Bibliographic record
Abstract
Leafcutting bees (Megachile: Megachilidae) cut leaves from various trees, shrubs, wildflowers and grasses to partition and encase brood cells in hollow plant stems, decaying logs or in the ground. The identification of preferred plant species via morphological characters of the leaf fragments is challenging and direct observation of bees cutting leaves from certain plant species are difficult. As such, data are poor on leaf preference of leafcutting bees. In this study, I use DNA barcoding of the rcbL and ITS2 regions to identify and compare leaf preference of three Megachile bee species widespread in Toronto, Canada. Nests were opened and one leaf piece from one cell per nest of the native M. pugnata Say (N=45 leaf pieces), and the introduced M. rotundata Fabricius (N=64) and M. centuncularis (L.) (N=65) were analysed. From 174 individual DNA sequences, 54 plant species were identified. Preference by M. rotundata was most diverse (36 leaf species, H'=3.08, phylogenetic diversity (pd)=2.97), followed by M. centuncularis (23 species, H'=2.38, pd=1.51) then M. pugnata (18 species, H'=1.87, pd=1.22). Cluster analysis revealed significant overlap in leaf choice of M. rotundata and M. centuncularis. There was no significant preference for native leaves, and only M. centuncularis showed preference for leaves of woody plants over perennials. Interestingly, antimicrobial properties were present in all but six plants collected; all these were exotic plants and none were collected by the native bee, M. pugnata. These missing details in interpreting what bees need offers valuable information for conservation by accounting for necessary (and potentially limiting) nesting materials.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".