NOAA Technical Memorandum NMFS-AFSC-137

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Alaska Fisheries Science Center

July 2003

by

J. M. Maselko, A. C. Wertheimer, and J. F. Thedinga

Selection and Application of a

Mark-and-Recapture Technique for

Estimating Pink Salmon Escapements
NOAA Technical Memorandum NMFS

The National Marine Fisheries Service's Alaska Fisheries Science Center

uses the NOAA Technical Memorandum series to issue informal scientific and

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are not appropriate or feasible. Documents within this series reflect sound

professional work and may be referenced in the formal scientific and technical

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the Northwest Fisheries Science Center.

This document should be cited as follows:

Maselko, J. M., A. C. Wertheimer, and J. F. Thedinga. 2003. Selection

and application of a mark-and-recapture technique for estimating pink

salmon escapements. U.S. Dep. Commer., NOAA Tech. Memo.

NMFS-AFSC-137, 44 p.

 

Reference in this document to trade names does not imply endorsement by

the National Marine Fisheries Service, NOAA.
July 2003

NOAA Technical Memorandum NMFS-AFSC-137

by

J. M. Maselko, A. C. Wertheimer, and J. F. Thedinga

Selection and Application of a

Mark-and-Recapture Technique for

Estimating Pink Salmon Escapements

Alaska Fisheries Science Center

7600 Sand Point Way N.E.

Seattle, WA 98115

www.afsc.noaa.gov

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

National Oceanic and Atmospheric Administration

Vice Admiral Conrad C. Lautenbacher, Jr., U.S. Navy (ret.), Under Secretary and Administrator

National Marine Fisheries Service

William T. Hogarth, Assistant Administrator for Fisheries
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iii

ABSTRACT

As part of a larger study to estimate straying rates of pink salmon (Oncorhynchus

gorbuscha), escapements of pink salmon were estimated for eight streams in lower Chatham

Strait, southeastern Alaska. Prior to stream sampling, computer simulations were used to

compare stratified Schaefer and Petersen population estimators. The Petersen estimator was

chosen for superior performance and used in conjunction with an efficient sampling design to

calculate escapement for the eight streams. Live fish entering each stream were tagged with

two opercular tags to account for tag loss, and streams were surveyed for tagged carcasses. 

Streams were sampled at least twice per week after fish were tagged. Petersen escapement

estimates varied from 8,609 fish at William Creek to 79,070 fish at Deep Cove Creek, and all

estimates had low coefficients of variation ( 0.11) and narrow confidence intervals. Some

of the tagged fish were observed later in streams other than where they were tagged. 

Estimates of these “probing” fish varied from 0 to 11.7% of the estimated escapements. We

used the estimates of probing to adjust the escapement estimates and variances.

v

CONTENTS

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

INTRODUCTION ......................................................... 1

METHODS .............................................................. 3

Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

 Simulation Methods ............................................ 3

Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Field Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Field Methods ................................................ 8

Estimation ................................................... 9

RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

CITATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

TABLES

Table 1.--Statistics: average, average error, and standard deviation from 1,000

simulations of escapement estimation for a hypothetical pink salmon

population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Table 2.-- Tagging statistics: number of pink salmon marked with opercular tags and

 carcasses recovered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Table 3.--Number of adult pink salmon tagged and number of fish that probed . . . . . . . . . 33

Table 4.--Estimated numbers and associated coefficients of variation (CV) of pink

salmon emigrating from streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

FIGURES

Figure 1.--The sampling design was applied to a hypothetical pink salmon escapement . . 35

Figure 2.--A typical history of all carcass recoveries used to calculate escapement

estimates for comparing the Schaefer and Petersen estimators . . . . . . . . . . . . . . . . 36

Figure 3.--Monte Carlo distributions of the estimated population sizes for the Petersen

and Schaefer estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 4.--Results of 1,000 iterations of the Petersen and Schaefer models . . . . . . . . . . . . 38

Figure 5.-- Map of study streams sampled within 35 km radius of Little Port Walter research

 hatchery. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 6.--Carcass recoveries at Borodino Creek and Lovers Cove Creek in 1997 . . . . . . 40
vi

Figure 7.--The relationship between the distance pink salmon probed and observed

number of fish that probed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 8.--Estimated escapements of pink salmon in eight study streams in

southeastern Alaska, adjusted for the expanded number of probers . . . . . . . . . . . . 42

Figure 9.--Probing rate and escapement of pink salmon into the eight study streams in

southeastern Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 10.--Comparison between peak aerial counts and Petersen estimates of pink

salmon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
INTRODUCTION

Fishery managers and researchers working with salmon (Oncorhynchus spp.) often

require estimates of escapements. Methods for estimating escapements include weirs, foot

and aerial surveys, and mark-recapture experiments. The choice of method is often

constrained by limited resources. As part of a research project to examine straying of pink

salmon (O. gorbuscha) (Wertheimer et al. 1999, Thedinga et al. 1999), we needed

escapement estimates for a number of streams. We also needed to examine all available

salmon carcasses in the streams to determine the number of fish marked as fry that spawned

in the streams. Weirs to enumerate and examine live fish or mark-recapture techniques

would have been suitable; however, weirs are expensive to build and operate, and we wanted

to examine post-spawning fish. Hence, we chose to tag live fish captured in a stream and

recover their carcasses after spawning.

Mark-recapture methods to estimate fish populations are based on either an open- or a

closed-population model. In a closed model, a fixed population size during sampling is

assumed. An open model allows for the population size to change during sampling. The

open-population model most often used is the Jolly-Seber model (Parker 1968, Sykes and

Botsford 1986, Schwarz et al. 1993), whereas the Petersen and Schaefer models are

commonly used for closed populations (Schwarz et al. 1993). The Schaefer model is

commonly used in populations not meeting all the assumptions under the Petersen model

(Law 1994, Boydstun 1994).

