NOAA Technical Memorandum NMFS-NWFSC-91

Biological Recovery Criteria

for the Oregon Coast Coho Salmon

Evolutionarily Significant Unit

May 2008

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
NOAA Technical Memorandum

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Service, NOAA.

This document should be referenced as follows:

Wainwright, T.C., M.W. Chilcote, P.W. Lawson, T.E.

Nickelson, C.W. Huntington, J.S. Mills, K.M.S. Moore, 

G.H. Reeves, H.A. Stout, and L.A. Weitkamp. 2008. Biological recovery criteria for the Oregon Coast coho salmon 

evolutionarily significant unit. U.S. Dept. Commer., 

NOAA Tech. Memo. NMFS-NWFSC-91, 199 p.
NOAA Technical Memorandum NMFS-NWFSC-91

Biological Recovery Criteria

for the Oregon Coast Coho Salmon

Evolutionarily Significant Unit

Thomas C. Wainwright, Mark W. Chilcote,1 Peter W. Lawson,

Thomas E. Nickelson,2

 Charles W. Huntington,3 Justin S. Mills,4

Kelly M.S. Moore,5

 Gordon H. Reeves,6 Heather A. Stout, and 

Laurie A. Weitkamp

Northwest Fisheries Science Center

Hatfield Marine Science Center

2032 Southeast OSU Drive

Newport, Oregon 97365

1 Oregon Department of Fish and Wildlife

3406 Cherry Avenue Northeast

Salem, Oregon 97303

2 75 Alydar Place

Paso Robles, California 93446

3 Clearwater Biostudies Inc.

23252 South Central Point Road

Canby, Oregon 97013

4 Frank Orth & Associates

4040 Lake Washington Boulevard Northeast, Suite 208

Kirkland, Washington 98033

5 Oregon Department of Fish and Wildlife

Corvallis Research Laboratory

28655 Highway 34

Corvallis, Oregon 97333

6 U.S. Forest Service

Pacific Northwest Research Station, Forestry Sciences Laboratory

3200 Southwest Jefferson Way

Corvallis, Oregon 97331

May 2008

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
ii

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Table of Contents 

List of Figures.............................................................................................................................................vii

List of Tables ...............................................................................................................................................ix

Executive Summary .....................................................................................................................................xi

Acknowledgments......................................................................................................................................xxi

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

Definition of Key Terms ..........................................................................................................................2

Population Structure and Associated Definitions.....................................................................................4

Important Issues............................................................................................................................................8

Habitat ......................................................................................................................................................8

Hatchery Fish............................................................................................................................................9

Environmental Fluctuations....................................................................................................................12

Density Dependence...............................................................................................................................13

Uncertainty .............................................................................................................................................14

Population Classification and Biogeographic Strata ..............................................................................15

Relationship to Criteria for other ESUs..................................................................................................15

Relationship of Biological Criteria to the ESA ......................................................................................16

Approach to Developing the Criteria..........................................................................................................18

Watershed-level Attributes.....................................................................................................................18

Population-level Attributes.....................................................................................................................18

Biogeographic Stratum-level Attributes.................................................................................................19

ESU-level Attributes ..............................................................................................................................19

Goals and Criteria...................................................................................................................................20

Use of a Decision Support System .........................................................................................................20

Introduction to the Criteria .....................................................................................................................26

Persistence ..................................................................................................................................................28

Population-level Persistence Criteria......................................................................................................28

Biogeographic Stratum-level Persistence Criteria..................................................................................41

ESU-level Persistence Criteria ...............................................................................................................41

Summary—Persistence...........................................................................................................................42

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Sustainability...............................................................................................................................................44

Population-level Sustainability Criteria..................................................................................................44

Biogeographic Stratum-level Criteria.....................................................................................................62

ESU-level Sustainability Criteria ...........................................................................................................65

Summary—Sustainability.......................................................................................................................80

Restoration ..................................................................................................................................................82

Environmental, Cultural, and Economic Benefits ..................................................................................82

Ecosystem Approach ..............................................................................................................................83

Suggested Criteria ..................................................................................................................................84

Summary—Restoration ..........................................................................................................................87

Conclusions.................................................................................................................................................89

Persistence ..............................................................................................................................................89

Sustainability ..........................................................................................................................................90

Restoration..............................................................................................................................................90

Limitations..............................................................................................................................................91

Glossary ......................................................................................................................................................93

References.................................................................................................................................................101

Appendix A: Use of a Knowledge-based DSS in Determining the Recovery Status of Oregon Coast 

Coho Salmon.............................................................................................................................................115

Introduction ..........................................................................................................................................115

Utilizing a DSS.....................................................................................................................................115

Implementation of the DSS ..................................................................................................................116

Network Development..........................................................................................................................117

Sensitivity Analysis..............................................................................................................................118

Data Testing..........................................................................................................................................120

Guidance for Interpreting Results.........................................................................................................123

Software and Script Description...........................................................................................................124

Appendix B: Genetic Effective Population Size for Oregon Coast Coho Salmon ...................................135

Introduction ..........................................................................................................................................135

Genetic Concepts..................................................................................................................................135

Ideal vs. Real Populations ....................................................................................................................136

Translating Effective Population Size to Spawner Counts...................................................................138

Spawner Abundance Criterion..............................................................................................................139

Appendix C: Population Persistence Models............................................................................................141

Introduction ..........................................................................................................................................141

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Density-dependent, Count-based PVA Model .....................................................................................146

SPAZ Hockey Stick Viability Analysis................................................................................................157

Bayesian Salmon Analysis Model........................................................................................................159

Stochastic Habitat-based Life Cycle Model .........................................................................................163

Appendix D: Population Abundance Data and Historical Reconstructions..............................................181

Contemporary Population Data for 1990–2004....................................................................................181

Reconstruction of Historical Population Abundance for 1958–1989...................................................190

v
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List of Figures 

Figure 1. Biogeographic strata and Independent Populations of the Oregon Coast Coho Salmon ESU. ....6 

Figure 2. Conceptual view of relationships of the four VSP parameters to habitat.....................................9 

Figure 3. Relationship between population productivity and average proportion of hatchery spawners 

in the population during 1990–2001. ..........................................................................................................11 

Figure 4. Relationship between biological status, other listing considerations, and listing decisions 

under the ESA.............................................................................................................................................17 

Figure 5. Overview of goals and criteria....................................................................................................21 

Figure 6. Terminology used in describing truth values resulting from application of the DSS.................22 

Figure 7. The relationship of DSS truth values to cost of restoration and risk of no action. .....................24 

Figure 8. Schematic of the conceptual relationships of population, strata, and ESU criteria in the DSS..27 

Figure 9. The decision network for the ESU Persistence Criterion ...........................................................29 

Figure 10. Truth membership function for the Population Productivity Criterion, PP-1...........................30 

Figure 11. Truth membership function for the Probability of Persistence Criterion, PP-2........................34 

Figure 12. Truth membership function for the Critical Abundance Criterion PP-3...................................38 

Figure 13. Persistence analysis results.......................................................................................................43 

Figure 14. The decision network for the ESU Sustainability Criterion .....................................................45 

Figure 15. Truth membership function for the Spawner Abundance Criterion, PD-1...............................46 

Figure 16. Population Sustainability Criterion analysis results. ................................................................48 

Figure 17. Truth membership function for the Artificial Influence Criterion, PD-2 .................................51 

Figure 18. Truth membership function for Criteria W-Sp, Spawner Watershed Occupancy, and W-Ju, 

Juvenile Watershed Occupancy. .................................................................................................................52 

Figure 19. Subsection of the decision network showing the Population Diversity Criteria. .....................57 

Figure 20. Truth membership function for the Population Functionality Criterion, PF. ...........................60 

Figure 21. Map showing estimated smolt-capacity truth values by population for the Population 

Functionality Criterion, PF .........................................................................................................................61 

Figure 22. Maps showing truth value scores for the Stratum Diversity, Stratum Functionality, and 

Stratum Sustainability criteria.....................................................................................................................63 

Figure 23. The Stratum Sustainability Criterion subsection of the ESU Sustainability Criterion 

decision network .........................................................................................................................................64 

Figure 24. ES-1, All Biogeographic Strata Sustainable, subsection of the ESU Sustainability Criterion 

decision network. ........................................................................................................................................65 

Figure 25. Truth membership function for ESU-level Diversity Criteria. .................................................66 

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Figure 26. ESU-level Diversity Criterion subsection of the ESU Sustainability Criterion decision 

network. .... .................................................................................................................................................78 

Figure 27. Hypothetical relative value of spawning escapement in terms of harvest, stream nutrient 

supply, and more general ecosystem benefits.............................................................................................85 

Figure 28. Conceptual illustration of the proportion of time a system is occupied as a function of the 

potential size of the population in that system............................................................................................88 

Figure A-1. A fuzzy membership function. .............................................................................................117 

Figure A-2. DSS network diagram. .........................................................................................................119 

Figure A-3. Response of the ESU-level ESU Persistence Criterion truth value to changes in the 

Probability of Persistence Criterion input data. ........................................................................................120 

Figure A-4. Response of the ESU-level ESU Sustainability Criterion truth value to changes in the 

Probability of Persistence Criterion input data. ........................................................................................121 

Figure C-1. Comparison of production relationships for the various models, all scaled to the same 

maximum production and rate of intrinsic increase..................................................................................143 

Figure C-2. Time series of naturally produced spawner abundance for the 21 Independent 

Populations ...............................................................................................................................................145 

Figure C-3. Generic example of Ricker recruitment curve and the modified version of this curve 

used to simulate recruitment in the PVA. .................................................................................................148 

Figure C-4. Hockey stick recruitment model...........................................................................................157 

Figure C-5. The production function used in the extinction risk model compared with a standard 

Beverton-Holt production function...........................................................................................................161 

Figure C-6. Results from the BaySAM model for all Independent Populations in the ESU ...................164 

Figure C-7. Relationship of extinction risk to quasi-extinction threshold for the Independent 

Populations................................................................................................................................................165 

Figure C-8. Box-plot time series of median spawner abundance for 34 generations ..............................173 

Figure C-9. Box-plot comparison of 12 model scenarios. .......................................................................176 

Figure C-10. Sensitivity of the base_ref scenario to assumption of quasi-extinction thresholds. ...........177 

Figure C-11. Sensitivity of the A13_ms_80 scenario to assumption of quasi-extinction thresholds ......178 

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List of Tables 

Table 1. Classification of Oregon Coast Coho Salmon ESU historical populations ...................................5 

Table 2. Population-level persistence criteria for Independent Populations of Oregon Coast coho 

salmon.........................................................................................................................................................31 

Table 3. Summary of DSS truth values for all populations, biogeographic strata, and ESU criteria ........32 

Table 4. Population Sustainability criteria results for Independent Populations of Oregon Coast coho 

salmon.........................................................................................................................................................47 

Table 5. Occupancy of watersheds by spawners and juveniles. ................................................................54 

Table 6. Summary of watershed level truth values of the knowledge-based DSS as to the relative 

distribution of coho salmon in the Oregon Coast ESU...............................................................................55 

Table 7. Summary of truth values for the ESU-level Diversity Criterion analysis broken down by 

subcriteria for Genetic Diversity, ED-1, and Phenotypic and Habitat Diversity, ED-2..............................67 

Table 8. Distribution of points by review panel members for the Genetic Structure Criterion, ED-1a.....69 

Table 9. Distribution of points by review panel members for the Effects of Selection Criterion, 

ED-1b........ .................................................................................................................................................69 

Table 10. Distribution of points by review panel members for the Effects of Migration Criterion, 

ED-1c ........ .................................................................................................................................................70 

Table 11. Distribution of points by review panel members for the Effects of Introgression, ED-1d ........72 

Table 12. Distribution of points by review panel members for the Phenotypic Diversity Criterion, 

ED-2a ........ .................................................................................................................................................74 

Table 13. Distribution of points by review panel members for the Habitat Diversity Criterion, ED-2b...76 

Table 14. Distribution of points by review panel members for the Small Populations Criterion, ED-3 ...77 

Table A-1. Operators used in the DSS.....................................................................................................118 

Table A-2. Results of model exclusion tests............................................................................................122 

Table A-3. DSS results for different levels of adult coho at the threshold for Independent 

Populations................................................................................................................................................124 

Table C-1. Comparison of the PVA models used....................................................................................142 

Table C-2. Estimated density-dependent PVA recruitment model parameters and associated 

information for 21 populations of Oregon Coast coho salmon.................................................................151 

Table C-3. Forecast probability from the DD-PVA model......................................................................154

Table C-4. Probability of extinction estimates for 21 populations of Oregon Coast coho salmon 

under different parameter error scenarios .................................................................................................156 

Table C-5. Relative Akaike Information Criterion for 18 Independent Populations of Oregon Coast 

ESU coho salmon......................................................................................................................................159 

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Table C-6. Probability of persistence of coho salmon populations estimated by the SPAZ-hs 

recruitment model .....................................................................................................................................160 

Table C-7. Probabilities of persistence under no harvest conditions for riverine Independent 

Populations of the Oregon Coast Coho Salmon ESU for five scenarios of future conditions using 

three quasi-extinction thresholds ..............................................................................................................174 

Table C-8. Probabilities of persistence under PFMC Amendment 13 harvest conditions for riverine 

Independent Populations of the Oregon Coast Coho Salmon ESU for five scenarios of future 

conditions using three quasi-extinction thresholds. ..................................................................................175 

Table D-1. Population estimates for Independent Populations in the Oregon Coast Coho Salmon 

ESU based on random spawning surveys. ................................................................................................182 

Table D-2. Regression equations used.....................................................................................................191 

Table D-3. Estimated abundance of adult fish with or without harvested numbers of Independent 

Populations of the Oregon Coast Coho Salmon ESU, 1958–1989 ...........................................................192

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Executive Summary 

Introduction 

This document outlines biological recovery criteria (also called viability criteria) for the 

Oregon Coast Coho Salmon Evolutionarily Significant Unit (ESU) as identified in the NOAA 

Fisheries Service status review for West Coast coho salmon (Oncorhynchus kisutch) (Weitkamp 

et al. 1995). This report was developed by the Oregon Coast Workgroup (“the workgroup” or 

“we”) of the Oregon and Northern California Coasts (ONCC) Technical Recovery Team (TRT). 

The criteria were developed as part of a joint federal and state effort to develop 

conservation goals for the ESU, and are intended to satisfy both federal and state conservation 

mandates. In developing these criteria, the workgroup reviewed many documents, including 

NOAA’s viable salmonid populations (VSP) report (McElhany et al. 2000), the draft reports of 

several other TRTs, the summary of the Oregon Scientific Workshop on Recovery Criteria (OSL 

2002a), and the summary report of the Oregon Joint Interim Task Force on Salmon Recovery 

(OSL 2002b). In addition, the workgroup has had substantial interactions with staff of the 

Oregon Department of Fish and Wildlife during its development of viability criteria for the 

state’s Oregon Coastal Coho Assessment (Chilcote et al. 2005), and considered reviews of that 

report by the Independent Multidisciplinary Science Team (IMST 2005) and Northwest Fisheries 

Science Center (NWFSC 2005). We also referred to guidance on ESU-level recovery 

approaches from the Independent Science Advisory Board (Bilby et al. 2005). 

The primary purpose of this report is to provide objective and measurable biological 

criteria for assessing progress toward recovery of the Oregon Coast Coho Salmon ESU. These 

criteria are also applied to an assessment of the current biological status of the ESU. Complete 

criteria are not provided for listing or delisting of the ESU under the U.S. Endangered Species 

Act (ESA), nor is there an evaluation as to whether the ESU should or should not be listed. In 

addition to evaluating the biological requirements presented here, listing and delisting decisions 

require evaluations regarding particular listing factors and conservation measures, which are 

beyond the scope of this report. Thus the biological criteria presented here provide a necessary, 

but not sufficient, set of criteria to determine the ESA listing status of the ESU. 

Key Terms 

There are a number of legal and technical concepts that came into play in developing the 

criteria, in particular the concepts of recovery and restoration. The term “recovery” has a 

number of meanings in common English, but a very narrow meaning within the context of the 

ESA. Because our criteria are intended to apply to broader mandates than the ESA, it is 

necessary to distinguish the ESA’s narrow-sense usage of “recovery” from the broader-sense 

usage by the State of Oregon in its conservation planning efforts. For this reason, the term 

xi
recovery is generally avoided in favor of the terms “restoration” and “delisting” as defined 

below. 

Restoration (broad-sense recovery) is a process leading to conditions such that the 

populations of naturally produced fish that make up the Oregon Coast Coho Salmon ESU are 

sufficiently abundant, productive, and diverse in terms of life histories and biogeographic 

distribution, and that the ESU as a whole will be self-sustaining and will provide environmental, 

cultural, and economic benefits (modified from Oregon Salmon Recovery Task Force, OSL 

2002b). 

Within this definition, a self-sustaining ESU is 1) able to survive prolonged periods of 

adverse climatic conditions without artificial support (including supplementation by hatchery 

fish) and 2) able to maintain its genetic legacy and long-term adaptive potential. Such a selfsustaining ESU will be composed of diverse interconnected populations. Environmental, 

cultural, and economic benefits include but are not limited to: providing nutrients to freshwater 

and terrestrial ecosystems; providing food resources for scavengers such as bears, eagles, and 

stream invertebrates; supporting ceremonial use by Native Americans; allowing viewing and 

nature study by coastal residents and tourists; and providing subsistence, recreational, and 

commercial harvest. 

Within the narrow sense of the ESA, our concept of delisting (narrow-sense recovery) is 

similar to the “self-sustaining” part of the definition of restoration, but has no reference to 

environmental, cultural, and economic benefits (which are not part of the ESA mandate). 

Delisting, based on the statutory language in the ESA, is a process resulting in the ESU 

being removed from the formal protections of the ESA as a consequence of no longer being an 

endangered or threatened species. The ESA defines those specific terms as follows (note that the 

legal term “species” differs from the common biological meaning, and includes ESUs): 

• An endangered species is “any species which is in danger of extinction throughout all or 

a significant portion of its range.” 

• A threatened species is “any species which is likely to become an endangered species 

within the foreseeable future throughout all or a significant portion of its range.” 

In addition to the above terms, two specialized terms are used to relate biological risk to 

threatened and endangered status under the ESA, persistent and sustainable: 

• A persistent population (or ESU) is one that is able to persist (i.e., not go extinct) over a 

100-year period without artificial support. This includes an ability to survive prolonged 

periods of adverse environmental conditions, which may be expected to occur at least 

once in the 100-year time frame. 

• A sustainable population (or ESU) is one that, in addition to being persistent, is also able 

to maintain its genetic legacy and long-term adaptive potential for the foreseeable future. 

This implies the availability of functional habitat and other conditions necessary for the 

full expression of the population’s (or ESU’s) life history diversity into the foreseeable 

future. 

xii
The terms persistent and sustainable are both part of the more general term “viable” as 

used in the viable salmonid populations report (McElhany et al. 2000). We use these two terms 

to distinguish distinct types and levels of risk. Persistence relates to the simple risk (or danger) 

of extinction, which is the primary determinant of endangered status under the ESA. 

Sustainability goes beyond this, requiring that population diversity (genetic and ecological) be 

sustained so that risk of extinction will not increase in the foreseeable future, thus relating to 

threatened status under the ESA. Neither term implies an evaluation of specific threats or a 

consideration of conservation measures required for listing determinations under the ESA, so 

these terms cannot be directly equated with thresholds of ESA listing status. 

Approach to Developing the Criteria 

The development of criteria began with the VSP approach (McElhany et al. 2000), which 

identifies four key population parameters that influence the persistence of populations. These 

attributes are: 

• Population size, the abundance of all life stages of the species 

• Population growth rate (productivity), production over the entire life cycle 

• Spatial structure, distribution of fish among habitat areas and connectivity among those 

areas 

• Diversity, variation in traits (phenotypic and genetic) among individuals in a population 

and among populations in the ESU 

The goal of recovery and restoration is not merely to meet a set of artificial criteria, but 

rather to restore or repair ecological processes that lead to long-term sustainability of the 

resource. Our approach views the ESU as a complex structure with important processes 

operating at scales ranging from individual spawning grounds (demes) up to the entire ESU. 

Along this continuum, we have identified four scales as important for defining attributes related 

to biological processes that define ESU status: watersheds, populations, biogeographic strata, and 

the entire ESU. We developed criteria at each of these scales. 

Goals and Criteria 

Because the Oregon Coast Workgroup is part of a joint federal-state process, there are 

multiple goals for defining the biological status of the ESU. As discussed earlier, we used the 

term restoration to encompass goals of the state processes, which have requirements beyond 

simple sustainability. This definition is also expected to cover the requirements of the U.S. 

Sustainable Fisheries Act. In contrast, the term delisting is used in the narrow sense of recovery 

under the ESA, which itself has two considerations: Is the species endangered or is the species 

threatened? Only when it is neither endangered nor threatened can it be delisted. 

These considerations led to a three-level structure of goals and criteria. The ultimate goal 

is restoration of the ESU in the sense defined by the Oregon Plan and the Joint Legislative Task 

Force (OSL 2002b). On the way to reaching that goal, it is necessary to also ensure meeting the 

goals of the ESA. Thus we focused on assessing condition of the ESU at two levels—

sustainability and persistence—and considered criteria to define restoration. 

xiii
To achieve restoration, coho salmon populations within the ESU must be robust enough 

to meet societal desires for environmental, cultural, and economic benefits. Restoration 

therefore requires sufficient abundance of the ESU and diversity among populations. Achieving 

ESA goals requires biological sustainability into the foreseeable future (as well as the absence of 

threats, which are not discussed in this report). This level has a number of ESU-level criteria, 

including the number and distribution of sustainable populations and the preservation of ESUlevel diversity. Avoiding endangerment requires biological persistence, that is, assurance that all 

significant parts of the ESU have a high likelihood of persisting for 100 years. Achieving each 

level implicitly requires meeting all criteria for the lower levels. 

Use of a Decision Support System 

A complete assessment of the biological condition of the ESU is necessarily multifaceted, 

including a variety of interrelated criteria, with varying data quality. These criteria relate to 

biological processes at a variety of time and space scales, with processes varying from individual 

stream reaches to the entire range of the ESU. To track this large suite of data and criteria in a 

transparent and logically consistent framework, we used a knowledge-based decision support 

system (DSS). 

A DSS is a computer-based tool that can analyze and compare numerous pieces of data, 

producing results that assist managers in making a decision (Turban and Aronson 2001). These 

systems allow decision makers to perform complex evaluations quickly, present a consistent 

assessment that draws from a variety of data sources, and track large sets of information 

accurately, thus improving the choices made by decision makers without overriding human 

judgment (Rauscher 1999). A system evaluating ecological conditions may contain substantial 

uncertainty about the precise conditions that are optimal for the target organisms because of gaps 

in information and the lack of perfect knowledge about the interrelationships among relevant 

factors (Reynolds et al. 2000). 

The DSS designed by the workgroup uses a network framework to link criteria at a 

variety of scales and aggregate them from fine-scale watershed-level criteria, through 

population-level criteria and biogeographic stratum-level criteria, to criteria for the entire ESU. 

The links take the form of logical operators that define specific relationships among the input 

values. In traditional Boolean logic (which evaluates propositions as either absolutely true or 

false), criteria are knife-edged, meaning that the result of an operation has only one of two 

values: true or false. In traditional Boolean logic, when the input values are near to the knifeedge criteria, the result is very sensitive to small changes in the input. In the knowledge-based 

system used here, a type of approximate logic extends our ability to work with the imprecise 

knowledge available for the system being assessed. The advantage of using this approximate 

logic is that it allowed us to evaluate and express the degree of certainty in an outcome, ranging 

from certainly false through uncertain to certainly true. Being able to work with a gradation of 

levels of certainty and uncertainty enables decision makers to evaluate the degree of risk inherent 

in a decision based on the strength of the conclusion from the DSS. 

In practice, applying the DSS begins with evaluating a number of primary biological 

criteria that are defined in terms of logical (true/false) statements about biological processes 

essential to the persistence or sustainability of the ESU. Evaluating these primary criteria with 

xiv
respect to available observations results in a “truth value” in the range from –1 (false) to +1 

(true). Intermediate values between these extremes reflect the degree of certainty of the 

statement given available knowledge, with a value of zero indicating complete uncertainty about 

whether the statement is true or false. These primary criteria are then combined logically with 

other criteria at the same geographic scale and then combined across geographic scales to result 

in an evaluation of ESU-wide criteria. Thus the end result is an evaluation of the biological 

status of the ESU as a whole, with an indication of the degree of certainty of that evaluation. 

Application of Criteria 

Our biological recovery criteria are framed within the context of the DSS. At the highest 

level, the DSS is structured to represent the hierarchical population structure of the ESU. 

Populations are grouped into biogeographic strata, which in combination make up the ESU. 

Within this structure, the goals of ESU persistence and ESU sustainability are evaluated using 

sets of logically linked and measurable criteria. The third goal, ESU restoration, is discussed but 

is not analyzed within this structure. Criteria for ESU persistence and sustainability are each 

presented in a separate section, and are presented at three levels of geographic aggregation: 

populations, biogeographic strata, and the entire ESU. Within each section, we present criteria at 

the population level first, then describe how these are combined into higher-level criteria for 

biogeographic strata and for the ESU as a whole. 

In addition to defining criteria and metrics, we provide an assessment of the current status 

of the ESU by applying each metric to the data available through 2005. This analysis is provided 

for illustration only to demonstrate how the criteria might be used in the future to assess progress 

towards recovery and restoration. 

Persistence 

Persistence refers to a population or ESU that has a high expectation of persisting (i.e., 

not going extinct) over a 100-year period without artificial support. This includes the ability to 

survive prolonged periods of adverse environmental conditions that may be expected to occur at 

least once during the 100-year time frame. In the ESU Persistence Criterion DSS structure, the 

lowest level population criteria form the basis of the analysis, with aggregations to the 

biogeographic stratum and ESU levels. Three criteria—Population Productivity, Probability of 

Persistence, and Critical Abundance—are evaluated, then combined to evaluate the overall 

Persistence for each population. Evaluations for individual populations are combined to arrive at 

a biogeographic stratum status (Stratum Persistence Criterion), then these results are combined to 

arrive at an ESU status (ESU Persistence Criterion). The workgroup then used recent 

observations of these population metrics to evaluate how certain it is that the Oregon Coast Coho 

Salmon ESU is persistent under recent conditions. 

Sustainability 

Sustainability refers to a population or ESU that, in addition to being persistent, is also 

able to maintain its genetic legacy and long-term adaptive potential for the foreseeable future. 

Sustainability implies stability of habitat availability and other conditions necessary for the full 

expression of the population’s (or ESU’s) life history diversity into the foreseeable future. In the 

xv
ESU Sustainability Criterion DSS structure, population-level criteria form the lowest level of the 

analysis. At this level, there are two watershed-level subcriteria—Spawner Watershed 

Occupancy and Juvenile Watershed Occupancy—which relate to two Population Diversity 

criteria (a Spawner Distribution Criterion and a Juvenile Distribution Criterion) evaluating the 

distribution of fish within river basins. These, along with two other Population Diversity criteria 

(a Spawner Abundance Criterion and an Artificial Influence Criterion) are combined to evaluate 

the overall Population Diversity Criterion, which is combined with a Population Persistence 

Criterion (above) to assess a Population Sustainability Criterion. These results for each 

population are then aggregated into biogeographic strata to evaluate a Stratum Diversity 

Criterion. A Population Functionality Criterion is evaluated based on available habitat capacity, 

and these criteria are combined to evaluate a Stratum Functionality criterion. The Stratum 

Diversity and Stratum Functionality criteria are combined to evaluate stratum sustainability. 

Stratum Sustainability Criterion values are aggregated to an ESU-level criterion (All Strata 

Sustainable), which is finally combined with an evaluation of an ESU-level Diversity Criterion 

to arrive at the overall ESU Sustainability Criterion. 

This framework evaluates a Population Diversity Criterion using objective measures of 

spawner abundance, artificial influence, spawner and juvenile distribution, and habitat capacity. 

In addition, we evaluated an ESU-level Diversity Criterion that includes a Genetic Diversity 

Criterion (a function of genetic structure and effects of selection, migration, and introgression), a 

Phenotypic and Habitat Diversity Ceiterion, and a Small Populations Criterion. Fully 

quantifiable criteria are not available for the ESU-level diversity measures, so an expert panel 

was used to evaluate them but the result was given half the weight of the more quantifiable 

population-level evaluations. The workgroup then used recent observations of these population 

metrics to evaluate how certain it was that the Oregon Coast Coho Salmon ESU is sustainable 

under recent conditions. 

Restoration 

ESU restoration (or broad-sense recovery) is a process leading to conditions such that the 

populations of naturally produced fish that make up the Oregon Coast Coho Salmon ESU are 

sufficiently abundant, productive, and diverse (in terms of life histories and geographic 

distribution) that the ESU as a whole is self-sustaining and will provide environmental, cultural, 

and economic benefits. 

Because these goals are fundamentally different from those for ESU persistence and ESU 

sustainability in having a substantial sociopolitical component beyond the scope of this report, 

we have not defined specific criteria, but rather have discussed general ecosystem considerations 

as background for restoration and have outlined types of criteria to measure progress toward 

restoration goals. 

Conclusions 

The workgroup developed and applied a means to evaluate the current biological status of 

the Oregon Coast Coho Salmon ESU. This evaluation is intended for use in the recovery 

planning process. The biological recovery criteria are organized into two coherent logical 

frameworks to evaluate persistence and sustainability. In addition to developing criteria to 

xvi
evaluate persistence and sustainability of the ESU, the workgroup also offers general guidelines 

regarding ESU restoration (or “broad-sense” recovery). 

Uncertainty within the logical structure of the ESU Persistence and ESU Sustainability 

criteria is accommodated through a knowledge-based DSS. This use reflects both uncertainty 

about “true” parameters and errors of measurement. The results of this assessment are expressed 

as the degree of confidence that two overall statements regarding the status of the ESU are true: 

1) the ESU is persistent, and 2) the ESU is sustainable. 

The logical networks and associated data within the DSS are designed to contribute to the 

decision-making processes associated with both federal recovery planning and Oregon State 

conservation planning. It was the intention of the workgroup to honor the multiple mandates of 

both federal and state requirements in developing the biological recovery criteria. One 

requirement of recovery planning is setting objective, measurable criteria for delisting. Toward 

this end, the ESU Persistence and ESU Sustainability criteria provide a means to assess 

biological conditions related to listing status under the ESA. This evaluation does not consider 

other factors, including conservation measures and expectations about future conditions, that 

factor into the ESA definition of threatened status. Incorporating such factors requires policy 

decisions not reflected in this analysis. Thus the criteria presented here provide only a partial 

assessment of status under the ESA. 

The workgroup has applied the DSS using available data, models, and best professional 

judgment to evaluate the current status of the ESU for two main goals: persistence and 

sustainability. 

Persistence 

The DSS framework includes criteria for Population Productivity, Probability of 

Persistence, and Critical Abundance. The workgroup used recent observations of these 

population metrics to evaluate how certain it is that the Oregon Coast Coho Salmon ESU is 

persistent under recent conditions. The results of the analysis lead the workgroup to conclude 

that, if recent past conditions continue into the future, it is moderately to highly certain that the 

ESU is persistent; that is, it is able to persist over a 100-year period without artificial support. 

This means that if recent habitat conditions, climate pattern, and management policies continue 

into the future, there is moderate to high certainty that the ESU and most of its constituent 

biogeographic strata and Independent Populations will persist for 100 years. The weakest strata 

in this assessment were the North Coast and Mid-Coast, which had only a low certainty of being 

persistent. The strongest strata were the Lakes and Mid-South Coast, which had high certainty 

of being persistent. To increase certainty that the ESU as a whole is persistent, restoration work 

should focus on those populations with low persistence, particularly those in the North Coast, 

Mid-Coast, and Umpqua strata. 

Sustainability 

A sustainable ESU is 1) able to survive prolonged periods of adverse climatic conditions 

without artificial support, and 2) able to maintain its genetic legacy and long-term adaptive 

potential. Additionally, a sustainable ESU is composed of diverse interconnected populations. 

xvii
To evaluate population diversity, the workgroup constructed a framework using objective 

measures of spawner abundance, artificial influence, spawner and juvenile distribution, and 

habitat capacity. In addition, ESU-wide diversity was evaluated by assessing genetic diversity, 

phenotypic and habitat diversity, and the status of small populations. The results of the analysis 

lead us to the conclusion that, if recent conditions continue into the future, there is a low to 

moderate certainty that the ESU is sustainable; that is, it is able to maintain its genetic legacy and 

long-term adaptive potential for the foreseeable future. This conclusion assumes that present and 

recent past habitat conditions, climate patterns, and management policies reflected in the data 

sets used by the workgroup will continue for the foreseeable future; no attempt was made to 

evaluate the effects of potential future changes in environmental conditions or conservation 

measures. 

The conclusion that there is low to moderate certainty that the ESU is sustainable can be 

interpreted further. This means the ESU may be sustainable if strong ecosystem, harvest, and 

hatchery protections are in place. Many Independent Populations, and all but one of the 

biogeographic strata, have moderate to high certainty of being sustainable, but many other 

Independent Populations have obvious problems for some criteria. On the positive side, the 

Lakes and Mid-South Coast strata have a high certainty of being sustainable, with most of their 

constituent Independent Populations having a high certainty of being sustainable. On the 

downside, the North Coast stratum has low certainty of sustainability, with two Independent 

Populations having a low certainty of being sustainable and two having low certainty of being 

unsustainable. 

An ESU at this level has good opportunities for recovery, but is also vulnerable to harvest 

and ecosystem alteration and destruction. Strong protections are needed to ensure that the status 

of populations and strata in moderate to good condition does not degrade, because the lower the 

status gets the more difficult and expensive it is to bring it back up. Overall ESU status can best 

be improved by focusing restoration efforts on those populations that scored low on 

sustainability, particularly those in the North and Mid-Coast strata. There should be 

opportunities for limited harvest during high productivity periods, and hatchery programs should 

be strictly managed to prevent risks to naturally produced fish. 

Restoration 

In addition to these analyses of persistence and sustainability, an outline has been 

presented for developing criteria for restoration (or broad-sense recovery), including suggestions 

for three types of criteria: ESU abundance, sustainable Independent Populations, and productive 

Dependent Populations. Metrics for these criteria depend on social, economic, and political 

considerations as well as biological requirements, so full metrics have not been developed at this 

time. 

Limitations 

The persistence and sustainability analyses deal only with current or recent conditions 

without addressing future conditions, threats, or limiting factors. This approach provides a 

systematic method for evaluating the current status of the Oregon Coast Coho Salmon ESU as 

part of a recovery or conservation plan, but is not sufficient to determine the ESA listing status of 

xviii
the ESU. For ESA evaluations, it is important to address not only present status, but also factors 

for decline, threats, and probable trends in land use patterns, harvest and hatchery management 

practices, and climate. These factors will play pivotal roles in the future sustainability of this 

ESU. Assessing these factors, which requires policy-level analysis of social and economic issues 

as well as a projection of climate trends, is beyond the scope of this report. 

Within the limited context of current and recent conditions, this assessment may be 

biased by including some threats that have been largely addressed and are not expected to 

continue into the future. These include intense fishing pressures and negative hatchery impacts, 

both of which have been substantially reduced. Some of the measures of persistence and 

sustainability include data collected before these factors for decline had been addressed, and 

therefore may have a pessimistic bias. However, this assessment reflects current (data through 

2004) levels of other threats (such as poor freshwater habitat conditions) that have not been fully 

addressed and can be expected to continue into the future. Thus the conclusions are limited to 

the conditions considered by the workgroup, and the status of the ESU hinges on future 

conditions that have not been addressed. 

xix
xx
Acknowledgments 

Development of biological recovery criteria for the Oregon Coast coho salmon 

(Oncorhynchus kisutch) evolutionarily significant unit was conducted by a team of scientists 

from the Northwest Fisheries Science Center (NWFSC); Oregon Department of Fish and 

Wildlife (ODFW), U.S. Forest Service (USFS) Pacific Northwest Research Station, Oregon 

Watershed Enhancement Board, and Clearwater Biostudies Inc. This workgroup is a committee 

of the Oregon and Northern California Coasts Technical Recovery Team (ONCC TRT). The 

workgroup relied on published literature, informational reports, and unpublished data made 

available by state, tribal, and federal agencies. The authors acknowledge the efforts of all who 

contributed to this process, especially ODFW and the Oregon Watershed Enhancement Board. 

Numerous scientists and fishery managers provided information that aided in preparation 

of this report and deserve recognition. We especially want to thank the Independent 

Multidisciplinary Science Team for its in-depth analysis of the State of Oregon’s Viability 

Assessment for Oregon Coast Coho Salmon. Thanks are also due to a team of scientists from 

NWFSC who analyzed the state’s viability and habitat assessments: Robin Waples, Paul 

McElhany, Martin Lierman, Rich Zabel, Michael Pollock, Tim Beechie, Peter Kiffney, Ashley 

Steel, Blake Fiest, and Phil Roni. These two reviews of a similar effort helped immeasurably in 

the development of this document. Members of the Oregon Coast Coho Project Stakeholders 

Team also provided helpful comments for this effort. In particular, we would like to thank those 

stakeholder team members who provided in-depth comments on earlier drafts: Bill Moshofsky, 

Wayne Hoffman, Paul Engelmeyer, and Kaitlin Lovell. Helpful public comments were received 

from the ODFW, the MidCoast Watersheds Council, the Native Fish Society, and Benjamin 

Stout. We particularly thank Jason Dunham and Eric Bjorkstedt for providing insightful peer 

review of the report. 

Thanks are also due for Rosemary Furfey and Jeff Lockwood of the NOAA Fisheries 

Service Northwest Regional Office (NWR), Bruce McIntosh, Ed Bowles, and Kevin Goodson of 

ODFW, Brian Spence of the Southwest Fisheries Science Center (SWFSC), Kaitlin Lovell of 

Trout Unlimited, and Wayne Hoffman of MidCoast Watersheds Council for useful comments 

and discussion. We particularly appreciate the diligent editorial and production assistance of 

Skye Hughes of ERT Inc. and Katie Phillips of Frank Orth & Associates. 

The ONCC TRT consists of Walt Duffy, U.S. Geological Survey, California Cooperative 

Fish Research Unit; Dave Hillemeir, Yurok Tribe fishery biologist; George Kautsky, Hoopa 

Valley Tribe fishery biologist; Thomas Lisle, USFS, Pacific Southwest Experiment Station; 

Mike McCain, Six Rivers National Forest; cochairs Tommy Williams, SWFSC, and Peter 

Lawson, NWFSC; Thomas Wainwright, NWFSC; Thomas Nickelson, ODFW (retired); Charles 

Huntington, Clearwater Biostudies Inc.; and Gordon Reeves, USFS, Pacific Northwest Research 

Station. Recovery coordinators are Rosemary Furfey of NWR and Greg Bryant of NOAA 

Fisheries Service Southwest Regional Office. Mike Rode (retired), California Fish and Game 

Region 1, was formerly on the ONCC TRT. 

xxi
xxii

The Oregon Coast Workgroup of the ONCC TRT consists of Peter Lawson, Gordon 

Reeves, Thomas Wainwright, Thomas Nickelson, and Charles Huntington. Adjunct members 

are Mark Chilcote and Kelly Moore of ODFW, and Laurie Weitkamp of NWFSC. Heather Stout 

of NWFSC staffs the workgroup. 
Introduction 

This document outlines biological recovery criteria (sometimes called viability criteria) 

for the Oregon Coast Coho Salmon Evolutionarily Significant Unit (ESU), as identified in the 

NOAA Fisheries Service status review for West Coast coho salmon (Oncorhynchus kisutch) 

(Weitkamp et al. 1995). This report was developed by the Oregon Coast Workgroup (hereafter 

“the workgroup” or “we”) of the Oregon and Northern California Coasts (ONCC) Technical 

Recovery Team (TRT). In developing the criteria, the workgroup reviewed several documents, 

including NOAA’s viable salmonid populations (VSP) report (McElhany et al. 2000), the Puget 

Sound TRT preliminary guidelines document (Puget Sound TRT 2002), the Willamette/Lower 

Columbia TRT revised report on viability criteria (Willamette/Lower Columbia TRT and ODFW 

2006), the Interior Columbia Basin TRT draft viability criteria (Interior Columbia Basin TRT 

2007), the summary of the Oregon Scientific Workshop on Recovery Criteria (OSL 2002a), and 

the summary report of the Oregon Joint Interim Task Force on Salmon Recovery (OSL 2002b). 

In addition, the workgroup has had substantial interactions with staff of the Oregon 

Department of Fish and Wildlife (ODFW) during ODFW’s development of viability criteria for 

the state’s Oregon Coastal Coho Assessment (Chilcote et al. 2005), and considered reviews of 

that report by the Independent Multidisciplinary Science Team (IMST 2005) and Northwest 

Fisheries Science Center (NWFSC 2005). The workgroup also referred to guidance on ESUlevel recovery approaches from the Independent Science Advisory Board (Bilby et al. 2005). 

The primary purpose of this report is to provide objective and measurable biological 

criteria for assessing progress toward the recovery of the Oregon Coast ESU. Secondarily, these 

criteria are applied to an assessment of the current biological status of the ESU. Complete 

criteria are not provided for listing or delisting of the ESU under the U.S. Endangered Species 

Act (ESA) of 1973, nor is an evaluation made of whether the ESU should or should not be listed. 

In addition to evaluating the biological requirements presented here, listing and delisting 

decisions require evaluations regarding particular listing factors and conservation measures (see 

Relationship of Biological Criteria to the ESA, page 16), which are beyond the scope of this 

report. Thus the biological criteria presented here provide a necessary, but not sufficient, set of 

criteria to determine the ESA listing status of the ESU. It is also not the intent of this report to 

suggest or analyze management measures; that is the role of a full ESA recovery plan. Certain 

past management actions, notably harvest and hatchery practices, have a predictable role in 

determining the current status of the ESU, and to that extent they have been accounted for in 

assessing current status. 

Several important terms must be defined before considering ESU-level and populationlevel criteria. For each criterion, it is first defined and possible metrics are identified to test 

against the criterion before ending with a discussion of issues and alternatives. 
Definition of Key Terms 

In planning for salmon recovery, the workgroup recognized that there are many 

considerations beyond the ESA. Both the National Marine Fisheries Service (NMFS or NOAA 

Fisheries Service) and the State of Oregon have mandated goals beyond simply preventing 

extinction of coho salmon. For this reason, we focused on defining recovery in a way that 

considers ESA requirements and the broader social goals associated with rebuilding populations 

beyond the point where ESA listing is warranted. This said, there is confusion regarding the 

term recovery. Under the ESA, the term has a very specialized usage, while usage is different 

under state mandates, and general public usage is different yet. For the present effort, however, 

recovery refers to the process leading to delisting of an ESU under the ESA. When broader 

societal goals are included, a more robust ESU status is required than merely a delisting under 

the ESA. The terms restoration or broad-sense recovery have been chosen to identify the process 

by which this second, more robust status is achieved. 

The concepts of recovery and restoration, as they are used here, come from a synthesis of 

several previous efforts and additional development by the TRT. In particular, a scientific 

workshop was held in July 2002 (OSL 2002a), cosponsored by the Oregon Independent 

Multidisciplinary Science Team, the Oregon Legislature’s Salmon Recovery Task Force (SRTF), 

and NOAA Fisheries Service. That workshop reached no firm consensus on a definition of 

recovery, but made several suggestions as to what should be included in such a definition. 

Following the workshop, the SRTF developed a definition of recovery within the Oregon 

legislative context (OSL 2002b). The workgroup reviewed the definition and concluded that it 

provided a good characterization of the concept of broad-sense recovery. However, to address 

TRT concerns about potential vagueness and inconsistency, the definition was modified 

somewhat. Starting from these prior efforts, the workgroup developed the following definitions 

for the Oregon Coast Coho Salmon ESU. 

Restoration (broad-sense recovery) is a process leading to conditions such that the 

populations of naturally produced fish that make up the Oregon Coast Coho Salmon ESU are 

sufficiently abundant, productive, and diverse (in terms of life histories and biogeographic 

distribution) that the ESU as a whole will be self-sustaining and will provide environmental, 

cultural, and economic benefits (modified from Oregon SRTF, OSL 2002b). 

A self-sustaining ESU is able to survive prolonged periods of adverse climatic 

conditions without artificial support (including supplementation by hatchery fish) and able to 

maintain its genetic legacy and long-term adaptive potential. A self-sustaining ESU will be 

composed of diverse interconnected populations. 

Environmental, cultural, and economic benefits include, but are not limited to: 

providing nutrients to freshwater and terrestrial ecosystems; providing food resources for 

scavengers such as bears, eagles, and stream invertebrates; supporting ceremonial use by Native 

Americans; allowing viewing and nature study by coastal residents and tourists; and providing 

subsistence, recreational, and commercial harvest. 

The workgroup’s concept of recovery (in the narrow sense of the ESA) is basically the 

same as for restoration, but with the deletion of the reference to environmental, cultural, and 

2
economic benefits. The term delisting is used to refer to recovery in this narrower sense, which 

only requires that a species (equivalent to an ESU) no longer be threatened or endangered, with 

no reference to the other societal values. This definition of delisting reflects the specific 

language of the ESA. 

Delisting (narrow-sense recovery), based on the statutory language in the ESA, is a 

process resulting in the ESU being removed from the formal protections of the ESA as a 

consequence of no longer being an endangered species or a threatened species. 

The ESA defines those specific terms as follows (note that the legal term “species” 

differs from the common biological meaning and includes ESUs). 

An endangered species is “any species which is in danger of extinction throughout all or 

a significant portion of its range.” 

A threatened species is “any species which is likely to become an endangered species 

within the foreseeable future throughout all or a significant portion of its range.” 

During the development of this document, NOAA Fisheries Service revised its policy on 

artificial propagation (NMFS 2005), which includes some relevant definitions. An effort was 

made to keep the definitions consistent with that policy; however, criteria also are proposed that 

fit within the state’s efforts to recover and restore coastal salmon, so definitions in Oregon’s 

Native Fish Conservation Policy (ODFW 2003) were reviewed. Its definitions for hatchery and 

naturally produced fish were adopted with only slight modifications. 

Naturally produced fish are fish that were spawned and reared in natural habitats, 

regardless of parental origin (modified from ODFW 2003). 

Hatchery-produced fish are fish incubated or reared under artificial conditions for at 

least a portion of their life cycle (ODFW 2003). 

In addition to the above terms, two specialized terms are used to relate biological risk to 

threatened and endangered status under the ESA: persistent and sustainable. 

A persistent population (or ESU) is one that is able to persist (i.e., not go extinct) over a 

100-year period without artificial support. This includes an ability to survive prolonged periods 

of adverse environmental conditions, which may be expected to occur at least once in the 100-

year time frame. 

A sustainable population (or ESU) is one that, in addition to being persistent, is also 

able to maintain its genetic legacy and long-term adaptive potential for the foreseeable future. 

This implies the availability of functional habitat and other conditions necessary for the full 

expression of the population’s (or ESU’s) life history diversity into the foreseeable future. This 

is similar to the definition of a self-sustaining ESU above. 

The terms persistent and sustainable are both part of the more generic term “viable” as 

used in the viable salmonid populations report (McElhany et al. 2000). The two terms are used 

to distinguish distinct types and levels of risk. Persistence relates to the simple risk (or danger) 

3
of extinction, which is the primary determinant of endangered status under the ESA. 

Sustainability goes beyond this, requiring that population diversity (genetic and ecological) be 

sustained so that risk of extinction will not increase in the foreseeable future, thus relating to 

threatened status under the ESA. Neither term implies evaluation of specific threats or 

consideration of conservation measures required for listing determinations under the ESA, so 

these terms cannot be directly equated with thresholds of ESA listing status. 

Population Structure and Associated Definitions 

The foundation for the biological criteria draws heavily from conservation principles for 

salmon and steelhead (Oncorhynchus mykiss) presented by McElhany et al. (2000). One of these 

principles is that demographic populations are the primary units of conservation within the ESU 

and that the status of the entire ESU is largely a function of the status of these individual 

populations. Population structure is not entirely discrete, but rather forms a hierarchical 

continuum from individual stream reaches where spawning or rearing occur, through watersheds 

and river basins, to the entire range of the ESU. For practical applications, the workgroup 

focused on the historical populations (Lawson et al. 2007) as fundamental units, and aggregated 

these populations into a number of biogeographic strata within the ESU. 

In Lawson et al. (2007), the workgroup relied on biological characteristics of fish and 

geographical and ecological characteristics of the landscape to establish historical population 

boundaries. A total of 56 historical populations were identified through this process, and were 

classified as either Dependent or Independent (Table 1, Figure 1). In addition to this populationlevel structure, there is also substantial genetic and geographic structure at larger scales within 

the ESU, with genetic similarities clustering into a few geographic units. ODFW recognized this 

structure by describing three gene conservation groups (GCGs) within the ESU (Kostow 1995). 

More recent genetic work has found similar structure, but with some indication of a fourth lake 

population group (Ford et al. 2004, Lawson et al. 2007). Subsequent to the 1995 report, ODFW 

established a set of four monitoring areas within the ESU, which are similar to GCGs, but with 

the northern unit divided into two monitoring areas based on geographic diversity. To account 

for this structure, Lawson et al. (2007) divided the ESU into five biogeographic strata, 

corresponding to the ODFW monitoring areas, but with the lake populations separated into a 

fifth stratum (Table 1, Figure 1). Because these units represent both biological diversity (genetic 

and ecological) and geographic variation, we assumed that preserving all of them will meet two 

goals: preserving the major genetic and life history variation in the ESU and spreading out the 

effects of risks due to catastrophes. 

Some of the definitions developed in Lawson et al. (2007) are relevant to the present 

work. 

An ESU represents a distinct population segment of Pacific salmon under the ESA that is 

substantially reproductively isolated from conspecific populations and represents an important 

component of the evolutionary legacy of the species (NMFS 1991, Waples 1991). 

4
Table 1. Classification of Oregon Coast Coho Salmon ESU historical populations. Modified from 

Lawson et al. (2007) and listed north to south within biogeographic strata. 

Stratum Population Type Stratum Population Type 

North Necanicum Independent Mid-Coast Alsea Independent 

Coast Ecola Dependent (continued) Big (near Alsea) Dependent 

Arch Cape Dependent Vingie Dependent 

Short Sands Dependent Yachats Dependent 

Nehalem Independent Cummins Dependent 

Spring Dependent Bob Dependent 

Watseco Dependent Tenmile Dependent 

Tillamook Bay* Independent Rock Dependent 

Netarts Dependent Big (near Siuslaw) Dependent 

Rover Dependent China Dependent 

Sand Dependent Cape Dependent 

Nestucca Independent Berry Dependent 

Neskowin Dependent Siuslaw Independent 

Mid-Coast Salmon Independent Lakes Sutton (Mercer Lake) Dependent 

Devils Lake Dependent Siltcoos Independent 

Siletz Independent Tahkenitch Independent 

Schoolhouse Dependent Tenmile Independent 

Fogarty Dependent Umpqua Lower Umpqua Independent 

Depoe Bay Dependent Middle Umpqua Independent 

Rocky Dependent North Umpqua Independent 

Spencer Dependent South Umpqua Independent 

Wade Dependent Mid-South Threemile Dependent 

Coal Dependent Coast Coos Independent 

Moolack Dependent Coquille Independent 

Big (near Yaquina) Dependent Johnson Dependent 

Yaquina Independent Twomile Dependent 

Theil Dependent Floras/New Independent 

Beaver Independent Sixes Independent 

*Includes coho salmon inhabiting all basins that drain directly into Tillamook Bay (Trask, Wilson, Tillamook, 

Miami, Kilchis, and other minor tributaries). 

5
Figure 1. Biogeographic strata and Independent Populations of the Oregon Coast Coho Salmon ESU. 

6
Biogeographic strata are groups of populations with biological (genetic, ecological) and 

geographical similarities. 

A population is a group of fish of the same species that spawns in a particular location at 

a particular season and does not interbreed substantially with fish from any other group (Lawson 

et al. 2007). There are two major classes of populations. 

Dependent Populations are populations that historically would not have had a high 

likelihood of persisting in isolation for 100 years. These populations relied upon periodic 

immigration from other populations to maintain their abundance (Lawson et al. 2007). 

Independent Populations are populations that historically would have had a high 

likelihood of persisting in isolation from neighboring populations for 100 years (Lawson et al. 

2007). 

Within populations, watersheds are geographical areas drained by one or more rivers and 

tributaries where fish spawn. In the context of this report, this term generally refers to fifth-field 

hydrologic units (HUC5, REO 2002). 

7
Important Issues 

Several general issues are important for the application of these criteria. These issues 

include: 

• The relationship of fish-based criteria to habitat 

• The role of hatchery fish in the ESU 

• The effects of fluctuations or trends in environmental conditions (particularly in 

freshwater habitat conditions and marine survival) 

• The role of density dependence in evaluating population-level risks 

• The role of uncertainty in making decisions based on these criteria 

• The consequences of assumptions made about population structure 

• The relationship of these criteria to those applied to other ESUs and to requirements of 

the ESA 

Habitat 

Habitat conditions, particularly in freshwater and associated upland areas, play a central 

role in the status of salmon populations (Figure 2). Habitats are diverse and dynamic, 

responding both to natural changes and to changes resulting from human management patterns 

(e.g., Burnett et al. 2007, Spies et al. 2007). Unfortunately habitat characteristics are difficult to 

relate quantitatively to population performance, so the criteria rely primarily on metrics related to 

fish performance rather than on habitat-based metrics, except for one criterion that uses a habitatbased estimate of smolt capacity. The status of habitat may be reflected in population metrics 

such as abundance, productivity, and distribution, although environmental variation, harvest, and 

artificial production can mask these relationships. Population abundance is directly related to the 

quantity (capacity) of habitat available, productivity (intrinsic smolts per spawner or adults per 

spawner) is most strongly tied to habitat quality, and distribution and diversity are both strongly 

influenced by the spatial arrangement and variety of suitable habitats within the population’s 

range. Habitat, of course, is not the only determinant of population status. Direct human 

intervention in the form of harvest and hatcheries also affects productivity, abundance, and 

diversity, but in the long term it is the condition of natural habitats that determine the ability of a 

population to tolerate any negative effects of harvest and hatcheries. 

There are risks in depending on fish-based criteria rather than including criteria that 

specifically address habitat condition. Fish populations will likely be the final signal of changes 

in habitat conditions which, because of time lags between changes in terrestrial conditions and 

changes in lotic habitat, are slow to affect fish populations and slow to recover. It may be that by 

the time serious changes in terrestrial habitat are detected in fish populations, it will be too late to 

do anything effective about them. Some threats, such as landslides, fires, and floods, cause local 

8
Diversity Productivity

Abundance

Distribution

Habitat

Quality

Quantity

Distribution

Diversity Productivity

Abundance

Distribution

Habitat

Quality

Quantity

Distribution

Figure 2. Conceptual view of relationships of the four VSP parameters to habitat. 

effects that could threaten a population but do not pose a threat to a biogeographic stratum or 

ESU. The large-scale threats would probably come from human development and climate 

change, which set the stage for widespread drought and habitat simplification and cause smaller 

changes to have more effect. Thus, merely following population trends without paying adequate 

attention to landscape-scale changes may not assure protection or recovery of the ESU. 

