Soil abiotic factors are not consistently associated with microbial diversity or organic matter removal intensity in regions of long-term reforestation
Bibliographic record
Abstract
Healthy soil structure is necessary for the sustainable management of forest ecosystems. As a major component of the Earth’s biosphere, these forest soil ecosystems play a key role in climate regulation, biogeochemical cycling, and the maintenance of biodiversity. Despite the importance of soil ecosystems, not much is known about the role of soil bacterial communities in mediating the health and productivity of the forest floor, especially in the context of deforestation. Here, we analyzed data obtained from a Long-Term Soil Productivity study to determine the effects of organic matter removal on soil abiotic factors, and the resulting impact on bacterial diversity 10 years after reforestation. By analyzing beta diversity, we found that both geographic location and soil depth were associated with differences in diversity between soil bacterial communities. Further statistical analysis also revealed significant relationships between soil depth and abiotic factors. Higher soil organic carbon, nitrogen content, and moisture content were associated with samples taken from the organic topsoil layer, and soil pH levels were more acidic in organic soil samples compared to mineral soil samples. Alpha diversity and taxonomic abundance analyses indicated that the distribution of bacterial phyla differed between geographic regions, with significantly lower diversity in British Columbia soil communities. We did not find organic matter removal to consistently impact the levels of soil organic carbon, nitrogen content, moisture content, or pH. Similarly, linear regression models for each region indicated minimal associations between soil abiotic factors and bacterial alpha diversity. Overall, these findings provide insight into the association between bacterial community composition and soil abiotic factors across a variety of geographic regions, and the impact of deforestation on these relationships.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".