1. FLR Fisheries 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: 1.Bayesian networks 2.WinBugs 3.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 Very simplified biological model ‘Juveniles’ and ‘Adults’ Quarterly time step - spawning Juveniles Adults Q1Q2Q3Q4

14. Simulation Two ‘fleets’ –Spawning aggregation fleet: greater catchability Juveniles Adults Q1Q2Q3Q4 –‘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 Constant F Increasing F Decreasing F Optimistic SRR

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

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!