Prepared in cooperation with the Bureau of Reclamation

Theoretical Life History Responses of Juvenile 

Oncorhynchus mykiss to Changes in Food Availability 

Using a Dynamic State-Dependent Approach

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2013–1154

Theoretical Life History Responses of Juvenile 

Oncorhynchus mykiss to Changes in Food Availability 

Using a Dynamic State-Dependent Approach

By Jason G. Romine, Joseph R. Benjamin, Russell W. Perry, Lynne Casal, Patrick J. Connolly, and 

Sally S. Sauter

Prepared in cooperation with the Bureau of Reclamation

Open-File Report 2013–1154

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS

For an overview of USGS information products, including maps, imagery, and publications,

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Suggested citation:

Romine, J.G., Benjamin, J.R., Perry, R.W., Casal, L., Connolly, P.J., and Sauter, S.S., 2013, Theoretical 

life history responses of juvenile Oncorhynchus mykiss to changes in food availability using a dynamic 

state-dependent approach: U.S. Geological Survey Open-File Report 2013-1154, 20 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

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

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

Study Area ................................................................................................................................................. 3

Methods......................................................................................................................................................... 5

Dynamic State Dependent Model .............................................................................................................. 5

Bio-Energetics Model................................................................................................................................. 7

Results........................................................................................................................................................... 9

Discussion ................................................................................................................................................... 14

Acknowledgments ....................................................................................................................................... 17

References Cited......................................................................................................................................... 17

Figures

Figure 1. Map of Methow River Basin showing location of study area in Beaver Creek, a tributary 

of the Methow River in north-central Washington ..........................................................................................4

Figure 2. Conceptual diagram of linkages between the bioenergetics model and the 

state-dependent life history model.................................................................................................................6

Figure 3. Graph showing diet composition throughout the year for a single coho salmon egg 

abundance scenario, Beaver Creek, a tributary of the Methow River in north-central Washington ...............8

Figure 4. Graphs showing simulated proportions of each life-history pathway as a function of 

maximum egg contribution to the diet at the peak of the spawning run .........................................................9

Figure 5. Graph showing mean daily growth by juveniles by life history pathway....................................... 10

Figure 6. Graph showing results of simulations showing predicted population structure for 

different values ofρ run under the baseline scenario................................................................................... 11

Figure 7. Graphs showing results of simulations showing relationship between freshwater 

survival, ρ, and smolt production for all values of SAR cap......................................................................... 12

Figure 8 .Graphs showing results of simulations showing relationship between SAR cap, ρ, and smolt 

production for all values of freshwater survival (S) ...................................................................................... 13
iv

Conversion Factors and Datums

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Flow rate

millimeter per day (mm/d) 0.03937 inch per day (in/d) 

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

Heat Capacity

joules per gram (j/g) 0.239 calories per gram (c/g)

Datums

Horizontal coordinate information is referenced to North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.
1

Theoretical Life History Responses of Juvenile 

Oncorhynchus mykiss to Changes in Food Availability 

Using a Dynamic State Dependent Approach

By Jason G. Romine, Joseph R. Benjamin, Russell W. Perry, Lynne Casal, Patrick J. Connolly, and 

Sally S. Sauter

Abstract

Marine subsidies can play an important role in the growth, survival, and migratory 

behavior of rearing juvenile salmonids. Availability of high-energy, marine-derived food sources 

during critical decision windows may influence the timing of emigration or the decision to 

forego emigration completely and remain in the freshwater environment. Increasing growth and 

growth rate during these decision windows may result in an altered juvenile population structure,

which will ultimately affect the adult population age-structure. We used a state dependent model 

to understand how the juvenile Oncorhynchus mykiss population structure may respond to 

increased availability of salmon eggs in their diet during critical decision windows. Our models 

predicted an increase in smolt production until coho salmon eggs comprised more than 

50 percent of juvenile O. mykiss diet at the peak of the spawning run. At higher-thanintermediate levels of egg consumption, smolt production decreased owing to increasing 

numbers of fish adopting a resident life-history strategy. Additionally, greater growth rates 

decreased the number of age-3 smolts and increased the number of age-2 smolts. Increased 

growth rates with higher egg consumption also decreased the age at which fish adopted the 

resident pathway. Our models suggest that the introduction of a high-energy food source during 

critical periods of the year could be sufficient to increase smolt production.

Introduction

Oncorhynchus mykiss (O. mykiss) shows a wide range of life histories (Thorpe, 2007). 

This species adopts both anadromous (steelhead) and non-migratory (rainbow trout) life histories 

with many variants in between. The many life-history trajectories used by O. mykiss represent a 

remarkably complex fitness-based cost benefit analysis. This has allowed O. mykiss to persist in 

the Pacific Northwest of the United States despite overfishing, loss of habitat, complete 

blockages to migratory pathways, and increased mortality during seaward migration (sensu

Hilborn and others, 2003). Typically, the cost of seaward emigration is higher mortality; 

however, the rewards are larger size and associated greater fecundity, a worthwhile gambit for 

some of these fish (steelhead). In contrast, fish that remain in small freshwater systems have 

lower mortality rates, but also have slower growth and an order of magnitude lower fecundity 

(rainbow trout) than their anadromous counterparts. The tradeoffs between survival probability 

and the genetic contribution to the next generation are driving forces behind the relative 

contribution of these pathways to the population. 
2

Life-history pathways of O. mykiss are influenced by genetics and environmental 

conditions, which in turn affect the physiology of the fish (Thorpe and others, 1998; Zimmerman 

and Reeves, 2000; Christie and others, 2011). Thorpe and others (1998) suggested that the 

diversity of life-history patterns of salmonids could be explained by growth patterns at critical 

periods or decision windows during the year relative to genotype. For example, if resources were 

adequate during a critical decision window and a fish was growing faster than its genetic 

predisposition, then the fish might continue the maturation process and remain resident rather 

than emigrate to the ocean. The basis for this hypothesis is that excess growth is converted to 

lipid stores, which in turn allow the fish to continue the maturation process by diverting excess 

growth to gonad development. Although growth rate can be a predictor of life-history 

trajectories, genetic variability also is involved, thereby diluting the absolutism of growth rate or 

lipid availability as predictive variables (Thorpe and others, 1998). 

