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:

Fish trawling and climate perturbations threaten the largest marine mercury sink

Nature Sustainability (2025)Cite this article

Subjects

Abstract

Global seafood demand is increasing while oceans continue to receive substantial anthropogenic mercury, heightening concerns about the toxic methylmercury bioaccumulation in seafood. Presently, the fate of mercury in the ocean remains uncertain, hindering comprehensive assessments of marine mercury dynamics and seafood safety. Here we leverage an observation-driven dataset to demonstrate that approximately \({1{,}290}_{-400}^{+680}\,{\rm{Mg}}\) of mercury is buried annually in continental shelves, substantially reducing its bioaccumulation potential in marine food webs. This flux is sixfold greater than that in the United Nations Environment Programme’s last report and twofold to sevenfold that of deep-sea sediment burial, making continental shelves the largest marine mercury sinks. Since industrialization, mercury levels in surface shelf sediments have tripled, indicating that most buried mercury is of anthropogenic origin. However, this sink is increasingly threatened by climate-related processes, bottom trawling and dredging, which physically remobilize mercury through diffusion, stirring, redistribution and off-shelf transport. Empirical extrapolations suggest that ongoing trawling, dredging and warming may transform coastal sediments from mercury sinks to net sources. This shift may have already occurred in parts of Europe’s shelves, though additional verification is required. Our findings highlight the urgent need to reduce anthropogenic mercury and greenhouse gas emissions and balance fishery demand with ecosystem conservation to sustain these critical mercury sinks.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Spatial controls on observed sediment mercury levels in continental shelves.
Fig. 2: Observed temporal trends of sediment mercury levels on continental shelves.
Fig. 3: Observation-based global distribution of mercury levels in surface sediments of continental shelves.
Fig. 4: Global distribution of mercury burial in continental shelves.
Fig. 5: Global patterns of fish trawling and climate-related impacts on coastal mercury sinks.

Similar content being viewed by others

Data availability

The underlying data for this study are available from the sources listed in the main text and Supplementary Information or can be found in the Supplementary Data.

Code availability

The data were collected and analysed in Microsoft Excel v.2020, R v.4.1.0, Python v.3.10.7, MATLAB v.R2020b and ArcGIS v.10.8. The R packages lme4 (ref. 87), MuMIn (ref. 88), lmerTest (ref. 89), strucchange (ref. 93) and trend (ref. 94) and the Python packages pyGAM (ref. 90), statsmodels (ref. 92), scikit-learn (ref. 96) and shap (ref. 97) were used to generate the results in this study.

References

  1. Driscoll, C. T., Mason, R. P., Chan, H. M., Jacob, D. J. & Pirrone, N. Mercury as a global pollutant: sources, pathways, and effects. Environ. Sci. Technol. 47, 4967–4983 (2013).

    Article  CAS  Google Scholar 

  2. Pirrone, N. et al. Global mercury emissions to the atmosphere from anthropogenic and natural sources. Atmos. Chem. Phys. 10, 5951–5964 (2010).

    Article  CAS  Google Scholar 

  3. Obrist, D. et al. Tundra uptake of atmospheric elemental mercury drives Arctic mercury pollution. Nature 547, 201–204 (2017).

    Article  CAS  Google Scholar 

  4. Streets, D. G. et al. Total mercury released to the environment by human activities. Environ. Sci. Technol. 51, 5969–5977 (2017).

    Article  CAS  Google Scholar 

  5. Selin, N. E. et al. Global 3‐D land–ocean–atmosphere model for mercury: present‐day versus preindustrial cycles and anthropogenic enrichment factors for deposition. Glob. Biogeochem. Cycles 22, GB2011 (2008).

    Google Scholar 

  6. Amos, H. M., Jacob, D. J., Streets, D. G. & Sunderland, E. M. Legacy impacts of all‐time anthropogenic emissions on the global mercury cycle. Glob. Biogeochem. Cycles 27, 410–421 (2013).

    Article  CAS  Google Scholar 

  7. Lamborg, C. H. et al. A global ocean inventory of anthropogenic mercury based on water column measurements. Nature 512, 65–68 (2014).

    Article  CAS  Google Scholar 

  8. Sonke, J. E. et al. Global change effects on biogeochemical mercury cycling. Ambio 52, 853–876 (2023).

    Article  CAS  Google Scholar 

  9. Mason, R. P., Fitzgerald, W. F. & Morel, F. M. The biogeochemical cycling of elemental mercury: anthropogenic influences. Geochim. Cosmochim. Acta 58, 3191–3198 (1994).

