Abstract
Abstract Pelagic fishes are a major source of protein and unsaturated fatty acids, and robust management is critical to avoid overfishing. Fisheries management is often supported by indices from scientific acoustic-trawl surveys, where vertically aligned echo sounders and trawl samples are used to provide an estimate of abundance. Survey biases may be introduced when fish are located near the sea surface or if they avoid the survey vessel. Horizontally scanning acoustic equipment, such as fish-detection sonars, have been proposed as a method to quantify such biases; however, manual interpretation of the data hamper further development. An automated method for identifying fish aggregations within large volumes of sonar data has been developed. It exploits the fact that near-stationary targets, i.e. a fish school, have distinct patterns through the data. The algorithm is not instrument specific, and was tested on data collected from several acoustic-trawl surveys in the Norwegian Sea. The automatic algorithm had a similar performance to manual interpretation, and the main cause of discrepancies was aggregations overlooked in the manual work. These discrepancies were substantially reduced in a second round of manual interpretation. We envision that this method will facilitate a labour efficient and more objective analysis of sonar data and provide information to support fisheries management for pelagic fish.
Highlights
Efficient fishing fleets around the world target pelagic fishes such as herring, pollock, mackerel, sprat, and anchovy(Watson et al, 2006), which are major components of several ecosystems and an important source of proteins for human consumption
Pelagic fish stocks are typically surveyed using acoustic-trawl surveys (MacLennan, 1990), resulting in independent indices of abundance that are used in the assessment models
Horizontally observing fisheries sonars have been proposed as a tool to augment the traditional echo sounder methodology since these sonars may have the capability to quantify these biases (Goncharov et al, 1989; Misund and Aglen, 1992; Mayer et al, 2002)
Summary
Efficient fishing fleets around the world target pelagic fishes such as herring, pollock, mackerel, sprat, and anchovy(Watson et al, 2006), which are major components of several ecosystems and an important source of proteins for human consumption. In some years, the estimates from the surveys are mismatching either the assessment or its own internal consistency (ICES, 2016) This inconsistency is often referred to as “year effects” by assessment biologists, which may be more precisely referred to as annual bias. These biases may be caused by many factors, but a variable amount of fish in the upper blind zone (Scalabrin et al, 2009; Totland et al, 2009) and different behavioural reactions (avoidance) (Hjellvik et al, 2008; De Robertis and Handegard, 2013) between years have been proposed as possible explanations. Horizontally observing fisheries sonars have been proposed as a tool to augment the traditional echo sounder methodology since these sonars may have the capability to quantify these biases (Goncharov et al, 1989; Misund and Aglen, 1992; Mayer et al, 2002)
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