Climate drove the fire cycle and humans influenced fire occurrence in the East European boreal forest
Bibliographic record
Abstract
Abstract Understanding long‐term forest fire histories of boreal landscapes is instrumental for parameterizing climate–fire interactions and the role of humans affecting natural fire regimes. The eastern sections of the European boreal zone currently lack a network of annually resolved and centuries‐long forest fire histories. To fill in this knowledge gap, we dendrochronologically reconstructed the 600‐year fire history of a middle boreal pine‐dominated landscape of the southern part of the Republic of Komi, Russia. We combined the reconstruction of fire cycle (FC) and fire occurrence with the data on the village establishment and climate proxies and discussed the relative contribution of climate versus human land use in shaping historic fire regimes. Over the 1340–1610 ce period, the territory had a FC of 66 years (with the 90% confidence envelope of 56.8 and 78.6 years). Fire activity increased during the 1620–1730 ce period, with the FC reaching 32 years (31.0–34.7 years). Between 1740–1950, the FC increased to 47 years (41.9–52.0). The most recent period, 1960–2010, marks FC's historic maximum, with the mean of 153 years (102.5–270.3). Establishment of the villages, often as small harbors on the Pechora River, was associated with a non‐significant increase in fire occurrence in the sites nearest the villages (p = 0.07–0.20). We, however, observed a temporal association between village establishment and fire occurrence at the scale of the whole studied landscape. There was no positive association between the former and the FC. In fact, we documented a decline in the area burned, following the wave of village establishment during the second half of the 1600s and the first half of the 1700s. The lack of association between the dynamics of FC and the dates of village establishments, and the significant association between large fire years and the early and latewood pine chronologies, used as historic drought proxy, indirectly suggests that the climate was the primary control of the landscape‐level FCs in the studied forests. Pine‐dominated forests of the Komi Republic may hold a unique position as the ecosystem with the shortest history of human‐related shifts in fire cycles across the European boreal region.
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 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".