    Article  CAS  Google Scholar 

  10. Mason, R. P. & Sheu, G. R. Role of the ocean in the global mercury cycle. Glob. Biogeochem. Cycles 16, 40-1–40-14 (2002).

    Article  Google Scholar 

  11. Lamborg, C. H., Fitzgerald, W. F., O’Donnell, J. & Torgersen, T. A non-steady-state compartmental model of global-scale mercury biogeochemistry with interhemispheric atmospheric gradients. Geochim. Cosmochim. Acta 66, 1105–1118 (2002).

    Article  CAS  Google Scholar 

  12. Semeniuk, K. & Dastoor, A. Development of a global ocean mercury model with a methylation cycle: outstanding issues. Glob. Biogeochem. Cycles 31, 400–433 (2017).

    Article  CAS  Google Scholar 

  13. Kawai, T., Sakurai, T. & Suzuki, N. Application of a new dynamic 3-D model to investigate human impacts on the fate of mercury in the global ocean. Environ. Modell. Softw. 124, 104599 (2020).

    Article  Google Scholar 

  14. Global Mercury Assessment 2018 (UNEP, 2019).

  15. Cossa, D. et al. (eds) Global and Regional Mercury Cycles: Sources, Fluxes and Mass Balances 229–247 (Springer, 1996).

  16. Sunderland, E. M. & Mason, R. P. Human impacts on open ocean mercury concentrations. Glob. Biogeochem. Cycles 21, GB4022 (2007).

    Article  Google Scholar 

  17. Zhang, Y. et al. Biogeochemical drivers of the fate of riverine mercury discharged to the global and Arctic oceans. Glob. Biogeochem. Cycles 29, 854–864 (2015).

    Article  CAS  Google Scholar 

  18. Outridge, P. M., Mason, R., Wang, F., Guerrero, S. & Heimbürger-Boavida, L. Updated global and oceanic mercury budgets for the United Nations Global Mercury Assessment 2018. Environ. Sci. Technol. 52, 11466–11477 (2018).

    CAS  Google Scholar 

  19. Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).

    Article  CAS  Google Scholar 

  20. Lavoie, R. A., Bouffard, A., Maranger, R. & Amyot, M. Mercury transport and human exposure from global marine fisheries. Sci. Rep. 8, 6705 (2018).

    Article  Google Scholar 

  21. Chen, L. et al. Mass budget of mercury (Hg) in the seawater of Eastern China Marginal Seas: importance of the sediment–water transport processes. Environ. Sci. Technol. 56, 11418–11428 (2022).

    Article  CAS  Google Scholar 

  22. Hammerschmidt, C. R. & Fitzgerald, W. F. Geochemical controls on the production and distribution of methylmercury in near-shore marine sediments. Environ. Sci. Technol. 38, 1487–1495 (2004).

    Article  CAS  Google Scholar 

  23. Liu, B. et al. Disturbance impacts on mercury dynamics in northern Gulf of Mexico sediments. J. Geophys. Res. Biogeosci. 114, G00C07 (2009).

    Article  Google Scholar 

  24. Seelen, E. A., Massey, G. M. & Mason, R. P. Role of sediment resuspension on estuarine suspended particulate mercury dynamics. Environ. Sci. Technol. 52, 7736–7744 (2018).

    Article  CAS  Google Scholar 

  25. Cossa, D., Dang, D. H. & Thomas, B. Mercury mobility in epibenthic waters of a deltaic environment. J. Geophys. Res. Biogeosci. 129, e2023JG007575 (2024).

    Article  CAS  Google Scholar 

  26. Amos, H. M. et al. Global biogeochemical implications of mercury discharges from rivers and sediment burial. Environ. Sci. Technol. 48, 9514–9522 (2014).

    Article  CAS  Google Scholar 

  27. Ribbe, J. & Holloway, P. E. A model of suspended sediment transport by internal tides. Cont. Shelf Res. 21, 395–422 (2001).

    Article  Google Scholar 

  28. Sunderland, E. M. et al. Response of a macrotidal estuary to changes in anthropogenic mercury loading between 1850 and 2000. Environ. Sci. Technol. 44, 1698–1704 (2010).

    Article  CAS  Google Scholar 

  29. Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).

    Article  CAS  Google Scholar 

  30. Liu, M. et al. Rivers as the largest source of mercury to coastal oceans worldwide. Nat. Geosci. 14, 672–677 (2021).

    Article  CAS  Google Scholar 

  31. Liu, M. et al. Observation-based mercury export from rivers to coastal oceans in East Asia. Environ. Sci. Technol. 55, 14269–14280 (2021).

