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SuperTree.html
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<div id="dating-a-super-tree-using-the-fbd-model"
class="section level1">
<h1>Dating a Super Tree using the FBD model</h1>
<div id="thanks-to-david-černý-for-his-help-developing-this-code."
class="section level6">
<h6>Thanks to <strong>David Černý</strong> for his help developing this
code.</h6>
<p>In paleobiology there are often situations where we want to date a
supertree, or any tree for which we do not have any character data
associated with taxa at the tips. This is commonly carried out in
MrBayes, though it is also possible to do in RevBayes.The following
tutorial provides sample code for dating a super tree of Theropod
dinosaurs in Revbayes using the Fossilized Birth death model. The data
is from <em>Tasimov et al. (in prep)</em> and is of 66 taxa with ages
ranging from Middle Jurassic to Late Cretaceous.</p>
<p>Note: This tutorial uses both R and RevBayes for steps of the
inference. RevBayes is under active development and will eventually
provide a approach to carry out the full analysis. Coming soon-ish……</p>
</div>
<div id="organise-you-code" class="section level2">
<h2>Organise you code</h2>
<p>As with any analysis it is important to set up you directories
correctly. You should have a scripts and a data folder. The trees and
fossils ages can be downloaded <a href="data/dino_data.zip">here</a></p>
</div>
<div id="time-scaling-the-tree-in-r" class="section level2">
<h2>1. Time-scaling the tree in R</h2>
<p>For this inference Revbayes requires a time-scaled tree as the input.
We can carry out this time-scaling in R studios. The exact method of
time scaling used here <em>should not</em> have an impact on the over
all inference results. We can use the R package <code>paleotree</code>
to do this. Additionally, if your tree has polytomies we need to provide
RevBayes with a fully resolved tree as the initial tree and the original
tree is then supplied as the so called backbone tree. In this way the
polytomies can be resolved by leveraging the temporal information during
the FBD analysis.</p>
<p>Load relevant r packages</p>
<pre class="r"><code>library(paleotree)
library(phytools)
library(phangorn)
library(ape)</code></pre>
<p>We first need to read in our age data and create a matrix of ages
required for the time scaling method</p>
<pre class="r"><code>taxa_ages <- read.table("data/ages.csv", header = T)
rownames(utaxa_ages) <- taxa_ages[,1]
dinoRanges <- taxa_ages[,2:3]</code></pre>
<p>We can read in our tree</p>
<pre class="r"><code>phy <- ape::read.tree("data/dino.tree")</code></pre>
<p>We then borrow a function from Liam Revell’s <a
href="http://blog.phytools.org/2017/06/generating-set-of-random-resolutions-of.html">website</a>
for randomly resolving polytomies</p>
<pre class="r"><code>resolveRandom<-function(tree){
while(!is.binary(tree)){
nodes<-1:tree$Nnode+Ntip(tree)
Nchildren<-function(node,tree) length(Children(tree,node))
nchilds<-sapply(nodes,Nchildren,tree=tree)
node<-nodes[which(nchilds>2)[1]]
tree<-sample(resolveNode(tree,node),1)[[1]]
}
tree
}
Binary_tree <- resolveRandom(phy)</code></pre>
<p>Using the function <code>timePaleoPhy</code> in the PaleoTree package
we can estimate branch length with units of time for our trees (the
original tree and the fully resolved, or binary, tree). The
<code>type</code> variable here refers to the time scaling appraoch
used. We are using “mbl” which ensures that there are no zero branch
lengths, which is a common result when using the “basic” method.</p>
<pre class="r"><code>tt_polytomy <- paleotree::timePaleoPhy(tree = phy, timeData = dinoRanges, type = "mbl", randres = FALSE,
ntrees = 1, plot= T, vartime = 2)
tt_binary <- paleotree::timePaleoPhy(tree = Binary_tree, timeData = dinoRanges, type = "mbl", randres = FALSE,
ntrees = 1, plot = T, vartime = 2)</code></pre>
<p>Now we can write the trees to file to use them for our FBD
inference</p>
<pre class="r"><code>write.tree(tt_polytomy,"data/dated_backbone.tre")
write.tree(tt_binary,"data/dated_initial.tre")</code></pre>
</div>
<div id="fbd-inference-in-revbayes" class="section level2">
<h2>2. FBD inference in RevBayes</h2>
<p>Below I will provide the code for estimating divergence times of a
super tree using the FBD model. It is important to note that this code
works by <strong>fixing the fossil ages</strong>. This has been shown to
be problematic, see <a
href="https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2020.00183/full">Barido-Sottani
et al 2020</a>, however is currently the only practical implementation.
