Skip to main content

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.

Shortfalls and opportunities in terrestrial vertebrate species discovery

Matters Arising to this article was published on 21 February 2024

Abstract

Much of biodiversity remains undiscovered, causing species and their functions to remain unrealized and potentially lost in ignorance. Here we use extensive species-level data in a time-to-event model framework to identify taxonomic and geographic discovery gaps in terrestrial vertebrates. Biological, environmental and sociological factors all affect discovery probability and together provide strong predictive ability for species discovery. Our model identifies distinct taxonomic and geographic unevenness in future discovery potential, with greatest opportunities for amphibians and reptiles, and for Neotropical and Indo-Malayan forests. Brazil, Indonesia, Madagascar and Colombia emerge as holding greatest discovery opportunities, with a quarter of potential discoveries estimated. These findings highlight the importance of international policy support for basic taxonomic research and the potential of quantitative models to aid species discovery.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Variation in observed and predicted discovery trends for the years 1759–2014 across the four terrestrial vertebrate groups.
Fig. 2: Joint effects of species-level attributes on discovery probability over different time periods.
Fig. 3: Predicted future discovery potential across major terrestrial vertebrate taxa.
Fig. 4: Global variation in predicted discovery potential, quantified as the percent of all global terrestrial vertebrate discoveries predicted to occur in a region.

Data availability

Data produced for this study are available as Supplementary Data files. Raw data to reproduce the analysis of this study are available at vertlife.org/data/discoverypotential.

Code availability

R scripts to reproduce the analysis of this study are available at vertlife.org/data/discoverypotential.

References

  1. Costello, M. J., May, R. M. & Stork, N. E. Can we name Earth’s species before they go extinct? Science 339, 413–416 (2013).

    CAS  PubMed  ADS  Google Scholar 

  2. Mora, C., Rollo, A. & Tittensor, D. P. Comment on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).

    CAS  PubMed  ADS  Google Scholar 

  3. Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the Ocean? PLoS Biol. 9, e1001127 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. May, R. & Beverton, R. J. H. How many species? Phil. Trans. R. Soc. B 330, 293–304 (1990).

    Google Scholar 

  5. Scheffers, B. R., Joppa, L. N., Pimm, S. L. & Laurance, W. F. What we know and don’t know about Earth’s missing biodiversity. Trends Ecol. Evol. 27, 501–510 (2012).

    PubMed  Google Scholar 

  6. Raven, P. H. & Wilson, E. O. A fifty-year plan for biodiversity surveys. Science 258, 1099–1100 (1992).

    CAS  PubMed  ADS  Google Scholar 

  7. Whittaker, R. J. et al. Conservation biogeography: assessment and prospect. Divers. Distrib. 11, 3–23 (2005).

    Google Scholar 

  8. Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).

    Google Scholar 

  9. Guide to the Global Taxonomy Initiative (Secretariat of the Convention on Biological Diversity, 2010).

  10. Costello, M. J., May, R. M. & Stork, N. E. Response to comments on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).

    CAS  PubMed  ADS  Google Scholar 

  11. Bebber, D. P., Marriott, F. H. C., Gaston, K. J., Harris, S. A. & Scotland, R. W. Predicting unknown species numbers using discovery curves. Proc. R. Soc. B 274, 1651–1658 (2007).

    PubMed  PubMed Central  Google Scholar 

  12. Edie, S. M., Smits, P. D. & Jablonski, D. Probabilistic models of species discovery and biodiversity comparisons. Proc. Natl Acad. Sci. USA 114, 3666–3671 (2017).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  13. Guenard, B., Weiser, M. D. & Dunn, R. R. Global models of ant diversity suggest regions where new discoveries are most likely are under disproportionate deforestation threat. Proc. Natl Acad. Sci. USA 109, 7368–7373 (2012).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  14. Blackburn, T. M. & Gaston, K. J. What determines the probability of discovering a species - a study of South-American Oscine Passerine birds. J. Biogeogr. 22, 7–14 (1995).

    Google Scholar 

  15. Costello, M. J., Lane, M., Wilson, S. & Houlding, B. Factors influencing when species are first named and estimating global species richness. Glob. Ecol. Conserv. 4, 243–254 (2015).

