Elucidating the genetic structure and ascertaining the natal origin of Golden Eagles (Aquila chrysaetos) are challenging for a number of reasons, including the lack of highly reproducible, variant genetic loci. Here, we developed a new high-quality Golden Eagle genome reference to serve as a computational atlas for future genetic investigations. We then generated unique genetic resources for the Golden Eagle by performing low-coverage genomic sequencing for 32 individuals ranging from Alaska to southern New Mexico and California to Nebraska. By aligning the reads from these 32 individuals to our Golden Eagle reference genome, we detected approximately 900,000 population variants in the form of Single Nucleotide Polymorphisms (SNPs). Using linkage disequilibrium and other quality filters, we next derived a set of 30,006 SNPs that were used to cluster our samples into three genetic groups. Although additional work is needed to fully characterize these loci, we provide a high-quality Golden Eagle genome reference and a comprehensive set of genetic markers for the conservation and management of Golden Eagles. Additionally, with a more comprehensive Golden Eagle genome assembly and associated transcriptomes, it is now possible to target specific genes or other biologically relevant regions for evaluating the effects of many anthropogenic stressors on Golden Eagle survival.
Delineating the biologically relevant boundaries within a species' range should constitute the first step in any conservation or management program (Palsbøll et al. 2007). This critical step informs wildlife managers, biologists, and policy makers of the “units” they are attempting to conserve or manage while also setting the biological and theoretical foundations for future decisions (Funk et al. 2012). For the past three decades, microsatellite loci served as the genetic marker of choice for delineating population boundaries; however, recent advances in speed and accuracy, and rapidly decreasing costs have facilitated Next Generation Sequencing (NGS) approaches for non-model organisms (Allendorf et al. 2010, Davey et al. 2011, Helyar et al. 2011). NGS facilitates the genotyping by sequencing approach that provides unprecedented resolution of segregating single nucleotide polymorphisms (SNPs) spanning the entire genome (Krück et al. 2013, Larson et al. 2014), thereby catalyzing the shift from microsatellite loci to SNPs for population genomic studies (van Bers et al. 2010, Pujolar et al. 2013, Malenfant et al. 2015). Although microsatellite loci typically possess greater allelic diversity than SNPs, the latter offer several advantages, including a high abundance, regular distribution throughout the genome (i.e., SNPs occur in both coding and noncoding regions), high reproducibility and transferability within and among laboratories, low scoring error rates, and a simple model of nucleotide evolution (Morin et al. 2004, Kraus et al. 2015). Moreover, when utilizing large numbers of SNPs (1000 or more), as few as four individuals per population provide accurate estimates of population differentiation (FST), whereas for typical microsatellite studies, the number of individuals required to obtain accurate FST estimates ranges between 25 and 30 (Hale et al. 2012).
Numerous advances have also been made over the past decade with regard to population genetic analyses. For example, a variety of statistical analyses, including BAPS (Corander et al. 2004), BayesAss+ (Wilson and Rannala 2003), GeneClass (Piry et al. 2004), GENELAND (Guillot et al. 2005a, 2005b), and STRUCTURE (Pritchard et al. 2000), make use of multilocus genotypes to determine the number of clusters from which the samples under study were collected or to assign individuals to predetermined clusters. These programs are useful for determining population structure as well as for detecting contemporary migrants; when used with large numbers of SNPs, even weak genetic structure can be revealed. For example, 586 American lobsters (Homarus americanus) from 17 locations were genotyped at 10,156 SNPs, revealing hierarchical genetic structure (Benestan et al. 2015). Not only could these loci separate northern lobsters from southern lobsters, but they also revealed 11 distinct populations and provided strong evidence for fine-scale genetic structuring within each region. Similarly, 494 eulachon (Thaleichthys pacificus) were collected from 12 sites and genotyped at 4104 SNP loci (Candy et al. 2015). Of these loci, 193 were putatively adaptive, so population differentiation was assessed using adaptive loci as well as neutral loci. Levels of population differentiation were similar based on either the 3911 neutral SNPs or the 193 putatively adaptive SNPs. However, the putatively adaptive SNPs provided greater resolution of stocks than the 3911 neutral SNPs. The authors proposed putative divergent selective pressures in the different freshwater and marine environments that acted on the regional populations of eulachon. More importantly, they posited that these adaptive differences should be given strong consideration when delineating genetic boundaries for conservation purposes. This study, as well as others that assessed Atlantic herring (Clupea harengus; Lamichhaney et al. 2012) and Pacific lamprey (Entosphenus tridentatus; Hess et al. 2013), highlights the potential for detecting adaptive variation using SNPs and represents an advantage over microsatellites. In fact, adaptive variation may be important for delineating biologically relevant units for management and conservation (Funk et al. 2012).
Delineating population boundaries of Golden Eagles (Aquila chrysaetos), if they exist, has proved challenging, due to Golden Eagles' capacity for long-distance movements, especially among immature individuals and during periods of nonbreeding. As Golden Eagles face increased anthropogenic pressures (e.g., wind energy development, electrocution, lead poisoning, habitat alteration and loss), it is paramount that biologically meaningful population boundaries be determined for their proper management and conservation (U.S. Fish and Wildlife Service 2009). Until recently, Golden Eagles in North America were managed using Golden Eagle Management Units that approximate the Bird Conservation Regions established by the North American Bird Conservation Initiative (U.S. Fish and Wildlife Service 2009, 2013). The correspondence of Golden Eagle Management Units and Bird Conservation Regions was based on the estimated natal dispersal distance for Golden Eagles (Millsap et al. 2014).
In one of the first studies to use genetic loci to evaluate genetic structure in North American Golden Eagles, Doyle et al. (2016) identified 159 autosomal SNPs by comparing a previous Golden Eagle genome assembly (Doyle et al. 2013) with our unpublished genome (described herein) of Golden Eagles, each bird