Phytobiomes and soil communities : assessing natural communities and the impact of anthropogenic change on plants, soils and their associated organisms
Bibliographic record
Abstract
Biodiversity on Earth is under immense threat and we must increase our understanding across systems to help research, conserve, manage and protect biodiversity for its own sake and human livelihoods worldwide. This thesis examined plant and microbial biodiversity across scales in three study systems: study system 1, a growth chamber experiment; study system 2, a field experiment with experimental nitrogen deposition and open top chamber warming in clear-cuts (CC) and forests edges (FE) at the subplot and plot scale, in a research forest in Canada; and study system 3, data mining across ecosystems in the continental United States of America. This thesis aimed to assess phytobiomes, plant performance, associated phytobiome communities (prokaryotic, fungal, nematode), and relationships (organism-pH, cross-taxon) to quantify richness and stochasticity and to use richness and stochasticity theory for synthesis across systems and scales. In study system 1, soil refugia treatments (bare soil vs. soil amended with plant roots of Arctostaphylos uva-ursi, Calamagrostis rubescens, or Pseudotsuga menziesii) did not impact P. menziesii seedling performance, but nematode abundance increased and root and shoot mycobiome stochasticity differed. In study system 2, simulated nitrogen deposition did not impact planted tree seedlings and associated understory vascular plant cover and there was no nitrogen and warming interaction effect, but warming increased plant growth in clear-cuts and the microenvironment impacted growth in both clear-cuts and forest edges. Plant and nematode richness and stochasticity were higher in clear-cuts than forest edges, but prokaryotic and fungal communities showed no trend. Stochasticity, not richness, had organism-pH and cross-taxon associations. In study system 3, plant and prokaryotic richness showed a linear organism-pH relationship across forests, but across different ecosystems only plant variables had a unimodal relationship with pH. Overall, this thesis finds that stochasticity and richness tend to be positively correlated. Stochasticity relationships were more common than richness relationships. For plants, lower stochasticity related to lower richness, local disturbance increased stochasticity and high determinism appeared with different ecosystem comparisons. Plant and nematode communities showed similar trends. Fungal communities generally showed higher stochasticity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".