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:

Limited protection and ongoing loss of tropical cloud forest biodiversity and ecosystems worldwide

Nature Ecology & Evolution volume 5, pages 854–862 (2021)Cite this article

Subjects

Abstract

Tropical cloud forests (TCFs) are one of the world’s most species- and endemism-rich terrestrial ecosystems. TCFs are threatened by direct human pressures and climate change, yet the fate of these extraordinary ecosystems remains insufficiently quantified. With discussions of the post-2020 biodiversity framework underway, TCFs are a defining test case of the success and promise of recent policy targets and their associated mechanisms to avert the global biodiversity crisis. Here we present a global assessment of the recent status and trends of TCFs and their biodiversity and evaluate the efficacy of current protection measures. We find that cloud forests occupied 0.4% of the global land surface in 2001 and harboured ~3,700 species of birds, mammal, amphibians and tree ferns (~15% of the global diversity of those groups), with half of those species entirely restricted to cloud forests. Worldwide, ~2.4% of cloud forests (in some regions, more than 8%) were lost between 2001 and 2018, especially in readily accessible places. While protected areas have slowed this decline, a large proportion of loss in TCF cover is still occurring despite formal protection. Increased conservation efforts are needed to avert the impending regional or global demise of TCFs and their unique biodiversity.

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 distribution of TCFs, their associated biodiversity and recent area loss.
Fig. 2: TCF species composition, area loss by PA status and biodiversity implications across main continental regions.
Fig. 3: Uniqueness in species composition of TCF-associated species between TCF regions.
Fig. 4: TCF-cover loss for PA status and accessibility level.
Fig. 5: Association between countries’ TCF-area loss and their respective stewardship for endemic species.

Similar content being viewed by others

Data availability

Biodiversity data related to this study are available at Map of Life (www.mol.org). Cloud forest predictions are available at http://www.earthenv.org/cloudforest.

Code availability

Codes are available at: https://gitlabext.wsl.ch/karger/cloudforests.

References

  1. Bruijnzeel, L. A., Scatena, F. N. & Hamilton, L. S. (eds) Tropical Montane Cloud Forests: Science for Conservation and Management (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384

  2. Mulligan, M. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 14–38 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.004

  3. Doumenge, C., Gilmour, D., Pérez, M. R. & Blockhus, J. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 24–37 (Springer-Verlag, 1995).

  4. Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).

    Article  Google Scholar 

  5. Bruijnzeel, L. A., Mulligan, M. & Scatena, F. N. Hydrometeorology of tropical montane cloud forests: emerging patterns. Hydrol. Process. 25, 465–498 (2011).

    Article  Google Scholar 

  6. Gentry, A. H. Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos 63, 19–28 (1992).

    Article  Google Scholar 

  7. Foster, P. The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Sci. Rev. 55, 73–106 (2001).

    Article  Google Scholar 

  8. Hamilton, L. S., Juvik, J. O. & Scatena, F. N. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 1–18 (Springer-Verlag, 1995).

  9. Ponce-Reyes, R. et al. Vulnerability of cloud forest reserves in Mexico to climate change. Nat. Clim. Change 2, 448–452 (2012).

    Article  Google Scholar 

  10. Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: challenges to conservation. BMC Ecol. 12, 1 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gould, W. A., González, G. & Rivera, G. C. Structure and composition of vegetation along an elevational gradient in Puerto Rico. J. Veg. Sci. 17, 653–664 (2006).

    Article  Google Scholar 

  12. Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12 (2014).

    Article  Google Scholar 

  14. Jarvis, A. & Mulligan, M. The climate of cloud forests. Hydrol. Process. 25, 327–343 (2011).

    Article  Google Scholar 

  15. Scatena, F. N., Bruijnzeel, L. A., Bubb, P. & Das, S. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 3–13 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.003

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

    Article  CAS  PubMed  Google Scholar 

  17. Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).

    Article  Google Scholar 

  18. Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).

