PyRate Manual – v. 0.570

PyRate Manual – v. 0.570
PyRate is a Python program to estimate speciation, extinction, and preservation rates from fossil
occurrence data using a Bayesian framework. The methods and the program are described here:
Silvestro, D., Schnitzler, J., Liow, L.H., Antonelli, A. & Salamin, N. (2014) Bayesian Estimation of
Speciation and Extinction from Incomplete Fossil Occurrence Data. Systematic Biology, 63, 349–
367.
Silvestro, D., Salamin, N., Schnitzler, J. (in review) PyRate: A new program to estimate speciation
and extinction rates from incomplete fossil record.
Please report suggestions and bugs to: pyrate.help@gmail.com
Table of Contents
Compatibility and installation!
2
Preparing input file for analysis!
3
Input files, working directory !
4
Main analysis settings!
5
Preservation (fossilization) model!
6
Birth-death model – constrained shifts!
7
Settings for trait correlated rates (Covar models)!
7
(Hyper)prior settings!
8
Description of the output files!
9
Plot/summarize results!
10
Advanced (BD)MCMC settings!
11
Tuning parameters!
12
Miscellaneous!
14
Acknowledgments!
15
References!
15
1
1. Compatibility and installation
PyRate has been tested under Unix operating systems (Mac OS 10.6 or higher and Cent OS 6)
and under Windows (XP and 7) using Python 2.6 – 2.7. The program requires the library
argparse, which needs to be installed manually under Python 2.6 (it can be found here: https://
code.google.com/p/argparse/), whereas it is part of the default installation in Python 2.7. Please
note that Python 3.x is currently not supported, Python 2.7.x can be found here: https://
www.python.org/downloads/.
The libraries numpy and scipy are required. Source files and installers are available here: http://
sourceforge.net/projects/numpy/files/ and http://sourceforge.net/projects/scipy/files/. Under
UNIX systems, PyRate will use the library multiprocessing and thread (if available) and
implement parallel computation of the likelihoods (see command -thread).
To launch a PyRate analysis on UNIX browse via Terminal to the PyRate directory and type:
./PyRate.py path_to_input_file/file_name.py [commands]
or
python PyRate.py path_to_input_file/file_name.py [commands]
To start PyRate on Windows, browse via Command prompt to the PyRate directory and type:
python PyRate.py path_to_input_file/file_name.py [commands]
If you are working under Windows, please make sure that the path to python.exe is included in
the PATH environment variables. To do so, edit the PATH environment variable and add the folder
in which Python 2.x is installed (e.g. ‘C:\python27’). An easy tutorial how to do that can be found
for example on the Java website: https://www.java.com/en/download/help/path.xml
The function -plot (see below) generates an R script that is used to produce a graphic output.
The script is automatically executed by PyRate using the shell command RScript. If you are
working under Windows, please make sure that the path to Rscript.exe is included in the
PATH environment variables (default in Mac/Linux). To do so, edit the PATH environment variable
and add the \bin\ folder of the R installation (e.g. ‘C:\Program Files\R\R-2.14.0\bin\i386’). An easy
tutorial how to do that can be found for example on the Java website: https://www.java.com/en/
download/help/path.xml
The R script pyrate_utilities.r was tested under R version 2.x and 3.x. The function
fit.prior requires the package fitdistrplus.
2
2. Preparing input file for analysis
The PyRate program requires a specific format for the input data, so please follow the next steps
carefully. A correctly formatted input file can be generated using an R function provided in the
script ‘pyrate_utilities.r’ starting from a table with the fossil occurrence data.
All fossil occurrences need to be provided in a table (a tab-delimited text file), with species
names, their status ("extant" or "extinct"), and minimum and maximum ages as the columns.
The minimum and maximum ages commonly correspond to the temporal boundaries of the
stage a particular fossil is assigned to and are generally available from the databases. At present,
PyRate can not deal with missing information in these four columns, so make sure that you
remove these entries beforehand. One additional column may be added providing a trait value,
if available, which can be used in the birth-death analysis (note that here, missing data are
allowed, and should be given as NA). A typical input file may look like this:
Species!
!
!
!
Ursus_etruscus! !
!
Ursus_etruscus! !
!
Ursus_etruscus! !
!
Agriotherium_insigne! !
Ursavus_brevirhinus! !
Ursavus_brevirhinus! !
Agriotherium_intermedium!
...! !
!
!
!
Status!
extinct!
extinct!
extinct!
extinct!
extinct!
extinct!
extinct!
