Automatic classification of acoustically detected krill aggregations: A case study from Southern Ocean. (May 2022) - Fontana, Ignazio; Barra, Marco; Bonanno, Angelo; Giacalone, Giovanni; Rizzo, Riccardo; Mangoni, Olga; Genovese, Simona; Basilone, Gualtiero; Ferreri, Rosalia; Mazzola, Salvatore; Lo Bosco, Giosué; Aronica, Salvatore
Abstract: Acoustic surveys represent the standard methodology to assess the spatial distribution and abundance of pelagic organisms characterized by aggregative behaviour. The species identification of acoustically observed aggregations is usually performed by taking into account the biological sampling and according to expert-based knowledge. The precision of survey estimates, such as total abundance and spatial distribution, strongly depends on the eﬃciency of acoustic and biological sampling as well as on the species identiﬁcation. In this context, the automatic identification of speciﬁc groups based on energetic and morphological features could improve the species identiﬁcation process, allowing to improve the precision of survey estimates or to overcome problems related to biases in biological sampling. In the present study, we test the use of well-known unsupervised clustering methods focusing on two important krill species namely Euphausia superba and Euphausia crystallorophias . In order to obtain a reference classiﬁcation, the observed echoes were ﬁrst classiﬁed according to speciﬁc criteria based on two parameters accounting for the acoustic response at 38 kHz and 120 kHz. Diﬀerent clustering methods combined with three distance metrics were then tested working on a wider set of parameters, accounting for the depth of insonified aggregation as well as for energetic and morphological features. The clustering performances were then evaluated by comparing the reference classiﬁcation to the one obtained by clustering. Obtained results showed that the k-means performs better than the considered hierarchical methods. Our ﬁndings also evidenced that working on a speciﬁc set of variables rather than on all available ones highly impact k-means performances. Highlights: Species identification is an important step to provide reliable information about marine organisms. Species identification is usually performed by looking at biological sampling and according to expert-based knowledge. Krill aggregations can be classified considering specific energetic features. Different clustering algorithms were used to identify the acoustically detected aggregations of two important krill species. K-means classification showed acceptable accordance with a regression methodology used for the same purpose.