suppl. Table S2 ). Temporal mean and standard deviations were then calculated for degree heating week (DHW; Skirving et al., 2020 ), sea surface temperature (SST; Skirving et al., 2020 ), chlorophyll (CHL) and suspended particulate matter (SPM), where CHL and SPM were obtained from the ‘OCEANCOLOUR_GLO_BGC_L4_MY_009_104’ dataset (accessed on 03-04-2014; E.U. Copernicus Marine Service Information (CMEMS), 2024 ). In addition to RECIFS environmental variables, we retrieved a geomorphic characterisation of the WIO reefs from the Allen Coral Atlas (ACA; Allen Coral Atlas, 2022 ). Available at 10m resolution, this variable was processed to calculate the proportion of reef slope, reef flat, back reef and reef plateau at each sampling site (250m radius around the sample site coordinates). A Spearman’s pairwise non-parametric rank correlation threshold of ρ<|0.8| was applied to retain uncorrelated variables for downstream analyses ( Table 1 ), where a principal component analysis (PCA) on scaled and centred environmental variable values was used to characterise the study sites ( Fig. 1b ). 2.4 DNA extraction and SNP genotyping DNA extraction and SNP genotyping followed methods of Selmoni et al. (2021) . Briefly, DNA was extracted using a DNeasy Blood and Tissue 96 kit (Qiagen) following the manufacturer’s instructions. DNA samples were sent to Diversity Arrays Technology (Canberra, Australia) for quality check screening and genotype-by-sequencing using the DArT-sequencing method (DArT-seq), using restriction enzymes Pst I and Hpa II ( Gawroński et al., 2016 ; Kilian et al., 2012 ; Selmoni et al., 2021 ). Sequencing read processing was performed by Diversity Arrays Technology using proprietary DArT analytical pipelines ( Sansaloni et al., 2011 ). To minimise technical bias and potential batch effects, targeted A . muricata and P. damicornis samples were kept separated and randomly distributed across the respective batches (e.g., 96-well plates, sequencing lanes) throughout the workflow (DNA purification, library preparation and sequencing). Raw sequence data have been deposited in the NCBI BioProject database under accession number PRJNA1277000. 2.5 SNP filtering The raw DArT-seq loci were aligned to each species’ reference genomes using BLAST to retain only the SNPs associated with the coral hosts (filtering thresholds: >70% percentage identity, >80% overlap identity, and >50 bitscore). The reference genome for A. muricata was the chromosome-level assembly of Acropora millepora (v2.1; GCF_013753865.1; Fuller et al., 2020 ), and the reference genome for P. damicornis was the scaffold-level assembly of P. damicornis (v1; GCF_003704095.1; Cunning et al., 2018 ). SNP filtering was performed for each species separately. First, we filtered out individuals sharing highly similar genotypes (Pearson pairwise correlations >0.95), retaining one individual in cases where genetic similarity was found. We then removed SNPs and individuals with high missing rates (>80%) before applying a filter to exclude rare alleles (minor allele frequency; MAF <5%). As both species potentially include cryptic species ( Gélin et al., 2018 , 2017 ; Grupstra et al., 2024 ; Johnston et al., 2017 ; Oury et al., 2024 ; Riginos et al., 2024 ), we used a principal coordinate analysis (PCoA) of the genotype matrix to cluster individuals based on genomic distance, before removing clusters with an F ST difference >0.3 with the other clusters. These individuals will hereafter be referred to as ‘cryptic’ individuals. We genetically confirmed the species identity of retained Pocillopora samples as P. damicornis using DArT sequences within the ITS-2 and 18S ribosomal DNA regions ( Suppl. Table S3 ). We repeated the missingness and MAF filtering steps on the retained individuals. Last, we assessed linkage disequilibrium (LD) amongst SNPs using functions from the dartR R package ( Mijangos et al., 2022 ) to create an LD map of the genome, with a maximum pairwise limit of 15 kbp (the distance of estimated LD decay to r 2 0.2. As we found minimal effect of LD pruning on population structure (assessed using a PCoA), nor evidence of SNPs clumping in a PCA (assessed using PCAdapt ; Luu et al., 2017 ; Privé et al., 2020 ), we did not prune for LD and retained all loci for downstream analyses. 2.6 Neutral genetic structure We assessed the neutral genetic structure of each species using two complementary methods: i) Principal Coordinate Analysis (PCoA) and ii) Sparse Nonnegative Matrix Factorization (sNMF). We first produced a neutral genomic dataset, where we identified and removed outlier loci using a genome scan of the filtered genotype matrix via the pcadapt R package (v4.3.5; Luu et al., 2017 ; Privé et al., 2020 ). In short, loci were attributed p-values based on their Mahalanobis distances along the retained axes of a PCA of the genotype matrix (2 PCs for A. muricata and 3 PCs for P. damicornis ). We calculated the false discovery rate (FDR; Storey and Tibshirani, 2003 ) to correct p-values for multiple testing using the qvalue R package (2.36.0; Storey et al 2024), with a q-value <0.05 threshold to identify and remove outlier loci for the neutral genotype matrix. We assessed contemporary population structure using a Gower PCoA clustering mixture model through the dartR R package (v2.9.7; Gruber et al., 2018 ; Mijangos et al., 2022 ) based on a distance matrix from allele frequency differences amongst individuals. We retained 2 PCo axes for A. muricata and 3 PCo axes for P. damicornis before allocating individuals to population groups using a hierarchical cluster analysis. We assessed ancestral population structure using sNMF algorithms with the LEA R package (v3.16.0; Frichot and François, 2015 ). Using a cross- entropy validation for K=1–10 over five repetitions with an alpha=100, we identified the number of ancestral populations to retain, using the K values resulting in the lowest average cross entropy to compute the sNMF. Membership assignment of each individual to the K ancestral populations was represented as proportions in a barplot. We calculated genetic structure between the sample sites using F- statistics (Wright, 1969, 1978) through the dartR R package ( Gruber et al., 2018 ; Mijangos et al., 2022 ), obtaining observed (H O ) and expected (H E ) heterozygosity levels to calculate the inbreeding coefficient (F IS ), alongside pairwise F ST between sample sites. 2.7 Connectivity analysis We ran a connectivity analysis to investigate how physical distances between reefs correspond to genetic separation between colonies, calculated using the pairwise F ST index (Section 2.6). Physical distance between reefs were 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 ). 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 ). Finally, we calculated the shortest sea distance (least-cost path) between pairs of sites in both directions from the transition matrix to obtain an asymmetrical square matrix of shortest sea distance. To determine the best predictor of genetic distance between reefs, we built a set of linear connectivity models to estimate F ST as a function of the shortest in-water sea distance between sample sites. We built one model per month, and one based on annual average sea currents (13 models total). We also investigated the predictive performance of Euclidean geographic distances as an independent comparison model calculated from cartesian coordinates of sampled reefs. Models were built using a log-log relationship between F ST and physical distance (i.e., sea distance and geographic distance). Their predictive powers were assessed using a leave-one- reef-out jackknife resampling approach, where model performance was assessed using coefficients of determination (R 2 ). Predictions of genetic connectivity among sites were obtained by translating the unit of sea distance into a unit of genetic separation (F ST ) through a linear model, using the connectivity predictor that best explained F ST values. 2.8 Genotype–environment associations (GEA) We performed GEAs using multivariate redundancy analyses (RDA) at the population-level following methods of Capblancq and Forester (2021) . The genotype response matrices were coded as allele frequencies, where missing data was imputed from the median of the locus- specific allele frequency across all populations (missingness per site for A. muricata : S04 with 0.05%, S05 with 0.04%, S07 with 0.14%; and for P. damicornis : S04 with 0.01%, S09 with 0.06%). The environmental variables used in the explanatory matrix were selected from the 10 uncorrelated variables ( Table 1 ) using an autonomised forward and backward stepwise model selection procedure with 10,000 permutations through the ordistep function of the vegan package. We first ran a preliminary RDA to identify environmental variables explaining neutral genomic variation. This procedure selected the standard deviation of monthly sea surface temperature (sSST) and average monthly maximal DHW (mDHW) as the variables that best explained neutral genetic variation independently for both species. To detect putative genomic regions under selection, we performed a multivariate GEA using a partial RDA on the entire genotype matrix and included reef-level population structure as a conditional variable to control for neutral genetic variation. Controlling for neutral genetic variation was done to reduce false- positive detections at the expense of losing power to detect true outlier loci along neutral gradients ( Excoffier et al., 2009 ; Lotterhos, 2023 ). The values representing population structure were obtained from the axes of a PCA of centred and scaled neutral allele frequencies using the rda function of the vegan R package, retaining the first K PCs that explained more than the mean explained variance. Outlier loci were identified from RDA loadings ( Capblancq et al., 2018 ). After retaining all constrained RDA axes ( K =2 for both species), we evaluated the significance of each SNP based on the extremeness of its Mahalanobis distance value compared to the distribution of the other SNPs in the RDA space. The Mahalanobis distances were computed using the dist_ogk function of the bigutilsr R package (v.0.3.4; Privé, 2021 ), corrected for genomic inflation factor ( François et al., 2016 ) and distributed along a chi-squared distribution with K degrees of freedom to assign a p-value to each SNP ( Luu et al., 2017 ). We applied an FDR threshold of q -value<0.05 to identify outlier loci. 2.9 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., those with q -value <0.05) detected with the two heat-related variables (sSST and mDHW) for both species, following Selmoni et al. (2021) . To facilitate comparability of annotation between species, we re-annotated genes in both reference genomes. For this, we retrieved transcript sequences of each gene, then ran a similarity search (blastx) against a manually curated database of protein sequences and annotations (Uniprot/swissprot, metazoan entries as accessed on November 2022; Boeckmann et al., 2003 ). Protein annotations were assigned to the coral genes when the E-score of the similarity search was <10 -6 . We then identified genes located within ±10 kbp of significant SNPs from the GEA analysis (hereafter “significant genes”; Selmoni et al., 2021 ). We evaluated gene-set enrichment of significant genes using the SetRank R package (version 1.0; Simillion et al., 2017 ), where genes are ranked based on ascending q -values from the GEA from which overrepresented GO terms associated with significant genes are identified. GO terms were deemed significant when they had SetRank p- values<0.01 and adjusted p -values<0.05 (i.e., corrected for multiple testing and overlap between gene-set categories). 