Functional diversity versus species diversity: relationships with habitat heterogeneity at multiple scales in a subtropical evergreen broad‐leaved forest
Bibliographic record
Abstract
Abstract Understanding the relationship between functional and species diversity as well as their association with habitat heterogeneity can help reveal the mechanisms of species coexistence in ecological communities. However, these interactions have been poorly studied in subtropical forests. In this paper, we evaluated functional diversity (as measured by Rao's Q) and traditional species diversity (based on Simpson's index) in a 24 ha forest plot in a subtropical evergreen broad‐leaved forest (EBLF) in China. We compared the sensitivities of functional and species diversity to topographic variables (elevation, convexity, slope and aspect) at multiple spatial scales based on 10 × 10, 20 × 20, 40 × 40 and 50 × 50 m quadrats. Functional and species diversity were found to have different distribution patterns along a topographical gradient, with functional diversity better explained by topography than was species diversity using a spatial autocorrelation regression error model. Furthermore, functional diversity had a significantly greater association with topographic variables than species diversity in both adult and young trees; in both cases, the strength of the diversity‐habitat association increased with quadrat size. We conclude that functional diversity reflects a greater diversity‐habitat association in EBLF than does species diversity, and that the association depends on the spatial scale and life stages of the woody plants under evaluation.
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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 | 0.001 |
| 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".