Saproxylic beetle tolerance to habitat fragmentation induced by salvage logging in a boreal mixed‐cover burn
Bibliographic record
Abstract
Abstract Saproxylic insect assemblages associated with burned forests are generally abundant and species rich, consisting of a mix of pyrophilous and secondary, opportunistic species depending on time elapsed since disturbance. Life‐history traits associated with each group suggest that they may respond differentially to habitat fragmentation caused by salvage logging, with pyrophilous species having a much higher dispersal potential. In a 2‐year‐old burn highly fragmented by pre‐ and post‐fire logging, we sampled saproxylic beetles in coniferous and broadleaf burned residual stands along a gradient of spatial context including intensity of fragmentation and isolation from source habitat using Lindgren multiple‐funnels traps. Beetle assemblages differed in composition between coniferous and broadleaf burned stands, with secondary users dominating the latter. Pyrophilous species increased in abundance with distance from the edge and avoided unburned patches within the fire. Secondary users did not respond negatively to fragmentation or isolation of burned habitats, with one exception, the alleculid I somira quadristriata (Couper), being overall diverse and abundant throughout the study area regardless of salvage logging prevalence. No deleterious effects of isolation were thus detected in the occurrence patterns of secondary users, even up to 8 km from the edge. Our results suggest that older burns, especially those having some broadleaf cover, are intensively used by non‐pyrophilous saproxylic species usually associated with dead wood in green forests and may contribute to maintain broader saproxylic assemblages than originally thought, especially when considering the importance of dead wood volume pulses associated with fire in boreal forests.
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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 | 0.000 | 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) | 0.001 | 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".