How the Functional Constitution of Plant Communities in Field Margins Affects Wild Bee Community Composition and Functional Structure
Bibliographic record
Abstract
Concerns about a global decline in pollinators have called for more knowledge about the drivers of wild pollinator abundance and diversity in agroecosystems. Maintaining flowering plants in agricultural field margins is often recommended as a cost-effective and efficient method of offering habitat for wild pollinator conservation. This research involved a three-year, multi-farm study, examining Mediterranean cereal field margins in order to investigate which general and functional characteristics of margin plant communities were important for sustaining wild bee abundance, diversity, community evenness and functional diversity. Wild bees were collected and identified to genus, and a database was compiled listing the morpho-physiological features and behaviours of the observed genera. A database was also compiled of the flowering plant species observed and relevant trait values. General and generalized linear models indicated that margins with a higher percentage of trees and shrubs and higher floral richness displayed positive effects on wild bee diversity and visits to flowers in Mediterranean cereal agroecosystems. They also indicated that high plant functional diversity, in terms of flower colour and morphology, as well as high nectar accessibility, were important to encourage bee visits and community evenness within wild bee assemblages in these field margins. This study stresses the importance of maintaining protected field margins and, when necessary to restore their functionality, sowing floral mixtures with diverse native species, including trees and shrubs, and providing plenty of accessible nectar and a diverse assortment of colours and shapes.
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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 | 0.000 | 0.000 |
| 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".