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:

Mangrove canopy height globally related to precipitation, temperature and cyclone frequency

Nature Geoscience volume 12, pages 40–45 (2019)Cite this article

Subjects

Abstract

Mangrove wetlands are among the most productive and carbon-dense ecosystems in the world. Their structural attributes vary considerably across spatial scales, yielding large uncertainties in regional and global estimates of carbon stocks. Here, we present a global analysis of mangrove canopy height gradients and aboveground carbon stocks based on remotely sensed measurements and field data. Our study highlights that precipitation, temperature and cyclone frequency explain 74% of the global trends in maximum canopy height, with other geophysical factors influencing the observed variability at local and regional scales. We find the tallest mangrove forests in Gabon, equatorial Africa, where stands attain 62.8 m. The total global mangrove carbon stock (above- and belowground biomass, and soil) is estimated at 5.03 Pg, with a quarter of this value stored in Indonesia. Our analysis implies sensitivity of mangrove structure to climate change, and offers a baseline to monitor national and regional trends in mangrove carbon stocks.

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: Global map of mangrove maximum canopy height and location of sampling sites (numbers) where in situ data were collected.
Fig. 2: Global distribution of maximum mangrove canopy height extent.
Fig. 3: Distribution of mangrove canopy height in the Gabon Estuary, on the Atlantic coast of Equatorial Africa.
Fig. 4: Latitudinal variation of maximum canopy height (m) and global environmental variables.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the Oak Ridge National Data Archive (ORNL DAAC; https://doi.org/10.3334/ORNLDAAC/1665) as GEOTIFF files and as an online webmapping tool (https://mangrovescience.earthengine.app/view/mangroveheightandbiomass). The in situ field data that have not been published previously are also available through the ORNL DAAC as .csv files listing individual tree measurements (https://doi.org/10.3334/ORNLDAAC/1665). The SRTM and ICESat/GLAS data sets used as input to generate the maps can be downloaded from https://lta.cr.usgs.gov/SRTM and https://nsidc.org/data/icesat/data.html, respectively. The global mangrove map26 is freely available at http://data.unep-wcmc.org/datasets/4. The tropical cyclone and SSS data are available from NOAA archives https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00834 (https://doi.org/10.7289/V5NK3BZP) and https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L3_SSS_CAP_MONTHLY_V4?ids=Platform&values=AQUARIUS_SAC-D (https://doi.org/10.5067/AQR40-3TMCS). The WorldClim data are available at http://worldclim.org/version2.

References

  1. McLeod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).

    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  Google Scholar 

  3. Alongi, D. M. Carbon cycling and storage in mangrove forests. Annu. Rev. Mar. Sci 6, 195–219 (2014).

    Article  Google Scholar 

  4. Bouillon, S. et al. Mangrove production and carbon sinks: a revision of global budget estimates. Global. Biogeochem. Cycles 22, GB2013 (2008).

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Polidoro, B. A. et al. The loss of species: mangrove extinction risk and geographic areas of global concern. PLoS ONE 5, e10095 (2010).

    Article  Google Scholar 

  7. Murdiyarso, D. et al. The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Change 5, 8–11 (2015).

    Article  Google Scholar 

  8. Richards, D. R. & Friess, D. A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl Acad. Sci. USA 113, 344–349 (2016).

    Article  Google Scholar 

  9. Krauss, K. W. et al. How mangrove forests adjust to rising sea level. New Phytol. 202, 19–34 (2014).

    Article  Google Scholar 

  10. Lovelock, C. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563 (2015).

    Article  Google Scholar 

  11. Sasmito, S. D., Murdiyarso, D., Friess, D. & Kurniato, S. Can mangroves keep pace with contemporary sea level rise? A global data review. Wetlands Ecol. Manage. 24, 263–278 (2016).

    Article  Google Scholar 

  12. Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738 (2016).

    Article  Google Scholar 

  13. Lovelock, C. E., Reuss, R. W. & Feller, I. C. CO2 efflux from cleared mangrove peat. PLoS ONE 6, e21279 (2011).

    Article  Google Scholar 

  14. Kauffman, J. B., Heider, C., Norfolk, J. & Payton, F. Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. Ecol. Appl. 24, 518–527 (2014).

    Article  Google Scholar 

  15. Hamilton, S. E. & Friess, D. A. Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nat. Clim. Change 8, 240–244 (2018).

    Article  Google Scholar 

  16. Hutchison, J., Manica, A., Swetnam, R., Balmford, A. & Spalding, M. Predicting global patterns in mangrove forest biomass. Conserv. Lett. 7, 233–240 (2014).

    Article  Google Scholar 

  17. Rovai, A. S. et al. Scaling mangrove aboveground biomass from site-level to continental-scale. Glob. Ecol. Biogeogr. 25, 286–298 (2016).

