This tutorial describes how to specify basic branching-process models in RevBayes; two variants of the constant-rate birth-death process (Yule 1925; Kendall 1948; Thompson 1975; Nee et al. 1994; Rannala and Yang 1996; Yang and Rannala 1997; Höhna 2015). The probabilistic graphical model is given for each component of this tutorial. After each model is specified, you will estimate speciation and extinction rates using Markov chain Monte Carlo (MCMC). Finally, you will estimate the marginal likelihood of the model and evaluate the relative support using Bayes factors.
You should read first the Introduction to Diversification Rate Estimation tutorial, which explains the theory and gives some general overview of diversification rate estimation.
Before evaluating the relative support for different models, we must
first specify them in
Rev. In this section, we will walk through
specifying a pure-birth process model and estimating the marginal
likelihood. The section about the birth-death process will be less
detailed because it will build up on this section.
The simplest branching model is the pure-birth process described by Yule (1925). Under this model, we assume at any instant in time, every lineage has the same speciation rate, $\lambda$. In its simplest form, the speciation rate remains constant over time. As a result, the waiting time between speciation events is exponential, where the rate of the exponential distribution is the product of the number of extant lineages ($n$) at that time and the speciation rate: $n\lambda$ (Yule 1925; Aldous 2001; Höhna 2014). The pure-birth branching model does not allow for lineage extinction (i.e., the extinction rate $\mu=0$). However, the model depends on a second parameter, $\rho$, which is the probability of sampling a species in the present time. It also depends on the time of the start of the process, whether that is the origin time or root age. Therefore, the probabilistic graphical model of the pure-birth process is quite simple, where the observed time tree topology and node ages are conditional on the speciation rate, sampling probability, and root age ().
We can add hierarchical structure to this model and account for uncertainty in the value of the speciation rate by placing a hyperprior on $\lambda$ (). The graphical models in and demonstrate the simplicity of the Yule model. Ultimately, the pure birth model is just a special case of the birth-death process, where the extinction rate (typically denoted $\mu$) is a constant node with the value 0.
For this exercise, we will specify a Yule model, such that the speciation rate is a stochastic node, drawn from a lognormal distribution as in . In a Bayesian framework, we are interested in estimating the posterior probability of $\lambda$ given that we observe a time tree.
In this example, we have a phylogeny of 233 primates. We are treating the time tree $\Psi$ as an observation, thus clamping the model with an observed value. The time tree we are conditioning the process on is taken from the analysis by Magnuson-Ford and Otto (2012). Furthermore, there are approximately 367 described primates species, so we will fix the parameter $\rho$ to $233/367$.
⇨ The full Yule-model specification is in the file called
Begin by reading in the observed tree.
T <- readTrees("data/primates_tree.nex")
From this tree, we can get some helpful variables:
taxa <- T.taxa()
Additionally, we can initialize a variable for our vector of moves and monitors:
moves = VectorMoves() monitors = VectorMonitors()
The model we are specifying only has three nodes
(). We can specify the birth rate $\lambda$, the
mean and standard deviation of the lognormal hyperprior on $\lambda$,
and the conditional dependency of the two parameters all in one line of
birth_rate_mean <- ln( ln(367/2) / T.rootAge() ) birth_rate_sd <- 0.587405 birth_rate ~ dnLognormal(mean=birth_rate_mean,sd=birth_rate_sd)
Here, the stochastic node called
birth_rate represents the speciation
birth_rate_sd are the prior
mean and prior standard deviation, respectively. We chose the prior mean
so that it is centered around observed number of species
(i.e., the expected number of species under a
Yule process will thus be equal to the observed number of species) and a
prior standard deviation of 0.587405 which creates a lognormal
distribution with 95% prior probability spanning exactly one order of
magnitude. If you want to represent more prior uncertainty by,
e.g., allowing for two orders of magnitude in
the 95% prior probability then you can simply multiply
by a factor of 2.
