Morphological variation associated with dispersal capacity in a tree‐killing bark beetle <i>Dendroctonus ponderosae</i> Hopkins
Bibliographic record
Abstract
Abstract Intrinsic factors influencing the dispersal of insect pests during outbreaks are poorly understood, yet these factors need to be quantified to parameterize dispersal in models that predict population spread. The present study related wing and body morphology of female mountain pine beetles ( Dendroctonus ponderosae ) to flight distance, as measured by flight mill bioassays. Beetles that flew long distances (> 11 km) had a greater body weight and larger wings than beetles that flew short distances (< 1 km). These heavier female beetles should also be more capable pioneers because other studies have shown that body weight is positively correlated with lipid content. Wing and body morphology of females are significant predictors of flight distance; heavy beetles with large wings generally flew further than smaller beetles, although this relationship is heteroscedastic. Dispersion of flight distance values increases with wing loading (weight/wing area) as a result of a cohort of ‘lazy’ individuals that fly short distances (< 1 km) regardless of flight phenotype. The observed morphology explained less than 20% of the variation in flight capacity, indicating a substantial contribution from other intrinsic factors that remain to be investigated. The present study may have implications for dispersal modelling, providing estimates of flight capacity using morphological measurements.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".