Spatiotemporal dynamics and interrelationships of fish assemblages and environment under stocking-based ecological fisheries practices: Insights from Qiandao Lake
Bibliographic record
Abstract
Qiandao Lake, a key drinking water reservoir and a national model for Stocking-based Ecological Fisheries (SEF) in China, has been intensively managed to balance fishery productivity with ecological health. We investigated the spatiotemporal dynamics of its fish assemblage and its interactions with water quality, phytoplankton, zooplankton, and macrobenthos. Non-Metric Multidimensional Scaling (NMDS) and Analysis of Similarities (ANOSIM) highlighted seasonal and spatial disparities in fish assemblage structure. Machine learning models demonstrated that water quality variables were strong predictors of fish and assemblage composition than biotic indicators. Phytoplankton and zooplankton density and biomass predominantly influenced fish abundance. Non-native species have steadily increased in abundance and biomass, coupled with a decline in native piscivorous fish, indicating the need to change fishing bans and enhance protected areas. • Fish communities stay stable under stocking-based ecological fisheries practices. •Non-native species dominate, reshaping fish communities' ecological composition. •RDA shows physio-chemical factors strongly shape fish community structure. •XGBoost shows biotic indicators predict fish diversity better than abiotic ones
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".