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 via your institution
Open Access articles citing this article.
Introducing the Food Value Framework (FVF) to empower transdisciplinary research and unite stakeholders in their efforts of building a sustainable global food system
Environment, Development and Sustainability Open Access 03 October 2023
BMC Ecology and Evolution Open Access 04 July 2023
Ambio Open Access 27 June 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Code used is available at https://doi.org/10.5061/dryad.jq2bvq87m.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Rodrigues, A. S. L. et al. Spatially explicit trends in the global conservation status of vertebrates. PLoS ONE 9, e113934 (2014).
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
The IUCN Red List of Threatened Species version 2018-1 (IUCN, 2018).
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).
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).
2019 Revision of World Population Prospects (United Nations, 2019).
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).
Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: the 2012 Revision ESA Working Paper No. 12-03 (FAO, 2012).
Bajželj, B. et al. Importance of food-demand management for climate mitigation. Nat. Clim. Change 4, 924–929 (2014).
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).
Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. https://doi.org/10.1111/conl.12159 (2016).
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
Travers, H. et al. A manifesto for predictive conservation. Biol. Conserv. 237, 12–18 (2019).
Visconti, P. et al. Future hotspots of terrestrial mammal loss. Phil. Trans R. Soc. Lond. B 366, 2693–2702 (2011).
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Integrated Assessment of Global Environmental Change with IMAGE 3.0 —Model Description and Policy Applications (PBL Netherlands Environmental Assessment Agency, 2014).
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).
Havlík, P. et al. Global land-use implications of first and second generation biofuel targets. Energy Policy 39, 5690–5702 (2011).
Global Agro-ecological Zones version 3.0 (FAO and IIASA, 2010).
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).
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).
The World Database on Protected Areas (UNEP-WCMC and IUCN, 2016); https://www.protectedplanet.net/en/thematic-areas/wdpa
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).
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).
Rondinini, C. et al. Global habitat suitability models of terrestrial mammals. Phil. Trans R. Soc. Lond. B 366, 2633–2641 (2011).
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).
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).
Dunn, R. R. Recovery of faunal communities during tropical forest regeneration. Conserv. Biol. 18, 302–309 (2004).
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).
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
Global Yield Gap and Water Productivity Atlas (Univ. Nebraska and Wageningen Univ., 2017); www.yieldgap.org
Folberth, C. et al. The global cropland-sparing potential of high-yield farming. Nat. Sustain. 3, 281–289 (2020).
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 2844 (2019).
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).
Phalan, B. T. What have we learned from the land sparing–sharing model? Sustainability 10, 1760 (2018).
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).
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).
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).
Foden, W. B. et al. Climate change vulnerability assessment of species. Wiley Interdiscip. Rev. Clim. Change 10, e551 (2019).
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).
Lobell, D. B. & Gourdji, S. M. The influence of climate change on global crop productivity. Plant Physiol. 160, 1686–1697 (2012).
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).
Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).
Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).
Green, J. M. H. et al. Local costs of conservation exceed those borne by the global majority. Glob. Ecol. Conserv. 14, e00385 (2018).
Dorward, A. & Chirwa, E. The Malawi agricultural input subsidy programme: 2005/06 to 2008/09. Int. J. Agric. Sustain. 09, 232–247 (2011).
Druilhe, Z. & Barreiro-Hurlé, J. Fertilizer Subsidies in sub-Saharan Africa ESA Working Paper No. 12-04 (FAO, 2012).
Cui, Z.-L. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 478, 363–366 (2018).
Hawkes, C. et al. Smart food policies for obesity prevention. Lancet 385, 2410–2421 (2015).
Vallgårda, S. Governing obesity policies from England, France, Germany and Scotland. Soc. Sci. Med. 147, 317–323 (2015).
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).
Choudhury, M. in Postharvest Management of Fruit and Vegetables in the Asia–Pacific Region (ed. Rolle, R. S.) 15–22 (APO and FAO, 2006).
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).
Phalan, B. et al. How can higher-yield farming help to spare nature? Science 351, 450–451 (2016).
Angelsen, A. Policies for reduced deforestation and their impact on agricultural production. Proc. Natl Acad. Sci. USA 107, 19639–19644 (2010).
2017 Revision of World Population Prospects (United Nations, 2017).
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).
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).
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).
FAOSTAT (FAO, 2017); http://faostat3.fao.org/home/E
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).
Gustavsson, J., Cedeberg, C. & Sonesson, U. Global Food Losses and Food Waste—Extent, Causes and Prevention (FAO, 2011).
IUCN Standards and Peititions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria version 13 (IUCN, 2017).
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).
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).
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).
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).
Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. Lond. B 278, 1633–1638 (2011).
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
Dudley, N. (ed.) Guidelines for Applying Protected Area Management Categories (IUCN, 2013).
Joppa, L. N., Loarie, S. R. & Pimm, S. L. On the protection of “protected areas”. Proc. Natl Acad. Sci. USA 105, 6673–6678 (2008).
Hijmans, R. J. raster: Geographic Data Analysis and Modeling v.3.3-13 (2015).
R Core Team R: A Language and Environment for Statistical Computing version 3.6.0 (R Foundation for Statistical Computing, 2019).
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
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).
Poore, J. A. C. Call for conservation: abandoned pasture. Science 351, 132–132 (2016).
Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).
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).
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.
The authors declare no competing interests.
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 Methods, Figs. 1–16 and Tables 1–3.
Explanatory variables, underlying data and rationale for their inclusion in our analyses.
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).
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.
Multinomial model coefficients for cropland. Coefficients are for predicting the log odds of an increase or decrease in a cell.
Multinomial model coefficients for pastureland. Coefficients are for predicting the log odds of an increase or decrease in a cell.
Model coefficients for cropland GLMs. Coefficients are for predicting the proportion of a cell that changes to or from cropland.
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).
Countries with at least 25 species losing ≥25% of their remaining habitat.
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.
Raster file containing spatial projections of agricultural extent in 2050 at a resolution of 1.5 × 1.5 km.
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.
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).
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.
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.
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.
About this article
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
This article is cited by
BMC Ecology and Evolution (2023)
Nature Sustainability (2023)
Communications Biology (2023)
Nature Food (2023)
Nature Food (2023)