AN ABSTRACT OF THE THESIS OF
Kenneth P. Currens for the degree of Doctor of Philosophy in Fisheries Science presented on April 30, 1997. Title: Evolution and Risk in Conservation of Pacific Salmon.
Abstract approved: Redacted for Privacy Redacted for Privacy
Carl B. Schreck Hiram W. Li
Identifying appropriate units for conservation requires knowledge of evolutionary patterns and risks of managing at different geographical and genetic scales. I examined genetic diversity at different geographical scales among 11,400 rainbow trout
(Oncorhynchus mykiss) from 243 locations in 13 major river basins throughout much of their range and among coho salmon (0. kisutch) from 31 watersheds in Oregon, Washington, and northern California. I also developed a model of genetic vulnerability of managed populations that links sources of potential technological hazards, protective mechanisms and responses, and potential losses, using artificial propagation of Pacific salmon as an example. Across the range of rainbow trout, allozyme differences between inland and coastal populations were more localized than previously acknowledged. In contrast, evolutionary continuity was most related to stability and persistence of major river systems, such as upper Sacramento, Kiamath, and Columbia rivers. Isolated, pluvial lake basins that contained divergent groups of redband trout (rainbow trout with plesiomorphic characteristics associated with cutthroat trout, 0. clarki) were sources of evolutionary diversity within large river systems. Human effects on genetic organization occurred in local breeding populations to regional metapopulations. In coho salmon, regional differences in mitochondrial DNA existed among fish from Puget Sound, Columbia River, northern Oregon coastal streams, and southern coastal streams. Differences within regions lacked obvious geographical patterns but were most likely due to recent fish translocations and genetic drift. In the Umatilla River, Oregon, significant genetic differences were detected among rainbow trout, but temporal differences at sites were as great as differences among sites within tributaries. In 10 of 12 locations, rainbow trout became more similar to anadromous hatchery fish. Although small breeding sizes suggested a role for genetic drift, episodic gene flow from hatchery fish most likely explained temporal genetic changes. Program-specific genetic risk assessment of a large artificial propagation program in the Columbia River revealed that artificial supplementation would result in fewer hatchery-reared fish returning to the wild than were taken from the wild for brood stock, that proximate safeguards for reducing vulnerability were not available and appropriate, but that use of genetic reserves strengthened the program. © Copyright by Kenneth P. Currens April 30, 1997 All Rights Reserved Evolution and Risk in Conservation of Pacific Salmon
by
Kenneth P. Currens
A THESIS
submitted to
Oregon State University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Presented April 30, 1997 Commencement June 1997 Doctor of Philosophy thesis of Kenneth P. Currens presented on April 30, 1997
APPROVED:
Redacted for Privacy
Co-Major Professor, representing Fisheries Science
Redacted for Privacy
Co-Major Professor, representing Fisheries Science Redacted for Privacy
Head or Chair of Department isheries and Wildlife
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Dean of Gra te School
I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request. Redacted for Privacy Kenneth P. Currens, Author ACKNOWLEDGEMENT
I wish to acknowledge the support and patience of my graduate committee: Dr.
Carl Schreck, Dr. Hiram Li, Dr. George Bailey, Dr. Arthur Boucot, Dr. Peter Dawson, and Dr. Douglas Markie. I especially wish to thank Dr. Carl Bond for encouraging my interest in redband trout and Dr. Craig Busack for providing the intellectual stimulus, encouragement, and opportunity to develop genetic risk assessment. The results of my research would have been much less without the support of the following individuals and organizations: Bill Bakke, Jerry Bauer, Dave Buchanan, Dan Farnsworth, John Fortune,
Janet Hanus, Alan Hemniingsen, Dr. Sharon Krueger, The Mazamas, Northwest Indian
Fisheries Commission, Oregon Department of Fish and Wildlife, Oregon Trout, Dr. Larry
Riggs, Cameron Sharpe, Neil Ward, Dr. Richard Williams, and Jeff Zakel. TABLE OF CONTENTS
Page
INTRODUCTION 1
EVOLUTIONARY ECOLOGY OF REDBAND TROUT 4
2.1. Abstract 4
2,2. Introduction 4
2.3. Materials and Methods 12
2.4. Results 25
2.5. Discussion 43
2.5.1. Phylogeny of Redband Trout 44 2.5.2. Biogeography of Major Groups 45 2.5.3. Implications for Conservation 51
GENETIC VARIATION OF WILD RA1N3OW TROUT UNI)ER HATCHERY SUPPLEMENTATION 53
3.1. Abstract 53
3.2. Introduction 53
3.3. Materials and Methods 58
3.4. Results 65
3.4.1. Genetic Variation Among Samples 86 3.4.2. Pattern of Temporal Variation 88
3.5. Discussion 92
MITOCHONDRIAL DNA VARIATION iN OREGON COHO SALMON 101
4.1. Abstract 101
4.2. Introduction 101 TABLE OF CONTENTS (Continued)
Page
4.3. Materials and Methods 103
4.4. Results 107
4.4.1. Within Basin Comparisons: Hatchery versus Wild 107 4.4.2. Within Basin Comparisons: Wild Aggregations 111 4.4.3. AmongBasinComparisons 111
4.5. Discussion 113
5. A FRAIVIEWORK FOR EVALUATING GENETIC VULNERABiLITY 119
5.1. Abstract 119
5.2. Preface 119
5.3. Introduction 121
5.3.1. The Problem: Wise Use and Technological Risks 121 5.3.2. Basis for Genetic Risk 122 5.3.3. Policy Directives for Genetic Risk Analysis 123 5.3.4. Overview of Document 123
5.4. Foundations of Genetic Risk Assessment 124
5.4.1. What Are The Important Questions? 124 5.4.2. What Are The Constraints? 124 5.4.3. Definitions and Concepts 127 5.4.4. Model Characteristics and Assumptions 135
5.5. Four Steps in Genetic Risk Assessment 136
5.5.1. Identify The Vulnerability System 136 5.5.2. Characterize Sources, Endpoints, and Safeguards 145 5.5.3. Inventory Safeguards 146 5.5.4. Describe Vulnerability 149
5.6. Genetic Variability of the Yakima Fishery Project 153
5.6.1. Purpose 153 TABLE OF CONTENTS (Continued)
Page
5.6.2. Materials and Methods 153 5.6.3. Results: Vulnerability of Yakima River Spring Chinook Salmon 159 5.6.4. Results: Vulnerability of Yakima River Summer Steelhead 171 5.6.5. Conclusions 186
6. CONCLUSION 189
BIBLIOGRAPHY 192
APPENDICES 213
Appendix A. Allele Frequencies and Samples Sizes (N) for Polymorphic Loci in Rainbow Trout 214 Appendix B. Ratings of Reliability Components for Yakima River Spring Chinook Salmon 263 Appendix C.Ratings of Reliability Components for Yakima River Steelhead 268 LIST OF FIGURES
Figures Page
2.1. Locations where samples were collected 13
2.2. Canonical variate analysis of allozyme variation among 16 major evolutionary groups of rainbow trout 26
2.3. Consensus cladogram of 29 equally parsimonious trees for rainbow trout based on allozyme characters 34
2.4. Phenogram of evolutionary relationships of rainbow trout based on neighbor-joining method and Cavalli-Sforza-Edwards chord distance 41
3.1. Umatilla River Basin 57
3.2. Similarity of mtDNA haplotypes and locations of rainbow trout in which they were found 83
3.3. Genetic similarity of rainbow trout in the Umatilla River based on allozyme variation 90
3.4. Genetic similarity of rainbow trout in the Umatilla River based on mitochondrial DNA variation 91
3.5. Relationship between genetic similarity of wild juvenile rainbow trout to 1992 hatchery brood fish and genetic variation between cohorts from the same location for 13 locations in the Umatilla River 93
4.1. Locations where coho salmon were collected 104
4.2. Phenogram of geographical mitochondrial DNA variation in coho salmon 112
4.3. Ordination of mitochondrial nucleotide divergence among coho salmon by nonmetric multidimensional scaling 114
5.1. The vulnerability system for technological hazards in natural resource management 138
5.2. The confine-and-control vulnerability system for technological hazards 139
5.3. Components of vulnerability control mechanisms in natural resource management 143 LIST OF FIGURES (Continued)
Figure Page
5.4. Fault tree analysis of collecting a representative sample brood stock from a wild population of fish 148
5.5. Relative probability of extinction in 100 years for Yakima River spring chinook salmon at different spawning population sizes with and without supplementation 162
5.6. Relative persistence (in generations) at 5% risk of extinction for Yakima River spring chinook salmon at different spawning population sizes with and without supplementation 163
5.7. Relative vulnerability for Yakima River spring chinook salmon 172
5.8. Relative probability of extinction in 100 years for Yakima River steelhead at different spawning population sizes with and without supplementation 176
5.9. Relative persistence (in generations) at 5% risk of extinction for Yakima River steelheadat different spawning population sizes with and without supplementation 177
5.10. Relative vulnerability for Yakima River steelhead 187
6.1. Hierarchical relationship of genetic organization over space and time 190 LIST OF TABLES
Table Page
2.1. Sample names, origins, and year of collection 15
2.2. International Union of Biochemistry (I.U.B.) enzyme names, Enzyme Commission (E.C.) numbers, loci examined in this study, tissues and buffers 21
2.3. Description of character states 23
2.4. Nested analysis of allele frequency variation amon and within basins 28
2.5. Character states for major groups of rainbow trout 30
2.6. Dominat patterns of character evolution in rainbow trout based on phylogenetic analysis 36
3.1. International Union of Biochemistry (T.U.B.) enzyme names, Enzyme Commission (E.C.) numbers, loci examined in this study, tissues and buffers 60
3.2. Rainbow trout mtDNA restriction fragment patterns for restrictionenzymes used in this study that revealed interpopulation variability 63
3.3. Allozyme frequencies and sample size (n) for polymorphic loci in Umatilla River rainbow trout 66
3.4. Composite haplotype definitions 82
3.5. Distribution of composite mtDNA haplotypes in rainbow trout in the Umatilla River 84
3.6. Contingency table analysis of genetic variation in Umatilla River rainbowtrout 87
3.7. Genetic differences between 1992 and 1994 cohorts of rainbow trout from 13 locations in the Umatilla River 89
4.1. Composite mtDNA haplotypes for coho salmon 108 LIST OF TABLES (Continued)
Table Page
4.2. Frequencies of mitochondrial DNA haplotypes and nucleotide diversity in coho salmon from different rearing aggregations 109
5 1. Hierarchy of components and criteria for assessing reliability of proximate control mechanisms in supplementation 157
5.2. Components and criteria of assessing reserves as ultimate control mechanisms 158
5.3. Reliability and resilience scores for proposed supplementation of Yakima River spring chinook 164
5.4. Reliability and resilience scores for proposed supplementation of Yakima River steelhead 178 EVOLUTION AND RISK IN CONSERVATION OF PACIFIC SALMON
1. INTRODUCTION
In 1981, the well-known fishery biologist Peter Larkin wrote, "it is rather surprising that there is currently no explicit working hypothesis of population genetics in the salmon management kit." Perhaps more surprising is that 16 years later the statement is still true. Larkin (1981) was concerned that without appropriate tools, fishery managers would be unable to address the need to conserve all genetically differentiated, spawning aggregations of Pacific salmon and trout (genus Oncorhynchus) while maintaining opportunities for harvest. The fundamental unit of fishery management was the "stock," which out of necessity was often an aggregation of different breeding populations
(Moulton, 1939). By 1981, however, an increasing body of biochemical genetic data indicated that genetic population structures of Pacific salmon and trout were much more complex than traditional taxonomy or life-history variation suggested (Withier et al.,
1982). Consequently, the fundamental problem for fish geneticists became describing stocks using genetic data (Berst and Simon, 1981; Allendorfet al., 1987; National
Research Council, 1995). Ironically, although this has resulted in nearly 25 years of extensive research on genetic variation in salmon, resource managers still lack genetic tools for deciding when or how to aggregate or split different groups for management purposes. In this thesis, I argue that the search for a fundamental genetic unit of fishery managementor stockis meaningless unless it occurs in a framework for evaluating genetic risks and hazards at different geographical, temporal, and evolutionary scales.
The stock concept was born from two very different parents. One was the need to 2 manage harvest of commercially important species. The second was the desire to understand the tremendous phenotypic diversity of salmon that were widely distributed throughout North America, migrated extensively in the ocean, yet returned to their natal streams to spawn, making both local adaptation and local fisheries and aquaculture possible. Out of this marriage came the realization that supply of salmon to fisheries depended on local populations, which managed as stocks were the fundamental units of fishery management (Moulton, 1939; Thompson, 1959, 1965; Larkin, 1972). Ricker
(1972) reviewed the morphological, behavioral, and physiological evidence for local adaptation in Pacific salmon and reworked the biological species concept of Mayr (1942,
1963) to define stocks as "the fish spawning ina particular lake or stream (or portion of it) at a particular season, which fish to a substantial degree do not interbreed with any group spawning in a different place, or in the same place at a different season." Ricker (1972) did not define a "substantial degree" of interbreeding. In contrast, as allele frequency data from protein electrophoretic studies becamemore available, other biologists began to define stocks more precisely as demes, randomly mating populations,or local populations based on genetic theory (Booke, 1981; Ihssen et al., 1981). In most fisheries, these groups were not easily identifiable. In addition, the growing impetus to protect declining distinct populations of Pacific salmon under the Endangered Species Actwas tempered by congressional direction to list populations sparingly (Waples, 1991, 1995). Geneticists had found a fundamental unit, but it was no longer thesame unit used for managing fisheries or for conservation.
The following four chapters provide the empirical evidence for considering genetic diversity at different hierarchical levels and usinga systematic approach to evaluate 3 genetic risks and hazards of different management actions. At the broadest level, Chapter
2 examines the relationships of evolutionary diversity in rainbow trout (Oncorhynchus my/ciss) to persistence of different kinds of habitat over thousands of years and a large portion of the species range. In contrast, Chapter 3 focuses on the detailed, temporal dynamics of genetic structure of rainbow trout within a single river basin under hatchery supplementation. Chapter 4 documents previously unrecognized genetic variation in
Oregon coho salmon (0. kisutch). Finally, Chapter 5 lays the conceptual foundation for evaluating genetic vulnerability and applies it to a large hatchery program in the Columbia
River. 4
2. EVOLUTIONARY ECOLOGY OF R}DBAND TROUT
2.1. Abstract
I examined genetic differences at 28 enzyme encoding loci among11,400rainbow trout (Oncorhynchus mylciss) from 243 locations in 13 major river basins throughout the range of rainbow trout. These included redband trout (geographical races of rainbow trout with plesiomorphic traits associated with cutthroat trout, 0.c/arid)in rivers and pluvial lake basins of the Northern Great Basin that were unglaciated but that have had largely internal drainage with no connection to the Pacific Ocean. Major genetic differences existed among rainbow trout of these basins and inland and coastal rainbow trout of Cascadia, the two geographical races previously thought to represent the greatest evolutionary divergence in rainbow trout. However, redband trout did not comprise a distinct monophyletic group. In contrast, evolutionary continuity of all groups was most related to stability and persistence of three major river systems: the upper Sacramento,
Klamath, and Columbia. Isolated, pluvial lake basins were sources of evolutionary diversity within large river systems rather than isolated refuges. Differences between inland and coastal rainbow trout, which have been largely attributed to isolation, extinction, and recolonization after Pleistocene glaciation, were localized to major rivers of Cascadia.
2.2. Introduction
Evolutionary diversity in fishes reflects different patterns of geographical isolation, ecological opportunities for dispersal, and long-term aquatic stability. Rainbow trout, distributed in streams and lakes along both eastern and western rims of the North Pacific 5
Ocean (Scott and Crossman, 1973; Okazaki, 1983, 1984, 1985), have evolved in highly dynamic landscapes (McPhail and Lindsey, 1986; Minckley et al., 1986). Large parts of this region were sculpted by glacial, volcanic, and tectonic forces during Pleistocene and
Recent times (McKee, 1972; Baldwin, 1981, Porter, 1983) that displaced fishes, much of the present distribution and evolutionary diversity of rainbow trout may have been forged by chance extinctions, recolonizations, and relatively recent geographical isolation. In contrast, broad patterns of diversity in North American freshwater fishes was associated with long periods of stable aquatic habitat (Smith, 1981),
In this study, which encompassed rainbow trout in habitats of the Columbia River and northern Great Basin, I examined the role of long-term stability of aquatic habitats on evolutionary diversity. Rainbow trout in this region may not have had access to persistent, stable habitats because of Pleistocene changes to the landscape. Alternatively, evolutionary diversity in these rainbow trout may reflect two sources of stable aquatic habitat that did persist: large river systems and large lakes. Both existed during Pliocene to Recent times in the geographical range of rainbow trout and its sister species.
Fossilized trout have been documented in Miocene to Recent deposits (Taylor and Smith,
1981; Allison and Bond, 1983; Stearly, 1989). Ancient, isolated lake basins still exist in northern Great Basin where rainbow trout of unknown origin, commonly known as redband trout, also occur. Redband trout are characterized by plesiomorphic traits associated with cutthroat trout, 0. clarki, (Currens et al., 1990; Behnke, 1992).
The backbone of this study area is the Cascade Range, which extends from southern British Columbia to Lassen Peak in northern California. These mountains formed a major barrier to westward draining streams as arc volcanics 12-14 million years 6 ago began lifting the High Cascades up to 1800m above the older crustal arch of the
Western Cascades (Hammond, 1979; Alt and Hyndman, 1995). Local faulting, volcanic eruptions of ash, and lava flows continued intermittently to Recent times throughout the
Cascades and interior basins to the east. These disturbances most likely extinguished many populations and isolated others behind lava dams or above barrier waterfalls.
However, three major rivers cut through the Cascade Range. The northern most, the
Columbia River, is the dominant river of Cascadiathe Pacific Northwest region characterized by Cordilleran topography and habitats that were largely covered by glaciers during the Pleistocene (McKee, 1972). Many freshwater fishes that colonized Puget
Sound and drainages further north as the glaciers receded came from the Columbia River
(McPhail and Lindsey, 1986). The southern most river, the Sacramento River, drains interior basins and eastern slopes of the Cascades through the Pit and McCloud rivers
(Baldwin, 1981). Large rivers, such as the Columbia and Sacramento, have apparently maintained most of their present courses for at least the last 14 my by cutting through lava dams, over waterfalls, and filled canyons (McKee et al., 1977; Swanson and Wright,
1979; Alt and Hyndman, 1995). The Snake River, however, which currently drains north to the Columbia River, was an independent tributary that flowed to the southwest
(Wheeler and Cook, 1954; Malde, 1965) and established temporary connections with the
Pit River, Kiamath River, and the Lahontan Basin (Taylor, 1960; Smith, 1981; Taylor and
Smith, 1981). The Kiamath River is the smallest of the three rivers that currently breach the Cascade Range. It drains Upper Kiamath Lake and then joins the lower Klamath River as it cuts its way westward through the older Kiamath Mountains (Baldwin, 1981).
Unlike late Ivliocene or Pliocene Columbia and Sacramento rivers, connection between the 7 older lower Klamath River and Upper Klamath Lake, which once had no outlet to the ocean (Russell, 1884; Hubbs and Miller, 1948), was established when the water levels in the lake rose and spilled over volcanic divide of more recent origin (Peacock, 193 1;
Anderson, 1941; Pease, 1965; Moyle, 1976).
East of the Cascades and south of the Columbia River, lie Oregon's desert lake basins. Two basinsFort Rock and Harneyoccur in the High Lava Plains, a physiographic province that has been covered repeatedly by lava flows and volcanic ash since Miocene rift volcanics began (Piper et al., 1939; Peterson and Groh, 1963; Axeirod,
1968; Green et al., 1972; Suppe et al., 1975). Five additional basins that contain rainbow troutUpper Klamath Lake, Goose Lake, Chewaucan, Warner Lakes, and Catlow
Valleyare a northwestern extension of the Basin and Range physiographic province of
Utah, Nevada, Arizona, and northeastern California into the Pacific Northwest (Hubbs and
Miller, 1948; Minckley et al., 1986; Sigler and Sigler, 1987). These cold, arid basins, most of which are above 1200m elevation, are characterized by shallow saline or playa lakes that formed in depressions or grabens between fault block mountains that rise to
2960 m (Russell, 1884; Fuller and Waters, 1929). Of these basins, only Upper Klamath
Lake drains to the sea, although Goose Lake has oveflowed into the Pit River during historical times (Hubbs and Miller, 1948; Baldwin, 1981).
Pleistocene glaciation affected the Columbia River and northern Great Basin differently. Large lakes were present in both areas, but they most likely had different consequences for fish, At maximum height of Fraser glaciation 15,000 years ago, one third of the entire Columbia River was covered by ice (Clague et al., 1980, Waitt and
Thorson, 1983). Tongues of ice extended down tributary valleys of the Columbia River 8 creating large glacial impoundments. The Okanogan ice lobe created Lake Columbia, which drained with enough force and volume to carve Grande Coulee (Flint and Irwin,
1939; McKee, 1972). Glacial impoundment of the Clark Fork near Pend Oreille, Idaho, created Lake Missoula, an icy lake at least 300 km long. When Glacial Lake Missoula drained in less than two weeks, it had catastrophic effects, scouring the scablands of eastern Washington and creating large, ephemeral lakes downstream where the river was constricted (Bretz, 1969; Baker and Barker, 1985). Cycles of ice and floods apparently occurred several times during the Pleistocene (Bunker, 1982). In contrast, Pleistocene glaciation in the northern Great Basin was largely confined to local montane areas
(Morrison, 1965; Baldwin, 1981). Vast pluvial lakes formed during that period
(Newberry, 1870, 1871; Russell, 1884; Waring, 1908; Feth, 1961; 1964; Snyder et al.,
1964) from increases in precipitation and cooler temperatures that reduced evapotranspiration rates (Morrison, 1965; Muffin and Wheat, 1979). These lakes expanded habitat for fishes. In fact, zoogeographical patterns offered the best evidence of drainage relationships that were obscured by Pliocene to Recent volcanics and faulting
(Snyder, 1908a; Hubbs and Miller, 1948; Minck!ey et al., 1986). Fluctuations of pluvial waterlevels in the northern Great Basin (Meinzer, 1922; Antevs, 1925; Snyder and
Langbein, 1962; Muffin and Wheat, 1979), development of soils, distribution of plant microfossils, dune activity, treelines, and peat formation (Morrison, 1961a-b, 1964, 1965;
Mehringer, 1977), however, indicated that climatic variability and associated environmental challenges to fishes during these times may have been as great as those of the present. 9
Based on limited fossil evidence, most western trouts probably originated near the
Pliocene-Pleistocene border (Miller, 1972), although it may have been earlier (Stearly,
1992). Rainbow trout were first described from coastal streams west of the Cascade
Mountains (Walbaum, 1792; Richardson, 1836). East of the Cascades, however, the morphological diversity of rainbow trout-like fishes found behind apparently ancient, formidable barriers to dispersal resulted in taxonomic confusion that persists to this day.
Rainbow trout east of the Cascades, currently referred to as redband trout, have been considered new or undescribed species (Girard, 1856, 1859; Suckley, 1860, 1874;
Fowler, 1912; Schreck and Behnke, 1971; Miller, 1972; Wilmot, 1974; Gold, 1977;
Behnke, 1979; Minckley et al., 1986), cutthroat trout, 0. clarki (Bendire, 1882; Cope,
1884, 1889; Evermann and Meek, 1989; Snyder, 1908a; Hubbs and Miller, 1948), or hybrids of rainbow and cutthroat trouts (Hubbs and Miller, 1948; Needham and Behnke,
1962). The most recent genetic and phylogenetic studies of redb and trout indicated that they are rainbow trout with plesiomorphic traits that have been lost in coastal rainbow trout (Currens et al., 1990; Behnke, 1992).
Because redband trout shared no unique biochemical or morphological traits throughout their range (Currens et al., 1990; Behnke, 1992), considerable debate has occurred over the geographical origins of these fish and whether they represent ecophenotypes; multiple lineages resulting from vicariance of a widely distributed species; or an ancient, single (but yet unsubstantiated) lineage of western trout. Many biologists combine all rainbow trout-like fishes into a single group (Mottley, 1934b; Needham and
Gard, 1959), recognizing that many morphological or behavior traits in trout could be environmentally labile (Mottley, 1934a, 1937; Taning, 1952; Garside, 1966; Kwain, 1975) 10 or the result of convergent or parallel evolution (Behnke, 1972; Allendorf, 1975; Wishard et al., 1984). Cope (1884) interpreted the desiccating Pluvial lakes in Oregon as remnants of a vast Oregon Lake, which provided an explanation for how rainbow trout became isolated in different basins. In contrast, Wilmot (1974) argued that dispersal of primitive trout along the north Pacific Coast from Asia led to one or more invasions of interior basins. In interior basins, the entrapment of freshwater resident populations led to the current diversity of rainbow trout, but populations remaining along the coast gave rise to coastal rainbow trout. Because the species of western trout with the most primitive traits are associated with the Gulf of California, however, Behnke (1992; Schreck and Behnke,
1971; Legendre et al,, 1972; Stearly, 1992) has argued that rainbow trout invaded from the south, entering the pluvial lake basins from the Sacramento River during high water periods, and finally arriving in the Columbia and Fraser rivers.
In contrast, population geneticists have attributed regional patterns of genetic variation to disruption caused by glacial advances, retreats, and local tectonic activity.
Allozyme surveys of geographical, genetic patterns in populations of rainbow trout with access to the ocean have documented large allelic frequency differences east and west of the Cascades.Inland rainbow trout in these rivers had morphological and karyotypic traits typical of redband trout (Behnke, 1981, 1992; Currens et al., 1990; Gold, 1977;
Thorgaard, 1983). However, they also had high frequencies of LDHB2*76 and low variation at sSOD-1 * compared to coastal populations west of the Cascades (Mlendorf
1975; Okazaki, 1984; Parkinson, 1984; Berg, 1987; Reisenbichier et al., 1992). Okazaki
(1984) noted that time of divergence of inland and coastal forms of rainbow trout, based on molecular inference, corresponded to the beginning of the last glacial period about ii
55,000 ybp. He attributed the difference to isolation and subsequent dispersal from two different glacial refligia: inland rainbow trout in ice free portions of the Columbia and the northern Great Basin and coastal rainbow trout near the Kamchatkan Peninsula in the eastern Pacific Ocean. Intermittent and partial barriers caused by volcanic and tectonic instability near the Cascade divide, as well as the tendency of migratory trout to return to their natal streams to spawn, may have maintained differences between inland and coastal forms in parapatry. Not all data support this pattern, however. Rainbow trout unlike either coastal or inland forms may have been isolated in a glacial refuge of the Athabasca
River, an Arctic Ocean drainage, for least 64,000 years (Carl et al., 1994).Additionally, limited data on northern Great Basin and Columbia River populations isolated by ancient barriers suggested major evolutionary divergence during long periods of isolation from inland populations that have had recent access to the ocean (Busack et al., 1980; Wishard et al., 1984; Berg, 1987; Currens et al., 1990). If ancient rainbow trout occurred in
Pacific coast drainages prior to the last glacial period, as data suggest,were populations in coastal streams really displaced by coastal forms from the north? In this study, I examine geographical patterns of variation in Columbia River and northern Great Basin populations to determine whether these reflect patterns of extinction, recolonization and recent isolation, as suggested by the glacial and tectonic history of the region,or long-term persistence major evolutionary lineages, as suggested by thepresence of ancient rivers and lakes. 12
2.3. Materials and Methods
Rainbow trout were collected from 241 locations in 13 major basins throughout
Washington, Oregon, and Idaho (Fig. 2.1, Table 2.1). Two non-native hatchery strains,
Oak Springs and Cape Cod, were included to represent cultured strains from coastal
California that have been released into streams in other states (Dollar and Katz, 1964;
MacCrimmon, 1971; Kinunen and Moring, 1978; Busack and Gall, 1980). In addition,
217 coastal cutthroat trout (Oncorhynchus ciarki clarki) were collected from 12 locations in the Coquille River, Oregon, to serve as an outgroup.
Specimens were frozen immediately on dry ice and stored at -20 or -80 C.
Procedures for protein electrophoresis were those of Aebersold et aL (1987).
Nomenclature for enzymes and loci (Table 2.2) followed Shaklee et al. (1990). Data for the two non-native hatchery samples and samples from the lower Deschutes River were published in Currens et al. (1990) but were included here with revised nomenclature and allele designations. Average heterozygosity was calculated for each locus using Hardy-
Weinberg expectations and averaged over all loci. Log likelihood ratio tests (G-test) were used to test for goodness of fit to Hardy-Weinberg expectations and allelic homogeneity among samples.
Four different analyses were used to examine geographical patterns of variation among rainbow trout. Nested G-tests were used to test homogeneity of samples among and within major basins and among and within tributaries within basins. Only loci with frequencies of the most common allele less than 0.95 were included; rare alleles were combined with more frequent classes. Canonical variates analysis (CVA), based on multivariate analysis of variance of arcsin square root transformed allele frequencies,was 13
Figure 2.1. Locations where samples were collected. Location numbers correspond to map numbers in Table 2.1. Upper case letters A-Q indicate major evolutionary groups of rainbow trout: A, Lower Columbia River; B, Mid-Columbia River; C, White River; D, Upper Columbia River; E, Clearwater River; F, Salmon River; G, Snake River; H, Harney Basin; I, Catlow Valley; J, Chewaucan Basin; K, Fort Rock Basin; L, Goose Lake Basin; M, Upper Kiamath Lake headwater populations; N, Upper Kiamath River and Lake; 0, Warner Valley; Q, Coastal Klamath Mountains. R is coastal cutthroat trout. 14
18
WASHINGTON IDAHO I
123- 25-126 127 12 1-122 C 74 36- 137 156 3 -33 157 8 30 6-105 158- 163 106- 138- 155 116
133- 134 35 7 166- 167
S
3 3-15 51-63 E
195- K 196 OREGON 205-206 219- 224 189- 200- ,L194 229- ,_204J C 243 f1 0 23 235-/1e' I 234 207- 240 p 197-199 211-218 210 188 225
CALIFORNIA J-I p'TJ I I I o 50 100 150 Kilometers
Figure 2.1. 15 Table 2.1. Sample names, origins, and year of collection. Map numbers correspond to locations in Figure 2.1. Codes for lineages: A, Lower Columbia River; B, Mid-Columbia River; C, White River; D, Upper Columbia River; B, Clearwater River; F, Salmon River; G. Snake River; H, Harney Basin; I, Catlow Valley; J, Chewaucan Basin; K, Fort Rock Basin; L, Goose Lake Basin; M, Upper Kiamath Lake headwater populations; N, Upper Kiamath River and Lake; 0, Warner Valley; P, Exotic Hatchery Strains; Q, Coastal Klamath Mountains; R, Coastal cutthroat trout.
Lineage Map Collection Code No. Year Origin A 1 Grays River 1985 Grays River A 2 Elochoman winter strain 1983 Beaver Creek Hatchery A 3 Big Creek winter strain 1983 Big Creek Hatchery A 4 Big Creek winter strain 1985 Big Creek Hatchery A 5 Big Creek winter strain 1983 Eagle Creek Hatchery A 6 Big Creek winter strain 1985 Eagle Creek Hatchery A 7 Cowlitz late winter strain 1983 Cowlitz Hatchery A 8 Cowlitz summer strain 1983 Cowlitz Hatchery A 9 Cowlitz winter strain 1983 Cowlitz Hatchery A 10 South Fork Toutle River 1985 Toutle River A 11 Coweeman River 1985 Coweeman River A 12 Skamania summer strain 1985 South Santiarn Hatchery A 13 Skamania summer strain 1984 Leaburg Hatchery A 14 Skamarna summer strain 1985 Leaburg Hatchery A 15 Skamama summer strain 1985 Mckenzie Hatchery A 16 Skamania summer strain 1983 Skamania Hatchery A 17 Skamania summer strain 1985 Skainania Hatchery A 18 Skamania winter strain 1985 Skamania Hatchery A 19 Eagle Creek winter strain 1983 Eagle Creek Hatchery A 20 Eagle Creek winter strain 1985 Eagle Creek Hatchery A 21 Willamette winter strain 1983 Marion Fork Hatchery A 22 Calapooia River 1983 Willamette River A 23 Calapooia River 1984 Willamette River A 24 Thomas Creek 1983 South Santiam, Willainette River A 25 Thomas Creek 1984 South Santiam, Willarnette River A 26 Thomas Creek 1985 South Santiam, Willamette River A 27 Wiley Creek 1984 South Santiam, Willarnette River A 28 Wiley Creek 1985 South Santiam, Willamette River A 29 Sandy River 1984 Sandy River A 30 Hamilton Creek 1985 Hamilton Creek A 31 Neal Creek 1985 Hood River A 32 Wind River 1984 Wind River A 33 Wind River 1985 Wind River B 34 Eightmile Creek #1 1993 Eightmile Creek B 35 Eightmile Creek #2 1993 Eightmile Creek B 36 Fifteenmile Creek 1983 Fifteenmile Creek B 37 Fifleenmile Creek 1984 Fifteemnile Creek B 38 Bakeoven Creek 1984, 1985 Deschutes River B 39 Buck Hollow Creek 1984, 1985 Deschutes River 16 Table 2.1. Continued.
Lineage Map Collection Code No. Year Origin B 40 Deschutes resident strain 1984, 1985 Deschutes River B 41 Deschutes River 1984, 1985 Deschutes River B 42 Lower Nena Creek 1984, 1985 Deschutes River B 43 Mid-Nena Creek 1984, 1985 Deschutes River B 44 Upper Nena Creek 1984, 1985 Deschutes River B 45 Big Log Creek 1984, 1985 Trout Creek, Deschutes River B 46 Lower East Foley Creek 1984, 1985 Trout Creek, Deschutes River B 47 Upper East Foley Creek 1984,1985 Trout Creek, Deschutes River B 48 Deschutes summer strain 1983 Round Butte Hatchery B 49 Deschutes summer strain 1985 Round Butte Hatchery B 50 Deschutes summer strain 1984 Round Butte Hatchery B 51 Crooked River gorge 1993 Crooked River, Deschutes River B 52 Lower Crooked River 1993 Crooked River, Deschutes River B 53 Bowman Dam 1993 Crooked River, Deschutes River B 54 Mckay Creek 1993 Crooked River, Deschutes River B 55 Ochoco Creek 1993 Crooked River, Deschutes River B 56 Marks Creek 1993 Crooked River, Deschutes River B 57 Horse Heaven Cr 1993 Crooked River, Deschutes River B 58 Pine Creek 1993 Crooked River, Deschutes River B 59 Lookout Cr 1993 Crooked River, Deschutes River B 60 Howard Creek 1993 Crooked River, Deschutes River B 61 Fox Canyon Cr 1993 Crooked River. Deschutes River B 62 Deep Creek 1993 Crooked River, Deschutes River B 63 Deer Cr 1993 Crooked River, Deschutes River B 64 Deardorif Creek 1984 Main stem Joim Day B 65 Deardorif Creek 1985 Main stem John Day B 66 Vinegar Creek 1984 Middle Fork John Day B 67 Vinegar Creek 1985 Middle Fork John Day B 68 Granite Creek 1984 North Fork John Day B 69 Meadow Creek 1985 North Fork John Day B 70 Grasshopper Creek 1987 South Fork John Day B 71 South Fork headwaters 1987 South Fork John Day B 72 Izee Falls 1987 South Fork John Day B 73 South Fork at Rockpile Ranch1987 South Fork John Day B 74 White Creek 1984 Klickitat River B 75 Willow Creek 1984 Columbia River B 76 North Fork Umatilla River 1992 Umatilla River B 77 North Fork Umatilla River 1994 Umatilla River B 78 Buck Creek 1992 Urnatilla River B 79 Buck Creek 1994 Uniatilla River B 80 Thomas Creek 1992 Umatilla River B 81 Thomas Creek 1994 Umatilla River B 82 South Fork Umatilla River 1992 Umatilla River B 83 South Fork Umatilla River 1994 Umatilla River B 84 Camp Creek 1992 Umatilla River B 85 Camp Creek 1994 Umatilla River 17 Table 2.1. Continued.
Lineage Map Collection Code No. Year Origin B 86 North Fork Meacham Creek 1992 Urnatilla River B 87 North Fork Meacham Creek 1994 Umatilla River B 88 Upper Meacham Creek 1992 Urnatilla River B 89 Upper Meacham Creek 1994 Urnatilla River B 90 Lower Squaw Creek 1992 Urnatilla River B 91 Upper Squaw Creek 1992 Umatilla River B 92 Squaw Creek 1994 lJmatilla River B 93 McKay Creek 1992 Urnatilla River B 94 McKay Creek 1994 Umatilla River B 95 East Birch Creek 1992 Uinatilla River B 96 East Birch Creek 1994 Umatilla River B 97 Pearson Creek 1992 Umatilla River B 98 Pearson Creek 1994 Urnatilla River B 99 West Birch Creek 1992 Umatilla River B 100 West Birch Creek 1994 Urnatilla River B 101 East Fork Butter Creek 1992 Umatilla River B 102 East Fork Butter Creek 1994 Umatilla River B 103 Bingham Springs 1983 Umatilla River B 104 Umatilla summer strain 1984 Oak Springs Hatcheiy B 105 Umatilla summer strain 1992 Threernile Dam, Urnatilla River B 106 Touchet River 1985 Walla Walla River B 107 Walla Walla River 1985 WaIla Walla River C 108 Lower White River 1984 White River C 109 Lower Tygh Creek 1984 White River C 110 Upper Tygh Creek 1984 White River C 111 Jordan Creek 1984 White River C 112 Little Badger Creek 1984 White River C 113 Threemile Creek 1984 White River C 114 Rock Creek 1984 White River C 115 Gate Creek 1984 White River C 116 Barlow Creek 1984 White River D 117 Fawn Creek 1984 Methow River D 118 Wells summer strain 1983 Wells Darn, Upper Columbia River D 119 Mad River 1984 Entiat River D 120 Peshastin Creek 1985 Wenatchee River D 121 Satus Creek 1983 Yakirna River D 122 Satus Creek 1984 Yakima River E 123 Mission Creek 1985 Clearwater River E 124 Big Canyon Creek 1985 Clearwater River E 125 Dworshak summer strain 1985 Dworshak Hatchery E 126 Pahsimeroi B strain 1985 Hagerman Hatchery B 127 Fish Creek 1985 Lochsa River B 128 Meadow Creek 1985 Selway River F 129 Sheep Cr 1985 Salmon River F 130 Chamberlain Creek 1985 Salmon River F 131 Horse Creek 1985 Salmon River 18 Table 2.1. Continued.
Lineage Map Collection Code No. Year Origin F 132 Indian Creek 1985 Middle Fork Salmon River F 133 Johnson Creek 1985 South Fork Salmon River F 134 Secesh River 1985 South Fork Salmon River F 135 Sawtooth strain 1985 Sawtooth Hatchery G 136 Tucannon River 1984 Tucannon River G 137 Tucannon River 1985 Tucannon River G 138 Fly Creek 1983 Grande Ronde River G 139 Fy Creek 1984 Grande Ronde River G 140 Limber Jim Creek 1983 Grande Ronde River G 141 Sheep Creek 1984 Grande Ronde River G 142 Chicken Creek 1984 Grande Ronde River G 143 Meadow Creek 1992 Grande Ronde River G 144 Ladd Creek 1992 Grande Ronde River G 145 Wallowa summer strain 1984 Wallowa Hatchery G 146 Wallowa River 1983 Grande Ronde River G 147 Wallowa River 1984 Grande Ronde River G 148 Lostine River 1983 Grande Ronde River G 149 Lostine River 1984 Grande Ronde River G 150 Broady Creek 1992 Grande Ronde River G 151 Horse Creek 1992 Grande Ronde River G 152 Jarboe Creek 1992 Grande Ronde River G 153 Little Lookingglass Creek 1992 Grande Ronde River G 154 Mottet Creek 1992 Grande Ronde River G 155 Swamp Creek 1992 Grande Ronde River G 156 Cook Creek 1992 Snake River G 157 Cherry Creek 1992 Snake River G 158 Gumboot Creek 1983,1984 linnaha River G 159 Grouse Creek 1983 Imnaha River G 160 Grouse Creek 1984 Imnaha River G 161 Big Sheep Creek 1983 Imnaha River G 162 Big Sheep Creek 1984 Imnaha River G 163 Imnaha summer strain 1984 Imnaha River G 164 Niagara summer strain 1985 Niagara Spring Hatchery G 165 McGraw Creek 1990 Snake River G 166 Conner Creek 1992 Pine Creek G 167 North Pine Creek 1992 Pine Creek G 168 Big Creek 1992 Powder River G 169 Indian Creek 1990 Powder River G 170 Summit Creek 1992 Powder River G 171 Sutton Creek 1992 Powder River G 172 Dixie Creek 1991 Burnt River G 173 Last Chance Creek 1990 Burnt River G 174 Lawrence Cr (above bamer) 1991 Burnt River G 175 Lawrence Cr (below barrier) 1991 Burnt River G 176 South Fork Dixie Creek 1991 Burnt River G 177 Snow Creek 1990 Burnt River 19 Table 2.1. Continued.
Lineage Map Collection Code No. Year Origin G 178 Black Canyon Creek 1988 Maiheur River G 179 Cottonwood Creek (Bully Cr.)1988 Maiheur River G 180 Cottonwood Creek 1989 Maiheur River G 181 Hog Creek 1991 Maiheur River G 182 South Fork Indian Creek 1989 Mallieur River G 183 Dinner Creek 1988 Maiheur River G 184 Calf Creek 1991 Maiheur River G 185 North Fork Squaw Creek 1991 Maiheur River G 186 Carter Creek 1989 Succor Creek G 187 Diy Creek 1989 Owyhee River G 188 West Little Owyhee River 1991 Owyhee River H 189 Deep Creek 1993 Blitzen River H 190 Indian Creek 1993 Blitzen River H 191 Bridge Creek 1989 Blitzen River H 192 Krumbo Creek 1989 Blitzen River H 193 Mud Creek 1989 Blitzen River H 194 Smyth Creek 1988 Smyth Creek H 195 Upper Sawmill Creek 1993 Silver Creek H 196 Lower Sawmill Creek 1993 Silver Creek I 197 Home Creek #1 1993 Home Creek I 198 Home Creek #2 1993 Home Creek I 199 Upper Home Creek 1993 Home Creek J 200 Augur Creek 1992 Chewaucan River J 201 Daily Creek 1992 Chewaucan River J 202 Bear Creek 1992 Chewaucan River J 203 Elder Creek 1992 Chewaucan River J 204 Witham Creek 1992 Chewaucan River K 205 Bridge Creek 1993 Bridge Creek K 206 Buck Creek 1993 Buck Creek L 207 Beaver Creek 1993 Goose Lake L 208 Camp Creek 1993 Goose Lake L 209 Cox Creek 1993 Goose Lake L 210 Thomas Creek 1993 Goose Lake M 211 Beaver Creek 1989 Jenny Creek M 212 Fall Creek 1989 Jenny Creek M 213 Jenny Creek #1 1989 Jenny Creek M 214 Jenny Creek #2 1989 Jenny Creek M 215 Johnson Creek #1 1989 Jenny Creek M 216 Johnson Creek #2 1989 Jenny Creek M 217 Shoat Springs 1989 Jenny Creek M 218 Willow Creek 1989 Jenny Creek M 219 Deming Creek 1987 Sprague River M 220 Paradise Creek 1987 Sprague River M 221 Paradise Creek 1990 Sprague River M 222 Deep Creek 1990 Upper Williamson River M 223 Williamson River #1 1990 Upper Williamson River 20 Table 2.1. Continued.
Lineage Map Collection Code No. Year Origin M 224 Williamson River #2 1987 Upper Williamson River N 225 Bogus Creek 1990 Kiarnath River N 226 Kiamath River 1987 Kiarnath River N 227 Spencer Creek 1987 Kiamath River N 228 Spencer Creek 1990 Klainath River N 229 Rock Creek 1987 Klainath Lake N 230 Wood Creek 1990 Kiamath Lake N 231 Spring Creek 1992 Lower Williamson River N 232 Spring Creek 1992 Lower Williamson River N 233 Trout Creek 1987 Sprague River N 234 Trout Creek 1990 Sprague River o 235 Honey Creek #1 1993 Honey Creek o 236 Honey Creek #2 1993 Honey Creek o 237 North Fork Deep Creek 1993 Deep Creek o 238 Deep Creek 1993 Deep Creek o 239 Willow Creek #1 1993 Deep Creek o 240 Willow Creek #2 1993 Deep Creek P 241 Cape Cod strain 1985 Introduced hatchery strain P 242 Oak Springs strain 1985 Introduced hatchery strain Q 243 Soda Creek 1989 Rogue River R 244 Coastal cutthroat trout 1991 Coquille River 21
Table 2.2.International Union of Biochemistry (I.U.B.) enzyme names, Enzyme Commission (E.C,) numbers, loci examined in this study, tissues and buffers. Tissues: M, muscle; L, liver, E, eye; H, heart. Buffers: ACE, an citrate-amine-EDTA gel and tray buffer pH 6.8; TBCLE, a Tris-citrate gel buffer and lithium hydroxide, borate-EDTA tray buffer pH 8.5; TBE, a Tris-borate-EDTA gel and tray buffer pH 8.5.
E. C, I.U.B Enzyme name Number Locus Tissue Buffer
Alcohol dehydrogenase 1.1.1.1 ADH* L ACE
Aconitate dehydratase 4.2.1.2 SAH* L ACE
Creatine kinase 2.7.3.2 CK-A1 * M TBCLE CKA2* M TBCLE Glucose-6-phosphate isomerase 5.3.1.9 GPI-A * M TBCLE
GPJ-B1 * M TBCLE
Glucose-6-phosphate isomerase 5.3.1.9 GPJ-B2 * M TBCLE Glycerol-3 -phosphate dehydrogenase 1.1.1.8 G3PDH-1 * M ACE G3PDH2* M ACE
Isocitrate dehydrogenase 1.1.1.42 mIDHP-1 * M,H ACE
mIDHP-2 * M,H ACE
sIDHP1,2* L ACE
Lactate dehydrogenase 1.1.1.27 LDH-B2 * L TBCLE
LDHC* E TBE
Malate dehydrogenase 1.1.1.37 sMDHA1,2* L ACE sMDHB1,2* IJ,M ACE
Malate dehydrogenase (NADP+) 1.1.1.40 ,nMEP-I * M ACE sMEPJ* M ACE
sMEP2* L ACE
Dipeptidase PEPA* M,E TBE
Tripeptide aminopeptidase 3.4.-.- PEPB-] * M,L1 TBE
Peptidase-C PEPC* E TBE
Proline dipeptidase 3.4.-.- PEPD-1 * M,I-1 TBE
Phosphoglucomutase 5.4.2.2 PGM-2 * M TBE
Superoxide dismutase 1.15.1.1 sSOD-1 * L ACE, TBLCE 22 also used to examine differences within and among basins. Patterns of character evolution among major evolutionary groups of rainbow trout were examined under criterion of parsimony (Kiuge and Fart-is, 1969; Farris, 1970) using the GLOBAL and MULPARS branch-swapping algorithms in the Phylogenetic Analysis Under Parsimony (PA[JP) computer program (Swofford, 1985). Geographical groups of rainbow trout that had unique combinations of character states were considered major evolutionary groups. Each locus was considered a character and different combinations of alleles (Table 2.3) represented different character states (Buth, 1984). Coastal cutthroat trout was the outgroup. A majority-rule consensus tree (majority> 0.6) was constructed from all equally parsimonious cladograms. Character states were treated as unordered in rainbow trout, except for CK_A1*. Structure divergence of CK_A1* and CKA2* in rainbow trout, but not in cutthroat trout, was assumed to be derived from an earlier isolocus (Allendorf and Thorgaard, 1984). Additionally, a phenogram depicting genetic similarity among major evolutionary groups was also constructed from a matrix of genetic distance values
(Cavalli-Sforza and Edwards, 1967) using the neighbor-joining tree algorithm (Saitou and
Nei, 1987). Character trait analysis is appropriate for inferring phylogenies when gene flow among lineages is minimal (Swofford et al., 1996), but it can also identify homoplasies that may indicate gene flow among lineages (Buth, 1984; Hillis et al., 1996).
However, transforming allele frequency data to character state data can be controversial
(Swofford et al., 1996). Phenetic analyses, which use similarity or distance measures calculated from frequency data, may suggest phylogenetic relationships when genetic similarity reflects evolutionary descent, but it does not identify potential homoplasies.
Using both analyses provides insights into patterns of evolution. Table 2.3. Description of character states. Character states are described by the relative mobilities of the allozymes observedat a locus.
Character state Character 1 2 3 4 5 6 7 8 9 10 1. ADH* -100 -100, -78 -100, -65 -100, -76, -65 2. sAII* 100, 85 100 100,85,72 100,85,112 100,85,72, 112 3. CK-A' isolocus divergered 4. G3PDH-1' -100 -100, 80 5. GPI-B1" 100, 152 100 100, 145 100, 138 100, 138, 145 100, 130 100, 130, 25 6. GPIB2* 100 100,25 100,25, 125 7. GPI_A* 100 100,110 100,110,93 100,93 8. mIDHP1* 100 100, -280 9. mIDHP-2' 100, 144 100 10. sIDHP1,2* 100, 42, 72 100, 42, 72, 100, 42, 72, 121 121,116 11. LDH-B2' 100,113 100 100,76 100, 113,76 12. LDH_C* 100 100, 97 13. sMDHAJ,2* 100 100,37 100,155 100,72 100,155,37, 100, 120,49 155,72, 120 14. sMDHB1,2* 100,83,116, 100 100,83 100,83,124 100,83,116, 100,83, 100,83, 100, 100,83, 100,83, 92,78 78 116,92, 116,92 116 70 116,70, 124 92,78, 120 15. mMEPI* 100 100,90 100, 90,115 16. sMEP1* 100 100,83 100,107 100, 107,83 17. sMEP2* 100,110 100 100, 83 100, 83, 110 18. PEPA* 100,111 100,111, 100 100,93 93 Table 2.3. Continued.
Character state Character 1 2 3 4 5 6 7 8 9 10 19. PEPB_1* 100,69 100 100,69,134 100,134 100,134,69, 50 20. PEPC* 100 100,110 100, 110, 92 21. PEPD1* 100 100, 93 22. PGM2* -100 -100, -120 -100, -10 -100, -120, -10 23. sSOD_1* 100,152 100,152, 100, 152,38, 38 187 25
2.4. Results
Genetic differences documented among the 11,400 rainbow trout in this study were based on 92 alleles segregating at 28 loci. Of these, 16 loci had an average frequency of the most common allele of 0.95 or less: ADH*, sAH*, G3PDH-1GPI-B1 mIDHP1*, mIDHP2*, sIDHP1,2*, LDHB2*, sMDHB1,2*, sMEP1*, sMEP2*,
PEP-A PEP-B] *, PEPC*, PGM-2 *, and sSOD-1 * (Appendix A). Only CK-A2 * was electrophoretically monomorphic in all samples. Less than 3% of the more than 1300 tests for Hardy-Weinberg equilibrium deviated from Hardy-Weinberg genotypic proportions.
Because this number of deviations would be expected by chance alone at the 5% level, each collection of fish was treated as a single sample in subsequent analyses.
Four major genetic groups of rainbow trout emerged from analysis of geographical variation: 1) Columbia River populations; 2) populations from Goose Lake, Warner
Lakes, and the Chewaucan Basin; 3) populations from Upper Kiamath Lake and River and the coastal Klamath Mountains; and 4) populations from pluvial lake basins in Oregon that were geographically and genetically intermediate between Columbia River and Kiamath groups (Fig 2. 2). Each of these four major groups included rainbow trout that were distinctly different from populations in other basins. Allelic frequency differences among rainbow trout samples from different basins far exceeded the differences among tributaries within basins and the differences among samples within tributaries (Table 2.4), although significant differences occurred at all levels of the analysis. Standardized index heterogeneity, G/df, was 55.3 for among-basin differences (Table 2.4). In contrast, it ranged from 1.6-3.7 for differences among tributaries within basins and 0.6-3.6 for differences among samples within tributaries. Only samples within upper Columbia River 26
Figure 2.2. Canonical variate analysis of allozyme variation among 16 major evolutionary groups of rainbow trout. Twelve alleles entered into the analysis: ADH*65, 5AH*1 12, GPI-B1 *138, mIDHP-2 *]44, sIDHP-i,2 * 72, LDH-B2 *76, sMDH-B], 2*83, PEP-A *100, PEP-A *]J], PEPB2*69, PEPC*i00, and sSOD-1 *152. 5
B L Columbia River
E L J J J 0 L J J 0 H 0 0 0 L 0 Transitional Basins Harney (H), Catlow (I), H Fort Rock(K) & N K Sacramento NN Goose Lake (L), Warner Lakes (0), M N H Chewaucan (J) G -5 - K ( 0 MN Kiamath N N N N Upper Kiamath N j
headwater populations M M (M), Upper Kiamath M M Lake and River (N), M M4 Coastal Kiamath M -10 - mountains (Q) M
-15
-30 -25 -20 -15 -10 -5 0 5 Canonical Variate I Figure 2.2 28 Table 2.4. Nested analysis of allele frequency variation among and within basins. Degrees of freedom (di) and G-test scores are totals over all loci.
Source of Variation df G Score P G/df AmongBasins 396 21881.0 0.000 55.3 Within Basins 7458 23099.6 0.000 3,1 A. Lower Columbia River 1056 3763.9 0.000 3.6 Among tributary samples 660 3210.6 0.000 4.9 Within tributary samples 396 553.4 0.000 1.4 B, C. Mid-Columbia River 2706 9179.1 0.000 3.4 Among tributary samples 330 4533.8 0.000 13.7 Within tributary samples 2376 4645.3 0.000 2.0 D. Upper Columbia River 165 313.3 0.000 1.9 Among tributary samples 132 272.8 0.000 2.1 Within tributary samples 33 40.5 0.173 1.2 E. Clearwater 165 269.8 0.000 1.6 F. Snake River 1683 4201.5 0.000 2.5 Among tributary samples 396 1682.5 0.000 4.2 Within tributary samples 1287 2519.0 0.000 2.0 G. Salmon River 198 407.1 0.000 2.1 H. Barney Basin 231 1038.7 0.000 4.5 Among tributary samples 66 439.6 0.000 6.7 Within tributary samples 165 599.2 0.000 3.6 I. Catlow Valley 33 47.9 0.045 1.5 J. Chewaucan Basin 132 201.1 0.000 1.5 Among tributary samples 66 112.3 0.000 1.7 Withintributarysamples 66 88.7 0.033 1.3 K. FortRockBasin 33 28.5 0.691 0.9 L. GooseLakeBasin 99 103.8 0.35 1 1.0 M, N. Upper Kiamath Lake Basin 792 3308.8 0.000 4.2 Among tributary samples 99 1040.6 0.000 10.5 Within tributary samples 693 2268.2 0.000 3.3 0. Warner Valley 165 236.2 0.000 1.4 Among tributary samples 66 176.1 0.000 2.7 Within tributary samples 99 60.1 0.999 0.6 29
tributaries, among streams of Fort Rock Basin and within streams of Goose Lake and
Warner Valley basins failed to show significant alielic differences (Table 2.4). In mid-
Columbia River and Upper Klamath Lake basins, however, G/df values foramong
tributary differences were conspicuously larger (13.7 and 10.5, respectively) than in other
regions, which suggested divergence of one or more groups within these regions.
Although allele frequencies of rainbow trout from a location could have changed over the
duration of this study, only 7 of 43 possible comparisons of temporal variation (8 from
hatcheries and 35 from wild locations) were significantly different, and six of thesewere
from hatchery locations: Big Creek strain at Eagle Creek Hatchery (G54.6, df= 33, P
= 0.010); Skamania strain at Leaburg Hatchery (G51,6, df= 33, P0.021 ) and at
Skamania Hatchery (G = 90.0, df= 33, P = 0.000); Eagle Creek strain and Eagle Creek
hatchery (G78.7, df= 33, P = 0.000); Umatilla strain at Umatilla Hatchery (G = 123.91,
df= 33, P = 0.000 ); Deschutes summer strain at Round Butte Hatchery (G= 228.39, df=
66, P = 0.000); and wild rainbow trout in Wiley Creek (G = 57.6, df33, P = 0.005).
Patterns of genetic differences at the basin level indicated each of the 14 maj or
drainages or basins in this study contained a different major evolutionarygroup of rainbow trout, with two exceptions. Rainbow trout within different drainages or basins had unique
combinations of alleles, or character states (Table 2.5), basedon presence or absence of alleles (Table 2.3). In addition, all pairwise comparisons of allelic frequency variation between major evolutionary groups were significant, ranging in magnitude from that between Fort Rock and Chewaucan groups (G = 131.9, df= 33, P= 0.0000) to that between mid-Columbia inland rainbow trout and lower Columbia River populations (G=
6674, df= 33, P = 0.0000). In the Columbia River, drainages associated with major Table 2.5. Character states for major groups of rainbowtrout. Characters in numerical order are ADH*, sAH*, CK-A 'K G3PDH-1GPI-B1GPI-B2 'K, GPI-AmIDHP-1 mIDHP-2sIDHP-1, 2'K, LDH-B2 'K, LDHC*, sMDH-A 1,2*, sMDH-B1, 2*, mMEP-1 'K, sMEP-1 sMEP-2 PEPAPEPB-1 PEPC*, PEPD-1 PGM-2 'K, sSOD-1 Descriptions of character states are in Table 2.3.
Characters Group 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1718 1920212223 A. Lower Columbia River 2322713122414732323 1121 B. Mid-Columbia River 4 5 2 2 2 3 3 1 1 3 4 2 5 10 3 4 3 2 5 1 2 4 3 C. White River 13222211223139123121121 D. Upper Columbia River 232221312241371 11121122 E. Clearwater River 132221412231381 12221 122 F. Salmon River 132161312241371 12241122 G. Snake River 23226131124261234232122 H. Harney Basin 432241 11 12321614211 1121 I. Catlow Valley 2 1 1 1 2 1 322 1 3 1 122323 1 1 1 3 1 J. Chewaucan Basin 3 12141 12123 123 1 123 1 1 K. Fort Rock Basin 121 442141 1 1 123 1 141 1241 1121 L. Goose Lake Basin 41214122223 12421 141 1 141 M. Upper Klamath Lake headwater populations 34213 1 1 1 121 1 151 12233 121 N. Upper Klamath River and Lake 1 1 3 5223 1 1 23 12 1 1 1423 2 1 2 1 0. Warner Valley 452151 12123 1 1421341 1 121 Q. Coastal Klamath Mountains 3 12121 1 1 1 13213 1222321 12 R. Coastal cutthroat trout 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 31
evolutionary groups were the lower Columbia River west of the Cascade Crest, the mid-
Columbia River to the confluence of the Snake River, the upper Columbia River, the
Clearwater River, the Snake River, and the Salmon River. In addition, Fort Rock and
Harney basins also contained a different group of rainbow trout. In the northern Great
Basin, each of the subbasins contained a different evolutionary group (Table 2.5). The
two exceptions to this pattern were mid-Columbia River and Upper Klamath Lake
regions, which had large G/df values. Among mid-Columbia drainages, one major
evolutionary group consisted of inland rainbow trout in tributaries that have had recent
access to the Pacific Ocean. A second major evolutionary group occurred above barrier
waterfalls in the White River of the Deschutes River (Currens et al., 1990). In Upper
Klamath Lake Basin, rainbow trout in headwater streams of the Sprague River,
Williamson River, and Jenny Creek had diverged from rainbow trout associated with
Upper Kiamath Lake and River or the Rogue River in the coastal Kiamath Mountains
(Table 2.5).
Interpretation of canonical variates analysis indicated that the major evolutionary
difference among rainbow trout demonstrated in this study occurred between populations
from Goose Lake, Warner Valley, and Chewaucan basins and all other populations (Fig.
2.2). This difference occurred along CVA axis I, which explained 69% of the observed variation. Variation at GPI-B1 PEP-B] PEP-A *, and sSOD-J * contributed most to
differences between Goose Lake-Warner Valley-Chewaucan populations from other
rainbow trout. Rainbow trout in these basins usually had high frequencies of GPI-
B] *138. It was absent, however, from populations in most other basins, except Harney
and Fort Rock basins where it was rare. Likewise, PEP-B] *69 was common in Goose 32
Lake and Chewaucan populations; it occurred at low frequency in Warner Valley, Fort
Rock, and coastal Kiamath Moutain rainbow trout; but it was rare or absent in other
basins. In contrast, PEPA* 111 was absent in Goose Lake, Warner, Chewaucan, Fort
Rock, and Catlow Valley populations, but it occurred at low levels in all other populations
with highest concentration in the Upper Columbia, Clearwater, and Salmon River
populations. High and sometimes moderate frequencies of sSOD-1 *152 occurred in
Goose Lake, Warner Valley, and some Harney Basin populations, although it was rare
among other populations of rainbow trout (Appendix A).
Major alielic frequency differences also occurred between Columbia River and
Kiamath populations (Fig. 2.2). This difference occurred largely along CVA axis II, which
explained 15% of the observed allelic variation. Upper Kiamath Lake headwater populations had moderate frequencies of sAH*112, an allele that was absent in all rainbow trout of other basins except Upper Klamath River, Fort Rock, and Warner Valley populations where it was rare. Populations from Upper Klamath Lake headwater locations, Upper Kdamath River and Lake, and coastal Kiamath Mountains generally also had greater variation at mIDHP-2 * and PEPC* than Columbia River populations. In contrast, frequencies ofsIDHP_1,2*72 were lower in Kiamath rainbow trout groups
(Appendix A).
