FLR Fisheries Library in ‘R’

1. FLRFisheries Library in ‘R’ Graham Pilling Phil Large, Finlay Scott, Mike Smith Cefas

2. Structure • Why FLR for data deficient stocks? • Background to FLR • Case studies • FLR’s strengths and weaknesses • Future of FLR

3. FLR & POORFISH • Idea: Use FLR for those case studies where ‘some’ data or knowledge were available • The third step on the methodological ladder framework: • Bayesian networks • WinBugs • FLR

4. Background to FLR • Extendable toolbox for implementing bio-economic simulation models of fishery systems • Open source = freely available • Uses the ‘R’ language: statistical modelling & graphics

5. Background to FLR • Collaborative approach (>10 organisations working on FLR components) • Inter-disciplinary (biology, economics, social science) • http://www.flr-project.org

6. More information

7. Many applications • Fit stock-recruitment relationships • Model fleet dynamics (incl. economics) • Stock assessments • Estimate biological reference points • Management strategy evaluations • HCRs and management procedures

8. Applying FLR to data deficient fisheries • Largely been used within data rich fisheries – e.g. EU HCR testing for North Sea cod • Three case studies undertaken in POORFISH project using FLR • Edible crab case study • Saronikos Gulf (Greece) fishery • Blue ling fishery

9. ‘Southern’ blue ling • Deepwater fishery • West of UK and France • Strong decline in CPUE seen over time • Overexploitation?

10. Why data poor? • CPUE data limited • One fleet (French trawlers from 1989) • One-way • Don’t know stock structure • No survey data • Some length data (mean length declining) • Biological knowledge limited

11. Proposed management • Spawning aggregations – interviews and surveys • Closed areas to protect spawning aggregations

12. The Question • Are closed areas within the spawning period sufficient to ‘recover’ the blue ling stock?

13. Simulation Q1 Q2 Q3 Q4 Adults Juveniles • Very simplified biological model • ‘Juveniles’ and ‘Adults’ • Quarterly time step - spawning

14. Simulation Q1 Q2 Q3 Q4 Adults Juveniles • Two ‘fleets’ • Spawning aggregation fleet: greater catchability • ‘General’ fleet (all year around) • Effort divided between fleets in Q1

15. Parameterising • Data limited • Available information from French fleet • Biological parameters • WGDEEP CPUE-based assessments • Starting population (juveniles, adults) • Fishery parameters (e.g. overall F) • Expert knowledge

16. Scenarios • Biological: • ‘Optimistic’ stock-recruitment • ‘Pessimistic’ stock-recruitment

17. Fishery & Management • No closure of spawning grounds • Closure of spawning grounds (i.e. fleet 1 cannot fish) • Constant F • 15% increase per annum • 15% decrease per annum • Up to 2x or 0.5x historical average F

18. Results - SSB Optimistic SRR • Constant F • Increasing F • Decreasing F

19. Results - Landings Optimistic SRR • Constant F • Increasing F • Decreasing F

20. Summary – blue ling • Not predictions – defined by assumptions • Changes in fleet F had greatest effect • Spawning ground closures will not recover the stock • Biological assumptions (particularly SRR) are important – but not critical (recovery doesn’t occur)

21. FLR – strengths & weaknesses • Currently, FLR is particularly good for identifying: • What you need to know (biology, economics, etc.) • Whether your controls/approach are robust to uncertainty inherent in data deficient situations • With simulations, there is a focus on parameterising models … data deficient?

22. FLR future & data-deficient cases • More ‘data deficient’ stock assessment approaches • E.g. stock-reduction models • E.g. PARFISH (participatory models; P. Medley) • E.g. Proto-moments model • Need to move beyond purely stock-assessment and MSE approaches

23. FLR and data-deficient cases • Key future development will be RISK • Likely to be higher in data deficient situations • Ecological risk assessment approaches (e.g. Australia/MSC) • Current EU and UK projects looking at risk-based approaches

24. Thanks for listening!