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.

Climate and land-use changes drive biodiversity turnover in arthropod assemblages over 150 years

Abstract

Long-term studies are essential to understand the impacts of global changes on the multiple facets of biological diversity. Here, we use distribution data for over 600 species of arthropods collected over 150 years from locations across Italy and test how multiple environmental stressors (climate, land use and human population density) influenced assemblage composition and functionality. By carefully reconstructing the temporal changes in these stressors, we explicitly tested how environmental changes can determine the observed changes in taxonomic and functional diversity. We found that rapid changes in precipitation destabilize the assemblages and maximize colonization and extinction rates, especially when coupled with changes in human population density (for taxonomy) or temperature (for functionality). Higher microclimatic heterogeneity increases the stability of biodiversity by reducing taxonomic and functional loss. Finally, changes in natural habitats increased colonization, influencing taxonomic nestedness and functional replacement. The integration of long-term datasets combining distributions, climate and traits may deepen our understanding of the processes underlying biodiversity responses to global-scale drivers.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Potential effects of environmental changes on the α- and β-diversity of assemblages.
Fig. 2: Temporal changes in climate, land use and human population density over the entire time series (1859–2003 ce).
Fig. 3: Density plots of the posterior distributions for the relationships between the rates of change of β-diversity, turnover and nestedness and the candidate environmental drivers.
Fig. 4: Relationships between β-diversity and the environmental drivers returning significant interactions.
Fig. 5: Density plots of the posterior distributions for the relationships between the rates of change in Dgain and Dloss and candidate environmental drivers.

Data availability

The data used to run the analyses are available at https://doi.org/10.6084/m9.figshare.14748057. Source data are provided with this paper.

Code availability

The R codes used to run the analyses are available at https://doi.org/10.6084/m9.figshare.14748057.

References

  1. Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).

    CAS  Article  PubMed  Google Scholar 

  3. Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).

    Google Scholar 

  4. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    CAS  Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  6. Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).

    Article  Google Scholar 

  8. Falaschi, M., Manenti, R., Thuiller, W. & Ficetola, G. F. Continental‐scale determinants of population trends in European amphibians and reptiles. Glob. Change Biol. 25, 3504–3515 (2019).

    Article  Google Scholar 

  9. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS  Article  PubMed  Google Scholar 

  10. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Jarzyna, M. A. & Jetz, W. Detecting the multiple facets of biodiversity. Trends Ecol. Evol. 31, 527–538 (2016).

    Article  PubMed  Google Scholar 

  12. Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).

    CAS  Article  PubMed  Google Scholar 

  13. Bell, J. R. et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century. Glob. Change Biol. 25, 1982–1994 (2019).

    Article  Google Scholar 

  14. Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).

    Article  PubMed  Google Scholar 

  15. van Strien, A. J., van Swaay, C. A., van Strien-van Liempt, W. T., Poot, M. J. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234, 116–122 (2019).

    Article  Google Scholar 

  16. Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—a macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).

    Google Scholar 

  18. Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).

    CAS  Article  PubMed  Google Scholar 

  19. Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).

    Article  Google Scholar 

  20. Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).

    Article  Google Scholar 

  21. Kondratyeva, A., Grandcolas, P. & Pavoine, S. Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution. Biol. Rev. 94, 1317–1337 (2019).

    Article  PubMed  Google Scholar 

  22. Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non‐native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).

    Article  Google Scholar 

  23. WallisDeVries, M. F. & van Swaay, C. A. A nitrogen index to track changes in butterfly species assemblages under nitrogen deposition. Biol. Conserv. 212, 448–453 (2017).

    Article  Google Scholar 

  24. Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).

    CAS  Article  PubMed  Google Scholar 

  25. Sgardeli, V., Zografou, K. & Halley, J. M. Climate change versus ecological drift: assessing 13 years of turnover in a butterfly community. Basic Appl. Ecol. 17, 283–290 (2016).

    Article  Google Scholar 

  26. van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2019).

    Article  CAS  Google Scholar 

  27. Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).

    CAS  Article  PubMed  Google Scholar 

  28. Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).

    Article  PubMed  Google Scholar 

  29. Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).

    CAS  Article  PubMed  Google Scholar 

  30. Marta, S. et al. ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna. Sci. Data 6, 195 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Koleff, P., Gaston, K. J. & Lennon, J. T. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).

    Article  Google Scholar 

  32. Legendre, P. A temporal beta‐diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecol. Evol. 9, 3500–3514 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Suggit, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).