Geographical divergence at LDH-B2 ', which has been considered evidence ofa major evolutionary break between rainbow trout east and west of the Cascade Mountains
(Allendorf, 1975; Okazaki, 1984; Parkinson, 1984; Berg, 1987; Reisenbichleret al.,
1992), was primarily limited to populations in the Columbia River system. Inland populations from the mid-Columbia River (excluding White River rainbow trout), Upper 33
Columbia River, Clearwater, Salmon, and Snake riverswere characterized by high
frequencies of LDH-B2 *76 In contrast, the allele was absent orrare in populations east
of the Cascades in White River, Goose Lake, and Upper Kiamath Lake headwater and
river populations and west of the Cascades in coastal Klamath Mountains. Similarly, only
low to moderate frequencies occurred in populations east of the Cascades in Chewaucan,
Warner, Harney, Catlow, and Fort Rock basins and west of the Cascades in lower
Columbia River populations (Appendix A).
Geographical distribution of different allelic combinations also indicated consistent
patterns of differences among major evolutionary groups associated with Klamath,
Columbia River, and Goose Lake drainages. However, consistent patterns of character
evolution based on parsimony were not apparent among these rainbow trout and major
evolutionary groups in many Oregon Lake basins, especially Chewaucan, Fort Rock, and
Catlow Valley basins (Figure 2.3). Patterns of character evolutionwere represented by 29
equally parsimonious cladograms. These analyses did not include samples for 18 locations because of independent evidence (stocking records, meristic traits, mitochondrial DNA, and allozyme loci not included in this analysis) of hybridization between native populations and exotic hatchery strains, which might have confounded interpretation. Excluded samples (with map codes) were Bowman Dam (53), Ochoco Creek (55), McKay Creek
(93, 94), Lower White River (108), Lower Tygh Creek (109), Jordan Creek (111), Rock
Creek (114), Mottet Creek (154), Conner Creek (166), Black Canyon Creek (178),
Cottonwood Creek (179), South Fork Indian Creek (182), Calf Creek (184),Krumbo
Creek (192), Fall Creek (212), Jenny Creek #1(213), Shoat Springs (217), and Willow
Creek (218). 34
Figure 2.3. Consensus cladogram of 29 equally parsimonious trees for rainbow trout based on allozyme characters. Character changes for each node supported by all 29 trees are listed in Table 2.6. Nodes are indicated by bars. 35
Transitional Kiamath SacramentoBasins Columbia ?)(
?<. 0 v 0 's $ \. s Cr (' . 271 28
1 25 26
23 24 2122
19 20_
7 17 18
910 15 16
12 13 14
Figure 2.3. Table 2.6. Dominant patterns of character evolution in rainbow trout based on phylogenetic analysis. Listed character changes include only those that supported nodes in all 29 equally parsimonious cladograms. Character suites that supported relationships between rainbow trout in Goose and Warner Lakes and between Hamey Basin and Columbia River rainbow trout in the consensus cladogram are indicated by asterisks. Nodes are illustrated in Figure 2.3. Complete descriptions of character states are in Table 2.3.
Character State Changes Node Group Character From To Description
1 Coastal cutthroat trout 11 1 3 Presence of LDH_B2*100 and 113 alleles without 76 allele (unpolarized) 2Rainbow trout 3 1 2Divergence of CK-A1 * and CK-A2 * from an isolocus 3 Coastal Kiamath mountains and 19 1 3 Gain of PEPB1*134 Upper Kiamath Lake Basin 20 1 2Gain of PEPC*110 4Coastal Klamath mountains 12 1 2Gain of LDHC*97 16 1 2Gain of sMEP1*83 23 1 2Gain of sSOD-1 *38 5Klamath River and Upper Klamath 2 1 5 Gain of sAH*72 and 112 alleles Lake Basin 5 2 3 Gain of GPIB1*I45 10 1 2Gain of sIDHP1,2*121 6Upper Klamath Lake Basin 2 5 4Loss of sAH*72 headwater populations 11 3 1 Loss of LDHB2*76 20 2 3 Gain of PEPC*92 7Upper Kiamath River and Lake 4 1 2 Gain of G3PDH-1 *80
13 1 2 Gain ofsMDHA1,2*I55 17 2 4Gain of sMEP2*83 and 110 alleles 8 Goose and Warner lakes* 15 1 2Gain of mMEPI *90 9Goose Lake 7 1 2Gain of GPIA*lI0 9 1 2Loss of mID HP2*l44 22 2 4Gain of PGM2*10 10 Warner Valley 2 1 5 Gain of sAH*72 and 112 alleles 5 4 5 Gain of GPIB1*145 11 Chewaucan Basin* 13 1 2 Gain of sMDFLAJ,2*37 8 1 2 Gain of ,nIDHP-1 *280 12 Fort Rock Basin 2 1 4Gain of 5AH*112 Table 2.6. Continued.
Character State Changes Node Group Character From To Description
13 Catlow Valley 2 1 2Loss of sAH*85 9 1 2Loss of mIDHP2*144 15 1 2 Gain of mMEP1*90
16 1 2GairiofsMEP1*107 14Past Columbia River 2 1 3 Gain of sAH*72 4 1 2 Gain of G3PDH-1 *80
15 Harney Basin 12 1 2Gain of LDHC*97 16 1 4Gain of sMEP_1*83 and 107 alleles 16Present Columbia River 9 1 2Loss of mIDHP-2 *J44 13 1 3 GainofsMDHA1,2*155 14 6 9 Gain of sMDHB1,2*70, 78 and 120 alleles and loss of 124 allele 19 1 2Loss of PEPB-1 *69
17 White River 6 1 2 Gain of GPI-B2 *25 16 1 2 Gain of sMEP-1 *83 17 2 3 Gain of sMEP2*83 18 7 1 4Gain of GPIA*93
18 1 2 Gain of PEPA*93
23 1 2 Gain of sSOD1*38 19 Clearwater River 14 9 8 Loss of sMDHB1,2*83 and 70 and gain of 116 alleles 20 7 4 3 Gain of GPIA*110 11 3 4 Gain of LDFLB2*113 14 9 7 Gain of sMDHB1,2*II6 and 92 alleles and loss of 70 allele 21 Salmon River 4 2 1 Loss of G3PDH-1 *80 5 2 6Gain of GPlB1*130 19 2 4 GainofPEPB1*134 22 1 1 2 Gain of ADH*78
23 Upper Columbia River 17 2 1 Gain of sMEP..2*I10 18 2 1 Loss of PEPA*93 Table 2.6. Continued.
Character State Changes Node Group Character From To Description 24 13 3 6Gain of sMDHA1,2*72 and 120 alleles 15 1 3 Gain of mMEP_1*90 and 115 alleles 16 1 4Gain of sMEP1*107 and 83 alleles 17 2 3 Gain ofsMEP2*83 19 2 3 Gain of PEPB1*69 and 134 alleles 25Lower Columbia River 5 2 7 Gain of GPI-B1 *130 and 25 alleles 13 6 4Loss of sMDHA1,2*155 and 120 alleles 16 4 2 Loss of sMEP1*107 23 2 1 Loss of sSOD-1 *38 26 9 2 1 Gain of mIDHP2*144 12 1 2Gain of LDHC*97 27Mid-Columbia River 1 2 4Gain of ADH*65 2 3 5 Gain of sAH*112 6 1 3 Gain of GPIB2*25 and 125 alleles 10 2 3 GainofsIDHP1,2*116 13 6 5Gain of sMDHAl,2*49 and 37 alleles and loss of 73 allele 19 3 5 Loss of PEPBI*5O 21 1 2Gain of PEPD-1 *93 22 2 4Gain of PGM2*I0 23 2 3 Gain of sSOD1*187 28 Snake River 5 2 6Gain of GPIB1*13O 15 3 2Loss of mMEP1*115 16 4 3 Loss of sMEP1*83 17 3 4Gain of sMEP2*110 20 1 2Gain of PEPC*110 39
In all cladograms, both Upper Kiamath Lake groups and coastal Kiamath
Mountain group were distinguished by gain of PEP-B] *]34 and PEP-C *110 alleles.
Upper Kiamath Lake groups in turn shared GPIB1*145, sIDHP1,2*112, and sAH*112 alleles (Fig. 2.3; Table 2.6). Likewise, in all cladograms, Columbia River populations were distinguished by loss of mIDHP-2 *]44 and PEP-B] *69 alleles and gain of alleles at sMDHA],2* and sMDH_B],2*. In 27 of 29 cladograms, presence of sAH*72 and
G3PDH-1 *80 alleles in both Columbia River and Harney major evolutionarygroups suggested a common ancestry (Fig. 2.3; Table 2.6).In contrast, no cladograms suggested that coastal rainbow trout of the lower Columbia River were a sister group of all inland forms. Among Goose Lake-Chewaucan-Warner Valley complex of rainbow trout, 25 of
29 cladograms supported a close evolutionary relationship between Goose Lake and
Warner Valley rainbow trout, based on presence of niMEP-1 *90 (Fig. 2.3; Table 2.6). In the other four cladograms, shared presence of sMDH_A],2*37 indicated a common ancestry of Goose Lake and Chewaucan rainbow trout with Warner Valley rainbow trout as the only immediate sister group. However, in the 25 cladograms supporting a close evolutionary relationship between Goose Lake and Warner Valley rainbow trout, shared allelic combinations also indicated Catlow Valley rainbow troutas a likely sister group.
Allelic combinations in Catlow Valley, Chewaucan, and Fort Rock groups reflected extensive homoplasies with major evolutionarygroups in Kiamath, Goose Lake, and Columbia River basins. In Catlow Valley rainbow trout, allelic combinations for mMEP-1 * and mIDHP-2 * were homoplasious with those in Goose Lake and Warner
Valley. In Chewaucan rainbow trout, allelic combinations involving rnIDHP-1 *.280 also occurred in Goose Lake and Warner Valley Basins. Chewaucan rainbow trout also had 40 allelic combinations involving sMDHA1,2*37 that distinguished Upper Kiamath Lake and
River rainbow trout in all cladograms. Similarly, Fort Rock rainbow trout expressed allelic combination involving sAH*1]2, which distinguished Upper Kiamath Lake groups
(Table 2.5, Table 2.6).
Homoplasies also occurred between northern Great Basin rainbow trout and major evolutionary groups east of the Cascade Mountains in the Columbia River and especially the Snake River (Table 2.5, Table 2,6). Allelic combinations for PEP-B] * and PEPC*, which distinguished all Klamath groups, and for MEP-2 <, which distinguished Upper
Klamath Lake and River rainbow trout, also occurred in Snake River rainbow trout.
Allelic combinations for mMEP-1 *, which occurred in Goose Lake, Warner Valley, and
Catlow Valley rainbow trouts, and sMEP-] *, found in Catlow Valley rainbow trout, were expressed in Snake River rainbow trout. Similarly, loss of variation at mIDHP-2 *, which occurred in Goose Lake and Catlow Valley rainbow trouts, also characterized rainbow trout of the White River, Upper Columbia, Clearwater, and Salmon rivers (Table 2.5,
Table 2.6).
Genetic relationships identified by neighbor-joining method (Fig. 2.4) supported the major geographical patterns of allelic frequency divergence identified by CVA (Fig.
2.2) and the patterns of character evolution identified by cladistic analysis (Fig. 2.3). A major evolutionary difference existed between Goose Lake-Warner Valley-Chewaucan
Basin rainbow trout and other rainbow trout. Among these latter rainbow trout, redband trout from Fort Rock and Catlow Valley basins were a distinct group from Klamath,
Columbia River, and exotic hatchery forms. In the Columbia River, coastal rainbow trout did not group with inland groups. Rainbow trout from inland drainages of the Columbia 41
Figure 2.4. Phenogram of evolutionary relationships of rainbow trout based on neighbor-joining method and Cavalli-Sforza-Edwards chord distance. N. Upper Kiamath River and Lake M. Upper Kiamath Lake headwater populations Q. Coastal Kiamath Mountains A. Lower Columbia River P. Exotic hatchery strains C. White River
D. Upper Columbia River F. Salmon River E. Clearwater River G. Snake River B. Mid-Columbia River H. Harney Basin
K. Fort Rock Basin Catlow Valley
Chewaucan Basin 0. Warner Valley L. Goose Lake Basin R. Coastal cutthroat trout
Figure 2.4. 43
River (excluding White River and including Harney Basin) formedone large geographical
group, whereas Lower Columbia River rainbow trout and exotic hatchery strains formed
another group. In contrast, rainbow trout east and west of the Cascades in Upper
Kiamath Lake Basin and coastal Klamath Mountains formeda single geographical group.
These patterns were the same for neighbor-joining trees rooted to the outgroupcoastal
cutthroat troutor to the midpoint.
2.5. Discussion
Patterns of genetic diversity in this study showed evolutionary continuity around
three major river systems that breached the crest of the Cascade Range: theupper
Sacramento River, the Klamath River, and the Columbia River. This strongly suggests
that large river systems must have provided long-termsources of stable, diverse aquatic
habitat that allowed rainbow trout to persist and evolve in the dynamic landscape of
Pleistocene and Recent times. Large, pluvial lake basins also persisted during this time.
However, the genetic similarities of endemic rainbow trout of pluvial lake basins to either
the upper Sacramento, Klamath,or Columbia rivers, suggested that these habitats were
sources of ecological and evolutionary diversity within large river systems rather than
completely independent habitats. In addition, the predominant effect of large rivers
indicated that genetic differences between coastal and inland forms of rainbowtrout,
which have been largely attributed to isolation and recolonization ofnew habitats from
different refugia since Pleistocene ice receded (Allendorf 1975; Okazaki, 1984; Behnke,
1992), may be more localized than previously acknowledgedor have been confounded by persistence of other lineages. In coastal streams associated withupper Sacramento and 44
Kiamath river system, which were beyond the advance of major glaciers, invading migratory coastal rainbow trout may have interbred with some populations that survived there but not with others, although I did not demonstrate that here. Rainbow trout from coastal southern California streams, for example, were a distinctly different major evolutionary lineage from coastal rainbow trout of Cascadia (Busby et al., 1996).
2.5.1. Phylogeny of Rainbow Trout
The genetic data I collected suggest that rainbow trout lineges have evolved around three major river systems: the Sacramento River, the Kiamath River, and the
Columbia River. No cladistic or neighbor-joining trees (Fig. 2.3, Fig. 2.4) indicated that rainbow trout from the arid, pluvial lake basins of Oregon and northern California, commonly known as redband trout, shared a common lineage distinct from coastal populations, such as those in the lower Columbia River, coastal Kiamath Mountains, or domesticated rainbow trout strains. In contrast, large genetic differences between upper
Sacramento River rainbow trout and other groups indicated by the CVA (Fig. 2.2) and neighbor-joining tree analysis (Fig. 2.4) suggested that Goose Lake, Warner Valley, and
Chewaucan Basin rainbow trout may have diverged very early or more rapidly from all other rainbow trout. Based on the neighbor-joining tree (Fig. 2.4), Fort Rock and Catlow
Valley rainbow trout may also have diverged early or rapidly from other rainbow trout groups, although this was not resolved by the cladistic analysis (Fig. 2.3). Both analyses suggested that White River and Harney Basin rainbow trout lineages diverged early from the other Columbia River groups. Likewise, both cladistic (Fig. 2.3) and neighbor-joining trees (Fig. 2.4) suggested that coastal rainbow trout from the Klamath Mountains were 45 more closedly related to other Klamath River populations than to coastal rainbow trout from the lower Columbia River.Including additional groups, such as California golden trout (0. aquabonita), McCloud River rainbow trout, southern California rainbow trout,
Oregon coastal rainbow trout populations, and Athabasca River rainbow trout in the analyses would provide a more complete description of evolutionary diversity in the species.
The differences suggested above might be explained by a geographical pattern of vicariance in rainbow trout that proceeded from south to north. However, the apparent genetic relationships among rainbow trout in isolated lake basins of the northern
Great Basin and those in tributaries of major river systems most likely also reflects the evolution of populations isolated in different habitats that had intermittent opportunities to disperse and interbreed. Extracting phylogenies from the intricate web of reticulate evolution that is indicated by the biogeography of these groups may be difficult.
2.5.2. Biogeography of Major Groups
Upper Sacramento River rainbow trout were comprised of populations from
Goose Lake, Pit River, Warner Valley, and Chewaucan Basin. Many investigators have recognized faunal and physiographic evidence for considering Goose Lake a disrupted part of the upper Sacramento River (Russell, 1884; Snyder, 1908a; Rutter, 1908; Van Wickle,
1914; Hubbs and Miller, 1948). Native fish fauna of Goose Lake were typical of
Sacramento River assemblages; Moyle, 1976; Minckley et al., 1986). Additionally, Berg
(1987) noted close genetic similarities between Goose Lake rainbow trout and Pit River rainbow trout. Until this study, however, evidence for considering Warner Valleyor 46
Chewaucan Basin as part of the upper Sacramento River system were limited or lacking.
Fish assemblages in both basins were dominated by three widely-distributed, persistent speciesGila bicolor, Rhinichthys osculus, and rainbow troutthat have been largely indistinguishable from forms in other basins. Consequently, it has been difficult to correlate fish distributions with hydrographic history. Genetic data from this study, in contrast, demonstrated geographical differences among rainbow trout and close association between forms from Goose Lake and Warner Valley and more limited association with Chewaucan Basin (Fig. 2.3, Fig. 2.4). Presence of mIDHP-1 *..28o in only Goose Lake, Warner Valley, and Chewaucan rainbow trout, presence of sMDH-
A1,2*37, and unusually high frequencies of GPIBI*I38 in these populations provided strong support for association of these basins (Table 2.5, Appendix A). Allelic frequencies at PEP-A *, PEP-B] *, and sSOD-1 * also indicated similarities among these groups that distinguished them from other groups of rainbow trout. The presence of Hesperoleucus symmetricus in Warner Valley, if indigeneous, would be additional strong evidence of connection to the Sacramento River, where it is endemic (Hubbs and Miller, 1948; Moyle,
1976; Minckley Ct al., 1986). Minckley et al. (1986) also suggested a close relationship between Catostomus warnerensis of Warner Valley and c rn/crops of the Pit River, although they presented few data.
Lacking other data from the Chewaucan, however, Minckley et al. (1986) speculated that Chewaucan fish might be most closely associated with adjacent Klamath,
Fort Rock, or Akali basins, based on morphological variation in G. b/color. That this does not correspond closely to genetic patterns for rainbow trout may reflect ecological differences between species or multiple connections with different basins. For example, 47 stream capture of rainbow trout between Goose Lake and Chewaucan streams might have occurred in several locations, such as between Thomas Creek in Goose Lake Basin and headwater streams of the Chewaucan River. Samples of rainbow trout were collected from both basins near this area. In contrast, association between Chewaucan and non-
Sacramento faunas based on differences in G. bicolor, which occupied different kinds of habitat than rainbow trout, may reflect patterns of extinction, recolonization, and isolation in different parts of the Summer Lake-Lake Abert-Chewaucan River complex that did not occur or that were obscuned in ecologically more restricted rainbow trout. More importantly, presence of sMDH_A1,2*37 in rainbow trout from Upper Kiamath Lake and rare occurrence of GPI-B1 *138 in Fort Rock rainbow trout may indicate that the
Chewaucan Basin has had multiple connections with different basins.
Kiamath rainbow trout were comprised of populations from coastal Klamath
Mountain streams (represented in these data by the Rogue River), lower Kiamath River, and Upper Klamath Lake Basin (Fig. 2.2, Fig. 2.3, Fig. 2.4). Within Upper Kiamath Lake
Basin major allelic differences existed between populations associated with Upper
Kiamath Lake and River and populations associated with Jenny Creek, which are isolated above ancient waterfalls, and headwaters of Sprague and Williamson rivers (Table 2.5,
Appendix A). In addition to data presented here, other evidence suggested that coastal
Kiamath Mountain streams that are presently separated from the Kiamath River may be considered part of a Kiamath River system. The Rogue River contained a closely related form of smallscale sucker (Catostomus rirniculus), which was endemic to the lower
Klamath River (Snyder, 1908b; Moyle, 1976; Minckley et al., 1986). Rainbow trout from this region also had a unique karyotype (Thorgaard, 1983). In addition, coastal steelhead 48
(anadromous rainbow trout) from Kiamath Mountains Province, which included Rogue and Kiamath rivers, were a distinct geographical race from coastal populations to the north or south, based on allozyme data (Busby et al., 1994).
Columbia River rainbow trout included all the major evolutionary groups within the extant Columbia River and Harney basins (Fig. 2.2, Fig. 2.3, Fig. 2.4). Although they are currently isolated, Harney Basin rainbow trout were almost certainly linked to
Columbia River populations in the past, probably through multiple hydrographic connections with Snake River or mid-Columbia River tributaries (Piper et al., 1939;
Bisson and Bond, 1971). Most fish of Harney Basin are typical Columbia River species
(Snyder, 1 908a; Minckiey et al., 1986), although Hubbs and Miller (1948) hypothesized that substantial isolation had led to four or five endemic subspecies.
Not all rainbow trout, such as those in Fort Rock and Catlow Valley, could be unambiguously assigned to associations with a single large river system, although the reasons differed (Fig. 2.2, Fig. 2.4). In Fort Rock Basin, ambiguity may reflect effects of multiple connections to different basins. Genetic, zoogeographical, and geological evidence supported intermittent connections to both Columbia and Kiamath River systems. Unlike other studies (Wilmot, 1974), I detected low frequencies of LDH-B2 *76 in Fort Rock populations, which could indicate previous connections to the Columbia
River, Kiamath, or Sacramento River systems. Pliocene fossils of Pacific salmon
(Oncorhynchus sp.), geological evidence of outflow to the Deschutes River, and close evolutionary relationship to both White River and mid-Columbia River rainbow trouts were evidence of substantial periods of association with the Columbia River (Allison,
1940, 1979; Cavender and Miller, 1972; Allison and Bond, 1983; Currens et al., 1990). In 49 contrast, expression of sAH*112 allele in Fort Rock rainbow trout is evidence of gene flow from Upper Kiamath Basin populations. Fort Rock rainbow trout had low frequencies of sAH*112. This allele was abundant in headwater populations of Upper
Kiamath Lake Basin but was extremely rare in Columbia River rainbow trout (Appendix
A). Geographically, the closest populations to Fort Rock rainbow trout were in headwaters of the Sprague River of Upper Kiamath Lake Basin, whichwere separated from Fort Rock streams by a low wetland divide at Sycan Marsh. This corridor between the two basins may also have allowed dispersal of G. bicolor and R. osculus (Hubbs and
Miller, 1948; Minckley et al., 1986). Fossil Chasmistes batrachops in Fort Rock Basin and extant Chasmistes brevirostris in Upper Klamath Lake (Minckley et al., 1986) also suggests earlier association between the basins.
More than any other group Catlow Valley rainbow trout populations appeared decoupled from any major river system. Genetically, Catlow Valley rainbow trout were more similar to Fort Rock rainbow trout than fish from other basins (Fig. 2.2, Fig.2.4), which may indicate a Columbia River association. Although other studies failed to detect the LDH_B2*76 allele (Wilmot, 1974; Wishard et al., 1984), I detected low frequencies in
Catlow Valley populations, which is abundant in the upper Columbia and Sacramento
River systems. Hubbs and Miller (1948) proposed that an overflow of pluvial Lake
Catlow into Harney Basin gave G. bicolor access to Catlow Valley. Although thiswas dismissed by Minckley et al. (1986) because the gradient would have been too precipitous for G. bicolor, it may not have been to steep for rainbow trout. Hubbs and Miller (1948) had assumed that all rainbow trout were introduced. In contrast, these data suggested that they were indigenous rainbow trout, and extinctions, genetic drift, and limited opportunity 50 of immigration as a result of the basin's isolated location and lack of large, persistent or interconnected streams may have obscurred relationships to major river systems. Like
Chewaucan and Warner Valley basins, Catlow Valley did not adjoin a major river basin.
Unlike the other two basins, which had complex networks of internal rivers and streams draining mountainous regions, rainbow trout in Catlow Valley have been restricted to the fate of two short, independent creeks (Hubbs and Miller, 1948; Minckley et al., 1986).
Fewer alleles per locus and fewer polymorphic loci in Catlow Valley rainbow trout compared to most other basins (Appendix A) indicated that Catlow Valley populations have remained small or were founded from few individuals with little exchange with other basins. Only two other fish speciesGAla bicolor and Rhinichthys osculus have persisted in the basin (Mincldey et al., 1986). This low species diversity may also reflect lack of long-term aquatic stability and ecological diversity.
The data examined here suggested that to greater or lesser degrees, isolated pluvial lake basins were sources of ecological and evolutionary diversity for rainbow trout within large river systems rather than unique, isolated habitats leading tonew species. Speciation by peripheral isolation (Mayr, 1963; Brooks and McLennan, 1991) would have depended on isolation that was strong enough to allow novel traits to become established and habitat that was stable enough to prevent extinctions. In contrast,one likely explanation for homoplasies in these data was that they reflected intermittent immigration andgene flow.
This would have helped maintain species cohesion while allowing divergence of geographical races. In addition, geologic evidence of fluctuating pluvial waterlevels
(Meinzer, 1922; Antevs, 1925; Snyder and Langbein, 1962; Pease, 1965; Muffin and
Wheat, 1979; Benson et al., 1996; Phillips et al., 1996) indicated that climatic variability 51 may have resulted in extinctions and recolonizations of populations in pluvial lake basins.
Smith (1981), for example, concluded that low species diversity in these regions could be explained by lack of long-term aquatic stability. Genetically, however, the process of isolation, extinction, and recolonization of peripheral populations in basins sets up an age distribution at the level of populations (or drainages) within large river systems.
Theoretical modeling of such scenarios indicated that this would have tended to increase the degree to which genetic variance is partitioned among populations (McCauley, 1991), while providing a buffer against extinction of all populations in the system.
2.5.3. Implications for Conservation
This study has implications for conservation and management of rainbow trout.
The most important is the recognition that unless large river systems are maintained as sources of aquatic stability, human intervention will be increasingly necessary and difficult to maintain only a fraction of the current genetic diversity. At an evolutionary scale, large river systems included both core and peripheral habitats, such as isolal;ed streams and pluvial lake basins. Given limited resources for intervention, conservation of rainbow trout without conserving large river systems may require difficult choices. Protection of core habitats may protect the long-term evolutionary potential of species, but it may also jeopardize unique races of fish that have evolved in peripheral habitats that can not be adequately protected. On the other hand, human intervention based on conserving the more genetically divergent groups may preempt protection of less divergent groups in core habitats. In addition, the results of this study indicated that identifying monophyletic lineages of rainbow trout for conservation purposes may be difficult because of 52 intermittent gene flow between largely isolated groups. More importantly, understanding the dynamics of core and peripheral habitats may provide a useful guide to making informed choices about how to manage genetically diverse salmonids in a changing landscape. 53
3. GENETIC VARIATION OF WILD RAINBOW TROUT UNDER HATCHERY SUPPLEMENTATION
3.1. Abstract
I examined geographical and temporal variation in allozymes and mitochondrial
DNA from wild rainbow trout (Oncorhynchus mykiss) from 12 steelhead (anadromous
rainbow trout) spawning tributaries and one resident rainbow trout stream in the Umatilla
River, Oregon, and the native and non-native steelhead strains used for hatchery
supplementation. Significant allele frequency differences were detectedamong samples
from different tributaries and among sites within tributaries. Cluster analysis, however,
showed no strong geographical pattern to the differences. Significant temporal differences
occurred between cohorts from the same location in two different years in 10 of 13
locations for either allozyme or mitochondrial DNA variation. Temporal differenceswere
as great as differences among sites within tributaries and were more easily detected by
allozyme variation than mtDNA variation. In 10 of 12 steelhead spawning streams,
naturally produced juvenile 0. mykiss became genetically more similar to adult hatchery
steelhead that had spawned in the basin. Although the small number of adult steelhead
spawning in the Umatilla River and the magnitude of temporal changes suggesteda role for genetic drift, episodic gene flow from hatchery fish spawning in the wildmost likely
explained the increasing similarity to hatchery steelhead.
3.2. Introduction
An increasingly important goal of releasing hatchery-reared fish into natural populations is to encourage successful reproduction between wild and hatchery fish, 54
thereby increasing numbers of breeders, and natural production. This use of artificial
production - known as supplementation- usually involvescapturing mature fish for
controlled matings from the wild, raising the offspring in an artificial environment, and
releasing them into streams with small or declining wild populations. Successful care of juvenile fish in hatcheries may offset high natural mortality rates of juveniles in the streams
and ultimately allow more adults to spawn in the wild.
Demographic advantages of supplementation, however, may also incur genetic
hazards, which are inherent in manipulating breeding structures of wild populations using
large numbers of artificially propagated fish. These may compromise the ultimate goal of
supplementation. First, natural patterns of genetic diversity between populations may be
lost if large numbers of fish from genetically different gene pools interbreed (Crow and
Kimura, 1970). Interbreeding may result from using fish from two or more populations as
a single brood stock, from translocations, or from straying of hatchery-reared fish into
other populations to reproduce. When frequency and magnitude of interbreeding are large
enough to disrupt local adaptation, supplementation may fail because wild fish have lost
the ability to meet local environmental challenges. Second, genetic diversity may also be
lost through founder effects when small numbers of brood fish are collected from local
populations (Allendorf and Ryman, 1987). Likewise, if small numbers of brood fish
contribute unequally large numbers of offspring to the subsequent generation, genetic
diversity is eroded (Ryman and Laikre, 1991). Loss of overall variability reduces the raw
material upon which natural selection acts (Fisher, 1932) and limits the potential of
populations to respond to changing environments. 55
In many areas of the Pacific Northwest, fishery managers have turned to supplementation as a means to rebuild declining wild populations of steelhead
(anadromous rainbow trout, Oncorhynchus mykiss) while conserving biological diversity
(Northwest Power Planning Council, 1987, 1992). Steelhead are economically and culturally important in the Pacific Northwest, but abundances of many remaining wild steelhead populations have declined and many populations are threatened by extinction
(Nehisen et al., 1991; Busby et al., 1996). Artificial production has been an important part of managing this resource for most of this century (Lichatowich and McIntyre, 1987). In recent years, annual hatchery production of steelhead in western North America has reached over 24 million juveniles (Light, 1989). Most of this production was intended for harvest to mitigate for declines in abundance from earlier overfishing, losses of spawning habitat due to agriculture, forestry and land development, and to dams that blocked migratory routes.
A crucial issue in supplementation programs, which was often not considered in traditional mitigation programs is where and when to collect adult brood fish to avoid losing genetic diversity. Equally important, however, fishery managers must be able to monitor and interpret genetic changes in supplemented populations. Steelhead, like other
Pacific salmon, usually return from the ocean to their natal streams to spawn (Sheer,
1939). Consequently, large genetic differences have evolved between geographically distant spawning aggregations (Allendorf 1975; Parkinson, 1984, Schreck et al., 1986;
Busby et al., 1996; Chapter 2). Among geographically proximate spawning aggregations, however, precision of homing and genetically effective populations sizes may vary from returning cohort to cohort, resulting in gene flow and random genetic drift.These can 56 produce statistically detectable genetic change, but interpreting such variation has been difficult. Waples and Tee! (1990), for example, noted significant allelic frequency changes in chinook salmon (0. tshawytscha) over 2-4 years in Oregon hatchery populations and explored genetic drift as an explanation. Allelic frequencies that fluctuate more between cohorts than between geographically different spawning aggregations or their offspring make it difficult to correctly identif,' different populations. Additionally, the dynamics of such fluctuations need to be understood to determine whether supplementation is working.
Despite their importance, however, very few genetic data have been available to guide such decisions about supplementation.
In this paper, I illustrate the potentially intricate dynamics of small breeding populations under hatchery supplementation by examining the magnitude and direction of genetic variation in naturally reproducing steelhead populations in the Umatilla River,
Oregon. Steelhead in the Umatilla River have had ample opportunity for genetic changes due to genetic drift and gene flow. Steelhead were once abundant in the Umatilla River, a tributary of the Columbia River that drains 6400 square km of semi-arid land in north- central and eastern Oregon (Fig.3.1). However, by the l920s, habitat degradation and construction of dams had reduced populations to low numbers. By the late 1 960s, steelhead returning to the Umatilla River had been reduced to 900-2000 fish annually
(Howell et al., 1984). Genetic effective population size (Ne) for the total population of
Umatilla Basin rainbow trout, may have ranged from 100-270, based on Waples (1990) relationship for age structure and Ne in Pacific salmon and unpublished demographic data from Oregon Department of Fish and Wildlife. If geographical genetic structure existed within the basin, Ne of local populations may have been much less. For example, most Threemile Dam
0 km 10
Figure 3.1. Umatilla River Basin. Codes for locations where rainbow trout were collected in 1992 are the following: 1, North Fork of the Umatilla River; 2, Buck Creek; 3, Thomas Creek; 4, South Fork of the Umatilla River; 5, Camp Creek; 6, North Fork of Meacham Creek; 7, upper Meacham Creek; 8, Squaw Creek; 9, McKay Creek; 10, East Birch Creek; 11, Pearson Creek; 12, West Birch Creek; 13, East Fork of Butter Creek; 14, Umatilla brood fish. 58
steelhead have spawned in Meacham Creek, Squaw Creek, and North and South forks
(Fig. 3.1). However, smaller spawning aggregations have persisted in other streams, even
though they account for less than than 5% of the total number of adult steelhead returning
to the Umatilla River (Oregon Department of Fish and Wildlife and Confederated Tribes
of the Umatilla Indian Reservation, unpublished data). Consequently, in many spawning
aggregations, effective population sizes of 10-20 may be common.
Gene flow among local populations and between cohorts within local populations
can confound the effects of genetic drift on allele frequencies. For example, different
cohorts within geographical spawning aggregations have regularly interbred.
Approximately 30% and 60% of the spawners in the Urnatilla River have been three and
four years old, respectively, with smaller proportions of two and five year olds (Oregon
Department of Fish and Wildlife, unpublished data). In addition, since1980,a portion of
all wild steelhead entering the Umatilla River have been trapped at Threemile Dam (Fig.
3.1) and used as a single brood stock for supplemenation. Non-native Skamania strain
steelhead (a hatchery derivative of steelhead from Washougal and Klickitat rivers,
Washington) were also introduced for four years until1970(Howell et al.,1985),but the
precision and success with which these hatchery-reared fish returned to spawn successfully
in different parts of the basin are unknown.
3.3. Materials and Methods
I collected wild yearling rainbow trout from 13 locations in the Umatilla River
Basin (Fig. 3.1) during Autumn, 1992, and again in1994.Steelhead have recently used all
locations, except McKay Creek, for spawning and rearing. McKay Creek, which has 59
received many introductions of non-native resident rainbow trout (Oregon Department of
Fish and Wildlife, unpublished data), supported resident rainbow trout but no steelhead
because of an impassable dam. I also sampled hatchery steelhead, including adult
steelhead that were trapped at Threemile Dam (Fig. 3. 1) in 1992, their hatchery-reared
offspring, and non-native Skamania strain steelhead. Naturally spawning steelhead of the
1992 adult cohort were the parents of wild juveniles collected in 1994. All samples were
frozen immediately and transported to Oregon State University. Muscle, liver, heart, and
eye tissues were removed from the right side of each fish and stored at -80 C for analysis
of allozyme and mitochondrial DNA (mtDNA) variation.
I examined genetic variation at 60 loci encoding 26 enzymes (Table 3.1) following
procedures for allozyme electrophoresis of Aebersold et al. (1987). To examine mtDNA
variation, I initially screened three mtDNA segments in rainbow trout from diverse
geographical origins for variability. NADH dehydrogenase-1 (ND-1), ND-2, and D-loop
segments of the mitochondrial genome were amplified using polymerase chain reaction
(PCR) from muscle tissue with primers developed by LGL Genetics, Inc. Procedures for
analysis of mtDNA polymorphisms using polymerase chain reaction (PCR) products were those of Cronin et al. (1993). Each segment was digested with twenty-four restriction
enzymes: Ase I, Ava II, Bfa I, Bgl I, Bgl II, BsaJ I, BstN I, BsIU I, Dde 1, Dpn II, EcoR I,
Hae II, Hae III, Hha I, Hinc II, Hind II, Hinf 1, Msp I, Nd I, Rsa I, Sazi96 1, SerF I, Taq
I, and Xba I. Fish screened were from nine locations:1) Umatilla Hatchery, 2) Marion
Forks Hatchery (Willamette River, Oregon), 3) Owyhee River (upper Snake River,
Oregon), 4) Malheur River (upper Snake River, Oregon), 5) Paradise Creek (Sycan
River, Oregon), 6) Spenser Creek (upper Kiamath River, Oregon), 7) Spring Creek 60
Table 3.1. International Union of Biochemistry (I.U.B.) enzyme names, Enzyme Commission (E.C.) numbers, loci examined in this study, tissues and buffers. Tissues: M, muscle; L, liver, E, eye; H, heart. Buffers: ACE, an citrate-amine-EDTA gel and tray buffer pH 6.8; TBCLE, a Tris-citrate gel buffer and lithium hydroxide, borate-EDTA tray bufferpH 8.5;TG, a Tris- glycine gel and tray buffer pH8.5.
E.C. I.U.BEnzyme name Number Locus Tissue Buffer Aspartate animotransferase 2.6.1.1 mAAT-] * M ACE mAAT2* M ACE mAAT3* M ACE sAATJ,2* M ACE sAAT3* L,E ACE sAAT4* L ACE Adenosine deaminase 3.5.4.4 ADA-i * L,M ACE ADA 2* L,M ACE Alcohol dehydrogenase 1.1.1.1 ADH* L ACE Aconitate dehydratase 4.2.1.2 sAH* L ACE Adenylate kiriase 2.7.4.3 AK* M ACE Alanine aminotransferase 2.6.1.2 ALAT* M ACE,TG Creatme kmase 2.7,3.2 CK-Ai * M TBCLE CKA2* M TBCLE CKB* E TG CKC1* E TG CKC2* E TG Fructose-bisphosphate aldolase 4.2.1.2 FBALD-I * E TG FBALD2* E TG Fumaratehydratase 4.2.1.2 FH* M ACE Glyceraldehyde-3 -phosphate dehydrogenase 1.2.1.12 GAPDH-3 * H ACE GAPDH4* B ACE GAPDH5* E ACE Guanine deaminase 3.5.4.3 GDA-1 L TBCLE
GDA2* L TBCLE Glucose-6-phosphate isomerase 5.3.1.9 GPI-A * M TBCLE
Glucose-6-phosphate isomerase 5.3.1.9 GPI-BI * M TBCLE GPIB2* M TBCLE Glycerol-3 -phosphate dehydrogenase 1.1.1.8 G3PDH-] * M ACE G3PDH2* M ACE 61
Table 3.1. Continued.
B.C. TuBEnzyme name Number Locus Tissue Buffer
Glutathione reductase 1.6,4.2 GR* M,H ACE
Isocitrate dehydrogenase 1.1.1.42 mJDHP]* M,H ACE
mIDHP2* M,JJ ACE sJDHP-1,2 * L ACE Lactate dehydrogenase 1.1.1.27 LDHA1* M TBCLE,TG LDH-A2 * M TBCLE,TG
LDH-B] * H,E TBCLE
LDH-B2 * L TBCLE LD11C* B TG
Malate dehydrogenase 1.1.1.37 sMDHA1,2* L ACE sMDHB1,2* H,M ACE Malate dehydrogenase (NADP+) 1.1.1.40 mMEP1* M ACE sMEP-] * M ACE sMEP-2 * L ACE Dipeptidase 3.4.-.- PEP-A * M,E TG Tnpeptide aminopeptidase 3.4.-.- PEP-B] * M,H TG
Peptidase-C 3.4.-.- FEB-C E TG Prolme dipeptidase 3.4.-.- PEP-D * M,H TG Phosphogluconate dehydrogenase 1.1.1.44 PGDH* M ACE Phosphoglucomutase 5.4.2.2 PGM]* M TG PGM2* M TG
Superoxide dismutase 1.15.1.1 5SOD_1* L ACE, TBLCE Triosephosphate isomerase 5.3.1.1 TPI_1* M,H TG
TPI2* M,JI TG
TPI-3 * M,T4 TG
TPI-4 * M,H TG 62
(Upper Kiamath Lake, Oregon), and 8) Wood River (Upper Kiamath Lake, Oregon), and
9) Bogus Creek, California (lower Klamath River, Oregon). From this initial screening, I
chose 14 restriction enzyme-mtDNA segment combinations (Table 3.2) for detailed study
of interpopulation mtDNA variation in Umatilla River rainbow trout and constructed
composite haplotypes from the combination of restriction fragment patterns.
I tested the hypotheses that genotypic frequencies at allozyme loci conformed to
Hardy-Weinberg proportions and that no allelic or haplotype frequency differences existed
among samples using log likelihood ratio tests (G-tests) in a nested contingency table
analysis. To examine the distribution of genetic diversity, I partitioned total allozyme variation hierarchically into differences among and within samples from major tributaries
(headwaters of the Umatilla River, Meacham Creek, Squaw Creek, McKay Creek, Birch
Creek, and Butter Creek, Fig. 3.1). Differences within tributarieswere further partitioned into differences among sites and differences between cohort from differentyears. Tests of
Hardy-Weinberg proportions included only loci where expected values could be calculated
(no isoloci were included) and with expected valueswere less than one. Tests of homogeneity were limited to loci withmean frequency of the common allele of less than
0.95 and rare alleles (<0.05) were combined withmore frequent classes. Significance levels were adjusted for multiple comparisons (Cooper, 1968). Because samplesizes for mtDNA haplotype frequencieswere too small to expect valid asymptotic probability values (p-values) from the chi-square distribution, I calculated exact permutational p-values and point probabilities and useda modified p-value (exact p-value minus one half point probability) to control for Type Ierror rates and conservatism (Mehta and Patel,
1992). Table 3.2. Rainbow trout mtDNA restriction fragment patterns for restrictionenzymes used in this study that revealed interpopulation variability. Patterns for digestion of ND-i with Ava II and Bgl II and D-loop with H/ia I and Mse I, which revealed restriction sites but no interpopulation variability, are not shown. Digestion of NID-2 with Hind III revealed no restriction sites. Numeral one indicates the restriction fragment was present; zero indicates it was absent.
Restriction fragments Restriction Fragment mtDNA segment enzyme pattern 1 2 3 4 5 6 7 8 9 10 11
ND-i AciI A 1 0 0 0 1 i 1 1 0 1 1
B 1 0 0 1 0 0 1 1 1 1 1
C 0 1 1 0 1 1 1 1 0 1 1
BsIUI A i 0 1 1 i 1 0 1
B 1 1 1 0 0 1 0 1
C 1 0 1 0 1 1 1 1
HaeII A 1 0 1 0 1 1 1 1 1 1 0
B 1 1 1 0 0 0 1 1 1 i 0
C 1 0 0 1 1 1 1 1 1 1 1
HindIII A 0 1 1
B 1 0 0
)v[spI A 1 1 1 0 1 1 1 1 1 1 1
B 1 1 1 1 1 i 1 0 1 0 1 Table 3.2. Continued.
Restriction fragments Restriction Fragment mtDNA segment enzyme pattern 1 2 3 4 5 6 7 8 9 10 11
ND-I TaqI A 1 0 0 1 1 0 1 1
B 0 1 0 1 1 1 1 1
C 1 0 1 1 0 0 1 0
ND-2 A/uI A 0 1 1 1 1 0 0 0 1 0 1
B 1 0 0 1 1 0 0 0 1 0 1
C 0 0 0 1 1 1 1 1 1 1 1
MseI A 1 1 0 0 1 1 1 1
B 1 0 1 1 1 1 1 1
D-loop Bgl II A 1 0 0
B 0 1 1
DpnII A 1 0 0 1 1 0
B 1 0 1 0 0 0
C 0 1 0 1 1 1
D 0 1 1 0 0 1 65
I examined patterns of geographical genetic similarity by constructing phenograms
from cluster analyses of pair-wise estimates of divergence between samples, using the
unweighted pair-group method with arithmetic averages (UPGMA) algorithm (Sneath and
Sokal, 1973). Nei's unbiased genetic distance (Nei, 1978) and Neis nucleotide diversity
(Nei, 1987) were used as measures of genetic divergence between populations for
allozyme and mtDNA data, respectively. Number of nucleotide substitutionsper site between different haplotypes (Nei, 1987) was used to examine distribution and similarity
of different haplotypes.
3.4. Results
I identified 116 alleles segregating at 60 allozyme loci in Umatilla River rainbow trout. Twenty-six loci were polymorphic in at least on sample (Table 3.3). Genotypic frequencies at three loci (GAPDH-3 GDA-1and sSOD-1 *) departed from Hardy-
Weinberg expectations in three samples (<5% of the tests),a number expected by chance.
Only five loci - sAH*, GAPDH-3 sIDHP-1, 2*, LDH-B2 PEP-A *- met the criterion of a minimum expected value for testing for allelic homogeneity.
In addition, I identified 16 mtDNA haplotypes in Umatilla River rainbow trout
(Table 3.4), representing three major lineages of mitochondrial DNA evolution (Fig. 3.2).
An additional haplotype, C17, that occurred in Skamania hatchery steelhead andsome wild populations where Skamatha steelhead have been introduced (author, unpublished data) was not present in Umatilla River steelhead (Table 3.5). Haplotypes inlineage A were widely distributed throughout the Umatilla River (Fig.3.2, Table3.5). Two haplotypes had a broad geographical distribution. Haplotype, Cl, prevailed in all Table 3.3. Allozyme frequencies and sample size (n) for polymorphic loci in Umatilla Riverrainbow trout. Frequencies are not given for mAAT-1 mAAT.2*90, sAAT1,2*125, sAAT3*140, ADH*78, sAH*72, AK*70, ALA T*106, CKB*89, FBALD- 2*107, GAPDH-3 *33, GDA-1 *JJ5, GDA-2 *87 GPI-A *93G3PDH-1 GR *121 mIDHP-2 *J44 sIDHP-1, 2*121, LDH- B2*113, sMDHA1,2*49, sMDHB*120, mMEP1*115, sMEP1*83,sMEP2*110, PEPA*93, PEP.B1*134, PGDH*92, PGM- 1 *.140 PGM-2 *..120, sSOD-1 *18 7.
Loci
mAAT1* mAAT2* sAAT1,2* sAAT-3 * ADH*
Population n-100 n-100 ii 100 n 100 69 n-100 -50
1. North Fork Umatjlla River 1992 1000.990 100 1.000 300 0.980 50 1.000 0.000 150 1.000 0.000
1. NorthForkUmatjllaRjver 1994 72 1.000 72 1.000 135 0.993 720.972 0.000 72 1.000 0.000
2. BuckCreek 1992 0 0.000 00.000 100 1.000 50 1.000 0.000 50 1.000 0.000
2. Buck Creek 1994 88 1.000 88 1.000 120 1.000 88 0,977 0.000 88 1.000 0.000
3. ThomasCreekl992 48 1.000 48 1.000 92 0.989 46 1.000 0.000 48 1.000 0.000
3. Thomas Creek 1994 70 1.000 70 1.000 20 1.000 70 1.000 0.000 700.986 0.000
4. SouthForkUmatjllaRjver 1992 0 0.000 0 0.000 0 0.000 50 1.000 0.000 50 1.000 0.000
4. SouthForkUmatjllaRjver 1994 66 1.000 66 1.000 132 0.992 66 0.970 0.030 660.985 0,000
5. Camp Creek 1992 46 1.000 460.978 100 0.970 24 1,000 0.000 46 1.000 0.000
5. Camp Creek 1994 82 1.000 82 1.000 164 1,000 82 0.976 0.012 820.963 0.000
6. NorthForkMeacham Creek 1992 48 1.000 48 1.000 96 1.000 48 1.000 0.000 48 1.000 0.000
6. NorthForkMeachamCreek 1994 90 1.000 90 1.000 132 1.000 16 1.000 0.000 90 0.989 0.000
7. Upper Meacham Creek 1992 0 0.000 0 0.000 28 1.000 48 1.000 0.000 48 1.000 0.000 Table 3.3. Continued.
Loci
mAAT1* mAAT2* sAAT1.2* sAAT-3 * AD11*
Population n-100 n-100 n 100 n 100 69 n-100 -50
7. Upper Meacham Creek 1994 76 1.000 76 1.000 128 1.000 48 0.979 0.021 76 1.000 0.000
8. Squaw Creek 1992 170 0.994 170 1.000 340 0.976 170 1.000 0.000 170 0.964 0
8. Squaw Creek 1994 86 1.000 86 1.000 120 1.000 1240.984 0.000 86 0.965 0.023
9. McKayCreek 1992 0 0.000 0 0.000 96 1.000 48 1.000 0.000 48 1.000 0.000
9. McKayCreek 1994 24 1.000 24 1.000 48 1.000 12 1.000 0.000 24 1.000 0.000
10. East Birch Creek 1992 50 1.000 50 1.000 100 1.000 0 0.000 0.000 50 0.980 0.000
10. East Birch Creek 1994 80 1.000 80 1.000 58 1.000 80 1.000 0.000 80 0.938 0.000
11. Pearson Creek 1992 44 0.955 44 1.000 88 0.989 44 1.000 0.000 44 0.886 0.000
11. Pearson Creek 1994 88 1.000 88 1.000 161 0.988 88 1.000 0.000 880.989 0.000
12. West Birch Creek 1992 56 1.000 56 0.982 112 1.000 56 1.000 0.000 56 1.000 0.000
12. West Birch Creek 1994 36 1.000 36 1.000 140 0.993 116 1.000 0.000 72 0.972 0.000
13. East Fork Butter Creek 1992 26 1.000 26 1.000 100 1.000 50 1.000 0.000 50 0.880 0.000
13. East Fork Butter Creek 1994 82 1.000 82 1.000 82 1.000 0 0.000 0.000 28 1.000 0.000
14. Umatilla brood 1992 260 0.965 260 1.000 7120.990 356 1.000 0.000 356 0.994 0.000 Table 3.3. Continued.
Loci
sAH* ALAT* CKB*
Population n 100 85 n 100 40 n 100 111 91 n 100
1. NorthForkUmatillaRiver 1992 150 0.853 0.147 150 1.000 0.000 150 1.000 0.000 0.000 150 0.980
1. NorthForkUmatjllaRjver 1994 680.750 0.191 72 1.000 0.000 00.000 0.000 0.000 72 1.000
2. BuckCreek 1992 500.880 0.120 50 0.960 0.040 50 1.000 0.000 0.000 50 1.000
2. BuckCreek 1994 82 0.829 0.171 88 0.977 0.023 00.000 0.000 0.000 88 1.000
3. Thomas Creek 1992 480.875 0.125 48 1.000 0.000 2 1.000 0.000 0.000 48 0.979
3. Thomas Creek 1994 660.894 0.106 70 1.000 0.000 0 0.000 0.000 0.000 70 1.000
4. SouthForkUmatillaRiver 1992 42 0.833 0.167 50 0.940 0.060 50 1.000 0.000 0.000 50 1.000
4. SouthForkUmatillaRiver 1994 58 0.914 0.086 66 1.000 0.000 0 0.000 0.000 0.000 66 1.000
5. Camp Creek 1992 460.804 0.130 46 1.000 0.000 46 1.000 0.000 0.000 46 0.978
5. Camp Creek 1994 82 0.841 0.159 82 1.000 0.000 0 0.000 0.000 0.000 82 1.000
6. NorthForkMeacham Creek 1992 48 0.854 0.125 48 1.000 0.000 48 1.000 0.000 0.000 48 1.000
6. NorthForkMeacham Creek 1994 90 0.889 0.111 90 1.000 0.000 0 0.000 0.000 0.000 90 1.000
7. UpperMeacham Creek 1992 480.833 0.167 48 1.000 0.000 48 1.000 0.000 0.000 48 1.000
7. Upper Meacham Creek 1994 680.838 0.162 76 1.000 0.000 0 0.000 0.000 0.000 76 1.000
8. Squaw Creek 1992 1700.905 0.088 170 1.000 0.000 62 1.000 0.000 0.000 1700.994
8. Squaw Creek 1994 86 0.895 0.08 1 86 1.000 0.000 0 0.000 0.000 0.000 86 1.000 Table 3.3. Continued
Loci
sAH* ALAT* CKB*
Population n 100 85 n 100 40 n 100 111 91 n 100
9. McKay Creek 1992 48 0.813 0.167 48 1.000 0.000 48 1.000 0.000 0.000 48 1.000
9. McKay Creek 1994 200.750 0.250 24 1.000 0.000 0 0.000 0.000 0.000 24 1.000
10. East Birch Creek 1992 50 0.660 0.300 50 1.000 0.000 50 1.000 0.000 0.000 500.980
10. East Birch Creek 1994 760.921 0.026 80 1.000 0.000 0 0.000 0.000 0.000 80 1.000
11. Pearson Creek 1992 440.727 0.250 44 1.000 0.000 44 1.000 0.000 0.000 44 1.000
11. Pearson Creek 1994 88 0.750 0.250 88 1.000 0.000 0 0.000 0.000 0.000 88 1.000
12. West Birch Creek 1992 560.893 0.107 56 1.000 0.000 56 1.000 0.000 0.000 56 0.964
12. West Birch Creek 1994 72 1.000 0.000 72 1.000 0.000 0 0.000 0.000 0.000 72 1.000
13. East Fork Butter Creek 1992 52 0.846 0.135 50 1.000 0.000 24 1.000 0.000 0.000 50 1.000
13. East Fork Butter Creek 1994 680.794 0.176 82 1.000 0.000 0 0.000 0.000 0.000 82 1.000
14. Umatilla brood 1992 2900.752 0.245 356 0.992 0.000 356 0.978 0.003 0.006 356 1.000 Table 3.3. Continued.
Loci FBALD2* GAPDH3* GDA1* GDA2* GPIA* G3PDH1*
Population n 100 n 100 n 100 n 100 n 100 110 n -100
1. NorthForkUmatillaRiver 1992 150 1.000 150 0.907 0 0.000 0 0.000 150 1.000 0.000 1500.973
1. NorthForkUmatillaRiver 1994 72 1.000 72 0.847 56 0.536 56 1.000 72 1.000 0.000 72 1.000
2. Buck Creek 1992 50 1,000 50 0.800 0 0.000 0 0.000 50 1.000 0.000 50 1.000
2. BuckCreek 1994 88 1.000 88 0.705 80 0.513 800.950 880.989 0.000 88 0.989
3. ThomasCreekl992 48 1.000 48 0.917 00.000 0 0.000 480.979 0.000 48 1.000
3. ThomasCreekl994 70 1.000 70 0.871 54 0.537 58 0.983 70 0.971 0.000 70 1.000
4. SouthForkUmatillaRiver 1992 50 1.000 50 0.960 00.000 0 0.000 50 1.000 0.000 50 1.000
4. SouthForkUmatilla River 1994 66 1.000 620.887 380.684 38 0.974 66 1.000 0.000 66 1.000
5. Camp Creek 1992 46 1.000 460.870 0 0.000 0 0.000 46 1.000 0.000 46 1.000
5. Camp Creek 1994 82 0.988 82 0.890 660.530 66 1.000 82 1.000 0.000 82 0.988
6. NorthForkMeacham Creek 1992 48 1.000 480.896 0 0.000 0 0.000 48 1.000 0.000 48 1.000
6. NorthForkMeacham Creek 1994 90 1.000 88 0.773 82 0.671 82 0.988 90 1.000 0.000 90 1.000
7. Upper Meacham Creek 1992 48 1.000 480.979 0 0.000 0 0.000 48 1.000 0.000 48 1.000
7. Upper Meacham Creek 1994 76 1.000 74 0.743 58 0.845 60 0.850 48 1.000 0.000 62 1.000
8. Squaw Creek 1992 170 1.000 170 0.917 0 0.000 0 0.000 1700.988 0.005 170 1.000
8. Squaw Creek 1994 86 1.000 860.872 600.600 600.969 86 1.000 0.000 86 0.977 Table 3.3. Continued.
Loci FBALD2* GAPDH3* GDA1* GDA2* GPIA* G3PDHl*
Population II 100 II 100 II 100 n 100 n 100 110 II -100
9. McKay Creek 1992 48 1.000 480.917 00.000 0 0.000 481.000 0.000 48 1.000
9. McKay Creek 1994 24 1.000 240.833 220.545 22 1.000 24 1.000 0.000 24 1.000
10. East Birch Creek 1992 50 1.000 50 0.980 0 0.000 0 0.000 50 1.000 0.000 50 1.000
10. EastBirch Creek 1994 80 1.000 80 0.863 660.515 660.985 80 0.975 0.000 80 1.000
11. Pearson Creek 1992 44 1.000 44 0.909 0 0.000 0 0.000 44 1.000 0.000 44 1.000
11. Pearson Creek 1994 88 1.000 88 0.898 780.590 74 1.000 88 1.000 0.000 88 1.000
12. West Birch Creek 1992 56 1.000 56 1.000 0 0.000 0 0.000 56 1.000 0,000 56 1.000
12. West Birch Creek 1994 72 1.000 72 0.958 660.545 58 0.93 1 58 1.000 0.000 72 1.000
13. East Fork Butter Creek 1992 50 1.000 50 0.920 0 0.000 0 0.000 50 1.000 0.000 50 1.000
13. East Fork Butter Creek 1994 82 1.000 82 0.915 760.618 76 0.961 82 1.000 0.000 82 1.000
14, Umatilla brood 1992 356 1.000 356 0.874 0 0.000 0 0.000 356 1,000 0,000 356 1.000 Table 3.3. Continued.
Loci mIDHP2* s1DHP1.2*
Population n 100 87 n 100 144 n 100 40 72 116
1. NorthForkUmatillaRiver 1992 150 0.980 0.020 1500.973 0.027 3000.700 0.170 0.127 0.000
1. NorthForkUmatillaRiver 1994 0 0.000 0.000 720.958 0.000 144 0.681 0.174 0.146 0.000
2. BuckCreek 1992 50 1.000 0.000 50 1.000 0.000 100 0.670 0.200 0.100 0.030
2. BuckCreekl994 0 0.000 0.000 880.989 0.000 156 0.724 0.128 0.147 0.000
3. Thomas Creek 1992 48 1.000 0.000 480.958 0.042 92 0728 0.098 0.174 0.000
3. Thomas Creek 1994 0 0.000 0.000 70 1.000 0.000 136 0.669 0.140 0.184 0.000
4. SouthForkUmatjllaRiver 1992 50 0.940 0.060 50 0.880 0.120 100 0.640 0.230 0.070 0060
4. SouthForkUmatjllaRjver 1994 0 0.000 0.000 66 0.985 0.000 128 0.719 0.078 0.195 0000
5. Camp Creek 1992 460.957 0.022 46 1.000 0.000 92 0.652 0.109 0.207 0.011
5. Camp Creek 1994 0 0.000 0.000 82 1.000 0.000 164 0.713 0.073 0.213 0.000
6. NorthForkMeacham Creek 1992 480.958 0.000 48 1.000 0.000 96 0.760 0.052 0.188 0.000
6. NorthForkMeacham Creek 1994 0 0.000 0.000 90 1.000 0.000 169 0675 0.089 0.219 0.000
7. Upper Meacham Creek 1992 480.896 0.104 48 1.000 0.000 96 0.646 0.146 0.208 0.000
7. Upper Meacham Creek 1994 00.000 0.000 76 1.000 0.000 140 0.714 0.100 0.179 0.000
8. Squaw Creek 1992 170 1.000 0.000 1700.988 0.005 340 0.758 0.113 0.128 0.000
8. Squaw Creek 1994 0 0.000 0.000 86 0.977 0.000 144 0.722 0.104 0.174 0.000 Table 3.3. Continued
Loci
GR* mIDHP2* sIDHIP-1 .2*
Population n 100 87 n 100 144 n 100 40 72 116
9. McKay Creek 1992 480.979 0.021 480.917 0.083 96 0.656 0.167 0.135 0.021
9. McKay Creek 1994 00.000 0.000 240.917 0.000 48 0.729 0.146 0.063 0.000
10. East Birch Creek 1992 500.980 0.020 50 1.000 0.000 960.802 0.021 0.177 0.000
10. EastBirchCreek 1994 0 0.000 0.000 80 1.000 0.000 156 0.679 0.135 0.167 0.000
11. Pearson Creek 1992 44 0.932 0.068 440.955 0.045 880.705 0.102 0.193 0.000
11. Pearson Creek 1994 0 0.000 0.000 88 1.000 0.000 182 0.698 0.110 0.192 0.000
12. WestBirchCreek 1992 56 1.000 0.000 56 1.000 0.000 1120.741 0.080 0.170 0.000
12. West Birch Creek 1994 0 0.000 0.000 72 1.000 0.000 132 0.674 0.159 0.159 0.000
13. EastFork Butter Creek 1992 50 1.000 0.000 50 0.980 0.020 1000.670 0.110 0.200 0.020
13. EastForkButter Creek 1994 0 0.000 0.000 82 1.000 0.000 1560.647 0.090 0.23 1 0.000
14. Umatilla brood 1992 356 1.000 0.000 356 0.978 0.022 6960.644 0.168 0.182 0.006 Table 3.3. Continued.
Loci LDHB2* sMDHA1,2*
Population n 100 76 n 100 155 120 37
1. North Fork Umatilla River 1992 150 0.387 0.613 3000.993 0.000 0.007 0.000
1. North Fork Umatilla River 1994 720.486 0.514 144 1.000 0.000 0.000 0.000
2. BuckCreek 1992 500.280 0.700 1000.980 0.000 0.020 0.000
2. Buck Creek 1994 88 0.500 0.489 176 1.000 0.000 0.000 0.000
3. Thomas Creek 1992 48 0.333 0.667 96 1.000 0.000 0.000 0.000
3. Thomas Creek 1994 700.400 0.586 140 0.979 0.000 0.000 0.000
4. South Fork Umatilla River 1992 50 0.260 0.740 100 1.000 0.000 0.000 0.000
4. South Fork Umatilla River 1994 660.455 0.545 132 1.000 0.000 0.000 0.000
5. Camp Creek 1992 460.326 0.609 92 1.000 0.000 0.000 0.000
5. Camp Creek 1994 82 0,378 0.622 165 0.994 0.000 0.000 0.000
6. North Fork Meacham Creek 1992 480,396 0.604 96 1.000 0.000 0.000 0.000
6. North Fork Meacham Creek 1994 90 0.356 0.611 180 0.989 0,000 0.000 0.000
7. Upper Meacham Creek 1992 48 0.229 0.771 96 0.948 0.000 0.052 0.000
7. Upper Meacham Creek 1994 76 0.342 0.632 152 0.987 0.007 0.000 0.000
8. Squaw Creek 1992 170 0.329 0.658 340 0.997 0.000 0.002 0.000
8. Squaw Creek 1994 86 0.302 0.698 168 1.000 0.000 0.000 0.000 Table 3.3. Continued.
Loci LDHB2* sMDHA1.2*
Population n 100 76 n 100 155 120 37
9. McKay Creek 1992 480.729 0.271 96 1.000 0.000 0.000 0.000
9. McKay Creek 1994 240.667 0.333 48 1.000 0.000 0.000 0.000
10. East Birch Creek 1992 50 0.580 0.420 100 1.000 0.000 0.000 0.000
10. East Birch Creek 1994 840.571 0.429 160 1.000 0.000 0.000 0.000
11. Pearson Creek 1992 440.455 0.545 88 1.000 0.000 0.000 0.000
11. Pearson Creek 1994 880.489 0.511 176 0.972 0.000 0.000 0.000
12. West Birch Creek 1992 56 0.464 0.536 112 1.000 0.000 0.000 0.000
12. West Birch Creek 1994 72 0.431 0.569 144 1.000 0.000 0.000 0.000
13. East Fork Butter Creek 1992 500.440 0.560 100 1.000 0.000 0.000 0.000
13. East Fork Butter Creek 1994 82 0.488 0.512 164 0.988 0.006 0.000 0.000
14. Umatilla brood 1992 356 0.444 0.553 7120.987 0.003 0.003 0.001 Table 3.3. Continued.
Loci
sMDH-B1 .2* mMEP1* s1v1EP1*
Population n 100 116 83 78 92 n 100 90 n 100 107
1. NorthForkUmatillaRiver 1992 300 0.997 0.000 0.000 0.000 0.003 150 0,993 0,007 150 0.987 0.013
1. NorthForkUmatilla River 1994 144 0.972 0.014 0.000 0.014 0.000 720.972 0.000 72 1.000 0.000
2. BuckCreek 1992 100 0.990 0.000 0.000 0.010 0.000 50 1.000 0.000 50 1.000 0.000
2. BuckCreek 1994 176 1.000 0.000 0.000 0.000 0.000 88 1.000 0.000 88 1.000 0.000
3. Thomas Creek 1992 96 1.000 0.000 0.000 0.000 0.000 48 1.000 0.000 48 1.000 0.000
3. Thomas Creek 1994 140 0.950 0.02 1 0.000 0.029 0.000 70 1.000 0.000 70 1.000 0.000
4. SouthForkUmatilla River 1992 100 1.000 0.000 0.000 0.000 0.000 50 1.000 0,000 50 0.980 0.020
4. SouthForkUmatillaRiver 1994 132 0.977 0.023 0.000 0.000 0.000 33 1.000 0.000 66 0.985 0.000
5. Camp Creek 1992 92 1.000 0.000 0.000 0.000 0.000 46 1.000 0.000 46 0.957 0.043
5. Camp Creek 1994 164 0.976 0.006 0.000 0.018 0.000 820.988 0.000 82 1.000 0.000
6. North Fork Meacham Creek 1992 95 0.969 0.021 0.000 0.000 0.000 48 1.000 0.000 48 1.000 0.000
6. NorthForkMeachamCreek 1994 178 0.949 0.022 0.011 0.017 0.000 90 1.000 0.000 90 1.000 0.000
7. Upper Meacham Creek 1992 96 0.979 0.000 0.010 0.010 0.000 48 1.000 0.000 48 1.000 0.000
7. Upper Meacham Creek 1994 1480.993 0.007 0.000 0,000 0.000 76 1.000 0.000 76 1.000 0.000
8. SquawCreekl992 3400.994 0,000 0.000 0.002 0.000 170 1.000 0.000 170 0,994 0.005
8. SquawCreekl994 172 0.983 0.006 0.000 0.012 0.000 86 1.000 0.000 86 1.000 0.000 Table 3.3. Continued.
Loci
sMDH-B 1.2* n1MEP1* sMEP1*
Population n 100 116 83 78 92 n 100 90 ii 100 107
9. McKay Creek 1992 960.990 0.000 0.000 0.000 0.010 480.979 0.021 48 1.000 0.000
9. McKay Creek 1994 48 1.000 0.000 0.000 0.000 0.000 24 1.000 0.000 220.955 0.000
10. East Birch Creek 1992 100 1.000 0.000 0.000 0.000 0.000 50 1.000 0.000 50 1.000 0.000
10. East Birch Creek 1994 160 0.988 0.000 0.013 0.000 0.000 80 1.000 0.000 80 1.000 0.000
11. Pearson Creek 1992 87 0.977 0.011 0.000 0.000 0.000 44 1.000 0.000 440.977 0.023
11. Pearson Creek 1994 176 0.983 0.000 0.017 0.000 0.000 88 1.000 0.000 88 1.000 0.000
12. West Birch Creek 1992 112 0.982 0.000 0.000 0.000 0.018 56 1.000 0.000 56 1.000 0.000
12. West Birch Creek 1994 144 1.000 0.000 0.000 0.000 0.000 72 1.000 0.000 72 1.000 0.000
13. East Fork Butter Creek 1992 100 1.000 0.000 0.000 0.000 0.000 50 1.000 0,000 50 1.000 0.000
13. East Fork Butter Creek 1994 164 1.000 0.000 0.000 0.000 0.000 82 1.000 0.000 82 1000 0.000
14. Umatilla brood 1992 711 0.989 0.006 0.000 0.006 0.000 356 1.000 0.000 356 1.000 0.000 Table 3.3. Continued.
Loci
sMEP2* PEPA* PEP-B 1 * PGDH*
Population n 100 83 n 100 111 n 100 69 n 100 104
1. NorthForkUmatillaRiver 1992 150 0.993 0.007 1500.927 0.073 150 0.973 0.020 150 1.000 0.000
1. NorthForkUmatilla River 1994 72 1.000 0.000 440.955 0.045 72 1.000 0.000 72 1.000 0.000
2. BuckCreekl992 50 1.000 0.000 50 0.880 0.120 50 1.000 0.000 50 1.000 0.000
2. Buck Creek 1994 40 1.000 0.000 48 0.958 0.042 88 0.977 0.000 88 1.000 0.000
3. ThomasCreekl992 480.958 0.000 48 0.958 0.042 48 1.000 0.000 48 1.000 0.000
3. Thomas Creek 1994 70 0.986 0.000 58 0.914 0.086 70 1.000 0.000 70 1.000 0.000
4. SouthForkUmatillaRiver 1992 50 1.000 0.000 50 0.940 0.060 50 0,980 0.000 50 1.000 0.000
4. SouthForkUmatillaRiver 1994 660.985 0.000 660.939 0.061 66 1.000 0.000 66 1.000 0.000
5. Camp Creek 1992 46 1.000 0.000 46 0.891 0.043 46 1.000 0.000 46 1.000 0.000
5. Camp Creek 1994 82 1.000 0.000 83 0.928 0.048 82 1.000 0.000 82 0.988 0.0 12
6. NorthForkMeacham Creek 1992 48 1.000 0.000 48 0.792 0.167 48 0.979 0.021 48 1.000 0.000
6. NorthForkMeachamCreekl994 90 1,000 0.000 97 0.835 0.082 90 1.000 0.000 90 1.000 0.000
7. UpperMeachamCreek 1992 48 1,000 0.000 480.833 0.167 480.979 0.000 48 1.000 0.000
7. Upper Meacham Creek 1994 760.974 0.000 74 0.946 0.027 76 1.000 0.000 76 1.000 0.000
8. Squaw Creek 1992 170 0.982 0.000 170 0.864 0.123 170 0.947 0.046 170 1.000 0.000
8. Squaw Creek 1994 86 0.988 0.000 88 0.920 0.080 86 0.977 0.000 86 1.000 0.000 Table 3.3. Continued
Loci
sMEP2* PEPA* PEPB1* PGDH*
Population n 100 83 n 100 111 ii 100 69 n 100 104
9. McKay Creek 1992 48 0.979 0.021 48 0.917 0.083 48 1.000 0.000 48 1.000 0.000
9. McKayCreek 1994 24 1.000 0.000 24 0.917 0.083 24 1.000 0.000 24 1.000 0.000
10. East Birch Creek 1992 50 1.000 0.000 50 0.920 0.060 50 0.980 0.020 50 1.000 0.000
10. East Birch Creek 1994 80 0.975 0.000 80 0.900 0.063 74 1.000 0.000 80 1.000 0.000
11. Pearson Creek 1992 44 0.977 0.000 44 0.841 0.091 440.955 0.023 44 1.000 0.000
11. Pearson Creek 1994 88 1.000 0.000 88 0.909 0.091 88 1.000 0.000 88 0.989 0000
12. West Birch Creek 1992 56 0.964 0.000 56 0.911 0.089 56 0964 0.000 56 1.000 0.000
12. West Birch Creek 1994 72 1.000 0.000 73 0.918 0.055 72 0.986 0.014 72 1.000 0.000
13. East Fork Butter Creek 1992 50 1.000 0.000 50 0.880 0.120 50 1.000 0.000 50 1.000 0.000
13. East Fork Butter Creek 1994 82 1.000 0.000 82 0.854 0.061 82 1.000 0.000 82 1.000 0000
14. Umatilla brood 1992 356 1.000 0.000 356 0.949 0.039 356 0.997 0.000 356 1.000 0.000 Table 3.3. Continued.
Loci PGM1* PGM2* sSOD1*
Population n-100 -85 n-100 n 100 152 38
1. North Fork Umatilla River 1992 150 0.993 0.007 150 1.000 1520.941 0.033 0.020
1. North Fork Umatilla River 1994 72 1.000 0.000 73 0.986 720.903 0.097 0.000
2. BuckCreek 1992 500.760 0.240 50 0.980 8 1.000 0.000 0.000
2. Buck Creek 1994 88 1.000 0.000 88 1.000 88 0.966 0.011 0.023
3. Thomas Creek 1992 48 1.000 0.000 48 1.000 480.979 0.000 0.021
3. Thomas Creek 1994 70 1.000 0.000 70 1.000 700.929 0.029 0.043
4. South Fork Umatilla River 1992 500.960 0.040 50 1.000 50 1.000 0.000 0.000
4. South Fork Umatilla River 1994 66 1.000 0.000 66 1.000 65 0.954 0.015 0.031
5. Camp Creek 1992 460.957 0.043 46 1.000 46 0.935 0.000 0.065
5. Camp Creek 1994 82 1.000 0.000 82 1.000 82 0.902 0.073 0.024
6. North Fork Meacham Creek 1992 48 0.958 0.042 48 1.000 480.958 0.000 0.021
6. North Fork Meacham Creek 1994 90 1.000 0.000 90 1.000 90 0.978 0.011 0.011
7. Upper Meacham Creek 1992 480.979 0.021 48 1.000 48 0.979 0.000 0.021
7. Upper Meacham Creek 1994 76 1.000 0.000 76 1.000 760.947 0.026 0.026
8. SquawCreek 1992 170 0.988 0.011 170 1.000 170 0.994 0.000 0.005
8. Squaw Creek 1994 860.988 0.012 86 1.000 86 0.988 0.000 0,012 Table 3.3. Continued.
Loci PGM1* PGM2* sSOD1*
Population n-100 -85 n-100 n 100 152 38
9. McKay Creek 1992 48 1.000 0.000 480.917 480.938 0.063 0.000
9. McKay Creek 1994 24 1.000 0.000 24 0.917 24 1.000 0.000 0.000
10. EastBirch Creek 1992 50 0.960 0.040 50 1.000 50 1.000 0.000 0.000
10. East Birch Creek 1994 80 1.000 0.000 80 1.000 800.988 0.000 0.013
11. Pearson Creek 1992 44 1.000 0.000 44 1.000 440.955 0,000 0.023
11. Pearson Creek 1994 88 1.000 0.000 88 1.000 88 0.966 0.000 0.034
12. West Birch Creek 1992 560.964 0.036 56 1.000 56 0.964 0.000 0.000
12. West Birch Creek 1994 72 1.000 0.000 72 1.000 72 0.944 0.028 0.028
13. East Fork Butter Creek 1992 50 0.980 0.020 50 1.000 50 1.000 0.000 0.000
13. East Fork Butter Creek 1994 82 1.000 0.000 82 0.988 82 1.000 0.000 0.000
14. Umatilla brood 1992 356 0.980 0.011 3560.994 356 0.986 0.008 0.006 Table 3.4. Composite haplotype definitions. No restriction sites for Hind IIIwere detected in ND-2 segment. Letters refer to the restriction fragment patterns in the Table 3.2.
Composite haplotype mtDNA Restriction segment enzyme Cl C2 C3 C4 C5 C6 C7 C8 C9 ClO CII C12 C13 C14 C15 C16 Cl?
ND-i AciI A A B A B A A A A A C A A A A A A Avail A A A A A A A A A A A A A A A A A BglII A A A A A A A A A A A A A A A A A BstUI A A B A B C A C A A A A A A A A A
HaelI A B A A A A A A A A A A A A A A C HindlII A A B A B B B A B A A A A A A A A
MspI A B A A A A A A A A A A A A A A A
TaqI A C B A B C B C A A A A C C C C A
D-loop BglII A A B B A B A B A B A A B A B A B
DpnI A A B C B C B C A D A A C A C A D
HhaI A A A A A A A A A A A A A A A A A MseI A A A A A A A A A A A A B A A A A
ND-2 AluI A A A A A B A A A A A C B A B A A
MseI A B B A B B B B A B A A B B B A B
HindIII - - - - Cl Alllocations C 11 Camp Creek C9 Buckend Butter creeks C14 McKay Creek C16 McKay Creek C12 Eight Umatille River locations C2 South Fork Umatilla River C4 McKay Creek C 1 0 Squaw Creek B C17 Skamania Hatchery C6 McKay Creek C8 McKay Creek C13 McKay Creek C15 McKay Creek C3 McKay Creek C C5 McKay Creek C7 McKay Creek I I I 0.0 16 0.008 0.000 Evolutionary Distance
Figure 3.2. Similarity of mtDNA haplotypes and locations of rainbow trout in which theywere found. Three major lineages are represented by clusters A, B, and C. Table3.5.Distribution of composite mtDNA haplotypes in rainbow trout in the Umatilla River. Numberof samples correspond to locations in Figure3.1.
Composite haplotype
Population Cl C2 C3 C4 C5 C6 C7 C8 C9 ClOCli C12 C13 C14 C15 C16 C17
1. North Fork Umatilla River 1992 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1. North Fork Umatilla River 1994 7 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
2.BuckCreekl992 7 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0
2.BuckCreekl994 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0
3. Thomas Creek 1992 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3. Thomas Creek 1994 6 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
4. South Fork Umatilla River 1992 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4. South Fork Umatilla River 1994 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5.CampCreekl992 8 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
5.CampCreekl994 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6. North Fork Meacham Creek 1992 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6. North Fork Meacham Creek 1994 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7. Upper Meacham Creek 1992 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7. Upper Meacham Creek 1994 7 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
8. SquawCreek 1992 25 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0
8.SquawCreekl994 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Table 3.5. Continued
Composite haplotype
Population Cl C2 C3 C4 CS C6 C7 C8 C9 ClO Cli C12 C13 C14 C15 C16 C17
9.McKayCreekl992 14 0 1 1 1 2 1 1 0 0 0 0 0 0 0 0 0
9. McKay Creek 1994 6 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0
10. EastBirch Creek 1992 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10. EastBirch Creek 1994 4 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
11. PearsonCreek 1992 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11. PearsonCreek 1994 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12. WestBirchCreek 1992 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12. West Birch Creek 1994 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0
13East Fork Butter Creek 1992 21 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0
13. East Fork Butter Creek 1994 19 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0
14. Umatilla brood 1992 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
14. Umatilla brood 1994 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15. Skamanaihatchery 1994 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 86
populations and 88% of the 380 steelhead examined, including the Skamania hatchery
steelhead; haplotype Cl 2 occurred in low frequencies in eight the 14 Umatilla River
samples. However, 13 haplotypes were unique to fish froma single location. Of these,
seven haplotypes comprised mtDNA lineage B, which was restricted to rainbow trout
from McKay Creek, the Skamania hatchery strain, and Squaw Creek,one of the major
spawning areas of hatchery-reared steelhead. A third, highly diverged line of
mitochondrial DNA was restricted only to McKay Creek. In all, 10 haplotypesoccurred
only in resident rainbow trout from McKay Creek (Table 3.5).
3.4.1. Genetic Variation Among Samples
Overall, very significant genetic differences existed at all levels of comparison within rainbow trout in the Umatilla River. Of 406 unplannedpair-wise tests, 236 (5 8%) were statistically significant. Greatest relative differences were among fish from major tributaries (headwaters of the Umatilla River, Meacham Creek, SquawCreek, McKay
Creek, Birch Creek, and Butter Creek) with lesser differencesamong fish from different locations within tributaries (Table 3.6). Standardized G2score (G2/df) for among tributary differences (3.9)was more than twice as large as within tributary differences
(1.7). Significant differences also occurredamong rainbow trout from different sites within the headwaters of the Umatilla River (North Fork, Buck Creek,Thomas Creek, and
South Fork) and Birch Creek (East and West Birch creeks and PearsonCreek) but not within other major tributaries (Table3 .6).
More interestingly, however, genetic change between cohortsfrom a location was as great or greater (G2/df = 1.8) than differencesamong fish from different spawning or Table 3.6. Contingency table analysis of genetic variation in Umatilla River rainbowtrout. Values for McKay and Butter creeks reflect only between year comparisons. Individual results of betweenyear comparsions within sites are in Table 8. Single asterisk indicates significance at a = 0.05 for a single test; double asterisks indicate statistical significancefor multiple comparisons.
Allozyme loci
sAH* GAPDF13* sIDHP1,2* LDUB2* PEPA* Total G2 mtDNA Source G2 df G2 dl G2 dl G2 dl G2 dl G2 dl fdf G2 P Total **12401 52 **8852 26 **20532 104 **11989 52 *8800 52 **62574 286 2.1 189.40 0.000 Among 14.53 10 **20.78 5 **76.5620 **71.83 10 **3573 10 **219.44 55 3.9 105.31 0.000 Within **1094842 **6773 21 **12876 84 48.06 42 52.2742 **40631 231 1.7 84.09 0.000 Umatilla 22.43 14 **2607 7 **63.6828 21.56 14 3.83 14 **13756 77 1.7 28.15 0.000 Among 9.72 6 **20.13 3 *2415 12 6.68 6 0.396 **61.07 33 1.8 13.14 0.001 **3953 Between 12.71 8 5.94 4 16 14.88 8 3.44 8 **76.49 44 1.7 15.000.001 Meacham 13.98 10 **2073 5 20.75 20 15.17 10 *22.53 10 **9316 55 1.6 10.950.029 Among 5.42 4 2.40 2 5.96 8 2.02 4 9.06 4 24.86 22 1.1 6.63 0.019 Between 8.56 6 **1833 3 14.79 12 *1316 6 *1347 6 **6830 33 2.0 4.32 0.058
Squaw 5.40 4 2.12 2 5.26 8 5.65 4 6.55 4 24.98 22 1.1 3.000,114
Among 1.81 2 0.14 1 2.45 4 4.48 2 1.89 2 10.77 11 0.9 0.00 1.000
Between 3.59 2 1.99 1 2.81 4 1.17 2 4.65 2 14.20 11 1.2 3.000.114
Mckay 1.25 2 1.07 1 5.13 4 0.30 2 0.00 2 7.75 11 0.7 16.05 0.046 Birch **6587 10 **1774 5 24.66 20 5.10 10 11.43 10 **12479 55 2.2 13.38 0.000 Among **33.23 4 *8,01 2 6.48 8 4.80 4 1.53 4 **54,Q5 22 2.4 5.92 0.012 Between **3264 6 *9.72 3 18.18 12 0.29 6 9.90 6 **7074 33 2.1 7.460 0.022
Butter 0.55 2 0.01 1 9.28 4 0.29 2 *794 2 18.07 11 1.6 12.56 0.001 88
rearing aggregations within tributaries (G/df = 1.7, Table 3.6).Significant genetic
change occurred in fish from 10 of 13 locations for either allozymeor mtDNA variation
(Table 3.7). Eight cohortswere significantly different when values were adjusted for
increase in Type I error from multiple comparisons. Only rainbow trout from Thomas
Creek, North Fork Meacham Creek, and Squaw Creek showedno genetic change over
time.
In general, results of statistical analyses of allozyme variation and mtDNA variation were similar. Both kinds of data revealed major differencesamong and within
groups (Table 3.6). However, allozyme variation was much more sensitive to temporal changes. For example, tests of between-cohort variation for allozyme variation revealed differences in rainbow trout fromseven locations. In contrast, mtDNA variation revealed a difference only in rainbow trout from East Fork Butter Creek, which was not detected by allozyme variation.
3.4.2. Pattern of Temporal Variation
Although statistical analyses indicated geographical geneticstructure among populations of rainbow trout from different major tributaries of the Umatilla River,cluster analyses of genetic similarity based on allozyme and mtDNA variation failedto reveal such a pattern (Fig. 3.1, Fig. 3.4). Only rainbow trout in McKay Creek stood out consistently as very different from fish from other locations in the Umatilla River. In contrast, allozyme variation displayed a striking temporalpattern. Except for rainbow trout from
Squaw Creek and North Fork Umatilla River, juvenile rainbowtrout collected in 1994 - which were predominantly offspring of fish spawning in1992 - were more similar to 1992 Table 3.7. Genetic differences between 1992 and 1994 cohorts of rainbow trout from 13 locations in the Umatilla River.Single asterisk indicates significance at a = 0.05 fora single test; double asterisks indicate significance after adjusting for multiple comparisons.
Allozyme Locus
GAPDH3* sIDHP1.2* LDHB2* PEPA* Total mtDNA
Location G2 df G2 df G2 df G2 df G2 df G2 df G2 P
1. NorthForkUmatjllaRjver **10502 1.65 1 1.11 4 1.97 2 0.46 2 15.68 11 3.020.235
2. BuckCreek 0.64 2 1.55 1 8.96 4 *6.52 2 2.10 2 *1976 11 7.21 0.016
3. Thomas Creek 0.10 2 0.61 1 2.14 4 1.692 0.88 2 5.42 11 3,320.103
4. SouthForkUmatillaRjver 1.47 2 2.13 1 **2731 4 4.71 2 0.00 2 **3562 11 1.45 0.500
5. CampCreek 6.35 2 0.12 1 7.32 4 *6.37 2 1.28 2 **21.44 11 1.07 0.300
6. NorthForkMeachamCreek 2.21 2 3.38 1 4.78 4 2.70 2 2.862 15.93 11 0.00 1.000
7. UpperMeachamCreek 0.01 2 **14.83 1 2.69 4 4.08 2 **933 2 **3094 11 3.25 0.105
8. Squaw Creek 3.59 2 1.99 1 2.81 4 1.17 2 4.652 14.20 11 3.00 0.114
9. MeKayCreek 1.25 2 1.07 1 5.13 4 0.30 2 0.002 7.75 11 16.05 0.046
10. EastBirchCreek **20l2 2 *6.17 1 **1454 4 0.01 2 0.34 2 **4119 11 2.20 0.179
11. PearsonCreek 2.22 2 0.04 1 0.044 0.14 2 *6.75 2 9.18 11 0.00 1.000
12. WestBjrchCreek **1030 2 3.51 1 3.61 4 0.15 2 2.81 2 **2037 11 5.260.041
13. EastForkButterCreek 0.55 2 0.01 1 9.28 4 0.29 2 *794 2 8.07 11 12.56 0.001 90
14. Umtfl1e brood 1. North Fork Umetilla River 1992 8. Squew Creek 1992 8. Squew Creek 1994 6. North Fork Meechem Creek 1994 11. Peerson Creek 1994 5. Cemp Creek 1994 13. Eest Fork Butter Creek 1 994 2. Buck Creek 1 994 10. Eest Birch Creek 1994 3. Thomes Creek 1 994 1. North Fork Umetille 1994 12. West Birch Creek 1994 4. South Fork Umetille River 1994 7. Upper Meechem Creek 1994
12. West Birch Creek 1992 1 3. Eest Fork Butter Creek 1 992 3. Thomes Creek 1992 6. North Fork Meechem Creek 1992 5. Cemp Creek 1992 4. South Fork Umetille River 1992 10. Eest Birch Creek 1992 2. Buck Creek 1992 11. Peerson Creek 1992 9. McKey Creek 1992 7. Upper Meechem Creek 1 992
9. McKey Creek 1994
0.032 0.016 0.000 NeFs Unbiased Genetic Distence
Figure 3.3. Genetic similarity of rainbow trout in the Umatilla River based on allozyme variation. 91
1. North Fork Umetille River 1992 3. Thomes Creek 1992 4. South Fork Umetille River 1994 5. Cemp Creek 1992 5. Cemp Creek 1994 6. North Fork Meechem Creek 1992 6. North Fork Meechem Creek 1994 7. Upper Meechem Creek 1992 8. Squew Creek 1994 10. Eest Birch Creek 1992 11. Peerson Creek 1994 11. Peerson Creek 1992 12. West Birch Creek 1992 14. UmetilIe hetcherj 1994 14. Umetille brood 1992 4. South Fork Umetille River 1992 8. Squew Creek 1992 2. Buck Creek 1992 13. Eest Fork Butter Creek 1992 1. North Fork Umetilla 1994 3. Thomas Creek 1994 East Birch Creek 1994 7. Upper Meachem Creek 1994 2. Buck Creek 1994 12. West Birch Creek 1994 1 3. East Fork Butter Creek 1 994 9. McKej Creek 1992 McKej Creek 1994
0.0006 0.0003 0.0000 Nucleotide Diversity
Figure 3.4. Genetic similarity of Umatilla River rainbow trout based on mitochondrial DNA variation. 92
Umatilla hatchery brood fish than they were to other cohort from the same stream (Fig.
3.3). This pattern was not obvious in mtDNA variation.
The relationship between genetic similarity to 1992 hatchery brood fish and the variation between cohorts of rainbow trout from different locations revealed several interesting observations (Fig. 3.5). First, the more similar a group of fish was to the hatchery brood fish, the less genetic difference occurred between cohorts (t5.36, df
=24, P=0.000). Second, in 10 of the 13 samples, the 1994 juveniles were more similar to
1992 hatchery brood fish than were the 1992 juveniles. Only three groups were less similar in 1994 - resident rainbow trout in McKay Creek, which are isolated by an impassable dam from wild spawning steelhead, and fish in Squaw Creek and the North
Fork Umatilla River.
3.5. Discussion
Historical genetic structure of steelhead populations is a template that can be used for recovery of wild populations. Unfortunately, in the Umatilla River and elsewherewe know little about genetic structure that existed prior to declines in abundance, habitat fragmentation, and introduction of hatchery programs in this century. Additionally, recent genetic variation in Umatilla River rainbow trout indicates a very complex and dynamic pattern of change influenced both by genetic drift andgene flow. Uncertainty of what was originally there and unpredictable dynamic processes of the presentare major challenges for using supplementation as a tool to rebuild wild populations and maintain genetic diversity. 0.040
0.032
0.024
0.016
0.008 0.005 0.010 0.015 0.020 0.025 Genetic Distance From Hatchery Brood Fish
Figure 3.5. Relationship between genetic similarity of wild juvenile rainbow trout to 1992 hatchery brood fish and genetic variation between cohorts from the same location for 13 locations in the Umatilla River. Open squares are the 1992 cohorts; filled squares are the 1994 cohort. Arrows indicate the direction of change from 1992 to 1994. 94
Considered together, lack of strong geographical structure relating neighboring populations and large numbers of genetically different samples suggested that steelhead in the Umatilla River were comprised of multiple breeding aggregations thatover short periods of time evolved independently. Only traces of hierarchical geographical structure were apparent. Standardized values for G2 tests (I G2Idf) suggested that steelhead may home with greater precision to major spawning tributaries than to different locations within tributaries (Table 3.6). However, analysis of genetic similarity basedon allozyme and mtDNA data indicated that rainbow trout from neighboring locations were no more similar to each than they were to those from more distant locations (Fig. 3.3, 3.4). Alone, lack of obvious geographical patterns has two possible explanations. First, fish from different geographical aggregations may have been part of a single randomly mating population. Alternatively, different locations may have supported distinct, independently evolving populations. Results of G2 tests for geographical genetic heterogeneity, which tested assumptions that steelhead chose spawning streams at random, clearly favored the latter explanation. Contingency table analyses indicated rainbow trout and steelhead in different major tributaries or even in different streams in headwaters of the Umatilla River or Birch Creek had not mated randomly (Table 3.7). In contrast, random mating was more likely within Meacham Creek and Squaw Creek, two tributaries that have supported most of the wild spawning within the basin.
Significant temporal variation in allozyme frequenciesmay reflect genetic drift.
Waples and Tee! (1990) noted that the probability of statistically significant differences in temporal samples increased with the ratio of sample size to effective number of breeders
(Nb, effective population size per year rather than per generation). In these data, for 95 example, greatest temporal change as measured by genetic distance from 1992 hatchery brood fish occurred in a resident rainbow trout population in McKay Creek (Fig. 3.5), but it was not statistically significant (Table 3.3, Table3.6). I detected significant temporal variation among other samples, however, despite samples sizes (12-44) that were generally much lower and consequently more conservative than those considered by
Waples and Teel (1990).
Opportunity for large amounts of random genetic drift existed within the basin.
Random genetic drift is most important in small populations with small Ne (Falconer,
1981). I did not estimate N for individual populations using genotypic data and indirect methods (Waples, 1989, Waples, 1990), because assumptions of no gene flow were violated in these data. However, based on historical records of numbers of spawners and assumptions I introduced earlier, N for the entire basin may have ranged from 100-270.
In 1992, over half the steelhead spawned in Meacham Creek; an additional 26% spawned in Squaw Creek (Oregon Department of Fish and Wildlife and Confederated Tribes of the
Umatilla Indian Reservation, unpublished data). Consequently, Ne in these streams has probably been much larger than spawning aggregations in other streams that received between 2-6% of the total returning steelhead.
Although genetic drift may have been acting on these populations, I concluded gene flow resulting from episodic influx of wild and hatchery-reared steelhead into small, isolated populations played a crucial role in the temporal changes in Umatilla River steelhead. Only small amounts of gene flow are necessary to overwhelm genetic drift
(Wright, 1931). Furthermore, in 10 of the 12 locations where gene flow from hatchery steelhead was possible, naturally produced juveniles become more similar to hatchery 96 steelhead (Fig. 3.5). Other agents of evolutionary change mutation or natural selectionmay also have been acting in these populations. Electrophoretically detectable mutation rates have been estimated at approximately 1 06 per locus per generation (Nei,
1987). Consequently, mutation was an unlikely actor. The role of natural selection on electrophoretically detectable alleles has not been well investigated, but given the potential for gene flow is not necessary to invoke natural selection to explain the changes.
Gene flow is most likely episodic in these river systems. Although only limited data are available, historical data did indicate that distribution of spawners among spawning areas has not been constant. For example, in earlier surveys nearly 27% of the total spawners used the North and South Forks of the Umatilla River (compared to 11% in 1992), while only5%(compared to 26% in 1992) used Squaw Creek (Oregon Game
Commission, unpublished data). Access to many spawning areas may change from year to year depending on water levels and temperatures in the streams or temporary blockages.
An alternative explanation for the temporal variation that I observed is that juveniles are tracking allele frequencies of hatchery brood fish or a some average allele frequencies of wild populations that are the primary source of spawners throughout the drainage. As allele frequencies of hatchery brood fish or a large wild population change, juvenile fish throughout the drainage reflect the change. Although age structure within spawning populations, should dampen such effects, Waples and Tee! (1990) have noted that even with 50% gene flow between year classes, allele frequencies can vary several percent from year to year because of random genetic drift.I did not have genetic data from 1990 hatchery brood fish or adult wild populations to test this hypotheses.
However, such a scenario is only possible if the samples are not independent. Results of 97
G2 tests indicate that many of that aggregations of rainbow trout in many locations are evolving independently of short periods of time.
The dynamic and unpredictable relationship between periods of isolation of many small, independent populations and episodes of gene flow - which I suspect has happened with rainbow trout and steelhead in the Umatilla River- presents several thorny problems for managing these and other similar populations. First, what is the appropriate unit of management? Second, to what extent does persistence of small, isolated populations depend on gene flow and immigration from other sources?
Since 1972, most fishery managers have been taught Ricker's (1972) classical definition of a stock - or genetically meaningful unit of management (Allendorf and
Ryman, 1987) - as "the fish spawning in a particular lake or stream (or portion of it) at a particular season, which.. .to a substantial degree do not interbreed with any group spawning in a different place, or in the same place at a different season.t This definition borrowed heavily from Mayr's (1942) emphasis on reproductive isolation in the formation of races and subspecies during speciation. Use of reproductive isolation to define species has been challenge scientifically (Ereshefsky, 1992, and articles therein). It becomes even more problematic to delimit evolutionary boundaries below the species level. As data from Umatilla River rainbow trout illustrated, however, nonrandom mating has occurred over different temporal and spatial scales. Consequently, two practical challenges face fishery biologists. First, they must identify appropriate temporal and spatial scales to lump or split genetically differentiated aggregations of fish.As Larkin (1981) noted, continuing to find genetic differences among groups of fish and treating them independently can greatly complicate fishery management. Second, however, fishery biologists must 98 determine risks of reducing the number or evolutionary independence of aggregations that comprise the management unit. A framework for genetic risk assessment has only recently become available to fishery managers (Currens and Busack, 1995).
The second problem facing fishery biologists is to determine to what extent the persistence of small, isolated populations depends on gene flow and immigration from other sources. While using supplementation to increase population abundance, fishery biologists must also find a balance between potentially homogenizing many small populations through gene flow and maintaining them as small, isolated populations, which are more prone to extinction (Soule, 1987; Lande, 1988). Data from different groups of rainbow trout in the Umatilla River suggested that they were part of a metapopulation.
Metapopulations (Levins, 1970) were orginally studied to understand patterns of local extinctions and subsequent recolonizations. Study of metapopulations can also illuminate how supplementation can work. As illustrated by Stacey and Taper (1992), extinction is not necessary for small, constituent populations to benefit from immigration. Small populations isolated over extended periods of time may be dependent on immigration from other populations in the metapopulation to moderate the effects of genetic drift and inbreeding, as well as to reduce risk of extinction, over much longer periods.
This type of metapopulation dynamic, consisting of source and sink populations
(Pulliam, 1988), can be imitated through supplementation. Supplementation can also accentuate sources or sinks. In the Umatilla River, for example, the more similar populations were to hatchery brood fish, the less they changed (Fig. 3.5). This suggested that wild fish collected for hatchery brood fish originated from populations with larger Nb.
These populations, such as Squaw Creek and Meacham Creek, then became thesource of 99 gene flow to smaller, isolated populations, which moderated stochastic effects of small Ne in smaller populations. On the other hand, we have previously shown that when hatchery- reared steelhead survive less well than wild spawning steelhead, even though much greater numbers of juvenile are produced by artificial propagation, the smaller wild populations is the source and supplementation is the sink (Currens and Busack, 1995).
Lacking a clear understanding of the historical genetic structure and metapopulation dynamics of Umatilla River steelhead, it is critical that fishery managers maintain an active monitoring program. As I have shown in this paper, genetic data can play an important role in this process. We used both allozyme and restriction fragment analysis of mtDNA variation. They had different strengths and weakness. Allozyme data were much more sensitive to temporal changes and differences among groups within drainages than were mtDNA data (Table 3.7, Table 3.8). Consequently, they may be more appropriate for monitoring random genetic changes in supplemented populations. This is not entirely unexpected. Although mtDNA evolves rapidly at the sequence level (Brown et al., 1979), it is inherited maternally (Hutchison et al., 1974) and individuals are haploid.
Consequently the effective population size for mtDNA is 1/4 that of nuclear DNA. In very small populations, rapid loss of unique haplotypes through random genetic drift can limit the amount of variation available for detecting differences. Additionally, analysis of restriction polymorphisms will normally detect fewer differences than direct sequencing might.
In contrast, mtDNA may be more powerful at detecting gene flow from non-native or geographically distant populations. For example, most steelhead in the Columbia River share allozyme alleles but vary in frequency (Schreck et al., 1986; Chapter 2). However, I 100 identified a mtDNA haplotype in Skamanian strain steelhead thatwas not present in any
Umatilla River steelhead (Table 3.5). Such haplotypes can be used as genetic tags.
Similarly, large number of unique haplotypes in McKay Creek- a small, resident rainbow trout population that has been isolated from steehead only during this century- compared to other rainbow trout populations in the drainage suggested a history of hybridization with introduced non-native strains. Incidence of lineage A haplotypes (Fig. 3.2) in only endemic Umatilla River populations suggested that these haplotypes evolved in this region of the Columbia River. On the other hand, lineage B haplotypeswere found in McKay
Creek, which has received extensive introductions, and Skamania strain steelhead, which was founded from geographically distant populations (Howell et al., 1985). The magnitude of sequence divergence of lineage C haplotypes, found only in McKay Creek rainbow trout, indicates that these were unlikely to have evolved in this region and have been introduced. These kinds of questions can be addressed with broader geographical surveys of rainbow trout. For fishery managers, however, choice of appropriate genetic data to monitor and model genetic consequences of metapopulation dynamics,as well as careful analyses, are necessary to understand andmanage genetic hazards of supplementation. 101
4. MITOCIIONDRIAL DNA VARIATION IN OREGON COHO SALMON
4.1. Abstract
I examined coho salmon from 46 rearing aggregations in nine hatcheries and 31 watersheds in Oregon, Washington, and northern California for mitochondrial DNA variation. Of the 17 haplotypes identified, 77% of the coho salmon examined had one of two common haplotypes. Greatest geographical genetic differences were among samples from four major regions: 1) Puget Sound, 2) Columbia River, 3) northern Oregon coastal streams, and 4) southern Oregon coastal streams. Differences within regions lacked obvious geographical patterns. This pattern of variation may be explained by biogeographical history and the history of recent fish translocations associated with aquaculture programs.
4.2. Introduction
As historical ranges and fishing opportunities for Pacific salmon shrink, identification and interpretation of geographical genetic variation has become essential for protecting these species. To qualifi for protection under the U.S. Endangered Species
Act (ESA), for example, a population or group of populations must be reproductively isolated from other populations and must represent an important component in the evolutionary legacy of the species (Waples, 1991). Misinterpretation or failure to obtain appropriate genetic data to judge the status of these populations can be one of the final acts of mismanagement of already mismanaged populations.
Genetic data are especially critical for coho salmon (Oncorhynchus kisutch). Coho salmon have historically spawned in North American streams from Point Hope, Alaska, to 102
Monterey Bay, California (Scott and Crossman, 1973). Although hatchery production has
increased extensively to support traditional and commercial fisheries, abundances of wild
populations of coho salmon in the southern part of the range have declined precipitously.
In the Pacific Northwest, the coho salmon is in danger of extinction along the Oregon
coast and most of California. It is already extinct from the eastern half of its range, the
tributaries of the middle and upper Columbia River Basin (Nehisen et al., 1991, Frissel,
1993). The few remaining wild populations of coho salmon in the lower Columbia River
were denied protection under the Endangered Species Act (Johnson et al., 1991), partly
because available genetic data did not demonstrate major geographical genetic differences
among wild coho salmon in the Columbia River, coastal populations, or hatchery strains
that support most of the commercial fisheries.
Lack of differences may have at least three explanations. First, it may reflect
natural population structure. Salmon generally return to their natal streams tospawn,
thereby providing an opportunity for genetic differentiation between fish in different
streams. However, if episodic natural catastrophes, such as earthquakes, floods,
volcanism, or glaciation, prevented salmon from returning to natal streams and forced
them to breed in other streams with other populations, differencesamong extant
populations might be minimal. Similarly, if extant populations have only recently
colonized these areas, too little time may have passed for genetic differences to have
evolved. Second, lack of differences may reflect lack of easily identifiable variation at
allozyme loci. Almost all available genetic data for Pacific salmonare from allozyme
studies. Allozyme variation has been extremely valuable for making inferences about breeding structure in other species of Pacific salmon (Allendorf and Utter, 1979; Schreck 103 et al., 1986; Utter, 1989; Reisenbichier et al., 1992; Kondzela et al., 1994; Phelps et al.,
1994; Shaklee and Varnavskaya, 1994; Wood et al., 1994). However, although similar variation has been documented for coho salmon (Weitkamp et al; 1995), it has been more difficult to measure (Allendorf and Utter, 1979; Utter et al., 1980), Finally, extensive human transfers of coho salmon among geographical regions may have reduced geographical, genetic distinctions through gene flow. Over 561 million transfers have occurred on the Oregon coast and Columbia River alone (Weitkamp et al., 1995).
Here, I report evidence for geographical, genetic differences in mitochondrial
DNA (mtDNA) among coho salmon in the lower Columbia River and coastal streams of
Oregon in spite of large scale fish transfer. Because the mitochondrial genome in vertebrates may evolve more rapidly than nuclear genes and is maternally inherited without recombination, mtDNA variation may demonstrate greater genetic differences among populations than allozyme variation when such differences still exist (Brown et al., 1979;
Avise, 1986). Although analysis of mtDNA variation is a powerful tool for identif,ing evolutionary patternsand genetic resources, it has not been used for detailed surveys of coho salmon.
4.3. Materials and Methods
Whole juvenile coho salmon and samples of fin were collected from 46 rearing aggregations in nine hatcheries and 31 watersheds in Oregon, Washington, and northern
California (Fig. 4.1) in 1992-1994. In the Nehalem, Siletz, Siuslaw, Umpqua, Coos, New,
Sixes, and Rogue River basins, coho salmon were collected from different tributaries within each river to test for geographical genetic differences within basins. Samples of 104
Figure 4.1. Locations where coho salmon were collected. Map codes: 1, Hood River; 2, Cascade Hatchery; 3, Multnomah Creek; 4, Sandy River; 5, Sandy Hatchery; 6, Clackamas River; 7, Clatskanie River; 8, Gnat Creek; 9, Big Creek Hatchery; 10, Klaskanine Hatchery; 11, Lewis and Clark River; 12, Nehalem River (Lousignont Creek); 13, main stem Nehalem River; 14, Nehalem Hatchery; 15, Miami River; 16, Wilson River; 17, Trask Hatchery; 18, Nestucca River; 19, Salmon River; 20, Siletz River (Fourth of July Creek); 21, Siletz River (Sunshine Creek); 22, Siletz River (Buck Creek); 23, Beaver Creek; 24, Alsea River; 25, Alsea Hatchery; 26, Siuslaw River (Wolf Creek); 27, Siuslaw River (Esmond Creek); 28, Tenmile Lake; 29, Smith River (Halfway Creek); 30, Rock Creek Hatchery; 31, North Umpqua River (Williams Creek); 32, main stem North Umpqua River; 33, North Umpqua River (Cavitt Creek); 34, South Umpqua River (Dumont Creek); 35, Millicoma River (Marlow Creek); 36, South Fork Coos River (Tioga Creek); 37, Coquille River; 38, New River (Bethel Creek); 39, New River (Morton Creek); 40, Sizes River (Keller Creek); 41, Sixes River (Edson Creek); 42, Elk River; 43, Rogue River (Silver Creek); 44, Illinois River (Grayback Creek); 45, Illinois River (Aithouse Creek); 46, South Fork Winchuck River; 47, Smith River (Mill Creek), California; 48, Skykomish Hatchery, Washington. 105
Figure 4.1. 106 hatchery and wild populations from the same basin were collected from the Sandy,
Nehalem, Alsea, and Umpqua rivers to test for divergence of hatchery and wild populations. Whole fish were preserved at -80°C; fin clips were preserved in ethanol until tissues were removed for analysis. From each fish, total genomic DNA was extracted from 0,5g of muscle tissue. Three segments of mtDNA-- NADH dehydrogenase subunit
I (ND-i), ND-516, and D-loop segments-- were amplified by polymerase chain reaction following methods of Cronin et al. (1993) using primers developed by LGL Genetics, Inc.
(1410 Cavitt St., Bryan, TX 77801). Seven unique restriction enzyme- mtDNA fragment combinations were selected for the survey (Table 4.1), based on preliminary digestions using 24 restriction enzymes on each segment. Restriction-fragment patterns were visualized by staining with ethidium bromide and photographed under ultraviolet light.
Composite haplotypes were constructed from restriction-fragment patterns and the frequency distribution of composite haplotypes was calculated for each sampling location.
Homogeneity of samples or groups of samples based on hap lotype frequencieswas tested by Monte Carlo simulations of chi-square analysis (Roff and Bentzen, 1989). When no significant differences existed in haplotype frequencies among samples withina stream, data were combined. Estimates of nucleotide diversity within samples (pr) and nucleotide divergence between samples were calculated following Nei and Tajima (1981) and (Nei
1987). Pair-wise estimates of nucleotide divergence were used togroup genetically similar samples based on the unweighted pair-group method with group centroids
(UPGMC) algorithm (Sneath and Sokal, 1973). Differencesamong populations were examined by ordination using nonmetric multidimensional scaling (Kruskal, 1964a,
1 964b). 107
4.4. Results
I identified 17 haplotypes from coho salmon in Oregon (Table 4.1). The most common haplotype was found in at least one salmon from every location except the
Illinois River, Sixes River (Edson Creek), and Skykomish hatchery (Table 4.2). In all,
77% of coho salmon examined shared one of two common haplotypes. However, several haplotypes had regional distributions. Haplotype 8 only occurred at low frequencies in
Columbia River samples. In contrast, haplotype 9 was unique to samples of the central coast of Oregon (Table 4.2). Haplotype 4 was the predominant type in coho from the
Puget Sound (Skykomish hatchery), although it was rare in samples from the Columbia
River and Oregon coast. Unique haplotypes occurred in Gnat Creek, Alsea Hatchery,
Tenmile Lake, Coquille River, and Coos River samples, although other rare haplotypes were shared by geographically distant rearing aggregations (Table 4.2).
4.4.1. Within Basin Comparisons: Hatchery versus Wild
Significant frequency differences existed between hatchery and wild samples from the same basin in three of four comparisons. Mitochondrial DNA variation in hatchery aggregations of juvenile coho salmon was significantly different from wild aggregations in the Sandy River (x2 = 16.26, df= 16, P = 0.005 ± 0.0022), Nehalem River( (x26.08, df16, P0.031± 0.0055), and AlseaRiver((2 = 13.02, df= 16, P= 0.002 ± 0.0014).
Coho salmon from Rock Creek Hatchery, however, were not different from collections from main stem, Williams Creek, or Cavitt Creek of the North Umpqua River,or from samples from Smith River or South Umpqua River. Table 4.1.Composite mtDNA haplotypes for coho salmon. Each letter representsa different fragment pattern observed for a polymorphic restriction enzyme- mtDNA segment combination.
Composite Haplotype mtDNA Restriction segment enzyme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ND-i BsIUI A A A A B A A A A A A A A A A A A ND-5/6 DdeI B A A C A B CAB B A C A A A B D TaqI A B A A A A A A A B A A A A A A A D-loop Avail A A A A A A A A A A A A A B C A A BsaJI B A A A A A B C B A B A B A A A A BstNI B A A A A A B A A A B B A A A B A Sau961 A A A A A A A A A A A A A B A A A Table 4.2. Frequencies of mitochondrial DNA haplotypes and nucleotide diversity in coho salmon from different rearing aggregations. Numbers designating populations correspond to locations in Figure 4.1. Haplotypes Nucleotide 3.MultnomahCreek2.CascadellatcheryLocation1.HoodRiver 1211 2 1 10 052 1911 3 0314 005 006 007 081 09 10 0 11 0 12 0 13 0 14 01 15 0 16 0 17 0 diversity 0.00220.01010.0087 5.SandyHatchery4.SandyRiver 271 02 381317 2 1 0 305 01 203 0 0 0 0 00 00 0 04 0 0.00590.0062 7.ClatskanieRiver9.BigCreekHatcbery8.GnatCreek6.ClackamasRiver 10 51 51 2712 7 401 0 05 01 30 0 010 01 00 0 0 0 0 0 0.00890.00640.00110.0019 14,NchalemHatchery12-13.NehalemRiver11.LewisandClarkRiver10. Kiaskanine Hatchery 272511 6 04 1711 02 02 20 04 0 0011 0 0 0 0 0 0 0 0 00 0.00220.00500.00310.0078 16.WilsonRiver15,MiamiRiver 1617 20 60 01 0 04 0 0 0 0 0 0 0 0 00 00 0 0.00340.0000 19.Sa!monRiver18.NestuccaRiver17.TraskHatchery 2014 5 0 83 01 20 20 0 1 0 0 0 0 0 0 0 00 0 00 0.00720.00650.0061 24.AlseaRiver23.BeaverCreek20-22.SiletzRiver 212223 00 12 0 034 00 73 00 0 21 0 0 0 0 0 0 00 00 0.00310.00360.0062 27.25.26. AlseaSjsaw SiuslawRiver(WolfCreek) Hatchery River (Esmond Creek) 1322 9 0 80 05 01 00 0 0 01 0 0 01 0 0 0 00 00 0.0060.00000.0041 9 30.RockCreekHatchery29.28.TenmileLake SniithRiver, Oregon 151318 0 10 98 02 30 21 01 0 0 0 01 0 02 0 00 00 00 0.00730.00660.0059 Table 4.2. Continued. Location 1 2 3 4 15 6 Haplotypes7 8 9 10 11 12 13 14 15 16 17 Nucleotide diversity 35-36.34.31-33.NorthUmpquaRiver South CoosRiver Umpqua River 223713 0 2715 4 041 0 41 03 0 01 0 03 0 0 00 00 00 200 0.00490.00650.0068 40.38-39,NewRiver37. SixesCoquille River River (Keller Creek) 35 021 0 101319 1 401 01 03 0 0 0 00 00 00 0 04 01 02 0 0.00170.00070.0073 44-45.43.RogueRiver42.ElkRiver41. Sixes Illinois River River (Edson Creek) 021 14 0 2714 2 0 0 24 0 0 0 00 0 0 0 0 0 0 0 0 0.00290.00160.0037 48.47.46.WinchuckRiver SkykomishSmith River, Hatchery California 1316 0 0 0 15 0 0 11 14 30 0 00 0 0 0 0 0 0 0 0 0,00270.00410.00260.0000 111
4.4.2. Within Basin Comparisons: Wild Agregations
Significant differences existed among wild coho salmon from different rearing aggregations within river basins in three of the eight comparisons. In the Sixes River, fish from Edson Creek differed from those in Keller Creek (x25.39, df= 16, P0.036 ±
0.0059). Likewise, coho salmon from the Illinois River were different from those in the
Rogue River (x2 = 19.07, df= 16, P = 0.000 ± 0.000), although coho salmon from
Grayback Creek and Aithouse Creek in the Illinois River were not different from each other. Within the Siuslaw River, fish from Wolf and Esmond creeks had different numbers and kinds of haplotypes (x29.16, df= 16, P = 0.005 ± 0.002; Table 4.2). Within
Nehalem, Siletz, Umpqua, Coos, and New River basins coho salmon from different rearing aggregations were alike.
4.4.3. Among Basin Comparisons
Despite widely-shared haplotypes among coho salmon from different locations, significant genetic differences in mitochondrial DNA haplotype frequencies existed among fish from different basins (Fig. 4.2). Greatest differences were among samples from four major areas: 1) Puget Sound, 2) Columbia River, 3) northern Oregon coastal streams, and
4) southern Oregon coastal streams (Fig.4.2). Of these, the greatest genetic difference occurred between the two groups that were separated by the greatest geographical distance: Skykomish hatchery coho salmon in the Puget Sound and Oregon coho salmon populations. Large geographical distances do not separate Columbia River or coastal populations, but most Columbia River populations were different from nearby coastal 112
1. HOOD RIVER COLUMBIA 0000r 10. KLASKANINE HATCHERY RIVER 9. BIG CREEK HATCHERY 0.00 1 2. CASCADE HATCHERY 4. SANDY RIVER 11. LEWIS AND CLARK RIVER NORTH & 5. SANDY HATCHERY CENTRAL 0.02918. GNAT CREEK COAST 17. TRASK HATCHERY 0.000 34. SOUTH UMPQUA RIVER 18. NESTUCCA RIVER 0.028 TENMILE LAKE SMITH RIVER, OREGON 19. SALMON RIVER 0.000 27. SIUSLAW RIVER 30. ROCK CREEK HATCHERY 0 000 35-36. COOS RIVER SOUTH 3 1-33. NORTH UMPQUA RIVER 1 COAST 20-22. SILETZ RIVER 0.000 47. SMITH RIVER, CALIFORNIA 42, ELK RIVER 12-13. NEHALEM RIVER 16. WILSON RIVER NORTH & 0.028 CENTRAL 23. BEAVER CREEK 46. WINCHUCK RIVER COAST 0.000 24. ALSEA RIVER 25. ALSEA HATCHERY 0.000 NEHALEM HATCHERY 38-39. NEW RIVER 0.342 MIAMI RIVER 26. SIUSLAW RIVER 3. MULTNOMALCREEK 40. SIXES RIVER 0.237 7. CLATSKANIE RIVER COLUMBIA RIVER & 0.027 6. CLACKAMAS RIVER SOUTH COAST 0 000 44-45. ILLINOIS RIVER 0.000 37. COQUILLE RIVER 41. SIXES RIVER PUGET SOUND 43. ROGUE RIVER 48. SKYKOMISH HATCHERY 0.006 0.004 0.002 0
NUCLEOTIDE DIVERGENCE
Figure 4.2, Phenogram of geographical mitochondrial DNA variation in coho salmon. Number designators for populations correspond to locations in Figure 4.1. Numbers at the nodes of the phenogram indicate the null probabilities that haplotype frequencies of the samples in that cluster were drawn from thesame statistical population. 113 populations. In addition, a major geographical boundary between southern and more northerly Oregon coastal populations also occurred near Cape Blanco in Oregon (Fig. 4.1,
Fig. 4.2).
Within geographical areas, coho salmon were also highly heterogeneous (Fig. 4.2).
The pattern of genetic differences between populations differed within major geographical areas, however. Within the Columbia River, rearing aggregations of coho salmon formed two discrete genetic groups. Salmon from the Clackamas River, Clatskanie River, and
Multnomah Creek were more similar to southern Oregon coastal streams than to salmon from other Columbia River streams and hatcheries (Fig. 4.2, 4.3). These three samples had strikingly low levels of nucleotide diversity (an estimate of genetic variation within populations) compared to other Columbia River populations. Nucleotide diversity in these samples ranged from 0.0011 to 0.0022, whereas nucleotide diversity in other
Columbia River populations was nearly five times greater, ranging from 0.0050 to 0.0101
(Table 4.2). In contrast, southern coastal populations did not form such cohesivegroups.
Although five of eight samples south of Cape Blanco and the Coquille River to the north were all part of the same cluster in the phenogram (Fig. 4.2), nucleotide divergence among southern coastal populations far exceeded the differences between populations in other groups and nearly equalled the divergence found among all Oregon populations (Fig. 4.3).
4.5. Discussion
My results clearly demonstrated genetic heterogeneity and population structure among rearing aggregations of coho salmon. The largest differences in these data existed between coho salmon from different regions: the Puget Sound, Columbia River, and 114
. . 0 S S . U 0000 0
37 . 0S U -1.6 00
-2.0 -1.0 0.0 1.0 2.0 I 1.0 .
0.5 - U 37 S U U S III0.0- 000 Q0 0 U . S 0 S. S -0.5- 0
-1.0
-1.6 -0.8 0.0 0.8 1.6 II Figure 4.3. Ordination of mitochondrial nucleotide divergence among coho salmon by nonmetric multidimensional scaling. Open circles are Columbia River populations; solid circles are Oregon coastal populations north of Cape Blanco; solid squares are Oregon and California coastal populations south of Cape Blanco; and open square is the Skykomish hatchery strain. Population 37 is the Coquille River. 115 coastal populations north and south of Cape Blanco, Oregon. Genetic differences among zoogeographical areas have also been well documented for other Pacific salmon
(Allendorf and Utter, 1979; Schreck et al., 1986; Utter, 1989; Reisenbichler et al., 1992;
Kondzela et al., 1994; Phelps et al., 1994; Shaklee and Varnavskaya, 1994; Wood et aL,
1994). Like other species of salmon, differences among coho salmon probably reflect reproductive isolation arising from the propensities of salmon to return to their natal streams to spawn and the effects of ancient glacial or tectonic barriers that further restricted exchange among populations. Certainly, differences between Puget Sound and
Oregon populations would have evolved since Pleistocene glaciers receded from the Puget
Sound. Fish from the Columbia River and southwestern Washington colonized Puget
Sound streams after glaciers receded (McPhail and Lindsey, 1986). However, subsequent exchange between salmon populations in the two regions was limited by loss of a dispersal route through the Chehalis River, which once drained the Puget Sound to the southwest, and by ocean distance between regions after the connection was lost. In addition, coho salmon from the two regions have evolved different oceanic migratory patterns (Weitkamp et al., 1995) that further limits the probability of exchange.
In contrast, the potential barriers to exchange between salmon populations north and south of the Cape Blanco region are not obvious. Cape Blanco marks a boundary between the Coast Range and Kiamath Mountain physiographic provinces (Dicken, 1965;
Franklin and Dyrness, 1973) and two ichthyofaunal regions for primary freshwater fishes
(Snyder, 1908; Mincke!y et al., 1986). Movement of coho salmon would not be restricted by seawater as it is for primary fishes. However, genetic data for steelhead (anadromous rainbow trout, 0. mykiss), which have similar life histories to coho salmon, also show 116 strong differences between populations north and south of Cape Blanco (Hatch, 1990;
Busby et al., 1994). This suggests that ecological or marine factors, rather than distance or geological barriers to migration, may be limiting genetic exchange.
Differences between Columbia River and Oregon coastal populations were surprising, because they suggested that coho salmon have apparently maintained some regional differences during a period of extensive human transfers of salmon between different basins. In constrast, the lack of obvious geographic patterns of differentiation within major geographical areas in these data may reflect effects of these practices.For example, our results indicated strong similarilities between coho salmon from Kiaskanine and Cascade hatcheries, as well as other Columbia River populations (Fig. 4.2). This may be partly the result of many years of moving fish within the Columbia River. Even in recent years (1981-1987), coho salmon were brought into the Kiaskanine Hatchery were from Big Creek, Cascade, and Sandy hatcheries (Oregon Department of Fish and Wildlife, unpublished data; Weitkamp et al. 1995). Interestingly, however, hatchery production of coho salmon on the Kiaskanine River began in 1911 with transfer of fish from the Trask
River (northern Oregon coast). This was followed by more introductions from coastal
Oregon streams, including the Trask (1926-1930 and 1955), Alsea (1927, 1928, 1955),
Nestucca (1930), and Siletz rivers (1939) and Tenmile Lake (1934-1937). Although records of fish transfers were unavailable for the 1960s and 1970s, fish transfers probably continued during that period. Yet, despite such transfers from coastal stocks, Kiaskanine coho salmon retained strong similarities to other Columbia River coho salmon.
Two factors may have prevented the complete homogenization of coho salmon in the Columbia River and the Oregon coast by gene flow from fiSh transfers. First, although 117 millions of fish were moved between streams that may have had different populations, most transfers were within major geographical regions (Weitkamp et al., 1995).
Consequently, some regional differences were preserved, while local geographic patterns, if they were originally present, were erased. One interesting exception to the practice of transferring fish within major geographical regions was introduction of Puget Sound coho salmon into hatchery strains in the Columbia River (Weitkamp et al., 1995) and the central and southern Oregon Coast (Oregon Department of Fish and Wildlife, 1982). It is possible that low frequencies of haplotype 4 in coho salmon from Columbia River hatcheries and central and southern Oregon coastal streams may be the result of these fish transfers from the Puget Sound. Second, natural selection may have maintained genetic differences. For example, many populations of coho salmon exhibited geographical patterns in genetic resistance to infection by Ceratomyxa shasta, a myxosporean parasite
(Hemmingsen et at., 1986; Hoffmaster et al., 1988). Consequently, coho salmon from coastal streams with little resistance to the parasite may not have survived well in the
Columbia River, where C. shasta occurred (Hoffmaster et al., 1988).
The most interesting deviation from an easily interpretable geographical genetic structure for coho salmon was the close similarity between wild fish with unusually low nucleotide diversity from Multnomah Creek and Clackamas and Clatskanie rivers in the
Columbia River and fish of southern Oregon coastal streams (Fig. 4.2). These Columbia
River populations are clearly different from other populations in that basin. Previous investigators have also noted phenotypic and genetic similarities between coho salmon in large coastal streams, such as the Rogue River, and some Columbia River tributaries.
They have suggested explanations ranging from parallel selection to undocumented fish 118 transfers (Hjort and Schreck, 1982; Moran, 1987). Our data may also be explained by two additional scenarios. The most likely is that genetic drift has shifted frequencies of haplotype 1 and haplotype 3, which are found in both Columbia River and coastal populations but in different frequencies (Table 4.2), away from typical Columbia River levels towards levels more common in southern coastal populations. The unusually low nucleotide diversities in Multnomah Creek, Clackamas, and Clatskanie populations indicate the possibility of one or more severe bottlenecks in population abundance during which large, rapid changes in haplotype frequencies might have occurred. Alternatively, similarities between these groups may reflect retention of ancestral genotypes that have been lost in other coastal or Columbia River populations.
These results suggest that population structure in coho salmon may be much more complex than previously acknowledged. My analysis has been focused on populations in
Oregon. Further analysis, including populations from the tributaries of the lower
Columbia River in Washington, Puget Sound, and northern California, will be necessary to answer some of the questions raised by this study. Similarly, an important question I did not examine was the extent of temporal variation in haplotype frequencies. Because coho salmo in many populations, unlike steelhead or chinook salmon (0. tshawytscha), reproduce at three years of age with little gene flow between year classes (Wright, 1970), considerable divergence between year classes could occur. These differences may be accentuated by episodic gene flow from hatchery fish or periods of small effective population sizes. Consequently, in some streams three genetically distinct populations of coho salmon may be using the same habitat in different years. Understanding this complexity will be essential for the management and recovery of this species. 119
5. A FRAMEWORK FOR EVAULATING GENETIC VULNERABILITY
5.1. Abstract
Genetic risk assessment is a tool for describing and communicating genetic risks and hazards of natural resource management objectively, on a case-by-case basis, to reduce conflicts and ambiguities that arise from generalized perceptions of such risks.
Here, I describe a general framework and some technical approaches to quantifying genetic risks and hazards. I develop a model of genetic vulnerability of managed populations that links sources of potential technological hazards, protective mechanisms and responses, and potential losses using artificial propagation of Pacific salmon in the
Columbia River as an example. Unlike other models of ecological risk assessment that emphasize containment of hazards, risk assessment must consider both the protective measures against genetic hazards (reliability) and the potential rate of recovery from failure of protective measures (resilience). When applied to a large artificial propagation program for salmon in the Columbia River, genetic risk assessment revealed that (1) artificial supplementation would result in fewer hatchery-reared fish returning to the wild than were taken from the wild for brood stock; (2) proximate safeguards for reducing vulnerability were not available and appropriate; but (3) use of genetic reserves was a major strength of the program.
5.2. Preface
In the Fall of 1992, I was hired by the Washington Department of Fisheries to produce a genetic risk assessment of the Yakima Fishery Project. This was a large, complex effort to use artificial propagation to rebuild natural populations of salmon 120 contributing to traditional and commercial fisheries. Genetic risk assessment was required under the Fish and Wildlife Program of the Northwest Power Act. Most managers, regional planning groups, and geneticists, however, were confused about what a genetic risk assessment was, how it should be conducted, and what it could be used for. Although descriptive assessment of a few projects had been completed, no standard for judging their quality existed. No models had been developed. Most terms had not been defined.
Biological, historical, and scientific constraints had not been characterized. Assumptions had not been identified.
Out of necessity, I have developed a framework for analyzing genetic vulnerability.
Many have hoped that this would be a model that would produce a set of numbers to tell us the probability that genetic harm will occur, or what the acceptable limits are. They will be disappointed. No model can do our job for us. Basing decisions on probability estimates implies that we have such a large number of trials or opportunities available that a low frequencies of failures will not matter. In contrast, I have worked from the fundamental assumption that every single opportunity we have left is too important to be left to chance.
Systematic, policy-oriented research into risk assessment and reduction began over
50 years ago with efforts to forecast and reduce vulnerability to flooding. Since then, research has continued in many other applied sciences. To develop this model, I have relied heavily on lessons learned from risk assessment, risk communications, and risk management of biotechnology, geophysical hazards, chemical and toxic wastes, and nuclear power. Although the technological hazards of natural resource management are different than those of other industries, the principal problems of forecasting harmful 121
events, reducing vulnerability, and resolving conflicting perceptions of risk among the
public are fundamentally the same.
This model provides a tool for managers, conservation biologists, and decision
makers. Like all tools, it can be improved. It can also be abused. It is designed to be
used iteratively as an integral part of monitoring and evaluating obstacles to achieving
supplementation. It requires some skill and training for those wishing to use it, but with
future refinement it should be accessible to geneticists and non-geneticists alike.
5.3. Introduction
5.3.1. The Problem: Wise Use and Technological Risks
As demand for resources grows so also does need to use technologies that exploit
resources more efficiently. Every technological innovation, however, creates risks as well
as benefits (Smith, 1992). In the Columbia River Basin, for example, technological
innovations in harnessing and distributing water supporta major energy industry,
production of 43% of the United States' supply of aluminum, and irrigation to eight
million acres of agricultural lands. But, these same innovations threaten communities and
peoples that have traditionally relied on the natural resources that the riveronce provided
(Northwest Power Planning Council, 1992): Wild salmon populations and fisheries have been decimated by dams, habitat changes, and overfishing (National Research Council,
1995).
Wise use of resources through technologies that sustain themor conservation
(Pinchot, 1910)depends on identifying resources and choosing appropriate technologies. Artificial propagation is an established technology of fishery science 122
(Everhart and Youngs, 1981). It is one possible solution to rebuilding wild populations of
salmon. However, considerable uncertainty exists over the risks and hazards of using
artificial propagation to sustain fishery resources (Hindar et al., 1991; Hilborn, 1992;
Meffe, 1992). Consequently, fishery managers are faced with thesame fundamental
challenges in assessing in a risky technologyas many other fields: (1) Hazards must be
identified, characterized, and forecast. (2) Vulnerability to hazards must be reduced. (3)
Conflicts over perceived risks and benefits of technological choices must be resolved.
Genetic risk assessment is a process by which genetic hazards, risks, and vulnerabilities
may be characterized and managed.
5.3.2. Basis for Genetic Risk
Fish are the product of their genes, their environment, and unique interactions
between the two. It is the genetic variation within andamong populations of fish that
determines their capacity to persist in changing environments. Consequently, long-term
production of populations or fisheries in changing environments dependson conserving
genetic variation. Manipulation of populations and their environments for short-term
gains creates genetic risks, because genetic diversitymay be lost.
Conservation of genetic variation is implied as the genetic objective of hatchery
supplementation in the Columbia River. Supplementation is explicitly definedas the use of artfIcialpropagation to maintain or increase natural production while maintaining the long-term fitness of the target population, and keeping ecological and geneticimpacts on non-target populations within specified biological limits. Long-term fitness is used synonymously with long-term performance, which is definedas "the capacity of a 123
population to persist in the face of environmental variability while undergoing natural
genetic change" (Regional Assessment of Supplementation Project, I 992a), which
requires maintaining genetic variation.
5.3.3. Policy Directives for Genetic Risk Analysis
The Columbia Basin Fish and Wildlife Program, whichwas established by the
Northwest Power Planning Council (NPPC) under Section 4(h) of the Pacific Northwest
Electric Power Planning and Conservation Act--Public Law 96-50 1, makes clear that risk
assessment is essential. Section 2. 1A(2) states the goal that management programs will
pose no appreciable risk to diversity and that the best available assessment tools should be
used to evaluate risk before proceeding. Section 6.2C(2) directs fisherymanagers to
prepare risk assessments for proposed supplementation projects. In addition, risk
inventory methodology will be necessary for Section 6.2C(13), which provides for
independent audits of hatchery performance to be conducted to improve, modif,',or terminate artificial propagation program. Maintenance of genetic integrity is includedas a performance standard in Section 6.2b(7).
5.3.4. Overview of Document
This document is divided into three major parts. First isa description of the foundations of genetic risk assessment. This section outlines the boundaries of what risk assessment can and can not do. This includes the following topics: (1) historical, scientific, and biological constraints; (2) definitions and key concepts; (3) characteristics of an ideal risk assessment model; and (4) operating assumptions. The second part of the document describes the details of the genetic risk assessment model in four keysteps, 124
including (1) identifying the structure of vulnerability; (2) characterizing thesources and
endpoints of genetic hazards; (3) evaluating the proximate and ultimate safeguardsagainst
hazards; and (4) describing vulnerability and presenting the results. Finally,the third part
of the document is an evaluation of the Yakima Fishery Project usingthe framework for
genetic risk assessment. Appendices contain thesummary data used for assessing genetic
vulnerability of different aspects of the Yakima Fishery Project.
5.4. Foundations of Genetic Risk Assessment
5.4,1. What Are The Important Questions?
Models exist for assessing and managing risks in fieldsas diverse as biotechnology,
geophysical hazards, chemical and toxic wastes, and nuclearpower, but not for risks of
artificial propagation in naturalresource management. To develop a framework for
artificial propagation, this section addresses four critical questions: (1) Whatare the biological and social constraints? (2) Whatare the key definitions and concepts? (3)
What are the characteristics of an ideal model? (4) What assumptions dowe need to make?
5.4.2. What Are The Constraints?
Tools work best if they are designed to work in the environments where theywill be used. Identifying biological and social constraints is importantbecause they shape the design, function, and usefulness of the model. They determine the placeswhere disruptions caused by failure of risky technologiescan be identified, measured, rated, or forecast. They also determine theways in which vulnerability can be reduced. For 125
artificial propagation of fish in the Columbia River, four major biologicaland social
parameters constrain the model of genetic vulnerability: (1) the history of fishery
management; (2) the relationship between genetic structure and function of the speciesto
be propagated; (3) the kind, quality, and quantity of available datato describe genetic
structure and function of the species to be propagated; (4) the system of vulnerability.
The most important immediate, historical constraints of fisherymanagement in the
Columbia River arise from creation of the Columbia Basin Fish and WildlifeProgram,
which is administered by the Northwest Power Planning Council under thePower
Planning and Conservation Act. These constraintsare the adoption of adaptive
management by NPPC and the ad hoc incorporation of risk assessment and population genetic concepts into fishery management goals and objectives inresponse to crises, as
opposed to well-formulated conservation policy. Adaptivemanagement is a policy that attempts to improve resource management by designing management actionsas experiments that can provide useful information for future actions(Northwest Power
Planning Council, 1 992b). Adaptive management allows for failureif the lessons learned can be applied to better scientific management. Application of adaptive management in recent NPPC (1 992b) guidelines has created three environments for genetic risk assessments: initial assessment of individual artificial propagationprograms, regular audits of hatchery performance, and cummulativeor system-wide assessments of proposed artificial propagation projects. In contrast, the ad hoc incorporation ofrisk assessment and population genetic concepts into fisherymanagement goals and objectives has created an environment where debate over the need, uses, and risks of artificial propagation has been hampered by confusing and inconsistentuse of common and technical vocabularies. 126
Definitions of important terms and concepts helps determine the environments in which
assessment can be successful.
The relationship between genetic structure and function is another constraint. The
extent to which genetic structure (organization) is related to genetic function (processes)
is important because risk assessment more easily focuses on vulnerability of populations to
losing genetic structure or diversity. Measuring function of genetic and ecological systems
is difficult in natural populations. Consequently, resource management generally
emphasizes protecting structure, which can be more easily estimated (Cairns and Pratt,
1986). The goal of maintaining long-term performance or fitness of populations, for
example, implies that we wish to manage for function, whereas conservation of
biodiversitywhether at genetic or ecosystem levelsis protection of structure. A variety of theoretical and empirical research suggests that some measurements of genetic
structure are intimately related to function (Allendorf and Leary, 1986; Quatro and
Vrijenhoek, 1989), although most studies suggest the relationship is weak (Lynch, 1996).
However, a consequence of focusing on measures of structure that are not closely related to function is that structure may be preserved while function is impaired.
The third constraint is the kind, quality, and quantity of available data. These
determine to what extent assessment can produce quantitative estimates of vulnerability.
Different kinds of genetic data have different strengths and weaknesses for making inferences about genetic structure or function (Antonovics, 1990, Hedrick and Miller,
1992; Lynch, 1996). For Pacific salmon in the Columbia River, allozyme data is the most available, reliable genetic data. Demographic datasuch as trends in historical abundance and straying patterns and ratesmay be useful for investigating functional parameters of 127
possible assessment models. However, these data have usuallynot been collected
systematically or by the same methods, and theymay range in quantity from abundant to
imaginary.
The four constraint is the system of vulnerability. Processes that linksources of
potential technological hazards, protective mechanisms andresponses, and potential
victims are not random. The relationship of thesecomponents provides the basic
framework for assessing vulnerability. For example, in biotechnology,nuclear power, or
toxic waste management, the mostcommon structure is based on confinement and control
of the hazard. The source is contained to make it useful,a series of protective, fail-safe
mechanisms are placed between thesource and the potential victims, and vulnerability is based on the probability that protective mechanism could fail and thepotential losses if they do (Wilson, 1991). Vulnerability in naturalresource management, however, does not follow the "confine-and-control" model because thevery resource we wish to protect from harm is also intentionally exposed to the potential hazard.Consequently, fail-safe mechanisms are of limited usefulness.
5.4.3. Definitions and Concepts
The ad hoc incorporation of riskassessment and population genetic vocabulary into fishery management goals and objectives inresponse to crisis, as opposed to well- formulated conservation policy, has createda fundamental problem. Lack of shared definitions has resulted in confusing and inconsistentuse of common and technical words.
This has two seriousconsequences. First, without defining the hazards or resources, they are unlikely to be brought under control and managed (Wilson, 1991). Second,confusion 128
and inconsistency among presumed authorities tendsto increase public perception of risk
(Smith, 1992). The purpose of this section is define key wordsand concepts associated
with risk assessment that have been missusedor are potentially confusing. Definitions of
genetic vocabulary used here may be found in basic population genetictextbooks (e.g.,
Falconer, 1981; Harti, 1981)or references for fishery managers (e.g., Tave, 1986,
Kapuscinski and Jacobson, 1987). Hazardsare the potentially adverse consequences
associated with an event or activity (Smith, 1992). Technologicalhazards are sequences
of events leading from human needs andwants to selections of specific technologies
resulting in adverse consequences (Hohenmeseret al., 1983). Genetic hazards are the
potentially adverse losses of genetic structureor function associated with an event or
activity. Risk is the probability that adverseconsequences of an event or activity will
occur (Smith, 1992). Genetic risk is the probability of genetic hazards. Faihue is the realization of a hazard. Vulnerability is the value of riskfor a given set of consequences.
Reliability is the potentialsuccess of protective measures against hazards (Smith, 1992).
Resilience is the potential rate ofrecovery from failure (Smith, 1992). Perceived vulnerability is the value of risk for potentialconsequences of an event of activity based on imperfect and personalized knowledge. Endpoints are the biogeographical dimensions where the magnitudes and durations of failure ofa technology can be measured, ranked, or assessed.
5.4.3.1. Risk
The concept of risk is potentially confusing because incommon usage it may include the probability ofan event occurring or the adverse consequences associated with 129
an event or activity or some product of the two. For example, in regional planning
documents for artificial propagation of fish in the Columbia River,genetic risk has been
defined as the probability of failing to meet genetic objectives (Anonymous,1992). It has
also been classified into four major types according to the potential lossesof genetic
structure and function (Currens and Busack, 1995). This ambiguity introducesan element
of confusion about the product of genetic riskassessment. Is it estimated probabilities of
harmful events? Or is it the kind and quantity of losses that mightoccur? To reduce this
confusion, the meaning of risk is here confinedto the definition above and concepts of
hazard, failure, and vulnerability are introduced. However, because "riskassessment' has been widely used to describe the process of analyzing vulnerability,both in regional planning documents and the scientific literature, it will also beused for that meaning here.
5.4.3.2. Hazards
Hazards can be recognizedas potential losses. Four basic hazards need to be recognized: (1) extinction; (2) loss of within-population genetic variability; (3)loss of between-population genetic variability; and (4) domesticationor the loss of fitness in the wild of fish propagated in an artificial environment. Hazards havemeaning only in an ecological context. Events or phenomena that insome situations might be beneficial or desirable will be hazardous in other situations, dependingon human location, needs, and perceptions. For example, inbreeding results in lost heterozygosity andincreased expression of homozygous genotypes (Falconer, 1981). Theseoften lead to reduced performance (Ryman, 1970; Kincaid, 1976a, 1976b, 1983; Allendorfand Leary, 1986).
Consequently, conservation genetic guidelines for artificialpropagation of fish to be 130
released into the wild frequentlywarn against using small breeding populations, which
increase the chance of inbreeding and genetic drift (Tave, 1986, GaIl,1987 Kapuscinski
and Miller, 1993). In completely different circumstances, however,conservation
geneticists have purposefully created small, inbred strains froma few endangered animals
to minimize the chance that genetic diversity of the whole species is lost (Templetonet al.,
1987). Likewise, inbreeding is often recommended in agriculturalprograms that wish to
increase the contribution of outstanding individualsto a strain.
5.4.3.3. Endpoints
Not all losses are equal, however. The value ofa hazard or loss is estimated at
different endpoints. Because technological hazards in naturalresource management arise from complex sequences of events and choicesat the interface of natural and human
systems, simple cause-and-effect or dose-response relationships rarelyoccur. Impacts cascade through different levels of biological organization. Forexample, extinction of a population of predatory fish may be measuredas a loss of unique genotypes, loss of diversity within an species, or loss of diversityor function within a community. Resilience may range from nonexistent (the same genotypes are unlikely to reevolve)to rapid
(invasion of a different predator in the community). Identificationof appropriate critical endpoints is one of the principal uncertainties of vulnerabilityassessment (Bartell et al.,
1992).
5.4.3.4. Vulnerability
The concept of vulnerability isnecessary because risks and hazards are not always equal. For example, in a given artificial propagationprogram, risk of domestication may 131
be high, relative to the risk of extinction. However, vulnerabilityto extinction, if the
program fails, may equal or surpass that for domestication because the value of the loss is
so much greater. Or, in another case, risk of domestication may be low compared to risk
of losing within-population variability when brood stockare collected from the wild.
However, vulnerability to domestication may be greater because it accumulatesover
multiple generations, whereas the vulnerability to the hazard of broodstock collection
only occurs once. The basic relationship between vulnerability (V), risk(r), hazard (L)
may be expressed as
V = rL (5.1) where L is the value of a hazard measuredas a loss (Smith, 1992). However, risk of a hazard with value of L results fromsequence of N number of independent events.
Additionally, perceptions of losses willvary. Consequently, this relationship may be further developed as
V= (1_fl(1_r1))Lx (5.2) where x is a power that dependson perceived vulnerability. The expected value of x is assumed to be 1 with complete, objective estimates of risksand losses (Smith, 1992).
When x does not equal 1, the difference between vulnerabilitiesbased on different values of x leads to the conflictsover appropriate tecimologies. Estimating and managing perceived vulnerability is part of the field of risk communications.
The above relationship illustrates why risk analysis isreally analysis of vulnerability. Estimating only ror L will not provide fishery managers with enough 132
information to reliably predict how their actions willreduce the value of risk for a given
set of hazards. The relationship also indicates that the safest naturalresource
technologies are those that either reduce the probability thatadverse consequences of a
management action will occur or those that limit losses froma hazard. Consequently,
vulnerability can be managed by controlling reliabilityand resilience (Smith, 1992). Where
hazards can be contained, reliability is useful.However, when hazards can not be
contained, resilience of the system becomes extremelyimportant.
5.4.3.5. Perceptions of Vulnerability
Different perceptions of vulnerabilityare an inevitable part of risk assessment,
because imperfect knowledge forces individuals andgroupsscientific and non-
scientificto simp1ify and personalize the situationto resolve the dilemma of how they should act (Simon, 1956; Kates, 1962). When thesesimplifications meet scientific criteria they are called models. Different models of vulnerabilityare based on different perceptions of both impending losses and risks. Perceptionsof losses range from well- defined and technical to poorly-understood andcomplex. For example, scientists generally view potential losses technically by how theycan be measured (e.g., mortality, loss of alleles, or loss of spawning grounds).Nonscientists tend to view lossesas much more complex, including harms such as social dismption and loss of valuesand history
(Gardner and Gould, 1989, Wachbroit, 1991).Although technical perceptions of lossmay be more quantifiable and repeatable, theyare not necessarily neutral (Tversky and
Kahneman, 1981) ormore valid and they tend to underestimate losses (Smith, 1992).
Even among scientists, different technicalapproaches may lead to different scientific 133 conclusion about vulnerability. A now-classic example is a debate in Science in which a respected ecologist and a respected geneticist use very different scientific models to arrive at very different conclusions about the risks of releasing genetically engineered organisms into the environment (Davis, 1987, Sharples, 1987). Disagreement among experts increases the perception of vulnerability among non-experts (Smith, 1992) and reinforces the complex view of loss.
Different perceptions of risk (probability) also contribute to differences in perceived vulnerability. In risk assessment two major problems are important. First is interpretation of the probability of occurrence of a single, immediate event (Wachbroit,
1991). Second is public acceptance of fallacies about probabilities (Tversky and
Kahneman, 1974). For example, what does it mean in the short term for a salmon population if experts conclude that the probability of extinction in the next 100 yrs is 1 o.
Such a small risk would probably not be seen as reason to prevent supplementation.
However, it is entirely possible and consistent with this probability that the next three populations supplemented might go extinct. A iOprobability tells us nothing about the frequency of extinction in the short term. Consequently, use of long-term frequencies as a basis for policy when every single case is important may not be advisable (Wachbroit,
1991). Likewise, an example of a commonly-accepted fallacy is the notion that deviations from random should get corrected (e.g. if you have three sons, odds are greater that the next child will be a girl).
One way to reduce differences in perceived vulnerability is to establish a shared standard. The default standard in risk management is the worst-case scenario (Wilson,
1991). Rather than debate probable events and consequences, assessment assumes a 134 worst-casethat a failure has happenedand evaluates the consequences. No guidelines exist for choosing the series of probable and improbable events that result in a worst-case.
Consequently, analysts need to explicitly define the de minimus standard they have choosen (Fiksel and Covello, 1986). Evaluation of worst-case scenarios is not neutral, but it can be an objective and responsible standard for convey information about vulnerability
(Wachbroit, 1991).
5.4.3.6. Distinctions Between Assessment and Management
The distinction between risk assessment and risk management can also be conflsing. When risk assessment was first applied to ecological systems, risk assessment was defined as the scientific process of collecting objective, value-free information which could be used by risk management in incorporating values and policy decisions (National
Academy of Sciences, 1983). For a variety of practical and theoretical reasons, however, most risk assessment usually incorporates evaluation of how well vulnerability can be reduced. Review of the scientific literature indicates that although quantitative measures of vulnerability should ideally support management decisions, most assessments in natural, ecological systems will include some decision making and risk management. For example, the National Academy of Sciences (1989) recently offered three criteria for assessing hazards of biotechnology: (1) How familiar are we with the organism to be released and the environment? (2) Can we confine or control the hazard? (3) What are the probable consequences of unintented effects? Of these, only the last fits the traditional definition of risk assessment. 135
The distinction between risk assessment and risk management in natural resource systems is inappropriate for two reasons. First, this distinction was based on inappropriate models. The most common model was the chemical risk-assessment model (National
Academy of Science, 1983), which relies on estimates of exposure, dose-response relationships, and predictable rates of entropic dissipation and decay of chemicals to estimate vulnerability. Although some microbiologists have attempted to use similar models (e.g., Fiksel and Covello, 1986; Strauss, 1991), organisms fundamentally do not behave as chemicals. Because organismsunlike chemicals or atomsmutate, adapt, reproduce, and interact with other organisms, the adequacy and usefulness of the chemical risk assessment model has been challenged (Sutor, 1985; Cairns and Pratt, 1986; Fiksel and Covello, 1986; Andow et al., 1987; Tiedje et al., 1989; Naimon, 1991; Sharples,
1991). Second, risk assessment of natural ecological systems leads to complex perceptions of vulnerability. Developing traditional risk assesssment models for ecosystems is difficult because of the large number of sequential events, complex interactions, and influence of environmental variation (Fiksel and Covello, 1986; Bartell et al., 1992). Consequently, assessments made in natural systems have great uncertainties attached to any estimates (Smith, 1992) and lack of scientific certainty introduces judgement into the assessment process (Russell and Gruber, 1987).
5.4.4. Model Characteristics and Assumptions
Based on the above considerations, the development of a genetic risk assessment model for artificial propagation should work towards these goals. It should providea systematic method for identifjing and evaluating genetic vulnerability. The methodology 136 should be well-specified, repeatable, and capable of using existing data or techniques. The model should be based on our best understanding of the structure and function of genetic systems at the appropriate levels of organization. Finally, the results should be easily understood and fit into decision-making processes, including initial risk assessments during the planning of the program, and subsequent hatchery audits, monitoring and evaluation.
The following assumptions, based on examining the constraints and concepts necessary to genetic vulnerability assessment, were made to develop the rest of the model.
The success of every single supplementation project is important. Genetic structure is tightly related to genetic function. Risk assessment should be based on worst-case scenarios. Risk assessment should emphasize the importance of resiliency as well as reliability in determining vulnerability. Principles and tools of technology assessment can be applied to genetic hazards and risks of artificial propagation.
5.5. Four Steps in Genetic Risk Assessment
Genetic risk analysis can be divided into four key steps each of which will be discussed in detailed below. First, identifij the structure of the vulnerability system.
Second, characterize the sources and endpoints of genetic hazards. Third, inventory the proximate and ultimate safeguards against hazards. Fourth, describe vulnerability and present the results.
5.5.1. Identify The Vulnerability System
The vulnerability system is the heart of genetic risk assessment and risk management. The system consists of the source of the hazard, control and protective mechanisms, and the endpoints, and the processes that link them. The structure of the 137 system determines how risks, hazards, and vulnerability can described and where managers should focus their efforts to reduce vulnerability. To assess natural resource technologies then, it is essential to understand the system of vulnerability.
The basic vulnerability system of a technological hazard in natural resource- management is illustrated in Figure 5.1. Five main components make up the system: (1) source of the hazard; (2) proximate safeguards; (3) endpoints; (4) ultimate safeguards; and
(5) failures. The organization of this system is very different from confine-and-control models of other kinds of technological hazards (Figure 5.2). In confine-and-control situations, source is separated from endpoints by a series of safeguards or control mechanisms that emphasize reliability of control mechanisms in preventing or controlling transfer and exposure to hazards. Vulnerability is reduced by reducing risk. For example, in laboratories using radioactive compounds, vulnerability is controlled by protective measures, such as proper training in handling compounds, wearing protective clothing, confining use to certified areas, and so on. Technicians wear radiation-sensitive safety badges to monitor radioactivity that escapes confinement. This information is then used to determine whether protective measures are working or need to be changed. In contrast, emphasizing ways to rehabilitate persons exposed to harmful levels of radiation, rather than confining and controlling the hazards, is not considered part of the system.
The crucial difference between the two systems is that vulnerability in natural resource systems is reduced by limiting loss through resilience provided by ultimate safeguards, as well as by reducing risk through proximate safeguards.
Emphasis on reliability of confine-and-control models, which focuses on transfer of a RELIABILITY RESILIENCE $ ULTIMATE SOURCE PROXIMATE RISK CONTROL OF CONTROL ENDPOINTS VALUE HAZARDS MECHANISMS MECHANISMS
- BROOD STOCK - STOCK TARGET - RESERVE EXPECTED LOSS: SELECTION IDENTIFICATION STOCK (A) SYSTEM - EXTINCTION NON-TARGETSTOCK BROOD STOCK - ARTIFICIAL OF SAME SPECIES (B) ADAPTIVE AMONG COLLECTION PROPAGATION MANAGEMENT POPULATION DIVERSITY NON-TARGET STOCKS MATING - PASSAGE OF NON-TARGET SPECIES (C) WITHIN POPULATION - HARVEST - NON-TARGET STOCKS - REARING DIVERSITY CONTROL OF OTHER TARGET SPECIES (D) -RELEASE HABITAT IMPROVEMENT - NON-TARGET STOCKS FITNESS IN THE OF TARGET SPECIES WILD - JUVENILE OUTSIDE OF BASIN (E) MIGRATION
ADULT MIGRATION
Figure 5.1. The vulnerability system for technologicalhazards in natural resource management. Below each component are examples from fishery supplementationprojects. PROXIMATE ULTIMATE SOURCE RISK RISK ENDPOINTS RISK OF CONTROL CONTROL VALUE HAZARD MECHANISM MECHANISM
USE OF PROPER rPROTECTIVE TECHNICIAN LOSS DUE TO RADIATION RADIOACTIVE I TRAINING CLOTHING COMPOUND POISOMNG LTEST CONFINEMENT TO TUBE CERTIFIED AREAS
GENETIC BIOLOGICAL TRAITS CONTROLLED USE IN WILD OR CULTURED LOSS DUE TO ENGINEERING OF THE ORGANISM AGRICULTURAL ORGANISMS SETI'INGS - ECOLOGICAL DISRUPTION
- GENETIC CHANGES
Figure 5.2. The confine-and-control vulnerability system for technologicalhazards. Below each component are examples from the use of radioactive compounds in laboratories and the useof genetic engineered organisms. 140 hazard, is clearly not appropriate when artificially-produced fish are intentionally raised and released to have a effect on a wild population. The model of vulnerability for natural resource systems, on the other hand, focuses on the biological capacity for homostasis and heterostasis. Although vulnerability may be reduced by proximate safeguards, such as adherence to genetic hatchery guidelines (e.g., Kapuscinski and Miller, 1993), managers must assume that such safe guards will sometimes fail.If proximate safeguards fail, the resource must still be able to respond to the hazard. Consequently, risk assessment for natural resource technologies, such as artificial propagation, must focus on the adequacy of safeguards that emphasize resilience, as well as reliability.
5.5.1.1. Sources of Hazard
Sources of hazards are the events or series of events where potentially adverse consequences might occur. The source of the hazards often becomes the focus of management attention rather than the presence or absence of effective safeguards. Seven general sources of hazards in the life-cycle of Pacific salmon from supplemented populations can be identified (Figure 5.1): (1) brood stock selection, (2) brood stock collection and holding, (3) mating, (4) rearing, (5) release, (6) juvenile migration, and (7) adult migration. Each of these can be divided into two or more specific events with associated hazards and mechanisms. For example, when brood stock are collected from the wild population (the event), non-representative sampling of the population (the mechanism) can result in a loss of within-population genetic diversity (the hazard). Even if the collection is representative, if the mortality in adults before spawning is non-random 141 with respect to genotype (the mechanism), then within-population genetic diversity can be reduced (the hazard).
5.5.1.2. Description of Endpoints
Hazards are meaningless without endpoints. Because impacts of technological hazards in ecosystems cascade through different levels of biological organization, determination of a single endpoint for risk assessment is not satisfactory (Cairns and Pratt,
1986). Genetic risk assessments of supplementation for Pacific salmon have at least five potential endpoints (Figure 5.1): (1) the target population (A); (2) non-target populations of the target species within the target area (B); (3) non-target species within the target area (C); (4) non-target populations of other target species (D); and (5) non-target populations of the target species outside the target area (E).
Consider an example where wild steelhead, chinook salmon, and cutthroat trout spawn within several nearby coastal rivers and artificial propagation is intended for steelhead and chinook salmon in one stream (the target area). With the classification above, two different sets of endpoints exist. For steelhead, the target population (A) is the specific spawning aggregation to be supplemented. However, if other genetically differentiated, spawning aggregations of steelhead occur in the river (B), they may be effected by supplementation, as might be the cutthroat trout (C) and the chinook salmon
(D). Because steelhead do not spend their entire life-cycle within the river, genetic hazards exist for steelhead in other coastal streams (E) as well. Similar endpoints would be constructed for the chinook salmon as well. 142
5.5.1.3. Proximate Safeguards
Proximate safeguards are components of reliability associated with the primary mechanisms by which sources of hazards are controlled to provide benefits to the resource or resource users while limiting vulnerability. Four primary control mechanisms presently exist for supplementation in the Columbia River. These are (1) artificial propagation, (2) control of passage through dams, (3) harvest regulation, and (4) habitat management.
Each of these control mechanisms is associated with one or more hazard sources and represents an opportunity for safeguards.
In most cases, proximate safeguards will include human, physical or logistical, and biological components. For example, Figure 5.3 illustrates four major components of proximate safeguards. Human components include the quality of the guidelines and ability of technicians to carrying out the guidelines. Guidelines include conservation guidelines, operating guidelines, and decision trees. Conservation guidelines are genetic or ecological guidelines based on first principles (e.g. collect brood fish randomly throughout run; release no more fish than the freshwater carrying capacity for that life-history stage).
Operating guidelines are the protocols and procedures that are actually used (e.g. collect every third fish over a weir). Decision trees are flow charts that allow a technician or manager to arrive at an appropriate decisions when unexpected problems arise. Decisions trees are just as needed for managers making conservation decisions as for hatchery biologists raising fish. Technician ability may be divided into having adequate training and skills to complete expected tasks and to make appropriate decisions when the unexpected happens. Logistical components include the availability of enough appropriate equipment and the ability to plan and coordinate the activity. Biological components include the PROXIMATE CONTROL OF GENETIC HAZARD
EQUIPMENT VARIABILiTY TECHNICIAN GUIDLELINES OVERIDE AND IN FISH LOGISTICS BEHAVIOR
TYPE I ERROR EQUIPMENT CHARACTERIZATION CONSERVATION GUIDELINES
TYPE II ERROR COORDINATION DETECTIONOF AND PLANNTNG CHANGES OPERATING GUIDELINES
DECISION TREES
Figure 5.3. Components of vulnerability control mechanisms in natural resource management. 144 anticipated variability in fish behavior upon which the guidelines are based and the ability to detect and recognize deviations.
5.5.1.4. Ultimate Safeguards
Ultimate safeguards are the components of resilience in the management of the natural resources. Like proximate safeguards, ultimate safeguards consist of biological and human components. Genetic reserves are the biological component. Adaptive management is the human component.
Identifying requirements for reserves has become an important focus of conservation biology and ecological research (e.g., Diamond, 1975; Simberloff, 1986;
Harris and Eisenberg, 1989). Likewise, the importance of genetic reserves is simply a logical extension of the fundamental genetic objective of supplementation to manage genetic diversity to maintain the capacity of populations to persist in the face of environmental variability using adaptative management (Northwest Power Planning
Council, 1992b, Regional Assessment of Supplementation Project, 1992a). The Columbia
Basin Fish and Wildlife Program acknowledges that a supplementation project may fail completely and directs that adaptive management be used encourage resilience. It follows, however, that the potential rate of recovery from failure (i.e. the capacity to persist) depends on the amount and structure of genetic diversity that remains. If proximate safeguards fail or don't exist, genetic reserves provide the most effective resilience, because some of the genetic structure and function that otherwise might have been lost would have been protected. 145
The link between biological and human components is crucial. Adaptive management increases resilience by providing a means to learn from failures (Holling,
1978; Walters and Hilborn, 1978; Walters, 1986). An often ignored constraint on adaptive management, however, is the availability of future opportunities to apply what has been learned. Understanding what we should have done is a hollow lesson when we've lost the resource we wanted to manage in the process. Reserves allow adaptive management to work.
5.5.2. Characterize Sources, Endpoints,, and Safeguards
Once components of the vulnerability system have been identified, the next step is to gather the data for the genetic risk assessment. This has two parts. First, identify and characterize each hazard source and its respective endpoints. Second, inventory the proximate and ultimate safeguards.
5.5.2.1. Ident/ication and Gharacterization of Sources and Endpoints
Because sources of hazards and endpoints are so intimately related, they may be characterized as part of a single process. This may be done systematically in two steps:
Identify sources and mechanisms for each possible hazard-endpoint combination, and
Describe the characteristics of the source that potentially create hazards.
One method of systematically organizing the sources and mechanisms of genetic hazards for each endpoint-hazard combination is to construct for each source a matrix of the four types of genetic hazards and the five types of endpoints and fill it in with the appropriate genetic or demographic mechanisms. Once the appropriate mechanisms have been identified, data are collected to describe the characteristics of the mechanisms that 146 potentially create hazards, including the types, duration, intensity, and amounts. For example, one cell In the matrix for brood stock collection might describe non-random sampling of spawners as the mechanism resulting in loss of within-population diversity in the target population. To characterize this possible hazard, we would want to gather data on the sampling procedures and variability of the endpoint: How many fish are to be taken? What is the sampling design? What kind of capture technique will be used? What is known about the variability in the wild, donor population?
5.5.3.Inventory of Safeguards
The second step in gathering data for genetic risk assessment is to inventory and describe the safeguards associated with each hazard source. The key to accomplishing this is to construct a complete diagram of the relationships of the major components of each safeguard. Once components have been identified, they can be characterized, either qualitatively or quantitatively, by descriptions, presence or absence, ratings, or results of empirical testing.
Three tools are often used in risk assessment for identifying and analyzing components of vulnerability: (1) relevance tree analysis, (2) fault tree analysis, and (3) decision trees. In relevance tree analysis, a central component or function is reduced into simpler elements that support it. Relevance trees are well-suited for analyzing hierarchical systems and assessing the relative importance of different components, when it is not critical to capture a dimension of time. Relevance trees are useful for analyzing both proximate and ultimate safeguards. Figure 5.3 is an example of a relevance tree. 147
When elements are organized sequentially, as they are in proximate safeguards, fault tree analysis may be more appropriate. Fault tree analysis uses flow charts to display all the possible independent elements that must work if the safeguard is to prevent a failure. Figure 5.4 is a simple example of a fault tree analysis for collecting brood stock representative of the wild population. Failure of any of the four main components will result in a failure to collect representative brood stock. First the guidelines must be correct. Next, the fish must behave in a way that was anticipated by the guidelines. Even if these two conditions hold, the procedure may fail if the collectors do not have enough available equipment adequate for the task. Finally, the technicians must be able to do the work.
Technician failure can happen in two ways (Wilson, 1991). First, they may simply be unable to do the work because of unavailable guidelines, knowledge of the natural variation of the fish, equipment, as well as lack of skill and training (Type I error).
Second, if they are aware that one of the previous components may have failed (e.g, the seine is unable to capture any portion of what appear to be the largest, oldest fish ina boulder-filled pool), which should lead to failure to accomplish the goal of representative samples, they may choose to overide the system and continue. If, in fact, their decision was correct, no harm was done, If, however, their decision was wrong (Type TI error), then they failed to get a representative sample for brood stock.
Decision trees are valuable in analyzing vulnerability associated with making different choices. Decision trees are maps of the choices that lead to specific actionsor conclusions. By formalizing judgements of an organizationor expert, decision trees allow 148
PROBABILiTY COMPONENTS OF SUCCESS
0.33 REPRESENTATIVE SAMPLE OF WILD SPAWNERS
0.70 TECHNICIAN OVERIDE
0.95 EQUIPMENT & LOGISTICS
0.50 VARIABILITY IN FISH BEHAVIOR
0.99 SAMPLING GUIDELINES
Figure 5.4. Fault tree analysis of collecting a representative sample brood stock from a wild population of fish. 149
different individuals to arrive at uniform conclusionsor actions. Because of this, future
responses to crisis can be anticipated and evaluated. In the example above, for example, a
well-developed decision tree might reduce risks of technician overides. Likewise,
evaluation of decision trees based on the different proposed managementresponses to
unsuccessful hatchery performances allows risks analysis of how well adaptive
management might operate to reduce vulnerability.
5.5.4. Describe Vulnerability
The last and most challenging step in risk assessment is to describe vulnerability
and present the results. Four different approaches may be possible: (1) Use of genetic and
demographic models; (2) comparative vulnerabilityscores; (3) qualitative description; and
(4) probabilistic descriptions. The emphasis of all these approaches ison identif,'ing the
components that contribute most to vulnerability and that can be corrected rather than on
estimating probabilities (risks) that genetic hazards will occur.
5.5.4.1. Genetic and Demographic Models
The primary purpose of using genetic and demographic models is to describe
potential genetic hazards simply and quantitatively in terms of loss of genetic structureor
function. These analyses can provide an estimate of potential losses and help identify
sources of hazards that were otherwise were not obvious. For example, given an estimate
of what gene flow might be among large, supplemented populations under different production strategies, it is possible to describe the between-population diversity that might be lost. Likewise, simple birth-and-death demographic modelscan be used to describe the growth or decline of a population as brood stockare continually taken from the wild and 150
more complex models can be used to describe extinction under a variety of scenarios. The
use of genetic and demographic models can be tailored to the specific project, based on
availability of data and preliminary qualitative assessment of which aspects of the project
are most vulnerable.
5.5.4.2. Comparative Vulnerability Scores
The primary purpose of comparative vulnerability scores is to calculate relative
values for different components of a supplementation effort that will allow project
managers to identifj the most vulnerable areas. The analysis is based on the inventories
and descriptions of sources, endpoints, and proximate and ultimate safeguards identified in
the previous step of risk assessment. In its simplest form, this is the procedure: At each
level and for each component identified in relevance tree analyses (Figure 5.3), the
performance of the supplementation project is rated from 1 (poor) to 5 (good) according
to predetermined criteria. For example, for brood stock collection, assessment might
begin with the guidelines. For each element (conservation guideline, operating guidelines,
decision trees), the project is given a score basedon whether the elements have been
developed, implemented, and how effective theyare. Lack of decision trees, for example,
might rate a 1, whereas well-developed decision trees might ratea 5. After all the
different elements of brood stock collection have been rated, thescores for each
component (e.g. all the elements under sampling guidelines) can be normalized (maximum
score of 100). The scores have no absolute value, unlike genetic or demographic calculations. However, completed across the whole project, it possibleto compare parts of the project and identifj theareas that are most vulnerable. 151
5.5.4.3. Qualitative Description
Qualitative descriptions are most useful when quantification is not always
appropriate or necessary. When probabilities and losses are poorly understood and
precision of the estimates is low, then qualitative descriptions of vulnerabilityare
appropriate. This is especially important when attempts to force the data intoa
quantitative model would provide an impression of precision that is not warranted.
Likewise, when probabilities and losses of specific mechanisms are well understood, but
precision is not needed for risk assessment (e.g. vulnerability ofeggs to light or
desiccation) then hazard descriptions are all that isnecessary (Fiksel and Covello 1986).
5.5.4.4. Probabilistic Description
Probabilistic descriptions are intended to be predictive. However, probabilistic
descriptions need to be used with caution in making decisions for two importantreasons.
First, probabilistic prediction relies on statistical theory and large amounts of accurate
historical data, which are generally not available for supplementation, if it is to generatea
probability that an event will occur that is not completely speculative and unreliable (Fiske!
and Covello, 1986). More importantly, if we can not afford to fail ina supplementation or recovery program, then long-term frequencies, which are the basis for probabilistic descriptions, are not a sound foundation for estimating probability ofsuccess for a given project or making policy decisions. However, fault tree analysis isone method which may be useful both for its predictive and heuristic value.
In fault tree analysis, a probability ofsuccess is estimated for each independent component. Because the probability of success of the whole is the product of probabilities 152
of success for each component, probability of failure for the whole process can be
calculated. For example, in Figure 5.4 the probability of successfully collecting a
representative sample for brood stock is 0.33or the product of the probabilities that
sampling guidelines are correct (0.99), that fish behave as anticipated (0.50), that it is
logistically possible to sample the fish (0.95), and that the technicians make all the correct
decisions (0.70). If an estimate of loss is available from genetic or demographic
descriptions then it is possible to calculate a value of risk using equation 1.2.
The difficulty in using fault tree analyses is in estimating probabilities. Estimates of
probabilities generally come from long-term frequencies of failures based on empirical
testing or analysis of historical records. Because in ecological systems it may be
impossible to obtain accurate estimates of the probabilities for each component, the use of
fault tree analyses provides one method of obtaining an upper limit on the probability of
success: if one or more components are amenable to empirical or historical assessment,
even if others aren't, the overall probability of success has to be lower than any single
probability or product of probabilities (Stich, 1978). When no historical data or empirical
assessments are available, however, estimates can be generated by a Delphi process. The
Delphi process attempts to exploit the opinions of a group of experts using a highly
structured format that preserves anonymity while allowing feedback to minimize adverse
effects of group dynamics (O'Keefe, 1982).
In addition to providing a simple method of calculating probability of success, fault tree analysis is extremely useful for identifying the components that contribute most to vulnerability that can be reduced. For example, if the hypothetical estimates in Figure 5.4
are reasonably accurate, then the analysis clearly indicates that the most likey source of 153
failure is because we don't understand variability in behavior and techniciansare likely to
make costly errors. Nothing can be done about variable behavior except by collecting
more informantion. However, the success of technicians can be improved by proper
training and providing them with decision trees. Spending large amounts of time and
money on other components may not provide the same benefits.
5.6. Genetic Vulnerability of the Yakima Fishery Project
5.6.1. Purpose
The purpose of this part of this report is to apply the fundamentals of genetic risk
assessment described above to evaluate genetic vulnerability of the Yakima Fishery
Project. Two levels of risk assessment are required for supplementation in the Columbia
River Basin (Columbia Basin Fish and Wildlife Authority, 1991). Level I risk assessment,
which had already been conducted, identifies genetic risks during the planning and
evaluation of production alternatives. Once alternatives have been selected, Level II risk
assessment is developed as part of operation plans and includes more quantitative analysis
of production measures and the contigency plans to prevent, terminate, and correct
undesirable genetic impacts. Here, I illustrate a Level II risk assessment of the Yakima
Fishery Project for two groups of fish: Yakima River spring chinook salmon (0.
tshawytscha) and Yakima River summer steelhead.
5.6.2. Materials and Methods
The worst-case scenario for this analysis is the null hypothesis that supplementation will be no different than conventional artificial propagation of salmon. 154
Choosing this level has two important advantages. First, it is based on realistic scenarios.
Second, unlike the speculation about what supplementation may accomplish, worst-cases
scenarios are described by considerable historical data.
I used the following sources of data for this analysis: (1) YakimalKlickitat
Production Project Preliminary Design Report & Appendices (Anonymous, 1990); (2)
YakimalKlickitat Fisheries Project Draft Project Planning Status Report (Yakima Fishery
Proj ect Science Team, 1992); (3)YakimalKlickitat Fisheries Project Planning Status
Report 1992, Vol. 1-8 (Anonymous, 1992); (4) Yakima Hatchery Experimental Design
(Busack et al. 1991); (5) Yakima Basin Subbasin Salmon and Steelhead Production Plan
(Confederated Tribes and Bands of the Yakima Indian Nation et al., 1990); (6) Yakima
River Spring Chinook Enhancement Study (Wasserman et al., 1984; Fast et al., 1985;
1986, 1987, 1988, 1989, 1991a, 1991b); (7) Yakima Fisheries Project
Operations/Procedures Manual (Hagar, In prep.); and (8) Yakima River Basin Fisheries
Project Draft Environmental Impact Statement (Bonneville Power Administration, 1992).
5.6.2.1. Describing Extinction
The purpose of this analysis was to examine differences in chance extinction of
salmon and steelhead in the Yakima River with and without supplementation. Itwas not
intended to provide absolute estimates of extinction probabilities. The relationships between chance extinction and demography were examined using the model by Goodman
(1987), which expresses persistence timeas a function of mean population growth rate and variance in population growth rate, Supplementation is expected to increase 155 population growth rate, because it decreases the death rate during the early life-history of the fish. However, it will not necessarily change variance in population growth rate.
Mean population growth rates (r) and variances for natural populations of spring chinook salmon and steelhead were calculated from historical trends in numbers of fish and from estimates of age structure of the spawners (Fast et al., 1991 a; Yakima Fishery
Project Science Team, 1992). Calculated values of r were assumed to represent the maximum limit of population growth rates expected of these populations under historical fishery management policies and environmental variation. Minimum population growth rates for populations which have persisted in Yakima River at low levels over the last 30 years were set at 1. Growth rates under supplementation were calculated fromproject estimates of fecundity of brood stock of different ages, prespawning mortality, egg-to- smolt survival, and smolt-to-adult survival of hatchery-reared fish under the following assumptions: (1) Brood fish were a representative sample of the natural population; (2)
Mating between wild and hatchery fish was random; (3) Relative fitness of the matings of hatchery and wild fish were 1 for wild x wild matings, 0.8 for wild x hatchery matings, and
0.5 for hatchery x hatchery matings, as used by the System Planning Model; (4) No fitness difference existed among hatchery and wild fish in the F2 generation; and (5) No more than
50% of the wild spawners could be used as brood stock. Variance in mean growth rate was assumed to be 12.08, based on the agreement between observed variances for three different stocks of Yakima spring chinook salmon for which we have the most complete long-term data set (Fast et al., 1991a) and Belovsky's (1987) estimate of 7.32r for high environmental variance. 156
5.6.2.2. Comparative Vulnerability Scores
Evaluation of proximate and ultimate safeguards was based on comparative vulnerability scores. Comparative vulnerability scores have no absolute value but rather provide a systematic means of identifying and describing patterns of vulnerability due to flaws in production measures or contingency plans to prevent, terminate, and correct undesirable genetic impacts. The reasons for relying on comparative vulnerability scores, rather than probabilistic or deterministics descriptions are discussed earlier in this chapter.
For each of the seven sources of genetic hazards (Fig. 5.1), potential scenarios that might lead to losses from any of the four kinds of genetic hazards at any of the five possible endpoints were identified. Then, for each source and each kind of hazard, the project was given a rating of 1-5 for the availability, appropriateness, and sufficiency of each of the essential components of proximate and ultimate safeguards that increase reliability and resilience and reduce vulnerability (Table 5. 1, Table 5.2, Appendix B,
Appendix C). A component was appropriate if it was consistent with the principles and actions recommended in the genetic guidelines. A component was sufficient if it was both appropriate for all endpoints and well-enough developed to allow monitoring and evaluation.Reliability or resilience scores (R) were computed as the proportion of the maximum possible score x 100, such that a score of 100 indicated the highest possible reliability and a score of 20 indicated the lowest possible reliability.To compare 157
Table 5.1. Hierarchy of components and criteria for assessing reliability of proximate control mechanisms in supplementation. Hazards are 1) extinction, 2) loss of within- population genetic diveristy, 3) loss of between-population genetic diversity, and 4) domestication. Key to scoring:1 =component is not available and not indicated in project planning documents; 2 = component is available but not appropriate or sufficient OR component is not available but project documents indicated that an appropriate safeguard is to be developed; 3 = component is available and appropriate but not sufficient; 5 = component is available, appropriate, and sufficient.
COMPONENTS HAZARD SCORES
HAZARD: 1 2 3 4
1.Guidelines a. Genetic Guidelines b. Operating Guidelines c. Decision Trees 2. Natural Variability a. Baseline Characterization b. Detection of Departures from Baseline 3. Logistics a. Equipment b. Coordination 4. Technician Ability & Judgment a. Type lError b. Type II Error 158
Table 5.2. Components and criteria of assessing reserves as ultimate control mechanisms.
C1UTERIA FOR EVALUATING POTENTIAL RESILIENCE FROM RESERVES SCORE
Availability
No genetic or ecological reserves identified = 1
Reserve identified but not implemented 3
Reserve identified and implemented 5
2. Appropriateness
Scoring: Neither of the criteria below apply 1 One of the two criteria apply =3 Both criteria apply = 5
Genetic structure is template for identifying the reserve.
Reserve represents ecological, aquatic diversity of area targeted for supplementation.
3. Sufficiency
Scoring: None of the criteria below apply = 1 One of the criteria apply = 2 Two or three of the criteria apply = 3 Four criteria apply = 4 All criteria apply = 5
Probability of extinction of target species in the reserve is less than 5% in 200 years.
Reserve protects genetic and ecological diversity of more than one stock.
Harvest management goals protect reserve.
Management goals (e.g. harvest, interagency agreements about habitat, water flows, migratory corridors, artificial propagation) are defined within a temporal hierarchy, beginning with the goat that the reserve should function for at least 200 years.
Reserve protects or restores historical complexity of migratory patterns of target species. 159 vulnerability of different hazards among different parts of the project, vulnerability was calculated by,
V= (1-0.01R.)L (5.4)
where N is the number of sequential events that combine to realize a loss and L is the value of the loss. Without empirical data to set the relative genetic losses of extinction, loss of within-population diversity, loss of among-population diversity, and domestication, all hazardsexcept extinctionwere arbitrarily given a value of 100. Loss due to extinction of a group of populations of the same species was set to 200, or the sum of losses of within-population diversity and among-population diversity. Total vulnerability was calculated as the sum of the vulnerability of the different proximate and ultimate control mechanisms:
V TOT=VDAP+VlAP+VHB+P+Vp7-i-p+VUCM (5.5) where VTQT = total vulnerability; VD = vulnerability of artificial propagation due to direct genetic effects; V= vulnerability of artificial propagation due to indirect ecological effects; VHB+p = vulnerability of habitat and passage management; VHV+p = vulnerability of harvest and passage management; and = vulnerability of reserves.
5.6.3. Results: Vulnerability of Yakima River Spring Chinook Salmon
Two special problems confront management of genetic vulnerability for spring chinook in the Yakima River. First, no practical method is available to avoid collecting 160
American River salmon while collecting Naches River spring chinook for brood stock.
Second, American River spring chinook will not be supplemented because they have been designated a genetic reserve, but low numbers of adult fish returning to this population give it the greatest probability of chance extinction.
5.6.3.1. Extinction
For every 100 spring chinook salmon taken as brood stock from the upper Yakima
River and Naches River, Yakima Fishery Project data indicate that on the average only 53 and 66 fish, respectively, will return. This rate of return is the product of 80% expected pre-spawning survival of brood stock (Hagar, Yakima Fisheries Project
Operations/Procedures Manual), expected mean fecundity of 4084 and 5067 eggs per female from the upper Yakima and Naches rivers, respectively (calculated from age structure and fecundity data in Fast et al. (1991 a)), expected 65% egg-to-smolt survival
(Hagar, Yakima Fisheries Project Operations/Procedures Manual), and 0.05% release-to- adult survival (Fast et al., 1991 a). No data presently support greater returns under supplementation. If population growth of wild-spawning spring chinook salmon isn't far enough above replacement levels to buffer against this loss, supplementation will lead to extinction of the entire population.
Under supplementation, mean growth rate of the population is reduced. Mean growth rates less than 1.0 indicate that numbers of adult salmon returning to reproduce are declining. From 1962 to 1991, mean growth rate of upper Yakima and Naches river spring chinook salmon was 1,65. Under simple, deterministic conditions of supplementation, mean growth rate of upper Yakima and Naches river chinook salmon 161 populations would be reduced to approximately 1.06 and 1.11, respectively. Assuming replacement of wild fish was 1, mean growth rate of upper Yakima and Naches chinook salmon under supplementation would be 0.71 and 0.75.
Figures 5.5 and 5.6 illustrate the relative effect of supplementation at high and low growth rates of the wild spawning fish on frequency of extinction. At mean population growth rates maintained by unsupplemented Yakima River spring chinook salmon over the last 25-30 years, probability of extinction in the next 100 years is consistently less for all population sizes than under supplementation. The smaller the spawning population size, the more pronounced is the difference (Figure 5.5). Similarly, at 5% probability of extinction (Fig. 5.6), unsupplemented spring chinook salmon populations are expected to persist for more generations than they would under supplementation. When very few spawners return per generation, however, the expected persistence times of the population are so short that differences are meaningless.
5.6.3.2. Reliability and Resilience
A reliability or resilience score of 60 or greater for proximate or ultimate control of vulnerability at any given source of genetic hazard indicates that over all, the essential components of that control mechanism were available and appropriate. For spring chinook salmon, the only proximate control mechanism that consistently scored over 60 was genetic stock identification (Table 5.3; Appendix B). Isolated scores of 100 in Table
5.3 (e.g., control of extinction during mating) reflect situations where the hazard was considered inappropriate; no control mechanism scored 100 because it was perfect. 162
4 6 8 10 12 14 16 Population Size (Thousands)
Figure 5.5. Relative probability of extinction in 100 years for Yakima River spring chinook salmon at different spawning population sizes with and without supplementation. Solid boxes indicate unsupplemented populations at high population growth rate; open boxes show unsupplemented populations at a low population growth rate and supplemented populations at a high population growth rate; triangles are supplemented populations at low population growth rate. 163
35
30
25
20
15
10-
0 0 2 4 6 8 10 12 14 16 Population Size (Thousands)
Figure5.6.Relative persistence in generations at 5% risk of extinction for Yakima River spring chinook salmon at different spawning population sizes with and without supplementation. Solid boxes indicate unsupplemented populations at high population growth rate; open boxes show unsupplemented populations at a low population growth rate and supplemented populations at a high population growth rate; triangles are supplemented populations at low population growth rate. 164
Table 5.3. Reliability andresilience scores for proposed supplementation of Yakima River spring chinook. A score of 100 indicated high reliability or resilience, whereas a score of 20 indicates low reliabilityor resilience. Genetic hazards are 1) extinction, 2) loss of within-population geneticdiversity, 3) loss of between-population genetic diversity, and 4) domestication.
Genetic Hazard
Controls Source of Hazard 1 2 3 4
Genetic stock Brood stock selection 91 91 91 91 identification
Artificial propagation Brood stock collection 40 44 53 29 Mating 100 49 49 44 Rearing 40 40 40 40 Release (direct effects) 38 36 36 40 Percent of maximum 5.5 2.8 3.4 1.9 reliability score
Release (indirect effects) 33 33 33 100 Percent ofmaximum 33.333.333.3 100 reliability score
Passage and Habitat Juvenile migration 33 33 100 100 Percent of maximum 33.3 33.3 100 100 reliability score
Passage and Harvest Adult migration 53 53 36 100 Percent of maximum 53.3 53.3 35.6 100 reliability score
Genetic reserves All of the above 53 53 53 53 Percent ofmaximum 53.3 53.3 53.353.3 reliability score 165
Three major factors contributed to low scores for supplementation of spring chinook salmon. First, operating procedures and protocols for how conservation guidelines will be implemented did not exist, were inconsistent with conservation guidelines, or have only been superficially developed. Second, very few decision trees have been developed to indicate what the contigency plans are for failure or unanticipated results of a control mechanism. Third, planning documents indicated no intentions to provide appropriate training to avoid type I and type II error by technicians or biologists.
Lack of appropriate training for technicians and biologists was conspicous for every source of genetic hazard. As a new application of artificial propagation, supplementation has such special problems that geneticists have been hired to identify and characterize them. Assuming that technicians and field biologists already have the training to implement this technology correctly is a major weakness. Control mechanisms of each of the potential sources of genetic hazards are discussed in detail below.
5.6.3.2.1. Genetic Stock Identfication
Selection of brood stock has a direct effect on vulnerability to all four major genetic hazards. Genetic stock identification for spring chinook salmon had the highest reliability score of any control mechanism for any species examined during this project.
Every component was judged available, appropriate, and sufficient, except for decision trees and the ability to detect departures from baseline stock identification (Appendix B).
Needed are explicit decision rules for how to proceed on selection of the Naches River stock as a brood stock if it continues to be impossible to avoid collecting American river adults with Naches River brood stock. 166
5.6.3.2.2. Artificial Propagation
For brood stock collection, the greatest weakness was the lack of operating guidelines for how brood stock would be collected and held to assurea representative sample of the upper Yakima River population. For example, no guidelines, decision trees, or monitoring procedures existed to control possible non-random mortality of brood stock while they are being held prior to spawning (Appendix B), yet potential effects of 20% non-random mortality should not be ignored. Likewise, although minimium effective population size (Ne) of natural populations should be at least 500 for geneticreasons
(Lande and Barrowclough, 1987), no decision rules were available for deciding how to proceed f Ne falls below 500 during supplementation or ifmore brood fish are being taken for brood stock than return from supplementation.
Reliability of controls for mating was the greatest of any source of genetic hazard associated with artificial propagation (Table 5.3), primarily because conservation guidelines were well-developed (Kapuscinski and Miller, 1993), operating guidelineswere to be consistent with conservation guidelines (Hagar, in prep.), and monitoring procedures were being developed. In contrast, low reliability of controls during rearing (Table 5.3) reflected two major weaknesses. First, operating guidelines for rearing fish accordingto the recommendations in conservations guidelines (Kapuscinski and Miller, 1993)were missing. Second, decision trees for how to respond to unexpected emergencies that might either compromise genetic goals or experimental goals have not been developed.
Experimental hypotheses and designs have been formulated for rearing, but without operating guidelines it was difficult to determine what the actual environments of the fish in the hatchery will be and whether they were appropriateor sufficient. Likewise, lack of 167 decision trees here is crucial. For example, if the health of fish in one experimental environment appears to be worsening due to unanticipated direct or indirect effects of the rearing regime, will the regime be changed or will it remain the same? If it is not changed, a large portion of the fish may be lost, resulting in lower overall returns to a natural population from which more brood stock may already be being taken then return from supplementation. This increases vulnerability to extinction and loss of within-population genetic diversity. However, if rearing is changed, the experiment is jeopardized.
Release of fish is both a direct and indirect source of genetic hazards.
Consequently, artificial propagation was judged least reliable in controlling hazards associated with acclimation and release (Table 5.3). Two components of this safeguard need work. First, operating guidelines were inconsistent with conservation guidelinesor poorly-developed. Second, decision trees have not been developed. Conservation guidelines recognized the uncertainty associated with time, place, and condition of releasing fish (Kapuscinski and Miller, 1993). However, present operating guidelines created several problems. For example, supplementation of Naches spring chinook salmon called for acclimation facilities on the Little Naches River (Anonymous, 1990). Although this site may be appropriate for imprinting Naches River spring chinook salmon, it is also very near the American River. Low levels of straying may occur naturally between neighboring wild chinook salmon in American River and Bumping River (Craig Busack,
Washington Department of Fisheries, unpublished data). However, untested acclimation procedures could increase numbers of potential Naches River salmon straying into the
American River, resulting in loss of among-population genetic diversity and ofa reserve.
Likewise, winter migration of wild spring chinook in the Yakima River isan important 168 behavioral trait (Fast et al., 1991a), but it was unclear how this will be altered in hatchery- reared salmon by release protocols. Finally, conservation guidelines recommended that the numbers of fish released be based on freshwater carrying capacity. However, actual numbers of fish appeared to be based on sample sizes necessary to detect statistical significance rather than on biological criteria (Anonymous, 1992).
No decision trees were available for situations involving release. What will be done with fish that do not choose to leave release facilities? If these fish are forcibly released, how will they be monitored. Very little baseline data existed to determine the reliability of operating guidelines for releasing fish in protecting genetic diversity ofnon- target species.
5.6.3.2.3. Management of Juvenile Migration and Habitat
Reliability of managing juvenile passage and habitat to control genetic hazards was the least of any component of artificial propagation (Table 5.3). Lack of reliability here is critical because mortality during passage through the Yakima River isa major source of poor smolt-adult survival rates (Fast et al., 1991 a).I found three major weaknesses of this proximate safeguard. First, conservation guidelines have not been well-developed. Second, operating guidelines were absentor poorly-developed. Finally, decision trees were missing.
First principles of conservation genetics and ecology have been applied to most other proximate controls, but specific guidelines are lacking for management of juvenile migration and habitat. Operating guidelines dealing withpassage, water flows, and predators are certainly available, but some are inappropriateor untested. For example, 169 one first principle is to protect or restore historical complexity of migratory patterns of target species (Currens et al., in review). Yet, although project returns of target species would increase by improving flows, the Yakima Fishery Project has an operating guideline of not affecting water in the Yakima Basin (Bonneville Power Administration, 1992).
Similarly, no appropriate proximate controls have been tested that would protect less productive, natural populations of fish from depletion due to increased natural harvest (i.e. predation) because large numbers of predators have been attracted byan abundance of hatchery-reared fish. Finally, contigency plans and decision rules have not been developed for problems associated with juvenile passage and habitat.
5.6.3.2.4. Management of Adult Migration and Harvest
Management of adult migration and harvest had relatively greater reliability to protect against extinction and loss of within-population diversity than to protect against loss of among-population diversity (Table 5.3). The higher scores for extinction and loss of within-population diversity (Appendix B) reflect intentions touse multiple-stock status- indexed harvest management that is based on protecting the less productive stocks
(Yakima Fishery Project Science Team, 1992).
A major conspicuous weaknesses of management of adult migration and harvest was the lack of sufficiently well-developed conservation guidelines to judge whether operating guidelines for harvest are appropriate. For example, although minimum Ne might be 500 for genetic reasons, it is not certain that this isan adequate number of spawners to protect against demographic risks of extinction (Figure 5.5, 5.6). American 170
River spring chinook most likely already have an Ne below 500 (Craig Busack,
Washington Department of Fisheries, unpubi. data).
Lower scores for loss of among-population diversity reflected the uncertainty and lack of decision trees associated with using a wier or other facility (Anonymous, 1992;
Yakima Fishery Project Science Team, 1992) to prevent strays from entering the
American River. Although this may prevent fish of other populations from spawning in the American River, it may also prevent American River salmon from entering the river.
This would effectively reduce Ne of the American River and could lead to American River salmon spawning with other populations.
5.6.3.2.5. Genetic Reserves
Recognition that genetic reserves are an essential component of reducing vulnerability is a maj or strength of the Yakima Fishery. The American River has been designated a reserve because it was genetically and ecologically unique in the Yakima
Basin. However, guidelines for reserves for anadromous fish are only beginning to be developed (Currens et al., in review). If this reserve were really to protect genetic diversity of American River salmon, three weaknesses need to be addressed, First, temporal and ecological dimensions of the reserve have not been defined and consequently were not protected. Second, the reserve protects only one of the potentially vulnerable and distinct populations in the Yakima River Basin. Third, American River spring chinook salmon have been fewer than any other populations (Fast et al., 1991 a) and therefore would be the most vulnerable to chance extinction (Figure 5.5). The reserve provides no useful purpose if the protected population becomes extinct. 171
5.6.3.3. Relative Vulnerability of Spring Chinook Salmon to Dfferent Genetic Hazards
The relationship between demography and chance extinction for spring chinook
salmon in the Yakima River and analysis of proximate and ultimate control mechanisms of
supplementation (Figure 5.7) both suggested that these populations are very vulnerable to
extinction and loss of within population genetic diversity. The possibility that
supplementation might lead to extinction has generally been ignored in Yakima Fishery
Project planning documents, because it was assumed that reproductivesuccess of hatchery
fish would be greater than wild fish. Likewise, extinction may have been ignored because
biologists assumed it was a lesser risk (probability) than other genetic hazards without
considering differences in potential loss. The analyses in this report suggest that these
assumptions should be reconsidered.
5.6.4. Results: Vulnerability of Yakima River Summer Steelhead
Three major problems confront management of genetic vulnerability for summer
steelhead populations in the Yakima River. First, differencesamong potentially different
populations have not been well described. Second, rainbow trout consist of multiple life-
history forms (including steelhead) for which the genetic basis and relationshipto genetic
structure are unknown. Third, the potential numbers of adult steelhead available for brood
stock from streams to be supplemented are low.
5.6.4.1. Extinction
For every 100 steelhead taken as brood stock in the Yakima River Basin, Yakima
Fishery Project data suggested that on theaverage only 61 will return. This rate of return
Yakima was the product of 80% pre-spawning survival of brood stock (Hagar, inprep; 172
700
600 >' 500
400
300
> 200
100
0 EXT LW LB D Genetic Hazards
DAP lAP HB+P HV+P UCM
Figure 5.7. Relative vulnerability of Yakima River spring chinook salmon. Codes for genetic hazards are the following: EXT = extinction; LW = loss of within population diversity; LB = loss of among population diversity; D = domestication or loss of fitness in the wild. Codes for components of vulnerability: DAP = direct effects of artificial propagation; TAP = indirect effects of artificial propagation; HB+P = juvenile habitat and passage management; HV+P = adult passage and harvest; UCM = genetic reserves. 173
Fisheries Project Operations/Procedures Manual), expected mean fecundity of 2560
(Confederated Tribes and Bands of the Yakima Indian Nation et al., 1990), 50% egg-to-
smolt (Confederated Tribes and Bands of the Yakima Indian Nation et a!,, 1990), and
0.12% smolt-to-adult survival (Yakima Fishery Project Science Team, 1992). Because
the Yakima Fishery Project has not collected data for different populations, these
calculations were applied to all potentially different populations. Unless population
growth of wild-spawning steelhead were far enough above replacement levels,
supplementation of Yakima River steelhead will lead to extinction of the populations.
Under supplementation, mean growth rate of the population was reduced. Mean
growth rates less than 1.0 indicated that the numbers of adult steelhead returning to reproduce were declining. For the short period from 1980 to 1992, the onlyyears for which good data was available, mean population growth ratewas 2.88. Under simple deterministic projections for supplementation, mean growth rate of Yakima River
steelhead was reduced to 2.0. Assuming that mean population growth rate of steelhead over the long-term was 1, mean population growth rate under supplementation would be reduced to 0.73.
Figures 5.8 and 5.9 illustrated the relative effect of this reduction of population growth rate on the frequency of extinction. At mean population growth rate sustained by unsupplemented populations over the last 12 years, probability of extinction in 100years or 30 generations was less than it was under supplementation. This was especially true when populations are as small as they are in the Yakima River. Likewise, at 5% probability of extinction, unsupplemented populations would be expectedto persist longer 174
than supplemented populations. These differenceswere meaningless, however, at iow
numbers of spawners because the expected persistence timeswere so short.
5.6.4.2. Reliability and Resilience
A reliability or resilience score of 60 or greater for proximate or ultimate control
of vulnerability at any given source of genetic hazard indicates that,over all, the essential
components of that control mechanism were available and appropriate. For Yakima
summer steelhead, the only proximate control mechanism that scored over 60 was genetic
stock identification (Table 5.4, Appendix C). Isolated scores of 100 in Table 5.4 (e.g.,
control of extinction during mating) reflected situations where the hazardwas considered
inappropriate; no control mechanism scored 100 because itwas perfect.
The same three major factors that contributed to low scores for supplementation of
spring chinook salmon also were the main factors contributing to lowscores of summer
steelhead. First, operating procedures and protocols for how conservation guidelines will be implemented did not exist, have only been superficial developed,or were inconsistent with conservation guidelines. Second,very few decision trees have been developed to indicate what the contigency plans are for failureor unanticipated results of a control mechanism. Third, planning documents indicatedno intentions to provide appropriate training to avoid type I and type II error by techniciansor biologists. Unlike spring chinook, inadequate operating procedures and protocols for steelhead partially reflected difficulty in setting refined genetic objectives until distinct populations have been identified. Like spring chinook, however, lack of appropriate training for technicians and biologists was conspicous for every source of genetic hazard. Asa new application of 175
artificial propagation, supplementation has such special problems that geneticists have
been hired to identify and characterize them. Assuming that technicians and field
biologists already have the training to implement this technology is a major weakness.
Control mechanisms of each of the potential sources of genetic hazards are discussed in
detail below.
5.6.4.2.1. Genetic Stock Identfication
Genetic stock identification had the highest reliability scores of any control
mechanism for summer steelhead, inspite of difficulties in identifying different populations
(Table 5.4). Every component was available and appropriate, except for absence of
decision trees (Appendix C). The Yakima Fishery Project has essentially postponed
making a decision about the relationship of Toppenish Creek steelhead to Satus Creek
steelhead, for example, until more data become available. However, at some point, rules
will be needed to decide whether to treat Toppenish Creek as a different population,
because the outcome of the decision may have an impact of the success of keeping Satus
Creek as a genetic refuge. Components such as baseline data, operating guidelines, and monitoring were considered available and appropriate, but not sufficient, because Yakima
Fishery Project geneticists believe that further analysis will resolve present amibiguities in
describing different populations.
5.6.4.2.2. Artficial Propagation
Two weaknesses were most prominent in the control of genetic hazards by brood
stock collection of summer steelhead. First, appropriate operating guidelines for hazards 176
1
0.9
0.8 C 0.7 0 C 0.6 w 0.5 -4-J>' 0.4 -o0 0 0.2
0.1
0 0 2 4 6 8 10 12 14 16 Population Size (Thousands)
Figure 5.8. Relative probability of extinction in 100 years for Yakima River steelhead at different spawning population sizes with and without supplementation. Solid boxes indicate unsupplemented populations at high population growth rate; plusses are supplemented populations at high population growth rate; open boxes show unsupplemented populations at a low population growth rate; triangles are supplemented populations at low population growth rate. 177
40
C35 0 0 . 30 w. 9-025 C')
o 20 o-... C) 4-J 15 0 C ci) U) U) ci) 5
I I 0 I I I 1 V 0 2 4 6 8 10 12 14 16 Population Size (Thousands)
Figure 5.9. Relative persistence in generations at 5% risk of extinction for Yakima River steelhead at different spawning population sizes with and without supplementation. Solid boxes indicate unsupplemented populations at high population growth rate; plusses are supplemented populations at high population growth rate; open boxes show unsupplemented populations at a low population growth rate; triangles are supplemented populations at low population growth rate. 178
Table 5.4. Reliability andresilience scores for proposed supplementation of Yakima River steelhead. A score of 100indicated high reliability or resilience, whereas a score of 20 indicates low reliability orresilience. Genetic hazards are 1) extinction, 2) loss of within- population genetic diversity, 3) loss of between-population genetic diversity, and 4) domestication.
Genetic Hazard
Controls Source of Hazard 1 2 3 4
Genetic stock Brood stock selection 71 71 71 71 identification
Artificial propagation Brood stock collection 40 38 47 33 Mating 100 47 40 44 Rearing 38 38 38 38
Release (direct effects) 33 33 31 38
Percent of maximum 3.6 1.6 1.6 1.5 reliability score
Release (indirect effects) 36 36 36 100 Percent of maximum 35.635.635.6 100 reliability score
Passage and Habitat Juvenile migration 36 36 31 100
Percent of maximum 35.635.6 3 1.1 100 reliability score
Passage and Harvest Adult migration 40 40 38 100 Percent of maximum 40.040.037.8 100 reliability score
Genetic reserves All of the above 53 53 53 53 Percent of maximum 53.3 53.3 53.3 53.3 reliability score 179 of loss of within-population diversity and domestication associated with proposed captive brood stock programs were lacking. Second, appropriate equipment and monitoring facilities for hazards of loss of within-population diversity and domestication associated with proposed captive brood stock programs were absent. Because so few mature steelhead return to Toppenish Creek, Upper Yakima Rvier, and Naches River, project biologists have proposed capturing smolts from individual streams and raising them in the hatchery until they can be used as brood stock (Yakima Fishery Project Science Team,
1992). Although this potentially alleviates the initial problem of having low genetically effective population size because of too few adult fish, it raises additional hazards that have not been addressed. First, no sampling guidelines existed for how to assure that collection ofjuvenile rainbow trout will be representative of wild adult steelhead.
Potentially increased reproductive success of a non-representative sample could lower Ne and reduce within-population diversity. This is especially critical because no reliable method exists for separating sympatric resident rainbow trout and steelhead as juveniles.
Likewise, no operating guidelines have been presented to control increased risk of capturing introduced, domesticated, non-native rainbow trout or their progeny, which have survived and bred with native rainbow trout (Campton and Johnston, 1985).
Furthermore, the special equipment and monitoring needs to collect a representative sample for captive brood stock and detect any departures from a representative sample were not indicated. If captive brood stock were not to be used, operating guidelines need to specify how minimum Ne will be achieved with so few adults.
The reliability of controls for mating of steelhead was the best of any source of genetic hazards associated with artificial propagation (Table 5.4). This reflected well- 180 developed conservation guidelines (Kapuscinski and Miller, 1993), operating guidelines that are consistent with conservation guidelines (Hagar, in prep.), and monitoring procedures that are being developed. A major weakness in planning for supplementation of steelhead was the intention to spawn all populations at the Nelson Springs Hatchery
(Anonymous, 1992; Hagar, in prep.). Historical evidence suggested that when different populations were kept at the same facility, gametes from the different populations were often mixed (Kinunen and Moring, 1978; Howell et al., 1985). This led to loss of among- population genetic diversity.
Control of genetic hazards during rearing of steelhead had relatively low reliability
(Table 5.4), compared to other aspects of artificial propagation. Two major areas of weakness explained these scores. First, operating guidelines for rearing the fish according to recommendations in conservation guidelines (Kapuscinski and Miller, 1993) were missing. Second, decision trees for how to respond to unexpected emergencies that might either compromise genetic goals or experimental goals have not been developed.
Experimental hypotheses and designs have been formulated for rearing, but without operating guidelines it was difficult to determine actual environments of fish in the hatchery and whether they are appropriate or sufficient. Similarly, if a juvenile, captive brood stock program were used for steelhead, operating guidelines need to be available for rearing the brood stock. Furthermore, it was not apparent from preliminary design reports that steelhead facilities were being designed with extensive captive brood stock rearing capacities, as well as juvenile rearing capacity (Anonymous, 1990).
Decision trees are crucial for rearing. The estimated 50% egg-to-smo!t survival rate for progeny of wild steelhead in the hatchery (Confederated Tribes and Bands of the 181
Yakima Indian Nation et al., 1990) indicated that fish health in the hatchery may often be compromised. The experience of this author in raising progeny of wild rainbow trout under different experimental environments suggested that when fish health is challenged, conflicts arise between experimental goals and conservation goals. A typical scenario was described earlier for spring chinook salmon.
Release of fish may have direct or indirect genetic impacts on target and non-target species. This was the least reliable component of any safeguard for steelhead in controlling genetic hazards. The most important weaknesses in strategies to release steelhead are listed below. First, operating guidelines were inconsistent with conservation guidelines or poorly-developed. Second, decision trees have not been developed. Third, adequate baseline data have not been collected to evaluate ecological effects of steelhead releases on resident rainbow trout populations.
An important omission from operating guidelines was whether steelhead smolts of different ages will be developed to mimic the complex structure of natural populations
(Busack et al., 1991; Confederated Tribes and Bands of the Yakima Indian Nation et al.,
1990) or whether they will be released as year-old fish. Kapuscinski and Miller (1993) indicated in the conservation guidelines that considerable uncertainty exists about the appropriate release of fish. However, they did recommend that numbers of fish released be based on freshwater carrying capacity. Actual numbers of steelhead to be released, however, appeared to be based on sample sizes necessary to detect statistical significance between treatments rather than on biological criteria (Anonymous, 1992).
Until careful behavioral, ecological, and genetic study is made of the aquatic communities of these drainages, it is will be impossible to estimate effects of releasing 182 large numbers of hatchery-reared fish or to develop reliable operating guidelines for supplementation. Adequate baseline data was not available, for example, to predict how rearing and release strategies may influence steelhead progeny to become resident rainbow trout. Although release was a source of genetic hazards on non-target endpoints -- such as existing resident rainbow trout populations -- reliability scores for indirect effects were actually higher for steelhead than for spring chinook salmon (Table 5.3, 5,4). In neither case did the scores indicate that controls were available and appropriate. However, the difference between species primarily reflected Yakima Fishery Project efforts to reduce vulnerability to rainbow trout by describing and monitoring steelhead-resident rainbow trout interactions. Similar efforts were missing for other species.
5.6.4.2.3. Management of Juvenile Migration and Habitat
Reliability of managing juvenile migration and habitat as a proximate safeguard against genetic hazards is crucial. Mortality during migration in the Yakima River can be a major source of poor smolt-to-adult survival (Fast et at., 1986). Because juvenile rainbow trout do not necessarily migrate towards the ocean, but rather may stray and take up freshwater residence until they mature and spawn, proximate safeguards for steelhead must also protect against loss of among-population genetic diversity. Three major weaknesses of this safeguard existed for steelhead (Appendix C). First, explicit conservation guidelines have not been developed. Second, operating guidelines were poorly-developed or missing. Third, decision trees or rules were missing.
First principles of conservation genetics and ecology have been applied to most other proximate controls, but specific guidelines for juvenile migrations and habitat were 183 lacking. Operating guidelines dealing with passage, water flows, and predators were available, but some were inappropriate or untested. For example, one first principle is to protect or restore historical complexity of migratory patterns of target species (Currens et al., in review). Yet, although projected returns of target species would be increased by improving flows, the Yakima Fishery Project has an operating guideline of not affecting water in the Yakima Basin (Bonneville Power Administration, 1992). Similarly, no appropriate proximate controls have been tested that would protect less productive, natural populations of fish from depletion due to increased natural harvest (i.e. predation) because large numbers of predators have been attracted by an abundance of hatchery- reared fish. Finally, contigency plans and decision rules have not been developed for problems associated with juvenile migration (or the lack of it) and habitat.
5.6.4.2.4. Management of Adult Migration and Harvest
Two principal weaknesses in management of adult migration and harvest explained low reliability of this safeguard (Appendix C). First, conservation guidelines were not sufficiently well-developed to judge whether operating guidelines for harvest were appropriate or sufficient. Second, baseline characterization of straying rates and patterns of geographical genetic differences among spawning aggregations have not been adequately collected to develop operating guidelines for preventing loss of among- population genetic diversity.
Unofficial documents indicated that harvest levels will be determined using a multiple-stock status-indexed harvest management that is based on protecting the less productive, reserve stocks (Yakima Fishery Project Science Team, 1992). The actual 184 critical levels that will be used were not available, however. To determine whether the values chosen are appropriate to minimize risks of extinction and loss of within-population genetic diversity, conservation guidelines need to be established. For example, although minimum N6 might be 500 for genetic reasons, this may not be an adequate number of spawners to protect against demographic risks of extinction (Figure 5.8, 5.9).
Conservation guidelines for restoring steelhead to streams where they were once abundant while preventing loss of genetic diversity among remaining anadromous and resident populations are needed to formulate appropriate operating guidelines. In addition, decision trees need to be developed. Limited allozyme data indicated that gene flow among spawning aggregations of steelhead has been more restricted than gene flow between resident rainbow trout and steelhead (Busack et al., 1991). However, inferred patterns of gene flow between resident rainbow trout and steelhead have been complicated by introduction of non-native strains of resident rainbow trout (Campton and Johnston,
1985) and construction of dams that disrupted traditional migratory life-histories and reduced Ne. Operating guidelines for using a trap to prevent hatchery-reared steelhead from breeding with the Satus Creek genetic reserve population have been suggested
(Yakima Fishery Project Science Team, 1992) but the potential effects of such a facility on
Satus Creek steelhead are unknown. Although further study may resolve some ambiguities about the relationship of steelhead spawning aggregations, it is possible that the preferred balance among resident rainbow trout and steelhead populationsmay be decided initially by non-genetic criteria. If so, then monitoring and decision trees will be crucial to allow biologists to respond appropriately to unexpect changes in the balance among resident rainbow trout and steelhead. 185
5.6.4.2.5. Genetic Reserves
A major strength of the Yakima Fishery Project has been the recognition that genetic reserves are an essential component of reducing vulnerability. Satus Creek has been designated a genetic reserve (Anonymous, 1992). However, guidelines for implementing reserves for anadromous fish are only beginning to be developed (Currens et al., in review) and no general conservation guidelines existed for the Yakima Fishery
Project. Two strengths of the designation of Satus Creek as a reserve deserved mention.
First, the reserve was based on a genetic template. Second, the reserve population, which accounts for nearly 50% of the total returns to the Yakima River Basin, will initially be protected from overharvest and has the lowest probability of extinction. In contrast, two weaknesses of using Satus Creek as planned as a reserve were the following. First, the temporal and ecological dimensions of the reserve protection were not well-defined and consequently were not protected. Second, the reserve protects only one of the potentially vulnerable and distinct populations in the Yakima Basin.
5.6.4.3. Relative Vulnerability of Summer Steelhead to Different Genetic Hazards
Both the relationship between demography and chance extinction for steelhead in the Yakima River and analysis of proximate and ultimate safeguards of supplementation
(Figure 5.10) suggested that these populations are very vulnerable to extinction and loss of within population genetic diversity. The possibility that supplementation might lead to extinction has generally been ignored in Yakima Fishery Project planning documents, because it was often assumed that reproductive success of hatchery fish will be greater than wild fish. Likewise, extinction may have been ignored because biologists assumed it 186 was a lesser risk (probability) than other genetic hazards without considering differences in potential loss. The analyses in this report suggest that these assumptions should be reconsidered.
5.6.5. Conclusions
Considered together, the results of this analysis suggest that under present plans, supplemented populations may be more vulnerable to extinction and loss of within- population genetic diversity than has been previously recognized. First, supplementation of spring chinook salmon and steelhead in the Yakima River will generally result in fewer hatchery-reared fish returning to the wild than were taken as brood stock, unless success of the program is better than present data suggest. Second, overall, proximate safeguards for reducing vulnerability were not available and appropriate. Third, lack of decision trees, decision rules, and contingency plans were a maj or weakness of proximate safeguards and the ultimate safeguard of adaptive management. Fourth, use of genetic reserves as an ultimate safeguard to reduce vulnerability is a major strength of the Yakima
Fishery Project that needs to be expanded. Finally, the Yakima Fishery Project was not ready for a Level II genetic risk assessment as it was defined by Northwest Power
Planning Council guidelines.
A major potential conflict exists between the use of the Yakima Fishery Project as experimental opportunity to test supplementation methodologies and the goal of rebuilding natural populations of salmon and steelhead in the Yakima River, if rebuilding includes maintaining the long-term fitness of the target population, and keeping ecological and genetic impacts on non-target populations within specified biological limits. 187
700
600 > 500
400
300
> 200
100
0 EXT LW LB D Genetic Hazards
DAP lAP HB+P
HV+P /UCM
Figure 5.10. Relative vulnerability of Yakima River steethead. Codes for genetic hazards are the following: EXT = extinction; LW = loss of within population diversity; LB = loss of among population diversity; D = domestication or loss of fitness in the wild. Codes for components of vulnerability: DAP = direct effects of artificial propagation; TAP = indirect effects of artificial propagation; T-IB+Pjuvenile habitat and passage management; HV+P = adult passage and harvest; UCM = genetic reserves. 188
Experiments must be allowed to fail to gain knowledge. However, supplementation in the
Yakima River must not be allowed to fail if these populations are to be rebuilt and genetic diversity of salmonids within the Columbia River Basin is to be maintained. Based on
Yakima Fishery Project data, even if pre-spawning and egg-to-smolt mortality were completely eliminated, the number of returning hatchery-reared adults from the Upper
Yakima population would just replace the numbers that were taken for brood stock.
Consequently, fate of the whole population would depend on fitness of wild fish, just as it does now. Considerable effort has gone into the developement of experimental designs to test different methods of reducing juvenile mortality of hatchery-reared fish in the Yakima
River. However, lack of explicit, well-developed operating guidelines, monitoring and evaluation plans, decision trees, and contingency plans, and emphasis on the development of operating guidelines based on statistical neeeds, provide no safeguards against failure of these experiments. 189
6. CONCLUSION
The results of my study of genetic variation in rainbow trout and coho salmon show that genetic diversity is largely organized hierarchically over space and time.
Conceptually, this is illustrated by Figure 6.1.In Chapter 2, for example, long-term evolutionary persistence of major geographical genetic groups of rainbow trout is associated with persistence of large rivers and associated basins. Within regions associated with large rivers, smaller genetically distinct groups have also existed, although they have been more vulnerable to changing landscapes (Chapter 2, Chapter 4). In contrast, at local levels genetic variation in isolated spawning aggregations can be very dynamic (Chapter 3). Human impacts on genetic organization of salmon, however, can occur at many levels from local breeding populations to metapopulations extending over whole regions (Chapter 3, Chapter 4).
A fundamental challenge for natural resource mangers is the proper identification and characterization of conservation units. In the future, if not now, the task of conserving genetic diversity with limited resources will require assessment of priorities.
By 2100, for example, the number of taxa requiring intensive management is expected to increase 100-fold (Wilcox 1990).jjjsitu conservation is the least costly tool for managing these groups, but lack of knowledge about genetic structure, its distribution over space and time, and the impacts of human intervention might be are major obstacties. Based on this research, there can be no single fundamental unit of conservation that is appropriate for widely distributed, phylopatric species such as salmon. Instead, I propose that an appropriate genetic conservation unit is the most-inclusive genetic, SPECIES
MAJOR EVOLUTiONARY GROUP
POPULATION
1,000,000 INDIVIDUAL
REDD 100,000 10,000 HEADWATE R TRIBUATARY 1000 STREAM 100 RIVER cr 10 BASIN RANGE 0
Figure 6.1. Hierarchical relationship of genetic organization over space and time. 191 geographic, and temporally persistent group of individuals of a species for which management action will not result in loss of genetic diversity of less-inclusive, less-widely distributed, and less-persistent groups that compose it. The key concept is that the long- term productivity and evolutionary potential of more-inclusive groups of salmon depend on the continued existence of multiple, less-inclusive groups, which maintain their evolutionary independence for shorter periods. No single level of organizationor stockshould be considered appropriate for all management activities.
The challenges of identifying conservation units then are two-fold. First is using appropriate genetic tools to identify the structure of local breeding populations. Once the smallest groups are identified, conservation units may be conveniently defined atmore- inclusive levels according to the potential impacts of management plans. The second challenge is assessing the risk and hazards of managing at different levels using well- defined methodology that is systematic, produces repeatable results,uses the best available information, and can be easily understood for decision making. In Chapter 5 of this thesis, I developed the conceptual framework for such risk assessment and explained why it is different from classical approaches to ecological risk assessment. Sucha framework has been lacking in fishery management. However, as I demonstrated in Chapter 5,many of the specific toolsgenetic and ecological models, fault tree analyses, and expert panelscan be borrowed from other fields. Combining knowledge of genetic population structure, evolutionary processes, and risk assessment will allow applied sciences, such as fishery science and conservation biology, to meet the challenges of the future. 192
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Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH* sAH* CK-A1 * G3PDH-1 * No. SampleName N -100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 1 GraysRiver 2001.0000.0000.0000.0000.0000.000 1740.8100.1490.040 0.0000.000 2001.0000.000 200 0.9900.010 2 Elochomanwinterstrain 2001.0000.0000.0000.000 0.0000.000 980.7240.1840.0920.0000.000 2001.0000.000 194 0.9690.031 3 BigCreekwinterstrain 2001.0000.0000.0000.000 0.0000.000 1960.9390.0610.0000.0000.000 2001.0000.000 200 0.9900.010 4 Big Creek winter strain 1901.0000.0000.0000.0000.0000.000 1900.8420.1000.0580.0000.000 1901.0000.000 190 0.9890.011 5 Big Creek winter strain 2001.0000.0000.0000.0000.0000.000 2020.9210.0400.0400.0000.000 2001.0000.000 1800.9720.028 6 BigCreekwinterstrain 1001.0000.0000.0000.0000.0000.000 980.8780.0610.0610.0000.000 1001.0000.000 1000.9800.020 7 Cowlitzlatewinterstrajn 1701.0000.0000.0000.0000.0000.000 1360.9120.0880.0000.0000.000 1981.0000.000 1660.9100.090 8 Cowlitz summer strain 1801.0000.0000.0000.0000.0000.000 1740.9200.0690.0110.0000.000 1601.0000.000 1510.9470.053 9 Cowlitzwinterstrajn 2001.0000.0000.0000.0000.0000.000 1920.8590.0990.0420.0000.000 1661.0000.000 184 0.9670.033 10 ToutleRiver 801.0000.0000.0000.0000.0000.000 1000.8600.1200.0200.0000.000 1001.0000.000 1000.9300.070 11 Coweeman River 1481.0000.0000.0000.0000.0000.000 1480.8640.0680.0680.0000.000 1481.0000.000 148 0.9800.020 12 Skamanja summer strain 1941.0000.0000.0000.0000.0000.000 1940.9790.0210.0000.0000.000 1941.0000.000 1920.9320.068 13 Skamanjasumsnerstrain 2001.0000.0000.0000.0000.0000.000 200 0.9900.0100.0000.0000.000 2001.0000.000 194 0.9180.082 14 Skamanjasummerstrain 1001.0000.0000.0000.0000.0000.000 1000.8600.1400.0000.0000.000 1001.0000.000 1000.9300.070 15 Skamaniasummerstrajn 1001.0000.0000.0000.0000.0000.000 1000.9300.0700.0000.0000.000 1001.0000.000 1000,8300.170 16 Skamaniasummerstrain 2001.0000.0000.0000.0000.0000.000 200 0.9500.0500.0000.0000.000 2001.0000.000 1900.7680.232 17 Skamaniasummerstrajn 2001.0000.0000.0000.0000.0000.000 1940.9790.0100.0100.0000.000 2001.0000.000 1960.9390.061 18 Skamania winter strain 200 0.9900.0000.0100.0000.0000.000 200 0.8700.0500.0800.0000.000 1981.0000.000 198 0.9900.010 19 Eagle Creekwinterstrain 2001.0000.0000.0000.0000.0000.000 1600.9880.0130.0000.0000.000 2001.0000.000 1020.9710.029 20 EagleCreekwinterstrain 2001.0000.0000.0000.0000.0000.000 194 0.9380.0520.0100.0000.000 1721.0000.000 1400.9570.043 21 Willamettewinterstrain 2001.0000.0000.0000.0000.0000.000 200 0.9900.0100.0000.0000.000 2001.0000.000 200 0.8500.150 22 CalapooiaRiver 1601.0000.0000.000 '0.0000.0000.000 2001.0000.0000.0000.0000.000 2001.0000.000 200 0.9300.070 23 Calapooia River 901.0000.0000.0000.0000.0000.000 901.0000.0000.0000.0000.000 941.0000.000 880.8520.148 24 Thomas Creek 1801.0000.0000.0000.0000.0000.000 1000.9800.0000.0200.0000.000 1001.0000.000 1000.9200.080 25 ThomasCreek 1101.0000.0000.0000.0000.0000.000 1100.9820.0000.0180.0000.000 1101.0000.000 800.9250.075 26 Thomas Creek 961.0000.0000.0000.0000.0000.000 48 0.958 0.0420.0000.0000.000 481.0000.000 961.0000.000 27 Wiley Creek 2001.0000.0000.0000.0000.0000.000 2000.9600.0200.0200.0000.000 2001.0000.000 200 0.7400.260 28 WileyCreek 1081.0000.0000.0000.0000.0000.000 54 0.9260.0560.0190.0000.000 541.0000.000 1080.8330.167 29 SandyRiver 2001.0000.0000.0000.0000.0000.000 1900.8680.1210.0110.0000.000 2001.0000.000 200 0.9600.040 30 HamiltonCreek 1061.0000.0000.0000.0000.0000.000 1050.8860.1140.0000.0000.000 1061.0000.000 106 0.8770.123 31 NealCreek 1001.0000.0000.0000.0000.0000.000 1000.8900.1100.0000.0000.000 1001.0000.000 100 0.9700.030 32 WindRiver 1001.0000.0000.0000.0000.0000.000 760.9470.0530.0000.0000.000 1001.0000.000 70 0.9570.043 33 WindRiver 501.0000.0000.0000.0000.0000.000 500.8200.1800.0000.0000.000 501.0000.000 500.9200.080 34 EightmileCr#1 380.9470.0260.0260.0000.0000.000 320.3130.6250.0000.0000.063 381.0000.000 381.0000.000 35 EightmileCr#2 341.0000.0000.0000.0000.0000.000 34 0.5290.3240.1470.0000.000 341.0000.000 341.0000.000 36 Fifieenmile Creek 1641.0000.0000.0000.0000.0000.000 1620.8520.0990.0490.0000.000 1641.0000.000 164 0.9880.012 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH* sAH* CK-A1 * G3PDH-1 * No. SampleName N -100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 37Fifteenmile Creek 1001.0000.0000.0000.0000.0000.000 1000.9400.0600.0000.0000.000 1001.0000.000 1000.9900.010 38Bakeoven Creek 396 0.9930.0000.0070.0000.0000.000 3720.7130.2720.0150.0000.000 3961.0000.000 396 0.9920.008 39BuckHollowCreek 3941.0000.0000.0000.0000.0000.000 2800.6030.3820.0150.0000.000 3941.0000.000 394 0.9880.012 40 Deschutesresidentstrajn 1801.0000.0000.0000.0000.0000.000 1700.6120.3880.0000.000 0.000 1801.0000.000 180 0.8940.106 41 Deschutes River 2541.0000.0000.0000.000 0.0000.000 2420.5620.434 0.0040.0000.000 2541.0000.000 254 0.9430.057 42 LowerNenaCreek 1400.9860.0000.014 0.000 0.0000.000 1060.6890.3110.0000.0000.000 1401.0000.000 1400.9930.007 43 Mid-Nena Creek 1340.977 0.0000.0230.000 0.0000.000 1180.6190.3480.0330.0000.000 1341.0000.000 134 0.9930.007 44 lJpperNena Creek 861.0000.0000.0000.000 0.0000.000 780.6540.3460.0000.0000.000 861.0000.000 861.0000.000 45 BigLogCreek 1401.0000.0000.0000.000 0.0000.000 1360.8750.1250.0000.0000.000 1401.0000.000 1401.0000.000 46 LowerEastFoleyCreek 301.0000.0000.0000.000 0.0000.000 280.8210.1790.0000.0000.000 301.0000.000 301.0000.000 47 UpperEastFoleyCreek 1521.0000.0000.0000.0000.0000.000 780.8720.1280.0000.0000.000 1521.0000.000 1521.0000.000 48 Deschutessummerstrajn 200 0.9900.0000.0100.0000.0000.000 1840.7280.2720.0000.0000.000 1860.9620.038 2000.9900.010 49 Deschutessummerstrajn 2001.0000.0000.0000.0000.0000.000 202 0.6830.2770.0400.0000.000 200 0.9600.040 2000.9900.010 50 Deschutessummerstrajn 200 0.9900.0000.0100.0000.0000.000 1840.7280.2720.0000.0000.000 2001.0000.000 2000.9950.005 51 Crooked River gorge 141.0000.0000.0000.0000.0000.000 161.0000.0000.0000.0000.000 161.0000.000 16 1.0000.000 52 LowerCrookedRiver 24 0.8750.0000.1250.0000.0000.000 281.0000.0000.0000.0000.000 281.0000.000 281.0000.000 53 BowmanDam 1660.9460.0360.0120.0000.0060.000 1681.0000.0000.0000.0000.000 1681.0000.000 1681.0000.000 54 Mckay Creek 641.0000.0000.0000.0000.0000.000 681.0000.0000.0000.0000.000 681.0000.000 681.0000.000 55 Ochoco Creek 48 0.938 0.0420.0210.0000.0000.000 661.0000.0000.0000.0000.000 661.0000.000 661.0000.000 56 Marks Creek 620.984 0.0000.0000.0000.0160.000 681.0000.0000.0000.0000.000 681.0000.000 681.0000.000 57 HorseHeavenCr 66 0.8030.0000.1970.0000.0000.000 661.0000.0000.0000.0000.000 661.0000.000 661.0000.000 58 Pine Creek 700.9710.0000.0290.0000.0000.000 721.0000.0000.000 ø000 0.000 721.0000.000 721.0000.000 59 Lookout Cr 621.0000.0000.0000.0000.0000.000 621.0000.0000.0000.0000.000 621.0000.000 621.0000.000 60 Howard Creek 1000.8000.0000.0500.0000.1500.000 741.0000.0000.0000.0000.000 741.0000.000 741.0000.000 61 FoxCanyonCr 761.0000.0000.0000.0000.0000.000 761.0000.0000.0000.0000.000 761.0000.000 761.0000.000 62 Deep Creek 721.0000.0000.0000.0000.0000.000 721.0000.0000.0000.0000.000 721.0000.000 721.0000.000 63 Deer Cr 580.9660.0000.0340.0000.0000.000 681.0000.0000.0000.0000.000 681.0000.000 681.0000.000 64 DeardorffCreek 381.0000.0000.0000.0000.0000.000 38 0.8420.1580.0000.0000.000 381.0000.000 381.0000.000 65 Deardorff Creek 301.0000.0000.0000.0000.0000.000 30 0.9330.0670.0000.0000.000 300.9330.067 301.0000.000 66 Vinegar Creek 781.0000.0000.0000.0000.0000.000 500.7200.2800.0000.0000.000 800.9880.013 781.0000.000 67 Vinegar Creek 360.9720.0000.0280.0000.0000.000 360.9170.0830.0000.0000.000 361.0000.000 361.0000.000 68 Granite Creek 841.0000.0000.0000.0000.0000.000 800.9130.0880.0000.0000.000 841.0000.000 841.0000.000 69 Meadow Creek 341.0000.0000.0000.0000.0000.000 30 0.9670.0330.0000.0000.000 341.0000.000 341.0000.000 70 Grasshopper Creek 1640.9700.0000.0060.0240.0000.000 1580.9180.0820.0000.0000.000 1641.0000.000 1341.0000.000 71 South Fork headwaters 1420.9720.0000.0280.0000.0000.000 1420.9370.0630.0000.0000.000 1421.0000.000 1420.9720.028 72 Izee FaIls 1560.9680.0000.026 0.0060.0000.000 1560.8010.1990.0000,0000.000 1561.0000.000 156 0.9620.038 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH* sAH* CK-A1 * G3PDH-1 * No. SampleName N -100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 73 SouthForkatRockpileRanc 1601.0000.0000.0000.0000.0000.000 1600.6810.3060.0000.0130.000 1601.0000.000 1600.994 0.006 74 KlickitatRiver 2001.0000.0000.0000.0000.0000.000 1880.8990.0800.0210.0000.000 2001.0000.000 1700.9000.100 75 Willow Creek 1601.0000.0000.0000.0000.0000.000 1360.7350.2650.0000.0000.000 1601.0000.000 1601.0000.000 76 NorthForkUmatillaRjver 1501.0000.000 0.0000.0000.0000.000 1500.8530.1470.0000.0000.000 1501.0000.000 1500.9730.027 77NorthForkUmatillaRjver 721.0000.0000.0000.0000.0000.000 680.7500.1910.0590.0000.000 721.0000.000 721.0000.000 78Buck Creek 501.0000.0000.0000.0000.0000.000 500.8800.1200.0000.0000.000 501.0000.000 501.0000.000 79 BuckCreek 881.0000.0000.0000.0000.0000.000 820.8290.1710.0000.0000.000 881.0000.000 880.9890.011 80Thomas Creek 481.0000.0000.0000.0000.0000.000 480.8750.1250.0000.0000.000 481.0000.000 481.0000.000 81 Thomas Creek 70 0.9860.0000.0140.0000.0000.000 66 0.8940.1060.0000.0000.000 701.0000.000 701.0000.000 82 SouthForkUmatjflaRjver 501.0000.0000.0000.0000.0000.000 420.8330.1670.0000.0000.000 501.0000.000 501.0000.000 83 SouthFork Umatilla River 66 0.9850.000 0.0150.0000.0000.000 58 0.914 0.0860.0000.0000.000 661.0000.000 661.0000.000 84 Camp Creek 461.0000.000 0.0000.0000.0000.000 46 0.8040.1300.0650.0000.000 461.0000.000 461.0000.000 85 Camp Creek 820.9630.000 0.0370.0000.0000.000 820.8410.1590.0000.0000.000 821.0000.000 820.9880.012 86 North Fork Meacham Creek 481.0000.000 0.0000.0000.0000.000 48 0.8540.1250.0210.0000.000 481.0000.000 481.0000.000 87 NorthForkMeachamCreek 900.9890.0000.0110.0000.0000.000 90 0.8890.1110.0000.0000.000 901.0000.000 901.0000.000 88 Upper Meacham Creek 481.0000.000 0.0000.0000.0000.000 48 0.8330.1670.0000.0000.000 481.0000.000 481.0000.000 89 Upper Meachain Creek 761.0000.000 0.0000.0000.0000.000 68 0.8380.1620.0000.0000.000 761.0000.000 621.0000.000 90 LowerSquawCreek 541.0000.0000.0000.0000,0000.000 54 0.8520.1300.0190.0000.000 541.0000.000 541.0000.000 91 UpperSquawCreek 1160.9480.0000.0520.0000.0000.000 1160.9310.0690.0000.0000.000 1161.0000.000 1161.0000.000 92 SquawCreek 880.9430.0000.0110.0000.0230.023 860.8950.0810.0230.0000.000 861.0000.000 860.9770.023 93 McKay Creek 481.0000.0000.0000.0000.0000.000 48 0.8130.1670.0210.0000.000 481.0000.000 481.0000.000 94McKay Creek 241.0000.0000.0000.0000.0000.000 20 0.7500.2500.0000.0000.000 241.0000.000 241.0000.000 95 East Birch Creek 500.9800.0000.0200.0000.0000.000 500.6600.3000.0400.0000.000 501.0000.000 501.0000.000 96 EastBirchCreek 800.9380.0000.0630.0000.0000.000 76 0.9210.0260.0530.0000.000 841.0000.000 801.0000.000 97PearsonCreek 440.8860.0000.1140.0000.0000.000 44 0.7270.2500.0230.0000.000 441.0000.000 441.0000.000 98PearsonCreek 880.9890.0000.0110.0000.0000.000 880.7500.2500.0000.0000.000 881.0000.000 881.0000.000 99WestBirchCreek 561.0000.0000.0000.0000.0000.000 560.8930.1070.0000.0000.000 561.0000.000 561.0000.000 100 WestBirchCreek 720.9720.0000.0280.0000.0000.000 721.0000.0000.0000.0000.000 721.0000.000 721.0000.000 101 EastForkButterCreek 500.8800.0000.1200.0000.0000.000 520.8460.1350.0190.0000.000 501.0000.000 501.0000.000 102 EastForkButterCreek 281.0000.0000.0000.0000.0000.000 68 0.7940.1760.0290.0000.000 821.0000.000 821.0000.000 103 BinghamSprings 2000.9900.0000.0100.0000.0000.000 1740.7180.2820.0000.0000.000 2001.0000.000 2001.0000.000 104 Umatillasummerstrajn 2000.9700.0000.0300.0000.0000.000 1960.8980.0310.0710.0000.000 2001.0000.000 2000.9900.010 105 Umatillasummerstrajn 3560.9940.0000.0060.0000.0000.000 2900.7520.2450.0030.0000.000 3561.0000.000 3561.0000.000 106 Touchet River 1001,0000.0000.0000.0000.0000.000 1000.7600.200 0.0400.0000.000 1001.0000.000 1001.0000.000 107 Walla Walla River 801.0000.0000.0000.0000.0000.000 800.6130.3630.0250.0000.000 801.0000.000 801.0000.000 108 LowerWhiteRiver 84 0.9640.0000.0360.0000.0000.000 720.8050.1950.0000.0000.000 841.0000.000 841.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName ADH* N -100 -65 -78 -123 -59 -50 SAJ1 N 100 85 72 112 123 CK-A1 * N 100 70 G3PDH-1 * N -100 80 112111110109 LittleBadgerCreekJordanCreekUpperLower Tygh Creek 100126136 62 1.0000.9761.000 0.000 0.0000.0240.000 0.000 0.000 0.0000.000 0.000 100116136 56 0.5700.9570.9560.732 0.4300.0430.0440.268 0.000 0.000 0.0000.000 0.000 100126136 62 1.000 0.000 100126136 0.984 620.016 0.9841.000 0.0000.016 116115114113 BarlowCreekGateRockThreemileCreek CreekCreek 116100 80 1.0000.964 0.000 0.000 0.0000.036 0.000 0.000 0.000 0.000 100116 9280 0.5600.9400.5650.700 0.4300.3000.0600.435 0.000 0.0100.000 0.0000.000 0.000 116100 80 1.000 0.000 116100 0.974 800.026 0.9880.9701.000 0.0000.0120.030 120119118117 PeshastinCreekMadRiverWellssummerstrajnFawnCreek 192100162116 0.9791.000 0.000 0.0210.000 0.000 0.000 0.000 0.000 189158108 96 0.7880.7710.7590.722 0.1800.1770.2410.269 0.052 0.0320.0000.009 0.000 0.000 192100162110 1.000 0.000 192100162116 0.9321.000 0.0680.000 124123121122 BigCanyonCreekMissionSatusSatusCreek Creek Creek 176 609896 1.000 0.000 0.000 0.000 0.000 0.000 0.000 174 0.730609084 0.6890.857 0.850 0.2590.1500.3110.143 0.0110.000 0.000 0.000 176 609896 1.0000.990 0.0000.010 176 609886 0.977 1.000 0.0000.023 127126125128 FishPahsimeroiDworshaksummerstrainMeadowCreek Creek B strain 196100146 1.000 0.000 0.000 0.000 0.000 0.000 178100144 94 0.7200.5530.6320.590 0.3990.2800.4470.368 0.0110.000 0.0000.000 0.000 196100146 1.000 0.000 196100146 0.9931.000 0.0000.007 132131130129 IndianHorseChamberlainCreekSheep CreekCreek Cr 316240102194 1.000 0.000 0.000 0.000 0.000 0.000 316240100194 0.687 0.7310.7700.771 0.0890.2200.2510.200 0.1800.0100.0620.029 0.0000.000 0.000 316240102194 1.000 0.000p.000 316240102194 1.000 0.000 136135134133 TucannonRiverJohnsonCreekSawtoothstrainSeceshRiver 226100122 1.000 0.000 0.000 0.000 0.000 0.000 0.000 214104 0.8509896 0.688 0.6220.621 0.1210.3570.3200.198 0.0280.0200.0580.115 0.0000.000 0.000 226100122 0.9911.0000.990 0.0090.0000.010 226100122 1.000 0.0000.000 140139138137 LimberFyCreekFlyTucannonRiver Creek JimCreek 100 6040 1.000 0.000 0.000 0.000 0.0000.000 0.000 100 4640 840.804 0.8100.8000.830 0.1960.1790.1750.160 0.0000.0120.0250.010 0.000 0.000 100 4060 1.0000.9901.000 0.0000.010 100 6040 0.9400.9501.000 0.0600.0500.0000.000 144143142141 LaddMeadowChickenCreekSheep Creek CreekCreek 506456 0.9801.000 0.000 0.0200.000 0.000 0.000 0.000 0.000 486444 500.8130.9580.977 0.960 0.0400.0420.1720.023 0.0000.016 0.000 0.000 506456 1,0001.000 0.000 506256 0.9600.9840.9821.000 0.0000.0400.0160.018 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH* sAH' CK-A] * G3PDH-1 * No. SampleName N -100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 145 Wallowasummerstrajn 2001.0000.0000.0000.0000.0000.000 200 0.7800.1500.0700.0000.000 200 0.990 0.010 1961.0000.000 146 Wallowa River 520.9810.000 0.0190.0000.0000.000 520.9230.0770.0000.0000.000 521.0000.000 521.0000.000 147 Wallowa River 741.0000.000 0.0000.0000.0000.000 68 0.8680.1320.0000.0000.000 741.0000.000 741.0000.000 148 LostineRiver 941.0000.0000.0000.0000.0000.000 94 0.8300.170 0.0000.0000.000 941.0000.000 941.0000.000 149 LostineRiver 501.0000.0000.0000.000 0.0000.000 48 0.8750.1250.0000.0000.000 501.0000.000 501.0000.000 150 BroadyCreek 541.0000.0000.0000.000 0.0000.000 54 0.8890.1110.0000.0000.000 520.9620.038 541.0000.000 151 Horse Creek 520.9620.0000.0380.000 0.0000.000 520.8080.1920.0000.000 0.000 521.0000.000 520.6730.327 152 Jarboe Creek 1380.9710.0000.0290.0000.0000.000 1380.7260.2740.0000.000 0.000 1381.0000.000 1381.0000.000 153 LittleLookingglassCreek 1000.9300.0000.0700.0000.0000.000 1000.8100.1900.0000.000 0.000 1001.0000.000 1001.0000.000 154 Mottet Creek 500.7200.0000.2800.0000.0000.000 501.0000.0000.0000.0000.000 501.0000.000 501.0000.000 155 Swamp Creek 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.0000.000 501.0000.000 501.0000.000 156 Cook Creek 500.9400.0000.0600.0000.0000.000 500.9400.0600.0000.0000.000 501.0000.000 501.0000.000 157 Cherry Creek 521.0000.0000.0000.0000.0000.000 521.0000.0000,0000.0000.000 521.0000.000 521.0000.000 158 Gumboot Creek 521.0000.0000.0000.0000.0000.000 480.9380.0630.0000.0000.000 521.0000.000 61.0000.000 159 Grouse Creek 561.0000.0000.0000.0000.0000.000 520.7310.2690.0000.0000.000 561.0000.000 561.0000.000 160 GrouseCreek 361.0000.0000.0000.0000.0000.000 360.8330.1390.0280.0000.000 361.0000.000 361.0000.000 161 BigSheepCreek 901.0000.0000.0000.0000.0000.000 840.7380.2380.0240.0000.000 901.0000.000 901.0000.000 162 Big Sheep Creek 741.0000.0000.0000.0000.0000.000 720.8190.1810.0000.0000.000 741.0000.000 741.0000.000 163 Imnahasumnierstrain 2001.0000.0000.0000.0000.0000.000 200 0.7900.2100.0000.0000.000 2001.0000.000 2001.0000.000 164 Niagarasummerstrain 2001.0000.0000.0000.0000.0000.000 200 0.7800.2100.0100.0000.000 2001.0000.000 2000.9900.010 165 McGraw Creek 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.0000.000 521.0000.000 520.7120.288 166 ConnerCreek 50L000 0.0000.0000.0000.0000.000 500.8400.1600.0000.0000.000 501.0000.000 501.0000.000 167 NorthPineCreek 501.0000.0000.0000.0000.0000.000 500.8400.1600.0000.0000.000 501.0000.000 501.0000.000 168 BigCreek 281.0000.0000.0000.0000.0000.000 280.8210.1790.0000.0000.000 281.0000.000 281.0000.000 169 Indian Creek 481.0000.0000.0000.0000.0000.000 36 0.9170.0830.0000.0000.000 481.0000.000 481.0000.000 170 SummitCreek 521.0000.0000.0000.0000.0000.000 520.8460.1540.0000.0000.000 521.0000.000 521.0000.000 171 Sutton Creek 361.0000.0000.0000.0000.0000.000 36 0.9170.0830.0000.0000.000 361.0000.000 361.0000.000 172 Dixie Creek 421.0000.0000.0000.0000.0000.000 38 0.7630.2370.0000.0000.000 421.0000.000 421.0000.000 173 La.stChanceCreek 441.0000.0000.0000.0000.0000.000 44 0.8180.1820.0000.0000.000 441.0000.000 441.0000.000 174 LawrenceCr(abovebarrier) 301.0000.0000.0000.0000.0000.000 24 0.7910.2090.0000.0000.000 301.0000.000 301.0000.000 175 LawrenceCr(belowbarrier) 201.0000.0000.0000.0000.0000.000 200.7000.3000.0000.0000.000 201.0000.000 201,0000.000 176 SouthForkDjxjeCreek 501.0000.0000.0000.0000.0000.000 500.9800.0200.0000.0000.000 501.0000.000 501.0000.000 177 SnowCreek 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.0000.000 521.0000.000 521.0000.000 178 BlackCanyonCreek 501.0000.0000.0000.0000.0000.000 500.6800.3200.0000.0000.000 501.0000.000 501.0000.000 179 Cottonwood Creek 521.0000.0000.0000.0000.0000.000 520.846 0.1540.0000.0000.000 521.0000.000 521.0000.000 180 Cottonwood Creek 501.0000.0000.000 0.0000.0000.000 501.0000.0000.0000.0000.000 501.0000.000 501.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH5 SAH* CK-A] * G3PDH-1 * No. SampleName N -100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 181 Hog Creek 501.0000.0000.0000.0000.0000.000 440.8670.1330.0000.0000.000 501.0000.000 501.0000.000 182 SouthForklndjanCreek 521.0000.0000.0000.0000.0000.000 520.8460.1540.0000.0000.000 521.0000.000 521.0000.000 183 DrnnerCreek 501.0000.0000.0000.0000.0000.000 500.8400.1600.0000.0000.000 501.0000.000 501.0000.000 184 CalfCreek 521.0000.0000.0000.0000.0000.000 420.9520.0480.0000.0000.000 521.0000.000 521.0000.000 185 NorthForkSquawCreek 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.0000.000 501.0000.000 501.0000.000 186 CarterCreek 521.0000.0000.0000.0000.0000.000 520.8460.1540.0000.0000.000 521.0000.000 521.0000.000 187 DiyCreek 501.0000.0000.0000.0000.0000.000 50 0.8400.1600.0000.0000.000 501.0000.000 501.0000.000 188 West Little Owyhee River 501.0000.0000.0000.0000.0000.000 46 0.8260.1740.0000.0000.000 501.0000.000 501.0000.000 189 Deep Creek 220.9090.0450.0450.0000.0000.000 201.0000.0000.0000.0000.000 281.0000.000 281.0000.000 190 Indian Creek 601.0000.000 0.0000.0000.0000.000 500.9600.0000.0400.0000.000 601.0000.000 601.0000.000 191 Bridge Creek 641.0000.0000.0000,0000.0000.000 64 0.9840.016 0.0000.0000.000 641.0000.000 641.0000.000 192 Krumbo Creek 921.0000.0000.0000.0000.0000.000 921.0000.000 0.0000.0000.000 921.0000.000 92 0.9890,011 193 Mud Creek 881.0000.0000.0000.0000.0000.000 881.0000.0000.0000.0000.000 881.0000.000 880.9550.045 194 SmythCreek 901.0000.0000.0000.0000.0000.000 900.7110.0110.2780.0000.000 901.0000.000 901.0000.000 195 UpperSawmillCreek 160.6880.0000.3130.0000.0000.000 200.9500.0500.0000.0000.000 201.0000.000 201.0000.000 196 Lower Sawmill Creek 380.6320.0000.3680.0000.0000.000 400.8750.0500.0000.0000.075 401.0000.000 401.0000.000 197 Home Creek #1 340.8530.1470.0000.0000.0000.000 301.0000.0000.0000.0000.000 401.0000.000 401.0000.000 198 Home Creek #2 200.9000.1000.0000.0000.0000.000 201.0000.0000.0000.0000.000 201.0000.000 201.0000.000 199 UpperHome Creek 281.0000.0000.0000.0000.0000.000 281.0000.0000.0000.0000.000 301.0000.000 301.0000.000 200 Augur Creek 281.0000.0000.0000.0000.0000.000 281.0000.0000.0000.0000.000 281.0000.000 281.0000.000 201 DairyCreek 321.0000.0000.0000.000 0.0000.000 320.9690.0310.0000.0000.000 321.0000.000 321.0000.000 202 Bear Creek 480.9790.0210.0000.0000.0000.000 640.9530.0470.0000.0000.000 641.0000.000 641.0000.000 203 ElderCreek 380.9740.0260.0000.0000.0000.000 360.8890.1110.0000.0000.000 381.0000.000 381.0000.000 204 Witham Creek 220.7730.2270.0000.0000.0000.000 340.6760.3240.0000.0000.000 341.0000.000 341.0000.000 205 Bridge Creek 400.8500.1250.0250.0000.0000.000 300.8670.1330.0000.0000.000 401.0000.000 401.0000.000 206 Buck Creek 600.8830.0670.0500.0000.0000.000 600.8670.0670.0000.0670.000 601.0000.000 601.0000.000 207 Beaver Creek 30 0.8330.1330.0330.0000.0000.000 301.0000.0000.0000.0000.000 301.0000.000 301.0000.000 208 Camp Creek 601.0000.0000.0000.0000.0000.000 401.0000.0000.0000.0000.000 601.0000.000 601.0000.000 209 Cox Creek 30 0.8000.2000.0000.0000.0000.000 340.9710.0290.0000.0000.000 361.0000.000 361.0000.000 210 Thomas Creek 301.0000.0000.0000.0000.0000.000 361.0000.0000.0000.0000.000 361.0000.000 361.0000.000 211 Beaver Creek 1381.0000.0000.0000.0000.0000.000 1380.2320.3990.0000.3700.000 1381.0000.000 1381.0000.000 212 Fall Creek 461.0000.0000.0000.0000.0000.000 461.0000.0000.0000.0000.000 461.0000.000 460.9350.065 213 Jenny Creek #1 780.974 0.0000.0260.0000.0000.000 780.8590.0000.0000.1410.000 781.0000.000 781.0000.000 214 JennyCreek#2 741.0000.0000.0000.0000.0000.000 680.7920.0000.0000.2080.000 741.0000.000 741.0000.000 215 JohnsonCreek#1 401.0000.0000.0000.0000.0000.000 400.7250.0000.0000.2750.000 401.0000.000 401.0000.000 216 JohnsonCreek#2 741.0000.0000.0000.0000.0000.000 66 0.6220.0300.0000.3480.000 741.0000.000 741.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ADH* &4H* CK-AJ * G3PDH-J * No. SampleName N-100 -65 -78 -123 -59 -50 N 100 85 72 112 123 N 100 70 N -100 80 217 Shoat Springs 1681.0000.0000.0000.0000.0000.000 1681.0000.0000.0000.0000.000 1681.0000.000 1680.9350.065 218 Willow Creek 1541.0000.0000.0000.0000.0000.000 154 0.9810.0060.0130.0000.000 1541.0000.000 1540.9870.013 219 Deming Creek 621.0000.0000.0000.0000.0000.000 620.5480.0000.0000.45 0.000 621.0000.000 621.0000.000 220 Paradise Creek 321.0000.0000.0000.0000.0000.000 32 0.3440.1880.0000.4690.000 321.0000.000 321.0000.000 221 Paradise Creek 201.0000.0000.0000.0000.0000.000 200.4000.1500.0000.4500.000 201.0000.000 201.0000.000 222 Deep Creek 120.7500.2500.0000.0000.0000.000 140.7140.1430.0000.1430.000 141.0000.000 41.0000.000 223 WillianisonRiver#1 40 0.9250.0750.0000.0000.0000.000 400.4750.3250.0000.2000.000 401.0000.000 401.0000.000 224 WilliamsonRiver#2 121.0000.0000.0000.0000.0000.000 120.3330.0000.0000.667.0.000 121.0000.000 121.0000.000 225 Bogus Creek 90 0.8220.1780.0000.0000.0000.000 900.9220.0780.0000.0000.000 901.0000.000 901.0000.000 226 KlamathRiver 36 0.7220.2780.0000.0000.0000.000 360.9440.0560.0000.0000.000 361.0000.000 36 0.8890.111 227 Spencer Creek 500.9200.080 0.0000.0000.0000.000 500.9600.0400.0000.0000.000 501.0000.000 50 0.9400.060 228 SpencerCreek 461.0000.0000.0000.0000.0000.000 460.9130.0870.0000.0000.000 461.0000.000 401.0000.000 229 Rock Creek 220.6820.3180.0000.0000.0000.000 221.0000.000 0.0000.0000.000 221.0000.000 22 0.9550.045 230 Wood Creek 561.0000.0000.0000.0000.0000.000 550.7450.0180.0000.2360.000 561.0000.000 561.0000.000 231 Spring Creek 520.9810.0190.0000.0000.0000.000 530.9250.0190.0000.0570.000 541.0000.000 541.0000.000 232 Spring Creek 380.9740.0260.0000.0000.0000.000 480.9170.0830.0000.0000.000 481.0000.000 481.0000.000 233 TroutCreek 500.9800.0200.0000.0000.0000.000 500.9200.0000.0400.0400.000 501.0000.000 50 0.9800.020 234 Trout Creek 820.9390.0610.0000.0000.0000.000 820.8900.024 0.0000,0850.000 821.0000.000 821.0000.000 235 HoneyCreek#1 300.9670.0000.0330.0000.0000.000 300.9330.067 0000 0.0000.000 301.0000.000 301.0000.000 236 HoneyCreek#2 321.0000.0000.0000.0000.0000.000 340.8530.147 0.0000.0000.000 321.0000.000 321.0000.000 237 NorthForkDeepCreek 301.0000.0000.0000.0000.0000.000 300.9330.0330.0000.0330.000 301.0000.000 301.0000.000 238 Deep Creek 240.7500.2500.0000.000 '0.0000.000 300.9330.067 0.0000.0000.000 301.000o.00b 301.0000.000 239 Willow Creek #1 380.8950.0000.1050.0000.0000.000 200.9000.0500.0500.000 0.000 401.0000.000 401.0000.000 240 Willow Creek #2 201.0000.0000.0000.0000.0000.000 201.0000.0000.0000.000 0.000 201.0000.000 201.0000.000 241 CapeCodstrain 1600.9940.0000.0060.0000.0000.000 1600.9870.0130.0000.000 0.000 1601.0000.000 1600.8810.119 242 OakSpringsstrain 1541.0000.0000.0000.0000.0000.000 620.9680.0320.0000.0000.000 1541.0000.000 154 0.9340.066 243 Soda Creek 800.9500.0500.0000.0000.0000.000 800.9870.0130.0000.0000.000 801.0000.000 801.0000.000 244 Coastal cutthroat trout 4341.0000.0000.0000.0000.0000.000 4340.9670.0330.0000.0000.000 4340.0001.000 4341.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName G3PDH2* N -100 150 GPI-BJ * N 100 130 25 138 145 152 GPIB2* N 100 125 25 70 GPI-A * N 100 110 93 432 1 BigBigCreekwinterstrainElochomanwjnterstrajnGraysRiver Creek winter strain 200190194 1.000 0.000 200190 1.0001.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 190200 1.000 0.0000.000 0.000 0.000 190200 0.9800.990 1.000 0.000 0.0000.0200.010 8765 CowljtzsummerstrajnCowlitzlatewinterstrainBigCreekwinterstrainBigCreekwmterstrain 151166100180 1.000 0.000 180182100196 1.000 0.000 0.000 0.000 0.000 0.0000.000 180182100196 1.000 0.000 0.000 0.000 180182100196 0.9780.9621.000 0.000 0.0220.0380.000 121110 9 SkamanjasunmerstrajnCoweemanRiverToutleCowljtz River winter strain 192148100184 1.0001.000 0.000 194148100190 1.0001.000 0.0000.000 0.000 0.0000.000 0.0000.000 0.000 194148100190 1.0001.000 0.000 0.0000.000 0.000 194148100190 0.948 0.8920.9600.958 0.0000.0070.010 0.0520.1010.0300.042 16151413 SkamaniaSkamanjaSkamanjasummerstrain summer strain 190100194 1.000 0.000 190100 96 1.000 0.000 0.000 0.000 0.000 0.000 190100 96 1.000 0.000 0.000 0.000 190 9698 0.9210.9270.9180.958 0.0000.073 0.0790.0000.0820.042 20191817 EagleCreekwinterstrainEagleSkamania Creek winter wintersummer strain strain strain 140102198196 1.000 0.000 200176160 1.000 0.000 0.0000.000 0.000 0.000 0.000 200176160 1.000 0.000 0.000 0.000 200176160194 0.990 0.938 1.000 0.000 0.0000.0100.062 24232221 ThomasCalapooiaWillamettewjnterstrain Creek River 100200 88 1.000 0.000 200 0.86094 1.000 0.0000.140 0.000 0.000 0.000 0.0000.000 200 94 1.000 0.000 0.000 0.000 200 94 1.0001,000 0.000 0.000 28272625 WileyThomasThomasCreek Creek Creek 108200 9680 1.000 0.000 200110 5448 1.000 0.000 0.000 0.000 0.000 0.0000.000 200110 5448 1.000 0.000 0.000 0.000 200110 5448 1.000 0.000 0.000 32313029 NealWindRiverHamiltonSandy Creek River Creek 200100106 70 1.000 0.000 200100106 0.9100.9911.000 0.000 0.0900.0000.009 0.000 0.000 0.000 100106200 1.000 0.0000.000 0.000 0.000 100200106 0.9200.9811.000 0.000 0.0800.0000.019 36353433 FifteenmileEightmileCr#2EightmileCr#1WindRiver Creek 164 343850 1.000 0.000 164 345018 0.9601.000 0.000 0.0000.040 0.000 0.000 0.000 164 0.957343850 1.000 0.0430.0000.000 0.000 0.000 164 343850 0.9401.000 0.000 0.0000.060 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map G3PDH2* GPI-B1 * GPI-B2 * GPI-A * No. Sample Name N -100 150 N 100 130 25 138 145 152 N 100 125 25 70 N 100 110 93 37Filteenmile Creek 1001.0000.000 1001.0000.0000.0000.0000.0000.000 960.9480.0520.0000.000 1001.0000.0000.000 38Bakeoven Creek 3961.0000.000 3961.0000.0000.0000.0000.0000.000 3961.0000.0000.0000.000 3961.0000.0000.000 39BuckHollowCreek 3941.0000.000 3941.0000.0000.0000.0000.000 0.000 3941.0000.0000.0000.000 3941.0000.0000.000 40Deschutesresidentstrain 1801.0000.000 1801.0000.0000.0000.0000.0000.000 1801.0000.0000.0000.000 1801.0000.0000.000 41 Desehutes River 2541.0000.000 2541.0000.0000.0000.0000.000 0.000 2541.0000.0000.0000.000 2541.0000.0000.000 42LowerNenaCreek 1401.0000.000 1401.0000.0000.0000.0000.000 0.000 1401.0000.0000.0000.000 1401.0000.0000.000 43 Mid-Nena Creek 1341.0000.000 1341.0000.0000.0000.0000.0000.000 1341.0000.0000.0000.000 1341.0000.0000.000 44UpperNenaCreek 861.0000.000 861.0000.0000.0000.0000.0000.000 861.0000.0000.0000.000 861.0000.0000.000 45 BigLogCreek 1401.0000.000 1401.0000.0000.0000.0000.0000.000 1401.0000.0000.0000.000 1401.0000.0000.000 46 LowerEastFoleyCreek 301.0000.000 301.0000.0000.0000.0000.0000.000 301.0000.0000.0000.000 301.0000.0000.000 47UpperEastFoleyCreek 1521.0000.000 1521.0000.0000.0000.0000.0000.000 1521.0000.0000.0000.000 1521.0000.0000.000 48 Deschutessummerstram 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 200 0990 0.0000.010 49 Deschutessummerstrain 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 50 Deschutessummerstrain 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 51 CrookedRivergorge 161.0000.000 161.0000.0000.0000.0000.0000.000 161.0000.0000.0000.000 161.0000.0000.000 52 Lower CrookedRiver 281.0000.000 281.0000.0000.0000.0000.0000.000 281.0000.0000.0000.000 180.8330.0000.167 53 Bowman Dam 1681.0000.000 1681.0000.0000.0000.0000.0000.000 1681.0000.0000.0000000 1700.9760.000 0.024 54 Mckay Creek 681.0000.000 681.0000.0000.0000.0000.0000.000 681.0000.0000.0000.000 680.9850.0000.015 55 Ochoco Creek 661.0000.000 661.0000.0000,0000.0000.0000.000 661.0000.0000.0000.000 621.0000.0000.000 56 Marks Creek 681.0000.000 681.0000.0000.0000.0000.0000.000 681.0000.0000.0000.000 661.0000.0000.000 57 Horse HeavenCr 661.0000.000 661.0000.0000.0000.0000.0000.000 661.0000.0000.0000.000 66 0.9850.0000.015 58 Pine Creek 721.0000.000 721.0O00.0000.0000.0000.0000.000 721.000 0000 0.0000.000 421.0000.0000.000 59 Lookout Cr 621.0000.000 621.0000.0000.0000.0000.0000.000 621.0000.0000.0000.000 500.7600.0000.240 60 Howard Creek 741.0000.000 741.0000.0000.0000.0000.0000.000 741.0000.0000.0000.000 980.7550.0000.245 61 FoxCanyonCr 761.0000.000 761.0000.0000.0000.0000.0000.000 761.0000.0000.0000.000 66 0.9550.0000.045 62 Deep Creek 721.0000.000 721.0000.0000.0000.0000.0000.000 721.0000.0000.0000.000 62 0.8390.0000.161 63 DeerCr 681.0000.000 681.0000.0000.0000.0000.0000.000 681.0000.0000.0000.000 301.0000.0000.000 64 DeardorffCreek 381.0000.000 381.0000.0000.0000.0000.0000.000 381.0000.0000.0000.000 381.0000.0000.000 65 DeardorffCreek 301.0000.000 301.0000.0000.0000.0000.0000.000 301.0000.0000.0000.000 301.0000.0000.000 66 Vinegar Creek 801.0000.000 801.0000.0000.0000.0000.0000.000 801.0000.0000.0000.000 801.0000.0000.000 67 Vinegar Creek 361.0000.000 361.0000.0000.0000.0000.0000.000 361.0000.0000.0000.000 361.0000.0000.000 68 Granite Creek 841.0000.000 841.0000.0000.0000.0000.0000.000 841.0000.0000.0000.000 841.0000.0000.000 69 Meadow Creek 341.0000.000 341.0000.0000.0000.0000.0000.000 341.0000.0000.0000.000 341.0000.0000.000 70 Grasshopper Creek 1641.0000.000 1641.0000.0000.0000.0000.0000.000 1641.0000.0000.0000.000 1641.0000.0000.000 71 SouthForkheadwaters 1421.0000.000 1421.0000.0000.0000.0000.0000.000 1421.0000.0000.0000.000 1421.0000.0000.000 72 Izee Falls 1561.0000.000 1561.0000.0000.0000.0000.0000.000 1560.9740.0000.0260.000 1521.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map G3PDH-2 * GPI-B1 * GPIB2* GPI-A * No. SampleName N -100 150 N 100 130 25 138 145 152 N 100 125 25 70 N 100 110 93 73 SouthForkatRockpileRanc 1601.0000.000 1601.0000.0000.0000.0000.0000.000 1601.0000.0000.0000.000 1601.0000.0000.000 74 Klickitat River 1701.0000.000 2001.0000.0000.0000.0000.000 0.000 2001.0000.0000.0000.000 200 0.9800.0150.005 75 Willow Creek 1601.0000.000 1601.0000.0000.0000.0000.0000.000 1601.0000.0000.0000.000 1601.0000.0000.000 76 NorthForkUmatjllaRiver 1501.0000.000 1501.0000.0000.0000.0000.0000.000 1501.0000.0000.0000.000 1501.0000.0000.000 77NorthForkUmatillaRiver 721.0000.000 721.0000.0000.0000.0000.000 0.000 721.0000.0000.0000.000 721.0000.0000.000 78Buck Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 79 BuckCreek 881.0000.000 881.0000.0000.0000.0000.0000.000 881.0000.0000.0000.000 880.9890.0000.011 80 Thomas Creek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 48 0.9790.0000.02 1 81 Thomas Creek 701.0000.000 701.0000.0000.0000.0000.0000.000 701.0000.0000.0000.000 700.9710.0000.029 82 SouthForkUmatillaRjver 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 83 SouthForkUmatillaRjver 661.0000.000 661.0000.0000.0000.0000.0000.000 661.0000.0000.0000.000 661.0000.0000.000 84 Camp Creek 461.0000.000 461.0000.0000.0000.0000.0000.000 461.0000.0000.0000.000 461.0000.0000.000 85 Camp Creek 821.0000.000 821.0000.0000.0000.0000.0000.000 821.0000.0000.0000.000 821.0000.0000.000 86 NorthForkMeachamCreek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 481.0000.0000.000 87 NorthForlcMeachamCreek 901.0000.000 901.0000.0000.0000.0000.0000.000 901.0000.0000.0000.000 901.0000.0000.000 88 UpperMeachamCreek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 481.0000.0000.000 89 UpperMeachamCreek 761.0000.000 761.0000.0000.0000.0000.0000.000 761.0000.0000.0000.000 481.0000.0000.000 90 LowerSquawCreek 541.0000.000 541.0000.0000.0000.0000.0000.000 541.0000.0000.0000.000 541.0000.0000.000 91 UpperSquawCreek 1161.0000.000 1161.0000.0000.0000.0000.0000.000 1161.0000.0000.0000.000 1160.9830.0090.009 92 Squaw Creek 861.0000.000 861.0000.0000.0000.0000.0000.000 861.0000.0000.0000.000 861.0000.0000.000 93 McKay Creek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 481.0000.0000.000 94 McKayCreek 241.0000.000 241.0000.0000.0000.0000.0000.000 241.0000.0000.0000.000 241.0000.0000.000 95 EastBjrchCreek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 96 EastBirchCreek 841.0000.000 841.0000.0000.0000.0000.0000.000 841.0000.0000.0000.000 800.9750.0000.025 97 PearsonCreek 441.0000.000 441.0000.0000.0000.0000.0000.000 441.0000.0000.0000.000 441.0000.0000.000 98 Pearson Creek 881.0000.000 881.0000.0000.0000.0000.0000.000 881.0000.0000.0000.000 881.0000.0000.000 99 West Birch Creek 561.0000.000 561.0000.0000.0000.0000.0000.000 561.0000.0000.0000.000 561.0000.0000,000 100 West Birch Creek 721.0000.000 721.0000.0000.0000.0000.0000.000 721.0000.0000.0000.000 581.0000.0000.000 101 East Fork Butter Creek 501.0000.000 501.0000.0000.0000.0000,0000.000 501.0000.0000.0000.000 501.0000.0000.000 102 East Fork Butter Creek 821.0000.000 821.0000.0000.0000.0000.0000.000 821.0000.0000.0000.000 821.0000.0000.000 103 Bingham Springs 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 104 Umatilla summer strain 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 105 Umatillasummerstrain 3561.0000.000 3561.0000.0000.0000.0000.0000.000 3561.0000.0000.0000.000 3561.0000.0000.000 106 Touchet River 1001.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.000 1001.0000.0000.000 107 WallaWallaRiver 801.0000.000 801.0000.0000.0000.0000.0000.000 801.0000.0000.0000.000 801.0000.0000.000 108 Lower White River 841.0000.000 841.0000.0000.0000.0000.0000.000 841.0000.0000.0000.000 841.0000,0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName G3PDH2* N -100 150 GPI-B1 * N 100 130 25 138 145 152 GPIB2* N 100 125 25 70 GPI-A * N 100 110 93 112111110109 LittleBadgerCreekJordanCreekUpperTyghCreekLowerTyghCreek 100126136 62 1.000 0.000 100126136 62 1.000 0.000 0.000 0.000 0.000 0.0000.000 100126136 62 1.000 0.000 0.000 0.000 100126136 62 1.0001.000 0.000 0.000 116115114113 BarlowCreekGateCreekRockCreekThreemileCreek 116100 80 1.000 0.000 116100 80 1.000 0.000 0.000 0.000 0.000 0.0000.000 116100 80 0.8880.9801.000 0.000 0.1120.0000.020 0.000 116100 80 1.0001.000 0.000 0.000 120119118117 PeshastinCreekMadRiverWellssummerstrainFawnCreek 192100162116 1.000 0.0000.000 192100162116 1.000 0.000 0.000 0.000 0.000 0.0000.000 100162116192 1.000 0.000 0.000 0.000 192100163116 0.957 0.9691.0000.975 0.0310.0000.0120.043 0.0000.012 124123122121 BigCanyonCreekMissionSatus CreekCreek 176 609886 1.000 0.0000.000 176 609896 1.000 0.000 0.000 0.000 0.000 0.000 176 609896 1.000 0.000 0.000 0.000 176 609896 1.000 0.000 0.000 128127126125 MeadowCreekFishPahsimeroiBDworshak Creek summer strain strain 196100146 1.000 0.0000.000 194100146 94 1.000 0.000 0.000 0.000 0.000 0.0000.000 100146194 1.000 0.0000.000 0.000 0.0000.000 100146194 0.990 1.000 0.000 0.0100.000 132131130129 IndianCreekHorseChamberlainSheep Creek Cr Creek 316240102194 1.000 0.000 316240100194 0.972 0.9901.000 0.0280.0000.010 0.000 0.000 0.000 0.000 316240102194 1.000 0.000 0.000 0.000 316240100194 0.9890.992 0.990 1.000 0.0110.0000.010 0.0000.008 136135134133 TucannonSawtoothJohnsonSecesh Creek strainRiver 226100122 1.000 0.000 226100122 1.0001.000 0.000 0.000 0.000 0.000 0.000 226100122 1.000 0.000 0.000 0.000 226100122 0.9921.000 0.0000.008 0.000 140139138137 LimberJimCreekFyCreekFlyTucannon Creek River 100 6040 1.000 0.000 100 6040 1.000 0.000 0.000 0.000 0.000 0.000 100 6040 1.000 0.000 0.0000.000 0.000 100 6040 1.000 0.000 0.000 144143142141 LaddMeadowChickenSheep Creek Creek Creek 506456 1.000 0.000 506456 1.000 0.000 0.000 0.000 0.000 0.000 506456 1.000 0.000 0.000 0.000 506456 0.9800.9840.9821.000 0.0000.020 0.0000.0160.018 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map G3PDH2* GPI-B1 * GPIB2* GPI-A * No. SampleNarne N -100 150 N 100 130 25 138 145 152 N 100 125 25 70 N 100 110 93 145 Wallowa summer strain 1961.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 146 Wallowa River 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 147 Wallowa River 741.0000.000 741.0000.0000.0000.0000.0000.000 741.0000.0000.0000.000 74L000 0.0000.000 148 Lostine River 941.0000.000 941.0000.0000.0000.0000.0000.000 941.0000.0000.0000.000 941.0000.0000.000 149 Lostine River 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 150 Broady Creek 541.0000.000 541.0000.0000.0000.0000.0000.000 541.0000.0000.0000.000 541.0000.0000.000 151 Horse Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 152 Jarboe Creek 1381.0000.000 1381.0000.0000.0000.0000.0000.000 1381.0000.0000.0000.000 1381.0000.0000.000 153 LittleLookingglassCreek 1001.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.000 1001.0000.0000.000 154 Mottet Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 155 Swamp Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 156 Cook Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 500.9800.0000.020 157 Cherry Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 158 Gumboot Creek 61.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 159 Grouse Creek 561.0000.000 561.0000.0000.0000.0000.0000.000 561.0000.0000.0000.000 561.0000.0000,000 160 Grouse Creek 361.0000.000 361.0000.0000.0000.0000.0000.000 361.0000.0000.0000.000 361.0000.0000.000 161 Big Sheep Creek 901.0000.000 901.0000.0000.0000.0000.0000.000 901.0000.0000.0000.000 901.0000.0000.000 162 Big Sheep Creek 741.0000.000 741.0000.0000.0000.0000.0000.000 741.0000.0000.0000.000 741.0000.0000.000 163 Jmnahasummerstrajn 2001.0000.000 2000.9000.1000.0000.0000.0000.000 2001.0000.0000.0000.000 2001.0000.0000.000 164 Niagarasummerstrain 2001.0000.000 2001.0000.0000.0000.0000.0000.000 2001.0000.0000.000 0.000 1900.9680.0320.000 165 McGraw Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 166 CoimerCreek 501.0000.000 501,0000.0000.0000.0000.000' 0.000 501.0000.0000.0000.000 501.0000.0000.000 167 North Pine Creek 500.9800.020 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 500.9400.0000.060 168 Big Creek 281.0000.000 281.0000.0000.0000.0000.0000.000 281.0000.0000.0000.000 281.0000.0000.000 169 Indian Creek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 481.0000.0000.000 170 Summit Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 171 Sutton Creek 361.0000.000 361.0000.0000.0000.0000.0000.000 361.0000.0000.0000.000 361.0000.0000.000 172 Dixie Creek 421.0000.000 421.0000.0000.0000.0000.0000.000 421.0000.0000.0000.000 421.0000.0000.000 173 Last Chance Creek 441.0000.000 441.0000.0000.0000.0000.0000.000 441.0000.0000.0000.000 441.0000.0000.000 174 Lawrence Cr (above barrier) 301.0000.000 301.0000.0000.0000.0000.0000.000 30 1.0000.0000.000 0.000 301.0000.0000.000 175 LawrenceCr(belowban-ier) 201.0000.000 201.0000.0000.0000.0000.0000.000 201.0000.0000.0000.000 201.0000.0000.000 176 South Fork Dixie Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 177 Snow Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 178 BlackCanyonCreek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 179 Cottonwood Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 180 Cottonwood Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbowtrout.
Map G3PDH-2 * GPI-B1 * GPIB2* GPI-A * No. SampleName N -100 150 N 100 130 25 138 145 152 N 100 125 25 70 N 100 110 93 181 Hog Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 182 SouthForkjndjanCreek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 183 Dinner Creek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 184 CalfCreek 521.0000.000 521.0000.0000.0000.0000.000 0.000 521.0000.0000.0000.000 521.0000.0000.000 185 NorthForkSquawCreek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 186 Carter Creek 521.0000.000 521.0000.0000.0000.0000.0000.000 521.0000.0000.0000.000 521.0000.0000.000 187 DryCreek 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 188 WestLittleowyheeRiver 501.0000.000 501.0000.0000.0000.0000.0000.000 501.0000.0000.0000.000 501.0000.0000.000 189 Deep Creek 281.0000.000 281.0000.0000.0000.0000.0000.000 281.0000.0000.0000.000 281.0000.0000.000 190 Indian Creek 601.0000.000 22 0.864 0.0000.0000.1360.0000.000 601.0000.0000.0000.000 221.0000.0000.000 191 Bridge Creek 641.0000.000 641.0000.0000.0000.0000.0000.000 641.0000.0000.0000.000 641.0000.0000.000 192 Krumbo Creek 921.0000.000 921.0000.0000.0000.0000.0000.000 92 0.9780.0220.0000.000 921.0000.0000.000 193 Mud Creek 881.0000.000 881.0000.0000.0000.0000.0000.000 881.0000.0000.0000.000 881.0000.0000.000 194 Smyth Creek 901.0000.000 901.0000.0000.0000.0000.0000.000 901.0000.0000.0000.000 901.0000.0000.000 195 Upper Sawmill Creek 201.0000.000 181.0000.0000.0000.0000.0000.000 201.0000.0000.0000.000 201.0000.0000.000 196 Lower Sasvmjll Creek 401.0000.000 401.0000.0000.0000.000 0.0000.000 401.0000.0000.0000.000 401.0000.0000.000 197 Home Creek #1 401.0000.000 401.0000.0000.0000.0000.0000.000 401.0000.0000.0000.000 401.0000.0000.000 198 Home Creek #2 201.0000.000 201.0000.0000.0000.0000.0000.000 201,0000.0000.0000.000 201.0000.0000.000 199 Upper Home Creek 301.0000.000 301.0000.0000.0000.0000.0000.000 301.0000.0000.0000.000 301.0000.0000.000 200 Augur Creek 281.0000.000 280.5360.0000.0000.4640.0000.000 281.0000.0000.0000.000 281.0000.0000.000 201 Dairy Creek 321.0000.000 320.4690.0000.0000.5310.0000.000 321.0000.0000.0000.000 321.0000.0000.000 202 Bear Creek 641.0000.000 00.0000.0000.0000.0000.0000.000 641.0000.0000.0000.000 641,0000.0000.000 203 Elder Creek 381.0000.000 280.2500.0000.0000.7500.0000.000 381.0000.0000.0000.000 381.0000.0000.000 204 Witham Creek 341.0000.000 00.0000.0000.0000.0000.0000.000 341.0000.0000.0000.000 341.0000.0000.000 205 Bridge Creek 401.0000.000 401.0000.0000.0000.0000.0000.000 401.0000.0000.0000.000 401.0000.0000.000 206 Buck Creek 601.0000.000 580.9480.0000.0000.0520.0000.000 601.0000.0000.0000.000 601.0000.0000.000 207 Beaver Creek 301.0000.000 30 0.2000.0000.0000.8000.0000.000 301,0000.0000.0000.000 301.0000.0000.000 208 Camp Creek 601.0000.000 600.0330.000 0.0000.9670.0000.000 601.0000.0000.0000.000 601.0000.0000.000 209 Cox Creek 361.0000.000 36 0.0000.000 0.0001.0000.0000.000 361.0000.0000.0000.000 361.0000.0000.000 210 Thomas Creek 361.0000.000 36 0.0280.0000.0000.9720.0000.000 361.0000.0000.0000.000 360.9440.0560.000 211 Beaver Creek 1381.0000.000 1381.0000.0000.0000.0000.0000.000 1381.0000.0000.0000.000 1381.0000.0000.000 212 Fall Creek 461.0000.000 461.0000.000 0.0000.0000.0000.000 461.0000.0000.0000.000 461.0000.0000.000 213 Jenny Creek #1 781.0000.000 781.0000.0000.0000.0000.0000.000 781.0000.0000.0000.000 781.0000.0000.000 214 JennyCreek#2 741.0000.000 741.0000.0000.0000.0000.0000.000 741.0000.0000.0000.000 741.0000.0000.000 215 JohnsonCreek#1 401.0000.000 401.0000.0000.0000.0000.0000.000 401.0000.0000.0000.000 401.0000.0000.000 216 JohnsonCreek#2 741.0000.000 741.0000.0000.0000.0000.0000.000 741.0000.0000.0000.000 741.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map G3PDH-2 * GPI-B1 * GPIB2* GPI-A * No. SampleName N -100 150 N 100 130 25 138 145 152 N 100 125 25 70 N 100 110 93 217 Shoat Springs 1681.0000.000 1681.0000.0000.0000.0000.0000.000 1681.0000.0000.0000.000 1681.0000.0000.000 218 Willow Creek 1541.0000.000 1541.0000.0000.0000.0000.0000.000 1541.0000.0000.0000.000 1541.0000.0000.000 219 DemingCreek 621.0000.000 580.8790.0000.0000.0000.1210.000 581.0000.0000.0000.000 581.0000.0000.000 220 Paradise Creek 321.0000.000 32 0.9380.0000.0000.0000.0630.000 321.0000.0000.0000.000 321.0000.0000.000 221 Paradise Creek 201.0000.000 201.0000.0000.0000.0000.0000.000 201.0000.0000.0000.000 201.0000.0000.000 222 Deep Creek 41.0000.000 41.0000.0000.0000.0000.0000.000 41.0000.0000.0000.000 41.0000.0000.000 223 Williamson River #1 401.0000.000 400.9750.0000.0000.0000.0250.000 401.0000.0000.0000.000 401.0000.0000.000 224 Williamson River #2 121.0000.000 121.0000.0000.0000.0000.0000.000 121.0000.0000.0000.000 121.0000.0000.000 225 Bogus Creek 901.0000.000 901.0000.0000.0000.0000.0000.000 901.0000.0000.0000.000 901.0000.0000.000 226 KlamathRiver 361.0000.000 36 0.9720.0000.0000.0000.0280.000 361.0000.0000.0000.000 361.0000.0000.000 227 Spencer Creek 501.0000.000 500.9400.0000.0000.0000.0600.000 500.9800.0000.0000.020 501.0000.0000.000 228 Spencer Creek 401.0000.000 241.0000.0000.0000.0000.0000.000 461.0000.0000.0000.000 461.0000.0000.000 229 Rock Creek 221.0000.000 220.9090.0000.0000.0000.0910.000 220.9090.0000.0000.091 221.0000.0000.000 230 Wood Creek 561.0000.000 561.0000.0000.0000.0000.0000.000 561.0000.0000.0000.000 561.0000.0000.000 231 SpringCreek 541.0000.000 541.0000.0000.0000.0000.0000.000 541.0000.0000.0000.000 541.0000,0000.000 232 Spring Creek 481.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.000 481.0000.0000.000 233 Trout Creek 501.0000.000 500.9800.0000.0000.0000.0200.000 501.0000.0000.0000.000 501.0000.0000.000 234 Trout Creek 821.0000.000 821.0000.0000.0000.0000.0000.000 821.000 0000 0.0000.000 821.0000.0000.000 235 Honey Creek #1 301.0000.000 280.2500.0000.0000.7500.0000.000 301.0000.0000.0000.000 301.0000.0000.000 236 Honey Creek #2 321.0000.000 320.1250.0000.0000.8750.0000.000 321.0000.0000.0000.000 321.0000.0000.000 237 North Fork Deep Creek 301.0000.000 00.0000.0000.0000.0000.0000.000 301.0000.0000.000 0000 301.0000.0000.000 238 DeepCreek 301.0000:000 300.4000.0000.0000.5670.0330.000 301.0000.000' 0.0000.000 301.0000.0000.000 239 WillowCreek#1 401.0000.000 360.4440.0000.0000.4440.1110.000 401.0000.0000.0000.000 401.0000.0000.000 240 Willow Creek #2 201.0000.000 200.2500.0000.0000.7000.0500.000 201.0000.0000.0000.000 201.0000.0000.000 241 Cape Cod strain 1601.0000.000 1601.0000.0000.0000.0000.0000.000 1601.0000.0000.0000.000 1601.0000.0000.000 242 Oak Springs strain 1541.0000.000 1541.0000.0000.0000.0000.0000.000 1541.0000.0000.0000.000 1541.0000.0000.000 243 Soda Creek 801.0000.000 801.0000.0000.0000.0000.0000.000 801.0000.0000.0000.000 801.0000.0000.000 244Coastalcutthroattrout 4341.0000.000 434 0.9220.0000.0000.0000.0000.078 4341.0000.0000.0000.000 4341.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-1 * nIDHP2* sIDHP1,2* LDHB2* No. SampleName N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 1 GraysRiver 20010000.0000.000 2001.0000.000 0.000 3750.6910.1200.0000.1890.0000.000 2000.8000.2000.000 2 Elochomanwinterstrain 2001.0000.0000.000 2001.0000.000 0.000 356 0.7500.1290.0110.1100.0000.000 1980.8480.1520.000 3 Big Creek winter strain 2001.0000.0000.000 2001.0000.0000.000 392 0.6990.1400.0050.1560.0000.000 2000.9600.0400.000 4 BigCreekwinterstrain 1901.0000.0000.000 1901.0000.0000.000 284 0.6410.1200.0110.2290.0000.000 1840.9510.0490.000 5 BigCreekwinterstrain 2001.0000.0000.000 2001.0000.0000.000 380 0.6390.2000.0610.1000.0000.000 200 0.9200.0800.000 6 Big Creek winter strain 1001.0000.0000.000 1001.0000.0000.000 188 0.6490.1220.080 0.1490.0000.000 1000.8100.1700.020 7 Cowlitzlatewinterstrajn 1981.0000.0000.000 1981.0000.0000.000 360 0.6810.1190.0110.1890.0000.000 1980.8990.1010.000 8 Cowljtzsunjmerstrajn 1601.0000.0000.000 1601.0000.0000.000 352 0.6590.1390.0110.1900.0000.000 1800.8780.1220.000 9 Cowlit.zwinterstrajn 1661.0000.0000.000 1661.0000.0000.000 400 0.6500.1600.0200.1700.0000.000 200 0.9000.1000.000 10 ToutleRiver 1001.0000.0000.000 1001.0000.0000.000 1560.6090.1920.0190.1790.0000.000 1000.8000.2000.000 11 CoweemanRiver 1481.0000.0000.000 1481.0000.0000.000 2880.6910.1600.0030.1460.0000.000 1480.8720.1280.000 12 Skamaniasunimerstrajn 1941.0000.0000.000 1941.0000.0000.000 356 0.6990.1800.0200.1010.0000.000 1920.8020.1980.000 13 Skamaniasummerstrain 2001.0000.0000.000 2001.0000.0000.000 388 0.6210.1910.0490.1390.0000.000 200 0.8800.1200.000 14 Slcamaniasummerstrain 1001.0000.000 0000 1001.0000.0000.000 200 0.6600.1500.0200.1700.0000.000 1000.8100.1900.000 15 Skamaniasummerstrain 1001.0000.0000.000 1001.0000.0000.000 177 0.7060.1810.0110.1020.0000.000 1000.7000.3000.000 16 Skamaniasummerstrajn 2001.0000.0000.000 2001.0000.0000.000 380 0.6390.2110.0050.1450.0000.000 1980.7980.2020.000 17 Skamaniasummerstrain 2001.0000.0000.000 2001.0000.0000.000 3050.6490.1900.0100.1510.0000.000 200 0.800 0200 0000 18 Skamaniawinterstrain 1981.0000.0000.000 1981.0000.0000.000 2760.6810.1090.0290,1810.0000.000 1980.9090.0910.000 19 EagleCreekwinterstrain 2001.0000.0000.000 2001.0000.0000.000 3750.7010.1200.0290.1490.0000.000 1600.7810.2190.000 20 EagleCreekwintersti-ain 1721.0000.0000.000 1721.0000.0000.000 2800.6110.1390.0610.1890.0000.000 1820.9120.0880.000 21 Willamettewinterstrain 2001.0000.0000.000 2001.0000.0000.000 377 0.6390.1300.0210.2100.0000.000 200 0.5300.4700.000 22 CalapooiaRiver 2001.0000.0000.000 2001.0000.0000.000 2720.7390.0400.0110.2100.0000.000 1960.4080.5920.000 23 Calapooia River 941.0000.0000.000 941.0000.0000.000 184 0.7120.1200.0000.1680.0000.000 940.4790.5210.000 24 Thomas Creek 2001.0000.0000.000 2001.0000.0000.000 232 0.7200.0390.0000.2410.0000.000 2000.6000.4000.000 25 ThomasCreek 1101.0000.0000.000 1101.0000.0000.000 2080.7310.1200.0000.1490.0000.000 1100.7090.2910.000 26 Thomas Creek 481.0000.0000.000 481.0000.0000.000 92 0.620 0152 0.0000.2280.0000.000 480.5000.5000.000 27 WileyCreek 2001.0000.0000.000 2001.0000.0000.000 104 0.6150.2210.0000.1630.0000.000 200 0.5500.4400.010 28 Wiley Creek 541.0000.0000.000 541.0000.0000.000 1040.6150.2210.0000.1630.0000.000 540.7590.2410.000 29 SandyRiver 2001.0000.0000.000 2001.0000.0000.000 387 0.7420.1290.0000.1290.0000.000 2000.9000.1000.000 30 Hamilton Creek 1061.0000.0000.000 1061.0000.0000.000 200 0.7200.1300.0000.1500.0000.000 1060.8770.1230.000 31 Neal Creek 1001.0000.0000.000 1001.0000.0000.000 1720.6220.1800.0000.1980.0000.000 1000.9100.0900.000 32 WindRiver 1001.0000.0000.000 1001.0000.0000.000 1680.6610.1790.0000.1610.0000.000 1000.7900.2000.010 33 Wind River 501.0000.0000.000 501.0000.0000.000 97 0.5770.3200.0100.0930.0000.000 500.7800.2200.000 34 EightmileCr#1 381.0000.0000.000 381.0000.0000.000 760.6710.0130.0260.2890.0000.000 380.0001.0000.000 35 EightniileCr#2 341.0000.0000.000 341.0000.0000.000 680.5880.0000.0000.4120.0000.000 340.0001.0000.000 36 Fjfteemnile Creek 1641.0000.0000.000 1641.0000.0000.000 328 0.6800.0910.0090.2200.0000.000 1620.6480.3520.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-1 * mIDHP2* sIDHPJ,2* LDH-B2 * No. SainpleName N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 37Fifteenmile Creek 1001.0000.0000.000 1001.0000.0000.000 200 0.7300.0800.0100.1800.0000.000 1000.5600.4400.000 38 BakeovenCreek 3961.0000.0000.000 3961.0000.0000.000 768 0.7190.1310.0000.1500.0000.000 396 0.404 0.5960.000 39 BuckHollowCreek 3941.0000.0000.000 3941.0000.0000.000 6800.704 0.1440.0050.1470.0000.000 388 0.3810.6190.000 40 Deschutes resident strain 1801.0000.0000.000 1801.0000.0000.000 2800.6680.0860.0000.2460.0000.000 1800.6670.3330.000 41 DeschutesRiver 2541.0000.0000.000 2541.0000.0000.000 4480.6180.1670.0010.2140.0000.000 254 0.5160.484 0.000 42 LowerNena Creek 1401.0000.0000.000 1401.0000.0000.000212.20.7020.1420.0000.1560.0000.000 1400.4360.564 0.000 43 Mid-Nena Creek 1341.0000.0000.000 1341.0000.0000.000 2600.6650.1460.0160.1730.0000.000 134 0.3730.6270.000 44 UpperNena Creek 861.0000.0000.000 861.0000.0000.000 1720.6570.1570.0170.1690.0000.000 860.3720.6280.000 45 BigLogCreek 1401.0000.0000.000 1401.0000.000 0.000 268 0.724 0.1270.0110,1380.0000.000 1380.3260.674 0.000 46 LowerEastFoleyCreek 301.0000.0000.000 301.0000.0000.000 60 0.5500.2170.0160.2170.0000.000 300.3330.667 0.000 47 UpperEastFoleyCreek 1521.0000.0000.000 1521.0000.0000.000 288 0.7150.1150.0030.1670.0000.000 1520.3290.6710.000 48 Deschutessummerstrain 1791.0000.0000.000 1791.0000.0000.000 3880.6800.1490.0000.1700.0000.000 2000.4400.5600.000 49 Deschutessunimerstrain 1921.0000.0000.000 1921.0000.0000.000 3800.6000.2000.0110.1890.0000.000 2000.4200.5300.050 50 Deschutessummerstrain 2001.0000.0000.000 2001.0000.0000.000 3880.6800.1520.0000.1680.0000.000 2000.4450.5550.000 51 CrookedRivergorge 161.0000.0000.000 140.9290.0710.000 280.6790.2140.000 0107 0.000 0000 16 0.3130.6880.000 52 Lower Crooked River 281.0000.0000.000 28 0.964 0.0360.000 510.4710.3730.0590.0980.0000.000 260.3850.6150.000 53 BowinanDam 1681.0000.0000.000 1680.8930.1070.000 3060.7680.0750.020 0137 0.0000.000 1660.6390.3610.000 54MckayCreek 681.0000.0000.000 681.0000.0000.000 1240.5890.1450.0000.2660.0000.000 660.7420.2580.000 55 Ochoco Creek 661.0000.0000.000 66 0.9700.0300.000 930.6130.1080.0220.2580.0000.000 660.6820.318 0000 56 MarksCreek 681.0000.0000.000 700.9570.0430.000 600.7000.1500.0000.1500.0000.000 74 0.5680.4320.000 57 HorseHeavenCr 661.0000.0000.000 660.9700.0300.000 1170.6840.0600.0000.256 0000 0.000 660.4390.5610.000 58' PineCreek 721.0000.0000.000 721.0000.0000.000 1160.7500.1900.0000.0600.0000.000 320.250 0.7500.000 59 LookoutCr 621.0000.0000.000 620.984 0.0160.000 1240.6210.0160.0000.3630.0000.000 620.1940.8060.000 60HowardCreek 741.0000.0000.000 1060.9910.0090.000 1960.6380.0000.0050.3570.0000.000 1060.1230.877 0.000 61 FoxCanyonCr 761.0000.0000.000 761.0000.0000.000 1320.7500.0000.0000.2500.0000.000 700.1710.8290.000 62 Deep Creek 721.0000.0000.000 72 0.9720.0280.000 1240.7260.0650.0650.1450.0000.000 720.5140.4860.000 63 Deer Cr 681.0000.0000.000 681.0000.0000.000 800.7500.2130.0000.0380.0000.000 660.7420.2580.000 64DeardorffCreek 381.0000.0000.000 381.0000.0000.000 720.7500.0970.0280.1250.0000.000 380.3420.6580.000 65 DeardorffCreek 301.0000.0000.000 301.0000.0000.000 600.6330.1170.0670.1830.0000.000 300.5330.4670.000 66 Vinegar Creek 801.0000.0000.000 361.0000.0000.000 600.6830.0830.0000.2330.0000.000 780.3080.6920.000 67 VinegarCreek 361.0000.0000.000 361.0000.0000.000 620.6130.2100.0000.1770.0000.000 360.3890.6110.000 68 Granite Creek 841.0000.0000.000 581.0000.0000.000 1400.7210.0860.0000.1930.0000.000 840.3450.6550.000 69 Meadow Creek 341.0000.0000.000 341.0000.0000.000 640.7500.0780.0000.1720.0000.000 34 0.2940.7060.000 70 GrasshopperCreek 1641.0000.0000.000 1140.9820.0180.000 224 0.6880.0850.0000.2280.0000.000 1640.4020.5980.000 71 SouthForkheadwaters 1421.0000.0000.000 1420.9150.0850.000 224 0.6160.1290.0000.2540.0000.000 1420.4300.5700.000 72 IzeeFalls 1521.0000.0000.000 1560.9290.0710.000 2960.6320.1990.0070.1620.0000.000 1500.6470.3530.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-J * mIDHP2* sIDHPJ2* LDHB2* No. Sample Name N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 73 SouthForkatRockpileRanc 1601.0000.0000.000 1600.9630.0380.000 304 0.6350.1940.0070.1640.0000.000 1580.3670.6330.000 74 KlickitatRiver 2001.0000.0000.000 2001.0000.0000.000 3680.7090.1300.0110.1490.000 0.000 2000.6000.4000.000 75 Willow Creek 1601.0000.0000.000 1601.0000.0000.000 2920.6990.178 0000 0.1230.0000.000 1600.2810.7190.000 76 NorthForklJmatjllaRjver 1501.0000.0000.000 1500.9730.0270.000 3000.7000.1700.0030.1270.0000.000 1500.3870.6130.000 77 NorthForkUmatillaRivei- 721.0000.0000.000 720.9580.0420.000 144 0.6810.1740.0000.1460.0000.000 720.4860.5140.000 78 Buck Creek 501.0000.0000.000 501.0000.0000.000 1000.6700.2000.0000.1000.0000.030 500.2800.7000.020 79BuckCreek 881.0000.0000.000 880.9890.0110.000 1560.7240.1280.0000.1470.0000.000 880.5000.4890.011 80 Thomas Creek 481.0000.0000.000 48 0.958 0.0420.000 920.7280.0980.0000.1740.0000.000 480.3330.6670.000 81 Thomas Creek 701.0000.0000.000 701.0000.0000.000 1360.6690.1400.007 0.184 0.0000.000 700.4000.5860.014 82 SouthForklJmatjllaRjver 501.0000.0000.000 500.8800.1200.000 1000.6400.2300.0000.0700.0000.060 500.2600.7400.000 83 SouthForkUmatillaRjver 661.0000.0000.000 660.9850.0150.000 1280.7190.0780.0080.1950.0000.000 660.4550.5450.000 84 Camp Creek 461.0000.0000.000 461.0000.0000.000 920.6520.1090.0220.2070.0000.011 46 0.3260.6090.065 85 Camp Creek 821.0000.0000.000 821.0000.0000.000 1640.7130.0730.0000.2130.0000.000 820.3780.6220.000 86NorthForkMeachamCreek 481.0000.0000.000 481.0000.0000.000 960.7600.0520.0000.1880.0000.000 48 0.3960.6040.000 87NorthForkMeachamCreek 901.0000.0000.000 901.0000.0000.000 1690.6750.0890.0180.2190.0000.000 90 0.3560.6110.033 88 Upper Meacham Creek 481.0000.0000.000 481.0000.0000.000 960.6460.1460.0000.2080.0000.000 48 0.2290.7710.000 89 UpperMeachamCreek 761.0000.0000.000 761.0000.0000.000 1400.7140.1000.0070.1790.0000.000 760.3420.6320.026 90 LowerSquawCreek 541.0000.0000.000 540.9810.0190.000 1080.6940.1480.0000.1570.0000.000 540.4260.5560.019 91 UpperSquawCreek 1161.0000.0000.000 1160.9910.0090.000 228 0.7890.0960.0000.1140.0000.000 116 0.2840.7070.009 92 Squaw Creek 861.0000.0000.000 860.9770.0230.000 1440.7220.1040.0000.1740.0000.000 860.3020.6980.000 93 MeKayCreek 481.0000.0000.000 480.9170.0830.000 960.6560.1670.0210.1350.0000.021 480.7290.2710.000 94 McKay Creek 241.0000.0000.000 240.9170.0830000 480.7290.1460.0630.0630.0000.000 240.6670.3330.000 95 EastBirchCreek 501.0000.0000.000 501.0000.0000.000 960.8020.0210.0000.1770.0000.000 500.5800.4200.000 96 EastBirchCreek 841.0000.0000.000 801.0000.0000.000 1560.6790.1350.0190.1670.0000.000 84 0.5710.4290.000 97 Pearson Creek 441.0000.0000.000 440.9550.0450.000 880.7050.102 0000 0.1930.0000.000 440.4550.5450.000 98 PearsonCreek 881.0000.0000.000 881.0000.0000.000 1820.6980.110 0000 0.1920.0000.000 880.4890.5110.000 99 WestBirchCreek 561.0000.0000.000 561.0000.0000.000 1120.7410.0800.0090.1700.0000.000 560.4640.5360.000 100 WestBirchCreek 721.0000.0000.000 721.0000.0000.000 1320.6740.1590.0080.159 0000 0,000 720.4310.5690.000 101 EastForkButterCreek 501.0000.0000.000 500.9800.0200.000 1000.6700.1100.0000.2000.0000.020 50 0.4400.5600.000 102 Ea.stForkButterCreek 821.0000.0000.000 821.0000.0000.000 1560.6470.0900.0320.2310.0000.000 820.4880.5120.000 103 Bingham Springs 2001.0000.0000.000 2001.0000.0000.000 3920.6610.1890.0000.1510.0000.000 1980.4190.5810.000 104 Umatillasummerstrain 2001.0000.0000.000 2001.0000.0000.000 360 0.6610.1190.0000.2190.0000.000 200 0.5700.4300.000 105 Umatilla summer strain 3561.0000.0000.000 3560.9780.0220.000 696 0.6440.1680.0000.1820.0000.006 356 0.4440.5530.003 106 TouchetRiver 1001.0000.0000.000 1001.0000.0000.000 1960.6120.1680.0000.2190.0000.000 1000.4500.5500,000 107 WallaWallaRjver 801.0000.0000.000 801.0000.0000.000 1600.6190.1630.0000.2190.0000.000 800.3630.6380.000 108 LowerWhiteRjver 841.0000.0000.000 841.0000.0000.000 1480.7160.1080.0000.1760.0000.000 840.8690.1310.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-J * mIDHP2* sIDHPJ,2* LDHB2* No. SampleName N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 109 LowerTyghCreek 621.0000.0000.000 621.0000.0000.000 1160.6900.1030.0000.2070.0000.000 621.0000.0000.000 110 UpperTyghCreek 1361.0000.0000.000 1361.0000.0000.000 212 0.7500.0050.0000.2450.0000.000 1361.0000.0000.000 111 JordanCreek 1261.0000.0000.000 1261.0000.0000.000 224 0.7280.1250.000 0.1470.0000.000 1261.0000.0000.000 112 LittleBadgerCreek 1001.0000.0000.000 1001.0000.0000.000 200 0.7150.0000.0000.2850.0000.000 1001.0000.0000.000 113 Threemile Creek 1001.0000.0000.000 1001.0000.0000.000 200 0.7150.0750.0050.2050.0000.000 100 0.9800.0200.000 114 RockCreek 1161.0000.0000.000 1161.0000.0000.000 204 0.7700.1030.049 0.0780.0000.000 1160.9830.0170.000 115 GateCreek 801.0000.0000.000 801.0000.0000.000 1080.7220.1020.0000.1760.0000.000 801.0000.0000.000 116 BarlowCreek 1161.0000.0000.000 1161.0000.0000.000 1760.7780.0050.007 0.2100.0000.000 1161.0000.0000.000 117 FawnCreek 1101.0000.0000.000 1101.0000.0000.000 2120.6600.1420.000 0.1980.0000.000 1160.2930.7070.000 118 Wellssummerstsajn 1621.0000.0000.000 1621.0000.0000.000 324 0.6600.1790.000 0.1600.0000.000 1620.2590.7410.000 119 MadRjver 1001.0000.0000.000 1001.0000.0000.000 2000.6000.1900.0050.2050.0000.000 1000.2900.6900.020 120 PeshastinCreek 1921.0000.0000.000 1921.0000.0000.000 2920.6200.2090.0100.1610.0000.000 1900.3790.6110.011 121 Satus Creek 961.0000.0000.000 961.0000.0000.000 1850.6490.1510.0220.1780.0000.000 960.6770.3230.000 122 Satus Creek 981.0000.0000.000 981.0000.0000.000 1830.6230.1580.0000.2190.0000.000 980.6120,3880.000 123 Mission Creek 601.0000.0000.000 601.0000.0000.000 1210.6360.1320.0000.2310.0000.000 60 0417 0.5830.000 124 BigCanyonCreek 1761.0000.0000.000 1761.0000.0000.000 3450.5800.1510.0000.2700.0000.000 1760.1590.8410.000 125 Dworshaksummerstrajn 1461.0000.0000.000 1461.0000.0000.000 2840.6510.2180.0000.1300.0000.000 1460.2330.7670.000 126 Pahsjmeroi B strain 1001.0000.0000.000 1001.0000.0000.000 1520.6780.0920.0130.2170.0000.000 1000.2900.7100.000 127 FishCreek 1001.0000.0000.000 1001.0000.0000.000 1720.6800.1220.0000.1980.0000.000 1000.2700.7300.000 128 MeadowCreek 1961.0000.0000.000 1961.0000.0000.000 3840.6200.1510.0000.2290.0000.000 196 0.3420.6580.000 129 Sheep Cr 2401.0000.0000.000 2401.0000.0000.000 3760.5690.1810.0000.2500.0000.000 240 0.2920.7000.008 130 ChamberlainCreek 1941.0000.0000.000 1941.0000.0000.000 3880.6700.1490.010 0.1700.0000.000 1940.2410.7280.031 131 Horse Creek 1021.0000.0000.000 1021.0000.0000.000 1600.6810.0690.0130.2380.0000.000 1000.2800.7200.000 132 Indian Creek 3161.0000.0000.000 3161.0000.0000.000 6320.6690.1500.0000.1800.0000.000 5540.3300.6590.011 133 JohnsonCreek 1001.0000.0000.000 1001.0000.0000.000 2000.5690.3300.0110.0900.0000.000 1000.2800.7200.000 134 SeceshRiver 1221.0000,0000.000 1221.0000.0000.000 224 0.6380.2410.0000.1210.0000.000 1220.2520.7480.000 135 Sawtoothstrain 1001.0000.0000.000 1001.0000.0000.000 1120.7320.0800.018 0.1700.0000.000 1000.4300.5600.010 136 TucannonRiver 2241.0000.0000.000 2241.0000.0000.000 424 0.6390.1700.0000.1910.0000.000 224 0.3300.6700.000 137 TucannonRiver 1001.0000.0000.000 1001.0000.0000.000 1960.6220.1890.000 0.1890.0000.000 1000.2900.7000.010 138 Fly Creek 401.0000.0000.000 401.0000.0000.000 800.7500.1500.0000.1000.0000.000 400.3250.6750.000 139 FyCreek 1001.0000.0000.000 1001.0000.0000.000 1560.7120.1150.0000.1730.0000.000 1000.4500.5500.000 140 LimberJimCreek 601.0000.0000.000 601.0000.0000.000 1200.6670.1500.0000.1830.0000.000 600.2030.7970.000 141 Sheep Creek 561.0000.0000.000 561.0000.0000.000 800.7130.1130.0000.1750.0000.000 560.3750.6250.000 142 ChjekenCreek 641.0000.0000.000 641.0000.0000.000 880.8070.1020.0000.0910.0000.000 44 0.3410.6590.000 143 Meadow Creek 501.0000.0000.000 501.0000.0000.000 960.7600.0830.0000.1560.0000.000 500.4600.5400.000 144 LaddCreek 501.0000.0000.000 501.0000.0000.000 640.7810.0470.0000.1720.0000.000 480.4380.5630.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-1 * mIDHP2* sIDHP1,2* LDHB2* No. Sample Name N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 145 Wallowa summer strain 1981.0000.0000.000 1981.0000.0000.000 369 0.6690.160 0.0000.1710.0000.000 200 0.2400.7600.000 146 WallowaRiver 521.0000.0000.000 521.0000.0000.000 104 0.8080.1250.0000.0670.0000.000 520.3850.6150.000 147 Wallowa River 741.0000.0000.000 741.0000.0000.000 1360.6910.1030.0070.1990.0000.000 74 0.4320.5680.000 148 LostineRiver 941.0000.0000.000 941.0000.0000.000 1880.7230.144 0.0000.1330.0000.000 94 0.3190.6810.000 149 Lostine River 501.0000.0000.000 501.0000.0000.000 920.7390.1410.0000.1200.0000.000 500.2600.7400.000 150 BroadyCreek 541.0000.0000.000 541.0000.0000.000 1080.7220.1110.0000.1670.0000.000 54 0.5000.5000.000 151 Horse Creek 521.0000.0000.000 521.0000.0000.000 104 0.7120.0290.0100.2500.0000.000 520.2880.7120.000 152 Jarboe Creek 1381.0000.0000.000 1381.0000.0000.000 276 0.8260.004 0.0000.1700.0000.000 1381.0000.0000.000 153 LittleLookingglassCreek 1001.0000.0000.000 1001.0000.0000.000 1000.6500.2000.0000.1500.0000.000 1000.0700.9300.000 154 Mottet Creek 501.0000.0000.000 50 0.9600.0400.000 1000.7200.0900.0000.1900.0000.000 500.7400.2600.000 155 Swamp Creek 501.0000.0000.000 501.0000.0000.000 1000.7300.0200.0000.2500.0000.000 500.1200.8800.000 156 Cook Creek 501.0000.0000.000 501.0000.0000.000 1000.7600.1000.0000.1400.0000.000 500.4000.5600.040 157 CherryCreek 521.0000.0000.000 521.0000.0000.000 1040.6920.1150.0000.1920.0000.000 520.4420.5580.000 158 GumbootCreek 521.0000.0000.000 521.0000.0000.000 1040.6920.1150.0000.1920.0000.000 520.2310.7690.000 159 Grouse Creek 561.0000.0000.000 561.0000.0000.000 1120.7050.1880.0000.1070.0000.000 560.2680.7320.000 160 GrouseCreek 361.0000.0000.000 361.0000.0000.000 720.7220.1530.0000.1250.0000.000 360.3890.6110.000 161 BigSheepCreek 901.0000.0000.000 901.0000.0000.000 1800.7000.1220.0000.1780.0000.000 900.3780.6220.000 162 BigSheepCreek 741.0000.0000.000 741.0000.0000.000 1440.7220.1250.0000.1530.0000.000 74 0.2030.7970.000 163 Imnahasummerstrain 2001.0000.0000.000 2001.0000.0000.000 3490.7390.0800.0030.1780.0000.000 1980.3890.6110.000 164 Niagarasummerstrain 2001.0000.0000.000 2001.0000.0000.000 2680.6310.1900.0000.1790.0000.000 2000.2100.7400.050 165 McGraw Creek 521.0000.0000.000 521.0000.0000.000 1041.0000.0000.0000.0000.0000.000 521.0000.0000.000 166 Cønner Creek 501.0000.0000.000 500.860 0.1400.000 920.7500.1960.0000.0540.0000.000 500.9000.0800.020 167 NorthPineCreek 501.0000.0000.000 501.0000.0000.000 960.7400.0830.0000.1770.0000.000 480.3960.6040.000 168 BigCreek 281.0000.0000.000 28 0.9290.0710.000 520.7120.1150.0000.1730.0000.000 280.5360.4640.000 169 IndianCreek 481.0000.0000.000 481.0000.0000.000 960.9580.0210.0000.0210.0000.000 480.5630.4380.000 170 Summit Creek 521.0000.0000.000 521.0000.0000.000 880.6590.0680.0000.2730.0000.000 520.2310.7690.000 171 Sutton Creek 361.0000.0000.000 361.0000.0000.000 680.6620.1470.0000.1910,0000.000 360.0001.0000.000 172 Dixie Creek 421.0000.0000.000 421.0000.0000.000 800.6250.1750.0000.2000.0000.000 420.4520.5480.000 173 Last Chance Creek 441.0000.0000.000 441.0000.0000.000 84 0.8690.0710.0000.0600.0000.000 44 0.4770.5230.000 174 LawrenceCr(abovebarrier) 301.0000.0000.000 301,0000,0000.000 600.5760.1860.0170.2200.0000.000 300.1670.8330.000 175 LawrenceCr(belowbarrier) 201.0000.0000.000 201.0000.0000.000 400.6250.1500.0250.2000.0000.000 200.4000.6000.000 176 SouthForkDixieCreek 501.0000.0000.000 501.0000.0000.000 1000.7400.1300.0000.1300.0000.000 500.5400.4600.000 177 Snow Creek 521.0000.0000.000 521.0000.0000.000 1040.9130.0290.0000.0580.0000.000 520.3080.6920.000 178 BlackCanyonCreek 501.0000.0000.000 501.0000.0000.000 500.6700.0800.0100.2400.0000.000 500.8400.1600.000 179 Cottonwood Creek 521.0000.0000.000 521.0000.0000.000 520.6920.1830.0000.1250.0000.000 520.9420.0580.000 180 CottonwoodCreek 501.0000.0000.000 501.0000.0000.000 1000.6900.0500.0000.2600.0000.000 500.0800.9200.000 N.) Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-1 * mIDHP2* sIDHPJ,2* LDHB2* No. SampleName N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 181 HogCreek 501.0000.0000.000 501.0000.0000.000 1000.7100.1400.000 0150 0.0000.000 500.540 0.4400.020 182 South Fork Indian Creek 521.0000.0000.000 521.0000.0000.000 104 0.4900.2500.0000.2600.0000.000 520.9600.0400.000 183 Dinner Creek 501.0000.0000.000 501.0000.0000.000 500.6000.0600.0200.3200.0000.000 500.5400.4600.000 184 Calf Creek 521.0000.0000.000 521.0000.0000.000 1000.6700.1700.0700.0900.0000.000 520.8270.1730.000 185 NorthForkSquawCreek 501.0000.0000.000 501.0000.0000.000 1000.7400.0200.0000.2400.0000.000 500.2800.7200.000 186 CarterCreek 521.0000.0000.000 521.0000.0000.000 104 0.5670.2210.0000.2120.0000.000 520.2500.7500.000 187 DryCreek 501.0000.0000.000 500.960 0.0400.000 1000.7130.1580.0300.0990.0000.000 500.3650.6350.000 188 WestLittleOwyheeRiver 501.0000.0000.000 501.0000.0000.000 960.7400.1150.0000.1460.0000.000 500.1600.8400.000 189 Deep Creek 281.0000.0000.000 281.0000.0000.000 560.6960.0890.000 0.214 0.0000.000 28 0.9290.0710.000 190 Indian Creek 601.0000.0000.000 601.0000.0000.000 560.6790.0710.0540.1960.0000.000 60 0.9500.0500.000 191 Bridge Creek 641.0000.0000.000 64 0.734 0.2660.000 1280.7660.0550.0000.1800.0000.000 64 0.7810.2190.000 192 Krumbo Creek 921.0000.0000.000 920.924 0.0760.000 1720.7670.1860.0000.0470.0000.000 921.0000.0000.000 193 Mud Creek 881.0000.0000.000 860.9530.0470.000 1680.7500.0360.0710.1430.0000.000 880.6700.3300.000 194 Smyth Creek 901.0000.0000.000 900.9560.044 0.000 1760.7220.0230,0400.2160.0000.000 900.8780.1220.000 195 Upper Sawmill Creek 201.0000.0000.000 200.8000.2000.000 400.7750.0250.0000.2000.0000.000 200.8000.2000.000 196 Lower Sawmill Creek 401.0000.0000.000 400.8250.1750.000 800.7250.0500.0630.1630.0000.000 40 0.900 0100 0.000 197 Home Creek #1 401.0000.0000.000 401.0000.0000.000 720.5690.0560.0000.3750.0000.000 400.9500.0500.000 198 Home Creek #2 201.0000.0000.000 201.0000.0000.000 400.5500.0750.0000.3750.0000.000 180.8890.1110.000 199 UpperllomeCreek 301.0000.0000.000 301.0000.0000.000 600.5670.1000.0000.3330.0000.000 300.8670.1330.000 200Augur Creek 280.8930.1070.000 281.0000.0000.000 540.7040.0000.0740.2220.0000.000 281.0000.0000.000 201 DairyCreek 321.0000.0000.000 321.0000.0000.000 600.6670.0000.067 0.267 0.0000.000 320.9060.0940.000 202 Bear Creek 641.0000.0000.000 641.0000.0000.000 1120.6430.0180.0800.2590.0000.000 64 0.8750.1250.000 203 Elder Creek 381.0000.0000.000 380.9740.0260.000 64 0.6090.0630.0000.3280.0000.000 360.7220.2780.000 204Witham Creek 341.0000.0000.000 34 0.9710.0290.000 600.6000.0670.0330.3000.0000.000 320.7500.2500.000 205 Bridge Creek 401.0000.0000.000 401.0000.0000.000 900.7890.0780.0000.1330.0000.000 400.9750.0250.000 206 BuckCreek 601.0000.0000.000 620.984 0.0160.000 1200.7420.0250.0420.1920.0000000 600.8500.1500.000 207BeaverCreek 301.0000.0000.000 301.0000.0000.000 600.7170.0330.0670.1830.0000.000 301.0000.0000.000 208 Camp Creek 60 0.9830.0170.000 601.0000.0000.000 1200.7080.0080.0000.2830.0000.000 601.0000.0000.000 209 Cox Creek 361.0000.0000.000 361.0000.0000.000 680.7500.0150.0000.2350.0000.000 361.0000.0000.000 210 Thomas Creek 361.0000.0000.000 361.0000.0000.000 64 0.7660.0000.000 0.234 0.0000.000 360.9440.0560.000 211 BeaverCreek 1331.0000.0000.000 1381.0000.0000.000 2720.8020.0110.0660.1210.0000.000 1381.0000.0000.000 212 Fall Creek 461.0000.0000.000 460.7390.2610.000 92 0.8470.0980.0220.0330.0000000 461.0000.0000.000 213 JennyCreek#1 781.0000.0000.000 780.7560.2440.000 1480.6690.1010.0340.1960.0000.000 781.000 0000 0.000 214 JennyCreek#2 741.0000.0000.000 74 0.9190.0810.000 1440.8120.0490.0000.1180.0210.000 741.000 0000 0.000 215 Jolmson Creek #1 401.0000.0000.000 400.9500.0500.000 760.5130.0000.0000.4870.0000.000 400.9750.0000.025 216 JohnsonCreek#2 741.0000.0000.000 74 0.8510.1490.000 1240.7910.0400.0000.1690.0000000 741.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIDHP-J * mIDHP2* sIDHP1,2* LDHB2* No. SampleName N 100 -280 -520 N 100 144 83 N 100 42 121 72 58 116 N 100 76 113 217 Shoat Springs 1681.0000.0000.000 1680.756 0.2440.000 3360.8270.1250.0030.0450.0000.000 1681.0000.0000.000 218 WillowCreek 1541.0000.0000.000 154 0.9810.0190.000 2480.6250.1130.1730.0890.0000.000 154 0.994 0.0060.000 219 Deming Creek 621.0000.0000.000 621.0000.0000.000 134 0.8800.0450.0000.0750.0000.000 621.0000.0000.000 220 Paradise Creek 321.0000.0000.000 321.0000.0000.000 64 0.9380.0470.0000.0160.0000.000 321.0000.0000.000 221 Paradise Creek 201.0000.0000.000 20 0.9500.0500.000 400.9000.0000.1000.0000.0000.000 201.0000.0000.000 222 Deep Creek 141.0000.0000.000 141.0000.0000.000 320.7190.0630.0000.2190.0000.000 141.0000.0000.000 223 Williamson River #1 401.0000.0000.000 400.8750.1250.000 800.9000.0130.0000.0880.0000.000 401.0000.0000.000 224 WilliamsonRiver#2 121.0000.0000.000 121.0000.0000.000 240.8750.0000.0000.1250.0000.000 121.0000.0000.000 225 Bogus Creek 90 0.9890.0000.011 901.0000.0000.000 1800.9440.0500.0000.0060.0000.000 90 0.9670.0330.000 226 Kiamath River 361.0000.0000.000 361.0000.0000.000 72 0.9580.0280.0000.0140.0000.000 361.0000.0000.000 227 SpencerCreek 501.0000.0000.000 501.0000.0000.000 1000.8100.1600.0000.0300.0000.000 501.0000.0000.000 228 Spencer Creek 461.0000.0000.000 460.9570.0430.000 420.9760.024 0.0000.0000.0000.000 461.0000.0000.000 229 Rock Creek 221.0000.0000.000 221.0000.0000.000 440.9090.0000.0000.0910.0000.000 221.0000.0000.000 230 Wood Creek 561.0000.0000.000 560.9820.0180.000 1120.8930.0450.0000.0630.0000.000 561.0000.0000.000 231 SpringCreek 541.0000.0000.000 541.0000.0000.000 1080.8610.1390.0000.0000.0000.000 541.0000.0000.000 232 Spring Creek 481.0000.0000.000 481.0000.0000.000 880.8640.1250.0110.0000.0000.000 481.0000.0000.000 233 Trout Creek 501.0000.0000.000 501.0000.0000.000 1000.9300.0500.0000.0200.0000.000 501.0000.0000.000 234 Trout Creek 821.0000.0000.000 821.0000.0000.000 164 0.9150.0790.0060.0000.0000.000 821.0000.0000.000 235 Honey Creek #1 30 0.8670.1330.000 30 0.9670.0330.000 600.8830.0330.0000.0830.0000.000 300.9330.067 0.000 236 HoneyCreek#2 32 0.6250.3750.000 321.0000.0000.000 64 0.9060.0310.0000.0630.0000.000 320.8750.1250.000 237 NorthForkDeepCreek 301.0000.0000.000 300,9000.1000.000 600.7000.1830.0170.1000.0000.000 300.6670.3330.000 238 Deep Creek ' 301.0000.0000.000 300.9000.1000.000 600.6330.1500.0170.2000.0000.000 300.8670.1330.000 239 WillowCreek#1 401.0000.0000.000 400.9750.0250.000 680.6760.0880.0000.2350.0000.000 380.8160.184 0.000 240 WillowCreek#2 201.0000.0000.000 201.0000.0000.000 400.7500.1750.0000.0750.0000.000 200.5500.4500.000 241 CapeCodstrain 1601.0000.0000.000 1601.0000.0000.000 3200.6470.2060.0220.1250.0000.000 1601.0000.0000.000 242 Oak Springs strain 1541.0000.0000.000 1541.0000.0000.000 2000.7350.0800.0450.1400.0000.000 1541.0000.0000.000 243 Soda Creek 801.0000.0000.000 80 0.9120.0750.013 1520.7830.1510.0000.0660.0000.000 800.9870.0130.000 244 Coastal cutthroat trout 4341.0000.0000.000 434 0.9860.0140.000 8140.2540.0650.0000.6810.0000.000 4320.9840.0000.016 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout, MapNo. SampleName LDHC* N 100 97 sMDHA1,2* N 100 155 72 37 120 49 sMDHB1,2* N 100 83 116 70 92 78 124 120 432 1 BigCreekwinterstrainBigCreekwinterstramElochomanwinterstramGraysRiver 200190198 1.000 0.000 380400 0.990 1.000 0.000 0.0000.010 0.0000.000 0.000 0.0000.000 368400376396 0.8990.9200.879 0.891 0.0950.0800.101 0.0000.0000.010 0.000 0.0050.0000.0290.010 0.000 0.000 0.000 8765 CowlitzsummerstrainCowljtzlatewjnterstrajnBigBigCreekwinterstrain Creek winter stram 200180198100 1.000 0.000 360396200400 1.000 0.000 0.000 0.0000.000 0.000 0.0000.000 354344200400 0.8190.9100.8600.890 0.1700.0900.110 0.0110.0000.030 0.000 0.0000.000 0.000 0.000 0.000 121110 9 SkamaniaCoweemanRiverToutleRiverCowlitz winter summer strain strain 200192148100 1.000 0.000 376296200400 0.9900.989 1.000 0.000 0.0110.0000.010 0.000 0.000 0.000 380296200396 0.9290.908 0.8690.880 0.0710.0400.110 0.0000.021 0.000 0000 0.000 0.0910.0100.000 0.000 0.000 0.000 16151413 SkamanjaSkamaniaSkamaniasummerstrain summer strain 200100 1.000 0.000 400200360 1.000 0.000 0.000 0.0000.000 0.000 0.000 376200400 0.8300.840 0.8190.870 0.1810.1200.1400.130 0.0000.0200.030 0.000 0.0000.0100.0100.000 0.000 0.000 0.000 20191817 EagleEagleCreekwinterstrainSkamanjawinterstrajnSkamania Creekwinterstrain summer strain 200160 1.000 0.000 400280 1.000 0.000 0.000 0.0000.000 0.000 0.0000.000 352380400 0.8410.8500.8800.910 0.0990.1390.1200.090 0.0000.060 0.000 0.0110.0000.000 0.000 0.000 0.000 24232221 ThomasCalapooiaCalapooiaRiverWillamette Creek River winter strain 200198 94 1.000 0.000 200400188 0.9800.990 1.000 0.000 0.0200.0000.010 0.000 0.000 0.000 388356400184 0.9890.9300.960 0.962 0.0700.0380.0110.040 0.000 0.000 0.000 0.000 0.000 0000 0.000 26282725 WileyThomasThomasCreek Creek Creek 200110 5448 1.000 0.000 400220108 0.98296 0.9721.000 0.000 0.0000.0180.0280.000 0.000 0.000 0.0000.000 388104184 0.899 0.92996 0.969 0.904 0.0100.0980.030.071 1 0.0000.003 0.000 0.0870.000 0.000 0.000 0.000 32313029 WindNealHamiltonSandy Creek River River Creek 200100106 1.000 0.000 200212400 0.9950.9911.000 0.000 0.0050.0000.009 0.000 0.000 0.000 200204392196 0.8920.921 0.9590.910 0.0410.0850.0590.077 0.0000.0050.0390.003 0.000 0.0000.010 0.000 0.000 0.000 36343335 FifteemnileEightmileCr#2EightmileCr#1Wind River Creek 164 343850 1.000 0.000 328100 7668 1.000 0.000 0.0000.000 0.000 0.000 0.000 328100 0.970 0.9706876 0.947 1.000 0.0090.0390.0000.000 0.0210.000 0.000 0.0000.030 0.000 0.000 0.0000.013 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map LDHC* sMDHA1,2* stVIDHB1,2* No. SampleName N 100 97 N 100 155 72 37 120 49 N 100 83 116 70 92 78 124 120 37Fifteenmile Creek 1001.0000.000 2001.0000.0000.0000.0000.0000.000 200 0.9600.0300.0100.0000.0000.0000.0000.000 38Bakeoven Creek 3961.0000.000 792 0.9990.0010.0000.0000.000 0.000 792 0.9330.0080.0020.0570.0000.0000.0000.000 39 Buck Hollow Creek 3941.0000.000 788 0.9700.014 0.0000.0160.0000.000 788 0.9720.0000.0030.0250.0000.0000.0000.000 40 Deschutes resident strain 1801.0000.000 3601.0000.0000.0000.0000.0000.000 344 0.9880.0120.0000.0000.0000.0000.0000.000 41 Deschutes River 2541.0000.000 5081.0000.0000.0000.0000.0000.000 496 0.9920.0060.0020.0000.0000.0000.0000.000 42 LowerNenaCreek 1401.0000.000 2801.0000.0000.0000.0000.0000.000 280 0.9640.0000.0000.0360.0000.0000.0000.000 43 Mid-Nena Creek 1341.0000.000 2561.0000.0000.0000.0000.0000.000 256 0.9760.0000.0000.024 0.0000.0000.0000.000 44UpperNena Creek 861.0000.000 1721.0000.0000.0000.0000.0000.000 1720.9940.0000.0000.0060.0000.0000.0000.000 45 BigLogCreek 1401.0000.000 280 0.9960.0040.0000.0000.0000.000 280 0.9500.0290.0000.0210.0000.0000.0000.000 46 Lower East Foley Creek 301.0000.000 601.0000.0000.0000.0000.0000.000 600.9830.0170.0000.0000.0000.0000.0000.000 47 UpperEastFoleyCreek 1521.0000.000 304 0.9940.0060.0000.0000.0000.000 304 0.9830.0040.0000.0130.0000.0000.0000.000 48 Deschutessummerstrain 2001.0000.000 4001.0000.0000.0000.0000.0000.000 392 0.959 0.0360.0050.0000.0000.0000.0000.000 49 Deschutessummerstrain 1981.0000.000 400 0.9900.0100.0000.0000.0000.000 400 0.9900.0100.0000.0000.0000.0000.0000.000 50 Deschutessunimerstrain 2001.0000.000 4001.0000.0000.0000.0000.0000.000 392 0.9540.0430.0030.0000.0000.0000.0000.000 51 Crooked River gorge 161.0000.000 641.0000.0000.0000.0000.0000.000 320.9690.0310.0000.0000.0000.0000.0000.000 52 Lower Crooked River 280.8930.107 48 0.9380.0000.0000.0630.0000.000 281.0000.0000.0000.0000.0000.0000.0000.000 53 BowmanDam 1681.0000.000 372 0.9760.0000.0000.0240.0000.000 326 0.9630.0340.0000.0000.0000.0030.0000.000 54 Mckay Creek 681.0000.000 1360.9410.0000.0000.0590.0000.000 401.0000.0000.0000.0000.0000.0000.0000.000 55 Ochoco Creek 661.0000.000 721.0000.0000.0000.0000.0000.000 1320.9090.0910.0000.0000.0000.0000.0000.000 56 Marks Creek 701.0000.000 1401.0000.0000.0000.0000.0000.000 1400.9290.0500.0070.0000.0000.0140.0000.000 57 Horse Heaven Cr 661.0000.000 1321.0000.0000.0000.0000.0000.000 1201.0000.0000.0000.0000.0000.0000.0000.000 58 Pine Creek 721.0000.000 721.0000.0000.0000.0000.0000.000 1440.9860.0140.0000.000 . 0.0000.0000.0000.000 59 Lookout Cr 621.0000.000 124 0.9840.0000.0000.0160.0000.000 1240.9600.0320.0000.0000.0000.0080.0000.000 60 Howard Creek 1060.9720.028 601.0000.0000.0000.0000.0000.000 212 0.9670.0330.0000.0000.0000.0000.0000.000 61 Fox Canyon Cr 141.0000.000 24 0.9170.0000.0000.0830.0000.000 1520.9800.0130.0070.0000.0000.0000.0000.000 62 Deep Creek 720.9580.042 1441.0000.0000.0000.0000.0000.000 1080.9810.0190.0000.0000.0000.0000.0000.000 63 DeerCr 681.0000.000 1120.9550.0000.0000.0450.0000.000 1361.0000.0000.0000.0000.0000.0000.0000.000 64 DeardorffCreek 381.0000.000 761.0000.0000.0000.0000.0000.000 760.9740.0000.0130.0000.0130.0000.0000.000 65 Deardorfi'Creek 301.0000.000 601.0000.0000.0000.0000.0000.000 600.9670.0170.0170.0000.0000.0000.0000.000 66 Vinegar Creek 8010000.000 1561.0000.0000.0000.0000.000 0.000 1480.9800.0140.0000.0000.0070.0000.0000.000 67 Vinegar Creek 361.0000.000 721.0000.0000.0000.0000.0000.000 720.9860.0140.0000.0000.0000.0000.0000.000 68 Granite Creek 841.0000.000 1681.0000.0000.0000.0000.0000.000 1681.0000.0000.0000.0000.0000.0000.0000.000 69 Meadow Creek 341.0000.000 681.0000.0000.0000.0000.0000.000 681.0000.0000.0000.0000.0000.0000.0000.000 70 Grasshopper Creek 1641.000 0000 3281.0000.0000.0000.0000.0000.000 3281.0000.0000.0000.0000.000 0000 0.0000.000 71 South Forkheadwaters 1421.0000.000 284 0.9930.0070.0000.0000.0000.000 2840.9960.0000.0040.0000.0000.0000.0000.000 72 Izee Falls 1521.0000.000 3120.9940.0060.0000.0000.0000.000 3120.9620.0130.0130.0060.0060.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map LDHC* sMDH-A1,24 sMDHB1,2* No. SampleName N 100 97 N 100 155 72 37 120 49 N 100 83 116 70 92 78 124 120 73 SouthForkatRockpileRanc 1601.0000.000 3201.0000.0000.0000.0000.0000.000 3200.9810.0130.0000.0060.0000.0000.0000.000 74 Klickitat River 2001.000 0000 4001.0000.0000.0000.0000.000 0.000 364 0.9090.0600.0190.0000.0110.0000.0000.000 75 Willow Creek 1601.0000.000 3201.0000.0000.0000.0000.0000.000 3200.9840.0 160.0000.0000.0000.0000.0000.000 76NorthForkUmatjllaRjver 1501.0000.000 3000.9930.0000.0000.0000.0070.000 2990.9970.0000.0000.0000.0030.0000.0000.000 77NorthForkUmatillaRiver 721.0000.000 1441.0000.0000.0000.0000.0000.000 1440.9720.0000,0140.0000.0000.0140.0000.000 78 Buck Creek 501.0000.000 1000.9800.0000.0000.0000.0200.000 1000.9900.0000.0000.0000.0000.0100.0000.000 79 Buck Creek 881.0000.000 1761.0000.0000.0000.0000.0000.000 1761.0000.0000.0000.0000.0000.0000.0000.000 80 Thomas Creek 481.0000.000 961.0000.0000.0000.0000.0000.000 961.0000.0000.0000.0000.0000.0000.0000.000 81 Thomas Creek 701.0000.000 1400.9790.0000.0000.0000.0000.021 1400.9500.0000.0210.0000.0000.0290.0000.000 82 SouthForkUmatillakjver 501.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.0000.0000.0000.0000.000 83 SouthForkUmatillaRiver 661.0000.000 1321.0000.0000.0000.0000.0000.000 1320.9770.0000.0230.0000.0000.0000.0000.000 84 Camp Creek 461.0000.000 921.0000.0000.0000.0000.0000.000 921.0000.0000.0000.0000.0000.0000.0000.000 85 Camp Creek 821.0000.000 164 0.9940.0000.0000.0000.0000.006 164 0.9760.0000.0060.0000.0000.0180.0000.000 86 NorthForkMeachamCreek 481.0000.000 961.0000.0000.0000.0000.0000.000 960.9690.0000.0210.0000.0000.0000.0000.010 87 NorthForkMeachamCreek 901.0000.000 1800.9890.0000.0000.0000.0000.011 1780.9490.0110.0220.0000.0000.0170.0000.000 88 UpperMeachamCreek 481.0000.000 96 0.9480.0000.0000.0000.0520.000 960.9790.0100.0000.0000.0000.0100.0000.000 89 UpperMeachamCreek 761.0000.000 1520.9870.0070.0000.0000.0000.007 1480.9930.0000.0070.0000.0000.0000.0000.000 90 LowerSquawCreek 541.0000.000 1080.9910.0000.0000.0000.0090.000 1080.9910.0000.0000.0000.0000.0090.0000.000 91 UpperSquawCreek 1161.0000.000 2321.0000.0000.0000.0000.0000.000 232 0.9960.0000.0000.0000.0000.0000.0000.004 92 SquawCreek 861.0000.000 1681.0000.0000.0000.0000.0000.000 1720.9830.0000.0060.0000.0000.0120.0000.000 93 McKay Creek 481.0000.000 961.0000.0000.0000.0000.0000.000 960.9900.0000.0000.0000.0100.0000.0000.000 94 McKay Creek 24 '1.0000.000 481.0000.0000.0000.0000.0000.000 481.0000.0000.0000.0000.0000.0000.0000.000 95 East Birch Creek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.0000.0000.0000.0000.000 96 East Birch Creek 841.0000.000 1601.0000.0000.0000.0000.0000.000 1600.9880.0130.0000.0000.0000.0000.0000.000 97 Pearson Creek 441.0000.000 881.0000.0000.0000.0000.0000.000 880.9770.0000.0110.0000.0000.0000.0000.011 98 Pearson Creek 881.0000.000 1760.9720.0000.0000.0000.0000.028 1760.9830.0170.0000.0000.0000.0000.0000.000 99 WestBirchCreek 561.0000.000 1121.0000.0000.0000.0000.0000.000 1120.9820.0000.0000.0000.0180.0000.0000.000 100 WestBirch Creek 721.0000.000 1441.0000.0000.0000.0000.0000.000 1441.0000.0000.0000.0000.0000.0000.0000.000 101 East Fork Butter Creek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.0000.0000.0000.0000.000 102 East Fork Butter Creek 821.0000.000 164 0.9880.0060.0000.0000.0000.006 1641.0000.0000.0000.0000.0000.0000.0000.000 103 Bingham Springs 1981.0000.000 400 0.9900.0100.0000.0000.0000.000 4000.9800.0030.0180.0000.0000.0000.0000.000 104 Umatillasummerstrain 2001.0000.000 4001.0000.0000.0000.0000.0000.000 4000.9800.0100.0100.0000.0000.0000.0000.000 105 Umatillasummerstrajn 3561.0000.000 712 0.9870.0030.0000.0010.0030.006 7120.9890.0000.0060.0000.0000.0060.0000.000 106 Touchet River 1001.0000.000 200 0.9900.0100.0000.0000.0000.000 2000.9700.0050.0050.0000.0200.0000.0000.000 107 WallaWallaRiver 801.0000.000 1600.9880.0000.0000.0130.0000.000 1600.9810.0190.0000.0000.0000.0000.0000.000 108 LowerWhiteRiver 841.0000.000 1681.0000.0000.0000.0000.0000.000 1680.8520.0530.0710.024 0.0000.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. Map No. SampleName LDHC* N 100 97 sMDHA1,2* N 100 155 72 37 120 49 sMDHBJ,2* IN 100 83 116 70 92 78 124 120 112111110109 LittleJordanCreekUpperlyghCreekLowerTyghCreek Badger Creek 100126136 62 1.000 0.000 200252272124 0.940 1.000 0.0600.000 0.000 0.0000.000 0.000 0.000 0.000 200252272124 0.917 1.0000.9111.000 0.0000.0600.032 0.0000.0150.057 0.000 0.0000.008 0.000 0.0000.000 0.000 0.0000.000 116115114113 BarlowGateCreekRockCreekThreemileCreek Creek 116100 80 1.000 0.000 232160200 1.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 232160232200 0.974 0.9260.985 0.975 0.0000.0250.0520.015 0.0000.000 0.0260.0000.022 0.000 0.0000.000 0.000 0.0000.000 120119118117 PeshastinMadRiverFawuCreekWellssummerstrain Creek 188100162116 1.0001.000 0.000 384200324232 0.981 0.9900.9911.000 0.0000.0100.0190.009 0.000 0.000 0.000 0.000 0.000 384200304232 0.9400.990 0.983 0.0210.0050.0000.009 0.0000.0050.0100.009 0.0000.000 0.0390.000 0.0000.000 0.000 0.000 124123122121 BigCanyonCreekMissionSatus CreekCreek 176 609896 1.000 0.000 352120196192 0.989 0.9920.9801.000 0.0110.0080.0200.000 0.000 0.000 0.000 0.000 352120196192 0.9791.000 0.0000.016 0.0000.005 0.0000.000 0.000 0.000 0.000 0.000 126128127125 MeadowFishPahsimeroiBDworshaksummerstrain Creek Creek strain 196100146 1.000 0.000 392200292 0.990 0.9901.000 0.0000.010 0.000 0.000 0.000 0.000 392200292 0.9950.990 0.9901.000 0.000 0.0050.0100.000 0.0000.000 0.000 0.000 0.000 0000 0.000 130129132131 IndianCreekHorseChamberlainSheep CreekCr Creek 238554100190 1.000 0.000 1108 388480200 1.0001.000 0.000 0.0000.000 0.000 0.000 0.000 1108 200388480 0990 0.000 0.9800.9790.985 0.0200.000 0.0000.0050.0100.015 0.0000.000 0.0000.0050.010 0.000 0.000 0.000 135134133136 TucaimonSawtoothSeceshJohnson River Creek strain River 226100122 1.000 0.000 452200244 0.996 1.0001.000 0.0000.004 0.000 0.0000.000 0.000 0.000 0.000 446200244 0.9800.9900982 0.009 0.0100.000 0.0090.0200.0000.000 0.0000.000 0.000 0.0000.000 0.000 0.000 140139138137 LimberJimCreekFyCreekFlyTucannonRiver Creek 100 6040 1.0001,000 0.000 200120 80 0.9831.0000.9750.980 0.0170.0000.0250.020 0.0000.000 0.000 0.000 0.000 200120 0.990 80 0.9800.9881.000 0.0000.0050.0130.010 0.0000.000 0.0000.000 0.0000.015 0.0000.000 0.000 0.000 144143142141 LaddMeadowChickenSheepCreek Creek Creek 506456 1.000 0.000 100128112 1.0000.990 0.0000.010 0.000 0.0000.000 0.000 0.000 0.000 100128112 1.000 0.000 0.000 0.0000.000 0.000 0.0000.000 0.000 0.000 oc Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. Map No. Sample Name LDHC* N 100 97 sMDHA1,2* N 100 155 72 37 120 49 sMDHBJ,2* N 100 83 116 70 92 78 124 120 148147146145 LostineWallowaWallowaRjvei-Wallowasummerstrain River River 200 947452 1.0001.000 0.000 188148104400 0.990 0.9891.0001.000 0.000 0.0110.0000.010 0.0000.000 0.000 0.000 0.0000.000 0.000 488400148104 0.9750.960 0.971 0.939 0.0060.0000.010 0.0180.0540.0290.030 0.0000.000 0.0000.007 0.000 0.000 0.000 0.000 0.000 152151150149 JarboeHorseBroadyLostine CreekCreek CreekRiver 138 525450 1.0001.000 0.000 276104108100 1.000 0.000 0.0000.000 0.000 0.000 0.0000.000 274104108100 0.990 0.9931.0000.970 0.0040.0000.010 0.0000.020 0.000 0.000 0.0040.000 0.000 0.0 0.00010 0.000 0.000 156155154153 CookMottetLittleLookingglassCreekSwamp Creek Creek Creek 100 5050 0.9801.000 0.0000.020 100200 0.9701.000 0.000 0.0000.000 0.000 0.0300.000 0.0000.000 200100 0.9900.9500.9851.000 0.0000.010 0.0000.040 0.000 0.0000.005 0.0100.0000.000 0.0000.000 0.000 160159157158 GrouseGrouseCreekGumbootCherry Creek Creek 36565252 1.000 0.000 112104 72 1.000 0.000 0.000 0.000 0.000 0.000 112104 72 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 164163162161 NiagarasummerstrainImnahasummerstrajnBigSheepCreek 200 7490 1.000 0.000 400148180 1.000 0.000 0.000 0.000 0.000 0.000 400148180 0.9801.000 0.0200.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 168167166165 BigNorthPineCreekConnerCreekMcGraw Creek Creek 285052 1.000 0.000 100104 9656 1.000 0.000 0.000 0000 0.000 0.000 0.000 0.000 100104 0.8855696 0.8750.9790.830 0.1250.0000.1700.115 0.0000.021 0.000 0000 0000 0.000 0.000 0.000 0.0000.000 0.000 172171170169 DixieSuttonCreekSummitCieckIndian Creek Creek 42365248 1.0001.000 0.000 104 729684 0.9860.9901.000 0.0000.0140.010 0.000 0.000 0.000 0.000 104 847296 0.9900.9581.000 0.0000.010 0.000 0.000 0000 0.000 0.0000.042 0.000 0.0000.000 0.000 176175174173 SouthForkDjxieLawrenceCr(belowbarrier)LawrenceCr(abovebarrier)LastChanceCreek Creek 50203044 1.0001.000 0.0000.000 100 406088 1.000 0.000 0.000 0.000 0.000 0.000 100 406088 0.9891.000 0.000 0.000 0.000 0.000 0.0000.011 0.0000.000 0.000 180179178177 CottonwoodBlackSnow CreekCanyon Creek Creek 5052 10001.0001.000 0.0000.000 100104 5250 1.000 0.0000.000 0.000 0.000 0.000 0.000 100104 0.956 0.9600.9500.981 0.0000.019 0000 0.000 0.0400.0150.000 0.000 0.0000.0290.050 0.000 0.0000.000 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map LDHC* .s*IDHA1,2* sMDHB1,2* No. SampleNaine N 100 97 N 100 155 72 37 120 49 N 100 83 116 70 92 78 124 120 181 Hog Creek 501.0000.000 1001.0000.0000.0000.0000.000 0.000 1000.9800.0200.0000.0000.0000.0000.0000.000 182 SouthForklndianCreek 521.0000.000 1041.0000.0000.0000.0000.0000.000 104 0.9800.0000.0200.0000.0000.0000.0000.000 183 DinnerCreek 501.0000.000 1000.8700.1300.0000.0000.000 0.000 1000.9700.0000.0000.0000.0300.0000.0000.000 184 CaffCreek 521.0000.000 1041.0000.0000.0000.0000.0000.000 104 0.7790.2210.0000.0000.0000.0000.0000.000 185 NorthFork Squaw Creek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.0000.0000.0000.0000.000 186 Carter Creek 521.000 0000 1041.0000.0000.0000.0000.000 0.000 1041.0000.0000.0000.0000.0000.0000.0000.000 187 DiyCreek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1000.9620.0380.0000.0000.0000.0000.0000.000 188 WestLittleOwyheeRiver 501.0000.000 1001.0000.0000.0000.0000.0000.000 1001.0000.0000.0000.0000.0000.0000.0000.000 189 Deep Creek 281.0000.000 561.0000.0000.0000.0000.0000.000 561.0000.0000.0000.0000.0000.0000.0000.000 190 Indian Creek 601.0000.000 1201.0000.0000.0000.0000.0000.000 1081.0000.0000.0000.0000.0000.0000.0000.000 191 Bridge Creek 64 0.9530.047 1281.0000.0000.0000.0000.0000.000 1280.9060.0550.0000.0000.0390.0000.0000.000 192 Krumbo Creek 921.0000.000 1841.0000.0000.0000.0000.0000.000 164 0.6890.2990.0000.0000.0120.0000.0000.000 193 MudCreek 881.0000.000 1761.0000.0000.0000.0000.0000.000 176 0.8980.017 0.074 0.0000.0110.0000.0000.000 194 SmythCreek 901.0000.000 1801.0000.0000.0000.0000.0000.000 1801.0000.0000.0000.0000.0000.0000.0000.000 195 Upper Sawmill Creek 201.0000.000 401.0000.0000.0000.0000.0000.000 400.7250.0250.0000.0000.0000.0000.2500.000 196 Lower Sawmill Creek 401.0000.000 801.0000.0000.0000.0000.0000.000 800.8630.0130.0000.0000.0000.0000.1250.000 197 Home Creek #1 401.0000.000 801.0000.0000.0000.0000.0000.000 801.0000.0000.0000.0000.0000.0000.000 0000 198 Home Creek2 201.0000.000 401.0000.0000.0000.0000.0000.000 401.0000.0000.0000.0000.0000.0000.0000.000 199 UpperHomeCreek 301.0000.000 601.0000.0000.0000.0000.0000.000 601.0000.00000000.0000.0000.0000.0000.000 200 AugurCreek 2810000.000 561.000 0000 0.0000.0000.0000.000 561.0000.0000.0000.0000.0000.0000.000 0000 201 Dairy Creek 321.0000.000 641.0000.0000.0000.0000.0000.000 64 0.9530.0470.0000.0000.0000.0000.0000.000 202 BeatCreek 641.0000.000 1280.934 0.0000.0000.0160.0000.000 1200.9170.0830.0000.0000.0000.0000.0000.000 203 Elder Creek 381.0000.000 721.0000.0000.0000.0000.0000.000 761.0000.0000000 0.0000.0000.0000.0000.000 204 Witham Creek 341.0000.000 681.0000.0000.0000.0000.0000.000 680.9410.0590.000 0000 0.0000.0000.0000.000 205 Bridge Creek 401.0000.000 801.0000.0000.0000.0000.0000.000 800.9880.0130.0000.0000.0000.0000.0000.000 206 Buck Creek 601.0000.000 1201.0000.0000.0000.0000.0000.000 1200.9670.0250.0000.0000.0000.0000.0080.000 207 Beaver Creek 301.0000.000 601.0000.0000.0000.0000.0000.000 600.9500.0500.0000.0000.0000.0000.0000.000 208 Camp Creek 601.0000.000 801.0000.0000.0000.0000.0000.000 1200.9920.008 0000 0.0000.0000.0000.0000.000 209 Cox Creek 361.0000.000 721.0000.0000.0000.0000.0000.000 720.9720.0140.0000.0000.0000.000 0014 0.000 210 Thomas Creek 361.0000.000 68 0.9850.0000.0000.0150.0000.000 721.0000.0000000 0.0000.0000.0000.0000.000 211 BeaverCreek 1381.0000.000 2761.0000.0000.0000.0000.0000.000 2761.0000.0000.0000.0000.0000.0000.0000.000 212 Fall Creek 460.9780.022 921.0000.0000.0000.0000.0000.000 920.9130.0870.0000.0000.0000.0000.0000.000 213 JeimyCreek#1 781.0000.000 1561.0000.0000.0000.0000.0000.000 1560.8970.1030.0000.0000.0000.0000.0000.000 214 JennyCreek#2 741.0000.000 1401.0000.0000.0000.0000.0000.000 148 0.9660.0000.034 0.0000.0000.000 0000 0000 215 JohnsonCreek#1 401.0000.000 801.0000.0000.0000.0000.0000.000 801.0000.0000.0000.0000.0000.0000.0000.000 216 JohnsonCreek#2 741.0000.000 1481.0000.0000.0000.0000.0000.000 1480.9930.007 0000 0.0000.0000.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map LDHC* sMDHA1,2* sMDHB1,2* No. SampleNanie N 100 97 N 100 155 72 37 120 49 N 100 83 116 70 92 78 124 120 217Shoat Springs 1680.9820.018 3361000 00000.0000.0000.0000.000 336 0.946 0.054 0.0000.0000.0000.0000.0000.000 218 WillowCreek 1541.0000.000 3081.0000.0000.0000.0000.0000.000 3080.8640.1360.0000.0000.0000.0000.0000.000 219 Deming Creek 621.0000.000 1241.0000.0000.0000.0000.0000.000 124 0.9350.0000.0000.0000.0000.0650.0000.000 220 Paradise Creek 321.0000.000 641.0000.0000.0000.0000.0000.000 640.8280.0000.0160.0000.0000.1560.0000.000 221 Paradise Creek 201.0000.000 401.0000.0000.0000.0000.0000.000 400.9000.0250.0500.0000.0000.0250.0000.000 222 Deep Creek 141.0000.000 281.0000.0000.0000.0000.0000.000 280.9640.0360.0000.0000.0000.0000.0000.000 223 WjlljamsonRjver#1 401.0000.000 801.0000.0000.0000.0000.0000.000 800.9250.0750.0000.0000.0000.0000.0000.000 224 Williamson River #2 12 1.0000.000 241.0000.0000.0000.0000.0000.000 24 0.9580.0000.0420.0000.0000.0000.000 0.000 225 Bogus Creek 901.0000.000 1801.0000.0000.0000.0000.0000.000 1800.8940.0000.0060.0000.1000.0000.0000.000 226 Kiamath River 361.0000.000 721.0000.0000.0000.0000.0000.000 720.9170.0000.0140.0000.0690.0000.0000.000 227 SpencerCreek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1000.9800.0100.0100.0000.0000.0000.000 0.000 228 Spencer Creek 281.0000.000 921.0000.0000.0000.0000.0000.000 920.9130.0430.0430.0000.0000.0000.0000.000 229 Rock Creek 221.0000.000 441.0000.0000.0000.0000.0000.000 440.9320.0000.0680.0000.0000.0000.0000.000 230 WoodCreek 561.0000.000 1121.0000.0000.0000.0000.0000.000 1120.9820.0000.0000.0000.0000.0180.000 0.000 231 Spring Creek 541.0000.000 1080.9910.0000.0000.0090.0000.000 1080.9810.0000.0190.0000.0000.0000.0000.000 232 Spring Creek 481.0000.000 96 0.9900.0000.0000.0100.0000.000 96 0.9790.0000.0210.0000.0000.0000.000 0.000 233 TroutCreek 501.0000.000 1001.0000.0000.0000.0000.0000.000 1000.9800.0100.0000.0000.0000.0100.0000.000 234 Trout Creek 821.0000.000 1641.0000.0000.0000.0000.0000.000 920.9240.0220.0000.0000.0000.0540.000 0.000 235 Honey Creek #1 301.0000.000 601.0000.0000.0000.0000.0000.000 600.8670.1330.0000.0000.0000.0000.0000.000 236 HoneyCreek#2 321.0000.000 641.0000.0000.0000.0000.0000.000 640.8750.1250.0000.0000.0000.0000.0000.000 237 NorthFork Deep Creek 301.0000.000 601.0000.0000.0000.0000.0000.000 600.9000.0830.0000.0000.0000.0000.0170.000 238 Deep Creek 301.0000.000 601.0000.0000.0000.0000.0000.000 600.8170.1000.0000.0000.0000.0000,0830.000 239 Willow Creek #1 401.0000.000 761.0000.0000.0000.0000.0000.000 800.9000.1000.0000.0000.0000.0000.0000.000 240 \VillowCreek#2 201.0000.000 401.0000.0000.0000.0000.0000.000 400.9250.0750.0000.0000.0000.0000.0000.000 241 Cape Cod strain 1601.0000.000 3201.0000.0000.0000.0000.0000.000 3200.7590.2410.0000.0000.0000.0000.0000.000 242 Oak Springs strain 1541.0000.000 3081.0000.0000.0000.0000.0000.000 308 0.9030.0970.0000.0000.0000.0000.0000.000 243 Soda Creek 800.9870.013 1601.0000.0000.0000.0000.0000.000 1600.9810.0190.0000.0000.0000.0000.0000.000 244 Coastalcutthroattrout 4341.0000.000 8681.0000.0000.0000.0000.0000.000 868 0.9200.0060.0060.0000.0240.0450.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mIvIEPI * .sMEP-1 * sAeIEP-2 * PEP-A * No. SampleName N 100 90 115 N 100 83 107 N 100 83 110 N 100 111 93 1 GraysRiver 1801.0000.0000.000 1800.8280.1720.000 2001.0000.0000.000 200 0.9900.0100.000 2 Elochomanwinterstram 2001.0000.0000.000 200 0.9500.0500.000 2001.0000.0000.000 1981.0000.0000.000 3 Big Creek winter strain 2001.0000.0000.000 200 0.8000.2000.000 2001.0000.000 0.000 2001.0000.0000.000 4BigCreekwinterstrain 1341.0000.0000.000 1340.9030.097 0.000 1901.0000.0000.000 1901.0000.0000.000 5 BigCreekwinterstrain 2000.9800.0000.020 196 0.8320.1680.000 2001.0000.0000.000 2001.0000.0000.000 6 Big Creek winter strain 94 0.9570.0000.043 90 0.9440.0560.000 1001.0000.0000.000 1001.0000.0000.000 7 Cowljtzlatewjnterstrain 1541.0000.0000.000 154 0.6170.3830.000 1981.0000.0000.000 1981.0000.0000.000 8 Cowljtzsunimerstrajn 1701.0000.0000.000 1700.7820.2180.000 1601.0000.0000.000 1801.0000.0000.000 9 Cowlitzwinterstrain 1961.0000.0000.000 196 0.8420.1580.000 1661.0000.0000.000 2001.0000.0000.000 10 Toutle River 1001.0000.0000.000 1000.6300.3700.000 1001.0000.0000.000 1001.0000.0000.000 11 Coweeman River 1481.0000.0000.000 1480.7970.2030.000 1481.0000.0000.000 1481.0000.0000.000 12 Skamania summer strain 1841.0000.0000.000 184 0.9620,0380.000 1941.0000.0000.000 194 0.9380.0620.000 13 Skamania summer strain 2001.0000.0000.000 200 0.9600.0400.000 2001.0000.0000.000 190 0.9480.0520.000 14 Skamanja summer strain 1001.0000.0000.000 1000.9200.0800.000 1001.0000.0000.000 94 0.9890.0110.000 15 Skamania summer strain 100 0.9900.0100.000 1000.9600.0400.000 1001.0000.0000.000 1000.9700.0300.000 16 Skamania summer strain 1841.0000.0000.000 1840.8210.1790.000 2001.0000.0000.000 200 0.9500.0100.040 17 Skamaniasummerstrajn 2001.0000.0000.000 200 0.9600.0400.000 2001.0000.0000.000 1780.9890.0110.000 18 Skamaniawinterstrajn 2001.0000.0000.000 2001.0000.0000.000 1981.0000.0000.000 2001.0000.0000.000 19 EagleCreekwinterstrain 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 20 EagleCreekwinterstrain 1261.0000.0000.000 1260.9760.0240.000 1721.0000.0000.000 2001.0000.0000.000 21 Willamettewinterstrain 2001.0000.0000.000 2001.0000.0000.000 2000.9950.0050.000 1800.9390.0610000 22 Calapooia River 1981.0000.0000.000 1981.0000.0000.000 1981.0000.0000.000 2001.0000.0000.000 23 CalapooiaRiver 941.0000.0000.000 941.0000.0000.000 941.0000.0000.000 94 0.9890.0110.000 24 Thomas Creek 1601.0000.0000.000 1601.0000.0000.000 1601.0000.0000.000 200 0.9900.0100.000 25 ThomasCreek 1101.0000.0000.000 1101.0000.0000.000 1101.0000.0000.000 1100.9640.0360000 26Thomas Creek 481.0000.0000.000 481.0000.0000.000 481.0000.0000.000 480.9380.0630.000 27 Wiley Creek 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 200 0.9600.0400.000 28 Wiley Creek 541.0000.0000.000 541.0000.0000.000 541.0000.0000.000 540.9630.0370.000 29 Sandy River 1901.0000.0000.000 1900.8790.1210.000 2000.9850.0150.000 200 0.9600.0400.000 30 HamiltonCreek 1061.0000.0000.000 1060.8870.1130.000 1061.0000.0000.000 1060.9720.0280.000 31 NealCreek 1001.0000.0000.000 1000.9400.0600.000 1001.0000.0000.000 1000.9900.0100.000 32 WindRiver 1001.0000.0000.000 1000.8400.1600.000 1001.0000.0000.000 1001.0000.0000.000 33 Wind River 501.0000.0000.000 500.8240.1760.000 501.0000.0000.000 501.0000.0000.000 34 EightmileCr#1 381.0000.0000.000 381.0000.0000.000 381.0000.0000.000 381.0000.0000.000 35 EightmileCr#2 341.0000.0000.000 341.0000.0000.000 341.0000.0000.000 341.0000.0000.000 36 Fifleenmile Creek 1001.0000.0000.000 1000.9400.0600.000 1641.0000.0000.000 1641.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map ,rMEP]* sMEP1* sMEP2* PEP-A5 No. SampleName N 100 90 115 N 100 83 107 N 100 83 110 N 100 111 93 37Fifteenmile Creek 1001.0000,0000.000 1001.0000.0000.000 1001.0000.0000.000 1000.9800.0200.000 38 BakeovenCreek 3961.0000.0000.000 3961.0000.0000.000 3961.0000.0000.000 3960.9070.0850.008 39 BuckHollowCreek 3941.0000.0000.000 3941.0000.0000.000 3941.0000.0000.000 3940.9230.0360.041 40 Desehutes resident strain 1801.0000.0000.000 1800.9670.0330.000 180 0.9670.0330.000 1600.9440.0060.050 41 Deschutes River 2541.0000.0000.000 2541.0000.0000.000 2541.0000.0000.000 2540.9290.047 0.024 42 LowerNena Creek 1401.0000.0000.000 1401.0000.0000.000 1401.0000.0000.000 1400.9640.0290.007 43 Mid-Nena Creek 1341.0000.0000.000 1341.0000.0000.000 1341.0000.0000.000 1340,9400.0600.000 44 UpperNena Creek 861.0000.0000.000 861.0000.0000.000 861.0000.0000.000 860.8020.1050.093 45 BigLogCreek 1401.0000.0000.000 1401.0000.0000.000 1401.0000.0000.000 1400.8640.0860.050 46 LowerEastFoleyCreek 301.0000.0000.000 301.0000.0000.000 301.0000.000 .000 301.0000.0000.000 47 UpperEastFoleyCreek 1521.0000.0000.000 1521.0000.0000.000 1521.0000.0000.000 1520.9070.0860.007 48 Deschutes summer strain 2001.0000.0000.000 2001.0000.0000.000 1791.0000.0000.000 194 0.9280.0720.000 49 Deschutessunnnerstrain 1961.0000.0000.000 1961.0000.0000.000 1921.0000.0000.000 1960.9080.0820.010 50 Deschutessummerstrain 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 200 0.9330.0670.000 51 CrookedRivergorge 141.0000.0000.000 141.0000.0000.000 160.9380.0630.000 161.0000.0000.000 52 LowerCrookedRiver 281.0000.0000.000 281.0000.0000.000 28 0.8930.1070.000 280.8570.1430.000 53 BowmanDam 1080.9540.0460.000 1680.9760.0240.000 168 0.9700.0300.000 1680.8870.1130.000 54 MckayCreek 461.0000.0000.000 681.0000.0000.000 681.0000.0000.000 680.8680.1030.029 55 Ochoco Creek 660.9850.0150.000 66 0.9550.0300.015 661.0000.0000.000 640.7810.2030.016 56 Marks Creek 700.9140.0860.000 700.9710.0140.014 681.0000.0000.000 700.9710.0000.029 57 HorseHeavenCr 660.9090.0910.000 661.0000.0000.000 661.0000.0000.000 660.7880.1970.015 58 Pine Creek 721.0000.0000.000 721.0000.0000.000 721.0000.0000.000 680.9560.0440.000 59 I,00koutCr 600.9830.0170.000 62 0.9520.0480.000 621.0000.0000.000 580.9480.0520.000 60 Howard Creek 961.0000.0000.000 104 0.9420.0580.000 1040.9710.0290.000 1060.9620.0280.009 61 FoxCanyonCr 581.0000.0000.000 76 0.9740.0260.000 76 0.9870.0130.000 761.0000.0000.000 62 Deep Creek 381.0000.0000.000 561.0000.0000.000 720.9860.0140.000 260.9620.0380.000 63 Deer Cr 681.0000.0000.000 681.0000.0000.000 681.0000.0000.000 521.0000.0000.000 64 Deardorfl'Creek 381.0000.0000.000 381.0000.0000.000 381.0000.0000.000 380.9740.0260.000 65 DeardorffCreek 301.0000.0000.000 30 0.9670.0330.000 301.0000.0000.000 300.9000.0670.033 66 VinegarCreek 421.0000.0000.000 421.0000.0000.000 781.0000.0000.000 780.8850.1150.000 67 Vinegar Creek 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 68 Granite Creek 581.0000.0000.000 581.0000.0000.000 841.0000.0000.000 840.9400.0600.000 69 Meadow Creek 341.0000.0000.000 341.0000.0000.000 341.0000.0000.000 340.9710.0290.000 70 GrasshopperCreek 1201.0000.0000.000 1200.9830.0170.000 1641.0000.0000.000 1640.9630.0370.000 71 SouthForkheadwaters 1421.0000.0000.000 1421.0000.0000.000 1421.0000.0000.000 1420.8520.1480.000 72 Izee Falls 1561.0000.0000.000 1560.9740.0260.000 1560.9940.0060.000 1560.9360.064 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName rn!vIEP-1 * N 100 90 115 sMEPI * N 100 83 107 sMEP2* N 100 83 110 PEP-A * N 100 111 93 76757473 NorthForkUmatillaRiverWillowKlickitatSouthForkatRockpileRanc Creek River 150160200 0.9931.000 0.0070.0000.000 0.0000.000 200150160 0.9871.000 0.000 0.0130.000 150200160 0.9881.000 0.0000.013 0.000 200150158160 0.910 0.8670.9270.944 0.0730.1330.050 0.040 0.0000.006 80797877 ThomasBuckNorthForkUmatillaRiver Creek Creek 488872 500.972 1.000 0.000 0.0000.028 48885072 1.000 0.000 0.000 48885072 1.000 0.000 0.000 485044 0.880 0.9550.958 0.0420.1200.045 0.000 0.000 84838281 CampSouthSouthForkUmatillaRiverThomas ForkCreek Creek Umatilla River 46665070 1.0001.000 0.000 0.000 46665070 0.9850.9800.9571.000 0.0000.015 0.0430.0000.020 46665070 1.000 0.000 0.000 4666505848 0.9400.9580.914 0.086 0.000 0.8910.939 0,0430.0610.060 0.000 0.0650.000 88878685 UpperMeachamCreekNorthNorthForkMeachamCreekCamp ForkCreek Meacham Creek 489082 1.0000.9881.000 0.000 0.0000.012 489082 1.000 0.000 0.000 489082 1.000 0.000 0.000 489682 0.8330.792 0.9280.835 0.0820.1670.048 0.0820.0420.024 92909189 SquawUpperSquawCreekLowerSquawCreekUpperMeachamCreek Creek 116 547686 1.0001.000 0.000 0.000 116 865476 0.9911.000 0.000 0.0000.009 116 865476 1.000 0.0000.000 0.000 116 74 88540.946 0.9200.8360.926 0.0800.1470.0740.0270.167 0.0170.0270000 0.000 '94969593 EastEastBirchCreekMcKay Birch Creek Creek 80244850 1.0000.9791.000 0.0000.021 0.0000.000 80502248 1.0000.955 0.0000.045 0.000 84502448 1.000 0.0000.000 0.000 2448 80500.917 0.9000.9200.917 0.0630.0600.083 0.0380.0200.000 100 999897 WestBirchCreekWestPearson Birch Creek Creek 72884456 1.000 0.000 0.0000.000 72568844 0.977 1.0001.000 0.000 0.0000.023 72568844 1.000 0.000 0.000 44 7256880.841 0.9180.9110.909 0.0550.0890.091 0.0000.0680.0270.000 104103102101 UmatillasummerstrainBinghamSpringsEastForkButterCreek 100 5082 1.000 0.0000.000 0.0000.000 100 8250 0.9901.0001.000 0.0100.000 0.000 200 8250 1.000 0.000 0.000 200196 0.950 8250 0.8980.8540.880 0.0500.1020.0610.120 0.0000.085 108107106105 LowerWallaWallaRiverTouchetUmatillasummerstrain White River River 200356 84 1.000 0.000 0.000 200356 84 0.988 1.0001.000 0.0120.000 0.000 356100 8480 0.988 0.9881.000 0.0120.0130.000 0.000 356 0.949 848094 0.8250.9261.000 0.0000.1750.0740.039 0.0000.011 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName SJDJ *N 100 90 115 N * 100 83 107 sIvIEP2* N 100 83 110 PEP-A * N 100 111 93 112111110109 LittleBadgerCreekJordanCreekUpperTyghCreekLowerTyghCreek 100126136 62 1.000 0.0000000 0.000 0.000 100126136 62 0.984 0016 0.000 0.9211.0001.000 0.0000.079 0.000 100126136 62 0.984 0.9681.000 0.0000.0320.016 0.000 100126136 62 0.95210001.000 0.0000.048 0.000 116115114113 BarlowGateCreekRockCreekThreemileCreek Creek 116100 80 1.000 0.0000.000 0.000 116100 0.99180 1.0000.9741.000 0.0090.0000.026 0.0000.000 116100 80 0.9910.9831.000 0.0090.0000.017 0.000 116100 80 0.9370.9910.9701.000 0.0000.0630.0090.030 0.000 120119118117 PeshastinMadRiverWellssummerstrainFawnCreek Creek 192 969880 1.000 0.0000.000 0.000 192 969880 1.0001.000 0.000 0000 0.000 192100162110 0.9551.000 0.000 0.0000.045 192162116 094898 0.052 0.000 0.9380.9590.907 0.0630.0410.093 0.000 124123122121 BigCanyonCreekMissionSatus Creek Creek 200124192100 1.000 0.0000.000 0.000 200124192100 1.000 0.000 0.000 176 609795 1.000 0.000 0.000 177 609896 0.8870.8000.8160.906 0.090 0.2000.1840.094 0.0230.000 128127126125 MeadowFishPahsimeroiDworshaksummerstrain Creek Creek B strain 196100146 1.0001000 0.0000.000 0.000 196100146 1.0001.000 0.000 0.000 100146196 1.000 0.000 0.000 196100146 92 0.5400.5410.8210.707 0.1790.2930.4600.459 0.0000.000 132131130129 IndianHorseChamberlainCreekSheepCr CreekCreek 554100122116 1.0001000 0.0000.000 OMOO 0.000 554100122116 1.0001.000 0.000 0.000 554122240102 1.000 0.000 0.000 554100184240 0.9600,9510.958 0.0400.0380.042 0.0000.0000.011 136135134133 TucannonRjverSawtoothstrajnSeceshJohnson River Creek 100200 80 1.000 0.0000.000 0.000 200100 80 1,0001.000 0.000 0.000 224100122 0.996 1.000 0.0040.000 0.0000.000 224100122 0.879 0.9500.9840.830 0.0500.0160.1600.116 0.0040.0000.010 140139138137 LimberFyCreekFlyTucannonRjver Creek Jim Creek 100 6040 1.000 0.000 0.000 100 6040 1.000 0.000 0.000 100 409960 1.0001.000 0.000 0.0000.000 100 6040 0.9330.8500.9250.900 0.1300.0500.0170.100 0.0500.0200.0250.000 144143142141 LaddMeadowChickenSheep Creek Creek Creek 506456 1.000 0.000 0.000 506456 1.000 0.000 0.000 506456 1.0001.000 0.000 0.000 506456 0.9600.9530.9641.000 0.0000.0400.0470.036 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mMEP-1 * sMEP-] * sMEP2* PEP-A * No. SampleName N 100 90 115 N 100 83 107 N 100 83 110 N 100 111 93 145 Wallowa summer strain 1461.0000.0000.000 1461.0000.0000.000 1981.0000.0000.000 200 0.9300.0600.010 146 Wallowa River 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 147 Wallowa River 741.0000.0000.000 741.0000.0000.000 741.0000.0000.000 740.9190.0810.000 148 Lostine River 941.0000.0000.000 941.0000.0000.000 941.0000.0000.000 941.0000.0000.000 149 Lostine River 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500.9400.0600.000 150 Broady Creek 541.0000.0000.000 541.0000.0000.000 54 0.9630.0000.037 54 0.9440.0560.000 151 Horse Creek 521.0000.0000.000 520.8850.0000.115 520.9810.0000.019 520.9230.0770.000 152 Jarboe Creek 1381.0000.0000.000 1381.0000.0000.000 1380.9930.0000.007 1760.8410.1590.000 153 Little Lookingglass Creek 1001.0000.0000.000 1001.0000.0000.000 1001.0000.0000.000 1000.9300.0700.000 154 Mottet Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500.9800.0200.000 155 Swamp Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 156 Cook Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500.8000.2000.000 157 ChertyCreek 521.0000.0000.000 521.0000.0000.000 520.9810.0000.019 521.0000.0000.000 158 Gumboot Creek 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 461.0000.0000.000 159 Grouse Creek 561.0000.0000.000 561.0000.0000.000 561.0000.0000.000 560.9640.0360.000 160 Grouse Creek 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 00.0000.0000.000 161 Big Sheep Creek 901.0000.0000.000 901.0000.0000.000 901.0000.0000.000 900.9560.0440.000 162 Big Sheep Creek 741.0000.0000.000 741.0000.0000.000 741.0000.0000.000 00.0000.0000.000 163 Imnaha summer strain 1881.0000.0000.000 1881.0000.0000.000 2001.0000.0000.000 2000.9900.0100.000 164 Niagarasummerstrain 200 0.9950.0050.000 2001.0000.0000.000 2000.9950.0050.000 1920.9480.0420.010 165 McGraw Creek 521.0000,0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 166 Conner Creek 501.0000.0000.000 501.0000.0000.000 50 0.9600.0000.040 500.9800.0200.000 167 Northpine Creek 481.0000.0000.000 481.0000.0000.000 480.9790.0000.021 480.8540.1460.000 168 Big Creek 28 0.964 0.0360.000 281.0000.0000.000 28 0.9640.0000.036 280.9290.0710.000 169 Indian Creek 481.0000.0000.000 481.0000.0000.000 481.0000.0000.000 481.0000.0000.000 170 Summit Creek 521.0000.0000.000 521.0000.0000.000 520.9810000 0019 520.9420.0580.000 171 Sutton Creek 361.0000.0000.000 361.0000.0000.000 360.9720.0000.028 361.0000.0000.000 172 Dixie Creek 421.0000.0000.000 421.0000.0000.000 421.0000.0000.000 420.9760.0240.000 173 Last Chance Creek 441.0000.0000.000 441.0000.0000.000 441.0000.0000.000 441.0000.0000.000 174 LawrenceCr(abovebarrier) 301,0000.0000.000 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 175 LawrenceCr(belowbarrier) 201.0000.0000.000 201.0000.0000.000 201.0000.0000.000 201.0000.0000.000 176 South Fork Dixie Creek 500.960 00400.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 177 Snow Creek 521.0000.0000.000 521.0000.0000.000 521.000 0000 0000 521.0000.0000.000 178 BlackCanyonCreek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 179 Cottonwood Creek 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 180 Cottonwood Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map mA'IEP-1 * sMEP-1 * avIEP2* PEP-A * No. SampleName N 100 90 115 N 100 83 107 N 100 83 110 N 100 111 93 181 HogCreek 500.9800.0200.000 501.0000.0000.000 500.9600.0400.000 500.9800.0200.000 182 SouthForklndianCreek 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 183 Dinner Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 184 CalfCreek 520.9420.0580.000 520.8650.1350.000 520.8650.1350.000 500.9200.0800.000 185 NorthForkSquawCreek 500.9800.0200.000 501.0000.0000.000 50 0.9200.0800.000 500.8000.2000.000 186 Carter Creek 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 187 DryCreek 501.0000.000 0.000 501.0000.0000.000 501.0000.0000.000 500.9230.0770.000 188 WestLittleOwyheeRiver 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 189 Deep Creek 281.0000.0000.000 281.0000.0000.000 281.0000.0000.000 281.0000.0000.000 190 Indian Creek 61.0000.0000.000 60.6670.0000.333 601.0000.0000.000 601.0000.0000.000 191 Bridge Creek 641.0000.0000.000 64 0.9380.0630.000 641.0000.0000.000 641.0000.0000.000 192 Krumbo Creek 921.0000.0000.000 920.7610.2390.000 92 0.8040.1960.000 921.0000.0000.000 193 Mud Creek 881.0000.0000.000 881.0000.0000.000 881.0000.000 0.000 881.0000.0000.000 194 SmythCreek 901.0000.0000.000 901.0000.0000.000 901.0000.0000.000 900.9890.0110.000 195 Upper Sawmill Creek 201.0000.0000.000 200.9500.0000.050 201.0000.0000.000 201.0000.0000.000 196 LowerSawmillCreek 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 197 Home Creek #1 400.9250.0750.000 401.0000.0000.000 401.000 0000 0.000 401.0000.0000.000 198 Home Creek #2 201.0000000 0.000 20 0900 0.0000.100 201.0000.0000.000 201.0000000 0.000 199 UpperllomeCreek 301.0000.0000.000 30 0.9330.0000.067 301.0000.0000.000 301.0000.0000.000 200 Augur Creek 281.0000.0000.000 281.0000.0000.000 281.0000.0000.000 281.0000.0000.000 201 Dairy Creek 321.0000.0000.000 321.0000.0000.000 321.0000.000 0.000 321.0000.0000.000 202 Bear Creek 0 0.0000.0000.000 00.0000.0000.000 641.0000.0000.000 641.0000.0000.000 203 Elder Creek 361.0000.0000.000 381.0000.0000.000 381.0000.0000.000 381.0000000 0.000 204 Witham Creek 00.0000.0000.000 00.0000.0000.000 341.0000.0000.000 341.0000.0000.000 205 Bridge Creek 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 380.9740.0000.026 206 Buck Creek 601.0000.0000.000 601.0000.0000.000 601.0000.0000.000 600.9500.0000.050 207 Beaver Creek 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 300.9670.0000.033 208 Camp Creek 600.9830.0170.000 601.0000.0000.000 601.0000.0000.000 600.9830.0000.017 209 CoxCreek 360.9720.0280.000 221.0000.0000.000 360.9440.000 0.056 361.0000.0000.000 210 Thomas Creek 00.0000.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 211 BeaverCreek 1381.0000.0000.000 1381.0000.0000.000 1381.0000.0000.000 1380.8840.1010.015 212 Fall Creek 461.0000.0000.000 1521.0000.0000.000 461.0000.0000.000 46 0.9780.0000.022 213 Jenny Creek #1 781.0000.0000.000 781.0000.0000.000 781.0000.0000.000 780.8970.1030.000 214 JennyCreek#2 741.0000.0000.000 741.0000.0000.000 741.000 0000 0.000 74 0.8780.0680.054 215 Johnson Creek #1 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 40 0.9000.0750.025 216 JohnsonCreek#2 741.0000.0000.000 741.0000.0000.000 741.0000.0000.000 740.9460.0270.027 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map nilvfEP-1 * sMEP..1 * sMEP-2 * PEP-A * No. SampleName N 100 90 115 N 100 83 107 N 100 83 110 N 100 111 93 217 Shoat Springs 1681.0000.0000.000 1161.0000.0000.000 1681.0000.0000.000 1680.9170.0830.000 218 WillowCreek 1541.0000.0000.000 1541.0000.0000.000 1541.0000.0000.000 1580.9170.0700.013 219 DemingCreek 621.0000.0000.000 621.0000.0000.000 621.0000.0000.000 620.9030.0970.000 220 Paradise Creek 321.0000.0000.000 321.0000.0000.000 321.0000.0000.000 320.7810.2190.000 221 ParadiseCreek 201.0000.000 0.000 201.0000.0000.000 201.0000.0000.000 200.8500.1500.000 222 Deep Creek 141.0000.000 0.000 141.0000.0000.000 141.0000.0000.000 141.0000.0000.000 223 WjfljarnsonRjver#J 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 80.7500.2500.000 224 WilliamsonRiver42 12 1.0000.0000.000 121.0000.0000.000 121.0000.0000.000 120.9170.0830.000 225 BogusCreek 901.0000.0000.000 901.0000.0000.000 901.0000.0000.000 900.9890.0110.000 226 Klainath River 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 360.9720.0280.000 227 Spencer Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500,9800.0200.000 228 Spencer Creek 461.0000.0000.000 461.0000.0000.000 46 0.9570.0430.000 460.9570.0430.000 229 Rock Creek 221.0000.0000.000 221.0000.0000.000 221.0000.0000.000 40.5000.5000.000 230 Wood Creek 561.0000.0000.000 561.0000.0000.000 561.0000.0000.000 560.9640.0360.000 231 Spring Creek 541.0000.0000.000 541.0000.0000.000 541.0000.0000.000 540.9630.0190.019 232 Spring Creek 481.0000.0000.000 481.0000.0000.000 48 0.9170.0420.042 480.9580.0210.021 233 Trout Creek 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500.9600.0400.000 234 Trout Creek 821.0000.0000.000 821.0000.0000.000 821.0000.0000.000 820.9630.0240.012 235 Honey Creek #1 301.0000.0000.000 301.0000.0000.000 300.9670.0330.000 300.9670.0000.033 236 Honey Creek #2 321.0000.0000.000 321.0000.0000.000 321.0000.0000.000 320.9060.0000.094 237 NorthForkDeepCreek 201.0000.0000.000 201.0000.0000.000 301.0000.0000.000 301.0000.0000.000 238 Deep Creek 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 301,0000.0000.000 239 Willow Creek #1 400.9750.0250.000 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 240 Willow Creek #2 201.0000.0000.000 201.0000.0000.000 201.0000.0000.000 200.9500.0000.050 241 Cape Cod strain 1601.0000.0000.000 1601.0000.0000.000 1601.0000.0000.000 1600.994 0006 0000 242 OakSpringsstrain 1541.0000.0000.000 1540.8310.1690.000 1540.8310.1690.000 1540.9870.0000.013 243 Soda Creek 801.0000.0000.000 800.9380.0630.000 801.0000.0000.000 800.9120.0630.025 244 Coastalcutthroattrout 4341.0000.0000.000 4341.0000.0000.000 434 0.0120.0000.988 434 0.0690.9310.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. Map No. SampleName PEPBJ * N 100 134 69 50 83 PEPC* N 100 110 92 PEPD-J * N 100 93 118 PGM2* N -100 -120 -10 432 1 BigCreekwinterstrainElochomanwinterstrainGrays River 200190180 1.000 0.0000.000 0.000 0.000 0.000 200190198 1.000 0.000 0.000 190200198 1.000 0.000 0.000 200190 1.000 0.000 0.000 8765 CowlitzsummerstrajnCowlitzlatewinterstrajnBigCreekwinterstrain 200180198100 0.9501.000 0.0000.050 0.000 0.000 0.000 200180198100 1.000 0.000 0.000 180200198100 1.000 0.000 0.000 200180198100 1.000 0.000 0.000 121110 9 SkamaniaCoweemanRiverToutleCowlitz River winter summer strain strain 200194146100 0.990 0.9931.0001.000 0.0000.007 0.0000.010 0.000 0.000 200192148100 1.000 0.000 0.000 192148100200 1.000 0.000 0.000 200148100 94 1.000 0.000 0.000 16151413 SkamaniasummerstrainSkamaniasunmierstrainSkamaniasummerstrajn 200100 86 1.000 0.000 0.000 0.000 0.000 200100 1.000 0.000 0.000 200100 1.000 0.000 0.0000.000 200100190 0.9701.000 0.0000.030 0.000 20191817 EagleEagleCreekwinterstrainSkamaniaSkamaniasummerstrain Creekwinterstrain winter strain 200190164 80 0.9420.9881.000 0.0580.000 0.0000.012 0.000 0.000 200160 1.000 0.000 0.000 200160 1.000 0.000 0.0000.000 200140 1.000 0.000 0.000 24232221 ThomasCalapooiaCalapooiaRiverWillamette Creek River winter strain 200170 94 1.000 0000 0.000 0.000 0.000 0.000 0.000 200198 94 1.000 0.000 0.000 200198 94 1.000 0.000 0.0000.000 200136 94 1.000 0.0000000 0.0000000 0.000 28272625 ThomasThomasCreekWiley Creek Creek 200110 5448 0.981 1.000 0.0190.000 0.0000.000 0.0000.000 0000 0.000 200110 5448 1.000 0.000 0.000 200110 5448 1.000 0.000 0.000 200110 4854 1.0001000 0000 0.000 0.000 0.000 32313029 NealHamiltonWindRiverSandy Creek RiverCreek 200100106 0.9900.9911.000 0.0000.0100.009 0.0000.000 0.000 0000 0.000 200100106 1.000 0.000 0.000 200100106 1.000 0.000 0.000 200100106 1.000 0.0000000 0.000 0.000 36353433 FifteenmileEightmileCr#2EightmileCr#1Wind River Creek 164 343850 0.9471.000 0.000 0.0000.053 0.000 0.000 164 343850 1.000 0.000 0.000 164 343850 1.000 0000 0.000 0.000 0.000 164 343850 1.000 0.000 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map PEPB-1 * PEP.C* PEPD-1 * PGM2* No. SampleName N 100 134 69 50 83 N 100 110 92 N 100 93 118 N -100 -120 -10 37Fifteenmile Creek 980.9800.0000.0200.000 0.000 1001.0000.0000.000 1001.0000.0000.000 1001.0000.0000.000 38 Bakeoven Creek 3961.0000.0000.0000.000 0.000 3961.0000.0000.000 3961.0000.0000.000 3961.0000.0000.000 39 Buckllollow Creek 3941.0000.0000.0000.000 0.000 3941.0000.0000.000 3941.0000.0000.000 3941.0000.0000.000 40 Deschutes resident strain 1801.0000.0000.0000.000 0.000 1801.0000.0000.000 1801.0000.0000.000 1801.0000.0000.000 41 Deschutes River 2541.0000.0000.0000.000 0.000 2541.0000.0000.000 2541.0000.0000.000 2541.0000.0000.000 42 LowerNena Creek 1401.0000.0000.0000.0000.000 1401.0000.0000.000 1401.0000.0000.000 1401.0000.0000.000 43 Mid-Nena Creek 1341.0000.0000.0000.0000.000 1341.0000.0000.000 1341.0000.0000.000 1341.0000.0000.000 44 UpperNena Creek 861.0000.0000.0000.0000.000 861.0000.0000.000 861.0000.0000.000 861.0000.0000.000 45 Big Log Creek 1401.0000.0000.0000.000 0.000 1401.0000.0000.000 1401.0000.0000.000 1401.0000.0000.000 46 LowerEastFoleyCreek 301.0000.0000.0000.000 0.000 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 47UpperEastFoleyCreek 1521.0000.0000.0000.000 0.000 1521.0000.0000.000 1521.0000.0000.000 1521.0000.0000.000 48 Deschutessummerstrain 2000.9900.0100.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 1560.9870.0130.000 49 Deschutessummerstrain 1860.9680.0000.0320.0000.000 1981.0000.0000.000 1981.0000.0000.000 2001.0000.0000.000 50 Deschutessummerstrain 2001.0000.0000.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 51 CrookedRivergorge 141.0000.0000.0000.0000.000 161.0000.0000.000 161.0000.0000.000 141.0000.0000.000 52 LowerCrookedRiver 201.0000.0000.0000.000 0.000 281.0000.0000.000 141.0000.0000.000 260.8850.1150.000 53 BowmanDam 1660.8730.1270.0000.000 0.000 1681.0000.0000.000 1540.9940.0060.000 1560.9810.0060.013 54 Mckay Creek 661.0000.0000.0000.0000.000 681.0000.0000.000 681.0000.0000.000 680.9120.0880.000 55 Ochoco Creek 660.8480.1520.0000.0000.000 661.0000.0000.000 221.0000.0000.000 620.9520.0160.032 56 Marks Creek 660.8640.0760.0610.0000.000 681.0000.0000.000 620.9840.0160.000 68 08090.0000.191 57 HorseHeavenCr 660.7730.2120.0150.000 0.000 661.0000.0000.000 661.0000.0000.000 480.6670.2290.104 58 Pine Creek 660.8790.1060.0150.000 0.000 721.0000.0000.000 181.0000.0000.000 721.0000.0000.000 59 Lookout Cr 620.9350.0650.0000.0000.000 621.0000.0000.000 541.0000.0000.000 581.0000.0000.000 60 HowardCreek 1080.9350.0460.0190.0000.000 741.0000.0000.000 1060.9720.0280.000 1000.8600.1000.040 61 Fox Canyon Cr 421.0000.0000.0000.0000.000 761.0000.0000.000 761.0000.0000.000 761.0000.0000.000 62 Deep Creek 720.9720.0280.0000.000 0.000 721.0000.0000.000 521.0000000 0.000 721.0000.0000.000 63 Deer Cr 320.9380.0310.0310.0000.000 681.0000.0000.000 341.0000.0000.000 681.0000.0000.000 64 DeardorffCreek 381.0000.0000.0000.0000.000 381.0000.0000.000 381.0000.0000.000 381.0000.0000.000 65 DeardorffCreek 301.0000.0000.0000.0000.000 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 66 Vinegar Creek 781.0000.0000.0000.000 0.000 801.0000.0000.000 801.0000.0000.000 781.0000.0000.000 67 Vinegar Creek 361.0000.0000.0000.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 68 Granite Creek 84 0.9880.0000.0120.0000.000 841.0000.0000.000 841.0000.0000.000 840.9880.0120.000 69 Meadow Creek 340.9410.0000.0000.0590.000 341.0000.0000.000 341.0000.0000.000 341.0000.0000.000 70 Grasshopper Creek 164 0.9570.0430.0000.0000.000 1641.0000.0000.000 1641.0000.0000.000 1641.0000.0000.000 71 SouthForkheadwaters 1420.9150.0850.0000.000 0.000 1421.0000.0000.000 1421.0000.0000.000 1420.9860.0140.000 72 Izee Falls 1560.942 0058 0.0000.000 0.000 1561.0000.0000.000 1561000 0.0000.000 1560.9940.0060.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. Map No. SampleName PEPB-1 * N 100 134 69 50 83 PEP-CS N 100 110 92 PEPD-1 * N 100 93 118 PGM-2 * N -100 -120 -10 76757473 NorthForkUmatillaRiverWillowKlickitatSouthForkatRockpileRanc Creek River 150160120134 0.9730.9881.000 0.0070.0130.000 0.0200.000 0.000 0.000 200150160 1.000 0.000 0.000 150160200 1.000 0.000 0.000 200150160 0.960 0.9811.000 0.0000.0400.019 0.000 79787780 ThomasBuckNorthForkUmatillaRiver Creek Creek 48728850 0.9771.000 0.0000.023 0.0000.000 0.000 0.000 48885072 1.000 0.000 0.000 48885072 1.0001.000 0.000 0.000 48885072 0.9800.9861.000 0.0000.0200.014 0.000 84828381 CampSouthForkUmatjflaRiverSouthForkUmatillaRiverThomas Creek Creek 46667050 0.980 1.000 0.0000.020 0.0000.000 0.000 0.000 46665070 1.000 0.000 0.000 46667050 1.000 0.000 0.000 46665070 1.000 0.000 0.000 88878685 UpperMeachamCreekNorthForkMeachamCreekCamp Creek 489082 0.979 1.000 0.0210.000 0.0000.0210.000 0.0000,000 0.000 489082 1.000 0.000 0.000 489082 1.0001.000 0.000 0.000 48489082 1.000 0.0000.000 0.000 92919089 UpperSquawCreekSquawLowerUpperMeachainCreek SquawCreek Creek 116 8676 560.977 0.9830.8751.000 0.0230.0000.018 0.0000.0170.1070.000 0.000 0.000 116 865476 1.0001000 0.000 0.000 0.000 116 768654 1.000 0.000 0.000 116 865476 1.000 0.000 0.000 96959493 EastMcKay Birch Creek Creek 74244850 0.9801.000 0.000 0.0000.0000.020 0,0000.000 0.000 84502448 1.000 0.000 0.000 48845024 1.000 0.000 0.000 80502448 0.9171.000 00000.0000.083 0.000 0.000 100 999897 WestPearson Birch Creek Creek 7244 56880.9860.955 0.9641.000 0.0000.0360.023 0.0140.0000.023 0.000 0.000 72568844 1.000 0.0000.000 0.000 72568844 1.0001000 0.000 0.000 0.0000.000 72568844 1.000 0.000 0.000 101104103102 UmatillasummerstrainBinghamEast Fork SpringsButter Creek 200 0.990 8250 1.000 0.0100.000 0.000 0.000 0.000 200198 8250 1.000 0.0000.000 0.000 200198 8250 1.000 0.000 0.0000.000 200 8250 0.9881.000 0.0000.012 0.000 108107106105 LowerWhiteRiverWallaWallaRiverTouchetUmatillasummerstrain River 356 0.99796 84800.979 0.9001.000 0.0000.0380.0210.003 0.000 0.0000.063 0.000 356100 8480 1.000 0.0000.000 0.000 100356 8480 1.000 0.000 0.0000.000 356100 8480 0.965 10001.0000.994 0.0350.0000.006 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. MapNo. SampleName PEPB-J * N 100 134 69 50 83 PEPC* N 100 110 92 PEPD-1 * N 100 93 118 PGM-2 N -100 -120 -10 112111110109 LittleBadgerCreekJordanCreekUpperTyghCreekLower TyghCreek 100126136 62 1.000 0.0000.000 0.000 0.000 0.000 100126136 62 1.000 0.000 0.000 126100136 62 1.000 0.000 0.0000.000 100126136 62 0.9801.000 0.0200.0000.000 0.000 116115114113 BarlowCreekGateRockCreekThreemileCreek Creek 116100 80 1.000 0.000 0.000 0.000 0.000 116100 80 1.000 0.000 0.000 116100 80 1.000 0.000 0.000 116100 80 0.9910,8880.9901.000 0.0090.0000.1120.010 0.000 120119118117 PeshastinCreekMadRiverWellssummerstrainFawnCreek 192162116 80 1.000 0.000 0.000 0.000 0.000 188100162116 1.000 0.000 0.000 188100162116 1.000 0.000 0.000 194100122116 0.9901.000 0.0000.0100.000 0.000 124123122121 BigCanyonCreekMissionCreekSatus Creek 176 609896 1.000 0.000 0.000 0.000 0.000 176 609896 1.000 0.000 0.000 176 609896 1.000 0.000 0.000 172100 6094 0.9840.9801.000 0.0000.0160.020 0.000 128127126125 MeadowFishPahsimeroiDworshaksummerstrain Creek Creek B strain 146196100 1.000 0.0000.000 0.000 0.000 0.000 196100146 1.000 0.000 0.000 196100146 1.000 0.000 0.000 194100146 0.9791.000 0.0210.000 0.000 132131130129 IndianCreekHorseChamberlainCreekSheep CreekCr 554240102194 0.9891.000 0.0110.000 0.000 0.000 0.000 238548100190 1.000 0.0000.O00 0.0000.000 238548100190 1.000 0.000 0.000 238554100194 1.000 0.000 0.000 136135134133 TucannonSawtoothstrainSeceshRiverJohnsonCreek River 224100114 0.9961.000 0,0040.000 0.000 0.000 0.000 226100122 1.000 0.000 0.000 226100122 1.000 0.0000.000 0.000 224100122102 1.0000.980 0.0000.020 0.000 140139138137 LimberFyFlyTucannonRiver Creek Creek Jim Creek 100 6040 0.9901.000 0.0000.010 0.000 0.000 0.000 100 6040 1.000 0.000 0.000 100 6040 1.000 0.000 0.000 100 6040 1.0001.000 0.000 0.0000.000 144143141142 LaddMeadowChickenSheep Creek Creek Creek 506456 0.9840.9821.000 0.000 0.0000.0160.018 0.000 0.000 506456 1.000 0.000 0.000 645056 1.000 0.000 0.000 645056 1.000 0.000 0.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map PEPB-J * PEPC* PEPD-1 * PGM2* No. SampleName N 100 134 69 50 83 N 100 110 92 N 100 93 118 N -100 -120 -10 145 Wallowasummerstrain 2001.0000.0000.0000.0000.000 2001.0000.0000.000 2001.000 0000 0.000 2001.0000.0000.000 146 WallowaRiver 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 147 Wallowa River 741.0000.0000.0000.0000.000 741.0000.0000.000 741.0000.0000.000 741.0000.0000.000 148 Lostme River 941.0000.0000.0000.0000.000 941.0000.0000.000 941.0000.0000.000 941.0000.0000.000 149 Lostine River 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 150 Broady Creek 541.0000.0000.0000.0000.000 541.0000.0000.000 541.0000.0000.000 541.0000.0000.000 151 Horse Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 152 Jarboe Creek 1381.0000.0000.0000.0000.000 1381.0000.0000.000 1381.0000.0000.000 1381.0000.0000.000 153 Little Lookingglass Creek 1001.0000.0000.0000.0000.000 1001.0000.0000.000 1001.0000.0000.000 1001.0000.0000.000 154 Mottet Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.000 0.000 155 Swamp Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 156 Cook Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 157 Cherry Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 158 Gumboot Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 159 Grouse Creek 561.0000.0000.0000.0000.000 561.0000.0000.000 561.0000.0000.000 561.0000.0000.000 160 Grouse Creek 361.0000.0000.0000.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 161 BigSheepCreek 901.0000.0000.0000.0000.000 901.0000.0000.000 901.0000.0000.000 901.0000.0000.000 162 BigSheepCreek 741.0000.0000.0000.0000.000 741.0000.0000.000 741.0000.0000.000 741.0000.0000.000 163 Jmnaha summer strain 2001.0000.0000.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 164 Niagara summer strain 2000.9900.0100.0000.0000.000 2001.0000.0000.000 2001.0000.0000.000 200 0.9950.0050.000 165 McGraw Creek 520.9420.0580.0000.0000.000 520.9230.0770.000 521.0000.0000.000 521.0000.0000.000 166 Conner Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.000' 0.0000.000 167 NorthPine Creek 481.0000.0000.0000.0000.000 481.0000.0000.000 481.0000.0000.000 48 0.9790.0210.000 168 Big Creek 280.964 0.0360.0000.0000.000 281.0000.0000.000 281.0000.0000.000 280.9290.0710.000 169 Indian Creek 481.0000.0000.0000.0000.000 481.0000.0000.000 481.0000.0000.000 481.0000.0000.000 170 Summit Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 171 Sutton Creek 361.0000.0000.0000.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 172 Dixie Creek 421.0000.0000.0000.0000.000 421.0000.0000.000 421.0000.0000.000 421.0000.0000.000 173 Last Chance Creek 441.0000.0000.0000.0000.000 441.0000.0000.000 441.0000.0000.000 441.0000.0000.000 174 LawrenceCr(abovebarrier) 301.0000.0000.0000.0000.000 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 175 LawrenceCr(belowbarrier) 201.0000.0000.0000.0000.000 201.0000.0000.000 201.0000.0000.000 201.0000.0000.000 176 SouthForkDixieCreek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 177 Snow Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 178 BlackCanyonCreek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 179 Cottonwood Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 520.9040.0960.000 180 Cottonwood Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map PEPB-J * PEPC* PEPD-1 * PGM2* No. SampleName N 100 134 69 50 83 N 100 110 92 N 100 93 118 N -100 -120 -10 181 Hog Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 182 SouthForklndianCreek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 183 Drnner Creek 501,0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 184 Calf Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 520.9230.0770.000 185 NorthForkSquawCreek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 186 Carter Creek 521.0000.0000.0000.0000.000 521.0000.0000.000 521.0000.0000.000 521.0000.0000.000 187 Dry Creek 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 500.9810.0190.000 188 WestLittleOwyheeRiver 501.0000.0000.0000.0000.000 501.0000.0000.000 501.0000.0000.000 501.0000.0000.000 189 Deep Creek 281.0000.0000.0000.0000.000 281.0000.0000.000 281.0000.0000.000 281.0000.0000.000 190 Indian Creek 540.9440.0000.0560.0000.000 601.0000.0000.000 461.0000.0000.000 601.0000.0000.000 191 Bridge Creek 641.0000.0000.0000.0000.000 641.0000.0000.000 641.0000.0000.000 641.0000.0000.000 192 Krumbo Creek 921.0000.0000.0000.0000.000 921.0000.0000.000 921.0000.0000.000 920.9780.0220.000 193 Mud Creek 881.0000.0000.0000.0000.000 881.0000.0000.000 881.0000.0000.000 880.9090.0910.000 194 SmythCreek 901.0000.0000.0000.0000.000 901.0000.0000.000 901.0000.0000.000 901.0000.0000.000 195 Upper Sawmill Creek 00.0000.0000.0000.0000.000 201.0000.0000.000 201.0000.0000.000 201.0000.0000.000 196 Lower Sawmill Creek 00.0000.0000.0000.0000.000 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 197 Home Creek #1 401.0000.0000.0000.0000.000 401.0000.0000.000 400.9750.0000.025 401.0000.0000.000 198 Home Creek #2 201.0000.0000.0000.0000.000 201.0000.0000.000 201.0000.0000.000 200.9500.0000.050 199 UpperHomeCreek 300.9670.0000.0330.0000.000 301.0000.0000.000 301.0000.0000.000 301.0000.0000.000 200 Augur Creek 180.5000.0000.5000.0000.000 281.0000.0000.000 281.0000.0000.000 280.8930.1070.000 201 Dairy Creek 300.9000.0000.1000.0000.000 321.0000.0000.000 321.0000.0000.000 321.0000.0000.000 202 Bear Creek 00.0000.0000.0000.0000.000 641.0000.0000.000 641.0000.0000.000 64 0.9840.0160.000 203 Elder Creek 140.2860.0000.7140.0000.000 381.0000.0000.000 360.9720.0000.028 361.0000.0000.000 204 WithamCreek 00.0000.0000.0000.0000.000 341.0000.0000.000 341.0000.0000.000 341.0000.0000.000 205 Bridge Creek 400.9250.0000.0750.0000.000 401.0000.0000.000 401.0000.0000.000 401.0000.0000.000 206 Buck Creek 380.9210.0000.0790.0000.000 601.0000.0000.000 601.0000.0000.000 60 0.9670.0330.000 207 Beaver Creek 180.2780.0000.7220.0000.000 301.0000.0000.000 361.0000.0000.000 300,9670.0000.033 208 Camp Creek 580.3620.0000.638 0.0000.000 601.0000.0000.000 601.0000.0000.000 600.9830.0000.017 209 Ccx Creek 36 0.194 0.0000.8060.0000.000 361.0000.0000.000 361.0000.0000.000 360.9720.0280.000 210 Thomas Creek 28 0.5360.0000.4640.0000.000 361.0000.0000.000 361.0000.0000.000 361.0000.0000.000 211 BeaverCreek 1381.0000.0000.0000.0000.000 1381.0000.0000.000 1381.0000.0000.000 138 0.9060.0940.000 212 FaliCreek 461.0000.0000.0000.0000.000 461.0000.0000.000 461.0000.0000.000 46 0.8700.1300.000 213 Jenny Creek #1 78 0.9490.0510.0000.0000.000 78 0.8720.0900.038 781.0000.0000.000 78 0.8460.1540.000 214JennyCreek#2 74 0.9730.0270.0000.0000.000 74 0.9320.0000.068 741.0000.0000.000 74 0.7700.2300.000 215 JohnsonCreek#1 401.0000.0000.0000.0000.000 40 0.9000.1000.000 401.0000.0000.000 401.0000.0000.000 216 JohnsonCreek#2 740.9730.0000.0270.0000.000 741.0000.0000.000 741.0000.0000.000 74 0.7970.2030.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout. Map No. SampleName PEPB-1 * N 100 134 69 50 83 PEPC* N 100 110 92 PEPD-1 * N 100 93 118 PGM2* N -100 -120 -10 220219218217 ParadiseDemingCreekWillowShoat Springs Creek Creek 154168 0.9873262 0.7101.000 0.0000.000 0.0000.2900.013 0.000 0.000 154168 3262 0.9420.9291.000 0.0000.0580.071 0.000 154168 0.9943262 1.000 0.0000.006 0.000 154168 3262 0.9841.000 0.0000.016 0.000 224223222221 WilliamsonRiver#2WilliamsonlRiver#1DeepParadise Creek Creek 12402014 0.7501.000 0.0000.000 0.0000.250 0.000 0.000 12402014 1.000 0.000 0.000 40201214 1.000 0.000 0.000 0.000 40201210 1.0000.950 0.0000.050 0.000 228227226225 SpencerSpencerCreekKlamathBogus Creek CreekRiver 46503690 0,9800.9720.9441.000 0.0000.0200.0280.022 0.0000.033 0.000 0.000 503690 0.9176 0.8401.000 0.0000.1600.083 0.000 46369050 1.000 0.000 0.000 46503690 0.9400.9440.9571.000 0.0430.0600.0560.000 0.000 232231230229 SpringSpringCreekWoodRock Creek CreekCreek 48545622 0.944 0.9381.000 0.0000.0210.019 0.0420.0370.000 0.000 0.000 0.000 48545622 0.9091.000 0.0000.091 0.000 48545622 1.000 0.000 0.000 48545622 0.982 1.0000.9091.000 0.0000.0180.091 0.000 235234233236 HoneyCreek#2HoneyCreek#1Trout Creek 32308250 0.9060.8670.9020.980 0.0000.0850.020 0.0940.1330.0120.000 0.000 0.000 0.000 32308250 1.000 0.0000,000 0.000 32308250 1.000 0.0000.000 0.000 32308250 0.8780.8801.000 0.0000.1220.120 0.000 240239238237 WillowWillowCreek#1DeepNorthForkDeepCreek Creek Creek #2 203012 0.9001.000 0.000 0.0000.100 0.000 0.000 204030 1.000 0.000 0.0000.000 204030 1.000 0.000 0.000 203038 0.974 1.000 0.0000.026 0.0000.000 244243242241 CoastalcutthroattroutSodaCreekOakCape Springs Cod strain strain 434154160 80 0.8710.9121.000 0.0000.063 0.0000.025 0.000 0.1290.000 434154160 80 1.0000.937 0.0000.063 0.000 434154160 80 1.000 0.000 0.000 434160154 0.81280 0.9941.000 0.0000.1880.006 0.0000.000 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map sSOD-J * Polymorphic Alleles/ Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosity 1 GraysRiver 192 0.6820.3180.0000.000 9 1.46 6 0.075 2 Elochomanwmterstrain 196 0.6790.3210.0000.000 9 1.46 5 0.070 3 BigCreekwinterstrain 196 0.6020.3980.0000.000 7 1.32 3 0.058 4 Big Creek winter strain 1720.5990.4010.0000.000 7 1.39 4 0.063 5 Big Creek winter strain 1980.6520.3480.0000.000 8 1.39 4 0.066 6 Big Creek winter strain 1000.7500.2500.0000.000 9 1.50 4 0.072 7 Cowlitzlatewinterstrain 1760.6080.3920.0000.000 8 1.36 2 0.078 8 Cowlit.z summer strain 1800.7780.2220.0000.000 8 1.43 4 0.072 9 Cowlit.z winter strain 200 0.6600.3400.0000.000 9 1.43 5 0.070 10 Toutle River 980.5820.4180.0000.000 9 1.50 6 0.090 11 CoweemanRiver 1320.6590.3410.0000.000 9 1.50 5 0.079 12 Skamarnasummerstrain 194 0.7220.2780.0000.000 10 1.46 5 0.065 13 Skamania summer strain 194 0.7780.2220.0000.000 9 1.43 5 0.065 14 Skamania summer strain 1000.6600.3400.0000.000 10 1.50 5 0.082 15 Skamaniasummerstrain 1000.6500.3500.0000.000 10 1.46 5 0.081 16 Skamaniasummerstrain 1920.8180.1820.0000.000 9 1.43 3 0.087 17 Skamaniasummerstrain 1960.6890.3110.0000.000 10 1.46 6 0.065 18 Skamaniawinterstrain 184 0.5980.4020.0000.000 8 1.39 4 0.059 19 Eagle Creekwinterstrain 1280.7420.2580.0000.000 6 1.32 4 0.055 20 Eagle Creekwinterstrain 1600.7380.2630.0000.000 8 1.43 3 0.062 21 Willamette winter strain 194 0.4480.5520.0000.000 9 1.39 4 0.080 22 Calapooia River 1180.5680.4320.0000.000 6 1.29 4 0.056 23 Calapooia River 94 0.5850.4150.0000.000 6 1.25 2 0.064 24 Thomas Creek 1160.5780.4220.0000.000 8 1.32 4 0.063 25 ThomasCreek 1100.6270.3730.0000.000 8 1.32 3 0.061 26 Thomas Creek 480.5630.4380.0000.000 6 1.25 2 0.064 27 Wiley Creek 200 0.6200.3800.0000.000 7 1.39 5 0.080 28 Wiley Creek 540.6480.3520.0000.000 9 1.43 5 0.076 29 Sandy River 1980.7580.2420.0000.000 9 1.43 5 0.062 30 Hamilton Creek 1060.7080.2920.0000.000 12 1.54 7 0.073 31 Neal Creek 940.7340.2660.0000.000 9 1.39 4 0.060 32 Wind River 1000.7000.3000.0000.000 10 1.43 4 0.075 33 Wind River 500.8200.1800.0000.000 9 1.39 3 0.078 34 EightmileCr#1 38 0.9470.0530.0000.000 6 1.39 6 0.049 35 EightmileCr#2 341.0000.0000.0000.000 2 1.11 0 0.038 36 Fifteenmile Creek 1520.9280.0590.0130.000 8 1.46 7 0.058 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map sSOD-1 * Polymorphic Alleles! Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosity 37 Fifteenmile Creek 1000.7900.1500.0600.000 9 1.46 6 0.059 38 Bakeoven Creek 3960.9070.0530.0400.000 9 1.54 8 0.066 39 Buck Hollow Creek 394 0.9090.0250.0660.000 8 1.54 10 0.067 40 Deschutes resident strain 1800.9060.0940.0000.000 41 Deschutes River 254 0.9250.0390.0360.000 7 1.46 8 0.069 42 LowerNena Creek 1400.9430.0500.0070.000 8 1.39 6 0.060 43 Mid-Nena Creek 1340.9110.0890.0000.000 8 1.39 5 0.066 44UpperNena Creek 86 0.977 0.0000.0230.000 6 1.32 3 0.065 45 Big Log Creek 1400.9790.0140.0070.000 7 1.43 6 0.053 46 Lower East Foley Creek 300.9670.0000.0330.000 5 1.25 3 0.051 47UpperEastFoleyCreek 152 0.947 0.0190.0340.000 7 1.43 7 0.051 48Deschutessummerstrain 200 0.9100.0500.0400.000 12 1.54 9 0.069 49 Deschutessummerstrain 1980.8680.0910.0410.000 10 1.57 9 0.077 50 Deschutes summer strain 200 0.9100.0550.0350.000 8 1.39 5 0.064 51 CrookedRivergorge 161.0000.0000.0000.000 5 1.21 1 0.044 52 LowerCrookedRiver 260.9620.0000.0380.000 11 1.46 2 0.096 53 Bowman Dam 1680.9400.0600.0000.000 15 1.75 14 0.074 54Mckay Creek 680.9710.0290.0000.000 7 1.32 3 0.055 55 OchocoCreek 680.9560.0440.0000.000 11 1.61 11 0079 56 Marks Creek 700.9430.0570.0000.000 12 1.61 8 0.077 57 HorseHeavenCr 661.0000.0000.0000.000 9 1.46 4 0.097 58 Pine Creek 320.9690.0310.0000.000 7 1.32 5 0.043 59 LookoutCr 380.7890.2110.0000.000 11 1.46 7 0.071 60 Howard Creek 1060.9720.0280.0000.000 14 1.68 12 0.080 61 Fox Canyon Cr 700.9860.0140.0000.000 8 1.32 6 0.037 62 Deep Creek 720.9860.0140.0000.000 10 1.43 7 0056 63 Deer Cr 661.0000.0000.0000.000 5 1.25 5 0.037 64 Deardorif Creek 360.9440.0280.0280.000 6 1.36 6 0048 65 Deardorif Creek 300.9330.0000.0670.000 8 1.43 4 0,062 66 Vinegar Creek 740.9590.0270.0140.000 7 1.36 5 0.059 67 Vinegar Creek 361.0000.0000.0000.000 5 1.21 2 0045 68 Granite Creek 840.9880.0000.0120.000 7 1.29 3 0.044 69 Meadow Creek 341.0000.0000.0000.000 5 1.21 2 0.037 70 Grasshopper Creek 1620.9380.0310.0310.000 9 1.43 8 0.053 71 SouthForkheadwaters 1400.964 0014 0.0210.000 12 1.50 7 0.069 72 Izee Falls 1500.8130.1470.0400.000 15 1.79 14 0.083 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbowtrout.
Map .cSOD-1 Polorphic Alleles! Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosity 73 SouthForkatRockpileRanc 1600.9190.0440.0380.000 10 1.57 11 0.067 74 Klickitat River 198 0.7780.2120.0100.000 9 1.61 9 0.075 75 Willow Creek 1600.8560.0060.1380.000 7 1.32 3 0.064 76 NorthForkUmatjllaRjver 1520.9410.0330.0200.007 12 1.61 12 0.059 77 North Fork Uinatjlla River 720.9030.0970.0000.000 9 1.43 6 0.066 78 Buck Creek 81.0000.0000.0000.000 7 1.36 5 0.052 79 BuckCreek 880.9660.0110.0230.000 9 1.43 8 0.053 80 Thomas Creek 480.9790.0000.0210.000 7 1.29 4 0.048 81 Thomas Creek 700.9290.0290.0430.000 9 1.50 9 0.061 82 South Fork Umatjlla River 501.0000.0000.0000.000 7 1.32 2 0.057 83 SouthForkUmatjllaRjver 650.9540.0150.0310.000 9 1.43 7 0.051 84 Camp Creek 460.9350.0000.0650.000 6 1.43 4 0.063 85 Camp Creek 820.9020.0730.0240.000 10 1.50 9 0.060 86 NorthForlcMeachamCreek 48 0.9580.0000.0210.021 7 1.43 7 0.059 87 NorthForlcMeachamCreek 90 0.9780.0110.0110.000 8 1.54 9 0.059 88 Upper Meacham Creek 48 0.9790.0000.0210.000 8 1.36 4 0.059 89 UpperMeachamCreek 76 0.9470.0260.0260.000 7 1.46 9 0.052 90 Lower Squaw Creek 541.0000.0000.0000.000 8 1.43 6 0.060 91 UpperSquawCreek 1160.9910.0000.0090.000 11 1.54 9 0.050 92 Squaw Creek 860.9880.0000.0120.000 10 1.54 10 0,054 93 McKay Creek 480.9380.0630.0000.000 9 1.46 5 0.067 94 McKay Creek 241.000. 0.0000.0000.000 7 1.32 1 0.064' 95 EastBirchCreek 501.0000.0000.0000.000 6 1.32 5 0.054 96 EastBirchcreek 800.9880.0000.0130.000 8 1.43 6 0.055 97Pearson Creek 440.9550.0000.0230.023 10 1.57 9 0.078 98 PearsonCreek 880.9660.0000.0340.000 8 1.32 4 0.060 99 West Birch Creek 560.9640.0000.0000.036 7 1.32 4 0,051 100 WestBirchCreek 720.9440.0280.0280.000 6 1.36 6 0.047 101 EastForkButterCreek 501.0000.0000.0000.000 6 1.32 3 0.061 102 EastForkButterCreek 821.0000.0000.0000.000 6 1.39 5 0,059 103 Bingham Springs 1920.9480.0000.0520.000 8 1.36 4 0.063 104 Umatillasummerstrain 200 0.9800.0200.0000.000 10 1.46 8 0.052 105 Umatillasummerstrain 356 0.9860.0080.0060.000 11 1.75 17 0.059 106 TouchetRjver 1000.9900.0000.0100,000 8 1.43 7 0061 107 WallaWallaRjver 800.8630.0130.1250.000 9 1.46 6 0.082 108 LowerWhjteRjver 84 0.8930.1070.0000.000 9 1.43 5 0.058 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci inrainbow trout.
Map sSOD-1 * Polymorphic Alleles/ Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosity 109 LowerTyghCreek 620.8870.1130.0000.000 8 1.36 5 0.051 110 UpperTyghCreek 1361.0000.000 0.0000.000 2 1.11 2 0.016 111 JordanCreek 1260.8490.1510.0000.000 8 1.39 6 0.043 112 LittleBadgerCreek 1001.0000.0000.0000.000 4 1.14 1 0.037 113 Threemile Creek 100 0.9700.0300.0000.000 9 1.39 8 0.044 114 RockCreek 1160.6980.3020.0000.000 11 1.50 8 0.054 115 GateCreek 800.9750.0250.0000.000 6 1.25 3 0.039 116 Barlow Creek 1160.9910.0090.0000.000 8 1.39 8 0.042 117 FawnCreek 1160.9740.0090.0170.000 9 1.46 8 0.061 118 Wells summer strain 162 0.8960.0120.0920.000 8 1.39 5 0.061 119 Mad River 980.9590.0000.0410.000 7 1.43 7 0.056 120 PeshastjnCreek 194 0.9070.0310.0620.000 10 1.57 9 0.072 121 Satus Creek 94 0.9 150.0430.0430.000 8 1.43 7 0.059 122 SatusCreek 98 0.857 0.0000.1430.000 8 1.32 3 0.075 123 Mission Creek 60 0.917 0.0670.0170.000 7 1.32 3 0.064 124 Big Canyon Creek 1760.9270.0110.0620.000 6 1.36 4 0.057 125 Dworshaksummerstrajn 1461.0000.0000.0000.000 7 1.29 3 0.067 126 PahsjmerojBstrajn 1001.0000.0000.0000.000 5 1.25 2 0.068 127 Fish Creek 1000.9000.0000.1000.000 7 1.29 2 0.068 128 Meadow Creek 1960.9080.0410.0510.000 8 1.39 5 0.072 129 SheepCr 2400.8710.0080.1210.000 7 1.39 6 0.062 130 ChamberlajuCreek 1940.9590.0100.0310.000 8 1.54 10 0.058 131 Horse Creek 1001.0000.0000.0000.000 5 1.32 5 0.048 132 Indian Creek 5540.9100.0110.0790.000 9 1.46 7 0.063 133 Johnson Creek 980.8880.0410.0710.000 8 1.46 6 0.072 134 SeceshRiver 1220.8930.0000.1070.000 8 1.36 4 0.060 135 Sawtooth strain 1000.9100.0100.0800.000 6 1.39 5 0.061 136 Tucannon River 226 0.9290.0620.0090.000 9 1.50 8 0.059 137 Tucanrion River 1000.940 0.0000.0600.000 9 1.43 6 0.059 138 Fly Creek 40 0.9250.0750.0000.000 8 1.39 4 0.058 139 FyCreek 1000.9500.0000.0500.000 7 1.39 4 0.063 140 LimberJjmCreek 60 0.8830.117 0.0000.000 6 1.29 2 0.054 141 Sheep Creek 560.9640.0180.0180.000 8 1.36 7 0.043 142 Chicken Creek 640.8910.0000.1090.000 8 1.36 5 0.052 143 Meadow Creek 500.9000.0800.0200.000 8 1.36 6 0.049 144 Ladd Creek 500.9000.0400.0600.000 5 1.25 4 0.041 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbowtrout.
Map sSOD-1 * Polymorphic Alleles! Rare Average No. SampleName N 100 152 38 187 Loci Locus Alleles Heterozygosity 145 Wallowasummersfrarn 200 0.990 0.000 0010 0.000 7 1.39 5 0.053 146 WallowaRiver 520.9420.0000.0580.000 7 1.29 3 0.042 147 Wallowakiver 74 0.8510.0540.0950.000 6 1.36 2 0.061 148 Lostine River 94 0.9570.0430.0000.000 6 1.29 4 0.047 149 Lostine River 500.9600.0000.0400.000 6 1.29 3 0.045 150 Broady Creek 54 0.9440.0190.037 0.000 7 1.32 4 0.053 151 Horse Creek 521.0000.0000.000 0.000 9 1.39 5 0.074 152 Jarboe Creek 1381.0000.0000.000 0.000 6 1.29 5 0037 153 Little Lookingglass Creek 1001.0000.0000.0000.000 6 1.29 2 0.044 154 Mottet Creek 500.7000.1200.1800.000 8 1.39 5 0.069 155 SwampCreek 501.0000.0000.0000.000 3 1.14 2 0.023 156 Cook Creek 500.7200.0200.2600.000 9 1.43 5 0.071 157 CheriyCreek 521.0000.0000.0000.000 3 1.14 1 0.036 158 Gumboot Creek 520.9420.0000.0580.000 4 1.18 0 0.038 159 Grouse Creek 56 0.9820.000 0.0180.000 5 1.21 2 0.048 160 Grouse Creek 36 0.9720.000 0.0280.000 5 1.26 2 0.046 161 Big Sheep Creek 90 0.9330.0110.0560.000 5 1.29 3 0.055 162 BigSheepCreek 74 0.8920.0270.0810.000 5 1.26 1 0.047 163 Imnahasummerstrain 1780.9100.0280.0620.000 6 1.32 3 0.057 164 Niagarasummerstrain 2000.9500.0100.0400.000 12 1.61 12 0.059 165 McGraw Creek 521.0000.0000.0000.000 4 1.14 0 0.031 166 ConnerCreek 500.6200.3800.0000.000 8 1.36 3 0.070 167 NorthPjneCreek 481.0000.0000.0000.000 9 1.36 4 0.060 168 BigCreek 280.8930.0710.0360.000 11 1.46 4 0.081 169 IndianCreek 481.0000.0000.000 0.000 4 1.18 3 0.029 170 Summit Creek 520.9810.0190.0000.000 8 1.32 4 0.047 171 Sutton Creek 361.0000.0000.0000.000 4 1.18 2 0.026 172 Dixie Creek 421.0000.0000.0000.000 4 1.18 1 0.052 173 LastChance Creek 441.0000.0000.0000.000 4 1.18 1 0.038 174 LawrenceCr(abovebarrjer) 301.0000.0000,0000.000 3 1.18 1 0.043 175 LawrenceCr(belowbarrjer) 20 0.9000.1000.0000.000 4 1.21 1 0.058 176 South Fork Dixie Creek 500.9600.0400.0000.000 5 1.21 3 0.040 177 Snow Creek 521.0000.0000.0000.000 3 1.14 2 0.022 178 Black Canyon creek 500.6200.3200.0600.000 5 1.29 1 0.064 179 Cottonwood Creek 520.5960.1540.2500.000 6 1.32 2 0.059 180 Cottonwood Creek 501.0000.0000.0000.000 3 1.14 1 0.024 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map sSOD-1 * Polymorphic Alleles/ Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosity 181 Hog Creek 500.9800.0200.0000.000 8 1.36 6 0.051 182 SouthForklndianCreek 520.884 0.0000.1160.000 5 1.21 2 0.043 183 Dinner Creek 500.8200.1800.0000.000 6 1.29 2 0.067 184 Calf Creek 520.7880.2120.0000.000 10 1.43 1 0.087 185 NorthForkSquawCreek 500.7600.0000.2400.000 6 1.25 2 0.060 186 CarterCreek 521.0000.0000.0000.000 3 1.14 0 0.044 187 Dry Creek 500.9810.0190.0000.000 8 1,36 5 0.056 188 WestLittleOwyheeRiver 500.9600.0000.0400.000 4 1.18 1 0.038 189 Deep Creek 281.0000.0000.0000.000 3 1.18 2 0.027 190 Indian Creek 58 0.9140.0860.0000.000 7 1.32 1 0.057 191 Bridge Creek 64 0.9530.0470.0000.000 8 1.36 4 0.058 192 Krumbo Creek 92 0.576 0.4240.0000.000 9 1.39 5 0.079 193 Mud Creek 881.0000.0000.0000.000 6 1.36 5 0.049 194 SmythCreek 901.0000.0000.0000.000 5 1.29 5 0.042 195 UpperSawmillCreek 201.0000.0000.0000.000 8 1.37 2 0.075 196 Lower Sawmill Creek 40 0.500 0.5000.0000.000 8 1.44 1 0.087 197 Home Creek #1 401.0000.0000.0000.000 5 1.21 1 0.038 198 Home Creek #2 201.0000.0000.0000.000 5 1.21 0 0.043 199 Upper Home Creek 300.5330.4670.0000.000 5 1.21 1 0.053 200AugurCreek 281.0000.0000.0000.000 5 1.21 0 0.065 201 Dairy Creek 32 0.9060.0940.0000.000 7 1.29 2 0.059 202 BearCreek 64 0.9690.0310.0000.000 12 1.41 6 0.047 203 Elder Creek 381.0000.0000.0000.000 8 1.32 3 0.073 204WithamCreek 34 0.9410.0590.0000.000 11 1.37 2 0.083 205 Bridge Creek 400.9500.0500.0000.000 8 1.36 4 0.043 206 Buck Creek 60 0.9330.0670.0000.000 11 1.57 6 0.062 207Beaver Creek 30 0.7000.3000.0000.000 8 1.39 4 0.075 208 Camp Creek 580.414 0.5860.0000.000 9 1.36 7 0.056 209 Cox Creek 36 0.4170.5830.0000.000 9 1.39 6 0.065 210 Thomas Creek 34 0.324 0.6760.0000.000 8 1.30 2 0.059 211 BeaverCreek 1381.0000.0000.0000.000 4 1.29 2 0.049 212 Fall Creek 42 0.8580.0710.0710.000 8 1.39 4 0.054 213 Jenny Creek #1 780.8210.1790.0000.000 10 1.46 3 0.086 214 JennyCreek#2 74 0,9190.0810.0000.000 9 1.43 4 0.063 215 JohnsonCreek#1 400.7250.2750.0000.000 7 1.29 2 0.064 216 JohnsonCreek#2 74 0.8650.1350.0000.000 8 1.39 6 0.065 Appendix A. Allele frequencies and samples sizes (N) for polymorphic loci in rainbow trout.
Map sSOD-1 * Polymorphic Alleles! Rare Average No. Sample Name N 100 152 38 187 Loci Locus Alleles Heterozygosily 217Shoat Springs 1600.7620.0000.2380.000 8 1.36 3 0.056 218 WillowCreelc 154 0.864 0.1360.0000.000 11 1.54 8 0.051 219 Deming Creek 62 0.9680.0320.0000.000 8 1.32 3 0.062 220 Paradise Creek 32 0.9690.0310.0000.000 6 1.32 4 0.056 221 Paradise Creek 20 0.800 0.2000.0000.000 7 1.36 2 0.072 222 Deep Creek 14 0.8570.1430.0000.000 5 1.25 1 0.056 223 WilliamsonRiver#1 40 0.9000.100 0.0000.000 9 1.39 2 0.072 224 Williamson River #2 120.9170.0830.0000.000 5 1.18 1 0.037 225 Bogus Creek 900.8670.1330.0000.000 9 1.43 7 0.042 226 KlamathRiver 36 0.8060.194 0.0000.000 11 1.46 6 0.060 227 SpencerCreek 50 0.8800.1200.0000.000 12 1.50 7 0.054 228 Spencer Creek 460.7830.217 0.0000.000 8 1.32 7 0.037 229 Rock Creek 220.6820.3180.0000.000 10 1.36 1 0.086 230 Wood Creek 560.9640.0360.0000.000 7 1.32 7 0.030 231 SpringCreek 541.0000.0000.0000.000 7 1.36 8 0.023 232 SpringCreek 481.0000.0000.0000.000 8 1.43 10 0.031 233 Trout Creek 500.8600.1400.0000.000 10 1.46 10 0.036 234 Trout Creek 820.6710.3290.0000.000 8 1.46 6 0.054 235 HoneyCreek#1 280.5360.4640.0000.000 12 1.46 5 0.082 236 Honey Creek #2 400.6000.4000.0000.000 9 1.36 1 0.085 237 NorthForkDeep Creek 300.9330.0670.0000.000 8 1.44 4 0.063 238 Deep Creek . 30 0.8670.1330.0000.000 9 1.46 2 0.096 239 WillowCreek#1 38 0.9470.0530.0000.000 10 1.46 3 0.078 240 WillowCreek#2 200.9000.1000.0000.000 6 1.29 0 0.063 241 CapeCodstrain 1600.8870.1130.0000.000 8 1.36 5 0.049 242 Oak Springs strain 1460.5410.4590.0000.000 9 1.39 3 0.078 243 Soda Creek 800.7490.1880,0630.000 12 1.61 7 0.060 244 Coastal cutthroat trout 4340.93 10.0690.0000.000 10 1.50 8 0.050 263
APPENDIX B
Table B. 1. Ratings of reliability components for Yakima River spring chinook salmon. Hazards are 1) extinction, 2) loss of within-population genetic diveristy, 3) loss of between-population genetic diversity, and 4) domestication. Ratings forreserves were the following: Availability = 3; Appropriateness= 3; Sufficiency = 2.
Hazards
Reliability Components 2 3 4 1.1. Brood Stock Selection
1.1.1. Guidelines
1.1.1.1. Genetic Guidelines 5 5 5 5
1.1.1.2. Operating Guidelines 5 5 5 5
1.1.1.3.Decision Trees 3 3 3 3 1.1.2. Natural Variability
1.1.2.1. Baseline Characterization 5 5 5 5
1.1.2.2. Detection of Departures from Baseline 3 3 3 3
1.1.3.Logistics
1.1.3.1. Equipment 5 5 5 5
1.1.3.2, Coordination 5 5 5 5 1.1.4. Tecimician Ability & Judgment
1.l.4.1.TypelError 5 5 5 5
1.1.4.2.TypellError 5 5 5 5
1.2. Brood Stock Collection
1.2.1. Guidelines
1.2.1.1. Genetic Guidelines 5 5 5
1.2.1.2. Operating Guidelines 1 2 2 1
1.2.1.3. Decision Trees 2 1 3 1.2.2. Natural Variability
1.2.2.1. Baseline Characterization 3 3 5 3
1.2.2.2. Detection of Departures from Baseline 1 3 3 1.2.3. Logistics
1.2.3.1. Equipment 2 2 2 2 264
Table B. 1. Continued.
Hazards
Reliability Components 1 2 3 4
1.2.3.2. Coordination 2 2 2 2 1.2.4. Technician Ability & Judgment
1.2.4.1.TypelError 1 1 1 1
1.2.4.2. Type II Error 1 1 1 1
1.3. Mating
1.31. Guidelines
1.3.1.1. Genetic Guidelines 5 5 5
1.3.1.2. Operating Guidelines 2 5 2
1.3.1.3.DecisionTrees 3 1 3 1.3.2. Natural Variability
1.3.2.1. Baseline Characterization 3
1.3.2.2. Detection of Departures from Baseline 3 3
1.3.3. Logistics
1.3.3.1. Equipment 2 2 2
1.3.3.2. Coordination 2 2 2 1.3.4. Technician Ability & Judgment
1.3.4.1.TypelError 1 1 1
1.3.4.2.TypellError 1 1 1
1.4. Rearing
1.4.1. Guidelines
1.4. 1.1. Genetic Guidelines 5 5 5 5
1.4.1.2. Operating Guidelines 1 1 1 1
1.4.1.3. Decision Trees 1 1 1 1 1.4.2. Natural Variability
1.4.2.1. Baseline Characterization 3 3 3 3
1.4.2.2. Detection of Departures from Baseline 2 2 2 2 265
Table B. 1. Continued.
Hazards
Reliability Components 1 2 3 4 1.4.3. Logistics
1.4.3.1. Equipment 2 2 2 2
1.4.3.2. Coordination 2 2 2 2 1.4.4. Technician Ability & Judgment
l.4.4.l.TypelError 1 1 1 1
1.4.4.2. Type II Error I I I
1.5. Release (direct genetic effects) 1 2 3 4 1.5.1. Guidelines
1.5.1.1. Genetic Guidelines 3 3 5 5
1.5.1.2. Operating Guidelines 2 2 1
1.5.1.3. Decision Trees 1 1 1
1.5.2. Natural Variability
1.5.2.1. Baseline Characterization 3 2 1 3
1.5.2.2. Detection of Departures fromBaseline 3 2 2 2 1.5.3. Logistics
1.5.3.1. Equipment 2 2 2 2
1.5.3.2. Coordination 2 2 2 2 1.5.4. Technician Ability & Judgment
l.5.4.l.TypelError 1 1 1 1
1.5.4.2.TypellError I I I
1.6. Release (indirect ecological genetic effects)
1.6.1. Guidelines
1.6.1.1. Ecological Genetic Guidelines 3 3 3
1.6.1.2.OperatingGuidelines 2 2 2
1.6.1.3. Decision Trees 1 1 1.6.2. Natural Variability
1.6.2.1. Baseline Characterization 2 2 2 266
Table B. 1. Continued.
Hazards
Reliability Components 1 2 3 4
1.6.2.2. Detection of Departures from Baseline 1 1
1.6.3. Logistics
1.6.3.1. Equipment 2 2 2
1.6.3.2. Coordination 2 2 2 1.6.4. Technician Ability & Judgment
1.6.4.1.TypelError 1 1
1.6.4.2.TypellError 1 1
1.7. Juvenile Migration
1.7.1. Guidelines
1.7.1.1. Genetic Guidelines I
1.7.1.2. Operating Guidelines 2 2
l.7.1.3.DecisionTrees I
1.7.2. Natural Variability
1.7.2.1. Baseline Characterization 3 3
1.7.2.2. Detection of Departures from Baseline 2 2 1.7.3. Logistics
1.7.3.1. Equipment 2 2
1.7.3.2. Coordination 2 2 1.7.4. Technician Ability & Judgment
1.7,4.l.TypelError 1
1.7.4.2.TypellBrror 1
1.8. Adult Migration
1.8.1. Guidelines
1.8.1.1. Genetic Guidelines 2 2
1.8.1.2. Operating Guidelines 3 3 3
1.8.1.3. Decision Trees 3 3 1.8.2. Natural Variability 267
Table B. 1. Continued.
Hazards
Reliability Components 1 2 3 4
1.8.2.1. Baseline Characterization 3 3 3
1.8.2.2. Detection of Departures from Baseline 3 3 3
1.8.3. Logistics
1.8.3.1. Equipment 5 5 2
1.8.3.2. Coordination 3 3 1
1.8.4. Technician Ability & Judgment
1.8.4.1. Type I Error 1 1 1
1.8.4.2.TypellError 1 1 1 268
APPENDIX C
Table C. 1. Ratings of reliability components for Yakima River steelhead. Hazardsare 1) extinction, 2) loss of within-population genetic diveristy, 3) loss of between-population genetic diversity, and 4) domestication. Ratings for reserves were the following: Availability3 ; Appropriateness = 3; Sufficiency2.
Hazards
Reliability Components 1 2 3 4 1.1. Brood Stock Selection
1.1.1. Guidelines
1.1.1.1. Genetic Guidelines 5 5 5 5
1.1.1.2. Operating Guidelines 3 3 3 3
1.1.1.3. Decision Trees 2 2 2 2 1.1.2. Natural Variability
1.1.2.1. Baseline Characterization 3 3 3 3
1.1.2.2. Detection of Departures from Baseline 3 3 3 3 1.1.3. Logistics
1.1.3.1.Equipment 3 3 3 3
1.1.3.2. Coordination 3 3 3 3 1.1.4. Technician Ability & Judgment
1.1.4.1.TypelError 5 5 5 5
l.1.4.2.TypellError 5 5 5 5
1.2.Brood Stock Collection
1.2.1.Guidelines
1.2.1.1. Genetic Guidelines 5 5 5 5
1.2.1.2. Operating Guidelines 2 1 3
1.2.1.3. Decision Trees 2 1 2 1.2.2. Natural Variability
1.2.2.1. Baseline Characterization 2 2 3 2
1.2.2.2. Detection of Departures from Baseline 1 3 3 1 1.2.3. Logistics
1.2.3.1. Equipment 2 1 1 1 269
Table C. 1. Continued.
Hazards
Reliability Components 1 2 3 4
1.2.3.2. Coordination 2 2 2 2 1.2.4. Technician Ability & Judgment
1.2.4.l.TypelError 1 1 1 1
1.2.4.2.TypellError 1 1 1 1
1.3. Mating
1.3.1. Guidelines
1.3.1.1. Genetic Guidelines 5 5 5
1.3.1.2. Operating Guidelines 2 2 2
1.3.1.3. Decision Trees 3 1 3 1.3.2. Natural Variability
1.3.2.1. Baseline Characterization 2
1.3.2.2. Detection of Departures from Baseline 3 3 1.3.3. Logistics
1.3.3.1. Equipment 2 2 2
1.3.3.2. Coordination 2 2 2 1.3.4. Technician Ability & Judgment
l.3.4.1.TypelError 1 1
1.3.4.2.TypeflError 1 1
1.4. Rearing
1.4.1. Guidelines
1.4.1.1. Genetic Guidelines 5 5 5 5
1.4.1.2. Operating Guidelines I I 1 1
1.4.1.3. Decision Trees 1 1 1 1 1.4.2. Natural Variability
1.4.2.1. Baseline Characterization 2 2 2 2
1.4.2.2. Detection of Departures from Baseline 2 2 2 2 270
Table C. 1. Continued.
Hazards
Reliability Components 1 2 3 4 1.4.3. Logistics
1.4.3.1. Equipment 2 2 2 2
1.4.3.2. Coordination 2 2 2 2 1.4.4. Technician Ability & Judgment
1.4.4.1.TypelError 1 1 1
1.4.4.2.TypellError 1 1 1 1
1.5. Release (direct genetic effects) 1 2 3 4 1.5.1. Guidelines
1.5.1.1. Genetic Guidelines 3 3 3 5
1.5.1.2. Operating Guidelines 1 1 1 1
1.5.1.3. Decision Trees 1 1 1 1 1.5.2. Natural Variability
I .5.2, 1. Baseline Characterization 2 2 1 2
1.5.2.2. Detection of Departures from Baseline 2 2 2 2 1.5.3. Logistics
1.5.3.1. Equipment 2 2 2 2
1.5.3.2. Coordination 2 2 2 2 1.5.4. Technician Ability & Judgment
1.5.4.1.TypelError 1 1 1 1
1.5.4.2. Type II Error 1 1 1 1
1.6. Release (indirect ecological genetic effects) 1 2 3 4 1.6.1. Guidelines
1.6.1.1. Ecological Genetic Guidelines 3 3 3
1.6.1.2. Operating Guidelines 2 2 2
1.6.1.3. Decision Trees 1 1 1
1.6.2. Natural Variability
1.6.2.1. Baseline Characterization 2 2 2 271
Table C. 1. Continued.
Hazards
Reliability Components 1 2 3 4
1.6.2.2. Detection of Departures from Baseline 2 2 2 1.6.3. Logistics
1.6.3.1. Equipment 2 2 2
16.3.2. Coordination 2 2 2 1.6.4. Technician Ability & Judgment
1.6.4.l.TypelError 1 1
1.6.4.2.TypellError 1 1
1.7. Juvenile Migration 1 2 3 4 1.7.1. Guidelines
1.7.1.1. Genetic Guidelines 1 1 3
1.7.1.2. Operating Guidelines 2 2 3
1.7.1.3. Decision Trees 1 1
1.7.2. Natural Variability
1.7.2.1. Baseline Characterization 3 3 2
1.7.2.2. Detection of Departures from Baseline 3 3 1 1.7.3. Logistics
1.7.3.1. Equipment 2 2 1
1.7.3.2. Coordination 2 2 1
1.7.4. Technician Ability & Judgment
1.7.4.l.TypelError 1 1 1
1.7.4.2.TypellError 1 1 1
1.8. Adult Migration
1.8.1. Guidelines
1.8.1.1. Genetic Guidelines I I
1.8.1.2. Operating Guidelines 3 3 3
1.8.1.3. Decision Trees 3 3 272
Table C. 1. Continued.
Hazards
Reliability Components 1 2 3 4 1.8.2. Natural Variability
1.8.2.1. Baseline Characterization 2 2 3
1.8.2.2. Detection of Departures from Baseline 3 3 3
1.8.3. Logistics
1.8.3.1. Equipment 2 2 2
1.8.3.2. Coordination 2 2 2
1.8.4. Technician Ability & Judgment
l.8.4.1.TypelError 1 1
1.8.4.2. Type II Error 1 1 1