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Opportunities and challenges of macrogenetic studies

Nature Reviews Genetics volume 22, pages 791–807 (2021)Cite this article

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Abstract

The rapidly emerging field of macrogenetics focuses on analysing publicly accessible genetic datasets from thousands of species to explore large-scale patterns and predictors of intraspecific genetic variation. Facilitated by advances in evolutionary biology, technology, data infrastructure, statistics and open science, macrogenetics addresses core evolutionary hypotheses (such as disentangling environmental and life-history effects on genetic variation) with a global focus. Yet, there are important, often overlooked, limitations to this approach and best practices need to be considered and adopted if macrogenetics is to continue its exciting trajectory and reach its full potential in fields such as biodiversity monitoring and conservation. Here, we review the history of this rapidly growing field, highlight knowledge gaps and future directions, and provide guidelines for further research.

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Fig. 1: What makes a macrogenetic study?
Fig. 2: Timeline of key advances underlying the emergence of macrogenetics.

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References

  1. Brown, J. H. & Maurer, B. A. Macroecology: the division of food and space among species on continents. Science 243, 1145–1150 (1989).

    CAS  PubMed  Google Scholar 

  2. Gaston, K. J., Robinson, D. & Chown, S. L. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).

    Google Scholar 

  3. Chown, S. L. & Gaston, K. J. Macrophysiology–progress and prospects. Funct. Ecol. 30, 330–344 (2016).

    Google Scholar 

  4. Avise, J. C. Phylogeography: the History and Formation of Species (Harvard University Press, 2000).

  5. Ebach, M. C. Origins of Biogeography. Vol. 13 (Springer, 2015).

  6. Brundin, L. On the real nature of transantarctic relationships. Evolution 19, 496–505 (1965).

    Google Scholar 

  7. Beheregaray, L. B. Twenty years of phylogeography: the state of the field and the challenges for the Southern Hemisphere. Mol. Ecol. 17, 3754–3774 (2008).

    PubMed  Google Scholar 

  8. Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).

    CAS  PubMed  Google Scholar 

  9. Gaston, K. J. & Blackburn, T. M. A critique for macroecology. Oikos 84, 353–368 (1999).

    Google Scholar 

  10. Lovegrove, B. G. The zoogeography of mammalian basal metabolic rate. Am. Nat. 156, 201–219 (2000).

    PubMed  Google Scholar 

  11. Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. Biol. Sci. 275, 1469–1478 (2008).

    PubMed  PubMed Central  Google Scholar 

  13. Kerr, J. T., Kharouba, H. M. & Currie, D. J. The macroecological contribution to global change solutions. Science 316, 1581–1584 (2007).

    CAS  PubMed  Google Scholar 

  14. Blanchet, S., Prunier, J. G. & De Kort, H. Time to go bigger: Emerging patterns in macrogenetics. Trends Genet. 33, 579–580 (2017). This study coined the term ‘macrogenetics’ and illustrated, through three study examples, how shifting toward macrogenetics should generate new perspectives and theories concerning genetic diversity patterns.

    CAS  PubMed  Google Scholar 

  15. Blanchet, S. et al. A river runs through it: the causes, consequences, and management of intraspecific diversity in river networks. Evol. Appl. 13, 1195–1213 (2020).

    PubMed  PubMed Central  Google Scholar 

  16. Frankham, R. Resolving conceptual issues in conservation genetics: the roles of laboratory species and meta-analyses. Hereditas 130, 195–201 (2004).

    Google Scholar 

  17. Arnqvist, G. & Wooster, D. Meta-analysis: synthesizing research findings in ecology and evolution. Trends Ecol. Evol. 10, 236–240 (1995).

    CAS  PubMed  Google Scholar 

  18. Paz-Vinas, I. et al. Systematic conservation planning for intraspecific genetic diversity. Proc. Biol. Sci. 285, 20172746 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Pelletier, T. A. & Carstens, B. C. Geographical range size and latitude predict population genetic structure in a global survey. Biol. Lett. 14, 20170566 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Miraldo, A. et al. An anthropocene map of genetic diversity. Science 353, 1532–1535 (2016). This paper is thought to be the first published study to massively repurpose public mtDNA sequences to explore global genetic patterns (100,791 sequences from >4,500 terrestrial mammal and amphibian species).

    CAS  PubMed  Google Scholar 

  21. Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021). This study found that adaptive IGV is higher at low latitudes and in smaller mammal species using repurposed MHC gene data from 93 mammal species.

    Google Scholar 

  22. Manel, S. et al. Global determinants of freshwater and marine fish genetic diversity. Nat. Commun. 11, 692 (2020). This study repurposed 58,565 public mtDNA sequences from 5,912 freshwater and marine fish to explore the effects of environmental drivers (temperature, species diversity) on intraspecific genetic diversity.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Theodoridis, S. et al. Evolutionary history and past climate change shape the distribution of genetic diversity in terrestrial mammals. Nat. Commun. 11, 2557 (2020). This study revealed a negative effect of past rapid climate change and a positive effect of interannual precipitation variability in shaping the genetic diversity of terrestrial mammals using 46,965 mtDNA sequences.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Barrow, L. N., da Fonseca, E. M., Thompson, C. E. P. & Carstens, B. C. Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13303 (2020).

