Local snow melt and temperature—but not regional sea ice—explain variation in spring phenology in coastal Arctic tundra
Bibliographic record
Abstract
The Arctic is undergoing dramatic environmental change with rapidly rising surface temperatures, accelerating sea ice decline and changing snow regimes, all of which influence tundra plant phenology. Despite these changes, no globally consistent direction of trends in spring phenology has been reported across the Arctic. While spring has advanced at some sites, spring has delayed or not changed at other sites, highlighting substantial unexplained variation. Here, we test the relative importance of local temperatures, local snow melt date and regional spring drop in sea ice extent as controls of variation in spring phenology across different sites and species. Trends in long-term time series of spring leaf-out and flowering (average span: 18 years) were highly variable for the 14 tundra species monitored at our four study sites on the Arctic coasts of Alaska, Canada and Greenland, ranging from advances of 10.06 days per decade to delays of 1.67 days per decade. Spring temperatures and the day of spring drop in sea ice extent advanced at all sites (average 1°C per decade and 21 days per decade, respectively), but only those sites with advances in snow melt (average 5 days advance per decade) also had advancing phenology. Variation in spring plant phenology was best explained by snow melt date (mean effect: 0.45 days advance in phenology per day advance snow melt) and, to a lesser extent, by mean spring temperature (mean effect: 2.39 days advance in phenology per °C). In contrast to previous studies examining sea ice and phenology at different spatial scales, regional spring drop in sea ice extent did not predict spring phenology for any species or site in our analysis. Our findings highlight that tundra vegetation responses to global change are more complex than a direct response to warming and emphasize the importance of snow melt as a local driver of tundra spring phenology.
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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 | 0.000 | 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 | 0.000 |
| 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 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".