New Results

Whole-genome sequences suggest long term declines of spotted owl (Strix occidentalis) (Aves: Strigiformes: Strigidae) populations in California

Zachary R. Hanna, John P. Dumbacher, Rauri C.K. Bowie, Jeffrey D. Wall
doi: https://doi.org/10.1101/343434

Abstract

We analyzed whole-genome data of four spotted owls (Strix occidentalis) to provide a broad-scale assessment of the genome-wide nucleotide diversity across S. occidentalis populations in California. We assumed that each of the four samples was representative of its population and we estimated effective population sizes through time for each corresponding population. Our estimates provided evidence of long-term population declines in all California S. occidentalis populations. We found no evidence of genetic differentiation between northern spotted owl (S. o. caurina) populations in the counties of Marin and Humboldt in California. We estimated greater differentiation between populations at the northern and southern extremes of the range of the California spotted owl (S. o. occidentalis) than between populations of S. o. occidentalis and S. o. caurina in northern California. The San Diego County S. o. occidentalis population was substantially diverged from the other three S. occidentalis populations. These whole-genome data support a pattern of isolation-by-distance across spotted owl populations in California, rather than elevated differentiation between currently recognized subspecies.

Introduction

Spotted owls (Strix occidentalis) in California are an important protected species that is declining throughout most of its range. Their status has enormous economic implications (Montgomery et al. 1994). The species is represented in California by two recognized subspecies, the northern spotted owl (S. o. caurina) and the California spotted owl (S. o. occidentalis). In California, S. o. caurina occurs from the northern border with Oregon, south along the west side of California’s Central Valley to the Golden Gate strait, and southeast to the region between the Pit River and Lassen Peak. The latter region constitutes the dividing line between S. o. caurina and S. o. occidentalis (Barrowclough et al. 2011; Tempel et al. 2017). Strix occidentalis occidentalis occurs from this line south to the southern Sierra Nevada and in mountainous areas of southern California, including the coastal ranges west of the Central Valley as far north as the Santa Lucia Mountains and Monterey Bay (Barrowclough et al. 1999, 2011 p. 2017; Davis and Gould 2008; Gutiérrez et al. 2017). Strix occidentalis caurina currently has a threatened listing status under the United States of America Endangered Species Act as well as the California Endangered Species Act. Strix occidentalis occidentalis is in decline throughout much of its range (Tempel et al. 2017). Although not presently listed under either endangered species act, S. o. occidentalis is currently designated by the California Department of Fish and Wildlife as a Species of Special Concern (Davis and Gould 2008).

There have been multiple excellent studies of the population genetics of Strix occidentalis caurina and S. o. occidentalis. Most of these relied on mitochondrial DNA (Barrowclough et al. 1999, 2005, 2011; Haig et al. 2004), which comprises a single genetic locus that does not always accurately represent the history of the entire nuclear genome. Additional studies analyzed microsatellites (Funk et al. 2008), RAPD markers (Haig et al. 2001), or a combination of mitochondrial DNA and microsatellites (Miller et al. 2017), but these mostly focused on S. o. caurina and northern populations of S. o. occidentalis. A whole genome analysis would test whether earlier conclusions based on mitochondrial genes and several nuclear markers are generalizable. By sequencing whole genomes, we can acquire data for thousands of independent loci and, even with one individual per population, we can obtain information for thousands of additional samples of the evolutionary history of a population as compared with previous S. occidentalis genetic studies.

The barred owl (Strix varia) has invaded the entire range of Strix occidentalis caurina and is in contact with S. o. occidentalis from the Lassen area to the southern Sierra Nevada (Keane 2017). Strix occidentalis and S. varia hybridize, but the frequency and primary directionality of introgression is unknown. In order to detect introgression on the leading edge of the S. varia expansion in California, we need a better understanding of the genetic variation present in pure S. occidentalis populations.

The recently assembled genome of a Strix occidentalis caurina individual from Marin County, California (Hanna et al. 2017d, 2017c) provides a useful resource for characterizing the genomic variation in S. occidentalis throughout California. Here we report data from high-coverage whole-genome sequences of that individual and new sequences of three additional S. occidentalis. Our sampling spans the northern and southern geographic extremes of S. o. caurina and S. o. occidentalis within California (Figure 1). We use these data to provide a broad-scale assessment of the genomic divergence of S. occidentalis in California and estimate long-term trends in population size.

Figure 1.
Figure 1. California map of the collection locations of the Strix occidentalis specimens from which we obtained whole-genome sequences for this study.

The yellow shading illustrates the distribution of Strix occidentalis in California. We have indicated the locations of the Pitt River and Lassen Peak. Owls to the north of the region between these two geographic features are considered northern spotted owls (S. o. caurina) while those to the south of this region are California spotted owls (S. o. occidentalis) (Barrowclough et al. 2011; Tempel et al. 2017).

Results

The nuclear reference genome contained 1,113,365,877 non-N nucleotides after masking. Our final set of variants included 486,582 biallelic sites. Across individuals, the mean coverage at the final set of variant sites ranged from approximately 33.2 to 56.7X (Table S1). Grouping the results by subspecies, we recovered a higher nucleotide diversity within Strix occidentalis occidentalis (1.676 × 10−4) than S. o. caurina (1.454 × 10−4) in California (Table 1). We calculated an FST of 0.1727 for the two subspecies. When we treated each sample as a separate population, additional patterns emerged.

