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:

Agricultural pesticide land budget and river discharge to oceans

Nature volume 620, pages 1013–1017 (2023)Cite this article

Subjects

Abstract

Pesticides are ubiquitous environmental pollutants negatively affecting ecosystem and human health1,2. About 3 Tg of pesticides are used annually in agriculture to protect crops3. How much of these pesticides remain on land and reach the aquifer or the ocean is uncertain. Monitoring their environmental fate is challenging, and a detailed picture of their mobility in time and space is largely missing4. Here, we develop a process-based model accounting for the hydrology and biogeochemistry of the 92 most used agricultural pesticide active substances to assess their pathways through the principal catchments of the world and draw a near-present picture of the global land and river budgets, including discharge to oceans. Of the 0.94 Tg net annual pesticide input in 2015 used in this study, 82% is biologically degraded, 10% remains as residue in soil and 7.2% leaches below the root zone. Rivers receive 0.73 Gg of pesticides from their drainage at a rate of 10 to more than 100 kg yr−1 km−1. By contrast to their fate in soil, only 1.1% of pesticides entering rivers are degraded along streams, exceeding safety levels (concentrations >1 μg l−1) in more than 13,000 km of river length, with 0.71 Gg of pesticide active ingredients released to oceans every year. Herbicides represent the prevalent pesticide residue on both land (72%) and river outlets (62%).

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Pesticide budget in the catchments and rivers.
Fig. 2: Pesticide mobility within catchments and discharge to oceans.

Data availability

We have included the land and river budgets of individual PAS as Supplementary Data. All material is available in the Figshare repository (https://doi.org/10.6084/m9.figshare.20452791) along with details available in the technical documentation. Any specific additional data used to conduct analyses and build figures in this work can be requested from the authors. Source data are provided with this paper.

Code availability

The BRTSim v.5.0b model is publicly accessible at https://sites.google.com/site/thebrtsimproject/home, with User Manual and Technical Guide, samples and basic scripts in @Matlab for model outputs and figure compilation. Codes used to preprocess inputs and postprocess outputs are not distributed because they are associated to a highly sparce data structure and computation workflow, which is dependent on the workstation operating system and the developers’ design of data management and manipulation comprising about 3.4 terabytes of data in compressed format. For simplicity, we distribute the model for one grid cell as an example and scripts that allow to compile simple figures. However, we have compiled selected outputs in NetCDF files to distribute the 30-yr 5-d time sequences of: total pesticide application rate (APR.nc); residue in the RZ (RESID.nc); direct runoff generation and overland routing (RNF.nc); pesticide concentration (CONC.nc) and discharge (DIS.nc) in river reaches; and an animation in .MP4 format of the above quantities. Additional specific data layers may be requested from the authors.

References

  1. Schulz, R., Bub, S., Petschick, L. L., Stehle, S. & Wolfram, J. Applied pesticide toxicity shifts toward plants and invertebrates, even in GM crops. Science 372, 81–84 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Landrigan, P. J. et al. The Lancet Commission on pollution and health. Lancet 391, 462–512 (2018).

    Article  PubMed  Google Scholar 

  3. FAO. Pesticides Use (FAOSTAT, accessed February 2023); https://www.fao.org/faostat/en/#data/RP.

  4. Ippolito, A. et al. Modeling global distribution of agricultural insecticides in surface waters. Environ. Pollut. 198, 54–60 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. FAO Land Use Statistics and Indicators. Global, Regional and Country Trends 1990–2019. FAOSTAT Analytical Brief Series No. 28 (FAO, 2021); https://www.fao.org/documents/card/en/c/cb6033en/.

  6. Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12, 7–21 (2019).

    Article  ADS  CAS  Google Scholar 

  7. FAO. Fertilizers by Nutrient (FAOSTAT, accessed August 2020); https://www.fao.org/faostat/en/#data/RFN.

  8. FAO. Pesticides Use (FAOSTAT, accessed August 2020); https://www.fao.org/faostat/en/#data/RP.

  9. FAO. Land Use (FAOSTAT, accessed August 2020); https://www.fao.org/faostat/en/#data/RL.

  10. Schlesinger, W. H. On the fate of anthropogenic nitrogen. Proc. Natl Acad. Sci. USA 106, 203–208 (2009).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Beusen, A. H., Bouwman, A. F., Van Beek, L. P., Mogollón, J. M. & Middelburg, J. J. Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum. Biogeosciences 13, 2441–2451 (2016).

