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:

Global biodiversity loss from outsourced deforestation

Nature volume 639, pages 389–394 (2025)Cite this article

Subjects

Abstract

Globalization increasingly allows countries to externalize the environmental costs of land use, including biodiversity loss1. So far, we have a very incomplete understanding of how countries cause biodiversity loss outside their own borders through their demand for agricultural and forestry products grown in other countries2. Here we quantify the global range losses to forest vertebrates from 2001 to 2015 caused by deforestation attributable to 24 developed countries by means of their consumption of products obtained through global supply chains. We show that these driver countries are responsible for much greater cumulative range loss to species outside their own borders than within them. These international impacts were concentrated geographically, allowing us to map global hotspots of outsourced losses of biodiversity. Countries had the greatest external impacts on species occurring in nearby regions. However, in a few cases, developed countries also inflicted disproportionate harm on vertebrates in distant countries.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

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

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

Fig. 1: Consumers drive distant biodiversity loss through global agricultural and forestry supply chains.
Fig. 2: International and domestic contributions to global forest biodiversity loss.
Fig. 3: Hotspots of attributable biodiversity loss by country.
Fig. 4: Impacts to species decline with distance from a driver country.

Similar content being viewed by others

Data availability

Data on forest cover and forest loss from the Hansen Global Forest Change v.1.7 (2000–2019) dataset are available in an online repository at https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html (ref. 17). Land-use attribution data are available from the authors of ref. 20 on request. Those data were calculated using data from the MRIO database (https://worldmrio.com), the previously mentioned forest cover and loss data, FAOSTAT (https://www.fao.org/faostat) and EliScholar (https://elischolar.library.yale.edu/yale_fes_data), all of which are publicly available. Range map data are available at the following online repositories: BirdLife International (http://datazone.birdlife.org/species/requestdis) and IUCN (https://www.iucnredlist.org/resources/spatial-data-download). Processed species-level data are available at Zenodo (https://doi.org/10.5281/zenodo.14030743)77.

Code availability

The code necessary to replicate the analysis and figures can be found in the following GitHub repository: https://github.com/AlexWiebe/Outsourced-Biodiversity-Loss.

References

  1. Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).

    Article  ADS  PubMed  MATH  CAS  Google Scholar 

  2. Kuemmerle, T., Kastner, T., Meyfroidt, P. & Qin, S. in Telecoupling: Exploring Land-Use Change in a Globalised World (eds Friis, C. & Nielsen, J. O.) 281–302 (Palgrave Macmillan, 2019).

  3. Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).

    Article  PubMed  MATH  CAS  Google Scholar 

  4. Pimm, S. L., Russell, G. J., Gittleman, J. L. & Brooks, T. M. The future of biodiversity. Science 269, 347–350 (1995).

  5. Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States: assessing the relative importance of habitat destruction, alien species, pollution, overexploitation, and disease. Bioscience 48, 607–615 (1998).

    Article  Google Scholar 

  6. Grooten, M. & Almond, R. E. A. Living Planet Report—2018: Aiming Higher (WWF, 2018).

  7. Bjelle, E. L., Kuipers, K., Verones, F. & Wood, R. Trends in national biodiversity footprints of land use. Ecol. Econ. 185, 107059 (2021).

    Article  Google Scholar 

  8. Hoang, N. T. et al. Mapping potential conflicts between global agriculture and terrestrial conservation. Proc. Natl Acad. Sci. USA 120, e2208376120 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Wilting, H. C., Schipper, A. M., Bakkenes, M., Meijer, J. R. & Huijbregts, M. A. J. Quantifying biodiversity losses due to human consumption: a global-scale footprint analysis. Environ. Sci. Technol. 51, 3298–3306 (2017).

    Article  ADS  PubMed  CAS  Google Scholar 

  10. Malik, A., Lenzen, M. & Fry, J. Biodiversity impact assessments using nested trade models. Environ. Sci. Technol. 56, 7378–7380 (2022).

    Article  ADS  PubMed  MATH  CAS  Google Scholar 

  11. Kitzes, J. et al. Consumption-based conservation targeting: linking biodiversity loss to upstream demand through a global wildlife footprint. Conserv. Lett. 10, 531–538 (2017).

    Article  PubMed  MATH  Google Scholar 

  12. Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 23 (2017).

