Local factors have a greater influence on the abundance of alfalfa weevil and its larval parasitoids than landscape complexity in heterogeneous landscapes
Bibliographic record
Abstract
Abstract Context The alfalfa weevil Hypera postica Gyllenhal (Coleoptera: Curculionidae) is one of the most destructive pests of alfalfa worldwide. Both local and landscape-scale factors can significantly influence crop pests, natural enemies, and the effectiveness of biological control services, but the relative influence of these factors is unclear. Objectives We investigated the influence of the local variables and surrounding landscape composition and configuration on the abundance of alfalfa weevil, and on the abundance and parasitism rates of its larval parasitoids, Bathyplectes spp. Methods We sampled 65 commercial alfalfa fields along the Ebro Basin, Spain, over a period of 3 years, recording the field characteristics and landscape structure at three buffer radii of 250, 500 and 1000 m from the center of each field. Results The abundance of weevil larvae was positively associated with the field perimeter and with the uncut alfalfa surrounding the pipes of the sprinkler irrigation system, but only one configuration variable was positively correlated: the alfalfa edge density. No local characteristics or landscape structures were associated with the abundance of adult weevils. The abundance of Bathyplectes spp. adults was positively associated to local factors such as the densities of alfalfa weevils and aphids. Few landscape structure variables, such as alfalfa edge density and Simpson’s Diversity Index, had explanatory value only at 250 m buffer radius. The rate of larval parasitism was affected by local variables, such as alfalfa weevil abundance and field age. Conclusion Our results provide, for the first time in the Mediterranean region and Europe, evidence of the relative importance of landscape structure and local factors on the abundance of the alfalfa weevil and its larval parasitoids, Bathyplectes spp. The strongest influences were based on local characteristics.
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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) | 0.001 | 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 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".