Survey of existing bioeconomic models Final report
April 2009
For: The European Commission Directorate-General for Maritime Affairs and Fisheries ii Survey of existing bioeconomic models
Final Report Studies and Pilot Projects for Carrying Out the Common Fisheries Policy No FISH/2007/07
Lot 5
Survey of existing bioeconomic models (SI2.507729)
Report prepared by,
AZTI-Tecnalia: Raúl Prellezo.
CEFAS: Alyson Little.
DTU-AQUA: Rasmus Nielsen and Bo Sølgaard Andersen.
FOI: Jesper Levring Andersen.
Wageningen IMARES: Christine Röckmann.
IREPA: Paolo Accadia.
LEI: Jeff Powell and Erik Buisman.
Disclaimer
This report has been prepared under contract FISH/2007/07 – Lot 5 – by AZTI-Tecnalia, CEFAS, DTU-Aqua, FOI, Wageningen IMARES, IREPA and LEI. It does not necessarily reflect the view of the European Commission.
Citation
Prellezo, R., Accadia, P., Andersen J. L, Little, A., Nielsen R., Andersen, B.S., Röckmann C., Powell J. and Buisman, E. (2009) Survey of existing bioeconomic models: Final report. Sukarrieta: AZTI-Tecnalia. 283 pages.
Survey of existing bioeconomic models Survey of existing bioeconomic models iii
Preparation of this document The sustainability of fisheries in terms of biological conservation and economic viability of the fleets is integrally linked to the approach followed in the management of the fisheries. For this reason, the Commission is seeking to assess different management options with a view to improving the overall effectiveness of fisheries management and so bioeconomic models can play a key role.
Under the scope of different research projects, where research has been done to support the introduction of various financial instruments or by personal initiatives, several models have been developed to evaluate options. Each of the models developed has specific tasks with more or less generality in terms of covering the specific characteristics of the fisheries and with more or less flexibility in order to deal with some other questions or other types of management advice.
These models are not clearly addressed in term of usability for the assessment of a range of management issues. In that sense it is necessary to explicitly address the advantages and limitations of each model when assessing a management alternative.
In that sense several initiatives have been carried out to review bioeconomic models, and of special interest to this tender is the SGECA report (SEC 2006c), where some of the operational bioeconomic models that could be used by the STECF were listed. In the review of that work (SEC 2006a) the STECF recommended the provision of documentation for each model according to the guidelines given in the Report of the Subgroup on bioeconomic models. With these guidelines as a starting point, but also considering the key issues explained above, a full review of the EIAA, TEMAS, MOSES, BEMMFISH, BIRDMOD, MEFISTO, AHF, EMMFID, SRRMCF, COBAS, ECOCORP, ECONMULT and EFIMAS models, has been undertaken.
This document presents the final report of this study, funded by the EC project “Survey of existing bioeconomic models”, (SI2.507729), and coordinated by AZTI-Tecnalia in collaboration with CEFAS, DTU-Aqua, FOI, IMARES, IREPA and LEI.
Survey of existing bioeconomic models iv Executive summary
Executive Summary A bioeconomic model is a theoretical construct that represents a system, with a set of variables and a set of logical and quantitative relationships between them. They are constructed to enable reasoning within an idealized logical framework about these economic processes, integrating biological processes and industry behaviour. They have played an important role in exploring diverse issues in fisheries management for the last 40 years. In fisheries there is an extensive range of models that provide a comprehensive impact assessment of different management alternatives as asked by the CFP.
This report presents a review of the existing operating bioeconomic models within the EU. In particular the following models have been reviewed; EIAA, TEMAS, MOSES, BEMMFISH, BIRDMOD (including Aladym), MEFISTO, AHF, EMMFID, SRRMCF, COBAS, ECOCORP, ECONMULT and FLR (EFIMAS).
The structure of the report as well as the review performed has focused on giving the reader a reference rather than something to be read cover to cover. The review has been based on the existing literature (reports and scientific papers), communication with model developers, and a feedback process among the project group and two external reviewers.
The possible tasks for which bioeconomic models produce advice, range from the general evaluation of the fisheries sector to the evaluation of specific regulations. Within these tasks, we have paid particular attention to the tasks performed within the EU STECF. For each analyzed task some specific issues need to be considered:
• Model orientation: If the model is output or input driven.
• If it is a simulation (what if) or an optimization model (what’s best).
• Characteristics of the economic and biological modules and the links between them.
• Data requirements: Biological and economic modules initializations. We have paid particular attention to the model requirements of stocks assessments and also analyzed data requirements in relation to whether the DCR provides sufficient information for each model.
• The output format and in particular the bioeconomic indicators that each model produces.
Based on the specific issues explained above and some others considered as relevant, a review framework consisting of specific and general tables has been created to facilitate a quick comparison and selection between models. In addition a summary table has been produced with the STECF tasks for which each model could produce advice.
Survey of existing bioeconomic models Executive summary v
Following the review provided, the next paragraphs summarise the main characteristics of each model, in terms of the objectives for which they were created, the advice they provide, the software used, the data requirements and the main limitations that they face.
AHF
AHF has been created to simulate the economic behavioural response of fishing fleets to the economic outcome in previous years of the fishery with response to the entry exit or invest- disinvest in the fishery changing fleet capacity. The model contains an economic and biological module.
AHF can assess TAC (total allowable catches) and effort regulations (switching between harvest and effort regulations), in addition to HCR (harvest control rules), discards, and cases of non- compliance (assuming that landings are proportional to quotas).
Implemented in FLR, AHF has been applied to North Sea Flatfish and Roundfish fisheries, and can be run with data provided by the DCR. In any case it should be taken into account that results are extremely sensitive to the calibration of the model.
BEMMFISH
BEMMFISH is a simulation model that projects biological and economic variables into the future to test different Mediterranean fisheries management and policies.
BEMMFISH can assess; effort regulations, vessel numbers and changes in taxation (by period), where it is possible to adjust effort for each species. Selectivity changes can also be assessed (but not directly).
Implemented in Java, the economic and biological models are linked, however the main limitations are that a maximum of 4 species and 3 fleets can be modelled and that BEMMFISH cannot be run only using DCR data.
BIRDMOD
BIRDMOD is a simulation model to predict effects of different management policies from a biological, economic and social perspective and consists of 4 modules; a biological, an economic, management and a state variation module.
BIRDMOD can assess restrictions on fishing effort (in terms of capacity and activity) and also provide advice in relation to changes in selectivity, taxes and subsidies.
Survey of existing bioeconomic models vi Executive summary
Implemented in R, BIRDMOD has been designed specifically for Mediterranean fisheries management. The applications analyzed use the Aladyn biological module and can be run with data provided by the DCR.
COBAS
COBAS is an option comparison model in which the effects of a particular policy are compared to the effect of the current management system.
COBAS provides assessment for management of the commercial and the recreational sector, changing of effort regulations, TACs, decommissioning schemes, levies, prices of fish, mesh sizes, area closures and post harvest policy options (traceability, ecolabelling,…).
Implemented in GAMS it has been used for fisheries in the English Channel, Celtic Sea and Western approaches. The main limitation of COBAS is that is does not include a biological model. Data supplied from the DCR data should be sufficient for running COBAS, excluding the environmental and recreational components.
ECOCORP
ECOCORP is a simulation model to assess the economic impacts of effort reductions imposed by the North Sea Cod recovery plan of the North Sea fishing fleet segments. It incorporates short-terms impacts and multi-species interactions.
ECOCORP provides advice in terms of effort regulation (e.g. decommissioning, cod fishing moratorium).
Implemented in a dynamic system framework, the model can be used with the data provided by the DCR given the MSVPA estimates to initialise the biological sub-model. Whilst ECOCORP includes multi-species interactions, the main limitation is that it is very case study specific.
ECONMULT
ECONMULT is a simulation model for the management of the Barents Sea fisheries using a multi-species and multi-fleet approach in which the user can define the dimensions.
ECONMULT provides advice in terms of input and output restrictions, including decommissioning schemes.
Implemented in Mathematica, fleets can be modelled at various aggregations. An important limitation of ECONMULT is that it does not include any biological model, even if it can easily be linked to a stock assessment model. In principle ECONMULT can be used with the data provided by the DCR however it will depend on the biological model used.
Survey of existing bioeconomic models Executive summary vii
EIAA
EIAA was originally created to assess the economic consequences of the TAC (ACFM advice). Currently it has been developed to include an optimization module to estimate the number of fishing days under different quota proposals.
EIAA provides advice for TAC changes, long-term management plans and days at sea restrictions in an extended version of the model.
Implemented in Excel, EIAA does not include a biological model. Nevertheless it has been used in many STECF tasks and can be used with the data provided by the DCR even if some adjustments are required.
FLR (EFIMAS)
FLR (EFIMAS) is a multi-stock and multi-fleet simulation toolbox to perform management strategy evaluation on a spatio-temporal disaggregated scale to enable simulation and evaluation of biological, social and economic consequences of a range of policy options.
FLR can provide advice on almost any kind of management plan or measure (including effort regulations, TACs, HCR, selectivity changes, fishery closures, enforcement effort and environmental variables).
Implemented in R, FLR has been used for North Atlantic, North Sea, Baltic Sea and Mediterranean fisheries management. FLR possess both biological an economic modules and includes several stock assessment models, and a key element is the inclusion of uncertainty into biological and economic “boxes”. In principle, the data requirements of the DCR are sufficient but it depends on the fishery to be analyzed. Users require expertise in R programming and in FLR.
EMMFID
EMMFID is an optimization and simulation model of the Danish fishery sector. Fishing activities are modelled over a one year period to clarify the economic consequences of fishery management regulations and industry activities.
EMMFID is able to provide advice in terms of the consequences of management plans of an entire national sector and also in terms of changes in the economic framework (fleet structure, employment, capital use, costs and prices).
Implemented in GAMS, EMMFID does not include a biological model even if it has been linked to one. Data provided by the DCR is not specific and detailed enough for running EMMFID.
Survey of existing bioeconomic models viii Executive summary
MEFISTO
MEFISTO produces bioeconomic simulations under alternative management scenarios to emulate fisheries management characteristic of the Mediterranean.
MEFISTO provides advice in relation to; fishing effort changes, selectivity changes, price changes, imports, dismissal price and fuel price. These measures can also be modelled as a user defined event, for which a value can be assigned for specified levels (country, fleet, vessel or cohort).
MEFISTO is a freeware stand-alone software package. It is possible to use MEFISTO with DCR data even if the original aggregation is at vessel level. The main model limitation comes from the assumption that the secondary species catches are proportional to a target species.
MOSES
MOSES was designed to produce the optimal distribution of fishing effort and to estimate the long-term effects of changes in allocation of fishing effort in terms of biomass, harvest and economic returns.
MOSES assesses fishing effort changes and prevents drastic changes in fishing effort distribution through an inertia constraint.
Implemented in FORTRAN MOSES is designed to replicate the specific characteristics of the Mediterranean fisheries, by optimizing value added as opposed to profit. MOSES does not have a link with any assessment models, (it has its own biological model) but does require biological initialization.
TEMAS
TEMAS is a simulation toolbox to perform multi-stock and multi-fleet-based, bioeconomic evaluations of management strategies, taking into account technical measures (e.g., MPAs) and fleet behaviour.
TEMAS includes both biological and economical modules and can on a spatio-temporal disaggregated scale evaluate almost any management measure including fishing effort and TAC regulations and HCRs. Furthermore, it provides modules representing stakeholder groups.
Implemented in Excel and Visual Basic, TEMAS has been applied to the Baltic cod, North Sea flatfish and Kattegat fisheries. Data provided by the DCR can be sufficient even if not in entirety. The main limitation of TEMAS is that currently it is no longer being maintained or developed.
Survey of existing bioeconomic models Executive summary ix
SRRMCF
SRRMCF was created to operationalise a strategic management plan for the commercial Swedish fishery with the aim of providing viable solutions for the structural problems in the fishing industry. SRRMCF is designed as an optimization model where the objective (for example resource rent or employment) can vary.
SRRMCF provides assessment of the overall strategic management plans including changes in TACs, quota allocations and restrictions in effort.
Implemented in GAMS, the main limitation of SRRMCF is that it does not include a biological ‘box’. However it can be run using DCR data.
From the models summarized above a key particularity that needs to be considered is the rationale behind the model development, as each model has its own objective. Some models deal with the specific problems associated with a dynamic change in fleet capacity (AHF) or the simulation of management strategies (BIRDMOD, FLR –EFIMAS-, COBAS,…), while other models evaluate particular advice (for example EIAA with the ACFM advice), or specify a more concrete problem (for example ECOCORP with the cod recovery plan). Whereas other models provide a more generalised economic evaluation (SRRMCF considers the whole Swedish fishing sector and EMMFID covers the entire commercial Danish fishery).
Noticeably many of the models are area-specific, i.e., they have been developed for a distinctive/specific region. The most obvious examples are those from the Mediterranean (MEFISTO, BEMMFISH, BIRDMOD, MOSES). In fact only those models based on FLR (including AHF) have case studies in both the Atlantic and Mediterranean (and also outside of the EU waters). The reason being is that FLR is not a model but a toolbox (framework) to be used for the construction of models. TEMAS is also a toolbox, but has not been applied to a Mediterranean case study.
Model orientation is also related to model focus and rationale. Given the management scheme of the Mediterranean, models such as MEFISTO, BEMMFISH, BIRDMOD and MOSES are input (effort) oriented. The Atlantic models are either input oriented (for example COBAS), output (catch) oriented (EIAA, many of the case specific models implemented in FLR) or input and output oriented (AHF, EcoCorP).
Simulation (what if) models are the main approach considered in the models reviewed with the exceptions of MOSES and SRRMCF in which an objective function is optimized (what’s best). To highlight the particular example of MOSES, value added is considered the objective function to be maximized, in order to meet the special characteristics of the Italian remuneration system.
Survey of existing bioeconomic models x Executive summary
Another exception to this classification is EMMFID which is both an optimization and a simulation model.
A trade off appears to exist between the generality and the complexity of the models. For example SRRMCF, EIAA and EMMFID do not have a biological module. They are all designed for advising, given a catch or/and a TAC, the overall fishing sector of one country and the overall ACFM advice affecting EU fleets, respectively. For example EIAA handles 60 stocks and 50 fleets (segments), whilst the SRRMCF model optimizes over 13 fleets and so it is apparent why a biological component is not included.
ECONMULT has been used with two biological models, MULTSIMP and AGGMULT, while BIRDMOD has been used with Aladyn. Some other models present limitations in terms of the dimensions they are capable of handling. The BEMMFISH model is a paradigmatic example, a maximum of 4 species and 3 fleets can be conditioned.
On the contrary there are models whose strength lies in precisely the biological component (FLR based models and BIRDMOD with Aladyn, for example), and with or without feedback between both components. For example AHF and ECONMULT are able to implement two management regimes such as the effort limitation and TACs (whatever is binding) by affecting the biological component.
The design and software implementation across the models is quite heterogeneous. GAMS, R, and Excel are the most common platforms used. R is supported by the constant development of routines that facilitate the evolution of models and stochastic based models. Furthermore, R is freeware and multiplatform characteristics are also advantageous (AHF, BIRDMOD, FLR - EFIMAS- are examples of R implementation). On the contrary, Excel is distributed worldwide (in scientific and the non scientific communities) with visual basic programming possibilities (TEMAS or EIAA in its long-run release) or not (EIAA in its base and extended release). GAMS (SRRMCF, EMMFID and COBAS) is also used; however a basic licence and some solvers are needed (also for Excel, Mathematica –ECONMULT- and Fortran –MOSES-). MEFISTO and BEMMFISH can be downloaded as a compiled programme and ECOCORP is based on dynamic systems which require a licence for implementation (VENSIM).
In terms of the quantity of data input needed, there are great differences among the models. Some models are more flexible and can be run with relatively small quantities of data, obviously reducing model performance (see for example TEMAS). Their relationship with the DCR is variable. Some models require all the data input from the DCR (for example EIAA, ECOCORP) whilst others do not. The reasons for the latter are diverse: Some models require data of sectors outside the scope of the DCR like environmental or regional indexes (COBAS for example), a number did not consider the DCR when developing the model (BEMMFISH
Survey of existing bioeconomic models Executive summary xi
and BIRDMOD), and others did not consider the DCR due to problems of relating the case study to the segmentation provided by the DCR (FLR-EFIMAS-), however, the FLR can be run with DCR data.
The “new” DCR is an improvement, due to the new segmentation provided (especially for fleet based economic data). In any case many of these models will require a lot of work to be conditioned before using the new data framework.
Survey of existing bioeconomic models xii Contents
Contents
1. Introduction...... 25 2. Bioeconomic models review in the literature...... 27 2.1 Results...... 27 2.1.1 Multiple-criteria decision-making ...... 28 2.1.2 Marine reserves ...... 28 2.1.3 Ecosystem approaches for managing fisheries...... 28 2.1.4 Capacity in fisheries ...... 28 2.1.5 Game theoretic models...... 28 2.1.6 New frameworks ...... 29 2.1.7 Social impact assessment ...... 29 3. Key Issues in bioeconomic models ...... 30 3.1 Objective of the tasks...... 30 3.2 Model orientation...... 31 3.3 Availability of stock assessment...... 32 3.4 Data availability...... 35 3.5 Bioeconomic indicators ...... 36 4. Key points to be reviewed...... 39 4.1 General description...... 39 4.1.1 Model objectives and dimensions ...... 39 4.1.2 Model structure...... 39 4.1.3 Types of advice and time range...... 40 4.2 Implementation details...... 40 4.2.1 Data requirements...... 40 4.2.2 Model language and platform characteristics...... 40 4.2.3 Producing advice ...... 41 4.2.4 Model use and full list of references ...... 41 4.2.5 Institute and key personnel...... 41 5. Review of models...... 42 6. Review framework ...... 45 6.1 Comparative tables for the review ...... 49 7. AHF ...... 62 7.1 General description...... 62 7.1.1 Model objectives and dimensions ...... 62 7.1.2 Model structure...... 63 7.1.3 Types of advice and time range...... 65
Survey of existing bioeconomic models Contents xiii
7.2 Implementation details...... 69 7.2.1 Data requirements...... 69 7.2.2 Model language and platform characteristics...... 70 7.2.3 Producing advice ...... 70 7.2.4 Model use and full list of references ...... 70 7.2.5 Institute and key personnel...... 72 8. BEMMFISH...... 73 8.1 General description...... 73 8.1.1 Model objectives and dimensions ...... 73 8.1.2 Model structure...... 73 8.1.3 Types of advice and time range...... 75 8.2 Implementation details...... 77 8.2.1 Data requirements...... 77 8.2.2 Model language and platform characteristics...... 77 8.2.3 Format of model output...... 77 8.2.4 Producing advice ...... 77 8.2.5 Model use and full list of references ...... 78 8.2.6 Institute and key personnel...... 79 9. BIRDMOD...... 80 9.1 General description...... 80 9.1.1 Model objectives and dimensions ...... 80 9.1.2 Model structure...... 81 9.1.3 Type of advice and time range ...... 82 9.2 Implementation details...... 84 9.2.1 Data requirements...... 84 9.2.2 Model language and platform characteristics...... 85 9.2.3 Format of model output...... 85 9.2.4 Producing an advice ...... 87 9.2.5 Model use and full list of references ...... 87 9.2.6 Institute and key personnel...... 89 10. COBAS...... 90 10.1 General description...... 90 10.1.1 Model objectives and dimensions ...... 90 10.1.2 Model structure ...... 91 10.1.3 Type of advice and time range...... 93 10.2 Implementation details...... 96 10.2.1 Data requirements ...... 96
Survey of existing bioeconomic models xiv Contents
10.2.2 Model language and platform characteristics...... 97 10.2.3 Format of model output...... 97 10.2.4 Producing an advice ...... 97 10.2.5 Model use and full list of references ...... 97 10.2.6 Institute and key personnel...... 98 11. ECOCORP...... 100 11.1 General description...... 100 11.1.1 Model objectives and dimensions ...... 100 11.1.2 Model structure ...... 100 11.1.3 Type of advice and time range...... 101 11.2 Implementation details...... 102 11.2.1 Data requirements ...... 102 11.2.2 Model language and platform characteristics...... 103 11.2.3 Format of model output...... 103 11.2.4 Producing an advice ...... 103 11.2.5 Model use and full list of references ...... 104 11.2.6 Institute and key personnel...... 105 12. ECONMULT ...... 106 12.1 General description...... 106 12.1.1 Model objectives and dimensions ...... 106 12.1.2 Model structure ...... 107 12.1.3 Type of advice and time range...... 108 12.2 Implementation details...... 110 12.2.1 Data requirements ...... 110 12.2.2 Model language and platform characteristics...... 110 12.2.3 Format of model output...... 110 12.2.4 Producing an advice ...... 111 12.2.5 Model use and full list of references ...... 111 12.2.6 Institute and key personnel...... 112 13. EIAA...... 113 13.1 General description...... 113 13.1.1 Model objectives and dimensions ...... 113 13.1.2 Model structure ...... 113 13.1.3 Type of advice and time range...... 115 13.2 Implementation details...... 117 13.2.1 Data requirements ...... 117 13.2.2 Model language and platform characteristics...... 118
Survey of existing bioeconomic models Contents xv
13.2.3 Format of model output...... 118 13.2.4 Producing advice...... 118 13.2.5 Model use and full list of references ...... 118 13.2.6 Institute and key personnel...... 120 14. EFIMAS ...... 121 14.1 General description...... 121 14.1.1 Model objectives and dimensions ...... 121 14.1.2 Model structure ...... 124 14.1.3 Type of advice and time range...... 127 14.2 Implementation details...... 131 14.2.1 Data requirements ...... 131 14.2.2 Model language and platform characteristics...... 133 14.2.3 Format of model output...... 133 14.2.4 Producing an advice ...... 134 14.2.5 Model use and full list of references ...... 136 14.2.6 Institute and key personnel...... 138 15. EMMFID...... 139 15.1 General description...... 139 15.1.1 Model objectives and dimensions ...... 139 15.1.2 Model structure ...... 140 15.1.3 Type of advice and time range...... 141 15.2 Implementation details...... 142 15.2.1 Data requirements ...... 142 15.2.2 Model language and platform characteristics...... 142 15.2.3 Format of model output...... 143 15.2.4 Producing an advice ...... 143 15.2.5 Model use and full list of references ...... 144 15.2.6 Institute and key personnel...... 144 16. MEFISTO ...... 145 16.1 General description...... 145 16.1.1 Model objectives and dimensions ...... 145 16.1.2 Model structure ...... 145 16.1.3 Type of advice and time range...... 147 16.2 Implementation details...... 149 16.2.1 Data requirements ...... 149 16.2.2 Model language and platform characteristics...... 152 16.2.3 Format of model output...... 153
Survey of existing bioeconomic models xvi Contents
16.2.4 Producing an advice ...... 153 16.2.5 Model use and full list of references ...... 154 16.2.6 Institute and key personnel...... 155 17. MOSES...... 156 17.1 General description...... 156 17.1.1 Model objectives and dimensions ...... 156 17.1.2 Model structure ...... 156 17.1.3 Type of advice and time range...... 158 17.2 Implementation details...... 159 17.2.1 Data requirements ...... 159 17.2.2 Model language and platform characteristics...... 160 17.2.3 Format of model output...... 161 17.2.4 Producing an advice ...... 161 17.2.5 Model use and full list of references ...... 162 17.2.6 Institute and key personnel...... 163 18. TEMAS...... 164 18.1 General description...... 164 18.1.1 Model objectives and dimensions ...... 164 18.1.2 Model structure ...... 165 18.1.3 Type of advice and time range...... 167 18.2 Implementation details...... 168 18.2.1 Data requirements ...... 168 18.2.2 Model language and platform characteristics...... 169 18.2.3 Format of model output...... 170 18.2.4 Producing advice...... 171 18.2.5 Model use and full list of references ...... 172 18.2.6 Institute and key personnel...... 174 19. SRRMCF...... 175 19.1 General description...... 175 19.1.1 Model objectives and dimensions ...... 175 19.1.2 Model structure ...... 176 19.1.3 Types of advice and time range ...... 178 19.2 Implementation details...... 179 19.2.1 Data requirements ...... 179 19.2.2 Model language and platform characteristics...... 179 19.2.3 Format of model output...... 179 19.2.4 Producing advice...... 179
Survey of existing bioeconomic models Contents xvii
19.2.5 Model use and full list of references ...... 180 19.2.6 Institute and key personnel...... 180 20. Others...... 181 20.1 ISIS- Fish...... 181 20.1.1 General description ...... 181 20.1.2 Implementation details...... 182 21. General references ...... 184 Appendix ...... 188 A. APPENDIX: Specific description of the models...... 189 A.1 Specific description: AHF ...... 189 A.1.1 Full specification of model equations...... 189 A.1.2 Full specification of model variables and model equations...... 194 A.1.3 Full list of model parameters...... 194 A.2 Specific description: BEMMFISH...... 197 A.2.1 Full specification of model equations...... 197 A.2.2 Full specification of model variables ...... 200 A.2.3 Full list of model parameters...... 201 A.3 Specific description: BIRDMOD...... 203 A.3.1 Full specification of model equations...... 203 A.3.2 Full specification of model variables ...... 209 A.3.3 Full list of model parameters...... 209 A.4 Specific description: COBAS ...... 211 A.4.1 Full specification of model equations...... 211 A.4.2 Full specification of model variables ...... 214 A.4.3 Full list of model parameters...... 216 A.5 Specific description: ECoCorp ...... 217 A.5.1 Full specification of model equations...... 217 A.5.2 Full specification of model variables ...... 221 A.5.3 Full list of model parameters...... 222 A.5.4 Model assumptions...... 223 A.6 Specific description: ECONMULT ...... 224 A.6.1 Full specification of model equations...... 224 A.6.2 Full specification of model variables ...... 225 A.6.3 Full list of model parameters...... 226 A.7 Specific description: EIAA...... 227 A.7.1 Full specification of model equations...... 227 A.7.2 Full specification of model variables ...... 233
Survey of existing bioeconomic models xviii Contents
A.7.3 Full list of model parameters...... 235 A.8 Specific description: EFIMAS...... 237 A.8.1 Full specification of model equations...... 237 A.8.2 Full specification of model variables and parameters ...... 242 A.9 Specific description: EMMFID ...... 244 A.9.1 Full specification of model equations...... 244 A.9.2 Full specification of model variables ...... 246 A.9.3 Full list of model parameters...... 246 A.10 Specific description: MEFISTO ...... 248 A.10.1 Full specification of model equations ...... 248 A.10.2 Full specification of model variables ...... 254 A.10.3 Full list of model parameters...... 255 A.11 Specific description: MOSES ...... 257 A.11.1 Full specification of model equations ...... 257 A.11.2 Full specification of model variables ...... 261 A.11.3 Full list of model parameters...... 261 A.12 Specific description: TEMAS...... 263 A.12.1 Full specification of model equations ...... 263 A.12.2 Full Specification of model variables ...... 263 A.12.3 Full list of model parameters...... 263 A.13 Specific description: SRRMCF ...... 281 A.13.1 Full specification of model equations variables and equations...... 281 A.14 Specific description: ISIS-FISH ...... 282 A.15 References used in the appendix...... 282
Survey of existing bioeconomic models List of tables xix
List of tables
Table 1. Model Type and Orientation ...... 32 Table 2. Links with the assessment when required...... 33 Table 3. Solutions to the cases that lack stock assessments (partly or completely)...... 34 Table 4. List of indicators that each model should provide ...... 37 Table 5. List of models considered ...... 42 Table 6. List of projects considered ...... 43 Table 7. Relationships between models and projects considered...... 44 Table 8. Generic table: General model features...... 49 Table 9. Specific Table: Biological model component...... 51 Table 10. Specific Table: Economic model component...... 53 Table 11. Specific Table: Linking economic and biological model component...... 56 Table 12. Implementation Table: Data requirements...... 57 Table 13. Implementation Table: Indicators ...... 58 Table 14. Implementation Table: Review of STECF tasks...... 60 Table 15. EFIMAS project case studies developed using FLR. Species and areas considered 122 Table 16. Dimensions of the models developed using FLR under EFIMAS...... 124 Table 17. Main classes and packages in FLR ...... 125 Table 18. Secondary stable packages in FLR...... 126 Table 19. Secondary not stable or under-development packages in FLR ...... 126 Table 20. Type of advice given by the models using FLR...... 128 Table 21. Regulatory measures and time scale. FLR...... 129 Table 22. Data required to initialise the model. FLR...... 131 Table 23. Relationship with the DCR. FLR ...... 132 Table 24. Requirements for applying the model. FLR...... 133 Table 25. Time requirements for running a model with one species and one fleet: an approximation. FLR...... 134 Table 26. Version of FLR evaluated...... 135 Table 27. Full list of references, including type of document, title and case study. EFIMAS.. 136 Table 28. Key persons and institutes for the models developed. FLR...... 138 Table 29. Performance measure indicators. MEFISTO ...... 148 Table 30. Species worksheet. MEFISTO ...... 149 Table 31. Cohort worksheet. MEFISTO ...... 150 Table 32. Recruitment worksheet. MEFISTO...... 150 Table 33. Interact worksheet. MEFISTO ...... 150 Table 34. Fleet worksheet. MEFISTO ...... 151
Survey of existing bioeconomic models xx List of tables
Table 35. Vessel worksheet. MEFISTO...... 151 Table 36. Market worksheet. MEFISTO...... 152 Table 37. Data required to initialise MOSES...... 160 Table 38. Five pairs of regime comparisons of the current TEMAS program...... 167 Table 39. Some of the many input tables in TEMAS ...... 169 Table 40. Full list of references, including type of document, and title for ISIS-Fish...... 182 Table 41. Parameters r and s for the Ricker stock–recruitment relationship and average recruitment, for plaice and sole in the North Sea...... 194 Table 42. Initialization data for the Dutch beam trawl fleet operating in the North Sea...... 195 Table 43. Economic variables. BIRDMOD ...... 209 Table 44. Biologic variables. BIRDMOD...... 209 Table 45. Economic parameters. BIRDMOD ...... 210 Table 46. Biologic parameters. BIRDMOD...... 210 Table 47. List of endogenous variables. COBAS ...... 214 Table 48. List of exogenous variables. COBAS ...... 215 Table 49. List of indices. COBAS...... 215 Table 50. List of endogenous variables. ECONMULT...... 225 Table 51. List of exogenous variables. ECONMULT...... 226 Table 52. List of indices. ECONMULT...... 226 Table 53. List of parameters. ECONMULT...... 226 Table 54. Types of stock-recruitment relationship. MEFISTO...... 249 Table 55. Groups of costs. MEFISTO...... 252 Table 56. Exogenous and endogenous variables. MEFISTO...... 255 Table 57. Full specification of the subscripts. MEFISTO...... 255 Table 58. Full list of model parameters. MEFISTO...... 256 Table 59. Types of labour contract in Italy ...... 258 Table 60. Full specification of the exogenous and endogenous variables. MOSES...... 261 Table 61. Full specification of the subscripts. MOSES ...... 261 Table 62. Full list of model parameters. MOSES ...... 262 Table 63. Tables in the stock input sheet. TEMAS...... 264 Table 64. The specific parameters for each of the four model choices. TEMAS ...... 265 Table 65. Tables in the fleet input sheet. TEMAS ...... 267 Table 66. Tables in the effort input sheet. TEMAS ...... 268 Table 67. Tables in the boats input sheet, which are actually used. TEMAS ...... 272 Table 68. Tables in the prices input section. TEMAS...... 274 Table 69. Tables in the economic section of TEMAS ...... 276 Table 70.Tables in the trip rules section of TEMAS...... 277
Survey of existing bioeconomic models List of tables xxi
Table 71. Independent variables in TEMAS...... 278 Table 72. Tables in the tuning section of TEMAS...... 278 Table 73.Tables in the observation input section of TEMAS ...... 279 Table 74. Tables in the technical measures section of TEMAS...... 279 Table 75. Tables in the harvest control section of TEMAS ...... 280
Survey of existing bioeconomic models xxii List of tables
Acknowledgements
The work has been carried out under the financial support of the Studies and Pilot Projects for Carrying out the Common Fisheries Policy of the European Commission's Directorate General for Maritime Affairs and Fisheries.
We would like to thank the review of the report provided by J.M da Rocha, Lee G. Anderson and the assistance and feedback of Ann Shriver.
Survey of existing bioeconomic models Acknowledgements xxiii
Abbreviations and acronyms
ACFM Advisory Committee for Fisheries Management AER Annual Economic Report CFP Common Fisheries Policy CLD Causal Loop Diagrams CPUE Catch Per Unit of Effort DCR Data Collection Regulation EP Extended Program (DCR) EU European Union GFCM General Fisheries Committee for the Mediterranean HCR Harvest Control Rule ICES International Council for the Exploration of the Seas IUU Illegal, Unregulated and Unreported MLS Minimum Landing Size HCR Harvest Control Rule MP Minimum Program (DCR) MPA Marine Protected Areas MSE Management Strategy Evaluation MSY Maximum Sustainable Yield MSVPA Multi Species Virtual Population Analysis OM Operating Model RAC Regional Advisory Committee SGECA STECF Sub Group on Economic Affairs RUM Random Utility Model SGMSNS Sub-Group on Economic Assessment SIA Social Impact Assessment STECF Scientific, Technical and Economic Committee for Fisheries SSB Spawning Stock Biomass TAC Total Allowable Catch TAE Total Allowable Effort VPA Virtual Population Analysis XSA Seasonal Extended Survivors Analysis ______See also Models Acronyms in Table 5. See also Projects Acronyms in Table 6.
Survey of existing bioeconomic models xxiv Abbreviations and acronyms
Methodology Followed
In order to systematically address the requirements outlined in the Terms of Reference the project has been divided into three different phases.
Phase 1: Project Inception
The main activities in the Inception Phase have been organizational. It has included a Kick-off meeting with the Commission and Consortium meeting held in Brussels on the 8th of October 2008. Minutes from this last meeting have been provided as an “unofficial” deliverable of it.
Phase 2: Analysis
This phase deals with the evaluation of the bioeconomic models with respect to their usability for assessing management options and feasibility in terms of data requirements. Divided into three tasks:
- Determine the main points to be reviewed.
- Review of the models (including the collection of the information and the design of the framework).
- Compiling the information.
Each model has been reviewed by an institute. It has been ensured that none of the developers of the models have done the review of the model that they have created. Afterwards, an overall review has been done by the entire consortium to anticipate any possible discrepancies. Subjectivity has been tried to be minimised, defining a clear scheme of how the review should be done and providing a commonly agreed review framework.
Phase 3: Reporting
This phase comprehends the reporting of the results obtained. It includes the production of an interim and a final report.
Before delivering this final report, a draft was produced. This draft report has been evaluated by two external reviewers and the corresponding unit(s) of the Commission. Their comments have been incorporated.
Survey of existing bioeconomic models Introduction 25
1. Introduction
Economy is one of the conditioning factors of fishing activity. It is not necessary for the resource to exist biologically, but there is an obvious economic interest in exploiting it. Economic analysis of the exploitation of natural resources applied to the fisheries is a relatively recent branch of economics. This speciality is known as bioeconomics, and has developed since the end of the 1950’s from the works of Gordon and Schaefer (Gordon 1953; Gordon 1954; Schaefer 1957).
There is a growing interest in using bioeconomic models as a tool for policy analysis to better understand pathways of development and to assess the impact of alternative policies on the natural resource base and human welfare. One of the potential benefits of these models is that one can get a better and more comprehensive indication of the feedback effects between human activity and natural resources. Modern computer power permits development of complex models far beyond what was possible only a few years ago. It has therefore become possible to make models that are theoretically more consistent and empirically more accurate.
The fundamental challenge of fisheries management is to take into account both the conservation of the resource base as well as the exploitation of it by the industry (extractive and processing sectors). In doing so conceptual models have been extensively used.
A conceptual model is a theoretical construct that represents a system, with a set of variables and a set of logical and quantitative relationships between them. They are constructed to enable reasoning within an idealized logical framework about these processes. Conceptual models and in particular bioeconomic models (integrating biological processes and industry behaviour) have played an important role in exploring diverse issues in fisheries management for the last 40 years. In fisheries there is an extensive range of models that provide a comprehensive impact assessment of different management alternatives as asked by the CFP (CR 2002). Each one has specific characteristics, making the selection of the model for a particular assessment a difficult task.
In this report some of these models are reviewed. This review has been structured as a guide with a double objective:
• To provide a model selection guide given the assessment to be made.
• To provide a tool to evaluate an assessment performed with a specific model.
These are two ambitious objectives. Firstly the aim is to provide a tool to select the model. In that sense managers should consider what are the species, fleet(s) or fisheries that they want to
Survey of existing bioeconomic models 26 Introduction
analyze, what is the exact analysis that they want to make, availability of data, etc… Based on all this information the guide must aid in selecting the appropriate models available for the requested purpose and in determining the necessities required, in terms of financial costs, human skills, etc.
From another perspective and as a second objective, this report should be able to provide a tool to critically review any kind of assessment made using one of the models reviewed, giving the main check points to analyze and the references of where to find model documentation that has to be used to follow the analysis performed.
In any case and for both objectives this report should be treated as a reference. The information provided and used in each model review has been cited in each section, and can be used to analyze more deeply each specific model.
Survey of existing bioeconomic models Bioeconomic models in the literature 27
2. Bioeconomic models review in the literature
As a starting point for the bioeconomic model review, a search has been conducted for possible reviews of the same nature as this one, but outside the EU. The main objective of this search was to obtain inspiration from other sources, when making a review of operational bioeconomic models.
Three of the most important worldwide fisheries management mailing lists have been approached:
- Fish-folk (Massachusetts Institute of Technology),
- IIFET (International Institute of Fisheries Economics and Trade)
- NAAFE (North American Association of Fisheries Economists)
Furthermore an indexed search in ONEFISH (www.onefish.org) has also been conducted.
2.1 Results
Worldwide there is too huge a list of operational models to tackle the review of them extensively. There is also an extensive list of papers, reports and books dealing with the theory and applications of bioeconomic models. Of note is the seminal paper of (Smith 1969) and the most recent book (Grafton 2008).
With respect to reviewing bioeconomic models, unfortunately only three have been found, all in the EU. Firstly a review made by (Bjørndal et al. 2004) also including aquaculture. Secondly the STECF reports (SEC 2006c) and (SEC 2006a) in which a review of some EU operational models was made. In fact this report has also been referenced in the proposal of this review and is well known by the potential target readers of it. A recent review of Nordic fisheries operational models has been found in (Tjeerd-Boom et al. 2008).
Given these limitations, it is also necessary to say that there have been attempts to provide guides for bioeconomic modelling, as in (FAO 1998), where apart from a review of the bioeconomic theory, the ALLOC operational model was reviewed and explained. A review of models (case specific models) was also made in (Conrad 1995), and there was also a review of bioeconomic models with environmental influences performed in (Knowler 2002).
In terms of the approaches and or topics inside the bioeconomic modelling literature of fisheries there are some reviews focusing on specific model features that have to be mentioned:
Survey of existing bioeconomic models 28 Bioeconomic models review in the literature
2.1.1 Multiple-criteria decision-making
Multiple-criteria decision-making (MCDM) is a technique to assist fishery managers in addressing conflicting goals as well as in providing a framework for devising proper decision support system. A first review of MCDM can be found in (Mardle and Pascoe 1999) and more recently an update of it in (Leung 2006).
2.1.2 Marine reserves
A marine reserve may be defined as a spatial area where some, or all, species receive long-term protection from harvesting. Reserves may exist in certain locations because of natural or physical features, but are also imposed as part of the management of marine resources, that is as a tool of fisheries management, which has lead to an extensive literature on the bioeconomic modelling of them. A review of these models can be found in (Grafton 2004).
2.1.3 Ecosystem approaches for managing fisheries
A comprehensive review of methods available for assessing the impacts of interactions between species and fisheries and their implications for marine fisheries management can be found in (Plagányi 2007).
In this report the following models are reviewed: ECOPATH with ECOSIM (EwE), ERSEM, SSEM, IGBEM, BM2 ATLANTIS, SEPODYM/SEAPODYM l, MRM, ESAM (Extended Single-species Assessment Models), MSVPA approach, MULTSPEC, BORMICON, GADGET, OSMOSE, INVITRO, CCAMLR, KPFM (Krill-Predator-Fishery Model), EPOC (Ecosystem Productivity Ocean Climate Model), Mori Butterworth multi-species, and SMOM.
Apart from the models review in the report some recommendations for moving forward in the development of multi-species and ecosystem models are also given.
2.1.4 Capacity in fisheries
Capacity and their relationship with fishing possibilities is an outstanding issue in fisheries policy. A full review of models and approaches can be found in (Pascoe 2003). Also even if not finalized yet the European project CAFE (contract nº: 022644) can give examples of applications of operational models to deal with this issue.
2.1.5 Game theoretic models
Understanding the cooperative and non cooperative behaviour of fishermen and their response to implantation of different management tools has been also covered by game theory. A review of game-theoretical fisheries models can be found in (Sumaila 1999).
Survey of existing bioeconomic models Bioeconomic models in the literature 29
2.1.6 New frameworks
Apart form the classical mathematical bioeconomic modelling there is a new current of assessing bioeconomic analysis in fisheries based on interactive tools such as Dynamic systems. A good example could be a system developed by the MIT (http://www.albany.edu/~potto/GCDC/index.htm) or an application for European fisheries in (Stouten et al. 2008).
2.1.7 Social impact assessment
Social impact assessment is an applied social science framework developed to systematically identify and address the potential social consequences of a wide range of anticipated actions. It has developed in some respects as a social equivalent of environmental impact assessment. In terms of fisheries it has been extensively reviewed in (Pollnac et al. 2006).
Survey of existing bioeconomic models 30 Key issues in bioeconomic models
3. Key Issues in bioeconomic models
3.1 Objective of the tasks
Model selection has to be done according to the task to be evaluated.
• General evaluations of the sector.
• Evaluation of specific applied regulations.
• Evaluation of management alternatives.
In that sense each model has been developed for a specific task. It implies that sometimes it cannot handle some specific regulations, and the model has to be developed further (if possible) or a different one must be selected.
In this report tasks of the different STECF subgroups have been explicitly considered, using the different terms of reference of each sub-group in the last two years. In particular the tasks considered are:
• Evaluation of management plans: The possibility that the model provides of dealing with the evaluation of a management plan, in whatever the way it is formulated (HCR, technical measures or effort limitations).
• Evaluation of HCR: If the model is capable of providing economic assessment for generic HCRs.
• Ecosystem based management economic evaluation: If the model considers an ecosystem approach both in terms of inputs and outputs.
• Marine protected areas: If the models can economically asses the implementation of an MPA.
• Discards. If the model can provide economic assessment of discards reduction programmes.
• Annual economic report. If the model provides economic assessment given the structure of the AER.
• Control system. If the model provides economic assessment for different enforcement scenarios.
• Illegal Unregulated and Unreported Fishing. If the model considers this issue and can provide economic assessment.
Survey of existing bioeconomic models Key issues in bioeconomic models 31
The implementation details of each model review present a more specific description of what the model can produce, and not only related to this list.
Beyond the advice that each model can provide some otherconsiderations have to be considered for the selection of the model:
• Model Orientation, in terms of if the model is output or input driven, but also if it is a simulation or optimization model.
• Availability of stock assessment, and the characteristics of the fishery (single or multi- species).
• Data availability. Some models are clearly much more complex than others but also represent a high data demanding characteristic. It has to be considered that some tasks do not always require a very complex model, or that immediate data availability can be limited.
• Bioeconomic indicators that the model produces.
The next sections describe more precisely these issues.
3.2 Model orientation A model is considered as output driven when the total production is considered as a restriction (TACs and quotas).
A model is considered as input driven when the total inputs are considered as a restriction (effort).
Simulation and optimization are different conceptual approaches. Optimization requires finding an optimal solution (maximization of profits, incomes, welfare, or minimization of costs or social costs) given a set of boundaries (constraints or parameter values). Simulation relies also on the same set of boundaries and parameter values but it can be seen as a set of rules that determines the dynamic consequences of a fishery.
Optimization models are normally constructed by using inequalities instead of equalities. In that sense the restrictions define a flexible area and given a pre-defined objective a solution is found within this area.
Survey of existing bioeconomic models 32 Key issues in bioeconomic models
Table 1. Model Type and Orientation
Model Type Orientation
AHF Simulation Input / Output BEMMFISH Simulation Input
BIRDMOD Simulation Input COBAS Simulation Input EcoCoRP Simulation Input / Output ECONMULT Simulation Output EIAA Simulation Output Optimization EMMFID Output or simulation FLR Simulation Output MEFISTO Simulation Input MOSES Optimization Input SRRMCF Optimization Output TEMAS Simulation Output
There are also other types of models known as endogenous optimization models (Arnason 2000). These models consider that an agent’s behaviour is not predetermined by exogenous behavioural rules. In any case these types of models are extremely computationally demanding and none of the models reviewed can be considered of this type.
Table 1 presents the type of model and orientation of each of the models reviewed.
3.3 Availability of stock assessment Bioeconomic models in fisheries can focus on a single species or multi-species (also in sequential fisheries) depending on the assessment they are provided. In either case, they require at least some measure of the stock(s)’ evolution. The following situations should be distinguished:
1. Stock assessment is available for the main species.
2. Stock assessments are available for some of the species
3. No stock assessments are available.
Situation 1 is desirable because it allows a deep (but not always complete) understanding of the stock; however, such assessments are not commonly available. Approaches to the treatment of the “biological box” differ greatly among models and range from simple surplus models to highly developed age-size structured models (normally based on VPA).
Survey of existing bioeconomic models Key issues in bioeconomic models 33
There is a large diversity among the models reviewed. Some of them are oriented towards giving advice for a single, primary stock and thereby largely ignoring other catches. Other models treat all stocks equally and require the same amount of input data for all species.
Another important issue is the link that each of the models have with the system that provides biological assessment. The most complex link is the direct one, that is, when the model incorporates the assessment of the stocks. The main advantage of this kind of model is that it may incorporate all the uncertainties arising from the biological box.
A less complex case is when the model requires the assessment of the stock but only to parameterize the “stock box”. The main advantage is that many sources of information can be used for doing so. These two cases are described in Table 2 as based on stock assessment.
Finally there are other models that parameterize the stock box just using a small quantity of data such as landings, effort or an initialization biomass. In this case it is said that the model is not based on stock assessment even if, for obtaining an initialization biomass a preliminary assessment could be required.
In Table 2 the relationships between the models and the assessment required is presented. Each model is reviewed in terms of whether an assessment model is included or not, whether biological input data is required, and the link with the assessment.
Table 2. Links with the assessment when required
Based on an Model Biological data required Link with assessment assessment model
AHF Yes ICES WG, ACOM incl. TAC Direct BEMMFISH No Biomass, Growth rate No
BIRDMOD No See section 9.2.1 - COBAS No See section appendix A No MSVPA runs (ICES) EcoCoRP Yes Parameters obtained See appendix A ECONMULT No Biomass No EIAA Yes Biomass TAC EMMFID No No endogenous biological component - EFIMAS Yes ICES, ICCAT or GFCM Direct One or more main species modelled MEFISTO Yes through VPA with optional S-R Parameters obtained relationship MOSES No See section 17.2.1 - SRRMCF No Catch and TAC - TEMAS Yes See Section 18.2.1 Parameters obtained
Survey of existing bioeconomic models 34 Key issues in bioeconomic models
Table 2 shows that there is a clear distinction between Atlantic and Mediterranean oriented models. FLR based models (EFIMAS and AHF) are based on previously provided assessments. The source and type of these assessments are case dependent, being the main sources of assessments ICES, ICCAT and CFCM. The link between the bioeconomic model and the assessment is direct in the sense that the assessment itself is directly included in the model. TEMAS does not directly require an assessment, but does require the estimation of typical biological parameters. EcoCoRP has similar requirements, but in its case the assessment is based on the MSVPA. The EIAA model requires an assessment, but the link with the bioeconomic model is made through the TAC advice and SSB. MEFISTO is the exception for the Mediterranean oriented model since it requires biological assessment.
The rest of the models including those oriented towards the Mediterranean are not linked to an assessment. This does not necessarily imply that biological parameters are not required. In fact, BIRDMOD, MOSES and BEMMFISH require an important amount of biological data. Finally, EMMFID does not contain an endogenous biological model.
Situation 2, i.e. stock assessment available for part of the species, is the most common case, at least in Atlantic waters. It is usual in these models to assume that there is a relationship between assessment and the commercial value of a species, even if that is not always the case. This holds especially true for models of the Mediterranean, English Channel and in the Atlantic coastal waters.
Table 3. Solutions to the cases that lack stock assessments (partly or completely)
Model Solution
AHF Requires analytical assessment and TAC information (see EFIMAS) BEMMFISH Surplus models with interactions (limited to 4 species)
BIRDMOD Depends on the data availability COBAS Stocks can be modelled by age structured or surplus models depending on the availability of data EcoCoRP Predator and prey species are modelled using VPA ECONMULT Cohorts can be added without loss of generality. EIAA Landings value of non quota species EMMFID Landings EFIMAS Surplus models or stochastic realizations of past landings MEFISTO Function of the target species. MOSES No requirements to parameterise the model. Using catch and effort data the biological sub-model estimates parameter for surplus or age structured model. SRRMCF It uses catch of all the species from the previous year, to determine the catch composition. TEMAS The model is flexible in terms of the biological data. It is able to consider all the species (assessed or not) with a minimum set of data.
Survey of existing bioeconomic models Key issues in bioeconomic models 35
The situation is addressed in the models reviewed in several different ways. As noted above, some models do require a certain level of data for at least one of the species considered (MEFISTO) and others do have limitations in terms of the number of species that can be incorporated (BEMMFISH).
For those models requiring an assessment, the solution is to run a preliminary assessment of the stock by using the available data (some hake stocks in the Mediterranean and Nephrops in the Atlantic, for the case of EFIMAS).
There is one model in which even given that there are assessed stocks they have been treated as in Situation 3 (Northern hake, EFIMAS). That is, even if assessment of these stocks is available, in order to simplify the model, it has not been used.
Situation 3, i.e. no stock assessment available, is the least desirable situation, but one found in some of the bioeconomic models studied. Solutions to the problem are based on the use of surplus models (catch and a time series of effort are collated from which a dynamic projection model is constructed) or by dividing species in terms of main species and secondary ones, where the latter are just a proportion of the main species. Other possible solutions are random realizations of past abundances and landings. Table 3 summarizes the approaches considered.
As it can be seen from the table above, many approaches are applied. Some models do require the same treatment as the target species, but the normal solution is to use landings as a proxy to determine future catch compositions. The possibility of selecting the complexity of the model is another solution. Normally models allow using a simpler surplus model for the non-target species. In any case, this solution will require parameterization.
In all of the models analyzed some knowledge of the accompanying species is required. The least data demanding situation is the case in which only the landings composition of the fleets is required, even if these kinds of approaches assume that availability of these species is not limited and that the fleets will not change catch composition. In the short term this can be a reasonable assumption, but in the long term it can cause problems. A small improvement is to consider the catch composition as stochastic. This has been done in the Northern Hake EFIMAS model. Finally, the rest of the solutions are more data demanding and require previous estimations or at least an endogenous “biological box” that given a time series of effort and catches perform these estimations (MOSES).
3.4 Data availability
Bioeconomic-models have to be fitted to data in order to provide assessment. Data can be provided in different aggregation levels, in terms of fleet segmentations, seasonality and geographical aggregation. A model should be flexible enough to adapt to the exiting data (if
Survey of existing bioeconomic models 36 Key issues in bioeconomic models
provision is comprehensive enough), always within the limits of the statistical inference, even if a more detailed information will make models run at their best.
In the EU a common framework for fisheries data collection (known as DCR) was established (CR 2000) and two programs (MP and EP) were designed (CR 2001) (amended by (CR 2004)). In 2009 this program has changed to (CR 2008). With these regulations fisheries segmentations as well as parameters to be collected are defined.
These two DCR frameworks, “old” (CR 2004) and “new” (CR 2008), are currently simultaneously in force, not in terms of collection, but in availability of the data for future bioeconomic assessments.
Given the DCR frameworks the review of the bioeconomic models will be done not only noting which is the data needed for running the models, but also if DCR (old and new) and or its uses (for example, the TACs and quotas) provide enough data to run the models.
In any case it should be remarked that even if a model copes with the data provision made under the DCR it does not necessarily ensure that the fishery can be adequately conditioned. The reason is that the segmentation provided in the DCR may not correspond to the one required to define a certain fishery. It cannot be set to a general rule for these kinds of problems, but the review provides a clear signal of the problem, if it exists, as well as the nature of it.
Some models require pre-analysis or arrangement of the root data provided by the DCR. In that sense it will be commented in each of the models specifying the task that has to be done before running the model.
3.5 Bioeconomic indicators Generally an indicator focuses on a small, manageable, and telling piece of a system to give people a sense of the bigger picture. The dimensions of the indicators that have to be considered are:
• The sub-systems.
• The scale.
• The time scale.
In bioeconomic models there are four clearly different systems or “boxes”. The biological box which reflects the biological situation of the system, the physical box which measures the system in terms of physical capital (vessels…), the economic box which measures the economic performance of the system and the social box which represents the social dimension of the system.
Survey of existing bioeconomic models Key issues in bioeconomic models 37
The scale is different depending on the sub-system considered. It is common to give the biological indicators by stock, but bigger scales like ecosystems, regions, seas or areas can be used. For the rest of the indicators the scales vary from unit of effort (CPUE), vessel, metiers, segments, fleets, countries, ….
Finally the time scale is important for the indicators. It is crucial for the economic part, given that it is not the same to measure the result of an activity (short term) and the performance of an investment. The latter, by nature, has to be a long term indicator.
Following this division of the systems that a bioeconomic model should cover, the scale and the time scale of a list of indicators that a model should provide are shown in Table 4. Two remarks have to be made on this table:
Table 4. List of indicators that each model should provide
Indicator Scale Description Biological Indicators SSB > Bpa Stock Comparison: full reproduction capacity Blim < SSB < Bpa Stock Comparison: being at risk of reduced reproductive capacity SSB < Blim: Stock Comparison: reduced reproductive capacity F < Fpa Stock Comparison harvested sustainably. Flim > F > Fpa Stock Comparison at risk of being harvested F> Flim Stock Comparison harvested unsustainably. Catches Stock Shows the total landings and discards Physical Indicators Landings Stock Shows the total landings obtained Number of vessels Segment Shows the evolution of the number of vessels Shows the average production of each vessel in terms of weight Vessel Physical Productivity Vessel of landings. Indicates average production in terms of weight of landings for Capacity Physical Productivity Vessel each capacity unit (GT) of the vessels. Shows the average production in terms of weight of landings for Power Physical Productivity Vessel each power unit (HP) of the vessels. Economic Short term Prices Stock Represents the average market price of landings. Income Segment Represents the income obtained by the segment. A positive cash flow indicates that they have fully recovered the Gross cash flow Segment operational costs Indicates the total profits obtained by the whole of the vessel Gross profit Segment owners, once the operating costs have been deducted. Indicates the total profits obtained by the whole of the vessel Net profit Segment owners, once the operating costs and the depreciation cost have been deducted Expresses the Added Value that the segment in question Gross Added Value (GAV), Segment contributes to the national economy. This includes: salaries, profits, opportunity cost and depreciations. Social Indicator FTE Segment Indicator of employment in full time equivalents Crew share Segment Indication of the remuneration to the employees. Economic Long term The difference between the sum of the discounted cash flows Net present value Vessel/segment which are expected from the investment, and the amount which is initially invested Economic sustainability indicator. Comparing the investment Return of Investment Vessel/segment profitability rate with the profitability of a risk free investment As a ratio of the optimal value (net or gross profit) that is Remuneration of Biomass Vessel/segment obtained relative to the maximum value that can be obtained
Survey of existing bioeconomic models 38 Key issues in bioeconomic models
The first remark is that Table 4 is just a list of minimum, many others can be provided, and some of them can be substituted by a similar one.
The second remark is that the list of indicators that have to be considered could vary depending on the assessment they aim to provide (even decreasing this list). In that sense according to the list of STECF tasks this list should be increased at least using the following indicators:
• Evaluation of management plans: An indicator of biological and economic risk is important (it will require stochastic simulations). For example the indicator can be the number of times that the stock is outside a safety threshold or the number of times that a short term and long terms economic indicator (e.g., cash flow) are negative or the trend of a social indicator.
• Evaluation of HCR: Similar to the evaluation of management plans.
• Ecosystem based management economic evaluation: Indication of ecosystem performance (e.g, trophic level index,…)
• Marine protected areas: Similar to the evaluation of management plans.
• Discards. Shadow value of the biomass.
• Annual economic report. Break-even revenue.
• Control system. Enforcement levels required.
• Illegal Unregulated and Unreported Fishing. Enforcement levels required.
Survey of existing bioeconomic models Key points to be reviewed 39
4. Key points to be reviewed
The operational bioeconomic models are reviewed and evaluated with respect to, among other things, their usability for assessing management options and feasibility in terms of data requirements. In order to provide an overall structure for the reviews, it is necessary to review each model using the same procedures.
Generally, the list of important model features needed to be considered can be divided into two overall sections:
1. General model description
2. Implementation details
Within each of these overall sections, more specific model features should be considered. In that sense the appendix of this report shows a specific description of model equations, variables, etc. of each model.