In a 1995 pilot study, a carcass mark-recapture sampling design was used to estimate

pink salmon escapement into Lovers Cove Creek (Wertheimer et al. 1997). The Jolly-Seber

model (Parker 1968, Sykes and Botsford 1986) was used to estimate escapement. However,
2

during floods, many carcasses were flushed out of the stream sampling area, causing the loss

of whole time strata from which no recoveries were made and which resulted in a biased

estimate of escapement (Parker 1968).

To compensate for the dynamic nature and frequent flooding of the streams, we

decided to tag live fish entering a stream and recover the marks later from carcasses after

spawning. We assumed the tagged fish would remain within the stream and would disperse

throughout the spawning area before dying. The Jolly-Seber estimator was no longer

appropriate because our design precluded multiple recaptures; marking and sampling were

not concurrent. Sykes and Botsford (1986) point out that in carcass-tagging, fresh carcasses

are sampled more easily than decayed ones, resulting in undercounting older, more decayed

tagged carcasses. Marking live fish addresses the problems associated with carcass tagging

and stream flooding, provided that the tagged fish mix with the escapement and flooding does

not remove them.

Pink salmon escapement may be regarded as a closed population for which all fish

can be examined as they enter a stream and again later as the entire spawning area is sampled

for marks. When marking and recovery sampling take place throughout the run and the

spawning area, respectively, all fish pass through the marking area and become carcasses in

the recovery sampling area. Using surveys at fixed intervals over the course of recovery

sampling, equal probability of recapturing all fish can be assumed. A fixed number of fish

escape into a stream system, and when the run has finished, this number will not change

unless immigration or emigration occur. In a mark-and-recapture study to estimate

escapement of coho salmon (O. kisutch), Schwartz et al. (1993) showed that the Petersen
3

closed-population model gives acceptable results similar to results from open-population

models when a constant proportion of carcasses is sampled (as expected with our recovery

surveys), or a constant proportion of the population is marked (unlikely with our tagging

schedule).

We therefore decided to use a closed population estimator. Prior to beginning field

sampling, we generated population estimates for simulated, hypothetical, pink salmon

spawning escapements to compare performance of the Schaefer model population temporally

(stratified) to that of the Petersen model population (unstratified). We simulated three

different levels of marking intensity to determine numbers of fish to mark and used both

models to estimate known simulated escapements.

METHODS

Simulation Model

Simulation Methods

A Monte Carlo model was used to simulate a large number of mark-and-recapture

experiments on a hypothetical pink salmon escapement of 30,003 fish, which was the average

expected escapement of the streams to be sampled. All the simulated fish entered the stream,

spawned, and died within 45 days. Live fish entry pattern was normal and occurred only

during the first 30 days. Carcasses were recovered until day 45. The escapement was divided

into two temporal distributions: live fish available for marking (marking distribution) and

carcasses by day after first entry (Fig. 1). The marking distribution equaled the live fish in

the stream minus the live fish that were already marked. When plotted by day, after first

entry, the available live fish produce the marking distribution. The distribution of the entry
4

timing was based on long-term averages of spawner returns to Sashin Creek in southeastern

Alaska (Heard 1991). Daily fish deaths were generated by assuming that the live fish had a

0.8 probability of dying 8 days after they had entered the stream (Heard 1991, Sharr and Bue

1993). Subsequent mortality was also 0.8 of the leftover fish per day after the initial 8 days. 

The carcasses available for sampling from which recoveries were made equaled the sum of

all daily mortalities minus previously recovered carcasses (Fig. 1).

A fixed number of fish were marked from the marking distribution during each of five

events, 4 days apart. The first marking event began when fish first entered the stream. The

last marking event was on day 31 of the run, when most fish were in the stream and effort

shifted to recovery of carcasses. To determine the sensitivity of the population estimates to

marking effort, three marking levels were examined: 100, 200, and 300 fish per event.

“Recoveries” were made from the carcass distribution. The random number of

recovered carcasses at a sampling event equaled the product of a uniform (0, 0.5) random

number and the number of available carcasses. The random number of recovered marked

carcasses equaled the product of a uniform (0, 1) random number, the marked proportion of

fish in the stream, and the number of carcasses recovered. This recovery model was intended

to simulate a range of possibilities expected under varying environmental conditions -- from

floods that could flush most carcasses out (resulting in few recoveries) to droughts (resulting

in high carcass retention and recoveries; Fig. 2). Recovery sampling did not take place until

after the second marking event, when tagged fish began to die and enter the carcass

distribution. Sampling continued every 4 days until day 44.
5

N , n mc

c c s

j i ij

j i j

u

i

t

= = = ⋅ ⋅

∑∑

1 1

Two mark-and-recapture population estimators, Schaefer (Pooled Petersen) and

Petersen (Seber 1982), were used. The Schaefer estimator stratifies the population by time,

generating an estimate for each marking event. The Schaefer estimate for population size, Ns,

is

where mi

 = number of fish marked at event i, cij = number of carcasses recovered at event j

that were marked at event i, c.i = total number of carcasses recovered that were marked at

event i, c.j

 = total number of carcasses recovered at event j, nj = total number of carcasses

examined at event j, t = total number of marking events, and u = total number of recovery

events.

Our purpose for modeling was to decide whether mark-and-recapture sampling should

be stratified throughout the escapement or treated as a single mark and recapture. Therefore,

the simple Petersen and the Schaefer estimators were compared to examine the

appropriateness of time stratification. To apply the Petersen estimator, the sequence of

marking and recapture events was considered as a single experiment. The number of marks

are summed for all marking events, and the number of carcasses and recaptures are summed

for all recapture events. The Petersen estimate for population size, Np, is
6

N .

m n

c

p

i j

j

u

i

t

ij

j

u

i

t = = =

= =

∑ ∑

∑∑

1 1

1 1

Both Schaefer and Petersen estimates were computed for each of 1,000 mark-and-recapture

simulations generated for each of the three levels of marking intensity. Monte Carlo means

and variances were computed for each marking level.

Simulation Results

As expected, precision of the escapement estimate was positively related to the

marking effort and the proportion of marked carcasses recovered (Robson and Regier 1964). 