    Google Scholar 

  16. Collen, B., Purvis, A. & Gittleman, J. L. Biological correlates of description date in carnivores and primates. Glob. Ecol. Biogeogr. 13, 459–467 (2004).

    Google Scholar 

  17. Diniz-Filho, J. A. F. et al. Macroecological correlates and spatial patterns of anuran description dates in the Brazilian Cerrado. Glob. Ecol. Biogeogr. 14, 469–477 (2005).

    Google Scholar 

  18. Costello, M. J., Houlding, B. & Joppa, L. N. Further evidence of more taxonomists discovering new species, and that most species have been named: response to Bebber et al. (2014). New Phytol. 202, 739–740 (2014).

    PubMed  Google Scholar 

  19. Meiri, S. Small, rare and trendy: traits and biogeography of lizards described in the 21st century. J. Zool. 299, 251–261 (2016).

    Google Scholar 

  20. Klein, J. P. & Moeschberger, M. L. Survival Analysis: Techniques for Censored and Truncated Data.(Springer, 2003).

  21. Essl, F., Rabitsch, W., Dullinger, S., Moser, D. & Milasowszky, N. How well do we know species richness in a well-known continent? Temporal patterns of endemic and widespread species descriptions in the European fauna. Glob. Ecol. Biogeogr. 22, 29–39 (2013).

    Google Scholar 

  22. Colli, G. R. et al. In the depths of obscurity: knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae). Biol. Conserv. 204, 51–62 (2016).

    Google Scholar 

  23. Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there? J. Mammal. 99, 1–14 (2018).

    Google Scholar 

  24. Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).

    PubMed  ADS  Google Scholar 

  25. Bellard, C. et al. Vulnerability of biodiversity hotspots to global change. Glob. Ecol. Biogeogr. 23, 1376–1386 (2014).

    Google Scholar 

  26. Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).

    CAS  PubMed  ADS  Google Scholar 

  27. Joppa, L. N., Roberts, D. L. & Pimm, S. L. How many species of flowering plants are there? Proc. R. Soc. B 278, 554–559 (2011).

    PubMed  Google Scholar 

  28. Giam, X. et al. Reservoirs of richness: least disturbed tropical forests are centres of undescribed species diversity. Proc. R. Soc. B 279, 67–76 (2012).

    PubMed  Google Scholar 

  29. Jetz, W. & Fine, P. V. A. Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment. PLoS Biol. 10, e1001292 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Gouveia, S. F., Villalobos, F., Dobrovolski, R., Beltrão-Mendes, R. & Ferrari, S. F. Forest structure drives global diversity of primates. J. Anim. Ecol. 83, 1523–1530 (2014).

    PubMed  Google Scholar 

  31. Oliveira, B. F. & Scheffers, B. R. Vertical stratification influences global patterns of biodiversity. Ecography 42, 249–249 (2019).

    ADS  Google Scholar 

  32. Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232–1244 (2016).

  33. Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).

    PubMed  Google Scholar 

  34. Garnett, S. T. & Christidis, L. Taxonomy anarchy hampers conservation. Nature 546, 25–27 (2017).

    CAS  PubMed  ADS  Google Scholar 

  35. Isaac, N. J. B., Mallet, J. & Mace, G. M. Taxonomic inflation: its influence on macroecology and conservation. Trends Ecol. Evol. 19, 464–469 (2004).

    PubMed  Google Scholar 

  36. Bremer, K., Bremer, B., Karis, P. & Källersjö, M. Time for change in taxonomy. Nature 343, 202 (1990).

    CAS  PubMed  ADS  Google Scholar 

  37. Raposo, M. A. et al. What really hampers taxonomy and conservation? A riposte to Garnett and Christidis (2017). Zootaxa 4317, 179–184 (2017).

    Google Scholar 

  38. Wake, D. B. Persistent plethodontid themes: species, phylogenies, and biogeography. Herpetologica 73, 242–251 (2017).

    Google Scholar 

  39. Tedesco, P. A. et al. Estimating how many undescribed species have gone extinct. Conserv. Biol. 28, 1360–1370 (2014).