    Article  PubMed  Google Scholar 

  19. Gillespie, R. G. et al. Long-distance dispersal: a framework for hypothesis testing. Trends Ecol. Evol. 27, 47–56 (2012).

    Article  PubMed  Google Scholar 

  20. Kreft, H., Jetz, W., Mutke, J. & Barthlott, W. Contrasting environmental and regional effects on global pteridophyte and seed plant diversity. Ecography 33, 408–419 (2010).

    Article  Google Scholar 

  21. Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Venter, Z. S., Cramer, M. D. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lawton, R. O., Nair, U. S., Pielke, R. A. & Welch, R. M. Climatic impact of tropical lowland deforestation on nearby montane cloud forests. Science 294, 584–587 (2001).

    Article  CAS  PubMed  Google Scholar 

  24. Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Guo, W.-Y. et al. Half of the world’s tree biodiversity is unprotected and is increasingly threatened by human activities. Preprint at bioRxiv https://doi.org/10.1101/2020/04.21.052464 (2020).

  26. Helmer, E. H. et al. Neotropical cloud forests and páramo to contract and dry from declines in cloud immersion and frost. PLoS ONE 14, e0213155 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  28. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).

  30. Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Beusekom, A. E. V., González, G. & Scholl, M. A. Analyzing cloud base at local and regional scales to understand tropical montane cloud forest vulnerability to climate change. Atmos. Chem. Phys. 17, 7245–7259 (2017).

    Article  Google Scholar 

  32. Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Gross, J. E., Goetz, S. J. & Cihlar, J. Application of remote sensing to parks and protected area monitoring: introduction to the special issue. Remote Sens. Environ. 113, 1343–1345 (2009).

    Article  Google Scholar 

  34. Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Di Minin, E. & Toivonen, T. Global protected area expansion: creating more than paper parks. BioScience 65, 637–638 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wetzel, F. T., Beissmann, H., Penn, D. J. & Jetz, W. Vulnerability of terrestrial island vertebrates to projected sea-level rise. Glob. Change Biol. 19, 2058–2070 (2013).

    Article  Google Scholar 

  37. Keil, P., Storch, D. & Jetz, W. On the decline of biodiversity due to area loss. Nat. Commun. 6, 8837 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Rybicki, J. & Hanski, I. Species–area relationships and extinctions caused by habitat loss and fragmentation. Ecol. Lett. 16, 27–38 (2013).

    Article  PubMed  Google Scholar 

  39. Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (WW Norton & Company, 2016).

  42. Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513–1516 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Schulze, K., Malek, Ž. & Verburg, P. H. Towards better mapping of forest management patterns: a global allocation approach. For. Ecol. Manage. 432, 776–785 (2019).

    Article  Google Scholar 

  44. Curtis, C. A., Pasquarella, V. J. & Bradley, B. A. Landscape characteristics of non-native pine plantations and invasions in southern Chile. Austral Ecol. 44, 1213–1224 (2019).

    Article  Google Scholar 

  45. Aldrich, M., Billington, C., Edwards, M. & Laidlaw, R. A Global Directory of Tropical Montane Cloud Forests (WCMC, 1997).

  46. Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad https://doi.org/10.5061/dryad.kd1d4 (2017).

  48. Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) Open-File Report No. 2011-1073 (USGS, 2011).

  49. Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186 (2000).

    Article  Google Scholar 

  50. Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    Article  PubMed  Google Scholar 

  51. Karmalkar, A. V., Bradley, R. S. & Diaz, H. F. Climate Change scenario for Costa Rican montane forests. Geophys. Res. Lett. 35, L11702 (2008).

    Article  Google Scholar 

  52. Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Heikkinen, R. K. et al. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geogr. 30, 751–777 (2006).

    Article  Google Scholar 

  55. Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).

    Article  Google Scholar 

  56. Fithian, W. & Hastie, T. Finite-sample equivalence in statistical models for presence-only data. Ann. Appl. Stat. 7, 1917–1939 (2013).

