NOAA Technical Memorandum NMFS-NWFSC-129

Viability Criteria for Steelhead 

within the Puget Sound 

Distinct Population Segment

May 2015

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Northwest Fisheries Science Center
NOAA Technical Memorandum

NMFS-NWFSC Series

The Northwest Fisheries Science Center of NOAA’s

National Marine Fisheries Service uses the NOAA Technical 

Memorandum NMFS-NWFSC series to issue scientific and 

technical publications. Manuscripts have been peer reviewed 

and edited. Documents published in this series can be cited 

in the scientific and technical literature.

The Northwest Fisheries Science Center’s NOAA Technical 

Memorandum NMFS-NWFSC series continues the NMFSF/NWC series established in 1970 by the Northwest and

Alaska Fisheries Science Center, which subsequently was 

divided into the Northwest Fisheries Science Center and the 

Alaska Fisheries Science Center. The latter center uses the 

NOAA Technical Memorandum NMFS-AFSC series.

Reference throughout this document to trade names does not 

imply endorsement by the National Marine Fisheries Service.

Reference this document as follows:

Hard, J.J., J.M. Myers, E.J. Connor, R.A. Hayman, 

R.G. Kope, G. Lucchetti, A.R. Marshall, G.R. Pess, and 

B.E. Thompson. 2015. Viability criteria for steelhead within 

the Puget Sound distinct population segment. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-129.
NOAA Technical Memorandum NMFS-NWFSC-129

Viability Criteria for Steelhead 

within the Puget Sound 

Distinct Population Segment

Jeffrey J. Hard, James M. Myers, Edward J. Connor,1

Robert A. Hayman,2 Robert G. Kope, Gino Lucchetti,3

Anne R. Marshall,

4 George R. Pess, and Bradley E. Thompson5

Northwest Fisheries Science Center

2725 Montlake Boulevard East

Seattle, Washington 98112

1

Seattle City Light

700 Fifth Avenue, Suite 3300

Seattle, Washington 98124

2

Skagit River System Cooperative

11426 Moorage Way

La Conner, Washington 98257

3

King County Department of Natural Resources and Parks

201 South Jackson Street, Suite 600

Seattle, Washington 98104

4

Washington Department of Fish and Wildlife

600 Capitol Way North

Olympia, Washington 98501

5

U.S. Fish and Wildlife Service

Washington Fish and Wildlife Office

510 Desmond Drive Southeast, Suite 102

Lacey, Washington 98503

May 2015

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
Most NOAA Technical Memorandums

NMFS-NWFSC are available at the 

Northwest Fisheries Science Center

Web site, http://www.nwfsc.noaa.gov

Copies are also available from the

National Technical Information Service

5285 Port Royal Road 

Springfield, VA 22161

Phone orders: 1-800-553-6847 

E-mail orders: orders@ntis.fedworld.gov

ii
iii

Table of Contents

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

List of Tables............................................................................................................................................xvii

Dedication..................................................................................................................................................xix

Executive Summary...................................................................................................................................xxi

Acknowledgments....................................................................................................................................xxix

Abbreviations...........................................................................................................................................xxxi

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

Viable Salmonid Population Criteria........................................................................................................1

Approaches to Assessing Viability...........................................................................................................4

Viability Assessment Methods......................................................................................................................7

Recent Abundance....................................................................................................................................7

Trends in Abundance................................................................................................................................7

Population Growth Rate ...........................................................................................................................8

Recruitment ..............................................................................................................................................9

Demographic Data and Analyses............................................................................................................10

Data from the Entire Series................................................................................................................10

Data Since 1995 .................................................................................................................................10

Data from the Most Recent 5 Years...................................................................................................14

Population Viability Analysis.................................................................................................................14

Stochastic Population Viability Analysis...........................................................................................18

Population Viability Analysis with Autoregressive State-Space Models ..........................................46

Analyses at the Major Population Group Level .....................................................................................53

DPS Viability Assessment Methods.......................................................................................................59

Abundance..........................................................................................................................................60

Productivity........................................................................................................................................60

Diversity.............................................................................................................................................60

Spatial Structure .................................................................................................................................61

Applying Viability Analyses to Multiple Spatial Scales........................................................................61

DPS Viability .....................................................................................................................................62
iv

MPG Viability....................................................................................................................................62

Decision Support Systems as Tools for Assessing Viability ......................................................................64

Bayesian Networks for Evaluation of Viability......................................................................................65

Use of Bayesian Networks to Incorporate Viable Salmonid Population Criteria into a Viability 

Assessment Framework..........................................................................................................................72

DIP Viability ......................................................................................................................................85

MPG Viability....................................................................................................................................93

DPS Viability ...................................................................................................................................105

Diversity Considerations for Steelhead Viability: Resident Life History and Iteroparity ........................109

Resident Fish as a Factor Influencing Viability ...................................................................................109

Iteroparity as a Factor Influencing Viability ........................................................................................113

Viability Criteria for Puget Sound Steelhead............................................................................................117

DIP Viability ........................................................................................................................................121

MPG Viability ......................................................................................................................................121

DPS Viability........................................................................................................................................121

Conclusions...............................................................................................................................................127

References.................................................................................................................................................129

Appendix A: State-Space Analyses of Viability of Puget Sound Steelhead DIPs....................................137

Appendix B: Interim Abundance-Based Viability Guidelines for Puget Sound Steelhead ......................159

Appendix C: Calculation of Intrinsic Potential Estimates for Puget Sound Steelhead DIPs....................161

Appendix D: Selected Steelhead Documents............................................................................................163

Appendix E: Description of Puget Sound Steelhead DIPs........................................................................169

The Four VSP Attributes......................................................................................................................169

Abundance and Productivity ............................................................................................................169

Diversity...........................................................................................................................................170

Spatial Structure ...............................................................................................................................171

Guidance ..........................................................................................................................................171

Summaries of Puget Sound Steelhead DIPs.........................................................................................172

Northern Cascades Steelhead Major Population Group...................................................................172

Central and South Puget Sound Major Population Group................................................................225

Hood Canal and Strait of Juan de Fuca Major Population Group....................................................252

Appendix F: Bayesian Networks Characterizing Viability of Puget Sound Steelhead DIPs....................279

Appendix G: Identifying Key Factors Influencing the Population Dynamics and Productivity of 

Wild Steelhead..........................................................................................................................................297

Appendix H: Responses to Reviewer Comments.....................................................................................311
v

Substantive Scientific Comments.........................................................................................................311

Development of Viability Criteria....................................................................................................311

Methods of Population Viability Analysis.......................................................................................316

Intrinsic Potential Model..................................................................................................................318

Minor Scientific Comments..................................................................................................................320

Editorial Comments..............................................................................................................................330

Policy-Related Comments....................................................................................................................331

Final Response to Reviewer Comments...............................................................................................331
vi
vii

Figures

Figure 1. Viability as a function of abundance and productivity...............................................................15

Figure 2. Hypothetical viability curves showing the relationship between levels of extinction risk 

and population persistence categories.........................................................................................................17

Figure 3. An example of the results of a population viability analysis (PVA) conducted with the 

program SPAZ ............................................................................................................................................21

Figure 4. PVA for the Samish River and Bellingham Bay Tributaries Winter Run ..................................22

Figure 5. PVA for the Skagit River Summer Run and Winter Run...........................................................23

Figure 6. PVA for the Stillaguamish River Winter Run ............................................................................24

Figure 7. PVA for the Pilchuck River Winter Run....................................................................................25

Figure 8. PVA for the Snohomish/Skykomish Rivers Winter Run ...........................................................26

Figure 9. PVA for the Snoqualmie River Winter Run...............................................................................27

Figure 10. PVA for the Tolt River Summer Run.......................................................................................28

Figure 11. PVA for the Cedar River Winter Run ......................................................................................29

Figure 12. PVA for the Green River Winter Run ......................................................................................30

Figure 13. PVA for the Puyallup/Carbon Rivers Winter Run ...................................................................31

Figure 14. PVA for the White River Winter Run ......................................................................................32

Figure 15. PVA for the Nisqually River WinterRun..................................................................................33

Figure 16. PVA for the South Hood Canal Tributaries Winter Run..........................................................34

Figure 17. PVA for the Tahuya River winter-run steelhead, part of the South Hood Canal 

Tributaries Winter-Run DIP .......................................................................................................................35

Figure 18. PVA for the East Hood Canal Tributaries Winter Run ............................................................36

Figure 19. PVA for the Skokomish River Winter Run..............................................................................37

Figure 20. PVA for the West Hood Canal Tributaries Winter Run...........................................................38

Figure 21. PVA for the Sequim/Discovery Bays Tributaries Winter Run.................................................39

Figure 22. PVA for the Dungeness River Summer Run and Winter Run..................................................40

Figure 23. PVA for the Strait of Juan de Fuca Tributaries Winter Run.....................................................40

Figure 24. PVA for the Morse Creek winter-run steelhead, part of Strait of Juan de Fuca 

Tributaries Winter-Run DIP .......................................................................................................................41

Figure 25. PVA for the Elwha River Winter Run......................................................................................41

Figure 26. PVA output from MARSS as applied to a time series of wild steelhead 

population abundance .................................................................................................................................49
viii

Figure 27. Graph of the trend in estimated total Puget Sound winter-run and one summer-run

steelhead population for a putative Northern Cascades MPG ....................................................................54

Figure 28. Plots of the residuals from regression of steelhead counts over time for five 

populations in the Northern Cascades MPG...............................................................................................55

Figure 29. Graph of the trend in estimated total Puget Sound winter-run steelhead for a putative 

Central and South Puget Sound MPG.........................................................................................................56

Figure 30. Plots of the residuals from regression of steelhead counts over time for five winter-run 

populations in the Central and South Puget Sound MPG ...........................................................................57

Figure 31. Graph of the trend in estimated total Puget Sound summer-run and winter-run steelhead 

for a putative Hood Canal and Strait of Juan de Fuca MPG.......................................................................58

Figure 32. Plots of the residuals from regression of steelhead counts over time for six summer-run 

and winter-run populations in the Hood Canal and Strait of Juan de Fuca MPG.......................................59

Figure 33. Three MPGs for the Puget Sound Steelhead DPS: Northern Cascades, Central and South 

Puget Sound, and Hood Canal and Strait of Juan de Fuca..........................................................................63

Figure 34. A simple Bayesian network (BN) depicting the influence of three discrete states of 

population abundance and five discrete states of average population growth on a dichotomous 

estimate of population viability ..................................................................................................................67

Figure 35. Top panel, the BN in Figure 34 instantiated at particular states of population abundance 

and growth, depicting certainty that the population is larger than the low-abundance threshold and 

increasing in size.........................................................................................................................................69

Figure 36. Most probable explanation for the BN in Figure 34.................................................................70

Figure 37. A BN to characterize the viability of a hypothetical DIP of winter-run steelhead in the 

Puget Sound DPS........................................................................................................................................73

Figure 38. A BN to characterize the viability of a hypothetical DIP of summer-run steelhead in the 

Puget Sound DPS........................................................................................................................................80

Figure 39. Comparisons of estimates of relative viability for Puget Sound steelhead DIPs between 

current and historical estimates of VSP parameters....................................................................................81

Figure 40. Dendrogram of Gower distances. Relationships among steelhead DIPs with the Puget 

Sound DPS based on a Gower similarity index ..........................................................................................83

Figure 41. Bar plot showing how the probabilities of viability for a representative DIP of Puget 

Sound steelhead vary with the contribution to risk from different combinations of VSP criteria..............87

Figure 42. Bar plot of the relative influences of the four VSP parameter estimates applied to the 

viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound DPS....................................88

Figure 43. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS 

as a function of their VSP parameter estimates of influence of abundance and productivity.....................89

Figure 44. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS 

as a function of their VSP parameter estimates of influence of abundance and diversity ..........................91

Figure 45. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS 

as a function of their VSP parameter estimates of influence of abundance and spatial structure...............92

Figure 46. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS

as a function of their VSP parameter estimates of influence of productivity and diversity.......................93
ix

Figure 47. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS

as a function of their VSP parameter estimates of influence of productivity and spatial structure ...........94

Figure 48. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the DPS

as a function of their VSP parameter estimates of influence of diversity and spatial structure.................95

Figure 49. BN used to characterize the viability of the Hood Canal and Strait of Juan de Fuca 

steelhead MPG............................................................................................................................................96

Figure 50. Highest-level BN used to characterize the viability of the Puget Sound Steelhead DPS.......106

Figure 51. Temporal changes between the 1970s and 1990s in the estimates of repeat spawning 

rates for seven winter-run steelhead populations from the Northern Cascades MPG and Central 

and South Puget Sound MPG ...................................................................................................................113

Figure 52. Empirical cumulative densities of 1,000 deterministic population projections over 

50 years for steelhead exposed to a 20% harvest rate under a 15% mean smolt survival ........................115

Figure 53. Viability criteria framework for DIPs of Puget Sound steelhead ...........................................119

Figure 54. Viability criteria framework for MPGs of Puget Sound steelhead based on the 

viability of component DIPs containing distinct life history types...........................................................120

Figure 55. Viability criteria framework for the entire Puget Sound Steelhead DPS................................120

Figure 56. A set of recommended viability criteria for a DPS of steelhead ............................................123

Figure 57. Estimates of current viability for the 32 DIPs of Puget Sound steelhead using the 

VSP framework.........................................................................................................................................124

Figure 58. Estimates of current viability for the 32 steelhead DIPs using the VSP framework, 

but assuming that all viability criteria without quantifiable information are given a score of 2...............125

Figure A-1. MARSS-based PVA for the Samish River winter-run steelhead .........................................138

Figure A-2. MARSS-based PVA for the Skagit River Summer Run and Winter Run............................139

Figure A-3. MARSS-based PVA for the Stillaguamish River Winter Run.............................................140

Figure A-4. MARSS-based PVA for the Pilchuck River Winter Run.....................................................141

Figure A-5. MARSS-based PVA for the Snohomish/Skyhomish Rivers Winter Run ............................142

Figure A-6. MARSS-based PVA for the Snoqualmie River Winter Run................................................143

Figure A-7. MARSS-based PVA for the Tolt River Summer Run..........................................................144

Figure A-8. MARSS-based PVA for the Cedar River Winter Run .........................................................144

Figure A-9. MARSS-based PVA for the Green River Winter Run .........................................................145

Figure A-10. MARSS-based PVA for the Puyallup River winter-run steelhead.....................................146

Figure A-11. MARSS-based PVA for the White River Winter Run .......................................................147

Figure A-12. MARSS-based PVA for the Nisqually River Winter Run .................................................148

Figure A-13. MARSS-based PVA for the South Hood Canal Tributaries Winter Run...........................149

Figure A-14. MARSS-based PVA for the Tahuya River winter-run steelhead.......................................149

Figure A-15. MARSS-based PVA for the East Hood Canal Tributaries Winter Run .............................150

Figure A-16. MARSS-based PVA for the Skokomish River Winter Run...............................................151
x

Figure A-17. MARSS-based PVA for the West Hood Canal Tributaries Winter Run............................152

Figure A-18. MARSS-based PVA for the Sequim/Discovery Bays Tributaries Winter Run..................153

Figure A-19. MARSS-based PVA for the Dungeness River Summer Run and Winter Run...................154

Figure A-20. MARSS-based PVA for the Strait of Juan de Fuca Tributaries Winter Run......................155

Figure A-21. MARSS-based PVA for the Morse Creek winter-run steelhead ........................................156

Figure A-22. MARSS-based PVA for the Elwha River Winter Run.......................................................157

Figure E-1. Map of Drayton Harbor Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................172

Figure E-2. Map of Drayton Harbor Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................173

Figure E-3. Map of Drayton Harbor Tributaries Winter-Run DIP area hydrology .................................174

Figure E-4. Map of Nooksack River Winter-Run DIP land cover and observed spawning area.............175

Figure E-5. Map of Nooksack River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................176

Figure E-6. Map of Nooksack River Winter-Run DIP area hydrology ...................................................177

Figure E-7. Map of South Fork Nooksack River Summer-Run DIP land cover and observed 

spawning area............................................................................................................................................178

Figure E-8. Map of South Fork Nooksack River Summer-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................179

Figure E-9. Map of South Fork Nooksack River Summer-Run DIP area hydrology ..............................180

Figure E-10. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP land cover 

and observed spawning area .....................................................................................................................181

Figure E-11. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP spatial 

structure, including migration barriers and spawning potential................................................................182

Figure E-12. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP area hydrology .183

Figure E-13. Map of Skagit River Summer-Run and Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................184

Figure E-14. Map of Skagit River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................186

Figure E-15. Map of Skagit River Summer-Run and Winter-Run DIP area hydrology..........................187

Figure E-16. Map of Nookachamps Creek Winter-Run DIP land cover and observed spawning area ...188

Figure E-17. Map of Nookachamps Creek Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................189

Figure E-18. Map of Nookachamps Creek Winter-Run DIP area hydrology..........................................190

Figure E-19. Map of Baker River Summer-Run and Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................191

Figure E-20. Map of Baker River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................192
xi

Figure E-21. Map of Baker River Summer-Run and Winter-Run DIP area hydrology...........................193

Figure E-22. Map of Sauk River Summer-Run and Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................194

Figure E-23. Map of Sauk River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................195

Figure E-24. Map of Sauk River Summer-Run and Winter-Run DIP area hydrology ............................196

Figure E-25. Map of Stillaguamish River Winter-Run DIP land cover and observed spawning area.....197

Figure E-26. Map of Stillaguamish River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................199

Figure E-27. Map of Stillaguamish River Winter-Run DIP area hydrology ...........................................200

Figure E-28. Map of Deer Creek Summer-Run DIP land cover and observed spawning area................201

Figure E-29. Deer Creek steelhead parr density changes over time. Average annual age 1+ parr 

per square meter density for 14 sites.........................................................................................................202

Figure E-30. Genetic relationship of steelhead collections from the Snohomish River basin, 

Puget Sound, and the Skamania Hatchery using Cavalli-Sforza and Edwards chord distance.................202

Figure E-31. Map of Deer Creek Summer-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................203

Figure E-32. Map of Deer Creek Summer-Run DIP area hydrology.......................................................204

Figure E-33. Map of Canyon Creek Summer-Run DIP land cover and observed spawning area ...........205

Figure E-34. Map of Canyon Creek Summer-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................206

Figure E-35. Map of Canyon Creek Summer-Run DIP area hydrology..................................................207

Figure E-36. Map of Snohomish/Skykomish Rivers Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................208

Figure E-37. Map of Snohomish/Skykomish Rivers Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................210

Figure E-38. Map of Snohomish/Skykomish Rivers Winter-Run DIP area hydrology...........................211

Figure E-39. Map of Pilchuck River Winter-Run DIP land cover and observed spawning area.............212

Figure E-40. Map of Pilchuck River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................213

Figure E-41. Map of Pilchuck River Winter-Run DIP area hydrology ...................................................214

Figure E-42. Map of North Fork Skykomish River Summer-Run DIP land cover and observed 

spawning area............................................................................................................................................215

Figure E-43. Map of North Fork Skykomish River Summer-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................216

Figure E-44. Map of North Fork Skykomish River Summer-Run DIP area hydrology ..........................217

Figure E-45. Map of Snoqualmie River Winter-Run DIP land cover and observed spawning area........218

Figure E-46. Map of Snoqualmie River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................220
xii

Figure E-47. Map of Snoqualmie River Winter-Run DIP area hydrology ..............................................221

Figure E-48. Map of Tolt River Summer-Run DIP land cover and observed spawning area..................222

Figure E-49. Map of Tolt River Summer-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................223

Figure E-50. Map of Tolt River Summer-Run DIP area hydrology ........................................................224

Figure E-51. Map of Cedar River Winter-Run DIP land cover and observed spawning area .................225

Figure E-52. Map of Cedar River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................226

Figure E-53. Map of Cedar River Winter-Run DIP area hydrology........................................................227

Figure E-54. Map of North Lake Washington and Lake Sammamish Winter-Run DIP land cover 

and observed spawning area .....................................................................................................................228

Figure E-55. State-space analysis of DIP viability ..................................................................................229

Figure E-56. Map of North Lake Washington and Lake Sammamish Winter-Run DIP spatial 

structure, including migration barriers and spawning potential................................................................230

Figure E-57. Map of North Lake Washington and Lake Sammamish Winter-Run DIP 

area hydrology ..........................................................................................................................................231

Figure E-58. Map of Green River Winter-Run DIP land cover and observed spawning area.................232

Figure E-59. Map of Green River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................234

Figure E-60. Map of Green River Winter-Run DIP area hydrology........................................................235

Figure E-61. Map of Puyallup/Carbon Rivers Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................236

Figure E-62. Map of Puyallup/Carbon Rivers Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................238

Figure E-63. Map of Puyallup/Carbon Rivers Winter-Run DIP area hydrology.....................................239

Figure E-64. Map of White River Winter-Run DIP land cover and observed spawning area.................240

Figure E-65. Map of White River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................241

Figure E-66. Map of White River Winter-Run DIP area hydrology........................................................242

Figure E-67. Map of Nisqually River Winter-Run DIP land cover and observed spawning area ...........243

Figure E-68. Map of Nisqually River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................244

Figure E-69. Map of Nisqually River Winter-Run DIP area hydrology..................................................245

Figure E-70. Map of South Puget Sound Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................246

Figure E-71. Map of South Puget Sound Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................247

Figure E-72. Map of South Puget Sound Tributaries Winter-Run DIP area hydrology ..........................248
xiii

Figure E-73. Map of East Kitsap Peninsula Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................249

Figure E-74. Map of East Kitsap Peninsula Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................250

Figure E-75. Map of East Kitsap Peninsula Tributaries Winter-Run DIP area hydrology......................251

Figure E-76. Map of East Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................252

Figure E-77. Map of East Hood Canal Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................254

Figure E-78. Map of East Hood Canal Tributaries Winter-Run DIP area hydrology..............................255

Figure E-79. Map of South Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................256

Figure E-80. Map of South Hood Canal Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................257

Figure E-81. Map of South Hood Canal Tributaries Winter-Run DIP area hydrology ...........................258

Figure E-82. Map of Skokomish River Winter-Run DIP land cover and observed spawning area.........259

Figure E-83. Map of Skokomish River Winter-Run DIP spatial structure, including migration 

barriers and spawning potential ................................................................................................................260

Figure E-84. Map of Skokomish River Winter-Run DIP area hydrology. ..............................................261

Figure E-85. Map of West Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area............................................................................................................................................262

Figure E-86. Map of West Hood Canal Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential................................................................................................264

Figure E-87. Map of West Hood Canal Tributaries Winter-Run DIP area hydrology ............................265

Figure E-88. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP land cover and 

observed spawning area ............................................................................................................................266

Figure E-89. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP spatial structure, 

including migration barriers and spawning potential................................................................................267

Figure E-90. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP area hydrology ..................268

Figure E-91. Map of Dungeness River Summer-Run and Winter-Run DIP land cover and 

observed spawning area ............................................................................................................................269

Figure E-92. Map of Dungeness River Summer-Run and Winter-Run DIP spatial structure, 

including migration barriers and spawning potential................................................................................270

Figure E-93. Map of Dungeness River Summer-Run and Winter-Run DIP area hydrology...................271

Figure E-94. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP land cover and 

observed spawning area ............................................................................................................................272

Figure E-95. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP spatial structure, 

including migration barriers and spawning potential................................................................................274

Figure E-96. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP area hydrology......................275
xiv

Figure E-97. Map of Elwha River Winter-Run DIP land cover and observed spawning area ................276

Figure E-98. Map of Elwha River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential .............................................................................................................................277

Figure E-99. Map of Elwha River Winter-Run DIP area hydrology .......................................................278

Figure F-1. BN for viability of the Drayton Harbor Tributaries Winter-Run DIP...................................279

Figure F-2. BN for viability of the Nooksack River Winter-Run DIP.....................................................280

Figure F-3. BN for viability of the South Fork Nooksack River Summer-Run DIP ...............................280

Figure F-4. BN for viability of the Samish River and Bellingham Bay Tributaries Winter-Run DIP.....281

Figure F-5. BN for viability of the Skagit River Summer-Run and Winter-Run DIP .............................281

Figure F-6. BN for viability of the Nookachamps Creek Winter-Run DIP .............................................282

Figure F-7. BN for viability of the Baker River Summer-Run and Winter-Run DIP..............................282

Figure F-8. BN for viability of the Sauk River Summer-Run and Winter-Run DIP ...............................283

Figure F-9. BN for viability of the Stillaguamish River Winter-Run DIP...............................................283

Figure F-10. BN for viability of the Deer Creek Summer-Run DIP........................................................284

Figure F-11. BN for viability of the Canyon Creek Summer-Run DIP...................................................284

Figure F-12. BN for viability of the Snohomish/Skykomish Rivers Winter-Run DIP............................285

Figure F-13. BN for viability of the Pilchuck River Winter-Run DIP.....................................................285

Figure F-14. BN for viability of the North Fork Skykomish River Summer-Run DIP ...........................286

Figure F-15. BN for viability of the Snoqualmie River Winter-Run DIP ...............................................286

Figure F-16. BN for viability of the Tolt River Summer-Run DIP .........................................................287

Figure F-17. BN for viability of the Cedar River Winter-Run DIP.........................................................287

Figure F-18. BN for viability of the North Lake Washington and Lake Sammamish Winter-Run DIP .288

Figure F-19. BN for viability of the Green River Winter-Run DIP.........................................................288

Figure F-20. BN for viability of the Puyallup/Carbon Rivers Winter-Run DIP......................................289

Figure F-21. BN for viability of the White River Winter-Run DIP.........................................................289

Figure F-22. BN for viability of the Nisqually River Winter-Run DIP ...................................................290

Figure F-23. BN for viability of the South Puget Sound Tributaries Winter-Run DIP ...........................290

Figure F-24. BN for viability of the East Kitsap Peninsula Tributaries Winter-Run DIP .......................291

Figure F-25. BN for viability of the East Hood Canal Tributaries Winter-Run DIP...............................291

Figure F-26. BN for viability of the South Hood Canal Tributaries Winter-Run DIP ............................292

Figure F-27. BN for viability of the Skokomish River Winter-Run DIP.................................................292

Figure F-28. BN for viability of the West Hood Canal Tributaries Winter-Run DIP..............................293

Figure F-29. BN for viability of the Sequim/Discovery Bays Tributaries Winter-Run DIP ...................293

Figure F-30. BN for viability of the Dungeness River Summer-Run and Winter-Run DIP....................294

Figure F-31. BN for viability of the Strait of Juan de Fuca Tributaries Winter-Run DIP .......................294
xv

Figure F-32. BN for viability of the Elwha River Winter-Run DIP ........................................................295

Figure G-1. PVA of wild Sashin Creek steelhead, based on a time series of adult escapement 

between 1986 and 2011 ............................................................................................................................298

Figure G-2. Contour plots summarizing for adult females and males the relationship between 

relative fitness, size, and stream residence time .......................................................................................299

Figure G-3. Estimated growth and survival functions for Sashin Creek steelhead..................................302

Figure G-4. IPM diagnostics applied to the steelhead survival and growth data, indicating an 

appropriate size range and resolution in number of size bins to estimate IPM matrices..........................303

Figure G-5. Estimated number of adult offspring as a function of size in Sashin Creek steelhead.........304

Figure G-6. Left, a graphical depiction of the survival-growth transition kernel P. Right, a view 

of the kernel from above ...........................................................................................................................304

Figure G-7. Estimated mean life expectancy and passage time to reach a target size of 750 mm 

as a function of size in year t.....................................................................................................................305

Figure G-8. Summary of some key results from IPM incorporating a simple logistic function 

describing survival and a simple polynomial function describing growth, with no covariates................306

Figure G-9. A few projections from the IPM described in Figure G-8....................................................308

Figure G-10. Graphical summary of the sensitivity and elasticity analyses of the estimate of finite 

rate of population increase ........................................................................................................................309
xvi
xvii

Tables

Table 1. Estimates of exponential trend in the natural logarithm of natural spawners for DIPs 

of steelhead in the Puget Sound DPS over the entire data series................................................................11

Table 2. Estimates of exponential trend in the natural logarithm of natural spawners for DIPs 

of steelhead in the Puget Sound DPS since 1995........................................................................................12

Table 3. Geometric means of natural spawners for DIPs of steelhead in the Puget Sound DPS 

over the most recent 5 years of available data ............................................................................................13

Table 4. Population persistence categories................................................................................................16

Table 5. Population abundance relative to persistence category and population size category.................16

Table 6. Description of WDFW natural escapement data available for Puget Sound steelhead 

populations and their relationship to the TRT-designated DIPs.................................................................42

Table 7. Estimated QETs for adult steelhead in Puget Sound ...................................................................52

Table 8. Conditional probability table (CPT) underlying the outcome node of population viability 

for the BN in Figure 34...............................................................................................................................68

Table 9. Results of a sensitivity analysis of the BN in Figure 34 ..............................................................72

Table 10. Titles and descriptions of the input, intermediate, and decision nodes in the BN 

describing population viability of steelhead ...............................................................................................75

Table 11. CPT for the input nodes describing risk for each VSP parameter for the BNs describing 

viability of Puget Sound steelhead populations..........................................................................................77

Table 12. CPT describing the influence of each of the four VSP parameters on DIP viability for 

the BNs describing viability of Puget Sound steelhead ..............................................................................78

Table 13. Results of a sensitivity analysis of the influence of three components of DIP abundance 

to the VSP risk posed by DIP abundance in the BN in Figure 38 ..............................................................86

Table 14. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 49 .......97

Table 15. CPT describing the influence of MPG viability on DPS viability for the BN in Figure 50 ....106

Table 16. Number of viable DIPs required for MPG viability in each of the Puget Sound steelhead 

MPGs with a requirement of 40% viable DIPs of each major life history type........................................122

Table B-1. Interim DIP abundance goals for steelhead in Puget Sound, based on a 4-year average.......160

Table C-1. Stream habitat rating matrix for streams for Puget Sound steelhead.....................................161
xviii
xix

Dedication

Robert August Hayman

27 February 1952–20 December 2011

In the midst of developing and drafting this document, the Puget Sound Steelhead 

Technical Recovery Team was saddened by the death of one of its members, Bob Hayman. His 

contributions to the TRT went beyond his extensive knowledge of the Skagit River basin; it was 

his determination that the TRT process be logical, consistent, and transparent that ensured its 

work would meet the highest standards. Bob’s good-natured and humble manner made him 

likeable even when he was challenging your thinking. He was a tireless worker, and the 

determination he displayed in both his professional endeavors and battling cancer was inspiring. 

We all miss him.
xx
xxi

Executive Summary

Introduction

Under the U.S. Endangered Species Act (ESA), the National Oceanographic and 

Atmospheric Administration’s National Marine Fisheries Service (NMFS) is required to identify 

measurable and objective delisting criteria as part of recovery planning. These delisting criteria 

must describe the conditions under which a listed species or distinct population segment (DPS) is 

no longer in danger of extinction (endangered) or likely to become so in the foreseeable future 

(threatened). We define a viable DPS as one that is unlikely to be at risk of extinction in the 

foreseeable future; for this purpose, we adopted the viable salmonid population (VSP) criterion 

of a 100-year timeline (McElhany et al. 2000, Viable Salmon Populations and the Recovery of 

Evolutionarily Significant Units, NOAA Tech. Memo. NMFS-NWFSC-42) to evaluate risk of 

extinction. Ultimately, the identification of delisting criteria requires the consideration of 

technical analyses relating to viability, which are contained in this report, and policy decisions 

such as acceptable levels of risk, which are not. It presents the biological viability criteria 

recommended by the Puget Sound Steelhead Technical Recovery Team (PSS TRT). The 

framework and the analyses it supports do not set targets for delisting, nor do they explicitly 

identify specific populations or groups of populations for recovery priority. Rather, the 

framework and associated analyses are meant to provide a technical foundation for those charged 

with recovery of listed steelhead (Oncorhynchus mykiss) in Puget Sound, from which they can 

develop effective recovery plans at the watershed scale (and higher) that are based on 

biologically meaningful criteria.

This technical memorandum develops viability criteria for the Puget Sound Steelhead 

DPS as identified in the NMFS status review (Hard et al. 2007, Status Review of Puget Sound 

Steelhead, NOAA Tech. Memo. NMFS-NWFSC-81). The DPS was listed as a threatened 

species under the ESA in May 2007. Under the ESA, 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” and an endangered species is “any species which is in danger of 

extinction throughout all or a significant portion of its range.” The viability of a threatened 

species is therefore at some risk, and in an attempt to quantify that risk, the current document 

was developed by the PSS TRT, which was composed of scientists from federal, state, tribal, and 

local government agencies with expertise in steelhead biology and management.

The primary purpose of this document is to recommend objective, measurable, biological 

criteria for assessing the recovery and progress toward recovery of the Puget Sound Steelhead 

DPS. In doing so, we applied these criteria to an assessment of the current biological status of 

the DPS. We do not provide recommendations for criteria for delisting of the DPS under the 

ESA, nor do we evaluate whether the DPS 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 
xxii

scope of this report. Thus the criteria presented here provide a necessary but not sufficient set of 

criteria to determine the ESA listing status of the DPS.

Approach to Developing the Criteria

We developed viability criteria based on the considerations laid out in the VSP document 

(McElhany et al. 2000, Viable Salmon Populations and the Recovery of Evolutionarily 

Significant Units, NOAA Tech. Memo. NMFS-NWFSC-42, online at http://www.nwfsc.noaa

.gov/assets/25/6190_06162004_143739_tm42.pdf), which identified four key population 

parameters that influence the persistence of populations.

• Size—the abundance of all life stages of the species (often only measured as adults);

• Growth rate (productivity)—production over the entire life cycle, often measured as 

recruits or returns per spawner, or as long-term population growth rate (λ);

• Spatial structure—distribution of individuals among spawning and rearing habitat areas, 

and connectivity among those areas; and

• Diversity—variation in traits (phenotypic and genotypic) among individuals within and 

among populations in the DPS.

The goal of recovery is not merely to meet a set of defined criteria, but rather to restore or 

repair ecological processes that lead to long-term sustainability of the resource. Our approach 

views the DPS as a complex structure with important processes operating at scales ranging from 

individual breeding aggregations to the entire DPS. Along this continuum, we identified three 

hierarchical units for defining attributes related to biological processes that characterize DPS 

status, from smallest to largest: demographically independent populations (DIPs), major 

population groups (MPGs), and the entire DPS. We developed VSP-based criteria that pertain to 

each of the 32 constituent DIPs within the three MPGs (Northern Cascades, Central and South 

Puget Sound, and Hood Canal and Strait of Juan de Fuca) that compose the Puget Sound DPS. 

These criteria were developed using the Puget Sound Steelhead DPS population structure the 

TRT identified in its previous publication (Myers et al. 2015, Identifying Historical Populations 

of Steelhead within the Puget Sound Distinct Population Segment, NOAA Tech. Memo. NMFSNWFSC-128).

Analysis of Viability

To achieve full recovery, steelhead populations in the Puget Sound DPS need to be robust 

enough to withstand natural environmental variation and even some catastrophic events, and they 

should be resilient enough to support harvest and habitat loss due to human population growth. 

Recovery therefore requires sufficient abundance and productivity of the DPS and diversity 

among and within its constituent populations distributed across the DPS’s range. Achieving 

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

threats, which are outside the scope of this report). DPS sustainability implies that the number 

and distribution of sustainable populations are sufficient and that DPS-level diversity is 

conserved. Avoiding endangerment requires biological persistence, that is, assurance that all 

significant parts of the DPS have a high likelihood of persisting over a VSP time horizon, which 
xxiii

is 100 years. Achieving viability at each level implicitly requires meeting all criteria for the 

lower levels.

Our analysis of steelhead viability rests on two primary components: one component 

focused on evaluating the quantitative abundance information available for individual 

populations, and the other component aimed at assessing viability at multiple hierarchical scales 

(DIP→MPG→DPS) by incorporating qualitative and quantitative information pertaining to all 

four VSP criteria. For the first component—a quantitative analysis of individual population 

data—we applied two distinct population viability analyses (PVAs) to the DIPs that have 

sufficient information on abundance and productivity to support them. The first of these PVAs,

based loosely on a conventional spawner-recruit analysis, estimates extinction risk from 

combinations of abundance and productivity across the parameter space. The second of these 

PVAs, based on an autoregressive state-space analysis of abundance time series, uses estimates 

of demographic stochasticity to predict future abundance and extinction risk.

For the second component, we developed a knowledge-based decision support system to 

incorporate these PVAs into a framework to characterize viability that can 1) accommodate 

quantitative and qualitative information, including habitat-based (rather than strictly biological) 

metrics, and 2) estimate viability at multiple hierarchical scales (i.e., DIPs, MPGs, and the entire 

DPS). This framework is based on a Bayesian network (BN, aka Bayesian belief network or 

Bayes net) of probabilities that links the key aspects of viability—abundance, productivity, 

diversity, and spatial structure—across the DPS and all its constituent populations. The network 

framework we designed links criteria at a variety of scales and aggregates them from populationlevel criteria, through MPG-level criteria, to criteria for the entire DPS.

Identification and Application of Viability Criteria

For each DIP, the components of population-level diversity are: 1) VSP abundance—

adult and juvenile abundance relative to estimated capacity, and the probability that abundance 

would reach a specified quasi-extinction threshold (an abundance considered to be tantamount to 

functional extinction) within 100 years; 2) VSP productivity—the number of smolts per spawner 

and the number of adults per smolt (both of which influence long-term population growth rate), 

and the frequency of repeat spawners; 3) VSP diversity—the degree of hatchery influence (using 

a simple propagule-pressure analysis), variation in adult run timing, the fraction of smolts from 

resident fish, and current age structure relative to the historic distribution; and 4) VSP spatial 

structure—the fraction of intrinsic potential rearing and spawning habitat occupied, where 

intrinsic potential is the area of habitat suitable for steelhead rearing or spawning, at least under 

historical conditions.

A BN constructed for each DIP estimated DIP viability from each of these VSP 

parameters using Bayesian probabilities. The BNs for each DIP were then combined to produce 

a composite BN for estimating the viability of each MPG, with weights provided to consider DIP 

representation of each major life history type (summer run versus winter run). The composite 

BNs for each MPG were then combined to estimate the viability of the DPS as a whole, with the 

contribution of each MPG weighted equally. In combination with the requirement that all MPGs 

within a viable DPS must be viable, this approach ensured that for an DPS to be viable, a 
xxiv

sufficient fraction of its constituent DIPs spread across all its MPGs, representing each major life 

history type, must be viable.

In defining viability criteria and metrics, we used this hierarchical decision support 

system to provide an assessment of the current status of the DPS by applying the metrics to data 

available through 2011. This analysis is provided for illustration to demonstrate how the criteria 

might be used in the future to assess progress toward recovery. The assessment framework 

evaluates viability using objective measures of spawner abundance, productivity, hatchery 

influence, spawner and juvenile distribution, and several measures of population diversity (e.g., 

resident fish contribution, spawn timing, and relative effective population size). Some of these 

measures were estimated from physical habitat data, in particular metrics extracted from 

Geographic Information System (GIS) data layers that measure intrinsic potential steelhead 

production. (The algorithm used by the TRT to estimate intrinsic potential spawning or rearing 

area and potential steelhead production is described in Appendix C of this report). Fully 

quantifiable criteria are not available for several metrics, so we used TRT members’ judgment to 

evaluate those. In evaluating DIP-level viability, we gave the combined diversity and spatial 

structure metrics equal weight of the more quantifiable abundance and productivity metrics. We 

then used recent observations of population performance (primarily trends in abundance) to 

evaluate how certain we can be that the Puget Sound Steelhead DPS is sustainable under current 

conditions.

The PSS TRT considered two issues specific to steelhead viability for which little 

information is available in Puget Sound populations: the demographic contributions of a resident 

life history and the degree of iteroparity (repeat spawning). In determining the viability of 

steelhead DIPs in the Puget Sound DPS, the TRT considered the potential influence of cooccurring resident O. mykiss on anadromous steelhead demographics. The TRT concluded that 

in basins where anadromous O. mykiss abundance is below the quasi-extinction threshold, the 

risk of extinction is not necessarily 100% if resident O. mykiss are present below natural, longstanding migration barriers. Inclusion of resident fish in the viability criteria is dependent, 

however, on several conditions. First, the abundance of resident fish must be large enough to be 

self-sustaining (this will be largely dependent on the numbers of resident females). Second, 

there must be some evidence of interbreeding between the anadromous and resident forms (this 

will most likely be established by genetic analysis of the relationship between the resident fish 

and the most proximate anadromous population). To accommodate these considerations, the 

TRT included in the BN for DIP viability a diversity node describing the proportional 

contribution of smolts from resident fish.

Iteroparity is another primary characteristic of O. mykiss, with potentially substantial 

demographic consequences. Simulation analyses of the demographic consequences of 

iteroparity were conducted to develop a suite of age-structured matrix population projection 

models with annual time steps that incorporate density dependence, varying levels of repeat 

spawning, and varying levels of density-independent stochasticity and fishing mortality. The 

stochastic models were used to evaluate the influence of varying levels of repeat spawning on 

population abundance and age structure under varying levels of fishing.

The modeling indicated that the average proportion of repeat spawners in an adult 

steelhead population is relevant to population abundance and stream capacity in two ways. First, 
xxv

relative to a population with no repeat spawners, for a given average capacity of juveniles, a 

population with repeat spawners will have a larger average adult spawning population. Second, 

a given average number of adult spawners can be sustained by fewer juveniles when repeat 

spawners are present than when they are not. It is therefore likely that viable populations (DIPs) 

of steelhead can be sustained in smaller stream basins than is the case for semelparous Pacific 

salmon (Oncorhynchus spp.). The model results also indicated that repeat spawning provides 

increased levels of resilience compared to populations without repeat spawning. From a 

population rebuilding and recovery perspective, it appears that for small population sizes such as 

the ones considered in this modeling exercise, the value of specific levels of repeat spawning to 

population resilience (and repeat spawning) are most likely to be realized under a no-fishing 

scenario, regardless of the level of environmental variation (at least for the range and kind of 

variation employed in the models).

While these analyses are exploratory, they and the Integral Projection Model analysis 

applied to a wild Alaskan steelhead population in Appendix G of this technical memorandum

tend to reinforce the TRT’s conviction that iteroparity is an important consideration in a 

comprehensive evaluation of viability for anadromous coastal steelhead. The team therefore 

included in the BN for DIP viability a productivity node describing the influence of iteroparity 

on aspects of viability.

Viability Criteria for Puget Sound Steelhead

The listed unit under the ESA for O. mykiss, including anadromous steelhead, is the DPS, 

and this is the unit that must be considered for delisting. Thus delisting criteria must ultimately 

address the extinction risk of the DPS. In approaching the development of criteria for assessing 

viability (and subsequent delisting) of DPSs, we have relied on the language in the ESA, 

information described in the listing decision, concepts outlined in the VSP document, and

published research describing salmon populations and their past or potential responses to 

environmental changes. The ESA in Section 4.2.1 lists five potential factors for decline that 

must be considered in species listing decisions:

1. The present or threatened destruction, modification, or curtailment of its habitat or range;

2. Overutilization for commercial, recreational, scientific, or educational purposes;

3. Disease or predation;

4. The inadequacy of existing regulatory mechanisms; and

5. Other natural or man-made factors affecting its continued existence.

NMFS identified all five factors as contributing to the endangerment of Pacific salmonids, 

including steelhead. In considering how viability criteria might inform population delisting 

requirements, the TRT also attempted to consider these factors for decline.

For the Puget Sound Steelhead DPS, the TRT developed in this report criteria for 

recovery of steelhead that share some similarities:

1. The viability (as reflected in abundance, productivity, diversity, and spatial structure) of a 

majority of steelhead populations in each of the MPGs across the DPS is detectably 

higher than currently, using conventional PVA.
xxvi

2. At least 40% of steelhead populations in each of the three MPGs within the DPS achieve 

viability, depending on the historical biological characteristics and acceptable risk levels 

for populations within each region. (The threshold of 40% corresponds to an average 

probability of DIP viability of 67% if the DIPs that are not viable have probabilities of 

viability <55%.)

3. At least 40% of populations from each major life history type (summer run, winter run) 

historically present within each of the MPGs are viable using these criteria.

4. Natural production of steelhead from tributaries to Puget Sound not identified as primary 

spawning or rearing habitat for any of the 32 identified populations is sustained to 

provide ecological diversity and productivity sufficient to support DPS-wide recovery.

From these considerations, the PSS TRT developed the following constructs for developing 

viability criteria at the three hierarchical levels of DIP, MPG, and DPS.

DIP Viability

1. Using a simple three-bin classification (not viable, intermediate, viable), a DIP is scored 

1, 2, or 3, depending on whether the probability of viability as computed by its viability 

BN is <40%, 40–85%, or >85%, respectively. For a DIP to be considered viable, its 

probability of viability must be at least 85%; the TRT considered this estimate to be 

sufficiently high to insure persistence over 100 years. To estimate this value, the mean 

abundance and productivity criteria for the candidate DIP are each double weighted; this 

produces DIP viability scores ranging from 6 to 18 points. DIPs with scores less than 11 

are considered not viable, those with scores between 11 and 14 are considered to have 

intermediate viability, and those with scores greater than 14 (>85%, rounded up) are 

considered viable.

MPG Viability

2. An MPG is considered viable if 40% (rounded up) of its DIPs (including extinct as well 

as extant historical populations) are viable and mean DIP viability exceeds a threshold for 

viability.

a. DIPs exhibiting distinct life history strategies (i.e., summer run versus winter run) 

will be considered separate components of the MPG. Therefore, a minimum of 40% 

of summer-run and 40% of winter-run populations within an MPG must be viable to 

achieve MPG viability.

b. DIPs containing both winter-run and summer-run subpopulations predominantly 

exhibit the winter-run life history strategy in Puget Sound and will be considered 

winter-run for the purpose of estimating 2a. This approach should be reevaluated as 

data on summer-run populations becomes available.

c. A viable MPG must, in addition to the criterion outlined in 2a and 2b, have a 

geometric mean (averaged over all its DIPs) score of at least 2.2 to be considered 

viable, using the 1–3 scale for individual DIPs described under the DIP Viability 

heading above.
xxvii

DPS Viability

3. A DPS is considered viable only if all its component MPGs are viable.

The TRT applied the criteria to the 32 DIPs in the Puget Sound Steelhead DPS to provide 

a status assessment of the current viability of these units. This assessment indicates low viability 

for many DIPs, all MPGs, and the DPS as a whole. Nearly all DIPs have insufficient current 

abundance and productivity scores to be considered viable. Most DIPs also have low scores for 

diversity and spatial structure, largely because of extensive hatchery influence, low breeding 

population sizes, and freshwater habitat fragmentation or loss.

The Puget Sound Steelhead DPS is not considered to be viable by the TRT. The TRT 

concluded that the DPS is currently at very low viability; most of the 32 DIPs, all three MPGs, 

and the DPS as a whole are at low viability. Nearly all DIPs in both the Central and South Puget 

Sound MPG and the Hood Canal and Strait of Juan de Fuca MPG are not viable. Most of the 

DIPs score low for all four VSP criteria, and nearly all DIPs have insufficient current abundance 

and productivity scores to be considered viable. Most DIPs also have low scores for diversity 

and intermediate scores for spatial structure. The pattern of low viability is widespread 

throughout Puget Sound, across all three MPGs, and includes both summer-run and winter-run 

populations. The populations with highest viability—and those with highest abundance and 

diversity—are in northeastern Puget Sound (Northern Cascades MPG).
xxviii
xxix

Acknowledgments

We are grateful to the National Marine Fisheries Service’s West Coast Region for its

support to the Puget Sound Steelhead Technical Recovery Team (PSS TRT) during the 

preparation of this report. Members of the Puget Sound Domain Team, including Elizabeth 

Babcock, Steve Leider, Amilee Wilson, Alison Agness, Samantha Brooke, and Tim Tynan, were 

especially helpful to the PSS TRT’s deliberations. We also acknowledge the advice and 

contributions of Susan Bishop, Bruce Crawford, Bob Donnelly, and Steve Stone, West Coast 

Region; Tristan Peter-Contesse and Jason Mulvihill-Kuntz, Puget Sound Partnership; and Lloyd 

Moody and Sara Laborde, Governor’s Salmon Recovery Office.

Several scientists at the Northwest Fisheries Science Center (NWFSC), including Krista 

Bartz, Barry Berejikian, Shallin Busch, Tom Cooney, Mike Ford, Elizabeth Holmes, Paul 

McElhany, John McMillan, Megan Moore, Mindi Sheer, Robin Waples, and Eric Ward, were 

generous with their time in reviewing particular aspects of the report or in providing technical 

assistance. We are especially grateful to Damon Holzer, NWFSC, for his extensive assistance 

with extracting information from Geographical Information System data layers and producing 

maps. His valuable contributions made much of the TRT’s work possible.

The able assistance and cooperation of several biologists and technicians from various 

agencies were very helpful to the PSS TRT’s efforts. For their help with steelhead 

(Oncorhynchus mykiss) information and comments on aspects of this report, we thank Brett 

Barkdull, Steve Foley, Annette Hoffman, Thom Johnson, Robert Leland, and Kris Ryding, 

Washington Department of Fish and Wildlife; Will Beattie, Northwest Indian Fisheries 

Commission; Ned Currence, Nooksack Tribe; Kit Rawson, Tulalip Tribe and the Recovery 

Implementation Technical Team; Alan Chapman, Lummi Tribe; Eric Beamer and Casey Ruff, 

Skagit River System Cooperative; Larry Wasserman, Swinomish Indian Tribal Community; 

Dave Pflug, Seattle City Light; Ian Courter, Cramer and Associates; Frank Thrower, Alaska 

Fisheries Science Center (ret.); Fred Goetz, U.S. Army Corps of Engineers; and Bruce Ward, 

Department of Fisheries and Oceans Canada (ret.).

We are grateful to Bill McMillan and Nick Gayeski, Wild Fish Conservancy, for 

substantial contributions to this report. Bill was instrumental in uncovering important historical 

data on steelhead for us, helping to answer many of the TRT’s questions, and compiling many of 

the historical references appended to this report. Nick generously contributed his time and effort 

to exploring the importance of iteroparity to steelhead population viability in an innovative 

modeling exercise summarized briefly herein. We are also grateful to Donald Campton, U.S. 

Fish and Wildlife Service; Mark Chilcote, U.S. Forest Service; John Reynolds, Simon Fraser 

University; Kit Rawson, Tulalip Tribe; and three anonymous reviewers for their very helpful 

comments on this document. Finally, we thank Ed Quimby, NWFSC, for technical editing and 

formatting this publication.
xxx
xxxi

Abbreviations

BN Bayesian network

BRT Biological Review Team

CPT conditional probability table

DIP demographically independent population

DPS distinct population segment

DSS decision support system

ESA U.S. Endangered Species Act

ESU evolutionarily significant unit

IP intrinsic potential

IPM integral projection model

MARSS multivariate autoregressive state-space

MPE most probable explanation

MPG major population group

NMFS National Marine Fisheries Service

PSS TRT Puget Sound Steelhead Technical Recovery Team

PVA population viability analysis

QET quasi-extinction threshold

SAS smolt-to-adult survival

SaSI Salmonid Stock Inventory

SPAZ Salmon Population AnalyZer

SSH summer-run steelhead

VSP viable salmonid population

WDFW Washington Department of Fish and Wildlife

WSH winter-run steelhead
xxxii
Introduction

The goal of recovery and restoration is to restore or repair ecological processes that lead 

to long-term sustainability of natural resources. Like other coastal steelhead (Oncorhynchus 

mykiss) in the southern portion of their range in the western United States, that is, California 

(Boughton et al. 2007) and Oregon (Chilcote 1997), steelhead in Puget Sound have shown 

declines in abundance for decades (Hard et al. 2007). The Puget Sound Steelhead Distinct 

Population Segment (DPS) was listed as a threatened species under the U.S. Endangered Species 

Act (ESA) in May 2007. The Puget Sound Steelhead Technical Recovery Team (PSS TRT), 

which convened in March 2008, is charged with developing criteria for viability of steelhead in 

Puget Sound that contribute to the long-term sustainability of these fish. Puget Sound steelhead 

have a complex, hierarchical structure affected by ecological processes operating at scales 

ranging from individual spawning aggregations up to the entire DPS. Along this continuum, the 

TRT members identified three scales as important for defining attributes related to biological 

processes that define status: demographically independent populations (DIPs), major population 

groups (MPGs), and the entire DPS. We developed viability criteria at each scale. The TRT also 

developed attribute sets for each scale to assess viability status.

The framework described in this report and the analyses it supports do not set 

biologically based targets for delisting or recovery of steelhead, nor do they explicitly identify 

specific populations or groups of populations for recovery priority. Rather, the framework and 

associated analyses are meant to provide a technical foundation for those charged with recovery 

of listed steelhead in Puget Sound, from which they can develop effective recovery plans at the 

watershed scale (and higher) that are based on biologically meaningful criteria.

Viable Salmonid Population Criteria

Our approach to assessing viability of Puget Sound steelhead adhered to the viable 

salmonid population (VSP) criteria originally described by McElhany et al. (2000) to address 

conservation and recovery of Pacific salmonids under the ESA. According to McElhany et al. 

(2000), four parameters form the key to evaluating viability status at the population scale. We 

slightly modify their description of these parameters here:

• Population size (abundance)—the abundance of all life stages of the species;

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

measured as recruits or returns per spawner, or as long-term population growth rate (λ);

• Diversity—phenotypic and genotypic variation in traits among individuals in a 

population and among populations in the conservation unit; and

• Spatial structure—distribution of individuals among spawning and rearing habitat areas, 

and connectivity among those areas.
2

In considering recovery of salmon and steelhead units (evolutionarily significant units 

[ESUs] in the case of Pacific salmon [O. spp.] and DPSs in the case of steelhead and Atlantic 

salmon [Salmo salar]), the National Marine Fisheries Service (NMFS) focuses on these 

parameters for three reasons. First, they are reasonable predictors of avoiding extinction risk, 

which reflects the ability of a population to persist into the future (i.e., its viability). We adopt 

here the VSP approach of considering a conservation unit’s viability primarily as its ability to 

persist over a 100-year time frame. Second, they reflect general processes that are important to 

all populations of all species. Third, the parameters are measurable.

Population size, or abundance, is recognized as an important parameter because, all else 

being equal, small populations are at greater risk of extinction than large populations, primarily 

because several processes that affect population dynamics operate differently in small 

populations than they do in large populations. These processes are deterministic density effects, 

environmental variation, genetic processes, demographic stochasticity, ecological feedback, and 

catastrophes. McElhany et al. (2000) provided guidelines relating minimum abundance to each 

of these processes at both the “viable” and “critical” levels, where a critical level implies a high 

risk of extinction over a short time period.

Population growth rate (i.e., productivity over the entire life cycle) and factors that affect 

population growth rate provide information on how well a population is “performing” in the 

natural habitats it occupies during the life cycle. Estimates of population growth rate that 

indicate a population is consistently failing to replace itself demonstrate increased extinction 

risk. Although the overall focus is on population growth rate over the entire life cycle, estimates 

of stage-specific productivity—particularly productivity during freshwater life history stages—

are also important to comprehensive evaluation of population viability. Other measures of 

population productivity, such as intrinsic productivity and the intensity of density dependence, 

may provide important additional information for assessing a population’s viability. The 

McElhany et al. (2000) guidelines for population growth rate are closely linked with those for 

abundance.

Several steelhead traits exhibit considerable diversity within and among populations, and 

this variation can have important effects on population viability (Boughton et al. 2007). In a 

spatially and temporally varying environment, there are three general reasons why biological 

diversity is important for population (and DPS) viability. First, it allows a population to use a 

wider array of environments than they could without it. Second, diversity protects against shortterm spatial and temporal changes in the environment. Third, genetic diversity provides the raw 

material for adapting to long-term environmental change.

Finally, when evaluating population viability, it is important to take spatial structure into 

account for two main reasons: 1) because there is a time lag between changes in spatial structure 

and species (DPS)–level effects, overall extinction risk at the 100-year time scale may be 

affected in ways not readily apparent from short-term observations of abundance and 

productivity, and 2) population spatial structure affects evolutionary processes and may therefore 

alter a population’s ability to respond to environmental change. Spatially structured populations 

in which subpopulations occupy habitat patches connected by some low to moderate migration 

rates are often generically referred to as metapopulations. A metapopulation’s spatial structure 

depends fundamentally on habitat quality, spatial configuration, and demographic dynamics, as 
3

well as the dispersal characteristics of individuals in subpopulations. Pacific salmonids such as 

steelhead are generally recognized to have some degree of metapopulation structure. Our criteria 

for steelhead population spatial structure attempt to account for this structure.

Population viability is typically estimated as some correlate of the probability that a lowabundance threshold, known as a quasi-extinction threshold (QET), will be reached within 100 

years. With sufficient quantitative data on abundance or productivity, this can be estimated 

using a variety of statistical approaches that involve analysis of time series. Incorporating 

information on population diversity and spatial structure is also important in assessing viability 

in a larger, holistic sense, but combining this information with abundance and productivity data 

in a statistical analysis of extinction risk remains a major challenge in evaluating viability in a 

conservation framework.

However, the ESA is not concerned with the viability of populations per se, but rather 

with the extinction risk faced by an entire conservation unit (i.e., an ESU or DPS). A key 

question is how the viabilities of a suite of constituent populations and major population groups 

are related to the sustainability of the entire unit. Three factors need to be considered when 

relating VSP parameters to viable conservation units: 1) catastrophic events, 2) long-term 

demographic processes, and 3) long-term evolutionary potential. McElhany et al. (2000) 

provided several guidelines related to these factors with an emphasis on risks from catastrophic 

events:

1. Conservation units should contain multiple populations. If an ESU (or DPS) is made 

up of multiple populations, it is less likely that a single catastrophic event will cause it to 

become extinct. Also, these conservation units may function as metapopulations over the 

long term and the existence of multiple populations would be necessary for the operation 

of sustainable population-level extinction/recolonization processes. In addition, multiple 

populations within a unit increase the likelihood that a diversity of phenotypic and 

genotypic characteristics will be maintained, thus allowing natural evolutionary processes 

to operate and increasing the unit’s viability in the long term. Obviously, this guideline 

does not apply to units that appear to contain a single population. In units containing a 

single population, Guideline 6 below becomes more important.

2. Some populations in a conservation unit should be geographically widespread.

Spatially correlated environmental catastrophes are less likely to drive a widespread unit 

to extinction. This guideline also directly relates to the ESA mandate of protecting a 

species in a “significant portion of its range.”

3. Some populations should be geographically close to each other. On long temporal 

scales, conservation units may function as metapopulations and having populations 

geographically close to one another facilitates connectivity among existing populations. 

Thus a viable unit composed of multiple populations requires both widespread (Guideline 

2) and spatially close populations.

4. Populations should not all share common catastrophic risks. A conservation unit 

containing populations that do not share common catastrophic risks is less likely to be 

driven to extinction by correlated environmental catastrophes. Maintaining 

geographically widespread populations is one way to reduce risk associated with 
4

correlated catastrophes (Guideline 2), but spatial proximity is not the only reason why 

multiple populations could experience a correlated catastrophic risk.

5. Populations that display diverse life histories and phenotypes should be maintained.

When a conservation unit’s populations have a fair degree of life history diversity (or 

other phenotypic diversity), the unit is less likely to go extinct as a result of correlated 

environmental catastrophes or changes in environmental conditions that occur too rapidly 

for an evolutionary response. In addition, assuming phenotypic diversity is caused at 

least in part by genetic diversity, maintaining diversity allows natural evolutionary 

processes to operate within a unit.

6. Some populations should exceed VSP viability criteria. In other words, some 

constituent populations should have a high probability of persistence for longer than the 

criteria required for viability of an entire conservation unit. Larger and more productive 

(resilient) populations may be able to recover from a catastrophic event that would cause 

the extinction of a smaller population. A conservation unit that contains some 

populations in excess of VSP threshold criteria for abundance and population growth rate 

is less likely to go extinct in response to a single catastrophic event that affects all 

populations. It is important to note that the abundance guidelines do not take 

catastrophes into account. If a unit consists of a single population or a small number of 

populations, the viability criteria for them should be stronger than the viability criteria for 

populations within a unit composed of many populations.

7. Evaluations of a conservation unit’s status should take into account uncertainty 

about unit-level processes. Our understanding of unit-level spatial and temporal process 

is very limited. Conservation units are believed to have been historically self-sustaining 

and the historical number and distribution of populations serves as a useful default goal in 

maintaining viable units.

Approaches to Assessing Viability

We considered several approaches to assessing the viability of the Puget Sound Steelhead 

DPS. Population viability analysis (PVA) (Beissinger and McCullough 2002) is a set of 

quantitative or semiquantitative methods used to estimate the probability that a population or 

collection of populations will persist for a defined period of time in a particular environment. 

The seminal papers by Dennis et al. (1991) and Thompson (1991) described some of these 

methods. PVA can assist with evaluating viability at the population level, but it can be

challenging to extend this analysis to evaluate viability at higher spatial scales involving multiple 

populations (yet see, e.g., Ward et al. 2010). We therefore explored additional tools, including 

decision support systems (DSSs), to conduct a more comprehensive and hierarchical assessment 

of Puget Sound steelhead viability (see the Decision Support Systems as Tools for Assessing 

Viability section of this technical memorandum).

In recent status review updates for Pacific salmon and steelhead (e.g., Good et al. 2005), 

biological review teams (BRTs) have adopted a risk assessment method that has been used for 

Pacific salmon recovery planning and is outlined in the VSP report (McElhany et al. 2000). In 

this approach, risk assessment is addressed first at the population level, then at the overall ESU 

or DPS level. In this approach, individual populations are assessed according to the four VSP 
5

criteria: abundance, growth rate/productivity, spatial structure, and diversity. The condition of 

individual populations is then summarized at the ESU or DPS level, and the patterns of 

abundance, distribution, and diversity of populations are considered in evaluating the status of 

the ESU or DPS as a whole. These larger scale patterns include the total number of viable

populations, geographic distribution of these populations (to ensure inclusion of major life 

history types and to buffer the effects of regional catastrophes), and connectivity among these 

populations (to ensure appropriate levels of gene flow and recolonization potential in case of 

local extirpations). The considerations are reviewed in McElhany et al. (2000).

The revised risk matrix integrates the four major population VSP criteria directly into the 

risk assessment process. After reviewing all relevant biological information for the ESU, each 

evaluator assigns a risk score (see below) to each of the four criteria. The scores are tallied and 

reviewed by the evaluation team before making its overall risk assessment. Although this 

process helps to integrate and quantify a large amount of diverse information, there is no simple 

way to translate the risk matrix scores directly into an assessment of overall risk. For example, 

simply averaging the values of the various risk factors would not be appropriate; an ESU or DPS 

at high risk for low abundance would be at high risk even if there were no other risk factors.

Scoring population viability criteria: Risks for each population viability factor are ranked

on a scale of 1 (very low risk) to 5 (very high risk):

1. Very low risk. Unlikely that this factor contributes significantly to risk of extinction 

throughout all or a significant portion of the range, either by itself or in combination with 

other factors.

2. Low risk. Unlikely that this factor contributes significantly to risk of extinction 

throughout all or a significant portion of the range by itself, but some concern that it may, 

in combination with other factors.

3. Moderate risk. This factor contributes significantly to long-term risk of extinction 

throughout all or a significant portion of the range, but does not in itself constitute a 

danger of extinction in the near future.

4. High risk. This factor contributes significantly to long-term risk of extinction 

throughout all or a significant portion of the range and is likely to contribute to short-term 

risk of extinction in the foreseeable future.

5. Very high risk. This factor by itself indicates danger of extinction throughout all or a 

significant portion of the range in the near future.