    Article  PubMed  Google Scholar 

  25. De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021). This study found weak support for latitudinal IGV gradients, taxonomic-specific effects of temperature stability and life-history traits, and higher IGV in animals compared to plants using microsatellite and amplified fragment length polymorphism data from 8,386 local populations from 727 animal and plant species.

    PubMed  PubMed Central  Google Scholar 

  26. Schmidt, C., Domaratzki, M., Kinnunen, R. P., Bowman, J. & Garroway, C. J. Continent-wide effects of urbanization on bird and mammal genetic diversity. Proc. Biol. Sci. 287, 20192497 (2020). This study used archived microsatellite data from 85 studies (66 species) to explore the effects of urbanization in mammals and birds.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Millette, K. L. et al. No consistent effects of humans on animal genetic diversity worldwide. Ecol. Lett. 23, 55–67 (2020). The authors of this article conducted spatial and temporal analysis of the effects of humans on animal genetic diversity worldwide, by repurposing 175,247 mtDNA sequences from >17,000 animal species.

    PubMed  Google Scholar 

  28. Taberlet, P. et al. Genetic diversity in widespread species is not congruent with species richness in alpine plant communities. Ecol. Lett. 15, 1439–1448 (2012). This paper reports a Class I macrogenetic study based on amplified fragment length polymorphism genetic data from 27 alpine plant species that tested whether genetic and species diversities co-vary.

    PubMed  Google Scholar 

  29. Manel, S. et al. Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol. Ecol. 21, 3729–3738 (2012).

    PubMed  PubMed Central  Google Scholar 

  30. Gugerli, F. et al. Relationships among levels of biodiversity and the relevance of intraspecific diversity in conservation – a project synopsis. Perspect. Plant. Ecol. Evol. Syst. 10, 259–281 (2008).

    Google Scholar 

  31. Schlaepfer, D. R., Braschler, B., Rusterholz, H.-P. & Baur, B. Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9, e02488 (2018).

    Google Scholar 

  32. González, A. V., Gómez-Silva, V., Ramírez, M. J. & Fontúrbel, F. E. Meta-analysis of the differential effects of habitat fragmentation and degradation on plant genetic diversity. Conserv. Biol. 34, 711–720 (2020).

    PubMed  Google Scholar 

  33. Ratnasingham, S. & Hebert, P. D. N. Bold: the barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Google Scholar 

  35. Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).

    Google Scholar 

  36. Theodoridis, S., Rahbek, C. & Nogues-Bravo, D. Exposure of mammal genetic diversity to mid-21st century global change. Ecography 44, 817–831 (2021).

    Google Scholar 

  37. Rissler, L. J. Union of phylogeography and landscape genetics. Proc. Natl Acad. Sci. USA 113, 8079–8086 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Hubbell, S. P. The unified neutral theory of biodiversity and biogeography (Princeton University Press, 2001).

  39. Haldane, J. B. S. A mathematical theory of natural and artificial selection, Part V: selection and mutation. Math. Proc. Camb. Philos. Soc. 23, 838–844 (1927).

    Google Scholar 

  40. Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Fisher, R. A. On the dominance ratio. Proc. R. Soc. Edinburgh 42, 321–341 (1922).

    Google Scholar 

  42. Kimura, M. & Weiss, G. H. The stepping stone model of population structure and the decrease of genetic correlation with distance. Genetics 49, 561–576 (1964).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kingman, J. F. C. The coalescent. Stoch. Process. Their Appl. 13, 235–248 (1982).

    Google Scholar 

  44. Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).

    CAS  PubMed  Google Scholar 

  45. Soulé, M. E. in Molecular Evolution (ed. Ayala, F. J.) 60–77 (Sinauer Associates, 1976).

  46. Brown, A. H. Isozymes, plant population genetic structure and genetic conservation. Tag. Theor. Appl. Genet. Theor. Angew. Genet. 52, 145–157 (1978).

    CAS  Google Scholar 

  47. Mullis, K. et al. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 51, 263–273 (1986).

    CAS  PubMed  Google Scholar 

  48. Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. & Johnson, E. A. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 17, 240–248 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Carroll, E. L. et al. Genetic and genomic monitoring with minimally invasive sampling methods. Evol. Appl. 11, 1094–1119 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270, 313–321 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).

    CAS  PubMed  Google Scholar 

  53. Gauthier, J. et al. Museomics identifies genetic erosion in two butterfly species across the 20th century in Finland. Mol. Ecol. Resour. 20, 1191–1205 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wandeler, P., Hoeck, P. E. A. & Keller, L. F. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22, 634–642 (2007).

