BIOTIC AND ABIOTIC REGULATION OF LIGHTNING FIRE INITIATION IN THE MIXEDWOOD BOREAL FOREST
Bibliographic record
Abstract
Lightning fire is the dominant natural disturbance of the western mixedwood boreal forest of North America. We quantified the independent effects of weather and forest composition on lightning fire initiation (a detected and recorded fire start) patterns in Alberta, Canada, to demonstrate how these biotic and abiotic components contribute to ecosystem dynamics in the mixedwood boreal forest. We used logistic regression to describe variation in annual initiation occurrence among 10,000-ha landscape units (voxels) covering a 9 million-ha study region over 11 years. At a voxel scale, forest composition explained more variation in annual initiation than did weather indices. Initiations occurred more frequently in landscapes with more conifer fuels (Picea spp.), and less in aspen-dominated (Populus spp.) ones. Initiations were less frequent in landscapes that had recently burned. Variation in initiation was also influenced by joint weather-lightning indices, but to a lesser degree. For each voxel, these indices quantified the number of days in the fire season when moisture levels were low and lightning was detected. Regional indices of fire weather severity explained substantial interannual variation of initiation, and the effect of forest composition was stronger in years with more severe fire weather. Our study is a conclusive demonstration of biotic and abiotic regulation of lightning fire initiation in the mixedwood boreal forest. The independent effects of forest composition emphasize that vegetation feedbacks strongly regulate disturbance dynamics in the 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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".