Fungal community dynamics and carbon mineralization in Populus tremuloides, Picea mariana, and Pinus banksiana coarse woody debris in two ecoregions of northern Ontario
Bibliographic record
Abstract
In Canadian silvicultural systems, intensifying biomass removal while harvesting for saw or \npulpwood is presently the only economically feasible way of increasing biomass feedstocks. \nAs a result, there is less slash and subsequently less coarse woody debris (CWD) pools in \ndifferent decay stages over time. The impacts on saproxylic fungi and ecosystem \nfunctioning are not well understood. Fungi are the primary agents of CWD decay in forests, \nplaying essential roles in nutrient cycling and carbon storage. We compared fungal \nbiodiversity and CWD mineralization across 3 CWD species (Pinus banksiana, Populus \ntremuloides, and Picea mariana), 5 decay classes, and 2 ecoregions representing 3W and 3E \nOntario. Fungal diversity metrics and community structure were examined via high-throughput sequencing of the ITS2 region. CWD pieces were incubated in a temperature controlled lab setting and C mineralization rates measured. Moisture content, density, and nutrient dynamics, including C, N, P, K, Ca, Mg, and Mn concentrations, C:N ratio, and N:P ratio of CWD were assessed. Fungal communities significantly differed among CWD species, decay class, and ecoregion and were primarily correlated with Ca, Mn, and K \nconcentrations, density, and C:N ratio. Species richness and diversity peaked in 3W Ontario \nand in decay classes 4 and 5. We observed a shift from pathogenic, colonizer fungi to an \nincreased abundance of wood decay fungi as decay class increased. Hardwood CWD had \nhigher abundances of white rot fungi than softwood CWD. C mineralization was higher in \nhardwood CWD, increased with decay class, and was primarily influenced by P \nconcentration and density. Data from this study will be used in developing forest \nmanagement strategies to preserve biodiversity and monitor carbon flux in northern \nboreal forests, particularly under intensified-bioenergy production silvicultural 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 | 0.001 |
| 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".