Background

The fossilized birth-death (FBD) process is a phylodynamic model that incorporates sampling through time and allows us infer trees with sampled ancestors (Stadler, 2010; Heath et al., 2014). This is important for phylogenies that incorporate fossils. In the original formulation of the model, the birth (speciation), death (extinction), and fossil sampling rates were treated as constant over time and across the tree. Since then, many model extentions have been published that relax the asummption of constant rates or uniform sampling (see (Mulvey et al., 2025) for an overview). The BEAST2 package BDMM-Prime (Vaughan & Stadler, 2025) incorporates several of these new models, including those that allow rates to vary across time or across different parts of the tree (Gavryushkina et al., 2014; Kühnert et al., 2016; Scire et al., 2022).

This tutorial will provide an introduction of how the BDMM-Prime package can be applied in macroevolution. For an example in epidemiology see the BDMM-Prime package website.

Pre-requisits

This tutorial assumes that you have already gained some experience using BEAST2. If you have not already done so, we recommend first completing the following tutorials:

Programs used in this exercise

BEAST2 - Bayesian Evolutionary Analysis Sampling Trees 2

BEAST2 is a free software package for Bayesian evolutionary analysis of molecular sequences using MCMC and strictly oriented toward inference using rooted, time-measured phylogenetic trees (Bouckaert et al., 2014). This tutorial uses BEAST v2.7.7.

BEAUti - Bayesian Evolutionary Analysis Utility

BEAUti2 is a graphical user interface tool for generating BEAST2 XML configuration files.

Both BEAST2 and BEAUti2 are Java programs, which means that the exact same code runs, and the interface will be the same, on all computing platforms. The screenshots used in this tutorial are taken on a Mac OS X computer; however, both programs will have the same layout and functionality on both Windows and Linux. BEAUti2 is provided as a part of the BEAST2 package so you do not need to install it separately.

Any programmer-friendly text editor

We will need to view our data input files to understand their format, for which we’ll need a text editor. It’s best to use one designed for programmers as these include nice features such as syntax highlighting, which makes the code more reader-friendly. Sublime Text is a good option which is available for MacOS, Windows and Linux.

Tracer

Tracer is used to summarise the posterior estimates of the various parameters sampled by the Markov Chain. This program can be used for visual inspection and to assess convergence. It helps to quickly view median estimates and 95% highest posterior density intervals of the parameters, and calculates the effective sample sizes (ESS) of parameters. It can also be used to investigate potential parameter correlations. We will be using Tracer v.

TreeAnnotator

TreeAnnotator is used to summarise the posterior sample of trees to produce a maximum clade credibility tree and summarize the posterior estimates of other parameters that can be easily visualized on the tree (e.g., node height). This program is also useful for comparing a specific tree topology and branching times to the set of trees sampled in the MCMC analysis.

Practical: BDMM-Prime for Macroevolution

BDMM-Prime is a package written for flexible phylodynamic inference. It combines the functionality of multiple other BEAST2 packages, including the BDMM, BDSKY, and SA packages, and can be applied to a wide range of scenarios in epidemiology and macroevolution. Due to the flexibility and the large variety of ways in which model parameters can be combined using BDMM-Prime there’s more options to consider when setting up your analysis.

The data

We’ll use a published dataset of amniotes that has been used previously to explore the early evolution of this group (Bouckaert et al., 2014).

The data comprises 294 binary and multi-state characters for 70 fossils, as well as their age and locality information, spanning ~70 myr from the late Carboniferous to the mid Triassic. The specimens can be categorised into three broad geographic areas associated with the northern, mid, and southern areas of the supercontinent Pangea, corresponding approximately to what would be known today as Eurasia, North America, and South Africa.

Download the data file amniotes.nex and open it in a text editor of your choice.

Figure 1: The nexus file containing the morphological data.

Note that the taxon labels contain information about the age of our samples (after the last underscore) and their geographic provenance (after the second underscore).

Installing the BDMM-Prime and MM packages

The first thing we need to do is install the BDMM-Prime package. This package is not yet listed in the Package Manager in BEAUTi, so we need to install it using the package URL.

Open BEAUTi and go to File > Manage Packages.

Select Package repositories at the bottom of this window. This will open the Package Repository Manager.

At the bottom click Add URL and enter the following address: https://tgvaughan.github.io/BDMM-Prime/package.xml

After you click ‘OK’ your window should look like this:

Figure 2: The Package Repository Manager window.