    PubMed  Google Scholar 

  55. Strasser, B. J. The experimenter’s museum: GenBank, natural history, and the moral economies of biomedicine. Isis 102, 60–96 (2011).

    PubMed  Google Scholar 

  56. Whitlock, M. C. Data archiving in ecology and evolution: best practices. Trends Ecol. Evol. 26, 61–65 (2011).

    PubMed  Google Scholar 

  57. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    PubMed  PubMed Central  Google Scholar 

  58. Deck, J. et al. The Genomic Observatories Metadatabase (GeOMe): A new repository for field and sampling event metadata associated with genetic samples. PLoS Biol. 15, e2002925 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. R Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing http://www.r-project.org/index.html (2021).

  60. Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol. 28, 614–621 (2013).

    PubMed  Google Scholar 

  61. Prunier, J. G., Colyn, M., Legendre, X., Nimon, K. F. & Flamand, M. C. Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses. Mol. Ecol. 24, 263–283 (2015).

    CAS  PubMed  Google Scholar 

  62. Stanley, R. R. E. et al. A climate-associated multispecies cryptic cline in the northwest Atlantic. Sci. Adv. 4, eaaq0929 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. Fenderson, L. E., Kovach, A. I. & Llamas, B. Spatiotemporal landscape genetics: investigating ecology and evolution through space and time. Mol. Ecol. 29, 218–246 (2020).

    PubMed  Google Scholar 

  64. Daza, J. M., Castoe, T. A. & Parkinson, C. L. Using regional comparative phylogeographic data from snake lineages to infer historical processes in middle America. Ecography 33, 343–354 (2010).

    Google Scholar 

  65. Riddle, B. R. Comparative phylogeography clarifies the complexity and problems of continental distribution that drove A. R. Wallace to favor islands. Proc. Natl Acad. Sci. USA 113, 7970–7977 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Carstens, B. C., Morales, A. E., Field, K. & Pelletier, T. A. A global analysis of bats using automated comparative phylogeography uncovers a surprising impact of Pleistocene glaciation. J. Biogeogr. 45, 1795–1805 (2018).

    Google Scholar 

  67. Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).

    PubMed  PubMed Central  Google Scholar 

  68. Smith, B. T. et al. The drivers of tropical speciation. Nature 515, 406–409 (2014).

    CAS  PubMed  Google Scholar 

  69. Ballin, M., Barcaroli, G., Masselli, M. & Scarnó, M. Redesign Sample for Land Use/Cover Area Frame Survey (LUCAS) 2018 (EU Publications, 2018).

  70. Buchhorn, M. et al. Copernicus global land cover layers — Collection 2. Remote. Sens. 12, 1044 (2020).

    Google Scholar 

  71. Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090-184. Ecology 90, 2648–2648 (2009).

    Google Scholar 

  72. Tedesco, P. A. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. Vellend, M. & Geber, M. A. Connections between species diversity and genetic diversity: species diversity and genetic diversity. Ecol. Lett. 8, 767–781 (2005).

    Google Scholar 

  74. Fourtune, L., Paz-Vinas, I., Loot, G., Prunier, J. G. & Blanchet, S. Lessons from the fish: a multi-species analysis reveals common processes underlying similar species-genetic diversity correlations. Freshw. Biol. 61, 1830–1845 (2016).

    Google Scholar 

  75. Bertin, A. et al. Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat. Mol. Ecol. 26, 431–443 (2017).

    PubMed  Google Scholar 

  76. Lawrence, E. R. & Fraser, D. J. Latitudinal biodiversity gradients at three levels: linking species richness, population richness and genetic diversity. Glob. Ecol. Biogeogr. 29, 770–788 (2020).

    Google Scholar 

  77. Schmidt, C., Dray, S. & Garroway, C. J. Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity. bioRxiv https://doi.org/10.1101/2020.06.03.132092 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).

    PubMed  Google Scholar 

  79. Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).

    PubMed  Google Scholar 

  80. Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).

    CAS  PubMed  Google Scholar 

  81. Schmidt, C. & Garroway, C. J. The conservation utility of mitochondrial genetic diversity in macrogenetic research. Conserv. Genet. 22, 323–327 (2021).

    Google Scholar 

  82. Gratton, P. et al. Which latitudinal gradients for genetic diversity? Trends Ecol. Evol. 32, 724–726 (2017). This response to Miraldo et al.20 identified a limitation of that article in that it did not account for the decay of genetic similarity with distance and represents the first critique of the downsides of the macrogenetic approach and the need for rigorous statistics.

    PubMed  Google Scholar 

  83. Loveless, M. D. & Hamrick, J. L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15, 65–95 (1984).

    Google Scholar 

  84. Hu, Y. et al. Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci. Adv. 7, eabd5725 (2021).

    CAS  PubMed  Google Scholar 

  85. Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).

    Google Scholar 

  86. Aguilar, R., Quesada, M., Ashworth, L., Herrerias-Diego, Y. & Lobo, J. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17, 5177–5188 (2008).

