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

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

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

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

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

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

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

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 2004-05 2005-06 2009-2010 2008-09 2010-2011 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

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

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

Defining operational management objectives Fisheries Management Avail. biomass (AB) > 0.4 AB0 > 50% of the time Commercial Fishing Industry Commercial CPUE > 80% 1994-1996 CPUE > 90% of the time Catch > 80% TAC >90% of the time Profits > Profit 2011 > 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

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

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

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

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

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

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

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

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