Analysis of cod catch data from Icelandic groundfish surveys using generalized linear models

Abstract Catch data from the Icelandic groundfish surveys are analyzed using generalized linear models (GLM). The main goal is to test the effects of environmental variables on the expected cod catch and to distinguish between the gamma and log-normal distributions for the error structure. Only positive catch data are included in this work, i.e. only the positive part of a delta–gamma or delta–log-normal distribution is examined. The distributions are compared via a Kolmogorov–Smirnov goodness of fit test. Polynomials are used to describe the relationship between each environmental variable and the cod catch and their effects are tested within the GLM framework (a continuous model). Finally, an attempt is made to locate temperature fronts in the ocean by estimating the temperature gradient vector at each data point. The effect of the size of this gradient vector is then tested within in the GLM framework. A stratification model with only spatial and time effects explains 80% of the variation but that comes with a high cost of degrees of freedom. Most of the tested effects are found to be significant but the continuous model only captures 45% of the total variation. The size of the temperature gradient vector is found to be statistically significant though only a small portion of the variation in the data is explained by this term.