U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2016–1211

Prepared in cooperation with the U.S. Army Corps of Engineers and the 

Oregon Department of Fish and Wildlife 

Development of a Study Design and Implementation Plan to 

Estimate Juvenile Salmon Survival in Lookout Point Reservoir 

and Other Reservoirs of the Willamette Project, Western Oregon
Cover: Background: Photograph of Lookout Point Reservoir, Oregon, July 22, 2010. 

Insets: Floating box trap (left) and (right) a juvenile Chinook salmon (right). 

Photographs by Fred Monzyk, Oregon Department of Fish and Wildlife, May 12 and May 17, 2012, respectively.
U.S. Department of the Interior

U.S. Geological Survey

Development of a Study Design and Implementation Plan 

to Estimate Juvenile Salmon Survival in Lookout Point 

Reservoir and Other Reservoirs of the Willamette Project, 

Western Oregon

By Tobias J. Kock, Russell W. Perry, Fred R. Monzyk, Adam C. Pope, and John M. Plumb

Prepared in cooperation with the U.S. Army Corps of Engineers and the 

Oregon Department of Fish and Wildlife

Open-File Report 2016–1211
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Kock, T.J., Perry, R.W., Monzyk, F.R., Pope, A.C., and Plumb, J.M., 2016, Development of a study design and 

implementation plan to estimate juvenile salmon survival in Lookout Point Reservoir and other reservoirs of the 

Willamette Project, western Oregon: U.S. Geological Survey Open-File Report 2016–1211, 25 p.,

https://doi.org/10.3133/ofr20161211.

ISSN 2331-1258 (online)
iii

Contents

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

Lookout Point Reservoir ............................................................................................................................................. 3

Study Designs ............................................................................................................................................................ 6

Staggered Release-Recovery Model ...................................................................................................................... 6

N-mixture Model...................................................................................................................................................... 9

Standard N-mixture Model with a Removal Sampling Protocol ........................................................................... 9

Adapting the N-Mixture Model to Estimate Fry Survival .................................................................................... 10

Assessment of the Parentage-Based Tagging N-Mixture Model....................................................................... 13

Implementation Plan................................................................................................................................................. 17

Fish Source, Parentage-Based Tagging, and Replication..................................................................................... 17

Fish Transport and Release.................................................................................................................................. 17

Reservoir Sampling............................................................................................................................................... 21

Critical Uncertainties................................................................................................................................................. 22

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

References Cited...................................................................................................................................................... 24

Figures

Figure 1. Map of the Middle Fork Willamette River showing Lookout Point Reservoir, Lookout Point Dam, 

Dexter Reservoir, and Dexter Dam, western Oregon .................................................................................................... 3

Figure 2. Floodplot of water temperatures in the forebay of Lookout Point Reservoir, western Oregon, 

March–November 2014. ................................................................................................................................................ 4

Figure 3. Graph showing fork length of juvenile Chinook salmon in Lookout Point Reservoir, western Oregon, 

March–November, 2010–16 .......................................................................................................................................... 5

Figure 4. Schematic of the staggered release-recovery model presented by Skalski (2016)....................................... 7

Figure 5. Schematic showing study designs of staggered release-recapture study and parentage-based

tagging N-mixture model study proposed for use in Lookout Point Reservoir, western Oregon, in 2017 ...................... 7

Figure 6. Posterior distributions of capture (p) and survival (S) probability for three simulations 

with p = 0.10, 0.05, and 0.01 ....................................................................................................................................... 12

Figure 7. Posterior distributions of capture (p) and survival (S) probability for the simulation with

two levels of capture probability and survival increasing over time.............................................................................. 15

Figure 8. Graph showing true survival relationship used to simulate capture data and estimated 

relationship using the parentage-based tagging N-mixture model............................................................................... 16

Figure 9. Orthoimage showing five release sites (white circles) for juvenile Chinook salmon in the 

upper part of Lookout Point Reservoir, western Oregon, during mid-April 2017.......................................................... 18

Figure 10. Photograph showing insulated fish-holding containers that will be used to transport juvenile 

Chinook salmon to Lookout Point Reservoir, western Oregon, during 2017................................................................ 19

Figure 11. Orthoimage showing five release sites (white circles) for juvenile Chinook salmon in 

Lookout Point Reservoir, western Oregon, during mid-June and mid-July 2017 ......................................................... 20

Figure 12. Photographs of sampling equipment, including boat electrofishing (A), gill nets (B), floating 

box traps (C), and Oneida Lake traps (D), that will be used in Lookout Point Reservoir, western Oregon, 2017 ........ 22
iv

Tables

Table 1. Release groups, target release dates, number of fish in group, and target fork length, for 

Chinook salmon releases at Lookout Point Reservoir, western Oregon, 2017 .............................................................. 8

Table 2. Estimated population of hatchery-produced juvenile Chinook salmon in Lookout Point Reservoir, 

western Oregon, 2014 ................................................................................................................................................ 21

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

International System of Units to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 0.2642 gallon (gal)

Salmon growth rate

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

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as °F = (1.8 × °C) + 32.

Abbreviations

ODFW Oregon Department of Fish and Wildlife

PBT parentage-based tagging

PIT-tags passive integrated transponder tags

Project Willamette Project

SFGPL Smith Farm Genetics and Performance Lab

USACE U.S. Army Corps of Engineers

WFSP Wild Fish Surrogate Program
1

Development of a Study Design and Implementation Plan 

to Estimate Juvenile Salmon Survival in Lookout Point 

Reservoir and Other Reservoirs of the Willamette Project, 

Western Oregon

By Tobias J. Kock1, Russell W. Perry1, Fred R. Monzyk2, Adam C. Pope1, and John M. Plumb1

Background

Estimates of juvenile salmon survival provide important monitoring data for fisheries resource 

managers working in impounded systems of the Western United States. Techniques have been 

developed to estimate survival of Pacific salmon (Oncorhynchus spp.) smolts during the past two 

decades, but these methods require the use of tags (passive integrated transponders [PIT-tags]) or active 

transmitters (radio and acoustic transmitters) and generally are applied to actively migrating fish

(Skalski and others, 1998; Muir and others, 2001; Perry and others, 2010; Skalski and others, 2016). 

However, in places like the Willamette River Valley, Oregon, resource managers require survival 

estimates for fry and parr (hereinafter “fry”) life stages. Estimation of fry survival is challenging 

because these fish are too small to be tagged with a PIT-tag or an active transmitter, and methods for 

estimating survival of fish in this size class have not been tested and proven.