    Article  CAS  Google Scholar 

  32. Aksentov, K. I. et al. Assessment of mercury levels in modern sediments of the East Siberian Sea. Mar. Pollut. Bull. 168, 112426 (2021).

    Article  CAS  Google Scholar 

  33. Liem-Nguyen, V. et al. Spatial patterns and distributional controls of total and methylated mercury off the Lena River in the Laptev Sea sediments. Mar. Chem. 238, 104052 (2022).

    Article  CAS  Google Scholar 

  34. Tesán Onrubia, J. A. et al. Mercury export flux in the Arctic Ocean estimated from 234Th/238U disequilibria. ACS Earth Space Chem. 4, 795–801 (2020).

    Article  Google Scholar 

  35. Kohler, S. G. et al. Distribution pattern of mercury in northern Barents Sea and Eurasian Basin surface sediment. Mar. Pollut. Bull. 185, 114272 (2022).

    Article  CAS  Google Scholar 

  36. Bianchi, T. S. et al. Anthropogenic impacts on mud and organic carbon cycling. Nat. Geosci. 17, 287–297 (2024).

    Article  CAS  Google Scholar 

  37. Kocman, D. et al. Toward an assessment of the global inventory of present-day mercury releases to freshwater environments. Int. J. Environ. Res. Public Health 14, 138 (2017).

    Article  Google Scholar 

  38. Qiu, X. et al. Declines in anthropogenic mercury emissions in the Global North and China offset by the Global South. Nat. Commun. 16, 1179 (2025).

    Article  CAS  Google Scholar 

  39. Zhang, Y., Jaeglé, L., Thompson, L. & Streets, D. G. Six centuries of changing oceanic mercury. Glob. Biogeochem. Cycles 28, 1251–1261 (2014).

    Article  CAS  Google Scholar 

  40. Hayes, C. T. et al. Global ocean sediment composition and burial flux in the deep sea. Glob. Biogeochem. Cycles 35, e2020GB006769 (2021).

    Article  CAS  Google Scholar 

  41. Bianchi, T. S. et al. Centers of organic carbon burial and oxidation at the land–ocean interface. Org. Geochem. 115, 138–155 (2018).

    Article  CAS  Google Scholar 

  42. Jickells, T. D. et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science 308, 67–71 (2005).

    Article  CAS  Google Scholar 

  43. Sun, X. et al. Mercury burial in modern sedimentary systems of the East China Marginal Seas: the role of coastal oceans in global mercury cycling. Glob. Biogeochem. Cycles 37, e2023GB007760 (2023).

    Article  CAS  Google Scholar 

  44. Outridge, P., Macdonald, R., Wang, F., Stern, G. & Dastoor, A. A mass balance inventory of mercury in the Arctic Ocean. Environ. Chem. 5, 89–111 (2008).

    Article  CAS  Google Scholar 

  45. Dastoor, A. et al. Arctic mercury cycling. Nat. Rev. Earth Environ. 3, 270–286 (2022).

    Article  CAS  Google Scholar 

  46. Rosati, G. et al. Mercury in the Black Sea: new insights from measurements and numerical modeling. Glob. Biogeochem. Cycles 32, 529–550 (2018).

    Article  CAS  Google Scholar 

  47. Liu, M. et al. Mercury export from mainland China to adjacent seas and its influence on the marine mercury balance. Environ. Sci. Technol. 50, 6224–6232 (2016).

    Article  CAS  Google Scholar 

  48. Hare, A. A. et al. Natural and anthropogenic mercury distribution in marine sediments from Hudson Bay, Canada. Environ. Sci. Technol. 44, 5805–5811 (2010).

    Article  CAS  Google Scholar 

  49. Žagar, D. et al. Mercury in the Mediterranean. Part 2: processes and mass balance. Environ. Sci. Pollut. Res. 21, 4081–4094 (2014).

    Article  Google Scholar 

  50. Cossa, D. et al. Mediterranean Mercury Assessment 2022: an updated budget, health consequences, and research perspectives. Environ. Sci. Technol. 56, 3840–3862 (2022).

    Article  CAS  Google Scholar 

  51. Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).

    Article  CAS  Google Scholar 

  52. Epstein, G., Middelburg, J. J., Hawkins, J. P., Norris, C. R. & Roberts, C. M. The impact of mobile demersal fishing on carbon storage in seabed sediments. Glob. Change Biol. 28, 2875–2894 (2022).

