A state-space spatial survey-based stock assessment (SSURBA) model to inform spatial variation in relative stock trends

An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-...

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Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Authors: Kumar, Rajeev, Cadigan, Noel G., Zheng, Nan, Varkey, Divya A., Morgan, M. Joanne
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2020
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Online Access:http://dx.doi.org/10.1139/cjfas-2019-0427
http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2019-0427
http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2019-0427
Description
Summary:An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-at-age research vessel (RV) survey indices that are assumed to be proportional to abundance. We model index catchability (q) as a logistic function of fish length, which varies with age, cohort, and the time of the survey; therefore, the model facilitates the estimation of q values that change spatially and temporally following changes in fish growth and survey gears. The SSURBA model produces division-level estimates of fishing mortality rates (F), stock productivity, and stock size relative to the logistic catchability assumption with q = 1 for fully selected ages. The spatial model allows us to include additional survey information compared with the space-aggregated assessment model (all of 3LNO) that is currently used to assess stock status. The model can provide estimates of relative catch, which we compare with reported catch trends to partially validate the model.