U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2017–1125

Prepared in cooperation with Sauk-Suiattle Indian Tribe

Skagit River Coho Salmon Life History Model—Users’ Guide
Cover: Photograph showing Sauk-Suiattle Tribe members gill net fishing on the Sauk River, 

Washington, 2016. Photograph by Grant Kirby, Sauk-Suiattle Indian Tribe, Darrington, Washington.
Skagit River Coho Salmon Life History Model—Users’ 

Guide

By Andrea Woodward, Grant Kirby, and Scott Morris

Prepared in cooperation with Sauk-Suiattle Indian Tribe

Open-File Report 2017–1125

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Acting Director

U.S. Geological Survey, Reston, Virginia: 2017

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Suggested citation:

Woodward, Andrea, Kirby, Grant, and Morris, Scott, 2017, Skagit River coho salmon life history model—

Users’ guide: U.S. Geological Survey Open-File Report 2017–1125, 57 p., 

https://doi.org/10.3133/ofr20171125.

ISSN 2331-1258 (online)
iii

Contents

Abstract ......................................................................................................................................................... 1

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

Purpose...................................................................................................................................................... 2

Skagit River Basin...................................................................................................................................... 3

Sauk-Suiattle Indian Tribe.......................................................................................................................... 3

Coho Salmon ............................................................................................................................................. 5

Model Overview............................................................................................................................................. 5

Data Sources................................................................................................................................................. 8

Model Details................................................................................................................................................. 9

Modeling “Predicted Smolts”...................................................................................................................... 9

Summary................................................................................................................................................ 9

Stock-Recruitment Curves...................................................................................................................... 9

Pink Salmon ......................................................................................................................................... 11

Physical Habitat.................................................................................................................................... 13

Modeling “Realized Smolts” ..................................................................................................................... 15

Summary.............................................................................................................................................. 15

Explanatory Flow Parameters .............................................................................................................. 15

Summer Low Flow................................................................................................................................ 16

Winter High Flow.................................................................................................................................. 17

Turbidity ............................................................................................................................................... 19

Scouring Flow....................................................................................................................................... 22

Modeling Escapement.............................................................................................................................. 23

Summary.............................................................................................................................................. 23

Ocean Survival..................................................................................................................................... 24

Harvest ................................................................................................................................................. 26

Model Validation .......................................................................................................................................... 27

Potential Uses of Model ........................................................................................................................... 35

Model Limitations ..................................................................................................................................... 37

Acknowledgments ....................................................................................................................................... 38

References Cited......................................................................................................................................... 38

Appendix A. Definitions of Model Terms..................................................................................................... 48

Appendix B. Accessing and Running the Model ......................................................................................... 50

Accessing the Model................................................................................................................................ 50

Running the Model................................................................................................................................... 50

Appendix C. Skagit Salmon Co-Managers Data Used in Model Development ........................................... 55

Appendix D. Model Equations .................................................................................................................... 56
iv

Figures

Figure 1. Map showing Skagit River Basin, northwestern Washington........................................................4

Figure 2. Schematic diagram showing Skagit River coho salmon life history mode.....................................6

Figure 3. Ricker stock-recruitment curve calculated from smolt and spawner data collected 

on the Skagit River, northwestern Washington, brood years 1989–2012.................................................... 10

Figure 4. Comparison of smolt recruitment curves expected in years with and without spawning pink 

salmon, brood years 1989–2012 ................................................................................................................. 12

Figure 5. Comparison of Ricker stock-recruitment curves calculated for early and late in the period of 

record, brood years 1989–96 and 2006–12, respectively............................................................................ 14

Figure 6. Regression showing fraction of smolt prediction from Ricker curve actually realized owing to 

summer flow conditions measured at Sauk River streamgage (USGS 12189500)...................................... 17

Figure 7. Graph showing effect of high flow during juvenile rearing period on residual deviation of 

smolt production from Ricker-based prediction after accounting for the effects of summer flow

measured at Sauk gage (USGS 12186000). ............................................................................................... 18

Figure 8. Regression showing relation of number of days with turbidity greater than (>)30 

Nephelometric Turbidity Units during the freshwater life stages of coho cohorts (February following 

spawning through April of following year) with residual deviation of actual from predicted smolts after 

summer low flow is accounted for................................................................................................................ 21

Figure 9. Comparison of model results with estimated number of smolts from counts at smolt 

trap when each year is initialized with estimated number of spawners (escapement), 1989–2012 ............. 28

Figure 10. Comparison of model results for estimated escapement predicted using Pacific Decadal 

Oscillation and smolts compared with data-based estimates when each year is initialized with estimated 

number of spawners (escapement), 1989–2012.......................................................................................... 29

Figure 11. Comparison of model results for estimated escapement predicted using North Pacific 

Gyre Oscillation and chlorophyll-a compared with data-based estimates when each year is initialized 

with estimated number of spawners (escapement), 2001–12...................................................................... 30

Figure 12. Comparison of model results with estimated number of smolts based on counts at 

smolt trap when the first 3 years of the model run are initialized with estimated number of spawners 

(escapement) and subsequently by model results....................................................................................... 32

Figure 13. Comparison of model results for estimated escapement predicted using Pacific Decadal 

Oscillation and smolts compared with data-based estimates when the first 3 years of the model run are 

initialized with estimated number of spawners (escapement) and subsequently by model results.............. 33

Figure 14. Comparison of model results for estimated escapement predicted using NPGO and

chlorophyll-a compared with data-based estimates when the first 3 years of the model run are

initialized with estimated number of spawners (escapement) and subsequently by model results.............. 34
v 

Tables

Table 1. Data sources used to develop Skagit River coho salmon life history model...................................8

Table 2. Model relations to determine “predicted smolts”.............................................................................9

Table 3. Model relations to determine “realized smolts”............................................................................. 15

Table 4. Best-ranked multiple regression models explaining smolt production .......................................... 16

Table 5. Model relations to determine “escapement” ................................................................................. 23

Table 6. Indicators of ocean condition determined to be associated with ocean mortality of coho

salmon in geographic areas near to or including the Skagit River, northwestern Washington.................... 25

Table 7. Results from leave-one-out cross-validation analysis................................................................... 35

Conversion Factors 

U.S. customary units to International System of Units

Multiply By To obtain

Area

acre 4,047 square meter (m2)

acre 0.004047 square kilometer (km2)

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2)

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F = (1.8 × °C) + 32.
vi 

Datums 

Vertical coordinate information is referenced to the North American Vertical Datum of 1929 (NAVD 29)].

Horizontal coordinate information is referenced to the Datum of 1983 (NAD 83)].

Elevation, as used in this report, refers to distance above the vertical datum.

Abbreviations

AICc Akaike's Information Criterion for small sample

BY brood year

CI confidence interval

ENSO El Nino Southern Oscillation

LOOCV leave-one-out cross-validation

MWMT maximum weekly maximum temperatures

NPGO North Pacific Gyre Oscillation

NTU Nephelometric Turbidity Unit

ONI Oceanic Nino Index

PDO Pacific Decadal Oscillation

r2 coefficient of determination

R2 coefficient of multiple determination

SST sea-surface temperature

TOCAS Treaty Online Catch Accounting System

USGS U.S. Geological Survey

WDFW Washington Department of Fish and Wildlife

WY water year
1 

Skagit River Coho Salmon Life History Model—Users’ 

Guide

By Andrea Woodward1, Grant Kirby2, and Scott Morris2 

Abstract

Natural resource management is conducted in the context of multiple anthropogenic stressors 

and is further challenged owing to changing climate. Experiments to determine the effects of climate 

change on complex ecological systems are nearly impossible. However, using a simulation model to

synthesize current understanding of key ecological processes through the life cycle of a fish population 

can provide a platform for exploring potential effects of and management responses to changing 

conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the 

effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions 

likely deviating in range and timing from conditions used to create the model so that the model is 

expected to become obsolete. In the meantime, however, the modeling process explicitly states 

assumptions, clarifies information gaps, and provides a means to better understand which relationships 

are robust and which are vulnerable to changing climate by observing whether and why model output 

diverges from actual observations through time. The purpose of the model described herein is to provide 

such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian 

Tribe of Washington State. 

The Skagit coho salmon model is implemented in a system dynamics format and has three 

primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are 

the number of smolts expected based on the number of spawners in any year and the Ricker production 

curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit River in odd years, and when they 

overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in nonpink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on 

whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based 

on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. 

Adverse conditions include scouring flow events that occur when redds are present; high-flow events 

during winter on juveniles, which can cause fish displacement and adverse water turbidity; and 

extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting 

for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, 

which are related to ocean biological productivity. Ocean survival also may have a density-dependent 

component such that lower survival is associated with higher numbers of smolts. The model allows the 

user to change certain model parameters and inputs, and choose among alternative predictors for certain 

modeled relations. 

 

1

U.S. Geological Survey. 

2

Sauk-Suiattle Indian Tribe, Darrington, Washington. 
2 

Introduction

Purpose

Natural resource management is conducted in the context of multiple anthropogenic stressors 

and is further challenged owing to changing climate. Experiments to determine the effects of climate 

change on complex ecological systems are nearly impossible. However, using a simulation model to

synthesize current understanding of key ecological processes through the life cycle of a fish population 

can provide a platform for exploring potential effects of and management responses to changing 

conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the 

effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions 

likely deviating in range and timing from conditions used to create the model so that the model is 

expected to become obsolete. In the meantime, however, the modeling process explicitly states 

assumptions, clarifies information gaps, and provides a means to better understand which relationships 

are robust and which are vulnerable to changing climate by observing whether and why model output 

diverges from actual observations through time. The purpose of the model described herein is to provide 

such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian 

Tribe of Washington State. 

Coho was selected as a candidate salmonid species for this modeling exercise because of life 

history attributes, distribution, and ongoing efforts. Coho life history provides a level of simplicity to 

the model because on average 97 percent of recruits return to the river as 3-year-olds. Therefore, the 

model does not have to account for variable life cycle lengths within cohorts as with most other salmon 

species, and whereas coho life history may become more complex in the future, we do not attempt to 

model potential changes. Coho also have the largest spatial distribution among salmon species in the 

Skagit River system. Coho spawning occurs in low-gradient conditions throughout the watershed, 

including urban drainage ditches, main-stem side channels, small and large tributaries, and low-gradient 

spawning habitat within high-gradient mountain streams. Fry to juvenile fish rear in main-stem fringe 

habitat, streams, wetlands, beaver ponds, and lakes. This extensive distribution will help with assessing 

effects of climate as variable changes are observed among habitat types. With ongoing and future coho 

habitat restoration planned (in particular, Barnaby Slough and culvert replacement), the model will 

serve as a tool to assess success. Additionally, we did not want to select a species that would duplicate 

the efforts of others. Presently (2017), much research and data compilation are being conducted 

regarding Skagit Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss) 

because both species are listed as threatened under the Endangered Species Act of 1973 (16 U.S.C. § 

1531 et seq.). Coho are not considered threatened or endangered in Puget Sound; however, there has 

been a steady decline in ocean survival of coho originating in the Salish Sea (Puget Sound, Strait of 

Juan de Fuca, and Georgia Strait) over several decades (Beamish and others, 2010; Zimmerman and 

others, 2015). 
3 

Skagit River Basin

The Skagit River Basin covers 8,270 km2, including the 1,849 km2 Sauk-Suiattle watershed, and 

drains the North Cascades Mountains of Washington and British Columbia. The Skagit River is the 

largest river between the Fraser River in British Columbia and the Columbia River, and it is the largest 

tributary to Puget Sound. The Skagit River Basin has a steep elevation range, from marine waters to 

3,275 m, and a steep annual precipitation gradient from 90 cm at sea level to 460 cm near Glacier Peak 

(Beechie and others, 2005). The river flows from the subalpine fir (Abies lasiocarpa) vegetation zone at 

high elevation, through the Pacific silver fir (Abies amabilis) zone, to the western hemlock (Tsuga 

heterophylla) zone in the lowlands (Franklin and Dyrness, 1973). Land uses and ownership include the 

State of Washington or private owners (19 percent), protected areas (44 percent), managed national 

forest (24 percent), and the Province of British Columbia (13 percent) (Beechie and others, 1994). 

