Stock-Recruitment Analysis for Escapement Goal Development: a Case Study of Pacific Salmon in...

8/13/2019 Stock-Recruitment Analysis for Escapement Goal Development: a Case Study of Pacific Salmon in Alaska

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*Corresponding author: [email protected] email address: [email protected]

Stock-Recruitment Analysis for Escapement Goal

Development: a Case Study of Pacific Salmon in Alaska

ROBERTA. CLARK*, DAVIDR. BERNARD1, ANDSTEVEJ. FLEISCHMAN

Alaska Department of Fish and Game, Sport Fish Division

Research and Technical Services

333 Raspberry Road, Anchorage, Alaska 99518, USA

American Fisheries Society Symposium 70:743757, 2009

2009 by the American Fisheries Society

Abstract.

The constitutional mandate to sustain yields along with regulatory guidancefrom the Sustainable Salmon and Escapement Goal policies of the State of Alaska pro-

vide the impetus for development and implementation of escapement goals for salmon.

When appropriate, a stock-recruitment analysis can provide vital insight on the popula-

tion dynamics of a salmon stock. This insight can greatly facilitate the development of

a scientifically defensible escapement goal to sustain yields from the stock. However,

the role of stock-recruitment analysis in escapement goal development is often misun-

derstood and misapplied. Although many analysts focus on the quantity and quality of

the data needed to conduct a stock-recruitment analysis, other factors such as the spe-

cies of salmon, type and size of fishery, management constraints, social and economic

constraints, and information content of the data are also important. Much of the confu-

sion about stock-recruitment analysis arises because, while the analysis is primarily astatistical procedure, the actual development and implementation of an escapement goal

is primarily a scientific and practical endeavor. Many see the ultimate goal of a stock-

recruitment analysis as the identification of the escapement that produces maximum

sustained yield, although in many cases it may identify much more than that, or less.

Several case studies are used to illustrate the potential uses of stock-recruitment theory

in the development of escapement goals.

Introduction

Article VIII, section 4 of the Alaska Con-

stitution states that (from Harrison 1992):

Fish, forests, wildlife, grasslands, and

all other replenishable resources belonging

to the State shall be utilized, developed, and

maintained on the sustained yield principle,

subject to preferences among benecial

uses.

This mandate for sustainable manage-ment of Pacic salmon Oncorhynchus spp.

provided the impetus for development of a

scientically defensible escapement goal

policy in Alaska. Along with the statutory

duties and powers of the Commissioner of

the Alaska Department of Fish and Game

(ADF&G) and relevant management plans

for salmon stocks, the development of es-

capement goals is regulated by the policy

for the management of sustainable salmon

sheries and the policy for statewide salmon

escapement goals (Title 5 of the Alaska Ad-ministrative Code, Chapter 39).

These two regulatory policies dene four


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744 Clark et al.

types of escapement goals, two of which are

most important to sustained yield manage-

ment of salmon stocks. The biological es-

capement goal (BEG) is dened as: the es-

capement that provides the greatest potentialfor maximum sustained yield (MSY). As an

alternative to management for MSY, the sus-

tainable escapement goal (SEG) is dened

as: the escapement that is known to provide

for sustained yield. Both of these escapement

goals must be described as ranges that take

into account our uncertainty in the data and

variation in stock productivity. The two regu-

latory policies also stipulate that BEGs and

SEGs for Pacic salmon be developed from

the best available data and be scientically

defensible.

The concept of scientic defensibility

is embodied in the well-known production

models and statistical formulations of stock-

recruitment theory. Much of the scientic dis-

course on salmon production and the mainte-

nance of yields is focused on model selection

(e.g., Schmidt et al. 1998), the effect of mea-surement error on model performance (e.g.,

Walters and Ludwig 1981), and the incorpo-

ration of accumulated knowledge about mod-

el parameters into the statistical formulation

(e.g., Hilborn and Liermann 1998) or what is

herein referred to as stock-recruitment analy-

sis. While these are important considerations

in the methodological development of the

theory, little of the peer-reviewed literature

on stock-recruitment analysis focuses on im-provements in data collection and the inter-

pretation of these data in relation to the sus-

tained yield principle in Alaskan law.

