TESTING FOR DIFFERENT RATES OF CONTINUOUS TRAIT EVOLUTION USING LIKELIHOOD

By Brian C. O'Meara, Cécile Ané, Michael J. Sanderson, Peter C. Wainwright. Evolution; international journal of organic evolution, 2006

686 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Genetics

View Paper: https://www.semanticscholar.org/paper/6fc9d3bb222c4864380b7f77722cb6b888775a53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology, Evolution, Behavior and Systematics

1. B. Hawkins
2. J. Diniz-Filho
3. Liam J. Revell
4. M. Rodríguez
5. Luis Mauricio Bini
6. R. Ricklefs
7. M. Á. Olalla‐Tárraga
8. Mark W. Chase

In Genetics

1. J. Losos
2. Liam J. Revell
3. Ole Seehausen
4. J. Diniz-Filho
5. Kenneth H Kozak
6. T. Jonathan Davies
7. Luke J. Harmon
8. Peter C. Wainwright

Citations in the corpus

• Functional Versus Morphological Diversity in Macroevolution (2007, methodology): “…among the lineages within a clade develops as a function of the time in the phylogeny, the amount of shared history between lineages, and the rate of evolution of the trait (Ackerly & Nyffeler 2004, Garland 1992, Gittleman et al. 1996, Mooers et al. 1999, O'Meara et al. 2006, Thomas et al. 2006).”
• Macroevolutionary patterns in the evolutionary radiation of archosaurs (Tetrapoda: Diapsida) (2010, neutral): “2009) and theoretical considerations (O’Meara et al. 2006; Ricklefs 2006; Sidlauskas 2007) show that this is not always the case.”
• Genome Size Dynamics and Evolution in Monocots (2010, methodology): “In contrast, Brownie uses maximum likelihood to analyse rates of genome size evolution across a phylogenetic tree and it can be applied to test for substantial differences in the rate of genome size-evolution between monocot clades.”
• phytools: an R package for phylogenetic comparative biology (and other things) (2012, methodology): “In the second, I simulate a stochastic discrete character history and a continuous character with different rate conditioned on the discrete character state, and then I fit a multi-rate Brownian character evolution model using the phytools function brownie.lite (O’Meara et al. 2006).”
• Testing quantitative genetic hypotheses about the evolutionary rate matrix for continuous characters (2008, background): “, 2000; see above); the intercept of regression and other statistical models can be estimated (Rohlf, 2001); and, relevant to this study, the likelihood equations for the evolutionary rate for a single character or rate matrix for many characters are readily derived (e.g. Felsenstein, 1973; O’Meara ”