The U.S. Army Corps of Engineers (USACE) operates the Willamette Project (hereinafter 

“Project”) in western Oregon, which includes 13 dams and reservoirs, about 68 km of revetments, and

several fish hatcheries. The primary purpose of the Project is flood risk management, but it also is 

operated to provide hydroelectricity, irrigation water, navigation, instream flows for wildlife, and 

recreation. The Project was determined to jeopardize Upper Willamette River spring Chinook salmon

(O. tshawytscha) and winter steelhead (O. mykiss) (National Oceanic and Atmospheric Administration,

2008), which has spurred a series of studies and actions to reduce the Project effects on these 

populations. Fish passage is one of the key issues in the Project. Passage for adult salmon and steelhead 

is accomplished using trap-and-haul procedures that provide spawning opportunities in free-flowing 

headwaters and tributaries upstream of Project reservoirs (Sard and others, 2015). Progeny of the 

transported adults move downstream and spend several months rearing in Project reservoirs because 

passage options are limited at the high-head dams in the system (Keefer and others, 2013; Beeman and 

others, 2014; Kock and others, 2015; Monzyk and others, 2015). Thus, fishery managers are faced with 

determining whether it is better to focus on developing fish passage options at the dams, or attempting

to capture fish near the head of the reservoirs. A key piece of information that will help with these 

1

U.S. Geological Survey. 2

Oregon Department of Fish and Wildlife.
2

decisions is understanding survival rates of salmon fry rearing in the reservoirs; high survival rates 

likely would result in decisions to focus on dam-based passage or collection efforts, whereas low 

survival rates may result in decisions to focus on collecting fish as they enter the reservoirs.

The need for study designs to estimate fry survival has been recognized and several potential 

options have been proposed. Skalski and others (2009) reviewed 20 fish-marking techniques and 16 

release-recapture study designs to identify approaches that would be useful for estimating fry survival. 

They determined that 11 of the study designs were capable of estimating survival parameters, 5 of the 

methods required unique fish marks, the 6 remaining methods used batch-specific marks, and all 

potential methods required the release of more than one group of marked fish (Skalski and others,

2009). The application of these methods within the Project was further refined in October 2015, when 

the USACE convened the Willamette Valley Downstream Fish Passage Research, Monitoring, and 

Evaluation Workshop. Participants at the workshop were familiar with the Project and identified five 

approaches that were of interest, along with several potential locations where fry survival data were

most needed. The approaches and locations that were identified in the workshop are presented in Skalski 

(2016).

Shortly after the workshop, the USACE requested the U.S. Geological Survey to develop a study 

design and implementation plan that could be used to implement a pilot study to estimate fry survival in 

Lookout Point Reservoir during 2017. Lookout Point Reservoir spans 16 km of the Middle Fork

Willamette River (fig. 1), between Hills Creek Dam (not shown in fig. 1) and Dexter Dam, and is in

need of downstream fish passage improvements. The reservoir supports abundant populations of several 

cool- and warm-water fish species that are known to prey on juvenile salmonids (Romer and Monzyk,

2014; Brandt and others, 2016). Given these factors, the assessment of fry survival in Lookout Point 

Reservoir was identified as a research priority by the USACE. However, assessing survival of salmon 

fry is challenging, so we were contracted to identify potential research approaches and to develop 

implementation strategies that provide the highest likelihood for success. Our goals in 2016 were to (1) 

assess and develop potential study designs, and (2) develop an implementation plan that could be used 

to evaluate fry survival in Lookout Point Reservoir during 2017.

The development of a study design and implementation plan for a pilot study in 2017 involves 

several components that include reviewing existing datasets and study designs, planning for and 

implementing juvenile salmon production to ensure that study fish are available, and developing 

procedures that will be used when the study is implemented. This report summarizes each of these 

elements and provides a recommendation to evaluate two potential study designs during a pilot effort in 

2017. Additionally, procedures related to fish rearing, fish releases, and field sampling are described.
3

Figure 1. Map of the Middle Fork Willamette River showing Lookout Point Reservoir, Lookout Point Dam, Dexter 

Reservoir, and Dexter Dam, western Oregon.

Lookout Point Reservoir

Lookout Point Reservoir is located on the Middle Fork Willamette River (fig. 1). It has an 

overall length of about 16 km, an average depth of 32 m, and a maximum depth of 71 m. Chinook 

salmon fry enter Lookout Point Reservoir during February–June each year, the peak occurs during 

April, and these fish primarily are distributed along the shorelines in the upper part of the reservoir 

during spring (Monzyk and others, 2015). The reservoir becomes thermally stratified in July, and 

Chinook salmon fry move offshore into deeper, cooler water as thermal stratification begins. Thermal 

stratification is substantial during July–September, when the upper 10 m of the water column commonly 

exceeds 20 °C (fig. 2). During these months, juvenile salmon generally are found in the middle and 

lower parts of the reservoir and reside near the thermocline, based on gill-net sampling by the Oregon 

Department of Fish and Wildlife (ODFW) (Monzyk and others, 2015). Thermal stratification abates 

during fall months when juvenile salmon exhibit a bi-modal spatial distribution with peak densities 

located at the upstream end of the reservoir and in the forebay of Lookout Point Dam (Monzyk and 

others, 2015). Growth rates in the reservoir are high. Mean fork length of Chinook salmon fry at the 

time of reservoir entry (March) is about 40 mm, and fish grow to nearly 200 mm by November 

(Monzyk and others, 2015; fig. 3). Estimated growth rates during this period exceeded 0.80 mm/d in 

2012, 2013, and 2014 (Monzyk and others, 2015). Peak outmigration of juvenile salmon from Lookout 

Point Reservoir occurs during November, December, and January each year (Keefer and others, 2013).
4

Figure 2. Floodplot of water temperatures in the forebay of Lookout Point Reservoir, western Oregon, March–

November 2014.
5

Figure 3. Graph showing fork length of juvenile Chinook salmon in Lookout Point Reservoir, western Oregon,

March–November, 2010–16. Data were collected by the Oregon Department of Fish and Wildlife. Line-of-best-fit 

and equation for that line (obtained using linear regression) also are shown.

A substantial predator community exists in Lookout Point Reservoir. Known piscivorous fish 

species include white crappie (Pomoxis annularis), black crappie (P. nigromaculatus), walleye (Sander 

vitreus), largemouth bass (Micropterus salmoides), northern pikeminnow (Ptychocheilus oregonensis), 

rainbow trout (O. mykiss), and cutthroat trout (O. clarkii). The black crappie population is believed to be 

relatively small, and northern pikeminnow, largemouth bass, and walleye preyed primarily on fish 

(Brandt and others, 2016). Piscivorous fish populations in Lookout Point Reservoir appear to be heavily 

influenced by strong year-classes that may affect predation influences over time. Sampling conducted 

by the ODFW during 2013–15 showed that crappie populations currently are dominated by a strong 

year-class from the 2010 brood year, walleye populations are dominated by a strong year-class from the 

2012 brood year, and largemouth bass populations have multiple strong year-classes (Brandt and others,

2016). Based on these data, predation rates on salmon fry likely are high in the reservoir, but the key 

predators may change from year to year, and predation rates are likely to change seasonally as juvenile 

Chinook salmon grow and metabolic demands of predators change.
6

Given this information, much is known about juvenile salmon and piscivorous fish communities 

in Lookout Point Reservoir, and these data are important for designing a study to rigorously estimate fry 

survival in the reservoir. The primary objective of the study is to estimate survival during the period 

when fry are believed to be most vulnerable to predation. This period begins when Chinook salmon fry

enter the reservoir during February–May and ends in June or July as fish grow and move offshore into 

cooler, deeper water. During this critical period, Chinook salmon fry are prey-sized (40–115 mm) and 

shoreline-oriented, which overlaps with the preferred habitats of several of the piscivorous fish species 

in the reservoir including crappie, largemouth bass, walleye, and northern pikeminnow (Brandt and 

others, 2016). The focus of this report, and the proposed pilot study in 2017, are to estimate survival of 

Chinook salmon fry in Lookout Point Reservoir during April–July.

