Prediction of Arctic plant phenological sensitivity to climate change from historical records
Bibliographic record
Abstract
Abstract The pace of climate change in the Arctic is dramatic, with temperatures rising at a rate double the global average. The timing of flowering and fruiting (phenology) is often temperature dependent and tends to advance as the climate warms. Herbarium specimens, photographs, and field observations can provide historical phenology records and have been used, on a localised scale, to predict species’ phenological sensitivity to climate change. Conducting similar localised studies in the Canadian Arctic, however, poses a challenge where the collection of herbarium specimens, photographs, and field observations have been temporally and spatially sporadic. We used flowering and seed dispersal times of 23 Arctic species from herbarium specimens, photographs, and field observations collected from across the 2.1 million km 2 area of Nunavut, Canada, to determine (1) which monthly temperatures influence flowering and seed dispersal times; (2) species’ phenological sensitivity to temperature; and (3) whether flowering or seed dispersal times have advanced over the past 120 years. We tested this at different spatial scales and compared the sensitivity in different regions of Nunavut. Broadly speaking, this research serves as a proof of concept to assess whether phenology–climate change studies using historic data can be conducted at large spatial scales. Flowering times and seed dispersal time were most strongly correlated with June and July temperatures, respectively. Seed dispersal times have advanced at double the rate of flowering times over the past 120 years, reflecting greater late‐summer temperature rises in Nunavut. There is great diversity in the flowering time sensitivity to temperature of Arctic plant species, suggesting climate change implications for Arctic ecological communities, including altered community composition, competition, and pollinator interactions. Intraspecific temperature sensitivity and warming trends varied markedly across Nunavut and could result in greater changes in some parts of Nunavut than in others.
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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) | 0.003 | 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".