A spatiotemporal model for combining survey and fishery data


Sustainable fisheries management requires the best stock assessments available in order to determine the current state of a stock and its trend over time. These estimates are typically based on fishery-independent surveys conducted specifically to address this need. However, there is potential to improve abundance estimates by combining fishery-independent and fishery-dependent data. Additionally, tracking changes in the spatial distribution of both abundance and fishing effort are important aspects of a stock assessment. Now that spatially-explicit catch and effort data is increasingly available, it seems natural to combine these data sources in a spatiotemporal model. The results of a simulation study that looks at the abundance estimate improvements that can be expected in such a model will be presented, as well as an application to fishery data. Including fishery data in stock assessments has the potential to improve abundance estimates and better inform the managers that use these estimates.

University of St. Andrews, St. Andrews, Scotland