Breakdown of brain–body allometry and the encephalization of birds and mammals

Abstract

The allometric relationship between brain and body size among vertebrates is often considered a manifestation of evolutionary constraints. However, birds and mammals have undergone remarkable encephalization, in which brain size has increased without corresponding changes in body size. Here, we explore the hypothesis that a reduction of phenotypic integration between brain and body size has facilitated encephalization in birds and mammals. Using a large dataset comprising 20,213 specimens across 4,587 species of jawed vertebrates, we show that the among-species (evolutionary) brain–body allometries are remarkably constant, both across vertebrate classes and across taxonomic levels. Birds and mammals, however, are exceptional in that their within-species (static) allometries are shallower and more variable than in other vertebrates. These patterns are consistent with the idea that birds and mammals have reduced allometric constraints that are otherwise ubiquitous across jawed vertebrates. Further exploration of ontogenetic allometries in selected taxa of birds, fishes and mammals reveals that birds and mammals have extended the period of fetal brain growth compared to fishes. Based on these findings, we propose that avian and mammalian encephalization has been contingent on increased variability in brain growth patterns.

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Fig. 1: Brain–body evolutionary allometry of six vertebrate classes.
Fig. 2: Allometric exponents at different taxonomic levels across vertebrate classes.
Fig. 3: Schematic illustration of two possible relationships between within- and among-species variation.
Fig. 4: Among- and within-species variation in relative brain size.
Fig. 5: Ontogenetic brain–body allometry of eight vertebrate species.

References

  1. 1.

    Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).

    Article  PubMed  Google Scholar 

  2. 2.

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

  3. 3.

    Amundson, R. The Changing Role of the Embryo in Evolutionary Thought: Roots of Evo-Devo (Cambridge Univ. Press, Cambridge, 2005).

  4. 4.

    Jerison, H. J. Evolution of the Brain and Intelligence (Academic Press, New York, 1973).

  5. 5.

    Striedter, G. F. Principles of Brain Evolution (Sinauer Associates, Sunderland, 2005).

  6. 6.

    Gould, S. J. Allometry in primates, with emphasis on scaling and the evolution of the brain. Contrib. Primatol. 5, 244–292 (1975).

    CAS  PubMed  Google Scholar 

  7. 7.

    Huxley, J. S. Problems of Relative Growth (Methuen & Co., London, 1932).

  8. 8.

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

    Article  PubMed  Google Scholar 

  9. 9.

    Grabowski, M. Bigger brains led to bigger bodies?: The correlated evolution of human brain and body size. Curr. Anthropol. 57, 174–196 (2016).

    Article  Google Scholar 

  10. 10.

    Riska, B. & Atchley, W. R. Genetics of growth predict patterns of brain-size evolution. Science 229, 668–671 (1985).

    Article  CAS  PubMed  Google Scholar 

  11. 11.

    Tsuboi, M. et al. Evolution of brain–body allometry in Lake Tanganyika cichlids. Evolution 70, 1559–1568 (2016).

    Article  PubMed  Google Scholar 

  12. 12.

    Voje, K. L., Hansen, T. F., Egset, C. K., Bolstad, G. H. & Pelabon, C. Allometric constraints and the evolution of allometry. Evolution 68, 866–885 (2014).

    Article  PubMed  Google Scholar 

  13. 13.

    Pelabon, C. et al. On the relationship between ontogenetic and static allometry. Am. Nat. 181, 195–212 (2013).

    Article  PubMed  Google Scholar 

  14. 14.

    Snell, O. Die abhängigkeit des hirngewichtes von dem körpergewicht und den geistigen fähigkeiten. Eur. Arch. Psychiatry Clin. Neurosci. 23, 436–446 (1892).

    Google Scholar 

  15. 15.

    Yopak, K. E. Neuroecology of cartilaginous fishes: the functional implications of brain scaling. J. Fish. Biol. 80, 1968–2023 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. 16.

    Martin, R. Relative brain size and basal metabolic-rate in terrestrial vertebrates. Nature 293, 57–60 (1981).

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    Kleiber, M. The Fire of Life: An Introduction to Animal Energetics (John Wiley & Sons, New York, 1961).

