Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean

Abstract

In the relatively unproductive waters of the tropical ocean, islands can enhance phytoplankton biomass and create hotspots of productivity and biodiversity that sustain upper trophic levels, including fish that are crucial to the survival of islands’ inhabitants. This phenomenon, termed the island mass effect 66 years ago, has been widely described. However, most studies focused on individual islands, and very few documented phytoplankton community composition. Consequently, basin-scale impacts on phytoplankton biomass, primary production and biodiversity remain largely unknown. Here we systematically identify enriched waters near islands from satellite chlorophyll concentrations (a proxy for phytoplankton biomass) to analyse the island mass effect for all tropical Pacific islands on a climatological basis. We find enrichments near 99% of islands, impacting 3% of the tropical Pacific Ocean. We quantify local and basin-scale increases in chlorophyll and primary production by contrasting island-enriched waters with nearby waters. We also reveal a significant impact on phytoplankton community structure and biodiversity that is identifiable in anomalies in the ocean colour signal. Our results suggest that, in addition to strong local biogeochemical impacts, islands may have even stronger and farther-reaching ecological impacts.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Maps of IME detection in the tropical Pacific.
Fig. 2: Maps of island impacts on PHYSAT phenoclasses.
Fig. 3: Island impacts on phytoplankton community structure as depicted by PHYSAT phenoclasses.

Similar content being viewed by others

Data availability

The main outputs of this study, including the island database, IME and REF masks and IME impacts on Chl, primary production and PHYSAT, are available as a dataset hosted on Zenodo65. The PHYSAT climatology calculated for this paper is available there as well. PHYSAT is now being processed by ACRI (https://www.acri-st.fr/) and PHYSAT data will soon be publicly available from their website. Other data that support the findings of this study are available in various public repositories: https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1chlamday.html (MODIS Chl), http://orca.science.oregonstate.edu/2160.by.4320.monthly.hdf.vgpm.m.chl.m.sst.php (MODIS primary production), https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html (GSHHG coastline), https://www.gebco.net/data_and_products/gridded_bathymetry_data/ (GEBCO bathymetry). The island database from Nunn et al.52 is available as Additional file 1 at https://doi.org/10.1186/s40562-016-0041-8.

Code availability

The IME detection algorithm, along with datasets and example code to reproduce Fig. 1, Extended Data Fig. 1 and parts of Table 1, is available at https://github.com/messiem/toolbox_IME_detection and the corresponding release is available via Zenodo66.

References

  1. Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).

    Article  Google Scholar 

  2. Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).

    Article  Google Scholar 

  3. Ptacnik, R. et al. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc. Natl.Acad. Sci. USA 105, 5134–5138 (2008).

    Article  Google Scholar 

  4. Corcoran, A. A. & Boeing, W. J. Biodiversity increases the productivity and stability of phytoplankton communities. PLoS ONE 7, e49397 (2012).

    Article  Google Scholar 

  5. Arteaga, L., Pahlow, M. & Oschlies, A. Global patterns of phytoplankton nutrient and light colimitation inferred from an optimality-based model. Glob. Biogeochem. Cycles 28, 648–661 (2014).

    Article  Google Scholar 

  6. Lewis, M., Hebert, D., Harrison, W. G., Platt, T. & Oakey, N. S. Vertical nitrate fluxes in the oligotrophic ocean. Science 234, 870–873 (1986).

    Article  Google Scholar 

  7. McGillicuddy, D. J. J. et al. Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms. Science 316, 1021–1026 (2007).

    Article  Google Scholar 

  8. Duce, R. A. et al. Impacts of atmospheric anthropogenic nitrogen on the open ocean. Science 320, 893–897 (2008).

    Article  Google Scholar 

  9. Tang, W. et al. Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production. Nat. Commun. 10, 831 (2019).

    Article  Google Scholar 

  10. Letscher, R. T., Primeau, F. & Moore, J. K. Nutrient budgets in the subtropical ocean gyres dominated by lateral transport. Nat. Geosci. 9, 815–819 (2016).

