Simulating Long-Term Effects of Bioenergy Extraction on Dead Wood Availability at a Landscape Scale in Sweden
Bibliographic record
Abstract
Wood bioenergy may decrease the reliance on fossil carbon and mitigate anticipated increases in temperature. However, increased use of wood bioenergy may have large impacts on forest biodiversity primarily through the loss of dead wood habitats. We evaluated both the large-scale and long-term effects of different bioenergy extraction scenarios on the availability of dead wood and the suitability of the resulting habitat for saproxylic species, using a spatially explicit forest landscape simulation framework applied in the Swedish boreal forest. We demonstrate that bioenergy extraction scenarios, differing in the level of removal of biomass, can have significant effects on dead wood volumes. Although all of the scenarios led to decreasing levels of dead wood, the scenario aimed at species conservation led to highest volumes of dead wood (about 10 m3 ha−1) and highest connectivity of dead wood patches (mean proximity index of 78), whilst the scenario aimed at reaching zero fossil fuel targets led to the lowest levels (about 8 m3 ha−1) and least connectivity (mean proximity index of 7). Our simulations stress that further exploitation of dead wood from sites where volumes are already below suggested habitat thresholds for saproxylic species will very likely have further negative effects on dead wood dependent species.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".