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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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 to run comparable optimization analyses has been made available at https://github.com/Martin-Jung/NatureMapCode.
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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.
The authors declare no competing interests.
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 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.
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.
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.
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).
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 for data preparation and Table 1.
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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