Study Designs

Staggered Release-Recovery Model

The staggered release-recovery model was presented by Skalski (2016) as one approach for 

estimating fry survival when fish are present in Project reservoirs, but too small for marking with a PITtag. As presented, the study design includes the release of two groups of juvenile salmon (R1 and R2)

and the releases are timed to occur at the beginning (R1) and the end of the period of inference (R2). The 

release groups are allowed to mix for a period of time and then reservoir sampling is conducted several 

times to capture fish from each release group (Skalski, 2016; fig. 4). Survival is then estimated for the 

period of time between R1 and R2. The staggered release-recovery model has five primary assumptions 

that were summarized by Skalski (2016) and include:

1. All fish act independently.

2. Both release groups share the same recovery probabilities and intra-period survival probabilities 

after the second release occurs.

3. Sample sizes R1 and R2 are known without error.

4. Recovery numbers are correctly reported and assigned to the correct release group.

5. Fish do not lose their tags. 

To evaluate fry survival in Lookout Point Reservoir, Skalski (2016) proposed to conduct the R1 release 

during late March or early April and to release R2 fish in June when fry are still oriented along the 

shorelines of the reservoir and vulnerable to predation (Skalski, 2016; fig. 4). Using this approach, the 

staggered release-recovery model would yield a survival estimate (S; fig. 4) for the period of time 

between the release of the R1 and mid-June, when the R2 would be released.

We are proposing to implement a modified version of the staggered release-recovery model that 

would include three release groups (R1, R2 and R3) and provide survival estimates during two time 

periods (S1 and S2; fig. 5). The staggered release-recovery study design has not been field-tested, so the 

additional release group will allow us to obtain survival estimates from two time periods, and also may 

provide insights into the performance of the study design. Factors such as poor collection success (that 

is, low capture probability) and inadequate mixing of release groups could limit the capability of the 

staggered release-recovery study design, so lessons learned from the pilot effort in 2017 will be critical 

for assessing the approach. The third release group will increase the number of fish that are available for 

sampling, will provide an additional release group for assessing mixing between groups, and will allow 

us to estimate survival during the S2 period.
7

Figure 4. Schematic of the staggered release-recovery model presented by Skalski (2016). Schematic shows 

releases R1 at time t0 and R2 at time t1 to estimate survival (S) during the intervening period. Recovery sampling 

occurs k times following the paired releases with observed counts x1, . . ., xk from the R1 release and counts y1, . . ., 

yk from the R2 releases.

Figure 5. Schematic showing study designs of staggered release-recapture study and parentage-based tagging Nmixture model study proposed for use in Lookout Point Reservoir, western Oregon, in 2017. Schematic includes 

proposed release dates of hatchery-reared juvenile Chinook salmon (R1, R2, and R3), number of fish to be 

released, and average size of fish (FL) in the release groups. Intervals in which survival (S1 and S2) will be 

estimated and the timing of seven collection efforts (C1–C7) also are shown. Collection efforts C1–C3 are for the 

N-mixture study design and C4–C7 are for the staggered release-recovery and N-mixture study designs.
8

For the 2017 pilot study, we are proposing to release groups of fish during mid-April, mid-June, 

and mid-July. Sample sizes for each release group were determined by examining:

1. Anticipated mortality rates of Chinook salmon fry in the reservoir;

2. Anticipated capture rates during reservoir sampling periods;

3. Effects of sample sizes on the precision of survival estimates; and

4. Logistical constraints associated with rearing fish for the study, which included the number of 

adults available for spawning and the space available for rearing fry prior to release.

In our initial discussions, we determined that a maximum of 135,000 Chinook salmon fry would be 

available for the study in 2017. Based on this constraint, we used information on anticipated reservoir 

survival rates, anticipated reservoir capture rates, and sample size simulations using Program 

SampleSize (Lady and others, 2003) to identify numbers of fish to be released in each group. The midApril release will be comprised of 75,000 fish that are about 48 mm long (table 1). Their release

coincides with peak reservoir entry timing of naturally produced Chinook salmon fry into Lookout Point 

Reservoir (Monzyk and others, 2015). The mid-June release will be comprised of 50,000 fish that are 

about 97 mm long (table 1). Their release coincides with the time period when natural-origin NOR 

Chinook salmon begin moving offshore and are large enough that they are leaving the prey size 

category. Based on these releases, the first survival estimate (S1) will include the time period between 

mid-April and mid-June (fig. 5). The mid-July release will be comprised of 10,000 fish that are about

120 mm long (table 1). Their release will occur one month after the R2 release, and at that time the fish 

should be too large to be preyed upon by most piscivorous predators in the reservoir. The second 

survival estimate (S2) will apply to the time period between mid-June and mid-July (fig. 5). Naturalorigin fry also will be present in the reservoir during 2017. A total of 687 adult spring Chinook salmon 

were outplanted upstream of Lookout Point Reservoir in 2016 and genetic samples were obtained from 

each of these fish. This will allow us to identify natural-origin fry that are captured in the reservoir,

which will allow us to compare distributions of natural-origin and hatchery-origin fish. The presence of 

natural-origin fry will not negatively affect our study in 2017. Details for fish rearing, transport and 

release, and sampling methods are presented in section, “Implementation Plan.”

Table 1. Release groups, target release dates, number of fish in group, and target fork length, for Chinook salmon 

releases at Lookout Point Reservoir, western Oregon, 2017.

Release group Target release date Number of 

fish in group

Target fork length 

(millimeters)

R1 April 14, 2017 75,000 48

R2 June 16, 2017 50,000 97

R3 July 15, 2017 10,000 120
9

N-mixture Model

We developed and tested an alternative model to the staggered entry design for estimating fry 

survival. Our goal was to design a model that could estimate survival without using a paired release 

design (such as the staggered entry model) in order to avoid the strict assumptions required by the 

paired release model (namely, equal survival between groups after the second release). Our alternative 

model design is part of the class of models known as N-mixture models.

