Floral resources, body size, and surrounding landscape influence bee community assemblages in oak‐savannah fragments
Bibliographic record
Abstract
Fragmentation of natural habitats due to urban development is predicted to have negative impacts on species diversity. The surrounding landscape (or ‘matrix’) of urban or semi‐natural habitats can sometimes support biodiversity, but the amount of support will depend on species‐specific traits, and on the resources available in the fragment and the matrix. Using data on bees collected from 19 oak‐savannah fragments, the question of whether bee communities differ when fragments are embedded in different landscapes ( D ouglas‐fir forest vs. urban residential neighbourhoods) was investigated, and also whether these differences could be attributed to species‐specific traits of bees (e.g. body size, specialization) and/or within‐fragment floral resources. No differences were found in overall richness or abundance of bees, but there were distinct differences in plant and bee community composition between matrix types. Common wood‐nesters and late‐flying, small‐bodied bees tended to be found in urban‐associated fragments, which also had a lower availability of within‐fragment floral resources. Forest‐associated fragments, on the other hand, had a greater density and richness of early‐flowering native plant species, and supported a higher abundance of large‐bodied bee species. Bumble bee abundance, in particular, increased with increasing proportion of forest cover in the surrounding landscape. Large‐bodied bees appear to respond to increased availability of within‐fragment floral resources, but it was also hypothesised that nesting and floral resources in matrix habitat drive the differences in bee community assemblages.
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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 | 0.000 |
| 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".