Satterthwaite and others (2009) presented a model based on growth, fecundity, freshwater 

survival, and smolt-to-adult survival rate (SAR) to predict the life-history trajectories of juvenile 

O. mykiss in coastal California streams. This model provides a unique approach to predicting 

life-history pathways of juvenile O. mykiss under a given set of growth and survival rates. 

Although it was developed for California systems, it may be parameterized for other regions 

such as the Methow River (Benjamin and others, 2013). The model provides a platform to 

evaluate how changes in growth as a result of habitat change, food availability, and fish density 

may affect the age-structure of the juvenile population.

Reintroduction of extirpated species can reinstate key ecological processes that were 

previously absent. For instance, the reintroduction of wolves (Canis lupis) into Yellowstone 

National Park in the Western United States has resulted in altered herbivore feeding patterns and 

thereby changed patterns of woody plant regeneration over large areas (Ripple and Beschta, 

2004). Species introductions or reintroductions likely will affect the associated ecosystem. The 

reintroduction of coho salmon (Oncorhynchus kisutch) into the Methow River system inevitably 

will alter ecological relationships within this system. However, the manner in which those 

relationships will be altered remains unclear because of the complex ecological effects that 

anadromous salmonids have on aquatic biota (Gresh and others, 2000; Naiman and others, 2002; 

Schoonmaker and others, 2003).

Adult anadromous salmonids are an energy-rich, marine-derived subsidy to freshwater 

systems and can transfer significant quantities of nutrients such as carbon, nitrogen, and 

phosphorus from the ocean to freshwater environments where they can be diffused across trophic 

levels. However, there is little consensus regarding the effect of marine derived nutrients on the 

growth and survival of fishes within the freshwater environment (Bilby and others, 1998; Lang 

and others, 2006; Harvey and Wilzbach, 2010). This discrepancy may be linked partially to 

studies in which salmon carcasses have been added to streams as opposed to studies where 

natural salmon runs occur. For carcass-addition studies, nutrients may be transmitted to fish 

either directly through consumption of carcass material or indirectly through increased primary 

and secondary production (Merz and Moyle, 2006). In the studies of indirect nutrient 

transmission through increased production, direct consumption of eggs can also provide an 

important energy source that affects fish growth and life-history expression (Moore and others, 

2008; Armstrong and others, 2010). Therefore, reintroduction of extirpated salmon species may 

have positive effects on other target species. Coho salmon spawn in the fall throughout the 

Pacific Northwest. In the Methow River system, spawning coho salmon could provide a critical 

pre-winter food source in the form of eggs that would contribute to increased growth of juvenile 
3

O. mykiss and other resident fishes. Late-fall spawning also coincides with the hypothesized 

smolt decision window (Satterthwaite and others, 2009). Changes in growth during this period 

could influence whether juveniles adopt a resident or anadromous life-history pathway and the 

age at which a given pathway is followed.

In this study, our goal was to simulate the potential shifts in life-history decisions by 

juvenile O. mykiss in response to the addition of coho salmon eggs, a seasonally available, highenergy food source. We used linked life history (Satterthwaite and others, 2009) and 

bioenergetics models (Hanson and others, 1997) to examine how contribution of coho salmon 

eggs to the diet of O. mykiss influenced their life-history trajectory. We parameterized our model 

to emulate conditions in Beaver Creek, a small tributary of the Methow River where O. mykiss

are likely to interact with spawning coho salmon in the future. Our goal was not to predict 

outcomes for Beaver Creek, but, rather, to understand the potential effect of egg subsidies on 

O. mykiss in small streams similar to Beaver Creek where such interactions will play out. 

Additionally, predicting the relative contribution of eggs to the diet of O. mykiss is difficult 

owing to spatial (e.g., spawning versus non-spawning habitat) and temporal (e.g., seasonal and 

inter-annual) variation in abundance of both O. mykiss and coho salmon. We circumvented this 

complexity by simulating a wide range of relative contributions of eggs to the diet of O. mykiss. 

Although spatial and temporal variation in egg subsidies to O. mykiss will ultimately control the 

observed distribution of life-history trajectories, our approach allowed us to gain insights about 

the magnitude of egg subsidies required to alter life-history trajectories.

Description of Study Area

Historically, the Methow River in north-central Washington, USA, supported a large 

population of coho salmon with an estimated 20,000–30,000 adults returning to the river 

annually (Mullan, 1984; Mullan and others, 1992). This coho salmon run was blocked by dam 

construction at the mouth of the Methow River in 1915 (Wahle and Pearson, 1984). At the time 

of this study, coho salmon are being reintroduced to the Methow River Basin. Annual smolt 

releases have ranged from 250,000 to 1 million since inception of the program in 1997. The 

program has resulted in adult returns increasing from less than 1,000 to more than 20,000.

We used a population of O. mykiss in Beaver Creek to parameterize our models. Beaver 

Creek is a tributary to the Methow River and is at a mean altitude of 505 m (fig. 1). Beaver 

Creek is a third-order stream with a mean base flow of 0.05 m3/s (Martens and Connolly, 2010). 

Ichthyofauna in Beaver Creek is dominated by O. mykiss, but other native and non-native fishes 

are present, including Chinook salmon (O. tshawytscha), brook trout (Salvelinus fontinalis), and 

shorthead sculpin (Cottus confusus; Martens and Connolly, 2010).

