NOAA Technical Memorandum NMFS-NWFSC-101

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Determination of Independent

Populations and Viability Criteria

for the Hood Canal

Summer Chum Salmon

Evolutionarily Significant Unit

August 2009
NOAA Technical Memorandum

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Service, NOAA.

This document should be referenced as follows:

Sands, N.J., K. Rawson, K.P. Currens, W.H. Graeber, M.H. 

Ruckelshaus, R.R. Fuerstenberg, and J.B. Scott. 2009. 

Determination of independent populations and viability criteria for the Hood Canal summer chum salmon evolutionarily 

significant unit. U.S. Dept. Commer., NOAA Tech. Memo. 

NMFS-NWFSC-101, 58 p. 
NOAA Technical Memorandum NMFS-NWFSC-101

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Determination of Independent

Populations and Viability Criteria

for the Hood Canal

Summer Chum Salmon

Evolutionarily Significant Unit

Norma J. Sands, Kit Rawson,

1

 Kenneth P. Currens,

2

William H. Graeber,

3

 Mary H. Ruckelshaus,

Robert R. Fuerstenberg,

4

 and James B. Scott5

Northwest Fisheries Science Center

Conservation Biology Division

2725 Montlake Boulevard East

Seattle, Washington 98112

Tulalip Tribes

6700 Totem Beach Road

Tulalip, Washington 98271

Northwest Indian Fisheries Commission

6730 Martin Way East

Olympia, Washington 98516

Stillwater Sciences

Present affiliation: Entrix Inc.

148 Rogers Street Northwest, Suite 1

Olympia, Washington 98502

King County Department of Natural Resources and Parks

201 South Jackson Street, Suite 700

Seattle, Washington 98104

Washington Department of Fish and Wildlife

600 Capitol Way North

Olympia, Washington 98501

August 2009

1

2

3

4

5
ii

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Table of Contents 

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

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

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

Background...................................................................................................................................................1 

Identification of Independent Populations within the Hood Canal Summer Chum Salmon ESU................2 

Introduction ..............................................................................................................................................2 

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

Geographical Distribution....................................................................................................................4 

Migration..............................................................................................................................................7 

Genetic Attributes ................................................................................................................................8 

Life History and Phenotypic Characteristics......................................................................................15 

Population Dynamics .........................................................................................................................16 

Environmental and Habitat Characteristics........................................................................................16 

Remaining Uncertainties in Population Structure..............................................................................17

Conclusions ............................................................................................................................................20 

Independent Populations ....................................................................................................................20 

Genetic Characteristics.......................................................................................................................20 

Viability Criteria for the Hood Canal Summer Chum Salmon ESU and Populations................................23 

Introduction ............................................................................................................................................23 

Population Viability................................................................................................................................24 

Source Data for Viability Analyses....................................................................................................25 

Estimating Quasi-extinction for Summer Chum Salmon...................................................................28 

PVA for Abundance and Productivity Criteria ..................................................................................29

Incorporating Spatial Distribution Characteristics into Evaluation of Viability ................................41 

ESU Viability .........................................................................................................................................44 

Discussion and Recommendations .........................................................................................................45 

Abundance and Productivity ..............................................................................................................45 

Spatial Structure and Diversity ..........................................................................................................47 

Recommendations ..............................................................................................................................47 

iii
References...................................................................................................................................................53 

iv
List of Figures 

Figure 1. Extant and historical distribution of Hood Canal summer chum salmon .....................................5 

Figure 2. Geographical distribution of historical and extant spawning aggregations of summer chum 

salmon based on frequency of pair-wise distances. ......................................................................................7 

Figure 3. Relationship between proportions of marked hatchery summer chum salmon recovered as 

adults and distance from original release location. .......................................................................................8 

Figure 4. Relationship of 35 summer and fall chum salmon populations using Cavalli-Sforza and 

Edwards chord distance and neighbor-joining clustering ...........................................................................10 

Figure 5. Relationship between Nm and distance based on allozyme data ................................................12 

Figure 6. Relationship between Nm and distance based on microsatellite DNA data ...............................13 

Figure 7. Distribution of population independence scores from experts judging Puget Sound Chinook 

salmon for different levels of genetic exchange .........................................................................................15 

Figure 8. The two populations of the Hood Canal Summer Chum Salmon ESU, including the streams 

with spawning aggregations and seven ecological diversity groups...........................................................18 

Figure 9. Annual return abundance of natural-origin summer chum salmon of the Strait of Juan de 

Fuca region from 1974 to 2005...................................................................................................................25 

Figure 10. Annual return abundance of natural-origin summer chum salmon aggregations of the Hood 

Canal region from 1974 to 2005 .................................................................................................................25 

Figure 11. Strait of Juan de Fuca summer chum salmon population R/S for 1974–2001 brood years......27 

Figure 12. Hood Canal summer chum salmon population R/S for 1974–2001 brood years .....................27 

Figure 13. Results from the Dynamic Model for estimation of spawner-recruit functions for the Strait 

of Juan de Fuca summer chum salmon population .....................................................................................37 

Figure 14. Results from the Dynamic Model for estimation of spawner-recruit functions for the Hood 

Canal summer chum salmon population.....................................................................................................38 

Figure 15. Viability curves for the Strait of Juan de Fuca summer chum salmon population for no 

harvest and three levels of harvest and associated realized escapement levels over 100 years..................40 

Figure 16. Viability curves for the Hood Canal summer chum salmon population for no harvest and 

three levels of harvest and associated realized escapement levels over 100 years.....................................41 

Figure 17. Distribution of escapement for spawning aggregations within the Strait of Juan de 

Fuca summer chum salmon population for 1974–1978 and 2001–2005 ....................................................45 

Figure 18. Distribution of escapement for spawning aggregations within the Hood Canal summer 

chum salmon population for 1974–1978 and 2001–2005...........................................................................45 

Figure 19. Diversity indices for the Strait of Juan de Fuca summer chum salmon population from 

1974 to 2005 ...............................................................................................................................................46 

Figure 20. Diversity indices for the Hood Canal summer chum salmon population from 1974 to 2005 ..46 

v
vi
List of Tables 

Table 1. Geographical distance between likely historical spawning locations of summer chum salmon 

in Strait of Juan de Fuca and Hood Canal areas............................................................................................6 

Table 2. Seven proposed ecological diversity groups of the Hood Canal Summer Chum Salmon ESU 

by geographic regions and associated spawning aggregations. ..................................................................19 

Table 3. Evidence and strength of inference for different indicators of independent populations of 

summer chum salmon. ................................................................................................................................22 

Table 4. Estimated values of the variance and the natural logarithm of the growth rate for Hood 

Canal and Strait of Juan de Fuca summer chum salmon populations.........................................................31 

Table 5. Results of population projections using SimSalmon model ........................................................32 

Table 6. Summary of output results from the Dynamic Model used as input to the VRAP model for 

the Strait of Juan de Fuca and Hood Canal summer chum salmon populations.........................................36 

Table 7. A selection of viable spawner-recruit functions as defined by the intrinsic productivity and 

capacity parameters for specific exploitation rates for the Strait of Juan de Fuca summer chum salmon 

population ...................................................................................................................................................39 

Table 8. A selection of viable spawner-recruit functions as defined by the intrinsic productivity and 

capacity parameters for specific exploitation rates for the Hood Canal summer chum salmon 

population ...................................................................................................................................................39 

Table 9. Viability estimates of escapement from the two PVA analyses for 5% risk and 1% risk of 

extinction and no harvest for the Strait of Juan de Fuca and Hood Canal populations ..............................42 

vii
viii
Executive Summary 

The Puget Sound Technical Recovery Team identified two independent populations of 

summer chum salmon (Oncorhynchus keta) within the Hood Canal Summer Chum Salmon 

evolutionarily significant unit (ESU), which is listed as threatened under the U.S. Endangered 

Species Act. Our analysis was based on allozyme and microsatellite DNA variation, straying 

patterns, historical and present geographical distribution, and life history and ecological 

variations. Based on multiple lines of evidence, we concluded that the ESU contains two 

independent populations. The Strait of Juan de Fuca population spawns in rivers and streams 

entering the eastern Strait of Juan de Fuca and Admiralty Inlet. The Hood Canal population 

includes all spawning aggregations within the Hood Canal watershed. An analysis by state and 

tribal biologists in 2000 identified three extant stocks associated with the Strait of Juan de Fuca 

population and six extant stocks associated with Hood Canal population. However, we 

considered spawning aggregations to be independent populations only if the aggregations were 

isolated to such an extent that exchanges of individuals among the aggregations would not be 

expected to substantially affect the population dynamics or extinction risk of the different groups 

over a 100-year time frame. 

Genetic analyses showed that genetic differences among spawning aggregations followed 

a pattern of isolation by distance, where the amount of exchange between spawning aggregations 

was greatest among streams that were geographically close; independence among spawning 

aggregations increased as geographical distances increased. Independent populations most likely 

occurred when adjacent spawning aggregations were separated by at least 30 km. Given 

historical distributions and ecological conditions that presumably sustained a persistent ESU, 

these distances separate the ESU into two groups: the Strait of Juan de Fuca and the Hood Canal 

aggregations. Additionally, our analyses indicate that stocks identified by earlier analyses—as 

well as spawning aggregations that have disappeared from some streams—are important for the 

viability of these populations through their effects on spatial structure and diversity. 

For populations within salmon ESUs, the National Marine Fisheries Service defines 

viability as a 0.95 probability of population persistence over a 100-year time frame. Four main 

population parameters—abundance, productivity, spatial structure, and diversity—describe the 

attributes of a viable population. The abundance and productivity attributes are estimated 

through quantitative population models; spatial structure and diversity of viable populations are 

described more qualitatively. 

Population viability has been determined using two methods: one assuming densityindependent returns from spawners and the other using density-dependent functions. In order to 

have a less than 0.05 probability of extinction, the Strait of Juan de Fuca summer chum salmon 

population requires an escapement level of 12,500 fish, assuming density independence. 

Assuming density dependence and no harvest, a viable Strait of Juan de Fuca population would 

need escapements between 4,500 and 5,100 spawners, depending on the population’s 

ix
x

corresponding intrinsic productivity (α) and capacity (β), two parameters that describe a 

population viability curve using a Beverton-Holt stock recruit function. To support harvest, the 

population viability curves should have higher values of α and β than without harvest. Other 

solutions for a viable Strait of Juan de Fuca population are given in this report. 

The Hood Canal summer chum salmon population would have a less than 0.05 

probability of extinction if it had an escapement level of 24,700 fish, assuming density 

independence. Assuming density dependence and no harvest, a viable Hood Canal population 

would need escapements between 18,300 and 19,100 spawners, depending on assumptions about 

α and β of the population. To support harvest, the population viability curves should have higher 

values of α and β than without harvest. Other solutions for a viable Hood Canal summer chum 

population are given in this report. 

To maintain diversity and spatial structure to support population viability, the Hood 

Canal and Strait of Juan de Fuca populations will have persistent spawning aggregations in each 

of the major ecological diversity groups delineated within their boundaries. In addition, 

spawning aggregations need to be distributed across the historical range of the population and at 

less than specified maximum distances. Such a distribution of subpopulations within each 

population will enhance diversity, increasing the chances that each population will be resilient to 

future environmental and anthropogenic changes. 

Since the Hood Canal Summer Chum Salmon ESU has only two independent 

populations, both the Strait of Juan de Fuca population and the Hood Canal population would 

need to be viable to have a viable ESU at low risk of extinction. Viability is defined by all four 

viability parameters: abundance, productivity, spatial distribution (structure), and diversity. 
Background 

In the 1980s, the abundance of summer chum salmon (Oncorhynchus keta) returning to 

the Hood Canal and the Strait of Juan de Fuca regions of Puget Sound began to decline 

precipitously. Several spawning aggregations within these areas were extirpated during this 

period (Johnson et al. 1997). By 1992, state and tribal natural resource comanagers had 

increased protection for the fish and soon began to develop conservation strategies to restore 

summer chum salmon abundance and distribution. This planning led to completion of an 

implementation plan for recovery actions—the Summer Chum Salmon Conservation Initiative 

(WDFW and PNPTT 2000). 

In the meantime, the federal government had listed the Hood Canal Summer Chum 

Salmon evolutionarily significant unit (ESU) as threatened with extinction under the U.S. 

Endangered Species Act (ESA) (NMFS 1999). To help guide the ESA recovery response for the 

listed ESU, the Puget Sound Technical Recovery Team (PSTRT) was charged with 1) 

identifying conservation units within the species that would be the focus of recovery activities 

under the ESA and 2) developing population and ESU viability criteria. This paper describes the 

conservation units (independent populations) and viability criteria for the Hood Canal Summer 

Chum Salmon ESU and populations identified by the PSTRT. 
Identification of Independent Populations within 

the Hood Canal Summer Chum Salmon ESU 

Introduction 

Chum salmon have the widest natural geographic distribution of all salmon species, but 

are most abundant in North America from Kotzebue Sound in northwest Alaska to Tillamook 

Bay, Oregon (Salo 1991). The early spawning type or “summer” chum salmon have a much 

more limited distribution that extends from the Yukon River in the north to British Columbia and 

Washington in the south. In the Puget Sound region of Washington, some runs of chum salmon 

display this summer adult migratory timing. Entering freshwater as spawning adults in late 

summer, these salmon are uniquely adapted to exploit spawning habitat when river and stream 

levels are typically low and before most other populations and species of salmon return to spawn. 

The conservation units for recovery identified by the PSTRT are based on the concept of 

a viable salmonid population (VSP), defined by McElhany et al. (2000) 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, the conservation units of interest are independent populations, which are 

spawning aggregations that are isolated to such an extent that exchanges of individuals among 

the aggregations do not substantially affect the population dynamics or extinction risk of the 

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

Demographic independence of salmon varies over time and geography. Identification of 

different stocks of salmon implies a degree of reproductive independence that is useful for 

management (Moulton 1939, Larkin 1972, Ricker 1972), but no single standard exists for all 

stocks. To avoid confusion, we use “stock” to refer to any geographical spawning aggregations 

used for management; “population” refers specifically to an aggregation meeting the independent 

population criteria; and “subpopulation” refers to unique aggregations of salmon that may be 

independent for periods less than 100 years or whose likelihood of persistence depends on 

limited exchanges with other such groups. 

The definitive information needed to characterize the independence of different spawning 

aggregations is long-term migration rates between different aggregations and the demographic 

consequences of that migration. In practice, such information is rare. Consequently, we use 

different kinds of information that are proxies for understanding the degree of reproductive 

isolation between spawning aggregations within the Hood Canal Summer Chum Salmon ESU. 

Each type of information contributes to our understanding of population boundaries and 

2
structure, but none alone provides us complete confidence in our answer. In the following 

subsections, the PSTRT discusses these information types in order of the strength of inference 

about population structure, everything else being equal, beginning with the most powerful. 

1. Geography. Reproductive independence will be defined in part by the spatial distribution 

of 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 exchange between groups (such as distance between streams) 

can provide a useful starting point from which to look more closely at the attributes of 

groups of fish within geographic areas, but will not generally support strong inferences at 

finer scales (e.g., distinguishing separate populations within a small river basin.) In 

addition, biogeographical 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 return to spawn in nonnatal streams 

(straying) will affect the degree of reproductive isolation and, therefore, demographic 

independence between groups. Straying estimates are the primary indicators available of 

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. Data are usually 

not available to obtain estimates of the magnitude of their variation over long time 

periods (e.g., 100 years). 

3. Genetic attributes. In contrast to direct estimates of migration, indirect estimates based 

on the genetic differences among groups for neutral genetic traits can be used to estimate 

long-term average rates of straying. These should be interpreted with caution, because 

they may reflect recent anthropogenic sources of migration (e.g., hatchery practices or 

altered hydrological connections) or unknown extinctions and recolonizations, and they 

describe average straying rates at a single point in time when the rates may actually be 

changing. Adaptive genetic differences among groups of fish, as indicated by 

quantitative traits or molecular markers, are more difficult to document than neutral 

genetic traits, but they provide support for independence because they suggest that the 

groups have responded to local selection pressures or have maintained unique traits in 

spite of unknown amounts of straying. 

