DOI: 10.5061/dryad.2280gb5zj
title: Genomic offset, telomere length, and abundance trends in Yellow Warblers

description: 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 the specific aspects of climate contributing to fitness loss
 and population declines in the yellow warbler system. We focus on
 precipitation because previous research indicates precipitation changes
 are linked to genomic and morphological signals of local adaptation in
 this species (Bay et al. 2018, Bay et al. 2021). We predict that the
 correlation between past population decline and future genomic offset
 previously identified is due to a correlation between past and future
 precipitation change.
description: 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
 across the rest of the breeding range to form a continuous map of genomic
 offset. We selected sample sites for telomere
 measurements by using the continuous map of genomic offset and choosing
 areas across a gradient of genomic offset in addition to precipitation and
 elevation. Estimates of future genomic offset were then calculated for
 each specific sample site. We collected blood samples for telomere
 measurements from each sample location spanning the breeding range of the
 yellow warbler during the 2020 and 2021 breeding seasons. Birds were
 captured via mist-nets, and once in hand, individuals were banded,
 morphological measurements were taken, and age and sex were recorded.
 Between 10 µl and 30 µl of blood was collected using brachial venipuncture
 and stored on ice until reaching the lab, where they were stored in -20°C
 until analysis (Criscuolo et al. 2009). Birds that could not be sexed in
 the field were sexed using PCR with the primer set CHD1F and CHD1R (Cakmak
 et. al 2017). Our sampling resulted in blood samples from 451 yellow
 warblers spanning 39 sample sites across the breeding range of the
 species. DNA was isolated from whole blood samples
 using the DNeasy Blood and Tissue kit (Qiagen, Valencia, California)
 following the manufacturer’s protocol. DNA purity and concentration were
 assessed using a NanoDrop 8000 spectrophotometer (Thermo Scientific), and
 DNA integrity was assessed on an agarose gel (Eastwood et al. 2018).
 Following the protocol of Criscuolo et al. (2009), qPCR was used to
 measure telomere length relative to the glyceraldehyde-3-phosphate
 dehydrogenase control gene. Samples were run in triplicate, with high
 efficiencies, intra-assay, and inter-assay repeatability (Eastwood et al.
 2018) (SI Appendix, section 1). The distribution of relative telomere
 length was right-skewed, so log relative telomere length was used in
 subsequent analyses. Log relative telomere length was then Z-transformed
 to facilitate comparison to future studies (Verhulst 2020).
 To test the assumption that past climate is correlated to future
 climate, we analyzed the correlation between historic and future changes
 in climate for the top three climate variables found to be associated with
 yellow warbler genomic variation according to Bay et al. (2018). These
 climate variables were bio18 (mean monthly precipitation amount of the
 warmest quarter), bio15 (precipitation seasonality), and bio13
 (precipitation amount of the wettest month). Bioclimatic data for each
 sample site were extracted from the WorldClim database (Eyring et al.
 2015, Fick and Hijmans 2017) using historical and future climate layers.
 We used a Shared Socio-economic Pathway (SSP) of 585 and used an ensemble
 model consisting of averages from 24 global climate models to calculate
 our future climate dataset. Historical changes in climate were calculated
 by subtracting historic bioclimatic variables (1970-2000) from those
 bioclimatic variables calculated for 2021-2040. Similarly, future changes
 in climate were calculated by subtracting bioclimatic variables from
 2021-2040 from future bioclimatic variables (2041-2060).
description: # Genomic offset, telomere length, and abundance trends in Yellow Warblers
 --- 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. bio13_Hdiff:  the change in historic climate for bioclim variable 13
 (mm) 15. bio13_Fdiff: the change in future climate for bioclim variable 13
 (mm) 16. bio15_Hdiff: the change in historic climate for bioclim variable
 15 (mm) 17. bio15_Fdiff: the change in future climate for the bioclim
 variable 15 (mm) 18. bio18_Hdiff: the change in historic climate for
 bioclim variable 18 (mm) 19. bio18_Fdiff: the change in future climate for
 bioclim variable 18 (mm)