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Record W2948041883

The pollination ecology of highbush blueberry (Vaccinium corymbosum) in British Columbia

2018· dissertation· en· W2948041883 on OpenAlexaboutno aff
Kyle Bobiwash

Bibliographic record

VenueSummit (Simon Fraser University) · 2018
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsVacciniumPollinationBotanyBiologyEricaceaeEcologyHorticultureGeographyPollen
DOInot available

Abstract

fetched live from OpenAlex

Agricultural systems often support low beneficial insect diversity because they reduce habitat quality. Agricultural management increases landscape homogeneity resulting in low habitat and resource diversity. Crops that rely on wild pollinators for fruit production or predators and parasitoids for pest control may lose access to these services as the agroecosystem becomes increasingly managed. I used yield data from pollination experiments conducted over four years, along with insect surveys, to better understand the dynamics between insect communities in agroecosystems and their use of the agricultural landscape in highbush blueberry (Vaccinium corymbosum) in the Fraser Valley of southern British Columbia, Canada. Regional land use was identified as being an important component in structuring beneficial insect communities. Semi-natural habitat, such as pasture or fallow, was found to support greater abundances and diversity of all beneficial insects. Land use with greater disturbance, like conventional non-flowering agriculture, reduced pollinator species richness but increased the abundance of generalist predators. The differences between groups in their response to land use types might be driven by variability in access to resources (ex. floral resources or pest insects) in the larger agricultural landscape. However, surrounding landscape composition did not affect blueberry yield deficit, which was instead determined primarily by bumble bee visits and minimum daily temperatures. This finding highlights the importance of weather conducive to pollinator foraging for crop production. Despite the importance of bumble bees for reducing yield deficit, experimental introduction of two managed bumble bee species did not mitigate these deficits. Differences in bumble bee species characteristics associated with reproduction predicted pollen forager recruitment, which when coupled with differences in foraging preferences (blueberry pollen comprised 50% of pollen loads in one species, but less than 20% in the other and in managed honey bees) provides some insight into which managed species is best suited for further commercial development. My results highlight the complexity associated with predicting crop pollination levels and demonstrate how the impact of wild insects on production will vary with surrounding land-use, species characteristics, and abiotic factors. In crops highly reliant on wild pollinators, like highbush blueberry, understanding the needs of beneficial insects may allow farmers to modify practices to improve ecosystem services.

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 score0.976
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.187
Teacher spread0.173 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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