Identification of Spruce Budworm Natural Enemies Using a qPCR-Based Molecular Sorting Approach
Bibliographic record
Abstract
Annual monitoring of mortality agents in the course of a spruce budworm (Choristoneura fumiferana (Clemens) (Lepidoptera: Tortricidae)) population cycle is essential to understanding the factors governing the rise and collapse of outbreaks. To date, assessments of causes of budworm mortality have relied on laboratory rearing of field-collected larvae, followed by visual identification of emerging parasitoids and/or microscopic analysis of pathogens in larval carcasses. Although this approach has provided vital information on the abundance and identity of mortality agents, the procedure is labor-intensive and has limits in terms of accuracy. To overcome these shortcomings, we developed a molecular identification tool that makes use of real-time quantitative PCR (qPCR) and TaqMan® technologies. The tool relies on taxon-specific molecular variants (single nucleotide polymorphism [SNP] markers) found in mitochondrial (COI) and nuclear (28S rDNA) genes, for parasitoids, and in the nuclear SSU rDNA gene for microsporidian pathogens; these are then used as molecular signatures targeted by qPCR primers and TaqMan probes. Thus, the design of several sets of primers and probes deployed in multiplex format enables the identification of natural enemies via a molecular sorting process, bypassing barcode sequencing. Crude budworm DNA extracts are processed through a first module that detects dipteran and hymenopteran parasitoids, and microsporidian infections. Positive samples are then processed for species determination using three additional modules, enabling the identification of 20 common natural enemies of the spruce budworm. The tool has been fully validated using DNA samples from all comprised taxa, and both its sensitivity and accuracy compared favorably with the rearing-based method in an analysis of field-collected budworms. Using this tool, sample processing can be completed within two days, does not require larval rearing, provides accurate species identification, and can be conducted by technical staff without extensive molecular biology or insect taxonomy training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".