GOA Retrospective analysisModel use: hypothesis testing

• The system, the stories, and the “data”• The model: Elseas; like Ecosim but

more flexible for our purposes • The simple and clear hypotheses: what

drives species trends in the GOA?– It’s fishing– It’s climate (the PDO)– It’s everyone eating shrimp– It’s complicated…

Walleye pollock, Theragra chalcogramma

0

1,000,000

2,000,000

3,000,000

4,000,000

1960

1970

1980

1990

2000

year

biom

ass

(t)

stock assessment

trawl survey

euphausiids

copepods

euphausiids

shrimp

Juvenile diet

Adult diet

Pacific cod, Gadus macrocephalus

0

200,000

400,000

600,000

800,000

1960

1970

1980

1990

2000

year

biom

ass

(t)

stock assessment

trawl survey

pollock

benthic amphipods

shrimp bairdi

shrimp

Juvenile diet

Adult diet

pollock

Juvenile diet

shrimp

hermitcrabs

Pacific halibut, Hippoglossus stenolepis

0

200,000

400,000

600,000

800,000

196

0

197

0

198

0

199

0

200

0

year

bio

mas

s (t

)

stock assessment

trawl survey

Adult diet

Arrowtooth flounder, Atherestes stomias

0

500,000

1,000,000

1,500,000

2,000,000

1960

1970

1980

1990

2000

year

biom

ass

(t)

stock assessment

trawl survey

euphausiids

pollock

Juvenile diet

Adult diet

capelin

capelin

0

1,000,000

2,000,000

3,000,000

4,000,000

1960

1970

1980

1990

2000

year

biom

ass

(t)

PollockP. codArrowtoothHalibut

1990-1993 snapshot

Mass balance to dynamic simulation

BP/BQ/BDCEE

CatchBA

Bioenergetics and mass accounting

M2GEM0q

Vul

(Bstart)

Population rates (total mortality is

key)

Equilibrium built here, perturbed here

Alternate stable states possible??Alternate stable states possible??

ModelingRecruitment

– A delay-difference equation with juveniles divided into monthly pools:

• Fixed age at recruitment

• Adjustable relationship between food intake and fecundity

– Knife edge recruitment to fishery, spawning, and ontogenetic diet switch.

– Spawning biomass is not directly comparable to stock assessments (because stock assessments vary).

• “Surplus” production (compensation) is an absolute requirement for sustainable single-species fishing. As biomass decreases, production per biomass must increase.

• This can happen in more than one way…

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6

Predator Biomass

Pre

dat

or

Pro

du

ctio

n/B

iom

ass

P/B (Age-structured von Bertalanffy)

P/B (Ecosim foraging risk)

Model structure: alternative myths

• In von Bertalanffy (vB) models (MSVPA, single species), fishing compensation comes from increasing growth rates (conversion efficiency) of relatively younger fish in a fished population.

• In Ecosim, compensation comes from increased per-capita consumption: all at the expense of other species.

• By definition, in Ecosim there is no true energetic “surplus,” it all comes from other species. Conversely, in vB models there is no “bottom up control.”

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6

Predator Biomass

Pre

da

tor

gro

wth

eff

icie

nc

y

Growth efficiency (Age-structured von Bertalanffy)

Growth efficiency (Ecosim foraging risk)

Alternative myths II

Forcing alone (no fitting of Ecosim parameters) in the Northern California Current (Field 2004):

This run is forced by NPZ output time series (1967-1998) and fishing mortality derived from catches and stock assessments

Forcing: fishing only (big 4)

Forcing (Fishery) Fit to Catch Fit to Biomass

Fitting with fishing only, but add 60’s POP fishery

Forcing (Fishery)

Fit to Catch

Fit to Biomass

Fitting using fishing only—all GOA time series

Fitting using fishing, pollock recruitment—all series

Fitting using fishing and all recruitment—all series

Summary…

• Can’t explain system dynamics (species trends)– with fishing alone (unlike in other, more heavily

fished systems)– with simple climate (PDO) forcing of primary

production

• Reproducing “known” groundfish dynamics – OK when forcing with stock assessment “data”

• Recruitment variability dominates this system?