Steep elevation gradients and a maritime climate mean that the Pacific Northwest has wet 

winters and dry summers, with winter precipitation falling as snow at high elevations, mixed snow and 

rain at mid-elevations, and rain in the lowlands. Peak flows in snow-dominated areas occur in spring, 

owing to snowmelt, whereas peak flows in rain-dominated areas occur in early winter, owing to winter 

storms. Mixed areas, also known as the transient zone, have intermediate hydrographs (Beechie and 

others, 2006). Hydrologic patterns are predicted to shift as snow-dominated areas become transient and 

transient areas become rain-dominated because of climate change (Mantua and others, 2010). Pacific 

Northwest climate varies in concert with the El Nino Southern Oscillation (ENSO; period 2–7 years) 

and Pacific Decadal Oscillation (PDO; period of several decades) patterns of sea-surface temperature 

anomalies. Warm phases of each pattern are associated with warm and dry winter and spring weather, 

whereas cool phases are associated with 1° C lower average temperature and 20 percent higher 

precipitation (Mote and others, 2003). 

Sauk-Suiattle Indian Tribe

The Sauk-Suiattle Indian Tribe historically occupied the Sauk, Suiattle, and Cascade River 

watersheds, tributaries of the Skagit River in western Washington (fig. 1) with seasonal villages 

extending downriver to the marine nearshore. The main part of the Sauk-Suiattle Indian Reservation 

covers 28 acres in the flood plain of the Sauk River, about 10 km north of Darrington, Washington. 

Traditionally, Sauk-Suiattle Tribal members subsisted as fishers, hunters, and gatherers, and traveled 

downriver to Puget Sound by the Skagit and Stillaguamish Rivers to harvest fish and shellfish; to the 

mountains for food, herbs and other materials; and to eastern Washington on horseback for trade. The 

Tribe’s continuing interest in fish for commercial, subsistence, and ceremonial purposes is subject to the 

1974 Boldt Decision3

, which reaffirms the reserved right of Tribes to co-manage (along with the State 

of Washington) fisheries and hatchery programs in Washington State, and allocates one-half of the 

harvestable surpluses of fish runs to Tribes. Specifically, the Sauk-Suiattle, Swinomish, and Upper 

Skagit Indian Tribes are co-managers of Skagit salmon with Washington State. As such, they have input 

into management decisions regarding ocean fisheries made by the Pacific Fisheries Management 

Council and regarding Puget Sound fisheries. 

3

United States v. Washington, 384 F. Supp. 312 (W.D. Wash. 1974), aff’d, 520 F. 2d 676 (9th Cir. 

1975). 
4 

Figure 1. Map showing Skagit River Basin, northwestern Washington. (Source: USGS; horizontal datum, North 

American Datum of 1983). 
5 

Coho Salmon

Coho salmon in the eastern Pacific Ocean range from northern California to Alaska including the 

Skagit River system, where they are present from the river mouth to the Gorge Dam, which is their 

native range. They have a 3-year life cycle that begins when coho spawners return from the ocean to 

freshwater and arrive at spawning grounds, typically between late November to early February, when 

they build redds, lay eggs, and expire in smaller tributaries (Sandercock, 1991; Beechie and others, 

1994; Lestelle, 2007). Eggs hatch into alevins and emerge as fry in spring. Juveniles grow in the 

freshwater habitat through summer, autumn, and the following winter until they smolt in spring and 

migrate to the ocean, although small fish may remain in freshwater for another year (Seiler and others,

2001). After 18 months in the ocean, they return to freshwater as spawners to repeat the cycle (Groot 

and Margolis, 1991). However, some return after a few months in the ocean and others may remain in 

the ocean for 30 months (Shapovalov and Taft, 1954; Gallagher and Wright, 2012).

Although Puget Sound coho salmon are not currently (2017) considered threatened under the 

Endangered Species Act, all salmonid populations on the Skagit River are subject to a number of 

anthropogenic disturbances. Flow regimes, including summer low flows (Mathews and Olson, 1980; 

Quinn and Peterson, 1996), winter high flows (McMahon and Hartman, 1989; Shirvell, 1990), and 

scouring flows during spawning (Seiler and others, 2003) explain much of the variation in coho smolt 

production. Climate change also is a potential threat to Skagit River Basin coho (hereinafter “Skagit 

coho”) because flow amount and timing as well as water temperature are expected to be altered by 

climate change (Mote and others, 2003; Mantua and others, 2010). Moreover, ocean conditions, 

including sea-surface temperature, upwelling, and other climate-related parameters are associated with 

ocean survival for coho salmon (Scarnecchia, 1981; Nickelson, 1986; Beamish and others, 2010; 

Zimmerman, 2012). The purpose of this coho salmon life history model is to integrate the various 

factors hypothesized to affect coho survival and to enable managers to investigate how changes 

predicted because of climate change and potential management actions may affect the Skagit River coho 

population. 

Model Overview

The Skagit coho salmon life history model was built using a system dynamics format (Ford, 

1999), which consists of three basic elements: stocks, flows, and converters. Stocks are measurable 

quantities that can change over time, such as numbers of animals in populations. Flows are any 

processes or conditions that change the quantity in a stock over time and are expressed in the model as a 

rate of change. Converters are processes or conditions that change the rate of flows. Anything that 

influences the rate of change such as different environmental conditions, other external factors, or 

feedbacks from other stocks or flows, is modeled as a converter. For example, the number of fish in a 

population is appropriately modeled as a stock. Mortality is modeled as a flow because it is a rate of 

change over time that determines the number of fish in the population. The effect of flow regime is an 

example of a converter because it influences the rate of mortality. Together, the flows and converters 

change the number, or stock, of coho. The instructions embedded in the stocks, flows, converters, and 

the linkages among them are processed by the simulation software. Unfortunately, the standard terms 

used for system dynamic models can be confusing when used regarding fisheries, where the terms

stocks and flows have other meanings. 
6 

The Skagit coho salmon model is implemented using Stella Architect® version 1.1.2 system 

dynamics software (http://www.iseesystems.com, accessed September, 2017) and has three primary 

stocks, (1) predicted smolts, (2) realized smolts, and (3) escapement (fig. 2). “Predicted smolts” are the 

number of smolts expected based on the number of spawners in any year and the Ricker production 

curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit in odd years, and when they overlap 

with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years 

(Michael, 1995; Zimmerman, 2012; Nelson and Reynolds, 2014). Therefore, the model uses alternative 

Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink 

year. Ricker curves include parameters describing the non-density dependent productivity and carrying 

capacity of system. The model allows the user to modify the baseline carrying capacity to account for 

positive or negative changes in habitat availability owing to processes such as restoration, aggradation 

owing to mass sediment deposition, and unsuitable water temperatures. 

Figure 2. Schematic diagram showing Skagit River coho salmon life history model.
7 

Streamflow conditions at various fish life stages have been identified as the major environmental 

conditions affecting coho freshwater productivity (Mathews and Olson, 1980; Quinn and Peterson, 

1996; Seiler and others, 2003). The stock “realized smolts” is calculated based on the expected effect of 

streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions 

include scouring flow events that occur when redds are present (Seiler and others, 2003); high-flow 

events during winter on juveniles (McMahon and Hartman, 1989; Shirvell, 1990), which can cause fish 

displacement and adverse water turbidity (Gregory 1992); and extremely low flows in summer 

(Mathews and Olson, 1980; Quinn and Peterson, 1996). Details of these analyses follow in section, 

“Model Details.” 

The stock “escapement” represents the fish remaining after accounting for ocean mortality and 

harvest. Ocean mortality has been linked with indices of ocean conditions (Nickelson, 1986), which are 

related to ocean biological productivity. Ocean survival also may have a density-dependent component 

such that lower survival is associated with higher numbers of smolts (Emlen and others, 1990; 

Coronado and Hilborn, 1998). In this document, the terms spawners and escapement are used 

interchangeably because we do not have information regarding mortality between the last fishery 

interceptions and actual spawning. Ricker curve parameters describing productivity were developed for 

the number of smolts expected from total escapement rather than estimating the number of females.

The model includes a user-friendly interface to easily change certain model parameters and 

inputs. The interface allows users to conduct sensitivity analyses, incorporate future knowledge or 

changing conditions, and implement scenarios for various future conditions. The model can be initiated 

annually with number of spawners or it can use the number of spawners predicted from the last model 

run for subsequent generations. Environmental variables are provided by brood year (BY) rather than 

the calendar year. For example, summer flow in 1992 and ocean conditions in 1994 are associated with 

BY 1991, which is short for spawning that occurs in late 1991 and early 1992. Results can be viewed 

graphically or in a table in Stella Architect 1.1.2®, or results can be output to an Excel® spreadsheet; 

they also are represented by brood year rather than calendar year. Additionally, the user can choose 

between two flow gages and two alternative sets of ocean indices to drive the model. Definitions of 

model terms and detailed instructions for obtaining and running the model are in appendix A, section 

“Definitions of Model Terms” and appendix B, section “Running the Model.” 
8 

Data Sources

Fish-related data (for example, smolts, escapement, harvest; table 1) are compiled or calculated 

by Pete Kairis, resource manager for the Swinomish Indian Tribe on behalf of the Skagit Salmon Comanagers for use in influencing pre-terminal and terminal harvest levels and other management 

decisions (specific data used in model are excerpted in appendix C). These data are direct estimates or 

calculations from monitoring efforts of others. Smolt estimates and escapement are provided by 

Washington Department of Fish and Wildlife (WDFW). Pre-terminal harvest estimates are calculated 

based on expansions of coded wire tag data after accessing them from the Regional Mark Processing 

Center at the Pacific States Marine Fisheries Commission (http://www.rmpc.org, accessed June 2017). 

Results are updated regularly by the Skagit Salmon Co-managers as new records are added, sometimes 

several years after a given cohort has returned. Therefore, harvest estimates can change slightly over 

time. These data are preferred over pre-terminal harvest data produced in static annual reports by the 

Pacific Salmon Treaty Coho Technical Committee. Stock-specific terminal catch data are housed in the 

Treaty Online Catch Accounting System (TOCAS) database, serviced by the Skagit Salmon Comanagers.

Table 1. Data sources used to develop Skagit River coho salmon life history model.

Variable Source Time series

Smolt numbers Washington Department of Fish and Wildlife 1991–2014

Smolt fork lengths Washington Department of Fish and Wildlife 1990–2015

Escapement (spawners) Skagit Salmon Co-managers1 (appendix C) 1989–2016

Harvest Skagit Salmon Co-managers (appendix C) 1992–2016

Ocean Mortality Skagit Salmon Co-managers (appendix C) 1992–2016

Flow U.S. Geological Survey streamgages: Sauk above White Chuck (12186000), 

Sauk (12189500), Mount Vernon (12200500)

1990–2013

Turbidity Anacortes Water Treatment Plant 1998–2012

Pacific Decadal 

Oscillation (PDO)

http://research.jisao.washington.edu/pdo/PDO.latest.txt (accessed November, 

2016)

1900–2015

North Pacific Gyre 

Oscillation (NPGO)

California Current Ecosystem Long-term Ecological Research, 

http://www.o3d.org/npgo (accessed May 2017)

1991–2014

Chlorophyll-a Washington Department of Ecology Marine Waters Monitoring Program, 

Saratoga Passage site (SAR003) 

http://www.ecy.wa.gov/programs/eap/mar_wat/data.html (accessed May, 

2017)

2002–2014

Sea level at Neah Bay Permanent service for Mean Sea Level, http://www.psmsl.org (accessed 

November, 2016)

1990–2013

1

Sauk-Suiattle, Swinomish, and Upper Skagit Indian Tribes, and Washington Department of Fish and Wildlife.
9 

Model Details

Modeling “Predicted Smolts”

Summary

The model predicts annual smolt production based on the number of returning adults and

presence of pink salmon in the juvenile year by using Ricker stock-recruitment curves estimated from 

years with and without pinks present during the coho juvenile stage (table 2). Changes in the abundance 

of rearing habitat can be incorporated by adjusting the carrying capacity parameter of the Ricker curves. 

The rationale for each of these factors and how they are parameterized in the model follows. Regression 

analyses for these and other relationships in the model were conducted using the statistical software R 

version 3.4.0 (R Core Team, 2013). 

Table 2. Model relations to determine “predicted smolts.” 