    Article  Google Scholar 

  11. Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2019).

    Article  Google Scholar 

  12. Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).

    Article  Google Scholar 

  13. Lévy, M., Franks, P. J. S. & Smith, K. S. The role of submesoscale currents in structuring marine ecosystems. Nat. Commun. 9, 4758 (2018).

    Article  Google Scholar 

  14. Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences 17, 609–634 (2020).

    Article  Google Scholar 

  15. Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581 (2016).

    Article  Google Scholar 

  16. Doty, M. S. & Oguri, M. The island mass effect. ICES J. Mar. Sci. 22, 33–37 (1956).

    Article  Google Scholar 

  17. Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).

    Article  Google Scholar 

  18. Bakker, D. C., Nielsdóttir, M. C., Morris, P. J., Venables, H. J. & Watson, A. J. The island mass effect and biological carbon uptake for the subantarctic Crozet Archipelago. Deep Sea Res. Pt II 54, 2174–2190 (2007).

    Article  Google Scholar 

  19. Heywood, K. J., Stevens, D. P. & Bigg, G. R. Eddy formation behind the tropical island of Aldabra. Deep Sea Res. Pt I 43, 555–578 (1996).

    Article  Google Scholar 

  20. Palacios, D. M. Factors influencing the island-mass effect of the Galapagos archipelago. Geophys. Res. Lett. 29, 2134 (2002).

    Article  Google Scholar 

  21. Gilmartin, M. & Revelante, N. The ‘island mass’ effect on the phytoplankton and primary production of the Hawaiian Islands. J. Exp. Mar. Biol. Ecol. 16, 181–204 (1974).

    Article  Google Scholar 

  22. Signorini, S. C., McClain, C. R. & Dandonneau, Y. Mixing and phytoplankton bloom in the wake of the Marquesas Islands. Geophys. Res. Lett. 26, 3121–3124 (1999).

    Article  Google Scholar 

  23. Messié, M., Radenac, M.-H., Lefèvre, J. & Marchesiello, P. Chlorophyll bloom in the western Pacific at the end of the 1997-98 El Niño: the role of the Kiribati Islands. Geophys. Res. Lett. 33, L14601 (2006).

    Article  Google Scholar 

  24. Messié, M. & Radenac, M.-H. Seasonal variability of the surface chlorophyll in the western tropical Pacific from SeaWiFS data. Deep Sea Res. Pt I 53, 1581–1600 (2006).

    Article  Google Scholar 

  25. Le Borgne, R., Dandonneau, Y. & Lemasson, L. The problem of the island mass effect on chlorophyll and zooplankton standing crops around Mare (Loyalty Islands) and New Caledonia. Bull. Mar. Sci. 37, 450–459 (1985).

    Google Scholar 

  26. Messié, M. et al. The delayed island mass effect: how islands can remotely trigger blooms in the oligotrophic ocean. Geophys. Res. Lett. 47, e2019GL085282 (2020).

    Article  Google Scholar 

  27. Dandonneau, Y. & Charpy, L. An empirical approach to the island mass effect in the south tropical Pacific based on sea surface chlorophyll concentrations. Deep Sea Res. Pt A 32, 707–721 (1985).

    Article  Google Scholar 

  28. Shiozaki, T., Kodama, T. & Furuya, K. Large-scale impact of the island mass effect through nitrogen fixation in the western South Pacific Ocean. Geophys. Res. Lett. 41, 2907–2913 (2014).

    Article  Google Scholar 

  29. Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochem. Cycles 33, 391–419 (2019).

    Article  Google Scholar 

  30. Martinez, E., Rodier, M., Pagano, M. & Sauzède, R. Plankton spatial variability within the Marquesas archipelago, South Pacific. J. Mar. Syst. 212, 103432 (2020).

    Article  Google Scholar 

  31. Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).

    Article  Google Scholar 

  32. Laws, E. A., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).

    Article  Google Scholar 

  33. Messié, M. & Chavez, F. P. A global analysis of ENSO synchrony: the oceans’ biological response to physical forcing. J. Geophys. Res. 117, C09001 (2012).

