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Expansion of the mangrove species Rhizophora mucronata in the Western Indian Ocean launched contrasting genetic patterns

  • 1.

    Bryan-Brown, D. N., Brown, C. J., Hughes, J. M. & Connolly, R. M. Patterns and trends in marine population connectivity research. Mar. Ecol. Prog. Ser. 585, 243–256 (2017).

    ADS  Article  Google Scholar 

  • 2.

    Tomlinson, P. B. The Botany of Mangroves (Cambridge University Press, Cambridge, 2016).

    Google Scholar 

  • 3.

    Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669. https://doi.org/10.3390/rs10101669 (2018).

    ADS  Article  Google Scholar 

  • 4.

    Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosyst. Health Sustain. 2, 01211. https://doi.org/10.1002/ehs2.1211 (2016).

    Article  Google Scholar 

  • 5.

    Richards, D. R. & Friess, D. A. Rates of drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. USA 113, 344–349 (2016).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 6.

    Hermansen, T. D., Britton, D. R., Ayre, D. J. & Minchonton, T. E. Identifying the real pollinators? Exotic honeybees are the dominant flower visitors and only effective pollinators of Avicennia marina in Australian temperate mangroves. Estuar. Coast. 37, 621–635 (2014).

    Article  Google Scholar 

  • 7.

    Wee, A. K. S., Low, S. Y. & Webb, E. L. Pollen limitation affects reproductive outcome in the bird-pollinated mangrove Bruguiera gymnorrhiza (Lam.) in a highly urbanized environment. Aquat. Bot. 120, 240–243 (2015).

    Article  Google Scholar 

  • 8.

    Rabinowitz, D. Dispersal properties of mangrove propagules. Biotropica 10, 47–57 (1978).

    Article  Google Scholar 

  • 9.

    Drexler, J. Z. Maximum longevities of Rhizophora apiculataand R. mucronatapropagules. Pac. Sci. 55, 17–22 (2001).

    Article  Google Scholar 

  • 10.

    Nettel, A. & Dodd, R. S. Drifting propagules and receding swamps: genetic footprints of mangrove recolonization and dispersal along tropical coasts. Evolution 61, 958–971 (2007).

    CAS  PubMed  Article  Google Scholar 

  • 11.

    Takayama, K., Tamura, M., Tateshi, Y., Webb, E. L. & Kajita, T. Strong genetic structure over the American continents and transoceanic dispersal in red mangroves Rhizophora (Rhizophoraceae), revealed by broad-scale nuclear and chloroplast DNA analysis. Am. J. Bot. 100, 1191–1201 (2013).

    CAS  PubMed  Article  Google Scholar 

  • 12.

    Lo, E. Y., Duke, N. C. & Sun, M. Phylogeographic pattern of Rhizophora(Rhizophoraceae) reveals the importance of both vicariance and long-distance oceanic dispersal to modern mangrove distribution. BMC Evol. Biol. 14, 83. https://doi.org/10.1186/1471-2148-14-83 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  • 13.

    Van der Stocken, T. et al. A general framework for propagule dispersal in mangroves. Biol. Rev. 94, 1547–1575 (2019).

    PubMed  Article  Google Scholar 

  • 14.

    Thomas, L. et al. Isolation by resistance across a complex coral reef seascape. Proc. R. Soc. B Biol. Sci. 282, 20151217. https://doi.org/10.1098/rspb.2015.1217 (2015).

    CAS  Article  Google Scholar 

  • 15.

    Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Contrasting effects of historical sea level rise and contemporary ocean currents on regional gene flow of Rhizophora racemosain eastern Atlantic mangroves. PLoS ONE 11, e0150950. https://doi.org/10.1371/journal.pone.0150950 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 16.

    Wee, A. K. S. et al. Oceanic currents, not land masses, maintain the genetic structure of the mangrove Rhizophora mucronataLam. (Rhizophoraceae) in Southeast Asia. J. Biogeogr. 41, 954–964 (2014).

    Article  Google Scholar 

  • 17.

