Species and global change-assessing the extinction point of albacore stocks

Global change determines the environmental condition and leads to decide the carrying capacity. While carrying capacity determines the extinction of the species, it is an important issue to estimate the extinction point of the species, the minimal carrying capacity, or the tolerant limitation of the species. If it is possible to estimate the tolerant limitation of the species, it will be possible to control the global change. Applied the above idea to the albacore stocks, it revealed that extinction point was about 0.0018% of the present status. From these results, it implies that this method may also suitable to other species for estimating their carrying capacities.

Impact of Climatic Factors on Albacore Tuna <i>Thunnus alalunga</i>in the South Pacific Ocean

Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.

Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory(LSTM)Neural Network Model

Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.