Artificial water-level fluctuation modulates trophic niches of benthic fish assemblages in one of the world's largest reservoirs
Bibliographic record
Abstract
The impoundment of the Yangtze River and construction of the Three Gorges Dam and Reservoir created an unnatural flow regime with a 30 m water-level fluctuation and four artificial seasons that differ from the river's natural flow-regime timing. Using stable isotopes, we selected 15 common benthic fish species representing five trophic guilds to analyze seasonal variation in production sources, trophic niche width, and niche overlap. We hypothesized that, similarly to a natural flow regime, artificial hydrological seasons effectively influence fish trophic niches. We found consistency in seasonal shifts in the contribution of aquatic and terrestrial producers to fish biomass. δ13C and δ15N depicted seasonal changes in intra-guild trophic niche width and intra- and inter-guild niche overlap. Considering that the Three Gorges Reservoir's flow regime is artificially regulated, our findings suggest that water-level fluctuation is one of the key mechanisms driving such trophic shifts and allowing multiple species to coexist. Given the increased cascaded large reservoir construction and the concomitant creation of unnatural flow regimes in large rivers worldwide, these findings are timely. Our findings contribute to improving water-level management by optimizing rhythmicity while balancing flood control and fish protection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".