Spatial Analysis of Bering Sea Groundfish Survey Data Using Generalized Additive Models

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Abstract
Generalized additive models (GAM) are herein applied to trawl survey data in the eastern Bering Sea with an eye to (1) detecting trends in groundfish distributions and (2) improving abundance estimates by including the trend. GAM is a statistical method, analogous to regression, but without the assumptions of normality or linearity that relate a response variable (in this case, fish abundance) to location (latitude and longitude) and associated environmental variables (e.g. depth and bottom temperature). GAM provided reasonable (i.e. high r2) fits to the spatial distribution of five flatfish species and was able to define a spatial "signature" for each species, namely their preferred depth and temperature range. GAM also gave lower average abundance and abundance variability estimates for these five flatfish species than the stratified sampling procedure previously employed.

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