Hatchery Fish 

Hatchery production has been widely used for Pacific salmon to increase the abundance 

of fish, particularly to allow for harvest where natural production has become limited by other 

human activities. The biological criteria focus on the natural sustainability of populations, so 

attempts must be made to account for the influence of hatchery production in the criteria and 

metrics. Workgroup concerns relate to the effect of hatchery fish that spawn naturally along with 

wild fish and are twofold: 1) that the fitness (contribution to future generations) of hatchery fish 

spawning naturally may be less than that of wild fish (Einum and Fleming 2001, Fleming and 

Petersson 2001, Bisson et al. 2002, Berejikian and Ford 2004) and thus will influence estimates 

of the productivity of those wild populations whose adults spawn with hatchery fish, and 2) that 

hatchery fish may impact the sustainability of wild populations either through ecological 

interactions between hatchery and wild fish (Einum and Fleming 2001, Nickelson 2003) or 

through interbreeding of hatchery and wild fish that leads to reduced fitness of the wild 

population (Reisenbichler and McIntyre 1977, Bisson et al. 2002). The bases for these concerns 

are discussed below. 

9
Fitness of Hatchery Fish 

Several studies in the Pacific Northwest have documented reduced fitness of hatchery 

salmonids rearing in natural environments. For example, Reisenbichler and McIntyre (1977) 

studying Deschutes River hatchery and wild steelhead found that survival of the offspring of 

hatchery fish during the first year of life was significantly less than that of the offspring of wild 

fish in three of four streams (74%, 72%, and 49% of wild fish). Similarly, Fleming and Gross 

(1993) found that hatchery male coho salmon were only 62% as successful at breeding as were 

wild males, and hatchery females were 82% as successful as wild females. 

In a study of summer steelhead in the Kalama River (Chilcote et al. 1986, Leider et al. 

1990), nonnative Skamania hatchery stock produced smolts at 31% of the rate of wild Kalama 

River steelhead and adults at 11–13% of the rate of wild steelhead. This study has been 

criticized because the hatchery stock was not a local stock. However, after the smolts left the 

Kalama Basin, the marine survival of the hatchery fish was still only 35–42% that of the wild 

fish, a difference that cannot be attributed to being a nonnative stock. 

A more recent study of reproductive success of steelhead in Hood River (Araki et al. 

2007) found that steelhead reared in a controlled supplementation hatchery (spawning and 

rearing of locally captured wild stock held for less than one year) had fitness indistinguishable 

from that of wild fish, while steelhead from a traditional hatchery (nonlocal origin, multiple 

generations in a hatchery environment) had markedly reduced fitness. 

Nickelson et al. (1986) reported on the study of the supplementation of wild populations 

of Oregon Coast coho salmon with hatchery presmolts. Using their data, productivity of 

hatchery and wild fish can be calculated from summer juvenile abundance of hatchery and wild 

fish in 1980 and 1981 and summer juvenile abundance of wild fish three years later in stocked 

and unstocked streams. Results of this analysis provided estimates of productivity of hatchery 

coho salmon that were 6% and 24% that of wild coho salmon. 

On a broader scale, extensive literature reviews (Einum and Fleming 2001, Bisson et al. 

2002, Berejikian and Ford 2004) have documented variable, but usually reduced, fitness of 

hatchery fish compared to wild fish. Einum and Fleming (2001) in a review of more than 100 

peer-reviewed publications found that “the hatchery rearing of salmonids results in increased 

pre-adult aggression, decreased response to predators, and decreased survival.” Bisson et al. 

(2002) concluded that “hatchery-origin adults returning from the ocean and spawning in the wild 

produce fewer progeny than adults of wild origin spawning in the wild.” Finally, Berejikian and 

Ford (2004) suggested that when estimating population productivity, it would be prudent to 

assume a range of values for relative fitness of local, multigenerational hatchery fish—advice 

that was followed in the analyses reported here by making precautionary assumptions and testing 

sensitivity of results to these assumptions. 

Impact of Hatchery Fish on Wild Populations 

Four of the studies important to examining this issue are from Oregon. The 

Reisenbichler and McIntyre (1977) study of summer steelhead discussed above also compared 

survival of the offspring of hatchery-wild crosses with survival of the offspring of wild parents, 

10
thus simulating the effects of interbreeding of hatchery and wild fish in the wild. They 

concluded that a long-term effect of hatchery adults spawning with wild adults would be the 

production of fewer returning adults than had the same number of exclusively wild adults 

spawned. 

Nickelson (2003) reported a negative relationship between the productivity of wild 

populations of coho salmon on the Oregon coast and the number of hatchery smolts released in 

the basin. He hypothesized that the mechanism was increased mortality of wild smolts caused by 

predators attracted by large numbers of hatchery smolts. 

Chilcote (2003) found a negative relationship between the proportion of hatchery fish in 

naturally spawning populations of steelhead in Oregon and the productivity of the population. 

Possible causes of such a relationship include reduced fitness of hatchery spawners, ecological 

interactions between hatchery and wild fish, and reduced fitness of the offspring of hatchery and 

wild spawners. Examination of the data presented in this report on populations of coho salmon 

on the Oregon Coast shows a similar result (Figure 3). 

Araki et al. (2007) found that fitness in the wild of hatchery-wild crosses were reduced 

relative to wild/wild crosses for traditional hatchery fish, but that this was not the case for 

crosses with fish from a wild-stock supplementation hatchery. 

Again, a broader look at the issue supports the workgroup’s concerns. Einum and 

Fleming (2001) suggest that “many of the current stocking practices may be detrimental to the 

recipient population.” Following an extensive literature review, Fleming and Petersson (2001) 

y = -1.92x + 1.03

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0% 20% 40% 60% 80% 100%

Returns/Spawner(Natural Logarithm) Returns/spawner (natural logarithm) 

Pro Proportion Hatchery Spawners portion hatchery spawners

Figure 3. Relationship between population productivity (logarithm of returning spawners per spawner) 

and average proportion of hatchery spawners in the population during 1990–2001. 

11
concluded that even limited interbreeding between hatchery and wild fish “may pose a concern, 

particularly when the scale of hatchery introductions is significantly greater than that of natural 

production.” Finally, Bisson et al. (2002) stated that although limited, evidence from the 

literature suggests that interbreeding of hatchery and wild fish can reduce the fitness of wild 

populations. 

Relationship to the NMFS Hatchery Policy 

The NMFS hatchery policy (NMFS 2005) relates directly to listing decisions, not 

recovery, but is relevant here to the extent that recovery criteria should be compatible with listing 

determinations. The policy focuses evaluations of biological condition on the status of natural 

populations and natural production, which is consistent with the approach taken here. Under the 

policy, a separate analysis of the role of hatcheries in conservation efforts will be a part of listing 

decisions. However, conservation efforts as part of the biological recovery criteria are not 

evaluated in this report. 

Environmental Fluctuations 

A major problem in setting criteria is that salmon populations fluctuate widely in 

response to changing environmental conditions. Such fluctuations affect measures of abundance, 

productivity, and distribution of populations. Because of this, assessments of these variables at 

any specific point in time are not necessarily informative regarding population sustainability. 

For example, an observed increasing trend in abundance that coincides with a period of 

increasing marine survival says little about the ability of the population to survive when 

conditions worsen (Lawson 1993). This problem is addressed in two ways: 1) criteria are 

focused on measures that indicate long-term ecological sustainability, rather than short-term 

measures of single population variables, and 2) metrics are based on observations across multiple 

generations, rather than short-term measures. 

Neither of these approaches is entirely satisfactory. Environmental change is 

multifaceted; for example, changes in marine survival are accounted for, which is relatively 

easily measured, but do not account for correlated changes in freshwater survival, fecundity, or 

habitat utilization, any of which could be equally important to the populations but are not as 

easily measured (Lawson et al. 2004). Using multigenerational time spans helps by picking up 

some of the variation in the various environmental factors, but it does not help with longer term 

patterns of change, such as decadal-scale shifts in climate or the slow processes of freshwater 

habitat development. Using a multigenerational data window means that the metrics are more 

stable because they do not respond to year-to-year fluctuations, but that also means there will be 

time lags in detecting and responding to sudden changes in ESU status. 

Most of the criteria use a four-generation (12-year) data window, a time span that 

historically has captured a large portion of the total environmental variation. For retrospective 

assessments, the 12-year window includes the period of poor ocean conditions in the 1990s. The 

choice of a 12-year window is a compromise between two requirements: metrics need to be 

stable relative to environmental change so that status evaluations do not change unpredictably 

from year to year, but they also need to be responsive to short-term changes in management. 

12
Too long a window would mean long delays in detecting the response of the ESU to changing 

management. 

Change is also unpredictable. Model-based estimates of extinction risk are especially 

dependent on explicit assumptions about future conditions. In the current analysis of 

productivity and persistence, it is assumed that future conditions will be similar to observed past 

conditions, and in particular that future conditions will not be worse than what was observed 

during the 1990s. It is difficult to know if this assumption is valid, and no explicit analysis has 

been done of this issue. Local habitat conditions can be expected to change due to human 

population growth and shifts in land-use practices, and freshwater, estuarine, and ocean 

conditions are likely to be substantially influenced by large-scale climate change. None of these 

effects is readily predictable, although some simulations of habitat changes are available (e.g., 

Bettinger et al. 2005). To address this uncertainty in future conditions, we tested the sensitivity 

of a persistence model (the stochastic habitat-based life cycle model, see Appendix C) to various 

future-condition scenarios, which demonstrates substantial variation in predicted extinction risk 

depending on future conditions. However, a full analysis would require integrating human 

demographic prediction, climate change, and the effectiveness of management policies into 

evaluations of limiting factors. 

Density Dependence 

Density dependence in population parameters is a well-established principle of 

population ecology (cf Wilson and Bossert 1971 or any of a number of basic ecology textbooks). 

For salmon, such effects are fundamental to management theory. In terms of numbers of 

offspring per adult, there are two general categories of effects: compensation, where the number 

of offspring per adult decreases with increasing abundance or density of adults, and depensation, 

where the effect is opposite. Typically, compensation occurs as populations grow and resource 

limitations begin to reduce fecundity or survival or both. Depensation typically occurs at very 

small population abundances, where various population processes begin to break down; these are 

also known as Allee effects, reviewed by Dennis (1989). 

There is considerable evidence for coho salmon that the recruits per spawner metric is 

density dependent (Beidler et al. 1980, Nickelson and Lawson 1998, Nickelson 2003). For 

Oregon Coast coho salmon populations, the importance of this strong density-dependent 

relationship is threefold: 1) the recruit per spawner (R/S) ratio, highly variable depending on 

spawner density, is not a constant and should not be treated as such, 2) spawner-recruit models 

that assume a constant R/S ratio are poorly suited for forecasting the recruitment response of 

coho salmon populations when the density of spawners is low, and 3) as spawner abundance 

declines, the resistance to further population decline becomes greater because of the 

compensatory increase in the R/S ratio, although this would be offset by depensation at very low 

abundance. 

An effect of this strong density-dependent relationship is that even a relatively low 

number of spawners can produce enough smolts that, if these smolts experience good marine 

survival, the number of returning adults will rebound considerably. This pattern was observed 

throughout the Oregon Coast Coho Salmon ESU in the rebound of populations from the levels of 

the late 1990s, especially for those populations in the northern portion of the range. 

13
The workgroup attempted to account for these effects in its criteria by focusing on 

estimates of productivity below abundances where compensation is likely to be significant and 

by exploring a variety of density-dependent population models when models are used. 

Uncertainty 

All assessments relating to these criteria involve uncertainty. Uncertainty is a difficult 

and often ignored issue in resource conservation, and decisions often need to be made in the face 

of substantial uncertainty (for example, see Ludwig et al. 1993, Regan et al. 2005). The authors 

of the VSP paper (McElhany et al. 2000) recognize two responses to the problems of uncertainty: 

a precautionary approach and the use of adaptive management. We strongly encourage the use 

of both as recovery planning moves forward. 

For this report, three distinct types of uncertainty must be considered: scientific 

uncertainty as to what levels of the various metrics are needed to ensure persistence or 

sustainability (criterion uncertainty), statistical uncertainty in estimating the metrics from data 

(data uncertainty), and uncertainty in determining population structure of the ESU as it relates to 

specific criteria (structural uncertainty). These three types of uncertainty are addressed in 

different ways: 

• Criterion uncertainty is a fundamental part of this approach, which uses a formal 

decision support system (DSS) to integrate this uncertainty from the lowest level criteria 

(those relating directly to observations) up through the highest level ESU criteria. At the 

lowest level, this uncertainty is expressed by using continuous functions relating 

observations to risk levels, rather than knife-edge (pass-fail) criteria. 

• Data uncertainty is addressed in three ways. First, the statistical confidence (Type I 

error) is examined in estimating quantitative metrics from data. Second, this uncertainty 

has been implicitly incorporated into criteria by using precautionary values for some 

metrics. Third, a sensitivity analysis was conducted on the whole analysis, varying both 

data inputs and selected metric levels to examine the effects of uncertainty on the final 

high-level results (see Appendix A). 

• Structural uncertainty involves the classification of populations into Independent and 

Dependent categories and the definitions of biogeographic strata that were developed by 

Lawson et al. (2007), who discussed a number of the uncertainties involved in 

determining the population structure of the ESU. In the DSS for this report, these 

classifications are important in applying some of the diversity criteria and in aggregating 

results from the population level to the ESU level. Because the population-level criteria 

focus on Independent Populations, the habitat-based size limit chosen by Lawson et al. 

(2007) to separate Independent from Dependent Populations is important. If this limit 

were higher or lower, the number and average size of Independent Populations would 

change, and this would have an effect on the results of the analysis. In general, lowering 

this limit would result in more but smaller Independent Populations and would increase 

the apparent ESU-level risk in the analysis, while raising the limit would have the 

opposite effect (see Appendix A). 

14
Population Classification and Biogeographic Strata 

In the analysis, population-level criteria are combined into biogeographic stratum criteria 

and these are then combined to the ESU level. With this approach, how the populations and 

strata are defined could have a significant effect on the higher-level criterion values. This issue 

has not been explicitly addressed, but some of the effects can be described. In defining the 

Independent Populations that are the focus of population-level criteria, a specific lower limit to 

population size based on predicted intrinsic habitat production was chosen. Because some of the 

criteria relate to abundance, distribution, and habitat capacity, smaller basins will tend to have 

lower scores for these metrics. Thus if the population size limit in the definition of Independent 

Populations were shifted higher (or lower), it could be expected that on average population-level 

status scores would increase (or decrease). For this reason, it is important that the definition of 

Independent Populations be ecologically meaningful, that is, that the lower size limit actually 

corresponds closely to the definition of demographic independence (Lawson et al. 2007). 

At the stratum level, the logical structure of the analysis dictates how changes in stratum 

size would affect ESU-level criteria. Because ESU-level persistence and sustainability depend 

on achieving persistence or sustainability in every stratum, defining more numerous but smaller 

strata would result in more likely failure at the ESU level. This failure happens for two reasons: 

1) smaller strata with fewer populations would likely result in greater variation in stratum-level 

results, and 2) with more strata, the likelihood of one with very low persistence or sustainability 

increases. Thus it is important that the strata chosen actually reflect significant portions of the 

ESU that, if lost, would compromise the persistence or sustainability of the ESU. 

Relationship to Criteria for other ESUs 

NMFS formed TRTs for eight geographic recovery domains, covering all the listed 

Pacific salmon ESUs. Several other TRTs have released draft or final biological recovery 

criteria for various ESUs (Puget Sound TRT 2002, Willamette/Lower Columbia TRT 2006, 

Interior Columbia Basin TRT 2007, Lindley et al. 2007, Spence et al. 2007, Williams et al. 

2007). Although all the TRTs followed the same general principles outlined in the VSP report 

(McElhany et al. 2000), each adopted a different suite of specific criteria tailored to the particular 

species, ecosystems, and data sets specific to their recovery domain. The approach for this report 

differs somewhat from these approaches. In addition to a somewhat different organization 

reflecting the multiple mandates addressed, three differences are apparent: the role of abundance 

criteria, the role of population viability models, and the use of DSSs. 

Most other TRTs placed a strong emphasis on abundance as an indicator of population 

status. That factor has been deemphasized for this report. Abundance of salmon populations is 

known to fluctuate widely as environmental conditions change, and abundance is inextricably 

linked to other VSP parameters, particularly productivity and distribution. In fact, abundance per 

se may be the least important criterion, because abundance levels can and will vary dramatically 

as a result of variations in marine survival and density-dependent freshwater survival. For 

example, low abundance during unfavorable ocean conditions may be a more sustainable 

abundance than only moderate abundance levels during favorable ocean conditions. There were 

only two areas where a clear link was found between abundance and risks to sustainability: 

15
genetic diversity, where reproductive abundance can be directly related to losses of diversity 

over time, and small population demographics, where small-population effects (depensation or 

Allee effects [Dennis 1989]) may significantly reduce the probability of persistence. 

Population viability models have become a standard approach in evaluating status of 

species at risk, but they are not without controversy. For this report, population viability models 

were used directly as a measure of population persistence, as one of a number of criteria. Some 

other TRTs (e.g., Puget Sound TRT 2002, Willamette/Lower Columbia TRT 2006) chose to use 

simple population viability models to develop viability curves defining combinations of 

productivity and abundance that result in high or low risk, and then applied those productivity 

and abundance combinations as criteria. The models that these TRTs used are fairly generic, and 

reflect little of the biology of specific species or life history types. We tried this approach, but 

we found that results depend on assumptions that were not appropriate for Oregon Coast coho 

salmon populations. We concluded it was best to use viability models directly to estimate 

extinction risk, rather than the indirect approach of using models to generate viability curves and 

comparing measured parameters with those curves. We believe that for Oregon Coast coho 

salmon populations this approach makes clearer the assumptions and limitations of the analysis. 

The final area where the workgroup departed from the methods applied by other TRTs is 

the use of a formal DSS for organizing and evaluating the criteria. This approach provides a 

clear, logical framework for the various factors that influence extinction risk and recovery 

processes, and can provide explicit documentation of how criteria at different population scales 

are combined into ESU-wide criteria. It also provides a single quantitative evaluation of ESU 

condition, while allowing managers to examine the specific conditions that contribute to the 

overall assessment. Use of a DSS is more fully described beginning on page 20. 

Relationship of Biological Criteria to the ESA 

The ESA outlines requirements for recovery planning, which have been clarified in 

agency regulations and guidelines. The most pertinent requirements of the ESA for purposes 

here are that a recovery plan incorporate “objective, measurable criteria which, when met, would 

result in a determination ... that the species be removed from the list” (ESA Sec. 4(f)). The most 

recent guidelines available are NMFS’ interim recovery planning guidance (NMFS 2006). 

Those criteria note that recovery requires a number of objectives (NMFS 2006, Sec. 5.1.8.2): 

• Adequate reproduction to balance mortality 

• Sufficient genetic robustness to avoid inbreeding depression and to allow adaptation 

• Sufficient habitat for long-term population maintenance 

• Elimination or control of threats to the species 

Each of these objectives should have associated recovery criteria by which progress 

toward the objective may be measured. The guidance further suggests (NMFS 2006, Sec. 

5.1.8.3) that these criteria should address representation (breadth of genetic makeup to conserve 

adaptive capabilities), resiliency (ensuring that populations can withstand stochastic events), and 

redundancy (sufficient number of populations to withstand catastrophic events). 

16
In determining the ESA listing status of a species or ESU, a number of factors beyond 

biological conditions of the ESU must be considered (Figure 4). For example, listing 

determinations require consideration of conservation efforts, adequacy of existing regulations, 

and other risks affecting the continued existence of the ESU. For delisting, NMFS guidance 

requires that “recovery criteria must include the management or elimination of threats by specific 

mechanisms.” These requirements are not directly addressed in this report; thus, the biological 

criteria presented here do not provide a sufficient set of criteria to determine the ESA listing 

status of the ESU. Rather, this report focuses on biological recovery criteria: the characteristics 

of populations and their habitat that lead to biological sustainability. This is not to ignore the 

primary importance of removing threats in achieving recovery, but reflects the fact that analysis 

of threats is sufficiently complex that it deserves a more complete treatment than could be 

provided at this time. 

When this ESU was first proposed for listing in 1997, the impact of all factors for decline 

resulted in Oregon Coast coho salmon abundance that was approximately 5% of that documented 

for the early 1900s (Weitkamp et al. 1995). Before the ESU can be considered fully recovered, it 

is important to analyze whether these factors for decline and threats subsequently identified have 

been addressed. It is also crucial to identify limiting factors for each Independent Population 

and, from them, ascertain the impediments to recovery for each Independent Population and the 

ESU as a whole. 

Listing status

• Endangered

• Threatened

• Not Warranted

or Delisted

Other considerations

• Habitat conditions

• Overutilization

• Disease or predation

• Inadequacy of regulatory mechanisms

• Other factors

• onservation meas

• Threats/Listing Factors

C ures

Biological status

• Persistent

• Sustainable

• Restored

Other considerations

• Habitat conditions

• Overutilization

• Disease or predation

• Inadequacy of regulatory mechanisms

• Other factors

• nservation meas

•• Threats/Listing factors Threats/Listing Factors

Listing status

• Endangered

• Threatened

• Not Warranted

or Delisted

• CCo onservation measures ures

• Endangered 

• Threatened 

• Not warranted 

or delisted 

Figure 4. Relationship between biological status (criteria in this document), other listing considerations, 

and listing decisions under the ESA. Listing decisions are based on the biological status of the 

species and other considerations. 

17
Approach to Developing the Criteria 

The development of criteria began with the VSP approach (McElhany et al. 2000), which 

identifies four key population parameters that influence the persistence of populations. 

• Population size, the abundance of all life stages of the species 

• Population growth rate (productivity), production over the entire life cycle 

• Spatial structure, distribution of fish among habitat areas and connectivity among those 

areas 

• Diversity, variation in traits (phenotypic and genetic) among individuals in a population 

and among populations in the ESU 

While recognizing the importance of these parameters to population persistence and 

sustainability, some problems were found in using them as organizing principles for a consistent 

set of recovery criteria for an ESU. Two primary concerns were the lack of direct connection 

with habitat characteristics and the strong interdependencies among the four parameters. 

The approach taken views the ESU as a metapopulation, with substructure at a continuum 

of scales from individual spawning grounds (demes) up to the entire ESU. Along this 

continuum, four scales were identified as important for defining attributes related to ESU status: 

watersheds, populations, biogeographic strata, and the entire ESU. 

The goal of recovery and restoration is not merely to meet a set of artificial criteria, but 

rather to restore or repair ecological processes that lead to long-term sustainability of the 

resource. As the criteria used in these analyses were developed (see Introduction to the Criteria 

on page 26), the workgroup tried to focus on the connections between the criteria and those 

fundamental processes. Given this focus on process, attributes that contribute to sustainability at 

each of these four scales needed to be understood, as a first step. 

Watershed-level Attributes 

Many ecological processes occur at geographic scales smaller than that of the 

Independent Population scale defined above. For the most part, these considerations have been 

subsumed into population-level attributes, but two watershed (or subbasin) scale subcriteria were 

used to examine within-population diversity and distribution. 

Population-level Attributes 

The definition of restoration given on page 2 refers specifically to the status of the 

constituent populations of the ESU. Population status is multifaceted and there are a variety of 

attributes that contribute to population health. In the VSP report, McElhany et al. (2000) 

categorized these attributes into four parameters: population size, population growth rate, spatial 

18
structure, and diversity. These same four parameters appear in the Oregon Native Fish 

Conservation Policy. The four are not independent of one another and their relationship to 

sustainability depends on a variety of interdependent ecological processes. 

Biogeographic Stratum-level Attributes 

In the approach taken in evaluating ESU status, biogeographic strata play two important 

roles. First, they represent the largest pieces of habitat, genetic, and life history diversity within 

the ESU, thus ensuring that they preserve much of the within-ESU and among-population 

diversity. Second, by ensuring that all the strata are preserved, a hedge is provided against loss 

of the whole ESU from large-scale catastrophes. Issues related to catastrophes are discussed 

further in the ESU-level Attributes subsection below. 

The important attributes of strata are primarily the number and status of their constituent 

populations and associated habitats. Thus in this analysis, stratum-level criteria are simply 

combinations of the status criteria of their populations. 

ESU-level Attributes 

On the ESU scale, the VSP report focuses on catastrophes, long-term demographic 

processes, and long-term evolutionary potential, leaving short-term environmental, demographic, 

and genetic processes as the focus of population-scale assessments (McElhany et al. 2000). 

Catastrophes 

For purposes of this report, catastrophes are considered to be sudden events that severely 

reduce or eliminate one or more populations. Scales vary from local (affecting only one 

population) to regional (affecting the entire ESU), and with expected natural frequencies of less 

than 1 per 100 years. Human disturbance can increase the frequency of catastrophes such as 

major floods and landslides; see NRC (1996, ch. 7) and Bisson et al. (1997). Severe drought or 

other climate events could have effects at the entire ESU scale, and frequencies of these events 

are likely to change in the future in response to global climate change (ISAB 2007). The primary 

approach here to reducing risk to coho salmon from catastrophes is to ensure that multiple 

sustainable populations are distributed across the range of the ESU. Biogeographic strata are 

used to help accomplish this. 

Long-term Demographic and Evolutionary Processes 

Beyond the 100-year time frame, which is the focus of the ESU Persistence Criterion, 

larger scale processes are important to the sustainability of an ESU. At these time scales, withinESU population structure is likely to change as catastrophes, climate variation, and habitat 

changes cause changes in constituent populations, including such changes as local extinctions, 

increases or decreases in habitat capacity, and shifts in among-population migration rates. The 

goal here in managing coho salmon populations within the ESU is to maintain sufficient 

population and habitat structure such that these changes can occur without threatening the 

continued existence and evolutionary potential of the ESU. 

19
Goals and Criteria 

The Oregon Coast Workgroup is part of a joint federal-state process to develop a 

recovery and conservation plan that meets federal and state conservation requirements. Because 

of multiple mandates, there are multiple goals for defining the biological status of the ESU. As 

discussed earlier, the term restoration is used to encompass goals of the state processes, which 

have requirements beyond simple sustainability. This definition is also expected to cover the 

requirements of the U.S. Sustainable Fisheries Act. In contrast, the term delisting is used in the 

narrow sense of recovery under the ESA, which itself has two considerations: is the species 

endangered or threatened? Only when it is neither endangered nor threatened can it be delisted. 

These considerations led the workgroup to a three-level structure of goals and criteria 

(Figure 5). The ultimate goal is restoration of the ESU in the sense defined by the Oregon Plan 

and the Joint Legislative Task Force (OSL 2002b). On the way to reaching that goal, it will be 

necessary to achieve recovery according to the ESA, and to ensure that the ESU does not drop 

into endangered status. Each higher level requires meeting all the criteria of the lower levels, as 

well as its own. 

To achieve restoration, coho salmon populations within the ESU need to be robust 

enough to meet societal desires for environmental, cultural, and economic benefits. Restoration 

therefore requires sufficient abundance of the ESU and diversity among populations. Achieving 

ESA delisting requires biological sustainability into the foreseeable future (as well as removal of 

threats, which are not discussed in this report). This level has a number of ESU-level criteria, 

including the number and distribution of sustainable populations and the preservation of ESUlevel diversity. Avoiding endangerment requires biological persistence, that is, assurance that all 

significant parts of the ESU have a high likelihood of persisting for 100 years. Achieving each 

level implicitly requires meeting all criteria for the lower levels. 

Use of a Decision Support System 

The goals in developing these biological recovery criteria are to meet needs for: 

• A scientifically based assessment of the biological conditions of the entire ESU 

• Criteria that are measurable 

• An approach that is useful to decision makers throughout the recovery planning process 

• Logical consistency and transparency of the links from data to top-level evaluations 

A complete assessment of the biological condition of the ESU is necessarily multifaceted, 

potentially including a variety of interrelated criteria, with varying data quality. These criteria 

relate to biological processes at a variety of time and space scales, with spatial processes varying 

from individual stream reaches to the entire range of the ESU. To track this large suite of data 

and criteria in a transparent and logically consistent framework, a knowledge-based DSS was 

used. 

20
21

Goals ESU Criteria

Restoration

Sustainable, plus 

support for environmental, cultural, & 

economic values

Sustainability

Maintain genetic 

legacy and adaptive 

potential for the 

foreseeable future

Persistent ESU

Persistent populations 

in all strata

Restored ESU

zOverall abundance

zAll independent pop's 

sustainable

zProductive dependent 

populations

(Not Further 

Developed)

Population

Goals 

Persistence

High expectation of 

persistence over 100 

years

Criteria

Persistent 

Populations

zProductivity

zPersistence

zAbundance

Sustainable ESU

zSustainable 

populations in all strata

zESU-wide diversity

Sustainable 

Populations

zSpawner abundance

zArtificial influence

zSpawner distribution

zJuvenile distribution

ESU criteria 

Biogeographic Strata

Population criteria 

Diverse ESU

zGenetic diversity

zPhenotypic and 

habitat diversity

zSmall populations

Figure 5. Overview of goals and criteria. 

A DSS is a computer-based tool that can analyze and compare numerous pieces of data, 

producing results that assist managers in making a decision (Turban and Aronson 2001). These 

systems allow decision makers to perform complex evaluations quickly, present a consistent 

assessment that draws from a variety of data sources, and accurately track large sets of 

information accurately, thus improving the choices made by decision makers without overriding 

human judgment (Rauscher 1999). A system evaluating ecological conditions may contain 

substantial uncertainty about the precise conditions that are optimal for the target organisms, 

because of gaps in information and the lack of perfect knowledge about the interrelationships 

among relevant factors (Reynolds et al. 2000). 

The DSS designed for this analysis uses a network framework to link criteria at a variety 

of scales and aggregate them from fine-scale watershed-level criteria, through population-level 

criteria and biogeographic stratum-level criteria, to criteria for the entire ESU. The links take the 

form of logical operators that define specific relationships among the input values. In traditional 

Boolean logic (which evaluates propositions as either absolutely true or false), criteria are “knifeedged,” meaning the result of an operation has only one of two values: true or false. In this 

traditional Boolean logic, when the input values are near the knife-edge criteria, the result is very 

sensitive to small changes in the input. In the knowledge-based system used here, a type of 

approximate logic (referred to as “fuzzy logic” in Appendix A) extends the ability to work with 

the imprecise knowledge available for the system being assessed. The advantage of using this 
approximate logic is that it allows the degree of uncertainty to be evaluated and expressed in an 

outcome, ranging from certainly false through uncertain to certainly true. Being able to work 

with a gradation of levels of certainty and uncertainty enables decision makers to evaluate the 

degree of risk inherent in a decision based on the strength of the conclusion from the DSS. 

Details of the DSS and its implementation are given in Appendix A. 

The criteria in this analysis have metrics, which at the population or watershed level have 

data inputs. These metrics are evaluated on a scale from +1.0 (completely true) to –1.0 

(completely false) through the application of “truth membership curves,” that is, graphs that 

relate observed conditions (along the horizontal, or x-axis) to degree of truth (along the vertical, 

or y-axis). These curves were developed through analysis and application of the workgroup’s 

best professional judgment during the DSS network development. The truth membership curves, 

as discussed in greater detail in Appendix A, allow intermediate values between true and false, 

thus allowing the workgroup to capture uncertainty in the knowledge. 

A disadvantage of this system is that it results in numerical truth values ranging from –1 

to +1 that are difficult to interpret by those unfamiliar with the method. To aid in this 

interpretation, a set of verbal descriptions of the degree of certainty associated with a given 

metric (Figure 6) were adopted. Values near zero are considered “uncertain,” those near ±0.1 are 

considered to have “low” certainty, those near ±0.3 have “moderate” certainty, those near ±0.6 

have “high” certainty, and values near ±1.0 are considered fully certain. Combining the sign 

(which indicates truth or falsity) with the magnitude (certainty) of the value, statements can be 

made about the propositions. For example, if the statement “the sun will rise tomorrow” is 

evaluated with a truth value of +1.0, one would say that this is certain. As another example, if 

the statement “gas prices will fall on Memorial Day Weekend” is evaluated with a value of –0.3, 

one would say that that statement has moderate certainty of being false. Values intermediate 

Degree of certainty 

Truth value 

Figure 6. Terminology used in describing truth values resulting from application of the DSS. Degree of 

certainty is described using adjectives that relate to general ranges of truth values. 

22
between those given above are given compound adjectives, so that a value of +0.5 would be 

considered true with moderate-to-high certainty. 

Within the structure of the DSS network, separate criteria may be combined to determine 

the status of a unit (watershed, population, biogeographic stratum, or ESU). These units are then 

combined to determine the status of larger units (for example, the results of population criteria 

metrics are combined to determine biogeographic stratum status, which are then combined to 

determine ESU status). These combinations are performed using three logical operators: 

• “AND,” which evaluates to true if all of its antecedents are true (+1) and to false if any of 

the antecedents are false (–1). Use of this operator means that it is essential that criteria 

be met for all subcomponents or subcriteria. 

• “UNION” (U in the decision network diagrams), which treats its antecedents as 

contributing evidence in support of a proposition, approximately averaging the truth 

values of its antecedents. This operator is used where tradeoffs among subcomponents 

are acceptable, for example, a high score for one population could offset a low score for 

another. 

• “MEDIAN” (M in the decision network diagrams), which returns the median truth value 

of its antecedents. Use of this operator implies that it is desirable that criteria are met for 

a majority of the subcomponents. 

These logical operations are fully defined in Appendix A. 

We considered a number of alternatives to this DSS approach. First, consideration was 

given to an approach with no formal DSS, where each criterion is evaluated separately and ESUlevel status is decided by subjective integration of the various criteria. This approach was 

rejected for two primary reasons: 1) by leaving the integration of disparate criteria and the 

aggregation from populations to ESU open to subjective interpretation, it does not provide an 

objective means of evaluating ESU status, and 2) it lacks an explicit description of the logical 

connections among the criteria, thus reducing the reproducibility and transparency of the 

analysis. 

Second, a DSS-like structure with strict pass-fail criteria was considered, so that the end 

result would be a simple pass or fail result for the entire ESU. This approach ignores 

uncertainties in both the science and data available for defining and evaluating criteria, and 

requires a determination of the acceptable level of risk prior to conducting the assessment. The 

workgroup believes the choice of acceptable level of risk is a sociopolitical decision, not a 

biological/technical exercise. Therefore, a method was sought that provided an objective 

evaluation of biological risk, not a pass/fail method based on a single value of acceptable risk. 

Finally, other approaches to evaluating risk within the DSS framework were considered, 

notably statistical (Bayesian) risk assessment (e.g., Marcot et al. 2006). Such a probabilistic 

approach would provide a fully integrated measure of risk, resulting in a final status evaluation 

that is easy to interpret. However, this approach requires a number of complex assumptions 

regarding probability distributions of events, tending to hide the details of the analysis in 

complicated mathematics. The workgroup concluded the chosen logic-based DSS provided 

23
greater transparency than statistical approaches, while retaining a full description of the logical 

connections leading from low-level criteria to the whole ESU evaluation. 

Interpreting DSS Results 

Applying the DSS provides quantitative measures (truth values) of the biological 

condition of the ESU at population, biogeographic stratum, and whole ESU scales. These values 

can inform decisions about ESA listing and recovery, as well as more general conservation 

planning. However, the values do not translate directly to yes or no conclusions (except at the 

extreme values of +1.0 or –1.0). 

As described previously, any given criterion at a value of –1.0 indicates a particular 

biological requirement is not met, meaning both the risk of inaction and the expected costs for 

restoration will be very high (Figure 7). Similarly, a value of +1.0 means that the requirement is 

fully met, and both risks and costs are zero. However, as illustrated in Figure 7, risks and costs 

behave differently at intermediate values. Costs can be expected to decline exponentially as 

biological conditions improve, while risks remain high until truth values are strongly positive. 

To put this in more concrete terms, an example was considered for the ESU 

Sustainability Criterion describing typical biological conditions and needed restoration actions 

that correspond to each of the result categories shown in Figure 6: 

• Certain false (DSS truth value near –1.0): All populations and biogeographic strata in the 

ESU are in very poor shape and near extinction. Abundance is generally low and 

declining. Distribution of juveniles and spawners is very limited. Genetic integrity of 

many populations is severely compromised, with little heterozygosity remaining. 

Diversity in the ESU has been lost due to loss of Independent and Dependent Populations 

0.000

0.500

1.000

-1.0 -0.5 0.0 0.5 1.0

Relative Risk or Cost

Truth Value

High

Lo

R

Cost of 

Risk of no action isk of No Action

Relative risk or cost 

Cost of restoration 

Truth value 

Figure 7. The relationship of DSS truth values to cost of restoration (solid curve) and risk of no action 

(dashed line). 

24
or life history types, which probably means widespread destruction and alteration of 

ecological processes that create and maintain habitat has occurred and heroic measures 

are required for successful restoration to achieve a properly functioning ecosystem. 

There are no opportunities for harvest, and hatchery programs should be restricted to 

conservation activities (such as captive breeding) only. 

• High certainty false (near –0.6): Conditions similar to those for Certain false, but some 

populations and strata are functioning at a higher level. Effective ecosystem restoration 

would require extreme measures throughout the ESU over a long time frame, and the 

ESU can be expected to continue for a long time at low abundance with high 

vulnerability to extinction due to catastrophes or cycles of poor ocean conditions. 

Actions needed include protection of remaining habitat and ecosystems, projects to 

increase short-term smolt production, and other stopgap measures. Longer-term 

ecosystem restoration programs should be focused on areas where self-sustaining 

populations can be established. There are no opportunities for harvest, and hatchery 

activities could pose a major risk through reducing fitness and survival of naturally 

produced juveniles. 

• Moderate certainty false (near –0.3): Populations and strata are in generally poor 

condition, possibly with some in good condition. Strong and widespread ecosystem 

restoration efforts will be needed to bring populations up to overall adequate levels due to 

lack of habitat and properly functioning ecosystems. Harvest mortality must be kept to a 

very low level. 

• Low certainty false (near –0.1): This condition is close to the Uncertain category. Status 

of populations and strata are mixed, with a majority in poor condition for a number of 

specific biological criteria. The ESU is unlikely to be self-sustaining, and aggressive 

ecosystem protection and restoration efforts are needed to improve conditions and 

prevent further deterioration. 

• Uncertain (near 0.0): This status can result from either of two conditions—biological 

status of the ESU is in intermediate condition relative to the various DSS criteria, or there 

is widespread uncertainty about conditions for many criteria because of lack of data. 

Most likely this means that some populations and strata are in relatively good condition 

(positive truth values), while others are in poor condition (negative truth values). 

Aggressive actions should be taken to prevent further habitat and ecosystem degradation 

and to restore areas that can be returned to functional ecosystems in a relatively short 

time. Actions should be focused on improving ecosystem functions for those populations 

that have low DSS results for one or more criteria, while protecting key ecological 

process that create and maintain habitat in populations with better DSS scores. The ESU 

is not self-sustaining and is likely to become in danger of extinction unless aggressive, 

effective ecosystems, harvest, and hatchery management measures are in place. 

• Low certainty true (near +0.1): This condition is close to the Uncertain category. Status 

of populations and strata are mixed, with many in poor condition for a number of specific 

biological criteria. The ESU may be self-sustaining, but aggressive ecosystem protection 

and restoration efforts are needed to prevent further deterioration. Harvest opportunities 

are severely limited, and hatchery programs should be strictly managed to prevent risks to 

naturally produced fish. 

25
26

• Moderate certainty true (near +0.3): Many populations, and probably all strata, are 

sustainable, but some are not, and others have problems for some biological 

requirements. An ESU at this level has good opportunities for recovery, but is also 

vulnerable to harvest and ecosystem alteration and destruction. Strong protections are 

needed to ensure that status does not degrade, because the lower the status gets the more 

difficult and expensive it is to bring it back up. The ESU is likely to be sustainable 

unless there is a clear lack of ecosystem, harvest, and hatchery protections. There are 

opportunities for limited harvest during high productivity periods. 

• High certainty true (near +0.6): With few exceptions, most populations and strata are 

self-sustaining. There are large areas of functioning ecosystems distributed throughout 

the ESU. Salmon at all life stages are utilizing the available habitat. Substantial 

economic, cultural, and social benefits are realized. There is room for improvement 

through restoration activities, but the urgency is low. The ESU is capable of sustaining 

moderate harvest pressure in most years. 

• Certain true (near +1.0): Most populations and all biogeographic strata are selfsustaining. Substantial economic, cultural, and social benefits are realized. There is 

flexibility in ecosystem and harvest management. The emphasis should be on 

maintaining strong populations and functioning ecosystems throughout the ESU. 

Introduction to the Criteria 

Our biological recovery criteria are framed within the context of the DSS described 

above. At the highest level, the DSS is structured to represent the hierarchical population 

structure of the ESU. Populations are grouped into biogeographic strata, which in combination 

make up the ESU (Figure 8). The goals of ESU persistence and ESU sustainability are evaluated 

using sets of logically linked and measurable criteria within this structure. (The third goal, ESU 

restoration, is discussed but is not analyzed within this structure.) Criteria for ESU persistence 

and sustainability are each presented in a separate section, and are presented at three levels of 

geographic aggregation: populations, biogeographic strata, and the entire ESU. Within each 

section, we present criteria at the population level first, then describe how these are combined 

into higher-level criteria for biogeographic strata and for the ESU as a whole. 

In addition to defining criteria and metrics, an assessment is also provided of the current 

status of the ESU by applying each metric to data that was available in fall 2005. This analysis is 

provided for illustration only to demonstrate how the criteria might be used in the future to assess 

progress towards recovery and restoration. 
27

Figure 8. Schematic of the conceptual relationships of population, strata, and the ESU criteria in the DSS. 
Persistence 

Persistence refers to the ability of the ESU and its populations to persist through a period 

of 100 years without artificial support. The DSS network used to evaluate the ESU Persistence 

Criterion is illustrated in Figure 9. The lowest level criteria are at the bottom, with combinations 

to the population, biogeographic stratum, and ESU levels higher up the figure. Stacked boxes 

represent points where multiple populations or strata are combined. Starting at the base of 

Figure 9, three criteria—Population Productivity, PP-1; Probability of Persistence, PP-2; and 

Critical Abundance, PP-3—are evaluated for each population to determine overall Population 

Persistence Criterion, PP. Evaluations for individual populations are combined to arrive at a 

biogeographic Stratum Persistence Criterion, SP, determination. Biogeographic stratum results 

are then combined to arrive at an ESU Persistence Criterion, EP, determination. Each of these 

criteria is defined below, starting with the lowest level population criteria, then moving up to the 

stratum and ESU criteria. 

Population-level Persistence Criteria 

Primary Criteria 

Population persistence is evaluated on the basis of the following three primary criteria 

(Population Productivity, Probability of Persistence, and Critical Abundance), which are then 

combined into a single Population Persistence Criterion. 

PP-1, Population Productivity 

Criterion: Productivity at low abundance (i.e., that experienced when spawner densities 

are low and compensation is not substantially reducing productivity) is sufficient to sustain an 

Independent Population through an extended period of adverse environmental conditions (12 

years with marine survival equivalent to the 1990–2001 brood year average). 

Metric: Productivity at low abundance is estimated as the geometric mean of the natural 

return ratio (NRR) for brood years with spawner abundances below the median of the last 4 

generations (12 years). Specifically, the metric is the statistical probability (Student’s t-test) that 

NRR is greater than 1, based on available data. NRR is the ratio N/T, where N is naturally 

produced spawners and T is total (hatchery produced plus naturally produced) spawners in the 

previous generation. The truth value of the criterion is evaluated by a straight line relationship 

equating zero probability with false (–1.0) and a probability of one with true (+1.0) (Figure 10). 

Estimates of naturally produced and hatchery coho salmon spawners based on random 

spawning surveys conducted by ODFW (Jacobs et al. 2002) are available for the period of 1990–

2004 (Appendix D). To examine the current productivity (NRR = N/T) of each population, 

28 
C

B

SPA

. . .

3

2

PP1

. . .

PP

PP-1 PP-2 PP-3

M

SP

AND

EP

AND

Represents a point where 

data is loaded into the 

decision support system 

and evaluated with a truth 

membership function

Represents criteria 

evaluated from lowerlevel criteria

Represents the collection 

of lower-level results for 

watersheds, populations, 

or strata

Represents logical 

operators

Represents the final 

outputs of the decision 

support system

Figure 9. The decision network for the ESU Persistence Criterion. This figure shows the connections 

from population-level criteria, through stratum-level criteria, to the entire ESU. Criterion codes 

are: PP-1, Population Productivity; PP-2, Probability of Persistence; PP-3, Critical Abundance; 

PP, Population Persistence; SP, Stratum Persistence; and EP, ESU Persistence. 

estimates of total spawner abundance, T, for the 1990–2001 broods and estimates of naturally 

produced spawners, N, three years later (1993–2004) were used. Productivity of each population 

was examined as the geometric mean of recruits per spawner for the six years of lowest spawner 

abundance. A precautionary assumption was made that the fitness of hatchery spawners was 

equal to the fitness of naturally produced spawners, which is likely conservative given that a 

number of studies have found hatchery fish to have less fitness than naturally produced fish (see 

discussion of Hatchery Fish on page 9). If hatchery spawners are actually producing fewer 

29 
-1

0

1

0 0.25 0.5 0.75 1

Probability that natural return ratio is greater than 1

Truth Value ruth value 

T

Probability natural return ratio is greater than 1 

Figure 10. Truth membership function for the Population Productivity Criterion, PP-1. 

offspring than naturally produced spawners, then a greater proportion of offspring would come 

from naturally produced parents and natural production would be higher than estimated by this 

metric. 

Current assessment: Results of these analyses are reported as the mean recruits per 

spawner and the probability that the value is greater than 1 (Table 2). Three populations had 

average productivities less than 1: the Salmon, Alsea, and North Umpqua. In addition, the 

Nehalem population had an estimated average productivity of 1.17 but only a 57% probability 

that the true average was greater than 1. All four of these populations have experienced 

significant interactions with hatchery fish (Nickelson 2003). Estimated truth values for the 

Population Productivity Criterion, PP-1, are presented in Table 3 and range from –0.96 (high 

certainty of being false) for the North Umpqua population to +1.00 (true) for the Coos 

population. These truth values indicate the North Umpqua population does not have sufficient 

productivity to sustain it through an extended period of adverse conditions, while the Coos 

population does. 

Discussion: Adequate productivity ensures that a population has the fundamental 

productivity required to survive conditions similar to the worst in recent history. However, 

estimating productivity is problematic, especially when environmental conditions are fluctuating; 

measurements taken under one environmental regime cannot be directly related to expectations 

under some unknown future regime. Because this metric is based on the ratio of returning 

spawners to parental spawners rather than the more common measure of preharvest recruits per 

spawner, achieving this metric demonstrates that when spawning stock was below the fourgeneration average, the population was able maintain itself given the actual combination of 

environmental conditions and harvest during that period. It gives a total assessment of the life 

cycle under recent past conditions without making assumptions about the relationship between 

productivity and environmental conditions. However, it is of course possible that some future 

period could be worse than recent observations, and this metric cannot directly indicate whether 

a population would be sustainable under such conditions (see discussion of Environmental 

Fluctuations, page 12). 

30 
31 

Table 2. Population-level persistence criteria for Independent Populations of Oregon Coast coho salmon. 

For the Population Productivity Criterion, PP-1, the values in parentheses are the probability that 

recruits per spawner (R/S) are greater than 1. For the Probability of Persistence Criterion, PP-2, 

the two values are probabilities of persistence based on full extinction/quasi-extinction of 50. NA 

means result is unavailable. 

Independent 

Population (by 

biogeographic 

stratum) 

PP-1 

Productivity 

PP-2 

Persistence Probability (over 100 years) 

PP-3 

Critical 

Abundance

Metric: Mean (prob.) Model 1a Model 2b Model 3c Model 4d (Fish/mile) 

North Coast 

Necanicum 2.58 (0.98) 1.00/1.00 1.00/1.00 1.00/0.99 0.92/0.18 3.77 

Nehalem 1.17 (0.57) 1.00/0.99 1.00/1.00 0.99/0.99 1.00/1.00 6.27 

Tillamook Bay 1.83 (0.88) 0.95/0.94 1.00/1.00 1.00/0.99 0.99/0.93 3.76 

Nestucca 2.83 (0.95) 1.00/1.00 1.00/1.00 0.97/0.96 1.00/0.97 5.33 

Mid-Coast 

Salmon 0.64 (0.24) 0.00/0.00 NA 0.99/0.86 1.00/0.91 0.57 

Siletz 1.61 (0.79) 0.96/0.94 1.00/1.00 0.99/0.98 1.00/0.99 3.48 

Yaquina 4.82 (0.98) 1.00/1.00 1.00/1.00 0.96/0.95 1.00/1.00 6.26 

Beaver 1.89 (0.93) 1.00/1.00 1.00/1.00 0.97/0.93 1.00/0.95 22.94 

Alsea 0.66 (0.20) 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 3.18 

Siuslaw 2.30 (0.81) 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 4.66 

Lakes 

Siltcoos 1.92 (0.89) 1.00/1.00 1.00/1.00 1.00/1.00 NA 52.95 

Tahkenitch 1.96 (0.94) 1.00/1.00 NA 0.97/0.97 NA 62.63 

Tenmile 1.81 (0.96) 1.00/1.00 1.00/1.00 1.00/1.00 NA 118.06 

Umpqua 

Lower Umpqua 2.75 (0.94) 1.00/1.00 1.00/1.00 0.97/0.97 1.00/1.00 9.00 

Middle Umpqua 2.52 (0.91) 1.00/1.00 1.00/1.00 0.98/0.97 1.00/1.00 5.35 

North Umpqua 0.51 (0.02) 0.96/0.89 1.00/1.00 0.99/0.98 1.00/0.99 7.48 

South Umpqua 1.53 (0.75) 1.00/1.00 1.00/1.00 0.99/0.99 1.00/1.00 5.76 

Mid-South Coast 

Coos 5.95 (1.00) 1.00/1.00 NA 0.98/0.98 1.00/1.00 18.56 

Coquille 1.95 (0.96) 1.00/1.00 1.00/1.00 0.99/0.99 1.00/1.00 15.16 

Florase 2.63 (0.91) 1.00/1.00 NA NA 1.00/0.98 26.37 

Sixes 2.21 (0.92) 0.08/0.00 NA 0.99/0.97 1.00/0.97 3.10 

a

 Density-dependent count-based model (Chilcote et al. 2005). 

b

 SPAZ model with hockey stick recruitment function (McElhany and Payne 2005). 

c

 Bayesian salmon analysis model (Wainwright et al. in prep.). 

d

 Stochastic habitat-based life cycle model (Nickelson and Lawson 1998). 

e

 Assessment is based on a combination of random and supplemental coho salmon surveys and standard Chinook 

salmon (Oncorhynchus tshawytscha) surveys. 
Table 3. Summary of DSS truth values for all populations, biogeographic strata, and ESU criteria. Criterion abbreviations 

Productivity; PP-2, Probability of Persistence; PP-3, Critical Abundance; PP, Population Persistence; SP, Stratum P

Persistence; PD-1, Spawner Abundance; PD-2, Artificial Influence; PD-3, Spawner Distribution; PD-4, Juvenile DPopulation Diversity; PS, Population Sustainability; SD, Stratum Diversity; PF, Population Functionality; SF, StratStratum Sustainability; ES-1, All Biogeographic Strata Sustainable; ES-2, ESU-level Diversity; and ES, ESU Susta

Persistence Sustainability 

Stratum Population PP-1 PP-2 PP-3 PP SP EP PD-1 PD-2 PD-3a PD-4a PD PS SD PF SF Whole ESU: 0.40 North Coast 0.23 (–0.02) 0.53 

Necanicum 0.96 (–0.44) (–0.08) (–0.28) 0.00 0.44 0.31 0.73 0.19 (–0.19) 0.18

Nehalem 0.14 0.92 0.30 0.32 0.76 0.15 (–0.09) 0.42 0.09 0.15 1.00

Tillamook Bay 0.76 0.50 (–0.08) 0.14 0.11 0.45 (–0.37) 0.44 (–0.20) (–0.13) 1.00

Nestucca 0.90 0.74 0.18 0.43 0.15 0.81 (–0.25) 0.82 (–0.01) 0.10 0.89

Mid-Coast 0.20 0.25 0.47 

Salmon (–0.52) (–1.00) (–1.00) (–1.00) (–1.00) (–1.00) (–0.97) 0.00 (–1.00) (–1.00) 0.20

Siletz 0.58 0.66 (–0.17) 0.05 0.07 (–0.02) (–0.04) 0.75 0.07 0.05 0.98

Yaquina 0.96 0.76 0.30 0.54 0.27 0.83 0.83 0.86 0.54 0.54 1.00

Beaver 0.86 0.62 1.00 0.79 0.02 0.65 0.86 1.00 0.33 0.48 0.14

Alsea (–0.60) 0.96 (–0.27) (–0.47) 0.16 0.61 (–0.15) 0.92 0.08 (–0.40) 1.00

Siuslaw 0.62 0.98 0.09 0.35 0.93 0.88 0.38 0.76 0.62 0.44 1.00

Lakes 0.91 0.80 0.3

Siltcoos 0.78 1.00 1.00 0.91 0.43 0.92 1.00 1.00 0.72 0.80 0.32Tahkenitch 0.88 0.70 1.00 0.84 0.21 0.93 1.00 1.00 0.56 0.67 0.32

Tenmile 0.92 0.98 1.00 0.96 0.85 0.99 1.00 0.73 0.87 0.92 0.59

Umpqua 0.44 0.20 0.8

Lower Umpqua 0.88 0.86 0.67 0.78 0.75 0.66 0.55 0.55 0.61 0.68 1.00

Middle Umpqua 0.82 0.84 0.18 0.44 0.23 0.50 0.34 0.47 0.33 0.36 1.00

North Umpqua (–0.96) 0.52 0.46 (–0.94) (–0.74) (–1.00) 0.00 (–0.94) (–1.00) (–1.00) 0.67South Umpqua 0.50 0.94 0.23 0.43 0.19 0.46 –0.21 0.05 –0.08 0.04 1.00

Mid–South Coast 0.92 0.69 0.68 

Coos 1.00 0.86 0.97 0.94 1.00 0.94 0.91 1.00 0.96 0.95 1.00

Coquille 0.92 0.96 0.90 0.93 1.00 0.75 0.51 0.54 0.65 0.77 1.00

Floras 0.82 0.92 1.00 0.90 0.17 0.95 0.38 1.00 0.44 0.61 0.87

Sixes 0.84 (–1.00) (–0.30) (–1.00) (–0.96) (–0.57) (–0.44) (–1.00) (–1.00) (–1.00) 0.38

32

a Truth values at the watershed scale for PD-3 and PD-4 are in Table 6. b Truth values contributing to ES-2 are in Table 7. 
The chosen threshold for NRR of 1.0 is the most logical standard; it represents the point 

where a population would naturally replace itself, that is, the breakpoint between population 

increase and population decline. However, NRR cannot be measured without error, so if the 

observed value is slightly above 1.0, there is some statistical probability that the true value for 

the population is below the threshold. For that reason, instead of using the NRR directly the 

statistical probability that the NRR is greater than 1.0 as our metric has been used. Thus if the 

estimated NRR is exactly 1.0, the metric is a probability of 50% that the criterion would be met, 

which corresponds to a truth value of zero (uncertain). In addition to random statistical errors 

that go into the probability calculation, there are two important sources of possible bias: the 

proportion of hatchery fish on the spawning grounds and assumptions regarding their 

reproductive effectiveness. In places where the hatchery contribution is very low, these biases 

will have little influence on the estimates of NRR. To the extent that hatchery fish are less 

effective at spawning compared to naturally produced fish, the calculation of NRR is biased low; 

this is an intentionally conservative assumption. 

