Areas of global importance for conserving terrestrial biodiversity, carbon and water.
Journal
Nature ecology & evolution
ISSN: 2397-334X
Titre abrégé: Nat Ecol Evol
Pays: England
ID NLM: 101698577
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
10
12
2020
accepted:
07
07
2021
pubmed:
26
8
2021
medline:
19
1
2022
entrez:
25
8
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34429536
doi: 10.1038/s41559-021-01528-7
pii: 10.1038/s41559-021-01528-7
doi:
Substances chimiques
Carbon
7440-44-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1499-1509Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
pubmed: 31831642
doi: 10.1126/science.aax3100
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
pubmed: 32908312
doi: 10.1038/s41586-020-2705-y
Butchart, S. H. M., Miloslavich, P., Reyers, B. & Subramanian, S. M. in IPBES Global Assessment on Biodiversity and Ecosystem Services (eds Berkes, F. & Brooks, T.) Ch. 3 (IPBES, 2019).
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).
pubmed: 29078344
pmcid: 5676916
doi: 10.1073/pnas.1710465114
First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (CBD, 2021); https://www.cbd.int/meetings/WG2020-03
Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).
pubmed: 30819953
doi: 10.1126/science.aaw2741
Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).
pubmed: 31016243
pmcid: 6474764
doi: 10.1126/sciadv.aaw2869
Visconti, P. et al. Protected area targets post-2020. Science 364, eaav6886 (2019).
doi: 10.1126/science.aav6886
Soto-Navarro, C. et al. Mapping co-benefits for carbon storage and biodiversity to inform conservation policy and action. Philos. Trans. R. Soc. B 375, 20190128 (2020).
doi: 10.1098/rstb.2019.0128
Greve, M., Reyers, B., Mette Lykke, A. & Svenning, J.-C. Spatial optimization of carbon-stocking projects across Africa integrating stocking potential with co-benefits and feasibility. Nat. Commun. 4, 2975 (2013).
pubmed: 24352139
doi: 10.1038/ncomms3975
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).
pubmed: 33057198
doi: 10.1038/s41586-020-2784-9
Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61 (2006).
pubmed: 16825561
doi: 10.1126/science.1127609
Pouzols, F. M. et al. Global protected area expansion is compromised by projected land-use and parochialism. Nature 516, 383–386 (2014).
doi: 10.1038/nature14032
Allan, J. R. et al. Conservation attention necessary across at least 44% of Earth’s terrestrial area to safeguard biodiversity. Preprint at bioRxiv https://doi.org/10.1101/839977 (2019).
Fastre, S., Mogg, C., Jung, M. & Visconti, P. Targeted expansion of protected areas to maximise the persistence of terrestrial mammals. Preprint at bioRxiv https://doi.org/10.1101/608992 (2019).
Rinnan, D. S. & Jetz, W. Terrestrial conservation opportunities and inequities revealed by global multi-scale prioritization. Preprint at bioRxiv https://doi.org/10.1101/2020.02.05.936047 (2020).
Hannah, L. et al. 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography 43, 943–953 (2020).
doi: 10.1111/ecog.05166
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).
pubmed: 19470638
pmcid: 2685248
doi: 10.1073/pnas.0810306106
McInnes, L. et al. Do global diversity patterns of vertebrates reflect those of monocots? PLoS ONE 8, e56979 (2013).
pubmed: 23658679
pmcid: 3641068
doi: 10.1371/journal.pone.0056979
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).
pubmed: 28538726
doi: 10.1038/nature22368
Daru, B. H. et al. Spatial overlaps between the global protected areas network and terrestrial hotspots of evolutionary diversity. Glob. Ecol. Biogeogr. 28, 757–766 (2019).
doi: 10.1111/geb.12888
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).
pubmed: 31601772
doi: 10.1126/science.aaw3372
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
pubmed: 27418509
doi: 10.1126/science.aaf2201
Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. 6, 1080–1082 (2019).
pubmed: 34691979
pmcid: 8291457
doi: 10.1093/nsr/nwz136
Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (W. W. Norton, 2016).
Laffoley, D. et al. An introduction to ‘other effective area-based conservation measures’ under Aichi Target 11 of the Convention on Biological Diversity: origin, interpretation and emerging ocean issues. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 130–137 (2017).
doi: 10.1002/aqc.2783
IUCN Red List Categories and Criteria Version 3.1 (IUCN, 2012).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
doi: 10.1038/35002501
pubmed: 10706275
Venter, O. et al. Harnessing carbon payments to protect biodiversity. Science 326, 1368–1368 (2009).
pubmed: 19965752
doi: 10.1126/science.1180289
Strassburg, B. B. N. et al. Global congruence of carbon storage and biodiversity in terrestrial ecosystems. Conserv. Lett. 3, 98–105 (2010).