Several different sources have been consulted in order to secure that all aspects relevant to perform a review are considered. The primary source of inspiration are the (SEC 2006c) and (SEC 2006a). However, other reports from the STECF working groups (SGECA, SGRN) have also been consulted.
The specific questions addressed will be the following for each section:
4.1 General description
4.1.1 Model objectives and dimensions
• The purpose of developing the model.
• Is the model a simulation (what-if) or optimization (what’s-best) model.
• Dimensions (e.g. fleets, species, area etc.) included in the model.
4.1.2 Model structure
• Main components of the model.
• If there are separate components for, for instance, the biological and economic procedures.
• The links between the components, for instance, how are the economic and biological components linked to provide management advice.
Survey of existing bioeconomic models 40 Key points to be reviewed
• The basic causality/flow in the model.
4.1.3 Types of advice and time range
• Types of regulatory frameworks (e.g. TAC, effort) that the model can consider.
• Variables (e.g. stock, days at sea etc.) that the harvest control rule directly influences.
• If the model assesses the impacts of technical measures (e.g. mesh size, closed areas, discards) and how.
• Uncertainty, is it considered? And if so, in which parameters.
• If sensitivity tests can be performed. And if so, how/which method.
• Any limit values for which the model cannot function.
• List of bioeconomic indicators (e.g. economic and biological) produced.
• Malleability of arguments in a dimension (e.g. number of fleets).
• Time range.
4.2 Implementation details
4.2.1 Data requirements
• Data needed to initialise the model.
• Requirements related to the current and forthcoming DCR.
• Necessity of making any estimations or data processing before the model can be applied.
4.2.2 Model language and platform characteristics
• Programming language.
• Sort of license in which the software is distributed. The cost of it.
• Format of model output.
• Format of the output produced (type of file).
• Processing of output in order to setup tables.
• Graphical output.
Survey of existing bioeconomic models Key points to be reviewed 41
4.2.3 Producing advice
• Time to run the model.
• Time to produce and disseminate the advice.
• Procedure/steps to run the model and produce an advice.
• Financial cost of producing an advice (e.g. cost of computer programs).
• Skills needed (e.g. economist, biologist, or programming).
4.2.4 Model use and full list of references
• Use of the model and the version of it.
• Outcome of this use.
• Validation of the results.
• Is the model generally well documented?
4.2.5 Institute and key personnel
• Key institute(s) and key persons.
Finally in the Appendix of this report a specific description of each model can be found. It includes a full specifications of the model equations, variables (endogenous and exogenous) and parameters (including their assumed value).
Answering this array of questions will be based on all available and relevant literature ranging from publications describing the theoretical foundation of the models to practical applications in relation to giving an advice on a specific issue. The latest available model will be reviewed, and if not this will be specified.
It is important to note that the review of each model will consist of factual information, and thus the inclusion of subjective considerations will be minimized.
Survey of existing bioeconomic models 42 Review of models
5. Review of models
The models considered for the review are listed in Table 5.
Table 5. List of models considered
Acronym Name
AHF The Dynamic Capacity Change Model BIRDMOD Methodological Support for a Bioeconomic Model of Population Analysis of Demersal Resources BEMMFISH Bioeconomic Modelling of Mediterranean Fisheries COBAS A Dynamic bioeconomic model of the fisheries of the South West to determine the costs and benefits of sustainable fisheries management EcoCoRP Economic effects of the cod recovery plan on the mixed fisheries in the North Sea. ECONMULT Bioeconomic multiespecies model of the Barnet Sea fisheries EIAA Economic Interpretation of ACFM advice EMMFID Economic Management Model of Fisheries in Denmark FLR Fisheries Library in R ISIS-Fish A mixed-fishery simulation tool MEFISTO Mediterranean Fisheries Simulation Tool MOSES Models for Optimal Sustainable Effort in the Seas SRRMCF Swedish Resource Rent Model for Commercial Fishery TEMAS A fleet-based bioeconomic simulation soft-ware for management strategies accounting for fishers behaviour
Apart from those, in the other section, an overview of the ISIS-Fish model has also been provided.
Most of these models have been created under the framework of different EU funded research projects or tenders and national or regional funded research projects. These projects are listed in Table 6 and the relationship between the models and the projects is explained in Table 7.
Survey of existing bioeconomic models Review of models 43
Table 6. List of projects considered
Acronym Name Coordination Web-page
Bioeconomic Modelling of BEMMFISH CSIC - Mediterranean Fisheries Imperial CAFE Capacity and Fishing Effort https://cafe.jrc.ec.europa.eu/home Collegue Evaluating alternative, participatory CEVIS management models for EU IFM http://www.ifm.dk/cevis/ fisheries Costs and Benefits of Control https://maritimeaffairs.jrc.ec.europ COBECOS U. of Iceland Strategies a.eu/web/cobecos/1 Creation Of Multiannual COMMIT CEFAS http://www.commit-fish.info/ Management Plans for Commitment Economic Assessment of European EAEF FOI - Fisheries Economic effects of the cod EcoCorp recovery plan on the mixed fisheries CEMARE - in the North Sea Operational evaluation tools for EFIMAS DTU-AQUA http://www.efimas.org/ fisheries management options Framework for the Evaluation of FEMS CEFAS Management Strategies Fisheries Independent Survey Based http://www.ifremer.fr/drvecohal/fi FISBOAT IFREMER Operational Assessment Tools sboat/ http://www.investinfishsw.org.uk/ IiFSW Invest in Fish South West main.html Judgement and Knowledge in JAKFISH CEFAS Fisheries including Stakeholders Precautionary risk methodology PRONE U. of Helsinki http://prone-fish.eu/ in fisheries Understanding the Mechanisms of UNCOVER VTI-OSF http://www.uncover.eu/ Stock Recovery
Survey of existing bioeconomic models 44 Review of models
Table 7. Relationships between models and projects considered
Model Project Section in this report
AHF EFIMAS 7 BEMMFISH BEMMFISH 8 BIRDMOD (1) - FISBOAT (Aladyn) 9 COBAS IiFSW 10 EcoCoRP EcoCoRP (Tender) 11 ECONMULT (2) 12 EIAA EAEF 13 FLR EFIMAS; COMMIT, CEVIS, (EFIMAS) FISBOAT, CAFÉ, PRONE, 14 JAKFISH, COBECOS. EMMFID EMMFID 15 MEFISTO BEMMFISH 16 MOSES - 17 TEMAS EFIMAS, UNCOVER 19 SRRMCF (3) 19 (1) Italian Ministry of Forestry and Agriculture Policies project. (2) Norwegian Research Council project. (3) Swedish Board of Fisheries project.
The following sections present the review framework and the reviews of each model.
Survey of existing bioeconomic models Review framework 45
6. Review framework
A review framework has been created in order to facilitate the comparison among the different models. It is based on several tables following the structure explained below:
• Generic table: Focuses on the general model features. (Table 8)
• Specific tables:
o Specific table 1: Biological model component. (Table 9)
o Specific table 2: Economic model component. (Table 10)
o Specific Table 3: Linking economic and biological model component. (Table 11)
• Implementation table:
o Implementation table 1: Data requirements. (Table 12).
o Implementation table 2: Bioeconomic Indicators (Table 13)
The review framework has been done in terms of specific questions that have to be answered using a Y-N-P scheme. That is: “Yes” (Y), when the model captures the specific issue, “No” (N) when the model doesn’t or “Possible” (P) when the model allows users to choose the related option.
In some cases the questions ask for a specific clarification, beyond a simple Y-N-P answer. In these cases an abbreviation of the answer is provided, while the meaning of each acronym is explained at the bottom of the tables.
The following subsection presents the tables. The ideas behind and reasoning for asking the selected questions are described.
In Table 8, general model features are compared. Concerning the general purpose of a model, we ask whether it has been built to answer “what’s best” questions (optimisation), “what if” questions (simulation), or for both purposes? In the case of an optimisation, what variable is optimised (maximised/ minimised)? Possible optimisation objectives include: maximisation of profit, value added, harvest, fish stock; minimisation of costs; optimisation of employment, number of fishing vessels, number of days at sea. In the case of a simulation model, the following questions are addressed and compared: Do the models allow us to compare results of different scenarios, such as: implementation of different management policies (e.g. effort reduction, HCR, MPA), changes in market prices, changes in natural extrinsic drivers (e.g.
Survey of existing bioeconomic models 46 Review framework
climate change), changes in economic indicators (e.g. changes in fuel price, employment, wages, discount rate)?
The number of fishing fleets, fish species, fishing areas and time steps included in a model define the model dimensions, and hence the level of detail of a model:
- Fleet dimension: Is a model based on fishing activity of individual vessels (dimension: Vessel = Yes), or does the model aggregate data and results on the level of fishing fleets (dimension: Vessel = No)? Furthermore, it is compared whether the models treat multiple fishing fleets (multiple fleets = Yes) or only one single fishing fleet (multiple fleets = No).
- Species dimension: The species dimension depicts the characteristics of the modelled fisheries. Do they target multiple species (mixed fisheries = Yes), or do they have only one target species?
- Area dimension: The area dimension defines the spatial resolution of a model. Does a model incorporate a spatial structure? If yes, space in a model is disaggregated into subareas. Additionally, a model might have been constructed specifically for a particular area. If yes, the area is specified.
- Time dimension: The time dimension defines the temporal resolution of a model. The time steps of the two model subcomponents, the biological and the economic components, might be different and are thus specified in the table.
The last question of the generic table refers to the generality of the model. Is it easily applicable and adaptable to other regions, other case studies (yes, possibly, no). This questions aims to judge the possibility (data availability, work effort, time) to parameterise a model so that it can be used in other regions, applied to other case studies.
Table 9 serves as a checklist concerning the biological features that the models cover. It is difficult to draw general conclusions as to the use of highly detailed, complex biological models. Quality does not necessarily increase with the amount of detail (e.g. the number of biological processes included), but it is case specific. Therefore, this table gives an overview of a number of biological processes/ parameters/ variables, and shows whether the models incorporate these biological features or not.
It can make a big difference whether fish population dynamics are based on fish age or fish length. The advantages and disadvantages of both approaches are disputed. Originally, stock assessments had been set up based on fish age. However, with ecosystem based management it might become more suitable to look at size classes of species instead of age categories.
Survey of existing bioeconomic models Review framework 47
Table 10, the specific table focussing on the economic model component, addresses three concepts/dynamics that play a central role in bio-economic modelling, namely fleet, cost, and price dynamics.
Fleet dynamics:
The behavioural system (defined by fishers’ choices) is the starting point of an applied economic analysis. Rules defining choices could be purely based on observations of historic choices. In this case, we talk of a Random Utility Model (RUM). On the other side, choices that determine fleet dynamics could be derived from microeconomic theory. The quality of the bio- economic model then depends on the quality of the theory, whether the theory takes into account the decisive/crucial and important processes and interactions and does not include spurious interactions. Generally speaking, deriving fleet dynamics from microeconomic theory is more flexible and robust than applying historical rules, using RUMs.
A model that includes the possibility to make behavioural choices needs underlying assumptions. An entry-exit rule represents such an assumption that determines thresholds of vessel activity or inactivity. It defines, for instance, minimum revenue or minimum profit that is needed for vessel operation. Similar assumptions can be made with respect to (re-) investment of capital.
Technological creep refers to the fact that fishing technology is continually developing and improving, so that fishing becomes more efficient. In general, efficiency can be increased by increasing capacity (more vessels), but it can also be increased through technological creep, keeping capacity constant but improving the vessels’ efficiency.
Cost dynamics:
Costs can be divided into fixed and variable costs. Fixed costs include investment capital, insurance and maintenance costs, rent. Variable costs include all the costs necessary to operate a vessel. All bio-economic models that are reviewed distinguish between fixed and variable costs.
If a model includes an endogenous cost function, one can distinguish between a static and a dynamic cost function. A static equation should be an equation where all variables (dependent and independent ones) are considered at the same time, while in a dynamic equation at least one variable is considered at a different time, for example an autoregressive process.
Price dynamics
Fish prices can be either constant or variable. If prices are variable, price dynamics can be a function of various factors, for example time, demand, or supply at the market.
Survey of existing bioeconomic models 48 Review framework
Table 10 also compares which models can test which type of management measure. Fisheries management measures can be divided into input and output limitations. Output limitations restrict the amount of fish that fishers can take out of the sea, by setting total allowable catch (TAC) quotas or minimal landing sizes (MLS). Input limitations could be restrictions on days at sea, fishing gear and/or area access.
Table 11 describes how the different models link the biological with the economic subcomponents. It also provides an overview of the different dimensions (fishery, area, time) of both subcomponents of each model and compares the models’ applicability to simulations over different time horizons (short/ medium/ long term).
Table 12 sheds light on data requirements. Does the old or the new data collection regulations ensure that the required data are available? Data collection according to the DCR is often used to initialise models. Which initialisations are necessary for the biological and the economic subcomponents?
Table 13, reviews the indicators provided by each model according to the list presented in Section 3 of this report (Table 4). In this case P (possible) stands for the possibility of constructing the indicator given the outputs provided.
Finally Table 14 provides a guide of STECF tasks (according to the tasks explained in Section 3 of this report) can be performed using each model. If it can be done, the type of preparatory work (data manipulation or model adaptation), if needed1, is explained as well as the resources required for perfoming the task.
1 A typical STECF subgroup of five working days has been considered as reference for the need of preparatory work.
Survey of existing bioeconomic models Review framework 49
6.1 Comparative tables for the review
Table 8. Generic table: General model features.
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORPECONMULT EFIMAS General purpose Optimisation model: Y(1) N Y N N N N Y Y N N N N - Profit Y(1) N N N N N N Y Y N N N N maximisation - Harvest Y(1) N N N N N N P P N N N N maximisation Y(1) LB LB - Other (specify) SD;NV N VA N N N N N N N N OTH NV LB Simulation model Y Y N Y Y Y Y Y P Y Y Y Y - management Y Y N Y Y Y Y Y P Y Y Y Y policies - price changes Y P N Y Y N P N P P Y Y (market) - natural extrinsic N P N N N N N N N N N Y Y(BC) drivers - economic Y Y N Y N Y Y Y Y N Y N Y indicators Y: Yes; N: No; P: Possible; (1): One module of the model applicable for that; SD: Sea days; NV: Number of Vessels; LB: Labour; VA: Value added; OTH: Others; BC: Baltic Cod case study.
Survey of existing bioeconomic models 50 Review framework
Generic table: General model features. (cont.)
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Dimensions included Fleet dimension: - vessel N N N N N Y N Y N N N N N - single fleet N Y N Y N Y Y P P N N N Y - multiple fleets Y Y Y Y Y Y Y Y Y Y Y Y Y Species dimension: - one target species N Y N P N Y(2) N N N N N Y - mixed fisheries Y Y Y Y Y Y Y Y Y Y Y Y Y Area dimension: - spatially structured? Y Y Y N N N N N Y Y N NAP Y (3) ICES DK ICES ICES - Specify if area-specific. Y(1) N N N N N Y BS N IV NS VII IV Time dimension: - Time step (biological) NAP Flex Year Flex MTH Flex Year Year N Year QTR NAP Flex DAY - Time step (economic) Year Flex Year Flex Year Flex Year Year N Year Flex Flex YEAR Generality - Adaptable to other regions Y Y N P P N Y P N Y P Y Y Y: Yes; N: No; P: Possible; QTR: Quarter; MTH: Month; Flex: Flexible; (1): One module of the model applicable for that. (2): One target and one secondary. DK: Denmark; NS: NS flatfish case study; BS: Barents Sea; NAP: Not applicable. (3) Baltic Cod case study and North Sea Demersal Flatfish case study.
Survey of existing bioeconomic models Review framework 51
Table 9. Specific Table: Biological model component.
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Age or size structured (specify) NAP Both Age N Age Age NAP P (Age) N Age Age NAP Both Based on stock assessment model Y P Y Y Y Y Y P N Y Y NAP Y What biological information is included - Stock recruitment dynamics Y(1) Y Y N Y Y Y P N Y Y NAP Y - Maturation Y(1) Y N N Y Y Y P N N Y NAP Y - Fecundity Y(1) N N N Y N N N N Y N NAP P - Growth Y(1) Y Y Y Y Y N N N Y Y NAP Y - Condition factors Y(1) Y N N N N N N N N N NAP Y - Food web dynamics Y(1) N N N Y N N N N N Y NAP N - Predation mortality Y(1) N N N Y N N N N N Y NAP N - Natural mortality Y(1) Y Y N Y Y Y P N Y Y NAP Y - Fishing mortality Y(1) Y Y N Y Y Y P N Y Y NAP Y - Migration Y(1) Y N N N N N N N N N NAP Y(2) - other (specify) Y(1) N N SI N N N P (DCD) N N N NAP DCD Y: Yes; N: No; P: Possible; (1): One module of the model applicable for that; DCD: Discards; SI: Species Interaction; NAP: Not applicable. (2) Baltic Cod case study and North Sea Demersal Flatfish case study.
Survey of existing bioeconomic models 52 Review framework
Specific Table: Biological model component. (cont.)
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Are uncertainties in biological information considered? - Stock recruitment dynamics Y(1) Y N N Y Y P P N N Y NAP Y - Maturation Y(1) N N N Y N N N N N N NAP P - Fecundity Y(1) N N N Y N N N N N N NAP P - Growth Y(1) Y N P Y Y N N N N N NAP Y - Condition factors Y(1) Y N N N N N N N N N NAP Y - Food web dynamics Y(1) N N N N N N N N N Y NAP N - Predation mortality Y(1) N N N Y N N N N N Y NAP N - Natural mortality Y(1) N N N Y Y N N N N Y NAP P - Fishing mortality Y(1) N N P Y N Y N N N Y NAP Y - Migration Y(1) N N N N N N N N N N NAP Y - other (specify) Y(1) CTY N N N Catch N N N N N NAP N Y: Yes; N: No; P: Possible; CTY: Catchability; (1): One module of the model applicable for that; NAP: Not applicable.
Survey of existing bioeconomic models Review framework 53
Table 10. Specific Table: Economic model component
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Fleet dynamics based on: - Random Utility Model N Y N N N N N N N N N N P - Micro economic theory Y Y Y Y Y Y Y Y Y P N Y Y Possibility to make behavioural choices, entry/exit rules P Y N Y Y Y Y Y N Y P Y Y
Can fishers choose entry/exit? (Y)(1) Y N Y Y Y Y Y N N P Y Y Net exit model (no new entrants) N Y N N N Y N N N Y N N N (re-)investment rules N Y N Y Y Y Y Y N N N N Y Options to (re)allocate effort Can fishermen decide to adjust: - target fish species Y P N N N Y Y N N N N N Y(2);P - fishing grounds N P N N N N N N N N N N Y(2);P - fishing gear N P N N N N P P N N N N Y(2);P Triggered by: - change in catch/ pop. dynamics Y N N N N Y Y Y N N N N Y(2);P - change in demand/ fish price Y N Y N N Y Y P N N N N Y(2);P - change in costs (like fuel price) Y N Y N N Y Y Y N N N N Y(2);P Is the possibility of Y P N Y Y Y P Y N Y Y N P technological creep included - Possibility to change capacity? Y P N N Y Y Y Y N N N N Y - Possibility to change efficiency? Y P N Y Y Y Y N N Y P N P Y: Yes; N: No; P: Possible; (1): One module of the model applicable for that; (2): Baltic cod case study (at metier level).
Survey of existing bioeconomic models 54 Review framework
Specific Table: Economic model component. (cont.)
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Costs – Type of costs considered Fixed costs Y Y Y Y Y Y Y Y Y Y Y Y Y Variable costs Y Y Y Y Y Y Y Y Y Y Y Y Y Cost function Static Y (DK) (function of vessel Y Y Y Y Y Y N Y Y Y Y Y N (NS) activity) Y SD: L Dynamic VC:L Y FC: L Y (NS): (specify, function of N N N Y N N N N N FC: (EF) VC:CAP time which variable?) NV: L,SDPV Y: Yes; N: No; P: Possible; EF: Effort; CAP: Capacity; SD: Sea days; L: Landings; VC: Variable costs; FC: Fixed Costs; SDPV: Sea days per vessel; DK: Danish case study; NS: North Sea flatfish case study.
Survey of existing bioeconomic models Review framework 55
Specific Table: Economic model component. (cont.)
EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Price modeL and price dynamics Are prices variable? Y P N Y Y Y Y N N P P Y Y Price dynamics vary with - demand curve Y Y N Y Y Y Y N N P N Y Y - time (specify) N Y N N N N N N N N N N - market: auction (a) or other (o) N N N N N N N N N N N N N Possibility to include imposed management measures on fleet dynamics (input or output limitations): - output limitation: TAC Y Y N N N N Y Y Y P Y Y Y - output: MLS N Y N N N Y Y P (NS) N N P N P - input: gear restriction Y(1) Y N N Y P N P N Y P Y Y - input: days at sea restrictions Y Y Y P Y Y Y Y Y Y Y Y Y - input: area closures N Y N N N P N P (NS) P Y N N P Possibility to include other external factors - Effects of changes in Y P N P Y Y Y Y Y N P N Y fuel price (cf. costs) - stock dynamics Y Y Y N N N Y P (NS) N Y Y Y Y (cf. biol. component) Are uncertainties in economic information considered - price estimates Y(1) N N P N Y P SA P N P N Y - cost estimates Y(1) N N P N N P SA P N N N Y - price elasticities Y(1) N N P N Y P I N N P N P - cost elasticities Y(1) N N N N N N I N N N N P - other? (specify) Y(1) N N N N N N N N N N P Y: Yes; N: No; P: Possible; (1): One module of the model applicable for that; NS: North Sea flatfish case study; SA: Sensitivity analysis, using variation of detailed price and cost information; I: Indirectly through highly detailed price, cost and catch per day information.
Survey of existing bioeconomic models 56 Review framework
Table 11. Specific Table: Linking economic and biological model component EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Link between fishing effort and fishing mortality Linear N Y Y N Y Y N N N Y Y P Y CD Other (specify) P N Exp. N N C.D. CD N N N CD CD Baranov Dimensionality of interaction/ information flow between economy and biology Fishery - on individual ship level N N N N N Y N N N N N N N - on metier N Y N N Y Y Y P N Y N N P - on fleet level Y Y Y Y Y Y Y Y Y Y Y Y Y Area: - Spatial resolution equal N Y Y N Na Y Y Y N N Y - Y for both components Time: - Temporal resolution equal Y Y Y Y N Y Y P (NS) N Y Y Y Y for both components - temporal resolution of Year Year Year Survey of existing bioeconomic models Review framework 57 Table 12. Implementation Table: Data requirements EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Data requirements covered by - Old DCR Y P N N Y P Y N Y N Y Y P - New DCR Y P P N Y P Y P Y Y Y Y Y - Not available Prior estimations needed? e.g. calculation Y Y N Y Y Y Y Y N Y Y Y Y of: - production function Y Y N Y Y N Y N N Y Y Y MSVPA - initialisation of biology Y Y N Y Y Y Y Y N Y P Assessment outputs - initialisation of Fleet Fleets Y Y N Y Y Y Y Y Y Y Y economy sizes Conditioning RS; EL; FS,UTR All other DR;SR EII Depends on - Other? (specify) N N N N N N N N NQS parameters CDP REE the model NFI Y: Yes; N: No; P: Possible; QTR: Quarter; RS: Relative Stability; EL: Elasticity; FS: Fleet Shares, UTR: Up-take Ratios; NQS: Non quota Species; NFI: Non Fishery income; EII: Environmental Impact Index; REE: Recreational effort Elasticity; SR; Stock-Recruitment; CDP: Cobb Douglas parameters; DR: Discount rate. Survey of existing bioeconomic models 58 Review framework Table 13. Implementation Table: Indicators EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Biological Indicators N (DK) SSB > Bpa N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) Blim < SSB < Bpa N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) SSB < Blim: N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) F < Fpa N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) Flim > F > Fpa N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) F> Flim N Y P Y Y(1) P Y N N Y N Y Y (NS) N (DK) Catches N Y Y Y Y(1) Y N N N Y N Y Y (NS) Physical Indicators Landings Y Y Y Y Y Y N Y Y Y N Y Y Number of vessels Y Y Y Y Y Y Y Y Y Y Y Y Y Vessel Physical Y Y N P N P Y Y P Y N Y P Productivity Capacity Physical Y N N N Y N Y P N Y N Y P Productivity Power Physical Y N N N N N Y P N Y N Y P Productivity Y: Yes; N: No; P: Possible; (1): Using Aladyn; DK: Danish case study; NS: North Sea flatfish case study. Survey of existing bioeconomic models Review framework 59 Implementation Table: Indicators (cont) EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Economic Short term Prices Y Y Y Y Y P N Y Y Y Y Y Y Income Y Y Y Y Y Y Y Y Y Y Y Y Y Gross cash flow Y Y P Y N P Y P Y Y (1) Y Y Gross profit Y Y P Y N Y Y P Y Y Y Y Y Net profit Y Y P P Y P Y P Y Y (1) Y Y Gross Added Value (GAV), Y N Y N N P Y P N Y (1) Y Y Social Indicator FTE N Y N N N P N P N Y (1) (1) Y Crew share N Y N N N Y N Y Y Y Y (1) P Economic/long term Net present value Y N P Y N P Y P P Y Y Y P Return of Investment Y N N N N N Y P P N (1) (1) P Remuneration of Biomass Y N P N N N N P P N (1) (1) P Y: Yes; N: No; P: Posible; (1): Not found in the documentation referenced. Survey of existing bioeconomic models 60 Review framework Table 14. Implementation Table: Review of STECF tasks EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Evaluation of Management plans Can be done? Y Y Y Y Y Y Y P Y Y Y Y Y Preparatory work Y Y Y Y Y Y Y Y Y Y Y Y Y (Data) Preparatory work Y Y N Y* Y N N Y Y* Y N Y Y (Model Adaptation) Resources E,P,(B) E,P,B,L E,P,B E,(P), B E, P E,B E,(B) E,P,L E E,P,B E or B E,P,B E,P,B Evaluation of HCR Can be done? Y Y Y Y Y Y Y P N Y Y Y Y Preparatory work Y Y Y N Y Y Y Y Y Y Y N (Data) Preparatory work Y Y N Y* Y N N Y Y N Y Y(2) (Model Adaptation) Resources E,P,(B) E,P,B,L E,P,B E,B, (P) E, P E or B E,(B) E,P,L E,P,B E or B E,P,B E,P,B Ecosystem BM economic evaluation Can be done? Y N N N N N N P N N P or N N P Preparatory work N Y Y Y (Data) Preparatory work N Y* Y Y (Model Adaptation) Resources E,P,(B) P,B,L P,B,L E,P,B MPA Can be done? Y Y N N N N N P Y Y N N P Preparatory work N Y Y N Y Y (Data) Preparatory work N Y Y Y* Y Y (Model Adaptation) Resources E,P,(B) E,P,B,L E,P,B,L E E,P,B E,P,B P: Programmer; B: Biologist; E: Economists; L: Licence; (1): Has been done in the North Sea Plaice Box study, but indirectly, based on assumptions of plaice behaviour, not based on spatial modelling. (2) It will depend on the HCR and the fishery to be analyzed; * It could require some model adaptation. Survey of existing bioeconomic models Review framework 61 Implementation Table: Review of STECF tasks (cont) EIAA TEMAS MOSES BEMMFISH BIRDMOD MEFISTO AHF EMMFID SRRMCF COBAS ECOCORP ECONMULT EFIMAS Reduction of discards Can be done? Y Y N N N N Y (1) N N Y N Y Preparatory work Y Y Y Y Y (Data) Preparatory work Y Y Y Y Y (Model Adaptation) Resources E P,(B) (E) P,B E,(B) P, L E,P;B AER Can be done? Y N P N N P N Y Y N N Y N Preparatory work Y Y Y Y Y Y (Data) Preparatory work N Y N N N Y (Model Adaptation) Resources E,(B) E E E, L E E, P, B Control systems Can be done? N N N N N N N N N N N N P (2) Preparatory work Y (Data) Preparatory work Y (Model Adaptation) Resources E, P IUU Can be done? N N N N N N N N N N N N P Preparatory work Y (Data) Preparatory work Y (Model Adaptation) Resources E, P P: Programmer; B: Biologist; E: Economists; L: Licence; (1): Not found in the referenced documentations; (2): Implemented for the Northern hake case study Survey of existing bioeconomic models 62 AHF 7. AHF 7.1 General description 7.1.1 Model objectives and dimensions The Dynamic Capacity Change Model, also known as the ‘AHF model’, evaluates the dynamic change in fleet capacity from one time period to the next, given expectations about future earnings from the fishery. The aim of the AHF model has been to construct a model that includes full feed back procedures between fish stocks and fishing fleets and that can work with two independent types of constraints such as output constraints in terms of TAC/quota and input constraints in terms of sea days and number of vessels. The model comprises aspects from models such as EIAA, TEMAS, BIRDMOD, MOSES, MEFISTO, and ECONMULT. Concepts such as recursivity and causality have formed strong basis for the model construction. The model and model report outlines bioeconomic models, which are designed to shed light on the efficiency of different management tools in terms of quota or effort restrictions given the objectives of the Common Fisheries Policy (CFP) about sustainable and economical viable fisheries. The complexities of biological and economic interaction in a multi-stock and multi- fleet framework are addressed and consistent mathematical models are outlined. The model has been developed as a contribution to the EFIMAS Project. Given the mixed nature of the management scheme aimed to be evaluated with the model, the model can switch between harvest and effort restrictions. This switching mechanism is an improvement of existing bioeconomic models of fisheries, which usually consider either harvest or effort control. One of the aims of the model is to assess the economic and biological effects of recovery plans under the new CFP, taking into account the causal relationship between quota restrictions set by the recovery scheme and the additional limitations on sea days. The model is a simulation (what-if) model simulating the economic behavioural response of fishing fleets to the economic result in previous years of the fishery with respect to entry- exit or investment-disinvestment in the fishery changing fleet capacity. The AHF models simulate a number of fleet segments exploiting a number of species. With an individual fleet segment is meant a collection of approximately similar (homogeneous) vessels, i.e. it is assumed that the vessels in the segment have approximately equal physical characteristics in terms of length, gear type, gross tonnages and engine power equalised to the average values of all vessels in the segment. The model has a biological and an economic Survey of existing bioeconomic models AHF 63 operation module, the former simulating the development of the fish populations (stocks) in question and the establishment of quotas each year, and the latter simulating the economic dynamics of the fleet, i.e. fleet catches and earnings, fleet effort, and capital investment / dis- investment. As such the dimensions of the model contain several species and stocks and several fleets. 7.1.2 Model structure Currently, several European fishing fleets are regulated through a combination of harvest and effort control. The two regulation schemes are interrelated, i.e. a given quota limit will necessarily determine the effort used, and vice versa. It is important to acknowledge this causality when assessing combined effort and harvest regulation systems. The AHF-model is a bioeconomic feedback model that takes into account the causality between effort and harvest control by switching back and forth between the two, depending on which is the binding rule. The model consists of a biological and an economic operation module, the former simulating stock assessment and quota establishment, i.e. the development of the fish populations in question and the establishment of quota each year, and the latter simulating the economic dynamics of the fleets, i.e. fleet catches and earnings, fleet effort, and capital investment/disinvestment. When harvest control is binding, catch is evaluated using the biological projection formula, whereas the economics-based Cobb–Douglas production function is used when effort is binding. Given the mixed nature of many management schemes considered, the model can switch between harvest and effort restrictions. This switching mechanism is an improvement of existing bioeconomic models of fisheries, which usually consider either harvest or effort control. The AHF model is a dynamic feedback model with annual time-steps. It switches between quota and effort restrictions on the fleet, depending on which control is binding. The model contains two dynamic bio-economical models, both of which are especially constructed to model the economic development of a number of fishing fleets, given the influence of different management procedures on the fish stocks. Both models cover the economic response of a number of fleet segments to the harvest of a number of fish stocks. The economic response is modelled through a selling/buying behaviour in the fishing fleet, i.e. whether capacity (number of fishing vessels) is bought or sold as a result of the economic outcome (entry/exit-model). The fleet responses are as such modelled through an investment/disinvestment function simulating economic behavioural response to the economic result in previous years of the fishery. The development of this investment/disinvestment object and how to link this to general bioeconomic fishery models in FLR has been the main aim of this work under EFIMAS. Survey of existing bioeconomic models 64 AHF As mentioned above several European fishing fleets are regulated through a combination of harvest and effort control. The two regulation schemes are interrelated, i.e. a given quota limit will necessarily determine the effort used, and vice versa. It is important to acknowledge this causality when assessing combined effort and harvest regulation systems. The causal relationship in the following diagram is based on TAC/quotas being the only harvest control rule (HCR). However, if the fleet effort fixed by an effort harvest control rule is lower than the estimated (required) to catch the TAC/quotas the model assess, it shifts to use the HCR-effort in the model calculations. Stock (Ns,y) in year y evaluated from observed Ns,y-1 and Fs,y-1 [Equation (7.2)] Species TACs in year y (Qs,y) evaluated from recovery plan and stock (Ns,y) (Equations (7.4) to 7.6)) Fleet quotas (qb,s,y) evaluated from TACs, country, and fleet shares (Equation (7.7) Fleet efforts (Eb,y) determined from quotas, fisher decisions and effort regulation rule (Equations (7.8) to (7.10)) Fleet landings (Lb,s,y) determined from fleet effort and quotas Equation (7.11) Final species catches at the end of year y (Cs,y) determined from fleet landings (Equation (7.12)) Final species fishing mortality rates for year y (Fs,y) determined from species stock and catches (Equation (7.2)) Figure 1. Diagram showing causality and relationships between main elements. AHF Therefore, the two models included in the AHF-Model consider two different management schemes, namely output control (quotas) in the AHF-Q model, and input (days at sea) control in the AHF-E model. It could be argued that both types of management should be included in the Survey of existing bioeconomic models AHF 65 same model, as a combination of these two schemes has been the situation for a number of EU fishing fleets since 20042, where regulation of sea days was introduced alongside quotas and limited entry in terms of number of fishing licences. It must, however, be stressed that output (harvest) and input (effort) controls are interrelated, i.e. a given harvest will necessarily determine the effort used or, correspondingly, a given effort used will necessarily determine the harvest. It is, thus, important to acknowledge the issues of causality and recursivity in the modelling process. In order to implement harvest and effort regulation as two independent management initiatives in the same model of a fishery it is necessary that they include and use constrained optimisation procedures or switching procedures as mentioned above. For reasons of interdependence between harvest and effort it is complicated to construct a model that accommodates control rules on harvest and effort at the same time in the same model. More precisely, it is difficult to construct analytical recursive models in which both harvest and effort control rules are included. Such models have to be constructed as “switching” models between harvest and effort controls. 7.1.3 Types of advice and time range The AHF-model can assess TAC systems, effort regulation systems, and the causality between effort and harvest control, i.e. combined effort and harvest regulation systems. The two dynamic bio-economical modules in the AHF-Model consider two different management schemes, namely output control (quotas), the AHF-Q sub-model, and input (days at sea) control, the AHF-E sub-model. The bio-economic quota management assessment module evaluates the dynamic response of a number of fishing fleet segments to harvest control rules (HCR). In this module harvest and stock growth is assessed through the well-known biological age-structured model (Mohn and Cook 1993). Discard is included in the model through the assumption that discard is proportional to total harvest. The harvest control rule is included through individual quotas for each fleet segment, and non-compliance catches are assumed to be proportional to the quotas. As mentioned above harvest and effort are interrelated, so in the quota module the number of sea days are calculated, given the HCR. Thus, the recursive flow of the module goes from fish stocks to fishing mortality rates to fishing effort, and the causality implies that total allowable catch is determined before the effort executed to catch the quotas is calculated, the AHF-Q sub- model. 2 Introduced in 2003, but not fully effective Survey of existing bioeconomic models 66 AHF The economic response of the fishing fleet segments to the HCR is modelled through an economic entry/exit function that evaluates how much the different fleet segments will invest/disinvest given previous years incomes from the fishery. The income includes profit from the fishery and possible decommissioning grants. The quota module comprises first an initialization3 of the model in the start year and secondly the dynamic recursive modelling for the following years. If harvest control rules such as TACs and sea days are used in combination and if sea days become binding it is necessary to change the causality of the model as the number of sea days is a function the TACs. In this case biomass and landings become a function of sea days, the AHF- E sub-model. To overcome the complexities of this model a simplified procedure has been applied taking into account the restrictions of both the TACs and the sea days. By use of a feed back loop the fleet segment shares and hence landings of the pertinent fleet segment are scaled down according to the sea days limitation in proportion to the number of sea days required to catch the TAC. The bio-economic management assessment module evaluates the dynamic response of a number of fishing fleet segments to effort control rules, expressed through control of sea days. In this module, harvest is modelled via the Cobb-Douglas economic production function, as a function of stock biomass and effort, where the latter is expressed through the total number of sea days for the fleet segment in question. Stock growth is evaluated through a combination of the well- known biological age-structured model (Mohn and Cook 1993) and the harvest given by the Cobb-Douglas function. The effort control rule is included through limitations on the number of sea days given to each vessel in the fleet segments. It is seen that in this module harvest is evaluated and limited through the allowed effort. Thus the recursive flow of this model goes from fishing effort to fish stocks and landing (catches), and the causality implies that effort in terms of sea days and capacity is determined before the landings. The economic response of the fishing fleet segments to the effort control driven fishery is modelled in the same way in both modules through the economic entry/exit function described above. The latter module comprises first an initialization of the model in the start year and secondly the dynamic recursive modelling for the following years. The same notation is used in both modules. 3 Sometimes also called calibration or conditioning. Survey of existing bioeconomic models AHF 67 The modules are very similar with the only difference being that in the AHF-E sub-model the number of sea-days is determined before the harvest is evaluated, while in the AHF-Q sub- model the number of sea-days is evaluated from the harvest. As mentioned above discard is included in the model through the assumption that discard is proportional to total harvest. The harvest control rule is included through individual quotas for each fleet segment, and non-compliance catches are assumed to be proportional to the quotas. The model does not directly evaluate different mesh sizes and selectivity parameters as direct input, and is not directly area disaggregated. The AHF-Model does not directly include observation and measurement error and uncertainty for the input parameters and variables. Neither does the model include process error and uncertainty directly. As such the model does not include parametric and structural error and uncertainty in the system. However, the AHF-Model is a simulation model. In principle, the model will be able to simulate the effect of errors and bias, by stochastic simulations. Stochastic simulation is simply to repeat the same calculations a large number of times, each time with new parameter-values drawn by a random number generator. The stochastic simulation requires specifications of probability distributions of those parameters which are considered stochastic variables. The principle is to perform simulation a large number of times and each time draw parameters and initial condition variables by random number generators and executes a simulation over a series of years. The parameters of the probability distributions of parameters are given as input. At the end it retrieves the results of all simulations and converts them into, for example, frequency diagrams of the given measure of performance evaluated given the input parameters and parameter settings simulated over. The distribution of output (i.e. the output distribution of the given Measure of Performance) from the many simulations produces uncertainty distributions (of different types) and error estimates for each of the processes. The AHF-model is a simulation model and sensitivity analyses can be directly performed by carrying out simulations using different parameter values and percentage deviations for all input parameters to the model. Also, sensitivity analyses can be performed through scenario analyses of different percentage deviations or values of input variables such as SSB, calculated effort through the Cobb-Douglas function, etc. In the implementation of the AHF-model for the North Sea flatfish fisheries (Hoff and Frost, 2008) several sensitivity analyses with the model was performed. Here sensitivity analyses were performed for recruitment to the stock(s), variable and fixed fleet costs, depreciation, and the scaling parameters β and γ in the CPUE relationship. Percentage deviations from the base case simulations of profits and SSB after 30 years obtained by varying the parameters were evaluated. Constant recruitment (R) was set to the 25% and 75% quartiles of the average Survey of existing bioeconomic models 68 AHF historical recruitment data. It was investigated how sensitive profit and SSB values after 30 years showed up to be to the values of R. Variable costs (G) in year 1 were set to 0.5× and 2× the base-case values, fixed costs (K) in year 1 was set to 0.5× and 2× the base-case values, the discount rate was set to 0.1 and 0.15 (compared with the base-case value of 0.05), and the cpue (U) scaling factor was set to 0.01 and 9, and the biomass (U) scaling factor to 0.1 and 1 from which it was investigated in separate sensitivity analyses how sensitive the profits by fleet segment and SSB by stock was after 30 years to the variation in each of these parameters. It is data demanding to initialize and calibrate the complete model as it requires both age- structured biological information as well as price, cost and production technology information on fleet segment level. Fleet segments are only allowed to exploit species for which they have recorded landings (production technology restriction). General TAC/quotas set to zero do not influence the functioning of the model. A minimum level at one for the number of vessels is included in the model, however, to allow vessels in a fleet segment to re-enter the fishery after having been driven out due to low profit. Based on an initial calibration the AHF-model produces projections of annual SSB by stock, annual profit by fishing fleet, annual effort by fleet with respect to the number of sea days, and finally the fleet capacity measured in number of vessels in each fleet. Further, the effort maximizing the annual profit is projected (Optim). In relation to effort: An example of a scenario evaluation with the AHF model is the ‘Min’ scenario, where the effort chosen by the vessels is set to the number of sea days necessary to take the most restrictive of the quotas of a mixture of species. In scenario ‘Max’, the selected effort is set to the sea days necessary to exhaust all quotas, and in scenario ‘Optim’, the average number of sea days per vessel is set to the number maximizing the average vessel (and therefore fleet) profit. The number of stocks and fleets, i.e. the number of arguments in a dimension, can be changed. The model is a dynamic feedback model with annual time-steps. It switches between quota and effort restrictions on the fleet, depending on which control is binding. The number of vessels in given year and in given fleet is based on previous years’ profit. The model can be run over several to many years and has as such no time limits. Simulations with projections over a 30 year scale have been performed. Survey of existing bioeconomic models AHF 69 7.2 Implementation details 7.2.1 Data requirements Initialization data used in the AHF-Model applied for the North Sea flatfish fisheries, i.e. for the Dutch beam trawl fleet operating in the North Sea. The input data to the model are all provided through or can be directly calculated from the data in the “new” DCR sampling schemes. Many of the input data are available from the AER. The fish stocks are initialized using cohort and catch data from the ICES stock assessment working group reports. Natural mortality and maturity rates are obtained from the stock annexes produced under the ICES stock assessment working groups in relation to the yearly working group reports. Stock-recruitment functions, e.g. Ricker stock–recruitment functions (non-linear least squares fits) or constant recruitment equal to the long term average values of R or other, has to be made based on the historical SSB (S) and recruitment (R) variations. These data R and SSB data for the stocks are obtained from the ICES stock assessment working group reports. Fishing fleet units have to be identified and defined. The AHF models simulate a number of fleet segments exploiting a number of species. With an individual fleet segment is meant a collection of approximately similar (homogeneous) vessels, i.e. it is assumed that the vessels in the segment have approximately equal physical characteristics in terms of length, gear type, gross tonnages and engine power equalised to the average values of all vessels in the segment. Fishing fleet units are initialized using data from AER. The price flexibility α in the price function has to be calculated and estimated or obtained from external sources. In case of the North Sea flatfish fisheries implementation price flexibilities has been obtained from work by the working group of the flatfish recovery plan (SEC 2006b). The parameter values of the Cobb–Douglas function can be obtained from (Danielsson et al. 1997) and (Eide et al. 2003). A value for the discount rate has to be used or estimated. In case of the implementation of the model for the flatfish fisheries in the North Sea a discount rate at of 5% was used. From a fisher’s perspective, this might bee too low, while from the perspective of society, it tends to be too high. The effect of an increase in the discount rate is that investments and disinvestments will be lower at a given profit, leading to less fluctuation in the capital stock over time. Survey of existing bioeconomic models 70 AHF Finally, the fleet share (percentage in relative stability) of the given TAC the fleet is fishing on for given stock has to be obtained. For EU relative stability coefficients are not published, but originate from a Council Resolution in 1976. 7.2.2 Model language and platform characteristics The model is implemented in FLR (Kell et al. 2007), which is a collection of tools in R for the construction of bioeconomic simulation models of fisheries and ecological systems (http://flr- project.org/doku.php). The software is open source and freely available from the internet. Output is provided in terms of R-files, including R-tables and figures and numbers in *.txt format. R is open source and freeware available from the internet. It must not be processed in order to setup tables and provides graphical output using the basic features in the R- environment. 7.2.3 Producing advice The time for running the model is highly variable. It takes from half an hour to several hours depending of the input data material size. The steps that have to be followed for producing the advice are: Calculate input parameters as described above an initialize the model with the above described parameters provided from e.g. ICES and STECF working groups. The costs for using and running the model will only be salary to the scientists running the model and providing the input data and calculation of input parameters to this. In order to run, condition and initialize the model needs skills as an economist or biologist with some background computer simulation knowledge. 7.2.4 Model use and full list of references The dynamic capacity change module has at present been used in three different settings: • www.efimas.org: EFIMAS CS2: Demersal Roundfish fisheries in the North Sea (see Hoff and Frost, 2006). • EFIMAS CS9: International Baltic cod fisheries (Bastardie et al. (submitted)). www.efimas.org • Danish Seiners catching Cod in the North Sea (EFIMAS DocuWiki and www.efimas.org). • Dutch Beam Trawlers catching Plaice and Sole in the North Sea. (Hoff and Frost, 2008). Survey of existing bioeconomic models AHF 71 In the two latter cases special emphasis has been put on modelling the combined quota and effort (sea days) control imposed in many European fisheries in the North Sea and the Baltic Sea. The models put special emphasis on that harvest (quota) and effort are necessarily interrelated, e.g. that a given harvest (quota) taken will necessarily determine the effort used, and correspondingly, that a given effort limit imposed will determine the harvest taken. Thus, one of the two regulations will always be the limiting factor in a combined quota-effort regulation system. The two last bio-economic models take this casual relationship between harvest and effort into account, and as such switch between quota and effort control depending on which is the limiting factor. The results have been validated through the EFIMAS ECONOWS work and expert group evaluation; also they have been tested in the case study groups through the case specific implementation under EFIMAS for North Sea flatfish and roundfish fisheries as well as in the Baltic Sea cod fisheries. The results for the flatfish fisheries have been published in a high ranking international peer reviewed scientific journal paper. The model has been documented through the www.efimas.org and EFIMAS DocuWiki case specific implementations, in the EFIMAS ECONOWS Report http://wiki.difres.dk/ efimas/lib/exe/fetch.php?id=efimas1%3Awp4%3Acs2%3Amain&cache=cache&media=efimas 1:wp3:3-6:econows:econows_draft_final_260608.doc as well as through the following publications: Anonymous, 2008. EFIMAS ECONOWS Final Report, 2008: Report from Economist Meetings, ECONOWS 2004-2008. EFIMAS Report June 2008. Available at EFIMAS DocuWiki, http://wiki.difres.dk/efimas; ECONOWS (2008). ECONOWS: Report from the Economic Workshops of EFIMAS. Hoff, A. and H. Frost. 2006. Economic Response to Harvest and Effort Control in Fishery. Fødevareøkonomisk Institut Report nr. 185, Copenhagen 2006. http://www.foi.life.ku.dk/ Publikationer/Rapporter/~/media/migration%20folder/upload/foi/docs/publikationer/rapporter/r apport_185.pdf. Hoff, A. and Frost, H. 2007. Modelling economic response to combined Harvest and Effort Control in Fishery. http://www.univ.brest.fr/gdr-amure/eafe/eafe-conf/2007/hoff_frost_eafe- 2007.pdf. Hoff, A. and H. Frost 2008 Modelling combined harvest and effort regulations: the case of the Dutch beam trawl fishery for plaice and sole in the North Sea. ICES Journal of Marine Science Advance Access published April 21, 2008. Survey of existing bioeconomic models 72 AHF Hoff, A., Frost, H. (2008). Modelling economic response to harvest and effort control in the North Sea cod fishery. Aquat. Living Resour., 21 (forthcoming). - and to more limited extent in: Bastardie, F., Nielsen, J.R., and Kraus, G. (Submitted). Management strategy evaluation framework for the Eastern Baltic cod fishery to test robustness of management against environmental conditions and fleet response scenarios. (Submitted ICES J. Mar. Sci.) Hamon, K., Ulrich, C., Hoff, A. and Kell, L. 2007. Evaluation of management strategies for the mixed North Sea roundfish fisheries with the FLR framework Presentation to MODSIM07 Conference, 10-13 December 2007, Christchurch, New Zealand with Hamon et al. peer-review publication in conference proceedings. 7.2.5 Institute and key personnel The key institute having developed the model and is main user of it is the Fødevareøkonomisk Institut (FOI), University of Copenhagen (KU), Denmark. The developers are A. Hoff and H. Frost being the key persons in relation to the model. The model has been furthermore been extensively used by DTU AQUA, i.e. the Danish Technical University of Denmark, National Institute for Aquatic Resources, Denmark (see e.g. www.efimas.org or Bastardie et al. (submitted)). Survey of existing bioeconomic models BEMMFISH 73 8. BEMMFISH 8.1 General description 8.1.1 Model objectives and dimensions The BEMMFISH 1.0 model (Bioeconomic Modelling of Mediterranean Fisheries) has been developed with the purpose of testing the effects of different fisheries policies and management measures for Mediterranean fisheries. The model aims at being sufficiently flexible to accommodate the realities of most Mediterranean fisheries, and include several species and their interactions, multiple fleets and gear types. The BEMMFISH 1.0 model may be seen as the first stage in an ongoing process towards a more complete and complex fisheries management program. The model is a simulation model designed to make projections of a set of biological and economic variables into the future according to a set of initial variables and parameters that represent the base bioeconomic conditions of the fisheries in the initial year. The model is dynamic simulating of the development of the two basic state variables in the model; biomass and the number of vessels in each fleet. The dimensions included in the model are fish species and fishing fleets. Biomasses evolve over time according to the biomass growth and harvests, and the number of vessels in each fishing fleet evolves according to investments undertaken by the fishing industry. The model is aggregated as fish stocks are specified as one biomass for each species and fishing fleets as a collection of identical fishing vessels. The model thus does not distinguish between cohorts of fish or different kinds of fishing vessels in each fleet. However, the model allows for up to 4 different fish species and 3 different fishing fleets. Three basic fisheries management control variables are included a) fishing effort by fleet and species, b) taxation of landings, and c) limitation on vessel numbers. 8.1.2 Model structure The BEMMFISH model contains two interacting sub-models; a biological sub-model, which includes the dynamics of the resource and its interaction with human activity in the form of fishing mortality, and an economic sub-model taking into account the dynamics of fleets, markets, and fishermen's behaviour. Fishing mortality provides the input from the economic sub-model to the biological sub-model. The outcome of the biological sub-model is fish catch, which in turns serves as input to the economic sub-model. Survey of existing bioeconomic models 74 BEMMFISH The model employs a number of biological and economic functions. These functions determine the following variables or relationships: 1) Biomass development including stock and growth functions (the growth functions include species interaction), 2) fishing mortality (including by- catch estimations), 3) harvest, 4) harvesting costs, 5) price formation, 6) profit calculation, 7) estimation of entry to or exit from the fishery, and 8) a function that models the dynamics of catchability. A set of biological and economic variables/parameters representing the base bioeconomic condition of the fishery must be defined for initial year. The parameters are fixed throughout the simulation horizon (e.g. growth parameters) or change dynamically according to the equations used in the model (endogenous variables). The parameters can be changed however if so desired in a new simulation. Three control variables; effort, taxation on landings and limitation on vessel numbers for each fleet, are also set for each simulation. The value of control variables can be changed by the user during the course of the simulation horizon. As explained above, the data set of a given scenario defines the initial conditions for both the biological and economic sub-models. Figure below shows the relationship between the main elements of the two sub-models. The model is run through a time horizon chosen by the model user. The fishing mortality at each time period (which is a result of effort and catchability) determines the fraction of the stock which goes into the economic sub-model (the harvest). It is assumed that catchability develops over time according to a defined endogenous technological progress depending in part on the economic result. Similarly, the stock develops in accordance with the initial stock, the defined growth parameters and the harvest. The harvest of “target” species and possibly also of secondary species together with the fish price determine the revenues and partly the costs. The costs depend furthermore on the fishing effort applied and economic controls, such as taxes. The costs can be split in fixed costs (independent of effort) and variable costs (dependent on effort and/or catch). In accordance with the net result of the difference between revenues and cost (profits or fisheries rent), the fishermen take decision on how to modify the fishing effort and thereby fishing mortality for the following time period, assuming that the behaviour is concerned with maximization of the profit. These investment decisions are reflected in the number of vessels in the following period. This modification can be further altered by the manager in the form of measure controls, such as effort control and control of the number of vessels. Survey of existing bioeconomic models BEMMFISH 75 Initial conditions, t = 0 Biological sub-model (stock Secondary Price i Catch t = t +1 Revenues Costs F Economic controls e.g. taxes Outcom e Fishermen’s behavioral Economic sub-model Control measures on effort and selectivity Adopted from Guillen, Maynou and Sole ( 2004). Figure 2. Diagram showing causality and relationships between main elements. BEMMFISH The BEMMFISH model does not directly link to any current biological models used for stock assessments. It has its own biological module, and may indirectly use some of the information in the current biological models. 8.1.3 Types of advice and time range The model does not take the utilization of harvest control in the form of TAC into consideration. It is adopted to the characteristic of the Mediterranean and therefore mainly concerned with controlling effort and numbers of fishing vessels. Harvest control rules can be implemented by setting the following control variables: • Effort for each species and fleet. It is possible to adjust effort for each species, fleets and time periods. • Taxation on landings for each species. The tax rate can be adjusted for each period. Limitations on vessel numbers for each fleet is the third control variable. Obviously, in order to have an effect on the outcome, the limitation for a particular fleet and time period must be lower than the number resulting from the fleet adjustment which is influenced by the profit for that same period. Survey of existing bioeconomic models 76 BEMMFISH Technical measures such as mesh sizes, closed areas and discards are not included as direct options for harvest control. However, selectivity changes can be included indirectly by adjusting effort, catchability and by-catch parameters, if the relationship between the parameters and the technical measures is known. Uncertainty can be taken into account by running the model several times with relevant changes in the selected parameters. In addition, it is possible to define the biomass variable, the stock, as a stochastic variable with 5 possible stochastic magnitudes. (± 1 %, ± 5 %, ± 10 %, ± 20 %, ± 50 %). Sensitivity test is not included directly, but again it can be conducted by running the program with changes in the concerned parameters and control variables. Each new version is easy and quick to perform. The model will function for all variable and parameter values that are acceptable by common mathematical rules. Clearly, positive values need to be given for the number of vessels, the effort, initial stock, initial catchability and most parameters in order to produce useful results. The main results produced by the model produces as basis for the advice are: • A project summary with the present value of profit for all species as well as the present value of the total tax revenue and the corresponding present value of the total fisheries (profit plus tax), the final biomass for each species and the final number of vessels in each fleet, • Development of stock and growth for each species over the time periods (in a separate spreadsheet), • The harvest for each species, fleet and time periods (in a separate spreadsheet), • Profit and present value for each fleet and time period, in a spreadsheet together with: • The size of each fleet is determined for each time period, measured by the number of vessels. The following arguments in a dimension can be changed: • Effort per species and fleet, • A total of up to 4 species and 3 fleets are feasible, • Tax rate per species and time period, • The starting number and maximum number of vessels per fleet can be selected, • The model can cover up to 100 time periods. Survey of existing bioeconomic models BEMMFISH 77 8.2 Implementation details 8.2.1 Data requirements The data needed to initialize the model are the number of species and fleets, the number of periods, effort for each species, tax on landings, vessel restrictions of each fleet and the discount rate. However, also all the different parameters as well as the initial stock and initial catchability must be specified for the model to yield meaningful results. Therefore it is necessary to make estimations of these parameters and calibrate them in the accordance with the model in order to obtain the results which are applicable to real situations. Default values are included for all parameters as well as the initial conditions, but these values do not necessarily produce useful results. Hence, the parameters need to be adapted to the specific situation. The data requirements do not relate to the “old” or “new” DCR. 8.2.2 Model language and platform characteristics The program is adoptable to Windows, Linux or Macintosh platforms as a JAR file written in Java Version 1.4. No specific license is needed in order to use the program as the program is free. Changes in the program will therefore require Java program skills. 