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Strategic approaches to restoring ecosystems can triple conservation gains and halve costs

Nature Ecology & Evolution volume 3, pages 62–70 (2019)Cite this article

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An Author Correction to this article was published on 23 April 2020

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Abstract

International commitments for ecosystem restoration add up to one-quarter of the world’s arable land. Fulfilling them would ease global challenges such as climate change and biodiversity decline but could displace food production and impose financial costs on farmers. Here, we present a restoration prioritization approach capable of revealing these synergies and trade-offs, incorporating ecological and economic efficiencies of scale and modelling specific policy options. Using an actual large-scale restoration target of the Atlantic Forest hotspot, we show that our approach can deliver an eightfold increase in cost-effectiveness for biodiversity conservation compared with a baseline of non-systematic restoration. A compromise solution avoids 26% of the biome’s current extinction debt of 2,864 plant and animal species (an increase of 257% compared with the baseline). Moreover, this solution sequesters 1 billion tonnes of CO2-equivalent (a 105% increase) while reducing costs by US$28 billion (a 57% decrease). Seizing similar opportunities elsewhere would offer substantial contributions to some of the greatest challenges for humankind.

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Fig. 1: Spatial configurations and outcomes for climate change mitigation, avoided extinctions and total costs of selected scenarios.
Fig. 2: Impacts of economic and ecological efficiencies of scale on cost-effectiveness.
Fig. 3: Impacts of economies of scale and of spatial prioritization for reducing opportunity and restoration costs across different scenarios.

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Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request. A free online platform for integrated land-use planning including these datasets will be available at www.iis-rio.org/ilup from 2019.

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  • 23 April 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. United Nations Sustainable Development Goals (United Nations, accessed 17 November 2017); http://www.un.org/sustainabledevelopment/sustainable-development-goals/

  2. Griggs, D. et al. Sustainable development goals for people and planet. Nature 495, 305–307 (2013).

    CAS  PubMed  Google Scholar 

  3. Chazdon, R. L. et al. A policy‐driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).

    Google Scholar 

  4. Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).

    PubMed  PubMed Central  Google Scholar 

  5. Crouzeilles, R. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Sci. Adv. 3, e1701345 (2017).

    PubMed  PubMed Central  Google Scholar 

  6. Verdone, M. & Seidl, A. Time, space, place, and the Bonn Challenge global forest restoration target. Rest. Ecol. 25, 903–911 (2017).

    Google Scholar 

  7. Smith, P. et al. How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Change Biol. 19(8), 2285–2302 (2013).

    Google Scholar 

  8. Gourevitch, J. D. et al. Optimizing investments in national-scale forest landscape restoration in Uganda to maximize multiple benefits. Environ. Res. Lett. 11, 114027 (2016).

    Google Scholar 

  9. Zwiener, V. P. et al. Planning for conservation and restoration under climate and land use change in the Brazilian Atlantic Forest. Divers. Distrib. 23, 955–966 (2017).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  12. Possingham, H. P., Bode, M. & Klein, C. J. Optimal conservation outcomes require both restoration and protection. PLoS Biol. 13, 1–15 (2015).

    Google Scholar 

  13. Laurance, W. F. Conserving the hottest of the hotspots. Biol. Conserv. 142, 1137 (2009).

    Google Scholar 

  14. Mittermeier, R. A. et al. Hotspots Revisited: Earth’s Biologically Richest and Most Endangered Terrestrial Ecoregions (Conservation International, 2004).

  15. Latawiec et al. Creating space for large‐scale restoration in tropical agricultural landscapes. Front. Ecol. Env. 13, 211–218 (2015).

    Google Scholar 

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

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

  19. Crouzeilles, R. & Curran, M. Which landscape size best predicts the influence of forest cover on restoration success? A global meta-analysis on the scale of effect. J. Appl. Ecol. 53, 440–448 (2016).

    Google Scholar 

  20. Crouzeilles, R., Beyer, H. L., Mills, M., Grelle, C. E. V. & Possingham, H. P. Incorporating habitat availability into systematic planning for restoration: a species-specific approach for Atlantic Forest mammals. Divers. Distrib. 21, 1027–1037 (2015).

