Time‐varying relationships between ocean conditions and sockeye salmon productivity
Bibliographic record
Abstract
Abstract Environmental change is occurring at unprecedented rates in many marine ecosystems. Yet, environmental effects on fish populations are commonly assumed to be constant across time. In this study, I tested whether relationships between ocean conditions and productivity of North American sockeye salmon ( Oncorhynchus nerka ) stocks have changed over the past six decades. Specifically, I evaluated the evidence for non‐stationary relationships between three widely used ocean indices and productivity of 45 sockeye salmon stocks using hierarchical Bayesian models. The ocean indices investigated were the Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), and sea surface temperature (SST). I found partial support for time‐varying salmon–ocean relationships. Non‐stationary relationships were strongest for the NPGO and weaker for the SST and PDO indices. Productivity–NPGO correlations tended to shift gradually over time with opposite trends for stocks in British Columbia (B.C.) and western Alaska; for B.C. stocks, the NPGO correlations shifted from significantly negative prior to 1980 to significantly positive after 1990, whereas for western Alaska stocks, the correlations shifted from positive to negative. Productivity–SST correlations showed declining trends for B.C. and Gulf of Alaska stocks, that is, correlations became more negative (B.C.) or less positive (Gulf of Alaska) over time. For the PDO, correlations weakened during the 1980s for western Alaska and B.C. stocks. Overall, these results provide evidence for time‐varying relationships between salmon productivity and environmental conditions over six decades, highlighting the need to recognize that historical responses of salmon populations to environmental change may not be indicative of future responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".