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GOA Retrospective analysis
“fitting”
Model use: hypothesis testing
• The system, the trends, and the “data”
• The simple and clear hypotheses: what
drives species trends in the GOA?
–
–
–
–
It’s
It’s
It’s
It’s
fishing
climate (the PDO)
everyone eating shrimp
complicated…
Focus on the GOA…
Eastern Bering Sea
Gulf of Alaska
Walleye pollock, Theragra chalcogramma
Adult
diet
shrimp
euphausiids
4 ,0 0 0 ,0 0 0
sto ck a sse ssme n t
tra w l su rve y
Juvenile diet
2 ,0 0 0 ,0 0 0
copepods
1 ,0 0 0 ,0 0 0
euphausiids
ye a r
2000
1990
1980
1970
0
1960
b io m a ss (t)
3 ,0 0 0 ,0 0 0
Pacific cod, Gadus macrocephalus
Adult diet
pollock
sto ck a sse ssme n t
8 0 0 ,0 0 0
shrimp
tra w l su rve y
Juvenile diet
6 0 0 ,0 0 0
4 0 0 ,0 0 0
shrimp
2 0 0 ,0 0 0
benthic amphipods
ye a r
2000
1990
1980
1970
0
1960
b io m a ss (t)
bairdi
Pacific halibut, Hippoglossus stenolepis
Adult diet
pollock
sto ck a sse ssme n t
8 0 0 ,0 0 0
tra w l su rve y
Juvenile diet
4 0 0 ,0 0 0
shrimp
2 0 0 ,0 0 0
hermit
crabs
ye a r
2000
1990
1980
1970
0
1960
b io m a ss (t)
6 0 0 ,0 0 0
Arrowtooth flounder, Atherestes stomias
Adult diet
pollock
2 ,0 0 0 ,0 0 0
capelin
sto ck a sse ssme n t
tra w l su rve y
1 ,5 0 0 ,0 0 0
1 ,0 0 0 ,0 0 0
capelin
5 0 0 ,0 0 0
euphausiids
ye a r
2000
1990
1980
1970
0
1960
b io m a ss (t)
Juvenile diet
4 ,0 0 0 ,0 0 0
P o llo c k
P. cod
A rro wto o th
Halib ut
1990-1993
snapshot
2 ,0 0 0 ,0 0 0
1 ,0 0 0 ,0 0 0
ye a r
2000
1990
1980
1970
0
1960
b io m a ss (t)
3 ,0 0 0 ,0 0 0
Standard Ecosim practice
•
•
•
•
Set up food web
Run forward with known fishing effort time series
Add other forcing functions as necessary (optional)
Fit to time series by adjusting vulnerability parameters
(also optional—see next example)
• Another standard practice is to “spin up” the model with
no fishing for a number of years to get to “unfished”
equilibrium state, then fish down and proceed as above
Forcing alone (no fitting
of Ecosim parameters)
in the Northern
California Current
(Field 2004):
This run is forced by NPZ
output time series (19671998) and fishing mortality
derived from catches and
stock assessments
Forcing: GOA fishing only, no spin up
Forcing (Fishery)
Fit to Catch
Fit to Biomass
GOA fitting experiment 1: spin up + fishing
• Spin up with no fishing (1930 -1959)
• Fishing with all gears (1960-2002)
• Iterative fitting procedure
–
–
–
–
1st convergence with predator vulnerabilities
2nd with prey vulnerabilities
3rd with predator vulnerabilities again
4th with prey vulnerabilities again
Experiment 1 Summary…
• Spin up does not achieve start biomass for POP, flatfish
• Resulting fishing and bycatch mortality initially results in
– Extinction of POP (not validated…)
– High bycatch of arrowtooth flounder, which makes a fish-out
followed by recovery look plausible,
– Which makes part of the pollock time series look plausible
• But by the time the model “converges”
– POP are alive, thornyheads are flourishing
– Herring now extinct (not validated…)
– Fits to large flatfish, pollock, sablefish, and others are gone
• Conclusion: the model cannot reconcile the time series
by varying only top down forcing (fishing), need to vary
bottom up forcing as well
Possibilities for bottom up forcing
• System wide primary production forcing
– Make an assumption about climate  phytoplankton production
• PDO is the usual suspect
• Could use anything else…Aleutian Low? ENSO? Sun spots?
– Fit a “primary production anomaly” that explains all time series
• Selective bottom up forcing
– Known or estimated time series of low TL group production
– Known or estimated time series of juvenile production
aka RECRUITMENT from stock assessments
• Which ones?
• How many are necessary?
Reconstructing climate history
4
3
2
1
0
-1
-2
-3
-4
Fitting with fishing and PDO—all series
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”
– Not entirely satisfactory…
• We still need a bottom up forcing series (or two)
– Actual low TL group series would be ideal
– Try fitting to primary production anomaly (or two? More?)
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
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)
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