2.10 Adaptive Potential Index (API) The potential of corals to adapt to temperature increases depends on (i) the thermal history at the reef (and therefore local adaptation of populations) and (ii) the arrival of propagules from neighbouring heat-adapted reefs ( Matz et al., 2020 ; Selmoni et al., 2020a ). Building on previous adaptation indices, including the Population Adaptive Index ( Bonin et al., 2007 ) and the Landscape Adaptive Index ( Zhang et al., 2019 ), we calculated a spatially explicit ‘Adaptive Potential Index’ (API) by combining results from the GEA (Section 2.7) and the connectivity analyses (Section 2.9). To summarise potential for local adaptation, first we calculated an ‘Adaptive Score’ to link putatively adapted alleles with the thermal history of reefs across the WIO ( Capblancq et al., 2020 ). For this, we ran an RDA associating the outlier loci from the GEA (Section 2.7) with the thermal explanatory variables (i.e., sSST and mDWH). The environmental scores from these RDA axes were extracted and normalised to a scale of 0-1 to obtain an Adaptive Score ( Steane et al., 2014 ). The Adaptive Score was then projected across the WIO reefs using thermal history data for each reef of the region. Second, we calculated the ‘Inbound Connectivity Index’ (ICI) and ‘Outbound Connectivity Index’ (OCI) for each reef, which represent the total neighbouring reef areas located downstream or upstream of a target cell, respectively, limited to a sea distance equivalent to the species’ F ST ≤0.1 (Section 2.9). Finally, we calculated the Adaptive Potential Index for each reef cell as the total area of inbound connected reefs with an Adaptive Score >0.8. 3. Results 3.1 Site characteristics and genomic filtering Across the 15 reefs selected for coral sampling, six sites were sampled in the Seychelles (three each at Mahé and Praslin), six in Mauritius and three in Rodrigues ( Fig. 1a ). These reefs represent contrasting environmental conditions ( Fig. 1b ), as characterised by 10 uncorrelated environmental variables ( Table 1 ; Suppl. Fig. S2 ). The northern-most sampled reefs were those from the Seychelles (represented in red), comprising predominantly of back reefs located near deep water with less thermal stress (i.e., low sSST and low mDHW), generally further away from human activity (i.e.., cropland, human population and boat activity). Most Mauritian reefs (represented in blue) are characterised by reef flats near land and human activity (i.e., cropland and boats) that experience more variable thermal conditions. The exception is the more isolated northern-most Mauritian site on Ile Plate (S12 in dark blue), which is associated with deeper waters and more stable temperatures. Finally, the three reefs at Rodrigues (represented in green) are isolated reef flats located near deeper water that experience hotter (higher mDHW) and more variable SST conditions (sSST). Between 8–29 individuals per species were sampled for genotyping at each site ( Fig. 1a ; Suppl. Table S1 ). The DArT-seq analytical pipeline resulted in 73,253 bi-allelic SNPs genotyped for 345 A. muricata individuals and 65,708 SNPs for 403 P. damicornis individuals (values of SNPs and individuals retained at each step are summarised in Suppl. Table S4 ). We removed 48 A. muricata and 261 P. damicornis individuals identified as clones, followed by 49 A. muricata and 8 P. damicornis individuals with too much missing data and low MAF, and a further 43 A. muricata and 14 P. damicornis individuals identified as cryptic individuals (i.e., F ST >0.3 between PCoA clusters). We used pairwise F ST and PCoA biplots to confirm that these cryptic individuals were removed from the dataset ( Suppl. Fig. S3 ). After filtering for rare variants, missing values, clones and cryptic individuals, our final genotype matrix comprised of 205 A. muricata individuals with 5,757 SNPs and 120 P. damicornis individuals with 12,953 SNPs. This resulted in 4–7 A. muricata individuals retained from 14 sites (no individuals were left at Curieuse Reef (S06) in Seychelles), and 3–14 P. damicornis individuals retained across all 15 sites ( Suppl. Table S1 ). 3.2 Neutral genetic structure Outlier loci were identified and removed using a genome scan via the pcadapt R package to produce a neutral genotype matrix for assessing population structure across the WIO. For the genome scan, we retained K =2 PCs for A. muricata and K =3 PCs for P. damicornis , identifying 74 and 138 outlier SNPs (q-value<0.05), respectively. The final neutral genotype matrices comprised of 5,683 SNPs for A. muricata and 12,815 SNPs for P. damicornis ( Suppl. Table S4 ). We found strong population structuring for A. muricata , where PCoA and sNMF models indicate the presence of three genetic clusters strongly associated with geographic distributions ( Fig. 2a ). Furthermore, we find evidence of high gene flow between reefs within regions contrasted with stronger genetic differentiation between regions. Indeed, the first two PCoA axes (capturing almost 20% of neutral genetic variation) assigned individuals into groups closely reflecting sampled regions ( Fig. 2ai ), with sNMF models confirming that individuals are genetically similar within regions with low gene flow between regions ( Fig. 2aii-iii ). These patterns are statistically supported by AMOVAs, where genomic variance is significantly higher between regions than expected by chance ( p <0.01; Suppl. Fig. S4a ). In contrast, variance between sites within regions and variance between subregions were not significant. Low pairwise F ST values (i.e., F ST ≤0.01) further substantiates substantial gene flow with limited genetic differentiation between reefs of the same region ( Table 2 ). In contrast, pairwise F ST values between certain regions exceeded 0.20, signalling restricted gene flow and increased genetic divergence between more distant regions ( Table 2 ); larger F ST values (i.e., F ST ≥0.20) were between reefs at the Seychelles with Rodrigues, as well as between site S04 on the western side of Mahé (SEY) with reefs around the main islands of Mauritius. Last, high inbreeding coefficients (F IS >0.20) were observed between reefs of the Seychelles, Rodrigues, and the northern and southern reefs of Mauritius ( Suppl. Table S5a ), further pointing towards restricted gene flow between reefs of different regions. Download figure Open in new tab Download figure Open in new tab Figure 2. Population structure analyses of (A) A. muricata and (B) P. damicornis , assessed by a principal coordinate analysis (PCoA) and sparse nonnegative matrix factorization (sNMF), with K =3 and K =4 groups identified for each species respectively in both methods. i) Individual scores on PCoA axes 1 and 2 grouped by geographic location (shapes indicate reef and colours indicate subregion; left) and hierarchical cluster grouping of PCoA results (coloured by assigned grouping; right). ii) Maps indicating the proportion of individuals at each sampled reef allocated to K ancestral populations using sNMF admixture models, where pie chart size indicates the relative number of sampled individuals at each reef and coloured boxes indicate sub-regions. iii) Proportion of sampled individuals (columns) allocated to K ancestral populations using sNMF admixture models, with individuals grouped by sub-region. Sub region abbreviations: PRA = Praslin, MAH = Mahé, IPL = Ile Plate, MW = West Mauritius, ME = East Mauritius, ROD = Rodrigues. View this table: View inline View popup Download powerpoint Table 2. Pairwise F ST between study sites for A. muricata (lower triangle) and P. damicornis (upper triangle) based on neutral SNP genotype matrices. Sites are grouped and coloured by region and subregion. F ST >0.20 are highlighted in yellow. Download figure Open in new tab Download figure Open in new tab Figure 3. Adaptive potential indices and predictive sea currents for (A) A. muricata and (B) P. damicornis . i) Connectivity model performance boxplots, measured as R 2 , for models describing the relationship between log 10 of the genetic distance (F ST ) and log 10 of the geographic distance, where boxplots show the spread of R 2 values of connectivity models built with leave-one-reef-out jackknife analyses, assessing the Euclidean geographic distance (‘geo’) and sea current averaged over each month (01–12) and the year (13). ii) Log 10 -log 10 relationship between the genetic distance (F ST ) and sea distance obtained from the strongest connectivity predictor (December sea currents for A. muricata in (A) ; annual sea currents for P. damicornis in (B) ). The blue dotted lines indicate the sea distance that corresponds to F ST =0.1. iii) Adaptive Potential Index (API in km 2 ) across the whole WIO region, calculated by combining Inbound Connectivity and Adaptive Scores. Larger API indicates more probability of hosting adaptive genotypes up-current of connected reefs. Arrows indicate sea current velocity of the strongest connectivity predictor. Download figure Open in new tab Figure 4. Genotype–environment association results for (A) A. muricata and (B) P. damicornis , using multivariate partial RDA (redundancy analyses) at the reef-level. i) RDA biplots projecting loci (dots) and environmental variables (arrows, where sSST is the standard deviation of sea surface temperature and mDHW is the mean of the degree heating weeks). Locus scores are rescaled by a factor of 12 to improve visibility. ii) Manhattan plots with the distribution of −log10(q-values) in the genomes. For the first two panels, loci that were identified as outliers (q- value<0.05) are coloured in orange, where outlier loci found within ±10kbp of significantly enriched genes (determined using SetRank) are labelled as diamonds and colour-coded by protein function. iii) Tables summarising the significantly enriched gene ontology (GO) terms and their associated protein names and descriptions, alongside SetRank results and the associated genes. Protein names and GO terms are colour coded to match panels i) and ii) . Four genetic clusters were identified for P. damicornis across the three geographic regions using PCoA (first two axes capturing 23% of neutral genetic variance) and sNMF. The genetic clusters roughly matched geographic location, with evidence of some gene flow between geographic regions ( Fig. 2b ). AMOVA analyses indicate that genomic variance is significantly higher between sites ( p <0.01) and between regions (p=0.03) than expected by chance, while variance between subregions were not significant ( Suppl. Fig. S4b ). There appears to be strong genetic similarities between individuals in the northern and southern Mauritius reefs with individuals in Rodrigues, as well as with some individuals in the northern Seychelles (Praslin) reefs (yellow clusters in Fig. 2b ). Within regions, reefs had varying levels of genetic differentiation, with pairwise F ST values between -0.01 and 0.16. Most individual genetic similarity was between the Rodrigues reefs, while the largest differences were between the Mauritius island reefs ( Table 2 ). Between regions, the largest differences were between the eastern and western reefs of Mauritius with the Seychelles (F ST ≥0.15), while the most similar regions are between those of the Mascarene Islands (i.e., Mauritius and Rodrigues; F ST <0.15). Finally, most sites had evidence of panmixia, though higher than expected homozygosity (F IS ≥0.20) was observed at the eastern-most reef of the Seychelles (S07), as well as at the northern (S12) and southern (S01) Mauritius reefs ( Suppl. Table S5b ). 3.3 Connectivity Analysis Genetic connectivity between A. muricata populations was well explained by sea currents and geographic distance, with a median R 2 of 0.80 (range of median R 2 0.67–0.87) across all predictors following a leave-one-reef-out jackknife analysis ( Fig. 3ai ). The best predictor of F ST was mean sea current in December with a median R 2 of 0.87 (IQR 0.86–0.87; Fig. 3ai ). Genetic connectivity between P. damicornis populations was also well explained by sea currents, albeit to a lesser degree than for A. muricata . Monthly sea currents all had similar strengths to predict patterns of genetic connectivity of P. damicornis across the WIO, with associations producing a median R 2 of 0.36 (range of median R 2 0.26–0.38; Fig. 3bi ). Geographic distance again explained less genetic connectivity than sea currents, with a median R 2 of 0.26 (IQR 0.25–0.40). We therefore chose to model the genetic connectivity of P. damicornis using the average annual sea current, which explained 36% of the F ST variation (median R 2 of 0.36; IQR 0.35–0.39; Fig. 3bi ). The species-specific log 10 –log 10 relationships between F ST and optimised sea distance (based on December currents for A. muricata and annual currents for P. damicornis ) indicate a stronger relationship by approximately 3.5 times for A. muricata than for P. damicornis , with respective regression coefficients of 0.885 ( Fig. 3aii ) and 0.238 ( Fig. 3bii ). These results reiterate patterns of neutral gene flow observed between regions for both species (see Section 3.2), where A . muricata individuals were more genetically similar between closer reefs and more genetically dissimilar with further reefs, while the weaker relationship for P. damicornis highlights that physical distance does not necessarily correspond with genetic similarity amongst individuals. 