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.
K E Y W O R D S :Adaptive radiation, common principal components analysis, convergent evolution, genetic constraint, Mantel test, phenotypic variance-covariance matrices, random skewers.Phenotypic diversification for quantitative traits during an adaptive radiation can be viewed as the result of the interplay between two multivariate processes, the multidimensional adaptive landscape and the G-matrix (Arnold et al. 2001. The adaptive landscape is a function relating the mean fitness of a population to the phenotypic trait means. The shape of the landscape represents underlying evolutionary features, such as correlational, stabilizing, or disruptive selection, and reveals where small fitness changes per unit of trait change exist, resulting in multivariate axes where peak movement is likely, termed the "selective lines of least resistance" (Arnold et al. 2001). It also clearly predicts how peak movement within the landscape induces directional selection and how the curvature and orientation of a moving peak generates nonlinear selection. In contrast, long-term stability of 3 6 0 8No Claim to original U.S. government works. Evolution 65-12: 3608-3624