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. 2026 May 14;16(5):e73651. doi: 10.1002/ece3.73651

Genomic and Mitonuclear Patterns of Divergence Among Recently Diverged White‐Crowned Sparrow Subspecies

Patricia B Osagie 1,✉, Juan Enciso‐Romero 1, Theresa M Burg 1
PMCID: PMC13175713  PMID: 42145887

ABSTRACT

Genetic divergence and subsequent speciation are possible within the range and habitat of a population. White‐crowned sparrows are comprised of five subspecies that thrive in a wide range of habitats, latitudes, and elevations across North America, suggesting the ability for local adaptation and rapid divergence. We assessed genome‐wide genetic differentiation in four populations of white‐crowned sparrow ( Zonotrichia leucophrys gambelii , Z. l. oriantha (northern and southern), and Z. l. pugetensis) and compared patterns of genetic structure using outlier single nucleotide polymorphisms (SNPs), including mitochondrial (255 SNPs) and Z chromosome (580 SNPs) loci. When these four populations are assessed using the full SNP dataset, three genetic clusters are identified: pugetensis, southern oriantha, and northern oriantha grouped with gambelii. However, four genetic clusters are supported by the Z chromosome outlier SNPs corresponding to three subspecies and a north–south split in Z. l. oriantha. Our analyses pointed to consistent divergence of Zonotrichia leucophrys pugetensis from other populations and mitochondrial SNPs showed overlap of southern Z. l. oriantha and Z. l. gambelii. Interestingly, northern Z. l. oriantha populations are more genetically similar to Z. l. gambelii as opposed to its sister population, the southern Z. l. oriantha. We found a region on the Z chromosome with highly elevated F ST among all the populations. The genetic clusters identified in our study may be suggestive of evolutionary events such as selection on the Z chromosome, and this may be driving divergence in white‐crowned sparrows.

Keywords: divergence, outlier SNPs, population, white‐crowned sparrow, Z chromosome

The study investigates genetic divergence among white‐crowned sparrow subspecies across North America, revealing three to four distinct genetic clusters based on genome‐wide SNP analysis, particularly highlighting divergence in the Z chromosome. Notably, Z. l. pugetensis consistently diverges from other populations, and a north–south split within Z. l. oriantha suggests localized adaptation and potential speciation events. Elevated genetic differentiation on the Z chromosome may indicate selection‐driven evolutionary processes contributing to this divergence.

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1. Introduction

Advances in genomic sequencing techniques have opened the door to a wide range of genetic studies including population structure and signatures of selection (Andrews et al. 2016; Jeffries et al. 2016; Hodel et al. 2017; Morgan et al. 2017; Bohling et al. 2019). Genome‐scale data are not limited to just neutral genetic variation; they also allow us to detect loci under selection (Hedrick 1999; Primmer 2009) and identify potential loci of interest for local adaptation (Steiner et al. 2013). Further, genomics is useful in studying how populations adapt and evolve in response to local environmental conditions. For example, genome‐wide scans have identified evidence of selection and differentiation resulting from local adaptation to salinity in Baltic Sea herring (Guo et al. 2016); and urban land use in both white‐footed mouse (Munshi‐South et al. 2016) and red‐tailed bumblebee (Theodorou et al. 2021).

Species with large geographic distributions often inhabit diverse habitat types and encounter a range of environmental conditions and physical barriers to dispersal. Claramunt et al. (2025) found populations within microhabitats may experience isolation. Similarly, high latitude species experienced large range shifts during the Pleistocene glaciations, adapting to lower latitudes and elevations where they survive in refugia and evolved in isolation, sometimes resulting in morphological, behavioral, and genetic differences (Weir and Schluter 2004; Pielou 2008; Hewitt 2000).

The white‐crowned sparrow, Zonotrichia leucophrys , has a wide distribution and exhibits phenotypic diversity across its range (Chilton et al. 1990; Dunn et al. 1995; Morton 2002) suggesting adaptation may have resulted from isolation in different refugia during the last glacial maximum (LGM) (Taylor et al. 2021; Welke et al. 2021). The present‐day populations of white‐crowned sparrows are found at high latitudes or high elevations in Canada and the US (Figure 1): Z. l. gambelii in northern and western Canada and Alaska; Z. l. leucophrys in northeastern Canada; Z. l. oriantha in the southern Rocky Mountains and Sierra Nevada; Z. l. nuttalli and Z. l. pugetensis west of the Cascade Mountain Range (Rand 1948). The five subspecies have several distinguishing features: Z. l. nuttalli and Z. l. pugetensis, the two subspecies found in scrub habitat along a narrow strip of the Pacific Coast (Figure 1), have pale lores, a yellow beak, drabber underparts, shorter wings and duller head stripes. Z. l. gambelii, Z. l. oriantha and Z. l. leucophrys are grouped as boreal and montane birds. They are generally grayish below with deep reddish, and pale gray stripes on the back. Z. l. gambelii from the western boreal forest has white/gray lores and an orange bill whereas Z. l. oriantha from the interior west/Rocky Mountains has black lores with a pink bill. Z. l. leucophrys from the eastern boreal is very similar to Z. l. oriantha and may be difficult to distinguish in the hand though they have distinct, non‐overlapping breeding areas (Banks 1964). In addition to morphology, there is ecological and behavioral variation among white‐crowned sparrow subspecies. Z. l. nuttalli is non‐migratory (Hafner and Petersen 1985); and the other four subspecies are migratory. Both Z. l. oriantha and Z. l. gambelii have strong fidelity to their wintering sites in the southwestern US and northern Mexico and breeding sites in the north (Morton 2002).

FIGURE 1.

FIGURE 1

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Breeding season range map of white‐crowned sparrow subspecies indicating distribution and intergradation. The two Pacific groups: Z. l. nuttalli (orange) and pugetensis (red) have an overlapping range in northern California. Two other areas of overlap are shown on the map with crosshatching between Z. l. oriantha (teal), Z. l. gambelii (blue‐gray) and Z. l. leucophrys (navy blue). From Welke et al. (2021). Black circles represent sampling locations, and the circle size corresponds to the number of samples.

Several studies using mitochondrial, microsatellite and single nucleotide polymorphism datasets have pointed to the divergence of white‐crowned sparrow subspecies with varying degrees of support. Mitochondrial DNA show the presence of some unique haplotypes within Z. l. nuttalli (Taylor et al. 2021); however, there is some overlap with the other subspecies and the other four subspecies show no differentiation (Taylor et al. 2021; Welke et al. 2021). In contrast, using nuclear markers there is clear separation of western subspecies Z. l. pugetensis and Z. l. nuttalli from the other subspecies; however, among Z. l. gambelii, Z. l. oriantha and Z. l. leucophrys differentiation is either weak or patterns are inconclusive (Taylor et al. 2021; Welke et al. 2021). Furthermore, Welke et al. (2021) found that population structure in the Canadian Rocky Mountains between Z. l. gambelii and Z. l. oriantha corresponded to habitat differences, not subspecies. One reason for the inconsistent patterns of genetic differentiation with the use of mitochondrial and nuclear markers could be incomplete lineage sorting from recent separation or hybridization in areas of range overlap especially the three eastern subspecies (Figure 1).

Whole genome sequencing provides a more comprehensive picture of divergence patterns and can allow us to focus on specific parts of the genome that may differ among closely related groups of individuals (Taylor et al. 2021; Wang et al. 2021; Oyler‐McCance et al. 2015). Sex chromosomes, especially the Z chromosome, show more rapid divergence than autosomes (Hooper and Price 2017; Irwin 2018), and some song and plumage coloration genes are found on the Z chromosome (Choe and Jarvis 2021; Cumer et al. 2024), that could contribute to reproductive isolation. Given the low levels of differentiation found among some of the subspecies, the higher resolution whole genome sequencing may be better able to resolve the differences. In addition, subspecies of white‐crowned sparrows show differences in lore coloration (gray in gambelii and black in pugetensis), bill coloration (orange in gambelii, dark reddish pink in oriantha, and dull yellow in pugetensis), call note characteristics, and migratory behavior (Dunn et al. 1995; Banks 1964; Hafner and Petersen 1985), we hypothesize that potential loci under selection may have roles in pigmentation, auditory processes, and skeletal muscle development. Our study used low coverage whole genome sequencing (lcWGS) data and outlier analyses to study three of the recently diverged subspecies, Z. l. gambelii, Z. l. oriantha and Z. l. pugetensis, and examine genes that may be under selection. Based on results from previous studies, we predict the presence of four genetic groups corresponding to the three subspecies with additional differences between northern and southern populations of oriantha and that these differences will correspond to genes under selection.

