Drivers of the fish community size spectrum and its utility as an indicator of fish productivity: insights from three decades of monitoring data from a small boreal lake
Bibliographic record
Abstract
Freshwater fisheries monitoring programs require indicators of fish community and population productivity and abundance. Two potential indicators include the slope and height of the community size spectrum, which are often interpreted as proxies for trophic dynamics and community abundance (or production), respectively. Previous studies have lacked alternative estimates of fish production and abundance to compare with community spectral parameters and have had limited limnological data to understand drivers of variability in spectral parameters among lakes. Using ∼30 years of monitoring data from a boreal lake, we showed that the height of the fish community size spectrum was related to independent mark/recapture-based estimates of abundance for the dominant large-bodied species, white sucker, but not to their biomass or annual production estimates. Further, limnological properties including the duration of ice cover, light, temperature, and the biomass of zooplankton prey were associated with inter-annual variation in spectral slopes and heights, often nonlinearly. Our results indicate that spectral parameters derived from fish communities can reflect long-term environmental change and provide new insights on drivers of fish community size structure.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".