Modeling Landscape-Level Spatial Variation in Sex Ratio Skew in the Mountain Pine Beetle (Coleoptera: Curculionidae)
Bibliographic record
Abstract
Through their influence on effective population sizes, sex ratio skew affects population dynamics. We examined spatial variation in female-biased sex ratios in the mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in western Canada to better understand how environmental context affects sex ratio skew. Our specific objectives were to: 1) characterize spatial variation in mountain pine beetle sex ratio; 2) test previously asserted hypotheses that beetle sex ratio varies with tree diameter and year in outbreak; and 3) develop predictive models of sex ratio skew for larval and adult populations. Using logistic regression, we modeled the probability that an individual beetle (n = 2,369) was female as a function of multiple environmental variables across 34 stands in British Columbia and Alberta, Canada. We identified a consistent female-biased sex ratio with significantly greater skew in adults (2:1, n = 713) than in larvae (1.76:1, n = 1,643). We found that the proportion of larval females increased with decreasing tree size and with outbreak age. However, adults did not respond to tree size and larvae did not respond to outbreak age. Predictive models differed between larvae and adults. All identified models perform well and included predictors related to weather, tree diameter, and year in outbreak. Female-biased sex ratios appear to originate from differential male mortality during development rather than from sex-biased oviposition, suggesting sex ratio skew is not the cause of outbreaks, but rather a consequence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".