Ecology

Gigantic trees occur in only a few regions on Earth. Some of the world’s largest eucalypts, for example, are on the island of Tasmania, off southeastern Australia. As wildfires increase in severity and frequency as a result of climate change, we urge the authorities to protect these trees by adopting measures similar to those applied to safeguard California’s redwood forests.
Competing Interests
The authors declare no competing interests. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Liu, W. et al. African origin of the malaria parasite Plasmodium vivax. Nat. Commun. 5, 3346 (2014).PubMed 

Google Scholar 
Liu, W. et al. Multigenomic delineation of Plasmodium species of the Laverania subgenus infecting wild-living chimpanzees and gorillas. Genome Biol. Evolution 8, 1929–1939 (2016).CAS 

Google Scholar 
Liu, W. et al. Single genome amplification and direct amplicon sequencing of Plasmodium spp. DNA from ape fecal specimens. Protocol Exchange 1–14 (2010).Liu, W. et al. Wild bonobos host geographically restricted malaria parasites including a putative new Laverania species. Nat. Commun. 8, 1635 (2017).PubMed 
PubMed Central 

Google Scholar 
Prugnolle, F. et al. African great apes are natural hosts of multiple related malaria species, including Plasmodium falciparum. Proc. Natl Acad. Sci. USA 107, 1458–1463 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharp, P. M., Plenderleith, L. J. & Hahn, B. H. Ape origins of human malaria. Annu. Rev. Microbiol. 74, 39–63 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, W. et al. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature 467, 420–425 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Otto, T. D. et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat. Microbiol. 3, 687–697 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boundenga, L. et al. Diversity of malaria parasites in great apes in Gabon. Malar. J. 14, 1–8 (2015).CAS 

Google Scholar 
Délicat-Loembet, L. et al. No evidence for ape Plasmodium infections in humans in gabon. Plos One 10, e0126933 (2015).PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Plasmodium falciparum-like parasites infecting wild apes in southern Cameroon do not represent a recurrent source of human malaria. Proc. Natl Acad. Sci. USA 110, 7020–7025 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Junker, J. et al. Recent decline in suitable environmental conditions for African great apes. Diversity Distrib. 18, 1077–1091 (2012).
Google Scholar 
de Nys, H. M. et al. Age-related effects on malaria parasite infection in wild chimpanzees. Biol. Lett. 9, 20121160 (2013).PubMed 
PubMed Central 

Google Scholar 
de Nys, H. M. et al. Malaria parasite detection increases during pregnancy in wild chimpanzees. Malar. J. 13, 413 (2014).PubMed 
PubMed Central 

Google Scholar 
Kaiser, M. et al. Wild chimpanzees infected with 5 Plasmodium species. Emerg. Infect. Dis. 16, 1956–1959 (2010).PubMed 
PubMed Central 

Google Scholar 
Paupy, C. et al. Anopheles moucheti and Anopheles vinckei are candidate vectors of ape Plasmodium parasites, including Plasmodium praefalciparum in Gabon. PLoS ONE 8, e57294 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl Acad. Sci. USA 113, 5329–5334 (2016).Loy, D. E. et al. Investigating zoonotic infection barriers to ape Plasmodium parasites using faecal DNA analysis. Int. J. Parasitol. 48, 531–542 (2018).Martin, M., Rayner, J., Gagneux, P., Barnwell, J. & Varki, A. Evolution of human–chimpanzee differences in malaria susceptibility: Relationship to human genetic loss of N-glycolylneuraminic acid. Proc. Natl Acad. Sci. USA 102, 12819–12824 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Scully, E. J., Kanjee, U. & Duraisingh, M. T. Molecular interactions governing host-specificity of blood stage malaria parasites. Curr. Opin. Microbiol. 40, 21–31 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat. Commun. 7, 11078 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wanaguru, M., Liu, W., Hahn, B. H., Rayner, J. C. & Wright, G. J. RH5-Basigin interaction plays a major role in the host tropism of Plasmodium falciparum. Proc. Natl Acad. Sci. USA 110, 20735–20740 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ngoubangoye, B. et al. The host specificity of ape malaria parasites can be broken in confined environments. Int. J. Parasitol. 46, 737–744 (2016).PubMed 

Google Scholar 
Mapua, M. I. et al. A comparative molecular survey of malaria prevalence among Eastern chimpanzee populations in Issa Valley (Tanzania) and Kalinzu (Uganda). Malar. J. 15, 423 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, D. F. et al. Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees. Malar. J. 17, 1–5 (2018).CAS 

