NOAA Technical Memorandum NMFS-NWFSC-96

Identification of an Independent

Population of Sockeye Salmon

in Lake Ozette, Washington

January 2009

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
NOAA Technical Memorandum

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Reference throughout this document to trade names does 

not imply endorsement by the National Marine Fisheries 

Service, NOAA.

This document should be referenced as follows:

Currens, K.P., R.R. Fuerstenberg, W.H. Graeber, K. Rawson, 

M.H. Ruckelshaus, N.J. Sands, and J.B. Scott. 2009. 

Identification of an independent population of sockeye 

salmon in Lake Ozette, Washington. U.S. Dept. Commer., 

NOAA Tech. Memo. NMFS-NWFSC-96, 18 p.
NOAA Technical Memorandum NMFS-NWFSC-96

Identification of an Independent

Population of Sockeye Salmon

in Lake Ozette, Washington

Kenneth P. Currens,

1

 Robert R. Fuerstenberg,

2

William H. Graeber,

3

 Kit Rawson,

4

Mary H. Ruckelshaus, Norma J. Sands,

and James B. Scott5

Northwest Fisheries Science Center

Conservation Biology Division

2725 Montlake Boulevard East

Seattle, Washington 98112

1 Northwest Indian Fisheries Commission

6730 Martin Way East

Olympia, Washington 98516

2 King County Department of Natural Resources and Parks

201 South Jackson Street, Suite 700

Seattle, Washington 98104

3 Washington State Department of Natural Resources

1111 Washington Street Southeast

Olympia, Washington 98504

4 Tulalip Tribes

6700 Totem Beach Road

Tulalip, Washington 98271

5 Washington Department of Fish and Wildlife

600 Capitol Way North

Olympia, Washington 98501

January 2009

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
ii

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Northwest Fisheries Science Center

web site (http://www.nwfsc.noaa.gov)

Copies are also available from:

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Table of Contents 

List of Figures...............................................................................................................................................v 

List of Tables ..............................................................................................................................................vii 

Executive Summary .....................................................................................................................................ix 

Introduction...................................................................................................................................................1 

Definition of a Population Used in This Approach ..................................................................................1 

Indicators of Population Structure ............................................................................................................2 

Data Quality..............................................................................................................................................3 

Methods and Results.....................................................................................................................................4 

Geographic Distribution ...........................................................................................................................4 

Genetic Attributes.....................................................................................................................................4 

Past Analyses .......................................................................................................................................4 

New Analyses—Methods.....................................................................................................................6 

New Analyses—Results.......................................................................................................................7 

Life History Characters and Spatial Synchrony .....................................................................................10 

Habitat Characteristics............................................................................................................................11 

Population Structure Decisions ..............................................................................................................12 

Conclusions.................................................................................................................................................16 

References...................................................................................................................................................17 

iii
iv
List of Figures 

Figure 1. Sockeye salmon spawning areas in the Lake Ozette ESU............................................................5 

Figure 2. Genetic similarity of sockeye and kokanee salmon aggregations in Lake Ozette 

based on nine microsatellite loci...................................................................................................................6 

Figure 3. Multidimensional scaling of coancestry genetic distances between Lake Ozette 

sockeye salmon. ............................................................................................................................................9 

Figure 4. Timing and abundance of lakeshore spawning sockeye salmon in the 1978–1979 

spawning season..........................................................................................................................................11 

Figure 5. Seasonal changes in temperature for Lake Ozette in 1994.........................................................13 

Figure 6. Water temperatures in the Ozette River and three inlet tributaries of Lake Ozette....................14 

v
vi
List of Tables 

Table 1. P-values from pairwise chi-square tests for allelic homogeneity for nine microsatellite loci 

and FST values with 95% confidence intervals for O. Nerka in Lake Ozette. ...............................................8 

Table 2. Migrants per generation and time since divergence for sockeye salmon in Lake Ozette ............10 

Table 3. Evidence and strength of inference for population structure in Lake Ozette sockeye salmon ....13 

vii
viii
Executive Summary 

The first task of the Puget Sound Technical Recovery Team (TRT) in developing a 

recovery plan for Lake Ozette sockeye salmon (Oncorhynchus nerka) was to delineate 

independent populations within the evolutionarily significant unit. In this context, NOAA 

Fisheries Service defines an independent population following Ricker’s (1972) definition of a 

stock, which is a group of fish of the same species that spawns in a particular lake or stream (or 

portion thereof) at a particular season and which, to a substantial degree, does not interbreed with 

fish from any other group spawning in a different place or time. For the TRT’s purpose, to “not 

interbreed” to a “substantial degree” means that spawning aggregations are isolated to such an 

extent that exchanges of individuals among the populations do not substantially affect the 

population dynamics or extinction risk of the different groups (independent populations) over a 

100-year time frame. 