    Article  PubMed  Google Scholar 

  57. Nelder, J. A. & Wedderburn, R. W. M. Generalized linear models. J. R. Stat. Soc. Ser. A 135, 370–384 (1972).

    Article  Google Scholar 

  58. Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability, 1990).

  59. Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012) .

    Article  Google Scholar 

  60. Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Article  Google Scholar 

  61. Aide, T. M. et al. Deforestation and reforestation of Latin America and the Caribbean (2001–2010). Biotropica 45, 262–271 (2013).

    Article  Google Scholar 

  62. Aide, T. M., Ruiz-Jaen, M. C. & Grau, H. R. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 101–109 (Cambridge Univ. Press, 2011).

  63. Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).

    Article  Google Scholar 

  64. Bubb, P. et al. Cloud Forest Agenda (UNEP-WCMC, 2004); https://www.unep-wcmc.org/cloud-forest-agenda

  65. Bockor, I. Analyse von Baumartenzusammensetzung und Bestandes-struckturen eines andinen Wolkenwaldes in Westvenezuela als Grundlagezur Wald-typengliederung. PhD thesis, Univ. Göttingen (1979).

  66. The State of the World’s Forests 2020: Forests, Biodiversity and People (FAO & UNEP, 2020); https://doi.org/10.4060/ca8642en

  67. Ribas, L. G., dos, S., Pressey, R. L., Loyola, R. & Bini, L. M. A global comparative analysis of impact evaluation methods in estimating the effectiveness of protected areas. Biol. Conserv. 246, 108595 (2020).

    Article  Google Scholar 

  68. Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2020).

    Article  PubMed  Google Scholar 

  69. Khandker, S., B. Koolwal, G. & Samad, H. Handbook on Impact Evaluation: Quantitative Methods and Practices (World Bank, 2009).

  70. Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209 (2014).

    Article  Google Scholar 

  71. Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Laurance, W. F. et al. Predictors of deforestation in the Brazilian Amazon. J. Biogeogr. 29, 737–748 (2002).

    Article  Google Scholar 

  73. Etter, A., McAlpine, C., Wilson, K., Phinn, S. & Possingham, H. Regional patterns of agricultural land use and deforestation in Colombia. Agric. Ecosyst. Environ. 114, 369–386 (2006).

    Article  Google Scholar 

  74. Geist, H. J. & Lambin, E. F. What drives tropical deforestation? LUCC Report Series No. 4 (LUCC, 2001).

  75. Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73 (2011).

    Article  Google Scholar 

  78. The IUCN Red List of Threatened Species version 2016.1 (IUCN, 2016); http://www.iucnredlist.org

  79. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    Article  CAS  PubMed  Google Scholar 

  80. Storch, D., Keil, P. & Jetz, W. Universal species–area and endemics–area relationships at continental scales. Nature 488, 78–81 (2012).

    Article  CAS  PubMed  Google Scholar 

  81. Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on species–area relationships. Ecol. Lett. 9, 215–227 (2006).

    Article  PubMed  Google Scholar 

  82. Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to A. Ranipeta and J. Wilshire for help with web visualizations. D.N.K. acknowledges funding from the Swiss Federal Research Institute for Forest, Snow and Landscape Research internal grant exCHELSA, ClimEx, the Joint BiodivERsA COFUND Call on ‘Biodiversity and Climate Change’ (project ‘FeedBaCks’) with the national funder Swiss National Foundation (20BD21_193907), the ERA-NET BiodivERsA–Belmont Forum with the national funder Swiss National Foundation (20BD21_184131) (part of the 2018 Joint call BiodivERsA–Belmont Forum call; project ‘FutureWeb’), and the Swiss Data Science Projects SPEEDMIND and COMECO. W.J. acknowledges support from NSF grant DEB-1441737, NASA grants 80NSSC17K0282 and 80NSSC18K0435, and the EO Wilson Biodiversity Foundation.