Within Beaver Creek, O. mykiss smolt at age 2 to age 4. Most juvenile steelhead 

(66 percent) smolt at age 2, while approximately 30 and 4 percent smolt at age 3 and age 4, 

respectively (P.J. Connolly, U.S. Geological Survey, unpub. data, 2010). Along with the 

migratory population, resident O. mykiss compose approximately 20 percent of the population in 

Beaver Creek. These fish mature at age 1 to age 4.
4

Figure 1. Map of Methow River Basin showing location of study area in Beaver Creek, a tributary of the 

Methow River in north-central Washington. Darker water bodies represent Beaver Creek and Methow 

River. Base map created from the National Hydrography Dataset (NHD). NHD provided by U.S. Geological 

Survey in cooperation with U.S. Environmental Protection Agency, USDA Forest Service, and other 

Federal, State and local partners.
5

Methods

Dynamic State-Dependent Model

We followed the methods of Satterthwaite and others (2009) to simulate life-history 

patterns of juvenile O. mykiss in Beaver Creek under different scenarios of coho salmon egg 

availability. Satterthwaite and others (2009) developed a dynamic state-dependent model 

(DSDM) that incorporated both proximal and ultimate approaches to predict age at smolting and 

becoming resident. We parameterized the model to emulate demographics of O. mykiss in northcentral Washington (growth, survival, and fecundity). The DSDM relies on growth information 

during critical “decision windows” throughout the year (fig. 2). At the end of the critical decision 

windows, the optimal life-history strategy is selected based on the size and growth of the fish. At 

each decision window, three choices are assessed by the model—wait until the next window, 

begin to mature, or begin the smoltification process. We based the timing of the windows on 

field observations and previously published literature for the area. Decision windows were 

assumed to be fixed periods in time when physiological, morphological, and behavioral changes 

may begin, depending on the size of the fish (Thorpe and others, 1998; Satterthwaite and others, 

2009). These windows included an average spawning date of May 1, an average date of fry 

emergence from the gravel of June 15, an average date of smolt emigration of June 1 (Peven and 

others, 1994; Snow and others, 2008), and discrete periods of time when smoltification or 

maturation are initiated. In the life-history model, the optimal fitness pathway for a fish is 

selected based on growth during the window and length at the end of each decision window. We 

estimated that the smolting decision window for juvenile O. mykiss in Beaver Creek was 

between October 1 and December 31, about 6 months prior to emigration as was suggested by 

Thorpe and others (1998) and Satterthwaite and others (2009). The decision window for 

maturing fish at age 1 and older was set between May 1 and June 1. This decision was assumed 

to occur about 1 year before spawning, owing to the metabolic cost of the maturation process. 

The maturation decision window for young-of-the-year fish was based on growth from 

emergence to October 1.

Fitness estimation was based on survival in freshwater, SAR, and size-dependent 

fecundity (Satterthwaite and others, 2009). Data were unavailable for the egg-to-emergence 

survival rate within the Methow River Basin; therefore, we estimated that rate at 6.5 percent 

based on a coastal steelhead population in southwest British Columbia (Ward and Slaney, 1993). 

The remaining freshwater survival rates were estimated at 50 percent annually, which is within 

the range of survival rates of other Oncorhynchus species throughout the Columbia River Basin 

(Bradford, 1995).
6

Figure 2. Conceptual diagram of linkages between the bioenergetics model and the state-dependent life 

history model. The bioenergetics model simulates the length of each fish (l) at time (t). The state-dependent 

model then predicts life-history trajectories either to mature and spawn as a rainbow trout (s) or to emigrate 

as a steelhead smolt (e) as a function of expected fitness, growth over discrete time windows (ts1 to ts2 or te1

to te2), and length at the start of the window. The dashed line indicates no trajectory has been selected and 

individual fish loop through the model until a trajectory is chosen.

Length dependent, SAR (σ(l)) was estimated from the release of 50,000 O. mykiss in the 

Methow River using a Cormack-Jolly Seber mark-recapture model: 

σ(l) = 0.36 e−6.171+0.03843l

1+e−6.171+0.03843l . (1)

Length-specific fecundity of resident female rainbow trout (φ(l)) was estimated from Hodge 

(2010) for O. mykiss in the Klamath River Basin,

 φ(l) = 0.2128 ∗ l

2.4301. (2)
7

This model provided a good fit to fecundity data from Wells Dam (Snow and others, 

2008) which is located near the mouth of the Methow River. Average lifetime fecundity of

anadromous females was estimated at 5,545 based on returns of wild steelhead to Wells Dam 

from 2000 to 2006 (Snow and others, 2008). Although a small percentage of steelhead spawn in 

multiple years, we considered the contribution of repeat spawning events to be negligible and, 

therefore, only one spawning event was considered in the fitness estimation.

The parameter ρ, which is a measure of how much recent growth experience influences 

the decision process, was set at 0. A value of 0 indicates that recent growth during a decision 

window does not influence the decision; whereas, a value of 1 indicates recent growth has a very 

high influence on the decision process (Satterthwaite and others, 2009). We set the value to 0 to 

accommodate the weight loss experienced by juveniles as they enter the early winter months. 

Satterthwaite and others (2009) set ρ =0.3 for their simulation based on an O. mykiss population 

in central California, USA. Additionally, model simulations were run for values of ρ from 0 to 

0.6 to show the sensitivity of the model to changes in this parameter. To further explore 

sensitivity to parameter values, we also varied the SAR cap from 0.33 to 0.39 and freshwater 

survival from 0.47 to 0.53.

