Improving area swept estimates from bottom trawl surveys

Estimation of area swept is a key component for standardizing catch per unit effort (CPUE) data from fishery independent bottom trawl surveys and survey trawl gear experiments. Given technological advances and the proliferation of data streams from net mensuration equipment and global positioning system (GPS), techniques for estimating survey effort can be improved. Here we investigate new analytical techniques for improving the accuracy and precision of survey effort estimation. Sources of error and bias associated with two of the components used to compute area swept as a measure of fishing effort, distance fished by the trawl and net spread, are systematically examined and their influence quantified using both simulated and survey data. New analytical methods, a cubic spline smoothing algorithm to smooth GPS and net spread data, a haversine great circle algorithm to calculate distance between smoothed GPS track points, and a sequential outlier rejection algorithm to diminish the influence of noise on mean net spread estimates are shown to reduce or even eliminate the influence of biased observations on area swept estimators.

► Sources of error and bias associated with area swept estimates, are investigated. ► New analytical methods to reduce errors and biases in effort estimation are proposed. ► Cubic spline algorithm is recommended for smoothing GPS and net spread data. ► Haversine great circle algorithm is recommended to calculate distance between smoothed GPS points. ► Sequential outlier rejection algorithm diminishes the influence of noise and bias on mean net spread estimates.