    Wee, A. K. S. et al. Genetic structures across a biogeographical barrier reflect dispersal potential of four Southeast Asian mangrove plant species. J. Biogeogr. 47, 1258–1271 (2020).

    Article  Google Scholar 

  • 18.

    Lessios, H. A. & Robertson, D. R. Crossing the impassable: genetic connections in 20 reef fishes across the eastern Pacific barrier. Proc. R. Soc. B: Biol. Sci. 273, 2201–2208 (2006).

    CAS  Article  Google Scholar 

  • 19.

    Ng, W. L., Chan, H. T. & Szmidt, A. E. Molecular identification of natural mangrove hybrids of Rhizophora in Peninsular Malaysia. Tree Genet. Genomes 9, 1151–1160 (2013).

    Article  Google Scholar 

  • 20.

    Guo, Z. et al. Genetic discontinuities in a dominant mangrove Rhizophora apiculata (Rhizophoraceae) in the Indo-Malaysian region. J. Biogeogr. 43, 1856–1868 (2016).

    Article  Google Scholar 

  • 21.

    Yan, Y.-B., Duke, N. & Sun, M. Comparative analysis of the pattern of population genetic diversity in three Indo-West Pacific Rhizophora mangrove species. Front. Plant Sci. 7, 1434. https://doi.org/10.3389/fpls.2016.01434 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  • 22.

    Triest, L., Hasan, S., Motro, P. R. & De Ryck, D. J. R. Geographical distance and large rivers shape genetic structure of Avicennia officinalis in the highly dynamic Sundarbans mangrove forest and Ganges Delta region. Estuar. Coast. 41, 908–920 (2018).

    Article  Google Scholar 

  • 23.

    Do, B. T. N., Koedam, N. & Triest, L. Avicennia marina maintains genetic structure whereas Rhizophora stylosa connects mangroves in a flooded, former inner sea (Vietnam). Estuar. Coast. Shelf Sci. 222, 195–204 (2019).

    ADS  Article  Google Scholar 

  • 24.

    He, Z. et al. Speciation with gene flow via cycles of isolation and migration: insights from multiple mangrove taxa. Natl. Sci. Rev. 6, 272–288 (2019).

    Google Scholar 

  • 25.

    Pil, M. W. et al. Postglacial north-south expansion of populations of Rhizophora mangle (Rhizophoraceae) along the Brazilian coast revealed by microsatellite analysis. Am. J. Bot. 98, 1031–1039 (2011).

    PubMed  Article  Google Scholar 

  • 26.

    Cerón-Souza, I. et al. Contrasting demographic history and gene flow patterns of two mangrove species on either side of the Central American Isthmus. Ecol. Evol. 5, 3486–3499 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 27.

    Sandoval-Castro, E. et al. Post-glacial expansion and population genetic divergence of mangrove species Avicennia germinans (L.) Stearn and Rhizophora mangle L. along the Mexican coast. PLoS ONE 9, 93358. https://doi.org/10.1371/journal.pone.0093358 (2014).

    ADS  CAS  Article  Google Scholar 

  • 28.

    Kennedy, J. P. et al. Contrasting genetic effects of red mangrove (Rhizophora mangleL.) range expansion along West and East Florida. J. Biogeogr. 44, 335–347 (2017).

    Article  Google Scholar 

  • 29.

    Francisco, P. M., Mori, G. M., Alves, F. A., Tambarussi, E. V. & de Souza, A. P. Population genetic structure, introgression, and hybridization in the genus Rhizophora along the Brazilian coast. Ecol. Evol. 8, 3491–3504. https://doi.org/10.1002/ece3.3900 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  • 30.

    Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Hidden founders? Strong bottlenecks and fine-scale genetic structure in mangrove populations of the Cameroon Estuary complex. Hydrobiologia 803, 189–207 (2017).

    Article  Google Scholar 

  • 31.

    Ngeve, M. N., Van der Stocken, T., Sierens, T., Koedam, N. & Triest, L. Bidirectional gene flow on a mangrove river landscape and between-catchment dispersal of Rhizophora racemosa (Rhizophoraceae). Hydrobiologia 790, 93–108 (2017).

    Article  Google Scholar 

  • 32.

