Trends in post-disturbance recovery rates of Canada’s forests following wildfire and harvest
Bibliographic record
Abstract
The recovery of forests following stand-replacing disturbance is of widespread interest; however, there is both a lack of definitional clarity for the term “recovery” and a dearth of empirical data on the rates of forest recovery associated with different disturbance types. We conducted a quantitative review of literature to determine recovery times following wildfire and timber harvest and to evaluate variation in recovery rates across Canada’s diverse forest ecosystems. Recovery was assessed according to the rate of change associated with certain forest structural attributes that have traditionally been used as indicators of forest growth and productivity. The recovery of forest canopy cover, tree height, and stand basal area varied at rates that depended on disturbance type, forest biome, and ecozone. We found that, on average, it took 5–10 years, depending on factors such as location and species, for most forest ecosystems of Canada to attain a benchmark canopy cover of 10% after wildfire or harvest. Similarly, regenerating stands in Canada’s boreal forests were capable of attaining average heights of 5 m within five to ten years after wildfire or harvest. Stands in the Boreal Plains ecozone post-harvest reached stand basal area, benchmarked at 10 m2 ha−1, faster than those in the Boreal Shield, attributable to differences in tree species composition and the rich mineral deposits of the Boreal Plains. Overall, recovery of canopy cover, tree height, and stand basal area was similar or more rapid following wildfire than harvest. Our review provides temporal benchmarks for gauging recovery times after disturbance. Building upon these temporal benchmarks, and conditioned by disturbance type, site conditions, and location, we present opportunities for using dense time series of remotely sensed data to inform on regional and national trends in forest recovery following disturbance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".