To estimate the value of $\lambda$, we assign a proposal mechanism to
operate on this node. In RevBayes these MCMC sampling algorithms are
called moves. We need to create a vector of moves and we can do this
by using vector indexing and our pre-initialized iterator
mi. We will
use a scaling move on $\lambda$ called
moves.append( mvScale(birth_rate,lambda=1,tune=true,weight=3) )
Our prior belief is that we have sampled 233 out of 367 living primate species. To account for this we can set the sampling parameter as a constant node with a value of 233/367
rho <- T.ntips()/367
Any stochastic branching process must be conditioned on a time that represents the start of the process. Typically, this parameter is the origin time and it is assumed that the process started with one lineage. Thus, the origin of a birth-death process is the node that is ancestral to the root node of the tree. For macroevolutionary data, particularly without any sampled fossils, it is difficult to use the origin time. To accommodate this, we can condition on the age of the root by assuming the process started with two lineages that both originate at the time of the root.
We can get the value for the root from the Magnuson-Ford and Otto (2012) tree.
root_time <- T.rootAge()
Now we have all of the parameters we need to specify the full pure-birth
model. We can initialize the stochastic node representing the time tree.
Note that we set the
mu parameter to the constant value
timetree ~ dnBDP(lambda=birth_rate, mu=0.0, rho=rho, rootAge=root_time, samplingStrategy="uniform", condition="survival", taxa=taxa)
Here we are fixing the value of the time tree to our observed tree from Magnuson-Ford and Otto (2012).
Finally, we can create a workspace object of our whole model using the
model() function. Workspace objects are initialized using the
operator. This distinguishes the objects used by the program to run the
MCMC analysis from the distinct nodes of our graphical model. The model
workspace objects makes it easy to work with the model in
creates a wrapper around our model DAG. Because our model is a directed,
acyclic graph (DAG), we only need to give the model wrapper function a
single node and it does the work to find all the other nodes through
mymodel = model(birth_rate)
model() function traverses all of the connections and finds all of
the nodes we specified.
For our MCMC analysis, we need to set up a vector of monitors to
record the states of our Markov chain. The monitor functions are all
\* is the wildcard representing the monitor type.
First, we will initialize the model monitor using the
function. This creates a new monitor variable that will output the
states for all model parameters when passed into a MCMC function.
monitors.append( mnModel(filename="output/primates_Yule.log",printgen=10, separator = TAB) )
Additionally, create a screen monitor that will report the states of
specified variables to the screen with
monitors.append( mnScreen(printgen=1000, birth_rate) )
With a fully specified model, a set of monitors, and a set of moves, we
can now set up the MCMC algorithm that will sample parameter values in
proportion to their posterior probability. The
mcmc() function will
create our MCMC object:
mymcmc = mcmc(mymodel, monitors, moves)
We may wish to run the
.burnin() member function,
i.e., if we wish to pre-run the chain and
discard the initial states. Recall that the
specifies a completely separate preliminary MCMC analysis that is used
to tune the scale of the moves to improve mixing of the MCMC analysis.
Now, run the MCMC:
When the analysis is complete, you will have the monitored files in your output directory.
Rev file for performing this analysis:
Tracer: What is the mean posterior estimate of the
birth_rateand what is the estimated HPD?
underPrior=TRUEin the function
mymcmc.run()) Are they different (e.g., )? Is the posterior mean outside the prior 95% probability interval?
With a fully specified model, we can set up the
analysis to create a file of ‘powers’ and likelihoods from which we can
estimate the marginal likelihood using stepping-stone or path sampling.
This method computes a vector of powers from a beta distribution, then
executes an MCMC run for each power step while raising the likelihood to
that power. In this implementation, the vector of powers starts with 1,
sampling the likelihood close to the posterior and incrementally
sampling closer and closer to the prior as the power decreases. For more
information on marginal likelihood estimation please read the
Bayesian Model Selection Tutorial
First, we create the variable containing the power posterior. This
requires us to provide a model and vector of moves, as well as an output
file name. The
cats argument sets the number of power steps.
pow_p = powerPosterior(mymodel, moves, monitors, "output/Yule_powp.out", cats=127, sampleFreq=10)
We can start the power posterior by first burning in the chain and and discarding the first 10000 states.
