Temperature-specific repeatability of evolution and its implications for genomic predictions of adaptation to warming
Data files
Mar 20, 2025 version files 1.83 GB
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altDP.txt
735.60 MB
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IDlist.txt
228 B
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Nes.txt
349 B
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pheno_dists.txt
215 B
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pheno_diverge.txt
1.41 KB
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pheno_theta.txt
1.35 KB
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README.md
3.88 KB
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shared_snps_selection.txt
254.36 MB
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TotalScaled_newerr.txt
13.82 KB
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totDP.txt
836.53 MB
Abstract
Climate warming is threatening biodiversity by increasing temperatures beyond the optima of many ectotherms. Due to the inherent non-linear relationship between temperature and the rate of cellular processes, such shifts towards hot temperature are predicted to impose stronger selection compared to corresponding shifts toward cold temperature. This suggests that when adaptation to warming occurs, it should be relatively rapid and predictable. Here, we tested this hypothesis from the level of single-nucleotide polymorphisms to life-history traits in the beetle Callosobruchus maculatus. We conducted an evolve-and-resequence experiment on three genetic backgrounds of the beetle reared at hot or cold temperatures. Indeed, we find that phenotypic evolution was faster and more repeatable at hot temperatures. However, at the genomic level, adaptation to heat was less repeatable when compared across genetic backgrounds. As a result, genomic predictions of phenotypic adaptation in populations exposed to hot temperatures were accurate within, but not between, backgrounds. These results seem best explained by genetic redundancy and an increased importance of epistasis during adaptation to heat, and imply that the same mechanisms that exert strong selection and increase repeatability of phenotypic evolution at hot temperature, reduce repeatability at the genomic level. Thus, predictions of adaptation in key phenotypes from genomic data may become increasingly difficult as climates warm.
This dataset consists of phenotypic data from an experiment used to quantify the predictability of evolution in the seed beetle Callosobruchus maculatus under different thermal regimes. Three main R scripts were used to run the analyses.
Dataset Version and Release History
- Current Version:
- Number: 1.0.0
- Date: 2024-9-23
- Persistent identifier: DOI:
- Summary of changes: n/a
Description of the Data and file structure
Phenotypic data:
The raw population-mean phenotypic data for 7 life-history traits (amongst other traits not used in the analysis) are in the text file titled TotalScaled_newerr.txt. Each row is a population, designated by its geographic origin (pop), replicate number (rep), and thermal regime (selection). The relevant columns for the analysis are lifetime reproductive success (LRS: total offspring produced), scaled metabolic rate (CO2/mass: CO2 ml/min/mg), adult weight (aw: mg), number of eggs laid during early fecundity (eggs), water loss during early fecundity (wvp: Pascals), and development time (devtime: days). The standard errors for each trait are given in separate columns whose names are appended with .SE (e.g. LRS.SE). Phenotypic measures of repeatability are output from the script “pheno_repeatability_submission.R” for use in the script “genomic_repeatability_submission.R”. These output files are titled pheno_dists.txt, pheno_diverge.txt, and pheno_theta.txt. The distance output is given as a vector, while the divergence and theta estimates are given as matrices, whose IDs are given in the repeatability scripts when created or loaded in.
Allelic depth data
Allele frequencies were calculated from the ratio between alternate allele depth in file altDP.txt and the total allele depth totDP.txt. Each file is the same dimension, with rows corresponding to a particular SNP at a position (pos) along a chromosome (chr). Each column is a population whose order is designated in the file IDlist.txt. In the ID file, populations are designated as origin_replicate_regime (e.g. ca_2_cold is a California population, replicate number 2, evolved in the cold regime). These allele frequencies are used to identify SNPs putatively under selection in the R scripts genomic_repeatability_submission.R and Offsets_Submission.R, which also incorporate information on the effective population size of each population in the file Nes.txt.
Selection coefficient data:
Selection coefficients were calculated for each population and SNP, present in the R object file sels_offset.rds. When loaded in R, it contains a list of vectors. Each vector contains estimated selection coefficients for each SNP (whose order along the genome is given in the file shared_snps_selection.txt) for each population (in the same order as IDlist.txt).
Script for analysis
The R scripts used to analyze the data and generate the plots for the response projections are in the base directory labelled “pheno_repeatability_submission.R”, “genomic_repeatability_submission.R”, and “Offsets_Submission.R”. R version 4.2.2 was used for this analysis.
The following R packages were used in the analysis:
data.table (v. 1.14.8)
vcfR (v. 1.14.0)
poolSeq (v. 0.3.5)
scales (v. 1.2.1)
SIBER (v. 2.1.8)
gplots (v. 3.1.3)
vegan (v. 2.6-4)
RColorBrewer (v. 1.1-3)
ComplexHeatmap (v. 2.15.4)
Sharing/Access Information
All scripts and supplementary files comply with the CC0 license below.
- License: Use of these data and scripts are covered by the following license:
- Title: CC0 1.0 Universal (CC0 1.0)
- Specification: https://creativecommons.org/publicdomain/zero/1.0/