Proximity to non-crop vegetation increases estimates of predation frequency but not beetle numbers
Bibliographic record
Abstract
Removal of non-crop vegetation (e.g., forest and grassland patches) to expand crop fields is detrimental to the natural enemies of crop pests, like the ground beetle family (Coleoptera: Carabidae), that use these areas for foraging and overwintering. An argument for maintaining non-crop vegetation is its potential to support the supply of ecosystem services, like pest control, to the surrounding crop. Pest control services from non-crop areas are usually measured using the activity-density of natural enemy species, a proxy measure for predatory activity. Evidence that predators such as ground beetles are attacking pests in crops has been less frequently studied. We distributed sentinel prey (fake ‘caterpillars’ made to look like potential prey items) along with pitfall traps at 300 stations using a spatially and temporally replicated design across 20 field sites in Alberta, Canada. The potential for pest attacks by carabids was estimated through bite marks on the sentinels. Using generalized additive modelling, we found that non-crop vegetation positively impacts carabid predation, but activity density of carabids does not explain the relationship. Instead, the non-crop vegetation affects predation frequency, likely by promoting predatory behaviours. These findings exemplify the importance of closer indicators of ecosystem services delivery than activity-density and provide evidence of the potential that ground beetles have in supplying pest control ecosystem services within agroecosystems. • Sentinel prey used as proxy measure for biocontrol ecosystem services by carabids. • Linear increase in carabid activity-density with distance from non-crop vegetation. • Positive effect on predation activity with proximity to non-crop vegetation. • Activity-density is a poor proxy of service delivery, sentinel prey is better.
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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 | 0.001 |
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".