Ecological correlates of roe deer fawn survival in a sub-Mediterranean population
Bibliographic record
Abstract
We investigated the effect of body mass, spring and summer total rainfall, birth period, and local population density on the survival of 130 roe deer ( Capreolus capreolus (L., 1758)) fawns captured over seven fawning seasons (1997–2003) and radio-monitored daily. We modelled survival using the program MARK, incorporating biological questions into different models, following a priori hypotheses. The best model was selected using Akaike’s information criterion. The population was surveyed by counts and estimates were obtained using mark–resight methods. Weekly survival of roe deer fawns exhibits a pseudo-threshold time trend. Probability of survival is low (0.33 ± 0.0046) in summer, increases (0.79 ± 0.0021) in fall, and approaches the highest value typical of adult survival (0.90 ± 0.00091 and 0.96 ± 0.00021 by the end of March and May, respectively) during early spring following birth. The final model predicts that survival of roe deer fawns is positively affected by total precipitation in spring and body mass under a pseudo-threshold time variation pattern. In contrast with other studies, we did not detect any effect of population density on survival of fawns. This study contributes to the scarce knowledge about the performance of roe deer populations in Mediterranean ecosystems, providing evidence that climate variables and individual characteristics shape the vital rates of roe deer populations.
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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 | 0.001 | 0.002 |
| 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".