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When simplicity is not parsimonious: a priori and a posteriori methods in historical biogeography

Despite using the same null hypothesis, a priori and a posteriori approaches in historical biogeography differ fundamentally. Methods such as Component Analysis (CA) and Reconciled Tree Analysis (RTA) may eliminate or modify input data in order to maximize fit to the null hypothesis, by invoking assumptions 1 and 2. Methods such as Brooks Parsimony Analysis (BPA) modify the null hypothesis, if necessary, to maintain the integrity of the input data, as required by assumption 0. Two exemplars illustrate critical empirical differences between CA/RTA and BPA: (1) CA rather than BPA may select the incorrect general area cladogram for a set of data (2) BPA, not RTA, provides the most parsimonious interpretation of all available data and (3) secondary BPA, proposed in 1990, applied to data sets for which dispersal producing areas with reticulate histories is most parsimonious, provides biologically realistic interpretations of area cladograms. These observations lead to the conclusion that BPA and CA/RTA are designed to implement different research programmes based on different conceptual frameworks. BPA is designed to assess the biogeographic context of speciation events, whereas CA/RTA are designed to find the best fitting pattern of relationships among areas based on the taxa that inhabit them. Unique distributional elements and reticulate (hybrid) histories of areas are essential for explaining complex histories of speciation. The conceptual framework for BPA, thus, assumes biogeographical complexity, relying on parsimony as an explanatory tool to summarize complex results, whereas CA/RTA assumes biogeographical simplicity, assuming conceptual parsimony a priori.