Abstract
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.
Highlights
The biophysical environment plays an essential role in controlling the distribution and abundance of top predators, such as tuna, in the ocean
Understanding the effects of environmental conditions on fish catch rates through fishing condition and habitat preference model studies is an essential step for the ecosystem-based management of fisheries, which is increasingly becoming a standard approach in management policy
Mainly the observer record data (O-data) covered only few vessels operating in the tropical Pacific Ocean (TPO), and seasonal variations were not concentrated in the TPO, in the range of 0°–20°S and 150°E–160°W and extended obvious because ofthe thesecond lack of data.the spatial distribution of O-data (Figure 2) indicated to 160°W in quarter
Summary
The biophysical environment plays an essential role in controlling the distribution and abundance of top predators, such as tuna, in the ocean. The integrity of large marine ecosystems has been compromised because of the depletion of the ocean’s top predators by large-scale fishing operations [1,2]. Since the 1950s, large-scale fisheries have targeted tuna, billfish, and other large predators in the ecosystem of the tropical Pacific Ocean (TPO) [3]. Changes in marine environments affect fishery resources at different spatial and temporal scales [6,7]. The availability of oceanographic and biological information concerning large predators will improve fishing operations as well as the management of fishery resources [8,9,10]
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