    Google Scholar 

  21. Groeneveld, J. et al. The impact of fragmentation and density regulation on forest succession in the Atlantic rain forest. Ecol. Model. 220, 2450–2459 (2009).

    Google Scholar 

  22. Soares-Filho, B. et al. Cracking Brazil’s forest code. Science 344, 363–364 (2014).

    CAS  PubMed  Google Scholar 

  23. Kennedy, C. M. et al. Bigger is better: improved nature conservation and economic returns from landscape-level mitigation. Sci. Adv. 2, e1501021 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).

    CAS  PubMed  Google Scholar 

  25. Scarano, F. R. Ecosystem-based adaptation to climate change: concept, scalability and a role for conservation science. Perspect. Ecol. Conserv. 15, 65–73 (2017).

    Google Scholar 

  26. Brancalion, P. H. S., Viani, R. A. G., Strassburg, B. B. N. & Rodrigues, R. R. Finding the money for tropical forest restoration. Unasylva 63, 239 (2012).

    Google Scholar 

  27. Mitchell, M. G. E. et al. Reframing landscape fragmentation’s effects on ecosystem services. Trends Ecol. Evol. 30, 190–198 (2015).

    PubMed  Google Scholar 

  28. Banks-Leite, C. et al. Using ecological thresholds to evaluate the costs and benefits of set-asides in a biodiversity hotspot. Science 345, 1041–1045 (2014).

    CAS  PubMed  Google Scholar 

  29. PLANAVEG: The National Vegetation Recovery Plan Federal Decree No. 8.972/2017 (Brazilian Ministry of Environment, 2017).

  30. Lemes, P., Melo, A. S. & Loyola, R. D. Climate change threatens protected areas of the Atlantic Forest. Biodivers. Conserv. 23, 357–368 (2014).

    Google Scholar 

  31. Global Biodiversity Information Facility Database (GBIF, accessed 15 March 2017); www.gbif.org

  32. SpeciesLink (SpeciesLink, accessd 15 March 2017); http://splink.cria.org.br/

  33. Oliveira-Filho, A. T. NeoTropTree, Flora Arbórea da Região Neotropical: Um Banco de Dados Envolvendo Biogeografia, Diversidade e Conservação (Universidade Federal de Minas Gerais, 2017); http://www.neotroptree.info/

  34. Flora do Brasil 2020 Under Construction (Jardim Botânico do Rio de Janeiro, accessed 20 March 2017); http://floradobrasil.jbrj.gov.br/

  35. Carvalho, G. Package ‘flora’ 2016: Tools for Interacting with the Brazilian Flora 2020 R Package Version 0.3.0 (R Foundation for Statistical Computing, 2017); https://cran.r-project.org/web/packages/flora/flora.pdf

  36. Robertson, T. et al. The GBIF integrated publishing toolkit: facilitating the efficient publishing of biodiversity data on the internet. PLoS ONE 9, 102623 (2014).

    Google Scholar 

  37. Stotz, D. F., Fitzpatrick, J. W., Parker, T. A. III. & Moskovits, D. K. Neotropical Birds: Ecology and Conservation. (Univ. Chicago Press, Chicago, 1996).

  38. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Google Scholar 

  39. Jones, P. G. & Thornton, P. K. Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agric. Syst. 114, 1–5 (2013).

    Google Scholar 

  40. Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).

    Google Scholar 

  41. Cruz-Cárdenas, G., López-Mata, L., Villaseñor, J. L. & Ortiz, E. Potential species distribution modelling and the use of principal component analysis as predictor variables. Rev. Mex. Biodivers. 85, 189–199 (2014).

    Google Scholar 

  42. Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Google Scholar 

  43. Barbet‐Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo‐absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).

    Google Scholar 

  44. Barve, N. et al. The crucial role of the accessible area in ecological niche modelling and species distribution modelling. Ecol. Model. 222, 1810–1819 (2011).

    Google Scholar 

  45. Stokland, J. N., Halvorsen, R. & Støa, B. Species distribution modelling-effect of design and sample size of pseudo-absence observations. Ecol. Model. 222, 1800–1809 (2011).

    Google Scholar 

  46. Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).

    PubMed  Google Scholar 

  47. Sánchez-Tapia, A. et al. Model-R: a framework for scalable and reproducible ecological niche modeling. In High Performance Computing Fourth Latin American Conference, CARLA 2017. Comm. Comp. Inform. Sci. 796, 218–232 (2017).

