Skip to main content

Thank you for visiting 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.

The consequences of craniofacial integration for the adaptive radiations of Darwin’s finches and Hawaiian honeycreepers


The diversifications of Darwin’s finches and Hawaiian honeycreepers are two text-book examples of adaptive radiation in birds. Why these two bird groups radiated while the remaining endemic birds in these two archipelagos exhibit relatively low diversity and disparity remains unexplained. Ecological factors have failed to provide a convincing answer to this phenomenon, and some intrinsic causes connected to craniofacial evolution have been hypothesized. The tight coevolution of the beak and the remainder of the skull in diurnal raptors and parrots suggests that integration may be the prevalent condition in landbirds (Inopinaves). This is in contrast with the archetypal relationship between beak shape and ecology in Darwin’s finches and Hawaiian honeycreepers, which suggests that the beak can adapt as a distinct module in these birds. Modularity has therefore been proposed to underpin the adaptive radiation of these groups, allowing the beak to evolve more rapidly and freely in response to ecological opportunity. Here, using geometric morphometrics and phylogenetic comparative methods in a broad sample of landbird skulls, we show that craniofacial evolution in Darwin’s finches and Hawaiian honeycreepers seems to be characterized by a tighter coevolution of the beak and the rest of the skull (cranial integration) than in most landbird lineages, with rapid and extreme morphological evolution of both skull regions along constrained directions of phenotypic space. These patterns are unique among landbirds, including other sympatric island radiations, and therefore counter previous hypotheses by showing that tighter cranial integration, not only modularity, can facilitate evolution along adaptive directions.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: How integration and selection direct phenotypic evolution.
Fig. 2: Pattern and tempo of craniofacial evolution in landbirds.
Fig. 3: Evolutionary integration between the beak and the skull in landbirds.
Fig. 4: Strength of cranial integration across landbirds and maximum phenotypic distance in each family or subfamily.

Data availability

All relevant data are available via the University of Bristol’s DataBris repository at


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

    Article  CAS  PubMed  Google Scholar 

  2. Cooney, C. R. et al. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature 542, 344–347 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Burns, K. J., Hackett, S. J. & Klein, N. K. Phylogenetic relationships and morphological diversity in Darwin’s finches and their relatives. Evolution 56, 1240–1252 (2002).

    Article  PubMed  Google Scholar 

  4. Arbogast, B. S. et al. The origin and diversification of Galapagos mockingbirds. Evolution 60, 370–382 (2006).

    Article  PubMed  Google Scholar 

  5. Lovette, I. J., Bermingham, E. & Ricklefs, R. E. Clade-specific morphological diversification and adaptive radiation in Hawaiian songbirds. Proc. R. Soc. Lond. B 269, 37–42 (2002).

    Article  Google Scholar 

  6. Pratt, H. D. & Conant, S. The Hawaiian Honeycreepers: Drepanidinae (Oxford Univ. Press, 2005).

  7. Tokita, M., Yano, W., James, H. F. & Abzhanov, A. Cranial shape evolution in adaptive radiations of birds: comparative morphometrics of Darwin’s finches and Hawaiian honeycreepers. Phil. Trans. R. Soc. B 372, 20150481 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Darwin, C. The Zoology of the Voyage of HMS Beagle: Under the Command of Captain Fitzroy, RN, During the Years 1832 to 1836: Published with the Approval of the Lords Commissioners of Her Majesty’s Treasury (Smith, Elder and Company, 1839).

