Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ulian, T. et al. Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Cazalis, V. et al. Bridging the research–implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).
Google Scholar
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).
Google Scholar
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Wong, J. S. Y. et al. Comparing patterns of taxonomic, functional and phylogenetic diversity in reef coral communities. Coral Reefs 37, 737–750 (2018).
Google Scholar
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).
Google Scholar
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).
Google Scholar
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).
Google Scholar
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).
Google Scholar
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).
Google Scholar
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Tomlinson, P. B. The uniqueness of palms. Bot. J. Linn. Soc. 151, 5–14 (2006).
Google Scholar
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Google Scholar
Muscarella, R. et al. The global abundance of tree palms. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Google Scholar
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.2.0.3.2: 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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Cámara–Leret, R. & Dennehy, Z. Indigenous knowledge of New Guinea’s useful plants: a review. Econ. Bot. 73, 405–415 (2019).
Google Scholar
Macía, M. J. et al. Palm uses in Northwestern South America: a quantitative review. Bot. Rev. 77, 462–570 (2011).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Brown, C. hash v.2.2.6.1: 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).
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