DOI: 10.5061/dryad.bzkh189kd
title: Temperature-specific repeatability of evolution and its implications for genomic predictions of adaptation to warming

description: 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. 
description: # Temperature-specific repeatability of evolution and its implications for
 genomic predictions of adaptation to warming --- 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/](https://creativecommons.org/publicdomain/zero/1.0/)