Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Chang, Sarah Lu
Master of Science - MSc
University of British Columbia
The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the resident freshwater form of sockeye salmon (Oncorhynchus nerka), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Additionally, stocking programs have been implemented to re-establish extirpated species to their historical ranges and can affect recipient ecosystems. Here, we developed and applied two Genotyping-in-Thousands by Sequencing (GT-seq) panels with targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada, as well as further investigation of the genetic outcomes of an anadromous sockeye salmon (Oncorhynchus nerka) stocking program in Skaha Lake, British Columbia. The broad-scale GT-seq panel exhibited high self-assignment accuracy (93.3%), perfect assignment of individuals not included in the baseline to their geographic basin and life history form. The panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan® SNP genotyping assays and improving resolution, extending a long-term time series, and demonstrating the scalability of this approach for this system and others. The O. nerka hybrid panel exhibited high efficiency, accuracy, and power (>94.5%) of assignment to pure-stock sockeye/kokanee, F₁ F₂ and B₂ backcross-sockeye/kokanee. We genetically re-assigned 91 individuals that had previously been assessed with otolith microchemistry analysis and TaqMan® SNP assay genetic assignment to evaluate panel power with known individuals and found that our panel was able to reclassify individuals previously assigned as F₁ hybrids to pure-stock and B₂ backcross classes. We then estimated hybrid proportions and the progression of introgression in 2019, revealing that hybrids now comprise of 31% of the population. Our results expand upon previous evidence of hybridization and intermediate morphology between O. nerka migratory forms, as well as providing a novel genomic tool able to detect advanced introgression for continued monitoring of the Skaha Lake restocking initiative.
Genotyping-in-Thousands by sequencing panel development and application for informing multi-scaled kokanee salmon (Oncorhynchus nerka) fisheries management by Sarah Lu Chang B.Sc., University of California Los Angeles, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2021 © Sarah Lu Chang, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Genotyping-in-Thousands by sequencing panel development and application for informing multi-scaled kokanee salmon (Oncorhynchus nerka) fisheries management submitted by Sarah Lu Chang in partial fulfillment of the requirements of the degree of Masters of Science . Dr. Michael Russello, Department of Biology, Irving K. Barber Faculty of Science Supervisor Dr. Hillary Ward, British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Penticton, BC Supervisory Committee Member Dr. Robert Lalonde, Department of Biology, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Federic Menard, Department of Chemistry, Irving K. Barber Faculty of Science University Examiner iii Abstract The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the resident freshwater form of sockeye salmon (Oncorhynchus nerka), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Additionally, stocking programs have been implemented to re-establish extirpated species to their historical ranges and can affect recipient ecosystems. Here, we developed and applied two Genotyping-in-Thousands by Sequencing (GT-seq) panels with targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada, as well as further investigation of the genetic outcomes of an anadromous sockeye salmon (Oncorhynchus nerka) stocking program in Skaha Lake, British Columbia. The broad-scale GT-seq panel exhibited high self-assignment accuracy (93.3%), perfect assignment of individuals not included in the baseline to their geographic basin and life history form. The panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan SNP genotyping assays and improving resolution, extending a long-term time series, and demonstrating the scalability of this approach for this system and others. The O. nerka hybrid panel exhibited high efficiency, accuracy, and power (>94.5%) of assignment to pure-stock sockeye/kokanee, F1, F2, and B2 backcross-sockeye/kokanee. We genetically re-assigned 91 individuals that had previously been assessed with otolith microchemistry analysis and TaqMan SNP assay genetic assignment to evaluate panel power with known individuals and found that our panel was able to reclassify individuals previously assigned as F1 hybrids to pure-stock and B2 backcross classes. We then iv estimated hybrid proportions and the progression of introgression in 2019, revealing that hybrids now comprise of 31% of the population. Our results expand upon previous evidence of hybridization and intermediate morphology between O. nerka migratory forms, as well as providing a novel genomic tool able to detect advanced introgression for continued monitoring of the Skaha Lake restocking initiative. v Lay Summary Sockeye salmon (Oncorhynchus nerka) exhibit tremendous life history variation encompassing different migratory (anadromous sockeye and resident freshwater kokanee) and reproductive ecotypes (shore/stream/deep-spawning). Genetics-based approaches can improve on results reported with the use of traditional survey methods. We designed and optimized a genetic tool to enable kokanee identification by geographic basin and ecotype across BC. We applied this to Wood Lake, historically among the most productive kokanee stocks in the world, to inform ongoing monitoring of ecotype composition after a population crash in 2011. We designed a second genetic panel to enable hybrid identification in Skaha Lake to investigate the impacts of a reintroduction program. Here, we investigate the ability of targeted genetic markers to identify fish by ecotype, basin, and hybrid class. We additionally provide general considerations and techniques for panel design and implementation for any species that require accurate genetic tools for a variety of fisheries management goals. vi Preface Many individuals have contributed to Chapters 2 and 3 of this thesis. The study design for Chapters 2 and 3 were collaborations between Dr. Michael Russello, Dr. Hillary Ward and I. Samples for both chapters were provided by personnel from the BC Ministry of Forests, Lands, Natural Resource Operations, and Rural Development. For Chapters 2 and 3, I was primarily responsible for sample processing, DNA extractions, genetic data collection, data analysis, and initial manuscript preparation. Previously extracted DNA samples were provided by Lucas Elliott, Bryson Sjodin, Dr. Evelyn Jensen, Dr. Andrew Veale, and Karen Frazer. Danielle Schmidt and Bryson Sjodin provided guidance on panel design, sequencing technique, and lab work optimization. Ty Russell (University of Alberta) provided and assisted with custom code for hybriddetective sensitivity analyses. A manuscript reporting the results from Chapter 2 has been completed with Dr. Michael Russello and Dr. Hillary Ward as co-authors and submitted to PLOS ONE for publication entitled: “Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales.” A manuscript encompassing the results from Chapter 3 has been completed with Dr. Michael Russello, Lucas Elliott, and Dr. Hillary Ward as co-authors and submitted to Molecular Ecology Resources for publication entitled: “Genotyping-in-Thousands by sequencing (GT-seq) panel to monitor kokanee-sockeye salmon introgressive hybridization associated with a long-term reintroduction program”. Dr. Michael Russello, Dr. Hillary Ward, and Dr. Robert Lalonde provided suggestion and feedback for the thesis proposal and final version of this thesis. Dr. Michael Russello provided guidance and suggestions throughout the entirety of this thesis. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 The Collapse of Freshwater Fisheries ........................................................................... 1 1.2 Oncorhynchus nerka ..................................................................................................... 2 1.3 Freshwater Fisheries Management ............................................................................... 5 1.3.1 Conventional Methods for Stock Assessment ........................................................ 5 1.3.2 Genetics Based Tools .............................................................................................. 6 1.4 Study Objectives ............................................................................................................... 7 Chapter 2: Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales.................................................................................................................................9 2.1 Background ................................................................................................................... 9 2.2 Methods....................................................................................................................... 11 2.2.1 British Columbia-wide GT-seq SNP Panel design .................................................. 11 2.2.2 GT-seq Test Library Preparation .......................................................................... 13 2.2.3 GT-seq Genotyping and Primer Optimization ...................................................... 14 2.2.4 Self-assignment analysis of Panel Utility ............................................................. 14 viii 2.2.5 Assignment of novel individuals across British Columbia ................................... 15 2.2.6 Wood Lake kokanee case study ............................................................................ 15 2.3 Results ......................................................................................................................... 17 2.3.1 Panel Optimization................................................................................................ 17 2.3.2 Self-assignment and Province-wide accuracy ...................................................... 17 2.3.3 GT-seq British Columbia individual assignment .................................................. 18 2.3.4 GT-seq SNP genotyping Wood Lake .................... 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Bookmark not defined. 2.4 Discussion ................................................................................................................... 19 Chapter 3: Genotyping-in-Thousands by sequencing panel development and application for high-resolution monitoring of introgressive hybridization ................................................26 3.1 Background ................................................................................................................. 26 3.2 Methods....................................................................................................................... 30 3.2.1 Study system and samples .................................................................................... 30 3.2.2 GT-seq SNP panel design ..................................................................................... 31 3.2.3 GT-seq test library preparation ............................................................................. 32 3.2.4 GT-seq genotyping and primer optimization ........................................................ 33 3.2.5 Panel Performance ................................................................................................ 33 3.2.6 Re-analysis of 2016-2017 spawners ..................................................................... 34 3.2.7 Assignment of 2019 samples ................................................................................ 34 3.2.8 Morphometric Analyses ........................................................................................ 35 3.3 Results ......................................................................................................................... 35 3.3.1 Initial panel ability ................................................................................................ 35 3.3.2 SNP panel assignment efficacy ............................................................................. 36 ix 3.3.3 Re-analysis of 2016-2017 Okanagan River spawners .......................................... 36 3.3.4 Assignment of 2019 Okanagan River spawners ................................................... 37 3.4 Discussion ................................................................................................................... 38 3.4.1 GT-seq panel development and evaluation ........................................................... 39 3.4.2 Management Implications ..................................................................................... 40 Chapter 4: Conclusion .................................................................................................................43 4.1 Research findings and significance ............................................................................. 43 4.2 Limitations and future studies ..................................................................................... 46 Bibliography .................................................................................................................................61 x List of Tables Table 1. Kokanee and anadromous sockeye salmon samples selected for assignment to migratory form and reproductive ecotypes across historical drainages. ........................................................ 49 Table 2. Confusion matrices of simulated individuals’ proportional assignment with NEWHYBRIDS based on data from: A). Top 300 Fst SNPs; B). Top 600 Fst SNPs; C). Random 350 SNPs from the top 600 Fst SNPs (Random 350 Fst); and D) 342 final SNP panel. .............. 50 Table 3. Stock composition of spawners by sample year assessed with the TaqMan 32 SNP assay and 342 SNP GT-seq Panel. Stock proportions indicated in parentheses. .......................... 51 xi List of Figures Figure 1. Map of British Columbia with baseline Oncorhynchus nerka populations used in initial panel design obtained from Veale & Russello (2017b). Fish image size represents migratory form (large = anadromous sockeye salmon, small = resident kokanee) and color represents reproductive ecotype (red = stream-spawning, brown = shore-spawning, black = deep-spawning kokanee). All map units in decimal degrees. ................................................................................ 52 Figure 2. Workflow of initial Oncorhynchus nerka multi-use panel design and primer removal. 53 Figure 3. Assignment accuracies estimated using the Support Vector Machine algorithm with Monte-Carlo cross-validation implemented in assignPOP (Chen et al., 2018). Sampling of five subsets of high θ loci (All loci, blue; top 75%, green; top 50%, orange) are crossed by two levels of training individuals (50%, 90%). Outliers are shown as black circles. The horizontal red line indicates 90% assignment rate. (Results grouped by: A: geographic basin, B: migratory form, C: reproductive ecotype.)................................................................................................................... 54 Figure 4. Map of the Columbia River displaying Skaha Lake, Okanagan River (extending southward originating at McIntyre Dam), and downstream lakes and dams. Size comparison of an average sockeye male and kokanee male included. ................................................................. 55 Figure 5. Workflow of Oncorhynchus nerka sockeye and kokanee GT-seq hybridization panel design. ........................................................................................................................................... 56 Figure 6. Accuracy and efficiency with simulated individuals of different hybrid classes for the top 300 Fst panel and the final optimized 342 SNP GT-seq panel. .............................................. 57 Figure 7. Body length of spawning male and female fish in Okanagan River Channel 2019. ..... 58 Figure 8. Tukey Test conducted on Okanagan River Channel 2019 for analysis of significance in body size difference between groups. Statistically significant groups did not have the 95% confidence level bar overlap with zero difference in mean levels of class. .................................. 59 xii Figure 9. Power evaluated with simulated individuals of different hybrid classes for the top 300 Fst panel and the final optimized 342 SNP GT-seq panel. ........................................................... 60 xiii Acknowledgements I am grateful for the important nuances and broader impacts involved in manuscript synthesis provided by Dr. Hillary Ward for this project. Additionally, I would like to thank Dr. Bob Lalonde for providing a broader viewpoint for the synthesis of my proposal and thesis. I would like to thank my advisor, Dr. Michael Russello, for all your essential guidance throughout the data collection, data analysis, and writing aspects throughout my degree. Over the past two years, I have experienced a well-rounded graduate experience, including, but not limited to: your critical perspective as a research and academic mentor, the tight-knit, trivia-busting family that is the Ecological and Conservation Genetics Lab, and the delicious ribs you have cooked up for us. It has truly been a joy to have you as my advisor for this project. To my lab mates past and present, the members of the Ecological and Conservation Genomic Laboratory from the University of British Columbia (Okanagan campus), from the bottom of my heart, thank you for your unwavering support for me in times of joy and despair. Luke, for helping me navigate the early stages of graduate school, finally get me to finish Fez, and inviting me into a wonderful group of supportive friends; Brock, for helping me discover the joy of frosty orange juice and the days in the lab idly listening to music while sharing in bioinformatics, TA, and lab work frustrations; Farida, for being my strong, fearless friend as we fend off hungry ducks, and contributing to my love of hidden gem restaurants; Chris, for joining my many adventures across British Columbia, indulging me when I am most angsty, and just always being down for a great time; Bryson, for the camaraderie in the way we both experience the world, and for all of the good music, beer, and conversations (only) past the witching hour; and last but not least Danielle, my bioinformatics, GT-seq, cooking, baking, and lovely all-around champion, it has truly been a pleasure co-hosting food events for the ECGL across various Canadian and U.S. xiv holidays and providing you the soups, stews, and sauces you require for your projects and general wellbeing. Finally, I would like to acknowledge the support of the Freshwater Fisheries Society of British Columbia and British Columbia’s freshwater anglers for helping fund recreational fisheries projects, including this one, through their license feeds. I would also like to thank the staff and contractors of the BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the University of British Columbia. 