The Puget Sound TRT reviewed geographical, migration, genetic, life history, 

demographic, and habitat characteristics of anadromous sockeye salmon in Lake Ozette. The 

team concluded that extant spawning aggregations in Lake Ozette are different subpopulations 

within a single population. It seems likely that differences also existed historically among other 

subpopulations, which have since been extirpated, either at different spawning beaches or in 

tributaries. In addition, significant genetic differences may have evolved between fish from 

different brood cycles as a result of the temporal isolation between spawning adults that are 

almost exclusively 4 years old. 

Genetic data provided the best evidence of isolation among aggregations, but we 

considered it weak evidence of independence because of the magnitude of the differences. 

Estimates of FST (a measure of genetic divergence) and migrants per generation between beach 

spawning aggregations ranged 0.007–0.015 and 31–73, respectively. By comparison, differences 

between fish from the same spawning area but different brood cycles were as large or larger. 

Times since divergence between different natural spawning aggregations from the same brood 

cycle on Olsen’s Beach and Allen’s Bay (3–12 generations) were generally similar to estimates 

between Umbrella Creek hatchery fish and Olsen’s Beach, where most of the brood stock 

originated 1–3 generations ago. 

Estimates of time since divergence from the genetic data corresponded closely with 

known times of divergence between Olsen’s Beach 1996 and Umbrella Creek 2000, and between 

Olsen’s Beach 1996 and Olsen’s Beach 2000, which gave us confidence in other estimates. 

Independent populations under our definition would have diverged approximately 25 or more 

generations ago. Both recently collected DNA data and older allozyme data, however, showed 

measurable divergence between spawning aggregations. In addition, although the inferences 

were necessarily weaker, potential differences in peak spawning time between beach spawning 

aggregations and differences in incubation temperatures between beach and tributaries suggested 

that subpopulations exist in Lake Ozette now and probably were more extensive historically. 

ix
x
Introduction 

The Puget Sound Technical Recovery Team’s (TRT) first task in developing salmon 

recovery plans has been to delineate independent populations within each of the three 

evolutionarily significant units (ESUs) assigned to it. Understanding the size and spatial extent 

of populations is critical for the viability analyses that are a necessary step in recovery planning 

and conservation assessments for any species. This report describes the delineation of 

populations and subpopulations for the Lake Ozette sockeye salmon (Oncorhynchus nerka) ESU. 

The TRT has completed this delineation for Puget Sound Chinook salmon (O. tshawytscha) and 

an additional report will address Hood Canal summer chum salmon (O. keta). 

The populations identified represent what we believe to be the historical populations of 

sockeye salmon within the Lake Ozette ESU. Anecdotal evidence suggests that spawning 

aggregations of sockeye salmon in different areas of the lake and its tributaries may have gone 

extinct. Little data is available to determine whether these were independent populations. For 

each of the independent populations identified in this document, the TRT will in future 

documents characterize its present and historical status and viability and describe targets for 

abundance, productivity, life history and phenotypic diversity, and spatial distribution for 

spawning and rearing. These are necessary to answer the question: What are necessary 

population characteristics that will add up to persistence of the ESU? 

Definition of a Population Used in This Approach 

The definition of a population that we use in this report is described in detail in the viable 

salmonid population (VSP) document (McElhany et al. 2000). The VSP document defines a 

viable salmonid population as: 

an independent population of any Pacific salmonid (genus Oncorhynchus) that has 

a negligible risk of extinction due to threats from demographic variation (random 

or directional), local environmental variation, and genetic diversity changes 

(random or directional) over a 100-year time frame. 

In this context, NOAA Fisheries Service defines an independent population following 

Ricker’s (1972) definition of a stock, which is a group of fish of the same species that spawns in 

a particular lake or stream (or portion thereof) at a particular season and which, to a substantial 

degree, does not interbreed with fish from any other group spawning in a different place or in the 

same place at a different season. For our purposes, to “not interbreed” to a “substantial degree” 

means they are isolated to such an extent that exchanges of individuals among the populations do 

not substantially affect the population dynamics or extinction risk of the different groups 

(independent populations) over a 100-year time frame. 

In many animals, reproductive isolation (or the level of “not interbreeding”) can vary 

from very little, which occurs between pairs of fish in the same spawning aggregation, to nearly 
complete and permanent isolation, which occurs between different biological species. The 

“independent population” defined here, therefore, is a group of fish that can be identified by 

looking at measures of reproductive isolation. Within independent populations, for example, 

groups of fish that are separated geographically or temporally may show some level of 

reproductive isolation but they are not isolated enough to meet the criteria for independence used 

here. The distribution and differences among these groups of fish, which we consider 

“subpopulations,” have important consequences for characterizing the whole VSP, because they 

affect the spatial distribution and diversity of the entire population, which are two of the key 

parameters for evaluating viability. 