Productivity is a function of life cycle fecundity, survival, and growth rates that vary with 

a number of factors, including freshwater habitat quality, spawner density, interactions with 

other species, effects of hatchery fish, and harvest. However, a major factor determining 

productivity is the quality of freshwater habitat (Nickelson and Lawson 1998). Because marine 

survival (and thus recruitment) can vary greatly from year to year, the ratio of smolts to females 

provides a more stable measure of freshwater productivity than does the ratio of adult recruits to 

spawners. However, the number of adult recruits per spawner is easier to measure and better 

represents the total life cycle. 

The chosen metric is intended to provide a conservative proxy for intrinsic productivity, 

which could be estimated in a variety of ways. It may be estimated from spawner data series 

using stock-recruit analysis, although long data series are needed to achieve high confidence in 

estimates. Direct measures of smolt production and marine survival may be obtained from life 

cycle monitoring stations, and these data can be used to estimate smolt-production parameters; 

however, this would require that data is representative of the freshwater habitat available to the 

populations. Finally, habitat survey data can be used to estimate capacity based on statistical 

modeling of juvenile production from habitat characteristics. Several alternative approaches to 

estimating intrinsic productivity of the populations were considered, including simple spawnerto-spawner ratios, recruits-per-spawner, and smolts-per-spawner. 

An empirical estimate of productivity was chosen here so that this criterion is 

independent of the model-based analyses used for the Probability of Persistence Criterion metrics 

(PP-2, below). Only the lowest 50% of spawner abundance were used as a model-independent 

means to estimate a lower bound on the intrinsic slope of a spawner-recruit relationship. This 

estimate assumes a compensatory smolt production curve, but doesn’t specify its shape. Where 

habitat capacity is not substantially limiting population abundance, the lower 50% of 

observations are likely to be in the ascending range of the smolt production curve. Thus this 

method is likely to provide a result somewhat less than or equal to the true intrinsic productivity 

of the population. The approach is not fail-safe. If a population reaches its habitat capacity 

before delisting, then this method is likely to produce “false negative” errors. An exception 

should be considered if stock-recruit analysis gives high confidence that the stock is stable near 

maximum production levels. 

33
PP-2, Probability of Persistence 

Criterion: The population has a high likelihood of persisting over the next 100 years. 

Metric: To evaluate extinction risk, a variety of population viability models are applied 

to estimate the expected probability of extinction for the population. To evaluate the truth of the 

criterion, the results of all the models for each population were averaged. A persistence 

probability of 80% is considered to result in a truth value of false (–1.0), 95% to be uncertain (0), 

and 100% to be true (+1.0) (Figure 11). 

Persistence of each population was examined using four different extinction-risk models. 

For each model, two different extinction thresholds were used: absolute extinction (abundance of 

0 or 1), or a quasi-extinction threshold (QET) of 50 in 100 years. This use of multiple thresholds 

reflects uncertainty as to how well the models represent realistic population dynamics at 

extremely low abundances. 

All four models use the same general approach: Based on a particular stock-recruit 

model, multiple simulations of a 100-year period are run for each population, with a different 

pattern of environmental variation affecting recruitment in each model run. In addition to 

environmental variation, two of the models incorporate variation in the parameters of the stockrecruit relationship (parameter uncertainty). This approach basically follows methodologies 

described by Burgman et al. (1993) and Morris and Doak (2002) where random fluctuations in 

environmental conditions are added to an assumed population recruitment model and spawner 

abundance is simulated for a specified period of time into the future. Each run of the simulation 

is classified as either extinction (if the modeled population drops below the extinction threshold) 

or nonextinction (if the population remains above the threshold). The four models differ in 

important characteristics that affect the dynamics of modeled populations: 

• The form of the relationship between stock (spawners in one generation) and recruitment 

(returning adults in the next generation), including how per capita recruitment varies with 

stock density at high abundance (compensatory density-dependence) and at very low 

abundance (depensatory density-dependence) 

-1

0

1

0 0.25 0.5 0.75

Probability of Persistence

Truth Value Truth value 

1

Figure 11. Truth membership function for the Probability of Persistence Criterion, PP-2. 

34
• The sources of risk included in the model (environmental variation, demographic 

stochasticity, genetic stochasticity, and parameter uncertainty) 

• The assumed form of environmental variability (uncorrelated random, autocorrelated 

random, or cyclic processes affecting marine survival) 

The four extinction-risk models are described briefly below. Details of the model 

applications are in Appendix C. 

Model 1: Density-dependent, count-based population viability analysis (PVA) 

model. This model assumes that coho salmon recruitment conforms to a Ricker compensatory 

recruitment function, modified to remove the descending limb of the recruitment curve at higher 

spawner abundance levels. At low abundances, the function is truncated at low spawner 

densities so that no recruits are produced when spawner densities fall to less than 1 fish per mile 

(0.6 per km) (depensation). Parameters were estimated via multiple linear regression where the 

response variable was the natural log of recruits per spawner (ln(R/S)) and the predictor variables 

were spawner abundance and an index of marine survival. To simulate marine survival during 

each of the model runs, the values for the marine survival index observed during the period of 

record (1958 to present) were replicated as necessary to obtain a 100-year sequence of indices 

(cyclic environment). To address the issue of autocorrelation between years with respect to 

marine survival, the sequence of values observed from 1958 to present was maintained in all 

100-year marine survival sequences used in the model runs. Although this meant the pattern of 

survival rates was the same, each simulation was initiated with a different (randomly selected) 

starting point in this generalized cycle of survival rates. 

Model 2: SPAZ hockey stick viability model. The second model is a stochastic 

viability model developed by McElhany and Payne (2005) as part of a suite of models known as 

“SPAZ.” The model uses a “hockey stick” form of the stock-recruitment relationship, with a 

linear increase in recruitment at low stock abundance, and constant recruitment when the stock is 

at or above habitat capacity. This model has no decrease in per capita production at very low 

stock abundance (no depensation). The model estimates stock-recruitment parameters from 

spawner-recruit data. The model uses simple uncorrelated random environmental variation in 

simulations and also incorporates random variation in parameter estimates (parameter 

uncertainty). 

Model 3: Bayesian Salmon Analysis Model. This is an application of the Bayesian 

Salmon Analysis Model (BaySAM) (Wainwright et al. in prep.). The model combines densitydependent freshwater production with density-independent, environmentally driven marine 

survival to predict future population abundance and estimate risk of extinction. Recruitment is 

modeled as a Beverton-Holt compensatory relationship with spawner density modified with an 

exponential decline in recruits (depensation) at very low spawner densities. Stock-recruit 

parameter estimates incorporate both information from recent stock-recruit data for each 

population and prior information from a meta-analysis of production in other coho salmon 

populations (similar to that in Bradford et al. 2000) via a nonlinear regression technique. In 

addition to autocorrelated environmental variation, the model incorporates randomization in 

parameters to characterize parameter uncertainty. 

35
Model 4: Stochastic habitat-based life cycle model. The habitat-based life cycle model 

(Nickelson and Lawson 1998) was adapted to simulate all the river populations in the ESU. In 

this model, the dynamics of each population are driven by the quantity and quality of winter 

habitat. Instead of using a simple stock-recruit function, the model steps through the life cycle of 

coho salmon and includes both compensation and depensation at different life stages. Egg-toparr survival is density dependent. Habitat quality determines smolt capacity and overwinter 

survival. Environmental and demographic stochasticity enters the model at each life stage. The 

basic model was structured as described by Nickelson and Lawson (1998) with three exceptions: 

the stray rate between reaches was 20% instead of 5%, a more resilient egg-to-parr survival 

function was used based on recent data, and the marine survival cycle was based on historical 

survival of Oregon Production Area hatchery coho salmon survival. This model also simulated 

harvest as specified by Amendment 13 to the Pacific Coast Salmon Fishery Management Plan 

(PFMC 1999). 

Current assessment: Results of the analysis are reported in Table 2. For most 

populations, the four models suggest very high persistence probabilities (above 95%). The three 

models that could be applied to the Salmon population suggested a relatively high risk of quasiextinction; this is consistent with an observed recruitment of zero in 2000 and 2001. The four 

models were consistent in their evaluations of most populations. Notable exceptions were the 

Necanicum, Salmon, North Umpqua, and Sixes populations. Estimated truth values for the 

Probability of Persistence Criterion, PP-2, are presented in Table 3. The Salmon and Sixes 

populations have truth values of –1.0, which predicts a low likelihood of persistence over the 

next 100 years. Conversely, the Siltcoos at a truth value of +1.0 has a very high likelihood of 

persistence over the next 100 years. 

Discussion: Risks associated with demographic and environmental variation are often 

assessed using stochastic population models, commonly termed PVA models. In these models, 

short-term extinction risk is largely determined by four factors: current abundance, the intrinsic 

rate of population change, the capacity of habitat, and variability in fecundity, growth, or 

survival (Lande and Orzack 1988, Lande 1993). Estimating extinction risk with such models 

requires specifying a time frame for the analysis; for this report a 100-year time frame was 

chosen as suggested in the VSP report (McElhany et al. 2000), which is consistent with the 

chosen definition of persistence and with viability definitions used by other TRTs (e.g., Puget 

Sound TRT 2002, Willamette/Lower Columbia TRT 2006). 

This criterion also requires selection of a specific level of acceptable risk. A 95% 

probability of persistence was chosen as a definition of “high likelihood of persisting” for 100 

years. This is consistent with the level of acceptable risk suggested in the VSP report and used 

by other TRTs. To some extent, setting this level should depend on input from policy makers, 

but lacking a forum for such input, a commonly used value was selected. 

The use of PVA models raises a number of concerns. First, they are inherently based on 

assumed population dynamics models, usually derived from some form of stock-recruit analysis. 

Results are dependent on the form of the model used, and there is no clear agreement on which 

models are most appropriate for salmon. Several forms of stock-recruit relationships have been 

used for salmon (including the Ricker, Beverton-Holt, and hockey stick formulations), any of 

which may be combined with various forms of depensation (reduction in production at extremely 

36
low densities). A subsidiary concern here is that all the models used assume that model 

parameters are constant through time, which is equivalent to assuming that habitat and 

environmental conditions have had no trends during the period for which parameters are 

estimated; this assumption is not likely to be true. Second, PVA models are subject to statistical 

error and bias. Spawner abundance estimates for individual populations have relatively high 

measurement error, and the combination of high measurement error and environmental variation 

leads to the potential for bias in parameter estimates and resulting errors in extinction probability 

estimates. Third, as described above, there are a large number of factors that affect extinction 

risk, and most models include only a few of these factors, thus giving an incomplete assessment 

of risk. Fourth, because the analyses involve projections 100 years into the future, relevance of 

the results depends critically on assumptions about future conditions, and it is unknown what the 

future will be like. Future conditions depend on possible changes in underlying conditions of 

habitat, marine survival, fishery management, etc. 

Model 4 is able to simulate a variety of future conditions, and some sensitivity analyses 

have been conducted (see Appendix C). The model results used in this analysis assumed stable 

freshwater habitat and marine survival and a continuation of the current fishery management 

system. It is likely that these conditions will not be stable and that freshwater habitat condition, 

in particular, will decline over the next century in response to population growth pressures and as 

a legacy of past land use practices. Results from a larger set of simulations that explore the 

importance of freshwater habitat, marine survival, and harvest to the viability of Oregon Coast 

coho salmon are reported in Appendix C. 

Finally, obtaining reliable absolute predictions of extinction probabilities is difficult, 

causing some authors to recommend against ever using PVA in situations demanding absolute 

answers (Reed et al. 2002). Coulson et al. (2001) note two necessary conditions for accurate 

PVA: 1) data must be of sufficient quality that estimates of the statistical distributions (shape, 

mean, variance, and autocorrelation) of vital rates are accurate, and 2) future distributions of 

these rates must be similar to those estimated, or changes in rates must be predictable. They 

argue that there are few real situations where these conditions can be met and conclude: 

“... PVAs could be useful for comparing the consequences of different 

management or conservation strategies, and for exploring theoretically the 

implication of model assumptions on extinction probabilities and population 

dynamics. However, we doubt the general claim that they can be accurate in 

their ability to predict the future status of wild populations” (Coulson et al. 2001, 

p. 221). 

In contrast, other authors (Brook et al. 2000, 2002) conclude that PVAs are reliable in 

classifying and managing populations despite their shortcomings, but note that: 

“the results of a PVA are just one factor in any social and political decisionmaking context and should be a necessary precursor to good judgment” (Brook et 

al. 2002, p. 263). 

Despite these problems, PVA is presently the only tool available to quantify the risk of 

extinction to populations. In addition, the Probability of Persistence Criterion is the only one 

that integrates the multiple factors of habitat quality, habitat quantity, life cycle survival, and 

37
recruitment variability. To deal with problems of model structure and statistical issues, we used 

a variety of models with different structural assumptions; to the degree that results of these 

models agree, confidence is gained in the estimated level of risk. 

PP-3, Critical Abundance 

Criterion: Population abundance is maintained above levels where small-population 

demographic risks (depensation or Allee effects) are likely to become significant, even during 

periods of adverse environmental conditions. 

Metric: This is measured by the average peak spawner density (peak adults per mile of 

occupied spawning habitat) in the lowest 3 of the last 12 years. Any value of this metric below 1 

adult per mile (0.6 per km) is equated with false (–1.0), 4 per mile (2.5 per km) is uncertain (0), 

10 per mile (6 per km) is highly certain (+0.8), and 20 or more per mile (12 per km) is true 

(+1.0) (Figure 12). 

To evaluate the Critical Abundance Criterion, average density of naturally produced 

spawners in occupied random survey sites was calculated for each population, each year, during 

the 12-year period 1993–2004. For the North Umpqua, where survey data was unavailable, the 

count of naturally produced fish at Winchester Dam was divided by the total spawning miles to 

estimate density. For each population, the density for the 3 lowest years was then averaged. 

Current assessment: Values for the Critical Abundance Criterion metric are presented in 

Table 2, with estimated truth values in Table 3. The truth value for the Salmon population (–1.0: 

false) predicts it will not be possible to maintain abundance above levels where small-population 

demographic risks (depensation or Allee effects) are likely to become significant during some 

periods. The Beaver, Siltcoos, Tahkenitch, Tenmile, and Floras/New populations, with truth 

values of +1.0 (true), are predicted to be able to avoid small-population demographic risks. 

Discussion: Abundance has been used as a main indicator of population status for many 

years, beginning with the initial concept of “minimum viable populations” developed by Shaffer 

(1978, 1981, 1987). Small-population effects include both directional effects and random 

effects. Directional effects include two contrasting density-dependent effects: compensation (a 

decrease in population productivity as population density increases) and depensation (a decrease 

-1

0

1

0 5 10 15 20

Average fish per mile in lowest three years

Truth Value Truth value 

Average fish per mile in lowest 3 years 

Figure 12. Truth membership function for the Critical Abundance Criterion, PP-3. 

38
in productivity as density decreases). Typically, compensation is important at high density, and 

depensation at very low density, so there is in theory a crossover point in dominance of the 

effects. Dennis (1989) thoroughly reviewed the theory of depensation, and there have been 

several reviews of empirical evidence for such effects in salmon (Peterman 1987, Myers et al. 

1995, Liermann and Hilborn 1997). Random effects include probabilistic variation in family 

size, sex ratios (Gabriel and Bürger 1992), ability of individuals to find mates (Dennis 1989), and 

similar processes (Lande 1998). 

There is a risk that this idea of critical abundance may be confused with two other smallpopulation concepts: minimum viable population (MVP) and QET. It is synonymous with 

neither. The abundance level chosen addresses only depensation effects, which are only one of 

several factors in evaluating extinction risk. MVPs are abundance levels sufficient to ensure a 

high persistence probability over some particular period of time, and their assessment should 

include consideration of all extinction risk factors; thus, an MVP level would be higher than the 

criterion used here. QETs are thresholds defined primarily for convenience in population 

viability modeling and may be based on a number of considerations including Allee effects and 

uncertainties in population functional response at very low abundance. In evaluating the 

Probability of Persistence Criterion (above), a QET of 50 fish per population was used, which 

may be above or below this criterion depending on the amount of available spawning habitat for 

any given population. 

One issue that should be considered here is the appropriate spatial scale for assessing this 

criterion. The data used by Sharr et al. (2000) was similar in scale to Independent Populations as 

was that of Barrowman et al. (2003), so in that sense metrics developed here are consistent with 

the studies that support them. However, the actual processes leading to Allee effects (Dennis 

1989) tend to operate on much smaller scales, such as that of individual redds or spawning areas. 

For salmon, the scales of interaction can be quite small, and their connectivity is important 

(Neville et al. 2006, Isaak et al. 2007). While this suggests that measuring spawner density at 

finer spatial scales might better reflect the processes the workgroup wishes to capture, it is aware 

of no published studies that would provide appropriate numerical criteria at those scales, and it 

would be very difficult to effectively sample at those scales. 

To reflect this finer scale of depensatory processes, the criterion is based on the density of 

spawners in occupied survey reaches. At this scale it seems clear that when a single spawner is 

present in a 1-mile reach there is a strong risk that both sex ratio and mate-finding problems will 

be a factor. With four fish per mile (2.5 per km), it is unlikely that mate finding is a problem, but 

there is still some chance that there will be a skewed sex ratio resulting in fewer matings than at 

higher densities. As densities increase above this level, these risks can be expected to decline 

rapidly. For comparison, Barrowman (2000, Barrowman et al. 2003) found that, for natural 

populations of coho salmon, depensation may be a factor when spawner densities are less than 1 

female per km. Sharr et al. (2000) found that 4 adults per mile (2.5 per km) was the approximate 

threshold of depensation in a model of Oregon Coast coho salmon. 

Based on these considerations, the workgroup concluded that small-population 

demographic risks were very likely to be significant (–1.0: false) when spawner density was 

below 1 spawner per mile (0.6 per km), uncertain (0) at 4 spawners per mile (2.5 per km), 

39
unlikely (+0.80: high degree of certainty of truth) at 10 per mile (6 per km), and very unlikely 

(+1.0: true) above 20 per mile (12 per km) (Figure 12). 

By using the average of the 3 lowest of 12 years, our aim is to be precautionary by having 

a criterion that will identify potential demographic risks before it is too late to respond. Three 

years were chosen to reflect a single generation for coho salmon, although it is not required that 

the 3 years be consecutive. This allows the criterion to reflect two different risk scenarios. First, 

if spawners are low during all of the 3 most recent years, this would indicate that the entire 

population may be at elevated risk, and this would be immediately reflected in the metrics. 

Second, if spawners are low every third year across three or four generations, this could indicate 

risk of loss of a single brood cycle out of the approximate 3-year life cycle. 

PP, Population Persistence (Combined Criterion) 

Criterion: The population will persist for the next 100 years. 

Metric: This requires that populations meet each (DSS “AND” operator) of three 

subcriteria: Population Productivity, Probability of Persistence, and Critical Abundance 

(Figure 9). 

Current assessment: The combined truth values for the Population Persistence Criterion, 

PP, range from –1.00 (false) for the Salmon and Sixes populations to +0.96 (high certainty of 

being true) for the Tenmile population (Table 3). These results predict that the Salmon and Sixes 

populations have a very low likelihood of persisting over the next 100 years. Conversely, the 

Tenmile is predicted to have a very high likelihood of persisting over the next 100 years. In 

addition to the Salmon and Sixes populations, three other populations had negative truth values: 

Necanicum, Alsea, and North Umpqua, which raises red flags as to the likelihood of persistence 

of these populations over the next 100 years as well. The remaining 16 populations had truth 

values greater than 0 (uncertain), with 9 greater than +0.50 (moderately to highly certain that the 

population will persist for the next 100 years). 

Discussion: The Population Persistence Criterion is focused on the ability of a population 

to persist over a short to moderate time frame (≈ 100 years). The main determinants of 

persistence are productivity, abundance (or capacity), and variability. These factors are all 

interrelated, and it is impossible to set independent criteria for all. However, there are clear 

minimum acceptable levels for productivity and abundance. First, intrinsic productivity (i.e., the 

returns per spawner at low spawner density) must be sufficient for the population to replace itself 

under any likely conditions. This minimum ensures that the population is capable of surviving 

through periods of poor environmental conditions. Second, there is some level of abundance 

below which small population risks become significant, and populations that drop below that 

critical abundance are at significant risk of extinction. Finally, extinction modeling is used as a 

tool to integrate all relevant variables (abundance, productivity, and variability) into a persistence 

probability. All three of these criteria are necessary to ensure population persistence. 

40
Biogeographic Stratum-level Persistence Criteria 

SP, Stratum Persistence 

Criterion: Most of the historically Independent Populations in the stratum are persistent 

according to the Population Persistence Criterion, PP, defined above. 

Metric: This is measured as the median Population Persistence Criterion truth value 

(DSS “MEDIAN” operator) of Independent Populations in the stratum. 

Current assessment: The results are presented in Table 3. The Mid-Coast 

biogeographic stratum had the lowest Stratum Persistence Criterion value (+0.20: low to 

moderate certainty that most of the historically Independent Populations in the stratum are 

persistent) and the Mid-South Coast had the highest Stratum Persistence Criterion value (+0.92: 

high certainty that most of the historically Independent Populations in the stratum are persistent). 

Discussion: By using the median of population values, we ensure that the majority of 

Independent Populations in the stratum will persist. An alternative approach would be to use the 

average truth value (DSS “UNION” operator), which would indicate that the populations within 

a stratum meet the criteria on average. This was considered, but the workgroup concluded this 

would allow one or two highly persistent populations to compensate for a large number of 

populations in poor conditions (or conversely, for one or two populations in extremely poor 

condition to prevent the stratum from meeting the criteria). The important consideration here is 

to spread risk by ensuring that multiple populations within each stratum are persistent. Given 

that the number of Independent Populations per stratum ranges from three to six, this criterion 

guarantees that two or three populations are persistent per stratum, depending on the geographic 

extent of the stratum. 

ESU-level Persistence Criteria 

EP, ESU Persistence 

Criterion: Persistence of the ESU requires that all of the biogeographic strata be 

persistent. 

Metric: This requires that all (DSS “AND” operator) biogeographic strata meet the 

Stratum Persistence criteria. 

Current assessment: Combining the Biogeographic Stratum Persistence Criterion values 

yields an ESU Persistence Criterion truth value of +0.40 (moderate to high certainty that all of 

the biogeographic strata are persistent) (Table 3). 

Discussion: The ESU Persistence Criterion relates to the current danger of extinction for 

the ESU. The emphasis here is on protecting all significant parts of the range and avoiding risks 

of local catastrophes, rather than longer-term genetic diversity issues that are addressed under the 

ESU Sustainability Criterion, ES, (below). Accepting the 100-year time frame suggested in the 

VSP report (McElhany et al. 2000), the workgroup sought to ensure a high likelihood of 

41
persistence of all the biogeographic strata over 100 years. At a coarse scale, this criterion 

ensures persistence of populations throughout the range of the ESU. Requiring multiple 

persistent populations per stratum reduces risk from changes in local conditions and populationscale catastrophes. By meeting the Population Persistence Criterion for multiple populations 

within each stratum, it is ensured that the ESU is not in danger of extinction throughout its range. 

Summary—Persistence 

Persistence refers to a population or ESU that has a high expectation of persisting (i.e., 

not going extinct) over a 100-year period without artificial support (including supplementation 

by hatchery fish). This includes the ability to survive prolonged periods of adverse 

environmental conditions that may be expected to occur at least once during the 100-year time 

frame. In the ESU Persistence Criterion DSS network, the lowest level population criteria form 

the basis of the analysis, with aggregations to the biogeographic stratum and ESU levels moving 

upward in the network (see Figure 9). Three criteria—Population Productivity, PP-1; Probability 

of Persistence, PP-2; and Critical Abundance, PP-3—are evaluated, then combined to evaluate 

the overall Population Persistence Criterion, PP, for each population. Evaluations for individual 

populations are combined to arrive at a biogeographic Stratum Persistence Criterion, SP. 

Biogeographic stratum results are then combined to arrive at an ESU Persistence Criterion, EP. 

The workgroup used recent observations of these population metrics to evaluate how 

certain it was that the Oregon Coast Coho Salmon ESU is persistent under recent conditions. 

Results of the current assessment for persistence are mapped in Figure 13. The results of the 

analysis lead to the conclusion that, if recent past conditions continue into the future, there is a 

moderate to high certainty that the ESU is persistent. 

42
Figure 13. Persistence analysis results. Individual population-level criteria results are shown in maps A, 

B, and C and are combined to produce the Population Persistence Criterion scores (map D). 

These scores are aggregated by biogeographic strata to evaluate the Stratum Persistence Criterion 

(map E). The biogeographic stratum results are aggregated to evaluate the ESU Persistence 

Criterion (map F). These maps are color coded to represent the numerical scores found in 

Table 3. 

43
Sustainability 

Sustainability refers to the ability of the ESU and its populations to maintain their genetic 

legacy and long-term adaptive potential for the foreseeable future. This definition implies 

stability of habitat availability and other conditions necessary for the full expression of life 

history diversity into the foreseeable future. To evaluate sustainability, the workgroup used the 

ESU Sustainability Criterion DSS network shown in Figure 14. The lowest level criteria are at 

the bottom, with combinations to the population, biogeographic stratum, and ESU levels higher 

up the figure. The criteria are divided into two main branches: All Strata Sustainable, ES-1, and 

ESU-level Diversity Criterion, ES-2. 

The first branch requires that all biogeographic strata meet a Stratum Sustainability 

Criterion, SS, which in turn requires a Stratum Diversity Criterion, SD, and a Stratum 

Functionality Criterion, SF. SD in turn depends on the Population Sustainability Criterion, PS, 

for populations within each stratum, which requires meeting both the Population Persistence 

Criterion, PP (page 40), and the Population Diversity Criterion, PD. Within PD, there are four 

criteria: Spawner Abundance, PD-1; Artificial Influence, PD-2; Spawner Distribution, PD-3; and 

Juvenile Distribution, PD-4. The last two also have watershed-level subcriteria. The Stratum 

Functionality Criterion, SF, depends on the Population Functionality Criterion, PF. 

The ESU-level Diversity Criterion branch has three ESU-level criteria: Genetic Diversity, 

ED-1; Phenotypic and Habitat Diversity, ED-2; and Small Populations, ED-3. The Genetic 

Diversity Criterion has four subcriteria: Genetic Structure, ED-1a; Effects of Selection, ED-1b; 

Effects of Migration, ED-1c; and Effects of Introgression, ED-1d. The Phenotypic and Habitat 

Diversity Criterion has two subcriteria: Phenotypic Diversity, ED-2a, and Habitat Diversity, 

ED-2b. 

Population-level Sustainability Criteria 

Primary Population Diversity Criteria 

Population diversity is evaluated on the basis of the following four primary criteria: 

Spawner Abundance, Artificial Influence, Spawner Distribution, and Juvenile Distribution, 

which are then combined into a single Population Diversity Criterion. 

PD-1, Spawner Abundance 

Criterion: The population has sufficient naturally produced spawners to prevent loss of 

genetic variation due to random processes over a 100-year time frame. 

Metric: Spawner abundance is measured as the long-term harmonic mean of naturally 

produced spawners (both 3-year-old adults and 2-year-old jacks) based on annual surveys. 

“Long-term” means including all available data (up to 100 years). The harmonic mean is 

44
*See Persistence 

criteria 

Figure 14. The decision network for the ESU Sustainability Criterion; the symbols are described in more 

detail in Figure 9 and criterion abbreviations are defined in the text. 

45
calculated as the reciprocal of the mean of reciprocals (i.e., n/[1/x1 + 1/x2 + ... + 1/xn], where xi is 

the abundance in year i, and n is the number of years of data). Any mean value below 85 

naturally produced spawners is equated with false (–1.0), a value of 450 is equated with 

uncertain (0), and any value above 5,000 is equated with true (+1.0) (Figure 15). 

This metric requires annual estimates of both adult (age 3) and jack (precocious age-2 

male) spawners for each population. Such data is available for the majority of the Independent 

Populations of the Oregon Coast Coho Salmon ESU only for the period 1990–2003 (except the 

North Umpqua population, for which data is available for 1946–2003). Most of this short period 

was likely the lowest period of spawning abundance in the last 100 years. Thus it does not 

provide a very good picture of long-term spawner abundance. As an alternative, estimates were 

made of adult spawners for most individual populations (exceptions: Salmon, Floras/New, and 

Sixes) for the period 1958–2004 by developing calibrations between peak counts in standard 

spawning surveys (or in the case of the Middle and South Umpqua, Winchester Dam counts) for 

the period 1990–2004 and applying them to counts from 1958 to 1989 (Appendix D). These 

estimates were then modified to account for the presence of hatchery fish in the natural spawning 

population (see Appendix D for details). This 47-year data set provides a much better basis from 

which to draw conclusions about the long-term harmonic mean spawner abundance, although it 

provides an underestimate because it does not include jacks. Therefore, the harmonic mean was 

adjusted upward by dividing by one minus the average proportion of jacks observed in the 

standard surveys during the period 1981–2003. 

Current assessment: Harmonic mean spawner abundances are presented in Table 4. No 

long-term data are available for the Salmon, Floras/New, and Sixes populations; for these 

populations only the 1990–2004 data were used. Estimated truth values are presented in Table 3 

and Figure 16, and range from –1.0 (false: the population does not have sufficient naturally 

produced spawners to prevent loss of genetic variation due to random processes over a 100-year 

time frame) for the Salmon population to +1.0 (true: the population has sufficient naturally 

produced spawners to prevent loss of genetic variation due to random processes over a 100-year 

time frame) for the Coos and Coquille populations. 

-1

0

1

0

Truth Value Truth value 

1000 2000 3000 4000 5000 6000

Harmonic mean spaw ner abundance

Figure 15. Truth membership function for the Spawner Abundance Criterion, PD-1. 

0 1,000 2,000 3,000 4,000 5,000 6,000

Harmonic mean Spawner Abundance 

46
Table 4. Population Sustainability criteria results for Independent Populations of Oregon Coast coho 

salmon. 

PD-1 

Spawner Abundance 

PD-2 

Artificial Influence 

PF 

Population 

Functionality 

Independent 

Population 

Metric 

Harmonic mean 

abundance of adults and 

jacks (1958–2004) 

Average proportion of 

naturally produced 

spawners in last 6 years 

Smolt capacity 

estimated from habitat 

survey data 

North Coast 

Necanicum 463 0.944 63,200 

Nehalem 3,921 0.915 1,039,000 

Tillamook Bay 939 0.945 505,100 

Nestucca 1,126 0.981 225,500 

Mid-Coast 

Salmon 23* 0.338 68,600 

Siletz 763 0.891 245,000 

Yaquina 1,691 0.983 481,700 

Beaver 540 0.965 54,700 

Alsea 1,173 0.961 618,500 

Siuslaw 4,664 0.988 1,080,600 

Lakes 

Siltcoos 2,419 0.992 94,400 

Tahkenitch 1,422 0.993 95,000 

Tenmile 4,319 0.999 157,000 

Umpqua 

Lower Umpqua 3,847 0.966 712,400 

Middle Umpqua 1,509 0.950 616,200 

North Umpqua 179 0.277 174,900 

South Umpqua 1,297 0.946 639,100 

Mid-South Coast 

Coos 6,025 0.994 840,300 

Coquille 5,151 0.975 595,400 

Floras/New 1,238* 0.995 220,800 

Sixes 99* 0.672 110,000 

*1990–2004 adults only. 

47
Figure 16. Population Sustainability Criterion analysis results. These results are shown in maps A, B, C, 

and D, and are combined to produce the Population Diversity Criterion, PD, scores (map E). 

These scores are aggregated with the Population Persistence Criterion, PP, (Figure 13D) to 

produce the Population Sustainability Criterion, PS (map F). These maps are color coded to 

represent the numerical scores found in Table 3. 

48
Discussion: This criterion recognizes that genetic diversity is essential to long-term 

sustainability. The central (0 = uncertain) value used in the metric is based on an analysis of the 

effective population size needed to avoid inbreeding and maintain natural levels of genetic 

heterozygosity in a coho salmon population (see Appendix B). In Appendix B, we estimated the 

population size needed to maintain 95% of heterozygosity in the population over a 100-year 

period. Since that analysis was completed, new theory on the effective population size of 

fluctuating populations has become available (Kalinowski and Waples 2002, Nunney 2002, 

Waples 2002, Waples 2006), which suggests that the analysis presented in Appendix B is not 

entirely correct. 

The analysis in Appendix B begins with calculation of the number of effective breeders 

per generation needed to preserve 95% of heterozygosity over a 100-year period based on 

observed Oregon Coast coho salmon spawner age distributions. This calculation resulted in 

target values for effective population size (Ne*) of between 332 (river populations) and 351 (lake 

populations). This is the number of spawners that would be required if the population structure 

is ideal, that is, there is an equal probability of reproductive success for all members of the 

population. 

To translate this theoretical target into actual spawner numbers (N) for a real population, 

adjustments need to be made for a variety of factors. First, there are within-year effects that 

result in variation among individual spawners within a year in reproductive success (measured 

by contribution of offspring to the next generation). Estimating this effect from data on family 

size variation for salmonids results in a ratio of annual Ne to N of about 0.53 (see Appendix B). 

Second, there are between-year effects that result from year-to-year variation in both spawner 

numbers and survival to adulthood, thus causing year-to-year variation in the annual average 

reproductive success of spawners. 

This effect can be largely accounted for by taking the harmonic mean of annual spawner 

numbers (Waples 2002), but this approach typically still results in an underestimate of effective 

population size for salmon (Waples 2006). Waples (2006) concludes between-year effects are 

mainly controlled by two factors: relative variation in mean reproductive success across years, 

and correlations between annual mean reproductive success and spawner abundance (e.g., 

density dependence). 

Simulations by Waples (2006, Table 3) suggest the density-independent effects on the 

ratio might range from 0.71 to 0.91, with the lower values representing populations with lower 

variation in spawner age or higher variation in mean reproductive success. Because coho salmon 

have both low variation in spawner age and high variation in annual recruits per spawner, this 

suggests a correction of about 0.71. In addition, coho salmon are strongly density dependent (see 

Appendix C), which should result in an even lower ratio of Ne to N, but there is insufficient 

information to estimate the magnitude of this effect. For now, it will be assumed that the 

between-year effect (combining both density-dependent and density-independent effects) to be in 

the range 0.40 to 0.67. Thus the target abundance, measured as the harmonic mean of annual 

spawner abundance (Ñ*), can be estimated as (see Appendix B): 

Ñ* = Ne* / [(within-year effect) × (between-year effect) × (mean generation time)] (1) 

49
Substituting values of Ne* and generation times for both lake (2.78 year) and river (2.94 

year) populations, combined with the range in estimates for between-year effects, results in Ne to 

Ñ ratios ranging from 0.59 to 1.05 and values for Ñ* ranging from 316 to 595. A value near the 

midpoint of this range (450) is taken as the uncertain (0) point in the criterion. 

The extreme values corresponding with false (–1.0) and true (+1.0) were derived from 

guidelines for gene conservation, as reviewed by Allendorf and Ryman (2002). They suggest 

that an effective population size per generation of 50 is the minimum needed to prevent shortterm losses due to inbreeding, and this value is used as the basis of the “false (–1.0)” level. They 

also suggest a range in effective population per generation of 500 to 5,000 to maintain genetic 

integrity of a population over the long term and cite some controversy in the conservation 

genetics literature regarding which of these two values is appropriate, but there is agreement that 

populations with effective size above 5,000 will experience very little random genetic change, so 

this value was chosen as the “true (+1.0)” level. Translating these values from effective 

spawners per generation to harmonic mean annual spawners using the Ne to Ñ ratios derived 

above, the workgroup obtained a range for the lower limit of 50 to 85, and for the upper limit of 

5,000 to 8,500. Because density-dependent effects are more likely to be important when 

population abundance is low, the larger density-dependence adjustment were used for the lower 

limit, and the smaller adjustment for the upper limit, resulting in values of 85 and 5,000 for the 

false (–1.0) and true (+1.0) metrics. 

Using the long-term harmonic mean incorporates the long-term effects of potential 

genetic bottlenecks during the 1990s; some populations may need long periods of fairly high 

abundance to overcome this deficit. As an alternative, a criterion of 1,000 spawners was 

considered based on the advice of Allendorf and Ryman (2002) and approaches used by Canada 

for upper Fraser River coho salmon viability standards (Interior Fraser Coho Recovery Team 

2006), but it was decided that an analysis based specifically on coho salmon life history 

characteristics was more appropriate. 

The workgroup also considered the issue of including hatchery fish in this criterion. 

There are divergent views on this issue. One argument is that hatchery fish also contribute to 

genetic variation; thus, basing this criterion on all spawners in the population would be most 

consistent with the genetic diversity goal. Another argument is that, because of artificial 

selection and reduced fitness among hatchery fish, their genetic contribution should not be 

considered here. The latter course was adopted, including only naturally produced fish in the 

metric. This leads to a more conservative criterion than one that included hatchery fish. 

PD-2, Artificial Influence 

Criterion: The abundance of naturally spawning hatchery fish will not be so high as to 

be expected to have adverse effects on natural populations. 

Metric: This is measured as the six-year (two-generation) mean of annual estimates of 

the proportion of naturally produced fish (one minus the proportion of hatchery fish) in spawning 

surveys for the population. Only years with adequate data to provide a reliable estimate are 

included in the average (see Appendix D). (Pending further statistical analysis, adequate data in 

any year is defined as at least 10 fish observed for marks denoting hatchery origin.) The 

50
associated truth value is false (–1.0) for values below 50%, uncertain (0) for a value of 90%, and 

true (+1.0) at 100% (Figure 17). 

The proportion of hatchery fish in the natural spawning population has been estimated 

each year since 1990 (Jacobs et al. 2002), originally from scale patterns and since 1998 from 

adipose fin clips, as most hatchery fish are now marked with an adipose fin clip. See Appendix 

D for a description of how the proportion of hatchery spawners was determined for each 

population. 

Current assessment: Average proportions of naturally produced spawners are presented 

in Table 4, and range from 0.277 for the North Umpqua to 0.999 for Tenmile. Estimated truth 

values are presented in Table 3 and Figure 16. Truth values range from –1.0 (false: the 

abundance of naturally spawning hatchery fish is so high as to be expected to have adverse 

effects on natural populations) for the Salmon and North Umpqua populations, to +0.99 (true: the 

abundance of naturally spawning hatchery fish is not so high as to be expected to have adverse 

effects on natural populations) for the Tenmile population. 

Discussion: Issues regarding the effects of hatchery fish on population sustainability 

were discussed in the Approach to Developing the Criteria section on page 18. This criterion is 

similar to that in Oregon’s Native Fish Conservation Policy (ODFW 2003), but uses an average 

proportion over 6 years rather than the ODFW approach of meeting the 90% criterion in 3 of the 

most recent 5 years. A 6-year time frame is consistent with the other criteria, which measure 

time in terms of 3-year coho salmon generations. The ODFW criterion was problematic in that, 

by requiring the criterion be met in only 3 of 5 years, there was no restriction on genetic 

introgression during the other 2 years. Such unlimited introgression could result in genetic 

swamping of the native population in those years. 

There are many statistical problems with estimating the hatchery-to-natural proportions 

for populations from observations of marks during spawner surveys, including observation bias, 

adjusting for location of samples within basins relative to hatchery release and acclimation sites, 

and small sample sizes. A full analysis of these problems should be conducted to develop the 

best statistical approach for this criterion, but that has not yet been done. As an interim measure, 

estimates are restricted to those years with at least 10 adult fish observed for marks; this is the 

-1

0

1

0 0.25 0.5 0.75 1

Proportion naturally produced spaw s ner

Truth Value uth value Tr

Proportion naturally produced spaw ers n

Figure 17. Truth membership function for the Artificial Influence Criterion, PD-2. 

51
minimum sample size that could reasonably detect 10% hatchery-origin fish in a sample. Using 

a higher sample size limit would result in more reliable estimates, but would also mean that there 

would be no estimates for many populations. 

PD-3, Spawner Distribution 

Criterion: On average, the historically occupied watersheds in the population’s range 

have spawners occupying the available spawning habitat. 

Metric: Evaluating this measure at the population level requires first evaluating a 

subcriterion regarding occupancy of each watershed within the population (Spawner Watershed 

Occupancy [W-Sp]). The truth value for the population is then the average (DSS “UNION” 

operator) of the watershed values. 

Subcriterion W-Sp, Spawner Watershed Occupancy 

Criterion: Spawners occupy a high proportion of the available spawning habitat within 

the watershed. 

Metric: Watershed occupancy is measured by the average occupancy rate of watersheds 

during the most recent 12 years. The distribution of spawners is analyzed by fifth-field 

hydrologic units (HUC5) (REO 2002) for each population by examining data from randomly 

selected survey reaches (Jacobs and Nickelson 1998). Average occupancy is the 12-year average 

of the annual fraction of habitat occupied, calculated within each watershed (HUC5) as the 

proportion of surveyed reaches in any year that have a minimum peak count (highest count on 

any single survey date) of at least four naturally produced spawners per mile (2.5 per km). Truth 

values for each watershed are evaluated as follows: false (–1.0) for occupancy less than 20%, 

uncertain (0) for occupancy of 50%, and true (+1.0) for occupancy greater than 80% (Figure 18). 

For the current assessment, the proportion of occupied sites within each HUC5 was 

averaged for 1993–2004. No data were available for one HUC5 in the South Umpqua and the 

three HUC5s of the North Umpqua because spawning surveys have not been routinely conducted 

in the area above Winchester Dam, where passage is enumerated. 

-1

0

1

0 0.25 0.5 0.75

Truth value Truth Value

1

Proportion watershed occupied by juveniles or spawners Proportion of watershed occupied by juveniles or spawners

Figure 18. Truth membership function for the Spawner Watershed Occupancy Criterion, W-Sp, and the 

Juvenile Watershed Occupancy Criterion, W-Ju,. 

52
53

Current assessment: Watershed-level occupancy rates are presented in Table 5, with 

corresponding truth values in Table 6. Population-level truth values are presented in Table 3 and 

Figure 16. Truth values range from –0.97 (false: spawners do not occupy a high proportion of 

the available spawning habitat within the watershed) for the Salmon population, to +1.0 (true: 

spawners occupy a high proportion of the available spawning habitat within the watershed) for 

the three lake populations (Siltcoos, Tahkenitch, and Tenmile). 

Discussion: Spawning distribution can give a good idea of the expansion and contraction 

of coho salmon populations during various ocean productivity cycles. The choice of four 

generations (12 years) provides a period that can be expected to include wide variation in 

environmental conditions. The goal here is to measure occupancy rates of spawning pairs, but 

available data do not distinguish gender. By defining occupancy as a peak count of at least four 

fish, it is guaranteed that at least four fish were observed in close proximity at the same time, and 

four fish are very likely to have at least one male and one female. This definition also guarantees 

that the total annual spawner density for a given reach is higher than four spawners per mile (2.5 

spawners per km), which is the density level below which depensation may be important (Sharr 

et al. 2000). The four-spawners-per-mile metric is used only to define occupancy for each of the 

sample reaches, it does not represent a target abundance level for distribution. Analysis of coho 

salmon spawner distribution during the 1989–2000 low-abundance cycle established that 

occupancy at this level in 50% of the watersheds maintained an adequate spatial distribution of 

spawners in habitats that helped anchor the populations (Talabere and Jones 2001). 

Additionally, populations that maintained occupancy at this level demonstrated rapid increases in 

spatial extent during the 2001–2003 period of improved ocean survival (Chilcote et al. 2005). 

This metric relies on information that directly measures the occupation of habitats 

throughout the spawning distribution of Oregon Coast coho salmon. The sampling program that 

supports this criterion relies on a spatially balanced design that can be used to assess both trends 

and status of distribution. However, there are problems with this approach. In particular, the 

short time frame for the evaluation—7 years under current levels of implementation—makes it 

difficult to evaluate long-term risk. Observations during a period of good environmental 

conditions may not predict occupancy patterns during future, less-favorable periods. 

The Oregon Plan assessment of occupancy patterns, although based on a less robust data 

set, demonstrated that this metric is sensitive to changes in spawner distribution over a range of 

environmental conditions, including the period of poor ocean survival during the 1990s. The 

minimum interpretation of this criterion would be that there is a distribution of habitat that is 

functional under very recent environmental conditions. A population with a spawner distribution 

that has a moderate or high score for this criterion during a period of high marine survival might 

experience severe difficulties the next time marine survival rates are low. Issues of fluctuating 

environmental conditions were discussed in the Approach to Developing the Criteria section on 

page 18. 
Table 5. Occupancy of watersheds (HUC5) by spawners, W-Sp, and juveniles, W-Ju. See text, pages 52 and 56, for defini

ND = no data. 

Percentage of 

sites occupied 

Percentage of 

sites occupied 

Population HUC W-SP W-Ju Population HUC W-SP W-Ju Population HNecanicum 1710020101 59 73 Alsea 1710020501 41 89 North 17100

1710020502 40 72 Umpqua 17100

Nehalem 1710020201 64 74 1710020503 70 75 17100

1710020201 55 72 1710020504 22 75 

1710020201 58 94 South 17100

1710020201 38 29 Siuslaw 1710020601 58 58 Umpqua 17100

1710020201 22 58 1710020602 60 100 17100

1710020603 67 100 17100

Tillamook 1710020301 40 69 1710020604 63 52 1710

Bay 1710020301 46 69 1710020605 71 79 17100

1710020301 47 65 1710020606 67 85 17100

1710020301 41 63 1710020607 64 92 17100

1710020301 21 50 1710020608 41 72 17100

17100

Nestucca 1710020301 49 92 Siltcoos 1710020701 81 83 17100

1710020302 36 69 

Tahkenitch 1710020701 92 83 Coos 17100

Salmon 1710020408 21 50 17100

Tenmile 1710030403 92 72 17100

Siletz 1710020405 58 75 

1710020407 40 70 Lower 1710030304 45 100 Coquille 17100

Umpqua 1710030305 60 50 17100

Yaquina 1710020401 72 100 1710030306 68 88 17100

1710020402 84 93 1710030307 80 100 17100

1710020403 72 67 1710030308 80 42 17100

17100

Beaver 1710020505 76 100 Middle 1710030301 56 96 

Umpqua 1710030302 79 40 Floras/New 17100

1710030303 46 72 

Sixes 17100

54
Table 6. Summary of watershed (HUC5) truth values of the knowledge-based DSS as to the relative distribution of coho sa

Coast ESU. 

Population HUC W-SP W-Ju Population HUC W-SP W-Ju Population HUNecanicum 1710020101 0.31 0.73 Alsea 1710020501 (–0.31) 1.00 North 171003

1710020502 (–0.04) 1.00 Umpqua 171003

Nehalem 1710020201 0.46 0.80 1710020503 0.68 0.83 171003

1710020202 0.16 0.73 1710020504 (–0.93) 0.83 

1710020203 0.25 1.00 South 171003

1710020205 (–0.41) (–0.70) Siuslaw 1710020601 0.27 0.27 Umpqua 171003

1710020206 (–0.93) 0.27 1710020602 0.34 1.00 171003

1710020603 0.56 1.00 171003

Tillamook 1710020303 (–0.33) 0.63 1710020604 0.42 0.10 171003

Bay 1710020304 (–0.14) 0.63 1710020605 0.69 0.97 171003

1710020305 (–0.11) 0.50 1710020606 0.58 1.00 171003

1710020306 (–0.30) 0.43 1710020607 0.48 1.00 1710031710020307 (–0.97) 0.00 1710020608 (–0.31) 0.73 171003

171003

Nestucca 1710020301 (–0.03) 1.00 Siltcoos 1710020701 1.00 1.00 171003

1710020302 (–0.47) 0.63 

Tahkenitch 1710020701 1.00 1.00 Coos 171003

Salmon 1710020408 (–0.97) 0.00 171003

Tenmile 1710030403 0.73 0.73 171003

Siletz 1710020405 0.26 0.83 

1710020407 (–0.34) 0.67 Lower 1710030304 (–0.16) 1.00 Coquille 171003

Umpqua 1710030305 0.33 0.00 171003

Yaquina 1710020401 0.75 1.00 1710030306 0.61 1.00 171003

1710020402 1.00 1.00 1710030307 1.00 1.00 171003

1710020403 0.74 0.57 1710030308 0.99 (–0.27) 171003

171003

Beaver 1710020505 0.86 1.00 Middle 1710030301 0.21 1.00 

Umpqua 1710030302 0.97 (–0.33) Floras/New 171003

1710030303 (–0.15) 0.73 

Sixes 171003

55
PD-4, Juvenile Distribution 

Criterion: On average, the historically occupied watersheds in the population’s range 

have juveniles occupying the available juvenile habitat. 

Metric: Evaluating this measure at the population level requires first evaluating a 

subcriterion regarding occupancy of each watershed within the population (Juvenile Watershed 

Occupancy [W-Ju]). The truth value for the population is then the average (DSS “UNION” 

operator) of the watershed values. 

Subcriterion W-Ju, Juvenile Watershed Occupancy 

Criterion: Juveniles occupy a high proportion of the available rearing habitat within the 

watershed. 

Metric: Juvenile occupancy is measured by the average occupancy rate of surveyed 

reaches in each watershed during the most recent 12 years. As with spawners, the distribution of 

juvenile coho salmon during summer was analyzed by HUC5s for each population by examining 

data from randomly selected survey reaches sampled in 1998–2004. Occupied is defined as the 

presence of fish in at least two pools within any survey reach that contains two or more pools; 

survey reaches with fewer pools are excluded from the statistics. Truth values for each 

watershed are evaluated as follows: false (–1.0) for occupancy less than 20%, uncertain (0) for 

occupancy of 50%, and true (+1.0) for occupancy greater than 80% (Figure 18). 

Current assessment: Watershed-level occupancy rates are presented in Table 5, with 

corresponding truth values in Table 6. Population-level truth values are presented in Table 3 and 

Figure 16. Values range from –1.0 (false: juveniles do not occupy a high proportion of the 

available rearing habitat within the watershed) for the Sixes population to +1.0 (true: juveniles 

occupy a high proportion of the available rearing habitat within the watershed) for five 

populations (Beaver, Siltcoos, Tahkenitch, Coos, and Floras/New). 

Discussion: Juvenile distribution can give a good idea of the expansion and contraction 

of coho salmon populations during various ocean productivity cycles and reflects the ability of 

spawning habitat to produce juveniles. The definition of occupancy used here reflects local 

biologists’ opinion that 1) survey reaches with only a single pool do not provide adequate habitat 

for juvenile coho salmon, and 2) it is extremely rare to find fish in one pool with nearby pools 

unoccupied. Issues discussed under the adult Spawner Distribution Criterion, PD-3, apply here 

as well. 

There are problems with this approach. In particular, the short time frame for the 

evaluation makes it difficult to relate to long-term risk; observations during a period of good 

environmental conditions tell us little about what occupancy patterns might be during a poor 

period (such as the 1990s). The minimum interpretation of this criterion would be that there is a 

distribution of habitat that is functional under very recent environmental conditions. A 

population with a juvenile distribution that barely met this criterion during a period of high 

marine survival might experience severe difficulties the next time marine survival rates are low. 

Another issue is that available data are generally for summer rearing, which may be spatially 

56
separated from winter rearing areas. Issues of fluctuating environmental conditions were 

discussed further in “Environmental Fluctuations” on page 12. 

PD, Population Diversity (Combined Criterion) 

Criterion: The population has sufficient diversity and distribution to ensure continued 

fitness in the face of environmental change. 

Metric: This requires that the population meet each (DSS “AND” operator) of four 

subcriteria: Spawner Abundance, PD-1; Artificial Influence, PD-2; Spawner Distribution, PD-3; 

and Juvenile Distribution, PD-4 (see Figure 19). 

Current assessment: The combined truth values for PD-1 through PD-4 ranged from 

–1.00 (false: the population does not have sufficient diversity and distribution to ensure 

continued fitness in the face of environmental change) for the Salmon, North Umpqua, and Sixes 

populations, to +0.96 (true: the population has sufficient diversity and distribution to ensure 

continued fitness in the face of environmental change) for the Coos population (Table 3, Figure 

16). In addition to the Salmon, North Umpqua, and Sixes populations, three other populations 

had negative values: Tillamook Bay, Nestucca, and South Umpqua. 

Discussion: Maintaining sufficient within-population genetic diversity is an issue of both 

short-term and long-term survival of the population. Such diversity is controlled by a variety of 

forces including: evolutionary legacy, immigration from other populations, mutation, 

PD

AND

PD-3 PD-4

U U

3 2 W-Sp1

. . . 3 2 W-Ju1

. . .

PD-1 PD-2

W-Sp W-Ju

The collection of 

results for lower-level 

criteria

Criteria evaluated from 

lower- level criteria

Logical operators

Data is loaded into the 

DSS and evaluated with 

a truth membership 

function

Figure 19. Subsection of the decision network showing the Population Diversity criteria. 

57
selection (both natural and artificial), and random loss of genetic variation due to small 

population size. Within-population diversity reflects phenotypic differences among groups of 

individuals and is a product of underlying genetic diversity and its interaction with the existing 

environmental diversity. These differences provide the flexibility for the population as a whole 

to respond successfully to short-term environmental variations. The genetic underpinning of 

diversity is the basis by which populations are able to adapt and evolve as conditions within their 

home range go through changes that are more permanent. Natural levels of genetic variation and 

its phenotypic expression are vital to maintaining the evolutionary potential of individual 

populations and the ESU. There are two important aspects of within-population diversity: 

maintaining adaptive potential and maintaining local adaptations. 

The genetic consequences of small population size and numerous approaches to defining 

minimum population abundance thresholds have been investigated widely (Soulé 1980, Lande 

1995, Franklin and Frankham 1998, Rieman and Allendorf 2001). In nearly all cases, this 

becomes an exercise of identifying a rate at which genetic variation can be lost without causing a 

risk to a population’s short-term or long-term persistence. Our first genetic diversity criterion 

(PD-1) reflects this approach. 

Equally important to maintaining local adaptation is ensuring that natural selective 

processes are able to operate without undue human influence. Interbreeding of hatchery and 

natural fish can reduce natural local adaptations (see discussion of Hatchery Fish on page 9). 

