12th IMR\PINRO Symposium Applying the Bayesian approach in assessment of red king crab (Paralithodes camtschaticus) and northern shrimp (Pandalus borealis) stocks in the Barents Sea by Sergey Bakanev and Boris Berenboim PINRO, Murmansk, Russia Introduction Bayesian’s theorem has been applied in fishery biology since 1990s. At present these models are widely used to describe the status of stocks and in forecasting. The estimates of the stock status made by this method have been supported by CCAMLR, FAO, NAFO, NEAFC, ICES. Goal to review the possibility of using this method for assessing the population dynamics and TAC of northern shrimp and red king crab in the Barents Sea Methods The Bayesian approach was used to construct a posterior model parameters distributions p( | data) p(data|)p() p( | data) is the posterior probability distributions ? of parameters?, is the likelihood function of difference between p(data |) s urvey indices and absolute biomass, is the estimated or assumed prior probability p( ) distribution of unobservable parameters (catchability coefficients, MSY, carrying capacity, etc.). Methods State equations Production model for red king crab and shrimp stocks m1 B B B B C V MSY t 1 t t1 t t t K K Cohort model Catch Survey Analysis (CSA) for red king crab m a Rt1 PRt *molt *GPR,R *e *e m ( y1)*m b Pt1 ((Pt Rt PRt *molt *GPR,P )*e Ct *e )*e Cohort model Length-Based Analysis (LBA) for red king crab ll1 Mt ( y1)Mt nl Nl1,t1 [Pl,l1(Nl,t Ol,t )e Cl,te ]ml,t *e Rl1,t1 l1 Mt ( y1)Mt ol Ol1,t1 [( Nl1,t Ol1,t )e Cl1,te ](1 ml1,t )*e Shrimp input data Russian trawl survey data, Fishery data Norwegian trawl survey data, Ecosystem trawl survey data Area-swept method (observed abundance by Russian and Norwegian CPUE, year) total catch Consumption by cod (AFWG) Crab input data Trawl survey data Fishery data Area-swept method Catch in numbers (observed abundance by (by length, shell condition, length, sex, shell condition, year) year) Tagging data (length increment per moult, moulting probability) Results for shrimp Comparison of observed and model estimated values: CPUE and survey stock biomass indices 2 2.5 1.8 1.6 2 1.4 1.2 1.5 1 0.8 1 Bt/Bmsy 0.6 0.4 0.5 CPUEIndecies Survey and 0.2 0 0 1982 1986 1990 1994 1998 2002 2006 R-CPUE N-CPUE N-Surv Eco-Surv B(est) Results for shrimp Dynamics of northern shrimp stock in the Barents Sea by management zone applying the precautionary approach for 1982-2005 (dark area – safe zone) 2.5 2 2006 1982 1.5 2000 1990 1 1994 BMSY 1995 Relative biomass Relative (Bt/Bmsy) 0.5 Blim Zlim 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Relative mortality(Zt/Zmsy) Results for shrimp The cumulative probability of exceeding MSY 1 0.75 0.5 0.25 Riskexceedingof MSY 0 0 200 400 600 800 1000 1200 Removal by fishery and cod ('000 t) Results for red king crab Fitted (lines) and observed survey index (circles) of legal stock using production, CSA, LBA models in REZ of the Barents Sea 18000 10000 16000 9000 8000 14000 7000 12000 6000 10000 5000 8000 4000 6000 3000 4000 2000 2000 1000 0 0 Index of legal stock, '000 spec. Abandunce Abandunce of legal stock, '000 spec. 1994 1996 1998 2000 2002 2004 2006 Production CSA LBA Survey index Results for red king crab Forcasted legal stock with annual catch of 2 million (А), 3 million (B) and 4 million (C) in 2007-2009 according to production (1), CSA (2) and LBA (3) models 11 11 11 1 А B C 10 10 10 2 3 9 9 9 8 8 8 000 spec. 7 7 7 6 6 6 Abandunce of legal stock, '000 2007 2008 2009 2007 2008 2009 2007 2008 2009 Conclusions The Bayesian approach can be used for estimating the population dynamics of northern shrimp and red king crab in the Barents Sea and Spitsbergen area. The dynamics of the Barents Sea red king crab population parameters is extremely unstable. But the presence of strong year-classes make it possible to forecast the stock dynamics and develop appropriate management strategies..