Indicators of Population Structure 

Based on our definition of independence, the definitive information needed to identify 

populations is long-term migration rates between different geographical spawning aggregations 

and the demographic consequences of those migration rates. In practice, information on such 

migration (or “straying” as it is commonly known) is rare. Consequently, we used other kinds of 

information as potential indicators of reproductive isolation. Each type of information 

contributes to our understanding of where independent populations might occur, but none alone 

provides us with a definitive answer. We describe these below. Depending on the quality of the 

data and the genetic and demographic history of salmon in different regions, the usefulness of 

these indicators in any one area varies. 

1. Geography. The boundaries of a salmon population are defined in part by the spatial 

distribution of its spawning habitat. Physical features such as a river basin’s 

topographical and hydrological characteristics dictate to a large degree where and when 

salmon can spawn and delimit the spatial area over which a single group of fish can be 

expected to interact. Geographic constraints on population boundaries (such as distance 

between streams) can provide a useful starting point from which to more closely examine 

the attributes of fish groups within circumscribed geographic areas, but will not generally 

support strong inferences at finer scales (e.g., distinguishing separate populations within a 

small river basin.) Biogeographic characteristics and historical connections between 

river basins on geological time scales can also be informative in defining population 

boundaries. 

2. Migration rates. The extent to which adults move between sites affects the degree of 

reproductive isolation and, therefore, demographic independence between sites. Straying 

estimates are the primary indicators available for the amount of connectivity between 

spawning aggregations. Stray rate estimates are particular to a group of fish and the 

season and streams in which they are made; thus they provide useful information about 

straying under current conditions. In contrast, data are not available to obtain estimates 

of the magnitude of their variation over long time periods (e.g., 100 years). Compared to 

mark-recapture and other direct estimates of straying, indirect estimates, such as 

genetically based estimates of intergroup isolation, can be used to estimate straying that 

has occurred between groups of fish, integrated over longer time periods. 

3. Genetic attributes. Neutral genetic markers are useful in identifying salmon populations 

because they indicate the extent of reproductive isolation among groups over longer 

periods of time. The observed patterns of variation in neutral markers can be difficult to 

2
interpret, because they may reflect anthropogenic sources of migration from hatchery 

practices or offer a characterization at a single point in time of populations that are 

changing; consequently, they should be interpreted with caution. Adaptive genetic 

differences among groups of fish (as indicated by quantitative traits or molecular 

markers) are more difficult to document than discrete marker differences, but they may 

offer good supporting evidence for distinct populations. 

4. Patterns of life history and phenotypic characteristics. Phenotypic traits based on 

underlying genetic variation (rather than environmentally induced variation) are 

informative in identifying populations. Although most life history and phenotypic traits 

are influenced by environmental variation, they are also controlled by genetic differences. 

Consequently, phenotypic variation may be used as a proxy for genetic variation and may 

also indicate similarities in the selective environments experienced by salmon in different 

streams. 

5. Population dynamics. Abundance data can be used to explore the degree to which 

demographic trajectories of two groups of fish are independent of one another. All else 

being equal, the less correlated those time series of abundance are between two groups of 

fish, the less likely they are to be part of the same population. Complicating the 

interpretation of correlations in abundance between groups of fish is the potentially 

confounding influence of correlated environmental characteristics. When groups of fish 

that are in close proximity are not correlated in abundance over time, it is likely that they 

are not linked demographically. The reverse is not always the case—when correlations in 

abundance between groups of fish are detected, more work is needed to rule out 

confounding sources of correlation. 

6. Environmental and habitat characteristics. The biotic and abiotic characteristics of 

occupied salmonid habitat may also help define a population, if these ecological 

characteristics are associated with different selective environments that could lead to 

isolation between groups of salmon. If different groups of salmon experience different 

selective environments and there is very little migration between those groups, we expect 

the fitness and phenotypic characteristics of the groups in each of those environments to 

diverge. The relative strength of inference for this kind of information though is weak, 

because we generally do not know which environmental variables affect selection or 

whether those effects will be observed at the population level. 

Data Quality 

An important first step in analyzing and interpreting any of the population structure 

indicators above is to carefully screen the data and information for potential sources of error or 

bias. To minimize such error, we consulted with biologists familiar with the local geography, 

adult and juvenile sampling and enumeration methods and calculations, and the history of 

hatchery releases. 

3
Methods and Results 

Geographic Distribution 

Lake Ozette sockeye salmon spawn in the Lake Ozette watershed, which defines the 

geographic boundary of the ESU (Gustafson et al. 1997). Sockeye salmon historically spawned 

on beaches throughout the lake and may also have spawned in its tributaries. Currently, nearly 

all spawning in the lake occurs on two beaches: Olsen’s Beach (referred to as Elk Creek in some 

reports), located on the eastern shore north of Siwash Creek, and Allen’s Bay, north of Allen’s 

Slough on the western shore (Figure 1). Limited or intermittent spawning may also occur on the 

south shore of Baby Island (Meyer and Brenkman unpubl. manuscr.), Umbrella Point, and 

Ericson’s Bay (Dlugokenski et al. 1981, Makah Fisheries Management 2000). The presence of 

ready-to-spawn sockeye salmon in Boot Bay near Quinn Creek may suggest intermittent or 

historical spawning near there also (Dlugokenski et al. 1981, Jacobs et al. 1996). No known 

geographical or ecological barriers exist to migration of sockeye salmon among these locations. 

