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  1. National Taiwan Ocean University Research Hub
  2. 海洋科學與資源學院
  3. 環境生物與漁業科學學系
Please use this identifier to cite or link to this item: http://scholars.ntou.edu.tw/handle/123456789/21412
Title: Hotspot Habitat Modeling of Skipjack Tuna (Katsuwonus pelamis) in the Indian Ocean by Using Multisatellite Remote Sensing
Authors: Vayghan, Ali Haghi
Lee, Ming-An 
Keywords: ALBACORE THUNNUS-ALALUNGA;WESTERN NORTH PACIFIC;RUTILUS-FRISII-KUTUM;BIGEYE TUNA;SPECIES DISTRIBUTIONS;STANDARDIZING CATCH;CASPIAN KUTUM;TEMPERATURE;MIGRATION;DYNAMICS
Issue Date: Sep-2022
Publisher: CENTRAL FISHERIES RESEARCH INST
Journal Volume: 22
Journal Issue: 9
Source: TURK J FISH AQUAT SC
Abstract: 
Skipjack tuna (SKJ) is one of the most targeted fish species globally, especially in the Indian Ocean. SKJ fishery data from Iranian purse seiners and multisatellite remote sensing data were used for hotspot habitat modeling from 2010 to 2018. Spatial and temporal variables were the most important predictors in the generalized additive model (GAM), and 58.6% of the variance was explained. In the MaxEnt model, sea surface temperature (SST), eddy kinetic energy (EKE), and sea surface height (SSH) were the most important predictors of SKJ hotspot habitat suitability in the tropical Indian Ocean between 2 degrees S and 2 degrees N. Furthermore, of the total studied area in the Indian Ocean defined as optimal habitat (habitat suitability index 0.6), 6.8% and 5.3% exhibited ordinary habitat suitability (AUC=0.934, P<0.01) and hotspot habitat suitability (AUC=0.952, P<0.01), respec tively. Iranian purse seiners are distributed mainly in tropical areas, and in the present study, SKJ habitat was affected by environmental variables, as determined using multisatellite remote sensing data. In general, for effective regional monitoring and management strategies to ensure sustainable fisheries, diverse datasets compiled using satellite datasets and habitat modeling can help identify potential hotspot habitats, thereby enabling more accurate suitable habitat zone predictions and more efficient stock management.
URI: http://scholars.ntou.edu.tw/handle/123456789/21412
ISSN: 1303-2712
DOI: 10.4194/TRJFAS19107
Appears in Collections:13 CLIMATE ACTION
14 LIFE BELOW WATER
環境生物與漁業科學學系

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