    CAS  PubMed  Google Scholar 

  40. Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).

    PubMed  Google Scholar 

  41. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    CAS  PubMed  ADS  Google Scholar 

  42. Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).

    PubMed  Google Scholar 

  43. Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563.e3 (2019).

    PubMed  Google Scholar 

  45. Moura, M. R. et al. Geographical and socioeconomic determinants of species discovery trends in a biodiversity hotspot. Biol. Conserv. 220, 237–244 (2018).

    Google Scholar 

  46. Gaston, K. J., Blackburn, T. M. & Loder, N. Which species are described first? The case of North-American butterflies. Biodivers. Conserv. 4, 119–127 (1995).

    Google Scholar 

  47. Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci. Data 4, 170123 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).

    Google Scholar 

  49. Hallmann, K. & Griebeler, E. M. An exploration of differences in the scaling of life history traits with body mass within reptiles and between amniotes. Ecol. Evol. 8, 5480–5494 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Slavenko, A., Itescu, Y., Ihlow, F. & Meiri, S. Home is where the shell is: predicting turtle home range sizes. J. Anim. Ecol. 85, 106–114 (2016).

    PubMed  Google Scholar 

  51. Regis, K. W. & Meik, J. M. Allometry of sexual size dimorphism in turtles: a comparison of mass and length data. PeerJ 5, e2914 (2017).

    PubMed  PubMed Central  Google Scholar 

  52. Itescu, Y., Karraker, N. E., Raia, P., Pritchard, P. C. H. & Meiri, S. Is the island rule general? Turtles disagree. Glob. Ecol. Biogeogr. 23, 689–700 (2014).

    Google Scholar 

  53. Faurby, S. & Svenning, J.-C. Resurrection of the island rule: human-driven extinctions have obscured a basic evolutionary pattern. Am. Nat. 187, 812–820 (2016).

    PubMed  Google Scholar 

  54. Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).

    Google Scholar 

  55. Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204A, 23–31 (2016).

  56. Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).

    Google Scholar 

  57. Gaston, K. J., Blackburn, T. M. & Lawton, J. H. Interspecific abundance–range size relationships: an appraisal of mechanisms. J. Anim. Ecol. 66, 579–601 (1997).

    Google Scholar 

  58. Borregaard, M. K. & Rahbek, C. Causality of the relationship between geographic distribution and species abundance. Q. Rev. Biol. 85, 3–25 (2010).

    PubMed  Google Scholar 

  59. IUCN Red List of Threatened Species. Version 2018 (IUCN, 2018).

  60. Freitag, S., Hobson, C., Biggs, H. C. & Jaarsveld, A. S. Testing for potential survey bias: the effect of roads, urban areas and nature reserves on a southern African mammal data set. Anim. Conserv. 1, 119–127 (1998).

    Google Scholar 

  61. Kier, G. & Barthlott, W. Measuring and mapping endemism and species richness: a new methodological approach and its application on the flora of Africa. Biodivers. Conserv. 10, 1513–1529 (2001).

    Google Scholar 

  62. Vilela, B. & Villalobos, F. letsR: a new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234 (2015).

    Google Scholar 

  63. Papavero, N. Essays on the History of Neotropical Dipterology: With Special Reference to Collectors: 1750–1905: Vol. I (Museu de Zoologia da Universidade de São Paulo, 1971).

  64. Baselga, A., Lobo, J. M., Hortal, J., Jiménez-Valverde, A. & Gómez, J. F. Assessing alpha and beta taxonomy in eupelmid wasps: determinants of the probability of describing good species and synonyms. J. Zool. Syst. Evol. Res. 48, 40–49 (2010).

    Google Scholar 

  65. Yang, W., Ma, K. & Kreft, H. Environmental and socio-economic factors shaping the geography of floristic collections in China. Glob. Ecol. Biogeogr. 23, 1284–1292 (2014).

    Google Scholar 

  66. Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. R Core Team R: A Language and Environment for Statistical Computing Version 3.5.3 (R Foundation for Statistical Computing, 2019).

  68. Hijmans, R. J. raster: Geographic Data Analysis and Modeling https://cran.r-project.org/package=raster (2015).

  69. Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).