    Article  Google Scholar 

  18. Saenger, P. & Snedaker, S. C. Pantropical trends in mangrove above-ground biomass and annual litterfall. Oecologia 96, 293–299 (1993).

    Article  Google Scholar 

  19. Twilley, R. R. & Rivera-Monroy, V. H. in Coastal Wetlands: An Integrated Ecosystem Approach (eds Perillo, G. M. E. et al.) Ch. 23 (Elsevier, Amsterdam, 2009).

  20. Thom, B. G. in Mangrove Ecosystems in Australia (ed. Clough, B. F.) 3–17 (Australian National Univ. Press, Canberra, 1982).

  21. Danielson, T. M. et al. Assessment of Everglades mangrove forest resilience: implications for above-ground net primary productivity and carbon dynamics. Forest Ecol. Manage. 404, 115–125 (2017).

    Article  Google Scholar 

  22. Twilley, R. R. et al. in Mangrove Ecosystems: A Global Biogeographic Perspective (eds Rovera-Monroy, V. H. et al.) Ch. 5 (Springer, Basel, 2017).

  23. Simard, M., Rivera-Monroy, V. H., Mancera-Pineda, J. E., Castañeda-Moya, E. & Twilley, R. R. A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: application to Ciénaga Grande de Santa Marta, Colombia. Remote Sens. Environ. 112, 2131–2144 (2008).

    Article  Google Scholar 

  24. Castañeda-Moya, E., Twilley, R. R. & Rivera-Monroy, V. H. Allocation of biomass and net primary productivity of mangrove forests along environmental gradients in the Florida coastal Everglades, USA. Forest Ecol. Manage. 307, 226–241 (2013).

    Article  Google Scholar 

  25. Balke, T. & Friess, D. A. Geomorphic knowledge for mangrove restoration: a pan-tropical categorization. Earth Surf. Process. Landforms 41, 231–239 (2016).

    Article  Google Scholar 

  26. Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159 (2011).

    Article  Google Scholar 

  27. Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).

    Article  Google Scholar 

  28. Pan, Y., Birdsey, R. A., Phillips, O. L. & Jackson, R. B. The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 44, 593–622 (2013).

    Article  Google Scholar 

  29. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

  30. Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosyst. Health Sustain. 2, e01211 (2016).

    Article  Google Scholar 

  31. Farfán, L. M., D’Sa, E. J., Liu, K.-b & Rivera-Monroy, V. H. Tropical cyclone impacts on coastal regions: the case of the Yucatán and the Baja California Peninsulas, Mexico. Estuar. Coasts 37, 1388–1402 (2014).

    Article  Google Scholar 

  32. Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017).

    Article  Google Scholar 

  33. Terray, L. et al. Near-surface salinity as nature’s rain gauge to detect human influence on the tropical water cycle. J. Clim. 25, 958–977 (2012).

    Article  Google Scholar 

  34. Twilley, R. R. in Maximum Power: the Ideas and Applications of H.T. Odum (ed. Hall, C. A. S.) 43–62 (Univ. Press Colorado, Niwot, 1995).

  35. Kauffman, J. B. & Bhomia, R. K. Ecosystem carbon stocks of mangroves across broad environmental gradients in West-Central Africa: global and regional comparisons. PLoS ONE 12, e0187749 (2017).

    Article  Google Scholar 

  36. Rovai, A. S. et al. Global controls on carbon storage in mangrove soils. Nat. Clim. Change 8, 534–538 (2018).

    Article  Google Scholar 

  37. Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environ. Res. Lett. 13, 055002 (2018).

    Article  Google Scholar 

  38. Bouillon, S. Carbon cycle: storage beneath mangroves. Nat. Geosci. 4, 282–283 (2011).

    Article  Google Scholar 

  39. Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15, 257–265 (2017).

    Article  Google Scholar 

  40. Fatoyinbo, T., Feliciano, E. A., Lagomasino, D., Lee, S. K. & Trettin, C. Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta. Environ. Res. Lett. 13, 025012 (2017).

    Article  Google Scholar 

  41. Atwood, T. B. et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Change 7, 523–528 (2017).

    Article  Google Scholar 

  42. Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).

    Article  Google Scholar 

  43. Siikamäki, J., Sanchirico, J. N. & Jardine, S. L. Global economic potential for reducing carbon dioxide emissions from mangrove loss. Proc. Natl Acad. Sci. USA 109, 14369–14374 (2012).

    Article  Google Scholar 

  44. Twilley, R. R., Chen, R. H. & Hargis, T. Carbon sinks in mangroves and their implications to carbon budget of tropical coastal ecosystems. Water Air Soil Pollut. 64, 265–288 (1992).