We found no divergence (FST = 0) between northern spotted owl populations in California. The Marin County population had a lower nucleotide diversity (1.374 × 10−4) than the Humboldt County population (1.591 × 10−4); between them, these two populations had the lowest nucleotide diversity (1.399 × 10−4) of any population pair (Table 1).

View this table:
Table 1.

Population and subspecies diversity and divergence statistics.

The San Diego County population harbored the lowest nucleotide diversity of all of the populations (1.189 × 10−4). We calculated genome-wide FST values ranging from 0.1634 to 0.3515 for pairwise comparisons of the San Diego County population with the other three populations (Table 1). The lowest of these (0.1634) was the FST between San Diego County and Nevada County. However, we found lower differentiation between Nevada County and each of the Strix occidentalis caurina populations (FST = 0.0672 between Nevada and Humboldt County; FST = 0.1371 between Nevada and Marin County) than between the Nevada and San Diego County populations of S. o. occidentalis.

Across all individuals, the proportion of the genome comprised by runs of homozygosity (ROH) ranged from approximately 6.08-36.23%, with the lowest proportion found in the Humboldt County population and the highest in the San Diego County population (Table S2). We found the two longest tracts in the Marin and San Diego County populations (7.5 and 8.9 Mb, respectively) (Figure 2). As the majority of the tracts across all individuals were less than 4 Mb long and none of the ROH exceeded 10 Mb (Figure 2), we found no evidence for very recent inbreeding (e.g. within the last three generations). Rather, the frequency and length of the ROH suggest low historic population sizes, especially in the Marin and San Diego County populations.

Figure 2.
Figure 2. Histogram of runs of homozygosity. The x-axis provides length bins for runs of homozygosity (ROH) in each population.

Each bin contains ROH greater than or equal to the length in megabases (Mb) indicated by the left tick mark and less than the length of the right tick mark. The y-axis expresses the total length of all ROH in a length bin as a percentage of the length of the reference genome.

Our estimates of the effective population size (Ne) of each population indicate that all four populations have undergone long-term population declines over the past approximately 70,000 generations. Of note, the Ne of the Strix occidentalis caurina populations grew about an order of magnitude larger approximately 3,000 to 700 generations ago before declining again (Figure 3). Taken with the caveat that recent population size changes are difficult to estimate accurately using the SMC++ method or other similar methods, especially with low sample sizes (Terhorst et al. 2017), our most recent estimates suggest that the San Diego County population has the lowest current Ne (~ 350 individuals) and that of the Marin County population (~ 500 individuals) is only slightly higher. We estimated the current Ne of the Nevada County population at ~ 1,100 individuals. The Humboldt County population Ne (~ 4,900 individuals) is approximately one order of magnitude greater than that of both the Marin and San Diego County populations. The Ne of the S. o. caurina populations appear to have remained very similar until the last 700 to 800 generations when they diverged; the Marin County effective population size dropped by over an order of magnitude while the Humboldt County population declined less drastically. The Ne of the S. o. occidentalis populations has also been similar over time, but less so than that of the S. o. caurina populations. The San Diego County population has been undergoing a steady decline in Ne for approximately the past 8,000 generations.

Figure 3.
Figure 3. Effective population size through time of each Strix occidentalis population.

The time on the x-axis is in generations before present. Both the x and y axes are logarithmically scaled.

Discussion

Early genetic studies of spotted owls (Strix occidentalis) utilizing mitochondrial DNA (mtDNA) sequence data concluded that S. o. caurina and S. o. occidentalis are largely reciprocally monophyletic, but that there are migrants across the subspecies’ boundaries and evidence of incomplete lineage sorting (Barrowclough et al. 1999, 2005; Haig et al. 2004). Funk et al. (2008) utilized a panel of ten nuclear-genome-derived microsatellite loci and they found two genetic clusters nearly exclusive to S. o. caurina and a third in which all of their S. o. occidentalis samples had high membership, but their results also included many S. o. caurina with membership in this third cluster. They interpreted their results as supporting the current division of subspecies, but with substantial gene flow between S. o. caurina and S. o. occidentalis in northern California and southern Oregon (Funk et al. 2008). Studies with denser sampling in northern California near the S. o. caurina - S. o. occidentalis boundary found multiple S. o. caurina genetic clades, which they attributed to incomplete lineage sorting, along with evidence of a differentiated S. o. caurina population in northern California (Barrowclough et al. 2011; Miller et al. 2017). The Miller et al. (2017) investigators further concluded that gene flow is predominantly occurring from the range of the S. o. occidentalis into that of S. o. caurina.

We found higher nucleotide diversity in the Nevada County Strix occidentalis occidentalis population than in the Humboldt County S. o. caurina population. This contrasts with the findings of prior researchers, who have reported higher nucleotide diversity in Humboldt County S. o. caurina than in Sierra Nevada S. o. occidentalis (Barrowclough et al. 1999; Miller et al. 2017), although Barrowclough et al. (2005) found higher diversity in Lassen County than in both Humboldt County S. o. caurina and El Dorado County S. o. occidentalis. El Dorado County is approximately 90 km southeast of the location of our Nevada County sample. Higher genetic diversity in Nevada County was contrary to what we expected if gene flow was predominantly northward out of the range of S. o. occidentalis and into that of S. o. caurina in northern California, as Miller et al. (2017) suggested. Indeed, the nucleotide diversity of the Nevada County population was the highest of all four of our populations.