Our second criterion (Artificial Influence, PD-2) addresses this concern. 

Criteria for protecting within-population adaptive diversity are not straightforward. 

There is little information on the differences, if any, among individuals that spawn in different 

parts of their respective basins, let alone what differences may have existed historically. 

However, the assumption is made that if coho salmon spawners and juveniles are distributed in 

high quality habitats across the landscape, there are potential differences that have the 

opportunity to be expressed. Adaptive diversity criteria can thus be addressed by documenting 

the distribution of fish and habitat throughout the basin or basins occupied by the population. 

For long-term sustainability, spatial distribution of fish throughout their natural range must be 

ensured; doing so will help secure the existing genetic diversity of the population. Some aspects 

of diversity (such as life history type or run timing) may be related to subbasin structure, but may 

also be expressed within subunits as much as between them. For this reason, we chose criteria 

that do not depend on specific subbasin structure. Distribution criteria were developed for two 

important life history stages: spawners and freshwater juveniles. These two criteria (Spawner 

Distribution, PD-3, and Juvenile Distribution, PD-4) are based on the distribution of fish among 

watersheds within the historical range of the population. 

These distribution criteria are intended to achieve two goals. The first goal is to maintain 

natural rates and levels of movements and migrations within populations. This criterion is 

intended to minimize the likelihood that populations will be lost due to local catastrophes (e.g., 

flood, fire, drought, or accidental introduction of toxic pollutants), to maintain natural rates of recolonization within the population, and to maintain other functions that depend on the 

population’s spatial structure. 

58
The second goal is to maintain patterns of variation. This goal serves to ensure that the 

populations can withstand environmental variation in both the short term and the long term. 

Mechanisms to achieve these goals include maintaining natural phenotypic expression, 

maintaining natural patterns of gene flow, maintaining occupancy in a natural variety of 

available habitat types, and maintaining integrity of natural systems (this mechanism focuses 

directly on observed genotypic and phenotypic variation within populations and on changes in 

that variation). By ensuring that naturally produced fish are abundant and widespread across 

available habitats, it is ensured that populations maintain the potential for these mechanisms. 

Changes in these natural patterns are possible evidence that the population is at risk for diversity. 

Two other approaches to distribution were considered: 1) spawner and juvenile 

distribution among habitat types, and 2) direct assessment of the amount and distribution of 

different habitat types (such as estuarine and lowland wetlands). The workgroup also discussed 

more direct ways to assess life history diversity. However, for coho salmon this type of diversity 

is expressed across larger spatial scales rather than single populations, so these issues are 

addressed at the whole ESU level (see ESU-level Diversity Criterion, ES-2, on page 77). 

PS, Population Sustainability 

Criterion: The population is able to sustain itself into the foreseeable future. 

Metric: This requires that the population is persistent and has sufficient diversity and 

distribution (DSS “AND” operator on the Population Persistence Criterion, PP, and the 

Population Diversity Criterion, PD). 

Current assessment: The Population Persistence Criterion and Population Diversity 

Criterion values were combined into a Population Sustainability Criterion value for each 

population (Table 3 and Figure 16). These values ranged from –1.00 (false: the population is not 

able to sustain itself into the foreseeable future) for the Salmon, North Umpqua, and Sixes 

populations, to +0.95 (high certainty that the population is able to sustain itself into the 

foreseeable future) for the Coos population. In addition to the Salmon, North Umpqua, and 

Sixes populations, three other populations had negative truth values (moderate to high certainty 

that the population will not be able to sustain itself into the foreseeable future): Necanicum, 

Tillamook Bay, and Alsea. The remaining 15 populations had Population Sustainability 

Criterion truth values greater than 0, with eight greater than +0.50. 

Discussion: Population sustainability, beyond simple persistence, requires maintaining 

sufficient diversity and distribution to ensure continued fitness of populations in the face of 

environmental change. Fitness is determined by the interaction between genetics and 

environment. To ensure continued fitness, both basic genetic diversity and local adaptations to 

diverse environments must be preserved. The sustainability criteria reflect these two 

requirements. 

PF, Population Functionality Criterion 

Population functionality is evaluated on the basis of a single primary criterion describing 

habitat capacity. 

59
Criterion: Habitat quality and quantity are adequate to support sufficient abundance to 

maintain long-term genetic integrity of the population. 

Metric: This criterion is measured as the estimated smolt capacity for the basin as 

estimated by the ODFW limiting factors model (Habitat Limiting Factors Model [HLFM] 

version 6.0 [Nickelson et al. 1992, Nickelson 1998, Nickelson1]). Truth of the criterion is 

evaluated according to whether this smolt capacity is sufficient to produce spawners to meet the 

Population Diversity Criterion, PD-1, under poor ocean conditions (i.e., at 2% marine survival). 

Thus the criterion for smolt capacity is 50 (=1/0.02) times the PD-1 spawner criterion. This 

results in truth values of false (–1.0) for smolts below 4,250, uncertain (0) at 22,500 smolts, and 

true (+1.0) for greater than 250,000 smolts (Figure 20). 

For the current assessment, the HLFM model was used to estimate smolt capacity for all 

populations (except the lakes) based on ODFW aquatic inventory data. The model applies 

juvenile densities to estimated quantities of different types of fast- and slow-water habitats and 

converts total numbers to smolts via a survival rate. For the purpose of this application, winter 

habitat was assumed to be limiting, which has been found to typically be the case in previous 

applications of the model (Nickelson 1998). For the lake populations, where much of the 

juvenile habitat is in lakes to which the HLFM model doesn’t apply, the highest recent (since 

1980) recruit abundance was divided by 10% marine survival to estimate smolt capacity. This 

calculation assumes that habitats for the lake populations were fully seeded and that marine 

survival was 10% for the highest-producing cohort during that time period. 

Current assessment: Capacity estimates ranged from 36,600 to 1,080,600 smolts (Table 

4). Estimated truth values for the Population Functionality Criterion, PF, are presented in Table 

3 and Figure 21. The values ranged from +0.14 (low certainty that habitat quality and quantity 

are adequate to support sufficient abundance to maintain long-term genetic integrity of the 

population) for the Beaver population, to +1.00 (true: habitat quality and quantity are adequate to 

support sufficient abundance to maintain long-term genetic integrity of the population) for 10 

populations. 

-1

0

1

0 50 100 150 200 250 300

Truth value Truth Value

Smolt Capacity (1000s)

Figure 20. Truth membership function for the Population Functionality Criterion, PF. 

Smolt capacity (1,000s) 

 

1

 T. E. Nickelson, Oregon Dept. Fish and Wildlife, Corvallis Research Laboratory, Corvallis. Pers. commun., 

15 August 2005. 

60
PF

Population 

Functionality

Figure 21. Map showing estimated smolt-capacity truth values by population for the Population 

Functionality Criterion, PF. This map is color coded to represent the numerical scores found in 

Table 3. 

Discussion: Even though a population may not be sustainable (for a variety of reasons), 

this criterion is intended to ensure that its base of habitat would be sufficient for sustainability. 

Sustainability requires both population processes and habitat. To some degree, population 

parameters reflect habitat adequacy, but that relationship can be masked by fluctuations in 

environmental conditions (e.g., ocean conditions, drought, and temperature). Here, the adequacy 

of habitat is addressed directly, by keying this criterion to observations of in-stream habitat 

conditions. 

There are three significant problems with this approach, which should be addressed in the 

future. First, the data used in this evaluation is primarily upland stream habitat, thus missing 

important habitat types such as lowland and estuarine wetlands and likely underestimating 

overall smolt potential. Second, the limiting factors model provides an approximate estimate of 

carrying capacity based on the assumption that winter habitat limits smolt production. For 

populations where this is not a good assumption, model estimates will be in error. Third, the 

limiting factors method does not apply to lake-rearing populations, and a potentially inaccurate 

method was used to estimate capacity for these populations. The workgroup encourages future 

improvements of both habitat monitoring and models relating habitat to fish productivity and 

capacity. A possible approach is that used by the Coastal Landscape Analysis and Modeling 

Study, which uses mapped physical characteristics of streams to estimate potential high-quality 

habitat (Burnett et al. 2007). 

61
Criteria relating to specific types of habitats are not specified, even though these may be 

important for sustaining the ESU. For example, recent work in Oregon estuaries (Miller and 

Sadro 2003, Simenstad et al. 2000, Bottom,2

 van de Wetering,3 Adamus 2006) has demonstrated 

the utilization of tidal riverine-sourced habitats (stream-estuary ecotone) for parr-stage coho 

salmon juveniles, as well as the utilization of marine-sourced marshes for coho salmon parr and 

smolt rearing and migration. However, the impact of these areas relative to population extinction 

risk has not been quantified and not including these areas in the estimates of habitat will result in 

underestimates of smolt potential. 

Also, connectivity among habitats needed by different life history stages is undoubtedly 

important. However, we believe that sufficient connectivity will be demonstrated by meeting the 

criteria specified here; if connectivity were lacking, this would be reflected in the distributions of 

spawners or juveniles (criteria PD-3 and PD-4). 

Biogeographic Stratum-level Criteria 

SD, Stratum Diversity 

Criterion: Most historically Independent Populations in the stratum (as defined in 

Lawson et al. 2007) are at present sustainable according to the population-level sustainability 

criteria. 

Metric: This is measured as the median Population Sustainability Criterion, PS, truth 

value (DSS “MEDIAN” operator) of Independent Populations in the stratum. 

Current assessment: Truth values for the strata are presented in Table 3 and Figure 22. 

Values range from –0.02 (uncertain whether most of the historically Independent Populations in 

the stratum [as defined in Lawson et al. 2007] are at present sustainable according to the 

Population Sustainability Citerion) for the North Coast stratum, to +0.69 (highly certain that 

most of the historically Independent Populations in the stratum are at present sustainable 

according to the Population Sustainability Criterion) for the Mid-South Coast stratum. 

Discussion: Requiring multiple sustainable populations per stratum reduces each 

stratum’s risk from changes in local conditions and population-scale catastrophes. By basing this 

criterion on the median Population Sustainability Criterion values within a stratum, it can be 

ensured that the majority of populations within the stratum are sustainable. 

SF, Stratum Functionality 

Criterion: All of the historically Independent Populations in the stratum are functional 

according to the Population Functionality Criterion, PF, criteria defined on page 59. 

Metric: This is measured by applying the DSS “AND” operator to the Population 

Functionality Criterion truth values for all Independent Populations in the stratum. 

 

2

 D. Bottom, NWFSC, Newport Research Station, Newport, OR. Pers. commun., April 2004. 3

 S. van de Wetering, Confederated Tribes of Siletz Indians of Oregon, Siletz. Pers. commun., April 2004. 

62
Figure 22. Maps showing truth value scores for the Stratum Diversity, Stratum Functionality, and 

Stratum Sustainability criteria. The scores for the Stratum Diversity Criterion, SD, are shown in 

map A. These are combined with scores for the Stratum Functionality Criterion, SF, shown in 

map B, with the resulting scores shown in the Stratum Sustainability Criterion, SS, in map C. 

These maps are color coded to represent the numerical scores found in Table 3. 

Current assessment: Truth values for the biogeographic strata are presented in Table 3 

and Figure 22. Values range from +0.38 (a low degree of certainty that all of the historically 

Independent Populations in the stratum are functional according to the population criteria defined 

above) for the Lakes stratum to +0.88 (a high degree of certainty that all of the historically 

Independent Populations in the stratum are functional according to the population criteria defined 

above) for the Umpqua stratum. 

Discussion: This criterion provides assurance that Independent Populations that may not 

be sustainable still have sufficient habitat to allow them to contribute to the evolution of the 

ESU. Within the ESU, it is anticipated that the condition of populations will shift over time, so 

that populations that are in poor condition at one time may later become sustainable, and vice 

versa. This variation in condition reflects the historical condition (Reeves et al. 1995) where the 

habitat reflected a dynamic landscape. There were areas that had lower habitat quality for 

periods of time. However, all populations retained the capacity to redevelop habitat quality 

through the processes of natural disturbance. The workgroup assumed this historical template is 

still appropriate and those populations not presently sustainable should retain the capacity to 

become sustainable in the future. This criterion ensures a minimum level of habitat for all 

Independent Populations so that all are able to respond to shifting environmental conditions and 

those presently in poor condition are available for restoration should an effort be made to 

improve their status. 

63
Nonsustainable populations may also provide a refuge in case of major catastrophes 

affecting other more sustainable populations in the stratum. Note that Dependent Populations are 

not included here, even though they may have an important ecological and evolutionary role—

occupancy standards for those areas were initially included here, but we concluded that the role 

of these populations is more important on a whole-ESU basis, rather than within biogeographic 

strata, because some strata have no Dependent Populations, and several Dependent Populations 

are near stratum boundaries, so are probably connected to two (or more) strata. Also, at this time 

there is insufficient data to estimate smolt capacity for these populations. For these reasons, 

criteria relating to Dependent Populations are included in ESU Diversity, ES-2, below (page 77). 

SS, Stratum Sustainability 

Criterion: The stratum is self-sustaining (in terms of both diversity and functionality) 

into the foreseeable future. 

Metric: This requires that the stratum meets both the Stratum Diversity Criterion, SD, 

and the Stratum Functionality Criterion, SF, (DSS “AND” operator) illustrated in Figure 23. 

Current assessment: The Stratum Sustainability Criterion, SS, is a combination of the 

Stratum Diversity and Stratum Functionality criteria, which respectively depend on the 

Population Sustainability Criterion, PS, and the Population Functionality Criterion, PF. The 

estimated truth values are shown in Table 3 and Figure 22. North Coast had the lowest value 

Figure 23. The Stratum Sustainability Criterion subsection of the ESU Sustainability Criterion decision 

network. 

64
at +0.11 (low certainty that the stratum is self-sustaining, in terms of both diversity and 

functionality, into the foreseeable future). Mid-South Coast had the highest value at +0.68 (high 

certainty that the stratum is self-sustaining, in terms of both diversity and functionality, into the 

foreseeable future). 

Discussion: By requiring that these two criteria are met, it is guaranteed that 1) there are 

sufficient sustainable populations within the stratum to maintain genetic and ecological diversity, 

and 2) there remains functional habitat in all populations (whether sustainable or not) so that 

population dominance can shift over time as local environmental conditions change (see 

discussion under Stratum Functionality on page 63). 

ESU-level Sustainability Criteria 

ES-1, All Biogeographic Strata Sustainable 

Criterion: All of the biogeographic strata within the ESU are sustainable. 

Metric: This is evaluated by applying the DSS “AND” operator to all Stratum 

Sustainability Criterion, SS, truth values shown in Figure 24. 

Current assessment: Combining the Stratum Sustainability Criterion values across strata 

resulted in a truth value of +0.28 (low to moderate certainty that all of the biogeographic strata 

within the ESU are sustainable) for this criterion (Table 3). 

Discussion: At a coarse scale, this criterion ensures sustainability of populations 

throughout the range of the ESU. The historical genetic structure of the ESU is not yet fully 

described, thus the division of the ESU into these strata should be considered tentative. In 

addition to providing for diversity and distribution, preserving these large strata should also meet 

the “significant portion of the range” language of the ESA. Requiring multiple sustainable 

populations within each biogeographic stratum provides for security of the stratum against 

catastrophes, as discussed by the Willamette/Lower Columbia TRT (Willamette/Lower 

Columbia TRT 2006). 

Figure 24. ES-1, All Biogeographic Strata Sustainable, subsection of the ESU Sustainability Criterion 

decision network. 

65
ESU Diversity—Evaluation Methods 

The following ESU-wide diversity criteria (ED-1 to ED-3) are used to evaluate the 

degree of genetic and phenotypic diversity present at the ESU level to ensure the continued 

fitness of the ESU in the face of environmental change. Given the threats to diversity discussed 

above, these criteria fall into three categories: genetic diversity, phenotypic and environmental 

diversity, and small population effects. Within each category, criteria are used to evaluate both 

the degree of diversity present in the ESU and the factors that may have degraded it, using 

healthy ESUs or historical conditions (pre-European-settlement) as the standard for comparison. 

Because these criteria are not as readily quantified as the previous criteria, a different 

method has been used to evaluate them. The method is a modification of the risk matrix 

approach (Wainwright and Kope 1999) used by NMFS Biological Review Teams for evaluating 

the biological status of ESUs. For each criterion, a 5-point scoring scale ranging from healthy or 

historical condition (score 5) to poor condition (score 1) was developed, with the third choice 

(score 3) representing either intermediate conditions or no information. Members of the 

workgroup examined all information relating to each criterion, and each member assigned a 

score for each criterion. To account for uncertainty in evaluating risks, a “likelihood-point” 

method referred to as the “FEMAT (Forest Ecosystem Management Assessment Team) method” 

was used because it is similar to that used to evaluate options under former President Clinton’s 

Forest Plan (FEMAT 1993). Under this method, each member was directed to distribute 100 

points to the five possible scores for each diversity criterion. Members were instructed to 

distribute their points to reflect their professional judgment of the status of the ESU for each 

criterion, using all 100 points. Thus if an evaluator was certain that a single score was 

applicable, all 100 points could be placed on that score; if there was great uncertainty, points 

could be evenly distributed across all categories. A similar method has been used for all NMFS 

Pacific salmon status reviews since 1999. Mean scores resulting from all awarded points were 

used to calculate truth values; a mean criterion score of 5.0 had a truth value of +1.0 (true), while 

a mean score of 1.0 had a truth value of –1.0 (false) (Figure 25). 

ED-1, Genetic Diversity 

Criterion: ESU-level genetic diversity is sufficient for long-term sustainability of the 

ESU. 

-1

0

1

1234

Truth Value Truth value 

5

Average Score Average score 

Figure 25. Truth membership function for all ESU-level diversity criteria. 

66
Metric: This is evaluated as the average (UNION) of four subcriteria: Genetic Structure, 

ED-1a; Effects of Selection, ED-1b; Effects of Migration, ED-1c; and Effects of Introgression, 

ED-1d. 

Current assessment: Truth values for the subcriteria and full criterion are presented in 

Table 7. The average score for this criterion was +0.22, indicating low certainty that ESU-level 

genetic diversity is sufficient for long-term sustainability of the ESU. 

Discussion: Genetic diversity within and among populations provides the raw material 

for surviving long-term environmental change. Actions that affect the natural processes that 

form and maintain genetic diversity (e.g., mutation, selection, genetic drift, recombination, and 

migration) have the potential to reduce or alter adaptive patterns of diversity. The following 

criteria first evaluate the level of genetic diversity currently present in the ESU, then consider 

how the processes of selection, migration, and introgression may have reduced genetic diversity. 

ED-1a, Genetic Structure 

Criterion: Genetic diversity within the ESU is comparable to healthy coho salmon ESUs 

(e.g., Olympic Peninsula, Southeast Alaska) and forms the basis for some life history diversity. 

Metric: This is evaluated by answering the question: Are populations genetically 

distinguishable (based on allozyme or DNA variation) from each other such that they form 

groups based on geography, ecology, or life history types? The five possible scores are: 

5. All populations within the ESU are genetically distinct, overall genetic diversity is 

comparable or exceeds that of healthy ESUs, and genetic information indicates many 

groups that have common geography, ecology, or life history. 

4. Most populations within the ESU are genetically distinct, overall genetic diversity is 

almost as high as healthy ESUs, and genetic information indicates some groups that have 

common geography, ecology, or life history. 

Table 7. Summary of DSS truth values for the ESU-level diversity criteria, broken down by subcriteria 

for Genetic Diversity, ED-1, and Phenotypic and Habitat Diversity, ED-2. The Small Populations 

Criterion, ED-3, has no subcriteria. 

Subcriterion 

ED-1 

Genetic 

Diversity 

ED-2 

Phenotypic and 

Habitat Diversity 

ED-3 

Small Populations 

a 0.28 0.30 

b 0.00 (–0.20) 

c 0.26 

d 0.34 

Value for 

full criterion: 0.22 0.05 0.40 

67
3. Some populations within the ESU are genetically distinct, overall genetic diversity is 

modest compared to healthy ESUs, and genetic information indicates that a few groups 

may be present that have common geography, ecology, or life history, or no information 

is available. 

2. Only a few populations within the ESU are genetically distinct, overall genetic diversity 

is low compared to healthy ESUs, and genetic information provides slight evidence for 

groups that have common geography, ecology, or life history. 

1. Few populations within the ESU are genetically distinct, overall genetic diversity is 

extremely low comparable to healthy ESUs, and genetic information provides little 

evidence for groups that have common geography, ecology, or life history. 

Genetic data considered to address this criterion included the 1995 status review 

(Weitkamp et al. 1995), recent published genetic results (Ford et al. 2004), and discussion of 

initial results of ongoing genetic analyses (Johnson and Banks 2008). Preliminary results of 

recent genetic analyses using microsatellites suggest that most genetic diversity in coho salmon 

occurs within Independent Populations (Lawson et al. 2007), rather than between populations. 

This pattern has also been observed in California and British Columbia.4

 The possible causes for 

high within-basin diversity were discussed with respect to their implications for ESU-level life 

history and genetic diversity and the definitions of populations. 

Current Assessment: The mean score for this criterion was 3.55, resulting in a truth 

value of +0.28 (low certainty that genetic diversity within the ESU is sufficient) (Table 7). The 

full point distributions from the panel are in Table 8, showing that the majority of votes were cast 

for scores of 4 and 3, and no votes were cast for 1. 

Discussion: This criterion indicates whether genetic diversity exists within the ESU and 

the amount of diversity relative to baseline conditions (healthy ESUs). It indirectly assesses 

whether factors such as mutation, genetic drift, introgression, or migration have compromised 

genetic diversity within the ESU. The presence of genetically based groupings indicates a 

genetic basis for at least some of the observed diversity in life history types (the other major 

source of life history diversity results from environmental variation). Such groupings point to 

extant genetic diversity that is important for long-term sustainability of the ESU. 

ED-1b, Effects of Selection 

Criterion: Human-driven selection is not sufficient to decrease genetic diversity. 

Metric: This is evaluated by answering the question: Has selection decreased genetic 

diversity? The five possible scores are: 

5. Genetic diversity has not been decreased by selection relative to the historical template. 

4. Genetic diversity has been mildly decreased by selection. 

3. Genetic diversity has been moderately decreased by selection or no evidence is available 

to determine the degree of alteration by selection. 

 

4

 See footnote 3. 

68
Table 8. Distribution of points by review panel members for the Genetic Structure Criterion, ED-1a. 

Score 

Panel member 5 4 3 2 1 

1 0 30 50 20 0 

2 0 80 20 0 0 

3 0 20 70 10 0 

4 0 50 50 0 0 

5 50 50 0 0 0 

6 0 40 50 10 0 

Total 50 270 240 40 0 

2. Genetic diversity has been fairly severely decreased by selection. 

1. Genetic diversity has been severely decreased by selection to the point that they have 

little in common with the historical template. 

There was little information available to indicate whether selection had affected life 

history or genetic diversity. The workgroup was able to point to many human activities that 

likely select for or against life history types and genetic diversity, although little, if any, evidence 

exists to document such selection. The activities considered included fishing, which may select 

for particular run timing, size, or age classes (i.e., jacks have much lower harvest rates than 

adults), disease resistance, a variety of hatchery practices, and habitat alterations, which may be 

selective for or against particular groups of fish. 

Current assessment: The mean score for this criterion was 2.98, resulting in a truth 

value of –0.01 (uncertain that human-driven selection is not decreasing genetic diversity) 

(Table 7). The full point distributions from the panel are in Table 9, showing that the vast 

majority of votes were cast for 3, and no votes were cast for 5 or 1. 

Discussion: Selection refers to the differential fitness expressed by individuals exhibiting 

one version of a trait in comparison to individuals exhibiting a different version of the trait. 

Selection due to either natural or human-caused factors may alter genetic diversity. For example, 

Table 9. Distribution of points by review panel members for the Effects of Selection Criterion, ED-1b. 

Score 

Panel member 5 4 3 2 1 

1 0 30 40 30 0 

2 0 25 50 25 0 

3 0 10 70 20 0 

4 0 0 100 0 0 

5 0 0 100 0 0 

6 0 15 70 15 0 

Total 0 80 430 90 0 

69
drought conditions extending into late fall may favor coho salmon with late river entry and 

spawning timing over those with earlier run timing. Alternately, advancement of spawn timing 

associated with hatchery practices may increase mortality for early emerging fry during spring 

freshets to a greater degree than for later emerging fry (Nickelson et al. 1986). 

ED-1c, Effects of Migration 

Criterion: Genetic diversity is not compromised by changes in the movements of fish. 

Metric: This is evaluated by answering the question: Have normal movement patterns 

been restricted or altered either within or between populations? The five possible scores are: 

5. Normal movement patterns within and between populations have not been altered; they 

represent the historical template. 

4. Normal movement patterns within and between populations have been mildly altered. 

3. Normal movement patterns within and between populations have been moderately altered 

or no information is available to determine whether they have been altered. 

2. Normal movement patterns within and between populations have been fairly severely 

altered. 

1. Normal movement patterns within and between populations have been severely altered; 

they no longer represent the historical template. 

There was little information available to indicate whether migration patterns had changed 

beyond natural levels. The workgroup was able to point to several dams in the ESU that block 

coho salmon access, but concluded that such blockages, although locally important, were of very 

minor importance at the ESU scale. 

Current assessment: The mean score for this criterion was 3.52, resulting in a truth 

value of +0.26 (low certainty that genetic diversity is not compromised by changes in the 

movements of fish) (Table 7). The full point distributions from the panel are in Table 10, 

showing that the majority of votes were cast for 3 and 4, and no votes were cast for 1. 

Discussion: Straying and gene flow strongly influence patterns of diversity within and 

among populations (McElhany et al. 2000). Rates of straying are usually estimated from 

Table 10. Distribution of points by review panel members for the Effects of Migration Criterion, ED-1c. 

Score 

Panel member 5 4 3 2 1 

1 0 25 50 25 0 

2 20 50 30 0 0 

3 0 15 70 15 0 

4 10 50 40 0 0 

5 10 90 0 0 0 

6 0 40 60 0 0 

Total 40 270 250 40 0 

70
experiments that involve monitoring the spawning location of fish that are marked or tagged in 

their home area as juveniles (e.g., Labelle 1992). The rate of gene flow among populations is 

estimated by fitting population genetic models to observed patterns of molecular genetic 

variation (Slatkin 1985). Because such experiments and models are not widely available for 

Oregon Coast coho salmon, movement patterns within and between populations were used as a 

proxy for potential changes to genetic diversity due to straying and gene flow. In doing so, it 

was assumed that changes in movement patterns likely have some genetic effect on population 

structure, and that the effect is proportional to the change in movement patterns. Accordingly, 

major changes in movement patterns are expected to have greater impact on genetic diversity 

than minor changes in movements. 

Metric: This is evaluated by answering the question: Have populations been genetically 

altered due to introgression of exotic genes (e.g., from use of out-of-basin or out-of-ESU 

hatchery stocks)? The five possible scores are: 

5. Populations are completely unaltered by introgression of exotic genes and represent the 

historical template. 

4. Populations have been mildly altered by introgression of exotic genes. 

3. Populations have been moderately altered by introgression of exotic genes, or no 

evidence is available to indicate the degree of alteration. 

2. Populations have been fairly severely altered by introgression of exotic genes. 

1. Populations have been completely altered by introgression of exotic genes and no longer 

represent the historical template. 

Information considered when addressing this criterion included the genetic dendrogram 

in the West Coast status review (Weitkamp et al. 1995), recent genetic analyses (Ford et al. 

2004), a summary of the potential effects of hatchery production on Oregon Coast coho salmon 

(Weitkamp 1997), and the proportion of naturally spawning hatchery fish by basin for Oregon 

Coast coho salmon populations (Appendix D). The workgroup concluded that the opportunity 

for introgression had been high, based on the numbers of hatchery fish released, the stocks used, 

the percent of hatchery fish spawning naturally, and the genetic distinctiveness of older hatchery 

stocks. 

However, evidence indicating whether introgression occurred was much more difficult to 

determine. It was suggested that there are few examples from coho and other Pacific salmon 

indicating that introgression of exotic genes from hatchery fish has occurred, despite numerous 

opportunities to do so. For example, the extensive releases of Puget Sound stocks from the 

Oregon Aqua Foods facility in the Yaquina basin apparently had little effect on the genetics of 

local coho salmon populations. The only example of introgression in the Oregon Coast Coho 

Salmon ESU comes from the Nehalem population of coho salmon, where a reduction in 

resistance of some wild coho salmon to the parasite Ceratomyxa shasta (Wade 1987) has been 

attributed to introgression from hatchery fish with a significant out-of-basin ancestry. Whether 

introgression remains in the Nehalem population has not been determined. 

Current assessment: The mean score for this criterion was 3.68, resulting in a truth 

value of +0.34 (low to moderate certainty that genetic diversity is not altered by the introgression 

71
of exotic genes) (Table 7). The full point distributions from the panel are in Table 11, showing 

that the majority of votes were cast for 4 and 3, and no votes were cast for 1. 

Discussion: One clear risk to genetic diversity is the introgression of exotic genes. These 

exotic genes come from fish that are adapted to a different suite of environmental conditions than 

those in which they are located. The degree of difference ranges from slight (i.e., fish originating 

from an adjacent basin) to extreme (fish originating from a different ESU). The most common 

vector for the introgression of exotic genes into natural populations are hatchery fish, which 

either inadvertently or deliberately spawn with natural fish. Although most Oregon Coast coho 

salmon hatchery stocks were derived from local populations, there has been considerable 

movement of fish between hatcheries and between ESUs (Weitkamp et al. 1995). These 

widespread exotic releases provide the potential for deleterious genetic impacts to natural 

populations. 

ED-2, Phenotypic and Habitat Diversity 

Criterion: ESU-level phenotypic and habitat diversity are sufficient for long-term 

sustainability of the ESU. 

Metric: This is evaluated as the average (DSS “UNION” operator) of two subcriteria: 

Phenotypic Diversity, ED-2a, and Habitat Diversity, ED-2b. 

Current assessment: Truth values for the subcriteria and full criterion are presented in 

Table 7. The average score for this criterion was +0.05, indicating uncertainty whether ESUlevel phenotypic and habitat diversity are sufficient for long-term sustainability of the ESU. 

Discussion: Phenotypic diversity is expressed as variable life history traits such as run 

timing, spawn timing, smolt age, and ocean distribution patterns. Phenotypic diversity allows 

species to use a wider array of environments and protects species against short-term spatial and 

temporal changes in environment. Phenotypic diversity is strongly influenced by environmental 

variation, which allows unique phenotypes to be expressed when the proper conditions (e.g., 

particular habitat types) are available. Consequently, not only is detectable phenotypic diversity 

Table 11. Distribution of points by review panel members for the Effects of Introgression Criterion, 

ED-1d. 

Score 

Panel member 5 4 3 2 1 

1 0 45 50 5 0 

2 25 75 0 0 0 

3 0 10 80 10 0 

4 40 40 20 0 0 

5 0 100 0 0 0 

6 0 30 60 10 0 

Total 65 300 210 25 0 

72
important for sustainability of ESUs, but habitat diversity is also important to allow expression 

of a wide range of phenotypes. 

ED-2a, Phenotypic Diversity 

Criterion: Phenotypic diversity is present within the ESU at levels comparable to healthy 

ESUs or the historical template. 

Metric: This is evaluated by answering the question: Do populations display variation in 

life history traits such as smolt age, age and size at maturity, juvenile run timing, adult run 

timing, and spawn timing (do upstream and downstream migrations extend for more than 2 

months), comparable to healthy ESUs or the historical template? The five possible scores are: 

5. There is extensive variation in life history traits comparable to healthy ESUs and the 

historical template. 

4. There is considerable variation in life history traits, although slightly less than in healthy 

ESUs or the historical template. 

3. There is moderate variation in life history traits, somewhat diminished from healthy 

ESUs or the historical template, or there is no evidence to indicate changes in life history 

diversity. 

2. Life history trait variation has been reduced and is well below that of healthy ESUs or the 

historical template. 

1. Life history trait variation has been greatly reduced and no longer represents either 

healthy ESUs or the historical template. 

Life history data provided in the original status review (Weitkamp et al. 1995) and the 

population document (Lawson et al. 2007) formed the basis of discussions about current levels of 

phenotypic variation. We concluded that available life history data on parameters such as run 

timing or size were unremarkable, in the sense that most variables are strongly influenced by 

environmental variation and the level of spatial and temporal variation in the traits along the 

Oregon Coast was similar to other ESUs. Data on other life history traits that might be more 

informative for addressing the criterion are not readily available for Oregon Coast populations. 

The general consensus of the group was that Oregon Coast coho salmon were highly variable in 

phenotypes. 

Current assessment: The mean score for this criterion was 3.61, resulting in a truth 

value of +0.30 (low to moderate certainty that phenotypic diversity is present within the ESU at 

levels comparable to healthy ESUs or the historical template) (Table 7). The full point 

distributions from the panel are in Table 12, showing that the majority of votes were cast for 4 

and 3, and no votes were cast for 1. 

Discussion: Compared to other Pacific salmon species with their spring and fall runs or 

complex age structure, coho salmon generally display relatively little variation in most life 

history traits. For example, the vast majority of coho salmon in the Pacific Northwest enter the 

ocean as yearling smolts, become sexually mature after 6 months (jacks) or a year (adults) in the 

ocean, enter rivers in the fall and spawn shortly afterwards (Sandercock 1991, Weitkamp et al. 

73
Table 12. Distribution of points by review panel members for the Phenotypic Diversity Criterion, ED-2a. 

Score 

Panel member 5 4 3 2 1 

1 0 30 40 30 0 

2 20 50 30 0 0 

3 0 40 60 0 0 

4 5 35 40 20 0 

5 10 80 10 0 0 

6 40 30 30 0 0 

Total 75 265 210 50 0 

1995). Despite this similarity at the gross scale, there may be unappreciated fine-scale life 

history variation that is generally overlooked because it is extremely difficult to measure. For 

example, Nielsen (1994) identified four discrete life history types in northern California streams 

that combined unique microhabitat distributions, foraging behavior, growth, and developmental 

patterns. Similarly, discrete stream-rearing and lake-rearing life history types have been 

identified in the Tenmile Lake basin (Reimers 1989). 

We assumed that some life history types are more successful than others as conditions 

vary (e.g., Reimers 1973). Accordingly, the more variation there is in life history diversity, the 

greater the chances that some life history pathways will be successful each year, leading to 

greater sustainability. However, without an extensive research effort or development of new 

technologies (e.g., otolith microchemistry), it is impossible to determine how many life history 

types are present in each basin or how this number has varied over time. Because of this lack of 

data, the workgroup was forced to examine variation in readily measured traits, such as juvenile 

run timing, age and size at maturity, and adult run and spawn timing. While variation in these 

traits may not be exceptionally informative with regards to variation in life history pathways 

present within the ESU, it is the best available information on phenotypic variation. 

Consequently, although moderately high diversity in these life history traits may or may not be 

associated with high life history pathway diversity, lack of diversity in these traits would clearly 

be a cause for concern. 

ED-2b, Habitat Diversity 

Criterion: Habitats are sufficiently productive, diverse, and accessible to promote 

phenotypic plasticity. 

Metric: This is evaluated by answering the question: Do multiple habitat types exist 

within basins, are they accessible to coho salmon, and are they structured by processes that 

support and maintain each habitat type? These habitat types include lakes, ponds, tributary pools 

and glides, mainstem rearing areas, off-channel pools, low-salinity estuarine habitats, tributary 

junctions, and active floodplains. The five possible scores are: 

5. Habitats within basins are as accessible, productive, and diverse as those of healthy ESUs 

or the historic template. 

74
4. Habitats within basins are slightly less accessible, productive, or diverse as those of 

healthy ESUs or the historic template. 

3. Habitats within basins are not as accessible, productive, or diverse as those of healthy 

ESUs or the historic template, or nothing is known about habitat productivity or diversity 

or coho salmon access. 

2. Habitats within basins are considerably less accessible, productive, or diverse as those of 

healthy ESUs or the historic template. 

1. Habitats within basins bear little resemblance to healthy ESUs or the historic template 

with respect to accessibility, productivity, or diversity. 

The information considered for making this assessment included: 

• Estimates of existing or filled estuarine habitats for 22 basins in the Oregon Coast ESU 

(Scranton 2004) 

• Estimates of the change in total area for Oregon Coast estuaries between 1870 and 1970 

(OSP 2000) 

• Categories of riparian vegetation and land use for the Coast Range (OSP 2000) 

• Estimates of old growth forests in the nineteenth century and at present in the Coast 

Range (OSP 2000) 

• Information about culverts (e.g., barriers to passage, quality of habitat upstream of 

culvert, etc.) by county for western Oregon (IMST 2002) 

• Water quality conditions for western Oregon (e.g., miles listed as 303(d), miles meeting 

standards, parameters out of compliance, etc.) (IMST 2002) 

• Water quality as determined by the Oregon Water Quality Index for coastal Oregon 

streams (IMST 2002) 

• ODFW habitat benchmarks for pools, riffles, shade, large woody debris, and riparian 

conifers (WPN 1999) 

Team members discussed the adequacy of the available information for determining 

habitat diversity, productivity, and accessibility and found it generally lacking. For example, 

from the available information on culverts it was not possible to determine the extent of coho 

salmon habitat above impassable culverts. Similarly, information on changes in estuarine areas 

did not consider how such modifications affected fish. Overall, the group concluded that current 

conditions have been altered compared to the historical template, although the degree of 

alteration was difficult to determine. The greatest habitat alterations have occurred in lowland 

portions of basins. The most significant water quality issue is high temperatures. With respect 

to the benchmarks, few if any basins meet the criteria for large woody debris, which is likely 

related to the fact that most old growth forests have been replaced by much younger stands. As a 

whole, habitats may be considered to be more uniform than they were historically. 

It was generally assumed that a wide range of life history patterns could still be expressed 

with current habitat. However, the production from some habitat types has been greatly reduced 

75
because habitat degradation or alteration has reduced the carrying capacity for life history 

patterns that rely on those habitat types. 

Current assessment: The mean score for this criterion was 2.60, resulting in a truth 

value of –0.20 (low to moderate certainty that habitats are not sufficiently productive, diverse, 

and accessible) (Table 7). The full point distributions from the panel are in Table 13, showing 

that the majority of votes were cast for 3, and no votes were cast for 5. 

Discussion: There is compelling evidence that patterns of phenotypic diversity within 

and among Pacific salmon populations are strongly influenced by the environments these 

populations inhabit (reviewed by Ricker 1972, Taylor 1991, Healey and Prince 1995). Nielsen 

(1994) identified four discrete coho salmon life history types, yet found little genetic basis for the 

variation. Accordingly, providing access to a diversity of productive habitat types allows 

expression of phenotypes that may not otherwise occur. Although it is extremely difficult to 

determine which particular life history type is successful in a given year, the expectation is for 

some life history types to be more successful than others. Consequently, by ensuring that a wide 

range of productive habitats are accessible to coho salmon, the opportunity is provided for 

greater expression of life history diversity, which, in turn, should increase the chances that at 

least some coho salmon life history types will be successful. 

ED-3, Small Populations 

Criterion: Dependent Populations within the ESU are not permanently lost. 

Metric: This is evaluated by answering the question: Is there any evidence that Small 

Populations have been permanently lost from the ESU? The five possible scores are: 

5. All Dependent Populations that were historically present in the ESU still currently exist. 

4. Most Dependent Populations that were historically present in the ESU still currently 

exist. 

3. Some Dependent Populations that were historically present in the ESU still currently 

exist, or no information exists to indicate whether Dependent Populations have been lost. 

2. Only a few Dependent Populations that were historically present in the ESU still 

currently exist. 

Table 13. Distribution of points by review panel members for the Habitat Diversity Criterion, ED-2b. 

Score 

Panel member 5 4 3 2 1 

1 0 0 0 50 50 

2 0 10 60 30 0 

3 0 30 60 10 0 

4 0 0 40 50 10 

5 0 0 100 0 0 

6 0 20 40 40 0 

Total 0 60 300 180 60 

76
1. All Dependent Populations that were historically present in the ESU have been lost. 

Information used to evaluate the persistence of small populations consisted of presence or 

absence of coho salmon recorded in the ODFW spawner surveys in years with productive ocean 

conditions (ca. 2000–2002), known restrictions to salmon access (e.g., Rocky Creek), and data 

for midcoast streams provided by the MidCoast Watersheds Council (MidCoast Watersheds 

Council 2005). For many Dependent Populations, no information was available to indicate 

whether these populations persist, resulting in considerable uncertainty. There was some 

discussion that the absence of coho salmon during spawner surveys does not necessarily mean no 

coho salmon were present. This apparent conflict stems from such zero counts occurring in 

systems in which some juvenile coho salmon are observed the following summer (e.g., Cummins 

Creek). There was also discussion that the random spawner surveys typically conducted in these 

small populations may not provide a complete picture of spawner abundances because they are 

designed to be random and therefore are not conducted in the same location each year, nor do 

they necessarily occur in the best spawning areas. 

Current assessment: The mean score for this criterion was 3.80, resulting in a truth 

value of +0.40 (moderate certainty that Dependent Populations within the ESU are not 

permanently lost) (Table 7). The full point distributions from the panel are in Table 14, showing 

that the vast majority of votes were cast for 4, and no votes were cast for 2 or 1, suggesting fairly 

high confidence that most small populations in the ESU have not been lost. 

Discussion: This evaluation only applies to identified Dependent Populations (Lawson 

et al. 2007) that have a historical production potential of at least 2,000 smolts (see Population 

Structure and Associated Definitions on page 4). It was considered that populations smaller than 

this threshold (often consisting of less than 50 spawners under moderate ocean conditions) were 

not likely to persist long enough to fix alleles and provide unique diversity to the ESU. 

These populations are assumed over the long term to be supported by larger nearby 

populations, but may also serve an important evolutionary role as being more efficient in 

retaining new mutations than larger populations. Small populations, by their small size, 

generally contribute little to overall ESU abundance and may be temporarily extirpated when 

environmental conditions are unfavorable and recolonized when conditions improve. However, 

they are important to evaluating the status of the ESU for three reasons: 

Table 14. Distribution of points by review panel members for the Small Populations Criterion, ED-3. 

Score 

Panel member 5 4 3 2 1 

1 0 75 25 0 0 

2 10 70 20 0 0 

3 0 60 40 0 0 

4 0 90 10 0 0 

5 0 90 10 0 0 

6 0 75 25 0 0 

Total 10 460 130 0 0 

77
• They may serve a critical role in ESU diversity by providing reservoirs of potentially 

adaptive diversity. 

• They may be important for adaptive radiation (e.g., Wright 1948) by providing habitat 

important to certain life history types and providing a locus for adaptive mixing. 

• Small populations are most susceptible to overexploitation (Hilborn 1985, Halupka et al. 

2000) and other sources of mortality, and thus may be important indicators of successful 

management. 

Although the exact degree to which these populations support ESU diversity is unknown, 

assuming they are important for ESU diversity seems the most far-sighted approach to their 

management. Because of these considerations, and in the face of substantial uncertainty 

regarding the role of these populations in the ESU as a whole, a precautionary approach suggests 

the importance of preserving as many of the pieces of the historical ESU as is feasible. 

ES-2, ESU-level Diversity 

Criterion: The ESU has sufficient broad-scale diversity to maintain its ecological and 

evolutionary functions into the foreseeable future. 

Metric: This requires meeting each (DSS “AND” operator) of three criteria: the Genetic 

Diversity Criterion, ED-1; Phenotypic and Habitat Diversity Criterion, ED-2; and Small 

Populations Criterion, ED-3, shown in Figure 26. 

Current assessment: The analysis combined the results of the analyses of the Genetic 

Diversity Criterion, ED-1; Phenotypic Diversity Criterion, ED-2; and Small Populations 

Figure 26. ESU-level Diversity Criterion subsection of the ESU Sustainability Criterion decision 

network. 

78
Criterion, ED-3 (Table 3). The resulting truth value was +0.14 (low certainty the ESU has 

sufficient broad-scale diversity to maintain its ecological and evolutionary functions into the 

foreseeable future). 

Discussion: For Pacific salmon, diversity is expressed by variable life history traits, 

including anadromy, morphology, fecundity, run timing, spawn timing, juvenile behavior, smolt 

age, age at reproduction, egg size, development rate, ocean distribution patterns, spawning 

behavior, physiology, and molecular genetic characteristics (McElhany et al. 2000). Diversity is 

important for ESU sustainability because 1) it allows species to use a wider array of 

environments than they otherwise could, 2) it protects species against short-term spatial and 

temporal changes in the environment, and 3) genetic diversity provides raw material for 

surviving long-term environmental change (McElhany et al. 2000). 

Life history traits are typically the product of both genetic and environmental variation. 

These traits may be affected by both naturally occurring and anthropogenic forces. For example, 

genetic diversity within and among populations is affected by mutation, selection, genetic drift, 

recombination, and migration. Phenotypic diversity is influenced by genes that guide 

development, by random environmental effects, and by phenotypic plasticity. To protect against 

the deleterious effects of these factors on species or population sustainability, the following 

guidelines were developed (McElhany et al. 2000): 

1. Human-caused factors such as habitat changes, harvest pressures, artificial 

propagation, and exotic species should not substantially alter variation in traits (e.g., 

run timing, age structure, size, etc.). 

2. Natural processes of dispersal should be maintained. Human-caused factors should 

not substantially alter the rate of gene flow among populations. 

3. Natural processes that cause ecological variation should be maintained. 

4. Population status evaluations should take uncertainty about requisite levels of 

diversity into account. 

At the ESU level, it is also important to consider the role played by small or peripheral 

populations. While small populations are less likely to persist than large populations and make 

only minor contributions to overall abundance, they may serve a critical role in ESU diversity by 

providing reservoirs of potentially adaptive diversity. Although it is impossible to predict 

whether the potential diversity provided by small populations will contribute to ESU 

sustainability, prudent management would hedge bets by avoiding the loss of these populations 

(Bilby et al. 2005). Accordingly, the persistence of small populations is viewed as beneficial to 

the overall diversity of the ESU. 

This ESU-level Diversity Criterion assessment differs from the Population Diversity 

criteria (PD 1–4 and PF), which evaluate the diversity of each population independently. While 

genetic and phenotypic diversity can also be evaluated at a population level, it is unrealistic to 

expect that every population should contain a large number of life history types or have access to 

all possible habitat types, making such evaluations difficult. Rather, it is genetic, phenotypic, 

and habitat diversity and the existence of small populations within the ESU as a whole that are 

essential for ESU sustainability. Accordingly, while the Population Diversity criteria consider 

79
population attributes such as abundance, fish distributions, hatchery influence, and habitat 

capacity as measures of individual population diversity, the criteria presented here evaluate the 

diversity of the ESU as a single unit. 

ES, ESU Sustainability 

Criterion: The ESU is self-sustaining into the foreseeable future. 

Metric: The ESU Sustainability Criterion requires (DSS “AND” operator) sustainability 

of the biogeographic strata (All Biogeographic Strata Sustainable, ES-1) and the ESU-level 

Diversity Criterion, ES-2, but with reduced weight placed on ES-2. Thus the truth value is 

derived by combining the biogeographic Stratum Sustainability Criterion, SS, values into ES-1, 

then combining the resulting value with the ES-2 value, with a weight of 0.5 placed on the ES-2 

value. The entire sustainability network is illustrated in Figure 14 (page 45). 

Current assessment: The resulting ESU Sustainability Criterion, ES, truth value is 

+0.19 (low to moderate certainty that the ESU is self-sustaining into the foreseeable future) 

(Table 3). 

Discussion: This criterion ensures a resilient and diverse ESU in two ways. First, 

Stratum Sustainability ensures broad geographic and ecological distribution of fish throughout 

the range of the ESU, and this distribution provides fundamental ecological diversity as well as 

insurance against local catastrophes. Second, the ESU Diversity criteria ensure maintenance of 

broad-scale diversity that may not be reflected in the stratum structure. The weight on the ESUlevel Diversity Criterion was reduced because of the greater level of uncertainty in assessing the 

subcriteria via professional judgment. 

Summary—Sustainability 

Sustainability refers to a population or ESU that, in addition to being persistent, is also 

able to maintain its genetic legacy and long-term adaptive potential for the foreseeable future. 

Sustainability implies stability of habitat availability and other conditions necessary for the full 

expression of the population’s (or ESU’s) life history diversity into the foreseeable future. In the 

ESU Sustainability Criterion DSS network, population-level criteria form the lowest level of the 

analysis. At this level, there are two watershed-level subcriteria (a Spawner Watershed 

Occupancy Criterion, W-Sp, and a Juvenile Watershed Occupancy Criterion, W-Ju), which relate 

to two Population Diversity criteria (a Spawner Distribution Criterion, PD-3, and a Juvenile 

Distribution Criterion, PD-4). 

These, along with two other Population Diversity criteria (a Spawner Abundance 

Criterion, PD-1, and an Artificial Influence Criterion, PD-2) are combined to evaluate a 

Population Diversity Criterion, PD. The Population Diversity Criterion, PD, and the Population 

Persistence Criterion, PP, combine to assess a Population Sustainability Criterion, PS. 

Populations are then aggregated into biogeographic strata to evaluate a biogeographic Stratum 

Diversity Criterion, SD. A Population Functionality Criterion, PF, is evaluated and populations 

are aggregated to evaluate a Stratum Functionality Criterion, SF. The Stratum Diversity, SD, 

and Stratum Functionality, SF, criteria are combined to evaluate stratum sustainability. Stratum 

80
Sustainability Criterion, SS, values are aggregated to the ESU level (All Biogeographic Strata 

Sustainable, ES-1), which is combined with an evaluation of the ESU-level Diversity Criterion, 

ES-2, to arrive at the ESU Sustainability Criterion, ES. 

This framework evaluates population diversity using objective measures of spawner 

abundance, artificial influence, spawner and juvenile distribution, and habitat capacity. In 

addition, the workgroup evaluated the ESU-level Diversity Criterion that includes a Genetic 

Diversity Criterion (a function of genetic structure and effects of selection, migration, and 

introgression), a Phenotypic and Habitat Diversity Criterion, and a Small Populations Criterion. 

Fully quantifiable criteria are not available for the ESU-level diversity measures, so an expert 

panel was used to evaluate them but the result was given half the weight of the more quantifiable 

population-level evaluations. 

Recent observations of these population metrics were used to evaluate how certain it is 

that the Oregon Coast Coho Salmon ESU is sustainable under recent conditions. Results of the 

current assessment for the ESU Sustainability Criterion are mapped in Figure 13 on page 43. 

The results of the analysis lead to the conclusion that, if recent conditions continue into the 

future, there is low to moderate certainty that the ESU is sustainable. This conclusion depends 

strongly on the assumption that recent conditions continue into the future. Because this report 

focuses on biological conditions, and future conditions depend to a large extent on 

socioeconomic factors, this assumption has not been evaluated. To the extent that freshwater 

habitat conditions, ocean conditions, harvest management, and hatchery practices may change in 

the future, so may the sustainability of the ESU. 

81
Restoration 

Restoration (or broad-sense recovery), as defined in the Introduction on page 2, includes 

sustainability as well as the provision of environmental, cultural, and economic benefits. Thus, 

restoration extends beyond the requirements of the ESA, and criteria involve social, economic, 

and political considerations as well as biological requirements. The TRT’s work is limited to 

scientific and technical considerations, and for that reason a full set of restoration criteria with 

clearly defined metrics was not developed. Rather, only general guidelines regarding the 

structure of restoration criteria are provided. 

The general approach taken was to 1) define the types of environmental, cultural, and 

economic benefits that may be desired beyond minimal sustainability, 2) describe an ecosystem 

approach to ESU conservation that should be considered to ensure those benefits, and 3) describe 

a minimal set of criteria that could be used to assess progress toward restoration. 

Environmental, Cultural, and Economic Benefits 

Environmental benefits of coho salmon include such things as providing nutrients for 

stream ecosystems (e.g., aquatic invertebrates, other fishes, streamside vegetation), providing 

food for wildlife (e.g., bears, eagles, osprey, etc.), and providing an indication of water quality 

and watershed function. In addition, conservation measures for coho salmon have an influence 

on other sensitive species (e.g., brook lamprey [Lampetra richardsoni]), spring-run Chinook 

salmon [Oncorhynchus tshawytscha]). To provide these benefits, the abundance of the ESU 

should be a substantial portion of the historical abundance, with abundant populations distributed 

throughout the historical range of the ESU. The best example of a fully functional coho salmon 

ESU for this region is the historical ESU as it existed prior to major landscape changes following 

European settlement. Thus historical abundance and distribution can serve as an idealized 

template against which to compare restoration criteria. 

Cultural benefits are harder to define. These include use of salmon for Native American 

ceremonies, availability of salmon for viewing and nature study, and the intangible psychological 

benefits of the abundance of wild salmon and healthy stream ecosystems. As an example of the 

potential role of salmon in society, there is strong evidence that the abundance of salmon in the 

Pacific Northwest was a major factor in structuring Northwest Coast tribal societies (Netting 

1977). There is also a distinct culture surrounding the sport and commercial salmon fishing 

communities that have developed since European settlement, cultures that have also been 

influenced by the strength of salmon runs. 

Economic benefits include both direct and indirect values. The most obvious direct 

benefits are those resulting from subsistence, commercial, and sport harvest, including benefits 

that coho salmon conservation may have on harvest of other coastal salmonids, notably Chinook 

salmon and steelhead. There is also direct economic benefit from subsistence harvest for all 

salmonids. Indirect benefits include the economic multiplier effects of direct harvest benefits, 

82
plus less tangible benefits such as the increase in local land values resulting from environmental 

or cultural benefits associated with salmon restoration. 

Ecosystem Approach 

Conservation programs, such as the one that will lead to the restoration of Oregon Coast 

coho salmon, need to be tailored to the features and behaviors of the species of concern, and to 

the landscape within which habitat for the species is embedded. Because interactions between 

disturbance and recovery processes make landscapes dynamic (Lugo et al. 1999), habitat suitable 

for a given species will be found in a mosaic of patches whose ecological conditions vary 

spatially and over time (White and Pickett 1985). Holling (1973) noted that attempts to view and 

manage ecosystems and associated resources in a static context may increase the rate of 

extirpation or extinction of some organisms. Biotic populations persist in dynamic landscapes in 

two basic ways. One strategy is to be an ecological generalist capable of persisting in a wide 

range of conditions (Huff and Raley 1991). An alternative strategy is to be a specialist that 

moves between patches of favorable habitat (May 1994). 

The historic landscape of the Oregon Coast, including the aquatic component, was very 

dynamic in space and time because of the natural disturbance regime. Coho salmon populations 

likely persisted in this environment as ecological specialists, occupying areas of suitable habitat 

for periods of time and then moving to new areas of similar habitat as such habitat developed or 

as occupied areas became less suitable over time (Reeves et al. 1995). Collections of interacting 

populations of specialists, such as that just described for the coho salmon of the Oregon Coast, 

are referred to as metapopulations. Metapopulation issues are apparent at a variety of scales 

from individual spawning grounds (e.g., Neville et al. 2006, Isaak et al. 2007) to interconnections 

among basin-scale populations (e.g., Hilborn et al. 2003, Hastings and Botsford 2006). For 

salmonids, metapopulation processes can be quite variable depending on species, life history, 

and landscape patterns (see review by Rieman and Dunham 2000). 