    PubMed  Google Scholar 

  87. Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).

    PubMed  Google Scholar 

  88. Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12, 1505–1512 (2019). This study estimated the magnitude of the loss of genetic variation over a century-scale using microsatellite data from 91 species.

    PubMed  PubMed Central  Google Scholar 

  89. Schmidt, C. & Garroway, C. J. The population genetics of urban and rural amphibians in north America. Mol. Ecol. https://doi.org/10.1111/mec.16005 (2021).

    Article  PubMed  Google Scholar 

  90. Bazin, E., Glémin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).

    CAS  PubMed  Google Scholar 

  91. Galtier, N., Nabholz, B., Glémin, S. & Hurst, G. D. D. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Mol. Ecol. 18, 4541–4550 (2009).

    CAS  PubMed  Google Scholar 

  92. Allio, R., Donega, S., Galtier, N. & Nabholz, B. Large variation in the ratio of mitochondrial to nuclear mutation rate across animals: implications for genetic diversity and the use of mitochondrial DNA as a molecular marker. Mol. Biol. Evol. 34, 2762–2772 (2017).

    CAS  PubMed  Google Scholar 

  93. Almeida-Rocha, J. M., Soares, L. A. S. S., Andrade, E. R., Gaiotto, F. A. & Cazetta, E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: a global meta-analysis. Mol. Ecol. 29, 4812–4822 (2020).

    CAS  PubMed  Google Scholar 

  94. Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).

    CAS  PubMed  Google Scholar 

  95. Paz-Vinas, I. et al. Macrogenetic studies must not ignore limitations of genetic markers and scale. Ecol. Lett. 24, 1282–1284 (2021).

    PubMed  Google Scholar 

  96. Crandall, E. D. et al. The molecular biogeography of the Indo-Pacific: testing hypotheses with multispecies genetic patterns. Glob. Ecol. Biogeogr. 28, 943–960 (2019).

    Google Scholar 

  97. Excoffier, L. & Foll, M. fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics 27, 1332–1334 (2011).

    CAS  PubMed  Google Scholar 

  98. Guillaume, F. & Rougemont, J. Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22, 2556–2557 (2006).

    CAS  PubMed  Google Scholar 

  99. Phillips, J. D., French, S. H., Hanner, R. H. & Gillis, D. J. HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves. PeerJ Comput. Sci. 6, e243 (2020).

    PubMed  PubMed Central  Google Scholar 

  100. Gratton, P. et al. A world of sequences: can we use georeferenced nucleotide databases for a robust automated phylogeography? J. Biogeogr. 44, 475–486 (2017).

    Google Scholar 

  101. Kimura, M. On the probability of fixation of mutant genes in a population. Genetics 47, 713–719 (1962).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Baguette, M. & Van Dyck, H. Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal. Landsc. Ecol. 22, 1117–1129 (2007).

    Google Scholar 

  103. Crow, J. F. & Aoki, K. Group selection for a polygenic behavioral trait: estimating the degree of population subdivision. Proc. Natl Acad. Sci. USA 81, 6073–6077 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Lanner, R. Why do trees live so long? Ageing Res. Rev. 1, 653–671 (2002).

    PubMed  Google Scholar 

  105. Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).

    PubMed  PubMed Central  Google Scholar 

  106. Lasne, C., Heerwaarden, B., Sgrò, C. M. & Connallon, T. Quantifying the relative contributions of the X chromosome, autosomes, and mitochondrial genome to local adaptation. Evolution 73, 262–277 (2019).

    PubMed  Google Scholar 

  107. Phillips, J. D., Gillis, D. J. & Hanner, R. H. Incomplete estimates of genetic diversity within species: implications for DNA barcoding. Ecol. Evol. 9, 2996–3010 (2019).

    PubMed  PubMed Central  Google Scholar 

  108. Humphries, P. & Winemiller, K. O. Historical impacts on river fauna, shifting baselines, and challenges for restoration. BioScience 59, 673–684 (2009).

    Google Scholar 

  109. Stoffel, M. A. et al. Demographic histories and genetic diversity across pinnipeds are shaped by human exploitation, ecology and life-history. Nat. Commun. 9, 4836 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Collier-Robinson, L., Rayne, A., Rupene, M., Thoms, C. & Steeves, T. Embedding indigenous principles in genomic research of culturally significant species: a conservation genomics case study. N. Z. J. Ecol. 43, 3389 (2019).

    Google Scholar 

  111. Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).

    PubMed  Google Scholar 

  112. Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).

    PubMed  Google Scholar 

  113. Pope, L. C., Liggins, L., Keyse, J., Carvalho, S. B. & Riginos, C. Not the time or the place: the missing spatio-temporal link in publicly available genetic data. Mol. Ecol. 24, 3802–3809 (2015).

    PubMed  Google Scholar 

  114. Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Sibbett, B., Rieseberg, L. H. & Narum, S. The genomic observatories metadatabase. Mol. Ecol. Resour. 20, 1453–1454 (2020).

