Improved bootstrap confidence limits in large-scale phylogenies, with an example from Neo-Astragalus (Leguminosae).
Phylogenetic analyses of large data sets pose special challenges, including the apparent tendency for the bootstrap support for a clade to decline with increased taxon sampling of that clade. We document this decline in data sets with increasing numbers of taxa in Astragalus, the most species-rich angiosperm genus. Support for one subclade, Neo-Astragalus, declined monotonically with increased sampling of taxa inside Neo-Astragalus, irrespective of whether parsimony or neighbor-joining methods were used or of which particular heuristic search algorithm was used (although more stringent algorithms tended to yield higher support). Three possible explanations for this decline were examined, including (1) mistaken assignment of the most recent common ancestor of the taxon sample (and its bootstrap support) with the most recent common ancestor of the clade from which it was sampled; (2) computational limitations of heuristic search strategies; and (3) statistical bias in bootstrap proportions, especially that from random homoplasy distributed among taxa. The best explanation appears to be (3), although computational shortcomings (2) may explain some of the problem. The bootstrap proportion, as currently used in phylogenetic analysis, does not accurately capture the classical notion of confidence assessments on the null hypothesis of nonmonophyly, especially in large data sets. More accurate assessments of confidence as type I error levels (relying on iterated bootstrap methods) remove most of the monotonic decline in confidence with increasing numbers of taxa.
Section of Evolution and Ecology, University of California, Davis, California 95616, USA. mjsanderson@ucdavis.edu
M J
MJ
Sanderson
M F
MF
Wojciechowski
D000465:Algorithms; D002965:Classification / Q000379:methods; D016001:Confidence Intervals; D007887:Fabaceae / Q000145:classification / Q000235:genetics*; D015233:Models, Statistical; D010802:Phylogeny