8.2.3 Format of model output Each BEMMFISH model run is considered as a project. Each project requires input and it generates output. The basic biological and economic fishery specifications in a new project can be saved in a file. The results can be saved as a plain text file or an excel file. The output is not provided in any graphical form. 8.2.4 Producing advice The practical implementation of the model starts by initiating a new project. The user is guided through the steps which are needed to insert all the necessary data. During this part the user can load the fisheries specifications (the biological and economic data) from a data file created earlier, or to insert new specifications according to the steps given by the program. The last step is to set the control variables. The options are effort, tax on catch and vessel restrictions. At the beginning the controls are set to non restrictive values, i.e. they are not binding. When starting a new project, it is necessary to save the data, if it is to be used again for old projects. Hereafter the run time options of the project are set. The discount rate, the number of time periods, if the stochastic component is to be used the relevant stochastic magnitudes, the starting Survey of existing bioeconomic models 78 BEMMFISH values for the biomass and the size of the fishing fleets. Specific values can be attached to the different years for each control variable; effort, tax on catch and vessel restrictions, where relevant. It is fast and easy to run the model if the parameters have been pre-defined and tables with the results are produced right away. The model can be executed using an ordinary PC. The financial cost of producing an advice can be divided into three parts: The first part is related to getting the program. This is freely available and can be used immediately. However, if more fleets and species are required for the analysis, this will require reprogramming of the model, and thus be costly. The size of these costs is dependent on the level of changes. The second part is concentrated on estimating the parameters and subsequently testing the model for a specific situation. This would require knowledge and experience regarding the concerned fisheries both with respect to fishing economics and biology, but not any programming skills. This part is considered to be quite costly and time consuming, if no previous knowledge is available. The third part running the model and analysing the results and these are considered to be minor. The model in its present form is difficult to use directly for the management of fisheries and defining harvest control measures. A weakness of the model is that is relatively simple and only allows for up to 4 species and 3 fleets. Also, it is simplified in the sense that it is aggregated with respect to fish stocks. Another weakness is that the model is not developed to directly use real data, but needs adjustment to the real cases via estimation and calibration of the parameters. The model is useful for testing different scenarios such as the effect on the fish stock, profit or the fleet sizes by changes in effort, prices, tax rates on landings or vessel restrictions. 8.2.5 Model use and full list of references The BEMMFISH model has not been used with real data. The model is documented in the documents mentioned below, particularly in Arnason and Koholka (2004). The other two documents contain background information and the theoretical foundation. The references are: Arnason, R. and Koholka, A. 2004: Bemmfish 1.0. Manual and Functional Specifications. In Bemmfish Project. Third Progress Report. Section 8.3, p. 47-54. Arnason, R. and Koholka, B. 2003: Bemmfish 1.0. M-country, N-fleet, L-stock fishery: A modelling outline. In Bemmfish Project. Second Progress Report, 17 pp. Theoretical background for the Bemmfish model. Guillen, J., Franquesa, R., Maynou, F. and Sole,I. 2004: The Bemmfish Bioeconomic Model. In Proceedings from IIFET 2004, Japan. Survey of existing bioeconomic models BEMMFISH 79 8.2.6 Institute and key personnel The institute, Instituto de Ciencias del Mar, CSIC, Barcelona, has been responsible for the development of the model (which was one of the outputs of a larger BEMMFISH project) together with a number of partners. The project BEMMFISH project coordinator, Dr. Francesc Maynou, from the above institute, is the contact person regarding the model. Survey of existing bioeconomic models 80 BIRDMOD 9. BIRDMOD 9.1 General description BIRDMOD is a bioeconomic model that consists of four main modules: The biological module, the economic module, the management module and the state variation module. The biological module consists of two biological components. The main component is the Aladym model, which is used to simulate single species (target species) dynamically and a component to simulate the dynamics for the group of other species. This review will consider the general structure of the BIRDMOD model and its main biological component, the Aladym (Age-Length Based Dynamic Model) model. 9.1.1 Model objectives and dimensions The main objective of the BIRDMOD model is to simulate the effects of different management policies from a biological, economic and social point of view. The management measures considered are mainly restrictions on the fishing effort in terms of activity and capacity, but also technical and economic measures, such as variations in gear selectivity and introduction of taxes and subsidies. Dimensions of the BIRDMOD model: • Multi-gear. • Multi-species. • Fleet segments. • Fishing activity by month. Aladym is an age-length based biological simulation model. Objective of this model is to predict the effects of different fishing pressure scenarios on a single population. Aladym simulation model belongs to the group of “dynamic pool models”. The objective is to predict, through simulations, the effects of changes of biological (e.g. size at first maturity, growth, and recruitment), pressure (e.g. total mortality) and management (e.g. size at first capture, fishing activity) parameters on single fish population dynamics. The model uses the classical equations from fish population dynamics. This is planned to evolve at a very detailed time scale (1 month), using vectors by age and size and accounting for differences by sex in growth, maturity and mortality. Also the natural mortality can be modulated as a vector by age- length. This is an important issue for Mediterranean fisheries, where fish populations are exploited starting at an early stage in the fish life. Dimensions of the Aladym model: Survey of existing bioeconomic models BIRDMOD 81 • One species (population). • Selectivity of gear: two options for ogive parameters. • Fishing activity by month. • Time intervals: monthly. 9.1.2 Model structure The BIRDMOD model is arranged into the following four main modules listed above. The biological module simulates the evolution of the state of the biomass among the stocks exploited by the fishing activity. The economic module simulates the evolution of the state of the fleet within the geographic area of interest. The management module enables to reproduce the Public Administration’s intervention in the sector and measurement of the effects of the different management policies. The state variation module permits to draw the dynamic relations between the overall variables of the model by means of predetermined behaviour rules. The structure of the BIRDMOD model is presented in the figure below. Figure 3. BIRMOD model structure The ALADYM core-model consists of four parts (“steps”): 1. Input and initialisation: In this step an unbiased initial population is formulated by specifying the number of runs (i.e. 100), randomly varying recruitment, the growth and the size at maturity parameters according to the values and distributions specified by the user. 2. The start loop or “seed run” where the dynamics are formulated following the evolution of several cohorts at a monthly scale. The start loop runs for a number of years that is equal to a multiple of two sex life spans. Survey of existing bioeconomic models 82 BIRDMOD 3. The simulation loop which runs for a period required by the user. 4. Output generated from the simulation loop. The model calculates: 1. Total mortality and fishing mortality for males, females and the whole population or each month and year in the simulation. 2. Yield in tons per month. 3. Exploited and unexploited population per month by sex and by age. 4. Average length and age of exploited and unexploited populations per month. From the core-model, three complementary but independent tools have been derived: The quasi-deterministic dynamic tool Aladym-r. The tuning tool Aladym-z. The stochastic dynamic tool Aladym-q. The structure of the model is presented in the Figure 4. 9.1.3 Type of advice and time range Management measures implemented in BIRDMOD are mainly restrictions on the fishing effort in terms of activity and capacity, but also technical and economic measures, such as variations in gear selectivity and introduction of taxes and subsidies: • Variation in selectivity: among a maximum of four gears considered, the model considers a selectivity function for each gear. The input parameters that define this function can be modified by the user for a given year of the simulation. This permits the simulation of the management measures directed at increasing the selectivity of gears. Survey of existing bioeconomic models BIRDMOD 83 R A) Input and initialisation i.e. 100 runs w Se Mat M F Z N UN l UN UN … UN 1,1 1,2 1,m N 1,1 N 1,2 …N1,m S- UN 2,1 UN 2,2 N N B) 2,1 2,2 UN UN N N Start loop 3,1 3,2 3,1 3,2 UN UN ……… N N …… (seed run) 4,1 4,2 4,1 4,2 i.e. 20 years … … UN n,1 …UNn,m N n,1 …Nn,m t=1,n t=1,n t=1,n t=1,n Z = [Zt, j ] j=1,m M = [M t, j ] j=1,m F = [Ft, j ] j=1,m Y = [Yt, j ] j=1,m C) t=1,n t=1,n t=1,n Simulation loop N = [Nt, j ] j=1,m B = [Bt, j ] j=1,m SSB = [SSBt, j ] j=1,m i.e.40 years t=1,n t=1,n t=1,n UN = [UNt, j ] j=1,m UB = [UBt, j ] j=1,m USSB = [USSBt, j ] j=1,m D) OUTPUTS B SSB L age SS L SS age UB USSB U L U age USS L USSage Z SSB/USSB Y C L C age Figure 4. Scheme of the Aladym-r tool (from documentation) R=recruitment; w=individual weight; Sel=selectivity; Mat=maturity; M=natural mortality; F=fishing mortality, Z=total mortality; N=exploited population, UN=unexploited population, B=exploited biomass, SSB=exploited spawning stock biomass, UB=unexploited biomass, USSB=unexploited spawning stock biomass, S-R=stock-recruitment relationship, Y=yield, t=time, j=cohort. • Temporary withdrawal: this measure is implemented within the model in relation to a single fleet segment. Its implementation is structured into monthly levels and allows determining the maximum number of fishing days foreseen for each month and fleet segment. Survey of existing bioeconomic models 84 BIRDMOD • Permanent withdrawal: the model simulates this measure by means of a proportional reduction in the number of the vessels that use a specific gear. The percent variation on the gear specified by the user is applied on all the fleet segments that use the same gear. • Levy variations: this is an economic measure that can be implemented either as an increase or a reduction in the taxes imposed on the fishing activity. • Variation subsidies: as with the taxation, this is an economic measure whose implementation allows evaluating the effects of subsidy variations on the sector. The initial value is nil. • Moratorium: this measure represents a ban on a specific gear or the introduction of a ban on its use. • Gear suspension: this measure can be coupled with temporary moratorium. To strengthen its effect, this measure can be associated to a temporary withdrawal. Indeed, temporary withdrawal is implemented with reference to the fleet segment, whilst suspension concerns the gear. The simultaneous selection of both allows to simulate a withdrawal affecting a whole fleet segment and all those vessels using the prevalent gear of the fleet segment considered, though they belong to different fleet segments. Uncertainty can be considered within the biological module by using the Aladym-q tool. This tool deals with stochastic representation of some input parameters in order to evaluate corresponding distribution functions of output variables using a Monte Carlo approach. In this way indicators and reference points can be associated with a confidence interval. The documentation of the model does not specify a maximum or minimum time range, but the description of the Aladym model (biological) suggests 40 years of start loop and 20 years of simulation. 9.2 Implementation details 9.2.1 Data requirements Input parameters for BIRDMOD include: • Parameters of prices and costs functions. • Flexibility coefficients of the state variation equations. Input parameters to the Aladym-r model are: • Von Bertalanffy growth parameters by sex with associated variability, Survey of existing bioeconomic models BIRDMOD 85 • length-weight relationship parameters by sex, • maturity ogive parameters by sex (Lm50% and Lm25%-Lm75% range), • natural mortality by sex (a constant value or a vector), • seed values (minimum, maximum, ln-mean and ln-standard deviation) of recruitment by sex, • proportion of offspring entering the stock by month, • stock-recruitment relationship parameters or a vector of recruit numbers by month both with associated variability, • time elapsing from spawning to birth, • sex-ratio (female/total) of offspring, • Fmax by month (option 2) or from the model (option 1), • QZ by sex, • selection ogive parameters (2 options) of the gear used by the fleet (L50% and L25%-L75% range, D50% in case of the selectivity option 2), and • fishing activity coefficient by month (0, in case of absence of fishing activity). 9.2.2 Model language and platform characteristics Both BIRDMOD and Aladym are written in the R language and licensed as open source under GPL2. The data and parameters feeding the Aladym model can be entered using an Excel data sheet. This sheet is the same for the three tools: 1. The quasi-deterministic dynamic tool named Aladym-r. 2. The tuning tool Aladym-z. 3. The stochastic dynamic tool named Aladym-q. 9.2.3 Format of model output The final output of BIRDMOD is composed of the historical series simulated for the biological and economic variables included in the logical-conceptual pattern of the model. An overall evaluation of the management measure simulated by the model is also produced as output. The evaluation is performed by considering the general economic outcome. This is calculated in Survey of existing bioeconomic models 86 BIRDMOD terms of net profits, management expenses incurred by the public administration to impose the measure, and social costs due to the measure adopted. The social costs are considered exclusively in terms of variation in the number of workers employed in the sector. The result is then compared to the biological costs measured in terms of biomass variation. The outputs automatically produced by the simulations of Aladym-r can be summarised in the following items. Export data file (*.dou): 1. exploited and unexploited population by sex, per month and age; 2. exploited and unexploited biomass by sex, per month and age; 3. exploited and unexploited population of females belonging to the spawning stock per month; 4. total mortality Z calculated by the model for females, males and the whole population in each month and year of the simulation as follows: ⎛ ∞ ⎞ ⎜ ∑ N t, j ⎟ 1 j=1 ; Z = ln⎜ ⎟ t ∆t ⎜ ∞ ⎟ ⎜ ∑ N t+∆t, j ⎟ ⎝ j=2 ⎠ 5. exploited and unexploited biomass per month; 6. exploited and unexploited spawning stock biomass per month; 7. ratio between exploited and the unexploited spawning stock biomass per month; 8. average length and age of exploited and unexploited populations per month; 9. average length and age of exploited and unexploited spawning populations per month; 10. yield in tons per month; 11. average length and age of catches per month; 12. fishing mortality per month calculated as; ⎛ ∞ ⎞ ⎜ ∑ N t, j ⎟ 1 j=1 , F = ln⎜ ⎟ t ⎜ ∞ ⎟ ∆t F ⎜ ∑ N t+∆t, j ⎟ ⎝ j=2 ⎠ F 13. where N t+∆t, j is the number of survivors at the time t+∆t under the hypothesis that only fishing mortality is acting; Survey of existing bioeconomic models BIRDMOD 87 14. biomass of natural losses and total biological production per month. Plots per year of the outputs listed from items 4 to 13 are also produced. • Some other outputs are also made available to the user: • average length at age and age by sex; • natural mortality at age/length by sex; • weight at age/length by sex; • proportion of mature individuals at age/length by sex. 9.2.4 Producing an advice The model is designed to predict, through simulations, the effects of different fishing pressure scenarios on a single population, in terms of different metrics and indicators. Removals are simulated on the basis of the total mortality rate modulated using harvesting pattern and a fishing activity coefficient. Aladym can work in the absence of fishery-dependent data, although its predictive capability for real catch levels can be verified using information on commercial catches or fishing activity per month. The results of the simulation are stored into three export files (.din for inputs, *.dou for outputs, *.RData for the R workspace) and saved in the same directory where R is started using the basename of the input sheet. To give an idea of the running time, Aladym-r requires about 25 seconds (assuming 40 years of start loop and 20 years of simulation) with a Intel (R) Pentium (R) personal computer with a processor of 1.70 GHz and 1 GB RAM. The tool Aladym-z requires about 2.6 hours (assuming 40 years of start loop and 20 years of simulation) with a Intel (R) Pentium (R) personal computer with a processor of 1.70 GHz and 1 GB RAM. The software can be downloaded from the FISBOAT web-site, where also a detailed description of the input sheet for user help is available. 9.2.5 Model use and full list of references The BIRDMOD model has been developed as a part of a project financed by The General Directorate for Fisheries and Aquaculture (under the Italian Ministry of Forestry and Agriculture Policies). Survey of existing bioeconomic models 88 BIRDMOD The Aladym model has been developed and applied within the project FISBOAT. The project case studies scanned four different stocks across European waters in the demersal domains with different vital traits, stock histories and survey methodologies. The case studies were: red mullet in the central-southern Tyrrhenian Sea, hake in the Bay of Biscay and in the Aegean Sea, and cod in the Baltic Sea. The Aladym model has thus been used to test, through simulations, the consequences of changes of pressure parameters (mortality) and management strategy (e.g. fishing activity, size at first capture) on the fish population dynamics of the target stocks. The full list of references is presented below: Accadia,Paolo, Massimo Spagnolo, a bioeconomic simulation model for the Italian fisheries, IIFET proceedings, 2006 References of the Aladym model: Lembo G., A. Abella, F. Fiorentino, S. Martino and M.T. Spedicato (2007), Simulating population dynamics. Aladym model (v 08) – May 2007, Italy Lembo G, M.T. Spedicato (2006a). ALADYM – Age-Length based dynamic Model. In: Petitgas P. (coordinator). Fisboat Activity Report, Appendices: 113-114 p. Lembo P. and M.T. Spedicato (2006b). ALADYM Age-Length Based Dynamic Model. MEDITS Coordination Meeting, Kavala, Greece, April 2006. Lembo G., Spedicato M.T. (2006c). Red mullet assessment in the GSA 10 using Aladym simulation model. Sub-Committee Stock Assessment SAC-GFCM, FAO Headquarters, 11-14 September 2006. 6 pp. Lembo G., Spedicato M.T., Abella A., Fiorentino F, Martino S. (2006d). Aladym description (version 06). Fisboat Meeting, IJmuiden 06-10 November 2006, 11 pp. Lembo G., Spedicato M.T. (2006e). Aladym: un modello previsionale di valutazione e gestione per le risorse della pesca nel Mediterraneo. 5° Convegno Nazionale delle Scienze del Mare, promosso ed organizzato da CoNISMa in collaborazione con A.I.O.L., S.I.B.M. e S.It.E. Viareggio, 14-18 Novembre 2006. abstract. Lembo G., Martino S., Abella A.J., Fiorentino F. and M.T. Spedicato. 2007. ALADYM (Age- Length Based Dynamic Model): a stochastic simulation tool to predict population dynamics and management scenarios using fishery-independent information. Scientific Advisory Committee- Sub-Committee Stock Assessment. Workshop on trawl survey based monitoring fishery system in the Mediterranean, Rome, Italy, 26-28 march 2007. 6 pp. Survey of existing bioeconomic models BIRDMOD 89 9.2.6 Institute and key personnel BIRDMOD has been developed by Paolo Accadia and Massimo Spagnolo. Their institute is IREPA Onlus, The main developers of the biological model Aladym are M.T. Spedicato and P. Lembo. Their institute is named COISPA. Survey of existing bioeconomic models 90 COBAS 10. COBAS 10.1 General description 10.1.1 Model objectives and dimensions COBAS model has been developed as part of the Invest in Fish South West (IiFSW) project from 2004 to 2006 by the Centre for the Economics and Management of Aquatic Resources (CEMARE) and the Centre for Environment, Fisheries & Aquaculture Science (CEFAS). The model simulates the interactions between fish stocks, the size and effort of the fishing fleet and regional output and employment within the South West. This is a dynamic simulation model designed to compare the effects of different management measures to the baseline of what is expected to happen if no action is taken. Therefore, it is not a forecasting model, but an “option comparison” model, where the effects of a particular policy are compared to the effects of the current management system. The COBAS or IiFSW model is aimed to evaluate the effects of different management options on the stocks, fishers and regional economy in terms of costs and benefits analysis. Therefore, the model answers the question “what happen if…” over a 15 years time horizon. The model is designed to include the areas of the English Channel, Celtic Sea and Western Approaches (i.e. ICES divisions VIId – VIIj). All key stocks and key fleets are considered in the model. Stocks are modelled by age-structured biological model or surplus production model depending on the data available for each species. Active fleets in the geographical areas under analysis are primarily of UK and French boats. As a consequence, data in the model are also segmented by country. Fisheries in the area are generally multi-species and multi-gear. Therefore, fishing activities, named metiérs, can be classified by country, gear used and area. The dimensions of the model can be listed as follows: • Country; • sub-fleet; • fishing activity (metier); • species; • age of individuals of a species; • year. Survey of existing bioeconomic models COBAS 91 10.1.2 Model structure The following description of the model structure is derived from the methodology report “DBM-WS A dynamic bioeconomic model of the fisheries of the southwest to determine the costs and benefits of sustainable fisheries management” of the Invest in Fish South West project produced by CEMARE and CEFAS. Comparing this report with the simulations outputs resulted from the project and available on the website www.investinfishsw.org.uk, few discrepancies arise. These differences are described at the end of this section. The key components of the model include the commercial fishing sector, the recreational sector and the regional economy. Moreover, the impacts of the fishing activities on the environment also are considered. The commercial fishing sector can be sub-divided in two different components, the biological and the economic ones. Model’s components interact each other and each endogenous variable is updated year by year. The bio-economic model COBAS includes a biological component where stocks are modelled by age-structured biological model or surplus production model depending on the data available for each species. Therefore this model is able to produce management advice in terms of both economic and biological indicators. The current biological models used for management advice cannot be used in combination with the COBAS model, but the effects of management measures on a set of biological indicators estimated by COBAS can be compared with the results produced by the biological models. The main components of the model can be listed as follows: • Commercial fishing sector: o biological component, o economic component; • recreational sector; • regional economy; • environment. The model’s components are interlinked. The output of a component is an input for the other components. The biological component is basically aimed to estimate the stocks dynamic. At each time step, this receives inputs from both the commercial sector economic component and the recreational sector. The levels of catches produced by the commercial and recreational fisheries have a direct impact on the stock surviving the year. Inputs to the biological component comes also from the environment as the status of the habitat can affect the natural processes associated to the Survey of existing bioeconomic models 92 COBAS recruitment, the growth and the natural mortality of the stocks. The outputs of the biological component are mainly directed to the economic component as a change in biomass will produce a change in the commercial landings in the next years. Changes in biomass will affect also recreational fisheries as the number of recreational angling trips is supposed to increase when biomass increases. The outputs of the economic component are not directed only to the biological component, but also to the environmental component as the “quality” of the fishing effort can have an impact upon habitat, cetaceans, and commercial and non-commercial by-catch. The effects on the environment depend on the fishing gears used. Therefore, changes in the composition and the level of fishing effort can produce changes in the status of the environment. This effect is estimated in the model by an ad hoc Environmental Impact Index (EII). Outputs from the economic component are directed also to the regional economy not only in terms of profits and incomes, but also as fishing costs and levels of employment. Regional economy receives inputs not only from the commercial sector, as described above, but also from the recreational sector. In particular, the fishing costs estimated in terms of travel costs in the recreational sector represent an input to the regional economy. Management operates directly on the economic component of the commercial sector via effort, estimated in terms of days at sea, fleet (number of vessels) and commercial catch. Management can be directed also to the recreational sector by the implementation of specific programmes. Figure 5 shows the dynamic relationships between the different components of the model. Each component consists of a number of variables represented in boxes. A box is a variable or a group of variables receiving inputs and producing outputs from and to the other boxes in the model. As reported above, some discrepancies exist between the methodology report and the output produced by the model in the IiFSW project. It seems that not all the links reported in Figure 5 are actually implemented in the model equations. In particular: The link between “Habitat” and the regeneration variables of the stock is not implemented. The environmental component consists in the EII index. Therefore, this is only a simulation output non producing any input to other model’s components. The regional economy is a simulation output as well. The project outputs do not show any dynamic interaction between this component and the model. Given the revenue and employment generated by commercial and recreational fishing, regional economy component estimates the total output and employment deriving also from fish processing, wholesale, retail, boat repair, etc. by a multiplier process. Survey of existing bioeconomic models COBAS 93 The model is designed primarily as an input driven model, where management measures impact directly on fishing effort in terms of days at sea and number of vessels. Other management measures can be simulated (see next section), but no management option on the recreational sector has been simulated in the project. Figure 5. Representation of the bio-economic model COBAS (IiFSW) 10.1.3 Type of advice and time range The model is to be used to assess a range of management options. Types of options can be divided in different groups based on the suitability to the model structure. A first group includes the options that can be readily modelled as these have a direct and measurable impact on specific parameters in the bio-economic model. Therefore, changing the values of the parameters, the following management options can be simulated: • days at sea limits (including tie-ups); • decommissioning schemes; • limits/bans of particular gear types; • restrictions on engine power, boat size, etc; • changes in TACs; • levies (e.g. management cost recovery, industry funded buyback); • price intervention. Survey of existing bioeconomic models 94 COBAS A second group of policy options can be modelled by changing key model coefficients. This group can include the following options: • Mesh size restrictions and other technical measures; • seasonal/area closures; • permanent area closures (e.g. MPAs), and • post harvest options (e.g. traceability, ecolabelling). The change of a key coefficient should be based on specific assumptions to be analysed and tested by ad hoc investigations. For instance, modelling technical measures, like mesh size restrictions, requires assumptions about the changes in catchability for the different age classes. Seasonal or area closures, as well as MPA, require assumptions about the changes in exploitable biomass of certain age groups and/or different species. Other management measures can also be simulated, but their implementation in the bio- economic model requires assumptions to be made about the behaviour of the fishing industry. For instance, the implementation of ITQs or ITEQs. All the management options listed above can be potentially implemented in the model as reported in the methodology report of the IiFSW project, but only some of them have been actually simulated and the resulting advices made available by the project. Based on the simulations reported on the project website, it is possible to identify the variables or parameters directly influenced by specific policy options: Decommissioning scheme is modelled by reducing the number of fishing vessels. This is implemented as a “one-off” percentage reduction in size of the fleet reducing the number of active vessels across all metiérs. Days at sea limits or effort reduction is simulated by reducing the number of days at sea. The size of the fleet is assumed to stay at its current levels, but the number of days at sea is reduced by a given percentage across all metiérs. Increase the minimum mesh size for a specific fleet segment is modelled in terms of changes in catchability and in the amount of fish taken. For this simulation, assumptions have been made on the effects of the policy option on catchability and landings. Other measures simulated in the project are related to the area closures, and seasonal and area closures. As for these simulations, the variables directly affected in the model are not specified. Notwithstanding, it seems likely the use of an approach similar to that used for technical restrictions. Survey of existing bioeconomic models COBAS 95 As reported above, the model has been used to simulate technical measures. However, the results of these simulations highlighted that the limited information on the effects of this types of management options do not allow the model to produce realistic outcomes. In particular, the simulation of the square mesh panels for beam trawlers shows to have no impact upon fish stocks as the model is not able to take into account the effects on discards, both in terms of undersized commercial species and non-target or valueless species. Also regarding areas-based measures, the bio-economic model shows no relevant impact upon fish stocks. The model structure is suitable to produce ‘fine scale’ local effects, but only where good data exists. Beside the difficulties in simulating technical measures, any type of simulation is affected by a degree of uncertainty. As COBAS is essentially a deterministic model, uncertainty is not specifically considered. This means that no parameter has been introduced in the model with a probability distribution, but as a single value. However, the problem of uncertainty or, more properly, how to allow policy makers to take decision based on advices in light of uncertainty has been broadly discussed during the IiFSW project. Given the outcomes of the project, the solution adopted in the model seems to be based on the sensitivity analysis. Actually, simulation of specific management options can be produced under different scenarios, where each scenario is defined by a set of assumptions on particular parameters or specific behavioural rules. When a policy strategy remains superior to the other over a wide range of different scenarios, that strategy can be reliably considered preferable to the others. By each simulation, the advice provided by the COBAS model consists of six graphs resuming the effects of the management option under analysis on the model’s components. These model outputs are shown ‘relative to the baseline’. Therefore, the graph does not show the predicted values year by year, but the difference between the impacts of a specific management option and the impacts of the current regulation (i.e. the baseline) on a set of bio-economic indicators. For each of the simulations produced in the IiFSW project, the following graphs are reported: • The level of spawning stocks (Demersal, Pelagic and Crustacean/Shellfish). • Overall impact on the environment. • The value of revenue by port. • Boat profitability (overall and by gear activity). • The value of recreational angling expenditure. • Regional output and employment. The bio-economic model will simulate the impacts of fisheries management changes on a year- to-year basis over a period of time of 15-20 years. Simulations produced in the project show the effects of a range of management options over a period of 12 years, from 2007 to 2018. Survey of existing bioeconomic models 96 COBAS The biological component of the COBAS model is based on an age-structured biological model. As this is very data demanding, the model foresees an alternative approach for species where stock assessments are not available or biological data is not complete. For those species a surplus production model can be used to estimate the levels of landings. 10.2 Implementation details 10.2.1 Data requirements The complex structure of the model needs many data to be used. Data to initialise the model is represented by the list of exogenous variables or parameters defined above which should be estimated before the model is used. Also the number of vessels by sub-fleet, size class and country, and the number of individuals by age for each of the age-structured stocks should be collected at least for one year (the simulation starting point). Data used in the project IiFSW has been obtained from a number of different sources. Economic, regional and environmental data has been collected through survey, while the structure and the activity of the commercial fishing fleets have been obtained through logbooks. Other biological data has been collected by previous biological investigations. All parameters estimated by metiér have been obtained through trip level data using trips of vessels landing in rectangles of ICES Area VII. Some parameters are estimated through a pair wise comparison survey using multi-criteria techniques. As an example, to measure the relative impact of fishing gears on habitat, cetaceans, commercial and non-commercial by-catch, an expert opinion survey where comparisons have been made between the relative impact of one gear type against the others has been used. COBAS data requirements are strictly related to the specific model components. Data to simulate the environmental and the recreational fisheries components cannot be collected by DCR, as this does not cover these sectors. On the contrary, the most part of the data needed to simulate the commercial sector in the model can be obtained directly or by elaborations on the data to be collected by the “new” DCR. Indeed, one of the main differences between the “old” and the “new” DCR is represented by the introduction of metiérs as a new level of aggregation for many data. This is also one of the most important dimensions of the model. Therefore, most of the parameters in the model cannot be estimated by data collected in the “old” DCR, but they can by the “new” DCR. Survey of existing bioeconomic models COBAS 97 10.2.2 Model language and platform characteristics The bioeconomic model COBAS is developed in the GAMS framework. The General Algebraic Modelling System (GAMS) is a high-level modelling system for mathematical programming and optimization. The COBAS code is open source and freeware, but it needs the mathematical programming system GAMS which is shareware. 10.2.3 Format of model output The model should be able to produce graphical output of results, but this cannot be verified as, at the moment, the COBAS code is not available. 10.2.4 Producing an advice As the COBAS code is not available, questions on model time consuming and model procedures to be followed for producing an advice cannot be answered. As for the financial costs of producing an advice, the only known cost is related to the GAMS license. It consists of a language compiler, which price is US$ 640, and a set of integrated high-performance solvers, which prices can vary from US$ 320 to 1920US$. As the model has not been compiled in a software package, GAMS programming skills are needed to operate on the model. Knowledge of biological and economic modelling are also needed to produce an advice by this model. Bio-economic models are generally used to evaluate the effects of management measures on a set of biological, economic and social indicators. The use of the COBAS model instead of other models depends on the specific features of the fisheries under analysis. COBAS is a suitable model to represent multi-species and multi-fleet fisheries where management measures are mainly based on effort control. Notwithstanding, each model is characterised by a set of assumptions on each of the model’s components. These assumptions should be verified on the data related to the fisheries under investigation. When all model assumptions are accepted, the model code is available and the data needed to run the model has been collected, an advice by the COBAS model can be immediately produced. On the contrary (and this is the most frequent case), when some assumptions are not verified, some arrangements are needed to make the model suitable for producing a reliable advice. Depending on the relevance of the model adaptations, this activity can be quite time consuming. 10.2.5 Model use and full list of references The model has been used in the IiFSW project to produce a number of simulations based on different scenarios. The model has been developed to simulate the effects of a range of Survey of existing bioeconomic models 98 COBAS management measures in the areas of the English Channel, Celtic Sea and Western Approaches (i.e. ICES divisions VIId – VIIj). The management options simulated for the project have been mainly input and technical measures. Input management measures have been simulated by reducing fishing effort in terms of number of vessels or days at sea. The model has shown that the implementation of these measures can produce a significant improvement in the levels of spawning stock biomass for demersal, pelagic and shellfish stocks. From an economic and social point of view, the reduction in the number of vessels will determine decreasing profit and employment in the short and medium run. On the contrary, a reduction in the average days at sea can result in a temporary decrease in profit and employment for the first years, and an increase in the medium run. A positive impact of these management measures on the environmental indexes and the value of the recreational sector are also registered. The simulation of technical measures, like the introduction of square mesh panels for beam trawlers or area-based measures, have highlighted that the limited information on the effects of this types of management options do not allow the model to produce realistic outcomes. No specific validation process on the results of the simulations is reported in the project documents. However, a set of validation exercises were carried out on the COBAS model. These exercises consisted in comparing the model outputs with real data on observed landings in the year 2003. The comparison showed that the total catches of the UK industry in the South West estimated by the COBAS model matched very closely reported landings. This review of the bio-economic model COBAS has been based on the results of the IiFSW project (www.investinfishsw.org.uk), and on the following papers and documents: Mardle, S. and Pascoe S. (Eds) 2003. Multiple objectives in the management of EU fisheries: multi-objective modelling. CEMARE Report No. 65, University of Portsmouth. Pascoe, S. and Mardle, S. 2001. Optimal fleet size in the English Channel: a multi-objective programming approach. European Review of Agricultural Economics 28(2) 161-183. CEMARE and CEFAS, March 2005. DBM-WS - A dynamic bioeconomic model of the fisheries of the southwest to determine the costs and benefits of sustainable fisheries management. Methodology Report. Invest in Fish South West project. 10.2.6 Institute and key personnel COBAS model has been developed by the Centre for the Economics and Management of Aquatic Resources (CEMARE) and the Centre for Environment, Fisheries & Aquaculture Survey of existing bioeconomic models COBAS 99 Science (CEFAS). Key personnel include Dr. Simon Mardle (CEMARE) and Dr. Sean Pascoe (CEMARE). In any case the contact person should be Prof. Trond Bjørndal the director of CEMARE. Survey of existing bioeconomic models 100 ECOCORP 11. ECOCORP 11.1 General description 11.1.1 Model objectives and dimensions The EcoCoRP model was built within the framework of an EU tender. The primary aim of the study was to determine the likely economic impacts on different North Sea fishing fleet segments resulting from the implementation of effort reductions imposed by cod recovery measures. The key components of the study were: • assessment of the short term impact of the introduction of the effort controls in 2003 (a before and after analysis); • development of a dynamic bioeconomic model of the fisheries including multi-species interactions; • development of a range of simulations of alternative scenarios (agreed with the Commission), taking into account the uncertainty in the system by undertaking Monte Carlo simulations; and • development of a user-friendly interactive interface to enable increased stakeholder interaction with the model. The EcoCoRP model includes the following dimensions: • fish species (i), • time (t ), • fish age (a), • fleet, • country (j), • gear type (k ). The biological component of the model is age structured. 11.1.2 Model structure The bioeconomic model developed for EcoCoRP is a state-of-the-art “dynamic bioeconomic model” of the North Sea fisheries. It consists of a biological component and an economic component, linked through the catch relationship by effort controls. Catch is a function of fleets and species. The dynamics of effort are related to fishing mortality. Fishing mortality (F) is Survey of existing bioeconomic models ECOCORP 101 estimated from the total catch within MSVPA. Since catch per fleet is known (or given), then partial F per fleet can be computed: C f F′ = F (11.1) C t where F’ is partial F, Cf is catch of the fleet under consideration and Ct is total catch. Partial F per fleet is further scaled by the exploitation pattern of a sub-fleet where the sub-fleet is a fleet with a particular mesh size. The exploitation pattern is the equivalent of a selection pattern/curve which is length dependent. In the 4M model (“Multi-species, Multi-fleet, Multi-area Model”), effort (e.g. days fished) is stored in a normalised form and the assumption is that a change in fishing mortality results in a proportional change in effort. In effect, the catchability coefficient (q) is equal to 1 ignoring units. In order to extend this relationship to include economic relationships, effort must be explicitly incorporated. For example, the evaluation of the economic activity of a fleet is highly dependent on the description of that fleet’s effort. Variable costs (including fuel cost) cannot be realistically incorporated otherwise. The EcoCoRP model has been developed in the Vensim simulation package. Vensim is based on the System Dynamics methodology which represents elements within the systems in terms of “Stocks” and “Flows”. In system dynamics modelling, the dynamic behaviour in the system occurs when flows accumulate in stocks. An example of a stock within the model would be the population estimate of 3 year old cod. Over time, this will change due to four flows: • Rate that fish are caught • Rate that fish die. • Rate that fish age from 3 year old to 4 year old • Rate that fish age from 2 year old to 3 year old At the start of the simulation, estimates are required for the initial value of the 3 year old cod stock. The majority of the biological stock initialisation values in the EcoCoRP model are derived from the output estimates from the MSVPA model runs for 2003. The stock initialisations for the financial aspects of the model such as fleet sizes are drawn from the AER 2004. Furthermore, input constants are required as initial model setup. They control the flows between the stocks. 11.1.3 Type of advice and time range Advice can be produced by comparing the outcomes of different management scenarios. Survey of existing bioeconomic models 102 ECOCORP Management scenarios need to be defined first to carry out simulation runs. The simulation runs can be compared to each other as well as to a baseline scenario. The baseline scenario in the EcoCoRP study is a purely deterministic simulation run and assumes that fleet sizes and effort levels are maintained at those for 2003. The EcoCoRP study carried out simulation runs for scenarios of effort reduction, harvest control rules, decommissioning, cod fishing moratorium. The time range of predictions can be short to long term, i.e. from 1 year to many years. The model depends on age-structured stock assessment data as input. Changing the model structure would involve high monetary costs (purchasing a Vensim DSS licence; costs: £1437 plus vat). Hence, it is not recommended to use the EcoCoRP model in cases where stock assessment data are not available. 11.2 Implementation details 11.2.1 Data requirements The biological component of the bioeconomic model was primarily developed by CEFAS, based on the ICES multispecies study group assumptions. The economic and fleet dynamics components were developed by CEMARE. The economic component includes the most recent cost information from the AER, as well as incorporating models of price changes based on published price flexibilities. Species specific flexibilities are used instead of a single common price flexibility. Assumptions about changes in costs also need to be made, particularly fuel costs. The model also allows for discarding behaviour if quotas are not compatible. The available cost information determines the fleet structure in the model. The fleet structure for the EcoCoRP model has been described based on an evaluation of the fleets detailed in the annual economic report 2005. Data requirements are covered by the DCR (CR 2001). Fleet dynamics largely focus on likely participation in the fishery and effort levels, taking into account the restrictions but not necessarily forcing the vessels to operate at these levels. Work was undertaken to consider entry/exit (participation) in the TECTAC project, and these results were available to the project team. The methods for incorporating efficiency change in the model used by (Ulrich et al. 2002) and the (Unit 2004) were adapted to allow for efficiency improvements in the fishery over time. The required model input consist of two Excel spreadsheets that contain the vast majority of the input data, one for the biological side, the other for the econometrics of the fishing fleet. The Survey of existing bioeconomic models ECOCORP 103 latter requires more interpretation to update and match currently available data with the fleet and gear types used in the model. 11.2.2 Model language and platform characteristics The model has been developed in the System Dynamics software VENSIM (Ventana Systems 1998-2008). Vensim is a state-of-the-art business simulation tool developed by Ventana Systems, Inc. To run the model will not incur costs. If one wants to change any of the structure (not data inputs) then one would need to acquire a Vensim DSS licence (costs: £1437 plus vat). The main advantage of the package is in its presentational features, and the possibility of developing a user-friendly simulation interface that will allow the simulations to be run easily by different stakeholders, e.g. the Commission or members of a RAC. The software was used by (Unit 2004) in order to present the model results to a wide variety of stakeholders, most of whom had little technical knowledge of modelling. 11.2.3 Format of model output The model output is presented in a user-friendly way and the main components of the user interface are well explained in the EcoCoRP final report. Model output, derived from sensitivity runs or simulation (scenario) runs, could include the following figures and tables for the different effort reduction scenarios: • Sensitivity of various individual parameters (e.g. recruitment) and the effect on fleet profitability. • Stock size, age structure, predation sources, annual catch, fleet specific profit. • Across scenario comparison. 11.2.4 Producing an advice A management advice can be produced by defining management scenarios and comparing the outcomes of the different scenario simulation runs. In the baseline scenario, fishing effort follows levels of fishing mortality as estimated by the biological component (in the base year 2003). The Baseline scenario provides the model forecasts of cod stocks if the fleet sizes and effort levels are maintained at those for 2003. The baseline scenario is a purely deterministic simulation run, with no stochastic variation in recruitment or any of the other model inputs. This assumes results based on “average” expectations. However, stock recruitment is the main driver of stock levels in the model. Survey of existing bioeconomic models 104 ECOCORP Running a model simulation takes a couple of minutes. No costs are involved and programming skills are not necessary either. Input data can also be changed without any additional costs except for the time required to gather all the relevant data. Changing the econometric input data of the fishing fleet requires more interpretation to match the currently available data with the fleet and gear types used in the model. As the project was funded using EU money, the model is free for anyone to use. However, as stated above, any changes to the model structure (not the data inputs) would require the purchase of a Vensim DSS licence (costs: £1437 plus vat). 11.2.5 Model use and full list of references The model was developed and used to address the likely economic impacts of the management measures that resulted from the implementation of the longer-term cod recovery plan. This management plan, agreed on in December 2003 by the Agriculture and Fisheries Council, specifies target minimum biomass levels of mature cod for specific areas and management measures how to achieve these targets. These measures include upper (based on expected fishing mortality) and lower limits on TACs, restrictions on the degree to which TACs can change from one year to the next, and complementing effort limits. The ICES advice for the past six years has been to stop cod fishing in the North Sea. The effects of doing so are uncertain for the stock. The EcoCoRP model simulations show that it is likely that some increase in stock could be seen over time. The likely economic consequences on the different fishing fleet segments which are fishing in ICES division IV resulting from effort reductions imposed by cod recovery measures have been evaluated, as well as their impact on the profitability of these fishing fleets. The results presented in the EcoCoRP final report show clearly that the economic consequences on the current fleet are worsening into the future. A key feature of the EcoCoRP model is that it has been designed for maximum usability by non-experts. The recently formed North Sea RAC is an obvious potential user of the model. The main reference for this model is: Mardle S., Pinnegar J., Hill A. (2008) Economic effects of the cod recovery plan on the mixed fisheries in the North Sea (EcoCoRP). Final Report for the European Commission, Directorate-General for Fisheries. February 2008. Survey of existing bioeconomic models ECOCORP 105 11.2.6 Institute and key personnel EcoCoRP model has been developed by the Centre for the Economics and Management of Aquatic Resources (CEMARE) and the Centre for Environment, Fisheries & Aquaculture Science (CEFAS). Key personal include Dr. Simon Mardle (CEMARE), Dr. John Pinnegar (CEFAS) and Andy Hill (technical programmer), (Ventana Systems UK). In any case the contact person should be Prof. Trond Bjørndal the director of CEMARE. Survey of existing bioeconomic models 106 ECONMULT 12. ECONMULT 12.1 General description 12.1.1 Model objectives and dimensions ECONMULT is a fleet model developed since 1991 as a part of the Multispecies Management Program by the Norwegian Research Council at the Norwegian College of Fishery Science (University of Tromsø, Norway). The main purpose of ECONMULT was to make available a tool for the management of the Barents Sea fisheries by a multi-species and multi-fleet approach. ECONMULT is an economic simulation model to be used in combination with a biological model. The first version of ECONMULT was used with the biological model MULTSIMP as a component of the bio-economic model ECONSIMP. More recently, the model has been combined with the biological model AGGMULT. The ECONMULT model is generally described as a framework where fleet models can be developed at different aggregation levels. This is due to its flexible structure characterised by the possibility for the user to define all the model dimensions. Each definition of the following dimensional variables can identify a different fleet model: • Number of vessel groups. • Number of targeted species. • Number of separate biomass units (cohorts). • Time unit. Relationships between model dimensions are flexible as well. Each vessels group can harvest one or more stocks, and each stock can consist of one or more cohorts. In addition, some features of the model can be activated or deactivated by using specific Boolean variables. Survey of existing bioeconomic models ECONMULT 107 Fleet group 1 …………….………….. m Stock unit 1 Stock unit 1 Stock unit 2 Stock unit 2 …. …. …. …………………………………………….….…. …. …. …. …. …. Stock unit u Stock unit u Structural variables Target species Target …………….………. …………………….…. Stock unit 1 Stock unit 1 Stock unit 2 Stock unit 2 s…. …………… 1 …. …. ……………………………………………………. …. …. …. …. …. Stock unit u Stock unit u Figure 6. Main dimensions in the ECONMULT model 12.1.2 Model structure As reported above, ECONMULT is a single component model non-including any biological component, but only an economic section. As a consequence, the model requires a biological sub-model to be used. The diagram in Figure 7 shows the basic flow of a bio-economic model consisting in ECONMULT and AGGMULT as the economic and biological components respectively. Using the economic model in combination with a biological sub-model, ECONMULT receives the level of biomass by species or cohort as an input from the biological model, and estimates the level of landings by a Cobb-Douglas production function. Landings estimated by ECONMULT are used both to calculate economic indicators, like contribution margin and profit, and as an output directed to the biological model, where contributes with other variables to calculate the biomass for the next time step. As the model ECONMULT does not include a biological component, the current biological models used for management advice can be combined with ECONMULT to produce management advice in terms of both economic and biological indicators. ECONMULT represents a flexible fleet model which can be easily associated to the biological models used for management advice, like the ICES models. What variable is exogenous or endogenous in the model and what is the model output depends on the management measures to be simulated. As an example, when quotas are defined, the associated level of effort can be an output of the model. Management can affect directly the level of landings or the contribution margin. Survey of existing bioeconomic models 108 ECONMULT Output •Effort (or Quotas) •Harvest •Contribution margin Management means Harvest Industry Model Profit Fishing days or Quotas Contribution Taxes Margin Closed seasons Catch per Biomass Closed areas unit of effort after catch Limited entry AggMult •Cod •Phytoplankton •Capelin •Zooplankton •Herring Figure 7. Representation of a bioeconomic model consisting of ECONMULT and AGGMULT as the economic and the biological components respectively. 12.1.3 Type of advice and time range ECONMULT simulations have been performed for the Barents Sea fisheries where harvest control rules (HCR) to determine total allowable catches (TAC) for the three shared stocks cod, haddock and capelin are annually defined by the Joint Norwegian-Russian Fisheries Commission. Given the management regime actually in force in this area, the model simulates the effects of input management measures in combination with output management measures. The effects of the following combinations of management options can be assessed by the simulation model ECONMULT: • Days at sea limits and catch quota per vessel, • days at sea limits and TAC changes, • decommissioning schemes and days at sea limits, • decommissioning schemes and catch quota per vessel, and • decommissioning schemes and TAC changes. Survey of existing bioeconomic models ECONMULT 109 Each of the combinations of management measures listed above can be easily implemented in the model by changing specific variables or parameters values. As fishing effort is calculated in terms of days at sea, days at sea limits can be simulated by changing the fishing effort per vessel. Decommissioning schemes are implemented by changing the number of vessels of one or more vessels groups. TAC and catch quota per vessel are simulated by introducing constraints to the associated variables in the model. Therefore, the model can simulate both input and output management measures, but it is not able to simulate technical measures like mesh size restrictions or areas closures. The ECONMULT model is a deterministic model. This means that uncertainty is not considered in the model. However, the model can be used for producing a number of different scenarios for each of the management options simulated, where a scenario is defined by a set of assumptions on particular parameters or specific behavioural rules. For instance, it is possible to activate a fleet dynamic rule based on the level of profit, or assuming that the fishing activity will not be produced when the contribution margin is negative. Evaluating a management policy over a wide range of different scenarios can reduce uncertainty and provide a measure of the model outputs reliability. The advice provided by the ECONMULT model consists in a number of histograms used to compare the effects of different management options or different scenarios on a set of indicators. The main bio-economic indicators produced as simulation outputs by the combined use of ECONMULT and AGGMULT can be listed as follows: • SSB, • stock biomass of fishes aged at least 3 years, • TAC allocation (as a simulation output based on specific HCR), • gross revenues, • total wage paying ability (possible labour remuneration), • Total contribution margin (possible capital remuneration). As reported above, the flexible structure of the model allows the user to change the number of arguments in all the ECONMULT dimensions. Number of vessel groups, number of species, number of cohorts, and time unit can be defined by the user to develop his own fleet model. The flexibility in the model dimensions can be used to overcome problems of data availability. For instance, biological data should be aggregated at cohort level. However, when stock assessment is available only at stock level, the model can be used by imposing the number of cohorts equals to one. This determines equivalence between cohort and stock. Survey of existing bioeconomic models 110 ECONMULT The bio-economic model can simulate the impacts of fisheries management changes over a period of time of several years. Simulations produced in the past have covered periods of a maximum of 30 years. 12.2 Implementation details 12.2.1 Data requirements Data to initialise the model is represented by the exogenous variables defined in the Section A.6.1 of the appendix, which should be estimated before the model is used. As described above, the model is characterised by a very flexible structure. As a consequence, the model is suitable for use in several areas with different structures of available data. For instance, even if biological data should be aggregated at cohort level, imposing the number of cohorts equals to one is equivalent to use data at stock level. Similarly, economic data should be collected at vessels group level, but the number of vessels groups can be set to one. Moreover, the combination of fleet–targeted species identifying a fishery is defined by the user. The flexibility in the model structure determines also a wide flexibility in data requirements. Consequently, data needed to use the fleet model ECONMULT can be obtained directly or by elaborations on the data to be collected by both the current and the forthcoming DCR. Even though ECONMULT can be easily adapted to the available data, its use is limited by the need to be combined with a compatible biological model. Therefore, the possible use of ECONMULT depends also on the relationships between the biological model data requirements and the current and forthcoming DCR. 12.2.2 Model language and platform characteristics The fleet model ECONMULT is developed and available in Mathematica code. Mathematica by Wolfram Research is a technical computing environment that integrates numeric and symbolic computations, interactive document capabilities and an advanced programming language. The ECONMULT code is open source and freeware, but it needs the mathematical programming language Mathematica which is shareware. 