1 Chapter 1: Introduction 1.1 The Collapse of Freshwater Fisheries For centuries, humanity have depended on fisheries as a primary source of food and a basis for coastal settlement. However, subsequent overfishing, invasive species, and water quality deterioration are a few of the many factors that are contributing to the collapse of nearly one in four fisheries over the last fifty years (Carlson et al., 2019; Holmes, 1994; Lyons et al., 1998; Mullon et al., 2005; Riddell et al., 2004). Efforts to recover fish stocks by reducing exploitation rate have not been successful (Walters et al., 2019), and could be due to management policies that do not consider conserving life history variation that prevents the loss of population diversity and can therefore stabilize productivity (Tamario et al., 2019). For example, skewed harvesting could lead to fluctuations in ecotype proportions (Pauli & Sih, 2017) and ultimately affect total catch volume. It is thus important to carefully investigate underlying population genetic diversity that may be integral to evaluating fluctuations in fish stocks. Anadromous fish species, such as Pacific salmon (Oncorhynchus spp.) are being affected by anthropogenic changes such as warming lake temperatures that can affect life histories, such as migration to the ocean at a younger age, a longer maturation period in the ocean, and reduced age structure diversity within a population (Cline et al., 2019). These impacts can lead to reduced overall population sustainability and a decrease in genetic diversity when age at maturity is delayed (Martinez et al., 2018). Rising temperatures also decrease oxygen solubility and increase water acidification, causing elevated fish mortality (Wood & McDonald, 1997). For example, fish larvae reared at projected acidity levels caused by elevated CO2 developed into mature fish with an impaired ability to distinguish olfactory stimuli, ultimately impacting homing sensitivity that is critical for migration success (Munday et al., 2009). Warmer winter sea surface temperatures can also cause northwards displacement that can increase migration distance and subsequently a delay 2 in return timing (Levy, 2006). These cumulative effects can significantly influence survival from juvenile to adult stages in salmonids, with late migrating individuals surviving at much lower rates (Scheuerell et al., 2009). As a result of these effects, spawning runs of salmon have declined in the Pacific Northwest of the United States of America and western Canada, leading to cascading effects on the ecosystem. Apex predators, such as brown bears, rely on salmon to survive the winter (Allan et al., 2005) and decaying fish after spawning runs are critical for bacteria and fungi downstream that recycle nitrate and phosphorus (Richey et al., 1975). Ultimately, careful management of inland salmonid fisheries is of crucial importance as they contribute to food production, angler satisfaction, economic growth, and are key contributors to overall ecosystem health. 1.2 Oncorhynchus nerka Sockeye salmon (Oncorhynchus nerka) are found throughout the Northern Pacific Ocean and have a remarkably unique life cycle for a teleost fish. The classic anadromous sockeye salmon life cycle encompasses both freshwater and saltwater environments. In autumn, salmon females deposit eggs into a depression in the gravel called a redd (McDonald, 1960) that is covered with substrate after spawning is complete to protect the eggs from predators. Newly hatched young known as alevins therefore stay protected and subsist from an attached yolk sac. In the spring, they become fry and initiate nocturnal migration from their hatch site to a freshwater nursery area. Typically, juvenile anadromous sockeye salmon spend one to three years in lake habitats after hatching (Groot, 1991). After this period, juveniles begin to develop into an intermediary stage called smolts. Smolt transformation requires increased salinity tolerance in preparation for migration and comes with high energetic demands (McCormick & Saunders, 1987) to allow for successful migration to the ocean to mature for one to four years before utilizing natal homing to migrate back to their natal freshwater habitat to spawn in late summer (Groot, 1991; Lohmann & 3 Lohmann, 2019). This complex life cycle of the O. nerka anadromous form diverges further into various life history forms within the species. Another form of O. nerka is represented by the resident ecotype known as kokanee that spend their entire life cycle in freshwater and differ from sockeye salmon behaviorally and morphologically (Nelson, 1968). Anadromous sockeye salmon have a greater swimming velocity than kokanee of the same body size, potentially because kokanee have lost the need for swimming performance with the loss of migration (Taylor & Foote, 1991). Genetic differences have also been found linked to body growth and egg size and are representative of the rich evolutionary history in O. nerka (Wood & Foote, 1990). Sockeye salmon have been proposed to have experienced multiple, independent divergences between polyphyletic forms (Foote et al., 1989). Separate analyses with mitochondrial DNA (mtDNA) and Single Nucleotide Polymorphisms (SNPs) found a clear separation between two genetic groups characterized by historical glacial refugia separation that provide evidence for historical allopatric divergence: the northwestern group consisting of western Alaska and the Skeena River drainage, and a southern group characterized by the Columbia-Fraser River drainages (Taylor et al., 1996; Veale & Russello, 2017b). Within these basins, the current geographic distribution of kokanee and sockeye salmon suggest parallel evolutionary origins of kokanee throughout the North Pacific (Taylor et al., 1996). The kokanee and lake spawning-sockeye salmon ecotypes are hypothesized to have diverged from the “river-type” sockeye salmon that survived multiple glaciations during the Pleistocene (Wood et al., 2008). Thus, kokanee have evolved from the sockeye anadromous form several times independently in major river systems (Taylor et al., 1997). Kokanee populations have been found to be genetically more similar to sockeye salmon within the same system than to other kokanee range-wide and vice versa, suggesting that the two life history forms are not monophyletic (Taylor 4 et al., 1996) and provide evidence of recurrent evolution of these separate lineages with niche exploitation that can lead to similar traits across populations (Dolloff et al., 1994). Other reproductive ecotypes in sockeye salmon include shore-spawners, stream-spawners, and deep-spawners. Stream-spawners move to tributary streams to spawn in free-flowing water (Taylor et al., 1997), whereas the shore ecotype spawn on lake beaches, and tend to be larger at age, but are less fecund than stream-spawners (Blair et al., 1993). No physical traits distinguish stream and shore- spawners other than the number of anal fin rays, which is greater in stream-spawners (Taylor et al., 1997). The evolutionary history of these reproductive ecotypes reflects the availability of separate reproductive niches within the same geographic region. Kokanee shore- and stream-spawning ecotypes in Okanagan Lake, British Columbia (BC), Canada were characterized by mtDNA haplotypes and allozyme alleles that are rare or apparently absent from other populations, and that the post-glacial time period of 11,000 years was appropriate for the level of phenotypic differentiation between the reproductive ecotypes if both were diverged from a common ancestral sockeye salmon (Taylor et al., 1997). This hypothesis was further supported by a subsequent study across multiple lakes employing microsatellites linked to expressed regions of the genome that failed to identify common candidate loci under selection across lakes (Frazer & Russello, 2013). However, recent findings revealed an extremely differentiated SNP between shore- and stream-spawning kokanee and river-and beach spawning sockeye salmon from pairs sampled across the entire Pan-Pacific distribution, providing evidence for an ancient selective sweep between alleles that are closely linked to underlying spawning behavior (Veale & Russello, 2017b). Deep-spawners, also known as black kokanee, spawn at depths between 20-70m in Lake Saiko, Japan, and in the Anderson and Seton lakes in British Columbia (Moreira & Taylor, 2015). 5 Fish of this ecotype gain black coloration at maturity, in contrast to the typical green and red coloration of mature sockeye salmon (Nakabo et al., 2011). Black kokanee are also known as ‘Gwenish’ or ‘floaters’, as they float to the surface with distended swim bladders after spawning in the spring (Levy, 2012). Unique populations of deep-spawners have been found in the Anderson and Seton Lakes in the southwestern interior of British Columbia and in Lake Saiko, Japan. (Moreira & Taylor, 2015). Black kokanee have been hypothesized to have had a diphyletic origin in the North Pacific basin, characterized by two separate divergence events: 1) two populations of black kokanee in the Anderson and Seton lakes were found to be genetically distinct from one another and from sympatric populations of sockeye salmon in interconnected creeks and 2) black kokanee in Lake Saiko were found to be genetically distinct from sympatric kokanee (Moreira & Taylor, 2015; Nakabo et al., 2011). 1.3 Freshwater Fisheries Management 1.3.1 Conventional Methods for Stock Assessment A variety of methods have been implemented for conducting fish stock counts that are critical for effective management. Fish counting fences funnel migratory fish to a trap box and often extend across the entire river to provide accurate counts, but is often expensive and can be impacted negatively by river topography (Porter et al., 2017). Video recordings and observation from boats or directly from the shore can circumvent the cost of fish counting fences, but proximity to the fish, light conditions, water depth, agitation of the water column, and a high density of individuals may obscure counts (Lucas & Baras, 2000). Dual-frequency identification sonar has been used to improve salmon counts and directly measure fish length for in-season management (Martignac et al., 2015), but ideally only one species of interest needs to be within range, actively migrating, and swimming over a consistent river bottom topography for the best results (Maxwell 6 et al., 2007). Additionally, unintended behavioral responses to sonar have been recorded, where fish have been observed to immediately turn and actively swim in the opposite direction when encountering a certain frequency or avoid the survey boat itself (Knudsen et al., 1994; Xie et al., 2008). Beaming position and placement has been shown to underestimate total counts by 20-100% and may also have varied results depending on geographic location and time of day (Knudsen & Sægrov, 2002). Stationary hydroacoustic monitoring stations are an alternative to research vessels, but differences in the spatial distribution of migrating fish can skew results (Ransom et al., 1998). Ultimately, these methods may lead to inaccurate ecotype identification, as the overlap in morphological differences between ecotypes are difficult to visually verify. Though conventional population assessment methods reveal useful and high-resolution information for species-level management, they are not ideal for within-species kokanee ecotype enumeration. Low visibility conditions and a strong overlap in morphology between ecotypes have resulted in inaccurate estimates (Oke et al., 2019; Ward et al., 2019). A potential solution is to use molecular genetic techniques for Genetic Stock Identification (GSI), a technique that is identified as a robust method for implementing stock priorities from an evolutionary standpoint (Begg et al., 1999). 1.3.2 Genetics Based Tools GSI can be used to inform management decisions, the recovery of extirpated populations and sustainability of current stocks. Initially, allozymes were effective at estimating groups of individual fish stocks, with estimates within 5-10% of confirmed contributions (Shaklee et al., 1999). However, the use of 18 alloyzyme loci and two minisatellite DNA loci did not reveal significant differences in allele frequency between kokanee reproductive ecotypes in Okanagan 7 Lake (Taylor et al., 1997). Microsatellite loci have been used as candidate markers for fish assessment, but parentage testing conducted with four microsatellite loci in Red sea bream (Pagrus major) led to only 40% of the samples matching with a parental stock (Perez-Enriquez & Taniguchi, 1999). Additionally, nine microsatellite loci used to assess introgression between indigenous and domesticated Brown trout (Salmo trutta) revealed poor resolution, with the probability of individuals representing the correct number of known populations being almost zero (Hansen, 2002). Dense panels of highly informative genetic markers can help mitigate this problem and provide appropriate power for fine-scale genetic differentiation. The development of Next-Generation Sequencing (NGS) technologies has allowed for millions of small DNA fragments to be sequenced in parallel and provided access to thousands of SNPs distributed across the genome in a relatively short time. Outlier SNP loci in particular demonstrate significantly higher genetic differentiation than neutral loci for detecting ecotype-level divergence (Russello et al., 2012). Previous population genomic studies have revealed a genetic basis for O. nerka ecotypes and marked population structure among spawning locations across BC, providing an opportunity to develop a genetics-based tool for identifying reproductive ecotype, migratory ecotype, and geographic basins (Columbia, Skeena, Fraser) within BC (Ferguson et al., 2019; Lemay & Russello, 2015; Veale & Russello, 2017b). 1.4 Study Objectives There is currently a considerable need for scalable, cost-effective, and accurate tools for high-resolution management of O. nerka stocks in British Columbia. First, we will evaluate and optimize a broad-scale GT-seq SNP panel for use in the identification of O. nerka in British Columbia that will be able to genetically distinguish individuals by 8 migratory form (anadromous sockeye salmon, resident freshwater kokanee), reproductive ecotype (shore-, stream-, deep-spawning), and geographic basin (Columbia, Fraser, Skeena). We will apply the province-wide broad scale GT-seq SNP panel to Okanagan Basin samples to estimate stock proportions between Wood Lake shore- and stream-spawners. Additionally, we will evaluate the information content of varying panel sizes and compositions for the ability to group relevant populations for identification. Second, we will evaluate and optimize a second GT-seq O. nerka hybrid SNP panel with the ability to identify advanced-introgression classes between kokanee and recently reintroduced sockeye salmon related to the Skaha Lake sockeye salmon reintroduction program. We will use the GT-seq O. nerka hybrid panel to investigate the genetic and morphological outcomes from sockeye salmon and kokanee hybridization. Our findings will allow for managers to integrate life-history variation into stock assessments across the province, as well as provide fundamental information for sockeye salmon reintroduction impacts to native kokanee. 9 Chapter 2: Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales 2.1 Background Freshwater fish populations have declined 83% over the past forty years as a result of the cumulative effects of overfishing, habitat degradation, climate change, dams and other migration barriers (Bailey et al., 2018; Dudgeon, 2019; Reid et al., 2019). Today, fisheries managers face challenges maintaining a balance between harvesting commercially or recreationally valuable stocks and conserving productive populations in fisheries with diverse demographics (Hendry et al., 2017; Walters et al., 2019). Mixed-stock fisheries present a further challenge for management as stock specific parameters are required to set harvest targets while meeting spawning escapement goals (Gaines et al., 2018). For mixed-stock fish populations, traditional survey methods can be inaccurate due to overlaps in morphology between closely-related life history forms (Ward et al., 2019). In response, genetic stock identification (GSI) techniques have been developed to assess stock proportions by delineating individuals by genetic origin and considering barriers to gene flow (Beacham et al., 2017; Begg et al., 1999; Utter & Ryman, 1993). Genetic stock identification panels have previously featured allozymes, microsatellites, and mitochondrial DNA as genetic markers of choice, but suffer from limited statistical power for precise estimates of stock composition within some species (Beacham et al., 1998; Hauser & Seeb, 2008; Ovenden, 1990; Waples et al., 2008a). Recent advances in massively parallel DNA sequencing (also known as next generation sequencing; NGS) have made great strides in improving speed and reducing costs of obtaining genetic data, providing opportunities for the development and application of novel genotyping approaches ideal for fisheries management applications (Van Nimwegen et al., 2016). 10 Genotyping-in-Thousands by sequencing (GT-seq) is a multiplex amplicon sequencing approach that is able to simultaneously generate genotypes for thousands of individuals at hundreds of single nucleotide polymorphism (SNP) markers (Campbell et al., 2015). This technique can increase the number and vary the composition of diagnostic loci in GSI panels relative to traditional markers, subsequently increasing the statistical power needed to distinguish sub-populations within the same system (Araujo et al., 2014; Helyar et al., 2011). Delineating mixed-stock fish populations requires careful consideration of candidate loci, taking into account detectable genetic variation and the level of tolerable error as key considerations (Begg et al., 1999; Kochzius, 2009). For example, loci exhibiting putative signatures of selection can provide more information than neutral loci for individual assignment, especially in systems with more recent divergence or where low levels of gene flow persist between co-occurring stocks (Russello et al., 2012). Kokanee, the resident freshwater form of sockeye salmon (Oncorhynchus nerka), is a species of economic, ecological, and cultural value. Kokanee are the 3rd most valuable freshwater fishery in British Columbia, Canada, represent a primary prey species for sustaining lucrative rainbow trout fisheries ($555 million/year in British Columbia), and serve important cultural and spiritual roles within some First Nations communities (Jacob et al., 2010; Nelson, 1968; Nguyen et al., 2013; Scheuerell et al., 2007). Kokanee have evolved multiple times independently across their pan-Pacific distribution, where three reproductive ecotypes have been observed including: 1) stream-spawners that move to tributaries to spawn in free-flowing water (Taylor et al., 1997); 2) shore-spawners that spawn in angular gravel along lake shores (Blair et al., 1993); and 3) deep-spawners (also referred to as black kokanee) that spawn at depth in lakes (Moreira & Taylor, 2015). Population genetic and genomic analyses have reconstructed neutral population 11 structure largely by lake and geographic basin (Frazer & Russello, 2013; Taylor et al., 1996), as well as revealing an underlying genetic basis associated with reproductive ecotypes and migratory forms (Lemay & Russello, 2015; Nichols et al., 2016; Russello et al., 2012, p. 212; Veale & Russello, 2017a, 2017b). This underlying genetic divergence across geographic basin, migratory and reproductive ecotypes provides an excellent framework for developing a multi-scaled GSI tool for informing kokanee fisheries management, and can be used as a guide for the development of genetic panels for other fish species with co-occurring ecotypes such as lake trout (Salvelinus namaycush)(Perreault-Payette et al., 2017). Here, we developed a GT-seq panel for use in informing kokanee fisheries management at multiple scales (geographic basin, migratory ecotype, and reproductive ecotype). We validated the panel using sensitivity analyses and self-assignment across systems spanning three distinct geographical basins in British Columbia and comprising locations where migratory forms and reproductive ecotypes co-occur or reside in isolation. We subsequently genotyped kokanee individuals that were distributed across British Columbia to evaluate individual assignment accuracy and applied this new tool to assess stock proportions of co-occurring shore- and stream-spawning kokanee in Wood Lake, Canada’s highest-use wild stock kokanee fishery. More broadly, our results highlight important considerations when developing and applying GT-seq panels for fisheries management. 