  18. 18.

    Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. Proc. Natl Acad. Sci. USA 113, 2532–2537 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. 19.

    MacLean, E. L. et al. The evolution of self-control. Proc. Natl Acad. Sci. USA 111, E2140–E2148 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Roth, G. & Dicke, U. Evolution of the brain and intelligence. Trends Cogn. Sci. 9, 250–257 (2005).

    Article  PubMed  Google Scholar 

  21. 21.

    Finarelli, J. A. & Flynn, J. J. Brain-size evolution and sociality in Carnivora. Proc. Natl Acad. Sci. USA 106, 9345–9349 (2009).

    Article  PubMed  Google Scholar 

  22. 22.

    Boddy, A. M. et al. Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling. J. Evol. Biol. 25, 981–994 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Holekamp, K. E., Swanson, E. M. & Van Meter, P. E. Developmental constraints on behavioural flexibility. Phil. Trans. R. Soc. B 368, 20120350 (2013).

    Article  PubMed  Google Scholar 

  24. 24.

    Montgomery, S. H. et al. The evolutionary history of cetacean brain and body size. Evolution 67, 3339–3353 (2013).

    Article  PubMed  Google Scholar 

  25. 25.

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

    Article  Google Scholar 

  26. 26.

    Lynch, M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45, 1065–1080 (1991).

    Article  PubMed  Google Scholar 

  27. 27.

    Riska, B. Regression-models in evolutionary allometry. Am. Nat. 138, 283–299 (1991).

    Article  Google Scholar 

  28. 28.

    Hansen, T. F. & Bartoszek, K. Interpreting the evolutionary regression: the interplay between observational and biological errors in phylogenetic comparative studies. Syst. Biol. 61, 413–425 (2012).

    Article  PubMed  Google Scholar 

  29. 29.

    Pagel, M. D. & Harvey, P. H. The taxon-level problem in the evolution of mammalian brain size—facts and artifacts. Am. Nat. 132, 344–359 (1988).

    Article  Google Scholar 

  30. 30.

    Hansen, T. F. & Houle, D. Measuring and comparing evolvability and constraint in multivariate characters. J. Evol. Biol. 21, 1201–1219 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. 31.

    Noreikiene, K. et al. Quantitative genetic analysis of brain size variation in sticklebacks: support for the mosaic model of brain evolution. Proc. R. Soc. B 282, 20151008 (2015).

    Article  PubMed  Google Scholar 

  32. 32.

    Rogers, J. et al. Heritability of brain volume, surface area and shape: an MRI study in an extended pedigree of baboons. Hum. Brain Mapp. 28, 576–583 (2007).

    Article  PubMed  Google Scholar 

  33. 33.

    Kotrschal, A. et al. Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain. Curr. Biol. 23, 168–171 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S. & Poll, H. E. H. Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum. Brain Mapp. 28, 464–473 (2007).

    Article  PubMed  Google Scholar 

  35. 35.

    Cheverud, J. M. et al. Heritability of brain size and surface-features in rhesus macaques (Macaca-Mulatta). J. Hered. 81, 51–57 (1990).

    Article  CAS  PubMed  Google Scholar 

  36. 36.

    Airey, D. C., Castillo-Juarez, H., Casella, G., Pollak, E. J. & DeVoogd, T. J. Variation in the volume of zebra finch song control nuclei is heritable: developmental and evolutionary implications. Proc. R. Soc. B 267, 2099–2104 (2000).

    Article  CAS  PubMed  Google Scholar 

  37. 37.

    Hansen, T. F., Pelabon, C. & Houle, D. Heritability is not evolvability. Evol. Biol. 38, 258–277 (2011).

    Article  Google Scholar 

  38. 38.

    Hansen, T. F., Pienaar, J. & Orzack, S. H. A comparative method for studying adaptation to a randomly evolving environment. Evolution 62, 1965–1977 (2008).

    PubMed  Google Scholar 

  39. 39.

    Grabowski, M., Voje, K. L. & Hansen, T. F. Evolutionary modeling and correcting for observation error support a 3/5 brain–body allometry for primates. J. Hum. Evol. 94, 106–116 (2016).

