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Global plant diversity as a reservoir of micronutrients for humanity

Matters Arising to this article was published on 21 December 2022

Abstract

With more than two billion people suffering from malnutrition and diets homogenizing globally, it is vital to identify and conserve nutrient-rich species that may contribute to improving food security and diversifying diets. Of the approximately 390,000 vascular plant species known to science, thousands have been reported to be edible, yet their nutritional content remains poorly characterized. Here we use phylogenetic information to identify plants with the greatest potential to support strategies alleviating B-vitamin deficiencies. We predict the B-vitamin profiles of >6,400 edible plants lacking nutritional data and identify 1,044 species as promising key sources of B vitamins. Several of these source species should become conservation priorities, as 63 (6%) are threatened in the wild and 272 (26%) are absent from seed banks. Moreover, many of these conservation-priority source species overlap with hotspots of malnutrition, highlighting the need for safeguarding strategies to ensure that edible plant diversity remains a reservoir of nutrition for future generations, particularly in countries needing it most. Although by no means a silver bullet to tackling malnutrition, conserving a diverse portfolio of edible plants, unravelling their nutritional potentials, and promoting their sustainable use are essential strategies to enhance global nutritional resilience.

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Fig. 1: Phylogenetic trees of terrestrial angiosperm edible plant species with their corresponding B-vitamin profiles.
Fig. 2: In situ threat and ex situ conservation status of all documented terrestrial edible angiosperm species and predicted B-vitamin source species.
Fig. 3: Percentages of B-vitamin source species that are globally threatened in situ and not conserved ex situ in 244 countries and island states.

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Data availability

All data used were accessed from publicly available databases and are indicated in the Methods. B-vitamin predictions are available in Supplementary Data 1 and 2. Species predicted to be B-vitamin sources under the conservative and ‘best-case’ scenarios are listed in Supplementary Data 3, with the names of the countries in which each consensus source species is found. Supplementary Data 4 contains, for each country, the number of source species, percentages of source species that are threatened in situ and not conserved ex situ, and the prevalence of each B-vitamin deficiency. A list of the nutritionally known edible species used is available in Supplementary Table 8.

Code availability

R scripts for testing for phylogenetic signal, and performing the predictions for the nutritionally known and unknown species are available in Supplementary Software 14.

References

  1. Beal, T., Massiot, E., Arsenault, J. E., Smith, M. R. & Hijmans, R. J. Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes. PLoS ONE 12, e0175554 (2017).

    Article  Google Scholar 

  2. The State of Food Security and Nutrition in the World 2020. Transforming Food Systems for Affordable Healthy Diets (FAO, IFAD, UNICEF, WFP & WHO, 2020); https://doi.org/10.4060/ca9692en

  3. Gernand, A. D., Schulze, K. J., Stewart, C. P., West, K. P. & Christian, P. Micronutrient deficiencies in pregnancy worldwide: health effects and prevention. Nat. Rev. Endocrinol. 12, 274–289 (2016).

    Article  CAS  Google Scholar 

  4. Pilling, D., Bélanger, J. & Hoffmann, I. Declining biodiversity for food and agriculture needs urgent global action. Nat. Food 1, 144–147 (2020).

    Article  Google Scholar 

  5. Nelson, G. et al. Income growth and climate change effects on global nutrition security to mid-century. Nat. Sustain. 1, 773–781 (2018).

    Article  Google Scholar 

  6. Lachat, C. et al. Dietary species richness as a measure of food biodiversity and nutritional quality of diets. Proc. Natl Acad. Sci. USA 115, 127–132 (2018).

    Article  CAS  Google Scholar 

  7. Siddique, K. H. M., Li, X. & Gruber, K. Rediscovering Asia’s forgotten crops to fight chronic and hidden hunger. Nat. Plants 7, 116–122 (2021).

    Article  Google Scholar 

  8. 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 

  9. Powell, B. et al. Improving diets with wild and cultivated biodiversity from across the landscape. Food Secur. 7, 535–554 (2015).

    Article  Google Scholar 

  10. Hunter, D. et al. The potential of neglected and underutilized species for improving diets and nutrition. Planta 250, 709–729 (2019).

    Article  CAS  Google Scholar 

  11. Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).

    Article  CAS  Google Scholar 

  12. Magrach, A. & Sanz, M. J. Environmental and social consequences of the increase in the demand for ‘superfoods’ world-wide. People Nat. 2, 267–278 (2020).

    Article  Google Scholar 

  13. Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).