    Article  Google Scholar 

  45. Feka, N. Z. & Ajonina, G. N. Drivers causing decline of mangrove in West-Central Africa: a review. Int. J. Biodiv. Sci. Ecosys. Serv. Manage. 7, 217–230 (2011).

    Article  Google Scholar 

  46. Omo-Irabor, O. O. et al. Mangrove vulnerability modelling in parts of Western Niger Delta, Nigeria using satellite images, GIS techniques and Spatial Multi-Criteria Analysis (SMCA). Environ. Monit. Assess. 178, 39–51 (2011).

    Article  Google Scholar 

  47. Hinson, A. L. et al. The spatial distribution of soil organic carbon in tidal wetland soils of the continental United States. Glob. Chang. Biol. 23, 5468–5480 (2017).

    Article  Google Scholar 

  48. Farr, T. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45, RG2004 (2007).

    Article  Google Scholar 

  49. Simard, M. et al. Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogramm. Eng. Remote Sens. 72, 299–311 (2006).

    Article  Google Scholar 

  50. Fatoyinbo, T. E., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique's mangrove, forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci. 113, G02S06 (2008).

    Article  Google Scholar 

  51. Fatoyinbo, T. E. & Simard, M. Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM. Int. J. Remote Sens. 34, 668–681 (2013).

    Article  Google Scholar 

  52. Lagomasino, D., Fatoyinbo, T., Lee, S. & Feliciano, E. A. Comparison of mangrove canopy height using multiple independent measurements from land, air, and space. Remote Sens. 8, 327 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Rodríguez, E., Morris, C. S. & Belz, E. A global assessment of the SRTM performance. Photogramm. Eng. Remote Sens. 72, 249–260 (2006).

    Article  Google Scholar 

  55. Stringer, C. E. et al. The Zambezi River Delta Mangrove Carbon Project: A Pilot Baseline Assessment for REDD+ Reporting and Monitoring. Final Report 1–56 (United States Forest Service, 2014).

  56. Trettin, C. C., Stringer, C. E. & Zarnoch, S. J. Composition, biomass and structure of mangroves within the Zambezi River Delta. Wetl. Ecol. Manag. 24, 173–186 (2015).

    Article  Google Scholar 

  57. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  58. Wang, X., Chao, Y., Shum, C. K., Yi, Y. & Fok, H. S. Comparison of two methods to assess ocean tide models. J. Atmos. Ocean. Technol. 29, 1159–1167 (2012).

    Article  Google Scholar 

  59. Yueh, S. H., Tang, W., Hayashi, A. K. & Lagerloef, G. S. E. L-band passive and active microwave geophysical model functions of ocean surface winds and applications to Aquarius retrieval. IEEE Trans. Geosci. Remote Sens. 51, 4619–4632 (2014).

    Article  Google Scholar 

  60. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. Proc. 9th Python Sci. Conf. 57–61 (2010).

  61. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project (2018); https://www.osgeo.org/projects/qgis/

  62. Bunting, P., Clewley, D., Lucas, R. M. & Gillingham, S. The Remote Sensing and GIS Software Library (RSGISLib). Comput. Geosci. 62, 216–226 (2014).

    Article  Google Scholar 

  63. Tange, O. GNU Parallel 2018 (2018); https://doi.org/10.5281/zenodo.1146014

  64. Aslan, A., Rahman, A. F., Warren, M. W. & Robeson, S. M. Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data. Remote Sens. Environ. 183, 65–81 (2016).

    Article  Google Scholar 

  65. Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Chang. Biol. 20, 3177–3190 (2014).

    Article  Google Scholar 

  66. Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).

    Article  Google Scholar 

  67. Hiraishi, T. et al. 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands. Methodological Guidance on Lands with Wet and Drained Soils, and Constructed Wetlands for Wastewater Treatment (IPCC, 2014).

  68. Komiyama, A., Ong, J. E. & Poungparn, S. Allometry, biomass, and productivity of mangrove forests: a review. Aquat. Bot. 89, 128–137 (2008).

