Supplementary Material KMQ_final:This word file includes 3 tables and the figure legend for supplementary figure 1. from Maternal effects and <i>Symbiodinium</i> community composition drive differential patterns in juvenile survival in the coral <i>Acropora tenuis</i>

Coral endosymbionts in the dinoflagellate genus <i>Symbiodinium</i> are known to impact host physiology and have led to the evolution of reef-building, but less is known about how symbiotic communities in early life-history stages and their interactions with host parental identity shape the structure of coral communities on reefs. Differentiating the roles of environmental and biological factors driving variation in population demographic processes, particularly larval settlement, early juvenile survival and the onset of symbiosis is a key to understanding how coral communities are structured and to predicting how they are likely to respond to climate change. We show that maternal effects (that here include genetic and/or effects related to the maternal environment) can explain nearly 24% of variation in larval settlement success and 5–17% of variation in juvenile survival in an experimental study of the reef-building scleractinian coral, <i>Acropora tenuis</i>. After 25 days on the reef, <i>Symbiodinium</i> communities associated with juvenile corals differed significantly between high mortality and low mortality families based on estimates of taxonomic richness, composition and relative abundance of taxa. Our results highlight that maternal and familial effects significantly explain variation in juvenile survival and symbiont communities in a broadcast-spawning coral, with <i>Symbiodinium</i> type A3 possibly a critical symbiotic partner during this early life-stage

Supp.Fig.1.weights_surv This TIFF is supplementary figure 1. from Maternal effects and <i>Symbiodinium</i> community composition drive differential patterns in juvenile survival in the coral <i>Acropora tenuis</i>

Coral endosymbionts in the dinoflagellate genus <i>Symbiodinium</i> are known to impact host physiology and have led to the evolution of reef-building, but less is known about how symbiotic communities in early life-history stages and their interactions with host parental identity shape the structure of coral communities on reefs. Differentiating the roles of environmental and biological factors driving variation in population demographic processes, particularly larval settlement, early juvenile survival and the onset of symbiosis is a key to understanding how coral communities are structured and to predicting how they are likely to respond to climate change. We show that maternal effects (that here include genetic and/or effects related to the maternal environment) can explain nearly 24% of variation in larval settlement success and 5–17% of variation in juvenile survival in an experimental study of the reef-building scleractinian coral, <i>Acropora tenuis</i>. After 25 days on the reef, <i>Symbiodinium</i> communities associated with juvenile corals differed significantly between high mortality and low mortality families based on estimates of taxonomic richness, composition and relative abundance of taxa. Our results highlight that maternal and familial effects significantly explain variation in juvenile survival and symbiont communities in a broadcast-spawning coral, with <i>Symbiodinium</i> type A3 possibly a critical symbiotic partner during this early life-stage

C lunulatus microsatellite data

Allele frequences for 12 microsatellite loci. Arelquin format data file. Genomic DNA extracted from fin clip tissue samples

Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral

<div><p>Can genetic adaptation in reef-building corals keep pace with the current rate of sea surface warming? Here we combine population genomics, biophysical modeling, and evolutionary simulations to predict future adaptation of the common coral <i>Acropora millepora</i> on the Great Barrier Reef (GBR). Genomics-derived migration rates were high (0.1–1% of immigrants per generation across half the latitudinal range of the GBR) and closely matched the biophysical model of larval dispersal. Both genetic and biophysical models indicated the prevalence of southward migration along the GBR that would facilitate the spread of heat-tolerant alleles to higher latitudes as the climate warms. We developed an individual-based metapopulation model of polygenic adaptation and parameterized it with population sizes and migration rates derived from the genomic analysis. We find that high migration rates do not disrupt local thermal adaptation, and that the resulting standing genetic variation should be sufficient to fuel rapid region-wide adaptation of <i>A</i>. <i>millepora</i> populations to gradual warming over the next 20–50 coral generations (100–250 years). Further adaptation based on novel mutations might also be possible, but this depends on the currently unknown genetic parameters underlying coral thermal tolerance and the rate of warming realized. Despite this capacity for adaptation, our model predicts that coral populations would become increasingly sensitive to random thermal fluctuations such as ENSO cycles or heat waves, which corresponds well with the recent increase in frequency of catastrophic coral bleaching events.</p></div

mtDNA alignment

mtDNA 566bp cytochrome b alignment of 88 individual

Phylogenetic trees

NJ, MP, ML and BI trees used to provide support on the MST in Figure 3

The population setting.