It is argued here that current salmon pro-

duction theory is sufcient to address the

question of sustaining yields of Pacic salm-

on in Alaska via the development of BEGs or

SEGs. When used judiciously, stock-recruit-

ment analysis is an important tool in this de-

velopment. It is also argued that factors such

as the species of salmon, type and size of sh-

ery, management constraints, social and eco-

nomic constraints, and information content

of the production data are as important as the

availability of data and sophistication of the

statistical machinery used for stock-recruit-

ment analyses. Four case histories from sh-eries on salmon stocks throughout Alaska are

presented to illustrate these often neglected

considerations in escapement goal analysis.

Methods

Brood year tables were constructed from

escapements (S) and subsequent production

(R) from the following stocks that likely rep-resent the range of information content and

shing power typical of salmon sheries in

Alaska (Table 1; Figure 1): early-run Chignik

River sockeye salmon O. nerka(Ruggerone

et al. 1999; Witteveen et al. 2005; Table 2),

Chilkat River Chinook salmon O. tshawyts-

cha (Ericksen and McPherson 2004; Table

3); Kenai River sockeye salmon, (Hasbrouck

and Edmundson 2007; Table 4); and Good-

news River Chinook salmon (Brannian et al.2006; Table 5).

Simple stock-recruitment analyses were

performed on each brood table using the lin-

earized form of the Ricker relationship with

multiplicative process error (Hilborn and

Walters 1992) to estimate parameters (equa-

tion (1) and reference points (equations (2)

through (4)). Beginning with the familiar

nonlinear form of the stochastic Ricker equa-

tion,

( ) ( )= expexp SSR , (1a)

and then dividing by S and taking natural logs

to form the linear regression recipe

( )2,0~;lnln +=

NSS

R(1b)

A linear regression of ln(R/S) on Swill

estimate the parameters ln (y-intercept),


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745Stock-Recruitment Analysis for Escapement Goal Development

TABLE1.Salmon stocks used to illustrate the different techniques for developing an escapement goal

based on the number of years of stock-recruit data, information content of these data, and the shing

power of the sheries on these stocks.

Stock n(years) Information Content Fishing Power

Chignik sockeye 75 high high

Chilkat Chinook 7 low very low

Kenai sockeye 28 low very high

Goodnews Chinook 17 high low

Chilkat River

Goodnews River

Chignik River

Kenai River

Chilkat River

Goodnews River

Chignik River

Kenai River

FIGURE1.The state of Alaska with the location of major communities and four river systems discussed

in this paper.


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746 Clark et al.

TABLE

2.Escapementsandsubsequentproductionofearly-runChignikRiversockeyesalmonOncorhy

nchusnerkaforthe1922-1996

broodyears

(fromRuggeroneetal.

1999andWitteveenetal.

2005).