Google Scholar 
Craig, M., le Sueur, D. & Snow, B. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol. Today 15, 105–111 (1999).CAS 
PubMed 

Google Scholar 
Mordecai, E. A. et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 16, 22–30 (2013).PubMed 

Google Scholar 
Paaijmans, K. P. et al. Influence of climate on malaria transmission depends on daily temperature variation. Proc. Natl Acad. Sci. USA 107, 15135–15139 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Parham, P. E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, 620–626 (2010).PubMed 

Google Scholar 
LaPointe, D. A., Goff, M. L. & Atkinson, C. T. Thermal constraints to the sporogonic development and altitudinal distribution of avian malaria Plasmodium relictum in Hawai’i. J. Parasitol. 96, 318–324 (2010).PubMed 

Google Scholar 
Vanderberg, J. P. & Yoeli, M. Effects of temperature on sporogonic development of Plasmodium berghei. J. Parasitol. 52, 559–564 (1966).Macdonald, G. The Epidemiology and Control of Malaria (Oxford University Press, 1957).Ryan, S. J. et al. Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne Zoonotic Dis. 15, 718–725 (2015).PubMed 
PubMed Central 

Google Scholar 
Gemperli, A. et al. Mapping malaria transmission in West and Central Africa. Tropical Med. Int. Health 11, 1032–1046 (2006).
Google Scholar 
Gething, P. W. et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasites Vectors 4, 92 (2011).PubMed 
PubMed Central 

Google Scholar 
Weiss, D. J. et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000–2012: a high-resolution spatiotemporal prediction. Malar. J. 13, 171 (2014).PubMed 
PubMed Central 

Google Scholar 
Lyons, C. L., Coetzee, M. & Chown, S. L. Stable and fluctuating temperature effects on the development rate and survival of two malaria vectors, Anopheles arabiensis and Anopheles funestus. Parasites Vectors 6, 104 (2013).PubMed 
PubMed Central 

Google Scholar 
Paaijmans, K. P., Wandago, M. O., Githeko, A. K. & Takken, W. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One 2, e1146 (2007).PubMed 
PubMed Central 

Google Scholar 
Faust, C. & Dobson, A. P. Primate malarias: diversity, distribution and insights for zoonotic Plasmodium. One Health 1, 66–75 (2015).PubMed 
PubMed Central 

Google Scholar 
Tucker Lima, J. M., Vittor, A., Rifai, S. & Valle, D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 372, 20160125 (2017).
Google Scholar 
Borner, J. et al. Phylogeny of haemosporidian blood parasites revealed by a multi-gene approach. Mol. Phylogenetics Evolution 94, 221–231 (2016).CAS 

Google Scholar 
Emery Thompson, M., Muller, M. N., Machanda, Z. P., Otali, E. & Wrangham, R. W. The Kibale Chimpanzee Project: over thirty years of research, conservation, and change. Biol. Conserv. 252, 108857 (2020).
Google Scholar 
Langergraber, K. E., Mitani, J. C. & Vigilant, L. The limited impact of kinship on cooperation in wild chimpanzees. Proc. Natl Acad. Sci. USA 104, 7786–7790 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arandjelovic, M. et al. Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol. Ecol. Resour. 9, 28–36 (2009).CAS 
PubMed 

Google Scholar 
Herbert, A. et al. Malaria-like symptoms associated with a natural Plasmodium reichenowi infection in a chimpanzee. Malar. J. 14, 220 (2015).PubMed 
PubMed Central 

Google Scholar 
Torres, J. R. Therapy of Infectious Diseases 597–613 (2003).Trampuz, A., Jereb, M., Muzlovic, I. & Prabhu, R. M. Clinical review: severe malaria. Crit. Care 7, 315 (2003).PubMed 
PubMed Central 

Google Scholar 
Akim, N. I. et al. Dynamics of P. falciparum gametocytemia in symptomatic patients in an area of intense perennial transmission in Tanzania. Am. J. Tropical Med. Hyg. 63, 199–203 (2000).CAS 

Google Scholar 
Mackinnon, M. J. & Read, A. F. Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi. Evolution 53, 689–703 (1999).PubMed 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
Prugnolle, F. et al. African monkeys are infected by Plasmodium falciparum nonhuman primate-specific strains. Proc. Natl Acad. Sci. USA 108, 11948–11953 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ayouba, A. et al. Ubiquitous Hepatocystis infections, but no evidence of Plasmodium falciparum-like malaria parasites in wild greater spot-nosed monkeys (Cercopithecus nictitans). Int. J. Parasitol. 42, 709–713 (2012).PubMed 
PubMed Central 

