Is understory plant species diversity driven by resource quantity or resource heterogeneity?
Bibliographic record
Abstract
What maintains plant species diversity has been the subject of much debate with no general consensus. In forest ecosystems in which understory plants account for the majority of floristic diversity, a crucial question is whether understory plant diversity is driven by resource quantity or resource heterogeneity. This study sought to reconcile the two hypotheses in relation to their effects on understory plant diversity in forest ecosystems. A database of studies that investigated the effects of resources on understory plant diversity was compiled and analyzed using log-linear models. Whether resource quantity or resource heterogeneity is the determinant of understory plant diversity in individual studies was dependent on stand successional stage(s), presence or absence of intermediate disturbance, and forest biome within which the studies were conducted. Resource quantity was found to govern species diversity in both young and mature stands, whereas resource heterogeneity dominated in old-growth stands. Resource quantity remained the important driver in both disturbed and undisturbed forests, but resource heterogeneity played an important role in disturbed forests. We argue that neither resource quantity nor heterogeneity alone structures species diversity in forest ecosystems, but rather their influences on understory plant diversity vary with stand development and disturbances in forest ecosystems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".