Now execute the run with the
Once the power posteriors have been saved to file, create a stepping-stone sampler. This function can read any file of power posteriors and compute the marginal likelihood using stepping-stone sampling.
ss = steppingStoneSampler(file="output/Yule_powp.out", powerColumnName="power", likelihoodColumnName="likelihood")
write("Stepping stone marginal likelihood:\t", ss.marginal() )
Path sampling is an alternative to stepping-stone sampling and also takes the same power posteriors as input.
ps = pathSampler(file="output/Yule_powp.out", powerColumnName="power", likelihoodColumnName="likelihood")
write("Path-sampling marginal likelihood:\t", ps.marginal() )
Rev file for performing this analysis:
|Marginal likelihood Yule ($M_0$)|
|Marginal likelihood birth-death ($M_1$)|
The pure-birth model does not account for extinction, thus it assumes that every lineage at the start of the process will have sampled descendants at time 0. This assumption is fairly unrealistic for most phylogenetic datasets on a macroevolutionary time scale since the fossil record provides evidence of extinct lineages. Kendall (1948) described a more general branching process model to account for lineage extinction called the birth-death process. Under this model, at any instant in time, every lineage has the same rate of speciation $\lambda$ and the same rate of extinction $\mu$. This is the constant-rate birth-death process, which considers the rates constant over time and over the tree (Nee et al. 1994; Höhna 2015).
Yang and Rannala (1997) derived the probability of time trees under an extension of the birth-death model that accounts for incomplete sampling of the tips () (see also Stadler (2009) and Höhna (2014)). Under this model, the parameter $\rho$ accounts for the probability of sampling in the present time, and because it is a probability, this parameter can only take values between 0 and 1.
In principle, we can specify a model with prior distributions on speciation and extinction rates directly. One possibility is to specify an exponential, lognormal, or gamma distribution as the prior on either rate parameter. However, it is more common to specify prior distributions on a transformation of the speciation and extinction rate because, for example, we want to enforce that the speciation rate is always larger than the extinction rate.
In the following subsections we will only provide the key command that
are different for the constant-rate birth-death process. All other
commands will be the same as in the previous exercise. You should copy
mcmc_Yule.Rev script and modify it accordingly. Don’t forget to
rename the filenames of the monitors to avoid overwriting of your
We have some good prior information about the magnitude of the diversification. The diversification rate represent the rate at which the species diversity increases. Thus, we just use the same prior for the diversification rate as we used before for the birth rate.
diversification_mean <- ln( ln(367.0/2.0) / T.rootAge() ) diversification_sd <- 0.587405 diversification ~ dnLognormal(mean=diversification_mean,sd=diversification_sd) moves.append( mvScale(diversification,lambda=1.0,tune=true,weight=3.0) )
Unfortunately, we have less prior information about the turnover rate. The turnover rate is the rate at which one species is replaced by another species due to a birth plus death event. Hence, the turnover rate represent the longevity of a species. For simplicity we use the same prior on the turnover rate but with two orders of magnitude prior uncertainty.
turnover_mean <- ln( ln(367.0/2.0) / T.rootAge() ) turnover_sd <- 0.587405*2 turnover ~ dnLognormal(mean=turnover_mean,sd=turnover_sd) moves.append( mvScale(turnover,lambda=1.0,tune=true,weight=3.0) )
The birth and death rates are both deterministic nodes. We compute them by simple parameter transformation. Note that the death rate is in fact equal to the turnover rate.
birth_rate := diversification + turnover death_rate := turnover
All other parameters, such as the sampling probability and the root age are kept the same as in the analysis above.
Initialize the stochastic node representing the time tree. The main difference now is that we provide a stochastic parameter for the extinction rate $\mu$.
timetree ~ dnBDP(lambda=birth_rate, mu=death_rate, rho=rho, rootAge=root_time, samplingStrategy="uniform", condition="survival", taxa=taxa)
Rev file for performing this analysis:
Tracer. What can you say about the diversification, turnover, speciation and extinction rates? How high is the extinction rate compared with the speciation rate?