Transcript Villy Cristensen: Using ecosystem modeling for fisheries

Using ecosystem modeling
for fisheries management
Villy Christensen
IncoFish WP4 Workshop
Cape Town, September 2006
Are
ecosystem
models useful
for fisheries
management?
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“One of those really smart quotes”
“We believe the food web
modelling approach is
hopeless as an aid to
formulating management
advice; the number of
parameters and assumptions
required are enormous.”
Hilborn and Walters
(1992, p. 448)
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Willie asked the right question...
• Why don’t the fish eat them all, dad?
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A key aspect of EwE modeling:
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Prey behavior limits predation
(foraging arena assumptions)
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Organisms are not chemicals!
Ecological interactions are highly organized
Reaction vat model
Prey
eaten
Foraging arena model
Prey
eaten
Predator handling
limits rate
Prey density
Prey behavior
limits rate
Prey density
Big effects from small changes in space/time scale
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Foraging arena
Predator, P
aVP
Available prey, V
Unavailable prey
B-V
v = behavioral exchange rate (‘vulnerability’); predator-prey specific;
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based on foraging arena theory (Walters and Juanes, 1993)
Time predictions from an ecosystem model of the
Georgia Strait, 1950-2000
With mass-action (Lotka-Volterra) interactions only:
With foraging arena interactions:
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A critical parameter: vulnerability
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Top-down/bottom-up “control” & carrying capacity
Predation mortality: effect of vulnerability
Predicted
predation
mortality
V=
=2
Ecopath
baseline
0
Carrying
capacity
Predator abundance
Top-Down
High v
Bottom-up
Low v
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So how do we get estimates of
carrying capacity?
• Surveys
• Assessments
Numbers (x 1000)
– Stock reduction analysis
Blue whales
Fin whales
Year
Year
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Christensen, LB, 2006
Evaluation of simulations
• Can the model
– replicate historic trends?
– make plausible extrapolations to novel
situations?
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Fitting to time series:
learning from ecosystem history
• A proliferation of ecosystem
modeling activities has in recent
years produced many apparently
credible models that fit historical data
well and make reasonable policy
predictions
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Ecosystems where EwE models have
been tested using historical trend data
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E Bering Sea
Aleutian Islands
W&C GoAlaska
E GoAlaska
W Vancouver Island
Hecate Strait
British Columbia Shelf
Strait of Georgia
NE Pacific
CN & ET Pacific
NWHI, Hawaii
Gulf of California
Central Chile
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Bay of Quinte
Oneida Lake
Scotian Shelf
Chesapeake Bay
Tampa Bay
S Brazil Bight
Norwegian Sea
North Sea
Baltic
S Benguela
Gulf of Thailand
South China Sea
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Modeling process: fitting & drivers
Formal estimation
Fishing
(Diet0)
(Z0)
( BCC/B0)
Ecosystem model
(predation,
competition,
mediation,
age structured)
Climate Nutrient
loading
Predicted C,
B, Z, W, diets
Log
Likelihood
Observed
C,B,Z,W, diets
Habitat
area
Search
Judgmental evaluation
Choice of parameters
to include in final
estimation (e.g., climate
anomalies)
Error
pattern
recognition
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Confounding of fishery, environment, and
trophic effects: monk seals in NWHI
Fishing effort:
Initial Ecosim runs: fishing &
trophic interactions together
could not explain monk seal
decline.
Predicted lobster recovery
Satellite chlorophyll data
indicate persistent ~40%
decline in primary production
around 1990. ‘Explains’ both
continued monk seal decline
and persistent low lobster
abundance
1970
2000
Low Chl
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Are seals
causing fish
declines in
the Georgia
Strait?
Is it fishing?
Is it
environmental
change?
Or, is it all
three?