Distances between locations range from 3 to 12 km. 

No historical abundance data exist for sockeye salmon in the tributaries of Lake Ozette, 

but the Makah Tribe and others have documented anecdotal information of sockeye salmon in 

some tributaries (reviewed in Jacobs et al. 1996). A spawning run of sockeye salmon has 

successfully colonized one tributary, Umbrella Creek, as a result of accidental (and later 

intentional) releases of artificially propagated sockeye salmon from the beach-spawning 

aggregations (Makah Fisheries Management 2000). This supports the hypothesis that sockeye 

salmon could have spawned in the tributaries as well as beaches. Small numbers of adult 

sockeye salmon have also recently been observed in Big River prior to any hatchery 

introductions to Big River, which occurred in 2000. 

Kokanee salmon, a nonanadromous life history variant of O. nerka, also occur in Lake 

Ozette and spawn in the smaller tributaries, such as South, Siwash, Quinn, and Crooked creeks. 

Genetic evidence indicates that kokanee salmon are reproductively isolated from the anadromous 

sockeye salmon (Gustafson et al. 1997), but both life history forms occupy the same habitat as 

post-emergent fry and while rearing in the pelagic zone of Lake Ozette until the sockeye salmon 

migrate seaward. Kokanee salmon are not part of the Lake Ozette ESU. 

Genetic Attributes 

Past Analyses 

Genetic data provide the strongest inferences about population structure in Lake Ozette. 

Analyses based on allozyme variation indicate that Lake Ozette sockeye salmon were genetically 

distinct from other sockeye salmon populations in Puget Sound (Winans et al. 1996) and the 

Washington coast (Gustafson et al. 1997). Within the Lake Ozette ESU, Hershberger et al. 

4
Ozette

River

Coal

Cr.

Umbrella

Creek

Trout Creek

River

Big

Current & historical

Reintroduced

Intermittent, limited

Key to spawning 

areas

?

N

Umbrella 

Bay

Swan Bay

Crooked

Creek

Quinn Creek

Siwash

Ericson’s Bay

Rocky Pt.

Garden 

Island

Lake 

Ozette

Allen’s

Bay

Olsen’s 

Beach Tivoli 

Island

? ?

?

?

Pacific

Ocean

012

Kilometers South 

Creek

Baby 

Island

Washington

State

Lake 

Ozette

Figure 1. Sockeye salmon spawning areas in the Lake Ozette ESU. 

(unpubl. manuscr.) documented genetic variation at 2 of 37 allozyme loci and, based on variation 

at PGM-1*, suggested that two genetically different groups of sockeye salmon were present in 

Lake Ozette. They hypothesized the groups might be isolated by differences in run timing. 

Gustafson et al. (1997) documented significant differences in allozyme frequencies among the 

Allen’s Bay and Olsen’s beach-spawning aggregations in samples collected in 1995. Significant 

difference also existed between these 1995 samples and two samples collected in other years. 

However, because the spawning destinations of the fish in the samples taken at the Lake Ozette 

weir were unknown, they could not determine whether these differences were due to interannual 

variation or geographical differences among spawning aggregations. 

5
Introductions of sockeye salmon and kokanee salmon into Lake Ozette do not appear to 

confound interpretations of current genetic patterns, but they remain an important consideration. 

Fingerling sockeye salmon were introduced from Baker Lake in 1937 and Lake Quinault in 

1983; kokanee salmon were introduced from Lake Crescent in 1940 and from an undocumented 

source in 1958. Despite these introductions, sockeye and kokanee salmon appear to have 

retained distinctive genetic characteristics (Gustafson et al. 1997) (Figure 2 below). 

New Analyses—Methods 

To add to these existing analyses, the Makah Tribe and the Washington Department of 

Fish and Wildlife analyzed samples from sockeye salmon collected from Olsen’s Beach in 1996, 

1999, and 2000; Allen’s Bay in 2000, and Umbrella Creek in 2000 at nine microsatellite DNA 

loci (One-100, One-101, One-102, One-103, One-105, One-108, One-110, One-114, and One115). Data and results of their analyses are available in Crewson et al. (2001). 

For the TRT’s analysis of these data, we calculated six indicators of independence of the 

spawning aggregations. These were: 1) pairwise P-values from chi-square tests for significant 

differences in allele frequencies; 2) Reynolds coancestry d genetic distances (Reynolds et al. 

