Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Apr 19;14(4):e1007220.
doi: 10.1371/journal.pgen.1007220. eCollection 2018 Apr.

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

Mikhail V Matz  1 , Eric A Treml  2 , Galina V Aglyamova  1 , Line K Bay  3
Affiliations

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

Mikhail V Matz et al. PLoS Genet. .

Abstract

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 Acropora millepora 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 A. millepora 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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The population setting.
(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–Acropora millepora). Centroid labels are initial letters of population names as in panel A. (D) ADMIXTURE plot of ancestry proportions with K = 2 (the lowest cross-validation error was observed with K = 1). Analyses on panels C and D were based on 11,426 SNPs spaced at least 2.5 kb apart and not including FST outliers.
Fig 2
Fig 2. Estimated demography of A. millepora populations on the GBR.
(A) Arc-plot of migration rates among populations reconstructed from population genomic data. Inset: ∂a∂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 r = 0.58, P = 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 stairwayPlot 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.
Fig 3
Fig 3. Modeling coral metapopulation persistence under warming.
(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). (A, B): Settings for the most efficient selection (perfect heritability, narrow tolerance). (C, D): Settings for the least efficient selection (low heritability, broad tolerance). (E, F): Intermediate heritability and tolerance settings (Esd = 1, σ = 1) with no migration. Warm-adapted populations (W and M) are shown as red-tint traces, populations from mild thermal regime (S and O) are green-tint traces, and the cool-adapted population (K) are the blue traces. Note close similarity between traces for pairs of populations pre-adapted to the same temperature (W, M and S, O). (G) Sensitivity of populations to random thermal anomalies increases under warming. Modeled temperature anomalies are shown as grey line, fluctuations in populations’ fitness–as colored lines (residuals from loess regression over fitness traces at Esd = 1, σ = 1; Wilkie: orange line, Keppel: blue line). The sign of temperature anomalies is inverted to better reveal the correspondence between rise in temperature and drop in fitness. Mutation rate was 1e-6 per locus per gamete in all simulations shown.
Fig 4
Fig 4. Effect of mutation rate on population persistence.
(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 (mu) 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: Esd = 1, σ = 1.
Fig 5
Fig 5. Larger population size and finer genetic architecture facilitate population persistence under warming.
(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 (N qtl) and population sizes (Ne) 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: Esd = 1, σ = 1, and mu = 1e-6.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Baker AC, Glynn PW, Riegl B. Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuar Coast Shelf Sci. 2008;80: 435–471. doi: 10.1016/j.ecss.2008.09.003 - DOI
    1. Logan CA, Dunne JP, Eakin CM, Donner SD. Incorporating adaptive responses into future projections of coral bleaching. Glob Chang Biol. 2014;20: 125–139. doi: 10.1111/gcb.12390 - DOI - PubMed
    1. Hughes TP, Baird AH, Bellwood DR, Card M, Connolly SR, Folke C, et al. Climate change, human impacts, and the resilience of coral reefs. Science 2003;301: 929–933. doi: 10.1126/science.1085046 - DOI - PubMed
    1. Pandolfi JM, Connolly SR, Marshall DJ, Cohen AL. Projecting coral reef futures under global warming and ocean acidification. Science. 2011;333: 418–22. doi: 10.1126/science.1204794 - DOI - PubMed
    1. Dixon GB, Davies SW, Aglyamova GV, Meyer E, Bay LK, Matz MV. Genomic determinants of coral heat tolerance across latitudes. Science 2015;348: 1460–1462. doi: 10.1126/science.1261224 - DOI - PubMed

Publication types