Evaluation of fish habitat suitability using a coupled ecohydraulic model: Habitat model selection and prediction
Bibliographic record
Abstract
Abstract The selection of an approach to evaluate habitat suitability for a specific fish or life stage has been a matter of concern in habitat quality modelling studies. This study has taken Jinshaia sinensis, a commercially valuable fish endemic to the Jinsha River, China, as the target fish species. One‐ and two‐dimensional hydrodynamic models were coupled and combined with fish habitat models for a middle reach of the Jinsha River. The resulting ecohydraulic model was used to predict the changes in hydrodynamics and spawning habitat suitability that resulted from the operation of an under‐construction reservoir downstream of the study area. The preference function (product, arithmetic mean, geometric mean, and minimum value) and fuzzy logic habitat evaluation methods were compared to predict the spawning habitat suitability of the fish. The model was validated using the numbers of spawning eggs, and the results show that both the arithmetic mean and fuzzy logic method can be used to predict spawning habitat suitability. The model predictions show that the hydrodynamics of the study area would be altered if the impoundment water level exceeded 969 m. During the spawning season, the spawning habitat suitability would increase from April to early June and has little change from early June to July under the impact of the reservoir impoundment. The optimal river discharge rate for fish spawning is ~3,500 m3/s, and this would not change after the reservoir begins operation. This research can benefit other regions that will be affected by planned dams by predicting the impacts of reservoir operation on fish habitat quality, and the results will help decision makers protect the health of rivers and the overall ecosystem.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".