12.2.3 Format of model output The model seems to be able to produce graphical outputs of results, as the outcomes of model simulations are generally reported by a set of histograms. However, this has not been verified by running the model as the reviewers are not licensed to use the Mathematica programming language. For the same reason, other details on the format of model output cannot be produced. Survey of existing bioeconomic models ECONMULT 111 12.2.4 Producing an advice As for the financial costs of producing an advice, the only known cost is related to the Mathematica license. The licence for Windows, Macintosh or Linux has a cost of 3.185 €. As the model has not been compiled in a software package, Mathematica programming skills are needed to operate on the model. Also economic knowledge is needed to use the model. Information on model time consuming and model procedures to be followed for producing an advice are not available as the reviewers are not licensed to use the Mathematica programming language. Bio-economic models are generally used to evaluate the effects of management measures on a set of biological, economic and social indicators. The use of the ECONMULT model instead of other fleet models depends on the specific features of the fisheries under analysis. ECONMULT is a suitable model to represent multi-species and multi-fleet fisheries where management measures are mainly based on harvest control, like TAC. Notwithstanding, each model is characterised by a set of assumptions on each of the model’s components. These assumptions should be verified on the data related to the fisheries under investigation. When all model assumptions are accepted, the model code is available and the data needed to run the model has been collected, an advice by the ECONMULT model can be immediately produced. On the contrary (and this is the most frequent case), when some assumptions are not verified, some arrangements are needed to make the model suitable for producing a reliable advice. Depending on the relevance of the model adaptations, this activity can be quite time consuming. 12.2.5 Model use and full list of references The ECONMULT model has been developed to simulate the effects of a range of management measures and different harvest control rules on the Barents Sea fisheries. Both input and output management measures have been simulated by ECONMULT in combination initially with the biological model MULTISIMP, and more recently with AGGMULT. The last ECONMULT version is running on Mathematica version 6.0. As in Eide and Flaaten (1994), ECONMULT in combination with MULTISIMP was used to perform nine simulations over the period 1992-2019 based on different management options and different scenarios. The simulations for cod and capelin fisheries highlighted that, given the management regime in force and some specific assumptions on fleet behaviour, the best economic results in terms of present value of profit could be obtained by an increase of 50% in fishing effort for both fisheries. Few years later, a set of ten simulations was performed over the period 1996-2025 by the combined model ECONMULT-AGGMULT, where the main objective was to test several levels Survey of existing bioeconomic models 112 ECONMULT of TAC for cod fisheries in the Baltic Sea under a range of different scenarios. The best economic result in terms of present value of the contribution margin was associated to the lowest level of TAC equals to 375 thousands tonnes. The simulations produced by the model have been able also to verify the effectiveness of the different levels of TAC in constraining the level of landings under different assumptions on fleet behaviour. ECONMULT and AGGMULT have been used also to study the economic implications of the 3 years HCR for the Northeast Arctic cod decided in November 2002 by the Joint Norwegian- Russian Fisheries Commission. The effects of the 3 years HCR on a number of bio-economic indicators have been simulated by the model and compared to the results obtained by the simulations of other five HCRs. The outcome of this study has shown that the 3 years HCR agreed in 2002, when compared to other HCRs, will determine the highest level of spawning stock biomass and total fishable stock, and the lowest levels in the economic indicators. No specific validation process on the results of the simulations is reported in the available documents. This review of the bio-economic model ECONMULT is based on the materials available on the website www.nfh.uit.no/prosjektvis.aspx?id=60, and on the following papers and documents: Eide A. and Flaaten O. Bioeconomic multispecies models of the Barents Sea fisheries. University of Tromsø, Norway. Eide, A and Flaaten O., 1994. Bioeconomic multispecies modelling of the Barents sea fisheries, In Antona M., Catanzano J. and Sutinen J.G. (eds.) Proceedings of the Intern. Inst. of Fish. Econ. and Trade's Conf. July 6-9, 1992, Paris. Volume I, pp. 183-191- Issy-les-Moulineaux. IFREMER. Eide A. and Heen K., 2002. Economic Impacts of Global Warming. A Study of the Fishing Industry in North Norway, Fisheries Research 56, 261-274. Eide A., 2007. Economic Impacts of Global Warming: the case of the Barents Sea fisheries, Natural Resource Modeling 20, 2, 199-221. 12.2.6 Institute and key personnel The ECONMULT model has been developed at the Norwegian College of Fishery Science, University of Tromsø, Norway, by Arne Eide and Ola Flaaten. Survey of existing bioeconomic models EIAA 113 13. EIAA 13.1 General description 13.1.1 Model objectives and dimensions The EIAA model (Economic Interpretation of ACFM Advice) was developed in 1999 under the Concerted Action “for Economic Assessment of European Fisheries”. The main objective of the model was to use economic information to evaluate the economic consequences of the TAC proposal formulated by ACFM. The EIAA is a deterministic economic model, where in its basic form (“base-version” or “1999 version”) a relative simple and straightforward model framework performing a single simulation producing four economic indicators in terms of gross value of landings, crew remuneration, gross cash flow and net profit. The model has since 2002 been used by STECF as an economic assessment tool for evaluating the economic repercussion of different TAC/quota scenarios for selected EU fleet segments. Since 1999 the model has gradually been extended with extra economic indicators, relaxing the assumption of fixed number of vessel and number of fishing days by adding an optimisation module to estimate number of fishing days under different quota proposal by maximizing the profit. A more detailed chronological development of the model can be viewed in (Frost and Levring 2009). The EIAA model is divided into four sets of dimensions: 1) Country, 2) fleets segments 3) Stocks (included management area), and 4) Year. Each country (member state) has divided the national fleet into a number of the fleets segments. The fleet segments are assumed to have similar physical characteristic in terms of vessel length, gear type, gross tonnage and engine power. For the 2007 STECF assessment (SEC 2008), the number of fleets segments for each country varies between 1-5, including the most important fleets. The model is designed to include information of all quotas within EU waters (113 stocks), there around 60 stocks biological assessment is performed and TAC has been proposed. The area dimension is implicitly included in the stock definition. The main model development and updating is currently lead by the Danish Institute of Food and Resource Economics. 13.1.2 Model structure The EIAA is an economic output driven model with focus on estimating the economic performance for selected EU fishing fleets segments under different quota scenarios. The main component is the “fleet activity module” or “effort module”, used to estimate effort economic Survey of existing bioeconomic models 114 EIAA dependent variables such as variable cost. No separate biological component is included in the model accounting for the dynamic of the stocks over time. Biological information in terms of spawning stock biomass (SSB) and proposed quotas is extracted from ICES expert group- and ACFM reports and given as input to the model. A flow diagram of the model structure of the base version is illustrated in Figure 8. On the left hand is given information of input data to parameterise and initialise the model. Input data to initialise the model is based on an average of the three pervious years (the baseline period). The first step in the simulation is to translate the proposed quota into landings in weight (or volume), where the landings of each stock is calculated for each fleet segment. To allocate the total TAC to national fleet segments, the total TAC is split by country (member state) using the relative stability matrix, afterwards the country shares is distributed nationally by using the national fleet segment shares defined in the baseline period. The future prices are calculated from the baseline prices, where it assumed that the price of each stock is a function of changes in the quota relative to the baseline period. The applied price function includes a price flexibility rate which is parameterised outside the model. The gross revenue (or total revenue) is based on the calculated landings and prices, and revenue from non quota species. The corner stone of the model is the estimation of the effort used to adjust the variable cost. Effort is not fixed or given as input. An inverse Cobb-Douglas production function is used to estimate the effort, by assuming that the level of effort is influenced by changes in the stock prices and stock biomass (in terms of SSB) relative to the baseline period. The exponential form of the biomass component in the Cobb-Douglas function contributes, in most cases, to a nonlinear relationship between effort and stocks size. It is within the Cobb-Douglas function where the biological and economical component is linked. In the base version profit is calculated independent of the number of vessel (number of vessel is not an issue in the base version). In Figure 8 (left) shows a diagram of the extended model, where the assumption of constant number of vessel is relaxed, an optimisation module is included to estimate the number of fishing days under different TAC scenarios. Assuming an optimal number of days at sea per vessel, the total capacity (in terms of number of vessel) can be estimated. Additionally, a number of extra economic indicators are estimated in the extended version. Survey of existing bioeconomic models EIAA 115 Y(-1,-3) Y(1) current and coming year Y(-1,-3) Y(1) current year calculations Y(n) future calculations EIAA model Observations calculations Observations Fishing mortality Hans Frost Stock Stock Stock FFStock Stock rate (decision rule) FOI F TAC TAC TAC TAC TAC Quota Quota Quota Quota Quota Decision Rule Prices Prices Prices Prices Prices Revenue Revenue Revenue Revenue Revenue Allocation Production function factor Effort Production function factor Production function factor on Member drivers States and fleets Var.cost Var.cost Var.cost Var.cost Var.cost Sea days Sea days Sea days Sea days Sea days Total catch Catch per day Total catch Fixed costs Fixed costs Fixed costs Fixed costs No vessels No vessels No vessels No vessels Days per vessel Days per vessel Days per vessel Profit Profit Profit No vessels No vessels Fixed costs Fixed costs Net present Profit excl. stock rent Profit excl. stock rent profit Profit incl. stock rent Profit incl. stock rent Figure 8. Flow diagram of the EIAA model. Left: the “Base version”. Right: the “Extended version”. 13.1.3 Type of advice and time range The EIAA model is designed as an output orientated management model to assess the economic consequences of the TAC proposed by ACFM. The management advice is given for three years: 1. Current year (y). 2. Coming year (y+1). 3. The long run where all stocks are assumed to have recovered to sustainable level. The data input is lagged and average data over three previous years are used to level out natural variation (the baseline period). An alternative long run calculation is disregarded when results are extracted from latter models into the long run version. Five nested models then make it possible to perform calculations for ten years. After ten years, the long run model calculates the economic performance for the following 20 years under the assumption that the years 11 to 30 are the same as the one in year ten. It is assumed that most stocks are able to recover within ten years given that the fishing mortality rates are set properly. The input to the long run EIAA model is a projections of stock abundances and correspondent yield. The model can use any stock projections as long as the stocks have recovered before year eleven. The output from the “long run version” is evaluated by net present of the gross cash flow and the net present value of the profit. Survey of existing bioeconomic models 116 EIAA The EIAA model has primarily been developed for evaluating output systems in terms of restriction in the TAC given by the Harvest Control Rule used by ICES. It is not designed to handle input management measures such as days at sea restrictions. In situations where days at sea restrictions have been enforced, the EIAA model will overestimate the landings and effort, and thereby the economic performance (SEC 2006b). In the extended version, an effort optimisation module has recently been implemented to account for days at sea restrictions on yearly basis (Frost and Levring 2009). The effort optimisation module can e.g. estimate the minimum effort required to catch the lowest quota for a stock for any of the selected stocks for a fleet segment. In mixed/multi species fisheries it is assumed that stocks are independent and in order to calculate fishing effort across stock fished, the EIAA uses (as a default) the weighted mean effort on all the species. Setting a harvest control rule in an effort based system is absolutely not trivial, and will not further be discussed here (e.g. (SEC 2006b)). To calculate the landings in an effort restricted system (number of effort days is set by exogenous decisions) information on catch per unit of effort (CPUE) is needed for individual stocks. The CPUE is expressed as a function of SSB and changes in effort relative to the baseline period (see page 37 in (Frost and Levring 2009)). The EIAA model does not explicitly take account for changes in spatial fleet activity or technical information of mesh size, discard or other technical measures. Neither does it directly include any stochastic simulation or sensitive option. However, the model is designed so that all the parameters can be changed without any major modification of the program. Visual basic codes have been made to perform sensitive analysis on selected parameters (Thomas Thøgersen pers. Comm.). The EIAA model is a flexible model in terms to change number of fleet segments and stocks. It is developed with the aim of having a relative simple model set-up and user interface, where input information’s can easily be copied from reports etc. and pasted directly into a predefine Excel working sheets. However, as many other models, assumptions have to be made due to lack of information (see (Frost and Levring 2009)). Main output indicators: In the base-version four main economic indicators are used for evaluating the economic performance: • Gross revenue, • Crew remuneration, • Gross cash flow, and • Net profit. Survey of existing bioeconomic models EIAA 117 For each quota/TAC scenario performed, a “classification” indicator is presented for each fleet segment based on the operation profit margin of the base line period compared with current year and forthcoming year: • Profitable: Net profit/gross value of landings > 5%. • Stable: -5% < net profit/gross value of landings ≤ 5%. • Unprofitable: Net profit/gross value of landings ≤ -5%. In the extended version (or the “2007” version) a number of extra output indicators are specified: • The break-even revenue (the gross revenue required to cover fixed costs exactly with the given variable cost), • Overcapacity (break-even revenue-current revenue/break-even revenue, see figure 5 in Frost and Levring, 2009). • Changes in the number of vessel and fixed cost. 13.2 Implementation details 13.2.1 Data requirements To initialise the model, a baseline period is set based on average over the three previous years. Input data required for the baseline period are divided up in five categories: 1) technical details of fleet segments, 2) landings by stock, 3) prices by stock, 4) cost information for fleet segments, and 5) ACFM advice for landings by management stocks. These data are extracted from the annual economic report, quota proposal from the European Union and different ICES reports. It is not necessary to estimate any of the input parameter before the model can be run, a default value is predefined for each of the input parameters. However, it is relatively unproblematic to alter the default value, if alternative estimates are available. Until 2006, the cost and earnings information was extracted from Annual Economic Report and the database CAClient hosted by LEI. The introduction of the new data collection program (DCR) in 2006 resulted in a new segregation of the fleet segments. At the STECF meeting in 2008 it was investigated whether the implementation of the new DCR data format could be used in the calculations. The conclusion was that it is possible to work on the new platform but further elaboration and experience with the data processing including relevant adjustments of the model is important to save time during the advisory process. Survey of existing bioeconomic models 118 EIAA 13.2.2 Model language and platform characteristics The EIAA model is constructed in a Microsoft Excel spreadsheet. In the base-version (from 1999) data is organised in seven worksheets, whereas the extended version (“2007-version”) data is organised in 23 sheets. The programming of the equations is done by use of cell reference, named arrays and if- sentence. This relative simple and straightforward programming must be regarded as open source, as no macros or virtual basic programming (except for the “long run version” module) is used. The model can be run on all standards PC with a Microsoft Office package. 13.2.3 Format of model output The results are directly presented in an Excel working sheet. Predefine tables and figures visualise the result, which can be copied directly into e.g. Microsoft Word or other word- processing packages. 13.2.4 Producing advice The process time of running a single simulation and producing tables and figures only takes seconds, then all input data sheets have been fill in correctly. It is the preparation of input data that is the time consuming part of running the model. Depending on the resolution of the case study in terms number of stocks and segregation level of national fleet segments, it takes between one day to weeks of producing and disseminating short and long term advice on the economic consequences proposed (e.g. by ACFM) quotas. To run the model, it minimum requires a standard pc installed with Microsoft Office package. Due to the simplicity of the model structure, no specialist skills is needed for running the EIAA model, besides basics knowledge of fisheries economy. However, interpretation of the results may need practical skill within fishery economy and biology. As in many similar management models, a number assumption have been made to keep the simplicity and transparency in the model structure and these assumptions are essential to account for interpretation of the results. 13.2.5 Model use and full list of references The EIAA model was developed for evaluating the short and long term economic consequence of the ACFM advice under FAIR CT97-3541. Since 2002 the “base-version” has been used to give an annually economic assessment based on quota proposal forwarded in late autumn by AFCM, and then producing and disseminating advice before the Council of minister meeting in December. Survey of existing bioeconomic models EIAA 119 Comprehensive applications of the extended version of EIAA have been applied in evaluating the EU proposed management plans for plaice and sole in the North Sea and the northern hake recovery programs under STECF (SEC 2006b; SEC 2008). In parallel, the EIAA has been used in several national projects for evaluating the economic performance of selected Danish fleets. The main development of EIAA is currently done by the Danish Institute of Food and Resources Economics. The use of EIAA as common tool for assessing the economic performance of the most important European fleets indicates a common acceptance of the model among fishery economist in Europe. A recently published working document by (Frost and Levring 2009) gives a more detailed description of the model. The EIAA model have under STECF meetings/workshops and other EU projects been used and described by several fisheries economist with special expertise in bioeconomic modelling etc. (see e.g. SEC (2004, 2008)), so the model is consider (or assumed) to be well documented and validated. Relevant references: Economic performance of selected European fishing fleets in 2007. The Potential Economic Impact on Selected Fishing Fleet Segments of TACs Proposed by ACFM and reviewed by SGRST for 2007 (EIAA-model calculations). Economic Assessment of EU fleets and economic impact of ACFM advice, SGECA-06-04 Brussels 23-27 October 2006. http://stecf.jrc.cec.eu.int/meetings/sgeca/0604/eiaa.php EIAA Specimen Report. Example of Application of the Methodology. Document no. 5 (1998) of the concerted action (FAIR CT97-3541): Promotion of common methods for economic assessment of EU fisheries. (Available from the Fødevareøkonomisk Institut, FOI). EAFE-AC REPORT (2001) The Potential Economic Impact on Selected Fishing Fleet Segments of TACs Proposed by ACFM for 2002 (EIAA-model calculations). A Report of the European Association of Fisheries Economists’ Advisory Committee. Frost H., J. Kjærsgaard (2003) Numerical allocation problems and introduction to An Economic Management Model for Fisheries in Denmark (EMMFID). FOI report no. 159. Institute of Food and Resource Economics, Copenhagen Frost, H., Levring, J.A., Hoff, A. and Thøgersen, T. (2009). The EIAA model: Methodology, definitions and model outline. Working document Hoff, A. and H. Frost (2006) Economic Response to Harvest and Effort Control in Fishery. FOI Report no. 185. Institute of Food and Resource Economics, Copenhagen. Survey of existing bioeconomic models 120 EIAA Salz, P. and H. Frost (2001) Model for economic interpretation of the ACFM advice (EIAA) in: Lindebo, E and N. Vestergaard (edt.) Proceedings of the XIIth Annual Conference of the European Association of Fisheries Economists (EAFE). University of Southern Denmark SEC (2004) 1710 The Potential Economic Impact on Selected Fishing Fleet Segments of TACs Proposed by ACFM for 2005 (EIAA-model calculations). Report of the Scientific, Technical and Economic Committee for Fisheries (STECF), Subgroup on Economic Assessment (SGECA) (Brussels 27-29 October 2004). Commission Staff Working Paper, Brussels, 23.12.2004. http://ec.europa.eu/fisheries/publications/factsheets/legal_texts/sec_2004_ 1710_en .pdf SEC (2004) 1711 Scientific, Technical and Economic Committee for Fisheries (STECF), Subgroup on Review of Scientific Advice on Stocks (SGRST) - Mixed Fisheries, Commission Staff Working Paper, Brussels 2004. http://ec.europa.eu/fisheries/publications/ factsheets/ legal_texts/sec_2004_1711_en.pdf. SEC (2005) 259 Report of the Joint SGRST-SGECA sub-group on Further improvements of the EIAA model including long term perspective and effect of recovery plans, Brussels, 14 – 16, June 2004. Commission Staff Working Paper, Brussels, 15.2.2005. http://ec.europa.eu/fisheries/publications/factsheets/legal_texts/sec_2005_259_en.pdf SEC (2006) Impact assessment of long term management plans for sole and plaice. SGECA- SGRST-06-05. Brussels 26-29 September 2006. Commission staff Working Paper. SEC (2007) Impact assessment: plaice and sole long-term management. SGECA-SGRST -07- 01. Copenhagen, 20 -23 March 2007. Commission Staff Working Document. SGECA-SGRST- 07-01: long-term management of sole and plaice SEC (2008) Northern hake long-term management plan impact assessment. SGBRE-07-05, Brussels 3-6 December 2007. Commission Staff Working Document 08. January 2008. Final Report with STECF opinion 13.2.6 Institute and key personnel The main development is done by FOI, lead by Hans Frost. Addtionally a number of key persons within same institute is also involved in the development of the model: Jesper Levring Andersen, Ayoe Hoff and Thomas Thøgersen . Survey of existing bioeconomic models EFIMAS 121 14. EFIMAS 14.1 General description Even if not a model itself the EFIMAS project has developed a management evaluation tool FLR (Fisheries Library in R being an object oriented simulation framework) which can be considered as an operational and generic fisheries management evaluation framework (MEF), incorporating biological and economic (among others) dynamics inside. The developed framework allows simulating and evaluating, respectively, the biological, social and economical consequences of a range of proposed fishery management options and objectives within different management regimes that can compare and predict the outcomes of alternative scenarios before potentially being implemented. This framework has also benefited from the projects COBECOS, CEVIS, AFRAME, FISBOAT, JAKFISH, PRONE and UNCOVER. It includes simulated data collection using existing databases and calculated variance in data, perform assessment of the system (with use of output from currently applied descriptive models and analysis tools, alternative existing models/tools, or modified existing –alternative- models/tools for fisheries/stock evaluation), and provide advice according to harvest control rules, management options and objectives. Simulations are mainly performed using an integrated suite of software facilities with implementation of a common language (main basis being R/FLR) and interface, i.e. a common simulation frame, which can handle output and results from a variety of descriptive models and analysis tools for analyzing different management scenarios, options and objectives. FLR has to be seen as a tool to create models. This tool provides a common framework based on R, common methods to perform analysis and a common structure to incorporate data. With this tool specific models (based on case studies) have been developed, all with different modelling and, in some cases, objectives. 14.1.1 Model objectives and dimensions The major objective of the models developed in EFIMAS is to create operating models4 that can be used for evaluating different types of management measures. It is based on FLR, which in principle is generic, but is specifically suited for the construction of simulation models for evaluations of fisheries management strategies. 4 An OM is a mathematical-statistical model used to describe the underlying resource dynamics and to generate future data when projecting forward based on a plausible hypothesis about population dynamics. Survey of existing bioeconomic models 122 EFIMAS The purpose has been to develop and build a Management Strategy Evaluation (MSE) framework that allows the performance of alternative management strategies to be evaluated prior to implementation and their robustness to be tested across a range of plausible hypotheses about the dynamics of the stocks, fisheries and fleets. Consequently, this has been used to evaluate results and output generated from a broad range of software packages (descriptive fisheries/fleets/stock assessment models as well as other evaluation and analysis tools), analyses, and existing databases being used for production of advice to management bodies, and has been applied to important EU fisheries (see Table 15). Table 15. EFIMAS project case studies developed using FLR. Species and areas considered Species Area Demersal Flatfish (Plaice and Sole) North sea Demersal Roundfish North sea Swordfish Mediterranean Hake Mediterranean Nephrops East Atlantic Hake East Atlantic (northern stock) Cod Baltic Salmon Baltic All these models have been implemented for different purposes using the FLR even if differences can be found in terms of the implementation and the level of development. There are some other models developed using FLR outside the scope of the EFIMAS project: • Greenland halibut (Reinhardtius hippoglossoides) in NAFO Subarea 2 and Divisions 3LKMNO: A reference set of 20 possible operating models have been performed using FLR. A suite of performance statistics for this stock are used to assess these management strategies. The results of this MSE exercise are evaluated in the context of the NAFO approach to fisheries management and the potential for further progress with regard to the application of MSE on this stock in general is considered. It cannot be considered a bioeconomic model. • North Australia Prawn CCAMLR: A set of different operating models for this stock have been developed using FLR. It cannot be considered a bioeconomic model. • Anchovy in the Bay of Biscay: Two different operating models (Biomass model and age structured model) for this stock have been developed using FLR. They have been basically created to perform MSE, and in Survey of existing bioeconomic models EFIMAS 123 fact they have been used in the STECF sub group SGBRE-08-01 Long term management of Bay of Biscay Anchovy. But given that the economic part has not followed the FLR (Flecon) structure it cannot be considered (yet) a bioeconomic model. • Bluefin Tuna in the East Atlantic and Mediterranean: Scenarios corresponding to alternative plausible hypotheses about the stock dynamics are used to evaluate alternative management strategies. • Herring West of North sea: In this model demographic data to examine for evidence of compensatory and depensatory mechanism has been used. • Herring West of British Isles: A biological set of simulations (in a MSE framework) have been developed for this case study under different scenarios, including constant fishing mortality in all fisheries, a one off increase in F in a single fishery; regime shifts, and diffusion between populations, all of them under two different assessment options. In principle these models are purely biological, but the implementation using FLR allows the possibility of performing economic analysis using the existing classes and packages. FLR has also been used in some other context such as STECF (SGRST) Harvest Control Rules I & II in 2007 and STECF (SGRST) Harvest Control Rules III in 2008. All the models developed using FLR are simulation models. They have been primarily developed for biological (assessment and biological management evaluation) purposes even if they include an economic component. The dimensions (species considered, area covered and fleets considered) of these models can be seen in Table 15. Regarding the flexibility of these dimensions the main characteristics are: Species cannot be easily changed, given that they will require at least an operating model for them. In some cases for the bioeconomic part some other considerations like the use of specific production functions have been used for considering multi-species fisheries. In general it implies that fleets behaviour will be assessed in terms of multiple species, but a full assessment of the other species is not considered. In fact it implies that the effort is driven by a single (main) species. The two exceptions for these assumptions are those models using the AHF or the Fcube models. Further explanations of these two models are given below (AHF has been fully reviewed under Section 7 of this report). Survey of existing bioeconomic models 124 EFIMAS The number of fleets can be easily changed and the only requirement will be to condition them, that is, to obtain the data and to implement into the model. Table 16. Dimensions of the models developed using FLR under EFIMAS Case study Area Fleets Species Demersal Flatfish in the • Plaice • North sea • Beam Trawlers North Sea • Sole • Belgium, Denmark, England, Demersal Roundfish in the Netherlands, • Cod • North sea North Sea Norway Scotland • Haddock • Including Seiners, trawlers (<24m) and gillnetters • Offshore Longline • Coastal Trapnet Salmon Fisheries in the River Fishery • Baltic sea • Salmon Baltic Sea • Finland • Sweden • Denmark • North sea • bottom trawls Nephrops fisheries in the • Irish sea (mainly) • Nephrops East Atlantic • Portuguese coast • Bay of Biscay • ICES Divisions • Spanish trawlers Northern hake in the East VIIIa,,b,d and netters and • Hake Atlantic Subareas VI and VII. longliners Swordfish fisheries in the • Surface longlines • Mediterranean • Swordfish Mediterranean and gillnets • Aegean sea • Northern alboran sea • Bottom trawls Hake in the Mediterranean • Hake • Gula of Lion • Longlines • Ligurian sea Cod fisheries in the Baltic • Demersal trawls • Baltic sea • Cod Sea • Gillnets 14.1.2 Model structure The MSE framework comprises an operating model which simulates the system to be managed, and a management procedure that models any management measures to be applied, which include models or algorithms used to process the data sampled from the system to be managed (as represented by the operating model). The model structure can be found in the following figure. Survey of existing bioeconomic models EFIMAS 125 Figure 9. FLR Structure The main predefined types and formats for inputs and outputs are referred to as core classes (FL Core package). The core classes allow a variety of objects, corresponding to fish stocks and fleets to be created and also contains methods to access, summarize and manipulate them. The main classes are: Table 17. Main classes and packages in FLR Class Description FLQuant An array used to store data of one particular type FL Biol Represents a biological population FL stock The observed data and population dynamics of a fish stock FL SR Stock-recruit dynamics FL Catch Species specific information on landings, discards and catches Dynamics of fleets in models and is able to accommodate multispecies FL Fleet and multigear fleets, including both catches and basic cost data FL Index Abundance indices Then, secondary packages are found which can be divided into the stable ones and those not stable or continuously under development. Survey of existing bioeconomic models 126 EFIMAS Table 18. Secondary stable packages in FLR Package Description Explanatory data analysis of the data available for stock FLEDA assessment For performing a extended survivor analysis Stock FlXSA Assessment methods FLICA For performing an integrated catch analysis FLBayes For running Bayesian interpretations of fisheries models FLSTF Performs a deterministic short term forecast FLBRP Calculates biological reference points FLFlash For solving non linear equations (multi fleets,…) Table 19. Secondary not stable or under-development packages in FLR Package Description FLEcon Bioeconomic models FLHCR Not used, substituted by FLFlash FLISIS A translation of the model underlying the ISIS-Fish Software FLOE Stochastic analyses FLOgive Fits ogives to data FLOM Operating model specifically developed for Northern hake FLSURBA Survey based stock assessment FLutils Useful but not essential utilities Following the model structure and the structure of classes and packages it can be said that the FLR and in general all the models developed follow the following structure Operating Model STOCK (OM) & FISHERY DATA ADVICE COLLETION ASSESSMENT Management PROCESS Procedure (MP) Figure 10. General flow chart in the models developed. FLR. In that sense there is a clear link between the dynamics of the system and the management procedure but the link with economics is not there. Up to the current level of development of the models, there is not a feedback between the economic part and the management procedure or the operating model, however some exceptions can be found. Newer and more case specific classes developed as a part of FLecon include the AHF, FLCompliance and FLFCube. The idea is that as economic modelling develops, functions that are commonly used will be combined Survey of existing bioeconomic models EFIMAS 127 into more general classes. Models using these classes will have a feedback (not necessarily complete) between the biological and the economical part. More specific classes are discussed below: FLFCube: The Fcube method has been implemented in the Demersal Roundfish fisheries in the North Sea case study. Its objective is to explore and evaluate the potential overquota catches arising from inconsistent single-species TAC, based on simple assumptions about fleet’s effort distribution. Its strength is the relative simplicity of data needs while being able to account for a great diversity of fleets, metiers and stocks. AHF: The Dynamic Capacity Change Model, also known as the ‘AHF model’, evaluates the dynamic change in fleet capacity from one time period to the next, given expectations about future earnings from the fishery. The dynamic capacity change module has at present been used in the following models: • Demersal roundfish fisheries in the North Sea. • Demersal flatfish fisheries in the North Sea. FLCompliance: The effectiveness of management regulations in achieving the desired objectives depends on fishermen’s compliance with those regulations. In particular, management measures imposing restrictions on catch and effort are generally subjected to a degree of violation, which can reduce their effectiveness in preserving stocks biomass. The Compliance model, integrated in a class named “FLCompliance”, was initially developed for the project COMMIT and then finalized in the project EFIMAS. The Compliance model is aimed to consider the probability of non-compliance with different management regimes. There has not been found any documentation of the implementation of this class to any model. In any case FLR is dynamic network in continuous development. It is being extended outside the scope of EFIMAS, in two directions: Using FLR to test some case studies not considered in this project and creating new classes and packages that allow more extensive and precise evaluation of the management strategies for the case studies considered in EFIMAS. Finally it has to be said that the models developed are complex in terms of their understanding/development and future uses. 14.1.3 Type of advice and time range In principle FLR provides flexibility to perform any kind of analysis, for a single stock/fleet or even for multi-species, multi fleet cases. Forecasting time range can be easily changed and adjusted to the type of advice required. Nevertheless the current development of the models (not Survey of existing bioeconomic models 128 EFIMAS of the FLR) is limited in the sense that a limited number of models have been developed and all of them for different proposes. Table 20. Type of advice given by the models using FLR Case Study Type of advice MSE of management plans (long term and RAC’s proposals), effort reductions and Demersal Flatfish in the North Sea changes in mesh sizes. MSE for changing the single species based reference point to multi- species ones. TAC and TAE combinations effects. Discarding. Demersal Roundfish in the North Sea Recovery plan. Testing different fleet behaviour scenarios in a multi-species fishery Comparing the relative performance of management plans. Salmon Fisheries in the Baltic Sea Effect of environmental variables Effect of different reference points. Testing different trawl nets design to capture Nephrops fisheries in the East Atlantic the interactions with cod haddock and whiting. MSE of management plans (long term and Northern hake in the East Atlantic RAC’s proposals) and changes in mesh sizes. MSE approximation for different scenarios of temporal closures and reduction of fishing Swordfish fisheries in the Mediterranean effort. Used by ICCAT during the last assessment to provide a bioeconomic evaluation of management. MSE for selectivity changes and effort Hake in the Mediterranean reductions. Comparison of TAC versus Effort system s Cod fisheries in the Baltic Sea (including spatial closures) under the innovative regulation system. In terms of the regulatory measures almost all of them provides advice for the use of TAC as a regulatory measure (except the Mediterranean models), some of them also consider effort, and finally some of them can handle selectivity changes and temporal or spatial closures. In that sense the application of AHF permits switching from output driven to effort driven, whatever restriction is binding (for those fisheries which are regulated using TACs and effort limitations). FLR has also been used by ICCAT during the last assessment of the Mediterranean Swordfish to evaluate management in bioeconomic terms (see, Tserpes et al, 2009). Currently under the COBECOS project there are some applications using FLR that assess the bioeconomic consequence of changing the enforcement effort, even if by the time this report has been delivered they were under development. There is no limit for the time scale when forecasting and the model can easily be updated (when conditioning) for new information available. Considering some other regulatory measures will Survey of existing bioeconomic models EFIMAS 129 need coding time, this makes it, at least, not straightforward. Finally there are not limit values (i.e set a TAC to zero) in use for all of them. Table 21. Regulatory measures and time scale. FLR Regulatory Variable Time Range Case study Measure (s) influenced Conditioning Forecasting TAC Catches Demersal Flatfish in the North Sea <2005 <2010 TAE Effort Demersal Roundfish in the North TAC Catches 2002-2004 2005-2022 Sea TAE Effort TAC Catches Delayed Salmon Fisheries in the Baltic Sea opening of the 1992-2007 2008-2037 Effort coastal fishery Nephrops fisheries in the East MPA Catch at age (1) <2010 Atlantic Gear change Catch at age TAC Catches Northern hake in the East Atlantic MPA Catch at age 2000-2005 2006-2040 Gear change Catch at age Swordfish fisheries in the Gear change Catch at age 2003-2005 <2030 Mediterranean Time closure Recruitment Hake in the Mediterranean 2004 2004-2023 Capacity Effort TAC Catches TAE Effort Gear change Catch at age Cod fisheries in the Baltic Sea 1980-2004 2005-2024 Catch at age MPA and Effort distribution (1) There is not a clear time range. Data of different years have been used. FLR is based on stochastic simulation techniques and can take into account a range of uncertainties (parametric as well as structural uncertainty) and allows a variety of sensitivity analysis and risk assessments to be conducted. The success of the MSE approach depends on the extent to which the true range of uncertainty can be identified and represented in operating models. These uncertainties include the following: • Process error – natural variation in dynamic processes such as recruitment, somatic growth, natural mortality, and the selectivity of the fishery; • Observation error – related to collecting data from a system (e.g. age sampling, catches, surveys); • Estimation error – related to estimating parameters, both in the operating model, and, if a model-based management procedure is used, in the assessment model within the management procedure that leads to the perception of current resource status. It can be Survey of existing bioeconomic models 130 EFIMAS applied to any variable or parameter incorporated in the model (including all the economic variables). • Model error – related to uncertainty about model structure (e.g. causal assumptions of the models), both in the operating model and in the management procedure; and • Implementation error –management actions are never implemented perfectly and may result in realized catches that differ from those intended. It can be said that the possibility of including uncertainty in any process or variable is one of the main advantages of using FLR. In all models developed using FLR this has been the case. The economic part is not always like that, given that some variables and parameters do consider uncertainty explicitly and others not. In any case an advantage of FLR is that the economic part can easily handle the biological uncertainty in the economic part. In term of indicators that can be produced those that come from the biological side and the economic side must be separated: In biological terms, all the indicators that are produced in the stock assessments are or can be provided, for example, catch, stock, landings and discards can be compared with the biological reference points (if they exist). In economic terms the list of indicators that the models produce are much more restricted: • Variable costs. • Fixed costs. • Value of landings per species. • Total value of landings. • Crew share. • Gross cash flow. • Net profits. • Gross value added. All these indicators can be found in the class ComInd which was initially developed to be used for the Demersal North Sea flatfish model. However, it is generic and can be easily expanded to store additional indicators. With the AHFInvest function dynamic fleet capacity change given historical profit for the fleet can be evaluated. Survey of existing bioeconomic models EFIMAS 131 14.2 Implementation details 14.2.1 Data requirements Data requirement to initialise the model differ from model to model (summarized in Table 22). In general it can be said that the biological part requires an assessment or at least the data needed for assessment, considering the particularities of each stock. Data for fleets involved are also required, both in terms of catches and their cost structure. The models do not require in principle, fleet dissaggregation, that is the fleets that want to be assessed can be specifically modeled and the rest can form a group of “others”, but their landings (and discards in some cases) have to be accounted for in the model. Table 22. Data required to initialise the model. FLR Case study Initial data requirements Demersal Flatfish in the North Sea • Biological data as ICES working groups. • Fleet data • Fleet cost structure data Demersal Roundfish in the North Sea • Biological data as ICES working groups. • Effort and catches as in STECF assessment of effort regimens. • Economic data as in the AER. Salmon Fisheries in the Baltic Sea • ICES assessment • Data from the catches and landings of the offshore, coastal and inshore fisheries. • Also data from Trout and Sprat assessment given the biological links with these species. Nephrops fisheries in the East Atlantic • Biological data as ICES working groups. Northern hake in the East Atlantic • Biological data as ICES working groups. • Fleet and Economic data (not DCR). • Production function estimations for other key species. Swordfish fisheries in the Mediterranean • ICCAT assessment. • Current DCR data. Hake in the Mediterranean • Not all the stocks are assessed which requires a preliminary assessment for them. • DCR data. Cod fisheries in the Baltic Sea • An age structured biological model. • Environment specific set of biological models. • Summed effort, landings, cost structure. • Logbook data. • Catching power. • Gear selectivity. Survey of existing bioeconomic models 132 EFIMAS In relation to data requirements and the DCR, the evaluation has been done in terms of the models applied. In general the current DCR applies well for the biological data and assessment possibilities. The set of variables for economical analysis are also adequate, thus, the main problem comes for the fleet disaggregation level which creates problems of identifying which segments correspond to which stocks. It is expected that the forthcoming DCR will improve, through the reduced aggregation of fleet segments, this situation. Table 23. Relationship with the DCR. FLR Case study “Old” DCR “New” DCR Demersal Flatfish in the North Sea It can be run even if some fleet It can be run even if some fleet aggregations are not adequate. aggregations are not adequate. Demersal Roundfish in the North It can be run even if some fleet It can be run, except for the part Sea aggregations are not adequate. related to Norway. The part of Norway is not covered. Salmon Fisheries in the Baltic Sea No. It has to be taken into No. It has to be taken into account that there is link with account that there is link with the river fishery not covered by the river fishery not covered by the DCR. the DCR. Nephrops fisheries in the East Yes but currently there is not a Is not a bioeconomic model. If Atlantic bioeconomic model. In order to a bioeconomic model is perform it the aggregation of performed Forthcoming DCR fleets is not adequate. data level and aggregation should be enough. Northern hake in the East Atlantic No the aggregation of fleets is Yes with some expert not adequate. knowledge. Swordfish fisheries in the Yes for EU countries (Italy and Yes for EU countries (Italy and Mediterranean Greece). Greece). It creates the necessity of data It creates the necessity of data outside the scope of the DCR. outside the scope of the DCR. Hake in the Mediterranean Yes. Yes. Cod fisheries in the Baltic Sea Environment specific set of Environment specific set of biological models required biological models required No fleet information is required Data still too aggregated. by season and by area Expert knowledge will be needed. Survey of existing bioeconomic models EFIMAS 133 Table 24. Requirements for applying the model. FLR Case study Requirements Demersal Flatfish in the North Sea • ICES assessment • Fleet data • Cost structure for the fleets explicitly modelled. Demersal Roundfish in the North Sea • Some aggregations of fleets needed. • Estimations of price flexibilities required. Salmon Fisheries in the Baltic Sea • Assessment of Salmon, Sprat and Trouts are required. • Information of the three fisheries (offshore coastal and river. • Fleet cost structure. Nephrops fisheries in the East Atlantic • ICES assessment. Northern hake in the East Atlantic • ICES assessment • Fleet data • Biomass of key species (other than hake) • Catches of key species (other than hake) • Fleet cost structure. Swordfish fisheries in the Mediterranean • ICCAT assessment. • Fleet data. • Cost structure for the fleets explicitly modelled. Hake in the Mediterranean • Landings and discards • Fleet data including economics. Cod fisheries in the Baltic Sea • Effort standardization • Gear selectivity and hand sorting ogives, • Calibration specific catchability (Observed vs. simulated landings). 14.2.2 Model language and platform characteristics All the models have been developed using FLR, so they have been programmed using R which is available as free software under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS. It does not have to be purchased. 14.2.3 Format of model output In principle there are no limits for the model output. Tables can be arranged in plain text (*.txt) or in coma separated (*.csv) or even as Microsoft Excel files (*.xls). Figures are also available as (encapsulated) postscript (*.ps, and *.eps), Joint Photographic Experts Group (*:JPEG and *.JPG), Graphics Interchange Format (*.gif) or even bitmap (*.bmp). In practical terms not all these types of outputs have been implemented for all the case studies, so they have to be decided on before running the code which takes coding time. If the output Survey of existing bioeconomic models 134 EFIMAS decided on has not been previously implemented it will have to be coded. The same is applicable for the graphical outputs. 14.2.4 Producing an advice In principle the FLR is able to provide any kind of advice. However it has been designed to perform MSE approximations, which makes it an extraordinary powerful tool for dealing with the economic evaluation of management/recovery/emergency plans. FLR can be run on all complexity levels with different types of input being a full flexible model framework and being object oriented, i.e. it can be adapted/modified/informed to whatever level needed. As a toolbox, it also provides a framework to accomplish the evaluation of generic harvest control rules as in STECF (SGRST) Harvest Control Rules I & II in 2007 and STECF (SGRST) Harvest Control Rules III in 2008. It is fully integrated with the assessment which implies that discards reduction programs can easily be assessed. Furthermore, fishing management imnplementation systems can also be assessed however, this part is still under development in COBECOS. Running time for these kinds of models is dependant on the hardware and the software. Considering as standard in 2009 an Intel Core 2 DUO (2.2 Ghz) processor with 2 G of memory and run on a Microsoft Windows platform. The running time can be approximated as described in the following table Table 25. Time requirements for running a model with one species and one fleet: an approximation. FLR. Type of advise Time by year and iteration Simulation of the population , with fleet dynamics, running and XSA, 4 seconds and economic indicators + Economic behaviour feed back 8 seconds using linear relationships + Economic behaviour feed back 16 seconds using linear relationships A standard MSE approximation for a prediction of 20 years using a stochastic simulation (100 iterations) takes from 4 hours to 8 hours if only one fleet is considered. It has to be said that there are other possibilities like running it in computer grids which reduce the computing time. Furthermore, FLCore is still under development (see below the FLR 2.0 release) which will Survey of existing bioeconomic models EFIMAS 135 make the models more efficient in terms of computing time (10 times quicker according to STECF, subgroup on Harvest control rules, 2008). The steps to be considered for producing the advice are: • Condition the model. Introduce the information (data) needed. • Code the management advices that want to be provided. • Test if the model works using a small number of iterations and short term prediction. • Run the model. The most recent stable version of FLCore is The Golden Jackal (FLCore 1.4.4), released: 1 February 2007 for R 2.4.1 and R 2.5.0. By the time this report is finalised FLR 2.0 is to be released. It improves the previous versions as well as adds new features such as: • The need to run multiple iterations for stochastic analyses. • The diversity of OM and MP addressed, calling for more flexible and extended “lego blocks”. • The increased focus on multiple units, e.g. several stocks, several fleets. • The increased focus on economic and fleet behaviour issues. • The diversity and complexity of HCR to be evaluated. • The need for improved computing speed and documentation. Table 26. Version of FLR evaluated. Case study FLCore Demersal Flatfish in the North Sea 2.2-1 Demersal Roundfish in the North Sea 1.99-8 Salmon Fisheries in the Baltic Sea 2.0-2 Nephrops fisheries in the East Atlantic 1.99-8 and FLIsis Northern hake in the East Atlantic 1.99-3 Swordfish fisheries in the Mediterranean 1.4-3 Hake in the Mediterranean 1.4-3 Cod fisheries in the Baltic Sea 1.4-4 The software needed (R) is free and it can be run in a Windows, Apple or Linux/Unix platform so there is not any financial cost. For performing an advice, it is necessary to have at least one FLR programmer (an advanced R user will need time to get into the FLR) an expert on the concrete stock assessment and management and an expert on interpreting the results obtained (biological and economic results). In any case a FLR programmer is a key person in all this procedure. Survey of existing bioeconomic models 136 EFIMAS 14.2.5 Model use and full list of references Full list of references, including reports, working documents and scientific papers are listed below. Table 27. Full list of references, including type of document, title and case study. EFIMAS. Type Title Model CS WEB EFIMAS WIKI General. http://wiki.difres.dk/efimas/doku.php?id=efimas All case studies WEB FLR WIKI General. http://www.flr-project.org/doku.php All case studies Report ECONOWS (2008). ECONOWS: Report from the Economic Workshops General. of EFIMAS. All case studies Available at http://wiki.difres.dk/efimas/doku.php?id=efimas1:wp3:3- 6:econows:main&s=econows Report ICES SGMixMan 2006, 2007, 2008 General. All case studies Scientific Kell, L.T., Mosqueira, I., Grosjean, P., Fromentin, J-M., Garcia, D., General. Journal Hillary, R., Jardim, E., Mardle, S., Pastoors, M. A., Poos, J.J., Scott, F., All case studies Scott, R.D. 2007. FLR: an open-source framework for the evaluation and development of management strategies. ICES J. Mar. Sci. 64, 640–646. Scientific Hamon, K., Ulrich, C., Hoff, A. and Kell, L. 2007. Evaluation of Demersal Journal management strategies for the mixed North Sea roundfish fisheries with Roundfish in the the FLR framework Presentation to MODSIM07 Conference, 10-13 North Sea december 2007, Christchurch, New Zealand with Hamon et al. peer- review publication in conference proceedings. Scientific Hoff, A., Frost, H. (2008). Modelling economic response to harvest and Demersal Journal effort control in the North Sea cod fishery. Aquat. Living Resour., 21 Roundfish in the (forthcoming). North Sea Working ICES, 2007. Results of the evaluation of the biological effects of North Demersal document Sea cod recovery plans in relation to various management measures using Roundfish in the the modelling FLR to evaluate management plans under the umbrella of North Sea current North Sea cod recovery plans are presented in ICES WGNSSK (2006), section 16 and Working Document 18: Working ICES, 2007. Results of the evaluation of North Sea haddock management Demersal document plan in relation to various management measures using modelling in Roundfish in the F/RLR is presented in ICES WGNSSK Report (2006), section 16: ICES North Sea WGNSSK 2006, section 16 and Working Document 18. ICES C.M. 2007/ACFM:35 Working Needle, C. L. (2006). Evaluating harvest control rules for North Sea Demersal document haddock using FLR. Working Paper for the ICES Working Group on Roundfish in the Methods of Stock Assessment, Galway, Ireland, 21-26 June 2006. North Sea Working Needle, C. L. (2006). Further evaluations of harvest control rules for Demersal document North Sea haddock using FLR. Discussion document for FRS and Roundfish in the SEERAD. North Sea Working Needle, C. L. (2006). Revised FLR-based evaluation of candidate harvest Demersal document control rules for North Sea haddock. Working paper for the ICES Roundfish in the Advisory Committee for Fisheries Management, Copenhagen, October North Sea 2006. Scientific Catchpole, T.L., Tidd, A.N., Kell,L.T., Revill, A.S. and Dunlin,G. (2007), Nephrops Journal The potential for new Nephrops trawl designs to positively effect North Sea stocks of cod, haddock and whiting Fisheries Research Volume 86, Issues 2-3, September 2007, Pages 262-267 Scientific Kell,L.T., Dickey-Collas,M., Nash,R.D.M., Pilling,G.M. and Roel,B.A. Herring Journal (submitted). Lumpers or splitters? evaluating recovery and management plans for metapopulations of herring, ICES J Marine Sci. Survey of existing bioeconomic models EFIMAS 137 Scientific Nash R.D.M., Dickey-Collas, M., Kell L.T. (in press). Stock and Herring Journal recruitment in North Sea herring (Clupea harengus); compensation and depensation in the population dynamics. ICES J Marine Sci. Scientific Pilling,G.M., Kell,L.T, Hutton,T., Bromley,P.J.,, Tidd,A.N., and Demersal Flatfish in Journal Bolle,L.J. 2008, Can economic and biological management objectives be the North sea achieved by the use of MSY-based reference points? A North Sea plaice (Pleuronectes platessa) and sole (Solea solea) case study. ICES Journal of Marine Science: Journal du Conseil 2008 65(6):1069-1080; Scientific Oostenbrugge, J.A.E. van, Powell, J.P., Smit, J.P.G., Poos, J.J., Kraak, Demersal Flatfish in Journal S.B.M., Buisman, E.F.C. 2008. Linking catchability and fisher behaviour the North sea under effort management. Aquatic Living Resources 21 (3) 265-273 Scientific Tserpes, G. and Peristeraki, P., 2007. Effects of a seasonal closure of the Mediterranean Journal Mediterranean swordfish fisheries on the stock production levels. ICCAT Swordfish Collective Volume of Scientific Papers, 60: 2059-2062. Working Tserpes, G., Tzanatos, E., Peristeraki, P., Placenti, V. and Kell, L., 2008. Mediterranean document A bioeconomic evaluation of different management measures for the Swordfish Mediterranean swordfish. ICCAT SCRS/2008/026. Scientific Tserpes,G., Tzanatos, E., Peristeraki1, P., Placenti,V. and Kell,L.T. Mediterranean Journal (2009). A bioeconomic evaluation of different management measures for Swordfish the Mediterranean swordfish. Fisheries Research 96: 160-166. Report Grift, R., W. Dekker, O. van Keeken, S. Kraak, B. van Marlen, M. Demersal Flatfish in Pastoors, J.J. Poos, F. Quirijns, A. Rijnsdorp, I. Tulp. 2005. Evaluation of the North sea management measures for a sustainable plaice fishery in the North Sea. RIVO report C019/05. Report STECF 2006. Impact assessment of long-term management plans for sole Demersal Flatfish in and plaice. http://old- the North sea stecf.jrc.it/meetings/sgeca/0605/stecfreportanannex.pdf Report Poos, J.J., Machiels, M.A.M., Pastoors, M.A. 2006. Investigation of some Demersal Flatfish in management scenarios for North Sea sole and plaice in 2006 and beyond. the North sea CVO report 06.004. Report Pastoors, M.A., Poos, J.J., Machiels, M.A.M. 2006. Evaluation of a Demersal Flatfish in proposed management plan for Northsea flatfish. http://flr- the North sea project.org/doku.php?id=applications:nsrac Report Machiels, M.A.M. Kraak, S.B.M., Poos, J.J. 2008. Biological evaluation Demersal Flatfish in of the first stage of the management plan for fisheries exploiting the the North sea stocks of plaice and sole in the North Sea according to Council Regulation (EC) no 676/2007 IMARES report C031/08 Report Machiels, M.A.M., Kraak, S.B.M., van Beek, F.A. 2007. Evaluation of a Demersal Flatfish in management plan as proposed by the European Commission in 2006 for the North sea fisheries exploiting stocks of plaice and sole in the North Sea. IMARES report C011/07 Working J. Haralabous, CD Maravelias, G. Tserpes & C. Papaconstantinou. 2007. Mediterranean hake paper Developing a FLR operational model for evaluation of fisheries management strategies: an application to Mediterranean hake fishery. 38th CIESM Congress, 9-13 April 2007, Istanbul, Turkey. Scientific Catchpole, T.L. , A.N. Tidd, L.T. Kell, A.S. Revill and G. Dunlin. 2007. Nephrops Journal The potential for new Nephrops trawl designs to positively effect North Sea stocks of cod, haddock and whiting Fisheries Research Volume 86, Issues 2-3, September 2007, Pages 262-267. Scientific Bastardie, F., Nielsen, J.R., and Kraus, G. 2008. Management Strategy Baltic cod Journal Evaluation framework for the Eastern Baltic cod fishery to test robustness of management against environmental conditions and fleet response scenarios. (Submitted ICES J. Mar. Sci.) submitted paper Working Horbowy, J. 2005. Cod assessment model with tuning to survey estimates Baltic cod paper of total mortality. Working paper to the WGBFAS, Hamburg, 12-21 April, 2005 Scientific Kraus, G., Pelletier, D., Dubreuil, J., Moellmann, C., Hinrichsen, H.H., Baltic cod Journal Bastardie, F., Vermard, Y., and Mahevas, S. 2008. A model-based evaluation of marine protected areas for fishery management in the case of strong environmental forcing – the example of Eastern Baltic cod (Gadus Survey of existing bioeconomic models 138 EFIMAS morhua callarias L.). (Accepted; DTU-Aqua). Scientific Nielsen, J.R., Bastardie, F., Nielsen, J.N., and Pedersen, E.M.F. (2008, In Baltic cod Journal review). Whole fishery selectivity, fishing patterns, and fleet catchability dynamics in international Baltic Sea cod fisheries – from observed spatio- temporal patterns in resource availability and fleet specific selection, relative fishing power, and fisherman sorting behaviour. (In review) ICES J. Mar. Sci. Working García, D. and Mosquiera, I. 2005. FLR: A Framework for Fisheries N. Hake paper Management in R. An Application to Northern Hake. WD presented at the WGHMM hold in Lisbon 10-19 May 2005. Working García, D., and Mosqueira, I. 2006. “A generic Operating Model using N. Hake paper FLR: An aplication to Northern Hake”. Working Document, ICES WGHMM,2006. Working García Dorleta. 