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.

  • Article
  • Published:

Rules of teeth development align microevolution with macroevolution in extant and extinct primates

Nature Ecology & Evolution volume 7, pages 1729–1739 (2023)Cite this article

Subjects

Abstract

Macroevolutionary biologists have classically rejected the notion that higher-level patterns of divergence arise through microevolutionary processes acting within populations. For morphology, this consensus partly derives from the inability of quantitative genetics models to correctly predict the behaviour of evolutionary processes at the scale of millions of years. Developmental studies (evo-devo) have been proposed to reconcile micro- and macroevolution. However, there has been little progress in establishing a formal framework to apply evo-devo models of phenotypic diversification. Here we reframe this issue by asking whether using evo-devo models to quantify biological variation can improve the explanatory power of comparative models, thus helping us bridge the gap between micro- and macroevolution. We test this prediction by evaluating the evolution of primate lower molars in a comprehensive dataset densely sampled across living and extinct taxa. Our results suggest that biologically informed morphospaces alongside quantitative genetics models allow a seamless transition between the micro- and macroscales, whereas biologically uninformed spaces do not. We show that the adaptive landscape for primate teeth is corridor like, with changes in morphology within the corridor being nearly neutral. Overall, our framework provides a basis for integrating evo-devo into the modern synthesis, allowing an operational way to evaluate the ultimate causes of macroevolution.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Integrative evolutionary modelling framework used in the present study.
Fig. 2: Principal component analysis of the full-sample covariance matrix for the three morphospaces.
Fig. 3: Primate phylogenetic tree of the 480 species included in this study painted by the best regime combination found on the phylogenetic mixed-model search for the individual molar areas.
Fig. 4: Graphical representation of the best selected model (\({\mathrm{OU}}{}_{{\varSigma} \propto G}^{\mathrm{D}}\)) for the ICM variables based on molar ratios m2/m1 and m3/m1.
Fig. 5: LGGD used to measure the node-specific rate of evolution throughout Primate divergence and diversification.

Similar content being viewed by others

Data availability

All of the data analysed during this study are included in the Supplementary Data.

Code availability

All of the code used for this paper is available at https://github.com/MachadoFA/PCMkappa and https://github.com/MachadoFA/PrimateTeethProject.

References

  1. Dobzhansky, T. Genetics and the Origin of Species 11 (Columbia Univ. Press, 1982).

  2. de Santis, M. D. Misconceptions about historical sciences in evolutionary biology. Evol. Biol. 48, 94–99 (2021).

    Google Scholar 

  3. Stanley, S. M. A theory of evolution above the species level. Proc. Natl Acad. Sci. USA 72, 646–650 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Gould, S. J. The Structure of Evolutionary Theory (Harvard Univ. Press, 2002).

  5. Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Natl Acad. Sci. USA 108, 15908–15913 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Houle, D., Bolstad, G. H., van der Linde, K. & Hansen, T. F. Mutation predicts 40 million years of fly wing evolution. Nature 548, 447–450 (2017).

    CAS  PubMed  Google Scholar 

  7. Lynch, M. The rate of morphological evolution in mammals from the standpoint of the neutral expectation. Am. Nat. 136, 727–741 (1990).

    Google Scholar 

  8. Machado, F. A., Marroig, G. & Hubbe, A. The pre-eminent role of directional selection in generating extreme morphological change in glyptodonts (Cingulata; Xenarthra). Proc. R. Soc. B https://doi.org/10.1098/rspb.2021.2521 (2022).

  9. Hlusko, L. J. Integrating the genotype and phenotype in hominid paleontology. Proc. Natl Acad. Sci. USA 101, 2653–2657 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Hlusko, L. J., Schmitt, C. A., Monson, T. A., Brasil, M. F. & Mahaney, M. C. The integration of quantitative genetics, paleontology, and neontology reveals genetic underpinnings of primate dental evolution. Proc. Natl Acad. Sci. USA 113, 9262–9267 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Mongle, C. et al. Developmental processes mediate alignment of the micro- and macroevolution of primate molars. Evolution 76, 2975–2985 (2022).

