Abstract
Extensive ecosystem restoration is increasingly seen as being central to conserving biodiversity1 and stabilizing the climate of the Earth2. Although ambitious national and global targets have been set, global priority areas that account for spatial variation in benefits and costs have yet to be identified. Here we develop and apply a multicriteria optimization approach that identifies priority areas for restoration across all terrestrial biomes, and estimates their benefits and costs. We find that restoring 15% of converted lands in priority areas could avoid 60% of expected extinctions while sequestering 299 gigatonnes of CO2—30% of the total CO2 increase in the atmosphere, or 14% of total emissions, since the Industrial Revolution. The inclusion of several biomes is key to achieving multiple benefits. Cost effectiveness can increase up to 13-fold when spatial allocation is optimized using our multicriteria approach, which highlights the importance of spatial planning. Our results confirm the vast potential contributions of restoration to addressing global challenges, while underscoring the necessity of pursuing these goals synergistically.
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Data availability
All input datasets are available from the references cited. All output datasets generated during the current study are available from the corresponding author upon request.
Code availability
R codes developed for and used in this analysis are available upon request from the corresponding author.
Change history
30 August 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41586-022-05178-y
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Acknowledgements
B.B.N.S. acknowledges that this work was supported by the Serrapilheira Institute (grant number Serra-1709-19329). We acknowledge inputs from the Secretariat of the Convention of Biological Diversity and experts from the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. We are very grateful for the support provided by F. Gomes, J. Krieger, I. Leite, R. Capellão, G. Duarte, L. Martinez, L. Oliveira and D. Rocha in the preparation of this manuscript.
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Contributions
B.B.N.S. conceived the study, coordinated the development of the multicriteria approach, led the analyses and wrote the first version of the paper. A.I., H.L.B. and B.B.N.S. led the multicriteria modelling. T.M.B., R.C., R.L.C. and S.H.M.B. helped with the development of the multicriteria approach. A.I., H.L.B., C.L.C., E.L., C.C.J., A.B.J., R.C., K.-H.E. and B.B.N.S. developed input datasets. S.H.M.B., G.B., P.F.D., K.-H.E. and C.P. contributed data. D.C., C.A.d.M.S. and F.R.S. helped with the interface with policy applications. B.B.N.S., A.I., H.L.B., C.L.C., R.C., C.C.J., A.B.J., E.L., A.E.L., A.B., T.M.B., S.H.M.B., R.L.C., P.B., D.C., S.D., V.K., L.M., D.L., M.O. and P.V. analysed the results. All authors provided input into subsequent versions of the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Converted lands and their estimated original ecosystem type.
a, Percentages of converted areas in each planning unit; current croplands and pasturelands are included as potentially restorable areas. b–f, Percentages of converted lands within each original ecosystem type: forests (b), natural grasslands (c), shrublands (d), wetlands (e) and arid areas (f). Areas in darker grey in b–f represent the current extent of each ecosystem type.
Extended Data Fig. 2 Benefits of ecosystem restoration for biodiversity conservation, the mitigation of climate change and associated costs.
a, Benefits for biodiversity were calculated as the number of avoided extinctions per hectare for all species combined. The map represents the starting situation with current vegetation cover before any restoration takes place. b, Benefits for climate change are calculated as the difference between the potential carbon stored after ecosystem restoration and the carbon currently stored in the agricultural lands. ‘Stock’ refers to carbon in the above- and belowground biomass and down to 30 cm in the soil, include above and belowground biomass and soil carbon sequestration. c, Costs consist of opportunity costs, based on the foregone agricultural benefits of areas allocated for restoration, and restoration implementation costs.
