Spatiotemporal Synchrony of Climate and Fire Occurrence Across North American Forests (1750–1880)
Bibliographic record
Abstract
ABSTRACT Aim Increasing aridity has driven widespread synchronous fire occurrence in recent decades across North America. The lack of historical (pre‐1880) fire records limits our ability to understand long‐term continental fire‐climate dynamics. The goal of this study is to use tree‐ring reconstructions to determine the relationships between spatiotemporal patterns in historical climate and widespread fire occurrence in North American forests, and whether they are stable through time. This information will address a major knowledge gap required to inform projections of future fire. Location North American Forests. Time Period 1750–1880 CE. Major Taxa Studied Trees. Methods We applied regionalisation methods to tree‐ring reconstructions of historical summer soil moisture and annual fire occurrence to independently identify broad‐ and fine‐scale climate and fire regions based on common inter‐annual variability. We then tested whether the regions were stable through time and for spatial correspondence between the climate and fire regions. Last, we used correlation analysis to quantify the strength of the fire‐climate associations through time. Results We found that broad‐scale historical patterns in climate and fire have strong spatial coherence. Although climate and fire regions vary over time, large core areas of the regions were stable. The association between climate and fire varied through time and was strongest in western North America, likely due to a combination of factors, such as the magnitude of drought frequency and severity, as well as varying use of fire by human communities. Main Conclusions The historical perspective gained through tree‐ring reconstructions of climate and fire patterns and their association suggests that climate‐driven synchrony of fire across large areas of the continent in recent decades is not unprecedented, will likely continue into the future, and may exhibit similar spatial patterns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".