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Data assimilation in APECOSM-E

Data assimilation in APECOSM-E. Sibylle Dueri, Olivier Maury. Tropical tuna exploitation in the Indian Ocean. Targeted tuna species. Bigeye (BET). Yellowfin (YFT). Skipjack (SKJ). Industrial fisheries start in 1983, rapid increase. Catch tons/year 1983 2009 SKJ  70 000  400 000

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Data assimilation in APECOSM-E

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  1. Data assimilation in APECOSM-E Sibylle Dueri, Olivier Maury

  2. Tropical tuna exploitation in the Indian Ocean Targetedtunaspecies Bigeye (BET) Yellowfin (YFT) Skipjack (SKJ) Industrial fisheries start in 1983, rapid increase Catch tons/year 1983 2009 SKJ 70 000 400 000 YFT 60 000 300 000 BET 40 000 120 000 From IOTC – Report 2009

  3. Skipjack tuna fishery: spatial distribution Main fishing gears: purse seine, bait boat, gillnet and long line From IOTC – Report 2009

  4. Skipjack fishery: available data • Fishing gears • Purse seiners (French, Spanish and other) • Maldivian Baitboat • Data • Monthly catch 1x1 degree grid • Monthly size frequency 5x5 degree grid • Monthly fishing effort 1x1 degree grid From IOTC – Report 2009

  5. APECOSM-E • Dynamic, deterministic spatially explicit and size structured model • Single species of top predator (i.e. skipjack tuna) • Devoted to parameter estimation • State variable  Tuna biomass f(x,y,z,t,V) • Partial differential equation, discretized and solved numerically • 48 parameters: 19 related to fisheries, 8 energetic (DEB), 21 ecological parameters

  6. Discretisation • 1º x 1º horizontal grid of the Indian Ocean (4230 cellules) • 20 vertical layers, 10 m interval in the first 150m and max depth of 500m • 83 size classes, from 1mm to 1m • 1 day time step

  7. APECOSM-E – Model Structure

  8. Fishing mortality • 4 fleets : French, Spanish and other (Seychelles, Ile Maurice, NEI) purse seiners and Maldivian Bait boat • Monthly value of spatial effort ek is applied on simulated biomass • Fishing power pk multiplies by an exponential function representing the increase in fishing efficiency due to technological development • Fish size and fishing depth selectivity functions

  9. Observed catches vs distribution of simulated biomass Mars 1993 Total biomass Vertical transect of biomass distribution along the Equator Exploitable biomass + observed catch Observed catch Vertical transect along the Equator

  10. Data assimilation • 3 Steps • Identifiability of the model parameters (Hessian Matrix) • Parameter estimation • Sensitivity analysis of non-estimated parameters • Cost function combines • –log likelihood of catches JCAPT(K) • –log likelihood of size frequencies JFDT(K) • –log likelihood of parameters JPAR(K) • Minimization algorithm requires the derivation of the code  automatic differentiation tool (TAPENADE-Inria) • Derives the exact gradient of the cost function with respect to model parameters

  11. Data assimilation: convergence Data assimilation over 10 year period: 84-93 Optimization of fishery related parameters: fishing power, size and depth selectivity, … Iterations

  12. Overall catch: simulatedvsobserved Temporal window used for optimisation

  13. Skipjack tuna fisheries: fishing strategy Peaks of catches occurring in association with Fish Aggregation Devices (FADs) are difficult to represent Associative behavior of fishes Increase the catchability? Ecological trap? (Marsac et al. 2000, Hallier and Gaetner 2008)

  14. Free-school catch

  15. Size frequencies Bimodal distribution

  16. Size frequencies: temporal dynamics mean

  17. Sensitivity analysis Based on the evaluation of the gradient of the cost function The gradient represents the variation of the cost function for a change in parameters Local sensitivity, changes in time 3 x 6 year periods 1984-1989 1990-1995 1996-2001 Ecological parameters DEB Param.

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