    Article  CAS  Google Scholar 

  53. Restreppo, G. A., Wood, W. T. & Phrampus, B. J. Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale. Geo-Mar. Lett. 40, 755–763 (2020).

    Article  Google Scholar 

  54. Kim, E.-H., Mason, R. P. & Bergeron, C. M. A modeling study on methylmercury bioaccumulation and its controlling factors. Ecol. Model. 218, 267–289 (2008).

    Article  CAS  Google Scholar 

  55. Ferré, B., De Madron, X. D., Estournel, C., Ulses, C. & Le Corre, G. Impact of natural (waves and currents) and anthropogenic (trawl) resuspension on the export of particulate matter to the open ocean: application to the Gulf of Lion (NW Mediterranean). Cont. Shelf Res. 28, 2071–2091 (2008).

    Article  Google Scholar 

  56. Churchill, J. H. The effect of commercial trawling on sediment resuspension and transport over the Middle Atlantic Bight continental shelf. Cont. Shelf Res. 9, 841–865 (1989).

    Article  Google Scholar 

  57. Swift, D. J. in The Geology of Continental Margins (eds Burk, C. A. & Drake, C. L.) 117–135 (Springer, 1974).

  58. Collie, J. S., Hall, S. J., Kaiser, M. J. & Poiner, I. R. A quantitative analysis of fishing impacts on shelf‐sea benthos. J. Anim. Ecol. 69, 785–798 (2000).

    Article  Google Scholar 

  59. García-Ordiales, E. et al. Mercury and arsenic mobility in resuspended contaminated estuarine sediments (Asturias, Spain): a laboratory-based study. Sci. Total Environ. 744, 140870 (2020).

    Article  Google Scholar 

  60. Hiddink, J. G. et al. Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance. Proc. Natl Acad. Sci. USA 114, 8301–8306 (2017).

    Article  CAS  Google Scholar 

  61. Zhang, Y., Soerensen, A. L., Schartup, A. T. & Sunderland, E. M. A global model for methylmercury formation and uptake at the base of marine food webs. Glob. Biogeochem. Cycles 34, e2019GB006348 (2020).

    Article  CAS  Google Scholar 

  62. Schartup, A. T. et al. Freshwater discharges drive high levels of methylmercury in Arctic marine biota. Proc. Natl Acad. Sci. USA 112, 11789–11794 (2015).

    Article  CAS  Google Scholar 

  63. Wu, P. et al. Atmospheric monomethylmercury: inferred sources constrained by observations and implications for human exposure. Environ. Int. 193, 109127 (2024).

    Article  CAS  Google Scholar 

  64. Guo, W. et al. Warming-induced vegetation greening may aggravate soil mercury levels worldwide. Environ. Sci. Technol. 58, 15078–15089 (2024).

    Article  CAS  Google Scholar 

  65. Zhou, J., Obrist, D., Dastoor, A., Jiskra, M. & Ryjkov, A. Vegetation uptake of mercury and impacts on global cycling. Nat. Rev. Earth Environ. 2, 269–284 (2021).

    Article  Google Scholar 

  66. Liu, M. et al. Substantial accumulation of mercury in the deepest parts of the ocean and implications for the environmental mercury cycle. Proc. Natl Acad. Sci. USA 118, e2102629118 (2021).

    Article  CAS  Google Scholar 

  67. Pauly, D. & Zeller, D. (eds) Catch reconstruction: concepts, methods and data sources. SeaAroundUs https://www.seaaroundus.org/catch-reconstruction-and-allocation-methods/ (2015).

  68. Pacyna, J. M. et al. Current and future levels of mercury atmospheric pollution on a global scale. Atmos. Chem. Phys. 16, 12495–12511 (2016).

    Article  CAS  Google Scholar 

  69. De Simone, F. et al. The GOS4M Knowledge Hub: a web-based effectiveness evaluation platform in support of the Minamata Convention on Mercury. Environ. Sci. Policy 124, 235–246 (2021).

    Article  Google Scholar 

  70. Bianchi, T. S. et al. What global biogeochemical consequences will marine animal–sediment interactions have during climate change? Elem. Sci. Anthr. 9, 00180 (2021).

    Article  Google Scholar 

  71. Jönsson, A., Gustafsson, Ö., Axelman, J. & Sundberg, H. Global accounting of PCBs in the continental shelf sediments. Environ. Sci. Technol. 37, 245–255 (2003).

    Article  Google Scholar 

  72. Covelli, S., Faganeli, J., Horvat, M. & Brambati, A. Mercury contamination of coastal sediments as the result of long-term cinnabar mining activity (Gulf of Trieste, northern Adriatic sea). Appl. Geochem. 16, 541–558 (2001).

