Advertisement

Dynamic Simulation of Pelagic Longline Retrieval

  • Liming Song
  • Yukun Qi
  • Jie Li
  • Zhibin Shen
  • Xinfeng Zhang
  • Xi Shen
Article
  • 2 Downloads

Abstract

To improve fishing gear efficiency, it is important to understand the interactions among sea current, fishing vessel, line hauler, and catches during pelagic longline gear retrieval. In this study, fishing gear configuration parameters, operational parameters, and 3D ocean current data were collected from Indian Ocean. Dynamic models of pelagic longline gear retrieval were built using the lumped mass method and solved using the Euler-Trapezoidal method. From the results, the pulling force of line hauler exerted on the gear was 2800–3600 N. There were no significant differences (P > 0.05) between the time of the hook retrieval measured at sea and that obtained from the simulation. The absolute values of the movement velocity at representative nodes along the X, Y, and Z axes were 0.01–25.5 m s−1. These results suggest that the dynamic model of longline fishing gear retrieval can be used to analyze the interactions among sea current, fishing vessel, line hauler, longline gear, and catches, and to acquire the basic data for optimizing the design of the line hauler. Moreover, the model can serve as a reference to study the hydrodynamic performance of other fishing gears during the hauling process.

Key words

pelagic longline retrieval dynamic simulation visualization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The project is funded by the National High Technology Research and Development Program of China (No. 2012 AA092302), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20113104110004), and Shanghai Municipal Education Commission Innovation Project (No. 12ZZ168). We thank Mr. Daochang Zheng, Fei Lin and the crews of longliner ‘Xinshiji No. 85’ of Zhejiang Ocean Family Co., Ltd. for their support. Gratitude also goes to Professor Yong Chen at University of Maine for reviewing the manuscript. Moreover, we thank the anonymous referees for their valuable comments and suggestions.