1983); 3) Nei’s unbiased genetic distances (Nei 1978); 4) FST, a measure of genetic population 

structure, as estimated by θ (Weir and Cockerham 1984); 5) M, the number of migrants per 

generation, which is the pairwise analog of Nem (Slatkin 1993); and 6) t, the time since 

divergence of two populations from a common ancestral population (Wier 1996). We examined 

patterns of genetic differences by constructing dendrograms based on the genetic distances and 

Crooked Creek

Siwash Creek

Olsen’s Beach 1999

Olsen’s Beach 1996

Allen’s Bay 2000

Olsen’s Beach 2000

Umbrella Creek 2000

Nei’s unbiased genetic distance

0.10 0.075 0.050 0.025 0.00

100

87

63

67

100

Figure 2. Genetic similarity of sockeye and kokanee salmon aggregations in Lake Ozette based on nine 

microsatellite loci. Numbers on branches are the percent bootstrap support. Similar results were 

obtained using Reynolds coancestry distance. 

6
7

the UPGMA (unweighted pair group method with arithmetic mean) clustering algorithm (Sneath 

and Sokal 1973) and by ordination of Reynolds coancestry distance (Reynolds et al. 1983) using 

the multidimensional scaling algorithm (Lessa 1990). We examined the robustness of the 

dendrograms by generating bootstrap values for each node based on 1,000 resamplings of the 

allele frequencies. 

We estimated the number of migrants per generation, M, using M = (1–θ )/ 2θ (Slatkin 

1993) and the divergence time of each pair of populations, t, by t = –2Neln(1–θ ) following Wier 

(1996). Calculation of time since divergence assumes that the populations have been completely 

isolated since diverging from a common ancestral population t generations ago, that mating is 

random within populations, that sockeye have discrete generations, and that θ is estimated from a 

neutral genetic trait. Violations of these assumptions mean that exact estimates of t are difficult 

to obtain. 

Limited or episodic gene flow is likely to have occurred between spawning aggregations, 

which biases estimates of t. Ozette sockeye salmon return, spawn, and die almost exclusively as 

four-year-olds with little reproductive exchange between brood years (Makah Fisheries 

Management 2000). Most Pacific salmonids, however, do not have discrete generations, but 

Waples (1990a) showed that violating this assumption is not likely to greatly affect estimates of t

over long time periods. The microsatellite loci used to estimate θ were presumed to be neutral 

and unlinked to loci under selection, but this assumption has not been tested. Comparisons of t 

for divergence of Umbrella Creek hatchery fish and Olsen’s Beach based on genetic data 

provided an internal control for our estimates, however, because the historical time of divergence 

is known. 

We estimated the genetic effective sizes, Ne, of the populations in two ways and used 

these to calculate a range for t. From demographic data in the Lake Ozette Hatchery Genetic 

Management Plan, we estimated the joint inbreeding effective size of all populations, using 

Ne = gNb, where g is the mean generation time and Nb is the harmonic mean of the annual 

effective breeding number of spawners. We calculated the annual effective breeding number 

from N, the annual census number of spawners to Lake Ozette, while assuming Nb:N = 0.3, 

which is in the middle of the range of Nb:N values in the scientific literature (Waples 1990a, 

Waples et al. 1993, Ford et al. 2001). We also calculated a variance effective population size for 

Olsen’s Beach sockeye salmon–based genetic drift over one generation (1996–2000) following 

Waples (1990b) and doubled it to estimate the joint effective population size between two 

different geographical aggregations. 

New Analyses—Results 

All genetic analyses showed major evolutionary divergence between sockeye salmon and 

kokanee salmon in Lake Ozette (Table 1, Figure 2). Kokanee and anadromous sockeye salmon 

aggregations consistently formed distinct groups in cluster analyses. Genetic divergence of the 

two forms of O. nerka was nearly 10 times greater than divergence of anadromous sockeye 

salmon aggregations from each other. This supported the conclusion that Lake Ozette kokanee 

salmon are not in the same ESU as anadromous Lake Ozette sockeye salmon. The following 

discussion therefore focuses only on genetic differences among spawning anadromous sockeye 

salmon aggregations in Lake Ozette. 
Table 1. P-values from pairwise chi-square tests for allelic homogeneity for nine microsatellite loci (below the diagonal) a

confidence intervals (above the diagonal) for O. Nerka in Lake Ozette. Unshaded entries are anadromous sockeye 

are kokanee. 

