Developing a new spatio-temporal framework for assessment of the Nova Scotia Inshore Sea Scallop (Placopecten magellanicus) Fishery

Abstract

The current stock assessment model model used to assess areas of the Nova Scotia Inshore Sea Scallop (Placopecten Magellanicus) Fishery is a delay-difference model. However, this model has been found to be unable to reliably model the Scallop Production Area 3. The focus of this work is to incorporate spatial information into this assessment model in two steps. The first step involves reconsidering abundance indices to reduce the amount of necessary pre-processing and directly model all intra-annual variability, while simultaneously accounting for the large number of zeroes. The second step involves explicitly modeling the location of survey tows and commercial fishing by modifying the error structure of the model by using Gaussian Markov Random Fields such that a spatio-temporal model results.

The new framework for abundance indices is shown to better capture population changes and can be viewed as a hybrid between a traditional temporal model and a spatio-temporal version. The full spatio-temporal stock assessment framework is further able to capture both local population changes reliably and population trends for the entire area of interest. This novel framework shows promise to improve the reliability of scientific advice given to fisheries managers while opening up new possibilities for spatial management.