2. Methods

2.1. Sample Collection

A total of thirty‐nine samples were initially collected from sites across North America (Figure 1). Z. l. oriantha (n = 6), Z. l. gambelii (n = 17) and one unknown sample were from both western and southern North America (Crowsnest Pass, Cypress Hills, Lethbridge, Banff, Mackenzie, Revelstoke, and Okanagan), with additional southern Z. l. oriantha (n = 12) from Gunnison County, Colorado, and Z. l. pugetensis (n = 3) from Oregon (Table 1). Samples were selected to avoid effects of habitat on variation for Z. l. gambelii and Z. l. oriantha that co‐occur in both alpine coniferous and riparian deciduous habitat types as Welke et al. (2021) found genetic differences among birds from the two habitats. Only samples from riparian deciduous habitat for these two subspecies were included in the analyses.

TABLE 1.

Sample location and size where study samples were collected.

Sample location Subspecies (# of sample) Latitude Longitude
MA (Mackenzie) gambelii (3) 55.31 −123.12
BA (Banff) gambelii (3), northern oriantha (1) 51.17 −115.56
OK (Okanagan) gambelii (3) 49.34 −119.57
RV (Revelstoke) gambelii (2), unknown (1) 50.99 −118.19
CNP (Crowsnest Pass) northern oriantha (3), gambelii (1) 49.73 −114.60
LE (Lethbridge) gambelii (4) 49.69 −112.83
CH (Cypress Hills) northern oriantha (2), gambelii (1) 49.56 −110.13
OR (Oregon) pugetensis (3) 45.18 −123.47
CO (Colorado) Southern oriantha (12) 38.98 −107
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Sample collection occurred during the breeding season between late May and July (2017–2021) using 12 m mist net and bird song playback. Up to 50 μL of blood was collected from the brachial vein of each bird, or a tail feather was collected. Blood samples were placed in 99% ethanol and stored at −20°C upon return to the lab; feather samples were placed in individual tubes and preserved at −20°C. Before being released, birds were photographed in case we needed to look at plumage, and banded with a numbered metal band to avoid resampling.

2.2. DNA Extraction, Library Preparation, and Sequencing

The DNA extraction followed a similar protocol of Aljanabi and Martinez (1997) with a salt extraction to obtain high yield DNA. We modified the DNA protocol by adding glycol blue to our supernatant after the precipitation step to allow enhanced visibility of the DNA pellet. We also did two ethanol washes to ensure all of the salts were removed. For low coverage whole genome sequencing, a shotgun PCR free library was prepared with 8 bp unique barcode tagged to each sample. Sequencing was done at Genome Quebec on a paired‐end run on Illumina Novaseq 6000 S4 PE 150.

2.3. LcWGS Pipeline

Our samples were sequenced at 6.2 to 8.9× depth of coverage. BWA‐MEM implemented in BWA v0.7.15 (Li 2013) was used to align the reads to the annotated genome of zebra finch Taeniopygia guttata (Warren et al. 2010; Rhie et al. 2021; reference genome version bTaeGut2.pat.W.v2, GenBank accession number GCA_008822105.2). After filtering out samples with low quality sequences or excessive missing data, 33 samples were retained. We used picard tools v.2.26.3 (http://broadinstitute.github.io/picard/) to remove PCR duplicates and unmapped reads. Reads were then left‐realigned around indels using the bamleftalign command from freebayes v1.3.6 (Garrison and Marth 2012), and we clipped overlapping reads using the clipOverlap function in bamUtils (Jun et al. 2015). We estimated genotype likelihoods using samtools (Li et al. 2009) and bcftools (Li 2011; Danecek et al. 2021) via the mpileup function. We used the bcftools call function for SNP and genotype calling with QUAL > 20. The output of this pipeline was used in pairwise F ST and DAPC based structure analysis.

2.4. Genome‐Wide F ST Scan and Outlier Loci

We ran sliding window F ST scans to identify outliers. We used ANGSD v0.933 (Korneliussen et al. 2014) to first estimate the folded allele frequency spectrum using the GATK genotype likelihood model, with minimum base and mapping qualities of 20, and then we ran the realSFS program and the Bhatia F ST estimator (Bhatia et al. 2013) using a window of size 25,000 and a step size of 10,000. To identify outlier SNPs and loci that are putatively under selection, we used a quantile‐based outlier analysis implemented in R, setting a quantile threshold of 0.999 over the sliding window F ST estimates from ANGSD (Korneliussen et al. 2014). Candidate loci are defined as those falling in the extreme tails of the empirical genome‐wide distribution of differentiation statistics. This empirical framework minimizes model assumptions and is more robust to complex or non‐equilibrium demographic histories under which Bayesian F ST‐outlier methods such as BayeScan can exhibit elevated false‐positive rates. Quantile‐based methods are also computationally efficient and transparent, making them well suited for exploratory genome scans in large SNP datasets (Lotterhos and Whitlock 2014; Foll 2010).

2.5. Evidence of Genetic Structure and Differentiation (Whole Genome vs. Mitochondrial vs. Z Chromosome)

Datasets with outlier SNPs on the Z chromosome or mitochondrial genome were used to test for genetic structure for both DAPC (Jombart et al. 2010) in Adegenet 2.0.0 implemented in RStudio v.1.3.1093 (R Core Team 2020) and PCoA implemented in GenAlEx 6.51b2 (Peakall and Smouse 2012). Our DAPC program first transformed individual multilocus genotypes into principal components (PCs) to reduce dimensionality; we retained PCs based on trade‐offs between explanatory power and overfitting following recommendations in Adegenet. We used the retained PCs as input for discriminant analysis. We inferred the number of genetic clusters (K) using the find.clusters function. We further inferred population structure with the whole dataset using ANGSD v0.933 (Korneliussen et al. 2014) to get major and minor alleles, estimate allele frequencies, and retain sites with minor allele frequencies (MAF) > 0.05. Site allele frequencies and population genetic were inferred from genotype likelihoods across individuals, and this formed our input file for the PCA and NGSAdmix analyses. For PCA, we used the sampling approach implemented in ANGSD v0.933 to calculate the genetic covariance matrix for the full dataset. We calculated genotype likelihoods with the samtools model (−GL 1) using a cutoff p‐value for SNPs of 2 × 10−6, a minimum base quality of 20 and mapping quality of 30 Phred score units, setting a minimum of 29 individuals to support genotype likelihood inference, removing low quality reads, re‐calculating base alignment quality (Li 2011), and adjusting mapping quality for excessive mismatches. We then computed the spectral decomposition in base R (R Core Team 2020, version 1.3.1093). We estimated individual admixture proportions in NGSadmix (Skotte et al. 2013) using genotype likelihoods in Beagle format pre‐computed in ANGSD v0.933 (Korneliussen et al. 2014) and the same filtering settings used for PCA. We varied the assumed number of genetic clusters (K) from two to four and ran ten iterations for each K using different starting seeds. We then used CLUMPAK (Kopelman et al. 2015) to summarize results across each K and plot our results.

To assess regions of the genome contributing to subspecies differentiation, the complete genome output file from the samtools and bcftools pipeline was analyzed for patterns of genetic differentiation. As a previous study showed two genetically distinct groups within oriantha corresponding to northern and southern groups (Welke et al. 2021), we also categorized our oriantha samples into northern (BC and AB) and southern (CO) groups. VCF tools v0.1.13 per‐site functionality vcftools ‐‐vcf sample.vcf ‐‐weir‐fst‐pop1.txt ‐‐weir‐fst‐pop2.txt (Danecek et al. 2011) was used to compute Weir and Cockerham F ST values for all pairwise combinations among subspecies and groups: Z. l. gambelii, Z. l. pugetensis; northern Z. l. oriantha and southern Z. l. oriantha to give an overview of chromosomes that may be contributing to divergence and have high values of F ST. Both the Z chromosome and mtDNA show higher rates of evolution and different modes of inheritance relative to autosomal chromosomes (Hooper and Price 2017; Irwin 2018; McCallum et al. 2024). To explore the degree of genetic differentiation between the subspecies, we calculated pairwise F ST in Arlequin v3.5.2 (Excoffier and Lischer 2010) using the whole genome dataset. To estimate chromosome‐level differentiation and map the distribution of genetic variation, we estimated nucleotide diversity using VCFtools v0.1.13 (Danecek et al. 2011).