    PubMed  Google Scholar 

  116. Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2020).

    Google Scholar 

  117. Cornwell, W. K., Pearse, W. D., Dalrymple, R. L. & Zanne, A. E. What we (don’t) know about global plant diversity. Ecography 42, 1819–1831 (2019).

    Google Scholar 

  118. Li, X. et al. Plant DNA barcoding: from gene to genome. Biol. Rev. 90, 157–166 (2015).

    PubMed  Google Scholar 

  119. Vasquez-Gross, H. A. et al. CartograTree: connecting tree genomes, phenotypes and environment. Mol. Ecol. Resour. 13, 528–537 (2013).

    PubMed  Google Scholar 

  120. Lawrence, E. R. et al. Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database. Sci. Data 6, 14 (2019). This paper reports a compilation of georeferenced vertebrate microsatellite data, summary statistics and meta-data across the Americas for 897 species and 9,090 genetically distinct populations.

    PubMed  PubMed Central  Google Scholar 

  121. Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).

    PubMed  Google Scholar 

  122. Barber, P. H. et al. Advancing biodiversity research in developing countries: the need for changing paradigms. Bull. Mar. Sci. 90, 187–210 (2014).

    Google Scholar 

  123. Bork, P. et al. Tara Oceans. Tara Oceans studies plankton at planetary scale. Introduction. Science 348, 873–873 (2015).

    CAS  PubMed  Google Scholar 

  124. Lotterhos, K. E. & Whitlock, M. C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).

    PubMed  Google Scholar 

  125. Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol. Conserv. 248, 108654 (2020).

    Google Scholar 

  126. Holmes, M. W. et al. Natural history collections as windows on evolutionary processes. Mol. Ecol. 25, 864–881 (2016).

    PubMed  PubMed Central  Google Scholar 

  127. Boukhdoud, L. et al. First DNA sequence reference library for mammals and plants of the Eastern Mediterranean Region. Genome 64, 39–49 (2021).

    CAS  PubMed  Google Scholar 

  128. Colella, J. P. et al. The Open-Specimen movement. BioScience 71, 405–414 (2020).

    Google Scholar 

  129. Wright, S. Correlation and causation. J. Agric. Res. 20, 557–585 (1921).

    Google Scholar 

  130. Fourtune, L. et al. Inferring causalities in landscape genetics: an extension of Wright’s causal modeling to distance matrices. Am. Nat. 191, 491–508 (2018).

    PubMed  Google Scholar 

  131. Paz-Vinas, I., Loot, G., Stevens, V. M. & Blanchet, S. Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems. Mol. Ecol. 24, 4586–4604 (2015).

    CAS  PubMed  Google Scholar 

  132. Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025–2035 (2002).

    PubMed  PubMed Central  Google Scholar 

  133. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Google Scholar 

  134. Proença, V. et al. Global biodiversity monitoring: From data sources to Essential Biodiversity Variables. Biol. Conserv. 213, 256–263 (2017).

    Google Scholar 

  135. Ve˅trovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 5142 (2019).

    PubMed  PubMed Central  Google Scholar 

  136. Hanson, J. O. et al. Conservation planning for adaptive and neutral evolutionary processes. J. Appl. Ecol. 57, 2159–2169 (2020).

    Google Scholar 

  137. Xuereb, A., D’Aloia, C. C., Andrello, M., Bernatchez, L. & Fortin, M. Incorporating putatively neutral and adaptive genomic data into marine conservation planning. Conserv. Biol. 35, 909–920 (2021).

    PubMed  Google Scholar 

  138. Carvalho, S. B., Torres, J., Tarroso, P. & Velo-Antón, G. Genes on the edge: a framework to detect genetic diversity imperiled by climate change. Glob. Change Biol. 25, 4034–4047 (2019).

    Google Scholar 

  139. Adams, W. M. & Sandbrook, C. Conservation, evidence and policy. Oryx 47, 329–335 (2013).

    Google Scholar 

  140. Laikre, L. et al. Post-2020 goals overlook genetic diversity. Science 367, 1083.2–1085 (2020).

    Google Scholar 

  141. Thomson, A. I. et al. Charting a course for genetic diversity in the UN Decade of Ocean Science. Evol. Appl. 14, 1497–1518 (2021).

    PubMed  PubMed Central  Google Scholar 

  142. Hoban, S. M. et al. Bringing genetic diversity to the forefront of conservation policy and management. Conserv. Genet. Resour. 5, 593–598 (2013).

    Google Scholar 

  143. Carroll, S. R. et al. The CARE principles for indigenous data governance. Data Sci. J. 19, 43 (2020).

    Google Scholar 

  144. Fargeot, L. et al. Patterns of epigenetic diversity in two sympatric fish species: genetic vs. environmental determinants. Genes 12, 107 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Gaggiotti, O. E. et al. Diversity from genes to ecosystems: a unifying framework to study variation across biological metrics and scales. Evol. Appl. 11, 1176–1193 (2018).