    Article  Google Scholar 

  69. Castañeda-Moya, E. et al. Patterns of root dynamics in mangrove forests along environmental gradients in the Florida coastal Everglades, USA. Ecosystems 14, 1178–1195 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

This work was conducted by the Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech), under contract with the National Aeronautics and Space Administration (NASA). This work was funded by the NASA Land-Cover/Land-Use Change (LCLUC) Program project ‘Vulnerability Assessment of Mangrove Forest Regions of the Americas’, the NASA Carbon Monitoring System (CMS) project ‘Total Carbon Estimation in African Blue Carbon Ecosystems’ (14-CMS14-0028), the Florida Coastal Everglades Long-Term Ecological Research program (FCE-LTER; grants nos. DBI-0620409 and DEB-1237517) and the Department of Interior, South-Central Climate Science Center. We furthermore acknowledge funding by the United States Agency for International Development SilvaCarbon Program, the United States Department of Agriculture – Forest Service International Programs and the Center for International Forestry Research (CIFOR) Sustainable Wetlands Adaptation Mitigation (SWAMP) Program for field data collection in Mozambique and Bangladesh. The authors thank M. Rahman (Kyoto University, Japan), Z. Iqbal (SilvaCarbon Bangladesh and Bangladesh Forest Service), I. Ahmed (Bangladesh Forest Service) and the Bangladesh Forest Service for providing access to the Sundarbans Mangrove Carbon Assessment field data. The authors also thank the Agence Nationale des Parcs Nationaux (ANPN) and Centre National de la Recherche Scientifique et Technologique (CENAREST) in Gabon, in particular, K. Jeffery, A. Flore, K. Pambo and L. White for providing field permits and access to Pongara National Park for the validation of canopy heights. The authors thank the Ministerio del Ambiente, Energia y Telecomunicacion (MINAET) for providing the field permits for data collection in Costa Rica, and the Organization of Tropical Studies (Costa Rica), E. Medina (Instituto Venezolano de Investigaciones Cientificas, Venezuela), V.E. Mena Mosquera (Universidad Tecnológica del Chocó, Colombia), M.M. Pozo Cajas (Escuela Superior Politecnica del Litoral, Ecuador), J.E. Pineda Mancera (Universidad Nacional de Colombia, Colombia), M. Obiang (Université Omar Bongo, Gabon) and the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO, Mexico) for in situ field support. Thanks also go to N. Pinto and M. Denbina (JPL, Caltech), R. Aguilar (Vascular Plants of the Osa Peninsula, Costa Rica), A. Armstrong (United Space Research Association/NASA GSFC, USA), P. Montesano (Sigma Space Applications International/NASA GSFC, USA), G. Sun (University of Maryland and NASA GSFC, USA), A. Vega (AMBICOR, Costa Rica), J. Corcoran (JPL, Caltech, USA), C. Trettin (USDA Forest Service, USA), L. Duncanson (University of Maryland/NASA GSFC), A. Williams (Louisiana State University, USA), H. Tavera (MarViva, Colombia), P. Walfir Souza-Filho, M. Ferreira Cougo and R. Salum (Universidade Federal do Pará, Brazil), I. Longa (Agence Gabonaise d’Etudes et Observations Spaciales, Gabon), S.K. Lee (University of Maryland/GSFC, USA) and D. Lagomasino (University of Maryland/GSFC, USA) for help in the field. The authors thank X.C. Wang (JPL, Caltech) for providing the tidal range data and S.K. Lee, R. Lucas (University of Aberystwith, Wales) and D. Lagomasino for providing edits to the manuscript. Conservation International, through the Blue Carbon initiative, supported identification of knowledge gaps and local stakeholders.

Author information

Author notes

  1. Edward Castañeda-Moya

    Present address: Southeast Environmental Research Center, Florida International University, Miami, FL, USA

Authors and Affiliations

  1. Radar Science and Engineering Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Marc Simard, Charlotte Smetanka & Tom Van der Stocken

  2. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Lola Fatoyinbo & Nathan Thomas

  3. Department of Applied Geomatics, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Charlotte Smetanka

  4. Department of Oceanography and Coastal Sciences, College of the Coast and Environment, Lousiana State University, Baton Rouge, LA, USA

    Victor H. Rivera-Monroy & Edward Castañeda-Moya

  5. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Nathan Thomas

Authors
  1. Marc Simard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lola Fatoyinbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charlotte Smetanka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Victor H. Rivera-Monroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edward Castañeda-Moya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nathan Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tom Van der Stocken
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S. and L.F. conceived and designed the study. M.S. led the SRTM and ICESat/GLAS data processing, with assistance from C.S., L.F. and N.T. Field data processing and biomass model development were led by L.F., with assistance from M.S. Field experimental design and data collection were carried out by M.S., L.F., V.R.-M., E.C.-M. and N.T. The interpretation of data and generation of results were performed by M.S., L.F., C.S., V.R.-M., E.C-M., T.V.D.S. and N.T. The writing of the paper was led by M.S. and L.F., with input from all co-authors.

Corresponding authors

Correspondence to Marc Simard or Lola Fatoyinbo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–6 and Supplementary Tables 1–9.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simard, M., Fatoyinbo, L., Smetanka, C. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geosci 12, 40–45 (2019). https://doi.org/10.1038/s41561-018-0279-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-018-0279-1

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