1. Sophie Gourguet, O. Thébaud
Multiple management objectives: defining viability thresholds in a context of economic uncertainty
Sophie Gourguet, O. Thébaud
©Tonya Van Der Velde
MSEAS th May - 3rd June 2016, Brest, France

2. Stochastic co-viability approach
Background: Need of sustainable fisheries management I
Some challenges:
Proposed approach:
Biological
Stochastic co-viability approach
Political
Non- target species conservation
Used in: Baumgärtner and Quaas, 2009; De Lara and Martinet, 2009; Doyen et al, 2012; Cissé et al, 2013; Gourguet et al, 2013; Maynou, 2014; Gourguet et al, 2015…
Multiple objectives
=> works in terms of probability that the system is satisfying multiple constraints
Social
Marine fisheries management is characterized by multiple, often conflicting objectives and it’s important to understand the trade-offs between these various objectives
And uncertainties
Face to the need for alternative approaches that can help to deal with multiple objectives under uncertainty the viability approach has been proposed
(an approach that can help dealing with multiple objectives under uncertainty)
This approach aims at identifying decision rules or controls for dynamical systems (in particular non-linear control problems), such that the systems are maintained at each instant inside a given set of admissible states of diverse nature, called the viability constraints set.
In particular, the stochastic co-viability approach works in terms of probability that the system is satisfying multiple constraints
Economic
Uncertainties
S. Gourguet – MSEAS 2016

3. Stochastic co-viability approach
Background: Need of sustainable fisheries management I
Proposed approach:
Stochastic co-viability approach
Used in: Baumgärtner and Quaas, 2009; De Lara and Martinet, 2009; Doyen et al, 2012; Cissé et al, 2013; Gourguet et al, 2013; Maynou, 2014; Gourguet et al, 2015…
=> works in terms of probability that the system is satisfying multiple constraints
The co-viability probability corresponds to the likelihood of respecting multiple constraints at each time
Co-viability probability (CV): likelihood of respecting multiple constraints at each time
Example of constraints:
Biological: spawning stock biomass of targeted species are above biological viability threshold
Economic: annual profit of the fishery is above an economic viability threshold
By-catch species conservation: by-catch is below a by-catch viability threshold
S. Gourguet – MSEAS 2016

4. Case study: The Australian Northern Prawn Fishery
Objectives II
1/ How the viability approach can help to make decisions when managing mixed fisheries? How can we define the viability thresholds?
2/ Assessing and comparing results from viability analyses when accounting for uncertainty in economic variables
To go further: are the results robust when integrating economic uncertainties?
=> here, we propose to compare the results when accounting for various economic scenarios
Application of stochastic co-viability analyses to a bio-economic model with different economic scenarios
Case study: The Australian Northern Prawn Fishery
S. Gourguet – MSEAS 2016

5. Applied case study: the Northern Prawn Fishery
III
Multi-fishing strategies
Multi-species trawl fishery
By-catch issues
managed by input controls
non-target species
uncertain resource
strong year to year variability
The fishery can be separate in 2 sub-fisheries: banana, tiger and is currently managed using input controls, especially the number of vessels involved in the fishery
And we have a more predictable resource comprising grooved and brown tiger prawns and blue endeavour prawn which are modelled through a weekly sex- and size-structured population dynamic model with ricker type stock-recruitment function
The fishery is characterized by a high proportions of by-catch and interactions with protected and endangered species
The amount of by-catch species caught in prawn trawl nets has been significantly reduced since 2000 through the mandatory introduction of Turtle Excluder Devices(TEDs) and By-catch Reduction Devices (BRDs).
However, it reduced the catches of sea snakes (Hydrophiidae) by only 5%.
So we decided to include sea snakes. So we modelled the impacts of trawling on sea snakes by estimating their catches by the fishery from the fishing efforts of the tiger and banana prawn sub-fisheries
NPF trawlers
more predictable resources
weekly, size-and sex-structured population dynamic model with Ricker type stock-recruitment function (with uncertainty)
adapted from Gourguet et al, Ecological Economics

