Impact of Abnormal Climatic Events on the CPUE of Yellowfin Tuna Fishing in the Central and Western Pacific

Abstract

To explore the impact of climate change on fishery resources, the temporal and spatial characteristics of the thermocline in the main yellowfin tuna purse-seine fishing grounds in the western and central Pacific Ocean during La Niña and El Niño years were studied using the 2008–2017 Argo grid data (BOA_Argo) and the log data of commercial fishing vessels. A generalized additive model (GAM) was used to analyze the variables affecting yellowfin tuna fishing grounds. The results showed that in La Niña years, the catch per unit effort (CPUE) moved westward as the high-value zone of the upper boundary contracted westward to 145° E, and in the El Niño years this moved eastward to 165° E. Compared with normal years, the upper boundary depth difference of the thermocline on the east and west sides of the equatorial Pacific was larger in La Niña years, and the upper boundary depth of 80–130 m shifted westward. The thermocline strength was generally weaker in the west and stronger in the east. The thermocline had two band-like distribution structures with an axis at 15° N and 15° S. The CPUE was distributed from 120 m to 200 m. The CPUE distribution was dense when the temperature range of the upper boundary of the thermocline was 27.5–29.5 °C, and the intensity was 0.08–0.13 °C·m−1. The upper-boundary temperature had the greatest impact on the CPUE. The eastward shift of the CPUE during El Niño and the westward shift during La Niña were associated with the optimal thermocline parameter values. The factor of year had a fluctuating effect on the CPUE, and the influence of the La Niña years was greater. The areas with high abundance were 5° N–5° S and 150° E–175° E. The results showed that the changes in the thermocline caused by abnormal climate events significantly affected the CPUE.

Feature Selection for Explaining Yellowfin Tuna Catch per Unit Effort Using Least Absolute Shrinkage and Selection Operator Regression

“…This study found that among all climate data, the El Niño-Southern Oscillation (ENSO) index had the greatest impact on yellowfin tuna CPUE. This is consistent with the findings of Zhou et al [44], who observed that during El Niño events, yellowfin tuna CPUE shifted eastward, while during La Niña events, it shifted westward, thereby affecting the distribution of yellowfin tuna [45]. Additionally, this study found that the North Pacific Gyre Oscillation (NPGO) index is also one of the important features, while the Pacific Decadal Oscillation (PDO) index is not an important feature.…”

How climate change and climate variability affected trip distance of a commercial fishery

“…The vertical overlap of bigeye tuna swimming depth and deep-set longline gear has a direct impact on bigeye catch rates in the Hawaii longline fishery [28,40,44,45]. With increasing ocean temperatures and ENSO events changing the thermal structure and oxygen concentration in different parts of the Pacific Ocean [7,29,33,46,47] and altering the spatial distributions of tuna species [6, 7, 11, 17-20, 28, 29], it is reasonable to expect that changes in climate factors would also impact tuna catch rates and spatial operations of the Hawaii longline fleet.…”

“…In particular, spatial distribution of fishing is influenced by the interactions of "physical, biological, and economic mechanisms" [24]. Rising ocean temperatures and El Niño-Southern Oscillation (ENSO) events could influence the spatial distribution [1, 6, 7, 11, 17-20, 28, 29], abundance [1,7,9,17,20], and catchability [1,6,11,[19][20][21][30][31][32][33] of highly migratory species like tuna as different species have different spatial responses to climate change and variability due to their unique physiological adaptations and preferred habitats [6,19,[34][35][36]. As a result, fisheries that target different tuna species could be affected correspondingly by climate change and variability [6,19].…”

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

“…Additionally, there is multicollinearity among marine environmental factors, which often leads these methods to overlook the spatial heterogeneity between the catch situations of yellowfin tuna in different ocean areas and changes in environmental factors or to omit some important influencing factors. Existing research frequently avoids the impact of limitations on model construction by limiting the scope of the research area or using limited fishing data from a specific commercial fleet [5,11,12,15,[17][18][19][20][21][22][23][24][25][26]. This artificially disconnects the spatial connection of yellowfin tuna resources, which is not conducive to the macro-analysis research of yellowfin tuna in ocean areas.…”