    PubMed  PubMed Central  Google Scholar 

  146. Waples, R. S., Antao, T. & Luikart, G. Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197, 769–780 (2014).

    PubMed  PubMed Central  Google Scholar 

  147. Waples, R. S. & Yokota, M. Temporal estimates of effective population size in species with overlapping generations. Genetics 175, 219–233 (2007).

    PubMed  PubMed Central  Google Scholar 

  148. Antao, T., Pérez-Figueroa, A. & Luikart, G. Early detection of population declines: high power of genetic monitoring using effective population size estimators. Evol. Appl. 4, 144–154 (2011).

    PubMed  Google Scholar 

  149. Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Phillips, J. D., Gwiazdowski, R. A., Ashlock, D. & Hanner, R. An exploration of sufficient sampling effort to describe intraspecific DNA barcode haplotype diversity: examples from the ray-finned fishes (Chordata: Actinopterygii). DNA Barcodes 3, 66–73 (2015).

    Google Scholar 

  151. Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123, 597–601 (1989).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Jordan, R., Breed, M. F., Prober, S. M., Miller, A. D. & Hoffmann, A. A. How well do revegetation plantings capture genetic diversity? Biol. Lett. 15, 20190460 (2019).

    PubMed  PubMed Central  Google Scholar 

  153. Holderegger, R. & Di Giulio, M. The genetic effects of roads: a review of empirical evidence. Basic. Appl. Ecol. 11, 522–531 (2010).

    Google Scholar 

  154. Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS One 7, e45170 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Jackson, T. M., Roegner, G. C. & O’Malley, K. G. Evidence for interannual variation in genetic structure of Dungeness crab (Cancer magister) along the California Current System. Mol. Ecol. 27, 352–368 (2018).

    CAS  PubMed  Google Scholar 

  156. Hoban, S. et al. Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion. Evol. Appl. 7, 984–998 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Anderson, C. N. K., Ramakrishnan, U., Chan, Y. L. & Hadly, E. A. Serial SimCoal: a population genetics model for data from multiple populations and points in time. Bioinformatics 21, 1733–1734 (2005).

    CAS  PubMed  Google Scholar 

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

    Google Scholar 

  159. Elbrecht, V., Vamos, E. E., Steinke, D. & Leese, F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6, e4644 (2018).

    PubMed  PubMed Central  Google Scholar 

  160. Shum, P. & Palumbi, S. R. Testing small-scale ecological gradients and intraspecific differentiation for hundreds of kelp forest species using haplotypes from metabarcoding. Mol. Ecol. https://doi.org/10.1111/mec.15851 (2021).

    Article  PubMed  Google Scholar 

  161. Yamahara, K. M. et al. In situ autonomous acquisition and preservation of marine environmental DNA using an autonomous underwater vehicle. Front. Mar. Sci. 6, 373 (2019).

    Google Scholar 

  162. Breed, M. F. et al. Mating patterns and pollinator mobility are critical traits in forest fragmentation genetics. Heredity 115, 108–114 (2015).

    CAS  PubMed  Google Scholar 

  163. Hoban, S., Gaggiotti, O. & Bertorelle, G. Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples. Methods Ecol. Evol. 4, 299–303 (2013).

    Google Scholar 

  164. Peck, S. L. Simulation as experiment: a philosophical reassessment for biological modeling. Trends Ecol. Evol. 19, 530–534 (2004).

    PubMed  Google Scholar 

  165. Reid, B. N., Naro-Maciel, E., Hahn, A. T., FitzSimmons, N. N. & Gehara, M. Geography best explains global patterns of genetic diversity and postglacial co-expansion in marine turtles. Mol. Ecol. 28, 3358–3370 (2019).

    PubMed  Google Scholar 

  166. Kardos, M., Luikart, G. & Allendorf, F. W. Measuring individual inbreeding in the age of genomics: marker-based measures are better than pedigrees. Heredity 115, 63–72 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Willing, E.-M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS One 7, e42649 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Shafer, A. B. A. et al. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol. Evol. 8, 907–917 (2017).

    Google Scholar 

  169. Cariou, M., Duret, L. & Charlat, S. How and how much does RAD-seq bias genetic diversity estimates? BMC Evol. Biol. 16, 240 (2016).

    PubMed  PubMed Central  Google Scholar 

  170. De-Kayne, R. et al. Sequencing platform shifts provide opportunities but pose challenges for combining genomic data sets. Mol. Ecol. Resour. 21, 653–660 (2021).

    PubMed  Google Scholar 

  171. Leigh, D. M., Lischer, H. E. L., Grossen, C. & Keller, L. F. Batch effects in a multiyear sequencing study: false biological trends due to changes in read lengths. Mol. Ecol. Resour. 18, 778–788 (2018).