BroodYear

Escapement

Produ

ction

BroodYear

Escapement

Production

Broo

dYear

Escapement

Prod

uction

192

2

86,4

21

96

3,8

14

1947

2,3

86,7

33

181,1

12

1

972

326,3

20

995,9

71

192

3

4,6

42

38

0,3

59

1948

3

84,6

37

327,2

95

1

973

538,4

62

1,1

72,4

28

192

4

121,9

83

1,32

2,5

57

1949

2

13,2

69

308,5

34

1

974

364,6

03

607,5

96

192

5

386,3

64

11

7,6

62

1950

2

06,2

70

392,6

27

1

975

319,8

90

655,8

27

192

6

289,0

09

53

0,1

94

1951

1

25,1

26

625,6

89

1

976

548,9

53

982,3

61

192

7

857,8

81

2,67

7,1

84

1952

34,1

55

230,8

20

1

977

364,5

57

2,2

33,7

83

192

8

507,3

53

82

0,9

81

1953

1

68,3

75

357,6

07

1

978

419,7

32

968,2

98

192

9

995,8

32

1,05

4,1

67

1954

1

84,9

53

142,4

21

1

979

491,4

67

3,6

12,1

07

193

0

92,9

55

37

7,4

85

1955

2

56,7

57

554,4

95

1

980

369,5

80

1,7

32,7

41

193

1

96,2

01

1,12

8,2

31

1956

2

89,0

96

208,1

68

1

981

570,2

10

1,8

33,4

32

193

2

2,1

51,7

34

34

1,2

98

1957

1

92,4

79

350,5

12

1

982

616,1

17

2,3

23,6

43

193

3

223,9

13

62

1,4

00

1958

1

20,8

62

242,3

70

1

983

426,1

78

564,1

74

193

4

866,8

90

1,65

8,4

66

1959

1

12,2

26

340,9

46

1

984

597,7

13

636,0

40

193

5

194,6

36

41

9,7

09

1960

2

51,5

67

774,7

56

1

985

373,0

40

772,9

20

193

6

548,0

39

64

5,9

85

1961

1

40,7

14

571,6

45

1

986

557,7

72

2,5

23,2

15

193

7

205,6

13

80

9,5

50

1962

1

67,6

02

693,4

73

1

987

589,2

99

1,4

34,0

36

193

8

175,9

72

1,02

5,5

70

1963

3

32,5

36

698,7

03

1

988

420,5

80

1,6

58,4

60

193

9

1,1

42,8

52

48

9,2

32

1964

1

37,0

73

755,7

26

1

989

384,0

01

1,7

06,4

00

194

0

176,3

07

50

5,3

79

1965

3

07,1

92

1,9

48,1

44

1

990

434,5

50

1,5

26,8

44

194

1

374,4

20

1,58

3,5

79

1966

3

83,5

45

1,3

03,5

67

1

991

662,6

60

1,4

95,5

03

194

2

442,9

81

3,05

9,1

05

1967

3

28,0

00

240,7

12

1

992

360,6

81

929,7

59

194

3

701,8

59

70

0,6

58

1968

3

42,3

43

1,2

10,9

27

1

993

364,2

61

903,5

37

194

4

291,8

44

33

4,0

93

1969

3

66,5

89

476,2

82

1

994

769,4

65

2,0

38,9

09

194

5

217,8

82

24

5,5

34

1970

5

36,2

57

441,6

85

1

995

366,4

95

2,1

17,1

30

194

6

774,1

30

22

4,1

63

1971

6

71,6

68

1,3

83,4

55

1

996

464,7

48

1,5

70,3

75

Average

430,2

54

983,9

14

SD

380,9

63

740,9

58


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TABLE 3.Escapements and subsequent production of Chilkat River Chinook salmon Oncorhynchus

tshawytschafor the 19911997 brood years (from Ericksen and McPherson 2004).

Brood Year Escapement Production

1991 5,897 12,932

1992 5,284 5,542

1993 4,472 3,231

1994 6,795 1,645

1995 3,790 4,348

1996 4,920 5,637

1997 8,100 7,081

Average 5,608 5,774

SD 1,465 3,619

TABLE4.Escapements and subsequent production (in thousands) of Kenai River sockeye salmon Onco-

rhynchus nerkafor the 19681995 brood years (from Hasbrouck and Edmundson 2007).

Brood year Escapement Production

1968 82 916

1969 52 409

1970 72 520

1971 289 863

1972 302 2,1861973 358 1,995

1974 144 665

1975 129 895

1976 353 1,187

1977 664 2,811

1978 350 3,451

1979 246 1,111

1980 398 2,346

1981 359 2,268

1982 566 8,9301983 557 8,697

1984 310 3,252

1985 396 2,246

1986 400 1,741

1987 1,333 9,531

1988 839 2,120

1989 1,334 3,898

1990 439 1,334

1991 376 3,926

1992 752 3,469

1993 670 1,2871994 895 2,511

1995 521 1,421

Average 471 2,714

SD 328 2,455


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748 Clark et al.

(slope), and2 (residual error). Then ln is

adjusted for lognormal process error (Hilborn

1985),

2

2

lnln

+= (1c)

and to estimate the relevant reference points

for salmon management from the regressionparameters:

=

lnEQS

, (2)

ln07.05.0EQMSY SS , and (3)

ln07.05.0lnMSY . (4)

Statistical uncertainty about the param-

eters and reference points was assessed with

a bootstrap technique (Efron and Tibshirani

1993); resampling the residuals of the linear

regression with replacement, calculating all

parameter estimates and reference points for

each bootstrap replicate, and using percentiles

of the bootstrap values to obtain interval es-timates. Here, for comparison among stocks

a nonparametric analog of the coefcient of

variation (NPCV) is also calculated for each

parameter and reference point (Prager and

Mohr 1999):

; (5)

where an NPCV of 25% or less was consid-ered precise.

TABLE5.Escapements and subsequent production of Goodnews River Chinook salmon Oncorhynchus

tshawytschafor the 19811997 brood years (from Brannian et al. 2006).

