Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?
Data files
Apr 05, 2024 version files 548.53 KB
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Effect_size_README.html
142.92 KB
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Effect_size_README.Rmd
14.18 KB
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G_matrix_data.zip
45.48 KB
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Meta_analysis_README.html
305.12 KB
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Meta_analysis_README.Rmd
23.97 KB
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mod_data.csv
76 B
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phylo_matrix.csv
3.42 KB
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phylogeny.R
3.44 KB
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README.md
4.62 KB
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SDV_effect_sizes.csv
5.23 KB
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taxa_group_data.csv
83 B
Abstract
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.
README: 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.
Usage notes
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.
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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.