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:

Overlooked and extensive ghost forest formation across the US Atlantic coast

Nature Sustainability volume 8, pages 1445–1454 (2025)Cite this article

Subjects

Abstract

Rising sea levels have driven widespread coastal tree die-off, forming large swaths of standing dead trees known as ‘ghost forests’. While reports of coastal forest loss are accumulating, its true severity and factors determining the underlying mortality risks remain poorly understood. Here we mapped over 10 million individual dead trees across the US Atlantic region, a sea level rise hotspot, using deep learning and sub-metre aerial imagery. Our analysis reveals disproportionate and pervasive tree mortality in many previously unrecognized ghost forest hotspots, with over 6 million dead trees concentrated in low-lying (<5 m) forests, primarily driven by salinization rather than flooding alone. Notably, man-made flood obstructions (roads and levees), when present, have reduced forest loss by 40% and 79% respectively, but they can only provide temporary protection against rising seas. These findings highlight the alarming decline in coastal forests and the need for strategic long-term planning to preserve the critical services that coastal ecosystems provide.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Heterogeneous ghost forest formation across the US Atlantic coast.
Fig. 2: Low-lying forests experienced substantial and disproportionate mortality.
Fig. 3: Ghost forest emergence in saline and frequently inundated environments can be exacerbated by disturbances, but their impacts vary spatially.
Fig. 4: Man-made obstructions protected forests from flooding and saltwater intrusion.

Similar content being viewed by others

Data availability

The coastal tree mortality and hotspot maps can be viewed at https://ee-chihgyeung.projects.earthengine.app/view/treedeath-atlanticus. All derived products, along with metadata, are available via Zenodo at https://doi.org/10.5281/zenodo.16380867 (ref. 78). The NAIP images are freely available and downloaded directly from Google Earth Engine. The sources of all datasets used in the driver analysis are presented in Supplementary Table 2 and are all publicly available.

Code availability

The deep learning model was built on an open-source Python framework for tree segmentation and is available via Zenodo at https://doi.org/10.5281/zenodo.3978185 (ref. 52). We modified the model architecture using ‘segmentation-models’, available via GitHub at https://github.com/qubvel/segmentation_models. The codes for all spatial analyses are available via Code Ocean at https://doi.org/10.24433/CO.4780620.v1. Please contact H.C.H.Y. or X.Y. for more information.

References

  1. Adame, M. F. et al. All tidal wetlands are blue carbon ecosystems. BioScience 74, 253–268 (2024).

    Article  Google Scholar 

  2. Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).

    Article  CAS  Google Scholar 

  3. Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).

    Article  Google Scholar 

  4. O’Donnell, K. L. et al. Saltwater intrusion and sea level rise threatens US rural coastal landscapes and communities. Anthropocene 45, 100427 (2024).

    Article  Google Scholar 

  5. Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).

    Article  CAS  Google Scholar 

  6. Chambers, J. Q. et al. Hurricane Katrina’s carbon footprint on US Gulf Coast forests. Science 318, 1107–1107 (2007).

    Article  CAS  Google Scholar 

  7. Kirwan, M. L. & Gedan, K. B. Sea-level driven land conversion and the formation of ghost forests. Nat. Clim. Change 9, 450–457 (2019).

    Article  Google Scholar 

  8. McDowell, N. et al. Processes and mechanisms of coastal woody-plant mortality. Glob. Change Biol. 28, 5881–5900 (2022).

    Article  CAS  Google Scholar 

  9. Ury, E. A., Yang, X., Wright, J. P. & Bernhardt, E. S. Rapid deforestation of a coastal landscape driven by sea-level rise and extreme events. Ecol. Appl. 31, e02339 (2021).

    Article  Google Scholar 

  10. White, E., Ury, E. A., Bernhardt, E. S. & Yang, X. Climate change driving widespread loss of coastal forested wetlands throughout the North American coastal plain. Ecosystems 25, 812–827 (2022).

    Article  Google Scholar 

  11. Williams, K., MacDonald, M. & Sternberg, L. daS. L. Interactions of storm, drought, and sea-level rise on coastal forest: a case study. J. Coast. Res. 19, 1116–1121 (2003).

    Google Scholar 

  12. Conner, W. H. Ecology of Tidal Freshwater Forested Wetlands of the Southeastern United States (Springer, 2007).

    Book  Google Scholar 

  13. Powell, E. et al. Spaceborne lidar observations reveal impacts of inundation on coastal forest structure across the US mid-Atlantic. Estuar. Coast. Shelf Sci. 323, 109372 (2025).