    Article  Google Scholar 

  34. Baselga, A., Bonthoux, S. & Balent, G. Temporal beta diversity of bird assemblages in agricultural landscapes: land cover change vs. stochastic processes. PLoS ONE 10, e0127913 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).

    Google Scholar 

  36. Mason, N. W., de Bello, F., Mouillot, D., Pavoine, S. & Dray, S. A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients. J. Veg. Sci. 24, 794–806 (2013).

    Article  Google Scholar 

  37. Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).

  38. Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).

    Article  Google Scholar 

  39. Brunetti, M., Maugeri, M., Monti, F. & Nanni, T. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345–381 (2006).

    Article  Google Scholar 

  40. Terzago, S., von Hardenberg, J., Palazzi, E. & Provenzale, A. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models. Cryosphere 11, 1625–1645 (2017).

    Article  Google Scholar 

  41. Beniston, M. et al. The European mountain cryosphere: a review of its current state, trends and future challenges. Cryosphere 12, 759–794 (2018).

    Article  Google Scholar 

  42. Wang, J. et al. Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. 11, 528 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with nonstationary climate–fire models. Nat. Commun. 9, 3821 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jacobson, A. R., Provenzale, A., von Hardenberg, A., Bassano, B. & Festa-Bianchet, M. Climate forcing and density dependence in a mountain ungulate population. Ecology 85, 1598–1610 (2004).

    Article  Google Scholar 

  45. Imperio, S., Bionda, R., Viterbi, R. & Provenzale, A. Climate change and human disturbance can lead to local extinction of Alpine rock ptarmigan: new insight from the Western Italian Alps. PLoS ONE 8, e81598 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hoffmann, S., Beierkuhnlein, C., Field, R., Provenzale, A. & Chiarucci, A. Uniqueness of protected areas for conservation strategies in the European Union. Sci. Rep. 8, 6445 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Article  Google Scholar 

  48. Queiroz, C., Beilin, R., Folke, C. & Lindborg, R. Farmland abandonment: threat or opportunity for biodiversity conservation? A global review. Front. Ecol. Environ. 12, 288–296 (2014).

    Article  Google Scholar 

  49. Falcucci, A., Maiorano, L. & Boitani, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).

    Article  Google Scholar 

  50. Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. Ranganathan, S., Swain, R. B. & Sumpter, D. J. T. The demographic transition and economic growth: implications for development policy. Palgrave Commun. 1, 15033 (2015).

    Article  Google Scholar 

  52. Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience 53, 941–952 (2003).

    Article  Google Scholar 

  53. Lacasella, F. et al. From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability. Ecol. Appl. 27, 575–588 (2017).

    Article  PubMed  Google Scholar 

  54. Ficetola, G. F. & Maiorano, L. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia 181, 683–693 (2016).

    Article  PubMed  Google Scholar 

  55. Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).

    CAS  Article  PubMed  Google Scholar 

  56. Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).

    Article  PubMed  Google Scholar 

  59. Poff, N. L. et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change 6, 25–34 (2017).

    Article  Google Scholar 

  60. Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).

    Article  PubMed  Google Scholar 

  61. Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. Galland, T. et al. Colonization resistance and establishment success along gradients of functional and phylogenetic diversity in experimental plant communities. J. Ecol. 107, 2090–2104 (2019).

    Article  Google Scholar 

  63. Lister, A. M. et al. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).

    Article  PubMed  Google Scholar 

  64. Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. B 345, 101–118 (1994).

    CAS  Article  Google Scholar 

  65. Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).

    Article  Google Scholar 

  66. Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019).

  67. Chazdon, R. L., Colwell, R. K., Denslow, J. S. & Guariguata, M.R. in Forest Biodiversity Research, Monitoring and Modeling: Conceptual Background and Old World Case Studies (eds. Dallmeir, F. & Cominsky, J. A.) 285–309 (Parthenon, 1998).

  68. Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).

    Article  Google Scholar 

  69. van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).

    Article  Google Scholar 

  70. Osborn, T. J. & Jones, P. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst. Sci. Data 6, 61–68 (2014).

    Article  Google Scholar 

  71. New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000).

    Article  Google Scholar 

  72. Brunetti, M. et al. Projecting north eastern Italy temperature and precipitation secular records onto a high resolution grid. Phys. Chem. Earth. 40, 9–22 (2012).