Conservation of metapopulations requires sufficient numbers of patches of suitable 

habitat through time and the potential for dispersal among the patches (Harrison 1994). Where 

there are currently insufficient numbers of patches of high quality habitat, it is important to 

protect existing high quality patches in the near term (Frissell 1997). Minimizing or eliminating 

external threats increases the likelihood of these patches persisting (Meffe and Carroll 1997) so 

that they can serve as sources of individuals to colonize new patches of favorable habitat as they 

develop. Development of future patches of favorable habitat, another important component of 

conservation, requires the protection or restoration of critical ecological processes that create 

such habitat over time (Meffe and Carroll 1997). The key to any conservation program will 

therefore be to protect against losses of existing patches of favorable habitat, to establish a 

program that leads to the development of favorable habitat in the future by focusing on the 

ecological processes that create such habitat, and to maintain and develop conditions that allow 

movement between the favorable patches. The importance of these dynamic landscape processes 

on the Oregon Coast has been demonstrated by recent work of the Coastal Landscape Analysis 

and Monitoring Study (Spies et al. 2007, Burnett et al. 2007). 

Habitat losses can result from human activities that directly destroy habitats or that 

change the long-term dynamics of landscapes and their embedded ecosystems (Rapport et al. 

83
1985, Webb and Thomas 1994). For coho salmon and other anadromous salmonids, naturally 

variable ocean conditions increase the importance of good freshwater habitat (FEMAT 1993, 

Lawson 1993, Nickelson and Lawson 1998) and the consequences of reduced habitat abundance 

and quality. Past approaches to management of the freshwater habitats of these fish have 

focused almost solely on mitigating losses rather than preventing them. This strategy has 

generally not been successful (Bisson et al. 1992) and large-scale habitat improvement and 

meaningful increases in fish numbers are slow to occur. Past attempts were generally 

unsuccessful because the focus was on local areas of in-stream habitat and not on ecosystem 

processes at a watershed scale (Moyle and Sato 1991, Naiman et al. 1992, Williams et al. 1989). 

As a consequence of the coho salmon’s dependence on freshwater habitats and the 

extensive amount of habitat degradation that has occurred within Oregon Coast watersheds, 

protection and restoration of upslope and fluvial processes that create and maintain habitats must 

be an integral component of any recovery program for the species. Recent proposals for 

restoring and protecting habitats of at-risk fishes (e.g., FEMAT 1993, Moyle and Yoshiyama 

1994, Reeves et al. 1995) addressed habitat destruction, primarily through the establishment of 

watershed-level reserves in which human impacts would be minimized, as advocated by Sheldon 

(1988) and Williams et al. (1989). Williams et al. (1989) called for recovery efforts to restore 

and conserve ecosystems rather than habitat attributes. 

Efforts to restore Oregon Coast coho salmon have begun to reflect a broader, watershedlevel and regional perspective on the species’ needs, but continued substantial improvement will 

be required to restore the natural ecosystem processes needed to sustain high quality freshwater 

habitats. This is particularly true if the intent is to move beyond avoidance of species 

imperilment and to broad-sense recovery. For example, while actions being taken on federal 

public lands managed under the President’s Forest Plan, and some actions taken on private and 

state-owned public lands under the funding or guidance of the Oregon Plan, will protect or 

restore natural processes linked to high-quality coho salmon habitat, ongoing activities in other 

areas may be less helpful. 

Conventional management practices on private and state-owned forest lands limited only 

by the state’s Forest Practices Act do not fully protect or restore such processes (Botkin et al. 

1995, IMST 1999). Conventional agricultural, rural-residential, or urban practices allowed in 

lowland areas also fall short, and sometimes substantially so, of what is needed to protect or 

restore the habitat of Oregon Coast coho salmon in those areas (IMST 2002). This is particularly 

important for coho salmon, as land ownership patterns and past land use policies have 

concentrated high-impact land uses in areas of high potential for coho salmon, and these patterns 

are likely to limit coho salmon production into the foreseeable future (Burnett et al. 2007). 

Further improvements will be necessary in watersheds across the Oregon Coast and on lands 

managed for a variety of uses. 

Suggested Criteria 

Considering the desire for environmental, cultural, and economic benefits within the 

context of an ecosystem approach, we suggest that a minimum of three types of criteria be 

considered: overall ESU abundance, the sustainability of Independent Populations, and the 

productivity of Dependent Populations. These criteria focus on measures of fish abundance and 

84
sustainability rather than directly on the ecosystem processes described above, but it should be 

recognized that goals for fish populations can only be achieved by restoring the ecosystem 

processes that support those populations. 

ESU Abundance 

ESU spawning abundance must be sufficient to meet economic, cultural, and 

environmental benefits. Overall abundance of the ESU is an important determinant of all three 

types of benefits and is an indicator of ecosystem function. In a natural system, abundance will 

fluctuate widely, depending on changing conditions in both the freshwater and marine 

environments. As a result, thresholds for this criterion need to allow for substantial year-to-year 

variation, while ensuring that habitats are sufficiently healthy to ensure that ecosystem functions 

will be met over the long term. Because this criterion is based on economic, cultural, and 

environmental benefits, the exact level is clearly a policy choice and should be chosen after 

weighing the value of societal benefits against costs of achieving those benefits. 

Some economic and environmental benefits can be quantified. For example, Figure 27 

shows hypothetical relationships between salmon abundance and three categories of benefits: 

commercial harvest, where surplus production theory predicts that economic return is maximized 

at some intermediate level of spawner abundance; stream nutrients, which start out very low but 

increase to a saturation value at moderate to high abundances; and general ecosystem benefits 

(food for wildlife, other interactions with the broader coastal ecosystem), which may not reach a 

maximum until abundance approaches that of pristine conditions. 

Sp

Re

Low High

None

Max

awner Abundance

lative Benefit

Commercial

Harvest

G

E

ream

trients

eneral

cosystem

St

Nu

Sp

Re

Low High

None

Max

awner Abundance

lative Benefit

Commercial

Harvest

G

E

ream

trients

eneral

cosystem

St

Nu

Commercial 

h

General 

arvest ecosystem 

Relative benefit 

Stream 

nutrients 

Spawner abundance 

Figure 27. Hypothetical relative value of spawning escapement in terms of harvest, stream nutrient 

supply, and more general ecosystem benefits. 

85
Commercial harvest benefits can be estimated based on stock-recruit analysis and 

surplus-production theory, resulting in a dome-shaped value curve where value increases with 

abundance up to a maximum value, then decreases because harvest rates would need to be 

reduced to achieve higher escapements. Alternatively, it could be assumed that harvest 

management will continue into the foreseeable future according to Amendment 13 to the Pacific 

Salmon Fishery Management Plan (FMP) (PFMC 2003), and benefits could be calculated on that 

basis given the relationship between average run size and allowable harvest rate. A minimum 

level of 125,000 spawners is suggested during periods of poor ocean conditions, which would 

essentially meet the fishery management plan rebuilding goal, estimated to be 126,700 spawners 

(Table 3-3 in PMFC 2003). This would ensure that harvest would not be restricted by spawner 

abundance under the FMP (but may still be restricted by marine survival conditions). Managing 

the ESU for levels greater than the 125,000 fish threshold is largely a policy choice of balancing 

benefits with costs of achievement. If the minimum criterion is met during poor ocean 

conditions, something likely to require about a four-fold increase in high-quality habitat for 

Oregon Coast coho salmon, substantially higher abundance under moderate or good ocean 

conditions, would be expected. 

Coho salmon are an important contributor to stream nutrient supply, and Bilby et al. 

(2001) estimated the density of coho salmon carcasses that would maximize nutritional benefit to 

juvenile coho salmon at approximately 120 carcasses per km of spawning habitat. Given about 

6,900 km (4,300 miles) of spawning habitat in the range of the ESU, this translates to an 

escapement of about 830,000 spawners to saturate the stream nutrient supply. 

Another approach to ensuring cultural, economic, and environmental benefits could be 

restoring salmon abundance to a specified substantial portion of historical abundance. We 

suggest that somewhere between 25% and 75% of historical abundance may be sufficient to meet 

these goals. Given a range of estimated historical peak abundance of 1 million to 2 million 

spawners (Appendix C in Lawson et al. 2007), this would translate to a goal of between 250,000 

and 1.5 million spawners during periods of high marine survival. 

To reduce year-to-year variation in the metric, the workgroup suggests that abundance be 

measured as a 4-generation (12-year) average of naturally produced spawners for the entire ESU 

estimated from ODFW’s annual spawner surveys. By using a 12-year average, this metric is 

likely to span a range of conditions in both ocean and freshwater environments. However, long 

periods of extreme environmental conditions could make this metric hard to interpret in terms of 

health of the ESU. The issue of environmental fluctuations is discussed more thoroughly in the 

Important Issues section on page 8. 

Sustainable Independent Populations 

To maintain the distribution of coho salmon populations as close to the historical 

template as possible, all of the historically Independent Populations should meet Population 

Sustainability Criterion, PS, defined under Sustainability (page 59). By ensuring widespread 

distribution of sustainable populations among varied habitats, this would help maintain the longterm adaptive potential of the ESU. We also expect that society would wish to ensure that the 

benefits of restoration accrue across the range of the ESU and to populations of diverse 

character, not just in portions of the largest populations. Requiring that all Independent 

86
Populations are sustainable into the foreseeable future provides assurance that some economic, 

cultural, and ecological benefits are available in all of the occupied basins. 

Productive Dependent Populations 

Habitats that historically supported Dependent Populations should be intact and 

accessible to fish. Ensuring intact habitats (as evidenced by occupancy and moderate 

production) for the smaller Dependent Populations would further distribute benefits and provide 

additional insurance for the ESU. The role of small populations in the ESU is not well 

understood, but was discussed under the Small Populations Criterion, ED-3 (page 76). 

Populations that were historically dependent on other populations for long-term 

persistence are, nonetheless, important to the health of the ESU. These smaller populations are 

likely to experience periodic local extinctions and be maintained, at times, by the immigration of 

spawners from other, usually independent, populations. They play a role in genetic diversity and 

may be sources of novel adaptations that can more easily arise in small populations, and then 

find their way into the larger ones, facilitating more rapid adaptation to changing conditions. 

While it is difficult to set conservation standards for these populations, effort should be directed 

toward ensuring that most of the existing systems with the potential to support such small 

populations (see Lawson et al. 2007) are accessible to fish, and that habitat in these systems is 

relatively intact. 

Because basins that support Dependent Populations, by definition, have limited habitat, 

even small amounts of habitat loss can seriously affect their ability to support coho salmon; 

therefore it is important to retain an adequate quantity and variety of habitat, including spawning, 

summer rearing, and winter rearing areas. Habitat loss could be expressed as the proportion of 

historical habitat, stratified by spawning and rearing, that is currently available. A baseline 

assessment of productivity, as expressed by smolts per spawner, could be used to confirm habitat 

assessments. 

Simple presence or absence of fish has limited usefulness as a criterion for assessing the 

health of Dependent Populations because periodic extinction and recolonization events are 

expected. Smaller systems will likely experience more frequent extinctions, while larger basins 

should be populated a greater proportion of the time (see Figure 28). While the number of 

occupied systems is expected to vary, a downward trend in occupancy would be a concern. Such 

trends could be identified over time through the use of periodic, comprehensive, rapid 

bioassessment (RBA) surveys. Interpretation of survey results must account for periodic 

variations in marine survival conditions. 

Summary—Restoration 

ESU restoration (or broad-sense recovery) is a process leading to conditions such that the 

populations of naturally produced fish that make up the Oregon Coast Coho Salmon ESU are 

sufficiently abundant, productive, and diverse (in terms of life histories and geographic 

distribution) that the ESU as a whole is self-sustaining and will provide environmental, cultural, 

and economic benefits. 

87
Figure 28. Conceptual illustration of the proportion of time a system is occupied as a function of the 

potential size of the population in that system. Larger systems are expected to be occupied for a 

greater proportion of the time. 

Because these goals are fundamentally different from those for persistence and 

sustainability in having a substantial sociopolitical component beyond the scope of this report, 

specific criteria have not been defined. Rather, general ecosystem considerations have been 

discussed as background for restoration, and a few types of criteria have been outlined to 

measure progress toward restoration goals. 

88
Conclusions 

The workgroup developed and applied a means to evaluate the current biological status of 

the Oregon Coast Coho Salmon ESU that is intended for use in the recovery planning process. 

The biological recovery criteria are organized into two coherent logical frameworks to evaluate 

persistence and sustainability. In addition to developing criteria to evaluate persistence and 

sustainability of the ESU, the workgroup also offers general guidelines regarding ESU 

restoration (or broad-sense recovery). 

Uncertainty within the logical structure of the ESU Persistence and ESU Sustainability 

criteria is accommodated through a knowledge-based DSS. This use reflects both the uncertainty 

about “true” parameters and errors of measurement. The results of this assessment are expressed 

as the degree of confidence that two overall statements regarding the status of the ESU are true: 

1) the ESU is persistent, and 2) the ESU is sustainable. 

The logical networks and associated data within the DSS are designed to contribute to the 

decision-making processes associated with both federal recovery planning and Oregon State 

conservation planning. It was the intention of the workgroup to honor the multiple mandates of 

both federal and state requirements in developing the biological recovery criteria. One 

requirement of recovery planning is setting objective, measurable criteria for delisting. Toward 

this end, the ESU Persistence and ESU Sustainability criteria provide a means to assess 

biological conditions related to listing status under the ESA. This evaluation does not consider 

other factors, including conservation measures and expectations about future conditions, that 

factor into the ESA definition of threatened status. Incorporating such factors requires policy 

decisions not reflected in this analysis. Thus the criteria presented here provide only a partial 

assessment of status under the ESA. 

The workgroup has applied the DSS using available data, models, and best professional 

judgment to evaluate the current status of the ESU for two main goals: persistence and 

sustainability. 

Persistence 

The DSS framework includes criteria for Population Productivity, Probability of 

Persistence, and Critical Abundance. The workgroup used recent observations of these 

population metrics to evaluate how certain it is that the Oregon Coast Coho Salmon ESU is 

persistent under recent conditions. The results of the analysis lead the workgroup to conclude 

that, if recent past conditions continue into the future, it is moderately to highly certain (truth 

value +0.40) that the ESU is persistent; that is, it is able to persist over a 100-year period without 

artificial support. This means that, if recent habitat conditions, climate patterns, and 

management policies continue into the future, there is moderate to high certainty that the ESU 

and most of its constituent biogeographic strata and Independent Populations will persist for 100 

years. The weakest strata in this assessment were the North Coast and Mid-Coast, which had 

89
only low certainty of being persistent. The strongest strata were the Lakes and Mid-South Coast, 

which had high certainty of being persistent. To increase certainty that the ESU as a whole is 

persistent, restoration work should focus on those populations with low persistence, particularly 

those in the North Coast, Mid-Coast, and Umpqua strata. 

Sustainability 

A sustainable ESU is 1) able to survive prolonged periods of adverse climatic conditions 

without artificial support, and 2) able to maintain its genetic legacy and long-term adaptive 

potential. Additionally, a sustainable ESU is composed of diverse interconnected populations. 

To evaluate population diversity, the workgroup constructed a framework using objective 

measures of spawner abundance, artificial influence, spawner and juvenile distribution, and 

habitat capacity. We also evaluated ESU-wide diversity by assessing genetic diversity, 

phenotypic and habitat diversity, and the status of small populations. The results of the analysis 

lead us to the conclusion that, if recent conditions continue into the future, there is low to 

moderate certainty (truth value +0.19) that the ESU is sustainable; that is, it is able to maintain 

its genetic legacy and long-term adaptive potential for the foreseeable future. This conclusion 

assumes that present and recent past habitat conditions, climate patterns, and management 

policies reflected in the data sets used by the workgroup will continue for the foreseeable future; 

no attempt was made to evaluate the effects of potential future changes in environmental 

conditions or conservation measures. 

The conclusion that there is low to moderate certainty that the ESU is sustainable can be 

interpreted further. This means the ESU may be sustainable if strong ecosystem, harvest, and 

hatchery protections are in place. Many Independent Populations, and all but one of the 

biogeographic strata, have moderate to high certainty of being sustainable, but many other 

Independent Populations have obvious problems for some criteria. On the positive side, the 

Lakes and Mid-South Coast strata have high certainty of being sustainable, with most of their 

constituent Independent Populations having high certainty of being sustainable. On the 

downside, the North Coast stratum has low certainty of Sustainability, with two Independent 

Populations having low certainty of being sustainable, and two having low certainty of being 

unsustainable. 

An ESU at this level has good opportunities for recovery, but is also vulnerable to harvest 

and ecosystem alteration and destruction. Strong protections are needed to ensure that the status 

of populations and strata in moderate to good condition does not degrade, because the lower the 

status gets the more difficult and expensive it is to bring it back up. Overall ESU status can best 

be improved by focusing restoration efforts on those populations that scored low on 

sustainability, particularly those in the North and Mid-Coast strata. There should be 

opportunities for limited harvest during high productivity periods, and hatchery programs should 

be strictly managed to prevent risks to naturally produced fish. 

Restoration 

In addition to these analyses of persistence and sustainability, an outline has been 

presented for developing criteria for restoration (or broad-sense recovery), including suggestions 

for three types of criteria: ESU abundance, sustainable Independent Populations, and productive 

90
Dependent Populations. Metrics for these criteria depend on social, economic, and political 

considerations as well as biological requirements, so full metrics have not been developed at this 

time. 

Limitations 

The persistence and sustainability analyses deal only with current or recent conditions 

without addressing future conditions, threats, or limiting factors. This approach provides a 

systematic method for evaluating the current status of the Oregon Coast Coho Salmon ESU as 

part of a recovery or conservation plan, but is not sufficient to determine the ESA listing status of 

the ESU. For ESA evaluations, it is important to address not only present status, but also factors 

for decline, threats, and probable trends in land use patterns, harvest and hatchery management 

practices, and climate. These factors will play pivotal roles in the future sustainability of this 

ESU. Assessing these factors requires policy-level analysis of social and economic issues, as 

well as a projection of climate trends, and is beyond the scope of this report. 

Within the limited context of current and recent conditions, this assessment may be 

biased by including some threats that have been largely addressed and are not expected to 

continue into the future. These include intense fishing pressures and negative hatchery impacts 

that have been substantially reduced. Some of the measures of persistence and sustainability 

include data collected before these factors for decline had been addressed, and therefore may 

have a pessimistic bias. However, the assessment reflects current (data through 2004) levels of 

other threats (such as poor freshwater habitat conditions) that have not been fully addressed and 

can be expected to continue into the future. Thus the conclusions are limited to the conditions 

considered by the workgroup, and the status of the ESU hinges on future conditions that have not 

been addressed. 

91
92
Glossary 

abundance. The number of fish in a population. 

AIC. For Akaike Information Criterion. A statistical criterion for selecting among models of 

different complexity, based on the difference between the number of parameters in the model 

and the goodness of fit to a particular data set. 

allele. Any one of a number of alternative forms of a gene that can occur at the same location 

(locus) on a chromosome. A population can have many alleles for a particular locus. See 

also DNA, heterozygosity, and mutation. 

allozyme. One of several alternative forms of an enzyme that have the same function, but are 

produced by different genetic alleles. 

arithmetic mean. The average of a set of values, calculated as the sum of the values divided by 

the number of values in the set. 

artificial propagation. Hatchery spawning and rearing of salmon, usually to the smolt stage. 

AUC. For area under the curve. A statistical technique for estimating an annual total number of 

spawners from periodic spawner counts. 

Bayesian analysis (or Bayesian estimation). A branch of statistics concerned with estimating 

parameters for models from multiple sources of information. Bayesian analysis divides 

information into two classes: prior information that is known before considering a new set of 

observations, and posterior information that incorporates both the prior information and the 

new information contained in the new observations. 

best professional judgment. A process of analyzing a problem by utilizing an expert or expert 

panel to use collective experience in relation to the problem. 

biogeographical strata. Groups of populations with genetic, ecological, and geographical 

similarities. 

Boolean logic. A system of logic that allows organizing things in sets using AND, OR, UNION, 

and other operators. Any item is either fully a member of a particular set or not at all a 

member of that set. In this type of logic, statements are typically described as true or false. 

bootstrapping. A statistical method for estimating the variability of a parameter for a 

population where a small sample is repeatedly resampled and analyzed. This allows for 

statistical analysis using a relatively small sample compared to analysis on a traditional 

single sample from the population. 

carrying capacity. The number of individuals that the resources of a habitat can support. 

93
catastrophic events. Sudden events that disastrously alter large areas of landscape. These can 

include floods, landslides, forest fires, and volcanic eruptions. 

CV. For coefficient of variation. In describing variation in a population, the standard deviation 

is expressed as a fraction of the mean. 

compensation. A decrease in productivity with increasing abundance, which results in an 

approach to some maximum level of production. 

compensatory smolt production curve. A decrease in smolt productivity with increasing 

abundance, which results in an approach to some maximum level of production. 

DSS. For decision support system. A computer application that assists users in using data and 

models to solve problems (Scott Morton 1971). Typically, decision support systems are 

computer programs that analyze many pieces of data or models, producing results that aid in 

decision making rather than replacing human judgment (Turban and Aronson 2001). 

delisting (or narrow-sense recovery). A process resulting in the ESU being removed from the 

formal protections of the ESA as a consequence of no longer being endangered nor likely to 

become endangered within the foreseeable future in a significant portion of its range. 

demographic. Pertaining to the processes of birth, development, growth, and mortality that 

control the dynamics of populations. 

demographic stochasticity. “Chance events in the survival and reproductive success of a finite 

number of individuals” (Shaffer 1981). 

dendrogram. A branching diagram, sometimes resembling a tree, that provides one way of 

visualizing similarities between different groups or samples. 

Dependent Populations. Populations that rely upon immigration from surrounding populations 

to persist. Without these inputs, Dependent Populations would have a lower likelihood of 

persisting over 100 years (Lawson et al. 2007). 

depensation. The effect where a decrease in spawning stock leads to reduced survival or 

production of eggs through either 1) increased predation per egg given constant predator 

pressure, or 2) the “Allee effect” (the positive relationship between population density and 

the reproduction and survival of individuals) with reduced likelihood of finding a mate. 

DNA. For deoxyribonucleic acid. A complex molecule that carries an organism’s heritable 

information. 

effective population size. The size of an idealized (panmictic) population that would have the 

same rate of loss of heterozygosity as the real (nonpanmictic) population. 

endangered species. In the context of the ESA, any species which is in danger of extinction 

throughout all or a significant portion of its range. 

ESA. For U.S. Endangered Species Act of 1973. 

escapement. Usually refers to adult fish that “escape” from fisheries and natural mortality to 

reach the spawning grounds. 

94
estuarine habitat. Areas available for feeding, rearing, and smolting in tidally influenced lower 

reaches of rivers. These include marshes, sloughs and other backwater areas, tidal swamps, 

and tide channels. 

ESU. For evolutionarily significant unit. An ESU represents a distinct population segment of 

Pacific salmon under the ESA that 1) is substantially reproductively isolated from 

conspecific populations, and 2) represents an important component of the evolutionary 

legacy of the species. 

exploitation rate. The proportion of adult fish in a population that die as a result of fisheries. 

extinction. The loss of a species or ESU. May also be used for the extirpation of local 

populations. 

extinction risk. The probability of a species going extinct within a particular time period. 

factors for decline. These are factors identified that cause a species to decrease in abundance 

and distribution and become threatened or endangered. 

fecundity. The number of offspring produced per female. 

fishery mortality rate. The number of fish that die when intercepted by a fishery in relation to 

the number of fish as a whole. 

freshwater habitat. Areas available for spawning, feeding, and rearing in freshwater. 

fry. Young salmon that have emerged from the gravel and no longer have an egg sack. 

fully seeded. Having enough spawners to fully occupy available juvenile habitat with offspring. 

fuzzy logic. A type of logical analysis that allows a system to use imprecise information in 

evaluating conditions (Zadeh 1965, Reynolds et al. 2000). It is discussed in Appendix A and 

referred to as “approximate logic” in the body of this technical memorandum. 

gamma distribution. A continuous probability distribution usually used to describe the amount 

of time until the nth occurrence of a process with a Poisson distribution. 

genetic drift. Random changes in gene frequencies of populations. 

genetic stochasticity. Random changes in the genetic makeup of populations. 

geometric mean. A measure of central tendency similar to the arithmetic mean, except that the 

computation uses multiplication instead of addition to summarize data values. It is a useful 

statistic for summarizing highly skewed data values. For two numbers the geometric mean 

equals the square root of the product of the two numbers, i.e. (x1x2)

-0.5. 

habitat quality. The suitability of physical and biological features of an aquatic system to 

support salmon in the freshwater and estuarine system. 

harmonic mean. The harmonic mean is another way of calculating an average. It is the 

reciprocal of the arithmetic mean of reciprocals of the data. For n numbers x1, x2, ... xn, the 

harmonic mean equals n/(1/x1 + 1/x2 + ... + 1/xn). 

95
hatchery. A facility for the artificial propagation of fish. Salmon hatcheries typically spawn 

adults in captivity and raise the progeny in fresh water for release into the natural 

environment. 

hatchery fish. Fish incubated or reared under artificial conditions for at least a portion of their 

life cycle (ODFW 2003). 

heterozygosity. The variation in alleles at the same locus on a chromosome. Loss of 

heterozygosity in populations could be damaging to the ability of a population to adapt to 

changes in the environment. 

historical abundance. The number of fish that were produced before the influence of Western 

settlement. 

HUCs. For hydrologic unit codes. “The United States is divided and subdivided into 

successively smaller hydrologic units which are classified into four levels: regions, 

subregions, accounting units, and cataloging units. Hydrologic units are arranged within 

each other, from the smallest (cataloging units) to the largest (regions). Each hydrologic unit 

is identified by a unique hydrologic unit code (HUC) consisting of two to eight digits based 

on the four levels of classification in the hydrologic unit system” (online at 

http://water.usgs.gov/GIS/huc.html). 

immigration. The movement of individuals into a population that they were not born into. 

inbreeding depression. Reduced survival rates of individuals in a population suffering from the 

effects of harmful recessive genes through matings between close relatives. Inbreeding 

depression may become a problem when populations become very small. 

Independent Population. A population that historically would have a high likelihood of 

persisting in isolation from neighboring populations for 100 years (Lawson et al. 2007). 

intrinsic growth rate. The growth rate of a population in the absence of compensation. 

intrinsic productivity. Productivity of a population in the absence of compensation, estimated 

as the mathematical limit of population productivity as abundance approaches zero. 

introgression. Introduction of genes from one population or species into another. 

isolation. Being unaffected by migration to and from other populations. 

jack. A male coho salmon that matures at age two and returns from the ocean to spawn one or 

more years before full-sized adults return. For coho salmon in California, Oregon, 

Washington, and southern British Columbia, jacks are 2-years-old, having spent 6 months in 

the ocean, in contrast to adults, which are 3-years-old after spending 11⁄2 years at sea. 

juvenile. A fish that has not matured sexually. 

juvenile capacity. The capacity of habitat to provide conditions for rearing for nonadult fish 

that have not smolted and subsequently migrated to sea. 

life history. The specific life cycle of a fish from egg to adult. 

96
limiting factors. Factors that limit survival or abundance. They are usually related to habitat 

quantity or quality at different stages of the life cycle. Harvest and predation may also be 

limiting factors. 

marine survival rate. The proportion of smolts entering the ocean that survive to be harvested 

or return to freshwater. 

mean. One of several methods of calculating an average, usually referring to an arithmetic 

mean. 

metric. A clearly defined measurement of some quantity or quality. 

migration. Movement of fish from one population to another. 

migration rate. The proportion of spawners that migrate from one population to another. 

Monte Carlo simulation. A method for solving a statistical problem by generating suitable 

pseudorandom numbers and observing that fraction of the numbers obeying some property or 

properties. 

mutation. A change in an allele at a locus. This can happen through random events or exposure 

to chemicals and radiation. 

NRR. For natural return ratio. The ratio N/T, where N is naturally produced spawners in one 

generation and T is total (hatchery produced + naturally produced) spawners in the previous 

generation. 

naturally produced fish. Fish that were spawned and reared in natural habitats, regardless of 

parental origin (see also wild fish). 

OCN coho. For Oregon Coast naturally produced coho salmon. 

ODFW. For Oregon Department of Fish and Wildlife. 

ONCC TRT. For Oregon and Northern California Coasts Technical Recovery Team. 

Oregon Plan. “In 1997, with the support and participation of a wide spectrum of stakeholders 

from all sectors and regions of the state, the Oregon Legislature and governor established the 

Oregon Plan for Salmon and Watersheds. Motivated at first by the conviction that Oregon 

must devise its own response to listings of coho salmon and other salmon species under the 

federal Endangered Species Act, the plan quickly evolved and expanded into an 

unprecedented statewide program to preserve and profit from Oregon’s natural legacy” 

(online at http://www.oregon-plan.org). 

OPI. For Oregon Production Index. An estimate of coho salmon adult abundance for the 

marine area extending from Leadbetter Point, Washington, to the U.S.-Mexico border. 

OWEB. For Oregon Watershed Enhancement Board. 

parr. The life stage of salmonids that occurs after fry and is generally recognizable by dark 

vertical bars (parr marks) on the sides of the fish. 

97
persistent population (also referred to as ESU). One that is able to persist (i.e., not go extinct) 

over a 100-year period without artificial support. This includes an ability to survive 

prolonged periods of adverse environmental conditions, which may be expected to occur at 

least once in the 100-year time frame. 

phenotypic diversity. Variation in the set of observable characteristics of an individual or group 

as determined by the genotype and the environment. 

phenotypic plasticity. The ability to vary the set of observable characteristics of an individual 

or group as determined by the genotype and the environment generally in response to 

environmental change. 

Poisson distribution. A discrete statistical probability distribution, expressing the probability of 

a number of independent events occurring in a fixed interval of time or region of space. 

population. A group of fish of the same species that spawns in a particular locality at a 

particular season and does not interbreed substantially with fish from any other group. 

population dynamics. Changes in the number, age, and sex of individuals in a population over 

time, and the factors that influence those changes. Five components of populations that are 

the basis of population dynamics are birth, death, sex ratio, age structure, and dispersal. 

population structure. The distribution of characteristics (such as age, size, or physiological 

state) of individuals within a population. 

PVA. For population viability analysis. “[A] set of analytical and modeling approaches for 

assessing the risk of extinction. Life history, demography, and genetics of a species are 

integrated with environmental variability to project the future course of populations” 

(Beissinger and McCullough 2002, p. xiv). 

posterior distribution. The statistical distribution of a parameter (or set of parameters) that is 

obtained by combining the prior distribution with information resulting from a new set of 

observations (see Bayesian analysis). 

prior distribution. The statistical distribution of a parameter (or set of parameters) that is 

known before incorporating a new set of observations (see Bayesian analysis). 

production. The number of fish produced by a population in a year. 

productivity. The rate at which a population is able to produce reproductive offspring. 

QET. For quasi-extinction threshold. A threshold representing the minimum allowable 

population abundance in population viability modeling, below which the population is 

assumed to be essentially extinct. It is defined primarily for convenience and may be based 

on a number of considerations including Allee effects and uncertainties in population 

functional response at very low abundance. 

reach. A segment of a stream with uniform physical characteristics. 

98
recovery. A general term for the reestablishment or restoration of populations at risk. Here, it is 

used in two senses: a “narrow sense” as defined in the ESA (see delisting), and a “broad 

sense” to include efforts that extend beyond the requirements of the ESA (see restoration). 

recovery domain. The geographic area for which a technical recovery team is responsible. 

recovery plan. Under the ESA, a document identifying actions needed to improve the status of 

a species or ESU to the point that it no longer requires protection. 

recruit. A preharvest maturing adult salmon, that is, an adult in the ocean that would be 

expected to return to spawn in freshwater if it is not harvested. 

recruitment. 1) The number of recruits during a given year or generation. Typically estimated 

as the number of adults that return to spawn divided by the total fishery escapement rate (one 

minus the total harvest rate). 2) The number of recruits in one generation per spawner in the 

previous generation (also “recruitment rate”). 

restoration (or broad-sense recovery). A process leading to conditions such that the 

populations of naturally produced fish that make up the Oregon Coast Coho Salmon ESU are 

sufficiently abundant, productive, and diverse (in terms of life histories and biogeographic 

distribution) that the ESU as a whole: 1) will be self-sustaining, and 2) will provide 

environmental, cultural, and economic benefits (modified from Oregon SRTF, OSL 2002b). 

run timing. The time of year (usually identified by week) when spawning salmon return to the 

spawning beds. 

salmonid. Any of the species of fish in the family Salmonidae, including salmon, trout, and 

char. 

selection. An evolutionary process that drives adaptation to environmental change through 

differential reproductive success of organisms that have variability in their genetic makeup. 

smolt. A life stage of salmon that occurs just before the fish leaves freshwater. Smolting is the 

physiological process that allows salmon to make the transition from freshwater to salt water. 

smolt capacity. The maximum number of smolts a basin can produce. Smolt capacity is related 

to habitat quantity and quality. 

spawner. A reproductive adult fish. 

spawner surveys. Efforts to estimate the number of adult fish on spawning grounds. Spawner 

surveys utilize counts of redds and fish carcasses to estimate spawner escapement and 

identify spawning habitat. Annual surveys can be used to compare the relative magnitude of 

spawning activity between years. 

spawner-to-spawner ratio. A measure of the productivity of salmon populations, based on the 

ratio of the number of spawners in one generation to the number of spawners in the previous 

generation. A spawner-to-spawner ratio of 1.0 indicates that, on average, each spawner 

produced one offspring that survived to spawn. 

99
species. In the ESA, either a recognized biological species, or any recognized subspecies, or (for 

vertebrate fish or wildlife) any distinct population segment which interbreeds when mature. 

By NOAA policy, the last definition includes ESUs of salmon. 

STEP. For Salmon and Trout Enhancement Program of the Oregon Department of Fish and 

Wildlife. 

stochastic. Influenced by random variation. 

SRS. For stratified random survey. A statistical design used to sample subgroups more 

effectively with fewer samples needed than if the entire population was randomly sampled. 

stray rate. The proportion of spawning adults that return to a stream other than their natal 

stream within a basin. (See also Migration rate.) 

stream-estuary ecotone. The area where the physical characteristics of a stream change to the 

physical characteristics of an estuary. This is where water becomes brackish, there is mixing 

of stream and estuary nutrients, and the physical attributes are affected by tides and ocean 

water influences. This tends to be a very productive area due to phosphorus and nitrogen 

inputs from both oceanic and upland influences. 

Student’s t-test. A statistical test for the significance of the difference between a sample mean 

and a specified criterion. 

sustainable population (or ESU). One that, in addition to being persistent, is also able to 

maintain its genetic legacy and long-term adaptive potential for the foreseeable future. 

“Sustainable” implies stability of habitat availability and other conditions necessary for the 

full expression of the population’s (or ESU’s) life history diversity into the foreseeable 

future. 

threatened species. Under the ESA, any species which is likely to become an endangered 

species within the foreseeable future throughout all or a significant portion of its range. 

TRT. For technical recovery team. 

upwelling. The upward transport of cold, nutrient-rich water to the surface; conditions that favor 

the survival and growth of salmon and other fish species. Upwelling along the Oregon Coast 

occurs in spring and summer, driven by winds from the Northwest. 

variance. A statistical measure of how spread out a distribution is. For example, a small 

variance would mean that the observations were similar to each other, while a large variance 

would mean that observations were less similar to each other. 

viability. The likelihood that a population will sustain itself over a 100-year time frame. 

viability criteria. A prescription of the biological conditions for populations, biogeographic 

strata, and ESUs that together imply that the ESU will have a negligible risk of extinction 

over a 100-year time frame. 

wild fish. Fish whose ancestors have always lived in natural habitats, i.e., those with no 

hatchery heritage (see also naturally produced fish). 

100
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114
Appendix A: Use of a Knowledge-based 

DSS in Determining the Recovery Status 

of Oregon Coast Coho Salmon 

Introduction 

While developing technical recovery analyses, technical recovery teams have been asked 

by policy makers to develop measurable criteria for evaluating the status of evolutionarily 

significant units (ESUs). The usual approach to developing measurable criteria has been to 

combine modeled goals with expert opinion assessments of the status of populations in relation 

to these goals. This development process, however, is difficult to apply consistently over many 

populations and is difficult to document. The Oregon Coast Workgroup of the Oregon and 

Northern California Coasts Technical Recovery Team chose to use a combination of aids to 

assist in this development, including data analysis, computer models, and expert opinion 

integrated into a decision support system (DSS). The DSS assists in ensuring that expert opinion 

will be well-documented, repeatable, and transparent. Results from the DSS can provide 

guidance to policy makers in evaluating the status of the Oregon Coast Coho Salmon 

(Oncorhynchus kisutch) ESU. This appendix introduces the DSS concept and provides guidance 

for interpreting the results. 

Utilizing a DSS 

Typically, DSSs are computer programs that analyze and compare numerous pieces of 

data, producing results that assist decision makers in making a decision (Turban and Aronson 

2001). Rather than specifying outcomes, these systems serve as aids to decision makers by 

allowing them to perform complex calculations quickly, presenting a consistent analytical 

product from many data sources and enhancing their ability to understand and recall information 

accurately. DSSs can improve the choices made by decision makers without overriding human 

judgment (Rauscher 1999). 

DSSs have been developed for use in medicine, groundwater pollution, and conservation 

biology (Rauscher 1999, Turban and Aronson 2001), among other fields. Using a DSS (Mills 

and Lawson in prep.), several members of the workgroup participated in the rating of habitat 

parameters in 82 fifth field Hydrologic Unit Codes (REO 2002) and found the DSS process 

superior to the Delphi method (Linstone and Turoff 1975) for capturing best professional 

judgment. 

A system evaluating ecological conditions may contain substantial uncertainty about the 

precise conditions that are optimal for the target organisms, because of uncertainty in both the 

expert knowledge and the associated monitoring data. Fuzzy logic (referred to as “approximate 

logic” in the body of this technical memorandum) is a type of logical analysis that allows a 

115
system to use such imprecise information in evaluating conditions (Zadeh 1965, Reynolds et al. 

2000). DSSs based on fuzzy logic can evaluate data with infinite gradations based on the degree 

to which the data satisfy a condition, while traditional rule-based systems only evaluate whether 

a condition is true or false. The resulting value reflects how strongly the expert panel judges 

whether the statement being evaluated is true. A fuzzy logic function will evaluate the degree to 

which the proposition is true. For this project, the workgroup used this degree of truth to 

indicate how certain they were that each criterion was fulfilled. 

The workgroup chose to use a DSS based on a fuzzy logic knowledge base because it 

allowed us to summarize and synthesize information from disparate data sets over a variety of 

scales in a uniform way. The DSS also facilitated documentation of the methods used and the 

logical structure of the decision process. Fuzzy logic allowed the workgroup to express not only 

its assessment of the ESU status, but also its confidence in the assessment. Policy makers can 

thereby be informed of the uncertainty associated with the biological assessment in their 

decision-making process. For the purposes of recovery planning, the fuzzy logic DSS can 

accommodate analysis of the full range of options for assessing whether the populations and 

ESU are persistent and the ESU is sustainable or restored. 

Implementation of the DSS 

Initially, the viability committee of the workgroup developed a system of pass/fail criteria 

for testing ESU status. Dissatisfaction with the arbitrary nature of some of the knife-edged 

criteria led the workgroup to translate the criteria into a fuzzy logic DSS. This translation also 

allowed us to incorporate an expression of uncertainty in our criteria, addressing one of the 

criticisms (IMST 2005, NWFSC 2005) of a similar product created by the State of Oregon 

(Chilcote et al. 2005). 

Knowledge Base Development 

The viability committee constructed the knowledge base with review and comment from 

the rest of the workgroup. Members designed fuzzy membership functions to reflect 

mathematical analyses, benchmarks, or best professional judgment for each criterion. Each of 

these functions was quantified as a curve that rates the chosen metric between –1.0 and +1.0, 

with –1.0 being entirely false (no membership) and +1.0 being entirely true (total membership). 

For example, the workgroup evaluated the statement “the population has sufficient naturally 

produced spawners to prevent loss of genetic variation due to random processes over a 100-year 

time frame.” The metric was the long-term harmonic mean of naturally produced spawner 

abundance. An abundance of 85 or less evaluated to a truth value of –1.0 (certainly not 

sufficient) (see Figure A-1), 450 evaluated to 0.0 (the value that, in our pass/fail system, was the 

minimum acceptable abundance and therefore represented uncertain status), and 5,000 or above 

evaluated to +1.0 (certainly sufficient). Intermediate values of the metric produced intermediate 

truth values. 

116
-1

0

1

Truth Value uth value Tr

0 1000 2000 3000 4000 5000 6000

Harmonic mean spaw ner abundance

Figure A-1. A fuzzy membership function. This function evaluates the statement “the population has 

sufficient naturally produced spawners to prevent loss of genetic variation due to random 

processes over a 100-year time frame.” Input data values are indicated on the x-axis, and the 

truth values returned are indicated on the y-axis. 

Network Development 

The viability committee originally developed recovery criteria reflecting important 

components of biological persistence and sustainability based on the viable salmonid populations 

document (McElhany et al. 2000) designed with pass/fail tests. Once the decision to convert to a 

DSS was made, the committee fit these criteria as well as new ones into a logic network. Each 

measurable criterion was reexamined in the context of the new decision structure. Based on 

data, analysis, and expert opinion, each criterion was mapped to a truth value as described above. 

The individual values were then linked using logical operators. 

We used three fuzzy logic operators in the DSS that are analogs to their like-named 

Boolean logic operators, and we used one operator chosen specifically for this task (Table A-1). 

Each type of logic implies a different relationship among the input variables. The UNION 

operator specifies a simple arithmetic average of the inputs. This means that, in terms of the 

network, the inputs can be present in any combination. For example, with a union of two 

populations in a geographical stratum (Figure A-2), the stratum evaluates the same whether the 

individual values are 1 and –1 or 0.2 and –0.2. A UNION operator allows the network to 

balance weak populations with corresponding strong ones. The AND operator requires that all 

inputs are at least partially “true,” (i.e., greater than –1). In other words, AND means all of the 

inputs are needed and none of them can be entirely false. This operator returns a value that is 

strongly weighted toward the lowest of the inputs. The OR operator returns the value of the 

highest input, as if to say at least one of these is needed, and the stronger that one is, the better. 

The MEDIAN operator returns the median of the inputs, allowing the workgroup to require most 

of the inputs to fulfill the criterion. While the MEDIAN operator is not a traditional fuzzy logic 

operator, it was found useful for this application as a way to address the distribution of scores 

within a group. 

By combining these analyses according to the network, two evaluations of ESU status 

were obtained: ESU Persistence and ESU Sustainability criteria. The structure of the network 

documents the logical relationship of the individual analyses. The fuzzy knowledge base 

Harmonic mean Spawner Abundance 

0 1,000 2,000 3,000 4,000 5,000 6,000 

117
Table A-1. Operators used in the DSS. These operators are based on those in the NetWeaver DSS 

development system. The abbreviations shown in parentheses appear in the knowledge base 

diagrams. 

Operator Equation Use 

OR 

Returns the 

antecedent truth 

value closest to 

fully true. 

AND 

⎛ ⎞ ⎛ ⎞ × − ⎜ ⎟ ⎜ ⎟ − × − ⎝ ⎠ ⎝ ⎠

∑

∑ mi n

i i

mi n

mi n

i

X F A L S E (x w

N D )

(x)= x + x

w T R U E F A L S E

OR(x) = max(x1, x2, ... xi)

A

Serves as a 

limiting factors 

analysis; returns 

FALSE if any one 

input is entirely 

false. 

MEDIAN 

(M) 

Returns the 

median antecedent 

truth value; tests 

for majority 

satisfying 

criterion. 

UNION 

(U) ( ) i i

i

(x w )

w

⎛ ⎞ × =⎜ ⎟

⎝ ⎠

∑

∑

U

MEDIAN(x) = median(x1, x2, ... xi)

NI O N x

Returns the 

weighted average 

of inputs. 

documents the relationship between the metrics and the contribution of that metric to the status 

of the ESU as a whole. The network includes tests for diversity and persistence at the 

population, biogeographic strata and ESU scales (for example, see Figure A-2). 

Sensitivity Analysis 

The workgroup tested the DSS’s sensitivity to changes in the data and the criteria, the 

effects of removing specific models used in the Probability of Persistence Criterion, PP-2, and 

the effects of changing the lower size limit in the definition of Independent Populations. A series 

of testing programs in R (R Development Core Team 2003) (an open-source statistical data 

analysis and plotting software package) were constructed that recorded the ESU-level results 

while changing the DSS inputs and criteria. While these tests did not evaluate every condition 

the DSS might encounter, they did explore how sensitive the DSS results are to changes in the 

on-the-ground conditions (changing data inputs) or changes in the state of science (changes in 

criteria and models). 

In general, the DSS results changed proportionally to the changes in the inputs to the 

DSS. Small changes to the population data caused small changes in the ESU-level results. 

Large changes to the ESU-level data caused large changes. The structure of the fuzzy 

membership functions lead to higher sensitivity in some criteria than others. For example, the 

Probability of Persistence Criterion, PP-2, becomes positive at values high in the available range 

118
AND

ES

ES-2

AND

ED-1 ED-3

U

ED-1a ED-1b ED-1c ED-1d

ED-2

SF

3 2

PF1

. . .

AND

PF

PD PP*

AND

*See Persistence 

Criteria

PD-3 PD-4

U U

3 2 W-Sp1

. . . 3 2 W-Ju1. . .

PD-1 PD-2

W-Sp W-Ju

AND

PS

M

SD

3 2

PS1

. . .

SS

AND

C B

SSA

. . .

AND

ES-1

U

ED-2a ED-2b

ESU Criteria

Stratum Criteria

Population Criteria

Watershed 

Criteria

Final outputs of the 

DSS

The collection of 

results for lower-level 

criteria

Criteria evaluated from 

lower- level criteria

Logical operators

Data is loaded into the 

DSS and evaluated with 

a truth membership 

function

ESU criteria 

Stratum criteria 

Po

pulation criteria 

Watershed 

criteria 

Figure A-2. The DSS network diagram. The DSS and knowledge base fuzzy membership functions 

document data analysis and the application of best professional judgment consistently and 

transparently on population, biogeographic strata, and ESU scales. 

119
(see Figure 11), and responds most strongly to very high data values. Thus it responds strongly 

to changes in either the data or the criteria. 

Data Testing 

To test how the DSS would respond to change in the environmental data, an R script was 

applied to increase and decrease the data for each criterion requiring such input and the DSS was 

run on the resulting data. For each criterion input, all values were increased and decreased (e.g., 

each population’s value) by 20%, in 0.5% increments. Each criterion input was tested 

individually. 

As an example of this process, the program selected the Spawner Abundance, PD-1, 

column, decreased each population’s value for the harmonic mean annual spawners by 20% (of 

their original value), and ran the DSS to return truth values for the ESU Persistence Criterion, 

EP, and ESU Sustainability Criterion, ES. The program recorded these results, then increased 

the values by 0.5% and ran the DSS again. The program repeated this until the values had been 

increased by 20%. It then began processing the next criterion requiring data, the Artificial 

Influence Criterion, PD-2. 

Most changes to the environmental data had small effects on the ESU Sustainability and 

ESU Persistence criteria scores. As the results from the DSS were near zero, small variations in 

the truth value lead to misleadingly large percent changes. 

The most influential changes were those to the PP-2 data (the average persistence 

probabilities from the population viability analysis [PVA] models). A 10% decrease in the 

average persistence probability lead to a sharp decrease in the ESU Persistence Criterion, EP 

(from about +0.40 to –0.25; see Figure A-3) and the ESU Sustainability Criterion, ES, (from 

about +0.19 to 0.00; see Figure A-4). This result is unsurprising, given the steep slope of the PP2 membership function. For example, increasing a value of 0.95 by 5% increases the resulting 

truth value from 0.0 to +1.0, or uncertain to fully true. This behavior is by design, as the 

workgroup considered high probability of persistence to be essential to support the proposition 

that a population is persistent, and designed the criterion’s membership function accordingly. 

-1

0

1

-20% -10% 0% 10% 20%

% Change

e Truth Valu Truth value 

Figure A-3. Response of the ESU-level ESU Persistence Criterion, EP, truth value to changes in the 

Probability of Persistence Criterion, PP-2, input data. 

Percent change 

120
-1

0

1

-20% -10% 0% 10% 20%

Truth value Truth Value

Percen e % Change t chang

Figure A-4. Response of the ESU-level ESU Sustainability Criterion, ES, truth value to changes in the 

Probability of Persistence Criterion, PP-2, input data. 

Criteria Testing 

The workgroup also tested how changing the membership functions for each criterion 

would affect the ESU-level results. We did so with another R program that adjusted the true, 

uncertain, and false points individually for each criterion. This program increased and decreased 

the input data value for each point by 20% in 0.5% increments. This showed the workgroup how 

sensitive the DSS would respond to changes in scientific knowledge, expert opinion, or other 

factors influencing the membership functions. 

The workgroup also tested how changing the membership functions for each criterion 

would affect the ESU-level results. We did so with another R program that adjusted the true, 

uncertain, and false points individually for each criterion. This program increased and decreased 

the input data value for each point by 20% in 0.5% increments. This showed the workgroup how 

sensitive the DSS would respond to changes in scientific knowledge, expert opinion, or other 

factors influencing the membership functions. 

As with the data testing, the membership function testing showed that changes to single 

points defining the membership functions had small effects on the ESU-level results. Again, the 

notable effects were in the Probability of Persistence Criterion, PP-2. Increasing the point where 

the PP-2 function returns a truth value of 0.0 (uncertain) from 95% to 98% caused the ESU 

Persistence Criterion, EP, to drop from 0.40 to 0.36, while increasing it to 99% caused EP to 

drop to 0.28. Changes in the other direction had less of an effect; reducing this critical point 

from 95% to 90% resulted in an increase of EP to 0.41. 

As with the data testing, the membership function testing showed that changes to single 

points defining the membership functions had small effects on the ESU-level results. Again, the 

notable effects were in the Probability of Persistence Criterion, PP-2. Increasing the point where 

the PP-2 function returns a truth value of 0.0 (uncertain) from 95% to 98% caused the ESU 

Persistence Criterion, EP, to drop from 0.40 to 0.36, while increasing it to 99% caused EP to 

drop to 0.28. Changes in the other direction had less of an effect; reducing this critical point 

from 95% to 90% resulted in an increase of EP to 0.41. 

Model Testing 

The high sensitivity of the Probability of Persistence Criterion, PP-2, to both input data 

and membership function changes led us to conduct further testing on the influence of each PVA 

model. While the DSS averaged all of the PVA model results for each population to calculate 

the value of PP-2, some models appeared to have results consistently different from others. We 

built another program in R that tested the effect of removing each PVA model individually from 

the DSS inputs. Both were tested, removing just one of the eight model runs at a time, as well as 

removing both model runs of the same type (SPAZ, Beverton-Holt, etc.). 

The high sensitivity of the Probability of Persistence Criterion, PP-2, to both input data 

and membership function changes led us to conduct further testing on the influence of each PVA 

model. While the DSS averaged all of the PVA model results for each population to calculate 

the value of PP-2, some models appeared to have results consistently different from others. We 

built another program in R that tested the effect of removing each PVA model individually from 

the DSS inputs. Both were tested, removing just one of the eight model runs at a time, as well as 

removing both model runs of the same type (SPAZ, Beverton-Holt, etc.). 

The results of these tests were highly variable (Table A-2). The ESU Persistence 

Criterion, EP, was less sensitive to model removal than the ESU Sustainability Criterion, ES, 

with each test resulting in the EP truth values varying by less than 0.010. In general, effects on 

truth values for ES were less responsive, but ES responded more strongly to removals of 

The results of these tests were highly variable (Table A-2). The ESU Persistence 

Criterion, EP, was less sensitive to model removal than the ESU Sustainability Criterion, ES, 

with each test resulting in the EP truth values varying by less than 0.010. In general, effects on 

truth values for ES were less responsive, but ES responded more strongly to removals of 

121
Table A-2. Results of model exclusion tests. 

Model(s) 

excluded QET 

ESU 

Persistence 

(EP) 

EP 

change 

ESU 

Sustainability 

(ES) 

ES 

change 

1a 1 0.401 0.002 0.194 0.000 

" 50 0.405 0.006 0.194 0.000 

" Both 0.407 0.008 0.194 0.000 

2b 1 0.396 -0.003 0.193 0.000 

" 50 0.396 -0.003 0.193 0.000 

" Both 0.392 -0.007 0.192 -0.001 

3c 1 0.399 0.000 0.194 0.000 

" 50 0.401 0.002 0.194 0.001 

" Both 0.407 0.008 0.194 0.001 

4d 1 0.395 -0.004 0.193 0.000 

" 50 0.403 0.004 0.224 0.031 

" Both 0.400 0.001 0.224 0.030 

Nonee 0.399 0.194 

a

 Density-dependent count-based model (Chilcote et al. 2005). 

b

 SPAZ model with hockey stick recruitment function (McElhany and Payne 2005). 

c

 Bayesian salmon analysis model (Wainwright et al. in prep.). 

d

 Stochastic habitat-based life cycle model (Nickelson and Lawson 1998). 

e

 Raw DSS results, as returned with all models present. 

Model 4, varying by as much as 0.030 in single run removals and 0.031 when the entire model 

was removed. The different responses of EP and ES result from the way the population scores 

are aggregated. For EP, the Population Persistence Criterion, PP, scores are aggregated directly 

to the Stratum Diversity Criterion level, SD, via the MEDIAN operator. For ES, PP scores are 

combined first with the Population Diversity Criterion, PD, scores via the AND operator to 

compute the Population Sustainability Criterion, PS, score, and then aggregated to the SD via the 

MEDIAN operator. Because the MEDIAN operator depends on the ranking of populations 

within each stratum, and rankings for PD were substantially different than those for PP, the 

effect of sensitivity of a single population to exclusion of a particular model can result in greater 

changes for ES than for EP. 

The model testing indicates the DSS responds conservatively to the model inputs. While 

declining input values for PP-2 will lower the ES and EP results, increasing input values will not 

substantially increase the results. This has the effect of insulating the ESU-level results from 

over-estimation in the model results, in keeping with the conservative approach to risk favored 

throughout this process. 

122
Population Definition Testing 

As a final set of sensitivity tests, the workgroup considered the response of the DSS to 

changes in the lower size limit for Independent Populations, as defined by Lawson et al. (2007). 

The original size limit was defined on the basis of historical potential habitat, and was set at 24 

km of habitat (equivalent to 15,000 adult spawners at 10% marine survival). Variations of 50% 

about this limit (i.e., values of 7,500 and 22,500 adult spawners) were considered to test the 

effects of this choice on numbers of Independent Populations (Figure 25 in Lawson et al. 2007). 

