Fire ecology of a tree glacial refugium on a nunatak with a view on Alpine glaciers
Bibliographic record
Abstract
In paleoecology, the function of biomass as a fire driver has become a focus of attention in cold ecosystems, and concerns have been raised about climate in this context. Little is known about the fire frequency and fire-plant relationships during glaciation when woodlands were limited and the climate was cold. Fire history and tree biomass were reconstructed from sedimentary charcoal and macroremains, respectively, archived in lake sediments from the western Alps. Two nunataks were investigated, both with lacustrine sediments covering the last 21 000 yr at least. During the Last Glacial Maximum (LGM) and the Lateglacial, fires occurred only on the nunatak sheltering woody plants. Cembra pine (Pinus cembra) and larch (Larix decidua) survived above glaciers during the LGM, thus evidencing a biological refugium and supporting the nunatak theory. We highlighted a long-term relationship between fires and dominant trees over the last 21 000 yr, where fire frequencies track the global climate and the local changes in tree biomass. Glacial climate (dry, cold) does not rule out fires. Fuel load and composition were significant fire drivers, with cembra pine dominating during colder periods with rare fires, and larch during the warmer Holocene with frequent fires. These findings increase knowledge of fire ecology in cold environments, and open perspectives in tree population genetics by considering new areas of tree glacial refugia in Europe.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".