Zhang, G. et al. Comparative genomics reveals insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014).
Sackton, T. B. et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science 364, 74–78 (2019).
Whitney, O. et al. Core and region-enriched networks of behaviorally regulated genes and the singing genome. Science 346, 1256780 (2014).
Yusuf, L. et al. Noncoding regions underpin avian bill shape diversification at macroevolutionary scales. Genome Res. 30, 553–565 (2020).
Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature 587, 252–257 (2020).
Stiller, J. et al. Complexity of avian evolution revealed by family-level genomes. Nature 629, 851–860 (2024).
Wright, N. A., Gregory, T. R. & Witt, C. C. Metabolic “engines” of flight drive genome size reduction in birds. Proc. Biol. Sci. 281, 20132780 (2014).
Organ, C. L., Shedlock, A. M., Meade, A., Pagel, M. & Edwards, S. V. Origin of avian genome size and structure in non-avian dinosaurs. Nature 446, 180–184 (2007).
Pfenning, A. R. et al. Convergent transcriptional specializations in the brains of humans and song-learning birds. Science 346, 1256846 (2014).
Mason, N. A. et al. Song evolution, speciation, and vocal learning in passerine birds. Evolution 71, 786–796 (2017).
Toomey, M. B. et al. High-density lipoprotein receptor SCARB1 is required for carotenoid coloration in birds. Proc. Natl Acad. Sci. USA 114, 5219–5224 (2017).
Aguillon, S. M., Walsh, J. & Lovette, I. J. Extensive hybridization reveals multiple coloration genes underlying a complex plumage phenotype. Proc. Biol. Sci. 288, 20201805 (2021).
Abzhanov, A. et al. The calmodulin pathway and evolution of elongated beak morphology in Darwin’s finches. Nature 442, 563–567 (2006).
Bravo, G. A., Schmitt, C. J. & Edwards, S. V. What have we learned from the first 500 avian genomes? Annu. Rev. Ecol. Evol. Syst. 52, 611–639 (2021).
Gregory, T. R., Andrews, C. B., McGuire, J. A. & Witt, C. C. The smallest avian genomes are found in hummingbirds. Proc. Biol. Sci. 276, 3753–3757 (2009).
Price-Waldman, R. & Stoddard, M. C. Avian coloration genetics: recent advances and emerging questions. J. Hered. 112, 395–416 (2021).
Smith, S. D., Pennell, M. W., Dunn, C. W. & Edwards, S. V. Phylogenetics is the new genetics (for most of biodiversity). Trends Ecol. Evol. 35, 415–425 (2020).
Lanfear, R., Ho, S. Y. W., Love, D. & Bromham, L. Mutation rate is linked to diversification in birds. Proc. Natl Acad. Sci. USA 107, 20423–20428 (2010).
Berv, J. S. et al. Genome and life-history evolution link bird diversification to the end-Cretaceous mass extinction. Sci. Adv. 10, eadp0114 (2024).
Bromham, L. Why do species vary in their rate of molecular evolution? Biol. Lett. 5, 401–404 (2009).
Weber, C. C., Nabholz, B., Romiguier, J. & Ellegren, H. Kr/Kc but not dN/dS correlates positively with body mass in birds, raising implications for inferring lineage-specific selection. Genome Biol. 15, 542 (2014).
Botero-Castro, F., Figuet, E., Tilak, M.-K., Nabholz, B. & Galtier, N. Avian genomes revisited: hidden genes uncovered and the rates versus traits paradox in birds. Mol. Biol. Evol. 34, 3123–3131 (2017).
Berv, J. S. & Field, D. J. Genomic signature of an avian Lilliput effect across the K–Pg extinction. Syst. Biol. 67, 1–13 (2018).
Montoya, P., Cadena, C. D., Claramunt, S. & Duchêne, D. A. Environmental niche and flight intensity are associated with molecular evolutionary rates in a large avian radiation. BMC Ecol. Evol. 22, 95 (2022).
Iglesias-Carrasco, M., Jennions, M. D., Ho, S. Y. W. & Duchêne, D. A. Sexual selection, body mass and molecular evolution interact to predict diversification in birds. Proc. Biol. Sci. 286, 20190172 (2019).