Olsen’s Beach Allen’s Bay Umbrella Creek Crooked Cr

Sample location 1996 1999 2000 2000 2000 1995 Olsen’s Beach 

1996 

— 0.0222 

(0.0069, 0.0431)

0.0069 

(–0.0060, 0.0239)

0.0136 

(0.0003, 0.0313)

0.0002 

(–0.0081, 0.0113) 

0.1146 

(0.0709, 0.016

Olsen’s Beach 

1999 

0.000 — 0.0072 

(–0.0004, 0.0138)

0.0155 

(0.0079, 0.0238)

0.0193 

(0.0107, 0.0288) 

0.1101 

(0.0760, 0.14

Olsen’s Beach 

2000 

0.183 0.000 — 0.0068 

(0.0006,0.0150) 

0.0067 

(0.0004, 0.0158) 

0.0877 

(0.0537, 0.12

Allen’s Bay 

2000 

0.001 0.000 0.000 — 0.0094 

(0.0003, 0.0249) 

0.1061 

(0.0676, 0.14

Umbrella Creek 

2000 

0.138 0.000 0.000 0.001 — 0.093 

(0.0576, 0.13

Crooked Creek 

1995 

0.000 0.000 0.000 0.000 0.000 — 

Siwash Creek 

1995 

0.000 0.000 0.000 0.000 0.000 0.648 

8
Microsatellite allele frequencies were significantly different between sockeye salmon 

from Olsen’s Beach, Allen’s Bay, and Umbrella Creek hatchery fish (Table 1). Significant 

temporal differences also occurred between sockeye salmon from different brood cycles on 

Olsen’s Beach but not between generations of the same four-year brood cycle. Allele 

frequencies were also significantly different between sockeye salmon from Umbrella Creek and 

Allen’s Bay and between Umbrella Creek and Olsen’s Beach in two out of three comparisons. 

The magnitude of the statistical differences between Olsen’s Beach and Allen’s Bay, as judged 

by p-values, was similar to the differences between Umbrella Creek hatchery fish and sockeye 

salmon from Olsen’s Beach and Allen’s Bay, which provided the source brood stock for the 

hatchery program less than 20 years ago. 

Pairwise FST values ranged from 0.0002 to 0.022. The largest FST values were between 

sockeye salmon from different brood cycles on Olsen’s Beach and between different brood 

cycles from Olsen’s Beach and Umbrella Creek. In contrast, the smallest FST values were 

between sockeye salmon from the same four-year brood cycle on Olsen’s Beach (1996–2000) 

and likewise between the same brood cycles from Olsen’s Beach and Umbrella Creek. 

The dendrogram of genetic similarity based on Nei’s genetic distance illustrates a similar 

pattern of diversity (Figure 2). Umbrella Creek hatchery fish and Olsen’s Beach sockeye salmon 

from the same 1996–2000 brood cycle are genetically most similar. These fish are more similar 

to sockeye salmon from Allen’s Bay in 2000 than to sockeye salmon from Olsen’s Beach in 

1999. This same pattern is represented in two dimensions by multidimensional scaling of 

genetic distances (Figure 3). 

Allen’s Bay 

2000

Olsen’s Beach 

1999 Olsen’s Beach 

2000

Olsen’s Beach 

1996

Umbrella 

Creek 2000

1.0

0.0

-2.0

-2.5 0.0 1.5

Figure 3. Multidimensional scaling of coancestry genetic distances between Lake Ozette sockeye salmon. 

9
The number of pairwise migrants per generation, M, ranged from 22 to 2,500 (Table 2). 

The greatest independence was detected between Olsen’s Beach sockeye salmon in 1996 and 

1999, and least independence was detected between Umbrella Creek hatchery fish and Olsen’s 

Beach, which was the main source of brood stock for the hatchery program. The number of 

migrants per generation between Olsen’s Beach and Allen’s Bay ranged from 36 to 73. Based 

on demographic data from 1988–1999, the mean inbreeding effective population size for all 

sockeye salmon in Lake Ozette was 850. In contrast, the variance effective population size for 

Olsen’s Beach based on genetic drift over one generation was 128. 

Using these effective population sizes to calculate time since divergence, the shortest 

divergence times were between Umbrella Creek hatchery fish and Olsen’s Beach from the same 

brood cycle (0–1 generations). This estimate corresponds closely with the known founding of 

the Umbrella Creek hatchery strain. Olsen’s Beach sockeye salmon were the principal source of 

brood stock for the Umbrella Creek hatchery strain 1–3 generations ago. The longest divergence 

times were between sockeye from different brood cycles from Olsen’s Beach (8–27 generations) 

and different brood cycles of Umbrella Creek hatchery fish and Olsen’s Beach sockeye (10–33 

generations). Over all brood cycles, divergence time between sockeye from Olsen’s Beach and 

Allen’s Bay ranged from 3 to 27 generations. Divergence time estimates between Allen’s Bay 

and Umbrella Creek varied from 5 to 16 generations. 

Life History Characters and Spatial Synchrony 

Historically, most demographic data on adult sockeye salmon have been collected as fish 

pass a weir on the Ozette River while entering the lake. Without means to distinguish fish from 

Olsen’s Beach, Allen’s Bay, or other spawning areas, it has been impossible to look for temporal 

synchrony between locations in spawner abundance and differences in adult migratory behavior. 