Click Close. The BDMM-Prime package should now appear in your list of available packages.

Select the BDMM-Prime package and click Install/Upgrade at the bottom of the window. This should bring up a message that confirms the package has been successfully installed.

If you haven’t done so already, you will also need to install the follow two packages:

MM for models of morphological evolution

BEAST_CLASSIC for the relaxed lognormal clock model

These packages should already appear on the same list in the Package Manager. You just need to click Install/Upgrade for each one.

Restart BEAUTi to set up an analysis using BDMM-Prime.

Part 1: The constant rate FBD process

The Partitions panel

Make sure you have downloaded the nexus file associated with this tutorial, amniotes.nex.

To input your data into BEAUTi, either drag and drop the file into the Partitions panel and select Add Morphological Data and then OK or go to File > Add Morphological Data.

This will bring up a window that asks, “Would you like to condition on recording variable characters only (Mkv)?” Since we typically don’t collect non-variable morphological characters this option makes sense for our dataset.

Click Yes.

Figure 5: Options for importing morphological data.

Your Partitions panel should now look like this:

By default the characters are separated into four partitions. This means that characters will be assigned to a substitution rate matrix where the number of possible states corresponds to the maximum observed state for each character. Our dataset contains characters with a maximum observed state of one, two, three and four, hence we have four data partitions. The number of characters in each partition is listed under Sites.

Note that the Site Model is unlinked across partitions, while the Clock Model and Tree are linked by default. This means that we will allow each partition to evolve under an separate substitution process, but we will assume that the underlying tree is the same and that the same clock model is shared across partitions.

The Tip Dates panel

Navigate to the Tip Dates panel and select Use tip dates.

Here we can extract information about the age of our samples from the tip labels. For our analysis, the time units are in millions of years, so we’ll leave year selected in the first drop down menu.

From the second drop down menu, change Since some time in the past to Before the present.

Next, select Auto-configure. Leave use everything selected and from the drop down menu, select after last, leaving the delimiter as “_”. This will extract age information from the end of your taxon labels.

Figure 7: Options for extracting age information from taxon labels.

Note that although our samples span an interval in the past, internally BEAST rescales the tree such that the youngest sample in the tree is at t = 0. The rescaled values are shown under the Age/Height column.

To make sure everything is set up correctly, if you scroll down, the youngest fossil, Candelaria_barbouri_NAm_239.5, should have a “0.0” next to it under the Age/Height column.

The Site Model panel

Under the Site Model panel we can specify the model of morphological evolution. For this exercise we’ll use the simple Mkv Lewis model for each of our partitions and leave everything as it is.

The Clock Model panel

Navigate to the Clock Model panel and select Relaxed Clock Log Normal from the drop down menu. This will allow rates of evolution to vary across branches in our tree.

The Priors panel

Here we will set up the tree model and all the prior parameters associated with our analysis.

Navigate to the Priors panel.

Clock model parameters

Let’s start with clock model parameters, the overall mean on theh clock rate (ucldMean) and the standard deviation of the clock rates (ucldStdev), which you should already see listed here.

Expand the options for ucldMean by clicking the triangle to the left of this parameter.

For the mean on the overall clock rate we will use a normal prior, following the original study, with a mean of 0.001 and a variance of 0.01. Rates cannot be negative so the distribution is truncated at zero.

Figure 11: The prior on the overall clock rate, ucldMean.

Select the initial value option next to the distribution and change Value to 0.001. This ensures our analysis starts in a reasonable part of the parameter space. Click OK.

Figure 12: Setting the initial value for the overall clock rate, ucldMean.

For the standard deviation of the distribution of clock rates, ucldStdev, we’ll use an exponential prior with a mean of 0.1.

Figure 13: The prior on the standard deviation of clock rates, ucldStdev.

These prior choices match our expectations about rates of morphological evolution estimated in previous analyses of terrestrial vertebrates, i.e., the rates tend to be relatively low, with some degree of variation across branches.

BDMM-Prime

Next let’s set up the phylodynamic model.

Select BDMMPrime from drop down menu at the top of the Priors panel next to Tree.t:amniotes and expand the options by clicking on the triangle to the left.

There are a lot of options for combining parameters using BDMM-Prime, so we need to be careful to select parameter combinations that make sense for our dataset.