2007. Northern and Southern Hake Recovery Plans A N. Hake paper Preliminary Analysis. WD presented at the WGHMM2007 in Vigo. May 2007. Working García D., Prellezo R. & Marina Santurtún. Update on EFIMAS Project: N. Hake paper Evaluation tool for Alternative scenarios for Northern Hake fisheries management (Management Strategies Evaluation (MSE)) NWW RAC Focus Group on Northern Hake Long Term Management 21st February 2008, Bilbao Working García D., Prellezo R. & Arantza Murillas 2008. Management Strategy N. Hake paper Evaluation of Northern Hake and associated fisheries: TAC versus Effort based Management. Symposium of Oceanography. Donostia. Spain. Working García, D., Santurtún M., Iriondo, A. and Quincoces I. (2008). Northern N. Hake paper Hake Long Term Management Plan Evaluation. Working Document presented in ICES WGHMM, Copenhagen, May 2008. Scientific Prellezo R, Garcia D, Santurtún M, Motos L (2008) Anticipando la N. Hake Book eficacia de modelos de gestión alternativos mediante simulación: el caso del stock norte de merluza. In: Laxe FG (ed) Lecciones de Economía Pesquera. Netbiblo, La Coruña, pp 101-118 14.2.6 Institute and key personnel Many institutes are found as developers and users of this framework. From those highlight: - DTU-Aqua and the EFIMAS project coordinator: Rasmus Nielsen. - CEFAS and the COMMIT project coordinator: Lawrence Kell. - FOI and Hans Frost as chairman of the ECONOWS workshops. - LEI and Jeff Powel as reporter and compiler of the ECONOWS report. For the models developed in the case studies the relevant contacts are: Table 28. Key persons and institutes for the models developed. FLR. Case study Institute Person Demersal Flatfish in the North Sea IMARES Sarah Kraak Demersal Roundfish in the North Sea DTU-Aqua Rasmus Nielsen Salmon Fisheries in the Baltic Sea FGFRI Matti Salminen Nephrops fisheries in the East Atlantic CEFAS Andy Revill Northern hake in the East Atlantic AZTI-Tecnalia Dorleta Garcia Swordfish fisheries in the Mediterranean HCMR George Tserpes Hake in the Mediterranean European University Cyprus Costas Papaconstantinou Cod fisheries in the Baltic Sea DTU-Aqua Rasmus Nielsen Survey of existing bioeconomic models EMMFID 139 15. EMMFID 15.1 General description 15.1.1 Model objectives and dimensions The Economic Management Model for Fisheries in Denmark (EMMFID) was developed as an all-inclusive bioeconomic numerical allocation model for the fisheries in Denmark (Frost and Kjærsgaard 2003). The aim of the EMMFID project was to model fishing activities over a one year model period in order to clarify the economic consequences of fishery management regulations and industry activities while considering sustainability of stocks. The focus of EMMFID thus lies on the economic analysis. The original model description (Frost and Kjærsgaard 2003) does not include a biological model component; hence the name: economic management model. EMMFID calculates the optimal number of vessels and days at sea, resolved on the level of five model dimensions (species, fleet, month, area, homeport of vessel) for the entire Danish fishery. The model has been designed to consider economic variables such as economic rent, employment and use of capital as the objectives subject to environmental constraints including minimum levels of fish stocks based on biological advice. The choice of objective function depends on which scenario is analysed. The objective function represents the criterion that is optimized. An optimal solution is found as a feasible combination of number of vessels and number of days at sea where the value is at maximum. Examples of different objectives could be: • Maximise contribution margin (short term) / profit (long term). • Maximise employment. • Maximise fleet size. The original EMMFID model version covers the entire commercial fishery in Denmark, disaggregated with respect to the five model dimensions: • Fleet segments (index f, 26 alternatives) • Homeports, aggregated on county level (index c, 14 alternatives) • Fishing areas (index a, 34 alternatives) • Species (index s, 118 alternatives) • Seasons by month (index m, 12 alternatives). Time, in terms of infinite time horizon, and fish stock dimensions are not included in the EMMFID approach, as the model comprises numerous stocks with unknown time/stock Survey of existing bioeconomic models 140 EMMFID interactions, especially in the long run. Instead the EMMFID model uses fish stock inputs based on biological scientific models and advice. Hence, an endogenous biological component is missing. The EMMFID model framework has so far been applied to two case studies: An economic analysis of the Danish fisheries (Frost and Kjærsgaard 2005, in Danish) and an analysis of effort allocation of flatfish fisheries in and around the North Sea plaice box (Kjærsgaard and Frost 2008). The latter does contain a distinctive age-structured biological model component. 15.1.2 Model structure EMMFID is a numerical allocation model, set up in a static framework as a linear constrained optimization programme. It models the decision process that gives rise to behavioural relationship, i.e. fishermen maximise profit subject to a number of constraints imposed by the management regime. This type of behavioural modeling implies determining which vessels are going to be operating and the scale of operation. Hence, the endogenous variables in the model are fleet size (which vessels are going to be operating) and fleet activity (i.e. fishing effort, scale of operation), measured in number of vessels and number of days at sea, respectively. The average number of days at sea is determined implicitly (for all vessels with respect to the relevant vessel fleet segment); the total number of days at sea can be subsequently derived. Profit is maximized for the whole industry. The focus of EMMFID lies on the economic analysis, and the original model (Frost and Kjærsgaard 2003) does not include a biological model component. Nonetheless, Kjærsgaard and Frost (2008) have shown that it is possible to link the economic EMMFID model framework to a biological component. Figure 11 shows an example of the bio-economic model structure, i.e., when the economic model component is coupled to a biological component (Kjærsgaard and Frost 2008). Survey of existing bioeconomic models EMMFID 141 Figure 11. Outline of a bio-economic model, following the EMMFID approach 15.1.3 Type of advice and time range EMMFID is meant as an analysing tool of fleet and effort behaviour. The model is static, which means that a model-run covers the behaviour in one given year (the model period), and there are no built-in dynamics. The comparative static makes it possible to compare scenarios and indicate directions with respect to future fleet structure adjustments, i.e. comparative static analyses are performed. The path of how to reach the optimum is not investigated. The main purpose is to serve as a tool in ‘what-if’ (simulation) and ‘what’s best’ (optimization) analysis. EMMFID is designed to shed light on management questions such as, what is best if certain objectives are pursued subject to technological and biological restrictions. EMMFID can calculate the potentially required restructuring of the entire Danish fishing fleet (in terms of species, landings, fleets, costs), if any change in quotas is implemented. The time horizon that can be investigated ranges from short to long term. In the original EMMFID setup for the Danish fishery, optimal fleet size and optimal effort level are calculated for a chosen year. It is possible to compare myopic year to year changes in effort and fleet adjustments, but it is also possible to investigate medium to longer term behaviour, e.g. studying time frames of 30 years (Kjærsgaard and Frost 2008). As EMMFID does not include an endogenous biological component, it does not depend on existing biological stock assessment models. However, highly detailed input on catches, costs and earnings is needed, if the model is to be applied to other case studies. Survey of existing bioeconomic models 142 EMMFID 15.2 Implementation details 15.2.1 Data requirements A large general fisheries database DFAD (Danish Fisheries Analytical Database) has been constructed in collaboration between the Danish Fisheries Directorate (FD), the Danish Institute for Fisheries Research (DIFRES), and the Fødevareøkonomisk Institut (FOI). For the specific Danish fishery model, data from the year 2000 serve as input. The two primary data sources are: 1. Databases prepared and maintained by Danish Fisheries Directorate (FD). 2. Account statistics prepared by the Statistical Department at the Fødevareøkonomisk Institut (FOI). The FD databases supply information on vessel characteristics, activity, catch and catch value. The FOI account statistics deliver information on average costs per vessel, such as the following: Fuel and lubricants, maintenance, sales cost, insurance and crew payment. Assets and liabilities are calculated, and activity is related to capacity. Furthermore it contains information on family income, consumption and savings, for participants of the fishery. Days at sea are available on a trip-basis. During a trip a vessel most likely catches several different species, some of which are not related (via quotas) to the same management areas. Activity is then allocated to catches and management areas according to the respective share of the total catch value. The data preparation for EMMFID is extensive. In the Danish fisheries version, it uses a lot of information in particular the cost per unit effort down to five dimensions. This high level of specific and detailed data input is not necessary, though. The model can also run with fewer dimensions and less information for each dimension (fewer species, fleets, etc.), as has been shown in the North Sea flatfish case. In this case, the model is easier to set up and faster to work with. Normally, a month or more by guess is required to organize information after it has been collated on the very detailed level that EMMFID works on. Time can be saved if the necessary data is collected and edited regularly each year. 15.2.2 Model language and platform characteristics All the models are programmed in the optimization software GAMS (General Algebraic Modelling System). The price for one license is around 1200 € (Hans Frost, personal communication). A person who wants to run EMMFID should have programming skills and experience, as well as an understanding of economics, since the focus of the model lies on an economic analysis. Survey of existing bioeconomic models EMMFID 143 FOI has similar but less detailed models available in Excel. 15.2.3 Format of model output In the case of the Danish fishery (Frost and Kjærsgaard 2005), results were presented in the form of tables, indicating, for instance, the optimal fleet size (number of vessels per fleet segment) for different scenarios for a given year. In the North Sea flatfish case study, the model runs over 30 years. This setup makes it possible to investigate temporal trends. Kjærsgaard and Frost (2008) presented graphs showing the annual development of spawning stock biomass (SSB) and profit for different scenarios. The following model output, derived from either optimisation or simulation runs, could be compared: Optimal fleet size (number of vessels per fleet segment) Optimal vessel activity (days at sea) Economic indicators, e.g. value of landings, costs, contribution margin/profit Biological indicators, e.g. SSB (when coupled to a biological component) Scenario-specific results can also be compared among each other, e.g. differences in economic indicators of the different fleet segments within one scenario. 15.2.4 Producing an advice A management advice can be produced by defining management scenarios and comparing the outcomes of the different scenario runs. These could be based on both, optimization or simulation. The data preparation for EMMFID is extensive, but the time for preparing and running the model depends on the level of detail is wanted. The Danish fisheries model version uses a lot of very detailed information and data and it thus very time demanding. Running the model with fewer dimensions and less information for each dimension (as has been done in the North Sea flatfish case), it is much easier and faster to set up and run the model. Normally, a month or more by guess is required to organize information after it has been collated on the very detailed level that EMMFID works on. Time can be saved if the necessary data is collected and edited regularly each year. Programming skills and experience with the programming language GAMS are needed to run the model. The model code can be obtained from the authors Frost and Kjærsgaard. Survey of existing bioeconomic models 144 EMMFID In terms of the financial costs, EMMFID requires purchasing a GAMS licence that costs around 1200 € (Hans Frost, personal communication). GAMS consists of a language compiler (ca. US$ 640), and a set of integrated high-performance solvers (prices can vary from US$ 320 to 1920US$). 15.2.5 Model use and full list of references Model description: Frost, Hans and Kjærsgaard, Jens (2003) Numerical Allocation Problems and Introduction to the Economic Management Model for Fisheries in Denmark – EMMFID. Fodevareokonomisk Institut, Report. Nr. 159, Copenhagen 2003. http://www.foi.life.ku.dk/Publikationer/Rapporter/~/media/migration%20folder/upload/foi/docs/ publikationer/ rapporter/nummererede%20rapporter/150-159/159.pdf.ashx. Model application to the Danish fishery (in Danish): Frost, Hans and Kjærsgaard, Jens (2005) Overkapaciteten i den danske fiskerflåde. (Section 7). Fodevareokonomisk Institut, Report Nr. 175, Copenhagen 2005. http://www.foi.life.ku.dk/Publikationer/Rapporter/~/media/migration%20folder/upload/foi/docs/ publikationer/rapporter/nummererede%20rapporter/170-179/175.pdf.ashx. Model application to the North Sea flatfish fishery, coupled to an age-structured biological model component (time dimension: year): Kjærsgaard, Jens and Frost, Hans (2008) Effort allocation and marine protected areas: is the North Sea Plaice Box a management compromise? ICES Journal of Marine Science Vol. 65 pp. 1203-1215.http://icesjms.oxfordjournals.org/cgi/reprint/fsn088?ijkey=v0znUeWyRVv9cjt& keytype =ref 15.2.6 Institute and key personnel FOI (Fødevareøkonomisk Institut) is the key institute involved in the development of EMMFID and key personnel include Hans Frost and Jens Kjærsgaard. Survey of existing bioeconomic models MEFISTO 145 16. MEFISTO 16.1 General description 16.1.1 Model objectives and dimensions The purpose of the MEFISTO (MEditerranean FIsheries Simulation Tool) model is to produce bio-economic simulations under alternative management scenarios to emulate fisheries management characteristic of the Mediterranean. Originally MEFISTO was developed jointly by the Insitut de Ciències del Mar, (CSIC) and University of Barcelona (UB), Spain between 1998-2000 this review will evaluate MEFISTO version 3.0 which was further developed under the framework of the European Union funded research project BEMMFISH (Bio-economic analysis of Mediterranean fisheries) (BEMMFISH, 2004). Mediterranean fisheries are predominantly artisanal and target multi-species stocks. Since effort control is primarily used to control fishing activity in the Mediterranean, MEFISTO adopts effort as input control given that are no regular stock assessments are undertaken in the Mediterranean. Besides effort limitation MEFISTO can assess other technical and economic management measures. The dimensions of MEFISTO are multi-species (more formally multi- stock) and multi-fleet to reflect the catch composition, multiple fleets and technical gear interactions indicative of Mediterranean fisheries. 16.1.2 Model structure MEFISTO consists of a biological and an economic sub-model of which there are three main modules; the stock box, market box and fisherman box (Figure 12).These three modules incorporate stock dynamics (biological sub-model), market and fisher behaviour (economic sub- model) and are linked via various functions; harvesting, fishing mortality, price formation, cost of harvesting, investment, entry-exit dynamics, fishing effort and catchability. Survey of existing bioeconomic models 146 MEFISTO Biological sub-model (Multi-stock) STOCK Effort, Catchability Catches (Multi-gear) (Prices) FISHERMAN MARKET Revenues Economic sub-model Figure 12. Conceptual representation of MEFISTO Biological sub-model: The stock box simulates multi-species age-structured stock dynamics, in which there are several main target species (whose behaviour, biomass, reproduction and growth dynamics are known) and several secondary species (of which the dynamics are unknown but catches are proportionate to one of the main target species). MEFISTO does not incorporate biological or predation interactions but rather technical fleet interactions. The main target species are modelled explicitly using age-structured stock-recruitment relationship of which there are four pre-programmed functions; constant, linear, Beverton and Holt or Ricker. Dynamics of the secondary species are not known and hence the harvest is a function of one of the targeted species. Spatial management cannot be considered as the species are not considered in space, all of the stocks are assumed to be equally available to fishers through fishing mortality. The stock box receives effort and catchability information from the fisherman box and generates catches which are fed into the market box. Economic sub-model: The biological and economic sub-models are linked to provide management advice. The economic sub-model is based on behavioural rules attributable to Mediterranean fishers. Survey of existing bioeconomic models MEFISTO 147 MEFISTO manages vessels by fleet and country/area, individual vessel behaviour and strategy is simulated by allowing changes in efficiency to modify fishing mortality. The market box converts catches into fisheries rent via a flexible price function and revenue is then fed into the fishermen box. Fishers’ economic behaviour is simulated in the fishermen box via a set of behavioural rules (Figure 13) of which effort (E) and catchability (q) are an output which subsequently feed into the stock box to complete the cycle for one unit in time. The fisherman and stock box are linked linearly via catchability and effort where fishing mortality (F) is F=q*E. Effort is defined in fishing time and is limited by legislation (but can be modified the ‘fisheries manager’), fishers can only increase catchability (efficiency) via capital investment in new technology (catchability is assumed to be a function of investment and time). Fishers are assumed to exhibit profit maximising behaviour and hence choose the most efficient combination of factors to achieve this objective. The behaviour rules in the fisherman box are set to imitate the micro-scale economic behaviour of Mediterranean fishers (vessels). The fishermen box simulates individual vessels entry-exit and investment decisions based upon the profit generated in the past. Fishers follow a set of behavioural rules and make investment and effort decisions based upon profit levels (‘outcomes’). Investment is a function of profit, such that positive profits allow firms to invest in fishing activity and make further profits, whilst a loss may see firms leave the industry or reduce effort accordingly (see Lleonhart et al., 2005 for more detailed account of behavioural rules and ’outcomes’). Profit achieved in period t is output from the market box and gets fed into the fisher box as the input in the decision process (behavioural rules) in year t+1. Effort and catchability are the output from the behavioural rules in the fisher box and are input into the stock box. Interaction between the economic- and biological-sub models is on an individual and métier level. 16.1.3 Type of advice and time range Mediterranean fisheries managers are unable to control landings since an array of management measures are adopted, also management is not adaptive as regular stock assessments are not conducted and so MEFISTO mimics effort control. The harvest control rule directly influences the days-at-sea a vessel may fish through a legal maximum that can be modified by the fisheries manager. Fishers are permitted to fish for a maximum number of days as specified by law and also as restricted by revenue through the behavioural rules. In addition MEFISTO is capable of assessing the impact of nine other economic and technical management measures; Technical measures: Survey of existing bioeconomic models 148 MEFISTO • Change in annual fishing time (days), • change in daily fishing time (hours), and • change in selectivity pattern. Economic measures: • Change in market fish price, • volume of fish imports, • change in decommissioning vessel price (dismissal price), • withdrawal of vessels through decommissioning (dismissal), • change in fuel price, and • subsidies. The impact of these technical and economic measures can be simulated as either a cyclical or single user defined ‘event’, to which a value can be assigned for specified levels; country (fuel price), fleet (subsidies, dismissal price, fishing hours, annual fishing days), vessel (active or deactivate vessels), cohort (selectivity and imports) to act as an exogenous shock. Several events may run simultaneously and these ‘events’ can be used to simulate management objectives. MEFISTO can run either deterministic or stochastic simulations. MEFISTO considers stochasticity by producing a set of possible outcomes with a probability distribution. The user can determine the number of iterations (default is 100) for each projection year, value for the random seed and set the confidence interval (default is 95%). Natural mortality, stock- recruitment, catch vector and fish price can be specified to vary stochastically around a mean value with a user specified distribution function (uniform, normal or lognormal). Sensitivity analysis cannot be performed in MEFISTO, but alternatively two different simulations could be run and the results contrasted and analysed. Table 29. Performance measure indicators. MEFISTO Stock indicators Cohort indicators (by species or age) Mean stock biomass Natural mortality Spawning stock biomass Initial number Recruits Mean number Catch Mean biomass Mean number Catch Average fishing mortality Fishing mortality Average total mortality Catch by gear Catch by gear Catchability Fleet indicators Vessel indicators (by fleet or vessel) Total costs Trade costs Profits Daily costs Effort Labour costs Survey of existing bioeconomic models MEFISTO 149 Catchability Average salary Capital Annual costs Number of vessels Annual variable and fixed costs MEFISTO can produce performance measure indicators from the simulations to allow comparison between management scenarios (table above). Besides the number of simulations, iterations and management scenarios (or ‘events’), the following arguments in a dimension can be changed and specified by the user; • Number of species (target and secondary species permitted). • Number of fleets. • Multiple vessels per fleet. The biological and economic sub-model both operate on the temporal scale of one year. A number of the variables can be defined in hours a day or days a year to facilitate data entry but no mathematical functions are on a temporal resolution of less than a year. MEFISTO has been shown to work over the short-term (Maynou et al., 2006) but can also be used for medium- to long-term simulation but as with all models the reliability and certainty in model simulation results is likely to decrease over the time horizon. 16.2 Implementation details 16.2.1 Data requirements MEFISTO requires good biological and economic data to run. The fishermen box comprises of parameters at the country, fleet and vessel level. Where possible data should be disaggregated to individual vessels as MEFISTO can simulate individual vessel entry-exit and investment decisions and therefore operates best with individual vessel data but has flexibility to be run with fleet segment data. The input data required for MEFISTO comprises of the following seven worksheets (which can easily be imported into MEFISTO) and defines the initial conditions (period t0 ) for the biological and economic sub-models. Table 30. Species worksheet. MEFISTO Excel name Description Notation a a parameter of the length-weight relationship A b b parameter of the length-weight relationship B Linf L∞ parameter of von Bertalanffy growth function L∞ K k parameter of von Bertalanffy growth function k t0 t0 parameter of von Bertalanffy growth function t0 Ncohorts number of cohorts for each stock -- stockname Name of the stock -- Survey of existing bioeconomic models 150 MEFISTO Table 31. Cohort worksheet. MEFISTO Excel name Description Notation stockname name of the stock -- age age of cohort index a Number number of individuals in the cohort Na Mat proportion of mature individuals at age Ia M natural mortality coefficient at age Ma Table 32. Recruitment worksheet. MEFISTO Excel name Description Notation stockname name of the stock -- type integer indicating the type of stock-recruitment function; ε 0 = Constant recruitment, R = N0e ε 1 = Linear recruitment, R = αSSBt −k e 0, 1, 2 or 3 α1SSBt −k ε 2 = Beverton and Holt model, N0,t+1 = e 1+ β1SSBt −k −β 2 SSBt−k ε 3 = Ricker model, N0,t +1 = α2SSBt −k e e rec1 parameter N0, α, α1, α2 in stock-recruitment function N0, α, α1, or α2 rec2 parameter β1, β2 in stock-recruitment function β1 or β2 k age of recruitment k epsilon normally distributed random variable N ~ (0, σ2); eε for lognormal ε recruitment distribution Table 33. Interact worksheet. MEFISTO Excel name Description Notation stockname name of the stock -- age age of cohort index a F1 – F6 fishing mortality (by fleet or fishing gear, age class) from fleet 1 to Fag fleet 6 S1 – S6 selectively factor of each fleet by age class from fleet 1 to fleet 6, Sag usually 1 d1 – d6 proportion of discards by fleet and by age class dag qa1 – qa6 initial catchability coefficient by age for fleet 1 to fleet 6, usually 1 qag Survey of existing bioeconomic models MEFISTO 151 Table 34. Fleet worksheet. MEFISTO Excel name Description Notation fleetname name of fleet -- NV number of vessels in fleet -- dismissal price paid for decommissioning (€/GT, euros per gross tonnage) disg Part share of total revenues belonging to owner after discounting trade c3g and fuel costs annincq annual increment of catchability due to technological progress τg ModKq increment of catchability due to investment in capital hg expect Proportion of profits invested in capital ug NHDmax Activity: Max number of hours a day by law of physically possible NHDmax,g NFDmax Activity: Max number of days a year by law of physically possible NFDmax,g NHD Activity: Average number of hours a day NHDg NFD Activity: Average number of days a year NFDg ice daily consumption of ice (€/day, euros per day) iceg CommCost commercial or trade cost, (% paid to fish market for sale of fish) clg maxcredit maximum amount of money lent by the bank as percentage of capital dg fuelprice price of fuel (€/l, euros per litre) paid by each fleet fpg oppC opportunity costs (costs of using capital invested) c6 finC financial costs (cost of paying the debt incurred with the bank) c7 varEff proportion of effort increase when profits are positive ∆Effg Table 35. Vessel worksheet. MEFISTO Excel name Description Notation vesselname name of vessel -- fleetname name of fleet -- K capital of the vessel Kν gt capacity expressed as gross tonnage (GT) GTν credit debt owed to the bank at time 0 Dν,0 consfuel fuel consumption (l/day, litres per day) fcν crewsize crew size of vessel (including owner if worker) csν otherDC daily costs other than fuel and ice oDCν annualC costs paid annually (engine repair, shipyard fees, mooring, fishing ACg license) percFC percentage of annual costs that are fixed or obligatory in order to FAC remain in fishery (mooring, licence etc). g percVC percentage of annual costs that are not obligatory, not met when profits are negative (costs that correspond to depreciation of capital i.e. VACg painting, repairs etc). active boolean value (0-1) indicating if the vessel was active at year 0 of the -- simulation pEff effort of vessel in days per year Eν q relative catchability of each vessel, relative fishing power of an Qν average vessel is 1 Survey of existing bioeconomic models 152 MEFISTO Table 36. Market worksheet. MEFISTO Excel name Description Notation vesselname name of vessel -- fleetname name of fleet -- g1 base or average price of main species γ1 g2 age-modifier of price to reflect that larger fish fetch higher prices γ 2 g3 offer-modifier of price related to catch to reflect that when quantity γ 3 on the market is high the price diminishes g4 offer-modifier of price related to imports γ 4 delta event-modifier of price (control variable) δ funct2sp function relating main species (i) to secondary species (j) 1: Cj = µij + νij Cj 1 or 2 νij 2: Cj = µijCj mu parameter µ in market relationship between main species (i) and µij secondary species (j) nu parameter ν in market relationship between main species (i) and νij secondary species (j) price average price of secondary species (€/kg, euros per kilogram) pj Biological data for target species can be taken directly from the DCR, for secondary species not covered by the DCR additional biological information can be collected by port sampling. Very little economic data for MEFISTO can be collected under the framework of “old” DCR (fuel costs and fish prices), as it is does not stipulate individual vessel data collection, the “new” DCR does not appear to be adopting this approach either. Ideally individual vessel data from fishing firms is required to run MEFISTO, but data can be aggregated to fleet level. Market information (species weights and prices) may be available through individual boat sales invoices and information surrounding vessel characteristics (fleet segments, power, tonnage etc) and fishing vessels (crew, gear, investment, costs, subsidies, and fishing effort) can be collected through individually designed surveys. The “old” DCR requires data to be collected on a species-by-species, the “new” DCR will place more emphasis on data collection from point of view of an ecosystem and fleet approach, under which regional fishing and fleet segments will have greater prominence which will help address the data requirements of MEFISTO. 16.2.2 Model language and platform characteristics MEFISTO 3.0 is a freeware stand-alone software package developed with a user-friendly drop down menu driven interface, the software can be downloaded at no financial cost with accompanying user guide from; www.mefisto.info. Survey of existing bioeconomic models MEFISTO 153 16.2.3 Format of model output Output can both be viewed and analysed directly in MEFISTO, alternatively graphics can be saved as Enhanced Window Metafiles or data can be exported into Microsoft Excel. To set-up tables data can be exported to Microsoft Excel and processed accordingly. Standard or customised graphical output can be produced for stock, cohort, fleet or vessel level analysis, or alternatively computed output can be produced for: • Mean biomass • Spawning stock biomass • Recruits • Average fishing mortality • Catch by stock • Catch by fleet • Effort • Catchability • Capital • Number of vessels • Profits • Total revenues • Total costs 16.2.4 Producing an advice Parameter estimation and data processing are required before MEFISTO can be applied. After data input files have been created (MEFISTO 3.0 provides blank Microsoft Excel template data file) the time taken to run the MEFISTO model is minimal (under a minute), but will increase relatively with number of simulations and iterations. To run MEFISTO 3.0 simply download it from www.mefisto.info and save the zip file to your computer, then proceed to install the model by double clicking on setup launcher (setup.exe) file which starts up the step-by-step installation process. To begin MEFISTO a shortcut icon will appear in the user’s Desktop or the model is accessible from the Start menu. There is no financial cost involved in running MEFISTO as the software package is freely and publicly available on the Internet. MEFISTO 3.0 was designed for Windows 98 or higher operating systems and a text editor; ideally Microsoft Excel should be used for the creation of data input files. Survey of existing bioeconomic models 154 MEFISTO Due to the user-friendly nature of the interface, no programming skills are necessary to run MEFISTO, however skills in economics and biology are required to parameterise and interpret the results. 16.2.5 Model use and full list of references MEFISTO 3.0 is the most recent version developed from MEFISTO 2.0 (COPEMED, 2001) under the framework of the European research project BEMMFISH funded by the Quality of Life Program of the European Union (Q5RS-2001-01533) to analyse the effect of alternative management measures controlling fishing effort. Three cases studies have been applied to MEFISTO version 2.0; Catalonia Hake, Tarragona sardine and anchovy and sardine and anchovy of Málaga all of which can be accessed at http://www.faocopemed.org/en/activ/ infodif/mefisto.htm, more recently adaptations of MEFISTO have been applied to; the Catalonian Hake fishery (Lleonart et al., 2003), a Brazilian hand-line and gillnet coastal fishery (Mattos et al., 2006), the Catalan red shrimp fishery (Maynou et al., 2006) and the Greek hake and red mullet fisheries (Merino et al., 2007). The list of references for this model is: BEMMFISH. 2004. Final Report. Bio-Economic Modelling of Mediterranean Fisheries. EU Research Program. Q5RS-2001-01533. COPEMED. 2001. MEFISTO: MEditerranean FIsheries Simulation TOol: A bioeconomic model for Mediterranean fisheries. (Online) Available at: .http://www.faocopemed.org/ en/activ/infodif/mefisto.htm. Lleonart, J., Maynou, F., Recasens, L. and Franquesa, R. 2003. A bioeconomic model for Mediterranean fisheries, the hake off Catalonia (western Mediterranean) as a case study. Scientia Marina, 67(1); 337-351. Lleonart, J., Franquesa, R , and Maynou, F. 2005. MEFISTO 3.0 – User’s Guide. Mediterranean Fisheries Simulation Tool: A bioeconomic model for Mediterranean fishers. (Online) Available at: http://www.mefisto.info/MEFISTO_userguide.pdf Mattos, S., Maynou, F. and Franquesa, R. 2006. Bioeconomic analysis of hand-line and gillnet coastal fisheries of Pernambuco State, north-eastern Brazil. Scientia Marina, 70(2); 335-346. Merino, G., Karlou-Riga, C., Anastopoulou, I., Maynou, F. and Lleonart, J. 2007. Bioeconomic simulation analysis of hake and red mullet fisheries in the Gulf of Saronikos (Greece). Scientia Marina, 71(3); 525-535. Survey of existing bioeconomic models MEFISTO 155 Maynou, F,. Sardà, F., Tudela., S., and Demestre., M. 2006. Management strategies for red shrimp (Aristeus antennatus) fisheries in the Catalan sea (NW Mediterranean) based on bioeconomic simulation analysis. Aquatic Living Resources. 19; 161–171. 16.2.6 Institute and key personnel MEFISTO is undergoing further development in collaboration with Marine Institute of Barcelona and Faculty of Economics at the University of Barcelona, the key personnel are Dr. Francesc Maynou (modeller/programmer/biologist) and Professor Ramon Franquesa (economist). Survey of existing bioeconomic models 156 MOSES 17. MOSES 17.1 General description 17.1.1 Model objectives and dimensions The purpose of MOSES (Models for Optimal Sustainable Effort in the Seas) is to provide estimates of the optimal distribution of fishing effort by gear and area. MOSES was developed by the Italian institute IREPA (Istituto Ricerche Economiche per al Pesca e l’Acquacolura) to emulate the multi-species and multi-gear artisanal fleets characteristic of Italy and this review examines the original MOSES model developed by IREPA in 1984-1985. Italian fisheries are predominantly artisanal (90% of fishing vessels are individual or family firms) and target multi-species stocks. Effort control is predominately used to control fishing activity, Triennial Plans are used to identify were fishing effort should be diminished in case of decreasing economic returns as result of overexploitation. The objective of MOSES is to produce the optimal distribution of fishing effort and to estimate the long-term effects of changes in allocation of fishing effort in terms of biomass, harvest and economic returns. In Italy factors of production (labour and capital) cannot easily be separated and so MOSES does not consider income distribution, consequently value added (remuneration of factors of production) is maximised according to fleet segments. MOSES is a dynamic multi-species, multi-gear, and multi-area bio-economic model characteristic of Mediterranean fisheries which can be used for simulation and optimisation. To estimate the optimal distribution of fishing effort (by gear and area) for various management scenarios, the objective function maximises value added subject to biological and socio- economic constraints given prices, unit cost, effort and catch dynamics (to avoid over or under exploitation) of the fishery. 17.1.2 Model structure MOSES is comprised of two main components; a biological and an economic sub-model. Biological sub-model: The biological sub-model is a catch-effort model capable of considering two different class of catch-effort relationship; a surplus production model (Schaefer or exponential) or a partially age-structured (Deriso-Schnute) model. Characteristic of the multi-species and multi-gear Mediterranean fisheries, the biological sub- model was developed to enable species to be caught by more than one gear (linked via a Survey of existing bioeconomic models MOSES 157 technical matrix). The input to MOSES is fishing effort by gear, species and area, whilst the output is optimal distribution of fishing effort. Fishing effort is defined as combination of fishing effort per gear and for catch of each species. Fishing mortality is related to fishing effort by means of a catch-effort model (age-structured or surplus production model). Economic sub-model: The economic sub-model represents the harvesting sector and is characteristic of Italian fisheries by encompassing artisianality and accounting for remuneration of factors of production. Using time series of catch and effort data and reference period data for unit fishing costs and prices the economic sub-model finds the optimal distribution of fishing effort given costs, prices, effort and catch dynamics. MOSES estimates the optimal fishing effort allocation (and optimal number of fishers) between fleet and regional segmentation (Tingley, 2005). Given the constraints are independent from the decision of the single fisher, MOSES can estimate the distribution of fishing effort (and therefore number of fishers) among different regions to take into account re-conversion of the system. MOSES maximises value added as opposed to profit, because as typical of Italian fisheries factors of production (capital and labour) are mixed and not easily separable. Value added corresponds to remuneration of factors of production and so the economic sub-model considers a production approach rather than income distribution. MOSES maximises value added by fleet according to gear/area and not by individual vessel, hence all fishers are assumed to behave homogenously and are price takers, the additive micro-economic behaviour of all fishing vessels is taken into consideration by the optimisation algorithm (SEC, 2006). The economic sub-model incorporates three types of labour contracts prevalent in Italian fisheries; wage contract, share contract and self-governing management contract. Given the dominance of share contract (80% of labour contracts (Arnason et al., 1997)) in Italy it is not appropriate to maximise profit since it is not possible to separate the remuneration of the vessel owner from that of the crew (is combined for calculation of value added). MOSES estimates the optimal distribution of fishing effort (from the catch-effort model) of the whole fleet, which maximises economic returns (value added), subject to biological constraints (species conservation) and an inertia constraint (socio-economic aspects). MOSES optimises using non-linear constrained optimisation techniques to estimate the optimal allocation of fishing effort and size of fleet for management policy given prices, unit costs, effort and catch dynamics. The optimisation procedure (Augmented Lagrangian approach with Quasi-Newton minimisation) is also used to determine the optimal distribution of fishing effort; in order to have the maximum economic result compatible with the following two constraints: Survey of existing bioeconomic models 158 MOSES • Biological constraint: to avoid over fishing and depopulation • Inertia constraint: to select realistic effort redistribution solutions MOSES can estimate current, simulated, optimal and sub-optimal levels of fishing effort. Given a level of fishing effort, the biological sub-model estimates long-term landings and the economic sub-model estimates long-term value added by fleet and area. Interaction between the economic- and biological-sub model is at fleet level. Catch and fishing effort link the biological and economic sub-models; catch and effort are used as input into the economic sub-model as predicted from the biological sub-model and used to determine value added in the economic sub-model. The two main modules within MOSES consist of CEMOD (Catch-Effort MODelling), a module for specification of the catch-effort relationship, and FISHOPT (global FISHery OPTimisation) a module for optimising distribution of fish effort by gear and area given the economic, biological and re-conversion objectives and constraints. The basic causality and flow of MOSES is represented below in Figure 13. Figure 13: Diagrammatic representation of MOSES. 17.1.3 Type of advice and time range MOSES was developed as an advisory tool for fisheries managers in the Mediterranean and operates under the regulatory framework of an effort-controlled fishery, MOSES cannot consider TAC (total allowable catch) management control and does not have the capability to assess the impact of technical measures. Survey of existing bioeconomic models MOSES 159 The harvest control rule in MOSES directly influences fishing effort (days-at-sea), MOSES ensures realistic effort distribution solutions and prevents drastic changes in fishing effort distribution through the inertia constraint, in addition the biological constraint is used to avoid over-fishing to ensure that the fishery is not fished at the maximum (see section A.11.1 in the Appendix of this report for specification of the constraints). Essentially MOSES is deterministic as it was developed in the framework of classical statistics. Uncertainty is not explicitly included in MOSES, it is only implicitly incorporated into the data used for model initialisation and also model results both of which are difficult to quantify. MOSES can produce the following performance indicators from the optimisation analysis aggregated by total or disaggregated by area, fleet, or species; • Fishing effort. • Catch. • Average prices. • Value added. • Revenue. • Fishing costs. • Biological term. • Inertia term. • Re-conversion model results – optimal effort distribution of area/fleet and associated unit fishing costs. The dimensions of the model can be adjusted accordingly by the user i.e. the number of fishing areas, fleets and species. MOSES is based upon discrete annual time intervals, in which the biological sub- model is based on annual increments and the economic sub-model can encompass daily or annual time steps depending upon the units specified by the user (the model does not consider seasonal adjustments). MOSES can be used for short- to long-term analysis but as with many models prediction reliability may decrease through the time horizon. 17.2 Implementation details 17.2.1 Data requirements MOSES was developed to manage the problems associated with data poor Italian fisheries and therefore data required is aggregated as MOSES is not a data intensive model. The framework of MOSES allows either Excel or Lotus data files to be used for data input. Input data required Survey of existing bioeconomic models 160 MOSES to run MOSES (Table 37) is required on an area, fleet or species level for either a reference period or time series. Table 37. Data required to initialise MOSES Model Data Temporal scale Level General Fishery areas -- Areas Fishing systems -- Fleets Catch-effort model Catch Time series By area and fleet Effort Time series By area and fleet Recruitment Reference period By age and species (for Deriso-Schnute model) Economic sub- Unit costs Reference period By area and fleet model Species technical matrix Reference period Species by fleet and recruitment age Prices Reference period By species and area Catch and effort data can be collected under the framework of the “old” DCR. The surplus production models are relatively less data intensive than the partially age structured Deriso- Schnute model which are reliant upon recruitment at age information supplied from biological surveys. Whilst economic species price information can be collected under the framework of “old” DCR, unit fishing costs must be collated through fleet surveys. The “old” DCR requires data to be collected on a species-by-species basis, the “new” DCR will place more emphasis on data collection from ecosystem and fleet perspective, so under the “new” DCR regional fishing and fleet segments will have greater emphasis which will help address data requirements of MOSES better. There is no requirement to parameterise the model before it is applied. The simulation tool within the biological sub-model endogenously estimates parameters in the surplus production and partially age-structured models using the time series of catch and effort data by means of non-linear squares estimation. 17.2.2 Model language and platform characteristics MOSES was built in Fortran (FORmula TRANslating system) for the intensive numerical and scientific computation within a version of the STATSOFT programme and the user-interface was developed in QuickBasic which also aids the real time model analysis. The MOSES software code is free and available upon request from the developers. Survey of existing bioeconomic models MOSES 161 17.2.3 Format of model output Most of pre- and post- data processing is integrated within MOSES itself, all tables and graphical analysis of figures are created using the software interface programme QuickBasic. The user is able to export output produced from MOSES in CSV file format for further analysis. MOSES can produce optimisation results graphically in bar charts and output can be aggregated to whole fishery or disaggregated to area, fleet or species level for: • Catch, effort, catch per unit effort • Value added, revenue, fishing costs, average prices, re-conversion costs • Biological terms • Inertia terms Biological and economic results can be produced for effort associated with; current, simulated effort and optimised levels. Optimisation output can be produced under the following four scenarios; • No constraints • Biological constraint • Inertia constraint • Biological and inertia constraint 17.2.4 Producing an advice The length of time taken to run the model depends upon the user’s operating system. Originally MOSES was built for a minimum of a 486 DX4 PC (100 MHz) for which the computational run time was approximately 20 minutes for the biological catch-effort model and 10 minutes required for the production catch-effort model. Computational time to run the model is considerably quicker on faster operating systems. Both Fortran and QuickBasic must be purchased to produce advice and to run the model. To run MOSES the software should be compatible with any Fortran complier, for instances Fortran PowerStation 4.0 for Windows can be used. In addition, a text editor is required to handle the data input files. To run MOSES it is recommended that a user directory called ‘Moses’ is created in which all the QuickBasic files are extracted. After which, Fortran should be installed and the ‘Moses’ directory set as the default path of the code. A sub-directory of ‘Moses’ called ‘Italy’ should be created in which the contents of data input files are extracted. Survey of existing bioeconomic models 162 MOSES Familiarity with Fortran computer-programming is useful as it is not a simple procedure to compile the MOSES code. Whilst MOSES was not built with emphasis on user-friendliness a menu driven user-interface was developed in QuickBasic which facilitates the operation of the model for the targeted end-user (fisheries manager). Proficiency in economics and biology are useful for analysis and interpretation of output to examine the economic and biological effects of management measures. 17.2.5 Model use and full list of references This review has evaluated the original MOSES developed by IREPA in 1984/1985 using Fortran and QuickBasic to provide policy support for Italian fisheries managers but since MOSES has been applied to other Mediterranean fisheries (French and Spanish). More recently a new model called IBEM (Italian Bio-Economic Model) was developed between 1995-1998 funded by the European Commission’s BEMMFISH (Bio-Economic models for the Management of Multi-species and Multi-gear FISHeries) project (FAIR-CT95-0561) based upon the principles and theory of MOSES, this is well documented from the BEMMFISH project. Whilst IBEM originates from MOSES it is a substantially simplified version developed in Matlab and has been presented at the BEMMFISH conference in October 2004. References for MOSES are: Arnason, R., Coccorese, P., Olafsson, S., Plancenti, V., Rizzo, G. 1997. Comparison of Icelandic (UI) and Itlaian (IREPA) Fisheries Management Models, FAIR-CT95-0561 Project, Discussion Paper, DP-1. BEMMFISH. 2004. Final Report. Bio-Economic Modelling of Mediterranean Fisheries, Q5RS- 2001-01533 project. Coccorese, P., Placenti, V and Rizzo, G. 1998a. The MOSES program: Software Implementation and User Interface Development, FAIR-CT95-0561 Project, Discussion Paper, DP-26. Coccorese, P., Placenti, V and Rizzo, G. 1998b Global Bio-economic optimisation of fishing effort for Italian fisheries, considering re-conversion costs, FAIR-CT95-0561 Project, Discussion Paper, DP-24. Tingley, D., 2005. UK fleet futures modelling (2004-2013) – literature review. CEMARE Report No. 67, pp. 23. Survey of existing bioeconomic models MOSES 163 17.2.6 Institute and key personnel IREPA is the key institute involved in the development of MOSES and key personnel include Paolo Accadia and Michele Sacco. In addition Professor Gianfranco at the University of Salerno (Co-ordinator of FAIR CT95-0561) is also a key personnel, other key institutes involved in the European Commission’s Innovative integrated bio-economic models for the management of multi-species and multi-gear fisheries (FAIR-CT95-0561) included; IREPA, Italy, the University of Iceland in Reykjavik, Iceland, MRAG Ltd and Imperial College, London, UK. Survey of existing bioeconomic models 164 TEMAS 18. TEMAS 18.1 General description TEMAS5 (technical management measures) is software developed to perform fleet-based, bioeconomic evaluations of management strategies taking into account technical measures (e.g., MPAs) and fleet behaviour. For example, it is possible to use TEMAS to compare TAC and effort-quota management regimes. TEMAS is a simulation model in that it is designed to evaluate potential management scenarios. A strength of the approach used in TEMAS is that it allows users to compare potential scenarios; its developers6 warn that it should not be used to obtain precise quantitative predictions for a single model. 18.1.1 Model objectives and dimensions As will become apparent, TEMAS is an extremely flexible model that can be used to run simulations across any commonly conceivable dimension including the following: • Different stocks, species • Age groups for each stock • Fleets • Vessel age groups for each fleet • Areas • Years • Different time steps within a year • Metier TEMAS can be used to run either short or long-term fleet dynamics. An important recent extension of the model is the distinction between physical groups of vessels and metiers which describe the activities (gear, mesh size, area fished) of a fleet in a given time. Furthermore, it's possible to specify a simulation period of less than a year. The following figure from the TEMAS model shows the main interface for adding input data and, in general, how interaction with TEMAS occurs. Notice that the column “Basic 5 This review is based largely on Per Sparre’s TEMAS user manual for the July 2007 version of the model (available on upon request from DTU-Aqua), Ulrich C, Pascoe S, Marchal P, Sparre P, De Wilde J-W (2002) Influence of trends in fishing power on bio-economics in the North Sea flatfish fishery regulated by catches - or by effort quotas. Canadian Journal of Fisheries and Aquatic Science 59: 829- 843, other references listed at the end of this document, and correspondence with the Danish Institute for Fisheries Research (DTU-Aqua). 6 Per Sparre, who worked for the Danish Institute for Fisheries Research (DTU-Aqua) is the main author of TEMAS. However, others at DTU-Aqua have contributed to the code and derivations of TEMAS (see references section). Survey of existing bioeconomic models TEMAS 165 Dimensions” includes the maximum sizes for some of the dimensions, e.g., up to five stocks can be included. The number of periods, here shown as twelve, is unlimited, twelve is the default value. Figure 14. Screenshot in which dimensions are specified Estimated Fish Stock. TEMAS 18.1.2 Model structure According to the TEMAS developers, TEMAS is not really a model, but rather a toolbox from which modules can be selected or added to create a model, the modelling features are therefore kept very general. The basic model structure is subdivided into operational (OM) and management (MP) models. The operational model is a simplified version of the biological system under consideration and incorporates the effects of changes to the management model. It simulates the “true” system as it is presumed to operate “under the water”, while the management procedure mimics the errors that invariably occur when a manager inaccurately estimates the true system, for example, inaccurate stock assessments. In short, the management module contains all of the steps of the perceived system, from data collection to management controls. The link between the OM and the MP is made through the sampling procedure, in which samples are taken from the true catches of the OM to provide the catch-at-age matrix used in assessment. The assessment is a simplified virtual population analysis procedure (Ulrich et al. 2002). The separation of the biological model from the management models within TEMAS means that it is relatively easy to incorporate biological data from various sources to run simulations. For example, in the case of management regime comparisons, the operational model remains the same in all comparisons. The operational model simulates fish stocks, fishing fleets, etc., and from those calculated figures it simulates input data which is sent to the management model(s). In short, the operating model produces input for the management model for year “y”, while the management models produces management regulations for year “y+1”, etc. Survey of existing bioeconomic models 166 TEMAS OPERATING SYSTEM DATA DATA MANAGEMENT MANAGEMENT REGIME A REGIME B Comparison Update dynamics of operating system Update dynamics of operating system Year = year+1 Year = year+1 Evaluation of alternatives Figure 15. Basic model of the flow of information within TEMAS The diagram below contains a more detailed overview of the flow of information within TEMAS. In the following sections some basic elements of the model are discussed: Figure 16. Diagram showing the operating model of TEMAS The first step in the procedure is to specify the underlying biological parameters and functions to be used in the model. As is typical in TEMAS, data can be incorporated at different levels of aggregation, for instance, the biological module allows growth of juveniles up to the age of 2 to be specified by quarter or month, allowing the impacts of technical changes to be analyzed in detail; in addition, a box model allows analysis of the impacts of migration. The model allows stochastic assumptions to be made for a number of variables, various biological models to be specified and both short-term and long-term behaviours to be modelled. Survey of existing bioeconomic models TEMAS 167 18.1.3 Type of advice and time range As previously stated, TEMAS can model and compare most conceivable management measures, e.g., TAC and effort constraints, over virtually any practical time scale. TEMAS has both short- term (tactical) and a long-term (structural) fleet behaviour modules. The short-term module covers effort allocation over metiers and areas for each period under consideration, while effort can be either user defined or derived endogenously. Long-term behaviour relates to the decision to enter, exit or stay in an existing fleet. TEMAS also allows for a number of economic models, each representing a group of stakeholders (fishing industry, government funds, society, etc.). TEMAS can be used to analyze a number of regulatory frameworks. For instance, it can incorporate technical management measures such as minimum landing size restrictions, mesh size, gear selection, and the maximum number of days as sea across fleets, areas, gear and areas. Various combinations of both effort and TAC restrictions can be modeled at numerous levels of analysis. The version of the model dated July 2007 includes the means to examine a harvest control rule for the precautionary approach. The model allows single deterministic simulations or multiple stochastic simulations. The multiple stochastic simulations executes a number of single deterministic simulations (say 1000 simulations), each of which is based on parameters drawn by a random number generator. The most recent version of TEMAS allows, in its standard implementation, the ability to analyze the following five pairs of alternative management regimes: Table 38. Five pairs of regime comparisons of the current TEMAS program Regime Comparisons Regime A Regime B 1 Scientific advice / ACFM Advice (TAC No ACFM Advice (TAC based on No scientific advice based on harvest control last years landings, and selected rule) CPUE trends) 2 TAC regime with ACFM Advice (TAC Misreporting (Various No misreporting / based on harvest control assumptions, effect of regulations With misreporting rule) on misreporting) No misreporting 3 With / without new TAC (With current, TAC with NEW Technical Technical management Technical management management measures. E.g. closed measures. E.g. closed measures, except for seasons and/or closed areas seasons and/or closed areas closed areas and seasons) (MPA). (MPA). 4 TAC / Effort regimes with TAC (based on the current Effort, An alternative regime, ACFM’s harvest control HCR of ACFM) management by effort regulations. rule. Both regimes based on the current HCR of ACFM" 5 TAC / Effort regimes with TAC (based on the current Effort, An alternative regime, NEW harvest control rule. HCR of ACFM) management by effort regulations. Based on an alternative HCR, (mixed fisheries, - fleet based …) From TEMAS Manual February 18th, 2008. Survey of existing bioeconomic models 168 TEMAS TEMAS accounts for a number of different types of “errors” in the system, where an error is defined by the TEMAS developers as a “deviation from the model” or “something that can go wrong”. The errors include: • Measurement errors. Errors in input data, such as catch at age data, caused by data being estimated from samples and not from complete enumeration. • Estimation errors. Errors caused by the method used to estimate parameters or erroneous assumption about the data. • Model misspecification errors. Errors caused by incomplete or wrong understandings of the mechanism behind the system dynamics. The assumed Stock/recruitment relationships may be candidates for model misspecifications. • Implementation errors. The errors caused by regulations not being reacted to as assumed. The fishers may find ways to implement regulations which do not lead to the achievements of the intentions of regulations. 18.2 Implementation details 18.2.1 Data requirements Given the flexibility of TEMAS, it is possible to run models with limited amounts of data as well as models requiring very detailed information. As it has previously shown, data can include specific information on effort use, fleet capacities, stock information, price details, and trip and structural rules. TEMAS also includes many helper functions which allow users to build models based on relatively small amounts of data. However, in order to run the complete model as it stands in July 2007, a vast amount of data is necessary and data processing would be necessary. A complete model specification would involve, for example: • Inputting migration data between areas, natural mortality data. • Stock data including growth and maturity and recruitment parameters, conditioning factors, recruitment distribution data over periods, information on low and high spawning areas, recruitment trends, stock numbers, weighing factors and discards. • Fleet data including catchability per gear and vessel group size, technical and gear effects, mesh size, gear selection factor, discard data, effort data on areas per vessel group and gear and effort multipliers. • Boat characteristics including vessel age, tonnage, watts and horsepower, crew size, maximum days at sea, disinvestment and attrition information, and decommissioning data. Survey of existing bioeconomic models TEMAS 169 • Price data, both aggregate and by age, relative price and price flexibility information. • Cost data related to operations, salaries, handling, license fees and insurance, investment costs and decommissioning information. Revenue data, tax rates, opportunity costs and sales costs. • Information of trips characteristics including historical and recent data on when a fisher decided to go fishing and when he decided to stay in port (for RUM), the expected value of landings, catches and revenues, cash flows. • Information related to tuning including mortality and biomass, abundance and indexes of abundance. Table 39. Some of the many input tables in TEMAS Dimensions of case study Stock input (input independent of the fleet structure) Fleet input (which may or may not be fleet structured) Effort input (optional) Boats input Prices input Economic input Trip rules input (Parameters of the short term behaviour algorithms) Structural rules input (Parameters of the long term behaviour algorithms) Tuning data, for fish stock assessment Observation used for model calibration List of demonstration examples List of all tables in TEMAS_INPUT Parameters of technical management measures Parameters of harvest control rule 18.2.2 Model language and platform characteristics According to the license holder, the software implementation of TEMAS is intended to become a ‘public’ software package. That is, to become a professional standard, with extensive documentation and a user-friendly design. The authors of TEMAS claim that is intended to be “100% open source software”. That said it does not use an open source license as that term is commonly understood. Wanting to avoid the complications of coding a large model directly in Microsoft Excel, the authors decided to program using Visual Basic (VB) modules, VB is the macro language of Excel. The advantage of using an Excel framework is that users are in a familiar environment and can use all the facilities of Excel for data input, pre-processing, further processing and presentation of results from TEMAS. The authors note that running TEMAS requires the participation of one or more people who can program in VB because the program crashes. They tell users that, most often, the problems causing the crash are associated with reading the data files from the hard disk, such problems Survey of existing bioeconomic models 170 TEMAS are relatively easy for an experienced VB-programmer to fix. In addition, VB has powerful debugging facilities which help experienced programmers to quickly solve problems. According to the authors, users (scientists) with a minimal knowledge of computer programming should be able to check the code, and modify it. More experienced users should be able to supply new elements to the system. “The system should be accessible to persons with a minimal knowledge of fisheries and computers.” (TEMAS Manual 21 June 2007, by Per Sparre). Given its malleability, it's difficult to predict the size of the TEMAS package, but the authors tell us that for most applications 20 Mb of hard disk should be sufficient. As TEMAS consists of three Excel workbooks, their size depends on how much is contained in the cells of the worksheets. 