Differential Host-Finding Abilities by a Weed Biocontrol Insect Create Within-Patch Spatial Refuges for Nontarget Plants
Bibliographic record
Abstract
Many modern weed biocontrol insects exhibit transient "spillover" nontarget herbivory when and where insects are in high density, such as following biocontrol releases, or around dense target weed infestations. Understanding spatial patterns of herbivory is important for predicting efficacy and safety of biocontrol, as refuges from herbivory can buffer plants from population-level impacts. Here, we demonstrate that differential host-finding and arrestment behaviors by an oligophagous biocontrol insect lead to spatial refuges from nontarget herbivory around insect release points within mixed patches of target and nontarget plants. We created transient insect outbreaks by releasing large numbers of Mogulones crucifer Pallas (Coleoptera: Curculionidae) into naturally occurring rangeland patches of the nontarget plant Hackelia micrantha (Eastwood) J.L. Gentry with varying densities of its target weed Cynoglossum officinale L., and monitored spatial patterns of herbivory around release points after 4-7 wk. In complement, we conducted a mark-release-recapture (MRR) experiment to compare M. crucifer's target and nontarget host-finding and arrestment behaviors. For rangeland releases, 95% of nontarget herbivory occurred within 4.25 m of release points, independent of target plant density. Target herbivory occurred throughout our evaluation radii (up to 14 m), where maximum density of diffusing M. crucifer was 1/10 of that in the nontarget herbivory radius. In the MRR experiment, more weevils were recaptured on C. officinale (but not H. micrantha) than expected by chance. M. crucifer's lack of specialized nontarget host-finding and arrestment behaviors means that spatial refuges from herbivory are created for H. micrantha just meters away from sources of high weevil density.
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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 | 0.000 | 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 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".