A pan-Canadian assessment of empirical research on post-disturbance recovery in the Canadian Forest Service
Bibliographic record
Abstract
Information about post-disturbance regeneration success and successional dynamics is critical to predict forest ecosystem resistance and resilience to disturbances and climate change. Our objective was to identify and classify post-disturbance empirical research conducted by the Canadian Forest Service (CFS) of Natural Resources Canada and their collaborators to provide guidance on future research needs, improving our understanding of post-disturbance recovery in a Canadian context. To achieve our objective, we collected and classified peer-reviewed and non-published literature produced by the CFS between 1998 and 2020 that concerned post-disturbance ecology. We focused on research addressing natural or anthropogenic disturbances, such as wildfires, pest outbreaks, windthrows, forest management, seismic lines, and those that studied processes related to soil, vegetation, fauna, hydrology, and microbial communities. We found that forest harvesting was the disturbance most studied by CFS between 1998 and 2020, followed by fire. Despite the fact that large, forested areas are affected annually by pests, studies on recovery after pest outbreaks were scarce. Other disturbances, such as mining and seismic lines or other abiotic disturbances were rare in CFS literature. Most studies (70%) examined changes in vegetation related to forest management and fire and they were mainly focussed on post-disturbance tree regeneration success. Post-disturbance changes in understory species diversity were also well-studied. Our results provide a geographic overview of CFS research on post-disturbance recovery in Canada and enable the identification of key knowledge gaps. Notably, research focusing on recovery after natural disturbances was underrepresented in the assessed literature compared to studies centered around harvesting. Long-term research sites, chronosequences that substitute space for time, and studies focused on consecutive disturbances are especially important to maintain and establish sustainable forest management strategies in the face of climate change.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.061 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".