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Record W2944205373 · doi:10.1111/1365-2656.13003

Specialist foragers in forest bee communities are small, social or emerge early

2019· article· en· W2944205373 on OpenAlexaff
Colleen Smith, Lucia R. Weinman, Jason Gibbs, Rachael Winfree

Bibliographic record

VenueJournal of Animal Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Manitoba
FundersNatural Resources Conservation ServiceGarden Club of America
KeywordsGeographyEcologyBiology

Abstract

fetched live from OpenAlex

Individual pollinators that specialize on one plant species within a foraging bout transfer more conspecific and less heterospecific pollen, positively affecting plant reproduction. However, we know much less about pollinator specialization at the scale of a foraging bout compared to specialization by pollinator species. In this study, we measured the diversity of pollen carried by individual bees foraging in forest plant communities in the mid-Atlantic United States. We found that individuals frequently carried low-diversity pollen loads, suggesting that specialization at the scale of the foraging bout is common. Individuals of solitary bee species carried higher diversity pollen loads than did individuals of social bee species; the latter have been better studied with respect to foraging bout specialization, but account for a small minority of the world's bee species. Bee body size was positively correlated with pollen load diversity, and individuals of polylectic (but not oligolectic) species carried increasingly diverse pollen loads as the season progressed, likely reflecting an increase in the diversity of flowers in bloom. Furthermore, the seasonal increase in pollen load diversity was stronger for bees visiting trees and shrubs than for bees visiting herbaceous plants. Overall, our results showed that both plant and pollinator species' traits as well as community-level patterns of flowering phenology are likely to be important determinants of individual-level interactions in plant-pollinator communities.

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.

How this classification was reachedexpand

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.891

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)0.0010.000

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 head0.072
GPT teacher head0.242
Teacher spread0.170 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2019
Admission routes1
Has abstractyes

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