Diversity and assembly of weed communities: contrasting responses across cropping systems
Bibliographic record
Abstract
Summary Diversity and weed community composition of mid‐season plant stands and autumn seedbanks were examined in spring barley–red clover cropping systems that varied according to crop rotation, tillage and weed management. Weed plant and seed density data collected over 4 years were used in the calculation of species richness (number of species), evenness (Shannon's E) and diversity (Shannon's H′), and in multivariate analysis (canonical discriminant analysis) of weed communities. Weed diversity indices were low (H′ < 2.0) but sensitive to management practices. Evenness had intermediate values (E = 0.4–0.8), suggesting little evidence of truly dominant species, particularly in the seedbanks. The difference in the number of species between treatments was never large (approximately two to four species). Overall, diversity indices were highest in the low disturbance treatments, particularly those with minimum weed management. Factors affecting ordination were somewhat different from those affecting diversity. Tillage had little effect on weed diversity indices but had a more major role in determining weed community composition. Seedbanks in no‐till and monoculture‐chisel plough treatments appeared to have more distinctive species composition compared with other treatments. Weed species assembly in seedbanks showed little discrimination across treatments and over time, confirming the ability of seedbanks to buffer disturbances across a variety of cropping systems. The use of diversity indices revealed part of the complexity of weed communities associated with disturbance in cropping systems, whereas ordination singled out species–cropping systems associations, which may be more meaningful to weed management.
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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".