2.2 Methods 2.2.1 British Columbia-wide GT-seq SNP Panel design We used previously published genotypic data at 7,347 SNPs (Veale & Russello, 2017b) collected via restriction site-associated DNA sequencing (RADseq from stream-, shore- and deep-spawning kokanee and anadromous sockeye salmon distributed across three geographic basins (Columbia, Skeena, and Fraser) in British Columbia. These systems encompass those where 12 reproductive ecotypes co-occur (e.g., Okanagan Lake, Wood Lake), migratory forms overlap (e.g., Skaha Lake kokanee-Okanagan River sockeye; Anderson/Seton Lake kokanee-Portage Creek sockeye), and/or within-lake geographic population structure has been previously inferred (e.g., North versus West Arm Kootenay Lake kokanee) (Figure 1). This initial pool of candidate SNPs (hereafter referred to as RADseq_kokanee) was previously filtered for outliers, deviations from Hardy-Weinberg equilibrium, and putative paralogs (Veale & Russello, 2017b). The initial step in panel design was to remove loci with insufficient flanking sequence for primer design, ensuring that the SNP of interest was located between the 40th and 70th base pairs of the contigs. We calculated Weir and Cockerham's (1984) unbiased estimates of pairwise genetic differentiation (θ) as implemented in VCFtools (Danecek et al., 2011; Weir & Cockerham, 1984) across different groupings in RADseq_kokanee. We then selected a total of 550 informative SNPs from a sequentially ranked list by θ values for each comparison based on previous trends of genetic divergence, with some loci exhibiting high θ across multiple comparisons: 1) geographic basin (Columbia v. Fraser v. Skeena) (200 SNPs); 2) migratory form (anadromous sockeye v. resident kokanee) (100 SNPs); and 3) kokanee reproductive ecotype (stream- v. shore- v. deep-spawning) (250 SNPs) (Table 1, Fig 1). All loci pairs were assessed for deviation from linkage equilibrium using a Fisher’s Exact Test as implemented in GENEPOP version 4.7 (Rousset, 2008); in cases of significant association, we removed the less informative locus after correction for false discovery rate using the Benjamini-Hochberg procedure (Benjamini & Hochberg, 1995). To evaluate our initial pool of candidate loci, we used the Bayesian clustering method implemented in STRUCTURE 2.3.3 (Pritchard et al., 2000) to test their ability to reveal the same inferred number of genetic units as RADseq_kokanee from (Veale & Russello, 2017b). We used 13 a run length of 200,000 MCMC iterations after a burn-in period of 100,000. The analysis was carried out using correlated allele frequencies under a straight admixture model and the LOCPRIOR option. The most likely number of clusters was determined using the ∆K (Evanno et al., 2005) method as implemented in STRUCTURE HARVESTER (Earl & vonHoldt, 2012). The optimal value of K was then combined across ten independent runs with CLUMPP (Jakobsson & Rosenberg, 2007). We also assessed individual assignment accuracy with the Rannala and Mountain (1997) estimator as implemented in ONCOR with 100 simulated individuals per population based on empirical genotype frequencies with recom-sim (Anderson et al., 2008; Elliott et al., 2020; Rannala & Mountain, 1997). After assessment, we sent the full RAD tag sequences associated with the pool of candidate SNPs to GTseek LLC (https://gtseek.com/) for custom locus‐specific primer design. 2.2.2 GT-seq Test Library Preparation We constructed a GT-seq test library with previously extracted DNA samples from (Veale & Russello, 2017b) that were not included in the RADseq_kokanee baseline to estimate assignment accuracy (Table 1), including shore- (n = 25), stream- (n = 30) and deep-spawning (n = 8) kokanee across the Columbia, Fraser and Skeena River basins, as well as anadromous sockeye salmon from the Columbia (n = 10) and Fraser River basins (n = 3). Additionally, we included repeated samples from the RADseq_kokanee baseline (n = 10) to assess genotyping error between RADseq and GT-seq, with five baseline samples repeated to assess within GT-seq genotyping error (Table 1). Extracted DNA was quantified with a Qubit 3.0 Fluorometer and the Qubit dsDNA High Sensitivity DNA Quant Kit (Invitrogen). Library preparation followed standard GT-seq protocol (Campbell et al., 2015), with the exception that we diluted the PCR1 product to 1:10. The PCR2 14 product was quantified with PicogreenTM (Molecular Probes, Inc.) and each sample was normalized to a concentration of 10 ng/µL. The pooled library was purified with a MinElute PCR Purification Kit (Qiagen) and eluted into a final volume of 25µl. Test libraries were sequenced using a single lane of Illumina MiSeq paired-end 150 bp sequencing at the McGill University and Genome Quebec Innovation Centre. 2.2.3 GT-seq Genotyping and Primer Optimization Demultiplexed raw sequencing files were processed with the GT-seq pipeline available on GitHub (https://github.com/GTseq/GTseq-Pipeline). We removed loci that met these criteria: 1) primers with unequal counts indicating non-specificity of in silico probes; 2) candidates exhibiting >2% of the raw read count; 3) loci with no counts indicating off-target amplification or in silico probe variation; 4) loci contributing to potential PCR artefacts; and 5) observed primer dimers. A second test library was prepared with an optimized primer pool and the same samples as library 1 according to the protocols detailed above for sample preparation, sequencing, and primer dropout. Genotypes were called, concatenated across libraries, and compiled into one file that was converted to a .ped file with GTseq_ped_converter3.py (https://github.com/schmanii). Individuals and loci with >30% missing data were removed from downstream analyses with PLINK (Purcell et al., 2007). We then calculated genotyping error rates within and among RADseq and GT-seq sequences by comparing individual genotypes between replicate samples with the custom script genoerrorcalc.py (https://github.com/bsjodin/genoerrorcalc). 2.2.4 Self-assignment analysis of Panel Utility We conducted self-assignment tests using the leave-one-out procedure and the Rannala and Mountain (1997) likelihood estimation method as implemented in ONCOR (Anderson et al., 15 2008; Rannala & Mountain, 1997). Additionally, we employed a machine-learning framework using the Support Vector Machine algorithm with Monte-Carlo cross-validation using the R package assignPOP (Chen et al., 2018) to evaluate the reliability of genetic datasets for population assignment and to assess panel informativeness for assignment of individuals to geographic basin, migratory form, reproductive ecotype and geographic location at multiple scales. Analyses were run with 30 iterations at varying proportions of the top θ loci (50%, 75%, 100%) and varying proportions of training individuals (50%, 90%). 2.2.5 Assignment of novel individuals across British Columbia We filtered genotypic data by individual and locus with a 30% missing data threshold in PLINK (Purcell et al., 2007). Individuals were then assigned to RADseq_kokanee pure stock populations with the method of Rannala and Mountain (1997) as implemented in ONCOR and the support vector machine algorithm as implemented in assignPOP (Anderson et al., 2008; Chen et al., 2018; Rannala & Mountain, 1997). 2.2.6 Wood Lake kokanee case study To compare assignment results across methods, we selected samples from (Ward et al., 2019) that were previously genotyped at 10 microsatellite loci (2012 trawl survey, n = 105) and at 24 SNP loci via TaqMan genotyping assays (2014 angler survey, n = 84). We genotyped these samples using the final optimized GT-seq panel and a Mid Output Reagent Kit (300 cycles) on an Illumina MiniSeq. To extend the Wood Lake time-series, operculum punches were collected from annual angler surveys conducted by personnel from the British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development in 2018 (n = 65) and 2019 (n = 100). We extracted DNA with Chelex according to manufacturer’s protocols and constructed a GT-seq 16 library with the final optimized panel and the methods detailed above. The Wood Lake samples were sequenced using a partial lane of Illumina HiSeq paired-end 150 bp sequencing at the McGill University and Genome Quebec Innovation Centre. All individuals were genotyped by locus and individual, and filtered using a 50% missing data threshold in PLINK (Purcell et al., 2007). In order to stay consistent with the stock assignment approaches used in the long-term time-series (Ward et al., 2019), we calculated the probability that an individual belongs to Wood Lake shore- or stream-spawning kokanee using the reference populations from the RADseq_kokanee baseline and the Rannala and Mountain (1997) method in ONCOR (Anderson et al., 2008; Rannala & Mountain, 1997). In addition, we used a Bayesian method to assign individuals to Wood Lake shore or stream-spawning kokanee as implemented in STRUCTURE 2.3.3 (Evanno et al., 2005; Pritchard et al., 2000). We used a run length of 1,000,000 MCMC replicates after a burn-in period of 500,000. The analysis was carried out using correlated allele frequencies under a straight admixture model and the LOCPRIOR option for the use of sampling location information to delineate closely related populations. The maximum number of clusters was set at K = 2 to assign individuals to the stream or shore cluster following (Ward et al., 2019). Membership coefficients for each sample were averaged across 10 replicates with CLUMPP 1.1.2 (Jakobsson & Rosenberg, 2007). Individuals were assigned to the shore- or stream-spawning ecotype if membership coefficients were >0.80 following (Veale & Russello, 2016). Mixed stock proportions were estimated for each year with 95% confidence intervals calculated from 10,000 bootstrap replicates using the conditional maximum likelihood-based approach as implemented in ONCOR and reference baseline data from pure stock Wood Lake shore- (n = 33) and stream-spawning (n = 34) kokanee in RADseq_kokanee (Anderson et al., 2008). 17 2.3 Results 2.3.1 Panel Optimization The finalized SNP pool (n = 547) before primer design included those that exhibited the highest divergence across geographic basin (n = 102), migratory form (n = 87), reproductive ecotype (n = 182), or overlapped across multiple comparisons (n = 176). Bayesian clustering analyses conducted with the initial 547 candidate loci revealed an optimal value of K = 8, mirroring results conducted from the full set of neutral SNPs from (Veale & Russello, 2017b). Individual assignment accuracy of 100 simulated individuals per population was >99%. Of the 547 candidate SNPs, primers were successfully designed for 448 loci after in silico testing. Following multiplex amplicon sequencing and subsequent primer pool optimization, we retained a final optimized panel of 288 SNPs consisting of loci informative at the following scales: geographic basin (n = 49), migratory form (n = 46), reproductive ecotype (n = 87), and across multiple scales [n = 106 total consisting of: geographic basin and migratory form (n = 17); geographic basin and reproductive ecotype (n = 51); migratory form and reproductive ecotype (n = 21); geographic basin, migratory form and reproductive ecotype (n = 17)] (Figure 2). Sixty of these loci were outliers previously detected for pairwise ecotype comparisons across the province (Veale & Russello, 2017b). We found the average genotyping discordance within GT-seq (n = 3) to be 1.07% and between RADseq and GT-seq (n = 5) to be 0.81%. 2.3.2 Self-assignment and Province-wide accuracy The optimized GT-seq panel exhibited a self-assignment accuracy of 97.7% based on leave-one-out tests using the Rannala and Mountain (1997) estimator (Rannala & Mountain, 1997). The machine-learning model as implemented in assignPOP (Chen et al., 2018) revealed an overall self-assignment accuracy of 93.3% when the analysis was conducted with every population analyzed simultaneously at all loci and 90% proportion of individuals. Self-assignment for 18 comparisons by geographic basin, migratory form and system-specific comparisons mostly exhibited >90% assignment accuracy when using 0.75 individuals and 0.75 loci in the training set (Figure 3). The exceptions were comparisons by reproductive ecotype pooled as deep-spawning kokanee (Anderson and Seton Lakes), shore-spawning kokanee (Wood Lake, Okanagan Lake, Tchesinkut Lake) and stream-spawning kokanee (Wood Lake, Okanagan Lake, Skaha Lake, Kootenay Lake North Arm), which exhibited slightly lower assignment accuracies when using 0.70 proportion of individuals and 0.75 of the loci in the training set, but none were <0.80 (Figure 3). 2.3.3 GT-seq British Columbia individual assignment After filtering for missing data, we retained 56 novel individuals at 260 loci (average read depth = 117.8; genotyping rate = 90.1%). Assignment accuracy was 100% with the Rannala and Mountain (1997) estimator, and 98% with the support vector machine algorithm to the correct geographic location, migratory form, and reproductive ecotype (S1 Table). 2.3.4 GT-seq SNP genotyping Wood Lake We successfully genotyped Wood Lake individuals at 243 SNPs after filtering for missing data (average read depth = 177.5; genotyping rate = 93.3% ) that were previously analyzed in (Ward et al., 2019) using microsatellites (2012 trawl; n = 99) and TaqMan SNP genotyping assays (2014 angler caught; n = 51). Overall assignment to Wood Lake shore- or stream-spawning kokanee was 99.3% concordant between ONCOR (Anderson et al., 2008) and STRUCTURE (Pritchard et al., 2000) across all samples (S2 Table). Additionally, ecotype assignment based on the GT-seq panel was 98.0% concordant with previous designations at 24 SNPs and 98.9% concordant with samples previously assessed at 10 microsatellites. Four samples that were not 19 confidently assigned originally using the 10 microsatellites were assigned with high confidence to shore- (n = 1) and stream-spawner (n = 3) (S2 Table). We successfully genotyped Wood Lake individuals from 2018 (n = 62) and 2019 (n = 92) at 282 SNPs after filtering for missing data (average read depth = 591.2; genotyping rate = 92.9%). Individual assignment was 98.7% concordant between ONCOR (Anderson et al., 2008) and STRUCTURE (Pritchard et al., 2000) across all samples across years (S3 Table). Mixed-stock proportions varied between years, revealing shore-spawner proportions of 0.3893 (95% CI = 0.274-0.517) in 2018, and 0.2080 (95% CI = 0.131-0.304) in 2019, results that were highly consistent with the proportions of shore-spawners estimated using individual assignment in 2018 (0.390) and in 2019 (0.219) (S3 Table). 2.4 Discussion The ability to accurately differentiate life history variants such as migratory form and reproductive ecotype is vital for estimating fisheries management parameters such as recruitment and mortality in mixed-stock fisheries (Carvalho & Nigmatullin, 1998). To our knowledge, this is the first GT-seq panel demonstrated to provide effective stock identification spanning across broad-scale geographic basins to finer-scale life history variants such as migratory form and reproductive ecotypes, even in locations where they co-occur. The performance of this panel was demonstrated by way of high self-assignment accuracy (93.3%), high assignment accuracy (>98%) of novel individuals to their corresponding location, migratory form and reproductive ecotype, as well as contributing rapid, accurate and scalable stock identification to a long-term time series in Wood Lake, where stream- and shore-spawning kokanee co-occur. More broadly, our results highlight important considerations when applying this panel and developing those for other species and systems. 