Singular and Combined Effects of Blowdown, Salvage Logging, and Wildfire on Forest Floor and Soil Mercury Pools
Bibliographic record
Abstract
A number of factors influence the amount of mercury (Hg) in forest floors and soils, including deposition, volatile emission, leaching, and disturbances such as fire. Currently the impact on soil Hg pools from other widespread forest disturbances such as blowdown and management practices like salvage logging are unknown. Moreover, ecological and biogeochemical responses to disturbances are generally investigated within a single-disturbance context, with little currently known about the impact of multiple disturbances occurring in rapid succession. In this study we capitalize on a combination of blowdown, salvage logging and fire events in the sub-boreal region of northern Minnesota to assess both the singular and combined effects of these disturbances on forest floor and soil total Hg concentrations and pools. Although none of the disturbance combinations affected Hg in mineral soil, we did observe significant effects on both Hg concentrations and pools in the forest floor. Blowdown increased the mean Hg pool in the forest floor by 0.76 mg Hg m(-2) (223%). Salvage logging following blowdown created conditions leading to a significantly more severe forest floor burn during wildfire, which significantly enhanced Hg emission. This sequence of combined events resulted in a mean loss of approximately 0.42 mg Hg m(-2) (68% of pool) from the forest floor, after conservatively accounting for potential losses via enhanced soil leaching and volatile emissions between the disturbance and sampling dates. Fire alone or blowdown followed by fire did not significantly affect the total Hg concentrations or pools in the forest floor. Overall, unexpected consequences for soil Hg accumulation and by extension, atmospheric Hg emission and risk to aquatic biota, may result when combined impacts are considered in addition to singular forest floor and soil disturbances.
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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 | 0.000 | 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".