Relation Function/process

Ricker initial slope parameter Pink years, 4.292; non-pink years, 3.844

Ricker carrying capacity Pink years, 1,460,832; non-pink years, 1,044,869

Stock recruitment with pinks Predicted smolts = spawners x exp(4.29- 0.00001841 x spawners)

Stock recruitment without pinks Predicted smolts = spawners x exp(3.84- 0.00001643 x spawners)

Habitat carrying capacity 0.31 smolts per square meter (default)

Effect of change in habitat on stock 

recruitment —pink years

Carrying capacity parameter is adjusted by (4.292 -1)/(habitat change x 

habitat capacity + 1,460,832) where habitat change is area of habitat 

restored or lost

Effect of change in habitat on stock 

recruitment— non-pink years

Carrying capacity parameter is adjusted by (3.844 -1)/(habitat change x 

habitat capacity + 1,044,869) where habitat change is area of habitat 

restored or lost (m2)

Stock-Recruitment Curves

Stock-recruitment relations are used by fisheries managers to predict the number of smolts 

expected from a given number of spawners in a particular area. Two commonly used relations are the 

Ricker (Ricker, 1954) and Beverton-Holt (Beverton and Holt, 1957) curves. Although they have 

different formulas and shapes, each has a parameter describing density-independent recruitment, which 

describes survival when densities are low, and a parameter describing density-dependent processes, 

which limits productivity with increasing numbers of spawners (Maceina and Pereira, 2007). The 

relations differ in that the Beverton-Holt curve approaches an asymptote of productivity with higher 

numbers of spawners, whereas the Ricker curve declines at higher spawner numbers, indicating overcompensation owing to processes such as cannibalism or predation. 
10 

Choosing which stock-recruitment model is appropriate for a given species or stock is 

problematic. Zhou (2007) used artificially generated datasets with differing underlying stockrecruitment models to indicate that the correct model may not be distinguishable based on statistical 

methods or information criteria. Caution against using statistical inferences to evaluate spawnerrecruitment relations is advised (de Valpine and Hastings, 2002; Maceina and Pereira, 2007). Rather, 

knowledge of biology and behavior of specific species may provide insight into which model is 

appropriate (Zhou, 2007). It has been argued that that the Beverton-Holt relation is more appropriate to 

describe coho behavior than the Ricker curve (Barrowman and others, 2003). Specifically, little densitydependent mortality is expected until the habitat fills because coho juveniles are territorial (Sandercock, 

1991); after habitat fills, productivity is expected to stabilize. However, coho are not territorial in winter 

(Sandercock, 1991), suggesting that populations that are more limited by winter habitat than summer 

habitat may show Ricker-type dynamics (Barrowman and Myers, 2000). Moreover, even if all stages of 

the lifecycle indicate compensatory dynamics (that is, Beverton-Holt dynamics), if density dependence 

from early stages also affects later stages (concurrent density dependence), the resulting dynamics will 

be over-compensatory (that is, Ricker dynamics, Bjorkstedt, 2000). Because concurrent density 

dependence is expected in salmon populations (Bjorkstedt, 2000), and coho biology does not necessarily 

support use of the Beverton-Holt equations, we based the coho model on the Ricker stock-recruitment 

curve (fig. 3).

Figure 3. Ricker stock-recruitment curve calculated from smolt and spawner data collected on the Skagit River, 

northwestern Washington, brood years 1989–2012. 
11 

The structure of this dataset is such that the year with highest escapement (2004; rightmost point 

in figure 3) also happens to have the lowest summer flow the and lowest number of pink salmon for a 

cohort with pinks present. Thus, there are multiple factors potentially lowering recruitment that year and 

perhaps exaggerating the downward curve of the Ricker curve, and, therefore, exaggerating typical 

density dependence. If the Ricker curve is estimated without that point, parameters describing initial 

recruitment rate and carrying capacity are 0.962 and 1.02 times those estimated from the entire dataset. 

We conclude that the Ricker curve calculated from the entire dataset is robust to the situation of this 

point. 

Because most fish return to spawn 3 years after they hatch, there are three mostly distinct 

cohorts. However, some fish deviate from the 3-year cycle by remaining in freshwater for 30 months or 

returning from the ocean at age 2 (jacks) (Sandercock, 1991). These less common life histories are not 

considered in the model because they are relatively rare in the Skagit system. Jacks comprised on 

average 3 percent of the returns to the Marblemount hatchery from 1977 to 2012, in agreement with the 

observation that jacks become less common on a south-to-north gradient (18.4 percent, Wadell Creek, 

California; 0-2 percent, sites in British Columbia) in data compiled by Drucker (1972). Juveniles 

remaining in freshwater for 30 months also are most common in Alaska and are associated with colder 

water temperatures causing slower growth; very few 2-year-old coho smolts occur in the region south of 

British Columbia, including the Skagit River (Sandercock, 1991). 

Pink Salmon

Pink salmon spawn in odd-number years in Puget Sound, numbering in the tens of thousands to 

over a million in the Skagit River. Pinks spawn in September–October then immediately migrate to 

saltwater upon fry emergence, usually in March–April (Heard, 1991; Michael, 1995). Coho production 

is greatly enhanced for cohorts that are juveniles during pink compared with non-pink years

(Zimmerman, 2012) owing to several hypothesized mechanisms. These include disturbance of the 

stream bottom by spawning pinks to displace insects or previously spawned eggs, thereby creating 

easier prey; decomposing pink carcasses, eggs, and emerging fry as a direct food resource for juvenile 

coho; and general fertilization of the stream food web (Michael, 1995; Wipfli and others, 1999; Nelson 

and Reynolds, 2014). To account for the productivity disparity in pink compared with non-pink years, 

the coho model uses different curves to predict smolts from spawners in pink (odd-numbered) compared 

with non-pink (even-numbered) brood years (fig. 4). Estimated carrying capacity based on the Ricker 

model is more than 1.46 million coho smolts in pink years compared with about 1.04 million in nonpink years. Productivity in pink years was not significantly related to number of pinks (regression of 

smolts on pinks, coefficient of determination [r2

] = 0.16, p > 0.05) in this dataset where numbers of 

pinks always exceeded 60,000. If future pink runs are less than 60,000, number of pinks may need to be 

considered rather than simply presence-absence of pinks. A study of coastal streams in central British 

Columbia also showed juvenile coho abundance to be related to the number of pinks present just prior to 

coho spawning (Nelson and Reynolds, 2014). However, pink salmon returned to these small streams 

annually, so fish population dynamics were quite different than those on the Skagit River. 
12 

Figure 4. Comparison of smolt recruitment curves expected in years with and without spawning pink salmon, 

brood years 1989–2012. 
13 

Physical Habitat

Coho are unusual among salmon species because they have an affinity for low-velocity habitat 

throughout their freshwater existence (Lestelle, 2007). Juvenile coho also prefer water depths of 0.46–

1.2 m (Beecher and others, 2002). In the Skagit River basin, spawning occurs in tributaries upstream 

from Burlington (Washington Department of Fish and Wildlife, 2016), with about 12 percent of 

documented spawning occurring in the Sauk River Basin (Washington Department of Fish and Wildlife,

2017). Beechie and others (1994) estimate that spawning habitat in the Skagit River Basin is not 

limiting coho salmon abundance such that summer habitat can be fully seeded even if survival to 

emergence is low. Emergent fry typically stay in slow-velocity stream margins or backwaters of their 

natal stream (Sandercock, 1991) or move downstream to avoid competition (Chapman, 1962), owing to 

displacement by high flows (Hartman and others, 1982), or in search of more suitable habitat (Au, 

1972). Even when present in the main stem Skagit River, coho occupy low-velocity habitats such as 

banks and backwaters (Beechie and others, 2005). In other systems, coho are known to move in order to 

avoid high (>15 °C to 19 °C) stream temperatures or low flows (Konecki and others, 1995; Kruzic, 

1998; Sutton and Soto, 2012), conditions that are not yet prevalent in the Skagit River but may become 

more common because of climate change (Mote and others, 2003; Mantua and others, 2010). Coho also 

may redistribute in autumn, sometimes great distances, to avoid areas with high flows and extremely 

cold water (Fedorenko and Cook, 1982; Scarlett and Cederholm, 1984). Their preferred winter habitats 

include off-channel ponds, beaver ponds, and side channels (Peterson and Reid, 1984; Scarlett and 

Cederholm, 1984), especially near complex wood cover (for example, rootwads) (Beechie and others, 

2005). 

Physical habitat for fish in rivers of the Pacific Northwest was greatly altered from 1880 to 1960 

as settlers exploited natural resources for hydropower, forest products, and agriculture (Beechie and 

others, 1994). Activities included hydromodification (diking, ditching, dredging) associated with 

agriculture and urban lands, which accounts for 73 percent of coho summer habitat loss and 91 percent

of winter habitat loss since before European settlement. Additionally, poorly functioning culverts on 

small tributaries have caused 13 percent loss of summer habitat and 6 percent loss of winter habitat, and 

forestry activities are responsible for 9 percent of summer habitat loss and 3 percent of winter habitat 

loss (Beechie and others, 1994). The large-scale activities have diminished since passage of the National 

Environmental Policy in 1970, but incremental losses continued (Beechie and others, 1994). 

Changes in habitat availability should manifest as changes in ecosystem carrying capacity.

Organized efforts to improve fish habitat in the Skagit River watershed began in 1990 with the

establishment of the non-profit organization Skagit Fisheries Enhancement Group (Skagit Council of 

Governors, 2009). Momentum increased in 1997 with the formation of the Skagit Watershed Council, a 

consortium of private, county, State and Federal agencies (Skagit Council of Governors, 2009). 

Restoration in the Skagit River Basin was estimated as 590,733 m2 in 2004 (Washington Department of 

Fish and Wildlife, 2004). If these areas were located in side channels and distributaries, they are 

expected to yield 0.391 smolts/m2 in summer (Beechie and others, 1994) or 0.141 million smolts. If they 

were located in tributaries, they are expected to yield 0.298 smolts/m2 in summer (Beechie and others, 

1994) or 0.123 million smolts. Ricker curves calculated from data in the first one-third of the record 

compared with the last one-third indicate an increased carrying capacity (fig. 5).
14 

Figure 5. Comparison of Ricker stock-recruitment curves calculated for early and late in the period of record, 

brood years 1989–96 and 2006–12, respectively. 

The model allows the user to increase or decrease available habitat to represent predicted or 

actual changes owing to habitat restoration or degradation, loss of habitat owing to sediment deposition, 

or exclusion of fish from areas owing to changes in water quality such as might result from climate 

change-caused increases in water temperature. Projections of future average August stream temperatures 

at the 1-km scale owing to climate change can be obtained from the NorWeST project 

(http://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html, accessed December 2016). Available 

coho habitat can be adjusted based on the range of observations of coho tolerance reported in the 

literature. Eaton and Sheller (1996) noted that coho avoid temperatures greater than 23.4 °C. Frissell 

(1992) observed coho to be rare or absent where temperatures exceeded 21 °C. Areas with maximum 

weekly maximum temperatures (MWMT) of 18 °C (Welsh and others, 2001) or those ranging from 23 

to 27 °C (Madej and others, 2006) also have been devoid of coho. Results from the NorWeST project 

for streams classified as “documented rearing habitat” (Washington Department of Fish and Wildlife, 

2017) indicate that mean August stream temperature will be 18–20 °C in 24 km of the South Fork 

Skagit and main-stem Skagit Rivers, in 2–3 km of lower Hansen Creek, in about 10 km of Day Creek, 

and in about 13 km of lower Finney Creek in the 2080s using the A1B climate-change scenario 

(Pachauri and Reisinger, 2008). At the same time, stream temperatures are projected to be 20–30 °C on 

about 5 km of the Nookachamps Creek. This is a small fraction of the estimated 756 km of river 

accessible to salmon in the Skagit system (Washington Department of Fisheries, 1975). 
15 

Modeling “Realized Smolts” 

Summary

The model determines “realized smolts” by adjusting the number of smolts predicted by the 

stock-recruitment relation to account for environmental conditions through the freshwater life stages 

(table 3). We tested the explanatory ability of several flow parameters and then developed relations for 

inclusion in the model. Summer low flow was used to determine the fraction of smolts predicted by 

Ricker curves that would actually be realized. Cumulative turbidity is used as a surrogate for high flows 

during the freshwater stage and as a direct stressor. Scouring flows are too infrequent to develop a 

statistical relation, so the basis for this relation primarily is anecdotal and it is modeled as a percentage 

decrease in smolt production in the case of a flow event exceeding a threshold. The rationale for and 

development of these relations follows. 

Table 3. Model relations to determine “realized smolts.” 

