Trophic specialization at the individual level in a terrestrial generalist salamander
Bibliographic record
Abstract
Information on individual trophic specialization may be relevant to better understand the ecological adaptation of populations to their environment and the evolution of their realized trophic niche. In this study, we analysed the trophic specialization at the individual level in a population of the plethodontid Northwest Italian Cave Salamander (Speleomantes strinatii (Aellen, 1958)), a terrestrial generalist predator. Salamanders were sampled in northwestern Italy on the forest floor in autumn (n = 49) and spring (n = 47) along with their available prey. In autumn, when trophic resources showed a twofold reduction in abundance, the population trophic niche width (TNW = 2.58) was significantly broader than during spring (TNW = 2.25), and in both seasons, individual specialization (IS) was significantly higher than expected by chance (P = 0.001). There were no sexual or ontogenetic differences in IS within each season, but IS in autumn was significantly higher than in spring (IS = 0.34 and IS = 0.41, respectively; P = 0.01). These findings are in accordance with the niche variation hypothesis, which predicts a positive relationship between TNW and IS. Therefore, while the population became more generalist, individual salamanders shifted towards a more specialized diet by adapting their feeding behaviour to changes in prey availability.
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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".