Global priority areas for ecosystem restoration

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 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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Fig. 1: Global priorities for restoration according to various criteria.
Fig. 2: Outcomes of restoration for biodiversity, mitigating climate change and minimizing opportunity costs.
Fig. 3: Outcomes for species conservation, the mitigation of climate change and costs for selected scenarios.
Fig. 4: The role for various criteria of the distribution of major ecosystem types that could be restored.

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.

References

  1. 1.

    IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  2. 2.

    IPCC. An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes In Terrestrial Ecosystems (SRCCL) (World Meteorological Organization, 2019).

  3. 3.

    Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).

    ADS  Google Scholar 

  4. 4.

    Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).

    Google Scholar 

  5. 5.

    IPBES. The IPBES Assessment Report on Land Degradation and Restoration (IPBES Secretariat, 2018).

  6. 6.

    Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).

    ADS  CAS  Google Scholar 

  7. 7.

    Chazdon, R. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).

    ADS  CAS  Google Scholar 

  8. 8.

    Temperton, V. M. et al. Step back from the forest and step up to the Bonn challenge: how a broad ecological perspective can promote successful landscape restoration. Restor. Ecol. 27, 705–719 (2019).

    Google Scholar 

  9. 9.

    Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).

    Google Scholar 

  10. 10.

    Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).

    ADS  Google Scholar 

  11. 11.

    Mappin, B. et al. Restoration priorities to achieve the global protected area target. Conserv. Lett. 12, e12646 (2019).

    Google Scholar 

  12. 12.

    Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61 (2006).

    ADS  CAS  Google Scholar 

  13. 13.

    Joppa, L. N., Visconti, P., Jenkins, C. N. & Pimm, S. L. Achieving the convention on biological diversity’s goals for plant conservation. Science 341, 1100–1103 (2013).

    ADS  CAS  Google Scholar 

  14. 14.

    Montesino Pouzols, F. et al. Global protected area expansion is compromised by projected land-use and parochialism. Nature 516, 383–386 (2014).

    ADS  CAS  Google Scholar 

  15. 15.

    Ando, A., Camm, J., Polasky, S. & Solow, A. Species distributions, land values, and efficient conservation. Science 279, 2126–2128 (1998).

    ADS  CAS  Google Scholar 

  16. 16.

    Naidoo, R. et al. Global mapping of ecosystem services and conservation priorities. Proc. Natl Acad. Sci. USA 105, 9495–9500 (2008).

    ADS  CAS  Google Scholar 

  17. 17.

    Beyer, H. L., Dujardin, Y., Watts, M. E. & Possingham, H. P. Solving conservation planning problems with integer linear programming. Ecol. Modell. 328, 14–22 (2016).

    Google Scholar 

  18. 18.

    Cabeza, M. & Moilanen, A. Design of reserve networks and the persistence of biodiversity. Trends Ecol. Evol. 16, 242–248 (2001).

    CAS  Google Scholar 

  19. 19.

    European Space Agency. Climate Change Initiative (ESA CCI). https://www.esa-landcover-cci.org/?q=node/158 (accessed May 2018).

  20. 20.

    Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).

    Google Scholar 

  21. 21.

    Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).

    ADS  CAS  Google Scholar 

  22. 22.

    Strassburg, B. B. N. et al. Impacts of incentives to reduce emissions from deforestation on global species extinctions. Nat. Clim. Chang. 2, 350–355 (2012).

    ADS  CAS  Google Scholar 

  23. 23.

    Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. 1, 0099 (2017).

    Google Scholar 

  24. 24.

    IUCN. The IUCN Red List of Threatened Species. Version 2019-3 http://www.iucnredlist.org (accessed 10 December 2019).

  25. 25.

    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 

  26. 26.

    Erb, K.-H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).

    ADS  CAS  Google Scholar 

  27. 27.

    Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl Acad. Sci. USA 114, 9575–9580 (2017).

    CAS  Google Scholar 

  28. 28.

    IPCC. in Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) 3–24 (World Meteorological Organization, 2018).

  29. 29.

    Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).

    ADS  CAS  Google Scholar 

  30. 30.

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

    ADS  CAS  Google Scholar 

  31. 31.

    Strassburg, B. B. N. et al. Increasing Agricultural Output While Avoiding Deforestation—A Case Study for Mato Grosso, Brazil (International Institute for Sustainability, 2012).

  32. 32.

