In review Do NOT CITE The role of climate change in forecasts of Pacific salmon population dynamics Mark Scheuerell [email protected].

In review • Do NOT CITE

The role of climate change in forecasts of Pacific salmon population dynamics

Mark [email protected]


DISCLAIMER

The results presented herein are currently "in review" and therefore should not be distributed or cited until further notice.

If you have any questions, please contact

Mark ScheuerellNorthwest Fisheries Science CenterNOAA Fisheries(206) [email protected]

September 30, 2004


Acknowledgments

John Williams (NOAAF)

Countless others fromNOAAF, USFWS, IDFG,ODFW, WDFW


Looking toward the future

• Society is faced with an uncertain future from increasing global change

• Scientists and policy makers agree that future “success” rests with the capacity to anticipate

• Increasingly important as the human population grows

• Ecological forecasting represents a step toward predicting ecosystem services using specified uncertainties under future scenarios

Clark et al. (2001) Science


A caution on forecasting

Pielke & Conant (2003) Ecology

• In Feb 1997, forecasters predicted that the Red River of the North would see flooding greater than anything previously recorded

• At Grand Forks, ND forecasters predicted a flood crest of 49 ft.

• In April, the river crested at 54 ft. & inundated several cities, causing $2 billion in damages

• Local, state, & federal officials cited the inaccuracy of the forecast as the problem

• Reality: The forecast was within the long-term 10% error

• Bottom line: everyone needs to understand the uncertainty involved


Forecasting in fisheries

• It’s done all the time in fisheries management (but not very well)

• We often use simple models like stock-recruit relationships

• More recent incorporation of more complex mathematics & environmental effects (e.g., Logerwell et al. 2003; Lawson et al. 2004)

• Salmon represent a good case study because of their high economic, social, and ecological value (Ruckelshaus et al. 2002)


Columbia R.Columbia R.

Snake R.Snake R.

OregonOregon IdahoIdaho

WashingtonWashington

NE PacificNE Pacific

CanadaCanada


Spring/Summer Chinook Salmon“Stream type” life history

Adults return to spawn & die in April-July (year t)

Parr emerge and rear in natal creeks & rivers (year t+1)

Smolts emigrate in April-June (year

t+2)

Adults at sea (years t+3 to t+5)

freshwater

ocean


0

20

40

60

80

100

120

1960 1970 1980 1990 2000

The slide toward extinction

Year

Re

turn

ing

adu

lts (

100

0s)

ESA listing


Possible reasons for decline

Generally grouped under the “4 H’s”• Harvest• Hatchery operations• Habitat degradation• Hydroelectric (& other) dams

…but there are others too• Exotic species• Climate• Marine-derived nutrients



Smolt Adult

Assessing stock productivity


Smolt-to-adult survival rate (SAR)

Count smolts emigrating past dam Count adults returning 1-3 years later

tt Smolts

AdultsSAR 3t1t


0

1

2

3

4

5

1960 1970 1980 1990 2000

Year of ocean entry

4

5

6

7

8

Early trends in survivalN

um

be

r of dam

sS

AR

(%

)


It’s the dams, dummy!

http://www.cbr.washington.edu/crisp/hydro/hydroihr1.html

http://www.cbr.washington.edu/crisp/hydro/hydrolmn2.html

http://www.cbr.washington.edu/crisp/hydro/hydrolgs1.html

http://www.cbr.washington.edu/crisp/hydro/hydrolwg1.html


Recent trends in survival

0

1

2

3

4

5

1960 1970 1980 1990 2000

Year of ocean entry

4

5

6

7

8

Nu

mb

er of da

ms

SA

R (

%)


Is it really just the dams?


The Pacific Decadal Oscillation*

*Mantua et al. (1997); cited 568 times as of Sept 2004

A “shot in the arm” for fisheries, oceanography, climatology

-3

-2

-1

0

1

2

3

1900 1920 1940 1960 1980 2000

PD

O


A dynamic ocean environmentSalmon survival related to climate• PDO (Mantua et al. 1997)

• ALPI (Beamish et al. 1997)

• AFI (McFarlane et al. 2000)

• Upwelling (Botsford & Lawrence 2002)

• Various (Logerwell et al. 2003)

Other trophic levels as well• Zooplankton (Brodeur et al. 1999)

• Crabs (Zheng & Kruse 2000)

• Intertidal inverts (Sagarin et al. 1999)

• Seabirds (Jones et al. 2002)


0

1

2

3

4

5

1960 1970 1980 1990 2000

Year of ocean entry

SA

R (

%)

An ocean-climate effect?Regime

shiftRegime

shift



Snake R.Snake R.