    Article  Google Scholar 

  14. 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 

  15. Jones, S. K. et al. Agrobiodiversity index scores show agrobiodiversity is underutilized in national food systems. Nat. Food 2, 712–723 (2021).

    Article  Google Scholar 

  16. Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat. Sustain. 3, 974–976 (2020).

    Article  Google Scholar 

  17. Diazgranados, M. et al. World Checklist of Useful Plant Species (Royal Botanic Gardens, Kew & Knowledge Network for Biocomplexity, 2020).

  18. Castañeda-Álvarez, N. P. et al. Global conservation priorities for crop wild relatives. Nat. Plants 2, 16022 (2016).

    Article  Google Scholar 

  19. Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).

    Article  Google Scholar 

  20. Agrawal, A. A., Salminen, J. & Fishbein, M. Phylogenetic trends in phenolic metabolism of milkweeds (Asclepias): evidence for escalation. Evolution 63, 663–673 (2009).

    Article  CAS  Google Scholar 

  21. Albuquerque, T. G., Nunes, M. A., Bessada, S. M. F., Costa, H. S. & Oliveira, M. B. P. P. in Chemical Analysis of Food (ed. Pico, Y.) 609–656 (Elsevier, 2020).

  22. Şerban, P., Wilson, J. R. U., Vamosi, J. C. & Richardson, D. M. Plant diversity in the human diet: weak phylogenetic signal indicates breadth. Bioscience 58, 151–159 (2008).

    Article  Google Scholar 

  23. Dempewolf, H., Rieseberg, L. H. & Cronk, Q. C. Crop domestication in the Compositae: a family-wide trait assessment. Genet. Resour. Crop Evol. 55, 1141–1157 (2008).

    Article  Google Scholar 

  24. Saslis-Lagoudakis, C. H. et al. The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae). PLoS ONE 6, e22275 (2011).

    Article  CAS  Google Scholar 

  25. Meyers, L. D., Hellwig, J. P. & Otten, J. J. Dietary Reference Intakes: The Essential Guide to Nutrient Requirements (National Academies Press, 2006).

  26. Miller, J. W. & Rucker, R. B. Present Knowledge In Nutrition (eds. Marriott, B. P., Birt, D. F., Stallings, V. A. & Yates, A. A.) 273–287 (Academic Press, 2020).

  27. Pinela, J., Carvalho, A. M. & Ferreira, I. C. F. R. Wild edible plants: nutritional and toxicological characteristics, retrieval strategies and importance for today’s society. Food Chem. Toxicol. 110, 165–188 (2017).

    Article  CAS  Google Scholar 

  28. Gruber, K. Agrobiodiversity: the living library. Nature 544, S8–S10 (2017).

    Article  CAS  Google Scholar 

  29. Caproni, L., Raggi, L., Talsma, E. F., Wenzl, P. & Negri, V. European landrace diversity for common bean biofortification: a genome-wide association study. Sci. Rep. 10, 19775 (2020).

    Article  CAS  Google Scholar 

  30. Dwivedi, S. L. et al. Diversifying food systems in the pursuit of sustainable food production and healthy diets. Trends Plant Sci. 22, 842–856 (2017).

    Article  CAS  Google Scholar 

  31. Borelli, T. et al. Local solutions for sustainable food systems: the contribution of orphan crops and wild edible species. Agronomy 10, 231 (2020).

    Article  CAS  Google Scholar 

  32. ThreatSearch Online Database (Botanic Gardens Conservation International, 2020); www.bgci.org/threat_search.php (data accessed: 23/12/2020).

  33. Banking the World’s Seeds (Royal Botanic Garden, Kew’s Millennium Seed Bank Partnership); https://www.kew.org/science/our-science/projects/banking-the-worlds-seeds (data accessed: 23/12/2020).

  34. Genesys Global Portal on Plant Genetic Resources for Food and Agriculture (Global Crop Diversity Trust, 2018); https://www.genesys-pgr.org (data accessed: 23/12/2020).

  35. Lughadha, E. N. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).

    Article  Google Scholar 

  36. Vincent, H. et al. Modeling of crop wild relative species identifies areas globally for in situ conservation. Commun. Biol. 2, 136 (2019).

    Article  Google Scholar 

  37. Khoury, C. K. et al. Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets. Ecol. Indic. 98, 420–429 (2019).

    Article  Google Scholar 

  38. Quave, C. L. & Pieroni, A. A reservoir of ethnobotanical knowledge informs resilient food security and health strategies in the Balkans. Nat. Plants 1, 14021 (2015).

