Plant diversity dynamics over space and time in a warming Arctic
Bibliographic record
Abstract
The Arctic is warming four times faster than the global average, and plant communities are responding through shifts in species abundance, composition and distribution. However, the direction and magnitude of local plant diversity changes have not been explored thus far at a pan-Arctic scale. Using a compilation of 42,234 records of 490 vascular plant species from 2,174 plots at 45 study areas across the Arctic, we quantified how species richness and composition have changed over time during a period of up to four decades (1981 – 2022), and identified the geographic, climatic and biotic drivers behind these changes. Despite plant species richness being greater at lower latitudes and warmer plots, pan-Arctic species richness did not change directionally over time at the plot level. However, 99% of the plots experienced changes in species abundance, with 66% of plots either gaining or losing species. Species richness increased most where temperatures had warmed most over time, and shrub expansion led to greater species losses and decreasing richness. Yet, Arctic plant communities did not become more similar to each other over time, suggesting that no biotic homogenisation has occurred thus far. Overall, we found that Arctic plots changed in richness and composition in all possible directions, yet climate and biotic interactions still emerged as the main drivers of directional change. Our results show a variety of diversity trends, which could be precursors of future changes for Arctic plant biodiversity, ecosystem function, wildlife habitats and livelihoods for Arctic Communities.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".