First, we need to choose how we want to parameterize our model - that is, which parameters we want to constrain using prior information. There are three options for parameterising the birth-death model using BDMM-Prime. For macroevolution we can choose from two options:

The Canonical Parameterization, which is parameterized using the birth ( $\lambda$ ), death ( $\mu$ ), and sampling rates ( $\psi$ ).
The Fossilized Birth Death Parameterization, which is parameterized using diversification rate ( $d$ ), turnover rate ( $v$ ), and sampling proportion ( $s$ ).

The relationship between the parameterizations is shown in Table 1. Both are useful in macroevolution and your choice might depend on which parameters are of most interest to you or what prior information you have available. Note that after your analysis is complete, you can always go from one set of parameters to the other via the transformations shown in the table below.

Parameter	Transformation
Speciation ( $\lambda$ )	$\lambda = d / (1 - v)$
Extinction ( $\mu$ )	$\mu = (vd)(1-v)$
Sampling ( $\psi$ )	$\psi = (s/ (1-s))((vd)/(1-v))$
Net diversification ( $d$ )	$d = \lambda - \mu$
Turnover ( $v$ )	$v = \mu/\lambda$
Sampling proportion ( $s$ )	$s = \psi/(\mu + \psi)$

After you’ve chosen which parameterization to use, there are still a lot of options left for setting up the model in BDMM-Prime. Later in Part 2 we will assign taxa to “types” based on their geographic provenance but for the first analysis in this tutorial we’ll assume that birth, death, and sampling are all constant through time and across our tree.

In this tutorial we’ll use the Canonical Parameterization, which is the default. Alternatives can be selected from the drop down menu next to Parameterization.
The canonical option can be applied to scenarios in both epidemiology and macroevolution.

The only change we’ll make here is to the value for Birth Rate and Death Rate, next to ALL and under Epoch 1, from 1.0 to 0.1 - these are the starting values for these parameters. Double click on each value to edit it and make sure to press enter. Notice that Estimate values is checked for all three of these parameters, which means they will be estimated and not fixed during inference.

We’ll leave Number of change times for each of these parameters as 0.

Figure 14: Options for the birth, death, and sampling rates.

Scroll down to the next set of parameters of interest.

Rho Sampling is the probability of sampling a lineage at a given time. In macroevolution this is usually associated with the extant sampling probability at t = 0. In our case study, we have no sampling beyond the period of interest, i.e., the youngest sample included in our dataset is from the Triassic and is therefore $\psi$ -sampled. There are many amniotes that persisted beyond this interval until the present but these are not included in our dataset. This means we have no rho-sampled taxa and it makes sense to leave Rho = 0.0.

The next parameter of note is the Removal Prob - this is the probability that a lineage is removed from the population upon sampling and it makes more sense in an epidemiological context. We need to change this to 0.0.

Select the value listed next to ALL and below Epoch 1 for Removal Prob and change it to 0.0.

Figure 15: Options for the rho sampling and removal probability parameters.

For now you can leave everything else under this tab as it is. If you close this tab and scroll down you should now be able to see an additional set of parameters listed in the Priors panel for the birth, death, sampling rates and the origin time parameter.

For the birth, death, and sampling rate parameters we’ll use an exponential prior with a mean = 0.1. This matches values that are typically estimated for these parameters in paleobiology.

From the drop down menu next to birthRateCanonical select Exponential and expand the options. Change the mean of this distribution to 0.1.

Figure 16: Prior options for the birth rate parameter.

Go ahead and do the same for the death and sampling rate parameters.

Figure 17: Prior options for the death rate parameter.

Figure 18: Prior options for the death rate parameter.

For the origin time parameter, we’ll also use an exponential distribution following the original study, with an offset = 318.1 Ma and a mean = 327.5 Ma. However, recall that BEAST scales the tree such the youngest sample is always zero, so these values will be defined relative to t = 0.

Select Exponential from the drop down menu next to originBDMMPrime and expand the options. Change the offset to 81.1, which is 318.1 minus the youngest age of our sampling interval = 237, and change the mean to 10.0.

Figure 19: Prior options for the origin time parameter.

Finally, select the initial value box for the origin and change Value to 100.0. The origin time of the process needs to be older than all our fossils. Click OK.

Figure 20: Setting the initial value for the origin time parameter.

Adding taxonomic constraints

We’re going to use two taxonomic constraints for this analysis.

Scroll down to the bottom of the Priors panel and click +Add Prior. From the drop down menu select MRCA prior. Click OK. This will open a box where you can define groups of taxa.