4. Patterns of life history and phenotypic characteristics. Although most life history and 

phenotypic traits may be influenced by environmental variation, they may also reflect 

genetic variation, or genetic-by-environmental interactions. Phenotypic variation may be 

useful as a proxy for genetic variation. It may also indicate similarities or differences in 

the selective environments experienced by salmon in different streams that could lead to 

adaptive genetic differences. 

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 that time series of abundance is between two groups, the 

more likely it is that they are independent. Interpretation of these patterns can be 

confounded by correlated environmental characteristics that affect abundance. When 

trends in abundance of groups that are in close proximity (i.e., share similar 

3
environmental influence) are not correlated, it may indicate demographic independence. 

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 identify potentially independent groups if these 

ecological characteristics are associated with different selective environments. The 

relative strength of inference for this kind of information is weak, because we generally 

do not know which environmental variables affect selection or whether those effects will 

be observed at the population level. Differences in adaptive traits or phenotypes may 

help support these inferences. 

Methods and Results 

Geographical Distribution 

Previous Analyses 

The freshwater and estuarine distribution of the Hood Canal Summer Chum Salmon ESU 

is restricted to the eastern Strait of Juan de Fuca, Admiralty Inlet, and Hood Canal marine 

subbasins of western Washington (Figure 1). In the eastern Strait of Juan de Fuca, state and 

tribal biologists assessing the status of summer chum salmon in the early 1990s identified small 

but persistent natural spawning aggregations in three streams (Salmon, Snow, and 

Jimmycomelately). Spawning also was noted in the Dungeness River, although the size of the 

aggregation was unknown. Adult salmon had been observed in Chimacum Creek through the 

early 1980s, but that spawning aggregation became extirpated near the middle 1980s. In Hood 

Canal, spawning aggregations persist in most of the major rivers draining the Olympic 

Mountains on the western edge of the canal, including Big and Little Quilcene, Dosewallips, 

Duckabush, and Hamma Hamma rivers and Lilliwaup Creek. On the eastern side of Hood 

Canal, persistent spawning was restricted to the Union River (WDFW and PNPTT 2000). 

Historical information and habitat characteristics of other streams indicated summer 

chum salmon were once more broadly distributed within this region, especially in the streams 

draining the eastern shore of Hood Canal (Figure 1). Based on the historical size of the river and 

historical tribal fishing records, a major spawning aggregation may once have occurred in the 

Skokomish River. State and tribal biologists also identified recent extirpations in Big Beef 

Creek, Anderson Creek, Dewatto River, Tahuya River, and Finch Creek. Although state and 

tribal biologists could not identify the magnitude or frequency of spawning in other streams such 

as Seabeck, Stavis, and Big and Little Mission creeks, these smaller streams were likely 

historically used by summer chum salmon (WDFW and PNPTT 2000). 

New Analyses—Methods and Results 

To examine the potential change in spatial structure between historical spawning and 

extant spawning aggregations, we compared the distribution of geographical distances between 

all pairs of spawning aggregations. We measured pair-wise geographical distances (kilometers) 

as the shoreline distance between mouths of streams using the most direct passage over open 

4
1

2 3

4

5

6

7 8

11

12

9

10

13

14

15 16

17

18

19 20

Strait of Juan de Fuca

Hood Canal

Extant spawning aggregations

11. 12. Little and Big Quilcene rivers

1. Dungeness River

2. Jimmycomelately Creek

3. 4. Salmon and Snow creeks

9. Dosewallips River

10. Duckabush River

14. Hamma Hamma River

15. Lilliwaup Creek

20. Union River

Extinct spawning aggregations and possible 

historical spawning areas

5. Chimacum Creek

6. Big Beef Creek

7. Seabeck Creek

8. Stavis Creek

13. Anderson Creek

16. Dewatto River

18. Tahuya River

19. Mission Creek

17. Skokomish River

WASHINGTON

Olympic 

Peninsula

Kitsap 

Peninsula

Admiralty 

Inlet

123o00 ́

48o00 ́

47o30 ́

Figure 1. Extant and historical distribution of Hood Canal summer chum salmon. Recent recovery 

efforts are reintroducing summer chum to Chimacum Creek, Big Beef Creek, and Tahuya River, 

where they have been recently extirpated. 

water at a scale of 1:200,000 using DeLorme Topo 4.0 digital maps. We tested for differences 

between historical and extant distributions using log-likelihood ratio tests (G-test, Sokal and Rolf 

1995) after collapsing distance classes into short (0–80 km), moderate (80–160 km), and long 

(>160 km) distances. 

Our results show that extant aggregations of summer chum salmon currently are more 

isolated by geographical distance than under historical conditions. Historically, most summer 

chum aggregations were less than 80 km apart with the greatest proportion separated by 20–40 

km (Table 1, Figure 2). The mean and median distances separating historical spawning 

aggregations were 17 km and 13 km, respectively. Under historical conditions, the proportion of 

summer chum salmon populations separated by distances ranging from 80 to 220 km were less 

than those separated by 0–40 km, but the proportions were fairly evenly distributed among larger 

distance classes. 

In contrast, most extant summer chum salmon spawning aggregations still occur within 

20–40 km of each other, but extinctions of some spawning aggregations have led to a significant 

5
Table 1. Geographical distance (km) between likely historical spawning locations of summer chum salmon in Strait of Jua

Canal areas. Shaded cells indicate distances that are less than 38 km apart, which is the most likely minimum dista

independent populations occur. Locations are keyed to Figure 1. 

Location 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

1. Dungeness R 0 2. Jimmycomelately Cr 19 0 

3. Salmon Cr 52 33 0

4. Snow Cr 53 34 1 0

5. Chimacum Cr 98 79 46 45 0

6. Big Beef Cr 159 140 107 106 61 0

7. Seabeck Cr 163 144 111 110 65 5 0

8. Stavis Cr 166 147 114 113 68 12 7 0

9. Dosewallips R 165 146 113 112 67 9 8 8 0

10. Duckabush R 168 149 116 115 70 12 10 6 8 0

11. Little Quilcene R 177 158 125 124 79 21 23 16 22 30 0 

12. Big Quilcene R 176 157 124 123 78 22 22 15 21 29 1 0

13. Anderson Cr 180 161 128 127 82 26 21 14 18 10 30 29 0

14. Hamma Hamma R 184 165 132 131 86 40 27 31 24 16 46 45 6 0 6

15. Lilliwaup Cr 197 178 145 144 99 53 40 44 37 29 59 58 19 13 0

16. Dewatto R 197 178 145 144 99 43 38 31 35 27 47 46 17 12 5 0

17. Skokomish R 210 191 158 157 112 56 53 47 50 42 72 71 32 26 13 16 

18. Tahuya R 218 199 166 165 120 61 56 49 58 50 80 79 40 34 15 18 

19. Mission Cr 236 217 184 183 138 79 74 67 76 68 98 97 58 52 33 36 

20. Union R 239 220 187 186 141 82 77 70 79 71 101 100 61 55 36 39 
0

0.05

0.1

0.15

0.2

0.25

0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 180-200 200-220

Geographical distance between aggregations (km)

Frequency

Historical

Extant

Figure 2. Geographical distribution of historical and extant spawning aggregations of summer chum 

salmon based on frequency of pair-wise distances. 

increase in the proportion of aggregations isolated by 80–160 km (G = 39.58, df = 2, p < 0.001). 

Geographically, these extinctions occurred primarily on the Kitsap Peninsula (eastern Hood 

Canal), in the Admiralty Inlet catchment at the center of the geographic range of the ESU, and in 

the Skokomish River (Figure 1). This reduced by 60% the proportion of spawning aggregations 

separated by 60–100 km and doubled the proportion of spawning locations isolated by more than 

100 km. Practically, this increased the geographical isolation between the remaining 

aggregations in the Strait of Juan de Fuca, northern Hood Canal, and Union River at the southern 

edge of the spawning distribution. 

Migration 

No information exists for straying of natural-origin adult summer chum salmon to 

nonnatal streams within the Hood Canal Summer Chum Salmon ESU. Since 1992, however, 

when state and tribal biologists began using artificial propagation to rebuild threatened stocks 

and reintroduce summer chum to historical areas, they have monitored the returns of uniquely 

marked salmon to streams draining into the Strait of Juan de Fuca and Hood Canal. Analysis of 

expanded recoveries of otolith-marked and adipose-clipped adult salmon in 2001 and 2002 

(WDFW and PNPTT 2003) showed that straying was a nonlinear function of distance from the 

release location (Figure 3). 

Most summer chum salmon (≈70–95%) returned to the stream from which they were 

released. Of those fish not returning to release locations, most returned to a location within 50 

km of their release. This resulted in very little straying between the Hood Canal and the Strait of 

Juan de Fuca aggregations, although straying was greater from Strait of Juan de Fuca hatchery 

releases than from Hood Canal releases. Approximately 0.07% of releases in Hood Canal 

streams were recovered in Strait of Juan de Fuca streams, whereas approximately 1.5% of 

releases into Strait of Juan de Fuca streams were found in Hood Canal streams. Analysis of the 

earlier data (WDFW and PNPTT 2003) and recoveries from 2003 through 2005 (WDFW and 

7
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 20 40 60 80 100 120 140 160 180 200

Distance (km)

Proportion of recoveries

Figure 3. Relationship between proportions of marked hatchery summer chum salmon recovered as 

adults and distance from original release location. 

PNPTC 2006) suggest that the locations to which summer chum salmon return remain constant 

from year to year, but the proportions vary. 

Genetic Attributes 

Previous Analyses 

The lack of genetically distinct geographical groups of summer chum salmon has 

confounded past efforts to identify independent stocks. In 1993 state and tribal biologists 

identified four stocks of summer chum salmon based primarily on geographical location and a 

few samples with statistically different allozyme frequencies (WDFW and WWTIT 1994). Two 

stocks—Discovery Bay and Sequim Bay—were associated with streams flowing into the eastern 

Strait of Juan de Fuca. They identified two additional stocks in Hood Canal: a Union River stock 

and a west side Hood Canal stock that included the remaining spawning aggregations. 

Using expanded allozyme data, Phelps et al. (1995) reexamined the genetic differences 

among chum salmon throughout Puget Sound. They documented major divergence of nine 

samples of summer chum salmon of Hood Canal and the Strait of Juan de Fuca from other 

aggregations of chum salmon. Within the summer chum salmon lineage, Phelps et al. (1995) 

suggested there were two major groups, a Strait of Juan de Fuca group and a Hood Canal group, 

based on significantly different allele frequencies. Discovery Bay and Sequim Bay stocks 

comprised the Strait of Juan de Fuca group. Within the Hood Canal group, they proposed three 

stocks: a Union River stock, which had significantly different allele frequencies from the western 

Hood Canal (Olympic Peninsula) stock, and an eastern Hood Canal (Kitsap Peninsula) stock, 

which they believed was distinct and independent because it had been extirpated when other 

stocks had not. Johnson et al. (1997) reanalyzed data from Phelps et al. (1995) and confirmed 

the divergence of summer chum salmon, which they designated an ESU (Waples 1991) under the 

8
ESA. Although both studies detected allele frequency differences among summer chum salmon, 

cluster analyses did not show geographically distinct groups of spawning aggregations. 

To provide the foundation for the Summer Chum Salmon Conservation Initiative, state 

and tribal comanagers identified nine extant stocks of summer chum salmon, seven recently 

extirpated stocks, and additional geographical areas where summer chum may have occurred 

(Figure 1) (WDFW and PNPTT 2000). The nine extant stocks were delineated based primarily 

on geographical separation of spawning aggregations and apparent allozyme frequency 

differences between some samples. These differences were defined based on pair-wise tests of 

allelic heterogeneity that were uncorrected for the increase in Type I error from multiple 

comparisons (see Kassler and Shaklee 2003). 

Kassler and Shaklee (2003) expanded the number of samples and reanalyzed the pairwise comparisons after correcting for multiple comparisons. They concluded the data showed 

two stocks in the eastern Strait of Juan de Fuca—Discovery Bay and Sequim Bay—and three 

stocks in Hood Canal associated with Union River, Lilliwaup Creek, and the remaining 

aggregations in western Hood Canal tributaries. Most recently, Small and Young (2003) 

analyzed variations at 18 microsatellite loci in a similar group of samples from the Hood Canal 

Summer Chum Salmon ESU. They found significant genotypic differences between Hood Canal 

region and Strait of Juan de Fuca region aggregations and among Union, Lilliwaup, and Strait of 

Juan de Fuca aggregations, but the remaining aggregations did not show genotypic differences. 

Union River, Discovery Bay, and Sequim Bay fish could be precisely assigned to their collection 

locations based on individual genotypes, but classification of fish from other locations had 

greater error. 

These analyses provide only limited insight into demographic relationships of spawning 

aggregations of summer chum salmon. Analyses relying on pair-wise tests of allelic 

heterogeneity among different spawning aggregations showed some statistical differences in 

allele frequencies. P-values from pair-wise tests of allelic heterogeneity that WDFW used to 

infer stock structure were largely dependent on sample size and variability of the loci used and 

do not necessarily reflect demographic relationships among spawning aggregations. Cluster 

analyses, which group samples based on their genetic similarity, also failed to identify 

genetically discrete groups that might correspond to independent populations. Although nearly 

all dendrograms of genetic similarity in summer chum salmon showed a cluster formed by 

Sequim Bay aggregations, most displayed “chaining,” a pattern where the dendrogram forms by 

successive additions of branches associated with individual aggregations rather than distinct 

clusters (Figure 4). 

Interestingly, although Kassler and Shaklee (2003) did not comment on it, the differences 

in their data suggest a geographical pattern of isolation of subpopulations by geographical 

distance rather than geographical clustering of similar spawning aggregations. Although Kassler 

and Shaklee’s (2003) cluster analysis did not show distinct differences within Hood Canal 

because of chaining, the most genetically divergent aggregation—the Union River—was 

geographically most distant. Ignoring the Little Quilcene sample, which was small and not 

statistically different from Quilcene Bay samples, subsequent divergence followed the same 

geographical pattern (Figure 4). Chaining in dendrograms often reflects an underlying linear 

9
Figure 4. Relationship of 35 summer and fall chum salmon populations using Cavalli-Sforza and 

Edwards (1967) chord distance and neighbor-joining clustering (modified from Kassler and 

Shaklee 2003). Dendrogram shows chaining of Hood Canal summer chum salmon samples. Key 

to summer chum samples: 1 Union River, 2 Lilliwaup Creek, 3 Hamma Hamma River, 4 

Duckabush River, 5 Dosewallips River, 6 Big Beef Creek (introduced from Quilcene National 

Fish Hatchery), 7 Quilcene Bay, 8 Quilcene National Fish Hatchery (introduced from Quilcene 

Bay), 9 Little Quilcene River, 10 Snow Creek, 11 Salmon Creek, and 12 Jimmycomelately Creek. 

30 19

rather than hierarchical structure in the data, although it is also characteristic of clustering 

algorithms that use low levels of genetic similarity to define clusters (Dunn and Everitt 1982). 

New Analyses—Methods 

If little hierarchical structure exists among spawning aggregations of summer chum 

salmon from Hood Canal and Strait of Juan de Fuca streams, at what geographical scale do 

independent populations occur? Because there appeared to be few genetically discrete groups of 

summer chum associated with the major river systems, the PSTRT hypothesized that genetic 

exchange and independence might be related due to geographical proximity of many different 

subpopulations to each other, rather than due to homing to major river systems or environmental 

differences. When subpopulations occur geographically in line, for example, a one-dimensional 

stepping-stone pattern of straying (where most genetic exchange is between neighboring 

subpopulations) can lead to genetic differences between subpopulations that are several 

subpopulations apart, even though there are few or no differences between adjacent 

subpopulations. The stepping-stone process of migration leads to patterns of genetic isolation by 

distance that can be detected with empirical data (Wright 1943). 

