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Sea-level rise causes shorebird population collapse before habitats drown


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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Fig. 1: Relationship between the elevation of shorebird habitat and flooding risk under SLR.
Fig. 2: Differences in the critical rates of SLR that habitat and birds can withstand on islands T, A and S.
Fig. 3: The impact of SLR on habitat and oystercatcher population loss for different GHG emission and mining scenarios.

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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 (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 (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.


  1. Saintilan, N. et al. Widespread retreat of coastal habitat is likely at warming levels above 1.5 °C. Nature 621, 112–119 (2023).

    Article  CAS  Google Scholar 

  2. He, Q. & Silliman, B. R. Climate change, human impacts and coastal ecosystems in the Anthropocene. Curr. Biol. 29, R1021–R1035 (2019).

    Article  CAS  Google Scholar 

  3. Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).

    Article  CAS  Google Scholar 

  4. van de Pol, M. et al. Do changes in the frequency, magnitude and timing of extreme climatic events threaten the population viability of coastal birds? J. Appl. Ecol. 47, 720–730 (2010).

    Article  Google Scholar 

  5. Kirwan, M. L., Temmerman, S., Skeehan, E. E., Guntenspergen, G. R. & Fagherazzi, S. Overestimation of marsh vulnerability to sea level rise. Nat. Clim. Change 6, 253–260 (2016).

    Article  Google Scholar 

  6. FitzGerald, D. M. & Hughes, Z. Marsh processes and their response to climate change and sea-level rise. Annu. Rev. Earth Planet. Sci. 47, 481–517 (2019).

    Article  CAS  Google Scholar 

  7. Leuven, J. R. F. W., Pierik, H. J., Vegt, M. van der, Bouma, T. J. & Kleinhans, M. G. Sea-level-rise-induced threats depend on the size of tide-influenced estuaries worldwide. Nat. Clim. Change 9, 986–992 (2019).

    Article  Google Scholar 

  8. Pontee, N. Defining coastal squeeze: a discussion. Ocean Coast. Manag. 84, 204–207 (2013).

    Article  Google Scholar 

  9. Thorne, K. M., Buffington, K. J., Elliott-Fisk, D. L. & Takekawa, J. Y. Tidal marsh susceptibility to sea-level rise: importance of local-scale models. J. Fish. Wildl. Manag. 6, 290–304 (2015).

    Article  Google Scholar 

  10. Crosby, S. C. et al. Salt marsh persistence is threatened by predicted sea-level rise. Estuar. Coast. Shelf Sci. 181, 93–99 (2016).

    Article  Google Scholar 

  11. Galbraith, H. et al. Global climate change and sea level rise: potential losses of intertidal habitat for shorebirds. Waterbirds 25, 173–183 (2002).

    Article  Google Scholar 

  12. Schmidt, J. A., McCleery, R., Seavey, J. R., Cameron Devitt, S. E. & Schmidt, P. M. Impacts of a half century of sea-level rise and development on an endangered mammal. Glob. Change Biol. 18, 3536–3542 (2012).

    Article  Google Scholar 

  13. Leonard, P. B. et al. Landscape connectivity losses due to sea level rise and land use change. Anim. Conserv. 20, 80–90 (2017).

    Article  Google Scholar 

  14. Von Holle, B. et al. Effects of future sea level rise on coastal habitat. J. Wildl. Manag. 83, 694–704 (2019).

    Article  Google Scholar 

  15. Klingbeil, B. T. et al. High uncertainty over the future of tidal marsh birds under current sea-level rise projections. Biodivers. Conserv. 30, 431–443 (2021).

    Article  Google Scholar 

  16. Iwamura, T. et al. Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations. Proc. R. Soc. B 280, 20130325 (2013).

    Article  Google Scholar 

  17. Traill, L. W. et al. Managing for change: wetland transitions under sea-level rise and outcomes for threatened species: threatened populations and sea-level rise. Divers. Distrib. 17, 1225–1233 (2011).

    Article  Google Scholar 

  18. Pennings, S. C., Grant, M.-B. & Bertness, M. D. Plant zonation in low-latitude salt marshes: disentangling the roles of flooding, salinity and competition. J. Ecol. 93, 159–167 (2005).