    Article  CAS  Google Scholar 

  73. Wang, S. et al. Total mercury and monomethylmercury in water, sediments, and hydrophytes from the rivers, estuary, and bay along the Bohai Sea coast, northeastern China. Appl. Geochem. 24, 1702–1711 (2009).

    Article  CAS  Google Scholar 

  74. Spada, L., Annicchiarico, C., Cardellicchio, N., Giandomenico, S. & Di Leo, A. Mercury and methylmercury concentrations in Mediterranean seafood and surface sediments, intake evaluation and risk for consumers. Int. J. Hyg. Environ. Health 215, 418–426 (2012).

    Article  CAS  Google Scholar 

  75. Heimbürger, L.-E. et al. Natural and anthropogenic trace metals in sediments of the Ligurian Sea (northwestern Mediterranean). Chem. Geol. 291, 141–151 (2012).

    Article  Google Scholar 

  76. Kim, H. et al. Increase in anthropogenic mercury in marginal sea sediments of the Northwest Pacific Ocean. Sci. Total Environ. 654, 801–810 (2019).

    Article  CAS  Google Scholar 

  77. Song, S. et al. A global assessment of the mixed layer in coastal sediments and implications for carbon storage. Nat. Commun. 13, 4903 (2022).

    Article  CAS  Google Scholar 

  78. Zhou, C. et al. Warming-induced retreat of West Antarctic glaciers weakened carbon sequestration ability but increased mercury enrichment. Nat. Commun. 16, 1831 (2025).

    Article  CAS  Google Scholar 

  79. Zaferani, S., Pérez-Rodríguez, M. & Biester, H. Diatom ooze—a large marine mercury sink. Science 361, 797–800 (2018).

    Article  CAS  Google Scholar 

  80. Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Moalusi, T. A new global oceanic multi-model net primary productivity data product. Earth Syst. Sci. Data 15, 4829–4848 (2023).

    Article  Google Scholar 

  81. Lee, T. R., Wood, W. T. & Phrampus, B. J. A machine learning (kNN) approach to predicting global seafloor total organic carbon. Glob. Biogeochem. Cycles 33, 37–46 (2019).

    Article  CAS  Google Scholar 

  82. Graw, J., Wood, W. & Phrampus, B. Predicting global marine sediment density using the random forest regressor machine learning algorithm. J. Geophys. Res. -Solid Earth 126, e2020JB020135 (2021).

    Article  Google Scholar 

  83. Martin, K. M., Wood, W. T. & Becker, J. J. A global prediction of seafloor sediment porosity using machine learning. Geophys. Res. Lett. 42, 10640–10646 (2015).

    Article  Google Scholar 

  84. Dutkiewicz, A., Müller, R. D., O’Callaghan, S. & Jónasson, H. Census of seafloor sediments in the world’s ocean. Geology 43, 795–798 (2015).

    Article  CAS  Google Scholar 

  85. Chen, L. et al. Trans-provincial health impacts of atmospheric mercury emissions in China. Nat. Commun. 10, 1484 (2019).

    Article  Google Scholar 

  86. Laruelle, G. G. et al. Global multi-scale segmentation of continental and coastal waters from the watersheds to the continental margins. Hydrol. Earth Syst. Sci. 17, 2029–2051 (2013).

    Article  Google Scholar 

  87. Bates, D., Maechler, M., Bolker, B. & Walkeret, S. lme4: linear mixed-effects models using ‘Eigen’ and S4. R package version 1.1-37 https://doi.org/10.32614/CRAN.package.lme4 (2025).

  88. Bartoń, K. MuMIn: Multi-model inference. R package version 1.48.11 https://doi.org/10.32614/CRAN.package.MuMIn (2025).

  89. Kuznetsova, A., Brockhoff, P. B., Christensen, R. H. B. & Jensen, S. P. lmerTest: Tests in linear mixed effects models. R package version 3.1-3 https://doi.org/10.32614/CRAN.package.lmerTest (2020).

  90. Servén, D., Brummitt, C. & Abedi, H. dswah/pyGAM: v0.10.1. Zenodo https://doi.org/10.5281/zenodo.1208723 (2025).

  91. Médieu, A. et al. Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs. Proc. Natl Acad. Sci. USA 119, e2113032119 (2022).

    Article  Google Scholar 

  92. McKinney, W. Data structures for statistical computing in Python. scipy 445, 51–56 (2010).

    Google Scholar 

  93. Zeileis, A. et al. strucchange: testing, monitoring, and dating structural changes. R package version 1.5-4 https://doi.org/10.32614/CRAN.package.strucchange (2024).