Standard errors of both the Schaefer and Petersen estimators were reduced with increase in

the number of fish marked at a marking event (mi

) and the proportion of marked fish

recovered (Table 1; Fig. 3). Bias, or average error, decreased with number of fish marked

(Table 1) but was always negligible.

The Schaefer escapement estimator produced more outliers than the Petersen

estimator (Fig. 4). Errors in estimation were especially large when no recoveries (c.i

 = 0)

were made during a recovery event j of tagged fish released during a marking event i. The

standard deviations of the Schaefer estimates were almost twice those of the Petersen

estimates (Table 1). When no tagged fish were recovered from a release event (ci

 = 0), the

escapement calculation was based on the remaining marking events as we assumed that all

the carcasses from that stratum were flushed out of the stream. Overall, the Petersen
7

 1Murphy, M. L., J. F. Thedinga, and J. J. Pella. 1994. Bootstrap confidence intervals for trapefficiency estimates of migrating fish. Unpublished manuscript, Auke Bay Laboratory, 11305

Glacier Hwy., Juneau, Alaska, 99801-8626.

estimator was unaffected by whole strata losses because the total proportion of marked to

unmarked carcasses remained unchanged. 

The standard deviation of the Petersen population estimates was similarly affected by

marking and sampling intensity. The standard deviation increased almost threefold when mi

decreased from 300 to 100 marks per marking event at all tag-recovery proportions. The

standard deviation increased only 50% when mi

 was decreased from 300 to 200 marks. The

proportion of tagged fish recovered had an even greater effect on the error estimates (Fig. 4). 

Under actual field conditions of this study, tag recoveries are expected to range from 10% to

30%.

Based on this simulation, the Petersen model was selected to estimate pink salmon

escapement because it produced more consistent and precise estimates. Although both

estimators showed less than 0.1% bias, the Petersen model biased high and the Schaefer

model biased low, with the overall bias smaller for the Petersen model (Table 1). In the field,

however, we observed increased bias due to tag loss and fish leaving the streams. To account

for that bias, we reduced the original number of tagged fish to account for tag loss, and

subtracted estimated numbers of fish leaving the stream from escapement estimates. 

Escapement estimates were computed with Bailey’s modified Petersen estimator.1

 Precision

of estimates was determined by a combination of a parametric bootstrap method and the delta

method.
8

 2Scott Johnson, ADF&G, Douglas, Alaska; pers. commun. July 1996.

Field Application

Field Methods

The study streams consisted of eight of the most productive pink salmon spawning

streams in lower Chatham Strait within a 35-km radius of the Little Port Walter research

hatchery (LPW) on Baranof Island, southeast Alaska (Fig. 5). The study was based out of

LPW as it was part of a larger study effort to estimate straying rates of pink salmon released

from the LPW hatchery. Stream selection was based on aerial survey counts by personnel of

the Alaska Department of Fish and Game (ADF&G).2

Based on the simulation and available resources, the target tagging effort was set at

300 pink salmon per each of five marking events in each stream. Live fish were tagged above

the mean high water in the first pool with adequate numbers of fish. Fish were seined with a

beach seine or a pole seine, then tagged with a Ketchum No. 1 operculum tag on each

operculum. The tags, aluminum “staples” commonly used in the poultry industry to tag the

wings of chicks, were numbered with a 5-digit code, allowing stream and time of tagging to

be determined from recaptured carcasses. The tags were inconspicuous so that samplers (or

predators and scavengers) were not attracted to marked fish. Fish were handled carefully and

released immediately after they were tagged to minimize the possibility of stress-induced

straying (Thrower 1988).

Recovery consisted of hiking the streams and examining each observed carcass for a

tag on either operculum. Each carcass was counted as tagged or untagged and chopped in

half to avoid resampling. For each tagged carcass, both (if present) tag numbers were
9

recorded to estimate tag-loss rates. Numbers of marking and recovery events were equal;

both were usually done on the same day.

Streams were sampled from 1 September to 10 October, which effectively

encompassed the spawning period. Three crews of three or four people each sampled the

streams. In six streams, fish were tagged and carcasses were recovered twice per week. The

two main streams nearest LPW, Lovers Cove and Borodino Creeks, were sampled more

intensely to maximize recoveries of coded-wire tags from the associated straying studies

(Wertheimer et al. 1999, Thedinga et al. 1999). These two streams were sampled four times

weekly; in addition, carcass weirs were constructed across the outlets of these streams to

increase carcass recoveries. The weirs were checked daily until they were destroyed by

floods in early October.

In addition to the 8 streams sampled systematically, 28 streams and one hatchery

(Armstrong-Keta, Inc. (AKI)) within 50 km of LPW were also sampled for tagged fish. We

sampled the total return at the LPW hatchery and at the weir on Sashin Creek; the other

streams were sampled opportunistically, with no attempt to estimate the proportion of the

population sampled.

Estimation

The escapement to each stream was estimated using Bailey’s adjustment of the

Petersen estimator, which corrects for statistical bias of the uncorrected Petersen formula

(Seber 1982):
10

N ,

m n

c

b

i j

j

u

i

t

ij

j

u

i

t =

+ 



 





+





 





= =

= =

∑ ∑

∑∑

1

1

1 1

1 1

Var N( )

m nn c

c c

b

i

i

t

j

j

u

j ij

j

u

i

t

j

u

ij

j

u

i

t

ij

j

u

i

t

 = .





 



 + 



 



 − 



 





+ 



 



 + 



 





== = = =

= = = =

∑∑ ∑ ∑ ∑

∑ ∑ ∑ ∑

1

2

1 1 1 1

1 1

2

1 1

1

1 2

with a variance of

Because we tagged both opercula, we were able to estimate the number of carcasses

recovered that were missing both tags assuming that the losses were independent. Let the

probability of losing one tag be denoted by p, and let the numbers of carcasses recovered with

no tags, a single tag, and with both tags present be denoted by m0, m1, and m2, respectively. 