A “recent events” category considers events that have predictable consequences for ESU 

or DPS status in the future, but have occurred too recently to be reflected in the population data. 

Examples include a climatic regime shift or El Niño event that may be anticipated to result in 

increased or decreased marine productivity in subsequent years. It is scored as follows:

++ (double plus) expect a strong improvement in status of the ESU or DPS,

+ (single plus) expect some improvement in status,

0 neutral effect on status,

– (single minus) expect some decline in status,

– – (double minus) expect a strong decline in status.
6

This analysis of overall risk to the ESU or DPS throughout all or a significant portion of 

its range has used categories that correspond to definitions in the ESA: in danger of extinction, 

likely to become endangered in the foreseeable future, or neither. These evaluations do not 

consider protective efforts (conservation measures) and therefore are not recommendations 

regarding listing status. The overall risk assessment reflects professional judgment by each

evaluation team member and thus is necessarily somewhat subjective. This assessment is guided 

by the results of the risk matrix analysis as well as expectations about likely interactions among 

factors. For example, a single factor with a high risk score might be sufficient to result in an 

overall score of “in danger of extinction throughout all or a significant portion of the range,” but 

a combination of several factors with more moderate risk scores could also lead to the same 

conclusion.

To allow for uncertainty in judging the actual risk facing the ESU or DPS, the evaluation 

team has often adopted a “likelihood point” method, often referred to as the FEMAT method 

because it is a variation of a method used by scientific teams evaluating options under former 

President Clinton’s Forest Plan (Forest Ecosystem Management: An Ecological, Economic, and 

Social Assessment Report of the Forest Ecosystem Management Assessment Team, or FEMAT). 

In this approach, each team member distributes 10 likelihood points among the three ESU/DPS

risk categories, reflecting one’s opinion of how likely that category correctly reflects the true 

ESU/DPS status. Thus if a member were certain the ESU or DPS was in the “not at risk” 

category, all 10 points could be assigned to that category. A reviewer with less certainty about 

ESU/DPS status could split the points among two or even three categories. This method has 

been used in all status review updates for anadromous Pacific salmonids since 1999; although 

not strictly scientific, it does capture a range of scientific judgment and opinion about extant risk.

Assessing a population’s viability in a comprehensive way requires consideration of all 

four VSP criteria (see also Boughton et al. 2007). That being said, relating the contribution of 

lost diversity or eroded spatial structure quantitatively to extinction risk is no trivial matter. We 

therefore adopted methods that can accommodate both qualitative and quantitative information 

to assess viability and develop viability criteria. These methods are explained in detail below. 

But first we describe our approaches to evaluating current viability at the population level using 

quantitative criteria. For this effort we relied on two different methods: viability curve analysis 

and evaluation of abundance data time series.
7

Viability Assessment Methods

The PSS TRT adopted methods described by Good et al. (2005) to evaluate data that 

affect the four VSP parameters in the Puget Sound Steelhead DPS; these methods are described 

briefly below. State and tribal comanagers provided data on abundance, the fraction of hatchery 

origin spawners (where available), harvest, age structure, and hatchery releases to the TRT. Data 

on adult returns were obtained from a variety of sources, including time series of freshwater 

spawner surveys, redd counts, and historical catch data. Time series of estimated abundance 

were assembled and analyzed for each population that had sufficient data. Estimates of harvest 

were provided for several populations.

Recent Abundance

Recent abundance of natural spawners is reported as the geometric mean (and range) of 

the most recent data to be consistent with previous coast-wide status reviews of steelhead. 

Geometric means were calculated to represent the recent abundance of natural spawners for each

DIP within the DPS. Geometric means were calculated for the most recent 5 years; this time 

frame was selected to correspond with modal age at maturity. Zero values in the data set were 

replaced with a value of 1 and missing data values within a multiple-year range were excluded 

from geometric mean calculations. The geometric mean is the nth root of the product of the n

data

XG ���� = �N1N2N3 ... Nn n

where Nt = 1...n is the abundance of natural spawners in year t. Arithmetic means (and ranges) 

were also calculated for the most recent abundance data

XA��� = ��Nt

n

t=1

��n

where Nt is the abundance of natural spawners in year t.

Trends in Abundance

Short-term and long-term trends were calculated from time series of the total number of 

adult spawners. Short-term trends were calculated using data from 1995 to the most recent year

(2010). Long-term trends were calculated using all the data in a time series. Trend was 

calculated as the slope of the regression of the number of natural spawners (log-transformed) 

over the time series. To mediate for zero values, 1 was added to natural spawners before 

transforming the data. Trend was reported in the original units as exponentiated slope, such that 

a value greater than 1 indicates a population trending upward and a value less than 1 indicates a 

population trending downward. The regression was calculated as
8

ln(N + 1) = β0 + β1X + ε

where N is the natural spawner abundance, β0 is the intercept, β1 is the slope of the equation, and 

ε is the random error term.

Confidence intervals (95%) for the slope, in their original units of abundance, were

calculated as

exp(ln(b1) − t0.05(2),ddsb1) ≤ β1 ≤ exp(ln(b1) + t0.05(2),ddsb1)

where b1 is the estimate of the true slope, β1, t0.05(2),df is the two-sided t-value for a confidence 

level of 0.95, df is equal to n – 2, n is the number of data points in the time series, and sb1 is the 

standard error of the estimate of the slope, b1.

Population Growth Rate

In addition to analyses of trends in natural spawners, the median short-term population 

growth rate (λ) of natural-origin spawners was calculated where possible as a measure for 

comparative risk analysis. Lambda incorporates overlapping generations and calculates running 

sums of cohorts. Its interpretation can be difficult because there is no adjustment for density 

dependence, which is known to be a factor affecting productivity during the freshwater phase of 

the life cycle in steelhead. Nevertheless, it is widely applied in viability assessment, as most 

population extinctions are the result of steady declines, λ < 1. It has been developed for data sets 

with high sampling error and age-structure cycles (Holmes 2001). These methods have been 

extensively tested using simulations for both threatened and endangered populations, as well as 

for stocks widely believed to be at low risk (Holmes 2004), and cross-validated with time-series 

data (Holmes and Fagan 2002).

Ideally, the λ of natural-origin spawners would be calculated on the basis of natural 

production alone. Nearly all Puget Sound steelhead abundance data series are based on redd 

counts to which an average number of fish per redd is applied. Counts or proportions of hatchery 

fish contributing to natural production typically were not available. Thus it is likely that our 

computation of λ includes a mixture of hatchery and natural-origin spawners, which could bias 

the estimates of productivity of wild fish. A multistep process based on methods developed by 

Holmes (2001) and Holmes and Fagan (2002) and described in McClure et al. (2003) was used 

to calculate estimates for λ, its 95% confidence intervals, and its probability of decline, P(λ < 1). 

The first step was calculating 4-year running sums (R) for natural-origin spawners as

Rt = �Nt−i+1

4

i=1

where Nt is the number of natural-origin spawners in year t. A 4-year running sum window was 

used, as the analysis by McClure et al. (2003) indicates this is an appropriate window for a 

diverse range of salmonid life histories.

Next an estimate of μ, the rate at which the median of R changes over time (Holmes

2001), was calculated as
9

μ̂= mmm �l �

Rt+1

Rt

��

the mean of the natural log-transformed running sums of natural-origin spawners. The point 

estimate for λ was then calculated as the median annual population growth rate

λ̂ = μ�

Confidence intervals (95%) were calculated for λ̂ to provide a measure of the uncertainty 

associated with the growth rate point estimate. First an estimate of variability for each 

population was determined by calculating an estimate for using the slope method (Holmes 2001). 

The slope method formula is

σ�ppp

2 = slope of the regression of v v �l �

Rt+τ

Rt

��vs. τ

where τ is a temporal lag in the time series of running sums.

Individual population variance estimates were highly uncertain, so a more robust variance 

estimate, σ�aaa

2 , was obtained by averaging the σ�ppp2 estimates from all the populations in the 

DPS. This average variance estimate was then applied as the variance for every population in 

the DPS. The degrees of freedom associated with the average variance estimate are obtained by 

summing the degrees of freedom for each of the individual population variance estimates. The 

degrees of freedom for the individual population estimates were determined using the method of 

Holmes and Fagan (2002), which identifies the adjusted degrees of freedom associated with 

slope method variance estimates. The calculation for the adjusted degrees of freedom is

d = 0.212n − 1.215

where n is the length of the time series. Using the average variance estimate and the summed 

degrees of freedom, the 95% confidence intervals for λ were calculated as

exp(μ� ± t0.05(2),dd)�σ�ss

2 /(n − 4)

Recruitment

Recruits, or spawners in the next generation, from a given broodyear were calculated as

Ct = � Nt+iA(i)t+i

M M M

i=1

where Ct is the number of recruits from broodyear t, Nt is the number of natural-origin spawners 

in year t, and A(i)t is the fraction of age i spawners in year t. The estimate of preharvest recruits 

is similarly

C(p p )t = � Pt+iA(i)t+i

M M M

i=1
10

where C(preHarvest)t is the number of preharvest recruits in year t, Pt is the number of naturalorigin spawners that would have returned and appeared on the spawning grounds in year t had 

there not been a harvest, and A(i)t is the fraction of age i spawners in year t had there not been a 

harvest. (Because Pt is in terms of the number of fish, it can be quite difficult to estimate; thus

simplifying assumptions are often made. See Hard et al. 2007.)

Demographic Data and Analyses

The data considered in this report include estimates of steelhead natural escapement, as 

calculated from index redd count statistics obtained from the Washington Department of Fish 

and Wildlife (WDFW). These data are for winter‐run steelhead primarily (the sole summer‐run 

exception is from the Tolt River) and date from as early as 1977. The 20 populations under 

analysis are considered by the TRT to be potential DIPs. However, sufficient quantitative 

abundance data were not available for other populations among the 32 total candidate DIPs. The 

steelhead DIPs for which insufficient quantitative data were available for analysis are: Drayton 

Harbor Winter Run, Nooksack River Winter Run, South Fork Nooksack River Summer Run, 

Nookachamps Creek Winter Run, Baker River Summer Run and Winter Run, Sauk River 

Summer Run and Winter Run, Canyon Creek Summer Run, Deer Creek Summer Run, North 

Fork Skykomish River Summer Run, North Lake Washington Tributaries Winter Run, South 

Puget Sound Tributaries Winter Run, and East Kitsap Peninsula Winter Run. Basic analyses of 

natural escapement data are in Table 1 through Table 3; these analyses focus on data from the 

entire time series, data since 1995, and data from the most recent 5 years.

Data from the Entire Series

Since 1977, Puget Sound steelhead abundance has shown a widespread declining trend 

over much of the DPS (Table 1). None of the 21 populations evaluated exhibit estimates of long‐

term population growth rate (λ = R0 = e

r

, where R0 is the net birth rate and r is the intrinsic 

geometric growth rate) that are significantly positive; only two of these has a point estimate of

λ >1 (indicating positive population growth): Samish River winter run and Snohomish/

Skykomish Rivers Winter Run. In fact, only one estimate of population growth rate was

significantly different from 1, indicating little evidence for population trend, but statistical power 

is limited by missing data and high variability. In general, the highest growth rates over the 

entire series were estimated in the Northern Cascades MPG, and the lowest rates were estimated 

in the Central and South Puget Sound MPG. Data from redd counts in the Nooksack River in 

2010 and 2011 indicated abundances of 1901 and 1774 spawners, respectively, but no data series 

was available to estimate trend.

Data Since 1995

Puget Sound winter‐run steelhead abundance since 1995 has also shown a widespread 

declining trend over much of the DPS (Table 2). Only one estimate of population growth rate 

was significantly different from 1, indicating little evidence for population trend, but the 

statistical power of those estimates was even lower in this shorter data set. For most populations, 

the point estimates of population growth are even more negative in this recent period. Only one 

of the 20 populations evaluated exhibit a point estimate of growth rate that was significantly 

positive (West Hood Canal Tributaries Winter Run).
11

Table 1. Estimates of exponential trend in the natural logarithm (ln) of natural spawners (λ) for DIPs of 

steelhead in the Puget Sound DPS over the entire data series. The Tahuya River winter run is part 

of the South Hood Canal Tributaries Winter-Run DIP and the Morse Creek winter run is part of 

the Strait of Juan de Fuca Tributaries DIP. Where necessary, data were interpolated between 

adjacent years (max. n = 3) for some populations to estimate the 95% CIs. NC = not calculated.

Population Exp. trend ln(nat. spawners) (95% CI)

Northern Cascades (South Salish Sea) MPG

Drayton Harbor Tributaries Winter Run NC

Nooksack River Winter Run NC

South Fork Nooksack River Summer Run NC

Samish River winter runa (1979–2011) 1.031 (0.915–1.160)

Nookachamps Creek Winter Run NC

Baker River Summer Run and Winter Run NC

Sauk River Summer Run and Winter Run NC

Skagit R. Summer Run and Winter Run (1978–2011) 0.997 (0.921–1.079)

Canyon Creek Summer Run NC

Pilchuck River Winter Run (1981–2011) 0.984 (0.879–1.101)

Stillaguamish River Winter Run (1985–2011) 0.963 (0.879–1.055)

Deer Creek Summer Run NC

Snohomish/Skykomish Rivers Winter Run (1981–2011) 1.012 (0.887–1.156)

North Fork Skykomish River Summer Run NC

Snoqualmie River Winter Run (1981–2011) 0.967 (0.902–1.037)

Tolt River Summer Run (1985–2011) 0.973 (0.801–1.182)

Central and South Puget Sound MPG

Cedar River Winter Run (1981–2011) 0.774 (0.592–1.010)

N. Lake Washington and Lake Sammamish Winter Run NC

Green River Winter Run (1978–2011) 0.975 (0.885–1.074)

Puyallup/Carbon Rivers Winter Run (1983–2011) 0.939 (0.860–1.026)

White River Winter Run (1983–2011) 0.974 (0.888–1.068)

Nisqually River Winter Run (1980–2011) 0.938 (0.853–1.031)

South Puget Sound Tributaries Winter Run NC

East Kitsap Peninsula Tributaries Winter Run NC

Hood Canal and Strait of Juan de Fuca MPG

South Hood Canal Tributaries Winter Run (1998–2011) 0.904 (0.431–1.896)

Tahuya River winter runa (1981–2011) 0.991 (0.877–1.121)

East Hood Canal Tributaries Winter Run (1981–2011) 0.985 (NC)

Skokomish River Winter Run (1982–2011) 0.975 (0.867–1.097)

West Hood Canal Tributaries Winter Run (1997–2011) 1.237 (1.083–1.393)

Dungeness R. Summer Run and Winter Run (1988–96) 0.935 (NC)

Strait of Juan de Fuca Tribs. Winter Run (1998–2010) 0.921 (0.501–1.692)

Morse Creek winter runa (1984–2010) 0.972 (0.907–1.042)

Elwha River Winter Runb (1986–1997) 0.864 (NC)

a One component of a DIP

b Historically, a summer run may have been present in the Elwha River; however, in the aftermath of dam 

construction in the early 1900s, a native summer run in the Elwha River basin may no longer be present. Further 

work is needed to distinguish whether existing feral summer-run steelhead are derived from introduced Skamania 

Hatchery (Columbia River) summer-run steehead.
12

Table 2. Estimates of exponential trend in the natural logarithm (ln) of natural spawners (λ) for DIPs of 

steelhead in the Puget Sound DPS since 1995. The Tahuya River winter run is part of the South 

Hood Canal Tributaries Winter-Run DIP and the Morse Creek winter run is part of the Strait of 

Juan de Fuca Tributaries Winter-Run DIP. Where necessary, data were interpolated between 

adjacent years (max. n = 3) for some populations to estimate the 95% CIs. NC = not calculated.

Population Exp. trend ln(nat. spawners) (95% CI)

Northern Cascades (South Salish Sea) MPG

Drayton Harbor Tributaries Winter Run NC

Nooksack River Winter Run NC

South Fork Nooksack River Summer Run NC

Samish River winter runa 0.993 (0.592–1.666)

Nookachamps Creek Winter Run NC

Baker River Summer Run and Winter Run NC

Sauk River Summer Run and Winter Run NC

Skagit River Summer Run and Winter Run 0.966 (0.494–1.891)

Canyon Creek Summer Run NC

Pilchuck River Winter Run 0.928 (0.413–2.085)

Stillaguamish River winter‐run 0.895 (0.729–1.098)

Deer Creek Summer Run NC

Snohomish/Skykomish Rivers Winter Run 0.953 (0.220–4.123)

North Fork Skykomish River Summer Run NC

Central and South Puget Sound MPG

Cedar River Winter Run 0.678 (0.434–1.060)

N. Lake Washington and Lake Sammamish Winter Run NC

Green River Winter Run 0.902 (0.703–1.157)

Puyallup/Carbon Rivers Winter Run 0.928 (0.700–1.229)

White River Winter Run 0.987 (0.794–1.228)

Nisqually River Winter Run 0.965 (0.615–1.514)

South Puget Sound Tributaries Winter Run NC

East Kitsap Peninsula Tributaries Winter Run NC

Hood Canal and Strait of Juan de Fuca MPG

South Hood Canal Tributaries Winter Run 0.904 (0.431–1.896)

Tahuya River winter runa 0.974 (0.743–1.276)

East Hood Canal Tributaries Winter Run 0.985 (NC)

Skokomish River Winter Run 1.007 (0.720–1.408)

West Hood Canal Tributaries Winter Run 1.237 (1.083–1.393)

Sequim/Discovery Bays Tributaries Winter Run 0.887 (0.709–1.111)

Dungeness River Summer Run and Winter Run NC

Strait of Juan de Fuca Tributaries Winter Run 0.921 (0.501–1.692)

Morse Creek winter runa 0.948 (0.830–1.083)

Elwha River Winter Runb NC

a One component of a DIP. b Historically, a summer run may have been present in the Elwha River; however, in the aftermath of dam 

construction in the early 1900s, a native summer run in the Elwha River basin may no longer be present. Further 

work is needed to distinguish whether existing feral summer-run steelhead are derived from introduced Skamania 

Hatchery (Columbia River) summer-run steehead.
13

Table 3. Geometric means of natural spawners for DIPs of steelhead in the Puget Sound DPS over the 

most recent 5 years of available data. The Tahuya River winter run is part of the South Hood 

Canal Tributaries Winter-Run DIP and the Morse Creek winter run is part of the Strait of Juan de 

Fuca Tributaries Winter-Run DIP. Where necessary, data were interpolated between adjacent 

years (max. missing n = 1) for some populations to estimate the 95% CIs. NC = not calculated.

Population Geometric mean (95% CI)

Northern Cascades (South Salish Sea) MPG

Drayton Harbor Tributaries Winter Run NC

Nooksack River Winter Run (2010–2011) 1,820.8 (NC)

South Fork Nooksack River Summer Run NC

Samish River winter runa (2007–2011) 581.3 (467.7–694.9)

Nookachamps Creek Winter Run NC

Baker River Summer Run and Winter Run NC

Sauk River Summer Run and Winter Run NC

Skagit River Summer Run and Winter Run (2007–2011) 4,078.0 (3,578.2–4,577.8)

Canyon Creek Summer Run NC

Pilchuck River Winter Run (2007–2011) 512.0 (390.0–634.1)

Stillaguamish River Winter Run (2007–2011) 301.3 (240.5–362.1)

Deer Creek Summer Run NC

Snohomish/Skykomish Rivers Winter Run (2007–2011) 917.5 (785.3–1,049.7)

North Fork Skykomish River Summer Run NC

Snoqualmie River Winter Run (2009–2011) 591.9 (NC)

Tolt River Summer Run (2007–2011) 70.7 (58.4–82.9)

Central and South Puget Sound MPG

Cedar River Winter Run (2007–2011) 1.9 (0.6–3.2)

North Lake Washington and Lake Sammamish Winter Run NC

Green River Winter Run (2007–2011) 660.3 (456.6–864.1)

Puyallup/Carbon Rivers Winter Run (2007–2011) 394.2 (340.9–447.6)

White River Winter Run (2007–2011) 366.2 (275.9–456.5)

Nisqually River Winter Run (2007–2011) 367.8 (280.4–455.2)

South Puget Sound Tributaries Winter Run NC

East Kitsap Peninsula Tributaries Winter Run NC

Hood Canal and Strait of Juan de Fuca MPG

South Hood Canal Tributaries Winter Run (2007–2011) 101.8 (74.8–128.9)

Tahuya River winter runa (2007–2011) 84.3 (58.3–110.4)

East Hood Canal Tributaries Winter Run (2007–2011) 30.1 (15.5–44.7)

Skokomish River Winter Run (2007–2011) 407.9 (359.5–456.3)

West Hood Canal Tributaries Winter Run (2007–2011) 181.7 (141.4–222.0)

Sequim/Discovery Bays Tributaries Winter Run (2007–2011) 17.1 (11.4–22.9)

Dungeness River Summer Run and Winter Run (1992–1996)b 304.0 (289.4–318.6)

Strait of Juan de Fuca Tributaries Winter Run (2006–2010) 176.4 (133.8–219.0)

Morse Creek winter runa (2007–2011) 56.9 (NC)

Elwha River Winter Run (1993–1997)b 125.1 (82.0–168.1)

a One component of a DIP.

b These are the most recent 5 years of data.
14

Data from the Most Recent 5 Years

Over the most recent 5 years where data were available, Puget Sound winter‐run 

steelhead abundance has been low over much of the DPS, with a geometric mean less than 250 

fish annually for eight of the 20 populations evaluated (Table 3). One of these is in the Northern 

Cascades MPG (Tolt River Summer Run), one is in Central and South Puget Sound MPG (Cedar 

River Winter Run), and six winter-run DIPS are in the Hood Canal and Strait of Juan de Fuca

MPG (South Hood Canal Tributaries, East Hood Canal Tributaries, West Hood Canal

Tributaries, Sequim/Discovery Bays Tributaries, Strait of Juan de Fuca Tributaries, and Elwha 

River). Only seven populations had a geometric mean greater than 500 fish—Nooksack River, 

Samish River, Skagit River, Pilchuck River, Snohomish River/Skykomish Rivers, Snoqualmie 

River, and Green River winter runs—and all but one of these are in the Northern Cascades MPG. 

The Cedar River Winter-Run population is extremely low in abundance. Eleven populations 

have no quantitative abundance estimates, and an additional two—Dungeness River Summer 

Run and Winter Run and Elwha River Winter Run—have no recent quantitative estimates.

Historically, a summer run may have been present in the Elwha River; however, it is 

possible that the run was extirpated or residualized when the Elwha and Glines Canyon dams 

were constructed in the early 1900s at RKM 7.9 and RKM 21.6, respectively. Although 

summer-run steelhead have been observed in the pools below the Elwha Dam in recent years, it 

is most probable that these fish are the product of nonnative Skamania Hatchery summer-run 

steelhead releases into the Elwha River. The historical distribution of Elwha River summer-run 

steelhead is unknown. Further study is required to establish whether there is any legacy of a 

native summer run above or below the recently removed dams.

Collectively, these data indicate that several populations are at low abundance (13 of 20 

populations with fewer than 500 spawners annually) and suggest that nearly all populations 

exhibit declining trends (low power and high variability precludes detection of clear trends in 

many cases using these simple measures). Natural escapement of winter‐run steelhead 

throughout Puget Sound is currently far below historical estimates, particularly in southern Puget 

Sound and on the Olympic Peninsula.

Population Viability Analysis

The abundance trends and numbers described above alone do not always give a clear 

picture of population viability. Small salmonid populations can be viable if not trending 

downward. In addition, estimates of population growth rate and annual trend for steelhead, 

based largely on redd counts of a mixture of wild and feral hatchery fish, may be biased because 

of the extended freshwater life history of these fish, and due to density-dependent ecological 

interactions between hatchery and wild fish on spawning or rearing grounds. Finally, these data 

address only two of the four VSP criteria. Evaluating the viability of populations or aggregates 

of populations that are perceived to be at high risk of extinction—usually the case with 

threatened and endangered species—is typically challenging. Such populations are often 

characterized by meager data on abundance or productivity. Ideally, when such data do exist, a 

PVA, complete with estimates of extinction risk and minimum viable population sizes, will be 

conducted. But even if a detailed PVA is not done, an attempt to describe the population’s likely 

future trajectory is crucial to identifying population status and recovery options. Identifying a 
15

population or conservation unit’s current state and likely future state is valuable in informing 

managers about how much improvement in abundance and productivity is necessary to arrest a 

declining trend (Holmes 2001). Such an exercise can be instrumental in helping to identify 

priorities among different populations with different trends and to quantify the short-term risks 

until better information is acquired. A now widely used estimator for population extinction was 

proposed by Dennis et al. (1991), in which treating a time series of abundance as a diffusion 

process can yield estimates of extinction risk. A key advantage of this approach is that it 

requires only two parameters to estimate the state of the population into the future based on past 

censuses, namely, the arithmetic mean and variance of the log population growth rate; this 

variance accounts for sources of variability of abundance, including environmental and 

demographic stochasticity and observation error (Dennis et al. 1991). The approach makes 

analysis of time series tractable, in part because it treats a population’s trajectory as though it is 

at stochastic equilibrium, subject primarily to random variation, with a state at a particular time 

little influenced by previous states (especially as the interval increases). However, this method 

has difficulty capturing the ability of populations at high risk of extinction to recover from very 

low levels.

Evaluating the viability of DIPs of steelhead was made challenging by the lack of 

quantitative abundance and productivity data for many of them. Additionally, some data sets 

describe multiple DIPs within a single basin. For DIPs that had quantitative or semiquantitative 

abundance estimates, we employed a combination of viability curves developed from stockrecruitment analysis and autoregressive state-space models to analyze population viability. 

Viability curves such as those depicted in Figure 1 are useful in illustrating the current extinction 

Figure 1. Viability as a function of abundance and productivity. The dotted curves depict isopleths of 

equivalent extinction risk along different combinations of abundance and productivity and 

corresponding to different viability categories (1, 2, or 3). For a given abundance, viability 

increases with increasing productivity; conversely, for a given productivity, viability increases 

with increasing abundance. The black dot and solid lines estimate the current risk of extinction 

and its 50% and 95% confidence ellipses for a hypothetical population. (Adapted from 

McElhany et al. 2007 and Waples et al. 2010.)
16

or quasi-extinction risk of a population under recent conditions of abundance and productivity. 

They require a demographic model to forecast future population viability from available trends 

in abundance and productivity.

For 20 of the 32 steelhead DIPs, we used viability curves to characterize the relationship 

between population abundance, productivity, and quasi-extinction risk (or conversely, the 

probability of persistence; see Table 4 and Table 5). DIPs for which we could not conduct such 

analyses included Drayton Harbor Tributaries Winter Run, Nooksack River Winter Run, South 

Fork Nooksack River Summer Run, Sauk River Summer Run and Winter Run, Baker River 

Summer Run and Winter Run, Canyon Creek Summer Run, Nookachamps Creek Winter Run, 

North Fork Skykomish River Summer Run, Deer Creek Summer Run, South Puget Sound 

Tributaries Winter Run, and East Kitsap Peninsula Tributaries Winter Run. The viability curve 

approach was developed in an effort to establish recovery criteria for threatened salmon and 

steelhead populations and was first described in McElhany et al. (2003). The points that describe 

a viability curve reflect combinations of abundance and productivity that generate the same

probability of reaching or exceeding a QET (Figure 2). The QET is established for each 

Table 4. Population persistence categories (McElhany et al. 2006).

Population 

category

Probability of 

persistence

Probability of 

extinction Description of extinction risk in 100 years

0 0–40% 60–100% Either extinct or very high risk

1 40–75% 25–60% Relatively high risk

2 75–95% 5–25% Moderate risk

3 95–99% 1–5% Low (“negligible”) risk (VSP)

4 >99% <1% Very low risk

Table 5. Population abundance (number of spawners) relative to persistence category and population size 

category (after McElhany et al. 2007). The Chinook salmon (Oncorhynchus tshawytscha) data 

were used to construct the viability curves in Figure 2.

Species

Size 

category

Persistence category

0 1 2 3 4

Chum salmon Small <250 250–300 300–500 500–1,000 >1,000

(O. keta) Medium <500 500–600 600–700 700–1,000 >1,000

Large <700 700–850 850–1,000 1,000–1,200 >1,200

Chinook salmon Small <100 100–200 200–500 500–1,000 >1,000

Medium <350 350–450 450–600 600–1,000 >1,000

Large <600 600–750 750–1,000 1,000–1,300 >1,300

Coho salmon Small <500 500–700 700–1,000 1,000–1,300 >1,300

(O. kisutch) Medium <1,000 1,000–1,400 1,400–2,000 2,000–2,400 >2,400

Large <1,600 1,600–2,000 2,000–3,000 3,000–3,600 >3,600

Steelhead Small <100 100–200 200–500 500–750 >750

Medium <200 200–250 250–500 500–1,000 >1,000

Large <400 400–450 450–500 500–1,000 >1,000
17

Figure 2. Hypothetical viability curves showing the relationship between levels of extinction risk and 

population persistence categories. The example is based on the Chinook salmon values in 

Table 5. Each of the curves indicates a different risk level corresponding to a combination of 

abundance (y-axis) and productivity (x-axis, here as recruits per spawner). The numbers in the 

circles are the persistence categories associated with each region of the plot (i.e., the area between 

adjacent curves). For example, a population with a risk category of 0 is described as a population 

that is nearly extinct and a population with a risk category of 3 or 4 is described as viable 

(Table 4).

population by first considering the potential of the freshwater habitat to support steelhead rearing 

and spawning, then applying a minimal marine survival rate to estimates of smolt production that 

would produce an abundance of spawners considered to be too low for sustained viability. 

Populations with productivity and abundance combinations above (to the right of) the curve have 

a lower extinction risk than those along the curve, while those below (to the left of) the curve 

have a higher risk.

Relating abundance, productivity, and extinction risk is accomplished using a simulation 

model with a stochastic recruitment function having terms for productivity, carrying capacity, 

recruitment variability, age structure, future harvest rate, and a reproductive failure threshold. 

To estimate extinction risk for any particular set of input parameters, the model is run thousands 

of times to determine the fraction of simulations that drop below a critical risk threshold. A 

viability curve is drawn by determining combinations of productivity and capacity (abundance) 

that are associated with a given level of risk. Drawing the curve for any particular group of fish 

requires appropriate estimates of recruitment variability, age structure, future harvest rate, and 

reproductive failure threshold. Note that the curve is not calculated from empirical estimates of 

productivity and abundance—instead, a range of hypothetical abundances and capacities 

(abundances) are explored to produce a curve. The viability curve can be thought of as a target 
18

for population abundance and productivity to achieve a certain level of extinction risk; it is not a 

comprehensive evaluation of population status (McElhany et al. 2003). We apply it to Puget 

Sound steelhead to evaluate status relative to desirable levels of abundance and productivity and 

compare these results to other methods to assess population viability (described in the Population 

Viability Analysis with Autoregressive State-Space Models subsection below).

In order to evaluate a particular population’s status relative to a viability curve estimate, 

the population’s abundance and productivity must be estimated. We used the MeanRS method 

(McElhany et al. 2006) to estimate these parameters. Productivity is a measure of a population’s 

resilience or tendency to return to higher abundance if the population declines to low abundance. 

Using the MeanRS method, this tendency is estimated as the geometric mean recruits per 

spawner for the broodyears with the lowest half of spawner abundances. The abundance is 

estimated as the geometric mean recruitment over the time series. The characteristics of the 

MeanRS method compared to other possible approaches are described in McElhany et al. (2006). 

The MeanRS method is solidly based on the empirical data because it does not depend on 

extrapolation outside the observed ranges of recruitment and abundance.

Estimating a population’s abundance and productivity requires input data on population 

spawner abundance, the fraction of hatchery origin spawners, harvest rates, and the population 

age structure. All of these parameters are estimated with error, sometimes considerable error. 

This error is incorporated into the analysis by using a Monte Carlo approach of simulating many 

equally plausible data sets based on our understanding of the measurement errors, then 

calculating the MeanRS output for each simulated data set. This gives a distribution of possible 

abundance and productivity combinations for the current state of the population, which can be 

depicted in the form of probability contours. We used the Salmon Population AnalyZer (SPAZ) 

computer program, version 1.3.4, to generate viability curves and current status probability

contours (McElhany and Payne 2006).

Stochastic Population Viability Analysis

We used a stochastic population viability model incorporated into SPAZ to characterize 

viability of populations for which we had temporal estimates of relative abundance (typically, 

number of spawners estimated from redd counts), using estimates of population abundance and 

productivity under different demographic models. SPAZ can estimate extinction risk, minimum 

viable population size, and population change criteria. The program estimates stock-recruitment 

parameters from spawner-recruit data and incorporates environmental stochasticity, in the form 

of random uncorrelated environmental variation, and parameter uncertainty, in the form of 

random variation in parameter estimates. These estimates can be obtained in SPAZ using any of 

a variety of stock-recruitment models, including constant recruitment, random walk (with or 

without drift), stochastic exponential (with ceiling), stochastic hockey stick, running sum growth, 

stochastic (logistic) Ricker, and Beverton-Holt models (Myers et al. 1994, Barrowman and 

Myers 2000).

The SPAZ program has the capability of incorporating observation error and process 

error into the analyses. Observation error, assumed to be normally distributed in SPAZ, is 

measurement error that contributes to uncertainty in estimating abundance at any given time, but 

it does not accumulate or contribute to future uncertainty. Process error, on the other hand, is 
19

random error that contributes to temporal variation in population dynamics, but is not directly 

accounted for by the model. It can result from a number of sources, including genetic or 

demographic stochasticity (sampling effects) or environmental stochasticity (random 

environmental variation). It can accumulate and contribute to future uncertainty. In SPAZ it is 

treated with a lognormal distribution and is considered temporally autocorrelated.

We used SPAZ to generate estimates of relative risk of a population reaching a lowabundance threshold, termed a QET, under simple stock-recruitment models such as the random 

walk with drift and the stochastic hockey stick. Using these risk estimates, we generated 

viability curves for steelhead populations that depict the risk of these populations reaching a 

particular QET within 100 years, given a range of particular combinations of current abundance 

and population growth rate. An example of the results of this type of PVA is in Figure 3, 

showing how the joint estimate of population abundance and productivity, depicted by the 

colored probability contours at lower left, relates to a series of four viability curves representing 

different levels of abundance and productivity that correspond to the projections of the 

population reaching a QET (e.g., QET = 2, 20, 50, or 100 spawners in a given year) within 100 

years with 5% probability.

Of the 32 DIPs of Puget Sound steelhead the PSS TRT members identified (Table 1), we 

applied PVA to 16 of them for which we had sufficient abundance time series (Figure 4 through 

Figure 25). For each of these PVAs we made the following assumptions:

1. We based these PVAs on available natural escapement data, which were available for 20 

of the 32 DIPs for most years from 1977 to 2011 (with some notable exceptions), as too 

few populations provided total run size (harvest plus escapement) information. Such 

PVAs will tend to underestimate extinction risk if temporal declines in total run size are 

steeper than those for escapements. Additional characteristics of the escapement data are 

briefly summarized in Table 6.

2. We had empirical age structure data available for only three populations in the Puget 

Sound DPS. We used the two data sets that were longest to determine a fixed average 

age structure for each Puget Sound DIP and apply it across its abundance time series. For 

DIPs in northern and southern Puget Sound and Hood Canal drainages, we used the 

estimated average age structure from the Green River Winter-Run DIP, available from a 

WDFW 28-year time series between 1978 and 2005. The age structure applied was 0.1% 

age-2, 5.8% age-3, 49.8% age-4, 45.3% age-5, 4.0% age-6, and 0.3% age-7 adults. For 

DIPs in Strait of Juan de Fuca drainages, we used the estimated average age structure 

from the Snow Creek winter-run steelhead population, available from a WDFW 34-year 

time series between 1977 and 2011. This age structure was similar, except that more age4 and fewer age-5 fish are represented (0.2% age-2, 6.3% age-3, 70.2% age-4, 21.9% 

age-5, 1.4% age-6, and <0.1% age-7 adults). We applied normally distributed random 

error around these estimates.

3. For most of the DIPs, we applied a running sum growth model (McElhany and Payne 

2006) to initiate the analysis of abundance and productivity, which relied on 4-year 

running sums of spawner counts. We fitted a hockey stick recruitment model 

(Barrowman and Myers 2000) to these data. The estimation of both the growth 

parameter (μ) and the process error (σ

2

) using the slope method (Holmes 2001) was done 
20

on the (corrected) running sums. A MeanRS bootstrap method was then used to calculate 

recruits per spawner, a growth curve was fit to the data, and population projections were 

used to estimate extinction risk for a set of four fixed QETs. For Dungeness River

Summer Run and Winter Run, Strait of Juan de Fuca Tributaries Winter Run, and Elwha 

River Winter Run, we relied on a simpler, random-walk-with-trend model to estimate 

minimum viable population sizes from their shorter time series of abundance.

4. We did not account for differences in reproductive fitness between hatchery and natural 

spawners (see discussion in Araki et al. 2008), but simply assumed that all adults 

identified as natural spawners in the data were of wild origin.

5. We ran 100,000 bootstrap replicates using estimates of intrinsic growth rate, maximum 

number of recruits, and process error. These were drawn from uniform distributions with 

ranges of 0.5–4.0, 1–30, and 0.2–4.0, respectively.

6. We ran 1,000 risk projections for several QET values that always included 1 and a 

habitat-based estimate of QET that we derived from our intrinsic potential (IP) metrics 

(with 5% probability of reaching QET over a 100-year time horizon).

7. We did not account for nonanadromous (resident) recruits of steelhead spawners. For 

example, precocious male offspring of steelhead are known to participate in steelhead 

spawning and sire anadromous offspring. Most of the empirical escapement data we used 

are based on redd counts multiplied by a uniform spawners per redd number, which may 

or may not adequately account for resident spawners. Not accounting for resident 

recruits may underestimate productivity and potentially lead to overestimates of 

extinction risk.

The results of the SPAZ analyses indicate that two of the seven populations that could be 

evaluated in the Northern Cascades MPG are at moderate to high levels of risk of reaching QET. 

The two populations at moderate to high quasi-extinction risk in this MPG—Stillaguamish River 

Winter Run and Tolt River Summer Run—are at risk primarily because of high variance in

productivity. For the Central and South Puget Sound MPG, the SPAZ analyses indicate that a 

much higher fraction of populations are at high risk: four of the five populations that could be 

evaluated are at high levels of risk of reaching QET. The four winter-run populations at high 

quasi-extinction risk in this MPG—Cedar River, Puyallup/Carbon Rivers, White River, and 

Nisqually River—are at risk primarily because of very low productivity (the Cedar River and 

Lake Washington populations are also at very low abundance). For the Hood Canal and Strait of 

Juan de Fuca MPG, the SPAZ analyses indicate that seven of the eight populations that could be 

evaluated are at moderate to high levels of risk of reaching QET. The five winter-run 

populations clearly at high quasi-extinction risk in this MPG—South Hood Canal Tributaries, 

East Hood Canal Tributaries, West Hood Canal Tributaries, Skokomish River, and Sequim/

Discovery Bays Tributaries—are at risk primarily because of low productivity. Two others—

Dungeness River Summer Run and Winter Run and Elwha River Winter Run—are also at low or 

unknown recent abundance. Because the abundance data used in the SPAZ analyses are 

generally indexes of natural escapement rather than reasonably precise estimates of natural run 

size, these analyses are likely to underestimate risk to viability from low abundance or 

productivity. The conclusions are further tempered by the rather restrictive assumptions 

described above, which may not apply to all these populations except in broad terms.
21

Figure 3. An example of the results of a PVA conducted with the program SPAZ (McElhany and Payne 

2006). The plot depicts a joint estimate of population abundance (y-axis) and productivity 

(recruits per spawner, x-axis), represented by the connected open squares (showing the 

approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and estimated productivity data are 

given by the black squares (for the wild Snow Creek winter-run steelhead population in 

Washington State, 1978–2011). The joint estimates and the data are portrayed in relation to four 

viability curves representing different combinations of abundance and productivity that 

correspond to the projections of the population reaching a QET (equal to 2, 20, 50, or 100 

spawners in a given year) within 100 years with 5% probability. Assumptions of this PVA 

include no harvest and stationarity of conditions into the future. The PVA relies on a hockey 

stick recruitment model and uses the geometric mean of recruits per spawner to estimate 

productivity over the observed data. The bootstrapped estimates incorporate a range of uniform 

priors for population growth rate, number of recruits, and process error. Comparison with the 

values in Table 5 and Figure 2 suggest that this population is at a high risk of quasi-extinction 

over the next 100 years, and that this risk is posed by both low abundance and high variance in 

productivity.
22

Figure 4. PVA for the Samish River and Bellingham Bay Tributaries Winter Run, conducted with SPAZ

(McElhany and Payne 2006). The plot depicts the joint estimate of population abundance (yaxis) and productivity (recruits per spawner, x-axis), represented by the connected open squares 

(showing the approximate 50% and 95% probability centroids of projected abundance and 

productivity) overlain by colored probability contours. Actual abundance and productivity data 

are given by the solid black squares. The joint estimate and the data are overlaid onto four 

viability curves representing different combinations of abundance and productivity that 

correspond to a 5% risk of reaching a QET of 1, 10, 20, or 31 spawners (the habitat-based 

estimate of QET for this population) in a given year over a 100-year time horizon. The PVA 

relies on a hockey stick recruitment model and uses the geometric mean of running sums of 

recruits per spawner to estimate productivity over the observed data. Comparison with the values 

in Table 5 and Figure 2 suggests that this population is at a low risk of quasi-extinction over the 

next 100 years.
23

Figure 5. PVA for the Skagit River Summer Run and Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). The plot depicts the joint estimate of population abundance (y-axis) and 

productivity (recruits per spawner, x-axis), represented by the connected open squares (showing 

the approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto four viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 20, 50, or 157 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a low risk of quasi-extinction over the next 100 years.
24

Figure 6. PVA for the Stillaguamish River Winter Run, conducted with SPAZ (McElhany and Payne 

2006). The plot depicts the joint estimate of population abundance (y-axis) and productivity 

(recruits per spawner, x-axis), represented by the connected open squares (showing the 

approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto four viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 20, 50, or 67 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a very high risk of quasi-extinction over the next 100 years.
25

Figure 7. PVA for the Pilchuck River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto three viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 20, or 34 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model and uses 

the geometric mean of running sums of recruits per spawner to estimate productivity over the 

observed data. Comparison with the values in Table 5 and Figure 2 suggests that this population 

is at a very low risk of quasi-extinction over the next 100 years.
26

Figure 8. PVA for the Snohomish/Skykomish Rivers Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). The plot depicts the joint estimate of population abundance (y-axis) and 

productivity (recruits per spawner, x-axis), represented by the connected open squares (showing 

the approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto four viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 20, 50, or 73 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a low risk of quasi-extinction over the next 100 years.
27

Figure 9. PVA for the Snoqualmie River Winter Run, conducted with SPAZ (McElhany and Payne 

2006). The plot depicts the joint estimate of population abundance (y-axis) and productivity 

(recruits per spawner, x-axis), represented by the connected open squares (showing the 

approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto three viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 20, or 58 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a moderate risk of quasi-extinction over the next 100 years.
28

Figure 10. PVA for the Tolt River Summer Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto three viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, or 25 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model and uses 

the geometric mean of running sums of recruits per spawner to estimate productivity over the 

observed data. Comparison with the values in Table 5 and Figure 2 suggests that this population 

is at a high risk of quasi-extinction over the next 100 years.
29

Figure 11. PVA for the Cedar River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto three viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, or 35 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model and uses 

the geometric mean of running sums of recruits per spawner to estimate productivity over the

observed data. Comparison with the values in Table 5 and Figure 2 suggests that this population 

is at a very high risk of quasi-extinction over the next 100 years.
30

Figure 12. PVA for the Green River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto four viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, 50, or 69 spawners (the habitat-based estimate of QET for this population) in a 

given year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model 

and uses the geometric mean of running sums of recruits per spawner to estimate productivity 

over the observed data. Comparison with the values in Table 5 and Figure 2 suggests that this 

population is at a moderate risk of quasi-extinction over the next 100 years.
31

Figure 13. PVA for the Puyallup/Carbon Rivers Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). The plot depicts the joint estimate of population abundance (y-axis) and 

productivity (recruits per spawner, x-axis), represented by the connected open squares (showing 

the approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto three viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 10, or 58 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a very high risk of quasi-extinction over the next 100 years.
32

Figure 14. PVA for the White River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto four viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, 50, or 64 spawners (the habitat-based estimate of QET for this population) in a 

given year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model 

and uses the geometric mean of running sums of recruits per spawner to estimate productivity 

over the observed data. Comparison with the values in Table 5 and Figure 2 suggests that this 

population is at a very high risk of quasi-extinction over the next 100 years.
33

Figure 15. PVA for the Nisqually River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto three viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, or 55 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model and uses 

the geometric mean of running sums of recruits per spawner to estimate productivity over the 

observed data. Comparison with the values in Table 5 and Figure 2 suggests that this population 

is at a very high risk of quasi-extinction over the next 100 years.
34

Figure 16. PVA for the South Hood Canal Tributaries Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). The plot depicts the joint estimate of population abundance (y-axis) and 

productivity (recruits per spawner, x-axis), represented by the connected open squares (showing 

the approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto three viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 10, or 30 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a high risk of quasi-extinction over the next 100 years.
35

Figure 17. PVA for the Tahuya River winter-run steelhead, part of the South Hood Canal Tributaries 

Winter-Run DIP, conducted with SPAZ (McElhany and Payne 2006). The plot depicts the joint 

estimate of population abundance (y-axis) and productivity (recruits per spawner, x-axis), 

represented by the connected open squares (showing the approximate 50% and 95% probability 

centroids of projected abundance and productivity) overlain by colored probability contours. 

Actual abundance and productivity data are given by the solid black squares. The joint estimate 

and the data are overlaid onto three viability curves representing different combinations of 

abundance and productivity that correspond to a 5% risk of reaching a QET of 1, 10, or 30 

spawners (the habitat-based estimate of QET for this population) in a given year over a 100-year 

time horizon. The PVA relies on a hockey stick recruitment model and uses the geometric mean 

of running sums of recruits per spawner to estimate productivity over the observed data. 

Comparison with the values in Table 5 and Figure 2 suggests that this population is at a low risk 

of quasi-extinction over the next 100 years.
36

Figure 18. PVA for the East Hood Canal Tributaries Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). Only Dewatto River population data are available for this DIP. The plot depicts 

the joint estimate of population abundance (y-axis) and productivity (recruits per spawner, xaxis), represented by the connected open squares (showing the approximate 50% and 95% 

probability centroids of projected abundance and productivity) overlain by colored probability 

contours. Actual abundance and productivity data are given by the solid black squares. The joint 

estimate and the data are overlaid onto three viability curves representing different combinations 

of abundance and productivity that correspond to a 5% risk of reaching a QET of 1, 10, or 27 

spawners (the habitat-based estimate of QET for this population) in a given year over a 100-year 

time horizon. The PVA relies on a hockey stick recruitment model and uses the geometric mean 

of running sums of recruits per spawner to estimate productivity over the observed data. 

Comparison with the values in Table 5 and Figure 2 suggests that this population is at a high risk 

of quasi-extinction over the next 100 years.
37

Figure 19. PVA for the Skokomish River Winter Run, conducted with SPAZ (McElhany and Payne 

2006). The plot depicts the joint estimate of population abundance (y-axis) and productivity 

(recruits per spawner, x-axis), represented by the connected open squares (showing the 

approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto four viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 10, 20, or 50 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a high risk of quasi-extinction over the next 100 years.
38

Figure 20. PVA for the West Hood Canal Tributaries Winter Run, conducted with SPAZ (McElhany and 

Payne 2006). The plot depicts the joint estimate of population abundance (y-axis) and 

productivity (recruits per spawner, x-axis), represented by the connected open squares (showing 

the approximate 50% and 95% probability centroids of projected abundance and productivity) 

overlain by colored probability contours. Actual abundance and productivity data are given by 

the solid black squares. The joint estimate and the data are overlaid onto four viability curves 

representing different combinations of abundance and productivity that correspond to a 5% risk 

of reaching a QET of 1, 10, 20, or 32 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a hockey stick 

recruitment model and uses the geometric mean of running sums of recruits per spawner to 

estimate productivity over the observed data. Comparison with the values in Table 5 and Figure 

2 suggests that this population is at a very high risk of quasi-extinction over the next 100 years.
39

Figure 21. PVA for the Sequim/Discovery Bays Tributaries Winter Run, conducted with SPAZ

(McElhany and Payne 2006). Only Snow Creek population data are available for this DIP. The 

plot depicts the joint estimate of population abundance (y-axis) and productivity (recruits per 

spawner, x-axis), represented by the connected open squares (showing the approximate 50% and 

95% probability centroids of projected abundance and productivity) overlain by colored 

probability contours. Actual abundance and productivity data are given by the solid black 

squares. The joint estimate and the data are overlaid onto three viability curves representing 

different combinations of abundance and productivity that correspond to a 5% risk of reaching a 

QET of 1, 10, or 25 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a hockey stick recruitment model and uses 

the geometric mean of running sums of recruits per spawner to estimate productivity over the 

observed data. Comparison with the values in Table 5 and Figure 2 suggests that this population 

is at a high risk of quasi-extinction over the next 100 years.
40

Figure 22. PVA for the Dungeness River Summer Run and Winter Run, conducted with SPAZ

(McElhany and Payne 2006). The plot depicts the estimates of minimum viable population size 

(y-axis) as a function of population growth rate (x-axis). The estimates correspond to a 5% risk 

of reaching a QET of 1, 10, or 30 spawners (the habitat-based estimate of QET for this 

population) in a given year over a 100-year time horizon. The PVA relies on a random walk with 

trend model to estimate productivity over the observed data. Comparison with the available 

abundance data (from the 1980s and 1990s) and the values in Table 5 and Figure 2 suggests that 

this population is at a very high risk of quasi-extinction over the next 100 years.

Figure 23. PVA for the Strait of Juan de Fuca Tributaries Winter Run, conducted with SPAZ (McElhany 

and Payne 2006). The plot depicts the estimates of minimum viable population size (y-axis) as a 

function of population growth rate (x-axis). The estimates correspond to a 5% risk of reaching a 

QET of 1, 10, or 26 spawners (the habitat-based estimate of QET for this population) in a given 

year over a 100-year time horizon. The PVA relies on a random walk with trend model to 

estimate productivity over the observed data. Comparison with the available abundance data 

(from the 1980s and 1990s) and the values in Table 5 and Figure 2 suggests that this population is 

at a very high risk of quasi-extinction over the next 100 years.
41

Figure 24. PVA for the Morse Creek winter-run steelhead, part of the Strait of Juan de Fuca Tributaries 

Winter-Run DIP, conducted with SPAZ (McElhany and Payne 2006). The plot depicts the 

estimates of minimum viable population size (y-axis) as a function of population growth rate 

(x-axis). The estimates correspond to a 5% risk of reaching a QET of 1, 10, or 26 spawners (the 

habitat-based estimate of QET for this population) in a given year over a 100-year time horizon.

The PVA relies on a random walk with trend model to estimate productivity over the observed 

data. Comparison with the available abundance data (from the 1980s and 1990s) and the values 

in Table 5 and Figure 2 suggests that this population is at a very high risk of quasi-extinction over 

the next 100 years.

Figure 25. PVA for the Elwha River Winter Run, conducted with SPAZ (McElhany and Payne 2006). 

The plot depicts the estimates of minimum viable population size (y-axis) as a function of 

population growth rate (x-axis). The estimates correspond to a 5% risk of reaching a QET of 1, 

10, 20, or 41 spawners (the habitat-based estimate of QET for this population) in a given year 

over a 100-year time horizon. The PVA relies on a random walk with trend model to estimate 

productivity over the observed data. Comparison with the available abundance data (from the 

1980s and 1990s) and the values in Table 5 and Figure 2 suggests that this population is at a very 

high risk of quasi-extinction over the next 100 years.
42

Table 6. Description of WDFW natural escapement data available for Puget Sound steelhead populations and their relationship to the TRTdesignated DIPs. WSH = winter-run steelhead; SSH = summer-run steelhead, and SaSI = Salmonid Stock Inventory (a WDFW database 

accessible through its “Salmonscape” Web site at http://wdfw.wa.gov/mapping/salmonscape).

DIP name

Drainages included 

in DIP

Drainages represented in 

escapement data 

Important contrasts between 

DIP and escapement data Escapement data type

Nooksack River 

WSH

Mainstem Nooksack 

River and all major 

tributaries and many 

side channels

Mainstem, north fork, middle 

fork, south fork, tributaries, and 

side channels

Escapement data are from 

2010 and 2011 only, and 

2011 data were made by 

expanding off 2010 data

Escapement estimates for redds 

in mainstem index areas and 

associated tributaries based on 

aerial counts and Area Under the 

Curve estimation

Samish River 

WSH

Samish River and 

four creeks entering 

Bellingham Bay

Mainstem Samish and Samish 

tributary Friday Creek

— Total escapement estimates 

based on cumulative redd counts

Skagit River 

SSH/WSH

Mainstem Skagit 

River and all 

tributaries except 

Baker River, Sauk 

River, and 

Nookachamps 

Creek

Mainstem Skagit River (RM 

22.5 to 94.1), Alder, Diobsud, 

Rocky, O’Toole, Cumberland, 

Day, Sorenson, Hansen and 

Jones creeks, and WSH in Sauk 

and Cascade rivers

SaSI data include Sauk River 

escapement, and Sauk River 

SSH/WSH is a separate 

designated DIP

Total escapement estimates 

based on cumulative redd counts

Stillaguamish 

River WSH

Entire Stillaguamish 

Basin except Deer 

and Canyon creeks

North Fork Stillaguamish and 

tributaries upstream of Deer 

Creek

South Fork Stillagaumish 

escapements are not included 

in SaSI data

“Spawner counts”

Snohomish/ 

Skykomish WSH

Mainstem 

Snohomish, 

Skykomish Basin

Mainstem Snohomish to RM 

51.5 on South Fork Skykomish, 

Wallace River RM 0.0 to 5.8, 

Sultan River RM 0.0 to 15.0, 

east and west forks of Woods 

Creek, Proctor, Elwell/Young’s, 

Olney, Lewis, and Salmon 

creeks

— Total escapement estimates 

based on redd counts

Pilchuck River 

WSH

Pilchuck River Mainstem Pilchuck River from 

RM 0.0 to 15.3, and Worthy, 

Dubuque, and Little Pilchuck 

creeks

— Total escapement estimates 

based on redd counts
43

Table 6 continued. Description of WDFW natural escapement data available for Puget Sound steelhead populations and their relationship to the 

TRT-designated DIPs. WSH = winter-run steelhead; SSH = summer-run steelhead, and SaSI = Salmonid Stock Inventory (a WDFW 

database accessible through its “Salmonscape” Web site at http://wdfw.wa.gov/mapping/salmonscape).

DIP name

Drainages included 

in DIP

Drainages represented in 

escapement data 

Important contrasts between 

DIP and escapement data Escapement data type

Snoqualmie 

River WSH

Mainstem 

Snoqualmie River 

and its tributaries

Mainstem Snoqualmie upstream 

to Snoqualmie Falls (RM 40.5), 

mainstem Tolt River, north and 

south forks of Tolt, Raging 

River and Tokul, Cherry, Harris, 

Griffin, Patterson, Canyon, and 

Deep creeks

— Total escapement estimates 

based on redd counts

Tolt River SSH North and south 

forks of Tolt River

South Fork Tolt River, RM 3.3 

to 7.8

No estimates for North Fork 

Tolt in SaSI data

Total escapement estimates 

based on redd counts February 

15 through May. (Cumulative 

redd counts are multiplied by 

0.81 to account for multiple redd 

construction by females, then by 

2 to represent the spawning pair 

[female and male])

Cedar River 

WSH

Cedar River Cedar River and Issaquah and 

Bear creeks

Cedar and Lake Washington 

data combined

Total escapement estimates 

based on redd counts

North Lake 

Washington 

WSH

Bear, Issaquah, and 

Swamp creeks and 

other tributaries

Data are not available separately 

from Cedar River

— —

Green River 

WSR

Green River Green River main stem 

spawning areas and index 

reaches in Soos and Newaukum 

creeks totaling 12 miles

— Total escapement estimates 

based on cumulative redd counts

Puyallup/Carbon 

WSH

Puyallup (excluding 

White River) and 

Carbon rivers

Mainstem Puyallup River and 

tributaries, excluding White 

River, Carbon River

Puyallup and Carbon are 

separate SaSI stocks; data 

were combined to form the 

TRT data set

Total escapement estimates 

based on redd counts in all 

suitable spawning habitat of 

Puyallup River and tributaries, 

and of Carbon River
44

Table 6 continued. Description of WDFW natural escapement data available for Puget Sound steelhead populations and their relationship to the 

TRT-designated DIPs. WSH = winter-run steelhead; SSH = summer-run steelhead, and SaSI = Salmonid Stock Inventory (a WDFW 

database accessible through its “Salmonscape” Web site at http://wdfw.wa.gov/mapping/salmonscape).

DIP name

Drainages included 

in DIP

Drainages represented in 

escapement data 

Important contrasts between 

DIP and escapement data Escapement data type

White River 

WSH

White River Important note: data are from 

fish management staff, which 

combines SaSI data (steelhead 

counts at Buckley Dam trap) and 

escapement (redd counts) below 

the dam in mainstem White 

River and Boise Creek.

2009 to 2011 counts at the 

Buckley Dam trap included 

returning adults from the wild 

broodstock hatchery program; 

these fish are passed upstream 

so are potential spawners

Counts of steelhead at the 

Buckley Dam trap (RM 24.3) 

and escapement estimates below 

dam in White River and Boise 

Creek

Nisqually WSH Nisqually River Nisqually River and some 

tributaries such as Mashel River 

are now surveyed

— Total escapement estimates 

based on redd counts

East Hood Canal 

Tributaries WSH

Dewatto River, Big 

Beef and Anderson 

creeks, other small 

west Kitsap 

peninsula streams

Dewatto River only SaSI data for Dewatto River 

only

Index escapement estimates 

based on redd counts from RM 

0.0 to 3.75

South Hood 

Canal Tributaries 

WSH

Tahuya and Union 

rivers, Mission 

Creek, other small 

South Hood Canal 

streams

Tahuya and Union rivers. (Also, 

Tahuya data provided separately 

as a longer data series that may 

be useful for PVA-type 

analyses)

SaSI data for Tahuya and 

Union only, Tahuya and

Union are separate SaSI 

stocks, data were combined to 

form the TRT data set

Index escapement estimates 

based on redd counts from 

Tahuya RM 1.0 to 11.0, and 

from Union RM 0.3 to 4.0

Skokomish WSH Skokomish River Mainstem Skokomish, North 

Fork Skokomish, and South 

Fork Skokomish

— Total escapement estimates 

based on redd counts in index 

areas in mainstem Skokomish 

RM 0.0 to 9.0, N. F. Skokomish 

RM 9.0 to 13.0, and S. F. 

Skokomish RM 0.0 to 21.4
45

Table 6 continued. Description of WDFW natural escapement data available for Puget Sound steelhead populations and their relationship to the 

TRT-designated DIPs. WSH = winter-run steelhead; SSH = summer-run steelhead, and SaSI = Salmonid Stock Inventory (a WDFW 

database accessible through its “Salmonscape” Web site at http://wdfw.wa.gov/mapping/salmonscape).

DIP name

Drainages included 

in DIP

Drainages represented in 

escapement data 

Important contrasts between 

DIP and escapement data Escapement data type

West Hood Canal 

Tributaries WSH

Hamma Hamma, 

Duckabush, 

Dosewallips, Big 

and Little Quilcene 

rivers, Tarboo 

Creek

Hamma Hamma, Duckabush, 

Dosewallips, and Little Quilcene 

rivers

SaSI data do not include Big 

Quilcene River or Tarboo 

Creek

Index escapement estimates 

based on redd counts in Hamma 

Hamma RM 0.3 to 1.8, in 

Duckabush RM 0.0 to 2.6, in 

Dosewallips RM 0.2 to 12.0, and 

in Little Quilcene RM 0.2 to 5.3

Sequim/ 

Discovery Bays 

Tributaries WSH

Snow, Salmon, and 

Jimmycomelately 

creeks and other 

streams draining to 

Sequim and 

Discovery bays

Snow Creek (Discovery Bay 

stream)

SaSI data only for Snow 

Creek population

Total escapement estimates 

based on Snow Creek trap 

counts and on redd counts 

downstream of trap

Dungeness WSH Dungeness River Dungeness River (no recent data 

series available)

— Index escapements based on 

redd counts in index areas. 

Escapement estimates are often 

not made because of difficult 

survey conditions, such as high 

flows in May

Strait of Juan de 

Fuca Tributaries 

WSH

Ennis, White, 

Morse, Siebert, and 

McDonald creeks

Morse and McDonald creeks. 

(Also, Morse data provided 

separately as a longer data series 

that may be useful for PVA-type 

analyses)

SaSI data only for Morse and 

McDonald creeks

Total escapement estimates 

based on redd counts in Morse 

Creek from RM 0.0 to 4.7, and 

in index areas of McDonald 

Creek from RM 0.0 to 5.4

Elwha River 

WSH

Elwha River Elwha River within 4.9 miles 

downstream of (former) Elwha 

Dam. No recent SaSI data series 

available

— Total escapement estimates 

based on hatchery/wild ratios in 

commercial harvest and return to 

the Elwha Hatchery
46

If we accept the assumptions underlying these analyses as reasonable and conclude that 

estimates based on index redd counts for these steelhead populations are tightly correlated with 

total natural spawning abundance (and that for about the last 10 years spawning abundance and 

total run size have been similar), the analyses indicate that the majority (considerably more than 

half) of steelhead populations in the Puget Sound DPS are at high levels of quasi-extinction risk. 

These analyses have been applied to populations for which semiquantitative estimates of 

abundance and productivity exist (or can be roughly inferred). For populations for which we 

have no quantitative information, we hypothesize that they would likely show a similar pattern, 

because the few data available for these populations show them either to be very small or to have 

declined precipitously from historic sizes. The risk of quasi-extinction of steelhead in Puget 

Sound appears to be highest in the Central and South Puget Sound MPG and the Hood Canal and 

Strait of Juan de Fuca MPG. In the Northern Cascades MPG, two of the seven populations that 

could be evaluated are at high levels of risk of reaching QET. One of these is a summer-run 

population (Tolt River). For the Central and South Puget Sound MPG, four of the five 

populations that could be evaluated are at high levels of risk of reaching QET. The populations 

at high quasi-extinction risk are at risk primarily because of very low productivity (the Lake 

Washington population is also at very low abundance). For the Hood Canal and Strait of Juan de 

Fuca MPG, all eight populations appear to be at high levels of risk of reaching QET. These risk 

assessments may underestimate true risk levels because of the nature of the data available. It is 

also important to recognize that these analyses do not explicitly incorporate uncertainty into the 

risk assessment; they do not fully describe a risk “envelope.”

The populations at high QET risk are spread geographically across the DPS. Very little 

information exists for many populations in the DPS, in particular in Central and South Puget 

Sound DIPs where steelhead once occurred in abundance in smaller streams and rivers, based 

largely on punch card data collected by the Washington Department of Game in the first half of 

the twentieth century. Collectively, these analyses provide strong evidence that most steelhead 

populations in the Puget Sound DPS are at low levels of viability in terms of abundance and 

productivity.

