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Please refer to the TUTORIALS for updated documentation.
PyRate is a Python program to estimate macroevolutionary rates from fossil occurrence data using a Bayesian framework. It includes several methods to infer speciation, extinction, and dispersal rates, how they vary through time and whether they correlate with traits (e.g. body size) or respond to continuous variables (e.g. climate proxies) or competitive effects (through diversity dependence). Macroevolutionary rates are jointly estimated with preservation rates, describing processes of fossilization, sampling and identification of organisms.
Installation and requirements are described here. For questions, suggestions or bugs contact us!
The PyRate package includes several programs implementing different models. Simple tutorials demonstrating the most important features are available here
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PyRate.py implements the main functions to infer speciation and extinction rates through time and preservation rates (commands and options are detailed here). It also includes a model to infer trait dependent speciation and extinction.
A standard PyRate analysis requires three steps: (1) Prepare an input file; (2) launch PyRate providing an input file and analysis settings; and (3) Summarize the results and generate rates-through-time plots. A tutorial is available here.
The main methods implemented in 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. (2014) PyRate: A new program to estimate speciation and extinction rates from incomplete fossil record. Methods in Ecology and Evolution, 5, 1126–1131
PyRateContinuous.py [requires library pyrate_lib]
This program implements a birth-death model in which rates vary through time through linear or exponential correlations with a time continuous variable. A detailed command list is in preparation. The model implemented here is described in:
Silvestro D., Antonelli A., Salamin N., Quental T. B. (2015) The role of clade competition in the diversification of North American canids. PNAS, 112, 8684-8689.
PyRateMCDD.py [requires library pyrate_lib]
This program implements a Multiple Clade Diversity Dependence model (MCDD) in which speciation and extinction rates may change through time as the result of diversity dependence effects within and between clades. A detailed command list is in preparation. The MCDD model is described here:
Silvestro D., Antonelli A., Salamin N., Quental T. B. (2015) The role of clade competition in the diversification of North American canids. PNAS, 112, 8684-8689.
PyRateDES.py [requires library pyrate_lib]
This program implements a Dispersal Extinction Sampling model (DES), which uses fossil occurrences in two discrete areas to estimate rates of dispersal between areas and rates of local extinction within areas, while incorporating sampling biases. Correctly formatted input files can be generated using the script DES_input_preparation.R as described here. A detailed command list is in preparation. The MCDD model is described here:
Silvestro, D., Zizka A., Bacon C. D., Cascales-Minana B. and Salamin, N., Antonelli, A. (2016) Fossil Biogeography: A new model to infer dispersal, extinction and sampling from paleontological data. Phil Trans Roy Soc B, doi: 10.1098/rstb.2015.0225.
The library pyrate_lib] contains various functions that are used by several PyRate programs. The library does not require any installation. Simply, when you run a program requiring pyrate_lib (e.g. PyRateContinuous), the folder pyrate_lib needs to be in the same directory as the program.
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/. Alternatively, you can download the requirements.txt file and use pip install -r requirements.txt to install them from Terminal (UNIX systems). 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 using 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 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 using 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.