Relation Function/process

Summer low flow—Sauk gage Fraction of Ricker prediction realized = 0.0001403 x average summer 

flow April to Sept + .3700

Summer flow—Sauk above White Chuck 

gage

Fraction of Ricker prediction realized = 0.0004445 x average summer 

flow April to Sept + .5054

Turbidity—Sauk gage Change fraction of Ricker expected to be realized by -.000163 x (days 

with turbidity >30 NTU during freshwater stage)

2 + 0.0070 x (days 

with turbidity >30 NTU) – 0.0351

Turbidity—Sauk above White Chuck gage Change fraction of Ricker expected to be realized by -0.000098 x (days 

with turbidity >30 NTU during freshwater stage)

2 + 0.001308 x (days 

with turbidity >30 NTU) – 0.2559

Scour threshold Sauk gage: 50,000 ft3/s; Sauk above White Chuck gage: 20,000 ft3/s

Scour effect Fraction of Ricker prediction expected to be realized is reduced by 0.2

Explanatory Flow Parameters

Based on many years of forecasting coho returns to Puget Sound and Washington coastal rivers, 

Seiler and others (2003) observed that, in addition to the presence of pink salmon (accounted for in this 

model with choice of Ricker curve), inter-annual variation in coho productivity in the Skagit River 

primarily is driven by winter flows (affecting eggs), summer flows (determining summer rearing 

habitat), and autumn flows (determining spawner distribution). Seiler and others (2003) also anecdotally

attributed high smolt production in 2002 to low flow during spawning, incubation, and early rearing 

beginning in autumn 2000. They postulated that higher-gradient streams in large systems, which yield 

low numbers of fry in normal years, could be more productive under lower, less disturbing flows. 

Unfortunately, the resolution of salmon abundance data does not allow for description and validation of 

the spatial complexity of fish response to changing flows. 
16 

Potentially important flow variables were statistically tested for explanatory ability using the 

linear regression procedure (“lm”) in R (R Core Team, 2013). Because the natural logarithm (ln) of 

smolts per spawner is a linear function of spawners, ln of number of smolts per spawner was regressed

against number of coho spawners, pinks during brood and juvenile years, peak and mean flow during 

egg and alevin stage (October–March), 7-day averaged low, and mean flow during first summer (April–

September). Predictor variables were normally distributed and correlations were not significantly 

different from zero (p<0.05). Regression models were evaluated using Akaike’s Information Criterion 

for small samples (AICc; Hurvich and Tsai, 1989), and results indicate that numbers of pinks and 

spawners and the flow variables (mean summer flow and peak winter flow during the juvenile stage) 

were important (table 4).

Table 4. Best-ranked multiple regression models explaining smolt production.

[95-percent confidence set of best-ranked regression models (those with cumulative Akaike weight less than or equal to 0.95) 

examining effect of number of spawners, number of pinks, peak flow during egg and alevin stage (winter flow 1), 7-day 

averaged low and mean summer flow (summer flow), and peak and mean 7-day averaged winter flow (winter flow 2) on 

natural log of smolts per spawner. Abbreviations: R2

, coefficient of multiple determination; df, degrees of freedom; logLik,

logarithm of likelihood ; AICc, Akaike information criterion for small samples; delta, model AICc minus minimum AICc; 

weight, probability of being best model]

Model R2 df logLik AICc delta weight

Pinks, summer flow, winter flow 2, spawners 0.931 6 9.300 0.40 0 0.616

Pinks, summer flow, spawners, 0.906 5 6.292 2.03 1.631 0.273

Pinks, summer flow, winter flow 2, spawners, winter flow 1 0.937 7 10.18 3.82 3.482 0.111

Summer Low Flow 

Studies spanning many decades have documented that higher numbers of returning coho adults 

are associated with higher summer flows during freshwater rearing. The effect was reported for the 

Pacific Northwest by Neave (1949), for Puget Sound by Smoker (1955), and was reaffirmed by 

Hartman and Scrivener (1990) and Quinn and Peterson (1996). The positive effect of higher summer 

flows primarily is attributed to increased habitat availability (Zillges, 1977; Zimmerman, 2012). An 

estimated habitat loss for steelhead (Oncorhynchus mykiss) of 1-2 percent is associated with a 1–2 

percent loss in streamflow from the 90 percent exceedance flow in several Puget Sound Basins 

(Caldwell, 1999). Other potential effects of low summer flow on juvenile fish include high stream 

temperature, low dissolved oxygen, disease, stranding, overcrowding (Mathews and Olson, 1980), 

higher predation, and loss of connectivity. Summer low flow is one parameter used by the WDFW to 

estimate freshwater productivity in rivers without smolt counts to forecast the number of returning coho 

adults to Puget Sound 2 years later (Zimmerman, 2012). 

The Skagit River system has snow-dominated basins in the upper watershed where winter 

precipitation accumulates as snow (Hamlet and others, 2001; Mantua and others, 2010), causing 

summer flow to be sustained by melting snow. Climate change is projected to cause mixed rain and 

snow to fall in these basins in winter, resulting in lower summer flows (Mote and others, 2003; Mantua 

and others, 2010), and the duration of the summer low-flow period is projected to lengthen (Elsner and 

others, 2010). Specifically, the 7-day average low-flow magnitude with a 2-year return interval (7Q2) is 

projected to be 65–75 percent of historical levels (1970–99) for the Skagit by the 2080s using the A1B 

climate-change scenario (Mantua and others, 2010). 
17 

The coho model uses mean summer flow (daily April–September) to refine the estimate for 

number of smolts calculated as the model stock “predicted smolts” (fig. 2). The fraction that the Rickerbased prediction represents of the actual number of smolts for each brood year was regressed against the 

mean daily summer flow (April–September) at the Sauk (U.S. Geological Survey [USGS] 12189500, 

fig. 6) and the Sauk above White Chuck (USGS 12186000) streamgages. Data from tributary 

streamgages are used in the model because they may better represent conditions experienced by coho 

than those on the main-stem Skagit River. Results show that productivity increases with increasing 

summer flow such that productivity may exceed the average capacity predicted from the Ricker curve. 

This relation is used in the coho model to adjust the number of smolts predicted by the Ricker curve to 

indicate summer flow.

Figure 6. Regression showing fraction of smolt prediction from Ricker curve actually realized owing to summer 

flow conditions measured at Sauk River streamgage (USGS 12189500). 

Winter High Flow

Coho are morphologically adapted to swimming in low-flow environments by having laterally 

compressed bodies and large median and paired fins (Bisson and others, 1988). Therefore, they are 

vulnerable to high flows, which normally occur in winter in the Pacific Northwest. Winter mortality can

be substantial (Hartmann and others, 1987) and seems to be related to the availability of the sheltered 

habitat (Murphy and others 1984) that coho prefer in winter (McMahon and Hartmann, 1989; Shirvell, 

1990). Winter rearing habitat has been identified by many authors as an important limiting factor for 

coho across the Pacific Northwest (Reeves and others, 1989; Nickelson, 1998; Lawson and others, 

2004) and for the Skagit River Basin specifically (Beechie and others, 1994; Seiler and others, 2003). 

Adverse winter rearing conditions (that is, high flows and scouring) for coho are projected to become 

more frequent because of climate change (Mote and others, 2003). 
18 

The visual comparison between the deviation of actual/predicted productivity predicted by the 

Ricker recruitment curve compared with high-flow events indicates a threshold relation where flows 

greater than about 30,000 ft3/s at the Sauk streamgage are associated with lower productivity (fig. 7). A 

breakpoint regression analysis using the “strucchange” package in R (Zeileis and others, 2003) 

confirmed evidence of a breakpoint at 31,000 ft3

/s (Sauk streamgage) and 7,000 ft3/s (Sauk above White 

Chuck streamgage) but not at the p<0.05 significance level. This relation differs from the relation seen

by Lawson and others (2004) in coastal systems, which seems to indicate that highest survival is 

associated with an intermediate flow that balances habitat availability with detrimental flows. Perhaps 

the different dynamics can be explained because compared with Puget Sound systems, coastal systems 

are smaller, lower-gradient, and rain-dominated, and likely have less in-stream large woody debris 

because of intensive logging. The effect of winter high flows was thought to be a component of the 

effect attributed to turbidity (see section “Turbidity”) and was not modeled independently. 

Figure 7. Graph showing effect of high flow during juvenile rearing period on residual deviation of smolt production 

from Ricker-based prediction after accounting for the effects of summer flow measured at Sauk gage (USGS 

12186000).
19 

Turbidity

Many Pacific Northwest rivers naturally transport considerable amounts of suspended sediments, 

especially those fed by glacial meltwater (Lloyd and others, 1987). The most commonly used index of 

suspended sediment transport is turbidity, largely because of ease and low cost of measurement (DaviesColley and Smith, 2001). Because of inconsistent relations between turbidity and mass of suspended 

sediments as well as poor repeatability among turbidometers (Duchrow and Everhart, 1971), it would be 

better to use water clarity or actual measurement of suspended sediment mass to indicate the biological 

effect of suspended sediments (Henley and others, 2000; Davies-Colley and Smith, 2001). Nevertheless, 

turbidity is a relative measure and can be used as an index of suspended sediment concentration (Sigler 

and others, 1984; Davies-Colley and Smith, 2001).

Turbidity varies among and within watersheds based on geology and location in stream system, 

as well as seasonally in response to timing of precipitation and runoff (Servizi and Martens, 1987; Bash 

and others, 2001). Models of sediment supply and transport based on road density and natural and landuse-caused supply rates indicate that higher than natural levels exist in the Skagit because of clearcutting, road construction and clearing of log jams (Smith, 2003). Sediment loads are expected to further 

increase because of climate change effects on glacier retreat and flooding (Moore and others, 2009). The 

current sediment supply is estimated to be 1.5 times the natural rate in the Skagit River Basin in spite of 

the effect of dams on the upper Skagit River to trap sediment, with the greatest load on the lower Sauk 

River (Beamer and others, 2000). Sediment supply rate is of concern to the Sauk-Suiattle Tribe because 

of its potential effect on pocket estuaries, which are used as rearing habitat by Chinook salmon and to a 

lesser extent, coho salmon (Magnuson and Hilborn, 2003; Greene and Beamer, 2012). Therefore, the

Sauk-Suiattle Tribe has partnered with USGS to monitor sediment at several places on the Sauk-Suiattle 

river system since 2006. 

Turbidity can have diverse negative lethal, sublethal and behavioral effects on fish, even at 

relatively low values (18–70 Nephelometric Turbidity Units [NTU]; Gregory, 1992). Effects can be 

physiological (for example, gill trauma [Berg and Northcote, 1985], growth rate [Sigler and others, 

1984]); behavioral (for example, avoidance of area [Berg, 1982; Servizi and Martens, 1987], loss of 

territoriality [Berg, 1982; Berg and Northcote, 1985], reduced ability to forage [Gregory and Northcote, 

1993]); or habitat related (for example, smothering of redds [Cederholm and Salo, 1979], alterations of 

hyporheic flow [Sedell and others, 1990; Poole and Berman, 2001]). Effects seem to depend on level of 

exposure and duration (Newcombe and MacDonald, 1991). Alternatively, coho appear to prefer slightly 

to moderately turbid water for feeding (Sigler and others, 1984; Boehlert and Morgan, 1985; Gregory, 

1993; Gregory and Northcote, 1993). It seems that turbidity can substitute for cover to protect fish from 

predation (Gregory and Levings, 1996). Therefore, there may be a tradeoff between reduced 

vulnerability to predation in clear water and inability to feed in very turbid water. 
20 

Levels of turbidity observed to have deleterious effects for coho (including avoidance of habitat, 

slower growth rates, and reduced survival) vary among studies. These effects can be seen at relatively 

low turbidities (18–70 NTU; Lloyd and others, 1987; Gregory, 1992). Bisson and Bilby (1982) reported

that fish are displaced by greater than (>) 70 NTU. Gill flaring was seen at 60 NTU and ceased when 

levels decreased to 30 NTU (Gregory and Northcote, 1993). Berg (1982) reported that juvenile coho 

avoid areas with 60 NTU but return at 20 NTU. Sigler and others (1984) noted a significantly lower 

growth rate at 25 NTU compared with clear water, whereas newly emerged fry had a lower growth rate 

at 25—50 NTU. Determining harmful levels of turbidity is complicated by the need to incorporate 

duration of exposure as well as level (Newcombe and MacDonald, 1991). Effects also depend on the 

size and shape of particles and whether they are carrying pollutants (Lake and Hinch, 1999). Moreover, 

fish are adapted to survive in turbid waters at particular life stages (for example, migration though the 

estuary). Positive effects of turbidity to reduce predation on fish have been documented at 11—13 NTU 

(Abrahams and Kattenfeld, 1997). 