Google Scholar 
Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogenetics Evolution 47, 261–273 (2008).CAS 

Google Scholar 
Thurber, M. I. et al. Co-infection and cross-species transmission of divergent Hepatocystis lineages in a wild African primate community. Int. J. Parasitol. 43, 613–619 (2013).PubMed 
PubMed Central 

Google Scholar 
Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics (Cambridge University Press, 2008).Stanisic, D. I. et al. Acquisition of antibodies against Plasmodium falciparum merozoites and malaria immunity in young children and the influence of age, force of infection, and magnitude of response. Infect. Immun. 83, 646–660 (2015).PubMed 
PubMed Central 

Google Scholar 
Taylor, R. R., Allen, S. J., Greenwood, B. M. & Riley, E. M. IgG3 antibodies to Plasmodium falciparum merozoite surface protein 2 (MSP2): increasing prevalence with age and association with clinical immunity to malaria. Am. J. Tropical Med. Hyg. 58, 406–413 (1998).CAS 

Google Scholar 
World Malaria Report (World Health Organization, 2015).Shaman, J. Letter to the Editor: Caution needed when using gridded meteorological data products for analyses in Africa. Eur. Surveill. 19, 20930 (2014).
Google Scholar 
Tatem, A. J., Goetz, S. J. & Hay, S. I. Terra and Aqua: new data for epidemiology and public health. Int. J. Appl. Earth Observation Geoinf. 6, 33–46 (2004).
Google Scholar 
Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 

Google Scholar 
Carter, R. & Mendis, K. N. Evolutionary and historical aspects of the burden of malaria. Clin. Microbiol. Rev. 15, 564–594 (2002).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, D. P. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77, 171–192 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tarello, W. A fatal Plasmodium reichenowi infection in a chimpanzee? Rev. de. Med. Veterinaire 156, 503–505 (2005).
Google Scholar 
Taylor, D. W. et al. Parasitologic and immunologic studies of experimental Plasmodium falciparum infection in nonsplenectomized chimpanzees (Pan troglodytes). Am. J. Tropical Med. Hyg. 34, 36–44 (1985).CAS 

Google Scholar 
Krief, S., Martin, M., Grellier, P., Kasenene, J. & Sevenet, T. Novel antimalarial compounds isolated in a survey of self-medicative behavior of wild chimpanzees in Uganda. Antimicrobial Agents Chemother. 48, 3196–3199 (2004).CAS 

Google Scholar 
Cox-Singh, J. et al. Plasmodium knowlesi malaria in humans is widely distributed and potentially life threatening. Clin. Infect. Dis. 46, 165–171 (2008).CAS 
PubMed 

Google Scholar 
Singh, B. & Daneshvar, C. Human infections and detection of Plasmodium knowlesi. Clin. Microbiol. Rev. 26, 165–184 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brasil, P. et al. Outbreak of human malaria caused by Plasmodium simium in the Atlantic Forest in Rio de Janeiro: a molecular epidemiological investigation. Lancet Global Health 5, e1038–e1046 (2017).Krief, S. et al. On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from bonobos. PLoS Pathog. 6, e1000765 (2010).Pacheco, M. A., Cranfield, M., Cameron, K. & Escalante, A. A. Malarial parasite diversity in chimpanzees: the value of comparative approaches to ascertain the evolution of Plasmodium falciparum antigens. Malar. J. 12, 328 (2013).PubMed 
PubMed Central 

Google Scholar 
Etienne, L. et al. Noninvasive follow-up of simian immunodeficiency virus infection in wild-living nonhabituated western lowland gorillas in Cameroon. J. Virol. 86, 9760–9772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science 313, 523–526 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Increased mortality and AIDS-like immunopathology in wild chimpanzees infected with SIVcpz. Nature 460, 515–519 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. et al. Eastern chimpanzees, but not bonobos, represent a simian immunodeficiency virus reservoir. J. Virol. 86, 10776–10791 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neel, C. et al. Molecular epidemiology of simian immunodeficiency virus infection in wild-living gorillas. J. Virol. 84, 1464–1476 (2010).CAS 
PubMed 

Google Scholar 
Rudicell, R. S. et al. Impact of simian immunodeficiency virus infection on chimpanzee population dynamics. PLoS Pathog. 6, 1–17 (2010).
Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D. & Maechler, M. Lme4: linear mixed-effects models using s4 classes. Cran R Project Website (2010). More

