A likelihood approach to estimating phylogeny from discrete morphological character data.

By Paul O. Lewis. Systematic Biology, 2001

2,724 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/beca7bcffe22c1e715279748e6ab0ef29b7c4459

How researchers see this paper

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

In Genetics

1. I. Sanmartín
2. Richard H. Ree
3. Fredrik Ronquist
4. Alexei J. Drummond
5. John P. Huelsenbeck
6. J. Pramuk
7. Liam J. Revell
8. Bui Quang Minh

In Molecular Biology

1. Alexei J. Drummond
2. Richard H. Ree
3. I. Sanmartín
4. Ziheng Yang
5. Fredrik Ronquist
6. John P. Huelsenbeck
7. Bui Quang Minh
8. F. G. Marx

In Paleontology

1. I. Sanmartín
2. Fredrik Ronquist
3. Richard H. Ree
4. Liam J. Revell
5. Nate B. Hardy
6. Johan A. A. Nylander
7. John P. Huelsenbeck
8. Alexei J. Drummond

Citations in the corpus

• Bayesian Analysis of Molecular Evolution Using MrBayes (2005, methodology): “[Although the program MrBayes can analyze morphological data in combination with molecular data, using the approach described by Lewis (2001), we do not examine the morphological characters of the Kim et al. study in this chapter.”
• Investigating the Bivalve Tree of Life – an exemplar-based approach combining molecular and novel morphological characters (2014, methodology): “The morphological data matrix was further analysed in combination with molecular sequence data under parsimony in POY and under Bayesian inference in MrBayes v. 3.1.2 (Huelsenbeck and Ronquist 2005), where the morphological data partition was assigned a discrete equalrates model (Lewis 2001).”
• A subfamily‐level classification of mealybugs (Hemiptera: Pseudococcidae) based on integrated molecular and morphological data (2008, methodology): “The mk1 model, with gamma-distributed rates and coding set to variable, was applied to the morphological dataset (Lewis, 2001).”
• Morphological Evolution in the Mulberry Family (Moraceae) (2009, methodology): “Bayesian analysis was conducted with default priors and the Markov k model with a gamma distribution ( Lewis 2001 ) using MrBayes v3.”
• Bayesian estimation of ancestral character states on phylogenies. (2004, methodology): “We use a continuous-time Markov model (Pagel, 1994, 1997; Lewis, 2001), mathematically equivalent to models of gene-sequence evolution, but not limited to investigating traits with four states.”
• Phylogenetic trees do not reliably predict feature diversity (2014, background): “, 2005); the relationship between feature diversity and hypotheses of common ancestry is not monotonic as otherwise phenetic and parsimony tree-building methods would be unproblematic (Lewis, 2001; Sober & Steel, 2002; Felsenstein, 2004).”
• Conceptual and statistical problems with the DEC+J model of founder‐event speciation and its comparison with DEC via model selection (2018, methodology): “Up to this point, we have described cladogenetic range evolution in DEC and DEC+J as probabilistic (although not with respect to time) because range inheritance scenarios are considered to be stochastic outcomes of speciation events. However, it is worth noting that this is not strictly true for DEC”
• Cranial anatomy and systematics of Hypacrosaurus altispinus, and a comparative analysis of skull growth in lambeosaurine hadrosaurids (Dinosauria: Ornithischia) (2010, methodology): “For further information on Bayesian phylogenetic methods and terminology, see Lewis (2001), Huelsenbeck et al. (2002), Nylander et al. (2004), Ronquist (2004), and Brooks et al. (2007).”
• Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices (2016, positive): “We simulated 100 matrices of categorical character evolution with 55 binary characters using a Markov k-state 1 parameter (Mk1) model (Lewis 2001).”
• The phylogeny and evolution of Figitidae (Hymenoptera: Cynipoidea) (2007, methodology): “The Markov k model (Lewis, 2001), under the assumption that variable characters where sampled, was applied to the morphological and life history partitions and with rate variation modeled using the G distribution.”
• Use of Simultaneous Analyses to Guide Fossil‐Based Calibrations of Pinaceae Phylogeny (2008, positive): “The nonmolecular characters were treated as standard discrete data, with all changes between states equally probable (Lewis 2001).”
• Molecular markers for the phylogenetics of mites and ticks (2002, methodology): “Molecular methods permit the use of a number of more sophisticated methods of phylogenetic reconstruction than the strictly parsimony based methods which are available for the analysis of morphological data (but see Lewis (2001a) for the application of likelihood models to morphological data).”
• Evolutionary Perspectives on Seed Consumption and Dispersal by Fishes (2007, methodology): “We estimated the diet at each ancestral node (figure 2) using maximum-likelihood ancestral character reconstruction, following the algorithm of Pagel (1999), under an Mk1 optimization model (Lewis 2001) as implemented in Mesquite (Maddison and Maddison 2006).”
• The origins and diversification of C4 grasses and savanna‐adapted ungulates (2009, methodology): “To infer the nodes at which shifts in photosynthetic pathways and habitat preference occurred, we optimized these characters with the Mk1 model of evolution (Lewis, 2001), implemented in MESQUITE v1.”
• Rapid evolution of an ancient lake species flock: Freshwater limpets (Gastropoda: Ancylidae) in the Balkan Lake Ohrid (2006, methodology): “…types occupied by the species (bathymetric range of occurrence and substrate type) were treated as unordered characters and most parsimoniously mapped (MPR) on the inferred phylogeny using MacClade 4.0 (Maddison and Maddison 2000) by linear reconstruction of ancestral states with full ranges.”