First, an estimate of p is obtained from the observed m1 and m2. The probability that a fish

lost both tags is p2 if the losses are independent events, and so the probability the fish lost

either one or none of the tags is 1-p2

. If attention is restricted to recovered carcasses with one

or two tags present, these counts are binomially distributed with parameter, N = m1+m2, and
11

p .

m

m

m

=

+

1

1

2

2

2

( ) ( ) ~p . m m

m mm

m

m

m mm = +

+ + =

+

+ +

2

2

0 1 2

012

0

1

012

m . m

m

0

1

2

2 4 =

respective probabilities, 2p(1-p)/(1-p2) and (1-p)

2

/(1-p2). The corresponding maximum

likelihood estimate of p is

If the number of fish losing both tags, m0, were known, an estimate of p would equal the

fraction of total tags lost, or

Setting the two estimates of p equal and solving for m0 produces the estimate for carcasses

missing both tags as

Additional uncertainty was introduced by tag loss and by probing -- fish entering the

stream but subsequently leaving to spawn elsewhere. To account for this, escapement and

variance were then estimated with a parametric bootstrap procedure. This estimation 
12

procedure provides a bootstrap point estimate and bootstrap variance for the population at

tagging time, including fish that die in the stream (either after spawning or as pre-spawning

mortality due to factors such as predation and stranding) and probing fish. The average

estimated escapement and variance of escapement estimate from 1,000 simulations were used

as point estimates. Probing was evident in a particular stream, A, by the recovery in stream A

of fish tagged in another stream, B, (immigration of probing fish) and by the recovery in

stream B of fish tagged in stream A (emigration of probing fish).

Immigration and emigration of tagged fish were accounted for differently. Tagged

carcasses that originated in other streams were simply counted as unmarked fish for

estimating escapement to the stream where they ultimately spawned. Emigration of tagged

fish was more difficult to estimate than immigration. We estimated the number of fish

emigrating from stream A into stream B by using tag recoveries from stream B, and by

expanding the sampling effort in stream B, using the following variables: Rab = observed

number of fish tagged in stream A, and later recovered in stream B, Ta = number of fish

tagged in stream A, Tb = number of fish tagged in stream B, Rb = observed number of fish

tagged in stream B, and later recovered in stream B, Ca = number of carcasses examined in

stream A, Cb = number of carcasses examined in stream B, Na = unknown escapement to

source stream A, Nb = unknown escapement to target stream B, Nab = unknown number of

probers from stream A that went into stream B, and Tab = unknown number of fish tagged in

stream A that went into stream B.

Our two assumptions were that
13

   N , T

T a b N a b

a

= a



T R , T

R ab ab

b

b

=

  N . R T

R T a b N ab b

b a

= a

and (1)

T

T

N

N

a b

a

a b

a

= ,

 

 (2) T

R

T

R

a b

a b

b

b

= .

Assumption (2) ignores emigrants from stream B, and provided their fraction is small, the

assumption should provide a reasonable approximation for estimating Tab below. Therefore,

the estimated number of fish leaving stream A and arriving in stream B from Assumption(1)

was

where the estimated number of fish tagged in stream A that went into stream B from

Assumption (2) was

and the estimated escapement to stream A was . After substitution, we obtained the N

a

estimated number of probers from stream A that went into stream B as
14

( ) ( )

( ) Var x

kpq N k

N = −

−



 , 1

( )

( )

( )

( )

( ) Var R

C R

C

R

C

N C

N

R R

C

N C

N

a b

b

a b

b

a b

b

b b

b

a b

a b

b

b b

b

=





 



 − 



 



 −

− =

− 



 



 −

−

1

1

1

1







 ,

( )

( )

( )

( )

( ) Var R

C R

C

R

C

N C

N

R R

C

N C

N b

b

b

b

b

b

b b

b

b

b

b

b b

b

=





 



 − 



 



 −

− =

− 



 



 −

−

1

1

1

1









To calculate the variance estimator for the above term, we assumed that the number of tagged

fish captured was best described by a hypergeometric distribution, so that

where is the estimated population size, N k is the number of carcasses sampled, x is the

number of marked carcasses, p = x/k (marked fraction observed in sample), and q = 1 – p. 

Var(Rab) and Var(Rb) were calculated as 

and

.
15

Var N Var N ( ) R T

R T ab a

ab b

b a

  = ; 



 





( ) ( ) ( ) ( ) Var N R T

R T

Var N N T

T

Var R

R

R Var R

R a b

ab b

b a

a

a b

a

a b

b

ab b

b

   = . 



 



 +





 



 + 



 





2 2

2

2

4

T R , ab ab =

  N ;

R

T a b N a b

a

= a

We then used the delta method to estimate the variance of the probing population estimate,

: N

a b

substitution and simplification gave 

For streams where probing fish were recovered but the sampling fraction was either

100% (e.g., Sashin Creek and AKI Hatchery) or unknown, probing estimates assumed

so that

the estimate of is biased low for streams with unknown sampling fraction. Variance of N

a b

these estimates depends on the variance of and Rab, N

a
16

Var N( ) ( ) ( ) R

T

Var N N

T a b Var R a b

a

a

a

a

a b    = . 



 



 +





 





2 2

N , a b

i

n

i

=

∑

1

() () ( ) ( ) ( )

Var N N

R T

R T

Var N N Var N

T

T Var R

R

R

Var R

a ab

i

n

ab b

i b a

n

aa a

a

b ab

a b

b

b

i

n

i

i i

i

i i

i

i

i

 

  .