Standard N-mixture Model with a Removal Sampling Protocol

N-mixture models are a class of hierarchical models that estimate animal abundance and capture 

probability from repeated point counts replicated in space (Kéry and Royle, 2016). Specifically, let yij

represent the number of individuals sampled at site i (i = 1, ..., R) on the jth sampling occasion (j = 1, 

..., J). Under a removal sampling protocol, the sample counts arise from a multinomial distribution:

1 ( 1 0 )

1

, , , ~ Multinomial , , , ,

J

i iJ i ij i i iJ i

j

y yN y N =  −∑ π ππ (1)

where 

Ni is the abundance at site i, 

π ij is the probability that an individual at site i is first captured on the jth sample, and 

π i0 is the probability of not being captured over all J samples.

The primary assumption here is that the sampled population is closed to immigration, emigration, 

mortality, and recruitment over the J samples. The probability of first capturing an individual on sample 

j (π ij ) is a function of the per-sample capture probability:

( ) 1

1

1

1 j

j jj

j

p p

p

−

−

−

= − p

p (2)

where 

pj is the probability of capturing an individual on the jth removal sample, and

1 1 p = p .

Although Ni can be estimated directly for each site (Dorazio and others, 2005), N-mixture models 

leverage information across sites by assuming that distribution of site-specific abundance follows a 

Poisson distribution:

Ni ~ Poisson(λ ) . (3)

where 

λ is the mean abundance across R sites.
10

The integrated likelihood function for this multinomial-Poisson mixture model has a convenient 

computational form that reduces to the product of conditionally independent Poisson distributions 

(Royle, 2004a, Royle, 2004b):

( ) ( )

1 1

, Poisson

R J

ij

i j

f p = =y λ = ∏∏ λp (4)

where

y is the R × J matrix of sample counts.

Model parameters (λ and p) can be estimated in both maximum likelihood (Fiske and Chandler, 2011) 

and Bayesian frameworks (Kéry and Schaub, 2012).

This hierarchical model provides a flexible framework for estimating abundance. The mean 

abundance parameter (λ) can be modeled as a function of site-specific covariates to draw inference on 

factors affecting abundance. Additionally, capture probability can be modeled as a function of both sitespecific and occasion-level covariates associated with each of the j samples at site i.

Adapting the N-Mixture Model to Estimate Fry Survival

The N-mixture model as described in the preceding report section is couched in the context of 

site-level abundance following a Poisson distribution across sites. We considered this framework for 

estimating abundance at each trap site (for example, each trap or gill net site) in Lookout Point 

Reservoir by conducting repeated daily removal samples at each site. However, this approach posed 

numerous challenges. First, the set of J samples at each site cannot be considered a closed sample 

because fish can move freely in and out of the sampling area each day. Second, because trapping relies 

on fish moving through the traps or nets, the spatial area over which individuals are at risk of capture is 

unknown, making it difficult to estimate fish density (number of fish per unit area). Third, even if sitespecific density could be estimated, estimation of reservoir-wide abundance would necessitate 

extrapolating from sampled to unsampled areas. For these reasons, we determined that the N-mixture 

model, as previously described, could not be used to estimate reservoir-wide abundance and survival of 

fry.

The use of parentage-based tagging (PBT; see section, “Fish Source, Parentage-Based Tagging, 

and Replication”) methods for the Lookout Point Study provided an opportunity for recasting the Nmixture model by using information from each PBT mark. PBT identifies the offspring from each malefemale pairing, thereby providing a large number of unique batch marks. By viewing repeated sample 

counts as replicated across PBT batch marks instead of replicated across sampling sites, the N-mixture 

model can be used to estimate abundance of fry. Thus, if we now let yij represent the number of fry 

captured from PBT mark i on sample j over all reservoir sampling locations, then R is the total number 

of unique PBT marks, Ni is the abundance of fry with each PBT mark, λ is the mean abundance over all 

PBT marks, and Rλ is the expected value of the total abundance of fry in the reservoir.
11

To estimate survival from a release group of hatchery-reared fry, we adapted the N-mixture 

model to allow for a series of monthly primary sampling occasions with secondary occasions formed 

from removal samples taking place over consecutive days (fig. 6). Consider a release group where G is 

the total number of fry released into the reservoir, the number of unique PBT marks is known, the 

average fecundity is constant among females, and the population is sampled at some later date such that 

mortality occurs between release and sampling. The number of individuals with each PBT mark at the 

time of release, Ni,0, is unknown, but following from equation 3, Ni,0 can be assumed to arise from a 

Poisson distribution with mean G/R. However, because the release group size is known with certainty, 

Ni,0 must be viewed as a particular realization of this Poisson distribution with the constraint that 

,0

1

R

i

i

N G

=

∑ = . Thus, with a known release group size, Ni,0 for R PBT marks follows a multinomial 

distribution with R categories, sample size G, and equal cell probabilities of 1/R:

1,0 ,0

1 1 , , | ~ Multinomial , , , N NG R G

R R

        . (5)

Here, the expected value of Ni,0 is G/R, equivalent to the expected value of the Poisson distribution, but 

the multinomial distribution satisfies the constraint ,0

1

R

i

i

N G

=

∑ = .

For a single primary sampling occasion with S fraction of G surviving, the number of fish with 

each PBT mark surviving from release to recapture (Ni) is distributed binomially conditional on Ni,0:

N N i i | ~ Binomial , ,0 (N S i,0 ) . (6)

The capture counts for the ith PBT mark can be expressed using a multinomial distribution 

conditional on Ni and conditional on the total probability of capture over J secondary sampling 

occasions:

1

1

1 1

, , | ~ Multinomial , , , i iJ

i iJ i i J J

ij ij

j j

y yN N

= =

   

  ∑ ∑

  π π

π π

(7)

where π ij is the probability of first capturing an individual with PBT mark i on the jth sample, as 

described in equation 2.
12

Figure 6. Posterior distributions of capture (p) and survival (S) probability for three simulations with p = 0.10, 0.05, 

and 0.01. Vertical lines show the true parameter values used to simulate the capture data.

