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.

Proactive conservation to prevent habitat losses to agricultural expansion

Abstract

The projected loss of millions of square kilometres of natural ecosystems to meet future demand for food, animal feed, fibre and bioenergy crops is likely to massively escalate threats to biodiversity. Reducing these threats requires a detailed knowledge of how and where they are likely to be most severe. We developed a geographically explicit model of future agricultural land clearance based on observed historical changes, and combined the outputs with species-specific habitat preferences for 19,859 species of terrestrial vertebrates. We project that 87.7% of these species will lose habitat to agricultural expansion by 2050, with 1,280 species projected to lose ≥25% of their habitat. Proactive policies targeting how, where, and what food is produced could reduce these threats, with a combination of approaches potentially preventing almost all these losses while contributing to healthier human diets. As international biodiversity targets are set to be updated in 2021, these results highlight the importance of proactive efforts to safeguard biodiversity by reducing demand for agricultural land.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Projected extent of agricultural land in 2050 under business-as-usual.
Fig. 2: Projected changes in habitat area from 2010 to 2050 under business-as-usual.
Fig. 3: Severity of projected habitat losses from 2010 to 2050.
Fig. 4: Projected changes in mean habitat area from 2010 to 2050 under alternative scenarios.

Data availability

Data are available at https://doi.org/10.5061/dryad.jq2bvq87m and from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Code used is available at https://doi.org/10.5061/dryad.jq2bvq87m.

References

  1. 1.

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

    CAS  Google Scholar 

  2. 2.

    Rodrigues, A. S. L. et al. Spatially explicit trends in the global conservation status of vertebrates. PLoS ONE 9, e113934 (2014).

    Google Scholar 

  3. 3.

    Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).

    CAS  Google Scholar 

  4. 4.

    The IUCN Red List of Threatened Species version 2018-1 (IUCN, 2018).

  5. 5.

    Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).

    CAS  Google Scholar 

  6. 6.

    Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds) Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  7. 7.

    2019 Revision of World Population Prospects (United Nations, 2019).

  8. 8.

    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    CAS  Google Scholar 

  9. 9.

    Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: the 2012 Revision ESA Working Paper No. 12-03 (FAO, 2012).

  10. 10.

    Bajželj, B. et al. Importance of food-demand management for climate mitigation. Nat. Clim. Change 4, 924–929 (2014).

    Google Scholar 

  11. 11.

    Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    Google Scholar 

  12. 12.

    Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. https://doi.org/10.1111/conl.12159 (2016).

  13. 13.

    Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).

    Google Scholar 

  14. 14.

    Travers, H. et al. A manifesto for predictive conservation. Biol. Conserv. 237, 12–18 (2019).

    Google Scholar 

  15. 15.

    Visconti, P. et al. Future hotspots of terrestrial mammal loss. Phil. Trans R. Soc. Lond. B 366, 2693–2702 (2011).

    Google Scholar 

  16. 16.

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

    CAS  Google Scholar 

  17. 17.

    Integrated Assessment of Global Environmental Change with IMAGE 3.0 —Model Description and Policy Applications (PBL Netherlands Environmental Assessment Agency, 2014).

  18. 18.

    Dietrich, J. P. et al. MAgPIE - an open source land-use modeling framework, version 4.0. Zenodo https://doi.org/10.5281/zenodo.1418752 (2018).

  19. 19.

    Havlík, P. et al. Global land-use implications of first and second generation biofuel targets. Energy Policy 39, 5690–5702 (2011).

    Google Scholar 

  20. 20.

    Global Agro-ecological Zones version 3.0 (FAO and IIASA, 2010).

  21. 21.

    Friedl, M. A. et al. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).

    Google Scholar 

  22. 22.

    Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

    CAS  Google Scholar 

  23. 23.

    The World Database on Protected Areas (UNEP-WCMC and IUCN, 2016); https://www.protectedplanet.net/en/thematic-areas/wdpa

  24. 24.

    Brooks, T. M. et al. Measuring terrestrial area of habitat (AOH) and its utility for the IUCN Red List. Trends Ecol. Evol. 34, 977–986 (2019).

