Andrew P. Kinziger, Michael Hellmair, and David G. Hankin

Transcription

1 GENETIC STRUCTURE OF CHINOOK SALMON (ONCORHYNCHUS TSHAWYTSCHA) IN THE KLAMATH-TRINITY BASIN: IMPLICATIONS FOR WITHIN-BASIN GENETIC STOCK IDENTIFICATION Andrew P. Kinziger, Michael Hellmair, and David G. Hankin Department of Fisheries Biology Humboldt State University One Harpst Street, Arcata, CA THE GREAT SEAL OF THE HOOPA VALLEY TRIBE Produced Under Contract Agreement Between the Hoopa Valley Tribal Fisheries Department and the Humboldt State University Sponsored Programs Foundation. This project received financial support provided by U.S. Bureau of Reclamation, Klamath Basin Area Office Full Contract Title: GSI Klamath Chinook, project number December

2 TABLE OF CONTENTS Summary.. 3 Introduction.. 6 Methods.. 8 Results.. 17 Discussion.. 22 References.. 34 Table Table Table Table Table Table Table Table Table Table Table Table Figure Captions.. 54 Figure Figure Figure Figure Figure Figure Appendix I.. 62 Appendix II.. 71 Appendix III

3 SUMMARY We constructed a genetic baseline data set for Chinook salmon from the Klamath-Trinity basin composed of 12 populations and 17 microsatellite loci. (Microsatellite loci were used for baseline construction instead of single nucleotide polymorphisms because microsatellites were most cost-effective as detailed in our October 2007 preliminary report.) The baseline included collections from all drainages supporting appreciable numbers of returns, wild and hatchery populations, and spring- and fall-run populations. In addition, we reviewed 3614 hatchery records pertaining to the release of hatchery spawned Chinook salmon in California dating from 1943 to Data were analyzed to determine: (1) the degree of genetic differentiation and relationships among populations in the basin and how these patterns are related to out-plant history, geography, and run-timing, and (2) the utility of microsatellite loci for genetic stock identification within the basin. Major findings and management recommendations are: 1) Klamath-Trinity Chinook salmon exhibit substantial levels of genetic structuring despite the large amount of out-of-basin transplantation that has occurred in the basin. The only exception was fall-run from the South Fork Trinity River which appeared to have their genetic structure influenced by both IGH and TRH fall-run. This transplantation event was unique among the records reviewed because in no other case was the receiving stock so small (few hundred individuals) and transplanted number of fish very large (>900000). No future management efforts should involve transplantation of fish outside of their native basin if genetic structure is to be retained. 2) Four genetically differentiated and geographically separated groups of Chinook salmon appear present in the Klamath-Trinity basin below Iron Gate and Lewiston dams: (i) Upper Basin containing fall-run populations from Iron Gate Hatchery, Shasta River and 3

4 Scott River, (ii) Trinity containing spring- and fall-run from the Trinity River Hatchery and South Fork of the Trinity River, (iii) Salmon containing spring- and fall-run from the Salmon River, and (iv) Lower Basin containing fall-run from Horse Linto Creek, Terwer Creek and Blue Creek. 3) Spring- and fall-run Chinook salmon life-histories have evolved repeatedly and independently through a process of parallel evolution in the Salmon and Trinity Rivers. 4) The Klamath-Trinity 12 population 17 microsatellite baseline can be used to estimate the proportion of Chinook salmon in a mixed catch originating from: (i) the four genetically divergent geographic regions within the basin: Upper Basin, Salmon, Trinity and Lower Basin with a mean assignment accuracy of nearly 90%, and (ii) each of the 12 populations in the baseline with a mean assignment accuracy of 72%. Assignment accuracy to geographic region is at a level generally considered sufficient for management applications. 5) The Klamath-Trinity 12 population 17 microsatellite baseline can be used to estimate the population of origin for individuals derived from unknown sources. While confidence of assignments for some individuals may be very high, it should be expected that, for the majority individuals, confidence of individual assignments will be low due to hybridization and low levels of divergence of among populations. 6) In the future, the baseline data set could potentially be improved by increasing the sample size of baseline populations from Shasta and South Fork Trinity rivers above individuals, however it is possible that increasing sample sizes for these populations will result in no or little improvement in assignment accuracy. Adding additional loci to the baseline data set will likely provide only very minor improvements in assignment 4

5 accuracy. Thus, the assignment accuracy reported herein may be close to the maximum possible given the limited geographic scope and levels of genetic divergence among populations in the Klamath-Trinity basin. 7) Although our analyses detected no major changes in allele frequencies through time that would interfere with genetic stock identification, the baseline should be periodically updated if genetic identification methods are used, in the future, for stock identification in a management context. 5

6 INTRODUCTION Chinook salmon (Oncorhynchus tshawytscha) exhibit a substantial amount of population genetic structuring across its geographic range (Waples et al., 2004; Beacham et al., 2006). Population structuring in Chinook salmon is directly related to the strong propensity for this species to home to their natal spawning grounds and the tendency for between-population migration/straying to decrease as the geographic distance between populations increases. Thus, the largest levels of genetic differentiation in Chinook salmon are generally observed between major river drainages, presumably due to the relatively low levels of between-basin straying (Waples et al., 2004; Beacham et al., 2006). In contrast, lower levels of divergence exist between populations within drainages where straying is more commonplace. In addition to geography, Chinook salmon populations are genetically structured according to time of river entry. Chinook salmon enter freshwater during almost every month (Healey, 1991) and different run-times are often genetically divergent from one another (Waples et al., 2004). Interestingly, genetic analyses have shown that run-times from the same drainage, such as spring- and fall-run, are often more genetically similar than populations having the same run-timing from a different geographic location (Waples et al., 2004). Thus, different run-timings in Chinook salmon are likely to have evolved repeatedly and independently at different geographic locations, likely due to divergent selection on run-timing (Quinn et al., 1996; Quinn et al., 2001; Quinn et al., 2002; Waples et al., 2004). However, fine-scale analyses have revealed exceptions to these generalized patterns indicating that unique drainage-specific genetic structure often exist; for example, in the Columbia River basin spring-run are highly divergent from all other stocks in the basin (Gall et al., 1991; Waples et al., 2004; Beacham et al., 2006). Also, out-of-basin transplantation can substantially alter the genetic structure of Chinook salmon within river basins. In the 6

7 Sacramento River drainage, out-of-basin transplantation has resulted in genetic homogenization of fall-run Chinook salmon (Williamson and May, 2005). Thus, understanding genetic structure of Chinook salmon within a particular river-basin requires fine-scale sampling and analysis of stocks in that basin. The objective of this study was to evaluate the genetic structure of Chinook salmon in the Klamath-Trinity basin and explore the potential of using genetic markers for within-basin stock identification. Previous studies of genetic structure of Chinook salmon in the Klamath-Trinity basin have shown that populations in the upper Klamath-Trinity basin are more closely related to one another than they are to stocks from any other drainage (Gall et al., 1991; Waples et al., 2004; Beacham et al., 2006; Seeb et al., 2007). In contrast, coastal populations from the lower Klamath-Trinity basin (below the confluence of the Klamath and Trinity rivers) are genetically distinct from upper-klamath-trinity populations and instead more closely related to coastal populations in northern California and southern Oregon (Gall et al., 1991). Prior genetic studies of spring-run and fall-run Chinook salmon returning to the Trinity River, a major tributary to the Klamath River, have revealed hybridization between these two runs (Kinziger et al., 2008). Temporal comparison of the genetic structure of spring- and fall-run returning to the Trinity River over a 12 year time span (3-4 generations for Chinook salmon) suggested no significant increase in levels of hybridization (Malakauskas et al, In Prep), nevertheless hybridization may influence estimates of population structure in the Klamath- Trinity basin and the ability to use genetic methods to identify these stocks. Similarly, trapping and tagging data also suggests overlap and the potential for hybridization between spring- and fall-run returning to the South Fork Trinity River (Aguilar et al., 1995). No previous genetic analyses have been conducted on spring- and fall-run Chinook salmon stocks returning to South 7

8 Fork Trinity or stocks of spring- and fall-run populations in the Salmon River to assess genetic distinctiveness and the potential for hybridization between spring- and fall-run within these basins. In addition to hybridization, out-basin-transplantation may have substantially altered the genetic structure of Chinook salmon within the Klamath-Trinity basin. Review of 3614 hatchery records pertaining to the release of hatchery spawned Chinook salmon in California dating from 1943 to 1994 show that substantial numbers of hatchery-reared juvenile Chinook salmon have been released outside of their original basins (Table 1). The fate of these transplanted fish remains unknown, and it has been shown that return of salmon to original planting sites can be highly variable (Ricker, 1972), thus making predictions as to how out-of-basin transplantation has influenced genetic structure uncertain. Some outplantings involved releases of Upper Basin hatchery fish at the Klamath-Trinity estuary, a practice that decreases outmigrant mortality but increases straying in returning adult fish (Table 1). These practices may have artificially increased gene flow among populations and therefore reduced the level of genetic differentiation among populations within the basin. We constructed a comprehensive data set composed of genotypic data for all major populations of Chinook salmon returning to the Klamath-Trinity basin to assess: (1) the degree of genetic differentiation and relationships among populations in the basin and how these patterns are related to out-plant history, geography, and run-timing, and (2) the utility of microsatellite loci for genetic stock identification within the basin. MATERIALS AND METHODS Study Area 8

9 The Klamath-Trinity basin is the second largest river system in California flowing approximately 423 km (Figure 1). Within the Klamath-Trinity basin, Chinook salmon support commercial, recreational and tribal fisheries. The mean annual in-river run of Chinook salmon over the past 28 years in the Klamath-Trinity River is estimated at 120,000 (range 34, ,000; California Department of Fish and Game, personal communication), however, historically the number of Chinook returning to the basin is thought to be more than 500,000 (Radtke, pers. comm. cited in Gresh et al. 2000). Two hatcheries in the basin rear and release Chinook salmon, Iron Gate Hatchery (IGH) and Trinity River Hatchery (TRH). Both hatcheries were built to mitigate for loss of upstream spawning and nursery habitat due to dam construction. Iron Gate hatchery releases up to 6 million fall-run Chinook salmon and the TRH releases 1.4 million spring-run and 2.9 million fall-run Chinook salmon annually (Zajanc and Hankin, 1998; Chesney, 2007) and, together, TRH and IGH releases account for the majority of Chinook salmon returning to the Klamath basin. In addition to the hatchery stocks, there are wild stocks inhabiting several small tributaries in the basin each with low to moderate numbers of fish (350-20,000 fish). Tributaries containing notable runs of wild Chinook salmon include the Shasta and Scott Rivers in the upper Klamath River basin, Horse Linto Creek and South Fork Trinity River in the Trinity River drainage, and Blue Creek and Terwer Creek, two small tributaries occurring in the lower basin below the Klamath and Trinity rivers confluence. The Klamath-Trinity basin contains both spring- and fall-run Chinook salmon. Fall-run Chinook are currently more geographically widespread and more numerous than spring-run; however, historically spring-run Chinook may have been more abundant (Snyder, 1931). Spring-run populations are known from the Trinity River, South Fork Trinity River and Salmon River and all spring-run populations occur sympatrically with fall-run populations. The Trinity 9

10 River spring- and fall-run are primarily sustained by TRH with recent annual run size estimates ranging from 4,000-48,000 for spring-run and 14,000-64,000 for fall-run (Aguilar et al., 1995; Sinnen et al., 2004, 2005, 2006). Unlike the Trinity River populations, the South Fork Trinity River and Salmon River spring- and fall-run Chinook salmon are not artificially propagated at hatcheries and upstream passage is not impeded by a dam. However, the spring-run populations in the South Fork Trinity River and Salmon River occur in low abundance; the South Fork Trinity River population currently consists of a few hundred individuals declining from more than 11,000 in 1964 (Aguilar et al., 1995) and the Salmon River population also exists in low abundance ranging from individuals over the past 25 years (Salmon River Weak Stocks Assessment Report, 2006). Spring-run Chinook salmon once also occurred in the upper Klamath Basin (Hamilton et al., 2004) but this population was probably extirpated shortly after the construction of the first impassable dam on the mainstem Klamath River in Sample Collection To assess the genetic structure of Chinook salmon returning to the Klamath-Trinity basin we genotyped a total of 937 individuals from 12 populations at 17 microsatellite loci. Collections included all drainages supporting appreciable numbers of returns, wild and hatchery populations, and spring- and fall-run populations (Table 2). Tissues were collected during carcass surveys, weir-operations, directly from hatcheries or by electrofishing. Tissues were from adults except for collections from IGH, Terwer Creek, and Blue Creek, wherein tissues were taken from juveniles. Collected tissues included fins and scales which were preserved in 95% ethanol, dried or frozen until DNA extraction. Given the high fecundity of Chinook salmon ( 5000 eggs/female), it is possible that juvenile collections could represent progeny from just a few families and thus juvenile sampling 10

11 may not accurately represent the allele frequencies of the entire population thereby biasing estimates of interpopulation differentiation. For our juvenile collections we took precautions to minimize family sampling. Juvenile collections from Blue Creek were taken on a weekly basis (April to June) from an out-migrant trap and juvenile samples from IGH were taken from six different raceways. Collection from Terwer Creek, however, consisted of a single point sample. To minimize inclusion of hybrids between spring- and fall-run Chinook returning to TRH in the data set (see Kinziger et al., 2008), spring-run were sampled at the hatchery between September 13 th and October 4 th (2004), while fall-run fish from the same location were sampled between November 8 th and December 2 nd (2004). No fish were sampled for 35 days following the last sample of presumed spring-run fish and the first sample of presumed fall-run fish. These methods likely minimized the number of hybrids in the data but probably did not completely eliminate them. Molecular Analysis DNA was extracted using the Promega Wizard SV96 Genomic DNA Purification System, Qiagen DNeasy spin columns, or Chelex Resin protocol (Miller and Kapuscinski 1993). A total of 17 microsatellite loci were amplified via polymerase chain reaction (PCR) in 10-μL reaction volumes with the following reagent concentrations: 5 μl Promega PCR Master Mix (Catalog #M7505) containing 50 units/ml Taq DNA polymerase, supplied in a proprietary reaction buffer, 400µM each datp, dgtp, dctp, dttp and 3mM MgCl 2., μl water, μl of each primer (10 pmol/ μl) and 1.0 μl of template DNA (Table 3). Thermocycling conditions varied between loci and are available from the authors upon request. Reactions were carried out in 96-well plates using Applied Biosystems and MJ Research thermocyclers. Amplified loci were electrophoresed on denaturing polyacrylamide gel using a 11

12 Beckman Coulter CEQ 8000 Genetic Analysis System and visualized using Beckman Coulter CEQ 8000 Genetic Analysis System software. Phenotypes from electropherograms were scored and automatically using the Beckman Coulter CEQ 8000 Genetic Analysis System software and each was visually inspected for each individual to avoid errors caused by automatic scoring. Analysis of Genetic Population Structure To identify potential bias due to family sampling, among juvenile samples collected from IGH, Blue Creek and Terwer Creek, we calculated the mean pairwise relatedness, r qg, (Queller and Goodnight, 1989) between individuals within each population using GENALEX version 6 (Peakall and Smouse, 2006). Relatedness coefficients can range from 1 to -1, with positive values indicating pairs of individuals are related and negative values indicating pairs of individuals are unrelated (Konovalov and Heg, 2008). Four samples consisted of collections from different years, spring-run from the Salmon River and South Fork of the Trinity River and fall-run from the Salmon and Shasta rivers. To determine whether there was significant genetic differentiation between years at a given sample location, we used GENEPOP version 3.3 (Raymond and Rousset 1995) to conduct a Fisher s exact test of the null hypothesis that allelic distribution was identical between years. All multiyear samples that did not exhibit significant genetic differentiation were combined for further analysis. Tests for conformance to Hardy-Weinberg proportions were conducted for each locus within each population using GENEPOP version 3.3. GENEPOP was also used to estimate F is for each locus within each population. Estimates of Hardy-Weinberg expected heterozygosity, mean number of alleles at a locus, and private allelic richness within each population were 12

13 calculated using HP-RARE 1.0 (Kalinowski, 2005). The number of alleles sampled per population ranged from 38 to 246. Because larger samples are expected to have more alleles, we standardized our estimates of mean number of alleles at a locus and private allelic richness to 38 genes, the smallest number detected in our samples, using rarefaction methods as implemented in HP-RARE 1.0 (Kalinowski, 2004, 2005). The software FSTAT (Goudet, 1995) was used to estimate F st (following Weir and Cockerham, 1984) and to test whether estimates were significantly different from zero. Correction against Type I error was made using the Bonferroni method (Rice, 1989) for all statistical tests involving multiple comparisons. PHYLIP (Phylogeny Inference Package, version 3.68) was used to calculate Cavalli- Sforza genetic distance between population pairs and to construct an unrooted neighbor-joining tree (Felsenstein, 1993). Branch support was evaluated by conducting a bootstrap analysis using PHYLIP by generating replicate datasets and then calculating pairwise Cavalli-Sforza genetic distances and an unrooted neighbor-joining tree for each replicate dataset. A consensus of the individual trees provides a bootstrap estimate of branch support with branches appearing in more than 90% of the trees considered well-supported. Genetic Stock Identification Analysis Genetic stock identification analysis involves genotyping fish from populations of interest to construct a baseline data set, sampling fish of unknown origin, and assigning fish of unknown origin to the baseline population for which they have the highest probability of occurrence. To evaluate accuracy of the Klamath-Trinity Chinook salmon 12 population 17 microsatellite locus baseline for genetic stock identification we used simulation methods to construct populations of specified composition and estimated the composition of the simulated populations. The software ONCOR (Kalinowski et al., 2008) was used to conduct simulations 13

14 and estimate the composition of simulated populations. ONCOR simulates populations of known composition using leave-one-out cross validation, a method that provides nearly unbiased estimates of genetic stock identification accuracy (Anderson et al., 2008). ONCOR estimates the composition of the simulated populations using conditional maximum likelihood (Millar, 1987). Other genetic stock identification algorithms may provide slightly improved assignment accuracy (see Koljonen et al., 2005), these methods are computationally time intensive and impractical for simulation analyses. First, we conducted 100% simulation analyses. For this analysis single populations composed of individuals that all originated from the same population were simulated for each of the 12 populations in the baseline. We conducted one analysis using a baseline containing all 17 loci, and another analysis with OTSG68 removed, to evaluate the effect of loci exhibiting significant departures from Hardy-Weinberg expectations on genetic stock identification. Second, realistic fishery simulations were used to determine the ability of the baseline to detect wild stocks that are of low abundance in the basin. We compared estimates of stock composition in simulated catches that were composed entirely of IGH and TRH fish to estimates when catches were dominated by IGH and TRH fish and also included smaller numbers of wild and rare stocks such Blue Creek, Salmon River spring-run and South Fork Trinity spring-run. Stock composition estimates are typically biased towards 1/k, where k is the number of populations in the baseline, so no simulated stocks composed 1/12 or 8.3% of the total. For each simulated population, we generated 200 replicate data sets, each containing 200 individuals, allowing calculation of standard deviation and 95% confidence intervals. We evaluated assignment accuracy to both population of origin and to reporting group. We recognized four reporting groups within the Klamath-Trinity basin: (1) Upper Basin, (2) 14

15 Salmon, (3) Trinity, and (4) Lower Basin. Baseline populations assigned to each of these four reporting groups are listed in Table 2. Recognition of four reporting groups of Chinook salmon in the Klamath-Trinity basin is based upon geographic separation of these areas, and the neighbor-joining tree, which indicated genetic divergence between these four regions (Figures 1 and 2). We also evaluated accuracy of the Klamath-Trinity Chinook salmon 12 population 17 microsatellite locus baseline for genetic stock identification by independently resampling three baseline populations, treating these populations as unknowns, and then evaluating the proportion that assign to the correct population (repeat baseline sampling). Repeat baseline sampling allows for evaluation of accuracy in real fishery situations, unlike simulation methods, because it tests the assumption that allele frequencies in the baseline population are a true reflection of allele frequencies of the natural populations of interest. This assumption may be violated due to sampling errors or changes in allele frequencies through time within baseline populations. Independent repeat baseline samples were collected from three populations, TRH spring-run (n=23), TRH fall-run (n=23) and Bogus Creek fall-run (n=30). The TRH spring- and fall-run samples were collected 12 years earlier than the original baseline samples (1992 vs 2004) and were taken early and late in the run to minimize the potential inclusion of hybrids (Kinziger et al., 2008). Bogus Creek, which enters the Klamath River directly below IGH, contains a spawning population that has been historically composed of a large number of IGH strays (Knechtle, 2007; Walsh and Hampton, 2007), and thus was used to represent a repeat baseline sample for IGH. Samples from Bogus Creek were collected in 2006, while IGH baseline samples consist of the progeny of adults spawned at the hatchery in Fall of

16 The software cbayes (see Beacham et al., 2005) was used to estimate the stock composition and to perform individual assignments for each of the repeat baseline populations. cbayes employs Bayesian methods (Pella and Masuda, 2001) that improve accuracy of estimates of stock composition and individual assignment over those employed in many other software packages (but are computationally time intensive and thus impractical for simulation analyses) (Koljonen et al., 2005; Beacham et al., 2005). For the cbayes analysis ten independent chains were run for 10,000 iterations, the first 9000 iterations were discarded as burn-in, and the last 1000 iterations were retained for calculation of standard deviation and confidence intervals. Inspection of the stock estimates from the ten chains indicated 9000 iterations was sufficient for convergence. We evaluated assignment accuracy to both population of origin and to reporting group. A plot of the baseline population sample size versus accuracy of assignment to population and reporting group in 100% simulations was used to determine the extent to which baseline population sample size influenced our estimates. To determine how adding alleles to the baseline would improve assignment accuracy we conducted 100% simulations, as described above, by sequentially adding loci to the baseline starting with the locus hypothesized to be most informative for stock identification (i.e., the locus with the largest numbers of alleles) and subsequently adding loci with fewer alleles. Plots of mean assignment accuracy to population and reporting group versus number of alleles were used to evaluate how adding alleles to the baseline would improve assignment accuracy. Lastly, to evaluate whether assignment accuracy of individual loci for stock identification increased with the number of alleles at a locus (see Beacham et al., 2006 and 2008), we constructed single locus baselines and then used each single locus baseline to conduct 100% 16

17 simulation analyses as described above. Plots of the number of alleles at a locus versus assignment accuracy to population and reporting group were used to determine if accuracy of individual loci for stock identification increased with the number of alleles at a locus. RESULTS Genetic Population Structure The percent of missing genotypes per population ranged from 1.2% to 7.0% with a mean of 4.2% and the percent of missing genotypes per locus ranged from 3.6% to 12.9% with a mean of 8.2%. We suspect missing data resulted from a combination of technical errors during laboratory assays and degradation of tissue used for DNA extraction due to improper preservation methods and the collection of tissue from carcasses. Mean within-population pairwise relatedness was negative in all cases and ranged from to (Table 4). Relatedness coefficients can range from 1 to -1, negative relatedness coefficients indicate that two individuals share fewer alleles than expected on the basis of the corresponding Hardy-Weinberg allele frequencies (Konovalov and Heg, 2008) indicating individuals in our collections are unrelated and our sample collections are not biased due to family structure. No significant genetic differentiation was detected between baseline samples collected during different years from Shasta River and South Fork Trinity River and Salmon River fallrun; these yearly samples were therefore combined for all further analysis (Table 5). Salmon River spring-run collected from different years had significantly different allelic distributions in three of six comparisons; however, the neighboring-joining tree revealed that these Salmon River 17

18 spring-run collected from different years were more closely related to one another than they were to any other population justifying combining these samples for analysis (see below). Tests for conformance to Hardy-Weinberg proportions across all 17 loci within each of the 12 populations indicated several instances of nonconformance to expectations. The deviations from Hardy-Weinberg expectations did not appear to be characteristic of particular populations as there were no populations with a high proportion of loci that deviated from expectations (Table 6). Instead, deviations from Hardy-Weinberg expectations were symptomatic of a few loci, in particular, OTSG68 with 50% of the populations deviating Hardy- Weinberg proportions, and to a much lesser extent, OTS100, OTS101, OTSG311, ONE114 and OKI10 (Table 7). For all of these loci F is was positive indicating an excess of homozygotes (heterozygous deficit) in comparison to Hardy Weinberg expectations. All of these loci contained relatively high numbers of alleles (30-79), suggesting that large allele drop-out, due to competition between alleles during PCR amplification, may be partially responsible for the deviation from expectations (Table 3). For OTSG68 and OTSG311, both with F is > 0.10, null alleles also likely contributes to the homozygous excess. We performed some of the subsequent analyses with and without inclusion of OTSG68, the locus exhibiting the most dramatic departures herein and in other studies (Williamson and May, 2005), to evaluate how significant departures from Hardy-Weinberg expectations would influence our inferences. Mean Hardy-Weinberg expected heterozygosity within populations ranged from 0.72 to 0.79 with a mean of 0.75 (Table 6). Mean allelic richness within populations ranged from 9.4 to 13.1 with a mean of 11.1 and the mean number of private alleles ranged from 0.20 to 1.2 with a mean of Populations from Terwer Creek and Blue Creek had slightly elevated levels of private allelic richness in comparison to other populations in the basin. Pairwise comparison of 18

19 genetic differentiation (F st ) between all 12 populations ranged from to with mean of (Table 8). All pairwise comparisons of genetic differentiation were significant except for the comparison between the IGH and Shasta River populations. The neighbor-joining tree contained four genetically divergent groups that are geographically separated from one another: (i) Upper Basin containing fall-run populations from Iron Gate Hatchery, Shasta River and Scott River, (ii) Trinity containing spring- and fall-run from the Trinity River Hatchery and South Fork of the Trinity River, (iii) Salmon containing spring- and fall-run from the Salmon River, and (iv) Lower Basin containing fallrun from Horse Linto Creek, Terwer Creek and Blue Creek.. Three of the groups ( Upper Basin, Trinity and Lower Basin ) were well supported (bootstrap > 90%) whereas the remaining group, Salmon, was weakly supported (bootstrap = 63%). The only well-supported (bootstrap > 90%) relationship among the four geographic groups united Salmon with Trinity. Fall-run from Horse Linto Creek, a tributary to the Trinity River, was an exception to geographic clustering, as it was resolved as more closely related to lower basin populations than to Trinity River populations. Spring- and fall-run from the same river basin were more closely related to one another than they were to spring- and fall-run from other river basins. Within the Trinity River, the spring-run lineage from the South Fork of the Trinity River was well-supported (bootstrap > 90%) as the sister group to the TRH spring-run. The TRH and South Fork Trinity River fall-run populations are resolved as more closely related to spring-run populations from that basin than fall-run from outside of the basin. Similarly, within the Salmon River spring- and fall-run were more closely related to one another than they were to any other population in the basin. 19

20 The outcome of neighbor-joining analysis did not change when the genotypic data for OTSG68, the locus exhibiting the most significant departures from Hardy-Weinberg expectations, was removed the data set. Also, analyses wherein the four yearly samples from the Salmon River spring-run were treated as distinct populations, due to significant divergence between years, revealed that these samples were more closely related to one another than they were to any other population justifying combining these samples for analysis. Genetic Stock Identification Accuracy of stock composition estimates in the 100% simulations ranged from 21% to 97% with a mean of 72% for assignments to populations (Table 9). Mis-assignments typically involved populations that were geographically proximate to the target population or different run-timings, spring or fall, from the same river drainage (Table 10). For reporting groups, accuracy of stock composition estimates in the 100% simulations ranged from 48% to 99% with a mean of 89% (Table 9). Lowest accuracy, at both levels of resolution, was to South Fork Trinity fall-run, with 21% assigned to the correct population and 48% to the correct reporting group. After removal of OTSG68 from the baseline, the locus consistently deviating from Hardy-Weinberg expectations, mean accuracy of assignment to population in 100% simulations decreased from 72% to 71% while mean accuracy of assignment to reporting group remained the same at 89%. Because the removal of OTSG68 caused a slight reduction in assignment accuracy, and other studies have shown that loci that depart from Hardy-Weinberg proportions can actually improve stock identification accuracy (Beacham et al., 2006), all subsequent analyses were conducted with OTSG68 included. 20

21 In the realistic fishery simulations, which were designed to evaluate the ability to detect rare stocks, estimates noticeably increased when the rare stocks were present in the catch in comparison to when they were absent (Table 11). For example, when the simulated population contained no Salmon River spring-run the genetically estimated composition was nearly zero, however when the simulated population contained 5% Salmon River spring-run the estimated composition was 4%. Similar results were obtains for populations from Blue Creek and springrun from the South Fork Trinity River. Accuracy of stock composition estimates for the independent repeat baseline samples ranged from 81% to 95% with a mean of 88% for assignment to population and ranged from 87% to 96% with a mean of 92% for assignment to reporting group (Table 12). The percentage of individuals assigned to the correct population in the independent repeat baseline samples, estimated using the maximum posterior probability for each individual, was 100% for TRH spring-run, 96% for TRH fall-run and 100% IGH (Figure 3). However, the percentage of individuals with posterior probabilities exceeding 90% was 74% for TRH spring-run, 56% for TRH fall-run, and 97% for IGH. The relationship between baseline population sample size and accuracy of assignment to population and reporting group in 100% simulations indicated that when sample sizes were less than approximately 60, mean accuracy of assignment was highly variable, ranging from 21% to 97% whereas when sample sizes exceeded 60 mean accuracy of assignment were more stable ranging from 64% to 93% (Figure 4). Mean accuracy of assignment to population and reporting group in 100% simulations increased steadily when loci were sequentially added to the baseline, starting with the loci with 21

22 the highest number of alleles (Figure 5). Assignment accuracy increased for the first 274 alleles (five loci), after which, only small improvements in assignment accuracy were realized. Accuracy of individual loci for stock identification in single locus baseline 100% simulations was related to the number of alleles at locus (Figure 6). However, assignment accuracy did not increase once the number of alleles at a locus increased above 30. DISCUSSION Overall Genetic Structure The 12 populations of Chinook salmon from Klamath-Trinity basin exhibited substantial levels of genetic differentiation from one another at the 17 microsatellite loci assayed. All pairwise comparisons of genetic differentiation (F st ) between populations, except one, were significant and neighboring-joining trees indicated the presence of statistically well-supported groups. Further, the degree of genetic differentiation was large enough that genetic methods could be used to accurately estimate the proportion of individuals belong to each of the baseline populations from a sample of unknown composition and, in many cases, even assign individuals of unknown origin to their source populations. Thus, each population studied herein represented a genetically distinctive group. Two populations were, however, exceptions to this overall pattern: fall-run populations from the Shasta and South Fork Trinity rivers. The Shasta River population was not significantly genetically differentiated from IGH populations and a high proportion of Shasta River individuals assigned to IGH in 100% simulations. The South Fork Trinity River fall-run population exhibited surprisingly low levels of divergence when compared to Scott and Shasta rivers and TRH fall-run (Table 9). Also, South Fork Trinity River population assignment accuracy in 100% simulations was only 21%. False assignments were not dominated 22

23 by a single population but instead included all populations in the baseline, except those from Horse Linto Creek and South Fork Trinity spring-run. Influence of Out-Of-Basin Transplantation on Genetic Structure Despite the rather extensive history of out-of-basin transplantation within the Klamath- Trinity basin, Chinook salmon within the basin have retained a substantial degree of genetic structure. Nevertheless, we hypothesize that out-of-basin transplantation has influenced the genetic structure of fall-run Chinook salmon returning to the South Fork of the Trinity River. The South Fork Trinity River was subject of a very unusual transplantation event in comparison to the other histories reviewed, as no other sub-population was supplemented with such a large number of individuals, over , when the receiving stocks were of much smaller size (the South Fork Trinity fall-run is currently estimated to consist of only a few hundred individuals). While the fate of these transplanted fish is unknown, our genetic data suggests that the transplanted stocks have interbred with the small native stock and significantly altered the genetic makeup of this sub-population. In contrast, we expected out-of-basin transplantation to influence the genetic structure of IGH and TRH fish due to the large number and the pervasiveness of the transplantation efforts involving these populations (Table 1). However, no signature of these transplantation events was evident in our analysis. Populations from IGH and TRH were genetically divergent from one another and never confused in the 100% simulation analyses (Tables 8 and 10). Similarly, we expected that the large number of IGH and TRH fish released from the lower river estuary (Klamath Glen) had the potential to increase straying rates and genetically homogenize 23

24 populations in the basin; however, Chinook salmon in the basin have retained substantial levels of genetic differentiation and no clear signal of these transplantation events was detected. No attempt was made to quantify the influence of transplantation of Chinook salmon from outside Klamath-Trinity basin on genetic structure of fish within the basin. However, it is possible that these events have influenced the genetic structure of Chinook in the basin. For example, five million Sacramento River drainage Chinook salmon(battle Creek) were released in the Klamath-Trinity basin (Snyder, 1931), and because populations from other geographic regions are expected to be genetically divergent (see Waples et al., 2004), such efforts could artificially elevate the level of genetic differentiation among populations of Klamath-Trinity Chinook. Quantification of the influence of this history on genetic structure requires comparative analysis of Chinook salmon from outside of the Klamath-Trinity basin, and no such data were collected for this study. Genetic Population Structure: Geography The Klamath-Trinity Chinook salmon can be divided into four genetically differentiated and geographically separated groups: Upper Basin, Trinity, Salmon, and Lower Basin. All four groups were resolved in the neighbor-joining tree and in the 100% simulation analysis nearly 90% assignment accuracy to these groups was achieved (Figures 1 and 2, Table 9). Structuring according to geography indicates: (1) the largest levels of genetic divergence occurs between populations from these four geographic regions, and (2) populations within the regions are more closely related to each other than they are to populations from other regions. This latter pattern is particularly evident in the 100% simulations, where miss-assignment to population almost always involved geographically proximate populations or different run-times, spring or 24

25 fall, from the same river. Overall, the genetic structure of Klamath-Trinity Chinook salmon is in accord with the hypothesis that migration or straying decreases among populations as the geographic distance between populations increases, a pattern consistent with other studies of genetic structuring in this species (Waples et al., 2004; Beacham et al., 2006). Thus, Chinook salmon are effectively isolated by their innate homing behavior and site selection for spawning areas that are discontinuous. The population from Horse Linto Creek appeared to be an exception to geographic clustering of populations (Figures 1 and 2). Horse Linto Creek is a tributary to the Trinity River and was expected to group with Trinity River populations instead of populations form the lower basin. However, the placement with lower basin populations is not totally unexpected because Horse Linto Creek is nearly equidistant from TRH populations and lower basin populations (Figure 1). Chinook salmon were once propagated at a hatchery on Horse Linto Creek, but, according to hatchery records only local broodstock were used and no records of out-of-basin transplantation involving Horse Linto Creek were recovered in our search. Genetic Population Structure: Run-timing There are two alternative hypothesis for the evolution of spring- and fall-run run-timings in Chinook salmon: (i) spring-run evolved from fall-run in a single evolutionary event (or vice versa), or (ii) spring- and fall-run evolved repeatedly and independently through a process of parallel evolution (Waples et al., 2004). Under the first scenario spring- and fall-run populations would be expected to be more closely to related populations of the same run-timing than to populations of different run-timing. In the second model, spring- and fall-run populations from the within the same river drainage would be expected to be more closely related to another than there were to spring- and fall-run from other river drainages. In the Klamath-Trinity Chinook 25