Turbidity is monitored by the City of Anacortes, Washington, at the Anacortes Water Treatment 

Plant located in Mount Vernon, Washington (river kilometer 25). Readings have been taken at 15-

minute intervals since 1999 and integrate sources of suspended sediment from most of the Skagit 

watershed upstream of the estuary. Considering turbidity values associated with harm to fish and the 

need to incorporate duration as well as level of exposure, the number of days with maximum turbidity 

>30 NTU during the freshwater life stages from alevin to smolt (February–April of the following year) 

was selected as an index of exposure to turbid conditions. 

Usefulness of the index was determined by examining whether the index explained residual 

deviations of actual smolts from those predicted by spawners using the Ricker curve and after taking 

summer flow into consideration (fig. 6). Because collection of turbidity data began in BY 1998, the 

earlier turbidity regime was estimated based on the regression of daily average turbidity against daily 

average flow (USGS streamgage 12200500), both measured in Mount Vernon. We adopted a sequential 

approach by first predicting realized smolts expected from summer low flow (fig. 6). Next, the 

remaining deviation (actual smolts/corrected smolt prediction minus fraction predicted based on low 

flow) was regressed against turbidity (days with maximum turbidity >30 NTU). Results show a 

significant (model, p<0.05) relation with a quadratic shape (fig. 8), in keeping with literature accounts 

that both low and high levels of turbidity can have adverse consequences for fish. 
21 

Figure 8. Regression showing relation of number of days with turbidity greater than (>)30 Nephelometric Turbidity 

Units during the freshwater life stages of coho cohorts (February following spawning through April of following year)

with residual deviation of actual from predicted smolts after summer low flow is accounted for.

Turbidity rather than winter high flow was used as a predictor in the coho model for several 

reasons. First, we considered the strong ability of days with turbidity >30 NTU to explain deviations of 

predicted smolt numbers from actual smolt numbers after summer low flow (fig. 8). Second, there is a

poor association between flows in the overwinter rearing period and subsequent smolt abundance below 

a threshold (fig. 7), and few years in the record exceed the threshold flow where effects of higher flows 

can be detected. Finally, the relative strength of the relation between flow and turbidity indicates that 

they may include redundant information. Suspended sediment in streams, and, therefore, turbidity, 

naturally are a function of streamflow and resulting erosion and transport (Servizi and Martens, 1987). 

The relation between turbidity and flow (turbidity as a power function of flow) has an r2 of 0.77 at 

Anacortes (November through January for 1999 to 2009). Therefore, we use the relation between smolt 

abundance and turbidity in figure 8 to further modify the fraction of Ricker-predicted smolts already 

adjusted to account for summer flow. 
22 

Scouring Flow

The Skagit River Basin is termed rain-dominated in lower elevations and either snow-dominated 

or transient (mixed rain and snow) at higher elevations (Mote and others; 2003; Beechie and others 

2006; Mantua and others, 2010). Rain-dominated areas typically show peak flow coincident with 

highest precipitation, specifically early winter in the Pacific Northwest. Snow-dominated areas show a 

spring or early summer peak in flow coincident with snowmelt. Transient watersheds have both rain and 

snow during winter as well as two peaks in flow: early winter and spring/early summer (Hamlet and 

others, 2001). Climate change projections predict that the magnitude and frequency of flooding will 

increase most substantially in December and January for transient watersheds (Mote and others, 2003) 

because winter precipitation is projected to increase and more of it will fall as rain because of warmer 

air temperature. Climate projections also forecast that, by 2080, all snow-dominated basins in the Skagit 

watershed will become transient, thereby shifting peak flows from spring to winter (Mantua and others, 

2010). 

Greater magnitude and frequency of high flows lead to greater vulnerability of anadromous fish 

to scouring of redds (Mote and others, 2003; Goode and others, 2013) because of increased number and 

depth of scour events (Montgomery and others, 1999). This is especially true for autumn spawners such 

as coho in snow-dominated basins (Montgomery and others, 1999; Goode and others, 2013) because the 

timing of high flows is expected to become more coincident with incubating eggs. These effects may be 

somewhat mitigated if higher flows result in greater access to additional spawning habitat.

Scouring events occur relatively infrequently in the historical record and could not be detected as 

significant drivers using regression methods on this relatively short 24-year time series. Moreover, their 

effect may be obscured by other events during freshwater rearing. However, Tribal resource managers 

observed a scouring event in November 2006 (Scott Morris, Sauk-Suiattle Indian Tribe, oral commun.) 

when instantaneous peak flow at the Sauk above White Chuck streamgage (USGS 12186000) reached 

33,600 ft3

/s. 

The effect of scour on egg mortality is a function of the magnitude and duration of peak 

discharge, grain size of bed-surface, and location in the channel (Holtby and Healey, 1986; Hassan, 

1990; Montgomery and others, 1999). Without sufficient data to describe these relations in the Skagit 

River or its tributaries, the model uses a simple peak flow threshold to determine scour events and a 

simple fractional reduction of production to estimate effects. The threshold was estimated by first 

considering that bed mobility across the channel is thought to begin at bankfull flows (Carling, 1987). 

Bankfull flow has a recurrence interval of 1–2 years in the Pacific Northwest (Castro and Jackson, 

2001). Using 92 years of annual instantaneous peak flow data from the Sauk above White Chuck 

streamgage, the flow frequency curve indicates that a recurrence interval of 2 years corresponds to peak 

flow of about 8,960 ft3

/s. Holtby and Healey (1986) determined that 65-percent mortality of eggs owing 

to scour occurred at about 2.5 times bankfull flow at Carnation Creek on the west coast of Vancouver 

Island, British Columbia. Using 2.5 bankfull as a rough estimate of when significant mortality might 

occur, peak flows of 22,400 ft3/s at the Sauk above White Chuck streamgage may pose a threat to 

salmon productivity. Instantaneous flow events greater than 22,400 ft3/s during November–December in 

this dataset include water years4 (WYs) 1990, 1996, and 2007. The event in WY 1996 corresponds 

4

A water year is the 12-month period from October 1, for any given year, through September 30 of the 

following year. The water year is designated by the calendar year in which it ends. 
23 

with an average daily peak flow of 18,900 ft3/s, which did not appear to affect smolt mortality, whereas

an average daily peak of 22,000 ft3/s in WY 2007 was known to be a scouring flow (Scott Morris, SaukSuiattle Indian Tribe, oral commun., November 2016). Therefore, the threshold for scour effects was set 

at 20,000 ft3/s for average daily peak flows occurring in November and December when spawning 

typically occurs. The same process applied to data from the Sauk streamgage (12189500) produces a 

threshold of 59,750 ft3

/s. Because a flow of 54,400 ft3/s in WY 2007 was known to be a scouring flow, 

the model uses 50,000 ft3/s as the threshold for scour for the Sauk streamgage. 

The fraction of salmon production lost because of scouring flows was modeled as a loss of 0.2 

from the fraction of “predicted smolts” expected to be realized owing to other flow factors. This is based 

on unexplained, lower-than-predicted production following the one known scouring event in the dataset. 

Scour thresholds for flow at the Sauk and Sauk above White Chuck streamgages and the effect of 

scouring flow on smolt survival can be adjusted by the model user to test model response to alternative 

values. 

The imposition of a fixed loss of productivity above a simple threshold during a discrete time 

interval certainly is an oversimplification of complex relations among timing of spawning, flow, and 

depth and geographic location of redds. Coho tend to spawn over a relatively long period from midautumn to early winter (Lestelle, 2007), and when scour occurs during the spawning period will 

determine the severity of effects. Spawning depth is partially determined by size of female (van den 

Berghe and Gross, 1984; Crisp and Carling 1989), which varies by individual and year. Scouring depth 

is linearly related to the logarithm of discharge (Moring and Lanz, 1975) and depth determines how 

many eggs are lost (Montgomery and others, 1999). Finally, the effects of an annual flow regime will 

vary across the Skagit River Basin, with some areas experiencing scour while others do not. 

Nevertheless, the model will enable users to experiment with the potential effects of more frequent high 

peak flows than represented in the data time-series. 

Modeling Escapement

Summary

After considering smolt size, number of smolts (density dependence), and numerous ocean 

condition indices, we determined that ocean mortality was best explained by number of smolts and a 

quadratic relation with PDO index (table 5). Because WDFW and Skagit Salmon Co-managers are now

(2017) using North Pacific Gyre Oscillation (NPGO) index and (or) chlorophyll-a monitoring in Puget 

Sound as predictors, these predictors also are provided as options in the model. The proportion of the 

coho population subject to harvest, as estimated from Skagit Salmon Co-managers data (appendix C), is 

subtracted in the model. Details regarding development of these relations follow. 

Table 5. Model relations to determine “escapement.” 

Relation Function/process

Ocean survival—PDO and smolts Logit ocean survival = (-0.00000038 x realized smolts) – 0.1421 x PDO 

+ 0.1068 x PDO2 – 0.8007

Ocean survival—NPGO and chlorophyll-a 

density

Logit ocean survival = 0.289 x NGPO + .0445 x chlorophyll-a density –

1.691

Harvest Fraction harvested = 1- estimated escapement/estimated OA3
24 

Ocean Survival

Marine survival of coho has been in decline in the Salish Sea (Puget Sound, Strait of Juan de 

Fuca, and Strait of Georgia) since estimates using wild indicator stocks began in the mid-1970s, 

although declines have been less in Puget Sound than other regions (Shaul and others, 2007; Beamish 

and others, 2010; Zimmerman and others, 2015). Determining the factors that influence ocean survival 

of salmon has not been successfully accomplished (Dorner and others, 2013; Zimmerman and others, 

2015). Candidate parameters include salmon population characteristics such as smolt size and numbers; 

and physical and biological ocean condition indicators such as sea-surface temperature and pressure 

indices (for example, PDO, NPGO, Oceanic Nino Index [ONI]), timing and strength of upwelling, and 

various chemical and food web constituents) (Scarnecchia, 1981; Nickelson, 1986; Zimmerman, 2012; 

Araujo and others, 2013; Burke and others, 2013). Because salmon range widely through spatially and 

temporally dynamic, complex ecological environments and cannot feasibly be tracked, survival is 

unlikely to relate to single metrics in any one environment (Burke and others, 2013). 

Size. It makes intuitive sense that larger smolts should have an advantage over smaller ones for 

ocean survival. However, in several studies, large smolt size was not consistently associated with 

greater survival of coho among years or sites (Mathews and Ishida, 1989; Holtby and others, 1990; 

Tschaplinski, 2000, Irvine and others, 2013). Within individual years, larger size may confer greater 

survival, especially in years when ocean conditions are relatively poor (Mathews and Ishida, 1989; 

Holtby and others, 1990). Survival also may be related to time of ocean entry (Bilton and others, 1982; 

Thedinga and Koski, 1984; Morley and Station, 1988). Time of entry is indirectly related to smolt size 

because larger smolts tend to emigrate from freshwater to the marine environment earlier than smaller 

ones (Seiler and others, 1981, 1984). The size that fish attain during early ocean residence is correlated 

with ocean survival (Holtby and others, 1990). 

Density dependence. Ocean survival also may be subject to density dependence (Peterman, 

1982; Clark and McCarl, 1983; McCarl and Rettig, 1983; Coronado and Hilborn, 1998; Emlen and 

others, 1990), perhaps especially in years with low upwelling (Peterman and Routledge, 1983; McGie, 

1984). Others reported no density dependence for ocean survival (Peterman, 1981; Nickelson, 1986; 

Holtby and others 1990) or questioned whether the experimental designs used by others are sufficient to 

detect density dependence (Nickelson, 1986; Peterman, 1989; Pearcy, 1992). Hypothesized mechanisms 

for density-dependent mortality include increased predation of coho in the nearshore caused by large 

numbers of coho training piscine and avian predators to exploit the coho search image (Emlen and 

others, 1990), lower marine food supplies, faster spread of diseases and parasites in dense populations 

(Peterman, 1978), and larger numbers of fish owing to hatchery production. 

Ocean conditions. Many studies have reported correlations between marine survival of Pacific 

Northwest salmon and ocean conditions thought to indicate habitat quality in the Pacific Ocean. 

