Evaluating the Relationship between Evolutionary Divergence and Phylogenetic Accuracy in AFLP Data Sets

Using in silico amplified fragment length polymorphism (AFLP) fingerprints, we explore the relationship between sequence similarity and phylogeny accuracy to test when, in terms of genetic divergence, the quality of AFLP data becomes too low to be informative for a reliable phylogenetic reconstruction. We generated DNA sequences with known phylogenies using balanced and unbalanced trees with recent, uniform and ancient radiations, and average branch lengths (from the most internal node to the tip) ranging from 0.02 to 0.4 substitutions per site. The resulting sequences were used to emulate the AFLP procedure. Trees were estimated by maximum parsimony (MP), neighbor-joining (NJ), and minimum evolution (ME) methods from both DNA sequences and virtual AFLP fingerprints. The estimated trees were compared with the reference trees using a score that measures overall differences in both topology and relative branch length. As expected, the accuracy of AFLP-based phylogenies decreased dramatically in the more divergent data sets. Above a divergence of approximately 0.05, AFLP-based phylogenies were largely inaccurate irrespective of the distinct topology, radiation model, or phylogenetic method used. This value represents an upper bound of expected tree accuracy for data sets with a simple divergence history; AFLP data sets with a similar divergence but with unbalanced topologies and short ancestral branches produced much less accurate trees. The lack of homology of AFLP bands quickly increases with divergence and reaches its maximum value (100%) at a divergence of only 0.4. Low guanine-cytosine (GC) contents increase the number of nonhomologous bands in AFLP data sets and lead to less reliable trees. However, the effect of the lack of band homology on tree accuracy is surprisingly small relative to the negative impact due to the low information content of AFLP characters. Tree-building methods based on genetic distance displayed similar trends and outperformed parsimony at low but not at high divergences. However, the impact of using alternative phylogenetic methods on tree accuracy was generally small relative to the uncertainty arising from factors such as divergence, nonhomology of bands, or the low information content of AFLP characters. Nevertheless, our data suggest that under certain circumstances, AFLPs may be suitable to reconstruct deeper phylogenies than usually accepted.