    Article  Google Scholar 

  39. de Medeiros, P. M. et al. Local knowledge as a tool for prospecting wild food plants: experiences in northeastern Brazil. Sci. Rep. 11, 594 (2021).

    Article  CAS  Google Scholar 

  40. Sogbohossou, E. O. D. et al. A roadmap for breeding orphan leafy vegetable species: a case study of Gynandropsis gynandra (Cleomaceae). Hortic. Res. 5, 2 (2018).

    Article  Google Scholar 

  41. Pascual, U. et al. Biodiversity and the challenge of pluralism. Nat. Sustain. 4, 567–572 (2021).

    Article  Google Scholar 

  42. Amaya, N., Meldrum, G. & Padulosi, S. Promoting Chaya and Tepary Bean to Improve Diet Quality, Climate Resilience, and Incomes in Guatemala (Bioversity International, 2020); https://cgspace.cgiar.org/bitstream/handle/10568/109363/Guatemala%20Impact%20Brief%20A4.pdf

  43. Davis, D. R., Epp, M. D. & Riordan, H. D. Changes in USDA food composition data for 43 garden crops, 1950 to 1999. J. Am. Coll. Nutr. 23, 669–682 (2004).

    Article  CAS  Google Scholar 

  44. Hotz, C. & Gibson, R. S. Traditional food-processing and preparation practices to enhance the bioavailability of micronutrients in plant-based diets. J. Nutr. 137, 1097–1100 (2007).

    Article  CAS  Google Scholar 

  45. McCance, R. A. & Widdowson, E. M. McCance and Widdowson’s the Composition of Foods (R. Soc. Chem., 2014).

  46. FoodData Central (US Department of Agriculture, 2019); https://fdc.nal.usda.gov/fdc-app.html#/food-search

  47. Sivakumaran, S., Huffman, L. & Sivakumaran, S. The New Zealand food composition database: a useful tool for assessing New Zealanders’ nutrient intake. Food Chem. 238, 101–110 (2018).

    Article  CAS  Google Scholar 

  48. Standards Tables of Food Composition in Japan (MEXT, 2015); https://www.mext.go.jp/en/policy/science_technology/policy/title01/detail01/1374030.htm

  49. Vincent, A. et al. FAO/INFOODS Food Composition Table for Western Africa (2019). User Guide and Condensed Food Composition Table (FAO, 2019).

  50. Malawian Food Composition Table (MAFOODS, 2019).

  51. Longvah, T., An̲antan̲, I., Bhaskarachary, K., Venkaiah, K. & Longvah, T. Indian Food Composition Tables (National Institute of Nutrition, Indian Council of Medical Research, 2017).

  52. Dignan, C., Burlingame, B., Kumar, S. & Aalbersberg, W. The Pacific Islands Food Composition Tables (South Pacific Commission, 2004).

  53. Ray, A., Ray, R. & Sreevidya, E. A. How many wild edible plants do we eat—their diversity, use, and implications for sustainable food system: an exploratory analysis in India. Front. Sustain. Food Syst. 4, 56 (2020).

    Article  Google Scholar 

  54. R: A Language and Environment for Statistical Computing (R Core Team, 2020).

  55. Koyande, A. K. et al. Microalgae: a potential alternative to health supplementation for humans. Food Sci. Hum. Wellness 8, 16–24 (2019).

    Article  Google Scholar 

  56. Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).

    Article  Google Scholar 

  57. Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).

    Article  Google Scholar 

  58. Sayers, E. W. et al. GenBank. Nucleic Acids Res. 47, D94–D99 (2019).

    Article  CAS  Google Scholar 

  59. Magallón, S., Gómez‐Acevedo, S., Sánchez‐Reyes, L. L. & Hernández‐Hernández, T. A metacalibrated time‐tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 207, 437–453 (2015).

    Article  Google Scholar 

  60. 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).

    Article  Google Scholar 

  61. Cayuela, L., Granzow‐de la Cerda, Í., Albuquerque, F. S. & Golicher, D. J. Taxonstand: an R package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).

    Article  Google Scholar 

  62. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. 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 

  63. Wickham, H. et al. Package “ggplot2”, Create Elegant Data Visualisations Using the Grammar of Graphics, version 3.1.1. (Springer-Verlag New York, 2019).

  64. Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).

    Article  CAS  Google Scholar 

  65. Revell, L. J.phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  66. Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).

  67. Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).

