The effects of brown trout (<i>Salmo trutta</i> morpha <i>fario</i>) on habitat selection by larval Fire Salamanders (<i>Salamandra</i> <i>salamandra</i>): a predator-avoidance strategy
Bibliographic record
Abstract
Predatory fish can have a major impact on aquatic amphibian assemblages. Knowledge regarding the influence of habitat heterogeneity on predator–prey dynamics is extensive, but not much is published on how the habitat structure influences the co-occurrence of brown trout (Salmo trutta Linnaeus, 1758 morpha fario) and Fire Salamander (Salamandra salamandra (Linnaeus, 1758)). I examined the microhabitat distribution of larval salamanders relative to the presence of brown trout and stream morphology, hypothesizing that larval salamanders will increase their habitat use in the presence of trout to avoid predation. Fish and salamanders were sampled with an electroshocker in 62 instream habitat patches. In the stream zone populated by brown trout, larval salamanders avoided high-quality habitats such as pools, whereas they strongly preferred them in the fishless zone (their densities were ∼10 times lower in pools than in riffles). Brown trout mainly occupied deeper pools. The co-occurrence of larval Fire Salamanders with trout suggests the presence of an effective predator-avoidance strategy. The predator-avoidance response and habitat-use pattern decreased interspecific overlap, leading to the use of different instream spaces. Heterogeneous habitats enable habitat partitioning between larval salamanders and brown trout, which means that the natural characteristics of streams promote coexistence between fish and amphibians.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".