Modelling Post-Disturbance Successional Dynamics of the Canadian Boreal Mixedwoods
Bibliographic record
Abstract
Natural disturbances, such as fire and insect outbreaks, play important roles in natural forest dynamics, which are characterized over long time scales by changes in stand composition and structure. Individual-based forest simulators could help explain and predict the response of forest ecosystems to different disturbances, silvicultural treatments, or environmental stressors. This study evaluated the ability of the SORTIE-ND simulator to reproduce post-disturbance dynamics of the boreal mixedwoods of eastern Canada. In 1991 and 2009, we sampled all trees (including seedlings and saplings) in 431 (256 m2) plots located in the Lake Duparquet Research and Teaching Forest (western Quebec). These plots were distributed in stands originating from seven wildfires that occurred between 1760 and 1944, and which represented a chronosequence of post-disturbance stand development. We used the 1991 inventory data to parameterize the model, and simulated short- to long-term natural dynamics of post-fire stands in both the absence and presence of a spruce budworm outbreak. We compared short-term simulated stand composition and structure with those observed in 2009 using a chronosequence approach. The model successfully generated the composition and structure of empirical observations. In long-term simulations, species dominance of old-growth forests was not accurately estimated, due to possible differences in stand compositions following wildfires and to differences in stand disturbance histories. Mid- to long-term simulations showed that the secondary disturbance incurred by spruce budworm did not cause substantial changes in early successional stages while setting back the successional dynamics of middle-aged stands and accelerating the dominance of white cedar in late-successional post-fire stands. We conclude that constructing a model with appropriate information regarding stand composition and disturbance history considerably increases the strength and accuracy of the model to reproduce the natural dynamics of post-disturbance boreal mixedwoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".