Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data

Sandipan Mondal,Bambang Semedi,Ming-An Lee,Ali Haghi Vayghan,Yi-Chen Wang

doi:10.3390/rs13142669

Abstract

In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m−3 and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m−3 SSC_2. The selected habitat suitability index model displayed a high correlation (R2 = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO.

Highlights

Multisatellite remote sensing has been used to obtain data on sea surface temperature (SST), sea surface chlorophyll (SSC) [1,2,3], phytoplankton concentrations, and other variables since 1978
The current study focused on the feeding habitat zone of the southern Indian Ocean (SIO) immature albacore tuna
A previous study showed a better correlation between 1-month chlorophyll lag and albacore catch (r = 0.982) than chlorophyll with no lag (r = 0.907). It may take some time for phytoplankton patches to become prominent [24]; it may take some time for secondary producers to reach the high-chlorophyll zone, and it can take more time for albacore to reach secondary producers for feeding. These results indicate that a higher concentration of chlorophyll does not mean higher albacore biomass at the same time

Summary

Introduction

Multisatellite remote sensing has been used to obtain data on sea surface temperature (SST), sea surface chlorophyll (SSC) [1,2,3], phytoplankton concentrations, and other variables since 1978. Because of its largescale data collection, remote sensing provides valuable support in fisheries exploitation and management [7,8,9,10] It has expanded understanding of the factors influencing the habitats of tuna and similar species [4,11,12,13,14,15]. Tuna regional fisheries management organizations have provided new insights into ecosystem-based fisheries management (EBFM) using remote sensing tools [17] These tools are frequently used in structuring the habitat models of tuna and similar species [13,14,15,18,19,20,21] and standardization [22,23] of catch per unit effort (CPUE)

Methods

Results

Discussion

Conclusion