    Article  Google Scholar 

  68. Molina-Venegas, R. & Rodríguez, M. Á. Revisiting phylogenetic signal; strong or negligible impacts of polytomies and branch length information? BMC Evol. Biol. 17, 53 (2017).

    Article  Google Scholar 

  69. Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).

    Article  CAS  Google Scholar 

  70. Pinheiro, J. et al. Package ‘nlme’. Linear and Nonlinear Mixed Effects Model version 3 (2017).

  71. Bird, J. K., Murphy, R. A., Ciappio, E. D. & McBurney, M. I. Risk of deficiency in multiple concurrent micronutrients in children and adults in the United States. Nutrients 9, 655 (2017).

    Article  Google Scholar 

  72. Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).

    Article  CAS  Google Scholar 

  73. Abellán, P., Carrete, M., Anadón, J. D., Cardador, L. & Tella, J. L. Non‐random patterns and temporal trends (1912–2012) in the transport, introduction and establishment of exotic birds in Spain and Portugal. Divers. Distrib. 22, 263–273 (2016).

    Article  Google Scholar 

  74. Brummitt, R. K., Pando, F., Hollis, S. & Brummitt, N. World Geographical Scheme for Recording Plant Distributions (Biodiversity Information Standards (TDWG), 2001).

  75. Prener, C., Grossenbacher, T. & Zehr, A. biscale: Tools and Palettes for Bivariate Thematic Mapping (2020).

  76. Fisher, W. D. On grouping for maximum homogeneity. J. Am. Stat. Assoc. 53, 789–798 (1958).

    Article  Google Scholar 

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Acknowledgements

A.C.-J. was funded by the Basil Furneaux Memorial Fund, Imperial College London, and the Natural Environment Research Council (NE/S007415/1). B.V. received funding from the Wellcome Trust Our Plant, Our Health programme (Grant number: 106864MA). S.P. received funding from Royal Botanic Gardens, Kew pilot study fund (reference: 11492-100). We thank M. Soto Gomez, the Gill and Graystock lab group (Imperial College London), and the Science Directorate (Royal Botanic Gardens, Kew) for feedback on project ideas; M. Bidartondo for comments on this manuscript; N. Black, R. Govaerts, I. Ondo and R. Turner for help with accessing the World Checklist of Vascular Plants and Useful Plants data; D. Satori and T. Cossu for help with accessing conservation data; F. Fletcher for help with assembling the nutritional data; K. Kam for help with code; and P. Jones for proofreading.

Author information

Authors and Affiliations

Authors

Contributions

S.P. conceived the project. A.C.-J. and S.P. designed the study. A.C.-J. and J.B. collected the nutritional data. M.D. and T.U. compiled and provided the list of edible plant species. D.C. and B.V. developed and provided the code for the nutrient predictions. S.P. provided the data on plant distributions. A.C.-J. performed analyses with guidance from S.P., F.F., J.H., M.-J.R.H. and R.D. A.C.-J. wrote the manuscript with guidance from S.P. All authors provided feedback on the manuscript and gave authorization for publication.

Corresponding authors

Correspondence to Aoife Cantwell-Jones or Samuel Pironon.

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No competing interests.

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Nature Plants thanks Christine Foyer, Nora Castañeda, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Predicted versus observed values (and their relationships) for B vitamins in nutritionally known species.

The different colours represent the different B vitamins (see legend). The coloured lines are the relationships between predicted and observed values (using generalised least squares models). The dashed line represents a relationship where the predicted and observed values are equal. Circles denote observed nutritionally known values which did not lie within the 95% confidence intervals of their predicted values. Predicted and observed values for each B vitamin were standardised prior to plotting.

Extended Data Fig. 2 Summary of the method used to estimate the B-vitamin profiles of nutritionally unknown species.

1) The branches of the phylogenetic tree are rescaled (often resulting in shortening internal branches relative to terminal branches, as in the second tree), so that the B-vitamin profiles of the nutritionally known species match the distribution expected under a Brownian-motion model. The parameter λ is used for this rescaling and represents the strength of phylogenetic signal for a given B vitamin, with values close to 1 suggesting strong signal64. The parameter σ2 is the estimated variance of the Brownian-motion process for a given nutrient. 2) The B-vitamin concentrations of the most recent common ancestors are estimated using the fastAnc function from “phytools”65, data for nutritionally known species, and the rescaled phylogeny. 3) The B-vitamin concentrations for nutritionally unknown edible species (here represented by “?”) are approximated as the values of the most recent common ancestors between the nutritionally unknown species and its most closely related nutritionally known species. The standard deviation (SD) of the prediction is calculated as the product of the variance of the Brownian-motion (σ2) and the branch length of the nutritionally unknown species to its most recent common ancestor with a nutritionally known species (t). CI refers to confidence intervals.

