Data exploration in phylogenetic inference: scientific, heuristic, or neither

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 Wheeler's 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 synapomorphy 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 ambiguously 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. Sensitivity analysis evaluates the responsiveness of results to variation or errors in parameter values and assumptions. Sensitivity analysis is generally interpreted as providing a measure of support, where conclusions that are insensitive (robust, stable) to perturbations are judged to be accurate, probable, or reliable. As an alternative to that verificationist concept, we define support objectively as the degree to which critical evidence refutes competing hypotheses. As such, degree of support is secondary to the scientific optimality criterion of maximizing explanatory power. Quality analyses purport to distinguish good, reliable, accurate data from bad, misleading, erroneous data, thereby assessing the ability of data to indicate the true phylogeny. Only the quality analysis of character compatibility can be judged scientific—and a weak test at that compared to character congruence. Methods judged to be heuristic include Bremer support, long‐branch extraction, and safe taxonomic reduction, and we underscore the great heuristic potential of a posteriori analysis of patterns of transformations on the total‐evidence cladogram. However, of the more than 20 kinds of data exploration methods evaluated, the vast majority is neither scientific nor heuristic. Given so little demonstrated cognitive worth, we conclude that undue emphasis has been placed on data exploration in phylogenetic inference, and we urge phylogeneticists to consider more carefully the relevance of the methods that they employ. [T]he cult of impressive technicalities or the cult of precision may get the better of us, and interfere with our search for clarity, simplicity, and truth [Popper, 1983, p. 60. Empirical papers chosen for publication are judged to be of interest to a broad systematics audience because they represent exemplary case studies involving some important contemporary issue or issues. These may be unusually thorough explorations of data, applications of new methodology, illustrations of fundamental principles, and/or investigations of interesting evolutionary questions. [Systematic Biology: Instructions for authors, 2002; italics added]