    PubMed  Google Scholar 

  12. Marroig, G. & Cheverud, J. M. Did natural selection or genetic drift produce the cranial diversification of Neotropical monkeys? Am. Nat. 163, 417–428 (2004).

    PubMed  Google Scholar 

  13. McGlothlin, J. W. et al. Adaptive radiation along a deeply conserved genetic line of least resistance in Anolis lizards. Evol. Lett. 2, 310–322 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. Erwin, D. H. Macroevolution is more than repeated rounds of microevolution. Evol. Dev. 2, 78–84 (2000).

    CAS  PubMed  Google Scholar 

  15. Hautmann, M. What is macroevolution? Palaeontology https://doi.org/10.1111/pala.12465 (2019).

  16. Lande, R. The dynamics of peak shifts and the pattern of morphological evolution. Paleobiology 12, 343–354 (1986).

    Google Scholar 

  17. Charlesworth, B., Lande, R. & Slatkin, M. A neo-Darwinian commentary on macroevolution. Evolution 36, 474 (1982).

    PubMed  Google Scholar 

  18. Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112, 9–32 (2001).

    PubMed  Google Scholar 

  19. Polly, P. D. Developmental dynamics and G-matrices: can morphometric spaces be used to model phenotypic evolution? Evol. Biol. 35, 83–96 (2008).

    Google Scholar 

  20. Hansen, T. F. Macroevolutionary quantitative genetics? A comment on Polly (2008). Evol. Biol. 35, 182–185 (2008).

    Google Scholar 

  21. Melo, D., Porto, A., Cheverud, J. M. & Marroig, G. Modularity: genes, development and evolution. Annu. Rev. Ecol. Evol. Syst. 47, 463–486 (2016).

    PubMed  PubMed Central  Google Scholar 

  22. Machado, F. A. Selection and constraints in the ecomorphological adaptive evolution of the skull of living Canidae (Carnivora, Mammalia). Am. Nat. 196, 197–215 (2020).

    PubMed  Google Scholar 

  23. Laland, K. N. et al. The extended evolutionary synthesis: its structure, assumptions and predictions. Proc. R. Soc. B 282, 20151019 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. Thompson, D. W. On Growth and Form Vol. 2 (Cambridge Univ. Press, 1942).

  25. Waddington, C. H. The Strategy of the Genes (Routledge, 1957).

  26. Alberch, P., Gould, S. J., Oster, G. F. & Wake, D. B. Size and shape in ontogeny and phylogeny. Paleobiology 5, 296–317 (1979).

    Google Scholar 

  27. Alberch, P. Ontogenesis and morphological diversification. Am. Zool. 20, 653–667 (1980).

    Google Scholar 

  28. Love, A. C. Evolutionary morphology, innovation, and the synthesis of evolutionary and developmental biology. Biol. Phil. 18, 309–345 (2003).

    Google Scholar 

  29. Cheverud, J. M. Quantitative genetics and developmental constraints on evolution by selection. J. Theor. Biol. 110, 155–171 (1984).

    CAS  PubMed  Google Scholar 

  30. Salazar-Ciudad, I. & Jernvall, J. A computational model of teeth and the developmental origins of morphological variation. Nature 464, 583–586 (2010).

    CAS  PubMed  Google Scholar 

  31. Linde-Medina, M. & Diogo, R. Do correlation patterns reflect the role of development in morphological evolution? Evol. Biol. 41, 494–502 (2014).

    Google Scholar 

  32. Hether, T. D. & Hohenlohe, P. A. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance. Evolution 68, 950–964 (2014).

    PubMed  Google Scholar 

  33. Pearson, K. & Davin, A. G. On the biometric constants of the human skull. Biometrika 16, 328 (1924).

    Google Scholar 

  34. Raup, D. M. Geometric analysis of shell coiling: general problems. J. Paleontol. 40, 1178–1190 (1966).

    Google Scholar 

  35. Kavanagh, K. D., Evans, A. R. & Jernvall, J. Predicting evolutionary patterns of mammalian teeth from development. Nature 449, 427–432 (2007).

    CAS  PubMed  Google Scholar 

  36. Alba, V., Carthew, J. E., Carthew, R. W. & Mani, M. Global constraints within the developmental program of the Drosophila wing. eLife https://doi.org/10.7554/eLife.66750 (2021).

