Exploring the effects of weighting against homoplasy in genealogies of palaeontological phylogenetic matrices

Abstract

AbstractAlthough simulations have shown that implied weighting (IW) outperforms equal weighting (EW) in phylogenetic parsimony analyses, weighting against homoplasy lacks extensive usage in palaeontology. Iterative modifications of several phylogenetic matrices in the last decades resulted in extensive genealogies of datasets that allow the evaluation of differences in the stability of results for alternative character weighting methods directly on empirical data. Each generation was compared against the most recent generation in each genealogy because it is assumed that it is the most comprehensive (higher sampling), revised (fewer misscorings) and complete (lower amount of missing data) matrix of the genealogy. The analyses were conducted on six different genealogies under EW and IW and extended implied weighting (EIW) with a range of concavity constant values (k) between 3 and 30. Pairwise comparisons between trees were conducted using Robinson–Foulds distances normalized by the total number of groups, distortion coefficient, subtree pruning and regrafting moves, and the proportional sum of group dissimilarities. The results consistently show that IW and EIW produce results more similar to those of the last dataset than EW in the vast majority of genealogies and for all comparative measures. This is significant because almost all of these matrices were originally analysed only under EW. Implied weighting and EIW do not outperform each other unambiguously. Euclidean distances based on a principal components analysis of the comparative measures show that different ranges of k‐values retrieve the most similar results to the last generation in different genealogies. There is a significant positive linear correlation between the optimal k‐values and the number of terminals of the last generations. This could be employed to inform about the range of k‐values to be used in phylogenetic analyses based on matrix size but with the caveat that this emergent relationship still relies on a low sample size of genealogies.