15

31

16

10 12

11

1

2

3

4

5

6

7

8

9

33

35

34

32

13

17

28

18

14

27

29

20 22

23

24 26 21 25

83

83 93

75

76 89

76

91 Hood Canal summer 

chum salmon 

Strait of Juan de Fuca 

Sinclair Inlet and 

Colvos Passage 

Canadian fall 

chum salmon 

Puget Sound summer 

and fall chum salmon 

summer chum salmon 

10
We examined allozyme and microsatellite DNA data of summer chum salmon for 

evidence of isolation by distance. Allozyme data were from 28 loci and 43 collections in streams 

within the Hood Canal Summer Chum Salmon ESU (Kassler and Shaklee 2003). Microsatellite 

DNA data were from Small and Young (2003). After finding no significant differences among 

years within sample locations using Bonferroni adjusted log-likelihood ratio tests, data from 

different years within streams were combined for subsequent analyses. Pair-wise Fst values 

were calculated using GENEPOP (Raymond and Rousset 1995). Migrants per generation, Nm, 

were calculated from Fst values, using Nm = (1–Fst)/4 Fst. Nm values for Fst near zero were 

arbitrarily set to 250 to avoid undefined or unrealistically high values. Geographical distances 

(km) were measured as the shoreline distance between mouths of streams using the most direct 

passage over open water at a scale of 1:200,000 using DeLorme Topo 4.0 digital maps (Table 1). 

Under isolation by distance, independence between groups—as measured by estimates of 

genetically effective Nm—increases with increasing geographical distance. Genetic theory 

predicts that more than one migrant per generation can prevent substantial genetic divergence 

and that rates between 1 and 10 may be necessary to prevent extinction of fragmented 

populations (Mills and Allendorf 1996, Wang 2004). To statistically describe the relationship 

between genetic connectivity and geographic distance, Mantel tests for association between Nm

and distance and reduced major axis regressions were performed using IBD 1.4 (Bohonak 2002). 

Regression confidence limits were constructed from 1,000 bootstrap regressions over all points. 

No empirical analyses indicate the maximum rates of genetic exchange that we might 

expect to find between independent populations. To inform decisions about the geographical 

scale of independent populations formed by isolation by distance, we analyzed expert 

conclusions for Chinook salmon (O. tshawytscha) in Puget Sound where geneticists have 

identified independent populations based on genetic information as well as knowledge of 

migration rates, life history and phenotypic differences, population dynamics, and habitat 

differences. Chinook salmon in Puget Sound show evidence of isolation by distance and eight 

geneticists representing different institutions examined patterns of genetic differentiation among 

21 spawning aggregations of Chinook salmon (Ruckelshaus et al. 2006). They independently 

scored 34 pair-wise comparisons for independence on a scale of +4 (very independent) to –4 (no 

independence). 

Because Nm provides a genetic estimate of long-term, demographic exchange that is 

independent of species differences, we used the distribution of independence scores greater than 

one relative to the analysis of Nm to characterize scientific opinion about the largest rate of 

genetic exchange between independent populations. Under an isolation-by-distance relationship, 

this corresponds to the smallest spatial scale at which independent populations might occur, 

which would be conservative in protecting ESU population structure. To describe the 

uncertainty in the maximum Nm, we generated a mean and 95% confidence interval by randomly 

resampling a triangular distribution 1,000 times where the minimum of the distribution was the 

smallest Nm expert score that always reflected independence and the maximum was the largest 

Nm score associated with independence. 

11
New Analyses—Results 

We concluded that isolation by distance best explains the genetic structure in summer 

chum salmon. Using allozyme data collected from 1985 to 2001 from Kassler and Shaklee 

(2003) and microsatellite DNA data from Small and Young (2003), we found a strong 

relationship between Nm and geographical distance (Figure 5 and Figure 6). As geographical 

distances increased between aggregations, they exchanged fewer migrants. The isolation-bydistance relationships were indistinguishable between allozyme and microsatellite DNA data. 

Exchange of migrants ranged from approximately 250 per generation to 4 per generation over 

distances of 1 to 219 km. The greatest genetic exchange was between aggregations in the 

Hamma Hamma, Dosewallips, Duckabush, and Quilcene rivers of the west central Hood Canal 

region and between Discovery Bay (Salmon and Snow creeks) and Sequim Bay 

(Jimmycomelately Creek) aggregations in the Strait of Juan de Fuca. 

An interesting feature of this relationship is the apparent clustering of most Strait of Juan 

de Fuca comparisons at greater distances and fewer migrants per generation (Figure 5). This 

kind of pattern sometimes suggests greater demographic isolation and more rapid genetic 

Distance (km)

Log Nm (migrants/generation)

3.0

2.5

2.0

1.5

1.0

0.5

0 50 100 150 200 250

0.0

Figure 5. Relationship between Nm and distance based on allozyme data. Open diamonds include Strait 

of Juan de Fuca samples; solid squares represent Hood Canal samples. Mantel test indicates 

significant association between Nm and distance (Z = 3747.08, p ≤ 0.0000). The regression line 

(solid line) using reduced major axis regression is log(Nm) = 1.85 – 0.0076 km. R2

 = 0.60. 

Dash-dotted lines show 95% confidence limits based on 1,000 bootstrap regressions over all 

points. Dotted lines show distribution of scientific opinion for maximum Nm indicating 

population independence; light dotted lines show 95% confidence limits, the heavy dotted line is 

the mean. 

12
Distance (km)

0 50 100 150 200 250

Log Nm (migrants/generation)

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Figure 6. Relationship between Nm and distance based on microsatellite DNA data. Open diamonds 

include Strait of Juan de Fuca samples, solid squares represent Hood Canal samples. Mantel test 

indicates significant association between Nm and distance (Z = 2357.84, p ≤ 0.0000). The 

regression line (solid line) using reduced major axis regression is log(Nm) = 1.96 – 0.0084km. 

R2

 = 0.57. Dash-dotted lines show 95% confidence limits based on 1,000 bootstrap regressions 

over all points. Dotted lines show distribution of scientific opinion for maximum Nm indicating 

population independence; light dotted lines show 95% confidence limits, the heavy dotted line is 

the mean. 

differentiation than would be expected if there were equilibrium between gene flow and genetic 

drift (Slatkin 1993). In this case, however, the pattern appears to reflect an absence of sample 

comparisons between 60 and 100 km apart (Figure 5 and Figure 6), rather than a lack of gene 

flow–genetic drift equilibrium. Extirpation of summer chum salmon aggregations from streams 

along the northeastern Olympic Peninsula and the northwestern Kitsap Peninsula may explain 

this absence. 

As noted, the effect of these extinctions was to increase geographical isolation of the 

Strait of Juan de Fuca aggregations from central Hood Canal aggregations and the Union River 

aggregation. This isolation would have increased genetic drift and promoted allele frequency 

differences, which were detected by earlier genetic studies, among these aggregations. 

Consequently, the observation that Strait of Juan de Fuca, central Hood Canal, Lilliwaup Creek, 

and Union River aggregations are evolving more or less independently may be an artifact of 

recent extinctions rather than a reflection of long-term, viable population structure. 

The isolation-by-distance relationship we found in summer chum salmon suggests that 

the aggregations represent a single metapopulation. Figure 5 shows that the isolation-bydistance relationship for all Strait of Juan de Fuca comparisons including those at short distances 

13
(i.e., between Discovery Bay [Salmon and Snow creeks] and Sequim Bay [Jimmycomelately 

Creek] samples) is similar to the relationship for summer chum overall. 

Historically, all summer chum salmon spawning aggregations may have been more 

connected by migration and gene flow occurring through stepping-stone spawning aggregations 

in streams such as Chimacum, Big Beef, Seabeck, and Stavis creeks in northern Hood Canal and 

the Skokomish, Dewatto, and Tahuya rivers in southern Hood Canal. The mean and median 

distances separating historical spawning aggregations, for example, were 17 km and 13 km, 

respectively. Under historical conditions, demographic isolation between Salmon and Snow 

creeks (Strait of Juan de Fuca) and Dosewallips River (Hood Canal) summer chum salmon may 

have occurred in three steps of approximately 40 km, which is typical of the isolation of the 

largest proportion of historical spawning aggregations, rather than one step of 112 km, which 

exists now (Table 1). Under stepping-stone migration, gene flow would have limited the genetic 

differentiation that otherwise would have occurred under greater geographical isolation. 

Geographically intermediate populations may also have been genetically intermediate because of 

the increased opportunity for gene flow, which would have limited the clustering observed in 

recent analyses. 

Genetic Independence 

When genetic differences among populations reflect isolation by distance with no clear 

geographical or genetic discontinuities, identifying discrete, independent populations is 

problematic. Although at some geographic distance, spawning aggregations may be genetically 

different at a level that suggests demographic independence between two pairs of aggregations, if 

the distance is much larger than that separating the two pairs, they may exist independently only 

because of intermediate aggregations that are not independent. 

Based on an independent survey of geneticists asked to identify independent populations 

from measures of genetic differentiation, however, aggregations with Nm = 15 were always 

considered independent, aggregations with Nm equal to or less than 25 were sometimes 

independent, and aggregations with Nm values greater than 25 were never independent 

(Figure 7). Considering the uncertainty in scientific opinion over the magnitude of Nm that 

would reflect independence and the uncertainty in the isolation-by-distance relationship of Nm to 

distance, the average distance over which independence might occur for summer chum salmon 

was 77–83 km, based on allozyme and microsatellite DNA data, respectively (Figure 5 and 

Figure 6). At this scale, none of the aggregations of summer chum salmon in the Strait of Juan 

de Fuca and Hood Canal can be considered truly independent, although significant differences 

exist among aggregations. 

The most likely minimum distance separating two spawning aggregations that could be 

considered independent was 30–38 km, based on microsatellite DNA and allozyme data, 

respectively (Figure 5 and Figure 6). Within this distance, most adjacent historical spawning 

aggregations would not be independent populations (Table 1). Aggregations separated by 

several streams might be considered independent if there were no spawning aggregations acting 

as stepping-stones between them, but this is generally not the case. Strictly applying only the 

most likely minimum distance as a criterion, three independent populations might have occurred 

historically for summer chum salmon: Strait of Juan de Fuca aggregations, Chimacum Creek, 

14
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 >60

Migrants/generation (Nm)

Frequency of independence

 Scores >1

Extensive introduction 

of hatchery fish. 

Independence scores 

based on more than 

genetic differences.

Figure 7. Distribution of population independence scores from experts judging Puget Sound Chinook 

salmon for different levels of genetic exchange (Nm). For geographically distinct spawning 

aggregations with evidence of gene flow from introductions of nonindigenous hatchery fish, 

estimates of historical independence were not associated with Nm and were based on other 

indicators. 

and Hood Canal aggregations (Table 1). This scenario, however, ignores the capacity of 

available, stable historical habitat in these locations to support a viable, independent population. 

This is an important consideration for Chimacum Creek, which occupies a much smaller 

geographical area than either the Strait of Juan de Fuca (without Chimacum) or the Hood Canal 

aggregation. 

Life History and Phenotypic Characteristics 

Little information exists on stock-specific life history variation in summer chum salmon. 

Much of the available information is summarized in the Summer Chum Salmon Conservation 

Initiative and its appendices (WDFW and PNPTT 2000). Many life history traits of summer 

chum that have been studied tend to be similar among aggregations and may reflect a common 

adaptation to the demands of spawning in streams with low flows and early migration of 

juveniles from freshwater to estuarine areas. 

Some life history traits in Hood Canal summer chum salmon do vary among streams. 

Adults returning to Strait of Juan de Fuca streams may enter the strait earlier than Hood Canal 

aggregations. Entry of adults into freshwater, which occurs between late August and midOctober, however, is approximately a week earlier for the southernmost Hood Canal aggregation 

(Union River) than the northern Strait of Juan de Fuca populations (WDFW and PNPTT 2000). 

Adults spawn soon after entering freshwater, but development rates of juveniles may be 

different. Geographical variation in time to emergence of juveniles after incubation may reflect 

environmental differences (Bakkala 1970, Salo 1991) and genetic traits (Robison et al. 2001). 

Strait of Juan de Fuca aggregations, which occupy colder streams than Hood Canal aggregations, 

15
generally produce fry that emerge later than fry from Hood Canal aggregations (Tynan 1997). 

Likewise, summer chum salmon originating from the colder, mainly snow melt–fed west side 

Hood Canal watersheds emerge later than summer chum from warmer, rainfall-fed streams on 

the Kitsap Peninsula (WDFW and PNPTT 2000). 

A unique life history attribute is the substantial proportion of the Sequim Bay 

subpopulation that returns to spawn as small, 2-year-old fish in some years (WDFW and PNPTT 

2000, 2003, WDFW and PNPTC 2006). Such a distinct life history trait may be related to the 

significantly different genetic characteristics of summer chum salmon from Sequim Bay. Other 

subpopulations included in the ESU do not exhibit this early age at return. 

Population Dynamics 

Exploring population dynamics was not informative for identifying independent 

populations in the Hood Canal Summer Chum Salmon ESU, in part because of hatchery 

supplementation programs, which have contributed to returns since 1995. More details on 

population dynamics can be found in the Viability Criteria section of this report. 

Environmental and Habitat Characteristics 

To determine whether potential environmental differences exist between summer chum 

salmon habitats that might give rise to locally adapted, independent populations, we examined 

the distribution of summer chum across freshwater ecoregions and marine subbasins. Although 

differences in habitat characteristics among Puget Sound streams and marine subbasins are 

apparent, the biological significance of those differences to summer chum salmon is not yet well 

understood. 

The literature on Pacific salmonids suggests that population structure and diversity of the 

fish are closely linked to patterns of ecological diversity (Healey and Prince 1995, Hanski and 

Gilpin 1996, McElhany et al. 2000, Rieman and Dunham 2000, Waples et al. 2001, Beechie et 

al. 2006, Fresh et al. in press). The argument is that geographic differences in habitat 

characteristics within and among watersheds may provide insights into the different selective 

environments fish experience, and thus indicate the degree of selectively driven isolation that 

might arise among spawning groups. In this subsection, we discuss the relationships between 

patterns of environmental and habitat characteristics and the historically independent population 

delineations. This information is also useful for examining diversity and spatial structure 

attributes for the corresponding independent populations. 

Ecoregional boundaries can be a useful way to describe habitat differences that may be of 

selective relevance to salmon. Streams within an ecoregion share similar biotic and abiotic 

characteristics, including geology, physiography, vegetation, climate, soils, land use, hydrology, 

and fauna that affect instream environmental conditions (Omernik 1987, 1995, 2003). In 

addition, including larger-scale ecoregional patterns helps to integrate landscape and riverine 

processes and their affects on potential selective environments experienced by salmon (Berman 

2002). We examined the patterns of overlays between ecoregions identified by the 

Environmental Protection Agency (EPA) and each of the watersheds that contain the identified 

historical spawning aggregations. Ecological diversity at the EPA level III geographic scale 

16
corresponds to genetic and life history diversity of salmonid ESUs (McElhany et al. 2000, 

Waples et al. 2001). Level IV ecoregions, which are further subdivisions of level III units, are 

thus an appropriate level of ecological diversity to consider for discriminating potentially 

different selective environments among populations within an ESU. We have applied data sets 

relating the EPA level IV ecoregional units and sixth level hydrologic units (EPA 2004). 

We identified five distinct groupings of watersheds occurring in distinct combinations of 

ecoregions encompassed by the Hood Canal Summer Chum Salmon ESU boundaries. The 

Dungeness, Big Quilcene and Little Quilcene rivers have headwaters in the High Olympics, Low 

Olympics, and Coast Range Volcanics ecoregions and lower reaches in the Olympic Rainshadow 

ecoregion. A group consisting of Discovery Bay (Salmon and Snow creeks), Sequim Bay 

(Jimmycomelately Creek), Admiralty Inlet (Chimacum Creek), and Western Upper Hood Canal 

tributaries are all fully contained within the Olympic Rainshadow ecoregion. The Duckabush 

and Dosewallips rivers are fully contained within the High Olympics, Low Olympics, and Coast 

Range Volcanics ecoregions. The Hamma Hamma and Skokomish rivers and Lilliwaup Creek 

have headwaters in the High Olympics, Low Olympics, and Coast Range Volcanics ecoregions 

and lower reaches in the Central Puget Sound Lowlands ecoregion. The final group, 

encompassing the West Kitsap tributaries to Hood Canal and Admiralty Inlet, is fully contained 

within the Central Puget Sound Lowlands ecoregion. 

