Land use intensification in the humid tropics increased both alpha and beta diversity of soil bacteria
Bibliographic record
Abstract
Anthropogenic pressures on tropical forests are rapidly intensifying, but our understanding of their implications for biological diversity is still very limited, especially with regard to soil biota, and in particular soil bacterial communities. Here we evaluated bacterial community composition and diversity across a gradient of land use intensity in the eastern Amazon from undisturbed primary forest, through primary forests varyingly disturbed by fire, regenerating secondary forest, pasture, and mechanized agriculture. Soil bacteria were assessed by paired-end Illumina sequencing of 16S rRNA gene fragments (V4 region). The resulting sequences were clustered into operational taxonomic units (OTU) at a 97% similarity threshold. Land use intensification increased the observed bacterial diversity (both OTU richness and community heterogeneity across space) and this effect was strongly associated with changes in soil pH. Moreover, land use intensification and subsequent changes in soil fertility, especially pH, altered the bacterial community composition, with pastures and areas of mechanized agriculture displaying the most contrasting communities in relation to undisturbed primary forest. Together, these results indicate that tropical forest conversion impacts soil bacteria not through loss of diversity, as previously thought, but mainly by imposing marked shifts on bacterial community composition, with unknown yet potentially important implications for ecological functions and services performed by these communities.
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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".