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.
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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.
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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.
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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
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