Finding northernmost baselines: high variability of above-ground biomass on Eurasian polar desert islands
Bibliographic record
Abstract
As the Arctic rapidly warms, a major change in its vegetation and biomass is expected. Understanding the current state of Arctic plant biomass is crucial due to its role in the surface energy budget and ecosystem carbon storage yet challenging due to logistical and methodological limitations. Arctic polar deserts are one of the most vulnerable terrestrial biomes on Earth, highly sensitive to climate change, and likely also the most understudied. During the 2021 Arctic Century expedition, we performed vegetation surveys and collected aboveground plant and lichen biomass samples at 8 sites on the Severnaya Zemlya archipelago, Franz Josef Land, Vize, and Uedineniya Islands, filling an important spatial gap in biomass measurements in the Arctic. For these study sites, we explored three different methods for estimating plant and lichen biomass: using 1) in-situ species richness, 2) in-situ cover, and 3) remotely sensed NDVI and plant cover. We found no relationship between total in-situ species richness and in-situ biomass, but in-situ lichen species richness significantly predicted lichen biomass. Remotely sensed NDVI had a limited explanatory power for in-situ biomass. However, drone-derived plant cover predicted in-situ biomass measurements well and could thus be used to effectively estimate landscape-level biomass of Arctic polar deserts. Our findings reveal that biomass varies widely among the sites, with an almost complete absence of biomass on Graham-Bell and Komsomolets islands, intermediate levels on October Revolution Inland and Pioneer islands, and maximum biomass found on Vize Island, although the results for this site are highly uncertain. Our findings could be used as a baseline to document future biomass changes in polar deserts. We propose our cover-based approach as an alternative to NDVI-based estimates of polar desert plant and lichen biomass and discuss its uncertainties and limitations.
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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 | 0.001 | 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".