Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

A global sensitivity analysis was conducted on the algorithm implemented in the Echoview w software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.