Abstract
Sea-level rise will lead to widespread habitat loss if warming exceeds 2 °C, threatening coastal wildlife globally. Reductions in coastal habitat quality are also expected but their impact and timing are unclear. Here we combine four decades of field data with models of sea-level rise, coastal geomorphology, adaptive behaviour and population dynamics to show that habitat quality is already declining for shorebirds due to increased nest flooding. Consequently, shorebird population collapses are projected well before their habitat drowns in this UNESCO World Heritage Area. The existing focus on habitat loss thus severely underestimates biodiversity impacts of sea-level rise. Shorebirds will also suffer much sooner than previously thought, despite adapting by moving to higher grounds and even if global warming is kept below 2 °C. Such unavoidable and imminent biodiversity impacts imply that mitigation is now urgently needed to boost the resilience of marshes or provide flood-safe habitat elsewhere.
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Data availability
No new data were collected, nor field experiments conducted as part of this study. All input data for our models (see Extended Data Table 1 for overview and sources) are described and available via Dryad at https://doi.org/10.5061/dryad.wm37pvmth (ref. 51) to facilitate reproduction of all our model results.
Code availability
The R code that can reproduce all results (using R v.4.2.1) is available via Dryad at https://doi.org/10.5061/dryad.wm37pvmth (ref. 51). This repository includes a file with all model parameter values, functions which describe the abiotic and biotic submodels from Methods and code to simulate the overall stochastic model with replication and store model results of each scenario considered.
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Acknowledgements
We thank D. le Bars, B. Brinkman, S. Buesink, H. van Dobben, C. Kampichler, H. van der Kolk, M. van Puijenbroek and J. de Vlas for discussion on SLR, marsh sedimentation, deep soil subsidence and shorebirds and Natuurmonumenten and Cooperatie Neerlands Reid for access to the study areas. We are grateful to K. Oosterbeek, J. de Jong, R. Martig and many others for their contribution to fieldwork and to C. Both for long-term support for the oystercatcher research. Funding was provided by the Applied and Engineering Sciences domain of the Netherlands Organisation for Scientific Research (NWO-STW 14638) and cofunding by NAM Gas Exploration, Birdlife Netherlands, Royal Netherlands Air Force and Deltares. This research was cofunded by NAM Gas Exploration and Birdlife Netherlands, two stakeholders that do not always agree on the desirability of mining activities in this nature area. Ecologists working at these organizations provided feedback during half-yearly progress meetings under the guidance of the Netherlands Organisation for Scientific Research but the authors were solely responsible for the research and interpretation of results.
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M.v.d.P. conceived the study, with input from L.D.B., B.J.E., M.F., A.M.A., L.B., E.J. and H.d.K. M.v.d.P. constructed the models, performed analyses and led the writing. L.D.B., M.v.d.S., N.H., B.J.E. and M.v.d.P. contributed to field data collection. All authors contributed to paper editing. Funding was applied for by H.d.K., E.J., B.J.E. and M.v.d.P.
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Extended data
Extended Data Fig. 1 Schematic overview of models and data sources.
A schematic overview of how abiotic and biotic models and data sources are linked to quantify the impact of future emission scenarios on habitat and population size loss of shorebirds. Models (black rectangles) are parameterized and initialized (black arrows) using various data sources (white squares) Model output of a submodel, can feed into another submodel (black arrows) and ultimately be sued to quantify the response variable of interest (red triangles; habitat and population size loss). Specifically: greenhouse gas emission scenarios are translated into sea level rise using IPCC scenarios spatially downscaled to the situation of the Netherlands. Sea levels further vary among and within years by drawing from historical intra- and interannual variability in water levels (reflecting weather variability and tidal cycles) and from a lunar nodal cycle model52. Marsh elevation changes over time due to accretion from sedimentation during flooding (geomorphological model; on island A there is additional deep soil subsidence from gas mining), which together with sea level rise determines habitat loss (that is proportion of habitat submerging below mean high tide). Elevation and sea-level dynamics together determine the relative elevation of the marsh and combined with a flooding model–describing the amplification and attenuation of water along creeks and the marsh plane respectively—, determines the flooding risk at each location. Biotic models for habitat and nest-site selection determine the nesting locations of birds and thereby their flooding pattern. Finally, the flooding level during the breeding season affects the number of offspring produced each year and this birth rate feeds into a population dynamical model that projects changes in population size. Key abiotic and biotic feedback loops (dashed arrows) are (i) higher flooding levels/frequencies leading to higher sediment accretion of marshes and the resulting vertical growth lowers future flooding risk and (ii) lower reproduction due to more frequent flooding leading to fewer birds selecting these (low) territories for settlement, buffering future flooding impact on nests success.
