Microbial Colonizers in an Agroecosystem Under Diverse Cover Crop Treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cover crops are often incorporated between cash crop seasons to improve or maintain soil health. Although their effects on certain soil properties (e.g., erosion control) are well described, their potential to steer soil microbial composition and function remains poorly understood. Most studies use direct soil sampling to investigate this relationship, but long-dormant microorganisms and legacy DNA can mask treatment effects, leading to signals that may not reflect active contributors to key functions such as biogeochemical cycling and decomposition. In this study, we deployed microbial traps (i.e., sterile soil enclosed in permeable mesh) to contrast active recolonization with direct soil sampling across 11 cover crop treatments applied after fall cash crop harvests in the northeast United States. Bulk and recolonized soil were collected for 16S rRNA gene and internal transcribed spacer region amplicon sequencing before (i) winter and (ii) spring planting. We hypothesized that different cover crop mixtures would stimulate distinct pools of microbial colonizers, with stronger between-treatment effects in recolonized soil compared with bulk. Our results showed that crop treatments significantly influenced microbial composition of active colonizers; however, effect sizes were similar in both bulk and recolonized (explaining 12 to 18% of community variance). The presence or absence of plant cover was the strongest driver of compositional differences in both soil compartments, suggesting microbial traps and bulk soil can capture similar signals despite containing ecologically distinct microbiome subsets. Future work coupling community assembly in situ with functionally informative methods may further resolve whether active colonizers overlap with root-associated taxa and can lead to management-relevant outcomes. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it