Brood year Escapement Production

1981 11,454 16,538

1982 4,332 11,870

1983 20,420 9,479

1984 12,003 12,412

1985 10,810 6,030

1986 6,186 11,899

1987 6,762 10,150

1988 8,131 11,255

1989 4,806 18,849

1990 11,292 9,1341991 6,473 12,069

1992 3,757 7,466

1993 7,076 22,817

1994 11,722 7,006

1995 14,701 24,571

1996 8,907 9,532

1997 10,153 7,187

Average 9,352 12,251

SD 4,211 5,424

( )median

percentilepercentileNPCV

thth85.3015.69

=


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Apart from this analytical recipe for es-

timating the reference points from the linear

regression parameters, it is important to note

that SEQ

is an estimate of the carrying capaci-

ty of a salmon stock, where carrying capacityis dened as the average escapement of the

stock when it is not being shed (Figure 2).

Moreover, ln is an estimate of the intrinsic

rate of increase of the stock, where intrinsic

rate of increase is dened as the natural loga-

rithm of average recruits per spawner as Sap-

proaches zero.

These denitions also have relevance to

the rate of harvest on a stock while stock-

recruitment data are being collected. Walters

and Hilborn (1976) showed that in the case

of very low harvest rate most of the stock-

recruitment data pairs are on the right-hand

side of the stock-recruit curve, the carrying

capacity of a stock is known, but not the in-

trinsic rate of increase. Conversely, they also

showed that in the case of a very high harvest

rate when most of the stock-recruitment data

pairs are on the left-hand side of the stock-re-

cruitment curve, the intrinsic rate of increase

is known but not the carrying capacity.

Using information from each of the four

salmon stocks as examples and the aforemen-tioned principles of stock-recruitment analy-

sis, recommendations for establishing a BEG

or SEG were developed for each stock that

are scientically defensible based on infor-

mation content of the stock-recruit data, the

denitions of carrying capacity and intrinsic

rate of increase, and the analytical relation-

ship between the two.

Case Studies

Early run Chignik River sockeye

salmon.The Chignik River is located on

the Alaska Peninsula near the community of

Chignik (Figure 1). Sockeye salmon are pri-

marily harvested in a commercial seine sh-

ery (Bouwens and Poetter 2006). Although

a long history of exploitation exists on this

Escapement

Pro

duction

Escapement

Pro

duction

FIGURE2.Graphical representation of reference points (SMSY

, SEQ

, MSY

) on a Ricker stock-recruitment

curve. The diagonal line is the replacement line and the shaded region is the area of surplus produc-

tion.


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750 Clark et al.

stock, recent rates of annual exploitation are

moderate and range from 51 to 85%, aver-

aging 69% during 19592003 (Witteveen et

al. 2005). Recent stock assessment consists

of escapements estimated by weir and har-vests estimated by allocation of age classes to

stream of origin based on the relative magni-

tude of escapements (Witteveen et al. 2005).

The brood table consists of 75 years (1922

1996) of escapement and subsequent produc-

tion estimates (Table 2). Sufcient shing

power occurs in the commercial seine shery

to harvest all available surplus production so

that a BEG was desired.

A plot of subsequent production on

brood-year escapement indicates that a broad

range of escapements have occurred, and that

production has not replaced escapement (i.e.,

data points below the replacement line) in 11

out of 75 years, most notably at the highest

levels of escapement (Figure 3). The infor-

mation content of these data are high because

there is information about the intrinsic rate

of increase at high exploitation rates and on

carrying capacity at lower exploitation rates.

The stockrecruitment analysis indicated that

estimates of ln and were relatively precise

with NPCV of 8% and 14%, respectively (Ta-

ble 6). Estimates of SEQ, SMSY, and MSYwerealso precise (NPCV of 11%, 11% and 5%, re-

spectively), indicating that a BEG based on

SMSY

could be developed from this analysis

(Table 7). A BEG appears to be a good t to

this shery as there is close correspondence

between the observed average exploitation

rate (69%) and MSY

(69%; Table 6).

Chilkat River Chinook salmon.The

Chilkat River is located near the city of Haines

in southeast Alaska (Figure 1). Chinook

salmon are harvested in commercial and ma-

rine sport sheries located in Lynn Canal as

well as in the commercial troll shery (Erick-

sen and McPherson 2004). Although a long

history of exploitation exists on this stock,

recent rates of annual exploitation are low

and ranged from 8 to 19%, averaging 12%

during 19881991. Recent stock assessment

Escapement

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000

Produ

ction

Escapement

FIGURE3.Plot of subsequent production on brood year escapement and the estimated Ricker curve from

the early-run of Chignik River sockeye salmon Oncorhynchus nerka, 19221996 brood years.


