Forage‐mediated density and climate effects on body mass in a temperate herbivore: a mechanistic approach
Bibliographic record
Abstract
The interplay between density and climate in shaping the dynamics of herbivore populations is widely acknowledged, and current research is fueled by the identification of mechanisms underlying their effects on individuals and populations. We assessed whether forage availability mediated the effects of density and winter climate on body mass of white-tailed deer (Odocoileus virginianus) yearlings by experimentally reducing deer density to 7.5 and 15 deer/km2 during eight growing seasons, and by using causal (graphical) hierarchical models and Bayesian hierarchical modeling to assess relationships. The abundance of preferred forage decreased with deer density and varied quadratically (positive parabola) with winter North Atlantic Oscillation (NAO), whereas the fall mass of yearlings increased with forage abundance and spring mass. Fall mass did not differ between experimentally reduced deer densities, yet experimental yearlings were 30% heavier than yearlings harvested at ambient densities. Hence, forage abundance simultaneously mediated the effects of density and climate on fall body mass, which was also influenced by carry-over effects of spring body mass. Our findings increase our ability to anticipate how temperate large herbivores will respond to ongoing changes in intrinsic (e.g., large-herbivore density) and extrinsic (e.g., climate) factors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".