  37. Asahara, M. Unique inhibitory cascade pattern of molars in canids contributing to their potential to evolutionary plasticity of diet. Ecol. Evol. 3, 278–285 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. Halliday, T. J. D. & Goswami, A. Testing the inhibitory cascade model in Mesozoic and Cenozoic mammaliaforms. BMC Evol. Biol. 13, 79 (2013).

    PubMed  PubMed Central  Google Scholar 

  39. Bernal, V., Gonzalez, P. & Perez, S. I. Developmental processes, evolvability, and dental diversification of New World monkeys. Evol. Biol. 40, 532–541 (2013).

    Google Scholar 

  40. Carter, C. & Worthington, S. The evolution of anthropoid molar proportions. BMC Evol. Biol. 16, 18 (2016).

    Google Scholar 

  41. Hlusko, L. J., Maas, M.-L. & Mahaney, M. C. Statistical genetics of molar cusp patterning in pedigreed baboons: implications for primate dental development and evolution. J. Exp. Zool. B 302, 268–283 (2004).

    Google Scholar 

  42. Hlusko, L. J., Sage, R. D. & Mahaney, M. C. Modularity in the mammalian dentition: mice and monkeys share a common dental genetic architecture. J. Exp. Zool. B 316, 21–49 (2011).

    Google Scholar 

  43. Hardin, A. M. Genetic correlations in the dental dimensions of Saguinus fuscicollis. Am. J. Phys. Anthropol. 169, 557–566 (2019).

    PubMed  Google Scholar 

  44. Hardin, A. M. Genetic correlations in the rhesus macaque dentition. J. Hum. Evol. 148, 102873 (2020).

    PubMed  PubMed Central  Google Scholar 

  45. Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89 (2013).

    Google Scholar 

  46. Brevet, M. & Lartillot, N. Reconstructing the history of variation in effective population size along phylogenies. Genome Biol. Evol. https://doi.org/10.1093/gbe/evab150 (2021).

  47. Plavcan, J. M. Sexual Dimorphism in the Dentition of Extant Anthropoid Primates. PhD thesis, Duke Univ. (1990).

  48. Godfrey, L. R., Samonds, K. E., Jungers, W. L. & Sutherland, M. R. Teeth, brains, and primate life histories. Am. J. Phys. Anthropol. 114, 192–214 (2001).

    CAS  PubMed  Google Scholar 

  49. Delson, E., Harcourt-Smith, W. E., Frost, S. R. & Norris, C. A. Databases, data access, and data sharing in paleoanthropology: first steps. Evol. Anthropol. 16, 161–163 (2007).

    Google Scholar 

  50. Wisniewski, A. L., Lloyd, G. T. & Slater, G. J. Extant species fail to estimate ancestral geographical ranges at older nodes in primate phylogeny. Proc. R. Soc. B 289, 20212535 (2022).

    PubMed  PubMed Central  Google Scholar 

  51. Liam, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57, 591–601 (2008).

    Google Scholar 

  52. Schluter, D. Adaptive radiation along genetic lines of least resistance. Evolution 50, 1766 (1996).

    PubMed  Google Scholar 

  53. Schroeder, L. & von Cramon-Taubadel, N. The evolution of hominoid cranial diversity: a quantitative genetic approach. Evolution 71, 2634–2649 (2017).

    PubMed  Google Scholar 

  54. Ackermann, R. R. & Cheverud, J. M. Discerning evolutionary processes in patterns of tamarin (genus Saguinus) craniofacial variation. Am. J. Phys. Anthropol. 117, 260–271 (2002).

    PubMed  Google Scholar 

  55. Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341 (1997).

    PubMed  Google Scholar 

  56. Mosimann, J. E. Size allometry: size and shape variables with characterizations of the lognormal and generalized gamma distributions. J. Am. Stat. Assoc. 65, 930 (1970).