These two values were used to test the sensitivity of the DSS results to this limit. 

Increasing the limit to 22,500 resulted in removing a single population (Salmon River, 

Mid-Coast stratum) from the list of Independent Populations. Decreasing the limit to 7,500 

resulted in the addition of five Independent Populations: Sand Creek (North Coast stratum), 

Yachats River, Devils Lake, Sutton Creek (Mid-Coast stratum), and Twomile Creek (Mid-South 

stratum). Results of these tests are in Table A-3. Results for the ESU Persistence Criterion, EP, 

were highly sensitive to these changes, particularly to the addition of more small populations. 

This was largely due to a lack of data for these small populations, which resulted in truth scores 

of 0.0 for most population-level criteria for these populations. Results for the ESU Sustainability 

Criterion, ES, were less sensitive, largely because ES scores were nearer to zero to start with and 

population-specific scores are combined with ESU Diversity criteria, which buffers the ESUlevel result from changes in a few populations. 

These results are logically consistent with both the DSS structure and the data available. 

The Salmon River scores poorly in most of the criteria, so it would be expected that the EP and 

ES scores would increase somewhat with it removed. While this increase is slight, it is not 

unreasonable given the results of previous sensitivity testing that showed fair stability in the DSS 

scores. The much larger decrease in EP with the added populations is also expected. Most 

populations had high scores in the Persistence criteria and the presence of these new populations 

with scores of 0.0 in four of the five strata would have strong effects on the stratum-level scores, 

pulling the results toward zero. 

Guidance for Interpreting Results 

There are several advantages to using a DSS to guide the deliberation of an expert panel 

such as the workgroup. It allows us to document the consistent application of criteria across the 

ESU. It also allows the entire best professional judgment analysis to be as transparent as 

possible, so other scientists can apply the same information and get similar results. The truth 

membership curves are documented; therefore, when new information becomes available, 

application of the DSS may yield different results. 

However, one of the drawbacks of having a number between +1.0 and –1.0 as a result is 

the interpretation that is made. Unlike a pass/fail system, the results should be thought of in 

levels of uncertainty. For example, if the result is +1.0, then the proposition is completely true; 

conversely, if the result is –1.0, then the proposition is completely false. If the result is 0.0, then 

the proposition is uncertain, or unknown. 

123
Table A-3. DSS results for different levels of adult coho at the threshold for Independent Populations. 

Lower size limit for Independent 

Populations (adult spawners) EP ES 

7,500 0.25 0.19 

15,000 0.40 0.20 

22,500 0.44 0.21 

This example provides visual guidance for how to interpret the numerical scores for DSS 

output: 

Completely false Uncertain Completely true 

 –1 ————————————————— 0 ————————————————— +1 

 Rising certainty that proposition is false Rising certainty that proposition is true 

However, the results the DSS has returned are +0.400 for the ESU Persistence Criterion 

and +0.201 for the ESU Sustainability Criterion. 

What does this mean in the context of the –1.0, 0.0, +1.0 scale? In a pass/fail system, it 

could be expected that any value below 0.0 would fail and any value above 0.0 would pass. This 

is not the case with the DSS scale, which indicates the degree of certainty of support. For 

example, the ESU Sustainability Criterion result is just 13% above the totally uncertain or “don’t 

know” value of 0.0. What this result communicates is that the uncertainty is still high with 

respect to the proposition that “the ESU is sustainable.” The ESU Persistence Criterion result 

still communicates a moderate amount of uncertainty to the proposition that “the ESU is 

persistent.” If the results returned had been negative, for instance –0.5 for the ESU 

Sustainability Criterion and –0.1 for ESU Persistence Criterion, the communication would be 

that there is substantial uncertainty the “the ESU is not persistent.” Additionally, the –0.5 score 

would communicate moderate uncertainty that “the ESU is not sustainable.” Additional 

discussion of this issue is found beginning on page 20. Using this basis for evaluating the DSS 

results, the workgroup reached the conclusions in this document. 

Software and Script Description 

For the initial logic network construction, we used NetWeaver (Miller 2003) and ArcGIS 

(ESRI 2005), within the Ecosystem Management Decision Support (Reynolds et al. 2002) 

package. For flexibility and portability we subsequently recoded the network as a script for R (R 

Development Core Team 2003), an open-source statistical data analysis and plotting software 

package. R supports rapid development of data processing functions and is well suited to the 

needs of our complex logic network. The resulting system can be transmitted to decision makers 

for their use in future recovery analyses. The script to run the program follows. 

124
Script to Run Program 

############################## BiRDSS v. 4.5 ################################### 

# This R program implements the Biological Recovery Decision Support 

# System version 4.5 as designed by the Oregon Coast coho workgroup 

# of the ONCC Technical Recovery Team. Fuzzy logic operators (AND, OR, Median, 

# and UNION) are implemented as functions. The membership functions 

# are generated on the fly from the provided criteria. The model uses 

# a criteria parameter file, watershed, population, and ESU-level data 

# in other comma separated value files. The file formats required are 

# as follows: 

# 

# All files will be comma separated value text files, using quotation 

# marks (") to delimit text fields. 

# 

# Criteria file: Header row has one pair of columns, one each for X and Y 

# for each criterion. The columns will contain pairs of X and Y values. It is 

# allowable for there to be unequal numbers of pairs for different criteria. 

# That is, some criteria can have only two pairs (or points) defined while 

# others have three or more. Simply leave blanks where there are no points. 

# Example: 

# "W.SP.X","W.SP.Y","W.JU.X","W.JU.Y" 

# 0.2,-1,0.2,-1 

# 0.8,1,0.5,0 

# ,,0.8,1 

# 

# Watershed Data File: Header row has columns for population, watershed 

# (stored as HUC5 number), W-Ju (juvenile data) and W-Sp (spawning data). 

# Example: 

# "Population","Watershed","W.Ju","W.Sp" 

# "Necanicum","1710020101",0.723,0.592 

# "Nehalem","1710020201",0.746,0.636 

# "Nehalem","1710020202",0.828,0.548 

# 

# Population Data File: Header row has columns for Stratum, Population, 

# and various criteria (PP 1-3, PD 1-2, and PF). 

# Example: 

# "Stratum","Population","PP.1","PP.2","PP.3","PD.1","PD.2","PF" 

# "North Coast","Necanicum",0.98,0.8,3.77,480,0.944,63200 

# "North Coast","Nehalem",0.57,0.98,6.26,4141,0.915,1039000 

# "North Coast","Tillamook Bay",0.88,0.88,3.75,1003,0.945,505100 

# 

# Stratum Data File: Header row has columns for Stratum and criteria 

# (S-Ju, S-Sp). 

# Example: 

# "Stratum", "S.Ju", "S.Sp" 

# "North Coast",0.843,0.224 

# "Mid-Coast",0.522,0.128 

# 

# ESU Data File: Header row has columns for ESU, ED.1.A, ED.1.B, ED.1.C, 

# ED.1.D, ED.2.A, ED.2.B, ED.3 

# Example: 

# "ESU","ED.1.A","ED.1.B","ED.1.C","ED.1.D","ED.2.A","ED.2.B","ED.3" 

# "OC coho salmon",3,3,3,3,3,3,3 

# 

################################################################################ 

 

################# Settings for this model used throughout ##################### 

# In this section, you may set the working directory and file names. 

## Edit this line to change the working directory 

DIRECTORY.WORK <- "C:/BiRDSS" # Project working directory 

## The following lines are for filename constants 

# Inputs 

DIRECTORY.INPUT <- "input" # Input file directory 

FILE.CRITERIA <- "Criteria.csv" # The criteria file 

FILE.DATA.WATERSHED <- "WatershedData.csv" # The watershed data file 

FILE.DATA.POPULATION <- "PopulationData.csv" # The Population data file 

FILE.DATA.STRATUM <- "StratumData.csv" # The Stratum data file 

125
FILE.DATA.ESU <- "ESUData.csv" # The ESU-level data file 

# Outputs 

DIRECTORY.OUTPUT <- "output" # Output file directory 

FILE.RESULTS.WATERSHED <- "WatershedResults.csv" # Watershed Results file 

FILE.RESULTS.POPULATION <- "PopulationResults.csv" # Population Results file 

FILE.RESULTS.STRATUM <- "StratumResults.csv" # The Stratum Results file 

FILE.RESULTS.ESU <- "ESUResults.csv" # The ESU Results file 

## Fuzzy logic constants 

# defines TRUE for the purposes of this model 

FUZZY.TRUE <- 1 

# defines FALSE for this model 

FUZZY.FALSE <- -1 

# defines undetermined/intermediate for this model 

FUZZY.UNDETERMINED <- 0 

################## DO NOT MAKE ANY CHANGES BELOW THIS LINE ##################### 

startTime <- Sys.time() 

################### General functions for model processing #################### 

readDataFiles <- function() { 

 ## readDataFiles 

 ## This function (which takes no inputs other than preset variables) reads the 

 ## data files and stores them in data frames. 

 

 # Let the user know what it is doing 

 cat("Loading input data files from", paste(DIRECTORY.WORK, DIRECTORY.INPUT, sep = '/'), "\n") 

 # Create the file names to work from by concatenating the work directory and the 

 # file name. The paste function concatenates strings. The separator is the /, 

 # used to denote the directory structure. 

 filenameCriteria <- paste(DIRECTORY.WORK, DIRECTORY.INPUT, FILE.CRITERIA, sep = '/') 

 filenameWatershedData <- paste(DIRECTORY.WORK, DIRECTORY.INPUT, FILE.DATA.WATERSHED, sep = '/') 

 filenamePopData <- paste(DIRECTORY.WORK, DIRECTORY.INPUT, FILE.DATA.POPULATION, sep = '/') 

 filenameStratumData <- paste(DIRECTORY.WORK, DIRECTORY.INPUT, FILE.DATA.STRATUM, sep = '/') 

 filenameEsuData <- paste(DIRECTORY.WORK, DIRECTORY.INPUT, FILE.DATA.ESU, sep = '/') 

 

 # Create the list of file names to loop through for testing below 

 fileNames <- c(filenameCriteria, filenameWatershedData, filenamePopData, filenameStratumData, 

filenameEsuData) 

 # Loop through all the file names to test that they exist 

 for (i in 1:length(fileNames)) { 

 # Make sure the file exists 

 if (file.exists(fileNames[i])== FALSE) { 

 stop(paste(fileNames[i], "is missing.")) 

 } 

 } 

 

 # Read the data files into the appropriate data frames for later use. The 

 # double << is needed to ensure global scope for the objects being populated. 

 criteriaDf <<- read.table(filenameCriteria, header = TRUE, sep = ",") 

 watershedDataDf <<- read.table(filenameWatershedData, header = TRUE, sep = ",") 

 populationDataDf <<- read.table(filenamePopData, header = TRUE, sep = ",") 

 stratumDataDf <<- read.table(filenameStratumData, header = TRUE, sep = ",") 

 esuDataDf <<- read.table(filenameEsuData, header = TRUE, sep = ",") 

} 

writeOutputTables <- function() { 

 ## writeOutputTables 

 ## This function writes the DSS results to the appropriate data tables 

 

 directoryOutput <- paste(DIRECTORY.WORK, DIRECTORY.OUTPUT, sep='/') 

 

 # Test for the output directory's existence 

 if (file.exists(directoryOutput)== FALSE) { 

 stop(paste(directoryOutput, "is missing.")) 

126
 } 

 # Paste together the directory names 

 directoryFullOutput <- paste(directoryOutput, timeStamp, sep='/') 

 # Create the timestamp'ed output directory. 

 dir.create(directoryFullOutput) 

 # Write the output files as CSVs 

 write.csv(watershedResultsDf, file=paste(directoryFullOutput, FILE.RESULTS.WATERSHED, sep='/')) 

 write.csv(populationResultsDf, file=paste(directoryFullOutput, FILE.RESULTS.POPULATION, 

sep='/')) 

 write.csv(stratumResultsDf, file=paste(directoryFullOutput, FILE.RESULTS.STRATUM, sep='/')) 

 write.csv(esuResultsDf, file=paste(directoryFullOutput, FILE.RESULTS.ESU, sep='/')) 

 

 # Report the location of the output files 

 cat("Results stored in", directoryFullOutput, "\n") 

} 

calcFuzzyOr <- function(data) { 

 ## calcFuzzyOr 

 ## Implements the fuzzy OR function. Takes a vector (data) and returns the 

 ## maximum input in the vector. If you want to combine several pieces of data 

 ## that are not in a vector already, use the c() function to convert a comma- 

 ## seperated list into a vector. 

 ## Usage: resultVector <- calcFuzzyOr(inputVector) 

 ## Example: temp <- calcFuzzyOr(c(0.5, 0.3, 0.1)) returns vector temp = 0.5 

 # NAs need to be converted to undetermined to allow for missing data 

 data[is.na(data)] <- FUZZY.UNDETERMINED 

 out <- max(data) 

 out 

} 

calcFuzzyAnd <- function(data, isWeighted=FALSE) { 

 ## calcFuzzyAnd 

 ## Implements the fuzzy AND function. Takes a vector (data) and returns the 

 ## "limiting factor" based result. It is limited by how close the lowest 

 ## input is to being entirely false. If you want to combine several pieces of 

 ## data that are not in a vector already, use the c() function to convert a 

 ## comma-separated list into a vector. 

 ## If the results are to be weighted, use the isWeighted parameter set to 

 ## TRUE. You will need to make matrix with two columns, one for the data and 

 ## one for the weights. The simplest way to do this is with the matrix() 

 ## function. Just put all your data values followed by the weights, in 

 ## order of the matchingdata values, into a vector with the c() function. 

 ## Then, use the matrix()function with the vector, followed by the number of 

 ## data values (rows), and the number of columns (should always be 2, one for 

 ## data, one for weights). Please refer to the example below. 

 ## 

 ## Usage: resultVector <- calcFuzzyAnd(inputVector, isWeighted=TRUE|FALSE) 

 ## resultVector <- calcFuzzyAnd(inputVector, FALSE) 

 ## resultVector <- calcFuzzyAnd(matrix(inputVector, rows, 2), TRUE) 

 ## Example: If you want an unweighted AND of 0.5, 0.3, and 0.1, use: 

 ## temp <- calcFuzzyAnd(c(0.5, 0.3, 0.1), FALSE) 

 ## returns vector temp = 0.21 

 ## 

 ## For a weighted AND, using three rows, use: 

 ## Data|Weight 

 ## ----|------ 

 ## 0.5 | 1 # of rows 

 ## 0.3 | 1 Data Weights | 

 ## 0.1 | 0.5 ______|______ ____|____ | 

 ## temp <- calcFuzzyAnd(matrix(c(0.5, 0.3, 0.1, 1, 1, 0.5),3,2), TRUE) 

 ## returns vector temp = 0.232 

 # NAs need to be converted to undetermined to allow for missing data 

127
 data[is.na(data)] <- FUZZY.UNDETERMINED 

 

 #Calculate the unweighted (or equal weighted) AND 

 if (isWeighted == FALSE ) { 

 # NAs need to be converted to undetermined to allow for missing data 

 data[is.na(data)] <- FUZZY.UNDETERMINED 

 # Find the minimum input data value 

 inputMin <- min(data) 

 # Find the arithmetic mean of the inputs 

 inputMean <- mean(data) 

 

 # We use a different calculation if calculating a weighted AND 

 } else if (isWeighted == TRUE ) { 

 # Find the minimum input data value 

 inputMin <- min(data[,1]) 

 # The weighted.mean() function uses the data values from column 1, and 

 # weights from column 2 of the matrix created when calling the function. 

 inputMean <- weighted.mean(data[,1], data[,2]) 

 } 

 # Calculate the AND, then store the result to be returned later. Weighted 

 # and unweighted ANDs use the same calculation; it's the pre-processing the 

 # weighted mean that makes the difference. 

 out <- inputMin + (inputMean - inputMin)*(inputMin - FUZZY.FALSE)/(FUZZY.TRUE - FUZZY.FALSE) 

 # Return the data value 

 out 

} 

calcFuzzyUnion <- function(data) { 

 ## calcFuzzyUnion 

 ## Implements the fuzzy UNION function. Takes a vector (data) and returns the 

 ## arithmetic mean of the inputs. If you want to combine several pieces of 

 ## data that are not in a vector already, use the c() function to convert a 

 ## comma-separated list into a vector. 

 ## Usage: resultVector <- calcFuzzyUnion(inputVector) 

 ## Example: temp <- calcFuzzyUnion(c(0.5, 0.3, 0.1)) returns vector temp = 0.3 

 

 # NAs need to be converted to undetermined to allow for missing data 

 data[is.na(data)] <- FUZZY.UNDETERMINED 

 # Calculate the unweighted arithmetic mean and store for output 

 out <- mean(data) 

 # Return the data value 

 out 

} 

calcFuzzyMedian <- function(data) { 

 ## calcFuzzyMedian 

 ## Implements the fuzzy median function. Takes a vector (data) and returns the 

 ## median of the inputs. If you want to combine several pieces of data that 

 ## are not in a vector already, use the c() function to convert a comma- 

 ## separated list into a vector. 

 ## Usage: resultVector <- calcFuzzyMedian(inputVector) 

 ## Example: temp <- calcFuzzyMedian(c(0.5, 0.3, 0.1)) 

 ## returns vector temp = 0.3 

 

 # NAs need to be converted to undetermined 

 data[is.na(data)] <- FUZZY.UNDETERMINED 

 out <- median(data) 

 # Return the data value 

 out 

} 

128
calcFuzzyMembership <- function(data, criterion) { 

 ## calcFuzzyMembership 

 ## Implements the fuzzy membership functions. Takes a vector (data) for the 

 ## input data and applies the appropriate membership function. Takes a string 

 ## (criterion) of the name of the criterion to evaluate, e.g. "W.SP". Returns 

 ## a vector of truth value results. 

 ## Usage: resultVector <- calcFuzzyMembership(data, criterion) 

 ## Example: temp <- calcFuzzyMembership(0.3, "W.SP") returns the truth value 

 ## for 0.3 as evaluated against criterion W.SP. 

 

 # Get the points for the membership function, with NAs removed, ordered by X 

 # Get Xs and Ys 

 critPoints <- criteriaDf[, c(paste(criterion, c("X","Y"), sep = "."))] 

 # Sort by X column (the first column) 

 critPoints <- critPoints[order(critPoints[,1]),] 

 xColumn <- critPoints[,1] # Store the Xs in a vector 

 xColumn <- xColumn[!is.na(xColumn)] # Remove NAs from X vector 

 yColumn <- critPoints[,2] # Store the Ys in a vector 

 yColumn <- yColumn[!is.na(yColumn)] # Remove NAs from Y vector 

 # We need matching numbers of X and Y values for each criterion 

 if (length(xColumn) != length(yColumn)) { 

 # If the number of X and Y values differs, halt with an error 

 stop(paste("The number of X values does not match the number of Y values 

 provided for",criterion,' ')) 

 } else { 

 # If the counts match, count the number of points provided 

 numCriterionRows <- length(xColumn) 

 } 

 # Run the fuzzy membership function on each row of the data vector 

 # and store the results. This line does the work of calculating the truth 

 # value for each data point in the input table. 

 results <- sapply(data, function(dataInput) applyFuzzyCurve(dataInput, xColumn, yColumn, 

numCriterionRows)) 

} 

applyFuzzyCurve <- function(dataInput, xColumn, yColumn, numCriterionRows) { 

 ## applyFuzzyCurve 

 ## This will apply a fuzzy membership function to the data from the criterion 

 ## points supplied in the criteria file. Based on the dataInput value (the 

 ## input data for which you are calculating a truth value), the function first 

 ## checks whether the input is equal to or below the minimum X value in the 

 ## criterion. If so, it assigns the appropriate Y value. It then checks if 

 ## the input is equal to or greater than the maximum X value, then assigns 

 ## the Y value if appropriate. If the input value is between the minimum and 

 ## maximum specified X values, the function finds the bounding X values and 

 ## calculates the slope and intercept of the line connecting these values. It 

 ## then calculates the truth value of the input data from the slope and 

 ## intercept. 

 # The default value is undetermined 

 truthValue <- FUZZY.UNDETERMINED 

 

 # Check if dataInput is supplied; if it's NA, skip all this 

 # and go to the end of this function to return FUZZY.UNDETERMINED 

 

 if (!is.na(dataInput)) { 

 

 # Get the highest and lowest pairs of (X, Y). They are ordered by X value. 

 xMin <- xColumn[1] 

 yMin <- yColumn[1] 

 xMax <- xColumn[numCriterionRows] 

 yMax <- yColumn[numCriterionRows] 

 # Check against the smallest X value supplied. If the input (dataInput) is 

129
 # less than or equal to the smallest X value supplied, return the truth 

 # value for the smallest X 

 if (dataInput <= xMin) { 

 truthValue <- yMin 

 # Check against the greatest X value supplied. If the input (dataInput) is 

 # greater than or equal to the greatest X value supplied, return the truth 

 # value for the greatest X 

 } else if (dataInput >= xMax) { 

 truthValue <- yMax 

 # If the data input is between the smallest and greatest X values provided, 

 # step through the X values, find the appropriate bounding values, and 

 # calculate the truth value for dataInput. 

 } else { 

 # Loop through all the criterion rows provided 

 for (i in 1:numCriterionRows) { 

 # Check if dataInput is between the current and next points 

 if ((dataInput >= xColumn[i]) & (dataInput < xColumn[i+1])) { 

 # Calculate the slope of the curve 

 m <- (yColumn[i+1] - yColumn[i])/(xColumn[i+1] - xColumn[i]) 

 # Calculate the intercept of the curve 

 b <- (yColumn[i] - (m * xColumn[i])) 

 # Apply the membership function in the form mx + b = y 

 truthValue <- (m * dataInput) + b 

 # Exit the for loop; we have calculated the output 

 break 

 } 

 } 

 } 

 } 

 

 # Return the truth value and exit the function 

 truthValue 

} 

runDss <- function() { 

 #################### WATERSHED MEMBERSHIP FUNCTIONS ########################## 

 

 ## Create results data frame by copying the Population and Watershed fields 

 watershedResultsDf <<- data.frame(watershedDataDf["Population"], watershedDataDf["Watershed"]) 

 

 ## Process the watershed file for W-Ju and W-Sp 

 watershedResultsDf$W.Ju <<- calcFuzzyMembership(watershedDataDf$W.Ju, "W.JU") 

 watershedResultsDf$W.Sp <<- calcFuzzyMembership(watershedDataDf$W.Sp, "W.SP") 

 

 

 ####################### POPULATION MEMBERSHIP FUNCTIONS ###################### 

 

 ## Create results data frame 

 populationResultsDf <<- data.frame(populationDataDf["Stratum"], populationDataDf["Population"]) 

 

 ## Process the population data file for PP-1, -2, -3, and PD-1, PD-2, and PF 

 populationResultsDf$PP.1 <<- calcFuzzyMembership(populationDataDf$PP.1, "PP.1") 

 populationResultsDf$PP.2 <<- calcFuzzyMembership(populationDataDf$PP.2, "PP.2") 

 populationResultsDf$PP.3 <<- calcFuzzyMembership(populationDataDf$PP.3, "PP.3") 

 populationResultsDf$PD.1 <<- calcFuzzyMembership(populationDataDf$PD.1, "PD.1") 

 populationResultsDf$PD.2 <<- calcFuzzyMembership(populationDataDf$PD.2, "PD.2") 

 populationResultsDf$PF <<- calcFuzzyMembership(populationDataDf$PF, "PF") 

 

 ## Calculate PP, PD-3, PD-4, and PS for each population 

 

 for (i in 1:length(populationResultsDf$Population)) { 

 

 

 ## PD-3 - For each population, UNION the watershed results and 

 ## store them in PD-3 

 # The conversion to as.vector is required to prevent level errors 

130
 populationResultsDf$PD.3[i] <<- 

calcFuzzyUnion(watershedResultsDf[as.vector(watershedResultsDf$Population) == 

as.vector(populationResultsDf$Population[i]), "W.Sp"]) 

 

 ## PD-4 - For each population, UNION the watershed results and 

 ## store them in PD-4. 

 # The conversion to as.vector is required to prevent level errors 

 populationResultsDf$PD.4[i] <<- 

calcFuzzyUnion(watershedResultsDf[as.vector(watershedResultsDf$Population) == 

as.vector(populationResultsDf$Population[i]), "W.Ju"]) 

 

 # Attach the results table for easier access below 

 attach(populationResultsDf) 

 

 ## PP - For each population, AND the PPs 

 populationResultsDf$PP[i] <<- calcFuzzyAnd(c(PP.1[i], PP.2[i], PP.3[i])) 

 

 ## PD -- AND PD-1 through PD-4 

 # Build the vector of results to tack together 

 tempPD <- c(PD.1[i], PD.2[i], PD.3[i], PD.4[i]) 

 # Detach the results table so it will refresh 

 detach(populationResultsDf) 

 

 # Compute PD by AND-ing PD-1 through PD-4 

 populationResultsDf$PD[i] <<- calcFuzzyAnd(tempPD) 

 # Clean up 

 rm(tempPD) 

 

 ## PS -- AND PD, PP 

 populationResultsDf$PS[i] <<- calcFuzzyAnd(c(populationResultsDf$PD[i], 

populationResultsDf$PP[i])) 

 } 

 

 

 

 ##################### STRATUM MEMBERSHIP FUNCTIONS ########################### 

 # This section processes the stratum level results by stepping through a list 

 # of all the strata listed in the populationResultsDf table. This will draw 

 # data for the various stratum level operators from the same table, and store 

 # the results in the stratumResultsDf table. 

 

 ## Run the Stratum functionality for dependence membership functions 

 

 # Create results data frame 

 stratumResultsDf <<- data.frame(unique(populationResultsDf["Stratum"])) 

 

 # Attach the completed population results table for easier use 

 attach(populationResultsDf) 

 

 # Loop through all the strata 

 for (i in 1:length(stratumResultsDf$Stratum)) { 

 

 ## SP -- Run the stratum aggregation for SP by median-ing the PP values 

 ## within each stratum 

 stratumResultsDf$SP[i] <<- calcFuzzyMedian(PP[as.vector(Stratum) == 

as.vector(stratumResultsDf$Stratum[i])]) 

 

 ## SD -- Run the stratum aggregation for SD by median-ing PP and all PS 

 ## criteria 

 stratumResultsDf$SD[i] <<- calcFuzzyMedian(PS[as.vector(Stratum) == 

as.vector(stratumResultsDf$Stratum[i])]) 

 

 ## SF -- Run the stratum aggregation for SF by AND-ing PF 

 stratumResultsDf$SF[i] <<- calcFuzzyAnd(PF[as.vector(Stratum) == 

as.vector(stratumResultsDf$Stratum[i])]) 

 

 ## SS -- Run the stratum aggregation for SS by AND-ing SS-1 and SS-2 

 stratumResultsDf$SS[i] <<- calcFuzzyAnd(c(stratumResultsDf$SD[i], stratumResultsDf$SF[i])) 

 

131
 } # Close strata loop 

 

 # Detach the population results table as it is no longer needed 

 detach(populationResultsDf) 

 

 

 ######################## ESU MEMBERSHIP FUNCTIONS ############################ 

 

 # Prepare a data frame with the list of ESUs covered 

 esuResultsDf <<- data.frame(esuDataDf["ESU"]) 

 

 ## EP -- Calculate EP by AND-ing all stratum results for SP 

 esuResultsDf$EP[1] <<- calcFuzzyAnd(stratumResultsDf$SP) 

 

 ## ED -- Calculate fuzzy membership for ESU Diversity criteria; all use the 

 ## same ED criterion, so all refer to "ED" when calculating the fuzzy membership 

 ## value. 

 # Genetic -- Structure 

 esuResultsDf$ED.1.A <<- calcFuzzyMembership(esuDataDf$ED.1.A, "ED") 

 # Genetic -- Effects of selection on genetic diversity 

 esuResultsDf$ED.1.B <<- calcFuzzyMembership(esuDataDf$ED.1.B, "ED") 

 # Genetic -- Effects of migration on genetic diversity 

 esuResultsDf$ED.1.C <<- calcFuzzyMembership(esuDataDf$ED.1.C, "ED") 

 # Genetic -- Effects of introgression on genetic diversity 

 esuResultsDf$ED.1.D <<- calcFuzzyMembership(esuDataDf$ED.1.D, "ED") 

 # Phenotypic -- Phenotypic variation 

 esuResultsDf$ED.2.A <<- calcFuzzyMembership(esuDataDf$ED.2.A, "ED") 

 # Phenotypic -- Habitat diversity 

 esuResultsDf$ED.2.B <<- calcFuzzyMembership(esuDataDf$ED.2.B, "ED") 

 # Dependent Populations 

 esuResultsDf$ED.3 <<- calcFuzzyMembership(esuDataDf$ED.3, "ED") 

 

 # Attach ESU results table for easy access 

 attach(esuResultsDf) 

 

 ## ED-1 -- Calculate ED-1 by AND-ing ED-1A through D 

 esuResultsDf$ED.1 <<- calcFuzzyUnion(c(ED.1.A,ED.1.B, ED.1.C, ED.1.D)) 

 

 ## ED-2 -- Calculate ED-2 by AND-ing ED-2A and B 

 esuResultsDf$ED.2 <<- calcFuzzyUnion(c(ED.2.A,ED.2.B)) 

 

 # Detach ESU results table 

 detach(esuResultsDf) 

 

 ## ES-1 -- Calculate ES-1 by AND-ing all stratum results for SS 

 esuResultsDf$ES.1 <<- calcFuzzyAnd(stratumResultsDf$SS) 

 

 ## ES-2 -- Calculate ES-2 by AND-ing ED-1, -2, and -3 

 esuResultsDf$ES.2<<- calcFuzzyAnd(c(esuResultsDf$ED.1,esuResultsDf$ED.2,esuResultsDf$ED.3)) 

 

 ## ES -- Weighed AND of ES-1 (weight = 1) and ES-2 (weight = 0.5) 

 esuResultsDf$ES <<- calcFuzzyAnd(matrix(c(esuResultsDf$ES.1, esuResultsDf$ES.2,1,0.5),2,2), 

isWeighted=TRUE) 

} 

####################### BEGIN BiRDSS LOGIC NETWORK RUN ######################### 

# Current date and time for directory timestamp; The date is returned as 

# yymmdd_hhmmss and will be used to create a directory for the current model run 

timeStamp <- format(Sys.time(), "%y%m%d_%H%M%S") 

# Read the input data files into the appropriate data frames to start the run. 

readDataFiles() 

# Run the DSS 

cat("Starting BiRDSS run. \n") 

runDss() 

132
# Display some results 

cat("EP:", esuResultsDf$EP, "\n") 

cat("ES:", esuResultsDf$ES, "\n") 

# Write the output data files 

writeOutputTables() 

 

# Calculate the running time 

endTime <- Sys.time() 

elapsedTime <- difftime(endTime, startTime, units = "secs") 

cat("BiRDSS run complete. Processing time was", elapsedTime, "second(s).\n") 

# Clean up by removing all objects used 

rm(list=ls(all=TRUE)) 

####################### FINISH BiRDSS LOGIC NETWORK RUN ######################### 

133
134
Appendix B: Genetic Effective Population 

Size for Oregon Coast Coho Salmon 

[Note: This appendix was prepared in 2006, before the Oregon Coast Workgroup of the Oregon 

and Northern California Coasts Technical Recovery Team made revisions to some criteria and 

metrics. Some of the formulations described here are not consistent with those in the body of this 

technical memorandum, which was revised based on reviewer comments and more recent 

literature.] 

Introduction 

Within-population diversity is an important component of the sustainability criteria 

described in the body of this report. Such diversity results from phenotypic differences among 

individuals that provide the flexibility for the population as a whole to respond successfully to 

short-term environmental variations. It is also the basis by which populations are able to adapt 

and evolve as conditions within their home range go through changes that are more permanent. 

Therefore, maintaining sufficient within-population diversity is an issue for both short-term and 

long-term survival. 

Within-population diversity is controlled by a variety of forces, including evolutionary 

legacy, immigration from other populations, mutation, selection, and random loss of genetic 

variation due to small population size. However, it is this last factor that is most commonly 

recognized as a concern for species that are vulnerable to extinction. The genetic consequences 

of small population size and numerous approaches to defining minimum population abundance 

thresholds have been investigated widely (Soulé 1980, Lande 1995, Franklin and Frankham 

1998, Rieman and Allendorf 2001). In nearly all cases, this becomes an exercise in identifying a 

rate at which genetic variation can be lost without a causing a risk to a population’s short-term or 

long-term persistence. 

Genetic Concepts 

A large factor in maintaining the genetic diversity of a population is the abundance of the 

population, and our approach focuses on that factor. A common measure of within-population 

genetic diversity is heterozygosity, a measure of the average amount of genetic variation within 

individual loci within chromosomes. In an idealized population, rate of loss of heterozygosity is 

a simple function of population size, but no real populations meet the assumptions of the 

idealized theory. For this reason, geneticists have developed the concept of “genetic effective 

population size,” which is the size of an idealized population having the same loss rate as a 

corresponding real population. In general, the real population size will be larger than the 

corresponding effective population size. In what follows, theory is applied to determine the 

135
effective population size (Ne) needed to achieve a particular rate of loss of heterozygosity, then 

translate that number into the size of corresponding real populations. 

After considerable review of the genetics and conservation biology literature, it was 

concluded that maintaining at least 95% (or losing no more than 5%) of a population’s 

heterozygosity over a 100-year period, as recommended by Allendorf and Ryman (2002), would 

be used as a conservation threshold. Heterozygosity is used here as a metric to represent genetic 

diversity. However, having defined the threshold in these terms, a method to actually measure or 

forecast this heterozygosity loss rate for Oregon Coast coho salmon (Oncorhynchus kisutch) 

populations was needed. For this purpose, the workgroup used a well-known relationship 

between a theoretical population size and the rate at which heterozygosity is lost. For an “ideal” 

population, this relationship, developed by Wright (1969), can be described by the following 

equation: 

∆H = (–1/2Ne)(100%) (2) 

where ∆H is the expected amount of genetic variation lost in one generation (expressed as 

percent of heterozygosity lost), and Ne is the number of adults in a population, where each 

member has equal probability of contributing offspring to the next generation (expressed as the 

effective population size). Based upon this equation, the value for Ne necessary to retain 95% of 

the genetic variation after a certain number of generations (t) can be expressed as: 

 Ne = 1/[2(1 – 0.95(1/t))] (3) 

To solve the calculation of Ne in Equation 3 for coho salmon, a value for the number of 

generations expected for a coho population over a period of 100 years (the reference time period) 

is required. Although coho salmon spawn primarily at age 3, some precocious males (jacks) 

from each brood spawn at age 2. The proportion of jacks appears to vary among coastal 

populations, with stream populations averaging about 6% and lake populations averaging about 

22% (Chilcote et al. 2005). The result is that the average age at maturity for stream populations 

is 2.94 years (34.01 generations in 100 years), and is 2.78 years (35.97 generations in 100 years) 

for lake populations. Therefore, substituting these values for t into Equation 3, values for Ne of 

332 and 351 are obtained for stream and lake populations, respectively. In other words, if these 

values for Ne are obtained, no more than 5% of the population’s heterozygosity will be lost over 

a period of 100 years. 

Ideal vs. Real Populations 

The calculation of minimum values of Ne for Oregon Coast coho is mathematically valid 

only if coho populations meet the conditions of an “ideal” population. An “ideal” population, 

whose abundance is represented by the symbol Ne, must meet certain conditions, such as: no 

overlapping generations, equal sex ratio, constant population size, and equal probability of 

reproductive success for all members of the population. The problem is that nearly all 

populations (regardless of species) fail to meet these “ideal” constraints. Therefore, to estimate 

the rate of genetic loss based upon the number of spawners, a significant number of “corrections” 

need to be made. Essentially, the goal of these corrections is to build a metric that represents Ne

but which can be applied to the species and data in question. 

136
Typically, when these corrections are made, the number of individuals necessary to 

achieve the genetic behavior of an “ideal” population is greater than the size of the 

corresponding “ideal” population. By convention, Ne is used to represent the number of adults in 

an ideal population and N is the number of adults actually counted in the population. In most 

real life situations, the ratio of Ne/N is less than 1.0, (i.e., N is greater than Ne). For salmonids, 

Allendorf et al. (1997) suggest that Ne/N ratios in the range of 0.20 may be typical. Likewise, 

McElhany et al. (2000) suggest that a Ne/N ratio of 0.30 is a reasonable expectation. Rather than 

using these rough guidelines, factors more specific to Oregon Coast coho salmon populations 

were examined to estimate the Ne/N ratio, specifically considering variation in family size and 

the effects of overlapping generations. 

Family-size Variation 

One factor that can cause Ne to be less than N is unequal probabilities of producing 

offspring among individual spawners, resulting in variation in family sizes among spawners. In 

some extreme examples, only a few members of the spawning population produce all of the 

surviving offspring. In effect, the genetic contribution to the next generation comes from a much 

smaller number of individuals than a simple count of spawning individuals would suggest. 

Obviously, the challenge of estimating how many offspring each spawner in a wild population 

produces is a daunting proposition. However, several estimates of offspring produced per 

individual spawner have been made for salmonids in recent years (Simon et al. 1986, Geiger et 

al. 1997, Seamons et al. 2003). Results from these studies can be compared in a standardized 

manner by calculating VK /K, where VK is the variance in offspring produced per spawner, and K 

is the mean number of offspring produced per spawner. Among these studies, the values for 

VK/K are confined to a surprisingly narrow range from 2.63 to 2.96, with a mean of 2.77. We 

will use this value in the following calculation of effective population size. 

An “ideal” population, represented by Ne, is assumed to have a pattern of offspring per 

parent across all parents that follow a Poisson distribution characterized by a ratio of Vk/K ≈ 1. 

With salmonid populations, apparently having a ratio that is much greater than 1 (for example 

2.77 in the studies discussed above), there is a need to adjust Ne calculations to account for this 

difference. As described by Hartl (2000), the ratio of VK/K can be used to adjust the calculation 

of Ne for unequal probabilities of reproductive success using the following equation: 

 Ne = 2N/[1 + (VK/K)] (4) 

When a value of 2.77 (the mean of the three studies cited above) is substituted for VK/K 

in Equation 4, the relationship between N and Ne becomes: Ne = 0.53N. In other words, to 

account for unequal reproductive success probabilities, N should be reduced by 0.53 in the 

computation of the effective population size, Ne, or effective population size. Ne represents a 

value of about half of the number of adults that should be in the population (N). 

Overlapping Generations 

Another factor that needs to be considered in the estimation of Ne is the effect of year-toyear variation in recruitment for species with overlapping generations. For example, Waples 

(2002) suggests that such variability in Chinook salmon (Oncorhynchus tshawytscha) results in a 

137
40% to 60% reduction in the value calculated for Ne. Although coho salmon have less variation 

in age of maturity than Chinook salmon, Van Doornik et al. (2002) demonstrated that jacks (2-

year-olds) contribute enough to gene flow between different age-classes that genetically, coho 

should be treated as a species with overlapping generations. Therefore, even though the majority 

of the reproduction comes from 3-year-old fish, coho salmon should not be treated like pink 

salmon (Oncorhynchus gorbuscha), for which the year classes do not overlap. Based upon a 

synthesis of these two studies (Waples 2002, VanDoornik et al. 2002), an additional reduction of 

60% was applied to the calculation of Ne for Oregon Coast coho salmon. Although a 60% 

reduction is the upper portion of the range suggested by Waples (2002), his analysis was based 

on Chinook salmon, which have a greater overlap in generations than do coho salmon. 

Therefore, it seemed appropriate to use the higher reduction value (60%). The net result of the 

overlapping generation adjustment and the one for unequal reproductive contribution among 

families (Equation 4) is the following: 

 Ne = (0.53N)(1–0.60) = 0.212N (5) 

It is reassuring that this level of reduction (Ne = 0.212N) is similar to values suggested for 

salmonids by Allendorf et al. (1997) (Ne = 0.20N) and by McElhany et al. (2000) (Ne = 0.30). 

Translating Effective Population Size to Spawner Counts 

Spawner abundance estimates for salmon are typically collected on an annual basis. 

However, for a species with overlapping generations (such as salmon), the value for N, such as 

in Equation 5, needs to incorporate the fact that each year’s spawning population is composed of 

individuals from different year classes. Waples (1990) demonstrated that for Pacific salmon, this 

problem of overlapping generations can be accounted for in the calculation of Ne using the 

relationship described below: 

 Ne = g (Nb) (6) 

where g is the average age at maturity for the species and Nb is the harmonic mean of annual 

counts of spawners for the entire data set. As previously discussed, the average age at maturity 

was g = 2.78 years for lake populations (i.e., Siltcoos, Tahkenitch, and Tenmile) and g = 2.94 

years for all other populations (stream populations). The harmonic mean of annual spawner 

counts (Nb in Equation 6) is calculated from: 

 Nb = 1 / (1/t)( ∑(1/Ni)) (7) 

where t is the number of years counted and Ni is the number of spawners counted for each year. 

It should also be noted that using a harmonic mean to characterize spawner abundance 

essentially gives greater weight to the smaller numbers in the data set. This approach is 

generally accepted as a way to incorporate the errors that will occur in estimating Ne if 

population abundance fluctuates widely, as is frequently the case for coho salmon. Finally, 

bringing together the previous adjustments described by Equation 5 for estimating Ne with those 

of Equation 6 to account for overlapping generations yields the following equation: 

 Ne = 0.212 g (Nb) (8) 

138
Substituting the average maturity age for lake populations (g = 2.78) and stream 

population (g = 2.94), Equation 8 can be simplified to 

 Ne = 0.589 Nb (9) 

for lake populations and 

 Ne = 0.623 Nb (10) 

for stream populations. 

As presented earlier (see Equation 3 discussion), Ne values that must be achieved to 

retain at least 95% of the population’s heterozygosity over a 100-year time period are 351 for 

lake populations and 332 for stream populations. Equations 9 and 10 can be used to calculate Ne

values directly from an easily measurable quantity—annual spawner abundance, expressed as a 

harmonic mean over several years. These equations can also be rearranged such that given a 

desired Ne, the harmonic mean necessary to achieve this Ne can be calculated. Substituting these 

values for Ne into Equations 9 and 10 yields an Nb for lake populations of 351 / 0.589 = 595, and 

an Nb for stream populations of 322 / 0.623 = 533. 

Spawner Abundance Criterion 

To establish a single criterion applicable to all populations, the workgroup averaged the 

critical Nb values for the lake and stream populations (average = 564) and rounded this number 

to 600. This provides a conservative estimate of the harmonic mean annual spawner abundance 

required to meet our goal of retaining 95% of heterozygosity over a 100-year time period. 

139
140
141

Appendix C: Population Persistence Models 

Introduction 

One of the population-level criteria—Probability of Persistence, PP-2—requires the use 

of quantitative models to estimate the probability that populations will persist for the next 100 

years. In evaluating this criterion, population viability analysis (PVA) models were used to 

estimate population-scale extinction risk over a 100-year time frame. This appendix describes in 

detail the methods and results of those modeling exercises. 

PVA is a “... systematic process for species-, location-, and time-specific criteria for 

persistence ...” of populations (Soulé 1987, p. 5). In general, the term includes a variety of 

quantitative analyses to predict the future status of a population or collection of populations, 

especially to predict the risk of extinction (or quasi-extinction) over time. The history of PVA 

spans slightly more than 20 years, beginning with early applications in predicting minimum 

viable populations (Shaffer 1981). 

To assess population viability, four different extinction-risk models were applied to the 

Oregon Coast coho salmon (Oncorhynchus kisutch) Independent Populations. These were: 

• A density-dependent, count-based PVA model (DD-PVA) developed by Mark Chilcote 

and used by the State of Oregon in its Oregon Plan assessment (Chilcote et al. 2005) 

• The SPAZ hockey stick model (SPAZ-hs), which uses the “hockey stick” recruitment 

function option in the SPAZ population analysis software package developed by 

McElhany and Payne (2005) 

• A Bayesian salmon analysis model (BaySAM) developed by Wainwright et al. (in prep). 

• A stochastic habitat-based life cycle model (NL) developed by Nickelson and Lawson 

(1998) 

Comparison of the Models 

The four models used make different assumptions about important aspects of coho 

salmon population dynamics: the form of the stock-recruit relationship (including compensation 

and depensation), sources of risk, data used, future conditions, and uncertainty in parameter 

estimates. These differences are summarized in Table C-1 and discussed in the following 

paragraphs. 

Stock-recruit relationships 

Each of the models assumes a particular form of relationship between spawner abundance 

and subsequent production of either smolts or adult recruits. All of the models assume some 
Table C-1. Comparison of the PVA models used. 

Stock-recruit relationship Data used Future conditions 

Model 

Form of 

compensation 

Form of 

depensation

Sources 

of risk 

Stock-recruit 

data 

Habitat 

data 

Environmental 

variation 

Averag

conditio

Densitydependent, countbased 

Modified Ricker 

Logistic with threshold 

Environmental 1958–2004 

(Appendix D) 

None Cyclic + random

 variation 

Like 1958–

SPAZ hockey stick 

Linear increase with ceiling 

None Environmental, 

parameter 

1958–2004 (Appendix D) 

None Uncorrelated 

 random variation

Like 1958–

Bayesian salmon analysis 

Beverton-Holt Exponential Environmental, 

demographic, parameter 

1958–2004 (Appendix D) 

Estimated smolt capacity 

 Autocorrelated  random variation

Like 1958–

Habitatbased life cycle 

Complex eggto-parr and parr-to-smolt functions 

Complex, demographic and genetic 

Environmental, demographic, genetic 

None Reachscale 

habitat quality 

 Cyclic + random variation 

Harvest, hachange, lowmarine surv

142
form of compensation (i.e., a decrease in productivity with increasing abundance, resulting in an 

approach to some maximum level of production), and most assume some form of depensation (a 

decrease in productivity as abundance becomes very low, also known as Allee effects). These 

assumptions are compared in Table C-1, and stock-recruit curves for three of the four models are 

shown in Figure C-1. (The habitat-based life cycle model does not have a distinct stock-recruit 

curve.) Further details can be found in the model description subsections below. 

Sources of risk 

The extinction risk for a population is a function of the average conditions that affect 

population dynamics and variations in those conditions into the foreseeable future. In addition to 

these biological factors, to estimate this risk one must include the uncertainties in model 

structure, parameter estimates, and recent population conditions. Shaffer (1987) classified 

random variations (or stochasticity) affecting viability into four broad categories: 1) 

demographic (random events in the survival and reproduction of individuals), 2) environmental Recruits 

Figure C-1. Comparison of production relationships for the various models, all scaled to the same 

maximum production (Rmax) and rate of intrinsic increase. Solid curves are the relationships with 

depensation as used in the modeling work, dashed lines show the relationships without the effects 

of depensation. Spawners are scaled to the parameter k, which is equal to Rmax divided by 

intrinsic production (slope at the origin) of the nondepensatory relationships. 

Spawners

143
(unpredictable changes in environmental variables affecting the population), 3) catastrophic 

(large events such as floods, fires, or droughts that occur at unpredictable intervals), and 4) 

genetic (random changes in genetic makeup of populations). 

An ideal viability model would include all of these, but such models are rare. Of the 

models, two (DD-PVA and SPAZ-hs) include only environmental variation, one (BaySAM) 

includes both environmental and demographic variation, and the final model (NL) includes 

environmental, demographic, and genetic variation. None of the models include catastrophes, 

which are difficult to evaluate on the short (100-year) time scale being assessed; catastrophes are 

considered at the stratum and evolutionarily significant unit (ESU) levels in the criteria. 

Regarding model uncertainty, uncertainty in model structure was addressed by using a variety of 

models. Two of the models also account for parameter estimation uncertainty through the use of 

Bayesian analysis (a statistical technique that allows the mathematical combination of 

uncertainty in parameters with environmental variation to derive a single risk estimate, discussed 

further in Parameter uncertainty on page 146). 

Data used 

Three of the models base their predictions on stock-recruit curves fitted to spawner 

abundance time series. These three models all used the population reconstructions described in 

Appendix D as their primary data; these data are shown in Figure C-2. The habitat-based model 

does not use population abundance data, but rather is driven by fine-grain measures of habitat 

quality from field sampling (e.g., Flitcroft et al. 2002) along with statistical relationships relating 

juvenile capacity and survival to habitat quality (Nickelson and Lawson 1998). Finally, three of 

the models (DD-PVA, BaySAM, and NL) incorporate variation in the marine environment based 

on the Oregon Production Index (OPI) marine survival estimates.5

Future conditions 

Assumptions about future conditions (especially the mean and interannual variation in 

freshwater and marine survivals) are critical in estimating extinction risk. Each model takes a 

different approach to future conditions. The DD-PVA model incorporates variation in marine 

survival by repeating the observed time series of OPI marine survival estimates cyclically into 

the future, randomly choosing a different starting point in the series for each simulation. The 

SPAZ-hs model assumes environmental variation is temporally autocorrelated (i.e., the variation 

in a given year is more likely to be like the variation in the years preceding it than would be 

expected if drawn from a random normal sample). This tends to create runs of bad years or good 

years. BaySAM assumes that future marine survivals are temporally autocorrelated random 

variables, with mean, variance, and autocorrelation estimated from a combination of the 

historical OPI marine survival series with residuals from the stock-recruit model fit. 

All three of these models assume that future conditions will be on average like average 

conditions during 1958–2004, but they take different approaches to modeling the variation about 

average conditions. The final model (NL) incorporates marine survival by repeating the pattern 

 

5

 C. Melcher, Oregon Dept. Fish and Wildlife, Clackamas. Pers. commun., July 2005. 

144
Naturall

y produced spawners 

Year 

Figure C-2. Time series (1958–2004) of naturally produced spawner abundance (logarithmic scale) for 

the 21 Independent Populations, based on data and reconstructions in Appendix D. 

145
of OPI marine survivals, with variability, into the future. The average survival rate can be 

adjusted up or down to simulate possible changes in future ocean cycles. Environmental 

variability in freshwater is a random process that affects all reaches the same in a given year. 

Parameter uncertainty 

Only two of the models specifically address parameter uncertainty, and both incorporate 

this uncertainty in terms of Bayesian analysis to incorporate uncertainty in parameters directly 

into the estimate of extinction risk. The SPAZ-hs analysis includes uncertainty in three key 

parameters, while BaySAM incorporates uncertainty in all biological and environmental 

parameters. 

Cautions 

The use of viability modeling to estimate extinction probabilities is somewhat 

controversial, in particular regarding whether results should be interpreted as absolute or relative. 

If PVA is to be used, for example, for making decisions as to whether a population should be 

listed under the U.S. Endangered Species Act, then absolute predictions of the likelihood of 

extinction are necessary. On the other hand, if the analysis is used just to compare the merits of 

two or more management options, or to prioritize conservation efforts (for example, in Allendorf 

et al. 1997), then relative predictions may be adequate. 

Obtaining reliable absolute predictions of extinction probabilities is difficult, causing 

some authors to recommend against ever using PVA in situations demanding absolute answers 

(Reed et al. 2002). Coulson et al. (2001) note two necessary conditions for accurate PVA: 1) 

data must be of sufficient quality that estimates of the statistical distributions (shape, mean, 

variance, and autocorrelation) of vital rates are accurate, and 2) future distributions of these rates 

must be similar to those estimated, or changes in rates must be predictable. They argue that there 

are few real situations where these conditions can be met and conclude (p. 221) that “PVAs 

could be useful for comparing the consequences of different management or conservation 

strategies, and for exploring theoretically the implication of model assumptions on extinction 

probabilities and population dynamics. However, the workgroup doubts the general claim that 

they can be accurate in their ability to predict the future status of wild populations.” In contrast, 

other authors (Brook et al. 2000, 2002) conclude that PVAs are reliable in classifying and 

managing populations despite their shortcomings, but note that “the results of a PVA are just one 

factor in any social and political decision-making context and should be a necessary precursor to 

good judgment” (Brook et al. 2002, p. 263). 

Density-dependent, Count-based PVA Model 

Introduction 

A wide variety of viability models have been used by conservation biologists to estimate 

the vulnerability of populations to extinction (Shaffer 1981, Shaffer 1990, Murphy et al. 1990, 

Nickelson and Lawson 1998). The viability analyses used in this assessment to estimate 

population persistence were based upon methods described by Burgman et al. (1993) and Morris 

and Doak (2002). In general, such methods combine a deterministic recruitment model and a 

146
Monte Carlo simulation of random fluctuations in environmental conditions to forecast future 

population abundance. Forecasts are made for an extended period of time (often 100 years) and 

are examined to determine how many times or how frequently the forecast number of spawners 

decreased below an abundance threshold defined as the minimum abundance needed to conserve 

the species. 

One of the primary characteristics of PVAs is that population abundance forecasts over a 

period of future time are generated more than once. Random variation in the recruitment process 

is incorporated into the model via this repetition, which ensures that each forecast will differ 

slightly from another. It is not unusual in this type of analysis to perform 5,000 forecasts for 

each population. To summarize such results, the proportion of all of the forecasts that yield a 

prediction of extinction is calculated. This extinction proportion is then used as an estimate of 

the probability of extinction for the population. Populations with a low probability of extinction 

risk are considered to be viable. 

The forecasts are based upon the combination of a population recruitment model and 

environmental variation that has a certain degree of randomness. In this appendix, the 

recruitment model and the associated parameter estimates used are discussed for each 

population. Then the details of the PVA model itself are described and how it integrates with 

each population’s recruitment process. 

Recruitment Model 

The model 

The Ricker population recruitment relationship was chosen as the basis from which a 

DD-PVA was developed for Oregon coho salmon. In the most basic form of this recruitment 

model, the number of spawners is used to predict the number of offspring (recruits) in the next 

generation. The mathematical formula used to make these predictions (illustrated in Figure C-3) 

is described by: 

R = S × exp (a + BS) (11) 

where R is the total number of adult recruits (preharvest) produced from the spawners 3 years 

previously, S is the number of spawners that produced those recruits, a is the parameter for 

intrinsic population growth rate, and B is the parameter inversely proportional to habitat 

capacity. 

The Ricker relationship is one of several alternative recruitment models commonly used 

by fish conservationists. The Beverton-Holt (Beverton and Holt 1957) and hockey stick 

(Barrowman and Myers 2000) recruitment models also see frequent use. The decision in this 

case to use the Ricker model was based on an ad hoc assessment that it fit the data as well as 

either of the two alternatives and that it seemed most consistent with the observed performance 

of Oregon Coast coho salmon when the density of spawners was low. The ratios R/S observed 

for Oregon Coast coho salmon populations show an inverse relationship with spawner abundance 

(i.e., as spawner abundance decreases, R/S increases). This pattern is inconsistent with the fixed 

R/S assumption of the hockey stick model when spawner abundance is below full seeding. 

147
Ricker 

recruitment 

curve 

Persistence model 

recruitment curve 

2,500 

2,000 

1,500 

1,000 

500 

0 

Recruits 

0 500 1,000 1,500 2,000 2,500

Figure C-3. Generic example of Ricker recruitment curve and the modified version of this curve used to 

simulate recruitment in the PVA (persistence model). 

