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Evolution of coastal forests based on a full set of mangrove genomes

Abstract

Genomic studies are now poised to explore whole communities of species. The ~70 species of woody plants that anchor the coastal ecosystems of the tropics, collectively referred to as mangroves, are particularly suited to this exploration. In this study, we de novo sequenced the whole genomes of 32 mangroves, which we combined with other sequences of 30 additional species, comprising almost all mangroves globally. These community-wide genomic data will be valuable for ecology, evolution and biodiversity research. While the data revealed 27 independent origins of mangroves, the total phylogeny shows only modest increases in species number, even in coastal areas of active speciation, suggesting that mangrove extinction is common. A possible explanation for common extinction is the frequent sea-level rises and falls (SLRs and SLFs) documented in the geological record. Indeed, near-extinctions of species with extremely small population size (N) often happened during periods of rapid SLR, as revealed by the genome-wide heterozygosity of almost all mangroves. Reduction in N has possibly been further compounded by population fragmentation and the subsequent accumulation of deleterious mutations, thus pushing mangroves even closer to extinction. Crucially, the impact of the next SLR will be exacerbated by human encroachment into these mangrove habitats, potentially altering the ecosystems of tropical coasts irreversibly.

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Fig. 1: Genome-scale phylogeny of mangroves.
Fig. 2: Independent origins of mangrove lineages.
Fig. 3: Demographic histories of four mangrove species.
Fig. 4: Model of population subdivision and inferred histories of the PSMC method using simulated samples.

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Data availability

The sequences of this study have been deposited in National Genomics Data Center (NGDC), China National Center for Bioinformation. The 48 genome assembly sequences have been deposited in the Genome Warehouse (https://ngdc.cncb.ac.cn/gwh) in NGDC, under BioProject ID PRJCA004930. Detailed accession numbers of assemblies are listed in Supplementary Table 1. RNA-seq data from multiple species have been deposited in the Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa) in NGDC, under accession number CRA004363 with BioProject ID PRJCA005451. The genomic and RNA sequences were also deposited in National Center for Biotechnology Information under BioProject ID PRJNA817364.

References

  1. He, Z. et al. Speciation with gene flow via cycles of isolation and migration: insights from multiple mangrove taxa. Natl Sci. Rev. 6, 275–288 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Zhou, R. et al. Population genetics of speciation in nonmodel organisms: I. Ancestral polymorphism in mangroves. Mol. Biol. Evol. 24, 2746–2754 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Xu, S. et al. Genome-wide convergence during evolution of mangroves from woody plants. Mol. Biol. Evol. 34, 1008–1015 (2017).

    CAS  PubMed  Google Scholar 

  4. He, Z. et al. Convergent adaptation of the genomes of woody plants at the land–sea interface. Natl Sci. Rev. 7, 978–993 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lyu, H., He, Z., Wu, C.-I. & Shi, S. Convergent adaptive evolution in marginal environments: unloading transposable elements as a common strategy among mangrove genomes. New Phytol. 217, 428–438 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Xu, S. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl Sci. Rev. 4, 721–734 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Feng, X. et al. Molecular adaptation to salinity fluctuation in tropical intertidal environments of a mangrove tree Sonneratia alba. BMC Plant Biol. 20, 178 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Feng, X. et al. Genomic insights into molecular adaptation to intertidal environments in the mangrove Aegiceras corniculatum. New Phytol. 231, 2346–2358 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Angelini, C. et al. A keystone mutualism underpins resilience of a coastal ecosystem to drought. Nat. Commun. 7, 12473 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Atwood, T. B. et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Change 7, 523–528 (2017).

    Article  CAS  Google Scholar 

  11. Barbier, E. B. et al. Coastal ecosystem-based management with nonlinear ecological functions and values. Science 319, 321–323 (2008).

    Article  CAS  PubMed  Google Scholar 

  12. Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).

    Article  Google Scholar 

  13. Hensel, M. J. S. & Silliman, B. R. Consumer diversity across kingdoms supports multiple functions in a coastal ecosystem. Proc. Natl Acad. Sci. USA 110, 20621–20626 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tomlinson, P. B. The Botany of Mangroves 2nd edn (Cambridge Univ. Press, 2016).

  15. Rovai, A. S. et al. Global controls on carbon storage in mangrove soils. Nat. Clim. Change 8, 534–538 (2018).

    Article  CAS  Google Scholar 

  16. Alongi, D. M. Carbon sequestration in mangrove forests. Carbon Manag. 3, 313–322 (2012).

    Article  CAS  Google Scholar 

  17. Grant, K. M. et al. Sea-level variability over five glacial cycles. Nat. Commun. 5, 5076 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Guo, Z. et al. Extremely low genetic diversity across mangrove taxa reflects past sea level changes and hints at poor future responses. Glob. Change Biol. 24, 1741–1748 (2018).

    Article  Google Scholar 

  19. Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature 541, 212–216 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat. Genet. 45, 67–71 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Duke, N. C. in Mangrove Ecosystems: A Global Biogeographic Perspective (eds Rivera-Monroy, V. H. et al.) 17–53 (Springer, 2017).

