Soil communities following harvest have different early successional dynamics compared with post-wildfire patterns
Bibliographic record
Abstract
Stand-replacing wildfire is the primary natural disturbance in jack pine-dominated boreal forests; but clearcut harvest also emulates this natural renewal process. We used a 60-year clearcut harvest chronosequence to assess whether soil communities became more similar to those in wildfire-origin stands over time. We assessed convergence across disturbance types at each stand development stage and recovery compared to the wildfire mature stand development stage (~ 85 years). To evaluate cumulative effects, we also assessed a 20-year salvage harvest chronosequence where wildfire was followed by salvage logging of fire-killed trees. Beta diversity analyses showed different recovery times among soil taxa. Following clearcut harvest, bacteria converged to wildfire reference conditions more quickly, followed by arthropods, whereas fungi did not converge within the study period. Soil communities in salvage-logged sites diverged from clearcut harvest and wildfire references suggesting compounded disturbance effects. This work showcases how highly-scalable DNA metabarcoding and bioinformatic tools can be applied to simultaneously monitor a diverse array of soil biota. In future work, tracking fungal and arthropod soil communities may provide more insights into the longer-term effects of current forest management practices and provide guidance when comparing alternative approaches.
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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 | 0.001 |
| 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".