Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra
Bibliographic record
Abstract
Arctic wildlife is often presented as being highly at risk in the face of current climate warming. We use the long-term (up to 24 years) monitoring records available on Bylot Island in the Canadian Arctic to examine temporal trends in population attributes of several terrestrial vertebrates and in primary production. Despite a warming trend (e.g. cumulative annual thawing degree-days increased by 37% and snow-melt date advanced by 4-7 days over a 23-year period), we found little evidence for changes in the phenology, abundance or productivity of several vertebrate species (snow goose, foxes, lemmings, avian predators and one passerine). Only primary production showed a response to warming (annual above-ground biomass of wetland graminoids increased by 123% during this period). We nonetheless found evidence for potential mismatches between herbivores and their food plants in response to warming as snow geese adjusted their laying date by only 3.8 days on average for a change in snow-melt of 10 days, half of the corresponding adjustment shown by the timing of plant growth (7.1 days). We discuss several reasons (duration of time series, large annual variability, amplitude of observed climate change, nonlinear dynamic or constraints imposed by various rate of warming with latitude in migrants) to explain the lack of response by herbivores and predators to climate warming at our study site. We also show how length and intensity of monitoring could affect our ability to detect temporal trends and provide recommendations for future monitoring.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".