Glass eels collection (Anguilla anguilla, L.)Three hundred European glass eels ( More

External datasetsWoody biomass carbon dataThe dataset by ref. 16 maps annual global woody biomass carbon densities for 2000–2019 at a spatial resolution of ~10 km. The annual estimates represent averages for the tropical regions and growing-season (April–October) averages for the extra-tropical regions. Ref. 16 analyse global trends of gains and losses in woody biomass carbon for 2000–2019. Overall, they find that grid cells with (significant) net gains of vegetation carbon are by a factor of 1.4 more abundant than grid cells with net losses of vegetation carbon, indicating that there is a global greening trend when only considering the areal extent of biomass gains and not the magnitude of carbon gains. Their regionally distinct analysis of trends shows that almost all regions, except for the tropical moist forests in South America and parts of Southeast Asia, experienced net gains in biomass carbon. On the country scale, the largest net increase in biomass carbon is shown in China, which is mainly attributed to the large-scale afforestation programs in the southern part of the country and increased carbon uptake of established forests. On the other hand, the largest vegetation carbon losses are shown for Brazil and Indonesia, which is partly attributed to deforestation, degradation, and drought events. All of the mentioned trends have been found to be significant16. The decreasing carbon sink in Brazil is in line with ref. 44, who, considering both natural and anthropogenic fluxes, show that the southeastern Amazon has even turned from a carbon sink to a carbon source, mainly owing to fire emissions from forest clearing. Isolating carbon fluxes in intact, old-growth Amazonian rainforests (i.e., SLAND,B), ref. 45 also find evidence for a significantly decreasing carbon sink due to the negative effects of increasing temperatures and droughts on carbon uptake since the 1990s.The dataset was remapped to the BLUE resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging).ERA-5 dataThe ERA-5 variables were downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). Monthly air temperature (Ta) at 2 m height was averaged over each year, and annual precipitation was calculated by taking the sum of the monthly total precipitation (P). Both variables were regridded from the original resolution of ~0.1° to 0.25° resp. to the TRENDY resolution of 0.5° through conservative remapping.TRENDY dataWe used the TRENDY model ensemble version 8 (conducted for the 2019 GCB; ref. 8). We used net biome production (NBP) and annual vegetation carbon stocks (cVeg) for 2000–2018 from four different model setups (S2, S3, S5, and S6) and eight resp. 13 DGVMs (depending on the data available). The selection of DGVMs is done as in ref. 19 (Supplementary Tab. 3), but we included one additional model (ISAM) for the S2 simulations. The terrestrial biomass carbon sink (SLAND,B) was calculated for 13 DGVMs following the GCB 2020 approach, i.e., from the S2 simulation, which is the simulation without LULCC (i.e., fixed pre-industrial land cover) under transient environmental conditions (climate, nitrogen deposition, CO2 evolution). SLAND,B is the annual difference of cVeg and makes no statements about the further fate of biomass if cVeg decreases. SLAND,B, therefore, should not be interpreted as equivalent to the flux to/from the atmosphere, since parts of cVeg may be transferred to litter, dead wood, or soil. The same applies to our BLUE estimates of SLAND,B, ensuring comparability between our BLUE estimates and the TRENDY estimates. Increases (decreases) of cVeg between two years are a net uptake (release) of carbon from the terrestrial biosphere. The global sums of biomass carbon stocks under transient climate and CO2 were