    De Ryck, D. J. R. et al. Dispersal limitation of the mangrove Avicennia marina at its South African range limit in strong contrast to connectivity in its core East African region. Mar. Ecol. Prog. Ser. 545, 123–134 (2016).

    ADS  Article  CAS  Google Scholar 

  • 33.

    Duke, N. C., Lo, E. Y. Y. & Sun, M. Global distribution and genetic discontinuities of mangroves—emerging patterns in the evolution of Rhizophora. Trees Struct. Funct. 16, 65–79 (2002).

    Article  Google Scholar 

  • 34.

    Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves (Earthscan and James & James, 2010).

  • 35.

    Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017).

    Article  Google Scholar 

  • 36.

    Duke, N. et al. Rhizophora mucronataThe IUCN Red List of Threatened Species 2010: e.T178825A7618520.https://doi.org/10.2305/IUCN.UK.2010-2.RLTS.T178825A7618520.en (2010). Downloaded on 27 January 2020.

  • 37.

    Schouten, M. W., de Ruijter, W. P. M., van Leeuwen, P. J. & Ridderinkhof, H. Eddies and variability in the Mozambique Channel. Deep-Sea Res. II(50), 1987–2003 (2003).

    ADS  Google Scholar 

  • 38.

    Ternon, J. F., Roberts, M. J., Morris, T., Hancke, L. & Backeberg, B. In situ measured current structures of the eddy field in the Mozambique Channel. Deep-Sea Res. II 100, 10–26 (2014).

    Article  Google Scholar 

  • 39.

    Yokoyama, Y., Lambeck, K., De Deckker, P., Johnston, P. & Fifield, K. L. Timing of the Last Glacial Maximum from observed sea-level minima. Nature 406, 713–716 (2000).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 40.

    Van der Stocken, T., Carroll, D., Menemenlis, D., Simard, M. & Koedam, N. Global-scale dispersal and connectivity in mangroves. Proc. Natl. Acad. Sci. USA 116, 915–922 (2019).

    PubMed  Article  CAS  Google Scholar 

  • 41.

    Schott, F. A., Shang-Ping, X. & McCreary, J. P. Jr. Indian Ocean circulation and climate variability. Rev. Geophys. 47, RG1002. https://doi.org/10.1029/2007RG000245 (2009).

    ADS  Article  Google Scholar 

  • 42.

    Hume, J. P., Martill, D. & Hing, R. A. Terrestrial vertebrate palaeontological review of Aldabra Atoll, Aldabra Group. Seychelles. PLoS ONE 13, e0192675. https://doi.org/10.1371/journal.pone.0192675 (2018).

    CAS  Article  PubMed  Google Scholar 

  • 43.

    Braithwaite, C. J. R., Taylor, J. D. & Kennedy, W. J. The evolution of an atoll: the depositional and erosional history of Aldabra. Philos. Trans. R. Soc. Lond. B. 266, 307–340 (1973).

    ADS  Article  Google Scholar 

  • 44.

    Obura, D. The diversity and biogeography of Western Indian Ocean reef-building corals. PLoS ONE 7, e45013. https://doi.org/10.1371/journal.pone.0045013 (2012).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 45.

    Urashi, C., Teshima, K. M., Minobe, S., Koizumi, O. & Inomata, N. Inferences of evolutionary history of a widely distributed mangrove species, Bruguiera gymnorrhiza, in the Indo-West Pacific region. Ecol. Evol. 3, 2251–2261 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 46.

    Tomizawa, Y. et al. Genetic structure and population demographic history of a widespread mangrove plant Xylocarpus granatum J. Koenig across the Indo-West Pacific region. Forests 8, 480 (2017).

    Article  Google Scholar 

  • 47.

    van der Ven, R. M. et al. Population genetic structure of the stony coral Acropora tenius shows high but variable connectivity in East Africa. J. Biogeogr. 43, 510–519 (2016).

    Article  Google Scholar 

  • 48.

    Jahnke, M. et al. Population genetic structure and connectivity of the seagrass Thalassia hemprichii in the Western Indian Ocean is influenced by predominant ocean currents. Ecol. Evol. 9, 8953–8964 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 49.

