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Record W3205671183 · doi:10.26786/1920-7603(2021)641

The importance of wild bee communities as urban pollinators and the influence of honeybee hive density

2021· article· en· W3205671183 on OpenAlexvenueno aff
Julie A. Weissmann, Iris Rebecca Magdalena Walldorf, Hanno Schaefer

Bibliographic record

VenueJournal of Pollination Ecology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorBiologyPollinationHoney beeBeekeepingCropPEARApoideaStingless beeSpecies richnessEcologyAbundance (ecology)BotanyApidaePollenHymenoptera

Abstract

fetched live from OpenAlex

While urban beekeeping is on the rise, data on the role of wild bee communities as crop pollinators in cities is still scarce. We analysed wild bee visitation rates on apple, plum, cherry, pear, blackberry, raspberry, and strawberry in a Bavarian city with a very high honeybee density of c. 19 hives/km2. During 137.5 hours of observation time, we observed 52 wild bee species on the studied crop plants. During more than 50 h of observation time on fruit trees in flower, we found that wild bees provided 41% of the total bee visits, honeybees the remaining 59%. Honeybee hive density had a significantly negative effect on wild bee abundance. Bumblebees appeared more tolerant to poor weather conditions than all other bee groups. Wild bee species richness on apple flowers was not significantly impacted by flower diversity in the surroundings of the trees. Together, our results suggest that species-rich wild bee communities in urban areas are important for pollination success in common fruit crops, especially under unstable spring weather conditions. Bee-friendly management of urban spaces should be prioritised to support wild bee communities as well as the increasing number of honeybees in cities.

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)0.001
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 score0.046
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.216
Teacher spread0.201 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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