High‐resolution biomonitoring of plant pathogens and plant species using metabarcoding of pollen pellet contents collected from a honey bee hive
Bibliographic record
Abstract
Abstract The Canadian beekeeping industry is spread across the country, with the greatest proportion of managed honey bee colonies occurring in the Prairie Provinces. Nationally, the number of beekeepers has recently been trending upwards. Simultaneously, agronomic and environmental plant pest incidents are increasing due to a number of factors, including the introduction of exotic organisms through international trade, which is a major pathway for the introduction of potentially invasive alien species and quarantine pests. Therefore, regulatory agencies are interested in developing high‐throughput tools to achieve earlier detection of unwanted species in order to expedite application of mitigating measures to limit the impacts of their introduction. This study evaluates the potential of pollen pellet contents collected by honey bees to monitor plant pests using metabarcoding, a high‐throughput sequencing (HTS) approach for monitoring complex environmental samples. The study used the ITS1 intergenic region to target oomycetes and fungi, the ATP9‐NAD9 spacer to specifically target Phytophthora species, and the ITS2 region to target plant species. From the HTS results, a number of plants that were detected corresponded to known hosts of certain pathogens or species closely related to potentially invasive plant species. Genera including phytopathogenic species found in the pollen samples comprised Fusarium sp., Ophiostoma sp., Peronospora sp., Phytophthora sp., and Pythium sp. Correlations, high entropy, and co‐occurrences between certain plants and oomycetes or fungi were observed. The potential for using honey bee‐collected pollen pellets to study phytopathogens in a given environment is demonstrated here, and this concept could represent a promising complementary tool for the surveillance of phytopathogens or unwanted plants with previously described air and insect sampling methods if the protocol was applied with additional genetic markers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".