In addition to ecological diversity in spawning environments, watersheds containing 

spawning aggregations flow into distinct regions within the marine environment (Figure 8). We 

therefore postulate that outmigrants and returning adults from spawning aggregations 

experiencing different marine subregions represent additional increments of ecological diversity 

for the population structure of the ESU. The variety of regimes experienced by the spawning 

aggregations may influence the expression of freshwater and marine life history characteristics of 

summer chum salmon, resulting in significant local variation within a population, a result 

consistent with findings from studies on chum salmon in Russia and British Columbia and with 

the variations reported for Chinook salmon in the Puget Sound Salmon Recovery Plan (Kulikova 

1972, Beacham and Murray 1987, Shared Strategy 2007). Boundaries of biogeographically 

distinct marine subbasins within Puget Sound previously have been identified (PSWQAT 2002, 

Shared Strategy 2007). Other groupings that were also considered may be appropriate and 

warrant further investigation as more information becomes available relating to what marine and 

freshwater habitat attributes best predict summer chum diversity. 

The watersheds were thus binned into similar groupings of ecoregions nested within their 

respective marine catchments to fully capture the potentially distinct ecological conditions 

experienced by summer chum salmon in each group. Overlaying the watershed units grouped by 

similar ecoregions and the marine subbasins into which the streams flow results in seven 

ecological diversity groups (Table 2). Table 2 provides a listing of the ecological composition of 

each stratum and the associated historical and extant spawning aggregations of summer chum 

each contains. These are also mapped in Figure 8. 

Remaining Uncertainties in Population Structure 

We presently lack sufficient metapopulation structure data in proximity to the proposed 

boundary between the two populations to resolve whether the boundary may nest within the 

17
Figure 8. The two populations of the Hood Canal Summer Chum Salmon ESU, including the streams 

with spawning aggregations and seven ecological diversity groups. 

18
Table 2. Seven proposed ecological diversity groups of the Hood Canal Summer Chum Salmon ESU by geographic region

spawning aggregations. 

Geographic Proposed ecological Spaw

region diversity groups Freshwater ecoregions Freshwater ecoregions aggr

(population) (names) (headwaters) (lower reaches) Exta

Eastern Strait of Dungeness High Olympics Olympic Rainshadow Dun

Juan de Fuca Low Olympics (unk

Coast Range Volcanics 

Sequim-Admiralty Olympic Rainshadow Olympic Rainshadow Jimm

Salm

Snow Chim

Central Puget Sound Lowlands Central Puget Sound Lowlands Unk

Hood Canal Toandos Olympic Rainshadow Olympic Rainshadow Unk

Quilcene High Olympics Coast Range Volcanics Big 

Low Olympics Littl

Coast Range Volcanics 

Mid West Hood Canal High Olympics Coast Range Volcanics Dose

Low Olympics DuckCoast Range Volcanics 

West Kitsap Central Puget Sound Lowlands Central Puget Sound Lowlands Big 

Seab

Stav

AndDew

Tahu

Miss

Unio

Lower West Hood Canal High Olympics Central Puget Sound Lowlands Ham

Low Olympics Lilli

Coast Range Volcanics Skok

19
geographic regions as proposed or may instead follow ecoregional unit boundaries. Also, it 

appears unlikely that ecological conditions within the small independent watersheds adjacent to 

the proposed boundary line could have supported any persistent spawning aggregations that 

would merit equal consideration to the other ecological diversity groups comprising the ESU. 

Nevertheless, it appears plausible that even intermittent spawning within these areas may 

contribute to the connectivity between the populations and to ESU persistence. Therefore, we 

conclude that the above delineation will be adequate to guide current recovery planning needs. 

Further investigation of population characteristics is warranted as recovery planning and 

implementation proceeds to more confidently delineate the boundary between the populations 

and how that may relate to ecological diversity delineations in this transitional area. 

There is uncertainty about whether the Dungeness River represents a subpopulation or a 

minor spawning aggregation within the Strait of Juan de Fuca population. This could have 

consequences in setting viability criteria for the population (see the Viability Criteria section of 

this report). 

Conclusions 

Independent Populations 

For the Hood Canal Summer Chum Salmon ESU, migration rates and genetic attributes 

offer moderate to strong support for two independent populations: one in the Hood Canal and a 

second in the Strait of Juan de Fuca. A key finding from our analyses is that the historical 

interdependence of subpopulations was directly related to how far apart they were 

geographically. The greatest level of exchange and interdependence likely was between 

subpopulations that were geographically close to one another. The likelihood of exchanging 

migrants decreased and genetic differences increased as greater distances separated 

subpopulations. 

Data on current patterns of migration suggest little demographic exchange of fish 

between these two regions in recent years. Fuss and Hopley (1991) noted that hatchery-raised 

fall chum salmon in Hood Canal also have a high fidelity to their stream of origin. Although the 

data that we used were from artificially propagated fish derived from native broodstocks, 

exchange between wild aggregations may be even less than the estimated rates for hatchery 

summer chum salmon. Straying rates are a function of complex environmental and populationspecific characteristics (Hard and Heard 1999). Rearing and release strategies of hatchery-raised 

fish, for example, may interfere with homing of salmon to natal areas (Quinn 1993, Pasqual et al. 

1995) and elevate straying rates above natural levels. Likewise, appropriate broodstock selection 

strategies such as those used for Hood Canal and Strait of Juan de Fuca summer chum salmon 

have been shown to decrease straying of chum salmon over time (Tallman and Healey 1994). 

Genetic Characteristics 

Genetic characteristics of summer chum salmon support the evidence from migration rate 

estimates. The estimates of genetic exchange, which integrate the effects of straying among 

spawning aggregations over many generations, decreased predictably as geographical distance 

between spawning aggregations increased. One hundred and twelve km separate the 

20
geographically closest major spawning aggregations in Hood Canal (Dosewallips River) and the 

Strait of Juan de Fuca (Salmon and Snow creeks) (Table 1), and these aggregations were 

associated with a level of Nm that most geneticists concluded was too low to affect the 

demographics and persistence of these two groups. Chimacum Creek, which lies between these 

major spawning aggregations, may provide additional production to the Hood Canal and the 

Strait of Juan de Fuca populations, but the likely historical capacity in these locations most likely 

could not support a distinct independent population, because of their much smaller basin size. 

Although the genetic data allow excellent estimates of genetic exchange, we considered 

the overall strength of inference to be moderate. Precise theoretical or empirical relationships 

between Nm and demographic population independence are not available and thus we relied on 

expert knowledge to make such judgments. Evidence from other potential indicators of 

population structure—such as life history variation, population dynamics, and habitat 

characteristics—was consistent with our identification of two independent populations, but the 

strength of inference from these data was weak (Table 3). Figure 8 presents the proposed two 

historically independent populations together with the associated spawning streams and seven 

proposed ecological diversity units. 

The Summer Chum Salmon Conservation Initiative (WDFW and PNPTT 2000), 

developed by the comanagers, identified nine extant stocks of summer chum salmon and seven 

recently extirpated stocks as management units for the conservation and recovery of the ESU. 

Our analyses indicate that these are subpopulations, which contribute to either the Hood Canal or 

Strait of Juan de Fuca population depending on their geographical location. Furthermore, our 

analyses strongly support the use of these local stocks as management units for recovering the 

ESU. 

21
Table 3. Evidence and strength of inference for different indicators of independent populations of 

summer chum salmon. 

Strength of inference 

Indicator Evidence Population Subpopulation 

Geography No geographical barriers; distances 

between historical spawning 

aggregations are small but have 

increased because of local 

extirpations. Despite small distances, 

natural recolonization of areas where 

extirpations have occurred has not 

been observed. 

Weak Weak, but lack of 

recolonization is 

consistent with 

subpopulation 

structure 

Migration rates Marked summer chum raised in 

hatcheries show 0.07–1.5% straying 

rates between Hood Canal and Strait 

of Juan de Fuca; 70–95% of fish 

return to natal streams and most of the 

rest return to streams within 50 km. 

Strong support of 

independence 

between Hood Canal 

and Strait of Juan de 

Fuca fish 

Moderate; shows 

isolation-by-distance 

subpopulation 

structure 

Genetic 

attributes 

Sequim Bay fish (in Strait of Juan de 

Fuca) consistently different from 

other summer chum in genetic 

studies; PSTRT analyses show strong 

isolation by distance suggesting 

aggregations separated by more than 

30–38 km. 

Moderate support of 

independence 

between Hood Canal 

and Strait of Juan de 

Fuca fish 

Strong support of 

isolation-by-distance 

subpopulation 

structure 

Life history 

characteristics 

Possible differences in entry timing, 

emergence timing, and juvenile 

migration timing between summer 

chum streams in Hood Canal and 

Strait of Juan de Fuca. 

Weak, but possible Weak 

Population 

dynamics 

No correlation in trends of adult 

returns between Hood Canal and 

Strait of Juan de Fuca. 

Not informative Not informative 

Habitat 

characteristics 

Hood Canal and Strait of Juan de 

Fuca summer chum habitat 

characterized by different freshwater 

ecoregions and marine conditions. 

Weak, but possible Weak 

22
Viability Criteria for the Hood Canal Summer 

Chum Salmon ESU and Populations 

Introduction 

The PSTRT is charged with identifying the biological characteristics of a recovered ESU 

in support of developing delisting and recovery criteria. These biological characteristics are 

based on the collective viability of individual populations, their characteristics, and their 

distributions throughout the ESU. A viable ESU is defined as one that is naturally selfsustaining and has a negligible risk of extinction over a long period of time, likely time periods 

greater than 100 years (McElhany et al. 2000). Using ESU-wide characteristics, the PSTRT 

provides technical assistance as the comanagers and the Hood Canal Coordinating Council, in 

cooperation with the National Marine Fisheries Service (NMFS), implement a recovery plan to 

meet broad regional goals for recovery. 

As the PSTRT concluded in the previous section of this report, the Hood Canal Summer 

Chum Salmon ESU consists of two historically independent populations: the Strait of Juan de 

Fuca and the Hood Canal populations. 

The Strait of Juan de Fuca population spawns in rivers and streams entering the Eastern 

Strait of Juan de Fuca Geographical Region of Puget Sound and includes extant spawning 

aggregations of summer chum in the Dungeness River and in Salmon, Snow and 

Jimmycomelately creeks. The historical spawning aggregation in Chimacum Creek has been 

locally extirpated, but summer chum have been reintroduced through a now-terminated hatchery 

supplementation program (see the Identification of Independent Populations section, Figure 8 

and Table 2). 

The Hood Canal population includes all spawning aggregations in streams entering the 

Hood Canal Geographic Region of Puget Sound. Summer chum salmon spawning aggregations 

within the Hood Canal population historically persisted in most of the major rivers draining the 

Olympic Mountains on the western edge of the canal, including the Big and Little Quilcene, 

Dosewallips, Duckabush, Hamma Hamma, and Skokomish rivers and Lilliwaup and Finch 

creeks. On the eastern side of Hood Canal, summer chum spawned in the Big Beef and 

Anderson creeks and Union, Dewatto, and Tahuya rivers. Seabeck, Stavis, Big and Little 

Mission creeks, and others were likely historically used by summer chum (WDFW and PNPTT 

2000). Currently, sizable summer chum salmon spawning aggregations are found only in the Big 

and Little Quilcene, Dosewallips, Duckabush, and Hamma Hamma rivers and Lilliwaup Creek 

on the western side of Hood Canal, and in the Union River on the southeastern portion of the 

canal (See the Identification of Independent Populations section, Figure 8 and Table 2). Small 

numbers (<25 annually) are found in the Skokomish and Dewatto rivers. Summer chum in Big 

Beef Creek and Tahuya River are being reintroduced through hatchery supplementation 

programs. 

23
For the ESU to be viable, both populations must to be viable. Quantitative analyses have 

been conducted to estimate the abundance and productivity criteria for both Hood Canal Summer 

Chum Salmon ESU populations and to evaluate spatial structure and diversity. The PSTRT has 

developed a set of recommendations that describe guidelines for incorporating viability criteria 

in planning for a viable population; these are found at the end of this document. 

Population Viability 

The PSTRT evaluates population viability using four key characteristics as described in 

the VSP document (McElhany et al. 2000): abundance, productivity, spatial structure, and 

diversity. Abundance is the number of individuals in the population at a given life stage or time. 

Productivity (or growth rate), as used here, is the actual or expected ratio of abundance in the 

next generation to current abundance. Spatial structure refers to how the abundance at any life 

stage is distributed among available or potentially available habitats. Diversity is the variety of 

life histories, sizes, and other characteristics expressed by individuals within a population. These 

characteristics together describe a viable population. 

Population viability is estimated as a probability of population persistence over a defined 

period of time (McElhany et al 2000). “Viable” in this sense refers to a naturally self-sustaining 

population that has a negligible risk of extinction over a 100-year time frame. The acceptable 

level of risk of extinction is a policy decision informed by available technical guidance and 

analyses. In this case, the population viability criteria we have developed describe the 

abundance, productivity, and diversity of summer chum salmon required for a naturally selfsustaining population to have either a 0.95 or 0.99 probability of persistence over a 100-year 

time period. 

For Hood Canal Summer Chum Salmon ESU populations, we used two methods of 

population viability analyses (PVAs) for estimating minimum abundance levels associated with 

population persistence. The two methods we used incorporate uncertainty in the data (estimated 

returns per spawner) to determine minimum target levels of abundance and productivity such 

that the population does not go extinct in the face of environmental variation and anthropogenic 

factors. 

One method uses variability around replacement recruits per spawner (R/S = 1) to derive 

minimum target escapement levels needed to maintain the population above a quasi-extinction 

threshold (QET), assuming that the population time series approximates a Brownian motion 

(Dennis et al. 1991). The other method assumes that the R/S relationship is density dependent 

and uses a R/S function to estimate R/S for different spawning levels. The variability around the 

predicted recruits is used to generate a productivity curve (defined by intrinsic productivity and 

capacity) that results in the population remaining above a specified QET given a fixed 

exploitation rate (ER, fraction of return taken by fishery). Separate productivity curves are 

estimated for the Hood Canal and Strait of Juan de Fuca populations over an ER range of 0–0.3. 

The abundance and productivity targets for population viability assume that the 

populations maintain or, preferably, improve their spatial distribution and diversity. We used 

two diversity indices to measure spatial distribution of spawning aggregations within the 

populations; these indices take into account the number of streams used for spawning and the 

24
relative abundance of adults between spawning sites. We have no direct measures for life history 

diversity; but for the purposes of defining population viability, we assume that if spatial 

distribution remains diverse, then so will life history. 

Source Data for Viability Analyses 

For each of the spawning aggregations in the two populations in the Hood Canal Summer 

Chum Salmon ESU, the PSTRT compiled annual estimates of the number of summer chum 

spawning naturally (i.e., spawning escapement), broodstock take, and catch for the 1974–2005 

period (Figure 9 and Figure 10). Spawning aggregation refers to the level at which spawning 

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Escapement Broodstock Catch

Return year

Natural origin return abundance

Figure 9. Annual return abundance of natural-origin summer chum salmon of the Strait of Juan de Fuca 

region from 1974 to 2005 (from PSTRT abundance and productivity tables using data from 

WDFW and PNPTT 2000, 2003, WDFW and PNPTC 2004, 2005, 2006). 

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Escapement Broodstock Catch

Return year

Natural origin return abundance

Figure 10. Annual return abundance of natural-origin summer chum salmon aggregations of the Hood 

Canal region from 1974 to 2005 (from PSTRT abundance and productivity tables using data from 

WDFW and PNPTT 2000, 2003, WDFW and PNPTC 2004, 2005, 2006). 

25
escapement is reported by the comanagers and includes, for the Strait of Juan de Fuca 

population, Jimmycomelately, Snow, Salmon, and Chimacum creeks. For the Hood Canal 

population, the spawning aggregations include Big and Little Quilcene, Dosewallips, Duckabush, 

Hamma Hamma, Union, Dewatto, and Tahuya rivers and Anderson, Big Beef, and Lilliwaup 

creeks. Escapement estimates are not available for this period for the Dungeness River in the 

Strait of Juan de Fuca population or the Skokomish River in the Hood Canal population, so they 

have not been included in analyses at the spawning aggregation level. 

Natural spawning escapement estimates, broodstock take, and catch per fishing area were 

provided by the comanagers (see WDFW and PNPTT 2000 for escapement estimation methods). 

Total recruitment includes, for the purposes of these analyses, the number of adult summer chum 

salmon caught in Canadian and U.S. fisheries, fish that spawn naturally, and fish removed for 

broodstock take. Observed predation, normally by marine mammals, was not included since 

observations were sporadic. Recruitment is calculated from run reconstruction analyses after the 

method used by the comanagers (see WDFW and PNPTT 2000 for run reconstruction methods). 

