Genotype data for: Demographic and genetic consequences of a steelhead supplementation program
Data files
Sep 28, 2023 version files 4.31 MB
Abstract
Supplementation of naturally-spawning populations by the addition of hatchery-spawned individuals is commonly conducted for recovery of threatened and endangered populations and to support harvest opportunities. We present an analysis of steelhead, the anadromous form of Rainbow Trout (Oncorhynchus mykiss), returning to an integrated supplemented population in Southwest Washington over the course of 15 years. The goal of the supplementation program was to evaluate whether use of a juvenile captive broodstock and an integrated paradigm could be used to increase adult returns while avoiding negative genetic impacts to the population. Estimates of relative reproductive success (RRS) for fish spawned in the hatchery ranged from 2.4 for hatchery-origin females to 6.4 for natural-origin males, indicating that fish spawned in the hatchery produced more returning adult progeny than did fish allowed to spawn in the natural environment. We observed a slight reduction in reproductive success (RS) for hatchery-origin (relative to natural-origin) fish when spawning in the natural environment, but the difference was non-significant for males and marginally significant for females. In contrast to the relatively weak relationship between RS and origin (male P = 0.347, η2 = 0.008; female P = 0.066, η2 = 0.037), we observed a strong relationship between RS and return year (male P < 0.001, η2 = 0.896; female P < 0.001, η2 = 0.867) (i.e., hatchery- and natural-origin fish did well or poorly together each year). Hatchery-origin fish exhibited reduced genetic diversity, as well as evidence of increased temporal population structure among hatchery fish. We suspect the latter is an artifact of cultural practices that reduce diversity in age at smoltification. We conclude that the program was successful in achieving an increase in adult return, but not in avoiding negative genetic effects on the population, and that any lasting impacts of supplementation remain to be determined.
README: Genotype data for Demographic and genetic consequences of a Steelhead supplementation program
https://doi.org/10.5061/dryad.wstqjq2sx
Genotypes for 291 single nucleotide polymorphism loci in steelhead captured in Abernathy Creek, WA. The data were used to conduct parentage analysis and infer reproductive success of hatchery- and natural-origin fish spawning in the hatchery and natural environments.
Description of the data and file structure
Genotype data are presented in two-column format (i.e., each allele in its own column), with the locus names indicated in the header row for each column. The Category column indicates how each individual was classified at the time of collection. The Year column indicates spawn year for captive brood individuals, and return year for returning adults. Numeric allele calls are coded as follows: A = 1, C = 2, G = 3, T = 4, deletion = 5, and missing data = 0.
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Methods
Samples were genotyped using the Genotyping-in-Thousands by Sequencing (GT-Seq) method developed by Campbell et al. (2015). The GT-Seq protocol included pre-amplification of a primer pool of 379 SNP loci, (“Omy_379”; Shawn Narum, Columbia River Inter-Tribal Fish Commission, personal communication), and barcode amplification resulting in a unique barcode for each sample. This was followed by a normalization step using a SequalPrepTM Normalization Plate Kit (Thermo Fisher Scientific, Waltham, MA) and pooling the normalized product for amplicon size selection using AMPure XP beads (Beckman Coulter Brea, CA) and a magnetic rack. Quantification of libraries was done by qPCR using KAPA Library Quantification Kit for Illumina® platforms (Roche Basel, Switzerland). Libraries were then normalized to 4nM and verified using a Quibit Fluorometer (Invitrogen Life Technologies Waltham, MA) according to the manufacturer’s protocol, and were processed on a NextSeq 5500 (Illumina, Inc.).
To determine laboratory error rate, a quality assessment/quality control (QA/QC) was performed by selecting one column (eight samples) from each 96-well plate of extracted DNA, and building a new library based on those samples. The GT-Seq protocol was as described above, except that the QA/QC library was processed on a MiSeq (Illumina, Inc.). Genotype data were extracted from the resulting sequence data using the bioinformatic scripts developed by Campbell et al. (2015).