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consists of escapements estimated by mark-

recapture and harvests estimated by coded-

wire tag recoveries. The brood table consistsof seven years (19911997) of escapement

and subsequent production estimates (Table

3). Prior to this analysis no escapement goal

was available for this stock. Although la-

tent shing power is available that could be

brought to bear on this stock, the initial de-

sire was to develop an escapement goal (SEG

or BEG) for this stock so existing sheries

could be liberalized when surplus production

was realized.A plot of subsequent production on brood-

year escapement indicates that a narrow range

of escapements have occurred and that pro-

duction has not replaced escapement in three

out of seven years, indicating that this stock is

at or near carrying capacity (Figure 4). Infor-

mation content of these data are low because

there is no information on the intrinsic rate

of increase, but there is on carrying capacity.

The stock-recruitment analysis indicated thatestimates of ln and were imprecise with

NPCV of 67% and 70%, respectively (Table

6). Estimates of SEQ

and SMSY

were notably

more precise (NPCV of 19% and 25%, re-

spectively) because there was good informa-tion about carrying capacity. The estimate of

MSY

was poor (NPCV of 53%) because of the

lack of production data at low escapements

and the resulting lack of information about

intrinsic rate of increase. The estimate of SEQ

is defensible; it differs little from the average

of escapements observed while shing power

was low (average S= 5,608; Table 3). From

the estimate of SEQ

, a conservative estimate of

SMSYcould be developed by assuming ln was0 (assumes return-per-spawner is 1 at low es-

capements) and solving equation (3) for SMSY

.

Alternatively, a less conservative estimate of

SMSY

could be calculated by using an estimate

of average ln from Chinook salmon stocks

in southeast Alaska (average ln = 1.92) as

was done by Ericksen and McPherson (2004).

Although these methods produced an estimate

of SMSY

, information was still missing on re-

turns at escapements near SMSY

for this stock,so that a SEG was recommended instead of a

BEG (Table 7).

TABLE6.Summary of parameter estimates, estimated reference points, and nonparametric CVs (in pa-

rentheses) from linear regression and bootstrapping of stock-recruitment data from four salmon stocks

in Alaska.

Stock

ln 2 SEQ SMSY MSY

Chignik

sockeye

1.87

(8%)

1.68 10-6

(14%)

0.60

(10%)

1,114,168

(11%)

410,940

(11%)

69%

(5%)

Chilkat

Chinook

0.91

(67%)

1.38 10-4

(70%)

0.49

(43%)

6,590

(19%)

2,875

(25%)

40%

(53%)

Kenai

sockeye

2.11

(8%)

5.71 10-4

(51%)

0.29

(13%)

3,698a

(48%)

1,303a

(50%)

74%

(5%)

Goodnews

Chinook

1.39

(19%)

1.08 10-4

(23%)

0.19

(16%)

12,843

(10%)

5,169

(12%)

56%

(14%)

ain thousands.


10/16

752 Clark et al.

TABLE

7.Summaryoffourcasestudiesofsalmonescapementgoaldevel

opment.

Stock

n

Info

rmation

Fishing

ln

Goaltype

Basisforgoal

Con

tent

Power

Chignik

sockeye

75

high

high

kn

own

known

BEG

Rickerregression

to

estimateSMSY

ChilkatChinook

7

low

low

un

known

unknown

SEG

Discountfromca

rrying

capacitybasedon

averageescapement

Kenaiso

ckeye

28

low

high

kn

own

unknown

SEG

Exploitationrate

fromln

GoodnewsChinook

17

high

low

kn

own

known

BEGorSEG

Rickerregression

to

estimateSMSY


11/16


Kenai River sockeye salmon.The

Kenai River is located on the Kenai Pen-insula near the cities of Soldotna and Ke-

nai (Figure 1). Sockeye salmon are har-

vested in marine commercial, freshwater

sport, and personal-use fisheries (Shields

2006). Escapement is estimated with so-

nar near the mouth of the river, and ma-

rine harvests are estimated by allocation

of harvests by age-class to stream of ori-

gin based on the relative magnitude of

escapements (Hasbrouck and Edmundson2007). Stock assessments have occurred

since 1968, with annual exploitation

ranging from 48 to 94% and averaging

79%. The brood table consists of 28 years

(19681995) of escapement and subse-

quent production estimates (Table 4).