  9. Mayr, E. The zoogeographic position of the Hawaiian Islands. Condor 45, 45–48 (1943).

    Article  Google Scholar 

  10. Fleischer, R. C., James, H. F. & Olson, S. L. Convergent evolution of Hawaiian and Australo-Pacific honeyeaters from distant songbird ancestors. Curr. Biol. 18, 1927–1931 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Bright, J. A., Marugán-Lobón, J., Cobb, S. N. & Rayfield, E. J. The shapes of bird beaks are highly controlled by nondietary factors. Proc. Natl Acad. Sci.USA 113, 5352–5357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Abzhanov, A. The old and new faces of morphology: the legacy of D’Arcy Thompson’s ‘theory of transformations’ and ‘laws of growth’. Development 144, 4284–4297 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Goswami, A., Smaers, J., Soligo, C. & Polly, P. The macroevolutionary consequences of phenotypic integration: from development to deep time. Phil. Trans. R. Soc. B 369, 20130254 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Klingenberg, C. P. Studying morphological integration and modularity at multiple levels: concepts and analysis. Phil. Trans. R. Soc. B 369, 20130249 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Felice, R. N., Randau, M. & Goswami, A. A fly in a tube: macroevolutionary expectations for integrated phenotypes. Evolution 72, 2580–2594 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Olson, E. C. & Miller, R. L. Morphological Integration (Univ. Chicago Press, 1999).

  17. Villmoare, B. Morphological integration, evolutionary constraints, and extinction: a computer simulation-based study. Evol. Biol. 40, 76–83 (2013).

    Article  Google Scholar 

  18. Fisher, R. A. The Genetic Theory of Natural Selection (Dover, 1958).

  19. Kirschner, M. & Gerhart, J. Evolvability. Proc. Natl Acad. Sci. USA 95, 8420–8427 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wagner, G. P. & Altenberg, L. Perspective: complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).

    Article  PubMed  Google Scholar 

  21. Raff, R. A. The Shape of Life: Genes, Development, and the Evolution of Animal Form (Univ. Chicago Press, 2012).

  22. Wagner, G. Coevolution of functionally constrained characters: prerequisites for adaptive versatility. Biosystems 17, 51–55 (1984).

    Article  CAS  PubMed  Google Scholar 

  23. Marroig, G. & Cheverud, J. M. Size as a line of least evolutionary resistance: diet and adaptive morphological radiation in New World monkeys. Evolution 59, 1128–1142 (2005).

    Article  PubMed  Google Scholar 

  24. Hansen, T. F. Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. Biosystems 69, 83–94 (2003).

    Article  PubMed  Google Scholar 

  25. Felice, R. N. & Goswami, A. Developmental origins of mosaic evolution in the avian cranium. Proc. Natl Acad. Sci. USA 115, 555–560 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Bright, J. A., Marugán-Lobón, J., Rayfield, E. J. & Cobb, S. N. The multifactorial nature of beak and skull shape evolution in parrots and cockatoos (Psittaciformes). BMC Evol. Biol. 19, 104 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hackett, S. J. et al. A phylogenomic study of birds reveals their evolutionary history. Science 320, 1763–1768 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Jarvis, E. D. et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Gibbs, H. L. & Grant, P. R. Oscillating selection on Darwin’s finches. Nature 327, 511–513 (1987).

    Article  Google Scholar 

  31. Grant, P. R. & Grant, B. R. Evolution of character displacement in Darwin’s finches. Science 313, 224–226 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Smith, T. B., Freed, L. A., Lepson, J. K. & Carothers, J. H. Evolutionary consequences of extinctions in populations of a Hawaiian honeycreeper. Conserv. Biol. 9, 107–113 (1995).

    Article  Google Scholar 

  33. Darwin, C. & Wallace, A. On the tendency of species to form varieties; and on the perpetuation of varieties and species by natural means of selection. Zool. J. Linn. Soc. 3, 45–62 (1858).

    Article  Google Scholar 

  34. Klingenberg, C. P. Cranial integration and modularity: insights into evolution and development from morphometric data. Hystrix 24, 43–58 (2013).

    Google Scholar 

  35. Schluter, D. Adaptive radiation along genetic lines of least resistance. Evolution 50, 1766–1774 (1996).

    Article  PubMed  Google Scholar 

  36. Randau, M. & Goswami, A. Unravelling intravertebral integration, modularity and disparity in Felidae (Mammalia). Evol. Dev. 19, 85–95 (2017).

