Development of a <scp>qPCR</scp>‐based method for counting overwintering spruce budworm (<i>Choristoneura fumiferana</i>) larvae collected during fall surveys and for assessing their natural enemy load: a proof‐of‐concept study
Bibliographic record
Abstract
BACKGROUND: instar spruce budworm (Choristoneura fumiferana) larvae ('L2s') are carried out each fall to guide insecticide application decisions in the following spring. These surveys involve the collection of fir and spruce branches in selected stands, followed by the mechanical/chemical removal of larvae. The latter then are counted manually on filter papers, using a stereomicroscope. Considering the significant effort and difficulties which this manual counting entails, we developed a quantitative (q)PCR-based 'molecular counting' approach designed to make this step less tedious. RESULTS: Using the C. fumiferana mitochondrial cytochrome c oxidase 1 (COI) gene as a target for qPCR DNA quantification, we show that the amount of DNA in a larval extract is strongly correlated with the number of larvae used to generate that extract, and that molecular estimates of L2 counts are comparable to those generated using the manual approach. In addition, we used the same DNA extracts to monitor the microsporidian pathogen Nosema fumiferanae, and the hymenopteran parasitoids Glypta fumiferanae and Apanteles fumiferanae in overwintering L2s employing a subset of a TaqMan assay developed by Nisole et al. (2020) for the identification of budworm natural enemies. We show that the proportion of individuals affected by each natural enemy in samples containing a known number of larvae can be estimated from presence/absence data through the binomial probability distribution. CONCLUSION: The present proof-of-principle study shows that a molecular approach for counting L2s and assessing their natural enemy load is clearly possible and is expected to generate reliable results. © 2021 Her Majesty the Queen in Right of Canada. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Natural Resources Canada.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".