title: Consequences of Secondary Calibrations on Divergence Time Estimates.
abstract: Secondary calibrations (calibrations based on the results of previous molecular dating studies) are commonly applied in divergence time analyses in groups that lack fossil data; however, the consequences of applying secondary calibrations in a relaxed-clock approach are not fully understood. I tested whether applying the posterior estimate from a primary study as a prior distribution in a secondary study results in consistent age and uncertainty estimates. I compared age estimates from simulations with 100 randomly replicated secondary trees. On average, the 95% credible intervals of node ages for secondary estimates were significantly younger and narrower than primary estimates. The primary and secondary age estimates were significantly different in 97% of the replicates after Bonferroni corrections. Greater error in magnitude was associated with deeper than shallower nodes, but the opposite was found when standardized by median node age, and a significant positive relationship was determined between the number of tips/age of secondary trees and the total amount of error. When two secondary calibrated nodes were analyzed, estimates remained significantly different, and although the minimum and median estimates were associated with less error, maximum age estimates and credible interval widths had greater error. The shape of the prior also influenced error, in which applying a normal, rather than uniform, prior distribution resulted in greater error. Secondary calibrations, in summary, lead to a false impression of precision and the distribution of age estimates shift away from those that would be inferred by the primary analysis. These results suggest that secondary calibrations should not be applied as the only source of calibration in divergence time analyses that test time-dependent hypotheses until the additional error associated with secondary calibrations is more properly modeled to take into account increased uncertainty in age estimates.
authors.affiliation: Department of Biology, Georgia Southern University, Statesboro, Georgia, United States of America.
authors.forename: John J
authors.initials: JJ
authors.lastname: Schenk
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001683:Biological Clocks / Q000235:genetics; D002138:Calibration; D005580:Fossils; D049810:Genetic Speciation; D016013:Likelihood Functions; D009115:Muridae / Q000145:classification* / Q000235:genetics; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D013995:Time; D035501:Uncertainty
references.citation: Mol Phylogenet Evol. 2013 Aug;68(2):300-11
references.pmid: 23578597
references.citation: Mol Phylogenet Evol. 2013 Jul;68(1):35-41
references.pmid: 23523862
references.citation: Front Genet. 2014 May 26;5:138
references.pmid: 24904638
references.citation: Bioinformatics. 2008 Jan 1;24(1):129-31
references.pmid: 18006550
references.citation: Mol Phylogenet Evol. 2014 Sep;78:277-89
references.pmid: 24910154
references.citation: Mol Biol Evol. 2001 Mar;18(3):352-61
references.pmid: 11230536
references.citation: Syst Biol. 2002 Oct;51(5):689-702
references.pmid: 12396584
references.citation: Mol Biol Evol. 2000 Jul;17(7):1081-90
references.pmid: 10889221
references.citation: Gene. 2002 Oct 30;300(1-2):59-61
references.pmid: 12468086
references.citation: Mol Phylogenet Evol. 2005 Jun;35(3):569-82
references.pmid: 15878126
references.citation: Syst Biol. 2007 Aug;56(4):591-608
references.pmid: 17654364
references.citation: Trends Ecol Evol. 2005 Jun;20(6):320-7
references.pmid: 16701388
references.citation: Nat Rev Genet. 2003 Mar;4(3):216-24
references.pmid: 12610526
references.citation: Syst Biol. 2013 Nov;62(6):837-64
references.pmid: 23925508
references.citation: Philos Trans R Soc Lond B Biol Sci. 2004 Oct 29;359(1450):1477-83
references.pmid: 15519966
references.citation: PLoS Biol. 2006 May;4(5):e88
references.pmid: 16683862
references.citation: Evolution. 2011 Jul;65(7):1862-78
references.pmid: 21729044
references.citation: Am J Bot. 2009 Jan;96(1):349-65
references.pmid: 21628193
references.citation: Am Nat. 2008 Jun;171(6):726-42
references.pmid: 18462127
references.citation: Trends Genet. 2004 Feb;20(2):80-6
references.pmid: 14746989
references.citation: Trends Genet. 2004 May;20(5):242-7
references.pmid: 15109778
references.citation: Syst Biol. 2009 Jun;58(3):367-80
references.pmid: 20525591
references.citation: Mol Ecol. 2008 May;17(10):2321-8
references.pmid: 18422932
references.citation: Bioinformatics. 2003 Jan 22;19(2):301-2
references.pmid: 12538260
references.citation: Gene. 2009 Jul 15;441(1-2):132-40
references.pmid: 18809474
references.citation: Mol Biol Evol. 2011 Sep;28(9):2521-35
references.pmid: 21422243
references.citation: Syst Biol. 2012 Mar;61(2):346-59
references.pmid: 22105867
references.citation: Syst Biol. 2010 Jul;59(4):384-99
references.pmid: 20538759
references.citation: Mol Biol Evol. 1998 Dec;15(12):1647-57
references.pmid: 9866200
references.citation: PLoS Comput Biol. 2009 Sep;5(9):e1000520
references.pmid: 19779555
references.citation: Mol Biol Evol. 2002 Jan;19(1):101-9
references.pmid: 11752195
references.citation: Syst Biol. 2003 Oct;52(5):705-16
references.pmid: 14530137
references.citation: Syst Biol. 2012 Mar;61(2):289-313
references.pmid: 22201158
references.citation: Bioinformatics. 2004 Jan 22;20(2):289-90
references.pmid: 14734327
references.citation: Syst Biol. 2009 Feb;58(1):130-45
references.pmid: 20525573
references.citation: Syst Biol. 2007 Jun;56(3):453-66
references.pmid: 17558967
references.citation: BMC Evol Biol. 2007;7:214
references.pmid: 17996036