Applying a novel state-space stock assessment framework using a fisheries-dependent index of fishing mortality

Abstract

State-space models have a hierarchical framework that assumes the observed data are derived from a time series of unobserved latent states. State-space stock assessment models have emerged as an alternative framework to conduct stock assessment in Canada, the east coast of the United States of America, and in Europe though little research has investigated where and how they are optimally used and if they would be appropriate in other geographical contexts. We built a novel state-space stock assessment model with process variation in recruitment and time- and age- specific catchability. We fit the model to commercial trap net and gill net catch and effort data of Lake Michigan lake whitefish using a marginal likelihood that integrated over latent states. Compared to the previously employed statistical catch at age model, the state-space model estimated dome-shaped, rather than asymptotic selectivity for both fisheries, 15% lower average total instantaneous mortality, and 20% higher average recruitment. To our knowledge this is the first application of a state-space stock assessment model fit by maximum likelihood in the Laurentian Great Lakes and the first such model to not include a fisheries-independent survey. These results demonstrate the feasibility of employing a maximum likelihood state-space framework in fisheries that lack such fishery independent indices of abundance and instead use catch per unit effort as an index of abundance. This work presents a novel approach to applying state-space stock assessment modeling and offers insights and suggestions for future use in the Great Lakes and in similar circumstances of data availability.