Genomic offset, telomere length, and abundance trends in Yellow Warblers

One of the biggest challenges with genomic offset approaches is the difficulty in validating the relationships between genomic offset and fitness. We investigate the relationship between genomic offset and current fitness in the yellow warbler, using telomere length as a proxy for fitness. The yellow warbler is a migratory songbird that breeds in various habitats throughout North America and is an excellent system for this study because of the seminal work of Bay et al. (2018) that identified patterns of genomic offset across the species range. We measure telomere length in yellow warbler populations occupying regions with high and low genomic offset across the species range. We predict that if yellow warbler populations are locally adapted to climate on the breeding grounds and future offset is indicative of recent climate change impacts, a significant negative relationship will exist between future predicted genomic offset and current telomere length. Additionally, we will investigate..., We used restriction-site associated DNA sequencing (RAD-Seq) data from Bay et al. (2018) on 229 individuals from 39 locations across the yellow warbler breeding range. To estimate genomic offset, we then ran gradient forest (Fitzpatrick et al. 2021) on a subset of 1694 unlinked candidate single-nucleotide polymorphisms (SNPs) that were significantly associated with BIOCLIM variables based on LFMM analysis from the previous paper. We built a gradient forest model with average monthly precipitation values for the months of May through July as environmental response variables and the candidate SNPs as predictors. Precipitation data were obtained from the CRU-TS 4.06 dataset (Harris et al., 2020), downscaled with WorldClim 2.1 (Fick and Hijmans, 2017). We then used the predict function within gradient forest to weight the environmental response variables for both current and future predicted climates at 10,000 random locations across the yellow warbler breeding range. We then interpolated a..., , , # Genomic offset, telomere length, and abundance trends in Yellow Warblers

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Dryad DOI: https://doi.org/10.5061/dryad.2280gb5zj

## Description of the Data

The \"YEWA_TL_data_2023.csv\" file contains all of the necessary data to carry out the analysis between genomic offset, telomere length, and abundance trends in yellow warblers. The following fields are the columns in the data file:  

1. Sample: sample identification
2. Lat: latitude
3. Long: longitude
4. Location: sample site number where the individual was captured at
5. TS: the T/S ratio used for the telomere length of that individual.
6. Age: describes the age class of the individual, of which it could be HY (hatch year), SY (second year; one-year old), or ASY (after-second year; older than one year).
7. Sex
8. Year
9. Month
10. Day
11. Goffset: the genomic offset value for that location/sample
12. Elevation: the elevation of that location (m)
13. Abundance: the abundance trends for yellow warblers at each sample site
14. bio...