Sockeye salmon from Ozette return to freshwater between late April and mid-August, 

from 3 to 10 months before they spawn (Makah Fisheries Management 2000, Jacobs et al. 1996). 

The sockeye spawn from mid-November to as late as early February. Timing of entry into the 

lake may be affected by water temperatures in some years; high temperatures can delay entry 

(Jacobs et al. 1996). The percentage of sockeye entering during the daytime is strongly 

Table 2. Migrants per generation (above the diagonal) and time since divergence, t, (below the diagonal) 

for sockeye salmon in Lake Ozette. The upper range of t was estimated using Ne from the 

demographic data and the lower range was estimated using Ne from the genetic data from Olsen’s 

Beach. 

Olsen’s Beach Allen’s Bay Umbrella Creek

Sample Location 1996 1999 2000 2000 2000 

Olsen’s Beach 1996 — 22 71 36 2,500 

Olsen’s Beach 1999 (6–38) — 68 31 25 

Olsen’s Beach 2000 (2–12) (2–12) — 73 74 

Allen’s Bay 2000 (7–23) (8–27) (3–12) — 52 

Umbrella Creek 2000 (0–1) (10–33) (3–11) (5–16) — 

10
correlated with lake level. In addition, discrepancies between total abundances observed at the 

weir and numbers of fish on the spawning grounds suggested unexplained mortalities or 

undiscovered spawning areas. These challenges have prevented investigators from examining 

correspondence between entry into freshwater and spawning characteristics at different beaches. 

Dlugokenski et al. (1981) suggested that sockeye salmon spawning at Allen’s Bay and 

Olsen’s Beach might be different subpopulations based on spawn timing (Figure 4). Peak 

spawning on the eastern shore (Olsen’s Beach) was more than a month earlier than spawning on 

the western shore (Allen’s Bay), but 60% of the total spawning occurred at the same time 

between December and January. More recent observations of spawning on Olsen’s Beach and at 

Allen’s Bay suggests that spawning coincides at both beaches (summarized in Jacobs et al. 1996, 

Makah Fisheries Management 2000). 

Habitat Characteristics 

All spawning locations for Lake Ozette sockeye salmon are within the same ecoregion, 

which suggests that no major differences exist in regional habitat characteristics related to 

elevation, land cover, and geology. The major differences in spawning areas among streams and 

beaches are size, gradient, and substrate. We found no evidence in the literature that the 

magnitude of these differences is associated with boundaries of independent populations. Big 

River and Umbrella Creek are the largest systems where sockeye currently spawn in Ozette, 

Nov Dec Jan Feb March

0

20

40

60

80

100

120

140

160

East shore

West shore

North shore

Number of spawning adults

Figure 4. Timing and abundance of lakeshore spawning sockeye salmon in the 1978–1979 spawning 

season (adapted from Dlugokenski et al. 1981). 

11
ranging from 15 to 25 km in mainstem length and having multiple tributary creeks. The main 

stems are low and moderate gradients, declining an average of 3 m/km of length. Tributaries and 

headwaters are steeper, dropping 15 to 40 m/km. The smaller streams predominantly used by 

kokanee salmon, such as Siwash, South, Elk, and Crooked creeks, are shorter, averaging 2 to 10 

km in length, and have steeper gradients (Figure 13 in Jacobs et al. 1996). Streambeds in low to 

moderate gradients tend to be composed of gravel and sand, whereas streams with steeper 

gradients have more boulders and cobbles (Bortleson and Dion 1979). 

One potentially significant habitat difference for lake-type sockeye salmon is between 

tributary and lake spawning habitat. Burgner (1991) noted that with sockeye, inlet spawning 

typically occurred earlier than beach spawning or spawning in lake outlet streams, which usually 

have higher water temperatures. Strong selection could exist in some lakes for fry emergence 

timing that would allow fry from different incubation temperature regimes to migrate to the lake 

and exploit common feeding and rearing opportunities (Godin 1982, Brannon 1987, Burgner 

1991). Emergence time is heritable in salmonids and may be affected by genotype environment 

interactions (McIntyre and Blanc 1973) that could lead to adaptive differences among 

populations. 

In Lake Ozette, warming and stratification of the lake during the summer may lead to 

periods in October and November where, because of the differences in mass, shallower beach 

spawning waters have not cooled as rapidly as stream temperatures. By the time most spawning 

and incubation begins in late November or December, lake waters have cooled to 11°C 

(Figure 5) but will not reach their coolest temperatures of 7–8°C until February and March 

(Makah Fisheries Management 2000). 

