Altitudinal gradient of Eurasian otter (Lutra lutra) food niche in Mediterranean habitats
Bibliographic record
Abstract
The effects of geographical factors, such as latitude and altitude, on climate are known to influence the food habits of predator species distributed over a broad distribution range. A sharp latitudinal gradient in the diet of the Eurasian otter ( Lutra lutra (L., 1758)), that is an increase of trophic diversity from temperate to Mediterranean Europe, has been described recently. Altitude, affecting the diversity of fish assemblages and hence prey availability for otters, could also be a key factor influencing otter diet. With the aim of testing for the presence of an altitudinal gradient in otter diet in the Mediterranean area, we assessed the composition of diet at 24 sampling reaches spread in three main river catchments of southern Italy. The analysis of 1885 spraints revealed that fish were the main prey of otters, followed by amphibians, while other food items were of lesser significance, although crustaceans could be locally important. The altitude of the sampling station was negatively correlated with fish consumption and positively correlated with otter trophic diversity; at higher altitudes, alternative prey (particularly amphibians) were included in otter diet. The reduction of fish availability with elevation plays a major role in shaping this dietary pattern. The ability of otters to exploit alternative prey, such as amphibians, could allow the colonization of small rivers sustaining a low fish biomass.
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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 | 0.001 | 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".