BACKGROUND: Probabilistic methods have progressively supplanted the Maximum 
Parsimony (MP) method for inferring phylogenetic trees. One of the major reasons 
for this shift was that MP is much more sensitive to the Long Branch Attraction 
(LBA) artefact than is Maximum Likelihood (ML). However, recent work by 
Kolaczkowski and Thornton suggested, on the basis of simulations, that MP is 
less sensitive than ML to tree reconstruction artefacts generated by 
heterotachy, a phenomenon that corresponds to shifts in site-specific 
evolutionary rates over time. These results led these authors to recommend that 
the results of ML and MP analyses should be both reported and interpreted with 
the same caution. This specific conclusion revived the debate on the choice of 
the most accurate phylogenetic method for analysing real data in which various 
types of heterogeneities occur. However, variation of evolutionary rates across 
species was not explicitly incorporated in the original study of Kolaczkowski 
and Thornton, and in most of the subsequent heterotachous simulations published 
to date, where all terminal branch lengths were kept equal, an assumption that 
is biologically unrealistic.
RESULTS: In this report, we performed more realistic simulations to evaluate the 
relative performance of MP and ML methods when two kinds of heterogeneities are 
considered: (i) within-site rate variation (heterotachy), and (ii) rate 
variation across lineages. Using a similar protocol as Kolaczkowski and Thornton 
to generate heterotachous datasets, we found that heterotachy, which constitutes 
a serious violation of existing models, decreases the accuracy of ML whatever 
the level of rate variation across lineages. In contrast, the accuracy of MP can 
either increase or decrease when the level of heterotachy increases, depending 
on the relative branch lengths. This result demonstrates that MP is not 
insensitive to heterotachy, contrary to the report of Kolaczkowski and Thornton. 
Finally, in the case of LBA (i.e. when two non-sister lineages evolved faster 
than the others), ML outperforms MP over a wide range of conditions, except for 
unrealistic levels of heterotachy.
CONCLUSION: For realistic combinations of both heterotachy and variation of 
evolutionary rates across lineages, ML is always more accurate than MP. 
Therefore, ML should be preferred over MP for analysing real data, all the more 
so since parametric methods also allow one to handle other types of biological 
heterogeneities much better, such as among sites rate variation. The confounding 
effects of heterotachy on tree reconstruction methods do exist, but can be 
eschewed by the development of mixture models in a probabilistic framework, as 
proposed by Kolaczkowski and Thornton themselves.