DOI: 10.5061/dryad.dz08kps2x
title: Testing relationships between multiple regional features and biogeographic processes of speciation, extinction, and dispersal

description: The spatial and environmental features of regions where clades are
 evolving are expected to impact biogeographic processes such as
 speciation, extinction, and dispersal. Any number of regional features
 (such as altitude, distance, area, etc.) may be directly or indirectly
 related to these processes. For example, it may be that distances or
 differences in altitude or both may limit dispersal rates. However, it is
 difficult to disentangle which features are most strongly related to rates
 of different processes. Here, we present an extensible Multi-feature
 Feature-Informed GeoSSE (MultiFIG) model that allows for the simultaneous
 investigation of any number of regional features. MultiFIG provides a
 conceptual framework for incorporating large numbers of features of
 different types, including categorical, quantitative, within-region, and
 between-region features, along with a mathematical framework for
 translating those features into biogeographic rates for statistical
 hypothesis testing. Using traditional Bayesian parameter estimation and
 reversible-jump Markov chain Monte Carlo, MultiFIG allows for the
 exploration of models with different numbers and combinations of
 feature-effect parameters, and generates estimates for the strengths of
 relationships between each regional feature and core process. We validate
 this model with a simulation study covering a range of scenarios with
 different numbers of regions, tree sizes, and feature values. We also
 demonstrate the application of MultiFIG with an empirical case study of
 the South American lizard genus Liolaemus, investigating sixteen regional
 features related to area, distance, and altitude. Our results show two
 important feature-process relationships: a negative distance/dispersal
 relationship, and a negative area/extinction relationship. Interestingly,
 although speciation rates were found to be higher in Andean versus
 non-Andean regions, the model did not assign significance to Andean- or
 altitude-related parameters. These results highlight the need to consider
 multiple regional features in biogeographic hypothesis testing.
description: This dataset contains a simulation study performed using R and
 RevBayes. It consists of simulated regional feature values, simulated
 phylogenetic trees and present-day species ranges, and output from
 analyses with the MultiFIG model. The dataset also contains files relevant
 to a MultiFIG analysis of <em>Liolaemus</em>. Finally, the
 dataset contains PDF versions of figures and supplemental
 figures.
description: The data files can be opened with any text editor. For
 visualization, trees and logfiles can be opened in Tracer. Plots can be
 opened by any PDF reader.
description: ## Testing relationships between multiple regional features and
 biogeographic processes of speciation, extinction, and dispersal This
 dataset is associated with Swiston & Landis 2023
 ([https://doi.org/10.1101/2023.06.19.545613](https://doi.org/10.1101/2023.06.19.545613)). It contains two major elements. First is a simulation study performed using R and RevBayes associated with the validation of the MultiFIG model. Here, we provide simulated regional feature values, simulated phylogenetic trees and present-day species ranges, and output from analyses with the MultiFIG model. Second is an empirical analysis of the Liolaemus genus under the MultiFIG model. We also provide figures and supplemental figures associated with the manuscript. Description of the data and file structure Overview: The **code** .zip file contains all of the scripts necessary for generating and analyzing simulations for the simulation study (**sim**), running the empirical analysis of *Liolaemus* (**emp**), and plotting the results of both analyses (**plotting**). The **data** .zip file contains all of the simulated datasets for the simulation study in **sim** and empirical dataset for the analysis of *Liolaemus* in **emp**. The **output** .zip file contains all of the unprocessed and processed output from the simulation analyses (**sim**) and empirical analysis (**emp**), as well as all plots and supplemental figures from the associated manuscript (**plots**). * `code` Contains R and RevBayes code for MultiFIG simulation study and empirical analysis of *Liolaemus*, as well as plotting scripts. * `sim` Contains scripts for generating and analyzing simulated datasets under the MultiFIG model * `batchsim.sh` Shell script for submitting simulations using an LSF job scheduler (not required if simulating locally) * `sim.sh` Shell script that calls separate scripts for simulating geographies and trees under the MultiFIG model * `geosim.R` R Script for simulating "geographies", generating .csv data files in `data/sim/geo` representing simulated regional features and feature summaries * `sim.Rev` RevBayes script for generating phylogenetic trees, tip states, and