Classification trees for species identification of fish-school echotraces

Acoustic surveys provide valuable information on the abundance and distribution of many fish species, but are particularly effective for schooling pelagic fish of commercial importance. However, despite recent advances in multifrequency processing, the technique still requires some subjective judgement when allocating the acoustic data, fish-school echotraces, to particular species-the so-called "scrutiny process". This is assisted by "ground truth" trawling and operator experience of relating trawl data to echotraces of particular fish schools. In this paper, a method to identify species based on "classification trees" is applied to data from a component of the International North Sea Herring Acoustic Survey. Classification trees may be considered as a variant of decision trees, and have properties that are intuitive to biologists, because they are similar to the keys used for the biological identification of species. The method described here incorporates a multifrequency fish-school filter, image analysis to isolate fish-school echotraces, and finally, a classificationtree system using the multifrequency information from the ground-truthed echotraces that can be translated into a processing tool for objective species allocation. The classification-tree system is compared with the traditional method of expert-based scrutiny. Unlike the latter, however, a measure of uncertainty is attributed to the classification-tree approach and this could be propagated through the acoustic-survey estimation procedure as a component of the total uncertainty in the abundance estimate.