Influence of the surrounding landscape on crop colonization by a polyphagous insect pest
Bibliographic record
Abstract
Abstract Landscape composition plays an important, but poorly understood, role in the population dynamics of agricultural pest species with broad host ranges including both crops and weeds. One such pest, the generalist plant bugLygus hesperusKnight (Hemiptera:Miridae), is a key cotton pest that feeds on various hosts differing in quality inCalifornia'sSanJoaquinValley (USA). We investigated the effects of 15 common crops and uncultivated agricultural land onL. hesperuspopulations, by correlating the densities ofL. hesperusin focal cotton fields with the areas of the 16 crops in surrounding rings. Insect counts were provided by private pest‐control advisors, and spatial data were obtained fromKernCounty records. We first calculatedSpearman's partial correlation coefficients on an annual basis for each crop separately, and then performed a meta‐analysis of these correlations across years to describe the overall effect of a particular crop onL. hesperusafter the effects of the 15 other crops are removed. Consistent with studies conducted in other areas,L. hesperusdensity was positively correlated with safflower, and negatively with cotton.Lygus hesperusdensity was also correlated with several other crops that are often not considered in pest management, including grape, oat, and onion (positive correlations), and almond, pistachio, and potato (negative correlations).Lygus hesperusdensity was also found to be negatively correlated with alfalfa and positively correlated with uncultivated habitats, a relationship that receives mixed support in the literature. Several other crops tested were not significantly correlated withL. hesperusdensities in focal cotton fields, suggesting a neutral role for them inL. hesperusdynamics. The improved understanding of the effects of a greater variety of crops onL. hesperuspopulation dynamics will be useful in the design of agricultural landscapes for enhanced management of this important polyphagous pest.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".