<p>(A) Locations of sampled populations where mean midsummer month sea surface temperature differed by up to ~3°C. (B) Principal component analysis of water quality and temperature parameters at the sampled locations. Winter.T—10% quantile of winter temperature, Summer.T– 90% quantile of summer temperature, Daily.T– 90% quantile of daily temperature range, Phos–total dissolved phosphorus, Chl–chlorophyll, NO3 –nitrate, Secchi–Secchi depth (water clarity). Locations are colored according to summer temperature as in panel A. (C) Principal component analysis of genome-wide genetic variation (inset–<i>Acropora millepora</i>). Centroid labels are initial letters of population names as in panel A. (D) ADMIXTURE plot of ancestry proportions with <i>K</i> = 2 (the lowest cross-validation error was observed with <i>K</i> = 1). Analyses on panels C and D were based on 11,426 SNPs spaced at least 2.5 kb apart and not including <i>F</i>_ST outliers.</p

Effect of mutation rate on population persistence.

<p>(A, C, E): Mean fitness, relative to maximum attainable with perfect heritability. (B, D, F): Mean phenotype (thick lines) and modeled temperatures (thin noisy lines). Mutation rate (<i>mu</i>) per locus per gamete is listed above the graphs; effect sizes of new mutations were drawn from a normal distribution with mean 0 and standard deviation 0.2°C. Adaptation to local thermal conditions and initial adaptive response based on genetic rescue happen efficiently even under low mutation rate (1e-7), but further evolution is only possible at high mutation rate (1e-5). All simulations shown share intermediate selection efficiency settings: <i>Esd</i> = 1, <i>σ</i> = 1.</p

Larger population size and finer genetic architecture facilitate population persistence under warming.

<p>(A, C, E, G): Mean fitness, relative to maximum attainable with perfect heritability. (B, D, F, H): Mean phenotype (thick lines) and modeled temperatures (thin noisy lines). Number of QTLs (<i>N</i> qtl) and population sizes (<i>N</i>e) are listed above graphs (K population size is five-fold smaller in all cases). With 100 QTLs, their effect sizes are proportionally smaller to enable the same total genetic variance as with 10 QTLs. Both larger population size (C, D) and finer genetic architecture (E, F) improve population persistence, and combination of the two might enable populations to adapt indefinitely (G, H). All simulations shown share intermediate selection efficiency settings: <i>Esd</i> = 1, <i>σ</i> = 1, and <i>mu</i> = 1e-6.</p

Estimated demography of <i>A</i>. <i>millepora</i> populations on the GBR.

<p>(A) Arc-plot of migration rates among populations reconstructed from population genomic data. Inset: <i>∂a∂i</i> model used: ancestral population splits into two populations of unequal sizes (N1 and N2) some time T in the past, these populations exchange migrants at different rates depending on direction. (B) Migration rates according to the biophysical model. On panels A and B, the arcs should be read clockwise to tell the direction of migration; line thickness is proportional to the migration rate. (C) Correlation between log-transformed biophysical and genetic migration rates (Mantel <i>r</i> = 0.58, <i>P</i> = 0.05). (D) Box plot of effective population sizes inferred by the split-with-migration model (panel A) across all population pairs and bootstrap replicates. (E) Historical effective population sizes inferred by <i>stairwayPlot</i> for the Keppel population and pooled Sudbury, Orpheus and Magnetic populations (GBR). The line is median of 200 bootstrap replicates, light shaded area is 95% credible interval, dark-shaded area is 75% credible interval.</p