...! !
MinT! MaxT
1.9! 2.6!!
1.2! 1.8!!
2.6! 3.4!!
4.2! 5.3!!
8.2! 9.0!!
11.2! 15.2!!
3.4! 4.2!!
...! ...!!
Trait
90
90
90
285
80
80
NA
...!
This file can then be processed using the R function extract.ages from the R utilities script
provided with PyRate package. To load the extract.ages function, open an R console and
type:
> source(file = "/path_to_file/pyrate_utilities.r")
The extract.ages function needs the path to the file containing the raw data and has a few
options that can be specified.
replicates!
Examples:
This option allows the user to generate several replicates of the data set in
a single input file, each time re-drawing the ages of the occurrences at
random from uniform distributions with boundaries MinT and MaxT. The
replicates can be analyzed in different runs (see PyRate command -j) and
combining the results of these replicates is a way to account for the
uncertainty of the true ages of the fossil occurrences (see also Silvestro et
al. 2014).
replicates=1 (default, generates 1 data set)
!
replicates=10
(generates 10 random replicates of the data set)
3
cutoff!
Specify a threshold to exclude fossil occurrences with a high temporal
Examples:
uncertainty, i.e. with a wide temporal range between MinT and MaxT.
cutoff=NULL (default; all occurrences are kept in the data set)
!
cutoff=5
random!
Specify whether to take a random age (between MinT and MaxT) for each
Examples:
(all occurrences with a temporal range of 5 Myr or
higher are excluded from the data set)
occurrence or the midpoint age. Note that this option defaults to TRUE if
several replicates are generated (i.e. replicates > 1).
random = TRUE (default)
random = FALSE (use midpoint ages)
The extract.ages function can be called in an R console as follows:
> extract.ages(file = "/path_to_file/Ursidae.txt", replicates=10,
cutoff=5, random=TRUE)
This resamples 10 times the age of fossil occurrences randomly within the respective temporal
ranges and generates a Python file (here called 'Ursidae_PyRate.py') that can now be
imported in PyRate for diversification rate analyses.
3. Input files, working directory
<input file> Set input file including path and file name. The file is a Python file
with the fossil occurrence data (as list of Numpy arrays) and, optionally,
one or more continuous traits. Input files with correct formatting can be
generated using the R script ‘pyrate_utilities.R’ (see above).
Example:
python PyRate.py path_to_input_file/Ursidae_PyRate.py
-wd!
Define working directory where all output files will be saved. If not
specified, output files will be saved in the same directory as the input file.
Example:
-wd path_to_target_directory
-j
If the input file includes several data sets, e.g. generated using the R script
Example:
‘pyrate_utilities.R‘ to account for uncertainties of fossil ages, this
command defines which data set will be analyzed.
-j 1 (default; the first data set from the input file is analyzed)
4
-out
Example:
Add tag to default stem name of output files. This command is useful to
avoid overwriting output files when running several instances of the
same analysis.
-out run_2 (adds ‘run_2’ to the name of all output files)
4. Main analysis settings
-A
-A 0 run an MCMC for parameter estimation, i.e. with fixed number of
shifts in the birth-death model (see section ‘Birth-death model –
constrained shifts’). This analysis generates output files with posterior
samples of the parameters, e.g. speciation/extinction/preservation rates
and times of rate shifts (see section ‘Description of the output files’ for
more details).
-A 1 run an MCMC with thermodynamic integration (TI; Lartillot &
alternative
Philippe 2006) to estimate the fit of a birth-death model. This analysis
computes the marginal likelihood of a birth-death model (save to a
‘*_marginal_likelihood.txt’ file) that can be use to compare
models, e.g. with different number of rate shifts or trait correlations.
-A 2 run a BDMCMC analysis (Silvestro et al. 2014; Stephens 2000) to
Example:
jointly estimate the number of rate shifts in the birth-death process, their
temporal placement and the speciation and extinction rates between
shifts. This analysis generates output files with posterior samples of the
parameters e.g. preservation rates and speciation/extinction rates
through time (see section ‘Description of the output files’).
-A 2 (default)
-n
Number of MCMC or BDMCMC generations
Example:
-n 10000000
-s
MCMC sampling frequency
Example:
-s 1000
-p
MCMC print-on-screen frequency
Example:
-p 1000
-b
Set the number of iterations to be discarded (i.e. not logged) from the
analysis as burnin. Must be set to a reasonable number when estimating
(default)
(default)
5
marginal likelihood through TI (see command -A). This command is also
used to exclude burnin samples when summarizing MCMC results (e.g.