In contrast with our finding of the highest genetic diversity in the Nevada County population, we estimated that the effective size of the Nevada County population has been lower than that of the Humboldt County population for approximately the past two thousand generations. The Marin County and San Diego County populations, each of which represents the southern edge of the range of their respective subspecies [although an isolated Strix occidentalis occidentalis population exists south of San Diego County in the Sierra de San Pedro Mártir of Baja California (Bryant 1889)], have low genetic diversity and low recent effective population sizes. All population sizes are lower than their historic counterparts. We estimated that the Humboldt County population size has declined the least in comparison with historic levels. Both S. o. occidentalis populations appear to have been declining steadily with little to no evidence of periodic recovery. In contrast, the S. o. caurina populations show indication of a period of sustained population growth from approximately 3,000 to 700 generations ago. Depending on whether one uses two (Gutiérrez et al. 1995), five (Barrowclough and Coats 1985; Barrowclough et al. 1999), or ten years (Noon and Biles 1990; USDA Forest Service 1992) as the S. occidentalis generation time, the S. o. caurina effective population size trough and subsequent growth 3,000 generations ago could coincide roughly with the beginning of the retreat of glaciers in North America approximately 19,000-20,000 years ago after the last glacial maximum (Clark et al. 2009). However, it is important to consider that recent population crashes in some or all of these populations (Gutiérrez 1994; Davis and Gould 2008; Dugger et al. 2015; Tempel et al. 2017) may be affecting our ability to estimate historic population sizes accurately (Terhorst et al. 2017).

Our pairwise FST value between the populations grouped by subspecies appeared to be driven largely by the divergence of the Marin and Humboldt County Strix occidentalis caurina populations from the San Diego County S. o. occidentalis population. We estimated a higher genetic divergence between the Nevada and San Diego County S. o. occidentalis populations than between the Nevada County S. o. occidentalis population and each of the S. o. caurina populations. Our pairwise FST value comparing Nevada and Humboldt County (FST = 0.0672) was almost identical to that calculated by Funk et al. (2008) between their S. o. occidentalis and Humboldt + Del Norte County populations using 10 microsatellite loci (FST = 0.066). Notably, the Funk et al. (2008) S. o. occidentalis population grouped samples from the northern and southern end of the Sierra Nevada S. o. occidentalis range. Our recovery of a comparable FST value with our samples from Nevada and Humboldt counties suggests low differentiation across Sierra Nevada S. o. occidentalis, a preliminary finding that needs to be tested with further genomic sequencing.

Our whole-genome data show the San Diego County population being clearly diverged from the other three populations. The genetic diversity of the San Diego population was the lowest of the four. Previous investigators have reported low to no genetic diversity in the mitochondrial control region of Strix occidentalis occidentalis populations in southern California (Barrowclough et al. 1999, 2005; Haig et al. 2004). Haig et al. (2004) found no differentiation (FST = 0.00) between S. o. occidentalis from the Sierra Nevada and southern California, whereas we found substantial differentiation between S. o. occidentalis from Nevada and San Diego counties (FST = 0.1634). Our FST values for all pairwise comparisons with the San Diego County population exceeded all of the FST values calculated by the Funk et al. (2008) microsatellite study investigating the relationships between three spotted owl subspecies, even those involving comparisons of the Mexican spotted owl (Strix o. lucida) with S. o. occidentalis or S. o. caurina. The highest pairwise FST value calculated by the Funk et al. (2008) authors (FST = 0.141) was between their S. o. occidentalis and S. o. lucida populations; this was lower than the pairwise value we recovered between our S. o. occidentalis populations from Nevada and San Diego counties (FST = 0.1634). Our results suggest that the southern California S. o. occidentalis population is considerably differentiated from the Sierra S. o. occidentalis population.

In contrast with our finding substantial differentiation between the Strix occidentalis occidentalis populations, we found no genetic differentiation between the Marin and Humboldt County S. o. caurina populations. Studies utilizing mtDNA and microsatellites to compare the genetic divergence of S. o. caurina populations in Marin and Mendocino County (the latter located immediately south of Humboldt County in California) reported FST values of 0.14327 (Chi 2006) and 0.090 (Henke 2005) for mtDNA and microsatellite markers, respectively. A mtDNA study by Barrowclough et al. (2005) also found the Marin County S. o. caurina population to be divergent from other S. o. caurina populations. According to our estimate, the effective population size of the Marin County population was almost identical to that of the Humboldt County population until approximately 700 generations ago when the Marin County population appears to have undergone a severe reduction, suggesting that Marin County S. o. caurina were part of a much larger interbreeding group until fairly recently.

We purposefully chose samples from the northern extent of the California ranges of Strix occidentalis caurina and S. o. occidentalis, but outside of previously described S. o. caurina and S. o. occidentalis contact zones (Barrowclough et al. 2011; Miller et al. 2017). Plausible explanations for the finding of less divergence between the S. o. caurina populations and the Nevada County S. o. occidentalis population than between the Nevada and San Diego County S. o. occidentalis populations include the following: 1) the Barrowclough et al. (2011) S. o. caurina and S. o. occidentalis contact zone has shifted southward in the Sierra Nevada and our Nevada County sample was an admixed individual; 2) our Humboldt County sample was the result of admixture of S. o. caurina and S. o. occidentalis in northern California; 3) alleles are moving beyond the Barrowclough et al. (2011) and Miller et al. (2017) contact zones in northern California into the ranges of S. o. caurina and/or S. o. occidentalis; and/or 4) whole-genome sequences are picking up variation that was missed by the microsatellites and not present in the mitochondrial genome. We attempted to avoid sampling from the zone of admixture of S. o. caurina and S. o. occidentalis described by Miller et al. (2017) by sampling approximately 50 km west of the described contact region. Nevertheless, it is possible that our Humboldt County sample was an S. o. occidentalis that migrated into the range of S. o. caurina or a product of admixture of the two subspecies. Likewise, we attempted to avoid the contact zone described by Barrowclough et al. (2011) by sampling an individual collected approximately 150 km southeast of Lassen Peak, but it is possible that our Nevada County sample was a S. o. caurina that migrated into the range of S. o. occidentalis or it is the result of admixture of two differentiated subspecies.