    Article  PubMed  Google Scholar 

  37. Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature 457, 830–836 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Wright, N. A., Steadman, D. W. & Witt, C. C. Predictable evolution toward flightlessness in volant island birds. Proc. Natl Acad. Sci. USA 113, 4765–4770 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. van der Geer, A. A., Lyras, G. A., Mitteroecker, P. & MacPhee, R. D. From Jumbo to Dumbo: cranial shape changes in elephants and hippos during phyletic dwarfing. Evol. Biol. 45, 303–317 (2018).

    Article  Google Scholar 

  40. Grant, B. R. & Grant, P. R. Evolution of Darwin’s finches caused by a rare climatic event. Proc. R. Soc. Lond. B 251, 111–117 (1993).

    Article  Google Scholar 

  41. Fritz, J. A. et al. Shared developmental programme strongly constrains beak shape diversity in songbirds. Nat. Commun. 5, 3700 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Yuri, T. et al. Parsimony and model-based analyses of indels in avian nuclear genes reveal congruent and incongruent phylogenetic signals. Biology 2, 419–444 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Del Hoyo, J. et al. Handbook of the Birds of the World Alive (Lynx Editions, 2017).

  44. Rambaut, A. & Drummond, A. TreeAnnotator v1.7.0 (2013);

  45. Rohlf, F. tpsDig v.2.10 (Department of Ecology and Evolution, State Univ. New York at Stony Brook, 2006).

  46. Rohlf, F. tpsRelw, relative warps analysis (Department of Ecology and Evolution, State Univ. New York at Stony Brook, 2010).

  47. Perez, S. I., Bernal, V. & Gonzalez, P. N. Differences between sliding semi-landmark methods in geometric morphometrics, with an application to human craniofacial and dental variation. J. Anat. 208, 769–784 (2006).

    Article  PubMed  Google Scholar 

  48. Torcida, S., Perez, S. I. & Gonzalez, P. N. An integrated approach for landmark-based resistant shape analysis in 3D. Evol. Biol. 41, 351–366 (2014).

    Article  Google Scholar 

  49. Klingenberg, C. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).

    Article  PubMed  Google Scholar 

  50. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017);

  51. Pagel, M. & Meade, A. BayesTraits v.2.0 (Univ. Reading, 2013).

  52. Venditti, C., Meade, A. & Pagel, M. Multiple routes to mammalian diversity. Nature 479, 393–396 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Adams, D. C. & Collyer, M. L. Multivariate phylogenetic comparative methods: evaluations, comparisons, and recommendations. Syst. Biol. 67, 14–31 (2017).

    Article  Google Scholar 

  54. Adams, D. C. & Felice, R. N. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS ONE 9, e94335 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rohlf, F. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).

    Article  CAS  PubMed  Google Scholar 

  56. Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39, 40–59 (1990).

    Google Scholar 

  57. Bookstein, F. L. in Advances in Morphometrics. NATO ASI Series (Series A: Life Sciences) Vol. 284 (eds Marcus, L. F. et al.) 131–151 (Springer, 1996).

  58. Dryden, I. & Mardia, K. Statistical Analysis of Shape (Wiley, 1998).

  59. Siegel, A. F. & Benson, R. H. A robust comparison of biological shapes. Biometrics 38, 341–350 (1982).

    Article  CAS  PubMed  Google Scholar 

  60. Cardini, A. Integration and modularity in Procrustes shape data: is there a risk of spurious results? Evol. Biol. 46, 90–105 (2018).

    Article  Google Scholar 

  61. Chapman, R. E. Conventional procrustes approaches. In Proc. of the Michigan Morphometrics Workshop Vol. 2 (eds Rohlf, F. J. & Bookstein, F.) 251–267 (Univ. Michigan Museum of Zoology, 1990).

  62. Zelditch, M. L., Swiderski, D. L. & Sheets, H. D. Geometric Morphometrics for Biologists: A Primer (Academic, 2012).

  63. Klingenberg, C. P. & McIntyre, G. S. Geometric morphometrics of developmental instability: analyzing patterns of fluctuating asymmetry with Procrustes methods. Evolution 52, 1363–1375 (1998).

