Life history and environmental influences on population dynamics in sockeye salmon
Bibliographic record
Abstract
Understanding linkages among life history traits, the environment, and population dynamics is a central goal in ecology. We compared 15 populations of sockeye salmon (Oncorhynchus nerka) to test general hypotheses for the relative importance of life history traits and environmental conditions in explaining variation in population dynamics. We used life history traits and habitat variables as covariates in mixed-effect Ricker models to evaluate the support for correlates of maximum population growth rates, density dependence, and variability in dynamics among populations. We found dramatic differences in the dynamics of populations that spawn in a small geographical area. These differences among populations were related to variation in habitats but not life history traits. Populations that spawned in deep water had higher and less variable population growth rates, and populations inhabiting streams with larger gravels experienced stronger negative density dependence. These results demonstrate, in these populations, the relative importance of environmental conditions and life histories in explaining population dynamics, which is rarely possible for multiple populations of the same species. Furthermore, they suggest that local habitat variables are important for the assessment of population status, especially when multiple populations with different dynamics are managed as aggregates.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".