Bio-Energetics Model

We used the Wisconsin bioenergetics model (Hanson and others, 1997) to simulate fish 

growth based on diet and temperature. Baseline growth was established by fitting the model to 

growth data from passive integrated transponder (PIT)-tagged juvenile O. mykiss that were at 

liberty in Beaver Creek for more than 100 days. Parameters for the bioenergetics model were 

those developed by Railsback and Rose (1999). Diet was constructed from available data 

(Bellmore, 2011). From November 1 (day 305) to March 31 (day 90), aquatic insects constituted 

95 percent of the diet and terrestrial insects constituted 5 percent of the diet. From April 1 

(day 91) to October 31 (day 304), aquatic insects constituted 65 percent of the diet and terrestrial 

insects constituted 35 percent of the diet (fig. 3). Water temperature used in the model was 

developed following the methods of Mohseni and others (1998). This model assumes that the 

relationship between air temperature and water temperature incorporates factors that influence 

water temperature such as shading, hydrology, and atmospheric conditions. Water temperature 

was sampled daily in Beaver Creek 1–2 times per hour from (1) October 25, 2005, to April 23, 

2007; (2) March 27, 2009, to March 17, 2010; and (3) July 13 to December 31, 2010. Continuous 

average daily air temperature was recorded at a weather station near the mouth of the Methow 

River by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data 

Center (2013). Water temperature then was predicted using a 7-d moving average of air 

temperature. We used daily water temperature averaged across 5 years in the bioenergetics 

model. Based on this diet regime and water temperature profile, we estimated the proportion of 

maximum consumption (PCmax) for each fish. Mean proportion of maximum consumption was 

estimated at 0.34 (standard deviation of 0.14). This value was used to simulate baseline growth. 

The bioenergetics model then was used to simulate growth based on different levels of 

coho salmon egg availability. Coho salmon eggs were introduced into the diet by assuming a 

normal distribution of egg availability centered about a peak spawning date of October 15. The 

maximum proportion of coho salmon eggs in the diet was varied from 0 (baseline scenario) to 1 

in each simulation. For example, under the 0.50 coho salmon egg scenario, the proportion of 

coho salmon eggs in the diet was 0.50 at the peak of spawning. Diet prior to and after peak 
8

spawning in this scenario always consisted of less than 50 percent coho salmon eggs and was 

determined by the aforementioned normal distribution. The range scenarios evaluated could be 

equated to 0 spawners (0 scenario) to maximum number of spawners in the system (1 scenario) 

where superimposition is occurring at a greater frequency and eggs are readily available in high 

densities. Aquatic and terrestrial insects in the diet were discounted proportionally when coho 

salmon eggs became available (fig. 3). Gape limitation also was imposed to mimic realistic 

feeding opportunity. Only fish larger than 72 mm were able to exploit eggs as a food resource 

(Armstrong and others, 2010). Other than changes in coho salmon egg availability in the diet, we 

assumed diet composition was constant on an annual basis, with only daily ration varying 

randomly for each fish on each day of the simulation. Initial lengths for the model were taken 

from available data for Beaver Creek. Average length of young–of-the-year O. mykiss in Beaver 

Creek on August 1 was 55 mm (standard deviation of 0.14). To generate an initial distribution of 

lengths, we randomly sampled, with replacement, from a dataset of lengths for Beaver Creek. 

For each simulation, 2,000 random start lengths were drawn with replacement. This random 

draw simulated fish hatching at different times to mimic realistic spawn variability and 

subsequent emergence within the population. Asymptotic length was set at 400 mm based on 

empirical data from north-central Washington.

Figure 3. Graph showing diet composition throughout the year for a single coho salmon egg abundance 

scenario, Beaver Creek, a tributary of the Methow River in north-central Washington. Gray line represents 

daily average number of adult coho salmon passing Wells Dam from 2002 to 2012. 
9

Results

The baseline simulation (no egg contribution to the diet) predicted 80 percent of juveniles 

would smolt at age 2 and age 3, with the remaining juveniles becoming resident at age 1 and age 

2 (fig. 4). The base model simulation also predicted that there would be more age-3 smolts than 

age-2 smolts (a contradiction of the empirical data that has been collected for Beaver Creek), and 

approximately 20 percent of the juveniles would mature at age 2. As coho salmon eggs were 

increased from 0 to 60 percent in the diet, the proportion of age-2 smolts increased to a 

maximum of 84 percent, and then decreased. Age-3 smolts decreased to 0 as the contribution of 

coho salmon eggs increased in the diet. Juveniles were not predicted to undergo smoltification at 

ages greater than age 3.

Figure 4. Graphs showing simulated proportions of each life-history pathway as a function of maximum 

egg contribution to the diet at the peak of the spawning run. (Freshwater survival=0.51, SAR cap=0.36, 

ρ =0). 
10

Maturing fish primarily were age 1 and age 2. As coho salmon eggs increased in the diet, 

age-2 maturing fish increased slightly to 23 percent and then decreased to 3 percent (fig. 4). As 

age-2 maturing fish decreased, age-1 maturing fish increased from 1 percent at baseline to 28 

percent for the maximum coho salmon egg scenario. Juveniles were not predicted to undergo the 

maturation process at ages greater than age 2. As coho salmon eggs increased in the diet, mean 

growth rate increased for all categories. On average, fish that grew an order of magnitude faster 

than other fish (0.015 mm/d, fig. 5) matured at age 1. Fish that grew 0.006 mm/d began 

smoltification at age 2 in the optimal scenario for age-2 smolt production.