3.4 Genotype–Environment Associations to identify loci under selection The two thermal stress variables, sSST and mDHW, best explained reef-level neutral genetic variation for both A. muricata and P. damicornis , as determined using an automated forward and backward variable selection method. As a preliminary partial RDA revealed confounding effects of neutral genetic structure and thermal stress, we performed GEA analyses while accounting for neutral structure using 3 PCs for A. muricata (explaining 68.4% variance) and 4 PCs for P. damicornis (explaining 64.2% variance) to reduce false positive outlier loci. For A. muricata , the first and second pRDA axes respectively explained 21.0% and 5.8% of genetic variation ( Fig. 4ai ). Both axes were negatively associated with sSST and mDHW, with the second axis showing a stronger association with mDHW. A total of 380 outlier loci were identified ( q -value<0.05) throughout the genome ( Fig. 3aii ). SetRank analyses revealed that significant genes (i.e., those within ±10 kbp of significant SNPs) were associated with seven enriched GO terms (molecular functions) and encode five proteins: glycine receptor subunit alpha-3 (GO:0022852), putative beta-hexosaminidase (GO:0030247, GO:0004563), protein SGT1 homolog (GO:0051087), Sacsin (GO:0070628, GO:0030544) and Glutathione S- transferase omega-1 (GO: 0045174) ( Fig. 4aiii ). Interestingly, six genes significantly associated with temperature were annotated as Sacsin (DNAJC29) and SGT1 homolog proteins, which have the molecular function “chaperone binding activity” (of these, five genes were also associated with “Hsp70 protein binding activity”). The other significant genes included those associated with glycine-gated chloride ion channel activity (two genes), polysaccharide binding activity (specifically with beta-N-acetylhexosaminidase activity; four genes), and glutathione dehydrogenase activity (two genes). In P. damicornis , the first pRDA axis explained 17.8% of genetic variation and was negatively associated with sSST, while the second partial RDA axis explained 16.9% of genetic variation and was negatively associated with mDHW ( Fig. 4bi ). A total of 292 outlier loci were identified ( Fig. 3bii ). SetRank enrichment analyses identified only carbon-sulfur lyase activity (GO:0016846) as an enriched molecular function by four significant genes that are associated with centromere protein V ( Fig. 4biii ). 3.5 Adaptive Potential Index (API) across the West Indian Ocean We combined network-level connectivity models with spatial distributions of putatively heat- adapted genotypes to produce an Adaptive Potential Index (API) across the WIO for identifying donor reefs with the potential to disperse thermal-adapted genotypes to neighbouring reefs ( Table 3 ). For a given pixel, the API represents the surface area (in km²) of neighbouring reefs that (1) have a thermal history linked to the presence of candidate adaptive loci (Adaptive Score ≥0.8) and (2) are within a sea distance indicative of inbound connectivity (i.e., a distance that corresponds to an F ST ≤0.1 upstream of the target reef). For both species, Adaptive Scores (AS) and API were region-specific, with higher values in more southern reefs at Mauritius and Rodrigues and lower values at reefs near the equator at the Seychelles ( Fig. 3iii ; Table 3 ; Supp Fig. S5 ). View this table: View inline View popup Table 3. Adaptive potentials and connectivity metrics for A. muricata and P. damicornis at 15 coral reefs across the West Indian Ocean. The Adaptive Score (AS) indicates the relative level of adaptation to thermal stressors (sSST and mDHW) from 0 to 1 (low to high adaptive values), as determined by a GEA analysis. Inbound and Outbound Connectivity indices (ICI and OCI, respectively) indicate the respective area (in km 2 ) of reef up-current and down-current that fall within a sea distance corresponding to genetic connectivity of F ST 0.8). See Supp Fig. S5 for spatial representations. For A. muricata ( Fig. 3aiii ; Table 3 ), the reefs around Mauritius hosted moderately heat adapted corals (AS between 0.43–0.74), further receiving adapted propagules from an estimated 181–187 km 2 of other reefs. While A. muricata at the Rodrigues reefs had slightly reduced API values (136 km 2 ) due to fewer heat adapted propagules up-current, these corals had the highest Adaptive Scores (AS ≥0.80) of the studied reefs, supplying propagules to almost 700 km 2 of reefs downstream (OCI). In the northern WIO, A. muricata corals of the Seychelles had both lower thermal Adaptive Scores (AS 935 km 2 downstream (OCI). Similar AS estimates of coral adaptation to thermal stress and patterns of API were observed for P. damicornis , where connectivity measurements based on weaker F ST relationships with sea currents resulted in lower values for IBI, OCI and API ( Fig. 3biii ; Table 3 ). The reefs at Mauritius and Rodrigues appear to host the most heat adapted corals (AS between 0.41–0.81), respectively receiving heat adapted propagules from an estimated 102 km 2 and 94 km 2 of reef (API) and providing adapted propagules to approximately 300 km 2 and 450 km 2 of reef (OCI). In the Seychelles, P. damicornis corals had very low adaptive scores (AS <0.17) and were expected to receive no adapted propagules (API of 0 km 2 ) from the 811–870 km 2 of inbound connected reefs. 4. Discussion 4.1 Life-history traits and asymmetric ocean currents drive population structure across the WIO Coupling genomic information with environmental data revealed the importance of coral life- history strategies and asymmetrical ocean currents in driving spatial patterns of population structure across the WIO. The population structure uncovered in this study corroborates connectivity simulations and mapped genetic structuring of various other coral species across the WIO (e.g., Gélin et al., 2018 ; Kusumoto et al., 2020 ; Oury et al., 2024 ; Vogt-Vincent et al., 2024 ). A. muricata showed strong patterns of local genetic mixing with minimal between-region gene flow across the WIO, mirroring expectations for a synchronous broadcast spawning coral species ( Miller and Ayre, 2008 ). Indeed, high gene flow between reefs of a region within a relatively short distance has been found for numerous Acroporidae around the world (e.g., Adam et al., 2022 ; Fuller et al., 2020 ; Lukoschek et al., 2016 ; Nakajima et al., 2010 ; Selmoni et al., 2021 ). Genetic mixing is promoted by synchronous release of gametes ( Wijayanti et al., 2019 ), while a free-swimming larval stage allows propagule dispersal to neighbouring reefs ( Tabitha et al., 2021 ). Some gene flow occurs between southern reefs of the Mascarene Islands (Mauritius and Rodrigues) in a predominately westward direction along ocean currents ( Vogt-Vincent et al., 2024 ). However, corals in the Seychelles appear to be genetically isolated, where gene flow in the Austral summer during A. muricata spawning is blocked by the strong oceanographic barrier of the South Equatorial Countercurrent (at approximate latitude of 10° S; Phillips et al., 2021 ). Similar, albeit more complex, population structuring was found for P. damicornis with three region-specific genetic clusters and a fourth dispersed genetic cluster (yellow group in Fig. 2b ). The general pattern of gene flow between the Mascarene Islands reflects that observed for the A. muricata populations and that of previous investigations into P. damicornis Type β dispersal across the southern WIO ( Gélin et al., 2018 ). The genetic dissimilarity observed for our P. damicornis between the northern and southern regions can again be explained by strong ocean currents ( Phillips et al., 2021 ). The fourth genetic cluster poses an interesting complexity, potentially attributed to contemporary gene flow, remnants of a common ancestral population, and/or the inclusion of cryptic species or ecotypes in our analyses. Contemporary region-wide gene flow might be facilitated by a well provisioned larval stage with up to 14 weeks viability ( Gélin et al., 2018 , 2017 ; Richmond, 1987 ), where propagules can survive long distances, travelling passively by ocean currents ( Phillips et al., 2021 ), rafting ( Nikula et al., 2013 ), or in boat ballast water or hull fouling ( Gollasch, 2008 ). Alternatively, this anomalous genetic cluster could represent genetic remnants of a common ancestral population ( Oury et al., 2024 ) that expanded across the WIO following the penultimate glacial period (ca. 100,000 years ago) under altered sea current conditions ( Colleoni et al., 2016 ; Herbert et al., 2010 ). Evidence of ancestral genetic make-up could be preserved by high rates of selfing and asexual reproduction of P. damicornis (indeed, 65% of our samples were clones). Finally, the genetic cluster could simply represent the inclusion of hybridised individuals, either between closely related cryptic species occurring in sympatry ( Carr et al., 2025 ; Combosch and Vollmer, 2015 ; Gélin et al., 2018 ; Oury et al., 2024 ) or between colonies of genetically divergent and locally adapted ecotypes (e.g., shallow lagoon ecotypes mating with outer reef slope ecotypes; Oury et al., 2024 ). Regardless of the mechanism, population structure is rarely a simple function of reproductive mode or larval type ( Miller and Ayre, 2008 ; Prata et al., 2024 ; Severance and Karl, 2006 ). While genetic exchange seems to occur across the southern WIO from Rodrigues towards the other Mascarene Island reefs, and potentially beyond to Madagascar and the African east coast, the South Equatorial Countercurrent barrier is undoubtedly inhibiting gene flow between southern to northern WIO reefs near the equator (e.g., Seychelles). This can have important consequences for adapted genotype exchange across the WIO, with impacts on management strategies to be implemented in different regions ( Phillips et al., 2021 ; Vogt-Vincent et al., 2024 ). More wide-spread genetic sampling of corals is required to draw precise conclusions of population structure across the WIO region. 4.2 Sea currents as a proxy for genetic connectivity and dispersal Using sea currents to determine asymmetric distance between reefs was a better predictor of population connectivity than Euclidean geographic distance for both coral species, supporting the growing body of literature calculating gene flow for marine organisms (e.g, Benestan et al., 2016 ; Cooke et al., 2016 ; Hernawan et al., 2023 ; Krueck et al., 2020 ; Prata et al., 2024 ; Snead et al., 2023 ; Villastrigo et al., 2023 ; White et al., 2010 ). For synchronous broadcast spawning corals, we might expect that genetic distance would be best predicted using sea currents from a particular time of the year. Indeed, connectivity of A. muricata was best predicted by sea currents in December, reflecting timing of known mass spawning for this species ( Wijayanti et al., 2019 ). When no month’s sea currents clearly explain genetic distance, such as for P. damicornis , it may be that the species has asynchronous spawning and potentially complex reproductive modes. The correlation (r 2 ) and regression coefficients (m) of the connectivity models can also provide information about the pattern and strength of sea currents used to disperse the propagules across a reef network. A strong correlation coefficient seems to indicate more local recruitment of propagules, such as for A. muricata (r 2 =0.87), while weaker correlation coefficients indicate decoupling of gene flow from physical geographic distance, such as for P. damicornis (r 2 =0.37). The regression coefficient provides a rough estimate of length of larval stage, where the 3.5 times steeper regression coefficient of A. muricata vs P. damicornis (m =0.88 vs. m =0.24) mirrors the much shorter larval stage (3 weeks vs 14 weeks; Richmond, 1987 ; Tabitha et al., 2021 ). By using metrics derived from the connectivity model, hypotheses of life history strategies can be obtained for non-model coral species to facilitate the development of appropriate management strategies. 