A Rockfish Life History Simulator to Evaluate the Impacts of Sparse Data on Stock Assessment Accuracy

A vital issue in fisheries is understanding the sources and consequences of uncertainty in stock assessments and how uncertainty affects the implementation of fisheries management policy. There are two main sources of uncertainty in fisheries stock assessments: 1) uncertainty about the basic biology of the target species and 2) missing, sparse, and/or imperfect data. In the face of sparse data, fisheries biologists are often forced to make strong, simplifying assumptions about the biological processes of the fished species, yet we have little understanding of how these assumptions may affect the accuracy of stock assessments or which types of data will improve estimates of fisheries reference points. Here, I construct a life history simulator for exploited Pacific rockfish (Sebastes spp.) populations. The simulator models harvested rockfish populations under a variety of biological and fishery scenarios across a range of types and amounts of data that typify rockfish fisheries. I then apply a stock assessment model to quantify the error of population estimates under the different biological, fishery and data scenarios. Results show that the amount and types of data play a significant role in the accuracy of the stock assessment output, indicating that additional data is not necessarily beneficial if it is not appropriate for the assumptions inherent to the model and system. Additionally, the assumptions made during the stock assessment influence the accuracy of the results. In practice, this implies that utilizing simplified assumptions to account for deficient data can lead to poor estimates. For example, assuming length-independence on a rockfish species for characteristics such as natural mortality has significant impacts on model accuracy. This research provides feedback for where assessment models encounter problems or fail, leading towards improvements of assessment models especially in the case of data poor fisheries.