2.6. Gene Identification

We identified genes near outlier loci using the NCBI gene viewer (Brown et al. 2015). Genomic regions surrounding outlier loci were annotated using the NCBI Gene database and NCBI Genome Data Viewer. For each outlier locus, we defined chromosomal coordinates based on the zebra finch reference genome assembly. We considered genes to be proximal to an outlier locus if they were located within ±500 kb upstream or downstream. We determined the genes' function using gene ontology program, Database for Annotation, Visualization, and Integrated Discovery (DAVID) (Sherman et al. 2022). We further explored the functions and the possible roles of these genes in our populations based on supplemental literature searches.

3. Results

3.1. Population Genetic Structure in the Zonotrichia leucophrys Subspecies: Pugetensis, Gambelii and Oriantha

Our PCA analysis with the complete dataset (103,268 SNP) showed the separation of Z. l. pugetensis and southern Z. l. oriantha from the other two groups (Figure 2A) along the first and second axis (1.70% and 1.13%, respectively). As pugetensis showed greater differentiation from the other three groups, the PCA was done without pugetensis and further separation was found (Figure 2B). The northern oriantha and gambelii separated from southern oriantha on PC1 (1.16%); and, three individuals, two oriantha and one gambelii, from Cypress Hills, a sky island in southeast Alberta, formed a third cluster separating on PC2 (1.08%). In both PCA analyses, the unknown individual from Revelstoke, British Columbia grouped with northern oriantha and gambelii. The result from our admixture analysis provides additional support for the divergence of Z. l. pugetensis from the rest, and some level of differentiation occurring among the remaining three subspecies (Figure S1).

FIGURE 2.

FIGURE 2

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(A) PCA plot of Z. leucophrys using lcWGS data shows three genetic clusters: Pugetensis and southern oriantha each formed a distinct cluster and northern oriantha grouped with gambelii to form a third cluster. PC1 explains 1.7% of the variation and separates pugetensis from the other subspecies. PC2 explains 1.13% of the variation and separates the three groups. (B) PCA run without pugetensis. PC1 contains 1.16% of the variation and separates southern oriantha from the northern oriantha and gambelii while PC2 (1.08% variation) separates three individuals from Cypress Hills (lower right) from the other two groups. One sample (unk) from Revelstoke, British Columbia was not photographed and as a result, we cannot confirm the subspecies.

Using outlier SNPs from the mitochondrial (255 SNP) and the Z chromosome (580 SNP) datasets, PCoA results showed clustering corresponding to the subspecies and geography within oriantha. The mtDNA dataset showed loose clustering (Figure 3A,B). When all three subspecies are included (Figure 3A), pugetensis and northern oriantha separate from gambelii and southern oriantha on PCo1 (23.58%) and northern oriantha shows some separation on axis 2 (9.18%). When pugetensis is excluded, axis 1 (29.26%) separates northern oriantha from the other two groups (except one gambelii) and axis 2 (11.68%) separates some gambelii from southern oriantha, resulting in three loose clusters (Figure 3B). The Z chromosome dataset showed tighter clustering. PCo1 (25.63%) clearly separated pugetensis from the other subspecies and axis 2 (15.77%) helped separate the northern oriantha and gambelii from the other two groups. When pugetensis was removed, three clear groups were present: northern oriantha, gambelii and southern oriantha (Figure 3D, axis 1 29.19%, axis 2 17.42%). Separation of the four groups using DAPC was lower in the mtDNA dataset (PC1 19.97%, PC2 15.82%, Figure 4A) compared to the Z chromosome (PC1 24.74%, PC2 11.61%, Figure 4B), but both showed four groups and high separation of pugetensis. In the mtDNA dataset, gambelii and southern oriantha showed minimal overlap.

FIGURE 3.

FIGURE 3

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PCoA analyses for the four groups of Zonotrichia subspecies with outlier SNPs from the mitochondrial genome (A and B, 255 SNP) and Z chromosome (C and D, 580 SNP) with and without pugetensis (A and C, and B and D respectively). A. Clustering of the individuals based on mitochondrial genome all four groups included: Gambelii (orange), pugetensis (purple), southern oriantha (navy blue), and northern oriantha (teal blue). B. Clustering of the individuals based on mitochondrial genome excluding pugetensis samples. Clustering of the individuals based on Z chromosome SNPs, all four populations included (C) and excluding pugetensis (D).

FIGURE 4.

FIGURE 4

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DAPC analysis of (A) 255 outlier SNPs in the mitogenome and (B) 580 outlier SNPs in the Z chromosome.

Pairwise F ST analyses (Figure 5) show statistically significant relationships (p < 0.05) which are similar to the PCoA for the whole genome dataset with all comparisons, except one. The exception is the pugetensis/northern oriantha pair; while the pairwise F ST value for this pair is very high (0.80), the lack of significance (p = 0.09) may be due to a small sample size (n = 3 for each).

FIGURE 5.

FIGURE 5

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Pairwise F ST values (below the diagonal) with the lcWGS data for the four groups (Z chromosome outlier SNPs) and associated p values (above the diagonal). Significant p values are in red.

3.2. Genetic Differentiation Across the Whole Genome

F ST was elevated on the Z chromosome across the majority of the subspecies pairs (Figure 6). For the comparison between Z. l. gambelii and northern Z. l. oriantha, F ST values are more uniform across all chromosomes, except for very low values observed on chromosome 1A, 3, 5 and 8 and elevated values on the mitochondrial genome (Figure 6A). F ST values for Z. l. gambelii and southern Z. l. oriantha are relatively uniform across all chromosomes, but noticeably lower for the mitochondrial genome, chromosomes 1, 3, and W (Figure 6B) relative to other chromosomes. Our results showed that when Z. l. gambelii was compared to Z. l. pugetensis F ST values were elevated not only on the Z chromosome, but also on chromosomes 1, 1A, and 2; and lower on chromosome 3 (Figure 6C). For Z. l. pugetensis and northern Z. l. oriantha F ST values are elevated for the Z chromosome, and chromosomes 1A and 2 (Figure 6D). Z. l. pugetensis and southern Z. l. oriantha comparisons show highly elevated F ST values for the Z chromosome and chromosome 1A and lower on chromosomes 33 and 37 (Figure 6E). Lastly F ST values are elevated for chromosomes 29, 33 and 36 and for the Z chromosome, but lower in the mitochondrial genome for the comparison between southern Z. l. oriantha and northern Z. l. oriantha (Figure 6F). Within pairs of subspecies, most consistently show higher or lower F ST values on the Z chromosome and chromosomes 1, 1A and 2; and the values are higher for comparisons involving Z. l. pugetensis to all the other two subspecies, while lower F ST values on either chromosome 1 or 1A for comparisons with Z. l. gambelii, and either southern or northern Z. l. oriantha.

FIGURE 6.

FIGURE 6

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Boxplot of genetic differentiation (F ST) for comparison between the four populations. 42 chromosomes including mitochondrial, and the sex chromosomes Z and W were compared between the populations. (A) comparison between Z. l. gambelii and northern Z. l. oriantha shows that genetic differentiation is nearly uniform across the chromosomes except for a few chromosomes. (B) Between Z. l. gambelii and southern Z. l. oriantha genetic differentiation is lowest in the mitochondrial genome. (C) Comparison between Z. l. gambelii and Z. l. pugetensis shows differentiation to be highest in the Z chromosome followed by chromosomes 1, 1A and 2. (D) Genetic differentiation between Z. l. pugetensis and northern Z. l. oriantha is highest in the Z chromosome, followed by chromosomes 1A and 2, and relatively high in the mitogenome. (E) Genetic differentiation is highest in the Z chromosome for the Z. l. pugetensis and southern Z. l. oriantha pair. Differentiation is also high in chromosomes 1, 1A, 2 and 3. (F) Chromosomes 29 and 33 showed the highest value for genetic differentiation between the northern and southern oriantha.

