High conservation value forests for burn‐associated saproxylic beetles: an approach for developing sustainable post‐fire salvage logging in boreal forest
Bibliographic record
Abstract
Abstract Fire‐killed timber is considered as a loss of potential revenues and is thus increasingly salvaged, though not without concerns for biodiversity conservation. Indeed, a large diversity of burn‐associated saproxylic beetles use recently burned trees. This study intends to reduce potential impacts of salvage logging on biodiversity by identifying high conservation value forests ( HCVF s) for burn‐associated beetles, which are considered the most at risk. In five burns ignited naturally in 2010 in the eastern Canadian boreal forest, we selected 31 and 29 stands of black spruce and jack pine respectively. Three 50‐cm bole segments were retrieved from each stand and placed in emergence cages to measure tree utilisation by saproxylic beetles. This yielded 7235 beetles from 103 taxa, of which 67 were considered rare (<5% occurrence in logs) and 36 as common taxa (>5% occurrence in logs). Among the common taxa, we identified six groups of ecologically related species using co‐occurrence‐based hierarchical clustering, among which three were mainly formed by opportunistic species that are currently of little concern in a post‐fire logging context. The three other groups were formed by burn‐associated species that could be affected by salvage logging. HCVF s include jack pine stands and large trees of either tree species of low‐ to mid‐range burn severity. We also recommend retaining the periphery of burned stands, as ecotones are rich habitats used by several burn‐associated species that are found in low numbers in green forests but they benefit from burned habitats by increasing their populations significantly.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".