Variability of microbial taxonomic and functional diversities across management boundaries in a boreal podzol
Bibliographic record
Abstract
Land capability classification describes boreal podzols as soils with severe to moderately severe \nlimitations that restrict the capability of the land to produce crops. Nevertheless, they are used \nfor crop production and it is predicted that more boreal podzols will be converted from forestry \nuse to agricultural uses. This usually requires intensive conservation and fertility improvement \npractices aimed at correcting the excessively low pH and improving soil carbon parameters. \nUnder such management, it is expected that the biotic parameters and drivers of soil fertility \nwould be drastically affected. It is hypothesized that mass and energy fluxes across the edge of a \ncropped field, between natural and managed conditions of soil, will alter the diversity of \nmicrobial populations and their fertility relevant functions. \nTo verify this, I surveyed a cropped field and its immediate surrounding areas, located within a \nBoreal Forest Ecosystem in Western Newfoundland. The surrounding areas, outside the four \nfield edges covered four distinct non-cropped conditions, i.e. forested, wetland, grassland and \ngrassed farm road border. Bacterial taxonomic diversity was assessed via a 16S rRNA obtained \nthrough an Illumina MiSeq PE 250bp amplicon sequencing of the V4 hypervariable region. \nFungal taxonomic diversity was assessed on an ITS dataset obtained through an Illumina MiSeq \nPE 250bp amplicon sequencing of the ITS1-2 region. A predictive functional profiling of the \nbacterial community, based on the 16S rRNA results (PICRUSt) was then carried out. Results are \ncontextualized by standard abiotic soil parameters and compared to potential nitrogen mineralization rates \nalong a management intensity gradient, i.e. a gradient crossing from natural to cropped conditions. Both \nsurface and subsurface layers were considered. Standard and exploratory statistics were carried out and \nincluded an analysis of ecological indicators for population diversity. Statistical analysis was carried out \nseparately on soil physicochemical properties, microbial taxonomic diversity, and microbial functional \ndiversity. Correlational analyses between microbial diversity and physicochemical properties and were \ncarried out separately. It was found that, while the natural conditions tested had distinct diversities, the \nresults became increasingly similar towards the field centre, away from the natural edge. Thus, land \nmanagement affects the taxonomic and functional diversity of microorganisms and also found \nthat the shift in taxonomic and functional diversity is directly related to the distance from the \nnatural areas.
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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 | 0.001 | 0.001 |
| 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".