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Evaluating the relative merits of fishery independent data in fisheries management

Evaluating the relative merits of fishery independent data in fisheries management. M. Pomarede, R. Hillary, L. T. Kell, E. J. Simmonds, C. Needle and M. K. McAllister. ICES Symposium on Fisheries Management Strategies Galway – Ireland 27th – 30th June 2006. FISBOAT Project:.

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Evaluating the relative merits of fishery independent data in fisheries management

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  1. Evaluating the relative merits of fishery independent data in fisheries management M. Pomarede, R. Hillary, L. T. Kell, E. J. Simmonds, C. Needle and M. K. McAllister ICES Symposium on Fisheries Management Strategies Galway – Ireland 27th – 30th June 2006

  2. FISBOAT Project: • Develop fisheries independent stock assessment tools based on survey data and evaluate how these perform in providing fisheries management advice. • Development of a generic evaluation framework. • FLR: Fisheries Library in R • Case studies: diversity of European stocks and regional seas.

  3. IIIa IVa IVb IVc VIId Case study: North Sea herring

  4. Aim of this work • Evaluate fishery independent harvest control rules: • Crash? • Sustainability SSB > Blim/Bpa? • Variation of the TAC? • Evaluation framework: • Operating model. • Observation error model. • Harvest control rules model.

  5. A little bit of history…

  6. Specific components of the OM • Age-structured: • Population structured into 10 age groups: 0 to 9+. • Single stock/area model. • Yearly model. • Natural mortality rate at age: • Based on existing WG “estimates”. • Decreases with age. • Constant over years. • Stock-recruitment functions: • Ricker. • Hockey-stick or segmented regression. • Recruitment occurs when fish are aged 0. • Autocorrelation in recruitment: estimated and doubled. • Fraction mature at age: variable over time. • Catches at age.

  7. Fisboat Operating Model • Use of ICA results and WG settings to define the past (1960-2004). • Those data contain stock numbers at age, fishing mortality and biological information. • Use of fishing mortality to estimate a logistic selectivity ogive. • Fit stock recruit function to data available. • Define an age-plus group. • Run the model to have simulated recruitment, SSB time series…

  8. Specific component of the OEM • Age (non) aggregated relative abundance index, annual independent error term: • Iy,a is the simulated index in year y at age a. • qa is the long-term average constant of proportionality for this index at age a. • Ny,a is the surveyable stock in year y at age a. • ey is the independent and identically distributed random error term in year y and follows a lognormal distribution. • Error in CVs = 0 %, 15 % and 30%.

  9. Model free - based on observations: • Model free - fixed TAC: • Model based - Loess smoother: • Z Model free: Harvest control rules

  10. Z Model free: Harvest control rules

  11. Results: Model free - fixed TAC SSB Bpa Blim

  12. Results: Model free - ratio index SSB TAC

  13. Results: Model based - smoother SSB TAC

  14. Results: Z model free SSB TAC

  15. Results: P(B2014<Bpa) - CV(TAC2014)

  16. Conclusion • These fishery independent based methods do not display seriously bad behaviour: • Smoothed HCR does not provide expected results. • Z model free HCR is overly conservative. • Both fixed TAC and ratio index HCRs provide not too bad results. • No crash of the stock. • For all scenarios, SSB kept above Blim with a very high probability (p>0.98) and above Bpa with high probability (p>0.8). • Variation of the TAC is not unduly high.

  17. For the future • Beverton Holt stock recruitment function. • Run a seasonal model. • Include assessment methods: • ICA (Integrated catch at age). • SURBA (Survey-based assessment). • BREM (Biomass random effects model). • Length-based management. • Other HCRs.

  18. Thank you

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