title: The stability of P in coral reef fishes.
abstract: The constancy of phenotypic variation and covariation is an assumption that underlies most recent investigations of past selective regimes and attempts to predict future responses to selection. Few studies have tested this assumption of constancy despite good reasons to expect that the pattern of phenotypic variation and covariation may vary in space and time. We compared phenotypic variance-covariance matrices (P) estimated for populations of six species of distantly related coral reef fishes sampled at two locations on Australia's Great Barrier Reef separated by more than 1000 km. The intraspecific similarity between these matrices was estimated using two methods: matrix correlation and common principal component analysis. Although there was no evidence of equality between pairs of P, both statistical approaches indicated a high degree of similarity in morphology between the two populations for each species. In general, the hierarchical decomposition of the variance-covariance structure of these populations indicated that all principal components of phenotypic variance-covariance were shared but that they differed in the degree of variation associated with each of these components. The consistency of this pattern is remarkable given the diversity of morphologies and life histories encompassed by these species. Although some phenotypic instability was indicated, these results were consistent with a generally conserved pattern of multivariate selection between populations.
authors.affiliation: School of Marine Biology and Aquaculture, James Cook University, Townsville, Queensland 4811, Australia. e.game@uq.edu.au
authors.forename: Edward T
authors.initials: ET
authors.lastname: Game
authors.forename: M Julian
authors.initials: MJ
authors.lastname: Caley
mesh_terms: D000818:Animals; D037421:Anthozoa; D019143:Evolution, Molecular; D005399:Fishes / Q000235:genetics* / Q000502:physiology*; D040961:Genetic Drift; D014644:Genetic Variation; D005828:Genetics, Population; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D025341:Principal Component Analysis; D012641:Selection, Genetic