  23. Ellison, A. M., Farnsworth, E. J. & Merkt, R. E. Origins of mangrove ecosystems and the mangrove biodiversity anomaly. Glob. Ecol. Biogeogr. 8, 95–115 (1999).

    Google Scholar 

  24. Gee, C. T. The mangrove palm Nypa in the geologic past of the new world. Wetl. Ecol. Manag. 9, 181–203 (2001).

    Article  Google Scholar 

  25. Germeraad, J. H., Hopping, C. A. & Muller, J. Palynology of tertiary sediments from tropical areas. Rev. Palaeobot. Palynol. 6, 189–348 (1968).

    Article  Google Scholar 

  26. Graham, A. Paleobotanical evidence and molecular data in reconstructing the historical phytogeography of Rhizophoraceae. Ann. Missouri Bot. Gard. 93, 325–334 (2006).

    Article  Google Scholar 

  27. Mazer, S. J. & Tiffney, B. H. Fruits of Wetherellia and Palaeowetherellia (?Euphorbiaceae) from Eocene sediments in Virginia and Maryland. Brittonia 34, 300–333 (1982).

  28. Muller, J. Fossil pollen records of extant angiosperms. Bot. Rev. 47, 1–142 (1981).

    Article  Google Scholar 

  29. Srivastava, J. & Prasad, V. Evolution and paleobiogeography of mangroves. Mar. Ecol. 40, e12571 (2019).

  30. Hu, M.-J. et al. Chromosome-scale assembly of the Kandelia obovata genome. Hortic. Res. 7, 75 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jin, Y. & Qian, H. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).

    Article  Google Scholar 

  32. Zachos, J. C., Dickens, G. R. & Zeebe, R. E. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature 451, 279–283 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Handley, L., Crouch, E. M. & Pancost, R. D. A New Zealand record of sea level rise and environmental change during the Paleocene–Eocene Thermal Maximum. Palaeogeogr. Palaeoclimatol. Palaeoecol. 305, 185–200 (2011).

    Article  Google Scholar 

  34. Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Lu, J. & Wu, C.-I. Weak selection revealed by the whole-genome comparison of the X chromosome and autosomes of human and chimpanzee. Proc. Natl Acad. Sci. USA 102, 4063–4067 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lynch, M. et al. Perspective: spontaneous deleterious mutation. Evolution 53, 645–663 (1999).

    Article  PubMed  Google Scholar 

  38. Ohta, T. Slightly deleterious mutant substitutions in evolution. Nature 246, 96–98 (1973).

    Article  CAS  PubMed  Google Scholar 

  39. Ohta, T. The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23, 263–286 (1992).

    Article  Google Scholar 

  40. Liu, X. & Fu, Y. X. Exploring population size changes using SNP frequency spectra. Nat. Genet. 47, 555–559 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Liu, X. & Fu, Y.-X. Stairway Plot 2: demographic history inference with folded SNP frequency spectra. Genome Biol. 21, 280 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Krauss, K. W. et al. How mangrove forests adjust to rising sea level. New Phytol. 202, 19–34 (2014).

    Article  PubMed  Google Scholar 

  43. Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Frederiksen, N. O. Review of Early Tertiary Sporomorph Paleoecology (American Association of Stratigraphic Palynologists Foundation, 1985).

  45. Smith, D. E., Harrison, S., Firth, C. R. & Jordan, J. T. The early Holocene sea level rise. Quat. Sci. Rev. 30, 1846–1860 (2011).

    Article  Google Scholar 

  46. Bouillon, S. et al. Mangrove production and carbon sinks: a revision of global budget estimates. Glob. Biogeochem. Cycles 22, GB2013 (2008).

    Article  Google Scholar 

  47. Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).

    Article  CAS  Google Scholar 

  48. Hamilton, S. E. & Friess, D. A. Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nat. Clim. Change 8, 240–244 (2018).

    Article  CAS  Google Scholar 

  49. Hutchison, J., Manica, A., Swetnam, R., Balmford, A. & Spalding, M. Predicting global patterns in mangrove forest biomass. Conserv. Lett. 7, 233–240 (2014).

    Article  Google Scholar 

  50. Ouyang, X. & Lee, S. Y. Improved estimates on global carbon stock and carbon pools in tidal wetlands. Nat. Commun. 11, 317 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Richards, D. R., Thompson, B. S. & Wijedasa, L. Quantifying net loss of global mangrove carbon stocks from 20 years of land cover change. Nat. Commun. 11, 4260 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sanders, C. J. et al. Are global mangrove carbon stocks driven by rainfall? J. Geophys. Res. Biogeosci. 121, 2600–2609 (2016).

    Article  Google Scholar 

  54. Alongi, D. M. Carbon cycling and storage in mangrove forests. Ann. Rev. Mar. Sci. 6, 195–219 (2014).

    Article  PubMed  Google Scholar 

  55. Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: one of the world’s threatened major tropical environments. Bioscience 51, 807–815 (2001).

    Article  Google Scholar 

  56. Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).