Predictive Process: predict, communicate, use

• Between Prediction and Use– What ought to be predicted?– How are predictions actually used?

• Between Prediction and Communication– What does the prediction mean in operational

terms?– How reliable is the prediction, and how is

uncertainty conveyed?

• Between Use and Communication– What information is needed by the decision maker?– What content or form of communication leads to

the desired response?

Predictive Potential

• Single Species Stock Assessment Model– Unknown parameters fit using data, updated annually– Predict direct effects of fishing on target populations– Quantitative prediction, 1-2 years out

• Ecosystem Model– Predict direct effects of fishing on nontarget species– Predict indirect effects of fishing mediated by trophic

interactions– Predict consequences of ecosystem changes not

related to fishing, therefore beyond our control– Qualitative predictions, must incorporate uncertainty

Data requirements in a simple food web

Biomass (B)Population growth rate

or Production (P/B)Consumption (Q/B)Diet comp (DC)

For ALL groups!!

Alternative: solve for B assuming a fixed proportion of production is used in the system:“top down balance”

• Each systematically added group adds constraints as well as data requirements, does one outweigh the other?

Too complex—uncertainty overwhelms?

GOA data pedigree

Base arrowtooth trajectory

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5

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20

25

2004 2014 2024 2034 2044 2054year

ton

s/k

m^

2

baseGOA_1000 Arrowtooth_Adu

Results: “Base trophic uncertainty”

• Bars show 95% confidence interval for year-50 biomasses in accepted ecosystems; symbols show varied assumptions of functional responses

• Limited confidence of exactly where system will be in 50 years, but patterns do emerge...

-200%

0%

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800%

1000%

Tra

nsie

nt

kill

e

Sle

eper

shark

s

Salm

on s

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s

Tooth

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Ste

llar

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rockfish

Skate

s

Seals

Pacific

halib

ut

Macro

uridae

Arr

ow

tooth

fl.

Pis

c.

birds

Pacific

cod

Spin

y d

ogfish

Sculp

ins

Cephalo

pods

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en w

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Sable

fish

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all)

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Jelly

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ALL

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p

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Predicting trophically mediated fishing effects (and level of control in

a system):Try to fish out arrowtooth?

• What effect would a “magic” arrowtooth reduction have?

• What might a real increase in targeting of arrowtooth look like?

• Different tradeoffs…

Fish out arrowtooth “magically”

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2004 2014 2024 2034 2044 2054year

ton

s/km

^2

magicATF_FisM_1000 Arrowtooth_Adu

Scenario difference from base

-120

-100

-80

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-20

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2004 2014 2024 2034 2044 2054

year

Pe

rce

nt

ch

an

ge

magicATF_FisM_1000lessBase Arrowtooth_Adu

Fish out arrowtooth “magically” (F on arrowtooth increases with no

bycatch)

-1

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1

2

3

4

5

Her

ring_

Adu

W. P

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ock

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Pan

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Fish out arrowtooth “realistically”(increase flatfish fishery q for

arrowtooth)

-1

0

1

2

3

4

5

Her

ring_

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Dis

card

s

W. P

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Predicting fishing effects on nontarget species

• Can we use knowledge of some system components to learn about effects of fishing on nontarget species?

• Apply the same method to “small” Gulf of Alaska model…

• Perturbations are new: stop fishing, increase fishing on all, increase target fishing to MSY levels for major groundfish

No fishing (top), 2xF (bottom)

-200%

0%

200%

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800%

1000%

Tra

nsie

nt

kill

e

Sle

eper

shark

s

Salm

on s

hark

s

Tooth

ed w

hale

s

Ste

llar

sea lio

Oth

er

rockfish

Skate

s

Seals

Pacific

halib

ut

Macro

uridae

Arr

ow

tooth

fl.