    Article  Google Scholar 

  19. Saha, A. K. et al. Sea level rise and South Florida coastal forests. Clim. Change 107, 81–108 (2011).

    Article  Google Scholar 

  20. van der Kolk, H.-J. et al. Shorebird feeding specialists differ in how environmental conditions alter their foraging time. Behav. Ecol. 31, 371–382 (2020).

    Article  Google Scholar 

  21. Stillman, R. A. et al. Predicting effects of environmental change on a migratory herbivore. Ecosphere 6, art114 (2015).

    Article  Google Scholar 

  22. Pike, D. A., Roznik, E. A. & Bell, I. Nest inundation from sea-level rise threatens sea turtle population viability. R. Soc. Open Sci. 2, 150127 (2015).

    Article  Google Scholar 

  23. Field, C. R. et al. High-resolution tide projections reveal extinction threshold in response to sea-level rise. Glob. Change Biol. 23, 2058–2070 (2017).

    Article  Google Scholar 

  24. Roberts, S. G. et al. Preventing local extinctions of tidal marsh endemic Seaside Sparrows and Saltmarsh Sparrows in eastern North America. Condor 121, duy024 (2019).

  25. Bailey, L. D. et al. Habitat selection can reduce effects of extreme climatic events in a long-lived shorebird. J. Anim. Ecol. 88, 1474–1485 (2019).

    Article  Google Scholar 

  26. Benvenuti, B., Walsh, J., O’Brien, K. M. & Kovach, A. I. Plasticity in nesting adaptations of a tidal marsh endemic bird. Ecol. Evol. 8, 10780–10793 (2018).

    Article  Google Scholar 

  27. KNMI Klimaatsignaal’21. KNMI (2021).

  28. van Dobben, H. F., de Groot, A. V. & Bakker, J. P. Salt marsh accretion with and without deep soil subsidence as a proxy for sea-level rise. Estuar. Coasts 45, 1562–1582 (2022).

    Article  Google Scholar 

  29. Stark, J., Van Oyen, T., Meire, P. & Temmerman, S. Observations of tidal and storm surge attenuation in a large tidal marsh. Limnol. Oceanogr. 60, 1371–1381 (2015).

    Article  Google Scholar 

  30. IPCC Climate Change 2023: Synthesis Report (eds Core Writing Team, Lee, H. & Romero, J.) (IPCC, 2023).

  31. Bailey, L. D. et al. No phenotypic plasticity in nest-site selection in response to extreme flooding events. Philos. Trans. R. Soc. B 372, 20160139 (2017).

    Article  Google Scholar 

  32. Gaswinning Vanaf de Locaties Moddergat, Lauwersoog en Vierhuizen: Resultaten Uitvoering Meet- en Regelcyclus 2019 (NAM, 2020).

  33. Schrama, M., Kuijper, D. P. J., Veeneklaas, R. M. & Bakker, J. P. Long-term decline in a salt marsh hare population largely driven by bottom-up factors. Ecoscience 22, 71–82 (2015).

    Article  Google Scholar 

  34. Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).

    Article  Google Scholar 

  35. Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Article  Google Scholar 

  36. de Vlas, J. Samenvatting Monitoring Effecten van Bodemdaling op Ameland-Oost: Evaluatie na 30 Jaar Gaswinning (Waddenacademie, 2017).

  37. State of Conservation of Properties Inscribed on the World Heritage List (UNESCO, 2023);

  38. van de Pol, M. et al. Effects of climate change and variability on population dynamics in a long-lived shorebird. Ecology 91, 1192–1204 (2010).

    Article  Google Scholar 

  39. Allen, A. M. et al. The demographic causes of population change vary across four decades in a long‐lived shorebird. Ecology 103, e3615 (2022).

    Article  Google Scholar 

  40. Esselink, P. et al. in Wadden Sea Quality Status Report (eds Kloepper S. et al.) 1–40 (Common Wadden Sea Secretariat, 2017).

  41. Engelstad, A. et al. Observations of waves and currents during barrier island inundation. J. Geophys. Res. Oceans 122, 3152–3169 (2017).

    Article  Google Scholar 

  42. Marin‐Diaz, B. et al. Using salt marshes for coastal protection: effective but hard to get where needed most. J. Appl. Ecol. 60, 1286–1301 (2023).