If our Humboldt County sample was a Strix occidentalis occidentalis that migrated into the S. o. caurina range or an introgressed individual, it is difficult to explain why we recovered no genetic differentiation between the Humboldt and Marin County populations, especially given the differentiation of Nevada and Marin counties. Our Marin County sample was from a location approximately 250 km south of the Miller et al. (2017) contact zone. If our Nevada County sample was a S. o. caurina that migrated into the range of S. o. occidentalis, we would have expected to identify a lower level of genetic differentiation between the Marin and Nevada County samples than that which we detected, given the absence of differentiation of the Marin and Humboldt County S. o. caurina populations. If admixture between two previously diverged subspecies is occurring as far south into the range of S. o. occidentalis as Nevada County, it is hard to image how the entire 640 km length of the Sierra Nevada (Schoenherr 1992) would not be colonized by admixed individuals within a few generations, since juvenile dispersal distance can exceed 100 km in S. o. caurina (Forsman et al. 2002). Apart from the lack of divergence between the S. o. caurina populations, all other population comparisons with our whole-genome dataset seem to support a pattern of isolation by distance, rather than a distinct boundary between S. o. caurina and S. o. occidentalis.

Materials and Methods

Sequence data

We utilized whole genome sequencing data generated by a previous study (Hanna et al. 2017c) from a Strix occidentalis caurina sample from Marin County, California (NCBI Sequence Read Archive (SRA) run accessions SRR4011595, SRR4011596, SRR4011597, SRR4011614, SRR4011615, SRR4011616, and SRR4011617 for S. o. caurina sample CAS:ORN:98821; Table 2; Tables S3 and S4). We also obtained high-coverage whole genome sequences from another S. o. caurina (CAS:ORN:99418) and two S. o. occidentalis (samples CDFWN13249 and CDFWN14089) by sending genomic DNA to MedGenome (Foster City, California, U.S.A.) for preparation of whole genome libraries with a single, 8 bp index and a 420 bp insert size. MedGenome sequenced each of these libraries with 150 bp paired-end reads on a HiSeq X Ten (Illumina, San Diego, California, U.S.A.). The raw sequences are available from the NCBI SRA run accessions SRR6048828, SRR6048829, SRR6048848 (Table S4).

View this table:
Table 2.

Sample information.

Our four Strix occidentalis samples were from locations near the extremes of the California distributions of S. o. caurina) and S. o. occidentalis) (Figure 1): Marin County, the southernmost extent of S. o. caurina (sample CAS:ORN:98821); Humboldt County, near the northern extent of S. o. caurina (CAS:ORN:99418); San Diego County, the southern extent of S. o. occidentalis (CDFWN13249); and Nevada County, near the northern extent of S. o. occidentalis (CAFWN14089). Our Humboldt County sample was from a location approximately 50 km northwest of the California and northern spotted owl contact zone identified by Miller et al. (2017).

Alignment and filtering

We used Trimmomatic version 0.36 (Bolger et al. 2014) along with relevant adapter files (Bolger et al. 2014; Hanna 2018a) to remove adapter sequences and low quality leading and trailing bases. For all four samples we used BWA-MEM version 0.7.17-r1188 (Li 2013) to align the processed sequences to the S. o. caurina nuclear genome “StrOccCau_1.0_nuc.fa” (Hanna et al. 2017d, 2017c) with the mitochondrial genome sequence from Hanna et al. (2017a) added to the nuclear genome reference sequence. We merged and sorted the alignments and then marked duplicate sequences using Picard version 2.17.6 (http://broadinstitute.github.io/picard; Accessed 2018 Mar 22).

SNP calling and filtering

We called variants using the Genome Analysis Toolkit (GATK) version 3.8.0 HaplotypeCaller tool (McKenna et al. 2010; DePristo et al. 2011; Van der Auwera et al. 2013) to produce a genomic variant call format (gVCF) file for each sample. We then used the GATK version 3.8.0 GenotypeGVCFs tool to produce a VCF file containing the variant information for all samples. We used the GATK version 3.8.0 SelectVariants and VariantFiltration tools to filter the variants and remove those of suspect quality. We then followed an adapted version of the GATK guidelines for base quality score recalibration BQSR (https://gatkforums.broadinstitute.org/gatk/discussion/2801/howto-recalibrate-base-qualityscores-run-bqsr; Accessed 2018 Mar 15) and used the GATK version 3.8.0 BaseRecalibrator and PrintReads tools to recalibrate all of the alignment files based on our set of high-quality variants. We performed a new round of variant calling using the GATK version 3.8.0 HaplotypeCaller and GenotypeGVCFs tools. We filtered the variant file to retain only high-quality, biallelic single nucleotide polymorphisms (SNPs) that were not on the mitochondrial genome using the GATK version 3.8.0 SelectVariants and VariantFiltration tools as well as GNU Awk (GAWK) version 4.2.0 (Free Software Foundation 2017).