calculated from the S3 setup (LULCC under historical environmental conditions), whereas the S5 setup provides biomass carbon under constant present-day environmental forcing (closest to the classical bookkeeping approach). In line with the GCB, ELUC was calculated under historical environmental conditions as the difference in NBP between the S2 and S3 simulations (ELUC = NBP_S2 – NBP_S3). ELUC under constant present-day environmental forcing was calculated as the difference in NBP between the S6 (fixed pre-industrial land cover under present-day environmental forcing) and S5 simulations (ELUC = NBP_S6 – NBP_S5)19. All datasets were remapped to a common resolution of 0.5∘ through conservative remapping (area-weighted average) for the data analysis.Assimilation of observed woody biomass carbon in BLUEThe observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.Carbon transfer in the default setup of BLUEThe BLUE simulation is started in AD 850. Biomass and soil vegetation carbon densities are based on ref. 17, which are converted to exponential time constants. A detailed explanation of the exponential model can be found in ref. 5.While in the default setup, changes are only due to LULCC, our assimilation approach now introduces environmental effects on woody vegetation carbon by assimilating the observed woody biomass carbon densities in BLUE from 2000 onward according to the methodological considerations explained below.Calculation of woody biomass carbon densities for different land cover types and PFTsWithin each 0.25° cell of the global grid, the (remapped) woody biomass carbon density from ref. 16 must be the sum of woody biomass carbon stored in all woody PFTs of all woody land cover types. The distribution of the woody biomass carbon across PFTs and land cover types is achieved by distributing the observed (i.e., actual) woody biomass carbon densities (ρBa) from ref. 16 across the two land cover types (j) and the eight PFTs (l) that can be woody vegetation (primary land, called virgin, “v” in BLUE and secondary, “s”, land) according to the fraction of total woody biomass carbon (fB) contained in each land cover type and each PFT (fB,j,l) as estimated by BLUE. fB,j,l varies for different PFTs and land cover types, depending on their history of LULCC and their potential for carbon uptake (i.e., the potential carbon densities).fB,j,l is extracted from the default simulations for the first year of the time series (i.e., 2000) and calculated for subsequent years from the BLUE simulations using the assimilated woody vegetation carbon densities for that year:$${f}_{B,j,l}(t)=frac{{C}_{B,j,l}(t)}{{C}_{B}(t)}$$
(1)
where CB is the woody biomass carbon stock.Consequently, the assimilated woody biomass carbon stock per cover type and PFT (CB_as,j,l) at each time step can be calculated as:$${C}_{B_as,j,l}(t)={rho }_{Ba}(t);*;A;*;{f}_{B,j,l}(t)$$
(2)
with j{v, s}; l{1. . 8}; t{2000. . 2019}. A is the area per grid cell.Thresholds for excluding inconsistent woody biomass carbon densitiesWe eliminate unrealistically large values for woody biomass carbon densities that our assimilation framework produces. Woody biomass carbon densities in BLUE that exceed the highest value (~374 t ha−1) of the original dataset indicate inconsistencies between the observed woody biomass carbon estimates and the fractional grid cell areas per PFT and land cover types that BLUE simulates. To account for uncertainties related to the criteria for exclusion of grid cells, multiple threshold approaches are applied and the results are compared. To maintain a temporally and spatially consistent time series of woody biomass carbon, grid cells that are excluded according to the chosen threshold approach are interpolated through linear barycentric interpolation. A first approach relies on a uniform upper threshold of More

We focused on the Northern High Latitudes (NHL, latitude > 50°N, excluding Greenland) due to their importance for carbon (CO2-C, the same hereafter)-climate feedbacks in the Earth system. To minimize the potential human influence on the CO2 cycle, we excluded areas under agricultural management (croplands, cropland/natural vegetation mosaic, and urban types), and considered only pixels of natural vegetation defined from the MODIS MCD12Q1 (v006) based IGBP land cover classification. Our main focus was the NHL permafrost region because permafrost plays a critical role in the ecology, environment, and society in the NHL. Permafrost, or permanently frozen ground, is defined as ground (soil, sediment, or rock) that remains at or below 0 °C for at least two consecutive years. The occurrence of permafrost is primarily controlled by temperature and has a strong effect on hydrology, soils, and vegetation composition and structure. Based on the categorical permafrost map from the International Permafrost Association58, the permafrost region (excluding permanent snow/ice and barren land), including sporadic (10–50%), discontinuous (50–90%), and continuous ( >90%) permafrost, encompasses about 15.7 × 106 km2, accounts for 57% of the NHL study dominion, and is dominated by tundra (shrubland and grass) and deciduous needleleaf (i.e., larch) forest that is regionally abundant in Siberia. The NHL non-permafrost region covers about 11.9 × 106 km2 and is dominated by mixed and evergreen needleleaf boreal forests (Fig. S1).Atmospheric CO2 inversions (ACIs)ACIs provide regionally-integrated estimates of surface-to-atmosphere net ecosystem CO2 exchange (NEEACI) fluxes by utilizing atmospheric CO2 concentration measurements and atmospheric transport models59. ACIs differ from each other mainly in their underlying atmospheric observations, transport models, spatial and temporal flux resolutions, land surface models used to predict prior fluxes, observation uncertainty and prior error assignment, and inversion methods. We used an ensemble mean of six different ACI products, each providing monthly gridded NEEACI at 1-degree spatial resolution, including Carbon‐Tracker 2019B (2000-2019, CT2019)60, Carbon‐Tracker Europe 2020 (2000–2019, CTE2020)61, Copernicus Atmosphere Monitoring Service (1979–2019, CAMS)62, Jena CarboScope (versions s76_v4.2 1976–2017, and s85_v4.2 1985-2017)63,64, and JAMSTEC (1996–2017)65. The monthly gridded ensemble mean NEEACI at 1-degree spatial resolution was calculated using the available ACIs from 1980-2017. Monthly ACI ensemble mean NEEACI data were summed to seasonal and annual values, and used to calculate the spatial and temporal trends of net CO2 uptake, and to investigate its relationship to climate and environmental controls.Productivity datasetDirect observations of vegetation productivity do not exist at a circumpolar scale. We therefore used two long-term gridded satellite-based estimates of vegetation productivity, including gross primary production (GPP) derived using a light use efficiency (LUE) approach (LUE GPP, 1982–1985)21,66 and satellite observations of Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS NDVI, 1982–1985)67. LUE GPP (monthly, 0.5° spatial resolution, 1982–2015) is calculated from satellite observations of NDVI from the Advanced Very High-Resolution Radiometer (AVHRR; 1982 to 2015) combined with meteorological data, using the MOD17 LUE approach. LUE GPP has been extensively validated with a global array of eddy-flux tower sites68,69,70 and tends to provide better estimates in ecosystems with greater seasonal variability at high latitudes. Following66,71, we used the ensemble mean of GPP estimates from three of the most commonly used meteorological data sets: National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis; NASA Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); and European Center for Medium-Range Weather Forecasting (ECMWF). GIMMS NDVI (bimonthly, 1/12 spatial resolution, 1982–2015) provides the longest satellite observations of vegetation “greenness”, and is widely used in studies of phenology, productivity, biomass, and disturbance monitoring as it has proven to be an effective surrogate of vegetation photosynthetic activity72.The gridded GPP data were resampled to 1-degree resolution at monthly time scales, to be consistent with NEEACI, and used to test (H1) whether greater temperature sensitivity of vegetation productivity explains the different trends in net CO2 uptake across the NHL. LUE GPP was also used to calculate monthly total ecosystem respiration (TER) as the difference between GPP and NEEACI (i.e., TERresidual =  GPP– NEEACI) from 1982-2015, as global observations of respiration do not exist. The NEEACI, GPP and TERresidual were used as observation-constrained top-down CO2 fluxes to investigate mechanisms underlying the seasonal CO2 dynamics in the structural equation modeling and additional decision tree-based analysis.Eddy Covariance (EC) measurements of bottom-up CO2 fluxesA total of 48 sites with at least three years of data representing the major NHL ecosystems were obtained from the FLUXNET2015 database (Table S1 and Fig. S1). EC measurements provide direct observations of net ecosystem CO2 exchange (NEE) and estimate the GPP and TER flux components of NEE using other climate variables. Daily GPP and TER were estimated as the mean value from both the nighttime partitioning method73 and the light response curve method74. More details on the flux partitioning and gap-filling methods used are provided by75. Daily fluxes were summed into seasonal and annual values and used to compare with trends from ACIs (Fig. S7), to estimate the climate and environmental controls on the CO2 cycle in the pathway analysis (Fig. 5), and to calculate the net CO2 uptake sensitivity to spring temperature (Fig. S14).Ensemble of dynamic