    Google Scholar 

  34. Luo, Y.-W., Lima, I. D., Karl, D. M., Deutsch, C. A. & Doney, S. C. Data-based assessment of environmental controls on global marine nitrogen fixation. Biogeosciences 11, 691–708 (2014).

    Article  Google Scholar 

  35. Messié, M. & Chavez, F. P. Seasonal regulation of primary production in eastern boundary upwelling systems. Prog. Oceanogr. 134, 1–18 (2015).

    Article  Google Scholar 

  36. Mouw, C. B. et al. A consumer’s guide to satellite remote sensing of multiple phytoplankton groups in the global ocean. Front. Mar. Sci. 4, 41 (2017).

    Article  Google Scholar 

  37. Alvain, S., Moulin, C., Dandonneau, Y. & Bréon, F. M. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Res. Pt I 52, 1989–2004 (2005).

    Article  Google Scholar 

  38. Rêve-Lamarche, A.-H. et al. Ocean color radiance anomalies in the North Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00408 (2017).

  39. Alvain, S., Loisel, H. & Dessailly, D. Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters. Opt. Express 20, 1070–1083 (2012).

    Article  Google Scholar 

  40. Mackey, D. J., Blanchot, J., Higgins, H. W. & Neveux, J. Phytoplankton abundances and community structure in the equatorial Pacific. Deep Sea Res. Pt II 49, 2561–2582 (2002).

    Article  Google Scholar 

  41. Johnson, Z. I. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).

    Article  Google Scholar 

  42. Martiny, A. C., Kathuria, S. & Berube, P. M. Widespread metabolic potential for nitrite and nitrate assimilation among Prochlorococcus ecotypes. Proc. Natl. Acad. Sci. USA 106, 10787–10792 (2009).

    Article  Google Scholar 

  43. Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299 (2014).

    Article  Google Scholar 

  44. Dai, S. et al. The seamount effect on phytoplankton in the tropical western Pacific. Mar. Environ. Res. 162, 105094 (2020).

    Article  Google Scholar 

  45. Leitner, A. B., Neuheimer, A. B. & Drazen, J. C. Evidence for long-term seamount-induced chlorophyll enhancements. Sci. Rep. 10, 12729 (2020).

    Article  Google Scholar 

  46. Bowen, B. W., Rocha, L. A., Toonen, R. J. & Karl, S. A. The origins of tropical marine biodiversity. Trends Ecol. Evol. 28, 359–366 (2013).

    Article  Google Scholar 

  47. Worm, B., Lotze, H. K. & Myers, R. A. Predator diversity hotspots in the blue ocean. Proc. Natl. Acad. Sci. USA 100, 9884–9888 (2003).

    Article  Google Scholar 

  48. Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).

    Article  Google Scholar 

  49. Harrison, A.-L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).

    Article  Google Scholar 

  50. Pompa, S., Ehrlich, P. R. & Ceballos, G. Global distribution and conservation of marine mammals. Proc. Natl. Acad. Sci. USA 108, 13600–13605 (2011).

    Article  Google Scholar 

  51. Wessel, P. & Smith, W. H. F. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741––8743 (1996).

    Article  Google Scholar 

  52. Nunn, P. D., Kumar, L., Eliot, I. & McLean, R. F. Classifying Pacific islands. Geosci. Lett 3, 7 (2016).

    Article  Google Scholar 

  53. Hasegawa, D., Lewis, M. R. & Gangopadhyay, A. How islands cause phytoplankton to bloom in their wakes. Geophys. Res. Lett. 36, L20605 (2009).

    Article  Google Scholar 

  54. Platt, T. & Sathyendranath, S. Oceanic primary production: estimation by remote sensing at local and regional scales. Science 241, 1613–1620 (1988).

