Data from: The evolution of reproductive diversity in Afrobatrachia: a phylogenetic comparative analysis of an extensive radiation of African frogs

Afrobatrachia_Single_Tree_ACE_SIMMAPThis directory contains character codings, a phylogenetic tree, and R script for performing ancestral character reconstructions with ACE (ape) and stochastic character mapping (phytools). This includes model testing (ER, ARD, SYM) for each character in ACE, as well as performing reconstructions with ACE and simmap, and plotting reconstructions of nodes with each analysis type on the phylogeny.Single_Tree_ACE_SIMMAP.zipAfrobatrachia_Multi_Tree_SIMMAPThis directory contains character codings, a sample of trees (n=100) from BEAST run, and R script for performing stochastic character mapping (phytools) on multiple trees. To account for topological uncertainty, the simmap function is carried out with 100 replicates on each of 100 trees. The results are summarized across simulations for each character.Multi_Tree_SIMMAP.zipAfrobatrachia_Sequence_AlignmentA nexus format sequence alignment of the five nuclear markers (FICD, KIAA2013, POMC, RAG1, TYR) and 16S data, along with relevant MrBayes style partitions and models defined. The alignment contains 186 taxa and 3700 bp.Concatenated_nuc_16S.nexPower_Analyses_1_Data&CodeDirectory contains the character codings and tree used by the accompanying R script to simulate discrete character data. The script will simulate each of the four characters independently and combine these results in an output file, with 500 replicates, for a total of 500 output files.Power_Analyses_2_SimulationResultsThe resulting output files from the previous step. The directory contains 500 files, each with independent simulations for characters 1-4 (note number of states varies across characters) across all taxa included.Power_Analyses_3_ConvertToBayesTraits&InputsIncludes python script to convert simulation output files to BayesTraits binary input format (Simulation_Outputs_processing_for_BayesTraits.py). Instructions for usage and details of conversion are annotated at the top of the script. The results of this conversion process are in the directory "3_BayesTraits_inputs", which contains subdirectories of the relevant character comparisons, each with 500 input files. These subdirectories contain a tree file, and will need the "BayesTraitsV2" executable placed within. These subdirectories are the focus of the next bundle of scripts.Power_Analyses_4_BayesTraitsScripts&ResultsThere are two python scripts used to execute BayesTraits on the subdirectories in the previous bundle, "BayesTraits_wrapper_ML.py" and "BayesTraits_wrapper_Bayesian.py". Each will automatically generate the independent and dependent model files required to run BayesTraits, and automatically access the tree file and BayesTraits executable in the subdirectory. It will run serial analyses across all the input files in a subdirectory, using the maximum likelihood version or Bayesian version with stepping stone sampling. Instructions and details are annotated at the top of those scripts. The results can be summarized using the relevant python script, bayesian ("Summary_bf_testing.py") or ml ("Summary_lr_testing.py") version. These will open the output files from each of the 500 analyses in a subdirectory and perform either likelihood ratio tests or bayes factors to compare the independent and dependent models for each input file. The output of these scripts for our set of analyses is provided in the directory "4_Results".