Climate‐induced nonlinearity in pelagic communities and non‐stationary relationships with physical drivers in the Kuroshio ecosystem
Bibliographic record
Abstract
Abstract Climate‐induced nonlinearity in biological variability and non‐stationary relationships with physical drivers are crucial to understand responses of marine organisms to climate variability. These phenomena have raised concerns in the northeastern North Pacific, but are out of the spotlight in the northwestern North Pacific in spite of potential implications for this productive system under increased climate variability. Pelagic communities in the Kuroshio ecosystem have both ecological and economic importance. However, patterns of climate‐induced nonlinearity in pelagic communities are not well understood, and existence of non‐stationarity in their relationships with physical drivers remains obscure. Here, we compile large numbers of climatic, oceanic and biological long‐term time‐series data and employ diverse statistical techniques to reveal such climate‐induced nonlinearity and non‐stationarity. Results show that pelagic communities in the Tsushima and Pacific areas (major areas in the Kuroshio ecosystem) had regime shifts in the late 1990s and late 1980s, respectively. Winter sea surface temperatures in the Kuroshio Current path and in the eastern part of East China Sea, which are respectively affected by the Kuroshio Current and Siberian High, correlate with dominant variability patterns in their pelagic communities. Furthermore, non‐stationarity was identified with threshold years in the 1990s in the Tsushima area and in the 1980s in the Pacific area as a possible result of the declined variances in the Siberian High and Aleutian Low, respectively. Our findings provide insights on spatial differentiation of climate‐induced nonlinearity and non‐stationarity, which are valuable for the management of pelagic communities in the northwestern North Pacific under changing climatic conditions.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".