Disparate genetic divergence patterns in three corals across a pan-Pacific environmental gradient highlight species-specific adaptation

Abstract

Tropical coral reefs are among the most affected ecosystems by climate change and face increasing loss in the coming decades. Effective conservation strategies that maximize ecosystem resilience must be informed by the accurate characterization of extant genetic diversity and population structure together with an understanding of the adaptive potential of keystone species. Here we analyzed samples from the Tara Pacific Expedition (2016-2018) that completed an 18,000 km longitudinal transect of the Pacific Ocean sampling three widespread corals-Pocillopora meandrina, Porites lobata, and Millepora cf. platyphylla-across 33 sites from 11 islands. Using deep metagenomic sequencing of 269 colonies in conjunction with morphological analyses and climate variability data, we can show that despite a targeted sampling the transect encompasses multiple cryptic species. These species exhibit disparate biogeographic patterns and, most importantly, distinct evolutionary patterns in identical environmental regimes. Our findings demonstrate on a basin scale that evolutionary trajectories are species-specific and can only in part be predicted from the environment. This highlights that conservation strategies must integrate multi-species investigations to discern the distinct genomic footprints shaped by selection as well as the genetic potential for adaptive change.

Fig. 1. Biogeography of genetic delineations of the genera Pocillopora, Porites, and Millepora across their Pacific spread.

The number of samples belonging to a given genetic delineation are given for each of the genera. Reference reefs from the United Nations Environment Programme—World Conservation Monitoring Center’s (UNEP-WCMP) Global Distribution of Coral Reefs dataset^, are plotted in red using reefMapMaker.

Fig. 2. Genetic delineation of Pocillopora spp.

a SVDquartet tree. Node support ≥80% from 100 bootstraps is indicated with a black dot. Samples are grouped into five clades (SVD1-SVD5) based on tree morphology and sNMF analysis which form the secondary species hypotheses (SSHs). Species names resulting from subsequent analyses (see the relevant section of this study) have been annotated. Samples not included in the sNMF analysis (due to removal of clonal/multilocus lineages) are underlined. b Principal component analysis (PCA) based on genome-wide SNPs. The relative eigenvalues are shown in the subplots with the displayed eigenvectors shaded black. c Hierarchical genetic clustering using sNMF. Bars in each column represent the contribution of predicted ancestral lineages to each sample. Samples are ordered longitudinally by island.

Fig. 3. Genetic delineation of Porites spp.

a SVDquartet tree. Node support ≥80% from 100 bootstraps is indicated with a black dot. Samples are grouped into three main clades (K1-K3) and two, three, and four subclades, respectively (K1a, K1b, K2a, etc.) based on tree morphology and sNMF analysis with the main clades representing the three secondary species hypotheses (SSHs; annotated). b Principal component analysis (PCA) based on genome-wide SNPs. The relative eigenvalues are shown in the subplots with the displayed eigenvectors shaded in black. c Hierarchical genetic clustering using sNMF across all samples with main clades below and subclades above. Bars in each column represent the contribution of predicted ancestral lineages to each sample. Samples are ordered by cluster and then longitudinally by island.

Fig. 4. Genetic delineation of Millepora spp.

a SVDquartet tree. Node support ≥80% from 100 bootstraps is indicated with a black dot. Samples are grouped into six clades (SVD1-SVD6) based on tree morphology and sNMF analysis. Samples not included in the sNMF analysis (due to the removal of clonal/multilocus lineages) are underlined. b Principal component analysis (PCA) based on transcriptome-wide SNPs. The relative eigenvalues are shown in the subplots with the displayed eigenvectors shaded black. c Hierarchical genetic clustering using sNMF. Bars in each row represent the contribution of predicted ancestral lineages to each sample. Samples are ordered longitudinally by island.

Fig. 5. TreeMix consensus trees with migration events and residuals for Pocillopora and Porites.

a, d residuals indicating potential remaining admixture between samples in Pocillopora and Porites, respectively, after accounting for the three migration events annotated in the TreeMix tree. b, e Treemix consensus trees with migration events and their weights annotated. c, f Corresponding f₄ indices.

Fig. 6. Manhattan plot displaying the association to past temperature variation of the linkage disequilibrium-filtered genome-wide SNPs computed by RDA analysis.

a Pocillopora, b Porites. The x axis gives cumulative position in the genome with contigs sorted by decreasing size order. P values used to generate the y axis are from the RDA analysis. Circles: SNPs not included in genomic islands of differentiation between SSHs (GID); triangles: SNPs included in GIDs; dark gray: SNPs not significantly associated to past temperature variation; light gray: SNPs significantly associated to temperature variation; gold: top 100 temperature outlier SNPs; red: SNPs among the top 100 temperature outliers under divergent selection among species according to Flink analysis.