Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus) in the Atlantic Ocean

Abstract

The environmental processes associated with variability in the catch rates of bigeye tuna in the Atlantic Ocean are largely unexplored. This study used generalized additive models (GAMs) fitted to Taiwanese longline fishery data from 1990 to 2009 and investigated the association between environmental variables and catch rates to identify the processes influencing bigeye tuna distribution in the Atlantic Ocean. The present findings reveal that the year (temporal factor), latitude and longitude (spatial factors), and major regular longline target species of albacore catches are significant for the standardization of bigeye tuna catch rates in the Atlantic Ocean. The standardized catch rates and distribution of bigeye tuna were found to be related to environmental and climatic variation. The model selection processes showed that the selected GAMs explained 70% of the cumulative deviance in the entire Atlantic Ocean. Regarding environmental factors, the depth of the 20 degree isotherm (D20) substantially contributed to the explained deviance; other important factors were sea surface temperature (SST) and sea surface height deviation (SSHD). The potential fishing grounds were observed with SSTs of 22–28°C, a D20 shallower than 150 m and negative SSHDs in the Atlantic Ocean. The higher predicted catch rates were increased in the positive northern tropical Atlantic and negative North Atlantic Oscillation events with a higher SST and shallow D20, suggesting that climatic oscillations affect the population abundance and distribution of bigeye tuna.

Spatial variability of bigeye tuna habitat in the Pacific Ocean: Hindcast from a refined ecological niche model

“…Through their lateral vascular heat exchangers, they are capable of modulating blood flow patterns to the red muscles to control the efficiency of heat exchange, hence reducing heat loss to the environment as they descend to cool waters and absorbing heat during ascents, when the ambient temperature (25°C) is above their body temperature (17–18°C) (Bernal, Brill, Dickson, & Shiels, 2017; Hino, Kitagawa, Matsumoto, Aoki, & Kimura, 2019). Bigeye tuna also exhibit successive vertical excursions to the warm surface layer, at a depth of 50–150 m, during the daytime, presumably to rewarm their red muscles or to compensate for oxygen deficiency when utilizing deep habitats (Bernal et al., 2017; Holland et al., 1992; Lan, Lee, Chou, & Vayghan, 2017; Schaefer & Fuller, 2010).…”

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

“…No significant bycatch (more than 85% of the catch was detected as the target fish; Figure 1c) was assumed [37], and CPUE (individuals/1000 hooks) of the tuna longline fishery fleets was considered a reliable indicator of stock relative abundance in the fishing regions [9,10,23]. Monthly nominal CPUE was computed as the number of fish captured per 1000 hooks (individuals/10 3 hooks).…”

“…Nominal CPUE may be affected by the function of some set of covariates (e.g., year, month, longitude, latitude), leading to unintentional overestimation of a relative abundance index [10,20,47,48]. A GLM was therefore applied to standardize CPUE as follows:…”

“…where cpue is the nominal CPUE, c is a constant value of 0.1% or 10% of the overall mean of the nominal catch rates, which is commonly used in standardizations (e.g., [10,20]), µ is the intercept, and ε is a normally distributed variable with a mean equal to zero. Thus, in the forthcoming analysis, we used the standardized CPUE to remove the influence of covariates.…”

“…Since 1978, multisatellite remote sensing data have been used for obtaining images of ocean sea surface temperature (SST), thermal and chlorophyll-a fronts [1][2][3], and phytoplankton pigment concentration, all of which have been useful in fisheries management, fisheries oceanography, and operational fisheries oceanography [4][5][6]. Remote sensing, with its fast and large-scale collection of data, offers enormous potential for the support of fishery exploitation and management of pelagic species [7][8][9][10]. It can increase our understanding of tuna habitats and the influencing factors [4,[11][12][13].…”