1. ELFSim: a fisheries decision support tool for coral reef line fish on the Great Barrier Reef of Australia
Rich Little
MSEAS 2016
Oceans and Atmosphere

2. Collaborators Bruce Mapstone (James Cook U / CSIRO) Andre Punt (CSIRO)
Tony Smith (CSIRO)
Campbell Davies (James Cook U / CSIRO)
Olivier Thebaud (CSIRO)
Brigid Kerrigan (Fisheries Queensland)
Alex Campbell (Fisheries Queensland)
John Kung (Fisheries Queensland)
Rich Little MSEAS 2016

3. Human involvement:
Using human behavioural data to model human behaviour
Using human input to determine what is important to them
1000 km
Coral Reef Fin Fish Fishery, Great Barrier Reef
Primary target:
50 M AU$
3 sectors (commercial, charter, recreational)
Fishery is in the GBR WHA – federal jurisdiction
System of MPAs used for conservation purposes
Rich Little MSEAS 2016

4. Decision support for fisheries Management on the Great Barrier Reef of Australia
Simulation model: ELFSim
Stakeholder engagement
Rich Little MSEAS 2016

5. ELFSim: Effects of Line Fishery Simulator 3 components:
II. Stakeholder engagement:
Alternative Strategies,
Specific Objectives
3. Management Model
Fishery Performance CPUE, Harvest, size of fish, Effort Distribution
Stock Performance Relative Biomass, Population Structure
Fleet Dynamics Model
I. Modelling human behaviour
2. Fishery:
Mortality
Catch
1. Biology: Population Dynamics Model
Rich Little MSEAS 2016

6. 1. Biological model Queensland Individual Reef-based (> 3000 reefs)
Cairns
Townsville
Mackay
Rockhampton
Gladstone
Individual Reef-based (> 3000 reefs)
Larval movement among reefs
Rich Little MSEAS 2016

7. 2. 2. Fishery model (I. Modelling human behavior)
Effort after ITQs introduced
2. 2. Fishery model (I. Modelling human behavior)
Agent-based model (ABM)
Assigns effort to reefs by individual vessel
Agents trade quota
Vessel fishing behavior is governed by a Discrete Choice model
Actual behaviour
Simulated
behaviour
Cool part … .
Which
So here we have a predicted effort trajectories under different TAC levels.
And the actual effort experienced after ITQs were implemented.
Rich Little MSEAS 2016

8. Fisheries model data
A survey parameterised the agent-based model (Thebaud et al. 2014)
Fishing vessel characterisation
Profitability of fishing
whether to sell or buy quota
Quota trading networks
Type
vessel
Trip length
Species
Cost / y
Diversified fishers
3 days
$ 33K
Generalists
<15m
7 days
Dead coral trout
$ 130K
Dedicated live fishers
>15m
13 days
Live coral trout
$ 220K
Rich Little MSEAS 2016

9. 3. Management model 3. Management model
Area closures
Effort levels
Min. Legal Size
Gear Selectivity
Spawning closures
Commercial catch quota
combined with an ITQ model
Assessment model
Harvest control rules
Rich Little MSEAS 2016

10. II. Stakeholder engagement Management: Stakeholder Engagement
Multiple stakeholders (industry, managers, + others)
with different objectives (economic, legislative)
Asked: Where do you want fishery to go?
or: What are their objectives for the fishery?
Rich Little MSEAS 2016

11. Defining operational management objectives
Fisheries Management
Avail. biomass (AB) > 0.4 AB0 > 50% of the time
Commercial Fishing Industry
Commercial CPUE > 80% CPUE > 90% of the time
Catch > 80% TAC >90% of the time
Profits > Profit > 80% of the time
Conservation
Spawn biomass (SB)
in MPAs > 90%SB0 >80% of the time
on GBR > 50%SB0 >80% of the time
Charter fisheries
1 fish caught each trip > 50 cm
Recreational fishers
Catch the bag limit >50% of the time
Rich Little MSEAS 2016

12. Using ELFSim Defining operational management strategies
i.e. how do you want to get there?
Change TAC of
Coral Trout:
2. Fleet constraints:
1.0
0.7
0.5
Regionally - constrained vessels
- unconstrained
Rich Little MSEAS 2016

13. Using ELFSim Simulating outcomes
Conservation objective:
Probability (Spawning biomass [marine reserves] > 90% B0) > 80%
Probability
80%
Regionally constrained
Unconstrained (regionally)
Rich Little MSEAS 2016

14. Using ELFSim Computer simulations
Far North
Probability (SB > 90% B0)
Cairns
Townsville
Mackay
Cap-Bunkers
Swains
Regional implications in achieving this objective.
Using ELFSim Computer simulations
Conservation objective:
Probability (Protected spawning biomass [marine reserves] above 90% B0) > 80%
Rich Little MSEAS 2016

15. Using ELFSim Decision Table : Trade-offs
Management objectives
Conservation
Management
Industry
Recreation A B C
Management strategies
Rich Little MSEAS 2016

16. Using ELFSim Decision Table : Trade-offs
Management objective:
Economic Conservation
Management strategy
Management strategy
Rich Little MSEAS 2016

17. Summary Human involvement: Using human behavioural data to model
Using human input to determine what
is important to them
Rich Little MSEAS 2016

18. Thank you Division/Unit Name Rich Little t +61 3 6232 5006e w
Add Business Unit/Flagship Name

19. Using ELFSim Computer simulations
Economic objective:
Pr (Profitability in 2035 > Profitability in 2011) > 80%