20 Regarding initial panel development and optimization, selection of an appropriate population assignment method is important for determining panel composition and overall utility. For example, assignment results from the leave-one-out approach (self-assignment: 97.7%; assignment of unknown individuals: 100%) with the Rannala and Mountain (1997) estimator (Rannala & Mountain, 1997) were notably higher than that of a machine-learning double-cross validation framework in assignPOP (Chen et al., 2018) across all populations (self-assignment 93.3%; assignment of novel individuals: 98%), highlighting the tendency for the former to suffer from a systematic upward bias in predicted accuracy (Anderson, 2010). This is an important consideration, as overestimated accuracy can subsequently lead to misidentification and inaccurate estimates of stock size, ultimately affecting long-term sustainability in mixed-stock fisheries, especially in closely-related populations (Garcia-Vazquez et al., 2012). Additionally, the training- and test-set protocol implemented in assignPOP (Chen et al., 2018) contains intrinsic sensitivity analyses to examine loci efficacy. Our methodology for candidate loci selection considered the level of genetic divergence before determining a targeted number of markers needed to effectively discriminate between populations of interest (Koljonen, 1995). Candidate loci in the finalized 288 primer panel were initially selected based on their high θ signatures for reproductive ecotype, geographic basin, migratory form, or a combination of these categories. However, self-assignment accuracy from the machine learning framework was consistently >90% when analyzing data from the entire panel simultaneously, regardless of the specific level at which information was required; for example, inclusion of loci that were initially selected based on high θ at the among-geographic basin level did not reduce assignment accuracy between co-occurring reproductive ecotypes within single systems (e.g., Wood Lake). Although this cross-category signal may not necessarily be the case for multi-scaled SNP panels 21 developed in other species or systems, the ability to simultaneously use the full set of 288 SNPs provides enhanced power due to an increased number of loci while limiting filtering steps and facilitating downstream analyses. Though use of all loci in the panel can increase assignment accuracy, we found that population composition can affect panel resolution; for instance, grouping individuals from separate drainages in the same analysis can lead to inaccurate results. Here, we found that the reproductive ecotype self-assessment accuracy at 90% training individuals and 100% training loci between shore-, stream-, and deep-spawning kokanee across British Columbia (86.7%) may have been affected by noise due to the grouping of ecotypes across geographic basin within this analysis. This result could indicate that the genetic signature of geographic basin across the province is stronger than that of reproductive ecotype, where shore- and stream-spawning kokanee populations within the same lake were found to be more closely related to each other than to their corresponding ecotypes in other lakes (Veale & Russello, 2017b). The reproductive ecotype accuracy markedly improved when Okanagan Lake (shore and stream = 94.0%), Wood Lake (shore and stream = 95.0%), and Anderson/Seton Lakes (deep-spawners = 100.0%) were analyzed separately. Depending upon the level of accuracy required for a specific application, GSI may be most effective using reduced subsets of individuals informed by evolutionary history and ecology to provide system-specific signal to stock assignment, especially in mixed stock fisheries (e.g., Okanagan Lake kokanee; Figure 3C). Size of initial reference population baselines is another important consideration in GSI that can be effectively addressed using data from GT-seq. Minimum baselines for GSI have been suggested with increased sample sizes found to improve resolution in closely-related groups (Kalinowski, 2004). GT-seq data combined with population assignment analyses implemented in 22 assignPOP (Chen et al., 2018) can be used to inform minimum sample sizes needed for effective baselines in lake systems that span a variety of evolutionary histories. For example, low levels of genetic mixing between reproductive ecotypes in Okanagan Lake may reduce assignment accuracy. Here, we found that self-assignment of Okanagan Lake shore/stream-spawners improved with more test individuals (50% test individual level = 92% accuracy; 90% test individual level = 94% accuracy) (Figure 3C). In contrast, no improvement was demonstrated in the Kootenay North /West Arm between the 50% and 90% test individual levels (100% accuracy at all levels), indicating that populations that are geographically separated, even in the same system, may not require as many baseline individuals for accurate assessment (Figure 3A) (Veale & Russello, 2017b). GT-seq therefore provides a scalable approach for adding to reference population baselines that is otherwise inefficient by other techniques such as RADseq (Andrews et al., 2016). Comparison of the same SNPs can also be difficult in RADseq due to the combination of restriction enzyme digestion variation and bioinformatic assignment of RADtags, even with alignment to a reference genome. Though capture probes have been implemented into RADseq protocols to allow for sequence capture (e.g. Rapture)(Ali et al., 2016), these methods still require more expensive, labour-intensive library preparation when compared to the rapid turnaround that GT-seq offers at a lower price ($15/sample versus $6/sample)(Meek & Larson, 2019). The consistent targeted capture of specific loci with the use of forward and reverse primers in GT-seq further allows for longitudinal datasets processed by different researchers to be connectable without the use of specialized laboratory equipment required for Rapture (Ali et al., 2016). With population diversity found to be comparable between the same samples genotyped with both RADseq and GT-seq, this panel can be used to improve existing pure stock 23 baselines for kokanee and provides an advantage when considering a shift towards GT-seq informed GSI in other systems (Schmidt et al., 2020b). More generally, GT-seq panel designs have revealed potential trade-offs to consider depending on panel intent and species characteristics. For example, a large primer pool designed for optimal panel performance needs to be balanced with the increased likelihood for primer interaction as well as the cost of subsequent primer drop out (May et al., 2020). This observation has led to varied design methodologies in published GT-seq panels intended for individual assignment that are tailored to the needs of each study system. Four separate 300 loci GT-seq panels improved the assessment of closely related Chinook Salmon populations within a single river drainage in Alaska, but subsequently led to a higher overall cost associated with primers (McKinney et al., 2020). A smaller, dual use 172 loci panel designed for population assignment and pedigree reconstruction for O. nerka in Alaska found that a minimum of 43 markers was required to achieve >90% accuracy for population assignment across two creeks, revealing potential lower limits needed for high accuracy (Bootsma et al., 2020; May et al., 2020). Comparatively, our British Columbia-wide GT-seq panel of 288 SNP loci has demonstrated >90% self-assignment accuracy of fourteen different populations representing three geographic basins, two migratory forms, and three reproductive ecotypes across the province, even in systems with co-occurring ecotypes expressing little to no neutral genetic divergence. In addition to considerations related to GT-seq panel composition, optimization and quality control, the accuracy of any GSI tool in real-world applications is of paramount importance. Our multi-scaled GT-seq panel showed high accuracy (100%) to assign novel individuals not included in the original RADseq_kokanee baseline to specific stocks spanning geographic basin, migratory form, and reproductive ecotypes across British Columbia. 24 Moreover, we demonstrated a real-world application to Wood Lake, a valuable mixed-stock fishery of co-occurring shore- and stream-spawning kokanee that has been subject to long-term, genetic stock assessment using a range of different molecular markers, from expressed sequence tag-linked microsatellites to TaqMan SNP genotyping assays (Ward et al., 2019). Shore-spawning kokanee are especially difficult to discern from co-occurring stream-spawners using traditional visual count methodologies due to their general lack of morphological distinction (Taylor et al., 1997; Veale & Russello, 2017b), as well as the in-lake low visibility conditions and wide-ranging habitat area in Wood Lake specifically (Ward et al., 2019). Recent visual surveys have reported <100 shore-spawners in the system, with declines precipitated by anoxic conditions and lethal water temperatures (Ward et al., 2019). Previous findings conducted with 24 microsatellites revealed that the shore-spawner population in Wood Lake was four times greater than previously estimated by visual counts from 2014-2016 (Ward et al., 2019). With our GT-seq panel, we found the proportion of shore-spawners in 2018 (0.39) and 2019 (0.21) to be consistent with previous trends in the system (Ward et al., 2019). Moreover, we were able to assign four samples that were ambiguous based on earlier microsatellite-based GSI to a specific ecotype, reinforcing the ability of dense SNP panels to provide finer-scale resolution compared to more traditional markers (Hess et al., 2011). Additionally, the high GSI concordance (>98%) between different classes of molecular markers (e.g., microsatellites and SNPs) and additional power provided by the GT-seq panel can be leveraged to bolster previous assessments if warranted in the future. Overall, our results demonstrate that this multi-scaled GT-seq panel is highly informative for guiding kokanee fisheries management within the systems included in the baseline database (RADseq_kokanee). Although it remains unclear the degree to which this panel can be applied to 25 systems outside of British Columbia, at minimum we expect these markers to have the capacity to identify individuals to migratory form and reproductive ecotype given the inclusion of SNPs found to be informative for this purpose from previous studies (Veale & Russello, 2017a, 2017b). More broadly, the design methodology and multiple levels of robust quality control demonstrated here can be applied for informing the development of panels for other systems and species with multiple levels of genetic variation that would benefit from GSI. GT-seq panels are emerging as valuable GSI tools for informing a range of management applications such as ongoing monitoring of critically depleted fish stocks (Winans et al., 2001), inferences on migration (Waples et al., 2008b), conservation unit delimitation (Schmidt et al., 2020c), and invasive species management (Sjodin et al., 2020). Here, we have demonstrated the effectiveness of the British Columbia O. nerka GT-seq panel to inform temporal and spatial monitoring of mixed-stock kokanee fisheries moving forward. 26 Chapter 3: Genotyping-in-Thousands by sequencing panel development and application for high-resolution monitoring of introgressive hybridization 3.1 Background Global fish populations are in decline due to agricultural development, rapidly rising riverine water temperatures, and reduced connectivity (Galbreath et al., 2014; Graham, 1997; Kaushal et al., 2010; Schmidt et al., 2020a; Vörösmarty et al., 2010; Winston & Taylor, 1991). In particular, 80% of salmonid populations in the Columbia River have experienced losses linked to hydroelectric development and the destruction of spawning and rearing habitat (Kendall et al., 2017; Myers et al., 1998). In response, management strategies have included incorporating fish passage at dams to mitigate impacts to migratory fish life histories and supplementing populations in decline with hatchery fry for stock enhancement. For example, Coho Salmon (Oncorhynchus kisutch) have been restored in the lower Columbia River using hatchery stocking, resulting in the establishment of local naturalized populations (Galbreath et al., 2014; Molony et al., 2005). Additionally, the enhancement of spawning habitat has been an effective strategy to restore fish populations, where the replacement of river substrate improved water velocities, dissolved oxygen, and usage of the site by spawning Chinook salmon (O. tshawytscha; Merz and Setka 2004). Ultimately, the success of fish stocking programs that target species with diverse migratory and resident forms should include careful consideration of resulting hybridization dynamics and corresponding outcomes on recovery initiatives. Hybridization in fish species is well documented, including inter-specific hybrids such as rainbow (Oncorhynchus mykiss) and cutthroat trout (O. clarkii; Ostberg et al. 2015), as well as within-species hybrids between different life history forms, such as the case between anadromous sockeye salmon and freshwater resident kokanee (O. nerka; Veale and Russello 2016). The long-term effects of hybridization are often complex, with the potential for both 27 positive and negative outcomes (Fraser et al. 2008). Hybridization can be a powerful conservation tool harnessed to rescue populations with low genetic diversity and increase fitness through the integration of favorable traits such as larger body size, more offspring, and longer lifespans (Chan et al., 2019; Fraser et al., 2008; Weeks et al., 2017). On the other hand, detrimental effects have been observed such as introgression with maladapted gene complexes (Bekkevold et al., 2006), decrease in reproductive success (Muhlfeld et al., 2009), and negative impacts on growth (Harvey et al., 2016), with hybrid fitness theorized to decrease as the divergence between parental phenotypes increases (Edmands, 2002). Overall, the impacts of intra-specific hybridization in the wild is still not well understood, warranting further study, especially when observed as part of an active management program. Oncorhynchus nerka provides an excellent system for investigating the genetic and physiological outcomes of hybridization as it exhibits tremendous life history variation, natal homing behavior, and represents a valuable species targeted for population restoration through restocking (Johnson et al., 2019). This species exhibits two main migratory forms, including anadromous sockeye salmon (hereafter referred to as “sockeye salmon”) and freshwater resident kokanee (hereafter referred to as “kokanee”). Kokanee are much smaller than sockeye salmon (26cm versus >45 cm average adult fork length) and occur sympatrically in many lakes, but tend to exhibit different spawning habitat preferences and spawning periods (Hanson & Smith, 1967; Wood & Foote, 1996). Despite differences in spawning behavior, kokanee males are known to sneak on spawning sockeye salmon females (Foote, 1988), and size-selective mating has been observed between male sockeye salmon and female kokanee (Craig et al., 2005), allowing for gene flow between migratory forms. Sockeye-kokanee hybridization can lead to an increase in body size of resident hybrids that can increase angler satisfaction (Harvey et al., 2016) and 28 bolster the overall genetic diversity of O. nerka in the system (Chan et al., 2019). Conversely, hybrids can also experience negative impacts such as lower swimming capabilities than pure sockeye salmon (Taylor & Foote, 1991), medial seawater adaptabilities (Foote et al., 1992), intermediate maturation time (Wood & Foote, 1996), decreased egg survival rate (Wood & Foote, 1990), and the loss of the iconic red coloration that is key in sexual selection (Craig et al., 2005). With a broad range of potential outcomes, accurate assessment of the extent of introgression is critical for elucidating the long-term effects of hybridization associated with supplementation and reintroduction programs. To address large-scale declines in sockeye salmon populations in the Columbia River, an experimental reintroduction program was initiated in Skaha Lake within the Okanagan Basin of the Southern Interior of British Columbia, Canada. This program provides an excellent opportunity to investigate the extent and outcomes of intra-specific hybridization between O. nerka migratory and resident forms outside of a laboratory setting. Previous research examining hybridization between sockeye salmon and kokanee found O. nerka hybrids occupy intermediate morphologies and exhibit a largely resident life history, although this latter finding requires further investigation (Elliott et al., 2020; Veale & Russello, 2016). These studies employed genetic marker sets that were effective at differentiating pure-stock from hybrid, however, they had limited power for the identification of advanced hybrid classes that has become increasingly important for genetic monitoring as the reintroduction program matures. The detection of advanced hybrid classes within a species can be challenging due to the proximity of backcrosses to pure genotype frequencies. For example, allozyme loci were only able to reveal intermediate allelic frequencies suggesting hybridization between transplanted and native sockeye (Hendry et al., 1996), while microsatellite assays have the ability to detect introgression to the F1 level 29 (Praebel et al., 2013), but both marker types experience difficulty in the detection of advanced hybrid classes based on the conventional number of loci employed (Mcfarlane & Pemberton, 2019; Sanz et al., 2009). In that regard, traditional markers may not have the statistical power to identify advanced hybrid classes (Vähä & Primmer, 2006), with an estimated minimum of 70 markers required to discriminate between pure parental species and advanced backcrosses (Boecklen & Howard, 1997). A previous simulation-based sensitivity analysis found that a panel comprised of 300 highly differentiated SNPs has the resolution needed to detect advanced hybrid classes between sockeye salmon and kokanee in Skaha Lake (Elliott & Russello, 2018). With this in mind, advancements in massively parallel sequencing can be leveraged to improve the identification of hybridization in systems by pooling barcoded amplicons to increase the power of genetic panels for classification (Twyford & Ennos, 2012); Genotyping-in-Thousands by sequencing (GT-seq) is a particularly useful approach in cases where large sample sizes need to be cost-effectively genotyped (Campbell et al., 2015). Here, we demonstrate a workflow for developing, evaluating and deploying a GT-seq SNP panel with the power to detect advanced hybrid classes to assess the extent and trajectory of intra-specific hybridization, using the sockeye salmon reintroduction program in the Okanagan basin as a case study. Using previously published restriction site associated DNA sequencing (RAD-seq) collected for this system, we first simulated two pure (kokanee, sockeye) and four hybrid classes (F1, F2, B2 backcross-kokanee, B2 backcross-sockeye) with three different SNP datasets (300 highest Fst SNPs; 600 highest Fst SNPs; 350 random SNPs selected from the 600 highest Fst SNPs) to assess information content to inform panel construction. We then evaluated the accuracy, efficiency, and power of the optimized GT-seq panel using simulation analyses. We further assessed panel performance by re-genotyping individuals sampled in Skaha Lake in 30 2016 and 2017 that were previously genetically assigned as pure-stock or F1 hybrid at 32 SNPs and subjected to otolith microchemistry analysis to reconstruct migratory history (Elliott et al., 2020) . Finally, we genetically assigned O. nerka spawners sampled in 2019 to pure-stock or hybrid class (F1, F2, B2 backcross-kokanee, B2 backcross-sockeye) to estimate stock proportions and examine trends in this system over time. 3.2 Methods 3.2.1 Study system and samples Skaha Lake is located in the Canadian portion of the Okanagan Basin, and flows south to join the Columbia River (Figure 4). O. nerka spawn in the Okanagan River, upstream of Skaha Lake. Historically, sockeye salmon populations existed in the Okanagan Basin, however, the construction of a dam at McIntyre Bluff in 1921 blocked access to spawning grounds and channelization of the Okanagan River further degraded spawning habitat. This left Skaha Lake with a sole population of stream-spawning kokanee (Alexander & Pickard, 2009). In attempts to restore sockeye salmon to the Okanagan Basin, a re-introduction program was implemented in 2004. Through this initiative, upstream fish passage was created by making structural improvements to migration barriers, restoring stream habitat, and stocking sockeye salmon fry annually into Skaha Lake. This system now supports a self-sustaining population of sockeye salmon (Alexander & Pickard, 2009; McQueen et al., 2013). We genotyped tissue samples from O. nerka spawners in the Okanagan River upstream of Skaha Lake that were previously sampled in 2016 (n = 45) and 2017 (n = 59) in Elliott et al. (2018), and newly sampled in 2019 (n = 218; this study) as deadpitch by personnel from the BC Ministry of Forests, Lands, Natural Resource Operations, and Rural Development. The section of river that was sampled represents the only spawning habitat that exists for O. nerka populations from Skaha Lake and all forms spawn within this section of river. Samples were collected on 31 multiple dates that spanned the duration of the spawning run. Biological data (length and sex) and tissue samples (operculum punches preserved in ethanol) were collected from all sampled fish. Moreover, the 2016 and 2017 samples were previously genetically assigned to pure-stock or F1 hybrid using a panel of 32 TaqMan SNP assays and subjected to otolith microchemistry analysis in Elliott et al. (2020) to determine fish migratory history and investigate maternal migratory signatures that are passed transgenerationally to offspring (Hegg et al. 2019; Bath et al. 2000). 