References

  1. Bigelow, K. A., Hampton, J., and Miyabe, N., 2002. Application of a habitat–based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna (Thunnus obesus). Fisheries Oceanography, 11: 143–155.CrossRefGoogle Scholar
  2. Bigelow, K. A., Musyl, M. K., Poisson, F., and Kleiber, P., 2006. Pelagic longline gear depth and shoaling. Fisheries Research, 77: 173–183.CrossRefGoogle Scholar
  3. Boggs, C. H., 1992. Depth, capture time, and hooked longevity of longline caught pelagic fish: Timing bites of fish with chips. Fishery Bulletin, 90: 642–658.Google Scholar
  4. Cao, D. M., 2011. The dynamic simulation of tuna longline. Master thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
  5. Cao, D. M., Song, L. M., Li, J., Yuan, J. T., and Zhou, Y. Q., 2014. Determining the drag coefficient of a cylinder perpendicular to water flow by numerical simulation and field measurement. Ocean Engineering, 85: 93–99.CrossRefGoogle Scholar
  6. Jiang, L. B., Xu, L. X., and Huang, J. L., 2005. Relationship between vertical distribution of bigeye tuna (Thunnus obesus) and water temperature in Indian Ocean. Journal of Shanghai Fisheries University, 14 (3): 333–336 (in Chinese with English abstract).Google Scholar
  7. Johansen, V., 2007. Modelling of flexible slender systems for real–time simulation and control applications. PhD thesis. Norwegian University of Science and Technology, Trondheim.Google Scholar
  8. Lee, J. H., Lee, C. W., and Cha, B. J., 2005. Dynamic simulation of tuna longline gear using numerical methods. Fisheries Science, 71: 1287–1294.CrossRefGoogle Scholar
  9. Mizuno, K., Okazaki, M., and Miyabe, N., 1998. Fluctuation of longline shortening rate and its effect on underwater longline shape. Bulletin of the National Research Institute of Far Seas Fisheries, 35: 155–164.Google Scholar
  10. Mizuno, K., Okazaki, M., Nakano, H., and Okamura, H., 1999. Estimation of underwater shape of tuna longline with microbathythermographs. Inter–American Tropical Tuna Commission Special Report, 10.Google Scholar
  11. Miyamoto, Y., Uchida, K., Orii, R., Wen, Z., Shiode, D., and Kakihara, T., 2006. Three–dimensional underwater shape measurement of tuna longline using ultrasonic positioning system and ORBCOMM buoy. Fisheries Science, 72: 63–68.CrossRefGoogle Scholar
  12. Nakano, H., Okazaki M., and Okamoto, H., 1997. Analysis of catch depth by species for tuna longline fishery based on catch by branch lines. Bulletin of the National Research Institute of Far Seas Fisheries, 34: 43–62.Google Scholar
  13. Shen, Z. B., 2016. The numerical simulation of tuna longline operation. Master thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
  14. Song, L. M., 2008. Habitat environment integration index of bigeye tuna (Thunnus obesus) in the Indian Ocean–Based on longline survey data. PhD thesis. Shanghai Ocean University, Shanghai (in Chinese with English abstract).Google Scholar
  15. Song, L. M., 2015. Environmental Biology of Fishes and Gear Performance in the Pelagic Tuna Longline Fishery. Science Press, Beijing, 40–42.Google Scholar
  16. Song, L. M., and Gao, P. F., 2006. Captured depth, watertemperature and salinity of bigeye tuna (Thunnus obesus) longlining in Maldives waters. Journal of Fishery Sciences of China, 30 (3): 335–340 (in Chinese with English abstract).Google Scholar
  17. Song, L. M., Chen, X. J., and Xu, L. X., 2004. Relationship between bigeye tuna vertical distribution and the temperature, salinity in the central Atlantic Ocean. Journal of Fishery Sciences of China, 11 (6): 561–566 (in Chinese with English abstract).Google Scholar
  18. Song, L. M., Li, J., Gao, P. F., Zhou, J., and Xu, L. X., 2012. Modeling the hook depth distribution of pelagic longlining in the equatorial area of Indian Ocean. Journal of Ocean University of China, 11 (4): 547–556.CrossRefGoogle Scholar
  19. Song, L. M., Li, J., Xu, W. Y., and Zhang, X. F., 2015. The dynamic simulation of the pelagic longline deployment. Fisheries Research, 167: 280–292.CrossRefGoogle Scholar
  20. Song, L. M., Zhang, Y., Xu, L. X., Jiang, W. X., and Wang, J. Q., 2008. Environmental preferences of longlining for yellowfin tuna (Thunnus albacares) in the tropical high seas of the Indian Ocean. Fisheries Oceanography, 17 (4): 239–253.CrossRefGoogle Scholar
  21. Song, L. M., Zhang, Z., Yuan, J. T., and Li, Y. W., 2011a. Numeric modeling of the pelagic longline based on the finite element analysis. Oceanologia et Limnologia Sinica, 42 (2): 256–261 (in Chinese with English abstract).Google Scholar
  22. Song, L. M., Zhang, Z., Yuan, J. T., and Li, Y. W., 2011b. Numeric modeling of a pelagic longline based on minimum potential energy principle. Journal of Fishery Sciences of China, 18 (5): 1170–1178 (in Chinese with English abstract).CrossRefGoogle Scholar
  23. Suzuki, Z., Warashina, Y., and Kishida, M., 1977. The comparison of catches by regular and deep tuna longline gears in the western and central equatorial Pacific. Bulletin of the Far Seas Fisheries Research Laboratory, 15: 51–89.Google Scholar
  24. Walton, T., and Polachek, H., 1960. Calculation of transient motion of submerged cables. Mathematics of Computation, 14 (69): 27–46.CrossRefGoogle Scholar
  25. Wan, R., Cui, J. H, Song, X. F., Tang, Y. L., Zhao, F. F., and Huang, L. Y., 2005. A numerical model for predicting the fishing operation status of tuna longline. Journal of Fisheries of China, 29 (2): 238–245 (in Chinese with English abstract).Google Scholar
  26. Wan, R., Hu, F. X., Tokai, T., and Matuda, K., 2002. A method for analyzing the static response of submerged rope systems based on a finite element method. Fisheries Science, 68: 65–70.CrossRefGoogle Scholar
  27. Wilcoxon, F., 1945. Individual comparisons by ranking methods. Proceedings of the Biometrics Bulletin, 1 (6): 80–83.CrossRefGoogle Scholar
  28. Wu, Y. W., and Wu, Y. S., 2005. The application of catenary and parabola theories in tuna longline fishery. Marine Fisheries, 27 (1): 1–9.Google Scholar
  29. Yao, Y. M., Chen, Y. L., Zhou, H., and Yang, H. Y., 2016. A method for improving the simulation efficiency of trawl based on simulation stability criterion. Ocean Engineering, 117: 63–77.CrossRefGoogle Scholar
  30. Zhang, X. F., Cao, D. M., Song, L. M., Zou, X. R., Chen, X. J., Xu, L. X., Zhang, M., Zhang, J., and Zhou, Y. Q., 2012. Application of whole–implicit algorithm and virtual neural lattice in pelagic longline modeling. International Conference on Fuzzy Systems & Knowledge Discovery (FSKD 2012), May 29–31,2012, Chongqing, China, 9, 2616–2619.Google Scholar
  31. Zhou, Y. Q., Xu, L. X., and He, Q. Y., 2001. The Dynamics of Fishing Gear. China Agricultural Press, Beijing, 161pp (in Chinese).Google Scholar

Copyright information

© Science Press, Ocean University of China and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Liming Song
    • 1
    • 2
    • 3
    • 4
  • Yukun Qi
    • 1
  • Jie Li
    • 1
  • Zhibin Shen
    • 1
  • Xinfeng Zhang
    • 1
    • 2
    • 3
    • 4
  • Xi Shen
    • 1
  1. 1.College of Marine SciencesShanghai Ocean UniversityShanghaiChina
  2. 2.National Engineering Research Centre for Oceanic FisheriesShanghai Ocean UniversityShanghaiChina
  3. 3.Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Shanghai Ocean University)Ministry of EducationShanghaiChina
  4. 4.Collaboration Innovation Center for National Distant-Water FisheriesShanghaiChina

Personalised recommendations