Edaphic filters and the functional structure of plant assemblages in grasslands in southern <scp>B</scp>razil
Bibliographic record
Abstract
Abstract Questions We analysed trait convergence and trait divergence assembly patterns across a metacommunity of grassland types (dry, wet and rocky) occurring along an edaphic gradient. We asked whether (1) floristics and phylogenetic structures vary among grassland types; (2) there is convergence and/or divergence in plant traits along the gradient; (3) the functional structure is influenced by phylogeny; and (4) abiotic or biotic filters generate the assembly patterns. Location Campos Gerais region, Paraná State, southern Brazil (ca. 25°15′02″ S, 49°59′59″ W). Methods We sampled plant functional traits and soil variables at 81.1‐m2 quadrats in three natural grassland vegetation types across three different sites. We analysed the relationship between species composition (abundance), phylogenetic relationships, functional traits and soil characteristics using matrix correlations, where soil characteristics were the predictors of functional and phylogenetic assembly patterns. Results A total of 168 plant species were sampled on the three vegetation types. Wet grassland quadrats were more similar to each other in species composition and phylogeny than with those on dry and rocky grasslands. We found trait convergence (not phylogenetically constrained) and trait divergence (phylogenetically constrained) assembly patterns in the three vegetation types along the edaphic gradient. Traits that generated convergence and divergence are related to strategies for survival in dry and low nutrient availability soils; nutritional soil gradient determined trait differences at small scales. Conclusion Species composition and phylogenetic structure of communities occurring in different grassland types are related to edaphic gradient. The occurrence of both trait convergence and trait divergence patterns suggests, respectively, that environmental filters and biotic filters (competition) are structuring the plant assemblages.
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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".