The current escapement goal is an SEG

of 500800 thousand (Hasbrouck and Ed-

mundon 2007). There is sufficient fishingpower in the currently configured fisher-

ies to harvest all available surplus pro-

duction so that a BEG was desired.


brood year escapement indicates that a nar-row range of escapements have occurred

and that production has always replaced es-

capement, indicating a lack of information

about this stocks carrying capacity (Figure

5). Information content of these data are low

because there is information on the intrinsic

rate of increase, but little or none on carry-

ing capacity. The stock-recruitment analysis

indicated that the estimate of ln was pre-

cise (NPCV of 8%) as was the estimate of

MSY(NPCV of 5%; Table 6). Estimates of

(NPCV of 51%), SEQ

(NPCV of 48%), and

SMSY

(NPCV of 50%) were grossly impre-

cise (Table 6), which prohibited the devel-

opment of a defensible BEG. On the other

hand, the estimate of MSY

(74%) is lower

than the observed average exploitation

rate (81%), indicating that the current SEG

should be increased somewhat. Althoughno defensible BEG could be developed, ad-

vice to increase the current SEG is useful to

shery managers (Table 7). While likely not

Escapement

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000

Spawning Abundance

Production

Escapement

FIGURE4.Plot of subsequent production on brood year escapement from Chilkat River Chinook salmon

Oncorhynchus tshawytscha, 19911997 brood years.


12/16

754 Clark et al.

optimal in terms of producing MSY, the cur-

rent SEG has been shown to be defensible

and sustainable given that escapements ob-

served since 1968 have always yielded sur-

plus production.

Goodnews River Chinook salmon.The

Goodnews River is located near the commu-

nity of Goodnews Bay in southwestern Alaska

(Figure 1). Chinook salmon are harvested in

commercial, sport, and subsistence sheries(Jones and Linderman 2006) that primarily

target sockeye salmon. Escapement is esti-

mated with a weir, and marine harvests are es-

timated by allocation of harvests by age-class

to stream of origin based on the relative mag-

nitude of escapements (Brannian et al. 2006).

Recent stock assessments have occurred since

1981, with annual exploitation ranging from

16 to 71% and averaging 33%. The brood

table consists of 17 years (19811997) ofescapement and subsequent production es-

timates (Table 5). The initial desire was to

replace an SEG range based on escapements

indexed with aerial surveys with one (SEG or

BEG) developed from a brood table based on

weir-based counts of escapement.


brood-year escapement indicates that a broad

range of escapements have occurred, and that

production has not replaced escapement (i.e.,

data points below the replacement line) in 5

out of 17 years, most notably at the highest

level of escapement (Figure 6). Informationcontent of these data are high because there

is information on the intrinsic rate of increase

at high exploitation rates and on carrying ca-

pacity at lower exploitation rates. The stock

recruitment analysis indicated that estimates

of ln and were relatively precise with

NPCV of 19% and 23%, respectively (Table

6). Precise estimates of SEQ

, SMSY

, and MSY

were estimated from the parameters (NPCV

of 10%, 12% and 14% respectively), indicat-ing that a BEG based on S

MSYcould be devel-

oped from this analysis (Table 7). However,

Escapement

0

2,000

4,000

6,000

8,000

10,000

0 2,000 4,000 6,000 8,000 10,000

Production

Escapement

FIGURE5.Plot of subsequent production on brood year escapement from Kenai River sockeye salmon

Oncorhynchus nerka (in thousands), 19681995 brood years.


13/16


estimated SMSY

(5,169 sh) was much lower

than the observed average escapement for

this stock (9,352 sh), so that the currently

congured shery may be unable to harvest

all available surplus production leading to achronic inability to achieve the BEG. A bet-

ter option for this shery might be an SEG

constructed around the range of escapements

shown to produce sustained yields.

