Abstract
Environmental characteristics in the Taiwan Strait (TS) have been linked to variations in the abundance and distributions of Greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically significant species in ecosystems, and their spatial distribution patterns are a pivotal role in fisheries management and conservation. The purpose of this research was focused on modeling the spatiotemporal distribution pattern of S. dumerili in association with environmental factors, as well as its effects in the TS, which are still understudied. Therefore, the relationship between the catch rates and the influence of environmental changes on fish communities must be thoroughly investigated. To investigate the catch rates of S. dumerili with changes in oceanographic conditions within the TS, we applied generalized additive models (GAMs) to spatiotemporal fishery data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017), and we developed a species distribution model based on the best selected GAMs. The deviance explained (DE) indicated that high catch rates revealed that sea surface temperature (SST) was the most important factor influencing S. dumerili distributions, whereas mixed layer depth (MLD) was the least relevant factor. The model predicted that the S. dumerili would have a relatively high abundance of catch rates in the northwestern region of TS during summer, which would have extended to the coastal seas of mainland China, and that, despite having a comparatively higher catch rate would be widely distributed again in the winter. The targeted species were strongly influenced by biophysical environmental conditions, and the potential fishing areas have occurred along the waters of TS. The findings of this study were informative in determining how the S. dumerili responded to spatiotemporal environmental variables and predicting species distributions. Habitat preferences and distribution pattern of S. dumerili is the primary information that contributes to better knowledge and understanding of the environmental conditions of TS, which plays an important role and inform future priorities for conservation planning and management aspects.

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