Soil respiration response to prescribed burning and thinning in mixed-conifer and hardwood forests
Bibliographic record
Abstract
The effects of management on soil carbon efflux in different ecosystems are still largely unknown yet crucial to both our understanding and management of global carbon flux. To compare the effects of common forest management practices on soil carbon cycling, we measured soil respiration rate (SRR) in a mixed-conifer and hardwood forest that had undergone various treatments from June to August 2003. The mixed-conifer forest, located in the Sierra Nevada Mountains of California, had been treated with thinning and burning manipulations in 2001, and the hardwood forest, located in the southeastern Missouri Ozarks, had been treated with harvesting manipulations in 1996 and 1997. Litter depth, soil temperature, and soil moisture were also measured. We found that selective thinning produced a similar effect on both forests by elevating SRR, soil moisture, and soil temperature, although the magnitude of response was greater in the mixed-conifer forest. Selective harvest increased SRR by 43% (from 3.38 to 4.82 µmol·m–2·s–1) in the mixed-conifer forest and by 14% (from 4.25 to 4.84 µmol·m–2·s–1) in the hardwood forest. Burning at the conifer site and even-aged harvesting at the mixed-hardwood site did not produce significantly different SRR from controls. Mean SRR were 3.24, 3.42, and 4.52 µmol·m–2·s–1, respectively. At both sites, manipulations did significantly alter SRR by changing litter depth, soil structure, and forest microclimate. SRR response varied by vegetation patch type, the scale at which treatments altered these biotic factors. Our findings provide forest managers first-hand information on the response of soil carbon efflux to various management strategies in different forests.
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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) | 0.000 | 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".