3.3. Genes Potentially Linked to the Outlier SNPs

We identified 36 genes (Table S1) within 500 kb of our outlier loci (Figure S2), and produced a gene function table (Table 2) across all six comparisons. We noted two genes, LUZP2 and DYM, appeared in three of the comparisons, and MYO5B and CTIF appeared in two comparisons. The largest number of genes was identified in gambelii‐pugetensis (n = 13; 36%) followed by gambelii‐southern oriantha (n = 8; 22%); pugetensis‐northern oriantha (n = 8; 22%); gambelii‐northern oriantha (n = 6; 16%); and the lowest number of genes was found in pugetensis‐southern oriantha (n = 3; 8%) and southern oriantha‐northern oriantha (n = 2; 5%). Chromosome 1A and the Z chromosome contained the largest number of identified genes (8 and 15, respectively). Interestingly, the DYM gene linked to outlier loci on the Z chromosome was identified in three pairs of subspecies.

TABLE 2.

Genes found around the outlier loci, function and occurrence in the subspecies. The genes marked with an asterisk appear in more than one pairwise comparison and those in bold appear in three comparisons.

Chr Gene Function/Ref References Subspecies combination
1A SEMA3E Positive regulation of cell migration Sherman et al. 2022 gambelii_S.oriantha
EXOC4 Involved in exocytosis Sherman et al. 2022 gambelii_pugetensis
SUV39H2 Cellular response to hypoxia Sherman et al. 2022 pugetensis_N.oriantha
FAM107B Plays a role in spine formation Mu et al. 2017 pugetensis_N.oriantha
MEIG1 Essential for spermiogenesis Sherman et al. 2022 pugetensis_N.oriantha
DCLRE1C Plays a role in DNA repair Sherman et al. 2022 pugetensis_N.oriantha
HSPA14 Heat shock protein family, in birds it protects cells during environmental stress Feder and Hofmann 1999; Shehata et al. 2020; Sherman et al. 2022 pugetensis_N.oriantha
DMTF1 Transcription regulation Sherman et al. 2022 pugetensis_N.oriantha
3 TTC32 Protein binding Sherman et al. 2022 gambelii_S.oriantha
4 FAM193A unknown Sherman et al. 2022 gambelii_S.oriantha
RNF4 Transcription regulation Sherman et al. 2022 gambelii_S.oriantha
5 LUZP2* unknown gambelii_pugetensis; pugetensis_N.oriantha; pugetensis_S.oriantha
7 KALRN Plays a role in protein phosphorylation, locomotory behavior, social behavior Sherman et al. 2022 gambelii_pugetensis
8 DAB1 Plays a role in neuron migration Sherman et al. 2022 gambelii_pugetensis
LRP8 Ventral spine cord development Sherman et al. 2022 gambelii_pugetensis
22 KAT6A Transcription regulation Sherman et al. 2022 gambelii_pugetensis
ANK1 Plays a role in cytoskeleton development Sherman et al. 2022 pugetensis_S.oriatha
31 CACNA1F Plays a role in vision, photoreceptor and skeletal muscle An et al. 2015 pugetensis_S.oriantha
32 SLC25A11 Plays a role in mitochondrial metabolism Sherman et al. 2022 S.oriantha_N.oriantha
35 STK19 Functions in DNA repair van den Heuvel et al. 2024 gambelii_pugetensis
36 RNF31 May contribute to mechanisms of melanin internalization Moreiras et al. 2022 gambelii_pugetensis
Z DYM* Golgi organization, plays a role in bone development, auditory function Denais et al. 2011; Nagtegaal et al. 2012; Sherman et al. 2022 gambelii_pugetensis; gambelii_N.oriantha; pugetensis_S.oriantha
MYO5B* Actin cytoskeleton, microfilament motor activity, and auditory function Nagtegaal et al. 2012; Sherman et al. 2022 gambelii_pugetensis; gambelii_N.oriantha
HOOK3 Participates in protein transport Sherman et al. 2022 gambelii_pugetensis
GAK Protein phosphorylation and golgi organization gambelii_pugetensis
PIGG Plays a role in glycolipid biosynthesis Sherman et al. 2022 gambelii_pugetensis
ACAA2 Plays a role in cellular response to hypoxia, and auditory function Nagtegaal et al. 2012 gambelii_N.oriantha
SMAD7 Response to laminar fluid shear stress, auditory function. Nagtegaal et al. 2012; Sherman et al. 2022 gambelii_N.oriantha
CTIF* Translation of mRNA molecules, auditory function Nagtegaal et al. 2012; Sherman et al. 2022 gambelii_N.oriantha; S.oriantha_N.oriatha
ZBTB7C Transcription regulation Sherman et al. 2022 gambelii_N.oriantha
SMAD2 Regulates glucose response. In birds it plays a role in muscle development Saneyasu et al. 2019; Sherman et al. 2022 gambelii_S.oriantha
SKOR2 Transcription regulator Sherman et al. 2022 gambelii_S.oriantha
HDHD2 In birds it plays a role in response to environmental stress Subba et al. 2026 gambelii_S.oriantha
KATNAL2 In vertebrates, plays a role in pigmentation Willsey et al. 2018 gambelii_S.oriantha
ELAC1 Functions in tRNA repair Sherman et al. 2022 pugetensis_N.oriantha
ME2 Plays a role in mitochondrial function Sherman et al. 2022 pugetensis_N.oriantha
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4. Discussion

LcWGS data showed that the three subspecies are genetically distinct and Z. l. oriantha have a north–south split. While the complete genome dataset overall showed low support for genetic differences, higher resolution was found using outlier loci on the Z chromosome, and the mitochondrial genome provided lower resolution with some mixing of Z. l. gambelii and Z. l. oriantha.

Our study compared three of the five subspecies (Z. l. gambelii, Z. l. oriantha, Z. l. pugetensis) believed to have diverged in the last 18,000 years (Rand 1948) and the patterns we found using outlier loci support previous findings of Welke et al. (2021) and Taylor et al. (2021). Welke et al. (2021) used microsatellite data from 328 birds across their range and showed a genetic split among those three subspecies and within Z. l. oriantha separating northern (BC and AB) and southern (CO) populations. Our subspecies groups are similar to the PCA in Taylor et al. (2021) who showed separation of oriantha and gambelii using RADseq data, but not with their ADMIXTURE and maximum likelihood tree analyses. Their other analyses showed either two clusters with oriantha, gambelii and leucophrys (the three subspecies east of the Cascade and Coast Mountains) in both clusters or no monophyly for oriantha and gambelii. While the oriantha sampling sites from Taylor et al. (2021) are similar to ours (four samples listed in their appendix shows two from each of CO and AB), they had fewer oriantha samples and their gambelii samples were mostly from AK (9 of 10 samples) while ours were from BC, AB, CO and OR. Aside from differences in samples and the much larger number of samples in the microsatellite dataset, the lcWGS may provide higher resolution than RADseq and contribute to some of the differences in the patterns. Low‐coverage whole‐genome sequencing (lcWGS) provides genome‐wide representation making it well suited for population‐level inference, but this genome‐wide comes at the cost of increased genotype uncertainty at individual loci due to lower sequencing depth compared to RADseq (Widmayer et al. 2025; Lou et al. 2021). We also looked at outlier loci which the other studies did not, in part as either their dataset did not allow them to (Welke et al. 2021) or it was outside the scope of the paper (Taylor et al. 2021).

