Linking soil microbial genomic features to forest-to-pasture conversion in the Amazon
Bibliographic record
Abstract
Amazonian soil microbial communities are known to be altered by land-use change. However, attempts to understand these impacts have focused on broader community alterations or the response of specific microbial groups. Here, we recovered and characterized 69 soil bacterial and archaeal metagenome-assembled genomes (MAGs) from three forests and three pastures of the Eastern Brazilian Amazon and evaluated the impacts of land conversion on their genomic features. Pasture MAGs had significantly higher GC content (64.9% vs 60.2%), genome size (4.0 vs 3.1 Mbp), and number of coding sequences (4,058 vs 3,306) compared to forest genomes. Taxonomically, MAGs belonged to eight phyla; however, most (90%) had low similarity to previously known species, indicating potentially novel taxa at multiple levels. We also observed that the functional profiles associated with biogeochemical cycling and carbohydrate-active enzyme genes were impacted by forest conversion, with pasture MAGs exhibiting a notably higher number of both gene groups. Together, these data constitute the largest single-sourced genomic data set from upland soils of the Brazilian Amazon to date and increase the known MAG richness in these soils by 78%. Our data, therefore, not only add to a neglected yet emerging field but, importantly, highlight that land-use change has drastic impacts on the genomic characteristics and functional traits of dominant soil microbes.IMPORTANCEThe Brazilian Amazon is facing unprecedented threats, including increasing deforestation and degradation, which together impact half of the original forest area. Soil microorganisms are sensitive indicators of land-use change, linked to a rise in microbial methane emissions and antibiotic-resistance genes in the Amazon. However, most Amazonian soil microbes remain unknown, and little attention has been given to their genomes. Using sequencing and bioinformatics, we recovered and characterized 69 soil bacterial and archaeal genomes (metagenome-assembled genomes). These abundant members of the microbial communities diverged across forests and pastures in terms of taxonomic and functional traits. Forest conversion favors organisms with specific genomic features - increased GC content, genome size, and gene number - selecting for microorganisms that can thrive under altered conditions. Our paper helps us understand the intricate relationships between microbes and the environment, which are crucial pieces of information for comprehensive soil health assessments and future policy formulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".