The potential impact of pathogens on honey bee, Apis mellifera L., colonies and possibilities for their control
Bibliographic record
Abstract
Excessive honey bee colony losses all over the world are believed to be caused by multiple stressors. In this thesis, I characterized and quantified pathogen levels in honey bee colonies, studied their interactions with each other and with their associated parasite vectors, examined factors that influence their combined impacts on honey bees and developed methods to manage honey bee viruses so that colony losses can be minimized. My baseline study of virus prevalence and concentration in healthy and unhealthy (showing visible signs of disease) colonies in Canada showed that seven economically important viruses (DWV, BQCV, IAPV, KBV, SBV, ABPV, and CBPV) were all widely distributed in Canada. Differences in concentration and prevalence of some viruses were found between unhealthy and healthy colonies but these differences may have been due in part to seasonal or regional effects. Studies of the impact of viruses on worker bee populations over winter showed different factors were correlated with bee loss in different environments. Spring concentrations of DWV and mean abundance of Varroa (Varroa destructor) were positively correlated with bee loss and negatively correlated with spring population size in outdoor-wintered colonies. Fall concentration of IAPV was negatively correlated with spring population size of colonies in indoor-wintering environments but not in outdoor-environments. My study showed that it is important to consider location of sampling when associating pathogen loads with bee loss with Nosema and BQCV. Seasonal patterns of parasites and pathogens were characterized for each wintering methods (indoor and outdoor). My results revealed lower ABPV and Nosema ceranae prevalence and lower DWV concentration in genetically diverse than genetically similar colonies. I showed that within colony genetic diversity may be an important evolutionary adaptation to allow honey bees to defend against a wide range of diseases. In laboratory studies, I showed that feeding DWV to larvae in the absence of Varroa causes wing deformity and decreased survival rates of adult bees relative to bees not fed DWV. Finally, I showed that RNA silencing can be used to reduce DWV concentrations in immature and adult bees, reduce wing deformity in emerging adults, and increase their longevity relative to controls.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".