Catch-per-unit-effort and size spectra of lake fish assemblages reflect underlying patterns in ecological conditions and anthropogenic activities across regional and local scales
Bibliographic record
Abstract
Across broad geographic scales, ecological indicators for fish assemblages should represent causal ecological processes, be sensitive enough to show patterns across the landscape, and reflect underlying biotic or abiotic conditions that influence those patterns. We assessed the responses of commonly applied ecological indicators for lake fish assemblages (mean body size, catch-per-unit-effort (CPUE), and normalized length size spectrum (NLSS) slope) to regional (climate, water chemistry, and watershed stress due to human activities) and local (lake morphometry, water quality, and angling pressure) ecological and anthropogenic variables. The indicators were estimated using fish assemblage catch data acquired via a standardized gillnetting protocol implemented within 693 lakes in Ontario, Canada. To our knowledge, our study is the first size-based or catch-based indicator evaluation to include detailed observations of angling pressure on hundreds of inland lakes. Boosted regression tree models showed that CPUE of large-bodied organisms and NLSS slope best described underlying patterns in the regional and local variables. Models developed with a mix of regional and local variables performed better than models developed with regional or local variables alone. The relative influences of the variables and responses varied among indicators, but in general, ecological variables had greater influence on the indicators than anthropogenic variables. These results emphasize the complex and multiscaled nature of factors and ecological processes affecting body size, habitat-community production, and trophic dynamics in lake fish assemblages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".