Therefore, it would be expected that at low spawner density the hockey stick model would under 

predict recruitment performance (negative recruitment bias). In contrast to the hockey stick 

model, preliminary scoping with the Beverton-Holt recruitment model suggested it may over 

predict recruitment at lower spawner abundance (positive recruitment bias). In addition, earlier 

work by Zhou (2000) suggested the Ricker model was preferable to the Beverton-Holt model for 

modeling recruitment in populations of Oregon Coast coho salmon. 

Some authors (e.g., Caputi 1988, Quinn and Deriso 1999) have suggested that when the 

Ricker model is fitted to population recruitment data containing substantial measurement error, 

the resulting recruitment curve will be biased and overestimate R/S at low spawner density. 

However, Kehler et al. (2002) found that, in general, low density R/S was not overestimated in 

the fitting the Ricker recruitment model to “noisy” data. 

A potential weakness of using the Ricker model for coho salmon is that it predicts that a 

reduction in the number of recruits produced will occur when the parental spawner abundance is 

high (see Figure C-3). Most populations of Oregon coho salmon do not show a strong decrease 

in recruits when the spawner escapement level is high. The right-hand, descending limb of the 

Ricker recruitment curve does not seem to match these observations. However, for evaluating 

populations with respect to the question of vulnerability to extinction, the recruitment 

performance of populations at high spawner abundance is of less concern. The function of 

population recruitment at high spawner abundance is a more critical question to fishery 

management than to assessing the risk of extinction. For conservation purposes, it is more 

important that the recruitment predictions are as accurate as possible when the spawner levels are 

low. In this respect, the Ricker relationship was judged to have this advantage relative to the 

Beverton-Holt and hockey stick curves. 

148
One shortcoming on the use of any model that forecasts recruits solely on the basis of 

parental abundance is that it ignores one of the key factors that influence coho salmon 

abundance: ocean survival conditions. Variation in marine survival has a strong influence on the 

number of coho salmon returning to spawn in any particular year. As a consequence, the 

relationship between spawners and recruits is heavily modified by variations in marine survival. 

To account for this important factor, a marine survival variable was added to the recruitment 

model, modifying Equation 11 to the resulting form: 

R = S × exp (a + BS + cO) (12) 

where c is an additional parameter for marine survival and O is a standardized index of annual 

smolt-to-adult survival rates for ocean conditions from 1960 to 2002 based on the OPI. The 

value for each year’s index was expressed in terms of its deviation from the 1960 to 2002 

natural-log-transformed mean of smolt-to-adult survivals. This standardized index was 

calculated from the equation: 

 Oyr = ln(OPIyr) – ln(OPI1960-2002mean) (13) 

The OPI is based on the smolt-to-adult survival of hatchery coho salmon, primarily 

originating from the Columbia River. As such it is not a direct measurement of wild coho 

salmon survival; rather it is an observable indicator likely to have a correlation with the annual 

variations in the marine survival of wild coho salmon. 

Parameter estimation 

The values for the parameters a, B, and c of Equation 12 must be estimated for each 

population in order to fit the recruitment model. The data used to make these fits (i.e., estimate 

the recruitment model parameters) were derived from spawner and recruit data for Oregon Coast 

coho salmon as presented in this report. The number of recruits corresponding to each brood 

year was calculated by dividing the spawner abundance observed 3 years later by one minus the 

fishery mortality rate. For example, the number of recruits produced by the fish that spawned in 

1996 was calculated as the spawner abundance observed in 1999 divided by one minus the 

fishery mortality rate observed for 1999. Fishery-related mortality rates for wild Oregon Coast 

coho salmon as presented in this report are based on information provided by the Oregon 

Department of Fish and Wildlife (ODFW).6

To estimate R/S for each brood year, both wild and naturally spawning hatchery fish were 

counted as spawners (combined for the denominator, S), whereas only wild fish were counted as 

recruits (the numerator, R). For each population, a data table was constructed having a row for 

each brood year from 1958 to 2000 and three columns with data for ln(R/S), the number of 

parental spawners, and the index of marine survival for the recruits corresponding to each brood 

year. Recruitment model parameters were estimated via multiple regression using the following 

equation: 

 ln(R/S) = a + BS + cO (14) 

 

6

 See footnote 5. 

149
However, rather than estimate these parameters from a single multiple regression 

analysis, Monte Carlo bootstrapping methodology was used. This methodology is considered to 

be a more reliable means of estimating model parameters (Efron and Tibshirani 1993). The 

bootstrapping approach is based on a successive series of randomly resampling observed data, 

which generates a large number of pseudo data sets. In the case of Oregon coho salmon data, a 

program was written to randomly select brood years from the 43 years of data, drawing a specific 

value for ln(R/S), spawner abundance, and marine survival index with each brood year sampled. 

The sampling protocol was done with replacement and continued until 43 brood years of data 

were obtained. Because the sampling was done with replacement, it was nearly certain that data 

for a single brood year could be drawn more than once. Likewise, it was nearly certain that at 

least some of the brood years in the original data set would not be drawn at all. Once 43 

selections were made, these data were stored in an array for future computations. The program 

then repeated the selection process until 2,000 pseudo data sets (each having 43 brood years of 

data) were generated. Following this step, a multiple regression, based on Equation 14 was 

performed on each of the 2,000 pseudo data sets and results recorded. The mean values of a, B, 

and c from these multiple regression analyses were used as the estimated recruitment relationship 

parameters for each population. 

Population parameters 

Using the bootstrap method as described, recruitment equation parameters were estimated 

for each population. The average R2

 value for the multiple regressions across all of the 

populations was 0.54 and indicates that for most populations the combination of the two 

variables (spawner abundance and marine survival) may account for most of the observed 

variation in population recruitment (Table C-2). However, for the Salmon, Floras/New, and 

Sixes populations, the parameter estimates were obtained from multiple regression analyses that 

involved 11 or fewer data points. As cautioned by Burgman et al. (1993), estimation of 

recruitment parameters from data sets with fewer than 15 points is likely to give inaccurate 

results. Therefore, for these three populations the resulting recruitment equations may describe a 

population recruitment behavior that is substantially different from the true manner in which 

these populations function. Only with additional years of data will the accuracy of the present 

parameter estimates for these populations be known. 

Population Viability (PVA) Model 

Forecasting viability and persistence 

The DD-PVA model is a series of calculations implemented via a MS Excel Visual Basic 

program. The mathematical protocol used to forecast recruits over multiple generations was 

basically Equation 12 modified such that the value for the a parameter was randomly selected 

from a normal distribution having a mean equal to the estimated mean value. This change was 

represented by replacing a in Equation 13 with arand as follows: 

R = S × exp (arand + BS + cOyr) (15) 

Therefore, arand signifies that the parameter is a randomly picked number from a normal 

distribution having a mean equal to the estimated value for the Ricker a parameter and a standard 

150
Table C-2. Estimated density-dependent PVA recruitment model parameters and associated information 

for 21 populations of Oregon Coast coho salmon. 

Population a B c SD R2 n 

Necanicum 1.694 –0.000925 0.876 0.634 0.61 43 

Nehalem 1.871 –0.000110 1.271 0.634 0.76 43 

Tillamook 1.219 –0.000117 0.963 0.884 0.39 43 

Nestucca 1.723 –0.000223 0.882 0.883 0.36 43 

Salmon –0.126 –0.002565 0.310 0.936 0.68 11 

Siletz 1.550 –0.000535 0.782 0.724 0.53 43 

Yaquina 1.821 –0.000185 0.852 1.156 0.36 43 

Beaver 1.694 –0.000831 0.573 0.701 0.54 35 

Alsea 1.526 –0.000304 0.772 0.741 0.54 43 

Siuslaw 2.325 –0.000153 0.882 0.827 0.55 41 

Lower Umpqua 1.956 –0.000163 0.578 0.617 0.56 43 

Middle Umpqua 2.115 –0.000433 0.318 0.749 0.52 43 

North Umpqua 0.861 –0.000257 0.721 0.896 0.54 43 

South Umpqua 1.933 –0.000479 0.260 0.796 0.48 43 

Siltcoos 2.123 –0.000383 0.388 0.664 0.53 41 

Tahkenitch 1.991 –0.000558 0.473 0.761 0.51 41 

Tenmile 1.447 –0.000064 0.465 0.656 0.27 43 

Coos 2.432 –0.000181 0.368 0.710 0.59 43 

Coquille 1.564 –0.000078 0.603 0.681 0.45 43 

Floras* 2.087 –0.001649 0.675 0.455 0.89 7 

Sixes* 1.724 –0.007369 –0.183 0.702 0.71 11 

*Parameter estimates and associated statistics may be unreliable because of the small number of data records 

available from which to make these estimates. 

deviation (SD) equal to the SD of the multiple regression estimated from the fitting of observed 

population data as reported in Table C-2. For example, in the case of the Necanicum population, 

the estimated value for the a parameter and the SD for the multiple regression fit were 1.694 and 

0.634, respectively. In simulating recruitment, each time another forecast for recruits was made, 

the computer would execute a routine to pick a new value for arand by randomly selecting a value 

from a normal distribution having a mean of 1.694 and SD of 0.634. 

The minor notation change from O to Oyr for marine survival in Equation 14 signifies 

that, for each year of the model run, a different value for marine survival was assumed. Since 

most of the model runs were made for a time period of 100 years (about 33 generations), it was 

necessary to select a sample of marine survival rates in order to more accurately model 

recruitment. This could have been done in a manner similar to the process used to randomly pick 

values for the Ricker a parameter (i.e., randomly draw a number from a normal). However, this 

approach has problems in that marine survival does not occur in a random pattern, but is rather 

more cyclic. For example, if poor marine survival is observed in one year, the likelihood is that 

a poor marine survival will also occur in the next year. For a coho salmon population, the 

challenge of surviving poor marine survival conditions is not one of dealing with the occasional 

151
rare event, but rather facing these rare events in a block of years together. The latter situation 

results in a greater challenge to the survival of the population, and it was this likely situation that 

the viability model was modified to simulate. 

It is possible to correct for this problem of temporal autocorrelation with respect to 

annual environmental conditions with modifications to the structure of a viability model, using a 

variety of approaches including those proposed by Morris and Doak (2002). The approach used 

for Oregon Coast coho salmon, however, was basically to make this modification using the 

observed marine survival rates, in the order that they occurred between 1960 and 2002, as a 

template for the future 100-year period modeled. Because the observation period covers only 43 

years, it had to be replicated to provide enough points for the 100-year simulation of the model. 

The mechanics of how the model prepared the sequence of marine survival rates for the 

recruitment calculations was to first artificially string together four replications of the marine 

survival rates observed from 1960 to 2002. Essentially this created a cyclic string of 172 values 

having 4 high and 4 low periods of marine survival. For each 100-year simulation, a different 

starting place was randomly selected (from a uniform distribution) within the first 43 years of the 

172-year string. The first 100 years after this starting point were then used as the marine 

survivals (Oyr) for the model run. 

Another key element of the persistence simulation model was the assumption made about 

population recruitment when the number of spawners was extremely low (i.e., depensation 

effects). Barrowman (2000) suggested that for natural coho salmon populations, depensation 

may be a factor when spawner densities are less than the equivalent of 3.2 spawners per mile. 

For the purposes of the persistence model, it was assumed that an abundance level equal to 3.2 

spawners per mile was the depensation threshold, below which population recruitment would be 

adversely impacted. The persistence simulation model was constructed to incorporate these 

depensation thresholds, with explicit protocol for how recruitment would function once the 

number of spawners had fallen below these thresholds. This protocol worked in the following 

manner. When the persistence simulation model yielded a spawner abundance less than the 

depensation threshold, the number of recruits forecast was adjusted downward as described by: 

 If S ≤ 0.10SDepn, then Radj = 0.0, 

 If S > 0.10SDepn and S < SDepn, then (16) 

Radj = R × [Sobs – (0.10SDepn)] / [SDepn – (0.10SDepn)] 

where SDepn is the depensation threshold for the population, Radj is the depensation adjusted 

number of recruits, R is the number of recruits forecast by the recruitment model without 

depensation adjustment, and Sobs is the observed number of spawners. This protocol essentially 

treats any spawner escapement less than one-tenth of the depensation value the same as if no 

recruitment had taken place. Between this zero recruitment level and the depensation threshold, 

the number of recruits is scaled up proportionally such that when the spawner level reaches the 

depensation threshold (and above), no depensation discounting is applied to the forecast number 

of recruits (i.e., Radj = R). 

152
As noted, one possible shortcoming of using the Ricker function is that it yields 

decreasing number of recruits as the number of spawners increase beyond the level needed to 

achieve maximum recruits. While this feature may be relatively unimportant for assessments 

that are focused on the performance of the population when the abundance is at low or moderate 

levels, such as this one, this characteristic can cause problems for PVA models. Within the 

model runs, if a very large spawner number is generated, mathematically, the Ricker function 

will forecast a very low number of recruits because of the descending shape of the recruitment 

curve at high spawner abundance (Figure C-3). Particularly when the value for a parameter is 

greater than 2.0, this can set up extraneous oscillations in abundance that are an artifact of 

recruitment function and unlikely to be mimicked by a real populations (Sabo et al. 2004). 

Although the a parameter values for most of the populations were less than 2.0 (Table 

C-2) and therefore less susceptible to this “pathological” oscillation problem, modifications to 

the recruitment function within the persistence simulation model were made to prevent this 

behavior from occurring. This consisted of a step inserted into the program code to check 

spawner abundance before each use of the recruitment equation. If this check revealed a 

spawner abundance value greater than the number of spawners needed to fully seed the habitat 

(i.e. Smax, estimated as –1/B), then the spawner abundance was reset to a value equal Smax. The 

effective result of this change was to level out the recruitment curve at spawner densities greater 

than Smax. This modification, in addition to the modification for a presumed depensation effect at 

extremely low spawner densities, resulted in the population recruitment function used for PVA 

modeling to effectively assume more of a hockey stick appearance as illustrated in Figure C-3. 

Mechanics of the model runs 

To estimate the probability of extinction, 3,000 iterations of a 100-year recruitment 

simulation were completed for each population. Upon completion, the sequence of spawner 

escapements generated for each 100-year trial were inspected to determine if at any point in this 

sequence there was a 3-year consecutive string of spawner abundance that averaged less than the 

test threshold. Three test thresholds were examined: zero spawners, 50 spawners, and the 

equivalent of 1 fish per mile. These test thresholds were intended to represent effective (or 

absolute) extinction of the population. 

After inspection, those trials with spawner abundance output that contained a 3-year 

average spawner abundance less than the test threshold were classified as extinction events. 

After the results from all of the trials were inspected, the probability of extinction was calculated 

for the number of trials that were classified as extinction events divided by the total number of 

trials (i.e., 3,000). 

Results 

The DD-PVA model predicts that for most populations, if the environment conditions of 

the last 50 years are repeated for the next 100, the likelihood of extinction is low. Using test 

thresholds of zero, 50, and 1 fish per mile (1 fpm), the number of populations with a higher than 

5% risk of failure were only 3, 5, and 6, respectively (Table C-3). Further, there was a clear 

pattern that the more northerly populations were more vulnerable than the southerly populations 

(except the Sixes). It is also worth noting that populations comprised primarily of wild fish, with 

153
the exception of the Sixes, were all predicted to persist. In contrast, the populations that were at 

risk of failing the threshold happen to be the ones that also contain the highest proportions of 

naturally spawning hatchery fish. Although it is not clear if hatchery fish per se are the cause of 

this increased risk, a correlation between hatchery fish and extinction risk does appear to exist. 

Parameter estimation errors 

Assessing coho salmon populations using the DD-PVA model was dependent on 

obtaining reliable estimates for the parameters used to describe the recruitment equation for each 

population. Even though two factors thought to be important to population recruitment for coho 

salmon (spawner abundance and marine survival) were integrated into the DD-PVA model, 

rarely could more than 60% of the variation in population recruitment be explained by the model 

(Table C-2). Such results were a clear warning that even using the bootstrap estimation 

procedures, the probability of errors in estimating the correct values for the recruitment model 

parameters were likely. 

Table C-3. Forecast probability from the DD-PVA model. This forecast predicts that the 3-year average 

wild spawner abundance will fall below conservation thresholds of zero, 50, and 1 fish per mile 

(fpm) at least once within a simulated future 100-year period. 

Threshold 

Population 0 50 1 fpm 

Necanicum 0.000 0.002 0.002 

Nehalem 0.005 0.006 0.077 

Tillamook 0.052 0.057 0.155 

Nestucca 0.000 0.000 0.001 

Salmon 1.000 1.000 1.000 

Siletz 0.038 0.065 0.248 

Yaquina 0.000 0.001 0.005 

Beaver 0.000 0.000 0.000 

Alsea 0.003 0.003 0.034 

Siuslaw 0.000 0.000 0.000 

Lower Umpqua 0.000 0.000 0.000 

Middle Umpqua 0.000 0.000 0.000 

North Umpqua 0.042 0.115 0.115 

South Umpqua 0.000 0.000 0.000 

Siltcoos 0.000 0.000 0.000 

Tahkenitch 0.000 0.000 0.000 

Tenmile 0.000 0.000 0.000 

Coos 0.000 0.000 0.000 

Coquille 0.000 0.000 0.000 

Floras/New 0.000 0.000 0.000 

Sixes 0.923 0.998 0.998 

154
To test the first part of this question, the parameter values for each population were 

changed and the net impacts on the persistence assessment were determined. As a precautionary 

measure, this sensitivity analysis focused only on the concern that the risk of extinction 

determined for each population was higher than reported because of errors in estimating model 

parameters. In other words, the sensitivity analysis only examined recruitment parameter values 

that would result in increased extinction risk relative to that estimated with the base parameter 

values. 

Furthermore, in the interests of brevity, the sensitivity analysis results presented here 

represent only a small portion of the full combination of possible parameter values that could 

have been tested. However, the parameter values tested were intended to represent the likely 

outward boundaries of the magnitude of these errors. 

For each population, persistence model runs were made under four different parameter 

error scenarios. The first scenario evaluated the impact of reducing the value for the a parameter 

such that the R/S value used in the recruitment calculations was essentially reduced by 30%. 

Effectively, this resulted in a scenario where the true values for the a parameter yielded a 

recruitment rate that was 30% lower than that used to evaluate the persistence and diversity 

criteria. The second test focused on the value for the B recruitment equation parameter, 

adjusting it such that the maximum number of adults that could be produced by the population 

was reduced by 30%. In the third sensitivity test, the value for the marine survival parameter c

was increased such that the weighting given to changes in the OPI marine survival index was 

increased by 30%. This was anticipated to cause a weaker recruitment potential when the 

population cycled through the period of poor marine survival in the persistence model 

simulations. Finally, a fourth simulation was run wherein all of the adjustments to individual 

parameters were the same as in test one through three; however, rather than separate, all of these 

changes were combined for a “perfect storm,” scenario for recruitment parameter errors. 

As illustrated in Table C-4, the probability of extinction increased substantially for each 

of the parameter error scenarios. With the exception of the second sensitivity test involving the 

B parameter, a sufficient number of populations failed each test. Among the populations there 

was a clear north to south pattern. The model output under any of the sensitivity test scenarios 

was more likely to yield a change from a pass to a fail for the persistence for populations in the 

northern portion of the range. 

The results for the fourth sensitivity test, which assumed the errors for each of the three 

parameters occurred together, were predictable given the outcomes for the analyses when these 

parameters were considered separately. In this “perfect storm” scenario of parameter estimation 

bias and error, the model estimates of extinction were substantially larger than any of the first 

three scenarios. However, even in this case the north to south pattern of sensitivity to these 

errors was clearly evident. 

One interpretation of the results from these parameter sensitivity analyses might be that 

the persistence model forecasts are not reliable and may significantly underestimate risk. 

However, there are several counter arguments to this interpretation. First, the sensitivity 

analyses examined parameter errors in only the direction that were anticipated to increase 

155
Table C-4. Probability of extinction estimates for 21 populations of Oregon Coast coho salmon under 

different parameter error scenarios. Extinction probabilities for the Base column are the same as 

those reported for the 1 fpm abundance test threshold (Table C-3). Probabilities given for test 1, 

test 2, and test 3 represent, respectively, a 30% reduction in R/S (a), a 30% effective reduction in 

the maximum number of recruits that can be produced (B), and a 30% increase in the sensitivity 

of population recruitment to changes in the OPI marine survival index (c). The results in the last 

column combine parameter changes of test 1, test 2, and test 3 into a single, worst-case scenario 

(a + B + c). Values greater than 0.05 are highlighted in bold. 

Population Base 

a 

sensitivity 

test 1 

B 

sensitivity 

test 2 

c

sensitivity 

test 3 

a + B + c

sensitivity 

test 4 

Necanicum 0.002 0.125 0.012 0.124 0.962 

Nehalem 0.077 0.649 0.323 0.718 1.000 

Tillamook 0.155 0.713 0.358 0.608 0.987 

Nestucca 0.001 0.031 0.003 0.022 0.575 

Salmon 1.000 1.000 1.000 1.000 1.000 

Siletz 0.248 0.882 0.641 0.811 1.000 

Yaquina 0.005 0.075 0.011 0.072 0.555 

Beaver 0.000 0.000 0.000 0.000 0.047 

Alsea 0.034 0.501 0.192 0.413 0.987 

Siuslaw 0.000 0.004 0.001 0.022 0.557 

Lower Umpqua 0.000 0.000 0.000 0.000 0.018 

Middle Umpqua 0.000 0.000 0.000 0.000 0.000 

North Umpqua 0.115 0.747 0.288 0.545 0.986 

South Umpqua 0.000 0.000 0.000 0.000 0.043 

Siltcoos 0.000 0.000 0.000 0.000 0.000 

Tahkenitch 0.000 0.000 0.000 0.000 0.000 

Tenmile 0.000 0.000 0.000 0.000 0.000 

Coos 0.000 0.000 0.000 0.000 0.000 

Coquille 0.000 0.000 0.000 0.000 0.046 

Floras/New 0.000 0.000 0.000 0.000 0.635 

Sixes 0.998 1.000 1.000 0.995 1.000 

extinction probabilities. Second, the magnitude of these “one-way” errors was assumed to be at 

the upper end of what was plausible. Thirdly, while the individual population results could be 

strongly influenced by estimation bias in one or more of the recruitment parameters, it seems 

unlikely that this bias would occur equally for all 21 populations at the same time. Presumably, 

it is more likely that by chance, the errors would cause bias in the opposite direction for some of 

the populations and result in overestimates of risk, perhaps canceling the effect at the ESU level 

of underestimating risk for other populations. Finally, the probabilities of extinction for many of 

the populations equal one (certainty of extinction) or nearly so for several of scenarios tested, 

particularly for test 4. However, for such dire forecasts to be true it would be expected that many 

of the populations in the northerly portion of the range would have already become extinct over 

the last 50 years; an outcome that did not occur. 

156
SPAZ Hockey Stick Viability Analysis 

Introduction 

“SPAZ” (McElhany and Payne 2005)7

 is a computer program that analyzes the viability 

of salmon populations. It uses spawner abundance time series to estimate parameters for several 

population models and then uses the models to make projections of future population trajectories. 

These trajectories are then used to estimate extinction risk. In the current application of the 

program, a hockey stick recruitment model (Barrowman and Myers 2000, Bradford et al. 2000) 

was used to estimate extinction risk for 16 of 21 Independent Populations of the Oregon Coast 

Coho Salmon ESU. 

The hockey stick recruitment model is more conservative than Beverton-Holt and Ricker 

recruitment models because it assumes that recruits per spawner remains constant as spawner 

abundance decreases, rather than increasing as in the other two models (Barrowman and Myers 

2000). This model assumes linearity in the relationship between recruits and spawners up to 

some spawner level, above which recruits remain constant (Figure C-4). 

Methods 

SPAZ-hs uses the “Markov-Chain Monte-Carlo” (Gelman et al. 1995) Bayesian 

estimation method to estimate parameters for various recruitment curves, including the hockey 

stick, from adult abundance data. Models can be compared using the Akaike Information 

Criterion (AIC) (Ludden et al. 1994). Models with relative AIC values (the difference between 

the AIC for a particular model and the lowest AIC value for any model) of two units or less are 

considered to be contenders for the “best” model. 

Figure C-4. The hockey stick recruitment model. This model has a linear increase in recruits from zero 

spawners until maximum recruitment is reached. To the right of this point, recruitment is 

constant. 

 

7

 Note: A beta version of SPAZ was used, which means the program is still undergoing development and testing and 

may contain errors that affect the results presented here. 

157
A total of 500,000 repetitions are used to fit the model parameters. Prior distributions for 

the Bayesian model-fitting analysis were set as: 

• Intrinsic growth rate: a uniform distribution from 0.1 to 40 

• Carrying capacity: a uniform distribution with a range of 1,000 to 100,000 

• Process error: a log-normal distribution with a range of 0.01 to 5.0 

The range of the distribution for intrinsic growth rate represented an approximate 

doubling of the range of spawners per spawner observed at low abundance in our analysis of the 

Population Productivity Criterion, PP-1. The range of the distribution for carrying capacity was 

based on the range in the size of estimated historic abundance of the Independent Populations in 

the ESU (Lawson et al. 2007). The process error distribution was recommended by McElhany 

and Payne (2005). 

The model was applied to data on the abundance of wild and hatchery coho salmon adult 

spawners for the 18 Independent coho salmon populations identified for the Oregon Coast for 

which long-term data is available (1958–2003 [Appendix D]). Relative AIC was compared to 

determine whether the hockey stick model was a reasonable model to use in the forecasting 

portion of the risk analysis (Table C-5). Results show that the hockey stick model is an 

appropriate model for all populations except for the Tahkenitch and Coos populations. Thus 16 

of the 21 Independent Populations of the Oregon Coast Coho Salmon ESU were included in the 

analysis. 

During the risk analysis portion of the model runs, uncertainty about model parameters 

was dealt with by sampling the posterior distributions of the parameters. For each of 1,000 

simulated population trajectories, new values for growth rate, carrying capacity, and variance 

were randomly sampled from the saved posterior distribution of each parameter. 

Results 

Results of risk analysis 

The probability of persistence for 100 years based on the SPAZ-hs model was either 

100% or very near 100% for all populations tested (Table C-6). 

Sensitivity to quasi-extinction level 

Because of the uncertainty about demographic processes at very low abundances, true 

extinction (i.e., abundance of 0) was replaced by a quasi-extinction threshold (QET) (Ginzburg et 

al. 1982). QETs of 1 and 50 were tested for a period of 100 years. The frequency that any 3-

year average fell below the QET was considered the probability of extinction. Results using the 

two QETs were not significantly different (P=0.24). 

158
Table C-5. Relative AIC for 18 Independent Populations of coho salmon of the Oregon Coast ESU. The 

AIC for four alternative recruitment models is shown. Asterisks indicate populations where the 

hockey stick does not compete for the best model. Populations are grouped by biogeographic 

strata. 

Models tested 

Population 

Constant 

recruitment Beverton-Holt Ricker Hockey stick 

Necanicum 5.46 0.06 0.00 0.22 

Nehalem 0.00 1.58 3.01 2.00 

Tillamook 24.00 0.68 0.24 0.00 

Nestucca 20.10 1.49 1.47 1.54 

Siletz 1.62 0.00 0.45 0.27 

Yaquina 5.78 0.00 0.66 1.52 

Beaver 4.54 0.63 0.00 0.90 

Alsea 2.23 1.64 0.00 0.12 

Siuslaw 0.04 0.00 1.27 1.31 

Lower Umpqua 0.93 0.00 2.04 0.63 

Middle Umpqua 0.00 3.45 6.19 2.00 

North Umpqua 12.37 3.76 4.01 0.00 

South Umpqua 0.00 2.93 4.90 1.99 

Siltcoos 0.00 2.00 5.46 1.91 

Tahkenitch 3.82 5.60 0.00 4.13* 

Tenmile 22.07 0.05 0.04 0.00 

Coos 5.66 7.61 0.00 5.96* 

Coquille 5.64 0.66 0.49 0.00 

Sensitivity to fitness of hatchery fish 

Two assumptions were made as to the fitness of hatchery fish: 1) hatchery fish have the 

same fitness as wild fish, and 2) hatchery fish have half the fitness of wild fish. Results using the 

two different assumptions as to fitness of hatchery fish were not significantly different (P=0.47). 

Bayesian Salmon Analysis Model 

Introduction 

This analysis is an application of the BaySAM (Wainwright et al. in prep.). The model 

combines density-dependent freshwater production with density-independent, environmentally 

driven marine survival to predict future population abundance and estimate risk of extinction. 

Smolt production is modeled as a Beverton-Holt function of spawner density, modified with an 

exponential decline in recruits (depensation) at very low spawner densities (Figure C-5). Unlike 

159
Table C-6. Probability of persistence of coho salmon populations estimated by the SPAZ-hs recruitment 

model. Populations are grouped by diversity strata. 

QET* 

Population 1 50 

Necanicum 0.999 0.986 

Nehalem 1.000 1.000 

Tillamook 1.000 1.000 

Nestucca 1.000 1.000 

Salmon NA NA 

Siletz 1.000 1.000 

Yaquina 1.000 0.998 

Beaver 1.000 1.000 

Alsea 1.000 1.000 

Siuslaw 1.000 1.000 

Lower Umpqua 1.000 1.000 

Middle Umpqua 1.000 1.000 

North Umpqua 1.000 1.000 

South Umpqua 1.000 1.000 

Siltcoos 1.000 1.000 

Tahkenitch NA NA 

Tenmile 1.000 1.000 

Coos NA NA 

Coquille 1.000 1.000 

Floras/New NA NA 

Sixes NA NA 

* Quasi-extinction threshold. 

the other models applied here, this model produces a Bayesian estimate of extinction risk (Taylor 

1995, Ludwig 1996, Wade 2002) rather than a simple extinction probability. The model does not 

provide a full assessment of extinction risk as it excludes important issues such as population 

genetics, changes in habitat condition, the influence of hatchery production, and the influence of 

migration among populations. However, the results provided a basis for evaluating risks 

resulting from the major demographic factors of population abundance, productivity, and 

variability. 

Methods 

Biological model 

The BaySAM model assumes that all density-dependence occurs in freshwater habitats, 

while environmental variation has its strongest influence during early ocean life. Smolt 

production was modeled with a Beverton-Holt stock-recruitment curve modified with 

160
Figure C-5. The production function used in the extinction risk model (solid curve) compared with a 

standard Beverton-Holt production function (dashed curve). Dotted lines indicate the 

relationships of the model parameters to important biological indicators: pk is smolt capacity, k is 

the adult abundance that produces smolts equal to one-half of capacity, and n50 is the adult 

abundance where depensation reduces production to one-half of the standard Beverton-Holt 

function. 

exponential depensation. Starting with the number of spawning females (Nf) in brood year t, the 

number of female smolts (nf) produced by those females was calculated as: 

( )

50

ln 0 5

1 exp

1

ft ft

ft

ft

pN N .

n = N n

+

k

⎡ ⎤ ⎛ ⎞ ⎢ ⎥ − ⎜

⎜ ⎢⎣ ⎝ ⎟

⎟

⎠⎥⎦ (17) 

On the right-hand side, the first term is the traditional Beverton-Holt recruitment curve and the 

second term represents depensation at low abundance. The parameters of the standard 

(nondepensatory) Beverton-Holt relationship are p, the slope of the curve at low spawner 

abundance, and k, the female spawner abundance that produces one-half of the maximum 

number of smolts. Maximum smolt production is pk, which is approached asymptotically as 

161
spawner abundance increases. The depensation parameter, n50, is the spawner abundance at 

which depensation reduces smolt production to 50% of what would be produced with no 

depensation. The smolt-production model is illustrated in Figure C-5. Assuming an even sex 

ratio, the number of male smolts (nm) is equal to the number of female smolts (nf). 

Smolts predicted by this relationship then enter the ocean, where they face variable 

marine survival and harvest. In simplified form, the number of recruits (Rt) is 

 (18) ( )t − h 3 1 R =n m t tt −

where mt is marine survival and ht is harvest rate for the return year (t). To represent temporal 

autocorrelations in the marine environment, marine survival is modeled as a first-order 

autoregressive random process with lognormal errors (see Wainwright et al. in prep. for details). 

Parameter estimation 

In Bayesian analysis, statistical distributions of model parameters are estimated from a 

combination of preexisting (prior) information and the likelihood of the model predictions 

relative to new data, resulting in a posterior distribution of the parameter values. The prior 

information used in the original application (Wainwright et al. in prep.) came from two sources: 

a meta-analysis of smolt-recruit data from other coho salmon populations along the West Coast 

(Bradford et al. 2000, Barrowman et al. 2003) for distributions of intrinsic productivity and 

capacity parameters, and an analysis of environmental time series (Wainwright et al. in prep.) for 

distributions of the variance and autocorrelation parameters for marine survival. 

Data used included spawning population abundance, estimated harvest rates, and an 

index of marine survival. For most populations, the spawning data was the number of naturally 

produced spawners estimated from two time series: the ODFW random sampling spawner 

abundance estimates (1990–2003), and the longer index stream area-under-the-curve spawner 

abundance index (1981–2003). Exceptions were the North Umpqua, where counts at Winchester 

Dam were used, and the lake populations (Tahkenitch, Siltcoos, and Tenmile), where standard 

ODFW lake population estimates were used. 

Modifications 

The model bases its parameters on smolt and spawner density (i.e., number per unit of 

habitat) rather than total population abundance. The original application (Wainwright et al. in 

prep.) was designed to be generic, without detailed habitat data for the populations modeled. For 

that reason, parameters were based on a habitat base indexed to total accessible stream length in 

the occupied basin (i.e., population density was numbers per accessible kilometer). This metric 

was the basis for the parameter estimates used as priors in that model, derived from a previous 

meta-analysis of coho salmon freshwater production (Bradford et al. 2000). However, for the 

current analysis, initial model results using the original methodology were counter-intuitive, 

resulting in higher risks for some large basins with good quality habitat than for smaller, poor 

habitat basins. This was apparently the result of variation in the ratio of actual coho salmon 

habitat to total basin stream miles. For this reason, total smolt capacity (as estimated in Lawson 

et al. 2007) was used here as the habitat metric, and parameters are thus expressed as density of 

162
fish per thousand potential smolts. Prior distributions for the capacity, depensation, and intrinsic 

productivity parameters used by Wainwright et al. (in prep.) were adjusted to this new density 

metric. 

Results 

Extinction risks over a 100-year time frame were estimated by applying the model 

independently to each of the historically Independent Populations, with the exception of 

Floras/New Creek, where the Beverton-Holt model failed to adequately fit the data. These 

estimates are shown in Figure C-6. Results are shown for both absolute extinction (upper panel) 

and for quasi-extinction (defined as spawner abundance below 50 fish per generation at any time 

in the 100-year prediction period). The two results are essentially the same except for the 

smaller river systems (Necanicum, Salmon, Beaver, and Sixes). Mean extinction risk is quite 

low for all populations, but quasi-extinction risk is somewhat higher for the smaller river 

systems. However, there is high uncertainty in results for most populations, as demonstrated by 

the long upper tails of the extinction probability distributions in Figure C-6. 

Sensitivity to Quasi-extinction Thresholds 

To test sensitivity of these results to the choice of QET, risk was estimated for each 

population using a range of QET values from 0 to 1,000 spawners per generation (Figure C-7). 

There is apparently little sensitivity of results over this range of QET values for most 

populations; however, results for the populations in smaller basins are quite sensitive. As an 

example, the Salmon risk goes from near zero for absolute extinction (QET=0) to more than 90% 

risk for a QET of 300. This appears to be related to the ratio of QET to the capacity of the 

system: estimated risk does not increase substantially until QET is a substantial fraction of 

estimated spawner capacity. For the Salmon, the model estimates a median smolt capacity of 

only 66,000 (range 20,000 to 320,000), which would produce a median of 660 adults at 1% 

marine survival. The combination of environmental variation and variation in estimated capacity 

(parameter uncertainty) results in a high likelihood that this population will drop below QETs of 

a few hundred fish in the 100-year simulation time frame. 

Stochastic Habitat-based Life Cycle Model 

Introduction 

PVA is intended to quantify the likelihood that a population will persist into the future. 

Implicit in projecting a population’s future are assumptions about future conditions that affect 

the population. Healthy populations are expected to persist through a wide variety of future 

conditions, especially if those conditions are similar to historical conditions the population has 

experienced. If conditions have changed from the historical, or a population is compromised in 

its viability, changing conditions may have a greater effect on future prospects. One role of PVA 

is to explore the effects of possible changes in future conditions on a population’s viability. 

The model used here, based on the habitat-based life cycle model of Nickelson and 

Lawson (1998), enables the exploration of three factors with potentially important implications 

for the future viability of Oregon Coast coho salmon: freshwater habitat conditions, marine 

163
Extinction risk

Quasi-extinction risk

Figure C-6. Results from the BaySAM model for all Independent Populations in the ESU. Diamonds 

mark the mean risk of extinction, box plots illustrate the distribution of extinction probability 

estimates as a function of parameter uncertainty. The upper panel is for absolute extinction, 

lower panel is for a quasi-extinction threshold of 50 spawners per generation. The horizontal line 

marks the 5% risk threshold. 

164
Risk 

Quasi-extinction threshold 

Figure C-7. Relationship of extinction risk to QET, for the Independent Populations. 

survival, and harvest. In this analysis, a baseline condition was established reflecting the best 

assessment of current conditions. Hypothetical future conditions were then modeled by 

applying, singly and in combination, a harvest regime, two trends in habitat quality (increasing 

and decreasing), and a generally poorer marine survival regime. Extinction probabilities are 

increased markedly with the addition of harvest, declining habitat trend, and poorer ocean 

survival. 

Stochastic Habitat-based Life Cycle Modeling 

The habitat-based life cycle model (NL) of Nickelson and Lawson (1998) simulates a 

coho salmon life cycle using data based on fine-grained measurements of habitat quality. 

165
Habitat is divided into river reaches. Each reach has an egg-to-smolt survival rate and smolt 

capacity determined by habitat quality and based on field sampling of habitat. Spawning adults 

produce eggs that survive to parr at a rate that is dependent on the density of parr. These parr 

survive to a number of smolts that is dependent on habitat quality and capacity. Smolts enter the 

ocean where they experience variable marine survival and harvest, then return as spawning 

adults to their natal reach. In the model a constant proportion of returning adults strays to other 

reaches, allowing repopulation of depleted reaches. The model works with a single brood cycle, 

so the basic time step is 3 years. The model uses a stochastic Monte Carlo technique in which 

most model parameters have associated error distributions. As the model is run, error values are 

chosen at random from the distributions and added to the fixed parameter values. This occurs 

with every time step. For this analysis, 33 time steps were modeled, following one brood cycle 

through 99 years. The model was then run 1,000 times and a range of possible results was 

produced that represents the uncertainty about salmon biology, future environmental conditions, 

and (for some model runs) management actions (Nickelson and Lawson 1998). 

The NL model is based on the principle that habitat quality and quantity govern the 

production of coho salmon. Population dynamics in the model include compensation, 

depensation, and straying. The structure of the model is flexible enough to incorporate a variety 

of factors such as harvest, variable marine survival, and changes in habitat quality over time. It 

also explicitly includes uncertainty arising from random (stochastic) environmental factors and 

uncertainty in estimating various biological parameters. It does not include any effects of 

hatchery fish. The habitat database does not include some of the lower reaches that are thought 

to have been the most productive coho salmon habitat in the past, so this potential source of 

rearing habitat is not part of the simulation. Despite these omissions, model results provide a 

good basis for exploring the relative importance of harvest, habitat, and ocean conditions to the 

viability of naturally produced coho salmon on the Oregon Coast. 

The model used for the current assessment differs from the version reported in Nickelson 

and Lawson (1998) in several respects: 

• The stray rate was increased from 5% to 20% in response to more recent information 

about actual stray rates8

 and comparisons of model behavior with observations. 

• The river reach database was updated with new information from more recent habitat 

surveys. In addition, these reach data were expanded to represent the entire basin in 

proportion to the relationship between habitat type and land use. Thus there was no 

longer a need in the model to expand from sampled reaches to represent an entire basin. 

• The marine survival time series was based on OPI hatchery survival rates, similar to the 

method used in the density-dependent, count-based PVA model (see page 146). 

• All model runs used the same marine survival time series, starting at the same point in 

time, rather than starting at a random point in the series as was done in Nickelson and 

Lawson (1998). This reduced overall model variability somewhat, and made it easier to 

interpret results. 

 

8

 T. Nickelson, Oregon Dept. Fish and Wildlife, Corvallis. Pers. commun., 10 February 2002. 

166
• The model was modified to include all riverine Independent Populations on the coast, 

including the four Umpqua River populations. 

Together these changes serve to make the model more representative of Oregon Coast 

coho salmon, with population structure as defined by the technical recovery team (TRT) 

(modified from Lawson et al. 2007). Coastal lakes are not included in this simulation as their 

population dynamics are considerably different from the river populations. 

Because harvest is an important component of the ecology of modern salmon populations 

and may present a considerable risk to the viability of stocks, for this assessment the Nickelson 

and Lawson (1998) life cycle model was extended to include a model of the current harvest 

regime. This modification was first applied to the risk assessment of Amendment 13 to the 

Salmon Fishery Management Plan of the Pacific Fishery Management Council (PFMC 1999). 

For the PFMC assessment, the model was used to evaluate the relative risk of two management 

systems. In this assessment, only the adopted (Amendment 13) management system was 

modeled. 

Modeling of the Amendment 13 Management Regime 

Amendment 13 specifies total allowable exploitation rates (total fishery-related 

mortalities including marine and freshwater landed catch and nonlanded mortalities) for naturally 

produced coho salmon on the Oregon Coast. Allowable exploitation rate is a function of marine 

survival and parental spawner numbers. If either marine survival or parental escapement is low, 

then exploitation rate is low. The highest rate is allowed only when both marine survival and 

parental escapement are high. 

Management under Amendment 13 is based on geographically based “subaggregates” of 

Oregon Coast coho salmon. In order to simulate this regime, all populations had to be modeled 

simultaneously, and management “decisions” were made based on the performance of the 

subaggregates. The population structure used for fisheries management differs from that used by 

the TRT (modified from Lawson et al. 2007). In particular, the TRT has subdivided the Umpqua 

River into four populations. The four Umpqua River populations reported in this analysis were 

aggregated into a single population for the purposes of simulating harvest management. 

The simulation of Amendment 13 management recognizes that managers operate with 

imperfect information on stock size predictions and spawner escapement estimates. Amendment 

13 requires an index of marine survival based on the ratio of hatchery smolts released to jack 

returns in the Columbia River; spawner abundances are estimated from surveys. Thus the 

Amendment 13 harvest rate control rule is implemented with error, meaning that actual postseason harvest rates differ from those set by the agencies pre-season. These factors were 

included in the analysis as sources of variability arising from harvest management. In other 

words, the modeling simulated the management regime given the information actually available 

to the managers. 

167
Management Information 

Oregon Coast natural coho salmon predictor 

Oregon Coastal Natural (OCN) coho salmon are the natural production component of the 

Oregon Coast ESU. For the purposes of harvest management, their abundance is predicted from 

a multiple regression of spring upwelling and winter sea surface temperature. It was not 

practical to reproduce this predictor in simulation. As a simpler alternative, the workgroup 

started from the modeled abundance and added an error term sampled from a gamma distribution 

with a mean of the known abundance (from the model) and a coefficient of variation (CV) 

similar to that of the predictor. 

A gamma distribution was used because it will always return a positive integer. The 

right-hand tail is not as extreme as a standard normal distribution, so it conforms better to the 

observed distribution of abundance. Parameters for the gamma distribution were estimated from 

OCN recruitments reconstructed from catch data and Stratified Random Survey (SRS) 

escapement estimates applied to the years 1970–1997. The mean of SRS ocean recruits from this 

reconstruction was 176,000 with a CV of 0.84. The gamma distribution was given an alpha of 2, 

corresponding to a CV of 0.7071. This is a fairly pessimistic view of the power of prediction. 

However, the error is applied to the actual recruitment rather than to the mean, and gamma 

distributions with high CVs are skewed to the right, with the median values lower than the mean. 

This results in a predictor that is more often low than high. Over the past 10 years, OCN 

predictors have more often been high than low, so this method of approximating the predictor 

may result in lower modeled risk to the stocks than the actual management regime. 

Spawner estimates 

Administration of the harvest regime specified in Amendment 13 requires estimates of 

spawners in 13 coastal basins. These estimates are generated from spawner surveys in each 

basin. We simulated the spawner survey methodology by specifying a number of sampled 

reaches, based on a 10% sampling rate, in each basin.9 In the model, reaches were sampled at 

random from each basin, and spawners in the sampled reaches were expanded by the sampling 

fraction to estimate total spawners. This captures only the sampling errors, and does not include 

variability in numbers of spawners observed or in other statistical estimates that are applied to 

the survey data. As a result, spawner estimates in the model are more accurate and, potentially, 

less biased than in practice. 

Marine survival predictor 

Predicted marine survival of Columbia River hatchery coho is one of the control 

variables for Amendment 13 management. Marine survival is the ratio of adult recruits per smolt 

released. Adult returns are not available at the time the harvest regulations are set, so an index of 

marine survival is needed. The relationship between jacks per smolt in the Columbia River is 

correlated with adults per smolt. To arrive at an estimate of marine survival for Amendment 13 

management purposes, the PFMC uses a regression of log-transformed jacks per smolt on logtransformed adults per smolt for the years 1970–1996. The fitted regression was: 

 

9

 K. Moore, Oregon Dept. Fish and Wildlife, Corvallis. Pers. commun., 10 February 2005. 

168
ln(adults/smolt) = 2.2725 + 0.9093 × ln(jacks/smolt) (19) 

with r2 = 0.80 and a standard error of 0.4045. Indexed marine survival was estimated, with error, 

from the actual modeled marine survival: 

indexed_marine_survival = exp(ln(marine_survival) + 0.4045 × std_norm) (20) 

where std_norm is a random draw from a standard normal distribution. 

Post-season exploitation rate 

Exploitation rate targets that are set preseason are different from the actual exploitation 

rate that results from the fishery. However, there was no way to separate these fishery 

implementation errors from other sources of error (prediction, spawner estimation) being 

modeling. As a result, a statistical comparison of preseason vs. postseason exploitation rates 

overestimates harvest implementation errors. For this analysis, management implementation 

errors were not modeled. Sensitivity analysis indicated that modeling without this error 

produced a slight increase in modeled viability. The results reported here assume management 

targets are implemented without error (although they may be set incorrectly). Therefore, the 

modeled risk from harvest may be slightly lower than actual risk. 

Management Model 

The management model was implemented as target exploitation rates based on the rules 

set forth in Amendment 13 and the current state of the fishery, estimated with error. It is 

relatively simple in this kind of modeling to capture the harvest formulas. However, managers 

frequently use discretion to alter harvest regimes in a given year. This discretion can include 

variable interpretation of data, opening of terminal fisheries, or even changing predictors. These 

actions are more difficult to model in a risk assessment context; therefore, we have modeled only 

the prescribed regimes. 

Amendment 13 

Amendment 13 specifies an exploitation rate matrix based on parental escapements and 

predicted marine survival. We have only implemented the ocean portion of the amendment; 

there are in-river harvest opportunities that have not been modeled here. This leaves the 

potential for exploitation rates on some stocks to be higher than those modeled. The original 

Amendment 13 exploitation rate harvest rule was modified in a review of the amendment in 

2000. For simplicity the model of Amendment 13 implemented here was restricted to the 

original matrix, but with exploitation rates changed to reflect the newer values. For the purposes 

of risk assessment, this makes very little difference to the outcome because the increased 

complexity of the revisions would largely be balanced by increased implementation error. 

Parental escapement is based on “subaggregates,” geographical groupings of river basins. 

There are three subaggregates in Amendment 13; North Coast, North-Central Coast, and SouthCentral Coast. These differ from the TRT-defined strata. 

169
Amendment 13 was modeled using five escapement levels for management triggers. The 

specific rules modeled were: 

1. Full seeding of high-quality habitat. (Seeding refers to the number of eggs relative to 

the carrying capacity of the habitat.) In-river harvest is not to reduce the run below 

this level, based on predicted run size. 

2. 75% seeding of high-quality habitat. 

• Subaggregate parental escapement at this level triggers exploitation rate of 0.45 if 

predicted marine survival is also high. 

• Subaggregate parental escapement at this level triggers exploitation rate of 0.38 if 

predicted marine survival is medium. 

3. 50% seeding of high-quality habitat. 

• Subaggregate parental escapement at this level triggers exploitation rate of 0.30 if 

marine survival is high. 

• Subaggregate parental escapement at this level triggers exploitation rate of 0.20 if 

marine survival is medium. 

4. 38% seeding of high-quality habitat. 

• Subaggregate parental escapement above this level but below 50% seeding 

triggers exploitation rate of 0.15. 

• Subaggregate parental escapement below this level and low marine survival 

triggers exploitation rate of 0.08. 

• Subaggregate parental escapement below this level and medium or high marine 

survival triggers exploitation rate of 0.15. 

5. 10% seeding of high-quality habitat. Any basin escapement below this level reduces 

parental spawner status for the subaggregate by one level. 

Marine survival was divided into three levels based on the predicted marine survival. 

These are high (ms > 0.05), medium (0.05 ≥ ms > 0.021), and low (ms ≤ 0.21). 

To model Amendment 13 management, spawner estimates were produced for each basin 

and subaggregate. Marine survival was predicted and classified as high, medium, and low. 

Based on these estimates, an allowable exploitation rate for each subaggregate was chosen. The 

subaggregate with the lowest exploitation rate determined the exploitation rate for the aggregate. 

Modeled Scenarios 

A strength of the modeling approach employed here is the ability to explore potential 

effects of different future conditions. We tested different assumptions about future marine 

survival and future trends in habitat quality. Twelve scenarios were modeled using combinations 

of two levels of marine survival (historical average and reduced survival), three trends in habitat 

quality (stationary, increasing, and decreasing), and two levels of harvest (current regulations 

170
and no harvest). Scenarios with no harvest were prefixed “base,” while those with harvest were 

labeled “A13.” Scenarios modeled with and without harvest were: 

• Reference (ref): recent historical marine survival, based on stationary habitat and the 

observation that naturally produced coho salmon survive at a higher rate than OPI area 

hatchery fish. 

• Marine survival (ms): lower marine survival, as if naturally produced coho salmon 

survived at the same rate as hatchery fish or actual marine survival declined. 

• Habitat × 0.8 (80): habitat quality declined exponentially over the simulation to a final 

value 20% lower than starting values. 

• Habitat × 1.2 (120): habitat quality increased exponentially over the simulation to a final 

value 20% higher than starting values. 

• Marine survival and habitat × 0.8 (ms_80): lower marine survival and declining habitat. 

• Marine survival and habitat × 1.2 (ms_120): lower marine survival and improving 

habitat. 

Extinction Thresholds 

Populations at extremely low abundances or spawner densities are thought to depart from 

classical density-dependent population dynamics (Dennis 1989). At low abundance, random 

events become more important, so that a single mortality event such as a landslide or toxic spill 

may affect most or all of a small population. At low densities adult fish may not find each other 

on the spawning grounds. This results in “depensatory” population dynamics, where the 

population becomes less productive and more variable as numbers drop. While this model 

attempts to capture some of these low-density dynamics, uncertainty remains. 

PVA frequently specifies a threshold below which the population, if not technically 

extinct, has ceased to function as a population and is functionally extinct. Because of the 

difficulty (and undesirability) in making observations as populations spiral toward extinction, 

there is much uncertainty in specifying these “QETs.” We report results for three QETs: zero 

fish, 50 fish, and 1 fish per mile of spawning habitat as specified in Chilcote et al. (2005). In 

addition, the effect of choosing a range of QETs was examined. 

A QET of 50 fish is somewhat standard in the viability literature. However, smaller 

basins are expected to have smaller populations under normal circumstances, and some of the 

smaller Independent Populations on the Oregon Coast may be expected to fall below 50 

spawners at times. At the same time, a large population reduced to 50 spawners would be a 

serious situation. Results from this model and analysis by Barrowman et al. (2003) suggest that 

at densities of 1 fish per mile, depensatory dynamics are apt to occur. We explored the use of 

one fish per mile as a QET. 

Each scenario was modeled with 1,000 Monte Carlo simulations of 33 generations. If at 

any time during the simulation the number of spawners dropped below a specified QET, the 

population was counted as “extinct.” Once a population went extinct, as defined by QET, it was 

considered to remain extinct through the duration of a single 33-generation simulation. 

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Results 

Spawner abundance over the 33 generations of the model runs showed the cyclical 

pattern of marine survival (Figure C-8) for the reference run with and without Amendment 13 

harvest. Variability was quite large, indicating the natural variability of these populations as well 

as our uncertainty in their dynamics. Probabilities of persistence in 100 years for the base and 

Amendment 13 runs are reported in Table C-7 and Table C-8, respectively. Persistence 

probabilities were sensitive to all factors tested, including QET, habitat, marine survival, and 

harvest. Umpqua and South Coast populations showed generally higher persistence probabilities 

than North-central or North Coast populations. Necanicum and Salmon populations had the 

basins with the lowest persistence values. A QET of 50 substantially decreased persistence 

values compared with a QET of 0, and the 1 fish per mile criterion was the most severe. 

Decrease in probability of persistence was about equal for the habitat degradation (80) and lower 

marine survival (ms) scenarios, with marine survival having a slightly greater effect. As 

expected, the combined effect of 80 and marine survival was greater than either alone, while the 

habitat improvement scenario (120) provided some increase in persistence probability. The 

Amendment 13 harvest regime had roughly the same effect as the habitat degradation scenario 

(80). These patterns are also evident in Figure C-9, a box plot comparison of the number of 

spawners in the final generation for the 12 scenarios. 

Sensitivity to Quasi-extinction Threshold 

The QET is intended to reflect the uncertainty and increased risk of very low abundances. 

Selection of a QET depends on our knowledge (or lack thereof) of population dynamics at low 

densities, how well the model captures these dynamics, and how willing the workgroup is to take 

the risk of failing to identify resulting extinction events. The sensitivity of the model used here 

to the value selected for QET was tested by assessing the output of two scenarios (base_ref and 

A13_ms_80) using a range of QETs. 

For each population, the probability of quasi-extinction (pQE) was calculated for QETs 

of 0, 50, 100, 200, 400, 800, 1,600, and 1 fish per mile (1 fpm) (Figure C-10, Figure C-11; Table 

C-7, and Table C-8). For all populations, pQE rose rapidly as QET increased, leveling off at 

pQE = 1. The shape of the curves was approximately logistic; steep in the middle and flat on the 

ends. The rate of increase of pQE was dependent on population size, with smaller populations 

being much more sensitive to small increases in the threshold. The 1 fpm criterion, by 

definition, always placed QET at or above 50 fish. Larger basins had more gradually sloping 

curves, but higher QETs led to higher pQEs under this criterion. 