    Article  PubMed  Google Scholar 

  40. 40.

    Mink, J. W., Blumenschine, R. J. & Adams, D. B. Ratio of central nervous-system to body metabolism in vertebrates—its constancy and functional basis. Am. J. Physiol. 241, R203–R212 (1981).

    CAS  PubMed  Google Scholar 

  41. 41.

    Barton, R. A. & Capellini, I. Maternal investment, life histories, and the costs of brain growth in mammals. Proc. Natl Acad. Sci. USA 108, 6169–6174 (2011).

    Article  PubMed  Google Scholar 

  42. 42.

    Isler, K. & van Schaik, C. P. The expensive brain: a framework for explaining evolutionary changes in brain size. J. Hum. Evol. 57, 392–400 (2009).

    Article  PubMed  Google Scholar 

  43. 43.

    Iwaniuk, A. N. & Nelson, J. E. Developmental differences are correlated with relative brain size in birds: a comparative analysis. Can. J. Zool. 81, 1913–1928 (2003).

    Article  Google Scholar 

  44. 44.

    Martin, R. D. & Harvey, P. H. in Size and Scaling in Primate Biology (ed. Jungers, W. L.) Ch. 8 (Springer, New York, 1985).

  45. 45.

    Nealen, P. M. & Ricklefs, R. E. Early diversification of the avian brain: body relationship. J. Zool. 253, 391–404 (2001).

    Article  Google Scholar 

  46. 46.

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

    Article  CAS  PubMed  Google Scholar 

  47. 47.

    Halley, A. C. Minimal variation in eutherian brain growth rates during fetal neurogenesis. Proc. R. Soc. B 284, 20170219 (2017).

    Article  PubMed  Google Scholar 

  48. 48.

    Halley, A. C. Prenatal brain–body allometry in mammals. Brain Behav. Evol. 88, 14–24 (2016).

    Article  PubMed  Google Scholar 

  49. 49.

    Raff, R. A. The Shape of Life: Genes, Development, and the Evolution of Animal Form (Univ. Chicago Press, Chicago, 1996).

  50. 50.

    Bolstad, G. H. et al. Genetic constraints predict evolutionary divergence in Dalechampia blossoms. Phil. Trans. R. Soc. B 369, 20130255 (2014).

    Article  PubMed  Google Scholar 

  51. 51.

    Svensson, E. & Calsbeek, R. (eds) The Adaptive Landscape in Evolutionary Biology (Oxford Univ. Press, Oxford, 2012).

  52. 52.

    Walsh, B. & Blows, M. W. Abundant genetic variation plus strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu. Rev. Ecol. Evol. Syst. 40, 41–59 (2009).

    Article  Google Scholar 

  53. 53.

    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).

    Article  PubMed  Google Scholar 

  54. 54.

    Arnold, S. J., Burger, R., Hohenlohe, P. A., Ajie, B. C. & Jones, A. G. Understanding the evolution and stability of the G-matrix. Evolution 62, 2451–2461 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Jones, A. G., Arnold, S. J. & Burger, R. Evolution and stability of the G-matrix on a landscape with a moving optimum. Evolution 58, 1639–1654 (2004).

    Article  PubMed  Google Scholar 

  56. 56.

    Pavlicev, M., & Cheverud, J. M. Constraints evolve: context dependency of gene effects allows evolution of pleiotropy. Annu. Rev. Ecol. Evol. Syst. 46, 413–434 (2015).

    Article  Google Scholar 

  57. 57.

    Jones, A. G., Burger, R. & Arnold, S. J. Epistasis and natural selection shape the mutational architecture of complex traits. Nat. Commun. 5, 3709 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Willis, J. H., Coyne, J. A. & Kirkpatrick, M. Can one predict the evolution of quantitative characters without genetics? Evolution 45, 441–444 (1991).

    Article  PubMed  Google Scholar 

  59. 59.

    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).

    Article  CAS  PubMed  Google Scholar 

  60. 60.

    Williams, G. C. Natural Selection: Domains, Levels, and Challenges (Oxford Univ. Press, New York, 1992).