In order to utilize up-to-date repetitive sequence libraries to identify and remove variants in low-complexity and repetitive regions, we performed a new masking of repetitive sequences in the reference Strix occidentalis caurina genome StrOccCau_1.0_nuc.fa (Hanna et al. 2017d, 2017c) with the mitochondrial genome from Hanna et al. (2017a). We created a de novo model of the repeats in the reference genome using RepeatModeler version 1.0.8 (Smit and Hubley 2015), which utilized RepeatMasker version open-4.0.7 (Smit et al. 2013) with the Repbase RepeatMasker libraries version 20170127 (Jurka 1998, 2000; Jurka et al. 2005; Bao et al. 2015), RMBlast version 2.2.28 (Smit et al. 2015), tandem repeats finder (TRF) version 4.09 (Benson 1999), RECON version 1.08 (Bao and Eddy 2002), and RepeatScout version 1.0.5 (Price et al. 2005). We first performed a homology-based masking of repeats and low-complexity regions and then implemented a second round of masking using the de novo repeat model we had generated using RepeatMasker version open-4.0.7 with the Repbase RepeatMasker libraries version 20170127, the repeat databases of the DFAM library version 2.0 (Hubley et al. 2016), TRF version 4.09, NCBI BLAST+ version 2.7.1 (Camacho et al. 2009), RMBlast version 2.2.28, and HMMER version 3.1b2 (Eddy 1998; http://hmmer.org). We created a browser extensible data (BED) formatted file of the repeat and low-complexity regions using seqtk version 1.2-r94 (Li 2016), sorted them using sort (GNU coreutils) version 8.25 (Haertel and Eggert 2016), and then used BEDTools version 2.25.0 (Quinlan and Hall 2010) to remove variants from our VCF file that fell in those regions. Finally, we removed variants at sites where the unfiltered read depth exceeded the mean plus five times the standard deviation, as suggested by the GATK documentation (https://software.broadinstitute.org/gatk/documentation/article.php?id=3225; Accessed 2018 Mar 16), using dp_cov_script.sh from SPOW-BDOW-introgression-scripts version 1.1.1 (Hanna et al. 2017b). We calculated the mean and standard deviation of the coverage depth across variant sites using DP_sample_calc.sh from genetics-tools version 1.0.0 (Hanna 2018b).

Population size and diversity statistics

After compressing and indexing our variant and repetitive region files using the bgzip and tabix tools from HTSlib version 1.7 (Li 2011; Davies et al. 2018), we treated each of the four samples as its own population and estimated the effective population size of each through time using SMC++ version 1.12.2.dev10+gaf60af6 (Terhorst et al. 2017) with the per-generation rate of collared flycatcher (Ficedula albicollis) (Smeds et al. 2016). We filtered the SMC++ files using GAWK version 4.2.0 and the GNU core utilities find version 8.25 (Decker et al. 2016), zcat version 8.25 (Gailly et al. 2016), and rm version 8.25 (Rubin et al. 2016). We converted our variant file format using BCFtools version 1.6 (Li et al. 2017) with HTSlib version 1.6 (Davies et al. 2017) and GAWK version 4.2.0. To estimate nucleotide diversity and population differentiation, we calculated πBetween (Nei and Li 1979) and the genome-wide fixation index (FST) (Hudson et al. 1992) between Strix occidentalis caurina and S. o. occidentalis as well as between each of the population pairs using the tableFstPi script from diversity-divergence-stats version 1.0.0 (Wall and Hanna 2018) with cat (GNU coreutils) version 8.25 (Granlund and Stallman 2017). In addition, we calculated πWithin (Nei and Li 1979) for each of the subspecies and populations using the tablePiTheta script from diversity-divergence-stats version 1.0.0 with cat version 8.25. In order to check for evidence of recent inbreeding that could serve as an explanation for the elevated divergence of some population, we used GAWK version 4.2.0 and PLINK version 1.90b5.4 (Chang et al. 2015; Purcell and Chang 2018) to search for runs of homozygosity (ROH) in each individual. We only considered ROH that contained at least 100 SNPs, were at least 1 Mb long, and where the heterozygosity in the run was less than 10% of the genome-wide mean heterozygosity for the individual. Finally, we mapped our samples using QGIS version 2.18.2 (Quantum GIS Development Team 2017) with raster and vector files from Natural Earth (http://www.naturalearthdata.com) and a S. occidentalis range map from California Wildlife Habitat Relationships (Gould and CWHR Program 2008).

See full details of materials and methods at protocols.io (http://dx.doi.org/10.17504/protocols.io.qp2dvqe).

Additional information and declarations

Competing interests

The authors declare that no competing interests exist.

Author contributions

Conceptualization: ZRH, JDW; Data Curation: ZRH, JPD, RCKB, JDW; Formal Analysis: ZRH, JDW; Funding Acquisition: JDW; Investigation: ZRH, JDW; Methodology: ZRH, JDW; Project Administration: ZRH, JDW; Resources: ZRH, JPD, RCKB, JDW; Software: ZRH, JDW; Supervision: JDW; Validation: ZRH; Visualization: ZRH, JDW; Writing – Original Draft Preparation: ZRH; Writing – Review & Editing: ZRH, JPD, RCKB, JDW

Data availability

The raw sequences for Strix occidentalis samples CAS:ORN:99418, CDFWN13249, and CAFWN14089 are available from NCBI (SRA run accessions SRR6048848, SRR6048828, and SRR6048829, respectively).

Funding

Funding from the University of California President’s Research Catalyst Award to the UC Conservation Genomics Consortium to JDW supported this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supporting information

Table S1. Variant site coverage values for each sample. The site coverage is the average sequence coverage across all variant sites in our final variant set after filtering. “SD” stands for “standard deviation”.