Figure 5. Graph showing mean daily growth by juveniles by life history pathway. Only age-2 and age-3 

smolts were predicted. Maturing resident fish only were predicted to occur at age 1 and age 2.
11

Model predictions were very sensitive to changes in ρ (fig. 6). An increase in ρ of 0.1 

could shift a population from one almost entirely composed of maturing residents to a population 

entirely composed of smolts, and vice versa. For the baseline scenario simulation where ρ was 

varied from 0 to 0.6, predicted proportions of the population smolting varied widely (fig. 6). As 

ρ increased, the proportion of age-3 smolts generally decreased as the proportion of age-2 

smolts increased. Age-2 smolts then decreased and age-1 smolts increased to constitute the entire 

population at ρ =0.6. The percentage of the population predicted to mature followed a different 

pattern. As ρ increased, age-1 maturing fish increased, and then decreased as age-3 maturing 

fish increased. Age-3 maturing fish decreased as age-2 maturing fish increased, then 

subsequently decreased. Maturing fish generally decreased as ρ increased, and smolting fish 

increased as ρ increased for the conditions set in the simulation.

Figure 6. Graph showing results of simulations showing predicted population structure for different values 

ofρ run under the baseline scenario. Freshwater survival was set at 0.50 and SAR was capped at 0.36. 

Simulations of all possible combinations of ρ and the SAR cap showed somewhat similar 

trends in smolt production in relation to ρ (figs. 7 and 8). Low freshwater survival, high SAR 

cap values, and high ρ produced the greatest number of smolts. As freshwater survival 

increased, numbers of juveniles predicted to smolt decreased, but this could be offset by higher 

values of ρ. When freshwater survival was set at the highest value (0.53), SAR cap and ρ had to 

be at the highest levels to produce smolts; otherwise, no smolts were predicted to be produced. 
12

Figure 7. Graphs showing results of simulations showing relationship between freshwater survival, ρ, and 

smolt production for all values of SAR cap. Contours represent percentage of juvenile population smolting 

under different scenarios. Scale increases from dark (0=0 percent smolt production) to light (1=100 percent 

smolt production). 
13

Figure 8. Graphs showing results of simulations showing relationship between SAR cap, ρ, and smolt 

production for all values of freshwater survival (S). Contours represent percentage of juvenile population 

smolting under different scenarios. Scale increases from dark (0=0 percent smolt production) to light 

(1=100 percent smolt production). 
14

Discussion

For this study, we analyzed the extent to which the population structure of O. mykiss

could be altered by adding a high energy food source (salmon eggs) just prior to the onset of 

winter. Our model simulations show that increased availability of coho salmon eggs could 

change the life-history pathways of rearing O. mykiss. Both age-2 smolt production and the 

number of juveniles becoming resident at age 1were predicted to increase, which suggests the 

presence of an optimal threshold for producing greater numbers of smolts.

Our model predicted that the greatest percentage of smolts would occur at intermediate 

levels of coho salmon egg availability. At the highest levels of coho salmon egg availability, 

most fish began smoltification at age 2, but more than 20 percent of the juveniles were predicted 

to become mature at age 1. This should be expected given the much higher growth rates. Higher 

growth rates would mean greater lipid production and allow the maturation process to continue. 

Thorpe and others (1998) reported similar findings. These findings indicate that high 

contribution of eggs to the diet could tip the fitness balance towards adoption of a resident lifehistory strategy.

We did not explicitly model food item encounter rates within our simulations. For 

example, not all fish within the system would likely be able to take advantage of coho salmon 

eggs because of spatial mismatch between juvenile O. mykiss and spawning coho salmon. 

However, we randomly selected daily consumption for each fish throughout the simulations in an 

attempt to mimic encounter rates of food items. Therefore, even in the scenario where coho 

salmon eggs were available for 100 percent of the diet, a fish that had a very low consumption 

value would not benefit as much as a fish with a high consumption value. By taking this 

approach, we have attempted to account for spatial heterogeneity in food items (Moore and 

others, 2008).

The DSDM was very sensitive to small changes in survival. A 1 percent change in 

survival yielded large shifts in the predicted life-history pathways for the base scenario. Such a 

small change in survival is well within the confidence limits of most survival estimates. As such, 

the model likely is better suited for evaluating relative changes among alternative scenarios than 

predicting actual changes in populations given the inherent uncertainty in system response to 

coho salmon reintroduction. It is unlikely that a 1 percent change in survival would result in true 

population structure shifts as predicted by the model. 

Our simulation results show the sensitivity of this model to the ρ parameter, in that small 

changes in ρ can lead to complete shifts in the predicted population structure. Moreover, there 

are little data to support an appropriate value for this parameter. The use of ρ as a parameter in 

future scenarios should be limited to that of a tuning parameter for model adjustment and 

agreement. Satterthwaite and others (2009) state that the ρ parameter defines the extent to which 

recent growth influences the decision process. This is indeed the case because of the structure of 

the fitness function; however, there are no biological or empirical data to support the estimation 

of this parameter other than tuning ρ to fit the observed population structure. Future studies 

should be aimed at improving estimates of this value.

Density-dependent effects were not included in this model. An increase in the residual 

population could induce a density-dependent response in mortality, growth, or emigration. Given 

the fitness function in the state-dependent model, decreased freshwater survival owing to 

competition or lack of resources would result in greater selection for the anadromous life-history 
15

pathway (Satterthwaite and others, 2009). The decision windows or ultimate cues are based on 

genetically predetermined and relatively rigid thresholds when compared to proximal decisions 

influenced by environmental cues and population densities. These thresholds allow for a much 

wider range of variation in life-history pathways that were not modeled in this study. 

Likewise, we did not account for the potential competitive effects between juvenile coho 

salmon and juvenile O. mykiss. Competition for resources and piscivory could reduce freshwater 

survival, thereby pushing more juveniles towards the anadromous pathway. Although some 

studies have reported slight negative effects on sympatric salmonids resulting from the 

introduction of hatchery raised coho salmon, predicting the response to such interspecific 

competition is difficult (Murdoch and others, 2005; Satterthwaite and others, 2009; Pess and 

others, 2011). However, Pearsons and Temple (2007) and Temple and Pearsons (2012) found 

little negative effect on O. mykiss from supplementation of coho salmon in the Yakima River 

Basin. The trade-off between an increased high-energy food source and increased competition 

for that resource owing to the addition of coho salmon is not known and may negate the positive 

growth attributed to coho salmon egg consumption, resulting in no change in the population 

structure (Mangel and Satterthwaite, 2008).

