Bacterial community structure and resilience are partially restored after 30 years of rehabilitation of an agricultural riparian system
Bibliographic record
Abstract
Soil microbiomes play critical roles in maintaining soil ecosystem functions, and therefore, they can be indicators of ecosystem recovery during the rehabilitation of degraded land. This study compared microbial community structure and co-occurrence patterns of potentially active bacterial communities in soils from a disturbance gradient: disturbed agricultural land (AGR), previously disturbed rehabilitated agroforest (RHF), and undisturbed natural forest (UNF). We quantified DNA and cDNA using qPCR and performed high-throughput amplicon sequencing to target potentially active bacterial communities. Bacterial transcript abundance was significantly higher in UNF compared to AGR, and the composition of potentially active bacterial communities varied significantly along the disturbance gradient. Soil temperature, nitrate, pH, carbon-to-nitrogen ratio, and total carbon were key soil properties driving differences in bacterial community composition. Key taxa such as Burkholderiales, Haliangium, and Pseudomonas, were differentially abundant along the disturbance gradient. Network robustness was used to evaluate network resilience and was highest in UNF, lowest in AGR, and RHF was intermediate, suggesting partial recovery of RHF following disturbance. Hub taxa from AGR were oligotrophs mainly from the phylum Actinomyceota, while forest soils hubs were from the phylum Pseudomonodata. UNF was the only site to have copiotrophic hub taxa such as TRA3–20, reflecting a functionally diverse network assembly in the nutrient-rich and less disturbed conditions. These findings show that after 30 years of rehabilitation the RHF has a similarity to UNF in terms of microbial abundance, composition, and soil characteristics, suggesting a recovery in ecosystem functionality at the site.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".