Influence of local and long-distance dispersal on patterns of Bt resistance in cabbage loopers (Trichoplusia ni)
Bibliographic record
Abstract
Subdivision of populations can significantly impact the maintenance of genetic variation and a population’s response to selection pressures. Agroecosystems, in which habitats are subdivided and selection pressures from insecticide use vary, provide some of the best opportunities for examining the genetic consequences of local adaptation. The insect pest, Trichoplusia ni has developed resistance to the microbial insecticide, Bacillus thuringiensis (kurstaki) in vegetable greenhouses in British Columbia (BC), Canada. Trichoplusia ni is a subtropical insect and is thought to migrate each spring from California to BC; however it can also persist year-round in BC greenhouses. Here, I examine the local and regional patterns of Bt resistance and their relation to the population genetic structure of T. ni. Patterns of Bt resistance suggest that early season dispersal occurs from over-wintered, Bt treated greenhouse populations into neighbouring untreated greenhouse populations in BC. Field populations surveyed from California to BC remain susceptible to Bt, but have failed to dilute resistance in Bt resistant greenhouse populations. To examine the influence of the genetic structure on Bt resistance, DNA isolation procedures were developed and primer combinations screened for amplified fragment length polymorphism (AFLP) analysis. A lack of genetic structure was revealed at the regional scale and indicated that gene flow connects populations from California to BC. At the local scale, a surprising level of spatial and temporal genetic structure was detected. Greenhouse populations from the local areas of Delta, Langley, and Abbotsford formed genetically distinct groups, whereas the spring and summer field collections showed genetic similarity to long-range migrants. Temporal collections reveal a genetic shift between spring and summer and autumn collections that potentially could be attributed to the movement of long-range migrants into greenhouse and field populations in the autumn. Lastly, Bt resistance is positively correlated with genetic differentiation in populations and provides evidence that recurrent Bt sprays likely induce population bottlenecks in greenhouse populations. Overall, this study provides an example of human-induced changes in which geographically concentrated greenhouses that support year-round populations and strong selection from Bt use significantly alter the genetic structure of T. ni populations in BC.
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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 | 0.000 | 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".