    Google Scholar 

  57. Jolicoeur, P. The multivariate generalization of the allometry equation. Biometrics 19, 497–499 (1963).

    Google Scholar 

  58. Gould, S. J. Ontogeny and Phylogeny (Belknap Press, 1977).

  59. Weaver, T. D., Roseman, C. C. & Stringer, C. B. Close correspondence between quantitative- and molecular-genetic divergence times for Neandertals and modern humans. Proc. Natl Acad. Sci. USA 105, 4645–4649 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Gómez-Robles, A. Dental evolutionary rates and its implications for the Neanderthal–modern human divergence. Sci. Adv. 5, eaaw1268 (2019).

    PubMed  PubMed Central  Google Scholar 

  61. Monson, T. A., Fecker, D. & Scherrer, M. Neutral evolution of human enamel-dentine junction morphology. Proc. Natl Acad. Sci. USA 117, 26183–26189 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Felsenstein, J. Phylogenies and quantitative characters. Annu. Rev. Ecol. Syst. 19, 445–471 (1988).

    Google Scholar 

  63. Felsenstein, J. Phylogenies and comparative method. Am. Nat. 125, 1–15 (1985).

    Google Scholar 

  64. Melo, D. & Marroig, G. Directional selection can drive the evolution of modularity in complex traits. Proc. Natl Acad. Sci. USA 112, 470–475 (2015).

    CAS  PubMed  Google Scholar 

  65. Jones, A. G., Bürger, R., Arnold, S. J., Hohenlohe, P. A. & Uyeda, J. C. The effects of stochastic and episodic movement of the optimum on the evolution of the G-matrix and the response of the trait mean to selection. J. Evol. Biol. 25, 2210–2231 (2012).

    PubMed  PubMed Central  Google Scholar 

  66. Riedl, R. Order in Living Organisms: A Systems Analysis of Evolution (John Wiley & Sons, 1978).

  67. Watson, R. A., Wagner, G. P., Pavlicev, M., Weinreich, D. M. & Mills, R. The evolution of phenotypic correlations and ‘developmental memory’. Evolution 68, 1124–1138 (2014).

    PubMed  PubMed Central  Google Scholar 

  68. Polly, P. D. Development with a bite. Nature 449, 413–414 (2007).

    CAS  PubMed  Google Scholar 

  69. Monson, T. A. et al. Evidence of strong stabilizing effects on the evolution of boreoeutherian (Mammalia) dental proportions. Ecol. Evol. https://doi.org/10.1002/ece3.5309 (2019).

  70. Varela, L., Tambusso, P. S. & Fariña, R. A. Unexpected inhibitory cascade in the molariforms of sloths (Folivora, Xenarthra): a case study in xenarthrans honouring Gerhard Storch’s open-mindedness. Frühförderung interdisziplinär 76, 1–16 (2020).

    Google Scholar 

  71. Sadier, A. et al. Bat teeth illuminate the diversification of mammalian tooth classes. Nat. Commun. https://doi.org/10.1038/s41467-023-40158-4 (2023).

  72. Glowacka, H. & Schwartz, G. T. A biomechanical perspective on molar emergence and primate life history. Sci. Adv. 7, eabj0335 (2021).

    PubMed  PubMed Central  Google Scholar 

  73. Monson, T. A. et al. Keeping 21st century paleontology grounded: quantitative genetic analyses and ancestral state reconstruction re-emphasize the essentiality of fossils. Biology https://doi.org/10.3390/biology11081218 (2022).

  74. Riedl, R. A systems-analytical approach to macro-evolutionary phenomena. Q. Rev. Biol. 52, 351–370 (1977).

    CAS  PubMed  Google Scholar 

  75. Houle, D. & Rossoni, D. M. Complexity, evolvability, and the process of adaptation. Annu. Rev. Ecol. Evol. Syst. 53, 137–159 (2022).

    Google Scholar 

  76. Olson, E. C & Miller, R. L. Morphological Integration (Univ. Chicago Press, 1958).

    Google Scholar 

  77. Kavanagh, K. D. et al. Developmental bias in the evolution of phalanges. Proc. Natl Acad. Sci. USA 110, 18190–18195 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Young, N. M., Wislow, B., Takkellapati, S. & Kavanagh, K. Shared rules of development predict patterns of evolution in vertebrate segmentation. Nat. Commun. 6, 6690 (2015).