    CAS  PubMed  Google Scholar 

  172. Linck, E. & Battey, C. J. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resour. 19, 639–647 (2019).

    CAS  PubMed  Google Scholar 

  173. Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).

    PubMed  Google Scholar 

  174. Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature 587, 252–257 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).

    CAS  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge the support of the GEO BON Genetic Composition Working Group in the development of this manuscript. We thank B. Dauphin, L. Beheregaray, L. Di Santo, W. C. Funk, J. Fant, A. MacDonald, A. Strand, C. Grueber and C. Richards for their insightful comments on early versions of the manuscript. D.M.L. is funded by the SNSF grant IZHRZ0_180651, “Dynamics of virus infection in mycovirus-mediated biological control of a fungal pathogen”. This research was funded in part by a USGS Northwest Climate Adaptation Science Center award G17AC000218 to C.Bv.R. I.P-V. works in a laboratory supported by the ‘Laboratoire d’Excellence’ (LABEX) entitled TULIP (ANR-10-LABX-41). S.H. was supported by National Science Foundation grant 1759759. L.L. is supported by a New Zealand Rutherford Discovery Fellowship (RDF-20-MAU-001). M.F.B. is funded by Australian Research Council (ARC) grants LP190100051, LP190100484, DP210101932 and DP180100668. CS was funded by an NSERC Discovery Grant to Colin J. Garroway.

Author information

Author notes

  1. These authors contributed equally: Deborah M. Leigh, Charles B. van Rees, Katie L. Millette.

Authors and Affiliations

  1. Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    Deborah M. Leigh

  2. Flathead Lake Biological Station, Polson, MT, USA

    Charles B. van Rees, Brian K. Hand & Gordon Luikart

  3. Department of Biology, McGill University, Montreal, QC, Canada

    Katie L. Millette

  4. Secretariat of the Convention on Biological Diversity (SCBD), Montreal, QC, Canada

    Katie L. Millette

  5. College of Science and Engineering, Flinders University, Bedford Park, SA, Australia

    Martin F. Breed

  6. Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada

    Chloé Schmidt

  7. City College of New York, New York, NY, USA

    Laura D. Bertola

  8. Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Copenhagen, Denmark

    Laura D. Bertola

  9. U.S. Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA

    Margaret E. Hunter

  10. School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne, UK

    Evelyn L. Jensen

  11. Natural Resources Defense Council, New York, NY, USA

    Francine Kershaw

  12. School of Natural and Computational Sciences, Massey University Auckland, Auckland, Aotearoa, New Zealand

    Libby Liggins

  13. CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France

    Stéphanie Manel

  14. Research Institute for Nature and Forest, Geraardsbergen, Belgium

    Joachim Mergeay

  15. Aquatic Ecology, Evolution and Conservation, KULeuven, Leuven, Belgium

    Joachim Mergeay

  16. Department of Biological Sciences, University of Alberta, Edmonton, Canada

    Joshua M. Miller

  17. Chair of Wildlife Ecology and Management, University Freiburg, Freiburg, Germany

    Gernot Segelbacher

  18. Center for Tree Science, The Morton Arboretum, Lisle, USA

    Sean Hoban

  19. Laboratoire Evolution & Diversité Biologique, Université de Toulouse, Centre National pour la Recherche Scientifique, Institut de Recherche pour le Développement, Université Toulouse III Paul Sabatier, UMR5174 EDB, Toulouse, Toulouse, France

    Ivan Paz-Vinas

  20. Conservation Genetics Specialist Group, International Union for Conservation of Nature (IUCN), Gland, Switzerland

    Martin F. Breed, Laura D. Bertola, Margaret E. Hunter, Gordon Luikart, Joachim Mergeay, Gernot Segelbacher & Sean Hoban

  21. Group on Earth Observation Biodiversity Observation Network (GEO BON) Genetic Composition Working Group, Montreal, QC, Canada

    Deborah M. Leigh, Katie L. Millette, Martin F. Breed, Chloé Schmidt, Laura D. Bertola, Brian K. Hand, Margaret E. Hunter, Evelyn L. Jensen, Francine Kershaw, Libby Liggins, Gordon Luikart, Stéphanie Manel, Joachim Mergeay, Joshua M. Miller, Gernot Segelbacher, Sean Hoban & Ivan Paz-Vinas

  22. Group on Earth Observation Biodiversity Observation Network (GEO BON) Species populations and Freshwater BONs, Montreal, QC, Canada

    Charles B. van Rees

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Contributions

D.M.L., C.B.v.R., K.L.M., M.F.B., C.S., S.H. and I.P-V. contributed to all aspects of the article. L.D.B., B.K.H., M.E.H., E.L.J., F.K., L.L., G.L., S.M., J.M., J.M.M. and G.S. researched data for the article, made substantial contributions to discussions of the content, and reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Sean Hoban or Ivan Paz-Vinas.