    PubMed  PubMed Central  Google Scholar 

  70. Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).

    Google Scholar 

  71. Joppa, L. N., Roberts, D. L. & Pimm, S. L. The population ecology and social behaviour of taxonomists. Trends Ecol. Evol. 26, 551–553 (2011).

    PubMed  Google Scholar 

  72. Wickham, H. stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.3.1 http://stringr.tidyverse.org (2018).

  73. Mahto, A. splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values. R package version 1.4.6 http://github.com/mrdwab/splitstackshape (2018).

  74. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).

    PubMed  PubMed Central  Google Scholar 

  75. Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. Applied Linear Statistical Models (McGraw-Hill, 2004).

    Google Scholar 

  76. Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models https://cran.r-project.org/package=usdm (2017).

  77. von Linné, C. Systema Naturae https://doi.org/10.5962/bhl.title.542 (Impensis Direct Laurentii Salvii, 1758).

  78. Harrell, F. E. Regression Modeling Strategies (Springer, 2001).

  79. George, B., Seals, S. & Aban, I. Survival analysis and regression models. J. Nucl. Cardiol. 21, 686–694 (2014).

    PubMed  PubMed Central  Google Scholar 

  80. Jackson, C. flexsurv: a platform for parametric survival modeling in R. J. Stat. Softw. 70, 1–33 (2016).

    Google Scholar 

  81. Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).

  82. Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).

    PubMed  Google Scholar 

  83. Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.6 https://cran.r-project.org/package=MuMIn (2019).

  84. Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).

    PubMed  Google Scholar 

  85. Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).

    PubMed  PubMed Central  Google Scholar 

  86. Fisher, D. O. & Blomberg, S. P. Correlates of rediscovery and the detectability of extinction in mammals. Proc. R. Soc. B 278, 1090–1097 (2011).

    PubMed  Google Scholar 

  87. Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The worldwide variation in avian clutch size across species and space. PLoS Biol. 6, e303 (2008).

    PubMed  PubMed Central  Google Scholar 

  88. Jetz, W. & Rubenstein, D. R. Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72–78 (2011).

    CAS  PubMed  Google Scholar 

  89. Jetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002).

    CAS  PubMed  ADS  Google Scholar 

  90. Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.4 https://cran.r-project.org/package=data.table (2019).

  91. Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).

    Google Scholar 

  92. Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  93. Database of Global Administrative Areas Version 3.6 (GADM, 2019); http://www.gadm.org

Download references

Acknowledgements

We are grateful to S. Meiri, D. S. Rinnan, G. Reygondeau, N. Upham, M. Costello, D. Wake and J. Hortal for providing helpful comments on the research or manuscript drafts. We thank C. Haddad, L. C. Márquez, G. Singh and A. F. Meyer for providing pictures of the example species in Fig. 1. This work was produced, in part, with the support of the National Geographic Society through a partnership with the E.O. Wilson Biodiversity Foundation and its Half-Earth Project. W. J. also acknowledges support from NSF grant DEB-1441737 and NASA grants 80NSSC17K0282 and 80NSSC18K0435.

Author information

Authors and Affiliations

Authors

Contributions

M.R.M. and W.J. conceived the study, developed the figures and wrote the text; M.R.M. analysed the data.

Corresponding authors

Correspondence to Mario R. Moura or Walter Jetz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Joaquin Hortal, Stewart Edie and Lucas N. Joppa for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Top 30 tetrapod families with highest percentage of total future species discoveries.

a, Amphibians. b, Reptiles. c, Mammals. d, Birds. The horizontal lines denote the 95% confidence intervals. Taxon-level estimates are available through Supplementary Data 1.

Extended Data Fig. 2 Top 30 tetrapod families with highest standardized proportion of unknown species.

a, Amphibians. b, Reptiles. c, Mammals. d, Birds. The horizontal lines denote the 95% confidence intervals. Taxon-level estimates are available through Supplementary Data 1.

