Honey bee pollen foraging ecology across an urbanization gradient
Bibliographic record
Abstract
Abstract Understanding animal foraging ecology requires large samples sizes spanning broad environmental and temporal gradients. For pollinators, this has been hampered by the laborious nature of morphologically identifying pollen. Metagenetic pollen analysis is a solution to this issue, but the field has struggled with poor quantitative performance. Building upon prior laboratory and bioinformatic methods, we applied quantitative multi-locus metabarcoding to characterize the foraging ecology of honey bee colonies situated along an urban-agricultural gradient in central Ohio, USA. In cross-validating a subset of our metabarcoding results using microscopic palynology, we find strong concordance between the molecular and microscopic methods. Our results show that, relative to the agricultural environment, urban and suburban environments were associated with higher taxonomic diversity and temporal turnover of honey bee pollen forage. This is likely reflective of the fine-grain heterogeneity and high beta diversity of urban floral landscapes at the scale of honey bee foraging. Our work also demonstrates the power of honey bees as environmental samplers of floral community composition at large spatial scales, aiding in the distinction of taxa characteristically associated with urban or agricultural land use from those distributed ubiquitously across our landscape gradient.
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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 | 0.000 |
| 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".