Diel patterns in spatial distribution of fish assemblages in lentic and lotic habitat in a regulated river
Bibliographic record
Abstract
Abstract Fish assemblages in large rivers are governed by spatio‐temporal changes in habitat conditions, which must be accounted for when designing effective monitoring programmes. Using boat electrofishing surveys, this study contrasts species richness, catch per unit effort (CPUE), total biomass, and spatial distribution of fish species in the Saint John River, New Brunswick, Canada, sampled during different diel periods (day and night) and macrohabitats (hydropower regulated river and its reservoir) in the vicinity of the Mactaquac (hydropower) Generating Station. Taxa richness, total CPUE, and total biomass were significantly higher during night surveys, resulting in marked differences in community composition between the two diel periods. Furthermore, the magnitude of diel differences in catch rate was more pronounced in lentic than in lotic macrohabitats. The required sampling effort (i.e., number of sites) to increase accuracy and precision of CPUE estimates varied widely between fish species, diel periods, and macrohabitats and ranged from 15 to 185 electrofishing sites. Determining a correction factor to contrast accuracy and precision of day‐ with night‐time surveys provide useful insights to improve the design of long‐term monitoring programmes for fish communities in large rivers. The study also shows the importance of multihabitat surveys to detect differences in the magnitude of diel changes in fish community metrics.
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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 | 0.000 |
| 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) | 0.001 | 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".