CanadaCanada


The environmental driver

• Also known as the Bakun Index (Bakun 1990)

• Generated monthly by NOAA PFEL based on naval oceanographic data

• Spatially referenced at every 3° of lat from 21-60 N

• Spring upwelling promotes 1° & 2° production (Pearcy 1992, Brodeur & Ware 1992)

• Fall downwelling may decrease advection of important zooplankton prey (Mackas 2001)

• Related to salmon survival (Nickelson 1986, Botsford & Lawrence 2002, Logerwell et al. 2003)

Pacific Coastal Upwelling Index (CUI)


Narrowing the searchChoosing candidate predictor variables• Exhaustive search over all possible combinations of

12 months is daunting

• Chose index from 45N, 48N & 46.25N (interpolated)

• Used stepwise multiple regression to choose potential predictor months for time series model

The results• The index from 45N was far superior

reflects early ocean distribution?

• April, September & October were significant

transition periods important?


Time series of the CUI

-80

-40

0

40

80

-40

0

40

80

-80

-40

0

40

1960 1970 1980 1990 2000

m3 s

eaw

ater

/ 1

00 m

sho

relin

e /

sec

April

September

October


Time Series Analysis

Observation equation

Yt = Xt´t + vt

vt~N[0,Vt]Evolution equation

t = Gtt-1 + wt

wt~N[0,Wt]

Dynamic Linear Models

Recipe for DLMs

1) Make forecast using information up through previous year

2) Wait for current-year observation and then update all priors

3) Repeat steps 1-2 to the end of the time series

4) Assess overall model performance through Bayes Factors


Dynamic Linear ModelsA note on information discounting

• At each time step, there is a decay of information

• This leads to greater uncertainty

• Address this through discounting of the Bayesian priors

V[t|Dt-1] = -1 ·V[t-1|Dt-1 ] where (0,1]• Choose appropriate by minimizing NLL of model

• When small, parameters “evolve” quickly, but with decreased precision of the prediction

• In practice, 0.99 < < 0.8



Snake R.Snake R.



CanadaCanada


CUI

SAR


Forecasting climate-induced survival

1960 1970 1980 1990 2000

Year of ocean entry

3

1

0

4

5

6

2

model

obse

rved

R2 = 0.71

SA

R (

%)


The best statistical description

1960 1970 1980 1990 2000

Year of ocean entry

3

1

0

4

5

2

model

obse

rved

R2 = 0.91

SA

R (

%)


Linking all environments


Columbia R. flow at The Dalles

100

140

180

220

260

300

1880 1900 1920 1940 1960 1980 2000

Flo

w (

kcfs

)


Smolts

Adults Eggs

Parr

An early view of the life cycle

Ocean Freshwater


Adding up the drivers

The 4 H’s

Marine-derived nutrients

Exotic speciesClimate change in the oceans & on land


An improved view?

Atmosphere

Smolts

Adults

Human influence

Smolts

Adults Eggs

Parr

Ocean Freshwater


ConclusionsConclusions• Effective conservation and management requires Effective conservation and management requires

ecological forecastsecological forecasts• Environmental science has largely failed to produce theseEnvironmental science has largely failed to produce these• Pacific salmon provide a good case studyPacific salmon provide a good case study• We can use simple ocean-climate metrics to predict We can use simple ocean-climate metrics to predict

salmon survivalsalmon survival• We need to examine the “big picture” with respect to both We need to examine the “big picture” with respect to both

life history & environmental processeslife history & environmental processes• It’s time to move forward with an eye on the pastIt’s time to move forward with an eye on the past

In review Do NOT CITE The role of climate change in forecasts of Pacific salmon population dynamics Mark Scheuerell [email protected].

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