    Article  ADS  CAS  Google Scholar 

  12. Silva, V. et al. Pesticide residues in European agricultural soils—a hidden reality unfolded. Sci. Total Environ. 653, 1532–1545 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Stehle, S., Bub, S. & Schulz, R. Compilation and analysis of global surface water concentrations for individual insecticide compounds. Sci. Total Environ. 639, 516–525 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. de Souza, R. M. et al. Occurrence, impacts and general aspects of pesticides in surface water: a review. Process Saf. Environ. Prot. 135, 22–37 (2020).

    Article  Google Scholar 

  15. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature https://doi.org/10.1038/nature09440 (2010).

  16. Waterbase v2020—WISE6. EEA https://www.eea.europa.eu/data-and-maps/data/waterbase-water-quality-icm-1 (accessed September 2021).

  17. Deacon, J., Lee, C., Norman, J. & Reutter, D. Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1963–2016. US Geol. Surv. 10, F73777K4 (2017).

    Google Scholar 

  18. Freshwater Quality Surveillance Data—Pacific Basin. Government of Canada https://open.canada.ca/data/en/dataset/831e19c6-edb1-46bf-9df9-31e6052a66ce (accessed January 2023).

  19. Catchment Loads Monitoring Program Pesticide Reporting Portal: Water Quality and Investigations. Department of Environment and Science, Brisbane, Australia https://storymaps.arcgis.com/stories/c0f0c6d7d88a4fd3a5541fe59f41ff75 (accessed December 2022).

  20. Eichelberger, J. W. & Lichtenberg, J. J. Persistence of pesticides in river water. Environ. Sci. Technol. 5, 541–544 (1971).

    Article  ADS  CAS  Google Scholar 

  21. Gassmann, M. Modelling the fate of pesticide transformation products from plot to catchment scale—state of knowledge and future challenges. Front. Environ. Sci. 9, 717738 (2021).

  22. Morselli, M. et al. Predicting pesticide fate in small cultivated mountain watersheds using the DynAPlus model: toward improved assessment of peak exposure. Sci. Total Environ. 615, 307–318 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Bertuzzo, E., Thomet, M., Botter, G. & Rinaldo, A. Catchment-scale herbicides transport: theory and application. Adv. Water Res. 52, 232–242 (2013).

    Article  CAS  Google Scholar 

  24. Fohrer, N., Dietrich, A., Kolychalow, O. & Ulrich, U. Assessment of the environmental fate of the herbicides flufenacet and metazachlor with the SWAT model. J. Environ. Qual. 43, 75–85 (2014).

    Article  PubMed  Google Scholar 

  25. Wittmer, I. K., Bader, H. P., Scheidegger, R. & Stamm, C. REXPO: a catchment model designed to understand and simulate the loss dynamics of plant protection products and biocides from agricultural and urban areas. J. Hydrol. 533, 486–514 (2016).

    Article  ADS  Google Scholar 

  26. Maggi, F. BRTSim v5.0b, A General-Purpose Multiphase and Multispecies Computational Solver for Biogeochemical Reaction-Advection-Dispersion Processes in Porous and Non-porous Media: User Manual and Technical Guide (BRTSim project, 2022); archived versions at https://sites.google.com/site/thebrtsimproject/downloads?authuser=0.

  27. Flury, M., Leuenberger, J., Studer, B. & Flühler, H. Transport of anions and herbicides in a loamy and a sandy field soil. Water Resour. Res. 31, 823–835 (1995).

    Article  ADS  CAS  Google Scholar 

  28. Flury, M. Experimental evidence of transport of pesticides through field soils—a review. J. Environ. Qual. 25, 25–45 (1996).

