Covariation in Productivity ofMid-Columbia Steelhead

Populations

S.P. Cramer & Associates, Inc.600 N.W. Fariss RoadGresham, OR 97030www.spcramer.com

Brian Pyper & Steve Cramer

Mid – Columbia Study Area

Background

»Population abundance»Population growth rate

(productivity)»Spatial structure »Diversity

• Mid-Columbia steelhead ESU listed as threatened

• NMFS uses four measures to evaluate viable salmonid populations (McElhany et al. 2000):

Background

• “Lambda” analysis a key tool used by NMFS to assess productivity (Homes 2001; McClure et al. 2003)

• “Lambda” measures population growth rate and extinction risk using time series of escapement data (increasing or decreasing trend?)

• Model is not mechanistic

• Assumes no density dependence in spawner-recruit dynamics

Spawner-recruit analysis

• Examined spawner-recruit data for 8 populations (Cramer et al. 2005)

• Estimated intrinsic growth rates and capacity

• Compared 4 spawner-recruit models:» Density independent model» Ricker model» Beverton-Holt model» Hockey-stick model

• Used simulations to examine potential bias

Data• Dam counts of natural-origin

spawners :» Deschutes » Yakima» Umatilla

• Redd counts (index) for 5 John Day subpopulations:

» Upper and Lower Mainstem

» South, Middle, and North Forks

• Recruitment indices based on available harvest and age-structure data

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Spawning Year

Sp

awn

er A

bu

nd

ance

In

dex

Deschutes Yakima Umatilla

Population abundance of natural-origin steelhead in the Middle Columbia ESU,

1978-2004

0

2

4

6

8

10

12

14

16

18

Spawning Year

Sp

awn

er A

bu

nd

ance

In

dex

Upper John Day Lower John Day

Population abundance of natural-origin steelhead in the Middle Columbia ESU,

1978-2004

0

2

4

6

8

10

12

14

16

18

20

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Spawning Year

Sp

awn

er A

bu

nd

ance

In

dex

S. Fk John Day N Fk John Day M. Fk John Day

Population abundance of natural-origin steelhead in the Middle Columbia ESU,

1978-2004

Covariation in recruitment

• Escapement indices correlated (Avg. r = 0.63)

• Suggests shared influence of freshwater or marine conditions on survival

• Suggests limited measurement error

• Next step: Fit spawner-recruit models …

0 2 4 6 80

2

4

6

8

10

85

86 87

88

DI

RK

1:1

BH

HS

Spawner Index

Re

crui

t In

dex

Fits of the spawner-recruit models to the North Fork data set of the John Day population (DI = density-independent model, RK = Ricker model, HS = logistic hockey-stick model, and BH = Beverton-Holt

model).

Model comparisons• Used the AIC model-selection criterion

• Beverton-Holt and Hockey-stick models “best” across data sets

• But many unstable fits and unreasonably high estimates of intrinsic growth rate (alpha)

Range in Alpha (Recruits per spawner)

Beverton-Holt: 5.5 to 72.9 Hockey-stick: 2.4 to 20.8

Ricker: 2.6 to 5.2

Model comparisons

• Ricker model stable with biologically reasonable estimates of growth rate (alpha)

• Ricker fits much better than Density- Independent model for all 8 data sets

• Note: Estimates of capacity similar across forms (Ricker, Beverton-Holt, Hockey-stick)

• Density Independent model assumes no limit to capacity

Fits of the Ricker and Density-independent models

0 2 4 6 8

0

2

4

6

8

10

JD North Fork

0 2000 4000 6000 8000 10000

0

2000

6000

10000

Deschutes

0 1000 2000 3000

0

1000

2000

3000

Umatillla

0 500 1000 1500 2000 2500

0

1000

2000

3000

Yakima

Spawner Index

Rec

ruit

In

dex

85

86 87

88

85 8687

88

8586

87

88

85

86

8788

0 5 10 15

0

5

10

15

JD Upper Mainstem

0 2 4 6 8 10 12 14

0

5

10

15

JD Lower Mainstem

0 5 10 15 20

0

5

10

15

20

JD South Fork

0 5 10 15

0

5

10

15

JD Middle Fork

Spawner Index

Rec

ruit

In

dex

85

86 87

88

85

868788

85

86 8788

85

86

87

88

Fits of the Ricker and Density-independent models

Ricker estimates of intrinsic growth rate (alpha)Average = 3.4 recruits per spawner

