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Rapid seagrass meadow expansion in an Indian Ocean bright spot

Floyd, Matthew, East, Holly K., Traganos, Dimosthenis, Musthag, Azim, Guest, James, Hashim, Aminath S., Evans, Vivienne, Helber, Stephanie, Unsworth, Richard K. F., Suggitt, Andrew J.·2024·Scientific Reports
DOI 10.1038/s41598-024-61088-1
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Abstract

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000–2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km 2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km 2 year−1, r 2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass ‘bright spot’ highly worthy of increased attention across scientific, commercial, and conservation policy contexts.

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  1. Unsworth, R. K. F., Cullen-Unsworth, L. C., Jones, B. L. H. & Lilley, R. J. The planetary role of seagrass conservation. Science 1979(377), 609–613 (2022). · DOI
  2. Unsworth, R. K. F., Nordlund, L. M. & Cullen-Unsworth, L. C. Seagrass meadows support global fisheries production. Conserv. Lett. 12, e12566 (2019). · DOI · Paper page
  3. Lamb, J. B. et al. Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes, and invertebrates. Science 355, 731LP – 733 (2017). · DOI · Paper page
  4. Fonseca, M. S. & Cahalan, J. A. A preliminary evaluation of wave attenuation by four species of seagrass. Estuar. Coast. Shelf Sci. 35, 565–576 (1992). · DOI · Paper page
  5. East, H. K. et al. Seagrass meadows are important sources of reef island-building sediment. Commun. Earth Environ. 4, 33 (2023). · DOI
  6. Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509 (2012). · DOI · Paper page
  7. Dunic, J. C., Brown, C. J., Connolly, R. M., Turschwell, M. P. & Côté, I. M. Long-term declines and recovery of meadow area across the world’s seagrass bioregions. Glob. Change Biol. 27(17), 4096–4109 (2021). · DOI · Paper page
  8. Santos, C. B. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019). · DOI · Paper page
  9. Lefcheck, J. S., Wilcox, D. J., Murphy, R. R., Marion, S. R. & Orth, R. J. Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA. Glob. Chang. Biol. 23, 3474–3483 (2017). · DOI
  10. Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Chang. 8, 338–344 (2018). · DOI · Paper page
  11. Tsujimoto, R. et al. Damage to seagrass and seaweed beds in Matsushima Bay, Japan, caused by the huge tsunami of the great east Japan Earthquake on 11 March 2011. Int. J. Remote Sens. 37, 5843–5863 (2016). · DOI
  12. Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Nat. Acad. Sci. 106, 12377–12381 (2009). · DOI · Paper page
  13. James, R. K. et al. Climate change mitigation by coral reefs and seagrass beds at risk: How global change compromises coastal ecosystem services. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2022.159576 (2022). · DOI
  14. Cullen-Unsworth, L. C. & Unsworth, R. A call for seagrass protection. Science 361, 446–448 (2018). · DOI
  15. Blume, A., Pertiwi, A. P., Lee, C. B. & Traganos, D. Bahamian seagrass extent and blue carbon accounting using Earth observation. Front. Mar. Sci. vol. 10 Preprint at (2023). · DOI
  16. Traganos, D. et al. Spatially explicit seagrass extent mapping across the entire mediterranean. Front. Mar. Sci. 22, 9 (2022).
  17. Lyons, M. B. et al. Mapping the world’s coral reefs using a global multiscale earth observation framework. Remote Sens. Ecol. Conserv. 6, 557–568 (2020). · DOI
  18. Turschwell, M. P. et al. Anthropogenic pressures and life history predict trajectories of seagrass meadow extent at a global scale. Proc. Nat. Acad. Sci. 118, e2110802118 (2021). · DOI
  19. Roelfsema, C. M. et al. Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach. Remote Sens. Environ. 150, 172–187 (2014). · DOI
  20. Lyons, M. B., Phinn, S. R. & Roelfsema, C. M. Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia. ISPRS J. Photogramm. Remote Sens. 71, 34–46 (2012). · DOI