3.2.2 GT-seq SNP panel design We used previously published genotypic data collected via RAD-seq from Veale and Russello (2017) for: 1) Skaha Lake kokanee (n=20) collected in 2003 prior to the sockeye salmon restocking program; and 2) Okanagan River sockeye salmon (n=35) collected in 2012 downstream of historical migration barriers. Using the populations module in STACKS version 2.0 beta 8 (Catchen et al., 2013), we required all loci to be present in at least 60% of individuals in both populations, with a minor allele frequency greater than 0.05. Due to the salmonid whole genome duplication event, we filtered out suspected homeologs by removing any locus with a negative Fis or Hobs > 0.5 that occurred in both reference populations (Hohenlohe et al., 2011). The resulting SNP dataset was then filtered for quality using VCFtools (Danecek et al., 2011) to remove SNPs not in Hardy-Weinberg equilibrium and to calculate Weir and Cockerham (1984)’s θ, an unbiased estimate of Fst, between Skaha Lake kokanee and Okanagan River sockeye salmon following Elliott and Russello (2018). We removed loci with insufficient flanking sequence required for primer design by retaining loci with the SNP positioned between the 40 th and 70th base pairs of the RAD tag sequences and selected the top 650 loci exhibiting the highest 32 Fst. We then assessed all loci pairs for deviation from linkage equilibrium using GENEPOP 4.5 (Rousset, 2008). To examine panel informativeness of candidate loci and forecasting of panel ability after primer dropout, we simulated parental and offspring individuals (n=1000) for six hybrid classes (kokanee, sockeye, F1, F2, B2 backcross-kokanee, B2 backcross-sockeye) with recom-sim.py (https://github.com/salanova-elliott/recom-sim). Separate simulations were conducted at: 1) 300 highest Fst SNPs; 2) 600 highest Fst SNPs; and 3) 350 SNPs randomly selected from the 600 highest Fst SNPs. We assigned simulated individuals to parental or hybrid class by calculating the posterior probabilities of membership as implemented in NEWHYBRIDS (Anderson & Thompson, 2002) with the reference populations flagged as known genotypes with the “z” option, and constructed confusion matrices for each panel assessment. After in-silico assessment, we sent the full RAD tag sequences that were associated with the pool of candidate SNPs to GTseek LLC (https://gtseek.com/) for custom locus-specific primer design. 3.2.3 GT-seq test library preparation We constructed a GT-seq test library with the previously extracted DNA samples collected in 2016 (n=45) and 2017 (n=59) for which otolith microchemistry analysis was previously conducted (Elliott et al., 2020). Extracted DNA was quantified with a Qubit 3.0 Fluorometer and the dsDNA High Sensitivity Assay Kit (Invitrogen). Library preparation followed the protocol as outlined in Campbell et al. (2015), with the exception that we diluted the PCR1 product to 1:10. The PCR2 product was quantified with PicogreenTM (Molecular Probes, Inc.) and each sample was normalized to a concentration of 10 ng/µL. The pooled library was purified with a MinElute PCR Purification Kit (Qiagen) and eluted into a final volume of 25µL. Test libraries were sequenced using a Mid Output Reagent Kit (300 cycles) on an Illumina MiniSeq within the 33 Ecological and Conservation Genomics Laboratory at the University of British Columbia Okanagan. 3.2.4 GT-seq genotyping and primer optimization Demultiplexed raw sequencing files were processed with the GT-seq pipeline available on GitHub (https://github.com/GTseq/GTseq-Pipeline). We removed primers with non-specific in silico probes, candidates that were overrepresented (exhibiting >2% of the raw read count), observed primer dimers, potential PCR artefacts, off-target amplification or in silico probe variation following Schmidt et al. (2020b). A second and third test library with the same sample composition as the first test library was prepared with optimized primer pools using the protocols detailed above for sample preparation, sequencing, and primer dropout. To compile the final dataset based on the optimized SNP panel for downstream analyses, raw sequencing files from individuals were concatenated across sequencing runs and processed with the GT-seq pipeline available on GitHub (https://github.com/GTseq/GTseq-Pipeline). We filtered out individuals with >25% missing data using PLINK (Purcell et al., 2007). We decided on a cutoff of 25% to minimize the proportion of missing data that can negatively impact the visualization of population structure, particularly with the high resolution needed to elucidate hybrid classes within a species (Reeves et al., 2016). 3.2.5 Panel Performance We assessed the accuracy, efficiency and power of NEWHYBRIDS assignments with simulated individuals (n=100) using two pools (top 300 Fst SNPs, optimized SNP panel) with hybridpowercomp as implemented in the R package hybriddetective (Vähä & Primmer, 2006; Wringe et al., 2017). Specifically, we evaluated assignment probabilities based on: 1) accuracy (correct assignments/total assignments per category); 2) efficiency (correct assignments / total 34 number of individuals in a category); and 3) power (product of accuracy and efficiency). We also used hybriddetective (Vähä & Primmer, 2006; Wringe et al., 2017) to quantify error rates associated with individual assignment (top 300 Fst SNPs, optimized SNP panel) as follows: 1) Type I: false positive error rate (number of known pure individuals wrongly assigned to a hybrid genotype frequency class/total number of known pure individuals); and 2) Type II: false negative error rate (number of known hybrid individuals wrongly assigned to a pure genotype frequency class/total number of known hybrid individuals). 3.2.6 Re-analysis of 2016-2017 spawners We genetically assigned the 2016 and 2017 sampled individuals to pure-stock or hybrid class using NEWHYBRIDS (Anderson & Thompson, 2002), the genotypic data from the optimized SNP panel, and reference baseline genetic data from 2003 Skaha Lake kokanee and 2012 Okanagan River sockeye salmon (Veale & Russello, 2017b). Five genotype frequency classes (kokanee, sockeye, F1, F2, B2 backcross-kokanee, and B2 backcross-sockeye) were specified, and the analysis initiated with a burn-in period of 10,000 followed by 50,000 iterations. We applied the s and z flags to the reference sockeye salmon and kokanee populations to designate individuals of known genotype that were sampled separately from the test individuals. We then selected the maximum probability of assignment to assign individuals to the most likely class. Lastly, we compared the NEWHYBRIDS (Anderson & Thompson, 2002) genetic assignment to pure-stock or hybrid class to the migratory history of the individuals, as previously inferred in Elliott et al. (2020) by way of otolith microchemistry analysis. 3.2.7 Assignment of 2019 samples For the new samples collected in 2019 (n=218), DNA was extracted with a standard Chelex-based protocol in 96-well plates containing approximately 0.5 mm2 of tissue, 5 ul proteinase K 35 (10 mg/ml), and 195 ul 10% Chelex solution (Walsh et al., 1991). Plates were incubated for 4 hours at 55°C to digest tissue, and then 95°C for 15 minutes using an Applied Biosystems Veriti thermal cycler (Applied Biosystems, Foster City, CA, USA). Library preparation was conducted as above with the optimized SNP panel. The multiplexed pooled library was sequenced using a partial High Output Reagent Kit (300 cycles) on the Illumina MiniSeq within the Ecological and Conservation Genomics Laboratory at the University of British Columbia Okanagan. Samples with individual missing data >25% were filtered out for downstream analyses. Individual assignment to hybrid class was conducted using NEWHYBRIDS (Anderson & Thompson, 2002) and the same parameters as above. Fisher’s Exact Test was conducted in R (R Core Team 2019) to determine if proportions of hybrid class were significantly different within a sample year and between sample years. 3.2.8 Morphometric Analyses One-way ANOVA and post-hoc Tukey Tests were conducted in R (R Core Team 2019) to determine if mean fork length for the 2019 samples was significantly different between individuals assigned to pure-stock (kokanee, sockeye salmon) and the various hybrid classes (F1, F2, B2 backcross-kokanee, and B2 backcross-sockeye). 3.3 Results 3.3.1 Initial panel ability The simulated SNP panels provided comparable accuracy to assign individuals to hybrid class based on initial NEWHYBRIDS assignment of simulated individuals (Table 2). The top 300 Fst SNP panel performed best with high accuracy (>0.98), followed by the top 600 Fst SNP panel (>0.96) and the random 350 SNPs selected from the top 600 Fst SNPs (>0.92). From the pool of top 600 Fst SNPs, primers were successfully designed for 515 SNPs after in silico testing. Following three rounds of multiplex amplicon sequencing and primer pool 36 optimization, the optimized GT-seq panel consisted of 342 SNPs (Figure 5). Our finalized 342 SNP panel had an accuracy of >94% across all hybrid classes (Table 2). The lowest assignment accuracies were to F1 and F2 across all simulated panels (but all ≥0.92; Table 2), which is consistent with previous results in Elliott & Russello (2018). 3.3.2 SNP panel assignment efficacy The 342 SNP GT-seq panel performed slightly worse than the top 300 Fst SNPs, but still displayed high accuracy and efficiency in assigning simulated individuals to pure-stock and the different hybrid classes (Figure 6). Pure-stocks and B2 backcrosses were detected at >98% accuracy and >99% efficiency at a critical posterior probability threshold of 50% (Figure 6). F1 and F2 classes were detected at lower, but still meaningful levels, with efficiency at >92% and accuracy at >90% at the 50% critical probability threshold (Figure 6). The power of assignment was comparable between the 342 SNP GT-seq panel and top 300 Fst SNPs where pure-stocks and B2 backcrosses remained stable over a wide range of probability thresholds: >99% at a critical probability threshold of 50%, and >98% at a threshold of 90% (Figure 8). However, the power of assignment for F1 and F2 classes declined in performance at higher critical posterior probability thresholds: >90% at a critical probability threshold of 50% and >79% at a threshold of 90% (Figure 8). The Type I false positive error rate and Type II false negative error rate were <0.001% for both the 342 SNP GT-seq panel and top 300 Fst SNPs. 3.3.3 Re-analysis of 2016-2017 Okanagan River spawners We successfully genotyped 2016 (n=36) and 2017 (n=55) Okanagan River spawners with the 342 SNP GT-seq panel after filtering for 25% missing data across individuals (average read depth = 220.8; genotyping rate = 90.1%). Overall, assignments to pure-stock and hybrid class were generally similar between the different marker sets, although there was a slightly higher 37 proportion of individuals assigned to pure-stock using the 342 SNP GT-seq panel (Table 3; Table S1). Our 342 SNP GT-seq panel further refined assignments into multiple hybrid classes for those individuals previously classified as hybrids, both in 2016 (F1: 0.22, B2 backcross-kokanee: 0.03) and 2017 (F1: 0.20, B2 backcross-kokanee: 0.05) (Table 3). A small number of assignment differences were found between the TaqMan SNP assays (Elliott et al., 2020) and 342 SNP GT-seq panel. In 2016, four hybrids were reclassified as sockeye salmon with three instances of microchemistry conflict, where the latter results suggested that these individuals did not migrate to the ocean (Table S1). Additionally, one sockeye salmon was genetically reclassified to F1 hybrid. One individual that was genetically assigned as sockeye salmon with both the TaqMan SNP assay (Elliott et al., 2020) and 342 SNP GT-seq panel had conflicting microchemistry that suggested no history of anadromy or maternal anadromy (Table S1). Within the 2017 cohort, five individuals that were previously genetically assigned as hybrids were refined to: sockeye salmon (n=1) and kokanee (n=4) (Table 3; Table S1). All microchemistry was congruent, other than one individual that was genetically assigned as sockeye salmon by both TaqMan SNP assays (Elliott et al., 2020) and the 342 SNP GT-seq panel; in this case, microchemistry suggested a resident maternal parent, but anadromous migratory history. 3.3.4 Assignment of 2019 Okanagan River spawners We genotyped 2019 Okanagan River spawners (n=202) with the 342 SNP GT-seq panel after filtering for 25% missing data across individuals (average read depth =370.9; genotyping rate = 91.2%). The composition of the 2019 Okanagan River spawners revealed a higher proportion of kokanee (0.57; Table 3; Table S2) and lower proportion of sockeye salmon (0.12; Table 3; Table S2) than in 2016 and 2017 (kokanee: 0.42-0.44; sockeye salmon: 0.31-0.33; Table 3; Table S2). 38 In addition, the 2019 spawners contained substantially more numbers of detected backcrosses, including the first B2 backcross-sockeye individuals genetically identified (B2 backcross-kokanee: n=24, overall proportion=0.12; B2 backcross-sockeye: n=19; overall proportion 0.09; Table 3; Table S2). We also detected low proportions of F2 hybrids in 2019 (n=4, overall proportion=0.02) when compared to other hybrid classes. We found significant differences in proportions among hybrid classes within a year among all sample years (2016: P <0.001; 2017: P <0.001; 2019: P <0.001). Across sample years, we found that the proportions of kokanee (P= 0.080), F2 (P = 0.779) and B2 backcross-kokanee (P = 0.140) were not significantly different. The proportions of sockeye salmon (P<0.001), F1 (P<0.05), and B2 backcross-sockeye (P<0.05) were significantly different across sample years. In general, sockeye salmon exhibited larger mean lengths compared with kokanee, with hybrids generally occupying an intermediate range between kokanee and sockeye salmon mean lengths (Figure 9). However, it is notable that F2 hybrids seem to occupy the same size distribution as B2 backcross-kokanee, signifying smaller body sizes with advanced hybrid classes. The mean lengths between sockeye/kokanee, F1/sockeye, F2/sockeye, B2 backcross-kokanee/sockeye, and B2 backcross-sockeye/sockeye were significantly different (Figure 9). 3.4 Discussion The accurate identification of advanced hybrid classes is valuable for monitoring the extent of introgression and potential fitness impacts between multiple reproductive forms of a single species or where hybridization between species is possible. SNP panels have been developed for a broad range of taxa with the ability to accurately identify hybridization between closely related species within the same genus up to the third backcross hybrid generation (Russell et al., 2019; Thongda et al., 2020). However, to our knowledge, this is the first GT-seq SNP panel with the resolution to differentiate advanced hybrid classes up to the second backcross hybrid generation 39 across life history forms within-species. Our GT-seq SNP panel represents a valuable tool that may be used to examine the ongoing progression of intra-specific hybridization and potential fitness outcomes associated with an active sockeye salmon reintroduction program in the Okanagan Basin, providing information to guide on-going management strategies and offer a roadmap to other such programs for species with complex life histories. 3.4.1 GT-seq panel development and evaluation Regarding initial panel development and optimization, we effectively integrated an expected level of primer drop-out within our simulation analyses to inform locus selection and provide preliminary insights on panel performance. To that end, our simulations were consistent with previous work in this system (e.g. Elliott and Russello 2018; Elliott et al. 2020), and displayed high assignment accuracy across all hybrid classes (>94%), as well as comparable panel accuracy, efficiency, and power between the top 300 Fst SNP pool, random 350 SNP subset of the top 600 Fst SNP pool, and ultimately, our optimized 342 SNP panel (Figure 7, Figure 9). Subsequent error rates (<0.001% at all levels) exhibited by the optimized 342 SNP panel reflected those of the pilot analyses, effectively streamlining the GT-seq panel preparation process and helping to ensure that panel performance met management relevant benchmarks for accuracy, efficiency, and power. When applied to 2016 and 2017 Okanagan River spawners previously analyzed using TaqMan SNP genotyping assays and otolith microchemistry, the optimized 342 SNP panel did result in a small number of shifts in assignment of hybrids to adjacent pure-stock classes or B2 backcross classes. One previously classified pure-stock sockeye salmon had a refined assignment (>99% probability) to an F1 hybrid, consistent with microchemistry that revealed saltwater inhabitance for the individuals and maternal parent; this result suggests that that the paternal 40 parent was likely a kokanee (Table S1). Individuals that were refined from hybrid to pure-stock sockeye salmon (n=2) and kokanee (n=4) all exhibited a high probability of assignment (>75%) and consistent microchemistry (Table S1). Reclassification of F1 hybrids to pure-stock sockeye salmon did not display evidence of anadromy, suggesting they may be residual sockeye salmon with anadromous parentage, but did not migrate to sea (Ricker, 1938). 