Use of a logging road in a Costa Rican forest changes the composition and stability of soil microbial decomposer communities, and the conversion of organic carbon into biomass
Bibliographic record
Abstract
AIMS: The effects of a tropical forest logging road on soil C and N, and the compositions of Actinobacteria, Acidobacteria, and wood rot/lignin-degrading fungal (WRT/LD) decomposer communities were evaluated. METHODS AND RESULTS: Soils from a healthy Costa Rican old growth forest before Hurricane Otto and from an adjacent, recently formed logging road built after Hurricane Otto were collected over 4 years and assessed for C and N metrics, and characteristics of the three decomposer communities determined by Illumina amplicon sequencing methods. The Logging Road negatively impacted the soil total organic C, respiration, biomass C, qCO2, and total N, while the Actinobacterial and Acidobacterial communities changed from stable compositions of copiotrophic taxa in the rich forest soil to stable compositions of oligotrophic taxa in the poor logging road soil, and the WRT/LD community changed from stable compositions of copiotrophic taxa in the forest soils to an unstable community of oligotrophic taxa with almost no overlap in genera between logging road soils. CONCLUSIONS: The logging road negatively influenced 3 decomposer communities and associated C and N metrics, with the two bacterial communities taxonomically stabilizing, but the fungal community taxonomically diverging into an unstable composition over time. Monitoring efforts are on-going to provide local forest land managers with potential indicators of soil ecosystem damage and recovery.
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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".