Relations with sea-surface temperature as described by various indices (Nickelson, 1986; Francis and 

Hare, 1994; Peterman and others, 1998, Di Lorenzi and others, 2008), upwelling parameters (Nickelson, 

1986; Scarnecchia, 1981; Ryding and Skalski, 1999), salinity, dissolved oxygen, and biological 

properties such as chlorophyll-a density and copepod richness and abundance (Zimmerman, 2012; 

Araujo and others 2013) showed no consistent result. It also seems that important factors also might be 

operating at various spatial and temporal scales, whether annual (Ryding and Skalski, 1999; Teo and 

other 2009) or decadal (Beamish and Bouillon, 1993; Mantua and others, 1997), by ocean basin or subbasin (Francis and others, 1998; Hare and others, 1999; Teo and others, 2009; Zimmerman and others, 

2015) or individual stream (Ryding and Skalski, 1999; Mueter and others, 2002), and that groups of 

sites with coherent survival patterns have shifting membership through time (Zimmerman and others, 

2015). Moreover, some time periods in the marine life stage may be more critical than others. 

Specifically, several authors have reported that ocean conditions at the time of ocean entry or for several 
25 

months thereafter are particularly important (Hobday and Boehlert, 2001; Beamish and others, 2010; 

Zimmerman and others, 2015), perhaps determining early ocean growth, which is associated with ocean 

survival (Holtby and others, 1990).

The need for more accurate forecasts of coho returns has motivated analyses to identify 

predictive indicators of ocean condition even though functional understanding is lacking. Although 

multivariate indices may be most appropriate representations of the complex marine environment 

(Burke and others, 2013), efforts to find relations predicting survival of Puget Sound, Strait of Juan de 

Fuca, and Skagit coho have focused until recently on univariate models or have found univariate models 

to be best (Zimmerman, 2012). Sea-surface temperature anomalies, such as PDO and ONI, generally are

associated with ocean survival of salmon. PDO describes monthly sea-surface temperature (SST) 

anomalies in the northeastern Pacific Ocean, north of 20° N latitude, an area closer to the Pacific 

Northwest than ONI, which describes the 3-month running mean of SST anomalies in the east-central 

Pacific Ocean (5° S to 5° N latitude, 170° W to 120° W longitude). 

The focus since 2015 has shifted to multi-variate predictions, specifically using NPGO, a seasurface pressure anomaly measured in the same region as PDO, and chlorophyll-a density measured in 

Saratoga Passage for Skagit coho. We used these findings of others (table 6) to justify investigating 

PDO (May–September), ONI (January–June), sea level at Neah Bay, and chlorophyll-a density 

averaged over the top 20 m of water depth in Saratoga Passage as indicators of marine conditions. We 

excluded mixed layer depth because data are difficult to obtain. We did not consider sea-surface 

temperature off the Oregon coast because it is highly correlated with PDO. We considered both linear 

and quadratic relations because Ryding and Skalski (1999) noted concave and convex relations between 

ocean mortality and condition indicators. 

Table 6. Indicators of ocean condition determined to be associated with ocean mortality of coho salmon in 

geographic areas near to or including the Skagit River, northwestern Washington. 

Geographic region Indicator Source

Strait of Juan de Fuca June sea-surface temperature off Washington coast

Spring transition

Upwelling 48°N

Ryding and Skalski, 1999

Puget Sound Sea level

Mixed layer depth

Hobday and Boehlert, 2001

Baker River (Skagit) Sea-surface temperature at station NH 05 

(nearshore buoy, Oregon coast)

Pacific Decadal Oscillation (May–September)

Oceanic Niño Index (January–June)

Zimmerman, 2012

Baker River (Skagit) Oceanic Niño Index (January–June) https://data.noaa.gov/dataset/climateorediction-center-cpcoceanic-ninoindex (accessed September 2017)

Baker River (Skagit) North Pacific Gyre Oscillation, chlorophyll-a 

density in Saratoga Passage

Zimmerman, 2017
26

To identify indicators of coho ocean success, logit-transformed ocean survival estimates 

(appendix C) were regressed against individual predictors describing ocean conditions (PDO, ONI, 

NPGO, and sea level), smolt abundance (number of smolts), and smolt size (fork length at smolt trap) 

from BY 1989 to 2012. Missing values of ocean survival for BY 2006 and 2007 were estimated by 

regression against ocean survival estimated for Skykomish River coho. As a result, fork length, NPGO, 

ONI, and sea level were eliminated from consideration for having no significant (p<0.05) explanatory 

value. A multiple regression of transformed ocean survival against the remaining predictors (PDO and 

smolts) resulted in a model where coefficients for smolts and linear and quadratic terms for PDO were 

significant (p<0.05). Using the shorter time series for which chlorophyll-a data were available (BY 

2001–12), a multiple regression of transformed ocean survival on NGPO and chlorophyll-a produced 

significant (p<0.05) coefficients for both. 

A quadratic relation between coho ocean survival and sea-surface temperature (monthly 

averages from buoy off Washington coast) has been seen by others for fish originating in the Strait of 

Juan de Fuca but not for coastal Washington and Columbia River coho (Ryding and Skalski, 1999), 

however the result was not explained. Salmon generally show better survival when sea-surface 

temperatures are cool (cool phase of PDO; Mantua and others, 1997). Because Skagit coho appear to 

rear primarily near the southwestern tip of Vancouver Island, Puget Sound and the Strait of Georgia 

(Weitkamp and Neely, 2002), they normally inhabit the California Current. However, they also may 

have access to the cooler water of the Alaska Current, which flows just north of Vancouver Island, when 

the California Current is adversely warm. The potential for coho to demonstrate interannual variability 

in spatial patterns of ocean use was hypothesized by Hobday and Boehlert (2001). Moreover, a 

quadratic relation may be more generally applicable because it also was seen to explain ocean survival 

of coho returning to Oregon coastal rivers (Rupp and others, 2012).

Based on these analyses, the coho model predicts ocean survival from a multiple regression that 

includes the number of smolts, and linear and quadratic terms for PDO. Note that the relation describing 

ocean survival using smolts and PDO is based on data mostly post-1990 when productivity in the Salish 

Sea experienced a major shift to lower ecosystem productivity (Zimmerman and others, 2015). Perhaps 

density dependence is less important when productivity of the Salish Sea is higher, as it was in the 

1970s and 1980s. The user also is given the option to use NPGO and chlorophyll-a density as predictors 

of ocean survival to correspond with latest practices of WDFW and Skagit Salmon Co-managers.

Harvest

The proportion of the coho population lost to harvest is simply modeled by subtracting the 

harvest percentage from fish surviving the marine environment (column “Total wild OA3”, appendix 

C). Harvest percentage was calculated by dividing estimated escapement by estimated survival to ocean 

age 3 (OA3), which represents ocean survival prior to commercial and recreational harvest.
27

Model Validation

The model was validated by asking two questions—(1) How well do model results reproduce the 

data that were used to generate the model (descriptive ability; Shmueli, 2010)?, and (2) how well does 

the model predict new data (predictive ability)? The first question was asked of model results when 

spawner numbers were generated from actual escapement estimates at the beginning of each brood year

(“non-cycling”), and also when the first three brood years were initialized with data and then the model 

results were initialized in subsequent years (“cycling”). Results from the non-cycling model indicate 

how well the model predicts each year individually based on initial spawners; results from the cycling 

version indicate how well the model predicts multiple lifecycles based on a single initiation of each 

cohort. The second question was answered using leave-one-out cross-validation (LOOCV; Geisser, 

1993) where model parameters were generated using all years but one, and then results from the missing 

year were generated from the model. This process was repeated for all years, and the error rate from the 

entire model was compared with the error rate when each year was predicted from the other years.

When the model is initiated annually with estimated escapement (model does not “cycle”), it 

generally reproduces the pattern of smolt production over time (r2

=0.7747), although some points are 

outside the 95-percent confidence intervals (CIs) around the data (fig. 9A) and the 95-percent CI of the 

regression slope of the actual smolt data against predicted smolts does not include 1.0, meaning that the 

model tends to over-estimate smolt production (slope, 1.1143; 95-percent CI, 1.03–1.19; fig. 9B). There

is no pattern in the residuals (fig. 9C), suggesting that the errors are random.

Because of an inadequate understanding of key factors affecting marine survival and potentially 

incomplete harvest data, results for escapement predicted using smolts and PDO from the annually 

initiated model (r

2 =0.3259; fig. 10B) are weaker than results for smolts. The model does not explain 

some years with poor returns (notably BYs 2003 and 2012) and one year with very high production (BY 

2001) (fig. 10A). The 95-percent CI of the slope of the regression of escapement data against modeled 

escapement includes 1 (slope, 0.72; CI, 0.28–1.16; fig. 10B). Residuals from the model prediction tend 

to be negative at high numbers of predicted escapement (fig. 10C). Escapement is better predicted using 

NPGO and chlorophyll-a (fig. 11; r2=0.8203) than using PDO and smolts (fig. 10; r2 = 0.3259) and 

NPGO and chlorophyll-a density to explain the high escapement observed in BY 2001. The 95-percent 

CI of the regression slope of actual against predicted escapement includes 1 (slope, 0.7593; CI; 0.51–

1.0; fig. 11B). Again, the residuals from the model prediction are most negative at high numbers of 

predicted escapement.
28 

Figure 9. Comparison of model results with estimated number of smolts from counts at smolt trap when each year 

is initialized with estimated number of spawners (escapement), 1989–2012. (A) Times series of predicted smolts 

and smolt counts. Error bars indicate 95-percent confidence intervals around smolt estimates (data and confidence 

intervals provided by Joe Anderson and Clayton Kinsel, Washington Department of Fish and Wildlife, January 

2017). (B) Regression of smolt counts against predicted smolts. (C) Graph of residuals (difference) of predicted 

smolts from smolt counts.
29 

Figure 10. Comparison of model results for estimated escapement predicted using Pacific Decadal Oscillation and 

smolts compared with data-based estimates when each year is initialized with estimated number of spawners 

(escapement), 1989–2012. (A) Times series of predicted escapement and estimated escapement. (B) Regression 

of estimated escapement against predicted escapement. (C) Graph of residuals (difference) of predicted 

escapement from estimated escapement.
30 

Figure 11. Comparison of model results for estimated escapement predicted using North Pacific Gyre Oscillation

and chlorophyll-a compared with data-based estimates when each year is initialized with estimated number of 

spawners (escapement), 2001–12. (A) Times series of predicted escapement and estimated escapement. (B) 

Regression of estimated escapement against predicted escapement. (C) Graph of residuals (difference) of 

predicted escapement from estimated escapement.
31 

When escapement for each cohort is initialized based on actual estimates for the first three brood 

years and subsequent escapement values are generated by the model (the model “cycles”), performance 

at predicting smolts is inferior to the non-cycling version (fig. 12A). This is likely owing to prediction 

errors propagating to the next generation of fish. The regression of actual smolt data against modeled 

smolt results (fig. 12B) shows a weaker association between data and the model (r

2 = 0.7747, noncycling, compared to 0.5899, cycling), but a slope close to 1 (slope, 0.9978; 95-percent CI, 0.90–1.09, 

fig. 12B). The residual plot (fig. 12C) shows no pattern, suggesting there no systematic source of error. 

Results for escapement predicted by PDO and smolts also are inferior in the cycling (r2

, 0.2317; fig.

13B) compared to the non-cycling model (r2, 0.3259; fig. 10B). The slope of the regression of actual 

against predicted escapement is low (slope, 0.5248; 95-percent CI, 0.34–0.71; fig. 13B), indicating that 

the model underestimates escapement. The residuals show increasing negative deviations, with higher 

predicted escapement, and may indicate a linear effect that is missing from the model (fig. 13C). 

