No abstract

Trawlers involved in the Antarctic krill (Euphausia superba) fishery use different trawl designs, and very little is known about the size selectivity of the various gears. Size selectivity quantifies a given trawl's ability to catch different sizes of a harvested entity, and this information is crucial for the management of a sustainable fishery. We established a morphological description of krill and used it in a mathematical model (FISHSELECT) to predict the selective potential of diamond meshes measuring 5–40 mm with mesh opening angles (oa) ranging from 10 to 90°. We expected the majority of krill to encounter the trawl netting in random orientations due to high towing speeds and the assumed swimming capabilities of krill. However, our results indicated that size selectivity of krill is a well-defined process in which individuals encounter meshes at an optimal orientation for escapement. The simulation-based results were supported by data from experimental trawl hauls and underwater video images of the mesh geometry during fishing. Herein we present predictions for the size selectivity of a range of netting configurations relevant to the krill fishery. The methods developed and results described are important tools for selecting optimal trawl designs for krill fishing.

During the fishing process, fish react to a trawl with a series of behaviours that often are species and size specific. Thus, a thorough understanding of fish behaviour in relation to fishing gear and a scientific understanding of the ability of different gear designs to utilize or stimulate various behavioural patterns during the catching process are essential for developing more efficient, selective, and environmentally friendly trawls. Although many behavioural studies using optical and acoustic observation systems have been conducted, harsh observation conditions on the fishing grounds often hamper the ability to directly observe fish behaviour in relation to fishing gear. As an alternative to optical and acoustic methods, we developed and applied a new mathematical model to catch data to extract detailed and quantitative information about species- and size-dependent escape behaviour in towed fishing gear such as trawls. We used catch comparison data collected with a twin trawl setup; the only difference between the two trawls was that a 12 m long upper section was replaced with 800 mm diamond meshes in one of them. We investigated the length-based escape behaviour of cod (Gadus morhua), haddock (Melanogrammus aeglefinus), saithe (Pollachius virens), witch flounder (Glyptocephalus cynoglossus), and lemon sole (Microstomus kitt) and quantified the extent to which behavioural responses set limits for the large mesh panel’s selective efficiency. Around 85% of saithe, 80% of haddock, 44% of witch flounder, 55% of lemon sole, and 55% of cod (below 68 cm) contacted the large mesh panel and escaped. We also demonstrated the need to account for potential selectivity in the trawl body, as it can bias the assessment of length-based escape behaviour. Our indirect assessment of fish behaviour was in agreement with the direct observations made for the same species in a similar section of the trawl body reported in the literature.

FEMNET, a numerical tool based on the finite element method, was used to estimate the shapes of four different designs of trawl cod-ends during fishing operations. Compared to a traditional diamond-mesh cod-end the design differences were the following: (i) the netting orientation was turned by 90 • (T90), (ii) the number of meshes in the circumference was reduced by 50% or (i) and (ii) were combined. The cod-end shape estimates were then entered into the simulation tool PRESEMO, to estimate their influence on the selectivity processes in the cod-end. This enabled us to predict how these design alterations -alone or combined -may act on the selectivity of each cod-end under identical fishing conditions. For instance, we predict that for a 110 mm diamond-mesh cod-end the 50% retention length (L50) is increased by nearly 12 cm by both turning the mesh orientation and reducing the number of meshes in the circumference. Of this combined effect we predict that 24% of it is caused by only turning the mesh orientation whereas 71% of the effect stems from only reducing the number of meshes in circumference. The remaining 5% is due to the interaction between the two factors.

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