  94. Pohlert, T. trend: non-parametric trend tests and change-point detection. R package version 1.1.6 https://doi.org/10.32614/CRAN.package.trend (2023).

  95. Liu, M. et al. Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment. Nat. Geosci. 17, 896–904 (2024).

    Article  CAS  Google Scholar 

  96. scikit-learn developers. scikit-learn. Zenodo https://doi.org/10.5281/zenodo.14627164 (2025).

  97. Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Proc. Syst. 30, 4768–4777 (2017).

    Google Scholar 

  98. Sundararajan, M. & Najmi, A. The many Shapley values for model explanation. In International Conference on Machine Learning (eds Daumé, H. & Singh, A.) 9269–9278 (PMLR, 2020).

  99. Smith, R. W., Bianchi, T. S., Allison, M., Savage, C. & Galy, V. High rates of organic carbon burial in fjord sediments globally. Nat. Geosci. 8, 450–453 (2015).

    Article  CAS  Google Scholar 

  100. Shi, X., Annett, A. L., Jones, R. L., Middag, R. & Mason, R. P. Benthic deposition and burial of total mercury and methylmercury estimated using thorium isotopes in the high-latitude North Atlantic. Geochim. Cosmochim. Acta. 399, 191–204 (2025).

    Article  CAS  Google Scholar 

  101. Clarke, S. & Elliott, A. Modelling suspended sediment concentrations in the Firth of Forth. Estuar. Coast. Shelf Sci. 47, 235–250 (1998).

    Article  Google Scholar 

  102. Kalnejais, L. H., Martin, W. R., Signell, R. P. & Bothner, M. H. Role of sediment resuspension in the remobilization of particulate-phase metals from coastal sediments. Environ. Sci. Technol. 41, 2282–2288 (2007).

    Article  CAS  Google Scholar 

  103. Ravens, T. M. & Gschwend, P. M. Flume measurements of sediment erodibility in Boston Harbor. J. Hydraul. Eng. 125, 998–1005 (1999).

    Article  Google Scholar 

  104. Jing, L. & Ridd, P. V. Wave-current bottom shear stresses and sediment resuspension in Cleveland Bay, Australia. Coast. Eng. 29, 169–186 (1996).

    Article  Google Scholar 

  105. Bloesch, J. A review of methods used to measure sediment resuspension. Hydrobiologia 284, 13–18 (1994).

    Article  Google Scholar 

  106. Wiberg, P. L., Drake, D. E. & Cacchione, D. A. Sediment resuspension and bed armoring during high bottom stress events on the northern California inner continental shelf: measurements and predictions. Cont. Shelf Res. 14, 1191–1219 (1994).

    Article  Google Scholar 

  107. Harris, C. K. & Wiberg, P. Across‐shelf sediment transport: interactions between suspended sediment and bed sediment. J. Geophys. Res. Oceans 107, 8-1–8-12 (2002).

    Article  Google Scholar 

  108. Dias, J., Gonzalez, R., Garcia, C. & Diaz-del-Rio, V. Sediment distribution patterns on the Galicia-Minho continental shelf. Prog. Oceanogr. 52, 215–231 (2002).

    Article  Google Scholar 

  109. Griffin, J. D., Hemer, M. A. & Jones, B. G. Mobility of sediment grain size distributions on a wave dominated continental shelf, southeastern Australia. Mar. Geol. 252, 13–23 (2008).

    Article  Google Scholar 

  110. Gill, G. A. et al. Sediment−water fluxes of mercury in Lavaca Bay, Texas. Environ. Sci. Technol. 33, 663–669 (1999).

    Article  CAS  Google Scholar 

  111. Soerensen, A. L. et al. A mass budget for mercury and methylmercury in the Arctic Ocean. Glob. Biogeochem. Cycles 30, 560–575 (2016).

    Article  CAS  Google Scholar 

  112. Boudreau, B. P. The diffusive tortuosity of fine-grained unlithified sediments. Geochim. Cosmochim. Acta 60, 3139–3142 (1996).

    Article  CAS  Google Scholar 

  113. Hollweg, T., Gilmour, C. C. & Mason, R. Mercury and methylmercury cycling in sediments of the mid‐Atlantic continental shelf and slope. Limnol. Oceanogr. 55, 2703–2722 (2010).

