What is the effect of prescribed burning in temperate and boreal forest on biodiversity, beyond tree regeneration, pyrophilous and saproxylic species? A systematic review protocol
Bibliographic record
Abstract
Abstract Background Forests set aside from productive forestry are often considered best conserved by non-intervention. However, biodiversity is often maintained in natural forests by a background level of disturbance, which, in some forests, takes the form of forest fires. Set-aside forests may therefore benefit from continuation of such disturbances, which, in forests under protection, must be managed anthropogenically. While the effects of prescribed burning on tree regeneration and on pyrophilous and/or saproxylic species in some regions are well known, effects on other organisms are less clear and/or consistent. It would be valuable to broaden the knowledge of how prescribed burning affects forest biodiversity, particularly because this practice is increasingly considered as a conservation management intervention. The primary aim of the proposed systematic review is to clarify how biodiversity is affected by prescribed burning in temperate and boreal forests. The ultimate purpose of the review is to investigate whether and how such prescribed burning may be useful as a means of conserving or restoring biodiversity, beyond that of pyrophilous and saproxylic species, in forest set-asides. Methods The review will examine primary field studies of how prescribed burning affects biodiversity in boreal and temperate forests. We will consider studies made in such forests anywhere in the world, and will include forests both in protected areas and under commercial management. Non-intervention will be used as a comparator. Relevant outcomes will include a range of measures of biodiversity, including abundance and diversity, but not of pyrophilous and saproxylic species. Relevant studies will be taken from a recent systematic map of the evidence on biodiversity impacts of active management in forests set aside for conservation or restoration. Additional searches and a search update will be undertaken in a subset of databases from the systematic map, using a search string targeted to identify studies focused on prescribed burning interventions. Searches for additional literature will be made in the bibliographies of existing reviews of forest burning. Traditional academic literature and grey literature in English, French, Swedish and Finnish will be considered. Stakeholders who engage in prescribed burning will be asked to provide relevant grey literature.
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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 | 0.061 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".