Seasonal coat-colour moulting phenology of snowshoe hares in a Yukon boreal forest undergoing climate change
Bibliographic record
Abstract
Climate change is slowly influencing boreal forest ecosystems, with rising temperatures and altered snow conditions driving phenological shifts in many plant and animal species. Using 7 years (2016-2022) of camera trap data from the Kluane Lake region, Yukon, we quantified seasonal moulting phenology and coat-colour mismatch in snowshoe hares. Autumn moult started between 28 September and 3 October and completed between 5 and 11 November, with the mean moult duration ranging from 36 to 43 days. Spring moult initiated between 12 April and 27 April and completed between 16 May and 27 May, with moult duration ranging from 24 to 38 days. Contrary to our expectations, there was no evidence of delayed or advanced moulting phenology over this 7-year period. The mismatch between snowshoe hare coat colour and background showed an increasing trend and average whiteness of the snowshoe hare coat in autumn declined. Temperature and snow variables influenced various aspects of seasonal moulting phenology, in some cases in the opposite direction. Long-term studies utilizing intrinsic and high-resolution microclimatic data and behavioural observations are needed to understand how moulting phenology and mismatch affect predator-prey dynamics and snowshoe hare demography and population dynamics as climate change continues.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".