    Article  Google Scholar 

  14. Smart, L. S. et al. Aboveground carbon loss associated with the spread of ghost forests as sea levels rise. Environ. Res. Lett. 15, 104028 (2020).

    Article  CAS  Google Scholar 

  15. Smith, A. J. & Kirwan, M. L. Sea level-driven marsh migration results in rapid net loss of carbon. Geophys. Res. Lett. 48, e2021GL092420 (2021).

    Article  CAS  Google Scholar 

  16. Carmichael, M. J., Helton, A. M., White, J. C. & Smith, W. K. Standing dead trees are a conduit for the atmospheric flux of CH4 and CO2 from wetlands. Wetlands 38, 133–143 (2018).

    Article  Google Scholar 

  17. Martinez, M. & Ardón, M. Drivers of greenhouse gas emissions from standing dead trees in ghost forests. Biogeochemistry 154, 471–488 (2021).

    Article  CAS  Google Scholar 

  18. Williams, K., Ewel, K. C., Stumpf, R. P., Putz, F. E. & Workman, T. W. Sea-level rise and coastal forest retreat on the west coast of Florida, USA. Ecology 80, 2045–2063 (1999).

    Article  Google Scholar 

  19. Smith, I., Fiorino, G., Grabas, G. & Wilcox, D. A. Wetland vegetation response to record-high Lake Ontario water levels. J. Gt Lakes Res. 47, 160–167 (2021).

    Article  Google Scholar 

  20. Tully, K. et al. The invisible flood: the chemistry, ecology, and social implications of coastal saltwater intrusion. BioScience 69, 368–378 (2019).

    Article  Google Scholar 

  21. Bhattachan, A. et al. Evaluating the effects of land-use change and future climate change on vulnerability of coastal landscapes to saltwater intrusion. Elem. Sci. Anthr. 6, 62 (2018).

    Article  Google Scholar 

  22. Sallenger, A. H., Doran, K. S. & Howd, P. A. Hotspot of accelerated sea-level rise on the Atlantic coast of North America. Nat. Clim. Change 2, 884–888 (2012).

    Article  Google Scholar 

  23. Velasquez-Manoff, M. As sea levels rise, so do ghost forests. The New York Times (8 October 2019).

  24. Zee, G., Griswold, L., Manzo, D. & Pereira, I. ‘Ghost forests’ threaten New Jersey’s water, ecosystem. ABC News (29 November 2023).

  25. Cheng, Y. et al. Scattered tree death contributes to substantial forest loss in California. Nat. Commun. 15, 641 (2024).

    Article  CAS  Google Scholar 

  26. Dixon, D. J., Zhu, Y., Brown, C. F. & Jin, Y. Satellite detection of canopy-scale tree mortality and survival from California wildfires with spatio-temporal deep learning. Remote Sens. Environ. 298, 113842 (2023).

    Article  Google Scholar 

  27. Field, C. R., Gjerdrum, C. & Elphick, C. S. Forest resistance to sea-level rise prevents landward migration of tidal marsh. Biol. Conserv. 201, 363–369 (2016).

    Article  Google Scholar 

  28. Pasquarella, V. J., Elkinton, J. S. & Bradley, B. A. Extensive gypsy moth defoliation in Southern New England characterized using Landsat satellite observations. Biol. Invasions 20, 3047–3053 (2018).

    Article  Google Scholar 

  29. Bernhardt, E. Coastal freshwater wetlands squeezed between migrating salt marshes and working lands. Sci. Adv. 8, eadd1628 (2022).

    Article  Google Scholar 

  30. Chen, Y. & Kirwan, M. L. Upland forest retreat lags behind sea-level rise in the mid-Atlantic coast. Glob. Change Biol. 30, e17081 (2024).

    Article  Google Scholar 

  31. Tully, K. L., Weissman, D., Wyner, W. J., Miller, J. & Jordan, T. Soils in transition: saltwater intrusion alters soil chemistry in agricultural fields. Biogeochemistry 142, 339–356 (2019).

    Article  CAS  Google Scholar 

  32. Ury, E. A., Wright, J. P., Ardón, M. & Bernhardt, E. S. Saltwater intrusion in context: soil factors regulate impacts of salinity on soil carbon cycling. Biogeochemistry 157, 215–226 (2022).

    Article  CAS  Google Scholar 

  33. Ardón, M., Morse, J. L., Colman, B. P. & Bernhardt, E. S. Drought-induced saltwater incursion leads to increased wetland nitrogen export. Glob. Change Biol. 19, 2976–2985 (2013).