Our model did not explicitly include a metric for lipid stores, which has been shown to be 

positively correlated to the maturation process (for example, Thorpe and others, 1998; Thorpe, 

2007). Fat content estimates for fish typically are estimated from a function that includes both 

length and weight (Bull and others, 1996). As such, weight, which was derived from the bioenergetics model, could be viewed as a proxy for lipids given the linear relationship between 

weight and fat content as estimated by Bull and others (1996) and used by Thorpe and others 

(1998). If a term for lipids was included within our model, the outcome would be identical owing 

to the use of proximal changes in weight to determine life-history pathways. We predicted fish 

with the fastest growth rates to mature. This is consistent with the correlation of lipid stores and 

maturing status.

The effect of marine-derived nutrients (MDN) is somewhat difficult to disentangle from 

other pathways of trophic transfer. Uchiyama and others (2008) reported little-to-no positive 

effect of increased MDN on sockeye salmon (O. nerka) productivity and suggested that 

environmental conditions played a more important role. Harvey and Wilzbach (2010) reported 

no increase in growth of juvenile salmonids following carcass addition. This is likely owing to 

the lower energy density value of fish tissue (1,300 j/g) versus that of salmon eggs (7800 j/g). 

Lang and others (2006) found slight increases in growth of juvenile coho salmon owing to 

increased MDN and suggested little long-term benefit from increased MDN in the form of 

overwinter survival or increased smolt production. These studies did not project the potential 

long-term consequences or potential changes in life-history patterns that could arise from a more 

productive ecosystem over larger temporal scales. These studies are in contrast to the findings of 

Moore and others (2008), which found large changes in O. mykiss growth owing to increases in 

sockeye salmon egg subsidies. Although carcass subsidies are important within the system, they 

may have a lesser benefit to rearing salmonids than MDN in the form of eggs. Bilby and others 

(1998) reported increased densities of juvenile coho salmon and steelhead following carcass and 

egg addition. Furthermore, they reported up to a 39 percent increase in the proportion of marinederived nitrogen in juvenile muscle tissue following carcass addition. Scheuerell and others 

(2007) also found increased growth in juvenile O. mykiss upon the arrival of spawning salmonids 
16

owing to consumption of eggs rather than carcasses, as their diet was primarily comprised of 

eggs. Although we did not include fish flesh within the diet of modeled juveniles, our results 

would not likely change given the lack of immediate contribution of carcasses to fish growth and 

the relatively low energy density value. 

Although our baseline scenario was somewhat indicative of the actual population, age-4 

smolts were not predicted in any of the scenarios even though they occur within the study area. 

Given the resolution and movements of juveniles within the system, age-4 smolts may not be of 

Beaver Creek origin, but instead may come from the much cooler headwaters of another 

tributary and may grow much slower than our model predicted. During the fitting of the growth 

model to the mark recapture data, it was observed that fish in the upper reaches grew much 

slower than fish in the lower reaches or closer to the mouth of Beaver Creek where it drains into 

the Methow River. Additionally, our model did not predict any age-1 smolts despite higher 

growth rates. Satterthwaite and others (2009) predicted that an increased growth rate would lead 

to younger smolts, whereas our results suggest slightly increased numbers of resident fish. This 

likely is owing to differences in model parameterization, namely freshwater asymptotic length 

(250 mm in the American River versus 400 mm in the Methow River) and differential 

survivorship. These differences stem from the presence of larger, fluvial O. mykiss in the 

Methow River that are absent in California rivers.

Freshwater survival was empirically estimated; however, because of the dynamic nature 

of the system, resolution of the data only allowed for annual estimates. (An interesting approach 

for future studies would be to examine model sensitivity to seasonal changes in mortality.) 

Additionally, freshwater survival did not vary across fish sizes, but may be size-dependent. The 

study system is somewhat cold-limited compared to the American River (Satterthwaite and 

others, 2009), owing to heavy winter snows that ice over the entire system. Overwinter mortality 

likely is the current limiting factor in this fish population; however, given the dynamics of the 

system, determination of size-based overwinter survival on wild juveniles is not currently 

feasible. Departures from baseline growth owing to increased growth from feeding on eggs may 

or may not increase overwinter survival (Connolly and Petersen, 2003).

Our results reflect the importance and variability in growth of juvenile steelhead and the 

resultant life-history pathways. Our approach has linked fitness with environmental conditions 

and trophic conditions creating a predictive platform that may be easily altered to test hypotheses 

regarding changes in bioenergetics, stream temperatures, and flow regimes. When egg 

availability was high (greater than 70 percent), juveniles grew at a faster rate that resulted in 

increased numbers of fish maturing in freshwater and fewer numbers of smolts. This conclusion 

is consistent with hatchery findings that suggest extremely fast- and slow-growing juveniles will 

become resident, while moderately growing juveniles will tend to smolt at later ages (Berejikian 

and others, 2011). 

The coho salmon reintroduction program offers a unique opportunity to monitor the 

potential consequences of a renewed ecological interaction between coho and other salmonids 

within the system. Given the great changes within the ecosystem and the potential changes 

owing to climate change (Benjamin and others, 2013), it is unlikely that the renewed interaction 

will be representative of a past condition within the Methow. Coho salmon were once dominant 

in this system and displacement and competition are likely to occur, resulting in changes to the 

trophic dynamics of the system. Future monitoring of the system will afford the only means to 

evaluate the true predictive power of our approach. 
17

Acknowledgments

We would like to thank the many individuals responsible for data collection. They 

included Grace Eger, Teresa Fish, Brian Fisher, Kyle Koger, Kyle Martens, and Wesley Tibbits. 

