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The vulnerability of Indo-Pacific mangrove forests to sea-level rise


Sea-level rise can threaten the long-term sustainability of coastal communities and valuable ecosystems such as coral reefs, salt marshes and mangroves1,2. Mangrove forests have the capacity to keep pace with sea-level rise and to avoid inundation through vertical accretion of sediments, which allows them to maintain wetland soil elevations suitable for plant growth3. The Indo-Pacific region holds most of the world’s mangrove forests4, but sediment delivery in this region is declining, owing to anthropogenic activities such as damming of rivers5. This decline is of particular concern because the Indo-Pacific region is expected to have variable, but high, rates of future sea-level rise6,7. Here we analyse recent trends in mangrove surface elevation changes across the Indo-Pacific region using data from a network of surface elevation table instruments8,9,10. We find that sediment availability can enable mangrove forests to maintain rates of soil-surface elevation gain that match or exceed that of sea-level rise, but for 69 per cent of our study sites the current rate of sea-level rise exceeded the soil surface elevation gain. We also present a model based on our field data, which suggests that mangrove forests at sites with low tidal range and low sediment supply could be submerged as early as 2070.

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Figure 1: Map of the Indo-Pacific region study sites and a schematic of the SET–MH.
Figure 2: The relationship between mangrove soil-surface elevation gains and sediment availability.
Figure 3: Year in which mangroves are predicted to be submerged at sites with low (<2.5 g m−3) sediment availability over variation in tidal range and rates of SLR.
Figure 4: Mangrove forest distribution in the Indo-Pacific region.


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The Global Change Institute at The University of Queensland supported this collaboration, as did the Australian Research Council SuperScience grant number FS100100024 to the Australia Sea Level Rise Partnerships. D.R.C., G.R.G. and K.W.K. acknowledge support from the US Geological Survey Climate and Land Use Research and Development Program. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.

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All authors participated in a collaborative workshop or contributed field data, contributed to the conceptualization of models and edited the manuscript.

Corresponding author

Correspondence to Catherine E. Lovelock.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Frequency distributions of values of shallow subsidence and elevation deficits.

a, The frequency distribution of shallow subsidence over all the SET sites, calculated as (surface accretion) − (surface elevation gain). (The data presented here are available online from the Source Data of Fig. 2). b, The frequency distribution of surface elevation deficits relative to SLR from tide gauges (see Supplementary Table 1).

Extended Data Figure 2 Years until submergence (logarithmic scale) of the highest intertidal mangrove forest over variation in tidal range and for a range of elevation deficits.

The elevation deficit is the difference between the rate of local SLR and the rate of surface elevation gain. Submergence is assumed to occur when the cumulative elevation deficit is equivalent to the elevation capital (defined as half the tidal range). The mean elevation deficit in our study was 6 mm yr−1 (dashed line); other elevation deficits shown are 12 mm yr−1 (mean + SD = 6 + 6.3; long-dashed line), 1 mm yr−1 (minimum; dotted line) and 20 mm yr−1 (maximum; solid line). Categories of tidal range are coloured blue for microtidal, yellow for mesotidal and red for macrotidal.

Extended Data Figure 3 Schematic summary of the modelling process for estimating the decade of submergence of mangrove forests with SLR.

Extended Data Figure 4 Comparison of surface elevation gains measured over longer and shorter periods for three sites.

The three sites are New Zealand (N = 3), Micronesia (N = 13) and Moreton Bay, Australia (N = 18). The long-term mean record length is 5.5 years; the short-term mean record length is 2.1 years. Longer-term and shorter-term rates were highly correlated (R2 = 0.59) with a slope of 0.90 ± 0.13, which is not statistically different from 1 (t = 0.769, P = 0.45).

Extended Data Figure 5 The relationship between mean TSM in 2011 over the available TSM record (2002–2011).

In 2011, all sites were represented in the MERIS data set. The linear regression (solid line) of this relationship is (mean TSM in 2011) = (1.38 ± 0.94) + (0.58 ± 0.08) × (mean TSM over all available years), R2 = 0.64, P < 0.0001, F test, where the indicated uncertainties are standard errors. Dashed lines are 95% confidence intervals.

Extended Data Table 1 Summary of the relative influence of predictor variables on surface elevation change for BRT models
Extended Data Table 2 Parameters used in the model for estimating time to submergence of mangrove forests for different sediment availability (classes of TSM).

Supplementary information

Supplementary Data

This file contains descriptions of study sites within the Indo Pacific region where surface elevation table - marker horizon (SET-MH) data are derived for the analysis. Long term rates of sea level rise are derived from tide gauges of variable record length and from satellite altimetry. (PDF 139 kb)

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

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