    Article  Google Scholar 

  34. Kirwan, M. L. et al. Feedbacks regulating the salinization of coastal landscapes. Annu. Rev. Mar. Sci. 17, 461–484 (2025).

    Article  Google Scholar 

  35. Helton, A. M. et al. Over, under, and through: hydrologic connectivity and the future of coastal landscape salinization. Water Resour. Res. 61, e2024WR038720 (2025).

    Article  Google Scholar 

  36. Ohenhen, L. O., Shirzaei, M., Ojha, C., Sherpa, S. F. & Nicholls, R. J. Disappearing cities on US coasts. Nature 627, 108–115 (2024).

    Article  CAS  Google Scholar 

  37. Osland, M. J. et al. Migration and transformation of coastal wetlands in response to rising seas. Sci. Adv. 8, eabo5174 (2022).

    Article  Google Scholar 

  38. Maxwell, T. L. et al. Soil carbon in the world’s tidal marshes. Nat. Commun. 15, 10265 (2024).

  39. Aakala, T., Kuuluvainen, T., Gauthier, S. & De Grandpré, L. Standing dead trees and their decay-class dynamics in the northeastern boreal old-growth forests of Quebec. Ecol. Manag. 255, 410–420 (2008).

    Article  Google Scholar 

  40. Kearney, W. S., Fernandes, A. & Fagherazzi, S. Sea-level rise and storm surges structure coastal forests into persistence and regeneration niches. PLoS ONE 14, e0215977 (2019).

    Article  Google Scholar 

  41. McDowell, N. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).

    Article  CAS  Google Scholar 

  42. Lagomasino, D. et al. Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma. Nat. Commun. 12, 4003 (2021).

    Article  CAS  Google Scholar 

  43. White, E. & Kaplan, D. Restore or retreat? Saltwater intrusion and water management in coastal wetlands. Ecosyst. Health Sustain. 3, e01258 (2017).

    Article  Google Scholar 

  44. Fagherazzi, S. et al. The ecohydrology of coastal ghost forests. Ecohydrology 18, e70020 (2025).

    Article  Google Scholar 

  45. Kirwan, M. L. & Megonigal, J. P. Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504, 53–60 (2013).

    Article  CAS  Google Scholar 

  46. Neville, J. A., Emanuel, R. E., Ardón, M. & Pavelsky, T. Location and design of flow control structures differentially influence salinity patterns in small artificial drainage systems. J. Water Resour. Plan. Manag. 149, 05023002 (2023).

    Article  Google Scholar 

  47. van Zelst, V. T. M. et al. Cutting the costs of coastal protection by integrating vegetation in flood defences. Nat. Commun. 12, 6533 (2021).

    Article  Google Scholar 

  48. Fagherazzi, S. et al. Sea level rise and the dynamics of the marsh-upland boundary. Front. Environ. Sci. 7, 25 (2019).

    Article  Google Scholar 

  49. Du, L. et al. Drainage ditch network extraction from lidar data using deep convolutional neural networks in a low relief landscape. J. Hydrol. 628, 130591 (2024).

    Article  Google Scholar 

  50. Storm surge overview. NOAA https://www.weather.gov/phi/stormsurge (2025).

  51. National Agriculture Imagery Program (NAIP). USDA Farm Production and Conservation Business Center https://naip-usdaonline.hub.arcgis.com/ (2024).

  52. Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).

    Article  Google Scholar 

  53. Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).

    Article  Google Scholar 

  54. Kattenborn, T., Leitloff, J., Schiefer, F. & Hinz, S. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J. Photogramm. Remote Sens. 173, 24–49 (2021).

    Article  Google Scholar 

  55. Ouali, Y., Hudelot, C. & Tami, M. An overview of deep semi-supervised learning. Preprint at https://arxiv.org/abs/2006.05278 (2020).

  56. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Navab, N. et al.) 234–241 (Springer, 2015).

  57. Tan, M. & Le, Q. V. EfficientNet: rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning 6105–6114 (ICML, 2019).

  58. Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

  59. Abraham, N. & Khan, N. M. A novel focal tversky loss function with improved attention U-Net for lesion segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging 683–687 (IEEE, 2019).

  60. Detection surveys. US Forest Service https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/detection-surveys (2021).

  61. 3D Elevation Program. US Geological Survey https://www.usgs.gov/3d-elevation-program (2024).