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1950
2000
1950
2000
Strait of Georgia
1.25
31.6
Model Phytoplankton
Race Rocks Salinity
31.4
31.2
1.05
31.0
salinity (‰)
production anomaly
1.15
0.95
30.8
0.85
0.75
1950
30.6
30.4
1960
1970
1980
1990
2000
• EwE PP & Index of Fraser River runoff (March19
April salinity at two measuring stations) Dave Preikshot, UBC
FC
BC Shelf biomass changes
herring EwE
herring SA
4.0
P. cod EwE
P. cod SA
0.5
0.5
2.0
biomass (t/km2)
3.0
biomass (t/km2)
0.3
sablefish EwE
1.0
0.2
0.0
1950
0.1
1950
sablefish SA
1970
1980
1990
2000
1960
1970
1980
1.0
0.5
biomass (t/km2)
biomass (t/km2)
0.5
1980
2000
1990
1960
1970
1980
1990
2000
seals EwE
seal SA
0.08
1.0
0.5
0.3
chinook EwE
chinook B from catch
0.0
1950
1960
3.0
hake EwE
hake SA
1970
1980
1990
0.0
1950
2000
1960
1970
1980
1990
2000
POP EwE
POP SA
0.5
biomass (t/km2)
0.4
0.06
0.04
biomass (t/km2)
biomass (t/km2)
1970
1.5
0.3
2.0
1.0
0.02
0.00
1950
1960
0.8
0.2
biomass (t/km2)
2000
coho B from catch
0.4
0.10
1990
0.0
1950
coho EwE
halibut EwE
halibut SA
0.1
1950
0.2
0.1
sablefish B=C/F
1960
0.3
0.3
0.2
0.1
1960
1970
1980
1990
2000
0.0
1950
1960
1970
1980
1990
2000
0.0
1950
20
1960
1970
1980
1990
2000
Dave Preikshot, UBC FC
biomass (t/km2)
0.4
0.4
BC shelf: Upwelling index in May,
June, and July. ≥10 year period
-20
Model Phytoplankton
54ºN Upwelling
1.1
-10
0.9
0
0.7
1950
10
1960
1970
1980
1990
2000
upwelling (m3/100m/s)
production anomaly
1.3
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Dave Preikshot, UBC FC
Northeast Pacific biomass changes
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arrowtooth EwE
0.4
2
0.6
3
1960
1970
1980
1990
1
1950
2000
0.8
halibut EwE
1960
1970
1980
1990
0.3
0.4
1960
1970
1980
plaice EwE
1990
2000
POP EwE
POP SA
1.2
halibut SA
0.2
0.6
biomass (t/km2)
biomass (t/km2)
biomass (t/km2)
0.6
0.0
1950
2000
plaice SA
0.4
0.1
1970
1980
1990
0.2
1950
2000
0.8
sockeye EwE
1960
1970
1980
1990
biomass (t/km2)
0.4
0.2
0.4
0.5
chum EwE
chum B from catch
sockeye B from catch
0.8
0.0
1950
2000
0.4
biomass (t/km2)
1960
0.6
biomass (t/km2)
P. cod SA
0.8
0.2
0.4
0
1950
P. cod EwE
2
0.2
0.0
1950
1.0
4
biomass (t/km2)
biomass (t/km2)
arrowtooth SA
1.2
pollock EwE
pollock SA
biomass (t/km )
0.8
0.6
0.4
1960
1970
1980
1990
1980
1990
2000
pink EwE
pink B from catch
0.3
0.2
0.1
0
1950
1960
1970
1980
1990
2000
0.2
1950
1960
1970
1980
1990
2000
0
1950
1960
1970
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2000
Dave Preikshot, UBC FC
Northeast Pacific: PDO index
(Pacific Decadal Oscillation),
April to July. 50 year period
1.5
Model Phytoplankton
PDO index
1.0
1.3
0.5
1.1
0.0
0.9
0.7
1950
PDO index
production anomaly
1.5
-0.5
-1.0
1960
1970
1980
1990
2000
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Dave Preikshot, UBC FC
Why have Steller sea lions declined?
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Guenette, Heymans, Christensen & Trites (CJFAS Nov 2006)
Alaska
Fishing
Aleutian Islands
Predation
Abundance
40,000
Competitive Interactions
30,000
20,000
10,000
0
1960
Ocean Climate
Change
1980
2000
Guénette, Heymans, Christensen & Trites (MS)
General finding:
multiple factors impact ecosystem resources
(in all but the easiest cases)
Evaluating trends
1.
2.
3.
4.
Fishing pressure
Trophic impact, including competition
Environmental impact
Nutrient loading
As a rule: All of the above contribute
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Are we finally able to develop useful
predictive models for ecosystem
management?
• It’s beginning to look like it;
• We can with some credibility describe agents of
mortality and trophic interdependencies;
• Evaluation of relative impact of fisheries and
environmental factors is progressing;
• As a rule we need to invoke fisheries and
environmental drivers to fit models.
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When we have a model
that can replicate development over time
we can (with some confidence) use it for ecosy stem -based policy exploration.
