Evaluating bee (Hymenoptera: Apoidea) diversity using Malaise traps in coffee landscapes of Costa Rica
Bibliographic record
Abstract
Abstract Even though Arabica coffee (Coffea arabicaLinnaeus, Rubiaceae) can self-pollinate, bees are important pollinators, without which there is lower fruit quality and yield. We studied bee diversity in coffee agroecosystems in Costa Rica during two coffee flowering seasons (2005 and 2006). Malaise traps were used as a passive sampling method to collect bees during coffee blooms. We collected 1012 bee individuals from three different site types: nonagricultural fields and shaded and unshaded coffee farms. Unshaded coffee farms had significantly higher species richness (S) and number of bee individuals (n) than did the shaded coffee farms and nonagricultural sites. Overall bee diversity did not differ among site types but evenness (J′) was significantly lower in unshaded coffee farms. Using a more detailed community analysis, there was a significant association between functional groups and habitat type with more species and individuals of small-bodied ground-nesting bees (Lasioglossum(Dialictus) Robertson) associated with unshaded coffee farms. A large proportion (49%) of bees collected were of this subgenus, which was never before reported as common in coffee agroecosystems. Further studies should establish whetherDialictusis important in coffee pollination. We propose strategies involving conservation of native bees through simple habitat management for small-scale coffee farms that may improve crop quality and quantity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".