Direct effects of lightning in temperate forests: a review and preliminary survey in a hemlock–hardwood forest of the northern United States
Bibliographic record
Abstract
Lightning strikes millions of trees worldwide each year, yet structured lightning damage surveys are relatively rare. Estimates drawn from the literature suggest that lightning directly or indirectly kills up to 4% of large canopy trees in a stand annually. Lightning is a major cause of death for pines in southeastern US forests and for large cacti in some deserts, but its landscape-level effects exclusive of fire at higher latitudes are poorly known. We quantified damage to trees from lightning and other sources in hemlock–hardwood forests of the Huron Mountain Region of Michigan, USA. This region receives ca. 100 cloud-to-ground lightning flashes per year, with most occurring in May to August. We recorded abiotic and biotic damage on 309 trees distributed among nine transects, each >2 km long. None of the transect trees had lightning scars, and we observed only 14 clear cases of lightning damage among thousands of trees examined during associated meander surveys (each ca. 0.5 ha). This damage was more commonly associated with emergent stature (50% of struck trees) and higher rates of biotic damage (50%) than we observed in the 309 transect trees (22% emergent status and 16% incidence of biotic damage). Nearly all (93%) of the lightning damaged trees were conifers, suggesting that either susceptibility to, or response to, lightning strikes has a phylogenetic basis. These preliminary results provide a foundation for comparative studies in other forests. Accurate quantification of lightning-induced tree mortality will improve forest turnover models and facilitate predictions of future forest structure under conditions of increased lightning frequency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".