The importance of river connectivity in maintaining headwater brown trout ( <i>Salmo trutta</i> ) stocks in a New Zealand river—results from a 29-year study
Bibliographic record
Abstract
Globally, many large rivers are modified to meet human needs, often with adverse impacts on fish populations. In New Zealand, these large rivers often support important recreational brown trout ( Salmo trutta) fisheries but understanding the impacts of flow alterations on connectivity for trout is limited. We analysed the most comprehensive fish trap dataset collected in New Zealand (Glenariffe Stream 1965–1993). Annual brown trout spawning counts varied eight-fold; larger runs had higher proportions of small fish and first-time spawners. Return spawning fish sustained the run for years with smaller runs. There were sex-based differences in the size and timing of fish reaching spawning grounds although the larger males and females typically arrived two months later than initial smaller spawners. Recoveries of tagged trout showed the importance of longitudinal connectivity between spawning tributaries and lagoon habitats with adult females moving >100 km downstream to rapidly regain condition. With inherent annual variability in spawning runs, and the catchment-wide scale that brown trout population dynamics occur over, managers need to understand these upstream–downstream linkages when making river-modification decisions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".