Bacterial Subspecies Variation and Nematode Grazing Change P Dynamics in the Wheat Rhizosphere
Bibliographic record
Abstract
Low phosphorus soils are thought to constitute the majority of soils worldwide and cannot support intensive agriculture without high fertilizer inputs. Rhizospheric Rhizobacteria are well-known to modify P dynamics, and an increased bacterial diversity normally has a positive impact on process rate. However, it is not well known how variation in bacterial diversity at the subspecies level could influence trophic interactions in the rhizosphere and its consequences on plant P nutrition. We therefore hypothesized that the interactions between closely related P solubilizing bacteria and their grazing nematodes could improve plant P dynamics from an unavailable P source. We isolated four Pseudomonas poae strains and extracted nematodes from a Saskatchewan wheat field soil sample. The potential of all bacterial isolates and with and without nematodes combinations for increasing P availability in the wheat rhizosphere was tested in controlled microcosms with Ca3(PO4)2 as sole P source. Liberated P, phosphatase activity, plant P and bacterial abundance based upon phnX gene copies were determined. Phosphorus solubilization efficiency of isolates varied as P. poae 1 >P. poae 2 > P. poae 3 > P. poae 4, respectively whereas phosphatase enzyme activity was only detected under nematodes grazing and during the first 15 days of the experiment. Nematodes grazing upon individual Pseudomonas poae increased phosphatase enzyme activity, bacterial abundance, but decreased plant P concentration compared to non-grazed system. In contrast, the treatment combining all Pseudomonas poae isolates together with nematodes resulted in significant increases in P availability and plant P concentration. Diverse P-solubilizing efficiency and interaction with nematodes within the same bacterial “species” suggest that P dynamics might be linked to micro variation in soil diversity that would not accurately be picked up using common tools such as 16S rRNA gene sequencing.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".