DOI: 10.5061/dryad.pzgmsbcsc
title: Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

description: 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 present in a population’s ancestry. Despite
 sufficient statistical power, we detected no broadscale pattern of
 increased genetic variance in novel environments, finding the type of
 novel environment did not explain any significant variation in effect
 sizes. When effect sizes were partitioned by experimental design, we found
 increased genetic variation in studies based on broad-sense measures of
 variance, and decreased variation in narrow-sense studies, in support of
 previous research. Therefore, the source of genetic variance, not the
 definition of a novel environment, was key to understanding
 environment-dependant genetic variation, highlighting non-additive genetic
 variance as an important component of cryptic genetic variation and avenue
 for future research.
description: 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: 
 <strong>README.md</strong>: summary of
 database structure and contents.  
 <strong>Effect_size_README.Rmd</strong>: code used to generate
 effect sizes comparing the volume of G between ancestral and novel
 environments.<br>Inputs: G_matrix_data.zip 
 <strong>Meta_analysis_README.Rmd</strong>: 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. <strong>Phylogeny.R</strong>, used to
 create phylo_matrix.csv. 
description: # Does the definition of a novel environment affect the ability to detect
 cryptic genetic variation? --- ### 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 individual folders labelled
 with a unique file name corresponding to included articles (e.g. EK009).
 Where multiple G-matrices were extracted from the same study, the file
 name ends with a lower-case letter. Within each folder, there are three
 .csv files; * A_n=._G.csv: G-matrix corresponding to the ancestral
 environment. "n=" indicates sample size. * N_n=._G.csv: G-matrix
 corresponding to the novel environmental treatment. "n="
 indicates sample size. * Desc_data.csv: Additional descriptive data
 corresponding to the g-matrix data, comprising the following variables; *
 trait: phenotypic traits included in the G-matrices, * env_identifier:
 ancestral or novel environment; * treatment: environmental variable
 manipulated in experimental treatments, * mean, standard deviation (sd),
 and standard error (se) of phenotypic trait values, * Va : additive
 genetic variance, * Nind: number of individuals, * Nfam: number of
 families, * units: unit of measurement used for trait values * notes: any
 additional information. **2. Mod_data.csv** A csv file with the following
 variables: * moderator: variable predicted to affect the strength of the
 relationship between the dependent and independent variable, * number:
 number of instances the moderator variable features in the dataset. **3.
 Phylo_matrix.csv** A matrix of phylogenetic relatedness with variable
 names corresponding to species names represented in the dataset. **4.
 SDV_effect_sizes.csv** A csv file with the following variables: *
 study_code: unique study identifier, * effect_code: unique effect size
 identifier. Corresponds to names of folders in G-matrix_data.zip, * type:
 type of matrix, such as G-matrix or P-matrix. All matrices examined in
 this study are G-matrices, * ES: effect size value, * S_var: sampling
 variance value, * Precision: precision value, calculated by 1/SE, *
 Authors: study authors, * Year: year study was published, * type_novel:
 whether the novel environmental treatment corresponded to an extreme or
 absolute novel environment, * av_sample_size: average sample size, *
 design: experimental design of study, whether using broad or narrow sense
 measures of variance, * trait_no: number of phenotypic traits included in
 the G-matrix, * Group: taxonomic group of organisms examined, * Species:
 species name of organism examined, * species_full: species name of
 organism examined. **5. Taxa_group_data.csv** A csv file with the
 following variables: * group: taxonomic group, * number: number of
 instances the moderator variable features in the dataset. ### Code The
 code required to run this analysis requires R studio, and is designed to
 run using R version 4.2.3. The necessary R packages to perform the
 analysis are detailed in the R scripts below. **1.
 Effect_size_README.Rmd** This markdown file details the code used to
 generate effect sizes comparing the volume of G between ancestral and
 novel environments. The required inputs are **G-matrix_data.zip**. **2.
 Meta_analysis_README.Rmd** This markdown file details the code used to
 perform the meta-analysis and generate plots comparing the volume of G
 between ancestral and novel environments. The required inputs are:
 **SDV_effect_sizes.csv, phylo_matrix.csv, taxa_group_data.csv,
 mod_data.csv**. **3. Phylogeny.R** This R script contains the R script
 used to create **phylo_matrix.csv**, a covariance matrix of phylogenetic
 relatedness for the species represented in the analysis. This is used to
 account for the effects of phylogeny in meta-regression models.