Duchêne, D. A. et al. Linking branch lengths across sets of loci provides the highest statistical support for phylogenetic inference. Mol. Biol. Evol. 37, 1202–1210 (2020).
Snir, S., Wolf, Y. I. & Koonin, E. V. Universal pacemaker of genome evolution. PLoS Comput. Biol. 8, e1002785 (2012).
Lanfear, R., Welch, J. J. & Bromham, L. Watching the clock: studying variation in rates of molecular evolution between species. Trends Ecol. Evol. 25, 495–503 (2010).
Duchêne, D. A., Duchêne, S., Stiller, J., Heller, R. & Ho, S. Y. W. ClockstaRX: testing molecular clock hypotheses with genomic data. Genome Biol. Evol. 16, evae064 (2024).
Ohta, T. The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23, 263–286 (1992).
Bergeron, L. A. et al. Evolution of the germline mutation rate across vertebrates. Nature 615, 285–291 (2023).
Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The worldwide variation in avian clutch size across species and space. PLoS Biol. 6, 2650–2657 (2008).
Brown, W. M., Prager, E. M., Wang, A. & Wilson, A. C. Mitochondrial DNA sequences of primates: tempo and mode of evolution. J. Mol. Evol. 18, 225–239 (1982).
Welch, J. J., Bininda-Emonds, O. R. P. & Bromham, L. Correlates of substitution rate variation in mammalian protein-coding sequences. BMC Evol. Biol. 8, 53 (2008).
Jarvis, E. D. et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331 (2014).
Germain, R. R. et al. Species-specific traits mediate avian demographic responses under past climate change. Nat. Ecol. Evol. 7, 862–872 (2023).
Claramunt, S., Derryberry, E. P., Remsen, J. V. Jr & Brumfield, R. T. High dispersal ability inhibits speciation in a continental radiation of passerine birds. Proc. Biol. Sci. 279, 1567–1574 (2012).
Ksepka, D. T. et al. Tempo and pattern of avian brain size evolution. Curr. Biol. 30, 2026–2036.e3 (2020).
Harries, P. J. & Knorr, P. O. What does the ‘Lilliput effect’ mean? Palaeogeogr. Palaeoclimatol. Palaeoecol. 284, 4–10 (2009).
Brown, J. W., Rest, J. S., García-Moreno, J., Sorenson, M. D. & Mindell, D. P. Strong mitochondrial DNA support for a Cretaceous origin of modern avian lineages. BMC Biol. 6, 6 (2008).
Waters, P. D. et al. Microchromosomes are building blocks of bird, reptile, and mammal chromosomes. Proc. Natl Acad. Sci. USA 118, e2112494118 (2021).
Liu, J. et al. A new emu genome illuminates the evolution of genome configuration and nuclear architecture of avian chromosomes. Genome Res. 31, 497–511 (2021).
Germain, R. R. et al. Changes in the functional diversity of modern bird species over the last million years. Proc. Natl Acad. Sci. USA 120, e2201945119 (2023).
Bryant, D. & Hahn, M. W. in Phylogenetics in the Genomic Era (eds Scornavacca, C., Delsuc, F. & Galtier, N.) 3.4:1–3.4:23 (HAL, 2020).
Xu, Y. et al. Ecological predictors of interspecific variation in bird bill and leg lengths on a global scale. Proc. Biol. Sci. 290, 20231387 (2023).
Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).
Shakya, S. B., Edwards, S. V. & Sackton, T. B. Convergent evolution of noncoding elements associated with short tarsus length in birds. Preprint at bioRxiv https://doi.org/10.1101/2024.04.30.591925 (2024).
Rezatabar, S. et al. RAS/MAPK signaling functions in oxidative stress, DNA damage response and cancer progression. J. Cell. Physiol. 234, 14951–14965 (2019).
Cannell, I. G. et al. p38 MAPK/MK2-mediated induction of miR-34c following DNA damage prevents Myc-dependent DNA replication. Proc. Natl Acad. Sci. USA 107, 5375–5380 (2010).
