Fire scars reveal variability and dynamics of eastern fire regimes
Bibliographic record
Abstract
Fire scar evidence in eastern North America is sparse and complex but shows promise in defining the dynamics of these fire regimes and their influence on ecosystems. We review fire scar data, methods, and limitations, and use this information to identify and examine the factors influencing fire regimes. Fire scar data from studies at more than 40 sites in Eastern North America document fire regimes in forests with oak. Fire frequency was highly variable in both time and space even at regional scales (less than 500,000 ha). Many sites burned frequently (2- to 3-year mean fire intervals) while nearby sites (less than 40 km distant) burned infrequently (mean fire intervals more than 20 years). The fire scar record shows that major factors controlling temporal differences in fire regimes are changes in human population density, culture, and annual drought. Spatial differences in fire regimes are influenced by regional temperature, human population density, and topographic resistance to the spread of fire. Severe fire years (more than 10 percent of trees scarred at the sites) were associated with strong regional droughts that covered most of the Eastern United States and southern Ontario, Canada. Major fire years in Eastern North America occurred about 3.6 times per century before suppression efforts in forests with an oak component. Fire regimes with numerous human ignitions were more influenced by droughts. We synthesize mean fire intervals during the pre-European settlement period using an empirically derived regression model. The model was developed using two variables to predict broad scale spatial differences in fire frequency based on fire interval data derived from dendrochronologically dated fire scarred trees. Sixty-three percent of the variance in mean fire intervals was explained by mean maximum temperature and 12 percent by mapped human population density and historical documentation. The model is used to map coarse scale fire intervals in forested regions of the Eastern United States.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".