    Latawiec, A. E., Strassburg, B. B. N., Brancalion, P. H. S., Rodrigues, R. R. & Gardner, T. Creating space for large-scale restoration in tropical agricultural landscapes. Front. Ecol. Environ. 13, 211–218 (2015).

    Google Scholar 

  33. 33.

    Anderson, C. B. et al. Determining nature’s contributions to achieve the sustainable development goals. Sustain. Sci. 14, 543–547 (2019).

    Google Scholar 

  34. 34.

    Martín-López, B. et al. Nature’s contributions to people in mountains: a review. PLoS ONE 14, e0217847 (2019).

    Google Scholar 

  35. 35.

    Latawiec, A. E., Strassburg, B. B. N., Valentim, J. F., Ramos, F. & Alves-Pinto, H. N. Intensification of cattle ranching production systems: socioeconomic and environmental synergies and risks in Brazil. Animal 8, 1255–1263 (2014).

    CAS  Google Scholar 

  36. 36.

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

    ADS  CAS  Google Scholar 

  37. 37.

    Balmford, A. et al. The environmental costs and benefits of high-yield farming. Nat. Sustain. 1, 477–485 (2018).

    Google Scholar 

  38. 38.

    Garnett, T. et al. Sustainable intensification in agriculture: premises and policies. Science 341, 33–34 (2013).

    ADS  CAS  Google Scholar 

  39. 39.

    Erb, K.-H. et al. Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun. 7, 11382 (2016).

    ADS  CAS  Google Scholar 

  40. 40.

    Reyes-García, V. et al. The contributions of Indigenous Peoples and local communities to ecological restoration. Restor. Ecol. 27, 3–8 (2019).

    Google Scholar 

  41. 41.

    Possingham, H. P., Bode, M. & Klein, C. J. Optimal conservation outcomes require both restoration and protection. PLoS Biol. 13, e1002052 (2015).

    Google Scholar 

  42. 42.

    Beresford, A. et al. Minding the protection gap: estimates of species’ range sizes and holes in the protected area network. Anim. Conserv. 14, 114–116 (2011).

    Google Scholar 

  43. 43.

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

    Google Scholar 

  44. 44.

    Di Marco, M. et al. Synergies and trade-offs in achieving global biodiversity targets. Conserv. Biol. 30, 189–195 (2016).

    Google Scholar 

  45. 45.

    Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).

    CAS  Google Scholar 

  46. 46.

    Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).

    Google Scholar 

  47. 47.

    Joppa, L. N. et al. Filling in biodiversity threat gaps. Science 352, 416–418 (2016).

    ADS  CAS  Google Scholar 

  48. 48.

    Knight, A. T., Cowling, R. M. & Campbell, B. M. An operational model for implementing conservation action. Conserv. Biol. 20, 408–419 (2006).

    Google Scholar 

  49. 49.

    Ban, N. C. et al. A social–ecological approach to conservation planning: embedding social considerations. Front. Ecol. Environ. 11, 194–202 (2013).

    Google Scholar 

  50. 50.

    Halpern, B. S. et al. Achieving the triple bottom line in the face of inherent trade-offs among social equity, economic return, and conservation. Proc. Natl Acad. Sci. USA 110, 6229–6234 (2013).

    ADS  CAS  Google Scholar 

  51. 51.

    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    Google Scholar 

  52. 52.

    Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).

    ADS  Google Scholar 

  53. 53.

    Gibbs, H. K., Brown, S., Niles, J. O. & Foley, J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2, 045023 (2007).

    ADS  Google Scholar 

  54. 54.

    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang. 2, 182–185 (2012).

    ADS  CAS  Google Scholar 

  55. 55.

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    ADS  CAS  Google Scholar 

  56. 56.

    Erb, K.-H. et al. A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data. J. Land Use Sci. 2, 191–224 (2007).

    Google Scholar 

  57. 57.

    IPCC. Guidelines for National Greenhouse Gas Inventories (National Greenhouse Gas Inventories Programme, 2006).

  58. 58.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).

    Google Scholar 

  59. 59.

    Harrell, F. E. Jr et al. Hmisc: Harrell Miscellaneous. R package version 4.1-1. https://cran.r-project.org/web/packages/Hmisc/Hmisc.pdf (2018).

  60. 60.

    Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).

    Google Scholar 

  61. 61.

    Fonseca, W. et al. Carbon accumulation in the biomass and soil of different aged secondary forests in the humid tropics of Costa Rica. For. Ecol. Manage. 262, 1400–1408 (2011).