Focusing on differences with the mitogenome and Z chromosome outlier datasets, we see a similar pattern as the complete genome with pugetensis separating from the populations to the east (Figures 2, 3, 4). We get separation within the three groups east of the Rocky Mountains, however, with the mtDNA outlier loci we see some gambelii with higher affinities to each of the oriantha clusters than to the other gambelii. Sample sizes could be a factor; however, both of the previous studies also showed no differences in the mtDNA from these two subspecies. Evidence of intergradation has been reported between Z. l. gambelii and Z. l. oriantha especially along their contact zone in southwestern Alberta (Lein and Corbin 1990) and may explain the lower resolution with the mitochondrial data. It may also explain why our genomic data show the northern oriantha and gambelii as the two most genetically similar groups since many of the samples for those two groups are from Alberta. In contrast the Z chromosome outlier dataset showed clear separation of all four groups though separation among the eastern populations was more apparent when pugetensis was removed (Figures 3 and 4).

The divergence within the Zonotrichia leucophrys clade is linked to the historical processes associated with range expansions at the end of the Pleistocene (Zink et al. 1991; Morton 2002). Taylor et al. (2021) and Welke et al. (2021) pointed to divergence especially for western nuttalli and pugetensis separating from gambelii, oriantha and leucophrys with a combination of microsatellite and nuclear SNP markers corresponding to isolation in refugia on either side of the Rocky Mountains. However, there was no clear pattern to divergence among Z. l. gambelii, Z. l. oriantha and Z. l. leucophrys in Taylor et al. (2021) and aside from the PCA, their results show evidence of the three subspecies mixing. Our data support the isolation of pugetensis from both gambelii and oriantha likely being the result of geographic separation between pugetensis and the other two subspecies, and the high fidelity to their breeding sites (Morton 2002), but it also showed differences among gambelii and oriantha. The genetic structuring we observe between gambelii and oriantha despite ongoing gene flow suggests that even shallow geographical and ecological barriers can maintain incipient divergence.

Within oriantha, populations from the northern part of the range (Alberta and British Columbia) clustered separately from those in the south (Colorado). Welke et al. (2021) had previously documented this separation with microsatellite data using a larger number of samples. High fidelity of oriantha populations in Colorado, divergence time and geographic distance or the sampling gap may also factor into the genetic divergence between the two oriantha groups. We also saw separation of three birds (two northern oriantha and one gambelii) from Cypress Hills. Cypress Hills is a sky island in southeastern Alberta and southwestern Saskatchewan known to contain genetically distinct populations of several species (Haché et al. 2017; Dempsey et al. 2020; Carpenter et al. 2022) and contains habitat more similar to the Rocky Mountains than the surrounding prairie habitat. Additional sampling across the range is needed to better understand the separation within Z. l. oriantha populations using lcWGS data and the factors that have led to their divergence.

4.1. Genomic Differences

When examining all 42 chromosomes (Figure 6), we found elevated genetic differentiation in all comparisons involving pugetensis, and between northern and southern Z. l. oriantha, particularly on the Z chromosome. As the Z chromosome has a smaller effective population size, it is more sensitive to drift and may also be subject to sex‐linked selection dynamics (Charlesworth et al. 1987; Oyler‐McCance et al. 2015). McCallum et al. (2024) suggested that recurrent selection, including diversifying selection, has shaped divergence on the Z chromosome in Z. atricapilla and Z. leucophrys , particularly between pugetensis and gambelii.

A large block of elevated differentiation on chromosome 1A was also reported in the McCallum et al. (2024) study and hypothesized to result from reduced recombination and/or selection maintaining linkage among co‐adapted loci. We observed similar patterns when we mapped them to the zebra finch genome: all comparisons involving pugetensis showed elevated differentiation on chromosome 1A, whereas oriantha–gambelii comparisons did not. In other avian taxa, including warblers and robins, high differentiation on chromosome 1A has been attributed to mitonuclear coadaptation, as this chromosome is enriched for nuclear‐encoded mitochondrial genes (Morales et al. 2018; Wang et al. 2021).

Our mitochondrial genome analyses mapped to zebra finch genome revealed relatively low differentiation between northern and southern oriantha compared to the Z chromosome and autosomal loci. One possible explanation is historical mitochondrial introgression or positive selection maintaining shared haplotypes across geographically structured nuclear backgrounds (Bazin et al. 2006; Seixas et al. 2018; Pereira et al. 2021). While female‐biased dispersal could reduce mitochondrial divergence, this is unlikely to fully explain the pattern as previous microsatellite data showed clear population structure (Welke et al. 2021), suggesting limited recent gene flow.

In contrast, mitochondrial differentiation between gambelii and both oriantha groups was higher than expected under a scenario of mtDNA capture through past hybridization, which typically reduces divergence (Toews and Brelsford 2012). Instead, elevated F ST at mitochondrial outlier loci may reflect lineage‐specific adaptation to local environments, such as climate, consistent with mitonuclear coadaptation (Dowling et al. 2008; Morales et al. 2018; Bar‐Yaacov et al. 2015). This hypothesis is supported by the lack of corresponding nuclear differentiation, implying selection rather than drift or neutral gene flow is driving mitochondrial divergence.

4.2. Genes Associated With Regions of Differentiation

Our study identified 36 genes within 500 kb of our outlier loci across the genome; four of these genes were found in more than one subspecies combinations (LUZP, DYM, MYO5B, and CTIF). Some genes, such as DYM, FAM107, LRP8, CACNA1F, and SMAD2, have roles in bone formation and skeletal muscle development (Denais et al. 2011; Nagtegaal et al. 2012; Mu et al. 2017; Sherman et al. 2022), which may be important for locomotion in animals. In addition to DYM's gene role in bone and muscle development, it is reported to be involved in sound processing in mice, along with other genes such as CTIF, ACAA2, SMAD7, and MYO5B (Nagtegaal et al. 2012). RNF31 and KATNAL2 are reported to play a role in melanin internalization and pigmentation in humans and mice (Moreiras et al. 2022; Willsey et al. 2018). These genes may be relevant in the divergence of bill coloration and lore pigmentation in our study populations. Further, HDHD2, HSPA14, CTIF, and KALRN were found to be linked to other important functions, such as response to environmental stress, auditory function, and locomotory and social behavior, respectively (Sherman et al. 2022; Shehata et al. 2020; Nagtegaal et al. 2012). These genes may also contribute to the migratory behavior and adaptation to local environment in our study populations, such as gambelii, that are highly migratory. With the identification of two genes linked to pigmentation within the regions showing elevated differentiation, it would be interesting to see if these correspond to variation in the bill and lore colouration, which are two major phenotypic features used to differentiate these subspecies. It will also be informative to know if the genes linked to stress, hypoxia, skeletal, and muscle development contribute to migration in this population.

5. Conclusions

By integrating low‐coverage whole‐genome sequencing with mitochondrial and Z chromosome analyses, this study clarifies patterns of recent divergence in the white‐crowned sparrow ( Zonotrichia leucophrys ). Across all analyses, Z. l. pugetensis was consistently the most genetically differentiated lineage, whereas divergence among the Rocky Mountain subspecies was more complex. We identified a clear north–south split within Z. l. oriantha, with northern populations showing greater genetic similarity to Z. l. gambelii than to southern oriantha, consistent with recent divergence, incomplete lineage sorting, and localized gene flow.

Genetic differentiation was highly heterogeneous across the genome, with the strongest divergence concentrated on the Z chromosome, highlighting the disproportionate role of sex‐linked loci in structuring variation among closely related subspecies. In contrast, mitochondrial markers showed lower resolution and evidence of mitonuclear discordance, underscoring the importance of jointly analyzing multiple genomic compartments. Regions of elevated differentiation were associated with genes linked to pigmentation, skeletal and muscle development, auditory function, and environmental stress responses, suggesting that selection on a limited number of loci may contribute to phenotypic divergence.

Overall, our results demonstrate that substantial genetic structure can emerge in the absence of strong physical barriers, driven by a combination of sex‐linked selection, local adaptation, and historical demography. This work highlights the utility of lcWGS for resolving shallow divergence and provides a foundation for future studies linking genomic differentiation to phenotypic and ecological variation in widespread songbirds.

Author Contributions

Patricia B. Osagie: formal analysis (lead), funding acquisition (supporting), investigation (equal), methodology (equal), writing – original draft (equal), writing – review and editing (equal). Juan Enciso‐Romero: formal analysis (supporting), methodology (supporting), software (lead), writing – review and editing (equal). Theresa M. Burg: conceptualization (lead), funding acquisition (lead), project administration (lead), resources (lead), supervision (lead), writing – original draft (supporting), writing – review and editing (lead).

