Beaver impact on stream fish life histories: the role of landscape and local attributes
Bibliographic record
Abstract
The Eurasian beaver (Castor fiber) has been reintroduced into the Carpathian Mountains and has once again become an important factor for modifying streams. Forty-three sampling sites were compared with 10 reference sites in streams not settled by beavers. Models related to the impacts of beavers on various fish life history stages and a model presenting attributes influencing the ichthyofauna structure in streams with and without beavers were generated. Significant differences in the fish species composition were found between beaver ponds versus the running sections of streams. The changes associated with pond aging caused decrease of Siberian bullhead (Cottus poecilopus) density. For brown trout (Salmo trutta), beaver ponds were the only location where large individuals were found, while the upstream parts of the beaver complexes provided spawning habitat and an area for fry growth. Common minnow (Phoxinus phoxinus) and stone loach (Barbatula barbatula) had higher density in ponds than in streams. The decisive factors for the ichthyofauna in the mountain streams settled by beavers were local attributes related to beaver activity. Our results illustrate interactions among beaver, landscape context, and fish life history in influencing the response of the stream fish assemblages to beaver recolonization. It also helps answer the question of how Eurasian beaver influence stream fish assemblages, at a much larger scale than previous studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".