First Spatial Reconstruction of Past Fires in Temperate Europe Suggests Large Variability of Fire Sizes and an Important Role of Human-Related Ignitions
Bibliographic record
Abstract
The spatial component of past forest fires in temperate Europe has been little studied, despite the value of such data in quantifying human and natural factors driving fire activity and associated forest dynamics. Changes in fire regimes reported across a range of ecosystems call for a better understanding of variability in historic fires and may help define reference points that can be relied upon when discussing climate change effects. We provide the first dendrochronological reconstruction of historical fire sizes in Central Europe and analyze the minimum extent of fires during the last four centuries in a 9.2 km 2 (920 ha) conifer-dominated section of Białowieża Forest, one of the largest continuous lowland forests of the subcontinent. We recorded 82 fires between 1666 and 1946, using 275 sample trees, while 92% of fires (76 out of 82) spread beyond the studied area. Fires varied considerably in size, from events recorded at only one site (1–200 ha) to fires recorded in more than half of the studied area, thus exceeding 500 ha in size. The fire cycle was 11 years over the whole study period, with three distinct periods revealed by the regime shift analysis. In the years 1670–1750, the fire cycle averaged 12 years. It shortened to 7 years between 1755–1840 and increased to 22 years over the 1845–1955 period. In comparison with present day data, the reconstructed fire density of 3.2 fires per 100 km 2 (10 000 ha) and year exceeded lightning ignition density by one to two orders of magnitude, suggesting a significant contribution of human-related ignitions. Our results highlight the important role of fire disturbance in Białowieża Forest and provide critical baseline information to design biological conservation strategies for European forests.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".