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada and Alberta Conservation Association.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Gene occurrence across subspecies pairs, providing an overview of genes that are common to each pair.

Figure S1: NGSAdmix plot shows support for the divergence of Z. l. pugetensis from other subspecies and some level of differentiation for the other three groups at K = 3 and K = 4.

Figure S2: F ST scans depicting regions that may be contributing to divergence of the four groups as determined by peaks and or high values based on the lcWGS dataset. The red line is the 99.9% threshold set for the identification of the outlier SNPs. SNPs/genes above the line are considered outliers. Chromosomes are arranged in the order from left chr. 1, 1A … 28, Z, 29, 30, W, 31…37.

ECE3-16-e73651-s001.docx (628.8KB, docx)

Acknowledgements

We would like to thank and acknowledge Parks Canada and Alberta Environment and Protected Areas for support received to collect bird samples from the national and provincial parks. We are grateful for field assistance received from Brendan Graham, Danika Schramm, Ross Conover, James Rivers, and Catherine Welke. We wish to thank the Royal Alberta Museum and Royal British Columbia Museum for providing us with additional samples.

Data Availability Statement

The dataset for this work is archived in The Federated Research Data Repository. https://doi.org/10.20383/103.01131.

References

  1. Aljanabi, S. M. , and Martinez I.. 1997. “Universal and Rapid Salt‐Extraction of High Quality Genomic DNA for PCR‐Based Techniques.” Nucleic Acids Research 25, no. 22: 4692–4693. 10.1093/nar/25.22.4692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. An, J. , Zhang L., Jiao B., et al. 2015. “Cacna1f Gene Decreased Contractility of Skeletal Muscle in Rat Model with Congenital Stationary Night Blindness.” Gene 562, no. 2: 210–219. 10.1016/j.gene.2015.02.073. [DOI] [PubMed] [Google Scholar]
  3. Andrews, K. R. , Good J. M., Miller M. R., Luikart G., and Hohenlohe P. A.. 2016. “Harnessing the Power of RADseq for Ecological and Evolutionary Genomics.” Nature Reviews. Genetics 17: 81–92. 10.1038/nrg.2015.28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Banks, R. 1964. “Geographic Variation in the White‐Crowned Sparrow Zonotrichia leucophrys .” University of California Publications in Zoology 70: 1–123. [Google Scholar]
  5. Bar‐Yaacov, D. , Hadjivasiliou Z., Levin L., et al. 2015. “Mitochondrial Involvement in Vertebrate Speciation? The Case of Mito‐Nuclear Genetic Divergence in Chameleons.” Genome Biology and Evolution 7, no. 12: 3322–3336. 10.1093/gbe/evv222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bazin, E. , Glémin S., and Galtier N.. 2006. “Population Size Does Not Influence Mitochondrial Genetic Diversity in Animals.” Science 312, no. 5773: 570–572. 10.1126/science.1122033. [DOI] [PubMed] [Google Scholar]
  7. Bhatia, G. , Patterson N., Sankararaman S., and Price A. L.. 2013. “Estimating and Interpreting F ST: The Impact of Rare Variants.” Genome Research 23, no. 9: 1514–1521. 10.1101/gr.154831.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bohling, J. , Small M., Von Bargen J., Louden A., and DeHaan P.. 2019. “Comparing Inferences Derived From Microsatellite and RADseq Datasets: A Case Study Involving Threatened Bull Trout.” Conservation Genetics 20: 329–342. 10.1007/s10592-018-1134-z. [DOI] [Google Scholar]
  9. Brown, G. R. , Hem V., Katz K. S., et al. 2015. “Gene: A Gene‐Centered Information Resource at NCBI.” Nucleic Acids Research 43: D36–D42. 10.1093/nar/gku1055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Carpenter, A. M. , Graham B. A., Spellman G. M., and Burg T. M.. 2022. “Do Habitat and Elevation Promote Hybridization During Secondary Contact Between Three Genetically Distinct Groups of Warbling Vireo ( Vireo gilvus )?” Heredity 128, no. 5: 352–363. 10.1038/s41437-022-00529-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Charlesworth, B. , Coyne J. A., and Barton N. H.. 1987. “The Relative Rates of Evolution of Sex Chromosomes and Autosomes.” American Naturalist 130, no. 1: 113–146. 10.1086/284694. [DOI] [Google Scholar]
  12. Chilton, G. , Lein M. R., and Baptista L. F.. 1990. “Mate Choice by Female White‐Crowned Sparrows in a Mixed‐Dialect Population.” Behavioral Ecology and Sociobiology 27: 223–227. 10.1007/BF00180307. [DOI] [Google Scholar]
  13. Choe, H. N. , and Jarvis E. D.. 2021. “The Role of Sex Chromosomes and Sex Hormones in Vocal Learning Systems.” Hormones and Behavior 132: 104978. 10.1016/j.yhbeh.2021.104978. [DOI] [PubMed] [Google Scholar]
  14. Claramunt, S. , Sheard C., Brown J. W., et al. 2025. “A New Time Tree of Birds Reveals the Interplay Between Dispersal, Geographic Range Size, and Diversification.” Current Biology 35, no. 16: 3883–3895.e3884. 10.1016/j.cub.2025.07.004. [DOI] [PubMed] [Google Scholar]
  15. Cumer, T. , Machado A. P., San‐Jose L. M., et al. 2024. “The Genomic Architecture of Continuous Plumage Colour Variation in the European Barn Owl ( Tyto alba ).” Proceedings of the Biological Sciences 291, no. 2014: 20231995. 10.1098/rspb.2023.1995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Danecek, P. , Auton A., Abecasis G., et al. 2011. “The Variant Call Format and VCFtools.” Bioinformatics 27, no. 15: 2156–2158. 10.1093/bioinformatics/btr330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Danecek, P. , Bonfield J. K., Liddle J., et al. 2021. “Twelve Years of SAMtools and BCFtools.” GigaScience 10, no. 2: giab008. 10.1093/gigascience/giab008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dempsey, Z. W. , Goater C. P., and Burg T. M.. 2020. “Living on the Edge: Comparative Phylogeography and Phylogenetics of Oreohelix Land Snails at Their Range Edge in Western Canada.” BMC Evolutionary Biology 20, no. 1: 3. 10.1186/s12862-019-1566-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Denais, C. , Dent C. L., Southgate L., et al. 2011. “Dymeclin, the Gene Underlying Dyggve‐Melchior‐Clausen Syndrome, Encodes a Protein Integral to Extracellular Matrix and Golgi Organization and Is Associated With Protein Secretion Pathways Critical in Bone Development.” Human Mutation 32, no. 2: 231–239. 10.1002/humu.21413. [DOI] [PubMed] [Google Scholar]
  20. Dowling, D. K. , Friberg U., and Lindell J.. 2008. “Evolutionary Implications of Non‐Neutral Mitochondrial Genetic Variation.” Trends in Ecology & Evolution 23, no. 10: 546–554. 10.1016/j.tree.2008.06.008. [DOI] [PubMed] [Google Scholar]
  21. Dunn, J. L. , Garrett K. L., and Alderfer J.. 1995. “White‐Crowned Sparrow Subspecies: Identification and Distribution.” Birding 27: 182–201. [Google Scholar]
  22. Excoffier, L. , and Lischer H. E. L.. 2010. “Arlequin Suite ver. 3.5: A New Series of Programs to Perform Population Genetics Analyses Under Linux and Windows.” Molecular Ecology Resources 10, no. 3: 564–567. 10.1111/j.1755-0998.2010.02847.x. [DOI] [PubMed] [Google Scholar]
  23. Feder, M. E. , and Hofmann G. E.. 1999. “Heat‐Shock Proteins, Molecular Chaperones, and the Stress Response: Evolutionary and Ecological Physiology.” Annual Review of Physiology 61: 243–282. 10.1146/annurev.physiol.61.1.24. [DOI] [PubMed] [Google Scholar]
