Fish habitat modelling in large rivers: combining expert opinion and hydrodynamic modelling to inform river management
Bibliographic record
Abstract
Physical habitat models represent a widely used tool in river management, yet, there is a growing consensus—particularly for large rivers—that fundamental principles have limits, and it is evident that improved methodologies for assessment and design are needed. Here, we suggest a framework that takes steps towards resolving some of these issues, using changes of fish habitat in a large, regulated river as a case study. First, we propose using hydrodynamic modelling in combination with a fuzzy rule-based classification as a tool to delineate and quantify meso-scale fish habitat. Variability in spatial and temporal extent of mesohabitats can be modelled across a range of flows and under non-wadable conditions when standard mesohabitat surveys become largely unfeasible. Second, research effort and empirical data on habitat use and preference of fishes is typically focused on a small group of species and limited for many imperilled or elusive taxa; we suggest using expert knowledge to expand beyond one or a few species to build the biological models for a community-level assessment until empirical data becomes available. Third, sources of uncertainty that are linked to both fundamental elements of habitat models, namely the biological and hydrodynamic components, need to be quantified and reported in modelling outcomes. The steps described in our modelling framework represent key tools for river managers charged with developing environmental flows guidelines in large, regulated rivers.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".