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Open AccessArticle

Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

1
Department of Environmental Biology Fisheries Science, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung 20224, Taiwan
2
Graduate School of Science and Technology, Hirosaki University, 1 Bunkyo-cho, Hirosaki-shi 036-8560, Aomori-ken, Japan
3
Institute of Ocean Technology and Marine A ffairs, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Chung-Ru Ho, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2017, 9(5), 444; https://doi.org/10.3390/rs9050444
Received: 3 January 2017 / Revised: 21 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used generalized additive models (GAMs) fitted to two spatiotemporal fishery data sources, namely 1° spatial grid and observer record longline fishery data from 2006 to 2010, to investigate the relationship between catch rates of yellowfin tuna and oceanographic conditions by using multispectral satellite images and to develop a habitat preference model. The results revealed that the cumulative deviances obtained using the selected GAMs were 33.6% and 16.5% in the 1° spatial grid and observer record data, respectively. The environmental factors in the study were significant in the selected GAMs, and sea surface temperature explained the highest deviance. The results suggest that areas with a higher sea surface temperature, a sea surface height anomaly of approximately −10.0 to 20 cm, and a chlorophyll-a concentration of approximately 0.05–0.25 mg/m3 yield higher catch rates of yellowfin tuna. The 1° spatial grid data had higher cumulative deviances, and the predicted relative catch rates also exhibited a high correlation with observed catch rates. However, the maps of observer record data showed the high-quality spatial resolutions of the predicted relative catch rates in the close-view maps. Thus, these results suggest that models of catch rates of the 1° spatial grid data that incorporate relevant environmental variables can be used to infer possible responses in the distribution of highly migratory species, and the observer record data can be used to detect subtle changes in the target fishing grounds. View Full-Text
Keywords: tropical Pacific Ocean; yellowfin tuna; generalized additive models; habitat preference tropical Pacific Ocean; yellowfin tuna; generalized additive models; habitat preference
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Lan, K.-W.; Shimada, T.; Lee, M.-A.; Su, N.-J.; Chang, Y. Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean. Remote Sens. 2017, 9, 444.

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