  24. Foll, M. 2010. BayeScan v2.0 User Manual. University of Bern. https://cmpg.unibe.ch/software/BayeScan/files/BayeScan2.0_manual.pdf. [Google Scholar]
  25. Garrison, E. , and Marth G.. 2012. Haplotype‐Based Variant Detection From Short‐Read Sequencing arXiv:1207.3907 .
  26. Guo, B. , Li Z., and Merilä J.. 2016. “Population Genomic Evidence for Adaptive Differentiation in the Baltic Sea Herring.” Molecular Ecology 25: 2833–2852. 10.1111/mec.13657. [DOI] [PubMed] [Google Scholar]
  27. Haché, S. , Bayne E. M., Villard M.‐A., et al. 2017. “Phylogeography of a Migratory Songbird Across Its Canadian Breeding Range: Implications for Conservation Units.” Ecology and Evolution 7, no. 16: 6078–6088. 10.1002/ece3.3170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hafner, D. J. , and Petersen K. E.. 1985. “Song Dialects and Gene Flow in the White‐Crowned Sparrow, Zonotrichia leucophrys nuttalli .” Evolution 39, no. 3: 687–694. 10.1111/j.1558-5646.1985.tb00405.x. [DOI] [PubMed] [Google Scholar]
  29. Hedrick, P. W. 1999. “Perspective: Highly Variable Loci and Their Interpretation in Evolution and Conservation.” Evolution 53: 313–318. 10.1111/j.1558-5646.1999.tb03767.x. [DOI] [PubMed] [Google Scholar]
  30. Hewitt, G. M. 2000. “The Genetic Legacy of the Quaternary Ice Ages.” Nature 405, no. 6789: 907–913. 10.1038/35016000. [DOI] [PubMed] [Google Scholar]
  31. Hodel, R. G. J. , Chen S., Payton A. C., McDaniel S. F., Soltis P., and Soltis D. E.. 2017. “Adding Loci Improve Phylogeographic Resolution in Red Mangroves Despite Increased Missing Data: Comparing Microsatellites and RAD‐Seq and Investigating Loci Filtering.” Scientific Reports 7: 17598. 10.1038/s41598-017-16810-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hooper, D. M. , and Price T. D.. 2017. “Chromosomal Inversion Differences Correlate With Range Overlap in Passerine Birds.” Nature Ecology & Evolution 1, no. 10: 1526–1534. 10.1038/s41559-017-0284-6. [DOI] [PubMed] [Google Scholar]
  33. Irwin, D. E. 2018. “Sex Chromosomes and Speciation in Birds and Other ZW Systems.” Molecular Ecology 27, no. 19: 3831–3851. 10.1111/mec.14537. [DOI] [PubMed] [Google Scholar]
  34. Jeffries, D. L. , Copp G. H., Lawson Handley L., Olsén K. H., Sayer C. D., and Hänfling B.. 2016. “Comparing RADseq and Microsatellites to Infer Complex Phylogeographic Patterns, an Empirical Perspective in the Crucian Carp, Carassius carassius , L.” Molecular Ecology 25: 2997–3018. 10.1111/mec.13613. [DOI] [PubMed] [Google Scholar]
  35. Jombart, T. , Devillard S., and Balloux F.. 2010. “Discriminant Analysis of Principal Components: A New Method for the Analysis of Genetically Structured Populations.” BMC Genetics 11: 94. 10.1186/1471-2156-11-94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Jun, G. , Wing M. K., Abecasis G. R., and Kang H. M.. 2015. “An Efficient and Scalable Analysis Framework for Variant Extraction and Refinement From Population‐Scale DNA Sequence Data.” Genome Research 25, no. 6: 918–925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kopelman, N. M. , Mayzel J., Jakobsson M., Rosenberg N. A., and Mayrose I.. 2015. “Clumpak: A Program for Identifying Clustering Modes and Packaging Population Structure Inferences Across K.” Molecular Ecology Resources 15, no. 5: 1179–1191. 10.1111/1755-0998.12387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Korneliussen, T. S. , Albrechtsen A., and Nielsen R.. 2014. “ANGSD: Analysis of Next Generation Sequencing Data.” BMC Bioinformatics 15, no. 1: 356. 10.1186/s12859-014-0356-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lein, M. R. , and Corbin K. W.. 1990. “Song and Plumage Phenotypes in a Contact Zone Between Subspecies of the White‐Crowned Sparrow ( Zonotrichia leucophrys ).” Canadian Journal of Zoology 68, no. 12: 2625–2629. 10.1139/z90-366. [DOI] [Google Scholar]
  40. Li, H. 2011. “A Statistical Framework for SNP Calling, Mutation Discovery, Association Mapping and Population Genetical Parameter Estimation From Sequencing Data.” Bioinformatics 27: 2987–2993. 10.1093/bioinformatics/btr509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Li, H. 2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs With BWA‐MEM.” arXiv preprint arXiv:1303.3997 .
  42. Li, H. , Handsaker B., Wysoker A., et al. 2009. “The Sequence Alignment/Map Format and SAMtools.” Bioinformatics 25, no. 16: 2078–2079. 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lotterhos, K. E. , and Whitlock M. C.. 2014. “Evaluation of Demographic History and Neutral Parameterization on the Performance of F ST Outlier Tests.” Molecular Ecology 23, no. 9: 2178–2192. 10.1111/mec.12725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lou, R. N. , Jacobs A., Wilder A. P., and Therkildsen N. O.. 2021. “A Beginner's Guide to Low‐Coverage Whole Genome Sequencing for Population Genomics.” Molecular Ecology 30, no. 23: 5966–5993. 10.1111/mec.16077. [DOI] [PubMed] [Google Scholar]
  45. McCallum, Q. , Askelson K., Fogarty F. F., et al. 2024. “Pronounced Differentiation on the Z Chromosome and Parts of the Autosomes in Crowned Sparrows Contrasts With Mitochondrial Paraphyly: Implications for Speciation.” Journal of Evolutionary Biology 37, no. 2: 171–188. 10.1093/jeb/voae004. [DOI] [PubMed] [Google Scholar]
  46. Morales, H. E. , Pavlova A., Amos N., et al. 2018. “Concordant Divergence of Mitogenomes and a Mitonuclear Gene Cluster in Bird Lineages Inhabiting Different Climates.” Nature Ecology & Evolution 2, no. 8: 1258–1267. [DOI] [PubMed] [Google Scholar]
  47. Moreiras, H. , Bento‐Lopes L., Neto M. V., et al. 2022. “Melanocore Uptake by Keratinocytes Occurs Through Phagocytosis and Involves Protease‐Activated Receptor‐2 Internalization.” Traffic 23, no. 6: 331–345. 10.1111/tra.12843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Morgan, T. D. , Graham C. F., McArthur A. G., Raphenya A. R., Boreham D. R., and Manzon R. G.. 2017. “Genetic Population Structure of the Round Whitefish ( Prosopium cylindraceum ) in North America: Multiple Markers Reveal Glacial Refugia and Regional Subdivision.” Canadian Journal of Fisheries and Aquatic Sciences 75: 836–849. 10.1139/cjfas-2016-0528. [DOI] [Google Scholar]
  49. Morton, M. 2002. “The Mountain White‐Crowned Sparrow: Migration and Reproduction at High Altitude.” Studies in Avian Biology 24: 1–236. [Google Scholar]
  50. Mu, P. , Akashi T., Lu F., Kishida S., and Kadomatsu K.. 2017. “A Novel Nuclear Complex of DRR1, F‐Actin and COMMD1 Involved in NF‐κB Degradation and Cell Growth Suppression in Neuroblastoma.” Oncogene 36, no. 41: 5745–5756. 10.1038/onc.2017.181. [DOI] [PubMed] [Google Scholar]
  51. Munshi‐South, J. , Zolnik C. P., and Harris S. E.. 2016. “Population Genomics of the Anthropocene: Urbanization Is Negatively Associated With Genome‐Wide Variation in White‐Footed Mouse Populations.” Evolutionary Applications 9, no. 4: 546–564. 10.1111/eva.12357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nagtegaal, A. P. , Spijker S., Crins T. T. H., and Borst J. G. G.. 2012. “A Novel QTL Underlying Early‐Onset, Low‐Frequency Hearing Loss in BXD Recombinant Inbred Strains.” Genes, Brain and Behavior 11: 911–920. 10.1111/j.1601-183X.2012.00845.x. [DOI] [PubMed] [Google Scholar]
  53. Oyler‐McCance, S. J. , Cornman R. S., Jones K. L., and Fike J. A.. 2015. “Genomic Sequence Data Suggest Adaptive Divergence in the Gunnison Sage‐Grouse ( Centrocercus minimus ).” BMC Genomics 16: 1–14. 10.1186/s12864-015-1349-6.25553907 [DOI] [Google Scholar]
