Snag abundance 20, 30, and 40 years following fire and harvesting in boreal forests
Bibliographic record
Abstract
The retention of standing dead trees (snags) has become an important conservation concern, especially when forest management efforts attempt to emulate natural disturbance. We investigate the abundance of snags within Ontario's boreal forest following 10–20, 21–30, and 31–40 years of both fire and forest harvest disturbance over a 24 000-km2area. Fire frequency varied considerably, with 90% of the fires in the study area occurring in the 1970s. We did not detect differences in basal area of snags (m2/km2) between burned and harvested stands. However, differences occurred in dead-stem density (number/km2); the burned stands produced more snags in the 21- to 30-year post-disturbance class and the harvested stands produced more snags in the 31- to 40-year post-disturbance class. Similarly, the distribution of diameter classes of snags differed between the burned and harvested stands. In size classes greater than 32 cm (diameter at breast height), we found more snags in the harvested forests 21–40 years following disturbance. We did not find differences in the basal area of snags between disturbance types, whether they were hardwood or softwood. However, hardwood snags occurred in greater abundance in the larger diameter classes. Our findings are limited by the changing timber harvest treatments (selective harvest, clearcut, and ecological cut), the small number of disturbance events, and the variety of stand compositions. More research is required on the ecological factors influencing snag abundance to improve development of local forest management plans and to design landscapes that conserve forest structure and biodiversity. Key words: biodiversity, clearcut, conservation, coarse woody debris, dead trees, forest management, landscape, snags, wildlife
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".