    Google Scholar 

  25. 25.

    Beresford, A. E. et al. Poor overlap between the distribution of protected areas and globally threatened birds in Africa. Anim. Conserv. 14, 99–107 (2011).

    Google Scholar 

  26. 26.

    Rondinini, C. et al. Global habitat suitability models of terrestrial mammals. Phil. Trans R. Soc. Lond. B 366, 2633–2641 (2011).

    Google Scholar 

  27. 27.

    Ficetola, G. F., Rondinini, C., Bonardi, A., Baisero, D. & Padoa-Schioppa, E. Habitat availability for amphibians and extinction threat: a global analysis. Divers. Distrib. 21, 302–311 (2015).

    Google Scholar 

  28. 28.

    Santini, L., Isaac, N. J. B. & Ficetola, G. F. TetraDENSITY: a database of population density estimates in terrestrial vertebrates. Glob. Ecol. Biogeogr. 27, 787–791 (2018).

    Google Scholar 

  29. 29.

    Dunn, R. R. Recovery of faunal communities during tropical forest regeneration. Conserv. Biol. 18, 302–309 (2004).

    Google Scholar 

  30. 30.

    Isbell, F., Tilman, D., Reich, P. B. & Clark, A. T. Deficits of biodiversity and productivity linger a century after agricultural abandonment. Nat. Ecol. Evol. 3, 1533–1538 (2019).

    Google Scholar 

  31. 31.

    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    CAS  Google Scholar 

  32. 32.

    Global Yield Gap and Water Productivity Atlas (Univ. Nebraska and Wageningen Univ., 2017); www.yieldgap.org

  33. 33.

    Folberth, C. et al. The global cropland-sparing potential of high-yield farming. Nat. Sustain. 3, 281–289 (2020).

    Google Scholar 

  34. 34.

    Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 2844 (2019).

    Google Scholar 

  35. 35.

    Luskin, M. S., Lee, J. S. H., Edwards, D. P., Gibson, L. & Potts, M. D. Study context shapes recommendations of land-sparing and sharing; a quantitative review. Glob. Food Sec. https://doi.org/10.1016/j.gfs.2017.08.002 (2017).

  36. 36.

    Phalan, B. T. What have we learned from the land sparing–sharing model? Sustainability 10, 1760 (2018).

    Google Scholar 

  37. 37.

    Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Change 48, 119–135 (2018).

    Google Scholar 

  38. 38.

    van Asselen, S. & Verburg, P. H. Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Glob. Change Biol. 19, 3648–3667 (2013).

    Google Scholar 

  39. 39.

    Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. The ravages of guns, nets and bulldozers. Nature 536, 146–145 (2016).

    Google Scholar 

  40. 40.

    Foden, W. B. et al. Climate change vulnerability assessment of species. Wiley Interdiscip. Rev. Clim. Change 10, e551 (2019).

    Google Scholar 

  41. 41.

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

    Google Scholar 

  42. 42.

    Lobell, D. B. & Gourdji, S. M. The influence of climate change on global crop productivity. Plant Physiol. 160, 1686–1697 (2012).

    CAS  Google Scholar 

  43. 43.

    Akpoti, K., Kabo-bah, A. T. & Zwart, S. J. Review—agricultural land suitability analysis: state-of-the-art and outlooks for integration of climate change analysis. Agric. Syst. 173, 172–208 (2019).

    Google Scholar 

  44. 44.

    Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).

    Google Scholar 

  45. 45.

    Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).

    Google Scholar 

  46. 46.

    Green, J. M. H. et al. Local costs of conservation exceed those borne by the global majority. Glob. Ecol. Conserv. 14, e00385 (2018).

    Google Scholar 

  47. 47.

    Dorward, A. & Chirwa, E. The Malawi agricultural input subsidy programme: 2005/06 to 2008/09. Int. J. Agric. Sustain. 09, 232–247 (2011).

    Google Scholar 

  48. 48.

    Druilhe, Z. & Barreiro-Hurlé, J. Fertilizer Subsidies in sub-Saharan Africa ESA Working Paper No. 12-04 (FAO, 2012).