18.2.3 Format of model output As can be seen in the following in the following example figure, TEMAS produces Excel sheets as output from which it's possible to translate the data to forms readable by other systems. The output also makes it relatively easy to make graphs and figures, and cut-and-paste data into other Microsoft products. Figure 17. Example of TEMAS output Survey of existing bioeconomic models TEMAS 171 18.2.4 Producing advice On a modern, if not state of the art, pc using a model which for a large part depends on TEMAS defaults, TEMAS quickly produces output which can then be easily inserted into documents as needed. Because it only uses Excel, there are no additional costs required to run TEMAS given a standard computer system. The real costs of running a full TEMAS model will be in the collection of data and the time required to make any adjustments to the program. Any substantial changes to TEMAS will require a good to a very good knowledge of Visual Basic. TEMAS is a complex program, and using its full potential requires expert knowledge in fishery biology and an understanding of basic economic/business concepts. Changes to the program require expertise in VB and Excel. In short, the costs of using TEMAS will depend on the types of analysis required. For instance, given that the computer programming, biological and economic expertises are available, the costs to assessing management alternatives in the STECF framework will be largely dependent on the costs of collecting and analysing the required data. VB OUTPUT (VISUAL BASIC MODULE) OUTPUT HAND- TEXT FILES LING VB (VISUAL BASIC MODULE) DATA PROCESSING VB (VISUAL BASIC MODULE) INPUT HAND- INPUT LING DATA TEXT FILES Figure 18. Summary of how TEMAS works Furthermore, TEMAS developers and expert users from the Danish Institute for Fisheries Research (DTU-Aqua) wish to emphasize several important points concerning limits of the TEMAS model. First, several modules of the TEMAS model have not been calibrated or have been calibrated using different processes. Consequently, calibration of the entire model, a critical and time consuming aspect of running any type of simulation, will require advanced computer programming and fisheries expertise. Second, because TEMAS is a simulation model, all parameter estimation, including biological, fleet and behavioural parameters, must be conducted before running TEMAS. Third, at a more general level, the TEMAS model is very Survey of existing bioeconomic models 172 TEMAS complex allowing a vast number of options to be specified. The complexity and technical difficulties make running the model difficult in practice. 18.2.5 Model use and full list of references The following is based largely on an email correspondence with Dr.'s Clara Ulrich Rescan and Rasmus Nielsen and Mr. Bo Sølgaard Andersen (Ph.D. candidate) from the National Institute of Aquatic Resources from the Technical University of Denmark. A general presentation of TEMAS can be found at Sparre, 2008a,b and Ulrich et al. 2007 (see ”Published References” below), in addition, the TEMAS model and parts of the model have been used in many contexts, especially in other modelling approaches (e.g. F-Cube7). The TEMAS model was a further development of the FAO BEAM models (latest the BEAM 4 Model). The BEAM 4 model is described under the FAO web-pages and through FAO reporting. Fcube references: Ulrich, C., Andersen B.S., Hovgård H., Sparre P., Murta A., Garcia D., Castro J. 2006. Fleet- based short-term advice in mixed-fisheries – the F3 approach. ICES Symposium on Fisheries Management Strategies, June 2006, Galway. http://www.ices06sfms.com/presentations/index.shtml ICES, 2006a. Report of the Working Group on Workshop on Simple Mixed Fisheries Management Models. ICES CM 2006/ACFM:14 ICES, 2007. Report of the Study Group on Mixed Fisheries Management. ICES CM 2007/ACFM:02. Published TEMAS references: Sparre, P. J. 2008a. User’s Manual for the EXCEL Application “TEMAS” or “Evaluation Frame”. DTU-Aqua Report 190-08: 182 pp. ISBN 978-87-7481-077-3. Sparre, P. J. 2008b. Evaluation Frame for comparison of alternative management regimes using MPA and closed seasons applied to Baltic cod. DTU-Aqua Report 191-08: 298 pp. ISBN 978- 87-7481-079-7. 7 Fcube is an advance fleet and fisheries method/tool being developed and implemented to provide mixed- fisheries advice, see for instance, (Ulrich et al., 2006; ICES, 2006a, 2007) and Section 14.2.1 of this report. Survey of existing bioeconomic models TEMAS 173 Ulrich, C., Andersen, B.S., Sparre, P.J., and Nielsen, J.R. 2007. TEMAS: fleet-based bioeconomic simulation software to evaluate management strategies accounting for fleet behaviour. ICES J. Mar. Sci., 64: 647-651. Case Study Applications: A - North Sea Flatfish During the EU FP5 TECTAC project, the TEMAS model was applied to the North Sea consumer (flatfish) fisheries. This work was presented in the ICES symposium on fisheries management strategies, Galway 2006, the presentation is available (see above Galway reference). Dr. Ulrich and others are in the process of submitting a paper corresponding to that work which is also described on the EU FP6 EFIMAS Project DocuWiki under WP4 CS1 (see below). TECTAC is also described in the following: http://cordis.europa.eu/data/PROJ_FP5/ACTIONeqDndSESSIONeq112482005919ndDOC eq2628ndTBLeqEN_PROJ.htm. http://wiki.difres.dk/efimas/doku.php?id=efimas1:wp4:cs1:appr2:main (Password required) B - Kattegat A previous version and application of the TEMAS model can be found in Youen Vermard's master's thesis report under ENSAR (ENSAR-INSFA, Département Agro-Alimentaire, Laboratoire de Science des Aliments, Rennes, France) where TEMAS was to some extent applied to the mixed consumer fisheries in Kattegat. C - Baltic Sea For the case of the Baltic Sea a TEMAS manual is available of the model applied in the Baltic Sea cod fisheries case study under EFIMAS and PROTECT: http://wiki.difres.dk/efimas/doku.php?id=efimas1:wp4:cs9:main (Password required) There was cooperation between the EU-FP6-EFIMAS and EU-FP6-PROTECT projects in regards to the evaluation of effects of closed areas/closed seasons with the FLR-Model, the ISIS-Fish Model, and the TEMAS Evaluation Framework. Also, with respect to EFIMAS, in Case Study 9 and Case Studies 6 and 4 there was cooperation between the EFIMAS and PROTECT projects in relation to application of the ISIS-FISH Simulation Tool (FL-ISIS). The work under Case Study 9 approach 2 has also been done in cooperation with a Danish national government project, and the overall CS9 work has worked together with the ICES WGBFAS Survey of existing bioeconomic models 174 TEMAS Assessment Working Group mainly with respect to Approach 1, but also in close communication with Approach 2. D - others In addition, the TEMAS model is currently being used within the EU FP6 UNCOVER Project, contact CEMARE, UK for details. Finally, DIFRES reports that scientists at the National Institute of Aquatic Resources from the Technical University of Denmark have received a number of requests from third countries scientists for a copy of the software, following the publication of the Ulrich et al, 2007. Inspiration for other words: Parts of TEMAS have been used in the development of the Fcube approach presented under ICES WGMIXMAN 2006, and further developed upon in the ICES WGMIXMAN Reports 2007-2008 (see http://www.ices.dk/products/cooperative.asp and search using working group mixed fisheries.) See also EFIMAS DocuWiki WP4 CS2 Approach 1: http://wiki.difres.dk/efimas/doku.php?id=efimas1:wp4:cs2:main (Password required) Parts of the TEMAS model have informed the FLR ( http://flr-project.org/ ) models developed by the Danish researchers and implemented for the Baltic Cod Fisheries and the North Sea round fish fisheries under EFIMAS at DTU Aqua (see EFIMAS DocuWiki WP4 CS2 Approach 1 and WP4 CS2 Approach 2a (see above link)) as well as under the application of the ISIS Fish Model to the Baltic cod fisheries under the EU FP6 PROTECT and EU FP6 EFIMAS (see EFIMAS DocuWiki WP4 CS9 Approach 2b). 18.2.6 Institute and key personnel The institute responsible for TEMAS is the Danish Institute for Fisheries Research (DIFRES). Researchers there have been very helpful with comments and advice in regards to the TEMAS model and programs descended there from. However, the main author and inspiration for TEMAS, Per Sparre, has recently retired, and the software will no longer be directly maintained, although the ideas and concepts of TEMAS continue to be used in newer models. Survey of existing bioeconomic models SRRMCF 175 19. SRRMCF 19.1 General description 19.1.1 Model objectives and dimensions The SRRMCF model (Swedish Resource Rent Model for the Commercial Fishery) was developed in 2005 at the Swedish Board of Fisheries using data from the results of the Swedish fishing fleet’s activities in 2004. The model is currently being updated, revised and tested, and it is expected that a new version which allows for a higher degree of complexity will be ready within a few months. A draft of the new version was not available at the time of the review, why the present description is based on the 2005 model. The SRRMCF model was developed in order to operationalise a Strategic Plan for the Commercial Fishery in Sweden with the aim of providing viable solutions for the structural problems of fishing industry. Its basic objective is to achieve an optimal economic use of resources while maintaining fish stocks at a sustainable level. Therefore the model’s main function is to estimate the profit that arises by the utilization of the Swedish fishing resource - the resource rent. The resource rent of the Swedish fishing resource equals the total revenues minus the total costs. The model permits testing various scenarios such as price increases, cost increases, changes in the fishing quotas, and estimate the effect on the resource rent of the given changes. The model is also able to calculate the resource rent obtained from the Swedish fishery in the current situation with the view to evaluating it against the optimal use of resources in the fishing industry. The SRRMCF model is an optimisation model based on linear programming. It optimizes an objective function with a series of variables under a number of restrictions. Here the objective function is the resource rent, which is maximized under given restrictions. The objective function of the model can be changed. For example, the model can maximize employment instead of the resource rent provided that the resource rent is set to zero or another desired value. Other variables such as the number of vessels or days at sea can be maximized if relevant. Also, the model restrictions can be changed in order to test the outcome of various scenarios. The model was developed using 2004 as the base year. It includes the entire Swedish fishing industry and is based on around 40 different species, which are divided into 42 different groups in order to fit the model. Some species such as herring are divided into different groups depending on how and where they are caught. Also, a single group may contain more than one species. The active, licensed vessels - with a gross catch valued large enough to be considered Survey of existing bioeconomic models 176 SRRMCF of importance - have been divided into 13 fleets based on size, type of catch, and method of fishing. The number of species that each fleet is able to catch is defined in the model. Each type of fishery has a catch composition of one or more “target species” and a connected catch of one or more “by-catch species”. The catch of target species is connected to an amount of by-catch by a ratio-based span, and opposite around. The knowledge about the different fishing types is based on landing statistics from the previous year. Also, the possible catch by each fleet is divided into target species and by-catch species according to landing statistics based on previous years on catch composition. Limitations are included due to natural limitations of the equipment, the geographic dispersion of the species and spatial distribution. 19.1.2 Model structure The main focus of the model is the economic components, while the biological elements are limited to the utilization of the actual catch for the individual species from the previous year to determine the catch composition as well as the types of fishery. Also, it uses the TAC for the different species as restrictions. Hence, no data from stock assessments are included. The economic procedures depart as outlined above from the characteristics of each fleet with respects to fishing gear, target species and by-catch species as well as the spatial distribution of their activities. The 13 fleets are listed below. Several of the fleets are subdivided according to fishing location and/or catch distributions: • Demersal trawlers < 24 meters. • Demersal trawlers > 24 meters. • Norway lobster trawlers < 12 meters. • Norway lobster trawlers > 12 meters. • Vessels > 12 meters with passive gear. • Pelagic fishing vessels < 24 meters. • Pelagic fishing vessels > 24 meters. • Shrimp trawlers. • Net and Hook vessels in the Baltic Sea. • Eel fishing in the Western Sea. • Eel fishing in the Baltic Sea. • Other passive gear in the Western Sea. • Other passive gear in the Baltic Sea. The model is based on the simplification that all vessels within each fleet have homogenous characteristics. For the scenario corresponding to the extension of the current situation, the Survey of existing bioeconomic models SRRMCF 177 number of vessels used in the calculations is the actual number of vessels for each fleet. For the different optimization scenarios, which may be put forward, there are no limitations on the number of vessels - other than that the numbers must be whole numbers in order to create correct fixed costs. With respects to employment, it is assumed that the average crew number per vessel for each fleet is maintained at its current level. Hence, the number of crew members is then estimated based on the number of vessels that the model determines to be optimal depending on costs, revenues, capacity, etc. The actual number of days at sea is used when estimation the results of extending the actual situation, while the number is set to 200 days per year in the optimization scenarios unless otherwise specified. The catch per vessel is not to exceed the total capacity per vessel. The model uses catch per day as the capacity limit, calculated on the basis of total catch, number of days at sea, and type of vessel. The model takes into consideration restrictions such as natural restrictions on fishing periods and technical restrictions like fishing bans. The fixed costs consist of capital costs, depreciation costs, and a desired profit. The capital cost and the depreciation cost are each estimated at 3% of the acquisition value based on latest available figures. The desired profit is estimated at 5% of the capital value. Variable costs include fuel costs, vessel costs, personnel costs, and other flexible costs. The model relates the variable operating costs to the number of fishing days. The costs vary among the different fleets and not among the different species. The prices used in the model are the latest mean prices per species and fleet, gathered from the fishermen’s transaction notes, but with some adjustments where appropriate. The causality and flow of data in the model is shown in the diagram below. The model seeks the optimal allocation – among all possible allocations - of catch, fishing effort and number of vessels and days at sea that maximizes the objective function – the resource rent – with given values of the exogenous variable such as costs and prices and subject to a set of restrictions. It is able to operate with many restrictions. Important restrictions are quotas and maximum number of days at sea. Different scenarios can be formulated by changing the objective function, the restrictions and assumptions about exogenous variables and parameter values. Survey of existing bioeconomic models 178 SRRMCF Base year vessel characteristics: Vessel & gear types, capacity and crew numbers Catch composition and fishery type per fleet Restrictions on effort: days at sea TAC Other restrictions Fishing fleet Base year Segment 1 Costs Segment 2 Max. Resource Optimiza‐ rent Base year • tion: catch • effort/no. Prices vessels • Segment 13 Figure 19. Diagram showing causality of the main elements. SRRMCF. Besides utilising the TAC projections from current biological models, the SRRMCF model does not have any biological considerations. Thus there are no direct links with current biological models. 19.1.3 Types of advice and time range The harvest control rules which can be used directly as variables in the model include TAC (or maximum catches if no TAC is available) and quota allocations and restrictions on effort (days at sea). The impacts of technical measures concerning fishing gear such as mesh size cannot be assessed by the model. The model does not consider discards either. Fishing bans and seasonal fishing periods can be taken into account for certain species. Several bioeconomic indicators are produced as basis for the advice. The model does not include endogenous dynamics in fish stocks. However, it does calculate the catch which under most scenarios is much lower than the TAC. The main economic indicators estimated as output for each fleet are: revenue, costs, profits, catch, fishing capacity per fleet, number of vessels, employment and capital of the fleet. The model calculates marginal estimates for the included variables. Sensitivity analyses can be applied to the model. Survey of existing bioeconomic models SRRMCF 179 The model is currently adapted to the Swedish situation involving 13 fleets and 42 different species or groups of species. In terms of time range, the 2005-model version is static and estimates the outcome of a given set of conditions at one point in time. 19.2 Implementation details 19.2.1 Data requirements The primary data source is the annual cost and earning studies conducted by the Swedish Board of Fisheries. The data from this study is in accordance with the EU data collection regulation. Fleet segmentation, cost data and vessel characteristics, as well as the data on production, fit the minimum requirements since there is no direct regional differentiation. The second source of data is landing statistics from log-books collected by the Swedish Board of Statistics. 19.2.2 Model language and platform characteristics The 2005-model was designed for Lingo programming, but the coming version is expected to work in GAMS. Hence, the model will require acquisition of a GAMS license. It consists of a language compiler, which price is US$ 640, and a set of integrated high-performance solvers, which prices can vary from US$ 320 to 1920US$. 19.2.3 Format of model output The division of fishing activities with respect to fleets and species means that output can be produced with a considerable level of detail. The result that comes from the SRRMCF is comprehensive information that determines an optimal allocation of catch under given conditions. The main output for each fleet that is provided consists of: profit, cat composition, fishing capacity, number of vessels, the number of fishermen and capital of the fleets. 19.2.4 Producing advice The application of the model for producing an advice will first of all require an effort to collect and prepare the needed input data from annual cost and earning studies and the data on landing statistics from log-books, both sources provided by the Swedish Board of Fisheries. This would require knowledge and experience regarding the concerned fisheries characteristics and knowledge with respect to fishery economics. GAMS programming skills will be needed to operate the new, updated version. Based on current knowledge of the model, the costs of producing an advice will firstly require securing that the SRRMCF model is applicable to the country in question. If not, changes will be required, but these are considered of minor importance. The next part is related to including Survey of existing bioeconomic models 180 SRRMCF data in the model. If data is available, this is considered to take a week for a trained person. Running the model requires purchase of a GAMS license, which costs around €1000. The actual model execution and analysis of results, is considered to take some time, depending on the level of detail. This will require economic expertise. 19.2.5 Model use and full list of references The model was developed in 2005, but it has contributed with valuable information to the Swedish Board of Fisheries in the Swedish Strategic plan for the fishery in Sweden for the years 2007-2013. The model was used to outline the probable outcome of 10 different scenarios such like the extension of the current situation and the maximum resource rent with current price and cost levels as well as scenarios that test such effect as changes in prices and costs. A general description of the 2005-model and the outcome of the different scenarios are found in three documents listed below, most comprehensively in Paulrud (2006) and Paulrud (2005). A detailed description of model including equations, variables and parameters is however not available in the mentioned documents. List of references: Paulrud, Anton. 2006: The Resource Rent in Sweden’s Fishery, Paper presented at the IIFET 2006 conference, Portsmouth. 12 pp. Paulrud, A. 2005. Diskussions- och underlagsrapport för den strategiska planen. Resursräntemodell för det svenska yrkesfisket - ett verktyg för analys av det svenska yrkesfisket. (In Swedish). Fiskeriverket (Swedish Board of Fisheries). STECF, 2006: REPORT OF The Joint SGECA - SGRST sub-group meetings on bioeconomic modelling. Ispra, 4-6 October 2005 and 7 – 9 March 2006. 19.2.6 Institute and key personnel The model was developed at the Swedish Board of Fisheries with Anton Paulrud as the main responsible for the development work. It is currently being updated by Anton Paulrud and Staffan Waldo, both now working at the Swedish University of Agricultural Sciences, Umå and Lund respectively. Survey of existing bioeconomic models Others 181 20. Others 20.1 ISIS- Fish 20.1.1 General description Most fisheries are complex systems due to the diversity of both exploited resources (multispecies) and fishing activities (multifleet) in so-called mixed fisheries, and to spatial and seasonal heterogeneities in the distributions of resources and fishing activities. In mixed fisheries, resources are exploited either simultaneously or sequentially by fishing units resorting to different types of fishing activity. It is thus difficult to evaluate the dynamics of both resources and exploitation, and subsequent fishing mortality for each population. The diversity of fishing activities and resulting catches arises from the variety of fishing grounds exploited, species targeted and gears used, but also from other factors like individual fishers’ behaviour, economic or environmental conditions. In addition, fishermen are aware of large-scale spatio- temporal distributions of resources and allocate fishing effort accordingly. At large scales, fluctuations of these spatial distributions are mainly due to concentrations of particular demographic stages in some areas at certain seasons in relation with specific events of the life cycle (reproduction, feeding...), and corresponding migrations between these areas. In this context, considering the spatial and seasonal allocation of fishing effort among fishing grounds is essential to evaluate the dynamics of the fishery. In mixed fisheries, these aspects are all the more important since fishermen may switch not only fishing grounds, but also target species and fishing gears. Spatially-explicit models are deemed necessary to understand the dynamics of many biological systems, in particular ecosystems subject to human activities. In the light of this overall complexity, the dynamics of the system, even if expressed through mathematical equations, may not be investigated analytically, and a simulation tool is indispensable to be able to evaluate the dynamics of a complex fishery. ISIS-Fish is a spatial and seasonal simulation model describing the dynamics of resources, exploitation and management. It has been developed to explore the impact of a range of management measures upon fisheries dynamics. It enables one to compare the respective impacts of conventional management measures like catch and effort controls, and measures like Marine Protected Areas (MPA) in the wider sense, i.e. any spatial measure. The model allows for flexibility in several model assumptions (for instance stock-recruitment relationships, selectivity models,...) which makes it possible to use it for most demersal and/or benthic fisheries. Management measures and fisher’s responses to management measures and to economic conditions may be interactively designed through a Script language. Survey of existing bioeconomic models 182 Others The fishery model is designed to assess the performance of local and temporal management measures involving spatial and seasonal control variables for regulating exploitation, e.g. fishing effort and catches. 20.1.2 Implementation details The most recent version of the bioeconomic ISIS-Fish is version 3. It requires Java, and both can be easily downloaded free of charge from http://www.ifremer.fr/isis-fish/downloaden.php. Proficiency in biology and economic is required to condition the model and analyze the results. The list of references, including reports, working documents and scientific papers can be found in the table below. There is also a package that links FLR and ISIS-FISH, available from the FLR web page. Table 40. Full list of references, including type of document, and title for ISIS-Fish Type Title Report Pelletier, D. 2003. Dynamique spatiale et saisonnière de pêcheries démersales et benthiques : Caractérisation, modélisation, et conséquences pour la gestion par Zones Marines Protégées. Mémoire d’Habilitation à diriger les recherches, Université de Montpellier II. 281 p. Report Mahévas, S., & D. Pelletier. 2001. Un outil de simulation pour l’impact de mesures de gestion sur la dynamique d’une pêcherie complexe. Ecole de printemps COREV,Hyères, mai 2001. Report Pelletier, D., S. Mahévas, 2000. Selecting appropriate mathematical and computer tools for developing integrated models : the example of the dynamics of a mixed fishery, Groupe de travail «Integrated modelling and assessment» et «Complex, adaptive, hierarchical systems», Ecosummit 2000, Integrating the Sciences (International Society for Ecological Modelling), 18-22 juin 2000, Halifax, Canada. Communication Vermard, Y., Lehuta, S., Mahevas, S., Thebaud, O., Marchal, P., Gascuel, D., Including dynamic fishermen behaviour in a fisheries simulation model to assess the impact of environmental changes Communication D. Pelletier, S. Mahévas, M. Jarraya, M. Capoulade, H. Drouineau, Y. Vermard, F. Bastardie, 2005, Investigating the consequences of Marine Protected Areas upon fish populations and fisheries through ISIS-Fish, a generic simulation tool Communication Stéphanie Mahévas, Dominique Pelletier, Hilaire Drouineau, Paul Marchal, Olivier Guyader, Olivier Thebaud, Raúl Prellezo, Marina Santurtún , Ane Iriondo, Simulating with ISIS-Fish the dynamics of a North-East Atlantic mixed fishery subject to spatial closures Communication Drouineau, H., S. Mahévas, D. Pelletier, & B. Beliaeff, 2004. Assessing the impact of marine protected areas on the hake-nephrops fishery of the Bay of Biscay using ISIS-Fish. ICES CM 2004/Y:09. Communication D. Pelletier & S. Mahévas, 2004. Evaluating the dynamics of mixed fisheries in response to policy options: Match and mismatch between data resolution, model requirements and model objectives. 4th World Fisheries mebas o, May 2-6, Vancouver, Canada. Communication D. Pelletier & S. Mahévas, 2002. Modélisation de la dynamique de pêcherie complexe Intégration des informations pour la pêcherie du Plateau Celtique. Colloque du Défi Golfe de Gascogne, Brest, 11-13 décembre. Communication Mahévas S., D. Pelletier, 2001. Impact de zones marines protégées sur la dynamique d’une pêcherie complexe. Actes du Cinquième Forum Halieumétrique, Lorient, 26-28 juin 2001, p. 215. Scientific Pelletier D., S. Mahévas, B. Poussin, J. Bayon, P. André and J.C. Royer. 2001. A Journal conceptual model for evaluating the impact of spatial management measures on the dynamics of a mixed fishery, pp. 53-66 In: G. H. Kruse, N. Bez, T. Booth, M. Dorn, Survey of existing bioeconomic models Others 183 S. Hills, R. Lipcius, D. Pelletier, C. Roy, S. Smith, & D. Witherell (eds), Spatial Processes and Management of Marine Populations. University of Alaska Sea Grant, AK-SG-00-04, Fairbanks. Scientific Drouineau, H., S. Mahévas, D. Pelletier, B. Beliaeff. Assessing the impact of Journal different management options using ISIS-Fish: the French Merluccius merluccius – Nephrops norvegicus mixed fishery of the Bay of Biscay. Aquat. Living Resour. 19, 15–29 (2006) Scientific Pelletier D. & S. Mahévas. Fisheries simulation models for evaluating the impact of Journal management policies, with emphasis on marine protected areas.Fish and Fisheries DEC 2005; 6 (4): 307-349. Scientific Mahévas, S., & D. Pelletier, 2004. ISIS-Fish, a generic and spatially-explicit Journal simulation tool for evaluating the impact of management measures on fisheries dynamics. Ecological Modelling 171, 65-84. The main institute involved in the development is IFREMER (France) and the contact persons are Dominique Pelletier (IFREMER) and Stephanie Mahevas (IFREMER). Survey of existing bioeconomic models 184 General references 21. General references Arnason R (2000) Endogenous optimization fisheries models. Annals of Operations Research 94: 219-230. Bjørndal T, Conrad J (1987) Capital dynamics in the North Sea herring fishery. Marine Resource Economics 4: 63-74. Bjørndal T, Lane DE, Weintraub A (2004) Operational research models and the management of fisheries and aquaculture: A review. European Journal of Operational Research 156: 533-540. Conrad J (1995) Bioeconomic models of the fishery. In: Bromley D (ed) Handbook of environmental Economics. Blackwells, Oxford and Cambridge, pp 405-432. Conrad JM, Clark C (1994) Natural Resource Economics. Cambridge University Press, Cambridge. UK. CR (2000) Council Regulation (EC) No 1543/2000 of 29 June 2000 establishing a Community framework collection and management of the data needed to conduct the common fisheries policy. CR (2001) Commission Regulation (EC) No 1639/2001 of 25 July 2001establishing the minimum and extended Community programmes for the collection of data in the fisheries sector and lying down detailed rules for the application of Council Regulation (EC) No 1543/2000. CR (2002) Council Regulation (EC) No 2371/2002 of 20 December 2002 on the conservation and sustainable exploitation of fisheries resources under the Common Fisheries Policy. CR (2004) Commission Regulation (EC) No 1581/2004 of 27 August 2004 amending Regulation (EC) No 1639/2001 establishing the minimum and extended Community programmes for the collection of data in the fisheries sector and laying down detailed rules for the application of Council Regulation (EC) No 1543/2000. CR (2008) Council Regulation (EC) No 199/2008 of 25 February 2008 concerning the establishment of a Community framework for the collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy. Survey of existing bioeconomic models General references 185 Danielsson A, Stefansson G, Baldursson FM, Thorarinsson K (1997) Utilization of the Icelandic cod stock in a multispecies context. Marine Resource Economics 12: 329-344. Eide A, Skjold F, Olsen F, Flåten O (2003) Harvest functions: the Norwegian bottom trawl cod fisheries. Marine Resource Economics 18: 81-94. FAO (1998) Fisheries bioeconomics Theory, modelling and management. In: J.C. Seijo ODaSS (ed) FAO FISHERIES TECHNICAL PAPER, Rome, pp 108p. Frost H, Levring JA, Hoff, A. and Thøgersen, T (2009) The EIAA model: Methodology, definitions and model outline. Garza-Gil MD, Varela-Lafuente M, Suris-Regueiro JC (2003) European hake fishery bioeconomic management (southern stock) applying en effort tax. Fisheries Research 60: 199-206. Gordon HS (1953) An economic approach to the optimum utilization of fishery resources. Journal of Fisheries Research Board of Canada 10: 442-447. Gordon HS (1954) The economic theory of a common property. Jpurnal of Political Economics 62: 124-142. Grafton RQ, Adamowicz W, Dupont D, Nelson H, Hill R.J, Renzetti S. (2008) Bioeconomics of Fisheries The Economics of the Environment and Natural Resources, pp 95-128. Grafton RQ, Kompas T. (2004) The Bioeconomics of Marine Reserves: A Selected Review with Policy Implications, 0405. Kell L, Mosqueira I, Grosjean P, J-M. F, Garcia D, Hillary R, Jardim E, Mardle S, Pastoors M, Poos J, Scott F, Scott R (2007) FLR: an opensource framework for the evaluation and development of management strategies. ICES Journal of Marine Science 64: 640-646. Knowler D (2002) A Review of Selected Bioeconomic Models with Environmental Influences in Fisheries. Journal of Bioeconomics 4: 163-181. Leung P (2006) Multiple-criteria decision-making (MCDM). applications in fishery management. Int. J. Environmental Technology and Management 6: 96- 110. Mardle S, Pascoe S (1999) A review of applications of multiple–criteria decision-making techniques. Marine Resource Economics 14: 41–63. Survey of existing bioeconomic models 186 General references Mardle S, Pascoe S, Tamiz M, Jones D (2000) Resource allocation in the North Sea demersal fisheries: a goal programming approach. Annals of Operations Research 94: 321-342. Mardle SP, S. (2002) Trade-offs between long run and short run objectives in the North Sea. Journal of Environmental Management 65: 49-62. Mohn R, Cook R (1993) Introduction to sequential population analysis. NAFO Sci. Counc. Stud. 17. Pascoe S, Gréboval, D. (2003) Measuring Capacity in Fisheries. FAO, Rome. Pascoe SM, S; Steen, F; Asche, S. (1999) Interactions between farmed salmon and the North Sea demersal fisheries: a bioeconomic analysis. CEMARE Research Paper 144. Plagányi ÉE (2007) Models for an ecosystem approach to fisheries, Rome. Pollnac R, Abbott-Jamieson., Smith., Miller., Clay., Oles. (2006) Toward a model for fisheries social impact assessment. Marine Fisheries Review. Schaefer M (1957) Some Considerations of Populations dynamics and economics in relation to the management of marine fisheries. Journal of Fisheries research Board of Canada. 14: 669-681. SEC (2006a) 22nd Report of the Scientific, Technical and Economic Committee for Fisheries. SEC (2006b) Impact assessment of long-term management plans for sole and plaice., Ispra. SEC (2006c) Report of the Joint SGECA - SGRST sub-group meeting on bioeconomic modelling. Ispra 4-6 October 2005 and 7 – 9 March 2006. Commission Staff Working Paper, Brussels 2006.. SEC (2008) Northern hake long-term management plan impact assessment. SGBRE-07-05, Brussels. Smith VL (1969) On Models of Commercial Fishing. Journal of Political Economy 77: 181. Sparre PJ, Willmann R (1993) Software for bio-economic analysis of fisheries. BEAM 4. Analytical bio-economic simulation of space-structured multi-species and multi-fleet fisheries. Volume 1: Description of model. User’s manual. FAO, 3, Rome. Stouten H, Heene A, Gellynck X, Polet H (2008) The effect of restrictive policy instruments on Belgian fishing fleet dynamics. Aquatic Living Resources 21: 247–258. Survey of existing bioeconomic models General references 187 Sumaila UR (1999) A review of game-theoretic models of fishing. Marine Policy 23: 10. Tjeerd-Boom J, Frost H, Sørensen L-C (2008) Integrated modeling of fisheries. Development of a global view based on models and bioeconomic data. Nordic Council of Ministers, Copenhagen. Ulrich C, Pascoe S, Marchal P, Sparre P, De Wilde J-W (2002) Influence of trends in fishing power on bio-economics in the North Sea flatfish fishery regulated by catches - or by effort quotas. Canadian Journal of Fisheries and Aquatic Science 59: 829-843. Unit PMsS (2004) Net Benefits: A sustainable and profitable future for UK fishing. Ventana Systems I (1998-2008) Vensim. Survey of existing bioeconomic models 188 Appendix Appendix Survey of existing bioeconomic models Appendix 189 A. APPENDIX: Specific description of the models A.1 Specific description: AHF A.1.1 Full specification of model equations The ‘AHF model’ evaluates the dynamic change in fleet capacity from one time period to the next, given expectations about future earnings from the fishery. The central equation of the Capacity Change model is the evaluation of fleet capacity V (number of vessels) in year y for fleet segment b: Vy,b= Vy-1,b+y,b ; VMIN,b ≤ Vy,b ≤ VMAX,b Vy,b = IIN,b · Πy,b / pIN,b ; Πy,b ≥ 0 Vy,b = IOUT,b · Πy,b / pOUT,b ; Πy,b < 0 Πy,b is the capitalisation of future payments, that reflects the assumption that the fishermen base their investment / disinvestment decisions on expected future earnings. IIN,b and IOUT,b are the fractions of positive relatively negative expected profits that are used to invest / disinvest. pIN,b and pOUT,b are the prices per unit capacity of investment / disinvestment. It is assumed that the change in capacity is determined by the opportunity cost of capital including an option for asymmetry in entry and exit. The price of a vessel pIN,b transforms pecuniary capital into physical capital, and the reciprocal of pOUT,b includes the fisherman’s perception of opportunity costs. For more details on the investment / disinvestment function, refer to Hoff and Frost (2006). The investment/disinvestment equation leading to dynamic capacity change presented above, is only one part of the overall bio-economic feedback model constructed to evaluate a chosen management scheme. The model is based on an earlier multi-fleet multi-stock model (Hoff and Frost, 2006). It is a dynamic feedback model with annual time-steps. It switches between quota and effort restrictions on the fleet, depending on which control is binding. The number of vessels Vy,b in year y in fleet b based on previous years’ profit is: min max Vy,b =Vy−1,b + ∆Vy,b when Vb ≤Vy,b ≤Vb , (A.1.1.1) with Survey of existing bioeconomic models 190 Appendix ⎧ I + Π b y,b ; Π ≥ 0 ⎪ y,b τ ub ⎪ ιb (1− (1+ ρ) ) ⎛ 1 ⎞ . ∆Vy,b = ⎨ − ; Π y,b = ⋅⎜ P( y−1)−w −i,y ⎟ I Π ρ ⎜1+ u ∑ b ⎟ ⎪ b y,b ; Π < 0 ⎝ b i=0 ⎠ ⎪ y,b ⎩ οb min max Vb and Vb are the minimum and maximum numbers of vessels allowed in fleet b, and Π y,b is the average profit over ub +1 years discounted over τ years; it is used for evaluating capacity change. The parameters ιb and οb are the prices per unit capacity of investments or + − disinvestments, Ib and Ib are the shares of respectively positive and negative profits used for investment/disinvestment in capacity, wb is the lag in the investment decision, i.e. the number of years from decision to invest / disinvest until the change is actually put into force, Py,b the net profit, ρ the interest rate, and τ the expected lifetime of a vessel. It is assumed that there is no decommissioning of vessels. In Equation (A.1.1.1) it is assumed that changes in capacity are determined by the opportunity cost of capital including an option for asymmetry in entry and exit. The price of a vessel ιb transforms pecuniary capital into physical capital, and the reciprocal of ιb includes a fisher’s perception of opportunity costs (Bjørndal and Conrad 1987). Based on estimated stock Ns,a, y−1 and observed catches cs,a, y−1 (both in numbers) for species s of age a in year y–1, the resulting fishing mortality rate Fs,a,y-1 for species s at age a in year y–1 is estimated by solving the following cohort catch equation for F: Fs,a, y −1 cs,a, y −1 = N s,a, y −1[]1− exp(−M s,a − Fs,a, y −1) . (A.1.1.2) M s,a + Fs,a, y −1 Here, Ms,a is the natural mortality rate of species s at age a. Equation (A.1.1.2) can be solved numerically by most mathematical/statistical software packages. Next, the stock is projected one year forward: N s,a,y = N s,a−1,y−1 exp(−M s,a−1 − Fs,a−1,y−1 ) ; a > 1. (A.1.1.3) The number of recruits Ns,1,y depends on the stock–recruitment relationship; here, a Ricker function and constant recruitment are used. A TAC for the stocks in year y is set. In the case of the implementation of the model for the North Sea flatfish fisheries (Hoff and Frost 2008) the TAC for plaice and sole in year y were set according to a multi-annual recovery plan for plaice and sole. For both species the plan states that the TAC of species s in a given year should be the maximum of (i) the TAC that will result Survey of existing bioeconomic models Appendix 191 in a 10% decrease in the fishing mortality rate relative to the previous year, and (ii) the TAC that will result in a fishing mortality rate of around κs for ages 2–6 of the stock (κs = 0.3 for plaice, and κs = 0.2 for sole). Moreover, if the TAC for year y determined by these rules is 15% above or below the TAC of the previous year, the TAC must be set equal to 1.15 or 0.85 times the TAC in the previous year, respectively. In addition to this quota regulation, the plaice and sole fishery are subject to effort limitations. The estimated fishing mortality rate of year y–1 (Equation (A.1.1.2)) is thus scaled into two proposed fishing mortality rates for year y using rules (i) and (ii): ~ Fs,a,y,(i) = 0.9Fs,a,y−1 . (A.1.1.4) ~ κ s Fs,a,y,(ii) = Fs,a,y−1 max{}Fs,a,y−1 ; a = 2,...,6 A tilde over a parameter in this equation and subsequently indicates that it is a proposed and not a realized value. Rule (ii) has been translated into meaning the TAC that will result in a fishing mortality rate ≤κs for all ages of the stock. This has been done for modelling purposes, but also on the assumption that it will generally be difficult to implement a fishing mortality rate of exactly κs for all these ages. ~ The proposed fishing mortalities are then used to evaluate a total catch proposal Cs,y (in weight): ⎧ ⎛ F~ ⎞ ⎫ ~ ⎪ ⎜ ~ s,a,y,(I ) ⎟ ⎪ Cs,y = max⎨ ws,a N s,a,y []1− exp(−M s,a − Fs,a,y,(I ) ; I = (i),(ii)⎬ ∑⎜ M + F~ ⎟ ⎩⎪ a ⎝ s,a s,a,y,( I ) ⎠ ⎭⎪ , (A.1.1.5) ⎧ ~ ⎫ ≡ max⎨∑Cs,a,y,( I ) ; I = (i),(ii)⎬ ⎩ a ⎭ ~ where ws,a is the weight of age class a of species s, and Cs,a, y,(I ) is the proposed catch weight in year y for species s at age a, evaluated with the fishing mortality given by scenario I. The catch is the sum of landings and discards. In this case here (Hoff and Frost 2008) the ~ landings are assumed to be equal to the proposed TAC, Qs,y . It is assumed that the landings fraction is constant across years for each age class. Hence, the proposed catches are scaled up to ~ obtain the proposed TAC, Qs,y : ~ ~ L Q = C s,a, y=1 , (A.1.1.6) s,y ∑ s,a, y y=1,a a Cs Survey of existing bioeconomic models 192 Appendix which is further scaled up or down if necessary, ensuring that the TAC in year y is within ±15% of the TAC in year y–1. The TACs in year y = 1 are set equal to the observed landings of the two species. The quota of species s for fleet b, qb,s,y , is set using Qs,y , the country share δ, and the fleet share φ: Cb,s,a, y−1 qb,s,a,y = Qs,a, yδφb,s, y ; φb,s,y = . (A.1.1.7) ∑()Cb,s,a, y−1 b The country share δ is constant throughout the simulation period, but the fleet shares are based on fleet catches in the previous year. The effort (number of sea days) needed per vessel in fleet b to catch species s is qb,s,y ε b,s, y = , (A.1.1.8) V¨b, yU b,s,y where U b,s,y is the CPUE, given by β −γ ⎛ L ⎞⎛ B ⎞ b,s ⎛ q ⎞ b,s U = ⎜ b,s,1 ⎟⎜ s, y ⎟ ⎜ b,s,y ⎟ . (A.1.1.9) b,s, y ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎝Vb,1 ⋅ Eb,1 ⎠⎝ Bs,1 ⎠ ⎝ Lb,s,1 ⎠ Here, Eb,1 is the average observed effort (number of sea days) per vessel in fleet b in year y = 1, and B = w N is the stock biomass. This CPUE relationship assumes that landings s, y ∑a s,a s,a,y are determined by the conventional economic Cobb–Douglas production function (Conrad and Clark, 1994 ch.2), cf. section 3.1 below. β and γ are the parameters of this Cobb–Douglas function. Equation (A.1.1.8) gives the number of sea days necessary for a vessel in fleet b to catch its full ~ quota of each species s. The fisher’s choice of effort Eb,y can be either (i) the minimum of the two efforts, (ii) the maximum of the two efforts, or (iii) a third effort, e.g. from a profit maximizing perspective. The actual number of sea days used in year y by a vessel in fleet b is further determined by the effort limit E b, y , set by the regulation max Eb,y = min(max(εb,1,y , εb,1,y ), Eb,y ) Survey of existing bioeconomic models Appendix 193 ~ ~ ⎧E ; E ≤ E b,y b,y b,y . (A.1.1.10) Eb,y = ⎨ ~ ⎩E b,y ; Eb,y > E b,y E b, y is set according to the regulation in force. In this case here (Hoff and Frost 2008) it does not strictly follow the recovery plan for plaice and sole, where it is stated that “Each year, the Council shall decide (…) on an adjustment to the maximum level of fishing effort available for fleets where either or both plaice and sole comprise an important part of the landings (…)”. Not knowing the actual rule by which this annual effort has been set, it has been decided to follow the sea-days regulation set in connection with recovery plans for cod, inter alia, in the North Sea. The actual landings of species corresponding to the effort given by Equation (A.1.1.10) are determined by using the CPUE relationship (Equation (A.1.1.9)). 1 ⎛ E ⎞1+γ b,s L = q ⎜ b,y ⎟ , (A.1.1.11) b,s,a, y b,s,a, y ⎜ ⎟ ⎝ ε b,s, y ⎠ and the actual catches of species s are then ~ Cs,a, y = (Cs,a, y − ∑ qb,s,a, y ) + ∑ Lb,s,a, y b b 1 . (A.1.1.12) cs,a, y = Cs,a, y ws,a Figure 1 shows the steps of the biological operations model. Given the observed landings of the two species taken by the fleet (Equation (A.1.1.1)), it is straightforward to evaluate the total landings revenue Rb,y , the fixed and variable costs and profit of each fleet in year y. The revenue of fleet b is α s 1 ⎛ Q ⎞ R = p L ; p = p ⎜ s, y ⎟ , α ≤ 0 . (A.1.1.13) b, y ∑ s,a, y b,s,a, y s,a,y s,a, y=1 ⎜ ⎟ s λb s,a ⎝ Qs,y=1 ⎠ Here, ps,a,y is the price of species s at age class a in year y, αs is the price flexibility rate, and λ is the fraction which the catch value of plaice and sole constitutes of the total catch value. b The fixed costs ( Kb,y ) and variable costs (Gb,y ) in year y are Survey of existing bioeconomic models 194 Appendix ⎛ V ⎞ K = K ⎜ b, y ⎟ b, y b,y=1 ⎜ ⎟ ⎝Vb,y=1 ⎠ (A.1.1.14) ⎛ E ⋅V ⎞ G = G ⎜ b, y b, y ⎟ b, y b, y=1 ⎜ ⎟ ⎝ Eb,y=1 ⋅Vb, y=1 ⎠ This means that the fixed costs are scaled by the capacity, and the variable costs by the fleet effort evaluated in Equation (A.1.10). It is assumed that the crew’s share is included in the variable costs, i.e. that the catch of other species than plaice and sole (e.g. cod and shrimp) is taken in a mixed fishery together with plaice and sole. Finally, the total profit is Pb,y = Rb,y −Gb,y − Kb,y . (A.1.1.15) A.1.2 Full specification of model variables and model equations This is described and given under section A.1.1 above. A.1.3 Full list of model parameters In case of the North Sea flatfish fisheries implementation the parameters and parameter settings were the following: Table 41. Parameters r and s for the Ricker stock–recruitment relationship and average recruitment, for plaice and sole in the North Sea. Parameter Plaice Sole R 9.5659 5.11889 S 0.0321 ×10 −4 0.1295 ×10−4 R 1.05231 ×10 6 1.25656 ×105 R25% 638 477 60 547 6 R75% 1.22624 ×10 140 717 Note: The unit for r is thousand (recruits) per tonne, and the unit for s is t–1. The average recruitment is measured in thousands. Survey of existing bioeconomic models Appendix 195 Table 42. Initialization data for the Dutch beam trawl fleet operating in the North Sea. Value Parameter Beam trawlers <24 m Beam trawlers >24 m 1 Number of vessels ( NVFl ) 171 131 1 Fishing days per vessel ( EFl ) 135 182 Variable cost for the total fleet ( 1 ) 3 3 VCFl €41.4×10 €115.2×10 1 3 3 Fixed cost for the total fleet ( FCFl ) €21.2×10 €51.7×10 In 3 3 Price per unit capacity of investment (VFl ) €556×10 €2 366×10 Price per unit capacity of disinvestment ( OUT ) 3 3 VFl €695×10 €2 958×10 + Investment fraction ( IFl ) 12.5% 12.5% − Disinvestment fraction ( I Fl ) 12.5% 12.5% Lag in investment decision (LAGFl) 1 1 Number of year for averaging profit (LGT) 2 2 Discount rate (r) 5% 5% Expected lifetime (LT) 20 20 Catch value fraction (CV) 34% 80% Sales price, plaice (€ per kg) 1.87 1.87 Sales price, sole (€ per kg) 8.32 8.97 Maximum number of sea days ( y ) E max Fl 156 156 U Biomass scaling factor ( ) β Fl,s 0.8 0.8 U Catch scaling factor (γ Fl,s ) 1 1 Note: The scaling factors are assumed equal for the two species, as seen in the last two rows above. The relationship between catch per unit effort and the landings production function has to be calculated: The catch per unit effort (CPUE) formula given in Equation (A.1.1.9) is directly related to the assumption that the landings are given by the conventional economic Cobb–Douglas production function (Conrad and Clark 1994; Danielsson et al. 1997; Eide et al. 2003; Garza-Gil et al. 2003), depending on the spawning–-stock biomass and the effort. Equation (A.1.1.9) states that β −γ ⎛ B ⎞ b,s ⎛ L ⎞ b,s U = U ⎜ s,y ⎟ ⎜ b,s,y ⎟ . (A.1.3.1) b,s,y b,s,y=1⎜ ⎟ ⎜ ⎟ ⎝ Bs,y=1 ⎠ ⎝ Lb,s,y=1 ⎠ Here, Ub,s, y is the CPUE of species s for fleet b in year y, Bs, y the stock biomass of species s in year y, and Lb,s, y are the landings (weight) of species s by fleet b in year y. When the landings are evaluated using the economics-based Cobb–Douglas production function ( Eb,y being the total effort used by fleet b in year y): χb,s λb,s Lb,s, y = Ab,s,a (Bs, y ) (Eb, y ) , (A1.3.2) Survey of existing bioeconomic models 196 Appendix βFl,, s, γ Fl s it is shown below that the following relationship rules between and χb,s , λb,s : 1 χb,s γ b,s = −1 βb,s = . (A1.3.3) λb,s λb,s The standard formula for CPUE is Lb,s,y U b,s,y = . (A1.3.4) Eb,y Therefore, U L E b,s, y = b,s, y b, y =1 Ub,s, y =1 Lb,s, y =1 Eb, y 1/ λ ⎛ χ b,s b,s ⎞ Lb,s, y ⎜ ()Lb,s, y =1 (Bs, y =1) ⎟ = 1/ λ L ⎜ χ b,s b,s ⎟ b,s, y =1 ⎝ ()Lb,s, y (Bs, y ) ⎠ 1 χ b,s . (A1.3.5) L ⎛ L ⎞ λb,s ⎛ B ⎞ λb,s b,s, y ⎜ b,s, y =1 ⎟ ⎜ b,s, y ⎟ = ⎜ ⎟ ⎜ ⎟ Lb,s, y =1 ⎝ Lb,s, y ⎠ ⎝ Bb,s, y =1 ⎠ χ 1 b,s 1− ⎛ B ⎞ λb,s ⎛ L ⎞ λb,s = ⎜ b,s, y ⎟ ⎜ b,s, y ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ Bb,s, y =1 ⎠ ⎝ Lb,s, y =1 ⎠ Using the inverse of the landings function (A2) proves the relationship (A3). Notice that γ b,s > 0 when λb,s <1, i.e. that decreasing returns to scale in the effort imply that the CPUE decreases with increasing landings, and vice versa. Survey of existing bioeconomic models Appendix 197 A.2 Specific description: BEMMFISH A.2.1 Full specification of model equations There are a maximum of 4 species and 3 fleets in the current BEMMFISH model. The model equations presented below under point a-g, are interconnected in the following way: • The number of species (I) and number of fleets (J) as well as the number of periods are selected first, • The desired discount rate for calculation of the present value and the tax rate on landings for each species are chosen, • The initial conditions: Initial stock xo, fleet size n(j,t), the number of vessels for each of the fleets, effort e (t) and catchability (initial catchability qo), determine the fishing mortality in b) and subsequently the harvest in c), • The initial stock, the harvest in c) and the growth in a2) give the value of the new stock in a1), • The harvest in c) together with the effort, the cost in d) and the price function in e) gives the profit in f), • The profit in f) influences the number of vessels in the following period in g); • Catchability develops over time, b3). a) Biomass/stock The stock x(i,t) for species i for period t+1 is determined by the stock at time t plus the growth minus the harvest in the period t. a1) Stock x(i,t) J x(i,t+1) = x(i,t) + G(x(1,t), x(2,t), ... x(I,t);i) - ∑n( j,t) y(i, j,t)) , j =1 i=1,2,...,I. (A.2.3.6) J The term ∑n( j,t)y(i, j,t)) corresponds to the total harvest of species i at j=1 time t. a2) Growth G(x(i,t) Where G(x) is the biomass growth defined by the following function: Survey of existing bioeconomic models 198 Appendix G(x(1,t), x(2,t),... x(I,t);i) = α(i)⋅ x(i,t) - β(i) ⋅ x(i,t)i + (i',t)x(i',t)(i',i) x(i',t)(i',i) ∑i'(i) (A.2.3.7) If x(i,t+1) < 0 x(i,t+1) = 0 The term represents species interactions. For each species there are I-1 such interaction terms with 3 (I-1) parameters. Thus the total number of biological interaction parameters is 3⋅ I⋅ (I-1). b) Fishing mortality production Fishing mortality is a function of catchability, effort and stock. b1) Fishing mortality production f(i,j,t) f(i,j,t) = q(i,j,t) ⋅ ⋅ (A.2.3.8) b2) Total fishing mortality F(i,t) Total fishing mortality of species i: J F(i,t) = , ∑n( j,t) f (i, j,t)) j=1 I etot(i,j,t) = ∑n(i, S, j)e(i, j,t) 1 ≥ρ (i,s) ≥ 0, ρ(i,i) = 1, S=1 (A.2.3.9) ρ(i,s,j), i ≠ s represent by-catch parameters which may be expected to be small in most cases. I e(i,j,t) is directed fishing effort. Normally, ∑e(i, j,t) ≤ (i), where (i), is the i=1 maximum total effort by fleet j. Often it is convenient to set (i) = 1. b3) Catchability An endogenous technological progress in catchability q(t) for each species and fleet has been defined: q(i, j,t 1) q(i, j,t) ⋅ (1 ε( j,t 1)) , (A.2.3.10) Survey of existing bioeconomic models Appendix 199 ε (j,t+1) is the rate of technological growth in the “catchability” of fleet j. ε (j,t+1) is expected to be in the interval (0,0.03) per annum. ε (j,t) evolves over time according to the specification (S-curve): max( j) ε (j,t 1) = eB(π ( j,t)) , ⎡(max( j) − (0( j))⎤ 1+ ⎢ ⎥ ⎣ 0( j) ⎦ (A.2.3.11) where π(j,t) represents the profits of fleet j. c) Harvest The harvest per fleet y and the total harvest per species, Y(i,t), is a result of the stock and the fishing mortality: c1) The harvest per fleet y(i,j,t) y(i,j,t) = Y(i,t) ⋅ f(i,j,t)/F(i,t) (A.2.3.12) c2) Total harvest Y(i,t) Y(i,t) = x(i,t)⋅ (1- exp(-F(i,t))) (A.2.3.13) d) Cost c(j,t) For each species (variable cost are related to species) and fleet a specific cost function can be employed: c(j,t) = d(j) + (A.2.3.14) where d(j) is fixed cost and f(i,j) and e(i,j,t)g(i,j) are variable costs, related to effort e(i,j,t) . No cost externality is assumed. e) Price p(i,t): The price function is determined for each species. The functions take into account the price flexibilities. p(i,t) = h(i)⋅ exp(-ε (i)⋅ Y(i,t)) (A.2.3.15) Survey of existing bioeconomic models 200 Appendix Note: No cross species elasticity assumed. f) Profits π(j,t) Profits for each fleet are calculated as the difference between the revenues (harvest multiplied by the price) and the costs. π(j,t) = (A.2.3.16) g) Number of vessels n(j,t) The number of vessels in each fleet for the following period, t+1, depends on the existing number in period t and the profit: n(j,t+1) = n(j,t) +Φ(j)⋅ (π (j,t)-η (j)), n(j,t+1) ≥ 0, n is an integer. (A.2.3.17) There is no minimum restriction in the model, except that n is non-negative. h) Stochasticity The below function can define the biomass as a stochastic variable: x(i,t+1) = x(i,t+1)⋅ exp(u(i,t) - 0.5), u(t) ~Uni (0,1). (A.2.3.18) A.2.2 Full specification of model variables The basic variables used in the above functions of the BEMMFISH model are the following: • x(i,t) Biomass of species i at time t. • xo has to be selected for all species i. Default value, xo = 0.5. • y(i,j,t) Harvest per vessel of species i of fleet j at time t. • f(i,j,t) Fishing mortality of species i by vessels of fleet j at time t. • e(i,j,t) Directed fishing effort for species i by vessels of fleet j at time t. • c(j,t) Costs per vessel of fleet j at time t. • p(i,t) Price of fish of species i at time t. • π(j,t) Profits per vessel of fleet j at time t. • n(j,t) Number of vessels of fleet j at time t. • q(i,j,t) Fishing mortality production coefficient (catchability) of species i by fleet j. • qo has to be selected for all species i and fleets j. Default value, qo = 1. There are three sets of control variables: • Effort: e(i,j,t), • tax on landings: τ(i,j,t), • restrictions on numbers of vessels: n(j,t). Survey of existing bioeconomic models Appendix 201 Effort: The effort level is selected; e(i,j,t) for each species i and fleets j and can furthermore be adjusted for each time period t, if relevant. Effort is expressed as a fraction of the total allowable effort and specified by a number that takes the value between 0 (no effort) and it would normally not be higher than 1, but the model does work with values larger than 1. Tax on landings: p°(i,j,t) = p(i,j,t) (1 - τ(i,j,t)), Total tax revenues = TAX(t) = (A.2.3.19) where p° is price after tax and τ(i,j,t) is the tax rate. The tax rate takes values between 0 and 1. Restrictions number of vessels: The user specifies an upper limit on the number of vessels in each fleet, NRES(j,t). The default number is 100 for each fleet. The actual vessel number then is: n(j,t) ≤NRES(j,t). The following variables are exogenous variables: the discount rate, tax on landings, effort, the initial number of vessels, the fixed costs, as well as initial values of the variable costs and the prices. The remaining variables are endogenous. A.2.3 Full list of model parameters The following parameters are included in the equations of the previous section: a) Biological growth: - α(i) > 0 Intrinsic growth rate of species i. - β(i) > 0. - γ(i) > 1 (normally close to 2). b) Fishing mortality production: - a(i,j) > 0 (normally close to 1). - b(i,j) ∈ (0,1) Distributional parameter. - ε0(j) Basic rate of technical growth in the generation of fishing mortality by fleet j. - εmax(j) Maximum rate of technical growth in the generation of fishing mortality by fleet j. c) Costs: - d(j) ≥ 0 Fixed costs. - f(i,j) > 0 Variable costs. Survey of existing bioeconomic models 202 Appendix - g(i,j) ≥ 1 Variable costs. d) Price: - h(i) > 0. - ε (i) ≤0 Elasticity of price w.r.t. supply. e) Fleet adjustment: - Φ(j) ≥ 0 Adjustment parameter. - η(j) Reservation earnings (income wage), probably positive. f) Interaction parameters: - δ(i’,i), species i’s interaction with the I-1 other species, - κ(i’,i), species i’s interaction with the I-1 other species, - ν(i’,i), species i’s interaction with the I-1 other species, - ρ(i,s,j), by-catch parameter, for species i and each fleet j, the parameter determines the by catch of each other species s (i ≠ s). The users of the program have to provide or estimate the specific values of the parameters which fit the particular situation. It may require a lot of calibration of the different parameters in order to produce useful results by the model. Survey of existing bioeconomic models Appendix 203 A.3 Specific description: BIRDMOD A.3.1 Full specification of model equations Economic module: The BIRDMOD economic box is formulated by fleet segments and constant time intervals equal to a year. The main elements of the economic box are prices and costs functions. Prices are either considered constant ps, f ,t = ps, f . (A.3.1.1) or calculated as a function of the catches ⎛ C − C ⎞ p = p ⎜1+ α s, f ,t s, f ,t−1 ⎟ , (A.3.1.2) s, f ,t s, f ,t−1 ⎜ s, f ⎟ ⎝ Cs, f ,t−1 ⎠ where αs,f is the flexibility coefficient for a given species and fleet segment The price estimate by each commercial category is performed using a system of simultaneous equations subject to the restrictions on the relations between prices introduced by the user (Eq. A.3.1.4- A.3.1.6). The other limitation (Eq. A.3.1.3) constraints the species average price, given the quantities simulated per commercial category by the biological box, to be equal to the real price registered in the base year. Cs1 ps1 + Cs2 ps2 + Cs3 ps3 + Cs4 ps4 = C * p C = Cs1 + Cs2 + Cs3 + Cs4 (A.3.1.3) ps1 + − a1 ps4 = 0 a1 = ps1 ps4 (A.3.1.4) ps2 + − a2 ps4 = 0 a2 = ps2 ps4 (A.3.1.5) ps3 − a3 ps4 = 0 a3 = ps3 ps4 , (A.3.1.6) Costs: Costs, which are subtracted from the revenues to obtain the added value and the profits, are divided into the following groups: • Variable costs; • Fixed costs; • Labour costs; • Interests and amortizations. Survey of existing bioeconomic models 204 Appendix Variable costs are subdivided into three headings: cost of fuel and lubricant, commercial and others variable costs. The components related to fuel and lubricant and other variable costs are a function of the effort (GRT multiplied by average days at sea) while the component of the commercial costs is a function of the level of catches. Each group of costs considered by the model in the economic box is estimated by using a direct proportional relationship with the related independent variable. The ratios between dependent and independent variables are kept constant throughout the years of simulation. Fixed costs are a function of the GRT, both as regards maintenance expenses and other fixed costs. The labour costs are calculated as 50% of the difference between revenues and variable costs. Gross profit is then obtained from the difference between revenues and intermediate costs (the sum of variable and fixed costs, and the labour cost). Finally, to obtain net profit, amortizations and interests are subtracted from gross profit. Amortizations and interests are estimated as a function of the GRT of the fleet segment. Net profits are further adjusted by taking into account the effect of management policies, defined in terms of taxes and subsidies. State variation: As for the average fishing days and the overall fleet GRT, the model foresees two possible dynamics. The first assumes the average days and the GRT as constant and equal to the values registered for the base year: dd f ,t = dd f ; (A.3.1.7) GRT f ,t = GRT f . (A.3.1.8) The latter hypothesizes a relation between the variations in the average days and the variations in the profits made over the preceding year: Π f ,t−1 − Π f ,t−2 dd f ,t = dd f ,t−1 (1+ α f ) . (A.3.1.7a) Π f ,t−2 A relation between the overall tonnage variations and the variations in the profits made over the preceding year can also be assumed: Π f ,t−1 − Π f ,t−2 GRT f ,t = GRT f ,t−1 (1+ β f ) . (A.3.1.8a) Π f ,t−2 Distribution matrix: Within the model, the connection between the biological and the economic boxes has been managed using a series of matrices that allow the conversion of catches and fishing days per Survey of existing bioeconomic models Appendix 205 fleet into catches and fishing days per gear, and vice versa. In particular, for each month and species, the catches per gear are obtained by a linear combination of catches per fleet according to a series of coefficients which identify the percentages of catch distribution per fleet among the different gears. This equation system can be represented with the following matrix, which considers four fleet segments and four fishing gears: ⎡c f 1,g1 c f 1,g 2 c f 1,g3 c f 1,g 4 ⎤ ⎢ ⎥ ⎢c f 2,g1 c f 2,g 2 c f 2,g3 c f 2,g 4 ⎥ []C C C C = []C C C C , (A.3.1.9) f 1 f 2 f 3 f 4 ⎢c c c c ⎥ g1 g 2 g3 g 4 ⎢ f 3,g1 f 3,g 2 f 3,g3 f 3,g 4 ⎥ ⎢ ⎥ ⎣c f 4,g1 c f 4,g 2 c f 4,g3 c f 4,g 4 ⎦ Which can be written as: C * D = C f f ,g g (A.3.1.9a) Biological module: The biological module consists of two components A. Aladym model component to simulate the dynamics of a single species (target species) B. Global component to simulate the dynamic for the group of other species A: Aladym model Growth: −K ⋅(age−t0 ) Lage = L∞ ⋅ (1− e ). (AL.1) (L − L ) L = L + age age+∆t (AL.2) age ∞ K ⋅ ∆t b Wage = aLage (AL.3) Population dynamics: dN = −MN (AL.4) dt dN = −ZN (AL.5) dt −Mt, j ⋅∆t N(t+∆t), j = Nt, je (AL.6) N = N e−(Ft, j +Mt, j )⋅∆t (t+∆t), j t, j (AL.7) Survey of existing bioeconomic models 206 Appendix Maturity: 1 Mat(L) = 1+ e −r(L−Lm50%) (AL.8) 1 Mat = age ⎛ 2⋅Ln3 ⎞ ⎜ ⎟⋅()L −L ⎜ L −L ⎟ m50% age 1 + e⎝ m75% m25% ⎠ (AL.9) Biomass: B = N ⋅ w t, j t, j age (AL.10) SSB = N ⋅ w ⋅ Mat t, j t, j age age (AL.11) UB = UN ⋅ w t, j t, j age (AL.12) USSB = UN ⋅ w ⋅ Mat t, j t, j age age (AL.13) Initial recruitment and stock recruitment relationship: The number of individuals entering the population can be a vector or it can be estimated from one of the following user selected stock-recruitment relationships: Beverton & Holt: S R = (a + bS) (AL.14a) Ricker: (−bS ) R = a ⋅ S ⋅e (AL.14b) Shepherd : b R = a ⋅ S /[1+ (S / c) ] (AL.14c) Barrowman & Myers: ⎪⎧α ⋅ S if S < S * ⎨ * ⎪α ⋅ S * if S ≥ S * R = α ⋅ min(S, S ) = ⎩ (AL.14d) Survey of existing bioeconomic models Appendix 207 ⎧α ⋅ S ⎪ S ≤ S * ⋅(1−δ ) ⎪ (S − S * ⋅ (1− δ ))2 if α ⋅ (S − ) * * ⎨ * S ⋅(1−δ ) < S < S ⋅(1+ δ ) ⎪ 4δ ⋅ S if ⎪α ⋅ S * * R = ⎩ if S ≥ S ⋅(1+ δ ) (AL.14e) SSN = SSN t ∑ j t, j (AL.15) SSN = N ⋅ Mat t, j t, j age . (AL.16) Mortality: ⎧ K −K ⋅()t −t t ≤ tM ⎪1− e 0 M ()t = ⎨ K ⎪ t ≥ t ⎪a + a ⋅ t − t + a ⋅ t − t 2 M ⎩ 0 1 ()()M 2 M (AL.17) where, 1 t = − ln1− eKt0 + t M K 0 −K ⋅()tM −t0 a0 = 1− e −K ⋅()tM −t0 a1 = K ⋅ e 1 2 −K ⋅()tM −t0 a2 = − K ⋅ e 2 F(L) = F ⋅ S(L) m ax (AL.18) F(L) = F ⋅ S(L)⋅ f max act (AL.19) F = QZ − M m ax input min (AL.20) The probability of selection S(L) of the cohort j is calculated at time t from one of the two following user selected relationships: 1 S(L) = ⎛ 2⋅Ln3 ⎞ ⎜ ⎟⋅()L −L ⎜ L −L ⎟ 50% age 1+ e⎝ 75% 25% ⎠ ; (AL.21a) or 1 1 S(L) = ⋅ ⎛ 2⋅Ln3 ⎞ ⎛ −2⋅Ln3 ⎞ ⎜ ⎟⋅()L −L ⎜ ⎟⋅()D −L ⎜ L −L ⎟ 50% age ⎜ D −D ⎟ 50% age 1+ e⎝ 75% 25% ⎠ 1+ e⎝ 25% 75% ⎠ (AL.21b) Z = F + M t, j t, j t, j (AL.22) Survey of existing bioeconomic models 208 Appendix D = N − N = N ⋅ 1− e −Zt , j ⋅∆t t, j t, j t+∆t, j t, j ( ) (AL.23) BP = D ⋅ w t, j t, j age ; (AL.24) M BND = t, j ⋅ N ⋅(1− e−(Ft , j +M t , j )⋅∆t )⋅ w t, j Z t, j age t, j . (AL.25) Harvest control rules ∆t F C = F ⋅ N ⋅e−Z⋅τ dτ = N ⋅()1− e−Z ⋅∆t ∆t ∫ 0 Z 0 0 (AL.26) F Y = t, j ⋅ N ⋅(1− e−(Ft , j +M t , j )⋅∆t )⋅ w t, j Z t, j age t, j . (AL.27) ⎛ ∞ ⎞ ⎜ N ⎟ 1 ∑ t, j ` (AL.28) Z = ln⎜ j=1 ⎟ t ∆t ⎜ ∞ ⎟ ⎜ ∑ N t+∆t, j ⎟ ⎝ j=2 ⎠ ⎛ ∞ ⎞ ⎜ ∑ Nt, j ⎟ 1 ⎜ j =1 ⎟ (AL.29) Ft = ln ∞ ∆t ⎜ F ⎟ ⎜ ∑ Nt +∆t, j ⎟ ⎝ j =2 ⎠ Simulation of the dynamics for the group of other species Estimating catches of all fleet segments by a “modified” Schaefer model: C = (k E − k E 2 )(E E ) t 0 t 1 t t t−1 , (AL.30) Relative productivity of fleet segments i compared to standard fleet segment s Ci,t Ei,t RPi,t = , (AL.31) Cs,t Es,t The equivalent effort at time t is then obtained using the weighed sum of the total effort per fleet segment, where the weights are the relative productivity factors Eeq,t = ∑ RPi,t Ei,t i . (AL.32) Survey of existing bioeconomic models Appendix 209 A.3.2 Full specification of model variables Economic module: Table 43. Economic variables. BIRDMOD Variable Name of variable E Effort Ct Catch RPi Relative productivity for fleet segment i Ps,f Price of species for fleet segment f ddf,t Days at sea for fleet segment f in period t GRTf,t Number of GRT for fleet segment f in period t Πf,t Net profit fleet segment f in period t Biological module: Table 44. Biologic variables. BIRDMOD Variable Name of variable Aladym model R recruitment L Length w individual weight Sel selectivity Mat maturity M natural mortality F fishing mortality Z total mortality N exploited population UN unexploited population SSN Number of offspring D Number of individuals death for all causes B exploited biomass SSB exploited spawning stock biomass UB unexploited biomass USSB unexploited spawning stock biomass BP Biomass of those death for all causes BND Biomass of those death for all causes excluding fishing S Number of spawners R Number of recruits S-R stock-recruitment relationship, C catch Y yield, t time, j cohort. Global model component for other species E Effort Ct Catch RPi Relative productivity for fleet segment i A.3.3 Full list of model parameters Economic module: Survey of existing bioeconomic models 210 Appendix Table 45. Economic parameters. BIRDMOD Parameter Name of parameters Equation Price flexibility coefficient species s and fleet segment f α s, f 9.2 flexibility of the average days in comparison with the profits αf 9.7a for the fleet segment f. and the flexibility of the overall GRT in comparison with the βf 9.8a profits for the fleet segment f. Biological module: Table 46. Biologic parameters. BIRDMOD Parameter Name of parameters Equation L∞, K and t0 Parameters of growth equation AL.1 L∞= asymptotic length a, b Parameters of weight equation AL.3 r Ogive slope of maturity function AL.8 a, b, c, α, δ , S * Parameters of stock recruitment relation AL.14 t0 and K Parameters of Chen and Watanabe model for natural mortality AL.17 a0, a1, a2 and tM Parameters of Chen and Watanabe model for natural mortality, AL.17 depending on t0 and K tM represents the age beyond which the contribution of the fish of a given cohort can be considered negligible Fmax Maximum fishing mortality AL.18, AL.19, AL.20 S(L) Proportion of retained fish. AL.18, AL.19 QZ A Z proxy AL.20 fact Fishing activity coefficient AL.19 L50%, L75% and L25% Selectivity parameters. AL.21 D50%, D25%, D75% De-selection parameters of the model AL.21 k0 , k1 Parameters of the Schaefer equation AL.30 Survey of existing bioeconomic models Appendix 211 A.4 Specific description: COBAS A.4.1 Full specification of model equations The model COBAS has been developed as part of the IiFSW project. Unfortunately, the project did not produce any specific mathematical or conceptual description of the bio-economic model. Model equations could be derived also from the software code, but at the moment this is not available. Notwithstanding, based on previous versions of the bio-economic model for the Channel, a specification of the model equations has been produced and reported below. As the previous version of the model, described in Mardle and Pascoe (2003), was an optimization model, some equations have been necessarily adapted and some variables added to make it compatible with a simulation model. However, any change of the original equations is based on the methodology report of the IiFSW project. The COBAS model is designed primarily as an input driven model, where management measures impact directly on fishing effort in terms of days at sea and number of vessels. Fishing effort at time t by country c is estimated by metiér m as follows: Em,c,t = ∑∑ B f ,g,c,t d f ,g,c a f ,m fpm,g , (A.4.1.1) fg where the meaning of each variable and index is reported in the next section. Stocks are modelled by age-structured biological model or surplus production model depending on the data available for each species. For age-structured stocks, two categories of stocks are modelled: those that extend beyond the Channel into the North Sea, Irish Sea or Western Approaches (Category 1), and those that fall fully within the English Channel (Category 2). Category 1 stocks are modelled considering the impact of fishing effort on the portion of the stock that lies within the Channel (Category 1a), and the portion that lies outside the Channel (Category 1b). Fishing mortality for each species is aggregated across metiers by the product of metiér-specific catchability coefficients and the standardized fishing effort: (*) (*) a FM1s,a,c,t = ∑q1s,a,m Em,c,t for s ∈ S , (A.4.1.2) m a FM 2s,c,t = ∑q2s,m Em,c,t for s ∉ S , (A.4.1.3) m where FM1 and FM2 represent the fishing mortality for age-structured and surplus production models respectively. Total mortality for each species is obtained by adding the natural mortality (only for age- structured stocks), the fishing mortality generated by boats operating in the Channel from ports Survey of existing bioeconomic models 212 Appendix outside the Channel, the fishing mortality associated with metiérs not explicitly included in the model, and the fishing mortality for stocks extend beyond the Channel (only for age-structured stocks): (1a) (1a) (1a) (1a) a TM1s,a,t = ∑[FM1s,a,c,t + efm1s,a,c ]+ ms,a + ∑ofm1s,a,om for s ∈ S , (A.4.1.4) c om (1b) (1b) (1b) (1b) (1b) a TM1s,a,t = ∑[FM1s,a,c,t + efm1s,a,c ]+ ms,a + fm1s,a + ∑ofm1s,a,om for s ∈ S , c om (A.4.1.5) (2) (2) (2) (2) a TM1s,a,t = ∑[FM1s,a,c,t + efm1s,a,c ]+ ms,a + ∑ofm1s,a,om for s ∈ S , (A.4.1.6) c om a TM 2s,t = ∑[]FM 2s,c,t + efm2s,c + ∑ofm2s,om for s ∉ S . (A.4.1.7) c om Stock dynamic in the age-structured model can be represented by the following equations: −β s SSBs,t Ns,0,t +1 = α sSSBs,te , (A.4.1.8) (*) −TM 1s,a,t Ns,a+1,t +1 = Ns,a,te , (A.4.1.9) (*) (*) −TM1s,A−1,t −TM1s,A,t Ns, A,t +1 = Ns, A−1,te + Ns, A,te , (A.4.1.10) where A is the older age-class for species s, while the other variables and indexes are described in the next section. Equation (A.4.1.8) is the Ricker model which represents the most common recruitment function. The spawning stock biomass can be calculated by a maturity index applied to the biomass, while biomass is the catch numbers at age multiplied by the average weight-at-age (ws,a): SSBs,t = ∑mts,a X s,a,t , (A.4.1.11) a X s,a,t = ws,a Ns,a,t . (A.4.1.12) The catch of each species modelled by an age-structured model is estimated by A−1 (*) (*) ⎡ (*) FM1 ⎤ ⎡ (*) FM1 ⎤ (*) −TM1s,a ,t s,a,c,t −TM1s,A,t s,A,c,t . Cs,c,t = ∑⎢ws,a N s,a,t (1− e ) (*) ⎥ +⎢ws,AN s,A,t (− e ) (*) ⎥ a=0 ⎣ TM1s,a,t ⎦ ⎣ TM1s,A,t ⎦ (A.4.1.13) Stock dynamic in the surplus production model can be described by the following equation: λs X s,t +1 = X s,t + (γ s X s,t − δ s (X s,t ) )− Cs,t , (A.4.1.14) where the stock at time t+1 equals the stock at time t plus the natural growth of the stock minus the catch at time t. Survey of existing bioeconomic models Appendix 213 Given the stock at time t, the catch can be estimated as the product of fishing mortality and biomass: C s,c,t = FM 2s,c,t X s,t . (A.4.1.15) Equations from A.4.1.8 to A.4.1.15 have been added to the model description reported in Mardle and Pascoe (2003) to make the model suitable for simulation. The choice of these equations is based on the methodology report of the IiFSW project. As many of the Channel vessels also fish outside of the Channel, two categories of revenue are estimated by the model: that produced fishing inside of the Channel, and that arising from fishing outside of the Channel: Rc,t = ∑ ps,cCs,c,t , (A.4.1.16) s Rc,ext,t = ∑∑rf ,g,c B f ,g,c,t d f ,g,ca f ,ext . (A.4.1.17) fg Net revenue by country c is estimated by deducting the market levy paid in each country and the variable fishing costs from the revenue: ⎛ ⎞ NRc,t = ()Rc,t + Rc,ext,t ()1− lc − ∑∑B f ,g,c,t ⎜ ∑d f ,g,ca f ,mv f ,g,m ⎟ . (A.4.1.18) fg⎝ m ⎠ The economic profit is estimated as net revenue minus the crew share and the fixed costs: Pc,t = NRc,t (1− csc )− ∑∑ B f ,g,c,t f f ,g,c . (A.4.1.19) fg The total employment in each country is estimated by multiplying the number of boats by the average number of full-time equivalent crew employed on each boat: EM c,t = ∑∑ B f ,g,c,tcrf ,g,c . (A.4.1.20) fg The recreational sector is not modelled in the previous version of the Channel model. A potential approach is described in the methodology report of the IiFSW project. It is assumed that recreational angling trips are a function of the fish stock and fish size. The state equation for recreational effort with respect to changes in biomass is reported as follows: ⎧ ⎡ X t+1 − X t ⎤ REF ⎫ REFmo,t = REFmo,t−1 ⎨1+ ⎢ ⎥ε mo ⎬ , (A.4.1.21) ⎩ ⎣0.5()X t+1 + X t ⎦ ⎭ Survey of existing bioeconomic models 214 Appendix where mo represents the mode (shore, private, for-hire) of the recreational effort. However, it is not clear which modes are taken into account in the model and which species are considered in the equation (A.4.1.21). Changes in recreational effort will lead to a change in expenditure and associated employment. The recreational fishing costs are estimated by RFCt = ∑ zmo REFmo,t , (A.4.1.22) mo where z represents the net revenue (or fishing cost) per recreational trip. The environmental impacts are estimated by a set of indexes. Indexes to estimate the impact of different gears on habitat, cetaceans, commercial and non-commercial by-catch are calculated through a pair wise comparison survey using multi-criteria techniques. Specific equations on the dynamic of these indexes within the bio-economic model are not available. A.4.2 Full specification of model variables Endogenous and exogenous variables in the bio-economic model with description are listed below. Endogenous variables can change during the simulation period, while exogenous variables or parameters are supposed to be constant. However, parameters can be modified to simulate specific management measures as reported above. A list of indexes used in the model equations are reported as well. Table 47. List of endogenous variables. COBAS Endogenous Description Variables Em,c effective (standardised) effort expended in each métier B f ,g,c number of boats in each sub-fleet f in size class g in country c N s,a number of individuals of species s at age a X s,a biomass of species s at age a SSBs spawning stock biomass of species s (*) FM1s,a,c , FM 2 s,c fishing mortality of species (at age a for age-structured stocks) (*) TM1s,a , TM 2s total mortality of species (at age a for age-structured stocks) (*) Cs,c , Cs,c catch of each species in the Channel Rc , Rc,ext revenues of Channel boats (inside and outside) NRc total net revenue in country Pc economic profit of the Channel fleet by country EM c estimated employment in the Channel fleet by country REFmo recreational angling trips associated with mode mo RFC recreational fishing costs depending on the number of trips Survey of existing bioeconomic models Appendix 215 Table 48. List of exogenous variables. COBAS Exogenous Variables or Description parameters d f ,g,c average number of days fished by a boat a f ,m proportion of time each sub-fleet spends in each métier fpm,g relative fishing power of a boat (*) q1s,a,m , q2s,m catchability coefficients (of age a for age-structured stocks) ms,a natural mortality for the age-structured species (*) , efm1s, a, c efm2s,c fishing mortality generated by the external boats (*) , ofm1s, a, om ofm2 s,om fishing mortality associated with other metiers not specified in the model (1b) additional fishing mortality for the age-structured species whose stocks fm1 s,a extend beyond the Channel ws,a average weight at age of each species ps,c price of species rf ,g,c estimated average revenue per day from fishing outside of the Channel a f ,ext proportion of time each sub-fleet spends operating outside the Channel lc average market levy paid in each country v f ,g,m variable cost per day (trip cost) csc average crew share of net revenue f fixed cost associated with each boat (included the non-cash costs such as f ,g,c depreciation and the opportunity cost of capital) cr average number of full-time equivalent crew (included skipper) on each f ,g,c boat α s , βs parameters for the Ricker stock-recruitment model γ s , δ s , λs parameters for the (Schaefer or Fox) surplus production model mts,a maturity index for the age-structured species REF ε mo elasticity coefficient of the recreational angling trips zmo net revenue (or fishing cost) per recreational angling trip Table 49. List of indices. COBAS Indices Description m métier c country (i.e. UK and France) f sub-fleet g size class s species ( S a the set of age-structured species) a age of a group of individuals of a species t time (year) mo mode of recreational effort * (superscript) stock classifications of age-structured species Survey of existing bioeconomic models 216 Appendix A.4.3 Full list of model parameters Parameters in the bio-economic model are the same as exogenous variables listed above. Their values should be estimated before the model is used. Specific information on the values for these parameters is not available. Survey of existing bioeconomic models Appendix 217 A.5 Specific description: ECoCorp A.5.1 Full specification of model equations Biological component: The core of the ‘biological’ model is illustrated by the influence diagram in Figure 20. Fish Stock fish stock to total annual mortality age rate fish age Estimated Fish Stock overall By Age total mortality rate by mortality rate species total fish stock fish stock ricker estimated estimate recruitment rate recruitment Annual Recruitment IMPORT Rate INITIAL N Figure 20. Estimated fish stock. ECOCORP The influence diagram illustrates the structure within the model controlling the population of each of the prey species within one year age groups. The stock levels for each of the prey species are increased by a quarterly recruitment rate into the first age group. The initial recruitment rates are based on the latest 2003 estimates from the 4M model simulations. The annual recruitment rates thereafter are based on Ricker type forecasts based on the spawning stock biomass of each species in the previous year, and a stochastic element of randomness. As the EcoCoRP model is projected forward, the cohort from each age group is automatically aged until they reach the maximum age for that species. Within any of the age groups there is an overall mortality rate, calculated on a quarterly basis. This overall mortality rate is a combination of: • Natural Mortality (M1) • Predator Mortality (M2) • Fishing Mortality (F) Survey of existing bioeconomic models 218 Appendix The natural mortality (‘residual mortality’) is an input to the model and has individual values for each age group within each species. The predator mortality is the sum across all predators and predator age groups likely to eat the prey within that prey age group. The fishing mortality has initial values set to those values estimated by the most recent 4M model runs, though the values are ‘user defined’ and can be changed between forecast years. The single species VPA solves for each species, i, the following basic equation iteratively given a natural rate, Mia, and either population size or total mortality rate in the oldest age class: Niat = Nia(t=1) exp(Fia + M ia )(1− t) (A.5.1.1) where for age class, a, and time within a year (0< t < 1), Niat is abundance of species i at age a at time t, Nia(t=1) is abundance at the end of the year and, Fia is the fishing mortality rate. The standard equation for catch at age, Cia, is then: Fia Cia = Nia(t=1) []1− exp(−Fia − M ia ) (A.5.1.2) Fia + M ia The MSVPA process then resolves natural mortality into predation mortality, M2ia, and other mortality, M1ia. Hence, total mortality rate is: Zia = Fia + M1ia + M 2ia (A.5.1.3) Where information exists for predator and prey species, they are modeled using VPA. Stock numbers for other predators are given as input and for other prey species using a production function. Other food biomass is also given as input. The growth of VPA predators is modeled as a function of the amount of available food. Weight at age (W) at time t is defined as the weight at age in the preceding year plus a growth term that is based on average growth observed and the amount of available food (FoodAvail) relative to the average amount of food: FoodAvail t −1 * Growth t −1 W t = W t −1 + (A.5.1.4) FoodAvail t −1 The food intake (R) at time t is defined as “a ‘bioenergetic’ model taking basal metabolism, somatic growth and spawning into account”: ⎛ W −W PROPMAT *W * SPAWN ⎞ ⎜ MET _ Bt t+1 t t t t ⎟ (A.5.1.5) Rt = max ⎜ MET _ At *Wt + + , 0⎟ ⎝ CONVEFFt GCONVEFFt ⎠ where MET_A and MET_B are constants to assess intake for unchanged weight (basal metabolism), CONVEFF is food conversion efficiency (somatic growth), GCONVEFF is food conversion efficiency (spawning products), SPAWN is factor of body weight lost due to spawning, and PROPMAT is proportion mature. Survey of existing bioeconomic models Appendix 219 The estimated weight (W) and food intake (R) are estimated by iteration until convergence of the values of estimated weight. W is then used in the calculation of consumption (and total and spawning biomasses). Average biomass ( B ) of other food p in year y is assumed to be: K p *L p *consumption B py =e (A.5.1.6) where K is a constant expressing the log of the biomass of other food when predation is zero and L is a constant expressing the amount of change in biomass of other food per unit of consumption. At this stage in the assessment process, numbers at age (including plus groups) in the species population can be calculated. This is an equilibrium state. Multispecies tuning can then be undertaken. Prediction process: The prediction process is built upon the basic age-structured population model of the form: Nt+1 = Nt exp[− (F + M1+ M 2)] (A.5.1.7) Population biomass can be calculated as: B = N w t t t (A.5.1.8) Catch is estimated as Fia (A.5.1.9) Cia = N ia []1− exp(−Z ia ) Z ia For EcoCoRP, output data were obtained from the 2005 SGMSNS ‘key-run’ of the MSVPA model, and projected forward in time using modeled predator-prey ‘suitabilites’ and Ricker stock recruitment relationships. Economic component: The economic component of the North Sea dynamic bioeconomic model is based on earlier models developed by (Pascoe 1999), (Mardle et al. 2000) and (Mardle 2002). The mathematical description of the model is presented below. Endogenous variables are presented in upper case and exogenous variables (and parameters) in lower case. Number of vessels and days fished as well as activity of vessels are direct inputs into the system as shown in the equations below. The model is developed as a simulation model. While several criteria will be of interest, the key criterion will be the economic implications of different effort control scenarios. Foremost of these will be the net present value of profits. The calculation order is reversed in this section for Survey of existing bioeconomic models 220 Appendix simplification of discussion. The model is driven from effort to catch rather than the other way around. NPV is expressed as: t (A.5.1.10) NPV = ∑∑∑(PROF j,k ,t /(1+ δ ) tjk where NPV is the net present value of total fishery profits over time, while PROFj,k,t is the level of economic profits of boats using gear type k from country j (i.e., a given fleet segment in the fishery) in time t and δ is the discount rate. The profit of each fleet segment in each time period is given by the revenue less the costs. The revenue of each fleet segment is given by (A.5.1.11) REV j,k,t = ∑(p j,i LANDj,k,i,t )+ vby j,k DAYS j,k,t ,∀j,k i where REVj,k is the total revenue of the fleet segment in time t, and pj,i is the average price of species i in country j (assumed constant over time), LANDj,k,i,t is the total landings of species i by each fleet segment, and vbyj,k is the average value of other (bycatch) species landed each day. The costs of each fleet segment are broken down into four components: Fixed costs (FCOSTj,k,t), variable (trip/day) costs (VCOSTj,k,t), crew costs (CCOSTj,k,t) and capital costs (KCOSTj,k,t). The total costs (COSTSj,k,t), and individual cost components, of each fleet segment are given by COSTj,k,t = FCOSTj,k,t +VCOSTj,k,t + CCOSTj,k,t + KCOSTj,k,t (A.5.1.12) FCOSTj,k,t = fc j,k * BOATS j,k,t (A.5.1.13) VCOSTj,k,t = ( fpd j,k * fprice j + ocpd j,k )* DAYS j,k,t (A.5.1.14) CCOSTj,k,t = cs j,k * REV j,k ,t (A.5.1.15) KCOSTj,k,t = (d + δ )*cap j,k * BOATS j,k,t (A.5.1.16) where csj,k is the crew share for the fleet segment; the average running cost per day of boats in each fleet segment is given by fuel-use per day fpdj,k multiplied by fuel price (e.g. fpricej is say €0.5 per litre) plus other variable costs per day ocpdj,k; fcj,k is the average fixed cost per boat; d is the depreciation rate; δ is the discount rate (equivalent to the opportunity cost of capital), capj,k is the average capital value of the vessel; BOATSj,k,t are the number of boats operating in time period t and DAYSj,k,t is the total number of days expended by the fleet segment in time period t. An indication of average Kw*Days by fleet can also be indicated. Data for these will be obtained from the Annual Economic Reports. The economic forecast that the model provides can be viewed at different levels. First, the NPV indicator gives a long term view of the effects of a modeled management option. Second, as the Survey of existing bioeconomic models Appendix 221 model is run in the form of a dynamic simulation over time, a yearly (i.e., non-discounted) indication of economic profits as well as costs is provided. Bioeconomic link: The link between the biological and economic components of the models is fishing mortality. The partial fishing mortality of each species by each fleet is given by Fj,k,i,a,t = q j,k,i,a DAYS j,k,t ,∀j,k,i,t (A.5.1.17) where qj,k,I,a is the catchability coefficient, and Fj,k,I,,a,t is the partial fishing mortality such that Fi, a, t = ∑ ∑ F j, k, i, a, t jk The catch of each species in each year by each fleet segment (CATCHj,k,I,y) is given by: (A.5.1.18) CATCH j,k,i,t = ∑Fi,a,t Bi,a,t ,∀j,k,i,t a where Bi,a,t is the biomass in each age class of the stock in year t, and (ignoring subscripts t for convenience). Landings are given by Ci,a = ∑∑q j,k,i,a DAYS j,k Bi,a jk LAND j,k,i,t ≤ CATCH j,k,i,t ,∀j,k,i,t (A.5.1.19) where landings are also affected by the TACs. For the purposes of simplicity, it will be assumed that ∑ Ci,a CATCH a , C >TAC j ,k ,i ,t ∑a i,a i TACi (A.5.1.20) LAND j,k,i,t = CATCH , C ≤TAC j ,k ,i ,t ∑a i,a i That is, any over-quota catch is assumed to occur in equal proportion by each fleet segment. In order to calibrate catchability, catch-at age, by fleet and country was obtained from STECF and used (in a proportional way) to scale the overall landings by fleets. This data also included some discarding data, which was used (in a proportional way) to estimate discards at age for fleets and countries. The data was normalised, relative to the numbers or biomass at age in the population, thus resulting in a selection ogive where most of the remaining old fish could be selected, but where only a few of the younger ages could be selected. A.5.2 Full specification of model variables The key ‘control’ variables in the model are the number of days fished and the number of boats each year. To impose a common change in these variables on all fleet segments, the existing days/boat numbers in each fleet segment were multiplied by a common scaling factor. That is, DAYS j,k,t = sea_days j,k,*ESCALEt*BOATS j,k,t ,∀j,k (A.5.2.1) Survey of existing bioeconomic models 222 Appendix BOATS j,k,t = boats j,k * BSCALEt ,∀j,k (A.5.2.2) Where, sea_daysj,k is the (unconstrained) days at sea of a boat in each fleet segment, and boatsj,k is the current number of boats in each fleet segment. ESCALEt and BSCALEt are scaling factors that alter the number of days and boats in each fleet segment respectively. Recruitment is variable, assuming a Ricker function. A.5.3 Full list of model parameters Initialisation parameters (for initial model setup): Biological component: • Initial stock levels of VPA species – for cod as well as 9 other predator and prey species considered in the model; max. 11 age-classes. • Ricker parameters for all 10 VPA species. • Natural mortality: constant over time; species and age-group specific. • Predator mortality: non-VPA species predator population, consumption, average weight-at- age. • Maximum age of VPA species. • Maturity proportions for all 10 VPA species and 11 age-groups. Economic component: • Fleet proportions. • Fleet sizes modelled. • Average days at sea per vessel in 2003. • Price per kg: species specific and age-group specific. Constant over time. • Other income, from fishing outside of area IV: assumed constant, if effort in area IV is reduced, hence no effort reallocation. • Input values of costs from 2003 for initialisation, based on data from AER • Specific cost initialisation data: - Average crew cost, - average vessel cost, - average fuel cost, - average running costs, and - depreciation and interest costs as proportion of variable costs. Survey of existing bioeconomic models Appendix 223 A.5.4 Model assumptions • Fixed predator populations (the population sizes of non VPA species predators, for example grey seals), remain constant throughout each simulation. • As the age distribution changes, so do F values proportionally. As the balance of the age distribution changes within a species stock, the model also adjusts the F –Values proportionally. • Although “technology creep” can be modeled as an annual percentage increase in catch for the same amount of effort and stock levels, this configuration was not used in the scenarios tested. • No increase in fuel prices though out each simulation. • No change in price per fish received for catches. • Area IV effort proportions have been estimated based on known catch data. However, some subjective inputs have been required. • Costs fixed. Only fleet variable costs will change based on changes in days at sea or number of vessels. • Revenue from outside area/other species has been estimated from catch data, however, subjective reviewing was required. • Fixed average weight at age. The average weight of species by age does not change in the model simulations. • Fixed discard proportions. Discard proportions were estimated from 2003 landing and catch data. These are applied for each year simulated by species and gear type. There is no separation between countries. • Ricker noise. Stochastic variation on the recruitment forecasts was based on +/- 100% of the Ricker estimates. • Financial data for some fleets in the AER were not provided. In these cases, estimates were used based on calculations from similar national fleet data. • No “starvation effect” is modeled. Thus a lack of suitable prey will not cause a decline in predator species. • Environmental factors such as sea temperature are not modeled. • Natural mortality factors (M1) are the same across all age groups within a species. Survey of existing bioeconomic models 224 Appendix A.6 Specific description: ECONMULT A.6.1 Full specification of model equations In the ECONMULT model, a fishery is defined as a fleet - targeted species combination. The landings per vessel coming from a biomass unit (this can be one cohort of a stock or an entire stock if it is not sub-divided in cohorts) are estimated by a Cobb Douglas production function, where the production factors are the number of days at sea (fishing effort) and the biomass. Landings obtained by all the biomass units in a fleet - targeted species combination represent the total catches for that fishery. Omitting the indices as these depend on the level of aggregation defined in the model, the Cobb- Douglas harvest function for a fleet or a vessels group can be represented as follows: H = qEα X β , (A.6.1.1) where the meaning of each variable is reported in the next section. The total fishing effort can be expressed as the product of the number of vessels and the average number of days at sea per vessel (fishing effort per vessel): E = e n (A.6.1.2) Based on equations A.6.1.1 and A.6.1.2, catches per vessel can be expressed as follows: H qeα nα X β h = = = qeα nα −1 X β (A.6.1.3) n n Landings price is assumed to be a function of landings by the following linear relationship: p = a − bC . (A.6.1.4) From equations A.6.1.3 and A.6.14, it is possible to estimate the average total revenues per vessel: tr = p h . (A.6.1.5) As for the harvesting costs, these are divided in fixed and variable costs, where the latter are estimated multiplying a daily cost parameter by the average number of days at sea per vessel: tc = fc + vce . (A.6.1.6) Contribution margin per vessel is obtained as a difference between total revenues per vessel and total variable costs per vessel: Survey of existing bioeconomic models Appendix 225 cm = p h − vce . (A.6.1.7) Profit per vessel (including resource rent) is calculated by subtracting the fixed costs per vessel from the contribution margin per vessel: π = cm − fc (A.6.1.8) Finally, the total profit of a given fishery is estimated multiplying the profit obtained by equation A.6.1.8 by the number of vessels operating in that fishery, and the present value of profit is calculated by applying a social rate of discount to the profit: Π = nπ (A.6.1.9) PV = e −d t Π t (A.6.1.10) A.6.2 Full specification of model variables Endogenous and exogenous variables in the ECONMULT model with description are listed below. Some of the exogenous variables in a simulation can become endogenous in a different simulation. The definition of exogenous variable in this model is strictly related to the management option to be simulated. For instance, fishing effort or total catch, are defined as exogenous variables because management measures can fix them. Where these variables are not fixed a priori, they could change in the simulation like endogenous variables. A list of indices used in the model equations are reported as well. Table 50. List of endogenous variables. ECONMULT Endogenous Variables Description cmi, j Vessel contribution margin hi, j,k Vessel catch of one cohort hi, j Vessel catch of all the cohorts of a given stock pi, j,k Price of fish tci, j Total vessel costs tri, j Total vessel revenues π i, j Vessel profit Π i Vessel group profit PVi Present value of vessel group profit Survey of existing bioeconomic models 226 Appendix Table 51. List of exogenous variables. ECONMULT Exogenous Variables Description m Number of vessel groups s Number of stocks u Number of cohorts ni, j Number of vessels in each vessel group ei, j Average number of days at sea per vessel (fishing effort per vessel) X i,k Biomass of one cohort C Catches affecting the price Ei, j Total fishing effort of the vessel group Hi, j,k Vessel group catch of a given cohort Hi,k Total catch of a given cohort X i, j,k Biomass of a given cohort available in the fishing area of one vessels group d Social rate of discount Table 52. List of indices. ECONMULT Indices Description i Stock j Vessel group k Cohort A.6.3 Full list of model parameters The list of model parameters is reported below. Their values should be estimated before the model is used. Specific information on the values for these parameters is not available. Table 53. List of parameters. ECONMULT Model parameters Description αi, j,k Price function coefficient βi, j,k Price function coefficient vci, j Variable cost per day fci, j Vessel fixed costs qi, j,k Catchability parameter ai, j,k Effort - output elasticity bi, j,k Stock - output elasticity Survey of existing bioeconomic models Appendix 227 A.7 Specific description: EIAA A.7.1 Full specification of model equations The model equations have been copied from (Frost and Levring 2009). Main indexes: • Base period, three years average (0). • Time (t). • Fish species (i). • Fleet segment (j). • Management area (a). • Member State (country) (m). The equations of the base version and extended version of the EIAA are described below. Base version of EIAA The landing of stock, i, in future year, t, for fleet segment, j, is calculated as: ⎛ ⎞ ⎛ L ⎞ L = ⎜ Q ⋅ ns ⎟ ⋅ nu ⋅⎜ 0,i, j,m ⎟ (A.7.1.1) t,i, j,m ∑ t,i,a i,a,m i,m ⎜ L ⎟ ⎝ a ⎠ ⎝ 0,i,m ⎠ Where the degree to which the quota is exhausted is express by nui,m : ∑L0,i, j,m j (A.7.1.2) nui,m = ∑Q0,i,a,m a Prices: The price per stock per fleet segment is based on baseline prices, from the landing value and landing weight: TR P = 0,i, j (A.7.1.3) 0,i, j L 0,i, j Survey of existing bioeconomic models 228 Appendix ε ⎛ ⎞ i ⎜∑Qt,i,a ⎟ ⎝ a ⎠ (A.7.1.4) Pt,i, j = P0,i, j ⋅ ε ⎛ ⎞ i ⎜∑Q0,i,a ⎟ ⎝ a ⎠ , if εi ≤0 Where ε is the prices flexibility, which is fixed at -0.2. Gross value: GR 0, j (A.7.1.5) TRt, j =(∑ Pt,i, j ⋅Lt,i, j + Kt, j ) ⋅ i ∑ P0,i, j ⋅L0,i, j + K0, j i where Kt, j is defined as: (A.7.1.6) Kt, j =TR0, j − ∑ P0,i, j ⋅L0,i, j i and GR0, j is defined as: GR =TR + O (A.7.1.7) 0, j 0, j 0, j Variable cost: The fishing effort (or fleet activity), A, is a function of the quota/SSB and estimated endogenous using a Cobb-Douglas function. The function form of the production model for a fleet segment and a single species is given: α β L = a* A ⋅ SSB (A.7.1.8) The inverse production function is: 1 L1 / α A = ( )1 / α * (A.7.1.9) a SSB β / α Expanding the inverse production function (A7.1.9) to a normalised format in terms of time, species and fleet segment, following expression is applied: ( χ ) (−γ ) ⎛ L ⋅ P ⋅θ ⎛ L ⎞ i , j ⎛ SSB ⎞ i ⎞ ⎜ 0,i, j t,i, j t,i, j ⎜ t,i, j ⎟ ⎜ t,i ⎟ ⎟ A = ⋅ ⋅ (A.7.1.10) t, j ∑⎜ ⎜ ⎟ ⎜ ⎟ ⎟ i L ⋅ P ⋅θ ⎜ L ⎟ ⎜ SSB ⎟ ⎜ ∑ 0,i, j 0,i, j t,i, j ⎝ 0,i, j ⎠ ⎝ 0,i ⎠ ⎟ ⎝ i ⎠ This expansion implies that the fishing effort of the fleet segment is affected by changes in prices, landings and in stock abundance. The interpretation of these three elements are; the Price-element’ accounts for the incentives to reallocate effort as a function of changes in the relative fish prices. Note that future prices depend on the price flexibility rates, see equation A.7.1.3 and A.7.1.4.The ‘Landings-element’ accounts for technological accessibility (over- Survey of existing bioeconomic models Appendix 229 water accessibility). If χ (χ=1/α) is zero the fish is easily handled (good crew, good space, good weather, no bottlenecks etc.), and if handling becomes harder, χ increases. The default value in the model is χ = 1. The inclusion of this element makes it possible to distinguish between different handling procedures in particular for demersal and pelagic species and different fishing technologies. The SSB-element accounts for accessibility caused by stock abundance (under water accessibility). Setting γ = 0, (γ=β/α) implies there is no stock abundance effect on the activity. With full effect γ = 1. Default values used in the model are between 0.6 and 0.8 for demersal species and between 0.1 and 0.2 for pelagic species. The factor θ is included in order to determine the activity according to those species which are assumed to drive the vessel effort. Then the effort is estimated the total variable cost can be calculated: RC = RC ⋅ A (A.7.1.11) t, j 0, j t, j function of quota species only, or RC = RC ⋅ AA (A.7.1.12) t, j 0, j t, j function of all species. The activity coefficient (or fishing effort), AA, is calculated as: where ∑Pt,i, j ⋅Lt,i, j TRt, j − ∑Pt,i, j ⋅Lt,i, j AA = A ⋅ i + i (A.7.1.13) t, j t, j TR TR t, j t, j Crew share: The crew share is calculated in the model for baseline period by taking the cost of the crew relative to the gross revenue in period t. CC = cc TR (A.7.1.14) t, j 0, j t, j where cc0, j is defined as: CS cc = ⋅ 0, j (A.7.1.15) 0, j GR 0, j Fixed costs: Fixed costs are assumed constant, i.e. transferred from the baseline period to future years. The model distinguishes between fixed costs related to the operation of the vessel i.e. semi-fixed costs such as maintenance, insurance and administration and the fixed capital costs depreciation and interest: FC = FC (A.7.1.16) t, j 0, j Survey of existing bioeconomic models 230 Appendix DC = DC (A.7.1.17) t, j 0, j Indicators of economic performance: A number of economic indicators are calculated as shown by the subsequent expressions. a) Cash flow in year t for fleet segment j: GF =TR − (RC + CC + FC ) (A.7.1.18) t, j t, j t, j t, j t, j b) Net profit in year t for fleet segment j: NP =TR −(RC + CC + FC + DC ) (A.7.1.19) t, j t, j t, j t, j t, j t, j c) Operating profit margin in year t for fleet segment j: TR − (RC + CC + FC + DC ) OPM = t, j t, j t, j t, j t, j (A.7.1.20) t, j TR t, j d) Gross value added in year t for fleet segment j: GV = NP + CC + DC (A.7.1.21) t, j t, j t, j t, j Extended version of EIAA Break-even and overcapacity: The break-even concept is applicable for all periods in time. In case 1 for the base period, it has the form: DC BR = 0, j (A.7.1.22) 0, j gf 0, j Where the cash flow coefficient gf is calculated as: TR − (RC + CC + FC ) gf = 0, j 0, j 0, j 0, j (A.7.1.23) 0, j TR0, j Overcapacity is then defined and calculated for the base period and future periods as: break even revenue − current revenue Overcapacity = break even revenue Expression to calculate overcapacity OC is: Survey of existing bioeconomic models Appendix 231 TR OC =1− t , j (A.7.1.24) t , j BR t , j See more detailed description in Frost and Andersen 2009. Break-even revenue (BRLS) is in this case calculated as: ( DCt , j + ∑ SSBLCt ,i, j ) BRLS = i (A.7.1.25) t , j gf t , j The value share of the spawning stock biomass (SSBLC), is calculated for each fleet segment subject to quotas of each fleet segment and Member State: ⎛ ⎞ ⎛ L ⎞ SSBLC = rl ⋅ P ⋅ ⎜ SSB ⋅ ns ⎟ ⋅ nu ⎜ t,i, j,m ⎟ (A.7.1.26) t,i, j,m t,i, j,m ∑ t,i,a i,a,m i,m ⎜ L ⎟ ⎝ a ⎠ ⎝ 0,i,m ⎠ The overcapacity is then calculated as: TR OCLS =1− t, j (A.7.1.27) t, j BRLS t, j The value of other species (SSBNC) (non-assessed quota species) is calculated as: ∑ SSBLCt,i, j TR0, j − ∑ P0,i, j ⋅ L0,i, j i i (A.7.1.28) SSBNCt, j = rn ⋅ ⋅ rl ∑ P0,i, j ⋅ L0,i, j i The break-even revenue (BRTS) with all stock values included is then: DCt , j + ∑ SSBLCt ,i, j + ∑ SSBNCt ,i, j BRTS = i i (A.7.1.29) t , j gf t , j The overcapacity is therefore: TR OCTS =1− t, j (A.7.1.30) t, j BRTS t, j Change in number of vessels (fixed costs) The number of sea days (SD) for a fleet segment to catch the allocated quotas is calculated using the inverse production function (see equation A.7.1.9): SD = AA ⋅ SD (A.7.1.31) t, j t, j 0, j Survey of existing bioeconomic models 232 Appendix When an effort control rule (ecr) is applied, it allows a vessel to execute a certain number of sea days per year. The number of vessels (NV) and hence the fixed costs (FC) are then calculated: SD 0, j NV = ⋅ NV (A.7.1.32) t, j ecr 0, j SD j NV t, j FC = FC ⋅ (A.7.1.33) t , j 0, j NV 0, j Effort approach The CPUE can be derived as follows: L cpue = 0,i, j (A.7.1.34) 0,i, j SD 0, j α β ⎛ SD ⎞ ⎛ SD ⎞ i , j ⎛ SSB ⎞ i ⎜ 0, j ⎟ ⎜ t, j ⎟ ⎜ t,i ⎟ (A.7.1.35) cpue = cpue ⋅ ⎜ ⎟ ⋅⎜ ⎟ ⋅ ⎜ ⎟ t,i, j 0,i, j ⎜ SD ⎟ ⎜ SD ⎟ ⎜ SSB ⎟ ⎝ t, j ⎠ ⎝ 0, j ⎠ ⎝ 0,i ⎠ or −1+α β ⎛ SD ⎞ i, j ⎛ SSB ⎞ i ⎜ t, j ⎟ ⎜ t,i ⎟ (A.7.1.36) cpue = cpue ⋅ ⎜ ⎟ ⋅ ⎜ ⎟ t,i, j 0,i, j ⎜ SD ⎟ ⎜ SSB ⎟ ⎝ 0, j ⎠ ⎝ 0,i ⎠ Note, that if CPUE is a function of spawning stock biomass (SSB) and landings (L), the equation looks cf. the AHF model in Hoff and Frost (2006): 1 β 1− i α ⎛ L ⎞ i , j ⎛ SSB ⎞αi , j ⎜ t,i, j ⎟ t,i cpue = cpue ⋅ ⋅ ⎜ ⎟ t,i, j 0,i, j ⎜ L ⎟ ⎜ SSB ⎟ ⎜ 0,i, j ⎟ 0,i ⎝ ⎠ ⎝ ⎠ (A.7.1.36B) (A.7.1.37) Lt,i, j = cpuet,i, j ⋅ SDt, j For future periods, SD could be chosen arbitrarily subject to exogenous decisions. Such a decision could be that effort is limited to a minimum effort level (minSD) required to catch the lowest quota for a species or the quota for any other selected species. This implies the model solves the problem with respect to SD, see equation A.7.1.36- A.7.1.37: ⎛ ⎞ (A.7.1.38) maxTRt ,j = ⎜∑ Pt ,i,j ⋅ cpuet ,i,j ⋅ SDt ,j ⎟ ⎝ i ⎠ Subject to: Survey of existing bioeconomic models Appendix 233 cpue ⋅SD ≤ Q (A.7.1.39) t ,i,j t ,j t ,i,j Alternatively, an optimisation procedure can be applied to find the number of sea days that minimises the sum of the differences between landings and quota (LVD) of a fleet segment. The model then solves the problem in equation A.7.1.38- A.7.1.39 with respect to SD: ⎛ ⎞ (A.7.1.40) min LVDt, j = ⎜∑ Pt,i, j ⋅(cpuet,i, j ⋅ SDt, j −Qt,i, j )⎟ ⎝ i ⎠ min LVD ≥ 0 (A.7.1.41) t , j Long run version (long term forecast, economic indicators) The input to the long run EIAA model is projections of stock abundances and correspondent yield. The result is evaluated by calculating the net present value (NPV), where the first element on the right hand side calculates NPV for the first ten years while the second element calculates the NPV for the years after year ten: 10 1− (1+ r)n −t −10 (A.7.1.42) NPVGF j = ∑GFt, j ⋅(1+ r) +GF10, j ⋅ ⋅(1+ r) t=1 r 10 n −t 1− (1+ r ) −10 NPVNP j = ∑ NPt , j ⋅(1+ r ) + NP10, j ⋅ ⋅(1+ r ) (A.7.1.43) t=1 r A.7.2 Full specification of model variables Exogenous variables are given from outside; endogenous variables are calculated by the model. The following variables are used: At, j ‘Activity coefficient’ as a function of quota species in year t of fleet segment j; A0, j = 1 for the baseline period, (endogenous) AAt, j ‘Activity coefficient’ as a function of quota and non quota species in year t of fleet segment j, (endogenous) B Total Stock (is not used in the model, only in the model description) BRt, j Break-even in year t for fleet segment j. It is optional to include FC BRLSt, j Break-even in year t for fleet segment j including remuneration of quota species BRTSt, j Break-even in year t for fleet segment j including remuneration of quota species CCt, j Crew share in year t of fleet segment j (endogenous) CS0, j Crew share in base period of fleet segment j, (exogenous) DC0,j Depreciation and interest costs for fleet segment j in the base period, (endogenous or exogenous) DCt,j Depreciation and interest costs for fleet segment j in period t, (endogenous) Survey of existing bioeconomic models 234 Appendix F Fishing mortality (is not used in the model, only in the model description) FC0,j Fixed costs for fleet segment j in the base period, (exogenous) FCt,j Fixed costs for fleet segment j in period t, (endogenous) GFt,j Gross cash flow for fleet segment j in period t, (endogenous) GR0, j Gross revenue including non-fisheries specific income of segment j, (exogenous) GVt,j Gross value added by fleet segment j in period t, (endogenous) K0,j Landings value of other species than quota species by fleet segment j in base years, (exogenous) Kt, j Landings value in year t of other species than quota species of segment j, (endogenous) L0,i,,j m Landings in base years of species i caught by fleet segment j for Member State m (exogenous) Lt, i, j, m Landings in year t of species i caught by fleet segment j for Member State m (endogenous) LVDt,j Difference between the landing value and the quota value in year t for fleet segment j M Natural mortality (is not used in the model, only in the model description) NPt,j Net profit for fleet segment j in period t, (endogenous) NPVGFJ Net present value of cash flow for fleet segment j NPVNPJ Net present value of net profit for fleet segment j NV0,j Number of vessels in fleet segment j in the base period NVt,,j Number of vessels in fleet segment j in year t O0, j Income from non-fisheries specific activities of fleet segment j, (exogenous) OCt, j Overcapacity in year t for fleet segment j, (endogenous) OCLSt, j Overcapacity in year t for fleet segment j taking stock remuneration (resource rent) of quota species into account, (endogenous) OCTSt, j Overcapacity in year t for fleet segment j taking stock remuneration (resource rent) of quota and non quota species into account, (endogenous) OPMt,j Operating profit margin for fleet segment j in period t (coefficient), (endogenous) P0, i, j Fish prices in base years of species i by fleet segment j (endogenous), calculated by use of landing value and landing weight Pt, i, j Fish prices year t of species i by fleet segment j (endogenous) Q0, i, a, m Quota in base years of species i for Member State m (exogenous) Qt, i, a Quota for year t of species i in area a (exogenous) RCt, j Running costs in year t of fleet segment j, includes fuel and other costs dependent on sea days (endogenous variable) Survey of existing bioeconomic models Appendix 235 RC0, j Running costs in the baseline period for fleet segment j, which includes fuel and other costs dependent on sea days (exogenous variable) SDt,j Sea days in year t by fleet segment j SDecr,t,,j Sea days per vessel in fleet segment j in year t determined by an effort control rule (ecr) SSBt,i Spawning stock biomass in year t of species i (exogenous variable) SSBLCt, i, j Spawning stock biomass costs of quota species in year t of species i for fleet segment j SSBNCt, i, j Stock biomass costs of non quota species in year t of species i for fleet segment j TR0, i, j Total revenue of quota species in base years of species i by fleet segment j, (exogenous) TRt, j Total revenue in year t by segment j, (endogenous) A.7.3 Full list of model parameters Parameters are either exogenous or calculated in the model; they are, however, subject for possible change by the model user for sensitivity analyses. The following parameters are used: a: Coefficient (is not used in the model, only in the model description) cc0, j Crew share coefficient in base period of fleet segment j cpuet,i,j catch per sea day in year t of species i by fleet segment j gf0,j Gross cash flow coefficient for fleet segment j, (cash flow per unit gross revenue) in the base period gft,j Gross cash flow coefficient for fleet segment j, (cash flow per unit gross revenue) in year t n The number of years after year ten nft,j Fleet segment share for fleet j of species i nsi, a, m Relative stability i.e. the share of species i in area a for Member State m (parameter) nui, m Quota uptake ratio of species i for Member State m (parameter, calculated by the model). Can be changed for future years r Discount rate rl Remuneration percentage of the quota fish stocks rn Remuneration percentage of the non quota fish stocks α and β Parameters (flexibilities); α ≥ 0; and β ≥ 0 εi Price flexibility of quota species i Survey of existing bioeconomic models 236 Appendix θt, i, j Effort driver. Selects the species i that in year t drive the effort of segment j. θ = 0 or 1. χi, j Activity-landing flexibility rate’ of quota species i for fleet segment j (parameter) γi Activity - stock flexibility rate of quota species i (parameter) Survey of existing bioeconomic models Appendix 237 A.8 Specific description: EFIMAS A.8.1 Full specification of model equations Assessment models: Three assessment models have been implemented: XSA, Separable, catch-at-age analysis (ICA) but FLR is also prepared for including any other type of assessment model. Stock recruitment There are four stock-recruitment models that are fitted by maximum likelihood assuming that the residuals are multiplicative and log-normally distributed. These models are Berverton and Holt, Ricker, Segmented Regression and Quadratic Hockey Stick Production function The following production models can be fitted: 1. Cobb-Douglas: Catch = Catchability x stockstock x effort effort 2. CES (Constant Elasticity of Substitution model.): Catch = intercept x (multiplier x stock-power + (1-multiplier) x effort-power)-1/power 3. Translog: Survey of existing bioeconomic models 238 Appendix Catch = exp(TL0 + TLs x log(stock)+ TLe x log(effort)+ TLss x log(stock)2 + TLee x log(effort)2 + TLes x log(stock) x log(effort)) Prices: There are three different functional forms of price dynamics in the different EFIMAS case- studies. The simplest form assumes a constant price independent of the landings. Alternatively, prices can be assumed to be a function of landings. The functional relation between prices and landings is defined using a flexibility coefficient. The flexibility between prices and landings measures the percent variation in prices due to a 1% inter-annual variation in landings, such that Pt=Pt-1(1+α(Lt-Lt-1)/(Lt-1)), where Pt is the price of a given species in time t, Lt is the landings in time t, α is a flexibility coefficient less than zero, Pt-1 is the price in the previous year, and Lt-1 is the landings in the previous year. Alternatively, the price flexibility can be estimated against a “base year”, accordingly, the price at time t is calculated as: α Pt=P0( (Lt/L0) ), where P0 is the price in the base year and L0 stands for the landings of a species in the base year. The approach can be extended to include the effects of other (substitute) species, such that α β Pt=P0( (Lt/L0) )(St/S0) ), where S0 is the landings of a substitute species in the base year and St is the landings of the substitute species in time t. The Dynamic Capacity Change Equation from the AHF model: The Dynamic Capacity Change Model, also known as the ‘AHF model’, evaluates the dynamic change in fleet capacity from one time period to the next, given expectations about future earnings from the fishery. The central equation of the Capacity Change model is the evaluation of fleet capacity V (number of vessels) in year y for fleet segment b: Vy,b= Vy-1,b+y,b ; VMIN,b ≤ Vy,b ≤ VMAX,b Vy,b = IIN,b · Πy,b / pIN,b ; Πy,b ≥ 0 Vy,b = IOUT,b · Πy,b / pOUT,b ; Πy,b < 0 Survey of existing bioeconomic models Appendix 239 Πy,b is the capitalization of future payments, that reflects the assumption that the fishermen base their investment/disinvestment decisions on expected future earnings. IIN,b and IOUT,b are the fractions of positive relatively negative expected profits that are used to invest/disinvest. pIN,b and pOUT,b are the prices per unit capacity of investment/disinvestment. It is assumed that the change in capacity is determined by the opportunity cost of capital including an option for asymmetry in entry and exit. The price of a vessel pIN,b transforms pecuniary capital into physical capital, and the reciprocal of pOUT,b includes the fisherman’s perception of opportunity costs. The investment/disinvestment equation leading to dynamic capacity change presented above, is only one part of the overall bioeconomic feedback model constructed to evaluate a chosen management scheme. The dynamic capacity change module has at present been used in the following models: • Demersal roundfish fisheries in the North Sea. • Demersal flatfish fisheries in the North Sea. Emphasis has been put on modelling the combined quota and effort (sea days) control imposed in many European fisheries in the North Sea. The models put special emphasis on that harvest (quota) and effort are necessarily interrelated, i.e. that a given harvest (quota) taken will necessarily determine the effort used, and correspondingly, that a given effort limit imposed will determine the harvest taken. Thus one of the two regulations will always be the limiting factor in a combined quota-effort regulation system. The two last bioeconomic models shown above both take this casual relationship between harvest and effort into account, and as such switch between quota and effort control depending on which is the limiting factor. Section 7 of this report has provided a full review of the AHF model. The Fcube: Its objective is to explore and evaluate the potential over-quota catches arising from inconsistent single-species TAC, based on simple assumptions about fleet’s effort distribution. Its strength is the relative simplicity of data needs while being able to account for a great diversity of fleets, metiers and stocks. Algorithm used: Data are structured by fleet segment Fl (e.g. group of vessels), metier Mt (type of activity practiced by the fleet; a fleet can engage in several metiers over a year) and stock St. Catchability estimates by fleet and metier q, as well as effort distribution by fleet and metier Effshare in the TAC year Y is user-input, e.g. as historical average or use of alternative models. Then average catchability by fleet is estimated as: Survey of existing bioeconomic models 240 Appendix q(Fl,St,Y)=∑Mt(q(Fl,Mt,St,Y)*Effshare(Fl,Mt,Y)) The fishing mortality corresponding to the single-stock TAC (Ftarg) is converted into “Stock dependent fleet effort”. The “stock-dependent fleet effort” is the estimated effort a fleet should develop in order to catch its quota share for a particular stock. The total target fishing mortality Ftarget(St) is first divided across fleet segments (partial fishing mortalities) through assumptions about quota share (e.g. historical landing share or alternative model) These partial fishing mortalities are subsequently used for estimating the stock-dependent fleet effort: F(Fl,St,Y)=Ftarg(St,Y)*QuotaShare(Fl,St) E(Fl,St,Y)=F(Fl,St,Y)/q(Fl,St,Y) It is unlikely that the effort corresponding to each single-species TAC is the same across species, and the resulting effort is therefore a choice. The user can explore the outcomes of a number of assumptions or rules about fleets own behaviour (e.g. going on fishing after some quotas are exhausted) or management scenarios (e.g. all fisheries are stopped when the quota of a particular stock is reached). E(Fl,Y)=rule(E(Fl,St1,Y),E(Fl,St2,Y),E(Fl,St3,Y)...) Final effort by fleet is then used to recalculate the actual fishing mortality by stock and corresponding catches. Difference between catches and TAC is interpreted as overquota. It has been applied to the Demersal Roundfish fisheries in the North Sea. Full feed back model for mixed fisheries: The methodology incorporates fisher behaviour in a full feed back model for mixed fisheries. The methodology introduces a simple and practical algorithm for a nonlinear catch - input relationship based on accepted theory in economics (decreasing returns to effort). The basic assumptions is that when subject to effort restrictions fishermen will skip those trips from which they expect the lowest earnings per unit of effort. The methodology indicates a convex catch – input relationship in the case of effort management in contrast to a concave relationship when applied to TAC driven scenarios. The catchability was defined as: β qt=q0(Et/E0) Where q is catchabilty, E is effort and β is an estimated parameter. Estimation of the parameter β is based on cross sectional analysis of variation in results by vessel and by trip. Data were drawn from logbook data by vessel and by trip, prices by month and by species from landings Survey of existing bioeconomic models Appendix 241 statistics. The catchability equation and other economic equations fit into the standard procedures applied by ICES stock dynamics. The model has been applied for NS flatfish fisheries but is applicable for other EU fleet segments where stock assessment data and landings by trip are available. Fleet Dynamics: The long term fleet based behavioural model used to simulate the entry-exit depends on three related decisions: a) investment; b) decommissioning; c) selling in the second hand market. These decisions are simulated at each time t during the simulation period and each of them determines the change in the operating number of vessels. Decisions are based on the average value of vessels. Four average values are considered: 1) The profit value estimated by the sum of actualized profits expected along the lifespan of a vessel (VΠ); 2) The value of a decommissioned vessel which has been estimated as the decommissioning rate (€/GT) times the average GT per vessel (VDG); 3) The market value of an existing vessel in the second hand market estimated as the average price per GT paid by the market times the average GT per vessel (VSH); 4) the average investment value of a new vessel estimated as the average price of a new GT times the average GT per vessel (VIN). a) Investment: Investment (I+) is simulated only if the profit value is positive, VΠ>0. Thus,(I+) is an increasing function of the profit value. Its slope (0.000001145) has been estimated based on time series regression analysis. Thus, the number of new vessels can be approximated by equation (14.1). + IN ∆N´t+1= It Πt/Vt (A.8.1.1) b) Decommissioning: Since obviously the decommissioning value cannot be negative, the maximum number of vessels to be decommissioned (∆NDG) is calculated as the ratio of the decommissioning grant (DGt) for the fleet and the decommissioning value (VDG,t) per vessel. The number of vessels abandoning the fleet (∆N´´t+1) (14.2) is estimated as a linear function of the difference between the profit value (VDG,t) and the decommissioning value. The estimated coefficients for β and γ are 4.10 and 0.00000469 respectively. ∆N´´t+1= β-γ(VΠ,t - VDG,t) (A.8.1.2) c) Selling: The number of vessels potentially sold on the market (∆N´´´t+1) (14.3) is a function of the difference between the profit value (VΠ) and the market value (VSH). The higher this Survey of existing bioeconomic models 242 Appendix difference, the lower the number of vessels sold on the market. The estimated β’ and γ’ coefficients are respectively 0.3422 and 0.00000088. ∆N´´´t+1= β’-γ’(VΠ,t - VSH,t) (A.8.1.3) Based on a), b) and c) the simulated number of vessels at time t+1 is defined by equation A.8.1.4. ∆Nt+1= ∆N´t+1 - ∆N´´t+1 - ∆N´´´t+1 (A.8.1.4) Notice that since the number of vessels is an integer value, the partial changes due to investment, decommissioning and selling in the second hand market are approximated to the closer integer. The link between fleet and effort dynamics In many fisheries the fishing effort represents the main management control variable. In this case the fishing effort, estimated in terms of days at sea for each fleet, allows measuring the impact of the simulated variation in the number of vessels on the biological and economic indicators. It is defined by equation (A.8.1.5), where dst is days at sea and dst Nt is the number of vessels at time t. Et = dst/Nt (A.8.1.5) This modelling approach has been applied in the Northern hake, Mediterranean swordfish and Mediterranean Hake case studies. A.8.2 Full specification of model variables and parameters Even if in principle variables are case specific the main variables to be able to provide advice using FLR are: Catch data: • Catch at age by fleet • Effort by fleet. • Discards by fleet (if discards want to be included). Biological data: • Weight at age • Natural mortality art age (normally considered as constant = 0.2). Survey of existing bioeconomic models Appendix 243 • Maturity at age • Fecundity at age (optional). Cost and capacity by fleet data: • Total costs related to landing per year • Total costs of licence(s) per year • Fuel costs per year • Bait costs per year • Ice costs per year • Food costs per year • Insurance costs per year • Management costs per year • Leasing costs per year • Crew costs (salary) per year • Maintenance costs per year • Depreciation per year • Opportunity costs per year • Interest costs per year • Insurance costs per year • Miscellaneous costs per year • Catches by species. • Capacity Price data: • Prices by species (and by age) • Price flexibility8 8 The percentage change in the price of a good as demand increases by a certain percentage. Survey of existing bioeconomic models 244 Appendix A.9 Specific description: EMMFID A.9.1 Full specification of model equations The economic model component has a high level of detail. The indices employed are based on the five dimensions (fleet [f], area [a], county [c], species [s], month [m]) that determine the level of detail of the model, as depicted above. Every time the model is run, several data files are included. These files contain data matrices, one- or multidimensional, that are created outside the model from the required datasets. They give information on the number of vessels and days at sea, catches, prices and costs, disaggregated down to the five model dimensions. In the Danish fishery case study, the model runs are initialised based on data from 2000. Catch per day at sea, derived from catch per unit effort, and variable costs per day are then calculated by the model for each of the five model dimensions. Average catch per unit effort, i.e. per day at sea, of species s in area a, month m, by a vessel that CPUE has c as home county and belongs to fleet segment f ( f ,,amsc ,, ) is calculated as: SUMCATCH CPUE = famc,, , famsc,, ,, NOFDdata famc,, , (A.9.1.1) SUMCATCH where famc,, , is the total catch in area a, month m, of species s for all vessels that NOFDdata have c as home county and belong to fleet segment f. Likewise famc,, , is the observed days at sea in area a, month m, for all vessels that have c as home county and belong to fleet segment f. It is then possible to state a catch equation, i.e. the catch in area a, month m, of species s, for vessels that have c as home county and belong to fleet segment f: CATCH= CPUE⋅ NOFD famsc,, ,, famsc ,, ,, famc ,, , (A.9.1.2) NOFD famc,, , is the endogenous variable number of days at sea in area a, month m, for all vessels that have c as home county and belong to fleet segment f, as opposed to the exogenous, NOFDdata observed number of days at sea famc,, , . Eight different cost items are included in the data matrices. These are allocated to four different cost components that are used in the model. The calculations are described below: Survey of existing bioeconomic models Appendix 245 - Cost component 1: Average variable operating cost per day at sea: OPECOST=+()/ FUEL ICE TNOFDdata ffff (A.9.1.3) FUEL ICE where f and f are the total costs of “Fuel and lubricants” and “Ice”, provisions, TNOFDdata stores” respectively, for vessels in fleet segment f. f is the total number of days at sea for all vessels in fleet segment f. - Cost component 2: Average fixed costs (within fleet segments) are calculated as the sum of average fixed cost items contained in the data matrices. These are costs for maintenance, rent and tax on real property, insurance, and other services. - Cost component 3: Average variable landings cost per DKK per day: LANCOSTfamc,, ,=⋅⋅∑ CPUE famsc ,, ,, PRICES fs , SALE f s (A.9.1.4) PRICES where f ,s are the average price of species s observed in the dataset for