The effect of natural selection on the performance of maximum parsimony.
BACKGROUND
Maximum parsimony is one of the most commonly used and extensively studied phylogeny reconstruction methods. While current evaluation methodologies such as computer simulations provide insight into how well maximum parsimony reconstructs phylogenies, they tell us little about how well maximum parsimony performs on taxa drawn from populations of organisms that evolved subject to natural selection in addition to the random factors of drift and mutation. It is clear that natural selection has a significant impact on Among Site Rate Variation (ASRV) and the rate of accepted substitutions; that is, accepted mutations do not occur with uniform probability along the genome and some substitutions are more likely to occur than other substitutions. However, little is know about how ASRV and non-uniform character substitutions impact the performance of reconstruction methods such as maximum parsimony. To gain insight into these issues, we study how well maximum parsimony performs with data generated by Avida, a digital life platform where populations of digital organisms evolve subject to natural selective pressures.


RESULTS
We first identify conditions where natural selection does affect maximum parsimony's reconstruction accuracy. In general, as we increase the probability that a significant adaptation will occur in an intermediate ancestor, the performance of maximum parsimony improves. In fact, maximum parsimony can correctly reconstruct small 4 taxa trees on data that have received surprisingly many mutations if the intermediate ancestor has received a significant adaptation. We demonstrate that this improved performance of maximum parsimony is attributable more to ASRV than to non-uniform character substitutions.


CONCLUSIONS
Maximum parsimony, as well as most other phylogeny reconstruction methods, may perform significantly better on actual biological data than is currently suggested by computer simulation studies because of natural selection. This is largely due to specific sites becoming fixed in the genome that perform functions associated with an improved fitness.
Hang
Dehua
D
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA. hang@cabm.rutgers.edu <hang@cabm.rutgers.edu>
Torng
Eric
E
Ofria
Charles
C
Schmidt
Thomas M
TM
D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D019143:Evolution, Molecular; D008957:Models, Genetic; D009154:Mutation; D010802:Phylogeny; D012641:Selection, Genetic; D018709:Statistics, Nonparametric