Linking deadwood-associated beetles and fungi with wood decomposition rates in managed black spruce forests<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
Deadwood-associated insects and saprotrophic fungi are principal agents of wood decomposition in boreal forest. Silvicultural treatments that alter microclimate and availability of deadwood likely affect composition and growth rates of both insect and fungal communities, leading to changes in wood decomposition rates. Here, we relate both saproxylic beetle and dominant polypore assemblages with woody decomposition rates and environmental variables in experimental partial cuts, clearcuts, and uncut controls using a series of causal models to determine the relationship between stand structure, biodiversity, and ecosystem function in black spruce ( Picea mariana (Mill.) BSP) forests. Overall beetle and fungal composition differed between uncut stands and harvested stands. Main effects of harvesting included large increases in wood-feeding beetles and the fungus Gloeophyllum sepiarium (Wul.:Fr.) Karst. We suggest that these species were promoted by specific alterations in microhabitat conditions of deadwood. Within clearcuts specifically, changes in species composition and significantly more fungal degree-days resulted in significantly higher decomposition rates. We concluded that levels of partial cutting in the range of 15%–20% retention were not sufficient to maintain predisturbance communities but were sufficient to maintain wood decomposition rates similar to uncut stands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".