    Google Scholar 

  48. Lang, D. T. et al. XML: Tools for Parsing and Generating XML within R and S-Plus R Package Version 3.98-1.1 (R Foundation for Statistical Computing, 2013); https://rdrr.io/cran/XML/

  49. Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling R Package Version 1.1-4 (R Foundation for Statistical Computing, 2016); https://cran.r-project.org/web/packages/dismo/index.html

  50. Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the Geospatial Data Abstraction Library R Package Version 0.8-16 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgdal/index.html

  51. Becker, R., Wilks, A., Brownrigg, R., Minka, T. & Deckmyn, A. maps: Draw Geographical Maps R Package Version 3.1. 0 (R Foundation for Statistical Computing, 2016); https://cran.r-project.org/web/packages/maps/index.html

  52. Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine-Open Source (GEOS) R Package Version 0.3-8 (R Foundation for Statistical Computing, 2014); https://cran.r-project.org/web/packages/rgeos/index.html

  53. Liaw, A. & Wiener, M. Classification and regression by randomforest. R News 2, 18–22 (2002).

    Google Scholar 

  54. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch F. e1071: Misc Functions of the Department of Statistics (e1071) R Package Version 1–6 (R Foundation for Statistical Computing, 2014); https://rdrr.io/rforge/e1071/

  55. Poorter, L., Bongers, F. & Rozendall, D. M. A. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).

    CAS  PubMed  Google Scholar 

  56. TNC Maps (The Nature Conservancy, accessed 21 October 2016); http://maps.tnc.org/gis_data.html

  57. WorldClim - Global Climate Data (WorldClim, accessed 27 October 2016); http://www.worldclim.org/current

  58. Chave, L. et al. Improved allometric models to estimate the above ground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).

    Google Scholar 

  59. Mendes, M. S. et al. Look down—there is a gap—the need to include soil data in Atlantic Forest restoration. Restor. Ecol. https://doi.org/10.1111/rec.12875 (2018).

  60. Sanderman, J., Tomislav, H. & Gregory, J. F. Soil carbon debt of 12,000 years of human land use. Proc. Natl Acad. Sci. USA 114, 9575–9580 (2017).

    CAS  PubMed  Google Scholar 

  61. Ministério do Meio Ambiente Potencial de Regeneração Natural da Vegetação no Brasil (World Resources Institute: Brasil, Brasília, 2017).

  62. Anuário da Agricultura Brasileira: Agrianual 2015 (Informa Economics FNP, São Paulo, 2014).

  63. Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008).

    CAS  PubMed  Google Scholar 

  64. Holl, H. D. & Aide, T. M. When and where to actively restore ecosystems? Forest Ecol. Manag. 261, 1558–1563 (2011).

    Google Scholar 

  65. SOS Mata Atlântica & INPE Atlas dos Remanescentes Florestais da Mata Atlântica - Período de 2011 (Fundação SOS Mata Atlântica, São Paulo, 2012); mapas.sosma.org.br

Download references

Acknowledgements

The authors acknowledge the support and inputs from the Brazilian Ministry of the Environment, the Secretariat of the Convention of Biological Diversity and experts from the Intergovernmental Science—Policy Platform on Biodiversity and Ecosystem Services (IPBES). B.B.N.S. acknowledges that this work was supported by the Serrapilheira Institute (grant number Serra-1709-19329). B.B.N.S., R.C., A.I. and A.L. acknowledge the support of the German Ministry of the Environment’s International Climate Initiative. R.L. thanks the CNPq (grant number 308532/2014-7) and the O Boticário Group Foundation for Nature Protection (grant number PROG_0008_2013). F.B., M.F.S. and A.S.T. thank CNPq (grant numbers 441929/2016-8 and 461572/2014-1). M.F.S. and A.S.T. thank CAPES (grant number 88887.145924/2017-00). The authors also acknowledge the support of I. L. Lucas in the preparation of the final version of the manuscript.

