Influence of discharge, hydraulics, water temperature, and dispersal on density synchrony in brown trout populations (<i>Salmo trutta</i>)
Bibliographic record
Abstract
Environmental factors may cause synchronous density variations between populations. A better understanding of the processes underlying synchrony is fundamental to predicting resilience loss in metapopulations subject to environmental change. The present study investigated the determinants of synchrony in density time series of three age groups of resident brown trout (Salmo trutta) (0+, 1+, and adults) in 36 stream reaches. A series of Mantel tests were implemented to disentangle the relative effects on trout synchrony of geographical proximity, environmental synchrony in key environmental variables affecting trout dynamics (discharge, water temperature, hydraulics, and spawning substrate mobility), and density-dependent dispersal. Results indicated that environmental synchrony strongly explained trout synchrony over distances less than 75 km. This effect was partly due to a negative influence on 0+ trout of strong discharges during the emergence period and a more complex influence of substrate mobility during the spawning period. Dispersal between reaches had a weak influence on results. Juvenile and adult densities were strongly driven by survival processes and were not influenced by environmental synchrony. The results suggest that the environment can have general effects on population dynamics that may influence the resilience of metapopulations.
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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 | 0.001 |
| 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".