Escapement is better predicted using NPGO and chlorophyll-a than using PDO and smolts in the cycling 

model (r2, 0.7114; fig. 14), but not a good as for the non-cycling model (r2, 0.8203; fig. 11). The 95-

percent CI for the slope of the regression of actual compared to predicted escapement includes 1 (slope, 

0.7675; 95-percent CI, 0.42–1.11; fig. 14B). Residuals of actual from predicted escapement show no 

pattern, with the exception of the most negative residual occurring with the highest predicted 

escapement.
32 

Figure 12. Comparison of model results with estimated number of smolts based on counts at smolt trap when the 

first 3 years of the model run are initialized with estimated number of spawners (escapement) and subsequently by 

model results. (A) Times series of predicted smolts and smolt counts. Error bars indicate 95-percent confidence 

intervals around smolt estimates (data and confidence intervals provided by Joe Anderson and Clayton Kinsel, 

Washington Department of Fish and Wildlife, January 2017). (B) Regression of smolt counts against predicted 

smolts. (C) Graph of residuals (difference) of predicted smolts from smolt counts.
33 

Figure 13. Comparison of model results for estimated escapement predicted using Pacific Decadal Oscillation and 

smolts compared with data-based estimates when the first 3 years of the model run are initialized with estimated 

number of spawners (escapement) and subsequently by model results. (A) Times series of predicted escapement 

and estimated escapement. (B) Regression of estimated escapement against predicted escapement. (C) Graph of 

residuals (difference) of predicted escapement from estimated escapement.
34 

Figure 14. Comparison of model results for estimated escapement predicted using NPGO and chlorophyll-a 

compared with data-based estimates when the first 3 years of the model run are initialized with estimated number 

of spawners (escapement) and subsequently by model results. (A) Times series of predicted escapement and 

estimated escapement. (B) Regression of estimated escapement against predicted escapement. (C) Graph of 

residuals (difference) of predicted escapement from estimated escapement.
35 

The LOOCV predicts results for individual years based on a model developed from all other 

years. After this is repeated for all years, the error between observed and predicted values for results 

generated by omitting years is compared with results for the whole model using parameters based on all 

years. This analysis was run for the non-cycling (escapement initialized from actual estimates for each 

brood year) version of the model and for escapement predicted from PDO and smolts. 

The LOOCV results for smolts show that the error for the full model is greater than the error 

from cross-validation predictions (table 7). These results indicate that the results from the validation 

runs are not significantly (p<0.05) different than results from the whole model. This can be interpreted 

to show that the model has similar ability to fit the data used to create as it has to fit new data. For 

escapement, LOOCV results show a slightly higher error for the cross-validation predictions compared 

with the full model (table 7). A possible cause of the greater error in the cross-validation results is that 

the model includes more parameters than can be estimated by the relatively small dataset; hence, the 

model fits the data very well but is not as good at predicting the results of new data. One solution for 

this situation is to simplify the model. Another interpretation is that each year of data provides important 

information to the model and the model can best be improved by obtaining more years of data. Given 

the simplicity of the model, the complexity of the system it attempts to describe, and the small 

difference between the estimated errors, accumulating more data may be the most productive means to

improve the model. 

Results from model-validation analyses indicate that the model does a good job of predicting 

smolts, both when cohorts are initialized annually and to a lesser extent when the model results are used 

to initialize subsequent life cycles. In both cases, the regression of actual smolts against predicted smolts 

is close to 1 (although not within the 95-percent CI for the annually initiated version) and residual plots 

do not show a pattern that would indicate exclusion of a systematic factor. The cross-validation exercise 

(conducted only for the non-cycling version) also indicates that the model structure does not include 

extra parameters.

Results of model validation are not as strong for escapement as for smolts, especially when PDO 

and number of smolts are used to predict ocean survival. Although most 95-percent CIs of regression 

slopes of actual escapement against predicted escapement include 1, the slope estimates themselves are

uniformly less than one. Residual plots indicate that this is due to an underestimate of production during 

highly productive years for all versions of the escapement model. Performance of the escapement model 

that uses NPGO and chlorophyll-a density to predict ocean survival is much superior to using PDO and 

smolts but can only be evaluated for the years when chlorophyll-a density is available. The crossvalidation analysis for escapement indicates that the model may benefit from future incorporation of a 

longer time-series of data.

Table 7. Results from leave-one-out cross-validation analysis. 

[Abbreviations: LOOCV, leave-one-out cross validation; RMSE, root mean square error]

Life stage Years RMSE model RMSE LOOCV

Smolts 1991-2010 280,833 233,067

Escapement 1991-2009 27,002 27,967
36 

Potential Uses of Model

This system dynamics model of coho in the Skagit River is a synthesis of available data based on 

current hypotheses regarding interactions between salmon and their environment. It is built to be easily

manipulated by tribal staff and others, and could be used for the following purposes: 

• Determining relative differences between climatic regimes and (or) management actions. In 

an era of potentially greatly changing climate and given the possibility that restoration activities 

can improve salmon habitat, the model provides an opportunity to experiment with combinations 

of actions and climate scenarios to evaluate the relative importance of specific changes in each. 

Although the model was developed using flow values from specific time periods (for example, 

March–October for low flow), the user is nevertheless able to experiment with changing the 

hydrologic regime. Advantageous or adverse events can simply be added to the appropriate 

hydrograph that represents a particular life stage. 

• Showing how much of annual variation can be explained and how much is not understood.

Years when model predictions and reality diverge are years when new hypotheses about system 

drivers can be developed to explain why the model, which describes what we think we 

understand, is not correct. The model is expected to become obsolete as novel drivers owing to 

changing climate and other ecosystem stressors come to dominate. We may already be seeing 

this as PDO becomes less explanatory of ocean mortality than NPGO and chlorophyll-a density. 

• Validating model with future data. As monitoring data continue to accumulate, they will 

represent a wider range of environmental conditions, including new combinations of average 

events as well as more extreme events. By comparing modeled survival with monitored survival, 

the veracity of the model can be tested.

• Testing sensitivity of results to parameter values. Parameters in the model are based on 

relations developed from relatively short time series of data, literature values, or expert opinion. 

By changing parameters and re-running the model, the user can determine the sensitivity of 

model outcomes. Eventually, a formal sensitivity analysis could be conducted to determine 

which parameters have the greatest influence on fish survival. 

• Public outreach. The model could be used as a teaching tool with various audiences to explain 

how various factors interact to affect fish survival. 

• Improving monitoring program and restoration activities. Outcomes from model validation 

and tests of sensitivity to parameters likely will suggest additional data needs, data that may not 

need to be collected, or more effective ways to collect data. Ultimately, the model could be used 

to redirect monitoring and restoration efforts and as a tool to test potential scenarios for 

improving growth, survival, and production of juvenile salmon. There is an obvious need to 

effectively monitor habitat and understand the potential for habitat availability to mitigate 

adverse flow conditions.
37 

Model Limitations

All models necessarily simplify reality. Users of this model should be aware of its limitations 

and consider them when evaluating model outcomes. 

• Limits of conceptual model. The model represents the current thinking of researchers and 

resource managers regarding factors limiting coho production. Although this conceptual model 

is based on direct experience and scientific evidence, the system is not perfectly understood and 

the model may need to be modified as more information accumulates.

• Short time series. The model is based on data collected over a very short period of time, which 

did not include the full range and combination of possible environmental conditions. Therefore, 

many relations (for example, smolt productivity when pink returns are less than 60,000 or the 

effect of the “blob” of anomalously warm water in the eastern Pacific that formed in late 2013 

and persisted until late 2016) could not be described because these conditions did not occur 

during the sampled time period. Additionally, the short time series constrains even 

parameterized relations to be based on few observations. 

• Not all important factors were measured. Datasets were not available to describe the 

condition of environmental factors suspected to be important. Examples include predation 

pressure in the freshwater and marine environments. Some of these cases were addressed using 

literature values (for example, identifying limiting levels of turbidity).

• Interaction terms were not included. For example, the effects of flow depend on the 

availability of refugia created by large woody debris. Future versions of the model would benefit 

from adding this complexity.

• Unforeseen or unpredictable changes. Unforeseen limiting factors are likely to arise as the 

environment changes and produces novel combinations of conditions or conditions more 

extreme than expected. Changes in abundance and distribution of invasive species and exotic 

diseases are other potential ecosystem stressors. Moreover, many assumptions may not hold in 

the future. For example, we do not know how fish may alter their life cycle to adapt to future 

environments or whether the hierarchy of life history bottlenecks will change under future 

hydrologic and temperature conditions. A small fraction of the coho population already do not 

adhere to the 3-year life cycle.

• Simplistic representation of ocean conditions. Factors determining ocean survival are the least 

understood part of the coho life cycle, which comprises one-half of the time frame for each 

generation. Fish spend about 18 months in the ocean, during which time their migratory 

dynamics are only known from point interceptions of coded, wire-tagged fish in ocean fisheries, 

and monitoring of environmental conditions other than sea-surface temperature that may affect 

their survival also is primarily based on point measurements. 

• Spatial resolution is crude. Elevation range, land use patterns, geology, and other factors create 

substantial diversity among sub-basins of the Skagit River. However, the availability of longterm fish production data from only one smolt trap eliminates our ability to describe productivity 

owing to differing environmental conditions at the sub-basin scale, yet these differences likely 

will counter or exacerbate climate change.
38

• Temporal resolution is crude. Although the model considers some drivers relative to the 

periods in which they are relevant (for example, scouring flows are only considered during 

November and December), other drivers are summarized over long periods of time (for example 

days having turbidity over a threshold are summarized for most of the freshwater stage of the 

lifecycle). This approach does not capture the variation in outcome that the same event might 

have on fish of different ages. Moreover, the temporal duration of events is not considered when 

looking at merely a maximum or threshold value.

• Deterministic model. The model does not describe the stochasticity of the real world. 

Therefore, it does not describe the variation in outcome that results from the natural variation 

that is represented by a deterministic parameter value in the model. The user does not see the 

confidence interval that exists around the model prediction. However, features of the model 

software enable the user to vary individual parameters according to a selected range and 

distribution to see the distribution of predictions that would result (see appendix B, section 

“Running the Model”).

Acknowledgments

The authors thank members of the Sauk-Suiattle Indian Tribe, including Russell Hornback, 

Jason Joseph, Norma Joseph, Jan Mabee, Kathy Misanes, and Ray Misanes, who discussed the cultural 

and economic importance of salmon species to the Tribe and Tribal natural resource management, and 

helped to determine the topic of the model. Eric Beamer and Casey Ruff of the Skagit River System 

Cooperative engaged in several fruitful discussions throughout the development of the model, and 

Casey provided R code and some analytical results. Pete Kairis (Swinomish Indian Tribe) provided fish 

data and feedback on parts of the model. We also discussed the model with Joseph Anderson

(Washington Department of Fish and Wildlife), Robert Black (USGS), Chris Curran (USGS), Correigh 

Greene (National Oceanic and Atmospheric Administration) , Clay Kinsel (Washington Department of 

Fish and Wildlife), and Corey Phillis (National Oceanic and Atmospheric Administration). Joseph 

Anderson and Clay Kinsel provided fish data collected by Washington Department of Fish and Wildlife,

and Chris Curran provided turbidity data. Finally, we thank Mara Zimmerman (Washington Department 

of Fish Wildlife) and an anonymous reviewer for great suggestions for improving the manuscript and 

the model.

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48

Appendix A. Definitions of Model Terms

Table A1. Definitions of names of stocks (S), flows (F), and converters (C).

Term Type Definition

AvgQAprSeptSauk C Average daily flow during April–September of juvenile year measured at Sauk 

streamgage (in cubic feet per second)

AvgQAprSeptSaukWC C Average daily flow during April–September of juvenile year measured at Sauk above 

White Chuck streamgage (in cubic feet per second)

Chlor C Time series of chlorophyll-a densities

Cycle C Information from switch on model interface used to choose whether model output is 

used to initiate escapement

empty F Flow used to empty Escapement stock after each cohort is calculated

Escapement S Fish returning to spawn (no distinction is made between escapement and spawners)

Fraction harvested C Total harvest of adult fish

Fraction ocean survival C Converter used to calculate fraction ocean survival from logit ocean survival

Fraction predicted C Fraction of smolt numbers predicted by Ricker curve that is realized owing to 

environmental conditions

FractionScour C Fraction of smolt population lost to scouring flow events

FractionSummerFlow C Fraction of smolt numbers predicted by Ricker curve that is realized owing to low 

summer flow

FractionTurbidity C Fraction of smolt numbers predicted by Ricker curve that is realized owing to stream 

turbidity after accounting for summer flow

Habitat capacity C Smolt production per square meter of habitat

Habitat change C Area of habitat gained or lost from current baseline (in square meters)

Initial cohort C Time series of total number of spawners ( both males and females)

Logit Fraction ocean 

survival

C Converter used to calculate logit of fraction of ocean survival based on indices of ocean 

condition

MaxQNovDecSauk C Maximum daily flow during November and December during brood year measured at 

Sauk streamgage

MaxQNovDecSaukWC C Maximum daily flow during November and December during brood year measured at 

Sauk above White Chuck streamgage

NPGO C Time series of North Pacific Gyre Oscillation (NPGO) values

ocean mortality F Flow that removes fish from Realized Smolts stock owing to ocean conditions and 

harvest

ocean survival F Flow that transfers fish from Realized Smolts stock to Escapement stock to represent 

ocean survival

PDO C Time series of Pacific Decadal Ocillation (PDO) values

Pinks C Time series of number of pink salmon during juvenile year; determines which Ricker 

curve to use

Predicted Smolts S Stock of number of smolts predicted by Ricker curve based on escapement