    Article  Google Scholar 

  55. Hasegawa, D., Yamazaki, H., Ishimaru, T., Nagashima, H. & Koike, Y. Apparent phytoplankton bloom due to island mass effect. J. Mar. Syst. 69, 238–246 (2008).

    Article  Google Scholar 

  56. Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Glob. Biogeochem. Cycles 30, 1756–1777 (2016).

    Article  Google Scholar 

  57. Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).

    Article  Google Scholar 

  58. Alvain, S., Moulin, C., Dandonneau, Y. & Loisel, H. Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: a satellite view. Glob. Biogeochem. Cycles 22, GB3001 (2008).

    Article  Google Scholar 

  59. Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).

    Article  Google Scholar 

  60. Pielou, E. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).

    Article  Google Scholar 

  61. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).

    Article  Google Scholar 

  62. Colwell, R. K., Mao, C. X. & Chang, J. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85, 2717–2727 (2004).

    Article  Google Scholar 

  63. De Monte, S., Soccodato, A., Alvain, S. & d’Ovidio, F. Can we detect oceanic biodiversity hotspots from space? ISME J. 7, 2054–2056 (2013).

    Article  Google Scholar 

  64. Soccodato, A. et al. Estimating planktonic diversity through spatial dominance patterns in a model ocean. Mar. Geonom. 29, 9–17 (2016).

    Article  Google Scholar 

  65. Messié, M., Petrenko, A., Doglioli, A., Martinez, E. & Alvain, S. Data from: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6416130 (2022).

  66. Messié, M. Code for: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6494328 (2022).

Download references

Acknowledgements

M.M. was funded by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement SAPPHIRE no. 746530, and by the David and Lucile Packard Foundation. The project leading to this publication has received funding from European FEDER Fund under project no. 1166-39417 (M.M., A.P. and A.D.). We thank A.-H. Rêve-Lamarche and D. Dessailly for their help with extracting and using the PHYSAT outputs, and E. Pape for useful comments and text edits.

Author information

Authors and Affiliations

Authors

Contributions

M.M. conceived and designed the study, with input from A.P., A.D. and S.A. M.M. developed the IME algorithm with feedback from A.P. and A.D., performed the data analyses and led the interpretation and writing. S.A. provided the PHYSAT outputs. A.P., A.D., E.M. and S.A. provided substantial input on the text, and contributed to discussions that shaped the study and the manuscript.

Corresponding author

Correspondence to Monique Messié.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Jamison Gove and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Kyle Frischkorn and Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Example maps of IME and reference (REF) region detection.

Two contrasting months are shown for the Fiji/Tonga region: a) August (small IMEs but higher climatological enrichment across the region), and b) December (strong IMEs, with IMEs from the Fiji and Tonga island groups merging). Reference regions (blue) exclude IME regions from any island.

Extended Data Fig. 2 IME maps for different climatological months.

For each month, IME regions are contoured in different shades of red depending on Chl increase near islands relative to a reference region (similar to Fig. 1).

Extended Data Fig. 3 PHYSAT phenoclass richness vs Chl across IME and REF regions.

Phenoclass richness was normalized to 100 data points, and only regions with at least 100 PHYSAT data points were included.

Extended Data Fig. 4 Example of random permutation used to remove the Chl signal on the phenoclass richness increase observed in IME regions.

Top panel: identical to Fig. 3b left panels (that is, including all IME and REF regions); both Chl (left) and phenoclass richness (right) are significantly higher in IME than in REF regions (Mann-Whitney U-test). Bottom panel: example of a random permutation where 2/3 of the IME and REF regions were retained such that the Chl distributions do not significantly differ anymore. In this permutation, phenoclass richness remains significantly higher within the IME subset (right) even though Chl is (non-significantly) lower (left).

Extended Data Table 1 Additional metrics for IME impacts in the tropical Pacific (see Table 1)

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Tables 1 and 2.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Messié, M., Petrenko, A., Doglioli, A.M. et al. Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean. Nat. Geosci. 15, 469–474 (2022). https://doi.org/10.1038/s41561-022-00957-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-022-00957-8

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing