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I: fight... :1 2.! .r In I. .. ~ r itlndkflhhrH 4.4.4274 THESlS l LIBRARIES l lllll \lllllll ills ll llll _—_'———-- E This is to certify that the thesis entitled A GENETIC COMPARISON OF LAKE MICHIGAN CHINOOK SALMON (Oncorhynchus tshawytscha) TO THEIR SOURCE POPULATION presented by Julie Anne Weeder has been accepted towards fulfillment of the requirements for Master of Science degree in Fisheries and Wildlife 3%; 3 Major 10f ssor Date w 501‘l99? 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution A GENETIC COMPARISON OF LAKE MICHIGAN CHINOOK SALMON (ONCORHYNCHUS TSHAWYTSCHA) TO THEIR SOURCE POPULATION By Julie Anne Weeder A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Fisheries and Wildlife 1997 ABSTRACT A GENETIC COMPARISON OF LAKE MICHIGAN CHINOOK SALMON (ONCORHYNCHUS TSHAWYTSCHA) TO THEIR SOURCE POPULATION By Julie Anne Weeder To determine whether genetic drift has impacted the genetic diversity of Lake Michigan Chinook salmon since their transfer from Washington’s Green River in the late 1960’s, I surveyed the allozyme variation of Lake Michigan Chinook at 18 loci that were variable in a 1980’s survey of Green River Chinook salmon. The genetic diversity of Lake Michigan Chinook salmon was consistently less than that of their Green River conspecifics (2.17 vs. 2.56 alleles per locus, 17% of variable loci monomorphic in Lake Michigan fish). Lake Michigan Chinook salmon were more closely related to Green River Chinook than to those of a tributary of Washington’s Toutle River, a purported source population. The average yearly variance effective population size (Ne) of Lake Michigan Chinook from 1967 to 1995 was 378 individuals. This is less than 1% of the estimated average census size, indicating that genetic drift has impacted the Lake Michigan population. ACKNOWLEDGEMENTS I would like to acknowledge my major professor, Dr. John Epifanio, for his inspiration and guidance throughout the development of my Master’s research, and my committee members, Dr. Thomas Coon and Dr. Donald Hall, for their fresh perspectives and valuable advice. My faculty advisors’ enthusiasm toward research was a constant motivation. The Michigan Department of Natural Resources (MDNR) and Michigan State University provided funding for this research project, and MDNR staff, notably Jan Sapak and Kelley Smith, was of valuable assistance in implementation of many aspects of the project. Anne Marshall of the Washington Department of Fish and Wildlife has provided consistent encouragement and technical support throughout my research, and made it possible for me to obtain the most accurate data by inviting me out to her lab. The resort owners, charter boat captains, private fishermen and agency personnel who helped me obtain tissue samples were important to the success of this project, and I greatly appreciate their help. Eric Stockinger, Dan Kennedy and Dan Gaebel provided critical assistance in the development of this project and learned with me - thanks for all your help. To my fellow graduate students who became close friends, I haven’t had such fulfilling personal and professional relationships before, and I hope that you are an example of what I can look forward to in my career. iii To my husband, Dave, I can’t say how much you have enriched this important personal and professional period in my life. I can’t imagine doing this without you. iv TABLE OF CONTENTS List of Tables ............................................................................................................... viii List of Figures. ............................................................................................................. ix Chapter 1: The Genetic Diversity of Lake Michigan and Green River Chinook Salmon ........................................................................................................................... 1 Introduction ................................................................................................................... 1 Methods ......................................................................................................................... 6 Tissue Collection ................................................................................................ 6 Oxytetracycline (OTC) mark detection .............................................................. 8 Protein Electrophoresis ...................................................................................... 8 Data Analysis ................................................................................................... 11 Deviation fi'om panmictic conditions ...................................................... 1 1 Genetic comparison of the Lake Michigan and Green River populations ........................................................................................... 12 Results ......................................................................................................................... 13 Deviation from panmictic conditions in watersheds ........................................... 13 Genetic comparison of Lake Michigan and Green River populations ................. 14 Discussion ................................................................................................................... 23 Panmixia and Hardy-Weinberg equilibrium ....................................................... 23 Impacts of genetic drift ................................................................................... 25 Causes of drift in Michigan’s chinook salmon program .................................... 25 Breeding practices ............................................................................... 25 Population bottlenecks and founder efi‘ects .......................................... 27 Evidence from other salmonid populations ...................................................... 28 Management recommendations ....................................................................... 30 Conclusions of this study of a recently founded, closed Chinook salmon population ...................................................................................................... 33 Chapter Two: An Application of the “Variance” Effective Population Size Method... 34 Introduction ............................................................................................................... 34 Methods ..................................................................................................................... 37 Results ....................................................................................................................... 40 Discussion .................................................................................................................. 43 Ne/ NC of Lake Michigan Chinook salmon ......................................................... 44 Comparison to other Ne estimates ................................................................... 44 Salmonids ............................................................................................ 44 Plants and shellfish ............................................................................... 45 Sources of potential bias ................................................................................. 48 Conclusions ................................................................................................................ 48 Management recommendations ................................................................................... 49 Appendix A: Genotypic frequencies for variable loci, in individual watersheds and the Lake Michigan population ................................................................... 50 Appendix B: Clustering levels of difi‘erent populations plotted in Figures 2 and 3, respectively, based on Nei’s (1978) unbiased genetic distance ............... 62 Appendix C: Allelic frequencies and deviation from Hardy-Weinberg equilibrium for individual watersheds and Lake Michigan pooled population .................... 63 List of References, Chapter 1 ..................................................................................... 66 List of References, Chapter 2 ..................................................................................... 7O vii LIST OF TABLES Table l: Rivers that were sampled in 1995-1996 for this study. Various evidence for feral recruitment is provided for each river where appropriate ......................... 8 Table 2: Enzyme systems, loci, tissues, and electrophoretic conditions for chinook salmon protein electrophoresis ...................................................................... 10 Table 3: Variable loci allelic fi'equencies and deviation from Hardy-Weinberg equilibrium for individual watersheds, the Lake Michigan pooled population, and the Green River population .................................................. 18 Table 4: Inbreeding coefiicients and contingency table analysis of all loci across watersheds. F-statistics are described in Wright (1965, 1978), where F13 is the fixation index of individuals as compared to their subpopulation, FIT is the fixation index of individuals relative to the total population and F31 measures differentiation among subpopulations as compared to the total population ...................................... 22 Table 5: Ne as estimated with the variance method and as a proportion of Nc when t and sample composition are varied, where 80 = 353, t = the number of generations between samples, and juveniles were included or excluded .................................................................................................. 42 viii LIST OF FIGURES Figure 1: Lake Michigan sampling locations ................................................................ 7 Figure 2: Relatedness of chinook salmon from six Lake Michigan tributaries, based on the Unweighted Pair Group Method and Nei’s (1978) unbiased genetic distance ................................................................................ 16 Figure 3: Relatedness of chinook salmon from Lake Michigan and two Washington drainages. “Green River” refers to the source stock, a Puget Sound strain, and “Cowlitz River” is an outgroup from the Columbia River basin. Calculations are based on the Unweighted Pair Group Method and Nei’s (1978) unbiased genetic distance .............................................................................................. 17 Figure 4: Ne/Nc and 95% confidence intervals for various salmonid populations, where Ne is computed with two genetic methods ........................ 47 ix CHAPTER ONE introduction Chinook salmon (thmchus W113) were first successfirlly introduced into the Laurentian Great Lakes in 1967 to improve the sport fishery and to control populations of the invasive alewife (W) (Michigan Department of Natural Resources [MDNR] 1974). Approximately one million fertilized eggs were shipped to Michigan from Washington’s indigenous Green River chinook population for each of three years in the late 1960’s (1966-68). In 1969, the first mature cohort was successfirlly spawned in captivity and Michigan has since been self-sufiicient in chinook salmon egg production. Descendants of the three groups of transferred embryos were ultimately stocked throughout the Great Lakes by the MDNR and other state and federal agencies, and Great Lakes chinook salmon numbers have since been augmented by an artificial propagation program. The Great Lakes chinook salmon program has by many accounts been successful: by 1986, the standing stock in Lake Michigan alone approached 40 million pounds, and the lakewide harvest by recreational anglers approached 1 million pounds (over 600,000 fish; Francis 1996). In 1988, however, large numbers of dead chinook salmon washed up on the eastern shores of Lake Michigan, and the number of adults migrating up tributaries to spawn declined precipitously (Johnson and Hnath 1991). These losses were attributed to l 2 bacterial kidney disease (BKD). Whether BKD was the only factor in these mortalities is still debated, but no other direct causes of death have been identified. The population has not firlly recovered fiom this crash; from 1989 to 1994, returns to rivers and sport harvest have remained at less than 50% of pre-l988 levels (MDNR, unpublished data). The dramatic fluctuations in Great Lakes chinook salmon populations in recent years has prompted a closer examination of the dynamics of this population. Genetic drifi, or random change in allelic frequencies, has caused a loss of genetic diversity in other managed salmonid populations (e. g. Gharrett and Thomason 1987). To address the possibility of such a genetic loss in Lake Michigan chinook salmon, an understanding of the composition and structure of genetic variability in the Great Lake population(s) is necessary. Genetic diversity is a usefirl index of the health and stability of populations; low levels of diversity within a population have been linked to reduced disease resistance (e. g. in rainbow trout, Ferguson and Drahushchak 1990), slower development, reduced size-at-age, higher mortality, and reduced fertility (e. g. Smith and Chesser 1981, Mefl‘e and Carroll 1994). Furthermore, genetic variation within populations is a basic requirement for adaptation and the long-term persistence of the population in a changing environment (Soulé 1980, 1987). Thus, management for the fixture success of Great Lakes chinook salmon requires an understanding of the amount and structure of genetic variation in the population, in addition to an examination of the processes responsible for its present state. I used allozymes as markers in order to determine whether Lake Michigan chinook salmon show evidence of population subdivision and genetic drift. I surveyed the 3 allozyme variation of over two hundred chinook salmon from Lake Michigan, and I addressed two main issues. First, I tested for population subdivision of Lake Michigan chinook salmon using a null hypothesis of panmixia. The alternative hypothesis is that Lake Michigan chinook may have created river-specific or regional subpopulations due to their tendency to spawn in “na ” streams, which could reinforce lineage difl‘erences over time. Evidence for such reproduction in the Great Lakes has existed almost since chinook were introduced (Carl 1982, Keller et al. 1990, Hesse 1994). Feral spawning may account for 20 to 50% of chinook salmon production in Lake Michigan alone (Carl 1982, Hesse 1994) Despite substantial feral recruitment, hatchery-reared fish continue to play a prominent role in the persistence and management of Great Lakes chinook salmon. Stocking of such fingerlings is still extensive: 70% of the age-0 chinook salmon in Lake Michigan were probably stocked (Hesse 1994 and Carl 1982). Although highly variable, on average 4 million fingerlings (i454 SE.) have been stocked into Lake Michigan fi’om 1976 to 1987 [range 687,000 (1968) to 7.7 million (1984); MDNR unpublished data]. This estimate includes fish stocked by other states. Some of these fingerlings were stocked by other states bordering Lake Michigan, but because all chinook stocked into the Great Lakes were obtained from Michigan’s hatchery system, all of these fish are descendants of the same gene pool of 3 million embryos from the 1960’s. The MDNR stocks the majority of these fingerlings (although other Illinois, Wisconsin and Indiana have also participated in stocking), and although Michigan-stocked broodstock have been taken intermittently fiom other Michigan rivers, the vast majority of fingerlings stocked by the MDNR originate fi'om less than 2,500 brood fish captured each year from the Little 4 Manistee River (MDNR unpublished data). The stocking of millions of fish which represent the gene pool of only this one tributary could serve to maintain panmictic conditions lakewide, even if regional genetic difi‘erences would normally result from feral spawning in “na ” streams. I next determined if there has been genetic change in the chinook salmon population since its Great Lakes introduction by testing the null hypothesis that the allelic fiequencies of Lake Michigan and the source population, that of Washington’s Green River, are not significantly different. No genetic data was collected from this chinook salmon population when it was sampled in the late 1960’s. Instead, I used allelic data from the Green River population which was collected in the 1980’s as a surrogate for the allelic frequencies of the Green River population from 1966-68. There is a substantial indigenous run on this river every year, and the population is also supplemented with hatchery-reared juveniles immediately derived from this run. The Green River frequencies were based on a total of 400 fish (average 353 fish per locus) which were randomly collected, 100 each year, in 1981, 1987, 1988 and 1990 (Anne Marshall, personal communication [per. comm.]). The three later samples were of returning adult spawners, while the sample fi'om 1981 was a sample of hatchery-reared juveniles fi'om the 1980 brood year (Anne Marshall, per. comm.) There are several properties of this population which make it reasonable to assume that these allelic frequencies were representative of late 1960's Green River fish. First, the risk of stochastic changes in allelic frequencies due to low population size is low, because the Green River breeding population is large: it has ranged from 5,000 to 10,050 and averaged 7,600 from 1987 through 1991 (Washington Department of Fish and Wildlife (WDFW) 1993). Second, much of the breeding in the 5 Green River population occurs in the wild and therefore is not subject to potentially detrimental husbandry practices which could have caused genetic change during the past 20 years. Third, there were no significant differences between the allelic fiequencies of the four sampling years. Thus, this population was temporally stable during the 1980’s, which increases the likelihood that this population was also temporally stable in previous years. Finally, the Green River population has not been subjected to any natural disasters or dramatic human intervention since Michigan’s fish were transferred which could cause population-level genetic changes due to such factors as bottlenecks. If there were significant differences between the Lake Michigan and Green River populations, this would indicate a change in allelic frequencies and thus heterozygosity since 1966. Two factors in the Michigan chinook salmon management program could have caused such changes. Early genetic bottlenecks could have resulted in founder effects, which would reduce the genetic variability of the first and subsequent Michigan generations. In addition, chinook salmon in Lake Michigan (and other Great Lakes) have always been bred and raised using methods which can erode a population’s genetic variability over time. One or both of these factors could be critically important to the future genetic management of this species. Documentation of the genetic history and current status of Lake Michigan’s chinook salmon population can provide insight into the role each of these factors may have played in any changes in allelic frequencies of Lake Michigan chinook salmon. Methods Tissue collection I and collaborators collected 213 chinook salmon (184 adults, 29 age-0) from six Lake Michigan tributaries (Figure 1; Table 1). We sampled rivers that had large spawning runs and were likely to include feral-origin fish, and included the greatest possible range of geographic locations given sampling limitations (Table 1). We obtained adult tissue from recreational anglers on shore and on charter boats, and from MDNR and Fish and Wildlife Service personnel. Finally, we collected juveniles from the Muskegon River in the Spring of 1996, because sampling of adults the previous autumn was impossible (Table 1). Sample sizes (N = 13 - 46) varied due to difi‘erences in availability of suitable tissue (Table 1). 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Iz/Jazzrrwaaaznrrzzzrzr.Izzzrzrlrrra Inertia/Irazrxrrrzzzr:zzzrrrrnz.rrarrz/azr .zzJIIIJIIaazrrarrrrrrr Izzraluzrzx rzrzzrzrzzarlr. u. (zlzaaarxzzzearrrazzrzz rwrzizrarnrrrzzmzwrxzz. z;rzrz:r:zrrar.rrra ... .. Irene/2222212222}!!! . razsrruvzarrrz{rearraxIrrzxriarzza. .zzzlzwlzraaixrzr x\\\ .a urarzrzrrJ/rzzrr{22:23:21}. (arrrarrrrwarr nu raarzrlrarzlzaalzrzrrrne . Irilrlzr/zzr .2razzzrrzrrezIIr/xzexa. .araaarazal azirnr. ..222/212 r awrar/zrz 22;. 2a).. Irrrxrza urn . 22:12: :1}; mm (a: l” n m m M locations Lake Michigan sampling Figure l Table 1: Rivers that were sampled in 1995-1996 for this study. Various evidence for feral recruitment is provided for each river where appropriate. River Sample size Age Collected in Feral recruitment documented? Betsie 46 Adult Autumn 1995 yes" Little Manistee 60 Adult Autumn 1995 yes? Manistee 13 Adult Autumn 1995 yes“ Muskegon 28 Juvenile Spring 1996 yes~""‘ Platte 43 Adult Autumn 1995 yes“ Pere Marquette 22 Adult Autumn 1995 yes" *Carl (1982) recovered juveniles before the stocking of hatchery-reared fish "Large run with no recent history of stocking ~Juveniles recovered by MDNR before the stocking of hatchery-reared fish (MDNR unpublished data) Seelbach (1985) recovered juveniles before the stocking of hatchery-reared fish Oxytetracycline (OTC) mark detection The MDNR marked all stocked chinook salmon fingerlings with oxytetracycline from 1990 to 1993 (Hesse 1994). When this chemical is fed to young fish, a mark is created which is retained throughout the fish’s life. This mark can be detected with ultraviolet light (Weber and Ridgeway 1962). I determined whether the tributary samples included any feral fish by examining vertebrae from each individual for this mark. I and collaborators thawed, cleaned, and sectioned (a 10 mm middle cross section excised) vertebrae from each fish, and I illuminated the chemical mark with ultraviolet light (Weber and Ridgeway 1962). Fish exhibiting an OTC mark were of hatchery origin, while those without a mark were considered feral. Protein Electrophoresis Collaborators and myself prepared the tissues according to the methods of Aebersold et al. (1987) with minor modification. We used a Teflon grinding pestle to homogenize a ~0.5 g sample of each tissue from each fish in an equal volume of buffer (0.1 M tris HCl, pH = 7.0), centrifiiged the homogenates at 15,000 X gravity at 4°C for 9 12 minutes, and subjected the final supematants to vertical or horizontal starch gel electrophoresis using conditions described in Table 2. We sliced and histochernically stained the gels for a suite of 14 enzymes encoded by 18 loci. The WDFW previously identified these loci as polymorphic in the Green River population. We conducted electrophoresis on the Lake Michigan samples in both the MSU and WDFW laboratories to assure comparable allelic designations and genotypic interpretations. 10 Table 2: Enzyme systems, loci. tissues, and electrophoretic conditions for chinook salmon protein electrophoresis. Enzyme Enzyme Locus Tissue Buffer number analyzed system Adenosine deaminase 3.5.4.4 ADA-1* Muscle TG ADA-2* Muscle TG Aspartate aminotransferase 2.6.1. 1 MAT-1,2 * Muscle TC Liver CAME Glucose-6-phosphate isomerase 5.3.1.9 CPI-82* Muscle TG ' GPI-A * Muscle TG Glutathione reductase 1.6.4.2 GR * Muscle TC Eye CAME Isocitrate dehydrogenase 1.1.1.42 sIDHp-1* Muscle TC (NADP+) Eye, Liver CAME sIDHp-Z * Muscle TC Eye CAME L-Lactate dehydrogenase 1.1.1.27 LDH-C* Eye CAME Malate dehydrogenase 1 .1. 1.37 sMDH-BI,2 * Muscle TC Eye CAME Malic enzyme (N ADP-1») 1.1.1.40 sMEP-l * Muscle TC Mannose-6-phosphate 5.3.1.8 MPl-I * Muscle TG isomerase Phosphoglycerate kinase 2.7.2.3 PGK-2* Eye, Liver CAME Proline dipeptidase 3.4.13.9 PEPD-2* Muscle TG Tripeptide aminopeptidase 3.4.-.-. PEPE-1* Muscle TC, TG Peptidase (PepLT) 3.4.] 1-13 PEP-LT“ Muscle TC, TC Superoxide dismutase 1.15.1.1 sSOD-l * Muscle TG Liver TC 11 Data analysis Interpretation of protein banding patterns followed existing WDFW models or rules recommended by May (1992) and Ruth (1990). I calculated allele and genotype fi'equencies, plus descriptive diversity statistics, with the statistical program BIOSYS-l (Swofi‘ord and Selander 1981). I calculated these statistics for the following groupings: for the samples from each drainage system, for the pooled population (which included all Lake Mchigan fish), and for Green River data obtained fi'om WDFW. I initially treated each isolocus as two distinct loci and used a procedure by Waples (1988) to estimate maximum-likelihood allele frequencies for these “loci”. Based on these results, I ultimately assigned all the variation to the second ‘locus’ and treated the first as monomorphic. I treated these isoloci the same way in the Green River population, as per WDFW methods (Anne Marshall, per. comm). Deviation fi-om panmictic conditions I employed six approaches to test for deviation from panmictic conditions in the watersheds and in the Lake Michigan population. 1. I directly compared a variety of descriptive statistics between the tributaries to detect differences in specific diversity indices. I tested our results against Hardy-Weinberg expectations 2. within each drainage system and 3. for the Lake Michigan population as a whole with the log-likelihood ratio test (Sokal and Rohlf 1995) using the statistical program GENES IN POPULATIONS (May 1992). 4. I investigated genetic diversity among populations by testing a null hypothesis of allele frequency homogeneity among tributaries. To test this hypothesis, I calculated a population-by-allele (R X C) contingency table for each locus using a procedure in GENES IN POPULATIONS (May 12 1992). 5. I estimated average gene flow between drainages by calculating Wright’s fixation index (PST) (1965, 1978) with BIOSYS-l (Swofi‘ord and Selander 1981). 6. To depict genetic relationships among drainages, I calculated Nei’s (1978) unbiased genetic distance (D) between tributaries. These D values were based on 17 common loci; I excluded the mAAT-l * locus due to low sample sizes in some tributaries. I used these distances to construct a dendrogram using the Unweighted Pair Group Method (UPGMA). Genetic comparison of the Lake Michigan and Green River populations The tributaries were pooled to create one Lake Michigan population with an average of 186 fish per locus. I used two methods to test for difi‘erences in the genetic variability of Lake Michigan and Green River fish. 1. I quantified any increases or decreases in the frequency of specific alleles in the Lake Michigan fish as compared to the Green River population. 2. Ifthere had been no change in the Lake Michigan fish since 1966, there would be little genetic distance between these fish and their Green River source stock. To test this hypothesis, I calculated the genetic distance (D) between these two populations and illustrated the genetic relationships with a dendrogram, which included for comparison a population of more distantly related, Columbia river-derived Cowlitz hatchery fish (WDFW, unpublished data). 13 Beams Deviation from panmictic conditions in watersheds 1. I detected more than one allele in 15 of the 18 loci examined (Table 3). There was little range in variability estimates across watersheds (Table 3); mean heterozygosity values ranged from 0.195 (21:0.051 SE.) to 0.216 (21:0.057 SE), and on average there were 1.85 alleles per locus. The percent of polymorphic loci was more variable; in particular, the Manistee and Pere Marquette populations were considerably less polymorphic than the others (55.6 and 61.1 vs. overall mean of others 75). 2. I tested the observed allelic distributions against Hardy-Weinberg expectations within individual watersheds. Multiple tests of the same hypothesis increase the probability of a Type I error. To compensate for this error, 1 evaluated all G-statistics according to an adjusted p (p*), where the original cc was arbitrarily set at 0.05 (Rice 1989). None of the loci in any separate drainage deviated significantly from the expected Hardy-Weinberg distributions under this adjusted significance level. 3. Similarly, I compared the Lake Michigan population to Hardy-Weinberg predictions. Only the PEP-81* locus deviated from these predictions when compared to p“. Although the MPI'" locus did not deviate significantly from our expectations when compared to p“, it showed a statistically significant deficiency of heterozygotes under the less stringent p value. In contrast, PEP-31* showed significantly more heterozygotes than expected. 4. I tested for heterogeneity in allele fi'equencies across tributaries with a contingency table analysis. PEPE-1* allele frequencies were slightly more heterogeneous than others, although there was no significant heterogeneity at the p < 0.1 level for any loci, or for the 14 mean of all loci. Similarly, overall heterogeneity considering all loci but PEPB-l * was also low. All populations except that of the Manistee River showed more heterozygotes at the PEP-B1 * locus than expected, but in only three of the seven comparisons was the excess significant at the 0.05 level (Appendix A). 5. F 37, a fixation index which is a measure of genetic differentiation of subpopulations within a larger population (Wright 1965, 1978), ranged fi'om 0.006 (MPI*) to 0.057 (mAAT-l ‘) (Table 4). Because PEP-Bl“ is a sex-linked locus, F51 values for this locus are misleading. When this locus was excluded, the mean F31» value was 0.026 (Table 4). 6. There was no measurable genetic distance between any watersheds except for that between the Little Manistee and the Manistee, and that between these two and the Betsie River (Figure 2; Appendix B). Genetic comparison of Lake Michigan and Green River populations 1. I hypothesized that Lake Michigan and Green River chinook salmon would have similar genetic profiles. The mean number of alleles per locus for the Lake Michigan population was substantially less than that of Green River chinook (2.17 vs. 2.56). Nearly 17% of the loci variable in Green river fish were no longer variable in the Lake Michigan population (Table 3). Specifically, three loci were fixed in Lake Michigan stocks, two of which were strongly polymorphic in Green River stocks, where this is arbitrarily defined as when the less frequent allele occurred at 2% or greater (Table 3). 2. There was genetic distance (D = 0.00072) between Lake Michigan chinook salmon and the Green River population (Figure 3; Appendix B). D measures the extent of gene differences between two populations, and this low number is weak evidence against 15 the null hypothesis of similar genetic profiles for Lake Michigan and Green River fish. 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Locus F15 F" F51 G statistic D.F. p MAT-2* -0094 -0072 0.020 5.53 5 ** mAAT-l“ 0.027 0.082 0.057 8.26 10 ** ADA-1* -0042 0.026 0.015 6.39 5 ** ADA-2* -0.059 -0.035 0.023 7.30 5 ** CPI-3* 0013 -0004 0.009 3.11 5 ** GR“ .0020 -0.009 0.011 5.36 10 ** leHp-3* -0101 -0.066 0.031 28.54 40 ** sIDHp-4* 0.081 0.093 0.013 4.307 5 ** wont-32* -0.040 50.033 0.007 7.663 10 ** sMEP-l* 0.144 0.172 0.032 13.11 10 ** MPIJ“ 0.158 0.162 0.006 2.879 5 ** PGK—2* -0.076 -0.018 0.054 6.876 5 ** PEPD-2* 0.083 0.090 0.008 2.463 5 ** PEPE-1* -O.262 .0233 0.023 7.73 5 ** sSOD-1* -0.1'37 -0.131 0.005 12.62 10 ** Mean -0034 -0015 0.019 122.03 140 ** Mean wnnout ELF-51“ 0.004 0.029 0.026 114.30 130 ** ii not51gn1T1cant at the p < 0.] level 23 12' . Panmixia and Hardy- Weinberg equilibrium I concluded that chinook salmon in Lake Michigan constitute one randomly breeding population that is in Hardy-Weinberg equilibrium, based on several lines of evidence. 1. Fish fi'om different tributaries showed similar levels of genetic diversity and similar allelic profiles. River-specific difi'erences in these measures could be evidence of meaningful genetic differences between tributaries, if difi‘erent tributaries show different profiles. For example, if one river was notably less diverse than others, this could be attributed to a bottleneck only in that population, to difl‘erent mating conditions there than in the other populations, or to other conditions specific to that river. Similarly, if there was a heterogeneous distribution of the alleles at a particular locus across difi‘erent watersheds, this could indicate non-random mating among watersheds and perhaps that watersheds should be treated as difi‘erent genetic groups. All possible evidence of subdivision in the Lake Michigan population, however, can be attributed to simpler causes. Although the Manistee and Muskegon Rivers had considerably fewer polymorphic loci than the others (Table 3), however this is probably because their small sample sizes limited the potential for occurrence of less frequent alleles at some loci (Table 3). 2. No loci within rivers deviated from Hardy-Weinberg expectations, indicating the consistency of one gene pool across rivers. 3. There was no unexplained deviation from Hardy-Weinberg predictions within the Lake Michigan population, indicating that this population is functionally one gene 24 pool. Because the PEP-B1 * locus is sex-linked, it violates an assumption of the Hardy- Weinberg test and this explains the deviation from Hardy-Weinberg at this locus within the Lake Michigan population. 4. Because there was no significant heterogeneity at any loci even with a liberal p“ value (0.1), the test for heterogeneity did not provide any evidence against panmixia. 5. Although a few F31 values were large ( arbitrarily defined as 2 0.03), there was no trend toward large values which would indicate substantial subpopulation structure. 6. If a population is subdivided, it should show allelic differences that would be reflected in genetic distance (D). Although there were small D values between a few of the Watersheds, most showed no distance fi'om each other (Figure 2; Appendix B). I concluded that the distances were not statistically rigorous evidence against the hypothesized panmictic Lake Michigan population for three reasons. 1. If several branches have distance lengths as small as 0.004, the phylogeny is probably incorrect (Nei 1987), as such small genetic distances can probably be explained by experimental error. 2. The Betsie River population showed alleles which are either absent or rare in the other river populations (Table 3), which explains the distance between the Betsie and the Little Manistee/Manistee cluster. 3. The Manistee population is based on a very small number of fish (13), and its distance from the other populations is probably due to its small size, which limits the potential for occurrence of alleles. 25 Impacts of genetic drift I found evidence of genetic drifi in the Lake Michigan chinook salmon population. The genetic diversity of Lake Michigan chinook salmon was consistently lower than that of Green River chinook salmon; 17% of the loci that were variable in Green River fish were monomorphic in Lake Michigan fish, and this population had lost nine alleles present in the Green River population. In addition, there was genetic distance between Lake Michigan and Green River stocks. This distance is likely due both to the losses described above, as well as to the increased fi'equency of several alleles in the Lake Michigan population, and to the occurrence of two rare alleles at the sIDHp-l * locus which were not'found in the Green River population. These alleles were apparently retained by chance in Michigan’s gene pool, but did not occur in the Green River allozyme survey of the 1980’s. Causes of (km in Michigan ’3 chinook salmon program Breeding practices If there is a limited amount of genetic material early in a population’s history, less common alleles may be lost and generally depressed levels of genetic variability can result fiom a genetic bottleneck efi‘ect (Nei et al. 1975). The results I have described are consistent with such a bottleneck between Green River sources and the first Michigan chinook salmon introductions, which would have resulted in the loss of genetic variability. Because it was common practice in the late 1960’s to harvest large lots of eggs over only one or two days, to pool milt, and to allow high variance in female reproductive success, allelic variability was probably lost through genetic drift due to such factors (e. g. Gall 26 1987). From 1989 on, an average 5.82] sex ratio has been used at the Little Manistee Weir (MDNR unpublished data). Similarly, deriving the founding embryonic pool fiom a relatively small number of parents was probably another cause of genetic drift and loss of allelic diversity (e.g. Simon et al. 1986). The initial yearly harvest of one million Green River eggs destined for Michigan could have involved as few as 200 females if the average female produced 5000 eggs (a conservative estimate of female fecundity; Healey 1991), provided that the breeding operation was of sufficient capacity. Even if a 1:1 sex ratio were used, a maximum of 400 parents would have contributed to Michigan’s embryonic pool for each of three years if the fecundity of both sexes was maximized. This would represent only 5% of the estimated total source river yearly brood run. Because of these circumstances, the embryos that founded Michigan’s population were probably not wholly representative of the Green River population’s breeding gene pool and thus a genetic bottleneck probably occurred during the stock transfers. An additional breeding factor, variance in female reproductive success, was likely an important early cause of genetic drift. It is common practice in Michigan and other states to combine the fertilized eggs fiom multiple broods into common egg trays in the hatchery. When broods are poOled in this way, the reproductive success of individual broods cannot be determined and is presumed to be approximately equal; however, as there is great variance in brood success, all females used in breeding do not make an equal contribution to the resulting offspring pool. Such variance in female contribution is amplified by the extreme fecundity of most salmonids, because large numbers of individuals fiom a single brood can dominate even very large pools of stocked fingerlings. 27 Ultimately, the genetic contribution of the less successful females is lost. If the described methods were implemented early in the Great Lakes chinook salmon program, the genetic variability of these founding stocks was likely compromised. Population bottlenecks and founder effects Early Great Lakes chinook salmon populations were probably derived from low numbers of surviving fingerlings, due to mortality both in the hatchery and alter stocking. Losses of fry in the hatchery, and additional mortality of stocked fingerlings, meant that founding adult populations constituted a fraction of the original embryonic pool. Two tributaries of Lake Michigan (the Little Manistee and Muskegon Rivers) were stocked with a total of 714,000 fingerlings yearly during each of the first three years of planting (1967-69) (Parsons 1973). Thus, about 300,000 of the original 1,000,000 embryos likely died before stocking each year. The Little Manistee River was stocked with an average of 380,000 chinook fingerlings yearly. Post-stocking mortality cut the number of returning spawning adults at the LMW to about 2100 per year from 1970 to 1977 (MDNR unpublished records). Although more than twice as many fingerlings were stocked each year for the next three years (1970-72), the initial low numbers of stocked fingerlings probably created a genetic bottleneck. Since stock transfers ended in 1968, Michigan has collected nearly all of its eggs from a fraction of this spawning population. Milt pooling, egg-lot pooling, and restricted harvest dates (about 3 to 4 days of entire spawning run, which usually lasted 3-4 weeks) are currently part of the Michigan egg-take procedure (MDNR, unpublished data) and were likely also practiced early in the program. Thus, early chinook salmon populations likely experienced a decrease in allelic variability each year due to random genetic drifi resulting from these breeding and husbandry practices. 28 Feral spawning runs appeared in trout streams soon after initial stocking (Keller et al. 1990). Because these were likely founded by small numbers of individuals, there were likely similar, if not more pronounced, founder efi‘ects in these feral populations. Evidence fiom other salmonid populations The genetic circumstances of Michigan’s stock transfer on current chinook salmon populations are consistent with those documented in several other studies of Pacific salmon. Pink salmon (Oncorhynchus gorbuscha) were accidentally introduced into the Laurentian Great Lakes in 1956. Upon comparison to the source population, Gharrett and Thomason (1987) determined that these salmon have lost an average of 0.3 alleles per locus since introduction. These fish are entirely feral, and thus culture practices have not contributed to this loss. Instead, this loss of variability is attributed to repeated bottlenecks resulting from limited founding sizes and limited survival of early colonists. Chinook salmon were introduced into New Zealand from California’s Sacramento river at the turn of the century. 300,000 to 500,000 embryos were transferred each year from 1904 to 1907, and these four embryonic pools constituted the founding populations for all current New Zealand chinook salmon populations (Quinn et al. 1996). Because these populations have been self-sustaining since introduction, unlike Michigan stocks, New Zealand populations have suffered little effects of drill due to artificial breeding or an extensive culturing system. Nonetheless, these populations show less genetic diversity than the presumed source stocks, probably due to an early bottleneck at the time of transfer and to very small founding populations in many new Zealand tributaries which were subsequently colonized (Quinn et al. 1996). 29 Genetic analysis of other Great Lakes salmonids has documented the importance of the hatchery system to the success of these populations. Native lake trout (Salvefinusnamaygush) populations in Lake Ontario have been on the decline since the 1950’s due to sea lamprey W) predation, overfishing and the decline of suitable habitat. Efforts to re-establish self-sustaining populations have included the stocking of a variety of hatchery strains. A genetic evaluation of wild-bom fry from Stony Island reef demonstrated that 67-90% of these fi'y were descended from the Seneca strain, even though only a small proportion of the hatchery fry stocked in previous years were of this strain (Grewe et al. 1994). In contrast, other strains that were stocked in great abundance were poorly represented in the wild- bom fry. The authors concluded that such great variation in the success of hatchery- stocked strains, variation which was independent of stocking densities, was indicative of differences in the suitability of different strains for re-establishment of Lake Ontario lake trout populations, and that the relative genetic contribution of different strains should influence stocking priorities for these strains (Grewe et al. 1994). The importance of stocking policies to the success of Lake Ontario lake trout is similar to the prominent role that culturing and stocking practices have played in the gene pool of Lake Michigan chinook salmon. Rainbow trout (Oncorhmchusmykiss) were first introduced into Lake Superior from drainages of the Pacific Ocean during the late 1880’s, and widespread feral reproduction has resulted in the naturalization of this species throughout Lake Superior (Krueger et al. 1994). Two strains of rainbow trout have been stocked throughout Minnesota tributaries since the 1960’s, and since this time, the angler effort required per 30 fish caught has increased dramatically. The potentially detrimental effects of interbreeding between hatchery-stocked fish and their naturalized conspecifics are one potential cause of this change in the fishery (Krueger et al. 1994), as wild and stocked stocks could be adapted to different environmental conditions. Krueger et al. (1994) compared 1. the allozyme variation of trout from different Lake Superior tributaries in Minnesota, and 2. the variation in these tributaries to that of the hatchery strains stocked throughout the area. In general, trout in heavily stocked streams were genetically similar to the hatchery strains they were stocked with, indicating that the stocked fish may have interbred with the wild populations. There were genetic differences among tributaries; however, the extent of such differences may have been diluted by extensive stocking of a particular strain (the “Michigan” strain) of hatchery fish throughout the area (Krueger et al. 1994). The authors recommend that such stocking of this or other hatchery strains should be stopped if genetic differences between tributaries are to be maximized. The dilution of feral difi‘erences between tributaries due to the introgression of hatchery strain fish is also a possible explanation for the lack of tributary differences between Lake Michigan chinook salmon; this evidence emphasizes the potentially great genetic impacts of hatchery and stocking policies on feral populations of salmonids in the Great Lakes. Management recommendations Lake Michigan’s chinook salmon population shows the efi‘ects of genetic drift. This drift has resulted in the loss of the population’s genetic variability and was likely caused by specific husbandry practices, by founder effects and by early bottlenecks. While founder effects fiom historic events cannot be directly mitigated, current causes of diversity loss should be identified and their impacts reduced. Husbandry practices which 31 allow high variance in parental reproductive success are known to have deleterious genetic impacts, and these impacts should be guarded against in breeding programs. I recommend that every effort be made to increase the number of males contributing to breeding in order to equalize the sex ratio in the fertilization system. Even modest improvements can give genetic benefits, and may not appreciably decrease the emciency of breeding operations. In addition, a one-time experiment is needed wherein individual broods remain isolated throughout the rearing program and their success is tracked in order to quantify the extent of variance in brood success. If this variance is high, the culling of broods with particularly high survival rates could be a practical and effective method of reducing this variance and thus increasing the number of broods contributing to the pool of fish eventually stocked. There is both historic documentation, and genetic evidence, that the Green River was the source population for chinook salmon in the Great Lakes. WDFW records, which account for the three million embryos thought to be transferred from 1966 to 1968, indicate that the Green River was the only source population. This is contrary to previous accounts of possible transfer(s) fiom other drainages such as the Toutle River, a Columbia River strain (Keller et al. 1990) believed to have been a major source of Michigan’s chinook salmon by many authors (e. g. Keller et al. 1990). I ruled out this possible source through a genetic comparison of this stock to the Green River and Lake Michigan populations (Figure 3), which demonstrated that Lake Michigan chinook are closely related to the Green River population, but more distantly related to Toutle River chinook salmon (Appendix B). 32 The transfer of additional chinook from elsewhere to the Great Lakes in order to bolster genetic diversity would seem to be a possible response to the loss of genetic diversity in Lake Michigan’s chinook salmon. However, such a transfer could in fact make the situation worse. If the transferred fish were difl‘erent enough fi'om Great Lakes chinook, the success of their ofi‘spring could be reduced due to the combination of incompatible gene complexes, or outbreeding depression. However, if such supplementation were ever to occur, it is clear that the new fish should come from the Green River, so that the compatibility of the introduced and established populations would be maximized. Effective population size (N,) is a population genetics parameter that is usefirl for estimating the expected extent of drift impacts on a particular population, and for predicting the genetic impacts of particular demographic factors, such as those described above. The application of genetic-based Ne equations, such as Waple’s (1989) temporal method or linkage disequilibrium (e. g. Bartley et al. 1992), to the genetic data described in this thesis salmon population would be a robust way to quantify genetic drift effects. I suggest the implementation of a management plan which minimizes drift, maximizes genetic diversity, and allows for genetic monitoring (perhaps with N,) in order to maintain the Lake Michigan chinook salmon gene pool. The genetic trends I documented in this population are extremely relevant to other fish populations in the Great Lakes as well, especially those where management policies play a prominent role in a population’s life history and persistence. 33 Conclusions of this study of a recently founded closed chinook salmon population 1. Small, non-representative founding populations probably caused an early bottleneck which restricted the genetic information available to founding populations. Efi’orts should be made to ensure that robust numbers of founding individuals, which are representative of the genetic variability of the source population, be used in fisheries introductions. 2. Several breeding practices have likely eroded Michigan’s gene pool since inception of the chinook salmon program, and continued use of these methods will undermine any efforts to retain or restore the genetic variability of this population. The effects of artificial propagation programs on introduced (and native) stocks should be carefirlly considered, and a breeding plan designed to reduce the effects of genetic drift should be a priority in management of this and other fish species in the Great Lakes. 3. Michigan’s hatchery system may have eased early bottlenecks through the use of consistently large breeding populations. Hatcheries can facilitate the maintenance of variability through well-designed breeding and husbandry plans. The future genetic sustainability and success of this and other fish populations is still very much afi‘ected by management policy. CHAPTER TWO Chinook salmon (W W) were first successfully introduced into the Laurentian Great Lakes in 1967 to improve the sport fishery and to control populations of the invasive alewife (W) (Michigan Department of Natural Resources [MDNR] 1974). Approximately one million fertilized eggs were shipped to Michigan from Washington’s indigenous Green River chinook population for each of three years in the late 1960’s (1966-68). In 1969, the first mature cohort was successfirlly spawned in captivity and Michigan has since been self-sufficient in chinook salmon egg production. Descendants of the three groups of transferred embryos were ultimately stocked throughout the Great Lakes by the MDNR and other state and federal agencies, and Great Lakes chinook salmon numbers have since been augmented by an artificial propagation program. The Great Lakes chinook salmon program has by many accounts been successful: by 1986, the standing stock in Lake Michigan alone approached 40 million pounds, and the lakewide harvest by recreational anglers approached 1 million pounds (over 600,000 fish; Francis 1996). In 1988, however, large numbers of dead chinook salmon washed up on the eastern shores of Lake Michigan, and the number of adults migrating up tributaries to spawn declined precipitously (Johnson and Hnath 1991). These losses were attributed to bacterial kidney disease (BKD). Whether BKD was the only factor in these mortalities is 34 35 still debated, but no other direct causes of death have been identified. The population has not fully recovered fi'om this crash; from 1989 to 1994, returns to rivers and sport harvest have remained at less than 50% of pre-1988 levels (MDNR, unpublished data). Although Great Lakes chinook salmon populations are largely supported through artificial propagation, feral reproduction in Great Lakes tributaries is widespread and supports a significant portion of chinook salmon production. Feral fish may constitute 20- 30°/o of chinook salmon production in eastern Lake Michigan alone (Carl 1982 and Hesse 1994). In fact, many Michigan streams that were never stocked now support runs of feral- origin adult chinook salmon, which indicates that chinook can stray and colonize rivers with suitable spawning conditions (Carl 1982). Thus, each year, returns are likely to be a mix of recent strays and feral fish (Carl 1982). Hesse (1994) surveyed adult vertebrae for oxytetracycline, a chemical mark applied to all stocked fish, and estimated that 39-54% (:tS%) of the three-year-old chinook salmon returning to two major Lake Michigan tributaries in 1992-93 were not of direct hatchery origin. Hesse (1994) concluded that these represented feral-born fish, potentially fiom “naturalized” populations. Despite the significant contribution of feral reproduction to Great Lakes chinook salmon populations, Michigan’s hatchery system continues to play a prominent role in management. Although highly variable, on average 4 million fingerlings (i454 S.E.) have been stocked into Lake Michigan from 1976 to 1987 [range 687,000 (1968) to 7.7 million (1984); MDNR unpublished data]. By some estimates, 70% of the age-0 chinook salmon in Lake Michigan were probably of immediate hatchery origin (Carl 1982, Hesse 1994). Dramatic fluctuations in the chinook salmon population over the last decade have prompted closer examination of the dynamics of this population. In Chapter 1 of this 36 thesis, I tested the hypothesis that chinook salmon in difi‘erent watersheds have genetically diverged in the 30 years since introduction, but concluded that Lake Michigan chinook salmon are in fact operationally one “genetic” population. I also determined, through comparison to the source population, that Lake Michigan chinook salmon have lost genetic variability since their introduction (Chapter 1). Genetic diversity is a usefirl index of the health and stability of populations; low levels of diversity within a population have been linked to reduced disease resistance (e. g. in rainbow trout, Ferguson and Drahushchak 1990), slower development, reduced size-at-age, higher mortality, and reduced fertility (e. g. Smith and Chesser 1981, Mefi‘e and Carroll 1994). Furthermore, genetic variation within populations is a basic requirement for adaptation and the long- term persistence of populations in changing environments (Soulé 1980, 1987). Thus, the long-term persistence of Great Lakes chinook requires an understanding of the amount and structure of genetic variation in the population, in addition to an examination of the processes responsible for its present state. The influence of artificial propagation on the genetic diversity of fish stocks has been well documented: propagated stocks tend to have lower levels of genetic variability than founder sources, as indicated by changed allele frequencies since the implementation of hatchery programs (e. g. Gharrett and Thomason 1987). The loss of allelic variability due to less heterozygous parents is possible solely as a consequence of genetic drift. A managed gene pool can be firrther compromised when it is subjected to concerted or inadvertent directional or stabilizing selection, such as selection against jack males, or selections for faster growing fish. The genetic diversity of Lake Michigan chinook salmon 37 has been compromised by founder events and breeding and husbandry practices, and this could affect the long-term persistence of this population. My primary objective was to estimate the extent of genetic drift and its effects on Lake Michigan chinook salmon. Efi‘ective population size (N,) is a population genetics parameter used to estimate the potential impact of genetic drift on a population. When compared to the actual number of breeders in a population (N,), N, is usefirl in detecting decreases in a population’s genetic variability, or discrepancies between assumed and actual levels of allelic diversity. I used a variation of Waples’ (1989) “variance method” as the basis for my estimate of N, (Hedgecock et al. 1992). I estimated the variance between Lake Michigan and Green River chinook populations, the target population and its source population, and used this variance to estimate N, and the efi’ects of genetic drift. A similar approach was used by Hedgecock et al. (1992) for estimating the N, of several captively bred shellfish and shrimp populations. Great Lakes managers need complete information in order to make informed decisions on issues that have genetic impacts; to this end, I used our effective population size estimates to make recommendations for effective management of the genetic diversity of chinook salmon and other Great Lakes salmonids. Mods The variance method measures changes in a population’s allelic frequencies between two temporally distinct samples (S, and S.) to estimate N,. Use of this method assumes that the alleles examined are selectively neutral and not subject to segregation, that mutation and migration are negligible, and that allele fiequency estimates are unbiased. Because I did not have allele frequency data for Lake Michigan chinook salmon that was taken one or more generations apart, I substituted the frequencies from a sample 38 taken from the founding population as time 0. This approach is valid when two additional assumptions are met. First, the allele frequency estimates from the surrogate time 0 population must accurately represent frequencies at the time of initial embryonic transfer (1966-1968). My second assumption was that difi‘erences in allele fi'equencies between the two groups are due to drift in the Green River population that occurred after the founding events. I used the 1995 allelic data for Lake Michigan chinook salmon described in Chapter 1 as a sample at time t (8,). I used data collected from the Green River to approximate the allele frequencies of Lake Michigan chinook at the time of introduction (8,). These fi'equencies were based on a total of 400 fish (average 353 fish per locus) which were randomly collected, 100 each year, in 1981, 1987, 1988 and 1990 (Anne Marshall, personal communication [per. comm.]). The three later samples were of returning adult spawners, while the sample from 1981 was a sample of hatchery-reared juveniles from the 1980 brood year (Anne Marshall, per. comm). I averaged all of the years together for the Green River allelic fi'equencies, thus these frequencies represent Green River fish during the 1980’s. In my original survey of Lake Michigan chinook salmon (Chapter 1), I assayed loci that were variable in the Green River population in order to permit direct comparison between these two populations. I and collaborators conducted electrophoresis on the Lake Michigan samples in both the Michigan and WDFW laboratories, in order to ensure comparable allelic designations and genotypic interpretations of gel banding patterns. Because the allele frequencies of the Green River Hatchery population were temporally stable over the four years sampled in the 1980’s (Chapter 1), I concluded that 39 the Green River population was probably stable between 1966 and 1985. Furthermore, I determined that the Green River population was probably the only source of Michigan’s founding chinook salmon populations, because the genetic distance between the other purported source (the Toutle River, represented by its tributary the Cowlitz River) and Lake Michigan chinook salmon was quite large (Chapter 1). I estimated N, from Waples (1989): Pr = [WK-1)] * [£[[(x; - Ya)2] / [(Xi + Yr) / 2]] N,=t/[2[Fk-1/(2 s,)-1/(2 S.)+1/N]] where Fl, = the variance in allele frequency over t generations, S, and S. = the size of the sample taken at time 0 and time t respectively, t = the number of generations between S, and 8., N = the total breeding population size at the time of the initial sample, K = the number of segregating alleles and x; and y; = the allele fiequencies of the Green River and Lake Michigan populations. Although variable, the historical run on the Green River has averaged approximately 7,600 breeders (WDFW, 1993); therefore, I set the total source breeding population in the Green River (N) equal to 7,600. The Green River frequencies were based on an average of 353 fish per locus, so and I approximated S, at 400. Because the number of Lake Michigan individuals sampled for each locus differed, I calculated the harmonic mean of all the sample sizes and weighted for the number of alleles in order to calculate S. (W aples 1989). Similarly, because the number of segregating alleles (K) 40 varied over loci, I calculated F, for each locus and then calculated the weighted mean of the single-locus values (W aples 1989). The Lake Michigan data set included 23 alleles fiom 10 loci and was derived fi'om an average of 218 fish (Chapter 1). The PEPB-l "‘ and MP1" loci were excluded because they violated model assumptions (Chapter 1). All alleles which occurred at a frequency less than 0.02 in Lake Michigan or Washington were excluded to reduce bias due to rare allele efi’ects (W aples 1989). I ultimately calculated Fk with 23 alleles. Because chinook salmon life history includes overlapping generations, accurate estimation of t is difficult. As Lake Michigan spawners are mostly three and four years old (MDNR unpublished data), I estimated that the average generation time for breeding Lake Michigan chinook salmon is 3.5 years. If I assumed that S, is representative of a sample taken in 1967, t E 8, but under the assumption that S, represents fish in 1985, re 3. To evaluate the range of possible N, values based on difi‘erent assumptions, I calculated N, based on t E 3 through 9. I also examined the effect of including and excluding juvenile fish (N = 28, Muskegon River) as a source of bias described by Waples (1989). Finally, I calculated the ratio of N, to N,, where N, = 125,959, the average number of all 3 to 5 year old chinook salmon caught in Michigan waters of Lake Michigan, 1985-1994 (MDNR, unpublished records). I excluded the rare age-2 year class fiom N, in order to avoid an underestimate of the N,,/N, ratio which can result from an inflated N, value. Results Estimates of Fk and N, from variable t values and sample composition are summarized in Table 5. N, estimates increased by a factor of 0.33 as t increased fiom 3 to 9. When juveniles were excluded, N, estimates were 6-10% smaller and the mean Lake 41 Michigan sample size across alleles (8.) decreased fiom 218 to 196 (Table 5). N, was lowest (139) when t = 3 and juveniles were included, and highest (425) when t = 9 and juveniles were excluded (Table 5). Under the most realistic conditions, where t = 8 and juveniles are excluded, N, = 378. 42 Sad mmv mood mum mood 0mm mood mwm moodwmm mood aw. Sod N: 00— .206 cog—05 mood omv mood mum mood own «8.0 9% wood mmm .86 cm. Sod o3 9N wiod UotEuE ..sz Oz ...zmz 02 £2 02 02,02 uz u2,...2 oz u.zmz uz 022 oz .m :82 a 8:55 60.5.88 .6 nova—63 263 3:53;: new .8383 5253 mcouabcow ..o ..unEsc 0.5 u a .mmm u cm 223 fiat? v.5 :oEmanou 0398.... use . :23, oz he 52835 a we can 852: count? 2: £3, vofiEcmo 8 9Z ”n 633. 43 The ratio of N, to N, was never above 0.003, and was as low as 0.001. When t = 8 and juveniles were excluded, this ratio was 0.003 (Table 5). Thus, under the most realistic conditions, the effective breeding size of the Lake Michigan chinook salmon population was less than 1% of the estimated actual breeding population. In fact, any combination of t, S, and age composition resulted in a N,,/N, ratio less than 1% (Table 5). E' . Lake Michigan’s chinook population may be experiencing a greater amount of genetic drift than might be anticipated from such a demographically large population. Because the amount of drift is related to the efl’ective population size, it is helpful to consider N, rather than N, when managing genetic diversity. I determined that there were an average of 374 efi‘ective breeding individuals in Lake Michigan per year according to the most realistic estimate, but that N, could be as low as 139 depending on sample composition and various values of t and So. Because all of the t and So values included are historically possible, the efi'ective breeding population size is probably much less than 1,000 individuals (or 500 breeding pairs), the minimum N, required to maintain long-term genetic variation in an isolated population (Franklin 1980). I concluded that the efi‘ective size of Lake Michigan’s chinook salmon population is, at best, only 42% of the minimum effective size recommended for long-term evolutionary stability, and at worst, well below the level required to overcome the effects of genetic drift on long-term allelic frequencies. N,,/N, of Lake Michigan chinook salmon Despite a large-scale state breeding program which includes thousands of breeders per year, and despite substantial feral recruitment, the average efl’ective breeding population size of Lake Michigan’s chinook salmon population since 1968 has been less than 1% of the estimated number of breeding adults, even under the most liberal conditions (See Table 5). Even if the estimates of N, used are ten times too large, this ratio would still be less than 0.05, or 5% of the census size. Comparison to other N, estimates Salmonids In order to put these N,/N, values into perspective, I compared them to ratios fi'om several salmonid populations studied by Bartley et al. (1992) (Figure 4). These data were derived using the linkage disequilibrium estimate of N,. As there are no published estimates of temporal N, fi'om populations of salmonids, I could not directly compare the estimate to one derived in an identical fashion from a similar species. Although the basis for and data used in these two N, methods differ, both approaches rely on allozyme data, and both methods have been rigorously tested with theoretical models. In the absence of directly comparable temporal method N, estimates, the linkage N, estimates were useful for rough comparison of estimates. “Hatchery born - Sacramento River” represents a heavily managed population (Figure 4). “Wild-born - Sacramento River” consists of wild-born fish fiom a historically wild, population. Finally, “single-pair matings” is the N,/N, ratio based on the offspring of 17-20 single pair matings of rainbow trout (W). The effectiveness of this breeding program is readily apparent, as the effective population size (38.5) is very 45 close to the actual number of breeders used (N,). In contrast, both the hatchery-bred and wild populations from the Sacramento River showed efi’ective population sizes that were less than one tenth of the N, value. The wild-bom fish had a larger NJN, ratio than those born in the hatchery (0.044 vs. 0.013), although the difl’erence was not statistically significant (Figure 4). This larger ratio could result from a more equal number of males and females breeding in the wild population, and/or less variance in the success of wild- bom broods. “Hatchery and feral born - Lake Michigan” represents the N,/N, ratio of Lake Michigan chinook salmon as measured with the temporal method, where t = 8 and juveniles are excluded. This N,/N, ratio for Lake Michigan chinook salmon (0.003) is an order of magnitude smaller than that of even the hatchery-born fish fi'om the Sacramento river. N, is much greater in breeding systems using only single-pair matings, such as that of the Shasta hatchery, than in populations where the genetic contributions of individuals are highly variable, such as in the hatchery-bred Sacramento river population (Figure 4; e. g. Simon et al. 1986 (large variance in family size) and Waples and Teel 1990 (modified sex ratio)). However, the NJN, ratio of Lake Michigan’s chinook salmon was less than one tenth of the ratio of hatchery-bred Sacramento river fish, a population which was propagated using similar breeding and rearing methodologies. This order of magnitude difference suggests that Lake Michigan chinook salmon have suffered sizable impacts of genetic drift as compared to their Sacramento River conspecifics. Plants and Shellfish A comparison to variance method N, estimates for plant and shellfish species reveals even more dramatic differences. Hedgecock and Sly (1990) determined that the 46 NJN, ratio for two hatchery stocks of the Pacific oyster (Crassostrea gigas) ranged fi'om 0.082 to 0.39. This ratio for populations of an annual plant, Eichhomia paniculata, averaged 0.47 (Husband and Barrett 1992). While these species are taxonomically distant from salmonids, they share high fecundity with salmonids and thus are subject to similar genetic concerns, such as those due to high variance in parental reproductive success (Hedgecock et al. 1992, Chapter 1). The fact that all three ratios are exponentially larger than that of Lake Michigan population supports the contention that the efi'ective population size of Lake Michigan chinook salmon is very low. 47 s computed cI a salmoni populations, where N with two genetic methods ill'lOllS tervals for v In N,,/Nc and 95% confidence Figure 4 0.895 23-444 43> xxx-rah.» . .’ . _/ " .- 9’ " «in ”433‘ -- .-.> v v .1 "I? iv'mi 4:3; 5:- ' . . 'IHNLWK‘L'H _. ' /.' {/3 I /_--,- .43 ‘ 4"1/ -:-" ":1'5-'I r' "’ ’53-‘32 / 4(- _ . III/W 1' . ... A, . . , 3.4., _ , . --.- , .."I .. gm.“ . ' .‘I-l..l 7.9%:52' _2;'.;,_{¢¢”- 1-2'2. -. .' *3“ _. 3’9: fit:- .2293”: - 0.044 a, $3, 0.003 0.0l3 .,-,:"( .\ ..- l.0 0.9 w 0.8 r- 0.7 -— 0.6 ‘r 0.5 T 0.4 ._ 0.3 ._ hatchery and feral born - wild born - Sacramento River hatchery born- Sacramento ings ir mat le-pa srng Lake Michigan River 48 Sources of potential bias The N, value I used is an estimate of the number of fish capable of breeding, and it is probably an overestimate because not all potential breeders actually breed. However, as this N, includes only fish caught within Michigan waters of Lake Michigan, it underestimates the number of potential breeders. In addition, angling efi’ort was probably not equal for all age classes and may be a source of bias. Overall, however, this is the best available estimate of the average number of breeding chinook salmon in Lake Michigan based on existing records. My sample included a large number of individuals from six drainages. I determined that Lake Michigan chinook salmon had lost nine alleles that were present in the source population (Chapter 1); however, it is possible that the presence of these alleles went undetected due to sampling error. If this were the case, allelic differences between the temporal samples would be inflated, resulting in the underestimation of N,. I ultimately concluded, however, that the absence of these alleles can be satisfactorily explained by genetic drift fi'om several inferred population bottlenecks that occurred early in the chinook salmon program (Chapter 1). Even if one or two alleles were passed over in sampling, it is unlikely that the N, values would change by an order of magnitude, and thus our conclusions would remain unchanged. Conclusions The efi’ective population size of Lake Michigan chinook salmon was lower than recommended N, values across all the variables I explored, indicating that the population is at high risk for loss of allelic variability. Lake Michigan chinook salmon have a particularly low effective population size (adjusted for census size) as compared to 49 conspecific populations and other highly fecund plant and animal species, and are thus clearly subject to detrimental drift effects. This low N, could reflect historic bottlenecks early in the chinook salmon program, and it could also be indicative of genetic drift resulting fiom certain procedures used in Michigan’s husbandry and culture protocol (Chapter 1). Managementreaomrnendatiens The current Lake Michigan chinook salmon breeding and rearing protocol is expected to decrease the genetic variability of Lake Michigan’s chinook salmon, and I have demonstrated that this decrease has occurred (Chapter 1) and that genetic drift has caused this decrease. It is critical that managers consider the following recommendations (see Chapter 1 for additional background). First, the number of breeders used should remain consistently large, in order to avoid population bottlenecks and additional losses of uncommon alleles due to sampling error. Second, more males should be use on each breeding day, in order to equalize the breeding sex ratio. Finally, a study designed to quantify the extent of variance in female reproductive success should be carried out. All of these factors are expected to have particularly dramatic impacts on genetic variability, and the extent of such genetic drift effects should be explored in order to promote more effective genetic management. APPENDIX A APPENDIX A Genotypic frequencies for variable loci, in individual watersheds and the Lake Michigan population. POP I N NAME Pop 0 46 - Betsie River; Pop 1 60 - Little Maniaee; Pop 2 13 -Bingistee; Pop 3 29 - Muskegon; Pop 4 43 - Platte River; Pop 5 22 - Pere Marquette; Pop 6 213 - Lake Michigan Hardy Wienberg pop 0 locus aat2 Expech value for smallea class was made equal to one chi - 1.210 df - 1 o - 1.577 chal -334 genotype - 11 12 22(22) observed - 33 11 o 0 expected - 33.69 9.63 0.69 1.00 pop 0 locus maul Expected value for smallea class was made equal to one chi G genotype observed expected pop 0 locus adal 1.481 1.645 11 27 28.01 df chal 12 3 2.37 - 2 -5.99 13 22 23 14 0 0 12.62 0.05 0.53 Expected value for smallest class was made equalto one chi G genotype observed expected pop 0 locus ada2 1.008 0.182 11 42 42.09 df chal 12 4 3.83 - 1 - 3.84 22 (22) 0 0 0.09 1.00 Expected value for srnallea class wasrnade equal to one chi G genotype observed expected pop 0 locus gr 1.018 0.301 11 39 39.14 df chal 12 5 4.72 - 1 . 3.84 22 (22) 0 o 0.14 1.00 Expected value forsrnallea class was made equal to one chi G 8000‘?!” observed expected 0.000 -0 .01 1 11 45 45.01 df chal 1 2 - 1 - 3.84 13 22 23 1 - - 0.99 - - 50 33 (22,23) 1 0 1.42 1.00 33 (13.33) 0 1 0.01 1.00 51 pop 0 locus idh3 chi - 0.019 df - l G - 0.019 chal - 3.84 genotype - 1 1 12 13 14 15 16 17 18 23 24 25 26 27 28 29 33 34 37 38 39 44 45 46 47 48 49 57 58 59 66 67 68 69 77 78 89 99 (19.88.89.99) observed - 41 - - - - - - 4 0 0 1 expected - 41.14 - - - . - - 3.78 0.04 0.01 1.08 pop 0 locus idh4 Expectedvalueforsrnallestclasswasmadeequsltoonc chi - 1.003 df - 1 G - 0.101 chal - 3.84 genotype - 11 12 22 (22) observed - 43 3 0 0 expected - 43.05 2.90 0.05 1.00 pop 0 locus mdh4 Expectedvalueforsmallestelasswasmadeegraltoone chi - 1.003 df - 1 G - 0.101 chal - 3.84 genotype - 11 12 13 22 23 33 (33) observed - 43 - 3 . - 0 0 expected - 43.05 - 2.90 - - 0.05 1.00 pop 0 locus mdhpi chi - 0.730 df - 2 G - 0.726 chal - 5.99 genotype - 11 l2 13 22 23 33 (13,23,33) observed - 1 1 16 0 10 2 0 2 expected - 9.26 18.51 0.97 9.26 0.97 0.03 1.97 pop 0 locus mp1 chi - 0.252 df - 1 G - 0.255 chal - 3.84 genotype - 11 12 22 observed - 14 18 4 expected - 14.69 16.61 4.69 pop 0 locus pda chi - 0.400 df - 1 G - 0.403 chal - 3.84 genotype - 11 12 22 observed - 2 6 2 expected - 2.50 5.00 2.50 pop 0 locus (hepl Expectedvalueforunallestclasswasmadeequaltoone chi - 1.055 df - 1 G - 0.637 chal - 3.84 gemtype - 11 12 22 (22) observed - 35 7 0 0 expected . 35.29 6.42 0.29 1.00 pop 0 locus tapepl chi - 1.097 df - 1 G - 1.102 chal - 3.84 genotype - 11 12 22 observed - 9 26 10 expcaed - 10.76 22.49 1 1.76 52 pop 0 locus sodl chi - 0.080 df - 1 G - 0.079 chal - 3.84 genotype - 11 12 22 observed - 20 20 6 expected - 19.57 20.87 5.57 pop 1 Icons aat2 Expectedvalueforsmallestclasswasmadcequaltoonc chi o 1.748 df - l G - 0.831 chal - 3.84 genotype - 11 12 22 (22) observed - 48 8 2 2 expected - 46.62 10.76 0.62 1.00 pop 1 loans maatl Expectedvalueforsrnallestclasswasmadeequaltoone chi - 0.079 df - 2 G - 0.017 chal - 5.99 genotype - 11 12 13 22 23 33 (22.23) observed - 36 4 15 0 1 2 1 expected - 35.69 3.92 15.69 0.11 0.86 1.72 1.00 pop 1 locus adal Expectedvalueforsmallestclasswasmadcequaltoone chi - 1.002 df - 1 G - 0.081 chal - 3.84 genotype - 11 12 22 (22) obsa-ved - 54 3 0 0 expected - 54.04 2.92 0.04 1.00 pop 1 locus ada2 Expectedvalucforsmallestclasswasmadeequaltoone chi - 1.029 df - l G - 0.458 chal - 3.84 genotype - 11 12 22 (22) observed - 50 7 0 0 expected - 50.21 6.57 0.21 1.00 pop 1 locus gpi3 Expectedvaluc forsmallestclasswasmadeequaltoonc chi - 0.000 df - 1 G - 0009 chal - 3.84 gemtype - 11 12 22 (12,22) observed - 53 1 0 1 expected - 53.00 0.99 0.00 1.00 pop 1 locus idh3 Eimectcdvalueforsmallcstclasswasmadeequaltoone chi - 1.079 df - 1 G - 0.910 chal - 3.84 genotype - 11 12 13 14 15 16 17 18 24 25 26 27 28 33 34 35 36 44 45 46 47 48 55 56 57 58 68 77 78 88 (88) observed - 50 - - - - - - 10 - - - 0 0 expected - 50.42 - - - . - - 9.17 - - - 0.42 1.00 pop 1 locus M14 Expectedvalucforsrnallestclasswasmadecqualtoonc chi - 0.917 df - 1 G - -0.755 chal - 3.84 genotype - 11 12 22 (22) observed - 57 2 1 1 expected - 56.07 3.87 0.07 1.00 22 66 23 38 67 53 pop 1 locusrndrpi Expectedvalueforsmallenclasswasmadeequaltoone chi - 0.066 df - 2 G - 0.056 chsl - 5.99 genotype - 11 12 13 22 23 33 (13.23.33) observed - 11 24 0 15 l 0 1 expected - 10.37 24.80 0.45 14.83 0.54 0.00 1.00 pop 1 locusrwr chi - 3.477 df - 1 G - 3 312 «Val - 3.84 genotype - 11 12 22 observed - 30 16 7 erqaected - 27 25 21.51 4.25 pop 1 locus [add - 0.157 df - l O - 0.155 chal - 3.84 genotype - 11 12 22 observed - 26 20 5 expected - 25.41 21.18 4.41 pop 1 locus dpepl Expectedvalueforsrnallest classwasmadeequaltoone chi - 0.010 df - 1 G - -0.329 chal - 3.84 genotype - 11 12 22 (22) observed - 46 12 l 1 expected - 45.83 12.34 0.83 1.00 pop 1 locus tapepl ” Deviation ” chi - 18.072 df - l G - 19.439 chal - 3.84 genotype - 11 12 22 observed - 4 46 10 expected - 12.15 29.70 18.15 pop 1 locus sodl Expectedvalucforsmallestclasswasmadcequaltoonc chi - 2.570 df - 3 G - 1.873 chal - 7.81 genotype - 11 12 13 22 23 33 (33) observed - 21 28 3 5 3 0 0 expected - 22.20 24.94 3.65 7.00 2.05 0.15 1.00 pop 2 locus aat2 Expectedvalueforsmallestclasswasmadecqualtoone chi - 1.583 df - 1 G - 2.039 chal - 3.84 genotype - 11 12 22 (22) observed - 6 6 0 expeaed — 6.75 4.50 0.75 1.00 pop 2 locus maatl Expeaedvalueforsmallestclasswasmadeequaltoone chi - 0.179 df - 2 G - 0.139 chal - 5.99 genotype - 11 12 13 22 23 33 (12.22.23) observed - 6 0 4 0 1 1 1 erqtected - 5.33 0.67 4.67 0.02 0.29 1.02 1.00 i expected- 0.21 pop2locusrndh4 0.667 (if 0 652 chal 1 1 12 25 26 45 46 77 78 6 - 6.75 - 54 - 3.84 13 14 15 27 28 33 47 48 55 88 (16.66.68.88) 0 l 0.52 1.50 Expectedvalueforsrnallcflclasswasmadeequaltoone chi . pop2locuspgk2 0.000 df -0.038 chal 11 12 12 - 12.02 - 2.000 df 3.244 chal 1 1 12 0 4 0.44 2.67 1.160 df 1.146 chal 11 12 5 3 4.23 4.55 . l - 3.84 13 22 23 1 - . 0.96 - - - 3.84 13 22 23 0.44 4.00 1.33 - 1 - 3.84 22 2 1.23 Expectedvalueforsmallestclasswasmadeequaltoonc chr - G . 8900131” ‘ observed - expected - pop2locusdpep1 Expectedvaluefor chi . G . W " observed - expected - 0.003 df - l -0. 163 chal - 3.84 11 12 22 (12,22) 2 l 0 l 2.08 0.83 0.08 1.00 smallest class was made equal to one 0.000 df - 1 -0.045 chal - 3.84 11 12 22 (12.22) 10 l 0 1 10.02 0.95 0.02 1.00 0.737 df - 1 0.746 chal - 3.84 11 12 22 2 8 3 2.77 6.46 3.77 0.770 df - 1 0.768 chal - 3.84 11 12 13 22 23 5 6 1 0 1 5.56 4.58 1.31 0.94 0.54 16 17 18 34 35 36 56 57 58 1 - 5 0.75 - 3.75 33 (13.33) 0 1 0.02 1.00 33 (11.1333) 1 1 0.11 1.00 33 (22,2333) 0 1 0.08 1.56 22 66 23 67 pop 3 locus aat2 Expected value for unallest clan was made equal to one chi - 1.155 df - 1 G - 1.005 chal - 3.84 genotype - 11 12 22 (22) observed - 15 6 0 0 expected - 15.43 5.14 0.43 1.00 pop 3 loans adal Erqaected value for smallest clue was made equal to one chi - 1.005 df - 1 G - 0.105 chal - 3.84 genotype - 11 12 22 (22) observed - 18 2 0 0 expected - 18.05 1.90 0.05 1.00 pop 3 locus ada2 Expectedvalueforsmallestclasswasmadeequaltoone chi - 0.000 df - 1 G - -0.022 chal - 3.84 genotype - 11 12 22 (12,22) observed - 22 1 0 1 expected - 22.01 0.98 0.01 1.00 pop 3 locus ah Expectedvalueforsmallestclasswasmadeequaltoone chi - 1.042 df - l G - 0.422 chal - 3.84 genotype - 11 12 22 (22) observed - 17 4 0 expected - 17.19 3.62 0.19 1.00 pop 3 locus gr Expectedvalueforsmallestclasswasmadeequaltoone chi - 0.000 df - 1 G - -0.019 chal - 3.84 genotype - 11 12 13 22 observed - 26 - 1 - expected - 26.01 - 0.98 - pop 3 locus idh3 Expectedvalueformnllestclasswasmadeequaltoonc chi - 1.115 df - 1 G - 0.860 chal - 3.84 genotype - 11 12 13 14 24 25 26 27 28 44 45 46 47 48 68 77 78 88 (33.38.88) observed - 18 - 2 - - - - 0 0 expected - 18.38 - 1.75 - - - - 0.17 1.00 pop 3 locus M14 Expectedvalueforsmallestcluswasmadeequaltoone chi - - 1.004 df - 1 G - 0.087 chal - 3.84 gmotype - 11 12 22 (22) observed - 22 2 0 0 expected - 22.04 1.92 0.04 1.00 55 23 15 55 33 (13,33) 0 0.01 16 34 56 1 1.00 17 35 57 18 36 58 22 66 23 38 67 0.17 observed expected pop3locusdpep1 Expectedvahrcforsmallestclasswasmadeequaltoonc c111 G W observed expected pop3|ocustapep1 chi G W observed expected pop3locussod1”Deviation“ Expectedvalucforsmallenclasswasmadeequaltoone pop4locusmaat1 valueforsmallestclssswasmsdeequaltoone Expeaed chi G W observed expected 0.074 1 1 26 26.04 4.125 4.661 1.038 0.401 6.597 8.992 1 1 7 9.78 0.402 0.376 1 1 23 22.40 1.536 0.986 chal 12 (If chal 12 5 7.87 df chal 12 9 10.11 df chal 12 12 1 1.26 df chal 12 4 3.64 df chal 12 13 11.15 df chal 12 13 8.48 df chal 12 10 11.20 df chal 12 3 3.09 1.93 valucforsmallestclasswasmadeetpraltoone - 5.99 13 2 0.68 - 3.84 22 2.45 - 3.84 22 2 2.37 - 3.84 22 (22) 0 0.18 - 3.84 22 3 3.92 - 5.99 13 3 1.96 - 3.84 22 2 1.40 - 5.99 13 7 9.26 56 22 23 2 22 23 9 0 6.96 1.21 1 1 1 0 1.00 1 2 22 23 0 0 1.84 0.85 1 2 22 23 0 1 0.1 1 0.69 33 (33) 0 0 0.04 1.00 33 (13,33) 0 2 0.05 1.00 33 (23,33) 0 0 0.10 1.00 33 (22,23) 2 1 1.03 1.00 57 pop 4 locus ada2 Expeaedvalueforsrnallestclasswasrnadeerpraltoone chi - 1.016 df - 1 G - 0.258 chal - 3.84 genotype - 11 12 22 (22) observed - 29 4 O 0 expected - 29.12 3.76 0.12 1.00 pop 4 locus gpi3 Expectedvalueforsmallestclasswasmadeequaltoone chi - 0.000 df - 1 G - -0.015 chal - 3.84 genotype - 1 1 12 22 (12,22) observed - 32 1 0 l erqaected - 32.01 0.98 0.01 1.00 pop 4 locus gr Expectedvalueforsmallestclasswasmadeequaltoonc chi - 1.001 df - 1 G - 0.050 chal - 3.84 genotype - 11 12 13 22 23 33 (33) observed - 39 - 2 - - 0 0 expected - 39.02 - 1.95 - - 0.02 1.00 pop 4 locus M13 Expected valueforsrnallestclasswasmadeequaltoone chi - 1.242 df - 1 G - 1.620 chal - 3.84 genotype - 1 1 12 13 14 15 16 17 18 24 25 26 27 28 33 34 35 36 44 45 46 47 48 55 56 57 58 68 77 78 88 (88) observed - 26 - - - - - - 10 - - - 0 0 expected - 26.69 - - - - - - 8.61 - - - 0.69 1.00 pop 4 locus idh4 Expectedvalueforsrnallestclasswasn'radeequaltoonc chi - 1.004 df - 1 G - 0.117 chal - 3.84 genotype - 11 12 22 (22) observed - 37 3 0 expeaed - 37.06 2.89 0.06 1.00 pop 4 locus M14 Expectedvalueforsmallestclasswasmadeetpaltoone chi - 1.004 df - 1 G - 0.108 chal - 3.84 genotype - 1 1 12 13 22 23 33 (33) observed - 40 - 3 - ~ 0 expected - 40.05 - 2.90 - - 0.05 1.00 pop 4 locus mdhpi Expected value forsmallest classwasmadeequaltoone chi - 0.089 df - 1 G - -0.308 «Val - 3.84 genotype - 1 1 12 22 (1 1) observed - 1 2 2 1 expected - 0.80 2.40 1.80 1.00 pop 4 locus mp1 chi - 2.139 df - 1 G - 2.142 chal - 3.84 genotype - 1 1 12 22 observed - 13 1 1 7 expected - l 1.04 14.92 5.04 23 38 67 58 pop 4 locus pgk2 chi - 0.214 df - 1 G - 0.229 chal - 3.84 genotype - 11 12 22 observed - 21 12 1 expected - 21.44 1 1.12 1.44 pop 4 lows dpepl Expectedvalueforsmallestclmwasmadeequaltoone chi - 1.030 df - 1 G - 0.397 eerl - 3.84 genotype - 1 l 12 22 (22) observed - 29 5 0 0 expected - 29.18 4.63 0.18 1.00 pop 4 locus tapepl ” Devratron ” chi - 8.882 df - l G - 12.330 chal - 3.84 genotype - 1 1 12 22 observed - 0 23 1 1 expected - 3.89 15.22 14.89 pop 4 locus sodl Expectedvalueforsmallestclasswasmadeequaltoonc chi - 2.647 df - 3 G - 2.167 chal - 7.81 genotype - 11 12 13 22 23 33 (33) observed - 12 15 3 2 3 0 0 expected - 12.60 13.20 3.60 3.46 1.89 0.26 1.00 pop 5 locus aat2 Expectedvalueforsmallestclasswasmadeequaltom chi - 1.077 df - l G - 0.573 chal - 3.84 genotype - 11 12 22 (22) observed - 12 4 0 0 expected - 12.25 3.50 0.25 1.00 pop 5 locus maatl Emectedvalueforsmallestclasswasmadeequaltoonc chi - 1.158 df - 1 G - 0.931 chal - 3.84 genotype - 11 12 13 22 23 33 (22,23,33) observed - 1 1 2 3 0 0 0 0 expected - 1 1.39 1.69 2.53 0.06 0.19 0.14 1.00 pop 5 locus adal Expectedvalue forsrnallest classwasmadeequaltoone chi - 0.000 df - 1 G - -0.029 chal - 3.84 gasotype - 1 1 12 22 (12,22) observed - 16 1 0 1 expected - 16.01 0.97 0.01 1.00 pop 5 locus M13 chi - 0.373 df . 1 G - 0.363 chal - 3.84 genotype - 1 1 12 13 14 15 16 17 18 24 25 26 27 28 33 34 35 36 44 45 46 47 48 55 56 57 58 68 77 78 88 (16.66.68.88) observed - 13 - - - - 1 - 4 - - 2 expected - 12.64 - - - - 0.82 - 4 89 0.16 - - 0.47 1 46 23 38 67 59 pop 5 locus mdh4 Emectedvalucforsmalleflclasswasmadeequaltoonc chi - 1.005 df - 1 G - 0.100 chal - 3.84 genotype - 11 12 13 22 23 33 (33) observed - 19 - 2 - - 0 0 expected - 19.05 - 1.90 - . 0.05 1.00 pop 5 loans mdbpr Expeaedvalueforsmallestclasswasmadeequaltoone chi - 0.123 df - 1 G - -0 482 chal - 3.84 genotype - 11 12 22 (11) observed - 1 3 5 1 expected - 0.69 3.61 4.69 1 00 pop 5 locus mp1 chi - 0.062 df - l G - 0.062 chal - 3.84 genotype - 11 12 22 observed - 6 6 2 expected - 5.79 6.43 1.79 pop 5 locus pgk2 Expectedvalue forsmallestclasswasmadeequaltoone chi - 0.003 df - 1 G - -0.112 chal - 3.84 genotype - 11 12 22 (22) observed - 7 5 1 1 expected - 6.94 5.12 0.94 1.00 pop 5 locus dpepl Expectedvalue forsrnallestclasswasmadeequaltoonc chi - 1.941 df - 1 G - 1.940 chal - 3.84 genotype - 11 12 22 (22) observed - 16 0 1 1 expected - 15.06 1.88 0.06 1.00 pop 5 locus tapepl chi - 0.014 df - 1 G - 0.014 chal - 3.84 genotype - 11 12 22 observed - 3 8 6 expected - 2.88 8.24 5.88 pop 5 locus sodl Expectedvalue forsmallestclasswasmadeerpsal toonc chi - 0.070 df . 2 G - 0.040 chal - 5.99 genotype - 11 12 13 22 23 33 (13,23,33) observed - 7 7 0 2 1 0 1 expected - 6.49 7.41 0.62 2.12 0.35 0.01 1.00 pop 6 locus aat2 chi - 0.018 df - 1 G - 0.018 ch111 - 3.84 genotype - 11 12 22 observed - 137 45 4 emected - 136.78 45.45 3.78 pop 6 locus maatl Expectedvalueforsmallestclasswasmadeerpaltoone chi - 1.460 df - 3 G - 1.106 chal - 7.81 genotype - 11 12 13 22 23 33 (22) observed - 106 12 43 0 3 6 0 expected - 104.84 11.78 45.55 0.33 2.56 4.95 1.00 pop6locusada1 Expectedvalueforsmallestclasswasmadcequaltoonc G . W ' observed . expected - pop6locusada2 1.008 df 0.278 chal - 3. 84 11 12 22 (22) 175 10 0 175.14 9.73 0.14 Expectedvalueforlnallestclasswasmadeetprsltoonc o . W ' observed - expected - pop6loarsah 1. 038 (If - 0.814 chal - 3. 84 11 12 22 (22) 169 17 0 169.39 16.22 0.39 Expectedvalueforsmallestclasswasrnsdeequaltoone chi . G - W - observed - expected - pop6locusgpi3 1.042 df - 0.422 chal - 3.84 11 12 22 (22) 17 4 0 17.19 3.62 0.19 Expectedvalueforsmallestclasswasmadeequaltoone chi . G - W ' observed - expected - pop6locusgr 1.000 df - 0.011 chal - 3.84 11 12 22 (22) 177 2 0 177.01 1.99 0.01 Expected value for smallest class was made equal to one chi . G . SW' observed - expected- observed- expected- 0.10 pop6locusidh4 1 g—O g—O t—O 1. 000 df 0.039 chal - 3.84 1 1 12 13 205 - 4 205.02 - 3.96 0.800 df - 0.905 chal - 5.99 1 1 12 13 24 25 26 38 39 44 58 59 66 99 (19,33,363839, 66, 68, 69, 89 ,99) 154 - 2 0 0 - - - 0 0 1 155.46 - 1.78 0.20 0.01 - - - 0.01 0.00 1.41 Expeaedvalueforsrnallestclasswasmadeerpraltoone chi - G . W ' oherved - expected - 0.236 df -1.396 chal - 3.84 11 12 22 (22) 191 10 1 190.18 11.64 0.18 — 23 15 28 68 0.20 33 (33) 0 0.02 61 pop 6 locus mdh4 Expectedvalueforsmallestclasswasmadcequaltoone chi - 1.008 df - 1 G - 0.294 chal - 3.84 genotype - 11 12 13 22 23 33 (33) observed - 200 - 1 1 - - 0 0 expeaed - 200.14 - 10.71 - - 0.14 1.00 pop 6 loans mdhpi Emectedvalueforsrnallestclasswasmadeequaltoone chi - 2.486 df - 3 G - 0.709 chal - 7.81 genotype - 1 1 12 13 22 23 33 (33) observed - 27 54 2 45 3 1 1 expected - 22.92 61.25 2.92 40.93 3.90 0.09 1.00 pop 6 locus mpi “ Deviation ” chi - 4.138 df - l G - 4.070 chal - 3.84 genotype - 1 1 12 22 observed - 79 63 25 expected - 73.12 74.77 19.12 pop 6 locus pgk2 chi - 0.000 df - 1 G - 0.000 chal - 3.84 genotype - 11 12 22 observed - 71 56 1 1 expected - 71.02 55.96 11.02 pop 6 locus dpepl chi - 0.229 df - 1 G - 0.210 chal - 3.84 genotype - 11 12 22 observed - 154 29 2 expected - 153.47 30.06 1.47 pop6 locustapepl “ Deviation “ chi - 17.641 df - 1 G - 18.015 chal - 3.84 genotype - 1 1 12 22 observed - 25 124 43 expected - 39.42 95.16 57.42 pop 6 locus sodl Expectedvalueforsmallestclasswasmadeequaltoone chi - 4.854 df - 3 G - 4.754 chal - 7.81 genotype - 11 12 13 22 23 33 (33) observed - 72 89 10 15 8 0 0 expected - 76.09 79.54 1 1.27 20.78 5.89 0.42 1.00 APPENDIX B APPENDIX B Clustering levels of difi’erent populations plotted in Figures 2 and 3, respectively, based on Nei’s (1978) unbiased genetic distance. Figure 2: Population or cluster Clustering numbers joined level Cycle 2 4 .00000 1 2 6 .00000 2 3 5 .00000 2 2 3 .00023 3 1 2 .00361 4 where 1 = Betsie River, 2 = Little Manistee River, 3 = Manistee River, 4 = Muskegon River, 5 = Platte River and 6 = Pere Marquette River. Figure 3: Population or cluster Clustering numbers joined level Cycle 3 .00072 1 1 2 .03 046 2 where 1 = Cowlitz River, 2 = Green River, and 3 = Lake Michigan. 62 APPENDIX C 63 APPENDIX C Allelic frequencies and deviation from Hardy-Weinberg equilibrium for individual watersheds and Lake Michigan pooled p0pulation. Locus. Swan Michigan allele and River population statistic (N =44) (N =257) sMT-l "' - ’100 1.000 1.000 MAT-2+ - ‘100 0.917 0.868 ‘85 0.083 0.132 mAAT-l + £100 0.849 0.798 '-77 0.058 0.047 ‘-104 0.093 0.155 ADA-l + ‘IOO 0.966 0.972 ‘83 0.034 0.028 ADA-2+ ‘IOO 0.989 0.961 '105 0.01 1 0.039 Ali-1+ '100 0.000 0.905 ‘86 0.000 0.095 ‘112 0.000 0.000 CPI-l ' 8100 1.000 1.000 CPI-2+ ‘IOO 1.000 1.000 ‘60 0.000 0.000 CPI-3+ ‘100 1.000 0.995 ‘105 0.000 0.005 ’93 0.000 0.000 CPI-H ‘IOO 1.000 1.000 GR+ ‘100 0.977 0.988 ‘85 0.000 0.000 *I l0 0.023 0.012 [DH-3+ ‘IOO ‘74 '142 ’94 ‘129 '136 [DH-4+ - ‘I00 '127 ‘50 LDH-3 ‘100 LDH-4 ‘100 LDH-5+ ‘100 ‘90 MDH-l ' . ‘100 MDH-Z‘ - ‘IOO MDH-3" - ‘l00 MOI-[4+ - ‘l00 ‘IZI ‘70 MDHP-l+ ‘IOO '92 ‘105 MDHP-Z ‘100 MPH- ‘IOO ‘l09 ’95 FOX-2+ 'IOO ‘90 POM-1+ *100 *210 PDPEP-2+ ‘IOO ‘107 0.864 0.01 1 0.000 0.000 0.1 14 0.01 1 0.932 0.057 0.01 1 1 .000 l .000 l .000 0.000 1 .000 l .000 l .000 0.943 0.000 0.057 0.4 1 7 0.556 0.028 1 .000 0.629 0.37 1 0.000 0.697 0.303 1 .000 0.000 0.849 0.151 0.884 0.006 0.000 0.004 0. 102 0.004 0.963 0.035 0.002 1 .000 1 .000 1 .000 0.000 1 .000 1 .000 1 .000 0.969 0.000 0.03 1 0.4 1 7 0.557 0.027 1 .000 0.656 0.344 0 000 (p<0.05) 0.713 0.287 1 .000 0.000 0.899 0.101 TAPEP-l + ‘100 0.295 0.424 ‘130 0.705 0.576 £350 0.000 0.000 (p<0.001) (p<0.001) PEP-LT+ "IOO 1 .000 l .000 'I I 0 0.000 0.000 5500- l + "'- 100 0.443 0.592 *-260 0.500 0.359 '580 0.057 0.048 mSOD *l00 1 .000 1 .000 Mean sample size per locus 40.5 2203* 5.1:. 1 .7 8.1 Mean No. of alleles per locus l .7 l .8 S. E. . 0.2 0.2 Percent ofloci that were palmmrphic“ 46.7 53.3 Heterozygosity direct count 0.102 0.1 29 1 standard error) 0.031 0.035 ”anonymity expected 0. 1 l4 0. l 24 1 standard error) 0.033 0.034 ‘ ‘ a locus was considered polymorphic i f more than one allele was detected. isoloci presented as two loci with allelic frequencies estimated using a maximum-likelihood approach reported by Waples ( l 9891. LIST OF REFERENCES CHAPTER ONE LIST OF REFERENCES, CHAPTER 1 Aebersold, P. B., Wmans, G. A., T eel, D. J., Milner, G. B., and F. M. Utter. 1987. M - fr 1:. h 1‘ 'l‘vmrhrno' i' ‘ ahu 0 'Q' emu 0. r‘n'i V2-1; 'n. NOAA Technical Report MS 61. US. Department of Commerce, Washington DC. Bartley, D., Bagley, M., Gall, G. and B. Bentley. 1992. Use of linkage disequilibrium data to estimate effective size of hatchery and natural fish populations. Conservation Biology 6:3 365-375. Buth, D. G. 1990. Genetic principles and the interpretation of electrophoretic data. 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