-mProb, -plot functions). When set to a number between 0 and 1, it is
Examples:
interpreted as a fraction of the total number of MCMC generations.
-b 0 (default; all samples are logged)
-b 1000
(first 1,000 samples are discarded)
-b 0.10
(the first 10% of the samples are discarded)
NOTE: Additional commands are provided in the section ‘Advanced (BD)MCMC settings’.
5. Preservation (fossilization) model
-mHPP
Use homogeneous Poisson process for preservation rates instead of
non-homogeneous Poisson process (NHPP) with ‘hat-shaped’ (PERT)
distributed preservation rates (Liow et al. 2010). Note that the NHPP is
used unless differently specified.
Example:
-mHPP
-mG
Set the Gamma model, allowing heterogeneity of the mean preservation
rate across the taxa in a data set. Preservation rates under the Gamma
model will be assumed to be distributed according to a gamma
distribution with shape parameter (alpha) estimated from the data.
On most empirical data sets the Gamma model strongly outperforms the
alternative assumption of constant rates, and across-taxa rate
heterogeneity has been shown to Gamma model accounts to some
extent for temporal changes of the preservation rates (Silvestro et al.
2014). This option can be applied to both the NHPP and the HPP
preservation models.
Example:
-mG
-ncat!
Set the number of categories used to obtained the gamma distributed
preservation rates under the Gamma model. Increasing this number will
allow for more variability of the rates across taxa, with comparatively little
effect on the speed of the analysis. This command is used only when
running under a Gamma model of preservation (i.e. with -mG).
Example:
-ncat 4
-fixSE!
Fix the times of speciation and extinction of all taxa based on a previous
(default)
analysis. This command can be used to load a ‘mcmc.log’ from which
posterior mean times of speciation and extinction are calculated for all
6
taxa. PyRate will then run the analysis using these values, i.e. without
estimating them and without calculating the likelihood of the
preservation process (NHPP or HPP), but only sampling the parameters
of the birth-death. If set to -fixSE null first and last appearances are
used as fixed times of speciation and extinction.
Example:
-fixSE /path_to_file/Ursidae.mcmc.log
6. Birth-death model – constrained shifts
-mL
Set the number of speciation rates through time used in the MCMC
(with -A 0 or -A 1). In BDMCMC analyses (-A 2) this command is only
used to set the number of starting rates.
Example:
-mL 2
(set 2 speciation rates and 1 rate shift)
-mM
Set the number of extinction rates through time used in the MCMC
(with -A 0 or -A 1). In BDMCMC analyses (-A 2) this command is only
used to set the number of starting rates.
Example:
-mM 3
(set 3 extinction rates and 2 rate shifts)
-mC
Constrain the time frames of speciation and extinction rates to be equal.
This command is only used in birth-death models with at least one rate
shift (e.g. with -mL 2 and -mM 2) and
Example:
-mC
-fixShift
Fix number and time based on ages provided in a text file. This should be
simply a text file with the ages at which the rate shifts should be fixed (see
file ‘epochs.txt’ in PyRate’s example files). When this command is used,
the number and temporal placement of the rate shifts is fixed and set
identical for both speciation and extinction.
Example:
-fixShift path_to_file/epochs.txt
7. Settings for trait correlated rates (Covar models)
-mCov
Set Covar models in which the birth-death rates (and preservation
rate) vary across lineages as the result of a correlation with a continuous
trait, provided as an observed variable, based on estimated correlation
parameters (cov_sp, cov_ex, cov_q).
Examples:
-mCov 1
correlated speciation rate
7
-trait
Example:
-mCov 2
correlated extinction rate
-mCov 3
correlated speciation and extinction rates
-mCov 4
correlated preservation rate
-mCov 5
correlated speciation, extinction, preservation rates
If the input file includes several traits, this command defines which trait
will be analyzed.
-trait 1 (default; it takes first trait)
8. (Hyper)prior settings
-N
!
Set the number of extant species (regardless of whether or not they are
included in the fossil occurrence data). This is used to calculate the
hyperprior on the speciation and extinction rates, i.e. conditioning on
the present known diversity (Kubo and Iwasa 1995; Equations 12–15 in
Silvestro et al. 2014). If set to -N -1 or if not specified, a gamma prior will
be used instead with shape and scale parameters defined by the flags -pL
and -pM (see below). The latter option might be appropriate for clades
that have gone extinct or when the current diversity is doubtful.
