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MSE Part1-Chapter3
- 2. TimeTable Changes in the pathway: 2/25
- 3. ChapterIII Goal: Impact of misspecification model under a spatially-structured population, the Patagonian Toothfish in South-America Needs:· Operating Model v/s Assessment Model Explore some state variables Implementing a MSE process - - - 3/25
- 4. Outline Review Operating Model Candidate Harvest Control Rules and Performance Metrics · Structure in ADMB Conditioning operating model Simple example: Implications of recruitment process error List TODO - - - - · Designs of harvest control rules Uncertainties & scenarios Performance measures - - - 4/25
- 5. Review Operating Model Potential MSE under spatial population
- 8. SomeResults Let me show you some scenarios (/home/jcquiroz/Dropbox/utas-aad- research/Chapter%20-%20III/OM_toy_modelling/) to explain the ADMB structure. 8/25
- 9. SomestatisticfortheToyMode Realizations: 1.000 [maybe is too many] Scenarios: 2 [low sigmaR / high sigmaR] Control Rules: 1 [constant catch rate] Number of assessment (fit) per realization: 30 [yrs projection] Total of assessment: 60.000 Functions per assessment: 43 [real model > 200] Total functions evaluated: 2.580.000 Runtime in my laptop: 3 hours, 13 minutes, 44 sec · · · · · · · · 9/25
- 10. TODOinMSE Thinking in Chapter III (the Chilean case): Thinking in Chapter II (the Kerguelen case): · Identify toothfish conservation or management objectives Define operating model requirements (e.g. spatial population) Conditioning of hte operating model on the available data and knowledge Set up the management strategies or posible candidates Evaluate alternative performance measures - - - - - · Build an operating model according with the feedback from Phil and Paul Apply the same rational of Chilean case - - 10/25
- 11. Harvest Control Rules Performance Metrics
- 12. Currentknowledge Actual harvest Policy· Reference points (rp) defined following a Tier system (May, 2014) Four Tier categories based on quality and quantity data (1a > 1b > 2 > 3) Patagonian Toothfish (TOP) was clasified in Tier 1b rp biomass-based | Target: ; Limit: rp mortality-based | Target: ; Limit: - - - Method: Proxies for MSY, taking account of uncertainty in the stock assessment model and resilience of the specie - - SB40% SB20% - Fspr45% Fspr30% 12/25
- 13. Gaps&potentialcontributions No HCRs are explicitly defined for the fishery of TOP in Chile Although several methods exist for estimating rp, it is unclear which performs best. No stock management objectives: example at over simulated period No clear prejection period (objectives short - medium - long term) · · · SB > SB40% P > 0.8 · 13/25
- 14. OperatingModel The reference points are calculated by finding the value of that results in the zero derivative of catch equilibrium equation. This is accomplished numerically using a Newton-Raphson method where an initial guess for is set equal to . where spawning biomass per recruit. Fe Ce Fmsy M Fe+1 ∂Ce ∂Fe ∂C 2 e ∂F 2 e = −Fe ∂Ce ∂Fe ∂C 2 e ∂F 2 e = + +Re ϕq Feϕq ∂Re ∂Fe FeRe ∂ϕq ∂Fe = +ϕq ∂Re ∂Fe Re ∂ϕq ∂Fe ϕq 14/25
- 16. HCRsCatch-based Numerically solve the Baranov catch equation Time-consuming simulation Always better option stakeholders · · · 16/25
- 17. Simpleformulation Fishing Mortality - Based ( : linear; : non-linear ) Catch - Based γ = 0 γ ≠ 0 =F ~ y ⎧ ⎩ ⎨ ⎪ ⎪ ⎪ ⎪ 0, ,( ) βB0 By γ / −αBy B0 β−α Fmsy ,Fmsy / < αBy B0 α <= / < βBy B0 / >= βBy B0 =C ~ y ⎧ ⎩ ⎨ ⎪⎪ ⎪⎪ 0, C( , βB0 By / −αBy B0 β−α Fmsy )y C( ,Fmsy )y / < αBy B0 α <= / < βBy B0 / >= βBy B0 17/25
- 21. Getclosetarget: (images_hcr/fig4.png) P (SB >= 0.9 ⋅ S )Btarget 21/25
- 24. TODOinHCR Explore quantitatively the interactions between performance measures Evaluate the trade offs following some explicative modelling: for example: where, : process error; : implementation error; : estimation error; steepness and is any performance measure. = α + + + +P Mi β1 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ h i 1 h i 2 ⋮ h i n ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ β2 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ σ i r1 σ i r2 ⋮ σ i rn ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ e + ⎛ ⎝ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ β3 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ σ i c1 σ i c2 ⋮ σ i cn ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ β4 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ σ i E1 σ i E2 ⋮ σ i En ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎞ ⎠ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ εi σr σc σE h P M 25/26