Littoral Fish Community Response to Smallmouth Bass Removal from an Adirondack Lake
Bibliographic record
Abstract
Abstract Large‐scale observational studies in eastern Canada and the northeastern USA have concluded that introduced littoral predators are responsible for reductions in native fish diversity and abundance. To determine whether nonnative predator removal could increase native littoral fish abundance, we removed 47,682 smallmouth bass Micropterus dolomieu from a 271‐ha Adirondack lake during a 6‐year period. Two years after removal began, habitat‐stratified snorkel surveys indicated a greater than 90% reduction in smallmouth bass abundance. The relative abundances of six native littoral species increased (4‐90 times preremoval abundances) within 2 years of smallmouth bass removal. Decreased relative predation risk during the experiment reflected the reduction in littoral predators and identified seasonal differences in nearshore predation risk. The smallmouth bass population was resilient to removal, producing strong year‐classes throughout the experiment. Mechanical removal was successful at decreasing smallmouth bass abundance and increasing native fish abundance, but removal must be conducted on a yearly basis to maintain low smallmouth bass population abundance. Our results provide experimental evidence regarding the need to prevent littoral predator introductions in Adirondack waters and offer support for nonnative control wherever native fish species conservation is a management priority.
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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 | 0.000 |
| 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".