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ABSTRACT Human-induced hybridization among genetically distinct groups of fish is a widespread and complex problem in fisheries management. A particularly challenging facet of human-induced hybridization is deciding which fish should be prioritized for conservation action or investment, and which should not. The increasing availability of genomic data in fisheries management demands that explicit hybridization frameworks and associated hybridization thresholds be developed, as increasing resolution will inevitably demonstrate that small amounts of nonnative ancestry are present in populations or species that were previously thought to be nonhybridized. A key question then becomes, how do we make rational decisions regarding resource prioritization for populations or species with, for example, 10, 1, 0.1 or even 0.01% nonnative ancestry? We use extensive data from Westslope Cutthroat Trout Onchorhynchus lewisi to describe how objective, data-based decision frameworks can be developed to help managers conserve genetic variation, while minimizing nonnative ancestry and the risk of outbreeding depression. While the conservation implications of hybridization are nuanced and context-dependent, the approach described herein is general and can be extended to other species. 

ABSTRACT Genetic rescue, specifically translocation to facilitate gene flow among populations and reduce the effects of inbreeding, is an increasingly used approach in conservation. However, this approach comes with trade‐offs, wherein gene flow may reduce fitness when populations have adaptive differentiation (i.e., outbreeding depression). A better understanding of the interaction between isolation, inbreeding, and adaptive divergence in key traits, such as life history traits, will help to inform genetic rescue efforts. Stream‐dwelling salmonids, such as the westslope cutthroat trout (Oncorhynchus lewisi; WCT), are well‐suited for examining these trade‐offs because they are increasingly isolated by habitat degradation, exhibit substantial variation in life history traits among populations, and include many species of conservation concern. However, few genomic studies have examined the potential trade‐offs in inbreeding versus outbreeding depression in salmonids. We used > 150,000 SNPs to examine genomic variation and inbreeding coefficients in 565 individuals across 25 WCT populations that differed in their isolation status and demographic histories. Analyses of runs of homozygosity revealed that several isolated WCT populations had “flatlined” having extremely low genetic variation and high inbreeding coefficients. Additionally, we conducted genome scans to identify potential outlier loci that could explain life history differences among 10 isolated populations. Genome scans identified one candidate genomic region that influenced maximum length and age‐1 to age‐2 growth. However, the limited number of candidate loci suggests that the life history traits examined may be driven by many genes of small effect or phenotypic plasticity. Although adaptive differentiation should be considered, the high inbreeding coefficients in several populations suggest that genetic rescue may benefit the most genetically depauperate WCT populations. 

Abstract The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled “ConGen 2018” was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists.