Multiscale variation in drought controlled historical forest fire activity in the boreal forests of eastern Fennoscandia
Bibliographic record
Abstract
Abstract Forest fires are a key disturbance in boreal forests, and characteristics of fire regimes are among the most important factors explaining the variation in forest structure and species composition. The occurrence of fire is connected with climate, but earlier, mostly local‐scale studies in the northern European boreal forests have provided little insight into fire–climate relationship before the modern fire suppression period. Here, we compiled annually resolved fire history, temperature, and precipitation reconstructions from eastern Fennoscandia from the mid‐16th century to the end of the 19th century, a period of strong human influence on fires. We used synchrony of fires over the network of 25 fire history reconstructions as a measure of climatic forcing on fires. We examined the relationship between fire occurrence and climate (summer temperature, precipitation, and a drought index summarizing the influence of variability in temperature and precipitation) across temporal scales, using a scale space multiresolution correlation approach and Bayesian inference that accounts for the annually varying uncertainties in climate reconstructions. At the annual scale, fires were synchronized during summers with low precipitation, and most clearly during drought summers. A scale‐derivative analysis revealed that fire synchrony and climate varied at similar, roughly decadal scales. Climatic variables and fire synchrony showed varying correlation strength and credibility, depending on the climate variable and the time period. In particular, precipitation emerged as a credible determinant of fire synchrony also at these time scales, despite the large uncertainties in precipitation reconstruction. The findings explain why fire occurrence can be high during cold periods (such as from the mid‐17th to early‐18th century), and stresses the notion that future fire frequency will likely depend to a greater extent on changes in precipitation than temperature alone. We showed, for the first time, the importance of climate as a decadal‐scale driver of forest fires in the European boreal forests, discernible even during a period of strong human influence on fire occurrence. The fire regime responded both to anomalously dry summers, but also to decadal‐scale climate changes, demonstrating how climatic variability has shaped the disturbance regimes in the northern European boreal forests over various time scales.
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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 | 0.001 | 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".