| Popis: |
The vertical sea temperature (VST) in the 0–40 m depth range for bigeye tuna ( Thunnus obesus ) inhabitation affects production of the spawning eggs. We then incorporate this VST along with regime shift into the original Ricker spawner-recruit (SR) model with two extended methods, i.e., the regime SR model and the fuzzy logic SR model. The three models are compared and evaluated using the bigeye tuna ( T. obesus ) SR data in the Indian Ocean from 1960 to 2001 obtained from the age-specific production model analyses. In the preliminary data analyses, we searched the specific range of the VST in 0–40 m that would increase activities of spawners. Spawners are defined as the female fish with the gonad index (GI) > 3.1. Available gonad and depth-specific sea temperature data were used for the analyses. As a result, it was found that the higher average VST values in the 27.5–30.5 °C range produced higher GI values. Using its mean of 28.43 °C as a threshold value, we classified the SR data into two temperature regimes (cool or warm). Then, we applied the bigeye tuna SR data to three models. Based on Akaike Information Criteria (AIC) and r 2 (correlation coefficient) values, it was resulted that the best model was the fuzzy, then the regime and the traditional Ricker model as the last. One of the essential problems discovered in the SR model analyses is that the time series of the SR data frequently followed the trajectories of the loops or the spirals instead of the original SR model curve. Such phenomena implied that the SR data made only apparent fitness to the SR model in many cases because the SR data could not actually satisfy the assumptions behind (the density-dependent process under the same environmental conditions). In our study, such loop was also observed in the original SR data, but after separated into two regimes, the loops were almost disappeared in each regime. This concluded that the bigeye tuna SR data in the Indian Ocean followed the density-dependent process in each homogenous environment (cool or warm regime) and the Fuzzy logic incorporating these two regimes was considered to the most appropriate method. |
Full Text from ScienceDirect