Dispersed Variable-Retention Harvesting Mitigates N Losses on Harvested Sites in Conjunction With Changes in Soil Microbial Community Structure
Bibliographic record
Abstract
As an alternative to clear-cutting, variable-retention harvesting is now standard forest management practice on the coast of British Columbia and in temperate forests globally, due to the benefits associated with maintaining mature forest species and forest structural diversity. Although there is some evidence that variable-retention harvesting, particularly single-tree (dispersed) retention will mitigate the impacts of clear-cutting on soil microbial communities and nutrient cycling, findings have been inconsistent. We examined microbial community structure (phospholipid-fatty acid), and nutrient availability (PRSTM probes) in a large (aggregated) retention patch and over three harvesting treatments: dispersed retention, clear-cut and clear-cut edge 2 years after harvest. Unlike previous studies, we did not observe elevated nitrate in the harvested areas, instead ammonium was elevated. Availability of N and other nutrients were surprisingly similar between the dispersed-retention treatment and the retention patch. The microbial community, however, was different in the clear-cut and dispersed-retention treatments, mostly due to significantly lower abundance of fungi combined with an increase in bacteria, specifically Gram-negative bacteria. This was accompanied by lower δ13CPDB value of the Gram-negative PLFA's in these treatments, suggesting the decline in mycorrhizal fungal abundance may have allowed the dominant Gram-negative bacteria to access more of the recently photosynthesized C. This shift in the microbial community composition in the dispersed-retention treatment did not appear to have a major impact on microbial functioning and nutrient availability, indicating that this harvesting practice is more effective at maintaining generic microbial functions/processes. However, as Mn levels were twice as high in the retention patch compared to the harvested treatments, indicating the other “narrow” processes (i.e., those performed by a small number of specialized microorganisms), such as lignin degradation, catalyzed by Mn peroxidase, which concomitantly removes Mn from solution, may be more sensitive to harvesting regimes. The effect of harvesting on such narrow nutrient cycling processes requires further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".