Population Viability Analysis with Autoregressive State-Space Models

The above analyses make some assumptions about population demography that may be 

too restrictive in evaluating extinction risk. In particular, they assume that the population census 

data are accurate, and they apply a particular stock-recruitment model and an average estimate of 

adult age distribution that may not apply to all populations or reflect temporal changes in age 

structure. In addition, as described above, the steelhead data themselves are typically based on

indexes of redd counts rather than precise estimates of total run size. Consequently, we also 

evaluated population viability by applying approaches based on maximum-likelihood estimation 

to analyze the time series reflecting indices of abundance and infer underlying population 

dynamics without making such restrictive assumptions. We used the state-space framework 

described by Hinrichsen and Holmes (2009) and Ward et al. (2010), and implemented in the 

Multivariate Autoregressive State-Space (MARSS) Program, version 2.3, (Holmes and Ward 

2011) in the R statistical environment (R Development Core Team 2012) to estimate parameters 

describing population dynamics over time from basic abundance data. The state-space 

framework has a distinct advantage in evaluating ecological applications such as time series of 

abundance, because they can accommodate missing data and can account for both intrinsic 
47

biological factors (process error) and measurement error (nonprocess error) in terms of their 

contributions to the variability observed in the time series.

State-space models have some other advantages. They explicitly incorporate 

consideration of uncertainty in forecasts of future population trends, assuming that current 

conditions persist. They do not require an assumption of a specific underlying demographic 

structure (e.g., a specific spawner‐recruit relationship). The MARSS models used here are fit 

iteratively to the data via maximum likelihood, using a Kalman-filtered expectation‐

maximization algorithm. This algorithm is especially well-suited to dynamic systems where 

hidden random variables occur in the model. The Kalman filter, which is widely applied to the 

analysis of time series, uses diffusion approximation methods to solve for the expected values of 

the hidden states (of the multivariate autoregressive processes), conditioned on the data over the 

entire time series. This approach is appropriate for steelhead abundance data for Puget Sound 

because these data primarily include observed redd counts, often from index stream reaches, 

which makes estimation of total abundance challenging.

State-space models have been applied frequently to time series of ecological data because 

they are able to separate these sources of error, while requiring less information than some other 

methods (such as estimates of age structure) in the absence of estimates of observation error or 

replicated observations. Widely used in ecology using both maximum-likelihood and Bayesian 

frameworks, state-space models have focused primarily on analysis of single time series (e.g., 

Staples et al. 2004, Dennis et al. 2006), but are capable of handling higher dimension data as 

well. The basic form of a univariate state-space model with Gaussian (normally distributed) 

errors is

xt = xt−1 + μ + t

which is closely related to a stochastic, discrete-time Gompertz model (Ives et al. 2003, Dennis 

et al. 2006). In these equations xt is abundance in year t, xt−1 is abundance in the previous year, μ

is a parameter estimating population growth rate (e.g., intrinsic rate of increase), and et is the 

process error in year t (et is distributed multivariate normal with mean = 0 and variance = σ

2

). 

The term xt−1 can be multiplied by a parameter to reflect the strength of density dependence. The 

multivariate version of the MARSS model, which can accommodate n multiple populations, 

takes the form (Holmes and Ward 2011)

xt = xt−1 + μ + et

yt = a + Z t + εt

The first of these equations models the population process and the second the observation 

process. In these equations, xt and xt−1 are n × 1 vectors of true population sizes in years t and 

t − 1, μ is an n-element vector of population growth rates, et is an n-element vector of process 

errors in year t (et is distributed multivariate normal with mean = 0 and covariance matrix Q), yt

is an n-element vector of observations in year t, a is an n-element vector that represents bias in 

observation errors, Z is an n × n matrix of zeros and ones that translates the n sizes in year t to n

observations in year t, and εt is an n-element vector of observation errors in year t (εt is 

distributed multivariate normal with mean = 0 and covariance matrix R).

The state-space-based PVAs were computed from the same data used for the SPAZ

analyses: estimates of natural escapement (rather than run size) for most of the Puget Sound 
48

steelhead populations. The PVAs provide estimates of process and measurement error and use 

these to compute probabilities of extinction risk and associated confidence intervals. Process 

error is particularly significant in this context because it reflects variability resulting from 

intrinsic natural processes, such as demographic stochasticity. The PVAs estimated by MARSS 

do not account for density dependent effects on productivity and abundance, but this is a frequent 

assumption of PVA when applied to small or declining populations. If habitat capacity is 

changing or if Allee effects expressed at low abundance are important influences on population 

trends, they are not detected by these methods. Although missing data are not strictly limiting to 

the approach (so long as sufficient data are present in the time series), the PVAs do assume that a 

population is stationary through time; that is, trends are constant and environmental conditions 

affecting mortality and production (including harvest) persist. Because it is a state‐space 

approach, a MARSS analysis can provide more precision in estimates of trend because 

observation error is explicitly included in the analysis (ignoring observation error tends to lead to 

inflated estimates of process error). The state‐space framework partitions the total variance into 

process and observation variance, which can yield more constrained, realistic estimates of 

process error and, as a result, more precise estimates of viability metrics. The model also allows 

estimation of a risk “envelope” that directly incorporates uncertainty resulting from the 

variability inherent in the time series into the risk projection.

Some concern has been expressed with this approach (and with the slope method), 

centering on potential bias in overestimating the precision of population growth rate from the 

analysis of trend in abundance data. However, this concern is based primarily on an assumption

that an analysis is estimating the observation variance, not the process variance. The slope and 

state-space (e.g., MARSS) methods separate process and observation errors and estimate the 

process variance, so that extinction risk can be forecast from a model of population variability 

that is free of observation variance.

An example of the results of a PVA using MARSS applied to a time series of population 

abundance data is shown in Figure 26. The key features and assumptions of this analysis include 

the following:

• The state-space model uses a diffusion approximation approach,

• No specific demographic structure (e.g., age structure) is assumed,

• Process error due to demographic stochasticity etc. is partitioned from observation error

and estimated,

• Density independence of the population’s trajectory and stationarity (meaning that an 

underlying trend that is a function solely of time can be removed, leaving a stationary 

process) is assumed, and

• Dispersions around estimates are obtained from a Hessian matrix (a numerically 

estimated square matrix of second‐order partial derivatives of the function describing the 

population trajectory).

In this example, the abundance data are plotted over time with the fitted state-space 

estimate in the top left graph; MARSS estimated the annual change in abundance (μest) at −0.057, 

reflecting a decline of 5.7% per year and corresponding to a growth rate (λ = e

μest) of 0.944. In 
49

Figure 26. PVA output from MARSS (Holmes and Ward 2011) as applied to a time series of wild 

steelhead population abundance (Snow Creek winter-run steelhead on the Olympic Peninsula). 

Top left, time series of abundance data showing estimated annual change in abundance (μest, or 

ln(λ)) and process error (Qest or s 2

p ). Top right, estimated probability of reaching a QET of 10% 

of the current abundance (21 in 2009 and 10 in 2010). Middle left, plot of the probability density 

function of the time to reach QET, given that it is reached. Middle right, plot of the probability of 

reaching QET within 100 years as a function of variation in current abundance Ne (the red vertical 

line shows the actual data). Lower left, sample projections for the population as a function of 

years into the future. Lower right, plot of the risk envelope (Ellner and Holmes 2008) showing 

the expected rate of decline in abundance as a function of time projection in years. The red and 

green hatched areas represent parameter spaces where rates of population decline over specific 

time periods are estimated with 95% or higher confidence, with the green area reflecting 

minimum time horizons for specified rates of decline and the red area reflecting maximum time 

horizons for those rates. The gray areas encompass the uncertainty envelope for estimating 

extinction risk (P < 0.95). 

1977 1982 1987 1992 1997 2002 2007

50 100 150

Year

Pop. estimate

u est = -0.057 (95% CIs -0.15, 0.039)

Q est = 0.072

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 2

95% CI

75% CI

mean

0 50 100 150 200

0.000 0.010 0.020

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

1.0 1.5 2.0 2.5 3.0 3.5

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 20 40 60 80

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 33 mu = -0.057 s2.p = 0.072
50

the top right graph, MARSS estimated that the probability of reaching a QET of 2 over time was 

80% by about year 60. The lower left graph plots several sample projections for the population 

as a function of years into the future, showing low variability among the projections. In the 

lower right graph of Figure 26, MARSS has plotted a risk envelope, along with the estimates of 

annual change in abundance (μest) and process error (Qest or sp

2 [s2.p in the figure], a reflection of 

variability resulting from intrinsic natural processes, such as demographic stochasticity). In this 

plot, the y-axis can be considered to represent the expected rate of decline in abundance from 

current (Ne) to that at the end of the projection (N0), plotted against the time projection in years 

on the x-axis. The gray areas encompass the uncertainty in estimating probabilities of reaching 

the specified QET. So, for example, in this plot one could conclude with 95% confidence that an 

expected rate of decline of 50% in this population (y = −1.0) is not likely to occur within 100 

years; a rate of decline of 90% is not likely before 8–10 years (and a decline of 99% is not likely 

before 18–20 years). However, beyond the near term, the precise level of extinction risk is 

uncertain and highly uncertain after about 20 years (for a specified decline of 90%).

We applied autoregressive state-space models to abundance time series for 16 Puget 

Sound steelhead populations identified as DIPs. The PVA results for each DIP are described in 

Appendix A, where a full set of MARSS plots is provided. Summaries of the MARSS-based

PVA analyses—which evaluate the trends in estimated wild abundance for putative DIPs of 

Puget Sound steelhead over the entire estimated escapement data series, project population 

trends 100 years into the future, and where possible evaluate these projections against specified 

viability criteria—are provided in the following list.

1. For the Samish River winter-run population, see the top two plots of Figure A-1.

2. For the Skagit River Summer Run and Winter Run, see the top two plots of Figure A-2.

3. For the Stillaguamish River Winter Run, see the top two plots of Figure A-3.

4. For the Pilchuck River Winter Run, see the top two plots of Figure A-4.

5. For the Snohomish/Skykomish Rivers Winter Run, see the top two plots of Figure A-5.

6. For the Snoqualmie River Winter Run, see the top two plots of Figure A-6.

7. For the Tolt River Summer Run, see Figure A-7.

8. For the Cedar River Winter Run, see Figure A-8.

9. For the Green River Winter Run, see the top two plots of Figure A-9.

10. For the Puyallup River winter-run population, see the top two plots of Figure A-10.

11. For the White River Winter Run, see the top two plots of Figure A-11.

12. For the Nisqually River Winter Run, see the top two plots of Figure A-12.

13. For the South Hood Canal Tributaries Winter Run, see Figure A-13.

14. For the Tahuya River winter-run population, see Figure A-14.

15. For the East Hood Canal Tributaries Winter Run, see Figure A-15.

16. For the Skokomish River Winter Run, see the top two plots of Figure A-16.

17. For the West Hood Canal Tributaries Winter Run, see the top two plots of Figure A-17.
51

18. For the Sequim/Discovery Bays Tributaries Winter Run, see Figure A-18.

19. For the Dungeness River Summer Run and Winter Run, see the top two plots of Figure 

A-19.

20. For the Strait of Juan de Fuca Tributaries Winter Run, see the top two plots of Figure 

A-20.

21. For the Morse Creek winter-run population, see the top two plots of Figure A-21.

22. For the Elwha River Winter Run, see Figure A-22.

The QETs applied here (Table 7, see also Appendix B) are based on a low average of 24 

spawners for Snow Creek winter-run steelhead measured over 4 consecutive years, then scaling 

by the ratio of the estimate of IP for the watershed supporting the candidate DIP (IP is the area of 

habitat suitable for steelhead rearing or spawning, at least under historical conditions) to that of 

Snow Creek. The Snow Creek winter-run steelhead population was chosen because it is a 

natural anadromous population with sustained wild production in a relatively stable watershed, 

and it provides accurate estimates of adult escapement, smolt production, and IP to serve as a 

basis for estimating QETs throughout the Puget Sound Steelhead DPS.

In summary, as did the SPAZ analyses, the MARSS PVAs indicate that the majority of 

steelhead populations in the Puget Sound DPS are at moderate to high levels of quasi-extinction 

risk. The risk appears to be high throughout the DPS, but especially in the Central and South 

Puget Sound MPG and in tributaries to the Strait of Juan de Fuca. Although the variability in the 

data meant that nearly all the estimates of population growth did not differ significantly from 1

(i.e., a stable trend; the one exception in these analyses was the Cedar River Winter-Run DIP), 

collectively the trends in abundance point to declining abundance and low productivity. In the 

Northern Cascades MPG, all but one of the seven populations that could be evaluated show 

evidence of long-term negative population growth, based on the point estimates (λ < 1), and 

three of these populations exhibit probabilities of reaching their specified QET within 100 years 

that exceed 50%. In this MPG, the Stillaguamish River Winter-Run, Snoqualmie River WinterRun, and Tolt River Summer-Run populations had the lowest productivities and steepest declines 

in abundance. Only the Samish River winter-run population showed evidence of positive 

population growth and a low QET risk. For the Central and South Puget Sound MPG, all of the 

five populations that could be evaluated show evidence of long-term negative population growth, 

and four of these exhibit probabilities of reaching their specified QET within 100 years that 

exceed 50% (three of these are >90%). In this MPG, the Cedar River Winter-Run, 

Puyallup/Carbon Rivers Winter-Run, and Nisqually River Winter-Run populations had the 

lowest productivities and steepest declines in abundance. Only the White River Winter-Run 

population showed evidence of nearly neutral population growth (λ ≈ 1) and a low QET risk 

(≈40% in 100 years). For the Hood Canal and Strait of Juan de Fuca MPG, six of the eight 

populations show evidence of long-term negative population growth and these also exhibit 

probabilities of reaching their specified QET within 100 years that exceed 50%. In this MPG, 

the South Hood Canal Tributaries Winter Run, Strait of Juan de Fuca Tributaries Winter Run,

Dungeness River Summer Run and Winter Run, and Elwha River Winter Run had the lowest 

productivities and steepest declines in abundance. Only the East Hood Canal Tributaries Winter 

Run and West Hood Canal Tributaries Winter Run showed evidence of neutral or positive 

population growth (λ ≥ 1) and a low QET risk (≤40% in 100 years).
52

Table 7. Estimated QETs for adult steelhead in Puget Sound. SSH = summer-run steelhead and WSH =

winter-run steelhead. Intrinsic potential (IP) is total area across habitat categories suitable for 

steelhead use.

Puget Sound steelhead DIP IP (m2) Estimated QET

Drayton Harbor Tributaries WSH 597,409 26

Nooksack River WSH 11,119,563 73

South Fork Nooksack River SSH 795,382 27

Samish R. and Bellingham Bay Tribs. WSH 1,616,020 31

Nookachamps River WSH 870,466 27

Skagit River SSH/WSH 30,038,382 157

Baker River SSH/WSH 2,850,284 36

Sauk River SSH/WSH 17,894,638 103

Stillaguamish River WSH 9,814,400 67

Deer Creek SSH 1,575,597 31

Canyon Creek SSH 91,697 24

Snohomish/Skykomish Rivers WSH 11,175,208 73

Pilchuck River WSH 2,446,486 34

Snoqualmie River WSH 7,738,534 58

North Fork Skykomish River SSH 414,682 25

Tolt River SSH 231,883 25

N. Lake Wash. and Lake Sammamish WSH 2,840,214 36

Cedar River WSH 2,545,580 35

Green River WSH 10,170,477 69

Puyallup/Carbon Rivers WSH 7,779,880 58

White River WSH 9,070,004 64

Nisqually River WSH 7,069,169 55

South Puget Sound Tributaries WSH 4,253,116 42

East Kitsap Peninsula Tributaries WSH 256,617 25

East Hood Canal Tributaries WSH 676,924 27

South Hood Canal Tributaries WSH 1,496,762 30

Skokomish River WSH 6,041,453 50

West Hood Canal Tributaries WSH 1,815,949 32

Sequim/Discovery Bays Tributaries WSH 228,335 25

Dungeness River SSH/WSH 1,449,576 30

Strait of Juan de Fuca Tributaries WSH 627,758 26

Elwha River WSH 3,915,221 41

Examination of the uncertainty surrounding the risk projections in the MARSS plots in 

Appendix A indicates that precise characterization of viability is not possible for most 

populations after a couple of decades. Nevertheless, the overarching trends are clear and 

viability appears generally to be low throughout most of the DPS. Because these abundance data 

in most cases are indexes of natural escapement rather than reasonably precise estimates of 

natural run size, we emphasize that these estimates of viability for the DIPs are likely to 

overestimate their true viabilities in most cases.
53

The populations at high QET risk are spread geographically across the DPS. 

Collectively, these PVAs conducted with SPAZ and MARSS provide strong evidence that most 

steelhead populations in all three MPGs within the Puget Sound DPS are at low levels of 

viability with respect to abundance and productivity, and many populations—probably at least a 

quarter of them—are at high risk of reaching specified quasi-extinction thresholds.

Analyses at the Major Population Group Level

Figure 27 through Figure 32 examine the trends in estimated natural escapement for 

Puget Sound steelhead over the entire data series (1985–2011), including their residuals (Figure 

28, Figure 30, and Figure 32), for 20 DIPs combined into three putative MPGs in the DPS: 

Northern Cascades, Central and South Puget Sound, and Hood Canal and Strait of Juan de Fuca. 

In each case, the graphs plot the maximum‐likelihood estimate of log(total number of natural 

steelhead) for the candidate populations in the MPG against the observed data, assuming that 

1) each population time series follows a single MPG trajectory and is simply scaled up or down 

relative to it, and 2) variances in the observation errors for each time series are multivariate 

normal but allowed to be unique for each population. The approximate CIs were computed 

either using a numerically estimated Hessian matrix (a square matrix of second‐order partial 

derivatives of the function) or via parametric bootstrapping (see Holmes and Ward 2011). The 

relatively tight CIs arise because the estimate of composite process error is small and because all 

the time-series data are fit to a single “population” trajectory. The total MPG estimate accounts 

for the bias estimated for the first population time series.

The Northern Cascades MPG shows a clearly declining trend in natural spawner 

abundance (Figure 27). The average long‐term MPG growth rate (μest, equivalent to ln(λ); see 

Table 1 and Table 2) is estimated from the slope of the regression. This growth rate is negative 

(−0.039), corresponding to an estimated loss in abundance of 3.9% per year and a λ of 0.962. 

The process error (Qest), which is the temporal variability in population growth rate arising from 

demographic stochasticity, estimated from the variance of residuals around the regression line

(Figure 28), is 0.024. The Central and South Puget Sound MPG also shows a clearly declining 

trend in wild abundance (Figure 29 and Figure 30). Its estimated long‐term MPG growth rate is 

negative, with a loss of 6.9% per year (λ = 0.933), and its estimated process error is <0.001. The 

Hood Canal and Strait of Juan de Fuca MPG shows a negative long‐term population growth rate 

of 1.3% per year (λ = 0.987), with an estimated process error of 0.096 (Figure 31 and 32). All 

three MPGs contain populations with trending residuals in abundance (Figure 28, Figure 30, and 

Figure 32), which indicates that the population dynamics of these populations are following 

different demographic processes or trajectories, suggesting that each MPG is not functioning 

demographically as a single, cohesive metapopulation, but rather contains distinctly different 

populations that reflect the DIP identifications. This type of diversity is natural for a complex 

unit like an MPG and we do not further investigate its demographic substructure here.

In summary, all three steelhead MPGs exhibit declining trends in abundance and 

estimated composite long-term growth rates are negative, ranging from about 1 to 7% annually. 

For all but a few of the DIPs of steelhead in these MPGs, estimates of mean population growth 

rates obtained from observed spawner or redd counts are declining—typically 3 to 10% 

annually—and extinction risk within 100 years for most populations in the DPS is estimated to 
54

be moderate to high, especially for most populations in the Central and South Puget Sound MPG 

and the Hood Canal and Strait of Juan de Fuca MPG. Collectively, these analyses indicate that 

steelhead in the Puget Sound DPS remain at risk of extinction throughout all or a significant 

portion of their range in the foreseeable future, but are not currently in danger of imminent 

extinction.

Figure 27. Graph of the trend in estimated total Puget Sound winter-run and one summer-run (Tolt River) 

steelhead population for a putative Northern Cascades MPG. The graph plots the maximumlikelihood estimate of log(total no. steelhead) in the MPG against the observed data, assuming a 

single population model for the MPG. The estimate of the log(total MPG count) (solid black 

line) has been scaled relative to the Samish River population. The 95% CIs around the total MPG 

estimate are given by the red dashed lines. (Note that these are not the CIs around the observed 

data, which are expected to fall outside the CI, depending on population-specific nonprocess 

error, but are instead around the composite estimate per Holmes and Ward 2011.) 

1985 1990 1995 2000 2005 2010

0

2

4

6

8 10

Years

Index of log abundance

Samish WSH

Skagit WSH

Stillaguamish WSH

Snohomish WSH

Tolt SSH
55

Figure 28. Plots of the residuals from regression of steelhead counts over time for five populations in the 

Northern Cascades MPG. Note the increasing trend for Samish River winter-run steelhead and 

the declining trend for Stillaguamish River Winter-Run steelhead.

5 10 15 20

Residuals

-1.5 -1.0 -0.5 0.0 0.5

Index

Samish

5 10 15 20

-0.2 -0.1 0.0 0.1 0.2

Index

Residuals

Skagit

5 10 15 20

Residuals

-0.6 -0.4 -0.2 0.0 0.2 0.4

Index

Stillaguamish

5 10 15

-0.4 -0.2 0.0 0.2

Index

Residuals

Snohomish

5 10 15

-1.0 -0.5 0.0 0.5 1.0

Index

Residuals

Tolt
56

Figure 29. Plot of the trend in estimated total Puget Sound winter-run steelhead for a putative Central and 

South Puget Sound MPG. The graph plots the maximum-likelihood estimate of log(total no. 

steelhead) in the MPG against the observed data. The estimate of the log(total MPG count) (solid 

black line) has been scaled relative to the Lake Washington population. The 95% CIs around the 

total MPG estimate are given by the red dashed lines. (Note that these are not the confidence 

intervals around the observed data, which are expected to fall outside the CI, depending on 

population-specific nonprocess error, but are instead around the composite estimate per Holmes 

and Ward 2011.) 

1985 1990 1995 2000 2005 2010

0

2

4

6

8 10

Years

Index of log abundance

Lk Wash WSH

Green WSH

Puyallup WSH

White WSH

Nisqually WSH
57

Figure 30. Plots of the residuals from regression of steelhead counts over time for five winter-run 

populations in the Central and South Puget Sound MPG. Note the declining trends for Lake 

Washington, Puyallup River, and Nisqually River winter-run steelhead and the increasing trend 

for the Green River Winter Run. 

5 10 15 20

-3 -2 -1 0 1

Index

Residuals

Lk Wash

5 10 15 20

-0.5 0.0 0.5

Index

Residuals

Green

5 10 15 20

-1.0 -0.5 0.0 0.5

Index

Residuals

Puyallup

5 10 15 20

-0.4 -0.2 0.0 0.2 0.4

Index

Residuals

White

5 10 15 20

-1.0 -0.5 0.0 0.5 1.0

Index

Residuals

Nisqually
58

Figure 31. Graph of the trend in estimated total Puget Sound summer-run and winter-run steelhead for a 

putative Hood Canal and Strait of Juan de Fuca MPG. The graph plots the maximum-likelihood 

estimate of log(total no. steelhead) in the MPG against the observed data. The estimate of the 

log(total MPG count) (solid black line) has been scaled relative to the Elwha River Winter-Run 

population. The 95% CIs around the total MPG estimate are given by the red dashed lines. (Note 

that these are not the confidence intervals around the observed data, which are expected to fall 

outside the CI, depending on population-specific nonprocess error, but are instead around the 

composite estimate per Holmes and Ward 2011.) 

1985 1990 1995 2000 2005 2010

0

2

4

6

8 10

Years

Index of log abundance

EHoodCanal

Skokomish

WHoodCanal

Sequim

Dungeness

Elwha
59

Figure 32. Plots of the residuals from regression of steelhead counts over time for six summer-run and 

winter-run populations in the Hood Canal and Strait of Juan de Fuca MPG. Note the declining 

trends for Skokomish River Winter-Run and Dungeness River Summer-Run and Winter-Run 

steelhead, and the increasing trend for West Hood Canal Tributaries Winter-Run steelhead. 

DPS Viability Assessment Methods

The viability of a DPS is dependent on the viability of all of its component MPGs, which 

in turn are evaluated based on the combined status of their component DIPs. The PSS TRT

adopted methods described by Good et al. (2005) to evaluate data that affect the four VSP 

parameters for each DIP in the Puget Sound Steelhead DPS. State and tribal comanagers 

provided data on abundance, harvest, age structure, and hatchery releases. Data on adult returns 

5 10 15 20

Residuals

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Index

E Hood Canal

5 10 15

-0.5 0.0 0.5 1.0

Index

Residuals

Skokomish

2 4 6 8 10 12 14

-0.5 0.0 0.5

Index

Residuals

W Hood Canal

5 10 15 20

-0.5 0.0 0.5

Index

Residuals

Sequim

2 4 6 8 10

Residuals

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

Index

Dungeness

2 4 6 8 10 12

Residuals

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Index

Elwha
60

were obtained from a time series of freshwater spawner surveys and redd and trap counts.

Where possible, time series were assembled and analyzed for each population that had sufficient 

data.

In addition, a comprehensive assessment of viability at each level depends on 

consideration of diversity and spatial structure as well as abundance and productivity, but these 

factors are typically much more difficult to quantify. In this subsection, we review the four VSP 

components and describe the metrics the TRT considered most useful for each component. The 

utility of a metric is based on its biological relevance and its availability.

Abundance

Population size or abundance is recognized as an important parameter because, all else 

being equal, small populations are at greater risk of extinction than large populations, primarily 

because several processes that affect population dynamics operate differently in small 

populations than they do in large populations. These processes are deterministic density effects

(e.g., depensation at low density), environmental variation, genetic processes (e.g., genetic drift), 

demographic stochasticity, ecological feedback, and susceptibility to catastrophe. McElhany et 

al. (2000) provided guidelines relating minimum abundance to each of these processes at both 

the “viable” and “critical” levels, where a critical level implies a high risk of extinction over a 

short time period.

Metrics: adult spawners, expanded from redd counts; weir, trap, or dam counts of adult 

fish.

Productivity

Population growth rate (i.e., productivity over the entire life cycle) and factors that affect 

population growth rate provide information on how well a population is “performing” in the 

habitats it occupies during the life cycle. Estimates of population growth rate that indicate a 

population is consistently failing to replace itself are an indicator of increased extinction risk. 

Although the overall focus is on population growth rate over the entire life cycle, estimates of

stage-specific productivity—particularly productivity during freshwater life history stages—are 

also important to comprehensive evaluation of population viability. Other measures of 

population productivity, such as intrinsic productivity and the intensity of density dependence, 

may provide important information for assessing a population’s viability. The McElhany et al.

(2000) guidelines for population growth rate are closely linked with those for abundance.

Metrics: lambda, recruit/spawner estimates, smolt-or-parr-to-adult survival estimates, 

trend analysis; freshwater habitat-based IP (parr production) estimates.

Diversity

Several salmonid traits exhibit considerable diversity within and among populations, and 

this variation has important effects on population viability. In a spatially and temporally varying 

environment, there are three general reasons why diversity is important for species and 

population viability. First, diversity allows a species to use a wider array of environments than it 

could without diversity. Second, diversity protects a species against short-term spatial and 
61

temporal changes in the environment. Third, genetic diversity provides the raw material for 

surviving long-term environmental change. In order to conserve the adaptive diversity of 

salmonid populations, it is essential to 1) conserve the environment to which they are adapted, 

2) allow natural process of regeneration and disturbance to occur, and 3) limit or remove humancaused selection or straying that weakens the adaptive fit between a salmonid population and its 

environment or limits a population’s ability to respond to natural selection.

Metrics: age structure, repeat spawning rate, spawn timing range (within run types), and 

contribution of resident O. mykiss to anadromous production; hatchery introgression (in contrast 

to competition).

Spatial Structure

When evaluating population viability, it is important to take within-population spatial 

structure into account for two main reasons: 1) because there is a time lag between changes in 

spatial structure and species-level effects, overall extinction risk at the 100-year time scale may 

be affected in ways not readily apparent from short-term observations of abundance and 

productivity, and 2) population structure affects evolutionary processes and may therefore alter a 

population’s ability to respond to environmental change. Spatially structured populations in 

which subpopulations occupy habitat patches connected by low to moderate stray rates are often 

generically referred to as metapopulations. A metapopulation’s spatial structure depends 

fundamentally on habitat quality, spatial configuration, and dynamics, as well as the dispersal 

characteristics of individuals in the population. Pacific salmonids including steelhead are 

generally recognized as having metapopulation structure and the guidelines for spatial structure 

describe general rules of thumb regarding metapopulation persistence.

Metrics: accessible habitat (present vs. historical), proportion of spawning or rearing 

habitat occupancy.

Applying Viability Analyses to Multiple Spatial Scales

Assessing the viability of a DPS is rooted in the evaluation of DIP VSP parameters. 

From a recovery planning standpoint, recommended biological delisting criteria for Puget Sound 

steelhead are more easily understood from a DPS to DIP perspective. The PSS TRT has 

recommended a number of biological viability criteria for the DPS; individual DIP viability 

criteria are less fully developed, although the general assessment approach has been outlined. In 

this subsection, we present the DPS- and MPG-based viability criteria. These criteria were 

developed using the Puget Sound Steelhead DPS population structure the TRT identified in its 

previous publication, Identifying Historical Populations of Steelhead within the Puget Sound 

Distinct Population Segment (Myers et al. 2015). In that technical memorandum, the TRT 

identified three MPGs containing a total of 32 historically present steelhead DIPs (Figure 33). 

The existing configuration of the Puget Sound Steelhead DPS is based on information compiled 

to date, and it is possible that future analyses of new data will suggest additional changes. It is 

unlikely that these changes will affect the number or configuration of the MPGs, but most 

probably would result in identification of additional DIPs. The following DPS viability criteria 

would be unaffected by changes in the number of DIPs.
62

DPS Viability

1. The DPS is considered viable only if all of its component MPGs are considered viable.

MPG Viability

2. An MPG is considered viable if at least 40% (rounded up) of its component DIPs are 

considered viable.

a. DIPs exhibiting major life history strategies (e.g., summer run vs. winter run) will be 

considered separate components within an MPG. Therefore, 40% of summer-run 

populations and 40% of winter-run populations within an MPG that contains both life 

history types must be viable.

b. DIPs containing winter-run and summer-run subpopulations predominantly exhibit 

the winter-run life history strategy and will be considered winter run for the purpose 

of 2a.

3. Within an MPG, those DIPs that do not meet the VSP criteria for viability must be 

maintained at a level such that the probability of reaching a specified QET within 100 

years is no greater than 0.25 (the geometric mean of all populations in the MPG).

a. Where a DIP is below QET, but a self-sustaining resident population of O. mykiss is 

present in the accessible anadromous zone, the QET risk cannot be greater than 0.90.

4. Viability criteria for DIPs within an MPG cannot be allowed to degrade to the point 

where an MPG is no longer viable.
63

Figure 33. Three MPGs for the Puget Sound Steelhead DPS: Northern Cascades (green), Central and 

South Puget Sound (blue), and Hood Canal and Strait of Juan de Fuca (orange). Areas currently 

occupied by out-of-DPS steelhead introduced into historically inaccessible areas are in brown or 

purple (e.g., Chamber’s Creek winter-run steelhead introduced (via fish ladder) above Tumwater 

Falls in the Deschutes River); crosshatched DIPs identify summer-run populations.
64

Decision Support Systems as Tools for 

Assessing Viability

Evaluating the viability of Puget Sound steelhead at the level of MPGs and the entire 

DPS requires methods that can accommodate not only qualitative and quantitative information, 

but also information of varying quality. In the following subsections, we describe some of these 

methods.

DSSs are increasingly being employed to assess status and viability when uncertainty 

about variables influencing viability is considered extensive (McCann et al. 2006, Newton et al. 

2007, Newton 2010). A complete assessment of the biological condition of a complex entity 

such as a DPS 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 DPS. To 

track this large suite of data and criteria in a transparent and logically consistent framework, we 

used a knowledge-based DSS.

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

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

which include decision trees and approximate logic models, allow decision makers to perform 

complex evaluations quickly, present a consistent assessment that draws from a variety data 

sources, and track large sets of information accurately, thus improving the choices made 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).

One type of DSS incorporates approximate logic (Zadeh 1965, Reynolds et al. 2000), 

popularly known as fuzzy logic, to assist evaluation of conditions and functional relationships 

based on imprecise information. These DSSs typically rely on relationships between influence 

variables and response variables referred to as truth membership functions. These functions can 

take any form—from linear, to a threshold or sigmoidal relationship, to nonlinear—so long as 

one is confident that the available information supports that characterization.

Wainwright et al. (2008) used this type of DSS to identify viability criteria for threatened 

Oregon coast coho salmon (O. kisutch). Their DSS used a fuzzy logic network framework to 

link criteria at a variety of scales and aggregate them from fine-scale watershed-level criteria, 

through population-level criteria and MPG-level criteria, to criteria for the entire coho salmon 

ESU. We attempted to adapt this tool for Puget Sound steelhead viability but ultimately did not 

employ it, because the lack of quantitative information available for Puget Sound steelhead made 

it difficult to develop robust truth membership functions to parameterize the model.
65

The PSS TRT opted instead to develop a novel tool for this purpose, another DSS model 

based on Bayesian networks (BNs) (Marcot et al. 2001, Newton et al. 2007). BNs represent a 

decision tool that provides a transparent, graphical framework for characterizing relationships 

among a diverse set of variables. They are increasingly being used to evaluate viability and 

project future status of organisms of conservation concern (e.g., polar bears [Ursus maritimus], 

Amstrup et al. 2008, 2010; marbled murrelets [Brachyamphus marmoratus], Steventon et al. 

2003) and have been applied to salmonids (Rieman et al. 2001). Because these networks usually 

incorporate likelihoods (conditional probabilities) for each of the input variables, typically by 

using discrete uniform distributions to treat all values of a finite set of possible values as equally 

probable, they are effective models of reasoning under uncertainty (Jensen 2001). They also 

have the advantage that they can readily incorporate new information as it becomes available to 

reduce uncertainty in the decision process.

Bayesian Networks for Evaluation of Viability

Evaluating viability of Puget Sound steelhead at the level of DIP, MPG, and DPS 

requires an approach that can explicitly incorporate uncertainty into the evaluation and deal with 

available information of variable quality for all four VSP criteria. We sought a method that 

could accommodate these types of information in a transparent, consistent way. We elected to 

construct a series of BNs (aka Bayesian belief networks or Bayes nets per Newton et al. 2007) to 

assess viability at these multiple scales and ascertain the influence of factors on viability. A BN 

is a probabilistic graphical model (e.g., Lee and Rieman 1997, Marcot et al. 2001, Conroy et al. 

2008, Peterson et al. 2008) designed to efficiently represent a probability distribution underlying 

a set of variables that influence each other, but that is too complex to be represented using a 

series of tables and equations. A main feature of a BN is that there is only one probability 

distribution that satisfies the structure of the BN (as reflected in the constraints among variables 

in the form of probabilistic conditional dependencies), and this distribution can be estimated by 

testing the BN by locking values of the variables at particular probabilities (a process known as 

instantiation). The objective of a BN is to characterize the dependencies among its constituent 

variables (depicted by nodes) given the influences between them (depicted by edges).

A BN is composed of nodes and edges. Each node represents a variable that can take any 

of several values, either as discrete states or as represented by a continuous function. Each edge 

connecting two nodes represents probabilistic dependencies among these nodes (the arrow 

indicates the direction of influence). In a BN, the conditional probabilities that reflect these 

dependencies are estimated from Bayes’ theorem. In its simplest form, Bayes’ theorem states 

that the probability of event b occurring given that event a has occurred, P(b|a), is

P(b| ) = P( |b) x P(b)

P( )

where P(a) is the probability of a, P(b) is the probability of b, and P(a|b) is the probability of a

given that b has occurred (Bayes and Price 1763, Gelman et al. 1995). We can demonstrate this 

relationship with a simple example. Suppose that we know from previous studies that slowly 

growing (event b) juvenile steelhead (i.e., progeny of steelhead parents) adopt a freshwater 

resident life history (event a), P(a|b), about 10% of the time. Suppose we also know that the 

average probability of a growth rate sufficiently low to induce residency in this population, P(b), 
66

is 0.3, and the probability of sampling a resident O. mykiss in this population which has 

anadromous access to the ocean is 1 in 5 (so, P(a) is 0.2). We want to know how likely it is that 

a juvenile O. mykiss born of steelhead parents will become resident as a result of slow growth—

that is, how likely is it that depressed growth (b) will result in residency (a) in this population? 

We can use Bayes’ theorem to compute the probability that residency results from depressed 

growth, given this information

P(b| ) = 0.1 x 0.3

0.2 = 0.15

Under these conditions, we would predict that if a fish born of anadromous parents becomes 

resident, then the likelihood that this is caused by depressed growth is 15%.

Another way of expressing Bayes’ theorem is one that includes a hypothesis (H), 

evidence (E), and prior knowledge or past experience (K), so that the probability that a 

hypothesis is true given the evidence and past experience is

P( |E,K) = P( |K) x P(E| ,K)

P(E|K)

where P(H|K) is the probability of the hypothesis given prior knowledge, P(E|K) is the 

probability of the evidence given prior knowledge, and P(E|H,K) is the probability of the 

evidence given the hypothesis is true and prior knowledge. The term P(H|E,K) is the posterior 

probability, or the probability of hypothesis H after considering the effect of the evidence E on 

prior knowledge K. The term P(H|K) is the prior probability H given K alone. The term 

P(E|H,K) is the likelihood and gives the probability of E given that both H and K are true. The 

term P(E|K) is independent of H and is usually regarded as a normalizing or scaling constant 

(Gelman et al. 1995).

A BN is intended to directly incorporate estimates of uncertainty into evaluating 

relationships between variables. As a DSS, it can be as simple or as complex as one thinks is 

supportable. It has the advantage that its components—its criteria—can easily be constructed 

through simple verbal guidelines, actual data, or estimated functional relationships. A BN has 

some features that can favor its use.

• BNs can lay out parameters and how they interact transparently in a clear decision 

framework;

• BNs explicitly depict uncertainty as prior probabilities, which lend themselves well to 

risk analysis; and

• BNs can be used to help identify key factors that influence the outcome of interest or to 

help prioritize research, monitoring, or other actions.

BNs are not intended as a substitute for human judgment or as a means of making decisions, but 

rather to support decision-making processes by integrating available information and human 

judgment into a sensible conceptual framework.

Figure 34 depicts an example of a simple BN to evaluate viability of a population based 

on current abundance and estimated population growth rate. For this example and other analyses 

using BNs in this report, we used the program Netica v. 5.09 (Norsys Software Corp., 
67

Figure 34. A simple BN depicting the influence of three discrete states of population abundance and five 

discrete states of average population growth on a dichotomous estimate of population viability. 

The bars and values in the two parental nodes (Population abundance and Average population 

growth) show estimates of the probabilities of each state for these factors; the bars and values in 

the child or outcome (decision-aiding) node (Population viability) depict resulting estimates of 

the probabilities of each viability state. The joint probabilities of all possible combinations of 

these states of Population abundance and Average population growth, which are inputs from the 

user, are given in Table 8.

Vancouver, British Columbia) to construct and evaluate the networks, following the general 

guidelines recommended by Marcot et al. (2006). In this BN, one parent node, “Population 

abundance,” summarizes an assessment of the current abundance of the population relative to a 

low-abundance threshold that would trigger concern (or remedial action); the other parent node, 

“Average population growth,” summarizes an assessment of the population’s growth rate (e.g., 

an estimate of the annual percent change in abundance). In this simple BN, we assume that these 

two factors alone determine “Population viability,” the child node of interest, and they do so 

independently.

For each parent node in the BN, the black bars and corresponding numbers depict prior 

probabilities of each state. In this example, we are using discrete states to describe each variable 

in the BN. For example, the assessment of current population abundance reflected in Figure 34

is that current abundance is larger than the population’s low-abundance threshold with a 

probability of 85%. However, because this estimate is likely to be obtained from sampling, it 

incorporates some uncertainty. To capture this uncertainty, in this case we have estimated that 

the probability that the population is much larger than this threshold (say, 10 × the threshold 

abundance) is 5%, and the probability that the population is smaller than this threshold is 10%. 

The assessment of the population’s growth (based, say, on an estimate of linear trend in 

abundance) is that the population is stable, but with a probability of only 50%. We think that 

there is some likelihood that the population is increasing (e.g., 1–2% annually) or rapidly 

increasing (>5% annually), here estimated at 16%, and we also think that there is some 

likelihood that the population is declining, here estimated at 34%. Note that the probabilities of 

each state must sum to 1.

Population viability

Viable

Not viable

53.3

46.7

Population abundance

Much larger than threshold

Larger than threshold

Smaller than threshold

5.00

85.0

10.0

Average population growth

Rapidly increasing

Increasing

Stable

Declining

Rapidly declining

1.00

15.0

50.0

20.0

14.0
68

The Population viability node is an output node; it represents the results of a decisionaiding model. In Figure 34 we have depicted the probability that the population is viable or not, 

given the probability distributions for population abundance and growth rate described above. 

The Bayesian calculations underlying the BN, parameterized with the probabilities given in the 

parent nodes for Population abundance and Average population growth and with a set of 

conditional probabilities that represent all the combinations of states for abundance and growth 

(Table 8), result in the probabilities estimated for viability (53.3%) or nonviability (46.7%) given 

in the child node for Population viability. The prior information that the population is thought to 

be somewhat larger than the low-abundance threshold and stable, albeit with some uncertainty, 

leads to a BN outcome of uncertainty that the population is viable, with probability 53.3%.

What if we are more certain about the population’s state, for example, because of new 

information? One can instantiate the BN to correspond to particular states of the variables and 

examine how viability is affected by this additional information. Instantiation, the term used to 

indicate that node states are set at particular values, is important in conducting sensitivity 

analyses for a BN. For example, if additional information led us to conclude that the population 

is in fact larger than the low-abundance threshold (P = 100%) and its growth is positive (e.g., λ > 

1, P = 100%), the BN estimates that the probability the population is viable is now 70% (Figure 

35, top panel). It turns out that if we are certain that the population is much larger than the 

threshold and its growth is positive, the probability that the population is viable rises to 95%. If, 

on the other hand, additional information led us to conclude that the population is in fact smaller 

than the low-abundance threshold (P = 100%) and its growth is negative (e.g., λ < 1, P = 100%), 

the BN estimates that the probability the population is viable is now only 5% (Figure 35, bottom 

panel). From these results it is easy to see that the estimates directly reflect the corresponding

values in the conditional probability table (CPT) (Table 8).

Table 8. CPT underlying the outcome node of population viability for the BN in Figure 34, representing 

the joint probabilities of all possible combinations of states of population abundance and 

population growth described in the BN’s parent nodes.

Population abundance Average population growth Viable Not viable

Much larger than threshold Rapidly increasing 0.99 0.01

Increasing 0.95 0.05

Stable 0.90 0.10

Declining 0.75 0.25

Rapidly declining 0.50 0.50

Larger than threshold Rapidly increasing 0.80 0.20

Increasing 0.70 0.30

Stable 0.60 0.40

Declining 0.45 0.55

Rapidly declining 0.35 0.65

Smaller than threshold Rapidly increasing 0.50 0.50

Increasing 0.40 0.60

Stable 0.30 0.70

Declining 0.05 0.95

Rapidly declining 0.01 0.99
69

Figure 35. Top panel, the BN in Figure 34 instantiated at particular states of population abundance and 

growth, depicting certainty that the population is larger than the low-abundance threshold and 

increasing in size. Note how this additional knowledge has altered the assessment of viability, 

given these prior probabilities and the CPT in Table 8. Bottom panel, the BN in Figure 34

instantiated at different states for population abundance and growth, depicting certainty that the 

population is smaller than the low-abundance threshold and declining in size.

To determine the most likely configuration of probabilities in the network given the

evidence, we can estimate the most probable explanation (MPE) for this network. The MPE is a 

complete variable instantiation across the entire network that gives the highest probability, given 

the current evidence (distributions of conditional probabilities). The MPE is equivalent to the 

maximum-a-posteriori probability, which asks the question, Given the values at some nodes, 

what are the most likely findings across the network? In a Bayesian sense, it can be considered 

an estimate of the most plausible explanation of the data. It is computed as follows. The 

probability of a complete variable instantiation x is

Population viability

Viable

Not viable

70.0

30.0

Population abundance

Much larger than threshold

Larger than threshold

Smaller than threshold

 0

 100

 0

Average population growth

Rapidly increasing

Increasing

Stable

Declining

Rapidly declining

 0

 100

 0

 0

 0

Population viability

Viable

Not viable

5.00

95.0

Population abundance

Much larger than threshold

Larger than threshold

Smaller than threshold

 0

 0

 100

Average population growth

Rapidly increasing

Increasing

Stable

Declining

Rapidly declining

 0

 0

 0

 100

 0
70

Pr(x) = �θx|u

xu~x

where ~ is the compatibility among instantiations (i.e., xu is compatible with x). An MPE is a 

complete variable instantiation given evidence e that has the highest probability

MPE(e) ≝ . argx~e max Pr(x)

= argx~e max �θx|u

xu~x

Note that there may be more than one MPE for a network (i.e., more than one complete 

instantiations with the same probability).

For the network in Figure 34, the MPE is shown in Figure 36. It indicates that, given the 

data, our best guess is that the population abundance is larger than the threshold, that its 

dynamics are stable (growth rate near zero), and that the population is viable. However, 

although the MPE indicates that the population is most likely viable (i.e., that probability is 

instantiated at 100%), the estimated probability that the population is not viable is still rather 

high (66.7%) given the MPE, reflecting the considerable uncertainty about the population’s 

status in both size and, especially, growth rate. Note that under MPE the “probabilities” given 

within a node in a network are not additive. The reason for this is that in computing MPE across 

the network, Netica software represents the MPE probability within each node as a full bar (i.e., 

P = 100%; the actual probability for that state given the evidence is given by the uninstantiated 

network without computing MPE. Figure 34 indicates that this value is 53.3%. Figure 36

indicates that the MPEs for abundance, growth, and viability are Larger than threshold, Stable, 

and Viable, respectively. These states do correspond to the largest probabilities for these 

respective nodes (85%, 50%, and 53.3%, respectively) in the uninstantiated network in Figure 

Figure 36. MPE for the BN in Figure 34.

Population abundance

Much larger than threshold

Larger than threshold

Smaller than threshold

8.82

 100

13.7

Average population growth

Rapidly increasing

Increasing

Stable

Declining

Rapidly declining

2.67

35.0

 100

36.7

30.3

Population viability

Viable

Not viable

 100

66.7
71

34. In computing the MPE for the network, Netica software simply estimates the probabilities of 

the alternate states at each node, given that the other nodes are in their most probable 

configurations (and scaling by the same factor that was used to bring the MPE bar to 100%. 

Since MPE is computed and probabilities are adjusted at each node, complex networks can be 

difficult to characterize simply. This is the primary value of MPE in a BN—it identifies the most 

likely states of each variable given the evidence and quantifies their likelihoods relative to those 

of the other states.

As expected, the viability outcome directly depends on the set of probabilities used to 

parameterize the BN. It is important to remember that these probabilities, including those in the 

CPT, can represent estimates obtained from best judgement (informed opinion or belief), actual 

data, or a combination of these sources of information, and they can be revised when additional 

information becomes available (Marcot et al. 2006, Newton et al. 2007).

We can conduct a sensitivity analysis of the BN to determine how sensitive the 

population viability results are to changes in the states of population abundance and growth. 

This sensitivity is often measured by estimating the entropy reduction (or reduction in mutual 

information) or belief variance (expected reduction in real variance) resulting from the degree to 

which findings at a node are influenced by the findings at another node, given the findings that 

are entered into the network. In information theory, entropy is a measure of the uncertainty of a 

random variable(s). Mutual information corresponds to the reduction in the entropy of a variable 

achieved by learning the state of another variable. In a BN, entropy reduction (in the case of 

discrete or categorical variable states) or variance reduction (in the case of continuous variables) 

is the expected reduction in variation at an output node due to a finding at an input node (Marcot 

et al. 2006). It is calculated by

I = H(Q) − (Q|F) = ��P(q, f)log2[P(q, f)]

P(q)P(f)

q f

where I is the entropy reduction, H(Q) is the entropy of output node Q before any new findings 

from input node F, H(Q|F) is the entropy of Q after new findings from F, P(q,f) is the probability 

of finding q at node Q given finding f at node F, and P(q) and P(f) are the probabilities of 

findings q and f, respectively (Appendix B in Marcot et al. 2006). In structuring a BN, one seeks 

to maximize entropy of the entire network, which corresponds to maximizing the mutual 

information among the variables. For example, an entropy reduction of 5% at node Q resulting 

from a new finding at node F reflects a reduction in mutual information of 5%; this means that 

the uncertainty in the state of Q is reduced by 5% by knowing the state of F.

The results of a sensitivity analysis (Table 9) show that the estimate of viability is 

sensitive to variation in both population abundance and growth rate, but slightly more sensitive 

to changes in abundance for this particular BN; the entropy reduction for sensitivity of viability 

to change in abundance was 4.27%, whereas that for sensitivity to change in productivity was 

4.07%. This is largely because the population’s viability changes considerably when its size 

drops below the low-abundance threshold, as depicted by the probabilities in the CPT (Table 8).
72

Table 9. Results of a sensitivity analysis of the BN in Figure 34, showing the sensitivity of population 

viability to changes in the states of population abundance and growth rate.

Probability ranges Root mean 

Viability Minimum Current Maximum square change

Sensitivity to findings at abundancea

Viable 0.2264 0.533 0.8224 0.1179

Not viable 0.1776 0.467 0.7736 0.1179

Sensitivity to findings at growth rateb

Viable 0.3235 0.533 0.7795 0.1174

Not viable 0.2205 0.467 0.6765 0.1174

a Entropy reduction = 0.0426 (4.27%) and belief variance = 0.01389 (5.58%). b Entropy reduction = 0.0406 (4.07%) and belief variance = 0.01379 (5.54%).

Use of Bayesian Networks to Incorporate Viable Salmonid 

Population Criteria into a Viability Assessment Framework

We constructed a BN to characterize the influence of a number of features of coastal 

steelhead biology and estimates of the four VSP parameters—abundance, productivity, diversity, 

and spatial structure—for individual populations on viability at the level of the DIP, the MPG, 

and the entire DPS. In essence, a BN is a set of variables (nodes) linked by probabilities that 

represent their interdependence. Our objective in building the BN model was to evaluate the net 

effects of several features of steelhead populations related to VSP parameters and their habitat on 

the viability of populations across the entire Puget Sound DPS. We did so by synthesizing 

available information and expert knowledge on abundance, productivity, diversity, and spatial 

structure of Puget Sound steelhead. The BN that determines DPS viability is composed of three 

subnetworks that determine the viability of each MPG. Each of these MPG subnetworks is 

composed of a series of subnetworks that determine the viability of each DIP in the constituent 

MPG. A representative BN to characterize the viability of a typical winter-run steelhead 

population in Puget Sound is depicted in Figure 37.

Our goal was to construct each such network to minimize its complexity while still 

retaining the capacity to evaluate the influence of all four VSP parameters on viability. We 

aimed to keep the number of nodes small but include key variables for which we either had 

available information or else thought information might become available later that could assist 

with viability assessment and future recovery planning. Consequently, some nodes can be 

considered placeholders with underlying conditional probabilities that reflect considerable 

uncertainty. As indicated by Figure 37, the effect of DIP abundance on DIP viability 

incorporates influences of adult and juvenile abundance relative to capacity for production, and 

quasi-extinction risk (as estimated from a PVA, if available). The effect of DIP productivity on 

DIP viability incorporates influences of population growth rate and frequency of repeat 

spawning; in the network, population growth rate is influenced by both freshwater survival 

(smolts per spawner) and marine survival (adults per smolt). Next, the influence of DIP diversity 

on DIP viability incorporates influences of the distribution of run timing, influence of hatchery 

fish on natural diversity, the adult age distribution, and the proportion of migrant smolts 
73

Figure 37. A BN to characterize the viability of a hypothetical DIP of winter-run steelhead in the Puget 

Sound DPS. The network is intended to be minimally complex while estimating the influence of 

all four VSP parameters (abundance, productivity, diversity, and spatial structure) on population 

viability. The influence of the DIP’s abundance on its viability is represented by the “VSP risk: 

DIP abundance” node (lower left) that incorporates influences of adult abundance, juvenile 

abundance, and quasi-extinction risk (as estimated from a PVA, if possible) on DIP viability. The 

influence of the DIP’s productivity on its viability is represented by the “VSP risk: DIP 

productivity” node (upper left) that incorporates influences of population growth rate and 

frequency of repeat spawning on DIP viability; the node for population growth rate is itself 

influenced by freshwater survival (smolts per spawner) and marine survival (adults per smolt). 

The influence of the DIP’s diversity on its viability is represented by the “VSP risk: DIP 

diversity” node (upper right) that incorporates influences of the distribution of run timing, 

influence of hatchery fish on natural diversity, the adult age distribution, and the proportion of 

migrant smolts produced by resident adults. The influence of the DIP’s spatial structure on its 

viability is represented by the “VSP risk: DIP spatial structure” node (lower right) that 

incorporates influences of the fraction of IP habitat occupied by rearing juveniles and the fraction 

of IP habitat occupied by spawning adults. This network is a subnetwork that then determines the 

viability of the corresponding MPG, as indicated by the dashed arrow at bottom; similar 

subnetworks for the viability of each DIP in the MPG combine to influence the MPG’s overall 

viability. A description of the nodes in the network is in Table 10. The underlying CPTs for this 

subnetwork are in Table 11 and Table 12.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

13.2

27.2

59.5

33.33 ± 0.007

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

18.0

23.7

58.3

33.33 ± 0

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

26.2

24.7

49.1

37.2 ± 13

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.9

24.8

18.2

33.33 ± 0

Fraction of high I.P. spawn habitat

More than half

Less than half

10.0

90.0

74 ± 18

Fraction of high I.P. rear habitat

More than half

Less than half

20.0

80.0

44 ± 8

DIP viability

Viable

Not viable

45.4

54.6

50 ± 0

Probability to reach QET

Less than 5 percent

More than 5 percent

5.00

95.0

68 ± 8.7

Adult abundance vs capacity

More than half

Less than half

20.0

80.0

65 ± 20

Juvenile abundance vs capacity

More than half

Less than half

25.0

75.0

62.5 ± 22

Spawner age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Run timing 67.5 ± 18

Historical

Truncated

30.0

70.0

56 ± 14

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

17.1

83.0

69.8 ± 23

Adults per smolt

Greater than 1 percent

Less than 1 percent

10.0

90.0

82 ± 24

Smolts per spawner

Greater than 10

Less than 10

45.0

55.0

50.5 ± 5

Hatchery influence

Nominal

Extensive

40.0

60.0

46 ± 20

MPG viability

Viable

Not viable

50.0

50.0

50 ± 10

Productivity

Diversity

Spatial structure

Abundance
74

produced by resident adults. Finally, the influence of DIP spatial structure on DIP viability 

incorporates influences of the fraction of IP habitat occupied by rearing juveniles and the fraction 

of IP habitat occupied by spawning adults.

In this network, the values for the input nodes and the conditional probabilities 

underlying each node were assigned by TRT members after considering available data, opinions 

and comments from other steelhead biologists, reviews of other models and analyses, reports, 

and similar approaches in the scientific literature (see References section and Appendix A). In 

most cases, the intermediate and output conditional probabilities pertain to steelhead 

demographic and life history responses that are poorly understood; in these cases, we attempted 

to account for this uncertainty by assigning moderate to high levels of spread in the conditional 

probabilities. In general, probability values were chosen to produce a linear relationship between 

the score for a VSP category and the DIP viability (with a consequent distribution of probability 

of being viable ranging from 0 to 100%), with all VSP criteria weighted equally in the network. 

A description of the nodes in the network for DIP viability in Figure 37 and Figure 38 is in Table 

10. The probabilities that underlie the network are in Table 11 and Table 12.

We initially weighted abundance and productivity criteria equally with diversity and 

spatial structure criteria in our network CPTs, even though the evidence was that DIP viability 

for Puget Sound steelhead was somewhat more dependent on how current abundance compared 

with historical estimates, based on preliminary networks constructed before weighting (Figure 

39). Comparisons of estimates of relative viability for Puget Sound steelhead DIPs between 

current (“depressed”) and historical estimates of the four VSP parameters (abundance, 

productivity, diversity, and spatial structure) point to the primary importance of abundance to 

DIP viability. Therefore, after constructing each network, in assessing final categories of 

viability for each DIP we weighted mean abundance and productivity criteria more heavily than 

mean diversity and spatial structure criteria for this reason and because more quantitative 

demographic information was available (see Viability Criteria for Puget Sound Steelhead section 

of this technical memorandum).

Our approach to parameterizing the BNs for each DIP was as follows. For the abundance 

criterion, we first computed each DIP’s geometric mean abundance over the most recent 5 years 

from available abundance data. For DIPs that have no recent abundance data, we used the mean 

geometric mean abundance for all DIPs within the MPG, scaled by the estimate of total IP area 

for that DIP, as the input variable. (For the Northern Cascades MPG, we excluded the Skagit 

River basin for these calculations for DIPs with no abundance data because of its uniquely large 

size among drainages within the MPG.) We computed the probability that the DIP would reach 

its estimated QET abundance within 100 years; the QET for each DIP was based on the DIP 

habitat’s estimate of IP, the empirical Puget Sound steelhead parr densities computed by Gibbons 

et al. (1985), using an estimate of 0.0754 parr/m2

, and the Chapman (1981) estimate of parr-tosmolt survival of 0.30 to estimate smolt productivity at 0.023 smolts/m2

. For each DIP, we 

calculated the probability of reaching QET using MARSS algorithms applied to the abundance 

data (as described in the earlier Viability Assessment Methods section under the Population 

Viability Analysis With Autoregressive State-Space Models subsection). For those DIPs for 

which we could not calculate QETs, we used the mean QET computed for the associated MPG 

as the input variable.
75

Table 10. Titles and descriptions of the input, intermediate, and decision nodes in the BN describing 

population viability of steelhead. See the immediately previous text for a description of how the 

input probabilities were calculated for these nodes, and Table 11 and Table 12 for the final input 

probabilities used.

Node title Node description States

Input nodes: VSP abundance

Adult abundance vs. capacity The probability that the fraction of 

estimated spawner carrying capacity 

represented by the current geometric mean 

adult abundance in the DIP is <25% or 

>25%

Greater than 25%

Less than 25%

Juvenile abundance vs. capacity The probability that the fraction of 

estimated rearing capacity represented by 

the current mean parr or smolt abundance 

in the DIP is <25% or >25%

Greater than 25%

Less than 25%

Probability to reach QET The probability that the QET estimated for 

this DIP will be reached within 100 years 

(from a PVA, if available)

Greater than 20%

Less than 20%

Intermediate node: 

VSP abundance

The probability that the DIP’s abundance 

poses a risk to DIP viability 

Low risk

Moderate risk

High risk

Input nodes: VSP productivity

Mean population growth rate The probability that the mean population 

growth rate estimated for this DIP is <1 

(declining population size) or >1 

(increasing population size). This metric is 

influenced by two major components: 

smolts per spawner and adults per smolt 

(below)

Greater than 1

Less than 1

Smolts per spawner A measure of productivity in freshwater 

(and a component of population growth 

rate), it is the probability that the number of

smolts produced per spawner in the DIP is 

<50 or >50

Greater than 50

Less than 50

Adults per smolt A measure of productivity in the ocean

(and a component of population growth 

rate), it is the probability that the smolt to 

adult survival rate in the DIP is <2% or 

>2%

Greater than 2%

Less than 2%

Frequency of repeat spawners The probability that the frequency of repeat 

spawning in this DIP is historical (e.g., 10–

20%) or compressed (<10%)

Historical

Depressed

Intermediate node: 

VSP productivity

The probability that the DIP’s productivity 

poses a risk to DIP viability 

Low risk

Moderate risk

High risk
76

Table 10 continued. Titles and descriptions of the input, intermediate, and decision nodes in the BN 

describing population viability of steelhead. See the immediately previous text for a description 

of how the input probabilities were calculated for these nodes, and Table 11 and Table 12 for the 

final input probabilities used.

Node title Node description States

Input nodes: VSP diversity

Run timing The probability that the current distribution 

of spawn timing (within a run type) in the 

DIP is historical vs. truncated or altered

Historical

Altered

Hatchery influence The probability that hatchery influence 

from Chambers Creek WSH or Skamania 

SSH stock in the DIP is nominal or 

extensive (e.g., hatchery plants have 

occurred for >5 years or involved more 

than 500,000 fish)

Nominal

Extensive

Fraction of smolts from 

residents

The probability that the fraction of smolts 

produced by residents in the DIP is 

appreciable or negligible (e.g., the fraction 

of smolts produced by residents is >10% or 

<10%)

Appreciable

Negligible

Input Nodes: VSP spatial structure

Fraction of IP spawning habitat The fraction of estimated available 

steelhead spawning habitat area in the DIP 

(IP) under historical conditions utilized by 

the current distribution of spawners. In the 

network’s current form, this is the 

estimated fraction (%) of accessible 

spawning habitat that is occupied

Greater than 20%

Less than 20%

Fraction of IP rearing habitat The fraction of estimated available 

steelhead rearing habitat area in the DIP 

(IP) under historical conditions utilized by 

the current distribution of rearing juveniles. 

In the network’s current form, this is the 

estimated fraction (%) of accessible rearing 

habitat that is occupied

Greater than 20%

Less than 20%

Intermediate node: 

VSP spatial structure

The probability that the DIP’s spatial 

structure poses a risk to DIP viability 

Low risk

Moderate risk

High risk

Decision node: DIP viability The probability that the DIP is viable

(based on the collective influence of the 

four VSP parameters on DIP viability)

Viable

Not viable

Decision node: MPG viability The probability that the MPG is viable 

(based on the collective influence of the 

viabilities of each of its composite DIPs)

Viable

Not viable
77

Table 11. CPT for the input nodes describing risk for each VSP parameter for the BNs describing viability of Puget Sound steelhead populations 

(DIPs). The probabilities input for each DIP were estimated from data, PVA results, and expert opinion.

Abundance Productivity Diversity Spatial structure

Adult abund. vs. capacityJuvenile abund. vs. capacityP(QET)

Risk

Mean growth rateaFrequency repeats

Risk

Run timingFraction smolts from residentsAge structureHatchery influence

Risk

Fraction IP rearing habit

at

Fraction IP spawn habitat

Risk

LowModerateHighLowModerateHighLowModerateHighLowModerateHigh

>25 >25 <10 90 9 1 >1 Histb 90 9 1 Hist Appb Hist Nomb 90 9 1 >20 >20 70 25 5

>25 >25 >10 75 15 10 >1 Depb 70 20 10 Hist App Hist Extb 69 20 11 >20 <20 50 20 30

>25 <25 <10 60 15 25 <1 Hist 40 40 20 Hist App Comb Nom 60 30 10 <20 >20 50 20 30

>25 <25 >10 40 20 40 <1 Dep 1 9 90 Hist App Com Ext 54 25 21 <20 <20 5 25 70

<25 >25 <10 15 25 60 Hist Negb Hist Nom 40 40 20

<25 >25 >10 10 20 70 Hist Neg Hist Ext 39 30 31

<25 <25 <10 15 20 65 Hist Neg Com Nom 20 50 30

<25 <25 >10 1 9 90 Hist Neg Com Ext 19 40 41

Altb App Hist Nom 10 50 40

Alt App Hist Ext 9 40 51

Alt App Com Nom 10 40 50

Alt App Com Ext 9 30 61

Alt Neg Hist Nom 5 35 60

Alt Neg Hist Ext 4 25 71

Alt Neg Com Nom 5 20 75

Alt Neg Com Ext 1 9 90

a For calculating mean growth rate values (>1 and <1): if the adults per smolt is >2% and the smolts per spawner is >50%, then the probability that the growth 

rate is >1 is 95% and the probability that the growth rate is <1 is 5%; if the adults per smolt is >2% and the smolts per spawner is <50%, then the probability that 

the growth rate is >1 is 40% and the probability that the growth rate is <1 is 60%; if the adults per smolt is <2% and the smolts per spawner is >50%, then the 

probability that the growth rate is >1 is 20% and the probability that the growth rate is <1 is 80%; and if the adults per smolt is <2% and the smolts per spawner 

is <50%, then the probability that the growth rate is >1 is 5% and the probability that the growth rate is <1 is 95%. b Key: Hist = historical, Dep = depressed, Alt = altered, App =appreciable, Neg = negligible, Com = compressed, Nom = nominal, and Ext = extensive.
78

Table 12. CPT describing the influence of each of the four VSP parameters on DIP viability for the BNs 

describing viability of Puget Sound steelhead. Note that the values in the first four columns are 

risk values (e.g., a value of “Low” for DIP abundance implies a low risk to DIP viability posed by 

abundance in the DIP). The values were chosen to produce a linear relationship between VSP 

category and DIP viability (with a consequent distribution of probability of being viable ranging 

from 0 to 100%), with all VSP criteria weighted equally.

