EFIMAS I° ECONOMISTS MEETING 21-22 December 2004, Seville, Spain

1. EFIMAS I° ECONOMISTS MEETING 21-22 December 2004, Seville, Spain Integrated bio-economic model with special emphasis on the management of resources and social impacts of fisheries V. Placenti, Irepa Onlus, Italy

2. The Research Project • OBJECTIVES Improve and enrich existing bioeconomic models for the management of multi-species, multi-fleet, multi-gear fisheries developed in Italy in previous years; Contribute to the development of an overall methodology for constructing and applying such models to the practical management of European fisheries. MILESTONES • Achievement of an innovative biological catch-effort model for multi-species multi-gear fisheries. Development of a computer codes for an integrated bio-economic model for optimal management of fisheries.

3. The Methodology The methodology combines: • bio-economic modelling • classical and Bayesian statistical estimation techniques • extensive computer programming • numerical calculation • linear and non-linear optimisation techniques. A strong interdisciplinaryapproach is required

4. MOSES Italian Model (IREPA) Management Tools Input Control: Licences (Fishing Effort Limitation) Target: maximal economic rent subject to sustainable resource exploitation and inertia or re-conversion constraints.

5. MOSES Italian Model (IREPA) Model Characteristics Target: support to public administration management policy Means: optimal allocation of fishing effort by gear and area Methodology: resolution of a non-linear constrained optimisation problem (with embedded LP problem)

6. MOSES Italian Model (IREPA) Model Structure Biological parameters are endogenous Effort by gear and area is the decision variable Data are often fragmented (Bayesian approach)

7. Reconversion unit costs Fishing Unit Costs Species Prices Revenues Scenario Variables The MOSES Model - Flow Chart Unemployment Level Status quo Fishing Effort Distribution Optimiser Fishing Effort per Gears and Areas Ecology/Biology: Stock Biomass Model Parameters Technical Matrix Fishing Reconversion Model Catch-Effort Model Fishing Cost Reconversion Cost Impact on Ecology: Biological Reference Point Catch per Species and Areas Fishing Effort Transfer Flows per Gears and Areas Value Added Performance Index

8. Multi-Gear Multi-Species Fishery Each Fishing System concurring to the catch of more species Competition among species for food and predator-prey relations Economic and Biological aspects related to multi-species multi-gear fishery management can conflict

9. Input Data for the MOSES Model • Catch-Effort Model • Catch time series per species and area (yearly data); • Fishing effort time series per fishing system and area (yearly data); • Specification of fishing systems concurring to the catch of each species (Technical Matrix); • Recruitment age for the species; • Optimal Fishing Effort Model • Unit fishing costs, per fishing systems and area (last year); • Species prices, per species and area (last year). • Unit costs for fishing effort transfer and re-conversion, per area and system. • Unemployment level per areas.

10. The MOSES Model - Optimal Allocation of Fishing Effort minx[-U(x)] maximum profit (value added) VB(x)<VBmax biological constraint (biological reference point) VI(x)0 inertia constraint Decision Variable: x fishing effort distribution per gears and areas • equivalent to the following non linear constrained optimisation problem: • minx f(x) • Gi(x)  0 i=1,M • Solution: • Augmented Lagrangian Approach • Quasi-Newton minimisation techniques • DFP method for Hessian matrix estimate

11. The MOSES Catch-Effort Models • Three catch-effort models have been used: • Logistic: 1)Schaefer - Dynamic 2)Exponential - Dynamic Age-Structured: • 3)Schnute/Deriso • Each model has been applied over 470 area/species combinations. • An “Equivalent Effort” is introduced as weighted sum of the effort over the N systems concurring to the catch. • Model parameters are estimated via non linear constrained optimization. • The following 7 biological parameters are endogenous (natural mortality, catchability, growth coefficient, recruitment and pre-recruitment weight,  and  recruitment parameters). • Parameter significance are estimated via classical statistical techniques.

12. Optimisation Analysis - The Scenario’s Different Scenario’s can be defined combining economic, biological and social (re-conversion) aspects

13. Optimisation Analysis - Global Results Economic Outcomes (million euro x year) Biological constraint per areas

14. Optimisation Analysis: Fishing Effort Distribution per Areas The solutions suggest articulate fishing effort re-distributions over gears and areas. Distribution per Fishing Systems

15. To Proposed Distribution Fishing Effort Reconversion Model From Reference Distribution • Assumptions • The variation from the reference to the • proposed distribution can be obtained by • different combinations of resource transfers. • A fictions activity “Unemployment” is • introduced. • Not all transfers between the activities are • allowed (dotted lines). • For each elementary transfer, the re-conversion • cost is proportional to the resource transferred. • Objective: Minimise total re-conversion cost. • Constraints: • - For each activity, the difference between • outflow and inflow must equate the assigned • variation of the resource. • - For each activity, the total outflow can not be • greater than the starting amount of resources. Activities: Gears/Areas Combinations Unemployment Effort Transfer LP Problem

16. Effort ReconversionSome results for bottom trawler & multipurpose vessels • Case A01 • Unit cost of effort transfer to other areas = linked to the distance • Unit cost of effort transfer to unemployment = 10 • Limitation to effort movements = within two contiguous areas • Total reconversion cost = 42.2416 • Case A02 • Unit costs of effort transfer to other areas = linked to the distance • Unit costs of effort transfer to unemployment = linked to the unemployment level • Limitation to effort movements = within two contiguous areas • Total reconversion cost = 34.4295

17. 0.081 0.657 0.007 Unemployment Disoccupazione 0.407 0.166 Reconversion Riconversione Reallocation Riallocazione Reallocation & Reconversion 0.077 0.062 0.049 0.289 0.169 0.003 0. 485 0.341 0.544 0.205 0.181 0.224 0.264 Unemployment Disoccupazione 1.866 1.461 A01 0.089 8 6 8 7 4 4 3 3 6 5 2 5 2 10 10 1 1 9 7 9 Fishing Effort Re-conversion - Case A01 Multipurpouse vessels Sistemi polivalenti Bottom trawler Strascico

18. 0.117 0.032 Unemployment Disoccupazione 0.330 0.049 Reconversion Riconversione Reallocation Riallocazione Reallocation & Reconversion 0.077 1.267 0.146 1.047 1.516 0.289 0.205 0.385 0.493 0.225 0.616 0.668 0.224 0.787 0.490 0.057 0.518 0.241 0.261 Unemployment Disoccupazione 1.138 A02 0.880 9 9 7 7 4 4 3 3 6 6 2 5 2 10 10 1 1 8 8 5 0.800 Fishing Effort Re-conversion - Case A02 Multipurpouse vessels Sistemi polivalenti Bottom trawler Strascico

19. Conclusions • Economic, biological and social aspects related to fishery management may have complex and conflicting interactions. • The Bio-Economic models can represent a powerful tool for the integrated management of such aspects, and to predict and control their ecosystem impact. • Due to combination of economics, biology, mathematics, statistics and computational techniques, a strong interdisciplinary approach is needed. • MOSES is able to account for the relevant aspects of the Mediterranean fisheries and to provide advices to politicians and fishery managers.