Use of cast antlers to assess antler size variation in red deer populations: effects of mast seeding, climate and population features in Mediterranean environments
Bibliographic record
Abstract
Abstract Fundamental understanding of the factors influencing cervid antler size, development and investment has been traditionally drawn from harvest data. However, depending on the hunting tactic, harvest data may not represent a random sample of the population leading to possible inferential biases. Cast antlers may represent an alternative, cost‐effective and non‐invasive method. We used 4756 red deer (Cervus elaphus L.) cast antlers collected during a 10‐year period to evaluate the relationship between annual antler gross score and three key environmental components that determine habitat quality and resource availability in Mediterranean systems: (1) population traits (density and male age structure), (2) acorn yield and (3) a proxy of plant productivity [Real Bioclimatic Index(RBI)]. Population traits and acorn yield were measured before antler formation (autumn/winter) whereasRBIwas calculated before (autumn/winter) and during (spring) antler formation. Population traits explained the highest amount of variance in antler score, followed by acorn yield and springRBI, while no effect was found for autumn/winterRBI. Antler gross score was negatively related to population density but positively associated with acorn yield, springRBIand male age structure. Interestingly, a significant interaction between population traits and acorn yield suggests a disproportional effect of population traits on antler size during non‐mast years (poor acorn crops), whereas no significant population effect was observed during mast years. Similarly, we found a positive effect of springRBIon antlers when density was medium or low and/or age structure was balanced or older. These findings have important ecological implications in environments with high inter‐annual resources variability where high population densities lead to strong intraspecific competition during years of low food availability (e.g. during non‐mast years or drier springs), producing large antler size variation. Finally, although cast antlers reflect changes in environmental conditions we do not recommend their use unless reliable data on age structure is available.
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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 | 0.001 |
| 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) | 0.000 | 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".