Denning phenology and reproductive success of wolves in response to climate signals
Bibliographic record
Abstract
Abstract Arctic and boreal ecosystems are experiencing rapid changes in temperature and precipitation regimes. Subsequent shifts in seasonality can lead to a mismatch between the timing of resource availability and species’ life-history events, known as phenological or trophic mismatch. Although mismatch has been shown to negatively affect some northern animal populations, longer-term impacts across large regions remain unknown. In addition, animals may rely on climate cues during preceding seasons to time key life history events such as reproduction, but the reliability of these cues as indicators of subsequent resource availability has not been examined. We used remote sensing and gridded spatial data to evaluate the effect of climate factors on the reproductive phenology and success of a wide-ranging carnivore, the gray wolf ( Canis lupus ). We used global positioning system (GPS) location data from 388 wolves to estimate den initiation dates ( n = 227 dens within 106 packs) and reproductive success in eight populations across northwestern North America from 2000 to 2017. Spring onset shifted 14.2 d earlier, on average, during the 18-year period, but the regional mean date of denning did not change. Preceding winter temperature was the strongest climatic predictor of denning phenology, with higher temperatures advancing the timing of denning. Winter temperature was also one the strongest and most reliable indicators of the timing of spring onset. Reproductive success was not affected by timing of denning or synchrony with spring onset, but improved during cooler summers and following relatively dry autumns. Our findings highlight a disconnect between climate factors that affect phenology and those that affect demography, suggesting that carnivores may be resilient to shifts in seasonality and yet sensitive to weather conditions affecting their prey at both local and regional scales. These insights regarding the relationship between climate and carnivore demography should improve predictions of climate warming effects on the highest trophic levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".