Life‐history trade‐offs and environmental variability shape reproductive demography in a mountain ungulate
Bibliographic record
Abstract
Alpine ecosystems are changing rapidly with implications for the demography of alpine organisms. Here, we studied a sentinel species of mountain environments-the mountain goat (Oreamnos americanus)-to examine hypotheses about intrinsic and extrinsic drivers of reproductive demography using long-term data collected from individually marked animals across a broad spatiotemporal extent (n = 180 females, 3 study areas, 17 years) in coastal Alaska. Our analyses revealed the importance of life-history trade-offs and environmental variability on reproductive performance. The cost of reproduction, defined as the impact of reproducing the previous year on the probability of current year parturition, was high, especially for young, largely primiparous females (13%-32% reduction) and old, senescing individuals (27%-43% reduction); parturition of prime-aged individuals was relatively unaffected (2% reduction). Winter snow accumulation, which alters energetic expenditure and forage availability, exerted strong negative effects on reproduction (20%-35% reduction, depending on age). The relationship between temperature during the preceding summer's growing season and reproduction was likewise negative, although weaker and more variable (10%-15% reduction). Demographic modelling indicated that snow exerted stronger effects on population growth than summer temperature in part due to greater variability in snow versus temperature among years. Our analyses further revealed that reproductive performance did not affect subsequent survival of mothers or offspring, suggesting mountain goats employ a 'risk-sensitive', conservative reproductive strategy that prioritizes survival over reproduction. Taken together, these results fill an important knowledge gap by providing novel insights about the interplay between life-history trade-offs and environmental variation, and how they shape the reproductive demography of climate-sensitive wildlife.
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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 | 0.001 |
| 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 | 0.000 |
| 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".