4.3 Thermal stress drives signals of local adaptation Historical thermal variation was found to correlate with non-neutral genetic diversity of two coral species across the WIO. Multivariate genotype–environment associations (GEAs) suggest local adaptation to thermal stress, with 380 and 292 outlier SNPs for A. muricata and P. damicornis , respectively, significantly associated with standard deviations in sea surface temperatures (sSST ; Skirving et al., 2020 ) and mean degree heating weeks (mDHW; Skirving et al., 2020 ). In both species, corals from Rodrigues exhibited the strongest signals of thermal adaptation, likely due to the higher and more fluctuating temperatures of its lagoon and reef flat environments. In contrast, corals from the Seychelles showed the weakest adaptive signals, as their backreef and reef slope habitats are influenced by cooler upwelling waters. These findings reflect recent evidence of mean SST as a strong predictor of global genetic diversity in P. damicornis , with highest nucleotide diversity at reefs experiencing higher temperatures ( Carr et al., 2025 ). Extreme and variable marine thermal conditions can trigger coral bleaching when SST fluctuations exceed seasonal averages ( Hughes et al., 2017b ; Liu et al., 2003 ), a pattern observed across the Indian Ocean (Shlesinger and van Woesik, 2023). Recurrent mass-bleaching events over decades exert strong selective pressures ( Hughes et al., 2017b ; Skirving et al., 2020 ), likely driving molecular signatures of thermal adaptation and tolerance in affected populations ( Lachs et al., 2023 ). SST variation and mean DHW capture both the duration and intensity of thermal anomalies ( Humanes et al., 2022 ; Liu et al., 2003 ; Skirving et al., 2020 ; van Hooidonk et al., 2016 ), which may explain the high number of outlier loci associated with heat stress detected here. While environmental data were extracted at 5 km resolutions, we acknowledge that reef systems can exhibit substantial microhabitat variation at much finer spatial scales (e.g., <100m), potentially influencing highly localised patterns of coral adaptation (e.g., Bay and Palumbi, 2014 ; Bozec et al., 2025 ). To mitigate this effect, we standardised sampling depth between sites and used variables from the Allen Coral Atlas (at 10m resolution) to refine the ecological characterisation of each sample site. Nonetheless, the potential for undetected fine-scale environmental heterogeneity remains a limitation here. 4.4 Candidate molecular targets of heat stress Genes near SNPs significantly associated with historical heat stress exposure (sSST and mDHW) had molecular functions that were previously observed in coral thermal responses. Interestingly, we found significant enrichment for heat shock proteins and molecular chaperones in A. muricata , driven by the presence of five significant genes encoding the Sacsin protein. Sacsin is a co-chaperone for the heat-shock protein Hsp70 ( Ménade et al., 2018 ; Parfitt et al., 2009 ) and is significantly up-regulated when Acropora and Pocillopora species are exposed to high temperatures in lab conditions ( Cunning et al., 2018 ; Cunning and Baker, 2014 ; Hemond et al., 2014 ; Mayfield et al., 2018 ). Sacsin has previously been identified as a key candidate protein involved in thermal responses of A. millepora in the Great Barrier Reef ( Fuller et al., 2020 ), while another seascape genomic study on A. millepora in New Caledonia found significant enrichment of chaperone binding proteins associated with bleaching alert frequency ( Selmoni et al., 2021 ). Three other molecular functions were significantly enriched for A. muricata in association with thermal stress. The glutathione S-transferase omega-1 (EC 2.5.1.18) enzyme was detected with two significant genes associated with sSST and mDHW. This enzyme is upregulated in some coral species following long-term (60 day) heat stress exposure ( Dias et al., 2019 ). This enzyme plays a key role in coral cellular antioxidant and immune defence responses ( Morrow et al., 2012 ), likely responding to reactive oxygen species induced by enhanced metabolic rates ( Burdon et al., 1990 ; Richier et al., 2006 ). We also found enrichment for the lysosomal enzyme beta-hexosaminidase (EC 3.2.1.52) from four genes. This is an important factor involved in a range of functions, including glycosylation of proteins, cellular signalling, protein folding, and mycobacterium defence ( Koo et al., 2008 ; Lundgren et al., 2013 ; Sproles et al., 2019 ). As stressed corals have increased susceptibility to disease ( Littman et al., 2010 ; Mydlarz et al., 2010 ; Thurber et al., 2009 ), an adaptive increase in immune responses due to increased temperatures might be expected ( Lundgren et al., 2013 ). Last, the glycine receptor (subunit alpha-3) protein associated with glycine-gated chloride ion channel activity was detected by two genes, which is involved in synaptic signalling of the central nervous system of vertebrates ( Ceder et al., 2024 ) that might alter mobility and movement for feeding in heat-adapted corals. Among heat-associated SNPs in P. damicornis , only one molecular function was significantly enriched: carbon-sulphur lyase activity. To our knowledge, this function has not been previously associated with heat stress and may represent a false signal. Given that previous studies on Pocillopora spp. identified candidate genes involved in immunity, cellular homeostasis, metabolism, and signalling responses to environmental stress ( Lundgren et al., 2013 ; Mayfield et al., 2018 ; Poquita-Du et al., 2024 ; Selmoni et al., 2021 ), we expected a greater number of significant functional associations. The limited detection of enriched functions may be attributed to the strong population structure observed, which we attempted to control for by including neutral genetic structure in our RDA analysis. In this way, we targeted shared genomic pathways involved in thermal stress responses amongst diverged individuals, where we cannot rule out the retention cryptic or hybrid individuals in our dataset that inhabit different niches and express different responses to stressors ( Johnston et al., 2017 ). Alternatively, a lack of signal may be attributed to the high proportion of unique, predominantly unannotated genes in P. damicornis , which comprise up to a quarter of its genome ( Cunning et al., 2018 ). We stress caution in over-interpreting results of GEA and GO term analyses. These models generate hypotheses to facilitate understandings of environmental influence on genotypes ( Lasky et al., 2023 ; Lotterhos, 2023 ; Selmoni et al., 2024 ). Candidate genes should therefore be treated as preliminary results requiring experimental or in-field validations (reviewed in Selmoni et al., 2024 ). Furthermore, numerous analytical factors can affect GEA results. Here, we traded GEA power with reduced false positive rates when we corrected for population structure ( Forester et al., 2018 ; Lotterhos, 2023 ). GEAs also have reduced power to detect quantitative trait nucleotides ( Lotterhos, 2023 ); because polygenic variation is expected to drive coral thermal adaptation ( Rose et al., 2018 ), important genomic regions under selection are likely missed. Additional methods, such as genome-wide association studies (GWAS) or quantitative trait locus (QTL) mapping, could help strengthen conclusions drawn from seascape genomic studies ( Lasky et al., 2023 ; Lotterhos, 2023 ), as could the inclusion of other genomic variants that are expected to play a major role in facilitating adaptation (e.g., structural variants; Mérot et al., 2020 ; Pokrovac and Pezer, 2022; Wellenreuther et al., 2019 ). Nevertheless, our results provide valuable hypotheses of genomic regions conserved across populations in response to thermal pressures across a discontinuous reef system to guide future coral research and assist regional coral reef management. 4.5 Local-level reef management is recommended across the West Indian Ocean Coral reef ecosystems across the WIO are vulnerable to collapse under projected climatic conditions ( Obura et al., 2022 ). As local scale stressors can affect regional level community structure ( Green et al., 1987 ), we echo recommendations for coordinated local level management across the WIO to maintain overall coral reef health ( Obura et al., 2022 ; Stefanoudis et al., 2023 ). We found that the large distances between reefs reduced the importance of regional gene flow for facilitating thermal adaptation. Indeed, the reef regions investigated represent two independent ecoregions ( Obura, 2012 ; Spalding et al., 2007 ). Furthermore, coral propagule dispersal simulations across the WIO estimates that there are over 100 generational steps of separation (about 500 years) between the Mascarene Islands and the Seychelles ( Vogt-Vincent et al., 2024 ). As congruent spatial pattens of the adaptive potential index (API) were observed for both species despite differences in life-history traits, demography and adaptation, we recommend similar management strategies for both species. Understandings of source locations of thermally adapted corals and connectivity with other reefs in the region can help guide conservation planning ( Bozec et al., 2025 ; Selmoni et al., 2024 , 2020a ). For instance, reefs hosting corals with higher adaptive potentials could be placed in a marine protected area ( Abelson et al., 2016 ; De Clippele et al., 2023 ), while reefs with low adaptive potentials might require assisted gene flow from adapted reefs ( Baums, 2008 ; Hagedorn et al., 2021 ; Van Oppen et al., 2015 ). The Mascarene Islands are important sources of heat adapted corals due to exposure to higher temperatures with large daily fluctuations across reef lagoons and flats. As the Mascarene Islands are located up-current of Madagascar and the east African coast along the South Equatorial Current ( Phillips et al., 2021 ; Vogt-Vincent et al., 2024 ), these reefs may serve as important sources of thermal resilience for west WIO populations ( Matz et al., 2020 ; Oury et al., 2024 ). In contrast, the Seychelles Archipelago appears to host fewer heat adapted corals, likely due to proximity with deep oceans and upwelling that bring cooler and more consistent water temperatures (Shlesinger and van Woesik, 2023). With their relative isolation, coral reefs of the Seychelles may require assisted gene flow, importing propagules from heat adapted reefs such as east African reefs that have already experienced recurrent mass bleaching ( Elma et al., 2023 ). Going forward, insights obtained from these seascape genomic analyses can be integrated into management frameworks that must be co-developed with local stakeholders ( Stefanoudis et al., 2023 ). This is important to facilitate collaboration between local managers across the regional network to standardise research and management methods, share resources and undertake research that is directly relevant to making decisions for the overall health of the coral reef ecosystem in the WIO. Extrapolations of genetic-based adaptive indices to unsampled regions can be biased, particularly if predictor variables covary differently in training vs extrapolated reefs ( Yates et al., 2018 ), or if genetic variation is not well distributed across the environmental landscape ( Capblancq and Forester, 2021 ). Future work will need to include seascape genomic analyses across the larger WIO network prior to making more definitive conclusions and preparing management strategies. 