− 



 



 =

− 













 ++ +































































=

= =

∑

∑ ∑

1

1

2

2

2

2

1

1 1

To determine the spawning escapement in stream A, the population in stream A at tagging

time was adjusted by subtracting the estimated number of probing emigrants, ( ) N

a

recovered in all n streams to which fish tagged in stream A emigrated. Variance estimators

were originally summed for all a Var N( ) nd added to to compute the variance a bi

 Var N( ) a 

estimate for the estimated spawning escapement (Maselko et al. 1999). However, because

N and are not independent, we used the delta method to obtain the combined variance a N

a bi

of . N N  

a ab

i

n

i − 



 



 =

∑

1

Because we obtain by the delta method: N N   R T

R T ab a

ab b

i b a

n

i i

i

= =

∑

1
17

RESULTS

In the eight streams combined, a total of 12,859 live pink salmon (range was

553–2,978 for individual streams) were tagged, and 62,784 carcasses were examined for

marks (range was 1,302 - 21,803 for individual streams) (Table 2). Number of fish tagged

varied between streams due to sampling effort, run size, and physical configuration of the

streams. The most fish were tagged at Borodino and Lovers Cove Creeks, where fish were

sampled and tagged four times per week. For the other streams, the total number of fish

tagged biweekly was roughly proportional to the escapement magnitude, except that fewer fish

were tagged in Pile Driver Creek (estimated escapement about 50,000) than in Parry Creek

(estimated escapement about 18,000). Because Parry Creek had limited amounts of upstream

spawning habitat and a single deep holding pool just upstream of tidewater, our sampling gear

caught more live pink salmon there than in Pile Driver Creek. The number of fish tagged in a

stream on a particular date varied from 50 to 350, depending on availability. The number of

fish tagged during a particular tagging event occasionally exceeded the 300-fish target, to

allow tagging crews to complete tagging a seine haul.

Although fish were tagged in lower stream reaches in pools near mean high tide,

tagged carcasses were found throughout the stream systems from the intertidal zone to the

upper reaches of spawning habitat, supporting our assumption of random dispersion of tagged

fish throughout the spawning population. The number of carcasses recovered in a stream

varied from about 20 to 4,000 per day. Variability in recoveries (e.g., Fig. 6) was due to an

interaction of run timing and stream conditions, which affected both the longevity of spawning
18

fish and the probability of recovery. Stream flows in the sampling area fluctuate widely due to

high and irregular precipitation (NOAA 1996). The high variability in carcass recoveries was

consistent with the widely varying probabilities of carcass recovery on any particular day that

were used in the simulation (Fig. 2).

Double-tagging provided the opportunity to measure tag loss. The “chick” tag proved

to be reasonably reliable. Estimated loss of both tags averaged 1.4% in all streams combined

(range 0.4-4.7%; Table 2); variation was due primarily to differences between tagging crews

(crew means were 0.5%, 0.8%, and 2.6%).

After adjusting the number of tags released in each stream for tag loss, we estimated

the escapement, or the number of pink salmon entering the stream (population at time of

tagging) and the adjusted escapement (number of fish that entered and remained to spawn,

adjusted for probers leaving the stream). The number of fish entering the streams (unadjusted

escapement) ranged from 8,609 in Williams Creek to 79,070 in Deep Cove Creek (Table 2). 

The precision of the escapement estimates, as represented by the coefficients of variation,

ranged from 0.024 to 0.110.

We recovered 139 tagged fish in streams other than those in which they were tagged

(Table 3). Although tagged fish were observed as far as 51 km from the tagging site, probing

was most frequent between neighboring streams: 97% of tagged probers were recovered in the

nearest systematically sampled stream (Fig. 7) to where they were tagged.

Minimum estimates of the number of fish probing each stream ranged from 0 to 1,868

fish (Table 4). The coefficient of variation of these estimates tended to be large due to the

small sample sizes on which they were based. Probing fish may not have been detected in
19

streams where we sampled opportunistically, and we could not adjust for the sampling fraction

in these streams because their population size was unknown.

Where we quantified escapement, probers averaged 2.4% of the fish entering the

stream (Table 4). Probers made up a small proportion (< 3%) of the escapements (Fig. 8)

except in Borodino Creek (11.7%).

The sexes differed in probing (Table 3). After adjusting for different sex recoveries,

the rate of male probers (6.3%) was twice that of female probers (2.9%; P = 0.0001, chisquare test; Table 3). This difference was weighted heavily by the large number and the sex

distribution of fish probing Borodino Creek, where 82% of the probers were recovered with

three times as many males as females (29.8% vs. 10.8%; P < 0.0001). Frequency of probing

in the other streams was much lower for both sexes; males remained the more frequent

probers (1.3% vs. 0.9%), but the difference was much less and not statistically significant (P >

0.5).

The physical characteristics of streams and possibly the magnitude of the pink salmon

escapement affected probing. The streams with the highest probing rates, Borodino and Parry

Creeks, were both lake-fed systems with large stream discharges, limited intertidal spawning

habitat, and little upstream spawning habitat due to barrier falls (Borodino Creek) and steep

gradient (Parry Creek). The nearest systematically sampled neighbors to each stream, Lovers

Cove Creek near Borodino Creek and Deep Cove Creek near Parry Creek, had the greatest

escapements. These streams had much more extensive spawning habitat, escapements more

than four times as great, and substantially lower probing rates than their nearest neighbor

(Table 4). Probing frequency differed significantly between Borodino and Lovers Cove
20

Creeks (P < 0.001, chi-square); sample size was too small to test for statistical significance

between Parry and Deep Cove Creeks.

The estimated spawning escapements, adjusted for probing fish, ranged from 8,440 to

79,070 fish (Table 4). The precision of the estimates (CV) averaged 6%, ranging from 2% to

11%. These CVs were consistent with those previously reported by Maselko et al. (1999)

even though some of the variances are lower because we accounted for covariance in the

estimates of and rather than simply summing the variances. For all streams except N

a N

a bi

Borodino Creek, the computational change caused small (0-5%) changes in the variance

estimates. Because of the relatively large number of probers at Borodino Creek, the change in

computation method reduced the estimate of variance by 28%, from 454,036 to 354,265,

reducing the CV for the population estimate from 4.2% to 3.7%. The high uncertainty of the

probing estimates thus did not greatly increase the CV of the spawning escapement estimates

because of the relatively low percentage of the initial escapement estimates that were

composed of probing fish. Numbers of tags recovered were more important in determining

the precision of the estimates; the two streams with the lowest number of tag recoveries, Deep

Cove and William Creeks, had the highest CV and relatively widest 95% confidence intervals

(Table 4).