In contrast, inlet and outlet streams have already cooled to 7–8°C (Figure 6) (Bortleson 

and Dion 1979). It is unclear whether this difference could have led to adaptive differences 

between historical tributary and beach spawning populations. The successful introduction of 

lake spawning fish into Umbrella Creek, however, suggests that adaptive differences for this trait 

may not have been large enough to prevent successful reproduction of lake spawning fish in 

tributaries. This is another factor that supports the hypothesis that there has been in the past a 

tributary spawner component to the population and that the ESU contains one population, not 

several isolated populations. 

Population Structure Decisions 

Table 3 summarizes the available evidence for and against independent populations in 

Lake Ozette. Strong evidence of population structure for any of indicators 1–4 would provide 

reliable inference of independent populations in Lake Ozette. Corroborating evidence from 

multiple indicators would also strengthen our inference. Similar logic applies to evidence of 

subpopulation structure. 

Strength of inference for independent populations in Lake Ozette is weak in four of the 

six indicators and data do not exist to test for it in two indicators. Genetic data provided the best 

evidence of differences among aggregations, but we considered it weak evidence of independent 

populations because of the magnitude of the differences. Estimates of FST and the related 

12
Feb

Apr

Jul

Aug

Oct

Dec

5 10 15 20 25

0

5

10

15

20

25

30

Depth (m)

Temperature (oC)

Figure 5. Seasonal changes in temperature for Lake Ozette in 1994 (figure from Jacobs et al. 1996, data 

from Meyer and Brenkman unpubl. manuscr.). 

Table 3. Evidence and strength of inference for population structure in Lake Ozette sockeye salmon. 

Strength of inference 

Indicator Evidence Population Subpopulation 

1. Geography No geographical barriers; 

distances between aggregations 

small 

Weak Weak 

2. Migration rates No data available – – 

3. Genetic attributes Statistically significant divergence 

of aggregations; divergence recent 

(<100 yrs); genetic migration 

rates high 

Weak Strong 

4. Life history 

characteristics 

Possible difference in peak 

spawning among aggregations 

Weak Weak, but 

possible 

5. Population 

dynamics 

No data available – – 

6. Habitat 

characteristics 

Possible 4–5°C difference in 

incubation temperatures between 

tributary and lake 

Weak Weak, but 

possible 

13
JFMAMJJASONDJFMAMJJAS

5

10

15

20

5

10

15

20

5

10

15

20

5

10

15

20 Ozette River

Big River

Crooked Creek

Umbrella Creek

Incubation Incubation

Incubation Incubation

Incubation Incubation

Incubation Incubation

oC

oC

oC

oC

1976 1977

Figure 6. Water temperatures in the Ozette River and three inlet tributaries of Lake Ozette (adapted from 

Bortleson and Dion 1979). 

14
estimates of migrants per generation, as well as time since divergence between different natural 

spawning aggregations on Olsen’s Beach and Allen’s Bay, were generally similar to estimates 

between Umbrella Creek hatchery fish and Olsen’s Beach, where most of the brood stock 

originated 1–3 generations ago. Estimates of time since divergence from the genetic data 

corresponded closely with known times of divergence between Olsen’s Beach 1996 and 

Umbrella Creek, and between Olsen’s Beach 1996 and Olsen’s Beach 2000, which gave us 

confidence in other estimates (Table 2). In contrast, independent populations under our 

definition would have diverged approximately 25 generations ago. 

Strength of inference for subpopulation structure among spawning aggregations is much 

stronger. Both recently collected DNA data (Table 2) and older allozyme data (Gustafson et al. 

1997) showed measurable divergence between spawning aggregations. In addition, although the 

inferences were necessarily weaker, potential differences in peak spawning time between beach 

spawning aggregations and differences in incubation temperatures between beach and tributaries 

suggested that subpopulations exist in Lake Ozette now and probably were more extensive 

historically. 

The strongest evidence of genetic differences among anadromous sockeye salmon in 

Lake Ozette occurred between different brood cycles (Table 2). Sockeye salmon in Lake Ozette 

return to spawn and die almost exclusively as four-year-olds, which limits potential genetic 

exchange between fish in the four different four-year brood cycles that return to the lake. 

Temporal isolation maintained by the age structure of these fish may have allowed genetic 

differences to evolve between fish in different brood cycles. It would be useful to continue to 

monitor the age structure for any divergence in the predominately 4-year-old return pattern. 

15
Conclusions 

Based on the genetic data, we conclude that extant spawning aggregations in Lake Ozette 

are different subpopulations within a single population. It seems likely that differences also 

existed historically among other subpopulations that have since been extirpated and 

subpopulations occurring at different spawning beaches or tributaries. 

If geographical subpopulation structure were real, we would expect year-to-year 

differences among generations to be smaller than differences between geographical areas. 

Existing data support this, but only one between-generation comparison was possible (Olsen’s 

Beach 1996 and Olsen’s Beach 2000). More between-generation comparisons are needed. 

Likewise, more comparisons between brood cycles will allow us to determine the temporal 

genetic structure within the population. Both of these may be possible using DNA from archived 

scales or tissues. 

16
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