Evolutionary divergence in the pan‐Atlantic mangrove Avicennia germinans

Abstract

Cuticular hydrocarbon composition was determined for 18 populations of Avicennia germinans (Avicenniaceae) collected from West Africa, Florida, the Pacific coast of Mexico and French Guyana. Variation in covariance structure among the hydrocarbons was evaluated from correlations among the traits for each of the populations and from a hierarchical common principal components analysis. Principal components ordinations of populations based on the 171 z* transformed correlation coefficients, onto which a minimal spanning tree was superimposed, suggested a network in which French Guyanan populations formed a nexus from which African populations differentiated biochemically from Floridan and Mexican groups. Common principal components analysis provided further support for the differentiation in covariance structure among these biochemical traits of African populations. Pacific coast Mexican populations appeared to have diverged less in covariation among traits from Atlantic American sources than did the northern (Floridan) and southern (French Guyanan) Atlantic American populations from one another. The complete isolation of Pacific coast sources after emergence of the Central American isthmus during the Pliocene provides a reference for comparison of levels of differentiation among regions that suggests an underlying pattern of divergence in this species. Partial Mantel matrix tests revealed significant geographic effects, with no significant climatic effects. Our results provide support for recognition of a major differentiation between Old and New World sources within the taxon and indicate the need for a full revision of this species.

From Micro- To Macroevolution Through Quantitative Genetic Variation: Positive Evidence From Field Crickets

“…Latitudinal gradients have been shown to be correlated with the additive genetic variance of photoperiodic response and developmental time in the pitcher-plant mosquito Wyeomyia smithii (Hard et al 1993;Bradshaw et al 1997), with the additive genetic variation of two morphological traits in two crickets of the genus Allonemobius , and with the additive genetic variance of wing characters in the fruit fly Drosophila melanogaster (van't Land et al 1999). In New World monkeys, P matrix studies have shown that matrix variation for cranial morphology was weakly correlated with diet (Marroig and Cheverud 2001), and the variation of a P matrix corresponding to hydrocarbon composition was correlated with geographical distribution in the mangrove Avicennia germinans (Dodd et al 2000). Blows and Higgie (2003) have concluded through an experimental sympatry experiment using two Drosophila species that natural selection has produced changes in a G matrix composed of cuticular hydrocarbons.…”

Interpretation of the Results of Common Principal Components Analyses

“…The CPC method can be used for the comparison of an arbitrary number of variance-covariance matrices (''covariance matrices'' in our shorthand) and has recently been extended to covariance component matrices Phillips and Arnold 1999). The CPC approach has become the most widely used method for comparing phenotypic (Steppan 1997a;Ackermann and Cheverud 2000;Badyaev and Hill 2000;Dodd et al 2000) and genotypic Phillips and Arnold 1999) covariance matrices and for the study of ontogenetic morphological integration (Klingenberg and Zimmermann 1992;Klingenberg and Spence 1993;Klingenberg et al 1996;Badyaev and Martin 2000).…”

Phenotypic Constraints and Phenotypic Hitchhiking in a Promiscuous Enzyme

“…The answer depends strongly on the study species, their phylogenetic diversity, and on the traits studied. It ranges from completely conserved to completely diverged covariation structure [20,27,28,[43][44][45][46][47]72]. Pertinent studies often involve species that have diverged many generations ago, and through incompletely understood evolutionary forces.…”

“…Complex phenotypes are not univariatevariable along one scalar axis -but multivariate. Examples include the shapes of vertebrate skulls and jaws, whose description involves dozens of morphometric landmarks, cuticular hydrocarbon composition of plants, or an individual's fitness itself, which can be decomposed into multiple scalar life history traits, such as survivorship or fecundity [20][21][22]. The individual scalar traits that compose such complex phenotypes typically do not vary independently, but show covariation within a population.…”

“…That is, natural selection can change several covarying traits in some directions but not in others, at least on short evolutionary timescales. On longer evolutionary timescales, this covariance itself can change, such that traits that show strong positive covariance in one species, may covary less, not at all, or even negatively in another species [20,21,[27][28][29][43][44][45][46][47]. For multiple univariate phenotypic traits P 1 , …, P n trait covariance is usually described by a phenotypic covariance matrix P whose entries describe the covariance between traits P i and P j .…”