Pis

c.

birds

Pacific

cod

Spin

y d

ogfish

Sculp

ins

Cephalo

pods

Bale

en w

hale

s

Sable

fish

Short

raker/

roug

Thorn

yheads

pollo

ck (

all)

oth

er

dem

ers

al

PO

P/n

ort

hern

/du

Jelly

fish

SM

ALL

Pacific

herr

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on

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mm

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sandla

nce

bath

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s

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bairdi

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o

Kin

g c

rab

Shrim

p

EP

IFA

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A

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Benth

ic A

mph.

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roductio

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pla

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noFlessBase

median

-400%

-300%

-200%

-100%

0%

100%

200%

Tra

nsie

nt

kill

e

Sle

eper

shark

s

Salm

on s

hark

s

Tooth

ed w

hale

s

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sea lio

Oth

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rockfish

Skate

s

Seals

Pacific

halib

ut

Macro

uridae

Arr

ow

tooth

fl.

Pis

c.

birds

Pacific

cod

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y d

ogfish

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pods

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en w

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fish

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pollo

ck (

all)

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/du

Jelly

fish

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ALL

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herr

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on

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p

EP

IFA

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INF

AU

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Benth

ic A

mph.

Copepods

Ocean p

roductio

Phyto

pla

nkto

n

2FlessBase

median

Predicting effects beyond our control

• Changes in species or group production• Evaluate system structure, relative

predictabilityGOA

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

W. P

ollo

ck

W. P

ollo

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uv

P. H

alib

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Ste

ller

Sea

Lio

n

Res

iden

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ls

Ste

ller

Sea

Lio

n_Ju

v

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l

Rou

ghey

e R

ock

Sho

rtra

ker

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k

FH

. Sol

e

Sho

rtsp

ine

Tho

rns

Gre

enlin

gs

Mis

c. fi

sh s

hallo

w

Gre

nadi

ers

Sho

rtsp

ine

Tho

rns_

Juv

Species affected by a 10% increase in W. Pollock production

Per

cen

t ch

ang

e in

eq

uil

ibru

im p

rod

uct

ion

EBS

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

Ala

ska

ska

te

P. H

alib

ut

Offa

l

Sa

ble

fish

Oth

er

ska

tes

Sh

ort

rake

r R

ock

Re

sid

en

t Kill

ers

Ka

mch

atk

a fl

.

W. P

ollo

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Win

teri

ng

se

als

He

rrin

g

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mch

atk

a fl

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v

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ller

Se

a L

ion

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v

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od

Species affected by a 10% increase in W. Pollock production

Per

cen

t ch

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e in

eq

uil

ibri

um

pro

du

ctio

n

Conclusions

• Predictive potential?– Most powerful when considering uncertainty– Error bars incorporate both data quality and

predictability– Direction of change a robust indicator– The GOA and the EBS may have different levels of

predictive potential—useful information for management

• Implications for policy– Keep active policy options for changing fishing

mortality– Explore new policy options for preparing for the

unexpected (system change will happen)

Discussion: What controls recruitment variability?

• Ideas:– The difference between the single species

models’ recruitment predictions and the ecosystem model’s may reflect the effect of predation

– So, these models can measure the proportion of recruitment variability due to trophic effects

• Next step:– Fit to series of diet composition to identify prey

switching, quantify mortality due to predation– Time series of low trophic level production

would help—output from NPZ model as in NCC

Discussion: When does fishing matter?

• Is there a threshold where dynamics switch from “recruitment dominated” to “fishing dominated”?– How much fishing, and on whom?– Is threshold dependent on system characteristics?

• The tradeoff:– Cross the line, and you can explain dynamics– Stay below it but live with low predictive power– Either way you may have less fish!!

• The policy implications…