For the first constraint, type ‘ingroup1’ into the box next to Taxon set label. From the list of taxa on the left click on any taxa and use select all (e.g., cmd + A on Mac or Ctrl + A on Windows) to highlight all of them. Scroll down to Gephyrostegus_bohemicus_Eu_309 and deselect this taxon using cmd + shift or Ctrl + shift. Click the » button. Now all taxa except Gephyrostegus should appear on the right. Alternatively, you can select all of them, move them to the right and simply move Gephyrostegus back to the left using the « button.

Figure 21: Creating the first taxonomic constraint.

Click OK and then select the checkbox that says monophyletic that appears next to this constraint.

To add the next constraint, click +Add Prior. From the drop down menu select MRCA prior. Click OK.

For the second constraint, type ‘ingroup2’ into the box next to Taxon set label. This next constraint will include all taxa except Gephyrostegus_bohemicus_Eu_309 and Seymouria_spp_NAm_288.51.

Figure 22: Creating the second taxonomic constraint.

Click OK and then select the checkbox that says monophyletic that appears next to this constraint.

Figure 23: The final set of options for the Priors panel.

Use this screen shot to ensure all your prior distributions and initial values have been set up as described.

The MCMC panel

Navigate to the MCMC panel and change the Chain Length to 1,000,000 (just delete one of the zeros).

BDMM-Prime allows us to output additional tree files using the typedTree and nodeTypedTree loggers - these are not useful for our initial analysis and make the analysis more computationally expensive, so we will disable these options for now (but see Part 2 below).

Uncheck the boxes next to Enable Logger for typedTreeLogger and nodeTypedTreeLogger.

Figure 25: Switching off the typed tree and node typed tree loggers.

Go to File > Save as to save your xml input file. Call it something like amniotes_constant_rates_FBD.xml or anything you like.

You can leave BEAUti open for the next part of the exercise! However, if you do close BEAUTi you can simply go to File > Load and select your xml file - all the BDMM-Prime specifications should be reloaded.

Running BEAST

To run your analysis open the BEAST application and navigate to the right working directory. Select your xml file and click Run.

This analysis should only take about a minute to run.

Examining the output

This analysis should have generated the following results files:

amniotes_constant_rates_FBD.log - this file contains all the numerical parameters sampled during MCMC.
amniotes.trees - this file contains the trees sampled during MCMC.

First open the .log file in Tracer and explore the output. It should look something like this.

Figure 26: The tracer window for the constant rates FBD model.

Of particular interest are the parameters birthRateSPCanonical, deathRateSPCanonical, and samplingRateSPCanonical. The speciation and extinction rates are both ~0.35, while the sampling rate is ~0.01. This suggests that sampling is low relative to the overall diversity of the group.

Note that this analysis hasn’t fully converged but you can download the pre-cooked output for the same analysis that’s been run a bit longer.

If you want you can also generate a consensus tree using the standard .trees file (amniotes.trees) as the input and the approach described in previous tutorials for trees with sampled ancestors. For the Target tree type stick with the default Maximum clade credibility tree and for the Node Heights select Median heights from the drop down menu.

Open the program IcyTree in your internet browser. Drag and drop your summary tree file onto the page or go to File > Load from file….

To generate an image like the one below you can select the following options:

Style > Mark singletons (to show sampled ancestors)

Style > Internal node text > posterior

Style > Label precision limit > 2 sig. figures

Style > Node height error bars > height_95%_HPD

Style > Axis > Age

Style > Set axis offset… and enter 237 in the box, which is end of our sampling window. Click Ok.

Figure 28: Summary tree plotted in IcyTree.

Part 2: The multi-type birth-death process

In the second part of this tutorial we will take geographic provenance of the fossils into account and allow birth, death, and sampling rate to vary across geographic areas. To achieve this we will incorporate a migration rate parameter into the model and in addition, we will allow migration rate to vary through time, to acknowledge the observation that the interconnectedness of the geographic areas changes considerably over the course of our sampling interval.

Create a new folder to run this next analysis.

The Priors Panel

Return to the Priors panel in BEAUti. Next we’re going to extract information about geographic area. Expand the tree model options and scroll down to Type Trait Set. Click Auto-configure. Select split on character and from the drop down menu next to and take group(s): select 3.

Figure 29: Options for extracting type information from taxon labels.

Click OK. You should now be able to see the geographic area associated with each fossil in the Type column, corresponding to Eurasia, North America, and South Africa.

Figure 30: Type information associated with each sample.