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Related links

California Conservation Genomics Project: https://www.ccgproject.org/

Chelsa: https://chelsa-climate.org/

CryoArks: https://www.cryoarks.org/

Distributed System of Scientific Collections (DiSSCO): https://www.dissco.eu

DRYAD: https://datadryad.org/

Figshare: https://figshare.com/

GenBank: https://www.ncbi.nlm.nih.gov/genbank/

GeoNames: https://www.geonames.org/

GitHub: https://github.com

Global Biodiversity Information Facility (GBIF): https://www.gbif.org/

International Nucleotide Sequence Database Collaboration INSDC: http://www.insdc.org/

International Union for Conservation of Nature (IUCN): https://www.iucn.org

MIxS standards: https://gensc.org/mixs/

NASA’s Earth observatory platform: https://Earthobservatory.nasa.gov/

Otlet: https://otlet.io

Supplementary information

Glossary

Phylogeography

The study of how historical events have helped to shape the current geographical distribution of genetic lineages within and among closely related species.

Biogeography

The study of the spatiotemporal distribution of species, communities and ecosystems.

Macroecology

The study of broad-scale ecological patterns and processes, including topics such as metabolic scaling, extinction risk and diversity gradients.

Macrophysiology

The study of variation in physiological traits for multiple species over large geographic and temporal scales and the ecological implications of this variation.

Intraspecific genetic variation

(IGV). Genetic variation observed at the DNA level within a species, including within-population genetic diversity and among-population genetic differentiation. It can be measured using many metrics, including gene diversity, allelic richness and nucleotide diversity.

Landscape genetics

The study of the effects of the environment including recent global change (such as climate or land-use change) on genetic patterns, and of how species will adapt to these changes on ecological timescales.

Fixation index

A metric indicating the nearness of fixation (from 0 to 1) of a subpopulation (S) relative to the total sampled population (T), which is frequently used to assess genetic differences among populations.

Effective population sizes

(Ne). A concept that helps represent how fast a given population is expected to lose genetic diversity; it is often only 10–20% of the population census size.

Unified neutral theory of ecology and biogeography

A model inspired by the neutral theory of molecular evolution that explains species biodiversity patterns assuming ecologically equivalent species.

Wright–Fisher model

A selectively neutral mathematical model that describes allele frequency change across discrete generations in an idealized population.

Stepping-stone model

A statistical model of metapopulation connectivity in which each subpopulation can only exchange migrants with its nearest neighbours. This constraint leads to a pattern of genetic isolation by distance.

Coalescent theory

A theory developed to model how allele copies sampled from a population originate from (coalesce in) a common ancestor and used to develop neutral expectations and infer the demographic history of populations.

Neutral theory of molecular evolution

A model of evolution that assumes that most genetic diversity at the molecular level in populations and species is the result of neutral (non-selective) processes such as genetic drift and mutation.

Restriction site-associated DNA sequencing

A genotyping method whereby thousands of short regions (100–300 bp) of DNA surrounding a restriction enzyme site are sequenced and variants are identified.

DNA barcoding

A method of identifying what species a DNA sample belongs to by comparing a particular DNA sequence with a database containing reference sequences of many species.

COI

A mitochondrial DNA gene sequence that encodes cytochrome C oxidase subunit I and is frequently used for species identification via DNA barcoding in Metazoa.

rbcL (or cbbL)

A plant chloroplast gene sequence that encodes ribulose bisphosphate carboxylase large chain and is frequently used for species identification via DNA barcoding in plants.

Interoperability

In the context of genetic and genomic data, refers to the ability of different datasets to be connected and integrated at present and in the future owing to standardized formats, storage, metadata and accessibility.

Species–genetic diversity correlation concept

A concept that suggests patterns of species and intraspecific genetic diversity are correlated because they both are influenced by the same underlying processes (such as stochasticity, selection, dispersal, speciation or mutation) and environmental variation.

Shifting baselines

The phenomenon whereby each generation of humans loses perception of biodiversity change by assuming that the biological state they observed at early stages of their lives or careers was the norm. Working under these misassumptions could fuel the use of incorrect baselines in temporal studies.

Wallacean shortfall

The scientific knowledge gap on the geographical species distributions, driven by the unequal global species presence or absence in formal survey efforts.

Linnean shortfall

The scientific knowledge gap for described species, that is, the gap between the number of formally described species and the greater number that actually exist.

Red Queen hypothesis

An evolutionary hypothesis that states that antagonistically interacting species constantly co-evolve in order to adapt to each other’s attack and defence strategies.

Pooled sequencing

A method of high-throughput DNA sequencing in which DNA extracts from groups of individuals are pooled together for sequencing, rather than each individual being sequenced independently.

Museomics

DNA sequencing of historical specimens archived in museums, herbaria and other natural history collections. It typically refers to samples that may be decades to centuries old.

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Leigh, D.M., van Rees, C.B., Millette, K.L. et al. Opportunities and challenges of macrogenetic studies. Nat Rev Genet 22, 791–807 (2021). https://doi.org/10.1038/s41576-021-00394-0

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