Fuel‐reduction management alters plant composition, carbon and nitrogen pools, and soil thaw in Alaskan boreal forest
Bibliographic record
Abstract
Abstract Increasing wildfire activity in Alaska's boreal forests has led to greater fuel‐reduction management. Management has been implemented to reduce wildfire spread, but the ecological impacts of these practices are poorly known. We quantified the effects of hand‐thinning and shearblading on above‐ and belowground stand characteristics, plant species composition, carbon (C) and nitrogen (N) pools, and soil thaw across 19 sites dominated by black spruce (Picea mariana) in interior Alaska treated 2–12 years prior to sampling. The density of deciduous tree seedlings was significantly higher in shearbladed areas compared to unmanaged forest (6.4 vs. 0.1 stems/m2), and unmanaged stands exhibited the highest mean density of conifer seedlings and layers (1.4 stems/m2). Understory plant community composition was most similar between unmanaged and thinned stands. Shearblading resulted in a near complete loss of aboveground tree biomass C pools while thinning approximately halved the C pool size (1.2 kg C/m2compared to 3.1 kg C/m2in unmanaged forest). Significantly smaller soil organic layer (SOL) C and N pools were observed in shearbladed stands (3.2 kg C/m2and 116.8 g N/m2) relative to thinned (6.0 kg C/m2and 192.2 g N/m2) and unmanaged (5.9 kg C/m2and 178.7 g N/m2) stands. No difference in C and N pool sizes in the uppermost 10 cm of mineral soil was observed among stand types. Total C stocks for measured pools was 2.6 kg C/m2smaller in thinned stands and 5.8 kg C/m2smaller in shearbladed stands when compared to unmanaged forest. Soil thaw depth averaged 13 cm deeper in thinned areas and 46 cm deeper in shearbladed areas relative to adjacent unmanaged stands, although variability was high across sites. Deeper soil thaw was linked to shallowerSOLdepth for unmanaged stands and both management types, however for any givenSOLdepth, thaw tended to be deeper in shearbladed areas compared to unmanaged forest. These findings indicate that fuel‐reduction management alters plant community composition, C and N pools, and soil thaw depth, with consequences for ecosystem structure and function beyond those intended for fire management.
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) | 0.000 | 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".