Spatial analysis of Varroa destructor and the relationship with surrounding landscape types in Southern Ontario
Bibliographic record
Abstract
Elevated colony losses have continued to be an issue for Canadian beekeepers for more than a decade. Numerous studies have identified unmanaged Apis mellifera colony infestation by the Varroa destructor mite as a main cause of the problem. V. destructor spread externally of the hive through a phoretic stage in their life cycle. Consequently, their movement outside the hive is influenced by honey bee flight behaviours, which can range to multiple kilometers from the originating hive in any direction. V. destructor are therefore of regional concern as neighboring colonies and yards share nearby forage which can serve as fomites. Additionally, mites can be transmitted through bee behaviours such as robbing and drifting, thus impacting surrounding colonies. Understanding the distribution of mites across a population is key for surveillance and equitable allocation of resources. Spatial patterns of V. destructor infestations in Southern Ontario, Canada, were investigated using a combination of cluster analysis, scan statistics, and geostatistical modelling, using 5 years of provincial apiary inspection data, from 2015 to 2019. A collection of disease clusters of V. destructor infestations was identified and found to be stable over multiple years with several other individual clusters occurring sporadically throughout Southern Ontario during the same study period. Universal kriging was applied to the V. destructor data in combination with regional colony density, and land use data as covariates, producing an isopleth map of the prevalence risk for V. destructor infestation. No substantial link between V. destructor infestation and environmental factors was found. This study highlights the need for more data and investigation to determine the cause of the identified clusters and areas of elevated risk. These results are hypothesis-generating but simultaneously provide information for government agencies, industry organizations, and beekeepers into the spatial distribution of V. destructor at a macro scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".