Acoustic Classification of Juvenile Pacific Salmon (Oncorhynchus spp) and Pacific Herring (Clupea pallasii) Schools Using Random Forests

Abstract

Acoustic surveys are the standard approach for evaluating many fish stocks around the world. The analysis of such survey data requires the accurate echo-classification of target species. This classification is often challenging as many organisms exhibit overlapping characteristics in terms of shape, acoustic amplitude, and behavior. In this study, a random forest approach was used to distinguish juvenile Pacific salmon (Oncorhynchus spp) from Pacific herring (Clupea pallasii) aggregations using the acoustic and morphological characteristics of their echo traces. The acoustic data was collected with an autonomous, multi-frequency echosounder deployed on the seafloor in the Discovery Islands, British Columbia from May to September 2015. The model was able to differentiate juvenile Pacific salmon from Pacific herring with a 98% accuracy. School depth and school mean volume backscattering strength were the most important predictors in determining the school classification. This study supports other publications suggesting that random forests represent a promising approach to acoustic target classification in fisheries science.