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Applications and analytical approaches using imaging sonar for quantifying behavioural interactions among aquatic organisms and their environment

期刊

ICES JOURNAL OF MARINE SCIENCE
卷 -, 期 -, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/icesjms/fsad182

关键词

acoustics; adaptive resolution imaging sonar (ARIS); behaviour; dual-frequency identification sonar (DIDSON); fish; meta-analysis review

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Behavioral information is crucial for understanding the ecosystem interactions of aquatic animals. However, obtaining such information is challenging. Advancements in imaging sonar technology provide new possibilities for studying aquatic animal behavior.
For many aquatic animals, distributional patterns, trophodynamic relationships, and reproductive activity are driven by complex biotic and abiotic ecosystem interactions that influence behaviour. Linking behavioural information to environmental stimuli and stressors can, therefore, help to anticipate population and ecosystem responses to changing conditions and inform management. However, behavioural information is challenging to obtain because many sampling gears do not provide adequate spatial or temporal resolution, or potentially alter behaviours. Traditionally, most behavioural studies have been laboratory experiments, while behaviour in situ has often been inferred indirectly. Advancements in imaging sonar technology enable the study of in situ behaviours with the potential to address many understudied relationships. In this review we discuss applications of imaging sonar among a meta-analysis of 155 studies of aquatic organisms in their environments. We evaluate the performance of imaging sonar for studying inter- and intra-specific interactions, associations with complex and sensitive habitats and low-visibility environments, and to evaluate traditional fisheries sampling gears. We examine the data processing and analytical methods used to refine taxonomic resolution, manage time use and autocorrelation through sub-sampling, extract behavioural metrics applied to ecological processes, and for automating abundance estimates and image classification with the goal of providing a resource for researchers.

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