Behavioral buffering of extreme weather events in a high‐Arctic herbivore
Bibliographic record
Abstract
Abstract As global warming advances, there is a growing concern about the impact of extreme weather events on ecosystems. In the Arctic, more frequent unseasonal warm spells and rain‐on‐snow events in winter cause changes in snow‐pack properties, including ground icing. Such extreme weather events are known to have severe effects across trophic levels, for instance, causing die‐offs of large herbivores. However, the extent to which individuals and populations are able to buffer such events through behavioral plasticity is poorly understood. Here, we analyze responses in space use to rain‐on‐snow and icing events, and their fitness correlates, in wild reindeer in high‐Arctic Svalbard. Range displacement among GPS ‐collared females occurred mainly in icy winters to areas with less ice, lower over‐winter body mass loss, lower mortality rate, and higher subsequent fecundity, than the departure area. Our study provides rare empirical evidence that mammals may buffer negative effects of climate change and extreme weather events by adjusting behavior in highly stochastic environments. Under global warming, behavioral buffering may be important for the long‐term population persistence in mobile species with long generation time and therefore limited ability for rapid evolutionary adaptation.
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 | 0.000 | 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".