Multidecadal trends in brown trout populations in France reveal a decline in adult abundance concomitant with environmental changes
Bibliographic record
Abstract
Most studies of brown trout ( Salmo trutta) populations in headwater streams have focused on the year-to-year variability in recruitment and survival, but only few analyzed long-term trends in trout densities, under both control and regulated flow conditions. Here, we conducted trend analyses of brown trout age class densities on 36 stream reaches over the 1990–2020 period, including reaches located in a bypassed section. We also investigated long-term trends in a panel of key environmental variables (water temperature, stream flow, current velocity, and habitat suitability). We found that annual water temperatures significantly increased by a median of +0.21 °C per decade. Analyses of stream flow revealed only a few significant trends, including a general increase in median values in spring and a general decrease in fall. A significant general decline in adult trout densities was observed, although disparities between geographic areas were highlighted. This decline is likely due to multifactorial effects, including possible interacting factors. Our results highlight the need to maintain and extend long-term monitoring of trout populations, which should be combined with extensive environmental monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".