The new release of RevBayes will allow you to estimate these ages, so
keep an eye out for that!</p>
<p>As with any analysis it is helpful to give it a name</p>
<pre class="r"><code>analysis_name = "Palss_2024"</code></pre>
<p>We can then read in our age information</p>
<pre class="r"><code>### read in the age infromation
taxa <- readTaxonData("data/ages.csv")</code></pre>
<p>Read in our backbone tree (i.e., our original tree with
polytomies)</p>
<pre class="r"><code>## read in the backbone tree - the one with ploytomies
bb <- readTrees("data/dated_backbone.tre")[1]</code></pre>
<p>As there is no explicit information in our newick string about the
age of the tree we need to offet it by the age of the youngest
fossil</p>
<pre class="r"><code>## offset the ages by the age of the youngest tip
bb.offset(98)</code></pre>
<p>We can do the exact same for the starting tree</p>
<pre class="r"><code>init <- readTrees("data/dated_initial.tre")[1]
init.offset(98)</code></pre>
<p>We initialize vectors for our moves and monitors</p>
<pre class="r"><code>moves = VectorMoves()
monitors = VectorMonitors()</code></pre>
<p>We can then set the priors on our FBD paramters: speciation,
extinction, and sampling</p>
<pre class="r"><code>speciation_rate ~ dnExponential(10)
extinction_rate ~ dnExponential(10)
moves.append( mvScale(speciation_rate, lambda=0.01, weight=1) )
moves.append( mvScale(speciation_rate, lambda=0.1, weight=1) )
moves.append( mvScale(speciation_rate, lambda=1.0, weight=1) )
moves.append( mvScale(extinction_rate, lambda=0.01, weight=1) )
moves.append( mvScale(extinction_rate, lambda=0.1, weight=1) )
moves.append( mvScale(extinction_rate, lambda=1, weight=1) )
psi ~ dnExponential(10)
moves.append( mvScale(psi, lambda=0.01, weight=1) )
moves.append( mvScale(psi, lambda=0.1, weight=1) )
moves.append( mvScale(psi, lambda=1, weight=1) )</code></pre>
<p>We can keep track of parameters of interest during the analysis</p>
<pre class="r"><code>diversification := speciation_rate - extinction_rate
turnover := extinction_rate/speciation_rate</code></pre>
<p>As our tree is fully extinct we can set rho, the probability of
extant species sampling, to zero</p>
<pre class="r"><code>rho <- 0</code></pre>
<p>The origin age is set using a uniform prior on the start of the
Triassic and the oldest fossil</p>
<pre class="r"><code>origin_age ~ dnUniform(170.9, 251)
moves.append( mvSlide(origin_age, delta=0.1, weight=5) )</code></pre>
<p>Having defined all our parameters we can create our FBD object</p>
<pre class="r"><code>fbd_dist = dnFBDP(originAge=origin_age, lambda=speciation_rate, mu=extinction_rate, psi=psi, rho=rho,taxa=taxa)</code></pre>
<p>The FBD tree is constrained using both of the time trees</p>
<pre class="r"><code>fbd_tree ~ dnConstrainedTopology(fbd_dist, backbone=bb, initialTree=init)
moves.append( mvFNPR(fbd_tree, weight=15.0) )
moves.append( mvCollapseExpandFossilBranch(fbd_tree, origin_age, weight=6.0) )
moves.append( mvNodeTimeSlideUniform(fbd_tree, weight=40.0) )</code></pre>
<p>Record the number of sampled ancestors</p>
<pre class="r"><code>num_samp_anc := fbd_tree.numSampledAncestors()</code></pre>
<p>Define our model</p>
<pre class="r"><code>mymodel = model(fbd_tree)</code></pre>
<p>Define some monitors</p>
<pre class="r"><code># 1. for the full model #
monitors.append( mnModel(filename="output/" + analysis_name + "_dino.log", printgen=10, exclude = ["F"]) )
# 2. the tree #
monitors.append( mnFile(filename="output/" + analysis_name + "_dino.trees", printgen=10, fbd_tree) )
# 3. and a few select parameters to be printed to the screen #
monitors.append( mnScreen(printgen=1000, origin_age) )</code></pre>
<p>Finally, initialise and run our inference</p>
<pre class="r"><code># Initialize the MCMC object #
mymcmc = mcmc(mymodel, monitors, moves, nruns=2, combine="mixed")
mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.run(generations=10000)</code></pre>
<p>Create a summary tree</p>
<pre class="r"><code>trace = readTreeTrace("output/" + analysis_name + "_dino.trees")
mccTree(trace,"output/" + analysis_name + "_dino_mcc.tre", positiveBranchLengths = TRUE)</code></pre>
