Development of integrated habitat indices for bigeye tuna, Thunnus obesus, in waters near Palau

We have extracted information on the habitats of bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis) and yellowfin (Thunnus albacares) in the Eastern Tropical Pacific Ocean by matching the spatial‐temporal distribution of catch and effort of purse seine and longline fleets collected by the Inter‐American Tropical Tuna Commission with oceanographic conditions and subjecting the matched data to Quotient Analysis and General Additive Models (GAMs). These analyses yielded the following results. The habitats defined by the GAM analysis of young fish differ significantly between two periods, one before and one after the introduction of fish aggregation devices (FADs). This was not true for the older fish caught by longline. We speculate that these changes were caused by the extensive use of FADs. Younger bigeye and yellowfin caught by the purse seine fleet have a different preference of environmental variables compared to older fish caught by longline. This is to be expected since tuna of different age groups have different sizes, metabolic capabilities and swimming skills. Moreover, as revealed by GAMs, the habitats of young fish differ between species to a much larger degree than those of older fish. Our results indicate the fundamental differences between fishing methods, targeted species, and operating region of the two fisheries. Specifically, young bigeye occupy equatorial waters farther from the coast and where the hypoxic layer is deeper, young skipjack occupy more productive waters associated with equatorial and coastal upwelling, and young yellowfin occupy broad areas where waters are underlain by a shallow hypoxic layer.

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“…This avoidance is in agreement with other studies such as Sund et al. (), Brill () and Li, Song, Nishida, and Gao ().…”

Section: Discussionsupporting

confidence: 94%

“…For example, both our GAM and Quotient Analysis suggested that bigeye and skipjack avoid waters where the concentrations of oxygen at 150 m depth are less than 1 ml/L. This avoidance is in agreement with other studies such as Sund et al (1981), Brill (1994) and Li, Song, Nishida, and Gao (2012).…”

Section: Discussionsupporting

confidence: 92%

Habitat analysis of the commercial tuna of the Eastern Tropical Pacific Ocean

Harrison

Hinton

et al. 2018

Fisheries Oceanography

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“…In addition, as we tried to kept environmental predictors according to their ecological significance, some correlated variables were retained. For example, we used the vertical shear of ocean current and dissolved oxygen concentration as they were demonstrated the effect of bigeye tuna habitat depths (Brill, 1994; Li et al, 2013), but these variables were correlated with each other and also correlated with the vertical temperature. Though the MaxEnt model could implicitly deal with feature selection based on its own inbuilt method ( e .…”

Section: Discussionmentioning

confidence: 99%

A comparative study on habitat models for adult bigeye tuna in the Indian Ocean based on gridded tuna longline fishery data

Zhang

Song

Yuan

et al. 2021

Fisheries Oceanography

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Using the gridded fishery data to estimate the habitat preferences of bigeye tuna (Thunnus obesus) in the Indian Ocean is challenging, as it is still not clear what type of model is appropriate to make reliable habitat predictions. In this study, we tested two classes of habitat models: Generalized Additive Models (including Gaussian distribution GAM, Poisson distribution GAM, Negative Binomial distribution GAM, Tweedie class distribution GAM, and Zero‐inflated distribution GAM) and Maximum Entropy Model (MaxEnt) for forecasting the habitat of bigeye tuna in the Indian Ocean, using the 5°×5° monthly gridded fishery data from 2008 to 2015 provided by Indian Ocean Tuna Commission (IOTC) and the environmental factors (i.e., the vertical temperature, salinity, dissolved oxygen concentration, thermocline depth, vertical shear of ocean current, concentration of sea surface chlorophyll‐a (chl‐a), and the eddy kinetic energy (EKE)). We compared the models’ fitting ability, predictive performance, predicted results, and the ecological interpretation within the models. The results showed (1) GAMs provided better model fit and predictive performance than MaxEnt; (2) GA‐GAM showed the best model fit performance by using the log transformation of the standardized CPUE as response variable; (3) TW‐GAM was suitable for predicting the habitat of adult bigeye tuna with the over‐dispersed fishery datasets. (4) Results suggested that the vertical temperature, dissolved oxygen concentration, and thermocline depth could be used as reliable predictors in forecasting the spatial‐temporal distribution of the adult bigeye tuna in the Indian Ocean.

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