Does local feeding specialization exist in Eurasian badgers?
Bibliographic record
Abstract
Several local populations of the otherwise trophic-generalist Eurasian badger (Meles meles) have been defined as specializing locally on temporally variable food resources such as earthworms (Lumbricus spp.), olive fruits (Olea europaea), or young rabbits (Oryctolagus cuniculus), owing to a lack of correlation between resource availability and use. However, theoretical models predict that temporal variation in resources reduces the probability of diet specialization. To understand the relationship between temporal resource variability and local feeding specialization, we studied temporal variation in diet composition and diversity (using fecal analysis), the availability of a temporally stable key resource, and the relation between consumption and availability of rabbits (key prey) and invertebrates (secondary prey) for a badger population previously described as specialized on young rabbits. We found strong variations in the use of different resources (including young rabbits) and in diet diversity among seasons and years. The main food resource was young rabbits during winter and spring, fruits in autumn, and reptiles in summer. Diet diversity was inversely related to consumption of young rabbits and directly related to consumption of secondary prey (invertebrates). Consumption of rabbits (both young and adults) was correlated with their abundance in the field, with a type 3 functional response in the consumption of young rabbits, which is typical of a generalist to whom alternative prey are available. There was no relationship between the abundance of invertebrates and their consumption. Our results show that badgers in the study area were not locally specialized, therefore care should be taken when referring to a population as specialized without an adequate test of the predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".