0.06 0.10 0.14

0 20 40 60

p1

0.06 0.10 0.14

0 10 30 50

p2

0.06 0.10 0.14

0 10 20 30 40

p3

0.06 0.10 0.14

0 5 10 20

p4

0.0 0.2 0.4 0.6 0.8 1.0

0 2 4 6 8 10

S1

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4 5 6

S2

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3

S3

0.0 0.2 0.4 0.6 0.8 1.0

0.0 1.0 2.0 3.0

S4

Density

p = 0.10 per day, 0.343 over 4 days

0.04 0.06 0.08 0.10 0.12

0 20 40 60 80

p1

0.04 0.06 0.08 0.10 0.12

0 10 20 30 40

p2

0.04 0.06 0.08 0.10 0.12

0 10 20 30

p3

0.04 0.06 0.08 0.10 0.12

0 5 15 25

p4

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4

S1

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4

S2

0.0 0.2 0.4 0.6 0.8 1.0

0.0 1.0 2.0 3.0

S3

0.0 0.2 0.4 0.6 0.8 1.0

0.0 1.0 2.0

S4

Density

p = 0.05 per day, 0.185 over 4 days

0.00 0.04 0.08 0.12

0 20 40 60 80

p1

0.00 0.04 0.08 0.12

0 10 20 30

p2

0.00 0.04 0.08 0.12

0 5 15 25

p3

0.00 0.04 0.08 0.12

0 5 10 20

p4

0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.5 1.0 1.5

S1

0.0 0.2 0.4 0.6 0.8 1.0

0.0 1.0 2.0

S2

0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.5 1.0 1.5

S3

0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.5 1.0 1.5

S4

Density

p = 0.01 per day, 0.0390 over 4 days
13

To extend this study design over K primary sampling occasions, the likelihood must account for 

removal of fry from the population at previous sampling occasions such that:

, ,1 , 1 ,, 1

1

| ~ Binomial ,

J

ik ik i k i jk k

j

N N N y S − − −

=

    −

  ∑ (8)

where yi,j,0 = 0 for all i and j. Extending the multinomial distribution for the removal sample to K

primary sampling occasions, we have

,1, , ,

,1, , ,

, , , ,

1 1

, , | ~ Multinomial , , , i k iJk

i k i J k ik ik J J

i jk i jk

j j

y yN N

= =

   

  ∑ ∑

  π π

π π

. (9)

The likelihood of the data (yijk) given the parameters (Sk and pk) of this model is the product of 

equation 5 with the product of equations 8 and 9 over all K primary occasions and R PBT marks. This 

model can be fit in either a maximum likelihood or Bayesian framework. Because the conditional 

likelihood formulation involves the unobserved latent abundance of each PBT mark (Nik), we decided to 

construct the model in a Bayesian framework where the latent abundances can be directly simulated and 

the parameters estimated using Markov Chain Monte Carlo (MCMC) techniques.

There are numerous assumptions under this form of the model. First, the number of individuals 

with each PBT mark at the time of release is not known with certainty. Given a constant average 

fecundity and constant survival in the hatchery, we assume that the number of individuals with each 

PBT mark at the time of release follows a Poisson distribution with mean G/R. Second, the model 

assumes equivalent reservoir survival and capture probabilities among PBT marks. These assumptions 

should be fulfilled if PBT marks are well mixed in the reservoir such that the distribution of PBT marks 

is similar among sampling locations. Because yij represents the total number of captures over all 

reservoir sampling sites, p now represents the proportion of each PBT marked group in the reservoir 

first captured on sample j. Thus, closure in this case means that (1) fish remain in the reservoir and are 

available for capture, and (2) no mortality occurs over the J days of sampling. 

Assessment of the Parentage-Based Tagging N-Mixture Model

To determine whether survival could be estimated without bias, we conducted a series of 

simulation experiments where true parameter values were assumed, capture data were simulated given 

the true parameter values, and the PBT N-mixture model was then fit to the simulated data. Given the 

expected study design to release 75,000 fry in the first release group, our goals were to (1) determine the 

minimum per-sample capture probability (p) required to estimate unique capture and survival 

probabilities for each survival interval of a K-occasion experiment, and (2) identify constrained models 

(for example, constant p or S across occasions) for cases in which very low p caused sparse data that led 

to bias in estimates of occasion-specific p and interval-specific S. For all simulations, we assumed that a 

release size of G = 75,000 fry was produced from R = 40 unique PBT marks, which could be 

approximately achieved by spawning 2 males with each of 20 females having mean fecundity of 4,000 

eggs (80,000 eggs, with some expected loss before release). This leads to an expected abundance at time 

of release of 1,875 fry per PBT mark.
14

For the first set of simulations, we simulated data for a survival study with K = 4 primary 

sampling occasions, J = 4 days of removal sampling, and S = 0.75 in each of the four time periods 

between primary occasions. We then simulated capture data for three scenarios with p = 0.10, 0.05, and 

0.01 for each secondary sampling occasion. We fit the PBT N-mixture model to these three simulated 

datasets in a Bayesian framework by using a Beta(1,1) prior distribution for both pk and Sk, and 

estimated unique survival and capture probabilities for each primary occasion.

We determined that the PBT N-mixture model was able to estimate unique detection and 

survival probabilities for p = 0.10 and 0.05, but not for p = 0.01 (fig. 6). For p = 0.10, the posterior 

distributions of p and S were centered around the true values. With an overall capture probability of 0.34 

over four secondary sampling occasions, uncertainty in the parameters increases with each primary

sampling occasion owing to a considerable fraction of the population being removed. At p = 0.05, the 

posterior distributions bracket the true values but are not centered about the true values. Additionally, 

the posterior distributions are much broader (for example, S1 spans 0.6–1.0 in fig. 6). Parameter 

estimates for this scenario may be unbiased, and the off-center posterior distributions may be due to the 

particular dataset that was simulated. Nonetheless, the simulations indicate difficulty in precisely and 

accurately estimating p and S when p = 0.05. At p = 0.01, the posterior distributions for survival are 

essentially uninformative about true survival because the distributions span the range from 0 to 1 and 

shows some influence of uniform prior distribution.

Given that we expect capture probabilities on the order 0.01–0.02 over a 4-day capture period (p

= 0.0025–0.005), the PBT N-mixture model cannot be used to estimate occasion-specific p and intervalspecific S. The simulations noted earlier in this section treat each primary capture occasion and survival 

interval as unique experiments. However, if p is similar among primary sampling occasions or survival 

varies systematically with covariates such as fish size or time, then information across multiple primary 

sampling occasions may be used to fit simpler models that could potentially estimate parameters 

without bias when capture probabilities are low. To test these ideas, we simulated an experiment with 

two levels of p, hypothesizing that capture probabilities would differ between gear types used for 

capturing fish when they inhabited nearshore compared to pelagic habitats. Additionally, we 

hypothesized that monthly survival probabilities would increase over time as fish grow over the spring 

and summer. We simulated an experiment with K = 6 primary occasions with p = 0.0025 for the first 

two occasions, p = 0.0050 for the last four primary occasions, and survival increasing over time from 

0.268 in the first monthly interval to 0.817 in the last monthly interval. All other inputs were set to the 

same values as the previous simulations.

We fit the PBT N-mixture model to this simulated data using a logit link function for both p and 

S:

( )

( )

,0 ,1 ,

,0 ,1 ,

logit

logit

k p p pk

k S S Sk

p b bx

S b bx

= +

= +

(10)

where 

bp,0 and bp,1 are the slope and intercept for capture probability, 

xp,k is a binary covariate set to 0 for k = (1, 2) and 1 for k = (3, 4, 5, 6), 

bS,0 and bS,1 are the slope and intercept for survival, and 

xS,k is a hypothetical covariate set to k-1.

