Habitat- and size-related variations in exotic trout impacts on native galaxiid fishes in New Zealand streams
Bibliographic record
Abstract
The factors controlling the effects of introduced trout on native galaxiid fishes in New Zealand streams were investigated by quantitative and qualitative electrofishing. Habitat assessments indicated that bed stability was closely related to total fish biomass. Exotic trout were not present at the most unstable sites but inhabited most medium to large streams with stable beds. Galaxiids (Galaxias vulgaris, Galaxias brevipinnis, and Galaxias paucispondylus) occurred at sites that spanned almost the entire range of habitat conditions and co-occurred with trout more frequently at intermediate levels of bed stability. However, galaxiids were absent from all sites with large (>150-mm fork length (FL)) brown (Salmo trutta) and rainbow (Oncorhynchus mykiss) trout. These streams tended to be larger, with more stable beds. In a field tank experiment, large brown trout consumed Canterbury galaxias (G. vulgaris), ranging in size from 48- to 94-mm FL, at a much higher rate than did small trout. Their predation of galaxiids appeared not to be size selective, so there was no size refuge for galaxiids from predation by large trout. These results indicate that predation by large trout has likely eliminated small-bodied galaxiids from many streams but that trout impact is limited by the availability of habitats suitable for large individuals.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".