    Muths, D., Tessier, E. & Bourjea, J. Genetic structure of the reef grouper Epinephelus merra in the West Indian Ocean appears congruent with biogeographic and oceanographic boundaries. Mar. Ecol. 36, 447–461 (2015).

    ADS  Article  Google Scholar 

  • 50.

    Mori, G. M., Zucchi, M. I. & Souza, A. P. Multiple-geographic-scale genetic structure of two mangrove tree species: the roles of mating system, hybridization, limited dispersal and extrinsic factors. PLoS ONE 10, 0118710. https://doi.org/10.1371/journal.pone.0118710 (2015).

    CAS  Article  Google Scholar 

  • 51.

    Hancke, L., Roberts, M. J. & Ternon, J. F. Surface drifter trajectories highlight flow pathways in the Mozambique Channel. Deep-Sea Res. II(100), 27–37 (2014).

    Google Scholar 

  • 52.

    Gamoyo, M., Obura, D. & Reason, C. J. C. Estimating connectivity through larval dispersal in the Western Indian Ocean. J. Geophys. Res. Biogeo. 124, 2446–2459. https://doi.org/10.1029/2019JG005128 (2019).

    Article  Google Scholar 

  • 53.

    Silva, I., Mesquita, N. & Paula, J. Genetic and morphological differentiation of the mangrove crab Perisesarma guttatum (Brachyura Sesarmidae) along an East African latitudinal gradient. Biol. J. Linn. Soc. 99, 28–46 (2010).

    Article  Google Scholar 

  • 54.

    Madeira, C., Alves, M. J., Mesquita, N., Silva, I. & Paula, J. Tracing geographical patterns of population differentiation in a widespread mangrove gastropod: genetic and geometric morphometrics surveys along the eastern African coast. Biol. J. Linn. Soc. 107, 647–663 (2012).

    Article  Google Scholar 

  • 55.

    Fatoyinbo, E. T., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeo. 113, G02S06. https://doi.org/10.1029/2007JG000551 (2008).

    ADS  Article  Google Scholar 

  • 56.

    Lutjeharms, J. R. E. & Da Silva, A. J. The Delagoa bight eddy. Deep-Sea Res. 35, 619–634 (1988).

    ADS  Article  Google Scholar 

  • 57.

    Quartly, G. D. & Srokosz, M. A. Eddies in the southern Mozambique Channel. Dee-Sea Res. II: Top. Stud. Oceanogr. 51, 69–83 (2004).

    ADS  CAS  Article  Google Scholar 

  • 58.

    Paula, J., Dray, T. & Queiroga, H. Interaction of offshore and inshore processes controlling settlement of brachyuran megalopae in Saco mangrove creek, Inhaca Island (South Mozambique). Mar. Ecol. Prog. Ser. 215, 251–260 (2001).

    ADS  Article  Google Scholar 

  • 59.

    Singh, S. P., Groeneveld, J. C., Hart-Davis, M. G., Backeberg, B. C. & Willows-Munro, S. Seascape genetics of the spiny lobster Panulirus homarus in the Western Indian Ocean: understanding how oceanographic features shape the genetic structure of species with high larval dispersal potential. Ecol. Evol. 8, 12221–12237 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  • 60.

    Ngeve, M., Koedam, N. & Triest, L. Runaway fathers? Limited pollen dispersal and mating system in Rhizophora racemosa populations of a disturbed mangrove estuary. Aquat. Bot. 165, 103241. https://doi.org/10.1016/j.aquabot.2020.103241 (2020).

    Article  Google Scholar 

  • 61.

    Kondo, K., Nakamura, T., Tsuruda, K., Saito, N. & Yaguchi, Y. Pollination in Bruguiera gymnorrhiza and Rhizophora mucronata (Rhizophoraceae) in Ishigaki Island, The Ryukyu Islands, Japan. Biotropica 19, 377–380 (1987).

    Article  Google Scholar 

  • 62.