Percentage of carcasses recovered varied greatly among streams. The highest rates

were in Borodino and Lovers Cove Creeks. These streams had carcass weirs and were

sampled more frequently than the other streams. For the other six streams, percentage of

carcasses recovered varied with stream gradient. In Joyce Creek, which had a low gradient,
21

we recovered 23% of the tagged fish, whereas in Deep Cove Creek, which had the steepest

gradient, we recovered 8% of the tagged fish (Table 2).

DISCUSSION

The bias-corrected Petersen estimator had a number of advantages for estimating pink

salmon escapement. Not only was this estimator cost-effective, it also provided precise

escapement estimates with well-defined confidence intervals. Live-marking with carcassrecapture can be adapted readily to the Petersen closed-population model, especially when

precautions are taken to account for emigrants and tag loss. This design is especially relevant

when carcasses must be sampled for tags. In Schwarz et al. (1993), the closed-population,

single-sample, Petersen estimator gave population estimates for coho salmon similar to our

pink salmon estimates, as did stratified open-population estimates based on live-marking and

live recoveries.

The dynamic, unpredictable nature of carcass recoveries in some pink salmon streams

makes stratification difficult. Although Parker (1968) did not think flushing of pink salmon

carcasses was a problem in a British Columbia stream, he did not estimate the rate of carcass

retention. Wertheimer et al. (1997) observed all fish of certain entry dates were flushed from

streams, thus violating the assumption of equal catchability of carcasses. We also observed

such flushing events during our field study, in particular for Deep Cove Creek, where carcass

recovery rates were less than 10%. The Petersen estimator is less affected by these flushing

events than either the open-population Jolly-Seber estimator that Wertheimer et al. (1997)

used in their study, or the stratified, closed-population Schaefer model. We observed tagged

fish migrating from the tagging stream to another stream to spawn, which violated this closed-
22

population assumption. However, we were able to estimate the magnitude of this probing and

adjust our escapement estimates accordingly. The estimates of probing caused only marginal

increases in the variance for spawning escapements; however, the probing estimates

themselves were broad. To estimate probing more precisely, intensive tagging and recovery

efforts would be required because relatively few fish probe.

Although salmon straying has been the subject of numerous studies (Blair 1968, Boyd

1964, Mortensen et al. 1999, Quinn 1984, Tallman and Healey 1994, Thedinga et al. 1999,

Wertheimer et al. 1999), probing has been poorly documented (Thrower 1988, Berman and

Quinn 1991) and had not been estimated previously. For seven of our eight streams, probing

rates were low -- less than 3% of the number of fish entering the stream. However, for

Borodino Creek the rate was more than 10%. Such high rates emphasize the need to

understand the degree of probing in pink salmon streams, especially where escapements are

enumerated by one-way weirs, which retain probers.

We could not determine the natal origin of the fish that we marked in the streams. 

Therefore, we could not determine whether 1) fish enter their natal stream, and upon finding

unfavorable conditions (e.g., flow rates and access, density of spawners, predation pressure),

leave for another stream; or 2) fish probe other streams in search of favorable conditions

before they settle on their natal stream, or 3) both types of behavior occur. We noted that

streams with large discharges but limited spawning habitat had a much higher proportion of

probers than their near neighbors that had substantially greater spawning areas but lower flow

rates and larger spawning escapements, suggesting that the high flow rates attracted fish. Such

behavior could be highly adaptive for colonizing new habitats and for avoiding catastrophic
23

flow conditions in natal streams, such as low-oxygen events (Murphy 1985). Due to their 2-

year life cycle, pink salmon lack an age structure to buffer against unfavorable stream

conditions when they return to spawn; probing that leads to straying might compensate for

unforeseen environmental events that would otherwise result in extinction (Quinn 1984,

Ricker 1972, Thedinga et al. 1999).

Male pink salmon probed at a higher rate than females did. Competition among males

on spawning grounds may induce some to leave a stream. Males may also not home so

precisely as females, which could lead to more searching of non-natal streams. More studies

are needed to discern the driving force behind probing of male versus female pink salmon.

Hard and Heard (1999) found that male chinook salmon were more likely to stray than

females. However, Thedinga et al. (1999) did not find any differences in straying rates

between male and female pink salmon.

Thrower (1988) found that pink salmon tagged at one stream were more likely to leave

and spawn in another stream if fish were tagged off a stream mouth rather than in a stream

itself. Thrower attributed this movement to stress, speculating that less-mature fish holding in

saltwater were more stressed by capture and tagging. Alternatively, a fish in a school off the

stream mouth may be more likely to have originated from another stream than a fish that has

entered the stream. Based on Thrower’s (1988) observations, we felt that tagging in the

stream instead of the stream mouth was important to minimize the tagging of strays and the

emigration of tagged fish. Because substantial intertidal spawning occurred in almost all our

streams, tagging pools were chosen in the stream, but within the reaches of the upper intertidal
24

zone. In the field, tagged carcasses were found throughout the intertidal zone as well as

upstream, confirming that the intertidal spawners were included.

If tagging does induce emigration, the capture-recapture experiments become biased. 

We had no way to determine whether tagging stress induced emigration. However, we did

observe highly variable rates of probing between pairs of streams, even though the fish were

handled and tagged in the same manner in each stream, indicating that factors other than

tagging were the primary determinants of the probing.

Aerial surveys are most commonly used to estimate or index pink salmon escapements

(Dangel and Jones 1988, Bue et al. 1998). Unless factors such as observer efficiency and fish

visibility are accounted for, aerial surveys can be highly biased (Dangel and Jones 1988, Jones

1995). Alaska Department of Fish and Game’s peak aerial escapement counts varied from

more than 100% of our estimated escapement (Lovers Cove Creek) to 5% (Joyce Creek; Fig.

