Root Fragment Amendments Increase Nematode Density and Mycobiome Stochasticity in Douglas-Fir Seedlings
Bibliographic record
Abstract
Relatively little is known about whole-plant fungal communities (mycobiome) and associated soil nematodes, especially with respect to woody plant seedlings and disturbance caused by forest harvesting. In a growth chamber experiment, we tested simulated clear-cut soil conditions on shoot biomass, total soil nematode density, and the shoot and root mycobiome of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, seedlings. Soil treatments included unamended bare soil and soil amended with root segments of kinnikinnick, Arctostaphylos uva-ursi (L.) Spreng., pinegrass, Calamagrostis rubescens Buckley, or P. menziesii seedlings. We used next-generation Illumina sequencing and the PIPITS pipeline to obtain fungal taxa used for mycobiome community richness and Jaccard-based taxonomic normalized stochasticity ratio to assess mycobiome community assembly stochasticity. Total nematode density, measured from Baermann funnel extractions, increased in soils supplemented with A. uva-ursi or C. rubescens root segments. Root mycobiomes were more stochastic in the A. uva-ursi than P. menziesii or the bare conditions, whereas the shoot mycobiome was more stochastic in the C. rubescens treatment than in the P. menziesii treatment. Our results suggest that refugia plants impact the phyto-biome, in this case plant-associated nematodes and the stochasticity of root and shoot mycobiome community assembly, while not showing noticeable impacts on above-ground plant growth.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".