Extended Data Fig. 2 Elevation and bird distribution maps of the study islands.
Elevation maps of islands (a) Schiermonnikoog and (b) Terschelling for the years (i) 1986 and (ii) 2125 under the intermediate RCP4.5 emission scenario. In the map of 1986, the distribution of oystercatchers’ territories is shown with a black dot for each territory (based on breeding census data). Elevation is in metres above mean high tide (MHT) where the MHT each year is adjusted with the amount of sea level rise (but removed of interannual fluctuations due to weather and the lunar nodal cycle). Coordinates on the axis are from the Dutch national RD grid (kilometre units). For similar maps of island A, see Fig. 1 in the main text.
Extended Data Fig. 3 Changes in population and habitat size over time for each of the three study islands.
Shown are changes under emission scenarios (a) RCP2.6, (b) RCP4.5 and (c) RCP8.5. Population size loss is calculated relative to the population size in the reference of a scenario where SLR would not have accelerated (‘no SLRA’) to determine the impact of additional SLR due to greenhouse gas emissions (for example \(\frac{{{\rm{n}}}_{{\rm{y}},{\rm{RCP}}4.5}-{{\rm{n}}}_{{\rm{y}},{\rm{noSLRA}}}}{{{\rm{n}}}_{{\rm{y}},{\rm{noSLRA}}}}\)). The double arrows highlight the amount of population loss already occurring at the point in time where habitat is first lost. Island T is the lowest island (median elevation of territories and study area is 0.35 m and 0.74 m above MHT, respectively) and therefore loses most habitat, even though island A has the lowest critical rate of SLR (Fig. 2a in main text; the median elevation of territories and study area of island A is 0.67 m and 1.21 m above MHT, respectively).
Extended Data Fig. 4 The weak association between habitat and population loss at a given point in time across simulations and emission scenarios.
For a given amount of habitat loss the median (solid line) and the range (2.5% to 97.5% quantile; grey area) of population loss is plotted, indicating that habitat loss is a poor (and nonlinear) predictor of population loss. For example, when habitat loss reached 13%, population loss varied between 10% and 90% across simulations (95% prediction interval).
Extended Data Fig. 5 Uncertainty in model projections.
Each greenhouse gas emission scenario has large uncertainty in the rate and amount of sea level rise27, as shown for the (a) low RCP2.6, (b) intermediate RCP4.5 and (c) very high RCP 8.5 scenario. This implies for example that within the low scenario, there is a small possibility that cumulative SLR will substantially exceed the SLR of the median projection for the intermediate scenario (95% RCP2.6 = 1.05 m, 50% RCP4.5 = 0.84 m in 2125). (d) This uncertainty in climate projections also leads to large uncertainty in the amount of projected population and habitat loss, as depicted by the error bars reflecting the mean loss for the 5% and 95% percentiles of climate projection within each emission scenario (n = 100 replicate simulations). For example, within RCP4.5 the median projection is 21% habitat loss in 2125, but there is a small (5%) probability that habitat loss will be 60% on these islands under this scenario (a threefold difference). (e) Uncertainty in population and habitat loss projections due to stochastic processes (environmental and demographic stochasticity) is much smaller, as can be seen from the error bars which reflect the 5%-95% confidence intervals around the mean projected population and habitat loss (across the n = 100 replicate model runs for each island and scenario using median climate projections).
Extended Data Fig. 6 The influence of soil subsidence due to gas mining on marsh elevation over time.
Changes in median elevation of the breeding habitat of the oystercatcher population of island A in a scenario with and without gas mining for the (a) low (RCP2.6), (b) intermediate (RCP4.5) and (c) very high (RCP8.5) emission scenarios (See Fig. 3a for corresponding rates of SLR). Elevations are relative to the sea level of each year. The double arrows show the island elevation in the year 2050 (when deep soil subsidence due to gas mining is projected to stop) and how many years it would have taken to get to this elevation if there had been no mining. The blue zones reflect the different tidal flooding mechanisms acting at different elevation zones.
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van de Pol, M., Bailey, L.D., Frauendorf, M. et al. Sea-level rise causes shorebird population collapse before habitats drown. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-02051-w
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DOI: https://doi.org/10.1038/s41558-024-02051-w