4.6 Conclusions Local thermal conditions seem to drive thermal tolerance across reefs of the West Indian Ocean. We found strong molecular signals of heat stress adaptation in corals from thermally variable environments, such as at the Rodrigues and Mauritius Islands. The genetic variants potentially implicated in thermal adaptation were associated with genes coding for proteins implemented in heat stress response, notably the heat-shock protein co-chaperone Sacsin. However, large sea distances and strong oceanographic barriers inhibit the genetic exchange of adapted genotypes across the WIO, calling for local-level management of reefs within a framework of a larger regional management plan. Further research into phenotypic expressions of hypothesised target genes will need to be carried out to validate adaptive potentials of corals during stressed conditions. Insights from this research contribute to a growing understanding of coral adaptation to thermal stress that can inform conservation strategies aimed at preserving thermally tolerant genotypes across the WIO in the face of ongoing climate change. Data availability statement Data for this study will soon be made available on Dryad. Conflict of Interest statement The authors declare no conflict of interests Acknowledgements This study was conducted with funding provided under the Coral Restoration Project (PIMS 5736) by the Adaptation Fund and implemented by the United Nations Development Programme Mauritius and Seychelles. 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 Parks and Gardens Authority, and Shoals Rodrigues. We also thank the United Nations Development Programme Mauritius and Seychelles for their support in coordinating activities throughout the project. Funder Information Declared United Nations Development Programme Mauritius and Seychelles , PIMS 5736 Footnotes Manuscript authorship and funding information has been updated References 1. ↵ Abelson , A. , Nelson , P.A. , Edgar , G.J. , Shashar , N. , Reed , D.C. , Belmaker , J. , Krause , G. , Beck , M.W. , Brokovich , E. , France , R. , Gaines , S.D ., 2016 . Expanding marine protected areas to include degraded coral reefs . Conserv. Biol . 30 , 1182 – 1191 . doi: 10.1111/cobi.12722 OpenUrl CrossRef PubMed 2. ↵ Adam , A.A.S. , Thomas , L. , Underwood , J. , Gilmour , J. , Richards , Z.T ., 2022 . Population connectivity and genetic offset in the spawning coral Acropora digitifera in Western Australia . Mol. Ecol . 31 , 3533 – 3547 . doi: 10.1111/mec.16498 OpenUrl CrossRef 3. ↵ Ainsworth , T.D. , Heron , S.F. , Ortiz , J.C. , Mumby , P.J. , Grech , A. , Ogawa , D. , Eakin , C.M. , Leggat , W ., 2016 . Climate change disables coral bleaching protection on the Great Barrier Reef . Science ( 80 -.). 352, 338–342. doi: 10.1126/science.aac7125 OpenUrl Abstract / FREE Full Text 4. ↵ Allen Coral Atlas , 2022 . Imagery, maps and monitoring of the world’s tropical coral reefs [WWW Document] . doi: 10.5281/zenodo.3833242 OpenUrl CrossRef 5. ↵ Baums , I.B ., 2008 . A restoration genetics guide for coral reef conservation . Mol. Ecol . 17 , 2796 – 2811 . doi: 10.1111/j.1365-294X.2008.03787.x OpenUrl CrossRef PubMed Web of Science 6. ↵ Bay , R.A. , Palumbi , S.R ., 2014 . Multilocus adaptation associated with heat resistance in reef- building corals . Curr. Biol . 24 , 2952 – 2956 . doi: 10.1016/j.cub.2014.10.044 OpenUrl CrossRef PubMed 7. ↵ Benestan , L. , Quinn , B.K. , Maaroufi , H. , Laporte , M. , Clark , F.K. , Greenwood , S.J. , Rochette , R. , Bernatchez , L ., 2016 . Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus) . Mol. Ecol . 25 , 5073 – 5092 . doi: 10.1111/mec.13811 OpenUrl CrossRef 8. ↵ Boeckmann , B. , Bairoch , A. , Apweiler , R. , Blatter , M.C. , Estreicher , A. , Gasteiger , E. , Martin , M.J. , Michoud , K. , O’Donovan , C. , Phan , I. , Pilbout , S. , Schneider , M ., 2003 . The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003 . Nucleic Acids Res . 31 , 365 – 370 . doi: 10.1093/nar/gkg095 OpenUrl CrossRef PubMed Web of Science 9. ↵ Bonin , A. , Nicole , F. , Pompanon , F. , Miaud , C. , Taberlet , P ., 2007 . Population adaptive index: A new method to help measure intraspecific genetic diversity and prioritize populations for conservation . Conserv. Biol . 21 , 697 – 708 . doi: 10.1111/j.1523-1739.2007.00685.x OpenUrl CrossRef PubMed Web of Science 10. ↵ Bozec , Y. , Adam , A.A.S. , Nava , B.A. , Cresswell , A.K. , Haller-Bull , V. , Iwanaga , T. , Lachs , L. , Matthews , S.A. , McWhorter , J.K. , Anthony , K.R.N. , Condie , S.A. , Halloran , P.R. , Ortiz , J.-C. , Riginos , C. , Mumby , P.J ., 2025 . A rapidly closing window for coral persistence under global warming . bioRxiv Prepr . doi: 10.1101/2025.01.23.634487 OpenUrl Abstract / FREE Full Text 11. ↵ Bridge , T.C.L. , Cowman , P.F. , Quattrini , A.M. , Bonito , V.E. , Sinniger , F. , Harii , S. , Head , C.E.I. , Hung , J.Y. , Halafihi , T. , Rongo , T. , Baird , A.H ., 2023 . A tenuis relationship: traditional taxonomy obscures systematics and biogeography of the ‘Acropora tenuis’ (Scleractinia: Acroporidae) species complex . Zool. J. Linn. Soc . 1 – 24 . doi: 10.1093/zoolinnean/zlad062 OpenUrl CrossRef 12. ↵ Bruno , J. , Siddon , C. , Witman , J. , Colin , P. , Toscano , M ., 2001 . El Niño related coral bleaching in Palau, Western Caroline Islands . Coral Reefs 20 , 127 – 136 . doi: 10.1007/s003380100151 OpenUrl CrossRef Web of Science 13. ↵ Burdon , R.H. , Gill , V. , Evans , C.R. , 1990 . Active oxygen species and heat shock protein induction , in: Schlesinger, M.J., Santoro, M.G., Garaci, E. (Eds.). Springer Berlin Heidelberg , Berlin, Heidelberg , pp. 19 – 25 . 14. ↵ Capblancq , T. , Fitzpatrick , M.C. , Bay , R.A. , Exposito-Alonso , M. , Keller , S.R ., 2020 . Genomic Prediction of (Mal)Adaptation across Current and Future Climatic Landscapes . Annu. Rev. Ecol. Evol. Syst . 51 , 245 – 269 . doi: 10.1146/annurev-ecolsys-020720-042553 OpenUrl CrossRef 15. ↵ Capblancq , T. , Forester , B.R ., 2021 . Redundancy analysis: A Swiss Army Knife for landscape genomics . Methods Ecol. Evol . 12 , 2298 – 2309 . doi: 10.1111/2041-210x.13722 OpenUrl CrossRef 16. ↵ Capblancq , T. , Luu , K. , Blum , M.G.B. , Bazin , E ., 2018 . Evaluation of redundancy analysis to identify signatures of local adaptation . Mol. Ecol. Resour . 18 , 1223 – 1233 . doi: 10.1111/1755-0998.12906 OpenUrl CrossRef PubMed 17. ↵ Carr , M. , Kratochwill , C. , Daly-Engel , T. , Crombie , T. , van Woesik , R ., 2025 . Geographical patterns of intraspecific genetic diversity reflect the adaptive potential of the coral Pocillopora damicornis species complex . PLoS One 20 , 1 – 13 . doi: 10.1371/journal.pone.0316380 OpenUrl CrossRef 18. ↵ Ceder , M.M. , Magnusson , K.A. , Weman , H.M. , Henriksson , K. , Andréasson , L. , Lindström , T. , Wiggins , O. , Lagerström , M.C ., 2024 . The mRNA expression profile of glycine receptor subunits alpha 1, alpha 2, alpha 4 and beta in female and male mice . Mol. Cell. Neurosci . 131 . doi: 10.1016/j.mcn.2024.103976 OpenUrl CrossRef PubMed 19. ↵ Colleoni , F. , Wekerle , C. , Näslund , J.O. , Brandefelt , J. , Masina , S ., 2016 . Constraint on the penultimate glacial maximum Northern Hemisphere ice topography (≈140 kyrs BP) . Quat. Sci. Rev . 137 , 97 – 112 . doi: 10.1016/j.quascirev.2016.01.024 OpenUrl CrossRef 20. ↵ Combosch , D.J. , Vollmer , S. V ., 2015 . Trans-Pacific RAD-Seq population genomics confirms introgressive hybridization in Eastern Pacific Pocillopora corals . Mol. Phylogenet. Evol . 88 , 154 – 162 . doi: 10.1016/j.ympev.2015.03.022 OpenUrl CrossRef PubMed 21. ↵ Cooke , G.M. , Schlub , T.E. , Sherwin , W.B. , Ord , T.J ., 2016 . Understanding the spatial scale of genetic connectivity at sea: Unique insights from a land fish and a meta-analysis . PLoS One 11 , 1 – 25 . doi: 10.1371/journal.pone.0150991 OpenUrl CrossRef PubMed 22. ↵ Cooke , I. , Ying , H. , Forêt , S. , Bongaerts , P. , Strugnell , J.M. , Simakov , O. , Zhang , J. , Field , M.A. , Rodriguez-Lanetty , M. , Bell , S.C. , Bourne , D.G. , van Oppen , M.J.H. , Ragan , M.A. , Miller , D.J. , 2020 . Genomic signatures in the coral holobiont reveal host adaptations driven by Holocene climate change and reef specific symbionts . Sci. Adv . 6 . doi: 10.1126/sciadv.abc6318 OpenUrl FREE Full Text 23. ↵ Costanza , R. , de Groot , R. , Sutton , P. , van der Ploeg , S. , Anderson , S.J. , Kubiszewski , I. , Farber , S. , Turner , R.K. , 2014 . Changes in the global value of ecosystem services . Glob. Environ. Chang . 26 , 152 – 158 . doi: 10.1016/j.gloenvcha.2014.04.002 OpenUrl CrossRef 24. ↵ Cunning , R. , Baker , A.C ., 2014 . Not just who, but how many: The importance of partner abundance in reef coral symbioses . Front. Microbiol . 5 , 1 – 10 . doi: 10.3389/fmicb.2014.00400 OpenUrl CrossRef PubMed 25. ↵ Cunning , R. , Bay , R.A. , Gillette , P. , Baker , A.C. , Traylor-Knowles , N ., 2018 . Comparative analysis of the Pocillopora damicornis genome highlights role of immune system in coral evolution . Sci. Rep . 8 , 1 – 10 . doi: 10.1038/s41598-018-34459-8 OpenUrl CrossRef PubMed 26. ↵ De Clippele , L.H. , Díaz , L.A. , Andradi-Brown , D.A. , Lazuardi , M.E. , Iqbal , M. , Zainudin , I.M. , Prabuning , D. , van Hooidonk , R. , Hakim , A. , Agung , F. , Dermawan , A. , Hennige , S.J. , 2023 . Evaluating annual severe coral bleaching risk for marine protected areas across Indonesia . Mar. Policy 148 , 105428 . doi: 10.1016/j.marpol.2022.105428 OpenUrl CrossRef 27. ↵ De Mita , S. , Thuillet , A.-C. , Gay , L. , Ahmadi , N. , Manel , S. , Ronfort , J. , Vigouroux , Y. , 2013 . Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations . Mol. Ecol . 22 , 1383 – 1399 . doi: 10.1111/mec.12182 OpenUrl CrossRef Web of Science 28. ↵ Dias , M. , Ferreira , A. , Gouveia , R. , Madeira , C. , Jogee , N. , Cabral , H. , Diniz , M. , Vinagre , C ., 2019 . Long-term exposure to increasing temperatures on scleractinian coral fragments reveals oxidative stress . Mar. Environ. Res . 150 , 104758 . doi: 10.1016/j.marenvres.2019.104758 OpenUrl CrossRef PubMed 29. ↵ Dixon , G. , Davies , S. , Aglyamova , G. , Meyer , E. , Bay , L. , Matz , M ., 2015 . Genomic determinants of coral heat tolerance across latitudes . Science (80-.). 348 , 2014 – 2016 . OpenUrl 30. ↵ Donovan , M.K. , Burkepile , D.E. , Kratochwill , C. , Shlesinger , T. , Sully , S. , Oliver , T.A. , Hodgson , G. , Freiwald , J. , van Woesik , R. , 2021 . Local conditions magnify coral loss after marine heatwaves . Science (80-.). 372 , 977 – 980 . doi: 10.1126/science.abd9464 OpenUrl Abstract / FREE Full Text 31. ↵ Drury , C. , Lirman , D ., 2021 . Genotype by environment interactions in coral bleaching . Proc. R. Soc. B Biol. Sci . 288 . doi: 10.1098/rspb.2021.0177 OpenUrl CrossRef 32. ↵ Dziedzic , K.E. , Elder , H. , Tavalire , H. , Meyer , E ., 2019 . Heritable variation in bleaching responses and its functional genomic basis in reef-building corals (Orbicella faveolata) . Mol. Ecol . 28 , 2238 – 2253 . doi: 10.1111/mec.15081 OpenUrl CrossRef PubMed 33. ↵ E.U. Copernicus Marine Service Information (CMEMS) , 2024 . OCEANCOLOUR_GLO_BGC_L4_MY_009_104. Mar. Data Store. doi: 10.48670/moi-00281 (Accessed on 03-04-2024) OpenUrl CrossRef 34. ↵ Elder , H. , Weis , V.M. , Montalvo-Proano , J. , Mocellin , V.J.L. , Baird , A.H. , Meyer , E. , Bay , L.K ., 2022 . Genetic variation in heat tolerance of the coral Platygyra daedalea indicates potential for adaptation to ocean warming . Front. Mar. Sci . 9 , 1 – 15 . doi: 10.3389/fmars.2022.925845 OpenUrl CrossRef PubMed 35. ↵ Elma , E. , Gullström , M. , Yahya , S.A.S. , Jouffray , J.B. , East , H.K. , Nyström , M ., 2023 . Post-bleaching alterations in coral reef communities . Mar. Pollut. Bull . 186 . doi: 10.1016/j.marpolbul.2022.114479 OpenUrl CrossRef 36. ↵ Excoffier , L. , Hofer , T. , Foll , M ., 2009 . Detecting loci under selection in a hierarchically structured population . Heredity (Edinb ). 103 , 285 – 298 . doi: 10.1038/hdy.2009.74 OpenUrl CrossRef PubMed Web of Science 37. ↵ Forester , B.R. , Lasky , J.R. , Wagner , H.H. , Urban , D.L ., 2018 . Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations . Mol. Ecol . 27 , 2215 – 2233 . doi: 10.1111/mec.14584 OpenUrl CrossRef 38. ↵ François , O. , Martins , H. , Caye , K. , Schoville , S.D ., 2016 . Controlling false discoveries in genome scans for selection . Mol. Ecol . 25 , 454 – 469 . doi: 10.1111/mec.13513 OpenUrl CrossRef PubMed 39. ↵ Frichot , E. , François , O ., 2015 . LEA: An R package for landscape and ecological association studies . Methods Ecol. Evol . 6 , 925 – 929 . doi: 10.1111/2041-210X.12382 OpenUrl CrossRef 40. ↵ Fuller , Z.L. , Mocellin , V.J.L. , Morris , L.A. , Cantin , N. , Shepherd , J. , Sarre , L. , Peng , J. , Liao , Y. , Pickrell , J. , Andolfatto , P. , Matz , M. , Bay , L.K. , Przeworski , M ., 2020 . Population genetics of the coroal Acropora millepora: Toward genomic prediction of bleaching , in: Science. doi: 10.1126/science.aba4674 OpenUrl Abstract / FREE Full Text 41. ↵ Gawroński , P. , Pawełkowicz , M. , Tofil , K. , Uszyński , G. , Sharifova , S. , Ahluwalia , S. , Tyrka , M. , Wędzony , M. , Kilian , A. , Bolibok-Brągoszewska , H ., 2016 . DArT markers effectively target gene space in the rye genome . Front. Plant Sci . 