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Report card: Using models to address
ecosystem management questions
CONCERN
Bycatch impacts
GRADE COMMENT
We are not bad at predicting direct
Aeffect of fishing in general
C
Trophic effects of fishing can be
classified as ‘top down’ or ‘bottom up’
with respect to where management
controls are exerted
- on valued prey
B
Changes in M for prey species already
subject to assessment
- on ‘rare’ prey
F
Outbreaks of previously rare species
Top-down effects
(of predator culling or
protection)
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Modeling report card (cont.)
CONCERN
Bottom-up effects
(effects of prey harvesting on
predator stocks)
Multiple stable
states
Habitat damage
Selective fishing
practices/policies
Production regime
changes
Regime shifts
GRADE COMMENT
Uncertainty here is about flexibility of
C
predators to find alternative food sources
when prey are fished
B
‘Cultivation-depensation’ mechanism
appears to be main mechanism that
could cause ‘flips’
D
Lack of understanding about real habitat
dependencies, bottlenecks
F
We have not yet looked closely at
options in this area!
B
Models look good when fitted to data,
but have not stood test of time
C
Policy adjustments in response to 30
ecosystem-scale productivity change
So are ecosystem models actually
used for fisheries management?
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Use of EM for fisheries management
• Multispecies models
– Estimating predation mortality for stock assessment;
– Limit harvest of prey species to meet consumer
demands;
– Impact of changing mesh size, North Sea roundfish;
– Minke whale and harp seal culling?
– Environmental Impact Assessment (EIA), Alaska
groundfish;
– Target species response to TACs, Bering Sea.
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Use of EM for fisheries management
• EwE
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Evaluate impact of shrimp trawling, GoCalifornia;
Evaluate impact of bycatch, GoCalifornia;
Evaluate impact of predators on shrimp, GoMexico;
Demonstrate ecological role of species, GoMexico;
Impact of proposed fisheries interventions, Namibia?
EIA of proposed fisheries interventions, Bering Sea;
EIA of alternative TAC’s, Bering Sea and GoAlaska;
Target species response to TACs, Bering Sea
Closed area sizing, Great Barrier Reef, Australia
Valuation of cormorant impact, Ortobello, Italy
South Africa pelagic fisheries: in progress.
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So why aren’t ecosystem models
used more for management?
• Lack of experience using ecosystem models for
predictive purposes;
• Ecosystem modeling is for strategic management,
and supplements the tactical single species
assessment;
• Fisheries management process is trapped in
tactical management;
• Strategic decisions are virtually non-existing.
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Data gap for modeling
• We need longer-term data than typical in assessments
to avoid shifting baselines,
e.g., 1950-present;
– Data mining is required;
– There is much more information out there:
Catches, CPUE, w, …
• Assessments should be expanded back in time:
– Stock Reduction Analysis;
• Biggest information gaps for:
– Mid-TL forage fishes;
– Novel conditions (vampires in the basement)
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– Estimates of mortality rates.
Our empirical knowledge is limited
• Habitat and environmental changes (including
those caused by fishing) and intensive fishery
removals are creating novel situations, which we
can only handle with difficulty:
– We do not to understand the ‘mechanics’ of
ecological response well enough to be able to
predict all important responses to these novel
situations;
– Make models one can play with;
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Our capability to provide advice
about large-scale dynamics is limited
• We cannot resolve uncertainty about how
ecosystems change based on models and
time-series data only;
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Predictive approaches are uncertain,
for some obvious reasons
• Lack of long-term monitoring data on non-target
species and life stages;
• Concentration of interaction effects (trophic, habitat) on
early life stages (recruitment) that are difficult to
monitor;
• Confounding of fishery, environmental, and trophic
effects in historical data;
• Failure to anticipate new problems (‘vampires in the
basement’) due to unpredictable changes in system
structure, (exotic invasions, fisheries inventions);
• Unpredictable pre-adaptations to habitat alterations. 38
Ecosystem modeling for adaptive
management requires a very different
approach to assessment
• Modelers must attempt to uncover alternative models
that equally well explain historical data but imply
different policy choices:
– Environmental vs. fisheries vs. trophic effects;
• Policy options would include diagnostic management
experiments to distinguish between the alternative
models:
– Spatial closures to test recovery predictions;
– Ecosystem modification to test trophic interaction effects.
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Models are not like religion
– you can have more than one
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The new Ecopath with Ecosim
• Four year project funded
through Lenfest Ocean Program
• Lenfest Ocean Futures Project:
– New generation of EwE to be
released Sep 07
– Single-player game version 2008
– Multi-player game version 2009
• Customized versions facilitated
• User Ownership
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