vessels SALE in fleet segment f. f is the “sales cost” coefficient for a vessel in fleet segment f. - Cost component 4: Average variable crew cost per day: CREWCOST= TREV⋅ CRW famc,, , famc ,, , f (A.9.1.5) CRW where f is the “crew cost” coefficient for a vessel in fleet segment f. TREV f ,,amc , is the average total revenue in area a, month m, for a vessel in fleet segment f originating from county c, that is: TREVfamc,, ,=⋅∑ CPUE famsc ,, ,, PRICES fs , s (A.9.1.6) Finally, the choice of the objective function depends on which scenario is analysed. Assuming that the contribution margin is being maximised, the objective function is: CM=⋅∑ CMNOFDfamc,, , NOFD famc ,, , famc,, , (A.9.1.7) CMNOFD where f ,,amc , is the contribution margin per fishing day in area a, month m, for a vessel in fleet segment f, originating from county c. That is: Survey of existing bioeconomic models 246 Appendix CMNOFD=− TREV OPECOST − LANCOST − CREWCOST f ,,amc , f ,, amc , f f ,, amc , f ,, amc , (A.9.1.8) The original model (Frost and Kjærsgaard 2003) does not contain an endogenous biological component, and there is thus no specification of a bio-economic interaction part. The connection between the economic component and the fish (catch) is via landings. Landings are not related linearly to effort (days at sea). A Cobb Douglas production function is applied, which gives the model a high degree of flexibility with respect to the constraints within fisheries. In the applied, bio-economic case study of the North Sea flatfish fishery (Kjærsgaard and Frost 2008, the choices of days at sea (effort) influence the rates of fishing mortality. A non-linear relationship between effort and fishing mortality is applied, depending on prices and biomasses). The relationship ensures that the composition of catches across cohorts varies with abundance. The effort-fishing mortality flexibility rate describes the degree to which a change in effort is mirrored in the fishing mortality. Different scenarios can be formulated in terms of restrictions and objectives. The objectives could relate to overall profit, total stock size, fleet segment profit, and income distribution, and structural and technical restrictions may be imposed. A.9.2 Full specification of model variables Fleet size and fishing effort, measured in number of vessels and number of days at sea, respectively, are the endogenous decision variables. A.9.3 Full list of model parameters The data matrices included in the model are used to calculate the cost per day components. The following cost items are considered in the Danish fishery account statistics at FOI: a) Fuel and lubricants b) Ice, provisions, stores c) Maintenance d) Sales costs e) Rent and tax on real property f) Insurance g) Other services h) Crew payment Eight matrices corresponding to the items (a) – (h) are included in the model. The “Sales cost” matrix contains coefficients corresponding to the share of the average sales cost relative to the average value of landings. Likewise the crew payment matrix contains coefficients Survey of existing bioeconomic models Appendix 247 corresponding to a share of revenue. The cost items are allocated to four different cost components that are used in the model. Catch per day (the matrix of equation (A.9.1.1)) together with prices and days at sea in the dataset, are used to transform costs into per-day measures. Assumptions: In the Danish fishery case, it is assumed that the Danish supply does not affect fish prices; therefore the Danish fishermen are price-takers, adapt the supply, and prices are assumed to be constant. Survey of existing bioeconomic models 248 Appendix A.10 Specific description: MEFISTO A.10.1 Full specification of model equations Biological sub-model equations: A dynamic age-structured approach underlies the biological sub-model: Number of individuals of cohort in age-structured model; −Z a ,t 1 ≤ a ≥ m Na +1,t +1 = Na,te , where; Initial data with class+, the following equation is implemented; N = N e−Z a ,t + N e−Z a+1,t a +1,t +1 a,t a +1,t , where; a +1 = m Total mortality ( Za,t ) corresponding to age-class a at time t Za,t = Fa,t + (M a,t + ε ) , ε ~ U, or, ε Za,t = Fa,t + (M a,tε ) , ε ~N where; M a,t is instantaneous natural mortality of age-class a and time t Fishing mortality ( Fa,g ,t ) at age a, with gear g and at time t is; F = q E a,g,t a,g,t g,t where qa,g,t is catchability of age-class a, by gear g and at time t, and Eg,t is fishing effort by gear g and at time t. Total fishing mortality is defined as; G Fa,g,t = ∑ Fa,g,t g=1 F' = F − F'' a,g,t a,g,t a,g,t F'' = F d a,g,t a,g,t a,g,t Survey of existing bioeconomic models Appendix 249 where, F'a,g,t is landings mortality by age-class a, by gear g and at time t, F''a,g,t is discard mortality by age-class a, by gear g and at time t and da,g,t is proportion of discards at sea by age-class a, by gear g and at time t Average number of individuals at age-class is: −Z a ,t N a,t = N 1− e / Z a,t ( ) a,t The von Bertalanffy growth model is: l = L 1− e−k (a−t0 ) eε a ∞ ( ) Biomass and catch: Relative growth weight relationship: w = Al Beε a a Mean biomass of age-class a and at time t: Ba,t = N a,t wa Total mean biomass for whole cohort: m Bt = ∑ Ba,t a=1 Catch of individual age-class a with gear g at time t: C = F Ba,t a,g,t a,g,t The stock-recruitment relationship can be dynamic in the sense that it links the population of the stock at time t with population at time t+1 (except in the case of constant stock-recruitment relationship), one of these following four pre-programmed functions is used to model the underlying stock-recruitment relationship: Table 54. Types of stock-recruitment relationship. MEFISTO Specification Function ε Constant recruitment R = N0e ε Linear recruitment R = αSSBt k e α1SSBt k ε Beverton and Holt N0,t+1 = e 1 + β1SSBt k −β 2 SSBt−k ε Ricker N0,t +1 = α2SSBt −k e e Survey of existing bioeconomic models 250 Appendix m SSBa,t = B a,t I a,t and ∑ SSBa,t a=1 Where, SSBa,t is spawning stock biomass at age-class a and time t and Ia,t is proportion of mature fish at age-class a and time t The biological and economic sub-models are linked via fishing mortality (F), catchability (q) and fishing effort (E) via the linear relationship F=qE. Fishing effort at time t is a fraction of the total allowable effort and catchability is dependent on technological investment (increase in capital) and time. The catchability ( qν ,t ) for each vessel ν at time t is given by: −hKν ,t t 1− e qν ,t = Qν ,0τ 1− e−hkν ,0 Where Qν ,0 is initial vessel-specific catchability constant, τ is fraction of catchability variation with time, h is proportion of catchability constant to capital Kν ,t is capital of vessel ν at time t and Kν ,0 is initial capital of vessel ν τ takes value τ >0, and if τ ≥1 there is time dependence but τ = 1 then there is no time dependence. h takes value h>0. If h is close to 0 then capital is significant and does affect catchability, but if h is high then capital does not affect catchability, Maximum catchability for infinite capital is defined by: Q 0 (1− exp(−h ⋅ K0 )) Catchability is also affected by gear specification and can be modelled using the selectivity parameter (S) which can be altered as an ‘event’. Fa,g,t = qa,g,t Eg,t Sa,g,t Where Sa,g,t is selectivity of age-class a, by gear g and at time t Economic sub-model equations: The economic sub-model represents the harvesting sector, fishing mortality generates a harvest (Yv,t ) from the stock for each vessel in accordance with relative fishing effort ( Ev,t ) and catchability ( qv,t ) of each vessel in the fleet (g) linearly: Survey of existing bioeconomic models Appendix 251 Ev,tqv,t Ev,t qv,t Yv,t = Yg,t Ya,v,t = Ya,g,t ∑∑Ev,t qv,t Fa,g,t vv The flexible fish price function ( Pg,t ) produces a value of the harvest and is a function of harvest and the vector Z (other explanatory variables besides harvest including aggregate supply of fish and substitutes). Pg,t = ƒ(Yg,t ,Z) The static price function pi,g,t , for the main target species i of fleet g at time t is: γ 2 γ 3 γ 4 ε pi,g,t = γ1ϖ i,g,t Ci,g,t impi,t δi,g,t ⋅e Where; γ1 is base price, i.e. the price when all other variables are equal to 1, Ci,g,t ϖ i,g,t is mean weight of main species i caught by fleet g at time t, (ϖ i,g,t = ), Ni,g,t γ 2 is size (length or age) modifier of price of average weight of fish caught by fleet, ϖ i,g,t , Ci,g,t is catch of main species i, by fleet g, at time t. γ 3 is offer modifier of price of catch, Ci,g,t , impi,t is imports of stock i at time t, γ 4 is offer modifier of price with imports, impi,t , δi,g,t is a price modifier to imitate the effect of unexpected exogenous price adjustment to the main species i by fleet g at time t, ε is the normally distributed error term to stimulate stochastic price variation The harvest cost function (Cov,t ) represents the harvest operating costs and is function of harvest (Y.,v,t ) and vectors k and w which describe individual vessel entity characteristics (vessel size, engine power, equipment, fishing gear, crew size etc) and input prices respectively. Cov,t = ƒ(k, Y.,v,t ,w) The catch of the secondary species (C j ) is related to the catch of the main species (Ci ). ⎪⎧C j = µij +ν ijCi ⎨ νij ⎩⎪C j = µijCi Survey of existing bioeconomic models 252 Appendix Where µij and ν ij are revenue-catch relationship parameters for the secondary species and are estimated from the data. Total revenues (gross value of production) Pν by vessel are: I J Pν = ∑Ci pi + ∑C j p j + Oν i =1 j =1 Where Oν is other sources of income e.g. subsidies. Hence net revenues ( RTν ) are total revenues ( Pν ) minus the harvest cost function (Coν ); RTν = Pν − Coν The methodology to model costs is based upon the structure of AER of which costs are divided into the following seven groups; Table 55. Groups of costs. MEFISTO Cost Type Name Variable Description Trade costs Co1 Function of catch Variable costs Labour costs Co3 Function of effort Daily costs Co2 Short-term costs Maintenance costs Co5.1 Function of profits Fixed costs Co5.2 Constant Compulsory costs Co4 Constant Opportunity costs Co6 Price of money Long-term costs Financial costs Co7 Interest rates Trade costs: Co1ν = clg ⋅ Pν Trade costs ( Co1ν ) are a proportion ( clg is constant for vessels in fleet g) of total revenues ( Pν ) and include VAT, taxes and sale process etc. Daily costs: Co2ν = NFDν (ƒpg ⋅ ƒcν + iceg + oDCν ) Daily costs ( Co2ν ) are the costs incurred from fishing and includes fuel consumption ( ƒcν ), ice ( iceg ) net mending, food and other daily costs (oDCν ) but exclude labour costs, where NFDν is average number of fishing days in one year. Labour costs (Co3ν ) is crew share and is expressed as percentage share ( c3g ) of revenue after trade and daily costs have been deducted. Co3ν = c3g (Pν − Co1ν − Co2ν ) Survey of existing bioeconomic models Appendix 253 ‘Monte menor’: MMν = Pν − Co1ν − Co2ν Average wage: AWg = Co3ν crewnumber Where, c3g typically averages around 50% but can vary between fleets. MM (‘Monte mennor’) is divided between owner and crew. Compulsory costs ( Co4ν ) are annual costs corresponding to legal fees such as harbour costs, licensing, insurance etc and are not dependent on fishing effort or catch. Maintenance costs (Co5ν ) are annual costs necessary to maintain the fishing vessel and constitute of reinstatement of used capital, repairs etc split into two sub-groups (Co5.1 and Co5.2) Opportunity costs ( Co6ν ) are the costs incurred from using the capital invested and is a function of public debt ( c6 ) and total investment in the vessel ( Kν ) and represents the lost revenue from investing in fishing. Co6ν = c6 ⋅ Kν Financial costs. Co7ν = c7 ⋅ Dν Capital (K) is dynamic and changes over time as a result of investment (I) and capital depreciation (δ ) K = K −δ K + I t+1 t t t t Where, I t is investment in capital at time t, δt is capital depreciation parameter at time t. Fishing firms will investment (either positively or negatively) when expected profits from investment (taking uncertainty and risk into account) are greater than with expected profits without investment and so the optimal investment behavioural rule is; V (K + ∆K) > V (K) If V (K) represents the firm’s value function, i.e. the maximal attainment of its objectives with capital and ∆K represents new investment (positive or negative). Hence, it is optimal to invest when the maximal expected profits with investment V (K + ∆K) are greater than maximal expected profits without investment V (K) . Survey of existing bioeconomic models 254 Appendix Figure 21: Diagrammatic representation of the fishermen box behavioural rules It is assumed that each firm maximises an objective function (profit) subject to biological constraints of the fishery. A set of fisher behavioural rules and responses are depicted in the figure above and this flow diagram represents the micro-economic scale behaviour associated with Mediterranean vessels. Fishing activity in period t+1 depends on fishing effort and revenue at end of period t because revenue from the previous period is used to cover costs at the beginning of the subsequent period. A set of possible ‘outcomes’ arise depending on whether firms make positive or negative profits (Lleonart et al., 2005). Investment is a function of profits; when profits are positive the firm will make further (positive) investment, however if profits are negative the firm may leave the industry and raise revenue from alternative avenues from capital already invested. A.10.2 Full specification of model variables Full specification of MEFISTO model exogenous and endogenous variables are detailed below in and Table 56 associated subscripts in the next table. Due to nature of simulation analysis most of the variables become endogenous. Survey of existing bioeconomic models Appendix 255 Table 56. Exogenous and endogenous variables. MEFISTO. Exogenous Variable Description Unit /endogenous C Endogenous Catch Tonne/year SSB Endogenous Spawning stock biomass Tonne w Exogenous Weight of species g E Endogenous Fishing effort Days/year p Exogenous Price of species €/tonne P Endogenous Total revenues (Gross Value of Production) €/year R Endogenous Recruitment -- RT Endogenous Rent, net revenues €/year N Exogenous Number of individuals -- Z Endogenous Total mortality -- F Exogenous Fishing mortality -- q Exogenous Catchability -- K Exogenous Capital € Co Endogenous Costs €/year Co1 Endogenous Trade costs €/year Co2 Endogenous Daily costs €/year Co3 Endogenous Labour costs €/year Co5.1 Endogenous Compulsory maintenance costs €/year Co5.2 Endogenous Avoidable maintenance costs €/year O Endogenous Other income (e.g. subsidies) €/year oDC Endogenous Other daily costs used in Co2 €/day MM Endogenous “monte menor” €/year AC Endogenous annual costs other than daily costs €/year AW Endogenous average wage €/year l Exogenous Size (weight) in the price function g imp Exogenous Species imports tonne NHD Exogenous Average number of hours a day Hours/day NFD Exogenous Average number of days a year Days/year Table 57. Full specification of the subscripts. MEFISTO Subscript Description Units t Time Year T Maximum time Year i Target species -- j Secondary species -- a Age-class interval Year k Age of recruitment Year g Fleet -- v Individual vessel -- A.10.3 Full list of model parameters A full list of model parameters and associated constraints are in Table 58. Survey of existing bioeconomic models 256 Appendix Table 58. Full list of model parameters. MEFISTO Parameter Description Unit Constraints A Length-weight relationship parameter g/cm B Length-weight relationship parameter -- von Bertalanffy maximum growth parameter L∞ cm von Bertalanffy growth rate parameter k Per year k0 ≠ 0 von Bertalanffy growth age at length 0 growth t 0 year I Proportion of maturity % M Natural mortality coefficient -- Beverton and Holt stock-recruitment parameter α1 Per tonne Beverton and Holt stock-recruitment parameter α 2 Per tonne β1 Beverton and Holt stock-recruitment parameter Per tonne Ricker stock-recruitment parameter Per tonne β2 S Selectivity -- 0 ≤ Sa,g,t ≥ 1 d Discard proportion % Initial relative catchability coefficient of each Q -- 0 fleet by age class h Capital-catchability parameter Per € h>0 Maximum number of hours a day by law and as Hours a NHD max physically possible day Maximum number of days a year by law and as Days a NFD max physically possible year δt Capital depreciation -- τ Time-catchability relationship parameter -- τ >0 Revenue-catch relationship parameter µ Per Tonne (secondary species) Revenue-catch relationship parameter ν Per tonne (secondary species) Base price or average price of target species €/tonne γ1 γ 2 Size (weight) price modifier -- Offer (catch) price modifier γ 3 -- Offer (imports) price modifier γ 4 -- δ Exogenous event price officer -- Maximum percentage of lend d g % FAC Percentage of unavoidable annual costs % fp Fuel price €/litre VAC Percentage avoidable annual costs % dis price paid for decommissioning €/GT share of total revenues belonging to owner after c3 % discounting trade and fuel costs u Proportion of profits invested in capital % ice Daily consumption of ice €/day commercial or trade cost, (percentage paid to cl % fish market for sale of fish) Co6 Opportunity costs €/year Co7 Financial costs €/year proportion of effort increase when profits are ∆Eff % positive cs Crew size of vessel -- D debt owed to the bank at time 0 € GT Capacity expressed as gross tonnage GT fc Fuel consumption per day Litre/day Survey of existing bioeconomic models Appendix 257 A.11 Specific description: MOSES A.11.1 Full specification of model equations Biological sub-model: Surplus production or age-structured stock assessment methods can be applied to the catch- effort data in the biological sub-model. 1/ Surplus production models: MOSES applies dynamic catch-effort equations for the Schaefer growth function and the exponential surplus production model. To address the issue of data poor fisheries, the surplus production models use aggregated fishing activity data, equivalent fishing effort ( Eeq,i ) is calculated as the weighted sum of fishing effort for the catch of each species. The functional form of the surplus production models are; For the Schaefer model: C = k E − k E 2 s,t 0 eq,t 1 eq,t And for the Exponential equilibrium model: C = k E e−k1Eeq,t s,t 0 eq,t A dynamic approach is applied to the above surplus production models; in the sense that catch in period t is expressed as weighted sum of equilibrium catch Cs and short-term catch Cc , which is derived from catch and effort in period t-1 (assuming a constant catch per unit effort, CPUE), the surplus production models have memory of one year. Eeq,t Ct = S.Cs,t + (1− S).Cc,t where; Cc,t = Ct −1 Eeq,t −1 The steady-state parameter S determines the number of years required to reach asymptotically equilibrium conditions for a given level of effort. If S is high, long-term predictions coincide with short-term predictions. Therefore, the surplus production models adopt a dynamic approach because the catch in a given year is the weighted sum of the long- and short-term catch (SEC 2006c). The following assumptions hold for the surplus production models (SEC 2006c): • Population dynamics are determined by density-dependent factors. • Age-structure is disregarded. • Time lags are not taken into account during production. Survey of existing bioeconomic models 258 Appendix • Stock biomass is homogenously distributed geographically. • Stability in immigration and emigration of stock. • Constant catchability and fishing activity patterns. 2/ Partially age-structured model MOSES can apply a flexible form of the partially age-structured Deriso-Schnute model: 1 C C C C ⎡ C ⎤γ t +1 = (1+ ρ)e−M t − ρe−2M −qEt t −1 + αV t +1−k 1− βγ t +1−k −qEt−1 qEt qEt−1 qEt+1−k ⎢ qEt+1−k ⎥ e e −1 e −1 e −1 ⎣ ()e −1 ⎦ 1 C ⎡ c ⎤γ − ρανe−M −qEt t −k 1− βγ t −k qEt−k ⎢ qEt−k ⎥ e −1 ⎣ ()e −1 ⎦ Catch (C) in year t is a function of fishing effort (E) in year t and also catch and fishing effort in k previous years (model is of order k). Economic sub-model There are three types of labour contract in Italy; wage, share and self-governing management contract (see BEMMFISH review in Section 8 of this report for a full specification and description). Table 59. Types of labour contract in Italy Labour contract Specification Description Wage contract VA = GVP – OC Labour costs are considered with other GCF = VA – W – SC costs and are paid in accordance with the National Collective Agreement, where the employer must fund a salary and social contributions. Share contract VA = GVP – OC Labour costs are considered as share of = WS + OS value added. The labour wage is linked to OS = VA – WS production as type of risk-sharing contract, = VA – (WP*VA) a share of value added is divided between = (1 – WP)*VA the crew and vessel-owner. GCF = OS – SC Self-governing management VA = GVP – OC Contract is primarily used by small vessels contract GCF = VA – SC with only a few crew Where; GVP is Gross Value of Production, OC is Operating Costs, GCF is Gross Cash Flow, W is Wages, SC is Ship-owner Costs, WS is Wage Share, OS is Owner Share, TPC is Total Production Costs and NP is Net Profit. Survey of existing bioeconomic models Appendix 259 Biological and inertia constraints: A biological index correlated to over-fishing, VBi is defined in the catch-effort models as: ⎧VBi = Ei − Em,i if Ei > Em,i ⎨ ⎩VBi = 0 if Ei ≤ Em,i Where; Em,i is fishing effort corresponding to the maximum catch in biological equilibrium. The Schaefer equilibrium model is: k0 Em,i = 2k1 And the Exponential model equilibrium 1 Em,i = k1 A ratio of theVBi terms for each species and total fishing effort Ei in each geographical area is given by; NSP ∑VBi i=1 VB = NSP ∑ Ei i=1 The VB index represents the proportion of total effort exceeding the conditions corresponding to maximum catch for each species, i and is suitable for area-based fisheries management and effort control. The inertia constraint VIi is defined to measure the difference between proposed effort solutions and actual effort for the i-th fishing system: ⎧ * E − E* > ∆E ⎪VIi = Ei − Ei − ∆Ei if i i i ⎨ if * ⎩⎪VIi = 0 Ei − Ei ≤ ∆Ei where; ∆Ei = StDev(Ei )F The term ∆Ei represents the maximum allowed variation in fishing effort around effort * reference value ( Ei ) and is proportional to standard deviation of i-th fishing effort in given area and time. The factor F can be changed according to the management scenarios and objectives during optimisation and allows ‘elasticity’ to be considered for each fleet during the previous Survey of existing bioeconomic models 260 Appendix period by area and fishing system and is therefore related to micro-economic behaviour in fisheries and technological improvement in the long-run. The inertia term is used to limit deviations in effort distribution to prevent unrealistic effort allocations and it also concerns the least re-conversion effort or cost. NFS VI = ∑VIi i=1 Optimisation: MOSES solves the optimal distribution of fishing effort using non-linear constrained optimisation (Augmented Lagrangian algorithm and Quasi-Newton minimisation), the optimisation procedure is used to determine the optimal distribution of fishing effort corresponding to maximum value added compatible with the following constraints: • Biological constraint – to avoid over fishing and depopulation • Inertia constraint – to select realistic effort redistribution solutions. MOSES estimates the optimal distribution of fishing effort (from the catch-effort model), which maximises economic returns (value added) subject to a biological constraint (species conservation) and an inertia constraint (socio-economic aspects). Value added is defined as revenue (R) minus total operating cost (C), VA = R – C where revenue is the total value of catches (by species and area) multiplied by selling price and total operating costs are the sum of fishing costs (by species and area) excluding labour: MaxxVA(x) subject to: VB(x) > VB max VI(x) ≤ 0 Where; x is a vector of effort distribution in the reference year, VB(x) >VBmax is the biological constraint to avoid over fishing VI(x) ≤ 0 is the inertia constraint to ensure realistic fishing effort distribution solutions. The inertia constraint limits variations in effort distribution and the optimisation procedure chooses the equivalent effort associated with minimum re-conversion effort or cost. The re- conversion model lies inside the optimisation procedure and links minimum re-conversion costs Survey of existing bioeconomic models Appendix 261 to prospective fishing effort distribution (Coccorese et al., 1998b). For full specification of the statistical estimation and optimisation procedure see Arnason et al., 1997. A.11.2 Full specification of model variables Full specification of exogenous and endogenous MOSES variables are detailed in Table 59 and associated subscripts in Table 60. During the optimisation procedure fishing effort becomes endogenous since MOSES forecasts the optimal distribution of effort (SEC, 2006). Table 60. Full specification of the exogenous and endogenous variables. MOSES. Exogenous/ Sub model Description Units Endogenous Fishing effort Effort unit (boat days) Catch kg Biological sub-model Exogenous Technical matrix Species/fleet Recruitment Year Unit fishing cost €/effort unit Exogenous Average prices €/kg Economic sub-model Total revenues € Endogenous Value added € Table 61. Full specification of the subscripts. MOSES Subscript Description Units t Time Years k Fish age Years i Fish species -- s Short-term catch kg c Equilibrium catch kg eq Equivalent effort Effort unit A.11.3 Full list of model parameters A full list of model parameters and associated constraints are shown in the next table. Survey of existing bioeconomic models 262 Appendix Table 62. Full list of model parameters. MOSES Parameter Description Units Constraints M Natural mortality -- 1/(boat q Catchability q > 0 days) Ford’s growth ρ -- 0 < ρ < 1 coefficient Pre-recruitment αν > 0 ν kg weight at age k-1 αV > 0 Recruitment weight αV > 0 V kg at age k 0 <ν < V Recruitment α productivity Fish/kg αν > 0 parameter Recruitment β 1/kg optimality parameter Recruitment γ -- limitation parameter Schaefer/exponential k -- k > 0 0 parameter 0 k1 > 0 Schaefer/exponential k -- k 1 parameter 0 k1 > 10En Steady-state S -- 9 parameter 0 ≤ S ≥ 1 Natural mortality σ -- survival from year t 0 < σ < 1 Further parameter constraints in the Schaefer model: αV > αν σ = e−M αV + e−M > 1 αV + σ > 1 ⎛ Ct +1−k ⎞ ⎜1− βγ qE ⎟ > 0 ⎝ ()e t+1−k −1 ⎠ ⎛ Ct −k ⎞ ⎜1− βγ qE ⎟ > 0 ⎝ ()e t−k −1 ⎠ t = k +1,...,T Where T is the number of years for which effort data are available. 9 S=0 for constant CPUE and equilibrium conditions at infinite time. S=1 for pure steady state with fast response time. Survey of existing bioeconomic models Appendix 263 A.12 Specific description: TEMAS In the sections to follow details of the TEMAS model will be reviewed to the degree possible given the breadth of the model and its modular design. To repeat, its modular design allows a practically limitless number of scenario implementations and, importantly, the ability to compare models with a user specified base model. It is possible to run a basic model with a limited amount of input data but, a strength of TEMAS is the degree to which detailed data can be easily incorporated into the model using the Excel ”sheets” provided and the use of “helper” functions. A.12.1 Full specification of model equations Rather than specifying all of the equations available within TEMAS in a separate section the equations as they appear in the various TEMAS modules are discussed. This is a more natural approach given the structure of TEMAS. A.12.2 Full Specification of model variables The variables used in TEMAS will be discussed as they appear in the major modules (see immediately below), this approach fits better with TEMAS’s structure and flexibility in that it’s often up to the modeler to decide what is an endogenous and exogenous variable. A.12.3 Full list of model parameters In the following sections the major TEMAS modules will be presented and discussed. Stock Inputs: The following table is taken from the TEMAS manual dated the 21st of June, 2007, Part II, written by Per Sparre who worked for the Danish Institute for Fisheries Research (DIFRES) (currently DTU-Aqua). It shows the incredible degree of detail that is available in the TEMAS model. The tables are created from a demonstration involving four periods, two stocks, two countries, five areas and ten years. Aspects of the model in relationship to the demonstration are discussed. The “Index” and “EXCEL Table” columns in the table below refer to internal TEMAS tables, the third column, entitled “Caption”, lists the names of stock inputs. Tables are presented to show the degree to which a model can be specified and the manner in which data is organized and can be entered by a modeler. Survey of existing bioeconomic models 264 Appendix Table 63. Tables in the stock input sheet. TEMAS Index EXCEL Table Caption 23 Table 2.1.1. Growth And Maturity Parameters 24 Table 2.1.2. Condition Factor 25 Table 2.2.1. Recruitment Parameters 26 Table 2.2.2. Recruitment Distribution On Periods 27 Table 2.2.3. Recruitment Distribution On Areas 28 Table 2.2.4. Low Spawning Success On Areas 29 Table 2.2.5. High Spawning Success On Areas 30 Table 2.2.6. Recruitment Trend Over Years 31 Table 2.3.1. Stock Numbers First Period Of First Year - Age 0- 1 32 Table 2.3.2. Stock Numbers First Period Of First Year - Age 2+ 33 Table 2.4.1. Weighting Factors For Mean F Calculation - Age 0- 1 34 Table 2.4.2. Weighting Factors For Mean F Calculation - Age 2+ 35 Table 2.5.1. West Cod: Migration - Age Gr.0-1 36 Table 2.5.2. West Cod: Migration - Age Gr.2+ 37 Table 2.5.3. East Cod: Migration - Age Gr.0-1 38 Table 2.5.4. East Cod: Migration - Age Gr.2+ 39 Table 2.6.1. Natural Mortality - West Cod 40 Table 2.6.2. Natural Mortality - East Cod Table 2.1.1 (Index number 23 above) concerns the typical growth and maturity parameters, e.g., it contains the three von Bertalanffy growth parameters by species. An interesting feature is the inclusion of a parameter for the fraction of the annual recruitment which occurs in a period and a condition factor which is the exponent in the model for the relationship between length and weight. Table 2.1.2 contains the condition factors which are assumed to depend on the time of the year, meaning that the user has the option to let the condition factor vary over seasons of the year, while the condition exponent is assumed to remain constant during the year. Table 2.2.1 contains the parameters of the chosen stock and recruitment model. There are four options, and the last column “Model Choice” points at the selected model. The four stock recruitment model options are: the Beverton and Holt, Hockey stick, Ricker and Deriso-Schnute stock recruitment models. Survey of existing bioeconomic models Appendix 265 Table 64. The specific parameters for each of the four model choices. TEMAS Column name Explanation BH1 (1) Beverton & Holt BH2 (1) Beverton & Holt H-S Biom. (2) Critical biomass in Hockey stick model H-S Const.Rec. (2) Constant recruitment in Hockey stick model H-S Slope (2) Slope of line in Hockey stick model Ricker coeff. (3) Coefficient in Ricker model Ricker Exp. (3) Exponent in Ricker Model D-S-Coeff.(1) (4) First coefficient in Deriso-schnute model D-S-Coeff.(2) (4) Second coefficient in Deriso-schnute model D-S-Exp. (4) Exponent in Deriso-schnute model RelStDev(R) Relative standard deviation of recruitment Freq.Outst.Yrs Frequency of outstanding years Mag.Outst.Yrs Magnitude of outstanding years Autocorr.Outst.Yrs Autocorrelation of outstanding years Model Choice 1,2,3 or 4 (B&H, Hockey Stick, Ricker, Deriso-Schnute) Tables 2.2.2 and 2.2.3 contain the distribution of recruitment over periods and areas respectively. After the total stock recruitment is derived, it is subsequently distributed to areas and over time periods. Tables 2.2.4 and 2.2.5 contain the spawning success parameters, for low success and high success respectively. These parameters are used to define the SSB in two alternative cases, namely when the year is an inflow year and when it is not an inflow year. Table 2.2.6 is the last recruitment parameter table. It contains the exogenous recruitment trend parameters. TEMAS allows for the analysis of the impact of a “recruitment trend”, that is, analysis of the effects of an increasing or decreasing recruitment trend. Table 2.3.1 and 2.3.2 contain the initial stocks in each area for juveniles and adults respectively. The initial stock numbers can be given as input or they can be computed by the program under the assumptions of equilibrium and constant fishing mortality. Tables 2.4.1 and 2.4.2 show the weighting factors (WF) in the calculation of stock mean F. These weighting factors can be used to compute the traditional mean F as presented by ICES Working Groups. Tables 2.5.1 and 2.5.4 contain the migration coefficients. Migration is modeled in a time discrete manner: Survey of existing bioeconomic models 266 Appendix • Migration takes place at the end of each time period and the process of migration takes zero time. • During a time period the fish/shrimps are assumed to be homogeneously distributed within the area. • The "Migration Coefficient" (MC) from area A to area B is defined as the fraction of the animals in area A which move to area B. In this definition, the "movements" include the "move" from area A to area A, i.e., the event that the animal does not move. Finally, there are two tables for each species. Separate migration coefficients can be specified for age groups 0 and 1 and for age groups 2 to and above. Tables 2.6.1 and 2.6.2 contain the natural mortality for each species. It is possible to let natural mortality depend on area, time and age. Finally, it is possible to quickly specify many of the model parameters using the following helper function. This allows a user to quickly run a more general model. Figure 22, helper functions for stock inputs Boat Inputs: Once again, the following table is taken from the TEMAS Manual dated the 21st of June, 2007, Part II, by Per Sparre. The tables are created from the same demonstration described above and concern four periods, two stocks, two countries, five areas and ten years. Aspects of the model in relationship to the demonstration are discussed. The “Index” and “EXCEL Table” table columns refer to TEMAS tables, the third column, entitled “Caption”, lists the names of boat inputs. It has only been considered each unique table, ignoring the equivalent table for the second fleet. Survey of existing bioeconomic models Appendix 267 Table 65. Tables in the fleet input sheet. TEMAS Index EXCEL Table Caption 41 Table 3.1.1. Baltistan : Absolute Catchability 42 Table 3.1.2. Baltistan : Parameters In Model For Catchability 43 Table 3.1.3. Scandinavia : Absolute Catchability 44 Table 3.1.4. Scandinavia : Parameters In Model For Catchability 45 Table 3.2.1. Baltistan : Mesh Size (Generalized Concept) 46 Table 3.2.2. Baltistan : Gear Selection Factor 47 Table 3.2.3. Baltistan : Gear Selection Range 48 Table 3.2.4. Baltistan : Discards L50% 49 Table 3.2.5. Baltistan : Discards L75% 50 Table 3.2.6.1. Baltistan : West Baltic Relative (Period) Catchability 51 Table 3.2.6.2. Baltistan : East Baltic Relative (Period) Catchability 52 Table 3.2.6.3. Baltistan : Not Baltic Relative (Period) Catchability 53 Table 3.2.6.4. Baltistan : Bornholm Relative (Period) Catchability 54 Table 3.2.6.5. Baltistan : Gotland Relative (Period) Catchability 55 Table 3.2.7 Scandinavia : Mesh Size (Generalized Concept) 56 Table 3.2.8 Scandinavia : Gear Selection Factor 57 Table 3.2.9 Scandinavia : Gear Selection Range 58 Table 3.2.10 Scandinavia : Discards L50% 59 Table 3.2.11. Scandinavia : Discards L75% 60 Table 3.2.12.1. Scandinavia : West Baltic Relative (Period) Catchability 61 Table 3.2.12.2. Scandinavia : East Baltic Relative (Period) Catchability 62 Table 3.2.12.3. Scandinavia : Not Baltic Relative (Period) Catchability 63 Table 3.2.12.4. Scandinavia : Bornholm Relative (Period) Catchability 64 Table 3.2.12.5. Scandinavia : Gotland Relative (Period) Catchability Tables 3.1.1 to 3.1.4 contain the absolute catchability data for species and areas by fleet, vessel size, country and rigging. It's possible to set catchability parameters for ranges of biological, technical and rigging changes in catchability. Tables 3.2.1-3.2.3 contain the parameters for the gear selection ogive. As is common practice, the logistic curve is used to model the selection of fishing gears Tables 3.2.4-3.2.5 contain discard data for 25% and 50% discards. The discard parameters can be modified in any time period of any year. The remaining tables give the parameters in the model that link effort to total area fishing mortality. Finally, just as for species data, there is a way to simplify the input process by making one or more general assumptions. Survey of existing bioeconomic models 268 Appendix Figure 23, helper functions for fleet parameters The five simplifying options are: • Make parameters equal for all years. This option will take the value for the first year and apply it to all other years, for all yearly dependent parameters. • Make parameters equal for all time periods. This option will take the value for first year and apply it to all other years. • Assign 1.0 to all multipliers and relative catchabilities. • Assign standard values to catchability. • Assign zero to catchability standard deviations for technical development, biomass and rig effects. Effort: Table 66. Tables in the effort input sheet. TEMAS Index EXCEL Table Caption 65 Table 4.1. Reference Effort (Maximum Possible Effort) Not Input 66 Table 4.2. Effort Distribution On Areas 67 Table 4.3. Resulting Effort After Distribution On Areas Not Input 68 Table 4.4. Effort Distribution On Rigs (After Distribution On Areas) 69 Table 4.5. Resulting Effort After Distribution On Rigs And Areas Not Input 70 Table 4.6. Effort Multipliers 71 Table 4.7. Resulting Effort Distribution On Rigs (After Distribution On Areas) In general, the TEMAS offers two ways of entering data: (1) Input effort directly; (2) Let the “Effort-rule” calculate the effort. The second method makes the direct input of effort data optional. When the option to let the effort be determined by effort rules is chosen (the short term and long term behaviour models also called “trip-behaviour” and “structural behaviour”) a modeler does not need to give effort as input. The effort exerted (the actual number of days at Survey of existing bioeconomic models Appendix 269 sea) is then a function of the effort entered (in a previous input table) as well as the number of vessels (boats) previously entered. The number of vessels defines an upper limit for the number sea days (effort) that can be exerted. The effort capacity of a vessel, EYMAX, is the maximum number of fishing effort units (fishing days or sea days) that a fleet can exert in a time period. The total effort exerted by fleet, vessels, and country during a time period is effort summed over riggings and areas. According to the definition of EYMAX, effort does not depend on the rigging used. The reference effort is in Table 4.1 (TEMAS numbering) below, note that this data is not inputted but calculated on the basis of previously inputted data. As stated above, they are the product the number of vessels and the maximum number of days per period. Figure 24. Example of calculated effort Figure 25. Effort distribution over areas Survey of existing bioeconomic models 270 Appendix Input effort in the reviewed version of TEMAS is E(Fl (fleet), Vs (vessel), Ct (country), y (year), q (fraction of year)), that is, the total effort summed over areas, together with the relative distribution of effort over areas (Ar): E ( Fl ,Vs ,•, Ct , y, q, Ar ) E ( Fl ,Vs ,•, Ct , y, q, Ar ) = Area − Dist E ( Fl ,Vs ,•, Ct , y, q,•) The effort distribution can be given as input for each period of each year in the case in which behaviour rules are not applied. Thus, effort is derived from the product of the two input parameters, E(Fl, Vs, •, Ct, y, q,) and EArea-Dist(Fl, Vs, • ,Ct, y, q, Ar) Which in turn gives the effort distribution on fleets, vessels sizes and countries: E(Fl, Vs , Ct, y, q, Ar) = E(Fl, Vs, , Ct, y, q)* EArea-Dist (Fl, Vs, •, Ct, y, q, Ar) The next step in calculating the distribution of effort is the distribution over riggings for given area: E(Fl, Vs, Rg, Ct, y, q, Ar) = E(Fl, Vs, ,Ct, y, q, Ar)* ERig-Dist(Fl, Vs, Rg, Ct, y, q, Ar) The definition of effort distribution on riggings for given area, Ar is: E ( Fl ,Vs , Rg , Ct , y , q , Ar ) E ( Fl ,Vs , Rg , Ct , y , q , Ar ) = Rig − Dist E ( Fl ,Vs ,•, Ct , y, q , Ar ) To summarize the distribution, the complete model of effort distribution on areas, and on rigs for given area is: E ( Fl ,Vs , Rg , Ct , y , q , Ar ) = E REF ( Fl ,Vs ,•, Ct , y , q ,•) * E Rig − dist ( Fl ,Vs , Rg , y , q , Ar ) * E Area − dist ( Fl ,Vs ,•, Ct , y , q , Ar ) Figure 26. Helper functions for effort data The figure above shows the user form for the pre-processing of effort input. Option 1 sets effort data equal from year to year, but allows for variations between periods. Option 2 makes data Survey of existing bioeconomic models Appendix 271 equal for all periods, but allows for variations between years. Option 3 makes the multipliers equal and 1.0 for all periods and years. Figure 27. Examples of effort tables Boats Input: The following tables are some of the parameters which can be specified in the boat section of TEMAS. The reader may notice that the column “Index” skips over some numbers, this is because if certain data is not specified by the user the table is omitted. Survey of existing bioeconomic models 272 Appendix Table 67. Tables in the boats input sheet, which are actually used. TEMAS Index EXCEL Table Caption 72 Table 5.1. Number Of Fleet Characteristics - Level And Index Of Max Total Characteristics 73 Table 5.2. Names Of Fleet Characteristics 74 Table 5.3. Overall Multiplier For Number Of Boats And Effort 75 Table 5.4.1. Baltistan: Initial Vessel Age Distribution And Investments (New Vessels) 76 Table 5.4.2. Baltistan: Number Of New Boats Mults 77 Table 5.4.3. Baltistan: Crew Per Vessel 78 Table 5.4.4. Baltistan: Max Days/Period 79 Table 5.4.5. Baltistan: Number Of Dis-Investment (Withdrawal) Vessels 80 Table 5.4.6. Baltistan: Number Of Attrition Vessels 81 Table 5.4.7. Baltistan: Number Of Decommissioned Vessels 82 Table 5.4.8. Baltistan: Resulting Vessel Age Distribution 83 Table 5.4.9. Baltistan: Resulting Number Of Decommissioned Vessels 84 Table 5.4.10. Baltistan: Number Of Vessels (Summary) 85 Table 5.4.11. Scandinavia: Initial Vessel Age Distribution And Investments (New Vessels) 86 Table 5.4.12. Scandinavia: Number Of New Boats MultS 87 Table 5.4.13. Scandinavia: crew per vessel 88 Table 5.4.14. Scandinavia: Max Days/Period 89 Table5.4.15. Scandinavia: number of dis-investment (withdrawal) vessels 90 Table 5.4.16. Scandinavia: Number Of Attrition Vessels 91 Table 5.4.17. Scandinavia: Number Of Decommissioned Vessels 92 Table 5.4.18. Scandinavia: Resulting Vessel Age Distribution 93 Table 5.4.19. Scandinavia: Resulting Number Of Decommissioned Vessels 94 Table 5.4.20. Scandinavia: Number Of Vessesls (Summary) 95 Table 5.5.1. Baltistan: Fleet Characteristics: Length 96 Table 5.5.2. Baltistan: (Start Number Of Vessels)* (Fleet Characteristics): Length 100 Table 5.5.6. Baltistan: Fleet Characteristics: Tonnage 101 Table 5.5.7. Baltistan: (Start Number Of Vessels)* (Fleet Characteristics): Tonnage 102 Table 5.5.8. Baltistan: Maximum Total Allowed Fleet Characteristics (By Country): Tonnage 105 Table 5.5.11. Baltistan: Fleet Characteristics: KgWat 106 Table 5.5.12. Baltistan: (Start Number Of Vessels)* (Fleet Characteristics): KgWat 120 Table 5.5.26. Scandinavia : Fleet Characteristics: Length 121 Table 5.5.27. Scandinavia : (Start Number Of Vessels)* (Fleet Characteristics): Length 125 Table 5.5.31. Scandinavia : Fleet Characteristics: Tonnage 126 Table 5.5.32. Scandinavia: (Start Number Of Vessels)* (Fleet Characteristics): Tonnage 127 Table 5.5.33. Scandinavia: Maximum Total Allowed Fleet Characteristics (By Ctry): Tonnage 130 Table 5.5.36. Scandinavia: Fleet Characteristics: KgWat 131 Table 5.5.37. Scandinavia: (Start Number Of Vessels)* (Fleet Characteristics): KgWat The number of boats or vessels in TEMAS is composed of “vessel age groups”. It's possible to specify the number of vessels decommissioned, lost to attrition, and withdrawn from the fleet. Tables 5.1 and 5.2 contain the number and names of the fleet characteristics. The values of the fleet characteristics are given in subsequent Excel tables. Examples of fleet characteristics are “Vessel tonnage”, “Length of vessel” and “KgW of engine”. The TEMAS model allows a user to select a number of fleet characteristics which can be used in two ways: • The definition of fisheries regulations (as in the example with tonnage above) • Measures of fleet features used in output tables, as additional information and explanation. Survey of existing bioeconomic models Appendix 273 The column “Level Max Characteristics” in EXCEL Table 5.1 indicates “Level 1”, that it, the country level. Thus, there is an upper limit for the total tonnage of all vessels of each country in the demonstration used. EXCEL Table 5.2 shows that there are options for up to 5 different fleet characteristics in the reviewed version of TEMAS, but only three of them are used. Table 5.3 contains the overall multiplier for new boats. The number of new vessels (investments) is created from a “reference number” multiplied by a “multiplier”. The multiplier is composed of two factors, the first factor is independent and applies to all fleets in all time periods, while the second factor is dependent on fleet and time periods. Table 5.4.1 contains the initial vessel age distribution and investments in new ships by type of boat and the time the investment was made. Table 5.4.2 contains the number of new boat multiplier. This multiplier will also be applied to the initial number of vessels in the first period of the first year. For the initial fleet, the multiplier applies to all vessel age groups. Tables 5.4.3 and 5.4.4 contain, respectively, the number of crew per vessel and the maximum number of days per month. The variable “crew per vessel” is used to compute employment through multiplication with the number of vessels, and to compute other indicators of performance in the economic model. Tables 5.4.5 and 5.4.6 and 5.4.7 include, respectively, the number of disinvestments (withdrawals) of vessels per period, the number of attritions, and the number of decommissioned vessels. When calculating the resulting number of vessels, the chronological order is (1) decommission (2) attrition (3) disinvestment. Table 5.4.8 contains the resulting number of vessels after vessel recruitment (investment) the execution of the algorithm involving decommissions, attritions, and disinvestments, and after application of multipliers to number of new vessels Table 5.4.2. Table 5.4.9 contains the resulting number of decommissions. Table 5.4.10 summarizes the resulting number of vessels, in that it gives results (new vessels, decommissions, attritions and disinvestments) summed over vessel age groups. The purpose is to illustrate the vessel number manipulations and to produce a table for presentation in reports. Table 5.5.1 contains the vessel lengths and Table 5.5.2 contains the total characteristics (length of vessels times starting number of vessels) for the fleets. Tables 5.5.3-5 are the tables for regulations, that is, the maximum level of the total characteristics. There are three possible levels for the regulation: (1) By Country Survey of existing bioeconomic models 274 Appendix (2) By (Fleet, Country) (3) By (Fleet, Vessel size, Country). Table 5.5.8 allows a maximum total allowed tonnage to be specified by country. Table 5.5.11 allows the KgWatts to be specified per fleet per period. In short, TEMAS allows vessel length, tonnage and engine strength to be specified across time. The maximum number of sea days, EYMAX(Fl, Vs, Ct, y, q, Ar), multiplied by the number of vessels, gives the upper limit of the effort (sea days) that can be exerted. We define the “reference effort” or the “maximum effort” by (terms as defined above): E REF ( Fl ,Vs , Ct , y , q , Ar ) = NU Vessel ( Fl ,Vs , Ct , y , q ,•) * EY Max ( Fl ,Vs , Ct , y , q , Ar ) Prices Input: Table 68. Tables in the prices input section. TEMAS Index EXCEL Table Caption 145 Table 6.2.1. Baltistan: Maximum Price (Over Age Groups) 146 Table 6.2.2. Baltistan: Relative Price (Over Age Groups) 147 Table 6.2.3. Baltistan: price flexibility 148 Table 6.3.1. Scandinavia: Maximum Price (Over Age Groups) 149 Table 6.3.2. Scandinavia: Relative Price (Over Age Groups) 150 Table 6.3.3. Scandinavia: Price Flexibility The price concept used in TEMAS is the “Ex-vessel price”, that is the price of the landings assigned to the vessel (the vessel owner). They are given as a maximum price over age groups and a relative price by age, Tables 6.2.1 and 6.2.2 allow the maximum prices to be entered and summed over age groups and the relative price to be entered per age group. Table 6.2.3 allows a price flexibility to be specified. Figure 28. Prices helper function Survey of existing bioeconomic models Appendix 275 As usual, TEMAS provides a way to simplify the analysis by providing a “helper” function. Users can specify the following simplifications: 1) "Make prices equal for all years". This option applies to the maximum price only (the relative price is not dependent on year). It takes price for the first year and assigns that value to all the later years. If that option is selected then the content of the cells for years after first year becomes irrelevant. 2) "Make prices equal for all time periods". This applies to the relative price only (the maximum price is not dependent on period). It takes price for the first period and assigns that value to all the later periods. If that option is selected then the content of the cells for periods after first period becomes irrelevant 3) "Make prices equal for all fleets". This option applies to the relative price and the maximum price. It takes price for the fleet first year and assigns that value to all the later periods. Note that between (year, period) variation is maintained. If that option is selected then the content of the cells for fleets after the first fleet become irrelevant 4) "Apply common factor to all prices". This option lets you read a common price multiplier, X, by an 'input-box'. The multiplier is then applied to the maximum price. Economic Input There are three economic models in the July, 2007 version of TEMAS, reflecting the views of three groups of stakeholders: • Financial analysis of fleets, from the point of view of vessel owners. • Government budget, the impact of the fleets on the government coffers. • Economic analysis, the economic performance of the economy as a whole. Survey of existing bioeconomic models 276 Appendix Table 69. Tables in the economic section of TEMAS Index EXCEL Table Caption 151 Table 7.1. Rate Of Discount 152 Table 7.2.1.1.1. Baltistan: Ob Trawler-Baltistan - Small Costs 153 Table 7.2.1.1.2. Baltistan: Ob Trawler-Baltistan - Medium Costs 154 Table 7.2.1.1.3. Baltistan: Ob Trawler-Baltistan - Large Costs 155 Table 7.2.1.2.1. Baltistan: Gillnett-Baltistan - Small Costs 156 Table 7.2.1.2.2. Baltistan: Gillnett-Baltistan - Medium Costs 157 Table 7.2.1.2.3. Baltistan: Gillnett-Baltistan - Large Costs 158 Table 7.2.2.1.1. Scandinavia: Ob Trawler-Scandinavia - Small Costs 159 Table 7.2.2.1.2. Scandinavia: Ob Trawler-Scandinavia - Medium Costs 160 Table 7.2.2.1.3. Scandinavia: Ob Trawler-Scandinavia - Large Costs 161 Table 7.2.2.2.1. Scandinavia: Gillnett-Scandinavia - Small Costs 162 Table 7.2.2.2.2. Scandinavia: Gillnett-Scandinavia - Medium Costs 163 Table 7.2.2.2.3. Scandinavia: Gillnett-Scandinavia - Large Costs 164 Table 7.2.2.2.3.1. Revenue From Other Species The Visual Basic code of the TEMAS program for the economy has been constructed so as to provide flexibility. Meaning, that the economic model can be modified, extended or reduced, should a special application require it. It is a relatively simple thing for the programmer to change the number of economic models. All three models operate with the same concepts for costs, earnings and investments, but with the possibility of specifying different parameters for each model. The economic model calculates the cash flow (revenues – costs) for each time period and it eventually computes the net present value over the time horizon simulated. The economic model was designed by Mr. Rolf Willmann, of the fisheries department of FAO, Rome (Sparre and Willmann 1993). The key performance measures of project analysis are the net present value (NPV), equal to the discounted net cash flow. The NPV is defined: y last Value NPV(r)= y ∑ y− y first y=y first (1+r) where “r” is a user defined input parameter, the “discount rate”. Table 7.1 allows the discount rate per country to be specified. Table 7.2.1.1.1 is composed of three subsections. Survey of existing bioeconomic models Appendix 277 Economic model A, Finance: including operating costs, crew salary, handling costs, sales costs, crew share, license and insurance fees, other fixed costs and investments per new vessels. Financial investment cost in harvesting capacity. Economic model B, Government treasury: includes taxes and subsidies on operating costs, revenue tax rates, license fees and decommission costs per crew and vessel. Economic model C, Economic model (for the Society): includes cost per weight, crew salary, salary per unit effort, operating costs of harvesting, landing costs, opportunity cost rate and the cost of one new vessel. Table 7.2.2.2.3.1 shows the “Revenue from other stocks”. The revenue from other stocks is a lump sum accounting for the revenue generated by landings of species which are not modeled explicitly in TEMAS. The revenue from other stocks is a (year, period, fleet, vessel size, rigging, country, area)-specific constant. The simplifying helper function includes the possibility to: • Make all parameters equal for all years. This option will take the values for first year and apply it to all years. • Multiply all costs with a common multiplier. This option will present a form where in which a common factor can be entered and then all costs will be multiplied by that factor. Trip rules input: TEMAS includes the possibility to conduct RUM analyses of the short-term behaviour of fishers. The model is mathematically the same as that for long term behaviour. TEMAS uses the logit form of RUM. There are four trip related behaviour models in the July, 2007 version of the TEMAS model: • Model for fishing/not fishing (Effort rule) • Model for choice of area (fishing grounds) • Model for choice of rigging • Model for discarding Table 70.Tables in the trip rules section of TEMAS Index EXCEL Table Caption 165 Table 8.1. Names Of Trip Behaviour Rules And Choices 166 Table 8.2. Baltistan: Trip Behaviour Coefficients Of RUM. 167 Table 8.3. Scandinavia: Trip Behaviour Coefficients Of RUM. Survey of existing bioeconomic models 278 Appendix Table 8.1 allows the names of the behavioural rules and choices to be specified. Table 8.2 contains the trip characteristics (areas, gear, etc.) and attributes used to perform RUM analyses. In relationship to the RUM model, there are two types of independent variables that can be estimated as can be specified in the following table: Two types of independent variables to model: Table 71. Independent variables in TEMAS Independent variable Features of variable Characteristics Dependent of choice-maker Independent of choice Attributes Independent of choice-maker Dependent of choice Tuning Inputs: Table 72. Tables in the tuning section of TEMAS Index EXCEL Table Caption 171 Table 10.1.1. West Cod: Tuning Fishing Mortality And Biomass Index 172 Table 10.1.2. West Cod: Index Of Abundance (Numbers At Age) 173 Table 10.2.1. East Cod: Tuning Fishing Mortality And Biomass Index 174 Table 10.2.2. East Cod: Index Of Abundance (Numbers At Age) TEMAS allows tuning, meaning the processes of finding the “reference simulation” of TEMAS. The reference simulation is the situation (scenario) relative to which all the other simulations are made and compared when addressing What-if-then-questions. Figure 29. Tuning helper functions. The option "Make Tuning F’s equal for all areas" takes the tuning F of first area, and assigns that value to all other areas. The option "Make Tuning F’s equal for all time periods" takes the tuning F of first time period, and assigns that value to all other periods. Survey of existing bioeconomic models Appendix 279 Observations Input The “observation” in question is the “Total landings” by stock, fleet, area, year and time period. Total landings means landings in units of (whole body wet) weight summed over age groups. This kind of data is often available from the annual statistics of fisheries. Such data may be used to tune TEMAS, that is, to modify selected parameters of TEMAS so that TEMAS is able to reproduce the observed landings as output from the simulation. Table 73.Tables in the observation input section of TEMAS Index EXCEL Table Caption 175 Table 11.1.1. Baltistan - West Cod: Observed Landings 176 Table 11.1.2. Baltistan - East Cod: Observed Landings 177 Table 11.2.1. Scandinavia - West Cod: Observed Landings 178 Table 11.2.2. Scandinavia - East Cod: Observed Landings Figure 30, helper functions for the tuning section Input of Technical Management Measures: These tables are not discussed in the user's manual; therefore, it's only possible to guess at the precise meaning of these parameters. Fortunately, the “Captions” appear to be self-explanatory. Table 74. Tables in the technical measures section of TEMAS Index EXCEL Table Caption 179 Table 14.1. Minimum Landing Size 180 Table 14.2.1.1. Baltistan - West Baltic Maximum Number Of Sea Days 181 Table 14.2.1.2. Baltistan - East Baltic Maximum Number Of Sea Days 182 Table 14.2.1.3. Baltistan - Not Baltic Maximum Number Of Sea Days 183 Table 14.2.1.4. Baltistan - Bornholm Maximum Number Of Sea Days 184 Table 14.2.1.5. Baltistan - Gotland Maximum Number Of Sea Days 185 Table 14.2.2.1. Scandinavia - West Baltic Maximum Number Of Sea Days 186 Table 14.2.2.2. Scandinavia - East Baltic Maximum Number Of Sea Days 187 Table 14.2.2.3. Scandinavia - Not Baltic Maximum Number Of Sea Days 188 Table 14.2.2.4. Scandinavia - Bornholm Maximum Number Of Sea Days 189 Table 14.2.2.5. Scandinavia - Gotland Maximum Number Of Sea Days Survey of existing bioeconomic models 280 Appendix Figure 31. Helper function for technical measures Input of Harvest Control Rules Just as above, these tables are not discussed in the user's manual; therefore it's only possible to guess at the precise meaning of these parameters. Once again, the “Caption” column appears to make the input self-explanatory. Table 75. Tables in the harvest control section of TEMAS Index EXCEL Table Caption 190 Table 15.1. Harvest Control Rules Of Precautionary Approach 191 Table 15.2.1.1. Relative Stability (Harvest Control Rules) - West Cod - West Baltic 192 Table 15.2.1.2. Relative Stability (Harvest Control Rules) - West Cod - East Baltic 193 Table 15.2.1.3. Relative Stability (Harvest Control Rules) - West Cod - Not Baltic 194 Table 15.2.1.4. Relative Stability (Harvest Control Rules) - West Cod - Bornholm 195 Table 15.2.1.5. Relative Stability (Harvest Control Rules) - West Cod - Gotland 196 Table 15.2.2.1. Relative Stability (Harvest Control Rules) - East Cod - West Baltic 197 Table 15.2.2.2. Relative Stability (Harvest Control Rules) - East Cod - East Baltic 198 Table 15.2.2.3. Relative Stability (Harvest Control Rules) - East Cod - Not Baltic 199 Table 15.2.2.4. Relative Stability (Harvest Control Rules) - East Cod - Bornholm 200 Table 15.2.2.5. Relative Stability (Harvest Control Rules) - East Cod - Gotland 201 Table 15.3.1.5. Relative Stability (Harvest Control Rules, Summed Over Areas) - West Cod 202 Table 15.3.2.5. Relative Stability (Harvest Control Rules, Summed Over Areas) - East Cod 203 Table 15.4.1.5. Relative Stability (Harvest Control Rules, By Country) - West Cod 204 Table 15.4.2.5. Relative Stability (Harvest Control Rules, By Country) - East Cod Survey of existing bioeconomic models Appendix 281 A.13 Specific description: SRRMCF A.13.1 Full specification of model equations variables and equations The model optimizes the value of catch, fishing effort and number of vessels that gives the maximal resource rent subject to a set of constraints and the given set of values for the exogenous variables. Due to the fact that the model is undergoing major changes and has not yet been completed and tested, model equations, variables and parameters cannot be presented. The main variables and the restrictions in the 2005-model are presented below to facilitate the perception of the model structure. Main variables and their type: • Prices: Exogenous. • Costs: Exogenous. • Employment per vessel: Exogenous. • Capacity per vessel: Exogenous. • Fishing type (catch composition): Exogenous. • Sea days: Endogenous. • Number of vessels: Endogenous. • Total employment: Endogenous. • Catch: Endogenous. Restrictions included: • Quotas. • Seasonal fishing for certain species. • Number of sea days: Max. pr. year or month. • Number of vessels: Unrestricted or Max/Min. • Market restrictions if appropriate. • Fishing stops for certain species. • Catch composition: Max. and Min. ratio. • Seal predation effects. Survey of existing bioeconomic models 282 Appendix A.14 Specific description: ISIS-FISH It is based on three sub-models, namely a population dynamics model, a model for fishing activity and a model for management measures. Each sub-model is spatially and seasonally explicit. Figure 32. Isis-Fish conceptual diagram. The fishery takes place in a region defined by its contour and a regular grid. The spatial resolution of the grid in latitude and longitude is chosen with respect to the dynamics being described, and depending on the precision of available information. Within the region, zones (i.e. sets of contiguous grid cells) are defined independently for each population, each fishing activity and each management measure. The model has a monthly time step. Seasons (i.e. sets of successive months) are also defined independently for each population, each fishing activity and each management measure. Within each zone and season, relevant variables such as fishing effort for a specific activity or abundance of a given population, are assumed to be homogeneous and uniformly distributed. A.15 References used in the appendix Bjørndal T, Conrad J (1987) Capital dynamics in the North Sea herring fishery. Marine Resource Economics 4: 63-74. Conrad JM, Clark C (1994) Natural Resource Economics. Cambridge University Press, CambridgeUK. Danielsson A, Stefansson G, Baldursson FM, Thorarinsson K (1997) Utilization of the Icelandic cod stock in a multispecies context. Marine Resource Economics 12: 329-344. Survey of existing bioeconomic models Appendix 283 Eide A, Skjold F, Olsen F, Flåten O (2003) Harvest functions: the Norwegian bottom trawl cod fisheries. Marine Resource Economics 18: 81-94. Frost H, Levring JA, Hoff, A. and Thøgersen, T (2009) The EIAA model: Methodology, definitions and model outline. Garza-Gil MD, Varela-Lafuente M, Suris-Regueiro JC (2003) European hake fishery bioeconomic management (southern stock) applying en effort tax. Fisheries Research 60: 199- 206. Mardle S, Pascoe S, Tamiz M, Jones D (2000) Resource allocation in the North Sea demersal fisheries: a goal programming approach. Annals of Operations Research 94: 321-342. Mardle SP, S. (2002) Trade-offs between long run and short run objectives in the North Sea. Journal of Environmental Management 65: 49-62. Pascoe SM, S; Steen, F; Asche, S. (1999) Interactions between farmed salmon and the North Sea demersal fisheries: a bioeconomic analysis. CEMARE Research Paper 144. SEC (2006) Report of the Joint SGECA - SGRST sub-group meeting on bioeconomic modelling. Ispra 4-6 October 2005 and 7 – 9 March 2006. Commission Staff Working Paper, Brussels 2006. Sparre PJ, Willmann R (1993) Software for bio-economic analysis of fisheries. BEAM 4. Analytical bio-economic simulation of space-structured multi-species and multi-fleet fisheries. Volume 1: Description of model. User’s manual. FAO, 3, Rome. Survey of existing bioeconomic models