Spatial distribution of Atlantic salmon (Salmo salar) breeders: among- and within-river variation and predicted consequences for offspring habitat availability
Bibliographic record
Abstract
The spatial distribution of breeders within populations may have important implications for offspring habitat availability in species where mobility of early life stages is restricted. Here we address this issue using time series of spawner distributions from eight Norwegian Atlantic salmon ( Salmo salar ) populations. Whereas spawners distribute themselves uniformly along the length of the rivers in some populations, others show a heavily skewed distribution. Linear mixed models identified pronounced and temporally consistent among-population differences in spawner distributions. Using a model of limited juvenile dispersal from nests following emergence, we show that the observed spawner distributions are predicted to result in significant proportions of rivers being inaccessible for young of the year during early life stages, and the magnitude of this effect differs among populations (e.g., ranging from 27% to 59% for dispersal distance of 250 m). Thus, assuming population regulation during early but not later juvenile stages, consistent differences in spawner distributions among populations such as those observed here may translate into differences in productivity (i.e., carrying capacity) as well as egg densities required for populations to reach their spawning targets.
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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 | 0.000 |
| 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".