VSP risk DIP viability

DIP abundance DIP productivity DIP diversity DIP spatial structure Viable Not viable

Low Low Low Low 100.0 0.0

Low Low Low Moderate 91.7 8.3

Low Low Low High 83.3 16.7

Low Low Moderate Low 91.7 8.3

Low Low Moderate Moderate 83.3 16.7

Low Low Moderate High 75.0 25.0

Low Low High Low 83.3 16.7

Low Low High Moderate 75.0 25.0

Low Low High High 66.7 33.3

Low Moderate Low Low 83.3 16.7

Low Moderate Low Moderate 75.0 25.0

Low Moderate Low High 66.7 33.3

Low Moderate Moderate Low 75.0 25.0

Low Moderate Moderate Moderate 66.7 33.3

Low Moderate Moderate High 58.3 41.7

Low Moderate High Low 66.7 33.3

Low Moderate High Moderate 58.3 41.7

Low Moderate High High 50.0 50.0

Low High Low Low 66.7 33.3

Low High Low Moderate 58.3 41.7

Low High Low High 50.0 50.0

Low High Moderate Low 58.3 41.7

Low High Moderate Moderate 50.0 50.0

Low High Moderate High 41.7 58.3

Low High High Low 50.0 50.0

Low High High Moderate 41.7 58.3

Low High High High 33.3 66.7

Moderate Low Low Low 83.3 16.7

Moderate Low Low Moderate 75.0 25.0

Moderate Low Low High 66.7 33.3

Moderate Low Moderate Low 75.0 25.0

Moderate Low Moderate Moderate 66.7 33.3

Moderate Low Moderate High 58.3 41.7

Moderate Low High Low 66.7 33.3

Moderate Low High Moderate 58.3 41.7

Moderate Low High High 50.0 50.0

Moderate Moderate Low Low 66.7 33.3

Moderate Moderate Low Moderate 58.3 41.7

Moderate Moderate Low High 50.0 50.0

Moderate Moderate Moderate Low 58.3 41.7

Moderate Moderate Moderate Moderate 50.0 50.0
79

Table 12 continued. CPT describing the influence of each of the four VSP parameters on DIP viability 

for the BNs describing viability of Puget Sound steelhead. Note that the values in the first four 

columns are risk values (e.g., a value of “Low” for DIP abundance implies a low risk to DIP 

viability posed by abundance in the DIP). The values were chosen to produce a linear 

relationship between VSP category and DIP viability (with a consequent distribution of 

probability of being viable ranging from 0 to 100%), with all VSP criteria weighted equally.

VSP risk DIP viability

DIP abundance DIP productivity DIP diversity DIP spatial structure Viable Not viable

Moderate Moderate Moderate High 41.7 58.3

Moderate Moderate High Low 50.0 50.0

Moderate Moderate High Moderate 41.7 58.3

Moderate Moderate High High 33.3 66.7

Moderate High Low Low 50.0 50.0

Moderate High Low Moderate 41.7 58.3

Moderate High Low High 33.3 66.7

Moderate High Moderate Low 41.7 58.3

Moderate High Moderate Moderate 33.3 66.7

Moderate High Moderate High 25.0 75.0

Moderate High High Low 33.3 66.7

Moderate High High Moderate 25.0 75.0

Moderate High High High 16.7 83.3

High Low Low Low 66.7 33.3

High Low Low Moderate 58.3 41.7

High Low Low High 50.0 50.0

High Low Moderate Low 58.3 41.7

High Low Moderate Moderate 50.0 50.0

High Low Moderate High 41.7 58.3

High Low High Low 50.0 50.0

High Low High Moderate 41.7 58.3

High Low High High 33.3 66.7

High Moderate Low Low 50.0 50.0

High Moderate Low Moderate 41.7 58.3

High Moderate Low High 33.3 66.7

High Moderate Moderate Low 41.7 58.3

High Moderate Moderate Moderate 33.3 66.7

High Moderate Moderate High 25.0 75.0

High Moderate High Low 33.3 66.7

High Moderate High Moderate 25.0 75.0

High Moderate High High 16.7 83.3

High High Low Low 33.3 66.7

High High Low Moderate 25.0 75.0

High High Low High 16.7 83.3

High High Moderate Low 25.0 75.0

High High Moderate Moderate 16.7 83.3

High High Moderate High 8.3 91.7

High High High Low 16.7 83.3

High High High Moderate 8.3 91.7

High High High High 0.0 100.0
80

Figure 38. A BN to characterize the viability of a hypothetical DIP of summer-run steelhead in the Puget 

Sound DPS. The primary differences between this BN and that for winter-run steelhead are in the 

conditional probabilities underlying abundance, productivity, and diversity (especially the run 

timing and hatchery influence nodes). See the Figure 37 caption for details.

We used the IP metrics (i.e., the sum of low, medium, and high IP areas in reaches 

accessible by steelhead) and the juvenile steelhead densities estimated by Gibbons et al. (1985) 

and Chapman (1981) for each DIP to calculate parr and smolt densities and spawner abundances,

based on 1%, 5%, and 20% marine survival rates; we considered these minimum, viable, and 

capacity abundances, respectively (see Appendix C). We then parameterized the influence of 

abundance on probabilities of viability in the BNs for each DIP using a compound rule: if the 

estimated geometric mean abundance was less than 25% of the adult abundance calculated from 

occupied IP area at a density of at least 1 fish/km at a marine survival rate less than 1%, the 

probability of viability was 20%; if the abundance was less than that for 1 fish/km at a marine 

survival rate less than 5%, the probability was 40%; if the abundance was less than that for 1 

fish/km at a marine survival of 20%, the probability was 80%; otherwise, the probability was 

90%. Using this rule, the probabilities of DIP viability based on abundance alone ranged from 

20 to 90%. In addition, we used the output from the PVAs for available DIPs to estimate the 

probability that a population was near its QET abundance. We did so in this way: if the ratio of 

the recent (last 5 years) geometric mean abundance to the QET was greater than 10, the 

probability that abundance was near QET was 10%; if this ratio was greater than 8, the 

probability was 20%; if this ratio was greater than 6, the probability was 40%; if this ratio was 

greater than 4, the probability was 60%; if this ratio was greater than 2, the probability was 80%;

Productivity

Diversity

Spatial structure

Abundance

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

8.87

26.4

64.7

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

18.0

23.7

58.3

33.33 ± 0

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

14.7

24.7

60.6

40.6 ± 12

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.7

24.9

18.4

33.33 ± 0

Fraction of high I.P. spawn habitat

More than half

Less than half

10.0

90.0

74 ± 18

Fraction of high I.P. rear habitat

More than half

Less than half

20.0

80.0

44 ± 8

DIP viability

Viable

Not viable

40.7

59.3

50 ± 0.0086

Probability to reach QET

Less than 5 percent

More than 5 percent

1.00

99.0

69.6 ± 4

Adult abundance vs capacity

More than half

Less than half

10.0

90.0

70 ± 15

Juvenile abundance vs capacity

More than half

Less than half

10.0

90.0

70 ± 15

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

35.0

65.0

Run timing 57.5 ± 24

Historical

Truncated

10.0

90.0

62 ± 9

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

16.1

83.9

70.3 ± 22

Adults per smolt

Greater than 1 percent

Less than 1 percent

10.0

90.0

82 ± 24

Hatchery influence

Nominal

Extensive

30.0

70.0

42 ± 18

Smolts per spawner

Greater than 10

Less than 10

40.0

60.0

51 ± 4.9

MPG viability

Viable

Not viable

50.0

50.0

50 ± 0
81

VIABILITY

1.2

1.0

0.8

0.6

0.4

0.2

0.0

VIABILITY

1.2

1.0

0.8

0.6

0.4

0.2

0.0

VIABILITY

1.2

1.0

0.8

0.6

0.4

0.2

0.0

VIABILITY

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Depressed Depressed

Depressed Depressed

Historical Historical

Historical Historical

Abundance Abundance

Abundance Productivity

Figure 39. Comparisons of estimates of relative viability for Puget Sound steelhead DIPs between current 

(“depressed”) and historical estimates of VSP parameters (abundance, productivity, diversity, and 

spatial structure). The red ovals encompass the highest estimates of DIP viability; the blue ovals 

encompass the lowest estimates. Note that the blue ovals tend to be shifted to the left of the red 

ovals, indicating the important contribution of abundance to DIP viability. 

otherwise, the probability was 90%. We applied these probabilities to both adult and juvenile 

abundances, because we had almost no information on historical or current juvenile abundances. 

These probabilities were then entered into each BN’s CPT for the abundance criterion and 

combined with the other probabilities in Table 11 to determine the overall contribution of 

abundance to DIP viability. 

For the productivity criterion, we relied primarily on the long-term population growth 

rate (λ) estimated by the PVAs from MARSS. For DIPs that we could calculate this metric for, 

we used the mean growth rate computed for all DIPs within the MPG. We parameterized the 

influence of productivity on probabilities of viability in the BNs for each DIP in this way: if the 

point estimate of growth rate was greater than 1.1, the probability of viability was 90%; if the 

growth rate was between 1.0 and 1.1, the probability of viability was 80%; if the growth rate was 

between 0.99 and 1.0, the probability was 70%; if the growth rate was between 0.95 and 0.99, 

the probability was 50%; if the growth rate was between 0.90 and 0.95, the probability was 30%; 

if the growth rate was between 0.85 and 0.9, the probability was 20%; otherwise, the probability 

was 10%. We selected values of smolts per spawner and adults per smolt to approximate the 

specified growth rates estimated from the PVAs, so that freshwater and marine influences on 
82

productivity could be accounted for separately in the network. Probabilities for the influence of 

frequency of iteroparity on viability through productivity were determined by consensus among 

PSS TRT members. All these probabilities were then entered into the CPTs in Table 11 to 

determine the overall contribution of productivity to DIP viability.

For the diversity criterion, we relied primarily on two considerations: 1) the potential 

influence of hatchery-produced steelhead, most of which are either highly domesticated 

(Chambers Creek winter run) or out-of-basin source stocks (Skamania River summer run), on 

wild fish; and 2) evidence for an alteration in natural spawn timing from historical patterns. 

Iteroparity was under consideration as a factor influencing diversity because of its importance as 

a temporal risk-spreading strategy, but the TRT decided to consider iteroparity under 

productivity because of its strong influence on population growth rate. To assess hatchery 

influence, for each DIP we looked at the number of years of hatchery plants and the cumulative 

number of hatchery fish planted, based on historical hatchery records. We parameterized the 

influence of this diversity element on probabilities of viability in the BNs for each DIP in this 

way: if the number of years of hatchery plants was fewer than 5, the probability of extensive 

hatchery influence was 10%; if there were no hatchery plants, the probability was 5%. However,

if the number of years of hatchery plants was 5 or more, we calculated the average annual 

number of hatchery fish planted by dividing the cumulative number of hatchery fish planted by 

the number of years planted. This number was then divided by the estimated IP area and 

multiplied by 1,000. If this metric was greater than 60, the probability of extensive hatchery 

influence from this diversity criterion was 90%; if it was between 40 and 60, the probability was 

80%; if it was between 30 and 40, the probability was 60%; if it was between 20 and 30, the 

probability was 40%; if it was between 10 and 20, the probability was 20%; otherwise, the 

probability was 10%.

To assess whether spawn timing was altered, we examined current spawn timing 

distributions relative to available documentation for historical distributions from unpublished 

reports and the published literature (in addition to the References section, see Appendix D). We 

parameterized the influence of this diversity element on probabilities of viability in the BNs for 

each DIP in this way: if there was evidence that the current distribution had changed (e.g., 

shifted earlier or become compressed) relative to the historical distribution for a particular DIP, 

the probability of viability was 60%; if there was no evidence for change, the probability was 

95%. Probabilities for other diversity factors affecting viability, fraction of smolts from resident 

fish and age structure, were determined by consensus among TRT members. There are no 

available data on the first factor for Puget Sound steelhead, but the TRT considered it important 

to include because resident fish that are sympatric with anadromous fish are known from studies 

of several steelhead populations to be capable of producing smolts, which could provide a 

demographic buffer, especially for small populations. There are some age data available for 

selected Puget Sound steelhead populations. All these probabilities, including those for effects 

of hatchery fish production, were then combined with those in Table 11 to determine the overall 

contribution of diversity to DIP viability.

Finally, for the spatial structure criterion, we first examined how steelhead DIPs tended 

to be related according to habitat characteristics, estimating a Gower similarity coefficient 

(Figure 40) that incorporated maximum elevation, current spawnable area, mean bankfull width, 

mean stream gradient, maximum mean temperature, and presence of permanent snowpack in the 
83

Figure 40. Dendrogram of Gower distances. Relationships among steelhead DIPs with the Puget Sound 

DPS based on a Gower (1971) similarity index. Three primary clusters are evident in the 

dendrogram: one for high elevation areas of moderate to high gradient and influenced by 

snowpack (left), one for low gradient areas with little or no snowpack influence (right), and one 

for streams with intermediate characteristics (middle).

watersheds harboring the DIPs. The Gower index clustered the DIPs with an agglomerative 

coefficient of 0.93. We determined probabilities of viability in the BNs for each DIP by first 

determining occupancy of adult steelhead in reaches within distinct habitat classes incorporating 

steelhead IP area as described in Appendix C. We further categorized stream reaches by whether 

they were main stem or tributary, and whether the hydrograph was influenced by snowpack or 

not (intersection of the reach with a 0°C contour in January). We then performed a series of 

regressions and multivariate analyses (using principal component analysis) to examine the 

relationships between observed steelhead productivity and habitat occupancy. These analyses 

revealed weak but significant relationships between drainage-wide productivity, as measured by 

population growth rate (λ) or exponential trend in abundance, and steelhead occupancy of 

smaller mainstem and larger tributary reaches influenced by snowpack. Linear regressions of λ 

or exponential trend in abundance on occupancy of particular habitat classes incorporating 

steelhead IP area revealed that these productivity metrics increased most strongly with 
84

occupancy of tributary reaches influenced by snowpack; the relationships were relatively weak 

(r2 = 0.1–0.2), but this reflected primarily a rise in productivity when a small fraction of the total 

IP represented by that habitat class was occupied (10–15% or more).

For example, a principal component analysis (with varimax rotation of principal 

components) of spawner occupancy (fraction of total IP area occupied by spawning steelhead) 

and population growth rate across various habitat categories (main stem vs. tributary, three 

stream width classes, three stream gradient classes, and snowpack vs. no snowpack influence) 

produced eight eigenvectors with eigenvalues greater than 1.0, explaining 85.8% of the variation. 

Although the relationships were complex, many of the higher loadings on the first 2–3 principal 

components were associated with snowpack (more positive values) vs. no snowpack (more 

negative values), with higher productivity also associated with occupancy of stream reaches of 

particular widths and gradients.

A principal component analysis of a subset of the data focusing on smaller mainstem and 

larger tributary reaches (3–20 m) of moderate to high gradient (>0.25%) revealed two principal

components that explained 73.5% of the variation and showed that population growth rate was 

most closely associated with steelhead occupancy of reaches influenced by snowpack. A 

principal component analysis of tributary reaches alone revealed two components that explained 

77.9% of the variation and showed that population growth rate was highly associated with 

steelhead occupancy of reaches with snowpack. Finally, a principal component analysis of 

tributary reaches influenced by snowpack revealed two components that explained 84.6% of the 

variation and showed that population growth rate was highly associated with steelhead 

occupancy of larger reaches with moderate to high gradient.

We used this information to parameterize the influence of the spatial structure element on 

probabilities of viability in the BNs for each DIP in a simple way: if the proportion of occupied 

IP habitat area in mainstem reaches less than 20 m bankfull width and influenced by snowpack 

and in tributary reaches greater than 3 m bankfull width and influenced by snowpack was less 

than 20%, the probability of viability was 40%; if the proportion was 20% or more, the 

probability of viability was 80%. We used 20% of historical area as our baseline, because the 

regressions of population growth rate on occupancy rate indicated a positive change in 

productivity of steelhead if occupancy of these habitat areas exceeded this rate. We applied 

these probabilities to both spawning and rearing occupancies, because we had almost no 

information on rearing occupancy. These probabilities were then combined with those in Table 

11 to determine the overall contribution of spatial structure to DIP viability.

We determined the overall viability of each DIP from each DIP-level BN using the 

conditional probabilities shown in Table 12. Supplementary data describing each DIP are 

summarized in Appendix E. For many DIPs, some specific viability assessments are based on 

little or no quantitative information. Although some critics might argue that the criteria these 

assessments are assigned to can be considered “placeholders” and should be left out of a 

Bayesian network framework until better information becomes available, we include them for 

three reasons:

• Eliminating such factors from the network, when they are thought to be important to an 

overall assessment, defeats the purpose of creating an informative network.
85

• Bayesian networks are designed to accommodate both factors that can be quantified and 

those that are evaluated based on collective judgement in the absence of more definitive 

information, which can be updated when more information becomes available.

• Eliminating such factors tends to place higher importance on factors with better 

information, regardless of whether those factors are more critical to viability or not.

DIP Viability

The conditional probabilities underlying the BN for the hypothetical winter-run steelhead 

population in Puget Sound (Figure 37, Table 11, and Table 12) lead to the conclusion that the 

viability of this DIP is relatively low (45.4%); the probability that the DIP is viable should be on 

the order of 90% or higher to be confident of viability. If additional information led us to 

conclude that the population is in fact larger than the low-abundance threshold (adult abundance, 

P = 100%) and its growth is positive (i.e., λ > 1, P = 100%), the BN estimates that the 

probability that the population is viable rises to 60.4%. On the other hand, if additional 

information led us to conclude that the population is in fact smaller than the low-abundance 

threshold (P = 100%) and its growth is negative (λ < 1, P = 100%), the BN estimates that the 

probability that the population is viable drops to 41.9%. Certainty that hatchery influence is 

nominal and that resident fish produce an appreciable fraction of smolts, with no instantiation at 

any other node in the network, prompts the estimate of probability that the population is viable to 

increase to 48.9%. Certainty that more than half the high IP spawning habitat is at capacity for 

adults, with no instantiation at any other node, prompts the probability that the population is 

viable to rise to 51.2%. Meeting most of the criteria for the positive contribution of VSP 

parameters in the network to DIP viability goes a long way to increasing probability that the DIP 

is viable; for example, if all of the lowest-level parent nodes in the network except for iteroparity 

and resident smolt production are instantiated at the highest levels, the probability that the DIP is 

viable rises to 84.2%. If all such criteria are satisfied, the probability that the DIP is viable is 

92.5%. The reason it is no higher is because of uncertainty in the overall contribution of risk for 

each VSP parameter, reflected by the distribution of probabilities there. This uncertainty is not 

zero because not all factors that can influence risk for each VSP parameter are considered in the 

network.

Similarly, the conditional probabilities underlying the BN for the hypothetical summerrun steelhead population in Puget Sound (Figure 38, Table 11, and Table 12) lead to the 

conclusion that the viability of this DIP is low (40.7%). Instantiating the network at the adult 

low-abundance threshold (P = 100%) and positive population growth (λ > 1, P = 100%) causes 

the probability that the population is viable to rise to 58.8%. Instantiating at the adult lowabundance threshold (P = 100%) and negative population growth (λ < 1, P = 100%) causes the 

probability the population is viable to drop to 38.3%. As for the winter-run steelhead BN, 

meeting most of the criteria for the positive contribution of VSP parameters in the network to 

DIP viability goes a long way to increasing probability that the DIP is viable; if all of the lowestlevel parent nodes in the network except for iteroparity and resident smolt production are 

instantiated at the highest levels, the probability that the DIP is viable rises to 85.1%. (cf. 84.2% 

for the winter-run case). If all such criteria are satisfied, the probability that the DIP is viable is 

again 92.5%. The results of the sensitivity analyses for this BN are essentially identical to those 

for the winter-run case (Table 13).
86

Table 13. Results of a sensitivity analysis of the influence of three components of DIP abundance to the 

VSP risk posed by DIP abundance in the BN in Figure 38.

Probability ranges Root mean 

Minimum Current Maximum square change

Sensitivity to findings at juvenile abundancea

Low risk 0.1565 0.262 0.5785 0.182700

Moderate risk 0.2349 0.247 0.2510 0.006972

High risk 0.1866 0.491 0.5925 0.175800

Mean of real value 27.95 37.20 40.29 5.343

Sensitivity to findings at adult abundanceb

Low risk 0.1806 0.262 0.5875 0.16280

Moderate risk 0.1949 0.247 0.2600 0.02605

High risk 0.2176 0.491 0.5594 0.13670

Mean of real value 28.48 37.20 39.38 4.361

Sensitivity to findings at P(QET)c

Low risk 0.2550 0.262 0.3950 0.03051

Moderate risk 0.2400 0.247 0.3795 0.03040

High risk 0.2255 0.491 0.5050 0.06092

Mean of real value 30.56 37.20 37.55 1.523

a Variance reduction = 28.54 (16.7%), entropy reduction = 0.1335 (8.85%), and belief variance = 0.0247 (6.03%). b Variance reduction = 19.02 (11.1%), entropy reduction = 0.0945 (6.26%), and belief variance = 0.01889 (4.61%). c Variance reduction = 2.321 (1.36%), entropy reduction = 0.01133 (0.751%), and belief variance = 0.001594 

(0.389%).

The MPE for a BN is an estimate of the most likely network, given the current evidence 

(i.e., the distributions of conditional probabilities). In other words, it is the most plausible 

explanation of the data given the evidence. The MPEs for these BNs indicate that neither of 

these DIPs is viable. The MPEs also indicate that for each network, the risk of DIP abundance to 

viability is high, the risk of DIP productivity to viability is low, the risk of DIP diversity to 

viability is high, and the risk of DIP spatial structure to viability is high. These MPE estimates 

are consistent with the probability distributions for these nodes depicted in Figure 37 and Figure 

38. Under MPE, the uncertainty that each DIP is not viable is relatively high; the probability that 

each DIP is viable is 49.9%.

Figure 41 shows how the four different VSP criteria combine to affect the probability that 

a representative steelhead DIP in Puget Sound is viable. In this example, abundance and 

productivity have the greatest influences on viability, approximately twice those of diversity and 

spatial structure. The contributions of the four VSP criteria to the probability of viability for all 

32 candidate DIPs, as estimated from the BNs, are summarized in Figure 42.

The BNs for viability for each of the 32 candidate DIPs the TRT identified for the Puget 

Sound DPS indicate that population viabilities vary appreciably both within and among MPGs, 

but these viabilities are all low (each of the 32 BNs is in Appendix F). Over the entire DPS, the 

probabilities that a DIP was viable are estimated from the BNs to range from 33.4 to 60.2%, with 
87

Figure 41. Bar plot showing how the probabilities of viability for a representative DIP of Puget Sound 

steelhead vary with the contribution to risk from different combinations of VSP criteria. 

Probabilities were derived from the BN model of steelhead population viability. In this example, 

abundance and productivity criteria are weighted twice those for diversity and spatial structure. 

A = abundance, P = productivity, D = diversity, and S = spatial structure.

an average of 44.1%. Average DIP viability is 47.9% in the Northern Cascades MPG, 39.9% in 

the Central and South Puget Sound MPG, and 40.6% in the Hood Canal and Strait of Juan de 

Fuca MPG. Average DIP viability for winter-run populations (or those that include both winterrun and summer-run fish) across the DPS is 42.7%; average viability for summer-run populations 

across the DPS is 52.7%. In most cases, these viabilities reflect the substantial influence of all 

four VSP criteria, but especially of abundance and diversity, as components of the BN models.

To illustrate how viability of the 32 candidate steelhead DIPs varies with respect to the 

influence of each of the four VSP criteria, we plotted estimated DIP viability as a function of the 

influence of pairs of these criteria. Figure 41 depicts the influence of the values of abundance 

and productivity on the estimate of viability. Higher values of the VSP components indicate 

higher influence on viability. Viabilities are computed from the DIP-level BNs in Appendix F.

The plots reveal interesting geographical patterns in population viability and how it varies 

with the VSP criteria. With respect to the combination of VSP abundance and productivity, 

viability is generally highest for DIPs in the Northern Cascades MPG (Figure 43). The BN 

models indicate that viability with respect to abundance and productivity is highest for a few 

Northern Cascades DIPs (e.g., Canyon Creek Summer Run and North Fork Skykomish River 

A

A+D

A+D+S

A+P

A+P+D

A+P+S

A+S

ALL

D

D+S

P

P+D

P+D+S

P+S

S

VSP criteria

10

20

30

40

50

60

70

80

90

100

110

Probability of viability
88

Figure 42. Bar plot of the relative influences of the four VSP parameter estimates (abundance, productivity, diversity, and spatial structure) 

applied to the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound DPS. These values are estimated from the 

respective BNs. SSH = summer-run steelhead and WSH = winter-run steelhead.

0

10

20

30

40

50

60

70

Influence

Population

VSP Components of Viability

Abundance

Productivity

Diversity

Spatial Structure
89

Figure 43. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of abundance and productivity. 

Viabilities are computed from the DIP-level BNs in Appendix F. Abundance and productivity 

scores are estimated from intermediate metrics computed by the DIP-level BNs. Three-letter DIP 

codes (SSH = summer-run steelhead and WSH = winter-run steelhead) are: dra = Drayton Harbor 

Tributaries WSH, nks = Nooksack River WSH, sns = South Fork Nooksack River SSH, sam = 

Samish River and Bellingham Bay Tributaries WSH, ska = Skagit River SSH and WSH, nka = 

Nookachamps Creek WSH, bkr = Baker River SSH and WSH, sau = Sauk River SSH and WSH, 

stl = Stillaguamish River WSH, der = Deer Creek SSH, cny = Canyon Creek SSH, snk = 

Snohomish/Skykomish Rivers WSH, pil = Pilchuck River WSH, nfs = North Fork Skykomish 

River SSH, snq = Snoqualmie River WSH, tlt = Tolt River SSH, cdr = Cedar River WSH, lkw =

North Lake Washington and Lake Sammamish WSH, grn = Green River WSH, puy = Puyallup/

Carbon Rivers WSH, wht = White River WSH, nsq = Nisqually River WSH, ssd = South Puget 

Sound Tributaries WSH, ekt = East Kitsap Peninsula Tributaries WSH, ehc = East Hood Canal 

Tributaries WSH, shc = South Hood Canal Tributaries WSH, sko = Skokomish River WSH, whc 

= West Hood Canal Tributaries WSH, seq = Sequim/Discovery Bays Tributaries WSH, dng = 

Dungeness River SSH and WSH, sjf = Strait of Juan de Fuca Tributaries WSH, and elw = Elwha 

River WSH.

Summer Run, probabilities of viability = 55–60%). The viabilities of these DIPs are influenced 

more by productivity than by abundance. Most other populations in this MPG with lower 

viabilities are influenced more by low abundance. Viabilities are lower in the Central and South 

Puget Sound MPG and the Hood Canal and the Strait of Juan de Fuca MPG, in general, and were 

lowest in the Central and South Puget Sound MPG, where most populations are at low 

abundance (e.g., Cedar River Winter Run and Puyallup/Carbon Rivers Winter Run, probabilities 

of viability 30–40%). In the latter MPG, only the White River Winter-Run DIP shows modest 

viability. The decline in DIP viability with declining abundance and productivity is clearly 

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

30

40

50

60

70

Abundance

40

50

60

70

Productivity

40

50

60

70

P(viable)

lkw

cdr

grn

puy

wht

nsq

ssd

ekt

ehc

shc

sko whc

seq

dng

sjf

elw

dra nks snk

sam ska bkr nka sau

stl

der

cny

sns pil

nfs

snq

tlt
90

nonlinear, and viability seems to be somewhat more sensitive to productivity than to abundance. 

Most DIPs in the DPS exhibit low probabilities of viability with respect to abundance and 

productivity (40–50%).

With respect to the combination of VSP abundance and diversity, there is less variation 

evident for the influence of diversity on DIP viability (Figure 44). For DIPs in both the Central 

and South Puget Sound MPG and the Hood Canal and Strait of Juan de Fuca MPG, where 

viabilities are lower, populations are influenced by both low abundance and low diversity. DIPs 

in the Northern Cascades MPG tend to show a wider range of viabilities and, on average, 

viabilities are higher with respect to these criteria. The change in DIP viability with abundance 

and diversity is highly variable. As for abundance and productivity, most DIPs in the DPS 

exhibit low probabilities of viability with respect to abundance and diversity (40–50%).

Regarding the combination of VSP abundance and spatial structure, the two summer-run 

DIPs with highest viabilities (Canyon Creek and North Fork Skykomish River) show a strong 

influence of spatial structure (Figure 45). Only the Nookachamps Creek Winter Run and the 

Tolt River Summer Run in the Northern Cascades MPG and the Dungeness River Summer Run 

and Winter Run in the Hood Canal and Strait of Juan de Fuca MPG show a relatively modest 

influence of spatial structure on viability. Most DIPs throughout the DPS show an appreciable 

influence of both abundance and spatial structure on viability.

The influences of VSP productivity and diversity on DIP viability are more variable than 

those for the previous combinations of VSP parameters (Figure 46). The DIPs with the lowest 

viabilities (e.g., Sequim/Discovery Bays Tributaries Winter Run, Elwha River Winter Run, 

Cedar River Winter Run) indicate the very high influence of low productivity. Those with the 

highest viabilities (e.g., Canyon Creek Summer Run and North Fork Skykomish River Summer 

Run) show a more consistent influence of productivity than of diversity. Viability appears to be 

most sensitive to low productivity.

When considered with productivity, spatial structure has a modest influence on 

population viability; for DIPs with a high influence of spatial structure on viability, the DIPs 

with lowest viabilities were influenced heavily by low productivity (Figure 47). In general, DIP 

viability declines steeply when productivity is low. For the few DIPs that have a modest 

influence of spatial structure on viability (Nookachamps Creek Winter Run, Tolt River Summer 

Run, and Dungeness River Summer Run and Winter Run), viability tends to be moderate. The 

pattern of viability with respect to the combination of VSP diversity and spatial structure is 

similar to the pattern with respect to productivity and spatial structure (Figure 48).

According to the BN models, a typical DIP’s viability is lowest whenever abundance and 

productivity are limiting (probabilities of viability < about 30%). As a representative example, 

for the Samish River and Bellingham Bay Tributaries Winter-Run DIP in the Northern Cascades 

MPG, the sensitivity of viability to DIP abundance (entropy reduction 6.7%), DIP productivity 

(entropy reduction 5.2%), DIP diversity (entropy reduction 1.3%, primarily a result of hatchery 

fish influence), and DIP spatial structure (entropy reduction 1.6%) indicated that Samish River 

steelhead viability is limited primarily by both abundance and productivity. For the abundance 

criterion, the most important contributing factor was adult abundance (entropy reduction 2.1%), 

followed by juvenile abundance (entropy reduction 0.1%) and by the probability of reaching the 
91

Figure 44. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of abundance and diversity. 

Viabilities are computed from the DIP-level BNs in Appendix F. Abundance and diversity scores 

are estimated from intermediate metrics computed by the DIP-level BNs. Three-letter DIP codes 

are as in Figure 43.

specified QET (entropy reduction <0.1%). For the productivity criterion, the most important 

contributing factor was population growth rate (entropy reduction 2.0%), followed by iteroparity 

(entropy reduction 0.3%). Other factors contributing to DIP viability included marine survival 

rate (entropy reduction 1.2%), spatial structure criteria (total entropy reduction for spawning and 

rearing area occupied, 0.5%), and altered spawn timing (entropy reduction 0.3%). Hatchery 

influence and alteration of age structure had minor influences on viability. The MPE for the 

viability of this DIP is that it is not viable, but there is considerable uncertainty around this MPE: 

the probability that this DIP could be viable is estimated at 69.2%.

By comparison, for the Nisqually River Winter-Run DIP in the Central and South Puget 

Sound MPG, the sensitivity of viability to DIP abundance (entropy reduction 6.0%), DIP 

productivity (entropy reduction 6.9%), DIP diversity (entropy reduction 1.3%), and DIP spatial 

structure (entropy reduction 1.7%) indicated that Nisqually River steelhead viability is limited 

more by productivity. For the abundance criterion, the most important contributing factor was 

adult abundance (entropy reduction 1.2%), followed by the probability of reaching the specified 

QET (entropy reduction 0.2%) and by juvenile abundance (entropy reduction <0.1%). For the 

productivity criterion, the most important contributing factor was population growth rate 

(entropy reduction 2.8%), followed by iteroparity (entropy reduction 1.4%). Other factors 

contributing to viability included marine survival rate (entropy reduction 1.5%), spawn timing 

(entropy reduction 0.3%), and spatial structure criteria (total entropy reduction for spawning and 

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

30

40

50

60

70

45

Abundance

50

55

60

Diversity

40

50

60

70 P(viable)

lkw

cdr

grn

puy

wht

nsq ssd

ekt

ehc

shc

sko

whc

seq

dng sjf

elw

dra nks

snk

sam

ska

nka

bkr

sau

stl

der

cny

sns

pil

nfs

snq

tlt
92

Figure 45. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of abundance and spatial 

structure. Viabilities are computed from the DIP-level BNs in Appendix F. Abundance and 

spatial structure scores are estimated from intermediate metrics computed by the DIP-level BNs. 

Three-letter DIP codes are as in Figure 43.

rearing area occupied, 0.5%). Hatchery influence and alteration of age structure had minor 

influences on viability. The MPE for the viability of this DIP is that it is not viable, and there is 

little uncertainty around the MPE, because the probability that it could be viable is estimated at 

only 11.9%.

As an example for a summer-run population, for the Tolt River Summer-Run DIP in the 

Northern Cascades MPG, the sensitivity of viability to DIP abundance (entropy reduction 6.2%), 

DIP productivity (entropy reduction 6.8%), DIP diversity (entropy reduction 1.3%), and DIP 

spatial structure (entropy reduction 1.1%) indicated that Tolt River steelhead viability is limited 

by both abundance and productivity. For the abundance criterion, the most important 

contributing factor was adult abundance (entropy reduction 2.1%), followed by juvenile 

abundance (entropy reduction 0.3%) and the probability of reaching the specified QET (entropy 

reduction 0.1%). For the productivity criterion, the most important contributing factor was 

population growth rate (entropy reduction 3.2%), followed by iteroparity (entropy reduction 

0.9%). Other factors contributing to viability included marine survival rate (entropy reduction 

1.8%) and spawn timing (entropy reduction 0.3%); hatchery influence, age structure, and adult 

and juvenile occupancy of IP areas had minor influences on viability. The MPE for the viability 

of this DIP is that it is not viable, but this MPE has a high level of uncertainty: the probability 

that it could be viable is estimated to be as high as 62.8%.

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

30

40

50

60

70

40

Abundance

45

50

55

Spatial Structure

40

50

60

70 P(viable)

lkw

cdr

grn

puy

wht

nsq

ssdekt

ehc

shc

sko

whc

seq

dng

sjf

elw

dra

nks

snk

sam

ska

nka

bkr

sau

stl

der

cny

sns

pil

nfs

snq

tlt
93

Figure 46. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of productivity and diversity. 

Viabilities are computed from the DIP-level BNs in Appendix F. Productivity and diversity 

scores are estimated from intermediate metrics computed by the DIP-level BNs. Three-letter DIP 

codes are as in Figure 43.

The BNs for the remaining 29 steelhead DIPs yield similar conclusions for DIP viability, 

as well as the range of influence of VSP parameters on DIP viability. Throughout the DPS, the 

probability that a DIP is viable does not exceed about 60%. For the DIP with the highest 

estimated viability, the Canyon Creek Summer Run in the Northern Cascades MPG, while the 

MPE is that this DIP is viable, there is considerable uncertainty around the MPE: the probability 

that this DIP could be nonviable is 85%.

MPG Viability

We evaluated the viability of each of the three Puget Sound steelhead MPGs by 

combining the DIP-level BNs for each MPG into an encompassing network with an edge 

connecting each node determining DIP viability to a single node for MPG viability. Figure 49

depicts such a network for one of the smaller MPGs—the Hood Canal and Strait of Juan de Fuca 

MPG. The CPT describing the influence of DIP viability on MPG viability for this BN is in 

Table 14 (similar CPTs, not shown, underlie the BNs for the Northern Cascades MPG and the 

Central and South Puget Sound MPG). In this table of probabilities and the corresponding 

values for other MPGs, the MPG is considered viable only if 40% or more of the constituent 

DIPs are considered viable as an outcome of the DIP viability BN analyses and, if summer-run 

DIPs are present, at least 40% of these populations are also considered viable.

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

40

50

60

70

45

Productivity

50

55

60

Diversity

40

50

60

70 P(viable)

lkw

cdr

grnpuy

wht nsq

ssd

ekt ehc

shc

sko

whc

seq

dng

sjf

elw

dra

nks

snk

sam

ska

bkrnka

sau

stl

der

cny

sns

pil

nfs

snq

tlt
94

Figure 47. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of productivity and spatial 

structure. Viabilities are computed from the DIP-level BNs in Appendix F. Productivity and 

spatial structure scores are estimated from intermediate metrics computed by the DIP-level BNs. 

Three-letter DIP codes are as in Figure 43.

Under these conditions, a BN analysis computes that the probability of viability for the 

Hood Canal and Strait of Juan de Fuca MPG as a whole is only 16.4% (Figure 49). Average DIP 

probability of viability is 40.6% in this MPG. The BN for the Hood Canal and Strait of Juan de 

Fuca MPG indicates that the probability that this MPG is viable would rise to 100% if at least 

one DIP from each of the four major regions of the MPG (i.e., Strait of Juan de Fuca [4 DIPs], 

East Hood Canal [1 DIP], South Hood Canal [2 DIPs], and West Hood Canal [1 DIP]) were 

viable with certainty (i.e., by instantiating certainty of viability for these DIPs). MPG viability 

would rise to 89.5% if all four DIPs on the Strait of Juan de Fuca were instantiated at fully 

viable. A sensitivity analysis (output not shown) indicates that the mutual information provided 

by the BN model for viability of this MPG was 0.64, which can be interpreted as meaning that 

the model reduced the uncertainty surrounding the MPG’s viability by about 64%. The entropy 

reduction contributed by the viability of its constituent DIPs varied from 5.2% (Sequim/

Discovery Bays Tributaries Winter Run) to 10.5% (Dungeness River Summer Run and Winter 

Run). The MPE for the viability of this MPG is that it is not viable and there is little uncertainty 

around this MPE: the probability that the MPG could be viable is 1.3%.

A BN analysis for the Central and South Puget Sound MPG shows that the probability of 

its viability is also low: 17.2%. Average DIP probability of viability in this MPG is 39.9%. The 

BN for the MPG indicates that the probability that this MPG is viable would rise to at least 

87.7% if at least one DIP from each of the four major regions of the MPG (i.e., Central Puget 

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

40

50

60

70

40

Productivity

45

50

55

Spatial Structure

40

50

60

70 P(viable)

lkw

cdr

grn

puy

wht

nsq

ssd

ekt ehc

shc

sko

whc

seq

dng

sjf

elw

dra

nks

sam snkska

nka

bkr

sau

stl

der

cny

sns

pil

nfs

snq

tlt
95

Figure 48. Scatter plot of the viabilities of each of the 32 candidate DIPs of steelhead in the Puget Sound 

DPS as a function of their VSP parameter estimates of influence of diversity and spatial structure.

Viabilities are computed from the DIP-level BNs in Appendix F. Diversity and spatial structure 

scores are estimated from intermediate metrics computed by the DIP-level BNs. Three-letter DIP 

codes are as in Figure 43.

Sound [3 DIPs], Southeast Puget Sound [3 DIPs], South Puget Sound Tributaries [1 DIP], and 

East Kitsap Peninsula Tributaries [1 DIP]) were viable. A sensitivity analysis (output not 

shown) indicates that the mutual information provided by the BN model for viability of this 

MPG was 0.66. The entropy reduction contributed by the viability of its constituent DIPs 

showed little variation, ranging from 6.6% (Cedar River Winter Run) to 6.7% (North Lake 

Washington and Lake Sammamish Winter Run). The MPE for the viability of this MPG is that it 

is not viable, and there almost no uncertainty around this MPE: the probability that the MPG 

could be viable is < 0.1%.

A BN analysis for the Northern Cascades MPG indicates a probability of viability of 

36.3%. Average DIP probability of viability in this MPG is the highest of the three MPGs at 

47.9%. Although this MPG contains twice as many DIPs as the other two in the DPS (16 versus 

8 for both the Hood Canal and Strait of Juan de Fuca MPG and the Central and South Puget 

Sound MPG), its viability is also low because not enough of its constituent DIPs within each 

major life history type are at reasonable levels of viability. The BN for this MPG indicates that 

its viability would rise to at least 88.7% if at least half of the DIPs in it were viable. Given the 

conditions specified by the underlying CPT, the BN also indicates that the probability that this 

MPG is viable would rise to 87.9% if at least half (6 of 11) of the winter-run DIPs and at least 

half (3 of 5) of the summer-run DIPs in the MPG were viable (spread geographically across the 

MPG, and including the Skagit River Summer Run and Winter Run). It indicates as well that the 

Northern Cascades

Hood Canal & SJF

Central & South Sound

MPG

45

50

55

60

Diversity

40

45

50

55

Spatial Structure

40

50

60

70 P(viable)

lkw

cdr

grn

puy

wht

nsq ssd

ekt

ehc

shc

sko

whc

seq dng

sjf

elw

dra

nks

snk

sam

ska nka

bkr

sau

stl

der

cny

sns

pil

nfs

snq

tlt
96 

Figure 49. BN used to characterize the viability of the Hood Canal and Strait of Juan de Fuca steelhead MPG. The CPT describing the influence 

of DIP viability on MPG viability is in Table 14.

Hood Canal & Strait of JdF MPG viability

Viable

Not viable

16.4

83.6

50 ± 0

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Skokomish R WSH viability

Viable

Not viable

42.7

57.3

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

80.0

20.0

38 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

22.5

18.2

59.3

39.6 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

South Hood Canal WSH viability

Viable

Not viable

35.2

64.8

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

20.0

80.0

62 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

14.4

14.1

71.5

42.9 ± 12

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

63.6

17.0

19.3

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

East Hood Canal tribs WSH viability

Viable

Not viable

45.5

54.5

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

10.0

90.0

66 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

80.5

19.5

31.7 ± 24

Adults per smolt

Greater than 2 percent

Less than 2 percent

85.0

15.0

22 ± 29

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

85.0

15.0

46.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

13.0

13.4

73.6

43.4 ± 11

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

63.6

17.0

19.3

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

West Hood Canal WSH viability

Viable

Not viable

48.3

51.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

80.5

19.5

31.7 ± 24

Adults per smolt

Greater than 2 percent

Less than 2 percent

85.0

15.0

22 ± 29

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

85.0

15.0

46.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

19.8

16.8

63.4

40.7 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.6

36.4

40.1

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Sequim/Discovery Bay tribs WSH viability

Viable

Not viable

34.8

65.2

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

10.0

90.0

66 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

95.0

5.00

68 ± 8.7

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

13.0

13.4

73.6

43.4 ± 11

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

SJF Independents WSH viability

Viable

Not viable

41.7

58.3

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

32.4

17.0

50.5

36.9 ± 14

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

61.0

23.4

15.6

33.33 ± 0.0058

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

80.0

20.0

32 ± 24

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

80.0

20.0

56 ± 8

Dungeness R SSH/WSH viability

Viable

Not viable

43.2

56.8

50 ± 0.0086

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.1

37.8

29.1

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

20.2

18.1

61.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Elwha R SSH/WSH viability

Viable

Not viable

33.6

66.4

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

20.0

80.0

62 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

11.9

88.1

72.9 ± 19

Adults per smolt

Greater than 2 percent

Less than 2 percent

15.0

85.0

78 ± 29

Hatchery influence

Nominal

Extensive

60.0

40.0

54 ± 20

Smolts per spawner

Greater than 50

Less than 50

15.0

85.0

53.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

14.4

14.1

71.5

42.9 ± 12
97

Table 14. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 49. 

Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and South 

Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern Cascades 

MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT to 

characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomishRiver WSHW. Hood Canal Tribs. WSHSequim/Disc. Bays WSHDungeness R. SSH & WSHStrait of J. de F. Tribs. WSH

Elwha River WSH

Viable

Not 

viable

V V V V V V V V 100 0

V V V V V V V NV 100 0

V V V V V V NV V 100 0

V V V V V V NV NV 100 0

V V V V V NV V V 100 0

V V V V V NV V NV 100 0

V V V V V NV NV V 100 0

V V V V V NV NV NV 100 0

V V V V NV V V V 100 0

V V V V NV V V NV 100 0

V V V V NV V NV V 100 0

V V V V NV V NV NV 100 0

V V V V NV NV V V 100 0

V V V V NV NV V NV 100 0

V V V V NV NV NV V 100 0

V V V V NV NV NV NV 100 0

V V V NV V V V V 100 0

V V V NV V V V NV 100 0

V V V NV V V NV V 100 0

V V V NV V V NV NV 100 0

V V V NV V NV V V 100 0

V V V NV V NV V NV 100 0

V V V NV V NV NV V 100 0

V V V NV V NV NV NV 100 0

V V V NV NV V V V 100 0

V V V NV NV V V NV 100 0

V V V NV NV V NV V 100 0

V V V NV NV V NV NV 100 0

V V V NV NV NV V V 100 0

V V V NV NV NV V NV 100 0

V V V NV NV NV NV V 100 0

V V V NV NV NV NV NV 0 100

V V NV V V V V V 100 0

V V NV V V V V NV 100 0

V V NV V V V NV V 100 0
98

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood CanalTribs. WSHS. Hood CanalTribs. WSHSkokomish Ri

ver WSH

W. Hood Canal

Tribs. WSH

Seq

uim/Disc.

Ba

ys WSH

Dungeness R. SSH &

WSH

Strait of J. de

F. 

Tribs. W

S

H

Elw

ha River

WSH

Viable

Not

viable

V V NV V V V NV NV 100 0

V V NV V V NV V V 100 0

V V NV V V NV V NV 100 0

V V NV V V NV NV V 100 0

V V NV V V NV NV NV 100 0

V V NV V NV V V V 100 0

V V NV V NV V V NV 100 0

V V NV V NV V NV V 100 0

V V NV V NV V NV NV 100 0

V V NV V NV NV V V 100 0

V V NV V NV NV V NV 100 0

V V NV V NV NV NV V 100 0

V V NV V NV NV NV NV 0 100

V V NV NV V V V V 100 0

V V NV NV V V V NV 100 0

V V NV NV V V NV V 100 0

V V NV NV V V NV NV 100 0

V V NV NV V NV V V 100 0

V V NV NV V NV V NV 100 0

V V NV NV V NV NV V 100 0

V V NV NV V NV NV NV 0 100

V V NV NV NV V V V 100 0

V V NV NV NV V V NV 100 0

V V NV NV NV V NV V 100 0

V V NV NV NV V NV NV 0 100

V V NV NV NV NV V V 100 0

V V NV NV NV NV V NV 0 100

V V NV NV NV NV NV V 0 100

V V NV NV NV NV NV NV 0 100

V NV V V V V V V 100 0

V NV V V V V V NV 100 0

V NV V V V V NV V 100 0

V NV V V V V NV NV 100 0

V NV V V V NV V V 100 0

V NV V V V NV V NV 100 0
99

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood CanalTribs. WSHS. Hood CanalTribs. WSHSkokomish Ri

ver WSH

W. Hood Canal

Tribs. WSH

Seq

uim/Disc.

Ba

ys WSH

Dungeness R. SSH &

WSH

Strait of J. de

F.

Tribs. W

S

H

Elw

ha River

WSH

Viable

Not

viable

V NV V V V NV NV V 100 0

V NV V V V NV NV NV 100 0

V NV V V NV V V V 100 0

V NV V V NV V V NV 100 0

V NV V V NV V NV V 100 0

V NV V V NV V NV NV 100 0

V NV V V NV NV V V 100 0

V NV V V NV NV V NV 100 0

V NV V V NV NV NV V 100 0

V NV V V NV NV NV NV 0 100

V NV V NV V V V V 100 0

V NV V NV V V V NV 100 0

V NV V NV V V NV V 100 0

V NV V NV V V NV NV 100 0

V NV V NV V NV V V 100 0

V NV V NV V NV V NV 100 0

V NV V NV V NV NV V 100 0

V NV V NV V NV NV NV 0 100

V NV V NV NV V V V 100 0

V NV V NV NV V V NV 100 0

V NV V NV NV V NV V 100 0

V NV V NV NV V NV NV 0 100

V NV V NV NV NV V V 100 0

V NV V NV NV NV V NV 0 100

V NV V NV NV NV NV V 0 100

V NV V NV NV NV NV NV 0 100

V NV NV V V V V V 100 0

V NV NV V V V V NV 100 0

V NV NV V V V NV V 100 0

V NV NV V V V NV NV 100 0

V NV NV V V NV V V 100 0

V NV NV V V NV V NV 100 0

V NV NV V V NV NV V 100 0

V NV NV V V NV NV NV 0 100

V NV NV V NV V V V 100 0
100

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomish River WSHW. Hood Canal Tribs. WSHSequim/Disc. Bays WSHDungeness R. SSH & WSHStrait of J. de F.Tribs. WSH

Elwha River WSH

Viable

Not 

viable

V NV NV V NV V V NV 100 0

V NV NV V NV V NV V 100 0

V NV NV V NV V NV NV 0 100

V NV NV V NV NV V V 100 0

V NV NV V NV NV V NV 0 100

V NV NV V NV NV NV V 0 100

V NV NV V NV NV NV NV 0 100

V NV NV NV V V V V 100 0

V NV NV NV V V V NV 100 0

V NV NV NV V V NV V 100 0

V NV NV NV V V NV NV 0 100

V NV NV NV V NV V V 100 0

V NV NV NV V NV V NV 100 0

V NV NV NV V NV NV V 0 100

V NV NV NV V NV NV NV 0 100

V NV NV NV NV V V V 100 0

V NV NV NV NV V V NV 0 100

V NV NV NV NV V NV V 0 100

V NV NV NV NV V NV NV 0 100

V NV NV NV NV NV V V 0 100

V NV NV NV NV NV V NV 0 100

V NV NV NV NV NV NV V 0 100

V NV NV NV NV NV NV NV 0 100

NV V V V V V V V 100 0

NV V V V V V V NV 100 0

NV V V V V V NV V 100 0

NV V V V V V NV NV 100 0

NV V V V V NV V V 100 0

NV V V V V NV V NV 100 0

NV V V V V NV NV V 100 0

NV V V V V NV NV NV 100 0

NV V V V NV V V V 100 0

NV V V V NV V V NV 100 0

NV V V V NV V NV V 100 0

NV V V V NV V NV NV 100 0
101

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomish River WSHW. Hood Canal Tribs. WSHSequim/Disc. Bays WSHDungeness R. SSH & WSHStrait of J. de F.Tribs. WSH

Elwha River WSH

Viable

Not 

viable

NV V V V NV NV V V 100 0

NV V V V NV NV V NV 100 0

NV V V V NV NV NV V 100 0

NV V V V NV NV NV NV 0 100

NV V V NV V V V V 100 0

NV V V NV V V V NV 100 0

NV V V NV V V NV V 100 0

NV V V NV V V NV NV 100 0

NV V V NV V NV V V 100 0

NV V V NV V NV V NV 100 0

NV V V NV V NV NV V 100 0

NV V V NV V NV NV NV 0 100

NV V V NV NV V V V 100 0

NV V V NV NV V V NV 100 0

NV V V NV NV V NV V 100 0

NV V V NV NV V NV NV 0 100

NV V V NV NV NV V V 100 0

NV V V NV NV NV V NV 0 100

NV V V NV NV NV NV V 0 100

NV V V NV NV NV NV NV 0 100

NV V NV V V V V V 100 0

NV V NV V V V V NV 100 0

NV V NV V V V NV V 100 0

NV V NV V V V NV NV 100 0

NV V NV V V NV V V 100 0

NV V NV V V NV V NV 100 0

NV V NV V V NV NV V 100 0

NV V NV V V NV NV NV 0 100

NV V NV V NV V V V 100 0

NV V NV V NV V V NV 100 0

NV V NV V NV V NV V 100 0

NV V NV V NV V NV NV 0 100

NV V NV V NV NV V V 100 0

NV V NV V NV NV V NV 0 100

NV V NV V NV NV NV V 0 100
102

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomish River WSHW. Hood Canal Tribs. WSHSequim/Disc.Bays WSHDungeness R. SSH & WSHStrait of J. de F.Tribs. WSH

Elwha River WSH

Viable

Not 

viable

NV V NV V NV NV NV NV 0 100

NV V NV NV V V V V 100 0

NV V NV NV V V V NV 100 0

NV V NV NV V V NV V 100 0

NV V NV NV V V NV NV 100 0

NV V NV NV V NV V V 100 0

NV V NV NV V NV V NV 0 100

NV V NV NV V NV NV V 0 100

NV V NV NV V NV NV NV 0 100

NV V NV NV NV V V V 100 0

NV V NV NV NV V V NV 0 100

NV V NV NV NV V NV V 0 100

NV V NV NV NV V NV NV 0 100

NV V NV NV NV NV V V 0 100

NV V NV NV NV NV V NV 0 100

NV V NV NV NV NV NV V 0 100

NV V NV NV NV NV NV NV 0 100

NV NV V V V V V V 100 0

NV NV V V V V V NV 100 0

NV NV V V V V NV V 100 0

NV NV V V V V NV NV 100 0

NV NV V V V NV V V 100 0

NV NV V V V NV V NV 100 0

NV NV V V V NV NV V 100 0

NV NV V V V NV NV NV 0 100

NV NV V V NV V V V 100 0

NV NV V V NV V V NV 100 0

NV NV V V NV V NV V 100 0

NV NV V V NV V NV NV 0 100

NV NV V V NV NV V V 100 0

NV NV V V NV NV V NV 0 100

NV NV V V NV NV NV V 0 100

NV NV V V NV NV NV NV 0 100

NV NV V NV V V V V 100 0

NV NV V NV V V V NV 100 0
103

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomish River WSHW. Hood Canal Tribs. WSHSequim/Disc. Bays WSHDungeness R. SSH & WSHStrait of J. de F.Tribs. WSH

Elwha River WSH

Viable

Not 

viable

NV NV V NV V V NV V 100 0

NV NV V NV V V NV NV 0 100

NV NV V NV V NV V V 100 0

NV NV V NV V NV V NV 0 100

NV NV V NV V NV NV V 0 100

NV NV V NV V NV NV NV 0 100

NV NV V NV NV V V V 100 0

NV NV V NV NV V V NV 0 100

NV NV V NV NV V NV V 0 100

NV NV V NV NV V NV NV 0 100

NV NV V NV NV NV V V 0 100

NV NV V NV NV NV V NV 0 100

NV NV V NV NV NV NV V 0 100

NV NV V NV NV NV NV NV 0 100

NV NV NV V V V V V 100 0

NV NV NV V V V V NV 100 0

NV NV NV V V V NV V 100 0

NV NV NV V V V NV NV 100 0

NV NV NV V V NV V V 100 0

NV NV NV V V NV V NV 0 100

NV NV NV V V NV NV V 0 100

NV NV NV V V NV NV NV 0 100

NV NV NV V NV V V V 100 0

NV NV NV V NV V V NV 0 100

NV NV NV V NV V NV V 0 100

NV NV NV V NV V NV NV 0 100

NV NV NV V NV NV V V 0 100

NV NV NV V NV NV V NV 0 100

NV NV NV V NV NV NV V 0 100

NV NV NV V NV NV NV NV 0 100

NV NV NV NV V V V V 100 0

NV NV NV NV V V V NV 0 100

NV NV NV NV V V NV V 0 100

NV NV NV NV V V NV NV 0 100

NV NV NV NV V NV V V 0 100
104

Table 14 continued. CPT describing the influence of DIP viability on MPG viability for the BN in Figure 

49. Similar CPTs were used to parameterize the Northern Cascades MPG and the Central and 

South Puget Sound MPG, which contain 16 and 8 steelhead DIPs, respectively. The Northern 

Cascades MPG contains twice as many DIPs as the other MPGs, but we used an analogous CPT 

to characterize the influence of DIP viability on MPG viability, which explicitly requires greater 

than 40% viable DIPs of each distinct life history type (summer run [SSH], winter run [WSH], or 

combined summer run and winter run) for MPG viability.

DIP viability MPG viability

E. Hood Canal Tribs. WSHS. Hood Canal Tribs. WSHSkokomish River WSHW. Hood Canal Tribs. WSHSequim/Disc. Bays WSHDungeness R. SSH & WSHStrait of J. de F.Tribs. WSH

Elwha River WSH

Viable

Not 

viable

NV NV NV NV V NV V NV 0 100

NV NV NV NV V NV NV V 0 100

NV NV NV NV V NV NV NV 0 100

NV NV NV NV NV V V V 0 100

NV NV NV NV NV V V NV 0 100

NV NV NV NV NV V NV V 0 100

NV NV NV NV NV V NV NV 0 100

NV NV NV NV NV NV V V 0 100

NV NV NV NV NV NV V NV 0 100

NV NV NV NV NV NV NV V 0 100

NV NV NV NV NV NV NV NV 0 100

probability that this MPG is viable would rise to 85.9% if all five summer-run DIPs and the 

Skagit River Summer-Run and Winter-Run DIP in this MPG were viable (100% if all five 

summer-run and three winter-run DIPs were viable). A sensitivity analysis (output not shown) 

indicates that the mutual information provided by the BN model for viability of this MPG was 

0.94. The entropy reduction contributed by the viability of its constituent DIPs showed 

considerable variation, ranging from 0.6% (Stillaguamish River Winter Run) to 7.6% 

(Snoqualmie River Winter Run and Tolt River Summer Run). The MPE for the viability of this 

MPG is that it is not viable and there little uncertainty around this MPE: the probability that the 

MPG could be viable is only 4.0%.

Despite the low uncertainty about viability for all three MPGs, we also assessed the 

reliability of the BNs to predict MPG viability by simulating 1,000 random networks in each

case. We used two metrics to assess the quality of the information provided by the networks: an 

error rate measured by a “confusion matrix,” which assesses how often the opinions or beliefs 

produced by the network fail to line up with the actual assignments to node categories based on 

the conditional probabilities; and a corresponding “quality of test,” which estimates the fraction 

of correctly predicted cases given a specified cutoff probability. In all cases we used the 

recommended value for the cutoff probability (40% for the Northern Cascades MPG, 20% for 

the Central and South Puget Sound MPG, and 20% for the Hood Canal and Strait of Juan de 

Fuca MPG). For the Northern Cascades MPG, the analysis indicated that the BN was a mediocre
105

predictor of MPG viability from the data. The confusion matrix indicated an error rate of 36.3% 

and the ability of the test to predict correct viability results was only 63.7% at a cutoff 

probability of 40% or higher (100% at a cutoff probability of 0%). For the Central and South

Puget Sound MPG, the BN was a better predictor of MPG viability; the confusion matrix 

indicated an error rate of 17.2% and the ability of the test to predict correct viability results was 

82.8% at a cutoff probability of 20% or higher. In the case of the Hood Canal and Strait of Juan 

de Fuca MPG, the BN’s predictive power for viability was similar to that for the Central and 

South Puget Sound MPG. The confusion matrix indicated an error rate of 16.4% and the ability 

of the test to predict correct viability results was 83.6% at a cutoff probability of 20% or higher. 

These results suggest that the networks for the two smaller MPGs provide reasonable 

frameworks that capture most of the variation contributing to MPG viability across these 

complex groups of constituent populations. The larger size and complexity of the Northern 

Cascades MPG contributes to the diminished capability of its network to predict how variation in 

its VSP parameters contributes to this MPG’s viability.

Collectively, these analyses indicate that all three steelhead MPGs are at very low 

viability. One implication of these analyses is that increasing the abundance and productivity of 

DIPs of both major life history types (summer run and winter run) and spread geographically 

across each MPG is essential to bringing all MPGs to viable condition. Maintaining life history 

diversity and the spatial distribution of steelhead in IP stream reaches across DIPs within the 

MPGs would also be essential to elevating viability in each of them.

DPS Viability

As previously described, the viability of the DPS as a whole critically depends on the 

combined viability of all its constituent MPGs; a DPS with even a single nonviable MPG cannot 

be viable. The BN depicted in Figure 50 shows how the viability of the entire DPS depends on 

MPG viability.

Combining the BNs for each MPG to create a network for the entire DPS (as outlined in 

Figure 50) and following the conditional probabilities in Table 15 indicates that the DPS is not 

viable: the probability of DPS viability computed by the network is 1.0%. A sensitivity analysis 

(output not shown) indicates that the mutual information provided by the BN model for viability 

of the DPS is only 0.082. The entropy reduction contributed by the viability of the Northern 

Cascades MPG is 18.3%, the entropy reduction contributed by the viability of the Central and 

South Puget Sound MPG is 32.0%, and that for the Hood Canal and Strait of Juan de Fuca MPG 

is 32.9%. We assessed the ability of these networks to predict DPS viability by simulating 1,000 

random networks in each case. The ability of the combined network to predict DPS viability was 

very high; the confusion matrix indicated an error rate of 1.0% and the ability of the test to 

predict correct viability results was greater than 98.9% at a cutoff probability of 2% or more. 

This result is not unexpected, as there is little uncertainty about the overall condition of each 

steelhead MPG constituting the Puget Sound DPS using these networks, and the DPS-level 

viability requires that all MPGs be viable.

We also used a simpler modification of these combined networks to evaluate the viability 

of the DIPs, MPGs, and DPS relying on only abundance and productivity criteria. The basis for 

this exercise was to address the question, What if we rely primarily on quantitative demographic
106

Figure 50. Highest-level BN used to characterize the viability of the Puget Sound Steelhead DPS. DPS 

viability requires that each MPG be viable (see Table 15); in this case, instantiation shows that 

one MPG that is not viable (e.g., Hood Canal and Strait of Juan de Fuca) leads to the situation 

where the DPS is also not viable. The viability of each MPG is estimated using BNs like that in 

Figure 49.

Table 15. CPT describing the influence of MPG viability on DPS viability for the BN in Figure 50.

