1. Full modeling versus summarizing gene- tree uncertainty: Method choice and species-tree accuracy L.L. Knowles et al., Molecular Phylogenetics and Evolution 65 (2012): 501-509

2. Full modeling versus summarizing gene- tree uncertainty: Method choice and species-tree accuracy

4. Two representative software examples STEM Maximum likelihood based estimation Needs known gene trees Less computationally intensive *BEAST Bayesian inference using full coalescent model Reads multi-locus nucleotide data Technically one of the fastest Bayesian approaches, but still quite costly in computational terms

5. Two representative software examples STEM Maximum likelihood based estimation Needs known gene trees Less computationally intensive *BEAST Bayesian inference using full coalescent model Reads multi-locus nucleotide data Technically one of the fastest Bayesian approaches, but still quite costly in computational terms

6. Two representative software examples ML-GT STEM Maximum likelihood based estimation ML- Gene trees computed using GARLI Less computationally intensive *BEAST Bayesian inference using full coalescent model Reads multi-locus nucleotide data Technically one of the fastest Bayesian approaches, but still quite costly in computational terms

7. Two representative software examples ML-GT STEM Maximum likelihood based estimation ML- Gene trees computed using GARLI Less computationally intensive *BEAST Bayesian inference using full coalescent model Reads multi-locus nucleotide data Technically one of the fastest Bayesian approaches, but still quite costly in computational terms Consensus-GT STEM Maximum likelihood based estimation Consensus gene tree computed using MrBAYES Less computationally intensive, although MrBAYES is slower than GARLI

8. percent accuracy 50 100 1N10N 0 ML-GT STEM consensus-GT STEM *BEAST Species tree accuracy using the three methods on datasets simulating evolutionary durations of 1N and 10N generations respectively

9. percent accuracy 50 100 1N10N 0 ML-GT STEM consensus-GT STEM *BEAST The authors' conclusion

10. percent accuracy 50 100 1N10N 0 ML-GT STEM consensus-GT STEM *BEAST The authors' conclusion The factor having the largest effect on the accuracy of a species-tree estimate is not the method of analysis or sampling design, but is the timing of divergence (sic)

11. 10N 1N sampling effort (individuals:loci)

12. (B) ML-GT STEM consensus-GT STEM *BEAST 10N 1N

13. Conclusion On small sample sizes, all methods yield similarly (in)accurate species trees Hence there is no justification for using computationally intensive approaches in these situations.

14. Conclusion Similarly, all methods yield similarly accurate trees independent of sample size when the analyzed data has evolved down a substantially deep tree. Therefore the less intensive methods would be preferable.

15. Conclusion When analyzing larger sample sizes containing recent speciation, there is a significant difference in species tree accuracy among the methods. Full coalescent model based inference methods (*BEAST for example) appear to perform best in these situations. In fact, the result on shorter trees rival those on deeper ones in this specific scenario.

16. In Short: Smaller Sample SizeLarger Sample Size Shorter TreeConsensus GT STEM* BEAST Deeper TreeML-GT STEM

17. Questions 1. How did running time compare for the two methods? Did the authors make any effort to adjust the degree of sampling time for one or the other? 2. Which one would you use for an analysis like this in the future based on what you’ve read? 3. How would you have improved the authors’ simulated dataset? 4. Why do you/the authors think sampling scheme has the opposite effect on species tree accuracy for late diverging versus recently diverging data? 5. A primary motivation of the paper is the way that gene tree estimation error is treated by the different methods. Did the time of divergence affect the amount of gene tree estimation error in either the maximum likelihood gene trees or the consensus gene trees? 6. What do Knowles et al mean by mutational variance? How is this different than coalescent variance? 7. What is the effect of incomplete gene trees on species tree estimation ? 8. Why did this paper not use summary methods in their analysis ?

18. Other Questions ?