Fisheries Science

, Volume 84, Issue 2, pp 309–321 | Cite as

The skipjack tuna fishery in the west-central Pacific Ocean: applying neural networks to detect habitat preferences

  • Jintao Wang
  • Xinjun Chen
  • Kevin W. Staples
  • Yong Chen
Original Article Fisheries
First Online:
Received:
Accepted:

Abstract

Spatial models for habitat selection were developed using neural networks. The model specifications were elucidated from model construction, training, validating, testing, and interpretation, and applied to skipjack tuna in the west-central Pacific Ocean. The model was created using commercial data from the Oceanic Fisheries Programme of the South Pacific Fisheries Commission and oceanic environmental data include sea surface temperature, horizontal gradient of sea surface temperature calculated from sea surface temperature, sea surface height, and chlorophyll-a. Local abundance indices for skipjack tuna were compiled using catch per unit effort, catch or effort. The optimal neural network models for each abundance index were selected by mean square errors and average relative variances. The predictive ability for optimal neural network models was evaluated by the R 2 value using a cross-validation approach. The accuracy and stability of the optimal models, the contribution of independent variables, and the distribution of spatial sensitivity analyses were shown to vary with the abundance index chosen as the response variable. Chlorophyll-a was the most significant oceanographic factor in habitat selection. These results improve our understanding of how best to apply neural networks for modeling habitat selection by skipjack tuna.

Keywords

Neural network Skipjack tuna West-central Pacific Ocean Fishing ground Oceanographic habitat 
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Notes

Acknowledgements

The authors thank the Oceanic Fisheries Programme (OFP) of the South Pacific Fisheries Commission for providing catch data and the National Oceanic and Atmospheric Administration for providing environmental data. Data analysis and writing of this paper were carried out at the School of Marine Sciences, University of Maine, supported by the Shanghai Ocean University and University of Maine. This work was funded by the Public Science and Technology Research Funds Projects of Ocean (20155014), Shanghai Leading Academic Discipline Project (Fisheries Discipline), and National Natural Science Foundation of China (31702343).

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Copyright information

© Japanese Society of Fisheries Science 2017

Authors and Affiliations

  1. 1.College of Marine SciencesShanghai Ocean UniversityShanghaiChina
  2. 2.Collaborative Innovation Center for Distant-Water FisheriesShanghaiChina
  3. 3.National Engineering Research Centre for Oceanic Fisheries, Shanghai Ocean UniversityShanghaiChina
  4. 4.Key Laboratory of Sustainable Exploitation of Oceanic Fisheries ResourcesMinistry of Education, Shanghai Ocean UniversityShanghaiChina
  5. 5.School of Marine SciencesUniversity of MaineOronoUSA

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