Some of the naturally spawning summer chum salmon aggregations in Hood Canal and 

the Strait of Juan de Fuca include hatchery-reared spawners in addition to those that originated 

from natural habitats. Including such hatchery-origin fish in counts of natural spawning can 

mask the true status of the natural-origin population. However, all hatchery-origin summer 

chum in the two regions are now mass marked, allowing for their differentiation from cooccurring natural-origin fish (WDFW and PNPTT 2000). Based on estimates of the numbers of 

natural-origin fish and hatchery-origin fish to natural spawning populations provided by the 

comanagers, we used only natural-origin fish in viability modeling. Broodstock removals to 

sustain the supplementation programs began in 1992 and the first 3-year-old, hatchery–origin 

chum returned to the region in 1995. Hatchery contributions to total adult recruitment between 

1995 and 2005 have ranged 38–74% in the Strait of Juan de Fuca population and 16–49% in the 

Hood Canal population. 

For the years 1999 to 2005, summer chum salmon from most of the spawning 

aggregations within each population were sampled for age, mark, and genetic composition. 

Sample sizes are good each year, with generally well over 100 fish per aggregation and from 300 

to 1,000 fish for the Strait of Juan de Fuca population and from 300 to 3,000 for the Hood Canal 

population (WDFW and PNPTT 2000, 2001, 2003, WDFW and PNPTC 2004, 2005, 2006). Age 

is reported separately for natural-origin and hatchery-origin fish. Adults return as age 2 to 5 

years; age-2 fish returning to the spawning grounds are considered mature fish. Age distribution 

estimates are available for each spawning aggregation. A weighted age distribution for each 

population is estimated based on the relative proportion of each spawning aggregation in the 

population each year. The estimated average age of returning adults is 3.4 years for the Strait of 

Juan de Fuca population and 3.6 years for the Hood Canal population. The annual age 

composition on the spawning grounds is used for cohort run reconstruction that is used in 

developing spawner-recruit functions. For years with age data, those data are used; for years 

without age data, the average age composition over available years is used for cohorts and is 

weighted by the relative total abundance for each contribution year to the cohort (Sands in 

prepa.). 

26
Productivity is measured as the number of natural-origin adult returns per natural 

spawner. Productivity has been estimated from the cohort run reconstruction and is shown in 

Figure 11 and Figure 12. While average R/S over the 1974 to 2001 cohort is 2.6 for the Strait of 

Juan de Fuca population and 3.1 for the Hood Canal population, variability around these 

averages is great. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Brood year

R/S

Figure 11. Strait of Juan de Fuca summer chum salmon population R/S for 1974–2001 brood years. The 

average R/S is 2.6 with a SD of 3.8. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Brood year

R/S

Figure 12. Hood Canal summer chum salmon population R/S for 1974–2001 brood years. The average 

R/S is 3.1 with a SD of 3.6. 

27
Estimating Quasi-extinction for Summer Chum Salmon 

The quasi-extinction threshold, NQET, is the population size below which extinction risk 

is mostly determined by depensatory effects and other factors not included in estimates of the 

variance of potential population growth rate (σ

2

) derived from populations at higher abundances. 

Several processes are important in setting NQET (Lande 1998) and there is no single accepted way 

to determine this parameter, but the outcome of PVA analysis is highly sensitive to its value. We 

used the potential loss of genetic diversity associated with small population size as a guide to 

setting NQET, because it addresses risks to three VSP criteria: abundance, spatial structure, and 

diversity. Conservation guidelines usually assume that populations cannot tolerate genetic 

effective sizes (Ne) less than 50 for more than short periods of time without significant risk 

(Soule 1980, Allendorf et al. 1997). Genetic drift begins to dominate natural selection in 

determining the genetic characteristics of populations smaller than 50 to 100 fish effective size 

(Gall 1987). Allendorf et al. (1997) recommended that Pacific salmon population effective sizes 

be above 500 to guard against long-term deleterious genetic change due to genetic drift and 

above 50 to guard against short-term change. 

Methods 

Because of the historical population genetic structure identified for summer chum salmon 

(see results of the population identification analyses, Figure 5 and Figure 6), maintaining genetic 

diversity requires that spawning aggregations be sustainable across the population’s range. The 

overall Ne for the whole population, which is used to determine a census number, NQET, for PVA 

analyses, depends on the sizes of the subpopulations and the amount of genetic migration among 

them. Consequently, we calculated the total population Ne by assuming a minimum 

subpopulation genetic effective size, Ne, of 50 for each of 5 persistent spawning aggregations in 

the Hood Canal population and 4 persistent spawning aggregations in the Strait of Juan de Fuca 

population. The number of spawning aggregations per population was based on the spawning 

area covered by the population and the distance between spawning aggregations so that it was 

consistent with the viability criteria for spatial structure. 

In the Strait of Juan de Fuca population, we considered two scenarios: 1) all 4 

aggregations are major spawning aggregations each with a minimum Ne of 50 and 2) 3 are major 

spawning aggregations and the fourth, the Dungeness River, is a satellite spawning aggregation 

with an Ne of 10. To estimate the degree of exchange, we assumed straying patterns for the 

natural spawning fish to be that observed from artificially propagated, otolith-marked, or 

adipose-clipped summer chum salmon in 2001 and 2002 (WDFW and PNPTT 2003). These 

observations showed that most hatchery fish homed to their natal streams and straying rates 

declined rapidly with distance from the natal stream. Based on these data, the proportion of 

adults, P, that return to their stream of origin or another stream was approximately 

PADULTS = 0.66 – 0.35log(km); R2

 = 0.73 (1) 

Assuming migration probabilities are similar to the observed straying patterns, we estimated the 

nonnatal migration probabilities using the above relationship and calculated homing probability 

as one minus the total nonnatal migration probability. We used the Tufto-Hindar algorithm 

(Tufto and Hindar 2003) to solve for an overall Ne for the population given the different 

28
subpopulations and migration rates. We converted Ne to NQET using the relationships in Waples 

(1990): 

B

N

N NN

c

e

QET e ÷÷= (2) 

where Ne was the total effective population size as calculated above based on the number of 

regions and distance between aggregates, Ne:Nc was the effective size to census size ratio of 0.2, 

and B was an average generation length (3.6 years for the Hood Canal population and 3.4 years 

for the Juan de Fuca population). 

Results 

Based on this approach, total Ne for the Hood Canal population should be 254; this 

converts to NQET ≈ 350. Likewise, assuming four major aggregations in the Strait of Juan de 

Fuca population, total Ne for the Strait of Juan de Fuca population should be 205, which converts 

to NQET ≈ 300. Alternatively, assuming under recovered conditions that spawning in the 

Dungeness is persistent but small, Ne for the Strait of Juan de Fuca population would be 109, 

which converts to NQET ≈ 160. 

PVA for Abundance and Productivity Criteria 

Two analyses were used for PVA to determine criteria for abundance and productivity. 

The first, based on a density-independent model based on work of Dennis et al. (1991) and 

Holmes (2001), assumes a stable growth rate and no harvest. We refer to this approach as 

SimSalmon, after the name of the computer program we used to implement this method (online 

at http://www.nwfsc.noaa.gov/trt/wlc/viability_simsalmon.cfm). The second analysis is based 

on density-dependent spawner–recruit curves and uses the simulation model Viability and Risk 

Assessment Procedure (VRAP, Sands in prepb.). VRAP also allows the incorporation of 

different exploitation rates in determining viable criteria. 

SimSalmon Methods 

The first PVA was based on the assumption that population dynamics are density 

independent, fluctuating only based on the variability of the population growth rate (Dennis et al. 

1991). The results from this method strongly rely on the estimated variance of the population 

growth rate. We applied the methods of Holmes (2001), Holmes and Fagen (2002), and 

McElhany and Payne (in prep.) to available Hood Canal Summer Chum Salmon ESU data to 

derive variance estimates, as was done for Puget Sound Chinook salmon (Rawson et al. in prep.). 

Given an observed time series of abundances, one can estimate the population growth rate (λ) 

and the variance (σ

2

) of the natural logarithm of the growth rate (μ). A stable population, neither 

increasing nor decreasing over time, would have a λ = 1 and a μ = 0. Assuming that the 

population dynamics will be restored in the future such that 1) the growth rate is stable or 

increasing (λ ≥ 1), and 2) the variance of the growth rate will remain the same as in the period of 

the observed time series, one can estimate the minimum initial population size necessary to 

maintain the probability or risk of extinction at or below a given level over a given number of 

years. Using this method, the viability criterion is for the population abundance to exceed the 

minimum size and for the growth rate to exceed λ = 1 over the specified time period. 

29
There are two steps in this analysis. The first is to estimate σ

2

 from the observed data, for 

which we used the slope method (Holmes 2001). The second step is to project extinction risk 

assuming that the time course of the population approximates a Brownian motion (Dennis et al. 

1991). 

As used in this PVA analysis, recruitment means the number of summer chum salmon 

that would have spawned naturally in the absence of any harvest.1 The PSTRT worked with the 

comanagers to develop estimates of the numbers of natural-origin and hatchery-origin recruits 

and spawners for the 1974–2005 period. All natural spawners are used for the parent spawner 

estimates and natural-origin spawners and harvest are used for the recruits in viability modeling. 

Parameter estimation—The slope method (Holmes 2001) was developed to separate 

process error (the parameter of interest) from measurement error in salmon time series. This 

method has been shown to produce reliable estimates of process error in the face of large 

measurement error when the population dynamics follow the assumptions of the model and also 

when the population dynamics follow other forms, including some of the density-dependent, 

spawner–recruit relationships commonly found in salmon (Holmes and Fagan 2002). The slope 

method, as we applied it, uses the 4-year running sums of abundance, expressed either as natural 

spawning escapement or total recruitment (average age at return is 3.4 years for the Strait of Juan 

de Fuca and 3.6 years for Hood Canal). 

We used estimated σ

2

 two ways for each population: using the time series of estimated 

natural spawning escapements and using the time series of total recruitment. The first estimate 

uses the Holmes (2001) method directly to compute the variance of log(Nt+1/Nt) for a time series, 

where Nt is the running sum of natural-origin spawning escapement at time t. The second 

estimate uses the estimate of total recruitment as Nt. In all cases we used 4-year-long 

unweighted running sums of the appropriate time series as the basic input data and lags (τ) of 1 

through 4 years (τ = 4 in Holmes’ notation) for the slope estimates. 

The length of the time series used for estimating σ

2

 is positively correlated with the 

parameter estimate. This is true even when the underlying process can be assumed to be 

Markovian (McElhany and Payne in prep.), as is the case for the simple model for which this 

technique was first developed. Given the well-known longer-term cycles and regime shifts that 

govern Pacific salmon population dynamics (Pearcy 1992, 1997, Mantua et al. 1997), this 

tendency is exacerbated, at least as time series length increases from very short to 30–40 years. 

The time periods chosen (see Table 4) were based on finding a stable period with respect to 

population abundance. 

Testing the assumptions of the model—To determine whether the assumptions 

underlying the slope method held for the Hood Canal Summer Chum Salmon ESU data, we 

chose time series from time periods with relatively stable population sizes, and tested for the 

linearity of the relationship between σ

2

 and τ using the square of the Pearson correlation 

coefficient (r2) and fit of the log(Nt+1/Nt) to the normal distribution with the Shapiro-Wilk test. 

 

1

 Used here, recruitment refers to calendar year recruitment, meaning fish of all ages returning in a single year. 

Other applications, such as our second PVA, use brood year recruitment, which counts all the fish in a single cohort 

returning over several years. 

30
Table 4. Estimated values of the variance (σ

2

) and the natural logarithm of the growth rate (μ) for Hood 

Canal and Strait of Juan de Fuca summer chum salmon populations computed on natural 

escapement and total recruitment time series for the indicated time periods. The stability notes 

refer to population abundance. Values in bold face are recommended for use in determining 

minimum viable population sizes. 

Natural escapement Total return 

Population: time period Years σ2 μ σ2 μ

Hood Canal–all years 1974–2003 0.416 –0.015 0.204 –0.035 

Hood Canal–stable, high 1974–1978 a* a a a 

Hood Canal–stable, low 1981–1994 0.020 –0.029 0.042 –0.076 

Hood Canal–decreasing 1978–1990 0.127 –0.213 0.056 –0.137 

Hood Canal–increasing 1990–2003 0.442 0.250 0.268 0.185 

Strait–all years 1974–2003 0.220 0.024 0.209 0.021 

Strait–stable, high 1974–1984 0.041 0.000 0.032 0.007 

Strait–stable, low 1989–2000 0.005 –0.056 0.013 –0.089 

Strait–decreasing 1982–1994 0.415 –0.117 0.136 –0.103 

Strait–increasing 1994–2003 0.096 0.387 0.092 0.372 

 * a = insufficient number of years available to estimate σ2. 

Population projection—To project a population’s extinction probability, we assumed 

that a population grows at a lognormally distributed growth rate with a mean of λ = 1 and 

variance around μ being σ

2

. We also simulated populations at several positive growth rates 

(λ > 1) in order to investigate the effect of positive growth rate on the required minimum 

population size. The straightforward approach we used is described by McElhany and Payne 

(in prep.) and we used their SimSalmon computer program to compute the results. This program 

repeatedly simulates population trajectories for Y years, starting with a population size of N. 

The trajectory is assumed to go extinct if the population size over a 4-year average ever falls 

below NQET during the Y years. Otherwise it is not extinct. This projection method includes the 

additional constraint that the population size can never exceed the initial population value. This 

provides a high estimate of extinction probability since, if this constraint is removed, extinction 

probabilities are much lower than with the constraint in place.2

SimSalmon Results 

Parameter estimation—Estimates of σ

2

 for the Hood Canal and Strait of Juan de Fuca 

summer chum salmon populations ranged from 0.005 to 0.442, depending on the time period 

chosen and whether the analysis was on the time series of natural escapement or total recruitment 

(Table 4). Based on visual examination of the data, we divided the time series into periods of 

increasing, decreasing, or stable population sizes. The rate of population increase or decrease is 

measured by μ, the natural logarithm of the average annual growth rate. We used estimates of σ

2

from the time segment for each population with the lowest value of μ (Table 4). Between using 

the time series of natural escapement or the total recruitment for σ

2

, we chose total recruitment 

because it is independent of the effect of harvest, which has been variable over the years. 

 

2

 P. McElhany, Northwest Fisheries Science Center, Seattle, WA. Pers. commun., January 2005. 

31
Visual examination of the relationship between σ

2

 and τ for the time series used suggests 

that the assumption of linearity is well founded. Analysis of these time series for normality of 

the distribution of log(Nt+1/N) showed both Shapiro-Wilk coefficients with p-values consistent 

with the assumption of a normal distribution (Hood Canal, p = 0.45; Strait of Juan de Fuca, p = 

0.85). 

Population projection—Determining minimum population sizes needed to ensure 

viability using the SimSalmon program requires the following input variables to be specified: 

potential population growth rate (λ or its natural logarithm μ), σ

2

, time period for the simulation 

in years (Y), extinction probability, and NQET. We assumed a stable population by setting μ = 0 

and simulated values of σ

2

 from 0.005 to 0.05 in increments of 0.005. We used 100 years for Y 

and 0.05 and 0.01 for the extinction probability following McElhany et al. (2000). 

With this input, one can produce a table of viable spawning populations sizes for a range 

of σ

2

 values, assuming μ = 0 (Table 5). Given μ = 0 and the recommended σ

2

 value of 0.03 for 

the Strait of Juan de Fuca population (Table 4), the viable spawning population size range 

(reflecting extinction probabilities of 1–5%) is about 13,000 to 31,000, assuming that the 

Dungeness River can support a major spawning aggregation, and about 6,000 to 20,000, 

assuming the Dungeness River supports a minor satellite spawning aggregation. For the Hood 

Canal population with a σ

2

 value of 0.04, the corresponding viability range is about 25,000 to 

85,000 summer chum spawners. 

VRAP Methods 

The second PVA analysis used is the VRAP model (Sands in prepb.). VRAP adds the 

dimensions of productivity, capacity, and exploitation rate over the single numeric estimate of 

spawning size from SimSalmon and assumes that population dynamics are density dependent. 

