Data Exploration in Phylogenetic Inference: Scientific, Heuristic, Or Neither
Cladistics Cladistics 19 (2003) 379–418 www.elsevier.com/locate/yclad Data exploration in phylogenetic inference: scientific, heuristic, or neither Taran Granta,b,* and Arnold G. Klugec,* a Division of Vertebrate Zoology, Herpetology, American Museum of Natural History, New York, NY 10024, USA b Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA c Museum of Zoology, University of Michigan, Ann Arbor, MI 48109, USA Accepted 16 June 2003 Abstract The methods of data exploration have become the centerpiece of phylogenetic inference, but without the scientific importance of those methods having been identified. We examine in some detail the procedures and justifications of WheelerÕs sensitivity analysis and relative rate comparison (saturation analysis). In addition, we review methods designed to explore evidential decisiveness, clade stability, transformation series additivity, methodological concordance, sensitivity to prior probabilities (Bayesian analysis), skewness, computer-intensive tests, long-branch attraction, model assumptions (likelihood ratio test), sensitivity to amount of data, polymorphism, clade concordance index, character compatibility, partitioned analysis, spectral analysis, relative apparent syna- pomorphy analysis, and congruence with a ‘‘known’’ phylogeny. In our review, we consider a method to be scientific if it performs empirical tests, i.e., if it applies empirical data that could potentially refute the hypothesis of interest. Methods that do not perform tests, and therefore are not scientific, may nonetheless be heuristic in the scientific enterprise if they point to more weakly or am- biguously corroborated hypotheses, such propositions being more easily refuted than those that have been more severely tested and are more strongly corroborated. Based on common usage, data exploration in phylogenetics is accomplished by any method that performs sensitivity or quality analysis.
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