The relationships between the thermocline and the catch rate of Thunnus albacares and Thunnus obesus in the high seas of the Indian Ocean
Bibliographic record
Abstract
It can improve our understanding of their behavior characteristics to analyze and identify the relationships between the thermocline and the vertical distribution of yellowfin tuna and bigeye tuna.It also provides critical information for fisheries to enhance the catch rate of the targeting species,fisheries management and resource conservation.Yellowfin tuna and bigeye tuna seem to exhibit nearly opposite approaches to their shared environment.Yellowfin tuna spend their days making excursions from around the top of the thermocline,both up and downward.Their nights are spent near the surface,diving down into the emergent scattering layer to feed.Bigeye tuna swims upward from the cooler depths to stay with their food resources.The depth which bigeye tuna inhabited is usually greater than that of yellowfin tuna.The difference in temperature between the surface layer and waters below the thermocline may be limiting the vertical movements of the tropical tuna.The thermocline is a water column at which the rate of decrease of temperature with increase of depth is much greater compared with those of above and below.The thermocline limits the vertical distribution of yellowfin tuna and bigeye tuna,so their catch rates are affected by the thermocline.A survey on tuna fishing ground has been carried out aboard of the longliners,Huayuanyu No.18 and No.19 in the high seas of the Indian Ocean from September 15th to Dec.12th,2005.The actual measured environmental data of the fishing area were obtained using Submersible Data Logger XR-620,TDR(2050)(RBR Co., Canada) and SBE37SM(CTD,SeaBird Co.,USA),the depth and intensity of the thermocline could be estimated by these data,and combined with the catch data recorded everyday,the catch rates of yellowfin tuna and bigeye tuna in two different depth layers(the thermocline and the deep water layer) were calculated respectively.The relationships between the thermocline and catch rate of yellowfin tuna and bigeye tuna were analyzed.The results showed that:(1) for 60.9% and 60.0% of all the surveying days of Huayuanyu No.18 and No.19 respectively,the catch rate of yellowfin tuna was higher in the thermocline.The average catch rates of Huayuanyu No.18 in and below the thermocline were 18.22 inds per 1000 hooks and 6.04 inds per 1000 hooks respectively,and that of Huayuanyu No.19 were 2.22 inds per 1000 hooks and 1.31 inds per 1000 hooks respectively.The catch rate of yellowfin tuna was higher in the thermocline,by t-Test paired two sample for means, the overall average catch rate of yellowfin tuna in and below the thermocline showed significant difference(P=0.020.05).(2) for 69.6% and 100% of all the surveying days of Huayuanyu No.18 and No.19 respectively,the catch rate of bigeye tuna was higher below the thermocline,the average catch rates of Huayuanyu No.18 in and below the thermocline were 4.18 inds per 1000 hooks and 4.88 inds per 1000 hooks respectively,and those of Huayuanyu No.19 were 0.10 inds per 1000 hooks and 2.57 inds per 1000 hooks respectively.By t-Test paired two sample for means,the catch rates of bigeye tuna was higher below the thermocline,the overall average catch rates of bigeye tuna in and below the thermocline showed no significant difference(P=0.070.05),but for Huayuanyu No.19,the average catch rates of bigeye tuna in and below the thermocline showed significant difference(P=0.000.05).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".