Kim, J. et al. β-Arrestin 1 regulates β2-adrenergic receptor-mediated skeletal muscle hypertrophy and contractility. Skelet. Muscle 8, 39 (2018).
Najafi, A., Sequeira, V., Kuster, D. W. D. & van der Velden, J. β-Adrenergic receptor signalling and its functional consequences in the diseased heart. Eur. J. Clin. Invest. 46, 362–374 (2016).
Lynch, M. & Kewalramani, A. Messenger RNA surveillance and the evolutionary proliferation of introns. Mol. Biol. Evol. 20, 563–571 (2003).
Wolin, S. L. & Maquat, L. E. Cellular RNA surveillance in health and disease. Science 366, 822–827 (2019).
Singh, P., Saha, U., Paira, S. & Das, B. Nuclear mRNA surveillance mechanisms: function and links to human disease. J. Mol. Biol. 430, 1993–2013 (2018).
Pan, S. et al. Convergent genomic signatures of flight loss in birds suggest a switch of main fuel. Nat. Commun. 10, 2756 (2019).
Oliveros, C. H. et al. Earth history and the passerine superradiation. Proc. Natl Acad. Sci. USA 116, 7916–7925 (2019).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
Binet, M., Gascuel, O., Scornavacca, C., Douzery, E. J. P. & Pardi, F. Fast and accurate branch lengths estimation for phylogenomic trees. BMC Bioinformatics 17, 23 (2016).
Ducatez, S. & Field, D. J. Disentangling the avian altricial-precocial spectrum: quantitative assessment of developmental mode, phylogenetic signal, and dimensionality. Evolution 75, 2717–2735 (2021).
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).
Lanfear, R. et al. Taller plants have lower rates of molecular evolution. Nat. Commun. 4, 1879 (2013).
Venditti, C. & Pagel, M. Speciation as an active force in promoting genetic evolution. Trends Ecol. Evol. 25, 14–20 (2010).
Duchêne, D. A., Hua, X. & Bromham, L. Phylogenetic estimates of diversification rate are affected by molecular rate variation. J. Evol. Biol. 30, 1884–1897 (2017).
Hoffman, M. & Gelman, A. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2011).
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1 (2017).
Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27, 2865–2873 (2008).
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis 2nd edn (CRC Press, 2003).
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).
Ho, L. S. T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593 (2010).
Jermiin, L. S., Jayaswal, V., Ababneh, F. M. & Robinson, J. Identifying optimal models of evolution. Methods Mol. Biol. 1525, 379–420 (2017).
Naser-Khdour, S., Minh, B. Q., Zhang, W., Stone, E. A. & Lanfear, R. The prevalence and impact of model violations in phylogenetic analysis. Genome Biol. Evol. 11, 3341–3352 (2019).
Goldman, N. Statistical tests of models of DNA substitution. J. Mol. Evol. 36, 182–198 (1993).
Duchêne, D. A., Duchêne, S. & Ho, S. Y. W. New statistical criteria detect phylogenetic bias caused by compositional heterogeneity. Mol. Biol. Evol. 34, 1529–1534 (2017).
Foster, P. G. Modeling compositional heterogeneity. Syst. Biol. 53, 485–495 (2004).
Duchêne, D. A., Duchêne, S. & Ho, S. Y. W. PhyloMAd: efficient assessment of phylogenomic model adequacy. Bioinformatics 34, 2300–2301 (2018).
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Björklund, M. Be careful with your principal components. Evolution 73, 2151–2158 (2019).
Mendes, F. K. & Hahn, M. W. Gene tree discordance causes apparent substitution rate variation. Syst. Biol. 65, 711–721 (2016).
Boyle, E. I. et al. GO::TermFinder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20, 3710–3715 (2004).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Luo, W. & Brouwer, C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830–1831 (2013).
Clopper, C. J. & Pearson, E. S. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934).
Duchêne, D. A. et al. Drivers of avian genomic change revealed by evolutionary rate decomposition. figshare https://doi.org/10.6084/m9.figshare.27323229 (2025).
Duchene, D. duchene/b10k_family_rates: v1.0. Zenodo https://doi.org/10.5281/zenodo.14848361 (2025).