3.4.2 Management Implications Sockeye salmon have been successfully re-introduced into the Okanagan Basin and comprised 41% of the O. nerka population by 2014 (Veale & Russello, 2016). The proportion of sockeye salmon in this system since that peak has significantly varied over time (12-33%), likely due to a combination of factors such as the number of spawners and fry stocked, variable ocean survival, and high water temperatures during upstream migration (Crossin et al., 2008). The re-establishment of wild spawning sockeye salmon created the possibility of hybridization with resident kokanee in later years; our results demonstrate that hybrids most recently comprised 31% of the population (2019: 8% F1, 2% F2, 12% B2 backcross-kokanee, 9% B2 backcross-sockeye; Table 3), with advanced hybrid classes in this system present since at least 2016. Given these trends, further hybrid classes (ex: B3 backcross sockeye/kokanee) may develop, while the occurrence of pure-stock sockeye salmon is likely to remain due to on-going stocking programs using broodstock collected downstream from Skaha Lake. Understanding the fitness outcomes of hybridization is important when considering the overall productivity of the system and long-term management goals. Our results and those of a previous study (Elliott et al., 2020) suggest that hybrid O. nerka can express a migratory life history with a small percentage of F1 hybrids spending time in a marine environment. Progressive hybrid classes may also prefer a resident life history (Elliott et al., 2020) with 41 similarities in size between F2 hybrids and B2 backcross-kokanee. However, it is still possible that backcrosses and F2 hybrids expressing anadromy were subject to increased mortality due to smaller size at migration, limiting their detection within the sampled pool of Okanagan River spawners. These trends are consistent with other salmonids, where cutthroat and steelhead trout hybrids have been found to exhibit intermediate migration behaviors when compared to parental species that may be maladaptive to their local environment (Moore et al., 2010). Such increased migration mortality in advanced hybrid classes expressing anadromy may signify a decrease in in overall productivity of the system. To examine the link between intermediate hybrid behavior and survival moving forward, a combination of identification and tracking methodologies may be appropriate, using passive integrated transponder (PIT) tags to mark juvenile movement, and subsequent comparison of recovered tags and hybridization classes to evaluate survival and age at maturity (McCutcheon et al., 1994). The population level outcomes arising from an intermediate body size in hybrids may ultimately be detrimental to the fitness of migratory hybrids compared to the larger-bodied sockeye salmon, as body size is often directly correlated with fecundity (Wood & Foote, 1990). Conversely, the overall increase in body size of the resident population resulting from the presence of hybrids in the system may be advantageous for some management strategies, especially those that target increasing recreational angling quality or harvest opportunities for First Nations, as larger bodied fish generally have increased survival and are often more valued as a food source. From a stock assessment perspective, our results emphasize that, though morphology and body size have been used historically to determine hybrid class in this system and others, advanced hybrid classes would be indistinguishable from pure-stock, with the B2 backcross-42 kokanee, F2 and kokanee classes having no significant mean size differences (Scribner et al., 2001). Therefore, if stock assessment programs and management goals require an estimate of stock composition, genetic tools such as GT-seq panels can be an effective enumeration method for their ability to accurately detect hybrid classes and expand sample sizes. Here, we demonstrated the effectiveness of the GT-seq for identifying advanced hybrid classes and, in this case, tracking the trajectory of sockeye salmon, kokanee, and hybrid stocks as part of a re-introduction program. Given the uncertainty in the long-term composition of the population and the range of potential outcomes and impacts on management goals, continued genetic monitoring of this system is recommended. More broadly, the GT-seq panel development workflow presented here could be applied to inform other sockeye salmon restoration initiatives in the Columbia and Fraser River systems, or in other species and systems where genetic stock identification and intra-specific hybridization are important management parameters. 43 Chapter 4: Conclusion 4.1 Research findings and significance As ongoing anthropogenic changes threaten freshwater fisheries, there is a need for the accurate identification of life history variation to assist with stock management and preserve genetic diversity across the range (Laikre, 2010). Here, we focused on the design and implementation of high-resolution GT-seq SNP panels to assist in kokanee stock assessment initiatives throughout British Columbia. By designing accurate tools with informative genetic marker pools, maintenance of kokanee by geographic basin, migratory ecotype, reproductive ecotype, and identification of kokanee-sockeye hybrids can allow for cost-effective monitoring of management outcomes. In Chapter 2, our findings emphasize that the selection of an appropriate population assignment method is important for determining acceptable panel assignment error rates for management goals. We found that the leave one out approach with the Rannala & Mountain (1997) estimator, historically the method of choice for individual assignment, had an upward bias in predicted accuracy at 97.7% when compared with an accuracy of 93.3% with the double-cross validation machine learning framework as implemented in assignPOP (Chen et al., 2018). Overestimated accuracy can lead to misidentification, inaccurate estimates, and ultimately affect management decisions that may have lasting impacts (Garcia-Vazquez et al., 2012). Additionally, we found an accuracy of >90% with the use of all loci, regardless of the specific level required for the analysis, confirming that cross-category signal can provide enhanced power within our 288 SNP panel that can also reduce additional filtering steps. With these considerations in mind, we applied the broad-scale panel to Wood Lake where stream- and shore- spawning kokanee co-occur and found that our panel was able to assign four samples that were previously ambiguous based on earlier analysis with microsatellite-based GSI, as well as 44 >99% concordance with previous assignments. Furthermore, we also found the proportion of shore spawners in 2019 (0.39) and 2019 (0.21) to be consistent with previous trends (Ward et al., 2019). Literature on sockeye salmon and kokanee hybridization in the wild is limited, and the Skaha Lake system provides an excellent opportunity to examine hybridization outcomes between ecotypes of the same species. Otolith microchemistry has been a historical standby for fisheries management that can provide information on migratory history, but can be costly to implement (Avigliano & Volpedo, 2016)., The GT-seq O. nerka hybrid SNP panel developed in Chapter 3 provides a much more affordable option for the ability to study hybrids that span life histories, especially in the context of fisheries management where large sample sizes are often required for accurate assessment. With hybrids comprising of large proportion of the Skaha Lake system, accurate evaluations are critical, as the success of population recovery initiatives have been shown to be more successful when hybrids do not out compete pure stocks (Lowe et al., 2015). Our GT-seq O. nerka hybrid SNP panel can differentiate advanced introgression classes up to the second generation between sockeye salmon and resident kokanee and allows for monitoring of ongoing genetic progression and morphological outcomes associated with a maturing sockeye salmon reintroduction program in Skaha Lake. Our panel displayed high assignment accuracy (>94%), associated error rates of <0.001% at all levels, and comparable panel performance exhibited by the optimized 342 loci panel and the top 300 Fst loci. We were able to use the panel to reassign individuals previously determined to be hybrids assessed with TaqMan SNP genotyping assays and otolith microchemistry analysis, with shifts to adjacent pure-stock or backcross classes. Most of the reclassifications exhibited a high probability of assignment and consistent microchemistry. 45 The proportion of sockeye salmon in the Skaha Lake system due to successful re-introduction into the Okanagan basin has widely varied over time from 12% to 41% (Elliott et al., 2020; Veale & Russello, 2016), likely due to stocking parameters, and environmental factors that affect survival such as migration water temperatures and variable ocean survival (Crossin et al., 2008). Our results in Chapter 3 provide valuable insights regarding reintroduction impacts on recipient ecosystems that are currently not well understood. We have found that kokanee at a >99% stock proportion in 2008 hit a low of 12% in 2015 but have since partially stabilized from 2016-2019 at 42-57%. However, with continued fluctuations and on-going management activities, these results are not necessarily predicative of future outcomes. Most recently, our panel revealed that hybrids comprise ~35% of the population as of 2019: 12% F1, 2% F2, 12% B2 backcross-kokanee, 9% B2 backcross-sockeye (Table 3). Given our findings, we predict that more advanced introgression classes may develop, with pure-stock sockeye salmon maintained by on-going stocking from brood stock collected downstream. Additionally, we found that the majority of introgression classes prefer a resident life history, as well as occupying similar body sizes between F2 and B2 kokanee. This may be due to increased mortality of advanced introgression classes that express a migratory life history. Our panel would thus be important for ongoing monitoring, especially since advanced introgression classes would be morphologically indistinguishable from pure stock, with B2 backcross-kokanee, F2, and pure stock kokanee classes not significantly different in size from one another. These findings emphasize the importance of accurate tools that can contribute to long-term time series for tracking stock fluctuations and potential losses in efficiency as such programs mature. 46 4.2 Limitations and future studies Salmonids provide an excellent framework as an evolutionary model species due to their diverse environment, life history variation, and natal homing behavior (Johnson et al., 2019). The broad-scale O. nerka GT-seq panel developed in Chapter 2 is the first GSI tool to our knowledge that can effectively identify O. nerka by migratory form, reproductive ecotype, and geographic basin, notably even in lake systems where life history forms co-occur. Our GT-seq O. nerka hybrid panel developed in Chapter 3 demonstrated high performance in efficiency, accuracy, and power (>94.5%) at identification of introgression classes across kokanee, sockeye salmon, F1, F2, B2 backcross-kokanee and sockeye salmon, and is the first GT-seq panel to our knowledge that has the power to elucidate advanced introgression classes within species. However, reclassifications with conflicting microchemistry may indicate an advanced introgression class that is not detectable by our panel, or potentially assignment error. In Chapter 2, we found with sensitivity analyses conducted for the broad-scale GT-seq panel that population composition analysis of different lake systems was best done separately, as genetic signatures of other signals such as geographic basin may be stronger than that of reproductive ecotypes in recently diverged systems. Overall, low levels of genetic mixing between populations of interest may reduce assignment accuracy and require more baseline individuals for accurate ascertainment. Our results demonstrate that GT-seq panels are highly informative for guiding kokanee fisheries management for specific applications within the systems included in their baseline databases, but it is unclear the degree to which our broad scale GT-seq panel and O. nerka hybrid panel can be applied to other systems. For example, we would not expect to be able to capture accurate estimates of hybridization outside of Skaha Lake due to 47 the use of baseline pure-stock samples highly specific to the region and population history due to recurrent evolution of the kokanee resident ecotype. Freshwater fish populations have been found to be more susceptible to introgression than anadromous populations (Utter, 2000). Our results in Chapter 3 show that the mixing of sockeye salmon and resident kokanee could result in the loss of adapted traits and lead to greater mortality, particularly in the F1 and F2 classes (Allendorf et al., 2001). With ongoing threats to the stability of freshwater fisheries potentially leading to the implementation of more reintroduction programs, detailed study on the impacts of hybridization is necessary to manage native and locally adapted gene pools and meet recovery objectives. For example, the incorporation of spawner age and age-0 proportions to hybrid class assignments can shed light on individual survival associated with stocking of fry and success of fish passage for anadromous migrants. With hybrids found to express intermediate migratory behaviors, individual-based simulation modeling with landscape and environmental data with different penalties on hybrid fitness and effects of riverine temperature on growth may allow for an examination of environmental consequences on intermediate morphologies. Though our GSI tool can allow for initial evaluations of genetic introgression, the integration of other stock assessment tools such as PIT tags or acoustic telemetry may be useful to directly track migratory history in order to examine the survival of hybrids and backcrosses. Our panels developed in Chapter 2 and 3 are alternatives to other sequencing methods such as Rapture that require more labor-intensive, expensive, and specialized equipment for library preparation (Meek & Larson, 2019). Additionally, for both GT-seq panels, the ability to leverage targeted capture of specific loci with forward and reverse primers allows for dataset connectability. Longitudinal time-series can therefore be easily maintained by researchers in 48 separate research facilities and can be used to improve existing pure stock baselines. Though the use of multiple sets of GT-seq panels have been found to improve the assessment of closely related populations within a single geographical drainage, this methodology can lead to a higher overall cost in development (McKinney et al., 2020) and the single GT-seq broad scale panel developed in Chapter 2 has displayed comparable identification ability. Ultimately, we have demonstrated that GT-seq panels are good alternatives to existing stock assessment tools for informing a range of management applications spanning from stock identification to hybridization outcomes and can provide detailed information critical for effective fisheries management. 49 Table 1. Kokanee and anadromous sockeye salmon samples selected for assignment to migratory form and reproductive ecotypes across historical drainages. Location Migratory Ecotype Reproductive Ecotype Historical Drainage Reference Sample Size Novel Sample Size Wood Lake Kokanee Shore Columbia River 34 4 Kokanee Stream Columbia River 34 4 Kootenay Lake Kokanee Shore (West Arm)a Columbia River 45 5 Kokanee Stream (West Arm) a Columbia River 48 3 Kokanee Stream (North Arm) Columbia River 22 3 Okanagan Lake Kokanee Shore Columbia River 48 12 Kokanee Stream Columbia River 48 8 Skaha Lake Kokanee Stream Columbia River 20 9 Okanagan River Sockeye Stream Columbia River 35 10 Portage Creek Sockeye Stream Fraser River 23 3 Anderson Lake Kokanee Deepb Fraser River 23 4 Seton Lake Kokanee Deepb Fraser River 23 4 Tchesinkut Lake Kokanee Shorec Skeena River 36 4 Kokanee Streamc Skeena River 36 4 The following reference populations were pooled for assignment analysis due to lack of structure in these systems as found in Veale & Russello (2017b): a Kootenay Lake West Arm shore- and stream-spawning kokanee; b Anderson Lake and Seton Lake deep-spawning kokanee; c Tchesinkut Lake shore- and stream- spawning kokanee. 50 Table 2. Confusion matrices of simulated individuals’ proportional assignment with NEWHYBRIDS based on data from: A). Top 300 Fst SNPs; B). Top 600 Fst SNPs; C). Random 350 SNPs from the top 600 Fst SNPs (Random 350 Fst); and D) 342 final SNP panel. A: Top 300 Fst Kokanee Sockeye F1 F2 B2 Kokanee B2 Sockeye Kokanee 1.000 0.000 0.000 0.000 0.000 0.000 Sockeye 0.000 1.000 0.000 0.000 0.000 0.000 F1 0.000 0.000 1.000 0.000 0.000 0.000 F2 0.000 0.000 0.020 0.980 0.000 0.000 B2 Kokanee 0.000 0.000 0.000 0.000 1.000 0.000 B2 Sockeye 0.000 0.000 0.000 0.000 0.000 1.000 B: Top 600 Fst Kokanee Sockeye F1 F2 B2 Kokanee B2 Sockeye Kokanee 1.000 0.000 0.000 0.000 0.000 0.000 Sockeye 0.000 1.000 0.000 0.000 0.000 0.000 F1 0.000 0.000 0.960 0.040 0.000 0.000 F2 0.000 0.000 0.020 0.980 0.000 0.000 B2 Kokanee 0.000 0.000 0.000 0.000 1.000 0.000 B2 Sockeye 0.000 0.000 0.000 0.000 0.000 1.000 C: Random 350 Fst Kokanee Sockeye F1 F2 B2 Kokanee B2 Sockeye Kokanee 1.000 0.000 0.000 0.000 0.000 0.000 Sockeye 0.000 1.000 0.000 0.000 0.000 0.000 F1 0.000 0.000 0.970 0.030 0.000 0.000 F2 0.000 0.000 0.060 0.920 0.020 0.000 B2 Kokanee 0.000 0.000 0.000 0.000 1.000 0.000 B2 Sockeye 0.000 0.000 0.000 0.010 0.000 0.990 D: 342 Final Kokanee Sockeye F1 F2 B2 Kokanee B2 Sockeye Kokanee 1.000 0.000 0.000 0.000 0.000 0.000 Sockeye 0.000 1.000 0.000 0.000 0.000 0.000 F1 0.000 0.000 0.950 0.050 0.000 0.000 F2 0.000 0.000 0.050 0.940 0.000 0.010 B2 Kokanee 0.000 0.000 0.010 0.020 0.970 0.000 B2 Sockeye 0.000 0.000 0.000 0.020 0.000 0.980 Rows represent true genetic classification, with columns as assigned introgression class. Highlighted values along the diagonal are correct assignment proportions. Subsets of candidate markers were tested for informativeness to assess potential primer dropout. 51 Table 3. Stock composition of spawners by sample year assessed with the TaqMan 32 SNP assay and 342 SNP GT-seq Panel. Stock proportions indicated in parentheses. Year n TaqMan 32 SNP assay 342 SNP GT-seq Panel Kokanee Hybrid Sockeye Kokanee B2Kok F1 F2 B2Sock Sockeye 2016 36 16 (0.44) 12 (0.33) 8 (0.22) 16 (0.44) 1 (0.03) 8 (0.22) 0 (0.00) 0 (0.00) 11 (0.31) 2017 55 19 (0.35) 19 (0.35) 17 (0.31) 23 (0.42) 3 (0.05) 11 (0.20) 0 (0.00) 0 (0.00) 18 (0.33) 2019 202 - - - 115 (0.57) 24 (0.12) 16 (0.08) 4 (0.02) 19 (0.09) 24 (0.12) 52 Figure 1. Map of British Columbia with baseline Oncorhynchus nerka populations used in initial panel design obtained from Veale & Russello (2017b). Fish image size represents migratory form (large = anadromous sockeye salmon, small = resident kokanee) and color represents reproductive ecotype (red = stream-spawning, brown = shore-spawning, black = deep-spawning kokanee). All map units in decimal degrees. Fish illustrations are a courtesy of Eileen Klatt. 53 Figure 2. Workflow of initial Oncorhynchus nerka multi-use panel design and primer removal. 7,347 SNPs 550 SNPs 547 SNPs 448 SNPs 357 SNPs 288 SNPs Removal of SNPs by base position; Selection of candidate SNPs Linked SNPs removed Primer design Primer optimization from Library 1 Primer optimization from Library 2 Loci Filtering Primer Optimization 54 Figure 3. Assignment accuracies estimated using the Support Vector Machine algorithm with Monte-Carlo cross-validation implemented in assignPOP (Chen et al., 2018). Sampling of five subsets of high θ loci (All loci, blue; top 75%, green; top 50%, orange) are crossed by two levels of training individuals (50%, 90%). Outliers are shown as black circles. The horizontal red line indicates 90% assignment rate. (Results grouped by: A: geographic basin, B: migratory form, C: reproductive ecotype.) 