Author information

Authors and Affiliations

  1. Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifícia Universidade Católica, Rio de Janeiro, Brazil

    Bernardo B. N. Strassburg, Renato Crouzeilles, Alvaro Iribarrem & Agnieszka Latawiec

  2. International Institute for Sustainability, Rio de Janeiro, Brazil

    Bernardo B. N. Strassburg, Renato Crouzeilles, Alvaro Iribarrem, Felipe Barros, Robin L. Chazdon, Toby A. Gardner & Agnieszka Latawiec

  3. Programa de Pós Graduacão em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Bernardo B. N. Strassburg, Renato Crouzeilles & Fabio Rubio Scarano

  4. Centre of Excellence for Environmental Decisions, School of Biological Sciences, University of Queensland, St Lucia, Queensland, Australia

    Hawthorne L. Beyer

  5. Botanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro, Brazil

    Marinez Ferreira de Siqueira & Andrea Sánchez-Tapia

  6. Department of Zoology, University of Cambridge, Cambridge, UK

    Andrew Balmford

  7. Department of Environmental Science, Instituto de Florestas, Federal Rural University of Rio de Janeiro, Seropédica, Brazil

    Jerônimo Boelsums Barreto Sansevero

  8. Departamento de Ciências Florestais—Esalq/USP, Piracicaba, Brazil

    Pedro Henrique Santin Brancalion

  9. Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA

    Eben North Broadbent

  10. Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA

    Robin L. Chazdon

  11. World Resources Institute, Global Restoration Initiative, Washington, DC, USA

    Robin L. Chazdon

  12. Department of Botanic, Federal University of Minas Gerais, Belo Horizonte, Brazil

    Ary Oliveira Filho

  13. Stockholm Environment Institute, Stockholm, Sweden

    Toby A. Gardner

  14. School of Global Urban and Social Studies, RMIT University, Melbourne, Victoria, Australia

    Ascelin Gordon

  15. Institute of Agricultural Engineering and Informatics, Faculty of Production and Power Engineering, University of Agriculture in Kraków, Kraków, Poland

    Agnieszka Latawiec

  16. School of Environmental Sciences, University of East Anglia, Norwich, UK

    Agnieszka Latawiec

  17. Laboratório de Biogeografia da Conservação, Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Brazil

    Rafael Loyola

  18. Department of Ecology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil

    Jean Paul Metzger

  19. Department of Life Sciences, Imperial College London, Ascot, Berkshire, UK

    Morena Mills

  20. The Nature Conservancy, Arlington, VA, USA

    Hugh P. Possingham

  21. The University of Queensland, St Lucia, Queensland, Australia

    Hugh P. Possingham

  22. Department of Biological Science, Escola Superior de Agricultura Luiz de Queiroz, University of São Paulo, Piracicaba, Brazil

    Ricardo Ribeiro Rodrigues

  23. Department of Ecosystems Conservation, Brazilian Ministry of the Environment (MMA), Brasília, Brazil

    Carlos Alberto de Mattos Scaramuzza

  24. The Brazilian Foundation for Sustainable Development, Rio de Janeiro, Brazil

    Fabio Rubio Scarano

  25. Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas, Federal University of ABC, Santo André, Brazil

    Leandro Tambosi

  26. Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA

    Maria Uriarte

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  1. Bernardo B. N. Strassburg
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Contributions

B.B.N.S. conceived the study, coordinated the development of the multicriteria approach and wrote the first version of the paper. H.L.B., B.B.N.S., R.C. and A.I. led the optimization modelling, while M.F.S., F.B. and A.S.-T. developed the environmental niche modelling. B.B.N.S., H.L.B., R.C., A.I., M.M., H.P.P., F.B., M.F.S., A.B., J.B.B.S., P.H.S.B., R.L.C., A.G., A.L., J.P.M., R.R.R., C.A.M.S., F.R.S., L.T., T.A.G. and M.U. developed the multicriteria prioritization approach. R.L., J.P.M. and A.O.F. contributed biodiversity data, and R.L.C. and E.N.B. developed the climate mitigation surface. C.A.M.S. coordinated the interface with policy applications. All authors analysed the results and provided input into subsequent versions of the manuscript.

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Correspondence to Bernardo B. N. Strassburg.

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Strassburg, B.B.N., Beyer, H.L., Crouzeilles, R. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat Ecol Evol 3, 62–70 (2019). https://doi.org/10.1038/s41559-018-0743-8

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