Protecting nature’s contributions to people requires accelerating extinction risk assessment and better integrating evolutionary, functional and used diversity with conservation planning. Here, we report machine learning extinction risk predictions for 1,381 palm species (Arecaceae), a plant family of high socio-economic and ecological importance. We integrate these predictions with published assessments for 508 species (covering 75% of all palm species) and we identify top-priority regions for palm conservation on the basis of their proportion of threatened evolutionarily distinct, functionally distinct and used species. Finally, we explore palm use resilience to identify non-threatened species that could potentially serve as substitutes for threatened used species by providing similar products. We estimate that over a thousand palms (56%) are probably threatened, including 185 species with documented uses. Some regions (New Guinea, Vanuatu and Vietnam) emerge as top ten priorities for conservation only after incorporating machine learning extinction risk predictions. Potential substitutes are identified for 91% of the threatened used species and regional use resilience increases with total palm richness. However, 16 threatened used species lack potential substitutes and 30 regions lack substitutes for at least one of their threatened used palm species. Overall, we show that hundreds of species of this keystone family face extinction, some of them probably irreplaceable, at least locally. This highlights the need for urgent actions to avoid major repercussions on palm-associated ecosystem processes and human livelihoods in the coming decades.
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Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).
van der Sande, M. T. et al. Biodiversity in species, traits, and structure determines carbon stocks and uptake in tropical forests. Biotropica 49, 593–603 (2017).
Grace, O. M. et al. Plant power: opportunities and challenges for meeting sustainable energy needs from the plant and fungal kingdoms. Plants People Planet 2, 446–462 (2020).
Howes, M. J. R. et al. Molecules from nature: reconciling biodiversity conservation and global healthcare imperatives for sustainable use of medicinal plants and fungi. Plants People Planet 2, 463–481 (2020).
Ulian, T. et al. Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445 (2020).
Brondizio, E., Diaz, S., Settele, J. & Ngo, H. T. (eds) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on biodiversity and ecosystem services. Zenodo https://doi.org/10.5281/zenodo.3831673 (2019).
Bennun, L. et al. The value of the IUCN Red List for business decision-making. Conserv. Lett. 11, e12353 (2018).
Betts, J. et al. A framework for evaluating the impact of the IUCN Red List of threatened species. Conserv. Biol. 34, 632–643 (2020).
Maira, L. et al. Achieving international species conservation targets: closing the gap between top-down and bottom-up approaches. Conserv. Soc. 19, 25–33 (2021).
IUCN Red List version 2022-2: Table 1a (IUCN, 2022); https://www.iucnredlist.org/resources/summary-statistics#Figure2
Rivers, M. The global tree assessment—red listing the world’s trees. BGjournal 14, 16–19 (2017).
Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).
Silva, S. V. et al. Global estimation and mapping of the conservation status of tree species using artificial intelligence. Front. Plant Sci. 13, 839792 (2022).
ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 12 October 2021); https://tools.bgci.org/threat_search.php
Bachman, S. P., Nic Lughadha, E. M. & Rivers, M. C. Quantifying progress toward a conservation assessment for all plants. Conserv. Biol. 32, 516–524 (2018).
Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN Red List. Conserv. Lett. 7, 126–130 (2014).
Cazalis, V. et al. Bridging the research–implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).
Dauby, G. et al. ConR: an R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecol. Evol. 7, 11292–11303 (2017).
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).
Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).
Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).
Pelletier, T. A., Carstens, B. C., Tank, D. C., Sullivan, J. & Espíndola, A. Predicting plant conservation priorities on a global scale. Proc. Natl Acad. Sci. USA 115, 13027–13032 (2018).
Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).
Walker, B. E., Leão, T. C. C., Bachman, S. P., Bolam, F. C. & Nic Lughadha, E. Caution needed when predicting species threat status for conservation prioritization on a global scale. Front. Plant Sci. 11, 520 (2020).
Lughadha, E. N. et al. The use and misuse of herbarium specimens in evaluating plant extinction risks. Philos. Trans. R. Soc. B 374, 20170402 (2019).