    Google Scholar 

  62. 62.

    Guo, L. B. & Gifford, R. M. Soil carbon stocks and land use change: a meta analysis. Glob. Change Biol. 8, 345–360 (2002).

    ADS  Google Scholar 

  63. 63.

    Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun. 10, 718 (2019).

    ADS  Google Scholar 

  64. 64.

    Mitchard, E. T. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manag. 8, 10 (2013).

    Google Scholar 

  65. 65.

    Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Google Scholar 

  66. 66.

    BirdLife International & NatureServe. Bird Species Distribution Maps of the World. Version 2018.1 http://datazone.birdlife.org/species/requestdis (BirdLife International and Handbook of the Birds of the World, 2018).

  67. 67.

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

    Google Scholar 

  68. 68.

    Staude, I. R. et al. Range size predicts the risk of local extinction from habitat loss. Glob. Ecol. Biogeogr. 29, 16–25 (2020).

    Google Scholar 

  69. 69.

    Carrasco, L. R., Webb, E. L., Symes, W. S., Koh, L. P. & Sodhi, N. S. Global economic trade-offs between wild nature and tropical agriculture. PLoS Biol. 15, e2001657 (2017).

    Google Scholar 

  70. 70.

    Naidoo, R & Iwamura, T. Global-scale mapping of economic benefits from agricultural lands: implications for conservation priorities. Biol. Conserv. 140, 40–49 (2007).

    Google Scholar 

  71. 71.

    Polasky, S. et al. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol. Conserv. 141, 1505–1524 (2008).

    Google Scholar 

  72. 72.

    Sulser, T. B. et al. in Beyond a Middle Income Africa: Transforming African Economies for Sustained Growth with Rising Employment and Incomes (ReSAKSS Annual Trends and Outlook Report 2014 (eds. Badiane, O. et al.) Ch. 2 (International Food Policy Research Institute (IFPRI), 2014).

  73. 73.

    Robinson, S. et al. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3 (IFPRI Discussion Paper 1483) (International Food Policy Research Institute (IFPRI), (2015).

  74. 74.

    IIASA & FAO. Global Agro-ecological Zones (GAEZ v.3.0) (IIASA & FAO, 2012).

  75. 75.

    Hoppe, R. A. Structure and Finances of U.S. Farms: Family Farm Report (EIB-132) (US Department of Agriculture Economic Research Service, 2014).

  76. 76.

    Baležentis, T. et al. Decomposing dynamics in the farm profitability: an application of index decomposition analysis to Lithuanian FADN sample. Sustainability 11, 2861 (2019).

    Google Scholar 

  77. 77.

    Statistic Canada. Table 32-10-0136-01, Farm Operating Revenues and Expenses, Annual. https://open.canada.ca/data/en/dataset/59ca6332-391b-4fdf-bb3a-31e5e45f6bb7 (2008).

  78. 78.

    De Groot, R. S. et al. Benefits of investing in ecosystem restoration. Conserv. Biol. 27, 1286–1293 (2013).

    Google Scholar 

  79. 79.

    International Labour Organization. ILOSTAT database. https://ilostat.ilo.org/data (accessed March 2020).

  80. 80.

    United Nations Statistics Division. UN Comtrade Database. https://comtrade.un.org/ (accessed March 2020).

  81. 81.

    Brancalion, P. H. S. et al. What makes ecosystem restoration expensive? A systematic cost assessment of projects in Brazil. Biol. Conserv. 240, 108274 (2019).

    Google Scholar 

  82. 82.

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

    ADS  CAS  Google Scholar 

  83. 83.

    Mueller, N. D. et al. Declining spatial efficiency of global cropland nitrogen allocation. Glob. Biogeochem. Cycles 31, 245–257 (2017).

    ADS  CAS  Google Scholar 

  84. 84.

    Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).

    ADS  CAS  Google Scholar 

  85. 85.

    Hornik, K. et al. SYMPHONY in R, an R interface to the SYMPHONY solver for mixed-integer linear programs. http://R-Forge.R-project.org/projects/rsymphony/ (2019).

  86. 86.

    Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).

    Google Scholar 

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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.

Corresponding author

Correspondence to Bernardo B. N. Strassburg.

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Peer review information Nature thanks Simon Ferrier and Robin Naidoo for their contribution to the peer review of this work.

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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. bf, 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 bf 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.

af, 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.

af, 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). cf, 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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