Short-term responses of soil decomposer communities to forest management: clear felling versus alternative forest harvesting methods
Bibliographic record
Abstract
We studied the short-term responses of decomposers to different forest harvesting methods in a boreal spruce forest (Picea abies (L.) Karst.). We hypothesised that the less intensive the forest harvesting method is, the fewer changes occur in the decomposer community. The treatments, in addition to untreated controls, were (1) selection felling (30% of the stand volume removed), (2) retention felling (tree patches retained), (3) clear felling, (4) gap felling without and (5) with harrowing. Microbial community structure (phospholipid fatty acids (PLFA) pattern) changed in the first year, microbial biomass and basal respiration decreased in the second year, and density of the enchytraeid worm Cognettia sphagnetorum (Vejd.) increased in the third year after the clear felling. The community of collembolans did not respond to forest harvestings. Although there were changes in the microbial community, the invertebrates at higher trophic levels did not parallelly respond to these changes. The selection felling had no influence on the decomposers, while the gap fellings induced an increase in the numbers of enchytraeids in harvested gaps. We conclude that the decomposers of the coniferous forest soils are well buffered against initial environmental changes resulting from forest harvesting, and also that the PLFA pattern is a sensitive indicator of changes in the microbial community induced by forest harvesting.
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 | 0.000 | 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 | 0.000 | 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".