carex: Carex section Ovales tree, chromosome data, and nodes

In maticce: Mapping Transitions in Continuous Character Evolution

Description

This is the Carex dataset used in Hipp (2007). It is a list with three items: a tree of 53 taxa for which chromosome counts are known; a vector of log-transformed mean chromosome counts for all taxa; and a list of eight taxon vectors.

Usage

data(carex)

Format

A list with four items:

• ovales.tree An ultrametric tree in phylo format with 53 tips

• ovales.data A named numeric vector of log-transformed mean chromosome counts for all 53 taxa

• ovales.nodes A list of eight taxon vectors defining the eight nodes studied.

• ovales.bayesTrees A list of 100 ultrametric trees in phylo format, subsampled from the MCMC analysis underlying carex$ovales.bayesTrees.

Details

Phylogeny (ovales.tree) was estimated for approximately 80 species (Hipp 2006), branch lengths optimized using penalized likelihood in ape, and then the tree was pruned down to only those taxa for which chromosome counts were available. Chromosome counts (ovales.data) were averaged by population, and the means log-transformed for analysis. Nodes were selected for analysis based on clade support and number of taxa in the clade.

Author(s)

Andrew L. Hipp <[e-mail]>

Source

Hipp, A.L. (2007) Non-uniform processes of chromosome evolution in sedges (Carex: Cyperaceae). Evolution 61:2175-2194.

References

Hipp, A.L., P.E. Rothrock, A.A. Reznicek, and J.A. Weber (2006) Phylogeny and classification of Carex section Ovales (Cyperaceae). International Journal of Plant Sciences 167:1029-1048.

Hipp, A.L. (2007) Non-uniform processes of chromosome evolution in sedges (Carex: Cyperaceae). Evolution 61:2175-2194.

Examples

  data(carex)
  # tree comes in in phylo format, but we need an ouchtree object
  trees <- lapply(carex$ovales.bayesTrees[1:2], ape2ouch) 
  contree <- ape2ouch(carex$ovales.tree)
  # for expedience, only tests for changes at up to 2 of the first 4 nodes 
  # on 2 trees visited in the MCMC phylogenetic analysis:
  trialBayesTrees <- runBatchHansen(trees, carex$ovales.data, carex$ovales.nodes[1:4], 
                                    maxNodes = 2) 
  summary(trialBayesTrees) # summarizes results using summary.hansenBatch 
                           # and displays the results using print.hansenSummary
  # same analysis as above, but on a single tree
  trialBayesConsensus <- runBatchHansen(contree, carex$ovales.data, 
                                        carex$ovales.nodes[1:4], maxNodes = 2)
  # compares five different models of character change at node 2
  multiModel(carex$ovales.tree, carex$ovales.data, carex$ovales.nodes[[2]])
  # simulates the evolution of the chromosome number under the model-averaged values
  trialSim <- ouSim(trialBayesConsensus, contree) 
  # plots the character simulation, with all branches black
  plot(trialSim) 
  # plots the character simulation, with branch colors changing at all 8 nodes
  plot(trialSim, colors = paintBranches(carex$ovales.nodes[1:4], contree)) 
  # plots the character simulation, with branch colors changing only at node 2
  plot(trialSim, colors = paintBranches(list(carex$ovales.nodes[[2]]), contree)) 

maticce documentation built on May 2, 2019, 6:13 p.m.