We would also like to thank the reviewers for their comments that improved the quality of this 

report.

References Cited

Armstrong, J.B., Schindler, D.E., Omori, K.L., Ruff, C.P., and Quinn T.P., 2010, Thermal 

heterogeneity mediates the effects of pulsed subsidies across a landscape: Ecology, v. 91, p. 

1445–1454.

Bellmore, J.R., 2011, The ecological importance of floodplains in montane river networks—

Implication for habitat restoration and salmon recovery: Pocatello, Idaho State University, 

Ph.D. dissertation, 212 p.

Benjamin, J.R., Connolly, P.J., Romine, J.G., and Perry, R.W., 2013, Potential effects of changes 

in temperature and food resources on life history trajectories of juvenile Oncorhynchus mykiss: 

Transactions of the American Fisheries Society, v. 142, p. 208–220.

Berejikian, B.A., Larsen, D.A., Swanson, P., Moore, M.E., Tatara, C.P., Gale, W.L., Pasley, 

C.R., and Beckman, B.R., 2011, Development of natural growth regimes for hatchery-reared 

steelhead to reduce residualism, fitness loss, and negative ecological interactions: 

Environmental Biology of Fishes, v. 94, p. 1–16.

Bilby, R.E., Fransen, B.R., Blisson, P.A., and Walter, J.K., 1998, Response of juvenile coho 

salmon (Oncorhynchus kisutch) and steelhead (Oncorhynchus mykiss) to the addition of 

salmon carcasses to two streams in southwestern Washington, U.S.A: Canadian Journal of 

Fisheries and Aquatic Sciences, v. 55, p. 1909–1918.

Bradford, M.J., 1995, Comparative review of Pacific salmon survival rates: Canadian Journal of 

Fisheries and Aquatic Sciences, v. 52, p. 1327–1338.

Bull, C.D., Metcalfe, N.B., and Mangel M., 1996, Seasonal matching of foraging to anticipated 

energy requirements in anorexic juvenile salmon: Proceedings of the Royal Society of London, 

v. 263, p. 13–18.

Christie, M.R., Marine, M.L., and Blouin, M.S., 2011, Who are the missing parents?—

Grandparentage analysis identifies multiple sources of gene flow into a wild population: 

Molecular Ecology, v. 20, p. 1263–1276.

Connolly, P.J., and Petersen, J.H., 2003, Bigger is not always better for overwintering young-ofyear steelhead: Transactions of the American Fisheries Society, v. 132, p. 262–274.

Gresh, T., Lichatowich, J., and Schoonmaker, P., 2000, An estimation of historic and current 

levels of salmon production in the northeast Pacific ecosystem: Evidence of a nutrient deficit 

in the freshwater systems of the Pacific Northwest: Fisheries, v. 25, p. 15–21.

Hanson, P.C., Johnson, T.B., Schindler, D.E., and Kitchell, J.F., 1997, Fish bioenergetics 3.0—

Sea Grant Technical Report WISCU-T-97-001: Madison, University of Wisconsin Sea Grant 

Institute, 113 p.

Harvey, B.C., and. Wilzbach, M.A., 2010, Carcass addition does not enhance juvenile salmonid 

biomass, growth, or retention in six northwestern California streams: North American Journal 

of Fisheries Management, v. 30, p. 1445–1451.
18

Hilborn, R., Quinn, T.P., Schindler, D.E., and Rogers, D.E., 2003, Biocomplexity and fisheries 

sustainability: Proceedings of the National Academy of Sciences of the United States of 

America, v. 100, p. 6564–6568.

Hodge, B., 2010. Life history variation in Oncorhynchus mykiss from the Klamath River Basin: 

Arcata, California, M.S. Humboldt State University, thesis.

Lang, D.W., Reeves, G.H., Hall, J.D., and Wipfli, M.S., 2006, The influence of fall-spawning 

coho salmon (Oncorhynchus kisutch) on growth and production of juvenile coho salmon 

rearing in beaver ponds on the Copper River Delta, Alaska: Canadian Journal of Fisheries and 

Aquatic Sciences, v. 63, p. 917–930.

Mangel, M., and Satterthwaite, W.H., 2008, Combining proximate and ultimate approaches to 

understand life history variation in salmonids with application to fisheries, conservation, and 

aquaculture: Bulletin of Marine Science, v. 83, p. 107–130.

Martens, K.D., and Connolly, P.J., 2010, Effectiveness of a redesigned water diversion using 

rock vortex weirs to enhance longitudinal connectivity for small salmonids: North American 

Journal of Fisheries Management, v. 30, p. 1544–1552.

Merz, J.E., and Moyle, P.B., 2006, Salmon, wildlife, and wine—Marine-derived nutrients in 

human dominated ecosystems of central California: Ecological Applications, v. 16, p. 999–

1009.

Mohseni, O., Stefan, H.G., and Erickson, T.R., 1998, A nonlinear regression model for weekly 

stream temperatures: Water Resources Research, v. 34, p. 2685–2692.

Moore, J.W., Schindler, D.E., and Ruff, C.P., 2008, Habitat saturation drives thresholds in 

stream subsidies: Ecology, v. 89, p. 306–312.

Mullan, J.S., 1984, Overview of artificial and natural propagation of coho salmon 

(Oncorhynchus kisutch) on the mid-Columbia River: Leavenworth, Washington, U.S. Fish and 

Wildlfe Service Report No. FRI/FAO-84-4, 37 p.

