Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesised to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by ‘novel environment’, an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation.  Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population’s evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments ..., , The code required in this analysis requires R studio and is designed to run in R version 4.2.3. The necessary R packages to perform the analysis are detailed in the accompanying R scripts.  
See README files for code and detailed usage notes: 

README.md: summary of database structure and contents. 

Effect_size_README.Rmd: code used to generate effect sizes comparing the volume of G between ancestral and novel environments.Inputs: G_matrix_data.zip

Meta_analysis_README.Rmd: code used to perform the meta-analysis and generate plots comparing the volume of G between ancestral and novel environments.Inputs: SDV_effect_sizes.csv, phylo_matrix.csv, taxa_group_data.csv, mod_data.csv. 

See also:
        3. Phylogeny.R, used to create phylo_matrix.csv. , # Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

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### Author: Camille L. Riley

This dataset contains the data and code necessary to perform the meta-analysis detailed in Riley et al. 2023; *Does the definition of a novel environment affect the ability to detect cryptic genetic variation?*.

Individual README files (.Rmd and .html) are provided, containing code and detailed usage notes. The methodology can be separated into two stages, the generation of effect sizes (effect_size_README.Rmd), and the meta-analysis of the effect sizes (meta_analysis_README.Rmd), involving the statistical analysis and generation of plots included in the article.

## Description of the data and file structure

### Datasets

The G-matrix data and associated metadata used in this analysis was sourced from eligible studies retrieved via systematic review. See supplementary table S1 for reference list.

**1. G-matrix_data.zip**

Zipped file comprised of i...