Drivers and demographic consequences of seasonal mass changes in an alpine ungulate
Bibliographic record
Abstract
We know little about the determinants and demographic consequences of the marked seasonal mass changes exhibited by many northern and alpine mammals. We analysed 43 years of data on individual winter mass loss (the difference between mass in early June and mass in mid-September the previous year) and summer mass gain (the difference between mass in mid-September and in early June of the same year) in adult bighorn sheep (Ovis canadensis). We calculated relative seasonal mass change as a proportion of individual body mass at the start of each season. We first examined the effects of weather and population density on relative changes in body mass. We then assessed the consequences of relative seasonal mass changes on reproduction. Mean April-May temperature was the main driver of relative seasonal mass changes: warm springs reduced both relative winter mass loss and summer mass gain of both sexes, likely partially due to a trade-off between growth rate of plants and duration of access to high-quality forage. Because these effects cancelled each other, spring temperature did not influence mass in mid-September. Mothers that lost relatively more mass during the winter had lambs that gained less mass during summer, likely because these females allocated fewer resources to lactation. Winter survival of lambs increased with their summer mass gain. In males, relative mass loss during winter, which includes the rut, did not influence the probability of siring at least one lamb, possibly indicating that greater mating effort did not necessarily translate into greater reproductive success. Our findings improve our understanding of how weather influences recruitment and underline the importance of cryptic mechanisms behind the effects of climate change on demographic traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".