We used diffuse normal prior distributions with a mean of 0 and standard deviation of 10 for all slope 

and intercept parameters.
15

We determined that the PBT N-mixture model was able to estimate all parameters without bias 

(fig. 7). All posterior distributions bracketed the true values of the parameters and were estimated with 

reasonable precision as evidenced by the breadth of the posterior distributions. For example, the halfwidth of the 95-percent credible interval ranged from 0.09 to 0.14 for the relation between survival and 

the hypothetical covariate (fig. 8). This analysis shows that the PBT N-mixture model can be used for 

capture probabilities as low as 0.01–0.02 over each primary sampling occasion by expressing p and S as 

a function of covariates likely to affect these parameters.

Figure 7. Posterior distributions of capture (p) and survival (S) probability for the simulation with two levels of 

capture probability and survival increasing over time. Vertical lines show the true parameter values used to 

simulate the capture data.

-2.0 -1.0 0.0 0.5

0.0 0.4 0.8

Intercept for S

0.0 0.4 0.8 1.2

0.0 1.0 2.0

Slope for S

-7.0 -6.5 -6.0 -5.5 -5.0

0.0 0.4 0.8 1.2

Intercept for p

-0.5 0.0 0.5 1.0 1.5

0.0 0.5 1.0 1.5

Slope for p

0.000 0.002 0.004 0.006

0 200 400 600

p1

0.000 0.005 0.010 0.015

0 50 150 250

p2

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4 5

S1

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4 5 6 7

S2

0.0 0.2 0.4 0.6 0.8 1.0

0 2 4 6 8

S3

0.0 0.2 0.4 0.6 0.8 1.0

0 2 4 6

S4

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4 5 6

S5

0.0 0.2 0.4 0.6 0.8 1.0

0 1 2 3 4 5 6

S6

Density

p = 0.0025 per day for primary occasions 1 and 2 
16

The PBT N-mixture study design has two potential advantages over the staggered releaserecovery study design. First, the PBT N-mixture study design only requires the release of one group of 

hatchery-reared Chinook salmon juveniles. This is advantageous because it allows us to release a single 

group of fish (R1) in the spring and monitor their survival through time. It also alleviates concerns 

associated with releasing the second and third groups of fish (R2 and R3) when release conditions are 

challenging, and assumptions related to their dispersal and mixing with the previous group. The second 

advantage is that the PBT N-mixture design can provide survival estimates between sampling occasions, 

rather than between periods when groups of fish are released, which will provide greater temporal 

resolution into key periods of mortality.

Figure 8. Graph showing true survival relationship used to simulate capture data and estimated relationship using 

the parentage-based tagging N-mixture model. The gray region shows the 95-percent credible envelope about the 

estimated relationship.

0 1 2 3 4 5

0.0 0.2 0.4 0.6 0.8 1.0

Hypothetical covariate val

Survival probability

True relationship

Estimated relationship
17

Implementation Plan

Fish Source, Parentage-Based Tagging, and Replication

Fish production details were planned and organized during 2016 to ensure that Chinook salmon 

fry were available for release into Lookout Point Reservoir for the 2017 pilot study. Planning occurred 

during May–August 2016, and spawning, incubation, and rearing are anticipated during September 

2016–April 2017. The Wild Fish Surrogate Program (WFSP) was identified as the lead entity 

responsible for producing salmon fry for the study. The WFSP produces juvenile salmon for research in 

the Willamette River Basin at the Smith Farm Genetics and Performance Laboratory (SFGPL) in 

Corvallis, Oregon. Sample size targets for our study exceeded their rearing capacity, so a plan was 

developed to split rearing efforts between the SFGPL and the Willamette Hatchery. Eggs and milt were 

to be collected from adult spring Chinook salmon at the Willamette Hatchery, and they would then be 

transported to the SFGPL for fertilization and incubation. All fish would hatch at the SFGPL and the R1

group would be reared there for about 4months, and then transported to Lookout Point Reservoir and 

released. Spawning for the R1 group is expected to occur during late September, hatching should occur 

in mid-December, and fry will be released in mid-April. The R2 and R3 groups will be reared at the 

SFGPL for about 4 months as well, and then transported to the Willamette Hatchery where they will be 

reared to their respective size and release date targets (table 1). Spawning for the R2 and R3 groups 

occurred on September 6, 2016, hatching should occur in mid-October, fry will be transported to the 

Willamette Hatchery in late-February, and releases will occur during mid-June and mid-July.

PBT methods will be used as the fish-marking technique for the pilot study (Steele and others,

2013; Sard and others, 2015). At the time of spawning, genetically distinct family groups will be created 

by mixing gametes from two male parents with gametes from one female parent. Genetic samples from 

every parent will be collected at the time of spawning. Family groups will then be assigned to specific 

replicates within each release group (R1, R2 or R3). The number of replicates within a given release 

group is based on the availability of individual rearing ponds at the SFGPL and the Willamette 

Hatchery— the R1 group will be comprised of five replicates of 15,000 fry, the R2 group will be 

comprised of two replicates of 25,000 fry, and the R3 group will be comprised of two replicates of 5,000 

fry. Replication within release groups will provide insights into the rearing, transportation, and release 

processes, so replicates will remain physically separated during these periods. Genetic samples also 

were collected from adult spring Chinook salmon that were outplanted upstream of Lookout Point 

Reservoir in 2016, so PBT also will allow us to identify naturally produced fry when they are 

encountered. 

Fish Transport and Release

Fish loading, transport, and release protocols are critical for survival studies because the total 

number of fish released must be known without error (Skalski, 2016), and mortality associated with 

handling, transportation, and release is unacceptable. These protocols ensure that fish are held and 

transported in appropriate densities, that fish are properly acclimated to water temperatures at the 

release site prior to release, and that the release strategy maximizes the likelihood that released fish will 

disperse and behave in a manner consistent with naturally produced fish in the system. Data from the 

ODFW show that naturally produced Chinook salmon enter the reservoir during February–May and that 

fish are dispersed throughout the upper part of the reservoir during spring (Monzyk and others, 2015). 
18

The R1 release group will be transported from the SFGPL to Lookout Point Reservoir where they 

will be released from a boat. On release days, a transport truck will arrive at the SFGPL to sort, load,

and haul fish to Lookout Point Reservoir. At the SFGPL, replicates will be held in circular fiberglass 

tanks (10-ft diameter) that contain 15,000 fish apiece. Fish will be released at five separate locations in 

the upper part of Lookout Point Reservoir (fig. 9), so each replicate will need to be divided into groups 

of 3,000 fish each prior to transport. These groups will then be loaded into 231-L insulated holding 

containers (fig. 10). In mid-April, the R1 fish are expected to be about 48 mm long and weigh 1.9 g 

apiece, so each holding container will maintain transport densities of about 18 g/L, well below the 

maximum standard of 50 g/L (Surgical Protocols Steering Committee, 2011). Water temperatures at the

SFGPL in mid-April are expected to be in the 10–12 °C range and surface temperatures in Lookout 

Point Reservoir are expected to be in the 10–11 °C range (fig. 2). Therefore, holding containers will 

receive oxygen during transport, but will not require heating or cooling measures to match conditions at 

the release sites. Transport time from the SFGPL to Lookout Point Reservoir is expected to be about 90 

minutes. Transport containers will be moved from the fish hauling truck to a boat on the reservoir and 

then taken upstream of Lookout Point, near Hampton Campground (fig. 9). The first group of 3,000 fish 

will be released at this location, in the middle of the river, and these fish will disperse downstream into 

the reservoir. The boat will then move downstream and release the remaining four groups at nearshore 

locations between the head of the reservoir and the mouth of School Creek (fig. 9). This release strategy 

is designed to distribute fish throughout the upper part of Lookout Point Reservoir to mimic fry 

distributions previously observed in the ODFW sampling (Monzyk and others, 2015). This process will 

be repeated for each replicate during the course of 2 days until all fish from the R1 group have been 

released.