global vegetation models (TRENDY simulations)The TRENDY intercomparison project compiles simulations from state-of-the-art dynamic global vegetation models (DGVMs) to evaluate terrestrial energy, water, and net CO2 exchanges76. The DGVMs provide a bottom-up approach to evaluate terrestrial CO2 fluxes (e.g., net biome production [NBP]) and allow deeper insight into the mechanisms driving changes in carbon stocks and fluxes. We used monthly NBP, GPP, and TER (autotrophic + heterotrophic respiration; Ra + Rh) from ten TRENDY v7 DGVMs76, including CABLE-POP, CLM5.0, OCN, ORCHIDEE, ORCHIDEE-CNP, VISIT, DLEM, LPJ, LPJ-GUESS, and LPX. We analyzed the “S3” simulations that include time-varying atmospheric CO2 concentrations, climate, and land use. All simulations were based on climate forcing from the CRU-NCEPv4 climate variables at 6-hour resolution. CO2 flux outputs were summarized monthly at 1-degree spatial resolution from 1980 to 2017. Monthly ensemble mean NBP, GPP, and TER were summed to seasonal and annual values, and then used to compare with observation-constrained ACI top-down CO2 fluxes (Figs. 4 and 5).Satellite data-driven carbon flux estimates (SMAP L4C)We also used a much finer spatio-temporal simulation of carbon fluxes from the NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon product (L4C) to quantify the temperature and moisture sensitivity of NHL CO2 exchange77. The SMAP L4C provides global operational daily estimates of NEE and component CO2 fluxes for GPP and TER at 9 km resolution since 2015; whereas, an offline version of the L4C model provides a similar Nature Run (NR) carbon flux record over a longer period (2000-present), but without the influence of SMAP observational inputs. The L4C model has been calibrated against FLUXNET tower CO2 flux measurements and shows favorable performance and accuracy in high latitude regions4,77. In this analysis, daily gridded CO2 fluxes at 9-km resolution from the L4C NR record were summed to seasonal and annual values, and used to calculate the sensitivity of net C uptake in response to spring temperature (Fig. S14).CO2 fluxes in this analysis are defined with respect to the biosphere so that a positive value indicates the biosphere is a net sink of CO2 absorbed from the atmosphere. The different data products described above use different terminology (e.g., NEE, NBP) with slightly different meanings; however, they all provide estimates of net land-atmosphere CO2 exchange78.Climate, tree cover, permafrost, and soil moisture dataMonthly gridded air temperatures at 0.5-degree spatial resolution from 1980 to 2017 were obtained from the Climate Research Unit (CRU TS v4.02) at the University of East Anglia79. Air temperature was summarized at seasonal and annual scales to calculate temperature sensitivities of net CO2 uptake and to investigate the mechanism underlying the seasonal CO2 dynamics.Percent tree cover (%TC) at 0.05-degree spatial resolution was averaged over a 35-year (1982-2016) period using annual %TC layers derived from the Advanced Very High-Resolution Radiometer (AVHRR) (Fig. 1a)42. %TC was binned using 5% TC intervals to assess its relation to net CO2 uptake, or aggregated at a regional scale (e.g., TC  > 50% or TC  90%), discontinuous permafrost (DisconP, 10% < P  90%), discontinuous (DisconP, 10% < P  0.05 indicate a good fitting model), Bentler’s comparative fit index (CFI, where CFI ≈ 1 indicates a good fitting model), and the root mean square error of approximation (RMSEA; where RMSEA ≤ 0.05 and p  > 0.1 indicate a good fitting model). The standardized regression coefficient can be interpreted as the relative influences of exogenous (independent) variables. The R2 indicates the total variation in an endogenous (dependent) variable explained by all exogenous (independent) variables.Direct and legacy effects of temperature on seasonal net CO2 uptakeBecause landscape thawing and snow conditions regulate the onset of vegetation growth and influence the seasonal and annual CO2 cycles in the NHL24,84, we also analyzed the legacy effects of spring (May–Jun) temperature on seasonal net CO2 uptake. We regressed seasonal and annual net CO2 uptake from the site-level EC observations, regional-level ACI ensemble, and the TRENDY NBP ensemble against spring (May-June) air temperature. For EC observations, net CO2 uptake (i.e., NEE) and air temperature were summarized from site-level measurements. For the ACIs and TRENDY ensemble, net CO2 uptake (i.e., NEEACI and NBP) was summarized as regional means from the ACIs and TRENDY ensemble outputs, and air temperature was summarized as regional means from CRU temperature. The slope of the regression line was interpreted as the spring temperature sensitivity of the CO2 cycle. Simple linear regression was used here mainly due to the strong influence of spring temperature on the seasonal and annual CO2 cycle in NHL ecosystems30. Temperature sensitivity (γ: g C m−2 day−1 K−1) is the change in net CO2 flux (g C m−2 day−1) in response to a 1-degree temperature change. The sensitivity of net CO2 uptake to warm spring anomalies was calculated for different seasons (EGS, LGS, and annual) and regions (i.e., permafrost and non-permafrost), and the T-test was used to test for the difference in γ among different regions, seasons, and datasets. Similarly, direct effects of temperature on net CO2 uptake were calculated using the same season data (Fig. S14).Observationally-constrained estimates (EC and ACIs) showed that the sensitivity of net CO2 uptake in the EGS to spring temperature is positive (γ  > 0) and not statistically different (p  > 0.05) between permafrost and non-permafrost regions (({gamma }_{{ACI}}^{{np}})=0.125 ± 0.020 gC m−2 d−1 K−1; ({gamma }_{{EC}}^{{np}}) = 0.052 ± 0.013 gC m−2 d−1 K−1). In contrast, the sensitivity of net CO2 uptake in LGS to spring temperature is negative (γ  More

Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 

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).Article 

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).Article 

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).Article 

Google Scholar 
Ulian, T. et al. Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445 (2020).Article 

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).PubMed 
PubMed Central 
Article 

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).Article 

Google Scholar 
IUCN Red List version 2022-2: Table 1a (IUCN, 2022); https://www.iucnredlist.org/resources/summary-statistics#Figure2Rivers, M. The global tree assessment—red listing the world’s trees. BGjournal 14, 16–19 (2017).
Google Scholar 
Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

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.phpBachman, S. P., Nic Lughadha, E. M. & Rivers, M. C. Quantifying progress toward a conservation assessment for all plants. Conserv. Biol. 32, 516–524 (2018).PubMed 
Article 

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).Article 

Google Scholar 
Cazalis, V. et al. Bridging the research–implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).PubMed 
Article 

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).PubMed 
PubMed Central 
Article 

Google Scholar 
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).PubMed 
PubMed Central 
Article 

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).PubMed 
Article 

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).Article 

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).CAS 
PubMed 
PubMed Central 
Article 

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).PubMed 
Article 

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).Article 

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).PubMed 
PubMed Central 
Article 

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).Article 

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).PubMed 
Article 

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).Article 

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).Article 

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).PubMed 

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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).CAS 
PubMed 
Article 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
PubMed 
Article 

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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
PubMed Central 
Article 
CAS 

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).PubMed 
Article 

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).CAS 
PubMed 
Article 

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).PubMed 
Article 
CAS 

Google Scholar 
Zona, S. & Henderson, A. A review of animal-mediated seed dispersal of palms. Selbyana 11, 6–21 (1989).
Google Scholar 
Kissling, W. D. et al. PalmTraits 1.0, a species-level functional trait database of palms worldwide. Sci. Data 6, 178 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Tomlinson, P. B. The uniqueness of palms. Bot. J. Linn. Soc. 151, 5–14 (2006).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
Muscarella, R. et al. The global abundance of tree palms. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

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).Article 

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).PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2021-2 (IUCN, accessed 12 October 2021); https://www.iucnredlist.orgBaker, W. J. & Dransfield, J. Beyond genera Palmarum: progress and prospects in palm systematics. Bot. J. Linn. Soc. 182, 207–233 (2016).Article 

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).PubMed 
PubMed Central 
Article 
CAS 

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).Article 

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).PubMed 
PubMed Central 
Article 

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).Article 

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).PubMed 
PubMed Central 
Article 
CAS 

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).Article 

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).
Google Scholar 
Voeks, R. A. Disturbance pharmacopoeias: medicine and myth from the humid tropics. Ann. Assoc. Am. Geogr. 94, 868–888 (2004).
Google Scholar 
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).Article 

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).Article 

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).Article 

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).Article 

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/109Brummitt, 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).Article 

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).PubMed 
PubMed Central 
Article 

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/H4M61H5FFick, 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).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 

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.phpChamberlain, 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).Article 

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).
Google Scholar 
Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

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).
Google Scholar 
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).PubMed 
Article 

Google Scholar 
Cámara–Leret, R. & Dennehy, Z. Indigenous knowledge of New Guinea’s useful plants: a review. Econ. Bot. 73, 405–415 (2019).Article 

Google Scholar 
Macía, M. J. et al. Palm uses in Northwestern South America: a quantitative review. Bot. Rev. 77, 462–570 (2011).Article 

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).Article 

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).PubMed 
Article 

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).PubMed 
Article 

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).CAS 
PubMed 
Article 

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).Article 

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). More

Bellot, S. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01858-0 (2022).Article 

Google Scholar 
Eiserhardt, W. L. et al. Ann. Bot. 108, 1391–1416 (2011).Article 

Google Scholar 
Muscarella, R. et al. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

Google Scholar 
Cámara-Leret, R. et al. Nat. Plants 3, 16220 (2017).Article 

Google Scholar 
The IUCN Red List of Threatened Species (IUCN, 2018).BGCI ThreatSearch Online Database (BGCI, 2018).Carlos-Júnior, L. A. et al. Divers. Distrib. 25, 743–757 (2019).Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Nature 546, 141–144 (2017).CAS 
Article 

Google Scholar 
Rice, J. et al. The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas (IPBES, 2018).Coelho de Souza, F. et al. Nat. Ecol. Evol. 3, 1754–1761 (2019).Article 

Google Scholar  More