DPS state

North Cascades 

MPG

Central & South 

Puget Sound MPG

Hood C. & Strait 

of J. de Fuca MPG 

Puget Sound 

DPS Viable

Not 

viable

Viable Viable Viable Viable 100 0

Viable Viable Not viable Not viable 0 100

Viable Not viable Viable Not viable 0 100

Viable Not viable Not viable Not viable 0 100

Not viable Viable Viable Not viable 0 100

Not viable Viable Not viable Not viable 0 100

Not viable Not viable Viable Not viable 0 100

Not viable Not viable Not viable Not viable 0 100

information to assess steelhead viability? To do so, we created subsets of each of the DIP BNs 

that included only the abundance and productivity nodes, and eliminated the iteroparity criterion 

from the productivity node. We used the same underlying CPTs for the abundance and 

productivity criteria as in the more comprehensive BNs and, as before, we used MPG-wide 

averages for abundance and productivity where these data were not available for individual DIPs. 

This analysis substantially increased the probabilities of viability at each level. Probabilities of 

Puget Sound DPS viability

Viable

Not viable

 0

 100

50 ± 0

Central & South Sound MPG viability

Viable

Not viable

 100

 0

50 ± 0

Northern Cascades MPG viability

Viable

Not viable

 100

 0

50 ± 0

Hood Canal & Strait of JdF MPG viability

Viable

Not viable

 0

 100

50 ± 0
107

viability for DIPs varied from a low of 64.9% for the Stillaguamish River Winter Run to a high 

of 86.8% for the Canyon Creek Summer Run in the Northern Cascades MPG. The MPE for the 

Stillaguamish River Winter Run was that it is viable, but there was a 75% probability that this 

DIP is not viable; the MPE for the Canyon Creek Summer Run was that it is viable, and there 

was only a 5.5% probability that this DIP is not viable. For the Central and South Puget Sound 

MPG, probabilities of DIP viability varied from a low of 61.3% for Cedar River Winter Run to a

high of 69.5% for White River Winter Run. The MPE for the Cedar River Winter Run was that 

it is viable, but there was a 75% probability that this DIP is not viable; the MPE for the White 

River Winter Run was that it is viable, but the probability that this DIP is not viable was 

estimated at 49.9%. For the Hood Canal and Strait of Juan de Fuca MPG, probabilities of DIP 

viability varied from a low of 61.8% for the Elwha River Winter Run to a high of 72.0% for 

West Hood Canal Tributaries Winter Run. The MPE for the Elwha River Winter Run was that it 

is viable, but there was a 75% probability that this DIP is not viable; the MPE for the West Hood 

Canal Tributaries Winter Run was that it is viable, but the probability that this DIP is not viable 

was estimated at 49.9%.

Under this analysis, the probabilities of viability for the three MPGs were 89.9% for the 

Northern Cascades MPG, 74.6% for the Central and South Puget Sound MPG, and 70.5% for the 

Hood Canal and Strait of Juan de Fuca MPG. The MPEs for MPG viability were that all three 

MPGs are viable, with probabilities that each MPG is not viable of 12.4%, 31.6%, and 56.2%, 

respectively. Finally, the probability of viability for the entire DPS was 42.3%; the MPE was 

that the DPS is viable, with a probability that the DPS is not viable equal to 56.2%.

This exercise illustrates how the structure of a BN can strongly influence the inference 

about viability at nodes throughout the network. If we rely on the more quantifiable and readily 

available of the VSP criteria alone, estimates of steelhead viability are higher throughout the 

DPS for two main reasons: the PSS TRT’s largely qualitative assessment of diversity and spatial 

distribution of steelhead is that the widespread state of these factors in Puget Sound steelhead 

poses risk to sustainability, and the TRT’s more quantitative assessment of demography 

(abundance and productivity) is conservative with respect to its influence on risk to persistence. 

If one were to accept this analysis (and its underlying probability theory) as a reasonable 

evaluation of the status of Puget Sound steelhead, one would still have to conclude that there is 

substantial uncertainty in the status of at least two of the MPGs and the entire DPS. 

Furthermore, the main problem with this analysis is that it is at odds with the threatened status of 

the DPS and with concerns about continued risks to persistence and sustainability of many of its 

populations. It also indicates that the existing BNs may be too benign with respect to risk of 

quasi-extinction; the viabilities for several DIPs and all the MPGs are at odds with the outcomes 

of several of the PVAs, in part because of the MPG-wide averaging for DIPs with no quantitative 

information. It is also probably due in part to the fact that this analysis does not consider the 

effects of factors that are known to be risks to wild Puget Sound steelhead, including limited 

availability of productive rearing and spawning habitat, the widespread use of Chambers Creek 

and Skamania River hatchery fish throughout the DPS, and perceived changes in aspects of 

diversity for several populations.

The TRT considers the networks that encompass all four VSP criteria to more accurately 

reflect the current status of Puget Sound steelhead. The vast majority of steelhead populations 

throughout Puget Sound do not appear to be viable, regardless of where one looks; most are 
108

declining steeply from historical levels and many are very small. Despite the lack of 

demographic information for many of the populations and the lack of clarity regarding the 

precise states of diversity and spatial structure for nearly all of them, these BNs appear to 

provide a framework that assesses the broad, overall view of steelhead viability with reasonable 

confidence. The TRT considers these networks to represent a reasonable starting point for 

identifying the major criteria for effective recovery of Puget Sound steelhead, and believes that 

they help to identify key gaps in knowledge that must be addressed if further declines in 

steelhead viability are to be arrested.
109

Diversity Considerations for Steelhead Viability: 

Resident Life History and Iteroparity

In this section, we briefly consider how the presence of freshwater resident 

Oncorhynchus mykiss and the degree of iteroparity may affect viability of steelhead. Both 

factors may mitigate extinction risk, but few analyses are available in the published literature to 

guide their consideration in a viability analysis of anadromous fish. We briefly describe 

NMFS’s previous considerations of these factors and outline an approach and justification for 

including them in our overall assessment of viability.

Resident Fish as a Factor Influencing Viability

O. mykiss exhibits varying degrees of anadromy. Nonanadromous forms are usually 

called rainbow trout. Although the anadromous and nonanadromous forms have long been 

taxonomically classified within the same species, in any given area the exact relationship 

between the forms is not well-understood.

The 2003 Biological Review Team (BRT) (Good et al. 2005) had to consider in more 

general terms how to conduct an overall risk assessment for a DPS that includes both resident 

and anadromous populations, particularly when the resident fish may outnumber the anadromous 

ones but their biological relationship is unclear or unknown. Some guidance is found in Waples 

(1991), which outlines the scientific basis for the NMFS ESU policy. That paper suggested that 

an ESU (or equivalently, a DPS) that contains both forms could be listed based on a threat to 

only one of the life history traits “if the trait were genetically based and loss of the trait would 

compromise the ‘distinctiveness’ of the population” (p. 16). That is, if anadromy were 

considered important in defining the distinctiveness of the ESU, loss of that trait would be a 

serious ESA concern. In discussing this issue, the NMFS ESU policy (NMFS 1991) affirmed the 

importance of considering the genetic basis of life history traits such as anadromy and 

recognized the relevance of a question one commenter posed: What is the likelihood of the 

nonanadromous form giving rise to the anadromous form after the latter has gone locally extinct? 

To focus the issue, Good et al. (2005) considered a hypothetical scenario that has varying 

degrees of relevance to individual steelhead ESUs/DPSs. In this scenario, the once-abundant and 

widespread anadromous life history is extinct, or nearly so, but relatively healthy native 

populations of resident fish remain in many geographic areas. The question the BRT (Good et 

al. 2005) had to consider was: Under what circumstances would one conclude that such a DPS 

was not in danger of extinction or likely to become endangered? The BRT identified the 

required conditions as:

• The resident forms are capable of maintaining connectivity among populations to the 

extent that the DPS’s historical evolutionary processes are not seriously disrupted.
110

• The anadromous life history is not permanently lost from the DPS but can be regenerated 

from the resident forms.

The task faced by the PSS TRT ideally involves a finer scale evaluation of the 

contribution of resident fish to the viability of individual anadromous DIPs. While Good et al. 

(2005) developed generalized guidelines to evaluate the relationship between the two life history 

forms, despite differences in that relationship for both coastal and interior steelhead DPSs, the 

TRT was able to focus on coastal O. mykiss in Puget Sound. Additionally, the TRT benefited 

from a number of recent studies on the interactions between Puget Sound resident and 

anadromous O. mykiss. In general, there appeared to be a relatively close relationship between 

sympatric resident and anadromous forms below long-standing natural barriers. This may be due 

in part to the relatively short geologic time period since the Pleistocene glaciations. It may also 

be that, below impassable barriers, truly resident populations do not exist. Rather, the degree of 

anadromy in an O. mykiss DIP may be somewhat plastic, with environmental and ecological cues 

influencing the relative rate of anadromy. For example, in the Lake Washington basin (including 

the Cedar River) the anadromous populations of O. mykiss and cutthroat trout (O. clarkii) have 

dwindled to near zero levels, yet resident fish of both species are widely abundant. This is 

thought to be due in part to changes (improvements) in the productivity of Lake Washington and 

Lake Sammamish.

In determining the viability of steelhead DIPs in the Puget Sound DPS, the TRT 

considered the potential influence of co-occurring resident O. mykiss on anadromous steelhead 

demographics. Based on recent studies (see References section, e.g., Zimmerman and Reeves 

2000, Thrower et al. 2004, Pearse et al. 2009), it is clear that there is some degree of interaction 

between resident and anadromous fish. This interaction can be both genetic and ecological 

(competition, predation, etc.) and has the potential to be a positive or negative influence on

steelhead viability. Interactions between resident and anadromous fish can be especially 

beneficial when the abundance of anadromous fish is especially low, near QET. Thus resident 

fish may be most important not in bringing a DIP to full viability, but in preventing a DIP from 

being extirpated. Any reduction in risk of extinction would be directly linked to the genetic 

relationship between resident and anadromous O. mykiss and the sustainability of the resident 

population. Resident O. mykiss could be the result of nonnative introductions from other basins 

within Puget Sound or from outside of the DPS (most prominently in northern California). 

Alternatively, resident fish may be emigrating into anadromous-accessible areas from upstream 

of stream structures impassable to upsteam migrants. In either case, where substantial genetic 

differences exist between resident and anadromous fish, it is unlikely that interbreeding between 

these two life history forms will be beneficial to the fitness of steelhead. Where genetic 

differences between the two forms are more moderate, the two life history forms may have 

recently diverged or may coexist with continued gene exchange.

If resident O. mykiss are a potential reservoir of the anadromous genetic legacy in a 

steelhead population, they can play an important role in the viability of that population. This is 

especially important in areas that historically contained anadromous O. mykiss, but have been 

rendered inaccessible to upstream migration post-European contact. The TRT concluded that in 

basins where anadromous O. mykiss abundance is below the QET threshold, the risk of 

extinction is not necessarily 100% if resident O. mykiss are present below long-standing 

migration barriers. Inclusion of resident fish in the viability criteria is dependent, however, on 
111

several conditions. First, the abundance of resident fish must be large enough to be selfsustaining (this will be largely dependent on the numbers of resident females). Secondly, there 

must be some evidence of interbreeding between the anadromous and resident forms (this will 

most likely be established by genetic analysis of the relationship between the resident fish and 

the most proximate anadromous population). Identifying resident fish that contain the genetic 

legacy of their historical anadromous population is an important step in potentially reestablishing 

anadromous O. mykiss to some basins and subbasins.

A comprehensive risk assessment must consider the effect of resident O. mykiss that have 

anadromous access on the viability of the entire DPS. This task is especially difficult because 

little or no information is available about the abundance and distribution of resident fish, or about 

the extent and nature of their interactions with anadromous populations. The 2003 BRT 

incorporated information about rainbow trout populations into their analyses of the four VSP 

criteria and their assessments of extinction risk for O. mykiss ESUs (Good et al. 2005). In 

several ESUs, Good et al. (2005) concluded the presence of relatively numerous rainbow trout 

populations reduced risks to ESU abundance. However, there is considerable scientific 

uncertainty regarding the potential of the resident form to contribute to the productivity, spatial 

structure/connectivity, and diversity of steelhead ESUs (Varanasi 2004).

Good et al. (2005) underscored the importance of the anadromous life history form in 

reducing risks to these latter three VSP parameters, and thus in contributing to a viable O. mykiss

ESU or DPS in total. Although there is the potential for rainbow trout populations to generate 

steelhead migrants, it may be short-lived if the reproductive success of steelhead offspring is 

low. Finally, the BRT concluded if the anadromous life history form in an ESU is extirpated or 

critically depressed, it is unlikely the resident life history form alone is capable of maintaining 

the productivity, connectivity, and diversity necessary for a viable O. mykiss ESU (Good et al. 

2005).

Subsequent to the conclusions of the 2003 BRT, NMFS solicited opinions from two 

expert panels to review the issue of viability in listing units that contain both rainbow trout and 

steelhead (note that the language below refers to ESUs, although listing units for O. mykiss are 

now considered DPSs). The independent Recovery Science Review Panel (RSRP) identified 

anadromy as “an evolutionarily significant component of O. mykiss diversity” (RSRP 2004). In 

its review of available information, the panel concluded “resident populations by themselves 

should not be relied upon to maintain long-term viability of an ESU.” Similarly, the Independent 

Scientific Advisory Board (ISAB) found the long-term consequences of the extirpation of a 

major life history form would have deleterious consequences on the entire ESU (ISAB 2005).

To be viable, an ESU or DPS needs more than simple persistence over time; it needs to 

be sustained in an ecologically and evolutionarily functional state. Evaluation of ESU/DPS

viability should not only rest on the numbers of component populations or on the abundance and 

productivity of those individual populations, but also should be based on the integration of 

population dynamics within the ecosystem as a whole. This concept of ESU/DPS viability does 

not accommodate the loss of populations or the anadromous or resident life history form from 

any given ESU/DPS, because that loss would represent a loss in diversity for the ESU/DPS that 

would put its long-term viability at risk.
112

Where both life history forms are present, the ISAB considered that the resident forms 

contribute to the overall abundance and diversity of an ESU/DPS, but was unsure of the 

contribution by resident fish to connectivity and spatial structure. Overall, the presence of both 

resident and anadromous life history forms is “critical for conserving the diversity of 

steelhead/rainbow trout populations and, therefore, the overall viability of ESUs.”

In a 2004 review of currently listed steelhead ESUs, the Northwest Fisheries Science 

Center concluded that “None of these ESUs is likely to persist in total into the foreseeable future,

because substantial parts of the ESUs are at risk of extinction” (Varanasi 2004). That review 

supported the 2003 BRT conclusions that the ESUs were at risk of extinction, now or in the 

foreseeable future, because the anadromous life history represented a “significant portion of the 

species ‘range,’ such that its loss is a direct threat to the ESUs” (Varanasi 2004).

Courter et al. (2010, 2013) conducted deterministic simulation modeling of population 

viability for an interior population of O. mykiss with both resident and anadromous life history 

types (Upper Yakima River) and concluded that extinction risk was low when production of 

steelhead from both resident and anadromous fish was accounted for. Viability was highly 

sensitive to the quasi-extinction risk thresholds for both resident spawners and anadromous 

steelhead, but Courter et al. (2010, 2013) found that natural variation in stage-specific survival 

rarely caused steelhead abundance to drop below 25 spawners over 4 consecutive years, yet

almost always caused abundance to drop below 100 spawners. The modeling indicated that after 

1,000 iterations, steelhead abundance dropped below a QET of 50 spawners in about a quarter of 

the simulated scenarios. Courter et al. (2010) stated that “our modeling as well as historic fish 

counts reveal that a reduction in steelhead below 50 spawners across 4 or more consecutive years 

does not lead to extinction when resident rainbow trout are abundant in the population.” They 

concluded that their data highlights the sensitivity of steelhead abundance to marine survival and 

demonstrates that when resident fish are highly abundant and contribute sufficient smolts, 

steelhead can be maintained in the population at low abundance.

These results are consistent with the evidence for plastic and genetic influences on 

migratory life history in this species. In at least some coastal systems, smolt production from 

resident fish has been documented, although the two forms can sometimes be genetically 

differentiated. Pearse et al. (2009) found evidence for rapid evolution of a resident life history in 

a coastal California steelhead population above a natural migration barrier in response to 

selection against seaward migration. In cases where anadromous steelhead abundance is very 

low in a population that also harbors a resident life history, smolt production from resident fish 

may contribute substantially to steelhead viability. In their controlled breeding study of O. 

mykiss from Sashin Creek, Alaska, Thrower et al. (2004) found that resident fish there can 

produce substantial numbers of smolts, albeit with lower marine survival (see also Thrower and 

Hard 2009). They concluded that “the judicious use of freshwater sequestration merits 

consideration as a temporary component of a comprehensive strategy for the maintenance of 

endangered anadromous populations of O. mykiss in cases where the likelihood of rapid 

restoration of freshwater habitats for anadromous fish is low.”

An O. mykiss population expressing a combination of migratory strategies (freshwater 

resident, anadromous) and a heritable propensity to produce both types of progeny means that 

residents can serve as a buffer when anadromous productivity is low—extinction risk is lower 
113

when residents are relatively abundant. But it is important to remember that while a population 

of residents may indeed provide a genetic reservoir to produce anadromous migrants, these fish 

may have reduced adaptive potential to cope with the marine environment and smolt production 

may entail a cost in fitness. This is an active area of research that merits immediate attention to

help guide development of conservation strategies for steelhead. For our analysis, we simply 

assumed that the presence of locally adapted, resident fish with an opportunity to interact with 

steelhead in Puget Sound (e.g., in the Cedar River watershed) limited extinction risk for 

steelhead to a level not exceeding 90% in 100 years.

Iteroparity as a Factor Influencing Viability

Steelhead are iteroparous anadromous salmonids. It is widely thought that this ability to 

reproduce repeatedly over the lifespan can contribute significantly to population productivity and 

provide a temporal buffer in demographic and environmental stochasticity. In the Pacific 

Northwest, the current frequency of repeat spawners appears to be low, generally less than 10% 

and frequently much lower, and rates appear to be declining in several populations in recent 

decades (e.g., Figure 51). A systematic analysis of the contribution of repeat spawning to 

population dynamics has not, to our knowledge, been undertaken for coastal steelhead. 

Nevertheless, a comprehensive evaluation of population viability must consider this contribution, 

Figure 51. Temporal changes between the 1970s and 1990s in the estimates of repeat spawning rates for 

seven winter-run steelhead populations from the Northern Cascades MPG and the Central and 

South Puget Sound MPG.

-0.03

0.02

0.07

0.12

0.17

0.22

0.27

1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994

Proportion of Spawners (M & F)

 

Nisqually Puyallup Skagit Snohomish Stillaguamish Green Nooksack
114

and that is a primary reason why iteroparity is a component of the BNs for viability developed in 

the previous section of this report.

In this subsection of the report, we summarize some preliminary analyses that incorporate 

the proportion of repeat spawners in adult populations as a viability parameter influencing 

productivity. These unpublished analyses, conducted by Nick Gayeski of the Wild Fish 

Conservancy in coordination with and reviewed by the PSS TRT, rely on an approach that 

develops a suite of age-structured matrix population projection models with annual time steps 

that incorporate density dependence, varying levels of repeat spawning, and varying levels of 

density-independent stochasticity and harvest mortality. The models were used to evaluate the 

influence of varying levels of repeat spawning on population abundance and age structure under 

varying levels of harvest.

The models incorporated iteroparity under deterministic and stochastic conditions, with 

and without harvest mortality. Gayeski’s models simulated a winter-run steelhead population

with six age-classes and three ages at maturation, with age of first maturation at age 4. The 

entire population was assumed to be anadromous and had no interactions with resident fish. 

Smolts were assumed to be all age 2, and repeat spawners were limited to one repeat spawning 

event in the year following the year of first reproduction. Therefore, repeat spawners were either 

5 or 6 years old. All harvest mortality occurred on mature fish following river entry and prior to 

spawning. Sex ratio was 0.5.

Female fecundity scaled with size and Kamchatka wild steelhead data were used to 

determine individual fecundities. First-time spawners were assumed to deposit more eggs than 

repeat spawners of the same age, reflecting the assumption that repeat spawners pay a cost in 

fecundity due to the costs of first spawning and to having less time available for growth in the 

ocean after spawning. This fecundity/growth cost is in addition to a survival cost that is paid for 

having matured and spawned at a younger age rather than remaining in the ocean and maturing 1 

or 2 years later.

Density-dependence was modeled as a Beverton-Holt functional response. There is a 

scarcity of data on age-specific and stage-specific survival or mortality rates for adult and 

juvenile steelhead. The model employed values from a few of the steelhead studies available and 

theoretical considerations based on life history theory and allometry to produce estimates for 

both juvenile and adult survival rates (Ward and Slaney 1993, McGurk 1996, Quinn 2005).

The effect of stochasticity was evaluated by modeling random variation in the smolt-toage-3 transition rate as a random variable drawn from a beta distribution with specific mean and 

coefficient of variation. Two ocean survival scenarios were evaluated that span reasonable levels 

of favorable and unfavorable variation in this transition rate.

Figure 52 summarizes some of the deterministic model results under a 20% harvest rate, 

assuming smolt-to-adult survival rates of 15% and a variety of repeat spawning rates. The 

median abundance declines from 330 for a population with 25% repeat spawners (which is more

than 100 adults lower than without harvest) to just above 250 with no repeat spawners.

From his modeling, Gayeski concluded that:
115

1. The average proportion of repeat spawners in an adult steelhead population is relevant to 

population abundance and stream capacity in two ways: first, relative to a population with 

no repeat spawners, for a given average capacity of juveniles, a population with repeat 

spawners will have a larger average adult spawning population; second, a given average 

number of adult spawners can be sustained by fewer juveniles when repeat spawners are 

present than when they are not. It is therefore likely that viable populations (DIPs) of 

steelhead can be sustained in smaller stream basins than is the case for Pacific salmon.

2. When population resilience to environmental variation or harvest mortality is measured 

in terms of either the probability of declining below specific levels of annual spawner 

abundance or the expected frequency of the spawning population declining below some 

threshold of concern (quasi-extinction), repeat spawning provides increased levels of 

resilience compared to populations without repeat spawning. However, the impact of 

harvest mortality on the average proportion of repeat spawning confounds understanding 

of precisely how much resilience a given mean level of repeat spawning (measured under 

deterministic conditions) affords a population, especially at low levels of total abundance. 

The significance of repeat spawning may be weakened (or underestimated) when small 

population sizes are considered.

3. When both harvest mortality and environmental variation in smolt survival are present, 

repeat spawning increases population resilience. The precise extent to which resilience is 

increased is sensitive to the harvest rate and to both the magnitude of the average value of 

life-stage survival rates subject to environmental variation and the amount of variation in 

the rate as measured by the coefficient of variation.

4. From a population rebuilding and recovery perspective, for small population sizes such as 

the ones considered in this modeling exercise, it appears that the value of specific levels 

of repeat spawning to population resilience (and repeat spawning) are most likely to be 

realized under a zero harvest scenario, regardless of the level of environmental variation 

(at least for the range and kind of variation employed in the models).

Figure 52. Empirical cumulative densities (CDF) of 1,000 deterministic population projections over 50 

years for steelhead exposed to a harvest rate of 20% under a mean smolt survival of 15% with a 

coefficient of variation of 50%. The different curves represent different repeat spawning rates 

ranging from 0 to 25%.

0 100 200 300 400 500 600 700 800 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

x

F(x)

Empirical CDF of 1000 population projections of 50 years. HR = 0.20. Mean smolt survival = 0.15, cv = 0.50. 

Percentage repeat spawners = 0, 10, 15, 20, and 25.

No repeats

Repeats = 10%

Repeats = 15%

Repeats = 20%

Repeats = 25%
116

These exercises are simulations with assumptions that may not be realistic for many 

natural steelhead populations, and demonstrating the effect of specific levels of repeat spawning 

on population resilience would realistically require a no-harvest regime considering the extent of 

natural environmental variation influencing survival, growth, and iteroparity. A comprehensive 

parentage would be required to fully address this question and provide empirical evidence to 

support these conclusions. That said, these analyses reinforce the TRT’s conviction that 

iteroparity is an important consideration in a comprehensive evaluation of viability for steelhead. 

Iteroparity is also arguably an important factor for diversity (and also for population persistence 

through temporal risk spreading), but the TRT did not consider this issue quantitatively. The 

TRT thinks that the degree of iteroparity is likely to be especially influential on viability in small 

populations during periods when marine mortality varies widely (see also the Integral Projection 

Model analysis of a small wild steelhead population described in Appendix G).
117

Viability Criteria for Puget Sound Steelhead

Under the ESA, NMFS is required to identify measurable and objective delisting criteria 

as part of recovery planning. These delisting criteria must describe the conditions under which a 

listed species or DPS is no longer in danger of extinction (endangered) or likely to become so in 

the foreseeable future (threatened). We define a viable DPS as one that is unlikely (with less 

than an estimated 5% probability) to be at risk of extinction in the foreseeable future; for this 

purpose, we adopt the VSP criteria of a specified QET and a 100-year timeline (McElhany et al. 

2000) to evaluate risk of extinction. Ultimately, the identification of delisting criteria requires 

the consideration of technical analyses relating to viability, which are contained in this steelhead 

document, and policy decisions such as acceptable levels of risk, which are not. This document 

presents the PSS TRT’s recommended biological viability criteria.

The listed unit under the ESA for Oncorhynchus mykiss including anadromous steelhead

is the DPS, and this is the unit that must be considered for delisting. Thus delisting criteria must 

ultimately address the extinction risk of the DPS. In approaching the development of criteria for 

assessing viability of (and, subsequently, delisting) DPSs, we have relied on the language in the 

ESA, information described in the listing decision, concepts outlined in the McElhany et al. 

(2000) VSP document, and published research describing salmon populations and their past or 

potential responses to environmental changes. The ESA lists five potential factors for decline 

that must be considered in species listing decisions (ESA Section 4.2.1):

1. The present or threatened destruction, modification, or curtailment of its habitat or range;

2. Overutilization for commercial, recreational, scientific, or educational purposes;

3. Disease or predation;

4. The inadequacy of existing regulatory mechanisms; and

5. Other natural or man-made factors affecting its continued existence.

NMFS identified all five factors as contributing to the endangerment of Pacific 

salmonids, including steelhead. In considering how viability criteria might inform biologically 

based population delisting requirements, the TRT also attempted to consider these factors for 

decline. The NMFS (2010) population recovery approach for threatened Puget Sound Chinook 

salmon (see also Ruckelshaus et al. 2006) identified six delisting criteria for this ESU.

1. The viability status of all populations in the ESU is improved from current conditions.

2. At least two and up to four Chinook salmon populations in each of five biogeographical 

regions (i.e., MPGs) within the ESU achieve viability, depending on the historical 

biological characteristics and acceptable risk levels for populations within each region.

3. At least one population from each major genetic and life history group historically 

present within each of the five biogeographical regions is viable.
118

4. Tributaries to Puget Sound not identified as primary freshwater habitat for any of the 22 

identified populations are functioning in a manner that is sufficient to support an ESUwide recovery scenario.

5. Production of Chinook salmon from tributaries to Puget Sound not identified as primary 

freshwater habitat for any of the 22 identified populations occurs in a manner consistent 

with an ESU recovery.

6. Populations that do not meet the viability criteria for all VSP parameters (i.e., abundance, 

productivity, diversity, and spatial structure) are sustained to provide ecological functions 

and preserve options for ESU recovery.

The TRT approach evaluated the extinction risks facing the DPS by assessing the 

viability of the individual populations (DIPs) and MPGs within that DPS. The TRT identified 

population-level viability criteria based on a combination of the four parameters indicated in the 

VSP document and information about the habitat requirements of the listed fish. From the 

analyses in this report and building from these population-level criteria, the TRT identified 

viability criteria for the Puget Sound steelhead DPS that take into account these key VSP 

parameters and incorporate hierarchical viability criteria for MPGs and for the entire listed unit. 

Some of these criteria for steelhead share similarities to NMFS population recovery approach 

criteria for Puget Sound Chinook salmon listed above.

1. The viability (as reflected in abundance, productivity, diversity, and spatial structure) of a 

majority of steelhead populations in each of the MPGs across the DPS is detectably 

higher than currently, using an accepted form of population viability analysis and 

additional tools like those described in this report.

2. At least 40% of steelhead populations in each of the three MPGs within the DPS achieve 

viability, depending on historical biological characteristics and acceptable levels for risk 

to population persistence within each region. To be viable, an MPG’s constituent DIPs 

must have a mean viability above a specified threshold (described in the DIP Viability 

subsection below).

3. A minimum of 40% of summer-run and 40% of winter-run populations historically 

present within each of the MPGs must be viable using the VSP-based criteria described in 

this report.

4. Natural production of steelhead from tributaries to Puget Sound not identified as primary 

spawning or rearing habitat in any of the 32 identified populations is sustained to provide 

sufficient ecological diversity and productivity to support DPS-wide recovery.

The viability analyses described earlier (in the Viability Assessment Methods section and 

the Decision Support Systems as Tools for Assessing Viability section) indicate that sufficient 

abundance and productivity from each major life history type in a sufficient fraction of 

populations spread spatially across each MPG are critical to the viability of each MPG, which 

DPS viability depends on. That said, diversity and spatial structure—which appear to be limited 

throughout the DPS, based on limited data—make essential contributions to MPG (and therefore 

DPS) viability as well.
119

From these considerations, the PSS TRT developed the simple viability criteria 

framework outlined below. The TRT developed these criteria using the Puget Sound Steelhead 

DPS population structure identified in its previous publication, Identifying Historical Populations 

of Steelhead within the Puget Sound Distinct Population Segment (Myers et al. 2015) and 

implemented here: a DPS composed of 3 MPGs containing a total of 32 historically present 

DIPs. The configuration of the Puget Sound Steelhead DPS adopted in this report is based on 

information compiled to date and it is possible, perhaps likely, that additional data will suggest 

future changes. These changes would most likely involve the number of DIPs identified rather 

than the number or configuration of the MPGs.

The criteria for viability for each unit within the DPS follow basic “traffic light” rule sets 

(summarized in Figure 53 through Figure 55). Using a three-bin classification (low = not viable, 

moderate = intermediate, and high = viable), the DIPs in an MPG are scored at each of the four 

VSP criteria with 1 (red inverted triangle), 2 (yellow square), or 3 points (green upright triangle), 

corresponding to the contributions of these scores to DIP viability. These scores reflect 

Figure 53. Viability criteria framework for DIPs of Puget Sound steelhead.

VSP Parameter

AbundanceProductivityDiversitySpatial structureDIP viability

   

6

10

10

8

8

14

12

12

12

12

10

16

16

14

14

18

Key:

Low < 40%

Moderate 40-85%

High > 85%
120

Figure 54. Viability criteria framework for MPGs of Puget Sound steelhead based on the viability of 

component DIPs containing distinct life history types (S = summer run and W = winter run). Key 

is in Figure 53.

Figure 55. Viability criteria framework for the entire Puget Sound Steelhead DPS. Key is in Figure 53.

probabilities that DIP viability is influenced by the VSP criteria, ranging from less than 40% to 

40–85% and greater than 85%, respectively (Figure 53).

MPG viability depends on two criteria. First, as indicated in Figure 54, a minimum of 

40% of the DIPs in the MPG that exhibit each of the two distinct life history strategies (summer 

run vs. winter run) must be viable using the same “traffic light” rule set (i.e., score = 3). (The 

threshold of 40% corresponds to an average probability of DIP viability of 67% if the DIPs that 

are not viable have probabilities of viability of 50%.) Second, the DIPs in the MPG must have a 

geometric mean score of at least 2.2, a value the TRT determined based on consideration of the 

first criterion and examining how MPG viability under the traffic light rule set varies. Following 

these two criteria, to be viable an MPG with 8 DIPs must have at least 4 viable DIPs (score = 3) 

and no more than 1 DIP that is considered not viable (score = 1). An MPG with 11 winter-run 

and 5 summer-run DIPs must have at least 5 viable winter-run and 2 viable summer-run DIPs 

and no more than 1 DIP of each life history type that is considered not viable.

<40% viable DIPs (S or W)

>40% viable DIPs (S)

>40% viable DIPs (W)>40% viable DIPs (S + W)MPG viability

- - -

- - -

- - -

- - -

MPG 1MPG 2MPG 3DPS viability
121

Finally, DPS viability depends only on one criterion: that each of its component MPGs is 

considered viable (Figure 55).

The viability criteria for each level within the DPS are summarized below.

DIP Viability

1. Using a simple three-bin classification (not viable, intermediate, viable), a DIP is scored 

1, 2, or 3, depending on whether the probability of viability as computed by its viability 

BN is less than 40%, 40–85%, or greater than 85%, respectively. For a DIP to be 

considered viable, its probability of viability must be at least 85%, as calculated by the 

BN (see Appendix F). To estimate this value, the mean abundance and productivity 

criteria for the candidate DIP are each double-weighted; this produces DIP viability 

scores ranging from 6 to 18 points. DIPs with viability scores less than 11 are considered 

not viable, those with viability scores between 11 and 14 are considered to have 

intermediate viability, and those with viability scores greater than 14 (>85% rounded up) 

are considered viable.

MPG Viability

2. An MPG is considered viable if 40% (rounded up) of its DIPs (including extinct as well 

as extant historical populations) are viable and mean DIP viability exceeds the specified 

threshold for viability (Table 16).

a. DIPs exhibiting distinct life history strategies (summer run vs. winter run) will be 

considered separate components of the MPG. Therefore, a minimum of 40% of 

summer-run and 40% of winter-run populations within an MPG must be viable to 

achieve MPG viability.

b. DIPs containing both winter-run and summer-run subpopulations predominantly 

exhibit the winter-run life history strategy in Puget Sound and will be considered 

winter run for the purpose of estimating 2a.

c. A viable MPG must, in addition to the criterion outlined in 2a and 2b, have a 

geometric mean score of at least 2.2 to be considered viable. This score is the 

minimum achieved across a range of MPG sizes (with number of DIPs ranging from 

8 to 16) with a sufficient fraction of DIPs having viabilities of greater than 85%.

DPS Viability

3. A DPS is considered viable only if all its component MPGs are viable.

A chart of representative viability criteria applied to the DPS to depict their relationships 

is in Figure 56. This figure emphasizes the hierarchical nature of the criteria across the levels of 

the components of the DPS. The framework partitions criteria at the DIP level between 

persistence and sustainability factors related to VSP components. For example, the framework 

considers spawner abundance, productivity, occupancy and density of suitable habitat by adults 

and juveniles, frequency of iteroparity, and sources of human-induced mortality as factors that 
122

Table 16. Number of viable DIPs required for MPG viability in each of the Puget Sound steelhead MPGs 

with a requirement of 40% viable DIPs of each major life history type. Some winter-run DIPs 

may include some summer-run fish as well, which is most common in the Northern Cascades 

MPG and along the Strait of Juan de Fuca.

MPG Life history type No. of DIPs No. viable

Northern Cascades Summer run 5 2

Winter run 11 5

Central and South Puget Sound Summer run 0 0

Winter run 8 4

Hood Canal and Strait of Juan de Fuca Summer run 0 0

Winter run 8 4

influence demography primarily and, therefore, population persistence. It considers effective 

population size, influence of hatchery fish (both genetic and ecological impacts), age variation in 

spawners, and variation in spawn timing as factors that influence diversity primarily and, 

therefore, population sustainability. The framework also conveys the importance of having 

populations distributed across major habitat types, having connectivity between resident and 

anadromous fish where they occurred historically, and minimizing risk to multiple DIPs due to 

catastrophic risk. It attempts to include factors considered important to viability, even if current 

information is inadequate to characterize their influence on viability estimates accurately.

Finally, it considers harvest mortality as a factor in the context of other human-induced risk to 

steelhead such as freshwater habitat loss or climate change.

The TRT applied the criteria to the 32 DIPs in the Puget Sound Steelhead DPS to provide 

a status assessment of the current viability of these units in Figure 57. The hierarchical BNs 

produced the viability values that were used to generate the scores. Because this figure

categorizes the viability scores for each criterion and for each DIP as a whole into 3 bins as 

described above, one of its primary purposes is to provide a readily visualized summary of 

patterns of viability for DIPs across the DPS. Another purpose of this figure is to highlight 

viability criteria that lack sufficient information, a key first step in outlining a monitoring 

program for recovery. That assessment, summarized in Figure 57, clearly indicates low viability 

for more than half of the DIPs, all MPGs, and the DPS as a whole. Nearly all DIPs have 

insufficient current abundance and productivity scores to be considered viable. Most also have 

low scores for diversity and spatial structure, largely because of extensive hatchery influence, 

low breeding population sizes, and freshwater habitat fragmentation or loss. In the Central and 

South Puget Sound MPG and the Hood Canal and Strait of Juan de Fuca MPG, nearly all DIPs 

are not viable. The mean score for the 16 DIPs in the Northern Cascades MPG is 11.9, just 

above the threshold for intermediate viability; however, no summer-run populations are viable 

using the assessment, so the MPG is not considered to be viable. The mean score for the 8 DIPs 

in the Central and South Puget Sound MPG is 9.5, and that for the 8 DIPs in the Hood Canal and 

Strait of Juan de Fuca MPG is 9.9; both scores are below the threshold for intermediate viability.

Under an alternative assessment in which all viability criteria for which data are 

insufficient are given an intermediate score (2), the outcome of viability changes surprisingly 

little, even at the level of individual DIPs (Figure 58). Most DIPs still have insufficient current
123 

Figure 56. A set of recommended viability criteria for a DPS of steelhead. The chart shows how viability at the scale of the DIP, MPG, and the 

entire DPS are related. For DIP viability, criteria are divided into those contributing to persistence (i.e., abundance and productivity, 

extinction risk, and effects of human-induced mortality) and those contributing to sustainability (i.e., diversity and spatial structure, 

influence of hatchery fish, age structure, and spawn timing). Human-induced mortality includes mortality from fishing, pollution, and 

habitat loss.
124

Figure 57. Estimates of current viability (low = not viable, moderate = intermediate, high = viable) for 

the 32 DIPs of Puget Sound steelhead (S = summer run and W = winter run) using the VSP 

framework. Note that many criteria are supported by insufficient data and in most (but not all) of 

those cases they were given an intermediate value with respect to influence on viability.

Spatial structure

VSP Parameter

Abundance Productivity Diversity Viability

DIP Adult

Juvenile

QETSmolts/sp.Adults/smolt

LambdaIteroparity

Hatchery

Spawn timing

Residents

Age

Spawn IP

Rear IP

DIP MPG DPS

N. Cascades MPG

Drayton Harbor trib. (W)

Nooksack R. (W)

S. Fk. Nooksack R. (S)

Samish R./Bellingham (W)

Skagit R. (S/W)

Nookachamps Cr. (W)

Baker R. (S/W)

Sauk R. (S/W) /

Stillaguamish R. (W)

Deer Cr. (S)

Canyon Cr. (S) /

Snohomish/Skykomish R. (W)

Pilchuck R. (W)

N. Fk. Skykomish R. (S)

Snoqualmie R. (W)

Tolt R. (S)

Central & S. Sound MPG

N. Lk. Washington trib. (W)

Cedar R. (W)

Green R. (W)

Puyallup/Carbon R. (W)

White R. (W)

Nisqually R. (W)

S. Sound trib. (W)

E. Kitsap Penin. (W)

Hood Canal & SJF MPG

E. Hood Canal (W)

S. Hood Canal (W)

Skokomish R. (W)

W. Hood Canal (W)

Sequim/Discovery Bay trib. (W)

Dungeness R. (S/W)

Strait of Juan de Fuca indep. (W)

Elwha R. (S/W)

Key:

Low < 40%

Moderate 40-85%

High > 85%

Ins. data
125

Figure 58. Estimates of current viability (low = not viable, moderate = intermediate, high = viable) for 

the 32 DIPs of Puget Sound steelhead (S = summer run and W = winter run) using the VSP

framework, but assuming that all viability criteria without quantifiable information are given a 

score of 2 (intermediate).

Spatial structure

VSP Parameter

Abundance Productivity Diversity Viability

DIP Adult

JuvenileQETSmolts/sp.Adults/smoltLambdaIteroparityHatcherySpawn timingResidents

Age

Spawn IPRear IP

DIP MPG DPS

N. Cascades MPG

Drayton Harbor trib. (W)

Nooksack R. (W)

S. Fk. Nooksack R. (S)

Samish R./Bellingham (W)

Skagit R. (S/W)

Nookachamps Cr. (W)

Baker R. (S/W)

Sauk R. (S/W)

Stillaguamish R. (W)

Deer Cr. (S)

Canyon Cr. (S)

Snohomish/Skykomish R. (W)

Pilchuck R. (W)

N. Fk. Skykomish R. (S)

Snoqualmie R. (W)

Tolt R. (S)

Central & S. Sound MPG

N. Lk. Washington trib. (W)

Cedar R. (W)

Green R. (W)

Puyallup/Carbon R. (W)

White R. (W)

Nisqually R. (W)

S. Sound trib. (W)

E. Kitsap Penin. (W)

Hood Canal & SJF MPG

E. Hood Canal (W)

S. Hood Canal (W)

Skokomish R. (W)

W. Hood Canal (W)

Sequim/Discovery Bay trib. (W)

Dungeness R. (S/W)

Strait of Juan de Fuca indep. (W)

Elwha R. (S/W)

Key:

Low < 40%

Moderate 40-85%

High > 85%

Ins. data
126

abundance and productivity scores to be considered viable, especially in the Central and South 

Puget Sound MPG and the Hood Canal and Strait of Juan de Fuca MPG. Most have intermediate 

scores for diversity and spatial structure. In the Central and South Puget Sound MPG and the 

Hood Canal and Strait of Juan de Fuca MPG, most (11 of 16) DIPs are not viable. The mean 

score for the 16 DIPs in the Northern Cascades MPG is 11.9, just above the threshold for 

intermediate viability; however, no summer-run populations are viable using the assessment, so 

the MPG is still not considered to be viable. The mean score for the 8 DIPs in the Central and 

South Puget Sound MPG is 10.7, and that for the 8 DIPs in the Hood Canal and Strait of Juan de 

Fuca MPG is 10.1; both scores are still below the threshold for intermediate viability. Twelve of 

the 32 DIPs lack sufficient information on the abundance and productivity criteria to be certain 

about their influences on viability, and all DIPs throughout the DPS have insufficient 

information on each of the diversity and spatial structure criteria.
127

Conclusions

Based on a combination of quantitative PVAs, collective judgment of qualitative 

information, and use of habitat-based metrics to estimate IP, the PSS TRT has developed and 

applied a framework to evaluate the current biological status of the Puget Sound Steelhead DPS. 

This framework is based on a series of hierarchical BNs intended to integrate the various sources 

of information that influence viability at the levels of the DIP, the biogeographic stratum or 

MPG, and the entire listed DPS. This information is biological (e.g., juvenile and adult 

abundance, population growth rate, freshwater and marine survival, iteroparity, spawn timing), 

related to habitat characteristics (e.g., habitat use and selection with respect to hydrograph, reach 

size and gradient, elevation), and related to management (e.g., hatchery influence, harvest 

effects). The TRT applied the framework to the Puget Sound Steelhead DPS to evaluate its 

status with the intent to assist in guiding recovery actions. Responses to reviewer comments on 

an earlier draft of this technical memorandum are in Appendix H.

The framework and the analyses it supports do not set recovery targets at any of the three 

levels, nor do they explicitly identify specific populations or groups of populations for recovery 

priority. Rather, the framework and associated analyses are meant to provide a technical 

foundation for those charged with recovery of listed steelhead in Puget Sound, from which they 

can develop effective recovery plans at the watershed scale (and higher) that are based on 

biologically meaningful criteria. They are also meant to highlight the data that are needed in 

future monitoring and evaluation studies to improve these efforts. It should be evident from this 

report that identifying the best way forward in recovery planning for steelhead would be 

enhanced considerably by additional information on the demography, diversity, and distribution 

of steelhead in watersheds throughout Puget Sound.

Consistent with its status as a threatened species under the ESA, the Puget Sound 

Steelhead DPS is not considered to be viable by the TRT. Using a comprehensive set of BNs 

that incorporate factors influencing all four VSP criteria (abundance, productivity, diversity, and 

spatial structure), nearly all of its 32 constituent DIPs and all 3 of its constituent MPGs are at low 

viability. Nearly all DIPs in the Central and South Puget Sound MPG and the Hood Canal and 

Strait of Juan de Fuca MPG are not viable. Most of the DIPs score low for all four VSP criteria 

and nearly all DIPs have insufficient current abundance and productivity scores to be considered 

viable. Most DIPs also have low scores for diversity and intermediate scores for spatial 

structure. The pattern of low viability is widespread throughout Puget Sound, across all three 

MPGs, and includes both summer-run and winter-run populations. The populations with highest 

viability—and those with highest abundance and diversity—are in northeastern Puget Sound 

(Northern Cascades MPG).

An analysis incorporating a simpler set of BNs that rely only on abundance and 

productivity criteria produced estimates of steelhead viability that are considerably higher 

throughout the DPS, but even under this analysis there is substantial uncertainty in the status of 
128

at least two of the MPGs and of the entire DPS. Despite the lack of quantitative information for 

several aspects of diversity and spatial structure, the TRT considers these VSP criteria to be 

essential for DPS viability into the future and recommends the use of the more comprehensive 

networks that encompass all four VSP criteria to evaluate the viability of Puget Sound steelhead.

The TRT’s BN framework provides a means of evaluating steelhead viability with 

explicit reference to estimates of historical abundance, productivity, diversity, and spatial 

distribution of steelhead in this region. This framework provides a systematic and transparent 

mechanism to evaluate viability of the DPS and its component MPGs and DIPs, and lends itself 

to evaluating alternative recovery scenarios and the effects of specific or alternative recovery 

actions, especially those operating at the watershed scale. The TRT’s companionate technical 

memorandum (Myers et al. 2015) describes the identification of the DIPs in this DPS that inform 

the framework for developing these viability criteria.
129

References

Amstrup, S. C., E. T. DeWeaver, D. C. Douglas, B. G. Marcot, G. M. Durner, C. M. Bitz, and D. A. 

Bailey. 2010. Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear 

persistence. Nature 468:955–960.

Amstrup, S. C., B. G. Marcot, and D. C. Douglas. 2008. A Bayesian network modeling approach to 

forecasting the 21st Century worldwide status of polar bears. In E. T. DeWeaver, C. M. Bitz, and 

L.-B. Tremblay (eds.), Arctic sea ice decline: Observations, projections, mechanisms, and 

implications, p. 213–268. Geophysical Monograph 180. American Geophysical Union, 

Washington, DC.

Araki, H., B. A. Berejikian, M. J. Ford, and M. S. Blouin. 2008. Fitness of hatchery-reared salmonids in 

the wild. Evol. Appl. 1:342–355.

Barrowman, N. J., and R. A. Myers. 2000. Still more spawner-recruitment curves: The hockey stick and 

its generalizations. Can. J. Fish. Aquat. Sci. 57:665–676.

Bayes, T., and R. Price. 1763. An essay towards solving a problem in the doctrine of chances. By the 

late Rev. Mr. Bayes, F.R.S., communicated by Mr. Price in a letter to John Canton, A.M.F.R.S. 

Phil. Trans. R. Soc. Lond. 53:370–418.

Beissinger, S. R., and D. R. McCullough (eds.). 2002. Population viability analysis. University of 

Chicago Press, Chicago, IL.

Boughton, D. A., P. B. Adams, E. Anderson, C. Fusaro, E. Keller, E. Kelley, L. Lentsch, J. Nielsen, K. 

Perry, H. Regan, J. Smith, C. Swift, L. Thompson, and F. Watson. 2007. Viability criteria for 

steelhead of the south-central and southern California coast. U.S. Dept. Commer., NOAA Tech. 

Memo. NMFS-SWFSC-407.

Busby, P. J., T. C. Wainwright, G. J. Bryant, L. Lierheimer, R. S. Waples, F. W. Waknitz, and I. V. 

Lagomarsino. 1996. Status review of West Coast steelhead from Washington, Idaho, Oregon, 

and California. U.S. Dept. Commer., NOAA Tech. Memo. NMFS‐NWFSC‐27.

Caswell, H. 2001. Matrix population models: Construction, analysis, and interpretation. Second edition. 

Sinauer, Sunderland, MA.

Cavalli-Sforza, L. L., and A. W. F. Edwards. 1967. Phylogenetic analysis: Models and estimation 

procedures. Evolution 21:550–570.

Chapman, D.W. 1981. Pristine production of anadromous salmonids–Baker River. Final report. 

Contract no. P00c14206448. Produced for the Bureau of Indian Affairs. Portland, OR.

Chilcote, M. W. 1997. Conservation status of steelhead in Oregon. Draft report, August 1997. Oregon 

Dept. Fish and Wildlife, Portland.

Conroy, M. J., R. J. Barker, P. W. Dillingham, D. Flether, A. M. Gormley, and I. A. Westbrooke. 2008. 

Application of decision theory to conservation management: Recovery of Hector’s dolphin. 

Wildl. Res. 35:93–102.
130

Coulson, T. 2012. Integral projection models, their construction and use in posing hypotheses in 

ecology. Oikos 121:1337–1350.

Coulson, T., T. G. Benton, P. Lundberg, S. R. X. Dall, B. E. Kendall, and G.-M. Gaillard. 2006. 

Estimating individual contributions to population growth: Evolutionary fitness in ecological time. 

Proc. R. Soc. B Biol. Sci. 273:547-555.

Coulson, T., D. R. MacNulty, D. R. Stahler, B. VonHoldt, R. K. Wayne, and D. W. Smith. 2011. 

Modeling effects of environmental change on wolf population dynamics, trait evolution, and life 

history. Science 334:1275–1278.

Coulson, T., S. Tuljapurkar, and D. Z. Childs. 2010. Using evolutionary demography to link life history 

theory, quantitative genetics, and population ecology. J. Anim. Ecol. 79:1226–1240.

Courter, I. I., D. B. Child, J. A. Hobbs, T. M. Garrison, J. J. G. Glessner, and S. Duerya. 2013. Resident 

rainbow trout produce anadromous offspring in a large interior watershed. Can. J. Fish. Aquat. 

Sci. 70:701–710.

Courter, I., B. Lister, S. Cramer, J. Vaughan, S. Duery, and D. Child. 2010. Evaluation of effects of 

resident rainbow trout and hatchery strays on steelhead production within the Middle Columbia 

River Oncorhynchus mykiss evolutionarily significant unit. Cramer Fish Sciences, Portland, OR.

Dennis, B., P. L. Munholland, and J. M. Scott. 1991. Estimation of growth and extinction parameters for 

endangered species. Ecol. Monogr. 61:115–143.

Dennis, B., J. M. Ponciano, S. R. Lele, M. L. Taper, and D. F. Staples. 2006. Estimating density 

dependence, process noise, and observation error. Ecol. Monogr. 76:323–341.

Easterling, M. R., S. P. Ellner, and P. M. Dixon. 2000. Size-specific sensitivity: Applying a new 

structured population model. Ecology 81:694–708.

Ellner, S. P., and E. E. Holmes. 2008. Commentary on Holmes et al. (2007): Resolving the debate on 

when extinction risk is predictable. Ecol. Lett. 11:E1–E5.

Ellner, S. P., and M. Rees. 2006. Integral projection models for species with complex life cycles. Am. 

Nat. 167:410–428.

Ford, M. J. (ed.). 2011. Status review update for Pacific salmon and steelhead listed under the 

Endangered Species Act: Pacific Northwest. U.S. Dept. Commer., NOAA Tech. Memo. NMFSNWFSC‐113.

Gelman, A., J. Carlin, H. Stern, and D. Rubin. 1995. Bayesian data analysis. Chapman and Hall/CRC, 

London.

Gibbons, R. G., P. K. J. Hahn, and T. H. Johnson. 1985. Methodology for determining MSH steelhead 

spawning escapement requirements. Fisheries Management Division report 85-11. Washington 

State Game Department, Olympia.

Good, T. P., R. S. Waples, and P. Adams (eds.). 2005. Updated status of federally listed ESUs of West 

Coast salmon and steelhead. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-66.

Gower, J. C. 1971. A general coefficient of similarity and some of its properties. Biometrics 27:857–

871.
131

Hard, J. J., J. M. Myers, M. J. Ford, R. G. Kope, G. R. Pess, R. S. Waples, G. A. Winans, B. A. 

Berejikian, F. W. Waknitz, P. B. Adams. P. A. Bisson, D. E. Campton, and R. R. Reisenbichler. 

2007. Status review of Puget Sound steelhead (Oncorhynchus mykiss). U.S. Dept. Commer., 

NOAA Tech. Memo. NMFS-NWFSC-81.

Hinrichsen, R. A., and E. E. Holmes. 2009. Using multivariate state-space models to study spatial 

structure and dynamics. In R. S. Cantrell, C. Cosner, and S. Ruan (eds.), Spatial ecology, p. 145–

166. Chapman and Hall/CRC, Boca Raton, FL.

Holmes, E. E. 2001. Estimation risks in declining populations with poor data. Proc. Natl. Acad. Sci. 

USA 98:5072–5077.

Holmes, E. E. 2004. Beyond theory to application and evaluation: Diffusion approximations for 

population viability analysis. Ecol. Appl. 14:1272–1293.

Holmes, E. E., and W. F. Fagan. 2002. Validating population viability analysis for corrupted data sets. 

Ecology 83:2379–2386.

Holmes, E. E., and E. J. Ward. 2011. Analysis of multivariate time series using the MARSS package. 

Version 2.3 (August 2011). (Available from E. E. Holmes, NWFSC, 2725 Montlake Blvd. E., 

Seattle, WA 98112.)

ISAB (Independent Scientific Advisory Board). 2005. Viability of ESUs containing multiple types of 

populations. Report ISAB 2005-2. (Available from the Northwest Planning Council, 851 SW 

6th Ave., Suite 1100, Portland, OR 97204.)

Ives, A. R., B. Dennis, K. L. Cottingham, and S. R. Carpenter. 2003. Estimating community stability and 

ecological interactions from time-series data. Ecol. Monogr. 73:301–330.

Jensen, F. V. 2001. Bayesian networks and decision graphs. Springer, New York.

Kassler, T. W., and S. Bell. 2011. Genetic analysis of steelhead (O. mykiss) juveniles found above and 

below a canyon in the South Fork Tolt River. October 2011, 26 p. Washington Dept. Fish and 

Wildlife, Olympia.

Kingsolver, J. G., R. Gomulkiewicz, and P. A. Carter. 2001. Variation, selection, and evolution of 

function-valued traits. Genetica 112–113:87–104.

Kraemer, C. 1999. Management brief: 1999 update on the status of the Deer Creek summer steelhead. 

Draft. Washington Dept. Fish and Wildlife, Olympia.

Lee, D. C., and B. E. Rieman. 1997. Population viability assessment of salmonids by using probabilistic 

networks. N. Am. J. Fish. Manag. 17:1144–1157.

Lefkovitch, L. P. 1965. The study of population growth in organisms grouped by stages. Biometrics 

21:1–18.

Leslie, P. H. 1945. On the use of matrices in certain population mathematics. Biometrika 33:183–212.

Leslie, P. H. 1948. Some further notes on the use of matrices in population mathematics. Biometrika 

35:213–245.

Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. M. Rowland, and M. J. Wisdom. 2001. Using 

Bayesian belief networks to evaluate fish and wildlife population viability under land 

management alternatives from an environmental impact statement. For. Ecol. Manag. 153:29–42.
132

Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006. Guidelines for developing 

and updating Bayesian belief networks applied to ecological modeling and conservation. Can. J. 

For. Res. 36:3063–3074.

McCann, R. K., B. G. Marcot, and R. Ellis. 2006. Bayesian belief networks: Applications in ecology and 

natural resource management. Can. J. For. Res. 36:3053–3062.

McClure, M. M., E. E. Holmes, B. L. Sanderson, and C. E. Jordan. 2003. A large-scale, multispecies 

status assessment: Anadromous salmonids in the Columbia River basin. Ecol. Appl. 13:964–989.

McElhany, P., T. Backman, C. Busack, S. Heppell, S. Kolmes, A. Maule, J. Myers, D. Rawding, D. 

Shively, A. Steel, C. Steward, and T. Whitesel. 2003. Interim report on viability criteria for 

Willamette and Lower Columbia Basin Pacific salmonids. Willamette/Lower Columbia 

Technical Recovery Team. (Available from P. McElhany, NWFSC, 2725 Montlake Blvd. E., 

Seattle, WA 98112.)

McElhany, P., C. Busack, M. Chilcote, S. Kolmes, B. McIntosh, J. Myers, D. Rawding, A. Steel, C. 

Steward, D. Ward, T. Whitesel, and C. Willis. 2006. Revised viability criteria for salmon and 

steelhead in the Willamette and Lower Columbia basins. Willamette/Lower Columbia Technical 

Recovery Team and Oregon Department of Fish and Wildlife. (Available from P. McElhany, 

NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

McElhany, P., M. Chilcote, J. Myers, and R. Beamesderfer. 2007. Viability status of Oregon salmon and 

steelhead populations in the Willamette and Lower Columbia basins. (Available from P. 

McElhany, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

McElhany, P., and J. Payne. 2006. User manual for SPAZ version 1.0 beta. Salmon population analysis. 

(Available from P. McElhany, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

McElhany, P., M. Ruckelshaus, M. J. Ford, T. C. Wainwright, and E. P. Bjorkstedt. 2000. Viable 

salmonid populations and the recovery of evolutionarily significant units. U.S Dept. Commer., 

NOAA Tech. Memo. NMFS-NWFSC-42. Online at http://www.nwfsc.noaa.gov/assets/25/6190

_06162004_143739_tm42.pdf [accessed 6 November 2014].

McGurk, M. D. 1996. Allometry of marine mortality of Pacific salmon. Fish. Bull. 94:77–88.

Metcalf, C. J. E., S. M. McMahon, R. Salguero-Gómez, and E. Jongejans. 2013. IPMpack: An R 

package for integral projection models. Methods Ecol. Evol. 4:195–200.

Myers, J. M., J. J. Hard, E. J. Connor, R. A. Hayman, R. G. Kope, G. Lucchetti, A. R. Marshall, G. R. 

Pess, and B. E. Thompson. 2015. Identifying historical populations of steelhead within the Puget 

Sound distinct population segment. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC128. Online at http://www.nwfsc.noaa.gov/assets/25/7505_04092015_155546

_HistPopsSteelheadTM128WebFinal.pdf [accessed 10 April 2015].

Myers, R. A., A. A. Rosenberg, P. M. Mace, N. J. Barrowman, and V. R. Restrepo. 1994. In search of 

threshold for recruitment overfishing. ICES J. Mar. Sci. 51:191–205.

Narum, S. R., C. Contor, A. Talbot, and M. S. Powell. 2004. Genetic divergence of sympatric resident 

and anadromous forms of Oncorhynchus mykiss in the Walla Walla River, USA. J. Fish Biol. 

65:471–488.

Newton, A. C. 2010. Use of a Bayesian network for red listing under uncertainty. Environ. Model. 

Softw. 25:15–23.
133

Newton, A. C., G. B. Stewart, A. Diaz, D. Golicher, and A. S. Pullin. 2007. Bayesian belief networks as 

a tool for evidence-based conservation management. J. Nat. Conserv. 15:144–160.

NMFS (National Marine Fisheries Service). 1991. Notice of policy: Policy on applying the definition of 

species under the Endangered Species Act to Pacific salmon. Federal Register [Docket 910248-

1244, 20 November 1991] 56(224):58612–58616.

NMFS (National Marine Fisheries Service). 2010. Puget Sound Chinook salmon population recovery 

approach (PRA). NMFS Northwest Region approach for distinguishing among individual Puget 

Sound Chinook salmon ESU populations and watersheds for ESA consultation and recovery 

planning purposes. Draft document. NMFS Northwest Region [now West Coast Region], Puget 

Sound Domain Team, Seattle WA.

Pearse, D. E., S. A. Hayes, M. H. Bond, C. V. Hanson, E. C. Anderson, R. B. MacFarlane, and J. C. 

Garza. 2009. Over the falls? Rapid evolution of ecotypic differentiation in steelhead/rainbow 

trout (Oncorhynchus mykiss). J. Hered. 100:515–525.

Pelletier, F., T. Clutton-Brock, J. Pemberton, S. Tuljapurkar, and T. Coulson. 2007. The evolutionary 

demography of ecological change: Linking trait variation and population growth. Science 

315:1571–1574.

Peterson, D. P., B. E. Rieman, J. B. Dunham, K. D. Fausch, and M. K. Young. 2008. Analysis of tradeoffs between threats of invasion by nonnative brook trout (Salvelinus fontinalis) and intentional 

isolation for native westslope cutthroat trout (Oncorhynchus clarkii lewisi). Can. J. Fish. Aquat. 

Sci. 65:557–573.

Phelps, S. R., S. A. Leider, P. L. Hulett, B. M. Baker, and T. Johnson. 1997. Genetic analyses of 

Washington steelhead: Preliminary results incorporating 36 new collections from 1995 and 1996. 

Progress report. Washington Dept. Fish and Wildlife, Olympia.

Quinn, T. P. 2005. The behavior and ecology of Pacific salmon and trout. University of Washington 

Press, Seattle.

R Development Core Team. 2012. R: A language and environment for statistical computing. R 

Foundation for Statistical Computing, Vienna, Austria. Online at http://www.R-project.org 

[accessed 1 October 2014].

Rauscher, H. M. 1999. Ecosystem management decision support for federal forests in the United States: 

A review. For. Ecol. Manag. 114:173–197.

Reynolds, K. M., M. Jensen, J. Andreasen, and I. Goodman. 2000. Knowledge-based assessment of 

watershed condition. Comput. Electron. Agric. 27:315–333.

Rieman, B., J. T. Peterson, J. Clayton, P. Howell, R. Thurow, W. Thompson, and D. Lee. 2001. 

Evaluation of potential effects of federal land management alternatives on trends of salmonids 

and their habitats in the interior Columbia River basin. For. Ecol. Manag. 153:43–62.

Riester, M., P. F. Stadler, and K. Klemm. 2009. FRANz: Reconstruction of wild multigeneration 

pedigrees. Bioinformatics 25:2134–2139.

RSRP (Recovery Science Review Panel). 2004. Report for the meeting held December 2004. Southwest 

Fisheries Science Center, Santa Cruz, CA.
134

Ruckelshaus, M. H., K. P. Currens, W. H. Graeber, R. R. Fuerstenberg, K. Rawson, N. J. Sands, and J. B. 

Scott. 2006. Independent populations of Chinook salmon in Puget Sound. U.S. Dept. Commer., 

NOAA Tech. Memo. NMFS-NWFSC-78.

Smith, C. J. 2002. Salmon and steelhead habitat limiting factors in WRIA 1, the Nooksack Basin. 

Washington State Conservation Commission, Lacey.

SSHAG (Salmon and Steelhead Hatchery Assessment Group). 2003. Hatchery broodstock summaries 

and assessments for chum, coho, and Chinook salmon and steelhead stocks within evolutionarily 

significant units listed under the Endangered Species Act. May 12, 2003 draft, 326 p. Northwest 

Fisheries Science Center, Seattle, WA, and Southwest Fisheries Science Center, La Jolla, CA.

Staples, D. F., M. L. Taper, and B. Dennis. 2004. Estimating population trend and process variation for 

PVA in the presence of sampling error. Ecology 85:923–929.

Steventon, J. D., G. D. Sutherland, and P. Arcese. 2003. Long-term risks to marbled murrelet 

(Brachyramphus marmoratus) populations: Assessing alternative forest management policies in 

coastal British Columbia. Tech. report 012. British Columbia Ministry of Forests, Forest 

Science Program, Victoria, BC.

StreamNet. No date. StreamNet database. Online at http://www.streamnet.org [accessed 9 March 2015].