Scroll up and you should see some additional options have been added for the tree model. In particular, options will have been added for the Migration Rate and Birth Rate Among Demes parameters.

The migration rate can be interpreted as the rate of change between types (in our case, the migration between geographic areas). We’ll estimate this parameter and allow it to vary over time (see below). The birth rate among demes can be interpreted as the rate of “spontaneous” change between types - in epidemiology this might be used together with types that are associated with specific mutations. This parameter doesn’t have an analogous meaning in our macroevolutionary context, so we’ll simply fix this parameter to zero.

Double click on the box for Birth Rate Among Demes next to ALL and enter 0.0.

Figure 31: The migration rate and birth rate among demes parameters.

In this analysis we want to allow birth, death, and sampling to vary across geographic areas.

Scroll back up. You’ll see that next to some of the parameters is a checked box called Scalar values - this indicates that a single rate applies to all types. Starting with Birth Rate, if you uncheck this box, a separate rate will appear next to each type. You can select Display epoch visualisation to make sure things are set up correctly.

Do the same for the Death Rate and Sampling Rate parameters.

Figure 33: Type specific death and sampling rates.

Over the course of our sampling interval, the late Carboniferous to the mid Triassic, we see major changes in continental configuration and the relative connectedness of geographic areas - to quantify the impact this has on migration we can allow the overall migration rate to change through time, assuming piece-wise constant variation in rate.

Return to the options associated with Migration Rate. First double click the the initial value for this parameter and change it to 0.1 and make sure the box next to Estimate values is checked. Next change Number of change times: to 2. Check the box that says Relative to process length and click Distribute evenly - this means that the time between the origin of the process and the end will be divided into 3 equal intervals and each one will be assigned an independent rate parameter. Select Display epoch visualisation to make sure things are set up correctly.

Figure 34: Time varying mirgration rates.

Note that we can specify the probability of each type at the beginning of the process, i.e., at the origin or root. By default these are set to equal but you can change these in the box next to Start Type Prior Probs. This probability can be estimated or you might have strong prior evidence for the root type, but we’ll leave this for now.

Finally, scroll down and assign an exponential prior with a mean of 0.1 to the migrationRateCanonical parameter. Each rate will be independently drawn from the same exponential prior distribution.

Figure 35: Prior options for mirgration rate.

The MCMC panel

Navigate to the MCMC panel. We’ll leave all the MCMC chain settings as above, i.e., we’ll use a chain length of 1,000,000 steps.

Now that we’ve added information about types, we can take advantage of some other output options. In particular, BDMM-Prime can use a stocastic mapping appraoch to generate edge-typed trees. These are trees that include information about the phylogenetic history of types. We can generate both typed trees, which show where along internal branches type changes (migration events) have occurred or node typed trees, which includes type information at internal nodes only. This can done using the typedTreeLogger and nodeTypedTreeLogger, respectively. In this analysis, we’re going to use the latter, as shown below, because node type trees are more computational efficient to generate and easier to summarise.

Expand the options next to nodeTypedTreeLogger and check the box next to Enable logger.

Figure 36: Options for the node typed tree logger.

Go to File > Save as to save your xml input file in your new folder. Call it something like amniotes_multi_type_FBD.xml.

Running BEAST

As above, run your analysis in the BEAST application and navigate to the right working directory. Select your xml file and click Run.

This analysis will take ~20 minutes to run. Ideally this analysis would be run for much longer, e.g., 10,000,000+ generations.

Examining the output

This analysis should have generated the following results files:

amniotes_multi_type_FBD.log - this file contains all the numerical parameters sampled during MCMC.
amniotes.trees - this file contains the trees sampled during MCMC.
amniotes_multi_type_FBD.typed.node.trees - this file contains the trees sampled during MCMC with ancestral types mapped onto internal nodes.

Open the .log file in Tracer and explore the output. It should look something like this.

You might need to expand and adjust the Tracer window so you can see parameter names.

Figure 37: The tracer window for the multi-type FBD model.

Now we have some additional parameters of interest. In particular, we have three estimates for birth, death, and sampling rates, associated with each type or geographic region.

Select the birthRateSPCanonical, deathRateSPCanonical, and samplingRateSPCanonical parameters for all three types simultaneously using the shift key. You should be able to higher relative rates of birth and death associated with North America.

Figure 38: The tracer window for the multi-type FBD model showing the birth rates associated with each type.

Scroll down and explore the estimates for the migration rate associated with different intervals. The intervals are labelled from youngest to oldest, i0, i1, and i2s.