  21. Knudby, A., Newman, C., Shaghude, Y. & Muhando, C. Simple and effective monitoring of historic changes in nearshore environments using the free archive of Landsat imagery. Int. J. Appl. Earth Observ. Geoinform. 12, S116–S122 (2010). · DOI
  22. McKenzie, L. J. et al. The global distribution of seagrass meadows. Environ. Res. Lett. 15, 74041 (2020). · DOI · Paper page
  23. Miller, M. & Sluka, R. Patterns of seagrass and sediment nutrient distribution suggest anthropogenic enrichment in Laamu Atoll, Republic of Maldives. Mar. Pollut. Bull. 38, 1152–1156 (1999). · DOI
  24. McHenry, J. et al. Geographic variation in organic carbon storage by seagrass beds. Limnol. Oceanogr. 68, 1256–1268 (2023). · DOI
  25. Lavery, P. S., Mateo, M.-A., Serrano, O. & Rozaimi, M. Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service) variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service. PLoS One 8, 73748 (2013). · DOI · Paper page
  26. Duarte, C. M. Seagrass depth limits. Aquat. Bot. 40, 363–377 (1991). · DOI · Paper page
  27. Potouroglou, M. et al. Measuring the role of seagrasses in regulating sediment surface elevation. Sci. Rep. 7, 11917 (2017). · DOI
  28. Unsworth, R. K. F. et al. High connectivity of Indo-Pacific seagrass fish assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 353, 213–224 (2008). · DOI
  29. Unsworth, R. K. F., Hinder, S. L., Bodger, O. G. & Cullen-Unsworth, L. C. Food supply depends on seagrass meadows in the coral triangle. Environ. Res. Lett. 9, 94005 (2014). · DOI
  30. Aoki, L. R., McGlathery, K. J. & Oreska, M. P. J. Seagrass restoration reestablishes the coastal nitrogen filter through enhanced burial. Limnol. Oceanogr. 65, 1–12 (2020). · DOI
  31. Unsworth, R. K. F., Collier, C. J., Henderson, G. M. & McKenzie, L. J. Tropical seagrass meadows modify seawater carbon chemistry: Implications for coral reefs impacted by ocean acidification. Environ. Res. Lett. 7, 24026 (2012). · DOI
  32. Brown, K. T., Bender-Champ, D., Bryant, D. E. P., Dove, S. & Hoegh-Guldberg, O. Human activities influence benthic community structure and the composition of the coral–algal interactions in the central Maldives. J. Exp. Mar. Biol. Ecol. 497, 33–40 (2017). · DOI
  33. Udy, J., Dennison, W., Long, W. & McKenzie, L. Responses of seagrass to nutrients in the great barrier reef Australia. Mar. Ecol. Prog. Ser. 185, 257 (1999). · DOI
  34. Kleypas, J. A., McManus, J. W. & Meñez, L. A. B. Environmental limits to coral reef development: Where do we draw the line?. Am. Zool. 39, 146–159 (1999). · DOI · Paper page
  35. Painter, S. C. et al. Anthropogenic nitrogen pollution threats and challenges to the health of South Asian coral reefs. Front. Mar. Sci. 10, 1187804 (2023). · DOI
  36. Statton, J., Kendrick, G. A., Dixon, K. W. & Cambridge, M. L. Inorganic nutrient supplements constrain restoration potential of seedlings of the seagrass Posidonia Australis. Restor. Ecol. 22, 196–203 (2014). · DOI
  37. Balestri, E. & Lardicci, C. Effects of sediment fertilization and burial on Cymodocea nodosa transplants: Implications for seagrass restoration under a changing climate. Restor Ecol 22, 240–247 (2014). · DOI
  38. East, H. K., Perry, C. T., Kench, P. S., Liang, Y. & Gulliver, P. Coral reef Island initiation and development under higher than present sea levels. Geophys. Res. Lett. 45, 11211–265274 (2018). · DOI
  39. Van Maren, D. S., Oost, A. P., Wang, Z. B. & Vos, P. C. The effect of land reclamations and sediment extraction on the suspended sediment concentration in the Ems Estuary. Mar. Geol. 376, 147–157 (2016). · DOI
  40. Van Maren, D. S., van Kessel, T., Cronin, K. & Sittoni, L. The impact of channel deepening and dredging on estuarine sediment concentration. Cont. Shelf Res. 95, 1–14 (2015). · DOI
  41. Robertson, B. P. & Savage, C. Thresholds in catchment nitrogen load for shifts from seagrass to nuisance macroalgae in shallow intertidal estuaries. Limnol. Oceanogr. 66, 1353–1366 (2021). · DOI
  42. Purvaja, R. et al. Seagrass meadows as proxy for assessment of ecosystem health. Ocean Coast. Manag. 159, 34–45 (2018). · DOI
