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Quality of fisheries data and uncertainty in stock assessment Chen, Y. (2003). Quality of fisheries data and uncertainty in stock assessment. Sci. Mar. (Barc.) 67(S1): 75-87. https://dx.doi.org/10.3989/scimar.2003.67s175 In: Scientia Marina (Barcelona). Consejo Superior de Investigaciones Científicas. Institut de Ciènces del Mar: Barcelona. ISSN 0214-8358; e-ISSN 1886-8134, meer Ook verschenen in: Ulltang, Ø.; Blom, G. (2003). Fish stock assessments and predictions: integrating relevant knowledge. SAP Symposium held in Bergen, Norway 4-6 December 2000. Scientia Marina (Barcelona), 67(S1). Institut de Ciències de Mar: Barcelona. 374 pp. https://dx.doi.org/10.3989/scimar.2003.67s1, meer

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Beschikbaar in	Auteur
VLIZ: Open access 67467 [ download pdf ]

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Trefwoorden

Characteristics > Uncertainty Collections > Data collections Data integrity > Data quality Stock assessment Uncertainty Uncertainty Marien/Kust

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Auteur		Top
Chen, Y.

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Abstract

The quality of fisheries data has great impacts on the quality of stock assessment, and thus fisheries management. In this paper, using a case study I evaluate the impacts of two types of error, biased error and atypical error, that can negatively affect the quality of fisheries data in stock assessment. These errors are commonly associated with fisheries data, and assumptions on their sources and statistical properties can have great impacts on the outcome of stock assessment. Although the sources and statistical properties of these errors differed, both of them could result in errors in stock assessment if estimation methods are not appropriate. Different statistical approaches used in fitting models differ in their robustness with respect to errors of different statistical properties in data. This study showed the importance of evaluating the quality of input data and the possibility of developing an approach that is robust to errors in data. Considering the likelihood of fisheries data being affected by errors of different statistical properties, I suggest that the robustness of a stock assessment be evaluated with respect to data quality.

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