Fungal community dynamics and carbon mineralization in coarse woody debris across decay stage, tree species, and stand development stage in northern boreal forests
Bibliographic record
Abstract
Abstract Fungi are primary agents of coarse woody debris (CWD) decay in forests, playing an essential role in nutrient cycling and carbon storage. Characterizing fungal communities within CWD will promote further understanding of the fungal controls on CWD decomposition. We compared fungal community assemblages using alpha and beta diversity metrics, carbon mineralization, and physical and chemical properties of CWD across 3 tree species (trembling aspen [ Populus tremuloides ], black spruce [ Picea mariana ] , and jack pine [ Pinus banksiana ]), 5 decay classes, and 2 stand development stages, differing in time-since-stand replacing disturbance (i.e., young/self-thinning and mature/steady-state) in Ontario’s boreal forest region. In total, we sampled 180 individual CWD logs from 6 independent stands, with 3 replicates per each species × decay class combination at each site. We found that fungal community structure significantly differed across tree species, decay stage, and stand age. Higher proportions of white rot fungi were found in trembling aspen CWD, whereas higher proportions of brown rot fungi were found in black spruce and jack pine CWD. Proportions of specialized wood decay fungi increased with decay stage and were higher in CWD located in mature forest stands. Fungal diversity was highest in decay class 4 CWD. We found that Mn and K concentrations, total carbon, C/N ratio, carbon mineralization (mg CO 2 g dry CWD -1 d -1 ), and moisture content were important predictors of fungal composition across CWD species and/or decay stage, though how CWD chemistry influences fungal species composition (and vice versa) is unknown. Carbon mineralization was highest in trembling aspen CWD and increased with decay stage, perhaps facilitated by increased N concentrations. This study suggests that forest management guidelines that consider both deadwood quantity and quality will support a broader range of fungal species and communities through post-disturbance stand development, thereby conserving biodiversity over the longer-term in our managed forest systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".