Assessment of environmental correlates with the distribution of fish stocks using a spatially explicit model

In this paper, we present a method for assessing the explanatory skill of environmental correlates with the distributions of commercial fish stocks using a simple analytical/numerical, spatially explicit model. We examined three environmental variables, temperature, bottom sediment type, and bottom depth, which have been shown by previous investigators to be environmental correlates of two species of groundfish, Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus), over Georges Bank, northwest Atlantic Ocean. Comparisons between modeled and observed distributions showed that bottom temperature alone accounts for between 0% and 35% of the total variance in monthly averaged distributions of both species. A smaller amount of the observed variance, 0–20%, is explained by bottom sediment type and bottom depth. As a benchmark, smoothed monthly maps computed by optimal interpolation (OI) of the data explained 15–75% of the observed variance. The model also showed that these same variables account for a smaller percent of the monthly catch variance observed in individual years. This suggests that while the environmental correlates examined can explain some of the variance in the observed distributions, historical monthly distributions are a better predictor of mean monthly distributions as well as monthly distributions within a given year.