    Article  PubMed  Google Scholar 

  64. Bookstein, F. L. in Image Fusion and Shape Variability Techniques: Proceedings (eds Gill, C. A. & Mardia, K. V.) 59–70 (Leeds University Press, 1996).

  65. Adams, D. C., Rohlf, F. J. & Slice, D. E. A field comes of age: geometric morphometrics in the 21st century. Hystrix 24, 7–14 (2013).

    Google Scholar 

  66. Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).

    Article  Google Scholar 

  67. Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: software for geometric morphometric analyses. R package version 3.0.7 (2018);

  68. Zelditch, M. L., Ye, J., Mitchell, J. S. & Swiderski, D. L. Rare ecomorphological convergence on a complex adaptive landscape: body size and diet mediate evolution of jaw shape in squirrels (Sciuridae). Evolution 71, 633–649 (2017).

    Article  PubMed  Google Scholar 

  69. Uyeda, J. C., Caetano, D. S. & Pennell, M. W. Comparative analysis of principal components can be misleading. Syst. Biol. 64, 677–689 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Chira, A. M. & Thomas, G. H. The impact of rate heterogeneity on inference of phylogenetic models of trait evolution. J. Evol. Biol. 29, 2502–2518 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Monteiro, L. R. Morphometrics and the comparative method: studying the evolution of biological shape. Hystrix 24, 25–32 (2013).

    Google Scholar 

  72. Adams, D. C. & Collyer, M. L. On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70, 2623–2631 (2016).

    Article  PubMed  Google Scholar 

  73. Mitteroecker, P. & Bookstein, F. The conceptual and statistical relationship between modularity and morphological integration. Syst. Biol. 56, 818–836 (2007).

    Article  PubMed  Google Scholar 

  74. Marroig, G., Shirai, L. T., Porto, A., de Oliveira, F. B. & De Conto, V. The evolution of modularity in the mammalian skull II: evolutionary consequences. Evol. Biol. 36, 136–148 (2009).

    Article  Google Scholar 

  75. Renaud, S., Auffray, J. C. & Michaux, J. Conserved phenotypic variation patterns, evolution along lines of least resistance, and departure due to selection in fossil rodents. Evolution 60, 1701–1717 (2006).

    Article  PubMed  Google Scholar 

Download references


We thank J. Cooper and J. White (NHM Tring), and C. M. Milensky and B. K. Schmidt (Smithsonian National Museum of Natural History) for access to specimens. We thank F. Blanco, M. Fabbri, I. Menéndez and L. Porras for discussions on the evolutionary implications of this research. We thank G. Thomas, T. Püschel, C. Klingenberg, A. Elsler, F. Babarović and S. de Esteban-Trivigno for insight and discussion on the methods. We thank Ó. Sanisidro and L. Balsa Pascual for design and technical advice that greatly improved the quality of the graphic support. G.N. was supported by a PG Scholarship/Studentship from the Alumni Foundation, University of Bristol, UK, and is currently supported by the ERC project ‘TEMPO’ (grant number 639791). J.M.-L. is supported by the Spanish MINECO, Project CGL-2013-42643. E.J.R. and J.A.B. were supported by BBSRC grant number BB/I011668/1. C.R.C. is supported by a Leverhulme Early Career Fellowship (grant number ECF-2018-101).

Author information

Authors and Affiliations



The focus and design of this research were developed by G.N., J.M.-L., J.A.B. and E.R.J. C.R.C. conducted the VRMAs. G.N. conducted the remaining analyses. G.N., J.M.-L., J.A.B., C.R.C. and E.R.J. wrote the manuscript.

Corresponding authors

Correspondence to Guillermo Navalón or Emily J. Rayfield.

Ethics declarations

Competing interests

The authors declare no competing interests.

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 Landmarks and semilandmarks used in this study for the beak (white) and skull (black) blocks.

