Effects of Dispersal, Predators (Acari: Phytoseiidae), Weather, and Ground Cover Treatments on Populations of <I>Tetranychus urticae</I> (Acari: Tetranychidae) in Apple Orchards
Bibliographic record
Abstract
In a 2-yr study of causes of mite outbreaks in apple (Malus spp.) orchards in Nova Scotia, we monitored immigration of Tetranychus urticae Koch from orchard ground cover into trees populated by the generalist phytoseiid predator Typhlodromus pyri Scheuten. In both years, T. urticae-days in the tree canopy increased with number of T. urticae caught in sticky bands on tree trunks. In 2000, T. urticae-days were negatively correlated with T. pyri-days. Lack of correlation in 2001 was attributed to higher rates of immigration, which would mask the effects of predation. Weather also affected mite dynamics. Rainfall in July and August was less in 2001 than in 2000. Heat units were sufficient for six generations of T. urticae in 2001 but only for five in 2000. Consequently, T. urticae-days in the tree canopy and immigration rates were significantly greater in 2001 than in 2000, despite three-fold greater use of miticides. We also tested the effects of herbicides on T. urticae immigration. Application of selective herbicides in laneways reduced coverage of reproductive hosts of T. urticae, but these changes did not reduce immigration. In 2001, application of a miticidal herbicide, glufosinate, in tree rows reduced captures of T. urticae on sticky bands in high immigration orchards but not in low immigration orchards. We conclude that generalist predators and modified herbicide use are insufficient remedies and that effective biological control of T. urticae in the ground cover by a specialist phytoseiid such as Amblyseius fallacis Garman is essential to prevent outbreaks.
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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) | 0.000 | 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 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".