  43. Altieri, A. H. et al. Tropical dead zones and mass mortalities on coral reefs. Proc. Nat. Acad. Sci. 114, 3660–3665 (2017). · DOI · Paper page
  44. Mumby, P. J., Hastings, A. & Edwards, H. J. Thresholds and the resilience of Caribbean coral reefs. Nature 450, 98–101 (2007). · DOI · Paper page
  45. Hughes, T. P. Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science 1979(265), 1547–1551 (1994). · DOI · Paper page
  46. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001). · DOI · Paper page
  47. Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: Region-wide declines in architectural complexity. Proc. R. Soc. B Biol. Sci. 276, 3019–3025 (2009). · DOI · Paper page
  48. Perry, C. T., Spencer, T. & Kench, P. S. Carbonate budgets and reef production states: A geomorphic perspective on the ecological phase-shift concept. Coral Reefs 27, 853–866 (2008). · DOI
  49. Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015). · DOI · Paper page
  50. Kench, P. Maldives BT- Encyclopedia of Modern Coral Reefs: Structure, Form and Process. in (ed. Hopley, D.) 648–653 (Springer Netherlands, Dordrecht, 2011). · DOI
  51. Planet Team. Planet application program interface: In Space for Life on Earth. Preprint at (2017).
  52. Unsworth, R. K. F. et al. Global challenges for seagrass conservation. Ambio 48, 801–815 (2019). · DOI · Paper page
  53. Stevens, G. M. W. & Froman, N. Chapter 10—The Maldives Archipelago. in (ed. Sheppard, C. B. T.-W. S. an E. E. (Second E.) 211–236 (Academic Press, 2019). https://doi.org/10.1016/B978-0-08-100853-9.00010-5. · DOI
  54. Pichon, M. & Benzoni, F. Taxonomic re-appraisal of zooxanthellate scleractinian corals in the Maldive Archipelago. Zootaxa 1441, 21–33 (2007). · DOI
  55. Roelfsema, C. M. et al. Workflow for the generation of expert-derived training and validation data: A view to global scale habitat mapping. Front. Mar. Sci. 8, 643381 (2021). · DOI
  56. Rodriguez-Ramirez, A. et al. A contemporary baseline record of the world’s coral reefs. Sci. Data. 7, 355 (2020). · DOI
  57. Mountrakis, G., Im, J. & Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 66, 247–259 (2011). · DOI · Paper page
  58. Potapov, P., Turubanova, S. & Hansen, M. C. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sens. Environ. 115, 548–561 (2011). · DOI
  59. Gómez, C., White, J. C. & Wulder, M. A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote Sens. 116, 55–72 (2016). · DOI · Paper page
  60. Schroeder, T. A., Cohen, W. B., Song, C., Canty, M. J. & Yang, Z. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens. Environ. 103, 16–26 (2006). · DOI · Paper page
  61. Schulz, D. et al. Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS J. Photogramm. Remote Sens. 178, 97–111 (2021). · DOI
  62. Xu, Y. et al. Tracking annual cropland changes from 1984 to 2016 using time-series landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa. Remote Sens. Environ. 218, 13–31 (2018). · DOI
  63. Jin, S., Yang, L., Zhu, Z. & Homer, C. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sens. Environ. 195, 44–55 (2017). · DOI
  64. Riley, S., Degloria, S. & Elliot, S. D. A terrain ruggedness index that quantifies topographic heterogeneity. Int. J. Sci. 5, 23–27 (1999).
  65. Brooks, M, E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalised linear mixed modelling. R J. 9(2), 378–400 (2017). · DOI · Paper page
  66. Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974). · DOI · Paper page
  67. Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed. R package version 0.4.6. Preprint at (2022).

Cited by

Metadata

Authors
Floyd, M., East, H. K., Traganos, D., Musthag, A., Guest, J., Hashim, A. S., Evans, V., Helber, S., Unsworth, R. K. F., & Suggitt, A. J.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-05-13
Volume
14
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-024-61088-1
Cited by
14
References
0
Language
en
Licence
Funders
  • Northumbria University (RDF)
  • Leverhulme Trust (RPG-2021-417)

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