Resource abundance and distribution drive bee visitation within developing tropical urban landscapes
Bibliographic record
Abstract
Urban landscapes include a mix of biotic and anthropogenic elements that can interact with and influence species occurrence and behaviour. In order to outline the drivers of bee (Hymenoptera: Apoidea) occurrence in tropical urban landscapes, foraging patterns and community characteristics were examined at a common and broadly attractive food resource, Tecoma stans (Bignoniaceae). Bee visitation was monitored at 120 individual resources in three cities from June 2007 to March 2009. Resource characteristics, spatial distribution, and other local and regional landscape variables were assessed and then used to develop descriptive regression models of forager visitation. The results indicated that increased bee abundance and taxon richness consistently correlated with increased floral abundance. Resource distribution was also influential, with more spatially aggregated resources receiving more foragers. Individual bee guilds had differential responses to the variables tested, but the significant impact of increased floral abundance was generally conserved. Smaller bodied bee species responded to floral abundance, resource structure, and proximity to natural habitats, suggesting that size-related dispersal abilities structure occurrence patterns in this guild. Larger bees favoured spatially aggregated resources in addition to increased floral abundance, suggesting an optimization of foraging energetics. The impact of the urban matrix was minimal and was only seen in generalist feeders (African honey bees). The strongly resource-driven foraging dynamics described in this study can be used to inform conservation and management practices in urban landscapes. download Appendix
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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 | 0.000 | 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) | 0.009 | 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".