Conclusion 

Assessment of extinction risk or, conversely, persistence probability, depends on 

assumptions about present and future conditions, and on assumptions about population dynamics 

at low abundances or densities. This modeling effort evaluated the persistence probabilities of 

Oregon Coast Coho Salmon ESU riverine Independent Populations using a habitat-based 

stochastic simulation model. Relative effects of harvest, habitat changes (improvement or 

degradation), and marine survival were assessed, as was the selection of a QET. One population, 

the Necanicum, was at high risk in most scenarios. This small system has some intact wetland 

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173

Spawners (x 1,000) Spawners (x 1,000) 

Generation 

Figure C-8. Box-plot time series of median spawner abundance for 34 generations. The boxes are 25th to 

75th percentile, the whiskers are 10th and 90th percentile, and dots are 5th and 95th percentile. 

The upper graph (base_ref) shows abundance without harvest. The lower graph (A13_ref) shows 

results with Amendment 13 harvest regulations. This figure illustrates the lower escapements 

expected as a result of harvest. 
Table C-7. Probabilities of persistence under no harvest (base) conditions for riverine Independent Populations of the Oreg

ESU for five scenarios of future conditions using three QETs. Scenarios include current conditions (ref), 20% hab20% habitat degradation (80), lower marine survival (ms), lower marine survival with habitat improvement (ms_12survival with habitat degradation (ms_80). QETs are 0 fish (0), 50 fish (50) and 1 fish per mile (fpm). Bold numb

< 90% probability of persistence. 

base_ref base_120 base_80 base_ms base_ms_120 

0 50 fpm 0 50 fpm 0 50 fpm 0 50 fpm 0 50 fpm 

Necanicum 97.4 68.7 68.7 99.0 75.8 75.8 96.1 51.6 51.6 90.6 23.5 23.5 93.2 39.2 39Nehalem 100.0 100.0 96.9 99.9 99.9 97.8 100.0 100.0 94.0 100.0 99.3 84.4 100.0 99.7 90

Tillamook 99.5 98.6 93.0 99.8 99.3 95.8 99.7 98.0 86.9 98.6 91.1 71.2 99.0 95.1 82

Nestucca 100.0 99.0 97.4 100.0 99.8 97.8 99.8 98.9 95.1 99.5 95.3 86.8 99.9 97.0 92

Salmon 100.0 96.4 96.4 100.0 97.1 97.1 100.0 93.8 93.8 99.8 85.4 85.4 99.8 90.9 90

Siletz 100.0 99.6 97.3 99.9 99.8 97.7 100.0 99.7 94.9 99.8 98.1 86.0 100.0 99.1 92Yaquina 100.0 99.9 99.4 100.0 99.9 99.8 100.0 100.0 99.6 100.0 99.6 98.3 100.0 99.9 99

Beaver 100.0 98.2 98.2 100.0 98.8 98.8 100.0 96.1 96.1 100.0 91.7 91.7 100.0 94.9 94

Alsea 100.0 99.9 99.1 100.0 99.9 99.8 100.0 99.9 99.0 100.0 99.6 97.1 100.0 99.8 97

Siuslaw 100.0 100.0 98.0 100.0 99.9 98.7 100.0 100.0 96.5 100.0 99.9 91.4 100.0 99.9 95

Lower Umpqua 100.0 99.9 98.7 100.0 99.9 99.2 99.9 99.9 98.0 99.9 99.8 93.7 100.0 99.8 95

Middle Umpqua 100.0 99.9 99.6 100.0 99.9 99.9 100.0 99.9 99.9 100.0 99.6 98.1 100.0 99.8 99

North Umpqua 100.0 99.2 99.2 100.0 99.9 99.9 100.0 99.4 99.4 100.0 98.1 98.1 100.0 98.8 98

South Umpqua 100.0 99.9 99.4 100.0 99.9 99.8 100.0 99.9 99.4 100.0 99.6 97.9 100.0 99.8 98

Coos 100.0 100.0 99.6 100.0 100.0 99.9 100.0 100.0 99.8 100.0 100.0 99.0 100.0 100.0 99

Coquille 100.0 99.9 98.7 100.0 99.9 99.5 100.0 99.9 98.5 100.0 99.7 95.5 100.0 99.9 96

Floras 100.0 98.8 98.8 100.0 99.6 99.6 100.0 98.8 98.8 100.0 96.7 96.7 100.0 97.4 97

Sixes 100.0 98.7 98.7 100.0 99.2 99.2 100.0 98.1 98.1 100.0 94.5 94.5 100.0 96.4 96

Total 100.0 100.0 98.8 100.0 100.0 99.8 100.0 100.0 98.6 100.0 100.0 95.1 100.0 100.0 96

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175

Table C-8. Probabilities of persistence under PFMC Amendment 13 harvest (A13) conditions for riverine Independent Pop

Coast Coho Salmon ESU for five scenarios of future conditions using three QETs. Scenarios include current condimprovement (120), 20% habitat degradation (80), lower marine survival (ms), lower marine survival with habitat and lower marine survival with habitat degradation (ms_80). QETs are 0 fish (0), 50 fish (50) and 1 fish per mile (highlight areas with less than 90% probability of persistence. 

A13_ref A13_120 A13_80 A13_ms A13_ms_120 

0 50 fpm 0 50 fpm 0 50 fpm 0 50 fpm 0 50 fpm 

Necanicum 92.3 18.3 18.3 94.6 32.3 32.3 88.3 8.8 8.8 71.6 2.6 2.6 77.0 5.6 5Nehalem 100.0 99.5 90.4 100.0 99.7 92.8 100.0 99.5 84.2 99.4 97.8 62.0 99.8 99.1 72

Tillamook 99.1 92.9 74.4 99.3 95.0 83.7 97.8 87.1 59.5 91.7 68.1 35.6 96.4 77.5 47

Nestucca 99.9 96.7 90.1 100.0 98.4 93.8 99.8 94.8 85.1 97.9 83.4 66.9 99.1 89.4 76

Salmon 99.9 90.7 90.7 99.9 94.3 94.3 99.9 85.4 85.4 98.4 70.5 70.5 99.6 80.0 80

Siletz 99.9 99.1 92.8 100.0 99.4 95.3 99.9 98.9 87.9 99.0 95.8 72.7 99.9 97.2 81

Yaquina 100.0 99.9 99.1 100.0 100.0 99.3 100.0 99.8 98.3 99.6 98.4 95.8 100.0 99.4 97

Beaver 100.0 95.4 95.4 100.0 96.7 96.7 100.0 93.7 93.7 99.5 85.8 85.8 100.0 89.4 89

Alsea 100.0 99.8 98.2 100.0 99.9 98.9 100.0 99.8 97.0 99.4 98.3 91.1 100.0 99.5 94

Siuslaw 100.0 99.8 95.0 100.0 100.0 96.5 100.0 99.8 92.2 99.8 98.5 80.9 100.0 99.6 86

L. Umpqua 100.0 99.8 96.5 100.0 99.8 98.0 100.0 99.7 94.0 99.4 98.1 85.1 99.8 99.3 90

M. Umpqua 100.0 99.8 99.2 100.0 99.9 99.4 100.0 99.9 98.6 99.4 98.1 96.3 100.0 99.1 97

N. Umpqua 100.0 99.0 99.0 100.0 99.0 99.0 100.0 98.5 98.5 99.4 95.2 95.2 99.9 97.2 97

S. Umpqua 100.0 99.9 99.1 100.0 99.9 99.1 100.0 99.7 97.9 99.4 98.1 94.5 99.9 99.0 96

Coos 100.0 100.0 99.7 100.0 100.0 99.6 100.0 100.0 99.7 99.9 99.6 97.9 100.0 100.0 99Coquille 100.0 99.9 97.6 100.0 100.0 98.4 100.0 99.8 95.8 99.4 99.0 91.3 100.0 99.6 93

Floras 100.0 98.2 98.2 100.0 98.8 98.8 100.0 96.3 96.3 99.8 93.9 93.9 100.0 95.6 95

Sixes 100.0 97.3 97.3 100.0 98.0 98.0 100.0 95.3 95.3 99.9 88.9 88.9 100.0 93.7 93

Total 100.0 100.0 97.7 100.0 100.0 98.4 100.0 100.0 95.9 99.9 99.9 89.4 100.0 100.0 92
Spawners (x 1,000) 

Scenario 

Figure C-9. Box-plot comparison of 12 model scenarios. These comparisons are grouped as base (no 

harvest) and A13 (harvest) scenarios. Scenarios are described in Modeled Scenarios, page 170. 

Spawner axis (y-axis) is a log scale. 

176
Figure C-10. Sensitivity of the base_ref scenario to assumption of QETs. Lines represent 18 Independent 

Populations. Dots show status relative to the 1 fpm threshold. Miles are in parentheses following 

basin names in legend. The lower graph shows a subset of the upper graph: the area near the 

origin. 

177
Figure C-11. Sensitivity of the A13_ms_80 scenario to assumption of QETs. Lines represent 18 

functional populations. Dots show status relative to the 1 fpm threshold. Miles are in parentheses 

following basin names in legend. The lower graph shows a subset of the upper graph: the area 

near the origin. 

178
habitat that was not represented in the model and would be likely to improve this population’s 

status. Other small systems were also more vulnerable to extinction or quasi-extinction than 

larger systems. 

Three factors were tested for effect on extinction probabilities: habitat change, decreased 

marine survival, and harvest; all had approximately equal levels of risk for the populations. This 

is the only model that includes an analysis of the Amendment 13 harvest regime that has been 

adopted by the PFMC as posing minimal risk to the viability of the Oregon Coast Coho Salmon 

ESU. However, this analysis shows that even conservative harvest management, by itself, can 

pose a substantial risk. The other PVA models do not include this factor and therefore will 

underestimate overall risk. 

Extinction risk in this model is very sensitive to assumptions about marine survival. If 

marine survival is good it seems there is little risk to the ESU. However, if current projections 

for warmer temperatures and accompanying lower marine survival are borne out, there could be 

serious implications for the status of the ESU. 

Habitat quality and quantity are the most visible factor affecting the Oregon Coast Coho 

Salmon ESU. While there are concerted efforts to protect and restore habitat, there is also strong 

pressure to relax land use regulations. This model shows that extinction risk for this ESU is 

directly tied to habitat. This is where restoration opportunities are greatest and where lasting and 

long-term harm can be inflicted. 

Given that harvest will continue, if marine conditions continue to degrade and habitat 

issues are not dealt with effectively, the status of this ESU could worsen. Conversely, with 

effective habitat protection and restoration activities and effective land use planning, the ESU 

could improve in status even given the pressures of harvest and declining marine survival. 

179
180
Appendix D: Population Abundance Data and 

Historical Reconstructions 

Contemporary Population Data for 1990–2004 

Estimation of Abundance of Coho Salmon for Independent Populations 

This appendix explains the methods of estimating populations abundance used in 

population productivity and spawner abundance analyses. Abundances of Independent 

Populations were estimated by expanding area-under-the-curve (AUC) estimates of spawner 

abundance in randomly selected survey sites (Jacobs and Nickelson 1998, Jacobs et al. 2002). 

Table D-1 presents the abundance estimates used in analyses in this report. The following notes 

describe differences in estimation methods from those described by Jacobs et al. (2002). 

Necanicum 

In past estimates of spawner abundance, the Necanicum and Ecola populations were 

combined into one estimate. For this assessment, the total abundance of adult coho salmon 

(Oncorhynchus kisutch) spawners in the Necanicum population was estimated independently by 

multiplying mean spawners per mile in random surveys by a stream length of 59.8 miles of 

spawning habitat. The number of wild fish each year was estimated by multiplying the total 

population by one minus the proportion of hatchery fish observed on the spawning grounds. 

Nehalem 

Because the preponderance of stray hatchery fish are found in the North Fork Nehalem 

River (the location of Nehalem Hatchery), AUC estimates for individual random survey reaches 

were adjusted for the different percentage of hatchery fish observed above and below the mouth 

of the North Fork. Estimates of mean adult spawners per mile were made for total fish before 

making the hatchery adjustment and for wild fish after making the adjustment. In each case, the 

population was estimated by multiplying mean spawners per mile by a stream length of 434 

miles of spawning habitat. 

Tillamook Bay 

Because the preponderance of stray hatchery fish are found in the Trask River (the 

location of the hatchery), AUC estimates for individual random survey reaches were adjusted for 

the different percentage of hatchery fish observed in the Trask and the rest of the basin (i.e., 

Miami, Kilchis, Wilson, and Tillamook rivers combined). Estimates of mean adult spawners per 

mile were made for total fish before making the hatchery adjustment and for wild fish after 

making the adjustment. In each case, the population was estimated by multiplying mean 

spawners per mile by a stream length of 249 miles of spawning habitat. 

181
Table D-1. Population estimates for Independent Populations in the Oregon Coast Coho Salmon 

Evolutionarily Significant Unit (ESU) based on random spawning surveys. 

Necanicum Nehalem Tillamook Bay Nestucca 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery

1990 126 84 1,158 5,464 80 145 160 29 

1991 752 501 6,837 3,012 1,577 1,498 618 110 

1992 133 89 1,392 2,373 176 142 604 80 

1993 512 342 3,049 14,014 571 1,146 340 61 

1994 269 179 2,844 2,712 1,105 817 266 47 

1995 181 120 1,700 2,118 341 755 1,537 274 

1996 416 277 527 3,766 733 246 440 79 

1997 97 64 1,187 1,351 437 44 230 41 

1998 575 383 1,206 51 358 26 202 36 

1999 351 19 3,555 600 1,831 147 2,357 0 

2000 359 19 14,462 118 2,178 299 1,219 50 

2001 4,832 280 21,928 414 1,944 175 4,164 207 

2002 2,047 96 17,164 698 13,334 373 16,698 55 

2003 2,377 158 32,517 284 13,008 121 10,194 109 

2004 2,262 145 20,471 95 3,975 66 6,402 100 

Salmon Siletz Yaquina Beaver 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery

1990 19 415 228 213 318 63 90 0 

1991 5 34 410 575 317 63 484 0 

1992 11 132 2,386 954 528 105 618 0 

1993 13 351 207 193 458 91 275 0 

1994 91 1,463 621 579 2,040 408 675 0 

1995 105 1,220 314 293 4,723 945 308 0 

1996 82 2,621 395 368 4,578 1,526 1,296 405 

1997 16 401 298 38 419 110 497 147 

1998 86 346 316 41 510 134 401 119 

1999 14 159 1,209 155 2,563 4 1,511 0 

2000 179 215 3,387 0 637 1 1,464 46 

2001 225 652 1,595 859 3,589 171 1,832 282 

2002 543 565 2,129 375 23,800 0 3,217 143 

2003 42 1,696 8,038 383 16,484 0 5,552 0 

2004 1,758 2,014 5,612 0 5,293 108 3,451 0 

182
Table D-1 continued. Population estimates for Independent Populations in the Oregon Coast Coho 

Salmon ESU based on random spawning surveys. 

Alsea Siuslaw Siltcoos Tahkenitch 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery

1990 775 422 2,269 674 1,578 44 1,085 0 

1991 1,011 550 2,808 1,434 2,868 27 1,215 0 

1992 6,273 2,214 3,554 1,717 385 6 317 1 

1993 694 377 4,600 1,278 3,569 53 954 0 

1994 828 451 3,159 1,904 1,302 124 1,056 6 

1995 441 240 6,161 1,406 4,415 82 1,577 50 

1996 1,060 577 7,234 4,136 4,707 68 1,627 0 

1997 601 327 501 174 2,653 0 1,842 16 

1998 108 1,624 1,020 362 3,122 0 2,817 0 

1999 1,341 730 2,980 213 2,756 63 3,664 105 

2000 3,363 0 6,532 0 3,835 0 634 0 

2001 3,228 692 10,606 0 5,104 0 3,510 16 

2002 9,073 181 55,445 250 4,636 113 3,480 7 

2003 10,281 0 29,003 56 6,628 0 3,188 15 

2004 6,328 0 9,036 0 8,025 0 3,496 0 

Lower Umpqua Middle Umpqua North Umpqua South Umpqua 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery

1990 1,678 95 1,222a 83a 355 1,258 2,934 530 

1991 3,123 0 4,546a 308a 1,301 2,430 2,233 992 

1992 1,797 0 5,275a 358a 1,579 3,170 435 79 

1993 7,877 161 2,947a 200a 906 1,513 3,723 673 

1994 2,762 156 2,162 147 899 990 1,081 1,442 

1995 10,854 0 3,250 0 1,293 1,756 4,715 555 

1996 7,985 450 5,086 345 1,069 3,743 7,040 7,040 

1997 1,257 188 563 38 577 1,379 937 187 

1998 4,552 0 1,257 79 765 3,379 3,177 1,430 

1999 2,623 85 1,748 166 1,194 1,979 3,011 198 

2000 5,781 115 4,555 164 1,677 7,585 2,581 329 

2001 11,639 1,233 8,940 877 2,634 14,094 11,871 1,554 

2002 18,881 906 10,738 931 3,368 6,695 10,517 0 

2003 16,494 35 11,090 0 2,862 8,884 4,337 0 

2004 9,778 70 5,668 52 4,025 6,836 12,077 454 

183
Table D-1 continued. Population estimates for Independent Populations in the Oregon Coast Coho 

Salmon ESU based on random spawning surveys. 

Tenmile Coos Coquille 

Year Wild Hatchery Wild Hatchery Wild Hatchery 

1990 1,687 0 2,243 30 2,589 123 

1991 3,033 108 2,426 1,387 4,782 869 

1992 1,271 6 16,722 257 2,033 82 

1993 5,544 25 14,932 1,140 7,291 260 

1994 3,354 0 14,500 707 5,119 0 

1995 5,092 0 10,302 145 2,034 82 

1996 7,092 0 12,128 0 15,814 355 

1997 4,092 0 1,112 15 5,720 0 

1998 5,169 0 2,985 0 2,412 0 

1999 6,123 0 4,818 0 2,667 0 

2000 8,278 0 4,704 0 6,253 0 

2001 10,990 49 33,595 664 13,833 1,832 

2002 13,861 0 33,120 145 7,676 190 

2003 6,260 0 25,761 189 22,403 162 

2004 7,148 18 22,046 106 22,408 42 

Floras/New Sixes 

Year Wild Hatchery Wild Hatchery

1990 NAb NA 58 5 

1991 NA NA 35 3 

1992 NA NA 92 8 

1993 NA NA 253 23 

1994 2,653 240 238 22 

1995 1,351 400 77 7 

1996 1,519 109 194 17 

1997 482 43 143 13 

1998 879 79 558 50 

1999 670 60 56 5 

2000 1,477 0 136 12 

2001 5,664 88 95 182 

2002 3,272 296 95 9 

2003 952 86 86 8 

2004 5,069 0 491 0 

a Estimate based on the relationship between Winchester Dam counts and abundance in Middle Umpqua (see 

Umpqua River subsection on Page 185). 

b

 NA = no estimate available. 

184
Beaver 

In past estimates of spawner abundance, the Beaver and Devils Lake populations were 

combined into one estimate. For this assessment, the total abundance of adult coho salmon 

spawners in the Beaver population was estimated independently by multiplying mean spawners 

per mile in random surveys (except in years with less than three samples when the standard 

survey was also used to calculate the mean) by a stream length of 22 miles of spawning habitat. 

The number of wild fish each year was estimated by multiplying the total population by one 

minus the proportion of hatchery fish observed on the spawning grounds. 

Siuslaw 

Because the preponderance of stray hatchery fish were found in the Lake Creek subbasin 

(the location of hatchery releases) in 1990–1998, AUC estimates for individual random survey 

reaches for those years were adjusted for the different percentage of hatchery fish observed in the 

Lake Creek subbasin and the rest of the basin. Estimates of mean adult spawners per mile were 

made for total fish before making the hatchery adjustment and for wild fish after making the 

adjustment. In each case, the population was estimated by multiplying mean spawners per mile 

by a stream length of 502 miles of spawning habitat. For 1999–2003, the standard method was 

used to estimate total abundance, which was then adjusted for hatchery fish.

Umpqua River 

Past estimates of spawner abundance have not divided the Umpqua River basin into the 

same four populations that are identified in this report: the Lower Umpqua, Middle Umpqua, 

North Umpqua, and South Umpqua. Therefore new estimates were made for these populations. 

Lower Umpqua: Annual estimates of mean spawner density (fish per mile) in random 

surveys were multiplied by 335 miles of spawning habitat. The number of wild fish each year 

was estimated by multiplying the total population by one minus the proportion of hatchery fish 

observed on the spawning grounds. 

Middle Umpqua: Annual estimates of mean spawner density (fish per mile) in random 

surveys were multiplied by 375 miles of spawning habitat. The number of wild fish each year 

was estimated by multiplying the total population by one minus the proportion of hatchery fish 

observed on the spawning grounds. 

North Umpqua: Abundance estimates for the North Umpqua population are based on 

counts of fish at Winchester Dam. Marked hatchery fish counted at the dam were expanded by 

mark rates to estimate passage of hatchery fish. These estimates were subtracted from the total 

to estimate wild fish. Hatchery and wild fish removed by the hatchery and the fishery were 

subtracted from their respective totals in years where such data was available. 

South Umpqua: Annual estimates of mean spawner density (fish per mile) in random 

surveys were multiplied by 612 miles of spawning habitat. The number of wild fish each year 

was estimated by multiplying the total population by one minus the proportion of hatchery fish 

observed on the spawning grounds. 

185
The Lakes 

Estimates of abundance of coho salmon for the Siltcoos, Tahkenitch, and Tenmile 

populations were calculated from standard spawner surveys following the methods described by 

Jacobs et al. (2002). 

Floras/New 

Spawning surveys for coho salmon were not conducted in the Floras/New (Fourmile) 

basin until 1994, when supplemental surveys began in several stream reaches. These reaches 

have been surveyed since 1994 on a hit-or-miss basis. Beginning in 1997, three to four randomly 

selected reaches have been surveyed each year in addition to the supplemental reaches. AUC 

data from all survey reaches were used to calculate mean spawner density (fish per mile), which 

was multiplied by 26.5 miles of spawning habitat to estimate the annual population. Since 1994, 

16–30% of the Floras/New population’s spawning habitat has been surveyed annually. 

Sixes 

Prior to 1994, when supplemental spawning surveys began, coho salmon data is available 

from two standard Chinook salmon (Oncorhynchus tshawytscha) surveys on Dry Creek that 

make up 11% of the coho salmon spawning habitat in the basin. Since 1994, five supplemental 

surveys have been surveyed in most years. In addition, during 1997–2002, a total of one to two 

random surveys were conducted in the Sixes Basin. AUC data from all survey reaches were 

used to calculate mean spawner density (fish per mile), which was multiplied by 32 miles of 

spawning habitat to estimate the annual population. 

Estimation of the Proportion of Hatchery Spawners 

The hatchery programs affecting the various populations went through changes in smolt 

releases of varying degrees midway through the 1990–2004 period. These changes affected the 

proportions of hatchery fish observed on the spawning grounds. Data on naturally spawning 

hatchery fish was collected from carcasses collected in random survey reaches. For 1990–1997, 

estimates of hatchery fish were based on interpretation of scale patterns (e.g., Borgerson 1999). 

For 1998–2004, estimates of hatchery fish were based on the number of carcasses that were fin 

marked, adjusted by the mark rate of the nearest hatchery. A minimum of 10 observations per 

population per year was used to estimate the proportion of hatchery spawners. For years with 

less than 10 observations, an average based on years with similar magnitude of smolt releases 

was applied. Details for each population follow. 

Necanicum 

Samples were inadequate to make an annual estimate of the proportion of hatchery 

spawners in each year from 1990 to 2000. For 1990–1998, the proportion of all observations 

during the period was applied to each year. Because production from Nehalem Hatchery (the 

nearest coastal hatchery) was greatly reduced beginning with 1999 returns, the proportion of 

hatchery fish in all observations during 1999–2003 was applied to 1999 and 2000. Marks 

observed in 1998–2003 were expanded for proportion of hatchery fish marked based on data 

from Nehalem Hatchery. 

186
Nehalem 

In the Lower Nehalem, sample sizes were inadequate in 1998–2000. As with the 

Necanicum, the proportion of hatchery fish in all observations during 1990–1998 was used for 

1998, and the proportion of hatchery fish in all observations during 1999–2003 was used for 

1999 and 2000. 

The Upper Nehalem was more complicated because in 1990, 1993, and 1996, hatchery 

fish returned to Fishhawk Lake. Scales determined to be hatchery fish that were found in the 

vicinity of Fishhawk Lake in other years were reclassified as wild fish because lake-reared fish 

can produce a scale pattern similar to hatchery fish. Sample sizes were inadequate in 1990 and 

1994–1996. The proportion of hatchery fish in all observations during 1990, 1993, and 1996 was 

used for 1990 and 1996. The proportion of hatchery fish in all observations during 1991, 1992, 

1994, 1995, 1997, and 1998 was used for 1994 and 1995. Marks observed beginning in 1998 

were expanded based on the proportion of hatchery fish marked at Nehalem Hatchery. 

Tillamook Bay 

In the Trask subbasin, sample sizes were inadequate in 1990–1992, 1998, 1999, and 

2001. Because the Trask Pond production was eliminated after 1995 returns, the proportion of 

hatchery fish in all observations during 1990–1995 was used for 1990–1992, and the proportion 

of hatchery fish in all observations during 1996–2003 was used for 1998, 1999, and 2001. 

Marks observed beginning in 1998 were expanded based on the proportion of hatchery fish 

marked at Trask Hatchery. 

In the remainder of the Tillamook Bay basin, sample sizes were inadequate in all years 

from 1990 to 1998, except 1994 and 1996. As with the Trask subbasin, the proportion of 

hatchery fish in all observations during 1990–1995 was used for 1990–1993 and 1995, and the 

proportion of hatchery fish in all observations during 1996–2003 was used for 1997 and 1998. 

Marks observed beginning in 1998 were expanded based on the proportion of hatchery fish 

marked at Trask Hatchery. 

Salmon 

Samples sizes were inadequate to make an annual estimate of the proportion of hatchery 

spawners in only 1995 and 1999. The proportion of hatchery fish in all observations during 

1990–1999 was used for both years. Smolt releases into the Salmon River from Salmon River 

Hatchery was reduced beginning with 2000 returns. Marks observed in 1998–2003 were 

expanded for the proportion of hatchery fish marked based on data from Salmon River Hatchery. 

Siletz 

Sample sizes were inadequate in all years from 1990 to 1999, except 1991 and 1992. 

Because of changes in the release of hatchery coho salmon into the Siletz River, the proportion 

of hatchery fish in all observations during 1990–96 was used for 1990 and 1993–1996, and the 

proportion of hatchery fish in all observations during 1997–2003 was used for 1997–1999. 

Marks observed in 1998–2003 were expanded for the proportion of hatchery fish marked based 

on data from fish released into the Siletz River. 

187
Yaquina 

Sample sizes were inadequate in all years from 1990 to 2000, except 1996. During this 

period, hatchery fish returned in 1996–1998 from releases into Yaquina Bay. Because of 

changes in the release of hatchery coho salmon into the Yaquina River, the proportion of 

hatchery fish in all observations during 1990–1995 was used for 1990–1995, the proportion of 

hatchery fish in all observations during 1996–1998 was used for 1997 and 1998, and the 

proportion of hatchery fish in all observations during 1999–2003 was used for 1999 and 2000. 

Marks observed in 1998–2003 were expanded for the proportion of hatchery fish marked based 

on data from fish released into the Yaquina River (1998) or the Siletz River (2001). 

Beaver 

Returns of hatchery fish released in the Yaquina River influence the proportion of 

hatchery fish found in Beaver Creek. Sample sizes were inadequate in all years from 1990 to 

2000, except 1996 and 1999. Because of changes in the release of hatchery coho salmon into the 

Yaquina River, the proportion of hatchery fish in all observations during 1990–1995 was used 

for 1990–1995, the proportion of hatchery fish in all observations during 1996–1998 was used 

for 1997 and 1998, and the proportion of hatchery fish in all observations during 1999–2003 was 

used for 2000. Marks observed in 1998–2003 were expanded for the proportion of hatchery fish 

marked based on data from fish released into the Yaquina River (1998) or the Siletz River 

(1999–2003). 

Alsea 

Sample sizes were inadequate in all years from 1990 to 1999, except 1992 and 1998. The 

release of hatchery coho salmon into the Alsea River was greatly reduced in 1998 brood, then 

terminated in 1999. Therefore, the proportion of hatchery fish in all observations during 1990–

1998 was used for 1991 and 1993–1997. Marks observed in 1998–2003 were expanded for the 

proportion of hatchery fish marked based on data from fish released into the Alsea River (1998–

1999) or the Siletz River (2000–2003). 

Siuslaw 

In the Lake Creek subbasin, sample sizes were inadequate in 1990–1993, 1997, and 1998. 

Hatchery fish returned in 1991–1998 from releases into Lake Creek. The proportion of hatchery 

fish in all observations during 1990–1998 was used for 1990–1993, 1997, and 1998. 

In the remainder of the Siuslaw Basin, sample sizes were inadequate from 1990 to 1998, 

except 1991 and 1996. The proportion of hatchery fish in all observations during 1990–1998 

was used for 1990, 1992–1995, 1997, and 1998. 

The percent of hatchery spawners in 1999–2003 were based on recoveries of carcasses in 

the entire basin. Marks observed in 1998–2003 were expanded for the proportion of hatchery 

fish marked based on the average mark rates for hatchery fish on the Oregon Coast each year. 

188
Umpqua River Populations 

Lower Umpqua: Sample sizes were inadequate in 1990, 1994, and 1996. The 

proportion of hatchery fish in all observations during 1990–2001 was used for 1990, 1994, and 

1996. Marks observed in 1998–2003 were expanded for the proportion of hatchery fish marked 

based on data from fish released from Rock Creek Hatchery. 

Middle Umpqua: Sample sizes were inadequate in all years from 1990 to 1997, except 

1995. The proportion of hatchery fish in all observations during 1990–2001 was used for 1990–

1994, 1996, and 1997. Marks observed in 1998–2003 were expanded for the proportion of 

hatchery fish marked based on data from fish released from Rock Creek Hatchery. 

North Umpqua: See Middle Umpqua above.

South Umpqua: Sample sizes were inadequate in 1990, 1992, and 1993. The proportion 

of hatchery fish in all observations during 1990–2001 was used for 1990, 1992, and 1993. 

Marks observed in 1998–2003 were expanded for the proportion of hatchery fish marked based 

on data from fish released from Rock Creek Hatchery. 

The Lakes 

Very few fish thought to be of hatchery origin have been observed in the lake populations 

and there is a strong possibility that some of those were actually wild fish with a scale pattern 

similar to hatchery fish. The proportion of hatchery fish in all observations during 1990–2003 in 

the Siltcoos Basin was used for 1992. The proportion of hatchery fish in all observations during 

1990–2003 in the Tahkenitch Basin was used for 1992, 2000, 2001, and 2003. The proportion of 

hatchery fish in all observations during 1990–2003 in the Tenmile Lakes Basin was used for 

1992 and 1993. Marks observed in 1998–2003 were expanded for the proportion of hatchery 

fish marked based on the average mark rates for hatchery fish on the Oregon Coast each year. 

Coos 

Sample sizes were inadequate in 1990 and 1997. The proportion of hatchery fish in all 

observations during 1990–2003 was used for 1990 and 1997. Marks observed in 1998–2003 

were expanded for the proportion of hatchery fish marked based on data from fish released from 

Noble Creek Salmon and Trout Enhancement Program Hatchery. 

Coquille 

Sample sizes were inadequate in 1992 and 1995. Releases of hatchery coho salmon were 

reduced beginning with the 1994 brood. Therefore, the proportion of hatchery fish in all 

observations during 1990–1996 was used for 1992 and 1995. Marks observed in 1998–2003 

were expanded for the proportion of hatchery fish marked based on data from fish released in the 

Coquille River. 

189
Floras/New 

Sample sizes were inadequate in all years from 1990 to 2003, except 1995, 1996, 2000, 

and 2001. Because hatchery fish were not released in the basin during this period, the proportion 

of hatchery fish in all observations during 1990–2003 was used for 1990–1994, 1997–1999, 

2002, and 2003. Marks observed in 1998–2003 were expanded for the proportion of hatchery 

fish marked based on data from fish released in the Coquille River. 

Sixes 

Sample sizes were inadequate in all years from 1990 to 2003, except 2001. Because 

hatchery fish were not released in the basin during this period and the data is limited to the four 

most recent years, the proportion of hatchery fish in all observations in the Floras Basin during 

1990–2003 was used for all years except 2001, the only year hatchery fish were observed. 

Marks observed in 1998–2003 were expanded for the proportion of hatchery fish marked based 

on data from fish released in the Coquille River. 

Reconstruction of Historical Population 

Abundance for 1958–1989 

Estimation of Abundance of Coho Salmon for Independent Populations 

Annual abundance during the period 1958–89 was estimated for 18 of the 21 Independent 

Populations that make up the Oregon Coast Coho Salmon ESU. Estimates were not made for the 

Salmon, Floras/New, and Sixes populations because data were not available. Also, it was not 

possible to make estimates for every year for some populations due to a lack of data. The 

primary method of estimation was to calibrate the total abundance estimate for a population (see 

Contemporary Population Data for 1990–2004 on page 181) to the average peak counts in 

standard spawner surveys found in the range of the population by fitting a linear regression with 

a zero intercept (Table D-2). The calibrations explained 38–92% of the variation in abundance 

during 1990–2003. In the case of the lake populations, methods described by Jacobs et al. 

(2002) were used. Abundance of the North Umpqua population was based on counts at 

Winchester Dam. Abundance of the Middle Umpqua and South Umpqua populations were 

calibrated to Winchester Dam counts. The proportion of hatchery fish in recent years was 

applied to the estimated total abundance each year by population as described below for each 

population. This likely resulted in an overestimate of the proportion of hatchery fish spawning 

naturally in coastal streams during the first few years, as hatchery production was not as 

successful prior to improvements in rearing procedures implemented in the early 1960s (ODFW 

1981). Table D-3 presents the abundance estimates used in analyses in this report. Notes on 

individual populations follow. 

Necanicum 

Standard spawning surveys have been conducted in the Necanicum Basin only since 1981 

and these were used in the calibration that was used to estimate abundance during 1981–1989. 

Estimates prior to 1981 were based on a calibration between Necanicum abundance estimates 

and average peak counts in the Nehalem Basin for the years 1990–2003. However, these 

190
Table D-2. Regression equations used. R2

 is the proportion of total variance explained by the regression; 

p is the significance probability for the slope of the regression. 

Population Period Equation R2 p 

Necanicum 1958–1980 Total Necanicum = 72.459 × Nehalem peak 

count 

0.30 0.035

1981–1989 Total Necanicum = 73.066 × Necanicum peak 

count 

0.44 0.008

Nehalem 1958–1989 Total Nehalem = 685.214 × Nehalem peak count 0.61 0.001

Tillamook 1958–1989 Total Tillamook = 212.341 × Tillamook peak 

count 

0.89 0.000

Nestucca 1958–1989 Total Nestucca = 517.279 × Nestucca peak 

count 

0.58 0.002

Siletz 1958–1989 Total Siletz = 82.390 × Alsea peak count 0.65 0.000

Yaquina 1958–1989 Total Yaquina = 134.350 × Yaquina peak count 0.92 0.000

Beaver 1958–1989 Total Beaver = 50.952 × Beaver peak count 0.69 0.000

Alsea 1958–1989 Total Alsea = 147.499 × Alsea peak count 0.88 0.000

Siuslaw 1958–1989 Total Siuslaw = 751.435 × Siuslaw peak count 0.82 0.000

Lower Umpqua 1958–1989 Total Lower Umpqua = 104.266 × Lower 

Umpqua peak count 

0.71 0.000

Middle Umpqua 1958–1989 Wild Middle Umpqua = 3.075 × Winchester 

Dam Wild 

0.89 0.000

South Umpqua 1958–1989 Wild South Umpqua = 2.629 × Winchester Dam 

Wild 

0.45 0.007

Coos 1958–1989 Total Coos = 340.616 × Coos peak count 0.65 0.001

Coquille 1958–1989 Total Coquille = 320.995 × Coquille peak count 0.68 0.000

191
Table D-3. Estimated abundance of adult fish with or without harvested numbers of Independent 

Populations of the Oregon Coast Coho Salmon ESU, 1958–1989. NA means no estimate is 

available. 

 Necanicum Nehalem Tillamook Bay Nestucca 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery 

1958 274 161 3,535 576 586 263 3,005 616 

1959 977 574 12,611 2,053 1,904 856 2,576 528 

1960 675 397 8,721 1,420 2,710 1,218 4,294 879 

1961 1,424 837 18,386 2,993 6,007 2,699 7,299 1,495 

1962 1,251 734 16,147 2,628 7,032 3,160 2,576 528 

1963 803 472 10,372 1,688 4,249 1,909 5,582 1,143 

1964 1,872 1,099 24,161 3,933 5,861 2,633 12,022 2,462 

1965 1,452 852 18,739 3,051 3,663 1,646 6,011 1,231 

1966 1,123 659 14,496 2,360 2,198 987 3,005 616 

1967 840 493 10,843 1,765 9,597 4,311 4,294 879 

1968 NA NA NA NA 4,689 2,106 2,146 440 

1969 648 381 8,368 1,362 2,710 1,218 1,717 352 

1970 1,022 601 13,200 2,149 8,717 3,917 7,299 1,495 

1971 1,634 960 21,097 3,434 6,447 2,896 5,582 1,143 

1972 356 209 4,597 748 2,637 1,185 1,288 264 

1973 593 349 7,661 1,247 7,472 3,357 4,723 967 

1974 338 198 4,361 710 5,714 2,567 2,576 528 

1975 NA NA NA NA 6,740 3,028 3,435 703 

1976 365 215 4,715 767 4,395 1,975 NA NA 

1977 457 268 5,893 959 293 132 859 176 

1978 593 349 7,661 1,247 4,249 1,909 2,146 440 

1979 365 215 4,715 767 4,103 1,843 3,864 792 

1980 512 300 6,600 1,074 1,539 691 3,005 616 

1981 230 135 3,418 556 1,392 625 2,290 469 

1982 2,255 1,325 8,368 1,362 1,978 889 4,580 938 

1983 322 189 1,296 211 403 181 4,007 821 

1984 644 379 16,500 2,686 2,271 1,020 4,007 821 

1985 599 351 6,600 1,074 1,575 708 6,154 1,260 

1986 874 514 10,607 1,727 1,575 708 4,865 997 

1987 599 351 2,946 480 879 395 1,002 205 

1988 920 541 2,631 1,754 809 518 1,145 234 

1989 736 433 8,469 5,646 1,145 235 2,004 410 

192
Table D-3 continued. Estimated abundance of adult fish with or without harvested numbers of 

Independent Populations of the Oregon Coast Coho Salmon ESU, 1958–1989. NA means no 

estimate is available. 

 Siletz Yaquina Beaver Alsea 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery 

1958 837 451 3,213 13 662 0 1,466 796 

1959 1,765 951 7,564 30 1,580 0 3,090 1,679 

1960 1,165 627 3,481 14 1,274 0 2,039 1,108 

1961 2,621 1,411 20,616 83 917 0 4,588 2,492 

1962 946 510 8,701 35 1,732 0 1,657 900 

1963 2,220 1,196 5,421 22 764 0 3,887 2,111 

1964 3,185 1,715 10,442 42 1,019 0 5,575 3,029 

1965 1,547 833 10,843 44 1,070 0 2,708 1,471 

1966 1,911 1,029 13,655 55 1,987 0 3,345 1,817 

1967 1,620 872 5,020 20 510 0 2,836 1,540 

1968 1,238 666 7,765 31 1,834 0 2,166 1,177 

1969 400 216 3,347 13 713 0 701 381 

1970 510 274 10,509 42 2,089 0 892 485 

1971 2,239 1,205 16,801 67 2,497 0 3,918 2,129 

1972 328 176 1,941 8 357 0 573 312 

1973 710 382 4,016 16 560 0 1,242 675 

1974 473 255 1,740 7 153 0 828 450 

1975 1,201 647 NA NA NA NA 2,103 1,142 

1976 382 206 NA NA NA NA 669 363 

1977 655 353 2,142 9 NA NA 1,147 623 

1978 874 470 1,607 6 NA NA 1,529 831 

1979 764 412 11,111 45 NA NA 1,338 727 

1980 852 786 2,124 3,185 727 853 1,467 1,409 

1981 906 837 1,559 2,339 258 302 1,561 1,500 

1982 480 444 1,398 2,097 469 550 827 795 

1983 328 302 457 685 258 302 564 542 

1984 601 554 1,478 2,218 563 660 1,034 994 

1985 2,173 2,006 4,140 6,209 1,336 1,568 3,742 3,596 

1986 1,210 1,478 3,387 8,710 2,147 2,846 2,077 2,643 

1987 871 557 2,837 1,397 141 165 2,081 426 

1988 1,594 590 2,758 871 164 193 1,572 2,263 

1989 1,976 61 2,064 423 662 0 3,362 215 

193
Table D-3 continued. Estimated abundance of adult fish with or without harvested numbers of 

Independent Populations of the Oregon Coast Coho Salmon ESU, 1958–1989. NA means no 

estimate is available. 

Siuslaw Siltcoos Tahkenitch Tenmile 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery 

1958 NA NA NA NA NA NA 12,043 61 

1959 NA NA NA NA NA NA 7,471 38 

1960 1,104 23 1,768 27 912 5 5,464 28 

1961 5,891 120 4,169 64 1,785 9 15,277 77 

1962 4,050 83 4,492 68 1,783 9 17,619 89 

1963 6,259 128 3,650 56 820 4 10,594 53 

1964 11,414 233 9,059 138 2,221 11 18,734 94 

1965 8,469 173 2,366 36 1,642 8 12,043 61 

1966 9,205 188 5,351 82 1,381 7 12,713 63 

1967 11,047 225 2,171 33 986 5 11,151 56 

1968 10,678 218 2,224 34 714 4 7,248 37 

1969 15,464 316 1,630 25 986 5 6,022 30 

1970 13,255 271 3,889 60 1,692 9 14,726 74 

1971 16,753 342 1,666 25 865 5 26,540 134 

1972 4,419 90 1,931 30 573 3 7,471 38 

1973 8,837 180 2,826 43 2,434 12 NA NA 

1974 3,498 71 1,497 23 699 4 4,143 21 

1975 6,186 126 2,817 43 420 2 2,338 12 

1976 13,991 286 NA NA NA NA 1,486 7 

1977 4,419 90 1,484 23 2,468 12 1,832 9 

1978 2,946 60 634 9 NA NA 957 5 

1979 6,996 143 2,652 40 3,297 16 627 3 

1980 11,783 240 1,719 26 1,099 5 1,816 9 

1981 3,682 75 NA NA NA NA 2,509 13 

1982 8,101 165 1,214 18 3,991 20 2,685 14 

1983 4,971 101 664 11 1,967 9 729 4 

1984 7,916 162 6,219 95 4,531 23 3,866 19 

1985 14,360 293 3,356 51 417 2 3,715 19 

1986 19,331 394 4,164 64 3,074 15 4,444 23 

1987 11,047 225 1,624 25 594 3 1,933 9 

1988 10,596 676 2,579 39 540 2 2,627 13 

1989 13,226 1,803 2,051 32 542 3 2,130 10 

194
Table D-3 continued. Estimated abundance of adult fish with or without harvested numbers of 

Independent Populations of the Oregon Coast Coho Salmon ESU, 1958–1989. NA means no 

estimate is available. 

 Lower Umpqua Middle Umpqua North Umpqua South Umpqua 

Year Wild Hatchery Wild Hatchery Wild Hatchery Wild Hatchery 

1958 3,300 0 1,356 0 441 0 1,159 0 

1959 3,648 0 1,937 0 630 0 1,656 0 

1960 1,285 0 818 0 266 0 699 0 

1961 3,509 0 1,258 0 409 0 1,075 0 

1962 7,157 0 1,298 0 422 0 1,109 0 

1963 3,926 0 2,906 0 945 0 2,484 0 

1964 20,671 0 2,761 0 898 0 2,361 0 

1965 14,314 0 5,357 0 1,742 0 4,580 0 

1966 5,698 0 2,171 0 706 0 1,856 0 

1967 9,693 0 3,066 0 997 0 2,621 0 

1968 3,439 0 3,899 0 1,268 0 3,334 0 

1969 7,365 0 1,335 0 434 0 1,141 0 

1970 7,504 0 483 0 157 0 413 0 

1971 4,933 0 1,510 0 491 0 1,291 0 

1972 2,362 0 996 0 324 0 852 0 

1973 6,254 0 1,242 0 404 0 1,062 0 

1974 2,988 0 956 0 311 66 818 0 

1975 3,231 0 821 0 267 214 702 0 

1976 2,745 0 772 0 251 82 660 0 

1977 1,459 0 1,319 0 429 7 1,128 0 

1978 1,529 0 1,141 0 371 107 975 0 

1979 6,254 0 1,292 0 420 0 1,104 0 

1980 1,424 0 744 0 242 1 636 0 

1981 1,320 0 166 0 54 0 142 0 

1982 7,195 379 2,079 133 676 1,981 1,777 314 

1983 1,585 83 NA NA 7 1,196 NA NA 

1984 3,267 172 NA NA 6 3,195 NA NA 

1985 4,720 248 2,577 164 838 4,023 2,203 389 

1986 5,281 278 2,958 189 962 9,581 2,529 446 

1987 2,971 156 3,023 193 983 2,165 2,584 456 

1988 11,321 596 1,845 118 600 1,196 1,577 278 

1989 3,927 207 4,975 318 1,618 2,734 4,254 751 

195
Table D-3 continued. Estimated abundance of adult fish with or without harvested numbers of 

Independent Populations of the Oregon Coast Coho Salmon ESU, 1958–1989. 

Coos Coquille 

Year Wild Hatchery Wild Hatchery

1958 3,403 0 6,195 466 

1959 8,167 0 24,554 1,848 

1960 2,155 0 3,060 230 

1961 12,818 0 7,986 601 

1962 13,498 0 14,429 1,086 

1963 6,239 0 3,134 236 

1964 11,003 0 15,464 1,164 

1965 6,012 0 17,076 1,285 

1966 7,146 0 7,284 548 

1967 6,579 0 6,344 477 

1968 4,424 0 5,015 378 

1969 6,012 0 11,105 836 

1970 4,197 0 7,762 584 

1971 7,373 0 11,224 845 

1972 4,991 0 6,866 517 

1973 5,445 0 4,478 337 

1974 9,868 0 6,448 485 

1975 6,125 0 5,522 416 

1976 10,549 0 24,031 1,809 

1977 8,167 0 1,194 90 

1978 2,892 0 3,508 264 

1979 6,806 0 5,971 449 

1980 6,431 715 4,080 307 

1981 3,879 431 2,746 207 

1982 9,188 1,021 3,940 297 

1983 2,450 272 4,359 328 

1984 4,389 488 11,224 845 

1985 5,411 601 3,582 270 

1986 5,819 647 3,582 270 

1987 4,638 693 4,657 351 

1988 10,880 1,484 4,475 982 

1989 5,366 873 1,849 1,233 

196
estimates are not very reliable because the calibration explained only 38% of the variation in 

abundance. The average proportion of hatchery fish observed during the period 1989–1998, 

prior to reductions in hatchery programs, was applied to the total abundance estimates prior to 

1990. 

Nehalem 

The average proportion of hatchery fish observed in the upper Nehalem Basin (where the 

standard surveys are located) during the period 1989–1998 (except for 1990, 1993, and 1996 

when hatchery fish returned to Fishhawk Lake) was applied to the total abundance estimates 

prior to 1990. 

Tillamook Bay 

The average proportion of hatchery fish observed in the Tillamook Bay subbasins except 

the Trask (no standard surveys are located in the Trask subbasin) during the period 1988–1995 (a 

period when Trask Pond was in operation) was applied to the total abundance estimates prior to 

1990. 

Nestucca 

The average proportion of hatchery fish observed during the period 1988–1998, prior to 

reductions in coastal hatchery programs, was applied to the total abundance estimates prior to 

1990. 

Siletz 

Standard spawning surveys have been conducted in the Siletz Basin only since 1981. A 

calibration using this data (one survey) was compared to a calibration using average peak count 

from the Alsea River Basin (four surveys). The Alsea calibration was used to estimate 

abundance for the Siletz population for all years prior to 1990 for three reasons: 1) the Alsea 

calibration was superior (R2

 = 0.65 compared to R2 = 0.45), 2) estimates derived from the two 

calibrations for 1981–1981 showed a similar trend, and 3) when doing analyses of the 

abundances there is an advantage to using a consistent calibration factor for all years. 

Determination of the proportion of hatchery fish in the Siletz also presented a problem because 

this population received strays from the private hatchery in Yaquina Bay and it had a state 

hatchery on a tributary during this period. Data from 1986 to 1988 were used to compute an 

average proportion of hatchery fish that was applied to 1980–1988. Because the state hatchery is 

no longer located in the basin, the proportion of hatchery fish in the Alsea River Basin during 

1990–1998 (a similar situation with a state hatchery on a tributary) was applied to the Siletz 

estimates prior to 1980. 

Yaquina 

The estimation of hatchery strays in the Yaquina during 1958–1989 is complicated by the 

private hatchery that was located in the basin for several years. Data from 1986 to 1988 were 

used to compute an average proportion of hatchery fish that was applied to 1980–1988. The 

197
average proportion of hatchery fish observed during the period 1990–2003, exclusive of hatchery 

return years 1996–1998, was applied to the total abundance estimates prior to 1980. 

Beaver 

Determination of the proportion of hatchery fish in Beaver Creek presented a problem 

because it received strays from the private hatchery in Yaquina Bay. Data from 1986 to 1988 

were used to compute an average proportion of hatchery fish that was applied to 1980–1988. 

The average proportion of hatchery fish observed during the period 1990–1995 was applied to 

the total abundance estimates prior to 1980. 

Alsea 

The Alsea River is similar to the Siletz in that it was influenced by private hatchery strays 

and had a state hatchery located on a tributary for many years that was ultimately closed. Data 

from 1986 to 1988 were used to compute an average proportion of hatchery fish that was applied 

to 1980–1988. The average proportion of hatchery fish observed during the period 1990–1998, 

prior to the closing of Fall Creek hatchery, was applied to the total abundance estimates prior to 

1980. 

Siuslaw 

The average proportion of hatchery fish observed during the period 1988–2003, in nonLake Creek subbasins, was applied to the total abundance estimates prior to 1990. This removes 

the influence of returns of hatchery smolts that were released in Lake Creek only in the mid1990s. 

Umpqua River 

Lower Umpqua: The first significant return from the hatchery program in the Umpqua 

River Basin was in 1982. Therefore, the average proportion of hatchery fish observed during the 

period 1990–2001, prior to the reduction in the hatchery program, was applied to the total 

abundance estimates during 1982–1989. Prior to 1982, hatchery strays were assumed to be 

insignificant. 

Middle Umpqua: The first significant return from the hatchery program in the Umpqua 

River Basin was in 1982. Therefore, the average proportion of hatchery fish observed during the 

period 1990–2001, prior to the reduction in the hatchery program, was applied to the total 

abundance estimates during 1982–1989. Prior to 1982, hatchery strays were assumed to be 

insignificant. 

North Umpqua: Hatchery and wild fish were determined based on counts at Winchester 

Dam. Hatchery fish were first recorded in 1974 but did not exceed 214 until 1982, when 1,981 

were counted. 

South Umpqua: The first significant return from the hatchery program in the Umpqua 

River Basin was in 1982. Therefore, the average proportion of hatchery fish observed during the 

period 1990–2001, prior to the reduction in the hatchery program, was applied to the total 

198
199

abundance estimates during 1982–1989. Prior to 1982, hatchery strays were assumed to be 

insignificant. 

The Lakes 

The average proportion of hatchery fish observed in Siltcoos, Tahkenitch, and Tenmile 

Lakes populations during the period 1990–2003 was applied to the total abundance estimates 

prior to 1990. 

Coos 

The Coos River population was influenced by private hatchery fish in the 1980s and by a 

STEP hatchery in recent years. The average proportion of hatchery fish observed in each 

population during the period 1986–1988 was applied to the total abundance in 1980–1988. Prior 

to 1980, hatchery strays were assumed to be insignificant. 

Coquille 

The average proportion of hatchery fish observed during the period 1988–1996, prior to 

reductions in Bandon Hatchery program, was applied to the total abundance estimates prior to 

1990. 

Recent NOAA Technical Memorandums

published by the

Northwest Fisheries Science Center

NOAA Technical Memorandum NMFS-NWFSC90 Ward, L., P. Crain, B. Freymond, M. McHenry, D. Morrill, G. Pess, R. Peters, J.A. Shaffer, B. Winter, and 

B. Wunderlich. 2008. Elwha River Fish Restoration Plan–Developed pursuant to the Elwha River Ecosystem and Fisheries Restoration Act, Public Law 102-495. U.S. Dept. Commer., NOAA Tech. Memo. NMFSNWFSC-90, 168 p. NTIS number pending.

89 Holt, M. 2008. Sound exposure and Southern Resident killer whales (Orcinus orca): A review of current 

knowledge and data gaps. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-89, 59 p. NTIS

 number pending.

88 Olson, O.P., L. Johnson, G. Ylitalo, C. Rice, J. Cordell, T. Collier, and J. Steger. 2008. Fish habitat use 

 and chemical contaminant exposure at restoration sites in Commencement Bay, Washington. U.S. Dept. 

 Commer., NOAA Tech. Memo. NMFS-NWFSC-88, 117 p. NTIS number pending.

87 Keller, A.A., B.H. Horness, V.H. Simon, V.J. Tuttle, J.R. Wallace, E.L. Fruh, K.L. Bosley,

 D.J. Kamikawa, and J.C. Buchanan. 2007. The 2004 U.S. West Coast bottom trawl survey of groundfish 

resources off Washington, Oregon, and California: Estimates of distribution, abundance, and length composition. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-87, 134 p. NTIS number pending.

86 Keller, A.A., V.H. Simon, B.H. Horness, J.R. Wallace, V.J. Tuttle, E.L. Fruh, K.L. Bosley,

 D.J. Kamikawa, and J.C. Buchanan. 2007. The 2003 U.S. West Coast bottom trawl survey of groundfish 

resources off Washington, Oregon, and California: Estimates of distribution, abundance, and length composition. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-86, 130 p. NTIS number pending.

85 Norman, K., J. Sepez, H. Lazrus, N. Milne, C. Package, S. Russell, K. Grant, R.P. Lewis, J. Primo,

 E. Springer, M. Styles, B. Tilt, and I. Vaccaro. 2007. Community profiles for West Coast and North 

Pacific fisheries–Washington, Oregon, California, and other U.S. states. U.S. Dept. Commer., NOAA Tech. 

Memo. NMFS-NWFSC-85, 602 p. NTIS number pending.

84 Brand, E.J., I.C. Kaplan. C.J. Harvey, P.S. Levin, E.A. Fulton, A.J. Hermann, and J.C. Field. 2007. 

A spatially explicit ecosystem model of the California Current’s food web and oceanography. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-84, 145 p. NTIS number PB2008-102578.

Most NOAA Technical Memorandums NMFS-NWFSC are available online at the

Northwest Fisheries Science Center web site (http://www.nwfsc.noaa.gov).