7 , 1 – 13 . doi: 10.3389/fpls.2016.01600 OpenUrl CrossRef PubMed 42. ↵ Gélin , P. , Fauvelot , C. , Mehn , V. , Bureau , S. , Rouzé , H. , Magalon , H ., 2017 . Superclone expansion, long-distance clonal dispersal and local genetic structuring in the coral Pocillopora damicornis type β in Reunion Island, South Western Indian Ocean . PLoS One 12 , 1 – 19 . doi: 10.1371/journal.pone.0169692 OpenUrl CrossRef PubMed 43. ↵ Gélin , P. , Pirog , A. , Fauvelot , C. , Magalon , H ., 2018 . High genetic differentiation and low connectivity in the coral Pocillopora damicornis type β at different spatial scales in the Southwestern Indian Ocean and the Tropical Southwestern Pacific . Mar. Biol . 165 . doi: 10.1007/s00227-018-3428-6 OpenUrl CrossRef 44. ↵ Goetze , J.S. , Wilson , S. , Radford , B. , Fisher , R. , Langlois , T.J. , Monk , J. , Knott , N.A. , Malcolm , H. , Currey-Randall , L.M. , Ierodiaconou , D. , Harasti , D. , Barrett , N. , Babcock , R.C. , Bosch , N.E. , Brock , D. , Claudet , J. , Clough , J. , Fairclough , D. V. , Heupel , M.R. , Holmes , T.H. , Huveneers , C. , Jordan , A.R. , McLean , D. , Meekan , M. , Miller , D. , Newman , S.J. , Rees , M.J. , Roberts , K.E. , Saunders , B.J. , Speed , C.W. , Travers , M.J. , Treml , E. , Whitmarsh , S.K. , Wakefield , C.B. , Harvey , E.S ., 2021 . Increased connectivity and depth improve the effectiveness of marine reserves . Glob. Chang. Biol . 27 , 3432 – 3447 . doi: 10.1111/gcb.15635 OpenUrl CrossRef PubMed 45. ↵ Nentwig , W Gollasch , S ., 2008 . Is ballast water a major dispersal mechanism for marine organisms? , in: Nentwig , W . (Ed.), Biological Invasions . Springer , Berlin, Heidelberg , pp. 49 – 57 . doi: 10.1007/978-3-540-36920-2_4 OpenUrl CrossRef 46. ↵ Gouezo , M. , Golbuu , Y. , Fabricius , K. , Olsudong , D. , Mereb , G. , Nestor , V. , Wolanski , E. , Harrison , P. , Doropoulos , C ., 2019 . Drivers of recovery and reassembly of coral reef communities . Proc. R. Soc. B Biol. Sci . 286 . doi: 10.1098/rspb.2018.2908 OpenUrl CrossRef 47. ↵ Graham , N.A.J. , Jennings , S. , MacNeil , M.A. , Mouillot , D. , Wilson , S.K ., 2015 . Predicting climate- driven regime shifts versus rebound potential in coral reefs . Nature 518 , 94 – 97 . doi: 10.1038/nature14140 OpenUrl CrossRef PubMed 48. ↵ Green , D.G. , Bradbury , R.H. , Reichelt , R.E ., 1987 . Patterns of predictability in coral reef community structure . Coral Reefs 6 , 27 – 34 . doi: 10.1007/BF00302209 OpenUrl CrossRef 49. ↵ Gruber , B. , Unmack , P.J. , Berry , O.F. , Georges , A ., 2018 . dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing . Mol. Ecol. Resour . 18 , 691 – 699 . doi: 10.1111/1755-0998.12745 OpenUrl CrossRef PubMed 50. ↵ Grupstra , C.G.B. , Gómez-Corrales , M. , Fifer , J.E. , Aichelman , H.E. , Meyer-Kaiser , K.S. , Prada , C. , Davies , S.W ., 2024 . Integrating cryptic diversity into coral evolution, symbiosis and conservation. Nat . Ecol. Evol . 8 , 622 – 636 . doi: 10.1038/s41559-023-02319-y OpenUrl CrossRef 51. ↵ Guest , J.R. , Baird , A.H. , Maynard , J.A. , Muttaqin , E. , Edwards , A.J. , Campbell , S.J. , Yewdall , K. , Affendi , Y.A. , Chou , L.M ., 2012 . Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress . PLoS One 7 , 1 – 8 . doi: 10.1371/journal.pone.0033353 OpenUrl CrossRef PubMed 52. ↵ Hagedorn , M. , Page , C.A. , O’Neil , K.L. , Flores , D.M. , Tichy , L. , Conn , T. , Chamberland , V.F. , Lager , C. , Zuchowicz , N. , Lohr , K. , Blackburn , H. , Vardi , T. , Moore , J. , Moore , T. , Baums , I.B. , Vermeij , M.J.A. , Marhaver , K.L ., 2021 . Assisted gene flow using cryopreserved sperm in critically endangered coral . Proc. Natl. Acad. Sci. U. S. A . 118 , 1 – 7 . doi: 10.1073/pnas.2110559118 OpenUrl CrossRef PubMed 53. ↵ Helgoe , J. , Davy , S.K. , Weis , V.M. , Rodriguez-Lanetty , M ., 2024 . Triggers, cascades, and endpoints: connecting the dots of coral bleaching mechanisms . Biol. Rev . 99 , 715 – 752 . doi: 10.1111/brv.13042 OpenUrl CrossRef 54. ↵ Hemond , E.M. , Kaluziak , S.T. , Vollmer , S. V ., 2014 . The genetics of colony form and function in Caribbean Acropora corals . BMC Genomics 15 , 1 – 21 . doi: 10.1186/1471-2164-15-1133 OpenUrl CrossRef PubMed 55. ↵ Herbert , T.D. , Peterson , L.C. , Lawrence , K.T. , Liu , Z ., 2010 . Tropical ocean temperatures over the past 3.5 million years . Science ( 80 -.). 328, 1530–1534. doi: 10.1126/science.1185435 OpenUrl Abstract / FREE Full Text 56. ↵ Hernawan , U.E. , van Dijk , K. jent , Kendrick , G.A. , Feng , M. , Berry , O. , Kavazos , C. , McMahon , K. , 2023 . Ocean connectivity and habitat characteristics predict population genetic structure of seagrass in an extreme tropical setting . Ecol. Evol . 13 , 1 – 14 . doi: 10.1002/ece3.10257 OpenUrl CrossRef 57. Hoegh-Guldberg , O. , Mumby , P.J. , Hooten , A.J. , Steneck , R.S. , Greenfield , P. , Gomez , E. , Harvell , C.D. , Sale , P.F. , Edwards , A.J. , Caldeira , K. , Knowlton , N. , Eakin , C.M. , Iglesias-Prieto , R. , Muthiga , N. , Bradbury , R.H. , Dubi , A. , Hatziolos , M.E ., 2007 . Coral reefs under rapid climate change and ocean acidification . Science (80-.) . 318 , 1737 – 1742 . doi: 10.1126/science.1152509 OpenUrl Abstract / FREE Full Text 58. ↵ Howells , E.J. , Abrego , D. , Liew , Y.J. , Burt , J.A. , Meyer , E. , Aranda , M ., 2021 . Enhancing the heat tolerance of reef-building corals to future warming . Sci. Adv . 7 . doi: 10.1126/sciadv.abg6070 OpenUrl FREE Full Text 59. ↵ Howells , E.J. , Abrego , D. , Meyer , E. , Kirk , N.L. , Burt , J.A ., 2016 . Host adaptation and unexpected symbiont partners enable reef-building corals to tolerate extreme temperatures . Glob. Chang. Biol . 22 , 2702 – 2714 . doi: 10.1111/gcb.13250 OpenUrl CrossRef PubMed 60. ↵ Hughes , T.P. , Anderson , K.D. , Connolly , S.R. , Heron , S.F. , Kerry , J.T. , Lough , J.M. , Baird , A.H. , Baum , J.K. , Berumen , M.L. , Bridge , T.C. , Claar , D.C. , Eakin , C.M. , Gilmour , J.P. , Graham , N.A.J. , Harrison , H. , Hobbs , J.A. , Hoey , A.S. , Hoogenboom , M. , Lowe , R.J. , Mcculloch , M.T. , Pandolfi , J.M. , Pratchett , M. , Schoepf , V ., 2020 . Spatial and temporal patterns of mass bleaching of corals in the Anthropocene 83 , 80 – 83 . OpenUrl 61. ↵ Hughes , T.P. , Barnes , M.L. , Bellwood , D.R. , Cinner , J.E. , Cumming , G.S. , Jackson , J.B.C. , Kleypas , J. , Van De Leemput , I.A. , Lough , J.M. , Morrison , T.H. , Palumbi , S.R. , Van Nes , E.H. , Scheffer , M. , 2017a . Coral reefs in the Anthropocene . Nature 546 , 82 – 90 . doi: 10.1038/nature22901 OpenUrl CrossRef PubMed 62. ↵ Hughes , T.P. , Kerry , J.T. , Álvarez-Noriega , M. , Álvarez-Romero , J.G. , Anderson , K.D. , Baird , A.H. , Babcock , R.C. , Beger , M. , Bellwood , D.R. , Berkelmans , R. , Bridge , T.C. , Butler , I.R. , Byrne , M. , Cantin , N.E. , Comeau , S. , Connolly , S.R. , Cumming , G.S. , Dalton , S.J. , Diaz-Pulido , G. , Eakin , C.M. , Figueira , W.F. , Gilmour , J.P. , Harrison , H.B. , Heron , S.F. , Hoey , A.S. , Hobbs , J.P.A. , Hoogenboom , M.O. , Kennedy , E. V. , Kuo , C.Y. , Lough , J.M. , Lowe , R.J. , Liu , G. , McCulloch , M.T. , Malcolm , H.A. , McWilliam , M.J. , Pandolfi , J.M. , Pears , R.J. , Pratchett , M.S. , Schoepf , V. , Simpson , T. , Skirving , W.J. , Sommer , B. , Torda , G. , Wachenfeld , D.R. , Willis , B.L. , Wilson , S.K ., 2017b . Global warming and recurrent mass bleaching of corals . Nature 543 , 373 – 377 . doi: 10.1038/nature21707 OpenUrl CrossRef PubMed 63. ↵ Hughes , T.P. , Kerry , J.T. , Connolly , S.R. , Baird , A.H. , Eakin , C.M. , Heron , S.F. , Hoey , A.S. , Hoogenboom , M.O. , Jacobson , M. , Liu , G. , Pratchett , M.S. , Skirving , W. , Torda , G ., 2019 . Ecological memory modifies the cumulative impact of recurrent climate extremes . Nat. Clim. Chang . 9 , 40 – 43 . doi: 10.1038/s41558-018-0351-2 OpenUrl CrossRef 64. ↵ Humanes , A. , Lachs , L. , Beauchamp , E.A. , Bythell , C. , Edwards , A.J. , Golbuu , Y. , Martinez , H.M. , Palmowski , P. , Treumann , A. , Steeg , E. Van Der , Hooidonk , R. Van , Guest , J.R. , 2022 . Within- population variability in coral heat tolerance indicates climate adaptation potential . Proc. R. Soc. B Biol. Sci . 289 , 20220872 . OpenUrl PubMed 65. Jin , Y.K. , Lundgren , P. , Lutz , A. , Raina , J.B. , Howells , E.J. , Paley , A.S. , Willis , B.L. , Van Oppen , M.J.H. , 2016 . Genetic markers for antioxidant capacity in a reef-building coral . Sci. Adv . 2 . doi: 10.1126/sciadv.1500842 OpenUrl FREE Full Text 66. ↵ Johnston , E.C. , Forsman , Z.H. , Flot , J.F. , Schmidt-Roach , S. , Pinzón , J.H. , Knapp , I.S.S. , Toonen , R.J ., 2017 . A genomic glance through the fog of plasticity and diversification in Pocillopora . Sci. Rep . 7 , 1 – 11 . doi: 10.1038/s41598-017-06085-3 OpenUrl CrossRef PubMed 67. ↵ Johnston , E.C. , Wyatt , A.S.J. , Leichter , J.J. , Burgess , S.C ., 2021 . Niche differences in co-occurring cryptic coral species (Pocillopora spp .). Coral Reefs . doi: 10.1007/s00338-021-02107-9 OpenUrl CrossRef 68. ↵ Kilian , A. , Wenzl , P. , Huttner , E. , Carling , J. , Xia , L. , Blois , H. , Caig , V. , Heller-Uszynska , K. , Jaccoud , D. , Hopper , C. , Aschenbrenner-Kilian , M. , Evers , M. , Peng , K. , Cayla , C. , Hok , P. , Uszynski , G ., 2012 . Diversity arrays technology: a generic genome profiling technology on open platforms . Methods Mol. Biol . 888 , 67 – 89 . doi: 10.1007/978-1-61779-870-2_5 OpenUrl CrossRef PubMed 69. ↵ Kirk , N.L. , Howells , E.J. , Abrego , D. , Burt , J.A. , Meyer , E ., 2018 . Genomic and transcriptomic signals of thermal tolerance in heat-tolerant corals (Platygyra daedalea) of the Arabian/Persian Gulf . Mol. Ecol . 27 , 5180 – 5194 . doi: 10.1111/mec.14934 OpenUrl CrossRef 70. ↵ Koo , I.C. , Ohol , Y.M. , Wu , P. , Morisaki , J.H. , Cox , J.S. , Brown , E.J ., 2008 . Role for lysosomal enzyme β-hexosaminidase in the control of mycobacteria infection . Proc. Natl. Acad. Sci. U. S. A . 105 , 710 – 715 . doi: 10.1073/pnas.0708110105 OpenUrl Abstract / FREE Full Text 71. ↵ Krueck , N.C. , Treml , E.A. , Innes , D.J. , Ovenden , J.R ., 2020 . Ocean currents and the population genetic signature of fish migrations . Ecology 101 , 1 – 13 . doi: 10.1002/ecy.2967 OpenUrl CrossRef 72. ↵ Kusumoto , B. , Costello , M.J. , Kubota , Y. , Shiono , T. , Wei , C.L. , Yasuhara , M. , Chao , A ., 2020 . Global distribution of coral diversity: Biodiversity knowledge gradients related to spatial resolution . Ecol. Res . 35 , 315 – 326 . doi: 10.1111/1440-1703.12096 OpenUrl CrossRef 73. ↵ Lachs , L. , Donner , S.D. , Mumby , P.J. , Bythell , J.C. , Humanes , A. , East , H.K. , Guest , J.R ., 2023 . Emergent increase in coral thermal tolerance reduces mass bleaching under climate change . Nat. Commun . 4939 . doi: 10.1038/s41467-023-40601-6 OpenUrl CrossRef 74. ↵ Lasky , J.R. , Josephs , E.B. , Morris , G.P ., 2023 . Genotype–environment associations to reveal the molecular basis of environmental adaptation . Plant Cell 35 , 125 – 138 . doi: 10.1093/plcell/koac267 OpenUrl CrossRef PubMed 75. ↵ Littman , R.A. , Bourne , D.G. , Willis , B.L ., 2010 . Responses of coral-associated bacterial communities to heat stress differ with Symbiodinium type on the same coral host . Mol. Ecol . 19 , 1978 – 1990 . doi: 10.1111/j.1365-294X.2010.04620.x OpenUrl CrossRef PubMed Web of Science 76. ↵ Liu , G. , Strong , A.E. , Skirving , W ., 2003 . Remote sensing of sea surface temperatures during 2002 barrier reef coral bleaching . Eos (Washington. DC ). 84 , 2002 – 2004 . doi: 10.1029/2003EO150001 OpenUrl CrossRef 77. ↵ Lotterhos , K.E ., 2023 . The paradox of adaptive trait clines with nonclinal patterns in the underlying genes . Proc. Natl. Acad. Sci . 120 , e2220313120 . doi: 10.1073/pnas.2220313120 OpenUrl CrossRef PubMed 78. ↵ Lotterhos , K.E. , Whitlock , M.C ., 2015 . The relative power of genome scans to detect local adaptation depends on sampling design and statistical method . Mol. Ecol . 