Realized Smolts S Stock of number of smolt production actually realized owing to environmental 

conditions

Ricker Capacity C Parameter of Ricker equation describing density-dependent capacity

Ricker Initial Slope C Parameter of Ricker equation describing non-density-dependent population growth

SaukScourThreshold C Flow that results in redd scour measured at Sauk streamgage

SaukWCScourThreshold C Flow that results in redd scour measured at Sauk above White Chuck streamgage

ScourEffect C Fraction of smolt population lost to scouring flow events

smolt mortality F Flow that removes fish from Predicted Smolts stock if environmental conditions 

indicate that the prediction will not be realized

smolt supplement F Flow that adds fish to Realized Smolts stock if environmental conditions indicate that 

more fish than predicted will be realized

smolt survival F Flow transferring fish from Predicted Smolts stock to Realized Smolts stock
49

Term Type Definition

spawn F Flow adding fish to Predicted Smolts for each cohort based on escapement and Ricker 

curve

Turbid30 C Time series of the number of days with average turbidity greater than 30 Formazin 

Nephelometric Units measured at the Anacortes Water Treatment Plant from 

February of brood year through smolting

UseNPGOChlor C Converter containing information from switch on interface used to determine whether to 

use NPGO and chlorophyll-a density or PDO to describe ocean conditions

UsePDO C Converter containing information from switch on interface used to determine whether to 

use Oceanic Nino Index (ONI) or PDO to describe ocean conditions

UseSauk C Converter containing information from switch on interface used to determine whether to 

use flow data from Sauk or Sauk above White Chuck streamgages

UseSaukWC C Converter containing information from switch on interface used to determine whether to 

use flow data from Sauk or Sauk above White Chuck streamgages
50 

Appendix B. Accessing and Running the Model

Accessing the Model

The Skagit River coho system dynamics simulation model can be run by accessing it using the 

isee® Exchange site at https://exchange.isee.systems.com/public/andreawoodward/skagit-coho. The 

model is set up to run using data from brood years (BYs) 1989–2012. The user can use alternative data by 

adjusting the inputs on the interface shown on the website and described in section, “Running the Model”. 

The online version of the software will not allow the user to modify the model structure or change 

parameters of equations. Equations used by the model are available in appendix D.

Running the Model

The isee® Exchange website provides an interface that enables the user to run a model 

without a personal copy of Stella Architect version 1.1.2®. The title page opens first and includes 

buttons to access the users’ guide (this document) and a description of model structure and 

elements (fig. B1). Paging down using the down arrow on the keyboard leads to the dashboard 

(fig. B2).

Figure B1. Screen capture showing title page of model in interface and buttons for accessing the users’ 

guide (this document), and a detailed description of model elements.
51 

Figure B2. Screen capture showing dashboard for running model. 

The dashboard enables the user to set model conditions and run the model (fig. B2). The 

user can decide whether to use PDO or NGPO and chlorophyll-a density as indicators of ocean 

conditions by clicking on the appropriate radio button. The same is true for selection of 

streamgage. The user also can decide whether the model will be initiated each year with 

observed estimates of escapement (model does not “cycle”) or whether the model will use its 

own output for subsequent escapement after each cohort has been initialized (model “cycles”). 

Important Note (available from “Note” button next to “Cycle” switch): Because the 

time series of chlorophyll-a density is not as long as the rest of the time series, the model will 

give spurious results if “UseNPGOChlor” and “Cycle” are used together. Results also are not 

legitimate for years that do not have chlorophyll-a data when “Cycle” is turned off (that is, first 

13 years of the time series).

Default escapement and environmental data for the model are from BYs 1989 to 2012, 

and are the data used to develop the model. The user can change these data to represent actual 

data collected in other years, to experiment with novel conditions, or to investigate the sensitivity 

of the model to changes in a single variable (fig. B3).
52 

Figure B3. Screen capture showing how to change data for graphical inputs. 

Changing data using the slider bars requires simply dragging the bar to the desired value. 

Changing graphical data requires first clicking on the appropriate graph. The graph will enlarge 

(fig. B3), and the data can be changed by clicking on the graph at the desired new value and then 

clicking on “Apply Changes”. 

After the data have been changed, clicking on “Run” will run the model. To reset the 

original, default values, click on “Restore”.

Output from the model displays on the third page of the model interface (fig. B4). It can 

be saved to an Excel® spreadsheet by right-clicking on the output table, and then clicking on the 

sign that reads “Copy Data”. Next, click Paste in an open spreadsheet. The model is set up to 

indicate annual results for the stocks Realized Smolts and Escapement.
53 

Figure B4. Screen capture showing model output page and opportunity to copy output to a spreadsheet.

Clicking on “Model Details” on the title page or dashboard takes the user to an image of 

the entire model structure followed by pages that define individual model elements (fig. B5). The 

full background on methods and rationale for model development is only available in the users’ 

guide (this document), which can be accessed from a button on the title page.

Stella version 1.1.2® software has many more features and capabilities than described 

here, but they are available only with the purchase of the software.
54 

Figure B5. Screen capture showing the first two pages of “Model Details.” 
55 

Appendix C. Skagit Salmon Co-Managers Data Used in Model Development

[This dataset is maintained by Pete Kairis, resource manager for the Swinomish Indian Tribe. Data in bold font are 

preliminary. Abbreviations: BY, brood year; RY, run year; OA3, estimated survival to ocean age 3; NA, not available] 

Brood 

year

Migration 

year

Return 

year

Total wild (Skagit + Baker)

Total wild BY 

smolts

Total wild RY 

esc

Total wild OA3

Total wild smoltto-OA3 survival

(percentage)

1989 1991 1992 587,000 27,793 64,860 11.05

1990 1992 1993 1,073,200 36,939 71,338 6.65

1991 1993 1994 619,868 53,815 103,715 16.73

1992 1994 1995 1,129,000 44,817 87,037 7.71

1993 1995 1996 720,000 48,997 82,922 11.52

1994 1996 1997 1,125,000 49,289 66,550 5.92

1995 1997 1998 1,136,268 89,338 109,911 9.67

1996 1998 1999 1,707,625 41,082 57,636 3.38

1997 1999 2000 677,779 58,600 83,480 12.32

1998 2000 2001 1,540,590 80,442 NA NA

1999 2001 2002 1,013,523 50,774 NA NA

2000 2002 2003 1,884,772 86,672 116,325 6.17

2001 2003 2004 1,382,479 116,706 158,950 11.5

2002 2004 2005 885,082 33,236 59,985 6.78

2003 2005 2006 1,017,519 7,392 13,736 1.35

2004 2006 2007 735,876 49,708 77,315 10.51

2005 2007 2008 747,491 21,408 31,610 4.23

2006 2008 2009 426,963 59,935 77,562 18.17

2007 2009 2010 1,475,065 28,134 56,233 3.81

2008 2010 2011 1,447,110 41,297 69,891 4.83

2009 2011 2012 951,990 92,687 128,837 13.53

2010 2012 2013 1,237,000 84,479 144,380 11.67

2011 2013 2014 1,409,000 24,820 44,788 3.18

2012 2014 2015 1,722,084 5,794 11,587 0.67

2013 2015 2016 665,087 35,080 41,901 6.30

2014 2016 2017 729,392
56 

Appendix D. Model Equations

Model equations as shown on the ‘Equation’ tab in Stella Architect version 1.1.2® software:

Escapement[BY](t) = Escapement[BY](t - dt) + (ocean__survival[BY] - empty[BY]) * dt 

INIT Escapement[BY] = 0

INFLOWS:

ocean__survival[BY] = (Realized_Smolts(BY)-ocean_mortality(BY)) 

OUTFLOWS:

empty[BY] = Escapement(BY)

Predicted_Smolts[BY](t) = Predicted_Smolts[BY](t - dt) + (spawn[BY] - smolt_mortality[BY] - 

smolt_survival[BY]) * dt 

INIT Predicted_Smolts[BY] = 0

INFLOWS:

spawn[BY] = if Cycle=0 then Initial_cohort(BY)*EXP(Ricker_Initial_Slope(BY)-

Ricker_Capacity(BY)*Initial_cohort(BY)) else (if BY>3 then Escapement(BY3)*EXP(Ricker_Initial_Slope(BY)-Ricker_Capacity(BY)*Escapement(BY-3))

else (Initial_cohort(BY)*EXP(Ricker_Initial_Slope(BY)-

Ricker_Capacity(BY)*Initial_cohort(BY))))

OUTFLOWS:

smolt_mortality[BY] = if fraction_predicted < 1 then (Predicted_Smolts(BY) * (1 - 

fraction_predicted)) else 0 

smolt_survival[BY] = Predicted_Smolts(BY) -smolt_mortality(BY)

Realized_Smolts[BY](t) = Realized_Smolts[BY](t - dt) + (smolt_survival[BY] + 

smolt_supplement[BY] - ocean__survival[BY] - ocean_mortality[BY]) * dt 

INIT Realized_Smolts[BY] = 0

INFLOWS:

smolt_survival[BY] = Predicted_Smolts(BY) -smolt_mortality(BY)

smolt_supplement[BY] = if fraction_predicted(BY) > 1 then (Predicted_Smolts(BY) * 

(fraction_predicted(BY) - 1)) else 0

OUTFLOWS:

ocean__survival[BY] = (Realized_Smolts(BY)-ocean_mortality(BY)) 

ocean_mortality[BY] = (Realized_Smolts(BY) * (1 -Fraction_ocean__survival)) + 

(Realized_Smolts(BY) * (Fraction_ocean__survival) * fraction_harvested(BY)) 

AvgQAprSeptSauk[BY] = GRAPH(BY)

AvgQAprSeptSaukWC[BY] = GRAPH(BY)

Chlor[BY] = GRAPH(BY)

Cycle = 0

FractionScour[BY] = if (UseSaukWC=1) then (if 

MaxQNovDecSaukWC>SaukWCScourThreshold then -ScourEffect else 0) else (if 

MaxQNovDecSauk>SaukScourThreshold then - ScourEffect else 0)

FractionSummerFlow[BY] = if (UseSaukWC=1) then 0.0004445*AvgQAprSeptSaukWC(BY) + 

.505373 else 0.00014034*AvgQAprSeptSauk(BY) + 0.37 
57 

FractionTurbidity[BY] = if (UseSaukWC=1) then -.000098*Turbid30(BY)^2 + 

.001308*Turbid30(BY) - 0.255861 else -0.000163*Turbid30(BY)^2 + 0.007*Turbid30(BY) - 

0.03507 

Fraction_harvested[BY] = GRAPH(BY) 

Fraction_ocean__survival[BY] = 

10^Logit_Fraction_ocean__survival/(1+10^Logit_Fraction_ocean__survival) 

fraction_predicted[BY] = FractionSummerFlow+FractionTurbidity+ FractionScour 

Habitat_capacity = 0.31

Habitat_change = 0

Initial_cohort[BY] = GRAPH(BY)

Logit_Fraction_ocean__survival[BY] = If (UsePDO=1) then (-

.0000003844*Realized_Smolts(BY)-0.1431*PDO+.1057*PDO(BY)*PDO(BY)-0.7959) else if 

(UseNGPOChlor=1) then(.28874*NGPO(BY) + .04462*Chlor(BY) - 1.69135) else 0 

MaxQNovDecSauk[BY] = GRAPH(BY)

MaxQNovDecSaukWC[BY] = GRAPH(BY)

NGPO[BY] = GRAPH(BY)

Ricker_Capacity[BY] = IF (Pinks(BY) > 0) 

THEN EXP(Ricker_Initial_Slope(BY) - 1)/(Habitat_change*Habitat_capacity+1460832) 

ELSE EXP(Ricker_Initial_Slope(BY) - 1)/(Habitat_change*Habitat_capacity+1044869) 

Ricker_Initial_Slope[BY] = if Pinks(BY) > 0 then 4.2919 else 3.8436 

SaukScourThreshold = 50000 

SaukWCScourThreshold = 20000 

ScourEffect = 0.2 

UseNGPOChlor = 0

UsePDO = 0

UseSauk = 0

UseSaukWC = 0

Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

Director, Forest and Rangeland Ecosystem Science Center

U.S. Geological Survey

777 NW 9th St., Suite 400

Corvallis, Oregon 97330 

https://fresc.usgs.gov
ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20171125

Woodward and others—Skagit River Coho Salmon Life History Model—Users’ Guide—Open-File Report 2017–1125