Convergent evolution of phenotypic integration and its alignment with morphological diversification in caribbean Anolis ecomorphs

Jason J. Kolbe, Liam J. Revell, Brian Szekely, Edmund D. Brodie, Jonathan B. Losos

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


The adaptive landscape and the G-matrix are keys concepts for understanding how quantitative characters evolve during adaptive radiation. In particular, whether the adaptive landscape can drive convergence of phenotypic integration (i.e., the pattern of phenotypic variation and covariation summarized in the P-matrix) is not well studied. We estimated and compared P for 19 morphological traits in eight species of Caribbean Anolis lizards, finding that similarity in P among species was not correlated with phylogenetic distance. However, greater similarity in P among ecologically similar Anolis species (i.e., the trunk-ground ecomorph) suggests the role of convergent natural selection. Despite this convergence and relatively deep phylogenetic divergence, a large portion of eigenstructure of P is retained among our eight focal species. We also analyzed P as an approximation of G to test for correspondence with the pattern of phenotypic divergence in 21 Caribbean Anolis species. These patterns of covariation were coincident, suggesting that either genetic constraint has influenced the pattern of among-species divergence or, alternatively, that the adaptive landscape has influenced both G and the pattern of phenotypic divergence among species. We provide evidence for convergent evolution of phenotypic integration for one class of Anolis ecomorph, revealing yet another important dimension of evolutionary convergence in this group. No Claim to original U.S. government works.

Original languageEnglish
Pages (from-to)3608-3624
Number of pages17
Issue number12
StatePublished - Dec 2011


  • Adaptive radiation
  • Common principal components analysis
  • Convergent evolution
  • Genetic constraint
  • Mantel test
  • Phenotypic variance-covariance matrices
  • Random skewers


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