“Comparing methods for standardizing commercial fishery catch rates of Northeast US Atlantic cod (Gadhus morhua)”

By: Luca McGinnis

Advisor
Gavin Fay (UMass Dartmouth)

Committee Members
Steven X. Cadrin (UMass Dartmouth) and Alex Hansell (NOAA Northeast Fisheries Science Center)

Monday December 8, 2025
3:00 PM
SMAST East 101-103
836 S. Rodney French Blvd, New Bedford
and via Zoom

Abstract:

Indices of abundance based on fishery independent survey data are preferred in stock assessments compared to fishery catch rates because they are designed to be representative of stock trends. However, fishermen collect data at a higher spatiotemporal resolution which, when standardized to remove effects of variables other than abundance, can potentially supplement information from a survey index. Updated understanding of the stock structure of Atlantic cod (Gadus morhua) supports shifting from two assessed management units to five populations. This change requires fine scale data, especially for populations with small sample size in the NEFSC Bottom Trawl Survey. We compare methods for producing indices of abundance for cod from vessel logbook data. First, data were divided into spatial units at the management level and the population structure. Generalized linear models (GLMs) were fit with covariates including combinations of year, month, depth, vessel horsepower, vessel tonnage, mesh size, and statistical area. An optimal model for each spatial unit was selected, and indices were produced from the transformed year effects. Trends in abundance differed between populations within the management units. Model fits were better for population units than management units, but predictive performance was poor and uncertainty was high for populations with low sample sizes. Next, whole-area models were tested to see if sharing information about covariate relationships could decrease uncertainty for data-poor stocks. The resulting indices produced for each population area were similar to those from the individual GLMs. Although uncertainty was reduced in some areas in some years, there was not enough improvement to justify the more complex modeling approach. Ultimately the coarse spatial resolution and distribution of datapoints among stocks were limiting factors in this analysis. Future work should explore other fishery-dependent datasets and leverage fishermen’s ecological knowledge to improve the application of the data they collect.

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https://umassd.zoom.us/j/93617858150

Meeting ID: 936 1785 8150

Passcode: 029892