Extended Data Fig. 3 Geographical discovery patterns for terrestrial vertebrates at different spatial resolutions.

a–c, Percent of total predicted discoveries across grid cells and their respective (d–f) uncertainty (± margin of error). g–i, Standardized proportion of undiscovered species across grid cells and their respective (j–l) uncertainty (± margin of error). Outlined and hatched regions designate grid cells holding values within respectively the top 10% and top 5% of the mapped metric. Maps drawn at spatial resolutions of 220, 440, 880 km. Assemblage-level estimates are available through Supplementary Data 2.

Extended Data Fig. 4 Geographical discovery patterns for amphibians at different spatial resolutions.

a–c, Percent of total discoveries across grid cells and their respective (d–f) uncertainty (± margin of error). g–i, Standardized proportion of undiscovered species across grid cells and their respective (j–l) uncertainty (± margin of error). Outlined and hatched regions designate grid cells holding values within respectively the top 10% and top 5% of the mapped metric. Maps drawn at spatial resolutions of 220, 440, 880 km. Assemblage-level estimates are available through Supplementary Data 2.

Extended Data Fig. 5 Geographical discovery patterns for reptiles at different spatial resolutions.

a–c, Percent of total discoveries across grid cells and their respective (d-f) uncertainty (± margin of error). (g–i) Standardized proportion of undiscovered species across grid cells and their respective (j–l) uncertainty (± margin of error). Outlined and hatched regions designate grid cells holding values within respectively the top 10% and top 5% of the mapped metric. Maps drawn at spatial resolutions of 220, 440, 880 km. Assemblage-level estimates are available through Supplementary Data 2.

Extended Data Fig. 6 Geographical discovery patterns for mammals at different spatial resolutions.

a–c, Percent of total discoveries across grid cells and their respective (d-f) uncertainty (± margin of error). g–i, Standardized proportion of undiscovered species across grid cells and their respective (j–l) uncertainty (± margin of error). Outlined and hatched regions designate grid cells holding values within respectively the top 10% and top 5% of the mapped metric. Maps drawn at spatial resolutions of 220, 440, 880 km. Assemblage-level estimates are available through Supplementary Data 2.

Extended Data Fig. 7 Geographical discovery patterns for birds at different spatial resolutions.

a–c, Percent of total discoveries across grid cells and their respective (d-f) uncertainty (± margin of error). g–i, Standardized proportion of undiscovered species across grid cells and their respective (j–l) uncertainty (± margin of error). Outlined and hatched regions designate grid cells holding values within respectively the top 10% and top 5% of the mapped metric. Maps drawn at spatial resolutions of 220, 440, 880 km. Assemblage-level estimates are available through Supplementary Data 2.

Extended Data Fig. 8 Biogeographical realms and biomes with higher percent of total future discoveries.

Biogeographic- and Biome-wide percent of total discoveries extracted from assemblages defined at (a) 220 km, (b) 440 km, and (c) 880 km of spatial resolution. Bioregion-level estimates are available through Supplementary Data 3.

Extended Data Fig. 9 Top 30 bioregions with higher percent of total future discoveries.

Bioregions-wide percent of total discoveries extracted from assemblages defined at (a) 220 km, (b) 440 km, and (c) 880 km of spatial resolution. A bioregion combines biogeographical realm and biome information. Bioregion-level estimates are available through Supplementary Data 3.

Extended Data Fig. 10 Top 30 countries with higher percent of total discoveries.

Country-wide percent of total discoveries extracted from assemblages defined at (a) 220 km, (b) 440 km, and (c) 880 km of spatial resolution. Country-level estimates are available through Supplementary Data 4.

Supplementary information

Supplementary Information

Supplementary Methods, Results, References, Tables 1–4 and Figs. 1–15.

Reporting Summary

Peer Review Information

Supplementary Data

Estimates of discovery potential at the levels of taxa, assemblages, bioregions and countries: TaxonLevelEstimates.zip (Supplementary Data 1), AssemblageLevelEstimates.zip (Supplementary Data 2), BioregionLevelEstimates.zip (Supplementary Data 3), CountryLevelEstimates.zip (Supplementary Data 4).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moura, M.R., Jetz, W. Shortfalls and opportunities in terrestrial vertebrate species discovery. Nat Ecol Evol 5, 631–639 (2021). https://doi.org/10.1038/s41559-021-01411-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01411-5

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing