timePaleoPhy: Timescaling of Paleo-Phylogenies

Description

Timescales an unscaled cladogram of fossil taxa using information on their temporal ranges, using various methods. Also can resolve polytomies randomly and output samples of randomly-resolved trees.

Usage

timePaleoPhy(tree, timeData, type = "basic", vartime = NULL, ntrees = 1, randres = FALSE, 
    add.term = FALSE, rand.obs = FALSE, node.mins = NULL, plot = FALSE)

bin_timePaleoPhy(tree, timeList, type = "basic", vartime = NULL, ntrees = 1,  nonstoch.bin=FALSE, 
    randres = FALSE, sites = NULL,  add.term = FALSE, rand.obs = FALSE, node.mins = NULL, plot = FALSE)

Arguments

tree

An unscaled cladogram of fossil taxa

timeData

Two-column matrix of first and last occurrances in absolute continous time, with rownames as the taxon IDs used on the tree

type

Type of time-scaling method used. Can be "basic", "equal", "aba", "zbla" or "mbl". Type="basic" by default. See the note below for more details

vartime

Time variable; usage depends on the method 'type' argument. Ignored if type = "basic"

ntrees

Number of time-scaled trees to output. If ntrees is greater than one and both randres and rand.obs are false, the function will fail and a warning is issued, as these arguments would simply produce multiple identical time-scaled trees.

randres

Should polytomies be randomly resolved? By default, timePaleoPhy does not resolve polytomies, instead outputting a time-scaled tree that is only as resolved as the input tree. If randres=T, then polytomies will be randomly resolved using multi2di() from t

add.term

If true, adds terminal ranges.By default, this function will not add the ranges of taxa when time-scaling a tree, so that the tips correspond temporally to the first appearance datums of the given taxa. If add.term=T, then the 'terminal ranges' of the tax

rand.obs

Should the tips represent observation times uniform distributed within taxon ranges? If rand.obs=T but add.term=F, the function fails and a warning is issued. If rand.obs=TRUE, tips are placed randomly within taxon ranges, as if uniformly distributed. Thi

node.mins

Minimum ages of nodes on the tree. The minimum dates of nodes can be set using node.mins; this argument takes a vector of the same length as the number of nodes, with dates given in the same order as nodes are they are numbered in the tree$edge matrix (no

plot

If true, plots the input and output phylogenies

timeList

A list composed of two matrices giving interval times and taxon appearance datums, as would be output by binTimeData. The rownames of the second matrix should be the taxon IDs

nonstoch.bin

If true, dates are not stochastically pulled from uniform distributions. See below for more details.

sites

Optional two column matrix, composed of site IDs for taxon FADs and LADs. The sites argument allows users to constrain the placement of dates in bin_timePaleoPhy by restricting multiple fossil taxa whose FADs or LADs are from the same very temporally rest

Value

The output of these functions is a time-scaled tree or set of time-scaled trees, of either class phylo or multiphylo, depending on the argument ntrees. All trees are output with an element $root.time. This is the time of the root on the tree and is important for comparing patterns across trees.

Details

This function is an attempt to unify and collect previously used and discussed methods for time-scaling phylogenies of fossil taxa. Unfortunately, it is difficult to attribute some time-scaling methods to specific references in the literature. A review of these time-scaling methods is forthcoming (Bapst, in prep). There are five method types that can be used by timePaleoPhy. Four of these use some value of absolute time, chosen a priori, to time-scale the tree. This is handled by the argument vartime, which is NULL by default and unused for type "basic". [object Object],[object Object],[object Object],[object Object],[object Object] These functions cannot time-scale branches relative to reconstructed character changes along branches, as used by Lloyd et al. (2012). These functions will intuitively drop taxa from the tree with NA for range or are missing from timeData or timeList. As with many functions in the paleotree library, absolute time is always decreasing, i.e. the present day is zero. timePaleoPhy can only be applied to datasets where taxon appearances are in continuous time. bin_timePaleoPhy is a wrapper of timePaleoPhy which produces timescaled trees for datasets which only have interval data available. For each output tree, taxon FADs and LADs are placed within their listed intervals under a uniform distribution. Thus, a large sample of time-scaled trees will approximate the uncertainty in the actual timing of the FADs and LADs. By setting the argument nonstoch.bin to TRUE, the dates are NOT stochastically pulled from uniform bins but instead FADs are assigned to the earliest time of whichever interval they were placed in and LADs are placed at the most recent time in their placed interval. This option may be useful for plotting. The sites argument becomes arbitrary if nonstoch.bin is TRUE.

References

Bapst, in prep. Time-scaling Trees of Fossil Taxa. To be submitted to Paleobiology Brusatte, S. L., M. J. Benton, M. Ruta, and G. T. Lloyd. 2008 Superiority, Competition, and Opportunism in the Evolutionary Radiation of Dinosaurs. Science 321(5895):1485-91488. Hunt, G., and M. T. Carrano. 2010 Models and methods for analyzing phenotypic evolution in lineages and clades. In J. Alroy, and G. Hunt, eds. Short Course on Quantitative Methods in Paleobiology. Paleontological Society. Lloyd, G. T., S. C. Wang, and S. L. Brusatte. 2012 Identifying Heterogeneity in Rates of Morphological Evolutio: Discrete Character Change in the Evolution of Lungfish(Sarcopterygii, Dipnoi). Evolution 66(2):330--348. Smith, A. B. 1994 Systematics and the fossil record: documenting evolutionary patterns. Blackwell Scientific, Oxford.

Examples

Run this code

#Simulate some fossil ranges with simFossilTaxa
set.seed(444)
taxa <- simFossilTaxa(p=0.1,q=0.1,nruns=1,mintaxa=20,maxtaxa=30,maxtime=1000,maxExtant=0)
#simulate a fossil record with imperfect sampling with sampleRanges
rangesCont <- sampleRanges(taxa,r=0.5)
#let's use taxa2cladogram to get the 'ideal' cladogram of the taxa
cladogram <- taxa2cladogram(taxa,plot=TRUE)
#Now let's try timePaleoPhy using the continuous range data
ttree <- timePaleoPhy(cladogram,rangesCont,type="basic",plot=TRUE)
#plot diversity curve 
phyloDiv(ttree)

#that tree lacked the terminal parts of ranges (tips stops at the taxon FADs)
#let's add those terminal ranges back on with add.term
ttree <- timePaleoPhy(cladogram,rangesCont,type="basic",add.term=TRUE,plot=TRUE)
#plot diversity curve 
phyloDiv(ttree)

#that tree didn't look very resolved, does it? (See Wagner and Erwin 1995 to see why)
#can randomly resolve trees using the argument randres
#each resulting tree will have polytomies randomly resolved in different ways using multi2di
ttree <- timePaleoPhy(cladogram,rangesCont,type="basic",ntrees=1,randres=TRUE,add.term=TRUE,plot=TRUE)
#notice well the warning it prints!
#now let's plot the first tree (both trees will be identical because we used set.seed)
phyloDiv(ttree)
#we would need to set ntrees to a large number to get a fair sample of trees

#if we set ntrees>1, timePaleoPhy will make multiple time-trees
ttrees <- timePaleoPhy(cladogram,rangesCont,type="basic",ntrees=9,randres=TRUE,add.term=TRUE,plot=TRUE)
#let's compare nine of them at once in a plot
layout(matrix(1:9,3,3))
for(i in 1:9){plot(ladderize(ttrees[[i]]),show.tip.label=FALSE,no.margin=TRUE)}
#they are all a bit different!

#can plot the median diversity curve with multiDiv
layout(1)
multiDiv(ttrees)

#compare different methods of timePaleoPhy
layout(matrix(1:6,3,2));parOrig <- par(mar=c(3,2,1,2))
plot(ladderize(timePaleoPhy(cladogram,rangesCont,type="basic",vartime=NULL,add.term=TRUE)))
    axisPhylo();text(x=50,y=23,"type=basic",adj=c(0,0.5),cex=1.2)
plot(ladderize(timePaleoPhy(cladogram,rangesCont,type="equal",vartime=10,add.term=TRUE)))
    axisPhylo();text(x=55,y=23,"type=equal",adj=c(0,0.5),cex=1.2)
plot(ladderize(timePaleoPhy(cladogram,rangesCont,type="aba",vartime=1,add.term=TRUE)))
    axisPhylo();text(x=55,y=23,"type=aba",adj=c(0,0.5),cex=1.2)
plot(ladderize(timePaleoPhy(cladogram,rangesCont,type="zlba",vartime=1,add.term=TRUE)))
    axisPhylo();text(x=55,y=23,"type=zlba",adj=c(0,0.5),cex=1.2)
plot(ladderize(timePaleoPhy(cladogram,rangesCont,type="mbl",vartime=1,add.term=TRUE)))
    axisPhylo();text(x=55,y=23,"type=mbl",adj=c(0,0.5),cex=1.2)
layout(1);par(parOrig)

#using node.mins
#let's say we have (molecular??) evidence that node #5 is at least 1200 time-units ago
nodeDates <- rep(NA,(Nnode(cladogram)-1))
nodeDates[5] <- 1200
ttree1 <- timePaleoPhy(cladogram,rangesCont,type="basic",
	randres=FALSE,node.mins=nodeDates,plot=TRUE)
ttree2 <- timePaleoPhy(cladogram,rangesCont,type="basic",
	randres=TRUE,node.mins=nodeDates,plot=TRUE)

#Using bin_timePaleoPhy to timescale with discrete interval data
#first let's use binTimeData() to bin in intervals of 1 time unit
rangesDisc <- binTimeData(rangesCont,int.length=1)
ttreeB1 <- bin_timePaleoPhy(cladogram,rangesDisc,type="basic",ntrees=1,randres=TRUE,add.term=TRUE,plot=FALSE)
#notice the warning it prints!
phyloDiv(ttreeB1)
#can also force the appearance timings not to be chosen stochastically
ttreeB2 <- bin_timePaleoPhy(cladogram,rangesDisc,type="basic",ntrees=1,nonstoch.bin=TRUE,randres=TRUE,add.term=TRUE,plot=FALSE)
phyloDiv(ttreeB2)

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