  49. 49.

    Cui, Z.-L. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 478, 363–366 (2018).

    Google Scholar 

  50. 50.

    Hawkes, C. et al. Smart food policies for obesity prevention. Lancet 385, 2410–2421 (2015).

    Google Scholar 

  51. 51.

    Vallgårda, S. Governing obesity policies from England, France, Germany and Scotland. Soc. Sci. Med. 147, 317–323 (2015).

    Google Scholar 

  52. 52.

    Colchero, M. A., Rivera-Dommarco, J., Popkin, B. M. & Ng, S. W. In Mexico, evidence of sustained consumer response two years after implementing a sugar-sweetened beverage tax. Health Aff. 36, 564–571 (2017).

    Google Scholar 

  53. 53.

    Choudhury, M. in Postharvest Management of Fruit and Vegetables in the Asia–Pacific Region (ed. Rolle, R. S.) 15–22 (APO and FAO, 2006).

  54. 54.

    Rolle, R. S. in Postharvest Management of Fruit and Vegetables in the Asia–Pacific Region (ed. Rolle, R. S.) 23–31 (APO and FAO, 2006).

  55. 55.

    Phalan, B. et al. How can higher-yield farming help to spare nature? Science 351, 450–451 (2016).

    CAS  Google Scholar 

  56. 56.

    Angelsen, A. Policies for reduced deforestation and their impact on agricultural production. Proc. Natl Acad. Sci. USA 107, 19639–19644 (2010).

    CAS  Google Scholar 

  57. 57.

    2017 Revision of World Population Prospects (United Nations, 2017).

  58. 58.

    KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Google Scholar 

  59. 59.

    O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).

    Google Scholar 

  60. 60.

    Grassini, P., Eskridge, K. M. & Cassman, K. G. Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4, 2918 (2013).

    Google Scholar 

  61. 61.

    FAOSTAT (FAO, 2017); http://faostat3.fao.org/home/E

  62. 62.

    Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179–204 (2009).

    Google Scholar 

  63. 63.

    Gustavsson, J., Cedeberg, C. & Sonesson, U. Global Food Losses and Food Waste—Extent, Causes and Prevention (FAO, 2011).

  64. 64.

    IUCN Standards and Peititions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria version 13 (IUCN, 2017).

  65. 65.

    Lambin, E. F., Geist, H. J. & Lepers, E. Dynamics of land-use and land-cover change in tropical regions. Annu. Rev. Environ. Resour. 28, 205–241 (2003).

    Google Scholar 

  66. 66.

    Veldkamp, A. & Fresco, L. O. CLUE: a conceptual model to study the conversion of land use and its effects. Ecol. Modell. 85, 253–270 (1996).

    Google Scholar 

  67. 67.

    Pfaff, A. S. P. What drives deforestation in the Brazilian Amazon? Evidence from satellite and socioeconomic data. J. Environ. Econ. Manag. 37, 26–43 (1999).

    Google Scholar 

  68. 68.

    Aguiar, A. P. D. et al. Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon. Glob. Change Biol. 22, 1821–1840 (2015).

    Google Scholar 

  69. 69.

    Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. Lond. B 278, 1633–1638 (2011).

    Google Scholar 

  70. 70.

    Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).

    Google Scholar 

  71. 71.

    Dudley, N. (ed.) Guidelines for Applying Protected Area Management Categories (IUCN, 2013).

  72. 72.

    Joppa, L. N., Loarie, S. R. & Pimm, S. L. On the protection of “protected areas”. Proc. Natl Acad. Sci. USA 105, 6673–6678 (2008).

    CAS  Google Scholar 

  73. 73.

    Hijmans, R. J. raster: Geographic Data Analysis and Modeling v.3.3-13 (2015).

  74. 74.

    R Core Team R: A Language and Environment for Statistical Computing version 3.6.0 (R Foundation for Statistical Computing, 2019).

  75. 75.

    Statutes of 5 October 1948, revised on 22 October 1996, and last amended on 27 September 2016 (including Rules of Procedure of the World Conservation Congress, last amended on 27 March 2019) and regulations revised on 22 October 1996 and last amended on 31 March (IUCN, 2019); https://doi.org/10.2305/IUCN.CH.2019.SR.01.en

  76. 76.

    Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).

  77. 77.

    Poore, J. A. C. Call for conservation: abandoned pasture. Science 351, 132–132 (2016).

    CAS  Google Scholar 

  78. 78.

    Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).

    Google Scholar 

  79. 79.

    Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).

    CAS  Google Scholar 

  80. 80.

    Plieninger, T., Hui, C., Gaertner, M. & Huntsinger, L. The impact of land abandonment on species richness and abundance in the Mediterranean basin: a meta-analysis. PLoS ONE 9, e98355 (2014).

    Google Scholar 

Download references

Acknowledgements

This research was made possible through support from the Wellcome Trust, Our Planet Our Health (Livestock, Environment and People, LEAP) award number 205212/Z/16/Z.

Author information

Affiliations

Authors

Contributions

M.C. and D.R.W. conceived the study, all authors planned the analysis, G.M.B., G.F.F. and C.R. provided data, M.C., D.R.W. and G.M.B. performed the analysis, M.C. and D.R.W. prepared the initial draft and all authors edited and revised the manuscript.

Corresponding authors

Correspondence to David R. Williams or Michael Clark.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Sustainability thanks Rebecca Chaplin-Kramer, Florian Zabel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–16 and Tables 1–3.

Supplementary Table 4

Explanatory variables, underlying data and rationale for their inclusion in our analyses.

Supplementary Data 1

Summary of changes in habitat for all species analysed. Data are summarized across taxa, regions, scenarios, and with or without habitat regrowth (see text for details).

Supplementary Data 2

Countries and regions included in analysis. Some countries are grouped for EAT-Lancet projections. We modelled agricultural expansion separately for each IUCN region (‘Region for modelling’) but grouped some of these for clarity in figures.

Supplementary Data 3

Multinomial model coefficients for cropland. Coefficients are for predicting the log odds of an increase or decrease in a cell.

Supplementary Data 4

Multinomial model coefficients for pastureland. Coefficients are for predicting the log odds of an increase or decrease in a cell.

Supplementary Data 5

Model coefficients for cropland GLMs. Coefficients are for predicting the proportion of a cell that changes to or from cropland.

Supplementary Data 6

Model coefficients for pastureland GLMs. Coefficients are for predicting the proportion of a cell that changes to pasture. Note, we did not predict any decreases in pasture extent, so only show coefficients for increases (see text for details).

Supplementary Data 7

Countries with at least 25 species losing ≥25% of their remaining habitat.

Source data

Source Data Fig. 1a

Raster files containing (1) agricultural land cover in 2010 at a resolution of 1.5 × 1.5 km, (2) spatial projection of agricultural land cover change from 2010 to 2050, at a resolution of 1.5 × 1.5 km.

Source Data Fig. 1b

Raster file containing spatial projections of agricultural extent in 2050 at a resolution of 1.5 × 1.5 km.

Source Data Fig. 2

Raster file of habitat loss by 2050 for each of amphibians, birds and mammals; data file containing estimated area of habitat loss from 2010 to 2050 for each species in each agricultural land-cover scenario.

Source Data Fig. 3a

Data file containing estimated area of habitat loss from 2010 to 2050 for each species in each agricultural land-cover scenario (same data file as for Fig. 2).

Source Data Fig. 3b

Raster file containing global distribution of the species projected to lose >25% of their remaining 2010 habitat by 2050 at a resolution of 1.5 × 1.5 km.

Source Data Fig. 3c

Raster file containing projected change in total habitat, calculated as mean habitat loss across all species in a cell multiplied by the species richness in that cell at a resolution of 1.5 × 1.5 km.

Source Data Fig. 4

Raster files containing projected mean habitat loss from 2010 to 2050 for each agricultural land-cover scenario at a resolution of 1.5 × 1.5 km.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Williams, D.R., Clark, M., Buchanan, G.M. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat Sustain 4, 314–322 (2021). https://doi.org/10.1038/s41893-020-00656-5

Download citation

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