0

2

4

6

8

10

12

14

UpperMainstem

LowerMainstem

South Fork

MiddleFork

North Fork

Deschutes Umatillla Yakima

Ric

ker

Alp

ha

(Re

crui

ts/S

paw

ner)

`

Ricker estimates of intrinsic growth rate (alpha)Average = 3.4 recruits per spawner

0

2

4

6

8

10

12

14

UpperMainstem

LowerMainstem

South Fork

MiddleFork

North Fork

Deschutes Umatillla Yakima

Ric

ker

Alp

ha

(Re

crui

ts/S

paw

ner)

`

Ricker

Average for Density-Independent models

= 1.4 Recruits/Spawner

Ricker estimates of capacity:unfished equilibrium spawner abundance

(S*)

0

2,000

4,000

6,000

8,000

10,000

Deschutes Umatillla Yakima

Ricker S*

`

Spa

wne

r A

bund

ance

Ricker estimates of capacity:unfished equilibrium spawner abundance

(S*)

0

2,000

4,000

6,000

8,000

10,000

Deschutes Umatillla Yakima

Recent 5-yr geometric mean

Ricker S*

`

Spa

wne

r A

bund

ance

Ricker estimates of capacity: John Day

0

5

10

15

20

UpperMainstem

LowerMainstem

South Fork Middle Fork North Fork

Recent 5-yr geometric mean

Ricker S*

Re

dd

s p

er

Mile

0 5 10 15

0

5

10

15

JD Upper Mainstem

0 2 4 6 8 10 12 14

0

5

10

15

JD Lower Mainstem

0 5 10 15 20

0

5

10

15

20

JD South Fork

0 5 10 15

0

5

10

15

JD Middle Fork

Spawner Index

Rec

ruit

In

dex

85

868788

85

868788

85

86 87 88

85

8687

88

Influence of 1985 – 1988 brood years:Density dependence or poor ocean survival?

• Removed years and re-fit Ricker models

• Similar results – still get strong evidence of density dependence (P < 0.01) for 8 data sets

• Consistent estimates of growth rate (alpha)

Influence of 1985 – 1988 brood years

Combined data (spawner indexstandardized so median = 1 for each data set)

Standardized Spawner Index

0 1 2 3 4 5

-2

-1

0

1

2

3

Log

[rec

ruits

per

spa

wne

r] 1985 -1988

Other years

Combined data (spawner indexstandardized so median = 1 for each data set)

Standardized Spawner Index

0 1 2 3 4 5

-2

-1

0

1

2

3

Log

[rec

ruits

per

spa

wne

r]

Density-independent

Ricker

• Possible bias in Ricker parameters related to:

»Short data sets»Measurement errors»Autocorrelation»Harvest rates

• Estimates of parameters uncertain• Strong concern for NMFS (McElhany et al. 2000)

• Can use simulations to assess potential bias

Potential problems withspawner-recruit analyses

• Simulated spawner-recruit data with same characteristics as Mid-Columbia data

» True alpha = 3» High autocorrelation» Low harvest rates

• Assumed measurement error in age structure and escapement estimates (CV = 30%)

• Estimated Ricker parameters for each simulated data set to assess potential bias

Simulations

Results (500 simulations)

0

10

20

30

40

50

60

1.0 2.0 3.0 4.0 5.0 6.0

Estimate of Ricker alpha

Num

ber

of S

imul

atio

nsTrue value = 3.0Median estimate = 3.2

• Bias in Ricker parameters was minimal (10 to 20%) for range of conditions typical of Mid-Columbia steelhead data sets

• Primary reason was low harvest rates (20% across most years)

• Significant bias expected for harvest rates = 40% or greater across years

Simulations results

• Widespread evidence of density dependence in Mid-Columbia steelhead data sets

• Consistent estimates of intrinsic growth rates (avg. = 3.4 recruits per spawner)

• No evidence that one or more populations experienced relatively poor productivity

• “Lambda” only useful as a red-flag indicator

• Intrinsic growth rates suggest resilience to short-term increases in mortality

Summary