doi: 10.1111/j.1755-263X.2009.00092.x
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
pubmed: 28608869
pmcid: 5451287
doi: 10.1093/biosci/bix014
Woodley, S. et al. A review of evidence for area-based conservation targets for the post-2020 global biodiversity framework. Parks 25, 31–46 (2019).
doi: 10.2305/IUCN.CH.2019.PARKS-25-2SW2.en
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. 5, eaaz0414 (2019).
pubmed: 31807712
pmcid: 6881168
doi: 10.1126/sciadv.aaz0414
Rapacciuolo, G. et al. Species diversity as a surrogate for conservation of phylogenetic and functional diversity in terrestrial vertebrates across the Americas. Nat. Ecol. Evol. 3, 53–61 (2019).
pubmed: 30532042
doi: 10.1038/s41559-018-0744-7
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).
pubmed: 24960185
pmcid: 4068989
doi: 10.1371/journal.pbio.1001891
Chauvenet, A. L. M., Kuempel, C. D., McGowan, J., Beger, M. & Possingham, H. P. Methods for calculating Protection Equality for conservation planning. PLoS ONE 12, e0171591 (2017).
pubmed: 28199341
pmcid: 5310882
doi: 10.1371/journal.pone.0171591
Waldron, A. et al. Reductions in global biodiversity loss predicted from conservation spending. Nature 551, 364–367 (2017).
pubmed: 29072294
doi: 10.1038/nature24295
Possingham, H. P., Bode, M. & Klein, C. J. Optimal conservation outcomes require both restoration and protection. PLoS Biol. 13, e1002052 (2015).
pubmed: 25625277
pmcid: 4308106
doi: 10.1371/journal.pbio.1002052
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat. Ecol. Evol. 2, 1042–1043 (2018).
pubmed: 29867100
pmcid: 6027986
doi: 10.1038/s41559-018-0573-8
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).
pubmed: 30858594
doi: 10.1038/s41559-019-0826-1
Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).
pubmed: 28389103
pmcid: 5489079
doi: 10.1016/j.tree.2017.02.002
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).
pubmed: 31534225
doi: 10.1038/s41586-019-1567-7
World Checklist of Vascular Plants (WCVP, 2020); http://wcvp.science.kew.org/
The IUCN Red List of Threatened Species Version 2019.2 (IUCN, 2019); www.iucnredlist.org
Bird Species Distribution Maps of the World Version 2019.1 (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdis
Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).
pubmed: 28993667
doi: 10.1038/s41559-017-0332-2
Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical informationnetwork to investigate the ecological impacts of global climate change on plant biodiversity. Preprint at PeerJ https://doi.org/10.7287/peerj.preprints.2615 (2016).
Maitner, B. S. et al. The BIEN R package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
doi: 10.1111/2041-210X.12861
Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).
doi: 10.1111/gcb.12712
Forest Inventory and Analysis National Program (US Forest Service, 2013); www.fia.fs.fed.us/
Peet, R., Lee, M., Jennings, M. & Faber-Langendoen, D. VegBank—a permanent, open-access archive for vegetation-plot data. Biodivers. Ecol. 4, 233–241 (2012).
doi: 10.7809/b-e.00080
Boyle, B. & Enquist, B. SALVIAS—the SALVIAS vegetation inventory database. Biodivers. Ecol. https://doi.org/10.7809/b-e.00086 (2012).
Wiser, S., Bellingham, P. & Burrows, L. Managing biodiversity information: development of New Zealand’s National Vegetation Survey databank. N. Z. J. Ecol. 25, 1–17 (2001).
DeWalt, S. J., Bourdy, G., ChÁvez de Michel, L. R. & Quenevo, C. Ethnobotany of the Tacana: quantitative inventories of two permanent plots of northwestern Bolivia. Econ. Bot. 53, 237–260 (1999).
doi: 10.1007/BF02866635
Dauby, G. et al. RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74, 1–18 (2001).
Fegraus, E. Tropical ecology assessment and monitoring network (TEAM Network). Biodivers. Ecol. 4, 287–287 (2012).
doi: 10.7809/b-e.00085
Oliveira-Filho, A. T. in Fitossociologia no Brasil—Métodos e Estudos de Caso Vol. 2 (eds. Eisenlohr, P. V. et al.) Ch. 19 (Editora UFV, 2015).
Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).
doi: 10.1111/conl.12158
Rondinini, C., Stuart, S. & Boitani, L. Habitat suitability models and the shortfall in conservation planning for African vertebrates. Conserv. Biol. 19, 1488–1497 (2005).
doi: 10.1111/j.1523-1739.2005.00204.x
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).
pubmed: 31324345
doi: 10.1016/j.tree.2019.06.009
Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).
pubmed: 32759943
pmcid: 7406504
doi: 10.1038/s41597-020-00599-8
Habitats Classification Scheme Version 3.1 (IUCN, 2012).
