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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".