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Areas of global importance for conserving terrestrial biodiversity, carbon and water

Nature Ecology & Evolution volume 5, pages 1499–1509 (2021)Cite this article

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An Author Correction to this article was published on 23 September 2021

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Abstract

To meet the ambitious objectives of biodiversity and climate conventions, the international community requires clarity on how these objectives can be operationalized spatially and how multiple targets can be pursued concurrently. To support goal setting and the implementation of international strategies and action plans, spatial guidance is needed to identify which land areas have the potential to generate the greatest synergies between conserving biodiversity and nature’s contributions to people. Here we present results from a joint optimization that minimizes the number of threatened species, maximizes carbon retention and water quality regulation, and ranks terrestrial conservation priorities globally. We found that selecting the top-ranked 30% and 50% of terrestrial land area would conserve respectively 60.7% and 85.3% of the estimated total carbon stock and 66% and 89.8% of all clean water, in addition to meeting conservation targets for 57.9% and 79% of all species considered. Our data and prioritization further suggest that adequately conserving all species considered (vertebrates and plants) would require giving conservation attention to ~70% of the terrestrial land surface. If priority was given to biodiversity only, managing 30% of optimally located land area for conservation may be sufficient to meet conservation targets for 81.3% of the terrestrial plant and vertebrate species considered. Our results provide a global assessment of where land could be optimally managed for conservation. We discuss how such a spatial prioritization framework can support the implementation of the biodiversity and climate conventions.

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Fig. 1: Global areas of conservation importance for terrestrial biodiversity, carbon and water.
Fig. 2: Implications of different relative weights given to carbon or water over achieving species conservation targets.
Fig. 3: Accumulation curves showing how the number of species targets met increases with the amount of land area optimally allocated with and without current protection.
Fig. 4: Change in global areas of biodiversity-only importance after adding plant species.

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Data availability

All maps will be made available through https://unbiodiversitylab.org/ and on a data repository (https://doi.org/10.5281/zenodo.5006332). The raw input data can be requested from the respective data providers (namely, IUCN, GARD, Birdlife International and Royal Botanic Gardens, Kew), and the predicted plant range data will be made available as part of the BIEN initiative47. The IUCN habitat type map used to construct the AOH is made available in the Supplementary Information. The carbon layers will be published openly in a separate data descriptor manuscript and are available upon request. Any additional raw data not listed can be made available from the authors upon reasonable request.

Code availability

Code to run comparable optimization analyses has been made available at https://github.com/Martin-Jung/NatureMapCode.

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Acknowledgements

This work was conducted by the Nature Map consortium. We thank R. Corlett and T. Brooks, who provided feedback on an earlier version of the manuscript. We further thank T. Hengl (OpenLandMap) for his advice on the soil organic carbon analysis. This study has benefited from a number of data providers and networks. We explicitly acknowledge all data providers in a separate Extended Acknowledgements owing to their length (Supplementary Information). The Nature Map project acknowledges funding from Norway’s International Climate and Forest Initiative (NICFI). The collection of the plant data used in this analysis has benefited from funding in the form of GEF grant no. 5810-SPARC, ‘Spatial Planning for Area Conservation in Response to Climate Change’. C.M. acknowledges funding from NSF (National Science Foundation) grant no. DBI‐1913673. R. Gallagher was supported by Australian Research Council DECRA Fellowship no. DE170100208. E.A.N. and X.F. were funded by the Bridging Biodiversity and Conservation Science Program of the University of Arizona. N.M.-H. was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant no. 746334. M.D.M. acknowledges support from the MIUR Rita Levi Montalcini programme. J.-C.S. considers this work a contribution to his VILLUM Investigator project, ‘Biodiversity Dynamics in a Changing World’, funded by VILLUM FONDEN (grant no. 16549), and his Independent Research Fund Denmark—Natural Sciences project, TREECHANGE (grant no. 6108-00078B). The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of the Food and Agriculture Organization of the United Nations.

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Authors and Affiliations

  1. Biodiversity and Natural Resources Program (BNR), International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Martin Jung, Matthew Lewis, Dmitry Schepaschenko, Myroslava Lesiv, Steffen Fritz, Michael Obersteiner & Piero Visconti

  2. UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UK

    Andy Arnell, Shaenandhoa García-Rangel, Jennifer Mark, Lera Miles, Corinna Ravilious, Oliver Tallowin, Arnout van Soesbergen, Valerie Kapos & Neil Burgess