    CAS  PubMed  Google Scholar 

  79. Carraco, G., Martins-Jesus, A. P. & Andrade, R. P. The vertebrate embryo clock: common players dancing to a different beat. Front. Cell Dev. Biol. 10, 944016 (2022).

    PubMed  PubMed Central  Google Scholar 

  80. Schroeder, L., Roseman, C. C., Cheverud, J. M. & Ackermann, R. R. Characterizing the evolutionary path(s) to early Homo. PLoS ONE 9, e114307 (2014).

    PubMed  PubMed Central  Google Scholar 

  81. Schroeder, L., Elton, S. & Ackermann, R. R. Skull variation in Afro-Eurasian monkeys results from both adaptive and non-adaptive evolutionary processes. Sci. Rep. 12, 12516 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Bookstein, F. L. Measurement, explanation, and biology: lessons from a long century. Biol. Theory 4, 6–20 (2009).

    Google Scholar 

  83. Houle, D., Pélabon, C., Wagner, G. P. & Hansen, T. F. Measurement and meaning in biology. Q. Rev. Biol. 86, 3–34 (2011).

    PubMed  Google Scholar 

  84. Machado, F. A., Hubbe, A., Melo, D., Porto, A. & Marroig, G. Measuring the magnitude of morphological integration: the effect of differences in morphometric representations and the inclusion of size. Evolution 73, 2518–2528 (2019).

    PubMed  PubMed Central  Google Scholar 

  85. Mitteroecker, P. The developmental basis of variational modularity: insights from quantitative genetics, morphometrics, and developmental biology. Evol. Biol. 36, 377–385 (2009).

    Google Scholar 

  86. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  Google Scholar 

  87. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016).

    PubMed  PubMed Central  Google Scholar 

  88. Mitov, V., Bartoszek, K. & Stadler, T. Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models. Proc. Natl Acad. Sci. USA 116, 16921–16926 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Lande, R. Natural selection and random genetic drift in phenotypic evolution. Evolution 30, 314 (1976).

    PubMed  Google Scholar 

  90. Lande, R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33, 402–416 (1979).

    PubMed  Google Scholar 

  91. Dennis, B., Ponciano, J. M., Taper, M. L. & Lele, S. R. Errors in statistical inference under model misspecification: evidence, hypothesis testing, and AIC. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00372 (2019).

  92. Schluter, D., Price, T., Mooers, A. Ø. & Ludwig, D. Likelihood of ancestor states in adaptive radiation. Evolution 51, 1699–1711 (1997).

    PubMed  Google Scholar 

  93. Hansen, T. F. & Martins, E. P. Translating between microevolutionary process and macroevolutionary patterns: the correlation structure of interspecific data. Evolution 50, 1404–1417 (1996).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank E. Delson and colleagues for access to the PRIMO dataset49, L. Godfrey and K. Samonds for access to their Strepsirrhine molar data48, M. J. Plavcan for providing access to a large sample of dental measurement data47, G. Burin for photographing specimens at the British Museum of Natural History, G. Garbino for measuring specimens at the Museu de Zoologia João Moojen of the Universidade Federal de Viçosa, A. Kurylyuk and R. M. Rodin for providing photographs of rare Microcebus specimens and M. Surovy for providing access to the American Museum of Natural History specimens. F.A.M., J.C.U., V.D. and A.S. were funded by NSF-DEB-1942717 to J.C.U. We thank V. Mitov for his help in making a fast version of the PCMkappa, and L. Hlusko, J. Jernvall, and D. Moen and his lab for providing feedback and suggestions that greatly improved this paper.