Example:
-N 24
-pL
Shape and scale parameters of the gamma distributed prior on the
speciation rate, which estimates the expected number of fossil
occurrences per lineage per Myr. This command if used only if the
number of extant taxa is not specified (i.e. -N -1).
Example:
-pL 1.1 1.1 (default)
-pM
Shape and scale parameters of the gamma distributed prior on the
extinction rate, which estimates the expected number of fossil
occurrences per lineage per Myr. This command if used only if the
number of extant taxa is not specified (i.e. -N -1).
Example:
-pL 1.1 1.1 (default)
-pP
Shape and scale parameters of the gamma distributed prior on the
preservation rate, which estimates the expected number of fossil
occurrences per lineage per Myr.
Example:
-pP 1.5 1 (default)
8
-pS
Shape parameter of the Dirichlet prior on the length of the time frames
Example:
relative to the total time span of the data set. If set to -pS 1 the prior is a
uniform distribution, whereas big values will favor equal sized time
frames.
-pS 2.5 (default)
-pC!
Standard deviation of the Normal prior on the correlation parameters
under the Covar model. The Normal priors are centered in 0.
Example:
-pC 1
(default)
9. Description of the output files
A typical PyRate analysis produces three output files:
*_sum.txt
Text file providing the complete list of settings used in the
analysis.
*_mcmc.log
Tab-separated table with the MCMC samples of the posterior,
prior, likelihoods of the preservation process and of the
birth-death (indicated by PP_lik and BD_lik, respectively), the
preservation rate (q_rate), the shape parameter of its gamma
distributed heterogeneity (alpha), the parameters of the Covar
model (cov_sp, cov_ex, cov_q), the number of sampled rate
shifts (k_birth, k_death; only logged in BDMCMC analyses),
the value of scaling factor used in TI analyses (beta), the time of
origin of the oldest lineage (root_age), the speciation/
extinction rates between shifts (lambda_0, lambda_1, ... and
mu_0, mu_1, ...; only logged under fixed number of shifts, i.e. with
-A 0 or -A 1), the times of rate shifts in speciation and
extinction (shift_sp_1, ... and shift_ex_1, ...; only logged
with -A 0 or -A 1), the total branch length (tot_length), and
the times of speciation and extinction of all taxa in the data set
(*_TS and *_TE, respectively). This file can be used to calculate
the sampling frequencies of birth-death models with different
number of rate shifts after a BDMCMC analysis using the function
-mProb (see section Plot/summarize results). Additionally, the
file can be opened in the program Tracer (Rambaut and
Drummond 2007) to check the efficiency and mixing of the
MCMC and the proportion of burnin.
*_marginal_rates.log
Tab-separated table with the posterior samples of the marginal
rates of speciation, extinction, and net diversification, calculated
within 1 time unit (typically Myr). This file can be used to
generate rates-through-time plots using the function -plot
(see section Plot/summarize results).
!
9
When running an analysis to estimate the marginal likelihood of a birth-death model by TI
(option -A 1) the ‘*_marginal_rates.log’ file is replaced by the following:
*_marginal_likelihood.txt
Text file providing the marginal likelihood of a birth-death
model estimated by TI. This value can be used to compare
the relative fit of different birth-death model (e.g. with
different number of shifts, fixed shift ages, trait-correlated
rates using the Covar model, ...). The calculation of Bayes
Factors to quantify the relative model support can be done
using the command -BF described below.
10. Plot/summarize results
-plot!
This function takes the marginal speciation and extinction rates logged
by a PyRate analysis in a file (named ‘*_marginal_rates.log’) and
generates a rates-through-time plot (RTT) using the scripting language R.
Two output files are generated: an R script named ‘*_RTTplot.r’ and a
pdf file named ‘*_RTTplot.pdf’. The former contains the source R code
for generating the graphic output saved in the pdf file. As for all the other
input files, by default these files will be save in the same directory as the
input file. Mean speciation, extinction, and net diversification rates
through time are plotted in 1 Myr time bins with the respective 95% HPD.
Several log files (e.g. from different replicates) can be loaded at ones and
combined in a single plot. The proportion of burnin to be excluded can be
specified using the command -b (by default set to 0). We recommend to
inspect the ‘*_marginal_rates.log’ file in Tracer to define the
appropriate proportion of burnin.
Example:
-plot file_name_marginal_rates.log
-mProb!
Takes the posterior samples logged in a BDMCMC analysis to a file
(named ‘*_mcmc.log’) to calculate the sampling frequencies of
birth-death models with different number of rate shifts after a BDMCMC
analysis. The proportion of burnin to be excluded can be specified
using the command -b (by default set to 0). We recommend to inspect
the ‘*_mcmc.log’ file in Tracer to define the appropriate proportion of
burnin.
Example:
-mProb file_name_mcmc.log
-BF!
Takes the marginal likelihoods calculated under two birth-death models
(from ‘*_marginal_likelihood.txt’ files) to calculate Bayes Factors
and quantify the support of one model against the other. The Bayes factor
10
is calculated as twice the difference of log marginal likelihood and the
degree of support divided into four categories: negligible, positive,
strong, very strong, based on Kass and Raftery (1995).
Example:
-BF path_to_file/file_1_marginal_likelihood.txt
!
path_to_file/file_2_marginal_likelihood.txt
11. Advanced (BD)MCMC settings
-r
Set the number of parallel ‘heated’ chains for Metropolis Coupled MCMC
(MC3) analysis. Each chain will use a different processor if available. The
number includes the ‘cold’ chain. This command is used only when
running MCMC analysis for parameter estimation (i.e. with -A 0).
Example:
-r 4 (for 1 cold and 3 heated chains)
-t
Set the ‘temperature’ parameter for the MC3 heated chains. This command
is used only when running MC3 analysis (i.e. with -r >1).
Example:
-t 0.03
(default)
-sw
Frequency of attempted swaps between chains in MC3 analysis. This
command is used only when running MC3 analysis (i.e. with -r >1).
Example:
-sw 100
(default)
-k
Number of scaling factors used for marginal likelihood estimation in TI.
Higher number of scaling factors will improve the accuracy of the
estimated marginal likelihood, but require longer computational time.
This command is used only when running TI analysis (i.e. with -A 1).
Example:
-k 10
-a
Shape parameter of the beta distributed scaling factors in TI analyses (cf.
Xie et al. 2011; Silvestro et al. 2014). This command is used only when
running TI analysis (i.e. with -A 1).
Example:
-a 0.3
-M!
Frequency of model update in BDMCMC analysis. This parameter
determines how frequently new birth-death models will be explored.
Reducing this number can improve the sampling of the number of shifts
in birth-death rates. This command is used only when running BDMCMC
analysis for parameter estimation (i.e. with -A 2).
(default)
(default)
11
Example:
-M 25
-B
Set the birth-rate at which the BDMCMC algorithm will propose new rate
shifts in the model. This also corresponds to the shape parameter a
Poisson distributed prior on the number of speciation/extinction rates in
the model. This command is used only when running BDMCMC analysis
for parameter estimation (i.e. with -A 2).
Example:
-B 1
-T
Set the time spent in updating the model in BDMCMC analysis. Increasing
this parameter has a similar effect to increasing the birth-rate (command
-B). This command is used only when running BDMCMC analysis for
parameter estimation (i.e. with -A 2).
Example:
-T 1
-S
Set the number of generations after which the BDMCMC algorithm will
start updating the model (e.g. after a burn-in phase). This command is
used only when running BDMCMC analysis for parameter estimation (i.e.
with -A 2).
-S 1000
Example:
(default)
(default)
(default)
12. Tuning parameters
-tT
Window size of updates of speciation/extinction times (uniform sliding
window).
Example:
-tT 1
-nT
Maximum number of speciation/extinction times updated at a time. If set
to 0, speciation/extinction times are set equal to first/last appearances
and the preservation model is automatically set to HPP.
Example:
-nT 5
-tQ
Window sizes of the preservation rate (q) and of the shape parameter of
the gamma distributed rate heterogeneity (alpha), respectively (uniform
sliding windows).
-tQ 0.33 3 (default)
Example:
(default)
(default)
12
-tR
Window size of updates of speciation/extinction rates (uniform sliding
window).
Example:
-tR 0.05
-tS
Window size of updates of shift times for speciation/extinction rates
(uniform sliding window).
-tS 1 (default)
Example:
(default)
-fS
Frequency of updating shift times, when updating birth-death
parameters (else rates are updated). The value will be automatically set to
0 when no rate shifts are being sampled or if the times of shifts are fixed
(with command -fixSE).
Example:
-fS 0.7
-tC
Window sizes of updates of correlation parameters, when using models
with rates covarying with a trait. Window sizes are given for covariation
with speciation, extinction, and preservation rates respectively. The
parameters will be updated (or not) depending on the Covar model
selected (see command -mCov).