    Google Scholar 

  57. Yang, G., Zhou, R., Tang, T. & Shi, S. Simple and efficient isolation of high-quality total RNA from Hibiscus tiliaceus, a mangrove associate and its relatives. Prep. Biochem. Biotechnol. 38, 257–264 (2008).

    Article  CAS  PubMed  Google Scholar 

  58. Wang, O. et al. Efficient and unique cobarcoding of second-generation sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and de novo assembly. Genome Res. 29, 798–808 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Liu, B. et al. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. Preprint at https://arxiv.org/abs/1308.2012v2 (2013).

  61. Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chin, C.-S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Xiao, C.-L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).

    Article  CAS  PubMed  Google Scholar 

  66. Chin, C.-S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).

    Article  CAS  PubMed  Google Scholar 

  67. Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 4, 30 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Tarailo‐Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics 25, 4.10.1–4.10.14 (2009).

    Article  Google Scholar 

  77. Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA 117, 9451–9457 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).

    Article  CAS  PubMed  Google Scholar 

  82. Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).

    Article  CAS  PubMed  Google Scholar 

  83. Birney, E. Genewise and genomewise. Genome Res. 14, 988–995 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Kent, W. J. BLAT—The BLAST-Like Alignment Tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2007).

    Article  PubMed  Google Scholar 

  89. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).

    Article  CAS  PubMed  Google Scholar 

  91. Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).

    Article  CAS  PubMed  Google Scholar 

  95. Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article  CAS  PubMed  Google Scholar 

  97. Reis, M. Dos & Yang, Z. Approximate likelihood calculation on a phylogeny for Bayesian estimation of divergence times. Mol. Biol. Evol. 28, 2161–2172 (2011).

    Article  PubMed  Google Scholar 

  98. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. GGTREE: an package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).

    Article  Google Scholar 

  99. Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19, 301–302 (2003).

    Article  CAS  PubMed  Google Scholar 

  100. Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).

    Article  PubMed  Google Scholar 

  101. Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).

    Article  CAS  PubMed  Google Scholar 

  102. Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).

    Article  CAS  PubMed  Google Scholar 

  103. Liang, Y. et al. Chromosome level genome assembly of Andrographis paniculata. Front. Genet. 11, 701 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Zhang, L. et al. The water lily genome and the early evolution of flowering plants. Nature 577, 79–84 (2020).

    Article  CAS  PubMed  Google Scholar 

  105. Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961–967 (2010).

    Article  CAS  PubMed  Google Scholar 

  106. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  108. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Miller, K. G. et al. The Phanerozoic record of global sea-level change. Science 310, 1293–1298 (2005).

    Article  CAS  PubMed  Google Scholar 

  111. Marçais, G. et al. MUMmer4: a fast and versatile genome alignment system. PLoS Comput. Biol. 14, e1005944 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Narasimhan, V. et al. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics 32, 1749–1751 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank P. H. Raven, X. He, W. Wang, Q. Qiu, J. Lu, J. Liu, K. Zeng, T. Tang and K. Huang for insightful comments. We also thank J. W. H. Yong, L. Chen, S. Jian, Y. Zhang, W. Lun Ng, M. Tracy, Y. Chen and S. Li for sample collection and Y. Sun and J. Ruan for technical support. This project was supported by the National Natural Science Foundation of China (grant nos. 31830005 to S. Shi and 31971540 to Z.H.); the National Key Research and Development Plan (grant no. 2017FY100705 to S. Shi); the Guangdong Basic and Applied Basic Research Foundation (grant no. 2019A1515010752 to Z.H.); the Science and Technology Project of Guangzhou (grant no. 202102020483 to Z.H.); and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (grant no. 311021006 to S. Shi).

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Contributions

S. Shi conceived of the study, while Z.H., C.-I.W. and S. Shi designed and conceptualized it. S. Shi, Z.H., X.F., Q.C., S.L., Z.G., M.L., S. Shao, X.M., W.X., X.W., R.Z., G.L., W.W., C.Z. and N.C.D. collected samples. X.F., S.L., M.L., S. Shao, W.W. and Z.Z. performed the experiments. Z.H., X.F., L.L., S.L., K.H., C.S., X.L. and G.F. sequenced and assembled the genomes. Z.H., X.F. and Q.C. performed the data analysis with assistance from S.L., J.W., S.X., S. Shao and W.W. Z.H., C.-I.W., R.E.R and S. Shi made the data interpretation and presentation. Z.H., C.-I.W. and S. Shi wrote the manuscript with input from X.F., Q.C. and Z.G. D.E.B and R.E.R revised the manuscript.

Corresponding author

Correspondence to Suhua Shi.

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Nature Ecology & Evolution thanks Richard Buggs, Abraham Morales-Cruz and Maheshi Dassanayake for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Phylogenetic tree of 61 species of mangroves, 23 mangrove associates, and 64 relatives of mangroves.

The types of sequences used to generate the phylogenetic tree are denoted to the right of the name of each species.

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He, Z., Feng, X., Chen, Q. et al. Evolution of coastal forests based on a full set of mangrove genomes. Nat Ecol Evol 6, 738–749 (2022). https://doi.org/10.1038/s41559-022-01744-9

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