Comparison of the distribution and phenology of Arctic Mountain plants between the early 20th and 21st centuries
Bibliographic record
Abstract
Arctic plants are adapted to climatic variability, but their long-term responses to warming remain unclear. Responses may occur by range shifts, phenological adjustments in growth and reproduction, or both. Here, we compare distribution and phenology of 83 arctic and boreal mountain species, sampled identically in the early 20th (1917-1919) and 21st centuries (2017-2018) from a region of northern Sweden that has warmed significantly. We test two compensatory hypotheses to high-latitude warming-upward shifts in distribution, and earlier or extended growth and reproduction. For distribution, we show dramatic upward migration by 69% of species, averaging 6.1 m per decade, especially boreal woodland taxa whose upward expansion has reduced arctic montane habitat by 30%. Twenty percent of summit species showed distributional shifts but downward, especially moisture-associated snowbed flora. For phenology, we detected wide inter-annual variability in the onset of leafing and flowering in both eras. However, there was no detectable change in growing-season length, relating to two mechanisms. First, plot-level snow melt data starting in 1917 demonstrated that melt date, rather than vernal temperatures, better predicts plant emergence, with snow melt influenced by warmer years having greater snowfall-warmer springs did not always result in earlier emergence because snowbeds can persist longer. Second, the onset of reproductive senescence between eras was similar, even when plant emergence was earlier by a month, possibly due to intensified summer heat stress or hard-wired 'canalization' where senescence occurs regardless of summer temperature. Migrations in this system have possibly buffered arctic species against displacement by boreal expansion and warming, but ongoing temperature increases, woody plant invasion, and a potential lack of flexibility in timing of senescence may foreshadow challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".