Extended Data Fig. 3 Prevalence of inadequate B-vitamin intake (%) and counts of B-vitamin-source species per country.

Inadequate B-vitamin intake is represented by the Prevalence of Inadequate Micronutrient Intake Index1. Light grey represents missing inadequate-intake data; blue, low inadequate intake and many source species; red, high inadequate intake and few source species; and brown, high inadequate intake and many source species. Brown countries are those that could benefit most, nutritionally, from their edible plant diversity. The axes labels on the legend in Extended Data Fig. 3a apply to the other subplots. The numerical ranges for each colour (indicated in the legends) were generated using Fisher natural breaks classification method76 and differ for each subplot for improved visualisation.

Extended Data Fig. 4 Prevalence of inadequate B-vitamin intake (%) and percentages of threatened B-vitamin-source species per country.

Inadequate B-vitamin intake is represented by the Prevalence of Inadequate Micronutrient Intake Index1. Light grey represents missing inadequate-intake data or no B-vitamin-source species; blue, low inadequate intake and a low percentage of threatened source species; dark grey, low inadequate intake and a high percentage of threatened source species; red, high inadequate intake and a low percentage of threatened source species; and black, high inadequate intake and a high percentage of threatened source species. Black countries are those that should prioritise most in-situ edible plant conservation. “Threatened species” are those that are possibly threatened, threatened or extinct in the wild, according to the Botanic Gardens Conservation International ThreatSearch database32. The axes labels on the legend in Extended Data Fig. 4a apply to the other subplots. The numerical ranges for each colour (indicated in the legends) were generated using Fisher natural breaks classification method76 and differ for each subplot for improved visualisation.

Extended Data Fig. 5 Prevalence of inadequate B-vitamin intake (%) and percentages of B-vitamin-source species that are not conserved ex situ, per country.

Inadequate B-vitamin intake is represented by the Prevalence of Inadequate Micronutrient Intake Index1. Light grey represents missing inadequate-intake data or no B-vitamin-source species; blue, low inadequate intake and a low percentage of source species not conserved ex situ; dark grey, low inadequate intake and a high percentage of source species not conserved ex situ; red, high inadequate intake and a low percentage of source species not conserved ex situ; and black, high inadequate intake and high percentage of source species not conserved ex situ. Black countries are those that should prioritise most ex-situ edible plant conservation. Species “not conserved ex situ” refers to those absent from Genesys34 and the Millennium Seed Bank Partnership databases33. The axes labels on the legend in Extended Data Fig. 5a apply to the other subplots. The numerical ranges for each colour (indicated in the legends) were generated using Fisher natural breaks classification method76 and differ for each subplot for improved visualisation.

Supplementary information

Supplementary Information

Supplementary Tables 1–10 and Methods.

Reporting Summary

Supplementary Data 1

Predicted values for nutritionally known species (using jackknifing) for thiamine, riboflavin, niacin, pantothenic acid and folate, following the approach of Vaitla et al.19.

Supplementary Data 2

Edible plant predictions (nutritionally unknown species) for thiamine, riboflavin, niacin, pantothenic acid and folate, following the approach of Vaitla et al.19.

Supplementary Data 3

Consensus and best-case B-vitamin source species. These plants were either predicted to be B-vitamin sources by both prediction methods or at least one prediction method, respectively. The countries in which the consensus source species are found are also given.

Supplementary Data 4

For each country, the number of source species, the percentages of source species that are threatened in situ and not conserved ex situ, and the prevalence of deficiency of thiamine, riboflavin, niacin and folate.

Supplementary Software 1

R script for measuring phylogenetic signal of B vitamins in nutritionally known species.

Supplementary Software 2

R script for predicting the B-vitamin concentrations for nutritionally unknown species, following the method of Vaitla et al.19.

Supplementary Software 3

R script for validating the prediction method of Vaitla et al.19 by predicting the B-vitamin concentrations of nutritionally known species.

Supplementary Software 4

R script for predicting B-vitamin source species using the ‘hot-node’ approach.

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Cantwell-Jones, A., Ball, J., Collar, D. et al. Global plant diversity as a reservoir of micronutrients for humanity. Nat. Plants 8, 225–232 (2022). https://doi.org/10.1038/s41477-022-01100-6

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