  3. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy

    Xavier de Lamo

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

    Matthew Lewis

  5. Department of Ecology and Evolutionary Biology, University of Connecticut, Stamford, CT, USA

    Cory Merow

  6. Royal Botanic Gardens, Kew, Richmond, UK

    Ian Ondo, Samuel Pironon & Rafaël Govaerts

  7. Botanic Gardens Conservation International, Richmondy, UK

    Malin Rivers

  8. Siberian Federal University, Krasnoyarsk, Russia

    Dmitry Schepaschenko

  9. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    Bradley L. Boyle, Brian J. Enquist, Brian Maitner & Erica A. Newman

  10. Department of Geography, Florida State University, Tallahassee, FL, USA

    Xiao Feng

  11. Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia

    Rachael Gallagher

  12. School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

    Shai Meiri & Gali Ofer

  13. Department of Geography, King’s College London, London, UK

    Mark Mulligan

  14. Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel

    Uri Roll

  15. CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, Vairão, Portugal

    Jeffrey O. Hanson

  16. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Walter Jetz & D. Scott Rinnan

  17. Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Walter Jetz & D. Scott Rinnan

  18. Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy

    Moreno Di Marco

  19. The Nature Conservancy, Arlington, VA, USA

    Jennifer McGowan

  20. Columbia University, New York, NY, USA

    Jeffrey D. Sachs

  21. School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Tasmania, Australia

    Vanessa M. Adams

  22. CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

    Samuel C. Andrew

  23. Department of Biology, University of Kentucky, Lexington, KY, USA

    Joseph R. Burger

  24. Betty and Gordon Moore Center for Science, Conservation International, Arlington, VA, USA

    Lee Hannah & Patrick R. Roehrdanz

  25. Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile

    Pablo A. Marquet

  26. Instituto de Ecología y Biodiversidad (IEB), Santiago, Chile

    Pablo A. Marquet

  27. Centro de Cambio Global UC, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile

    Pablo A. Marquet

  28. The Santa Fe Institute, Santa Fe, NM, USA

    Pablo A. Marquet

  29. Instituto de Sistemas Complejos de Valparaíso (ISCV), Valparaíso, Chile

    Pablo A. Marquet

  30. Manaaki Whenua—Landcare Research, Lincoln, New Zealand

    James K. McCarthy

  31. Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Naia Morueta-Holme

  32. Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

    Daniel S. Park

  33. Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus, Denmark

    Jens-Christian Svenning

  34. Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

    Jens-Christian Svenning

  35. CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, Montpellier, France

    Cyrille Violle

  36. Naturalis Biodiversity Center, Leiden, The Netherlands

    Jan J. Wieringa

  37. World Resources Institute, London, UK

    Graham Wynne

  38. Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifical Catholic University, Rio de Janeiro, Brazil

    Bernardo B. N. Strassburg

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

    Bernardo B. N. Strassburg

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

    Bernardo B. N. Strassburg

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

    Bernardo B. N. Strassburg

  42. Environmental Change Institute, Centre for the Environment, Oxford University, Oxford, UK

    Michael Obersteiner

  43. UN Sustainable Development Solutions Network, Paris, France

    Guido Schmidt-Traub

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  1. Martin Jung
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Contributions

M.J. and P.V. designed the study. M.J. led the analysis and interpretation of the data and drafted the manuscript. P.V. conceived the study and contributed to the analysis and drafting of the manuscript. J.O.H., B.L.B. and C.M. contributed to creating the software used in this work. A.A., C.R., S.G.-R., M. Lewis, D.S., A.v.S., M.M., J. Mark, S.P., I.O., C.M., B.J.E., X.F., P.R.R., B.L.B., B.M. and R. Gallagher contributed to the acquisition, analysis and interpretation of the data. B.B.N.S., J.O.H., M.D.M., J. Mark, W.J., D.S.R., J. McGowan, M.O., M.R. and X.d.L. contributed to the interpretation of the data. G.O., U.R., S.M., M. Lewis, R. Gallagher, M. Lesiv and O.T. contributed to the acquisition and interpretation of the data. X.d.L., V.K., L.M., N.B., G.W., S.F., J.D.S., G.S.-T. and M.O. contributed to the conception of the study. V.M.A., S.C.A., J.R.B., L.H., R. Govaerts, P.A.M., J.K.M., N.M.-H., E.A.N., D.S.P., P.R.R., J.-C.S., C.V. and J.J.W. provided data and contributed to the interpretation of the data. All authors contributed to revising the manuscript.

Corresponding authors

Correspondence to Martin Jung or Piero Visconti.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Richard Schuster 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.

Extended data

Extended Data Fig. 1 Uncertainty in ranks of areas of importance for biodiversity, carbon and water.

Calculated as coefficient of variation across optimal solutions with different representative sets. Expressed as percentage with lower values indicating higher precision of ranks. Map can be interpreted as overall confidence in the mapped ranks (Fig. 1), given existing biases in species range data. Maps are at 10 km resolution in Mollweide projection.