Walker, B. E., Leão, T. C. C., Bachman, S. P., Lucas, E. & Nic Lughadha, E. M. Evidence-based guidelines for developing automated assessment methods. Preprint at https://ecoevorxiv.org/zxq6s/ (2021).
Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).
Grenié, M., Denelle, P., Tucker, C. M., Munoz, F. & Violle, C. funrar: an R package to characterize functional rarity. Divers. Distrib. 23, 1365–1371 (2017).
Lindegren, M., Holt, B. G., MacKenzie, B. R. & Rahbek, C. A global mismatch in the protection of multiple marine biodiversity components and ecosystem services. Sci. Rep. 8, 4099 (2018).
Pollock, L. J. et al. Protecting biodiversity (in all its complexity): new models and methods. Trends Ecol. Evol. 35, 1119–1128 (2020).
Arnan, X., Cerdá, X. & Retana, J. Relationships among taxonomic, functional, and phylogenetic ant diversity across the biogeographic regions of Europe. Ecography 40, 448–457 (2017).
Wong, J. S. Y. et al. Comparing patterns of taxonomic, functional and phylogenetic diversity in reef coral communities. Coral Reefs 37, 737–750 (2018).
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).
Cámara-Leret, R. et al. Fundamental species traits explain provisioning services of tropical American palms. Nat. Plants 3, 16220 (2017).
Saslis-Lagoudakis, C. H. et al. Phylogenies reveal predictive power of traditional,medicinein bioprospecting. Proc. Natl Acad. Sci. USA 109, 15835–15840 (2012).
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).
Molina-Venegas, R., Rodríguez, M., Pardo-de-Santayana, M., Ronquillo, C. & Mabberley, D. J. Maximum levels of global phylogenetic diversity efficiently capture plant services for humankind. Nat. Ecol. Evol. 5, 583–588 (2021).
Molina-Venegas, R. Conserving evolutionarily distinct species is critical to safeguard human well-being. Sci. Rep. 11, 24187 (2021).
Zaman, W. et al. Predicting potential medicinal plants with phylogenetic topology: inspiration from the research of traditional Chinese medicine. J. Ethnopharmacol. 281, 114515 (2021).
Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural heritage. Sci. Adv. 5, eaaz1455 (2019).
Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 2267 (2022).
Johnson, D. V. Tropical Palms 2010 Revision Non-Wood Forest Products 10 (FAO, 2010).
Johnson, D. V. & Sunderland, T. C. H. Rattan Glossary and Compendium Glossary with Emphasis on Africa Non-Wood Forest Products 16 (FAO, 2004).
Ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).
Zona, S. & Henderson, A. A review of animal-mediated seed dispersal of palms. Selbyana 11, 6–21 (1989).
Kissling, W. D. et al. PalmTraits 1.0, a species-level functional trait database of palms worldwide. Sci. Data 6, 178 (2019).
Tomlinson, P. B. The uniqueness of palms. Bot. J. Linn. Soc. 151, 5–14 (2006).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Muscarella, R. et al. The global abundance of tree palms. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).
Dransfield, J. et al. Genera Palmarum: The Evolution and Classification of Palms (Kew Publishing, 2008).
Diazgranados, M. et al. World Checklist of Useful Plant Species (Royal Botanic Gardens, Kew, 2020).
Couvreur, T. L. P. & Baker, W. J. Tropical rain forest evolution: palms as a model group. BMC Biol. 11, 2–5 (2013).
Faurby, S., Eiserhardt, W. L., Baker, W. J. & Svenning, J. Molecular phylogenetics and evolution: an all-evidence species-level supertree for the palms (Arecaceae). Mol. Phylogenet. Evol. 100, 57–69 (2016).
The IUCN Red List of Threatened Species Version 2021-2 (IUCN, accessed 12 October 2021); https://www.iucnredlist.org
Baker, W. J. & Dransfield, J. Beyond genera Palmarum: progress and prospects in palm systematics. Bot. J. Linn. Soc. 182, 207–233 (2016).
