Disappearing green: Shrubs decline and bryophytes increase with nine years of increased snow accumulation in the High Arctic
Bibliographic record
Abstract
Abstract Question How does increased snow depth affect plant community composition of High Arctic tundra, and can the Normalized Differential Vegetation Index ( NDVI ) detect induced changes? Location Adventdalen, Spitsbergen, Svalbard (78°10′ N, 16°04′ E). Methods We manipulated snow depth on the tundra using fences, resulting in Deep , Medium , and Ambient snow regimes. Increased snow led to warmer winter soil temperatures, a delayed onset of growing season and wetter conditions during the early growing season. Plant community composition of living and dead plant material was recorded after nine years. NDVI was measured at the plot level using a handheld sensor. Results Community composition and the abundance of typically dominant shrub species were substantially different in the Deep compared to the Ambient regime. Deep had lower cover of live shrubs ( Cassiope tetragona, Dryas octopetala and Salix polaris ) and Luzula confusa , and higher cover of dead shrubs ( Cassiope and Dryas ) compared to the other snow regimes. Bryophyte cover was highest in Medium . NDVI was positively correlated to the cover of living vascular plants and negatively correlated to cover of dead vascular plants. Accordingly, Deep snow regime had reduced NDVI , reflecting the contribution of dead Cassiope and Dryas . Conclusion Snow regime strongly influenced community composition in High Arctic plant communities. Enhanced snow regimes had more dead shrubs, reduced Luzula and increased bryophyte cover than ambient conditions. These differences were detectable by handheld NDVI sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".