Plastic adaptations of foraging strategies to variation in forage quality in Alpine chamois (<i>Rupicapra rupicapra</i>)
Bibliographic record
Abstract
Foraging efficiency strongly affects individual fitness and is influenced by diverse factors such as food quality and quantity, as well as intra- and inter-specific interactions. We investigated whether Alpine chamois (Rupicapra rupicapra (Linnaeus, 1758)) in a protected area in the Swiss Alps adapted their foraging behaviour to forage availability and quality by modeling the bite and step rates of individuals on vegetation nitrogen content, relative plant cover, sex, daytime, air temperature, and slope. Vegetation characteristics were derived using remote sensing data from airborne imaging spectroscopy data sets and feeding locations determined using a theodolite. Chamois increased their bite rates with decreasing forage nitrogen content, decreasing slope, and increasing temperature. Step rates were higher at high temperatures and decreased with increasing relative plant cover. Males showed higher bite rates and lower step rates than females. Daytime had no influence on either bite or step rates. An increase in bite rate may represent a plastic adaptation of foraging behaviour to compensate for lower nutritional quality of the available vegetation. Our results show variability in foraging behaviour according to both vegetation characteristics and physical environment and emphasize the use of remote sensing data to investigate relationships between habitat and subtle behavioural adaptations in ungulates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".