DOI: 10.5061/dryad.tb2rbp064
title: Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq, reference genome assembly and filtered VCF

description: The impact of ongoing climate change on populations will be contingent
 upon their contemporary adaptive evolution. In this study, we investigated
 the contemporary evolution of four populations of the cold-water kelp
 Laminaria digitata by analysing their spatial and temporal genomic
 variation using ddRAD-sequencing. These populations were sampled from the
 center to the southern margin of its north-eastern Atlantic distribution
 at two-time points, spanning at least two generations. Through genome
 scans for local adaptation at a single time point, we identified candidate
 loci that showed clinal variation correlated with changes in sea surface
 temperature (SST) along latitudinal gradients. This finding suggests that
 SST may drive the adaptive response of these kelp populations, although
 factors such as species' demographic history should also be
 considered. Additionally, we performed a simulation approach to
 distinguish the effect of selection from genetic drift in allele frequency
 changes over time. This enabled the detection of loci in the southernmost
 population that exhibited temporal differentiation beyond what would be
 expected from genetic drift alone: these are candidate loci which could
 have evolved under selection over time. In contrast, we did not detect any
 outlier locus based on temporal differentiation in the population from the
 North Sea, which also displayed low and decreasing levels of genetic
 diversity. The diverse evolutionary scenarios observed among populations
 can be attributed to variations in the prevalence of selection relative to
 genetic drift across different environments. Therefore, our study
 highlights the potential of temporal genomics to offer valuable insights
 into the contemporary evolution of marine foundation species facing
 climate change.
description: Please refer to the Methods section in the published manuscript
 for detailed information.
description: # Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq,
 reference genome assembly and filtered VCF Feel free to contact Lauric
 Reynes ([lreynes@hawaii.edu](mailto:lreynes@hawaii.edu);
 [https://orcid.org/0000-0002-0223-4332](https://orcid.org/0000-0002-0223-4332) for any inquiries regarding this repository and data organization. Temporal genomics aids in deciphering neutral and adaptive patterns in the contemporary evolution of kelp populations. The ongoing climate change affects populations differently based on their adaptive evolution. The study focused on Laminaria digitata populations, revealing variations in genetic adaptation to sea surface temperature gradients. It highlights the importance of understanding the interplay between selection and genetic drift in different environments and underscores the value of temporal genomics in studying the contemporary evolution of marine species facing climate change. The preprint of the published manuscript is available at [https://doi.org/10.1101/2023.05.22.541724](https://doi.org/10.1101/2023.05.22.541724). Given the improvements in the published version, we recommend reading and referring to it. This repository contains: Paired-end sequencing reads (fq.gz) for the 221 samples generated by dd-RADseq. Sample sheet in Samples_temporal_genomics.csv, reporting sample ID, localization, time point, number of reads, % mapped to the genome. Analyses can be reproduced by performing read mapping on the reference genome assembly of the species using the Laminaria_digitata_Gernot_s_assembly.fa file. SNP calling can be performed following criteria reported in the main manuscript, thus generating the filtered VCF (Filtered_VCF_190_individuals_no_replicate.vcf) of 2,854 SNPs and 190 individuals. Analyses for the detection of temporal outliers can be reproduced following the Rmarkdown tutorial specifically created for this purpose,available at [https://lauricreynes.github.io/Temporal-genomics/](https://lauricreynes.github.io/Temporal-genomics/) with the associated DOI 10.5281/zenodo.10946281. Details on the R version, required packages, and dependencies are provided on the end page of this tutorial.