  54. Peakall, R. , and Smouse P. E.. 2012. “GenAlEx 6.5: Genetic Analysis in Excel. Population Genetic Software for Teaching and Research–An Update.” Bioinformatics 28, no. 19: 2537–2539. 10.1093/bioinformatics/bts460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Pereira, R. J. , Monahan W. B., and Wake D. B.. 2021. “Predictors for Reproductive Isolation in a Ring Species Complex Following Genetic and Ecological Divergence.” Molecular Ecology 30, no. 1: 1–14. 10.1111/mec.15789. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Pielou, E. C. 2008. After the Ice Age: The Return of Life to Glaciated North America. University of Chicago Press. [Google Scholar]
  57. Primmer, C. R. 2009. “From Conservation Genetics to Conservation Genomics.” Annals of the New York Academy of Sciences 1162: 357–368. 10.1111/j.1749-6632.2009.04444.x. [DOI] [PubMed] [Google Scholar]
  58. R Core Team . 2020. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.r‐project.org/. [Google Scholar]
  59. Rand, A. L. 1948. “Glaciation, an Isolating Factor in Speciation.” Evolution 2, no. 4: 314–321. 10.1111/j.1558-5646.1948.tb02749.x. [DOI] [PubMed] [Google Scholar]
  60. Rhie, A. , McCarthy S. A., Fedrigo O., et al. 2021. “Towards Complete and Error‐Free Genome Assemblies of All Vertebrate Species.” Nature 592, no. 7856: 737–746. 10.1038/s41586-021-03451-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Saneyasu, T. , Fukuzo S., Kitashiro A., Nagata K., Honda K., and Kamisoyama H.. 2019. “Central Administration of Insulin and Refeeding Lead to the Phosphorylation of AKT, but not FOXO1, in the Hypothalamus of Broiler Chicks.” Physiology & Behavior 210: 112644. 10.1016/j.physbeh.2019.112644. [DOI] [PubMed] [Google Scholar]
  62. Seixas, F. A. , Boursot P., and Melo‐Ferreira J.. 2018. “The Genomic Impact of Historical Hybridization With Massive Mitochondrial DNA Introgression.” Genome Biology 19: 91. 10.1186/s13059-018-1467-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Shehata, A. M. , Saadeldin I. M., Tukur H. A., and Habashy W. S.. 2020. “Modulation of Heat‐Shock Proteins Mediates Chicken Cell Survival against Thermal Stress.” Animals 10, no. 12: 2407. 10.3390/ani10122407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Sherman, B. T. , Hao M., Qiu J., et al. 2022. “DAVID: A Web Server for Functional Enrichment Analysis and Functional Annotation of Gene Lists (2021 Update).” Nucleic Acids Research 50, no. W1: W216–W221. 10.1093/nar/gkac194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Skotte, L. , Korneliussen T. S., and Albrechtsen A.. 2013. “Estimating Individual Admixture Proportions From Next Generation Sequencing Data.” Genetics 195, no. 3: 693–702. 10.1534/genetics.113.154138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Steiner, C. C. , Putnam A. S., Hoeck P. E. A., and Ryder O. A.. 2013. “Conservation Genomics of Threatened Animal Species.” Annual Review of Animal Biosciences 1: 261–281. 10.1146/annurev-animal-031412-103636. [DOI] [PubMed] [Google Scholar]
  67. Subba, P. , Mariette M. M., Palios K. A., et al. 2026. “Programming of Embryonic Blood Brain Barrier and Neurovascular Transcriptome by an Anticipatory Acoustic Signal of Heat in the Zebra Finch.” bioRxiv, 2026.2001.2023.701307. 10.64898/2026.01.23.701307. [DOI] [Google Scholar]
  68. Taylor, R. S. , Bramwell A. C., Clemente‐Carvalho R., et al. 2021. “Cytonuclear Discordance in the Crowned‐Sparrows, Zonotrichia atricapilla and Zonotrichia leucophrys .” Molecular Phylogenetics and Evolution 162: 107216. 10.1016/j.ympev.2021.107216. [DOI] [PubMed] [Google Scholar]
  69. Theodorou, P. , Baltz L. M., Paxton R. J., and Soro A.. 2021. “Urbanization Is Associated With Shifts in Bumblebee Body Size, With Cascading Effects on Pollination.” Evolutionary Applications 14, no. 1: 53–68. 10.1111/eva.13087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Toews, D. P. L. , and Brelsford A.. 2012. “The Biogeography of Mitochondrial and Nuclear Discordance in Animals.” Molecular Ecology 21, no. 16: 3907–3930. 10.1111/j.1365-294X.2012.05664.x. [DOI] [PubMed] [Google Scholar]
  71. van den Heuvel, D. , Rodríguez‐Martínez M., van der Meer P. J., et al. 2024. “STK19 Facilitates the Clearance of Lesion‐Stalled RNAPII During Transcription‐Coupled DNA Repair.” bioRxiv: The Preprint Server for Biology, 2024.07.22.604575. 10.1101/2024.07.22.604575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wang, S. , Ore M. J., Mikkelsen E. K., et al. 2021. “Signatures of Mitonuclear Coevolution in a Warbler Species Complex.” Nature Communications 12, no. 1: 4279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Warren, W. C. , Clayton D. F., Ellegren H., et al. 2010. “The Genome of a Songbird.” Nature 464, no. 7289: 757–762. 10.1038/nature08819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Weir, J. T. , and Schluter D.. 2004. “Ice Sheets Promote Speciation in Boreal Birds.” Proceedings. Biological Sciences 271, no. 1551: 1881–1887. 10.1098/rspb.2004.2803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Welke, C. A. , Graham B., Conover R. R., Rivers J. W., and Burg T. M.. 2021. “Habitat Linked Genetic Structure for White‐Crowned Sparrow ( Zonotrichia leucophrys ): Local Factors Shape Population Genetic Structure.” Ecology and Evolution 11: 11700–11717. 10.1002/ece3.7887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Widmayer, S. J. , Wooldridge L. K., Swanzey E., et al. 2025. “Low‐Coverage Whole‐Genome Sequencing Facilitates Accurate and Cost‐Effective Haplotype Reconstruction in Complex Mouse Crosses.” Mammalian Genome 36, no. 4: 1063–1080. 10.1007/s00335-025-10148-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Willsey, H. R. , Walentek P., Exner C. R. T., et al. 2018. “Katanin‐Like Protein Katnal2 is Required for Ciliogenesis and Brain Development in Xenopus Embryos.” Developmental Biology 442, no. 2: 276–287. 10.1016/j.ydbio.2018.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Zink, R. M. , Dittmann D. L., and Rootes W. L.. 1991. “Mitochondrial DNA Variation and the Phylogeny of Zonotrichia .” Auk 108: 578–584. https://digitalcommons.usf.edu/auk/vol108/iss3/11. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1: Gene occurrence across subspecies pairs, providing an overview of genes that are common to each pair.

Figure S1: NGSAdmix plot shows support for the divergence of Z. l. pugetensis from other subspecies and some level of differentiation for the other three groups at K = 3 and K = 4.

Figure S2: F ST scans depicting regions that may be contributing to divergence of the four groups as determined by peaks and or high values based on the lcWGS dataset. The red line is the 99.9% threshold set for the identification of the outlier SNPs. SNPs/genes above the line are considered outliers. Chromosomes are arranged in the order from left chr. 1, 1A … 28, Z, 29, 30, W, 31…37.

ECE3-16-e73651-s001.docx (628.8KB, docx)

Data Availability Statement

The dataset for this work is archived in The Federated Research Data Repository. https://doi.org/10.20383/103.01131.

Articles from Ecology and Evolution are provided here courtesy of Wiley

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