With the VRAP method, there is not an estimate for a viable escapement level independent of 

other considerations. Viability is expressed as the parameters of a spawner-recruit curve given a 

Table 5. Results of population projections using SimSalmon model for σ

2

 ranging from 0.005 to 0.05, 

μ = 0, extinction probabilities of 0.05 and 0.01, and NQET of 350 for Hood Canal and both 300 and 

160 for the Strait of Juan de Fuca. Based on the recommended values of σ

2

 (Table 4) and other 

assumptions (see text), the recommended ranges for the minimum viable annual population sizes 

for the Hood Canal and Strait populations are shown in bold face. 

Hood Canal, NQET = 350 Strait, NQET = 300 Strait, NQET = 160 

σ2 p = 0.05 p = 0.01 p = 0.05 p = 0.01 p = 0.05 p = 0.01

0.005 1,600 2,600 1,400 2,100 700 1,100 

0.010 2,900 5,000 2,400 4,400 1,400 2,400 

0.015 4,700 10,600 4,200 7,900 2,000 4,100 

0.020 7,500 16,700 6,100 13,200 3,200 7,600 

0.025 10,000 27,200 9,100 21,700 4,600 11,300 

0.030 15,900 35,300 12,500 30,800 5,600 20,200 

0.035 19,200 47,000 16,300 51,000 7,900 27,700 

0.040 24,700 84,500 21,300 61,600 12,000 39,700 

0.045 30,900 127,100 26,300 86,900 13,800 62,700 

0.050 40,200 156,400 43,200 142,900 17,500 67,700 

32
desired harvest level; the resulting escapement can be estimated from these parameters. VRAP 

is the same model that has been used to determine rebuilding exploitation rates (RER, i.e., the 

highest level of harvest that allows rebuilding of the population) for jeopardy determinations for 

Puget Sound Chinook salmon (NMFS 2001). The model has two modes of operation. 

• Harvest mode: Given a spawner-recruit function with parameters fit to available data, the 

model determines the RER that results in the population not going below a lower 

escapement threshold more than a chosen percent of the time and ends, at the end of the 

modeled time spawn of years, above an upper escapement threshold a chosen percent of 

the time. 

• Population mode: Given a fixed ER, the model allows one to determine from output risk 

tables the intrinsic productivity and capacity parameters of a chosen spawner-recruit 

function that results in the population not going extinct more than a chosen percent of the 

time over a given time period (e.g., 5% probability or risk of extinction over 100 years). 

This analysis of population viability for the Hood Canal Summer Chum Salmon ESU 

used the population mode of VRAP. 

VRAP is a stochastic simulation model that projects recruits and spawners over a period 

of years based on a spawner-recruit relationship chosen from Ricker (1975), Beverton-Holt 

(Ricker 1975), and hockey stick (Barrowman and Meyers 2000) functions, given a starting 

population size and a target exploitation rate. In VRAP, harvest mortality may be estimated by 

age for two fishery types: mixed maturity stock fishery and mature stock fishery. Input values 

for average simple harvest rates and average maturation rates by age are used to partition the 

target exploitation rate by age and fishery. Simple harvest rates are the portion of the cohort by 

age taken by the fishery. Maturation rates are the proportion of the cohort after the mixed 

maturity fishery that mature and migrate back to the spawning grounds. The magnitude of the 

harvest rates is adjusted until the desired total exploitation rate is reached while maintaining the 

age and fishery distribution of the harvest. All of the Hood Canal summer chum harvest is 

assumed to be taken on fish returning to the spawning grounds, that is, there is only a mature 

stock fishery on Hood Canal summer chum salmon. 

In this analysis, uncertainty in the projected number of recruits is introduced as process 

error and management error. The average ratio of estimated recruits (from cohort run 

reconstruction) to the predicted recruits from the spawner-recruit curve and the variance of that 

ratio (i.e., process error recruits) σ

2

 are used to stochastically determine recruits from the 

predicted R/S. These error terms are determined during the parameter estimation stage explained 

below. To address uncertainty about the ability to achieve a target exploitation rate (i.e., 

management error), we used data in comanager reports (WDFW and PNPTT 2001, 2003, 

WDFW and PNPTC 2004, 2005, 2006) to estimate the variability in the forecasted exploitation 

rate under the comanagers’ Base Conservation Regime compared to the actual exploitation rate 

achieved. However, the Base Conservation Regime exploitation rate “targets” are actually 

ceilings and the comanagers strive not to exceed those levels. Therefore, the coefficient of 

variance (with a mean value of one) around the actual versus forecasted levels is used in VRAP; 

this assumes that the comanagers will target an ER as summer chum populations become viable. 

33
Both these sources of variability—process error and management error—are expressed as 

gamma distributions around the predicted recruits, and the target ER as the distribution of these 

errors is skewed rather than normal. The two parameters of the gamma distribution are the shape 

parameter (gamma1) and the scale parameter (gamma2). The gamma distribution is a skewed 

distribution; the two parameters may be calculated from the mean and standard deviation of the 

data with gamma1 = mean-squared/variance and gamma2 = variance/mean. 

Parameter estimation—Prior to running VRAP, the population data is run through the 

Dynamics Model to determine the spawner-recruit relationship for the time span of data being 

used; this gives us the variability estimate of observed versus predicted recruits that will be used 

in the viability analyses. For summer chum salmon, no covariates for the environment have been 

found that are useful for explaining the variance, although an exhaustive search has not been 

made. By excluding environmental variables for now, the process error incorporates variability 

due to environmental factors as well as sampling and observation error. 

To estimate the parameters of the spawner-recruit function, a Microsoft Excel 

spreadsheet model developed by Jim Scott (WDFW) and adapted by Norma Sands (NMFS) was 

used. This model is referred to as the Dynamic Model and utilizes the solver function in Excel to 

iteratively solve for parameters that minimize the error between predicted calendar year 

escapement and the observed calendar year escapement. This approach allows one to solve for 

nonlinear functions, such as when covariates are appended to the spawner-recruit functions and 

to minimize error on escapement rather than on recruits. 

Process error—Minimizing the error between predicted and estimated calendar year 

escapement is used since the escapements are closer to being observed data than recruits, which 

are based on proportioning catch estimates according to relative escapement or terminal size for 

each spawning aggregation. In other words, there is undoubtedly greater error in our estimates 

of recruits than escapement, since the error for recruits includes the error inherent in escapement 

plus the error introduced from catch estimates and age estimates. The process error in estimating 

recruits is larger when minimizing on escapement than if we minimized on recruits, reflecting 

the fact that we are less certain in our estimates of recruits than escapement. 

The Dynamic Model uses the existing data of natural spawners, natural-origin spawners, 

and harvest rates as recorded in the PSTRT abundance and productivity tables, as well as 

maturation rates and recruits estimated from the cohort run reconstruction done within the 

abundance and productivity tables. Three spawner-recruit functions are run and compared: 

hockey stick, Ricker, and Beverton-Holt. For each function, the model iteratively picks 

spawner-recruit function parameters, predicts the cohort size at age 2 years (adult equivalent 

[AEQ] recruits divided by age-2 AEQ), and uses the age-specific harvest rates and maturation 

rates to conduct a cohort run reconstruction. From the run reconstruction, predicted calendar 

year escapements are constructed and tested against the observed escapements for minimizing 

error. Predicted AEQ recruits are compared with the AEQ recruits estimated in the abundance 

and productivity tables to determine the process error to supply the VRAP model. 

All three spawner-recruit functions were run using the Dynamic Model. The Dynamic 

Model calculates the F-statistic that can be used to test if the independent variable—spawners—

explains a significant amount of the variation in recruits and the probability from the 

34
F-distribution that the fit could happen from chance using methods from Kleinbaum and Kupper 

(1978). 

F = mean square regression/mean square residual = (N-2) × R2

 / (1-R2) (3) 

where R2

 is the square of the Pearson correlation of the predicted and estimated escapement or 

recruits. The F-statistic and associated probability can tell us if the independent variable—

spawners—significantly explains the variation in escapement or recruits, but cannot tell us which 

function to use if they all do a good job of explaining the variability. 

The Beverton-Holt function was chosen for both the Strait of Juan de Fuca and Hood 

Canal summer chum populations. The Beverton-Holt function is generally written as 

R = S / (c × S + p) (4) 

where p is the intrinsic productivity parameter (recruits per spawner approaches 1/p as spawners 

approach zero) and c is the capacity parameter (maximum recruits = 1/c) (Ricker 1975). In order 

to express them as recognizable values, VRAP uses the form 

R = S / (S/β + 1/α) (5) 

such that α presents the productivity at the origin and β represents maximum recruits. Returns 

per spawner are modified by the QET and depensation values provided to the model. Spawning 

levels less than the QET return no recruits and spawning levels between the QET and the 

depensation level return spawners according to a line between the recruits at depensation and no 

recruits at QET. 

A summary of the output data from the Dynamic Model that are used as input for the 

VRAP model is given in Table 6. The α and β parameters given in Table 6 represent values 

estimated over 1974–2001 brood year data. For the VRAP simulation runs, α was set to a fixed 

number and the model determined the probability of extinction for a range of β values. 

VRAP Results 

Parameter estimation—Management error was estimated as a gamma distribution with 

gamma1 = 4.082 and gamma2 = 0.245 (mean = 1, SD = 0.245 with a skewed rather than normal 

distribution). 

For the Strait of Juan de Fuca population, all three spawner-recruit functions determined 

from the Dynamic Model and estimated over 1974–2001 brood years resulted in parent natural 

spawners, significantly explaining the variability seen in progeny annual natural-origin spawners 

at the 1% level of error. The fit of the estimated recruits to the predicted recruit curve was less 

precise, as was expected since we were not minimizing on recruit error. None of the three 

spawner-recruit functions showed that the spawners significantly explained the variation seen in 

recruits. All three spawner-recruit functions indicated low productivity for the population as 

evidenced by the closeness of the left-hand portion of the curve to the replacement line (Figure 

13). The resulting estimate of variability of estimated recruits compared to predicted recruits 

35
Table 6. Summary of output results from the Dynamic Model used as input to the VRAP model for the 

Strait of Juan de Fuca (Strait) and Hood Canal (Canal) summer chum salmon populations. For 

each VRAP simulation run, intrinsic productivity (α) and ER were set to fixed values and the 

model output gave the probability of extinction for a range of capacity (β) parameter values. 

Parameter Strait Canal

Function (Beverton-Holt) 

α productivity 4.61 7.24

β capacity 1,590 8,340

Process error 

Gamma1 0.876 0.557

Gamma2 1.982 2.931

QET 300 350

Depensation 310 710

Management error 

Gamma1 4.082 4.082

Gamma2 0.245 0.245

Starting population size 

Age 1 27,072 207,808

Age 2 13,536 103,904

Age 3 8,082 62,289

Age 4 2,721 30,171

Age 5 35 603

Maturation rate 

Age 2 0.0244 0.0015

Age 3 0.5755 0.3857

Age 4 0.9757 0.9505

Age 5 1.0000 1.0000

Harvest rate (all considered mature catch) 

Age 2 0.0000 0.0000

Age 3 0.1281 0.3680

Age 4 0.1291 0.3683

Age 5 0.1271 0.3604

Recent average ER 0.0040 0.1420

was expressed as a gamma distribution with, for the Beverton-Holt function, parameters 0.876 

and 1.982 for gamma1 and gamma2. Expressed as a mean and SD, although the distribution is 

not normal, the values are mean = 1.74, SD =1.85. Note that with a mean of 1.74, the deviations 

greater than the predicted curve are greater than the deviations less than the curve (Figure 13). 

For the Hood Canal population, all three spawner-recruit functions, estimated over 1974–

2001 brood years, resulted in parent natural spawners significantly explaining the variability seen 

in progeny annual natural-origin spawners at the 1% level of error. The fit of the estimated 

recruits to predicted recruits, using the spawner-recruit parameters based on minimizing on 

return spawners, was better for the Hood Canal population than the Strait of Juan de Fuca 

population. The parent spawners significantly explained the variability in recruits at a 5% level 

for the Beverton-Holt and Ricker functions and at a 10% level for the hockey stick function. We 

chose the Beverton-Holt function for the analysis of the Hood Canal population to be compatible 

with the analysis for the Strait of Juan de Fuca population. 

36
0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Ric-ave

BH-ave

Hoc-ave

Replace

Est R

Observed spawners

Recruits

Figure 13. Results from the Dynamic Model for estimation of spawner-recruit functions for the Strait of 

Juan de Fuca summer chum salmon population. The stars represent actual estimated recruits 

from the cohort run reconstruction in the abundance and productivity tables. 

The Hood Canal population is more productive than the Strait of Juan de Fuca population 

as evidenced by the spawner-recruit graphs (Figure 13 and Figure 14). The resulting estimate of 

variability of estimated recruits to predicted recruits for the Hood Canal population expressed as 

a gamma distribution was 0.557 and 2.931 for the gamma1 and gamma2 parameters, 

respectively. Expressed as mean and SD, although the distribution is not normal, the values are 

mean =1.63, SD = 2.19 for the Hood Canal population. Again note that with a mean of 1.63, the 

deviations greater than the predicted curve are greater than the deviations less than the curve 

(Figure 14). 

Viability criteria—Viability criteria are presented as spawner-recruit functions and there 

is a family of curves that represent viable population conditions. The parameter α, as estimated 

by the Dynamic Model for the Beverton-Holt relationship over the 1974–2005 data set, was 4.6 

for the Strait of Juan de Fuca and 7.2 for the Hood Canal population. Therefore, we used a range 

for α of 3–6 and 5–9 for the Strait of Juan de Fuca and Hood Canal populations, respectively, for 

running the viability simulations. ERs of 0, 0.1, 0.2, and 0.3 were used in the model. 

Each VRAP run, with given α and ER levels, estimated extinction probability over a 

range of β values set by the user. The minimum value of β for viability was then chosen as the 

smallest level that consistently gave an extinction probability of 5% or less over 100 years. The 

condition of viability is thus defined by three parameters: α, β, and ER. The associated number 

37
0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000

Ric-ave

BH-ave

Hoc-ave

Replace

Est R

Observed spawners

Recruits

Figure 14. Results from the Dynamic Model for estimation of spawner-recruit functions for the Hood 

Canal summer chum salmon population. The stars represent actual estimated recruits from the 

cohort run reconstruction in the abundance and productivity tables. The data point 2000 is off the 

chart, being 73,500 recruits from a spawning of 8,000 spawners. 

of spawners (i.e., the resulting viable escapement level) needed to sustain an equilibrium rate of 

exploitation, given the α and β parameters, was calculated as the average annual escapement 

realized over the 100-year run. Each annual escapement is the average of 3,000 simulations. 

This viable escapement is termed resulting viable escapement since, on its own, the escapement 

level is not viable unless the function parameters α and β are met and the target ER is not 

exceeded. 

Several combinations of viable population parameters for the two populations in the 

Hood Canal Summer Chum Salmon ESU are shown in Table 7 and Table 8 and graphed in 

Figure 15 and Figure 16. In interpreting the results of the VRAP viability analyses, the viability 

level is a curve defined by the α and β parameters that supports a given ER; the viable 

escapement level is the average escapement that would result from the given ER. The population 

could be managed for a target ER from which one would expect the corresponding escapement 

level on average, or one could manage for the escapement level and, on average, obtain the 

corresponding ER. 

For the 1974–2005 period, α is 4.6, β is about 1,600, and average escapement is about 

2,000 summer chum salmon for the Strait of Juan de Fuca population; so the population is not 

currently viable. It appears that the recent 5-year average escapement of about 4,300 spawners 

38
Table 7. A selection of viable spawner-recruit functions as defined by the intrinsic productivity (α) and 

capacity (β) parameters for specific exploitation rates (ERs) for the Strait of Juan de Fuca summer 

chum salmon population (QET = 300). The simulation model, VRAP, estimates the resulting 

average escapement (Esc) for each combination of α, β, and ER value. VRAP runs were made 

using both a 1% and a 5% risk of extinction over 100 years. 

