Environmental drivers of <i>Lygus</i> species distribution in faba bean fields across Saskatchewan (2019–2023)
Bibliographic record
Abstract
This study evaluates the incidence and severity of Lygus infestations in faba bean fields across Saskatchewan from 2019 to 2023. Surveys were conducted in collaboration with Saskatchewan Pulse Growers, the Saskatchewan Ministry of Agriculture, and the Entomology Laboratory of the University of Saskatchewan. Lygus insects were collected using standard sweep nets at BBCH 72 stage of faba bean growth. Environmental data, including cumulative degree days, total precipitation, and geographical variables, such as soil climatic zones and crop district, were integrated to assess their influence on Lygus species composition and abundance. Statistical analyses, including PERMANOVA and non-metric multidimensional scaling, revealed that year significantly influenced Lygus community composition, accounting for 22.77% of the variation. A significant interaction between year and crop district explained 40.74% of the variation. Generalized linear mixed models showed species-specific responses to environmental factors: Lygus lineolaris showed a significant association with cumulative degree days and total precipitation, while Lygus borealis and L. elisus responded more to soil climatic zones and precipitation. In contrast, Lygus keltoni exhibited weaker responses to both temperature and precipitation. This study highlights the complex relationships between climate, geographic conditions, and species distribution, providing valuable insights for improving Lygus management in faba bean crops.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".