  13. Courchamp, F., Berec, L. & Gascoigne, J. Allee Effects in Ecology and Conservation (Oxford Univ. Press, 2008).

  14. Caughley, G. Directions in conservation biology. Conserv. Biol. 82, 195–210 (1994).

    MATH  Google Scholar 

  15. Soule, M. E. & Wilcove, B. A. (eds) Conservation Biology: An Evolutionary–Ecological Perspective (Sinauer, 1980).

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

    Article  PubMed  Google Scholar 

  17. Levers, C. & Müller, D. in Telecoupling: Exploring Land-Use Change in a Globalised World (eds Friis, C. & Nielsen, J. O.) 89–113 (Palgrave Macmillan, 2019).

  18. Mayer, A. L., Kauppi, P. E., Angelstam, P. K., Zhang, Y. & Tikka, P. M. Importing timber, exporting ecological impact. Science 308, 359–360 (2005).

    Article  PubMed  CAS  Google Scholar 

  19. Smith, S. B., Gotoh, T. & Greenwood, P. L. Current situation and future prospects for global beef production: overview of special issue. Asian-Australasian J. Anim. Sci. 31, 927–932 (2018).

    Article  MATH  CAS  Google Scholar 

  20. Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).

    Article  PubMed  MATH  Google Scholar 

  21. Grenyer, R. et al. Global distribution and conservation of rare and threatened vertebrates. Nature 444, 93–96 (2006).

    Article  ADS  PubMed  MATH  CAS  Google Scholar 

  22. Data Zone (BirdLife International); http://datazone.birdlife.org/species/requestdis (Accessed 31 January 2023).

  23. Red List of Threatened Species (IUCN, 2022); https://www.iucnredlist.org/resources/spatial-data-download (Accessed 31 January 2023).

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

    Article  ADS  PubMed  MATH  CAS  Google Scholar 

  25. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    Article  ADS  PubMed  CAS  Google Scholar 

  26. May, R. M. The future of biological diversity in a crowded world. Curr. Sci. 82, 1325–1331 (2002).

    MATH  Google Scholar 

  27. Andriamparany, J. N., Heritiana, J. T., Hänke, H., Kunz, S. & Schlecht, E. Market supply of livestock and animal products in north-eastern Madagascar—the role of the vanilla boom. Sci. Afr. 19, e01526 (2023).

  28. Hänke, H. et al. Socio-economic, Land Use and Value Chain Perspectives on Vanilla Farming in the SAVA Region (North-eastern Madagascar): The Diversity Turn Baseline Study (DTBS). (Georg-August-Universität Göttingen, 2018).

  29. Boone, C., Kaila, H. K. & Sahn, D. E. Posh spice or scary spice? The impacts of Madagascar’s vanilla boom on household well-being. SSRN Electron. J. https://doi.org/10.2139/ssrn.4085179 (2022).

  30. Burivalova, Z., Şekercioǧlu, Ç. H. & Koh, L. P. Thresholds of logging intensity to maintain tropical forest biodiversity. Curr. Biol. 24, 1893–1898 (2014).

    Article  PubMed  CAS  Google Scholar 

  31. Pendrill, F. et al. Disentangling the numbers behind agriculture-driven tropical deforestation. Science 377, eabm9267 (2022).

    Article  PubMed  CAS  Google Scholar 

  32. Kadoya, T., Takeuchi, Y., Shinoda, Y. & Nansai, K. Shifting agriculture is the dominant driver of forest disturbance in threatened forest species’ ranges. Commun. Earth Environ. 3, 108 (2022).

  33. Woodward, F. I., Lomas, M. R. & Kelly, C. K. Global climate and the distribution of plant biomes. Philos. Trans. R. Soc. B 359, 1465–1476 (2004).

    Article  MATH  CAS  Google Scholar 

  34. Mills, J. H. & Waite, T. A. Economic prosperity, biodiversity conservation, and the environmental Kuznets curve. Ecol. Econ. 68, 2087–2095 (2009).

  35. Andreoni, J. & Levinson, A. The simple analytics of the environmental Kuznets curve. J. Public Econ. 80, 269–286 (2001).

    Article  MATH  Google Scholar 

  36. Grossman, G. M. & Krueger, A. B. Economic growth and the environment. Q. J. Econ. 110, 353–377 (1995).

  37. Naidoo, R. & Adamowicz, W. L. Effects of economic prosperity on numbers of threatened species. Conserv. Biol. 15, 1021–1029 (2001).

    Article  MATH  Google Scholar 

  38. McPherson, M. A. & Nieswiadomy, M. L. Environmental Kuznets curve: threatened species and spatial effects. Ecol. Econ. 55, 395–407 (2005).

    Article  MATH  Google Scholar 

  39. Dietz, S. & Adger, W. N. Economic growth, biodiversity loss and conservation effort. J. Environ. Manag. 68, 23–35 (2003).