α = 3 α = 4 α = 5 α = 6 

 β Esc β Esc β Esc β Esc

≤5% risk 

ER = 0.0 4,300 5,100 3,700 4,800 3,300 4,500 3,300 4,700

ER = 0.1 5,300 5,400 4,500 5,100 3,900 4,700 3,700 4,600

ER = 0.2 6,500 5,500 5,300 5,100 4,700 4,800 4,500 4,800

ER = 0.3 9,500 6,400 7,100 5,600 6,300 5,400 5,500 5,000

≤1% risk 

ER = 0.0 8,300 9,700 6,500 8,400 6,300 8,600 5,700 8,000

ER = 0.1 9,100 9,200 7,900 8,800 6,900 8,200 6,300 7,800

ER = 0.2 13,000 10,900 10,000 9,500 8,300 8,500 7,900 8,500

ER = 0.3 18,500 12,300 13,000 10,500 10,500 8,900 9,500 8,500

Table 8. A selection of viable spawner-recruit functions as defined by the intrinsic productivity (α) and 

capacity (β) parameters for specific exploitation rates (ERs) for the Hood Canal summer chum 

salmon population (QET = 350). The simulation model, VRAP, estimates the resulting average 

Esc for each combination of α, β, and ER value. VRAP runs were made using both a 1% and a 

5% risk of extinction over 100 years. 

α = 5 α = 6 α = 7 α = 8 α = 9 

 β Esc β Esc β Esc β Esc β Esc 

≤5% risk 

ER = 0.0 17,000 20,600 15,000 19,100 14,000 18,400 13,500 18,300 13,000 17,900

ER = 0.1 20,500 21,500 18,500 20,400 17,500 20,000 15,500 18,300 15,500 18,600

ER = 0.2 27,000 23,900 23,500 22,000 21,000 20,700 20,000 20,200 17,500 18,200

ER = 0.3 35,000 25,300 29,000 22,500 27,500 22,300 25,500 21,500 24,000 20,800

≤1% risk 

ER = 0.0 41,500 48,500 37,000 45,500 32,500 41,300 30,500 39,700 29,500 39,200

ER = 0.1 53,500 54,200 42,000 45,100 38,500 43,000 33,000 38,100 33,000 38,800

ER = 0.2 66,500 57,100 55,000 50,200 51,000 48,800 43,500 43,000 42,000 42,500

ER = 0.3 97,500 68,000 77,500 58,500 66,000 52,300 61,000 50,400 57,000 48,300

(upper circle in Figure 15) is close to the escapement needed for a viable population and that the 

viable escapement levels do not vary widely over the ranges of productivity values modeled 

(Table 7, Figure 15). However, escapement or productivity or both alone do not define 

population viability. In particular, higher capacity values are needed for the Strait of Juan de 

Fuca population to maintain no more than a 5% probability of extinction over a 100-year time 

frame. 

Similar statements can be made for the Hood Canal summer chum population, that is, 

with current capacity (β = 8,300) and current productivity (α = 7.2), the population is not viable 

(Table 8, Figure 16) even though the recent 5-year average escapement is about 23,000 spawners 

39
0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

3456

0

0.1

0.2

0.3

0

0.1

0.2

0.3

01-05 ave esc

Not viable

Viable

74-05 beta value

74-05 ave esc

Intrinsic productivity parameter–alpha

Abundance

ER

Figure 15. Viability curves for the Strait of Juan de Fuca summer chum salmon population for no 

harvest, expressed as ER, and three levels of ER (lines) and associated realized escapement levels 

(symbols) using the 5% probability of extinction over 100 years. Viability curves are defined by 

function parameters (intrinsic productivity on the x-axis and capacity on the y-axis), not by 

escapement abundance. Also indicated on the graph, by circles, are the capacity (abundance = 

1,600) and average escapement (abundance = 1,800) at average productivity (4.6 on x-axis) 

determined over the period of the analysis (1974–2005). The recent (2001–2005) average 

escapement (abundance = 4,300, plotted at average 1974–2005 intrinsic productivity) is also 

shown. The population is not viable despite recent escapements of about 4,300 spawners as the 

capacity is too low for the associated productivity. 

(upper circle in Figure 16). Again, in particular, higher capacity values are needed to maintain 

no more than a 5% probability of extinction over a 100-year time frame. 

The difference in viable abundance for the two populations is—like the SimSalmon 

estimates—due to the greater QET and variability for the Hood Canal population. In the VRAP 

analysis, differences in depensation levels and maturation rate (Table 7 and Table 8) also 

contribute to the differences in viability levels; however, it is the process error (variability 

around predicted recruits) that attributes to most of the difference. 

Comparison of SimSalmon and VRAP results 

Results from the PVAs for no harvest are compared in Table 9 using the 5% and 1% risk 

of extinction criteria, respectively. As SimSalmon results are for no harvest, only the no harvest 

case for VRAP is compared here. Viability curves for various levels of harvest are in Table 7 

and Table 8. It is not surprising that VRAP viability escapements are much less than SimSalmon 

40
0

20,000

40,000

60,000

80,000

100,000

120,000

56789

0

0.1

0.2

0.3

0

0.1

0.2

0.3

74-05 beta value

01-05 ave 

Viable

Not viable

74-05 ave 

Intrinsic productivity parameter–alpha

Abundance

ER

Figure 16. Viability curves for the Hood Canal summer chum salmon population for no harvest, 

expressed as ER, and three levels of ER (lines) and associated realized escapement levels 

(symbols) using the 5% probability of extinction over 100 years. Viability curves are defined by 

function parameters (intrinsic productivity on the x-axis and capacity on the y-axis), not by 

escapement abundance. Also indicated on the graph, by circles, are the capacity (abundance = 

8,300) and average escapement (abundance = 8,600) at average productivity (7.2 on x-axis) 

determined over the period of the analysis (1974–2005). The recent (2001–2005) average 

escapement (abundance = 23,000, plotted at average 1974–2005 intrinsic productivity) is also 

shown. The population is not viable despite recent escapements of about 23,000 spawners as the 

capacity is too low for the associated productivity.

estimates, since VRAP assumes the robustness of the population to return more recruits per 

spawner at lower spawning levels than at higher spawning levels. 

From the SimSalmon analysis, higher escapement abundance levels are needed for 

viability, since the method assumes density independence and the relatively conservative 

population condition that productivity is at a level where one spawner produces only one adult 

fish in the subsequent generation (i.e., the population is just replacing itself). There are no 

positive density-dependent effects at low population sizes. From the VRAP analysis, lower 

escapement levels are possible for viability if the viable α and β can be estimated and met. This 

requires that data is sufficient to estimate and understand the population dynamics and that 

density dependence can be shown or assumed. 

Incorporating Spatial Distribution Characteristics into Evaluation of Viability 

All four VSP parameters are critical to the viability of salmon populations; all are 

interrelated, and levels of all four attributes in aggregate characterize extinction risk (Fresh et al. 

41
Table 9. Viability estimates of escapement from the two PVA analyses for 5% risk and 1% risk of 

extinction and no harvest for the Strait of Juan de Fuca and Hood Canal populations. Note that 

the VRAP viable escapement levels are only valid if the associated productivity (α) and capacity 

(β) parameters are also achieved. Other viable productivity and capacity pairs are possible; those 

given here are for intrinsic productivity equal or close to current intrinsic productivity for 

comparison with SimSalmon. 

Risk QET

SimSalmon 

Esc VRAP

Strait of Juan de Fuca

α = 3 α = 4 α = 5 

β Esc β Esc β Esc 

5% 300 12,500 4,300 5,100 3,700 4,800 3,300 4,500 

1% 300 30,800 8,300 9,700 6,500 8,400 6,300 8,600 

Hood Canal

α = 6 α = 7 α = 8 

β Esc β Esc β Esc 

5% 350 24,700 15,000 19,100 14,000 18,400 13,500 18,300 

1% 350 84,500 37,000 45,500 32,500 41,300 30,500 39,700 

in press). Developing approaches to evaluate spatial structure and diversity in recovery planning 

and implementation are needed so that recovery options can be completely assessed and possible 

outcomes of suites of actions fully evaluated (McElhany et al. 2000). A quantitative analysis of 

spatial distribution was conducted here to help further guide evaluations of the viability of the 

populations in terms of all four VSP characteristics and their interacting effects. The following 

analysis and results are presented not as a prescriptive solution but as quantitative information to 

help illustrate the relationships to be considered in evaluations of viability relative to the above 

described criteria. We have no direct measures for life history diversity, but for the purposes of 

defining population viability, we assume that if spatial distribution remains diverse, then so will 

life history diversity. 

Methods 

The diversity index is a single statistic that describes the number of components in a 

group and their relative abundance or evenness. Diversity is high when there are many 

components and their abundances are fairly even. Our components are spawning aggregations 

and their abundances are spawning numbers. Since spawning aggregations are spatially 

separated units, we are looking at the spatial structure of the population as well as relative 

abundances. We used both Simpson (1949) and Shannon (Shannon and Weaver 1949) measures 

of diversity. The latter has the advantage of being able to measure diversity of the ESU 

hierarchically by characterizing diversity of spawning aggregations within a population and 

populations within the ESU (Pielou 1969). 

The Simpson index (D) is formulated as 

D = 1-1/(N(N–1)) ∑j Nj(Nj–1) (6) 

42
where N is the total population (natural escapement) or sample from the population and Nj is the 

total number of N that belong to spawning aggregation j. If the probability of two individuals 

chosen at random belonging to the same spawning aggregation is high, then diversity is low. For 

a given set (number) of spawning aggregations, diversity is at a maximum when all spawning 

aggregations have the same abundance. 

The Shannon index (H) for a population is formulated as 

H = –∑j (Nj/N) ln(Nj/N) (7) 

For the ESU the multigroup index is formulated as 

H = H(ESU) + H(P) (8) 

where H(ESU) is the diversity based on the populations within the ESU and H(P) is the average 

of the diversities within each population. 

Data 

The annual spawning escapement for the Strait of Juan de Fuca summer chum salmon 

population has been monitored for four component spawning aggregations since 1974: 

Jimmycomelately, Salmon, Snow, and Chimacum creeks. Over the period 1974–2005, 60% of 

the natural spawners and 66% of the total return (spawners + broodstock + catch) were observed 

as returns to Salmon Creek. More extensive monitoring of returns to the Dungeness River, 

where summer chum are known to occur at very low levels (WDFW and PNPTT 2000), has been 

done during August through October since at least 1986 (i.e., at the same time as spawner 

surveys for Chinook salmon in the system). Estimated Dungeness River escapement represented 

1.5% of the total spawning for the Juan de Fuca population in 2004 and 0.02% in 2005. The 

Chimacum Creek stock was extirpated in the early 1980s and a hatchery-based reintroduction 

program using Salmon Creek spawning aggregation began in 1996 to reestablish summer chum 

spawning. 

The annual escapement for the Hood Canal summer chum salmon population has been 

monitored for 11 spawning aggregations: Little Quilcene, Big Quilcene, Dosewallips, 

Duckabush, Hamma Hamma, Dewatto, Tahuya, and Union rivers and Lilliwaup, Anderson, and 

Big Beef creeks. Over the period 1974–2005, 86% of the natural spawners and 80% of the total 

return (spawners + broodstock + catch) were from five spawning aggregations: Big Quilcene, 

Dosewallips, Duckabush, Hamma Hamma, and Union rivers. Returns to Big Beef and Anderson 

creeks declined to no fish in the 1980s; a hatchery reintroduction program was initiated on Big 

Beef Creek in 1996. Returns to the Dewatto and Tahuya rivers diminished to very low levels (<9 

annually for Dewatto and 1 annually for the Tahuya) in the 1990s and both native stocks are 

considered functionally extirpated (WDFW and PNPTT 2000); a hatchery reintroduction 

program was initiated on Tahuya River in 2003. Focused monitoring of annual summer chum 

escapements to the Skokomish River, where the native summer chum population was extirpated, 

began in 2004. 

The total number of fish escaping to spawn naturally is used as the measure of annual 

abundance for diversity estimation. 

43
Results 

The relative total abundance of spawning by the component spawning aggregations has 

changed over time. For the Strait of Juan de Fuca population, this has reduced the diversity of 

the population (Figure 17). In the early years of our data availability (1974–1978), there were 

relatively equal proportions spawning in Jimmycomelately, Snow, and Salmon creeks; in recent 

years the majority of the escapement for the population has been observed in Salmon Creek. 

Returns to Chimacum Creek were reestablished beginning in 1999 with a hatchery 

supplementation program using Salmon and Snow creek stock. Surveys of salmon on the 

Dungeness River suggest that this system during this time had few to no summer chum 

spawners; it represented only 1% of the total spawning in 2004. 

For the Hood Canal population, from 1974 to 1978 three spawning aggregations—the 

Dosewallips, Duckabush, and Hamma Hamma rivers—had more than 50% of the escapement 

(Figure 18). In most recent years, the Big Quilcene River has had on average 42% of the 

escapement followed by the Dosewallips and Union rivers. 

Results from the two diversity indices are very similar for both populations (Figure 19 

and Figure 20). For the Strait of Juan de Fuca population, the average over the early period of 

1974–1978, when the diversity was relative constant, is 63% for the Simpson index and 1.05 for 

the Shannon index. The diversity index, which reflects spatial distribution and relative 

abundance, declined during the 1980s and 1990s and now shows a recent increase. A good 

initial target level for viability would be the early year (1974–1978) average, as we know this is 

attainable by this population. The maximum values for a population with five components, all 

being equally abundant, are 80% and 1.61, respectively for the Simpson and Shannon indices. 

The averages for recent years (2001–2005), where an increase is observed, are 45% and 0.86, 

respectively, and appear to indicate a trajectory towards the earlier levels (Figure 19). As returns 

to Chimacum Creek and Dungeness River increase, diversity can surpass that of the earlier years 

when returns to these two systems were low to none. 

For the Hood Canal population, relatively high stable indices of diversity extended from 

1974 through 1985. Starting with data from 1974, the average over this relatively stable period 

is 80% for the Simpson index and 1.86 for the Shannon index. The diversity values declined 

during the 1980s and 1990s and show recent increases since about 2000. Again, a good initial 

target level for viability would be the early year (1968–1985) average. The averages for recent 

years (2001–2005), where an increase is observed, are 74% and 1.6, respectively, and appear to 

indicate a trajectory towards the early period averages. The maximum values for a population 

with 12 components, all being equally abundant, are 92% and 2.48, respectively for the Simpson 

and Shannon indices. 

ESU Viability 

The ESU viability criterion for the Hood Canal Summer Chum Salmon ESU is relatively simple: 

in order for the ESU to have a negligible risk of extinction, both the Strait of Juan de Fuca and 

Hood Canal populations need to be viable. Summer chum salmon exist today in each of these 

two historical populations in several spawning aggregations located in various rivers and creeks 

(Identification of Independent Populations section, Figure 8). 

44
Salmon

Snow

1974–1978

Jimmycomelately

 

Salmon

Chimacum

Snow

2001–2005

Jimmycomelately

Figure 17. Distribution of escapement for spawning aggregations within the Strait of Juan de Fuca 

summer chum salmon population for 1974–1978 and 2001–2005. The pie sections represent the 

average annual percentage of total population escapement represented by each spawning 

aggregation over the given time period. The Simpson Index associated with 1974–1978 is 0.63 

compared with 0.45 for 2001–2005. The comparable Shannon indices are 1.05 and 0.86 as of 20 

December 2006. 

Skokomish

Tahuya 

Dewatto

Lilliwaup

Hamma 

Hamma

Duckabush

Dosewallips

Anderson

Big Beef 

B. Quilcene

L. Quilcene Union

1974–1978

Skokomish

Tahuya 

Lilliwaup

Dewatto

Hamma 

Hamma

Duckabush Dosewallips Anderson

Big Beef 

B. Quilcene

L. Quilcene

Union

2001–2005

Figure 18. Distribution of escapement for spawning aggregations within the Hood Canal summer chum 

salmon population for 1974–1978 and 2001–2005. The pie sections represent the average annual 

percentage of total population escapement represented by each spawning aggregation over the 

given time period. The Simpson Index associated with 1974–1978 is 0.80 compared with 0.74 

for 2001–2005. The comparable Shannon indices are 1.86 and 1.60 as of 29 December 2006. 

Discussion and Recommendations 

Abundance and Productivity 

The range in viable population spawning abundances as determined from SimSalmon represents 

the range of the risk of extinction from 5 to 1 percent and gives a single escapement value 

assuming no density dependence in recruits per spawner, although the method of predicting 

returns does incorporate a maximum return. The VRAP method incorporates density 

dependence, assumes a function with productivity that changes with spawning level, and has an 

45
0%

10%

20%

30%

40%

50%

60%

70%

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Simpson Shannon

Simpson

Shannon

Figure 19. Diversity indices for the Strait of Juan de Fuca summer chum salmon population, 1974–2005. 

