Natural deadwood hosts more diverse pioneering wood‐inhabiting fungal communities than restored deadwood
Bibliographic record
Abstract
Deadwood can be recreated as a forest restoration measure to increase the amount of deadwood and assist deadwood‐dependent biodiversity. While deadwood restoration is known to have an overall positive effect on associated species in the long term, it remains poorly understood how and when wood‐inhabiting organisms colonize different kinds of deadwood, which is essential for developing efficient restoration frameworks. In this study, we use DNA metabarcoding to compare wood‐inhabiting fungal communities between fresh naturally fallen spruce logs and spruce logs felled for restoration. The results show that although pioneering fungal community composition greatly differs between natural and felled logs, with natural logs hosting more species‐rich and heterogeneous communities, felled logs still hold a relatively high fungal diversity. Responses to log type carried a strong phylogenetic signal, and orders Polyporales and Hymenochaetales including most species of conservation concern were more likely to occur in natural than in felled logs. Furthermore, we found that log type was more important for rarely recorded than commonly recorded taxa, suggesting that rare species might be more specialized in their habitat requirements than the common ones. Overall, while restored deadwood can hold a high fungal diversity, the results underline that freshly felled logs do not mimic fresh natural logs. Deadwood restoration should focus not only on increasing the quantity of deadwood but also on the quality of thereof, and most importantly, retaining the existing natural deadwood rather than artificially downing trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".