Figure 39: The tracer window for the multi-type FBD model showing the migration rates associated with each interval.

You should be able to see a larger estimate for migration in the final time bin, which we might expect based on the increase in connectivity between regions in the Triassic. Recall that we defined 3 equal intervals relative to the origin time of the process. The variables appended _endtime are the estimates end points of each interval relative to the origin age.

We can summarise information about ancestral types across our posterior distribution of trees using TreeAnnotor.

Open TreeAnnotator and select amniotes_multi_type_FBD.typed.node.trees as your Input Tree File.

For the Target tree type stick with the default Maximum clade credibility tree and for the Node Heights select Median heights from the drop down menu. Call your Output File something like amniotes_multi_type_FBD.typed.node.con.tre. See Figure 27.

Open the program IcyTree in your internet browser. Drag and drop your summary tree file onto the page or go to File > Load from file….

To generate an image like the one below you can select the following options:

Style > Mark singletons (to show sampled ancestors)

Style > Colour nodes by > type

Style > Internal node text > type.prob

Style > Label precision limit > 2 sig. figures

Style > Display legend

Style > Axis > Age

Style > Set axis offset… and enter 237 in the box, which is end of our sampling window. Click Ok.

Figure 40: Summary tree plotted in IcyTree showing the ancestral types.

We can use this tree to explore hypotheses about the geographic origins of different lingeages. Note that based on node typed trees, it is not possible to make any statements about where and when along a given branch a type change may have occurred. To learn more about options for exploring migration history check out the BDMM-Prime package website.

Useful Links

Bayesian Evolutionary Analysis with BEAST 2 (Drummond & Bouckaert, 2014)
BEAST 2 website and documentation: http://www.beast2.org/
BDMM-Prime package website

Relevant References

Stadler, T. (2010). Sampling-through-time in birth–death trees. Journal of Theoretical Biology, 267(3), 396–404. https://doi.org/10.1016/j.jtbi.2010.09.010
Heath, T. A., Huelsenbeck, J. P., & Stadler, T. (2014). The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences, 111(29), E2957–E2966.
Mulvey, L. P. A., Nikolic, M. C., Allen, B. J., Heath, T. A., & Warnock, R. C. M. (2025). From fossils to phylogenies: exploring the integration of paleontological data into Bayesian phylogenetic inference. Paleobiology, 51(1), 214–236. https://doi.org/10.1017/pab.2024.47
Vaughan, T. G., & Stadler, T. (2025). Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data. Molecular Biology and Evolution, 42(6), msaf130. https://doi.org/10.1093/molbev/msaf130
Gavryushkina, A., Welch, D., Stadler, T., & Drummond, A. J. (2014). Bayesian Inference of Sampled Ancestor Trees for Epidemiology and Fossil Calibration. PLoS Computational Biology, 10(12), e1003919. https://doi.org/10.1371/journal.pcbi.1003919
Kühnert, D., Stadler, T., Vaughan, T. G., & Drummond, A. J. (2016). Phylodynamics with migration: a computational framework to quantify population structure from genomic data. Molecular Biology and Evolution, 33(8), 2102–2116.
Scire, J., Barido-Sottani, J., Kühnert, D., Vaughan, T. G., & Stadler, T. (2022). Robust phylodynamic analysis of genetic sequencing data from structured populations. Viruses, 14(8), 1648.
Bouckaert, R., Heled, J., Kühnert, D., Vaughan, T., Wu, C.-H., Xie, D., Suchard, M. A., Rambaut, A., & Drummond, A. J. (2014). BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Computational Biology, 10(4), e1003537. https://doi.org/10.1371/journal.pcbi.1003537
Drummond, A. J., & Bouckaert, R. R. (2014). Bayesian evolutionary analysis with BEAST 2. Cambridge University Press.

Citation

If you found Taming the BEAST helpful in designing your research, please cite the following paper:

Joëlle Barido-Sottani, Veronika Bošková, Louis du Plessis, Denise Kühnert, Carsten Magnus, Venelin Mitov, Nicola F. Müller, Jūlija Pečerska, David A. Rasmussen, Chi Zhang, Alexei J. Drummond, Tracy A. Heath, Oliver G. Pybus, Timothy G. Vaughan, Tanja Stadler (2018). Taming the BEAST – A community teaching material resource for BEAST 2. Systematic Biology, 67(1), 170–-174. doi: 10.1093/sysbio/syx060