Climate drives body mass changes in a mountain ungulate: shorter winters lead to heavier Alpine ibex
Bibliographic record
Abstract
Climate affects seasonality and plant phenology, which can influence seasonal body mass dynamics of herbivores in temperate environments. We investigated long‐term trends of seasonal body mass changes in male Alpine ibex Capra ibex . We used SEM to test direct and indirect relationships between body mass, mass changes and environmental and climatic variables. Individually recognizable Alpine ibex were weighed repeatedly between 2000 and 2022 in Gran Paradiso National Park (Italy). Autumn mass increased substantially over these two decades, up to 15% in some age classes. Over the same time frame, both summer mass gain and winter mass loss decreased, suggesting that heavier autumn body mass was due to the cumulative effects of reduced mass loss over several winters. The environmental factor with the strongest effects on winter mass changes was the starting date of vegetation green‐up at low altitude, where ibex gather after winter to feed on new growth vegetation. Early springs led to lower winter mass loss, likely because ibex relied on stored fat for a shorter period and had greater access to forage. High population density also increased winter mass loss. Environmental conditions and resource availability, possibly also influenced by density in winter and early spring, seem therefore to directly affect the body mass dynamics of male Alpine ibex, while the effect of summer conditions appears less relevant. By affecting seasonal body mass dynamics, climate change may have consequences for life history and population dynamics of mountain herbivores, for example via earlier access of young males to reproduction.
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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 | 0.000 |
| 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".