    Article  MATH  Google Scholar 

  40. Tevie, J., Grimsrud, K. M. & Berrens, R. P. Testing the environmental kuznets curve hypothesis for biodiversity risk in the US: a spatial econometric approach. Sustainability 3, 2182–2199 (2011).

    Article  Google Scholar 

  41. Otero, I. et al. Biodiversity policy beyond economic growth. Conserv. Lett. 13, e12713 (2020).

  42. Mozumder, P., Berrens, R. P. & Bohara, A. K. Is there an environmental Kuznets curve for the risk of biodiversity loss? J. Dev. Areas 39, 175–190 (2006).

    Article  MATH  Google Scholar 

  43. USA Location Map (Geographic.Media, 1970); https://geographic.media/north-america/usa/usa-maps/usa-location-map.

  44. Cattle & Beef Statistics & Information (USDA, 2022).

  45. Major Uses of Land in the United States, 2012 (USDA, 2012).

  46. Wunsch, N.-G. Palm Oil Consumption in the United States from 2000 to 2022 (Statista, 2023); https://www.statista.com/statistics/301032/palm-oil-consumption-united-states/#:~:text=Americans.

  47. Neate-Clegg, M. H. C. & Şekercioǧlu, Ç. H. Agricultural land in the Amazon basin supports low bird diversity and is a poor replacement for primary forest. Condor 122, duaa020 (2020).

  48. Ritchie, H., Spooner, F. & Roser, M. Forests and Deforestation (OurWorldinData, 2021); https://ourworldindata.org/forests-and-deforestation.

  49. Grain: World Markets and Trade (USDA, 2022) (Accessed on 31 August 2023).

  50. FAOSTAT: Crops and Livestock Products (FAO, 2022); https://www.fao.org/faostat/en/#data/QCL (Accessed on 31 August 2023).

  51. Classification Schemes (IUCN, 2022); https://www.iucnredlist.org/resources/classification-schemes (Accessed on 31 January 2023).

  52. Hansen, A. et al. Global humid tropics forest structural condition and forest structural integrity maps. Sci. Data 6, 232 (2019).

  53. Moran, D. & Wood, R. Convergence between the Eora, Wiod, Exiobase, and Openeu’s consumption-based carbon accounts. Econ. Syst. Res. 26, 245–261 (2014).

    Article  MATH  Google Scholar 

  54. Godar, J., Persson, U. M., Tizado, E. J. & Meyfroidt, P. Towards more accurate and policy relevant footprint analyses: tracing fine-scale socio-environmental impacts of production to consumption. Ecol. Econ. 112, 25–35 (2015).

    Article  Google Scholar 

  55. Lenzen, M. et al. Compiling and using input-output frameworks through collaborative virtual laboratories. Sci. Total Environ. 485–486, 241–251 (2014).

    Article  ADS  PubMed  MATH  Google Scholar 

  56. Ritchie, H. & Roser, M. Land Use (Our World in Data, 2019); https://ourworldindata.org/land-use.

  57. 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  ADS  PubMed  CAS  Google Scholar 

  58. Hijmans, R. terra: Spatial data analysis. R package version 1.7-29 (2023).

  59. Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).

    Article  MATH  Google Scholar 

  60. Pebesma, E. & Bivand, R. Spatial Data Science: With Applications in R (Chapman and Hall/CRC, 2023).

  61. Hijmans, R. raster: Geographic data analysis and modeling. R package version 3.6-20 (2023).

  62. Ross, N. fasterize: Fast polygon to raster conversion. R package version 1.0.4 (2022).

  63. Lamiguero, O. P. & Hijmans, R. rasterVis. R package version 0.51.5 (2023).

  64. Bivand, R. & Rundel, C. rgeos: Interface to geometry engine—open source (GEOS). R package version 0.6-4 (2023).

  65. Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the geospatial data abstraction library. R package version 1.6-7 (2023).

  66. Csárdi, G. et al. remotes: R package installation from remote repositories, including GitHub. R package version 2.4.2 (2021).

  67. Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  ADS  MATH  Google Scholar 

  68. Wickham, H., François, R., Henry, L., Müller, K. & Vaughan, D. dplyr: A grammar of data manipulation. R package version 1.1.2 (2023).

  69. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  70. Massicotte, P. & South, A. rnaturalearth: World map data from Natural Earth. R package version 0.3.3 (2023).

  71. South, A. rnaturalearthdata: World vector map data from Natural Earth used in rnaturalearth. R package version 0.1.0 (2017).

  72. South, A., Michael, S. & Massicotte, P. rnaturalearthhires: High resolution world vector map data from Natural Earth used in rnaturalearth. R package version 0.2.1 (2023).

