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.
Highlights
A fisheries management system usually includes many components (Fig. 1)
When the normal method” (NM) was applied to the adjusted catchCPUE data, the derived posterior distributions were right skewed for all parameters
Bcur is current stock biomass; Ratio is calculated as Bcur/K, indicating stock depletion level; and MSY is maximum sustainable yield
Summary
Large errors in any of these components may result in mis-management of a fisheries stock, resulting in either over-exploitation of fisheries resources or unnecessary economic loss or social hardship for the coastal communities that depend upon fisheries (Hilborn and Walters, 1992; Walters, 1998). The impacts of errors occurring in some components of the management system (Fig. 1) have been evaluated in many studies (Hilborn and Walters, 1992; McAllister and Kirkwood, 1998; NRC, 1997, 1999). Of particular interest in this study, are the impacts of measurement errors in fisheries data on stock assessment and fisheries management. In some cases, errors associated with fisheries data can be non-random and biased. An example of this is catch statistics in a quota-managed fisheries system. Fishermen may try to maximise their profits for a given quota by high grading, a practice of discarding less valuable or desirable catch (usually small fish) while keeping more valuable or desirable catch (usually large fish)
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