Foraging preferences of leafcutter bees in three contrasting geographical zones
Bibliographic record
Abstract
Abstract Aim Leafcutter bees use plants as pollen and nectar sources, but also cut leaf discs and use them to line their nests. Which plant species they choose as nesting material and why they do so have remained obscure. We asked the following: (1) How are the plant species used by leafcutter bees distributed phylogenetically? (2) Does plant choice differ across geographical regions, and if so, in what ways? (3) Are the leaf plant species natives or exotics? (4) What plant and leaf traits predict selection of plant species by leafcutter bees? And (5) Does the abundance of individuals per species in the habitat influence leafcutter bees’ plant preferences? Location Tropical South Asia, temperate eastern Canada and US Sonoran Desert Methods We mapped taxa known to be used by leafcutter bees both from our own study and published literature onto the most recent angiosperm phylogeny. To determine what plant and leaf traits predict leaf selection, we monitored 6,120 individuals of 214 native and exotic plant species planted in a 3‐sq. km. arboretum in Arizona and recorded leaf damage inflicted by bees. Results Megachile showed a strong preference for species in the rosid clade, particularly the phylogenetic cluster of Fabales, Fagales and Rosales. Thirty‐two to forty‐five percentage of the leaf plant species were exotic to a given region. Membership in the rosid clade and Fabaceae family predicted plant preference, whereas the plant species’ local abundance and geographical origin did not. Leaf water content, morphotype and the presence of latex were important factors influencing plant choice, whereas leaf shape and size did not. Main conclusion These patterns point to plant groups whose availability should be monitored to assure persistence of leafcutter bees. Their preference for specific globally distributed plant clades, yet ability to readily adopt certain exotic plant species as nest resources, likely augments their ability to persist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".