Economic Risks from an Invasive
Species in the Black Sea
Presented by Duncan Knowler
School of Resource and Environmental Management
Simon Fraser University
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Overview
•  Case study background: Black Sea problem
•  Management objectives
•  Basic relationships and assumptions
•  Stochastic process: invasive spp outbreaks
•  Risk analysis using a bioeconomic model
•  Implications of alternative trigger mechanisms
•  Conclusions: risk analysis aspects
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3
Problem definition
•  Black Sea drains 50% of Europe; major tributary is
the Danube River
•  It is subject to severe environmental pressures, e.g.
eutrophication, toxics, overfishing, etc.
•  Invasion of the Black Sea by the comb jelly
“Mnemiopsis leidyi”
•  Cumulative impact on fisheries (e.g. anchovy) from
all pressures has been substantial
•  Coordination of several riparian countries to
address problems is difficult
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The Comb Jelly “Mnemiopsis leidyi”
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Management objectives
•  Need to address pollution pressures, especially
nutrient loads, and fisheries management
•  Some fish species (e.g. anchovy) have a complex
interaction with nutrients, a ‘mixed blessing’ effect
•  What triggers stochastic Mnemiopsis events: sea
temperature or nutrient loads?
•  Critical to assess the influence of nutrient
abatement policies under alternative triggering
assumptions
•  What escapement policy is optimal for modeling?
•  Response is to model this system using a
stochastic bioeconomic modeling approach
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Description of system processes
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Relationship
Between
Nutrients,
Mnemiopsis
and Anchovy
Population
Dynamics
(+)
(-)
Key uncertainties:
1.  What triggers
Mnemiopsis
outbreaks?
•
Sea temperatures or
nutrient load?
2. What should the
escapement level
be for the anchovy
stock?
•
Constant escapement
optimal?
?
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Some key modeling assumptions
•  Recurring Mnemiopsis outbreaks have identical
impacts on the anchovy stock
•  Outbreaks are determined by a time-varying
random threshold or trigger
•  Fishery managers know relevant probability
distributions and magnitudes and are risk neutral
•  Whether an outbreak will occur is unknown until
after fish escapement is decided for that period
•  Management is evaluated using expected
economic profits from the anchovy harvest, as
determined by an optimization procedure
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Stochastic process
•  Stochastic transition equation:
where X is anchovy stock, S is anchovy
escapement, P is phosphates and σi, Ri(S,P) are
survival and recruitment under state of the world i
•  State i = 1 is normal conditions while i = 2 refers to
an outbreak. [Note: derivative RP > 0]
•  Stochastic variable Zt is the unknown threshold at
time t that triggers a Mnemiopsis outbreak
•  Consider (i) sea surface temperature and
(ii) nutrient level as triggering mechanisms
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•  Following expression describes the probability
mass function governing the stochastic transition
equation:
•  Denote the first probability as Λ(Pt,Tt) and the
second as Φ(Pt,Tt)
•  It follows that Λ(Pt,Tt) = 1 - Φ(Pt,Tt), Φ’ = f(Zt) > 0
and Λ’ = -f(Zt) < 0.
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Black Sea anchovy-Mnemiopsis model
and nutrient abatement implies:
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Nutrient abatement under alternative
triggering mechanisms
1.  Sea temperature
2.  Nutrients
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Results for Stochastic Model for Varying Mean Threshold
Values, Assuming 50% Impact of Mnemiopsis and a 50%
Nutrient Abatement Policy (P = 2.75 µM; US$ 1989/90)
No Nutrient Abatement
Mean
Thres
–hold
(1)
Outbreak
probability
(2)
Optimal
escapement
('000 t)
50% Nutrient Abatement
(3)
Expected
profits
($'000/yr)
(4)
Outbreak
probability
(5)
Optimal
escapement
('000 t)
(6)
Expected
profits
($'000/yr)
(6) – (3)
Benefits of
abatement
($'000/yr)
Sea Surface Temperature Triggers Outbreaks
3.0°C
0.66
1233
7106
0.66
1082
5736
(1370)
3.5°C
0.60
1259
7887
0.60
1116
6405
(1482)
4.0°C
0.55
1281
8581
0.55
1146
7001
(1580)
Nutrient Loads Trigger Outbreaks
3 µM
0.84
1153
4779
0.60
1232
6393
1614
6 µM
0.60
1258
7874
0.37
1338
9438
1564
9 µM
0.46
1325
9981
0.26
1387
10,962
981
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Conclusions: risk analysis aspects
•  Indicators of risk
‘Expected value’ analysis
•  List of management options
Unspecified pollution control policies
•  Uncertainties analyzed quantitatively?
Key uncertainty is the triggering mechanism
•  Ways of taking into account uncertainties
about inputs to the analysis
Used exponential distribution to model risk
•  Analysis of sensitivity of conclusions
Varied key parameter values
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Thank You
School of Resource and Environmental Management
Simon Fraser University
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