Modelling functional fish habitat connectivity in rivers: A case study for prioritizing restoration actions targeting brown trout
Bibliographic record
Abstract
Abstract Throughout the world, decreased connectivity of fluvial habitats caused by artificial river channel alterations such as culverts, weirs and dykes is seen as an important threat to the long‐term survival of many aquatic species. In addition to assessing habitat quality and abundance, wildlife managers are becoming increasingly aware of the importance of taking into account habitat connectivity when setting priorities for restoration. In this paper, a new approach of spatial analysis adapted to rivers and streams is proposed for modelling 2D functional habitat connectivity, integrating distance, costs and risk of travelling between habitat patches (e.g. daily use, spawning, refuge) for particular fish species, size classes and life stages. This approach was applied to a case study in which brown trout (Salmo trutta) habitat accessibility was examined and compared under various scenarios of stream restoration in a highly fragmented stream in Ile‐de‐France. Probabilities of reaching spawning habitats were estimated from a trout‐populated area located downstream of the barriers and from potential daily‐use habitat patches across the stream segment. The approach successfully helped prioritize restoration actions by identifying options that yield the greatest increase in accessible spawning habitat areas and connectivity between spawning habitat and daily‐use habitat patches. This case study illustrates the practical use of the approach and the software in the context of river habitat management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".