24 , 1031 – 1046 . doi: 10.1111/mec.13100 OpenUrl CrossRef PubMed 79. ↵ Louis , Y.D. , Kaullysing , D. , Gopeechund , A. , Mattan-Moorgawa , S. , Bahorun , T. , Dyall , S.D. , Bhagooli , R ., 2016 . In hospite Symbiodinium photophysiology and antioxidant responses in Acropora muricata on a coast-reef scale: implications for variable bleaching patterns . Symbiosis 68 , 61 – 72 . doi: 10.1007/s13199-016-0380-4 OpenUrl CrossRef 80. ↵ Loya , Y. , Sakai , K. , Yamazato , K. , Nakano , Y. , Sambali , H. , Van Woesik , R. , 2001 . Coral bleaching: The winners and the losers . Ecol. Lett . 4 , 122 – 131 . doi: 10.1046/j.1461-0248.2001.00203.x OpenUrl CrossRef Web of Science 81. ↵ Lukoschek , V. , Riginos , C. , Van Oppen , M.J.H. , 2016 . Congruent patterns of connectivity can inform management for broadcast spawning corals on the Great Barrier Reef . Mol. Ecol . 25 , 3065 – 3080 . doi: 10.1111/mec.13649 OpenUrl CrossRef PubMed 82. ↵ Lundgren , P. , Vera , J.C. , Peplow , L. , Manel , S. , van Oppen , M.J.H. , 2013 . Genotype-environment correlations in corals from the Great Barrier Reef . BMC Genet . 14 . doi: 10.1186/1471-2156-14-9 OpenUrl CrossRef PubMed 83. ↵ Luu , K. , Bazin , E. , Blum , M.G.B ., 2017 . pcadapt: an R package to perform genome scans for selection based on principal component analysis . Mol. Ecol. Resour . 17 , 67 – 77 . doi: 10.1111/1755-0998.12592 OpenUrl CrossRef PubMed 84. ↵ Matz , M. V. , Treml , E.A. , Haller , B.C ., 2020 . Estimating the potential for coral adaptation to global warming across the Indo-West Pacific . Glob. Chang. Biol . 26 , 3473 – 3481 . doi: 10.1111/gcb.15060 OpenUrl CrossRef PubMed 85. ↵ Mayfield , A.B. , Chen , Y.J. , Lu , C.Y. , Chen , C.S ., 2018 . The proteomic response of the reef coral Pocillopora acuta to experimentally elevated temperatures . PLoS One 13 , 1 – 24 . doi: 10.1371/journal.pone.0192001 OpenUrl CrossRef PubMed 86. ↵ Ménade , M. , Kozlov , G. , Trempe , J.F. , Pande , H. , Shenker , S. , Wickremasinghe , S. , Li , X. , Hojjat , H. , Dicaire , M.J. , Brais , B. , McPherson , P.S. , Wong , M.J.H. , Young , J.C. , Gehring , K ., 2018 . Structures of ubiquitin-like (Ubl) and Hsp90-like domains of sacsin provide insight into pathological mutations . J. Biol. Chem . 293 , 12832 – 12842 . doi: 10.1074/jbc.RA118.003939 OpenUrl Abstract / FREE Full Text 87. ↵ Mérot , C. , Oomen , R.A. , Tigano , A. , Wellenreuther , M ., 2020 . A roadmap for understanding the evolutionary significance of structural genomic variation . Trends Ecol. Evol . 35 , 561 – 572 . doi: 10.1016/j.tree.2020.03.002 OpenUrl CrossRef 88. ↵ Mijangos , J.L. , Gruber , B. , Berry , O. , Pacioni , C. , Georges , A ., 2022 . dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture . Methods Ecol. Evol . 13 , 2150 – 2158 . doi: 10.1111/2041-210X.13918 OpenUrl CrossRef 89. ↵ Miller , K.J. , Ayre , D.J ., 2008 . Population structure is not a simple function of reproductive mode and larval type: Insights from tropical corals . J. Anim. Ecol . 77 , 713 – 724 . doi: 10.1111/j.1365-2656.2008.01387.x OpenUrl CrossRef PubMed 90. ↵ Moberg , F. , Folke , C ., 1999 . Ecological goods and services of coral reef ecosystems . Ecol. Econ . 29 , 215 – 233 . doi: 10.1016/S0921-8009(99)00009-9 OpenUrl CrossRef Web of Science 91. ↵ Morais , J. , Morais , R. , Tebbett , S.B. , Bellwood , D.R ., 2022 . On the fate of dead coral colonies . Funct. Ecol . 1 – 13 . doi: 10.1111/1365-2435.14182 OpenUrl CrossRef 92. ↵ Morrow , K.M. , Ritson-Williams , R. , Ross , C. , Liles , M.R. , Paul , V.J ., 2012 . Macroalgal extracts Induce bacterial assemblage shifts and sublethal tissue stress in Caribbean corals . PLoS One 7 . doi: 10.1371/journal.pone.0044859 OpenUrl CrossRef PubMed 93. ↵ Mumby , P.J. , Van Woesik , R. , 2014 . Consequences of ecological, evolutionary and biogeochemical uncertainty for coral reef responses to climatic stress . Curr. Biol . 24 , R413 – R423 . doi: 10.1016/j.cub.2014.04.029 OpenUrl CrossRef PubMed 94. ↵ Mydlarz , L.D. , McGinty , E.S. , Drew Harvell , C ., 2010 . What are the physiological and immunological responses of coral to climate warming and disease? J. Exp. Biol . 213 , 934 – 945 . doi: 10.1242/jeb.037580 OpenUrl Abstract / FREE Full Text 95. ↵ Nakajima , Y. , Nishikawa , A. , Iguchi , A. , Sakai , K ., 2010 . Gene flow and genetic diversity of a broadcast-spawning coral in northern peripheral populations . PLoS One 5 , e11149 . doi: 10.1371/journal.pone.0011149 OpenUrl CrossRef PubMed 96. ↵ Nikula , R. , Spencer , H.G. , Waters , J.M ., 2013 . Passive rafting is a powerful driver of transoceanic gene flow . Biol. Lett . 9 . doi: 10.1098/rsbl.2012.0821 OpenUrl CrossRef PubMed 97. ↵ Obura , D ., 2012 . The diversity and biogeography of Western Indian Ocean reef-building corals . PLoS One 7 . doi: 10.1371/journal.pone.0045013 OpenUrl CrossRef PubMed 98. ↵ Obura , D. , Gudka , M. , Samoilys , M. , Osuka , K. , Mbugua , J. , Keith , D.A. , Porter , S. , Roche , R. , van Hooidonk , R. , Ahamada , S. , Araman , A. , Karisa , J. , Komakoma , J. , Madi , M. , Ravinia , I. , Razafindrainibe , H. , Yahya , S. , Zivane , F. , 2022 . Vulnerability to collapse of coral reef ecosystems in the Western Indian Ocean . Nat. Sustain . 5 , 104 – 113 . doi: 10.1038/s41893-021-00817-0 OpenUrl CrossRef 99. ↵ Ortiz , J.C. , Pears , R.J. , Beeden , R. , Dryden , J. , Wolff , N.H. , Gomez Cabrera , M. del C ., Mumby , P.J ., 2021 . Important ecosystem function, low redundancy and high vulnerability: The trifecta argument for protecting the Great Barrier Reef’s tabular Acropora . Conserv. Lett . 14 , 1 – 18 . doi: 10.1111/conl.12817 OpenUrl CrossRef 100. ↵ Otto , S.P ., 2018 . Adaptation, speciation and extinction in the Anthropocene . Proc. R. Soc. B Biol. Sci . 285 . doi: 10.1098/rspb.2018.2047 OpenUrl CrossRef PubMed 101. ↵ Oury , N. , Mona , S. , Magalon , H ., 2024 . Same places, same stories? Genomics reveals similar structuring and demographic patterns for four Pocillopora coral species in the southwestern Indian Ocean . J. Biogeogr . 51 , 754 – 768 . doi: 10.1111/jbi.14788 OpenUrl CrossRef 102. ↵ Palumbi , S.R. , Barshis , D.J. , Traylor-Knowles , N. , Bay , R.A ., 2014 . Mechanisms of reef coral resistance to future climate change . Science (80-.) . 344 , 895 – 898 . doi: 10.1126/science.1251336 OpenUrl Abstract / FREE Full Text 103. ↵ Parfitt , D.A. , Michael , G.J. , Vermeulen , E.G.M. , Prodromou , N. V. , Webb , T.R. , Gallo , J.M. , Cheetham , M.E. , Nicoll , W.S. , Blatch , G.L. , Chapple , J.P ., 2009 . The ataxia protein sacsin is a functional co-chaperone that protects against polyglutamine-expanded ataxin-1 . Hum. Mol. Genet . 18 , 1556 – 1565 . doi: 10.1093/hmg/ddp067 OpenUrl CrossRef PubMed Web of Science 104. ↵ Phillips , H.E. , Tandon , A. , Furue , R. , Hood , R. , Ummenhofer , C.C. , Benthuysen , J.A. , Menezes , V. , Hu , S. , Webber , B. , Sanchez-Franks , A. , Cherian , D. , Shroyer , E. , Feng , M. , Wijesekera , H. , Chatterjee , A. , Yu , L. , Hermes , J. , Murtugudde , R. , Tozuka , T. , Su , D. , Singh , A. , Centurioni , L. , Prakash , S. , Wiggert , J ., 2021 . Progress in understanding of Indian Ocean circulation, variability, air-sea exchange, and impacts on biogeochemistry . Ocean Sci . 17 , 1677 – 1751 . doi: 10.5194/os-17-1677-2021 OpenUrl CrossRef 105. ↵ Pittman , S. , Yates , K. , Bouchet , P. , Alvarez-Berastegui , D. , Andréfouët , S. , Bell , S. , Berkström , C. , Boström , C. , Brown , C. , Connolly , R. , Devillers , R. , Eggleston , D. , Gilby , B. , Gullström , M. , Halpern , B. , Hidalgo , M. , Holstein , D. , Hovel , K. , Huettmann , F. , Jackson , E. , James , W. , Kellner , J. , Kot , C. , Lecours , V. , Lepczyk , C. , Nagelkerken , I. , Nelson , J. , Olds , A. , Santos , R. , Scales , K. , Schneider , D. , Schilling , H. , Simenstad , C. , Suthers , I. , Treml , E. , Wedding , L. , Yates , P. , Young , M ., 2021 . Seascape ecology: Identifying research priorities for an emerging ocean sustainability science . Mar. Ecol. Prog. Ser . 663 , 1 – 29 . doi: 10.3354/meps13661 OpenUrl CrossRef 106. Pokrovac , I. , Pezer, Ž., 2022 . Recent advances and current challenges in population genomics of structural variation in animals and plants . Front. Genet . 13 , 1 – 12 . doi: 10.3389/fgene.2022.1060898 OpenUrl CrossRef 107. ↵ Poquita-Du , R.C. , Huang , D. , Todd , P.A ., 2024 . Genome-wide analysis to uncover how Pocillopora acuta survives the challenging intertidal environment . Sci. Rep . 14 , 1 – 11 . doi: 10.1038/s41598-024-59268-0 OpenUrl CrossRef PubMed 108. ↵ Prata , K.E. , Bongaerts , P. , Dwyer , J.M. , Ishida , H. , Howitt , S.M. , Hereward , J.P. , Crandall , E.D. , Riginos , C ., 2024 . Some reef-building corals only disperse metres per generation . Proc. R. Soc. B Biol. Sci . 291 . doi: 10.1098/rspb.2023.1988 OpenUrl CrossRef 109. ↵ Privé , F ., 2021 . bigutilsr: Utility functions for large-scale data. R package version 0.3.4 . 110. ↵ Privé , F. , Luu , K. , Vilhjálmsson , B.J. , Blum , M.G.B. , Rosenberg , M ., 2020 . Performing highly efficient genome scans for local adaptation with R Package pcadapt Version 4 . Mol. Biol. Evol . 37 , 2153 – 2154 . doi: 10.1093/molbev/msaa053 OpenUrl CrossRef PubMed 111. ↵ Quigley , K.M. , Bay , L.K. , van Oppen , M.J.H. , 2020 . Genome-wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral . Mol. Ecol . 29 , 2176 – 2188 . doi: 10.1111/mec.15482 OpenUrl CrossRef 112. ↵ R Core Team , 2024 . R: A language and environment for statistical computing . 113. ↵ Rellstab , C. , Gugerli , F. , Eckert , A.J. , Hancock , A.M. , Holderegger , R ., 2015 . A practical guide to environmental association analysis in landscape genomics . Mol. Ecol . 24 , 4348 – 4370 . doi: 10.1111/mec.13322 OpenUrl CrossRef 114. ↵ Richier , S. , Sabourault , C. , Courtiade , J. , Zucchini , N. , Allemand , D. , Furla , P ., 2006 . Oxidative stress and apoptotic events during thermal stress in the symbiotic sea anemone, Anemonia viridis . FEBS J . 273 , 4186 – 4198 . doi: 10.1111/j.1742-4658.2006.05414.x OpenUrl CrossRef PubMed 115. ↵ Richmond , R.H ., 1987 . Energetic relationships and biogeographical differences among fecundity, growth and reproduction in the reef coral Pocillopora damicornis . Bull. Mar. Sci . 41 , 594 – 604 . OpenUrl 116. ↵ Riginos , C. , Crandall , E.D. , Liggins , L. , Bongaerts , P. , Treml , E.A ., 2016 . Navigating the currents of seascape genomics: How spatial analyses can augment population genomic studies . Curr. Zool . 62 , 581 – 601 . doi: 10.1093/cz/zow067 OpenUrl CrossRef PubMed 117. ↵ Riginos , C. , Popovic , I. , Meziere , Z. , Oliver Garcia , V. , Byrne , I. , Howitt , S.M. , Ishida , H. , Bairos- Novak , K.R. , Humanes , A. , Scharfenstein , H. , Richards , T. , Briggs , E.J. , Clark , V. , Lei , C. , Khan , M. , Prata , K.E ., 2024 . Cryptic species and hybridisation in corals: challenges and opportunities for conservation and restoration . Peer Community J . doi: 10.24072/pcjournal.492 OpenUrl CrossRef 118. ↵ Roff , G. , Doropoulos , C. , Rogers , A. , Bozec , Y.M. , Krueck , N.C. , Aurellado , E. , Priest , M. , Birrell , C. , Mumby , P.J ., 2016 . The Ecological Role of Sharks on Coral Reefs . Trends Ecol. Evol . 31 , 395 – 407 . doi: 10.1016/j.tree.2016.02.014 OpenUrl CrossRef PubMed 119. ↵ Rose , N.H. , Bay , R.A. , Morikawa , M.K. , Palumbi , S.R ., 2018 . Polygenic evolution drives species divergence and climate adaptation in corals . Evolution (N. Y ). 72 , 82 – 94 . doi: 10.1111/evo.13385 OpenUrl CrossRef PubMed 120. ↵ Sansaloni , C. , Petroli , C. , Jaccoud , D. , Carling , J. , Detering , F. , Grattapaglia , D. , Kilian , A ., 2011 . Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome- wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus . BMC Proc . 5 , P54 . doi: 10.1186/1753-6561-5-S7-P54 OpenUrl CrossRef 121. ↵ Schmidt-Roach , S. , Miller , K.J. , Woolsey , E. , Gerlach , G. , Baird , A.H ., 2012 . Broadcast Spawning by Pocillopora Species on the Great Barrier Reef . PLoS One 7 , 1 – 7 . doi: 10.1371/journal.pone.0050847 OpenUrl CrossRef PubMed 122. ↵ Schoepf , V. , Stat , M. , Falter , J.L. , McCulloch , M.T ., 2015 . Limits to the thermal tolerance of corals adapted to a highly fluctuating, naturally extreme temperature environment . Sci. Rep . 5 , 1 – 14 . doi: 10.1038/srep17639 OpenUrl CrossRef PubMed 123. ↵ Selkoe , K.A. , D’Aloia , C.C. , Crandall , E.D. , Iacchei , M. , Liggins , L. , Puritz , J.B. , Von Der Heyden , S. , Toonen , R.J. , 2016 . A decade of seascape genetics: Contributions to basic and applied marine connectivity . Mar. Ecol. Prog. Ser . 