Quick link to: EFIMAS home, WP4 General

Specific tasks in relation to WP4

3.1 Produce an overview of management systems and instruments (from the EFIMAS WP2 Knowledge Basis Book) that are applicable to the selected case studies. Describe the purpose of the management measures and their “observed” effects.

3.2 For each case study, identify alternative management systems and instruments which have not yet been implemented, but are deemed likely to emerge. For example:

		TAC (single- and multi-species based)
		Effort and capacity (fisheries based; single- and multi-fleet based)
		EcoQO (ecological quality objectives)
		Real time management (e.g. research survey based)
		Multiannual aspects
		Technical measures
		Spatial/temporal closures
		Economic measures (price intervention, tax, licenses, MAG, subsidies, credit 		schemes, decommission)	

3.3 Define scientific based objectives and hypotheses about the case study systems. This is done in relation to specific management procedures which includes methods for monitoring and assessing the status of the system.

3.4 Select appropriate descriptive models and analytical tools to address the objectives and hypotheses. This includes:

• production of an overview (annotated list) of relevant descriptive fisheries / stock assessment models and analytical tools (mainly from WP2)

• supplying the relevant software packages with descriptions of purpose, properties and requirements.

• criteria for selecting the descriptive models and analytical tools

• identification of key components (parameters and relationships).

Different types of descriptive models and analytical tools that could be considered in the evaluations are for example:

• VPA (XSA, length based assessments, etc.)

• (MSVPA (multi-species VPA))

• Mixed fisheries models (TEMAS, ISIS-Fish, Flatfish Model, North Sea Roundfish Model, etc.)

• Bio-economic models (EIAA, TEMAS, MOSES, MEPHISTO, BEMMFISH, AHF, etc.)

Box 1. Details and examples of the bioeconomic model development stages included

Determination of critical management issues addressed in the case studies and the descriptive models and methods by which they are simulated.

While the case studies in general are based on a common generic structure, the case specific evaluations by the framework and the descriptive models need to capture the particular features relevant to the policy options simulated in the operating model. For example, simulation of changes in technical measures (e.g. selectivity parameters) requires catchability by age/size class for the species affected. The measure can be simulated by changing the catchability coefficients to reflect catch size compositions with the new gear. Similarly, seasonal and/or area closures require a temporal and/or spatial elements to be developed in the descriptive models. However, such a feature is not necessary if these management options are not considered relevant to the fishery.

Assumptions are also necessary concerning fisher behaviour in response to the management changes. Fishers change their behaviour in response to the management measures and management system, and this needs to be factored into the analysis. For example, a TAC may result in over-quota catch or diversion of effort onto other species (or a combination of both). This is important as most fish are caught in mixed fisheries where a quota on one species has influence on the catch of all other species caught together with the quota-species. In an effort regulation system the mixed fisheries problem is that there is a high risk of conflicting effort levels by species where some stocks will be in risk of being over- fished and some under-fished, as usually only one stock can be fished on the single species optimal level. These types of problems and assumptions in relation to evaluation through models as well as the necessary knowledge basis behind this was informed from the review undertaken in WP2, as well as obtained through the continuous, iterative and cyclic feedback process between WP4, WP3 and WP5 (including regional stakeholder workshops). In this example the case study descriptive models need to allow for these (assumed) behavioural changes to be simulated.

Derivation of the biological relationships for inclusion in the case specific descriptive models

The key biological relationships incorporated into the descriptive models include stock numbers, stock-recruitment relationships, growth and weight at age, natural mortality and fishing mortality. A number of other key measures is also required, such as biomass and spawning stock biomass, which are derived from the models (e.g. as a function of stock numbers at age and weight at age). In addition, key reference points is also provided (e.g. Bpa or Blim).

Most of these parameters were estimated as part of previous stock assessments. However, not all of the information was available for all the key stocks (and fisheries), or some of the information were highly uncertain. Where the critical parameters were not available, assumed values were derived based on existing information relating to similar species and stocks (and fisheries) elsewhere, or ‘best guess’ estimates based on expert knowledge within the European scientific community (partly included in the continuous iterative and cyclic feedback system). It is possible that such values are elicited through subjective methods or empirically rigorous methods for operating model parameterisation applied where methods and data were available, i.e. for example where the uncertain parameter is related to ‘known’ parameters for other species and stocks. If that is not the case then approaches that apply expert judgment were applied as a last resort. While some of these techniques were not ‘state-of- art’ in the purest sense, such methods have been recently reviewed as useful tools for derivation of uncertain or unknown parameters in bio-economic models. Generally, operating models does in some cases incorporate the best available knowledge about the underlying population dynamics. Therefore, empirically rigorous methods for operating model parameterisation are for example in some cases applied where methods and data are available. Where existing information relating to similar species and stocks were applied, statistical analysis methods were utilized to identify plausible parameter values. In this overall context the attempt was that state of the art methodology were applied.

