Published November 20, 2023 | Version v1
Dataset Open

Western burrowing owl genomics

  • 1. University of California Los Angeles


Migration is driven by a combination of environmental and genetic factors, but many questions remain about those drivers. Potential interactions between genetic and environmental variants associated with different migratory phenotypes are rarely the focus of study. We pair low coverage whole genome resequencing with a de novo genome assembly to examine population structure, inbreeding, and the environmental factors associated with genetic differentiation between migratory and resident breeding phenotypes in a species of conservation concern, the western burrowing owl (Athene cunicularia hypugaea). Our analyses reveal a dichotomy in gene flow depending on whether the population is resident or migratory, with the former being genetically structured and the latter exhibiting no signs of structure. Among resident populations, we observed significantly higher genetic differentiation, significant isolation‐by‐distance, and significantly elevated inbreeding. Among migratory breeding groups, on the other hand, we observed lower genetic differentiation, no isolation‐by‐distance, and substantially lower inbreeding. Using genotype–environment association analysis, we find significant evidence for relationships between migratory phenotypes (i.e., migrant versus resident) and environmental variation associated with cold temperatures during the winter and barren, open habitats. In the regions of the genome most differentiated between migrants and residents, we find significant enrichment for genes associated with the metabolism of fats. This may be linked to the increased pressure on migrants to process and store fats more efficiently in preparation for and during migration. Our results provide a significant contribution toward understanding the evolution of migratory behavior and vital insight into ongoing conservation and management efforts for the western burrowing owl.



Funding provided by: California Energy Commission
Crossref Funder Registry ID:
Award Number: EVA-2023-004-OA.R2

Funding provided by: National Geographic Society
Crossref Funder Registry ID:
Award Number: WW-202R-17

Funding provided by: National Science Foundation
Crossref Funder Registry ID:
Award Number: NSF-1942313

Funding provided by: Santa Clara Valley Habitat Agency*
Crossref Funder Registry ID:
Award Number:


Details regarding sample collection, genome sequencing, and sequence processing may be found in Methods S1; but notably, we sequenced a reference genome to high coverage and 202 burrowing owl samples collected across their migratory and resident breeding range to low coverage. Because our resequencing dataset was low coverage, we used variant detection and analytical methods that largely did not require called genotypes. This included both genotype likelihoods as estimated in the program ANGSD (Korneliussen et al., 2014) and a single‐read‐sampling (SRS) method that randomly selects one read per variant to temper the bias of high variation in locus‐to‐locus depths. Using these methods, files were prepared for analyses as described below using the following four filtering and genotyping frameworks and conditions: (1) Using ANGSD to produce genotype likelihood files for all individuals in the BEAGLE format (‐doGlf 3) and a minor allele frequency file (‐domaf 1) with restrictive filtering that uses a conservative minimum minor allele frequency (‐minmaf 0.05), a low maximum likelihood of being polymorphic (‐SNP_pval 1e‐6), adjusting mapQ scores for excessive mismatches from the reference genome (‐C 50), and confirming variants using a base alignment quality estimation (‐baq 1). (2) For SRS analyses, we used the 'HaplotypeCaller' module in GATK (McKenna et al., 2010) to call genotypes for all individuals sequenced, filtered by removing insert/deletion variants, and kept only biallelic variants found in 50% of the individuals. (3) We used ANGSD to create population‐specific site frequency spectra (SFSs) from site allele frequency files using the reference genome to polarize allele calls (‐anc), adjusting frequencies using individual FIS (‐indF), and with strict filtering conditions including discarding reads without unique mapping (‐uniqueOnly 1), removing bad reads (‐remove_bads 1), using only reads for which mates are mapped (‐only_proper_pairs 1), discarding reads with low mapping quality (‐minMapQ 1), keeping reads with high base quality (‐minQ 20), dropping reads with low or high depth across samples (‐setMinDepth 10 ‐setMaxDepth 500), keeping only biallelic sites (‐skipTriallelic 1), and also previously described conditions (‐minMaf 0.05 ‐C 50 ‐baq 1). (4) Minor allele frequency files (MAFs) were also generated for each sample site (‐doMaf 1), sampling all the sites identified in the overall MAF file, and only generating minor allele frequencies for variants found in a minimum of four individuals in each population.



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Is cited by
10.1111/eva.13600 (DOI)