6. Sensitivity analysis on viability thresholds
Control => fleet size
Management strategies:
management strategies where the number of vessels is found as to maximize the co-viability probability (CV), i.e. the likelihood of respecting multiple constraints at each time
Constraints:
According the values of the different viability thresholds we will have different results in terms of fleet size which maximize the CVA
So we did sensitivity analyses on the viability threshold values
Biological: spawning stock size index of prawns are above a precautionary threshold
Economic: annual profit of the fishery is above an economic viability threshold
Sea snake conservation: annual sea snake catch is below a sea snake viability threshold 6

7. Co-viability probability
Sensitivity analysis on viability thresholds
IV.1
Control => fleet size
Management strategies:
Co-viability probability
management strategies where the number of vessels is found as to maximize the co-viability probability (CV), i.e. the likelihood of respecting multiple constraints at each time
Constraints:
According the values of the different viability thresholds we will have different results in terms of fleet size which maximize the CVA
So we did sensitivity analyses on the viability threshold values
more restrictive constraints
(correspond to 2010 levels of annual profit and sea snake by-catch)
Biological: spawning stock size index of prawns are above a precautionary threshold
Economic: annual profit of the fishery is above an economic viability threshold
Sea snake conservation: annual sea snake catch is below a sea snake viability threshold 7

8. Co-viability probability
Sensitivity analysis on viability thresholds
IV.1
Control => fleet size
Management strategies:
Co-viability probability
management strategies where the number of vessels is found as to maximize the co-viability probability (CV), i.e. the likelihood of respecting multiple constraints at each time
More restrictive economic constraint : harder to satisfy
Constraints:
According the values of the different viability thresholds we will have different results in terms of fleet size which maximize the CVA
So we did sensitivity analyses on the viability threshold values
more restrictive constraints
(correspond to 2010 levels of annual profit and sea snake by-catch)
Biological: spawning stock size index of prawns are above a precautionary threshold
Economic: annual profit of the fishery is above an economic viability threshold
Less restrictive economic constraint : satisfied with higher probabilities
Sea snake conservation: annual sea snake catch is below a sea snake viability threshold 8

9. Co-viability probability
Sensitivity analysis on viability thresholds
IV.1
Status quo: 52 vessels
Fleet size
Co-viability probability
More restrictive economic constraint : need more vessels to satisfy the constraint – in conflict with sea snake conservation constraint
According the values of the different viability thresholds we will have different results in terms of fleet size which maximize the CVA
So we did sensitivity analyses on the viability threshold values
more restrictive constraints
(correspond to 2010 levels of annual profit and sea snake by-catch)
Sea snake conservation constraint : not restrictive whatever its threshold value
=> Because number of vessels to satisfy less restrictive economic constraint is low enough 9

10. Co-viability probability
Sensitivity analysis on viability thresholds
IV.1
Minimum confidence level to guarantee: 90%
Fleet size
Co-viability probability
Comparison between these 2 graphs can assist stakeholders. For instance if they want to guarantee some constraints with sufficiently high probability, which viability thresholds should they choose? and in this case what are the associated fleet size?
Status quo: 52 vessels
max economic threshold: 6.2 AU$ million
associated fleet size: 30 vessels
min sea snake catch threshold: indv 10

11. Economic scenarios
IV.2
Results when considering a base case economic scenarios (fuel price and prawn prices considered constant over the time of the simulation)
Are the results sensitive to future changes in economic variables? Which implication in terms of management?
Economic scenarios tested here:
Fuel price:
progressive increase by 5% per year (as in Gourguet et al, 2014)
progressive decrease by 1% per year
Prawn prices:
progressive decrease by 2% per year (as in Gourguet et al, 2014)
progressive decrease by 3% per year
progressive increase until 110.6% (as in Punt et al, 2011)
S. Gourguet – MSEAS 2016

12. Increase (as in Punt et al, 2011)
Co-viability
PRAWN PRICE SCENARIOS
Increase (as in Punt et al, 2011)
Decrease by 3 %
Decrease by 2 %
Base case
Increase (by 5%)
FUEL PRICE SCENARIOS
Base case
Decrease (by 1%)