    Article  CAS  Google Scholar 

  29. Vonberg, D., Vanderborght, J., & Rüde, T. R. Atrazine in the Environment 20 Years After its Ban: Long-term Monitoring of a Shallow Aquifer (in Western Germany) and Soil Residue Analysis (Deutsche Nationalbibliothek, 2015); http://publications.rwth-aachen.de/record/484062/files/484062.pdf.

  30. EFSA. Conclusion on the peer review of the pesticide risk assessment of the active substance metam. EFSA J. 9, 2334 (2011).

  31. Larson, B. T., Capel, P. D., Goolsby, D. A., Zaugg, S. D. & Sandstrom, M. W. Relations between pesticide use and riverine flux in the Mississippi river basin. Chemosphere 31, 3305–3321 (1995).

    Article  ADS  CAS  Google Scholar 

  32. la Cecilia, D. et al. Continuous high-frequency pesticide monitoring to observe the unexpected and the overlooked. Water Res. X 13, 100125 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ippolito, A. & Fait, G. Pesticides in surface waters: from edge-of-field to global modelling. Curr. Opin. Environ. Sustain. 36, 78–84 (2019).

    Article  Google Scholar 

  34. Beketov, M. A., Kefford, B. J., Schäfer, R. B. & Liess, M. Pesticides reduce regional biodiversity of stream invertebrates. Proc. Natl Acad. Sci. USA 110, 11039–11043 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ryberg, K. R. & Gilliom, R. J. Trends in pesticide concentrations and use for major rivers of the United States. Sci. Total Environ. 538, 431–444 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  36. Sustainable Development Goals: Indicator 2.4.1—Proportion of Agricultural Area Under Productive and Sustainable Agriculture. FAO https://www.fao.org/sustainable-development-goals/indicators/241/en/ (accessed July 2022).

  37. Lehner, B., Verdin, K. & Jarvis, A. HydroRIVERS v1.0—Technical Documentation (HydroSheds, accessed August 2020); https://www.hydrosheds.org/page/hydrorivers.

  38. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles https://doi.org/10.1029/2007GB002947 (2008).

  39. Guglielmo, M., Tang, F. H., Pasut, C. & Maggi, F. SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014. Sci. Data 8, 263 (2021).

  40. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE https://doi.org/10.1371/journal.pone.0169748 (2017).

  41. de Sousa, L. M. et al. SoilGrids 2.0: producing quality-assessed soil information for the globe. Soil Discuss. 1, 10.5194 (2020).

    Google Scholar 

  42. Zhang, Y., Schaap, M. G. & Zha, Y. A high-resolution global map of soil hydraulic properties produced by a hierarchical parameterization of a physically based water retention model. Water Resour. Res. 54, 9774–9790 (2018).

    Article  ADS  Google Scholar 

  43. Brooks, R. H. & Corey, A. T. Hydraulic properties of porous media and their relation to drainage design. Trans. ASABE 7, 26–28 (1964).

    Article  Google Scholar 

  44. Dai, Y. et al. A global high-resolution data set of soil hydraulic and thermal properties for land surface modeling. J. Adv. Model. 11, 2996–3023 (2019).

    Google Scholar 

  45. Tang, F. H. & Maggi, F. Pesticide mixtures in soil: a global outlook. Environ. Res. Lett. 16, 044051 (2021).

    ADS  CAS  Google Scholar 

  46. Gruber, A., Scanlon, T., Schalie, R., Wagner, W. & Dorigo, W. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 11, 717–739 (2019).

    Article  ADS  Google Scholar 

  47. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    Article  ADS  Google Scholar 

  48. Dorigo, W. A. et al. Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone J. https://doi.org/10.2136/vzj2012.0097 (2013).

  49. Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  50. Menne, M. J. et al. Global Historical Climatology Network—Daily (GHCN-Daily), Version 3. NOAA/National Climatic Data Center https://doi.org/10.7289/V5D21VHZ (2018).

  51. Andreadis, K. M., Schumann, G. J. P. & Pavelsky, T. A simple global river bankfull width and depth database. Water Resour. Res. 49, 7164–7168 (2013).

    Article  ADS  Google Scholar 

  52. Harrigan, S. et al. GloFAS-ERA5 operational global river discharge reanalysis 1979–present. Earth Syst. Sci. Data 12, 2043–2060 (2020).