554 , 1 – 19 . doi: 10.3354/meps11792 OpenUrl CrossRef 124. ↵ Selmoni , O. , Bay , L.K. , Exposito-Alonso , M. , Cleves , P.A ., 2024 . Finding genes and pathways that underlie coral adaptation . Trends Genet . 1 – 15 . doi: 10.1016/j.tig.2024.01.003 OpenUrl CrossRef 125. ↵ 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 . doi: 10.1111/geb.13657 OpenUrl CrossRef 126. ↵ Selmoni , O. , Lecellier , G. , Magalon , H. , Vigliola , L. , Benzoni , F. , Peignon , C. , Joost , S. , Berteaux- Lecellier , V ., 2021 . Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species . Mol. Ecol . 30 , 1892 – 1906 . doi: 10.1111/mec.15857 OpenUrl CrossRef 127. ↵ Selmoni , O. , Rochat , E. , Lecellier , G. , Berteaux-Lecellier , V. , Joost , S ., 2020a . Seascape genomics as a new tool to empower coral reef conservation strategies: an example on north-western Pacific Acropora digitifera . Evol. Appl . 13 , 1923 – 1938 . doi: 10.1111/eva.12944 OpenUrl CrossRef PubMed 128. ↵ Selmoni , O. , Vajana , E. , Guillaume , A.S. , Rochat , E. , Joost , S ., 2020b . Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach . Mol. Ecol. Resour . 20 , 154 – 169 . doi: 10.1101/603829 OpenUrl CrossRef PubMed 129. ↵ Severance , E.G. , Karl , S.A ., 2006 . Contrasting population genetic structures of sympatric, mass- spawning Caribbean corals . Mar. Biol . 150 , 57 – 68 . doi: 10.1007/s00227-006-0332-2 OpenUrl CrossRef 130. Shlesinger , T. , van Woesik , R. , 2023 . Oceanic differences in coral-bleaching responses to marine heatwaves . Sci. Total Environ . 871 , 162113 . doi: 10.1016/j.scitotenv.2023.162113 OpenUrl CrossRef PubMed 131. ↵ Simillion , C. , Liechti , R. , Lischer , H.E.L. , Ioannidis , V. , Bruggmann , R ., 2017 . Avoiding the pitfalls of gene set enrichment analysis with SetRank . BMC Bioinformatics 18 , 1 – 14 . doi: 10.1186/s12859-017-1571-6 OpenUrl CrossRef PubMed 132. ↵ Skirving , W. , Marsh , B. , De La Cour , J. , Liu , G. , Harris , A. , Maturi , E. , Geiger , E. , Mark Eakin , C. , 2020 . Coraltemp and the coral reef watch coral bleaching heat stress product suite version 3.1 . Remote Sens . 12 , 1 – 10 . doi: 10.3390/rs12233856 OpenUrl CrossRef 133. ↵ Smith , E.G. , Hazzouri , K.M. , Choi , J.Y. , Delaney , P. , Al-Kharafi , M. , Howells , E.J. , Aranda , M. , Burt , J.A ., 2022 . Signatures of selection underpinning rapid coral adaptation to the world’s warmest reefs . Sci. Adv . 8 , 1 – 10 . doi: 10.1126/sciadv.abl7287 OpenUrl CrossRef 134. ↵ Smith , H.A. , Moya , A. , Cantin , N.E. , van Oppen , M.J.H. , Torda , G. , 2019 . Observations of simultaneous sperm release and larval planulation suggest reproductive assurance in the coral Pocillopora acuta . Front. Mar. Sci . 6 , 1 – 6 . doi: 10.3389/fmars.2019.00362 OpenUrl CrossRef PubMed 135. ↵ Snead , A.A. , Tatarenkov , A. , Avise , J.C. , Taylor , D.S. , Turner , B.J. , Marson , K. , Earley , R.L ., 2023 . Out to sea: ocean currents and patterns of asymmetric gene flow in an intertidal fish species . Front. Genet . 14 , 1 – 16 . doi: 10.3389/fgene.2023.1206543 OpenUrl CrossRef 136. ↵ Souter , D. , Planes , S. , Wicquart , J. , Logan , M. , Obura , D. , Staub , F ., 2021 . Status of coral reefs of the world: 2020 report , Global Coral Reef Monitoring Network (GCRMN) and International Coral Reef Initiative (ICRI ). doi: 10.59387/WOTJ9184 OpenUrl CrossRef 137. ↵ Spalding , M.D. , Fox , H.E. , Allen , G.R. , Davidson , N. , Ferdaña , Z.A. , Finlayson , M. , Halpern , B.S. , Jorge , M.A. , Lombana , A. , Lourie , S.A. , Martin , K.D. , McManus , E. , Molnar , J. , Recchia , C.A. , Robertson , J ., 2007 . Marine ecoregions of the world: A bioregionalization of coastal and shelf areas . Bioscience 57 , 573 – 583 . doi: 10.1641/B570707 OpenUrl CrossRef Web of Science 138. ↵ Sproles , A.E. , Oakley , C.A. , Matthews , J.L. , Peng , L. , Owen , J.G. , Grossman , A.R. , Weis , V.M. , Davy , S.K ., 2019 . Proteomics quantifies protein expression changes in a model cnidarian colonised by a thermally tolerant but suboptimal symbiont . ISME J . 13 , 2334 – 2345 . doi: 10.1038/s41396-019-0437-5 OpenUrl CrossRef PubMed 139. ↵ Steane , D.A. , Potts , B.M. , McLean , E. , Prober , S.M. , Stock , W.D. , Vaillancourt , R.E. , Byrne , M ., 2014 . Genome-wide scans detect adaptation to aridity in a widespread forest tree species . Mol. Ecol . 23 , 2500 – 2513 . doi: 10.1111/mec.12751 OpenUrl CrossRef Web of Science 140. ↵ Stefanoudis , P. V. , Talma , S. , Fassbender , N. , Swanborn , D. , Ochieng , C.N. , Mearns , K. , Komakoma , J.D. , Otwoma , L.M. , Mbije , N.E. , Osuka , K.E. , Samoilys , M. , Shah , N. , Samaai , T. , Trotzuk , E. , Tuda , A. , Zivane , F. , Wagner , D. , Woodall , L.C ., 2023 . Stakeholder-derived recommendations and actions to support deep-reef conservation in the Western Indian Ocean . Conserv. Lett . 16 , 1 – 11 . doi: 10.1111/conl.12924 OpenUrl CrossRef 141. ↵ Storey , J.D. , Tibshirani , R ., 2003 . Statistical significance for genomewide studies . Proc. Natl. Acad. Sci. U. S. A . 100 , 9440 – 9445 . doi: 10.1073/pnas.1530509100 OpenUrl Abstract / FREE Full Text 142. ↵ Storfer , A. , Patton , A. , Fraik , A.K ., 2018 . Navigating the interface between landscape genetics and landscape genomics . Front. Genet . 9 . doi: 10.3389/fgene.2018.00068 OpenUrl CrossRef PubMed 143. ↵ Sully , S. , Hodgson , G. , Woesik , R ., 2022 . Present and future bright and dark spots for coral reefs through climate change . Glob. Chang. Biol . 1 – 14 . doi: 10.1111/gcb.16083 OpenUrl CrossRef 144. ↵ Tabitha , S.N. , Moses , K.S. , Rajan , R ., 2021 . Observations of synchronized spawning, larval survival and settlement in corals of the genus Acropora in Lakshadweep, India . Curr. Sci . 121 , 133 – 140 . doi: 10.18520/cs/v121/i1/133-140 OpenUrl CrossRef 145. ↵ Thomas , L. , Kennington , W.J. , Evans , R.D. , Kendrick , G.A. , Stat , M ., 2017 . Restricted gene flow and local adaptation highlight the vulnerability of high-latitude reefs to rapid environmental change . Glob. Chang. Biol . 23 , 2197 – 2205 . doi: 10.1111/gcb.13639 OpenUrl CrossRef PubMed 146. ↵ Thomas , L. , Underwood , J.N. , Rose , N.H. , Fuller , Z.L. , Richards , Z.T. , Dugal , L. , Grimaldi , C.M. , Cooke , I.R. , Palumbi , S.R. , Gilmour , J.P ., 2022 . Spatially varying selection between habitats drives physiological shifts and local adaptation in a broadcast spawning coral on a remote atoll in Western Australia . Sci. Adv . 9185 , 1 – 12 . OpenUrl 147. ↵ Thurber , R.V. , Willner-Hall , D. , Rodriguez-Mueller , B. , Desnues , C. , Edwards , R.A. , Angly , F. , Dinsdale , E. , Kelly , L. , Rohwer , F ., 2009 . Metagenomic analysis of stressed coral holobionts . Environ. Microbiol . 11 , 2148 – 2163 . doi: 10.1111/j.1462-2920.2009.01935.x OpenUrl CrossRef PubMed Web of Science 148. ↵ van Etten , J. , 2017 . R Package gdistance: Distances and Routes on Geographical Grids . J. Stat. Softw . 76 , 1 – 21 . doi: 10.18637/jss.v076.i13 OpenUrl CrossRef 149. ↵ van Hooidonk , R. , Maynard , J. , Tamelander , J. , Gove , J. , Ahmadia , G. , Raymundo , L. , Williams , G. , Heron , S.F. , Planes , S. , 2016 . Local-scale projections of coral reef futures and implications of the Paris Agreement . Sci. Rep . 6 , 1 – 8 . doi: 10.1038/srep39666 OpenUrl CrossRef PubMed 150. ↵ Van Oppen , M.J.H. , Oliver , J.K. , Putnam , H.M. , Gates , R.D. , 2015 . Building coral reef resilience through assisted evolution . Proc. Natl. Acad. Sci. U. S. A . 112 , 2307 – 2313 . doi: 10.1073/pnas.1422301112 OpenUrl Abstract / FREE Full Text 151. ↵ van Woesik , R. , Kratochwill , C. , 2022 . A global coral-bleaching database, 1980–2020 . Sci. Data 9 , 1 – 7 . doi: 10.1038/s41597-022-01121-y OpenUrl CrossRef PubMed 152. ↵ Villastrigo , A. , Orenes-Salazar , V. , García-Meseguer , A.J. , Mirón-Gatón , J.M. , Mourre , B. , Millán , A. , Velasco , J ., 2023 . Oceanic currents maintain the genetic structure of non-marine coastal taxa in the western Mediterranean Sea. npj Biodivers . 2 , 1 – 9 . doi: 10.1038/s44185-023-00028-0 OpenUrl CrossRef PubMed 153. ↵ Virgen-Urcelay , A. , Donner , S.D ., 2023 . Increase in the extent of mass coral bleaching over the past half-century, based on an updated global database . PLoS One 18 , 1 – 17 . doi: 10.1371/journal.pone.0281719 OpenUrl CrossRef PubMed 154. ↵ Vogt-Vincent , N.S. , Burt , A.J. , van der Ven , R.M. , Johnson , H.L. , 2024 . Coral reef potential connectivity in the southwest Indian Ocean . Coral Reefs 43 , 1037 – 1051 . doi: 10.1007/s00338-024-02521-9 OpenUrl CrossRef 155. ↵ Voolstra , C.R. , Hume , B.C.C. , Armstrong , E.J. , Mitushasi , G. , Porro , B. , Oury , N. , Agostini , S. , Boissin , E. , Poulain , J. , Carradec , Q. , Paz-García , D.A. , Zoccola , D. , Magalon , H. , Moulin , C. , Bourdin , G. , Iwankow , G. , Romac , S. , Banaigs , B. , Boss , E. , Bowler , C. , de Vargas , C. , Douville , E. , Flores , M. , Furla , P. , Galand , P.E. , Gilson , E. , Lombard , F. , Pesant , S. , Reynaud , S. , Sullivan , M.B. , Sunagawa , S. , Thomas , O.P. , Troublé , R. , Thurber , R.V. , Wincker , P. , Planes , S. , Allemand , D. , Forcioli , D ., 2023 . Disparate genetic divergence patterns in three corals across a pan-Pacific environmental gradient highlight species-specific adaptation. npj Biodivers . 2 , 1 – 16 . doi: 10.1038/s44185-023-00020-8 OpenUrl CrossRef 156. ↵ Ward , S ., 1992 . Evidence for broadcast spawning as well as brooding in the scleractinian coral Pocillopora damicornis . Mar. Biol . 112 , 641 – 646 . doi: 10.1007/BF00346182 OpenUrl CrossRef 157. ↵ Weersing , K. , Toonen , R.J ., 2009 . Population genetics, larval dispersal, and connectivity in marine systems . Mar. Ecol. Prog. Ser . 393 , 1 – 12 . doi: 10.3354/meps08287 OpenUrl CrossRef Web of Science 158. ↵ Wellenreuther , M. , Mérot , C. , Berdan , E. , Bernatchez , L ., 2019 . Going beyond SNPs: the role of structural genomic variants in adaptive evolution and species diversification . Mol. Ecol . 28 , 1203 – 1209 . doi: 10.1111/mec.15066 OpenUrl CrossRef PubMed 159. ↵ White , C. , Selkoe , K.A. , Watson , J. , Siegel , D.A. , Zacherl , D.C. , Toonen , R.J ., 2010 . Ocean currents help explain population genetic structure . Proc. R. Soc. B Biol. Sci . 277 , 1685 – 1694 . doi: 10.1098/rspb.2009.2214 OpenUrl CrossRef PubMed Web of Science 160. ↵ Wijayanti , D.P. , Indrayanti , E. , Wirasatriya , A. , Haryanto , A. , Haryanti , D. , Sembiring , A. , Fajrianzah , T.A. , Bhagooli , R ., 2019 . Reproductive seasonality of coral assemblages in the Karimunjawa Archipelago , Indonesia. Front. Mar. Sci . 6 , 1 – 15 . doi: 10.3389/fmars.2019.00195 OpenUrl CrossRef 161. ↵ Yates , K.L. , Bouchet , P.J. , Caley , M.J. , Mengersen , K. , Randin , C.F. , Parnell , S. , Fielding , A.H. , Bamford , A.J. , Ban , S. , Barbosa , A.M. , Dormann , C.F. , Elith , J. , Embling , C.B. , Ervin , G.N. , Fisher , R. , Gould , S. , Graf , R.F. , Gregr , E.J. , Halpin , P.N. , Heikkinen , R.K. , Heinänen , S. , Jones , A.R. , Krishnakumar , P.K. , Lauria , V. , Lozano-Montes , H. , Mannocci , L. , Mellin , C. , Mesgaran , M.B. , Moreno-Amat , E. , Mormede , S. , Novaczek , E. , Oppel , S. , Ortuño Crespo , G. , Peterson , A.T. , Rapacciuolo , G. , Roberts , J.J. , Ross , R.E. , Scales , K.L. , Schoeman , D. , Snelgrove , P. , Sundblad , G. , Thuiller , W. , Torres , L.G. , Verbruggen , H. , Wang , L. , Wenger , S. , Whittingham , M.J. , Zharikov , Y. , Zurell , D. , Sequeira , A.M.M ., 2018 . Outstanding challenges in the transferability of ecological models . Trends Ecol. Evol . 33 , 790 – 802 . doi: 10.1016/j.tree.2018.08.001 OpenUrl CrossRef PubMed 162. ↵ Yuen , B. , Stuart , C.E. , Pittman , S.J. , Green , S.J. , Henderson , L.M. , Wedding , L.M ., 2023 . Habitat suitability models of elkhorn coral provide ecological insight to support coral reef restoration . Restor. Ecol . 31 , 1 – 15 . doi: 10.1111/rec.13953 OpenUrl CrossRef 163. ↵ Zhang , X.X. , Liu , B.G. , Li , Y. , Liu , Y. , He , Y.X. , Qian , Z.H. , Li , J.X ., 2019 . Landscape genetics reveals that adaptive genetic divergence in Pinus bungeana (Pinaceae) is driven by environmental variables relating to ecological habitats . BMC Evol. Biol . 19 , 1 – 13 . doi: 10.1186/s12862-019-1489-x OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 07, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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