model parameters in `data/sim/history` according to the MultiFIG model (based on simulated geographies) * `batchinf.sh` Shell script for submitting inference jobs on simulated datasets using an LSF job schedule (not required if performing inference locally) * `inf.sh` Shell script that calls `inf.Rev` on simulated datasets * `inf.Rev` RevBayes script for performing inference on simulated datasets under the MultiFIG model * `emp` Contains RevBayes scripts for performing an analysis of *Liolaemus* under the MultiFIG model * `batchinf.sh` Shell script for submitting inference jobs on *Liolaemus* dataset using an LSF job scheduler (not required if performing analysis locally) * `inf.sh` Shell script that calls `inf.Rev` on *Liolaemus* dataset * `inf.Rev` RevBayes script for performing inference on *Liolaemus* dataset under the MultiFIG model * `plotting` Contains R scripts for plotting results of simulation study and empirical analysis of *Liolaemus* * `cov_plots.R` R script for generating coverage plots and coverage table associated with the simulation study * `joint_plots.R` R script for generating joint posterior plots associated with the analysis of *Liolaemus* * `posterior_plots.R` R script for generating plots of Bayesian posteriors associated with the analysis of *Liolaemus* * `rj_sim_plots.R` R script for plotting reversible jump results associated with the simulation study * `state_plots.R` R script for plotting the ancestral state reconstruction for *Liolaemus* * `variance_plots.R` R script for plotting the variance of the empirical features against the variances of the simulated features * `data` Contains data for simulation study and empirical analysis of *Liolaemus* * `sim` Contains data associated with the simulation study * `geo` Contains data files for regional features over a set of simulated geographies, as well as summary files which describe the features used in each analysis (some analyses use different features in simulation versus inference) * The first element of the filename represents whether the analysis will be performed using reversible-jump (RJ) or without (NONRJ) * The second element of the filename represents the number of regions * The third element represents the experimental condition of the analysis (all 12 features are generated, but some are not used) * LESS: area & distance during simulation; area, distance, & altitude during inference * FULL: area, distance, & altitude during simulation; area, distance, & altitude during inference * MORE: area, distance, altitude, & temperature during simulation; area, distance, & altitude during inference * NOISY: area, distance, & taltitude (true altitude) during simulation; area, distance, & altitude during inference * The fourth element represents the tree size category: 25-49 = XSMALL, 50-99 = SMALL, 100-199 = MEDIUM, 200-349 LARGE (this information is not used in simulating geographies, but will be used in simulating trees) * The fifth element represents the index of the simulated geography (randomly assigned) * `SIM_feature_summary.csv` files contain a list of features to be used in the associated simulation * `INF_feature_summary.csv` files contain a list of features to be used in the associated inference * For all feature files: * The fifth element represents the type of data (c=categorical, q=quantitative, w=within-region, b=between-regions) * The sixth element is the geographical feature that the data represents: area, distance, altitude, true altitude, or temperature * Eg. `3.FULL.MEDIUM.1.cb_distance.csv` uses 3 regions, will use the feature set "FULL" when simulating a "MEDIUM" tree, is the simulated geography indexed 1, and contains a matrix of categorical distances between regions -- adjacency matrix * `history` Contains data files for simulated trees, tip states, and model parameters * Eg. `RJ.3.FULL.MEDIUM.1.tree.tre` Contains a Newick-string representation of a dated phylogeny, simulated based on the 3-region geography indexed 1 using the feature set "FULL", and targeting a medium tree size, to be analyzed using reversible jump * Eg. `RJ.3.FULL.MEDIUM.1.data.tsv` Contains tip states corresponding to the `RJ.3.FULL.MEDIUM.1` simulation * Eg. `RJ.3.FULL.MEDIUM.1.param.txt` Contains MultiFIG model parameters that produced the `RJ.3.FULL.MEDIUM.1` simulation * `emp` Contains empirical dataset associated with *Liolaemus* and 6 South American regions (AA = Altiplanic Andes, CA = Central Andes, PA = Patagonia, CC = Central Chile, AD = Atacama Desert, EL = Eastern Lowland) * `history` Contains the empirical dataset relating to *Liolaemus* * `liolaemidae.data.full.csv` Contains information about each species in the family Liolaemidae (including the genus *Liolaemus*), such as presence/absence in different regions, as well as other species traits that are not used for the MultiFIG analysis -- data from Esquerré et al. 2019 * `make_dat.py` A Python script for translating the data from `liolaemidae.data.full.csv` into data that is usable for the MultiFIG model (`liolaemidae.data.table.tsv` and `ranges.data.tsv`) * `state_labels_n6.txt` Relates binary presence/absence data to integer state numbers used by RevBayes for the MultiFIG analysis, used by the `make_dat.py` script * `liolaemidae.data.table.tsv` Contains present-day ranges of *Liolaemus*, representing presence in a region with (1) and absence with (0) * `ranges.data.tsv` Contains state numbers for present-day *Liolaemus* species * `tree.mcc.tre` Time-calibrated phylogeny of *Liolaemus* * `geo` Contains the empirical dataset relating to South America * `shapefiles` Contains shapefiles associated with the 6 regions used in the *Liolaemus* analysis, are not required for running the analysis * `RJ.6.HL.LIOLAEMUS.feature_summary.csv` Contains information about which features to use for the empirical analysis using highland/lowland classification * `altitudes` Contains regional features associated with altitude * `andean_classification.csv` Classifying regions as Andean or non-Andean * `andean_sameness.csv` Matrix describing whether regions share (1) or do not share (0) Andean classification * `classification.csv` Altitude classification of regions (1=high, 0=low) * `mean.csv` Mean altitudes of regions (m) * `mean_diff.csv` Differences in mean altitude between regions (m) * `sameness.csv` Matrix describing whether regions share (1) or do not share (0) altitude classification * `sd.csv` Standard deviation of altitudes of regions * `areas` Contains regional features associated with area * `areas.csv` Sizes of regions (km^2) * `classification.csv` Size classification of regions (1=large, 0=small) * `distances` Contains regional features associated with distance * `mean.csv` Mean distances between regions (km) * `adjacency.csv` Matrix of region adjacency * `equal` Contains vectors/matrices of equal features values for simplified analyses (removing feature effects) * Eg. `q_equal_vector.csv` A vector of equal feature values for simplified analysis -- in this case, a quantitative one-dimensional vector * `output` Contains output of simulated and empirical analyses * `sim` Contians output of simulation study * `output` Output of simulation analyses (logfiles for model parameters) * `data` Summaries of output for each analysis (estimates and HPD intervals) * `processed_data` Contains large output file of the coverage analysis, `coverages.csv` * `emp` Contains output of empirical analysis of *Liolaemus*, including 6 file types: `.ase.tre` (ancestral state tree), `.states.log` (ancestral state trace), `.stoch.log` (stochastic mapping), `.events.tsv` (list of state transitions and cladogenetic events), `.model_extras.log` (by-region rates of biogeographic processes), and `.model.log` (logfile of model parameters) * `concatenated.model.log` Logfile of model parameters used for results in the manuscript; created by concatenating the output of analyses 8 and 9 (numbering was arbitrary), after removing burnin -- done to ensure sufficient number of generations * `output` Contains other output files associated with analyses 8 and 9, including the ancestral state reconstruction generated from analysis 9 * `plots` Contains plots associated with Swiston & Landis 2023 ([https://doi.org/10.1101/2023.06.19.545613](https://doi.org/10.1101/2023.06.19.545613)) Sharing/Access information Links to other publicly accessible locations of the data: * [https://bitbucket.org/sswiston/multifig/src/master/](https://bitbucket.org/sswiston/multifig/src/master/) Data was derived from the following sources: * Esquerré, D., Brennan, I. G., Catullo, R. A., Torres-Pérez, F., & Keogh, J. S. (2019). How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America’s most species-rich lizard radiation (Squamata: Liolaemidae). Evolution; International Journal of Organic Evolution, 73(2), 214–230. * NASA. (2013). Shuttle Radar Topography Mission (SRTM) Global [Data set]. [https://doi.org/10.5069/G9445JDF](https://doi.org/10.5069/G9445JDF) * Swiston, S. K., & Landis, M. J. (2023). Testing relationships between multiple regional features and biogeographic processes of speciation, extinction, and dispersal. BioRxiv. [https://doi.org/10.1101/2023.06.19.545613](https://doi.org/10.1101/2023.06.19.545613) Code/Software * The dataset contains .R files for generating simulated geographies and plotting output. These files were designed to be run using R version 4.4.0. * The dataset also contains .Rev files for performing analyses in RevBayes. Correct versions of RevBayes and TensorPhylo can be found in Docker image `sswiston/rb_tp:7` [https://hub.docker.com/r/sswiston/rb_tp](https://hub.docker.com/r/sswiston/rb_tp). For a tutorial explaining the details of the MultiFIG analysis, visit [https://revbayes.github.io/tutorials/multifig/](https://revbayes.github.io/tutorials/multifig/). Version Changes: * 2024/07/22: Due to changes in RevBayes and TensorPhylo software, all scripts were overhauled, all analyses re-run, and all figures re-generated. New .zip files have been uploaded. * 2024/10/17: The term 'altitude' was changed to 'elevation' throughout the plotting scripts and figures.