Limited effects of stocked trout on littoral invertebrates in boreal foothills lakes
Bibliographic record
Abstract
Stocking lakes with trout for the purposes of recreational angling is a management strategy that introduces a new predator into these systems and thus deserves careful scrutiny. To assess the impact of non-native trout on littoral invertebrates in naturally fish-bearing lakes in the boreal foothills of Alberta, Canada, we compared their community composition, abundance, and size structure in stocked (n = 5) and unstocked (n = 6) lakes over a 2-year period. We detected no clear negative effects of introduced trout on invertebrate community composition and only few taxa-specific examples of decreased or increased invertebrate abundance. Furthermore, predation by trout had inconsistent direct effects on the size structure of invertebrate populations. Indirect effects were suggested by increased abundances and sizes of some invertebrate taxa in stocked lakes and might also contribute to the limited overall differences that we observed. We propose that net effects of stocked trout on littoral invertebrates are influenced by key characteristics of receiving ecosystems. In our boreal foothills lakes, dense macrophyte cover in warm littoral zones, high productivity, abundant forage fish, and limited densities of trout all likely combine to allow littoral invertebrate communities to withstand the impact of introduced trout with minimal effects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".