    Article  Google Scholar 

  73. Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. High-resolution temperature climatology for Italy: interpolation method intercomparison. Int. J. Climatol. 34, 1278–1296 (2014).

    Article  Google Scholar 

  74. Crespi, A., Brunetti, M., Lentini, G. & Maugeri, M. 1961–1990 high-resolution monthly precipitation climatologies for Italy. Int. J. Climatol. 38, 878–895 (2018).

    Article  Google Scholar 

  75. Peterson, T. C. et al. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517 (1998).

    Article  Google Scholar 

  76. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  77. Burnham, K. & Anderson, D. Model Selection and Multi-model Inference (Springer, 2002).

  78. Blonder, B & Harris, D. J. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12 (2019).

  79. Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n‐dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).

    Article  Google Scholar 

  80. Barros, C., Thuiller, W., Georges, D., Boulangeat, I. & Münkemüller, T. N‐dimensional hypervolumes to study stability of complex ecosystems. Ecol. Lett. 19, 729–742 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).

    Article  PubMed  Google Scholar 

  82. Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Community Ecol. 19, 77–83 (2018).

    Article  Google Scholar 

  83. Signorell, A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.40 (2021).

  84. Maclean, I. M. D., Suggitt, A. J., Wilson, R. J., Duffy, J. P. & Bennie, J. J. Fine-scale climate change: modelling fine-scale spatial variation in biologically meaningful rates of warming. Glob. Change Biol. 23, 256–268 (2017).

    Article  Google Scholar 

  85. Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article  Google Scholar 

  86. Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991).

    Article  Google Scholar 

  87. Bivand, R. S. & Wong, D. W. Comparing implementations of global and local indicators of spatial association. Test 27, 716–748 (2018).

    Article  Google Scholar 

  88. Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).

    Article  Google Scholar 

  89. Bivand, R. S., Gómez-Rubio, V. & Rue, H. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31 (2015).

    Google Scholar 

  90. Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).

    Article  PubMed  Google Scholar 

  91. R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

  92. Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We thank D. O’Brien for the revision of an early version of the manuscript. S.M. and G.F.F. are funded by the European Research Council under the European Community’s Horizon 2020 Programme, grant agreement no. 772284 (‘IceCommunities—Reconstructing community dynamics and ecosystem functioning after glacial retreat’).

Author information

Authors and Affiliations

Authors

Contributions

S.M., G.F.F. and R.M. designed the study. M.B. and A.P. associated climatic information to each distribution record, while S.M. retrieved the distribution and trait data and performed the analyses. S.M. and G.F.F. led the writing with substantial contributions from all the other authors.

Corresponding author

Correspondence to Silvio Marta.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Alistair Auffret and Marta Jarzyna for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Relationships between the rate of change of precipitation at slower, medium and faster changes of the human population density and taxonomic indices (βsim, βsne, Dgain, Dloss).

Relationships between taxonomic indices and the rate of change of precipitation (mm/year) at slower (−0.45), medium (0.72) and faster (1.82) changes of the human population density (cube-root transformed; (inhabitants/km2/year)1/3). In each plot, the thick red line represents the average predicted relationship on the link scale, while the grey lines represent 500 samples of the posterior distribution. a–c, turnover (βsim). d–f, nestedness (βsne). g–i, standardized gain (Dgain). j–l, standardized loss (Dloss).

Source data

Extended Data Fig. 2 Relationships between the rate of change of temperature with negative, stable and positive rate of changes of precipitation and functional indices (βsim, βsne, Dgain, Dloss).

Relationships between functional indices and the rate of change of temperature (°C/year) with negative (−10.06), stable (−1.96) and positive (5.4) rates of change in precipitation (mm/year). In each plot, the thick red line represents the average predicted relationship on the link scale, while the grey lines represent 500 samples of the posterior distribution. a–c, turnover (βsim). d–f, nestedness (βsne). g–i, standardized gain (Dgain). j–l, standardized loss (Dloss).

Source data

Supplementary information

Supplementary Information

Supplementary Table 1 and Note 1.

Reporting Summary

Supplementary Data 1

Trait dataset.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marta, S., Brunetti, M., Manenti, R. et al. Climate and land-use changes drive biodiversity turnover in arthropod assemblages over 150 years. Nat Ecol Evol 5, 1291–1300 (2021). https://doi.org/10.1038/s41559-021-01513-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01513-0

Further reading

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