10). The proportion of the escapement observed was highest for streams with predominately

intertidal spawning, where foliage did not obscure the fish, and pre-spawning fish held in the

estuary rather than in stream pools. A substantially smaller proportion of the escapement was

observed in streams where pink salmon held and spawned in upstream areas obscured by

dense riparian vegetation. Aerial counts are typically expanded to total escapement using the

counts, frequency of counts, the estimated stream life, and the estimate of observer efficiency

(Bue et al. 1998, Hilborn et al. 1999). Our data indicate that observer efficiency for pink

salmon varies strongly with stream type.
25

ACKNOWLEDGMENTS

Many people contributed to the success of this project. We thank the many finclippers, wire-taggers, stream-walkers, and humpy-slashers whose hard work and sense of

humor made this study both possible and enjoyable. We appreciate the efforts of the crew and

skippers of the M/V Alaska Adventurer, NOAA ship John N. Cobb, and M/V Explorer. We

especially thank Brad Weinlaeder for his commitment to make this project work. A special

thanks are due to Dr. Jerry Pella for his tireless review of this manuscript and especially the

statistical analysis and derivations.

The research described in this paper was supported by the Exxon Valdez Oil Spill

Trustee Council. However, the authors’ findings and conclusions are their own and do not

necessarily reflect the view or position of the Trustee Council.
26

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Boyd, F. C. 1964. Return of pink salmon to Robertson Creek shows promise of success. 

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salmon (Oncorhynchus tshawytscha) spawning run into Bogus Creek, California.

Calif. Fish Game 80(1): 1–13.

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and stream-life estimates: The Prince William Sound pink salmon example. N. Pac.

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357–362. Plenum Press, New York.

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29

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incubation substrate on survival and straying of wild pink salmon, pp. 7.1–7.20. 

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99801-8626.
31

Table 1.--Statistics: average, average error, and standard deviation from 1,000 simulations of

escapement estimation for a hypothetical pink salmon population of 30,003 fish. Petersen and

Schaefer population estimators were used. Tagging level is the maximum number of live fish

tagged per time period.

Average

Tagging Average error Standard

level N

^ (30,003 – N^ ) Deviation

Petersen

100 30,018 –15 111.75

200 30,006 –3 58.29

300 30,007 –4 39.31

Schaefer

100 29,987 +16 165.11

200 29,993 +10 91.10

300 30,000 +3 67.53
32

Table 2.--Number of live pink salmon marked with opercular tags, number of carcasses

examined with and without tags, the estimated proportion of fish that lost both tags, and the

Petersen escapement estimate with coefficient of variation (CV), by stream.

Carcasses Examined Both

 tags Petersen estimates

Live fish Double Single lost 

Stream tagged Unmarked mark mark (%) Escapement CV

Deep Cove 1,709 6,240 114 19 0.7 79,070 0.086

Parry 1,423 2,476 158 18 0.6 19,819 0.074

Joyce 1,693 14,268 323 70 1.0 60,761 0.044

Pile Driver 1,186 7,812 136 41 1.7 51,174 0.072

Wolf 1,152 2,136 130 73 4.7 11,494 0.068

William 553 1,225 65 12 1.3 8,609 0.110

Borodino 2,165 4,111 478 76 0.5 15,951 0.041

Lovers Cove 2,978 21,671 993 139 0.4 56,733 0.024

 Total 12,859 59,939 2,397 448 1.4 303,611 0.028
33

Table 3.--Number of adult pink salmon tagged and number of fish that probed.*

Tagged Probers

 

Stream Males Females Males Females

Deep Cove 969 736 0 0

Parry 1,104 317 2 1

Joyce 997 694 2 0

Pile Driver 779 396 1 0

Wolf 735 407 2 0

William 287 264 1 2

Borodino 1,425 740 84 30

Lovers Cove 1,716 1,262 8 6

 Total* 8,012 4,816 100 39

*

Note the total tagged numbers differ from those in Table 2 due to adjustment for tag loss in Table 2 and occasional

missing sex information.
34

Table 4.--Estimated numbers and associated coefficients of variation (CV) of pink salmon

emigrating from streams, their percentage of entrants (probing rate), estimated spawning

escapements adjusted for probing fish that left a stream to spawn in another stream, and

associated 95% confidence intervals (CI) for spawning escapements for eight streams in

southeastern Alaska, 1997.

Estimated*

 Probing Adjusted

Emigra- rate 

Stream tion CV (%) Escapement Variance 95% CI

Deep Cove 0 0 79,070 46,752,180 (65,668 – 92,472)

Parry 524 0.564 2.6 19,295 2,256,772 (16,351 – 22,239)

Joyce 2 < 0.1 60,759 7,265,669 (55,476 – 66,042)

Pile Driver 307 0.929 0.6 50,867 13,552,968 (43,651 – 58,082)

Wolf 119 0.660 1.0 11,375 617,364 (9,835 – 12,915)

William 169 0.643 2.0 8,440 913,285 (6,567 – 10,313)

Borodino 1,868 0.088 11.7 14,083 454,036 (12,763 – 15,404)

Lovers Cove 945 0.236 1.7 55,788 1,863,314 (53,113 – 58,464)

 Total 3,933 299,677 73,675,889 (263,424 – 335,931)

*

Includes tags found in streams where escapement was not estimated.
35

Total escapement

0

10,000

20,000

30,000

1 10 20 30 40

Live fish available for marking

0

10,000

20,000

30,000

1 10 20 30 40

Day

0

10,000

20,000

30,000

1 10 20 30 40

Carcasses available for sampling

Number of fish

Figure 1.--The sampling design was applied to a hypothetical pink salmon escapement. Fish

available for marking (marking distribution) and recovery are plotted against day after first

entry.
36

0

1,000

2,000

3,000

4,000

0 5 10 15 20 25

Day

Total carcasses

Figure 2.--A typical history of all carcass recoveries used to calculate escapement estimates for

comparing the Schaefer and Petersen estimators. The probability of recovering a carcass was

assigned at random; therefore, this graph changed with each of the 1,000 mark-recapture

simulations.
37

Mi=100

0

0.05

0.1

0.15

0.2

0.25

0.3

29500 29750 30000 30250 30500

Mi

=200

0

0.05

0.1

0.15

0.2

0.25

0.3

29500 29750 30000 30250 30500

Petersen

Schaefer

Mi =300

0

0.05

0.1

0.15

0.2

0.25

0.3

29500 29750 30000 30250 30500

Estimated Escapement

Probability Probability Probability

Figure 3.--Monte Carlo distributions of the estimated population sizes for the Petersen and