Example:
-tC 0.025 0.025 0.1
-fU
Example:
Update frequencies for preservation rate, birth-death parameters, and
correlation parameters under the Covar model, respectively. What is left is
used for updating speciation and extinction times.
-fU 0.02 0.18 0.08 (default under Covar model; updates
preservation parameters with frequency 2%, birth-death parameters with
frequency 18%, Covar parameters with frequency 8%, times of speciation
and extinction with frequency 72%)
-fR
Fraction of birth-death rates updated at a time (with frequency defined
(default)
(default)
by the command -fU). This command should be used to reduce the
fraction of updated rate parameters especially when running birth-death
models with many shifts, e.g. defined by the command -fixShift, to
improve the MCMC mixing.
Example:
-fR 1
(default)
13
13. Miscellaneous
-v!
Print program’s version
-h
Print command list
-cite
Print PyRate citation
-thread
Set the number of threads used for calculating the likelihood of the
birth-death process and the NHPP likelihood. When both values are set to
0, PyRate will use sequential computation of the likelihood (thus
slowing down a bit the analysis) and will not use the multiprocessing
python library. Under MS Windows operating systems the sequential
likelihood calculation is set by default.
Examples:
-thread 1 3
(default on UNIX operating systems)
-thread 0 0
(default on Windows operating systems)
The calibration of molecular phylogenies heavily relies on the assignment of prior distributions
on the ages of particular internal nodes (generally derived from first appearances of fossils that
belong to the clade of interest). However, the selection of appropriate prior distributions can be
difficult and is critical for the correct calibration of the tree (Heath 2012). The R function fit.prior
from the pyrate_utilities.r script can be used to derive a gamma distribution for the
estimated time of speciation of any species in the dataset.
To load the fit.prior function, open an R console and enter:
> source(file = "/path_to_file/pyrate_utilities.r")
The function fit.prior needs the path to the log file (i.e. the ‘*mcmc.log’ file containing
the posterior sample of the model parameters) and the name of the species of interest.
lineage!
Examples:
Name of the species for which a prior distribution should be generated.
The default (“root age”) is the age of origin of the diversification process,
i.e., the time of speciation of the oldest sampled species.
lineage=”Ursus_minimus” (generate a prior distribution for the time
of speciation for Ursus minimus)
The fit.prior function can be called in an R console as follows:
> fit.prior(file = "/path_to_file/Ursidae_mcmc.log", lineage =
“Ursus_minimus”)
14
This generates a text file with the shape and scale parameters as well as the offset of the gamma
distribution.
14. Acknowledgments
We are grateful to Susanne Fritz and Ingo Michalak for help testing the software on Windows
and to the VITAL-IT cluster of the Swiss Institute for Bioinformatics where we ran simulations and
analyses.
15. References
Kass, R.E. & Raftery, A.E. (1995) Bayes Factors. Journal of the American Statistical Association, 90,
773–795.
Kubo, T. & Iwasa, Y. (1995) Inferring the rates of branching and extinction from molecular
phylogenies. Evolution, 49, 694–704.
Lartillot, N. & Philippe, H. (2006) Computing Bayes factors using thermodynamic integration.
Systematic Biology, 55, 195–207.
Liow, L.H., Skaug, H.J., Ergon, T. & Schweder, T. (2010) Global occurrence trajectories of
microfossils: environmental volatility and the rise and fall of individual species. Paleobiology, 36,
224–252.
Rambaut, A. & Drummond, A.J. (2007) Tracer: Available from http://beast.bio.ed.ac.uk/Tracer.
Silvestro, D., Schnitzler, J., Liow, L.H., Antonelli, A. & Salamin, N. (2014) Bayesian Estimation of
Speciation and Extinction from Incomplete Fossil Occurrence Data. Systematic Biology, 63, 349–
367.
Silvestro, D., Salamin, N., Schnitzler, J. (in review) PyRate: A new program to estimate speciation
and extinction rates from incomplete fossil record.
Stephens, M. (2000) Bayesian Analysis of Mixture Models with an Unknown Number of
Components – an alternative to reversible jump methods. The Annals of Statistics, 28, 40–74.
Xie, W., Lewis, P.O., Fan, Y., Kuo, L. & Chen, M.H. (2011) Improving marginal likelihood estimation
for Bayesian phylogenetic model selection. Systematic Biology, 60, 150–160.
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