  61. 61.

    Finlay, B. L. & Darlington, R. B. Linked regularities in the development and evolution of mammalian brains. Science 268, 1578–1584 (1995).

    Article  CAS  Google Scholar 

  62. 62.

    Striedter, G. F. & Charvet, C. J. Developmental origins of species differences in telencephalon and tectum size: morphometric comparisons between a parakeet (Melopsittacus undulatus) and a quail (Colinus virgianus). J. Comp. Neurol. 507, 1663–1675 (2008).

    Article  PubMed  Google Scholar 

  63. 63.

    Koyabu, D. et al. Mammalian skull heterochrony reveals modular evolution and a link between cranial development and brain size. Nat. Commun. 5, 3625 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Iwaniuk, A. N. & Nelson, J. E. Can endocranial volume be used as an estimate of brain size in birds? Can. J. Zool. 80, 16–23 (2002).

    Article  Google Scholar 

  65. 65.

    Froese, R. & Pauly, D. (eds) FishBase (2016); http://www.fishbase.org

  66. 66.

    Crile, G. & Quiring, D. P. A record of the body weight and certain organ and gland weights of 3690 animals. Ohio J. Sci. 40, 219–260 (1940).

    Google Scholar 

  67. 67.

    Hrdlička, A. Brain Weight in Vertebrates Vol. 3 (Smithsonian Institution, 1905).

  68. 68.

    Mangold-Wirz, K. Cerebralisation und ontogenesemodus bei eutherien. Acta Anat. 63, 449–508 (1966).

    Article  CAS  PubMed  Google Scholar 

  69. 69.

    Isler, K. et al. Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set. J. Hum. Evol. 55, 967–978 (2008).

    Article  PubMed  Google Scholar 

  70. 70.

    Hrdlička, A. Weight of the brain and of the internal organs in American monkeys with data on brain weight in other apes. Am. J. Phys. Anthropol. 8, 201–211 (1925).

    Article  Google Scholar 

  71. 71.

    Gittleman, J. L. Carnivore brain size, behavioral ecology, and phylogeny. J. Mammal. 67, 23–36 (1986).

    Article  Google Scholar 

  72. 72.

    Matějů, J. et al. Absolute, not relative brain size correlates with sociality in ground squirrels. Proc. R. Soc. B 283, 20152725 (2016).

    Article  CAS  PubMed  Google Scholar 

  73. 73.

    Blinkov, S. M. & Glezer, I. A. I. The Human Brain in Figures and Tables: A Quantitative Handbook (Basic Books, New York, 1968).

  74. 74.

    Starck, J. M. Zeitmuster der Ontogenesen bei nestflüchtenden und-nesthockenden Vögeln. Cour. Forsch. Inst. Senckenb. 114, 1–319 (1989).

    Google Scholar 

  75. 75.

    R Core Team R: A Language and Environment for Statistical Computing v.3.4.0 (R Foundation for Statistical Computing, Vienna, 2017).

  76. 76.

    Jetz, W. et al. Global distribution and conservation of evolutionary distinctness in birds. Curr. Biol. 24, 919–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  77. 77.

    Bininda-Emonds, O. R. P. et al. The delayed rise of present-day mammals. Nature 446, 507–512 (2007).

    Article  CAS  PubMed  Google Scholar 

  78. 78.

    Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4, 1958 (2013).

    Article  CAS  PubMed  Google Scholar 

  79. 79.

    Pyron, R. A. & 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).

    Article  PubMed  Google Scholar 

  80. 80.

    Velez-Zuazo, X. & Agnarsson, I. Shark tales: a molecular species-level phylogeny of sharks (Selachimorpha, Chondrichthyes). Mol. Phylogenet. Evol. 58, 207–217 (2011).

    Article  PubMed  Google Scholar 

  81. 81.

    Zheng, Y. C. & Wiens, J. J. Combining phylogenomic and supermatrix approaches, and a time-calibrated phylogeny for squamate reptiles (lizards and snakes) based on 52 genes and 4162 species. Mol. Phylogenet. Evol. 94, 537–547 (2016).

    Article  PubMed  Google Scholar 

  82. 82.

    Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Benton, M. J. et al. Constraints on the timescale of animal evolutionary history. Palaeontol. Electron. 18, 1–106 (2015).

    Google Scholar 

  84. 84.

    Sanderson, M. J. Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach. Mol. Biol. Evol. 19, 101–109 (2002).

    Article  CAS  PubMed  Google Scholar 

  85. 85.

    Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R. F1000Res. 2, 191 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Cook, R. D. & Weisberg, S. Residuals and Influence in Regression (Chapman and Hall, New York, 1982).

  87. 87.

    Pinheiro, J. B. D., DebRoy, S., Sarkar, D. and R Core Team nlme: Linear and Nonlinear Mixed Effects Models v.3.1.131 (2017).

  88. 88.

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

    Article  CAS  PubMed  Google Scholar 

  89. 89.

    Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).

    Article  PubMed  Google Scholar 

  90. 90.

    Martins, E. Estimating the rate of phenotypic evolution from comparative data. Am. Nat. 144, 193–209 (1994).

    Article  Google Scholar 

  91. 91.

    Boettiger, C., Coop, G. & Ralph, P. Is your phylogeny informative? Measuring the power of comparative methods. Evolution 66, 2240–2251 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Oikawa, S. & Itazawa, Y. Relative growth of organs and parts of the carp, Cyprinus carpio, with special reference to the metabolism–size relationship. Copeia 1984, 800–803 (1984).

    Article  Google Scholar 

  93. 93.

    Kawabe, S., Matsuda, S., Tsunekawa, N. & Endo, H. Ontogenetic shape change in the chicken brain: implications for paleontology. PLoS ONE 10, e0129939 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Oikawa, S., Takemori, M. & Itazawa, Y. Relative growth of organs and parts of a marine teleost, the porgy, Pagrus-Major, with special reference to metabolism–size relationships. Jpn. J. Ichthyol. 39, 243–249 (1992).

    Article  Google Scholar 

  95. 95.

    Muggeo, V. M. Segmented: an R package to fit regression models with broken-line relationships. R News 8, 20–25 (2008).

    Google Scholar 

  96. 96.

    Tsuboi, M. et al. Brain mass and body mass datasets and phylogenies linked to brain–body allometry and the encephalization of birds and mammals. Figshare fileset. https://doi.org/10.6084/m9.figshare.6803276 (2018).

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Acknowledgements

We thank C. Pélabon and J. Fitzpatrick for helping us with data collection and T. Hansen for insightful advice in the comparative analysis and treatment on measurement errors. This study was funded by the Japanese Society for Promotion of Science (JSPS) Postdoctoral Fellowship for Young Scientist (2016-3238) and the Norwegian Research Council Norway–Japan researcher mobility grant (258580/H30) to M.T., Swedish Research Council grants (2009-5157, 2012-03624) to N.K., Canada Research Chairs Program to A.N.I. and the Australian Research Council to S.P.C.

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M.T., A.N.I. and N.K. conceived the study and wrote the manuscript. M.T., B.T.K., J.E., A.K., K.E.Y., S.P.C., A.N.I. and N.K. collected the data. M.T., W.v.d.B., B.T.K., K.L.V. and N.K. designed analytical protocols, and M.T., W.v.d.B. and B.T.K. analysed the data. All authors provided input to the manuscript.

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Correspondence to Masahito Tsuboi.

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Supplementary Information

Supplementary Figures 1–3, Supplementary Tables 1–7

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Supplementary Data 1

Summary of fossil taxa and node age used to calibrate supertrees of Amphibia and Chondrichthyes

Supplementary Data 2

Dataset of static allometric slopes and conditional variance of brain size

Supplementary Data 3

Description: Dataset of brain mass (g) and body mass (g) used for assessing ontogenetic allometry and static allometry of guppies

Supplementary Code

Description: Computer code to run SLOUCH version 2.0.0

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Tsuboi, M., van der Bijl, W., Kopperud, B.T. et al. Breakdown of brain–body allometry and the encephalization of birds and mammals. Nat Ecol Evol 2, 1492–1500 (2018). https://doi.org/10.1038/s41559-018-0632-1

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