26 salmon neighbor-joining tree, spring- and fall-run from within Trinity and Salmon rivers were more closely related to one another than they were to populations from the other drainage (Figure 2). Similar patterns are also evident in the 100% simulation analyses where misassignments typically involved different run-timings, spring or fall, from the same river drainage (Table 10). This pattern is consistent with repeated and independent evolution of spring-run populations in the Trinity and Salmon rivers, an evolutionary pattern observed throughout much of the geographic range of Chinook salmon (Waples et al., 2004). Potential for Adaptation to Wild Conditions For river systems undergoing habitat restoration, such as the Trinity River, an understanding of the patterns of gene flow between wild and hatchery fish is important because several recent studies have revealed that hatchery fish can have reduced performance in the wild and there is thereotical argument how and why we would expect a reduction in the performance of wild fish following interbreeding with hatchery fish (Lynch and O Hely, 2001; Ford, 2002; Berejikian and Ford 2004; Goodman, 2004; Araki et al., 2007a and b). Conditions under which fitness of wild fish will be reduced by hatchery fish typically occur when the number of naturalorigin fish used for hatchery broodstock is less than the number of hatchery-origin fish in the natural spawning grounds (Ford, 2002). These conditions appear to exist in the mainstem Klamath and Trinity Rivers in the 10 km of immediately below IGH and TRH. The proportion of hatchery-origin fish in the natural spawning areas ranges from approximately 0.20 to 0.60 for both IGH and TRH and the proportion of natural-origin broodstock used by both hatcheries ranges from approximately 0.10 to 0.20 (Sinnen 2006; Knechtle, 2007; Walsh and Hampton, 2007; Grove and Magneson, 2006; Wade Sinnen, California Department of Fish and Game, personal communication; Chesney, 2007). Due to the dominance of hatchery-origin fish in the 26

27 upper reaches of the Trinity and mainstem Klamath rivers we suspect that little to no genetic differentiation exists between hatchery stocks and those fish that spawn immediately below the hatcheries. The microsatellite data supports this hypothesis for the mainstem Klamath River, as no significant genetic divergence was detected between IGH and Bogus Creek, a tributary that enters the Klamath River immediately below IGH (F st = , p < 0.05). These data suggest that, in the river reaches immediately below the hatcheries, low potential exists for development of wild stocks that will be well-adapted to the natural riverine conditions. Downstream of the 10 river kilometers immediately below IGH and TRH, it is possible that the conditions are substantially different. Our sampling from the mainstem Klamath River included Shasta and Scott Rivers, which are approximately 20 and 75 kilometers downstream from IGH, respectively, allowing an evaluation of the extent of downstream influence of IGH fish. Pairwise estimates of genetic differentiation (F st ) between IGH and Shasta River was (p > 0.05) and between IGH and Scott River was (p < 0.05) indicating the degree of genetic differentiation from IGH increases with the distance from the IGH. Similarly, in the 100% simulations, the proportion assigned to IGH decreased downstream from 43% for Shasta River to 10% for Scott River (Table 10). This suggests that the potential for wild adaptation may exist in downstream reaches of mainstem Klamath, but, additional analyses that include collections stratified by their distance from IGH are required to fully evaluate the cline of hatchery influence. Genetic Stock Identification The 17 microsatellite 12 population Klamath-Trinity baseline exhibited good potential for research and management problems requiring estimates of stock composition of unknown samples. Mean accuracy of stock composition estimates for 100% simulations averaged 72% for 27

28 assignment to population. The overall mean accuracy of assignment to population was dragged down by low assignment accuracies for South Fork Trinity and Shasta River fall-run populations, which were 21% and 38%, respectively. Assignment accuracy to reporting group in 100% simulations was nearly 90%, a level generally deemed adequate for management and research applications. Further, simulations of real fishery catches indicated the baseline could detect natural stocks that are rare in the basin. For example, fall-run population from Blue Creek and spring-run from the South Fork Trinity River and the Salmon River could be detected when they composed only 5% of the catch in our simulations. Independent repeat baseline analysis, which provides a more rigorous evaluation of the accuracy of the baseline for stock identification, also indicated good potential for future applications of the baseline dataset for genetic stock identification. Our independent repeat baseline samples from IGH and TRH were collected in different years than the original baseline samples allowing an evaluation of how changes in allele frequencies in baseline populations through time and sampling variance may influence accuracy of stock composition estimates. Assignment accuracy of the repeat baseline samples was very similar to the 100% simulations ranging from 81% to 95% for the three populations, indicating the baseline is robust enough for practical applications in the Klamath-Trinity basin. Tests of accuracy for individual assignment using the repeat baseline samples also indicated the baseline dataset could be used to generate reasonable estimates of the population of origin for individuals derived from unknown sources. The proportion of individuals in the repeat baseline collections from TRH and IGH assigned to the correct population ranged from 96% to 100% (Figure 3). Interestingly, stock composition estimates calculated using the proportion of correct individual assignments was better than the straight stock composition estimates which 28

29 ranged from 81% to 95%. Similar findings were obtained by Koljonen et al. (2005) for Atlantic salmon (Salmo salar), suggesting individual assignments may be the best method for estimating stock composition of batches of fish of unknown origin in the future. The power to discriminate stocks using genetic methods can be reduced if the data set does not adequately describe the actual level of genetic differentiation between baseline populations. The ability of the baseline to reflect genetic variation within and between the baseline populations is related, in part, to the sample sizes of the populations composing the baseline. For the Klamath-Trinity Chinook baseline, population sample sizes ranged from 23 to 125, however, accuracy of assignment in 100% simulations was highly variable when sample sizes were less than 60, ranging from 21% to 97% whereas when sample sizes exceeded 60 mean accuracy of assignment were more stable ranging from 64% to 93% (Figure 4). Similarly, other genetic stock identification studies of Chinook salmon indicated high variability in assignment accuracy when baseline population sample sizes were less than 75, but when sample sizes were larger, assignment accuracy was generally above 90% (Beacham et al., 2006). For the Klamath- Trinity Chinook salmon baseline, two populations with samples sizes below 60, South Fork Trinity fall-run and Shasta River, had the lowest levels of assignment accuracy. For these populations, it is possible that increasing sample size may increase assignment accuracy however alternative explanations for the low assignment accuracy for these populations were presented previously. The ability of the baseline to adequately capture the actual level of genetic variation in the baseline populations is also related to the genetic markers used to characterize the stocks. The total number of alleles in the baseline data set is a key characteristic for predicting assignment accuracy for genetic stock identification (Kalinowski, 2002; Beacham et al., 2006). 29

30 Mean accuracy of assignment to population and reporting group in 100% simulations increased steadily when loci containing additional alleles were sequentially added to the baseline, starting with the loci with the highest number of alleles (Figure 5). Assignment accuracy increased to 63% for populations and 81% for reporting groups after 274 alleles (five loci) were added to the baseline. Addition of all remaining 236 alleles (12 loci) only increased assignment accuracy by about 10%. Thus, adding more loci/alleles to the existing 17 microsatellite locus baseline will likely result in only small improvements in assignment accuracy. Even if baseline population sample sizes and the chosen molecular markers adequately reflect the true level of genetic divergence among baseline populations, genetic stock identification will be limited by the degree of genetic divergence among baseline populations, which may be too low to allow for accurate assignments. Genetic divergence, and hence the potential for genetic stock identification, in Chinook salmon is generally related to geographic distance separating populations (Beacham et al., 2006, 2008). For example, assignment accuracy in 100% simulations for 19 Chinook salmon stocks from the Yukon River, which flows 3,200 km, was 93% in 100% simulations when only five microsallite loci were used (Beacham et al., 2008). In contrast, assignment accuracy to population for Klamath-Trinity basin, which flows 423 km, was 72% in 100% simulations when all 17 microsatellite loci were used. Thus, the assignment accuracy reported herein may be close to the maximum possible given the limited geographic scope and levels of genetic divergence among populations in the Klamath-Trinity basin. Genetic stock identification methods automatically assign individuals to one of the baseline population regardless of whether or not the fish actually originated from one of the stocks in the baseline. Thus, individuals originating from a Klamath-Trinity River population 30

31 not included in the baseline or strays from populations outside the basin will be erroneously assigned to one of the baseline populations. Because our baseline contains representatives of all of the major stocks in the basin, it is expected that errors resulting from automatic assignment of Klamath-Trinity populations not included in the baseline will be minimal. Further, because Klamath-Trinity Chinook salmon are structured by geography, it is expected that populations originating from within the basin but not represented in the baseline would be assigned to the correct geographic region within the basin. Strays, with origins outside the basin, are problematic because they will be erroneously assigned. The individual assignment algorithms employed in the software package STRUCTURE (Pritchard et al., 2000), have the ability to detect individuals that likely originated from populations not included in the baseline. Such methods hold promise for detecting strays from outside the basin. For genetic methods to be effective for stock identification, allele frequencies in baseline populations must remain constant through time. We tested for stability in allelic distributions in six Klamath-Trinity baseline populations for which we had temporally spaced collections. In all populations, with the exception of Salmon River spring-run, no significant difference in allelic distribution was detected (Table 5). The Salmon River spring-run stock of Chinook salmon has experienced a drastic decline in stock size in recent years (as low as 250 individuals), a bottleneck event that can alter the genetic structure of the population by reducing the genetic diversity through random loss of rare alleles caused by a drastic reduction in population size, which may explain the changes in allele frequencies through time in this population. An underlying assumption of the mathematic algorithms used for genetic stock identification is conformance of the genetic markers to Hardy-Weinberg equilibrium. The locus OTSG68, however, was not in Hardy-Weinberg equilibrium in 50% of the baseline populations. 31

32 Further, several other loci, OTS100, OTS101, OTSG311, ONE114 and OKI10, significantly departed from expectations but not to the degree of OTSG68. Removal of OTSG68 from the baseline decreased mean accuracy of assignment to population from 72% to 71% in 100% simulations, a finding consistent with Beacham et al. (2006), who found that inclusion of loci not conforming to Hardy-Weinberg equilibrium may increase the accuracy of stock identification (Beacham et al., 2006). Further, construction of the neighbor-joining tree with and without OTSG68 resolved identical topologies. Thus, our findings appear to be robust to deviations from Hardy-Weinberg proportions. Accuracy of individual loci for stock identification in single locus baseline 100% simulations was related to the number of alleles at a locus (Figure 6). However, assignment accuracy did not increase once the number of alleles at a locus increased above 30. Beacham et al. (2008) recovered a similar pattern in a study of genetic stock identification of Chinook salmon from the Yukon drainage. However, in stock identification studies encompassing the entire geographic range of Chinook salmon, assignment accuracy continued to increase as the number of alleles at a locus increased (Beacham et al., 2006). Thus, continued increase in stock identification accuracy with the number of alleles at a locus may depend on the level of genetic differentiation among baseline populations. Candidate Loci A large percentage of falsely assigned fish was due to reciprocal assignment of springand fall-run fish (10 27%, depending on the stock). To improve accuracy of assignments, two loci were included in the suite of 17 loci, OTSCLOCK1B and OTS515NWFSC, that have previously been reported as potentially linked to run-timing and thus provide increased resolution for distinguishing sympatric runs of Chinook salmon returning at different times of the 32

33 year (O Malley et al. 2007). These two candidate loci have resolved significant genetic differentiation of spring- and fall-run stocks that appear otherwise to be genetically homogenous based on data from neutral microsatellite markers (O Malley et al. 2007). In contrast, genetic differentiation between spring- and fall-run in our data set for the candidate loci are no more indicative of differences in run timing than neutral microsatellite markers. Pairwise F st values for spring- and fall-runs from the Trinity River, South Fork Trinity River and Salmon River were , and for OTSCLOCK1B, and 0.017, and for OTS515NWFSC. These values were not any greater than the average pairwise F st estimates for the 15 presumably neutral microsatellite loci from the same populations that ranged from to , with an average of ACKNOWLEDGEMENTS Chinook salmon tissue samples were collected by the California Department of Fish and Game, Salmon River Restoration Council, Hoopa Valley Tribe, and Yurok Tribe. This research was funded through a contract between the Humboldt State University Sponsored Programs Foundation and the Hoopa Valley Tribe. Thanks to George Kautsky, Billy Matilton, Petey Brucker, Amy Sprowles, Eric Loudenslager, Nat Pennington, Dave Hillemeier, and Andrew Antonetti. The methods used in this study were reviewed and approved by the Humboldt State University Institutional Animal Care and Use Committee, protocol number 05/06.F.108.E. 33

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39 Sinnen, W., M. Currier, M. Knechtle, and S. Borok Annual Report Trinity River Basin salmon and steelhead monitoring project season. California Department of Fish and Game. Sinnen, W., M. Currier, M. Knechtle, and S. Borok Annual Report Trinity River Basin salmon and steelhead monitoring project season. California Department of Fish and Game. Sinnen, W., M. Currier, M. Knechtle, and S. Borok Annual Report Trinity River Basin salmon and steelhead monitoring project season. California Department of Fish and Game. Smith, F.E Water development impact on fish resources and associated values of the Trinity River, California. Pages in J.F. Orsborg and C.H. Allman editors. Instream Flow Needs, Volume 2. American Fisheries Society, Bethesda, Maryland. Walsh, B., M. Hampton Shasta river fish counting facility, Chinook and coho salmon observations in 2006, Siskiyou County, CA. Weir, B.S. and C.C. Cockerham Estimating F-statistics for the analysis of population structure. Evolution 38: Williamson, K.S. and B. May Homogenization of fall-run Chinook salmon gene pools in the Central Valley of California. North American Journal of Fisheries Management 25: Zajanc, D. and D. Hankin A detailed review of the annual hatchery production cycle at Trinity Rivery Hatchery: with recommendations for changes in hatchery practices that would improve representativeness of marking and accuracy of estimation of numbers 39

40 released. Hoopa Valley Tribal Fisheries Department and Humboldt State University Foundation. 40

41 Table 1. Examples of out-of-basin transplantation events in the Klamath-Trinity basin. Records listed include those events involving a large number of individuals and thus expected to have the greatest potential impact on the genetic structure of Chinook salmon in the basin. Most of the 3614 records reviewed involved transplants of less than 5,000 individuals. While efforts were made to evaluate as many records as possible, it is impossible that some records were overlooked. TRH+IGH indicates mixed stocks of unknown proportions. Population abbreviations as in Table 2. Population Year Run- Timing Site of Propagation Site of Release Number TRH+IGH 71/77 fall TRH Trinity R 1,891,594 TRH+IGH 73 fall TRH South Fork Trinity R 930,900 IGH 75/83/85/86 fall IGH South Fork Salmon R 100,726 TRH 76 fall TRH Klamath R (Klamath 819,000 IGH 75-77, Glen, near estuary) fall IGH Klamath R (Klamath Glen, near estuary) 7,143,348 41

42 Table 2. Population, abbreviation, reporting group, total number of individuals assayed, collection year and life stage for the 12 populations of Chinook salmon from the Klamath-Trinity basin analyzed in this study. Population Run- Timing Abbreviation Total Number of Individuals Collection Year (number of individuals) Life Stage Reporting Group Iron Gate Hatchery Fall IGH (110) Juvenile Upper Basin Shasta River Fall SHST (18), 2003(35) Adult Upper Basin Scott River Fall SCOT (91) Adult Upper Basin Salmon River Spring SRS (22), 1997 (29), Adult Salmon 2006 (26), 2007 (8) Salmon River Fall SRF (87), 2006(44) Adult Salmon South Fork Trinity River Spring SFTS (8), 1993(34) Adult Trinity South Fork Trinity River Fall SFTF (46) Adult Trinity Trinity River Hatchery Spring TRHS (125) Adult Trinity Trinity River Hatchery Fall TRHF (119) Adult Trinity Horse Linto Creek Fall HLC (43) Adult Lower Basin Blue Creek Fall BC (69) Juvenile Lower Basin Terwer Creek Fall TC (23) Juvenile Lower Basin 42

43 Table 3. Locus, reference, primer sequences, GenBank accession numbers, number of alleles, and size range for the 17 loci assayed in Chinook salmon from the Klamath-Trinity basin. Dye labels and size ranges specific to the Beckman Coulter CEQ 8000 genetic analysis system. +A indicates GTTT added to the tail of the primer. Locus Reference Primer Sequence (5-3 ), Dye label OTS100 OTS311 Nelson and Beacham (1999) Williamson et al. (2002) D4 F: TGAACATGAGCTGTGTGAG R: ACGGACGTGCCAGTGAG D3 F: TGCGGTGCTCAAAGTGATCTCAGTCA R: TCCATCCCTCCCCCATCCATTGT OKI10 Smith et al. (1998) D3 F: GGAGTGCTGGACAGATTGG R: CAGCTTTTTACAAATCCTCCTG SSA197 OTS104 O Reilly et al. (1996) Nelson and Beacham (1999) D3 F: GGGTTGAGTAGGGAGGCTTG R: GTTTTGGCAGGGATTTGACATAAC(+A) D3 F: GCACTGTATCCACCAGTA R: GTAGGAGTTTCATTTGAATC(+A) ONE114 Olsen et al. (2000) D4 F: TCATTAATCTAGGCTTGTCAGC R: TGCAGGTAAGACAAGGTATCC OTSG68 Williamson et al. (2002) D2 F: TATGAACTGCAGCTTGTTATGTTAGT R: CATGTCGGCTGCTCAATGTA OTS101 Small et al. (1998) D2 F: ACGTCTGACTTCAATGATGTTT R:TATTAATTATCCTCCAACCCAG(+A) OTS107 OTSG432 Nelson and Beacham (1999) Williamson et al. (2002) D4 F: ACAGACCAGACCTCAACA R: ATAGAGACCTGAATCGGTA(+A) D3 F: TGAAAAGTAGGGGAAACACATACG R: TAAAGCCCATTGAATTGAATAGAA Genbank Accession Number Number of Alleles Size Range (nucleotides) AF AF AF U AF AF AF Unpublished AF AF

44 OTS515 NWFSC OKE2 Naish and Park (2002) Buchholz et al. (2001) D2 F: ACAGTGATGGAGCTTGATTC R:ACGATTTCTATTTGTCTCCG D4 F: AGATTGGTACCCTTATCTCTGTGTG R: ACTTCCTGTTTTGGTTCCCATA OTS2 Banks et al. (1999) D2 F: ACACCTCACACTTAGA R: CAGTGTGAAGGATATTAAA OKE4 Buchholz et al. (2001) D2 F: AGGCCCAAAGTCTGTAGTGAAGG R: GATGAATCGAGAGAATAGGGACTGAAT(+A) OKI11 Smith et al. (1998) D3 F: TCTGAGACAGGCAAATGCAC R: GTTTTAAACCTCACCATTGAGT(+A) µsat73 Estoup et al. (1993) D2 F: CCTGGAGATCCTCC R:CTATTCTGCTTGTAACTAGACCTA OTSCLOCK1B O Malley et al. (2007) D4 F: CCTGTGTTTGTCTCCAACAGCA R: CTGTCACTGCGAAATTACAGTCCT AY AF AF AF AF AB DQ

45 Table 4. Mean within-population pairwise relatedness coefficient (r qg ) of sample populations included in a 17-loci baseline for genetic stock identification in the Klamath-Trinity basin (*indicates baseline populations consisting entirely of juvenile collections.) Population r qg St. dev IGH* SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC* TC*

46 Table 5. Fisher-exact tests for genetic differentiation between samples collected in different years from South Fork Trinity spring-run (SFTS), Salmon River spring-run (SRS), Salmon River fall-run (SRF) and Shasta River fall-run (SHST). Bonferroni corrected p = Population (sample years) χ 2 df P-value SFTS (1992 and 1993) SHST (2002 and 2003) SRS (2006 and 2007) SRS (2006 and 1997) <0.001 SRS (2006 and 1994) SRS (2007 and 1997) SRS (2007 and 1994) SRS (1997 and 1994) <0.001 SRF (2002 and 2006)

47 Table 6. Percentage of missing genotypes, proportion of loci exhibiting significant deviation from Hardy-Weinberg expectations, mean F is, mean expected heterozygosity, mean allelic richness and mean number of private alleles for the 17 loci assayed in 12 populations of Chinook salmon from the Klamath-Trinity basin. Mean allelic richness and mean number of private alleles standardized to 38 genes. Percentage of Missing Genotypes Proportion of Loci Exhibiting Significant Deviation from Hardy-Weinberg Expectations Mean Private Allelic Richness Mean Mean Expected Mean Allelic Population F IS Heterozygosity Richness IGH SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC TC

48 Table 7. Percentage of missing genotypes, Hardy-Weinberg expected heterozygosity, proportion of populations exhibiting significant deviation Hardy-Weinberg expectations, and mean F is for the 17 loci assayed in 12 populations of Chinook salmon in the Klamath- Trinity basin. Percentage of Missing Genotypes Proportion of Populations Exhibiting Significant Deviation from Hardy-Weinberg Expectations Expected Locus Heterozygosity Mean F IS OTS OTS OKI SSA OTS ONE OTSG OTS OTS OTSG OTS515NWFSC OKE OTS OKE OKI µsat OTSCLOCK1B

49 Table 8. Mean pairwise F st between the 12 populations of Chinook salmon from the Klamath-Trinity Basin. Asterisk indicates significance at p = IGH SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC SHST SCOT * * SRS * * * SRF * * * * SFTS * * * * * SFTF * * * * * * TRHS * * * * * * * TRHF * * * * * * * * HLC * * * * * * * * * BC * * * * * * * * * * TC * * * * * * * * * * * 49

50 Table 9. Assignment accuracy in 100% simulations to population and reporting group. Simulated populations were composed individuals that all originated from the same population thus, the expectation is that 100% of the individuals should assign to the same population. Each population was simulated 200 times for calculation of standard deviation and 95% confidence intervals. Average Correct Assignment to Population Average Correct Assignment to Reporting Group Standard 95% Confidence Standard 95% Confidence Population Deviation Interval Deviation Interval IGH (0.7204, ) (0.9385, ) SHST (0.3325, ) (0.9000, ) SCOT (0.6876, ) (0.8312, ) SRS (0.5900, ) (0.9178, ) SRF (0.6868, ) (0.8327, ) SFTS (0.7592, ) (0.9363, ) SFTF (0.1676, ) (0.4222, ) TRHS (0.8121, ) (0.9312, ) TRHF (0.7746, ) (0.8913, ) HLC (0.7734, ) (0.8490, ) BC (0.8991, ) (0.9073, ) TC (0.9563, ) (0.9833, ) Mean

51 Table 10. Percent assignment in single population simulations to each of the 12 populations in the Klamath-Trinity Chinook salmon baseline. Simulated populations were composed individuals that all originated from the same population thus the expectation is that 100% of the individuals should assign to the same population. Stock estimates rounded to whole numbers. Simulated Population Assigned Population IGH SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC TC IGH SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC TC

52 Table 11. Assignment accuracy in three simulated catches to each of the 12 populations in the Klamath-Trinity Chinook salmon baseline. Population Simulated Mixture 1 Assignment to population Simulated Mixture 2 Assignment to population Simulated Mixture 3 Assignment to population IGH SHST SCOT SRS SRF SFTS SFTF TRHS TRHF HLC BC TC Group Upper Basin Salmon Trinity Lower Basin

53 Table 12. Assignment accuracy for independent repeat baseline sampling to population and reporting group. Population Correct Assignment to Population Standard Deviation 95% Confidence Interval Correct Assignment to Reporting Group Standard Deviation 95% Confidence Interval IGH (0.8478, ) (0.8550, ) TRHS (0.6770, ) (0.7460, ) TRHF (0.4690, ) (0.5379, ) Mean

54 Figure 1. Collection locations for Chinook salmon in the Klamath-Trinity Basin. Iron Gate Dam and Lewiston Dam are impassable and represent the extent of upstream migration for Chinook salmon in the Klamath and Trinity rivers, respectively. Iron Gate Hatchery is adjacent to Iron Gate Dam and Trinity River Hatchery is adjacent to Lewiston Dam. Approximate location of study populations from rivers too small to appear on map: Terwer Creek (TC), Blue Creek (BC), and Horse Linto Creek (HLC). Figure 2. (A) Unrooted neighbor-joining tree generated using PHYLIP. Branch lengths are equivalent to Cavalli-Sforza genetic distance. (B) Consensus of bootstrap neighborjoining trees constructed using Cavalli-Sforza genetic distance and rooted along the mid-point of the longest branch. The percentage of times particular branches occurred in the bootstrap replicates is above the branches. Figure 3. Posterior probability of assignment to source population for each individual sampled for independent repeat baseline analysis. Individuals are ranked from highest to lowest posterior probability. Figure 4. Relationship between baseline population sample size and mean assignment accuracy to population (filled circles) and reporting group (asterisk) in 100% simulations. Figure 5. Relationship between number of loci in the baseline and mean accuracy to population (filled circles) and reporting group (asterisk) in 100% simulations. Loci were sequentially added to the baseline starting with locus with the highest number of alleles first (Table 3). 54

55 Figure 6. Relationship between locus and mean assignment accuracy to population (filled circles) and reporting group (asterisk) in 100% simulations using single locus baselines. Loci are ranked from lowest (2) to highest number of alleles (79) as in Table 3. 55

56 Pacific Ocean! Oregon California Shasta R!!!!!! Iron Gate Dam! Klamath R TC BC Salmon R HLC Scott R! OR S Fk Trinity R Trinity R!! Lewiston Dam CA 100 Kilometers

57 A SFTS SFTF TRH TRHS TC SRS SRF IGH SHST SCOT BC HLC B IGH SHST Upper Basin SCOT SRS SRF TRHF Salmon SFTS TRHS SFTF Trinity HLC TC Lower Basin BC

58 Trinity River Hatchery spring run posterior probability Trinity River Hatchery fall run posterior probability Bogus Creek posterior probability Rank

59 Accuracy (%) Population sample size

60 Accuracy(%) Number of loci

61 Accuracy(%) OtsClock1b µsat73 Oke4 Oki11 Ots2 Oke2 Ots515NWFSC OtsG432 OTS107 Locus OtsG68 Ots101 One114 Ots104 Ssa197 Oki10 Ots311 Ots100

62 APPENDIX I MICROSATELLITE PROTOCOLS AND SCORING SPECIES: CHINOOK SET OF LOCI: HSU ELECTROPHORESIS PLATFORM: BECKMAN COULTER CEQ 8000 APPENDIX I, TABLE I: PRIMER SEQUENCES TABLE FORMAT FOR PRIMER ORDERS 1. LOCUS 2. TAG/FORWARD PRIMER SEQUENCE 3. REVERSE PRIMER SEQUENCE SERIES 1 (HSU) OTSG68 D2 TAT GAA CTG CAG CTT GTT ATG TTA GT CAT GTC GGC TGC TCA ATG TA OTS311 D3 TGC GGT GCT CAA AGT GAT CTC AGT CA TCC ATC CCT CCC CCA TCC ATT GT OTSG432 D3 TGA AAA GTA GGG GAA ACA CAT ACG TAA AGC CCA TTG AAT TGA ATA GAA ONE114 D4 TCA TTA ATC TAG GCT TGT CAG C TGC AGG TAA GAC AAG GTA TCC SERIES 2 (DFO) 62

63 OKE4 Reverse with +A D2 AGG CCC AAA GTC TGT AGT GAA GG GTT TGA TGA ATC GAG AGA ATA GGG ACT GAA T OTS2 (TM63)** D2 GCC TTT TAA ACA CCT CAC ACT TAG GTT TTT ATC TGC CCT CCG TCA AG SSA197 Reverse with +A D3 GGG TTG AGT AGG GAG GCT TG GTT TTG GCA GGG ATT TGA CAT AAC OTS100 (I1) Naming Convention* Reverse with +A D4 TGA ACA TGA GCT GTG TGA G GTT TAC GGA CGT GCC AGT GAG SERIES 3 (DFO) OTS101 (3G) Reverse with +A D2 ACG TCT GAC TTC AAT GAT GTT T GTT TTA TTA ATT ATC CTC CAA CCC AG OTS104 (A1) Reverse with +A D3 GCA CTG TAT CCA CCA GTA GTT TGT AGG AGT TTC ATT TGA ATC OTS107 (1B) Reverse with +A D4 ACA GAC CAG ACC TCA ACA GTT TAT AGA GAC CTG AAT CGG TA SERIES 4 (DFO and USFW) 63

64 OKE2 D4 AGG GCC AGA GAA AAG TCT CAC TAT GTC AGT CCT GCC CTC TGT GTC CTA OKI10 D3 GGA GTG CTG GAC AGA TTG G CAG CTT TTT ACA AAT CCT CCT G OKI11 Reverse with +A D3 TCT GAG ACA GGC AAA TGC AC GTT TTA AAC CTC ACC ATT GAG T μsat73 D2 CCT GGA GAT CCT CCA GCA GGA CTA TTC TGC TTG TAA CTA GAC CTA OTS515NWFSC D2 ACA GTG ATG GAG CTT GAT TC ACG ATT TCT ATT TGT CTC CG OTSClock1b D4 CCT GTG TTT GTC TCC AAC AGC A CTG TCA CTG CGA AAT TAC AGT CCT *In the sequence submitted to GenBank the primer designated by our lab as a Forward primer is the Reverse primer. The primer designated by our lab as a Reverse primer is the Forward primer in the GenBank sequence. **Redesign of Banks et al. original primers by DFO to allow poolplexing. 64

65 APPENDIX I, TABLE 2: LOCI OVERVIEW Locus Citation GenBank AC# DFO Modifications Repeat Range (bp)** OTSG68 Williamson, K.S., J.F. Cordes, and B. May Characterization of microsatellite loci in Chinook salmon (Oncorhynchus tshawytscha) and cross-species amplification in other salmonids. Mol. Biol. Notes 2: AF None (GATA)30(TAGA) OTS311 Williamson, K.S., J.F. Cordes, and B. May Characterization of microsatellite loci in Chinook salmon (Oncorhynchus tshawytscha) and cross-species amplification in other salmonids. Mol. Biol. Notes 2: AF None (GATA)30-GACA- (GATA)2- (GAGTGATA)7- GATA OTSG432 Williamson, K.S., J.F. Cordes, and B. May Characterization of microsatellite loci in Chinook salmon (Oncorhynchus tshawytscha) and cross-species amplification in other salmonids. Mol. Biol. Notes 2: AF None (GATA)3-GGAT- (GATA) ONE114 Olsen, J.B., S.L. Wilson, E.J. Kretschmer, K.C. Jones, and J.E. Seeb Characterization of 14 tetranucleotide microsatellite loci derived from sockeye AF None (TAGA)12N4 (TAGA)

66 salmon. Mol. Ecol. 9: Oke4 Buchholz WG et al Isolation and characterization of chum salmon microsatellite loci and use across species. Animal Genetics 32: AF R primer -A+ (ca)4 a (ca) Ots2 (TM63) Banks MA et al Isolation and inheritance of novel microsatellites in chinook salmon (Oncorhynchus tshawytscha). Journal of Heredity 90: AF R primer-a+ 2 (ca) Ssa197 O Reilly PT et al Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Canadian Journal of Fisheries and Aquatic Sciences 53: U43694 R primer-a+ 2,4 (gtga,tg,gt) Ots100 (I1) Nelson RJ et al Isolation and cross species amplification of microsatellite loci useful for study of Pacific salmon. Animal Genetics 30: AF Naming convention* R primer-a+ 2,4 (gaca, ga) Ots101 Small MP et al Discriminating coho salmon Unpublish ed R primer-a

67 (3g) (Oncorhynchus kisutch) populations within the Fraser River, British Columbia, using microsatellite DNA markers. Molecular Ecology 7: microsatel lite sequence (imperfect tet) Ots104 (A1) Nelson RJ et al Isolation and cross species amplification of microsatellite loci useful for study of Pacific salmon. Animal Genetics 30: AF R primer-a+ 2,4 (gtct,tg,tc) Ots107 (1B) Nelson RJ et al Isolation and cross species amplification of microsatellite loci useful for study of Pacific salmon. Animal Genetics 30: AF R primer-a+ 2,4 (gata,gt,gtct) Oke2 Buchholz WG et al Isolation and characterization of chum salmon microsatellite loci and use across species. Animal Genetics 32: Oki10 Smith CT et al Isolation and characterization of coho salmon (Oncorhynchus kisutch) microsatellites and their use in other salmonids. Molecular Ecology 7: Oki11 Smith CT et al Isolation and characterization of coho salmon (Oncorhynchus kisutch) microsatellites and their use in other salmonids. AF None 2 (tg) AF None 4 (ctgt) AF none 2 (gt)

68 Molecular Ecology 7: μsat73 Estoup A et al (CT)n AND (GT)n Microsatellites: A new class if genetic markers for Salmo trutta L. (Brown trout). Heredity 71: AB none Ots515 NWFSC Naish KA and LK Park Linkage relationships for 35 new microsatellite loci in Chinook salmon Oncorhynchus tshawytscha. Animal Genetics 33: AY none 2 (ca) OtsClock 1b O Malley KG et al Candidate loci revealing genetic differentiation between temporally divergent migratory runs of Chinook salmon (Oncorhynchus tshawytscha). Molecular Ecology 16: DQ none *In the sequence submitted to GenBank the primer designated by our lab as a Forward primer is the Reverse primer. The primer designated by our lab as a Reverse primer is the Forward primer in the GenBank sequence.**range in bp for all loci are from HSU analyses of Klamath-Trinity Basin Chinook salmon. 68

69 APPENDIX I, TABLE 3: PCR RECIPES (VOLUMES IN µl) FOR 1 REACTION. Promega PCR Master Mix (Catalog #M7505) contains 50 units/ml Taq DNA polymerase, supplied in a proprietary reaction buffer, 400µM each datp, dgtp, dctp, dttp and 3mM MgCl 2. Locus Name PCR Name Water Promega PCR Master Mix Equivalent F & R Primer 10pmol/µL DNA OTSG OTS OTSG ONE114 One Oke4 Oke Ots2 Ots Ssa197 Ssa Ots100 Ots Ots101 Ots Ots104 Ots Ots107 0ts Oke2 Oke Oki10 Oki Oki11 Oki µsat73 µsat Ots515 NWFSC Ots OtsClock1b Otsclock1b

70 APPENDIX I, TABLE 4: POOLPLEXING VOLUMES (A 2μL VOLUME OF COCKTAIL IS USED PER INJECTION). INJECTION PCR VOLUME (μl) SERIES 1 (HSU) 1 Otsg Ots Ots One SERIES 2 (DFO) 2 OKE OTS SSA OTS SERIES 3 (DFO) 3 Ots Ots Ots SERIES 4 (DFO and USFWS) 4 Oke Oki Oki μsat Ots OtsClock1b

71 APPENDIX II: EXAMPLE PHENOTYPES FOR EACH LOCUS OKE4: -dinucleotide repeat -minimal to moderate stutter -Important--An insertion/deletion also is present resulting in alleles 1 bp apart. These are most common in Yukon populations and must be scored. Example of het, het, homo. 71

72 Example of 1 bp het. 72

73 OTS2 (TM63): -dinucleotide -minimal stutter, stutter increases with larger alleles -allele range (eg. 127 bp) borders very closely on ots9 range so adjust genotyper category accordingly. Use peak intensity and morphology to make the distinction. Example of homo, het, het. 73

74 SSA197: -dinucleotide/tetranucleotide repeat -minimal stutter Examples of 2 bp het, het, homo, 4 bp het. 74

75 OTS100: -dincucleotide/tetranucleotide repeat -moderate stutter -variable intensity of 2-4 bp stutter before and after scored peak(s) Example of homo, het, 4 bp het. 75

76 OTS104: -dinucleotide/tetranucleotide repeat -moderate sutter Examples of homo, 4 bp het, het. 76

77 Example of 2 bp het. 77

78 OTS107: -dinucleotide/tetranucleotide repeat -moderate stutter -weaker samples have higher chatter/noise in background Example of het, het, homo. 78