Reduced representation sequencing detects only subtle regional structure in a heavily exploited and rapidly recolonizing marine mammal species

Abstract

Next‐generation reduced representation sequencing (RRS) approaches show great potential for resolving the structure of wild populations. However, the population structure of species that have shown rapid demographic recovery following severe population bottlenecks may still prove difficult to resolve due to high gene flow between subpopulations. Here, we tested the effectiveness of the RRS method Genotyping‐By‐Sequencing (GBS) for describing the population structure of the New Zealand fur seal (NZFS, Arctocephalus forsteri), a species that was heavily exploited by the 19th century commercial sealing industry and has since rapidly recolonized most of its former range from a few isolated colonies. Using 26,026 neutral single nucleotide polymorphisms (SNPs), we assessed genetic variation within and between NZFS colonies. We identified low levels of population differentiation across the species range (<1% of variation explained by regional differences) suggesting a state of near panmixia. Nonetheless, we observed subtle population substructure between West Coast and Southern East Coast colonies and a weak, but significant (p = 0.01), isolation‐by‐distance pattern among the eight colonies studied. Furthermore, our demographic reconstructions supported severe bottlenecks with potential 10‐fold and 250‐fold declines in response to Polynesian and European hunting, respectively. Finally, we were able to assign individuals treated as unknowns to their regions of origin with high confidence (96%) using our SNP data. Our results indicate that while it may be difficult to detect population structure in species that have experienced rapid recovery, next‐generation markers and methods are powerful tools for resolving fine‐scale structure and informing conservation and management efforts.

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

“…RE-RRS is used to obtain genotypes for parentage identification or genomic selection (i.e. to identify the individuals with the most favorable genotypes associated with phenotypes of interest) in a variety of species across livestock, plants and aquaculture [15][16][17][18], as well as population diversity studies, e.g. for conservation [17].…”

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

“…RE-RRS is used to obtain genotypes for parentage identification or genomic selection (to identify the individuals with the most favorable phenotypes) in a variety of species across livestock, plants and aquaculture (15-18), as well as population diversity studies, e.g. for conservation (19). RE-RRS holds promise as a technique for rapid, high-throughput and cost-effective sequencing of metagenome samples at a fraction of the cost of WGS.…”

Commonly used Hardy-Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data

“…In order to validate our results against empirical data and across multiple SNP calling pipelines, we selected three publicly available datasets as they represented a range of organisms, with a range of population structure: A DArTseq (Diversity Arrays Technology sequencing) dataset of a New Zealand isopod ( Isocladus armatus ) (Pearman et al, 2020), and two RADseq datasets of the New Zealand fur seal ( Arctocephalus forsteri ) (Dussex et al, 2018) and the Plains zebra ( Equus quagga ) (Larison et al, 2021). For the isopod dataset, the DArTseq genotypes were provided by diversityarrays™, who generated them using their proprietary SNP calling software with a de novo assembly (SRA: PRJNA643849, https://osf.io/kjxbm/).…”

“…SRA data (New Zealand fur seal: SRP125920, single-end data; and zebra: SRP288329, paired-end data) was obtained (using prefetch) and converted to fastq (using fastq-dump) with sratoolkit v2.9.6 (Leinonen et al, 2011). Metadata associated with these datasets (Dussex et al, 2018; Larison et al, 2021) was used to generate popmap files. Conspecific genomes were used as references, namely Antarctic fur seal for the New Zealand fur seal analyses (GCA_900642305.1_arcGaz3_genomic: Humble et al, 2018) and horse for the zebra analyses (GCF_002863925.1_EquCab3.0_genomic: Kalbfleisch et al, 2018).…”

“…SRA data (New Zealand fur seal: SRP125920, single-end data; and zebra: SRP288329, paired-end data) was obtained (using prefetch) and converted to fastq (using fastq-dump) with sratoolkit v2.9.6 (Leinonen et al, 2011). Metadata associated with these datasets (Dussex et al, 2018;Larison et al, 2021) was used to generate popmap files. Conspecific genomes were used as references, namely…”