Table S2. Homozygous fraction of genome. The total length of all runs of homozygosity (ROH) expressed as a fraction of the length of the reference genome for each population.

Table S3. Specimen institution data. We here provide information regarding the collections that archive specimens of the Strix occidentalis individuals we utilized in this study.

Table S4. Genomic sequence details. We here provide sample identifier information, including the NCBI Sequence Read Archive (SRA) run accessions of the raw genomic sequences we analyzed in our study.

Acknowledgements

We thank J. Mark Higley and Aaron Pole of Hoopa Valley Indian Reservation Tribal Forestry; WildCare, San Rafael; Krysta Rogers, Wildlife Investigations Laboratory, California Department of Fish and Wildlife; and the California Academy of Sciences for graciously providing us with genetic samples.

References

  1. ↵
    Bao, Z., and S. R. Eddy, 2002 Automated De Novo Identification of Repeat Sequence Families in Sequenced Genomes. Genome Res. 12: 1269–1276.
    OpenUrlAbstract/FREE Full Text
  2. ↵
    Bao, W., K. K. Kojima, and O. Kohany, 2015 Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6: 1–6.
    OpenUrlCrossRef
  3. ↵
    Barrowclough, G. F., and S. L. Coats, 1985 The demography and population genetics of owls, with special reference to the conservation of the spotted owl (Strix occidentalis), pp. 74–85 in Ecology and management of the spotted owl in the Pacific Northwest. Gen. Tech. Rep. PNW-185., U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station, Portland, OR.
  4. ↵
    Barrowclough, G. F., J. G. Groth, L. A. Mertz, and R. J. Gutiérrez, 2005 Genetic structure, introgression, and a narrow hybrid zone between northern and California spotted owls (Strix occidentalis). Mol. Ecol. 14: 1109–1120.
    OpenUrlPubMedWeb of Science
  5. ↵
    Barrowclough, G. F., R. J. Gutiérrez, and J. G. Groth, 1999 Phylogeography of Spotted Owl (Strix occidentalis) Populations Based on Mitochondrial DNA Sequences: Gene Flow, Genetic Structure, and a Novel Biogeographic Pattern. Evolution 53: 919–931.
    OpenUrl
  6. ↵
    Barrowclough, G. F., R. J. Gutiérrez, J. G. Groth, J. E. Lai, and D. F. Rock, 2011 The Hybrid Zone between Northern and California Spotted Owls in the Cascade—Sierran Suture Zone. The Condor 113: 581–589.
    OpenUrl
  7. ↵
    Benson, G., 1999 Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27: 573–580.
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    Bolger, A. M., M. Lohse, and B. Usadel, 2014 Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114–2120.
    OpenUrlCrossRefPubMedWeb of Science
  9. ↵
    Bryant, W. E., 1889 A catalogue of the birds of Lower California, Mexico, pp. 237–320 in Proceedings of the California Academy of Sciences, Second, California Academy of Sciences, San Francisco.
  10. ↵
    Camacho, C., G. Coulouris, V. Avagyan, N. Ma, J. Papadopoulos et al., 2009 BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
    OpenUrlCrossRefPubMed
  11. ↵
    Chang, C. C., C. C. Chow, L. C. Tellier, S. Vattikuti, S. M. Purcell et al., 2015 Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4: 7.
    OpenUrlCrossRefPubMed
  12. ↵
    Chi, T. Y., 2006 Genetic characterization of four populations in two subspecies of spotted owl [M.S.]: San Jose State University.
  13. ↵
    Clark, P. U., A. S. Dyke, J. D. Shakun, A. E. Carlson, J. Clark et al., 2009 The Last Glacial Maximum. Science 325: 710–714.
    OpenUrlAbstract/FREE Full Text
  14. ↵
    Davies, R., J. C. Randall, S. A. McCarthy, J. Bonfield, M. O. Pollard et al., 2017 HTSlib. Version 1.6. [Accessed 2018 Mar 20]. Available from: https://github.com/samtools/htslib.
  15. ↵
    Davies, R., J. C. Randall, S. A. McCarthy, J. Bonfield, M. O. Pollard et al., 2018 HTSlib. Version 1.7. [Accessed 2018 Mar 19]. Available from: https://github.com/samtools/htslib.
  16. ↵
    1. W. D. Shuford and
    2. T. Gardali
    Davis, J. N., and G. I. Gould Jr.., 2008 California spotted owl (Strix occidentalis occidentalis), pp. 227–233 in California Bird Species of Special Concern: A ranked assessment of species, subspecies, and distinct populations of birds of immediate conservation concern in California, edited by W. D. Shuford and T. Gardali. Studies of Western Birds 1, Western Field Ornithologists and California Department of Fish and Game (Sacramento, California), Camarillo, California.
  17. ↵
    Decker, E., D. MacKenzie, J. Plett, T. Wood, M. Rendell et al., 2016 find (GNU coreutils). Version 8.25. [Accessed 2018 Mar 19]. Available from: http://www.gnu.org/software/coreutils/coreutils.html.
  18. ↵
    DePristo, M. A., E. Banks, R. Poplin, K. V. Garimella, J. R. Maguire et al., 2011 A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43: 491–498.
