Lime amendment to chronically acidified forest soils results in shifts in prokaryotic communities
Bibliographic record
Abstract
A consequence of past acid rain events has been chronic acidification of Nova Scotian forests, leading to a loss of essential nutrients and subsequent decreases in forest productivity and biodiversity. Liming-supplementing forests with crushed limestone-can restore essential nutrients to acidified soils and increase the pH of soils and the carbon capture by forests through the promotion of tree growth. Liming treatments are often assessed through tree growth measurements, although little is known about how microorganisms respond to these changes in pH and nutrient availability. Understanding the impacts of liming on microbial communities will help determine whether liming is a good remediation strategy for Nova Scotia. A pilot study evaluating liming in acidified forests in Nova Scotia began in 2017. Microbiome analysis (prokaryotic 16S and fungal ITS2 rRNA gene amplicon sequencing) of three different horizons (depths; upper forest floor, lower forest floor, and upper B horizon) of soil in a softwood forest area showed significant differences between lime-treated and control soils for the prokaryotic but not fungal communities, particularly in the uppermost soil horizon. Several genera from the Alphaproteobacteria class were significantly higher in abundance in treated than control soils, whereas genera from the Acidobacteriia (previously Acidobacteriae) class were significantly lower in abundance in treated versus control soils. Soil chemistry analysis of the same three horizons showed a significant increase in base cations and pH of the uppermost soil horizon in control versus treatment sites.IMPORTANCEForests are increasingly being managed with an emphasis on understanding how forests function. Lime amendments are used to promote forest health and increase resilience to climate change. To date, only a handful of studies have analyzed the effects of liming on microbial communities in forest soils. Our study combines soil chemistry with prokaryotic and fungal communities of limed and control soils. Shifts in microbial composition that are coincident with liming may provide early indications of the effectiveness of liming and provide insight into the roles of microbes in forest health.
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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 | 0.001 | 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".