Tautz, A. F., B. R. Ward, and R. A. Ptolemy. 1992. Steelhead trout productivity and stream carrying 

capacity for rivers of the Skeena drainage. PSARC working papers S92-6 and S92-8. British 

Columbia Ministry of Environment, Land, and Parks, Fisheries Branch, Victoria, BC.

Thompson, G. G. 1991. Determining minimum viable populations under the Endangered Species Act. 

U.S. Dept. Commer., NOAA Tech. Memo. NMFS F/NWC-198.

Thrower, F. P., and J. J. Hard. 2009. Effects of a single event of close inbreeding on growth and survival 

in steelhead. Conserv. Genet. 10:1299–1307.

Thrower, F. P., J. J. Hard, and J. E. Joyce. 2004. Genetic architecture of growth and early life history 

transitions in anadromous and derived freshwater populations of steelhead. J. Fish Biol. 

65(Suppl. A):286–307.

Thrower, F. P., and J. E. Joyce. 2004. Effects of 70 years of freshwater residency on survival, growth, 

early maturation, and smolting in a stock of anadromous rainbow trout from southeast Alaska. In

M. J. Nickum, P. M. Mazik, J. G. Nickum, and D. D. MacKinlay (eds.), Propagated fish in 

resource management, p. 485–496. Symposium 44. American Fisheries Society, Bethesda, MD.

Turban, E., and J. E. Aronson. 2001. Decision support systems and intelligent systems. Prentice Hall, 

Upper Saddle River, NJ.

U.S. Army Corps of Engineers. 1988. Appendix F, Environmental: Part 1, Fish mitigation and 

restoration. Additional water storage project, Draft feasibility report and EIS, Howard Hanson 

Dam, Green River, Washington. U.S. Army Corps of Engineers, Seattle District, WA.

Varanasi, U. 2004. Extinction risk assessments for evolutionarily significant units (ESUs) of West Coast 

Oncorhynchus mykiss. Letter from NWFSC to D. R. Lohn and R. McInnis, 3 February 2004, 12 

p. (Available from J. Hard, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

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 
135

the Oregon coast coho salmon evolutionarily significant unit. U.S. Dept. Commer., NOAA Tech. 

Memo. NMFS-NWFSC-91.

Waples, R. S. 1991. Pacific salmon, Oncorhynchus spp., and the definition of “species” under the 

Endangered Species Act. Mar. Fish. Rev. 53(3):11–22.

Waples, R. S., M. M. McClure, T. C. Wainwright, P. McElhany, and P. W. Lawson. 2010. Integrating 

evolutionary considerations into recovery planning for Pacific salmon. In J. A. DeWoody, J. W. 

Bickham, C. H. Michler, K. M. Nichols, O. E. Rhodes, Jr., and K. E. Woeste (eds.), Molecular 

approaches in natural resource conservation and management, p. 239–266. Cambridge University 

Press, Cambridge, UK.

Ward, E. J., H. Chirakkal, M. González-Suárez, D. Aurioles-Gamboa, E. E. Holmes, and L. Gerber. 

2010. Inferring spatial structure from time-series data: Using multivariate state-space models to 

detect metapopulation structure of California sea lions in the Gulf of California, Mexico. J. Appl. 

Ecol. 47:47–56.

Ward, B. R., and P. A. Slaney. 1993. Egg-to-smolt survival and fry-to-smolt density dependence of 

Keogh River steelhead trout. In R. J. Gibson and R. E. Cutting (eds.), Production of juvenile 

Atlantic salmon, Salmo salar, in natural waters, p. 209–217. Can. Spec. Publ. Fish. Aquat. Sci. 

118.

Ward, B. R., and J. C. Wightman. 1989. Monitoring steelhead trout at the Keogh River as an index of 

stock status and smolt-to-adult survival: Correlations with other data sources. Fisheries 

management report 95. British Columbia Ministry of Environment, Recreational Fisheries 

Branch, Fisheries Research and Development Section, Victoria, BC.

WDF (Washington Department of Fisheries), Washington Department of Wildlife, and Western 

Washington Treaty Indian Tribes. 1993. 1992 Washington salmon and steelhead stock 

inventory. 212 p. + app. Olympia, WA.

WDFG (Washington Dept. Fisheries and Game). 1932. Fortieth and forty-first annual reports of the 

State Department of Fisheries and Game, Division of Fisheries, for the period from April 1, 1930 

to March 31, 1931. Washington Dept. Fisheries and Game, Olympia.

WDFW (Washington Dept. Fish and Wildlife). 2002. 2002 Washington salmonid stock inventory. 

(Available from J. Myers, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

WDFW (Washington Department of Fish and Wildlife). 2003. Age data submitted by Ann Blakeley 

(WDFW) to Jim Myers (NWFSC), 30 July 2003. (Available from J. Myers, NWFSC, 2725 

Montlake Blvd. E., Seattle, WA 98112.)

WDG (Washington Dept. Game). 1931–1940. Steelhead egg taking at twelve Washington streams. 

(Available from J. Myers, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

WDG (Washington Dept. Game). No date. User’s Guide. Catch statistics for winter and summer 

steelhead runs in the State of Washington, 1948–1972. (Available from J. Myers, NWFSC, 2725 

Montlake Blvd. E, Seattle, WA.)

Williams, R. F., R. M. Laramie, and J. J. Ames. 1975. A catalog of Washington streams and salmon 

utilization. Volume 1, Puget Sound region. Washington Dept. Fisheries, Olympia.
136

WSCC (Washington State Conservation Commission). 1999. Salmon habitat limiting factors. Final 

report. Water Resource Inventory Area 5. Stillaguamish watershed. Washington State 

Conservation Commission, Lacey.

Zadeh, L. A. 1965. Fuzzy sets. Inform. Contr. 8:338–353.

Zimmerman, C. E., and G. H. Reeves. 2000. Population structure of sympatric anadromous and 

nonanadromous Oncorhynchus mykiss: Evidence from spawning surveys and otolith 

microchemistry. Can. J. Fish. Aquat. Sci. 57:2152–2162.
137

Appendix A: State-Space Analyses of Viability of 

Puget Sound Steelhead DIPs

This appendix contains Figure A-1 through Figure A-22. Several of the figures have only 

the first two panels described below. For each demographically independent population (DIP), 

the results of the multivariate autoregressive state-space (MARSS) population viability analyses 

(PVAs) are summarized in up to six subplots in each figure. In each group of plots, the top left 

panel plots the observed counts and the fitted state-space estimate against year, giving the 

MARSS maximum-likelihood estimate of fit to the abundance data (red curve), the estimated 

long‐term population growth rate (μest, equivalent to ln(λ)), and the estimate of process error 

(Qest). The top right panel plots the probability that the population will reach a particular quasi‐

extinction threshold (QET) abundance within the next 100 years (with approximate 95% CIs). 

The QETs for each population applied here (Table 7) are based on an estimate derived from the 

intrinsic potential metrics described in the main text (the Use of Bayesian Networks to 

Incorporate Viable Salmonid Populations Criteria into a Viability Assessment Framework 

subsection of the Decision Support Systems as Tools for Assessing Viability section).

The middle left panel plots the probability density function (PDF) of the time in years to 

reach QET, given that it is reached within 100 years, and the middle right panel depicts the 

probability of reaching QET in 100 years, given as a function of the number of individuals at the 

end of the projection. The bottom left panel plots several of the sample population projections 

estimated by MARSS.

Finally, the bottom right panel depicts the regions of high certainty and uncertainty 

surrounding the population projections (an extinction risk “envelope,” see Ellner and Holmes 

2008). The green region is where the upper 95% CIs of the projections do not exceed P = 0.05 

(i.e., where the probability of the specified population decline is <5%). The red region is where 

the lower 95% CIs of the projections exceed P = 0.95 (i.e., where the probability of the specified 

population decline is >95%). The grey regions define less certain areas of parameter space 

between these extremes, with the dark grey region representing the region of highest uncertainty. 

Note that not all plots and corresponding estimates could be constructed for each population. For 

example, we were not able to calculate PVA estimates for putative winter‐run steelhead 

(Oncorhynchus mykiss) DIPs in the Nooksack River or in South Puget Sound tributaries, nor 

were we able to do so for any summer‐run steelhead populations in the Puget Sound DPS, except 

for that in the Tolt River.
138

Figure A-1. MARSS-based (Holmes and Ward 2011) PVA for the Samish River winter‐run population. 

Steelhead counts in the Samish River have varied considerably with a decline over the past 

decade, but have generally declined. The estimated mean population growth rate (μest) is 0.048 (λ 

= 1.049) and process error (Qest) is 0.260, with no significant evidence for population trend. 

Assuming that these counts are a reasonable reflection of spawner abundance, the estimated

probability that this population would decline to a QET of 31 fish is relatively low—about 30% 

within 100 years. We can be highly confident (P < 0.05) that a 90% decline in this population 

will not occur within the next 5–8 years and that a 99% decline will not occur within the next 15 

years. However, beyond the very near term (i.e., after about 20 years), we are uncertain about the 

precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

200 600 1000

Year

Pop. estimate

u est = 0.048 (95% CIs -0.13, 0.22) 

Q est = 0.26

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 31

95% CI

75% CI

mean

0 100 200 300 400 500

0.000 0.010

Years into future

Probability to hit threshold

PDF of time to threshold

 given it is reached

20 30 40 50 60

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0.0e+00 1.5e+09 3.0e+09

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = 0.048 s2.p = 0.26
139

Figure A-2. MARSS-based (Holmes and Ward 2011) PVA for the Skagit River Summer Run and Winter

Run. Steelhead counts in the Skagit River have been highly variable, but have declined since the 

early 1980s. The estimated mean population growth rate is −0.002 (λ = 0.998) and process error 

is 0.033, with no significant evidence for population trend. The estimated probability that this 

population would decline to a QET of 157 fish is very low—less than 10% within 100 years. We 

can be highly confident (P < 0.05) that a 90% decline in this population will not occur within the 

next 20 years and that a 99% decline will not occur within the next 45 years. However, beyond 

the near term (after just a few decades), we are uncertain about the precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

2000 6000 12000

Year

Pop. estimate

u est = -0.0018 (95% CIs -0.061, 0.058)

Q est = 0.033

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 157

95% CI

75% CI

mean

0 100 200 300 400 500

0.0000 0.0010

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

100 150 200 250 300

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 100000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.0018 s2.p = 0.033
140

Figure A-3. MARSS-based (Holmes and Ward 2011) PVA for the Stillaguamish River Winter Run. 

Steelhead counts in the Stillaguamish River (represented by north fork counts only) have declined 

steadily since the early 1980s. The estimated probability that this population would decline to a 

QET of 67 fish is high—about 90% within 25 years. With an estimated mean population growth 

rate of −0.075 (λ = 0.928) and process error of <0.001, we can be highly confident (P < 0.05) that 

a 90% decline in this population will not occur within the next 30 years and that a 99% decline 

will not occur within the next 55 years. However, a 50% decline is highly likely within 10 years

and a 90% decline within 35 years. There is little uncertainty about a decline in this population if 

current conditions continue. 

1977 1982 1987 1992 1997 2002 2007

500 1500 2500

Year

Pop. estimate

u est = -0.075 (95% CIs NA , NA ) 

Q est = 0.00019

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 67

16 17 18 19 20 21 22 23

0.0 0.2 0.4

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100 120

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 50 150 250

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.075 s2.p = 0.00019
141

Figure A-4. MARSS-based (Holmes and Ward 2011) PVA for the Pilchuck River Winter Run. Steelhead 

counts in the Pilchuck River have declined detectably from levels during the late 1980s and early 

1990s and been relatively low in most years since 2000. The estimated probability that this 

population would decline to a QET of 34 fish is relatively low—about 40% within 100 years. 

With an estimated mean population growth rate of −0.006 (λ = 0.994) and process error of 0.097, 

we can be highly confident (P < 0.05) that a 90% decline in this population will not occur within 

the next 6–8 years and that a 99% decline will not occur within the next 20 years. However, 

beyond the very near term (i.e., after about 20 years), we are uncertain about the precise level of 

extinction risk.

1977 1982 1987 1992 1997 2002 2007

500 1000

Year

Pop. estimate

u est = -0.0059 (95% CIs -0.12, 0.10)

Q est = 0.097

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 34

95% CI

75% CI

mean

0 100 200 300 400 500

0.000 0.004

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

20 30 40 50 60 70

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 5000 15000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.0059 s2.p = 0.097
142

Figure A-5. MARSS-based (Holmes and Ward 2011) PVA for the Snohomish/Skykomish Rivers Winter

Run. Steelhead counts in the Snohomish River have generally declined since the early 1990s, but 

have varied widely. The estimated probability that this population would decline to a QET of 73 

fish is low—about 40% within 100 years. With an estimated mean population growth rate of 

−0.005 (λ = 0.995) and process error of 0.120, we can be highly confident (P < 0.05) that a 90% 

decline in this population will not occur within the next 8–10 years and that a 99% decline will 

not occur within the next 20 years. However, beyond the very near term (i.e., after about 20 

years), we are uncertain about the precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

1000 3000 5000

Year

Pop. estimate

u est = -0.0046 (95% CIs -0.13, 0.12) 

Q est = 0.12

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 73

95% CI

75% CI

mean

0 100 200 300 400 500

0.000 0.004 0.008

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100 120 140

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 100000 250000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.0046 s2.p = 0.12
143

Figure A-6. MARSS-based (Holmes and Ward 2011) PVA for the Snoqualmie River Winter Run. 

Steelhead counts in the Snoqualmie River have declined since the early 1990s. The estimated 

probability that this population would decline to a QET of 58 fish is relatively high—nearly 70% 

within 100 years. With an estimated mean population growth rate of −0.027 (λ = 0.973) and 

process error of 0.030, we can be highly confident (P < 0.05) that a 90% decline in this 

population will not occur within the next 15 years and that a 99% decline will not occur within 

the next 35 years. However, beyond the next 30–40 years, we are uncertain about the precise 

level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

500 1500 2500

Year

Pop. estimate

u est = -0.027 (95% CIs NA , NA ) 

Q est = 0.03

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 58

0 100 200 300 400 500

0.000 0.004 0.008

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 500 1000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.027 s2.p = 0.03
144

Figure A-7. MARSS-based (Holmes and Ward 2011) PVA for the Tolt River Summer Run. Steelhead 

counts in the Tolt River have varied since the 1980s, declining from a modest high in the late 

1990s. The estimated probability that this population would decline to a QET of 25 fish is high—

about 80% within 100 years. The estimated mean population growth rate is −0.013 

(λ = 0.987) and the process error is 0.077. We are highly uncertain about the precise level of 

extinction risk for this population, but it is likely to be high because abundance is typically low 

yet highly variable.

Figure A-8. MARSS-based (Holmes and Ward 2011) PVA for the Cedar River Winter Run. Steelhead 

counts in the Lake Washington watershed, including the Cedar River, have declined sharply since 

the early 1980s and have been very low since the early 1990s. The estimated probability that this 

population would decline to a QET of 36 fish is high—at least 90% within the next few years. 

The estimated mean population growth rate is −0.210 (λ = 0.811) and process error is 0.280. We 

are uncertain about the precise level of extinction risk for this population, but it is clearly

alarmingly high due to very low abundance.
145

Figure A-9. MARSS-based (Holmes and Ward 2011) PVA for the Green River Winter Run. Steelhead 

counts in the Green River have been variable, but have exhibited a clear decline in recent years.

The estimated probability that this population would decline to a QET of 69 fish is moderately 

high over a VSP time frame—about 50% within 100 years. With an estimated mean population 

growth rate of −0.018 (λ = 0.982) and process error of 0.066, we can be highly confident (P < 

0.05) that a 90% decline in this population will not occur within the next 10 years and that a 99% 

decline will not occur within the next 25 years. However, beyond the near term (i.e., after about 

30 years), we are uncertain about the precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

500 1500 2500

Year

Pop. estimate

u est = -0.018 (95% CIs -0.11, 0.072)

Q est = 0.066

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 69

95% CI

75% CI

mean

0 100 200 300 400 500

0.000 0.004 0.008

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100 120 140

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 1000 3000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.018 s2.p = 0.066
146

Figure A-10. MARSS-based (Holmes and Ward 2011) PVA analysis for the Puyallup River winter‐run 

population. Steelhead counts in the Puyallup River have declined steadily since the 1980s. The 

estimated probability that this population would decline to a QET of 58 fish is high—about 90% 

within 30 years. With an estimated mean population growth rate of −0.071 (λ = 0.931) and 

process error of <0.001, we can be highly confident (P < 0.05) that a 90% decline in this 

population will not occur within the next 5–10 years and that a 99% decline will not occur within 

the next 25 years. However, a 50% decline is highly likely within 15 years and a 90% decline 

within 40 years. There is little uncertainty that the population will continue to decline within the 

foreseeable future if current conditions persist. 

1977 1982 1987 1992 1997 2002 2007

500 1500 2500

Year

Pop. estimate

u est = -0.071 (95% CIs NA , NA ) 

Q est = 9e-04

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 58

20 25 30

0.00 0.10 0.20

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 50 150 250

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.071 s2.p = 9e-04
147

Figure A-11. MARSS-based (Holmes and Ward 2011) PVA for the White River Winter Run. Steelhead 

counts in the White River have declined steadily since the 1980s. The estimated probability that 

this population would decline to a QET of 64 fish is relatively low—about 40% within 100 years. 

With an estimated mean population growth rate of −0.003 (λ = 0.997) and process error of 0.055, 

we can be highly confident (P < 0.05) that a 90% decline in this population will not occur within 

the next 20–15 years and that a 99% decline will not occur within the next 30–35 years. 

However, beyond the next few decades, we are uncertain about the precise level of extinction 

risk.

1977 1982 1987 1992 1997 2002 2007

500 1500

Year

Pop. estimate

u est = -0.0030 (95% CIs -0.11, 0.1)

Q est = 0.055

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 64

95% CI

75% CI

mean

0 100 200 300 400 500

0.000 0.003 0.006

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100 120

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 10000 25000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.0030 s2.p = 0.055
148

Figure A-12. MARSS-based (Holmes and Ward 2011) PVA for the Nisqually River Winter Run. 

Steelhead counts in the Nisqually River declined steadily since about 1990 and have remained 

low since then. The estimated probability that this population would decline to a QET of 55 fish 

is high—about 90% within 25 years. With an estimated mean population growth rate of −0.075 

(λ = 0.928) and process error of <0.001, we can be highly confident (P < 0.05) that a 90% decline 

in this population will not occur within the next 25–30 years and that a 99% decline will not 

occur within the next 55 years. However, a 50% decline is highly likely within 10 years and a 

90% decline within 40 years. There is little uncertainty that the population will continue to 

decline if current conditions persist.

1977 1982 1987 1992 1997 2002 2007

1000 3000

Year

Pop. estimate

u est = -0.075 (95% CIs NA , NA ) 

Q est = 0.00037

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 55

18 20 22 24 26

0.00 0.10 0.20 0.30

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 100

0.0 0.4 0.8

80

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 50 150 250

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.075 s2.p = 0.00037
149

Figure A-13. MARSS-based (Holmes and Ward 2011) PVA for the South Hood Canal Tributaries 

Winter Run (including the Dewatto and Tahuya rivers). Steelhead counts in South Hood Canal 

declined steadily since the late 1990s. The estimated probability that this population would 

decline to a QET of 30 fish is high—about 90% within 20 years. The estimated mean population 

growth rate is −0.097 (λ = 0.908) and process error is 0.049. We are uncertain about the precise 

level of extinction risk for this population, but it is clearly high because of low and steeply 

declining abundance.

Figure A-14. MARSS-based (Holmes and Ward 2011) PVA for the Tahuya River winter-run population, 

which we analyzed because of longer time series available compared to Tahuya and Union rivers 

combined data. Steelhead counts in the Tahuya River have varied and often been quite low, but 

there has been no distinct trend since the 1980s. The estimated probability that this population 

would decline to a QET of 21 fish is relatively high—over 80% within 100 years. The estimated 

mean population growth rate is −0.016 (λ = 0.984) and process error is 0.083. We are uncertain 

about the precise level of extinction risk for this population, but it is clearly relatively high 

because of low abundance.
150

Figure A-15. MARSS-based (Holmes and Ward 2011) PVA for the East Hood Canal Tributaries Winter

Run. Steelhead counts in East Hood Canal have varied and often been quite low, but there has 

been no distinct trend since the 1980s. The estimated probability that this population would 

decline to a QET of 27 fish is relatively low—about 40% within 100 years. The estimated mean 

population growth rate is 0.006 (λ = 1.006) and process error is <0.001. We are uncertain about 

the precise level of extinction risk for this population, but it is clearly neither safely low (because 

of the stable trend) nor very high (because of the very low abundance).
151

Figure A-16. MARSS-based (Holmes and Ward 2011) PVA for the Skokomish River Winter Run. 

Steelhead counts in the Skokomish River have declined since the 1980s. The estimated 

probability that this population would decline to a QET of 50 fish is relatively high—over 70% 

within 100 years. With an estimated mean population growth rate of −0.029 

(λ = 0.971) and process error of 0.042, we can be highly confident (P < 0.05) that a 90% decline 

in this population will not occur within the next 10 years and that a 99% decline will not occur 

within the next 30 years. However, beyond the next two or three decades, we are uncertain about 

the precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

500 1000 1500

Year

Pop. estimate

u est = -0.029 (95% CIs -0.12, 0.062)

Q est = 0.042

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 50

95% CI

75% CI

mean

0 100 200 300 400

0.000 0.006 0.012

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

40 60 80 100

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 500 1000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 33 mu = -0.029 s2.p = 0.042
152

Figure A-17. MARSS-based (Holmes and Ward 2011) PVA for the West Hood Canal Tributaries Winter

Run. Steelhead counts in West Hood Canal have tended to increase since the mid 1990s, but 

variability in abundance has been high. The estimated probability that this population would 

decline to a QET of 32 fish is low—less than 20% within 100 years. With an estimated (positive) 

mean population growth rate of 0.035 (λ = 1.096) and process error of 0.048, we can be highly 

confident (P < 0.05) that a 90% decline in this population will not occur within the next 20 years 

and that a 99% decline will not occur within the next 60 years. However, beyond the next few 

decades, we are uncertain about the precise level of extinction risk. One issue with this analysis 

is the inclusion of supplementation fish in the returns for the Hamma Hamma River, where the 

contribution of hatchery fish to overall abundance is out of proportion to the stream size. 

1977 1982 1987 1992 1997 2002 2007

100 200 300

Year

Pop. estimate

u est = 0.035 (95% CIs NA , NA ) 

Q est = 0.048

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 32

0 50 100 150 200 250 300 350

0.000 0.010 0.020

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

20 30 40 50 60

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 40000 80000

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = 0.035 s2.p = 0.048
153

Figure A-18. MARSS-based (Holmes and Ward 2011) PVA for the Sequim/Discovery Bays Tributaries 

Winter Run. Steelhead counts in this area have varied considerably since the 1980s, but generally 

have declined and recently have been very low. The estimated probability that this population 

would decline to a QET of 25 fish is high—about 90% within 100 years. The estimated mean 

population growth rate is −0.054 (λ = 0.947) and process error is 0.074. We are uncertain about 

the precise level of extinction risk for this population, but it is clearly high due to low abundance.
154

Figure A-19. MARSS-based (Holmes and Ward 2011) PVA for the Dungeness River Summer Run and 

Winter Run. Steelhead counts in the Dungeness River declined steeply between the late 1980s 

and the late 1990s. Accurate counts in more recent years are not available. The estimated 

probability that this population would decline to a QET of 30 fish is high—about 90% within 20 

years. With an estimated mean population growth rate of −0.064 (λ = 0.938) and process error of 

<0.001, this analysis indicates that we can be highly confident (P < 0.05) that a 90% decline in 

this population will not occur within the next 35 years and that a 99% decline will not occur 

within the next 65 years. However, a 50% decline is highly likely within 12 years and a 90% 

decline within 40 years. The analysis indicates that there is little uncertainty that the population 

would continue to decline if current conditions observed in the 1990s persisted into the future. 

However, the lack of data in recent years means that the population’s viability is far from certain.

1977 1982 1987 1992 1997 2002 2007

250 350 450

Year

Pop. estimate

u est = -0.064 (95% CIs NA , NA ) 

Q est = 9e-05

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 30

16 17 18 19 20 21 22

0.0 0.2 0.4

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

20 30 40 50 60

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 20 40 60 80

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.064 s2.p = 9e-05
155

Figure A-20. MARSS-based (Holmes and Ward 2011) PVA for the Strait of Juan de Fuca Tributaries 

Winter Run (represented by Morse and McDonald creeks data). Steelhead counts in this area 

have declined steeply since the late 1990s. Based on the limited count data, the estimated 

probability that this population would decline to a QET of 26 fish is high—about 90% within 60 

years. With an estimated mean population growth rate of −0.067 (λ = 0.935) and process error of 

0.046, we can be highly confident (P < 0.05) that a 90% decline in this population will not occur 

within the next 10 years and that a 99% decline will not occur within the next 25 years. 

However, we can be confident of a modest decline (perhaps 20%) within 100 years. 

1977 1982 1987 1992 1997 2002 2007

100 200 300 400

Year

Pop. estimate

u est = -0.067 (95% CIs NA , NA ) 

Q est = 0.046

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 26

0 20 40 60 80 100 120

0.00 0.02

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

20 30 40 50

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 50 150 250

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.067 s2.p = 0.046
156

Figure A-21. MARSS-based (Holmes and Ward 2011) PVA for the Morse Creek winter‐run population, 

part of the Strait of Juan de Fuca Tributaries Winter-Run DIP, which we analyzed because of the 

longer time series available compared to Morse and McDonald creeks combined data. Steelhead 

counts in Morse Creek have declined, albeit at a slower rate than Strait of Juan de Fuca 

Tributaries steelhead as a whole (Figure A-20), since the mid 1980s. The estimated probability 

that this population would decline to a QET of 11 fish is high—almost 90% within 100 years. 

With an estimated mean population growth rate of −0.041 (λ = 0.960) and process error of 0.037, 

we can be highly confident (P < 0.05) that a 90% decline in this population will not occur within 

the next 12 years and that a 99% decline will not occur within the next 30 years. However, 

beyond the next 30 years, we are uncertain about the precise level of extinction risk.

1977 1982 1987 1992 1997 2002 2007

50 100 150 200

Year

Pop. estimate

u est = -0.041 (95% CIs NA , NA ) 

Q est = 0.037

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 11

0 50 100 150 200

0.000 0.010 0.020

Years into future

Probability to hit threshold

PDF of time to threshold 

 given it is reached

5 10 15 20

0.0 0.4 0.8

Number of ind. at Ne

Probability to hit threshold

90% threshold

Prob. of hitting threshold in 100 yrs

0 20 40 60 80 100

0 40 80 120

Sample projections

Years into future

N

20 40 60 80 100

-2.0 -1.0 0.0

Projection interval T yrs

xe = log10(N0/Ne)

50%

90%

99%

nyrs = 35 mu = -0.041 s2.p = 0.037
157

Figure A-22. MARSS-based (Holmes and Ward 2011) PVA for the Elwha River Winter Run. Like 

Dungeness River steelhead counts, counts in the Elwha River declined steeply between the 1980s 

and late 1990s, after which data are not available. Based on the limited count data, the estimated 

probability that this population would decline to a QET of 41 fish is high—at least 90% currently. 

The analysis indicates that the estimated mean population growth rate is −0.17 (λ = 0.844) and 

the process error is 0.046. The analysis indicates that there is little uncertainty that the population 

would continue to decline if current conditions observed in the 1990s persisted into the future. 

However, the lack of data in recent years means that the population’s viability, while low, is far 

from precisely known. 
158
159

Appendix B: Interim Abundance-Based Viability 

Guidelines for Puget Sound Steelhead

Assessment of distinct population segment (DPS) viability is rooted in the evaluation of 

demographically independent population (DIP) viable salmonid population (VSP) parameters. 

From a recovery planning standpoint, however, viability criteria for Puget Sound steelhead 

(Oncorhynchus mykiss) are more easily understood from a top-down DPS to DIP perspective. 

In part this reflects the nature of the challenge the Puget Sound Steelhead Technical Recovery 

Team (PSS TRT) faced in identifying population-specific viability criteria for these steelhead. 

In contrast to Chinook salmon (O. tshawytscha), the quality and quantity of data on steelhead 

abundance and life history are much more limited. Although there have been a number of recent 

efforts by the comanaging agencies to consolidate existing databases and expand sampling 

(especially genetic sampling), the PSS TRT was only able to assess VSP status for roughly 50–

70% of the 32 DIPs in the DPS. In developing DIP-level viability criteria, the TRT had to 

compare the metrics that are most biologically relevant to viability with those that are available 

or may become available in the foreseeable future. While this process continues, there is a desire 

by public, governmental, and tribal entities to begin recovery efforts. As an interim measure, the 

TRT recommends the use of DIP abundance goals to support initial recovery planning. Existing 

DPS and major population group (MPG)–level viability criteria (described below) provide the 

framework for developing recovery scenarios. Interim abundance criteria were estimated using 

the intrinsic potential model and correspond to different levels of sustainability (Appendix C).

1. Those DIPs selected for achieving “viable” status should maintain a 4-year average 

spawner abundance specified by a 5% smolt-to-adult survival (SAS) rate (Table B-1).

2. Of the remaining populations in each MPG, at least 75% of the populations should 

maintain 4-year average spawner abundances specified by a 1% SAS rate (Table B-1).

3. Finally, the remaining 25% of the populations in each MPG should not be allowed to 

degrade below their current level of abundance.

4. Spawner counts will consider “naturally produced” adults only, not including those 

naturally produced fish that are known to be descended from out-of-DPS lineages.

In addition to these abundance thresholds, the TRT recommends the following actions to address 

information needs while recovery planning is pursued.

1. Juvenile and adult monitoring efforts should be expanded into DIPs with little or no 

current monitoring.

2. Genetics samples should be acquired in those DIPs that are currently not represented.

3. Selected DIPs (or subbasins within DIPs) should be intensively monitored for life stage 

survivals.

4. Selected DIPs (or subbasins within DIPs) should be monitored to estimate the 

contribution of resident O. mykiss to anadromous escapement.
160

5. Selected DIPs (or subbasins within DIPs) should be monitored to estimate the influence 

of hatchery introductions.

Given the current status of most DIPs in the Puget Sound Steelhead DPS, these abundance levels 

are well above current estimates. It falls upon the recovery planning entities to determine which 

populations are to be recovered to full viability. Under any potential scenario, it is likely that 

considerable time and effort will be required to reach the viability criteria. Under these 

circumstances, there appears to be little risk to establishing interim abundance goals.

Table B-1. Interim DIP abundance goals for steelhead in Puget Sound, based on a 4-year average. 

Abundance goals for summer-run fish (two creeks and three rivers in italics) are still under 

review. QET = quasi-extinction threshold, SAS = smolt-to-adult survival, low (minimum)

abundance = 100, and viable = 250.

Population basin Abundance

Population name

Area 

(km2)

Mean 

elev. (m)

Total stream 

length (m) QET

Low 

(1% SAS)

Viable 

(5% SAS)

Capacity 

(20% SAS)

Baker River 771 999 421,859 36 503 2,514 10,056

Canyon Creek 100 864 47,716 24 100 (12) 250 (60) 243

Cedar River 650 461 402,349 35 595 2,975 11,899

Deer Creek 180 761 105,313 31 157 786 3,144

Drayton Harbor Tributaries 223 37 206,057 26 243 1,213 4,852

Dungeness River 564 978 306,740 30 246 1,232 4,930

East Hood Canal Tributaries 342 99 174,736 27 127 635 2,540

East Kitsap Peninsula Tributaries 703 75 259,413 25 156 779 3,115

Elwha River 833 1,021 472,871 41 712 3,558 14,231

Green River 1,444 463 834,472 69 1,977 9,884 39,537

Nisqually River 1,991 524 1,030,771 55 1,533 7,665 30,660

Nookachamps Creek 183 252 159,503 27 123 616 2,462

Nooksack River 1,982 619 1,257,480 73 2,205 11,023 44,091

North Fork Skykomish River 156 1,195 117,602 25 100 (66) 331 1,325

N. Lake Washington Tributaries 978 119 441,887 36 527 2,634 10,536

Pilchuck River 356 253 242,383 34 519 2,597 10,386

Puyallup River 1,395 672 803,817 58 1,472 7,358 29,432

Samish R./Bellingham Bay Tribs. 661 203 453,694 31 319 1,596 6,386

Sauk River 1,897 1,132 1,079,263 103 2,323 11,615 46,460

Sequim/Discovery Bays Tribs. 557 197 234,042 25 100 (51) 256 1,024

Skagit River 5,543 1,098 2,815,113 157 6,478 32,388 129,551

Skokomish River 634 570 411,699 50 1,003 5,015 20,060

Snohomish/Skykomish River 1,595 420 1,021,690 73 2,139 10,695 42,779

Snoqualmie River 1,615 620 1,134,038 58 1,674 8,370 33,479

South Fork Nooksack River 172 926 99,347 27 114 568 2,273

South Hood Canal Tributaries 295 126 216,935 30 299 1,493 5,970

South Puget Sound Tributaries 1,860 84 582,451 42 985 4,927 19,709

Stillaguamish River 1,230 398 927,234 67 1,912 9,559 38,236

Strait of Juan de Fuca Tributaries 403 611 246,441 26 100 (73) 364 1,456

Tolt River 182 784 117,732 25 100 (32) 250 (160) 641

West Hood Canal Tributaries 1,433 715 842,382 32 361 1,804 7,217

White River 1,285 1,061 863,251 64 1,749 8,745 34,981

Totals: 1,462 30,449 153,194 613,662
161

Appendix C: Calculation of Intrinsic Potential 

Estimates for Puget Sound Steelhead DIPs

The sustainability of any demographically independent population (DIP) is based in large 

part on the maintenance of a minimum numbers of spawners over an extended period. In a 

simplified life cycle model of steelhead (Oncorhynchus mykiss), adult abundance is based on 

freshwater productivity and marine survival. Freshwater productivity in turn is based on habitat 

quality and stream characteristics. In order to estimate freshwater productivity, the Puget Sound 

Steelhead Technical Recovery Team (PSS TRT) generated estimates of stream length, stream 

area (wetted bankfull area), and stream gradient using GIS-based data layers obtained from the 

U.S. Geological Survey’s National Hydrography Dataset (at a scale of 1:100,000) for 200 m 

stream reaches. These data were integrated into an intrinsic potential (IP) model to estimate 

productivity, adapted from data in Gibbons et al. (1985) and the Interior Columbia TRT’s 

steelhead IP model, and based primarily on stream size, gradient (using the U.S. Geological 

Survey 10 m resolution digital elevation model), and hydrograph for reaches below impassable 

barriers. For Puget Sound steelhead, we adopted a model incorporating three stream widths and 

three stream gradient classes. Stream habitat was initially classified as having low, moderate, 

and high productivity (Table C-1). We then further categorized stream reaches by whether they 

were main stem or tributary, and whether the hydrograph was influenced by snowpack or not 

(intersection of the reach with a 0°C contour in January).

Adult steelhead occupancy of stream reaches was determined from the Washington 

Department of Fish and Wildlife’s Salmonscape database (online at http://wdfw.wa.gov/mapping

/salmonscape), steelhead spawning surveys where redd locations were documented, and subbasin 

reports produced by the Washington Conservation Commission.

Steelhead freshwater productivity has been estimated in a number of ways. Chapman 

(1981) estimated production under pristine conditions at 0.0877 parr/m2 (equivalent to 0.0263 

smolts/m2

). Gibbons et al. (1985) developed a more complex productivity model, based on 

Table C-1. Stream habitat rating matrix (below natural barriers) for streams for Puget Sound steelhead. 

Stream size and gradient categories were assigned by PSS TRT members based on consideration 

of the Interior Columbia TRT’s IP model and on expert opinion. The PSS TRT used these basin 

characteristics to calculate the IP of Puget Sound steelhead basins in order to establish whether a 

large enough population could be sustained into the foreseeable future.

Stream bankfull width

Stream gradient 0–3 m 3–20 m >20 m

0.00–0.25% High Moderate Low

0.25–4.00% Moderate High Moderate

>4.00% Low Low Low
162

observed parr densities categorized according to stream gradient and size, with parr productivity 

for Puget Sound streams varying from 0.05 to 0.12 parr/m2. Gibbons et al. (1985) found that 

small independent tributaries had some of the highest productivities. On average, western 

Washington stream productivity was 0.0717 parr/m2 with 0.0265 spawners/parr (Gibbons et al. 

1985). Similarly, the U.S. Army Corps of Engineers (1988) estimated potential steelhead 

freshwater productivity at 0.067 parr/m2 for streams and 0.041 parr/m2 for rivers. We combined 

an average estimate for parr productivity of 0.0754 parr/m2 with the Chapman (1981) parr-tosmolt survival of 0.30, to establish a 0.023 smolts/m2 level of productivity. Low productivity 

areas were not included in the estimate of potential parr numbers. This is similar to estimates for 

the Keogh River, 0.032 smolts/m2 (Tautz et al. 1992). Smolt-to-adult survival (SAS) was 

estimated using Keogh River studies (Ward and Wightman 1989), with a 20% survival rate 

selected to estimate average pre-European contact productivity.

Using the 20% SAS rate as an estimate of capacity provided a frame of reference for 

estimating two other abundance thresholds. First, a viability abundance threshold, where a 

population is sustainable over longer periods, specifically meets the 5% risk of reaching quasiextinction threshold over a 100-year period. For planning purposes, IP calculations using a 5% 

SAS provided a reasonable estimate of the abundance threshold for a viable population. 

Alternatively, a low abundance threshold provides demographic sustainability over periods less 

than 100 years and is large enough to prevent the loss of genetic diversity over a similarly short 

period of time. Using a 1% SAS rate in calculating the IP capacity provided an estimate of this 

low abundance threshold. Review of the IP calculations suggested that abundance thresholds for 

populations with higher gradient headwaters were too low; this was especially the case for 

summer-run populations. For all populations, the low abundance threshold was set at a 

minimum of 100 adults/year (4-year average), while the minimum viable abundance threshold 

was set at 250 adults/year (4-year average).
163

Appendix D: Selected Steelhead Documents

In addition to the documents cited in this technical memorandum and listed in the 

References section, the following is a bibliography of documents we found useful for various 

research aspects, such as general steelhead biology or information about particular watersheds.

Alaska Department of Fish and Game. 2003 and 2004. Alaska Department of Fish and Game Web site 

for Situk Weir counts as accessed in 2003 and 2004. Online at http://www.adfg.alaska.gov/sf/

FishCounts/index.cfm?adfg=main.home [accessed 9 March 2015].

Arterburn, J., K. Kistler, and R. Dasher. 2005. 2005 Okanogan Basin steelhead spawning surveys. BPA 

project no. 200302200. Bonneville Power Administration, Division of Fish and Wildlife, 

Portland, OR.

Bali, J. M. 1959. Scale analyses of steelhead trout, Salmo gairdnerii Richardson, from various coastal 

watersheds of Oregon. Master’s thesis. Oregon State University, Corvallis.

Bjornn, T. C. 1966. Annual completion report for Job No. 3: Steelhead trout production studies, Lemhi 

Big Springs Creek Investigations Project F-49-R-4, Salmon and steelhead investigations. Idaho 

Dept. Fish and Game, Boise.

Briggs, J.C. 1953. The behavior and reproduction of salmonid fishes in a small coastal stream. Fish 

bulletin no. 94. Scripps Institution of Oceanography Library, UC San Diego, CA.

British Columbia Ministry of Environment, Lands, and Parks and Dept. Fisheries and Oceans Canada. 

1998. Review of Fraser River steelhead trout (Oncorhynchus mykiss). British Columbia Ministry 

of Environment, Lands, and Parks and DFO Canada, Vancouver, BC.

Brown, E., and M. Lewis. 2011. Assessment of Oregon coast adult winter steelhead redd surveys 2010. 

The Oregon Plan for Salmon and Watersheds, Monitoring report no. OSPW-ODFW-2010-2009. 

Oregon Adult Salmonid Inventory and Sampling Project, Western Oregon Research and 

Monitoring Program. Oregon Dept. Fish and Wildlife, Corvallis.

Buchanan, D. V., A. R. Hemmingsen, D. L. Bottom, P. J. Howell, R. A. French, and K. P. Currens. 1991. 

Native Trout Project. Project no. F-136-R. Annual progress report, Fish Research Project 

Oregon. Oregon Dept. Fish and Wildlife, Portland.

Buchanan, D. V., A. R. Hemmingsen, and K. P. Currens. 1994. Native Trout Project. Project no. F-136-

R-07. Annual progress report, Fish Research Project Oregon. Oregon Dept. Fish and Wildlife, 

Portland.

Carl, G. C., and W. A. Clemens. 1948. The freshwater fishes of British Columbia. Handbook no. 5 

[Cowichan River steelhead spawn time in 1940s]. British Columbia Provincial Museum, 

Victoria, BC.
164

Cederholm, C. J. 1984. Clearwater River wild steelhead spawning timing. In J. M. Walton and D. B. 

Houston (eds.), Proceedings of the Olympic Wild Fish Conference (March 23–25, 1983), p. 257–

268. Peninsula College, Fisheries Technology Program, Port Angeles, WA.

Cobb, J. N. 1911. The salmon fisheries of the Pacific coast. Bureau of Fisheries document 751. Report 

of the commissioner of fisheries for the fiscal year 1910 and special papers. U.S. Bureau of 

Fisheries, Washington, DC.

Cramer, S. P., and R. C. Beamesderfer. 2006. Population dynamics, habitat capacity, and a life history 

simulation model for steelhead in the Deschutes River, Oregon. Portland General Electric, 

Portland, OR.

Crawford, B. A. 1979. The origin and history of the trout brood stocks of the Washington Department of 

Game. Fishery research report. Washington Dept. Game, Olympia.

de Leeuw, A. D. 1984. A fisheries management strategy for the Pallant Creek watershed, Queen 

Charlotte Islands. Skeena fisheries report no. 83-02. B.C. Ministry of Environment, Fisheries 

Branch, Smithers, BC.

de Leeuw, A. D. 1985. Pallant Creek steelhead 1983–84. In Skeena fisheries progress report no. SK-51, 

p. 19–21. B.C. Ministry of Environment, Fisheries Branch, Smithers, BC.

de Leeuw, A. D. 1986. Deena Creek steelhead: Some aspects of their life history, population size, and 

sport fishery, spring 1983. Fisheries progress report no. SK-53. B.C. Ministry of Environment, 

Fish and Wildlife Branch, Smithers, BC.

de Leeuw, A. D. 1987. Yakoun River steelhead: Some aspects of their life history, population size, and 

sport fishery, 1982–83. Skeena fisheries report no. SK-55. B.C. Ministry of Environment and 

Parks, Fish and Wildlife Branch, Smithers, BC.

de Leeuw, A. D., and M. R. Whately. 1983. Steelhead of the Yakoun River: Some aspects of their life 

history, population size, and the sport fishery, 1981–82. Skeena fisheries report no. 82-1. B.C. 

Ministry of Environment, Fish and Wildlife Branch, Smithers, BC.

Dodge, D. P., and H. R. MacCrimmon. 1970. Vital statistics of a population of Great Lakes rainbow 

trout (Salmo gairdneri) characterized by an extended spawning season. J. Fish. Res. Board Can.

27:613–618.

Dodge, D. P., and H. R. MacCrimmon. 1971. Environmental influences on extended spawning of 

rainbow trout (Salmo gairdneri). Trans. Am. Fish. Soc. 100:312–318.

Everest, F. H. 1973. Ecology and management of summer steelhead in the Rogue River. Fishery 

research report no. 7. Oregon State Game Commission, Corvallis.

Evermann, B. W. 1896. 2.–A preliminary report upon salmon investigations in Idaho in 1894. In

Bulletin of the United States Fish Commission. Vol. XV, for 1895, p. 253–284. Marshall 

McDonald, commissioner. Government Printing Office, Washington, DC.

Evermann, B. W., and E. L. Goldsborough. 1907. The fishes of Alaska. In G. M. Bowers (ed.), Bulletin 

of the U.S. Bureau of Fisheries, Vol. XXVI, 1906. Dept. Commerce and Labor, U.S. Bureau of 

Commercial Fisheries, Washington, DC.

Evermann, B. W., and S. E. Meek. 1898. A report upon salmon investigations in the Columbia River 

basin and elsewhere on the Pacific coast in 1896. Extracted from U.S. Fish Commission Bulletin 
165

for 1897. Article 2, p. 15–84, plates 1 and 2, and six text figures. U. S. Commission of Fish and 

Fisheries, Washington, DC.

Fergusson, I. 2011. Winter steelhead spawning ground surveys Salmonberry River (Nehalem Basin), 

Oregon. A STEP Project report. Online at https://nrimp.dfw.state.or.us/web%20stores/data

%20libraries/files/ODFW/ODFW_941_2_STEP%20Report-Salmonberry%20Spawning

%20Surveys.pdf [accessed 9 March 2015].

Fessler, J. L. 1973. An ecological and fish cultural study of summer steelhead in the Deschutes River, 

Oregon. Project no. F-88-R-2, Federal aid progress reports, Fisheries. Oregon Wildlife 

Commission, Research Division, Portland.

Hallock, R. J. 1989. Upper Sacramento River steelhead, Oncorhynchus mykiss, 1952–1983. U.S. Fish 

and Wildlife Service, Sacramento, CA.

Hemmingsen, A. R., and D. V. Buchanan. 1993. Native Trout Project. Project no. F-136-R-6, Annual 

progress report, Fish Research Project Oregon. Oregon Dept. Fish and Wildlife, Portland.

Hemmingsen, A. R., R. A. French, D. V. Buchanan, D. L. Bottom, and K. P. Currens. 1992. Native 

Trout Project. Project no. F-136-R, Annual progress report, Fish Research Project Oregon. 

Oregon Dept. Fish and Wildlife, Portland.

Johnson, R. E. 1991. Situk River steelhead studies, 1990. Fishery data series no. 90-47. Alaska Dept. 

Fish and Game, Anchorage.

Johnson, T. H., and R. Cooper. 1992. Snow Creek anadromous fish research, July 1, 1990–June 30, 

1991. Annual performance report no. 92-5. Washington Dept. Wildlife, Fisheries Management 

Division, Olympia.

Kendall, W. C. 1920. What are rainbow trout and steelhead trout? Trans. Am. Fish. Soc. 50:187–190.

Lewis, M., W. Clark, and members of the Corps of Discovery. 2002. The journals of the Lewis and 

Clark expedition (G. Moulton, ed.). University of Nebraska Press, Lincoln. Online at 

http://lewisandclarkjournals.unl.edu [accessed 7 October 2014].

Little, A. C. 1898. Ninth annual report of the state fish commissioner to the governor of the State of 

Washington. State of Washington, Olympia.

Mapes, L. V. 2012. Steelhead spawning in the Elwha. Field notes: A northwest nature blog. July 5, 

Seattle Times. Online at http://seattletimes.nwsource.com/html/fieldnotes/2018609640_steelhead

_spawning_in_the_elwha.html [accessed 7 October 2014].

McGregor, I. A. 1986. Freshwater biology of Thompson River steelhead (Salmo gairdneri) as 

determined by radio telemetry. Master’s thesis. University of Victoria, Dept. Biology, Victoria, 

BC.

McKenna, Rainey. 2012. Return of the kings. Olympic Park press release, 20 August 2012. Olympic 

National Park, Port Angeles, WA.

McMillan, B. 1996–2013. Puget Sound spawning surveys [three-ring binder of personal surveys]. B. 

McMillan, Concrete, WA.

McMillan, B. 2001. Males as vectors to hatchery/wild spawning interactions and the reshaping of wild 

steelhead/rainbow populations through fishery management. Washington Trout, Duvall, WA.
166

McMillan, J. R., S. L. Katz, and G. R. Pess. 2007. Observational evidence of spatial and temporal 

structure in a sympatric anadromous (winter steelhead) and resident rainbow trout mating system 

on the Olympic Peninsula, Washington. Trans. Am. Fish. Soc. 136:736–748.

Millenbach, C. 1972. Genetic selection of steelhead trout for management purposes. Washington State 

Dept. Game, Fishery Management Division, Olympia.

Moore, D. C., and W. R. Olmstead. 1985. An ecological study of steelhead trout (Salmo gairdneri) 

reproduction in Deadman River, B.C., 1984. Prepared for DFO Canada and the British Columbia 

Ministry of Environment. W. R. Olmstead and Associates, Inc., Vancouver, BC.

Mueller, R. P., and D. R. Geist. 1999. Steelhead spawning surveys near Locke Island, Hanford Reach of 

the Columbia River. Pacific Northwest National Laboratory (operated by Battelle for the U.S. 

Dept. Energy). Online at http://www.pnl.gov/main/publications/external/technical_reports/pnnl13055.pdf [accessed 7 October 2014].

Neave, F. 1949. Bulletin No. LXXXIV. Gamefish populations of the Cowichan River. Fisheries 

Research Board of Canada, Ontario.

Needham, P. R. 1938. Trout streams. Comstock Publishing Co., Ithaca, NY.

Nelson, T. C., M. L. Rosenau, J. Rissling, C. E. J. Mussell, and P. A. Caverhill. 2004. Run timing, 

migration, distribution, spawning locations, survival-to-spawning, and survival-to-kelt of anglercaught wild and hatchery adult steelhead in the Vedder/Chilliwack River, BC. LGL Ltd., 

Vancouver, BC.

Olsen, E. A., R. B. Lindsay, and W. A. Burck. 1992. Summer steelhead in the Deschutes River, Oregon. 

Oregon Dept. Fish and Wildlife, Corvallis.

Orcutt, D. R., B. R. Pullman, and A. Arp. 1968. Characteristics of steelhead trout redds in Idaho streams. 

Bureau of Commercial Fisheries, Boise, ID.

Oregon Fish Commission and Oregon State Game Commission. 1946. The Umpqua River study. 

Oregon Fish Commission and Oregon State Game Commission, Portland.

Oregon Wildlife Commission. 1972. 1972 annual report, Fishery Division (C. J. Campbell and M. K. 

Daily, eds.). Oregon Wildlife Commission, Portland.

Pearsons, T. N., G. A. McMichael, S. W. Martin, E. L. Bartrand, J. A. Long, and S. A. Leider. 1996. 

Yakima River species interactions studies annual report 1994. Report to U.S. Dept. Energy, 

project no. 89-105, contract no. DE-B179-93BP99852. Bonneville Power Administration,

Portland, OR.

Pflug, D. 2011. Skagit River steelhead cumulative redd count by year for the Rockport to Copper Creek 

Reach (figure of raw redd counts). Seattle City Light, Seattle, WA.

Riseland, J. L. 1907. 16th and 17th annual reports of the fish commissioner and game warden to the 

governor of the State of Washington. Washington Dept. Fisheries and Game, Olympia.

Schroeder, R. K., and L. H. Smith. 1989. Life history of rainbow trout and effects of angling regulations, 

Deschutes River, Oregon. Oregon Dept. Fish and Wildlife, Research and Development Section, 

Portland.
167

Snider, W. M. 1983. Reconnaissance of the steelhead resource of the Carmel River drainage, Monterey 

County. Administrative report no. 83-3. California Dept. Fish and Game, Environmental 

Services Branch, Sacramento.

Snow, C., B. Goodman, T. Seamons, and A. Murdoch. 2012. Monitoring the reproductive success of 

naturally spawning hatchery- and natural-origin steelhead in a tributary of the Methow River. 

Annual progress report for the period: 1 August 2010–31 July 2011. Washington Dept. Fish and 

Wildlife, Olympia.

Stone, L. 1872. VI. Report of operations in 1872 at the United States salmon-hatching station on the 

McCloud River, and on the California salmonidae generally, with a list of specimens collected. 

In S. F. Baird, 1874, Part II–Report of the commissioner for 1872 and 1873, A–Inquiry into the 

decrease in the food-fishes, B–The propagation of food-fishes in the waters of the United States,

with supplementary papers, p. 168–200. United States Commission of Fish and Fisheries, 

Washington, DC.

Stone, L. 1873. XX. Report of operations in California in 1873, A–Field work in the winter of 1872–3. 

In S. F. Baird, 1874, Part II–Report of the commissioner for 1872 and 1873, p. 377–381. United 

States Commission of Fish and Fisheries, Washington, DC.

Stone, L. 1883. XXV. Report of operations at the United States trout-breeding station on the McCloud 

River, California, during the year 1883. In Part II–Report of the commissioner for 1883, Part B, 

Appendix E, p. 1001–1006. United States Commission of Fish and Fisheries, Washington, DC.

Suring, M., and M. Lewis. 2008. Assessment of Oregon coast adult winter steelhead redd surveys 2007. 

The Oregon Plan for Salmon and Watersheds, monitoring report no. OSPW-ODFW-2007-2009, 

Oregon Adult Salmonid Inventory and Sampling Project, Western Oregon Research and 

Monitoring Program. Oregon Dept. Fish and Wildlife, Corvallis.

Susac, G. L., and S. E. Jacobs. 1999. Evaluation of spawning ground surveys for indexing the abundance 

of adult winter steelhead in Oregon coastal basins. Annual progress report, 1 July 1997 to 30 

June 1998. Oregon Dept. Fish and Wildlife, Salem.

Susac, G. L., and S. E. Jacobs. 2001. Assessment of the status of Nestucca River winter steelhead, 2001. 

Coastal Salmonid Inventory Project. Oregon Dept. Fish and Wildlife, Salem.

Susac, G. L., and S. E. Jacobs. 2003. Assessment of the status of Nestucca and Alsea River winter 

steelhead, 2002. Information report no. 2003-01. Oregon Department of Fish and Wildlife, 

Salem.

Troop, S., and D. Wicker. 1996. Necanicum River basin “3M” steelhead surveys, January-April, 1996. 

Oregon Dept. Fish and Wildlife, Portland.

Trotter, P. C. 1987. Cutthroat: Native trout of the West. Colorado Associated University Press, Boulder.

Van Dishoeck, P., T. Slaney, and J. Korman. 1998. Wild steelhead conservation planning in the lower 

mainland region, Chilliwack/Vedder River. ARL report no. 277-4. British Columbia Ministry of 

Environment, Lands, and Parks, Fish and Wildlife Management, Surrey, BC.

Van Dusen, H. G. 1903. Annual reports of the Department of Fisheries of the State of Oregon to the 

legislative assembly twenty-second regular session. Oregon Dept. Fisheries, Salem.
168

Van Dusen, H. G. 1905. Annual reports of the Department of Fisheries of the State of Oregon for the 

years 1903 and 1904 to the twenty-third Legislative Assembly (regular session). Oregon Dept. 

Fisheries, Salem.

Van Dusen, H. G. 1907. Annual reports of the Department of Fisheries of the State of Oregon to the 

legislative assembly twenty-fourth regular session. Oregon Dept. Fisheries, Salem.

Van Dusen, H. G. 1909. Annual reports of the Department of Fisheries of the State of Oregon to the 

legislative assembly twenty-fifth regular session. Oregon Dept. Fisheries, Salem.

Washington Dept. Fisheries, U.S. Fish and Wildlife Service, and Washington Dept. Game. 1973. Joint 

statement regarding the biology, status, management, and harvest of salmon and steelhead 

resources of the Puget Sound and Olympic Peninsula drainage areas of western Washington. 

(Available from Washington Dept. Fish and Wildlife, 600 Capitol Way N., Olympia, WA 98501.)

Washington Dept. Game. 1931–1940. Steelhead egg taking at twelve Washington streams. (Available 

from J. Myers, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112.)

Zimmerman, C. 2000. Ecological relation of sympatric steelhead and resident rainbow trout in the 

Deschutes River, Oregon. Doctoral dissertation. Oregon State Univ., Dept. Fisheries and 

Wildlife, Corvallis.
169

Appendix E: Description of Puget Sound 

Steelhead DIPs

This appendix presents in a condensed form the viable salmonid population (VSP) 

attributes (McElhany et al. 2000) of each of the steelhead (Oncorhynchus mykiss) 

demographically independent populations (DIPs) that existed historically in the Puget Sound 

Steelhead Distinct Population Segment (DPS), as well as hydrology data. Much of the fish data 

were obtained from the Washington State Salmon and Steelhead Stock Inventory (WDF et al. 

1993), the Washington State Salmonid Stock Inventory (WDFW 2002), the Salmon and 

Steelhead Hatchery Assessment Group report (SSHAG 2003), and the Pacific States Marine 

Fisheries Commission’s StreamNet database (StreamNet no date).

The Four VSP Attributes

Abundance and Productivity

Productivity and related trends in abundance reflect conditions that drive a population’s 

dynamics and thus determine its abundance. Changes in environmental conditions, including 

ecological interactions, can influence a population’s intrinsic productivity or the environment’s 

capacity to support a population or both. Such changes may result from random environmental 

variation over a wide range of temporal scales (environmental stochasticity). In this section, 

however, we are most concerned with measures of population growth and related parameters that 

reflect systematic changes in a population’s dynamics (McElhany et al. 2000).

Abundance estimates for each DIP are generally based on expansions of redd counts from 

index areas. In a few cases, fish passage facilities allow relatively precise counts of returning 

adults. In other areas, systematic surveys are not available and anecdotal reports alone establish 

the presence of steelhead. In these cases, it may be necessary to use adjacent DIPs as proxies to 

estimate abundance; however, except where accessibility is limited, it is likely that the absence 

of survey activity is related to low steelhead abundance.

Productivity is generally derived from abundance data series, either as lambda, trend, or 

spawner/recruit calculations. Given the relatively high uncertainty in abundance estimates 

(particularly expansions of index redd counts), productivity estimates include considerable 

uncertainty.

Several historical measures of abundance are included to provide context for present-day 

abundance estimates. These include a summary of salmon streams conducted in 1930 (WDG 

1931–1940). Populations were categorized as being large, medium, scarce, very scarce, and 

absent, with no numerical equivalents given. Punch card estimates are derived from Washington 

Department of Game estimates. These estimates are “corrected” by a factor of 0.60 to account 
170

for reporting bias. Peak catch estimates were selected from those return years that were not 

supplemented, based on data provided in WDG (no date). Punch card estimates do not include 

any adjustment for commercial or tribal catch. Finally, habitat based intrinsic potential (IP) 

estimates were developed to approximate historical production. It should be noted that this 

system was developed to establish whether or not the basin could support a DIP; it is not 

considered a precise estimate and likely underestimates potential productivity.

Diversity

In their VSP report, McElhany et al. (2000) identify three general reasons why diversity 

is important for species and population viability. First, diversity allows a species to use a wider 

array of environments than it could without it. For example, varying adult run and spawn timing 

allows several salmonid species to use a greater variety of spawning habitats than would be 

possible without this diversity. Second, diversity protects a species against short-term spatial 

and temporal changes in the environment. Fish with different characteristics have different 

likelihoods of persisting—depending on local environmental conditions. Therefore, the more 

diverse a population is, the more likely it is that some individuals would survive and reproduce

in the face of environmental variation. Third, genetic diversity provides the raw material for 

surviving long-term environmental changes. Salmonids regularly face cyclic or directional 

changes in their freshwater, estuarine, and ocean environments due to natural and human causes, 

and genetic diversity allows them to adapt to these changes.

Two major life history types of steelhead were historically and are presently found in 

Puget Sound: the summer run and winter run. The life histories of summer-run and winter-run 

steelhead have considerable overlap. Both rear in freshwater for 1–4 years prior to 

smoltification, select similar habitats for freshwater rearing, and spend 1–4 years in the ocean. 

However, substantial differences separate these races at the time of adult freshwater entry, 

degree of sexual maturity at entry, and spawning time.

Each year, the majority of naturally produced Puget Sound summer-run steelhead enters 

freshwater between May and October. These fish are still in the process of sexually maturing

upon return to their natal streams. The fish subsequently spawn between January and June, with 

peak spawning between late February and early April. In contrast, wild winter-run steelhead 

enter freshwater as sexually mature fish between December and May. Spawning occurs between 

February and June, with peak spawning time in late April and early May, almost 2 months later 

than wild summer-run steelhead.

On average, there is a 2-month difference in peak spawning time between winter-run and 

summer-run steelhead, although there is some temporal overlap in the spawning distribution 

(Busby et al. 1996). Within the same watershed, winter-run and summer-run steelhead maintain 

a high degree of reproductive isolation by spawning in geographically distinct areas; these areas 

are generally separated by temporal migration windows created by falls or cascades. In a few 

cases, winter-run and summer-run steelhead spawn in the same stream reach, but at slightly 

different times. Hatchery introductions, especially with nonnative steelhead, and modifications 

to barrier falls are a potential source for the breaking down of historical reproductive barriers and 

the erosion of locally adapted genotypes.
171

Diversity status is difficult to quantify due to the near absence of information on 

historical life history characteristics. Any actions that affect basic demographic and evolutionary 

processes (e.g., patterns of mutation, selection, drift, recombination, migration, and population 

turnover) have the potential to alter a species’ diversity (McElhany et al. 2000). At a minimum, 

information on hatchery introductions is available for most DIPs. In some cases, genetic 

information can substantiate the influence of hatchery fish on the naturally spawning population. 

Additionally, habitat diversity (the basis of life history diversity) has been assessed for most 

populations and there are estimates for the proportion of lost habitat diversity.

Regarding artificial propagation, hatchery releases for each DIP have been compiled for 

the most recent 15 years (3 to 4 generations). Releases of small O. mykiss fry (250 fish per lb, or 

≈2 g each) have been excluded. The contribution of these fish to steelhead abundance is thought 

to be minimal. Additionally, many fry releases were into lakes and not necessarily intended to 

enhance the anadromous population. Hatchery broodstock names have been retained, but where 

Chambers Creek or Skamania River hatchery-derived stocks are used, a notation of “CC” or 

“SK” has been added. Hatchery broodstocks derived from either of these sources are considered 

as being out-of-DPS stocks.

Spatial Structure

Spatial structure is defined by McElhany et al. (2000) as the geographic distribution of 

individuals in a population and the processes that create that distribution (i.e., habitat structure or 

migrational barriers). From a demographic perspective, spatial structure within a DIP prevents 

the stochastic extirpation of smaller breeding units through migration between units and the 

reduction in the effects of catastrophic events.

Steelhead within a DIP utilize a variety of habitats for spawning, rearing, and adult 

holding. Ecological differences among primary streams and their tributaries likely influence the 

expression of distinct life history strategies. Spatial structure can be viewed on an absolute scale:

How much of the historically accessible habitat is still available or usable? Alternatively, the 

distribution of accessible habitat may also affect spatial structure.

Guidance

In evaluating the four VSP attributes, some overlap is to be expected; however, one 

should be mindful to keep “double scoring” effects to a minimum. Each attribute should be 

considered for its contribution to population viability or conversely to its contribution to 

extinction risk. The quantity and quality of the information available will determine, in turn, the 

accuracy of the estimate of extinction risk. Uncertainty due to data quality should be reflected in 

VSP scores.
172

Summaries of Puget Sound Steelhead DIPs

Northern Cascades Steelhead Major Population Group

1. Drayton Harbor Tributaries Winter Run

Overview—This population (Figure E-1) includes steelhead that spawn in tributaries 

from the Canadian border to Sandy Point, primarily in Dakota and California creeks (Smith 

2002). Although steelhead redds are still reported in this DIP, abundance information is very 

limited. Spawning habitat is limited.

Figure E-1. Map of Drayton Harbor Tributaries Winter-Run DIP land cover and observed spawning area.
173

Abundance and productivity—

WDF survey Punch card max. IP estimate*

Abundance Medium-sized 67 2,426 4,852

Year 1930 1957 —

* The IP estimate for this drainage may overestimate capacity for the system, to an unknown degree, because the 

geology of this drainage appears to limit spawning habitat for salmonids here.

