Weakened portfolio effect in a collapsed salmon population complex
Bibliographic record
Abstract
Recent research has highlighted the importance of interpopulation diversity in fostering the stability of population complexes. Here we focus on California’s recently collapsed fall-run Chinook salmon ( Oncorhynchus tshawytscha ) and ask whether portfolio effect induced buffering is observed across the complexity hierarchy from individual populations to populations within a river basin (Sacramento, San Joaquin) to the entire Central Valley. Some buffering was observed when comparing the coefficient of variation in adult returns to a given river basin with its constituent populations but not when comparing returns to the entire Central Valley with its constituent basins because of disproportionately many fish returning to the Sacramento Basin. Moreover, we report that positive correlations in population dynamics between rivers were stronger in the last 25 years of the study compared with the first 25 years. Together, these results suggest evidence of only a weak portfolio effect that has deteriorated in recent years. Nonetheless, we also report that correlations between rivers decreased significantly with distance, suggesting that some biocomplexity remains. Our results suggest that the greatest potential for strengthening the portfolio effect would come through restoration of San Joaquin Basin populations, which at low abundance currently contribute little to the overall buffering capacity despite low cross-basin correlations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".