Historical abundance and spatial distributions of spawners determine juvenile habitat accessibility in salmon: implications for population dynamics and management targets
Bibliographic record
Abstract
Spatial distribution of spawning may have important ramifications for population dynamics in species where early life stages suffer from low mobility and high density-dependent mortality. Here, we use time series of spatial spawning distribution in Atlantic salmon (Salmo salar) to test for density-dependent behavioural effects on the spatial utilization of spawning sites and resulting juvenile habitat availability. The probability of utilizing spawning sites in a given year increased both with increasing spawner abundance and proximity to sites used the previous year. The accessible area for juveniles increased asymptotically with both current and time-lagged spawner abundance. Several nonexclusive mechanisms may be responsible for the observed direct and lagged density dependence of spawner distributions, including social aggregation, asymmetric competition for space, local homing, and habitat modification by the previous year’s spawners. Time-lagged density-dependent spawner distributions can be predicted to reduce the realized population growth rate. If such effects are not accounted for, this may lead to a downward bias in estimates of spawning targets or other associated conservation or management measures derived from population abundance time series.
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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 | 0.000 |
| 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".