Henderson, A. A revision of Calamus (Arecaceae, Calamoideae, Calameae, Calaminae). Phytotaxa https://doi.org/10.11646/phytotaxa.445.1.1 (2020).
Rakotoarinivo, M., Dransfield, J., Bachman, S. P., Moat, J. & Baker, W. J. Comprehensive red list assessment reveals exceptionally high extinction risk to Madagascar palms. PLoS ONE 9, e103684 (2014).
Cosiaux, A. et al. Low extinction risk for an important plant resource: conservation assessments of continental African palms (Arecaceae/Palmae). Biol. Conserv. 221, 323–333 (2018).
Johnson, D. & UICN/SSC Palm Specialist Group (eds) Palms, Their Conservation and Sustained Utilization—Status Survey and Conservation Action Plan (Union Internationale pour la Conservation de la Nature et de ses Ressources, 1996).
Bachman, S., Walker, B. E., Barrios, S., Copeland, A. & Moat, J. Rapid least concern: towards automating red list assessments. Biodivers. Data J. 8, e47018 (2020).
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. https://doi.org/10.1126/sciadv.aaz0414 (2019).
Vieilledent, G. et al. Combining global tree cover loss data with historical national forest cover maps to look at six decades of deforestation and forest fragmentation in Madagascar. Biol. Conserv. 222, 189–197 (2018).
Gaveau, D. L. A. et al. Rise and fall of forest loss and industrial plantations in Borneo (2000–2017). Conserv. Lett. 12, e12622 (2019).
Gamoga, G., Turia, R., Abe, H., Haraguchi, M. & Iuda, O. The forest extent in 2015 and the drivers of forest change between 2000 and 2015 in Papua New Guinea: deforestation and forest degradation in Papua New Guinea. Case Stud. Environ. 5, 1442018 (2021).
Cámara-Leret, R. & Bascompte, J. Language extinction triggers the loss of unique medicinal knowledge. Proc. Natl Acad. Sci. USA 118, e2103683118 (2021).
Henderson, A., Fischer, B., Scariot, A., Whitaker Pacheco, M. A. & Pardini, R. Flowering phenology of a palm community in a central Amazon forest. Brittonia 52, 149–159 (2000).
Olivares, I. & Galeano, G. Leaf and inflorescence production of the wine palm (Attalea butyracea) in the dry Magdalena river valley, Colombia. Caldasia 35, 37–48 (2013).
Voeks, R. A. Disturbance pharmacopoeias: medicine and myth from the humid tropics. Ann. Assoc. Am. Geogr. 94, 868–888 (2004).
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).
Govaerts, R., Dransfield, J., Zona, S. & Henderson, A. World Checklist of Arecaceae (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://wcsp.science.kew.org/
Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0 (2021).
Zizka, A. et al. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://www.plantsoftheworldonline.org/
South, A. rworldmap v.1.3-6: Mapping global data (2016).
Bivand, R. et al. maptools v.0.9-2: Tools for handling spatial objects (2017).
Arel-Bundock, V., Enevoldsen, N. & Yetman, C. countrycode: an R package to convert country names and country codes. J. Open Source Softw. 3, 848 (2018).
Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps v.3.3.0: Draw geographical maps (2018).
Pebesma, E. et al. sp v.1.2-7: Classes and methods for spatial data (2018).
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
Wickham, H., Hester, J. & Chang, W. devtools v.1.13.5: Tools to make developing R packages easier (2018).
World Geographic Scheme for Recording Plant Distributions Standard (TDWG, 2001); http://www.tdwg.org/standards/109
Brummitt, R. K. World Geographical Scheme for Recording Plant Distributions (Hunt Institute for Botanical Documentation, 2001).
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Moat, J. & Bachman, S. P. rCAT v.0.1.6: Conservation assessment tools (2017).
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 10 June 2020); http://www.plantsoftheworldonline.org/
Csárdi, G. & FitzJohn, R. progress v.1.2.2: Terminal progress bars (2019).
