Effects of fuelwood harvesting on biodiversity — a review focused on the situation in Europe<sup>1</sup>This article is one of a selection of papers from the International Symposium on Dynamics and Ecological Services of Deadwood in Forest Ecosystems.
Bibliographic record
Abstract
A continually increasing demand for energy and concerns about climate change, greenhouse gas emissions and peak oil have prompted countries to develop policies that promote renewable energy including forest-based bioenergy. In Europe, fuelwood-driven changes in forestry are likely to impact habitat conditions for forest biodiversity. We conducted a systematic literature overview based on 88 papers to synthesize research findings and gaps in knowledge. At the stand scale, but also on a landscape scale, deadwood availability and profile are altered by several practices: whole-tree harvesting and postharvest recovery of logging residues and stumps, for instance. Large-scale fuelwood removal may, on a landscape scale, jeopardize the amounts and diversity of substrate that saproxylic organisms require as food and habitat. Besides, bioenergy-related forest practices also affect nonsaproxylic biodiversity through physical (e.g., soil compaction and disturbance) and chemical changes in soil properties associated with fuelwood removal and increased machine traffic. Moreover, the extended density of internal edges threatens interior forest species populations. Important effects differ substantially between boreal and nemoral forests because of contrasts in management systems, structure of forest ownership, and ecological properties. Developing relevant operational guidelines to partially mitigate ecological damage on biodiversity should be based on our compiled cautionary statements but require further large-scale and long-term research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".