How many do we need? Meeting the challenges of studying the microbiome of a cryptic insect in an orchard
Bibliographic record
Abstract
The minimal sampling effort required to report the microbiome composition of insect surveyed in natural environment is often based on empirical or logistical constraints. This question was addressed with the white pine cone beetle, Conophthorus coniperda (Schwarz), a devastating insect pest of seed orchards. It attacks and stop the growth of the cones within which it will spend its life, on the ground. To survive, the bark beetle probably interacts with microorganisms involved in alimentation, cold adaptation, and dormancy stage. Deciphering the drivers and benefits of these microorganisms in an orchard first requires methodological development addressing variability of the white pine cone beetle microbiome. The number of insect guts integrated in composite samples prior to DNA extraction and the number of surveyed trees are two features expected to induce variability in recovered microbiome profiles. These two levels of heterogeneity were examined in an orchard experimental area where 12 white pine trees were sampled and 15 cones from each tree were grouped together. For each tree, 2, 3 and 4 insects were selected, their intestinal tract dissected, and the microbiome sequenced. The number of insects caused no significant incidence on the coverage of bacterial and fungal communities’ composition and diversity (p > 0.8). There was more variability among the different trees. A sampling effort including up to 33 trees in an area of 1.1 ha is expected to capture 98% of the microbial diversity in the experimental area. Spatial variability has important implications for future investigations of cryptic insect microbiome.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".