Weakening portfolio effect strength in a hatchery-supplemented Chinook salmon population complex
Bibliographic record
Abstract
Biocomplexity contributes to asynchronous population dynamics, buffering stock complexes in temporally variable environments, a phenomenon referred to as a “portfolio effect”. We previously revealed a weakened but persistent portfolio effect in California’s Central Valley fall-run Chinook salmon (Oncorhynchus tshawytscha), despite considerable degradation and loss of habitat. Here, we further explore the timing of changes in variability and synchrony and relate these changes to factors hypothesized to influence variability in adult abundance, including hatchery release practices and environmental variables. We found evidence for increasing synchrony among fall-run populations that coincided temporally with increased off-site hatchery releases into the estuary but not with increased North Pacific environmental variability (measured by North Pacific Gyre Oscillation), nor were common trends well explained by a suite of environmental covariates. Moreover, we did not observe a simultaneous increase in synchrony in the nearby Klamath–Trinity system, where nearly all hatchery releases are on-site. Wavelet analysis revealed that variability in production was higher and at a longer time period later in the time series, consistent with increased environmental forcing and a shift away from dynamics driven by natural spawners.
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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 | 0.003 |
| 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) | 0.001 | 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".