Microsoft Corporation & Weston, S. doParallel: Foreach parallel adaptor for the ‘parallel’ package. R package version 1.0.16 (2020).
Microsoft Corporation & Weston, S. foreach: Provides foreach looping construct. R package version 1.5.0 (2020).
Ooms, J., Lang, D. T. & Hilaiel, L. jsonlite v.1.7.2: A simple and robust JSON parser and generator for R (2020).
Wickham, H. httr v.1.4.2: Tools for working with URLs and HTTP (2020).
Global Human Footprint (Geographic), v2 (1995 – 2004) (SEDAC, accessed 14 May 2018); https://doi.org/10.7927/H4M61H5F
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Wickham, H. plyr v.1.8.6: Tools for splitting, applying and combining data (2021).
Wickham, H. & RStudio. tidyr v.1.1.4: Tidy messy data (2021).
Wickham, H., François, R., Henry, L. & Müller, K. dplyr v.1.0.7: A grammar of data manipulation (2021).
Bivand, R. et al. rgdal v.1.5-8: Bindings for the ‘geospatial’ data abstraction library (2020).
Greenberg, J. A. & Mattiuzzi, M. gdalUtils v.126.96.36.199: Wrappers for the Geospatial data Abstraction Library (GDAL) utilities (2020).
Hijmans, R. J. et al. raster v.3.1-5: Geographic data analysis and modeling (2020).
The IUCN Red List of Threatened Species (IUCN, accessed 22 March 2018); https://www.iucnredlist.org/
ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 1 March 2018); https://tools.bgci.org/threat_search.php
Chamberlain, S., ROpenSci & Salmon, M. rredlist: ‘IUCN’ Red List client (2020).
Wickham, H. stringr v.1.4.0: Simple, consistent wrappers for common string operations (2019).
Gagolewski, M. & Tartanus, B. stringi v.1.7.5: Character string processing facilities (2021).
Kuhn, M. caret: Classification and regression training. R package version 6.0-86 (2020).
Torgo, L. Data Mining with R, Learning with Case Studies (Chapman and Hall/CRC, 2010).
Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2020).
Stokely, M. HistogramTools: Utility functions for R histograms. R package version 0.3.2 (2015).
Sarkar, D. et al. lattice v.0.20-40: Trellis graphics for R (2020).
Wickham, H. ggplot2 Elegant Graphics for Data Analysis (Springer, 2016).
Auguie, B. & Antonov, A. gridExtra v.2.3: Miscellaneous functions for ‘grid’ graphics (2017).
Pruim, R., Kaplan, D. T. & Horton, N. J. mosaic v.1.6.0: Project MOSAIC statistics and mathematics teaching utilities (2020).
Meyer, D. & Buchta, C. proxy v.0.4-23: Distance and similarity measures (2019).
Wickham, H. & Seidel, D. scales v.1.1: Scale functions for visualization (2019).
Branco, P., Ribeiro, R. & Torgo, L. UBL v.0.0.6: An implementation of re-sampling approaches to utility-based learning for both classification and regression tasks (2017).
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).
Ripley, B. & Venables, W. nnet v.7.3-13: Feed-forward neural networks and multinomial log-linear models (2020).
Warnes, G. R. et al. gdata v.2.18.0: Various R programming tools for data manipulation (2017).
Wright, M. N., Wager, S. & Probst, P. ranger v.0.12.1: A fast implementation of random forests (2020).
Arya, S., Mount, D., Kemp, S. E. & Jefferis, G. RANN v.2.6.1: Fast nearest neighbour search (wraps ANN Library) using L2 metric (2019).
Meyer, D. et al. e1071 v.1.7-3: Misc Functions of the Department of Statistics, Probability Theory Group (formerly: E1071), TU Wien (2019).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
Greenwell, B. fastshap v.0.0.7: Fast approximate Shapley values (2021).
Greenwell, B. vip v.0.3.2: Variable importance plots (2020).