Mullan, J.S., Williams, K.R., Rhodus, G., Hillman, T.W., and McIntyre, J.D.,1992, Production 

and habitat of salmonids in mid-Columbia tributary streams: Leavenworth, Washington, U.S. 

Fish and Wildlife Service Monograph 1, 489 p.

Murdoch, K.G., Kamphaus, C.M., and Prevatte, S.A., 2005, Mid-Columbia coho reintroduction 

feasibility study: 2003 monitoring and evaluation report prepared by Yakama Nation Fisheries 

Resource Management for Project #1996-040-00, Bonneville Power Administration, Portland, 

Oregon, 205 p.

Naiman, R.J., Bilby, R.E., Schindler, D.E., and Helfield, J.M., 2002, Pacific salmon, nutrients, 

and the dynamics of freshwater and riparian ecosystems: Ecosystems v. 5, p. 399–417.

National Oceanic and Atmospheric Administration, 2013, National Climate Data Center: 

Website, accessed June 13, 2013, at http://www.ncdc.noaa.gov/.

Pearsons, T.N., and Temple, G.M., 2007, Impacts of early stages of salmon supplementation and 

reintroduction programs on three trout species. North American Journal of Fisheries 

Management, v. 27, p. 1–20.

Pess, G.R., Kiffney, P.M., Liermann, M.C., Bennett, T.R., Anderson, J.H., and Quinn,T.P., 2011, 

The influences of body size, habitat quality, and competition on the movement and survival of 

juvenile coho salmon during the early stages of stream recolonization: Transactions of the 

American Fisheries Society, v. 140, p. 883–897.

Peven, C.M., Whitney, R.R., and Williams, K.R., 1994, Age and length of steelhead smolts from 

the mid-Columbia River Basin, Washington: North American Journal of Fisheries 

Management, v. 14, p. 77–86.
19

Railsback, S.F., and Rose, K.A., 1999, Bioenergetics modeling of stream trout growth—

Temperature and food consumption effects: Transactions of the American Fisheries Society, 

v. 128, p. 241–256.

Ripple, W.J., and Beschta, R.L., 2004, Wolves, elk, willows, and trophic cascades in the upper 

Gallatin Range of Southwestern Montana, USA: Forest Ecology and Management, v. 200, 

p. 161–181.

Satterthwaite, W.H., Beakes, M.P., Collins, E.M., Swank, D.R., Merz, J.E., Titus, R.G., Sogard, 

S.M., and Mangel, M., 2009, Steelhead life history on California's central coast—Insights from 

a state-dependent model: Transactions of the American Fisheries Society, v. 138, p. 532–548.

Scheuerell, M.D., Moore, J.W., Schindler, D.E., and Harvey, C.J., 2007, Varying effects of 

anadromous sockeye salmon on the trophic ecology of two species of resident salmonids in 

southwest Alaska: Freshwater Biology, v. 52, p. 1944–1956.

Schoonmaker, P.K., Gresh, T., Lichatowich, J., and Radtke, H.D., 2003, Past and present Pacific 

salmon abundance—Bioregional estimates for key life history stages, in Stockner, J.G., ed., 

Nutrients in salmonid ecosystems—Sustaining production and biodiversity: American 

Fisheries Society symposium 34, Bethesda, Maryland, p. 33–40.

Snow, C., Frady, C., Fowler, A., and Murdoch, A., 2008, Monitoring and evaluation of Wells 

and Methow hatchery programs in 2007: Olympia, Washington Department of Fish and 

Wildlife, Report to Douglas County Public Utility District and Wells Habitat Conservation 

Plan Hatchery Committee.

Temple, G.M., and Pearsons, T.N., 2012, Risk management of non-target fish taxa in the Yakima 

River Watershed associated with hatchery salmon supplementation: Environmental Biology of 

Fishes, v. 94, p. 67–86.

Thorpe, J.E., 2007, Maturation responses of salmonids to changing developmental opportunities: 

Marine Ecology Progress Series, v. 335, p. 285–288.

Thorpe, J.E., Mangel, M., Metcalfe, N.B., and Huntingford, F.A., 1998, Modelling the proximate 

basis of salmonid life-history variation, with application to Atlantic Salmon, Salmo salar L.: 

Evolutionary Ecology, v. 12, p. 581–599.

Uchiyama, T., Finney, B.P., and Adkison, M.D., 2008, Effects of marine-derived nutrients on 

population dynamics of sockeye salmon (Oncorhynchus nerka): Canadian Journal of Fisheries 

and Aquatic Sciences, v. 65, p. 1635–1648.

Wahle, R.J., and Pearson, R.E., 1984, History of artificial propogations of coho salmon, 

Oncorhynchus kisutch, in the Mid-Columbia River system: Marine Fisheries Review, v. 46, 

p. 34–43.

Ward, B.R., and, Slaney, P.A., 1993, Egg-to-smolt survival and fry-to-smolt desnity dependence 

of Keogh River steelhead trout: Canadian Special Publication of Fisheries and Aquatic 

Sciences, v. 118, p. 209–217.

Zimmerman, C.E., and Reeves, G.H., 2000, Population structure of sympatric anadromous and 

nonanadromous Oncorhynchus mykiss—Evidence from spawning surveys and otolith 

microchemistry: Canadian Journal of Fisheries and Aquatic Sciences, v. 57, p. 2152–2162.
20

This page left intentionally blank
For additional information contact:

Director, Western Fisheries Research Center

U.S. Geological Survey

6505 NE 65th Street

Seattle, Washington 98115

http://wfrc.usgs.gov/

Publishing support provided by the U.S. Geological Survey

Publishing Network, Tacoma Publishing Service Center
Romine and others— Theoretical Life History Responses of Juvenile Oncorhynchus mykiss to Changes in Food Availability Using a 

Dynamic State-Dependent Approach—Open-File Report 2013–1154