Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

Use of endoparasitic helminths as tags in delineating stocks of American plaice (Hippoglossoides platessoides) from the southern Gulf of St. Lawrence and Cape Breton Shelf

Endoparasitic helminths were inventoried in 483 American plaice (Hippoglossoides platessoides) collected from the southern Gulf of St. Lawrence, NAFO (North Atlantic Fisheries Organization) division 4T, and Cape Breton Shelf (NAFO subdivision 4Vn) in September 2004 and May 2003, respectively. Forward stepwise discriminant function analysis (DFA) of the 4T samples indicated that abundances of the acanthocephalans Echinorhynchus gadi and Corynosoma strumosum were significant in the classification of plaice to western or eastern 4T. Cross validation yielded a correct classification rate of 79% overall, thereby supporting the findings of earlier mark-recapture studies which have indicated that 4T plaice comprise two discrete stocks: a western and an eastern stock. Further analyses including 4Vn samples, however, indicated that endoparasitic helminths may have little value as tags in the classification of plaice overwintering in Laurentian Channel waters of the Cabot Strait and Cape Breton Shelf, where mixing of 4T and 4Vn fish may occur.