    Islam, M. S., Lian, C., Kameyama, N., Wu, B. & Hogetsu, T. Development of microsatellite markers in Rhizophora stylosa using a dual-suppression-polymerase chain reaction technique. Mol. Ecol. Notes 4, 110–112 (2004).

    CAS  Article  Google Scholar 

  • 63.

    Takayama, K., Tamura, M., Tateishi, Y. & Kajita, T. Isolation and characterization of microsatellite loci in the red mangrove Rhizophora mangle (Rhizophoraceae) and its related species. Conserv. Genet. 9, 1323–1325 (2008).

    CAS  Article  Google Scholar 

  • 64.

    Takayama, K. et al. Isolation and characterization of microsatellite loci in a mangrove species, Rhizophora stylosa (Rhizophoraceae). Conserv. Genet. Resour. 1, 175. https://doi.org/10.1007/s12686-009-9042-7 (2009).

    Article  Google Scholar 

  • 65.

    Shinmura, Y. et al. Isolation and characterization of 14 microsatellite markers for Rhizophora mucronata (Rhizophoraceae) and their potential use in range-wide population studies. Conserv. Genet. Resour. 4, 951–954 (2012).

    Article  Google Scholar 

  • 66.

    Wee, A. K. S., Takayama, K., Kajita, T. & Webb, E. L. Microsatellite loci for Avicennia alba (Acanthaceae), Sonneratia alba (Lythraceae) and Rhizophora mucronata (Rhizophoraceae). J. Trop. For. Sci. 25, 131–136 (2013).

    Google Scholar 

  • 67.

    Ribeiro, D. O. et al. Isolation of microsatellite markers for the red mangrove, Rhizophora mangle (Rhizophoraceae). Appl. Plant Sci. 1, 1300003. https://doi.org/10.3732/apps.1300003 (2013).

    Article  Google Scholar 

  • 68.

    Goudet, J. FSTAT, version 2.9.3, a program to estimate and test gene diversities and fixation indices. (2001).

  • 69.

    van Oosterhout, C., Hutchison, W. F., Wills, D. P. M. & Shipley, P. Micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).

    Article  CAS  Google Scholar 

  • 70.

    Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Hered. 100, 106113 (2009).

    Article  CAS  Google Scholar 

  • 71.

    Campagne, P., Smouse, P. E., Varouchas, G., Silvain, J.-F. & Leru, B. Comparing the van Oosterhout and Chybicki-Burczyk methods of estimating null allele frequencies for inbred populations. Mol. Ecol. Resour. 12, 975–982 (2012).

    CAS  PubMed  Article  Google Scholar 

  • 72.

    Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 73.

    Hardy, O. & Vekemans, X. spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).

    Article  CAS  Google Scholar 

  • 74.

    Loiselle, B., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).

    Article  Google Scholar 

  • 75.

    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 76.

    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).

    CAS  PubMed  Article  Google Scholar 

  • 77.

    Earl, D. M. & von Holdt, B. M. Structure harvester: a website and program for visualizing Structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).

    Article  Google Scholar 

  • 78.

    Li, Y. L. & Liu, J. X. Structureselector: a web based software to select and visualize the optimal number of clusters using multiple methods. Mol. Ecol. Resour. 18, 176–177 (2018).

    PubMed  Article  Google Scholar 

  • 79.

    Manni, F., Guerard, E. & Heyer, E. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier’s algorithm. Hum. Biol. 76, 173190 (2004).

    Article  Google Scholar 

  • 80.

    Beerli, P. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22, 341–345 (2006).

    CAS  PubMed  Article  Google Scholar 

  • 81.

    Beerli, P. & Palczewski, M. Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185, 313–326 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  • 82.

    Cornuet, J. M. et al. DIYABC v2.0: a software to make approximate bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).

    CAS  PubMed  Article  Google Scholar 

  • 83.

    Lutjeharms, J. R. E., Biastoch, A., Van der Werf, P. M., Ridderinkhof, H. & De Ruijter, W. P. M. On the discontinuous nature of the Mozambique Current. S. Afr. J. Sci. https://doi.org/10.4102/sajs.v108i1/2.428 (2012).

    Article  Google Scholar 


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