Paper No. 82-2
Presentation Time: 1:15 PM
FOSSILS, PHYLOGENIES, AND MODELS OF TRAIT EVOLUTION: EXAMINING PHENOTYPIC EVOLUTION IN THE ORDOVICIAN BRACHIOPOD GENUS GLYPTORTHIS
WRIGHT, David F., School of Earth Sciences, The Ohio State University, 275 Mendenhall Laboratory, 125 South Oval Mall, Columbus, DC 43210
Phylogenetic paleobiology is an emerging, quantitative research program grounded in mathematical methods and models commonly referred to as phylogenetic comparative methods (PCM). PCMs are statistical approaches to studying the tempo and mode of trait evolution and diversification using phylogenetic trees of extant taxa. Similarly, phylogenetic paleobiology uses phylogenetic trees of fossil taxa as a template to conduct detailed, statistical analyses of the tempo and mode of trait evolution over geologic time. Because PCMs can be used to fit evolutionary models of trait evolution to time-calibrated fossil phylogenies, they provide a powerful analytical toolkit for testing hypotheses regarding rates and patterns of trait change throughout the history of life. The purpose of this presentation is to review conceptual and mathematical aspects of phylogeny-based models of trait evolution and demonstrate their utility for testing evolutionary patterns in the fossil record.
To illustrate the application of PCMs to paleontological data, I examine patterns of phenotypic and biogeographic evolution among North American species of the Ordovician brachiopod genus Glyptorthis. To test whether biogeographic expansion occurred concomitant with phenotypic changes in body size, four process-based models of phenotypic trait evolution were fit to a previously published time-calibrated phylogeny of Glyptorthis using maximum likelihood. Models of phenotypic evolution considered include: Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst, and directional change.
Model comparison using AICc overwhelmingly supports OU as the best fitting model, suggesting morphologic changes were constrained to occur within a single adaptive peak despite frequent changes in biogeographic distribution. In addition, the adequacy of the BM and OU models were further explored and support OU as the best fitting model.