Translocating deadwood in ecological compensation benefits saproxylic beetles, but effects are dependent on substrate density
Bibliographic record
Abstract
Ecological compensation is increasingly used to offset habitat and biodiversity loss resulting from changes in land use, large infrastructure projects (e.g., roads and railroads) or industrial expansions (e.g., mines, harbours), but the effectiveness of specific compensation strategies remain largely untested. When old-growth forest ecosystems are impacted by such projects, designated compensation areas may also require additional restoration or habitat enrichment. For organisms that rely on habitats that require decades to develop, such as advanced decayed wood, restoration will require novel approaches. We tested whether translocation of deadwood of various decay stages and large dimensions enhance saproxylic beetle communities within compensation areas in a large boreal forest landscape in Sweden. Experimental plots (50 m in diameter) within the compensation zone were enriched with 0, 16, or 48 deadwood substrates. We collected beetles using flight intercept traps prior to translocation and again 1 and 4 years after translocation and compared species richness, abundance and assemblage composition across treatment and over time. We showed that translocation of relatively high densities of deadwood (48 substrates per plot) increased species richness of saproxylic beetles. Increased beetle richness could have occurred from direct transport of beetles in experimental substrates and/or through attraction of beetles to the translocated substrates. Our results indicate that translocation of deadwood can serve as an important tool in ecological compensation and restoration if sufficient amounts of deadwood are translocated. While promising, the long-term success of restoring saproxylic biodiversity through translocation of deadwood depends on whether translocated substrates continue to provide suitable habitat for beetles over time and whether benefits for biodiversity can be enhanced through targeted translocation of specific combinations of deadwood.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".