Donoghoe, M. W. glm2 v.1.2.1: Fitting generalized linear models (2018).
Wickham, H. reshape2 v.1.4.4: Flexibly reshape data: a reboot of the reshape package (2020).
Robin, X. et al. pROC v.1.18.0: Display and analyze ROC curves (2020).
Warnes, G. R. et al. gplots v.3.0.3: Various R programming tools for plotting data (2019).
Müller, K. & Bryan, J. here v.1.0.1: A simpler way to find your files (2017).
Wickham, H., Hester, J., Francois, R., Jylänki, J. & Jørgensen, M. readr v.1.3.1: Read rectangular text data (2018).
Wickham, H. et al. readxl v.1.3.1: Read Excel files (2019).
Henry, L. & Wickham, H. purrr v.0.3.4: Functional programming tools (2020).
Lin Pedersen, T. ggforce v.0.3.1: Accelerating ‘ggplot2’ (2019).
Lin Pedersen, T. patchwork v.1.0.0: The composer of plots (2019).
Hester, J. glue v.1.3.1: Interpreted string literals (2019).
Ooms, J. & McNamara, J. writexl v.1.2: Export data frames to Excel ‘xlsx’ format (2019).
Horikoshi, M. et al. ggfortify v.0.4.8: Data visualization tools for statistical analysis results (2019).
Liaw, A. randomForest v.4.6-14: Breiman and Cutler’s random forests for classification and regression (2018).
Kassambara, A. ggpubr v.0.2.5: ‘ggplot2’ based publication ready plots (2020).
Gruca, M., Blach-Overgaard, A. & Balslev, H. African palm ethno-medicine. J. Ethnopharmacol. 165, 227–237 (2015).
Cámara–Leret, R. & Dennehy, Z. Indigenous knowledge of New Guinea’s useful plants: a review. Econ. Bot. 73, 405–415 (2019).
Macía, M. J. et al. Palm uses in Northwestern South America: a quantitative review. Bot. Rev. 77, 462–570 (2011).
Orme, D. et al. caper: Comparative analyses of phylogenetics and evolution in R. R package version 1.0.1 https://cran.r-project.org/package=caper (2018).
Kowarik, A. & Templ, M. Imputation with the R package VIM. J. Stat. Softw. 74, 1–16 (2016).
Alfons, A. & Templ, M. Estimation of social exclusion indicators from complex surveys: the R package laeken. J. Stat. Softw. 54, 1–25 (2013).
Milliken, W., Walker, B. E., Howes, M. J. R., Forest, F. & Nic Lughadha, E. Plants used traditionally as antimalarials in Latin America: mining the tree of life for potential new medicines. J. Ethnopharmacol. 279, 114221 (2021).
Fritz, S. A. & Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv. Biol. 24, 1042–1051 (2010).
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
Paradis, E. & Schliep, K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci. Data 8, 215 (2021).
Yu, G. ggplotify v.0.0.4: Convert plot to ‘grob’ or ‘ggplot’ object (2019).
Yu, G. aplot v.0.0.3: Decorate a ‘ggplot’ with associated information (2020).
Slowikowski, K. et al. ggrepel v.0.8.1: Automatically position non-overlapping text labels with ‘ggplot2’ (2019).
Schloerke, B. et al. GGally v.1.4.0: Extension to ‘ggplot2’ (2018).
Rubis, B. et al. hrbrthemes v.0.6.0: Additional themes, theme components and utilities for ‘ggplot2’ (2019).
Henry, L., Wickham, H. & Chang, W. ggstance v.0.3.3: Horizontal ‘ggplot2’ components (2019).
Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).
Brown, C. hash v.188.8.131.52: Full feature implementation of hash/associated arrays/dictionaries (2019).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
RStudio Team. RStudio: Integrated Development for R (RStudio, 2021).
Bellot, S. et al. Workflow and code used to perform palm extinction risk and regional palm use resilience analyses. Zenodo https://doi.org/10.5281/zenodo.6678122 (2022).