  62. NOAA Shoreline. NOAA https://shoreline.noaa.gov/med-res.html (2024).

  63. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  Google Scholar 

  64. Coastal Change Analysis Program (C-CAP) regional land cover. NOAA Office for Coastal Management https://coast.noaa.gov/digitalcoast/data/ccapregional.html (2024).

  65. Protected Areas Database of the United States (PAD-US). USGS https://doi.org/10.5066/P96WBCHS (2024).

  66. Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (1995).

    Article  Google Scholar 

  67. Caldas de Castro, M. & Singer, B. H. Controlling the false discovery rate: a new application to account for multiple and dependent tests in local statistics of spatial association. Geogr. Anal. 38, 180–208 (2006).

    Article  Google Scholar 

  68. Breiman, L. Random forest. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  69. Hoeppner, S. S., Shaffer, G. P. & Perkins, T. E. Through droughts and hurricanes: tree mortality, forest structure, and biomass production in a coastal swamp targeted for restoration in the Mississippi River Deltaic Plain. For. Ecol. Manag. 256, 937–948 (2008).

    Article  Google Scholar 

  70. Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).

    Article  Google Scholar 

  71. Zhu, M. et al. Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access 6, 4641–4652 (2018).

    Article  Google Scholar 

  72. Nembrini, S., König, I. R. & Wright, M. N. The revival of the Gini importance?. Bioinformatics 34, 3711–3718 (2018).

    Article  CAS  Google Scholar 

  73. Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R. J. 9, 421 (2017).

    Article  Google Scholar 

  74. National hydrography dataset. US Geological Survey https://www.usgs.gov/national-hydrography/national-hydrography-dataset (2024).

  75. TIGER/Line Shapefiles. US Census Bureau https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html (2024).

  76. National levee database. US Army Corps of Engineers https://levees.sec.usace.army.mil/ (2024).

  77. Ho, D. E., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).

    Article  Google Scholar 

  78. Yeung, H. C. H. et al. Overlooked and extensive ghost forest formation across the US Atlantic coast–data. Zenodo https://doi.org/10.5281/zenodo.16380867 (2025).

  79. Individual tree species parameter maps. US Forest Service https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/individual-tree-species-parameter-maps (2022).

Download references

Acknowledgements

This work is supported by the NASA Coastal Resilience Team (grant no. 80NSSC23K0127). N.M. was supported through the US National Science Foundation. We thank Research Computing at the University of Virginia for providing the computational resources that made this work possible.

Author information

Authors and Affiliations

  1. Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA

    Henry Chi Hang Yeung & Xi Yang

  2. Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC, USA

    Tamlin M. Pavelsky & Chao Wang

  3. Atmospheric Sciences and Global Change Division, Pacific Northwest National Lab, Richland, WA, USA

    Nate G. McDowell

  4. School of Biological Sciences, Washington State University, Pullman, WA, USA

    Nate G. McDowell

  5. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Ryan E. Emanuel & Emily S. Bernhardt

  6. Department of Biology, Duke University, Durham, NC, USA

    Emily S. Bernhardt

Authors
  1. Henry Chi Hang Yeung
    View author publications

    Search author on:PubMed Google Scholar

  2. Tamlin M. Pavelsky
    View author publications

    Search author on:PubMed Google Scholar

  3. Chao Wang
    View author publications

    Search author on:PubMed Google Scholar

  4. Nate G. McDowell
    View author publications

    Search author on:PubMed Google Scholar

  5. Ryan E. Emanuel
    View author publications

    Search author on:PubMed Google Scholar

  6. Emily S. Bernhardt
    View author publications

    Search author on:PubMed Google Scholar

  7. Xi Yang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

H.C.H.Y. and X.Y. designed the study with inputs from all co-authors. H.C.H.Y. performed all the analyses. H.C.H.Y. and X.Y. prepared the initial manuscript, with valuable comments and discussions from all authors. All authors contributed to the writing of the paper and approved the final version.

Corresponding authors

Correspondence to Henry Chi Hang Yeung or Xi Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Elisabeth Powell and the other, anonymous, reviewer(s) for their contribution to the peer review process.

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 Overview of training and evaluation sites.

The locations of the 1,519 training and 260 validation sites of random sizes (at least 256 × 256 pixels) used for model training, containing a total of 142,012 dead tree labels. We developed 350 1-ha independent evaluation sites that capture different elevation and dead tree density gradients, consisting of 9,624 dead trees. The background map shows the elevation (from 0 to 50 m above sea level) of the US Atlantic coast. Basemap © 2025 ESRI terrain. Basemap from Esri with state boundaries from the US Census Bureau.

