Monitoring native non-target Lepidoptera for three years following a high dose and volume application of<i>Bacillus thuringiensis</i>subsp.<i>kurstaki</i>
Bibliographic record
Abstract
Abstract The operational use of Bacillus thuringiensis subsp. kurstaki (Btk) to control the western spruce budworm in the mountainous terrain of British Columbia, Canada, gives variable results at the registered dose of 30 BIU ha − 1 in 2.4 L ha − 1. Btk (Foray 48B®, 60 BIU ha − 1 in 4.8 L ha − 1) was applied experimentally in 1996 over 150-ha of western spruce budworm-infested Douglas-fir forest to determine if increasing both the dose and volume of Btk applications gives more uniform and effective control. We sampled native caterpillars on the shrub Ribes cereum Douglas in this and a similar, untreated area 2 km away from 1996 to 1998 to assess the impact of this high dose and volume Btk application on non-target Lepidoptera. The numerically dominant lepidopteran species were Euhyponomeutoides gracilariella Busck (52%) and Gelechia ribesella Chambers (32%). The remaining 16% of the guild was comprised of numerous sparsely distributed species. Total larval abundance was significantly lower on plants that were sprayed with Btk than on plants that were covered to exclude Btk within the treatment area in the year of application. Geometrids appeared to be less affected by the treatment than other taxa. Both G. ribesella and E. gracilariella appeared to make a full recovery by the second year post-treatment, but we did not find a full recovery of the sparsely distributed species. Keywords: Non-target Lepidopterawestern spruce budwormChoristoneura occidentalisDouglas-firPseudotsuga menziesii var. glaucaaerial sprayBacillus thuringiensis subsp. kurstakiBtk Acknowledgements We wish to thank Heather McGowan and Angus Bridgman for their assistance with fieldwork; Robert Betts for counting the Btk spray droplets; and Nicholas Conder who has redrawn the figures for this manuscript. Dr. Roger Davidson, Department of Mathematics and Statistics, University of Victoria, recommended the experimental design and guided the statistical analysis. Funding was provided by the Canadian Forest Service and Forest Renewal British Columbia for the fieldwork and manuscript preparation, and Agriculture Canada, Eastern Cereal and Oilseed Research Centre funded Jean-François Landry, who provided taxonomic expertise. We thank the anonymous reviewers and the editors for their comments on this manuscript. This research was conducted in partial fulfilment of the requirements for the degree of Master of Science at the University of Victoria, Victoria, B.C. for T. J. Boulton.
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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 | 0.001 | 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".