Discussion

As illustrated by the four case studies,

the simple stock-recruitment analyses usedherein provide vital information regarding

population dynamics and information content

of the basic adult escapement and production

data. This information, when combined with

the practical considerations of the shery

such as shing power and prior knowledge of

a particular salmon species can help the ana-

lyst and shery manager develop a scienti-

cally defensible escapement goal that meets

the mandates of Alaskan law. As seen in the

example of Chignik sockeye salmon, shery

objectives and the information content of thedata match and all that is needed is a simple

stockrecruitment analysis to develop a BEG

that has the potential for producing MSY in

the long-term. More sophisticated models

and analytical techniques of these stock and

recruitment data are available to help rene

our analysis, but the basic premise of a de-

fensible BEG would remain. Our experience

with salmon stocks and sheries in Alaska is

that this situation is the exception rather than

the norm.

Situations similar to the Chilkat Chinook

salmon and Kenai sockeye salmon case stud-

ies are most often seen, where the results of

simple stock-recruitment analyses are in-

conclusive or partially conclusive, and the

Escapement

0

5,000

10,000

15,000

20,000

25,000

30,000

0 5,000 10,000 15,000 20,000 25,000 30,000

Pro

duction

Escapement

FIGURE6.Plot of subsequent production on brood year escapement and the estimated Ricker curve from

Goodnews River Chinook salmon Oncorhynchus tshawytscha, 19811997 brood years.


14/16

756 Clark et al.

analyst must use theoretical considerations

and past knowledge of similar stocks to de-

velop a scientically defensible escapement

goal. In these cases, an SEG is most often

recommended to managers because escape-ment that produces MSY cannot be specied

from the available information. In the case

of Chilkat Chinook salmon this outcome has

little consequence for a shery that has very

low shing power which likely would not

harvest all available surplus production even

with generous shing time. If additional sh-

ing power could be brought to bear on this

stock, then information might be gained on

production potential at levels of escapements

lower than currently observed.

The lack of a BEG is also self perpetu-

ating in the Kenai sockeye salmon shery

because current shing power is more than

sufcient to harvest at rates higher than what

might be specied at MSY. This results in

production information that is conned to the

existing SEG with little potential for gaining

information on the carrying capacity of thisstock. In this case, it can be argued that the

current SEG or one slightly higher has suc-

cessfully sustained high levels of harvest and

is therefore a defensible escapement goal.

Situations similar to Goodnews Chinook

salmon are also seen where knowledge of es-

capement that produces MSY may not be the

proper objective for the shery. In this example,

stock assessment data are collected from a sh-

ery that harvests multiple species where shingpower may be adequate to harvest surplus pro-

duction of one species (sockeye salmon in this

example) and not the other species (Chinook

salmon). Sufcient information is possessed to

develop a BEG from simple stock-recruitment

analysis, but may not be elected to set an es-

capement goal based on this analysis because

there is insufcient shing power to harvest all

available surplus production. In this case, the

tradeoffs in harvest of two species in a single

shery may best be handled by a BEG for one

species and a SEG for the other.

These case studies also illustrate the

need for independent corroborative evidence

of carrying capacity and intrinsic rate of in-

crease in escapement goal development, es-

pecially when one or both pieces of this in-formation are missing or inconclusive in the

stock-recruitment analysis. Existing habitat-

based and paleolimnological approaches for

describing carrying capacity (e.g., Bradford

et al. 1997 for coho salmon O. kisutch, Parken

et al. 2004 for Chinook salmon, Schindler et

al. 2005 for lake-rearing sockeye salmon) are

valuable tools for the development of escape-

ment goals. The development of these ap-

proaches is encouraged for other salmon spe-

cies such as river-rearing sockeye salmon and

chum salmon O. keta. Similarly, meta analy-

ses of intrinsic rates of increase for salmon

species (e.g., Myers et al. 1999) can provide

helpful information on production dynamics

and scientic defensibility of an escapement

goal, especially when shing power is low.

While the continued statistical development

of stock-recruitment analysis is laudable, it isargued that the science of setting escapement

goals and understanding of production dy-

namics of salmon would be better served by

an increase in the collection of high quality

stock assessment data across a broad range

of salmon species, geographic settings, and

shing power.

Acknowledgments

The authors would like to acknowledge

the helpful comments and suggestions on

the content and presentation of this mate-

rial from the many Alaska Department of

Fish and Game staff that participated in the

Escapement Goal Analysis workshops held

from March 2006 through February 2007

in Anchorage and Juneau. We also greatly

appreciate the helpful external peer reviewsprovided to us by the AYK SSI Editorial

Board.


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