Increasing ground-layer plant taxonomic diversity masks declining phylogenetic diversity along a silvicultural disturbance gradient
Bibliographic record
Abstract
Most plant diversity in temperate deciduous forests is found in the ground layer, but nearly all studies comparing plant community assembly using taxonomic, trait, and phylogenetic diversity indices are limited to woody plants. To examine the relationship between short-term ground-layer plant community assembly and disturbance severity, we leveraged a silvicultural experiment that applied a combination of harvest and site preparation treatments in a northern hardwood forest in Michigan, USA. We predicted that after two growing seasons, plant communities would be less sensitive to harvest treatments when compared with site preparation treatments that disturb the rhizosphere and modify rooting substrate. We also predicted that an increase in taxonomic diversity would accompany a decline in trait diversity and phylogenetic diversity. Instead, plant species composition responded similarly to harvest treatment and site preparation treatment. However, our measure of disturbance severity was positively correlated with both trait diversity and taxonomic diversity but negatively correlated with phylogenetic diversity, indicating that increasingly diverse traits and taxonomies along this disturbance severity gradient were comprised of more phylogenetically simple plant communities. Informed management decisions should therefore consider the underlying value of each diversity measure, as taxonomic diversity alone may not be the best metric for assessing plant community assembly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".