Schaefer estimators based on 1,000 iterations. Mi is the number of live fish tagged at each of

the five marking events.
38

Proportion of tags recovered

Estimated

Proportion of tags recovered

Mi =300 

Mi=200

Mi

=100

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 29,987

St. Dev = 165.11

Schaefer

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 30,118

St. Dev = 111.75

Petersen

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 30,006

St. Dev = 58.29

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 29,993

St. Dev = 91.10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 30,000

St. Dev = 67.53

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

29,000

29,500

30,000

30,500

31,000

Mean = 30,007

St. Dev = 39.31

N

Figure 4.--Results of 1,000 iterations of the Petersen and Schaefer models where Mi is the

number of live fish tagged at each tagging event. The escapement size N is 30,003 fish.
39

Gulf of Alaska

Lovers Cove 

Cr.

10 km

N

S

W E

Study area

Baranof 

Alaska

Island

Kuiu

Island

Borodino

Joyce Cr.

Wolf Cr.

William

Pile Driver Cr.

Parry

Deep Cove

Cr.

LPW

Cr.

Cr.

Cr.

Chatham Strait

AKI 

Hatchery

Figure 5.--Map of study streams sampled within a 35-km radius of the Little Port Walter

research hatchery.
40

0

500

1000

1500

2000

2500

5 Sep 13 Sep 21 Sep 29 Sep 7 Oct

Date

Carcasses recovered per survey

Borodino

Lovers Cove

29 Aug

Figure 6.--Carcass recoveries at Borodino Creek and Lovers Cove Creek in 1997. Due to their

close proximity to the Little Port Walter research station, carcasses were sampled in these

streams 4–6 times per week.
41

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60

Distance between streams (km) 

Number of pink salmon

Figure 7.--The relationship between the distance pink salmon probed and observed number of

fish that probed.
42

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

Deep Cove

Joyce

Lovers Cove

Pile Driver

Parry

Borodino

Wolf

William

Escapement

Probers

Escapement

Creek

Figure 8.--Estimated escapements of pink salmon in eight study streams in southeastern

Alaska, adjusted for the expanded number of probers.
43

0

2

4

6

8

10

12

14

Borodino

Parry

William

Lovers Cove

Wolf

Pile Driver

Joyce

Deep Cove

Probing rate (%)

Escapement

20,000

40,000

60,000

80,000

100,000

0

Probing rate

Escapement

Figure 9.--Probing rate and escapement of pink salmon into the eight study streams in

southeastern Alaska.
44

0

20,000

40,000

60,000

80,000

100,000

Borodino

Lovers Cove

Deep Cove

Parry

*Piledriver

*William

*Wolf

*Joyce

Peak Aerial Count

Petersen Estimate

Number of fish

Figure 10.--Comparison between peak aerial counts and Petersen estimates of pink salmon. 

Peak escapement counts were based on aerial surveys for all streams except Joyce Creek,

which was based on foot surveys. All streams were surveyed by Alaska Department of Fish

and Game, August 1997. Kuiu Island streams are denoted by an asterisk.
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AFSC136 BARBEAUX, S. J., and M. W. DORN. 2003. Spatial and temporal analysis of eastern Bering Sea echo

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135 DIETER, B. E., D. A. WION, and R. A. MCCONNAUGHEY. 2003. Mobile fishing gear effects on benthic

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134 ROBSON, B. W. (editors). 2002. Fur seal investigations, 2000-2001, 80 p. NTIS No. PB2003-103825.

133 ANGLISS, R. A., and K. L. LODGE. 2002. Alaska marine mammal stock assessments, 2002, 224 p.

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132 DOYLE, M. J.., M. S. BUSBY, J. T. DUFFY-ANDERSON, S. J. PICQUELLE, and A. C. MATARESE.

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131 SEASE, J. L., and C. J. GUDMUNDSON. 2002. Aerial and land-based surveys of Steller sea lions

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PB2003-102164.

130 AYDIN, K. Y., V. V. LAPKO, V. I. RADCHENKO, and P. A. LIVINGSTON. 2002. A comparison of the

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web models, 78 p. NTIS No. PB2003-100518.

129 FERRERO, R. C., and L. W. FRITZ. 2002. Steller sea lion research coordination: a brief history and

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128 WEINBERG, K. L., M. E. WILKINS, F. R. SHAW, and M. ZIMMERMANN. 2002. The 2001 Pacific

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length and age composition, 140 p. + Appendices. NTIS No. PB2002-108221.

127 MARQUETTE, W. M. 2002. Annotated bibliography of the bowhead whale, Balaena mysticetus,

1767-1983, 325 p. NTIS No. PB2002-108033.

126 HONKALEHTO, T., W. PATTON, S. DE BLOIS, and N. WILLIAMSON. 2002. Echo integration-trawl

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summer 2000, 66 p. NTIS No. PB2002-104686.

125 HONKALEHTO, T., N. WILLIAMSON, and S. DE BLOIS. 2002. Echo integration-trawl survey results

for walleye pollock (Theragra chalcogramma) on the Bering Sea shelf and slope during summer 1999,

77 p. NTIS No. PB2002-104686.

124 ANGLISS, R. P., D. P. DEMASTER, and A. L. LOPEZ. 2001. Alaska marine mammal stock

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123 ROBSON , B. W. (editor). 2001. Fur seal investigations, 1999, 52 p. NTIS No. PB2002-100418.

122 SEASE, J. L., W. P. TAYLOR, T. R. LOUGHLIN, and K. W. PITCHER. 2001. Aerial and land-based

surveys of Steller sea lions (Eumetopias jubatus) in Alaska, June and July 1999 and 2000, 52 p. NTIS