Fire is a stronger driver of forest composition than logging in the boreal forest of eastern Canada
Bibliographic record
Abstract
Abstract Aims Our study aimed to: (1) document the preindustrial (1925) forest composition prior to extensive logging; (2) document the magnitude of changes from 1925 to 2005; and (3) identify the relative influence of logging and natural disturbances as drivers of the present‐day forest composition. Location Boreal forest in central Quebec, eastern Canada. Methods We used a dense network of georeferenced historical (~1925) forest plots ( n = 30 033) to document preindustrial forest composition. We evaluated the magnitude of changes with the present‐day using modern plots (1980s to 2000s). We reconstructed a long‐term, spatially explicit history of logging, spruce budworm outbreaks ( Choristoneura fumiferana [Clem.], SBO ), and fire using historical maps and field surveys. Results In the preindustrial period, late successional coniferous taxa ( Abies balsamea and Picea spp.) dominated the landscape, whereas early successional deciduous taxa ( Betula spp. and Populus spp.) were confined to recently burned areas. In the present‐day landscape, large areas dominated by late successional coniferous taxa have been replaced by early successional deciduous taxa. Forest communities dominated by early successional deciduous taxa increased sharply throughout the study area. Logging has been a minor driver of these changes compared to fire and SBO s. Conclusions This study demonstrates the importance of documenting the long‐term history of both anthropogenic and natural disturbances in order to assess their relative contributions to the development of the present‐day forest ecosystems. Natural disturbances have remained the main drivers of forest composition during the 20th century, whereas logging played a less important role. In the current context of global change, long‐term experimental research is required to help forecast impacts of natural disturbances and forest management on boreal forest composition.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".