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Dryad

Adaptation to distinct habitats is maintained by contrasting selection at different life stages in sunflower ecotypes

Cite this dataset

Goebl, April M. et al. (2023). Adaptation to distinct habitats is maintained by contrasting selection at different life stages in sunflower ecotypes [Dataset]. Dryad. https://doi.org/10.5061/dryad.2jm63xstg

Abstract

Conspecific populations living in adjacent but contrasting microenvironments represent excellent systems for studying natural selection. These systems are valuable because gene flow is expected to force genetic homogeneity except at loci experiencing divergent selection. A history of reciprocal transplant and common garden studies in such systems, and a growing number of genomic studies, have contributed to understanding how selection operates in natural populations. While selection can vary across different fitness components and life stages, few studies have investigated how this ultimately affects allele frequencies and the maintenance of divergence between populations. Here, we study two sunflower ecotypes in distinct, adjacent habitats by combining demographic models with genome-wide sequence data to estimate fitness and allele frequency change at multiple life stages. This framework allows us to estimate that only local ecotypes are likely to experience positive population growth (λ>1) and that the maintenance of divergent adaptation appears to be mediated via habitat- and life-stage-specific selection. We identify genetic variation, significantly driven by loci in chromosomal inversions, associated with different life history strategies in neighbouring ecotypes that optimize different fitness components and may contribute to the maintenance of distinct ecotypes.

Methods

Full variant call format table for all filtered samples and SNPs as described in Goebl et al. 2022 (https://doi.org/10.1111/mec.16785). 

Usage notes

This VCF table is an input file for the R code used to conduct the analyses presented in Goebl et al. 2022 (https://doi.org/10.1111/mec.16785). The R code and other associated input files are located: https://github.com/aprilgoebl/GSD_CodeAndFiles

Funding

Natural Sciences and Engineering Research Council, Award: 327475

Natural Sciences and Engineering Research Council, Award: PGS‐D

National Science Foundation, Award: IGERT 1144807