Author information

Authors and Affiliations

  1. Swiss Federal Research Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland

    Dirk Nikolaus Karger

  2. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Dirk Nikolaus Karger & Walter Jetz

  3. Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Dirk Nikolaus Karger & Walter Jetz

  4. Department of Systematic and Evolutionary Botany, University of Zurich, Zürich, Switzerland

    Michael Kessler

  5. Martin Luther University Halle-Wittenberg, Halle, Germany

    Marcus Lehnert

Authors
  1. Dirk Nikolaus Karger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Kessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcus Lehnert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Walter Jetz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.N.K. and W.J. conceived of and planned the study, W.J. prepared the biodiversity data, D.N.K. prepared the climatic layers and ran the models and analyses, M.L. and M.K. contributed the tree-fern data and D.N.K. and W.J. wrote the paper.

Corresponding authors

Correspondence to Dirk Nikolaus Karger or Walter Jetz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Reuben Clements, Johanna Eklund and the other, anonymous, reviewer(s) 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 Exact TCF region delineation polygons for which TCF areas and biodiversity data has been extracted.

Numbers indicate the ID of the respective TCF region. For TCF region names see Extended Data Fig. 1.

Extended Data Fig. 2 Probability of cloud forests occurrence P(TCF) with respect to elevation based on a random sample of 55,000 1 km2 raster cells.

a, Elevational distribution of TCF loss with elevation. b, In both graphs, red lines are visual characterizations of the overall elevational trend, based on lowess regression.

Extended Data Fig. 3 Uniqueness in species composition of strictly TCF confined species among TCF regions for the four groups assessed by using the minimum Jaccard dissimilarity index for a given region compared to all other regions.

High values indicate that a TCF region is unique in their assemblage, while low values indicate that it shares a large amount of species with at least one other region. Numbers indicate the ID of the TCF region. For TCF region names see Fig. S4. For turnover between TCF associated species, see Fig. 3.

Extended Data Fig. 4 TCF area of within a region, compared to the number of species which are associated, or strictly confined to TCFs in said region separately for each group.

Yellow = Mammals, blue = Birds, green = Tree ferns, red = Amphibians. Numbers indicate the TCF region ID.

Extended Data Fig. 5 Comparison of tropical cloud forest (TCF) loss before the establishment of a protected area (PA) and after its establishment for 483 PAs that got established between 2002 and 2018.

The color indicates the year in which a shift in designation happened according to the World Databank of Protected Areas (WDPA).

Extended Data Fig. 6 Percentage change in TCF area in protected areas based on their respective IUCN category for the years 2001 − 2018.

Letters indicate significantly different groups based on a Tukey post-hoc testing for differences between IUCN categories. Colors indicate different groups. NA = Not assigned, NAp = Not applicable, NR = Not reported.

Extended Data Fig. 7 Variation among TCF regions in overall 2001 area (bars), relative protection (dark part = protected fraction, white part = unprotected fraction), 2001-18 area loss and corresponding expected region-level species loss.

TCF regions are organized by their expected species loss (from top = highest loss, to bottom lowest loss). TCF region names are given with their IDs (see Fig. 1) and colored by major continent (see bottom legend). On the left, labels provide the names (ISO3166 country codes51) and the relative (percentage) area countries hold of each TCF region (i.e their respective TCF region stewardship). On the right, species loss expectations are based on an EAR z value of 0.5 (violet boxes). The violet shading indicates the range of all possible outcomes of the EAR when species loss for all key TCF groups analyzed (birds, mammals, amphibians, treeferns) using a range of z = 0.1 to z = 0.9.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karger, D.N., Kessler, M., Lehnert, M. et al. Limited protection and ongoing loss of tropical cloud forest biodiversity and ecosystems worldwide. Nat Ecol Evol 5, 854–862 (2021). https://doi.org/10.1038/s41559-021-01450-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01450-y

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