CONFIRMED: DFO PhD Dissertation Defense presented by Cole Carrano
Department of Fisheries Oceanography "Modeling Index Selectivity for Fishery Stock Assessments" By: Cole Carrano Advisor Steven X. Cadrin (University of Massachusetts Dartmouth) Committee Members Pingguo He (University of Massachusetts Dartmouth), Gavin Fay (University of Massachusetts Dartmouth), Lisa Kerr (University of Maine) Monday January 6th, 2025 10:00 AM SMAST East 101-103 836 S. Rodney French Blvd, New Bedford and via Zoom Abstract: Abundance indices are crucial components of fishery stock assessments because they provide a time series of relative abundance for estimating absolute stock size, derived from the response of relative indices to the absolute magnitude of fishery removals. Selectivity is the relative vulnerability to a fishery or fishery-independent survey for each species or demographic group within a species (e.g., size or age class). In an age-based assessment model, selectivity parameters are needed to relate observed stock indices to model estimates of abundance at age. Thus, selectivity estimates must be carefully modeled to ensure an accurate depiction of the stock's age structure. The objectives of this research are to improve the accuracy and utilization of indices in fisheries stock assessment models by understanding the effect of alternative approaches to estimating index selectivity. Chapter One provides a general introduction to the topic and a review of the relevant literature. Chapter Two involves splitting a fishery-independent survey into two series to account for vessel and methodological changes by estimating distinct catchability and selectivity parameters for each series. Results indicated improvement in model performance for stocks with sufficient contrast in the new index, and no improvement for stocks with limited years of data or contrast in the recent indices. Chapter Three develops fleet-structured assessment models to improve selectivity estimates for fishery and the fishery-dependent indices. Splitting catch into fleets improves selectivity estimates for respective CPUE indices, but robust catch-at-age data is desirable for fleets that make up a large portion of the total catch. Chapter Four involves simulation cross-testing as a method to evaluate performance of assessments that assume a single index series that is calibrated for changes in survey technology vs. assuming separate indices in stock assessment models. Results from this chapter suggest that the consequences of assuming a split when there truly wasn't one were not severe, but that assuming there wasn't a split when there truly was one can produce significant biases in model results This work examines how decisions about modeling fleet structure or changes in survey systems affect the performance of an assessment model and how sensitive models are to these decisions. This research will emphasize the importance of selectivity estimates to stock assessment and advance our understanding of how to effectively utilize abundance indices in an assessment model. ************ Join Zoom Meeting https://umassd.zoom.us/j/94890073016 Note: Meeting passcode required, email contact below to receive ************** To request the Zoom passcode or for any other questions, please email Callie Rumbut at c.rumbut@umassd.edu
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