Temporal variation in sea trout Salmo trutta life history traits in the Erriff River, western Ireland
Bibliographic record
Abstract
The demographic and life history characteristics of sea trout Salmo trutta L. populations can be changed by a range of pressures in both freshwater and marine environments. Few long-term monitoring programmes are in place to assess temporal change in population dynamics. We analysed a 20 yr time series (1985-2004) using 15 sea trout population response variables in the Erriff River, western Ireland. Over this period, when time was considered as a categorical variable comprising 4 sequential periods of 5 yr, important life history changes were observed. The most dramatic of these changes corresponded with the period immediately after the commencement of salmon farming in the local estuary, with significant decreases in the number and length of sea trout kelts, the estimated number of eggs deposited, the sea trout rod catch, the proportion of older (1+ and 2+ sea age) fish and the frequency of repeat spawners. We found a significant positive relationship between the number of salmon lice Lepeophtheirus salmonis in the local salmon farm and the number of lice found on sea trout collected contemporaneously in local rivers. Results of this long-term monitoring programme demonstrate that significant changes in sea trout population structure with respect to quantitative life history traits can occur over a relatively short time period and suggest that the introduction of salmon farming into the local estuary most likely contributed to the observed changes in sea trout population dynamics.
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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 | 0.000 | 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".