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    Dugger, K. M., E. D. Forsman, A. B. Franklin, R. J. Davis, G. C. White et al., 2015 The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls. The Condor 118: 57–116.
    OpenUrl
  20. ↵
    Eddy, S. R., 1998 Profile hidden Markov models. Bioinforma. Oxf. Engl. 14: 755–763.
    OpenUrl
  21. ↵
    Forsman, E. D., R. G. Anthony, J. A. Reid, P. J. Loschl, S. G. Sovern et al., 2002 Natal and Breeding Dispersal of Northern Spotted Owls. Wildl. Monogr. 1–35.
  22. ↵
    Free Software Foundation, 2017 GNU Awk.
  23. ↵
    Funk, W. C., E. D. Forsman, T. D. Mullins, and S. M. Haig, 2008 Introgression and dispersal among spotted owl (Strix occidentalis) subspecies. Evol. Appl. 1: 161–171.
    OpenUrl
  24. ↵
    Gailly, J., M. Adler, J. Meyering, and P. Eggert, 2016 Gzip (GNU coreutils).
  25. ↵
    Gould, G., and CWHR Program, 2008 Spotted Owl Range - CWHR B270 [ds897]. [Accessed 2018 May 10]. Available from: https://www.wildlife.ca.gov/Data/CWHR.
  26. ↵
    Granlund, T., and R. M. Stallman, 2017 cat (GNU coreutils).
  27. ↵
    Gutiérrez, R. J., 1994 Changes in the Distribution and Abundance of Spotted Owls During the Past Century. Stud. Avian Biol. 15: 293–300.
    OpenUrl
  28. ↵
    1. A. Poole
    Gutiérrez, R. J., A. B. Franklin, and W. S. Lahaye, 1995 Spotted Owl (Strix occidentalis). The Birds of North America Online ( A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. [Accessed 2016 Oct 1]. Retrieved from the Birds of North America Online: https://birdsna.org/Species-Account/bna/species/spoowl.
  29. ↵
    Gutiérrez, R. J., P. N. Manley, and P. A. Stine, 2017 The California spotted owl: current state of knowledge. Gen Tech Rep PSW-GTR-254 Albany CA US Dep. Agric. For. Serv. Pac. Southwest Res. Stn. 254:.
  30. ↵
    Haertel, M., and P. Eggert, 2016 sort (GNU coreutils).
  31. ↵
    Haig, S. M., T. D. Mullins, and E. D. Forsman, 2004 Subspecific relationships and genetic structure in the spotted owl. Conserv. Genet. 5: 683–705.
    OpenUrl
  32. ↵
    Haig, S. M., R. S. Wagner, E. D. Forsman, and T. D. Mullins, 2001 Geographic variation and genetic structure in Spotted Owls. Conserv. Genet. 2: 25–40.
    OpenUrl
  33. ↵
    Hanna, Z. R., 2018a Adapter sequences used for trimming of genomic sequences in the assembly of the Northern Spotted Owl (Strix occidentalis caurina) genome assembly version 1.0. Version 1.0.0. Zenodo.
  34. ↵
    Hanna, Z. R., 2018b genetics-tools. Version 1.0.0. Zenodo.
  35. ↵
    Hanna, Z. R., J. B. Henderson, A. B. Sellas, J. Fuchs, R. C. K. Bowie et al., 2017a Complete mitochondrial genome sequences of the northern spotted owl (Strix occidentalis caurina) and the barred owl (Strix varia; Aves: Strigiformes: Strigidae) confirm the presence of a duplicated control region. PeerJ 5: e3901.
    OpenUrlCrossRef
  36. ↵
    Hanna, Z. R., J. B. Henderson, and J. D. Wall, 2017b SPOW-BDOW-introgression-scripts. Version 1.1.1. Zenodo.
  37. ↵
    Hanna, Z. R., J. B. Henderson, J. D. Wall, C. A. Emerling, J. Fuchs et al., 2017c Northern Spotted Owl (Strix occidentalis caurina) Genome: Divergence with the Barred Owl (Strix varia) and Characterization of Light-Associated Genes. Genome Biol. Evol. 9: 2522–2545.
    OpenUrlCrossRef
  38. ↵
    Hanna, Z. R., J. B. Henderson, J. D. Wall, C. A. Emerling, J. Fuchs et al., 2017d Supplemental dataset for Northern Spotted Owl (Strix occidentalis caurina) genome assembly version 1.0. Zenodo.
  39. ↵
    Henke, A. L., 2005 Spotted owl (Strix occidentalis) microsatellite variation in California [M.S.]: San Jose State University.
  40. ↵
    Hubley, R., R. D. Finn, J. Clements, S. R. Eddy, T. A. Jones et al., 2016 The Dfam database of repetitive DNA families. Nucleic Acids Res. 44: D81–D89.
    OpenUrlCrossRefPubMed
  41. ↵
    Hudson, R. R., M. Slatkin, and W. P. Maddison, 1992 Estimation of Levels of Gene Flow from DNA Sequence Data. Genetics 132: 583–589.
    OpenUrlAbstract/FREE Full Text
  42. ↵
    Jurka, J., 2000 Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 16: 418–420.
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    Jurka, J., 1998 Repeats in genomic DNA: mining and meaning. Curr. Opin. Struct. Biol. 8: 333–337.
    OpenUrlCrossRefPubMedWeb of Science
  44. ↵
    Jurka, J., V. V. Kapitonov, A. Pavlicek, P. Klonowski, O. Kohany et al., 2005 Repbase Update, a database of eukaryotic repetitive elements. Cytogenet. Genome Res. 110: 462–467.
    OpenUrlCrossRefWeb of Science
  45. ↵
    1. R. J. Gutiérrez,
    2. P. N. Manley, and
    3. P. A. Stine
    Keane, J. J., 2017 Threats to the viability of California spotted owls, pp. 185–238 in The California Spotted Owl: Current State of Knowledge, edited by R. J. Gutiérrez, P. N. Manley, and P. A. Stine. General Technical Report PSW-GTR-254, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany, California.
