The Effects of Weather on the Flight of an Invasive Bark Beetle, Pityophthorus juglandis
Bibliographic record
Abstract
The walnut twig beetle, Pityophthorus juglandis Blackman (Coleoptera: Scolytidae), vectors the fungus Geosmithia morbida, which has been implicated in thousand cankers disease of walnut. Little is known about the flight behavior of the insect across seasons, or about the variability in its flight patterns with weekly fluctuations in weather. We sampled flying adults weekly over a 142-week period (from 29 August, 2011 to 2 June, 2014) with 12-unit black plastic multiple funnel traps baited with a male-produced aggregation pheromone in California, USA. Up to 5000 beetles were captured per trap per week, although catches in most weeks were less than 100 insects. Trap catches were regressed against terms for precipitation, solar radiation, vapor pressure, air temperature, relative humidity, wind speed, and trap catches in preceding weeks. The number of beetles captured in each of the preceding two weeks explained most variation in a current week’s catch. This strong temporal autocorrelation was present in regression models developed for males, females, and both sexes pooled. These models were improved by including two environmental variables. Captures of P. juglandis increased with mean weekly air temperature and decreased with increasing mean minimum relative humidity. The percentage of variation in male, female, or total trap catch explained by the temporal variables and the two environmental variables in these multiple regression models ranged from 72% to 76%. While the flight of this invasive insect will likely be affected by site-specific factors as it spreads to new areas, the strong temporal correlation present in this system may provide a useful starting point for developing flight models for newly invaded areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".