Derivation of key economic relationships

Some key economic relationships included in the models are costs of fishing and prices received. Key costs incorporated into the models are variable costs (e.g. fuel, oil, etc.) that are a function of the level of effort and boat characteristics (e.g. size); and fixed costs (including capital costs). The fixed costs are costs that are incurred regardless of the level of fishing effort (e.g. onshore running costs, depreciation, administration, etc.). Fishing costs are derived from economic surveys that have been previously conducted where possible. Where data are not readily available, assumptions on cost structures based on similar types of boats in nearby or similar fisheries are used. As with the uncertain biological parameters, other (alternative) methods and techniques are applied where data are available for doing that to derive reasonable estimates of the costs. If data and methods are not available then approaches that apply expert judgment are applied as a last resort here as well, i.e. judgment based on expert opinion.

The prices received are, where appropriate, modelled in relation to total landings (i.e. a price dependent demand curve is specified in the model). This allow prices to vary with the level of output produced – critical when assessing stock recovery programmes or measures that restrict the level of output. Studies of price formation have been undertaken in a wide range of European fisheries for a wide range of species. These existing models have been used as the basis for the analyses, or, where an appropriate time series of data are available on prices and quantities landed, fishery specific models were developed. Price by size can also be examined, and where relevant, incorporated into the models (the models can be adapted to allow for variable price by size, although in many cases the different size classes may have the same unit price).

Effort dynamics are also built into the models. Fishers respond to changing conditions in the fishery through adjusting their own effort level, or diverting effort to alternative activities. Effort levels are linked to catch rates, prices and costs (i.e. the level of profitability in the activity). Other assumptions regarding fisher behaviour were applied as well.

Other important relationships and information that are needed and incorporated into the models are the fleet structure (size, gear type etc), efficiency change and capacity utilisation (related also to the effort dynamics model above). Changes in fleet size and structure affect the average efficiency of the fleet, thereby altering the relationship between nominal effort (i.e. observable effort such as days fished) and catch. Assumptions regarding efficiency change are developed based on studies of efficiency distribution in selected EU fisheries. Similarly, changes in fleet structure affect the level of capacity utilisation, which again changes the relationship between the level of fixed inputs and outputs (i.e. catch).

As the models are dynamic (variable by time), an allowance is required for the effects of technical change on efficiency. A number of studies have been undertaken on the rate of technical change and the effects on efficiency in European fisheries, although no consistent patterns have been identified. For example, the results of the EU funded project QLK5-CT1999-01295 (TEMEC) demonstrated varying trends in efficiency change over time. Also the EU FP5 funded TECTAC project examined this issue in detail and helped in informing this work package (WP4).

Combination of key biological and economical relationships

The different components of the descriptive models that have been used and developed separately have been combined. This has required high-level and extensive co-ordination between the biologists and economists involved in each case study and WP3-WP4. The general framework for combining the components has been established. This has among other been done with help from WP2 and mainly through the cooperation between WP3 and WP4 (in the cyclic cooperation and feed-back system).

3.5 Make existing national and international (e.g. ICES and STECF data) case specific data available according to needs. Which data are central in order to make high quality parameterisation, modelling and analysis for the different case studies? This included discussion and by data processing enhancement of the data quality. Appropriate choices in relation to specific use of data in relation to the quality and aggregation in data has been made and described (discussed) by case study, as well as appropriate choices in relation to data processing.

3.6 Perform scientific analyses of the current system. Apply descriptive models and analytical tools to provide a base-line description of the system and testing main hypotheses about the system in relation to the management evaluation framework. This has involved further development, modification or re-organization of existing descriptive models and analytical tools as well as con-current input to development of the generic evaluation framework in relation to this.

3.7 Develop operating models that describe the dynamics of the system. The scientific analysis hasve been used as the basis for the operating model of the evaluation framework (e.g. to parameterize the model and to identify sensitive parameters as well as identify central and necessary components of the evaluation framework).

3.8 Implement the operating model supported by the output from the analyses with the descriptive models and analytical tools in a full-feedback simulation model. This has been done in a cyclic feedback process between WP4 and WP3 and WP5 (including feedback from the regional stakeholder workshops).

3.9 Run simulation trials. If simulation trials are not feasibly due to lack of data, carry out qualitative evaluations. Also this has been done on an extensive basis.

3.10 Evaluation of the performance of the candidate management options. Recommendations on further research and proposals of alternative management options were developed in a cyclic feedback with WP3-5 (and regional stakeholder workshops).

3.11 Integrate evaluation of uncertainties in the dynamics and in the data collection, assessment, and advisory processes (under different management systems as well as examples of the ICES advisory process) has been performed.

J. Rasmus Nielsen 2007/01/31 16:26

efimas1/wp4/general/specwp4tasks.txt · Last modified: 2008/11/14 00:04 by admin
Except where otherwise noted, content on this wiki is licensed under the following license:CC Attribution-Noncommercial-Share Alike 3.0 Unported
Recent changes RSS feed Donate Powered by PHP Valid XHTML 1.0 Valid CSS Driven by DokuWiki