Plant community diversity and composition provide little resistance to <i>Juniperus</i> encroachment
Bibliographic record
Abstract
Widespread encroachment of the fire-intolerant species Juniperus virginiana L. into North American grasslands and savannahs where fire has largely been removed has prompted the need to identify mechanisms driving J. virginiana encroachment. We tested whether encroachment success of J. virginiana is related to plant species diversity and composition across three plant communities. We predicted J. virginiana encroachment success would (i) decrease with increasing diversity, and (ii) J. virginiana encroachment success would be unrelated to species composition. We simulated encroachment by planting J. virginiana seedlings in tallgrass prairie, old-field grassland, and upland oak forest. We used J. virginiana survival and growth as an index of encroachment success and evaluated success as a function of plant community traits (i.e., species richness, species diversity, and species composition). Our results indicated that J. virginiana encroachment success increased with increasing plant richness and diversity. Moreover, growth and survival of J. virginiana seedlings was associated with plant species composition only in the old-field grassland and upland oak forest. These results suggest that greater plant species richness and diversity provide little resistance to J. virginiana encroachment, and the results suggest resource availability and other biotic or abiotic factors are determinants of J. virginiana encroachment success.
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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 | 0.000 | 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) | 0.001 | 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".