Contrasting patterns in species and functional‐trait diversity of bees in an agricultural landscape
Bibliographic record
Abstract
Summary Land‐use change frequently reduces local species diversity. Species losses will often result in loss of trait diversity, with likely consequences for community functioning. However, the converse need not be generally true: management approaches that succeed in retaining species richness could nevertheless fail to maintain trait diversity. We evaluated this possibility using bee communities in a California agroecosystem. We examined among site patterns in bee species diversity and functional‐trait diversity in a landscape composed of a mosaic of semi‐natural habitat, organic farms and conventional farms. We sampled bees from all three habitat types and compiled a data base of life‐history (‘functional’) traits for each species. Although species richness was higher on organic farms than conventional farms, functional diversity was lower in both farm types than in natural habitat. Nesting location (below‐ground vs. above‐ground) was the primary trait contributing to differences in functional diversity between farms and natural habitat, reflecting observed differences in availability of nesting substrates among habitat types. Other traits, including phenology and sociality, were also associated with species' occurrences or dominance in particular site types. These patterns suggest that management practices common to all farms act as environmental filters that cause similarly low functional diversity in their bee communities. Synthesis and applications. Although our results support the value of organic farming in maintaining abundant and species‐rich bee communities, components of bee functional diversity are not well supported in farmed landscapes, regardless of farming practice. Maintenance of natural habitat, and/or the addition of natural habitat elements to farms, is therefore important for the retention of functionally diverse bee assemblages in agroecosystems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".