The implementation of the Common Fisheries Policy always causes a lot of reactions from the member states. That is why the European Commission wants to avoid discussion about the quality of the data and estimated parameters collected for stock assessment purposes. This study will develop a common “open source” tool for quality evaluation of these data and parameters within the framework of Data Collection Regulation.
Why this study?
The systematic collection of reliable basic data on fisheries is a cornerstone to fish stock assessment and scientific advise, and consequently for the implementation of the Common Fisheries Policy. In 2001, the Commission introduced a framework for the collection and management of such data as part of an integrated programme (DCR). In recent years quality issues of data collected through DCR have been identified. The Commission, responding to the need for reliable methods for data quality evaluation, initiated this study to produce a common tool for accessing the quality of data collected in the framework of DCR.
The principal objective is to develop a common “open source” tool (COST) for assessing the accuracy of data collected for stock assessment purposes within the framework of the DCR. This tool is intended to assess these data with reliable, comprehensive and internationally agreed methods.
The common “open source” tool-box will consist of different packages that will develop validated methods to assess discards, length and age structure of catches and landings, and biological parameters such as growth, maturity and sex-ratio. The estimates will have to be calculated according to one out of a fixed number of agreed procedures. A close cooperation with ICES is necessary in order to facilitate the use of the tool-box by the stock assessment Working Groups. The packages should include data administration, exploratory data analysis, parameter estimation and associated precision, and simulations. A common format of datasets should be adopted, and based on that format, appropriate exploratory analysis will be proposed, and algorithms and software will be developed to estimate statistical properties, to account for missing data and for external errors, and to enable the calculation of the number of samples and the number of individuals to achieve a targeted precision.