  46. ↵
    Li, H., 2013 Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv:1303.3997 Q-Bio. [Accessed 2016 Feb 16].
  47. ↵
    Li, H., 2016 Seqtk. Version 1.2-r94. [Accessed 2018 Mar 16]. Available from: https://github.com/lh3/seqtk.
  48. ↵
    Li, H., 2011 Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27: 718–719.
    OpenUrlCrossRefPubMedWeb of Science
  49. ↵
    Li, H., B. Handsaker, P. Danecek, S. McCarthy, J. Marshall et al., 2017 BCFtools. Version 1.6. [Accessed 2018 Mar 20]. Available from: https://github.com/samtools/bcftools.
  50. ↵
    McKenna, A., M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis et al., 2010 The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20: 1297–1303.
    OpenUrlAbstract/FREE Full Text
  51. ↵
    Miller, M. P., T. D. Mullins, E. D. Forsman, and S. M. Haig, 2017 Genetic differentiation and inferred dynamics of a hybrid zone between Northern Spotted Owls (Strix occidentalis caurina) and California Spotted Owls (S. o. occidentalis) in northern California. Ecol. Evol. 7: 6871–6883.
    OpenUrl
  52. ↵
    Montgomery, C. A., J. Brown Gardner M., and D. M. Adams, 1994 The Marginal Cost of Species Preservation: The Northern Spotted Owl. J. Environ. Econ. Manag. 26: 111–128.
    OpenUrl
  53. ↵
    Nei, M., and W. H. Li, 1979 Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. U. S. A. 76: 5269–5273.
    OpenUrlAbstract/FREE Full Text
  54. ↵
    Noon, B. R., and C. M. Biles, 1990 Mathematical Demography of Spotted Owls in the Pacific Northwest. J. Wildl. Manag. 54: 18–27.
    OpenUrl
  55. ↵
    Price, A. L., N. C. Jones, and P. A. Pevzner, 2005 De novo identification of repeat families in large genomes. Bioinformatics 21: i351–i358.
    OpenUrlCrossRefPubMedWeb of Science
  56. ↵
    Purcell, S. M., and C. C. Chang, 2018 PLINK. Version 1.90b5.4. [Accessed 2018 Apr 12]. Available from: https://www.cog-genomics.org/plink/1.9.
  57. ↵
    Quantum GIS Development Team, 2017 Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. [Accessed 2017 Sep 16]. Available from: http://qgis.org.
  58. ↵
    Quinlan, A. R., and I. M. Hall, 2010 BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841–842.
    OpenUrlCrossRefPubMedWeb of Science
  59. ↵
    Rubin, P., D. MacKenzie, R. M. Stallman, and J. Meyering, 2016 rm (GNU coreutils). Version 8.25. [Accessed 2018 Mar 19]. Available from: http://www.gnu.org/software/coreutils/coreutils.html.
  60. ↵
    Schoenherr, A. A., 1992 A Natural History of California. Berkeley: University of California Press.
  61. ↵
    Smeds, L., A. Qvarnström, and H. Ellegren, 2016 Direct estimate of the rate of germline mutation in a bird. Genome Res. 26: 1211–1218.
    OpenUrlAbstract/FREE Full Text
  62. ↵
    Smit, A. F. A., and R. Hubley, 2015 RepeatModeler Open-1.0. [Accessed 2018 Mar 16]. Available from: http://www.repeatmasker.org.
  63. ↵
    Smit, A., R. Hubley, and P. Green, 2013 RepeatMasker Open-4.0. [Accessed 2018 Mar 16]. Available from: http://www.repeatmasker.org.
  64. ↵
    Smit, A., R. Hubley, and National Center for Biotechnology Information, 2015 RMBlast. [Accessed 2016 Oct 1]. Available from: http://www.repeatmasker.org/RMBlast.html.
  65. ↵
    1. R. J. Gutiérrez,
    2. P. N. Manley, and
    3. P. A. Stine
    Tempel, D. J., R. J. Gutiérrez, and M. Z. Peery, 2017 Population distribution and trends of California spotted owls, pp. 75–108 in The California Spotted Owl: Current State of Knowledge, edited by R. J. Gutiérrez, P. N. Manley, and P. A. Stine. General Technical Report PSW-GTR-254, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany, California.
  66. ↵
    Terhorst, J., J. A. Kamm, and Y. S. Song, 2017 Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49: 303–309.
    OpenUrlCrossRefPubMed
  67. ↵
    USDA Forest Service, 1992 Final Environmental Impact Statement on Management for the Northern Spotted Owl in the National Forests. USDA Forest Service, National Forest System: Portland, Oregon. 2 vol.
  68. ↵
    Van der Auwera, G. A., M. O. Carneiro, C. Hartl, R. Poplin, G. del Angel et al., 2013 From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinforma. 11: 11.10.1–11.10.33.
    OpenUrl
  69. ↵
    Wall, J. D., and Z. R. Hanna, 2018 diversity-divergence-stats. Version 1.0.0. Zenodo.
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Whole-genome sequences suggest long term declines of spotted owl (Strix occidentalis) (Aves: Strigiformes: Strigidae) populations in California
Zachary R. Hanna, John P. Dumbacher, Rauri C.K. Bowie, Jeffrey D. Wall
bioRxiv 343434; doi: https://doi.org/10.1101/343434
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