No abstract

Abstract— Because they are designed to produced just one tree, neighbor‐joining programs can obscure ambiguities in data. Ambiguities can be uncovered by resampling, but existing neighbor‐joining programs may give misleading bootstrap frequencies because they do not suppress zero‐length branches and/or are sensitive to the order of terminals in the data. A new procedure, parsimony jackknifing, overcomes these problems while running hundreds of times faster than existing programs for neighbor‐joining bootstrapping. For analysis of large matrices, parsimony jackknifing is hundreds of thousands of times faster than extensive branch‐swapping, yet is better able to screen out poorly‐supported groups.

Patterns of diversification and timing of evolution within Neoaves, which includes almost 95% of all bird species, are virtually unknown. On the other hand, molecular data consistently indicate a Cretaceous origin of many neoavian lineages and the fossil record seems to support an Early Tertiary diversification. Here, we present the first well-resolved molecular phylogeny for Neoaves, together with divergence time estimates calibrated with a large number of stratigraphically and phylogenetically welldocumented fossils. Our study defines several well-supported clades within Neoaves. The calibration results suggest that Neoaves, after an initial split from Galloanseres in Mid-Cretaceous, diversified around or soon after the K/T boundary. Our results thus do not contradict palaeontological data and show that there is no solid molecular evidence for an extensive preTertiary radiation of Neoaves.

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Several aspects of current resampling methods to assess group support are reviewed. When the characters have different prior weights or some state transformation costs are different, the frequencies under either bootstrapping or jackknifing can be distorted, producing either under-or overestimations of the actual group support. This is avoided by symmetric resampling, where the probability p of increasing the weight of a character equals the probability of decreasing it. Problems with interpreting absolute group frequencies as a measure of the support are discussed; group support does not necessarily vary with the frequency itself, since in some cases groups with positive support may have much lower frequencies than groups with no support at all. Three possible solutions for this problem are suggested. The first is measuring the support as the difference in frequency between the group and its most frequent contradictory group. The second is calculating frequencies for values of p below the threshold under which the frequency ranks the groups in the right order of support (this threshold may vary from data set to data set). The third is estimating the support by using the slope of the frequency as a function of different (low) values of p; when p is low, groups with actual support have negative slopes (closer to 0 when the support is higher), and groups with no support have positive slopes (larger when evidence for and against the group is more abundant).