  73. Gearty, W. & Chamberlain, S. rredlist: IUCN Red list client. R package version 0.7.1 (2022).

  74. Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: Draw geographical maps. R package version 3.4.2 (2023).

  75. South, A. rworldmap: A new R package for mapping global data. R J. 3, 35–43 (2011).

    Article  MATH  Google Scholar 

  76. Stahel, W. Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler (Vieweg+Teubner, 2008).

  77. Wiebe, R. A. & Wilcove, D. S. Processed species-level data for ‘Global biodiversity loss from outsourced deforestation’ [Data set]. Zenodo https://doi.org/10.5281/zenodo.14030743 (2024).

Download references

Acknowledgements

We would like to thank C. Tarnita, C. Riehl, S. Robinson and Y. Zeng for offering valuable perspectives that informed the design and analyses of the study; N.T. Hoang and K. Kanemoto for providing access to their data; and the High Meadows Foundation and Princeton University for their support of this work.

Author information

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    R. Alex Wiebe & David S. Wilcove

  2. Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    David S. Wilcove

  3. High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA

    David S. Wilcove

Authors
  1. R. Alex Wiebe
    View author publications

    Search author on:PubMed Google Scholar

  2. David S. Wilcove
    View author publications

    Search author on:PubMed Google Scholar

Contributions

R.A.W. conceived of the study and R.A.W. and D.S.W. developed the methodology. R.A.W. conducted the data analysis and wrote the initial draft, with both authors contributing to subsequent revisions.

Corresponding author

Correspondence to R. Alex Wiebe.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Jean-Francois Bastin, Holger Kreft, Manfred Lenzen 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 Cumulative absolute habitat loss attributable to each driver country.

Values shown are the cumulative contributions of each driver country to habitat loss of forest-obligate vertebrates (km2), separated by outsourced (red) and domestic (blue) components.

Extended Data Fig. 2 Cumulative domestic and outsourced range loss, shown together for direct comparison.

Values shown are total contributions of driver countries to habitat loss of forest-obligate vertebrates (units of species range equivalents), separated by domestic (blue) and outsourced (red) components. All countries have nonzero domestic losses except for Saudi Arabia, but those losses are very small in some countries and not visible in this figure.

Extended Data Fig. 3 Magnitude of international impacts relative to domestic impacts.

Values shown are the natural logarithm of the ratio of cumulative international to domestic range loss attributable to driver countries.

Extended Data Fig. 4 Median contributions to range loss across all individual species.

Values shown are the median contributions to species’ range loss of each driver country, as a percentage of the total range loss of those species, internationally (red) and domestically (blue).

Extended Data Fig. 5 Hotspots of attributable biodiversity loss by country (alphabetical order: Argentina - India).

Maps of the distribution of forest vertebrate biodiversity loss attributable via consumption-driven deforestation to 24 driver countries. Pixel values are the sum of species present in that pixel, weighted by the proportion of their ranges lost and attributable to a given driver country (units of species range equivalents). Credit: Made with Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 6 Hotspots of attributable biodiversity loss by country (alphabetical order: Indonesia - Saudi Arabia).

Maps of the distribution of forest vertebrate biodiversity loss attributable via consumption-driven deforestation to 24 driver countries. Pixel values are the sum of species present in that pixel, weighted by the proportion of their ranges lost and attributable to a given driver country (units of species range equivalents). Credit: Made with Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 7 Hotspots of attributable biodiversity loss by country (alphabetical order: Singapore - United States of America).

Maps of the distribution of forest vertebrate biodiversity loss attributable via consumption-driven deforestation to 24 driver countries. Pixel values are the sum of species present in that pixel, weighted by the proportion of their ranges lost and attributable to a given driver country (units of species range equivalents). Credit: Made with Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 8 Global hotspots of long-distance biodiversity loss attributable to all driver countries combined.

Species-country data are included if the magnitude of attributable range loss exceeds the predicted values given the distance between that species’ range and the driver country. Thus, this map reflects the global distribution of exceptionally strong impacts of countries given their distance to species. Credit: Made with Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 9 Distribution of outsourced impacts to Critically Endangered species.

The distribution shows frequencies of species-specific values of range loss attributable to the combined international effects of all driver countries, for all Critically Endangered species.

Supplementary information

Supplementary Information

Methods and discussion of a sensitivity analysis, testing the sensitivity of our primary results to potential errors in forest cover data by comparing them with those calculated using an alternative dataset.

Reporting Summary

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

Wiebe, R.A., Wilcove, D.S. Global biodiversity loss from outsourced deforestation. Nature 639, 389–394 (2025). https://doi.org/10.1038/s41586-024-08569-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-024-08569-5

This article is cited by

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