Figure 9. Orthoimage showing five release sites (white circles) for juvenile Chinook salmon in the upper part of 

Lookout Point Reservoir, western Oregon, during mid-April 2017. Hampton Campground and School Creek also 

are shown as reference points.
19

Figure 10. Photograph showing insulated fish-holding containers that will be used to transport juvenile Chinook 

salmon to Lookout Point Reservoir, western Oregon, during 2017. In the foreground a single container is shown on 

a wheeled platform and, in the background, five containers are shown in a fish transport truck.

The R2 and R3 releases will be more challenging because they are scheduled for June and July, 

when Lookout Point Reservoir is thermally stratified (fig. 2). Juvenile salmon would be expected to 

avoid warm surface water in the reservoir during summer, and this has been confirmed by the ODFW in 

their sampling. During summer, gill nets are placed 30–60 ft below the water surface to target the 

segment of the water column being used by juvenile Chinook salmon (Monzyk and others, 2015). The 

R2 and R3 release strategy must account for warm surface temperatures and use release techniques that 

avoid thermally shocking released fish. Water temperatures at the Willamette Hatchery, where R2 and R3

fish will be held, are expected to be in the 13–15 °C range during June and July 2017 (Dan Peck, 

Oregon Department of Fish and Wildlife, written commun., August 8, 2016), and data collected by the 

ODFW suggest that juvenile salmon in the reservoir will be using water temperatures in the 15–17 °C

range. However, surface temperatures will be in the 18–21 °C range, so a release apparatus will be used

to release fish from a boat and inject them 30–60 ft below the surface, without exposing them to the 

warm surface water. This device will use a submersible pump to pull cool water from lower segments of 
20

the water column into a headbox that will feed water through a release pipe (10.2-cm diameter). The 

pipe will extend from the surface down through the warm surface water and can be adjusted to release 

fish at desired depths. 

The R2 and R3 releases will be conducted in the lower two-thirds of Lookout Point Reservoir, 

where Chinook salmon fry typically reside during summer (Monzyk and others, 2015). Releases of each 

group will occur at five locations to mimic distributions previously observed (fig. 11). The R2 fish will 

be held in two replicate groups containing 25,000 fish apiece at the Willamette Hatchery. In mid-June, 

fish are expected to be about 97 mm long (table 1) and weigh 11.4 g apiece, so each replicate will need 

to be sorted into smaller groups for transport on each release day. We plan to transport R2 fish in 1,000 

fish groups, in holding densities of 50 g/L. Each group will be loaded into an insulated holding 

container (fig. 10), transported by truck to the reservoir, and then transferred onto a boat. The boat will 

then take fish to the release sites (fig. 9) where they will be released using the release apparatus. The 

identical process will occur for R3 releases with the exception of numbers of fish transported in each 

holding container—R3 fish are expected to be 120 mm long and weigh 20.1 g, so fish will be sorted in 

500 fish groups (holding density = 44 g/L).

Figure 11. Orthoimage showing five release sites (white circles) for juvenile Chinook salmon in Lookout Point 

Reservoir, western Oregon, during mid-June and mid-July 2017. Lookout Point Dam also is shown as a reference 

point.
21

Reservoir Sampling

Sampling will occur 1 week per month during April–October 2017. To estimate catch rates for 

the 2017 pilot study, we examined data from Lookout Point Reservoir in 2014 when 67,313 hatcheryproduced Chinook salmon fry were outplanted. We used fish-length data from Chinook salmon fry in 

Lookout Point Reservoir (Monzyk and others, 2015) and length-based survival estimates from Zabel 

and others (2005) to estimate the number of fry that were alive in the reservoir at the end of each month 

during July–November (table 2). We then used monthly collection data from Monzyk and others (2015) 

to obtain estimates for the proportion of the live population that was captured during each monthly 

sampling period. This analysis determined that average catch rates were 2.1 fish per gill net set and

estimated that monthly capture probabilities were about 0.006 (table 2). Our goal for the 2017 pilot 

study is to develop a sampling strategy that will allow us to achieve total capture probabilities in the 

0.01–0.02 range. To accomplish this, we estimate that each sampling period will require about 40 gill 

net sets per night during the 4-day sampling interval. This would result in an overall effort of 160 gill 

net sets per month and will likely require four 2-person crews. On the first day of sampling each month, 

sampling gear (gill nets or shoreline traps) will be deployed in the reservoir and allowed to fish for 

about 24 h. Crews will remove fish daily and redeploy the sampling gear for a total of four consecutive 

sampling days. Collected fish will be enumerated, identified to species, and preserved for PBT 

processing after the study.

Table 2. Estimated population of hatchery-produced juvenile Chinook salmon in Lookout Point Reservoir, western 

Oregon, 2014.

[ODFW, Oregon Department of Fish and Wildlife

Month Estimated 

population

Number of gill 

nets placed by 

ODFW

Number of juvenile 

Chinook salmon

collected

Average number of 

fish captured per net

placed

Estimated proportion of 

population captured

July 40,232 60 195 3.3 0.005

August 26,090 43 125 2.9 0.005

September 17,737 46 66 1.4 0.004

October 12,579 56 76 1.4 0.006

November 9,425 56 74 1.3 0.008

Average 52.2 107.2 2.1 0.006

A combination of electrofishing, gill nets, and nearshore traps will be used to sample Chinook 

salmon fry in the reservoir during the pilot study (fig. 12). Monzyk and others (2015) reported that 72–

90 percent of the subyearling Chinook salmon captured during April and May in Lookout Point 

Reservoir were located in the upper part of the reservoir, whereas the proportion captured in the lower 

part of the reservoir was small (2–4 percent). We will use this information in our sampling strategy. 