    Article  Google Scholar 

  43. Babcock, M. & Booth, V. Tern Conservation Best Practice: Habitat Creation and Restoration (Life, 2020);

  44. Goss-Custard, J. The Oystercatcher: From Individuals to Populations (Oxford Univ. Press, 1996).

  45. Piening, H., van der Veen, W. & van Eijs, R. in Monitoring Effecten van Bodemdaling op Oost-Ameland (ed. de Vlas, J.) 9–25 (Waddenacademie, 2017).

  46. van de Pol, M. et al. A global assessment of the conservation status of the nominate subspecies of Eurasian Oystercatcher Haematopus ostralegus ostralegus. Int. Wader Stud. 20, 27–61 (2016).

    Google Scholar 

  47. van de Pol, M. et al. A silver spoon for a golden future: long-term effects of natal origin on fitness prospects of oystercatchers (Haematopus ostralegus). J. Anim. Ecol. 75, 616–626 (2006).

    Article  Google Scholar 

  48. Ens, B. et al. Chapter Eight—the study of career decisions: oystercatchers as social prisoners. Adv. Stud. Behav. 46, 343–420 (2014).

    Article  Google Scholar 

  49. Bruinzeel, L. et al. Local dominance and territorial settlement of nonbreeding oystercatchers. Behaviour 137, 473–530 (2000).

    Article  Google Scholar 

  50. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  51. van de Pol, M. Data and code for: Sea level rise causes shorebird population collapse before habitat drowns [Dataset]. Dryad (2024).

  52. Haigh, I. et al. Global influences of the 18.61 year nodal cycle and 8.85 year cycle of lunar perigee on high tidal levels. J. Geophys. Res. Oceans 116, C06025 (2011).

    Article  Google Scholar 

  53. Waterhoogte (Rijkswaterstaat Waterinfo, accessed 10 December 2023);!/kaart/waterhoogte/

  54. Schwalm, C. R. et al. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).

    Article  CAS  Google Scholar 

  55. Houston, J. R. & Dean, R. G. Accounting for the nodal tide to improve estimates of sea level acceleration. J. Coast. Res. 276, 801–807 (2011).

    Article  Google Scholar 

  56. Baart, F. et al. The effect of the 18.6-year lunar nodal cycle on regional sea-level rise estimates. J. Coast. Res. 280, 511–516 (2012).

    Article  Google Scholar 

  57. Stark, J. et al. Observations of tidal and storm surge attenuation in a large tidal marsh: tidal and storm surge attenuation in a marsh. Limnol. Oceanogr. 60, 1371–1381 (2015).

    Article  Google Scholar 

  58. AHN Viewer. Actuele Hoogetekaart Nederland (accessed 12 October 2023).

  59. Gombin, J. et al. concaveman: A very fast 2D concave hull algorithm. R package version 1 (2017).

  60. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  Google Scholar 

  61. van Wijnen, H. J. & Bakker, J. P. Long-term surface elevation change in salt marshes: a prediction of marsh response to future sea-Level rise. Estuar. Coast. Shelf Sci. 52, 381–390 (2001).

    Article  Google Scholar 

  62. Dijk, A. J. et al. Introduction of autocluster software in the breeding bird monitoring program. Limosa 86, 94–102 (2013).

    Google Scholar 

  63. Schlicht, L. et al. Thiessen polygons as a model for animal territory estimation. Ibis 156, 215–219 (2014).

    Article  Google Scholar 

  64. Pärt, T. & Doligez, B. Gathering public information for habitat selection: prospecting birds cue on parental activity. Proc. R. Soc. Lond. B 270, 1809–1813 (2003).

    Article  Google Scholar 

  65. Hallmann, C. & Ens, B. in Monitoring Effecten van Bodemdaling op Ameland-Oost 2005–2010 (ed. de Vlas, J.) 1–147 (Waddenacademie, 2011).

  66. Ens, B. J. et al. Territory quality, parental effort and reproductive success of Oystercatchers (Haematopus ostralegus). J. Anim. Ecol. 61, 703 (1992).

    Article  Google Scholar 

  67. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378 (2017).

    Article  Google Scholar 

  68. Caswell, H. Matrix Population Models Vol. 1 (Sinauer, 2000).

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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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Authors and Affiliations



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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Correspondence to Martijn van de Pol.

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Nature Climate Change thanks Chris Elphick, Neil Saintilan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Overview of the different submodels and input data sources and how they were related or derived from studies on the three islands

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).

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