Plant community assembly in semi‐natural grasslands and ex‐arable fields: a trait‐based approach
Bibliographic record
Abstract
Abstract Question The assembly of plants into communities is one of the central topics in plant community ecology. The objective of this study was to investigate how plant functional trait diversity and environmental factors influence community assembly in two different grassland communities, and if variation in these factors could explain the difference in species assembly between these communities. Location Six grazed ex‐arable fields and eight semi‐natural grasslands in southeast Sweden. Methods We estimated species abundance and measured soil attributes at each site. For each species within each site we measured specific leaf area (SLA), leaf dry matter content (LDMC) and seed mass. We analysed the data both for abundance‐weighted species values and species occurrence. Results Trait gradient analysis indicated random distribution of species among sites, while CCA analysis indicated that both soil phosphorus and moisture were related to species assembly at a site. Correlations and fourth‐corner analysis also revealed a relationship between measured species traits and soil phosphorus and moisture. There was a lower average seed mass and higher SLA of species in ex‐arable fields compared to species in semi‐natural grasslands. Conclusions Even though trait gradient analysis indicated that plant community assembly in the studied grasslands was random, other results implied that species occurrence and abundance was influenced both by environmental factors and species traits. Higher species richness in semi‐natural grasslands was associated with more large‐seeded species found there compared to ex‐arable fields, indicating that large‐seeded species establish in grasslands later than small‐seeded species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".