    Article  CAS  Google Scholar 

  114. Eigaard, O. R. et al. Estimating seabed pressure from demersal trawls, seines, and dredges based on gear design and dimensions. ICES J. Mar. Sci. 73, i27–i43 (2016).

    Article  Google Scholar 

  115. De Madron, X. D. et al. Trawling-induced resuspension and dispersal of muddy sediments and dissolved elements in the Gulf of Lion (NW Mediterranean). Cont. Shelf Res. 25, 2387–2409 (2005).

    Article  Google Scholar 

  116. Liu, M. et al. Rice life cycle-based global mercury biotransport and human methylmercury exposure. Nat. Commun. 10, 5164 (2019).

    Article  Google Scholar 

  117. Mayorga, E. et al. Global nutrient export from WaterSheds 2 (NEWS 2): model development and implementation. Environ. Modell. Softw. 25, 837–853 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded by the National Natural Science Foundation of China (grant nos 42476127, 42521004 and 42577530); Science and Technology Projects of Xizang Autonomous Region, China (grant no. XZ202501ZY0091); the China Postdoctoral Science Foundation (grant no. 2022M720005); Beijing Natural Science Foundation (grant no. 8244068); the public instrument platform of the College of Urban and Environmental Sciences at Peking University; and the High-Performance Computing Platform of Peking University. M.L. is also supported by the Fundamental Research Funds for the Central Universities (grant no. 7100604874) and the Laboratory for Earth Surface Processes, Ministry of Education, Peking University.

Author information

Author notes

  1. These authors contributed equally: Maodian Liu, Chengzhen Zhou, Qianru Zhang.

Authors and Affiliations

  1. Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Maodian Liu, Chengzhen Zhou, Xiaolong Li, Hehao Qin, Guofeng Shen, Dongqiang Zhu & Xuejun Wang

  2. School of the Environment, Yale University, New Haven, CT, USA

    Maodian Liu & Peter A. Raymond

  3. Institute of Ocean Research, Peking University, Beijing, China

    Maodian Liu

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

    Qianru Zhang

  5. Department of Marine Sciences, University of Connecticut, Groton, CT, USA

    Robert P. Mason

  6. Cary Institute of Ecosystem Studies, Millbrook, NY, USA

    Taylor Maavara

  7. School of Geography, University of Leeds, Leeds, UK

    Taylor Maavara

  8. Department of Earth Sciences—Geochemistry, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands

    Junjie Wang

  9. Department of Geological Sciences, University of Florida, Gainesville, FL, USA

    Thomas S. Bianchi

  10. Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA

    Thomas S. Bianchi

Authors
  1. Maodian Liu
    View author publications

    Search author on:PubMed Google Scholar

  2. Chengzhen Zhou
    View author publications

    Search author on:PubMed Google Scholar

  3. Qianru Zhang
    View author publications

    Search author on:PubMed Google Scholar

  4. Peter A. Raymond
    View author publications

    Search author on:PubMed Google Scholar

  5. Xiaolong Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Robert P. Mason
    View author publications

    Search author on:PubMed Google Scholar

  7. Taylor Maavara
    View author publications

    Search author on:PubMed Google Scholar

  8. Junjie Wang
    View author publications

    Search author on:PubMed Google Scholar

  9. Hehao Qin
    View author publications

    Search author on:PubMed Google Scholar

  10. Guofeng Shen
    View author publications

    Search author on:PubMed Google Scholar

  11. Dongqiang Zhu
    View author publications

    Search author on:PubMed Google Scholar

  12. Xuejun Wang
    View author publications

    Search author on:PubMed Google Scholar

  13. Thomas S. Bianchi
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.L., P.A.R. and X.W. designed the research. X.W., M.L. and Q.Z. acquired the funding needed to complete the study. C.Z. and Q.Z. collected the data. Q.Z., C.Z., X.L., H.Q. and M.L. conducted the data processing and modelling. M.L., Q.Z. and T.M. wrote the original manuscript in close discussion with T.S.B., R.P.M. and P.A.R. J.W., G.S. and D.Z. provided important data to help complete the work. M.L., C.Z., Q.Z., T.S.B., R.P.M., T.M., X.W., D.Z. and P.A.R. contributed to manuscript revision and completion.

Corresponding authors

Correspondence to Maodian Liu, Qianru Zhang or Xuejun Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Lars-Eric Heimbürger-Boavida and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Comparing the geospatial distribution of mercury and organic carbon in continental shelf sediments.

Organic carbon data from ref. 81. Boundaries of coastal oceans adapted from ref. 86 under a Creative Commons licence CC BY 4.0.

