https://github.com/niklashohmann/paradox_of_predictability
Supplementary code and data for "The paradox of predictability"
https://github.com/niklashohmann/paradox_of_predictability
evolutionary-prediction evolvability genotype-phenotype-map macroevolution phenotypic-integration phylogenetic-comparative-methods quantitative-genetics time-series-analysis
Last synced: 5 months ago
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Supplementary code and data for "The paradox of predictability"
- Host: GitHub
- URL: https://github.com/niklashohmann/paradox_of_predictability
- Owner: NiklasHohmann
- License: apache-2.0
- Created: 2024-04-18T09:43:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-11T17:43:29.000Z (over 1 year ago)
- Last Synced: 2024-08-11T19:12:28.397Z (over 1 year ago)
- Topics: evolutionary-prediction, evolvability, genotype-phenotype-map, macroevolution, phenotypic-integration, phylogenetic-comparative-methods, quantitative-genetics, time-series-analysis
- Language: R
- Homepage:
- Size: 310 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# paradox_of_predictability
Supplementary code and data for "The paradox of Predictability Provides a Bridge Between 1 Micro- and Macroevolution".
## Authors (no specific order)
__Masahito Tsuboi__
Lund University
Web page: [www.masahitotsuboi.com](https://www.masahitotsuboi.com/)
ORCID: [0000-0002-0144-2893](https://orcid.org/0000-0002-0144-2893)
__Niklas Hohmann__
Utrecht University
email: n.h.hohmann [at] uu.nl
Web page: [www.uu.nl/staff/NHohmann](https://www.uu.nl/staff/NHHohmann)
ORCID: [0000-0003-1559-1838](https://orcid.org/0000-0003-1559-1838)
__Kjetil L. Voje__
ORCID: [0000-0003-2556-3080](https://orcid.org/0000-0003-2556-3080)
__Melanie Hopkins__
ORCID: [0000-0002-3580-2172](https://orcid.org/0000-0002-3580-2172)
__Meghan Balk__
ORCID: [0000-0003-2699-3066](https://orcid.org/0000-0003-2699-3066)
__Sophie Nilén__
ORCID: [0009-0002-0996-0182](https://orcid.org/0009-0002-0996-0182)
__Erik I. Svensson__
ORCID: [0000-0001-9006-016X](https://orcid.org/0000-0001-9006-016X)
__Lee Hsiang Liow__
ORCID: [0000-0002-3732-6069](https://orcid.org/0000-0002-3732-6069)
__Gene Hunt__
ORCID: [0000-0001-6430-5020](https://orcid.org/0000-0001-6430-5020)
## Requirements
R (version >= 4) and the RStudio IDE
## Usage
Download the code from GitHub. In RStudio, open the file `paradox_of_predictability.Rproj`. This open the RProject, installs the `renv` package (if it is not already installed), and set the working directory correctly. Next, run
```r
renv::restore()
```
to install all required packages in the correct version. Now you are set up to interact with the code and data.
Then you can inspect and run the code in the directory `code/`. The main analysis is contained in the script `code/analysis.R`.
## Repository structure
* _code_ : folder with R code
* _data_ : folder with raw and modified data
* contains various files with raw time series data, named after publication and year
* _output_ : folder with data extracted from time series
* _var_div_dat_ : folder with data extracted from time series, with added data on timescale (paleo or neo). This data was added manually
* _figs_ : folder for figures
* _renv_ : `renv` package folder
* _.gitignore_ : untracked files
* _.Rprofile_ : R session settings
* _LICENSE_ : Apache 2.0 license file
* _paradox_of_predicability.Rproj_ : R Project file
* _README.md_ : readme file
* _renv.lock_ : lock file for `renv` package
## Data
Bibliographical references of the data are shown in Table S1 of the publication.
Raw data is stored in .RData files as list (of lists). The first layer of the list contains 10 variables of the following name and meaning.
$nsamp : number of time steps
$nvar : number of variables
$M : mean values of variables at each time step
$S : list of phenotypic variance-covariance matrices (P-matrices) at each time step
$nn : sample size at each time step
$tt : time lapse between steps (the first entry is 0)
$time.units : unit of time laper. Either "Myr" for million years or "yr" for years.
$taxon : taxon name (Genus and species)
$sex : sex. Either "male" or "female".
$reference : bibliographical reference
Note that the unit of raw data is measurement of size and shape in the ratio scale (length and area) except for the whorl count (ordinal) in Bellamya (see Table S1). Whorl count however essentially reflects length in the ratio scale, and thus treated here as a ratio scale trait. In all cases, traits in the original scales were natural-log transformed before estimating the P-matrix. Values presented in "$S" are therefore are mean-scaled variances and covariances, which are unit-less elasticities in percentage of the mean (see Hansen and Houle 2008, Voje et al 2023 for measurement theory behind this operation). For the trait units and further details about each measurements, please consult original sources.
## License
Apache 2.0, see LICENSE file for details.
## Funding information
This work was funded by
1. The Center for Advanced Studies in Norwegian Academy of Science and Letters to Thomas F. Hansen and Christophe Pélabon and
2. The Japanese Society for the Promotion of Science, Overseas Research Fellowship to Masahito Tsuboi (Grant identification number: 202270014)