13. Increase (as in Punt et al, 2011)
Co-viability
PRAWN PRICE SCENARIOS
Increase (as in Punt et al, 2011)
Decrease by 3 %
Decrease by 2 %
Base case
Max CVA = 74,9%
Increase (by 5%)
FUEL PRICE SCENARIOS
Base case
Decrease (by 1%)

14. Increase (as in Punt et al, 2011)
Fleet size
PRAWN PRICE SCENARIOS
Increase (as in Punt et al, 2011)
Decrease by 3 %
Decrease by 2 %
Base case
6 vessels
22 vessels
27 vessels
Max CVA = 74,9%
Increase (by 5%)
FUEL PRICE SCENARIOS
6 vessels
16 vessels
30 vessels
34 vessels
Base case
11 vessels
30 vessels
38 vessels
Decrease (by 1%)
18 vessels

15. Increase (as in Punt et al, 2011)
Fleet size
PRAWN PRICE SCENARIOS
Increase (as in Punt et al, 2011)
Decrease by 3 %
Decrease by 2 %
Base case
6 vessels
22 vessels
27 vessels
Max CVA = 74,9%
Increase (by 5%)
FUEL PRICE SCENARIOS
According to future changes in economic variables we might not be willing to select the same viability threshold values
And this will not be associated with the same fleet sizes in terms of management
6 vessels
16 vessels
30 vessels
34 vessels
Base case
11 vessels
30 vessels
38 vessels
Decrease (by 1%)
18 vessels

16. A promising way to represent trade-offs between multiple objectives
Discussion V
Results are sensitive to threshold values: we have moderate biological and economic risks if non restrictive constraints but higher risks if restrictive constraints
A promising way to represent trade-offs between multiple objectives
So what we can conclude, is that the results are sensitive to the threshold values.
Limiting biological and economic risks would require reductions in the fleet size compared to the status quo, which entails lost economic returns
What we called the cost of co-viability
This framework might help fisheries managers and stakeholders to find consensus when assessing management strategies
That’s might also help setting viability thresholds
This framework might help fisheries managers and stakeholders to find consensus when assessing management strategies
Might help setting viability thresholds 16
S. Gourguet – MSEAS 2016

17. Economic scenario analyses:
Discussion V
Economic scenario analyses:
results are sensitive to changes in economic variables, especially to the assumptions made on future evolution of prawn prices
viability thresholds should be set with regards to potential evolution of economic variables
So what we can conclude, is that the results are sensitive to the threshold values.
Limiting biological and economic risks would require reductions in the fleet size compared to the status quo, which entails lost economic returns
What we called the cost of co-viability
This framework might help fisheries managers and stakeholders to find consensus when assessing management strategies
That’s might also help setting viability thresholds
need to reduce the fleet size (52 vessels) to maximize the probability of co-viability (i.e. to limit ecological and economic risks) 17
S. Gourguet – MSEAS 2016

18. Climate change scenarios (especially for banana prawn biomasses)
Perspectives V
Incorporation of a broader set of objectives (e.g. explicit social constraint)
Climate change scenarios (especially for banana prawn biomasses)
Extension toward a biodiversity conservation objective (including a suite of groups, such as rays, sharks, sawfishes, turtles, etc.)
Ecological interactions (to better address the needs of ecosystem- based approaches to the sustainable harvesting of marine biodiversity)
Explicit Social constraint – which indicator? If reduction of number of vessels, would fishermen find easily a new job? Which social impacts?
In the context of the NPF, the analysis could be extended to include a suite of groups (such as rays, sharks, sawfishes, turtles, etc.) in the definition of a biodiversity conservation objective imposed on managing the fishery.
Promising future developments involve the incorporation of a broader set of objectives including social dimensions, as well as the integration of ecological interactions, to better address the needs of ecosystem-based approaches to the sustainable harvesting of marine biodiversity.
A further extension of this work could be to allow the number of vessels to change over time. However the dimension of the problem will then be very high, which would be very demanding in terms of computer time and estimation performance 18
S. Gourguet – MSEAS 2016

19. Thank you for your attention
A prawn trawler returning to Cairns from the Northern Prawn Fishery. Brian Cassey, CSIRO