Diversity—Anecdotal reports suggest the steelhead returning to this DIP are slightly 

smaller in size and spawn earlier than other nearby populations. There are no known Drayton 

Harbor Tributaries Winter Run hatchery releases (zero within and zero outside the DPS) since 

1995.

Spatial structure and hydrology—Figure E-2 and Figure E-3.

Figure E-2. Map of Drayton Harbor Tributaries Winter-Run DIP spatial structure, including migration 

barriers and spawning potential.
174

Figure E-3. Map of Drayton Harbor Tributaries Winter-Run DIP area hydrology.
175

2. Nooksack River Winter Run

Overview—This population (Figure E-4) includes winter-run steelhead in the north fork, 

middle fork, and south fork of the Nooksack River. The Salmonid Stock Inventory (WDFW 

2002) suggests that the Middle Fork Nooksack River may have supported a summer run of 

steelhead prior to the construction of the impassable diversion dam at RKM 11. Glacial 

conditions limit spawner surveys in the North Fork Nooksack River. Lowland tributaries 

(Fishtrap and Bertram creeks) presently contribute significantly to overall abundance.

Figure E-4. Map of Nooksack River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Multiple large & medium 2,114 22,045 44,091

Year 1930 1953 —
176

Tributary

200020012002200320042005200620072008200920102011

Mainstem north fork — — — — 1,574 — — — — — 1,308 1,069

South fork — — — — — — — — — — 524 425

Middle fork — — — — — — — — — — 69 104

Diversity—

Nooksack River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Kendall Cr. 1996–2011 14 Tokul/Bogachiel (CC) 0 1,413,238

M.F. Nooksack 1995–1998 4 McKinnon Pd (CC) 0 268,215

Total: 0 1,681,453

Spatial structure and hydrology—Figure E-5 and Figure E-6.

Figure E-5. Map of Nooksack River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential.
177

Figure E-6. Map of Nooksack River Winter-Run DIP area hydrology.
178

3. South Fork Nooksack River Summer Run

Overview—This population (Figure E-7) includes steelhead that spawn in tributaries 

from the headwaters to Sandy Point, primarily in Dakota and California creeks (Smith 2002). 

Although steelhead redds are still reported in this DIP, abundance information is very limited.

Figure E-7. Map of South Fork Nooksack River Summer-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Medium run 31 1,137 2,273

Year 1930 1964 —

Diversity—Hatchery releases for the Nooksack River Summer Run are zero within the 

DPS and zero outside the DPS.
179

Spatial structure and hydrology—Figure E-8 and Figure E-9.

Figure E-8. Map of South Fork Nooksack River Summer-Run DIP spatial structure, including migration 

barriers and spawning potential.
180

Figure E-9. Map of South Fork Nooksack River Summer-Run DIP area hydrology.
181

4. Samish River and Bellingham Bay Tributaries Winter Run

Overview—This population of steelhead (Figure E-10) exists in an independent tributary 

to Puget Sound. The Samish River drains into Samish and Bellingham bays, whereas the 

headwaters are more closely associated with the Skagit River basin. The majority of historical 

and present production is in the Samish Basin, with lesser contribution from the Bellingham Bay 

tributaries.

Figure E-10. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP land cover and 

observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Not surveyed 1,934 534 (389–732) 3,193 6,386

Year 1930 1951 2005–2009 —

The Samish River and Bellingham Bay Tributaries Winter‐Run DIP’s exponential trend 

in the natural logarithm of natural spawners (95% CI) from 1985 to 2009 is 1.008 (0.972–1.045).
182

For a state-space analysis of population viability, see the top two plots of Figure A-1 in 

Appendix A.

Diversity—

Samish River and Bellingham Bay Tributaries Winter Run Total releases

Watershed Duration Years Source Within DPS Outside DPS

Samish River 1995–2008 10 Whatcom R.Tokul (CC) 0 324,073

Whatcom Cr. 1995–2009 12 Whatcom H. (CC) 0 170,508

Total: 0 494,581

Spatial structure and hydrology—Figure E-11 and Figure E-12.

Figure E-11. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP spatial structure, 

including migration barriers and spawning potential.
183

Figure E-12. Map of Samish River and Bellingham Bay Tributaries Winter-Run DIP area hydrology.
184

5. Skagit River Summer Run and Winter Run

Overview—The population (Figure E-13) includes the mainstem Skagit and numerous 

tributaries. Major tributaries include: Cascade River, Finney Creek, Day Creek, Bacon Creek, 

and Illabot Creek. Historically, this DIP was likely one of the primary producers of summer-run 

and winter-run steelhead in Puget Sound.

Figure E-13. Map of Skagit River Summer-Run and Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Multiple large 

and medium

8,022 4,648 

(2,827–7,642)

64,775 129,551

Year 1930 1950 2005–2009 —
185

The Skagit River Summer-Run and Winter-Run DIP’s exponential trend in the natural 

logarithm of natural spawners (95% CI) from 1985 to 2009 is 0.969 (0.954–0.985).

For a state-space analysis of population viability, see the top two plots of Figure A-2 in 

Appendix A.

Diversity—

Skagit River Summer Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Skagit River 1995–1998 3 Stillaguamish H. 0 71,256

Total: 0 71,256

Skagit River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Hamilton Slough 1997–1999 2 Barnaby Slough (CC) 0 38,350

Barnaby Slough 1996–2008 12 Barnaby Slough (CC) 0 1,911,179

Skagit River 1995–2001 5 Bogachiel/Skagit (CC) 0 747,392

Cascade River 1995–2010 16 Marblemount H. (CC) 0 2,576,846

Grandy Creek 1996–2002 6 Barnaby/Marblemount 0 324,461

Total: 0 5,598,228
186

Spatial structure and hydrology—Figure E-14 and Figure E-15.

Figure E-14. Map of Skagit River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
187

Figure E-15. Map of Skagit River Summer-Run and Winter-Run DIP area hydrology.
188

6. Nookachamps Creek Winter Run

Overview—Nookachamps Creek (Figure E-16) was identified as a potential DIP for 

winter steelhead. This basin met the criteria for basin size and IP production. Very little 

information is available for this basin other than anecdotal reports of steelhead abundance. 

Spawning and rearing habitat is primarily in the forks, not the main stem.

Figure E-16. Map of Nookachamps Creek Winter-Run DIP land cover and spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Scarce NA 1,231 2,462

Year 1930 NA —

Diversity—Hatchery releases for the Nookachamps Creek Winter Run are zero within 

the DPS and zero outside the DPS.
189

Spatial structure and hydrology—Figure E-17 and Figure E-18.

Figure E-17. Map of Nookachamps Creek Winter-Run DIP spatial structure, including migration barriers 

and spawning potential.
190

Figure E-18. Map of Nookachamps Creek Winter-Run DIP area hydrology.
191

7. Baker River Summer Run and Winter Run

Overview—Historically, Baker River (Figure E-19) was likely a major contributor to 

Skagit River steelhead runs. Baker River is the second largest tributary to the Skagit River, with 

a basin size of 771 km2

. Access to historical spawning grounds is blocked by the upper and 

lower Baker dams. Trap and haul operations currently provide the only access to headwater 

regions.

Figure E-19. Map of Baker River Summer-Run and Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Medium NA 5,028 10,056

Year 1930 — —
192

Diversity—

Baker River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Baker River 1995–2010 14 Chambers/Baker/Skagit 0 679,322

Total: 0 679,322

Spatial structure and hydrology—Figure E-20 and Figure E-21.

Figure E-20. Map of Baker River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
193

Figure E-21. Map of Baker River Summer-Run and Winter-Run DIP area hydrology.
194

8. Sauk River Summer Run and Winter Run

Overview—This population (Figure E-22) includes both winter-run and summer-run 

steelhead in the Sauk River and its tributaries. Much of the basin consists of higher gradient 

reaches and some tributaries are glacially influenced. Historically important, recent returns have 

been relatively poor.

Figure E-22. Map of Sauk River Summer-Run and Winter-Run DIP land cover and spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Multiple medium 679 2,541 23,230 46,460

Year 1930 1951 2004–2006 —

Tributary

1997199819992000200120022003200420052011

Sauk (summer) — — — — — — — — — —

Sauk (winter) — — — — — — — 2,726 1,962 3,068
195

Diversity—

Sauk River Summer Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

— — — — — —

Total: 0 0

Sauk River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Sauk River 1995–2008 14 Skagit/Stillaguamish (CC) 0 323,918

Total: 0 323,918

Spatial structure and hydrology—Figure E-23 and Figure E-24.

Figure E-23. Map of Sauk River Summer-Run and Winter-Run DIP spatial structure, including migration 

barriers and spawning potential.
196

Figure E-24. Map of Sauk River Summer-Run and Winter-Run DIP area hydrology.
197

9. Stillaguamish River Winter Run

Overview—Winter-run steelhead (Figure E-25) spawn in the mainstem north fork and 

mainstem south fork of the Stillaguamish River and in numerous tributaries. Winter-run 

steelhead are considered distinct from the summer-run steelhead that spawn in Deer Creek and 

Canyon Creek above flow barriers (e.g., above RKM 0.8 in Canyon Creek) because of the likely 

geographic and temporal separation of spawners.

Figure E-25. Map of Stillaguamish River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large & medium 1,564 327 (100–1,067) 19,118 38,236

Year 1930 1954 2005–2009 —

The Stillaguamish River Winter‐Run DIP’s exponential trend in the natural logarithm of 

natural spawners (95% CI) from 1985 to 2009 is 0.910 (0.887–0.934).
198

For a state-space analysis of population viability, see the top two plots of Figure A-3 in 

Appendix A.

Diversity—

Stillaguamish River summer run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Stillaguamish 

River

1998–2005 5 Snohomish Hatchery

(SK)

0 178,558

North Fork 

Stillaguamish R.

1995–2011 16 Stillaguamish/ 

Snohomish H. (SK)

0 1,077,547

Total: 0 1,256,105

Stillaguamish River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Canyon Creek 1995–2003 9 Chambers/Tokul/Sky 

(CC)

0 132,365

Johnson Creek 1996–2001 3 Chambers/ 

Stillaguamish

0 35,267

Pilchuck Creek 1995–2009 12 Chambers/Snohomish 

(CC)

0 117,045

Stillaguamish 

River

1995–2011 17 Chambers/Bogachiel 

(CC)

0 2,048,991

Total: 0 2,333,668
199

Spatial structure and hydrology—Figure E-26 and Figure E-27.

Figure E-26. Map of Stillaguamish River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential.
200

Figure E-27. Map of Stillaguamish River Winter-Run DIP area hydrology.
201

10. Deer Creek Summer Run

Overview—This steelhead population (Figure E-28) spawns and rears in the upper 

portion of Deer Creek. Steep canyons and cascades from RKM 2.5 to 8 may present a temporal 

barrier to returning winter-run fish, although Deer Creek is accessible to summer steelhead up 

to approximately RKM 32. Habitat below these canyons and cascades, below RKM 4, is 

considered winter-run steelhead habitat. Even under pristine conditions, the steelhead run into 

Deer Creek may not have been very large, potentially 1,000 to 2,000 adults (WSCC 1999), 

although the 1929 survey classified Deer Creek as a large population (WDFG 1932).

Figure E-28. Map of Deer Creek Summer-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Escapement IP estimate

Abundance Large NA 460 1,572 3,144

Year 1930 — 1994 —
202

Figure E-29. Deer Creek steelhead parr density changes over time. Average annual age 1+ parr per 

square meter density for 14 sites. (Reprinted from Kraemer 1999.)

Diversity—Some Skamania Hatchery summer-run steelhead were released in the 1950s 

to 1970s, although there was thought to be little influence on the population. Hatchery releases 

for 1995 to the present are zero within the DPS and zero outside the DPS.

Figure E-30. Genetic relationship of steelhead collections from the Snohomish River basin, Puget Sound, 

and the Skamania Hatchery using Cavalli-Sforza and Edwards (1967) chord distance. Bootstrap 

values are shown at each node. (Reprinted from Kassler and Bell 2011.)
203

Spatial structure and hydrology—Figure E-31 and Figure E-32.

Figure E-31. Map of Deer Creek Summer-Run DIP spatial structure, including migration barriers and 

spawning potential.
204

Figure E-32. Map of Deer Creek Summer-Run DIP area hydrology.
205

11. Canyon Creek Summer Run

Overview—There is relatively little information available on the existing summer run of

steelhead in the Canyon Creek basin (Figure E-33). Information provided by local biologists 

indicates that a summer run is still present in the basin. Historically, Canyon Creek was 

identified as having a relatively good-sized run of steelhead.

Figure E-33. Map of Canyon Creek Summer-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Large 283* 121 243

Year 1930 1951 — * Punch card–based catch estimates did not differentiate between run times prior to 1960.
206

Diversity—

Canyon Creek Summer Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Canyon Creek 1995–2008 5 Skykomish/Snohomish 

Hatchery (SK)

0 40,596

Total: 0 40,596

Canyon Creek winter run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

— — — — — —

Total: 0 0

Spatial structure and hydrology—Figure E-34 and Figure E-35.

Figure E-34. Map of Canyon Creek Summer-Run DIP spatial structure, including migration barriers and 

spawning potential.
207

Figure E-35. Map of Canyon Creek Summer-Run DIP area hydrology.
208

12. Snohomish/Skykomish Rivers Winter Run

Overview—This population includes winter-run steelhead in the mainstem Snohomish, 

Skykomish, and Wallace rivers, the North Fork Skykomish River below Bear Creek Falls, the 

Sultan River, and several tributaries (Figure E-36). WDFW (2003) identified three winter-run 

populations in the Snohomish Basin based on geographic discreteness. This basin includes

lowland and higher elevation tributaries. Historically, it was one of the primary steelhead 

producers in Puget Sound.

Figure E-36. Map of Snohomish/Skykomish Rivers Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Multiple large 

and medium

4,801 4,573 

(500–41,865)

21,389 42,779

Year 1930 1951 2005–2009 —
209

The Snohomish/Skykomish Rivers Winter‐Run DIP’s exponential trend in the natural 

logarithm of natural spawners (95% CI) from 1985 to 2009 is 0.963 (0.941–0.985).

For a state-space analysis of Snohomish/Skykomish Rivers Winter‐Run DIP viability, see 

the top two plots of Figure A-5 in Appendix A.

Diversity—

Snohomish/Skykomish summer run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Silver Creek 1998–1999 2 Snohomish H. (SK) 0 19,334

Index Creek 2000 1 Snohomish H. (SK) 0 16,300

Skykomish R. 1995–2011 16 Sky/ Snohomish H. (SK) 0 2,400,055

Sultan River 1995–2009 15 Sky/ Snohomish H. (SK) 0 266,980

Total: 0 2,702,669

Snohomish/Skykomish Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Howard Creek 1997–2001 6 Tokul/Snohomish (CC) 0 173,317

Skykomish R. 1995–2011 17 Chambers/Tokul (CC) 0 2,464,540

Sultan River 1995–2009 15 Chambers/Tokul (CC) 0 344,781

Wallace River 1995–2011 17 Chambers/Tokul (CC) 0 309,863

Total: 0 3,292,501
210

Spatial structure and hydrology—Figure E-37 and Figure E-38.

Figure E-37. Map of Snohomish/Skykomish Rivers Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
211

Figure E-38. Map of Snohomish/Skykomish Rivers Winter-Run DIP area hydrology.
212

13. Pilchuck River Winter Run

Overview—The Pilchuck River (Figure E-39) flows through the Northern Cascades and 

Puget Lowlands ecoregions. The basin is relatively low gradient and low altitude and exhibits a 

rainfall-dominated flow pattern. There appears to be sufficient habitat (366 km2

) to support a 

sustainable population. The Puget Sound Steelhead Technical Recovery Team noted that run 

timing for this DIP was significantly earlier than other populations in the greater Snohomish 

Basin, providing further confirmation of its DIP status.

Figure E-39. Map of Pilchuck River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large 1,583 469.81 5,193 10,386

Year 1929 1952 2006–2010 —
213

Tributary

2001200220032004200520062007200820092010

Pilchuck 462 279 696 1,522 604 580 — 646 344 294

For a state-space analysis of population viability, see the top two plots of Figure A-4 in 

Appendix A.

Diversity—

Pilchuck River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Pilchuck River 1995–2009 15 Tokul/Stillaguamish/ 

Skagit (CC)

0 390,193

Total: 0 390,193

Spatial structure and hydrology—Figure E-40 and Figure E-41.

Figure E-40. Map of Pilchuck River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential.
214

Figure E-41. Map of Pilchuck River Winter-Run DIP area hydrology.
215

14. North Fork Skykomish River Summer Run

Overview—Summer-run steelhead in the North Fork Skykomish River (Figure E-42) 

spawn primarily above Bear Creek Falls (RKM 21) (WDFW 2002). Spawning habitat is limited 

above these falls and accessible habitat may terminate at RKM 31 (Williams et al. 1975). Falls 

and cascades may provide some level of reproductive isolation from winter-run steelhead in the 

Skykomish River, but probably also limit population abundance.

Figure E-42. Map of North Fork Skykomish River Summer-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Medium 229* 663 1,325

Year 1930 1963 —

* Includes only summer-run fish caught in the north fork and not those caught in the main stem.
216

Diversity—

North Fork Skykomish River Summer Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

North Fork 

Skykomish R.

1996–2006 11 Skykomish/ 

Snohomish H. (SK)

0 306,641

Total: 0 306,641

Spatial structure and hydrology—Figure E-43 and Figure E-44.

Figure E-43. Map of North Fork Skykomish River Summer-Run DIP spatial structure, including 

migration barriers and spawning potential.
217

Figure E-44. Map of North Fork Skykomish River Summer-Run DIP area hydrology.
218

15. Snoqualmie River Winter Run

Overview—This DIP (Figure E-45) includes steelhead in the mainstem Snoqualmie 

River and those in major tributaries: Tolt, Raging, and Tokul creeks. There are numerous 

historical references indicating that this basin sustained large runs of steelhead.

Figure E-45. Map of Snoqualmie River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large and 

medium

2,791 1,092.06 16,740 33,479

Year 1930 1951 2005–2010 —
219

Tributary

2001200220032004200520062007200820092010

Pilchuck 1,395 789 988 1,506 1,060 1,856 — — — 662

For a state-space analysis of Snoqualmie River Winter-Run DIP viability, see the top two 

plots of Figure A-6 in Appendix A.

Diversity—

Snoqualmie River summer run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Snoqualmie R. 1995–2008 12 Skykomish/ 

Snohomish H. (SK)

0 494,660

Tokul Creek 2001 1 Snohomish H. (SK) 0 28,330

Raging River 1999–2007 6 Snohomish H. (SK) 0 177,626

Total: 0 640,616

Snoqualmie River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Raging River 1995–2008 14 Tokul Creek H. (CC) 0 206,934

Snoqualmie R. 1995–2009 15 Tokul Creek H. (CC) 0 2,290,994

Tokul Creek 2009–2011 3 Tokul Creek H. (CC) 0 329,638

Tolt River 1995–2008 11 Tokul Creek 

Hatchery/Reiter(CC)

0 246,776

Total: 0 3,074,342
220

Spatial structure and hydrology—Figure E-46 and Figure E-47.

Figure E-46. Map of Snoqualmie River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential.
221

Figure E-47. Map of Snoqualmie River Winter-Run DIP area hydrology.
222

16. Tolt River Summer Run

Overview—Summer-run steelhead are found in the North Fork and South Fork Tolt 

River basins (Figure E-48). Both forks are typical of summer-run steelhead habitat and contain a 

number of falls and cascades, although the north fork is higher gradient with steeply sloped 

canyon walls (Williams et al. 1975). Genetically, Tolt River steelhead are similar to other 

Snohomish Basin steelhead (Phelps et al. 1997).

Figure E-48. Map of Tolt River Summer-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large and 

medium

304* 78.64 321 641

Year 1930 1964 2005–2010 —

* Includes only summer-run fish caught in the Tolt River and not those caught in the mainstem Snoqualmie or 

Snohomish rivers.
223

For a state-space analysis of population viability, see Figure A-7 in Appendix A.

Diversity—Hatchery releases for Tolt River Summer Run are zero within the DPS and 

zero outside the DPS.

Spatial structure and hydrology—Figure E-49 and Figure E-50.

Figure E-49. Map of Tolt River Summer-Run DIP spatial structure, including migration barriers and 

spawning potential.
224

Figure E-50. Map of Tolt River Summer-Run DIP area hydrology.
225

Central and South Puget Sound Major Population Group

17. Cedar River Winter Run

Overview—This population includes steelhead in the Cedar River and tributaries to the 

southern end of Lake Washington, primarily May and Coal creeks (Figure E-51). Diversion of 

the Cedar River from its confluence with the Black River to Lake Washington in the early 1900s 

may have impacted steelhead life history and abundance. The hydrology of the Cedar River 

(rain/snow) is distinct from other tributaries in the Lake Washington system. Also, until recently 

much of the upper watershed was blocked to anadromous access.

Figure E-51. Map of Cedar River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Scarce 353 5,949 11,899

Year 1930 1954 —
226

For a state-space analysis of population viability, see Figure A-8 in Appendix A.

Diversity— Hatchery releases for Cedar River Winter Run are zero within the DPS and 

zero outside the DPS.

Spatial structure and hydrology—Figure E-52 and Figure E-53.

Figure E-52. Map of Cedar River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential.
227

Figure E-53. Map of Cedar River Winter-Run DIP area hydrology.
228

18. North Lake Washington and Lake Sammamish Winter Run

Overview—This population includes steelhead that spawn in tributaries to North Lake 

Washington, the Sammamish River, and Lake Sammamish (Figure E-54). The majority of the 

tributaries drain lowland habitat, with the exception of Issaquah Creek. Given the major 

modifications in the Lake Washington system, primarily the rerouting of the outlet from the 

Black River to the Lake Washington Ship Canal and widespread land development, it is difficult 

to infer historical distribution from current abundance.

Figure E-54. Map of North Lake Washington and Lake Sammamish Winter-Run DIP land cover and 

observed spawning area.
229

Abundance and productivity—The North Lake Washington and Lake Sammamish 

Winter Run’s exponential trend in the natural logarithm of natural spawners (95% CI) from 1985 

to 2009 is 0.807 (0.770–0.845).

WDF survey Punch card max. Geometric mean IP estimate

Abundance Absenta 748b 12 (3–55) 5,268 10,536

Year 1930 1952 2005–2009 — a Surveys were not conducted in Swamp Creek, North Creek, or Issaquah Creek.

b Includes catch from the Lake Washington Ship Canal (which may have been destined for the Cedar River).

Figure E-55. State-space analysis of population viability. The left panel plots the observed counts and 

the fitted state-space estimate against year, giving the MARSS maximum-likelihood estimate of 

fit to the abundance data (red curve), the estimated long‐term population growth rate (μest, 

equivalent to ln(λ)), and the estimate of process error (Qest). The right panel plots the probability 

that the population will reach a particular quasi‐extinction threshold abundance within the next 

100 years (with approximate 95% CIs). 

Diversity— 

North Lake Washington Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Issaquah Creek 1998–2000 3 Lake Washington 39,516 0

Total: 39,516 0

1985 1989 1993 1997 2001 2005 2009

0 500 1500

Year

Pop. estimate

u est = -0.23 (95% CIs NA , NA ) 

Q est = 0.33

0 20 40 60 80 100

0.0 0.4 0.8

Years into future

Probability to hit threshold

Prob. to hit 36
230

Spatial structure and hydrology—Figure E-56 and Figure E-57.

Figure E-56. Map of North Lake Washington and Lake Sammamish Winter-Run DIP spatial structure, 

including migration barriers and spawning potential.
231

Figure E-57. Map of North Lake Washington and Lake Sammamish Winter-Run DIP area hydrology.
232

19. Green River Winter Run

Overview—This population includes steelhead that spawn in the Green River and its 

tributaries including the Duwamish River (Figure E-58). In the last 150 years, the Green River 

basin has undergone a number of considerable changes. Connectivity with the Black, Cedar, and 

White rivers has been lost (although on a geologic time scale these connections were likely 

transitory), as has access to much of the headwater areas of the Green River.

Figure E-58. Map of Green River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large 4,242 986 (401–2,428) 19,768 39,537

Year 1930 1950 2005–2009 —

The Green River Winter Run’s exponential trend in the natural logarithm of natural 

spawners (95% CI) from 1985 to 2009 is 0.992 (0.969–1.016).
233

For a state-space analysis of population viability, see the top two plots of Figure A-9 in 

Appendix A. 

Diversity— 

Green River summer run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Big Soos Cr. 2002–2011 10 Green River (SK) 0 355,017

Green River 1995–2010 16 Green River (SK) 0 1,012,567

Icy Creek 2010–2011 2 Green River (SK) 0 46,600

Total: 0 1,414,184

Green River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Big Soos Cr. 2011 1 Green River (CC) 0 55,033

Big Soos Cr. 2003–2011 9 Green River native 461,849 0

Crisp Creek 1999–2002 4 Tokul Creek H. (CC) 0 257,000

Crisp Creek 1998–2004 3 Green River native 134,300 0

Duwamish R. 2000–2001 2 Green River native 95,269 0

Friday Creek 1996–1997 2 Green River native 2,262 0

Green River 1995–2011 15 Bogachiel/Tokul (CC) 0 1,805,397

Green River 1995–2009 13 Green River native 1,005,555 0

Icy Creek 2011 1 Icy Creek (CC) 0 25,000

Icy Creek 2010–2011 2 Green River native 28,964 0

Sunday Creek 1995–2000 4 Green River native 46,585 0

Total: 1,774,784 2,142,430
234

Spatial structure and hydrology—Figure E-59 and Figure E-60.

Figure E-59. Map of Green River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential.
235

Figure E-60. Map of Green River Winter-Run DIP area hydrology.
236

20. Puyallup/Carbon Rivers Winter Run

Overview—This population includes two Salmonid Stock Inventory stocks (WDFW 

2002), the Puyallup and Carbon rivers (Figure E-61). The Puget Sound Steelhead Technical 

Recovery Team determined that the mainstem Puyallup below the confluence of the Puyallup 

River and White River was more closely associated with the Carbon River than the White River.

Figure E-61. Map of Puyallup/Carbon Rivers Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Multiple large 

and medium

9,190* 326 (178–596) 14,716 29,432

Year 1930 1950 2005–2009 —

* Catch from the mainstem Puyallup River and Carbon River, which may include White River steelhead.
237

The Puyallup/Carbon Rivers Winter Run’s exponential trend in the natural logarithm of 

natural spawners (95% CI) from 1985 to 2009 is 0.919 (0.899–0.938).

Tributary 

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

Mainstem Puyallup 119 78 52 91 64 139 91 133 51 74

Carbon River 358 248 235 410 98 323 418 367 190 398

For a state-space analysis of population viability, see the top two plots of Figure A-10 in 

Appendix A. 

Diversity— 

Puyallup/Carbon Rivers Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Clarks Creek 1996–2009 6 Puyallup H. (CC) 0 67,550

Diru Creek 1995–2000 6 Puyallup Tribal H. (CC) 0 592,091

Puyallup River 1995–1998 4 Tokul Creek H. (CC) 0 369,761

Viola Creek 2009 1 Voight Creek H. (CC) 0 187,550

Voight Creek 1995–2009 15 Voight Creek H. (CC) 0 2,214,396

Total: 0 3,431,348
238

Spatial structure and hydrology—Figure E-62 and Figure E-63.

Figure E-62. Map of Puyallup/Carbon Rivers Winter-Run DIP spatial structure, including migration 

barriers and spawning potential.
239

Figure E-63. Map of Puyallup/Carbon Rivers Winter-Run DIP area hydrology.
240

21. White River Winter Run

Overview—This population is one Salmonid Stock Inventory stock (WDFW 2002), the 

White River (Figure E-64). The Technical Recovery Team determined this population begins at 

the confluence of the White and Puyallup rivers. This DIP includes a number of glacially 

influenced tributaries.

Figure E-64. Map of White River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Medium 827* 265 (206–342) 17,490 34,981

Year 1930 1952 2005–2009 —

* Does not include White River steelhead caught in the mainstem Puyallup River.

The White River Winter Run’s exponential trend in the natural logarithm of natural 

spawners (95% CI) from 1985 to 2009 is 0.938 (0.923–0.952).
241

For a state-space analysis of population viability, see the top two plots of Figure A-11 in 

Appendix A.

Diversity—

White River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

White River 1995–2010 9 Puyallup H. (CC) 0 205,979

Total: 0 205,979

Spatial structure and hydrology—Figure E-65 and Figure E-66.

Figure E-65. Map of White River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential.
242

Figure E-66. Map of White River Winter-Run DIP area hydrology.
243

22. Nisqually River Winter Run

Overview—In contrast to the rest of the southern Puget Sound region, which is 

predominantly lowland stream habitat, the Nisqually River (Figure E-67) drains the glacial 

slopes of Mt. Rainier. The Technical Recovery Team considered that ecological differences 

between the Nisqually River and surrounding South Puget Sound tributaries are sufficient to 

result in life history and reproductive differentiation. Anecdotal information suggests that the 

Nisqually River steelhead population was quite large.

Figure E-67. Map of Nisqually River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Multiple medium 2,769 402 (178–908) 15,330 30,660

Year 1930 1952 2005–2009 —
244

The Nisqually River Winter Run’s exponential trend in the natural logarithm of natural 

spawners (95% CI) from 1985 to 2009 is 0.914 (0.890–0.940).

For a state-space analysis of population viability, see the top two plots of Figure A-12 in 

Appendix A.

Diversity—

Nisqually River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Lake St. Clair 1998 1 Skykomish Hatchery 0 11,494

Total: 0 11,494

Spatial structure and hydrology—Figure E-68 and Figure E-69.

Figure E-68. Map of Nisqually River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential. Historically, a series of cascades near the present site of the La Grande and 

Alder dams may have been a seasonal barrier, but they also could have posed a complete barrier 

to fish passage.
245

Figure E-69. Map of Nisqually River Winter-Run DIP area hydrology.
246

23. South Puget Sound Tributaries Winter Run

Overview—This population includes four Salmonid Stock Inventory stocks (WDFW 

2002): Chambers Creek, Eld Inlet, Totten Inlet, and Hammersley Inlet and Case/Carr Inlet—

effectively all of the lowland tributaries entering into South Puget Sound (Figure E-70). Little 

current or historical information is available on abundance or life history characteristics for this 

population.

Figure E-70. Map of South Puget Sound Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 85* 9,854 19,709

Year — — —

* Compiled from a number of different streams draining to South Puget Sound: Coulter Creek, Goldsborough Creek, 

Kennedy Creek, Mill Creek, Percival Creek, and Sherwood Creek.
247

Diversity—

South Puget Sound Tributaries Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Kennedy Creek 1996 1 Bogachiel H. (CC) 0 9,996

Deschutes R. 1996–2005 9 Bogachiel/Tokul (CC) 0 290,654

Chambers Cr. 1995–1996 2 Chambers Creek H. 0 90,887

Total: 0 391,537

Spatial structure and hydrology—Figure E-71 and Figure E-72.

Figure E-71. Map of South Puget Sound Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
248

Figure E-72. Map of South Puget Sound Tributaries Winter-Run DIP area hydrology.
249

24. East Kitsap Peninsula Tributaries Winter Run

Overview—This population includes small lowland independent tributaries on the east 

side of the Kitsap Peninsula (Figure E-73). There is limited information, other than presence, for 

the east side of the Kitsap Peninsula, with the exception of Curley Creek.

Figure E-73. Map of East Kitsap Peninsula Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 53 1,557 3,115

Year — 1963 —

Diversity—Hatchery releases for the East Kitsap Peninsula Tributaries Winter Run are 

zero within the DPS and zero outside the DPS.
250

Spatial structure and hydrology—Figure E-74 and Figure E-75.

Figure E-74. Map of East Kitsap Peninsula Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
251

Figure E-75. Map of East Kitsap Peninsula Tributaries Winter-Run DIP area hydrology.
252

Hood Canal and Strait of Juan de Fuca Major Population Group

25. East Hood Canal Tributaries Winter Run

Overview—This population includes winter steelhead spawning in small lowland 

independent tributaries on the west side of the Kitsap Peninsula (eastern shore of Hood Canal) 

from Point No Point to the southern end of Hood Canal (Alderbrook and Twanoh creeks). The 

primary streams in this DIP include Big Beef Creek, Anderson Creek, and the Dewatto River

(Figure E-76).

Figure E-76. Map of East Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Not available 327* 213 (122–372) 1,270 2,540

Year — 1958 2005–2009 —

* Includes estimates from Big Beef Creek and Dewatto River.
253

The East Hood Canal Tributaries Winter Run’s exponential trend in the natural logarithm 

of natural spawners (95% CI) from 1985 to 2009 is 1.022 (0.997–1.048).

Tributary 

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 

Dewatto River 30 18 39 23 53 28 49 15 13 92

For a state-space analysis of population viability, see Figure A-15 in Appendix A. 

Diversity— 

East Hood Canal Tributaries Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Dewatto River 2009–2010 2 Native 14,183 0

Total: 14,183 0
254

Spatial structure and hydrology—Figure E-77 and Figure E-78.

Figure E-77. Map of East Hood Canal Tributaries Winter-Run DIP spatial structure, including migration 

barriers and spawning potential.
255

Figure E-78. Map of East Hood Canal Tributaries Winter-Run DIP area hydrology.
256

26. South Hood Canal Tributaries Winter Run

Overview—This population includes winter steelhead spawning in independent 

tributaries on the southwest side of the Kitsap Peninsula (eastern shore of Hood Canal),

including the Tahuya and Union rivers to the southern end of Hood Canal (Alderbrook and 

Twanoh creeks). The primary streams in this DIP include the Tahuya and Union rivers (Figure 

E-79). Stream surveys conducted in 1932 give very general estimates of abundance with larger 

runs of steelhead in the Tahuya and Union rivers (WDG 1931–1940).

Figure E-79. Map of South Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 640 2,985 5,970

Year — 1952 —
257

Tributary

2002200320042005200620072008200920102011

Tahuya River 97 53 168 91 183 175 144 53 68 47

Union River 49 50 58 23 86 21 15 15 21 11

For a state-space analysis of South Hood Canal Tributaries Winter Run viability, see 

Figure A-13 in Appendix A. For a state-space analysis of Tahuya River winter-run population 

viability, see Figure A-14 in Appendix A.

Diversity—Hatchery releases for the South Hood Canal Tributaries Winter Run are zero 

within the DPS and zero outside the DPS.

Spatial structure and hydrology—Figure E-80 and Figure E-81.

Figure E-80. Map of South Hood Canal Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
258

Figure E-81. Map of South Hood Canal Tributaries Winter-Run DIP area hydrology.
259

27. Skokomish River Winter Run

Overview—This population contains native winter-run steelhead in the north and south 

forks of the Skokomish River (Figure E-82). Much of the North Fork Skokomish River is 

currently inaccessible beyond Cushman Dam No. 2 (RKM 27.8). Steelhead currently utilize 

both lowland tributaries (for example Vance Creek) and headwater areas of the South Fork 

Skokomish River. There has been considerable debate as to whether winter-run steelhead had 

access beyond the series of falls in the lower North Fork Skokomish River; steelhead may have 

had access at least to the Staircase Rapids at RKM 48.1 (Williams et al. 1975).

Figure E-82. Map of Skokomish River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Not available 693 355 (183–686) 10,030 20,060

Year — 1952 2005–2009 —
260

The Skokomish River Winter Run’s exponential trend in the natural logarithm of natural 

spawners (95% CI) from 1985 to 2009 is 0.956 (0.932–0.979).

For a state-space analysis of population viability, see the top two plots of Figure A-16 in 

Appendix A.

Diversity—

Skokomish River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

South Fork 

Skokomish R.

1995–2011 11 Eells Springs (CC) 0 450,198

Hunter Creek 1996–1997 2 Bogachiel H. (CC) 0 43,351

Total: 0 493,549

Spatial structure and hydrology—Figure E-83 and Figure E-84.

Figure E-83. Map of Skokomish River Winter-Run DIP spatial structure, including migration barriers 

and spawning potential.
261

Figure E-84. Map of Skokomish River Winter-Run DIP area hydrology.
262

28. West Hood Canal Tributaries Winter Run

Overview—This population combines winter-run steelhead from four Salmonid Stock 

Inventory stocks (WDFW 2002): Hamma Hamma, Duckabush, and Dosewallips rivers, and 

Quilcene/Dabob Bay (Figure E-85). WDFW (2002) identified these as distinct stocks based on 

their geographic separation; however, genetically, resident, parr, and smolt O. mykiss from the 

Duckabush and Dosewallips cluster together relative to steelhead populations on the east side of 

Hood Canal. All of these rivers drain the east slope of the Olympic Mountains, with falls or 

cascades blocking access to most of the higher elevation habitat.

Figure E-85. Map of West Hood Canal Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Large 982* 208 (118–366) 3,608 7,217

Year 1932 1952 2005–2009 —

* Hamma Hamma, Duckabush, Dosewallips, and Quilcene rivers; this year includes some hatchery returns.
263

The West Hood Canal Tributaries Winter Run’s exponential trend in the natural 

logarithm of natural spawners (95% CI) from 1985 to 2009 is 1.022 (0.997–1.048).

Tributary

2002200320042005200620072008200920102011

Hamma Hamma R. 230 134 214 123 73 193 198 81 42 45

Duckabush River 16 8 29 10 21 16 18 12 29 120

Dosewallips River 52 96 — — — 15 42 — — 31

Quilcene River 30 16 36 9 76 39 41 6 41 31

For a state-space analysis of population viability, see the top two plots of Figure A-17 in 

Appendix A.

Diversity—

West Hood Canal Tributaries Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Dosewallips River 1995–2003 7 Bogachiel Hatchery 0 80,142

Duckabush River 1995–2003 7 Bogachiel Hatchery 0 65,860

Duckabush River 2009–2010 2 Native 6,595 0

Hamma Hamma R. 2000–2005 5 Native 7,306 0

John Creek (HH) 2000–2005 4 Native 2,837 0

Total: 16,738 146,002
264

Spatial structure and hydrology—Figure E-86 and Figure E-87.

Figure E-86. Map of West Hood Canal Tributaries Winter-Run DIP spatial structure, including migration 

barriers and spawning potential.
265

Figure E-87. Map of West Hood Canal Tributaries Winter-Run DIP area hydrology.
266

29. Sequim/Discovery Bays Tributaries Winter Run

Overview—This population combines two Salmonid Stock Inventory stocks, Sequim 

Bay and Discovery Bay (Figure E-88), and includes winter-run steelhead that occupy streams in 

the Quimper Peninsula (Pt. Townsend) that were not included in the WDFW (2002) stock list. 

The entire population is located within the Puget Lowland Ecoregion and stream flows are rain 

dominated, with many streams lacking surface flow during summer. Although the 802 km2 basin 

size for this DIP is well above the minimum, the majority of the area contains relatively small,

independent streams.

-

Figure E-88. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 200 512 1,024

Year — 1962 —
267

For a state-space analysis of population viability, see Figure A-18 in Appendix A.

Diversity—Hatchery releases for the Sequim/Discovery Bays Tributaries Winter Run are 

zero within the DPS and zero outside the DPS.

Spatial structure and hydrology—Figure E-89 and Figure E-90.

Figure E-89. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
268

Figure E-90. Map of Sequim/Discovery Bays Tributaries Winter-Run DIP area hydrology.
269

30. Dungeness River Summer Run and Winter Run

Overview—This population includes steelhead spawning in the mainstem Dungeness 

and Grey Wolf rivers (Figure E-91). Much of the mainstem and upper headwaters are glacially 

influenced. Anecdotal information indicates that this population was historically quite abundant.

Figure E-91. Map of Dungeness River Summer-Run and Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 348 2,039 4,078

Year — 1946 —

The Dungeness River Summer-Run and Winter-Run DIP’s exponential trend in the 

natural logarithm of natural spawners (95% CI) from 1985 to 2009 is 0.926 (0.909–0.943).
270

For a state-space analysis of population viability, see the top two plots of Figure A-19 in 

Appendix A.

Diversity—

Dungeness R. Summer Run and Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Dungeness 1995–2010 16 Bogachiel/Dungeness (CC) 0 158,427

Total: 0 158,427

Spatial structure and hydrology—Figure E-92 and Figure E-93.

Figure E-92. Map of Dungeness River Summer-Run and Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
271

Figure E-93. Map of Dungeness River Summer-Run and Winter-Run DIP area hydrology.
272

31. Strait of Juan de Fuca Tributaries Winter Run

Overview—This population consists of steelhead spawning in small independent 

tributaries to the Strait of Juan de Fuca between the Dungeness and Elwha rivers, including 

Ennis, White, Morse, Siebert, and McDonald creeks (Figure E-94). While each of the tributaries 

is relatively small, collectively, the creeks cover a 410 km2 watershed.

Figure E-94. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP land cover and observed 

spawning area.

Abundance and productivity—

WDF survey Punch card max. Geometric mean IP estimate

Abundance Not available 258* 147 (53–405) 728 1,456

Year — 1958 2005–2009 —

* Includes Morse and McDonald creeks only.
273

The Strait of Juan de Fuca Tributaries Winter Run’s exponential trend in the natural 

logarithm of natural spawners (95% CI) is 0.964 (0.899–1.031).

Tributary 

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 

Morse Creek 71 84 121 — 124 118 — 24 — —

McDonald Creek 125 63 29 89 206 63 49 63 193 —

For a state-space analysis of the DIP’s viability, see the top two plots of Figure A-20 in 

Appendix A. For a state-space analysis of Morse Creek winter-run population viability, see the 

top two plots of Figure A-21. 

Diversity— 

Strait of Juan de Fuca Tributaries Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Morse Creek 1995–2004 10 Bogachiel Hatchery 0 60,115

Total: 0 60,115
274

Spatial structure and hydrology—Figure E-95 and Figure E-96.

Figure E-95. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP spatial structure, including 

migration barriers and spawning potential.
275

Figure E-96. Map of Strait of Juan de Fuca Tributaries Winter-Run DIP area hydrology.
276

32. Elwha River Winter Run

Overview—Winter-run steelhead were historically present in the Elwha River basin

(Figure E-97), although little is known of their distribution of life history diversity prior to the 

construction of the two Elwha River dams in the early 1900s. Until recently, only 8 km of 

lowland mainstem habitat was accessible in the lower Elwha River. Ongoing restoration 

activities after dam removal will allow access to the entire basin in the near future.

Figure E-97. Map of Elwha River Winter-Run DIP land cover and observed spawning area.

Abundance and productivity—

WDF survey Punch card max. IP estimate

Abundance Not available 746 7,116 14,231

Year — 1952 —

The Elwha River Winter Run’s exponential trend in the natural logarithm of natural 

spawners (95% CI) from 1985 to 2009 is 0.840 (0.749–0.943).
277

For a state-space analysis of population viability, see Figure A-22 in Appendix A.

Diversity—

Elwha River summer run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Elwha River 1995–2010 16 Bogachiel (SK) 0 85,445

Total: 0 85,445

Elwha River Winter Run hatchery releases Total releases

Watershed Duration Years Source Within DPS Outside DPS

Elwha River 1995–2010 16 Elwha/Bogachiel (CC) 0 2,036,243

Total: 0 2,036,243

Spatial structure and hydrology—Figure E-98 and Figure E-99.

Figure E-98. Map of Elwha River Winter-Run DIP spatial structure, including migration barriers and 

spawning potential. The two dams constructed in the early 1900s at RKM 7.9 and RKM 21.6 

were recently removed.
278

Figure E-99. Map of Elwha River Winter-Run DIP area hydrology.
279

Appendix F: Bayesian Networks Characterizing 

Viability of Puget Sound Steelhead DIPs

Appendix F contains Figure F-1 through Figure F-32. They show the Bayesian networks 

(BNs) for viability for each of the 32 candidate demographically independent populations (DIPs) 

of steelhead (Oncorhynchus mykiss) identified by the Puget Sound Steelhead Technical 

Recovery Team for the Puget Sound Steelhead Distinct Population Segment. In structuring a 

BN, one seeks to maximize entropy of the entire network. For a DIP to be considered viable, its 

probability of viability must be at least 85%, as calculated by the BN for viability. The figures 

indicate that population viabilities vary appreciably both within and among major population 

groups (MPGs), but these viabilities are all low. In most cases, these viabilities reflect the 

substantial influence of all four viable salmonid population criteria (i.e., abundance, growth rate, 

diversity, and spatial structure), but especially of abundance and diversity, as components of the 

BN models. A more detailed description of BNs is in the body of this technical memorandum.

Figure F-1. BN for viability of the Drayton Harbor Tributaries Winter-Run DIP in the Northern Cascades 

MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Drayton Harbor tribs WSH viability

Viable

Not viable

47.3

52.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14
280

Figure F-2. BN for viability of the Nooksack River Winter-Run DIP in the Northern Cascades MPG.

Figure F-3. BN for viability of the South Fork Nooksack River Summer-Run DIP in the Northern 

Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Nooksack R WSH viability

Viable

Not viable

43.2

56.8

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

SF Nooksack R SSH viability

Viable

Not viable

47.3

52.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14
281

Figure F-4. BN for viability of the Samish River and Bellingham Bay Tributaries Winter-Run DIP in the 

Northern Cascades MPG.

Figure F-5. BN for viability of the Skagit River Summer-Run and Winter-Run DIP in the Northern 

Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

22.7

33.2

44.2

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0.0041

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

63.6

17.0

19.3

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Samish R/Bellingham Bay WSH viability

Viable

Not viable

53.8

46.2

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

80.5

19.5

31.7 ± 24

Adults per smolt

Greater than 2 percent

Less than 2 percent

85.0

15.0

22 ± 29

Hatchery influence

Nominal

Extensive

60.0

40.0

54 ± 20

Smolts per spawner

Greater than 50

Less than 50

85.0

15.0

46.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.9

40.3

25.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Skagit R SSH/WSH viability

Viable

Not viable

45.3

54.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13
282

Figure F-6. BN for viability of the Nookachamps Creek Winter-Run DIP in the Northern Cascades MPG.

Figure F-7. BN for viability of the Baker River Summer-Run and Winter-Run DIP in the Northern 

Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Nookachamps Cr WSH viability

Viable

Not viable

47.3

52.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Baker R WSH viability

Viable

Not viable

47.3

52.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14
283

Figure F-8. BN for viability of the Sauk River Summer-Run and Winter-Run DIP in the Northern 

Cascades MPG.

Figure F-9. BN for viability of the Stillaguamish River Winter-Run DIP in the Northern Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.9

40.3

25.8

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Sauk R SSH/WSH viability

Viable

Not viable

42.5

57.5

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

40.0

60.0

54 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

17.1

15.5

67.5

41.8 ± 12

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Stillaguamish R WSH viability

Viable

Not viable

36.3

63.7

50 ± 0.0086

Probability to reach QET

Less than 20 percent

More than 20 percent

40.0

60.0

54 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

17.1

15.5

67.5

41.8 ± 12
284

Figure F-10. BN for viability of the Deer Creek Summer-Run DIP in the Northern Cascades MPG.

Figure F-11. BN for viability of the Canyon Creek Summer-Run DIP in the Northern Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.6

36.4

40.1

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0.0041

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Deer Cr SSH viability

Viable

Not viable

47.3

52.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

95.0

5.00

68 ± 8.7

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Canyon Cr SSH/WSH viability

Viable

Not viable

60.2

39.8

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

90.0

10.0

30 ± 15

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

90.0

10.0

30 ± 15

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

78.3

11.6

10.1

24.2 ± 9.2
285

Figure F-12. BN for viability of the Snohomish/Skykomish Rivers Winter-Run DIP in the Northern 

Cascades MPG.

Figure F-13. BN for viability of the Pilchuck River Winter-Run DIP in the Northern Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

21.6

29.5

48.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.3

17.2

26.5

33.33 ± 0.007

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Snohomish/Skykomish R WSH viability

Viable

Not viable

50.9

49.1

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

68.9

31.1

38.7 ± 28

Adults per smolt

Greater than 2 percent

Less than 2 percent

75.0

25.0

30 ± 35

Hatchery influence

Nominal

Extensive

20.0

80.0

38 ± 16

Smolts per spawner

Greater than 50

Less than 50

75.0

25.0

47.5 ± 4.3

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

36.5

17.9

45.6

35.5 ± 14

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

21.6

29.5

48.8

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0.0058

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.3

17.2

26.5

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Pilchuck R WSH viability

Viable

Not viable

46.8

53.2

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

68.9

31.1

38.7 ± 28

Adults per smolt

Greater than 2 percent

Less than 2 percent

75.0

25.0

30 ± 35

Hatchery influence

Nominal

Extensive

20.0

80.0

38 ± 16

Smolts per spawner

Greater than 50

Less than 50

75.0

25.0

47.5 ± 4.3

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13
286

Figure F-14. BN for viability of the North Fork Skykomish River Summer-Run DIP in the Northern 

Cascades MPG.

Figure F-15. BN for viability of the Snoqualmie River Winter-Run DIP in the Northern Cascades MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

21.3

28.6

50.0

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

NF Skykomish R SSH viability

Viable

Not viable

56.3

43.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

80.0

20.0

35 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

10.0

90.0

34 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

80.0

20.0

35 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

68.8

13.3

17.9

26.7 ± 11

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.3

17.2

26.5

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Snoqualmie R WSH viability

Viable

Not viable

47.6

52.4

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

68.9

31.1

38.7 ± 28

Adults per smolt

Greater than 2 percent

Less than 2 percent

75.0

25.0

30 ± 35

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

75.0

25.0

47.5 ± 4.3

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13
287

Figure F-16. BN for viability of the Tolt River Summer-Run DIP in the Northern Cascades MPG.

Figure F-17. BN for viability of the Cedar River Winter-Run DIP in the Central and South Puget Sound 

MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

21.3

28.6

50.0

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

61.0

23.4

15.6

33.33 ± 0.0058

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

80.0

20.0

32 ± 24

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

80.0

20.0

56 ± 8

Tolt R SSH viability

Viable

Not viable

46.9

53.1

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

20.0

80.0

62 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

10.0

90.0

34 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

27.0

15.8

57.1

38.7 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.9

40.3

25.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0.0041

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

20.2

18.1

61.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Cedar R WSH viability

Viable

Not viable

33.4

66.6

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

10.0

90.0

66 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

11.9

88.1

72.9 ± 19

Adults per smolt

Greater than 2 percent

Less than 2 percent

15.0

85.0

78 ± 29

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

15.0

85.0

53.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

13.0

13.4

73.6

43.4 ± 11
288

Figure F-18. BN for viability of the North Lake Washington and Lake Sammamish Winter-Run DIP in 

the Central and South Puget Sound MPG.

Figure F-19. BN for viability of the Green River Winter-Run DIP in the Central and South Puget Sound 

MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.6

39.5

26.9

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

N Lk Washington tribs WSH viability

Viable

Not viable

39.6

60.4

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

19.8

16.8

63.4

40.7 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.6

39.5

26.9

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Green R WSH viability

Viable

Not viable

44.6

55.4

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

80.0

20.0

38 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

22.5

18.2

59.3

39.6 ± 13
289

Figure F-20. BN for viability of the Puyallup/Carbon Rivers Winter-Run DIP in the Central and South 

Puget Sound MPG.

Figure F-21. BN for viability of the White River Winter-Run DIP in the Central and South Puget 

Sound MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Puyallup/Carbon R WSH viability

Viable

Not viable

37.4

62.6

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

19.8

16.8

63.4

40.7 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

56.3

17.2

26.5

33.33 ± 0.0058

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

White R WSH viability

Viable

Not viable

44.8

55.2

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

40.0

60.0

54 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

68.9

31.1

38.7 ± 28

Adults per smolt

Greater than 2 percent

Less than 2 percent

75.0

25.0

30 ± 35

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

75.0

25.0

47.5 ± 4.3

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

17.1

15.5

67.5

41.8 ± 12
290

Figure F-22. BN for viability of the Nisqually River Winter-Run DIP in the Central and South Puget 

Sound MPG.

Figure F-23. BN for viability of the South Puget Sound Tributaries Winter-Run DIP in the Central and 

South Puget Sound MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Nisqually R WSH viability

Viable

Not viable

38.7

61.3

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

80.0

20.0

38 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

22.5

18.2

59.3

39.6 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.6

39.5

26.9

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

South Sound tribs WSH viability

Viable

Not viable

40.7

59.3

50 ± 0.0086

Probability to reach QET

Less than 20 percent

More than 20 percent

80.0

20.0

38 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

22.5

18.2

59.3

39.6 ± 13
291

Figure F-24. BN for viability of the East Kitsap Peninsula Tributaries Winter-Run DIP in the Central and 

South Puget Sound MPG.

Figure F-25. BN for viability of the East Hood Canal Tributaries Winter-Run DIP in the Hood Canal and 

Strait of Juan de Fuca MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0.0041

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

E Kitsap Peninsula WSH viability

Viable

Not viable

40.3

59.7

50 ± 0.0086

Probability to reach QET

Less than 20 percent

More than 20 percent

30.0

70.0

58 ± 18

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

28.4

16.1

55.5

38.3 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

63.6

17.0

19.3

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

East Hood Canal WSH viability

Viable

Not viable

45.5

54.5

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

10.0

90.0

66 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

80.5

19.5

31.7 ± 24

Adults per smolt

Greater than 2 percent

Less than 2 percent

85.0

15.0

22 ± 29

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

85.0

15.0

46.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

13.0

13.4

73.6

43.4 ± 11
292

Figure F-26. BN for viability of the South Hood Canal Tributaries Winter-Run DIP in the Hood Canal 

and Strait of Juan de Fuca MPG.

Figure F-27. BN for viability of the Skokomish River Winter-Run DIP in the Hood Canal and Strait of 

Juan de Fuca MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

South Hood Canal WSH viability

Viable

Not viable

35.2

64.8

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

20.0

80.0

62 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

14.4

14.1

71.5

42.9 ± 12

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.4

35.9

40.6

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

42.8

17.5

39.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Skokomish R WSH viability

Viable

Not viable

42.7

57.3

50 ± 0.0086

Probability to reach QET

Less than 20 percent

More than 20 percent

80.0

20.0

38 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

47.5

52.5

51.5 ± 30

Adults per smolt

Greater than 2 percent

Less than 2 percent

55.0

45.0

46 ± 40

Hatchery influence

Nominal

Extensive

90.0

10.0

66 ± 12

Smolts per spawner

Greater than 50

Less than 50

55.0

45.0

49.5 ± 5

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

22.5

18.2

59.3

39.6 ± 13
293

Figure F-28. BN for viability of the West Hood Canal Tributaries Winter-Run DIP in the Hood Canal 

and Strait of Juan de Fuca MPG.

Figure F-29. BN for viability of the Sequim/Discovery Bays Tributaries Winter-Run DIP in the Hood 

Canal and Strait of Juan de Fuca MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

63.6

17.0

19.3

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

West Hood Canal WSH viability

Viable

Not viable

48.3

51.7

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

80.5

19.5

31.7 ± 24

Adults per smolt

Greater than 2 percent

Less than 2 percent

85.0

15.0

22 ± 29

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

85.0

15.0

46.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

19.8

16.8

63.4

40.7 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.6

36.4

40.1

33.33 ± 0.0041

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Sequim/Discovery Bay tribs WSH viability

Viable

Not viable

34.8

65.2

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

10.0

90.0

66 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

95.0

5.00

68 ± 8.7

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

13.0

13.4

73.6

43.4 ± 11
294

Figure F-30. BN for viability of the Dungeness River Summer-Run and Winter-Run DIP in the Hood 

Canal and Strait of Juan de Fuca MPG.

Figure F-31. BN for viability of the Strait of Juan de Fuca Tributaries Winter-Run DIP in the Hood Canal 

and Strait of Juan de Fuca MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

61.0

23.4

15.6

33.33 ± 0.0058

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

80.0

20.0

32 ± 24

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

80.0

20.0

56 ± 8

Dungeness R SSH/WSH viability

Viable

Not viable

43.2

56.8

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

90.0

10.0

34 ± 12

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

23.8

18.9

57.3

39.1 ± 13

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

23.2

35.0

41.8

33.33 ± 0.0058

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

30.7

17.8

51.4

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

SJF Independents WSH viability

Viable

Not viable

41.7

58.3

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

60.0

40.0

46 ± 20

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

60.0

40.0

47 ± 15

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

28.5

71.5

62.9 ± 27

Adults per smolt

Greater than 2 percent

Less than 2 percent

35.0

65.0

62 ± 38

Hatchery influence

Nominal

Extensive

80.0

20.0

62 ± 16

Smolts per spawner

Greater than 50

Less than 50

35.0

65.0

51.5 ± 4.8

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

40.0

60.0

55 ± 24

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

32.4

17.0

50.5

36.9 ± 14
295

Figure F-32. BN for viability of the Elwha River Winter-Run DIP in the Hood Canal and Strait of Juan 

de Fuca MPG.

VSP risk: DIP diversity

Low risk

Moderate risk

High risk

33.1

37.8

29.1

33.33 ± 0

VSP risk: DIP spatial structure

Low risk

Moderate risk

High risk

37.0

22.6

40.4

33.33 ± 0

VSP risk: DIP productivity

Low risk

Moderate risk

High risk

20.2

18.1

61.7

33.33 ± 0

Fraction of I.P. spawning habitat

More than 20 percent

Less than 20 percent

40.0

60.0

56 ± 29

Fraction of I.P. rearing habitat

More than 20 percent

Less than 20 percent

40.0

60.0

48 ± 9.8

Elwha R SSH/WSH viability

Viable

Not viable

33.6

66.4

50 ± 0

Probability to reach QET

Less than 20 percent

More than 20 percent

20.0

80.0

62 ± 16

Juvenile abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

Age structure

Historical

Compressed

45.0

55.0

48.5 ± 15

Fraction of smolts from residents

Appreciable

Negligible

15.0

85.0

Spawn timing 67.5 ± 18

Historical

Altered

95.0

5.00

36.5 ± 6.5

Frequency of repeat spawners

Historical

Depressed

30.0

70.0

58 ± 18

Mean population growth rate

Greater than 1

Less than 1

11.9

88.1

72.9 ± 19

Adults per smolt

Greater than 2 percent

Less than 2 percent

15.0

85.0

78 ± 29

Hatchery influence

Nominal

Extensive

60.0

40.0

54 ± 20

Smolts per spawner

Greater than 50

Less than 50

15.0

85.0

53.5 ± 3.6

Adult abundance vs capacity

More than 25 percent

Less than 25 percent

20.0

80.0

65 ± 20

VSP risk: DIP abundance

Low risk

Moderate risk

High risk

14.4

14.1

71.5

42.9 ± 12
296
297

Appendix G: Identifying Key Factors Influencing 

the Population Dynamics and Productivity of 

Wild Steelhead

The intrinsic and extrinsic factors that influence the abundance and demographic 

dynamics of coastal steelhead (Oncorhynchus mykiss) populations, which are often small and 

variable, are typically difficult to identify. Fish survival, growth, age, and fertility all affect 

individual fitness and, consequently, population productivity, but it is usually difficult to 

determine which of these factors is more important than another for a particular population. 

Although demographic trends can be estimated from time series of periodic (e.g., annual) census 

data, determining the mechanisms underlying these trends requires at a minimum estimating the 

basic vital rates of survival, growth, and fertility. Ascertaining these factors can help to 

illuminate particular aspects of the life history that are contributing to population decline or 

increasing the risk to viability. Determining whether a population is stable or declining is a 

primary objective of conservation, but uncovering the mechanisms underlying these states is 

critical to identifying sustainable conservation and recovery strategies.

In an attempt to provide some insight into the factors that might constrain viability or 

enhance productivity in coastal steelhead populations, in this section we apply a population 

viability analysis (PVA) to a population of wild steelhead to assess trend, dynamics, and 

extinction risk. We follow this with an analysis of these dynamics in combination with 

information on individual survival, size and estimated growth, and fertility. Recent theoretical 

advances in understanding how demography and adaptation affect population dynamics, 

combined with powerful genetic approaches to identify relationships among individuals, provide 

a much more powerful set of tools for achieving these aims than has previously been available. 

When coupled with careful, regular monitoring of population abundance and key characteristics 

of potential breeders, such as size, age, fecundity, and timing of breeding, the ability to 

effectively reconstruct molecular pedigrees for wild populations using DNA markers allows 

unprecedented opportunities to dissect the components of population dynamics.

To this end, we analyzed demographic and genetic data collected from a small wild 

population of steelhead from Sashin Creek, Baranof Island, Alaska (Thrower and Joyce 2004,

Thrower and Hard 2009). The population is enumerated annually as the individuals pass through 

a monitoring weir that permits collection of all migrating adults and juveniles. The data 

collected include size (length in millimeters), adult sex, and stream entry and exit timing. We 

estimated age and fecundity (number of returning adult offspring) from a molecular pedigree we 

reconstructed for the anadromous adults in the creek during this period from a set of 13 

polymorphic DNA microsatellite markers using FRANz 0.99 software (Riester et al. 2009). 

FRANz uses a full Bayesian probability model to assign parentage; for these data we set the 

threshold for parentage assignment at 95%.
298

The temporal trend in the size of the anadromous population between 1986 and 2011 is 

shown in the upper left panel of Figure G-1. A PVA applied to this time series with the 

Multivariate Autoregressive State-Space (MARSS) Program, version 2.3, (Holmes and Ward 

2011) in the R statistical environment (R Development Core Team 2012) indicates that, over this 

period, the population is declining at a rate of 3.4% annually, corresponding to a population 

growth rate of 0.967; the process variance (variance thought to arise from genetic or 

demographic stochasticity or from random environmental variation), σp

2

, is estimated at 0.00019. 

The MARSS model, described more fully in the main text of this technical memorandum, uses a 

state-space framework to project the population’s dynamics 100 years into the future. What this 

PVA indicates is that, based on the observed trend, the population is likely to reach a minimum 

threshold size of 10 adults within 20–25 years (Figure G-1, upper right and lower left panels); 

over the next 17–27 years, the population is expected to decline by up to 50% if current 

conditions persist (Figure G-1, lower right panel).

What the PVA alone does not—and cannot—tell us is what is driving these dynamics. 

To aid this effort, we need additional information and another approach. One approach is to 

examine how particular phenotypes contribute directly to population productivity. Coulson et al. 

(2006) and Pelletier et al. (2007) developed a method they called “de-lifing,” which essentially 

estimates how population growth rate varies when individuals are removed sequentially from the 

population with replacement. The method is akin to the jackknifing procedure common in 

Figure G-1. PVA of wild Sashin Creek steelhead, based on a time series of adult escapement between 

1986 and 2011.

Number of adults

Prob. to hit threshold

λ = 0.967 (μ = -0.034) Min. population threshold = 10

Prob. to hit threshold

n = 26 yrs, μ = -0.034, σ

p

2 = 0.00019

Expected rate of decline
299

statistical practice. De-lifing fits linear models that estimate the relationships between fitness, 

measured here as lifetime reproductive success, and phenotypic variation, then examines how 

population productivity changes with these variables. The analysis indicates that males residing 

longer in the stream make significantly higher contributions to population productivity (P < 

0.05); there is a tendency for larger females to contribute more to productivity, but these results 

are not significant (P > 0.05). The analysis is summarized graphically in Figure G-2. In this 

figure, contour plots of relative fitness for males and females sampled from the pedigree suggest 

that large females tend to have higher fitness; stream entry and residence time information 

obtained from the molecular pedigree suggests further that larger males that remain in the stream 

longer have higher fitness, while the large females that spawn and then return to the ocean within 

2–3 weeks tend to have higher fitness.

