Non‐stationary effects of multiple drivers on the dynamics of Japanese sardine (<i>Sardinops melanostictus</i>, Clupeidae)
Bibliographic record
Abstract
Abstract Non‐stationary driver‐response relationships are increasingly being recognized by scientists, underlining that a paradigm shift out of conventional stationary relationships is crucial. Japanese sardine ( Sardinops melanostictus , Clupeidae) is a typical small pelagic fish in the northwestern Pacific with considerable fluctuations in productivity, bringing about great economic and ecological concerns. Numerous studies suggest that the population dynamics of Japanese sardine is an integrated process affected by multiple density‐dependent, fishing and climatic drivers. However, little has hitherto been done to incorporate the non‐stationary effects of multiple drivers, impeding progresses in understanding the population dynamics and in developing management strategies. In this study, we adopted variable coefficients generalized additive models to reveal the non‐stationary effects of density dependence, fishing pressure and climatic conditions on the population dynamics of Japanese sardine. Results suggest that the dynamics of Japanese sardine from 1976 to 2018 could be divided into four periods: the 1980s when suitable climatic conditions from strong Siberian High pressure system sustained high abundance; the 1990s when negative density‐dependent effects and degrading climatic conditions due to temperature increase led to population collapse; the 2000s when negative triple effects, particularly high fishing pressure, restricted the population increase; and the 2010s when favourable climatic conditions with re‐strengthening Siberian High pressure system accompanied by low fishing pressure contributed to the population recovery. The study highlights that precise identifications of population status and climatic conditions are helpful to achieve good trade‐offs between resource exploitation and protection and to facilitate ecosystem‐based management for Japanese sardine fisheries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".