Anomalous snow events increase mortality for a winter-adapted species
Bibliographic record
Abstract
Winter is a costly time for animals, requiring individuals to adapt to increased energetic costs and reduced resources. Porcupines ( Erethizon dorsatum (Linnaeus, 1758)) confront winter by storing and catabolizing somatic stores. Increasing temperatures and attenuated snow conditions due to climate change increase porcupine survival, but impacts of greater weather variability have not been explored. In April of 2018, an anomalously heavy and late snowstorm occurred at our long-term study site in central Wisconsin followed by multiple mortalities among adult porcupines. We assessed cause of mortality and determined nutritional condition by extracting bone marrow and quantifying lipid content. Porcupines that died following the snow event had lower fat stores than the fall 2019 group and likely died of starvation. We estimated survival of female porcupines during the winters of 2012 and 2015–2018 to assess the effects of snow conditions and nutritional condition on survival. Survival declined with increased snow depth but increased with improved nutritional condition. The mass starvation event we observed in 2018 appeared to have resulted from deep snow increasing locomotive costs and reducing nutritional condition. As climate change increases the frequency of extreme weather events, including extreme snowfalls, we predict that the frequency of such clustered mortalities will increase.
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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 | 0.001 | 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) | 0.002 | 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".