<p>Note: this will not converge! The chain needs to be run for a lot
longer, but that is ok for the purposes of this tutorial.</p>
</div>
<div id="tree-visualization-in-r" class="section level2">
<h2>3. Tree visualization in R</h2>
<p>We can plot our time tree in R using the R package
<code>RevGadgets</code>. This R package provides lots of cool ways for
processing the output of a phylogentic inference in RevBayes, for more
information <a
href="https://revbayes.github.io/tutorials/intro/revgadgets.html">see</a></p>
<pre class="r"><code>library(RevGadgets)
file <- "output/Palss_2024_dino_mcc.tre"
FBD_tree <- RevGadgets::readTrees(paths = file)</code></pre>
<p>As before we need to include our offset, so we add 98 to the
posterior of tree</p>
<pre class="r"><code>FBD_tree[[1]][[1]]@data[["age_0.95_HPD"]]<-
lapply(FBD_tree[[1]][[1]]@data[["age_0.95_HPD"]], function(x) x + 98)</code></pre>
<p>Create our plot</p>
<pre class="r"><code>p <- plotFBDTree(tree = FBD_tree,
timeline = T,
geo_units = "epochs",
tip_labels_italics = T,
tip_labels_remove_underscore = T,
tip_labels_size = 1,
tip_age_bars = T,
node_age_bars = T,
age_bars_color = "blue",
label_sampled_ancs = TRUE,
line_width = 0.5,
age_bars_width = 0.5)</code></pre>
<p>Add our offset to the tree object and plot</p>
<pre class="r"><code>p$data$x <- p$data$x - 98
p</code></pre>
<p>You should end up with a tree like this:</p>
<p><img src="dino.png" width="50%" style="padding:5px" /></p>
<p>You can play with the plotting function to make this tree look nicer!
Download the complete scripts for this tutorial <a
href="scripts/dino.zip">here</a></p>
</div>
<div id="skyline-fbd" class="section level2">
<h2>4. Skyline FBD</h2>
<p>We can relax the assumption of constant speciation, extinction, and
fossil sampling in an FBD analysis by using a skyline model. This model
allows rates to vary in a piece-wise manner across different geological
intervals. Essentially, creating time-bins as in common with many
paleontological methods. To do this you need to define your timeline and
create a vector of your rates for each bin.</p>
<p>We can edit the existing FBD.rev file as as follows (copy the FBD.rev
file and rename it).</p>
<p>Update the name of the analysis.</p>
<pre class="r"><code>analysis_name = "Palss_2024_skyline"</code></pre>
<p>Define the time line specifying the intervals. We will set four time
bins here, representing the middle Jurassic, Late Jurassic, Early
Cretaceous, and Late Cretaceous.</p>
<pre class="r"><code>timeline <- v(100.5, 145, 163.5)</code></pre>
<p>You can remove how we defined speciation, extinction and fossil
sampling from this new skyline analysis. We can then loop through the
different time bins to estimate rates for each of them.</p>
<pre class="r"><code>alpha <- 10
for(i in 1:(timeline.size()+1))
{
extinction_rate[i] ~ dnExp(alpha)
speciation_rate[i] ~ dnExp(alpha)
psi[i] ~ dnExp(alpha)
diversification[i] := speciation_rate[i] - extinction_rate[i]
turnover[i] := extinction_rate[i]/speciation_rate[i]
moves.append( mvScale(extinction_rate[i], lambda = 0.01) )
moves.append( mvScale(extinction_rate[i], lambda = 0.1) )
moves.append( mvScale(extinction_rate[i], lambda = 1) )
moves.append( mvScale(speciation_rate[i], lambda = 0.01) )
moves.append( mvScale(speciation_rate[i], lambda = 0.1) )
moves.append( mvScale(speciation_rate[i], lambda = 1) )
moves.append( mvScale(psi[i], lambda = 0.01) )
moves.append( mvScale(psi[i], lambda = 0.1) )
moves.append( mvScale(psi[i], lambda = 1) )
}</code></pre>
<p>Our FBD model then uses a vector of values for these rates</p>
<pre class="r"><code>fbd_dist = dnFBDP(originAge=origin_age, lambda=speciation_rate, mu=extinction_rate, psi=psi, rho=rho, taxa=taxa, timeline=timeline)</code></pre>
<blockquote>
<p>Nothing else in the script needs to be changed for this analysis.
Here we have all three parameters varying, however, we could choose to
fix one, for example fossil sampling, and then just allows speciation
and extinction to vary though time.</p>
</blockquote>
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