Extended Data Fig. 2 Temporal variations in mercury levels and organic carbon contents within continental shelf sediment cores since industrialization.

Sediment cores used for the compilation are presented in Supplementary Table 1. Boundaries of ocean basins are according to Mayorga et al.117. Data are compared using linear regression models. Some sediment cores were excluded from the analysis because their time spans were too long to allow reliable temporal interpretation. Dot color, size, and shape represent P value, R2, and slope of regressions, respectively. The statistical results are presented in Supplementary Table 8. OC – organic carbon. Hg/OC – Hg and OC ratio. Hg~OC – linear relationship between Hg and OC.

Extended Data Fig. 3 Potential age of mercury in continental shelf sediments disturbed during fish trawling and dredging activities.

Data are inferred from sedimentation rates of grid cells and average penetration depths of fish trawling and dredging54,60. Boundaries of coastal oceans adapted from ref. 86 under a Creative Commons licence CC BY 4.0.

Extended Data Fig. 4 Global hotspots for mercury perturbation in continental shelf sediments due to fish trawling and dredging activities.

Boundaries of coastal oceans adapted from ref. 86 under a Creative Commons licence CC BY 4.0.

Extended Data Fig. 5 Relationship between seafloor temperature and modeled mercury diffusion rates in continental shelf sediments.

Shade areas represent 95% confidence intervals. The solid line depicts the linear regression fit. Dot size represents shelf surface areas (1,000 km2) among coastal oceans.

Extended Data Fig. 6 Mercury burial under natural conditions and fish trawling-induced off-shelf export of mercury to the open ocean in different continental shelves worldwide.

The delineation of global coastal regions is based on Laruelle et al.86. The bars show the median values from 10,000-iteration Monte Carlo simulations. Error bars represent the interquartile range (percentile 25th to 75th) from the Monte Carlo simulations that incorporate both two methods for off-shelf exports.

Extended Data Fig. 7 Comparison of the performance of five models for surface mercury concentrations in global continental shelf sediments.

GAM – Generalized Additive Model; KNN – K-Nearest Neighbors; LightGBM – Light Gradient Boosting Machine; XGBoost – Extreme Gradient Boosting.

Extended Data Fig. 8 Monthly variations in sediment mercury remobilization due to climatic and anthropogenic influences in the five leading coastal oceans.

The shaded bands show interquartile ranges from 10,000 Monte Carlo iterations, with their midlines marking the median simulation values. Boundaries of coastal oceans are according to Laruelle et al.86.

Extended Data Fig. 9 Updated mercury cycling in the global ocean.

Panel a. Updated based on the current study. Panel b. original budget sourced from UNEP14. In Panel a, the notation “a” placed at the top right of the numbers denotes that these fluxes are cited from our prior research30.

Supplementary information

Supplementary Information

Supplementary Texts 1–5, Tables 1–9 and references.

Reporting Summary

Supplementary Data

This compressed file contains a comprehensive collection of supplementary data supporting the study of mercury concentrations and fluxes in coastal sediments worldwide. The datasets include: (1) Mercury Concentrations: Observational data on mercury concentrations in surface sediments (Supplementary Data 1) and sediment cores (Supplementary Data 2), including variations, uncertainties, and geographical representation. (2) Environmental Parameters: Key environmental factors influencing mercury concentration variations (Supplementary Data 3), including the data sources for model training. (3) High-Spatial-Resolution Data: Datasets with global coverage of mercury concentrations in coastal sediments (Supplementary Data 4–6), mercury burial rates (Supplementary Data 7), and natural mercury diffusion (Supplementary Data 8). (4) Mercury Perturbation: Fish trawling (Supplementary Data 9) and dredging (Supplementary Data 10)-induced mercury perturbation data at the same spatial resolution. (5) Flux and Trend Data: Global summary of mercury fluxes (Supplementary Data 11) and historical trends in coastal mercury concentrations (Supplementary Data 15, 16), with associated uncertainties from Monte Carlo simulations. (6) Historical Variations: Monthly variation data for trawling- and dredging-induced mercury perturbations (Supplementary Data 13, 14) and mercury diffusion across coastal oceans (Supplementary Data 12). (7) Seabed Lithology and Grid Data: Seafloor lithology information (Supplementary Data 17), and grid cell areas for calculations (Supplementary Data 18), useful for flux and concentration conversions.

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

Liu, M., Zhou, C., Zhang, Q. et al. Fish trawling and climate perturbations threaten the largest marine mercury sink. Nat Sustain (2025). https://doi.org/10.1038/s41893-025-01642-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41893-025-01642-5

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene