Genetic population structure of central Oregon Coast coho salmon

(Oncorhynchus kisutch)

Michael J. Ford*, David Teel, Donald M. Van Doornik, David Kuligowski &Peter W. LawsonNOAA-Fisheries, Northwest Fisheries Science Center, Conservation Biology Division, 2725 Montlake Blvd E,Seattle, WA 98112, USA (*Author for correspondence: fax: 206-860-3335; e-mail: mike.ford@noaa.gov)

Received 9 March 2004; accepted 1 May 2004

Key words: conservation unit, effective population size, migration, salmon

Abstract

We surveyed microsatellite variation from 22 spawning populations of coho salmon (Oncorhynchus kisutch)from the Oregon Coast to help identify populations for conservation planning. All of our samples weretemporally replicated, with most samples obtained in 2000 and 2001. We had three goals: (1) to confirm thestatus of populations identified on the basis of spawning location and life history; (2) to estimate effectivepopulation sizes and migration rates in order to determine demographic independence at different spatialscales; and (3) to determine if releases of Washington hatchery coho salmon in the 1980’s into OregonCoast streams resulted in measurable introgression into nearby wild Oregon Coast coho populations. Forthe last question, our study included a hatchery broodstock sample from 1985, after the Puget Soundintroduction, and a 1975 sample taken from the same area prior to the introduction. Our results generallysupported previously hypothesized population structure. Most importantly, we found unique lake-rearinggroups identified on the basis of a common life-history type were genetically related. Estimates of immi-grant fraction using several different methods also generally supported previously identified populations.Estimates of effective population size were highly correlated with estimates of spawning abundance. The1985 hatchery sample was genetically similar to contemporary Washington samples, and the contemporaryOregon Coast samples were similar to the 1975 Oregon Coast sample, suggesting that introductions ofWashington coho salmon did not result in large scale introgression into Oregon populations.

Introduction

Identifying conservation units is an integral part ofconserving genetic diversity for many organisms(see a variety of perspectives in Nielsen 1995).Conservation efforts aimed at Pacific salmon(Oncorhynchus sp.) in the United States have beeninfluential in this area, particularly with regard todefining and identifying Evolutionarily SignificantUnits (Waples 1991; Waples 1995; Pennock andDimmick 1997; Waples 1998; ESUs; Ford 2003).Waples (1991) defines an ESU as a population orgroup of populations that is substantially repro-ductively isolated from other populations and

contributes substantially to the ecological or ge-netic diversity of the species as a whole. A series ofstatus reviews conducted in the 1990s by the Na-tional Marine Fisheries Service (NMFS) (e.g.,Matthews and Waples 1991; Weitkamp et al. 1995;Busby et al. 1996; Hard et al. 1996; Gustafsonet al. 1997; Johnson et al. 1997; Myers et al. 1998)resulted in the identification of 52 Pacific salmonESUs, of which 26 are listed as threatened orendangered under the U.S. Endangered SpeciesAct (ESA) (see http://www.nwr.noaa.gov/).

As an initial step in the recovery planningprocess for ESA-listed salmon ESUs, McElhanyet al. (2000) developed criteria for describing

Conservation Genetics 5: 797–812, 2004.� 2004 Kluwer Academic Publishers. Printed in the Netherlands.

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viable populations in terms of their abundance,intrinsic growth rate, spatial structure, and diver-sity. An important part of McElhany et al.’s(2000) framework is a precise definition of a pop-ulation, which they define as ‘‘any collection ofone or more local breeding units whose populationdynamics or extinction risk over a 100-year timeperiod are not substantially altered by exchangesof individuals with other populations.’’ The goalof this definition is to identify population units forwhich it is biologically meaningful to assessextinction risk from intrinsic factors such asdemographic or local environmental stochasticity.As a rough rule of thumb, McElhany et al. (2000)suggested (citing Hastings (1993)) that populationsexchanging less than �10% migrants per genera-tion should be considered to be demographicallyindependent. They suggested that a variety ofinformation could be used to assess populationstructure, including tagging information, spatialpatterns of breeding groups, and population ge-netic analysis. McElhany et al. (2000) were con-cerned with describing the criteria necessary forviable salmon populations, but the literature theydrew from was not restricted to any single groupof organisms and their approach is applicable tomany organisms (Fraser and Bernatchez 2001).

Coho salmon (Oncorhynchus kisutch) thatbreed in Oregon Coast basins north of CapeBlanco were identified by NMFS as an ESU basedon a synthesis of genetic, life history, biogeo-graphic, geologic, and environmental information(Weitkamp et al. 1995). In August of 1998, theESU was listed as threatened under the ESA(Department of Commerce (DOC 1998)), and inNovember 2002 a recovery team was formed todevelop a recovery plan for the ESU (see http://www.nwfsc.noaa.gov/cbd/trt/). The team’s initialtask was to identify historically independent pop-ulations (sensu McElhany et al. 2000) of cohosalmon along the Oregon Coast.

Like all Pacific salmon, coho salmon have ananadromous life-history. Breeding adults spawn infreshwater in the fall in relatively small coastalstreams, or small tributaries within major riversystems (Sandercock 1991). Eggs incubate in thestreambed for several months before hatching. Fryemerge from the gravel in the spring, and typicallyspend an entire year rearing in freshwater beforemigrating to the ocean as smolts the followingspring. In the southern part of their spawning range

(including the Oregon Coast), coho salmon typi-cally spend 18 months in the ocean, before return-ing to freshwater to spawn and then die, resulting ina 3 years life-span (Sandercock 1991). A sometimessizable fraction of the males follow an alternativelife-history strategy, which involves spending only�6 month at sea and returning to spawn as two-year-olds (‘‘jacks’’). Other life-history patterns havealso been described, but are infrequent in OregonCoast populations (reviewed by Sandercock 1991).Like all Pacific salmon, coho salmon have a ten-dency to return to their home stream to spawn,although straying among streams also occurs athighly variable rates (Labelle 1992; Quinn 1993).

As part of their status review of west coastcoho salmon, NMFS analyzed an allozyme datasetthat included 29 samples from Oregon Coastpopulations (Weitkamp et al. 1995) and reviewedearlier genetic studies that used variation in allo-zyme loci (Hjort and Schreck 1982; Olin 1984;Solazzi 1986), mitochondrial DNA (Currens andFarnsworth 1993), and growth hormone genes(Forbes et al. 1993). However, despite these stud-ies, the genetic population structure of OregonCoast coho salmon remains largely unknown.Weitkamp et al. (1995), for example, concludedthat although Oregon Coast coho north of CapeBlanco form a discrete group and had ‘‘relativelyhigh heterogeneity’’ there was ‘‘no clear geo-graphic pattern to the differentiation’’.

A recent report on Oregon Coast coho salmonby the Oregon Department of Fish and Wildlifeidentified twelve ‘‘population complexes’’ based ongeography, habitat and life history similarity thatwere intended to correspond to independent pop-ulations (Table 1 – Nickelson 2001). In this study,we use genetic data to examine population geneticstructure in five of the population complexes alongthe central Oregon Coast (Figure 1). Four of theseunits, the Siletz, Yaquina, Alsea, and Umpquacomplexes, contain spawning populations thatNickelson (2001) grouped by river basin. The fifthcomplex, the Lakes complex, is comprised of cohosalmon inhabiting three coastal watersheds havinglarge lake systems: Siltcoos, Tahkenitch, and Ten-mile Lakes (Figure 1). These lakes provide exten-sive habitat for juvenile rearing and the watershedsare considered among the most productive cohosalmon systems on the Oregon Coast (Nickelson2003). Nickelson (2001) grouped the coho salmonspawning in these lake basins into a single complex

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Table 1. Sample information

Region Population

complexaBasin Site Year Short name Life

stageb

Oregon coast Siletz Devil’s Lake Devil’s Lk. 2001 Devil_01 a

Siletz Devil’s Lake Devil’s Lk. 2002 Devil_02 a

Siletz Siletz River Siletz R. 2000 Siletz_00 a

Siletz Siletz River Siletz R. 2001 Siletz_01 a

Yaquina Yaquina River Wright Cr. 1975 Wright_75 a

NA Yaquina River Oregon Aqua Foods 1985 Aqua_85 a

Yaquina Yaquina River Yaquina R. 2000 Yaq_00 a

Yaquina Yaquina River Yaquina R. 2001 Yaq_01 a

Alsea Beaver Creek Beaver Cr. 2000 Beaver_00 a

Alsea Beaver Creek Beaver Cr. 2001 Beaver_01 a

Alsea Alsea River Alsea R. 1996 Alsea_96 j

Alsea Alsea River Alsea R. 2000 Alsea_00 a

Alsea Alsea River Alsea R. 2001 Alsea_01 a

Lakes Mercer Lake Sutton Cr. 2002 Sutton_02 a

Lakes Mercer Lake Mercer Lk. 2002 Mercer_02 a

Lakes Siltcoos Lake Siltcoos Lk. 2000 Siltcoos_00 a

Lakes Siltcoos Lake Siltcoos Lk. 2001 Siltcoos_01 a

Lakes Tahkenitch Lake Tahkenitch Lk. 2000 Tahk_00 a

Lakes Tahkenitch Lake Tahkenitch Lk. 2001 Tahk_01 a

Umpqua Umpqua River Mainstem Umpqua R. 2000 UmpMS_00 a

Umpqua Umpqua River Mainstem Umpqua R. 2001 UmpMS_01 a

Umpqua Umpqua River Smith R. 1994 Smith_94 a

Umpqua Umpqua River Smith R. 1996 Smith_96 j

Umpqua Umpqua River Smith R. 2000 Smith_00 a

Umpqua Umpqua River Smith R. 2001 Smith_01 a

Umpqua Umpqua River Elk R. 2000 Elk_00 a

Umpqua Umpqua River Elk R. 2001 Elk_01 a

Umpqua Umpqua River Calapooya R. 2000 Cala_00 a

Umpqua Umpqua River Calapooya R. 2001 Cala_01 a

Umpqua Umpqua River S. F. Umpqua R 2000 UmpSF_00 a

Umpqua Umpqua River S. F. Umpqua R 2001 UmpSF_01 a

Umpqua Umpqua River Rock Cr., N. Umpqua R 1996 Rock_96 j

Umpqua Umpqua River Rock Cr., N. Umpqua R 1997 Rock_97 j

Lakes Tenmile Lake Tenmile Lk. 1992 Ten_92 j

Lakes Tenmile Lake Tenmile Lk. 2000 Ten_00 a

Lakes Tenmile Lake Tenmile Lk. 2001 Ten_01 a

Coos Coos River S. FK. Coos R., Tioga Cr. 1993 Tioga_93 j

Coos Coos River S. FK. Marlow Cr., Millicoma R. 1993 Marlow_93 j

Coos Coos River S. FK. Millicoma R. 1997 Millicoma_97 j

Coquille Sixes River Sixes R. 1993 Sixes_93 j

Puget sound NA Green River Soos Cr. Hatchery 1999 Soos_99 a

NA Snoqualmie River Grizzly Cr. 1999 Grizzly_99 a

Washington

coast

NA Queets River Clearwater R. 1994 ClearW_94 j

NA Chehalis River Hope Cr. 1998 Hope_98 j

NA Chehalis River Hope Cr. 1999 Hope_99 j

a Population complexes identified in Nickelson 2001 (see Figure 1).b a – adult; j – juvenile.

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due to similarities in habitat and geographic prox-imity, but there was considerable uncertainty aboutwhether or not coho salmon spawning in thesediscrete drainages are a cohesive genetic or demo-graphic group. In particular, because the mouth ofthe Umpqua River is located between the outlets of

Tenmile and Tahkenitch Lakes and also because ofthe close proximity of the Siuslaw River it is plau-sible that each lake population might be geneticallymore similar to the nearest large river populationthan populations in the other lakes. One of theprincipal goals of this study was to confirm or

Figure 1. Sampling locations.

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refute the validity of the Lakes Complex as a dis-crete population group distinct from nearby rivers.

A second goal of this study was to use patternsof temporal and spatial variation to estimate ratesof gene flow among population complexes. Wewere particularly interested in determining if theestimates of gene flow among complexes were lessthan the 10% rule of thumb that McElhany et al.(2000) suggested was necessary for demographicindependence. To do this, we utilized three differ-ent methods to either simultaneously or sequen-tially estimate effective population size andmigration rates from patterns of spatial and tem-poral genetic variation.

The third goal of this study was to evaluate thegenetic effects of a non-native, artificially propa-gated population on nearby wild populations. Over107 million coho salmon smolts were released intothe Yaquina River (Figure 1) between 1974 and1990 as part of ‘‘ocean ranching’’ operations by theOregon Aqua-Foods (OAF) corporation (Borger-son 1991). Initially, OAF releases were made intoWright Creek, a tributary to the lower YaquinaRiver, using coho salmon obtained from the Siletzand Alsea rivers. In the late 1970s operationsshifted to a facility in Yaquina Bay and smolt re-leases were progeny of returning OAF adults sup-plemented with large infusions of stock importedfrom Puget Sound (Washington) hatcheries. Eventhough <1% of the fish released returned aspotentially breeding adults, substantial numbers ofOAF fish strayed into six coastal basins from theSalmon River to the Yachats River. Yaquina Rivertributaries had the highest density of strays, withOAF hatchery fish making up 90.6% of spawnersin 1985 (Jacobs 1988). Despite these high strayrates, the genetic effects of OAF releases have notbeen fully evaluated. In this study, we comparemicrosatellite allele frequencies of samples of adultreturns to Wright Creek prior to OAF imports ofPuget Sound stock, of the OAF hatchery brood-stock following the imports, and from contempo-rary Puget Sound and Oregon Coast populations.

Methods

Sample collection and genetic analysis

We analyzed 45 collections of tissue samples takenfrom natural spawning and hatchery populations

of coho salmon (Figure 1, Table 1). Approxi-mately half of the samples were scales collectedfrom 2000 to 2002 from carcasses of naturallyspawned fish in five putative population complexesalong the central Oregon Coast. Scales were col-lected in 2 years from coho salmon at multiplespawning sites in three of the complexes (Umpqua,Lakes, and Alsea) and at single sites in two com-plexes (Yaquina and Siletz) to provide a hierar-chical analysis of genetic variability at three levels:among population complexes, among samplingsites within complexes, and among temporalsamples within sites. To examine the effects ofPuget Sound stock releases into Yaquina Bay wealso analyzed scale samples from 1975 adult re-turns to Wright Creek and the 1985 OAF brood-stock and samples from four Puget Sound andWashington Coast populations (Figure 1, Ta-ble 1). Prior to laboratory analysis, scales weredried and stored between two sheets of paper.Samples of fin and muscle tissue were preserved inethanol. DNA was isolated from the tissue samplesusing a Promega Wizard DNA purification kit,following the manufacturer’s protocol.

Polymerase chain reactions (PCR) were used toamplify seven microsatellite loci (Table 2). ThePCR products were subjected to capillary electro-phoresis using an Applied Biosystems (ABI) 310genetic analyzer. Results of the electrophoreticruns were analyzed using GeneScan analysis soft-ware (ABI) to determine DNA fragment sizes.Genotyper software (ABI) was used to comparefragment sizes at each locus, to construct sizeranges (bins) for each allele, and to score each al-lele into the appropriate bin.

Statistical analysis

We used the Genetic Data Analysis (GDA) pro-gram of Lewis and Zaykin (2001) to estimate theproportion of genetic variation occurring at vari-ous spatial and temporal scales. Confidence inter-vals for these estimates were generated bybootstrapping over loci (1000 times) using thebootstrapping function in the GDA program.Departures from Hardy–Weinburg equilibriumexpectations were evaluated using the permutationmethod implemented in the program Genepop(Raymond and Rousset 1995). Statistical signifi-cance was corrected for multiple tests using thesequential Bonferroni method (Holm 1979).

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Relationships among samples were visualized byconstructing a population tree using Felsenstein’s(1981) maximum likelihood method, implementedby the program CONTML in the PHYLIP pack-age (Felsenstein 1993). Tree robustness wasdetermined both by determining if branch lengthswere significantly greater than zero under theassumptions of the model, and by comparing thestructure of the most likely tree found to that often other nearly equally likely trees.

Effective population sizes and migration rateswere estimated using three different methods. Thefirst method involved estimating the effectivenumber of breeders per year, Nb, from temporalvariation in allele frequencies using the methoddeveloped for Pacific salmon by Waples (1990).Waples found that N̂b ¼ b=2ðF̂ � 1=~sÞ, where b isanalogous to the number of generations betweensamples in a discrete generation model and can bethought of as the expected number of generationsbetween samples. Values of b among samples ta-ken a number of years apart can be calculated byassuming a constant age structure and using themethod of Tajima (1992) to calculate the expectednumber of generations between years. For cohosalmon, which primarily spawn at 2 or 3 years ofage, the expected number of generations betweentemporal samples is quite sensitive to the propor-tion of two-year-old spawners (Van Doornik et al.2002), so we bracketed our estimates by assuming

that the proportion of jacks ranged from 10 to25% of the total spawning population (20–50% ofthe male population, assuming an even sex ratio).All of our samples were taken one year apart,corresponding to values of b of 7.3 and 3.4 forassumptions of 10 or 25% two-year-old breeders,respectively. Note that Waples’s (1990) methodestimates the effective number of breeders per year,Nb, rather than the effective size per generation,Ne. The estimates of Nb can be converted toapproximate estimates of Ne by multiplying themby the expected generation time, calculated as themean age at maturity (2.9 or 2.75 years assuming10 or 25% two-year-olds, respectively). Waples’s(1990) method assumes that temporal changes inallele frequencies are not appreciably affected bymigration or selection. In order to estimatemigration rates, we therefore assumed migrationwas low and estimated the immigration fraction,m, into each population from the relationshipm̂ � ð1=4N̂ eÞð1=h� 1Þ (Wright 1978), where h isWeir and Cockerham’s (Weir and Cockerham1984; Weir 1996) estimate of FST and N̂ e is theestimate of effective population size for the popu-lation in question.

The second two methods are the moment andmaximum likelihood methods developed byWang and Whitlock (2003) to simultaneouslyestimate effective size and immigration propor-tion. Both estimates are based on the same

Table 2. Microsatellite loci

Locus Annealing temp. (�C) Primer sequences Primer reference

Ocl8 60 F: TAGTGTTTCGTGTTCGCCTG Condrey and Bentzen (1998)

R: CCCTGTCCCTTCCATCTCT

Oki10 60 F: GGAGTGCTGGACAGATTGG Smith et al. (1998)

R: AGCTTTTTACAAATCCTCCTG

Oki23 58 F: TGTGCTATAGGGTGAATGTGC A. Spidle, unpub. data. Genbank

accession # AF272822

R: AACACAGGCATCCCCACTAA

Ots3 47 F: CACACTCTTTCAGGAG Banks et al. (1999)

R: AGAATCACAATGGAAG

Ots103 54 F: AGGCTCTGGGTCCGTG Small et al. (1998)

R: TGATATGGTGTGATAGCTGG

Ots505 NWFSC 54 F: GTCTAGCAATGATCAACAACC Naish and Park (2002)

R: TGGGACCAACAGTTTACG

P53 58 F: TGACACATATCCTCGCTTTCTCC de Fromentel et al. (1992)

R: CAACTCTCTTGGTGAGGC

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model, which assumes one focal population ofsize Ne and proportion of immigrants m and aninfinitely large source population that contributesimmigrants to the focal population. Using com-puter simulations, Wang and Whitlock (2003)found that the methods are not very sensitive toviolation of the population structure assumption,and provide reasonably good estimates for islandand stepping stone models of population struc-ture under a discrete generation model. We ap-plied their methods by looking at individual sitesor population complexes sampled in 2000 and2001 as focal populations, and using all of theCentral Oregon Coast samples from 2000 to 2001combined (excluding the focal population) as thesource population. We used the values of b dis-cussed above, rounded to the nearest integer, asestimates of the number of generations betweensamples.

Results

Patterns of variation

The seven microsatellite loci were highly variablein all of the samples, with expected heterozygosi-ties >0.8 in each population (Table 3). Mostpopulations had genotypic frequencies consistentwith Hardy–Weinburg expectations after correc-tion for multiple tests. Assuming a bifurcatingpattern of population divergence and a pure driftmodel of evolution, the allele frequency data canbe used to estimate a phylogenetic tree of popu-lations using a maximum likelihood method (Fel-senstein 1981). The tree has some clear geographicstructure, although the internal branches con-necting the geographic groups are short comparedto the terminal branches, and not all brancheswere significantly greater than zero (Figure 2). Wecompared the structure of the tree illustrated inFigure 2 with ten other structures with approxi-mately equal likelihoods, and found that thetopological differences among the eleven trees in-volved only minor rearrangements within the welldefined clusters (data not shown). Four clusterswith coherent geographic groupings can be iden-tified. The samples from the Coquille and CoosComplexes form a ‘‘southern’’ Oregon geneticgroup. Twelve of the fourteen samples from theUmpqua River basin group together, and a large

cluster with little or no internal structure includesthe samples from Beaver Creek and the Alsea,Yaquina, and Siletz Rivers, the most northerncoastal Oregon populations we sampled. Finally,all of the Washington populations grouped to-gether in a single cluster. In addition to the tree’sgeographic patterns, there is a genetic affinityamong lake-rearing populations. The samplesfrom Siltcoos, Tahkenitch, Tenmile, and Mercerlake systems cluster together. Samples from Dev-il’s Lake, however, are not genetically similar tosamples of other lake-rearing populations andcluster with the other Northern Oregon samples.

To further explore patterns of variation, wecalculated the proportion of genetic variance ob-served at three hierarchical levels: among com-plexes, among sampling sites within complexes,and among temporal samples within sites. In eachcase, in order to use a consistent data set, only the2000 and 2001 sample years from the OregonCoast were included the analyses. Overall, �2.5%of the total genetic variance was found amongsamples. Of the among sample variance, 44%was found among the population complexes, 35%was among sites within complexes, and 21% wasamong temporal replicates within sampling sites,with all three levels significantly greater than zero(Table 4). The relative proportions of temporaland spatial genetic variance varied considerablyamong the population complexes. For example, inthe Lakes Complex the component of temporalvariation was not significantly >0, whereas in theAlsea Complex, spatial variation was not signifi-cantly >0 (Table 4). When the Alsea, Siletz andYaquina Complexes were considered on their own,the estimated genetic variance among Complexeswas not significantly >0, but the variance amongsampling sites and among years was significant(Table 4).

Estimating effective population sizes and migrationrates

We used the temporal samples from 2000 to 2001to estimate the proportion of migrants and theeffective size of each population and populationcomplex. The one exception was the Devil’s Lakepopulation, which was sampled in 2001 and 2002.The Siletz complex did not have multiple spatialsamples from the same years, so estimates were notmade for that complex as a whole. Based on the

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Table 3. Summary of genetic variation within samples

Sample n A He Ho f

Alsea 00 36.43 17 0.88 0.89 )0.01Alsea 01 31.86 16.43 0.89 0.88 0.01

Alsea 96 34 13.86 0.87 0.84 0.04*

Aqua 85 95.14 20.71 0.87 0.85 0.03*

Beaver 00 17 11.71 0.87 0.85 0.03

Beaver 01 49.29 16 0.87 0.87 0

Cala 00 19.86 12.14 0.88 0.88 0

Cala 01 17 11.29 0.88 0.9 )0.02ClearW 94 37.71 16.71 0.89 0.85 0.05

Devil 01 26.71 12.43 0.87 0.85 0.026*

Devil 02 35.57 14 0.86 0.83 0.04*

Elk 00 24.43 11.86 0.87 0.88 )0.02Elk 01 7.14 7.14 0.88 0.85 0.03

Grizzly 99 36 15.57 0.88 0.88 0.00*

Hope 98 41.29 11 0.82 0.8 0.02*

Hope 99 37 13.14 0.84 0.84 )0.01Marlow 93 19.71 13.14 0.89 0.87 0.03

Mercer 02 26.71 12.57 0.84 0.85 )0.01Millicoma 97 24.29 13.86 0.88 0.82 0.07

Rock 96 25.43 12.57 0.89 0.83 0.07

Rock 97 29.43 13.29 0.89 0.89 0

Siletz 00 60.57 18.43 0.9 0.89 0.01

Siletz 01 10.86 9.86 0.88 0.89 )0.02Siltcoos 00 27.86 13.57 0.83 0.83 0

Siltcoos 01 21.43 13.29 0.85 0.85 0

Sixes 93 24.86 9.29 0.8 0.88 )0.1Smith 00 39 16.14 0.88 0.89 )0.01Smith 01 31.14 15.14 0.88 0.89 0.00*

Smith 95 29.29 13 0.89 0.83 0.07

Smith 96 26.43 13.43 0.9 0.87 0.04

Soos 99 44.86 16.29 0.87 0.82 0.05

Sutton 02 44.57 14.29 0.84 0.84 0

Tahk 00 29.86 14 0.85 0.85 0

Tahk 01 24.14 12.43 0.85 0.9 )0.06Ten 00 28.29 12 0.83 0.81 0.03

Ten 01 25 12.14 0.84 0.8 0.05

Ten 92 34.43 13.43 0.84 0.8 0.06

Tioga 93 25.43 13.86 0.89 0.84 0.06

UmpMS 00 34.43 15.57 0.88 0.87 0.02

UmpMS 01 25.43 12.14 0.87 0.85 0.02

UmpSF 00 39 15.57 0.88 0.89 )0.01UmpSF 01 27.57 14.86 0.88 0.86 0.02

Wright 75 26.29 12.86 0.86 0.84 0.02

Yaq 00 41.14 16.57 0.88 0.88 0.01*

Yaq 01 25 14.43 0.87 0.84 0.04

n – arithmetic average sample size per locus; A – average number of alleles per locus; He – expected heterozygosity at Hardy–Weinburg

equilibrium ð¼P

i 6¼j2pipjÞ;Ho – observed heterozygosity in sample; f – the proportional deviation of Ho from He

*Indicates significant (P < 0.05). overall departure from Hardy–Weinburg expectations across loci (Fisher’s method) after sequential

Bonferroni correction.

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tree structure in Figure 2, which provided littlesupport for separate Alsea, Yaquina and Siletzpopulation complexes, we also estimated parame-ters for those three complexes combined (2000 and2001 samples only).

Using Waples’s (1990) estimator, we were ableto obtain estimates of Nb for every population andpopulation complex except for the Elk River,Tenmile Lake, and the Lakes Complex (Table 5).For the latter populations, the variance attribut-able to sampling was greater than the varianceattributable to drift, producing negative estimatesof Nb. Assuming 10% two-year-olds, the estimatesof Nb ranged from 152 (Devil’s Creek) to 1031(South Fork Umpqua River), and the estimates for

the population complexes ranged from 560 (AlseaComplex) to 927 (Umpqua Complex). The esti-mates made assuming 25% two-year-olds differ bythe 10% two-year-olds estimates by a factor of3.4/7.3 (¼47%), due to the difference in the as-sumed number of generations between samples.The estimates of immigration fraction, m, usingthe FST method are generally small, ranging from<1% to �10% depending on the age structureassumed (Table 5). Assuming 10% two-year-olds,no population had an upper 95% confidenceinterval of m>10%, and assuming 25% two-year-olds, only two populations, Devil’s Creek and theCalapooya River, had upper 95% confidenceintervals of m>10%.

0.01

Wright 75

Aqua 85

Hope 99

Hope 98

Clear W 94

Soos 99

Grizzly 99

Lakes Complex

Umpqua Complex

Siletz, Alsea, Yaquina Complexes

Coquille and Coos Complexes

WashingtonPopulations

Smith 00Smith 01

Smith 96

Rock 96

Rock 97

Smith 94UmpSF 00

UmpSF 01

Elk 01

Elk 00Cala 00

Cala 01Sutton 02

Mercer 02

Siltcoos 00Tahk 00

Tahk 01Siltcoos 01

Ten 00Ten 01Ten 92

UmpMS 00UmpMS 01

Millicoma 97

Sixes 93Tioga 93Marlow 93

Devil 01

Devil 02

Beaver 00Beaver 01

Yaq 01

Alsea 01Alsea 00Yaq 00

Siletz 00

Siletz 01

Alsea 96

Figure 2. Maximum likelihood tree of contemporary Oregon Coast, Washington Coast and Puget Sound samples. The 1985 OAFsample (Aqua 85) and the 1975 Wright Creek sample are shown in bold. Branches not significantly >0 are shown with thin dottedlines.

Table 4. Temporal and spatial variation with 95% confidence intervals

Structure Total among Temporal Sampling sites Complexes

Complete 2.47% (2.1–2.9%) 21% (12–32%) 35% (26–45%) 44% (35–53%)

Lakes 0.92% (0.04–1.5%) )9% ()45–32%) 109% (68–145%) –

Umpqua 1.22% (0.8–1.6%) 27% ()4–57%) 73% (43–104%) –

Alsea 1.25% (0.7–1.8%) 59% (26–102%) 41% ()2–74%) –

Siletz 2.95% (1.8–3.9%) 60% (41–90%) 40% (10–59%) –

Alsea/Siletz/Yaquina 1.7% (1.5–2.0%) 60% (39–82%) 44% (28–59%) )5% ()15–7%)

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Table 5. Estimates of the effective number of breeders, Nb, and immigration fraction, m

Fraction

2 years olds

Focal

population

Temporal FST methoda W&W momentb W&W likelihoodc

Nb m Nb m Nb m

0.1 Alsea/Sil/Yaq 638 (510, 781) 0.01 (0, 0.02)

Complex 920 0.05 285 (183, 459) 0.05 (0.03, 0.09)

Alsea Complex 560 (441, 693) 0.01 (0.01, 0.01) – 0.10 38 (32, 139) 1 (0.12, 1)

Siletz R 234 (182, 294) 0.03 (0.02, 0.04) 71 0.10 20 (16, 25) 0.36 (0.17, 1)

Devil Cr 152 (115, 194) 0.04 (0.03, 0.06) 192 0.16 13 (11, 15) 0.99 (0.65, 1)

Beaver Cr 248 (191, 315) 0.03 (0.02, 0.03) – 0.19 21 (17, 26) 1 (0.62, 1)

Alsea R 465 (362, 581) 0.01 (0.01, 0.02) 7589 0.10 22 (18, 28) 1 (0.6, 1)

Yaquina R 492 (379, 618) 0.01 (0.01, 0.01) 459 0.08 17 (14, 21) 1 (0.58, 1)

Umpqua Complex 927 (739, 1135) 0 (0, 0.01) 809 0.02 335 (189, 632) 0.04 (0.01, 0.07)

UmpMS 634 (487, 800) 0.01 (0.01, 0.02) – 0.11 15 (12, 18) 1 (0.61, 1)

Smith 384 (297, 483) 0.02 (0.01, 0.05) 193 0.11 18 (15, 23) 1 (0.56, 1)

Elk – – – 0.04 6 (6, 8) 1 (0.53, 1)

Cala 247 (185, 318) 0.03 (0.03, 0.04) 264 0.12 8 (7, 10) 1 (0.64, 1)

UmpSF 1031 (795, 1296) 0.01 (0.01, 0.01) – 0.06 17 (14, 21) 1 (0.63, 1)

Lakes Complex – – – 0.04 511 (229, 2113) 0.03 (0.01, 0.06)

Ten – – – 0.03 12 (10, 14) 1 (0.63, 1)

Siltcoos 652 (499, 825) 0.01 (0.01, 0.02) – 0.09 14 (12, 18) 1 (0.66, 1)

Tahk 527 (399, 674) 0.01 (0.01, 0.02) – 0.11 14 (12, 17) 1 (0.63, 1)

0.25 Alsea/Sil/Yaq 269 (215, 330) 0.02 (0.01, 0.04)

Complex 419 0.10 131 (94, 193) 0.13 (0.07, 0.19)

Alsea Complex 261 (205, 323) 0.02 (0.01, 0.04) – 0.21 55 (41, 77) 0.34 (0.23, 0.5)

Siletz R 109 (85, 137) 0.06 (0.04, 0.09) 35 0.21 19 (16, 24) 0.39 (0.21, 0.89)

Devil Cr 71 (54, 90) 0.1 (0.07, 0.15) 102 0.34 15 (12, 18) 0.47 (0.33, 0.77)

Beaver Cr 116 (89, 147) 0.06 (0.04, 0.09) – 0.38 22 (17, 29) 0.65 (0.46, 1)

Alsea R 196 (153, 245) 0.03 (0.03, 0.04) 3714 0.22 25 (19, 33) 0.5 (0.32, 1)

Yaquina R 229 (177, 288) 0.03 (0.02, 0.04) 219 0.18 18 (14, 25) 0.52 (0.31, 1)

Umpqua Complex 431 (345, 529) 0.01 (0.01, 0.02) 357 0.05 137 (95, 219) 0.1 (0.05, 0.17)

UmpMS 296 (227, 373) 0.03 (0.02, 0.06) – 0.23 15 (12, 19) 1 (0.53, 1)

Smith 179 (138, 225) 0.05 (0.03, 0.1) 95 0.23 21 (17, 29) 0.43 (0.26, 1)

Elk – – – 0.1 6 (6, 8) 0.56 (0.29, 1)

Cala 104 (78, 134) 0.08 (0.05, 0.18) 132 0.26 8 (7, 10) 1 (0.49, 1)

UmpSF 481 (371, 604) 0.02 (0.01, 0.04) – 0.14 20 (15, 27) 0.46 (0.29, 1)

Lakes Complex – – – 0.08 142 (85, 299) 0.1 (0.05, 0.17)

Ten – – – 0.08 15 (12, 20) 0.29 (0.16, 0.47)

Siltcoos 304 (233, 385) 0.02 (0.02, 0.03) – 0.19 14 (12, 18) 1 (0.52, 1)

Tahk 246 (185, 314) 0.03 (0.03, 0.03) – 0.23 14 (11, 17) 1 (0.34, 1)

a The temporal/FST method uses among complex estimates of FST for the population complex estimates of m and the within complex estimates

of FST for the within complexes estimates. The temporal/FST method reports estimates of Nb, but m was estimated using estimates of Ne (Nb *

expected generation time). 95% confidence limits on Nb are in parentheses and were calculated using the chi-square approximation described by

Waples (1990). Confidence limits on m were approximated by using the lower 95% confidence limit of h combined with the lower 95%

confidence limit of Nb to produce an upper confidence limit for m, and the upper confidence limit of h combined with the upper confidence limit

of Nb to produce a lower limit for m.b Wang and Whitlock (2003) moment method.c Wang and Whitlock (2003) likelihood method.

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Only 7 of the 17 samples produced non-negative estimates of Nb using the Wang andWhitlock (2003) moment estimator (Table 5). Thenon-negative estimates were fairly similar to thoseproduced by the temporal/FST method, particu-larly for the population complexes (Table 5). Themoment estimates of m were generally several foldlarger than the corresponding temporal/FST esti-mates, but were <10% for each of the populationcomplexes (Table 5).

The Wang and Whitlock maximum likelihoodestimates of Nb and m differed substantially fromthose obtained from the other two methods formost populations. In particular, the estimates ofNb tended to be much smaller than those obtainedwith the other two methods, and the estimates of mtended to be unrealistically large for most popu-lations (Table 5). The estimates for the populationcomplexes were generally more similar to thoseobtained from the other methods than were theestimates from the individual populations. Forexample, the ML estimates of m were <10% foreach population complex except for Alsea Com-plex, and the estimates of Nb for the populationcomplexes were in the same order of magnitude asthe estimates from the other methods.

Historical Alsea River and Oregon Aqua-Foodssamples

The sample from the 1985 OAF broodstock wasgenetically more similar to samples from PugetSound and the Washington Coast than it was toother Oregon Coast samples (Figure 2). The 1975Wright Creek (Yaquina River) sample clusteredwith the contemporary Devil’s Lake samples in agroup distinct from both the contemporaryYaquina River samples and the OAF andWashington samples (Figure 2).

Discussion

Major patterns of variation

The populations we surveyed were characterized bya high degree of variation within populations and arelatively low degree of variation among popula-tions. Overall, 97.5% of the genetic variance wasfound within populations, and only �2% of thevariance was found among geographic locations,

with the remainder found among temporal repli-cates within locations (Table 4). Weak geographicpopulation structure appears to be characteristic ofmany coho salmon populations, although theabsolute level of differentiationwe observed is lowerthan has been found for coho salmon from someother regions. For example, in a similar microsat-ellite study of coho salmon fromAlaska,Olsen et al.(2003) found that 10% of the variation was ob-served among all populations. Within regions ofcomparable geographic scale to the Oregon Coastpopulations of our study, Olsen et al. (2003) foundthat 2.6–17.2% of variation was observed amongpopulations. Similarly, in an allozyme study ofCalifornia coastal coho salmon, Bartley et al.(1992) estimated that 16%of the variation observedwas among populations. In contrast, in another al-lozyme study of coho salmon from California toBritish Columbia, Teel et al. (2003) found that only2.9% of the variation observed was due to variationamong populations within regions (with theOregonCoast considered one region), and Reisenbichlerand Phelps (1987), in a study of Washington Coastpopulations, found that 3.97% of the variation wasamong populations. In a microsatellite survey ofcoho salmon throughout British Columbia, Smallet al. (1998) estimated that 5.1% of the variationwas observed among populations. Despite the widevariation in levels of divergence amongpopulations,all of the studies cited above reported finding onlyweak patterns of geographic structure. One patternthat is apparent when these studies are compared isthat populations at the extremes of the species’range (California and Alaska) tend to be moredivergent than populations in the central portion ofthe range. Olsen et al. (2003) suggested that obser-vations of high levels of divergence among Alaskancoho population combined with weak geographicstructuring indicated that coho populations aresmall and isolated. The observation of lower levelsof divergence among population in the centralportion of the species range, including the OregonCoast, suggests that the central populations are ei-ther larger or less isolated than those in Alaska orCalifornia.

Evaluation of proposed Oregon Coast populationcomplexes

We used the genetic relationships visualized in thephylogenetic tree (Figure 2) and quantified by the

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variance analysis (Table 4) to evaluate five of theOregon Coast population complexes Nickelson(2001) identified using geographic, habitat, and lifehistory similarities. The genetic data producedrelatively little insight into appropriate populationgroupings for the northern most complexes weexamined. Consistent with earlier allozyme studies(Weitkamp et al. 1995; Teel et al. 2003), we foundthat while coho salmon from the Alsea, Yaquina,and Siletz rivers are genetically heterogeneous bothspatially and temporally, there is no evidence ofgenetic structure at the level of the proposed pop-ulation complexes (Table 4, Figure 2). This patternmay be the result of the large scale release ofcommon Oregon Coast hatchery stocks in theserivers, a practice that began several decades priorto the collection of our historical (1975) YaquinaRiver sample (Weitkamp et al. 1995). For example,large numbers of Nehalem, Trask, Siletz, and Alseahatchery stock, each derived largely from nativefish, have all been planted periodically in the Alsea,Yaquina, and Siletz rivers (Weitkamp et al. 1995).Assuming that a level of population structuretypical of coho salmon elsewhere also existed alongthe Northern Oregon Coast, these stocking histo-ries could explain the lack of differentiation amongthese putative population complexes. For the pur-pose of identifying independent populations,groups that are genetically similar to each othertoday due to past hatchery activities may be moredemographically isolated than one would inferfrom current patterns of genetic variation.

The large scale releases of Oregon Coast hatch-ery fish may have homogenized genetic variationamong some populations, but we did not detectstrong effects on contemporary allele frequenciesfrom the extensive introductions of non-local PugetSound coho salmon by the Oregon Aqua-Foods(OAF) corporation during the 1980s. Our sample ofadult returns to the OAF Yaquina Bay facility in1985 clustered with Puget Sound hatchery popula-tions, clearly indicating that during this period theearlier stock imports were contributing to the adultpopulationmigrating to freshwater.Recent samplesfrom adult spawners in the Yaquina River sampledin 2000 and 2001 clustered with Oregon Coastpopulations, however. The Oregon Coast samplesare genetically distinct from Puget Sound popula-tions, indicating that the fish imported from outsidethe ESU did not completely replace the native genepools. The lack of substantial introgression from

introduced non-native stock has also been observedin other anadromous salmonid populations (Utter2000). Utter (2000) suggested that populations aremuch more vulnerable to introgression when theintroduced population is from the same geneticlineage, as is primarily the case for Oregon Coastcoho salmon.

The genetic data provide stronger support forthe Umpqua Population Complex, the largestcomplex proposed by Nickelson (2001) having anestimated 1230 miles of coho salmon spawninghabitat. The most extensive sampling in our studywas in the Umpqua River basin and includedspawning populations in the Smith, South ForkUmpqua, Elk, and Calapooya rivers. With theexception of two samples from the mainstem, all ofthe samples from the Umpqua basin clustered to-gether (Figure 2).

The genetic similarity among populations in theTenmile, Tahkenitch, Siltcoos, and Mercer Lakesand their differentiation from populations in largerrivers clearly supports the Lakes Population Com-plex identified by Nickelson (2001) (Figure 2). Theoutlet streams from the lakes enter the ocean inde-pendently and in some cases closer to other majorrivers than to each other. However, coho salmon inthe Lakes Complex have a distinctive life-historypattern with juveniles rearing over the winter in aseries of shallow freshwater lakes formed behindsand dunes along the Oregon Coast. To ourknowledge, this is the first time that coho salmonpopulations have been found to be more geneticallycorrelated with a pattern of life-history variationthanwith spawning location, although the influenceof life history traits on population genetic structureis well documented in other Pacific salmon species(Waples et al. 2001). Samples from Devil’s Lake onthe north coast did not group with the other lakepopulations but instead grouped with the northernsamples, a result anticipated by Nickelson (2001)who placed theDevil’s Lake population in the Siletzpopulation complex (Figure 1).

Estimates of effective population size andimmigration rate

Using three different methods, we estimated botheffective population size and immigration fractionfor each of the populations and populationcomplexes for which we had temporal replicates.All three methods make some unrealistic and

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potentially problematic assumptions. The tempo-ral/FST method estimates the effective size andimmigration rates separately, using two differentmodels that make contradictory assumptions. Inparticular, the effective size estimates are based onthe expected temporal variance of allele frequenciesassuming that the only factors changing allele fre-quencies are genetic drift and sampling error. Themigration estimates are based on the expected var-iance of allele frequencies among populations undera model of migration/drift equilibrium amongpopulations. If the level of migration among pop-ulations is sufficiently large to appreciably bias theestimates of Nb, then the estimates of m will also bebiased, in the opposite direction.

Wang and Whitlock’s (2003) methods estimateNe andm simultaneously, potentially an improvementover the temporal/FST method. However, the Wangand Whitlock model also makes unrealistic assump-tions, including an infinitely large source populationand discrete generations. Wang and Whitlock (2003)showed that their estimators perform reasonably wellunder either island or stepping stonemigrationmodels(both of which violate the infinite source assumption),but their method has not been evaluated for a salmontype life-history consisting of semelparity andmultipleages at maturity.

Preliminary computer simulations (MJF,unpublished data) of salmon populations underan equilibrium island model indicate that thetemporal/FST method produces only slightlybiased estimates of Nb and m when m is small(<0.10). The same simulations indicated that theWang and Whitlock moment estimator producedonly slightly biased estimates of Nb for a widerange of migration rates, but tended to produceupwardly biased estimates of m when the truemigration rate was low (<0.20). The Wang andWhitock likelihood method was not evaluateddue to its computational complexity. The likeli-hood method did not appear to work well forour data, however, as it produced estimates of mthat seem implausibly large and estimates of Nb

that seem implausibly small for most populationssampled (Table 5). Whether these unrealisticestimates were due violation of model assump-tions or problems with small sample sizes is notclear. In either case, for these data, the tempo-ral/FST method and the Wang and Whitlockmoment method appear to have produced morerealistic estimates.

McElhany et al. (2000), citing Hastings (1993),suggested that when a population receives <10%immigrants from other populations it should beconsidered demographically independent. Nickel-son (2001) used patterns of geographic proximityand life-history diversity to identify populationcomplexes that he believed were likely to be demo-graphically independent. The estimates of m fromthe genetic data appear to largely support Nickel-son’s (2001) population complexes, as all threemethods of estimating m produced estimates below10% in most cases. The estimates of m obtainedfrom the temporal/FSTmethodswere< 10% for allof the individual populations as well, suggestingthat some of these populations might also be inde-pendent.

Using the temporal/FSTmethod, estimates of theeffective number of breeders for the populationcomplexes ranged from several hundred to nearly-thousands, depending on the assumed age structure(Table 5). When these estimates are compared toestimates of spawning abundance (Nickelson 2001),the ratios of effective number of breeders to actualnumber of potential breeders range from 0.12 to0.30, values similar to the 0.2–0.3 range which hasbeen previously believed to be typical of Pacificsalmon populations (Waples et al. 1993). The esti-mates of effective size and spawning abundance aresignificantly correlated (Figure 3), suggesting thateffective size scales approximately linearly withcensus size, at least for the range of population sizesin our study. The high degree of correlation alsoprovides some independent validation of the effec-tive size estimates.

Conservation implications

The first step in defining viability criteria for ESA-listed Pacific salmon ESU’s is to identify demo-graphically independent populations (McElhanyet al. 2000). The rationale is to identify biologicalunits for which it is meaningful to estimate extinc-tion risk and develop viability goals based on pop-ulation attributes such as abundance, growth rate,diversity and spatial structure. This study providesuseful information for identification of demo-graphically independent populations of OregonCoast coho salmon, and for evaluating the geneticimpacts of long term hatchery stocking. One par-ticularly important component of this studywas theuse of temporal samples, which allowed us to

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partition genetic variation into spatial and temporalcomponents. In addition, the temporal samples al-lowed us to estimate effective population size andgene flow among populations, parameters thatwould have been confounded without temporalsamples. These estimates must be viewed with somecaution due to the potential violation of modelassumptions, but they nonetheless provide supportfor the identified population complexes. Populationgenetic data is clearly useful for population identi-fication, but our study was not intended to be thesole source of information used this purpose. In ouropinion, a robust strategy is to use a variety of datasources, including geographic patterns of occur-rence, patterns of life-history variation, and infor-mation from tagging studies, as well populationgenetic data, to identify populations. This holisticapproach was implemented by the TechnicalRecovery Team charged with formally identifyingpopulations of Oregon Coast (Lawson et al. 2004).The next step in this process is to describe viabilitycriteria for these populations, followed by adescription of the number and composition ofpopulations necessary for a viable ESU.

A text file containing the genotypes used in thisstudy is available at www.nwfsc.noaa.gov.

Acknowledgments

Tom Nickelson encouraged the study and sug-gested its sampling design. Lisa Borgerson and the

Oregon Department of Fish and Wildlife collectedand provided the scale samples. Justin Mills cre-ated Figure 1. Robin Waples (Northwest FisheriesScience Center) and three anonymous provideduseful comments on an earlier version of the paper.

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0

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0 2000 4000 6000 8000 10000

Mean estimated spawning abundance

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ctiv

e br

eede

rs

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