When a generalist becomes a specialist: patterns of red fox predation on roe deer fawns under contrasting conditions
Bibliographic record
Abstract
The red fox ( Vulpes vulpes (L., 1758)) functional response to roe deer ( Capreolus capreolus (L., 1758) fawn density was investigated in two Norwegian study areas characterized by a 26-fold difference in prey density and contrasting habitat characteristics. In the southern area, characterized by a fragmented agricultural landscape and high prey density, red fox consumption rates were closer to the specialist end in the specialist–generalist continuum of feeding habits. Conversely, in the northern area, dominated by boreal forest and with low prey density, the foxes displayed a type-III functional response typical of archetypal generalist predators. We suggest that differences in the predators’ feeding habits reflect differences in prey profitability, which was much higher in the southern area owing to higher prey density and to the openness of the landscape that both favour the hunting of roe deer fawns. Different functional responses produced different predation rates (25% for the southern area and 13% for the northern area) and different temporal patterns (highest predation risk for fawns born at the beginning or at the end of the birth season). Even though the understanding of a predator’s functional response is crucial for interpreting predation rates and patterns, much remains to be understood regarding its plasticity in different ecological settings. In the fawn–fox system, this might be the key factor in addressing unsolved questions regarding the adaptive value of reproductive synchrony as an antipredator strategy. Given the flexibility of the functional response and the resulting different impacts of predation with respect to birth synchrony, we suggest that reproductive synchrony evolved primarily in response to habitat seasonality and not as an antipredator strategy. Finally, our results contribute to the debate on the additive or compensatory nature of neonatal predation.
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 | 0.001 |
| 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 | 0.001 |
| 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".