Role of maturation and mortality in portfolio effects and climate resilience
Bibliographic record
Abstract
The portfolio effect plays a critical role in population productivity and stability. Age structure of spawning salmon represents an example of portfolio effects such that the risks of experiencing unfavorable conditions are spread across time. However, the distribution of maturation ages for Pacific salmon ( Oncorhynchus spp.) is increasingly concentrated into fewer and younger ages, which may impact population resilience to climate change. We explored the population dynamics of Sacramento River fall-run Chinook salmon ( O. tshawytscha) under different age structure scenarios using a life-cycle model and compared two mechanisms that can underlie these changes—mortality and maturation. In addition, we tested whether age structure promotes resilience to drought. We found that high age structure diversity increased the stability of population size and harvest compared with low diversity. However, mean population size responded differently depending on the underlying mechanism. Reduced mortality of adult fish ages 4–5 increased escapement, whereas delayed maturation decreased escapement. Overall, high age structure diversity was able to buffer against the adverse effects of droughts by reducing the variability of population size and harvest compared with low diversity. Our results suggest that age structure promotes stability of salmon in an increasingly variable climate.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".