Recruitment of saproxylic beetles in high stumps created for maintaining biodiversity in a boreal forest landscape
Bibliographic record
Abstract
The active creation of coarse woody debris (CWD) has been suggested as a measure to preserve and restore biodiversity in managed forests. A common practice in Sweden is to create high stumps at final cutting. We evaluated the importance of high stumps for saproxylic (wood-dependent) beetles in a boreal forest landscape in central Sweden. The number of high stumps created on clearcuts was recorded and the beetle fauna under the bark of high stumps of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) was sampled. High stumps yielded only 0.13% of CWD volume and bark area in the landscape. Out of the 29 beetle species most frequently found in the landscape, high stumps were the major source of recruitment at the landscape level for only one, Hadreule elongatula (Gyllenhal). For the remaining 28 beetle species, less than 1% of the landscape's population occurred in high stumps on clearcuts. The abundance of H. elongatula increased with the area of the surrounding forest land that was covered by clearcuts within a radius of 1000 m. This is the first example of a saproxylic species associated with clearcuts, in contemporary forest landscapes, for which such an occurrence pattern has been documented.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".