April and May sampling will be conducted using floating box traps, Oneida Lake traps, and shoreline 

electrofishing because Chinook salmon fry are shoreline-oriented during this period (Monzyk and 

others, 2015). We will sample throughout the reservoir in April and May, but will apply greater effort 

(more sampling equipment) in the upper part of the reservoir where Chinook salmon fry are expected to 

be concentrated. June is a transition month when fry begin moving offshore while others remain along 

the shorelines. We will use a combination of sampling techniques during this period including nearshore 

traps, electrofishing, and gill nets. Less is known about the longitudinal distribution of Chinook salmon 

fry in the reservoir during this period because the ODFW has not traditionally sampled during June. 
22

Therefore, sampling will occur throughout the reservoir during June. We will sample exclusively with 

gill nets during July–October because juvenile Chinook salmon fry will be offshore during this period. 

Sampling will be conducted throughout most of the reservoir, but we will avoid areas upstream of 

School Creek in July and August as the ODFW has shown that Chinook salmon fry are absent from that

area of the reservoir in those months (Monzyk and others, 2015).

Figure 12. Photographs of sampling equipment, including boat electrofishing (A), gill nets (B), floating box traps 

(C), and Oneida Lake traps (D), that will be used in Lookout Point Reservoir, western Oregon, 2017.

Critical Uncertainties

In theory, the staggered release-recovery model and PBT N-mixture model are conceptually 

sound techniques for estimating fry survival in Lookout Point Reservoir, but field testing will be 

required to address a series of critical uncertainties and model assumptions. The staggered releaserecovery model will use three releases of hatchery-reared fish to generate survival estimates that will be 

used to make inferences about survival of Chinook salmon fry in the reservoir. However, one key 

assumption of the staggered release-recovery model is that survival of the two release groups is the 

same in the time period following release of the second group. It may be challenging to produce 

hatchery-reared Chinook salmon juveniles that match the body size, behavior, and dispersal patterns of 

naturally produced fish. Furthermore, the second release group will be naïve to the natural environment, 

whereas the survivors of the first release group will have already reared for a substantial period of time 

in the reservoir. Therefore, survival of the second release group may differ from that of the first group.

A B

C D
23

Another key assumption is that capture probabilities are the same among release groups, which 

implies that the release groups must be well-mixed such that they have a similar spatial distribution 

throughout the reservoir. We are planning a rigorous sampling effort that will include spatial and 

temporal replication, and we will assess for differences in dispersal patterns between the two 

populations to assess this assumption. Furthermore, hatchery-produced juveniles will be released into 

the reservoir in three groups, and these will be distinguishable using PBT methods. This will allow us to 

further assess differences in dispersal patterns among hatchery-produced release groups.

The PBT N-mixture model has assumptions and critical uncertainties that differ from those of

the staggered release-recovery design. The key assumption of the PBT N-mixture model is that capture 

and survival probabilities are similar among PBT mark groups, which implies that fish from all marks 

have a similar spatial distribution throughout the reservoir. This assumption can be achieved with 

careful rearing and release strategies that ensure that all PBT groups have a similar spatial distribution 

among release sites. Two critical uncertainties of the PBT N-mixture model are (1) whether the study 

will achieve capture probabilities of 1–2 percent over each primary sampling occasion, and (2) whether 

key covariates will be able to explain variation in survival and capture probability among sampling 

occasions.

Low capture probability of Chinook salmon juveniles is a primary concern for both approaches. 

We have examined existing datasets to estimate reservoir populations and related those datasets to catch 

data from previous years. These results were used to develop estimates for effort required to achieve our 

collection goals in 2017, and we plan to implement sampling at a level not previously attempted in 

Lookout Point Reservoir. However, the outcome of this sampling strategy remains to be seen. We 

believe that the planned efforts will achieve our sampling goals, but insufficient collection of Chinook

salmon fry will limit the ability to estimate survival using either of our proposed approaches.

By applying two analytical methods for estimating survival, each with different assumptions and 

uncertainties, we anticipate gaining much more information about survival than using either method 

alone. Estimates from each method can be compared to help understand and assess the performance of

each model. For example, because the PBT N-mixture model does not require multiple release groups, 

we may be able to assess the assumption of equal survival between the two release groups in the 

staggered release-recovery design. Likewise, because the PBT N-mixture model must rely on covariates 

to estimate variation in survival among primary sampling periods, these estimates can be compared to 

those from the staggered release-recovery model to evaluate whether the survival estimates are similar.

Summary

We plan to evaluate the staggered release-recovery and PBT N-mixture study designs during a 

pilot study in Lookout Point Reservoir, western Oregon, in 2017. Much is known about fish populations 

and distributions in the reservoir, but little is known about survival of Chinook salmon (Oncorhynchus 

tshawytscha) fry, and this information will be critical for fishery managers in the future. The two 

approaches seem to offer promising capabilities, but neither has been field-tested. The 2017 pilot study 

will provide information on the performance of these study designs, and also should yield important 

insights into sampling approaches because we will be implementing fish collection efforts at levels that 

are unprecedented in Willamette Project (Project) reservoirs. This level of collection effort was selected 

to provide the best opportunity for collecting sufficient numbers of Chinook salmon fry to obtain 

meaningful survival estimates. There may be additional benefits from these collection efforts as well. 

For example, we should be able to rigorously evaluate proportions of catch at each sampling location to 

determine if there are differences in distribution patterns of salmon fry from R1, R2, R3 and naturally

produced groups. Our sampling methods likely will preclude by-catch of adult piscivorous fish species, 
24

but we may collect juvenile piscivorous fish and could potentially learn more about their distribution 

within Lookout Point Reservoir. When encountered, we will enumerate juvenile piscivorous fish, 

identify them to species, collect fork-length data, and record their location of capture. We also will be 

lethally sampling fish to meet the removal requirements of the study designs we are testing, and to 

collect DNA samples for PBT analysis. Thus, we expect to have large numbers of samples available if 

other researchers are interested in examining isotope ratios within the fish community, or applying other 

tissue-based research methodologies in the future. The side-by-side comparison of the two approaches 

will allow us to evaluate strengths and weaknesses, and to compare survival estimates from each 

approach. This process will be insightful for future development of similar techniques that can be used 

throughout the Project and at other locations where survival information of salmon fry is desired.

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Report 2014-1177, 52 p.

Brandt, J.R., Monzyk, F.R., Romer, J.D., and Emig, R., 2016, Status and trends of predator species in 

Lookout Point Reservoir: Report by the Oregon Department of Fish and Wildlife to the U.S. Army 

Corps of Engineers, 44 p.

Dorazio, R.M., Jelks, H.L., and Jordan, F., 2005, Improving removal-based estimates of abundance by 

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For more information concerning the research in this report, contact the

 Director, Western Fisheries Research Center

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http://wfrc.usgs.gov/
ISSN 2331-1258 (online)

http://doi.org/10.3133/ofr20161211

Kock and others—A Study Design and Implementation Plan to Estimate Juvenile Salmon Survival in Lookout Point Reservoir, Oregon—Open-File Report 2016–1211