Extended Data Fig. 2 Examples of evaluation dataset.

Manually labelled dead trees were compared with predicted dead trees in 350 1-ha plots distributed across the Atlantic coast. We defined dead trees as overstory dead trees with visible shadow and white/grey appearance. Model accuracy was evaluated at plot-level (counting) and tree-level (localization). Last row showed an occasion where dead tree delineation by both human and deep learning model was difficult. Image from NAIP51.

Extended Data Fig. 3 Plot-level and tree-level evaluation.

a. Manually labeled dead trees were compared with predicted dead trees in 200 1-ha plots collected in the NAIP year used in this study. Here, the number of dead trees per ha was compared. Color indicates different states. Shaded area represents the 95% confidence interval. b. As in a, but using all 350 plots, including those state-year NAIP combinations that were not used in the study. c. Overall accuracy (F1 score) of localizing Individual trees across states. Each point represents the F1 score of a plot. The box plot shows the median (centerline) and quartiles (bounds). The whiskers extend to 1.5 × the interquartile range beyond the first and third quartiles. The figure only showed plots with > 10 manually labeled dead trees since F1 score is highly sensitive when the sample size is low. The overall F1 score considering all labels and predictions is 0.82.

Extended Data Fig. 4 Modelled total tree density and tree cover across elevation gradients.

Tree density data from the USFS tree density map in 2012, which was modeled using the Forest Inventory and Analysis (FIA) field plots and gridded environmental data79. Tree cover data from the Hansen tree cover map in 2000 was derived from Landsat63. It is important to note that these datasets are not directly comparable with our product due to differences in methodology and input data source. Instead, these datasets are used as a reference to assess how tree cover or total tree count changes with elevation. We found that tree cover remains stable across elevations, and although the total tree density slowly increases with decreasing elevation, it cannot explain the drastic increase in tree mortality density below 5 m in this study.

Extended Data Fig. 5 Comparison with existing coastal forest loss product.

a. Comparing ghost forest scene in NAIP (0.6 m), Sentinel (10 m), and Landsat (30 m). All images are in false-color. Dead trees and healthy vegetation are displayed in white/grey and red, respectively. b. An example comparing our tree mortality map with an existing Landsat-based coastal forest loss product from 2001 to 2016 (C-CAP)68, excluding human-driven land cover change (for example agriculture conversion). The star symbol represents the location of a. There was very little overlap between the high dead tree density area in our map and the high forest loss area in the existing product, since most mortality mapped in our study was not detected in the Landsat-based map. c. Mean forest loss from 2001 to 2016 across elevations (0.5 bin). This highlights that coarse-resolution product severely underestimates coastal forest mortality in low-lying areas. Comparing with forest loss data from 2001 to 2021 yielded similar results. Credit: satellite images in b, Esri. Image in a (left) from NAIP51. Data from NOAA64.

Extended Data Fig. 6 Quality of NAIP imagery in this study.

a. Spatial distribution of acquisition timing of NAIP imagery for our map. The quality assurance (QA) label was assigned based on state-averaged forest phenological timing using MODIS land surface phenology product. b. Eccentricity map showing median dead tree crown eccentricity per 500 m pixel. Generally, we advise careful use of data acquired during green-up, green-down, and with high eccentricity values. Basemaps from Esri with state boundaries from the US Census Bureau. Image in x from NAIP51.

Extended Data Fig. 7 Ghost forest hotspots in low-lying (< 5 m) areas.

Hotspots of clustered dead trees were identified using spatial autocorrelation statistics (Methods). Basemap from Esri with state boundaries from the US Census Bureau.

Extended Data Fig. 8 Site-matching performance for flood-controlled areas.

a. Matching for sites with and without roads. b. Matching for sites with and without levees. Absolute standardized mean difference (ASMD) refers to the difference between the matched and unmatched (all) data. ASMD values less than 0.1 indicate good matching.

Extended Data Table 1 Model performance across states
Full size table
Extended Data Table 2 Distribution of dead trees and hotspots across states in low-lying areas (< 5 m)
Full size table

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yeung, H.C.H., Pavelsky, T.M., Wang, C. et al. Overlooked and extensive ghost forest formation across the US Atlantic coast. Nat Sustain 8, 1445–1454 (2025). https://doi.org/10.1038/s41893-025-01691-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41893-025-01691-w

This article is cited by

Search

Advanced 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