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Global mangrove growth variability driven by climatic oscillation-induced sea-level fluctuations

Nature Geoscience volume 18, pages 488–494 (2025)Cite this article

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Abstract

Mangroves are a carbon-dense and highly productive ecosystem but can experience massive dieback under environmental extremes. Climatic oscillations, such as the El Niño–Southern Oscillation (ENSO), are major drivers of global climate variability, yet their impact on mangrove growth at the global scale remains uncertain. Here, using long-term satellite observations from 2001 to 2020, we show that more than 50% of global mangrove areas experience significant variations during ENSO events, exhibiting a seesaw-like pattern across the Pacific Basin where mangrove leaf area decreases in the western Pacific but increases in the eastern Pacific during El Niño, with the reverse occurring during La Niña. The Indian Ocean Dipole affects mangroves across the Indian Ocean similarly but with a lower magnitude relative to ENSO. These patterns are driven by corresponding sea-level fluctuations across the Pacific and Indian ocean basins, with local contributions from lunar nodal cycles. Our study highlights the crucial role of short-term sea-level fluctuations driven by climatic oscillations in dominating the variability of coastal wetland growth and, consequently, in influencing the blue carbon sink.

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Fig. 1: LAI anomaly in global mangroves during climatic events from 2001 to 2020.
Fig. 2: Zonal seesaw-like pattern of mangrove LAI anomaly corresponding to three climate oscillation events.
Fig. 3: Relationships between SLA and climatic oscillations.
Fig. 4: Attribution of mangrove LAI anomalies.

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Data availability

All data used in this study are publicly available. The GLASS V6 LAI data are available at http://www.glass.umd.edu/LAI/MODIS/250m/. The Global Mangrove Watch v3.0 data are available via Zenodo at https://doi.org/10.5281/zenodo.6894273 (ref. 61). Gridded climate data used in this study are presented in Supplementary Table 2.

Code availability

All code used in this study is available from the corresponding authors upon reasonable request.

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Acknowledgements

Y.L. acknowledges the support from the National Natural Science Foundation of China Grants (grant no. 42276232). D.F. thanks M. and M. Cochran for endowing the Cochran Family Professorship in Earth and Environmental Sciences at Tulane University. We thank W. Cai at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) for his feedback on an earlier version of this work.

Author information

Authors and Affiliations

  1. State Key Laboratory of Marine Environmental Science, Key Laboratory of Coastal and Wetland Ecosystems (Ministry of Education), College of the Environment and Ecology, Xiamen University, Xiamen, China

    Zhen Zhang & Yangfan Li

  2. Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA

    Zhen Zhang & Daniel A. Friess

  3. Department of Geography, National University of Singapore, Singapore, Singapore

    Xiangzhong Luo

  4. Center for Nature-Based Climate Solutions, National University of Singapore, Singapore, Singapore

    Xiangzhong Luo

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  1. Zhen Zhang
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Contributions

Z.Z. conceptualized the study. Z.Z. and Y.L. designed the study. Z.Z. performed the analysis and drafted the paper with contributions from all other co-authors. All authors contributed to the interpretation of the results. Y.L. is the main corresponding author of the study.

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Correspondence to Zhen Zhang or Yangfan Li.

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Nature Geoscience thanks Norman Duke, Melinda Martinez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Distribution of mangroves within six biogeographic regions.

This map was adopted from ref. 62.

Extended Data Fig. 2 Local-scale mangrove variation in response to ENSO.

ΔEVI (average EVI during ENSO event minus average EVI during neutral periods) in (a) Shoalwater Bay, Australia, and (b) Gulf of California, Mexico. The left panels show Landsat results at 30-m resolution. The other three-column panels show MODIS results at 250-m resolution with three different criteria to define mangrove extents. The right panels show results for pure mangrove pixels (mangrove coverage > 80%) at 250-m resolution, which is used in the main analysis. Areas with green color represent mangroves with enhanced growth, and red color indicates mangrove degradation during the ENSO event.

Extended Data Fig. 3 Observed spatial patterns of changes in climatic and oceanic factors corresponding to 2015-2016 El Niño.

Changes in anomaly (z-score) of mean monthly (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly are shown. Pie plots indicate the area-weighted proportion of grid cells with significantly increased LAI (green), significantly decreased LAI (purple), or no significant difference with neutral phases (grey) (P > 0.05).

Extended Data Fig. 4 Observed spatial patterns of changes in climatic and oceanic factors corresponding to 2010-2012 La Niña.

Changes in anomaly (z-score) of mean monthly (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly are shown. Pie plots indicate the area-weighted proportion of grid cells with significantly increased LAI (green), significantly decreased LAI (purple), or no significant difference with neutral phases (grey) (P > 0.05).

Extended Data Fig. 5 Observed spatial patterns of changes in climatic and oceanic factors corresponding to 2019 pIOD.

Changes in anomaly (z-score) of mean monthly (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly are shown. Pie plots indicate the area-weighted proportion of grid cells with significantly increased LAI (green), significantly decreased LAI (purple), or no significant difference with neutral phases (grey) (P > 0.05).

Extended Data Fig. 6 Each term in Eq. (4) plotted as a contribution to ΔLAI anomaly in the response to 2015-2016 El Niño.

The contribution is calculated as the product of mangrove sensitivity to (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly and El Niño-induced anomaly in these factors.

Extended Data Fig. 7 Each term in Eq. (4) plotted as a contribution to ΔLAI anomaly in the response to 2010-2012 La Niña.

The contribution is calculated as the product of mangrove sensitivity to (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly and La Niña-induced anomaly in these factors.

Extended Data Fig. 8 Dominant driver of sea level anomaly during climatic events.

The dominant component driving SLA during (a) 2015-2016 El Niño, (b) 2010-2012 La Niña, and (c) 2019 pIOD. In (a) and (b), the left pie charts represent results from Indo-Malesia and Australasia, while in (c), the left pie chart shows results from eastern Africa. The right pie charts in (a) and (b) depict results from western America, while in (c), the right pie chart shows results from Indo-Malesia and Australasia. The red areas in the charts denote regions where SLA is predominantly influenced by climatic oscillations, and the blue areas indicate regions where the nodal cycle is the dominant factor. Specific spatial details of SLA contributed by climatic oscillations and nodal cycle are shown in Supplementary Fig. 8.

Extended Data Fig. 9 Each term in Eq. (4) plotted as a contribution to ΔLAI anomaly in the response to 2019 pIOD.

The contribution is calculated as the product of mangrove sensitivity to (a) air temperature, (b) SPEI, (c) wind speed, (d) sea surface temperature, (e) sea surface salinity, and (f) sea-level anomaly and pIOD-induced anomaly in these factors.

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Supplementary Figs. 1–11 and Tables 1–3.

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Zhang, Z., Luo, X., Friess, D.A. et al. Global mangrove growth variability driven by climatic oscillation-induced sea-level fluctuations. Nat. Geosci. 18, 488–494 (2025). https://doi.org/10.1038/s41561-025-01701-8

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