    Article  ADS  Google Scholar 

  53. Maggi, F., Tang, F. H., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).

  54. Baker, N. T. Estimated Annual Agricultural Pesticide Use by Major Crop or Crop Group for States of the Conterminous United States,1992–2016. US Geological Survey https://water.usgs.gov/nawqa/pnsp/usage/maps/index.php (2018).

  55. FAO. Pesticides Use (FAOSTAT, accessed 2019); https://www.fao.org/faostat/en/#data/RP (accessed 2019).

  56. ISAAA’s GM Approval Database (ISAAA, 2018); http://www.isaaa.org/gmapprovaldatabase/

  57. PLANT EU Pesticides Database (European Commission, 2016); https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/start/screen/active-substances.

  58. Watts, M. PAN International Consolidated List of Banned Pesticides. Pesticide Network Action International http://pan-international.org/pan-international-consolidated-list-of-banned-pesticides/ (2019).

  59. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Google Scholar 

  60. Maggi, F., la Cecilia, D., Tang, F. H. & McBratney, A. The global environmental hazard of glyphosate use. Sci. Total Environ. 717, 137167 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  61. Trevisan, M., Di Guardo, A. & Balderacchi, M. An environmental indicator to drive sustainable pest management practices. Environ. Model. Softw. 24, 994–1002 (2009).

    Article  Google Scholar 

  62. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

  63. Dorigo, W. A. et al. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 15, 1675–1698 (2011).

    Article  ADS  Google Scholar 

  64. Rodell, M. et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    Article  ADS  Google Scholar 

  65. Hong, Y. & Adler, R. F. Estimation of global SCS curve numbers using satellite remote sensing and geospatial data. Int. J. Remote Sens. 29, 471–477 (2008).

    Article  Google Scholar 

  66. Edwards, M. Data Announcement 88-MGG-02: Digital Relief of the Surface of the Earth (NOAA, 988).

  67. Li, M. et al. The carbon flux of global rivers: a re-evaluation of amount and spatial patterns. Ecol. Indic. 80, 40–51 (2017).

    Article  CAS  Google Scholar 

  68. Corcoran, S. et al. Pesticides in surface waters in argentina monitored using polar organic chemical integrative samplers. Bull. Environ. Contam. Toxicol. 104, 21–26 (2020).

  69. Rico, A. et al. Ecological risk assessment of pesticides in urban streams of the Brazilian Amazon. Chemosphere 291, 132821 (2022).

  70. Acayaba, R. D. A. et al. Occurrence of pesticides in waters from the largest sugar cane plantation region in the world. Environ. Sci. Pollut. Res. 28, 9824–9835 (2021).

  71. Fang, W. et al. A critical review of synthetic chemicals in surface waters of the US, the EU and China. Environ. Int. 131, 104994 (2019).

  72. Xu, M. et al. Occurrence and ecological risk of pharmaceuticals and personal care products (PPCPs) and pesticides in typical surface watersheds, China. Ecotoxicol. Environ. Saf. 175, 289–298 (2019).

  73. Eissa, F., Al-Sisi, M. & Ghanem, K. Occurrence, human health, and ecotoxicological risk assessment of pesticides in surface waters of the River Nile’s Rosetta Branch, Egypt. Environ. Sci. Pollut. Res. 28, 55511–55525 (2021).

  74. Sarker, S. et al. Level of pesticides contamination in the major river systems: A review on South Asian countries perspective. Heliyon 7, e07270 (2021).

Download references

Acknowledgements

We thank A. Porporato for advice on the hydrological modelling and general overview of the manuscript structure and writing and A. McBratney for advice on soil quality and general editing of the manuscript. The authors acknowledge the use of the National Computational Infrastructure which is supported by the Australian Government and accessed through the Sydney Informatics Hub HPC Allocation Scheme, which in turn is supported by the Office of the Deputy Vice-Chancellor (Research) and through the NCMAS Allocation Scheme granted to F.M. (NCMAS-2021-78). The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of the FAO.

