Impacts of dead wood manipulation on the biodiversity of temperate and boreal forests. A systematic review
Bibliographic record
Abstract
Abstract Dead wood (DW) provides critical habitat for thousands of species in forests, but its amount, quality and diversity have been heavily reduced by forestry. Therefore, interventions aiming to increase DW might be necessary to support its associated biodiversity, even in protected forests, which may be former production forests. Our aim was to synthesize the current state of knowledge drawn from replicated experimental studies into solid quantitative evidence of the effects of DW manipulation on forest biodiversity, with a focus on protected forests. We conducted a full systematic review of effects of DW manipulation on forest biodiversity in boreal and temperate regions. We included three intervention types: creation of DW from live trees at the site, addition of DW from outside the site and prescribed burning. Outcomes included abundance and species richness of saproxylic insects, ground insects, wood‐inhabiting fungi, lichens, reptiles and cavity‐nesting birds. In total, we included 91 studies, 37 of which were used in meta‐analyses. Although meta‐analysis outcomes were heterogeneous, they showed that increasing the amount of DW (“DW enrichment”) has positive effects on the abundance and richness of saproxylic insects and fungi. The positive effect on saproxylic pest insect abundance tended to be less than that on saproxylic insects in general. No significant effects were found for ground insects or cavity‐nesting birds. Although reviewed studies were mainly short term, our results support that management that increases DW amounts has the potential to increase the abundance of DW‐dependent species and, in most cases, also their species richness. Studies of burning showed positive effects on the abundance of saproxylic insects similar to those of other interventions, even though burning on average resulted in a smaller enrichment of DW amounts. Policy implications. The findings of the review suggest that manipulating dead wood (DW) can be an effective part of conservation management to support biodiversity in protected areas. The findings also indicate that the diversity of DW types is important, a mix of DW qualities should be favoured. Burning seems to be an effective method to increase biodiversity but to benefit cavity‐nesting birds, snag losses need to be minimized.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".