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Prediction of shooting trajectory of tuna purse seine fishing.
- Source :
-
Fisheries Research . Dec2018, Vol. 208, p189-201. 13p. - Publication Year :
- 2018
-
Abstract
- Highlights • Shooting trajectories of purse seines for unassociated (FAD-free) tuna schools were predicted. • Shooting methods were proposed according to the speed of the fish school. • Shooting trajectory was suggested according to the net sinking depth. Abstract Purse seine fishing is very effective in catching large volumes of pelagic fish. In Korea, as in many other countries, it is frequently used in catching tuna, a fish with high added value. To make a successful catch in purse seine fishing, it is important to choose the initial gear shooting position and trajectory for the purse seine based on the speed and direction of the swimming fish and the speed of the ship. In the field, the initial shooting position typically depends on the experience of the captain. With increasingly strict global regulations for fish aggregating devices (FADs), greater precision in predicting the purse seine shooting trajectory is required to improve the catch success rate for unassociated (FAD-free) schools. In this study, we propose trajectories with high potential for application in purse seine fishing, based on the speed and direction of the fish school. Two distinct gear shooting methods are proposed according to the speed of the fish. The first gear shooting method is applicable for fish moving at a moderate speed and with less changes in direction; the second method can be applied to fast-swimming schools. In addition, when the depth covered by the sinker line is known through the analysis of the gear behavior, the gear shooting trajectory can be modified according to the depth of the sinker line. The results from this study may be used as a technological alternative to FAD operations by assisting in tuna resource management and reducing the capture of non-targeted species. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PURSE seining
*FISH schooling
*BYCATCHES
*PREDICTION models
*NUMERICAL analysis
Subjects
Details
- Language :
- English
- ISSN :
- 01657836
- Volume :
- 208
- Database :
- Academic Search Index
- Journal :
- Fisheries Research
- Publication Type :
- Academic Journal
- Accession number :
- 131945323
- Full Text :
- https://doi.org/10.1016/j.fishres.2018.07.019