Extended Data Fig. 2 Global areas of importance for conserving biodiversity, carbon or water only.

Ranked hierarchical maps by the most (1–10) and least important areas (90–100) to conserve all of (a) biodiversity, (b) carbon and (c) water globally. Maps are at 10 km resolution in Mollweide projection.

Extended Data Fig. 3 Global areas of importance for biodiversity and carbon or biodiversity and water.

Showing an optimization across 10 representative sets for either (a) biodiversity and carbon or (b) biodiversity and water. All assets were jointly optimized and ranked hierarchical by the most (1–10) and least important areas (90–100) to conserve globally. Maps are at 10 km resolution in Mollweide projection.

Extended Data Fig. 4 Varying weights and shortfalls.

We tested for various weights (points) given to either carbon or water and how it affected the trade-off with biodiversity conserved across a selection of different budgets (10%, 30%, 50%). We varied carbon or water weights across a range from none, for example equivalent to a single species, to equal, where weights are estimated as the sum of all other feature weights (all species + 1 other NCP) weighting (as shown in Fig. 2) with all assets (biodiversity, carbon and water). The x,y and z-axis show the shortfall as a percentage of their respective targets for either biodiversity, carbon or water.

Extended Data Fig. 5 Global areas of value for conservation and accumulation curves for terrestrial biodiversity, carbon and water without biome splits.

(a) All assets were jointly optimized with equal weighting and ranked hierarchical by the most (1–10) and least (90–100) important areas to conserve globally. The map is at 10 km resolution in Mollweide projection. (b–g) Proportion of species conservation targets reached for an optimal prioritization (b,d,f) and considering current protected areas (c,e,g). (b,c) Target accumulation curves for analysis variants including other assets; (d,e) for different taxonomic groups when optimizing biodiversity only to conservation; (f,g) for species classified as threatened or not (see Methods) when optimizing for biodiversity only.

Extended Data Fig. 6 Global areas of importance for biodiversity, carbon and water considering current protected areas.

All assets were jointly optimized and ranked hierarchical by the most (1–10) and least important areas (90–100) to conserve globally. The proportion of grid cells currently managed for conservation (https://www.protectedplanet.net) are considered to be part of the most important areas. Maps are at 10 km resolution in Mollweide projection.

Extended Data Fig. 7 Difference in the top-ranked 10% solution for varying vertebrate species weights.

For each biodiversity feature a weight was assigned equating to either no differential weight (red), current threat category (green) or evolutionary distinctiveness (ED) (blue). Comparison was made only for vertebrate species, where data on both threat category and evolutionary distinctiveness was available. Grid cells coloured in black were selected in all three solutions. Map in Mollweide projection at 10 km resolution. The line plot shows the amount of land area necessary for all species to reach all conservation targets, defined as the amount of land area needed for a species to be considered non-threatened (see Methods). Shown for either no weight (red), species weighted by threat status (green) and weighted by evolutionary distinctiveness (blue). The inset zoom highlights the difference among solutions at a budget of 10% terrestrial land area. The confidence bounds of accumulation curves indicate the uncertainty among representative sets.

Extended Data Fig. 8 Comparison of representative sets spatially and in range size distributions.

Compared to a full dataset, both subsampling at random and per WGSRPD region produces similar patterns in space and species area-size distributions. (a) Spatial map in Mollweide projection showing aggregated richness layers of all vertebrate species for the full dataset, a random sample and a representative sample by WGSRPD level 2 regions. Colours indicate low and high species richness (blue to brown). (b) Shows the log10-transformed Area of Habitat (AOH) of all species in the full dataset (dark blue) compared to representative subsets of species (other colours).

Extended Data Fig. 9 Accumulation curves showing how the number of species targets met increases with amount of land optimally allocated to conservation.

Estimates shown for representative subsets (dotted line) and for all species included (solid line).

Extended Data Fig. 10 Comparison of global areas of importance at 10 km and 50 km areas.

Comparisons in variants of areas of importance for conserving biodiversity only; biodiversity and carbon; and biodiversity, carbon and water. Colour scale of map as in Fig. 1. Inset graphs show how the number of species conservation targets met increases with amount of land optimally allocated to conservation for both 10 km (blue) and 50 km (orange).

Supplementary information

Supplementary Information

Supplementary information for data preparation and Table 1.

Reporting Summary

Supplementary Table 2

Priority rank per country for each of the main problem variants—namely, biodiversity only, biodiversity and carbon, biodiversity and water, and biodiversity, carbon and water. Calculated as either ordinary arithmetic or area-weighted rank per country.

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Jung, M., Arnell, A., de Lamo, X. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat Ecol Evol 5, 1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7

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