55 Figure 4. Map of the Columbia River displaying Skaha Lake, Okanagan River (extending southward originating at McIntyre Dam), and downstream lakes and dams. Size comparison of an average sockeye male and kokanee male included. Fish illustrations are a courtesy of Eileen Klatt. 56 Figure 5. Workflow of Oncorhynchus nerka sockeye and kokanee GT-seq hybridization panel design. 342 SNPs 9,942 SNPs 600 SNPs 550 SNPs 515 SNPs 430 SNPs Selection of high Fst candidate SNPs at base positions 40-60 Linked SNPs removed Primer design (GTseek LLC) Primer optimization from Library 1 Primer optimization from Library 2 Loci Filtering Primer Optimization 395 SNPs Primer optimization from Library 3 57 Figure 6. Accuracy and efficiency with simulated individuals of different hybrid classes for the top 300 Fst panel and the final optimized 342 SNP GT-seq panel. 58 Figure 7. Body length of spawning male and female fish in Okanagan River Channel 2019. 59 Figure 8. Tukey Test conducted on Okanagan River Channel 2019 for analysis of significance in body size difference between groups. Statistically significant groups did not have the 95% confidence level bar overlap with zero difference in mean levels of class. 60 Figure 9. Power evaluated with simulated individuals of different hybrid classes for the top 300 Fst panel and the final optimized 342 SNP GT-seq panel. 61 Bibliography Alexander, C. A., & Pickard, D. (2009). Skaha Lake Experimental Sockeye Reintroduction: Synthesis of first 4 of 12 years (2004-2007 brood years) (p. 110). Prepared by ESSA Technologies Ltd., Vancouver, B.C. for The Canadian Okanagan Basin Technical Working Group, c/o Okanagan Nation Alliance. Ali, O. A., Jeffres, C., & Miller, M. R. (2016). RAD Capture (Rapture): Flexible and Efficient Sequence-Based Genotyping. Genetics, 202, 16. Allan, J. D., Abell, R., Hogan, Z., Revenga, C., Taylor, B. W., Welcomme, R. L., & Winemiller, K. (2005). Overfishing of Inland Waters. BioScience, 55(12), 1041. https://doi.org/10.1641/0006-3568(2005)055[1041:OOIW]2.0.CO;2 Allendorf, F. W., Leary, R. F., Spruell, P., & Wenburg, J. K. (2001). The problems with hybrids: Setting conservation guidelines. Trends in Ecology & Evolution, 16(11), 613–622. https://doi.org/10.1016/S0169-5347(01)02290-X Anderson, E. C. (2010). Assessing the power of informative subsets of loci for population assignment: Standard methods are upwardly biased. Molecular Ecology Resources, 10(4), 701–710. https://doi.org/10.1111/j.1755-0998.2010.02846.x Anderson, E. C., & Thompson, E. A. (2002). A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data. Genetics, 160, 1217–1229. Anderson, E. C., Waples, R. S., & Kalinowski, S. T. (2008). An improved method for predicting the accuracy of genetic stock identification. Canadian Journal of Fisheries and Aquatic Sciences, 65(7), 1475–1486. https://doi.org/10.1139/F08-049 Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G., & Hohenlohe, P. A. (2016). Harnessing the power of RADseq for ecological and evolutionary genomics. Nature Reviews Genetics, 17(2), 81–92. https://doi.org/10.1038/nrg.2015.28 62 Araujo, H. A., Candy, J. R., Beacham, T. D., White, B., & Wallace, C. (2014). Advantages and Challenges of Genetic Stock Identification in Fish Stocks with Low Genetic Resolution. Transactions of the American Fisheries Society, 143(2), 479–488. https://doi.org/10.1080/00028487.2013.855258 Avigliano, E., & Volpedo, A. (2016). A Review of the Application of Otolith Microchemistry Toward the Study of Latin American Fishes. Reviews in Fisheries Science & Aquaculture, 24. https://doi.org/10.1080/23308249.2016.1202189 Bailey, C. J., Braun, D. C., McCubbing, D., Reynolds, J. D., Ward, B., Davies, T. D., & Moore, J. W. (2018). The roles of extrinsic and intrinsic factors in the freshwater life-history dynamics of a migratory salmonid. Ecosphere, 9(9), e02397. https://doi.org/10.1002/ecs2.2397 Beacham, T. D., Wallace, C., MacConnachie, C., Jonsen, K., McIntosh, B., Candy, J. R., … Withler, R. E. (2017). Population and individual identification of coho salmon in British Columbia through parentage-based tagging and genetic stock identification: An alternative to coded-wire tags. Canadian Journal of Fisheries and Aquatic Sciences, 74(9), 1391–1410. https://doi.org/10.1139/cjfas-2016-0452 Beacham, T., Margolis, L., & Nelson, R. (1998). A comparison of methods of stock identification for sockeye salmon (Oncorhynchus nerka) in Barkley Sound, British Columbia. North Pacific Anadromous Fishery Committee Bulletin, 1, 227–239. Begg, G. A., Friedland, K. D., & Pearce, J. B. (1999). Stock identification and its role in stock assessment and fisheries management: An overview. Fisheries Research, 43(1–3), 1–8. Bekkevold, D., Hansen, M. M., & Nielsen, E. E. (2006). Genetic impact of gadoid culture on wild fish populations: Predictions, lessons from salmonids, and possibilities for 63 minimizing adverse effects. ICES Journal of Marine Science, 63(2), 198–208. https://doi.org/10.1016/j.icesjms.2005.11.003 Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x Blair, G. R., Rogers, D. E., & Quinn, T. P. (1993). Variation in Life History Characteristics and Morphology of Sockeye Salmon in the Kvichak River System, Bristol Bay, Alaska. Transactions of the American Fisheries Society, 122(4), 550–559. https://doi.org/10.1577/1548-8659(1993)122<0550:VILHCA>2.3.CO;2 Boecklen, W. J., & Howard, D. J. (1997). Genetic Analysis of Hybrid Zones: Numbers of Markers and Power of Resolution. Ecology, 78(8), 2611–2616. https://doi.org/10.1890/0012-9658(1997)078[2611:GAOHZN]2.0.CO;2 Bootsma, M. L., Gruenthal, K. M., McKinney, G. J., Simmons, L., Miller, L., Sass, G. G., & Larson, W. A. (2020). A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms. Molecular Ecology Resources, 20(6), 1706–1722. https://doi.org/10.1111/1755-0998.13226 Campbell, N. R., Harmon, S. A., & Narum, S. R. (2015). Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Molecular Ecology Resources, 15(4), 855–867. https://doi.org/10.1111/1755-0998.12357 Carlson, A. K., Taylor, W. W., Kinnison, M. T., Sullivan, S. M. P., Weber, M. J., Melstrom, R. T., … Harrell, R. M. (2019). Threats to Freshwater Fisheries in the United States: 64 Perspectives and Investments of State Fisheries Administrators and Agricultural Experiment Station Directors. Fisheries, 44(6), 276–287. https://doi.org/10.1002/fsh.10238 Carvalho, G. R., & Nigmatullin, C. M. (1998). Stock structure analysis and species identification. FAO Fisheries Technical Paper, 199–232. Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A., & Cresko, W. A. (2013). Stacks: An analysis tool set for population genomics. Molecular Ecology, 22(11), 3124–3140. https://doi.org/10.1111/mec.12354 Chan, W. Y., Hoffmann, A. A., & Oppen, M. J. H. van. (2019). Hybridization as a conservation management tool. Conservation Letters, 12(5), e12652. https://doi.org/10.1111/conl.12652 Chen, K.-Y., Marschall, E. A., Sovic, M. G., Fries, A. C., Gibbs, H. L., & Ludsin, S. A. (2018). assignPOP: An r package for population assignment using genetic, non-genetic, or integrated data in a machine-learning framework. Methods in Ecology and Evolution, 9(2), 439–446. https://doi.org/10.1111/2041-210X.12897 Cline, T. J., Ohlberger, J., & Schindler, D. E. (2019). Effects of warming climate and competition in the ocean for life-histories of Pacific salmon. Nature Ecology & Evolution, 3(6), 935. https://doi.org/10.1038/s41559-019-0901-7 Craig, J. K., Foote, C. J., & Wood, C. C. (2005). Countergradient variation in carotenoid use between sympatric morphs of sockeye salmon (Oncorhynchus nerka) exposes nonanadromous hybrids in the wild by their mismatched spawning colour. Biological Journal of the Linnean Society, 84(2), 287–305. https://doi.org/10.1111/j.1095-8312.2005.00430.x 65 Crossin, G. T., Hinch, S. G., Cooke, S. J., Welch, D. W., Patterson, D. A., Jones, S. R. M., … Farrell, A. P. (2008). Exposure to high temperature influences the behaviour, physiology, and survival of sockeye salmon during spawning migration. Canadian Journal of Zoology, 86(2), 127–140. https://doi.org/10.1139/Z07-122 Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., … Durbin, R. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 Dolloff, C. A., Flebbe, P. A., & Thorpe, J. E. (1994). Salmonid Flexibility: Responses to Environmental Extremes. Transactions of the American Fisheries Society, 123(4), 606–612. https://doi.org/10.1577/1548-8659(1994)123<0606:SFRTEE>2.3.CO;2 Dudgeon, D. (2019). Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology, 29(19), R960–R967. https://doi.org/10.1016/j.cub.2019.08.002 Earl, D. A., & vonHoldt, B. M. (2012). STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources, 4(2), 359–361. https://doi.org/10.1007/s12686-011-9548-7 Edmands, S. (2002). Does parental divergence predict reproductive compatibility? Trends in Ecology & Evolution, 17(11), 520–527. https://doi.org/10.1016/S0169-5347(02)02585-5 Elliott, L. D., Ward, H. G. M., & Russello, M. A. (2020). Kokanee–sockeye salmon hybridization leads to intermediate morphology and resident life history: Implications for fisheries management. Canadian Journal of Fisheries and Aquatic Sciences, 77(2), 355–364. https://doi.org/10.1139/cjfas-2019-0034 66 Elliott, L., & Russello, M. A. (2018). SNP panels for differentiating advanced-generation hybrid classes in recently diverged stocks: A sensitivity analysis to inform monitoring of sockeye salmon re-stocking programs. Fisheries Research, 208, 339–345. https://doi.org/10.1016/j.fishres.2018.09.001 Evanno, G., Regnaut, S., & Goudet, J. (2005). Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology, 14(8), 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x Ferguson, A., Reed, T. E., Cross, T. F., Mcginnity, P., & Prodöhl, P. A. (2019). Anadromy, potamodromy and residency in brown trout Salmo trutta: The role of genes and the environment. Journal of Fish Biology, jfb.14005. https://doi.org/10.1111/jfb.14005 Foote, C. J. (1988). Male Mate Choice Dependent on Male Size in Salmon. Behaviour, 106(1/2), 63–80. Foote, C. J., Wood, C. C., Clarke, W. C., & Blackburn, J. (1992). Circannual Cycle of Seawater Adaptability in Oncorhynchus nerka: Genetic Differences between Sympatric Sockeye Salmon and Kokanee. Canadian Journal of Fisheries and Aquatic Sciences, 49(1), 99–109. https://doi.org/10.1139/f92-012 Foote, C. J., Wood, C. C., & Withler, R. E. (1989). Biochemical Genetic Comparison of Sockeye Salmon and Kokanee, the Anadromous and Nonanadromous forms of Oncorhynchus nerka. Canadian Journal of Fisheries and Aquatic Sciences, 46(1), 149–158. https://doi.org/10.1139/f89-020 Fraser, D. J., Cook, A. M., Eddington, J. D., Bentzen, P., & Hutchings, J. A. (2008). Mixed evidence for reduced local adaptation in wild salmon resulting from interbreeding with 67 escaped farmed salmon: Complexities in hybrid fitness. Evolutionary Applications, 1(3), 501–512. https://doi.org/10.1111/j.1752-4571.2008.00037.x Frazer, K. K., & Russello, M. A. (2013). Lack of parallel genetic patterns underlying the repeated ecological divergence of beach and stream-spawning kokanee salmon. Journal of Evolutionary Biology, 26(12), 2606–2621. https://doi.org/10.1111/jeb.12250 Gaines, S. D., Costello, C., Owashi, B., Mangin, T., Bone, J., Molinos, J. G., … Ovando, D. (2018). Improved fisheries management could offset many negative effects of climate change. Science Advances, 4(8), eaao1378. Galbreath, P. F., Bisbee, M. A., Dompier, D. W., Kamphaus, C. M., & Newsome, T. H. (2014). Extirpation and Tribal Reintroduction of Coho Salmon to the Interior Columbia River Basin. Fisheries, 39(2), 77–87. https://doi.org/10.1080/03632415.2013.874526 Garcia-Vazquez, E., Machado-Schiaffino, G., Campo, D., & Juanes, F. (2012). Species misidentification in mixed hake fisheries may lead to overexploitation and population bottlenecks. Fisheries Research, 114, 52–55. https://doi.org/10.1016/j.fishres.2011.05.012 Graham, K. (1997). Contemporary status of the North American paddlefish, Polyodon spathula. Environmental Biology of Fishes, 48, 279–289. Groot, G. (1991). Pacific Salmon Life Histories. UBC Press. Hansen, M. M. (2002). Estimating the long-term effects of stocking domesticated trout into wild brown trout (Salmo trutta) populations: An approach using microsatellite DNA analysis of historical and contemporary samples. Molecular Ecology, 11(6), 1003–1015. https://doi.org/10.1046/j.1365-294X.2002.01495.x 68 Hanson, A. J., & Smith, H. D. (1967). Mate Selection in a Population of Sockeye Salrnon (Oncorhynchus nerka) of Mixed Age-groups. Journal of the Fisheries Board of Canada, 24(9), 23. Harvey, A. C., Glover, K. A., Taylor, M. I., Creer, S., & Carvalho, G. R. (2016). A common garden design reveals population-specific variability in potential impacts of hybridization between populations of farmed and wild Atlantic salmon, Salmo salar L. Evolutionary Applications, 9(3), 435–449. https://doi.org/10.1111/eva.12346 Hauser, L., & Seeb, J. E. (2008). Advances in molecular technology and their impact on fisheries genetics. Fish and Fisheries, 9(4), 473–486. https://doi.org/10.1111/j.1467-2979.2008.00306.x Hegg, J. C., Kennedy, B. P., & Chittaro, P. (2019). What did you say about my mother? The complexities of maternally derived chemical signatures in otoliths. Canadian Journal of Fisheries and Aquatic Sciences, 76(1), 81–94. Helyar, S. J., Hemmer‐Hansen, J., Bekkevold, D., Taylor, M. I., Ogden, R., Limborg, M. T., … Nielsen, E. E. (2011). Application of SNPs for population genetics of nonmodel organisms: New opportunities and challenges. Molecular Ecology Resources, 11(s1), 123–136. https://doi.org/10.1111/j.1755-0998.2010.02943.x Hendry, A. P., Gotanda, K. M., & Svensson, E. I. (2017). Human influences on evolution, and the ecological and societal consequences. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1712), 20160028. https://doi.org/10.1098/rstb.2016.0028 Hendry, A. P., Quinn, T. P., & Utter, F. M. (1996). Genetic evidence for the persistence and divergence of native and introduced sockeye salmon (Oncorhynchus nerka) within Lake 69 Washington, Washington. Canadian Journal of Fisheries and Aquatic Sciences, 53, 823–832. Hess, J. E., Matala, A. P., & Narum, S. R. (2011). Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Molecular Ecology Resources, 11(s1), 137–149. https://doi.org/10.1111/j.1755-0998.2010.02958.x Hohenlohe, P. A., Amish, S. J., Catchen, J. M., Allendorf, F. W., & Luikart, G. (2011). Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Molecular Ecology Resources, 11(s1), 117–122. https://doi.org/10.1111/j.1755-0998.2010.02967.x Holmes, B. (1994). Biologists Sort the Lessons of Fisheries Collapse. Science, 264(5163), 1252–1253. https://doi.org/10.1126/science.264.5163.1252 Jacob, C., McDaniels, T., & Hinch, S. (2010). Indigenous culture and adaptation to climate change: Sockeye salmon and the St’át’imc people. Mitigation and Adaptation Strategies for Global Change, 15(8), 859–876. https://doi.org/10.1007/s11027-010-9244-z Jakobsson, M., & Rosenberg, N. A. (2007). CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics, 23(14), 1801–1806. https://doi.org/10.1093/bioinformatics/btm233 Johnson, B. M., Johnson, M. S., & Thorgaard, G. H. (2019). Salmon Genetics and Management in the Columbia River Basin. Northwest Science, 92(sp5), 346–363. https://doi.org/10.3955/046.092.0505 70 Kalinowski, S. T. (2004). Genetic polymorphism and mixed-stock fisheries analysis. Canadian Journal of Fisheries and Aquatic Sciences, 61(7), 1075–1082. https://doi.org/10.1139/f04-060 Kaushal, S. S., Likens, G. E., Jaworski, N. A., Pace, M. L., Sides, A. M., Seekell, D., … Wingate, R. L. (2010). Rising stream and river temperatures in the United States. Frontiers in Ecology and the Environment, 8(9), 461–466. https://doi.org/10.1890/090037 Kendall, N. W., Marston, G. W., & Klungle, M. M. (2017). Declining patterns of Pacific Northwest steelhead trout ( Oncorhynchus mykiss ) adult abundance and smolt survival in the ocean. Canadian Journal of Fisheries and Aquatic Sciences, 74(8), 1275–1290. https://doi.org/10.1139/cjfas-2016-0486 Knudsen, F. R., Enger, P. S., & Sand, O. (1994). Avoidance responses to low frequency sound in downstream migrating Atlantic salmon smolt, Salmo salar. Journal of Fish Biology, 45(2), 227–233. https://doi.org/10.1111/j.1095-8649.1994.tb01302.x Knudsen, F. R., & Sægrov, H. (2002). Benefits from horizontal beaming during acoustic survey: Application to three Norwegian lakes. Fisheries Research, 56(2), 205–211. https://doi.org/10.1016/S0165-7836(01)00318-6 Kochzius, M. (2009). Trends in Fishery Genetics. In The Future of Fisheries Science in North America (Vol. 31, pp. 453–493). Springer, Dordrecht. Koljonen, M.-L. (1995). Distinguishing between resident and migrating Atlantic salmon ( Salmo salar ) stocks by genetic stock composition analysis. Canadian Journal of Fisheries and Aquatic Sciences, 52(4), 665–674. https://doi.org/10.1139/f95-067 71 Laikre, L. (2010). Genetic diversity is overlooked in international conservation policy implementation. Conservation Genetics, 11(2), 349–354. https://doi.org/10.1007/s10592-009-0037-4 Lemay, M. A., & Russello, M. A. (2015). Genetic evidence for ecological divergence in kokanee salmon. Molecular Ecology, 24(4), 798–811. https://doi.org/10.1111/mec.13066 Levy, D. (2006). BC Sockeye Salmon Population Declines: Probable Causes and Recommended Response Strategies. The Sierra Club of Canada - BC Chapter. Levy, D. (2012). Implementation plan for the St’át’imc Government Services Fisheries Program: 2013 – 2017. Lillooet, BC: St’át’imc Government Services. Lohmann, K. J., & Lohmann, C. M. F. (2019). There and back again: Natal homing by magnetic navigation in sea turtles and salmon. The Journal of Experimental Biology, 222(Suppl 1), jeb184077. https://doi.org/10.1242/jeb.184077 Lowe, W. H., Muhlfeld, C. C., & Allendorf, F. W. (2015). Spatial sorting promotes the spread of maladaptive hybridization. Trends in Ecology & Evolution, 30(8), 456–462. https://doi.org/10.1016/j.tree.2015.05.008 Lucas, M. C., & Baras, E. (2000). Methods for studying spatial behaviour of freshwater fishes in the natural environment. Fish and Fisheries, 1(4), 283–316. https://doi.org/10.1046/j.1467-2979.2000.00028.x Lyons, J., Gonzalez-Hernandez, G., Soto-Galera, E., & Guzmdn-Arroyo, M. (1998). Decline of Freshwater Fishes and Fisheries in Selected Drainages of West‐Central Mexico. FISHERIES MANAGEMENT, 23(4), 9. Martignac, F., Daroux, A., Bagliniere, J.