To cope with climate change, species may shift their distributions or adapt in situ to changing environmental conditions. However, clear examples of genetic changes via adaptation are limited. We explore evolutionary responses to climate change in the endangered southwestern willow flycatcher (Empidonax traillii extimus) through whole-genome comparisons between historical specimens, collected from 1888 to 1909 near San Diego, California, United States, and contemporary individuals from across the breeding range. Genomic analyses revealed that introgression into San Diego increased adaptive potential over time and shifted genome-wide population structure towards that of neighbouring populations. In contrast, loci linked to climate (dew point temperature and precipitation) shifted away from neighbouring populations and in a direction consistent with adaptation to climate change in southern California. This research highlights the role of admixture in facilitating adaptive shifts through its impact on genome-wide genetic variation and represents one of the few studies to document climate adaptation in a wild population.
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The data generated for this study are available in the NCBI Sequence Read Archive (BioProject PRJNA957938) and Dryad Digital Repository (https://datadryad.org/stash/share/qlafA02RwIPjoptjefUaKbrMszEsMJWP8a_H9-e24MY)68.
Code associated with this study can be accessed on GitHub (https://github.com/sturbek/WIFL-Climate-Adaptation)69.
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This work was supported by a National Science Foundation (NSF) Postdoctoral Research Fellowship in Biology (2208881) to S.T. and an NSF CAREER grant (1942313) to K.R. We thank B. Forester for contributing funding for sequencing. We thank all of the individuals who contributed genetic samples, including T. Kita, B. Keith, R. Taylor and S. Birks. The following museums generously provided historical samples for the analyses presented in this study: the University of California Berkeley Museum of Vertebrate Zoology, California Academy of Sciences, Los Angeles County Natural History Museum, American Museum of Natural History, Buffalo Museum of Science, Charles R. Conner Museum, Cornell University Museum of Vertebrates, University of Kansas Biodiversity Institute & Natural History Museum, New York State Museum, Royal Ontario Museum, University of Colorado Museum of Natural History and the University of Michigan Museum of Zoology. All samples were collected under federal, state and IACUC permits necessary for the research. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US government.
The authors declare no competing interests.
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Extended Data Fig. 1 Genomic differentiation between historical and contemporary willow flycatchers in San Diego.
Manhattan plot showing mean FST, averaged over 25-kb windows, between historical (n = 17) and contemporary (n = 18) individuals breeding in San Diego. Colours (black and grey) alternate between scaffolds in the willow flycatcher reference genome.
Extended Data Fig. 2 Results from the gradient forest analysis of 500,000 loci and 11 contemporary populations across the breeding range of the southwestern willow flycatcher.
(A) PCA of the top three climate variables associated with genomic variation. The arrows indicate the loadings of the top-ranking environmental variables. Colours are based on the predicted genomic composition from the top climate variables at 100,000 random points. (B) Gradient forest-transformed environmental variables from the PCA mapped across the breeding range of the southwestern willow flycatcher. Sampling locations are indicated with black circles.
Extended Data Fig. 3 Redundancy analysis (RDA) showing the relationship between genomic variation and top-ranking climate variables for 221 contemporary willow flycatchers breeding across the United States.
The loadings on (A) axes one and two and (B) axes one and three for monthly mean dew point temperature (tdmean), monthly precipitation (precip), and monthly maximum temperature (tmax) are shown. Points are coloured by sampling location and the grey cloud in the center of each plot shows the 128,147 SNPs included in the analysis. The four subspecies of willow flycatcher are shown as different shapes.
Extended Data Fig. 4 Redundancy analysis (RDA) showing the candidate SNPs associated with top-ranking climate variables for 221 contemporary willow flycatchers breeding across the United States.
The loadings of 128,147 SNPs on (A) axes one and two and (B) axes one and three and their relationship with monthly mean dew point temperature (tdmean), monthly precipitation (precip), and monthly maximum temperature (tmax) are shown. Candidate SNPs (with loadings greater than three standard deviations from the mean) are coloured according to their associations, while non-candidate SNPs are shown in light grey.
Extended Data Fig. 5 Shifts in patterns of genomic differentiation over time in willow flycatchers breeding across the United States at climate-linked SNPs.
Principal component analysis (PCA) of loci associated with (A) monthly mean dew point temperature (n = 104), (B) monthly precipitation (n = 72), and (C) monthly maximum temperature (n = 56) across 238 contemporary and historical samples of the willow flycatcher. Details as in Fig. 1. Contemporary samples of the four subspecies are shown as squares, circles, diamonds, and triangles, while upside-down triangles indicate historical samples.
Extended Data Fig. 6 Allele frequency shifts within San Diego at climate-linked loci located near genes associated with avian thermal tolerance and bill morphology.
Number of loci linked to (A) monthly mean dew point temperature (n = 20), (B) monthly precipitation (n = 10), and (C) monthly maximum temperature (n = 7) in willow flycatchers breeding in San Diego that shifted in a direction consistent with climate adaptation. The p-values show the results for one-tailed binomial tests examining whether the observed proportion of loci that shifted in the expected direction (that is, consistent with climate adaptation) was significantly greater than expected by chance.
Supplementary Methods, Tables 1–5 and Figs. 1–15.
Fluidigm primer information (5’ to 3’) for the 96 SNP markers included in the SNP genotyping analyses. ASP1, the SNP allele detected with allele-specific primer 1; ASP2, the SNP allele detected with allele-specific primer 2; SNP_SEQ, the sequence of the amplified fragment containing the SNP; ASP1_SEQ, the sequence of allele-specific primer 1; ASP2_SEQ, the sequence of allele-specific primer 2; LSP_SEQ, the sequence of locus-specific reverse primer; STA_SEQ, the sequence of forward primer for specific target amplification; AMP_GC, the proportional GC content.
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Turbek, S.P., Bossu, C., Rayne, C. et al. Historical DNA reveals climate adaptation in an endangered songbird. Nat. Clim. Chang. 13, 735–741 (2023). https://doi.org/10.1038/s41558-023-01696-3
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