Landmark definition in Extended Data Table 1.

Extended Data Fig. 2

Landmarks used in this study. **Cracraft, J. The lacrimal-ectethmoid bone complex in birds: a single character analysis. American Midland Naturalist, 316-359 (1969).

Extended Data Fig. 3 Tempo and mode of craniofacial shape evolution in landbirds (labelled by major radiations).

Phylomorphospaces of the first three PCs (a) and rates of evolution (b) for the whole skull configurations. Dot colours in phylomorphospaces (a) correspond to each major landbird lineage (colour legend by each silhouette in B). Branch colours in B indicate relative rate of shape evolution. Inferred rate shifts with higher posterior probability than 0.7 are plotted in corresponding branches (circles) or nodes (triangles) in the phylogeny in B. Posterior probability of each inferred rate shift is indicated by size as indicated in the legend in b.

Extended Data Fig. 4 Tempo and mode of beak shape evolution in landbirds (labelled by major radiations).

Phylomorphospaces of the first three PCs (a) and rates of evolution (b) for the beak block configurations. Colours and legends as before.

Extended Data Fig. 5 Tempo and mode of skull shape evolution in landbirds (labelled by major radiations).

Phylomorphospaces of the first three PCs (a) and rates of evolution (b) for the skull block configurations. Colours and legends as before.

Extended Data Fig. 6 Shape differences associated with the first pair of PLS vectors (PLS1) for the beak block and the skull block for the main lineages of passerines.

Polar histograms summarizing angle comparisons between the PLS1 vectors for the beak (b) and skull (c) blocks. As orientation of PLS1 vectors is arbitrary, the maximum possible angle between PLS1 vectors is 90°. * indicates single angular comparison of the PLS1 vectors of Passeroidea excluding DF and HH.

Extended Data Fig. 7 Extreme morphologies and spread along lines of least resistance for each family within the parvorder Passeroidea in our sample.

Within-family maximum Procrustes distances for PLS1scores (situation 2) for both beak and skull blocks. Done for all the families that include two or more species in our sample. Legend for labels in Extended Data Table 2. Dot colours correspond to the ages of the most common recent ancestor (MRCA) for each of the focal families in our MCC tree.

Extended Data Fig. 8 Relationship between levels of cranial integration and evolutionary rates per clade.

Dotplot showing the relationship between mean and median log-rate per landbird/passerine clade (clades as defined in Figs. 3, 4 and Table 1) with mean clade zscore values (that is evolutionary covariation, situation 2). Dashed ellipses encompass the values for selected clades: 1, All landbirds; 2, Non-passerines; 3, Passeriformes; 4, Passeri; 5, Tyranni; 6, Psittaciformes; P.1, Passeroidea (including Darwin’s finches and Hawaiian honeycreepers); P.2. Passeroidea (excluding Darwin’s finches and Hawaiian honeycreepers).

Extended Data Fig. 9 Legend for family names.

Fig. 4c, d and Extended Data Figure 6.

Extended Data Fig. 10 Comparisons of the pattern of maximum covariation lines between Passeroidea and other selected passerine clades.

Angles (θ, degrees) for each pair of PLS1 vectors for the beak and skull block in situation 2 between Passeroidea (including and excluding DF and HH), Muscicapida (the parvorder that includes the passerine radiations sympatric to DF and HH) and Passeriformes, Passeri (all oscine passerines) and Tyranni (all suboscine passerines). As orientation of PLS1 vectors is arbitrary, the maximum possible angle between PLS1 vectors is 90°. * excluding DF and HH.

Supplementary information

Supplementary Information

Extended results, Supplementary Figs. 1–6, Tables 1–3 and references.

Reporting Summary

Supplementary Data

Supplementary Data Tables 1–5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Navalón, G., Marugán-Lobón, J., Bright, J.A. et al. The consequences of craniofacial integration for the adaptive radiations of Darwin’s finches and Hawaiian honeycreepers. Nat Ecol Evol 4, 270–278 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


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