Standing or downed dead trees — does it matter for saproxylic beetles in temperate oak-rich forest?
Bibliographic record
Abstract
For conservation of forest biodiversity, dead wood in the form of logs, snags, or cut high stumps is sometimes left or created when forests are harvested. In Scandinavia, such dead wood usually comes from conifers. For forests in temperate regions, few studies have analysed composition and species richness of beetles using dead wood of oaks ( Quercus spp). In this study in southern Sweden, I examined the occurrence of saproxylic beetles trapped at lying (logs) and standing (snags) dead wood of European oaks ( Quercus robur L. and Quercus petraea (Mattuschka) Liebl.) in 13 oak-rich mixed forests of relatively high conservation value. The assemblage of beetles differed strikingly between the lying and standing dead wood. Traps on lying dead wood, compared to traps on standing dead wood, had more fungivores and fewer primary and secondary wood boring species. Of 94 species tested for individual substrate preferences, 48 showed prevalence for different trap/substrate types. Absolute species richness was significantly higher on logs than snags, but a smaller proportion of the snag substrate or snag beetles may have been sampled. For red-listed beetles, no differences in their species richness were detected among substrates. These results suggest that logs of dead oaks are valuable and that both snags and logs of oak should be retained and, if needed, created in forestry, such that they are continuously available in stands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".