The indices are estimated over the five spawning aggregations in this population. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

0.0

0.5

1.0

1.5

2.0

2.5

Simpson Shannon

Simpson

Shannon

Figure 20. Diversity indices for the Hood Canal summer chum salmon population, 1974–2005. The 

indices are estimated over the 12 spawning aggregations in this population. 

average maximum recruit value (i.e., capacity). This second approach allows us to determine the 

productivity and capacity needed for different levels of harvest. The viable escapement level as 

estimated using VRAP is dependent on the population achieving the associated intrinsic 

productivity and capacity parameters of the viable spawner-recruit curve. 

The SimSalmon viability goals are simply escapement levels for a given level of risk and 

do not address the question of how much harvest the population can sustain and remain viable. 

Also, because the SimSalmon method does not incorporate density dependence, abundance 

levels resulting from this approach are generally higher than those resulting from models that 

46
assume that productivity increases as abundance decreases. This difference is apparent when 

comparing the SimSalmon and VRAP results (Table 9). 

Another method of examining viable escapement levels is to estimate population capacity 

and productivity under habitat conditions defined as properly functioning by NMFS (1996). This 

method was used for Puget Sound Chinook salmon populations using ecosystem diagnosis and 

treatment (EDT) (Rawson et al. in prep.). The Hood Canal Coordinating Council and the 

comanagers are conducting EDT analyses for stocks within the Hood Canal Summer Chum 

Salmon ESU; they are expected to be available in late 2009.3

Spatial Structure and Diversity 

The relative total abundance of spawning by the component spawning aggregations has 

changed over time for both populations, with one spawning aggregation in each population now 

contributing most of the total abundance (Figure 17 and Figure 18). This has reduced the 

diversity index for both populations, although the declines in the diversity measure seen in the 

late 1980s and 1990s have been reversed since around 2000 (Figure 19 and Figure 20). As 

formally extinct stocks are being reintroduced through hatchery supplementation programs, both 

populations have the opportunity to increase the number of spawning aggregations and, with 

time, increase the relative contribution of currently low abundance spawning aggregations. Both 

of these events would increase the diversity index and, by improving spatial structure, allow for a 

fuller expression of life history diversity types. 

The subpopulation structure of summer chum salmon has important implications for 

designing successful strategies to recover the spatial structure and diversity attributes necessary 

for viable salmon populations. Efforts to rebuild sustainable populations by recovering habitat 

and reintroducing summer chum salmon to tributaries where they have been extirpated will be 

most successful if they focus on streams with major spawning aggregations, which can act as 

core natural production areas, and the smaller tributaries that are needed to reestablish the 

connections among these larger groups. 

Although we found little information on adaptive differences among stocks across the 

range of summer chum differences that might guide reintroduction efforts, our analyses of spatial 

patterns of neutral genetic variation suggest that sources of broodstock for reintroductions should 

come from nearby spawning aggregations, as previously described in the Summer Chum Salmon 

Conservation Initiative (WDFW and PNPTT 2000) and implemented for the three reintroduction 

programs in the region. This would simulate the natural patterns of gene flow in summer chum 

salmon that we believe are most likely to promote successful reestablishment. 

Recommendations 

Recommendation 1: Abundance and productivity 

A viable population of summer chum salmon in the Strait of Juan de Fuca population has 

12,500 spawners, assuming a 1:1 replacement rate and density-independent dynamics at low 

population sizes. Spawner escapement numbers for a viable Strait of Juan de Fuca population 

 

3

 L. Lestelle, Biostream Environmental, Poulsbo, WA. Pers. commun., 20 August 2009. 

47
could be as low as 4,500 adults if we can assume that the population is driven by densitydependent dynamics and the intrinsic α and β parameters of the population’s viable spawnerrecruit curve can be estimated and achieved (i.e., for escapement = 4,500, then α = 5 and β = 

3,300). Similarly, a viable population of summer chum in the Hood Canal population has 24,700 

spawners, assuming a 1:1 replacement rate and density-independent dynamics at low population 

sizes. Spawner escapement numbers for a viable Hood Canal population could be as low as 

18,300 adults if we can assume that the population is driven by density-dependent dynamics and 

the corresponding intrinsic α and β parameters of the population’s viable spawner-recruit curve 

can be estimated and achieved (i.e., for escapement = 18,300, then α = 5 and β = 13,500). 

Estimates of spawner escapement consistent with viable summer chum populations under 

different assumptions of intrinsic productivity, capacity, and persistence probability are 

presented in Table 5 and Tables 7–9. 

Before the population achieves its viable state (where the population abundance is stable, 

or λ = 1), a useful benchmark for tracking progress in recovery is for the population growth rate 

for spawners (λ) to be greater than 1. 

Rationale: A population will have a low risk of extinction if it has sufficient abundance 

and productivity to withstand the natural variability in returns due to environmental and 

anthropogenic factors. Viability estimates are based on natural variation observed in returns per 

spawner or population growth rate, and specifying in a population projection that the population 

not go below a quasi-extinction level more than a small proportion of the time. The relationship 

between abundance and productivity for a particular population can be represented as a curve 

along which productivity required for viability decreases as abundance increases. 

In contrast, the higher viable abundance numbers that we present assume density 

independence and relatively conservative population conditions; that is, the productivity is at a 

level where one spawner produces only one adult fish in the subsequent generation (the 

population is just replacing itself) and there are no positive density-dependent effects at low 

population sizes. On the other hand, the lower viable escapement numbers we present are 

possible if we know more about the population dynamics and can assume density dependence; 

that is, if the population realized productivity is greater than replacement, the capacity of the 

population is known, and there are positive impacts of density dependence at low population 

sizes. Under conditions of higher productivity, the resilience of the population to environmental 

changes is increased, and fewer spawners than the equilibrium level are required to assure the 

viability of the population. 

Uncertainty: Significant scientific uncertainty exists in our ability to describe the 

characteristics of a viable summer chum salmon population. This uncertainty results from 1) our 

limited understanding of the interacting factors controlling population dynamics, 2) the quality 

and quantity of data available, and 3) our inability to predict the environmental and 

anthropogenic conditions that will affect each population in the future. The structure and 

parameters included in the viability models used by the PSTRT may result in underestimates or 

overestimates of the abundance and productivity criteria. Qualitatively evaluating the net bias of 

the models is difficult due to the large number of confounding factors that must be considered. 

Ecological interactions, the spatial distribution of the population, life history diversity, and 

variance of the estimate of the variability in the population growth rate were not incorporated 

48
into the analyses and are likely to result in underestimates in the abundance criteria. Therefore, 

depending on the biological conditions in an individual population, the accuracy of the estimated 

probability of persistence for a population at a given level of abundance is uncertain. 

A key uncertainty in the recovery abundance criteria for the Strait of Juan de Fuca 

population focuses on the role of the Dungeness River spawning aggregation. Although 

currently summer chum salmon are observed in small numbers in the Dungeness River, the 

historical size of this spawning aggregation is unknown. Likewise, although spawning in the 

Dungeness River is important to maintain the spatial distribution and diversity of the population, 

the potential contribution of summer chum spawning to abundance under recovered habitat 

conditions is unknown. This has important implications in determining recovery criteria for 

abundance for the Strait of Juan de Fuca population as shown by the different results of our PVA 

modeling. 

Given the uncertainty, we recommend using abundance criteria assuming that the 

Dungeness River would contain one of the major spawning aggregations for this population 

because it minimizes the likelihood that, at the recommended abundance levels, the population 

would go extinct. In keeping with the approach we used for other ESUs, such as Puget Sound 

Chinook salmon and Lake Ozette sockeye salmon (O. Nerka), we also recommend that when 

analyses of potential habitat capacity and information about the response of the Strait of Juan de 

Fuca population—including the Dungeness River—recovery actions become available, they be 

used to verify the recommended abundance criteria. For populations in other ESUs, we have 

used this information as part of a series of decision rules to moderate against setting abundance 

criteria unrealistically high. 

As with all viability goals for abundance and productivity, those presented in this 

document are based on observed variability in the returns of salmon related to the escapement. 

This estimate of variability will likely change over time as environmental conditions change and 

recovery actions are taken. As such, viability goals should be reevaluated on a regular basis. 

New analyses and methods should also be incorporated as they become available. 

Recommendation 2: Spatial structure 

A viable population contains multiple persistent spawning aggregations. The number of 

persistent aggregations needed for viability depends on the historical biological characteristics of 

the population and the historical distribution of spawning aggregations of the population. A 

population that meets the criteria below is likely to have a negligible risk of extinction over a 

100-year period (i.e., be viable): 

• Spawning aggregations are distributed across the historical range of the population. 

• Most spawning aggregations are within 20 km of adjacent aggregations. 

• Major spawning aggregations (spawning aggregations in rivers and creeks that have 

historically provided the most persistent habitat) are distributed across the historical 

range of the population and are not more than approximately 40 km apart. 

Rationale: A population with well-distributed spawning aggregations is less likely to go 

extinct than one with a severely limited distribution. Within populations that have a considerable 

49
range of distances between the most widely separated aggregations (such as those in the Hood 

Canal Summer Chum Salmon ESU), well-distributed spawning aggregations can “spread the 

risk” from disturbances, lending the population more resistance to severe local disturbances and 

catastrophes. Furthermore, should an aggregation suffer decline or extirpation, nearby 

aggregations can act as sources for rescue or recolonization, provided they are not too distant. In 

particular, major spawning aggregations in relative proximity throughout the population help to 

further maintain a balance between the benefits of connectivity and broad distribution. Finally, 

geographically well-distributed aggregations also provide important ecological contributions to 

freshwater and marine ecosystems. 

Although it may not be necessary to reestablish spawning aggregations in all rivers and 

streams where they historically occurred, meeting spatial structure population viability criteria in 

the Hood Canal and Strait of Juan de Fuca populations will require reestablishing spawning 

aggregations in many of the major rivers and the smaller streams and creeks where they have 

been extirpated. The loss of these intermediate stepping-stone spawning aggregations can 

increase the demographic isolation of subpopulations, which increases the likelihood of these 

subpopulations becoming extinct, and which in turn reduces the likelihood of population and 

ESU persistence. 

Based on the genetic analyses and historical distribution of summer chum salmon in the 

ESU, our analyses suggest that most spawning aggregations should be within 20 km of each 

other and that major spawning aggregations (e.g., in larger river systems) should not be separated 

by more than approximately 40 km. Particularly in the early stages of population and ESU 

recovery, production of summer chum from smaller streams may provide important contributions 

to the health of freshwater, estuarine, and marine ecosystems and to the maintenance of the 

viability of the populations. The presence of salmon in these habitats can provide stopgaps for 

ecological services and may provide temporary but significant cumulative production while 

degraded habitats in other rivers and creeks recover. 

The loss of the Skokomish River, the largest tributary to the geographic region, to the 

collection of spawning aggregations in the southwestern area of the Hood Canal catchment may 

represent a loss of both major historical ecological diversity and spatial connectivity to the 

remainder of the ESU. 

Recommendation 3: Diversity 

Depending on the geographic extent and ecological context of the population, a viable 

population includes one or more persistent spawning aggregations from each of the two to four 

major ecological diversity groups historically present within the two populations. 

Rationale: This recommendation is meant to help maintain the genetic and phenotypic 

diversity of the population. For any population, maintaining genetic and phenotypic diversity is 

critical for responding to future environmental variability and change. Such diversity also allows 

the population to occupy, and thus potentially adapt to, a wider range of environmental 

conditions and to colonize or recolonize newly available habitats. The conservation of this 

diversity is particularly important for recovery of a population that occupies a large area of 

diverse habitats as do the populations within the Hood Canal Summer Chum Salmon ESU. 

50
Maintaining and reestablishing functional subpopulations across the range of ecological diversity 

historically experienced by summer chum salmon may help promote the above diversity. 

Therefore, a viable distribution of spawning aggregations will contain persistent spawning 

aggregations within each of the major ecological diversity groups described below (see Table 2 

and Figure 8). We specifically exclude the Toandos group from this criterion until we have 

evidence that spawning aggregations within the unit may significantly contribute to ESU spatial 

structure. 

The spawning and early rearing range of the Strait of Juan de Fuca population is 

comprised of two ecological diversity groups with historical spawning aggregations in rivers and 

streams entering the Eastern Strait of Juan de Fuca Geographic Region of Puget Sound. The 

Dungeness unit has headwaters in the High Olympics, Low Olympics, and Coast Range 

Volcanics ecoregions and lower reaches in the Olympic Rainshadow ecoregion and constitutes 

the first ecological diversity group within the Eastern Strait of Juan de Fuca Geographic Region. 

The second unit is comprised mostly of the Sequim Bay, Discovery Bay, and Admiralty Inlet 

watersheds, which are all fully contained within the Olympic Rainshadow ecoregion. It is 

referred to as the Sequim-Admiralty ecological diversity group. Those West Kitsap watersheds 

that drain into Admiralty Inlet are also part of this second unit (Figure 8); they are all fully 

contained within the Central Puget Sound Lowlands ecoregion. 

Similarly, the early life history range of the Hood Canal population is comprised of five 

ecological diversity groups, four of which contain historical spawning in rivers and streams 

entering the Hood Canal Geographic Region of Puget Sound. The watersheds within the 

Quilcene unit have headwaters in the High Olympics, Low Olympics, and Coast Range 

Volcanics ecoregions and lower reaches in the Olympic Rainshadow ecoregion. The watersheds 

within the Mid West Hood Canal unit are fully contained within the High Olympics, Low 

Olympics, and Coast Range Volcanics ecoregions. The watersheds within the West Kitsap unit 

are fully contained within the Central Puget Sound Lowlands ecoregion. The watersheds within 

the Lower West Hood Canal unit have headwaters in the High Olympics, Low Olympics, and 

Coast Range Volcanics ecoregions and lower reaches in the Central Puget Sound Lowlands 

ecoregion. The Toandos unit has watersheds fully contained within the Olympic Rainshadow 

ecoregion. See Table 2 for a listing of the ecological composition of each stratum and the 

associated historical and extant spawning aggregations of summer chum salmon each contains. 

Some uncertainty remains regarding the appropriate geographic scale for delineation of 

ecological diversity as it relates to biological diversity of the ESU and the population units, 

particularly understanding the significance of marine biogeographic and ecoregional diversity to 

summer chum diversity. Regardless of the way ecological diversity is divided, the loss of 

spawning aggregations within three or four of the major ecological diversity groups demonstrates 

losses in spatial connectivity and distribution and the potential for selective differences within 

the ESU. 

Recommendation 4: Viable population 

A viable population has spawning, rearing, and migratory habitats that function in a 

manner that is consistent with population persistence. 

51
52

Rationale: In addition to providing spawning and freshwater rearing habitat, the 

biological characteristics and distribution of summer chum salmon suggest that rivers, streams, 

estuaries, and nearshore habitats within the Hood Canal Summer Chum Salmon ESU can be 

important for viability in other ways. 

• Conditions in freshwater tributaries will affect the estuarine and nearshore environments 

into which they empty. Poor water quality and other habitat degradation can create 

inhospitable or stressful local conditions for summer chum salmon utilizing the estuaries 

and nearshore. 

• Estuarine habitats associated with both spawning and nonspawning tributaries act as 

stepping-stone habitats for early marine outmigrants and returning migrants, thus 

potentially affecting the probability of successful dispersal and recolonization. 

Recommendation 5: ESU viability 

The Hood Canal Summer Chum Salmon ESU would have a negligible risk of extinction 

if both of the historical populations of summer chum achieve a low risk (i.e., viable) status. 

Rationale: Criteria for a viable ESU focus on minimizing risk and maximizing resiliency 

of the ESU to catastrophic events or to environmental changes that occur too rapidly for 

population adaptation. The VSP document (McElhany et al. 2000) suggests using historical 

patterns of population abundance, distribution, and diversity as a reference against which to 

evaluate ESU viability, since an historical ESU was very likely viable. The aim of the ESU 

viability guidelines is to describe the viability characteristics of the component populations that 

are necessary to ensure a high probability of ESU persistence. “Viable” in this sense refers to a 

naturally self-sustaining population that has a negligible risk of extinction over a 100-year time 

frame. 
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