The effect of simulated seasonal temperatures on <i>Metarhizium brunneum</i>‐associated mortality in <i>Agriotes</i> spp. click beetles, and a degree‐day infection model
Bibliographic record
Abstract
Abstract Entomopathogens tend to have a slow speed of kill when used for targeting agricultural insect pests. Relating temperature as a driver of this speed is important to predict pest mortality, and extending this to a degree‐day infection model has rarely been studied. Many species of wireworms (Coleoptera: Elateridae), the larvae of click beetles, are subterranean and generalist agricultural pests that can be difficult to control with pesticides. Targeting adult beetles, however, may be an effective method to reduce larval recruitment. Metarhizium brunneum Petch (Hypocreales), an entomopathogenic fungus, kills click beetles but the mortality rate and speed of kill are expected to vary according to temperature. Using a thermal gradient plate to simulate daily oscillating temperatures in Agassiz, British Columbia, Canada, for April, May, and June, the effectiveness of M. brunneum strains LRC112 and F52 in causing mortality to Agriotes obscurus (L.) and Agriotes lineatus (L.) click beetles was studied in the laboratory. Mortality was fastest in beetles exposed to June temperatures and slowest in those exposed to April temperatures, with differences among beetle species × M. brunneum strain combinations. Higher temperatures resulted in more rapid mycelial outgrowth and conidiation in beetle cadavers, with only A. obscurus infected with M. brunneum LRC112 attaining near 100% conidiation. The number of degree days required to kill 50% of the beetles (LDD50) was least for A. obscurus infected with M. brunneum LRC112 (176) followed by A. obscurus × M. brunneum F52 (212), A. lineatus × M. brunneum LRC112 (215), and A. lineatus × M. brunneum F52 (292). Hypothetical calculations showed that M. brunneum exposure earlier in the season resulted in a longer time to kill 50% of the beetles (LT50) but the earliest LT50 calendar date. Later M. brunneum exposure dates resulted in lower LT50's, but later LT50 dates. This conceptual work demonstrates that daily temperature oscillations, seasonality, and degree days must be considered to predict the efficacy and speed of kill of different fungal entomopathogen strains when targeting different click beetle species.
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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".