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Record W3019936491 · doi:10.1002/ecs2.3102

Characterizing the floral resources of a North American metropolis using a honey bee foraging assay

2020· article· en· W3019936491 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEcosphere · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
FundersNational Institute of Food and Agriculture
KeywordsForagingPollenSpecies richnessBiologyEcologyPhenologyBiodiversityResource (disambiguation)Geography

Abstract

fetched live from OpenAlex

Abstract Roughly a third of described insect species visit flowers, making the flower–insect interface one of the chief pillars of global biodiversity. Studying flower–insect relationships at the scale of communities and landscapes has been hindered, however, by the methodological challenges of quantifying landscape‐scale floral resources. This challenge is especially acute in urban landscapes, where traditional floral surveying techniques are ill‐suited to the unique constraints of built environments. To surmount these challenges, we devised a “honey bee foraging assay” approach to floral resource surveying, wherein continuous colony weight tracking and DNA metabarcoding of pollen samples are used to capture both the overall availability and taxonomic composition of floral resources. We deploy this methodology in the complex urban ecosystem of Philadelphia, Pennsylvania, USA. Our results reveal distinct seasonality of floral resource availability, with pulses of high availability in May, June, and September, and a period of prolonged scarcity in August. Pollen genus richness mirrored this pattern, with peak richness in May and June. The taxonomic composition of pollen samples varied seasonally, reflecting underlying floral phenology, with especially strong turnover between May and June samples and between August and September samples delineating well‐defined spring, summer, and fall floral resource communities. Trait analysis also revealed seasonal structure, with spring samples characterized by trees and shrubs, summer samples including a stronger presence of herbaceous “weeds”, and fall samples dominated by woody vines. Native flora predominated in spring, giving way to a preponderance of exotic flora in summer and fall. At a basic level, this yields insight into the assembly of novel urban floral resource communities, showcasing, for example, the emergence of a woody vine‐dominated fall flora. At an applied level, our data can inform urban land management, such as the design of ecologically functional ornamental plantings, while also providing practical guidance to beekeepers seeking to adapt their management activities to floral resource seasonality. Methodologically, our study demonstrates the potential of the honey bee foraging assay as a powerful technique for landscape‐scale floral resource surveying, provided the inherent biases of honey bee foraging are accounted for in the interpretation of the results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.215
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it