Author information

Authors and Affiliations

  1. Environmental Engineering, School of Civil Engineering, The University of Sydney, Sydney, New South Wales, Australia

    Federico Maggi & Fiona H. M. Tang

  2. Sydney Institute of Agriculture, The University of Sydney, Sydney, New South Wales, Australia

    Federico Maggi

  3. School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia

    Fiona H. M. Tang

  4. Statistics Division, Food and Agriculture Organization of the United Nations, Rome, Italy

    Francesco N. Tubiello

Authors
  1. Federico Maggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fiona H. M. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco N. Tubiello
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.M. conceptualized the main research subject and developed the modelling methods. F.H.M.T. curated all datasets for pre- and postmodelling analyses. F.N.T. provided contextual information on several aspects of datasets used for modelling and analyses. F.M., F.H.M.T. and F.N.T. equally contributed to data interpretation and writing of the manuscript.

Corresponding author

Correspondence to Federico Maggi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Stefan Reichenberger, Matthias Gassmann 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 figures and tables

Extended Data Fig. 1 Schematics of the assessment framework.

Process accounting in: (a) direct runoff and overland flow, (b) land surface; (c) belowground; and (d) river reaches.

Extended Data Fig. 2 Mass balance at the catchment land.

(a) Time sequence of PAS budget at the catchment surface and root zone (RZ, 0 to 100 cm) expressing losses (blue) and residue (red) as a percent of the cumulative PAS input mass, with an annual input of 0.94 Tg. (b) Contributions to losses from the catchment surface and RZ. (c) Contributions to residue in RZ. Percentages in parentheses refer to the end of the 30-year simulation, while the percent variability is expressed as one standard deviation (68% CI) across all PAS (see tables of the land and river budges of individual PAS distributed in ”Data and code availability”). The light grey shaded area represents the time frame of near-steady state where the relative contributions have an annual rate of change smaller than 5% relative to the previous year.

Source data

Extended Data Fig. 3 Mass balance within rivers.

Time sequence of PAS budget in rivers expressing losses (blue) and residue (red) as a percent of the cumulative PAS yield from land into rivers at an average annual mass of 0.73 Gg. Percentages in parentheses refer to the end of the 30-year simulation, while the percent variability is expressed as one standard deviation (68% CI) across all PAS (see tables of the land and river budges of individual PAS distributed in ”Data and code availability”). The grey shaded area represents the time frame of near-steady state where all represented variables have an annual rate of change smaller than 5% relative to the previous year.

Source data

Extended Data Fig. 4 Pesticide land budget within catchments.

Geographic distribution of: (a) to (d) contributions of PAS losses from land; and (e) to (h) contributions of PAS residue in the root zone (0 to 100 cm). All contributions are expressed as percent of the 30-year cumulative PAS input mass at an annual rate of 0.94 Tg.

Source data

Extended Data Fig. 5 Pesticide concentration in rivers.

Geographic location of monitoring stations of PAS concentrations in rivers in (a) Canada and the USA, (b) the EU, and (c) Australia. Locations may represent multiple stations when in close proximity (see Supplementary Table S4 for station aggregation and station-specific data density). (d) modelled versus observed average concentrations of PAS at locations in the USA, Canada, the EU, and Australia. Dark coloured markers above the detection concentration highlight observations exceeding the Regulatory Threshold Limits (RTLs) in Schulz at et., 2021 (reported in Supplementary Table S1).

Source data

Extended Data Fig. 6 PAS and Regulatory Threshold Limits (RTL).

River reaches in which the estimated concentration of individual PAS exceeded RTL values at least once during the simulated 30 years. The RTLs for aquatic invertebrates, aquatic plants, and fish from Schulz at et., (2021) are reported in Supplementary Table S1.

Source data

Extended Data Table 1 Pesticide land budget in the catchments of North America
Full size table
Extended Data Table 2 PAS concentration across multiple rivers and countries
Full size table
Extended Data Table 3 Analysis of principal and confounding contributions to PAS residue in soil (RES) and discharge to oceans (DIS)
Full size table

Supplementary information

Source data

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

Maggi, F., Tang, F.H.M. & Tubiello, F.N. Agricultural pesticide land budget and river discharge to oceans. Nature 620, 1013–1017 (2023). https://doi.org/10.1038/s41586-023-06296-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06296-x

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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