Predation as a probable mechanism relating winter weather to population dynamics in a North American porcupine population
Bibliographic record
Abstract
Abstract An abundance index of an eastern Quebec population of North American porcupines ( Erethizon dorsatum ) has cycled with superimposed periodicities of 11 and 22 years from 1868 to 2000. This cycle closely followed 11‐ and 22‐year cycles in solar irradiance and local weather (e.g., winter precipitation and spring temperature), generating the hypothesis that solar activity may affect porcupine abundance through effects on local weather. We investigated the mechanisms linking porcupine abundance to local weather conditions using a 6‐year study (2000–2005) involving individual mark‐recapture, radio tracking, seasonal survival analyses and identification of mortality causes. Summer (May–August) survival was high and constant over the study period, whereas winter (August–May) survival was lower and varied during the duration of our study. Variations in local winter precipitation explained 89% of the variation in winter survival. Porcupine predation rates appeared strongly related to snow conditions; 95% of depredated porcupines were killed when snow was covering the ground, and predation rates were higher in years with increased winter precipitation. Our data thus support the hypothesis that changes in predation rates under different snow conditions were the mechanism relating climate to porcupine population dynamics, via modifications of the local predator–prey interactions and impacts on porcupine winter survival. Our study adds to the growing body of evidence supporting an effect of climate on predator–prey processes. Also, it identifies one possible mechanism involved in the relationship between solar irradiance and porcupine population cycles observed at this study site over a 130‐year period.
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 | 0.001 |
| 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) | 0.001 | 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".