We are grateful to Naturalis Biodiversity Center (Leiden, the Netherlands) and Kew Herbarium (United Kingdom) for sharing their geographic coordinates databases, to O. A. Pérez-Escobar for insightful discussion and to T. Couvreur for insightful discussion and critical reading of an earlier version of the manuscript. A.A. is supported by funding from the Swedish Research Council and the Royal Botanic Gardens, Kew. W.D.K. acknowledges funding of palm research from the Netherlands Organisation for Scientific Research (grant no. 824.15.007).
The authors declare no competing interests.
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Nature Ecology & Evolution thanks Tinde van Andel, Rafael Molina-Venegas and Danilo Neves for their contribution to the peer review of this work. Peer reviewer reports are available.
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a. Model training and testing. b. Datasets. NI: not included; N-T: non-threatened; T: threatened. See Methods for further details.
a. Comparison between global estimates obtained with or without ML predictions in different areas of the world. Triangles: Estimates obtained by combining recent published assessments and ML predictions from the most accurate model (total evidence approach). Circles: Extrapolations based on published assessments only. Grey: all published assessments; Black: Only recent (that is ≤ 10-year-old) published assessments. America spans longitudes ∈ [−180°, −25°[, Africa / West Asia spans longitudes ∈ [−25°, 68°[, and East Asia / Pacific spans longitudes ∈ [68°, 180°]. b. Relationships between regional percentage of threatened species and total species number in the region (top) and between regional percentage of threatened species in different diversity and use categories (bottom). Percentages were obtained following the total evidence approach. Each dot represents a region with at least one palm species with data in the considered categories (n = 225, 93, 132, 224, 218 and 218 from top to bottom and left to right). The associations between the variables were measured using Pearson’s product moment correlation coefficient in two-sided Pearson’s correlation tests. r is Pearson’s correlation coefficient and p is the associated p-value. The grey error band corresponds to the 95% confidence interval of the correlation coefficient.
Extended Data Fig. 3 Changes in regional percentage of threatened species and associated region rank depending on the diversity category and the extinction risk information used.
‘total evidence’ is the combination of recent published extinction risk assessments and most accurate machine learning predictions. Each line corresponds to a region, and lines are coloured by the total number of palm species in the region. Regions with ≥40% threatened species of interest (that is top priorities) according to the total evidence approach are annotated, and the lines linking estimates from the total evidence approach and estimates based only on published assessments for these regions are thicker and dotted.
Extended Data Fig. 4 Replaceability of threatened utilized species and potential for substitution of non-threatened species.
The replaceability of a species is defined as the number of potential alternatives identified for that species. The potential for substitution of a species is defined as the number of threatened utilized species that may be substituted by that species. a. Regional replaceability of threatened utilized species across use categories. b. Global replaceability of threatened utilized species across use categories. The ‘violins’ represent the kernel density distributions. In a violin, the bold line represents the median value, the box spans values from the first to the third quartile, and the lines outside the box extend until the smallest and largest values, no further than 1.5 times the distance between the first and third quartiles. c. Functional traits and phylogenetic distribution of species replaceability and potential for substitution, for all uses taken together. The phylogenetic tree displayed was obtained by removing species without data from a maximum clade credibility consensus tree summarizing 750 palm phylogenetic trees estimated by Faurby et al.56 (see Methods and Supplementary Data).
a. Correlation between median number of alternatives per threatened utilized species and total palm species richness in the region. Each dot represents a region with at least one threatened utilized palm species (n = 92). The association between the variables was measured using Pearson’s product moment correlation coefficient in a two-sided Pearson’s correlation test. r is Pearson’s correlation coefficient and p is the associated p-value. The grey error band corresponds to the 95% confidence interval of the correlation coefficient. b. Regional percentages of threatened utilized species with alternatives.
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Bellot, S., Lu, Y., Antonelli, A. et al. The likely extinction of hundreds of palm species threatens their contributions to people and ecosystems. Nat Ecol Evol 6, 1710–1722 (2022). https://doi.org/10.1038/s41559-022-01858-0
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