-L., Ombredane, D., & Guillard, J. (2015). The use of acoustic cameras in shallow waters: New hydroacoustic tools for monitoring migratory 72 fish population. A review of DIDSON technology. Fish and Fisheries, 16(3), 486–510. https://doi.org/10.1111/faf.12071 Martinez, A. S., Willoughby, J. R., & Christie, M. R. (2018). Genetic diversity in fishes is influenced by habitat type and life-history variation. Ecology and Evolution, 8(23), 12022–12031. https://doi.org/10.1002/ece3.4661 Maxwell, S. L., Johnson, D., Knutzen, J., Augerot, X., O’Neill, T., & Pearsons, T. (2007). Hydroacoustics: Rivers. American Fisheries Society, 133–152. May, S. A., McKinney, G. J., Hilborn, R., Hauser, L., & Naish, K. A. (2020). Power of a dual-use SNP panel for pedigree reconstruction and population assignment. Ecology and Evolution, 10(17), 9522–9531. https://doi.org/10.1002/ece3.6645 McCormick, S. D., & Saunders, R. L. (1987). Preparatory Physiological Adaptations for Marine Life of Salmonids: Osmoregulation, Growth, and Metabolism. American Fisheries Society Symposium, 1, 211–229. McCutcheon, C. S., Prentice, E. F., & Park, D. L. (1994). Passive Monitoring of Migrating Adult Steelhead with PIT Tags. North American Journal of Fisheries Management, 14(1), 220–223. https://doi.org/10.1577/1548-8675(1994)014<0220:PMOMAS>2.3.CO;2 McDonald, J. (1960). The Behaviour of Pacific Salmon Fry During Their Downstream Migration to Freshwater and Saltwater Nursery Areas. Journal of the Fisheries Research Board of Canada, 17(5), 655–676. https://doi.org/10.1139/f60-051 Mcfarlane, S., & Pemberton, J. (2019). Detecting the True Extent of Introgression during Anthropogenic Hybridization. Trends in Ecology & Evolution, 34. https://doi.org/10.1016/j.tree.2018.12.013 73 McKinney, G. J., Pascal, C. E., Templin, W. D., Gilk-Baumer, S. E., Dann, T. H., Seeb, L. W., & Seeb, J. E. (2020). Dense SNP panels resolve closely related Chinook salmon populations. Canadian Journal of Fisheries and Aquatic Sciences, 77(3), 451–461. https://doi.org/10.1139/cjfas-2019-0067 McQueen, D., Hyatt, K. D., Stockwell, M., Wright, H., Bussanich, R., & Stevens, A. (2013). Evaluation of the experimental introduction of Sockeye Salmon (Oncorhynchus nerka) into Skaha Lake and assessment of Sockeye rearing in Osoyoos Lake. Westbank, BC: Okanagan Aquatic Enterprises Ltd. Meek, M. H., & Larson, W. A. (2019). The future is now: Amplicon sequencing and sequence capture usher in the conservation genomics era. Molecular Ecology Resources, 19(4), 795–803. https://doi.org/10.1111/1755-0998.12998 Merz, J. E., & Setka, J. D. (2004). Evaluation of a Spawning Habitat Enhancement Site for Chinook Salmon in a Regulated California River. North American Journal of Fisheries Management, 24(2), 397–407. https://doi.org/10.1577/M03-038.1 Molony, B. W., Lenanton, R., Jackson, G., & Norriss, J. (2005). Stock enhancement as a fisheries management tool. Reviews in Fish Biology and Fisheries, 13(4), 409–432. https://doi.org/10.1007/s11160-005-1886-7 Moore, M. E., Goetz, F. A., Doornik, D. M. V., Tezak, E. P., Quinn, T. P., Reyes-Tomassini, J. J., & Berejikian, B. A. (2010). Early Marine Migration Patterns of Wild Coastal Cutthroat Trout (Oncorhynchus clarki clarki), Steelhead Trout (Oncorhynchus mykiss), and Their Hybrids. PLOS ONE, 5(9), e12881. https://doi.org/10.1371/journal.pone.0012881 74 Moreira, A. L., & Taylor, E. B. (2015). The origin and genetic divergence of “black” kokanee, a novel reproductive ecotype of Oncorhynchus nerka. Canadian Journal of Fisheries & Aquatic Sciences, 72(10), 1584–1595. https://doi.org/10.1139/cjfas-2015-0145 Muhlfeld, C. C., Kalinowski, S. T., McMahon, T. E., Taper, M. L., Painter, S., Leary, R. F., & Allendorf, F. W. (2009). Hybridization rapidly reduces fitness of a native trout in the wild. Biology Letters, 5(3), 328–331. https://doi.org/10.1098/rsbl.2009.0033 Mullon, C., Fréon, P., & Cury, P. (2005). The dynamics of collapse in world fisheries. Fish and Fisheries, 6(2), 111–120. https://doi.org/10.1111/j.1467-2979.2005.00181.x Munday, P. L., Dixson, D. L., Donelson, J. M., Jones, G. P., Pratchett, M. S., Devitsina, G. V., & Døving, K. B. (2009). Ocean acidification impairs olfactory discrimination and homing ability of a marine fish. Proceedings of the National Academy of Sciences, 106(6), 1848–1852. https://doi.org/10.1073/pnas.0809996106 Myers, J., Bryant, G., & Lynch, J. (1998). Factors contributing to the decline of Chinook salmon: An addendum to the 1996 west coast steelhead factors for decline report. Portland, OR: NOAA Protected Resources Division, National Marine Fisheries Service. Nakabo, T., Nakayama, K., Muto, N., & Miyazawa, M. (2011). Oncorhynchus kawamurae “Kunimasu,” a deepwater trout, discovered in Lake Saiko, 70 years after extinction in the original habitat, Lake Tazawa, Japan. Ichthyological Research, 58(2), 180–183. https://doi.org/10.1007/s10228-011-0204-8 Nelson, J. S. (1968). Distribution and Nomenclature of North American Kokanee, Oncorhynchus nerka. Journal of the Fisheries Research Board of Canada, 25(2), 409–414. https://doi.org/10.1139/f68-032 75 Nguyen, V. M., Rudd, M. A., Hinch, S. G., & Cooke, S. J. (2013). Recreational anglers’ attitudes, beliefs, and behaviors related to catch-and-release practices of Pacific salmon in British Columbia. Journal of Environmental Management, 128, 852–865. https://doi.org/10.1016/j.jenvman.2013.06.010 Nichols, K. M., Kozfkay, C. C., & Narum, S. R. (2016). Genomic signatures among Oncorhynchus nerka ecotypes to inform conservation and management of endangered Sockeye Salmon. Evolutionary Applications, 9(10), 1285–1300. https://doi.org/10.1111/eva.12412 Oke, K. B., Motivans, E., Quinn, T. P., & Hendry, A. P. (2019). Sexual dimorphism modifies habitat-associated divergence: Evidence from beach and creek breeding sockeye salmon. Journal of Evolutionary Biology, 32(3), 227–242. https://doi.org/10.1111/jeb.13407 Ostberg, C. O., Chase, D. M., & Hauser, L. (2015). Hybridization between Yellowstone Cutthroat Trout and Rainbow Trout Alters the Expression of Muscle Growth-Related Genes and Their Relationships with Growth Patterns. PLOS ONE, 10(10), e0141373. https://doi.org/10.1371/journal.pone.0141373 Ovenden, J. (1990). Mitochondrial DNA and marine stock assessment: A review. Marine and Freshwater Research, 41(6), 835. https://doi.org/10.1071/MF9900835 Pauli, B. D., & Sih, A. (2017). Behavioural responses to human‐induced change: Why fishing should not be ignored. Evolutionary Applications, 10(3), 231–240. https://doi.org/10.1111/eva.12456 Perez-Enriquez, R., & Taniguchi, N. (1999). Use of Microsatellite DNA as Genetic Tags for the Assessment of a Stock Enhancement Program of Red Sea Bream. Fisheries Science, 65(3), 374–379. https://doi.org/10.2331/fishsci.65.374 76 Perreault-Payette, A., Muir, A. M., Goetz, F., Perrier, C., Normandeau, E., Sirois, P., & Bernatchez, L. (2017). Investigating the extent of parallelism in morphological and genomic divergence among lake trout ecotypes in Lake Superior. Molecular Ecology, 26(6), 1477–1497. https://doi.org/10.1111/mec.14018 Porter, T. R., Clarke, G., & Murray, J. (2017). Underwater Visual Survey Methodology Used to Count Atlantic Salmon (Salmo Salar) in Rivers of St. George’s Bay, Newfoundland (Canadian Technical Report of Fisheries and Aquatic Sciences No. 3126). Fisheries and Oceans. Praebel, K., Westgaard, J.-I., Amundsen, P.-A., Siwertsson, A., Knudsen, R., Kahilainen, K. K., & Fevolden, S.-E. (2013). A diagnostic tool for efficient analysis of the population structure, hybridization and conservation status of European whitefish (Coregonus lavaretus (L.)) and vendace (C. albula (L.)). Advances in Limnology, 64, 247–255. https://doi.org/10.1127/1612-166X/2013/0064-0026 Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945–959. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559–575. https://doi.org/10.1086/519795 Rannala, B., & Mountain, J. L. (1997). Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences, 94(17), 9197–9201. https://doi.org/10.1073/pnas.94.17.9197 77 Ransom, B. H., Johnston, S. V., & Steig, T. W. (1998). Review on monitoring adult salmonid (Oncorhynchus and Salmo spp.) escapement using fixed-location split-beam hydroacoustics. Fisheries Research, 35(1), 33–42. https://doi.org/10.1016/S0165-7836(98)00057-5 Reeves, P. A., Bowker, C. L., Fettig, C. E., Tembrock, L. R., & Richards, C. M. (2016). Effect of error and missing data on population structure inference using microsatellite data (preprint). Evolutionary Biology. https://doi.org/10.1101/080630 Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T. J., … Cooke, S. J. (2019). Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews, 94(3), 849–873. https://doi.org/10.1111/brv.12480 Richey, J. E., Perkins, M. A., & Goldman, C. R. (1975). Effects of Kokanee Salmon ( Oncorhynchus nerka ) Decomposition on the Ecology of a Subalpine Stream. Journal of the Fisheries Research Board of Canada, 32(6), 817–820. https://doi.org/10.1139/f75-109 Ricker, W. E. (1938). “Residual” and kokanee salmon in Cultus lake. Journal of the Fisheries Board of Canada, 27. Riddell, B., Pacific Fisheries Resource Conservation Council, Canada, Department of Fisheries and Oceans, & Pacific Scientific Advice Review Committee (Canada). (2004). Pacific salmon resources in central and north coast British Columbia. Vancouver: Pacific Fisheries Resource Conservation Council. Rousset, F. (2008). genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources, 8(1), 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x 78 Russell, T., Cullingham, C., Kommadath, A., Stothard, P., Herbst, A., & Coltman, D. (2019). Development of a Novel Mule Deer Genomic Assembly and Species-Diagnostic SNP Panel for Assessing Introgression in Mule Deer, White-Tailed Deer, and Their Interspecific Hybrids. G3: Genes|Genomes|Genetics, 9(3), 911–919. https://doi.org/10.1534/g3.118.200838 Russello, M. A., Kirk, S. L., Frazer, K. K., & Askey, P. J. (2012). Detection of outlier loci and their utility for fisheries management. Evolutionary Applications, 5(1), 39–52. https://doi.org/10.1111/j.1752-4571.2011.00206.x Sanz, N., Araguas, R. M., Fernández, R., Vera, M., & García-Marín, J.-L. (2009). Efficiency of markers and methods for detecting hybrids and introgression in stocked populations. Conservation Genetics, 10(1), 225–236. https://doi.org/10.1007/s10592-008-9550-0 Scheuerell, M. D., Moore, J. W., Schindler, D. E., & Harvey, C. J. (2007). Varying effects of anadromous sockeye salmon on the trophic ecology of two species of resident salmonids in southwest Alaska. Freshwater Biology, 52(10), 1944–1956. https://doi.org/10.1111/j.1365-2427.2007.01823.x Scheuerell, M. D., Zabel, R. W., & Sandford, B. P. (2009). Relating juvenile migration timing and survival to adulthood in two species of threatened Pacific salmon (Oncorhynchus spp.). Journal of Applied Ecology, 46(5), 983–990. https://doi.org/10.1111/j.1365-2664.2009.01693.x Schmidt, B. A., Tucker, T. R., Collier, J. J., Mayer, C. M., Roseman, E. F., Stott, W., & Pritt, J. J. (2020a). Determining habitat limitations of Maumee River walleye production to western Lake Erie fish stocks: Documenting a spawning ground barrier. Journal of Great Lakes Research, 46(6), 1661–1673. https://doi.org/10.1016/j.jglr.2020.08.022 79 Schmidt, D. A., Campbell, N. R., Govindarajulu, P., Larsen, K. W., & Russello, M. A. (2020b). Genotyping‐in‐Thousands by sequencing (GT‐seq) panel development and application to minimally invasive DNA samples to support studies in molecular ecology. Molecular Ecology Resources, 20(1), 114–124. https://doi.org/10.1111/1755-0998.13090 Schmidt, D. A., Govindarajulu, P., Larsen, K. W., & Russello, M. A. (2020c). Genotyping-in-Thousands by sequencing reveals marked population structure in Western Rattlesnakes to inform conservation status. Ecology and Evolution, 10(14), 7157–7172. https://doi.org/10.1002/ece3.6416 Scribner, K. T., Page, K. S., & Bartron, M. L. (2001). Hybridization in freshwater fishes: A review of case studies and cytonuclear methods of biological inference. Reviews in Fish Biology and Fisheries, 10, 293–323. Shaklee, J. B., Beacham, T. D., Seeb, L., & White, B. A. (1999). Managing Fisheries using genetic data: Case studies from four species of Pacific salmon. Fisheries Research, 34. Sjodin, B. M. F., Irvine, R. L., & Russello, M. A. (2020). RapidRat: Development, validation and application of a genotyping-by-sequencing panel for rapid biosecurity and invasive species management. PLOS ONE, 15(6), e0234694. https://doi.org/10.1371/journal.pone.0234694 Stewart, G. S., Morris, M. R., Genis, A. B., Szűcs, M., Melbourne, B. A., Tavener, S. J., & Hufbauer, R. A. (2017). The power of evolutionary rescue is constrained by genetic load. Evolutionary Applications, 10(7), 731–741. https://doi.org/10.1111/eva.12489 Tamario, C., Sunde, J., Petersson, E., Tibblin, P., & Forsman, A. (2019). Ecological and Evolutionary Consequences of Environmental Change and Management Actions for 80 Migrating Fish. Frontiers in Ecology and Evolution, 7. https://doi.org/10.3389/fevo.2019.00271 Taylor, E. B., & Foote, C. J. (1991). Critical swimming velocities of juvenile sockeye salmon and kokanee, the anadromous and non-anadromous forms of Oncorhynchus nerka (Walbaum). Journal of Fish Biology, 38(3), 407–419. https://doi.org/10.1111/j.1095-8649.1991.tb03130.x Taylor, E. B., Foote, C. J., & Wood’, C. C. (1996). Molecular Genetic Evidence for Parallel Life-History Evolution within a Pacific Salmon (Sockeye Salmon and Kokanee, Oncorhynchus nerka). Evolution, 50(1), 401–416. Taylor, E. B., Harvey, S., Pollard, S., & Volpe, J. (1997). Postglacial genetic differentiation of reproductive ecotypes of kokanee Oncorhynchus nerka in Okanagan Lake, British Columbia. Molecular Ecology, 6(6), 503–517. https://doi.org/10.1046/j.1365-294X.1997.00213.x Thongda, W., Lewis, M., Zhao, H., Bowen, B., Lutz-Carrillo, D. J., Peoples, B. K., & Peatman, E. (2020). Species-diagnostic SNP markers for the black basses (Micropterus spp.): A new tool for black bass conservation and management. Conservation Genetics Resources, 12(2), 319–328. https://doi.org/10.1007/s12686-019-01109-8 Twyford, A. D., & Ennos, R. A. (2012). Next-generation hybridization and introgression. Heredity, 108(3), 179–189. https://doi.org/10.1038/hdy.2011.68 Utter, F. (2000). Patterns of subspecific anthropogenic introgression in two salmonid genera. Reviews in Fish Biology and Fisheries, 10, 256–279. Utter, F., & Ryman, N. (1993). Genetic Markers and Mixed Stock Fisheries. Fisheries, 18(8), 12. 81 Vähä, J.-P., & Primmer, C. R. (2006). Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Molecular Ecology, 15(1), 63–72. https://doi.org/10.1111/j.1365-294X.2005.02773.x Van Nimwegen, K. J. M., Van Soest, R. A., Veltman, J. A., Nelen, M. R., Van der Wilt, G. J., Vissers, L. E. L. M., & Grutters, J. P. C. (2016). Is the $1000 Genome as Near as We Think? A Cost Analysis of Next-Generation Sequencing. Clinical Chemistry, 62(11), 1458–1464. https://doi.org/10.1373/clinchem.2016.258632 Veale, A. J., & Russello, M. A. (2016). Sockeye salmon repatriation leads to population re‐establishment and rapid introgression with native kokanee. Evolutionary Applications, 9(10), 1301–1311. https://doi.org/10.1111/eva.12430 Veale, A. J., & Russello, M. A. (2017a). An ancient selective sweep linked to reproductive life history evolution in sockeye salmon. Scientific Reports, 7(1), 1–10. https://doi.org/10.1038/s41598-017-01890-2 Veale, A. J., & Russello, M. A. (2017b). Genomic Changes Associated with Reproductive and Migratory Ecotypes in Sockeye Salmon (Oncorhynchus nerka). Genome Biology and Evolution, 9(10), 2921–2939. https://doi.org/10.1093/gbe/evx215 Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., … Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. https://doi.org/10.1038/nature09440 Walsh, P. S., Metzger, D. A., & Higuchi, R. (1991). Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. BioTechniques, 10(4), 506–513. 82 Walters, C., English, K., Korman, J., & Hilborn, R. (2019). The managed decline of British Columbia’s commercial salmon fishery. Marine Policy, 101, 25–32. https://doi.org/10.1016/j.marpol.2018.12.014 Waples, R. S., Punt, A. E., & Cope, J. M. (2008a). Integrating genetic data into management of marine resources: How can we do it better? Fish and Fisheries, 9(4), 423–449. https://doi.org/10.1111/j.1467-2979.2008.00303.x Waples, R. S., Punt, A. E., & Cope, J. M. (2008b). Integrating genetic data into management of marine resources: How can we do it better? Fish and Fisheries, 9(4), 423–449. https://doi.org/10.1111/j.1467-2979.2008.00303.x Ward, H. G. M., Askey, P. J., Weir, T., Frazer, K. K., & Russello, M. A. (2019). Genetic Stock Identification Reveals That Angler Harvest Is Representative of Cryptic Stock Proportions in a High-Profile Kokanee Fishery. North American Journal of Fisheries Management, 39(3), 415–425. Weeks, A. R., Heinze, D., Perrin, L., Stoklosa, J., Hoffmann, A. A., van Rooyen, A., … Mansergh, I. (2017). Genetic rescue increases fitness and aids rapid recovery of an endangered marsupial population. Nature Communications, 8(1), 1071. https://doi.org/10.1038/s41467-017-01182-3 Weir, B. S., & Cockerham, C. C. (1984). Estimating F-Statistics for the Analysis of Population Structure. Evolution, 38(6), 1358–1370. https://doi.org/10.2307/2408641 Winans, G. A., Viele, D., Grover, A., Palmer-Zwahlen, M., Teel, D., & Doornik, D. V. (2001). An Update of Genetic Stock Identification of Chinook Salmon in the Pacific Northwest: Test Fisheries in California. Reviews in Fisheries Science, 9(4), 213–237. https://doi.org/10.1080/20016491101753 83 Winston, M. R., & Taylor, C. M. (1991). Upstream Extirpation of Four Minnow Species Due to Damming of a Prairie Stream. Transactions of the American Fisheries Society, 120, 8. Wood, C. C., Bickham, J. W., John Nelson, R., Foote, C. J., & Patton, J. C. (2008). Recurrent evolution of life history ecotypes in sockeye salmon: Implications for conservation and future evolution. Evolutionary Applications, 1(2), 207–221. https://doi.org/10.1111/j.1752-4571.2008.00028.x Wood, C. C., & Foote, C. J. (1990). Genetic Differences in the Early Development and Growth of Sympatric Sockeye Salmon and Kokanee (Oncorhynchus nerka), and their Hybrids. Canadian Journal of Fisheries and Aquatic Sciences, 47(11), 2250–2260. https://doi.org/10.1139/f90-250 Wood, C. C., & Foote, C. J. (1996). Evidence for Sympatric Genetic Divergence of Anadromous and Nonanadromous Morphs of Sockeye Salmon (oncorhynchus Nerka). Evolution, 50(3), 1265–1279. https://doi.org/10.1111/j.1558-5646.1996.tb02367.x Wood, C. M., & McDonald, D. G. (1997). Global Warming: Implications for Freshwater and Marine Fish. Cambridge University Press. Wringe, B. F., Stanley, R. R. E., Jeffery, N. W., Anderson, E. C., & Bradbury, I. R. (2017). hybriddetective: A workflow and package to facilitate the detection of hybridization using genomic data in r. Molecular Ecology Resources, 17(6), e275–e284. https://doi.org/10.1111/1755-0998.12704 Xie, Y., Michielsens, C. G. J., Gray, A. P., Martens, F. J., & Boffey, J. L. (2008). Observations of avoidance reactions of migrating salmon to a mobile survey vessel in a riverine environment. Canadian Journal of Fisheries and Aquatic Sciences, 65(10), 2178–2190. https://doi.org/10.1139/F08-128 84
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Russello, Michael A.
Genotyping-in-Thousands by sequencing panel development and application for informing multi-scaled kokanee salmon (Oncorhynchus nerka) fisheries management
Text