Lesiv, M. et al. Global planted trees extent 2015. Zenodo https://doi.org/10.5281/zenodo.3931930 (2020).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
doi: 10.1016/j.rse.2017.06.031
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).
pubmed: 27250865
doi: 10.1111/ele.12624
Brummitt, R. K. World Geographical Scheme for Recording Plant Distributions (International Working Group on Taxonomic Databases for Plant Sciences, 2001).
Santoro, M. GlobBiomass—Global Datasets of Forest Biomass (PANGAEA, 2018); https://doi.org/10.1594/PANGAEA.894711
Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1. (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084
Buchhorn, M. et al. Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020).
doi: 10.3390/rs12061044
Bouvet, A. et al. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sens. Environ. 206, 156–173 (2018).
doi: 10.1016/j.rse.2017.12.030
Xia, J. et al. Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sens. 6, 1783–1802 (2014).
doi: 10.3390/rs6031783
Spawn, S. A., Lark, T., & Gibbs, H. New Global Biomass Map for the Year 2010 (American Geophysical Union, 2017).
Schepaschenko, D. et al. Improved estimates of biomass expansion factors for Russian forests. Forests 9, 312 (2018).
doi: 10.3390/f9060312
Eggleston, S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 5 (IPCC, 2006).
Hengl, T. & Wheeler, I. Soil organic carbon stock in kg/m
Hengl, T. & Nauman, T. Predicted USDA soil orders at 250 m (probabilities) (version v0.1). Zenodo https://doi.org/10.5281/zenodo.2658183 (2019).
Mulligan, M. WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally. Hydrol. Res. 44, 748–769 (2013).
doi: 10.2166/nh.2012.217
Mulligan, M. in The Impacts of Climate Change on Water Resources in Agriculture (eds Zolin, A. C. & Rodrigues, R. A. R.) 184–204 (CRC, 2014).
van Soesbergen, A. & Mulligan, M. Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru. Int. J. Water Res. Dev. 34, 150–165 (2018).
doi: 10.1080/07900627.2016.1259101
Van Soesbergen, A. & Mulligan, M. Uncertainty in data for hydrological ecosystem services modelling: potential implications for estimating services and beneficiaries for the CAZ Madagascar. Ecosyst. Serv. 33, 175–186 (2018).
doi: 10.1016/j.ecoser.2018.08.005
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).
pubmed: 31819059
pmcid: 6901482
doi: 10.1038/s41597-019-0300-6
Kukkala, A. S. & Moilanen, A. Core concepts of spatial prioritisation in systematic conservation planning. Biol. Rev. 88, 443–464 (2013).
pubmed: 23279291
doi: 10.1111/brv.12008
Adams, V. M., Pressey, R. L. & Naidoo, R. Opportunity costs: who really pays for conservation? Biol. Conserv. 143, 439–448 (2010).
doi: 10.1016/j.biocon.2009.11.011
Armsworth, P. R. Inclusion of costs in conservation planning depends on limited datasets and hopeful assumptions. Ann. N. Y. Acad. Sci. 1322, 61–76 (2014).
pubmed: 24919962
doi: 10.1111/nyas.12455
Eklund, J., Arponen, A., Visconti, P. & Cabeza, M. Governance factors in the identification of global conservation priorities for mammals. Philos. Trans. R. Soc. B 366, 2661–2669 (2011).
doi: 10.1098/rstb.2011.0114
McCreless, E., Visconti, P., Carwardine, J., Wilcox, C. & Smith, R. J. Cheap and nasty? The potential perils of using management costs to identify global conservation priorities. PLoS ONE 8, e80893 (2013).
pubmed: 24260502
pmcid: 3829910
doi: 10.1371/journal.pone.0080893
Carwardine, J. et al. Cost-effective priorities for global mammal conservation. Proc. Natl Acad. Sci. USA 105, 11446–11450 (2008).
pubmed: 18678892
pmcid: 2495010
doi: 10.1073/pnas.0707157105
Rodrigues, A. S. L. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643 (2004).
pubmed: 15071592
doi: 10.1038/nature02422
Arponen, A., Heikkinen, R., Thomas, C. D. & Moilanen, A. The value of biodiversity in reserve selection: representation, species weighting, and benefit functions. Conserv. Biol. 19, 2009–2014 (2005).
doi: 10.1111/j.1523-1739.2005.00218.x
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).
doi: 10.1016/j.ecolmodel.2016.02.005
Hanson, J. O., Schuster, R., Strimas-Mackey, M. & Bennett, J. R. Optimality in prioritizing conservation projects. Methods Ecol. Evol. 10, 1655–1663 (2019).
doi: 10.1111/2041-210X.13264
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0.3. (2020); https://CRAN.R-project.org/package=prioritizr
Gurobi Optimizer Reference Manual (Gurobi Optimization, 2019).