Another approach that is gaining traction in the ecological literature, called an integral 

projection model (IPM), is appealing because it can help to determine the direct influences of 

individual survival, growth, and reproduction on population productivity. An IPM is an 

innovative analytical tool designed to estimate the temporal dynamics of a population when the 

fates of its members depend on characteristics that are continuous or reflect a combination of 

discrete and continuous characteristics. These characteristics or states include traits such as size 

(length, weight), age, survival, fecundity, and distribution of offspring sizes. In much the same 

way that an analysis of function-valued traits related to an analysis of discrete traits (Kingsolver 

Figure G-2. Contour plots summarizing for adult females (left) and males (right) the relationship between 

relative fitness, estimated as individual contribution to population growth rate (z-axis, i.e., the 

contours), size in length (y-axis), and stream residence time in days (x-axis). The data are given 

by the open red circles and the contour lines represent contours of individual fitness (× 1,000). 

The highest fitness values are represented by the red peaks on the plots, with blues lines 

representing the valleys of lowest values of fitness and green lines slopes of intermediate values. 

The contributions to population productivity were estimated according to the formulas in Coulson 

et al. 2006.

 

-10 0 10 20 30 40 50 60

Stream residence (d)

500

600

700

800

900 Length (mm)

F

0.0

10.0

-10 0 10 20 30 40 50 60

Stream residence (d)

500

600

700

800

900

Length (mm)

M

0.0

0.0

0.0

10.0

10.0

20.0
300

et al. 2001), an IPM is analogous to a matrix population model (Leslie 1945, 1948, Lefkovitch 

1965, Caswell 2001), the classical approach to estimating vital rates that affect population 

productivity and persistence from demographic data on stage or age transitions. A key 

difference between an IPM and a matrix population model is that the IPM does not discretize 

state variables a priori, and its approach is pertinent to the study of population dynamics of 

species where an individual’s state (e.g., sexual maturity or reproductive lifespan) depends 

directly on its growth, size, or age. This is important because assuming that continuous 

underlying states are discrete can lead to bias in estimating population dynamic parameters 

(Metcalf et al. 2013). An IPM evaluates the distribution of individuals across these state 

variables between census times (typically annually) by projecting from models that estimate the 

underlying vital rates as a function of the continuous or quasi-continuous state variables

(Easterling et al. 2000, Ellner and Rees 2006). When combined with pedigree or marking 

methods that identify individuals and their relatives, an IPM can provide this information.

Why should we consider such approaches as the IPM? The answer is simple: When we 

cannot conduct robust, replicated experiments to ascertain how aspects of life history affect 

population dynamics, the best alternative is to apply appropriate models to the available 

information, then analyze these models with suitable quantitative methods that can identify these 

relationships. Indeed, if one could simply rely on correlations between demographic trends and 

changes in key life history parameters to confirm hypotheses about the factors that limit viability 

in natural populations, then methods such as IPMs and evolutionary models would be 

unnecessary in inferring causality. Unfortunately, this fact and the inability to conduct the 

relevant experiments that might answer these questions for natural populations in particular 

environments make such methods essential. The IPM is one rigorous and systematic way to try 

to uncover the relationships between these key demographic and evolutionary patterns. In 

essence, it is a formal framework for analyzing the dynamics of a system, such as a natural 

population, that is structured by a mix of discrete and continuous characters that can be 

measured. The study of the dynamics of these character distributions has the potential to afford 

insight into ecological dynamics across a range of levels of organization, including communities 

as well as populations (Coulson et al. 2010, 2011, Coulson 2012).

The data we considered for the demographic analysis constituted a subset of 144 

individuals from five consecutive broods and representing two complete generations (parents and 

their adult offspring) collected between 1997 and 2008. We applied the IPM to these data using 

IPMpack software, version 1.6 (Metcalf et al. 2013) in R version 2.15.2 (R Development Core 

Team 2012). We built a survival function for the IPM around a polynomial logistic regression 

relating size in year t to survival from year t to year t +1, and we built a growth function around a 

polynomial regression relating size in year t to survival from year t to year t +1.

An IPM is characterized by a kernel or function K that represents the probabilities of 

growth and survival between stages and the production of offspring and their recruitment

n(y,t + 1) = ∫ K(y, x)n(x,t)d U

L

where n(y, t + 1) is the distribution across size y of n individuals at census t + 1, n(x,t) is the 

distribution across size x of n individuals at census t, and L is the lower and U the upper size 

limit represented in the IPM (Metcalf et al. 2013).
301

K is itself composed of one kernel that defines survival and growth (the P kernel) and 

another kernel that defines reproduction (the F kernel)

∫ K(y, x)n(x,t)d = ∫ [P(y, x) + F(y, x)]n(x,t)d U

L

U

L

where the P kernel is given by

∫ P(y, x)n(x,t)d = ∫ ss (x)g gg h(y, x)d U

L

U

L

and the F kernel is given by

� F(y, x)n(x,t)d = � c1c2cz ... f 1(x)f 2(x) ... f z(y, x) d

U

L

U

L

where ci are constants for the functions relating size and fecundity and z indicates the stages or 

years (Metcalf et al. 2013).

Using the average and SD of adult length estimated from the data (706.54 and 67.68 mm, 

respectively), we first constructed survival and growth elements of the IPM. We did not have 

longitudinal size estimates for most (123) of the individuals, only estimates of average growth in 

length between years estimated from the pedigree. We fitted a polynomial regression to the 

longitudinal size data available for 22 adults to estimate the size in year t + 1 (e.g., a 6-year-old 

adult) from the size in year t (e.g., a 5-year-old adult) for all adults, based on a mean of 

approximately 9.3% annual growth in length and with normally distributed random error based 

on the variation observed in size in year t. The fitted regression was yt+1 = 533.2 − 0.0872 yt + 

0.0006 yt

2 (r2 = 0.778). Mean generation time was 6 years. We defined the survival component 

of the P kernel as a polynomial logistic regression relating size and size2 in year t to survival 

from year t to year t + 1 (ysurv), and the corresponding growth component as a polynomial

regression relating size and size2 in year t to size in year t + 1 (ygrowth). The models provided the 

following fits:

ysss = −13.52 + 0.00297 ∗ s s − 0.00001633 ∗ s s 2

y g h = 0.005988 − 0.02961 ∗ s s + 0.0007527 ∗ s s 2

These data are plotted in Figure G-3, with curves representing expected values (from the 

two equations immediately above) fitted to the points. The relationship between annual growth 

and initial size was nonlinear. There was no evidence that adults returning earlier or later than 

average had lower or greater growth or survival. The plot of survival on size suggests that the 

increase in annual survival with size is appreciable, perhaps 30–40% for fish greater than 800 

mm vs. 10% for smaller fish.

Diagnostics applied to the estimated P matrix, using a starting size of 550 mm and a 

target size of 750 mm, showed no evidence of outliers or that the size range or the number of size 

bins selected was insufficient (Figure G-4). These diagnostics do not indicate any serious 

problems in fitting vital rate functions to the data or estimating IPM components.
302

Figure G-3. Estimated growth and survival functions for Sashin Creek steelhead. The corresponding red 

curves are the fitted expected values. The black line indicates the 1:1 relationship of sizes in 

subsequent years.

We then defined a normally distributed fertility kernel F representing adult offspring 

production based on parental size in year t (yfec), using the observed data to define the parameters 

of that distribution. The general linear model estimate was

y f = 0.224535 + 0.001198 ∗ s s

Figure G-5 indicates no consistent pattern in adult progeny produced as a function of 

adult size. There is also no consistent pattern with adult run timing (data not shown). The 

maximum number of adult offspring produced by a spawning adult was four over the time series, 

produced by adults as small as 610 mm and as large as 750 mm.

Figure G-6 depicts the survival-growth transition kernel P that accounts for survival and 

growth transitions only (i.e., no covariates), between size in year t and size in year t + 1, using 70 

mesh points. The transition kernel estimates a weak curvilinear fit of survival and growth 

transitions to the size data, with the highest (and much higher) probability of surviving and 

growing to return the following year for the largest adults. From this transition kernel, the 

estimates of mean life expectancy and time to reach the target size across the range of mesh

points in the survival-growth transition kernel P are shown in Figure G-7. The left plot predicts 

a survival advantage for larger adults, as expected from the relationship observed in Figure G-3; 

the predicted life expectancy rises from about 1 year for adults less than 600 mm long to about 

1.75 years for fish 750 mm long. The right plot of passage time estimates that surviving 

550 650 750 850

700 750 800 850 900

Growth

Size at t

Size at t+1

550 650 750 850

0.0 0.2 0.4 0.6 0.8 1.0

Survival

Size at t

Survival to t+1
303

Figure G-4. IPM diagnostics applied to the steelhead survival and growth data, indicating an appropriate 

size range and resolution in number of size bins to estimate IPM matrices. There is no mismatch 

between the observed sizes and the range fitted in the P matrix. The red lines and blue bars 

indicate the results of increasing the size range and bin resolution, respectively, on the model 

fitting. The correspondence of the black and red lines in the Survival and Life expectancy plots 

indicate that the predictions of the discretized matrix do not depart appreciably from the fitted 

survival model over the range of observed values.
304 

Figure G-5. Estimated number of adult offspring as a function of adult size in Sashin Creek steelhead.

Figure G-6. Left, a graphical depiction of the survival-growth transition kernel P indicating the 

probability of a survival and growth transition (z-axis) as a function of sizes in adjacent annual 

censuses, showing a weak curvilinear fit to the size data. Right, a view of the kernel from above, 

with the red regions indicating low probabilities and yellow regions (upper right quadrant) higher 

probabilities of a transition.

Pmatrix

Y

Z

0.0 0.4 0.8

0.0 0.2 0.4 0.6 0.8 1.

0
305 

Figure G-7. Estimated mean life expectancy and passage time to reach a target size of 750 mm as a 

function of size in year t. 

steelhead take 2 years to grow to a target size of 750 mm from a length of 650 and 3 years if 

smaller than that. Growth at sea between successive spawning events has a clear influence on 

future survival and fertility. 

An IPM constructed from the data, assuming a target size of 750 mm and using a logistic 

model of survival and a linear model of growth with no covariates except for size, produced the 

following results for survival, growth, life expectancy, and passage time as functions of size 

(Figure G-8). Given the estimates of survival and growth, the IPM predicts that mean life 

expectancy increases from 1.0 ±0.5 year for adults less than 550 mm to nearly 2.0 ±1.0 year for 

fish greater than 850 mm long. The IPM predicts passage times to 750 mm of 2 years for adults 

greater than 600 mm long and up to 4 years for adults smaller than 450 mm. The P kernel at 

lower left indicates the probabilities of a survival-growth transition as a function of sizes at 

successive censuses (year t on the x-axis and year t + 1 on the y-axis). The F kernel at lower 

right indicates the probabilities of a reproduction transition as a function of those sizes. The 

probabilities of individual survival and growth transitions are relatively closely associated with 

size, but they are high only for very large fish (ca. 850 mm long). The probabilities of 

reproduction for surviving adults (production of adult offspring) are not closely linked to size 

and indeed can be high for fish of various sizes, but these probabilities tend to be lower for fish 

that grow less between spawning events. 

600 650 700 750

0.0 0.5 1.0 1.5 2.0

Size

Mean life expectancy

600 650 700 750

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Size at start

Time to reach chosen siz

e
306 

Figure G-8. Summary of some key results from IPM incorporating a simple logistic function describing 

survival and a simple polynomial function describing growth, with no covariates. The top four 

plots indicate predicted patterns of growth, survival, life expectancy, and passage time as 

functions of size. Fitted functions are given by the red curves for the first two plots. Ninety-five 

percent CIs for life expectancy bracket the predicted values along the solid curve. The bottom 

left plot illustrates the probability of a survival-growth transition (on the z-axis, color contours)

and sizes at two successive censuses (year t on the x-axis and year t + 1 on the y-axis). The 

bottom right plot illustrates the probability of a reproductive transition (on the z-axis, color 

contours) and sizes at two successive censuses (year t on the x-axis and year t + 1 on the y-axis). 

In both of these plots, low values of transition probability are represented by red regions and high 

values by yellow regions. The predicted transition for individual survival and growth is more 

closely linked to size; that for reproduction (production of adult offspring) is not closely linked to 

size.

550 650 750 850

700 800 900

Growth

Size at t

Size at t+1

550 650 750 850

0.0 0.4 0.8

Survival

Size at t

Survival to t+1

550 650 750 850

0.5 2.0 3.5

Life expectancy

Size

Mean life expectancy

300 400 500 600 700

2.0 3.0 4.0

Time to reach particular size

Size at start

Time to reach chosen size

600 650 700 750 800 850

600 750

Continuous (e.g., size) stage at t Continuous stage (e.g., size) at t

558.34 631.82 705.3 772.1 838.9

558.34 758.74

Continuous (e.g., size) stage at t+ 1

Continuous stage (e.g., size) at t+1
307

Combining the P and F kernels to construct a full IPM that integrates growth, survival, 

and fecundity and is fitted iteratively to the 1997–2008 data estimates the population’s net 

reproductive rate per generation, R0, at 1.80. This represents the mean number of adult females 

produced by a female over her lifetime. With a mean generation time of 6 years, this estimate of 

R0 corresponds to an estimate of the per capita growth rate or finite rate of increase, λ, of 1.10, 

and an estimate of the instantaneous rate of change, r, of 9.8% increase per year. The population 

is on average showing positive population growth over the 1997–2008 period. The overall weak 

declining trend of the population between 1986 and 2011 (Figure G-1) is driven largely by a 

population decline between the 1980s and the late 1990s.

Figure G-9 summarizes some projections of the IPM described in Figure G-8. The 

results indicate that population growth rate is sensitive to both growth and survival, but depends 

heavily on the reproductive success of larger adults, especially those larger than 750 mm.

Sensitivity analysis measures how changes in population growth rate are affected by small 

changes in survival-growth and reproduction transitions. Proportional changes in population 

growth rate relative to proportional changes in these transitions are examined through elasticity 

analysis, which standardizes sensitivities to minimize bias due to trait-scale effects. Figure G-10 

supports the pattern of Figure G-9, showing how sensitive population growth rate is to the 

nonlinear component of the relationship between survival and growth.

Taken together, the results of this analysis quantify an expected pattern: that survival, 

growth, and lifetime reproductive success of relatively large breeding adults are critical to higher 

productivity of this wild steelhead population. It appears that sufficiently high interannual 

survival of steelhead in the marine environment is essential to high productivity, implying the 

importance of iteroparity. It is also evident that rapid marine growth of adults can have 

substantial benefits for the survival as well as the fecundity of first-time spawners and repeat 

spawners.
308 

Figure G-9. A few projections from the IPM described in Figure G-8. The top two plots show the 

projected distributions of individual survival and growth (left) and fecundity (right) in the 

population over 10 years against the mesh points of size in the P kernel. The middle left plot 

projects that a size structure dominated by smaller (≈550 mm) adults will produce few larger 

adults in the future. The sensitivity plot (middle right) illustrates the predicted relationship 

between population growth rate (on the z-axis, color contours) and sizes at two successive 

censuses (year t on the x-axis and year t + 1 on the y-axis), showing that it is most sensitive to the 

reproductive success of adults initially between about 700 and 750 mm and subsequently greater 

than 750 mm, suggesting that population growth rate is sensitive to annual growth of breeders as 

well as iteroparity. The corresponding elasticity plot (lower right) suggests that population 

growth rate is most influenced by the reproductive success of adults between 700 and 750 mm. 

In these two latter plots, low sensitivities and elasticities are represented by red regions and high 

values by yellow regions.

600 650 700 750 800 850

600 750

Continuous (e.g., size) stage at t

Continuous (e.g

Continuous stage (e.g., size) at t

Continuous stag

558.34 631.82 705.3 772.1 838.9

558.34 758.74

550 600 650 700 750 800 850

0 400 1000

Size

n in each size c

current

future

600 650 700 750 800 850

600 750

Sensitivity

Continuous (e.g., size) stage at t

Continuous (e.g

600 650 700 750 800 850

600 750

Elasticity

Continuous (e.g., size) stage at t

Continuous (e.g Continuous (e.g. size) stage at t+1 Continuous (e.g. size) stage at t+1 n in each size class

Continuous (e.g. size) stage at t+1 Continuous (e.g. size) stage at t+1

current

future
309

Figure G-10. Graphical summary of the sensitivity and elasticity analyses of the estimate of finite rate of 

population increase (λ). The sensitivity plot indicates the large effect that the relationship 

between survival and the nonlinear growth component has on λ, suggesting that the survival of 

fast-growing fish is key to higher productivity. The corresponding elasticity plot indicates that 

the most influential factors for high productivity are the linear component of the relationship 

between survival and growth and the average production of adult offspring. The factors that tend 

to depress population productivity are low survival between years and the nonlinear component 

of the relationship between survival and growth that indicates disproportionately higher survival 

of larger adults, implying that poor marine survival and limited annual growth in some years 

could threaten population viability.
310
311

Appendix H: Responses to Reviewer Comments

In September 2013, we asked several scientists with expertise in steelhead 

(Oncorhynchus mykiss) biology and viability analysis to review the draft of this Viability Criteria 

for Steelhead within the Puget Sound Distinct Population Segment document that was completed 

in July 2013. We received comments from three peer reviewers and respond to them in this 

appendix. Where appropriate, we have incorporated these responses into this technical 

memorandum, representing the final review. Here we have summarized and organized the 

reviewers’ comments into categories relevant to issues raised by the Puget Sound Steelhead 

Technical Recovery Team (PSS TRT). The reviewers are not specifically identified in order to 

preserve their anonymity. We also include and respond to comments provided by the 

Washington Department of Fish and Wildlife (WDFW) and Washington tribal comanagers.

In general, the reviewers supported the conclusions of the TRT. However, some of the 

reviewers questioned aspects of the analytical methods the TRT employed to assess the status of 

populations within the Puget Sound Steelhead Distinct Population Segment (DPS), and provided 

additional information on specific steelhead populations that will be useful for recovery 

planning.

Substantive Scientific Comments

Development of Viability Criteria

Review

Several reviewers had concerns about the approach the TRT used to assess viability of 

Puget Sound steelhead, which focused on the lack of empirical support for parameters 

influencing VSP viability. These comments are summarized below.

In general, while we agree with the initial approach taken here to quantify viability (e.g., 

decision support system–Bayesian network analyses of viability) for each demographically 

independent population (DIP) within the Puget Sound DPS, we have significant concerns with 

the lack of empirical data support for many of the parameters listed under each viable salmonid 

population (VSP) criterion used in the final determination of viability. Specifically, while some 

stocks within Puget Sound have sufficient time-series data to assess contributions of abundance 

and productivity to overall viability, empirical data on parameters within the diversity and spatial 

structure are lacking for most if not all DIPs. For parameters within specific VSP criteria where 

the TRT has used a combination of limited data and expert knowledge to assign values, we 

recommend that these values be excluded from the current assessment of viability and remain as 

place holders until they can be informed by sufficient empirical data. However, we do not 

dispute the potential importance many of these data-limited parameters to the continued viability 

of Puget Sound steelhead.
312

While results from this study provide a reference point for technical and policy 

discussion regarding setting interim abundance and productivity goals for each TRT-proposed 

DIP, much of the data utilized in the assessment of each DIP is not supported by sound empirical 

studies, but rather is a combination of temporally sparse information and expert opinion from the 

TRT panel. Specifically, the TRT states that of the four VSP parameters (abundance, 

productivity, diversity, and spatial structure), abundance and productivity are the most supported 

by empirical data, whereas diversity and spatial structure have very little empirical support. 

Even so, sufficient time-series data to effectively assess temporal trends in abundance and 

productivity for many of the DIPs are lacking (page 32, lines 1182–1187 of the review draft). 

Furthermore, the authors specifically state they identified and used as placeholders parameters to 

quantify viability for each VSP category for which currently little is available is, but anticipate 

more may become available later through future studies (page 108, lines 1608–1613 of the draft). 

While we do not dispute that all four VSP criteria (and the many parameters used to assess 

viability for each category) are important to the future viability of the Puget Sound DPS, we have 

significant concerns about the use of parameters in the viability analyses for which little 

empirical data are available. We ask that these potentially important parameters for which 

empirical support is lacking be excluded from current viability results for specific DIPS, but still 

utilize parameters as place holders within the conceptual model framework for future inclusion 

in the viability analyses as they are informed by new studies.

The primary recommendation is that parameters for which almost quantitative 

information was available should be excluded from the viability assessment, and from the 

Bayesian network models for viability. In most cases, these parameters would especially include 

those associated with diversity and spatial structure.

Response

The TRT discussed this issue at length and took two actions to address it in the revision 

of this document. First, the TRT unanimously felt that it would be inappropriate to exclude these 

parameters from a decision-theoretic model like the Bayesian network (BN). The TRT’s charge 

was to evaluate viability of DIPs, major population groups (MPGs), and the DPS for all four 

VSP criteria—abundance, productivity, diversity, and spatial structure—with the best 

information available. We therefore included metrics for these criteria for three reasons (see 

page 121 of the draft):

• Eliminating such factors from the network when they are thought to be important to an 

overall assessment defeats the purpose of creating an informative network;

• Bayesian networks are designed to accommodate both factors that can be quantified and 

those that are evaluated based on collective judgement in the absence of more definitive 

information, which can be updated when more information becomes available; and

• Eliminating such factors tends to place higher importance on factors with available 

information, regardless of whether those factors are more critical to viability or not.

The TRT also investigated some scenarios that did what the reviewers asked for (e.g., page 149 

et seq.) to evaluate this issue further. In general, these scenarios resulted in substantial 

improvements in viability at each level. Probabilities of viability for DIPs throughout the DPS 

varied from 64.9% to 86.8%. Probabilities of viability for the three MPGs were 89.9% for the 
313

Northern Cascades MPG, 74.6% for the Central and South Puget Sound MPG, and 70.5% for the 

Hood Canal and Strait of Juan de Fuca MPG. Probability of viability for the entire DPS was 

42.3%; the most probable explanation was that the DPS is viable, with a probability that the DPS 

is not viable equal to 56.2%.

This exercise illustrates how the structure of a BN can strongly influence the inference 

about viability at nodes throughout the network. If we rely on the more quantifiable and readily 

available of the VSP criteria alone, estimates of steelhead viability are higher throughout the 

DPS for two main reasons: the TRT’s largely qualitative assessment of diversity and spatial 

distribution of steelhead is that the widespread state of these factors in Puget Sound steelhead 

poses risk to sustainability, and the TRT’s more quantitative assessment of demography 

(abundance and productivity) is conservative with respect its influence on risk to persistence. 

The primary problem with this analysis is that it is at odds with the threatened status of the DPS 

and with concerns about continued risks to persistence and sustainability of many of its 

populations. It also indicates that the existing BNs may be too benign with respect to risk of 

quasi-extinction; the viabilities for several DIPs and all the MPGs are at odds with the outcomes 

of several of the population viability analyses (PVAs), in part because of the MPG-wide 

averaging for DIPs with no quantitative information. It is also probably due in part to the fact 

that this analysis does not consider the effects of factors that are known to be risks to wild Puget 

Sound steelhead, including limited availability of productive rearing and spawning habitat, the 

widespread use of Chambers Creek and Skamania River hatchery fish throughout the DPS, and 

perceived changes in aspects of diversity for several populations.

The TRT considers the networks that encompass all four VSP criteria to more accurately 

reflect the current status of Puget Sound steelhead. The vast majority of steelhead populations 

throughout Puget Sound do not appear to be viable, regardless of where one looks; most are 

declining steeply from historical levels and many are very small. Despite the lack of 

demographic information for many of the populations and the lack of clarity regarding the 

precise states of diversity and spatial structure for nearly all of them, the TRT considers these 

networks to represent a reasonable starting point for identifying the major criteria for effective 

recovery of Puget Sound steelhead, and believes that they help to identify key gaps in knowledge 

that must be addressed if further declines in steelhead viability are to be arrested. The TRT 

therefore did not consider these alternative scenarios further in developing the viability criteria.

Review

Several reviewers provided comments on individual parameters within each VSP 

criterion regarding whether the parameter should be excluded from the current viability analyses 

due to lack of empirical data or whether we think that these parameters can be informed by 

studies currently being conducted in the Skagit Basin that are either at or near completion. They 

went on to say: It would be very helpful if the TRT outlined the data completeness and gaps,

caveats, and assumptions for each individual DIP included in the viability analysis.

Comments on productivity were as follows.

• Smolts per spawner: This information is available for many DIPs and is estimated 

annually using redd counts and smolt traps.
314

• Adults per smolt: Can be estimated using estimates of annual escapement and outmigrant 

smolt abundance.

• Mean population growth rate: Informed from the recent status of smolts per spawner and 

adults per smolt parameters.

• Frequency of repeat spawners: We agree that this parameter would have a direct 

influence on productivity by increasing recruitment. However, this parameter could also 

fit under the diversity node. Because the frequency of repeat spawners represents a 

relatively minor contribution to the overall spawning stock (see Figure 71 of the draft), 

we would view this life history characteristic more as an index of diversity vs.

productivity. Additionally, how did the TRT make the comparison between historical vs.

current? Based on the inclusion of this parameter under productivity, it appears the 

authors are using frequency of repeat spawners as an index of productivity for each DPS. 

Given how little information is currently available regarding the proper assessment of the 

importance of this parameter to overall viability, we suggest that this parameter be used 

as a placeholder and not be assigned a probability for inclusion in the viability analyses 

for each DIP at this point in time. To properly assess the contribution of repeat spawners 

to overall smolt production (which would then really be a productivity parameter rather 

than diversity parameter), a genetic-based parentage study would need to be conducted.

Comments on abundance were as follows.

• Probability to reach quasi-extinction threshold: Estimates from multivariate 

autoregressive state-space (MARSS) modeling of viability in previous section of study.

• Adult abundance vs. capacity: GIS-based assessment of abundance and habitat 

availability.

• Juvenile abundance vs. capacity: GIS-based assessment of abundance and habitat 

availability.

For any estimate of capacity, we have enough historical information from the Skagit to 

quantitatively assess current capacity by assessing spawner/recruit curves. Specifically, we 

should not rely on potentially out-of-date information from Gibbons et al. (1985) for estimates of 

optimal densities and for specific values of marine survival or coarse-scaled measurements of 

habitat availability (e.g., channel width and gradient) to understand adult or juvenile abundance 

vs. capacity. Rather, in the Skagit River system, we have an abundance of much finer scale 

habitat data that enables us to quantify habitat availability at the channel unit scale. In addition, 

the Skagit Yearling Salmonid Study is fairly close to being finished and will likely help inform 

current estimates of rearing habitat use vs. rearing capacity.

Comments on diversity were as follows.

• Hatchery influence: Poorly understood. For the Skagit, the Saltonstall-Kennedy study 

will provide Skagit-specific results for the degree of hatchery genetic influence on the 

wild/natural populations. We suggest using these results rather than using an assumed 

value of hatchery influence based on categories of smolt release size within the watershed 

of specific DIPs. Please mark as a placeholder until we can support this parameter with 

available data.
315

• Fraction of smolts from residents: Unless some new genetic studies have taken place 

within each DIP that we are unaware of, this information is contrived or produced from 

the literature. Either way, there is likely considerable variation in this parameter between 

DIPs that should be accurately accounted for. Given that our current understanding of 

this cannot accurately account for the likely significant variation in this parameter 

between DIPs, this should only be included as a place holder and not incorporated into 

the viability analysis until more information comes available.

• Run timing: Comparison between current and historical run timing is likely not possible 

for many DIPs. Therefore, please provide a clear explanation for how this comparison 

was made for individual DIPs. Otherwise, we need to come up with a more sound 

method to better assess this parameter as it relates to viability.

• Spawner age structure: Comparison between current age structure and historical is not 

possible for many DIPs. Similar to run timing, please provide a clear explanation for 

how this comparison was made for individual DIPs.

For parameters in which there is a probability split between a comparison of current vs. historical 

values for the specific parameter, we are unclear of the time frame that is used for this 

comparison. In fact, it would be good for the authors to include a discussion regarding what (if 

any) historical data were used in the historical vs. current status assessments for each of these 

parameters.

Comments on spatial structure were as follows.

• Fraction of intrinsic potential rearing habitat occupied by juveniles.

• Fraction of intrinsic potential spawning habitat occupied by adults.

These parameters do not appear to satisfy the spatial structure criteria. These help address the 

carrying capacity question for DIPs but do not help in the assessment of spatial structure. How 

are these different than those parameters included in the abundance category (e.g., adult 

abundance vs. capacity)? Another way to ask this question is to first ask what is the current 

diversity of habitat available to juveniles and adult steelhead within each basin (e.g.,

temperature/gradient combination)? Second, what percentage of each habitat type is currently 

occupied by juveniles and adults? Finally, we cannot assume that the proportion of available 

habitat occupied varies directly with abundance.

These appear to be quantified using GIS. This method can only provide ballpark 

estimates of these parameters. Additionally, viability was estimated based on the relationship 

between smolt abundance, densities (fish/km), and marine survival rates quantified for each DIP 

in Gibbons et al. (1985). We suspect that these relationships are entirely variable through time,

based on changes in rearing and spawning habitat availability. Again, juvenile occupancy can be 

informed by the nearly completed Skagit Yearling Salmonid Study.

Response

See the TRT’s previous response. Again, the TRT unanimously felt that it would be 

inappropriate to exclude these parameters from a decision-theoretic model like the BN, because 

it defeats the purpose of such a network, because these models can easily and transparently 
316

accommodate new or additional information, and because eliminating parameters tends to put 

greater weight on factors with available information, regardless of whether those factors are 

more critical to viability or not.

As stated earlier, the TRT investigated scenarios that did what the reviewers asked for to 

evaluate this issue further. In general, these scenarios resulted in improvements in viability at 

each level, which illustrated how the structure of a BN can strongly influence the inference about 

viability at nodes throughout the network. Under these alternative scenarios, estimates of 

steelhead viability are higher throughout the DPS. The primary problem with this analysis is that 

it is at odds with the threatened status of the DPS and with concerns about continued risks to 

persistence and sustainability of many of its populations. It also indicates that the existing BNs 

may be too benign with respect to risk of quasi-extinction. Therefore, the TRT considers the 

networks that encompass all four VSP criteria to more accurately reflect the current status of 

Puget Sound steelhead.

In the revised report, we responded directly to the reviewer’s comments about the spatial 

structure criteria; as described in the main text, we developed a spatially explicit method of 

determining spawning steelhead presence/absence from stream reaches suitable for the species 

(based on the TRT’s intrinsic potential criteria) and developed spatial structure scores from these 

data to make them more independent from the abundance criteria. We applied these scores to the 

rearing criterion due to the lack of quantitative juvenile data.

Finally, we note that the studies the reviewer referred to are incomplete and the TRT did 

not have access to those data to incorporate in this report.

Review

Reviewers had concerns about the use of quantitative thresholds to help prioritize 

populations (e.g., 15% of viability abundance or growth rates of >1), because they may not result 

in DIPs that meet these criteria across MPGs. They suggested a qualitative approach to 

characterize DIP viability criteria (e.g., increasing or stable growth rate, highest population 

growth rate over a given time period).

Response

The TRT adopted quantitative viability criteria for DIP abundance and productivity that 

could be 1) used to conduct PVAs at the DIP level and 2) discretized for use in the BN models 

for DIP viability. The TRT felt that this would more accurately reflect the patterns of viability 

across distinct MPGs and would at least partially address the reviewers’ concerns without 

essentially rescaling relative viabilities to a more ambiguous scale.

Methods of Population Viability Analysis

Review

A WDFW reviewer commented that the TRT should have used Dennis et al. (1991) 

methods for its PVAs instead of the “slope” methods based on Holmes (2001) and Holmes and 

Fagan (2002) to evaluate viability of individual steelhead populations. Detailed comments on 
317

this were provided in a memorandum from WDFW, which contained a tabular comparison of the 

Dennis et al. (1991) and Holmes (2001) methods. The primary thrust of these comments was: 

Based on analysis conducted by WDFW biometricians, variances and subsequent width of 

confidence intervals of median term rates of population change are underestimated, giving a false 

sense of precision in estimates of lambda (population growth), and the amount of information 

contained in the data.

Response

After review of the comments and consulting Dr. Holmes, the TRT elected to retain the 

multivariate autoregressive state-space analysis methods (Holmes and Ward 2011) for PVAs of 

individual steelhead populations in the DPS. (These methods were also used to evaluate trends 

in abundance and productivity by the National Marine Fisheries Service in the recent status 

review updates of all listed West Coast salmonids [Good et al. 2005, Ford 2011].) The TRT 

made this decision for two reasons.

First, there seems to be some confusion over what the slope method does; the reviewer 

subscribes to the notion that this method underestimates the combined process and observation 

variance. The point of the slope method is that it tries to estimate the process variance in a way 

that it can forecast extinction risk using a model of population dynamics that is free of 

observation variance. In doing so it accounts for observation variance, something that the 

Dennis et al. (1991) method does not do. The slope method is an extension of Dennis et al. 

(1991) that accounts for both process and nonprocess error. In fact, the Dennis et al. (1991) 

method actually tends to overestimate the process variance. There appears to be considerable 

controversy over the value of the variance estimates obtained by the Dennis et al. (1991) method 

for estimating extinction risk. Dr. Holmes provided a list of several recent publications that 

point toward a statistical consensus to avoid the Dennis et al. (1991) method for this purpose.

Second, the TRT incorporated these PVA results provided by MARSS modeling for 

individual populations and some population groups, after discretizing them into broad classes of 

abundance and productivity as they affect viability, into a hierarchical BN model to evaluate 

viability at the levels of a) DIP), b) major population groups (MPGs, or “strata”), and c) the DPS

(the listed unit) as a whole. This model is described in detail in the main body of this technical 

memorandum. As a consequence, most of any specific differences between the methods would 

have little bearing on these broader results. The WDFW reviewers did not comment on these 

approaches or results.

The TRT amended the text in the description of the PVA methods in the body of the 

report to reflect these changes.

Review

A reviewer commented that the use of lambda can be problematic in assessing 

productivity for steelhead because of their extended freshwater life history and expression of 

population dynamics that are so strongly density dependent.
318

Response

The TRT agrees with this concern, so it also used adult recruits per spawner as a metric in 

assessing productivity in steelhead populations. However, alternative metrics like adult recruits 

per spawner can be more sensitive to changes in life history that might be occurring during the 

longer time series, such as declining age at maturation as a result of increasing abundance of 

hatchery fish on spawning grounds, and therefore lambda was used as the primary metric. It is a 

conventional metric that can be applied to a wide variety of taxa with distinct life histories, and is 

appropriate when applying state-space models to time series of count data. Since the TRT 

discretized the lambda metric in applying its BN model approach to viability at the DIP scale, the 

TRT considers the use of this altered metric is suitable for this purpose.

Intrinsic Potential Model

Review

A WDFW reviewer proposed that the TRT did not apply results from the Gibbons et al. 

(1985) report correctly to estimate intrinsic potential (IP) for steelhead. The reviewer’s primary 

concern was the TRT’s assignment of moderate to high values for IP to very low-gradient 

reaches, especially for reaches less than 20 m in width, and low values to higher gradient reaches

regardless of width. The reviewer’s comments, with minor editorial changes, are presented 

below.

The Gibbons et al. (1985) work suggests that lumping 0–4% large river and tributary 

“mainstem” gradients together is not appropriate. Very low-gradient, large main stems have 

very low production potential. Gradients in the 0.25–3.0 range have greatly increased densities. 

In general, Gibbons et al. (1985) had much higher tributary parr densities compared to mainstem 

parr densities. If the TRT evaluated results from other parr density studies and choose 

parameters for Table C-1 based on “averaging” among studies, including Gibbons et al. (1985), 

that should be clearly described.

The Table C-1 matrix as it stands does not produce reliable numbers. Personal 

observations in some of the low-gradient streams (i.e., Nookachamps Creek, Drayton Harbor 

streams) have production potentials that are unrealistically high. On the flip side, some highgradient streams (i.e., Deer Creek, Nooksack River) are way too low. Even with current poor 

ocean survival rates and degraded habitat, both are probably producing more steelhead than the 

model would predict.

The reviewer suggested an alternative to Table C-1 that might better reflect the Gibbons 

et al. (1985) parr density estimates, giving higher IP ratings to higher gradient stream habitats 

and lower IP ratings to lower gradient stream habitats.

Stream habitat rating matrix (below natural barriers)

Stream bankfull width

Stream gradient 0–3 m 3–20 m >20 m

0.0–1.0% High Moderate Low

>1.0% Moderate High Moderate
319

Response

The information on density of O. mykiss parr contained in the Gibbons et al. (1985) 

report was only part of the information the TRT used to develop its IP model. For example, the 

TRT states in Appendix C that this matrix was developed from GIS data used to develop an IP

model that was adapted from data in Gibbons et al. (1985) and from the Interior Columbia TRT’s 

steelhead IP model; it incorporates information from both works. In addition, the TRT thought

that some of the concerns expressed by the reviewer would not necessarily apply generally to 

steelhead-supporting systems throughout the Puget Sound DPS. After consideration of these 

comments, the TRT modified the habitat rating matrix it used in Table C-1 to reflect this input as 

follows.

Stream habitat rating matrix (below natural barriers)

Stream bankfull width

Stream gradient 0–3 m 3–20 m >20 m

0.00–0.25% High Moderate Low

0.25–4.00% Moderate High Moderate

>4.00% Low Low Low

This change largely implements the suggested changes for lower stream gradient reaches,

but retains low IP for very high-gradient reaches. In part this reflects, in addition to Gibbons et 

al. (1985) and the above comments, the historical evidence the TRT examined for widespread 

production of steelhead in small, low-gradient streams, and the lack of evidence for production in 

very high-gradient reaches, where other species such as bull trout (Salvelinus confluentus) might 

be more common.

Review

A reviewer commented that it is critically important to account for naturally spawning 

hatchery fish to evaluate their impact on natural productivity. The reviewer was especially 

concerned that the simple use of redd counts does not directly lead to this accounting and could 

seriously affect estimates of productivity and, therefore, viability. This issue is particularly 

important because of the widespread reliance in the past on hatchery steelhead production in 

Puget Sound, employing broodstocks that are either highly selected for particular traits 

(Chambers Creek winter-run fish) or sourced from out-of-basin (Skamania River summer-run 

fish). Not only are some of the redds in the data considered here undoubtedly built by feral 

hatchery fish, but more importantly, the progeny of early spawning hatchery fish that survive to 

interact ecologically with progeny of later spawning wild fish are likely to have impacts on 

productivity over time.

Response

The TRT agrees with the reviewer about the need to account for naturally spawning 

hatchery fish to accurately assess the effect of feral hatchery fish on natural productivity, and 

agrees that the use of redd count data alone is not likely to fully account for this effect. The TRT 

is not sure how to deal with this issue using the available data. The ongoing studies of hatchery 

introgression in the Skagit River by WDFW should provide some insight into the magnitude of 
320

this problem. Given the widespread published evidence for reduced fitness of naturally 

spawning steelhead in the presence of feral hatchery fish, this is of considerable concern. 

However, the TRT considers the estimates of productivity to be conservative with respect to this 

factor. Moreover, a strong majority of the TRT thought that the metric it employed to 

characterize the influence of feral hatchery fish on viability (through reduction in diversity) was 

a reasonable proxy for this effect, using a straightforward “propagule pressure” approach. The 

TRT thought it was a rather conservative approach, in part because it did not influence 

productivity directly. An altered BN model that attempted to capture this influence would 

undoubtedly have produced an even grimmer assessment of steelhead population viability.

Minor Scientific Comments

Review

A reviewer commented that the fact that each MPG is weighted equally in assessing the 

viability of the DPS in total may bias the results.

Response

We explain why each MPG must be weighted equally in assessing DPS viability at 

several points in the document. The reason for this is that each MPG is considered an essential 

component of the DPS in all regards of VSP criteria such that the loss or sufficient degradation 

of any MPG means that the DPS no longer retains its essential biological characteristics and 

therefore cannot remain viable as a unit.

Review

A reviewer indicated that the TRT identified required conditions for resident life history 

to contribute to anadromous life history viability. These conditions need to state whether this 

information is or can even be quantified.

Response

In the Resident Fish as a Factor Influencing Viability subsection of the Diversity 

Considerations for Steelhead Viability section of this technical memorandum, the TRT makes it 

clear that this is an unresolved issue for most O. mykiss populations. This issue is an active area 

of research that merits immediate attention to help guide development of conservation strategies 

for steelhead. Therefore, this information is not quantified. However, it could be with a wild 

pedigree of known residents and anadromous fish and an appropriate demographic analysis of 

the anadromous population (e.g., with an integral projection model approach, as described in 

Appendix G).

Review

A reviewer indicated concern that the dependence of resilience of small populations on 

specific levels of repeat spawning can only be demonstrated under a no-harvest scenario,

regardless of environmental variation. Furthermore, a comprehensive genetic study on parentage 
321

would be required to correctly answer this question and therefore would provide empirical 

evidence to support such broad conclusions.

Response

We amended the text in this section on iteroparity to reflect this concern.

Review

A reviewer asked how the TRT came up with these threshold numbers to assign whether 

a DIP is viable or not.

Response

The TRT developed these criteria collectively after considering the results of the 

individual PVAs, the BN model outputs, information from the published literature, and 

discussion of alternative values. In short, the criteria reflect a mixture of quantitative and 

qualitative information and collective judgment by TRT members. These criteria could 

obviously be altered with new or improved information to reassess viability under different 

scenarios.

Review

A reviewer commented that the TRT says it used comanager-provided data on 

abundance/productivity, fraction of hatchery-origin spawners, harvest, age structure, and 

hatchery releases in assessment of viability of individual DIPs. The reviewer asked to ensure 

that current/correct data were used (especially for Skagit) in these analyses.

Response

The TRT made repeated requests to comanagers for this information and was assured it 

received the most current and correct data through 2011 for this document, including that 

reflected in Salmonid Stock Inventory (SaSI) and Salmonscape databases. This information was 

updated frequently during the drafting of this report. It should be noted that at least two TRT 

members served as liaisons with state and tribal comanagers (their employers).

Review

A reviewer noted that the highest observed growth rates over entire series were in the 

Northern Cascades MPG, whereas the lowest were estimated in the Central and South Puget 

Sound MPG. The reviewer also noted that this is not a surprising result here, given variation in 

habitat and hatchery influence throughout the DPS.

Response

No response is required.
322

Review

A reviewer commented that the authors state many times throughout the document that 

accurately quantifying viability of many DIPs is a challenging endeavor due to the lack of 

quantitative abundance and productivity data for many of the DIPs. Given the nature, size, and 

complexity of this document, the reviewer recommended that all assumptions, 

caveats/uncertainties, data limitations, etc., be stated in an individual section at the front of the 

document. While acknowledging that the TRT does a good job of this throughout the document, 

the reviewer thought that a short summary of this for each DIP would be helpful as an appendix.

Response

The TRT reasoned that this point is, as the reviewer noted, emphasized at several points 

in the document. In combination with the information it provided in the main text as well as in 

Appendix C, the TRT decided that the document’s information was sufficient to indicate where 

the uncertainties and limitations of the data at the DIP level are, and that an additional appendix 

was unnecessary.

Review

A reviewer acknowledged that the TRT did not account for nonanadromous recruits in 

their stochastic PVAs, but given that we have very little understanding of nonanadromous 

contributions to annual smolt production, the reviewer emphasized that these contributions must 

be quantified before its influence on viability can be assessed.

Response

The TRT does not disagree with this as an important step in quantitatively evaluating 

viability of an entire metapopulation, but argues in the document that, at a minimum, such 

nonanadromous recruits can serve to buffer steep declines in anadromous abundance during 

periods of low abundance and reduced marine survival.

Review

A reviewer commented that the language reflecting the use of index counts of natural 

escapement and not accounting for resident recruits in assessing productivity and extinction risk 

is contradictory to the statement about the potential overestimation of extinction risk from the 

exclusion of resident individuals to recruitment.

Response

The TRT was unclear about the meaning or intent of this comment and therefore made no 

changes to that section of the document.
323

Review

A reviewer indicated that the TRT utilizes SaSI for information on stock abundance and 

productivity. There needs to be a comparison between the SaSI tables and current comanager 

data to assess for discrepancies in data sources.

Response

See the response above about the information the TRT received from comanagers in this 

report. No further response is required.

Review

A reviewer indicated that the authors state that PVAs estimated by MARSS modeling do 

not account for the effects of density dependence on stock abundance and productivity. The 

reviewer noted that is a problem, given that we know that many populations for which the data 

are available are limited by density dependent effects. This assumes that viability is primarily 

related to abundance and not the ability of natal habitat to support adult spawning and juvenile 

rearing capacities required for persistence into the future.

Response

The TRT is aware of this limitation of the analyses, but there is not currently a way 

around it with this set of state-space models, which are among the most robust available to assess 

quasi-extinction risk from time-series data, such as that available. The TRT also wishes to point 

out that another likely influence of density dependence in many of these populations is probably 

depensatory at very low abundance (e.g., Allee effects), which could tend to depress the 

estimates of viability over those given in this report. Whether this depensation is more 

influential on the productivity of these depressed wild populations than the ecological effects of 

feral hatchery fish remains an open question.

Review

A reviewer noted that the assumption for the PVAs that population trends are linear and 

environmental conditions affecting mortality (e.g., marine and freshwater survival) and 

production are constant is not valid. The reviewer also noted that this is clearly not true for 

many DIPs within the Puget Sound DPS.

Response

The method does not assume that population trends are linear but, as the reviewer 

indicates, the assumption of stationarity is a property of the method.

Review

A reviewer noted that because a knowledge-based decision support system attempting to 

evaluate ecological conditions may lack precision due to data gaps and imperfect knowledge, it 

makes a clear statement of uncertainty due to the lack of data.
324

Response

The TRT concurs with this assessment, but thinks that this uncertainty is adequately 

captured in the decision support system (in this case, the BN model). Indeed, the calculations of 

most probable explanation and the use of the sensitivity analyses and confusion matrices provide 

for each model a quantitative estimate of this uncertainty over the entire framework.

Review

A reviewer noted that the viability outcome appears to be highly sensitive to how the 

parent nodes (e.g., VSP criteria) are parameterized. The main concern is that quality data 

representing significant time series for individual DIPs have significant limitations for 

quantifying the VSP criteria used in the decision support system viability analysis.

Response

The TRT understands the reviewer’s point, but does not agree that the viability outcome 

is highly sensitive to how the parent nodes are parameterized. In most cases, the parameters for 

all VSP criteria would have to be shifted to substantially higher values or entire criteria would 

have to be removed for a DIP to become viable. In fact, the TRT was sensitive to this potential 

problem and chose fairly broad, discrete categories for many of the variables to help alleviate 

this concern. That said, the TRT agrees with the reviewer that data quality is a significant 

limitation to accurately quantifying the VSP criteria at the individual DIP level in the BN

models.

Review

A reviewer commented that the TRT states that each network was constructed so as to 

minimize complexity without compromising the analytical ability to evaluate sensitivity of 

viability to the four VSP parameters; the reviewer was concerned that such oversimplification 

can bias the analysis, especially when these results can influence decisions that can significantly 

affect current conservation and management practices.

Response

The TRT agrees that finding a proper balance between model complexity and utility for 

informing conservation and management actions is difficult, but it is unclear to the TRT how 

additional model complexity without additional information on the key parameters would avoid 

this potential bias.

Review

A reviewer indicated that some of the nodes included in the BN models are placeholders

and should be viewed as such unless new information comes available that allows the proper 

assessment of these parameters.
325

Response

This is true. In those cases, we tried to give values to the nodes that implied considerable 

uncertainty in influences on viability; the discretization of the parameters also helps to reduce 

this influence.

Review

A reviewer noted that estimates of DIP diversity incorporate influence of run timing 

distribution, hatchery fish on natural diversity, the adult age distribution, and the proportion of 

migrant smolts produced by resident adults. The reviewer argued that two of these parameters,

hatchery influence and resident contribution to migrant smolts, lack empirical support from 

actual data. The authors need to make this very clear that data are lacking on these specific 

parameters and that they will be assessed more thoroughly/updated as empirical data to support 

estimates becomes available.

Response

The TRT is clear in the revised report about the methods used to derive these parameter 

values. In the case of hatchery influence, we used a systematic metric to provide a proxy for this 

effect, based on historical hatchery production (propagule pressure). In the case of resident 

contribution to migrant smolts, we agree that this lacks empirical support for Puget Sound O. 

mykiss populations (except potentially for the Cedar River). The TRT used broad categories of 

influence to estimate what that influence might be, and the TRT states repeatedly in the 

document that these data are lacking and the models should be updated as they become available.

Review

A reviewer pointed out that the TRT outlines the uncertainty in various parameters 

utilized in their analytical approach and how they dealt with this uncertainty in their analyses. 

For example, the TRT states that intermediate and output conditional probabilities pertain to 

demographic and life history responses that are poorly understood due to lack of empirical data. 

The TRT dealt with this significant uncertainty by double weighting probabilities of abundance 

and productivity (more well-known) compared to diversity and spatial structure (significant lack 

of empirical understanding), and by utilizing high levels of spread in conditional probabilities for 

parameters where empirical data are lacking. In addition, the reviewer felt that the contrived 

linear relationship between viability and VSP risk is perhaps an oversimplification of the 

influence of each VSP criterion on overall viability. The reviewer argued that, while this 

approach is valid in Bayesian methodology and for exploratory purposes, results from these 

analyses, especially with the inclusion of poorly understood parameters, should not influence 

firm “set-in-stone” policy decisions. Rather, the analyses should be meant to be adapted as new 

data becomes available (e.g., Skagit yearling and Saltonstall-Kennedy study).

Response

The TRT agrees with this statement in general, but is unsure what is meant by “these 

analyses...should not influence firm ‘set-in-stone’ policy decisions.” Because these analyses can 
326

and should be adapted as new data become available, why shouldn’t they influence policy 

decisions? Which policy decisions are immutable?

Review

A reviewer had a concern about the conditional probabilities used to describe risk for 

each VSP parameter. The TRT specifically states that probabilities for each parameter were 

estimated using a combination of empirical data, PVA results, and expert opinion. The 

conditional probabilities represent oversimplification of all the factors at play that could affect 

viability of individual DIPs. For example, under VSP parameter diversity, the categories for 

fraction smolts from residents include negligible vs. appreciable and categories for hatchery 

influence include nominal vs. extensive.

Response

The TRT thought that such simplification was necessary when data were insufficiently 

quantitative to provide greater resolution of influences on viability. Nevertheless, conditional 

probabilities are still required for the model and these were chosen based on that combination of 

information the reviewer describes.

Review

A reviewer argued that it is very unclear how the TRT estimated the influence of each 

parameter on viability of the Nookachamps Creek Winter-Run DIP, given that little or no 

historical data exist for this population.

Response

The TRT agrees with the reviewer that the Nookachamps Creek population is more 

problematic than some other populations to assess viability because of the lack of historical 

information. The TRT considered many of the parameters to likely be similar to those for other 

winter-run steelhead populations in low-elevation areas in the Northern Cascades MPG, such as 

the Samish River and Bellingham Bay Tributaries Winter Run.

Review

A reviewer commented that, in its inspection of the results of the BN analyses, the TRT 

implies that the results provide a complete explanation of fine-scale processes that drive 

population persistence. The reviewer went on to say that, “undoubtedly, increasing abundance 

and productivity of all stocks spread geographically across the DPS will increase the probability 

of persistence of the DPS. However, this does not take into account contributions of individual 

DIPs or entire MPGs to persistence of the DPS. I would imagine that given the high degree of 

variation in habitat quality between individual systems, some DIPs should have higher 

contributions to MPG and hence DPS persistence. However, I agree that both life history 

diversity and spreading risk across the entire geographic range of the DPS would increase the 

probability of persistence.” In addition, the reviewer pointed out that the TRT’s approach 

“demonstrates the importance of viability for each MPG to overall DPS viability.... I think this 
327

analysis is a bit coarse and does not take into account the scaled importance of individual DIPs to 

MPG viability and, similarly, the importance of individual MPGs to DPS viability.”

Response

The TRT agrees with the reviewer’s observation about variable contribution of DIPs to 

MPG and DPS viability, but did not consider this factor in the analyses. The TRT did not give 

higher weights to specific DIPs in terms of their influence on MPG and DPS viability, and this 

was deliberate. For DIPs of each major life history type (summer run vs. winter run), the 

influence of each DIP on MPG was considered to be equivalent (although each DIP had a 

different viability itself). As stated elsewhere in the document, DPS viability depended equally 

on the viability of each constituent MPG; this is considered an essential characteristic of an 

MPG.

Review

A reviewer noted that the conclusions depend heavily on the underlying conditional 

probabilities in the model and asked how they were derived.

Response

As the reviewer suspected, these probabilities were derived by a combination of data and 

expert opinion, and generally show simple, often linear relationships to viability. This protocol 

is described in detail in the report. More realistic relationships, informed by empirical data, 

could easily be implemented in the models.

Review

A reviewer asked which studies the TRT used to guide analysis of residence vs. 

anadromy.

Response

The TRT reviewed a variety of empirical studies and modeling investigations of both 

coastal and interior O. mykiss to guide its assessment of interactions between resident and 

anadromous forms of this species, including Zimmerman and Reeves (2000), Narum et al. 

(2004), Thrower et al. (2004), Pearse et al. (2009), Thrower and Hard (2009), and Courter et al. 

(2010, 2013). There is some debate over the extent of these interactions and their consequences, 

but some consensus seems to be emerging that these migratory forms are highly plastic, can be 

produced by parents of both types, and may have different fitnesses in different environments.

Review

A reviewer commented that the TRT made an assumption that in areas where there are 

known to be abundant populations of residents interacting with anadromous individuals, 

extinction risk was not allowed to exceed 90% in 100 years. The reviewer noted that this is 

likely to be a conservative estimate.
328

Response

The TRT adopted this value to reflect the likelihood that sufficient resident fish 

producing some smolts could ameliorate risk of extinction of an anadromous population in that 

drainage. The TRT concurs with the reviewer that this value would be case specific, and it could 

be higher or lower.

Review

A reviewer expressed concern that the simulation of the demographic effects of 

iteroparity on population persistence conducted by Nick Gayeski and summarized in this report 

might be too general to be applied to all DIPs within an MPG or to the entire DPS. The reviewer

requested a copy of the Gayeski analyses to further review its validity. The reviewer also noted 

that, to validate these conclusions, there need to be complementary empirical studies assessing 

the importance of repeat spawners within individual DIPs (i.e., a comprehensive genetic study in 

which outmigrant smolts are successfully assigned to parents to understand the relative 

contribution of repeat spawners to smolt production).

Response

The summary of Gayeski’s simulation modeling herein is the only report available, and it 

was reviewed and approved by the author. More comprehensive modeling work has not yet been 

published. The TRT agrees that the simulation results would not necessarily be applicable to 

individual DIPs in the Puget Sound DPS, and ultimately these results should be verified by 

empirical parentage studies. The TRT notes that Appendix G of this report is intended as a step 

in that direction, in describing the application of a demographic analysis to a molecular pedigree 

for a wild Alaskan steelhead population.

Review

A reviewer asked, in the TRT’s description of viability criteria and scoring methodology 

for each DIP using “traffic light” rule sets, and in doing so determining whether an MPG could 

be considered viable, how was a geometric mean of 2.2 for viability scores for all DIPs within an 

MPG arrived at?

Response

Using the “traffic light” rule set, the TRT settled on a geometric mean viability of 2.2 

based on an evaluation of viability scores for 8-DIP and 16-DIP MPGs, in which each DIP was 

given a rank score of 1, 2, or 3, corresponding to “not viable” (<40% probability of viability), 

“intermediate” (40–85% probability of viability), and “viable” (>85% probability of viability). 

Under a requirement of 40% viable DIPs in an MPG and no more than one DIP being not viable, 

the TRT adopted a minimum geometric mean viability score of 2.2 for the MPG to be considered 

viable.
329

Review

A WDFW reviewer commented on this statement in Appendix C: “Low productivity 

areas [those with gradients greater than 4%] were not included in the estimate of potential parr 

numbers.” The concern was that some of the highest parr densities observed by Gibbons et al. 

(1985) were in the higher gradient streams.

Response

We revised the estimates to consider the higher density reaches that had higher gradients 

and revised this statement accordingly.

Review

A WDFW reviewer thought that the IP estimate for the Drayton Harbor Tributaries 

Winter Run in Appendix E did not consider spawning habitat potential, indicating that this is the 

limiting factor for all salmonids in these drainages due to their geology.

Response

The TRT developed these estimates from the IP model in Appendix C and for 

consistency provided them in Appendix E. Because of the peculiar geology of this area, this IP 

estimate may overestimate the capacity of steelhead in this system, to be sure, but to an unknown 

degree. We added a statement to this effect to that appendix.

Review

A WDFW reviewer commented on the estimate of IP of steelhead in Nookachamps 

Creek given in Appendix E, stating that the estimate is “completely wrong.” The reviewer went 

on to say that the maps of spatial structure for several DIPs do not reflect reality, but did not 

provide specific information to show where they are inaccurate. The primary concern seems to 

be that some low gradient areas are given too high an estimate of IP, but spawning habitat is 

limiting in some of these areas.

Response

Without more detailed information to tailor the estimates to specific basins, it is difficult 

to address these comments specifically. We do not have specific punch card data for 

Nookachamps Creek, but for some systems we do have punch card data that indicate that 

steelhead were recovered from areas like these historically (i.e., in the first half of the twentieth 

century). The point is that current distribution should not be the primary guide to determining 

potential spawning or IP.

Review

A WDFW reviewer commented (on the spatial structure map in Appendix E) that in the 

Stillaguamish River, Canyon Creek above RKM 0.8 is summer-run steelhead territory, not 

winter-run steelhead territory.
330

Response

The distribution map in question considers both summer-run and winter-run steelhead.

We added a comment to this effect to the text.

Review

A WDFW reviewer commented that summer-run steelhead spawning and rearing habitat 

in Deer Creek should not include the lower 2.5 miles, which are winter-run steelhead habitat.

Response

We amended the text in Appendix E to reflect this comment.

Review

A WDFW reviewer commented on the IP estimate for Canyon Creek in Appendix E, 

indicating that the estimate does not include the main stem of the creek down to RKM 0.8.

Response

The area in question is included in the Stillaguamish River estimate.

Review

A WDFW reviewer recommended that we clarify language regarding risk of extinction to 

read “no more than a 5% risk of extinction.”

Response

We made the suggested change where needed or altered the language to reflect a more 

general intention regarding extinction risk.

Editorial Comments

Review

Peer reviewers 1 and 2 both commented that the Executive Summary was too lengthy and 

should be more concise. 

Response

We revised the Executive Summary substantially to meet this concern and incorporated 

many of the reviewers’ specific suggestions. In addition, we addressed each of the minor 

editorial comments made on the review draft by all reviewers and incorporated most of the 

suggested changes made by these reviewers throughout the entire document.
331

Policy-Related Comments

Review

A reviewer commented that delisting criteria should be clearly designated as 

recommended biological delisting criteria.

Response

We revised the document accordingly.

Review

A reviewer commented that the document differentiate the application of delisting criteria 

to extant vs. extinct populations.

Response

This technical memorandum does not address delisting criteria, but rather extinction risk 

and the criteria required for viability sufficient to minimize such risk. As the document focuses 

on DIPs that are all extant or their extirpation was recently documented (but may include extinct 

components), we focus herein on application of viability criteria to these (and more inclusive) 

units.

Review

A reviewer recommended that we further clarify the role of resident O. mykiss for 

steelhead viability.

Response

The revised document includes a complete section on the potential role of resident O. 

mykiss in viability of steelhead and its relevance to biological viability criteria.

Final Response to Reviewer Comments

The TRT’s approach to assessing steelhead viability described in this technical 

memorandum enlists a variety of analytical tools to evaluate information available on steelhead 

ecology, population dynamics, phenotypic diversity, and habitat use—information that varies 

considerably in content and quality as well as quantity. Many of the comments and concerns of 

the reviewers have driven home this point, and their comments have focused on the utility of the 

TRT’s hierarchical framework for assessing viability when empirical data are lacking, the 

validity of the analyses’ underlying assumptions, and the approach the TRT has taken to evaluate 

the dependence of DPS viability on the viability of individual populations. However, none of the 

reviewers identified an alternative to the approach the TRT adopted. Clearly, some aspects of 

this approach have required a number of important assumptions, detailed in the body of this 

report, that are often clearly violated. With this in mind, the TRT has strived to implement 

methods that can accommodate and combine information of varying content and quality and are 
332

likely to be more robust to violation of key assumptions. The decision-theoretic approach we 

developed here, in the form of BN models, attempts to assess viability in a quasi-quantitative 

way across all three hierarchical levels within the Puget Sound DPS, from DIP to MPG to the 

DPS as a whole, and in a way that uses as much relevant information as possible while 

acknowledging the nature of that information. By discretizing the information in broad but 

distinct categories that describe its influence on viability, the models have allowed the TRT to 

assess current viability, examine the influence of different VSP parameters on viability, and 

establish a reasonably robust framework that can readily accept new or improved information on 

factors that influence viability. At a minimum, the work summarized in this report should serve 

to highlight the importance of 1) collecting the appropriate information from natural populations 

to help better evaluate viability and its components in Puget Sound steelhead, and 2) developing 

and implementing a rigorous, systematic monitoring program to detect changes in these 

populations. Figure 56 provides a simple sketch of some of the key data to collect and monitor 

for viability. Through the approach the TRT developed here, it should also prove useful to 

recovery planning by providing a transparent, systematic means of evaluating different recovery 

actions at the watershed scale.
Recent NOAA Technical Memorandums

published by the

Northwest Fisheries Science Center

NOAA Technical Memorandum NMFS-NWFSC128 Myers, J.M., J.J. Hard, E.J. Connor, R.A. Hayman, R.G. Kope, G. Lucchetti, A.R. Marshall, 

G.R. Pess, and B.E. Thompson. 2015. Identifying historical populations of steelhead within the 

Puget Sound distinct population segment. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC128, 155 p. NTIS number PB2015-103741.

127 Roni, P., G.R. Pess, T.J. Beechie, and K.M. Hanson. 2014. Fish-habitat relationships and the 

effectiveness of habit restoration. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-127, 

154 p. NTIS number PB2014-108836.

126 Russell, S., and M.S. Ruff. 2014. The U.S. whale watching industry of Greater Puget Sound: A 

description and baseline analysis. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-126, 

171 p. NTIS number PB2014-105939.

125 Sloan, C.A., B.F. Anulacion, K.A. Baugh, J.L. Bolton, D. Boyd, R.H. Boyer, D.G. Burrows, D.P. 

Herman, R.W. Pearce, and G.M. Ylitalo. 2014. Northwest Fisheries Science Center’s analyses of 

tissue, sediment, and water samples for organic contaminants by gas chromatography/mass spectrometry 

and analyses of tissue for lipid classes by thin layer chromatography/flame ionization detection. U.S. 

Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-125, 61 p. NTIS number PB2014-104055.

124 Anderson, L.E., and S.T. Lee. 2013. Washington and Oregon saltwater sportfishing surveys: 

Methodology and results. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-124, 61 p. NTIS 

number PB2014-101405.

123 Ward, E.J., M.J. Ford, R.G. Kope, J.K.B. Ford, L.A. Velez-Espino, C.K. Parken, L.W. LaVoy, 

M.B. Hanson, and K.C. Balcomb. 2013. Estimating the impacts of Chinook salmon abundance and 

prey removal by ocean fishing on Southern Resident killer whale population dynamics. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-123, 71 p. NTIS number PB2013-110079.

122 Frame, E., and K. Lefebvre. 2013. ELISA methods for domoic acid quantification in multiple marine 

mammal species and sample matrices. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-122, 

20 p. NTIS number PB2013-109341.

121 Lian, C.E. 2012. West Coast limited entry groundfish cost earnings survey: Protocol and results for 

2008. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-121, 62 p. NTIS number PB2013-

104899.

Most NOAA Technical Memorandums NMFS-NWFSC are available at the

Northwest Fisheries Science Center Web site, http://www.nwfsc.noaa.gov