Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).
Google Scholar
Visconti, P. et al. Protected area targets post-2020. Science 364, eaav6886 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).
Google Scholar
Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61 (2006).
Google Scholar
Pouzols, F. M. et al. Global protected area expansion is compromised by projected land-use and parochialism. Nature 516, 383–386 (2014).
Google Scholar
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).
Google Scholar
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).
Google Scholar
McInnes, L. et al. Do global diversity patterns of vertebrates reflect those of monocots? PLoS ONE 8, e56979 (2013).
Google Scholar
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).
Google Scholar
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).
Google Scholar
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).
Google Scholar
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
Google Scholar
Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. 6, 1080–1082 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Venter, O. et al. Harnessing carbon payments to protect biodiversity. Science 326, 1368–1368 (2009).
Google Scholar
Strassburg, B. B. N. et al. Global congruence of carbon storage and biodiversity in terrestrial ecosystems. Conserv. Lett. 3, 98–105 (2010).
Google Scholar
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
Google Scholar
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).
Google Scholar
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. 5, eaaz0414 (2019).
Google Scholar
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).
Google Scholar
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).
Google Scholar
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).
Google Scholar
Waldron, A. et al. Reductions in global biodiversity loss predicted from conservation spending. Nature 551, 364–367 (2017).
Google Scholar
Possingham, H. P., Bode, M. & Klein, C. J. Optimal conservation outcomes require both restoration and protection. PLoS Biol. 13, e1002052 (2015).
Google Scholar
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat. Ecol. Evol. 2, 1042–1043 (2018).
Google Scholar
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).
Google Scholar
Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Rondinini, C., Stuart, S. & Boitani, L. Habitat suitability models and the shortfall in conservation planning for African vertebrates. Conserv. Biol. 19, 1488–1497 (2005).
Google Scholar
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
Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).
Google Scholar
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).
Google Scholar
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. Zenodo https://doi.org/10.5281/ZENODO.2536040 (2018).
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).
Google Scholar
Kukkala, A. S. & Moilanen, A. Core concepts of spatial prioritisation in systematic conservation planning. Biol. Rev. 88, 443–464 (2013).
Google Scholar
Adams, V. M., Pressey, R. L. & Naidoo, R. Opportunity costs: who really pays for conservation? Biol. Conserv. 143, 439–448 (2010).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Carwardine, J. et al. Cost-effective priorities for global mammal conservation. Proc. Natl Acad. Sci. USA 105, 11446–11450 (2008).
Google Scholar
Rodrigues, A. S. L. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643 (2004).
Google Scholar
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).
Google Scholar
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
Hanson, J. O., Schuster, R., Strimas-Mackey, M. & Bennett, J. R. Optimality in prioritizing conservation projects. Methods Ecol. Evol. 10, 1655–1663 (2019).
Google Scholar
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).
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