Author information

Authors and Affiliations

  1. Department of Integrative Biology, Oklahoma State University, Stillwater, OK, USA

    Fabio A. Machado

  2. Department of Anthropology, Stony Brook University, Stony Brook, NY, USA

    Carrie S. Mongle

  3. Turkana Basin Institute, Stony Brook University, Stony Brook, NY, USA

    Carrie S. Mongle

  4. Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA

    Graham Slater & Anna Wisniewski

  5. Department of Anthropology, University of Texas at San Antonio, San Antonio, TX, USA

    Anna Penna

  6. Department of Biology, Virginia Tech, Blacksburg, VA, USA

    Anna Soffin & Josef C. Uyeda

  7. Department of Anthropology, Florida Atlantic University, Boca Raton, FL, USA

    Vitor Dutra

Authors
  1. Fabio A. Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carrie S. Mongle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graham Slater
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Penna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna Wisniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anna Soffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vitor Dutra
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Josef C. Uyeda
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.A.M., C.S.M. and J.C.U. conceptualized the project. J.C.U. gathered the necessary funds. F.A.M., C.S.M., A.P., A.S. and V.D. gathered the dataset. A.W. and G.S. conducted the phylogenetic analysis. F.A.M. performed statistical analysis and produced the first draft. F.A.M., C.S.M., A.P., A.W., G.S. and J.C.U. wrote the following versions of the draft. All authors approved the last draft.

Corresponding author

Correspondence to Fabio A. Machado.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Pauline Guenser and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Variables used to construct morphospaces.

The Distance-space was built on the mesiodistal length (MD, vertical) and buccolingual breadth (BL, horizontal) taken from each molar. The Area-space was built by estimating the occlusal areas of each molar as the A=MDxBL. The ICM-space was built by calculating the relative area size for m2 and m3 in relation to m1.

Extended Data Fig. 2 Bayesian Information Criterion results for the Gaussian mixture models for different morphospaces.

Negative BIC values for the Gaussian mixture models for the linear distances, areas and ICM spaces for different numbers of clusters (i). EEV- Elipsoidal model with the same shape, same volume and different orientations VEE- Elipsoidal model with the same shape, different volumes and same orientation. Higher values of negative BIC suggest the best model for each morphospace.

Extended Data Fig. 3 Regimes for different runs of the heuristic search.

Regimes for different runs of the heuristic search for the Distance morphospace. Left- Best model (Search 5). Right- Model compatible with the best model for areas (Fig. 3 on the main text).

Extended Data Fig. 4 Regime-specific disparities for Three-regime model on morphospace.

Simulated regime-specific disparities for Three-regime model on morphospace. Regimes are described on the main text and illustrated on Fig. 3.

Extended Data Fig. 5 Disparity and phylogenetic signal under BM and OU models.

Simulated disparity (A) and phylogenetic signal (B) assuming the best model (OU) or a brownian motion (BM) model with the same rate parameters as the best OU model. Ellipses represent the covariance matrix of the simulated tip values, and thus do not represent any evolutionary parameter (for example Sigma, H, Omega, stationary variance, etc), but the phenotypic distributions of tips. Dots are observed species averages for comparison.

Extended Data Fig. 6 Phylogenetic half-lifes.

Phylogenetic half life for ICM ratios (m2/m1 and m3/m1) and components of the ICM model (Activation-Inhibition gradient and deviations from the ICM). Violin plots represent the distribution of values within the 95% confidence interval for the best model.

Extended Data Fig. 7 Traitgrams of the components of the ICM for Primates.

Black lines represent the phylogeny mapped to measured trait values (black points) and golden lines and golden dots represents each trait evolutionary optimum.

Extended Data Fig. 8 Comparison between the Monte Carlo and analytical approaches to estimate covariances of areas.

Differences between the Monte Carlo sampling approach for generating covariances for areas and the analytical approximation. Values are equal to the difference in coefficient of variation between matrices. Horizontal lines within violins highlights the 95% interval for each matrix cell. The first three entries are each area variance and the latter three are the areas covariances. The subscript indicates which trait (variances) or traits (covariances) are being compared.

Supplementary information

Supplementary Information

Supplementary Materials and Methods, Tables 1–8, references and data source references.

Reporting Summary

Peer Review File

Supplementary Data 1

Distances, areas and ICM measurements for living and extinct primate species.

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

Machado, F.A., Mongle, C.S., Slater, G. et al. Rules of teeth development align microevolution with macroevolution in extant and extinct primates. Nat Ecol Evol 7, 1729–1739 (2023). https://doi.org/10.1038/s41559-023-02167-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-023-02167-w

Search

Advanced 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