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Article

Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data

1
Department of Environmental Biology Fisheries Science, National Taiwan Ocean University, No. 2, Beining Rd., Zhongzheng Dist., Keelung City 20224, Taiwan
2
Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
3
Doctoral Degree Program in Ocean Resource and Environmental Changes, National Taiwan Ocean University, No. 2, Beining Rd., Zhongzheng Dist., Keelung City 20224, Taiwan
4
Center of Excellence for Oceans, National Taiwan Ocean University, No. 2, Beining Rd., Zhongzheng Dist., Keelung City 20224, Taiwan
5
Coastal and Offshore Resources Research Center of Fisheries, Research Institute, Kaoshiung City 80672, Taiwan
6
Department of Geography, School of Sciences, Netaji Subhas Open University, Kolkata 700064, West Bengal, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(20), 5278; https://doi.org/10.3390/rs14205278
Submission received: 15 August 2022 / Revised: 28 September 2022 / Accepted: 19 October 2022 / Published: 21 October 2022
(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

Abstract

This study evaluated the vertical distribution of immature albacore tuna (Thunnus alalunga) in the Indian Ocean as a function of various environmental parameters. Albacore tuna fishing data were gathered from the logbooks of large-sized Taiwanese longline vessels. Fishery and environmental data for the period from 1998 to 2016 were collected. In addition to the surface variable, the most influential vertical temperature, dissolved oxygen (OXY), chlorophyll, and salinity layers were found at various depths (i.e., 5, 26, and 53 m for SST; 200, 244, and 147 m for OXY; 508, 628, and 411 for SSCI; and 411, 508, and 773 m for SSS) among 20 vertical layers based on Akaike criterion information value of generalized linear model. Relative to the 20 vertical layers base models, these layers had the lowest Akaike information criteria. For the correlation between the standardized and predicted catch per unit effort (CPUE), the correlation values for the generalized linear model (GLM), generalized additive model (GAM), boosted regression tree (BRT), and random forest (RF) model were 0.798, 0.832, 0.841, and 0.856, respectively. The GAM-, BRT-, and RF-derived full models were selected, whereas the GLM-derived full model was excluded because its correlation value was the lowest among the four models. From March to September, a higher immature albacore standardized CPUE was mainly observed from 30°S to 40°S. A northward shift was observed after September, and the standardized CPUE was mainly concentrated at the south coast of Madagascar from November to January.
Keywords: albacore; ensemble species modelling; salinity; temperature; vertical distribution albacore; ensemble species modelling; salinity; temperature; vertical distribution

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MDPI and ACS Style

Mondal, S.; Wang, Y.-C.; Lee, M.-A.; Weng, J.-S.; Mondal, B.K. Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data. Remote Sens. 2022, 14, 5278. https://doi.org/10.3390/rs14205278

AMA Style

Mondal S, Wang Y-C, Lee M-A, Weng J-S, Mondal BK. Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data. Remote Sensing. 2022; 14(20):5278. https://doi.org/10.3390/rs14205278

Chicago/Turabian Style

Mondal, Sandipan, Yi-Chen Wang, Ming-An Lee, Jinn-Shing Weng, and Biraj Kanti Mondal. 2022. "Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data" Remote Sensing 14, no. 20: 5278. https://doi.org/10.3390/rs14205278

APA Style

Mondal, S., Wang, Y.-C., Lee, M.-A., Weng, J.-S., & Mondal, B. K. (2022). Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data. Remote Sensing, 14(20), 5278. https://doi.org/10.3390/rs14205278

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