Extended Data Fig. 3 Areas potentially available for restoration and their relative priority across subregions.
a–f, For each of the 17 subregions of IPBES, the horizontal bars show their relative priority percentile for the main scenarios focused on biodiversity (a), climate change mitigation (b), minimizing costs (c), biodiversity and climate change mitigation (d) and all three criteria (e); the last panel (f) shows absolute areas. South America has the greatest extent of converted lands that are relatively evenly distributed in the top 50% of global priorities, whereas the Caribbean has the smallest extent of areas potentially available for restoration—but almost all of them are in the top 10% of global priorities. The patterns in relative priority for restoration for each subregion change substantially across the different restoration scenarios, which further highlights the importance of using multicriteria optimizations that take into account several benefits of restoration simultaneously.
Extended Data Fig. 4 Cost-efficiency of climate change mitigation for main scenarios.
The curves show, for the 5 main scenarios and across 20 targets ranging from 5% to 100%, the carbon value required to cover both opportunity and restoration costs. These results underscore the cost effectiveness of restoration as a climate mitigation option, as carbon values are in the lower range of low and medium mitigation costs according to the IPCC1.
Extended Data Fig. 5 Distribution of major ecosystem types that could be restored.
Dominant estimated predisturbance ecosystem type in each cell; for the fraction of each ecosystem type per cell, see Extended Data Fig. 1.
Extended Data Fig. 6 Accuracy of original ecosystem-cover predictions.
a–f, The accuracy of the predictions of the original proportion of each ecosystem type in each planning unit was quantified using the root mean square error (r.m.s.e.). To better understand any heterogeneity in prediction accuracy, we calculated the r.m.s.e. separately for each of the five land-cover classes (forest, grassland, shrubland, wetland and desert) in addition to the overall r.m.s.e. Overall, predictive accuracy was excellent (total r.m.s.e 6.73%, f) with relatively little variation among the five land-cover types: forests, 4.0% (a); grasslands, 1.7% (b); shrublands, 4.3% (c); wetlands, 1.2% (d); and arid areas, 2.6% (e).
Extended Data Fig. 7 Fraction of converted lands available for restoration after closing yield gaps.
Combining yield gaps for croplands and pasturelands, the map indicates the fraction of a planning unit that could be spared if 75% of its yield gap were to be closed.
Extended Data Fig. 8 Global and national priority areas for restoration.
For the multiple benefits scenario and 15% restoration target, areas in green are selected both in the globally unconstrained scenario and in a scenario constrained by national boundaries; areas in red are selected only in the global scenario and areas in blue are selected only in the national version of the scenario. A substantial fraction (69%) of global priority areas would not be restored using uniform national targets. As most of these areas are in lower-income countries, the results reinforce the role that international cooperation mechanisms such as REDD+ can have in achieving cost-effective global outcomes through restoration.
Extended Data Fig. 9 Sensitivity analysis with future land-use change.
a, b, In the pessimistic regional rivalries SSP3 scenario83, substantial conversion would happen until 2050 (a), and—as a consequence—some priority areas would shift towards newly converted areas of high endemic and threatened biodiversity that are also rich in carbon, in particular in Africa (b). c–f, Despite this, the restored fraction in each planning unit would be very similar to those based on 2015 land-use (c) (r.m.s.e. = 13%), and 2050 outcomes for biodiversity (d), climate (e) and costs (f) would be within the uncertainty range of 2015 estimates. Although the reduction in extinction debt would be slightly lower in 2050 (55% versus 60%), the extinction debt itself would be 25% higher (10% versus 8% in 2015), so absolute extinctions avoided would be higher.
Extended Data Fig. 10 Comparisons between potential biomass carbon stocks calculated in this study and other estimates.
Comparisons between our estimates of potential carbon stocks in biomass (above and below-ground) and estimates from ref. 24: Forest Resources Assessment (FRA)-related map (FAO) and remote-sensing based map. Box plots are based on pixel-level estimates of carbon stocks per ha in each biome, have the same sample size (pixels) across maps, and show the median (vertical lines), the interquartile range (bounding boxes) minimum and maximum values (whiskers).
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Strassburg, B.B.N., Iribarrem, A., Beyer, H.L. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020). https://doi.org/10.1038/s41586-020-2784-9
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DOI: https://doi.org/10.1038/s41586-020-2784-9
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