A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden. (June 2022)

• Record Type: Journal Article
• Authors: Giacalone, Giovanni; Barra, Marco; Bonanno, Angelo; Basilone, Gualtiero; Fontana, Ignazio; Calabrò, Monica; Genovese, Simona; Ferreri, Rosalia; Buscaino, Giuseppa; Mazzola, Salvatore; Noormets, Riko; Nuth, Christopher; Lo Bosco, Giosuè; Rizzo, Riccardo; Aronica, Salvatore
• Is Part Of: Environmental modelling & software. Volume 152(2022)
• Publication Date: 2022-06
• Subjects: Fish school -- Multi-beam -- K-means -- 3D pattern -- Cluster; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105401
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Svalbard is one of the most intensively studied marine regions in the Artic; here the composition and distribution of marine assemblages are changing under the effect of global change, and marine communities are monitored in order to understand the long-term effects on marine biodiversity. In the present work, acoustic data collected in the Kongsfjorden using multi-beam technology was analyzed to develop a methodology for identifying and classifying 3D acoustic patterns related to fish aggregations. In particular, morphological, energetic and depth features were taken into account to develop a multi-variate classification procedure allowing to discriminate fish species. The results obtained from clustering suggest that from a mathematical point of view three distinct groups could be identified. The proposed approach, that allows to discriminate the acoustic patterns identified in the water column, seems promising for improving the monitoring programs of the marine resources, also in view of the ongoing climate changes. Highlights: Implementation of a methodology to identify any 3D acoustic pattern present in the water column. For each 3D pattern that represents a fish school, the geometric, energetic and positional parameters were calculated. Unsupervised clustering ( k -means) allowed to hypothesize to divide the dataset into three different groups of fish schools. From a biological point of view, the division into three clusters may not identify three distinct species, but two. In the Arctic area, the effects of global warming are more evident with consequences on climate and biodiversity.